Biological Evaluation of the Renewable
Fuel Standard Set Rule and Addendum
£% United States
Environmental Protect
Agency
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Biological Evaluation of the Renewable
Fuel Standard Set Rule and Addendum
Assessment and Standards Division
Office of Transportation and Air Quality
U.S. Environmental Protection Agency
United States
Environmental Protection
^1 Agency
EPA-420-R-23-029
May 2023
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Table of Contents
List of Acronyms and Abbreviations 4
I. Executive Summary 6
II. Consultation to Date 14
III. The Renewable Fuel Standard (RFS) 16
A. Statutory Requirements 16
B. Discretion Available under the Statute 20
C. Historical Production of Renewable Fuel 21
D. Factors Affecting Actual Production and Use of Renewable Fuel 24
IV. Description of the Action and the Action Area 26
A. The RFS Set Rule Action 26
1. Proposed Action 26
2. Final Action 29
B. The Action Area 31
1. Potential Locations that Comprise the Action Area 31
2. Crop-Based Feedstocks 31
3. Identifying the Area of Potential Land Use Change 32
4. Identifying Downstream Areas 36
5. Non-Crop-Based Feedstocks 37
V. Listed Species That Are Found Within the Action Area 40
VI. Changes in Land Use Attributable to the RFS Set Rule 75
A. Corn Production Potentially Attributable to the RFS Set Rule 75
1. Factors Contributing to Increased Ethanol Production in the Past 80
2. Potential Impacts of the RFS Set Rule on Ethanol Production 85
3. Historical Corn Production and Ethanol Production 90
4. Potential Impacts of Future RFS Standards on Corn Production and Land Use 92
5. Uncertainty in Estimating the Land Use Impacts of RFS-Driven Ethanol Consumption 99
B. Soybean Production Potentially Attributable to the RFS Set Rule 101
1. Historical Biodiesel Production and Use 102
2. Overview of Soybean Markets 104
3. Potential Impact of the RFS program on Soybean Oil Use for Biofuel Production 105
4. Interactions Between Biofuel Production and Domestic Soybean Production 108
5. Projecting the Potential Impact of the RFS program on Soybean Planting 112
C. Canola Production Potentially Attributable to the RFS Set Rule 121
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1. Description of EPA Agricultural Economic Modeling of Canola Oil-Based Fuels 122
2. Results of the Economic Modeling for Canola Oil Biofuels 124
3. Estimated Volume of Canola-Oil Based Fuels Attributable to the RFS Set Rule 130
4. Estimated Potential Impact of Increased Canola-Based Fuels on U.S. Cropland 132
VII. Land Use Change Potential Impacts on Listed Species 133
A. Potential Impacts from Increased Corn Production 133
1. Identifying Potential Locations of Acres Impacted (FWS Species) 133
2. Potential Impacts on Listed Species and Critical Habitat (FWS species) 136
3. Identifying Potential Locations of Acres Impacted (NMFS Species) 143
4. Potential Impacts on Listed Species and Critical Habitat (NMFS Species) 143
B. Potential Impacts from Increased Soybean Production 150
1. Identifying Potential Locations of Acres Impacted (FWS and NMFS species) 150
2. Potential Impacts on Listed Species and Critical Habitat (FWS species) 158
3. Potential Impacts on Listed Species and Critical Habitat (NMFS species) 165
C. Potential Impacts from Increased Canola Production 166
1. Identifying Potential Locations of Acres Impacted 166
2. Potential Impacts on Listed Species and Critical Habitat 167
D. Total Potential Impacts of Increased Biofuel Crop Production 168
VIII. Potential Impacts on Listed Species from Changes in Water Quality 172
A. Potential Impact of Increased Crop Production on Water Quality 172
1. Estimated Potential Impacts of Increased Fertilizer Use 173
2. Estimated Potential Impacts of Increased Pesticide Use 179
B. Ongoing Mitigation Efforts 182
IX. Framework and Species\Critical Habitat Determinations 185
A. Framework for Species\Critical Habitat Determinations 185
B. FWS Species and Critical Habitats 186
C. NMFS Species and Critical Habitats 222
X. Conclusions 231
Appendix A. Overview of how the RFS program could affect listed species and critical habitat via land
use change 236
References 243
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List of Acronyms and Vhbreviations
Numerous acronyms and abbreviations are included in this document. The most commonly
used ones are defined below.
BBD Biomass-Based Diesel, which includes biodiesel and renewable diesel qualifying
as advanced biofuel under the RFS program
CAA Clean Air Act
CNG Compressed Natural Gas
CRP Conservation Reserve Program
DOE U.S. Department of Energy
DPS Designated Population Segments
EISA Energy Independence and Security Act of 2007
EPA U.S. Environmental Protection Agency
EPAct Energy Policy Act of 2005
ESA Endangered Species Act
ESU Evolutionary Significant Units
FWS Fish and Wildlife Service, U.S. Department of the Interior
GHG Greenhouse Gas
GIS Geographic Information System
LCA Lifecycle Analysis
LCFS Low Carbon Fuel Standard
LNG Liquified Natural Gas
RPAs Reasonable and prudent alternatives
NMFS National Marine Fisheries Service, National Oceanic and Atmospheric
Administration
PBFs Physical and Biological features (PBFs)
PCEs Primary Constituent Elements (PCEs)
RFS Renewable Fuel Standard
RIA Regulatory Impact Analysis
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RIN Renewable Identification Number
RNG Renewable Natural Gas
RVO Renewable Volume Obligation
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I. Executive Summary
The Endangered Species Act requires federal agencies to consult with the Fish and
Wildlife Service (FWS) and the National Marine Fisheries Service (NMFS) (hereafter "the
Services") whenever the agency determines that a discretionary federal action may affect
endangered or threatened species or designated critical habitat. The U.S. Environmental
Protection Agency (EPA) has made such a determination for the Renewable Fuel Standard (RFS)
rulemaking titled, "Renewable Fuel Standard (RFS) Program: Standards for 2023-2025 and
Other Changes." A proposal for this rulemaking, also known as the "Set Rule," was published on
December 30, 2022 (US EPA, 2022), and proposes RFS volume requirements and associated
percentage standards for the years 2023, 2024, and 2025 as well as a series of important
modifications to strengthen the RFS program. We are preparing to finalize this action, with some
modifications from the proposal.
In this Biological Evaluation, we first provide a chronology of our interactions with the
Services towards completing ESA consultation on the Set Rule, followed by an overview of the
RFS program. We then describe in more detail the RFS Set Rule as well as the action area,
defined by 50 CFR § 402.02 as the area within the U.S. that will be affected directly or indirectly
by the Set Rule. EPA has determined that the production of crop-based feedstocks has the
potential to affect endangered and threatened species (also referred to as "listed species" in this
document) and critical habitat by contributing to land use changes that could, for example, lead
to habitat loss or water quality impairments via runoff from agricultural lands. Therefore, for all
species within the action area, we find that the Set Rule may affect listed species and critical
habitat; thus, we are engaging in an ESA section 7 consultation with the Services. The action
area we delineate is based on where crops of corn, soybean, and canola are currently grown in
the U.S. and where we project that land use changes may occur, as well as the associated
downstream areas that could be impacted by agricultural runoff and pollution from such crop
areas. We focused our analyses on these three crops because they are used to produce the bulk of
renewable fuel under the RFS: corn ethanol; soy biodiesel and renewable diesel; and canola
biodiesel and renewable diesel.1 Although the very broad scope of this analysis has required EPA
to make a significant number of assumptions that have resulted in considerable uncertainty in the
results, EPA found that the Set Rule action area overlaps with a total of 712 unique species: 672
FWS species, 32 NMFS species, and 8 that are both FWS and NMFS species. And because
multiple species have Designated Population Segments (DPSs) or Evolutionary Significant Units
(ESUs), a total of 810 populations are evaluated in this Biological Evaluation. The full list of
species and populations can be found in Section V.
To further assess how the Set Rule may affect these species, we followed a four-step
process to assess potential impacts, as depicted in Figure ES.l below.2 We followed the same
stepwise process to assess potential impacts from corn (corn ethanol), soy (soy biodiesel), and
1 Hereinafter, references to "soy biodiesel" and "canola biodiesel" will encompass both biodiesel and renewable
diesel.
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This process diagram is a general description of how we approached the analyses. In the context of the discussion
of potential changes in corn, soybean, and canola production we have provided process diagrams that are specific to
each of these feedstocks.
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canola (canola biodiesel). However, the methods used to complete each step varied to some
degree for each of these three feedstock crops, primarily because we leveraged existing data and
research that used different approaches and had varying levels of data availability.
Figure ES.l: Illustration of EPA's four-step process to assess potential impacts on
threatened and endangered species due to increases in each of the three feedstock crops
related to the RFS Set Rule. The sections of this Biological Evaluation that discuss each
step are indicated in bold in the figure.
1. Estimate land use
change (# of acres)
associated with
increases in biofuel
production
attributable to the
RFS Set Rule
Section VI
2. Identify potential
locations of acres
impacted due to
increases in biofuel
production
attributable to the
RFS Set Rule
Section VII
3. Assess potential
impacts on listed
species from land
use changes based
on locations
identified in #2
Section VII
4. Assess potential
impacts on listed
species from
changes in water
quality
Section VIII
Step 1. Estimate land use change impacts attributable to the RFS Set Rule
There are many factors, including various economic and policy drivers, that influence the
production of renewable fuels in the United States. In Section VI.A, for example, we discuss
how the increases in historical corn ethanol production were driven by a wide range of factors in
addition to the RFS Program.3 As Step 1 for this Biological Evaluation, we made an attempt at
estimating the land use changes associated with increases in the production of corn ethanol,
soybean biodiesel, and canola biodiesel that could be attributable to the Set Rule alone. For
comparison purposes, we estimated land use change in the U.S. from 2023-2025 for all crops,
not just for corn, soybeans, and canola.
It is important to note the significant assumptions and high uncertainty inherent in
estimating these acreage impact numbers at each and every step in the underlying causal
relationship between the RFS standards and the effects that could result from increased
production of crop-based feedstocks. For example, projecting the impact of increased biofuel
demand on corn, soybean, and canola production is complicated by the fact that the majority of
all three crops is used in non-biofuel markets; for further information on domestic use of corn
and soybeans, see Sections VI.A.3 and VI, B.2. There is thus uncertainty in attributing the
increased biofuel demand from the set rule to corn, soybean, and canola planting decisions.
Further, the potential impacts of any RFS volume standards on species would be indirect and
mediated through markets. The fact that farmers do not generally grow crops for specific end
uses (e.g., earmarked for biofuel production vs. animal feed) nor do biofuel producers specify
how much of the fuel they produce is attributable to the RFS rather than what they would have
produced in the absence of the RFS program make our projections of the potential impacts of the
Though this document in some cases looks at historical trends—for example, historical drivers of ethanol use—
this Biological Evaluation assesses only potential future impacts of the Set Rule proposed volumes.
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RFS program on species inherently uncertain. Without reliable data on these points, we cannot
identify with any specificity parcels of land that may be converted to cropland, or any changes in
water quality that may result from such conversion, that could be the result of the incremental
demand for biofuels created by the RFS program. Thus, the impacts presented in this Biological
Evaluation are somewhat hypothetical and are based on potential scenarios, which often
represent to the worst-case scenarios, to conservatively compensate for the absence of specific
land conversion and associated water quality impact data. Where present in our analyses, we
detail the uncertainty associated with the various inputs and interpretations of our projections.
We note as well that the increased demand for biofuels does not necessarily result in
increased plantings of corn, soybean, and canola for production. This is further explained in
EPA's recent external review draft of the Third Triennial Report to Congress at sections 6 and 7.
We are unable to quantify a discrete contribution of the RFS to increased crop-based feedstock
production, as any of these increases could be a result of other factors, like increased yields and
crop production for other non-biofuel uses. The discussion of estimates in this and later sections,
are therefore uncertain given the lengthy causal chain between EPA setting the volume
requirements under the RFS program, and any potential impacts on listed species and critical
habitat, and any break in the causal chain would mean that the RFS Set rule did not cause the
outcome contemplated by our analysis.
Our soybean biodiesel analyses provide an example of this uncertainty: out of the three
feedstock categories, increases in soybean biodiesel from the RFS Set Rule are expected to have
the greatest acreage impacts at an estimated 1.93 million acres of soybeans, which is -1.2 million
acres greater than the estimated acreage impacts from corn ethanol and canola biodiesel
combined (Table ES. 1). Importantly, however, our analyses suggest that the expected increased
demand for these types of biofuels, driven by the Set Rule alone, could be met fully by changes
in imports/exports or by projected increases in feedstock yields on existing soybean lands, rather
than by converting new lands to crops. This again illustrates how EPA's analysis is based on a
worst-case scenario.
To assess land use change impacts from corn ethanol, we leveraged analyses available in
published literature combined with updated data to estimate the change in corn acres and total
cropland per billion gallons of ethanol production. For soybean biodiesel, we developed a
methodology comparing the historical relationship between domestic meat production and
soybean crush to projected meat production and soybean crush; the difference between the two in
future years provided a best guess at a projected increase in soybean demand for soybean oil
used to produce biofuel. Finally, for canola biodiesel, we relied on modeling work from a recent
RFS rulemaking that approved a new canola renewable diesel pathway. The results, which are
based on a wide variety of assumptions, are shown in Table ES.l below.
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Table ES.l Maximum potential acreage impacts for all crops in the U.S. due to increases in
corn ethanol, soybean biodiesel, and canola biodiesel that can be attributed to EPA's Set
Rule.
Volume Increase in
Maximum Potential Acreage
RFS Set Rule
Increase
(bil
ion gallons)
All Crops (mi
lion acres)
2023
2024
2025
2023
2024
2025
Corn ethanol
0.71
0.78
0.84
0.39
0.44
0.46
Soybean biodiesel
1.95
1.92
1.89
1.57
1.78
1.93
Canola biodiesel
0.24
0.24
0.24
0.26*
0.26*
0.26*
*Projected to occur in the North Dakota region
Table ES.l indicates that the RFS Set Rule could potentially lead to an increase of as
much as 2.65 million acres of cropland by 2025. This would constitute approximately 1% of the
projected U.S. acreage for major field crops in 2025 (USDA, 2022). Again, it is important to
note the significant assumptions and high uncertainty inherent in estimating these nationwide
acreage impacts numbers, making this number, by definition, a worst-case scenario. The need for
multiple assumptions underlying this assessment, and the inherent uncertainty contributed to our
finding of NLAA, as described in Section IX
Step 2. Identify locations potentially impacted by increases in acres from the Set Rule
It was difficult to estimate with confidence the magnitude of the nationwide total
cropland acreage changes associated with the Set Rule under Step 1 and as summarized in Table
ES.l due to the multiple uses of biofuel crops and the potential for global trade and
substitutability of these crops in some markets. It was even more challenging to state with a high
degree of confidence where those acreage increases might occur in the United States given the
vast quantity of potential cropland in the U.S. and the multitude of factors that contribute to an
individual farmer's decision whether to bring additional land into crop production. Even so, in
order to conduct this Biological Evaluation, it was necessary for us to attempt to identify
locations to better understand potential impacts of the Set Rule on listed species and critical
habitat. This is the goal of Step 2.
For soybean biodiesel, we retained the services of a contractor (ICF) to develop a
soybean-specific land selection model that used a series of weighted factors to prioritize the
selection of lands for soybean production to provide a plausible best guess of where farmers
might expand soybean crops. For example, lands that were closer to existing soybean fields and
in states with larger soybean growth rates were weighted higher for selection. In contrast, areas
that are permanently protected from conversion as well as forestlands and wetlands were
assigned a lower weight in the land selection model. ICF used their model to select lands within
a constrained soybean expansion area for various biofuel volume scenarios. For this Biological
Evaluation, we focused on the two scenarios that most closely matched the 1.93 million acres
maximum value from Table ES.l. Although we defined our action area separately from ICF's
work, we found that greater than 99 percent of their modeled lands occurred within the action
area defined for this Biological Evaluation.
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In contrast, for the potential land-use changes associated with corn ethanol and canola
biodiesel, EPA was not able to develop a similar land selection model. This was partially due to
the fact that while the increase in demand for soybean oil is expected to result in increased
planting of soybeans, the published literature and modeling we used to estimate the impacts of
increased demand for corn and canola oil respectively suggest that any new cropland would not
be limited to these crops. Instead, the published literature found that increases in demand for
corn for ethanol production will result in an expansion of total cropland, and the modeling we
used to estimate the impact of increased demand for canola oil found the demand increase would
primarily result in cropland expansion for crops other than canola in the U.S. While there is
value in using a model to select specific areas that could be converted, and it is a robust approach
for assessing potential effects on listed species, it must be noted that economic-driven factors
could lead to land use changes on any available lands within the action area that are separate and
apart from any Set Rule volumes.
Faced with this high level of uncertainty, for corn ethanol and canola biodiesel we
developed a probabilistic approach to select available lands for potential conversion within the
action area. We defined four land cover classes: (1) shrubland, (2) grassland/herbaceous, (3)
pasture/hay, and (4) emergent herbaceous wetlands. For corn ethanol, we used ArcGIS Pro and
R4 to randomly select 500,000 acres from among available and suitable land, which is a
conservative approach given the 460,000 acres estimated in Table ES.l. We repeated the process
100 to 500 times to generate an estimated probability that any given acre of land would be
converted to growing additional corn for the purpose of producing ethanol.
We applied this same probabilistic approach to assess potential impacts from canola
biodiesel. To do so, we randomly selected 260,000 acres from among available and suitable land
based on the values in Table ES.l. In addition, we constrained our analysis to the state of North
Dakota based on the results of a separate modeling exercise that showed this state would be the
primary area affected by potential land use change impacts related to canola biodiesel.
These locations of potential land use change do not identify actual conversion as a result
of the RFS Set rule, but rather provide EPA with a tool to identify areas of potential impact.
Step 3. Identify potential impacts on listed species from land use changes
EPA's analysis of the potential impacts on listed species is based on predicting what
quantities of land use change might occur and where those land use changes might manifest
because of the Set Rule. This is because changes in the way that land is used to grow crops could
impact listed species in several ways: non-cropland that is converted to cropland could result in
adverse effects to the habitats or ranges of listed species; nearby habitats could be indirectly
affected by dust or runoff created during the land conversion; and, after conversion, new
cropland could affect listed species or habitat on both the land in question and nearby areas
through sediment, pesticide, or fertilizer runoff. Similarly, in cases where additional crops are
grown on existing cropland through various intensification measures such as double-cropping or
4 ArcGIS is a geographic information system software commonly used for mapping and spatial analyses. R is
software commonly used for statistical computing and graphics.
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increased fertilizer or pesticide use, there could be impacts on flora and fauna for that land and
nearby areas. In evaluating potential impacts to species with critical habitat, it is especially
important to assess whether effects would occur to essential Physical and Biological features
(PBFs) or Primary Constituent Elements (PCEs) found within the boundary of the critical
habitat. However, as described throughout this Biological Evaluation, the high level of
uncertainty associated with the many assumptions we were required to make in our analyses
prevented EPA from determining with reasonable certainty that any given listed species and/or
critical habitat will be indeed impacted in any of these ways. As such, throughout this Biological
Evaluation EPA uses terms such as "potential impacts" and "potential effects of the action."5
Despite these uncertainties, we conservatively used the maximum acreage impacts in
Table ES.l to assess the potential impacts that listed species and critical habitat might experience
due to land use changes. Using the locations identified for potential land use change driven by
increases in each feedstock crop (Step 2), we identified which listed species or critical habitat are
present in or near the locations subject to potential land use change. Many of the species or
populations that emerged as having relatively higher potential to be impacted in each of the
feedstock crop analyses are discussed in detail in Sections VII. A-VII.C, and we assessed the
aggregate impacts from all three analyses in Section VII.D of this Biological Evaluation. For the
10 species that had the greatest potential critical habitat impacts from land use changes alone, we
found that 0.57 to 4.62 percent of their critical habitats could be potentially impacted (Table
VII.D-1). When we added a 2,500-foot buffer to critical habitats, not surprisingly, the potential
impact numbers for the top 10 impacted species went up and ranged from 4 to 13.5 percent
(Table VII.D-2). Results also indicate that the top 10 species with ranges potentially impacted
could see 2 to 13.7 percent of their range impacted, whether the range had a buffer or not (Tables
VII.D-3 and VII.D-4). In total we identified 810 listed populations in the analyses. Of the 810
listed populations, we estimated that only 7 may have greater than 1% of their critical habitat
converted to cropland (38 species had greater than 1% of critical habitat plus buffer converted)
and 15 species may have greater than 1% of their range converted (14 species had greater than
1% of their range plus buffer converted). This "overlap analysis" is again, inherently uncertain,
as any species' range or critical habitat could have more or less overlap with converted lands
based on many unrelated factors that are impossible to more precisely define. Instead, this
analysis provides EPA with guidance as to the species and critical habitat for which further
analysis of taxa-specific and species-specific habitats, PBFs, and ranges is justified.
Since the numbers described above were estimated based on the maximum potential
acreage increases from the Set Rule, these numbers likely represent the maximum potential
acreage impacts to species' critical habitat and/or range. As discussed previously, we made
conservative assumptions in our analyses throughout Steps 1 through 3. These conservative
assumptions compound upon one another resulting in an overall very conservative analysis.
Thus, it is possible that there may be no land use impacts at all to species/populations and their
critical habitats due to the RFS Set Rule volumes. For example, there would likely be no impact
5 This use of "effects" is thus not equivalent to the regulatory term "effects of the action" in 50 CFR 402.02.
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were the volumes to be met through other means such as increasing imports or decreasing
exports of biofuels or feedstocks. Further, it is important to consider species' life history
information and, for critical habitat in particular, the PBFs/PCEs that are essential for the
conservation of the listed species and whether or not such features could be affected by the RFS
Set Rule. Clearly, not all land within the boundary of a critical habitat unit contains PCEs/PBFs.
We explore this uncertainty in more detail in Section IX of this Biological Evaluation.
Step 4. Assess potential impacts on listed species from changes in water quality
To estimate the impact on water quality of the potential land use changes attributable to
the Set Rule, we leveraged results from published literature that used the Soil and Water
Assessment Tool (SWAT) to estimate the water quality impacts from observed increases in
cropland in the Missouri River basin, and extrapolated those results to the Mississippi River
Basin.
In particular, we relied on the Chen study that applied the SWAT to the Missouri River
basin to estimate the water quality changes resulting from land use changes observed from 2008-
2016 (Chen et al., 2021). The total quantity of land converted to cropland in the Missouri River
basin during this time period was approximately 2.51 million acres, which is similar to the total
maximum land conversion we are expecting from this action (compare Table ES.l and Table
VIII. A-l). Results show that, at the outlet of the Missouri River, the conversion of non-cropland
to cropland could result in an increase in the total nitrogen (TN) and total phosphorus (TP) loads
of up to 6.4% and 8.7%, respectively. The modeled increases in total nitrogen and phosphorus
would represent increases of approximately 0.8% and 2.1% respectively at the Mississippi River
outlet, if we assume as a worst-case that the modeled increase in nitrogen and phosphorus at the
mouth of the Missouri River is equal to the increase in nitrogen and phosphorus at the
Mississippi River outlet.
The Chen study did not consider the impact of increased use of pesticides, and no
equivalent study on pesticide concentrations exists. While nitrogen, phosphorus, and suspended
solids are not perfect analogs to the pesticides, they do share some important similarities. The
application rates for both fertilizers (nitrogen and phosphorus) and pesticides are expected to be
related to the projected potential changes in cropland. For example, we expect that any new land
planted in corn would be treated with fertilizers and pesticides at the average national rate for all
corn acres. Total fertilizer and pesticide use would therefore depend on the amount of fertilizer
and pesticides used to produce the new crop relative to the previous use of the land (whether
cropland or non-cropland) and the total projected land use changes. We therefore estimate that
the increase in pesticides in aquatic environments would be approximately equal to the increases
in nitrogen and phosphorus projected in the Chen study using SWAT.
These analyses suggest that, even if the maximum projected acreage impacts from the Set
Rule (2.65 million acres total) were to occur, the water quality impacts will be small relative to
total nutrient, sediment, and pesticide effects already happening at the mouth of the Mississippi
and other larger water bodies within the action area. Also, effects are unlikely to negatively
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impact the NMFS species found within coastal regions and oceans, as the potential effects are
insignificant relative to baseline conditions or are discountable (e.g., through diffusion of
pollution before it reaches the areas of these species). Localized water quality impacts in
freshwater ecosystems within the action area are also likely to be discountable as it is unlikely
that species will be exposed to potential effects from the action caused by land use changes. EPA
completed a qualitative analysis for some NMFS species in Section XI of this Biological
Evaluation and this analysis supports this conclusion. We cannot say with certainty that impacts
would occur, and if they did occur we cannot say with certainty where they would take place,
despite our best efforts to assess this.
Finally, we note that EPA currently has several programs and funding opportunities
designed to improve water quality. We therefore expect that these efforts, discussed further in
Section VIII.B, will help to lessen any potential water quality impacts of increased cropland
attributable to the Set Rule, if indeed such cropland increases come to pass.
NLA A Conclusion
EPA's analyses support a determination that the Set Rule may affect, but is not likely to
adversely affect (NLAA), any of the 810 populations within the Set Rule action area or their
critical habitat. Specific species determinations are discussed in Section IX after considering life
history and PBF information by taxonomic groupings. Even if there could be impacts to certain
PBFs/PCEs, we cannot say with reasonable certainty that any particular species will be impacted,
again due to the numerous layers of uncertainty between the finalized RFS Set Rule volumes and
on-the-ground, localized land use changes. As such, we find that effects on all species and
critical habitat are discountable (i.e., extremely unlikely to occur) and/or insignificant, With the
submission of this Biological Evaluation, EPA respectfully requests the Services' concurrence on
EPA's effects determinations for the species and critical habitat detailed in Table V-l that are not
likely to be adversely affected by this federal action.
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IL ( onsultation to Date
The following chronology summarizes EPA's engagement with the Services in support of
this ESA section 7(a) consultation.
• March 23, 2021 - Meeting between EPA and the Services
• May 20, 2021 - Meeting between EPA and the Services
• August 24, 2021 - Meeting between EPA and the Services
• September 7, 2021 - Meeting between EPA and the Services
• September 21, 2021 - Meeting between EPA and the Services
• September 30, 2021 - Email from EPA to the Services requesting geospatial information related
to listed or proposed endangered or threatened species and designated critical habitat in the
potential action area.
• September 30, 2021 - Email from FWS to EPA providing information on the location of listed or
proposed endangered or threatened species and designated critical habitat.
• November 15, 2021 - Email from NMFS to EPA providing information on the location of listed
or proposed endangered or threatened species and designated critical habitat.
• November 16, 2021 - Meeting between EPA and the Services
• January 25, 2022 - Meeting between EPA and the Services
• February 22, 2022- Meeting between EPA and the Services
• March 8, 2022 - Meeting between EPA and the Services
• April 5, 2022 - Meeting between EPA and the Services
• May 3, 2022 - Meeting between EPA and the Services
• June 3, 2022 - Email from EPA to the Services to share ESA 7(d) Memorandum for the
Renewable Fuel Standard Program: RFS Annual Rules ("2020-2022 RFS Final Rule")
• June 21, 2022 - Email from EPA to the Services to share draft Biological Evaluation chapters and
request review
• June 28, 2022 - Meeting between EPA and the Services
• August 23, 2022 - Meeting between EPA and the Services
• September 6, 2022 - Meeting between EPA and the Services
• September 20, 2022 - Meeting between EPA and the Services
• October 18, 2022 - Meeting between EPA and the Services
• November 1, 2022 - Meeting between EPA and the Services
• November 15, 2022 - Meeting between EPA and the Services
• November 29, 2022 - Meeting between EPA and the Services
• December 1, 2022 - Email from EPA to the Services to share draft Biological Evaluation
chapters and request review
• December 7, 2022 - Meeting between EPA and NMFS
• December 13, 2022 - Meeting between EPA and the Services
• January 10, 2023 - Meeting between EPA and the Services
• January 24, 2023 - Meeting between EPA and the Services
• January 31, 2023 - Meeting between EPA and the Services
• January 31, 2023 - Submittal of draft complete Biological Evaluation to Services with Request
for Concurrence
• February 7, 2023 - Meeting between EPA and the Services
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• February 14, 2023 - Meeting between EPA and the Services
• February 21, 2023 - Meeting between EPA and the Services
• February 28, 2023 - Meeting between EPA and the Services
• March 7, 2023 - Meeting between EPA and the Services
• March 14, 2023 - Meeting between EPA and the Services
• March 21, 2023 - Meeting between EPA and the Services
• March 28, 2023 - Meeting between EPA and the Services
• April 4, 2023 - Meeting between EPA and the Services
• April 11, 2023 - Meeting between EPA and the Services
• April 18, 2023 - Meeting between EPA and the Services
• April 25, 2023 - Meeting between EPA and the Services
• May 2, 2023 - Meeting between EPA and the Services
• May 9, 2023 - Meeting between EPA and the Services
• May 16, 2023 - Meeting between EPA and the Services
• May 19, 2023 - Submittal of revised draft of Biological Evaluation to Services with Request for
Concurrence
15
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HI. I lie Renewable Fuel Standard (Rl S)
Congress created the Renewable Fuel Standard (RFS) to reduce greenhouse gas
emissions and enhance energy security through expanding the nation's use of renewable fuels.
This program was created under the Energy Policy Act of 2005 (EPAct), which amended the
Clean Air Act (CAA). The Energy Independence and Security Act of 2007 (EISA) further
amended the CAA by expanding the RFS program. Under Clean Air Act section 21 l(o), the RFS
program requires that certain minimum volumes of renewable fuel must be used in the
transportation sector, for all years after 2005, with the goal of replacing or reducing the quantity
of petroleum-based transportation fuel, heating oil, or jet fuel. 21 l(o) contains specific renewable
fuel volume targets through 2022 and provides EPA with the authority for setting volumes for
2023 and beyond. However, the statute also provides EPA with the discretion to waive the
volume requirements under specific circumstances. This section describes the operation of the
RFS program, the conditions associated with the agency's available discretion, the historical
production of renewable fuel, and factors affecting actual production and use of renewable fuel.
A. Statutory Requirements
The RFS program places an obligation on producers and importers of gasoline and diesel
(hereafter simplified to "refiners") to utilize certain amounts of renewable fuel to replace fossil-
based transportation fuels. The obligation is presented as a percentage standard that each refiner
multiplies by its gasoline and diesel production and importation to determine the volume of
renewable fuel for which it is responsible. The RFS program does not create an obligation for
any individual party to produce or use any amount or type of renewable fuel. Instead, renewable
fuel producers respond in typical market fashion to the demand for their products that is created
by the RFS blending obligations placed upon refiners.
The applicable standards under the RFS program fall into four broad categories that are
defined primarily by the estimated greenhouse gas (GHG) benefits achieved by each renewable
fuel relative to the petroleum-based fuels that they replace. Some categories are also defined by
additional criteria as shown below.
16
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Table III.A-1: Statutory criteria for renewable fuel under the RFS program
Category
Minimum GHG
reduction requirement
Other requirements and exclusions
Cellulosic biofuel
60%
Must be made from cellulose, hemi-cellulose, or
lignin
Biomass-based diesel
50%
Includes only biodiesel and renewable diesel, but
excludes any renewable fuel that is produced
through co-processing a feedstock with
petroleum
Advanced biofuel
50%
Excludes ethanol derived from corn starch
Renewable fuel
20%a
Must be made from qualifying renewable
biomass
a Does not apply to "grandfathered" renewable fuel produced in a facility that was operational or under
construction before December 2007.
As defined under the statute, these four categories are nested within one another. For
instance, both cellulosic biofuel and biomass-based diesel (BBD) also count as advanced biofuel,
and all advanced biofuel counts as renewable fuel. Since the nested nature of the categories
necessarily means that there is some overlap between them, it is sometimes helpful to decompose
the nested categories into mutually independent ones for discussion purposes. Thus, we may
speak of non-cellulosic advanced biofuel rather than advanced biofuel, and speak of
conventional renewable fuel rather than renewable fuel. The relationship between all categories,
both those defined in the statute and the decomposed categories having some additional practical
utility, are shown below.
17
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Figure III.A-1: Relationship between categories of renewable fuel under the RFSa
Total renewable fuel
(t±i\
Advanced biofue
N on-cellulosic ach/anced biofue
t
S
t
t
Cellulose
Other advanced
1
Conventional renewabb fuel
biofuel
BBD
a Categories in red are those defined in the statute. Categories in black are sometimes helpful in
differentiating between different renewable fuel types and their impacts.
Within the requirements and exclusions shown in Table III.A-1, the statute allows any
renewable fuel made from any renewable biomass feedstock to qualify under the RFS program.
Renewable biomass is defined in the statute. Thus, the applicable standards for each category do
not require the production and consumption of any particular type of renewable fuel. The
market's participants (refiners and importers of gasoline and diesel fuel, biofuel producers, fuel
blenders, consumers, etc.) determine the mix of renewable fuel and feedstock types that end up
being used as transportation fuel based on economic and other market factors in order to ensure
that the RFS obligations are met.
The statute specifies volume targets for each of the four component categories of
renewable fuel for years 2010 through 2022. These targets are shown below.
18
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Figure III.A-2: EISA 2007 Volume Targets
40
Cellulosic biofuel
Biomass-based diesel (BBD)
Other advanced biofuel
Conventional renewable fuel
BO
25
2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022
The statutory volume targets shown above represent the applicable volume requirements
unless EPA waives them in whole or in part as described in the next section. For years that the
statute does not specify volume targets (i.e., 2013+ for BBD and 2023+ for all other categories),
EPA must establish the applicable volume targets following certain processes, criteria, and
standards specified in the statute.
To ensure that the applicable volumes of renewable fuel are used each year, CAA section
21 l(o)(3)(B)(i) requires EPA to set annual percentage standards. Because EPA was sued over its
failure to meet the statutory deadline to set the 2023 standards, EPA is subject to a court-ordered
deadline to finalize the 2023 standards—which are part of the RFS Set Rule which is the subject
of this Biological Evaluation—by June 14, 2023 (Growth Energy v. Regan, 2022).
Notably, the RFS program does not regulate the conduct of farmers who plant crops that
can then be utilized as feedstock to create renewable fuel. In order to participate in the program,
renewable fuel producers must register with EPA, and keep records demonstrating only that the
renewable fuel meets the statutory requirements6 that the renewable fuel is produced from
renewable biomass and used as transportation fuel in the United States. However, the program
itself, and the standards promulgated in this action, do not require any action by or place any
other requirements on any particular renewable fuel producer or farmer. The production of
renewable fuels, their type, and the crop-based feedstocks used for many of them, like corn and
6 These statutory requirements do not include impacts on endangered species.
19
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soy, are not directly regulated by the RFS program. Instead these many decisions and the
resulting planting and crop marketing and sales decisions made by farmers are the result of many
market factors such as crop prices, demand for crops, and local conditions, meaning that farmers'
decisions regarding the best use of their crops can change from month to month, year to year.
B. Discretion Available under the Statute
The statutorily prescribed volume targets in the Clean Air Act (CAA) section
21 l(o)(2)(B) can be modified by EPA in specified circumstances. CAA section 21 l(o)(7)
provides EPA's waiver authorities. CAA section 21 l(o)(7)(A) provides EPA's "general waiver
authorities," allowing EPA to waive the national quantity of renewable fuel in the statute in
whole or in part upon a demonstration that the requirements would cause severe harm to the
economy or environment of a State, region, or the United States, or a determination that there is
an inadequate domestic supply. EPA can only waive volumes under this provision after notice
and opportunity for comment, and after consultation with USD A and DOE. The statute does not
prescribe how EPA is to determine the appropriate volume to waive.
EPA has never waived volumes on the basis of "severe harm" to the economy or the
environment under the general waiver authority. However, in December 2015 EPA did waive
volumes of total renewable fuel under a finding of "inadequate domestic supply." EPA was
challenged on that action, and after review the U.S. Court of Appeals for the D.C. Circuit ruled
that EPA had exceeded its authority in waiving volumes, holding that any waiver based on the
"inadequate domestic supply" prong of 21 l(o)(7) would only be appropriate if there was an
"inadequate domestic supply" of renewable fuel to obligated parties, and that EPA could not
consider demand side-factors, including the supply of renewable fuel to consumers.
EPA has also received a number of administrative petitions requesting that we use our
21 l(o)(7) general waiver authority to reduce the RFS standard volumes, but to date EPA has not
granted any of these petitions. As recently as January 2021, EPA sought comment on a number
of petitions to waive the volumes under a finding of severe economic harm or a finding a severe
environmental harm. In the notice, EPA referred to its prior interpretation of the statutory
provision, including that 1) the harm must be caused by implementation of the RFS program
itself; 2) the harm must be fairly certain to occur and not be merely speculative; 3) the harm must
be severe; 4) the harm must be to an entire state, region, or the U.S. and not to a single industry;
and 5) given the discretionary nature of the waiver authority, EPA will also consider benefits of
the program. EPA sought comment on the elements of that interpretation (86 FR 5182, 2021).
EPA reaffirmed that interpretation and denied the petitions for a waiver of the 2019 and 2020
RFS standards in a recent action (87FR 39600, 2022; 87FR 39620, 2022).
CAA section 21 l(o)(7)(D) provides EPA with additional waiver authority, commonly
referred to as the "cellulosic waiver authority." Under this authority, EPA shall reduce the
volume of cellulosic biofuel when the projected volume of cellulosic biofuel production is less
than the applicable volume of cellulosic biofuel. It also provides that EPA may reduce the
applicable volume of renewable fuel and advanced biofuel by the same or lesser volume. Courts
have indicated that EPA retains significant discretion in deciding whether to waive the advanced
biofuel and total renewable fuel volumes, and by how much (Americans for Clean Energy v.
-------
EPA, 2017; American Fuel & PetrochemicalMfrs. v. EPA, 2019). EPA has waived the cellulosic
biofuel volume every year since the RFS2 program began in 2010, and has made commensurate
reductions in the advanced biofuel and total renewable fuel requirements under the cellulosic
waiver authority beginning in the 2016 compliance year, and each subsequent year, with the
exception of the 2020-2021 compliance years.
Finally, CAA section 21 l(o)(7)(F) provides that EPA shall modify the applicable
volumes of renewable fuel if certain triggers are met. This is commonly referred to as the "reset
authority." In doing so, EPA is to analyze the same factors specified in CAA section
21 l(o)(2)(B)(ii). EPA used this authority in a rulemaking published on July 1, 2022 to modify
the 2020-2022 volume requirements for all fuel types except biomass-based diesel (87 FR
39600, 2022).
EPA's waiver authorities, found in CAA section 21 l(o)(7), provide EPA with the
discretion to waive the volume requirements if certain criteria are met as described above. While
EPA has used those waiver authorities in past years to reduce the volume requirements below the
targets specified in the statute, it does not intend to do so in the forthcoming rulemaking that will
establish volume requirements for 2023-2025. Instead, EPA intends to use the set authority.
Nevertheless, EPA retains the ability to reconsider promulgated rulemakings. EPA has, in the
past, based on new information and drastically changed circumstances, revised standards after
initially promulgating them using the waiver authorities.
We note that, regardless of the authority that EPA uses to establish nationwide volume
requirements, EPA can only set the overall applicable volumes of renewable fuel that are
required to be used. Which types of fuels from which feedstocks and in what quantities
ultimately are used are all left up to the market, making it difficult to predict with any accuracy
what each year's actual RFS fuel mix will be.
C. Historical Production of Renewable Fuel
The full list of renewable fuel types and feedstocks that qualify under the RFS is
provided in Table 1 to Section 1426, part 80, Title 40 of the Code of Federal Regulations. In
practice, however, certain renewable fuels have dominated the transportation fuels market while
others have been used in very small quantities or not at all. The figure and tables below show the
fuel types and feedstocks that were produced in the U.S. between 2016 and 2021 for cellulosic
biofuel, non-cellulosic advanced biofuel, and conventional renewable fuel, along with the
average contribution that each has made to the total volume over that timeframe (Figure III.C-1,
Tables III.C-1, III.C-2, and III.C-3). Also included is a table showing the average proportions for
crop-based versus non-crop-based feedstocks produced domestically (Table III.C-4). Fuel types
and feedstocks not shown were produced in only negligible quantities or not at all.
21
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Figure III.C-1: Fuel types that were produced in the U.S. from 2016-2021
¦ Cellulosic
¦ Non-cellulosic advanced
¦ Conventional
Table III.C-1: Fuel/feedstock combinations for cellulosic biofuel
that were produced in the U.S. from 2016-2021
Fuel type
Feedstock
Average contribution to
total
CNG/LNG3
Landfill
90%
CNG/LNG
Agricultural digester
6%
CNG/LNG
Waste treatment plant
3%
Ethanol
Agricultural Residues
1%
Ethanol
Annual Cover Crops
0.4%
a CNG = compressed natural gas; LNG = liquified natural gas
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Table III.C-2: Fuel/feedstock combinations for non-cellulosic advanced biofuel
that were produced in the U.S. from 2016-2021
Fuel type
Feedstock
Average contribution to
total
Biodiesel
Soybean Oil
42%
Biodiesel
Waste Oils/Fats/Greases
16%
Renewable diesel
Waste Oils/Fats/Greases
14%
Biodiesel
Corn oil
9%
Biodiesel
Canola Oil
8%
Renewable diesel
Corn oil
5%
Renewable diesel
Soybean Oil
4%
Ethanol
Separated Food Wastes
1%
Gasoline/naphtha
Separated Food Wastes
1%
Gasoline/naphtha
Corn oil
0.3%
Jet fuel
Waste Oils/Fats/Greases
0.1%
Gasoline/naphtha
Waste Oils/Fats/Greases
0.1%
Heating oil
Separated Food Wastes
0.1%
LPG
Waste Oils/Fats/Greases
0.1%
Table III.C-3: Fuel/feedstock combinations for conventional renewable fuel
that were produced in the U.S. between 2016 and 2021
Fuel type
Feedstock
Average contribution to
total
Ethanol
Corn starch
>99%
Ethanol
Grain Sorghum
<1%
Table III.C-4: Average proportions from 2016-2021 for domestically produced
feedstocks used to produced biofuel
Cellulosic
Non-cellulosic
advanced
Conventional
Crop-based
0.4%
55%
99.99%
Non-crop-based
99.6%
45%
0.01%
The historical proportions shown in the tables above do not necessarily represent what
might be expected in the future. As discussed more fully in Section III, our projections of the
possible mix of renewable fuel types that could be used in the near future are based on a
combination of information about historical trends and a knowledge of feedstock availability,
infrastructure, and various other opportunities and constraints for the future.
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I). Factors Affecting Actual Production and Use of Renewable Fuel
Once EPA determines and establishes the overall volume requirements and associated
percentage standards that will apply to RFS obligated parties (e.g., refiners and importers) for a
particular year, the market determines precisely what renewable fuels are used. While generally
one could expect that the market would supply the volumes that are required because this is the
purpose and design of the RFS program, nevertheless there are several reasons that actual
volumes consumed often differ from the regulated volumes.
The first is that the percentage standards are based on projected volumes of gasoline and
diesel consumption which typically deviate to some degree from what actually occurs. In the
event that the actual consumption of gasoline and diesel is lower than the projection that EPA
used to set the applicable percentage standard in a given year, the obligations applicable to
individual obligated parties are likewise lower, and the actual volumes of renewable fuel used as
transportation fuel will fall short of the volumes EPA assumed in setting the percentage
standards. Likewise, if the actual consumption of gasoline and diesel is higher than the projection
that EPA used to set the applicable percentage standards, the actual volumes of renewable fuel
used as transportation fuel will be higher. While the discrepancy typically falls within the range
of a few percent, it can be much higher, as occurred in 2020 due to the impacts of the COVID-19
pandemic on fuel markets.
Another reason that the actual renewable fuel volumes used in a given year vary from the
volumes at which EPA set the standards is related to the Renewable Identification Number (RIN)
credit system that is used to demonstrate compliance with the RFS program. Obligated parties
have the flexibility to over-comply in one year and bank RINs for future use, often called
"carryover RINs," and then use those RINs to demonstrate compliance in the subsequent year
rather than using RINs representing current year renewable fuel production. The nationwide total
of carryover RINs grew dramatically in the early years of the RFS program, and obligated parties
have at times drawn down this carryover RIN bank to help fulfill their obligations.
The third reason that the actual renewable fuel volumes in a given year may vary from
the volumes atzwhich EPA set the standards is the regulatory flexibility that obligated parties are
afforded through the statute to carry a deficit from one year to the next. This provision allows
them to fall short of their obligation to blend renewable fuel into their gasoline and diesel in one
year by as much as 20%, so long as they compensate for that shortfall in the following year.
Fourth, exemptions for small refineries due to disproportionate economic hardship may
result in actual consumption of renewable fuels falling short of the intended volume
requirements. These exemptions are permitted under CAA 21 l(o)(9)(B) and are evaluated on a
refinery-by-refinery basis. In cases where a small refinery hardship exemption was granted after
the applicable percentage standards were set, the percentage standards in years past remained
unchanged but were then applicable to a smaller number of parties. These small refinery
exemptions may have had some impact in some prior years, but are not anticipated to have any
appreciable impact in the future based on a series of actions recently taken by EPA regarding
implementation of the small refinery exemption program.
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Finally, there are many market factors beyond the RFS program itself that affect the
consumption of renewable fuel. These include crude oil prices, renewable fuel production costs
(which are in turn a function of feedstock and process heat and power costs), tax subsidies, and
the demand for renewable fuel created by other federal and state programs. The California Low-
Carbon Fuel Standard (LCFS) program in particular has and will continue to drive various
renewable fuel volumes, and several other states are now implementing their own LCFS
programs.
Combined, all of these factors make it very difficult for EPA to predict with certainty
exactly how many gallons of renewable fuel will be used for transportation in the United States
in a given year, let alone what the relative mix of the renewable fuels types will be. EPA is
required by statute to make an educated guess to implement the RFS program each year, but this
inherent uncertainty is yet another reason why EPA is unable to predict with certainty the
magnitude and location of potential land use changes that might be driven by the Set Rule
volumes.
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I\ . Description of the Action and the Action Area
A. The RFS Set Rule Action
This Biological Evaluation addresses the impacts of an EPA action that would establish
volume requirements for the use of renewable fuel in the transportation sector for years 2023-
2025 under the RFS program. This action is commonly referred to as the "Set Rule."
As described in Section I.A, the Clean Air Act does not provide volume targets for years
after 2022. Instead, CAA section 21 l(o)(2)(B)(ii) provides that EPA shall, for years beyond
those specified in the statute, determine the applicable volumes of each of the renewable fuel
types. This is commonly referred to as the "set authority." In doing so, EPA is to analyze a
specified set of factors, but provides limited additional guidance to EPA regarding how to
determine appropriate RFS volumes. For most of the fuel types, the statute provides no specific
numerical requirements. EPA has used this authority to establish the biomass-based
diesel volume beginning in 2013 and for each subsequent year. EPA will also do so for all other
fuel types beginning in 2023.
The statute requires that EPA establish these targets based on an analysis of the following
criteria:
The impact of the production and use of renewable fuels on the environment,
including on air quality, climate change, conversion of wetlands, ecosystems, wildlife
habitat, water quality, and water supply;
The impact of renewable fuels on the energy security of the U.S.;
The expected annual rate of future commercial production of renewable fuels,
including advanced biofuels in each category (cellulosic biofuel and BBD);
The impact of renewable fuels on the infrastructure of the U.S., including
deliverability of materials, goods, and products other than renewable fuel, and the
sufficiency of infrastructure to deliver and use renewable fuel;
The impact of the use of renewable fuels on the cost to consumers of transportation
fuel and on the cost to transport goods; and
The impact of the use of renewable fuels on other factors, including job creation, the
price and supply of agricultural commodities, rural economic development, and food
prices.
1. Proposed Action
EPA proposed applicable volume requirements for 2023-2025 on December 30, 2022 (87
FR 80582, 2022), and updated them for the final rule.
26
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Table IV.A-1: Proposed Volume Targets (billion RINs)a
2023
2024
2025
Cellulosic biofuel
0.72
1.42
2.13
Biomass-based dieselb
2.82
2.89
2.95
Advanced biofuel
5.82
6.62
7.43
Renewable fuel
20.82
21.87
22.68
Supplemental standard
0.25
n/a
n/a
a One RIN is equivalent to one ethanol-equivalent gallon of renewable
fuel.
b The BBD volumes are in physical gallons (rather than RINs).
Again, as noted above in Section II.B regarding the historical RFS volume requirements,
these proposed volume requirements are not requirements for the use of specific types of
renewable fuels, and thus regulated parties can and will use a variety of different renewable fuel
types as long as they meet the qualifications (see Table II.A-1). With the Set Rule, EPA will only
set the overall applicable volumes of renewable fuel that are required to be used in 2023, 2024,
and 2025. Which types of fuels from which feedstocks and in what quantities ultimately are used
is all left up to the market. The highly uncertain land-use changes and species impacts projected
in this Biological Evaluation are therefore built on top of significant uncertainty in these
projections.
EPA has nonetheless projected a plausible mix of renewable fuel types that might be used
to meet the proposed standards and plausible estimates of the portion of those individual fuel
types that could be attributable to the RFS program as discussed in the Set Rule (as opposed to
other economic, market, and regulatory factors). This potential mix is shown below.
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Table IV.A-2: Projected Fuel Types Attributable to the RFS Set Rule Proposed Volumes
(million gallons)9
2023
2024
2025
CNG/LNG from biogasa
87
82
289
Diesel/jet fuel from wood waste/MSW
0
3
6
Biodiesel from soybean oil
728
695
661
Biodiesel from canola oil
240
240
240
Biodiesel from FOGb
200
200
200
Biodiesel from corn oil
120
120
120
Renewable diesel from soybean oil
1,048
1,048
1,054
Renewable diesel from FOG
275
329
388
Renewable diesel from corn oil
80
86
91
Ethanol from corn
706
776
840
a Provided in ethanol-equivalent gallons.
b FOG = Waste fats, oils, and greases.
The volumes in the table above represent those volumes that we believe might be attributable to
the RFS program. They are less than the total volumes of these fuels that we project will actually
be used for transportation in the U.S.
There are also a number of proposed regulatory changes included in the Set Rule
intended to improve the operation of the RFS program. The regulatory changes relate to
recordkeeping, reporting, and credit generation, and are therefore not expected to have any
impact on volumes and therefore not on listed species or their habitats. These changes are:
• RFS Third-Party Oversight Enhancement
• Deadlines for Third-Party Engineering Reviews for Three-Year Updates
• RIN Apportionment in Anaerobic Digesters
• BBD Conversion Factor for Percentage Standards
• Flexibility for RIN Generation
• Changes to Tables in the CFR
• Prohibition on RIN Generation for Fuels Not Used in the Covered Location
• Biogas Regulatory Reform
• Separated Food Waste Recordkeeping Requirements
• Definition of Oceangoing Vessels
• Bond Requirement for Foreign RIN Generating Renewable Fuel Producers
• Definition of Produced from Renewable Biomass
• Limiting RIN Separation Amounts; and
• Technical Amendments.
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2. Final Action
The final rule includes the following volume targets.
Table IV.A-3: Set Rule Volume Targets (billion RINs)a
2023
2024
2025
Cellulosic biofuel
0.84
1.09
1.38
Biomass-based dieselb
2.82
2.89
3.20
Advanced biofuel
5.94
6.29
1.08
Renewable fuel
20.94
21.54
22.33
Supplemental standard
0.25
n/a
n/a
a One RIN is equivalent to one ethanol-equivalent gallon of renewable
fuel.
b The BBD volumes are in physical gallons (rather than RINs).
As discussed above, we have projected a plausible mix of renewable fuel types that might be
used to meet the final standards and plausible estimates of the portion of those individual fuel
types that could be attributable to the RFS program as discussed in the Set Rule (as opposed to
other economic, market, and regulatory factors).
Table IV.A-4: Projected Volumes of Renewable Fuel Types Attributable to the RFS Set
Rule Final Volumes
(million gallons)9
2023
2024
2025
CNG/LNG from biogasa
495
688
932
Diesel/jet fuel from wood waste/MSW
0
0
0
Biodiesel from soybean oil
841
757
755
Biodiesel from canola oil
292
307
323
Biodiesel from FOGb
-101
-92
-113
Biodiesel from corn oil
46
63
20
Renewable diesel from soybean oil
457
671
729
Renewable diesel from FOG
99
90
110
Renewable diesel from corn oil
130
-64
-20
Ethanol from corn
660
731
787
a Provided in ethanol-equivalent gallons.
b FOG = Waste fats, oils, and greases.
The volumes in the table above represent those volumes that we believe might be attributable to
the RFS program. They are less than the total volumesof each type of renewable fuel that we
29
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project will actually used for transportation in the U.S.. The table below shows the total
volumes, along with the fraction of that total that might be attributable to the RFS program.
Table IV.A-5: Total Projected Volumes of Renewable Fuel Consumed,
and Fraction Attributable to the RFS program
(million gallons)9
2023
2024
2025
CNG/LNG from biogasa
831 (59%)
1,039 (63%)
1,299 (68%)
Diesel/jet fuel from wood waste/MSW
0 (0%)
0 (0%)
0 (0%)
Biodiesel from soybean oil
982 (86%)
967 (78%)
953 (79%)
Biodiesel from canola oil
292 (100%)
307 (100%)
323 (100%)
Biodiesel from FOGb
321 (-32%)
303 (-31%)
285 (-40%)
Biodiesel from corn oil
115 (-40%)
89 (71%)
63 (32%)
Renewable diesel from soybean oil
457 (100%)
671 (100%)
883 (100%)
Renewable diesel from FOG
1,108 (9%)
1,074 (8%)
1,154(10%)
Renewable diesel from corn oil
205 (63%)
239 (-27%)
272 (-7%)
Ethanol from corn
13,845 (5%)
13,955 (5%)
13,779 (6%)
a Provided in ethanol-equivalent gallons.
b FOG = Waste fats, oils, and greases.
Our analysis was performed using our assessment of the volumes of renewable fuel that
would be supplied to meet the proposed volumes that is attributable to the RFS program. The
RFS volumes targets we are finalizing for 2023 - 2025 are slightly different than the proposed
volumes, as are the volumes of renewable fuel we project will be used to meet these volume
targets that are attributable to the RFS program. The differences between the attributable
volumes of canola biodiesel, soybean biodiesel, and corn ethanol from the proposed rule to the
final rule are summarized in Table IV.A-6.
Table IV.A-6: Biofuel Volumes Attributable the RFS Program in the Proposed and Final
RFS Set Rules (million gallons)
2023
2024
2025
Proposal
Final
Proposal
Final
Proposal
Final
Biodiesel from Canola
Oil
240
508
240
489
240
614
Biodiesel from Soybean
Oil
1,607
1,298
1,720
1,429
1,694
1,485
Ethanol from Corn
706
660
776
731
840
787
Although the total RFS volumes we are finalizing for 2023-2025 are higher than the
proposed volumes, the volumes of biodiesel produced from soybean oil and ethanol from corn
attributable to the RFS program are lower in the final rule. Theis means that our analysis, which
was conducted prior to and immediately after the proposal was issued, overestimates the impacts
of the RFS volumes in 2023-2025 from these fuels on listed species. Conversely the attributable
30
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volume of biodiesel from canola oil is higher in the final Set rule. While these higher volumes
would be expected to have directionally greater impacts on listed species, as we discuss in
Section , all or nearly all of the canola oil projected to be used for biofuel production in the
U.S. is projected to be imported from Canada, and thus is likely to have limited impacts on listed
species in the U.S. Despite these changes to the attributable volumes in the Set final rule, our
analyses are still applicable to the final volumes.
B. The Action Area
1. Potential Locations that Comprise the Action Area
The action area for a Biological Evaluation is the area within the U.S. where potential
effects are reasonably expected to occur. In the case of the RFS Set Rule covered by this
Biological Evaluation, the action area may encompass all locations in the United States where
feedstocks used for renewable fuel are produced, transported, and used to produce biofuel that is
consumed domestically. This potentially includes agricultural lands used for crop-based
feedstocks as well as agricultural lands where crops used for non-biofuel purposes might be
grown after being displaced from their current uses by the demand for biofuel production. It
could further include lands used for non-crop based feedstocks such as landfills, agricultural
digesters, waste treatment plants, restaurants, and other facilities where biogas and waste oils,
fats, and greases are collected and processed. We describe our close examination of these
potential components of the action area in the sections below.
While biofuels are also made from imported feedstocks, for the purposes of this
Biological Evaluation and consistent with Section 7 of the Endangered Species Act, we focus on
the potential effects to species and habitat caused by land use change impacts within, and not
outside of, the United States.
2. Crop-Based Feedstocks
Biofuels created from crop-based feedstocks may affect listed species and designated
critical habitat by contributing to habitat loss via land use change as well as water quality
impairments via runoff from agricultural lands. Appendix A illustrates the very complex causal
chain that would have to occur in order for this Biological Evaluation to conclude with certainty
that the Set Rule may negatively affect listed species and critical habitat. It is these causal chains
for each of the three feedstock crops—corn, soy, and canola—that are the focus of the following
chapters.
The majority of biofuels produced and consumed in the United States are from crop-
based feedstocks. As shown in Figure III.C-1, 80% of renewable fuel produced in the U.S. from
2016-2019 was made from conventional biofuel, of which nearly 100% constituted ethanol from
crop-based corn starch (Table III.C-3). Another 18% of renewable fuel was made from non-
31
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cellulosic advanced biofuel (Figure III.C-1). Soybean, corn, and canola oils from crop-based
feedstocks made up the majority (-68%) of the feedstocks used to produce non-cellulosic
advanced biofuel (Table III.C-2). These data are from the years 2016-2020 and are
representative of what was observed in 2021 and 2022 as well. In the proposed and final set rules
we projected the volumes of different renewable fuel attributable to the RFS Set rule (see Table
IV.A-2 and IV.A-4). The majority of the renewable fuel volumes attributable to the RFS
program are projected to be produced from corn, soybeans, and canola, with smaller volumes of
fuel produced from feedstocks not expected to impact listed species (biogas, MSW, and FOG).
To examine where effects on listed species or critical habitat might be reasonably
expected to occur, we focused the action area on the footprint of the U.S. where crop-based
feedstocks are currently grown and could be grown in response to the RFS Set Rule. In 2007,
EISA included the following in the definition of renewable biomass: "planted crops and crop
residue from agricultural land cleared or cultivated at any time prior to the enactment of this
sentence that is either actively managed or fallow, and non-forested." (EISA, 2007) In doing so,
Congress set limits on where crops for biofuel could be grown to prevent the conversion of other
lands for the sole purpose of producing crop-based feedstocks to meet the standards in the
statute. Therefore, one approach for this Biological Evaluation might be to assume that the action
area footprint includes all lands cleared or cultivated on or before 2007.
However, using the lands cultivated or cleared in 2007 would not account for potential
indirect land use impacts that may occur when non-biofuel crops on 2007 lands are displaced by
the demand for renewable fuel. Hypothetically, if such displacement were to occur, the demand
for crops for non-biofuel purposes could be met by increasing yields on existing cultivated lands
outside of the 2007 lands, or in yet-to-be cultivated lands. In delineating an action area to
account for all potential effects of the RFS Set Rule, as part of a worst-case scenario, we attempt
to capture these broader lands in the area of potential land use change as described in the
following section. As required under the Endangered Species Act, our assessment of potential
impacts on species and habitat is made in the context of a specific EPA action. This Biological
Evaluation is designed to address the Set Rule which will establish standards for 2023-2025. As
mentioned previously, we expect that corn, soybean, and canola will continue to be the
predominant crop-based feedstocks produced domestically to meet the 2023-2025 volumes. To
capture the broader action area that includes potential indirect land use impacts from
displacement for biofuel demand, we believe that focusing on areas where corn, soybean and
canola are currently grown or could be grown provides a more accurate depiction of the action
area rather than using the 2007 lands alone, an approach that would not account for potential
indirect land use impacts.
3. Identifying the Area of Potential I,and Use Change
To better understand which species may be affected by crop-based feedstocks and land
use impacts driven by the RFS program, we used ArcGIS Pro to delineate the area of potential
land use change. We used the Cropland Data Layer (CDL) from the United States Department of
Agriculture (USDA)'s National Agricultural Statistics Service and Agricultural Research Service
(NASS) to identify areas used to grow the predominant crop-based feedstocks used domestically
32
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for biofuel production (corn, soybean, and canola) in 2020. The CDL is a 30-meter raster, crop-
specific data layer produced annually based on satellite imagery and extensive agricultural
ground truth. The year 2020 was chosen as it was the most recent year available at the start of
our analysis. After downloading the 2020 CDL data layer, the corn, soy, and canola croplands
were extracted (Figure IV.B-1) and converted to vector (polygon) data.
Figure IV.B-1: Corn (A), Soybean (B), and Canola (C) croplands extracted from the 2020
Cropland Data Layer.
C.
We then applied a 15-acre minimum mapping unit (MMU) filter. Applying a MMU (also
known as minimum unit of change) filter is considered a best practice when using the USDA's
CDL for land use and land cover change analyses (Lark et al., 2017) and is an approach that has
been used widely by other researchers (R. & R., 2010) (Peterson et al., 2010) (Copenhaver et al.,
2021). The filter we used captures corn, soybean, and canola lands that are at least 15 acres in
size. Using a MMU helps avoid random errors in the CDL where 30-m pixels may be
misclassified. In addition, it removes small farm plot sizes that are unlikely to be used for
commercial scale farming operations needed to support biofuel production, and instead would
likely be planted for other reasons (e.g., hobby farms, deer feeding, etc.).
Applying a 15-acre MMU was also important to avoid compounding errors that would
result from applying a 5-mile buffer (described below) to the area where all corn, soy, and canola
are grown as captured by the CDL. For example, we noticed the CDL sometimes properly
characterizes small hobby farms and deer feeding plots in the Upper Peninsula of Michigan, but
in other instances erroneously characterizes such operations as large-scale farming operations,
which we determined because we know that large-scale farming and biofuel production do not
occur in that region. We wanted to apply a five-mile buffer as the next step in identifying our
action area and doing so without a 15 MMU filter would capture large areas near those smaller
plots of land where we are confident the RFS program does not play a role and therefore does not
affect threatened and endangered species.
To apply a buffer, we used the ArcGIS buffer tool, which creates areas around features to
a specified distance. It is used widely in a variety of environmental assessments, including
endangered species analyses. For example, the Office of Chemical Safety and Pollution
Prevention in EPA wrote a Revised Method (EPA, 2020) which describes applying a buffer of
2,600 feet to capture the off-site transport of pesticides.
-------
For our analyses, we chose to apply a five-mile buffer as a way to capture potential
indirect land use change effects that occur when lands that are not used to grow biofuel
feedstocks are displaced by the demand for biofuel production. The resulting area is shown in
Figure IV.B-2. In this Biological Evaluation, we refer to this area as the "area of potential land
use change." In defining this area, we also considered which crops are most likely to be
displaced by the demand for biofuel production by considering which crops are predominantly
grown on new croplands (Lark et al., 2020). found that corn was the predominant crop planted
on newly cultivated land from 2008-2016. Corn was most common in all years except 2014-
2015, when soybeans were more prevalent. Together with wheat, these three crops were the first
plantings on over 78% of all new croplands nationwide (Figure IV.B-3). Assuming the same
trends continue beyond 2016, the area we defined and depicted in Figure IV.B-2 includes all
corn and soy. Wheat is one of the most common crops to be replaced by corn or soybean, and we
calculated that the area of potential land use change also contains over 80% of the wheat land
cover from the 2020 CDL. Thus, this area of potential land use change with the five-mile buffer
likely covers the majority of wheat and other Midwestern crops that may be displaced through
indirect land use change effects.
To the extent that RFS volumes might have or will impact corn, soy, or canola plantings
in the future, the commercial viability of increasing such plantings is almost certainly in and
around the areas already being commercially harvested for these crops. Not only are the soil,
water, and other climate conditions likely to be applicable, but the available infrastructure for
planting, fertilizing, harvesting, storing, and transporting the crops is likely to be available in
such areas. As described in later sections, EPA hired a contractor to model where soybean
production expansion may occur in the future. The contractor used 2020 as the baseline year and
projected cropland potential expansion through 2025. We took the results from their work and
calculated that 99.89% of the projected expansion area fell within the area of potential land use
change in Figure IV.B-2.
Further, it is important to note that within this area of potential land use change, it is
possible that agricultural conversion would occur on lands that were once in cultivation,
managed under the Conservation Reserve Program, or used for other uses such as pastureland.
These lands already may not be suitable habitat with PBFs for species and therefore their
conversion may not affect species at all. Based on a study from the Economic Research Service,
81%) of former CRP land was put to some type of crop production, of which 57%> transitioned to
annual crop production, from 2013-2016. This conversion mainly occurred in the Corn Belt and
the most common annual crops grown on expired CRP were soybeans, corn, and wheat. (Peoples
Company, 2020).
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Figure IV.B-2: The geographical region where corn, soybean, and canola may be grown to
meet biofuel volumes as established by the RFS actions covering the years 2023-2025. This
region was identified by extracting corn, soybean, and canola croplands from the 2020
USD A Cropland Data Layer, applying a 15-acre minimum mapping unit filter, and
applying a five-mile buffer.
Figure IV.B-3 from Lark et al. (2020). Rates of net conversion calculated as gross cropland
expansion minus gross cropland abandonment from 2008-2016.
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4. Identifying Downstream Areas
If increased through land-use changes caused by the Set Rule, the production of crop-
based feedstocks could potentially affect threatened and endangered species and critical habitat
through agricultural non-point source pollution and water quality impacts. Generally, runoff
from agricultural lands can transport excess nutrients (nitrogen and phosphorus), sediment, and
pesticides (herbicides, insecticides, fungicides) into surrounding water bodies, contributing to
water quality impairments in streams, rivers, lakes, and groundwater (Bales et al., 2010)
(Mbonimpa et al., 2012) (Ryberg & Gilliom, 2015). These pollutants can persist in the
environment for a long time and accumulate in estuaries and coastal regions. For instance, excess
nutrients in the Gulf of Mexico and Chesapeake Bay contribute to dead zone (hypoxic)
conditions in the summertime (Twomey et al., 2009). Species that rely on healthy watersheds
and aquatic ecosystems may therefore be adversely impacted by such impairments.
To ensure that we properly considered the full potential effects from the RFS Set Rule,
we took the area of potential land use change (shown in Figure IV.B-2) and expanded it to
capture downstream regions that could be affected by agricultural non-point source pollution. To
do so, we used the National Hydrography Dataset (NHD) Version 2 Catchment Data, NHD
Version 2 Plus Attribute Flowline Value-Added Attributes, and the trace downstream tool on
ArcGIS Pro. Using ArcGIS tools, points were allocated throughout the area of potential land use
change and the following parameters and data were used to determine the downstream flow path
from those points: slope and elevation, stream order, and velocity of flow.
The resulting action area for the RFS Set Rule is shown in Figure IV.B-4. To find which
listed species and designated critical habitats are present within this area, we used the tabulate
intersection tool on ArcGIS Pro. We used ArcGIS shapefiles of listed species' ranges and critical
habitat that were provided to us by the U.S. Fish and Wildlife Service and the National Oceanic
and Atmospheric Administration's National Marine Fisheries Service. The listed species with
critical habitat and/or ranges within the action area are provided in the next section.
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5. Non-Crop-Based Feedstocks
It is also important to consider the potential role that the Set Rule may have in impacting
listed species and critical habitat through increases in non-crop-based feedstocks. As described
below, we have made the determination that the production of these feedstocks does not affect
listed species or critical habitat. We therefore did not alter the action area further to include
effects from these feedstocks.
Waste fats, oils, and greases (FOG)
A fairly large portion (-45%) of non-cellulosic advanced biofuel in the U.S. is made from
non-crop-based feedstocks (Table III.C-4). Non-crop-based feedstocks used to produce non-
cellulosic advanced biofuel from 2016-2020 came from food wastes, waste oils, fats, and greases
(Table III.C-2). Fats, oils, and greases (FOG) are generally byproducts of other food preparation
industries that properly manage to avoid these potentially troublesome materials from entering
drainpipes in the home or commercial food service operations. They are instead collected and
either disposed of in landfills, recycled to produce various commercial products such as
oleochemicals, or processed in wastewater treatment plants to produce biogas. An increasing
portion has been used to produce biodiesel due to its low cost in comparison to crop-based
feedstocks such as soybean oil and canola oil. However, this increasing use to produce renewable
fuel has not resulted in an increase in their production, but only a change in their use/disposition.
This can be seen in Table IV.A-4, as the no-RFS baseline analysis shows that, absent the RFS,
the only thing that would change about the use of FOG is the produced renewable fuel. As a
result, the production of FOG will not affect listed species or their habi tats because no
reasonably certain causal link can be established to the RFS Set Rule, and thus no causal impact
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on land use change or other environmental impact that can be attributed to the RFS Set Rule. As
a result of this determination, this feedstock was not analyzed further.
Biogas
Cellulosic biofuel, has historically reflected a small portion (2%) of all renewable fuel
produced in the U.S. (Figure III.C-1). Nearly all cellulosic biofuel produced from 2016-2020 was
CNG or LNG for use in natural gas vehicles, which utilizes non-crop based feedstock. We
anticipate that in the 2023-2025 Set Rule timeframe additional quantities of cellulosic biofuel
will continue to be produced from biogas sourced from landfills and digesters.
Biogas from landfills is the largest source of cellulosic biofuel (92%), followed by biogas
from agricultural digester and waste treatment plant sources (four and three percent, respectively;
Table III.C-1). Neither landfills nor wastewater treatment plants are built for the purpose of
producing biogas. Instead, they are built for the purpose of disposing of wastes, and biogas is a
byproduct of their operation. Therefore, it is likely that no landfills or wastewater treatment
plants will be built for the purpose of producing the biogas that would qualify as renewable fuel
under the RFS program. However, some portion of the biogas that is already being produced, or
would be produced in the 2023-2025 timeframe, is likely to be diverted from other uses to use as
a transportation fuel.
Agricultural digesters, in contrast, are typically built for the purpose of generating biogas.
Such digesters typically process manure or crop residue such as corn stover. We acknowledge
that new agricultural digesters may be built. If new agricultural digesters are built, they will most
likely be located on the grounds of existing farms on land already cleared and used for farming
activities.
Capturing biogas for the purposes of producing renewable fuel prevents GHGs from
escaping into the atmosphere. This is important as, for instance, municipal solid waste landfills
are the third-largest source of human-related methane emissions in the United States (US EPA,
2019). Further, emissions from landfills and other sources that produce biogas are regulated by
air quality standards.
As a result of these considerations, the production of biogas will not affect listed species
or their habitats because no reasonably certain causal link can be established to the RFS Set
Rule, and thus no causal impact on land use change or other environmental impact that can be
attributed to the RFS Set Rule. As a result of this determination, this feedstock was not analyzed
further.
Wood waste /Municipal Solid Waste (MSW)
These products are not produced for the purpose of providing feedstock for renewable
fuel production. Furthermore, there are not any volumes set for these pathways, so no RINs will
be generated on these products during the Set Rule years. Thus, there are no land-use change
impacts of this feedstock, and we do not anticipate that the processing of these feedstocks into
renewable fuel will result in impacts to listed species or their habitats. As a result, the production
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of wood wastes and MSW will not affect listed species or their habitats because no reasonably
certain causal link can be established to the RFS Set Rule, and thus no causal impact on land use
change or other environmental impact that can be attributed to the RFS Set Rule. As a result of
this determination, this feedstock was not analyzed further.
Corn oil
Corn oil is a byproduct of ethanol production from corn. Ethanol production is based on
fermentation of the starch found in corn, and the oil is not used in this process. Some oil is often
left in the distiller's grains which are commonly sold as a high protein livestock feed. Oil that is
extracted from the corn or distiller's grains has historically been used primarily for food products
such as cooking oil and margarine, with lesser amounts used in industrial products. Over the last
15 years, a greater portion of corn oil has been diverted to use as a feedstock for the production
of biodiesel due to its low cost. Similar to FOG, this increasing use to produce renewable fuel
has not resulted in an increase in its production, but only a change in its use. Thus, there are no
land-use change impacts of this feedstock, and we do not anticipate that the processing of these
feedstocks into renewable fuel will result in impacts to listed species or their habitats. As a result,
the production of corn oil will not affect listed species or their habitats because no reasonably
certain causal link can be established to the RFS Set Rule, and thus no causal impact on land use
change or other environmental impact that can be attributed to the RFS Set Rule. As a result of
this determination, this feedstock was not analyzed further.
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V. Listed Species I hat Vre Found Within the Action Area
Our analyses on ArcGIS found 712 unique species located within the action area shown
in Figure IV.B-4. Out of the 712 unique species, 672 are FWS species; 32 are NMFS species;
and eight are both FWS and NMFS species. The eight species that are both FWS and NMFS
species are the Gulf Sturgeon, Loggerhead Sea Turtle, Green Sea Turtle, Leatherback Sea Turtle,
Hawksbill Sea Turtle, Kemp's Ridley Sea Turtle, Olive Ridley Sea Turtle, and Atlantic Salmon.
Though there are 712 unique species, some species have multiple DPSs or ESUs,
culminating in a total of 810 unique populations found within the action area. It is important to
assess populations separately, where applicable, as different species' populations may be found
in different regions and could face different threats or rely on different PBFs or PCEs within
their critical habitat. All 810 populations assessed in this Biological Evaluation have a listing
status of endangered, threatened, proposed endangered, proposed threatened, candidate, or
experimental population. A listing status of candidate represents populations that are under
review for qualification under the ESA; experimental populations are reintroduced populations
that are geographically separated from nonexperimental populations. This Biological Evaluation
only includes experimental populations representing FWS species. Per guidance from NMFS,
EPA does not consider NMFS experimental populations in this Biological Evaluation.
Table V-l lists all populations and whether they have an associated designated critical
habitat, range, or both critical habitat and range. Species populations are identified in Table V-I
in the "DPS or ESU (if applicable)" column and listing status is also provided.
In the analyses supporting this Biological Evaluation, we evaluated not only the range of
potentially affected populations but also critical habitat. These two data types must be considered
separately. A range for a species is the geographical area where a particular species may be
found. In contrast, critical habitat includes geographic regions that contain PBFs or PCE that are
considered essential for the conservation of a listed species.
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Table V-l. The FWS and NMFS listed populations found within the action area, whether
they have an associated critical habitat (CH), range, or both CH and range, their
Designated Population Segment (DPS) or Evolutionary Significant Unit (ESU), if
applicable, and listing status 7
FWS or
NMFS
Species
CH,
Range,
or Both
Common Name
Scientific Name
DPS or ESU (if applicable)
Listing Status
FWS
Range
Large-fruited sand-
verbena
Abronia macrocarpa
Endangered
FWS
Both
San Mateo
thornmint
Acanthomintha obovata ssp.
duttonii
Endangered
FWS
Range
Gulf sturgeon
Acipenser oxyrinchus
(=oxyrhynchus) desotoi
Threatened
FWS
Range
White sturgeon
Acipenser transmontanus
Endangered
FWS
Range
Northern wild
monkshood
Aconitum noveboracense
Threatened
FWS
Range
Sensitive joint-
vetch
Aeschynomene virginica
Threatened
FWS
Range
Sandplain gerardia
Agalinis acuta
Endangered
FWS
Both
Cumberland elktoe
Alasmidonta atropurpurea
Endangered
FWS
Range
Dwarf
wedgemussel
Alasmidonta heterodon
Endangered
FWS
Range
Appalachian elktoe
Alasmidonta raveneliana
Endangered
FWS
Range
Sonoma
alopecurus
Alopecurus aequalis var.
sonomensis
Endangered
FWS
Range
Seabeach
amaranth
Amaranthus pumilus
Threatened
FWS
Range
Fat threeridge
(mussel)
Amblema neislerii
Endangered
FWS
Both
Ozark cavefish
Amblyopsis rosae
Threatened
FWS
Range
South Texas
ambrosia
Ambrosia cheiranthifolia
Endangered
FWS
Range
Reticulated
flatwoods
salamander
Ambystoma bishopi
Endangered
FWS
Both
California tiger
salamander
Ambystoma californiense
Central California
Threatened
FWS
Both
California tiger
salamander
Ambystoma californiense
Santa Barbara County
Endangered
FWS
Both
California tiger
salamander
Ambystoma californiense
Sonoma County
Endangered
FWS
Range
Frosted Flatwoods
salamander
Ambystoma cingulatum
Threatened
FWS
Range
Santa Cruz long-
toed salamander
Ambystoma macrodactylum
croceum
Endangered
FWS
Range
Florida
grasshopper
sparrow
Ammodramus savannarum
floridanus
Endangered
7 In this Table, the FWS species listed first, followed by the 40 NMFS species (72 populations total). The eight
species that are shared by the two agencies are listed more than once.
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FWS
Range
Little amphianthus
Amphianthus pusillus
Threatened
FWS
Range
Large-flowered
fiddleneck
Amsinckia grandiflora
Endangered
FWS
Range
Dixie Valley Toad
Artaxyrus williamsi
Endangered
FWS
Range
Painted snake
coiled forest snail
Anguispira picta
Threatened
FWS
Range
Sonoran
pronghorn
Antilocapra americana
sonoriensis
Endangered
FWS
Range
Madison Cave
isopod
Antrolana lira
Threatened
FWS
Range
Florida scrub-jay
Aphelocoma coerulescens
Threatened
FWS
Range
Price"s potato-
bean
Apios priceana
Threatened
FWS
Range
Point Arena
mountain beaver
Aplodontia rufa nigra
Endangered
FWS
Both
Lange's metalmark
butterfly
Apodemia mormo langei
Endangered
FWS
Range
Georgia rockcress
Arabis georgiana
Threatened
FWS
Both
McDonald's rock-
cress
Arabis macdonaldiana
Endangered
FWS
Range
Braun's rock-cress
Arabis perstellata
Endangered
FWS
Range
red tree vole
Arborimus longicaudus
Candidate
FWS
Both
Ouachita rock
pocketbook
Arcidens wheeleri
Endangered
FWS
Range
Dwarf Bear-poppy
Arctomecon humilis
Endangered
FWS
Range
Franciscan
manzanita
Arctostaphylos franciscana
Endangered
FWS
Range
Presidio Manzanita
Arctostaphylos hookeri var.
ravenii
Endangered
FWS
Range
Pallid manzanita
Arctostaphylos pallida
Threatened
FWS
Range
Marsh Sandwort
Arenaria paludicola
Endangered
FWS
Range
Sacramento prickly
poppy
Argemone pleiacantha ssp.
pinnatisecta
Endangered
FWS
Range
Mead's milkweed
Asclepias meadii
Threatened
FWS
Range
Prostrate
milkweed
Asclepias prostrata
Proposed
Endangered
FWS
Both
Welsh's milkweed
Asclepias welsh ii
Threatened
FWS
Range
American hart's-
tongue fern
Asplenium scolopendrium
var. americanum
Threatened
FWS
Range
Pecos assiminea
snail
Assiminea pecos
Endangered
FWS
Range
Shivwits milk-
vetch
Astragalus ampullarioides
Endangered
FWS
Range
Guthrie's (=Pyne's)
ground-plum
Astragalus bibullatus
Endangered
FWS
Range
Sentry milk-vetch
Astragalus cremnophylax
var. cremnophylax
Endangered
FWS
Both
Holmgren milk-
vetch
Astragalus holmgreniorum
Endangered
FWS
Range
Mancos milk-vetch
Astragalus humillimus
Endangered
FWS
Range
Peirson's milk-
vetch
Astragalus magdalenae var.
peirsonii
Threatened
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FWS
Range
Jesup"s milk-vetch
Astragalus robbinsii var.
jesupii
Endangered
FWS
Range
Coastal dunes
milk-vetch
Astragalus tener var. titi
Endangered
FWS
Range
Star cactus
Astrophytum asterias
Endangered
FWS
Range
Anthony's
riversnail
Athearnia anthonyi
Wherever found; Except
where listed as
Experimental Populations
Endangered
FWS
Range
Anthony's
riversnail
Athearnia anthonyi
U.S.A. (AL;The free-
flowing reach of the
Tennessee R. from the
base of Wilson Dam
downstream to the
backwaters of Pickwick
Reservoir [about 12 RM
(19 km)] and the lower 5
RM [8 km] of all
tributaries to this reach in
Colbert and Lauderdale
Cos.
Experimental
Population, Non-
Essential
FWS
Range
Anthony's
riversnail
Athearnia anthonyi
U.S.A. (TN - specified
portions of the French
Broad and Holston Rivers
Experimental
Population, Non-
Essential
FWS
Range
Texas ayenia
Ayenia limitaris
Endangered
FWS
Range
Hairy rattleweed
Baptisia arachnifera
Endangered
FWS
Range
Coffin Cave mold
beetle
Batrisodes texanus
Endangered
FWS
Range
Helotes mold
beetle
Batrisodes venyivi
Endangered
FWS
Range
Virginia round-leaf
birch
Betula uber
Threatened
FWS
Range
Sonoma sunshine
Blennosperma bakeri
Endangered
FWS
Range
Shale barren rock
cress
Boechera serotina
Endangered
FWS
Range
Decurrent false
aster
Boltonia decurrens
Threatened
FWS
Range
Rusty patched
bumble bee
Bombus affinis
Endangered
FWS
Range
Franklin's bumble
bee
Bombusfranklini
Endangered
FWS
Both
Columbia Basin
Pygmy Rabbit
Brachylagus idahoensis
Endangered
FWS
Both
Marbled murrelet
Brachyramphus marmoratus
Threatened
FWS
Both
Conservancy fairy
shrimp
Branchinecta conservatio
Endangered
FWS
Range
Longhorn fairy
shrimp
Branchinecta longiantenna
Endangered
FWS
Range
Vernal pool fairy
shrimp
Branchinecta lynchi
Threatened
FWS
Both
Hungerford's
crawling water
Beetle
Brychius hungerfordi
Endangered
43
-------
FWS
Range
Houston toad
Bufo houstonensis
Endangered
FWS
Range
Red knot
Calidris canutus rufa
Threatened
FWS
Range
Texas poppy-
mallow
Callirhoe scabriuscula
Endangered
FWS
Range
San Bruno elfin
butterfly
Callophrys mossii bayensis
Endangered
FWS
Range
Tiburon mariposa
Illy
Calochortus tiburonensis
Threatened
FWS
Range
Benton County
cave crayfish
Cambarus aculabrum
Endangered
FWS
Both
Big Sandy crayfish
Cambarus callainus
Threatened
FWS
Range
Slenderclaw
crayfish
Cambarus cracens
Endangered
FWS
Range
Hell Creek Cave
crayfish
Cambarus zophonastes
Endangered
FWS
Range
Slender
campeloma
Campeloma decampi
Endangered
FWS
Range
Ivory-billed
woodpecker
Campephilus principalis
Endangered
FWS
Range
Gray wolf
Canis lupus
U.S. - multiple states
Endangered
FWS
Both
Gray wolf
Canis lupus
Minnesota
Threatened
FWS
Range
Mexican wolf
Canis lupus baileyi
Wherever found; Except
where listed as
Experimental Populations
Endangered
FWS
Range
Mexican wolf
Canis lupus baileyi
U.S.A. (portions of AZ and
NM)
Experimental
Population, Non-
Essential
FWS
Range
Red wolf
Canis rufus
Wherever found; Except
where listed as
Experimental Populations
Endangered
FWS
Range
Red wolf
Canis rufus
U.S.A. (portions of NC and
TN)
Experimental
Population, Non-
Essential
FWS
Range
Small-anthered
bittercress
Cardamine micranthera
Endangered
FWS
Both
Loggerhead sea
turtle
Caretta caretta
Threatened
FWS
Range
Golden sedge
Carex lutea
Endangered
FWS
Both
Navajo sedge
Carex specuicola
Threatened
FWS
Range
Tiburon paintbrush
Castilleja affinis ssp.
neglecta
Endangered
FWS
Range
Fleshy owl's-clover
Castilleja campestris ssp.
succulenta
Threatened
FWS
Both
golden paintbrush
Castilleja levisecta
Threatened
FWS
Range
California
jewelflower
Caulanthus californicus
Endangered
FWS
Range
Gunnison sage-
grouse
Centrocercus minimus
Threatened
FWS
Range
Fragrant prickly-
apple
Cereus eriophorus var.
fragrans
Endangered
44
-------
FWS
Both
Hoover's spurge
Chamaesyce hooveri
Threatened
FWS
Both
Piping Plover
Charadrius melodus
Great Lakes Watershed
Endangered
FWS
Both
Piping Plover
Charadrius melodus
Atlantic Coast and
Northern Great Plains
Threatened
FWS
Both
Western snowy
plover
Charadrius nivosus nivosus
Threatened
FWS
Range
Shortnose Sucker
Chasmistes brevirostris
Endangered
FWS
Range
June sucker
Chasmistes liorus
Threatened
FWS
Range
Green sea turtle
Chelonia mydas
Threatened
FWS
Both
Pygmy fringe-tree
Chionanthus pygmaeus
Endangered
FWS
Range
Ben Lomond
spineflower
Chorizanthe pungens var.
hartwegiana
Endangered
FWS
Both
Monterey
spineflower
Chorizanthe pungens var.
pungens
Threatened
FWS
Range
Scotts Valley
spineflower
Chorizanthe robusta var.
hartwegii
Endangered
FWS
Range
Robust
spineflower
Chorizanthe robusta var.
robusta
Endangered
FWS
Range
Sonoma
spineflower
Chorizanthe valida
Endangered
FWS
Range
Laurel dace
Chrosomus saylori
Endangered
FWS
Range
Florida golden
aster
Chrysopsis floridana
Endangered
FWS
Range
Salt Creek Tiger
beetle
Cicindela nevadica
lincolniana
Endangered
FWS
Both
Ohlone tiger
beetle
Cicindela ohlone
Endangered
FWS
Both
Robber Baron Cave
Meshweaver
Cicurina baronia
Endangered
FWS
Both
Madia Cave
Meshweaver
Cicurina madia
Endangered
FWS
Range
Government
Canyon Bat Cave
meshweaver
Cicurina vespera
Endangered
FWS
Range
Fountain thistle
Cirsium fontinale var.
fontinale
Endangered
FWS
Range
Pitcher's thistle
Cirsium pitcheri
Threatened
FWS
Range
Sacramento
Mountains thistle
Cirsium vinaceum
Threatened
FWS
Range
Wright's marsh
thistle
Cirsium wrightii
Proposed
Threatened
FWS
Range
Florida perforate
cladonia
Cladonia perforata
Endangered
FWS
Range
Presidio clarkia
Clarki a fran ciscan a
Endangered
FWS
Range
Springville clarkia
Clarkia springvillensis
Threatened
FWS
Range
Morefield"s
leather flower
Clematis morefieldii
Endangered
FWS
Range
Alabama leather
flower
Clematis socialis
Endangered
FWS
Range
Pigeon wings
Clitoriafragrans
Threatened
FWS
Range
Yellow-billed
Cuckoo
Coccyzus americanus
Threatened
45
-------
FWS
Range
Etonia rosemary
Conradina etonia
Endangered
FWS
Range
Apalachicola
rosemary
Conradina glabra
Endangered
FWS
Range
Cumberland
rosemary
Conradina verticillata
Threatened
FWS
Range
Salt marsh bird's-
beak
Cordylanthus maritimus ssp.
maritimus
Endangered
FWS
Range
Soft bird's-beak
Cordylanthus mollis ssp.
mollis
Endangered
FWS
Range
Palmate-bracted
bird's beak
Cordylanthus palmatus
Endangered
FWS
Range
Ozark big-eared
bat
Corynorhinus (=Plecotus)
townsendii ingens
Endangered
FWS
Range
Virginia big-eared
bat
Corynorhinus (=Plecotus)
townsendii virginianus
Endangered
FWS
Range
Lee pincushion
cactus
Coryphantha sneedii var. leei
Threatened
FWS
Range
Sneed pincushion
cactus
Coryphantha sneedii var.
sneedii
Endangered
FWS
Range
Pygmy Sculpin
Cottus paulus (=pygmaeus)
Threatened
FWS
Range
Grotto Sculpin
Cottus specus
Endangered
FWS
Range
Ozark Hellbender
Cryptobranchus
alleganiensis bishopi
Endangered
FWS
Range
diamond Darter
Crystallaria cincotta
Endangered
FWS
Range
Okeechobee gourd
Cucurbita okeechobeensis
ssp. okeechobeensis
Endangered
FWS
Range
Spectaclecase
(mussel)
Cumberlandia monodonta
Endangered
FWS
Range
Santa Cruz cypress
Cupressus abramsiana
Threatened
FWS
Range
Gowen cypress
Cupressus goveniana ssp.
goveniana
Threatened
FWS
Range
Jones Cycladenia
Cycladenia humilis var.
jonesii
Threatened
FWS
Range
Guadalupe Orb
Cyclonaias necki
Proposed
Endangered
FWS
Range
Texas pimpleback
Cyclonaias petrina
Proposed
Endangered
FWS
Range
Utah prairie dog
Cynomys parvidens
Threatened
FWS
Range
Blue shiner
Cyprinella caerulea
Threatened
FWS
Range
Desert pupfish
Cyprinodon macularius
Endangered
FWS
Range
Western fanshell
Cyprogenia aberti
Proposed
Threatened
FWS
Range
Fanshell
Cyprogenia stegaria
Endangered
FWS
Range
Leafy prairie-
clover
Daleafoliosa
Endangered
FWS
Range
Monarch butterfly
Danaus plexippus
Endangered
FWS
Range
Beautiful pawpaw
Deeringothamnus pulchellus
Endangered
FWS
Range
Rugel's pawpaw
Deeringothamnus rugelii
Endangered
FWS
Range
Baker's larkspur
Delphinium bakeri
Endangered
FWS
Range
Yellow larkspur
Delphinium luteum
Endangered
46
-------
FWS
Range
Lost River sucker
Deltistes luxatus
Endangered
FWS
Range
Leatherback sea
turtle
Dermochelys coriacea
Endangered
FWS
Range
Valley elderberry
longhorn beetle
Desmocerus californicus
dimorphus
Threatened
FWS
Range
Longspurred mint
Dicerandra cornutissima
Endangered
FWS
Range
Lakela's mint
Dicerandra immaculata
Endangered
FWS
Range
Devils River
minnow
Dionda diaboli
Threatened
FWS
Range
Giant kangaroo rat
Dipodomys ingens
Endangered
FWS
Range
Fresno kangaroo
rat
Dipodomys nitratoides exilis
Endangered
FWS
Range
Tipton kangaroo
rat
Dipodomys nitratoides
nitratoides
Endangered
FWS
Range
Iowa Pleistocene
snail
Discus macclintocki
Endangered
FWS
Range
Dromedary
pearlymussel
Dromus dromas
Wherever found; Except
where listed as
Experimental Populations
Endangered
FWS
Range
Dromedary
pearlymussel
Dromus dromas
U.S.A. (AL;The free-
flowing reach of the
Tennessee R. from the
base of Wilson Dam
downstream to the
backwaters of Pickwick
Reservoir [about 12 RM
(19 km)] and the lower 5
RM [8 km] of all
tributaries to this reach in
Colbert and Lauderdale
Cos.
Experimental
Population, Non-
Essential
FWS
Range
Dromedary
pearlymussel
Dromus dromas
U.S.A. (TN - specified
portions of the French
Broad and Holston Rivers
Experimental
Population, Non-
Essential
FWS
Range
Eastern indigo
snake
Drymarchon couperi
Threatened
FWS
Range
Smooth
coneflower
Echinacea laevigata
Threatened
FWS
Both
Black lace cactus
Echinocereus reichenbachii
var. albertii
Endangered
FWS
Both
Acuna Cactus
Echinomastus erectocentrus
var. acunensis
Endangered
FWS
Range
Delta green
ground beetle
Elaphrus viridis
Threatened
FWS
Range
Spring pygmy
sunfish
Elassoma alabamae
Threatened
FWS
Range
Lacy elimia (snail)
Elimia crenatella
Threatened
FWS
Range
Puritan tiger
beetle
Ellipsoptera puritana
Threatened
FWS
Both
Chipola slabshell
Elliptio chipolaensis
Threatened
FWS
Range
Yellow lance
Elliptio lanceolata
Threatened
47
-------
FWS
Range
Altamaha
Spinymussel
Elliptic/ spinosa
Endangered
FWS
Range
Purple
bankclimber
(mussel)
Elliptoideus sloatianus
Threatened
FWS
Range
Southwestern
willow flycatcher
Empidonax traillii extimus
Endangered
FWS
Range
Southern sea otter
Enhydra lutris nereis
Threatened
FWS
Both
Cumberlandian
combshell
Epioblasma brevidens
Wherever found; Except
where listed as
Experimental Populations
Endangered
FWS
Range
Cumberlandian
combshell
Epioblasma brevidens
U.S.A. (AL;The free-
flowing reach of the
Tennessee R. from the
base of Wilson Dam
downstream to the
backwaters of Pickwick
Reservoir [about 12 RM
(19 km)] and the lower 5
RM [8 km] of all
tributaries to this reach in
Colbert and Lauderdale
Cos.
Experimental
Population, Non-
Essential
FWS
Range
Cumberlandian
combshell
Epioblasma brevidens
U.S.A. (TN - specified
portions of the French
Broad and Holston Rivers
Experimental
Population, Non-
Essential
FWS
Both
Oyster mussel
Epioblasma capsaeformis
Wherever found; Except
where listed as
Experimental Populations
Endangered
FWS
Range
Oyster mussel
Epioblasma capsaeformis
U.S.A. (AL;The free-
flowing reach of the
Tennessee R. from the
base of Wilson Dam
downstream to the
backwaters of Pickwick
Reservoir [about 12 RM
(19 km)] and the lower 5
RM [8 km] of all
tributaries to this reach in
Colbert and Lauderdale
Cos.
Experimental
Population, Non-
Essential
FWS
Range
Oyster mussel
Epioblasma capsaeformis
U.S.A. (TN - specified
portions of the French
Broad and Holston Rivers
Experimental
Population, Non-
Essential
FWS
Range
Curtis
pearlymussel
Epioblasma florentina curtisii
Endangered
FWS
Range
Yellow blossom
(pearlymussel)
Epioblasma florentina
florentina
Wherever found; Except
where listed as
Experimental Populations
Endangered
48
-------
FWS
Range
Yellow blossom
(pearlymussel)
Epioblasma florentina
florentina
U.S.A. (AL;The free-
flowing reach of the
Tennessee R. from the
base of Wilson Dam
downstream to the
backwaters of Pickwick
Reservoir [about 12 RM
(19 km)] and the lower 5
RM [8 km] of all
tributaries to this reach in
Colbert and Lauderdale
Cos.
Experimental
Population, Non-
Essential
FWS
Range
Tan riffleshell
Epioblasma florentina
walkeri (=E. walkeri)
Endangered
FWS
Range
Upland combshell
Epioblasma metastriata
Endangered
FWS
Range
Purple Cat"s paw
(=Purple Cat"s paw
pearlymussel)
Epioblasma obliquata
Wherever found; Except
where listed as
Experimental Populations
Endangered
FWS
Range
Purple Cat"s paw
(=Purple Cat"s paw
pearlymussel)
Epioblasma obliquata
U.S.A. (AL;The free-
flowing reach of the
Tennessee R. from the
base of Wilson Dam
downstream to the
backwaters of Pickwick
Reservoir [about 12 RM
(19 km)] and the lower 5
RM [8 km] of all
tributaries to this reach in
Colbert and Lauderdale
Cos.
Experimental
Population, Non-
Essential
FWS
Range
Southern
acornshell
Epioblasma othcaloogensis
Endangered
FWS
Range
Southern
combshell
Epioblasma penita
Endangered
FWS
Range
White catspaw
(pearlymussel)
Epioblasma perobliqua
Endangered
FWS
Range
Northern riffleshell
Epioblasma rangiana
Endangered
FWS
Range
Green blossom
(pearlymussel)
Epioblasma torulosa
gubernaculum
Endangered
FWS
Range
Tubercled blossom
(pearlymussel)
Epioblasma torulosa
torulosa
Wherever found; Except
where listed as
Experimental Populations
Endangered
49
-------
FWS
Range
Tubercled blossom
(pearlymussel)
Epioblasma torulosa
torulosa
U.S.A. (AL;The free-
flowing reach of the
Tennessee R. from the
base of Wilson Dam
downstream to the
backwaters of Pickwick
Reservoir [about 12 RM
(19 km)] and the lower 5
RM [8 km] of all
tributaries to this reach in
Colbert and Lauderdale
Cos.
Experimental
Population, Non-
Essential
FWS
Both
Snuffbox mussel
Epioblasma triquetra
Endangered
FWS
Range
Turgid blossom
(pearlymussel)
Epioblasma turgidula
Wherever found; Except
where listed as
Experimental Populations
Endangered
FWS
Range
Turgid blossom
(pearlymussel)
Epioblasma turgidula
U.S.A. (AL;The free-
flowing reach of the
Tennessee R. from the
base of Wilson Dam
downstream to the
backwaters of Pickwick
Reservoir [about 12 RM
(19 km)] and the lower 5
RM [8 km] of all
tributaries to this reach in
Colbert and Lauderdale
Cos.
Experimental
Population, Non-
Essential
FWS
Range
Kern mallow
Eremalche kernensis
Endangered
FWS
Both
Streaked Horned
lark
Eremophila alpestris strigata
Threatened
FWS
Range
Hawksbill sea
turtle
Eretmochelys imbricata
Endangered
FWS
Range
Willamette daisy
Erigeron decumbens
Endangered
FWS
Both
Spotfin Chub
Erimonax monachus
Wherever found; Except
where listed as
Experimental Populations
Threatened
FWS
Range
Spotfin Chub
Erimonax monachus
U.S.A. (TN-specified
portions of the Tellico
River
Experimental
Population, Non-
Essential
FWS
Range
Spotfin Chub
Erimonax monachus
U.S.A. (AL, TN-specified
portions of Shoal Creek
Experimental
Population, Non-
Essential
FWS
Range
Spotfin Chub
Erimonax monachus
U.S.A. (TN-specified
portions of the French
Broad and Holston Rivers
Experimental
Population, Non-
Essential
FWS
Range
Slender chub
Erimystax cahni
Threatened
FWS
Range
Umtanum desert
buckwheat
Eriogonum codium
Threatened
50
-------
FWS
Both
Gypsum wild-
buckwheat
Eriogonum gypsophilum
Threatened
FWS
Range
Clay-Loving wild
buckwheat
Eriogonum pelinophilum
Endangered
FWS
Both
San Mateo woolly
sunflower
Eriophyllum latilobum
Endangered
FWS
Range
Arizona eryngo
Eryngium sparganophyllum
Endangered
FWS
Range
Contra Costa
wallflower
Erysimum capitatum var.
angustatum
Endangered
FWS
Range
Menzies'
wallflower
Erysimum menziesii
Endangered
FWS
Range
Ben Lomond
wallflower
Erysimum teretifolium
Endangered
FWS
Range
Minnesota dwarf
trout lily
Erythronium propullans
Endangered
FWS
Range
bluemask darter
Etheostoma akatuio
Endangered
FWS
Range
Slackwater darter
Etheostoma boschungi
Threatened
FWS
Range
Vermilion darter
Etheostoma chermocki
Endangered
FWS
Range
Relict darter
Etheostoma chienense
Endangered
FWS
Range
Etowah darter
Etheostoma etowahae
Endangered
FWS
Range
Fountain darter
Etheostoma fonticoia
Endangered
FWS
Both
Yellowcheek
Darter
Etheostoma moorei
Endangered
FWS
Range
Niangua darter
Etheostoma nianguae
Threatened
FWS
Range
Candy darter
Etheostoma osburni
Endangered
FWS
Range
Duskytail darter
Etheostoma percnurum
Wherever found
Endangered
FWS
Range
Duskytail darter
Etheostoma percnurum
The Tellico River,
between the backwaters
of the Tellico Reservoir
(approximately Tellico
River mile 19 (30.4
kilometers) and Tellico
River mile 33 (52.8
kilometers), near the
Tellico Ranger Station,
Monroe County,
Tennessee.
Experimental
Population, Non-
Essential
FWS
Range
Duskytail darter
Etheostoma percnurum
U.S.A. (TN - specified
portions of the French
Broad and Holston Rivers
Experimental
Population, Non-
Essential
FWS
Both
Rush Darter
Etheostoma phytophilum
Endangered
FWS
Range
Bayou darter
Etheostoma rubrum
Threatened
FWS
Range
Cherokee darter
Etheostoma scotti
Threatened
FWS
Both
Maryland darter
Etheostoma sellare
Endangered
FWS
Range
Kentucky arrow
darter
Etheostoma spilotum
Threatened
FWS
Range
Cumberland darter
Etheostoma susanae
Endangered
FWS
Both
Trispot darter
Etheostoma trisella
Threatened
FWS
Range
Boulder darter
Etheostoma wapiti
Wherever found
Endangered
51
-------
FWS
Range
Boulder darter
Etheostoma wapiti
Shoal Creek (from Shoal
Creek mile 41.7 (66.7
km)) at the mouth of
Long Branch, Lawrence
County, TN, downstream
to the backwaters of
Wilson Reservoir (Shoal
Creek mile 14 (22 km)) at
Goose Shoals, Lauderdale
County, AL, including the
lower 5 miles (8 km) of all
tributaries that enter this
reach
Experimental
Population, Non-
Essential
FWS
Range
Island marble
Butterfly
Euchloe ausonides insulanus
Endangered
FWS
Both
Tidewater goby
Eucyclogobius newberryi
Endangered
FWS
Both
Florida bonneted
bat
Eumopsfloridanus
Endangered
FWS
Range
Smith's blue
butterfly
Euphilotes enoptes smithi
Endangered
FWS
Range
Telephus spurge
Euphorbia telephioides
Threatened
FWS
Both
Taylor's (=whulge)
Checkerspot
Euphydryas editha taylori
Endangered
FWS
Both
Salado Salamander
Eurycea chisholmensis
Threatened
FWS
Both
San Marcos
salamander
Eurycea nana
Threatened
FWS
Both
Georgetown
Salamander
Eurycea naufragia
Threatened
FWS
Range
Texas blind
salamander
Eurycea rathbuni
Endangered
FWS
Range
Barton Springs
salamander
Eurycea sosorum
Endangered
FWS
Both
Jollyville Plateau
Salamander
Eurycea tonkawae
Threatened
FWS
Both
Austin blind
Salamander
Eurycea waterlooensis
Endangered
FWS
Range
Northern
Aplomado Falcon
Fa! co femoralis
septentrionalis
Endangered
FWS
Both
Big Creek Crayfish
Faxonius peruncus
Proposed
Threatened
FWS
Both
St. Francis River
Crayfish
Faxonius quadruncus
Proposed
Threatened
FWS
Range
Gentner's Fritillary
Fritillaria gentneri
Endangered
FWS
Range
Tapered pigtoe
Fusconaia burkei
Threatened
FWS
Range
Shiny pigtoe
Fusconaia cor
Wherever found; Except
where listed as
Experimental Populations
Endangered
52
-------
FWS
Range
Shiny pigtoe
Fusconaia cor
U.S.A. (AL;The free-
flowing reach of the
Tennessee R. from the
base of Wilson Dam
downstream to the
backwaters of Pickwick
Reservoir [about 12 RM
(19 km)] and the lower 5
RM [8 km] of all
tributaries to this reach in
Colbert and Lauderdale
Cos.
Experimental
Population, Non-
Essential
FWS
Range
Shiny pigtoe
Fusconaia cor
U.S.A. (TN - specified
portions of the French
Broad and Holston Rivers
Experimental
Population, Non-
Essential
FWS
Range
Finerayed pigtoe
Fusconaia cuneolus
Wherever found; Except
where listed as
Experimental Populations
Endangered
FWS
Range
Finerayed pigtoe
Fusconaia cuneolus
U.S.A. (AL;The free-
flowing reach of the
Tennessee R. from the
base of Wilson Dam
downstream to the
backwaters of Pickwick
Reservoir [about 12 RM
(19 km)] and the lower 5
RM [8 km] of all
tributaries to this reach in
Colbert and Lauderdale
Cos.
Experimental
Population, Non-
Essential
FWS
Range
Finerayed pigtoe
Fusconaia cuneolus
U.S.A. (TN - specified
portions of the French
Broad and Holston Rivers
Experimental
Population, Non-
Essential
FWS
Both
Narrow pigtoe
Fusconaia escambia
Threatened
FWS
Range
Atlantic pigtoe
Fusconaia masoni
Threatened
FWS
Both
false spike
Fusconaia mitchelli
Proposed
Endangered
FWS
Range
Longsolid
Fusconaia subrotunda
Proposed
Threatened
FWS
Range
Blunt-nosed
leopard lizard
Gambelia silus
Endangered
FWS
Both
San Marcos
gambusia
Gambusia georgei
Endangered
FWS
Range
Pecos gambusia
Gambusia nobilis
Endangered
FWS
Range
Illinois cave
amphipod
Gammarus acherondytes
Endangered
FWS
Both
Noel's Amphipod
Gammarus desperatus
Endangered
FWS
Range
No common name
Geocarpon minimum
Threatened
FWS
Range
Spreading avens
Geum radiatum
Endangered
53
-------
FWS
Both
Humpback chub
Gila cypha
Threatened
FWS
Both
Bonytail
Gila elegans
Endangered
FWS
Range
Gila chub
Gila intermedia
Endangered
FWS
Range
Chihuahua chub
Gila nigrescens
Threatened
FWS
Range
Yaqui chub
Gila purpurea
Endangered
FWS
Both
Virgin River Chub
Gila seminuda (=robusta)
Endangered
FWS
Range
Monterey gilia
Gilia tenuiflora ssp. arenaria
Endangered
FWS
Range
Cactus ferruginous
pygmy-owl
Glaucidium brasilianum
cactorum
Proposed
Threatened
FWS
Range
Carolina northern
flying squirrel
Glaucomys sabrinus
coloratus
Endangered
FWS
Range
bog turtle
Glyptemys muhlenbergii
Threatened
FWS
Both
Desert tortoise
Gopherus agassizii
Threatened
FWS
Range
Gopher tortoise
Gopherus polyphemus
Threatened
FWS
Range
Yellow-blotched
map turtle
Graptemys flavimaculata
Threatened
FWS
Range
Ringed map turtle
Graptemys oculifera
Threatened
FWS
Range
Bartram's
stonecrop
Graptopetalum bartramii
Threatened
FWS
Both
Whooping crane
Grus americana
Wherever found; Except
where listed as
Experimental Populations
Endangered
FWS
Range
Whooping crane
Grus americana
U.S.A. (CO, ID, FL, NM,
UT, and the western half
of Wyoming)
Experimental
Population, Non-
Essential
FWS
Range
Whooping crane
Grus americana
U.S.A. (AL, AR, CO, FL, GA,
ID, IL, IN, IA, KY, LA, Ml,
MN, MS, MO, NC, NM,
OH, SC, TN, UT, VA, Wl,
WV, western half of WY)
Experimental
Population, Non-
Essential
FWS
Range
Whooping crane
Grus americana
U.S.A (Southwestern
Louisiana)
Experimental
Population, Non-
Essential
FWS
Both
Mississippi sandhill
crane
Grus canadensis pulla
Endangered
FWS
Range
North American
wolverine
Gulo gulo luscus
Proposed
Threatened
FWS
Range
Rock gnome lichen
Gymnoderma lineare
Endangered
FWS
Both
California condor
Gymnogyps californianus
Wherever found; Except
where listed as
Experimental Populations
Endangered
FWS
Range
California condor
Gymnogyps californianus
U.S.A. (specific portions
of Arizona, Nevada, and
Utah)
Experimental
Population, Non-
Essential
FWS
Range
Northeastern
beach tiger beetle
Habroscelimorpha dorsalis
dorsalis
Threatened
FWS
Range
Showy stickseed
Hackelia venusta
Endangered
54
-------
FWS
Both
Finelined
pocketbook
Hamiota altilis
Threatened
FWS
Range
Southern
Sandshell
Hamiota australis
Threatened
FWS
Range
Orangenacre
mucket
Hamiota perovalis
Threatened
FWS
Range
Shinyrayed
pocketbook
Hamiota subangulata
Endangered
FWS
Range
Harper's beauty
Harperocallis flava
Endangered
FWS
Range
Todsen's
pennyroyal
Hedeoma todsenii
Endangered
FWS
Range
Roan Mountain
bluet
Hedyotis purpurea var.
mo n tan a
Endangered
FWS
Range
Virginia
sneezeweed
Helenium virginicum
Threatened
FWS
Both
Pecos (=puzzle,
=paradox)
sunflower
Helianthus paradoxus
Threatened
FWS
Range
Schweinitz's
sunflower
Helianthus schweinitzii
Endangered
FWS
Both
Whorled
Sunflower
Helianthus verticillatus
Endangered
FWS
Range
Swamp pink
Helonias bullata
Threatened
FWS
Range
bog buck moth
Hemileuca maia
menyanthevora
Proposed
Endangered
FWS
Range
Cracking
pearlymussel
Hemistena lata
Wherever found; Except
where listed as
Experimental Populations
Endangered
FWS
Range
Cracking
pearlymussel
Hemistena lata
U.S.A. (AL;The free-
flowing reach of the
Tennessee R. from the
base of Wilson Dam
downstream to the
backwaters of Pickwick
Reservoir [about 12 RM
(19 km)] and the lower 5
RM [8 km] of all
tributaries to this reach in
Colbert and Lauderdale
Cos.
Experimental
Population, Non-
Essential
FWS
Range
Cracking
pearlymussel
Hemistena lata
U.S.A. (TN - specified
portions of the French
Broad and Holston Rivers
Experimental
Population, Non-
Essential
FWS
Both
Dakota Skipper
Hesperia dacotae
Threatened
FWS
Range
Marin dwarf-flax
Hesperolinon congestum
Threatened
FWS
Both
Comal Springs
riffle beetle
Heterelmis comalensis
Endangered
FWS
Range
Dwarf-flowered
heartleaf
Hexastylis naniflora
Threatened
FWS
Range
Neches River rose-
mallow
Hibiscus dasycalyx
Threatened
FWS
Range
Slender rush-pea
Hoffmannseggia tenella
Endangered
55
-------
FWS
Range
Santa Cruz tarplant
Holocarpha macradenia
Threatened
FWS
Both
Mountain golden
heather
Hudsonia montana
Threatened
FWS
Both
Rio Grande Silvery
Minnow
Hybognathus amarus
Wherever found; Except
where listed as
Experimental Populations
Endangered
FWS
Range
Rio Grande Silvery
Minnow
Hybognathus amarus
Rio Grande, from Little
Box Canyon
(approximately 10.4 river
miles downstream of Fort
Quitman, TX) to Amistad
Dam; and on the Pecos
River, from its confluence
with Independence Creek
to its confluence with the
Rio Grande
Experimental
Population, Non-
Essential
FWS
Range
Lakeside daisy
Hymenoxys herbacea
Threatened
FWS
Range
Texas prairie
dawn-flower
Hymenoxys texana
Endangered
FWS
Both
Delta smelt
Hypomesus transpacificus
Threatened
FWS
Both
Fender's blue
butterfly
Icaricia icarioidesfenderi
Endangered
FWS
Range
Mission blue
butterfly
Icaricia icarioides
missionensis
Endangered
FWS
Range
Yaqui catfish
Ictalurus pricei
Threatened
FWS
Range
Peter's Mountain
mallow
lliamna corei
Endangered
FWS
Range
Holy Ghost
ipomopsis
Ipomopsis sancti-spiritus
Endangered
FWS
Range
Dwarf lake iris
Iris lacustris
Threatened
FWS
Range
Louisiana quillwort
Isoetes louisianensis
Endangered
FWS
Range
Black spored
quillwort
Isoetes melanospora
Endangered
FWS
Range
Mat-forming
quillwort
Isoetes tegetiformans
Endangered
FWS
Range
Small whorled
pogonia
Isotria medeoloides
Threatened
FWS
Both
Koster's springsnail
Juturnia kosteri
Endangered
FWS
Range
Pink mucket
(pearlymussel)
Lampsilis abrupta
Endangered
FWS
Range
Guadalupe
Fatmucket
Lampsilis bergmanni
Proposed
Endangered
FWS
Both
Texas fatmucket
Lampsilis bracteata
Proposed
Endangered
FWS
Range
Higgins eye
(pearlymussel)
Lampsilis higginsii
Endangered
FWS
Range
Arkansas
fatmucket
Lampsilis powellii
Threatened
FWS
Range
Neosho Mucket
Lampsilis rafinesqueana
Endangered
FWS
Range
Speckled
pocketbook
Lampsilis streckeri
Endangered
56
-------
FWS
Range
Alabama
lampmussel
Lampsilis virescens
Wherever found; Except
where listed as
Experimental Populations
Endangered
FWS
Range
Alabama
lampmussel
Lampsilis virescens
U.S.A. (AL;The free-
flowing reach of the
Tennessee R. from the
base of Wilson Dam
downstream to the
backwaters of Pickwick
Reservoir [about 12 RM
(19 km)] and the lower 5
RM [8 km] of all
tributaries to this reach in
Colbert and Lauderdale
Cos.
Experimental
Population, Non-
Essential
FWS
Range
Banbury Springs
limpet
Lanx sp.
Endangered
FWS
Both
Carolina
heelsplitter
Lasmigona decorata
Endangered
FWS
Range
Burke's goldfields
Lasthenia burkei
Endangered
FWS
Both
Contra Costa
goldfields
Lasthenia conjugens
Endangered
FWS
Range
Eastern Black rail
Laterallus jamaicensis ssp.
jamaicensis
Threatened
FWS
Range
Beach layia
Layia carnosa
Threatened
FWS
Both
Fleshy-fruit
gladecress
Leavenworthia crassa
Endangered
FWS
Both
Kentucky glade
cress
Leavenworthia exigua
laciniata
Threatened
FWS
Range
Birdwing
pearlymussel
Lemiox rimosus
Wherever found; Except
where listed as
Experimental Populations
Endangered
FWS
Range
Birdwing
pearlymussel
Lemiox rimosus
U.S.A. (AL;The free-
flowing reach of the
Tennessee R. from the
base of Wilson Dam
downstream to the
backwaters of Pickwick
Reservoir [about 12 RM
(19 km)] and the lower 5
RM [8 km] of all
tributaries to this reach in
Colbert and Lauderdale
Cos.
Experimental
Population, Non-
Essential
FWS
Range
Birdwing
pearlymussel
Lemiox rimosus
U.S.A. (TN - specified
portions of the French
Broad and Holston Rivers
Experimental
Population, Non-
Essential
FWS
Range
Ocelot
Leopardus (=Felis) pardalis
Endangered
FWS
Range
Barneby ridge-
cress
Lepidium barnebyanum
Endangered
FWS
Both
Slickspot
peppergrass
Lepidium papilliferum
Threatened
57
-------
FWS
Range
Kemp's ridley sea
turtle
Lepidochelys kempii
Endangered
FWS
Range
Olive ridley sea
turtle
Lepidochelys olivacea
Threatened
FWS
Both
Vernal pool
tadpole shrimp
Lepidurus packardi
Endangered
FWS
Range
Scaleshell mussel
Leptodea leptodon
Endangered
FWS
Range
Mexican long-
nosed bat
Leptonycteris nivalis
Endangered
FWS
Range
Round rocksnail
Leptoxis ampla
Threatened
FWS
Range
Interrupted
(=Georgia)
Rocksnail
Leptoxisforemani
Endangered
FWS
Range
Plicate rocksnail
Leptoxis plicata
Endangered
FWS
Range
Painted rocksnail
Leptoxis taeniata
Threatened
FWS
Range
Prairie bush-clover
Lespedeza leptostachya
Threatened
FWS
Range
Lyrate bladderpod
Lesquerella lyrata
Threatened
FWS
Range
Spring Creek
bladderpod
Lesquerella perforata
Endangered
FWS
Range
San Francisco
lessingia
Lessingia germanorum (=L.g.
var. germanorum)
Endangered
FWS
Range
Heller's blazingstar
Liatris helleri
Threatened
FWS
Range
Huachuca water-
umbel
Lilaeopsis schaffneriana var.
recurva
Endangered
FWS
Range
Western lily
Lilium occidentale
Endangered
FWS
Range
Butte County
meadowfoam
Limnanthes floccosa ssp.
californica
Endangered
FWS
Range
Pondberry
Lindera melissifolia
Endangered
FWS
Range
Cylindrical lioplax
(snail)
Lioplax cyclostomaformis
Endangered
FWS
Range
Lee County cave
isopod
Lirceus usdagalun
Endangered
FWS
Both
Kincaid's Lupine
Lupinus sulphureus ssp.
kincaidii
Threatened
FWS
Range
Clover
(TidestronV's)
lupine
Lupinus tidestromii
Endangered
FWS
Range
Lotis blue butterfly
Lycaeides argyrognomon
lotis
Endangered
FWS
Range
Karner blue
butterfly
Lycaeides melissa samue/is
Endangered
FWS
Both
Canada Lynx
Lynx canadensis
Threatened
FWS
Range
Rough-leaved
loosestrife
Lysimachia asperulaefolia
Endangered
FWS
Range
White birds-in-a-
nest
Macbridea alba
Threatened
FWS
Both
Peppered chub
Macrhybopsis tetranema
Endangered
FWS
Range
Suwannee alligator
snapping turtle
Macrochelys suwanniensis
Proposed
Threatened
FWS
Range
Alligator snapping
turtle
Macrochelys temminckii
Proposed
Threatened
FWS
Range
Walker's manioc
Manihot walkerae
Endangered
58
-------
FWS
Range
Louisiana
pearlshell
Margaritifera hembeli
Threatened
FWS
Range
Alabama pearlshell
Margaritifera marrianae
Endangered
FWS
Range
Mohr's Barbara's
buttons
Marshallia mohrii
Threatened
FWS
Range
Royal marstonia
(snail)
Marstonia ogmorhaphe
Endangered
FWS
Range
Armored snail
Marstonia pachyta
Endangered
FWS
Both
Pacific Marten,
Coastal Distinct
Population
Segment
Martes caurina
Threatened
FWS
Range
Alameda
whipsnake
(=striped racer)
Masticophis lateralis
euryxanthus
Threatened
FWS
Range
Spikedace
Medafulgida
Endangered
FWS
Range
Alabama
moccasinshell
Medionidus acutissimus
Threatened
FWS
Range
Coosa
moccasinshell
Medionidus parvulus
Endangered
FWS
Range
Gulf moccasinshell
Medionidus penicillatus
Endangered
FWS
Range
Ochlockonee
moccasinshell
Medionidus simpsonianus
Endangered
FWS
Range
Suwannee
moccasinshell
Medionidus walkeri
Threatened
FWS
Both
Waccamaw
silverside
Menidia extensa
Threatened
FWS
Range
Spruce-fir moss
spider
Microhexura montivaga
Endangered
FWS
Range
Florida salt marsh
vole
Microtus pennsylvanicus
dukecampbelli
Endangered
FWS
Range
Michigan monkey-
flower
Mimulus michiganensis
Endangered
FWS
Range
MacFarlane's four-
o'clock
Mirabilis macfarlanei
Threatened
FWS
Range
San Joaquin wooly-
th reads
Monolopia (=Lembertia)
congdonii
Endangered
FWS
Range
Black-footed ferret
Mustela nigripes
Wherever found; Except
where listed as
Experimental Populations
Endangered
FWS
Range
Black-footed ferret
Mustela nigripes
U.S.A. (WY and specified
portions of AZ, CO, MT,
SD, and UT,
Experimental
Population, Non-
Essential
FWS
Range
Wood stork
Mycteria americana
Threatened
FWS
Range
Gray bat
Myotis g rise see ns
Endangered
FWS
Range
Northern Long-
Eared Bat
Myotis septentrionalis
Endangered
FWS
Both
Indiana bat
My otis sodalis
Endangered
FWS
Range
Spreading
navarretia
Navarretia fossalis
Threatened
59
-------
FWS
Range
Black warrior
(=Sipsey Fork)
Waterdog
Necturus alabamensis
Endangered
FWS
Both
Neuse River
waterdog
Necturus lewisi
Threatened
FWS
Range
Saint Francis' satyr
butterfly
Neonympha mitchellii
francisci
Endangered
FWS
Range
Mitchell's satyr
Butterfly
Neonympha mitchellii
mitchellii
Endangered
FWS
Range
Sand skink
Neoseps reynoldsi
Threatened
FWS
Both
Colusa grass
Neostapfia colusana
Threatened
FWS
Range
Riparian woodrat
(=San Joaquin
Valley)
Neotoma fuscipes riparia
Endangered
FWS
Range
Atlantic salt marsh
snake
Nerodia clarkii taeniata
Threatened
FWS
Range
Copperbelly water
snake
Nerodia erythrogaster
neglecta
Threatened
FWS
Range
American burying
beetle
Nicrophorus americanus
Wherever found; Except
where listed as
Experimental Populations
Threatened
FWS
Range
American burying
beetle
Nicrophorus americanus
In southwestern Missouri,
the counties of Cedar, St.
Clair, Bates, and Vernon.
Experimental
Population, Non-
Essential
FWS
Range
Britton's beargrass
Nolina brittoniana
Endangered
FWS
Range
Palezone shiner
Notropis albizonatus
Endangered
FWS
Range
Smalleye Shiner
Notropis buccula
Endangered
FWS
Range
Cahaba shiner
Notropis cahabae
Endangered
FWS
Both
Arkansas River
shiner
Notropis girardi
Threatened
FWS
Both
Cape Fear shiner
Notropis mekistocholas
Endangered
FWS
Range
Sharpnose Shiner
Notropis oxyrhynchus
Endangered
FWS
Both
Pecos bluntnose
shiner
Notropis simus pecosensis
Threatened
FWS
Both
Topeka shiner
Notropis topeka (=tristis)
Wherever found; Except
where listed as
Experimental Populations
Endangered
FWS
Range
Topeka shiner
Notropis topeka (=tristis)
U.S.A. (MO-specified
portions of Little Creek,
Big Muddy Creek, and
Spring Creek watersheds
in Adair, Gentry, Harrison,
Putnam, Sullivan, and
Worth Counties
Experimental
Population, Non-
Essential
FWS
Both
Smoky madtom
Noturus baileyi
Wherever found
Endangered
60
-------
FWS
Range
Smoky madtom
Noturus baileyi
The Tellico River,
between the backwaters
of the Tellico Reservoir
(approximately Tellico
River mile 19 (30.4
kilometers) and Tellico
River mile 33 (52.8
kilometers), near the
Tellico Ranger Station,
Monroe County,
Tennessee
Experimental
Population, Non-
Essential
FWS
Range
Chucky Madtom
Noturus crypticus
Endangered
FWS
Both
Yellowfin madtom
Noturus flavipinnis
Wherever found; Except
where listed as
Experimental Populations
Threatened
FWS
Range
Yellowfin madtom
Noturus flavipinnis
U.S.A. (TN-specified
portions of the Tellico
River
Experimental
Population, Non-
Essential
FWS
Range
Yellowfin madtom
Noturus flavipinnis
U.S.A. (TN, VA-specified
portions of the Holston
River and watershed
Experimental
Population, Non-
Essential
FWS
Range
Yellowfin madtom
Noturus flavipinnis
U.S.A. (TN - specified
portions of the French
Broad and Holston Rivers
Experimental
Population, Non-
Essential
FWS
Both
Carolina madtom
Noturus furiosus
Endangered
FWS
Both
Frecklebelly
madtom
Noturus munitus
Proposed
Threatened
FWS
Range
Neosho madtom
Noturus placidus
Threatened
FWS
Range
Pygmy madtom
Noturus stanauli
Wherever found
Endangered
FWS
Range
Pygmy madtom
Noturus stanauli
U.S.A. (TN - specified
portions of the French
Broad and Holston Rivers)
Experimental
Population, Non-
Essential
FWS
Range
Scioto madtom
Noturus trautmani
Endangered
FWS
Range
Chittenango ovate
amber snail
Novisuccinea
chittenangoensis
Threatened
FWS
Range
Eskimo curlew
Numenius borealis
Endangered
FWS
Both
Poweshiek
skipperling
Oarisma poweshiek
Endangered
FWS
Range
Choctaw bean
Obovaria choctawensis
Endangered
FWS
Range
Ring pink (mussel)
Obovaria retusa
Endangered
FWS
Range
Round hickorynut
Obovaria subrotunda
Proposed
Threatened
FWS
Range
Columbian white-
tailed deer
Odocoileus virginianus
leucurus
Threatened
FWS
Both
Antioch Dunes
evening-primrose
Oenothera deltoides ssp.
howellii
Endangered
FWS
Range
Apache trout
Oncorhynchus apache
Threatened
61
-------
FWS
Range
Lahontan
cutthroat trout
Oncorhynchus clarkii
henshawi
Threatened
FWS
Range
Greenback
Cutthroat trout
Oncorhynchus clarkii stomias
Threatened
FWS
Range
Rio Grande
cutthroat trout
Oncorhynchus clarkii
virginalis
Candidate
FWS
Range
Gila trout
Oncorhynchus gilae
Threatened
FWS
Range
Bakersfield cactus
Opuntia treleasei
Endangered
FWS
Range
Nashville crayfish
Orconectes shoupi
Endangered
FWS
Both
San Joaquin Orcutt
grass
Orcuttia inaequalis
Threatened
FWS
Both
Hairy Orcutt grass
Orcuttia pilosa
Endangered
FWS
Range
Slender Orcutt
grass
Orcuttia tenuis
Threatened
FWS
Both
Sacramento Orcutt
grass
Orcuttia viscida
Endangered
FWS
Range
Canby's dropwort
Oxypolis canbyi
Endangered
FWS
Range
Fassett's locoweed
Oxytropis campestris var.
chartacea
Threatened
FWS
Range
Squirrel Chimney
Cave shrimp
Palaemonetes cummingi
Threatened
FWS
Range
Alabama cave
shrimp
Palaemonias alabamae
Endangered
FWS
Both
Kentucky cave
shrimp
Palaemonias ganteri
Endangered
FWS
Range
Jaguar
Panthera onca
Endangered
FWS
Range
Papery whitlow-
wort
Paronychia chartacea
Threatened
FWS
Range
James spinymussel
Parvaspina collina
Endangered
FWS
Range
Tar River
spinymussel
Parvaspina steinstansana
Endangered
FWS
Range
Siler pincushion
cactus
Pediocactus
(=Echinocactus,=Utahia)
sileri
Threatened
FWS
Range
Brady pincushion
cactus
Pediocactus bradyi
Endangered
FWS
Range
San Rafael cactus
Pediocactus despainii
Endangered
FWS
Range
Knowlton's cactus
Pediocactus knowltonii
Endangered
FWS
Both
Fickeisen plains
cactus
Pediocactus peeblesianus
ssp. fickeiseniae
Endangered
FWS
Range
Littlewing
pearlymussel
Pegias fabula
Endangered
FWS
Range
Fisher
Pekania pennanti
Endangered
FWS
Range
Blowout
penstemon
Penstemon haydenii
Endangered
FWS
Range
White-rayed
pentachaeta
Pentachaeta bellidiflora
Endangered
FWS
Both
Amber darter
Percina antesella
Endangered
FWS
Range
Goldline darter
Percina aurolineata
Threatened
FWS
Range
Pearl darter
Percina aurora
Threatened
FWS
Both
Conasauga
logperch
Percina jenkinsi
Endangered
62
-------
FWS
Range
Leopard darter
Percina pantherina
Threatened
FWS
Range
Roanoke logperch
Perein a rex
Endangered
FWS
Range
Tricolored bat
Perimyotis subflavus
Proposed
Endangered
FWS
Both
Choctawhatchee
beach mouse
Peromyscus polionotus
allophrys
Endangered
FWS
Both
Alabama beach
mouse
Peromyscus polionotus
ammobates
Endangered
FWS
Range
Southeastern
beach mouse
Peromyscus polionotus
niveiventris
Threatened
FWS
Both
St. Andrew beach
mouse
Peromyscus polionotus
peninsularis
Endangered
FWS
Both
Perdido Key beach
mouse
Peromyscus polionotus
trissyllepsis
Endangered
FWS
Range
Red Hills
salamander
Phaeognathus hubrichti
Threatened
FWS
Range
Yreka phlox
Phlox hirsuta
Endangered
FWS
Range
Short-tailed
albatross
Phoebastria (=Diomedea)
albatrus
Endangered
FWS
Range
Blackside dace
Phoxinus cumberlandensis
Threatened
FWS
Range
Snake River physa
snail
Physa natricina
Endangered
FWS
Both
White Bluffs
bladderpod
Physaria douglasii ssp.
tuplashensis
Threatened
FWS
Range
Missouri
bladderpod
Ph ysari a filifo rmis
Threatened
FWS
Both
Short's bladderpod
Physaria globosa
Endangered
FWS
Both
Zapata bladderpod
Physaria thamnophila
Endangered
FWS
Range
Red-cockaded
woodpecker
Picoides borealis
Endangered
FWS
Range
Godfrey's
butterwort
Pinguicula ionantha
Threatened
FWS
Range
Whitebark pine
Pinus albicaulis
Threatened
FWS
Both
Yadon's piperia
Piperia yadonii
Endangered
FWS
Both
Black pinesnake
Pituophis melanoleucus
lodingi
Threatened
FWS
Both
Louisiana
pinesnake
Pituophis ruthveni
Threatened
FWS
Range
Ruth's golden
aster
Pityopsis ruthii
Endangered
FWS
Both
Woundfin
Plagopterus argentissimus
Wherever found; Except
where listed as
Experimental Populations
Endangered
FWS
Range
Woundfin
Plagopterus argentissimus
Gila R. drainage, AZ, NM
Experimental
Population, Non-
Essential
FWS
Both
Magnificent
ramshorn
Planorbella magnifica
Proposed
Endangered
FWS
Range
White fringeless
orchid
Platanthera integrilabia
Threatened
63
-------
FWS
Range
Eastern prairie
fringed orchid
Platanthera leucophaea
Threatened
FWS
Range
Western prairie
fringed Orchid
Platanthera praeclara
Threatened
FWS
Range
White wartyback
(pearlymussel)
Plethobasus cicatricosus
Endangered
FWS
Range
Orangefoot
pimpleback
(pearlymussel)
Plethobasus cooperianus
Wherever found
Endangered
FWS
Range
Orangefoot
pimpleback
(pearlymussel)
Plethobasus cooperianus
U.S.A. (TN - specified
portions of the French
Broad and Holston Rivers
Experimental
Population, Non-
Essential
FWS
Range
Sheepnose Mussel
Plethobasus cyphyus
Endangered
FWS
Range
Cheat Mountain
salamander
Plethodon nettingi
Threatened
FWS
Range
Shenandoah
salamander
Plethodon shenandoah
Endangered
FWS
Both
Canoe Creek
Clubshell
Pleurobema athearni
Endangered
FWS
Range
Clubshell
Pleurobema clava
Wherever found; Except
where listed as
Experimental Populations
Endangered
FWS
Range
Clubshell
Pleurobema clava
U.S.A. (AL;The free-
flowing reach of the
Tennessee R. from the
base of Wilson Dam
downstream to the
backwaters of Pickwick
Reservoir [about 12 RM
(19 km)] and the lower 5
RM [8 km] of all
tributaries to this reach in
Colbert and Lauderdale
Cos.
Experimental
Population, Non-
Essential
FWS
Range
Black clubshell
Pleurobema curtum
Endangered
FWS
Range
Southern clubshell
Pleurobema decisum
Endangered
FWS
Range
Dark pigtoe
Pleurobema furvum
Endangered
FWS
Range
Southern pigtoe
Pleurobema georgianum
Endangered
FWS
Range
Georgia pigtoe
Pleurobema hanleyianum
Endangered
FWS
Range
Flat pigtoe
Pleurobema marshalli
Endangered
FWS
Range
Ovate clubshell
Pleurobema perovatum
Endangered
FWS
Range
Rough pigtoe
Pleurobema plenum
Endangered
FWS
Range
Oval pigtoe
Pleurobema pyriforme
Endangered
FWS
Range
Fuzzy pigtoe
Pleurobema strodeanum
Threatened
FWS
Range
Heavy pigtoe
Pleurobema taitianum
Endangered
FWS
Range
Rough hornsnail
Pleurocera foremani
Endangered
FWS
Range
Slabside
Pearlymussel
Pleuronaia dolabelloides
Endangered
FWS
Range
Cumberland pigtoe
Pleuronaia gibber
Endangered
64
-------
FWS
Range
Gila topminnow
(incl. Yaqui)
Poeciliopsis occidentalis
Endangered
FWS
Range
Audubon's crested
caracara
Polyborus plancus audubonii
Threatened
FWS
Range
Lewton's polygala
Polygala lewtonii
Endangered
FWS
Range
Tiny polygala
Polygala smallii
Endangered
FWS
Range
Sandlace
Polygonella myriophylla
Endangered
FWS
Range
Scotts Valley
Polygonum
Polygonum hickmanii
Endangered
FWS
Range
Virginia fringed
mountain snail
Polygyriscus virginianus
Endangered
FWS
Range
Mount Hermon
June beetle
Polyphylla barbata
Endangered
FWS
Both
Texas Hornshell
Popenaias popeii
Endangered
FWS
Range
Fat pocketbook
Potamilus capax
Endangered
FWS
Range
Inflated
heelsplitter
Potamilus inflatus
Threatened
FWS
Range
Hickman's
potentilla
Potentilla hickmanii
Endangered
FWS
Range
Mexican blindcat
(catfish)
Prietella phreatophila
Endangered
FWS
Range
Maguire primrose
Primula maguirei
Threatened
FWS
Range
Alabama red-
bellied turtle
Pseudemys alabamensis
Endangered
FWS
Range
Plymouth Redbelly
Turtle
Pseudemys rubriventris
bangsi
Endangered
FWS
Range
Hartweg's golden
sunburst
Pseudobahia bahiifolia
Endangered
FWS
Range
San Joaquin adobe
sunburst
Pseudobahia peirsonii
Threatened
FWS
Range
Black-capped
petrel
Pterodroma hasitata
Proposed
Threatened
FWS
Range
Hawaiian petrel
Pterodroma sandwichensis
Endangered
FWS
Range
Harperella
Ptilimnium nodosum
Endangered
FWS
Range
Triangular
Kidneyshell
Ptychobranchus greenii
Endangered
FWS
Range
Southern
kidneyshell
Ptychobranchus jonesi
Endangered
FWS
Range
Fluted kidneyshell
Ptychobranchus subtentus
Endangered
FWS
Both
Colorado
pikeminnow
Ptychocheilus lucius
Wherever found; Except
where listed as
Experimental Populations
Endangered
FWS
Range
Colorado
pikeminnow
Ptychocheilus lucius
Salt and Verde R.
drainages, AZ
Experimental
Population, Non-
Essential
FWS
Range
Florida panther
Puma (=Felis) concolor coryi
Endangered
FWS
Range
Gulf Coast
jaguarundi
Puma yagouaroundi
cacomitli
Endangered
FWS
Range
Arizona Cliffrose
Purshia (=Cowania)
subintegra
Endangered
65
-------
FWS
Range
Bruneau Hot
springsnail
Pyrgulopsis bruneauensis
Endangered
FWS
Range
Chupadera
springsnail
Pyrgulopsis chupaderae
Endangered
FWS
Range
Socorro springsnail
Pyrgulopsis neomexicana
Endangered
FWS
Both
Roswell springsnail
Pyrgulopsis roswellensis
Endangered
FWS
Range
Rabbitsfoot
Quadrula cylindrica
cylindrica
Threatened
FWS
Both
Rough rabbitsfoot
Quadrula cylindrica
strigillata
Endangered
FWS
Range
Winged Mapleleaf
Quadrula fragosa
Wherever found; Except
where listed as
Experimental Populations
Endangered
FWS
Range
Winged Mapleleaf
Quadrula fragosa
U.S.A. (AL-specified
portions of the Tennessee
River
Experimental
Population, Non-
Essential
FWS
Range
Stirrupshell
Quadrula stapes
Endangered
FWS
Range
California clapper
rail
Rallus longirostris obsoletus
Endangered
FWS
Range
Yuma Ridgway"s
rail
Rallus obsoletus yumanensis
Endangered
FWS
Range
Chiricahua leopard
frog
Rana chiricahuensis
Threatened
FWS
Both
California red-
legged frog
Rana draytonii
Threatened
FWS
Both
Oregon spotted
frog
Rana pretiosa
Threatened
FWS
Both
dusky gopher frog
Rana sevosa
Endangered
FWS
Range
Southern
Mountain Caribou
DPS
Rangifer tarandus ssp.
caribou
Endangered
FWS
Range
Autumn Buttercup
Ranunculus aestivalis
(=acriformis)
Endangered
FWS
Range
Round Ebonyshell
Reginaia rotulata
Endangered
FWS
Range
Salt marsh harvest
mouse
Reithrodontomys raviventris
Endangered
FWS
Both
[no common
name] Beetle
Rhadine exilis
Endangered
FWS
Both
[no common
name] Beetle
Rhadine infernalis
Endangered
FWS
Range
Tooth Cave ground
beetle
Rhadine persephone
Endangered
FWS
Range
Leedy's roseroot
Rhodiola integrifolia ssp.
leedyi
Threatened
FWS
Range
Chapman
rhododendron
Rhododendron chapmanii
Endangered
FWS
Range
Michaux's sumac
Rhus michauxii
Endangered
FWS
Range
Knieskern's
Beaked-rush
Rhynchospora knieskernii
Threatened
FWS
Range
Miccosukee
gooseberry
Ribes echinellum
Threatened
FWS
Both
Everglade snail
kite
Rostrhamus sociabilis
plumbeus
Endangered
66
-------
FWS
Range
Bunched
arrowhead
Sagittaria fascicu lata
Endangered
FWS
Range
Krai's water-
plantain
Sagittaria secundifolia
Threatened
FWS
Both
Atlantic salmon
Salmo salar
Endangered
FWS
Both
Bull Trout
Salvelinus confluent us
Threatened
FWS
Range
Green pitcher-
plant
Sarracenia oreophila
Endangered
FWS
Range
Alabama
canebrake pitcher-
plant
Sarracenia rubra ssp.
alabamensis
Endangered
FWS
Range
Mountain sweet
pitcher-plant
Sarracenia rubra ssp. jonesii
Endangered
FWS
Range
Pallid sturgeon
Scaphirhynchus albus
Endangered
FWS
Range
Alabama sturgeon
Scaphirhynchus suttkusi
Endangered
FWS
Range
Clay reed-mustard
Schoenocrambe argillacea
Threatened
FWS
Range
Barneby reed-
mustard
Schoenocrambe barnebyi
Endangered
FWS
Range
Shrubby reed-
mustard
Schoenocrambe
suffrutescens
Endangered
FWS
Range
American
chaffseed
Schwalbea americana
Endangered
FWS
Range
Northeastern
bulrush
Scirpus ancistrochaetus
Endangered
FWS
Range
Tobusch fishhook
cactus
Sclerocactus brevihamatus
ssp. tobuschii
Threatened
FWS
Range
Pariette cactus
Sclerocactus brevispinus
Threatened
FWS
Range
Colorado hookless
Cactus
Sclerocactus glaucus
Threatened
FWS
Range
Mesa Verde cactus
Sclerocactus mesae-verdae
Threatened
FWS
Range
Uinta Basin
hookless cactus
Sclerocactus wetlandicus
Threatened
FWS
Range
Florida skullcap
Scu tellaria floridan a
Threatened
FWS
Range
Large-flowered
skullcap
Scutellaria montana
Threatened
FWS
Both
Ocmulgee skullcap
Scutellaria ocmulgee
Proposed
Threatened
FWS
Range
golden-cheeked
warbler
Setophaga chrysoparia
Endangered
FWS
Range
Keek's Checker-
mallow
Sidalcea keckii
Endangered
FWS
Range
Nelson's checker-
mallow
Sidalcea nelsoniana
Threatened
FWS
Range
Wenatchee
Mountains
checkermallow
Sidalcea oregana var. calva
Endangered
FWS
Range
Fringed campion
Silene polypetala
Endangered
FWS
Range
Spalding's Catchfly
Silene spaldingii
Threatened
FWS
Range
Eastern
Massasauga
(=rattlesnake)
Sistrurus eaten atus
Threatened
FWS
Range
White irisette
Sisyrinchium dichotomum
Endangered
67
-------
FWS
Range
Houghton's
goldenrod
Solidago houghtonii
Threatened
FWS
Range
Short's goldenrod
Solidago shortii
Endangered
FWS
Range
Blue Ridge
goldenrod
Solidago spithamaea
Threatened
FWS
Both
Hine's emerald
dragonfly
Somatochlora hineana
Endangered
FWS
Both
Buena Vista Lake
ornate Shrew
Sorex ornatus relictus
Endangered
FWS
Both
Alabama cavefish
Speoplatyrhinus poulsoni
Endangered
FWS
Range
Silverspot
Speyeria nokomis nokomis
Proposed
Threatened
FWS
Range
Behren's silverspot
butterfly
Speyeria zerene behrensii
Endangered
FWS
Range
Oregon silverspot
butterfly
Speyeria zerene hippolyta
Threatened
FWS
Range
Myrtle's silverspot
butterfly
Speyeria zerene myrtleae
Endangered
FWS
Both
Gierisch mallow
Sphaeralcea gierischii
Endangered
FWS
Range
Gentian pinkroot
Spigelia gentianoides
Endangered
FWS
Range
Virginia spiraea
Spiraea virginiana
Threatened
FWS
Range
Ute ladies'-tresses
Spiranthes diluvialis
Threatened
FWS
Range
Navasota ladies-
tresses
Spiranthes parksii
Endangered
FWS
Range
Longfin Smelt
Spirinchus thaleichthys
Candidate
FWS
Range
California least
tern
Sterna antillarum browni
Endangered
FWS
Range
Roseate tern
Sterna dougallii dougallii
Atlantic Coast south to
NC, Canada, Bermuda
Endangered
FWS
Range
Roseate tern
Sterna dougallii dougallii
Western Hemisphere and
adjacent oceans,
including USA(FL, PR, VI),
where not listed as
endangered
Endangered
FWS
Range
Flattened musk
turtle
Sternotherus depressus
Threatened
FWS
Both
Bracted
twistflower
Streptanthus bracteatus
Proposed
Threatened
FWS
Range
Tiburon
jewelflower
Streptanthus niger
Endangered
FWS
Both
Northern spotted
owl
Strix occidentalis caurina
Threatened
FWS
Both
Mexican spotted
owl
Strix occidentalis lucida
Threatened
FWS
Both
Peck's cave
amphipod
Stygobromus (=Stygonectes)
pecki
Endangered
FWS
Both
Comal Springs
dryopid beetle
Stygoparnus comalensis
Endangered
FWS
Range
California seablite
Suaeda californica
Endangered
FWS
Range
Riparian brush
rabbit
Sylvilagus bachmani riparius
Endangered
FWS
Range
California
freshwater shrimp
Syncaris pacifica
Endangered
68
-------
FWS
Range
Penasco least
chipmunk
Tamias minimus atristriatus
Proposed
Endangered
FWS
Range
Tooth Cave
pseudoscorpion
Tartarocreagris texana
Endangered
FWS
Range
Bliss Rapids snail
Taylorconcha serpenticola
Threatened
FWS
Both
Government
Canyon Bat Cave
spider
Tayshaneta microps
Endangered
FWS
Range
Tooth Cave spider
Tayshaneta myopica
Endangered
FWS
Range
Kretschmarr Cave
mold beetle
Texamaurops reddelli
Endangered
FWS
Range
Cokendolpher
Cave Harvestman
Texella cokendolpheri
Endangered
FWS
Range
Bee Creek Cave
harvestman
Texella reddelli
Endangered
FWS
Range
Bone Cave
harvestman
Texella reyesi
Endangered
FWS
Range
Cooley's
meadowrue
Thalictrum cooleyi
Endangered
FWS
Both
Northern Mexican
gartersnake
Thamnophis eques megalops
Threatened
FWS
Range
Giant garter snake
Thamnophis gigas
Threatened
FWS
Both
Narrow-headed
gartersnake
Thamnophis rufipunctatus
Threatened
FWS
Range
San Francisco
garter snake
Thamnophis sirtalis
tetrataenia
Endangered
FWS
Range
Cumberland
monkeyface
(pearlymussel)
Theliderma intermedia
Wherever found; Except
where listed as
Experimental Populations
Endangered
FWS
Range
Cumberland
monkeyface
(pearlymussel)
Theliderma intermedia
U.S.A. (TN - specified
portions of the French
Broad and Holston Rivers
Experimental
Population, Non-
Essential
FWS
Range
Cumberland
monkeyface
(pearlymussel)
Theliderma intermedia
U.S.A. (AL;The free-
flowing reach of the
Tennessee R. from the
base of Wilson Dam
downstream to the
backwaters of Pickwick
Reservoir [about 12 RM
(19 km)] and the lower 5
RM [8 km] of all
tributaries to this reach in
Colbert and Lauderdale
Cos.
Experimental
Population, Non-
Essential
FWS
Range
Appalachian
monkeyface
(pearlymussel)
Theliderma sparsa
Wherever found
Endangered
FWS
Range
Appalachian
monkeyface
(pearlymussel)
Theliderma sparsa
USA (TN - specified
portions of the French
Broad and Holston Rivers)
Experimental
Population, Non-
Essential
69
-------
FWS
Range
HowelT's
spectacular
thelypody
Thelypodium howellii ssp.
spectabilis
Threatened
FWS
Range
Alabama streak-
so rus fern
Thelypteris pilosa var.
alabamensis
Threatened
FWS
Range
Socorro isopod
Thermosphaeroma
thermophilus
Endangered
FWS
Both
Olympia pocket
gopher
Thomomys mazama
pugetensis
Threatened
FWS
Both
Tenino pocket
gopher
Thomomys mazama tumuli
Threatened
FWS
Range
Yelm pocket
gopher
Thomomys mazama
yelmensis
Threatened
FWS
Range
Ashy dogweed
Thymophylla tephroleuca
Endangered
FWS
Range
Loach minnow
Tiaroga cobitis
Endangered
FWS
Range
Florida torreya
Torreya taxifolia
Endangered
FWS
Range
Last Chance
townsendia
Townsendia aprica
Threatened
FWS
Range
Pale lilliput
(pearlymussel)
Toxolasma cylindrellus
Endangered
FWS
Both
West Indian
Manatee
Trichechus manatus
Threatened
FWS
Range
Showy Indian
clover
Trifolium amoenum
Endangered
FWS
Range
Monterey clover
Trifolium trichocalyx
Endangered
FWS
Range
Persistent trillium
Trillium persistens
Endangered
FWS
Range
Relict trillium
Trillium reliquum
Endangered
FWS
Range
Zayante band-
winged
grasshopper
Trimerotropis infantilis
Endangered
FWS
Range
Flat-spired three-
toothed Snail
Triodopsis platysayoides
Threatened
FWS
Both
Texas fawnsfoot
Truncilla macrodon
Proposed
Threatened
FWS
Both
Greene's tuctoria
Tuctoria greenei
Endangered
FWS
Both
Solano grass
Tuctoria mucronata
Endangered
FWS
Range
Tulotoma snail
Tulotoma magnifica
Threatened
FWS
Range
Attwater's greater
prairie-chicken
Tympanuchus cupido
attwateri
Endangered
FWS
Range
Grizzly bear
Ursus arctos horribilis
Threatened
FWS
Range
Bachman's warbler
(=wood)
Vermivora bachmanii
Endangered
FWS
Range
Rayed Bean
Villosafabalis
Endangered
FWS
Both
Purple bean
Villosa perpurpurea
Endangered
FWS
Range
Cumberland bean
(pearlymussel)
Villosa trabalis
Wherever found; Except
where listed as
Experimental Populations
Endangered
70
-------
FWS
Range
Cumberland bean
(pearlymussel)
Villosa trabalis
U.S.A. (AL;The free-
flowing reach of the
Tennessee R. from the
base of Wilson Dam
downstream to the
backwaters of Pickwick
Reservoir [about 12 RM
(19 km)] and the lower 5
RM [8 km] of all
tributaries to this reach in
Colbert and Lauderdale
Cos.
Experimental
Population, Non-
Essential
FWS
Range
Cumberland bean
(pearlymussel)
Villosa trabalis
U.S.A. (TN - specified
portions of the French
Broad and Holston Rivers
Experimental
Population, Non-
Essential
FWS
Range
Least Bell's vireo
Vireo bellii pusillus
Endangered
FWS
Range
San Joaquin kit fox
Vulpes macrotis mutica
Endangered
FWS
Range
Carter's mustard
Warea carteri
Endangered
FWS
Both
Razorback sucker
Xyrauchen texanus
Endangered
FWS
Range
Tennessee yellow-
eyed grass
Xyris tennesseensis
Endangered
FWS
Both
New Mexico
meadow jumping
mouse
Zapus hudsonius luteus
Endangered
FWS
Both
Preble's meadow
jumping mouse
Zapus hudsonius preblei
Threatened
FWS
Both
Texas wild-rice
Zizania texana
Endangered
NMFS
Range
Shortnose
sturgeon
Acipenser brevirostrum
Endangered
NMFS
Both
Sturgeon, Green
Acipenser medirostris
Southern
Threatened
NMFS
Both
Sturgeon, Atlantic
(Gulf subspecies)
Acipenser oxyrinchus
(=oxyrhynchus) desotoi
Threatened
NMFS
Both
Sturgeon, Atlantic
Acipenser oxyrinchus
oxyrinchus
Carolina
Endangered
NMFS
Both
Sturgeon, Atlantic
Acipenser oxyrinchus
oxyrinchus
Chesapeake Bay
Endangered
NMFS
Both
Sturgeon, Atlantic
Acipenser oxyrinchus
oxyrinchus
Gulf of Maine
Threatened
NMFS
Both
Sturgeon, Atlantic
Acipenser oxyrinchus
oxyrinchus
New York Bight
Endangered
NMFS
Both
Sturgeon, Atlantic
Acipenser oxyrinchus
oxyrinchus
South Atlantic
Endangered
NMFS
Both
Elkhorn coral
Acropora palmata
Threatened
NMFS
Range
Guadalupe fur seal
Arctocephalus townsendi
Threatened
NMFS
Range
Sei Whale
Balaenoptera borealis
Endangered
NMFS
Range
Blue Whale
Balaenoptera muscuius
Endangered
NMFS
Range
Fin Whale
Balaenoptera physa/us
Endangered
NMFS
Range
Rice's Whale
Balaenoptera ricei
Gulf of Mexico
Endangered
NMFS
Range
Oceanic Whitetip
Shark
Carcharhinus longimanus
Threatened
71
-------
NMFS
Both
Loggerhead Sea
Turtle
Caretta caretta
Northwest Atlantic Ocean
Endangered
NMFS
Range
Loggerhead Sea
Turtle
Caretta caretta
North Pacific Ocean
Endangered
NMFS
Range
Green Sea Turtle
Chelonia mydas
North Atlantic
Threatened
NMFS
Range
Green Sea Turtle
Chelonia mydas
East Pacific
Threatened
NMFS
Both
Leatherback Sea
Turtle
Dermochelys coriacea
Endangered
NMFS
Range
Hawskbill Sea
Turtle
Eretmochelys imbricata
Endangered
NMFS
Both
North Atlantic
Right Whale
Eubalaena glacialis
Endangered
NMFS
Range
North Pacific Right
Whale
Eubalaena japonica
Endangered
NMFS
Both
StellerSea Lion
Eumetopias jubatus
Western
Endangered
NMFS
Both
Abalone, black
Haliotis cracherodii
Endangered
NMFS
Range
Kemp's Ridley Sea
Turtle
Lepidochelys kempii
Endangered
NMFS
Range
Olive Ridley Sea
Turtle
Lepidochelys olivacea
All other areas
Threatened
NMFS
Range
Olive Ridley Sea
Turtle
Lepidochelys olivacea
Mexico's Pacific coast
breeding colonies
Endangered
NMFS
Range
Giant Manta Ray
Manta birostris
Threatened
NMFS
Both
Humpback Whale
Megaptera novaeangliae
Central America
Endangered
NMFS
Both
Humpback Whale
Megaptera novaeangliae
Mexico
Threatened
NMFS
Range
Humpback Whale
Megaptera novaeangliae
Western North Pacific
Endangered
NMFS
Both
Coho Salmon
Oncorhynchus (=Salmo)
kisutch
Central California coast
Endangered
NMFS
Both
Coho Salmon
Oncorhynchus (=Salmo)
kisutch
Lower Columbia River
Threatened
NMFS
Both
Coho Salmon
Oncorhynchus (=Salmo)
kisutch
Oregon coast
Threatened
NMFS
Both
Coho Salmon
Oncorhynchus (=Salmo)
kisutch
Southern Oregon &
Northern California
coasts (SONCC)
Threatened
NMFS
Both
Steelhead
Oncorhynchus (=Salmo)
mykiss
California Central Valley
Threatened
NMFS
Both
Steelhead
Oncorhynchus (=Salmo)
mykiss
Central California coast
Threatened
NMFS
Both
Steelhead
Oncorhynchus (=Salmo)
mykiss
Lower Columbia River
Threatened
NMFS
Both
Steelhead
Oncorhynchus (=Salmo)
mykiss
Middle Columbia River
Threatened
NMFS
Both
Steelhead
Oncorhynchus (=Salmo)
mykiss
Northern California
Threatened
NMFS
Both
Steelhead
Oncorhynchus (=Salmo)
mykiss
Puget Sound
Threatened
NMFS
Both
Steelhead
Oncorhynchus (=Salmo)
mykiss
Snake River Basin
Threatened
NMFS
Both
Steelhead
Oncorhynchus (=Salmo)
mykiss
South-Central California
coast
Threatened
72
-------
NMFS
Both
Steelhead
Oncorhynchus (=Salmo)
mykiss
Southern California
Endangered
NMFS
Both
Steelhead
Oncorhynchus (=Salmo)
mykiss
Upper Columbia River
Threatened
NMFS
Both
Steelhead
Oncorhynchus (=Salmo)
mykiss
Upper Willamette River
Threatened
NMFS
Both
Salmon, sockeye
Oncorhynchus (=Salmo)
nerka
Ozette Lake
Threatened
NMFS
Both
Salmon, sockeye
Oncorhynchus (=Salmo)
nerka
Snake River
Endangered
NMFS
Both
Chinook Salmon
Oncorhynchus (=Salmo)
tshawytscha
California coastal
Threatened
NMFS
Both
Chinook Salmon
Oncorhynchus (=Salmo)
tshawytscha
Central Valley spring-run
Threatened
NMFS
Both
Chinook Salmon
Oncorhynchus (=Salmo)
tshawytscha
Lower Columbia River
Threatened
NMFS
Both
Chinook Salmon
Oncorhynchus (=Salmo)
tshawytscha
Puget Sound
Threatened
NMFS
Both
Chinook Salmon
Oncorhynchus (=Salmo)
tshawytscha
Sacramento River winter-
run
Endangered
NMFS
Both
Chinook Salmon
Oncorhynchus (=Salmo)
tshawytscha
Snake River fall-run
Threatened
NMFS
Both
Chinook Salmon
Oncorhynchus (=Salmo)
tshawytscha
Snake River
spring/summer-run
Threatened
NMFS
Both
Chinook Salmon
Oncorhynchus (=Salmo)
tshawytscha
Upper Columbia River
spring-run
Endangered
NMFS
Both
Chinook Salmon
Oncorhynchus (=Salmo)
tshawytscha
Upper Willamette River
Threatened
NMFS
Both
Chum Salmon
Oncorhynchus keta
Columbia River
Threatened
NMFS
Both
Chum Salmon
Oncorhynchus keta
Hood Canal summer-run
Threatened
NMFS
Both
Coral, lobed star
Orbicella annularis
Threatened
NMFS
Both
Coral,
mountainous star
Orbicellafaveolata
Threatened
NMFS
Both
Boulder star coral
Orbicella franksi
Threatened
NMFS
Both
Whale, killer
Ore in us orca
Southern Resident
Endangered
NMFS
Range
Sperm Whale
Physeter macrocephalus (=
catodon)
Endangered
NMFS
Range
Smalltooth sawfish
Pristis pectin ata
U.S. portion of range
Endangered
NMFS
Range
False Killer Whale
Pseudorca crassidens
Main Hawaiian Islands
Insular
Endangered
NMFS
Range
Sunflower sea star
Pycnopodia helianthoides
Proposed
Threatened
NMFS
Both
Salmon, Atlantic
Salmo salar
Gulf of Maine
Endangered
NMFS
Both
Bocaccio
Sebastes paucispinis
Puget Sound/ Georgia
Basin
Endangered
NMFS
Both
Rockfish,
yelloweye
Sebastes ruberrimus
Puget Sound/ Georgia
Basin
Threatened
NMFS
Range
Scalloped
Hammerhead
Sphyrna lewini
Central & Southwest
Atlantic
Threatened
NMFS
Both
Eulachon
Thaleichthys pacificus
Southern
Threatened
73
-------
Upon further discussion with NMFS, EPA did not include many of the marine and
coastal NMFS species in additional analyses or discussions in this Biological Evaluation due to
discountable or insignificant effects potentially attributed to land use changes from the RFS Set
Rule. Potential consequences from the action pertaining to water quality (e.g., pesticide
exposure, as discussed in more detail in later sections) and species' responses to such potential
effects are discountable for populations that are found in offshore or circumpolar regions, far
enough away from the pollution source where exposure would not likely occur due to dilution in
marine waters.
This applies to the following cetaceans (and their associated critical habitats) which
receive NLAA determinations because effects, if any, are discountable: Sei Whale, Rice's Whale
Gulf of Mexico population, Blue Whale, Finback Whale, all listed Humpback Whale DPSs, and
Sperm Whale.
Other marine NMFS species that fully or partially reside or have critical habitat in more
shallow, coastal waters could be exposed to water quality effects caused by potential land use
changes driven by the RFS Set Rule. Although exposure is possible, we do not anticipate adverse
effects to these species because their responses to that exposure would be undetectable and not
measurable relative to baseline conditions and would not rise to the level of take. In the case of
pesticide exposure attributed to the RFS Set Rule, the risk of bioaccumulation and/or
biomagnification in larger animals (e.g., North Atlantic Right Whale) would be very low. For
species whose critical habitat PBFs include prey availability, potential impacts are insignificant
for species whose prey are wholly marine. Additionally, we anticipate that any potential effects
from the RFS Set Rule would not meaningfully reduce the prey populations and food sources
found in more freshwater or estuarine ecosystems, and thus not adversely affect the listed species
who rely on those food sources. For other PBFs, such as nearshore reproductive habitat (e.g., for
many sea turtles), potential effects would also be undetectable and not measurable.
Species that receive insignificant effects due to reasons as described above include: the
North Atlantic Right Whale, Killer Whale (Southern Resident population), North Pacific Right
Whale, False Killer Whale Main Hawaiian Islands Insular population). Olive Ridley Sea Turtle
(Mexico's Pacific coast breeding colonies and all other areas populations), Leatherback Sea
Turtle, Loggerhead Sea Turtle (Northwest Atlantic Ocean and North Pacific Ocean populations),
Green Sea Turtle (North Atlantic and East Pacific populations), Hawksbill Sea Turtle, Kemp's
Ridley Sea Turtle, Elkhorn Coral, Lobed Star Coral, Mountainous Star Coral, Boulder Star
Coral, Steller Sea lion (Western population), Guadalupe Fur Seal, Oceanic Whitetip Shark,
Scalloped Hammerhead Shark (Central & Southwest Atlantic population), and Giant Manta Ray.
Though the Southern Resident Killer whale depends on salmon for food (e.g., the Chinook
salmon), the potential effects of the RFS Set Rule on these salmon populations are also
considered to be discountable, as discussed in later sections. As such, this group of marine and
coastal species is not further evaluated in the ensuing analyses for this Biological Evaluation.
74
-------
YL Changes in I and Use Attributable to the HI S Set Rule
As discussed in the preceding sections, the RFS volume requirements for 2023-2025 may
affect listed species primarily by increasing demand for corn, soybean, and canola feedstock
crops used to produce biofuels. The increased demand for these crops could impact listed species
in two different but inter-related ways. First, increased demand for these crops could result in an
increase in the amount of land used to produce these crops. This increase in the amount of land
used to produce corn, soybeans, and canola could come from cropland currently being used to
produce other crops or from the conversion of non-cropland to cropland. These land use changes
could impact species with critical habitat or ranges on this land. Further, increased production of
these crops could result in an increase in the quantity of fertilizer, herbicides, pesticides, and
sediment in waterways that are close to or downstream of land used to produce these crops. This
could negatively affect species that live in or near the impacted waterways. Increased production
of these crops could also result in direct and indirect impacts to terrestrial species that use crop
lands or lands adjacent to them.
This section discusses a plausible projection of land use changes associated with the RFS
volume requirements for 2023-2025. In particular, for the purposes of carrying out this
Biological Evaluation, we projected the land use changes associated with increased demand for
corn, soybeans, and canola for biofuel production, and the portion of those land use changes
attributable to the Set Rule. To project land use changes associated with increased demand for
biofuels we used the best available data and analytical techniques from the published literature.
The types of data and information on the impact of biofuel demand on crop production that is
publicly available differs significantly for each of the three crops analyzed. These differences
have led to different methodologies to project the land use change associated with each of three
crops considered, though all methodologies were designed to address the same issues and steps
in connecting the Set Rule with potential impacts on species and habitats as shown in Figure
ES.l. We provide an overview of the methodologies used to project land use change associated
with increased demand for corn, soybean oil, and canola, and the results of these analyses. In
Sections VII and VIII, we discuss in more detail the potential impacts on listed species and
critical habitat from the estimated land use changes (Section VII), and the potential impacts on
listed species and critical habitat from changes in water quality (Section VIII).
A. Corn Production Potentially Attributable to the RFS Set Mule
Before assessing the impacts of the Set Rule on ethanol production and thus corn
production for the period 2023-2025, we first provide some historical context on the factors that
have contributed to the growth in domestic ethanol production since the RFS program began in
2006. This historical context helps to explain why only a small portion of ethanol production
(about 5%) in the 2023-2025 timeframe is reasonably certain to occur as a direct result of the
standards established through the Set Rule. Specifically, but for the RFS Set Rule, the volumes
of corn ethanol could be about 5% lower than those that we project will be consumed in the
2023-2025 timeframe.
75
-------
As shown in Figure II.C-1, conventional renewable fuel has represented about 85% of all
biofuel between 2016 and 2021. Table II.C-3 shows that the conventional renewable fuel pool is
comprised of greater than 99% corn-based ethanol. Ethanol production increased significantly
between 2006 and 2010, after which it increased more slowly due to constraints arising from
legal limits on the amount of ethanol that can be blended into gasoline, as well as other
constraints related to the vehicles permitted to use higher ethanol blends and the number of retail
service stations that offer those blends. More specifically, gasoline that can be used in any
vehicle or engine can contain no more than 10% ethanol. The "E10 blendwall" is the amount of
ethanol that would be consumed if all gasoline contained 10% ethanol. Total ethanol use can
exceed the El0 blendwall only insofar as consumers choose to buy either El5 (gasoline
containing 15% ethanol, which can only be used in model year 2001 or later light duty vehicles)
or E85 (fuel containing 51-83% ethanol which can only be used in flex-fuel vehicles). However,
the number of retail service stations offering El 5 and/or E85 has remained very low in
comparison to the total number of service stations offering E10. The graph below shows that
ethanol consumption has remained very close to the E10 blendwall since 2012.
Figure VLA-1: Historical Ethanol Production and Consumption
18,000
16,000
14,000
12,000
V)
c
= 10,000
Qfl
C
¦2 8,000
i
6,000
4,000
2,000
0
—
"""
f*
• Domestic
production
• Domestid
consumptic
in
ElOblendwal
1
2000
2001
2002
m
o
o
-------
Figure VI.A-2: Ethanol Consumption and RFS Volume Requirement
for Conventional Renewable Fuel3
16,000
14,000
12,000
c 10,000
_o
™ 8,000
0
1 6,000
4,000
2,000
0 '
^•"Domestic i
r.nnsumntifii
• Requirerru
renewable
lui i vet iuui idi
! fuel
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
2021
a For years 2006 - 2009, there was only a single RFS volume requirement that
applied to all renewable fuels. For 2010 and beyond, the values represent the
implied volume requirement for conventional renewable fuel that was the basis
for the applicable percentage standards.
While the growth in ethanol production over time coincided with the increase in
applicable standards under the RFS program, further analysis detailed below reveals that the RFS
standards were only responsible for a small portion of that growth, and that the level of growth
attributable to the RFS program differed from year to year. Other non-RFS factors and drivers
are responsible for most of the growth in ethanol production and consumption since 2000 (EPA,
2022a) (EPA, 2022b). Further, when looking to the future action addressed by this Biological
Evaluation, the analysis demonstrates that the RFS program would likely be responsible for only
about 5-6% of total ethanol production, namely that associated with consumption as El 5 and/or
E85; E10 would continue to be used even in the absence of the RFS program. These topics are
discussed in detail in the following sections.
There are four primary sources of evidence supporting the conclusions that the increase
in ethanol consumption shown in Figure VI.A-2 was not driven by the RFS program. The first
source of evidence is that ethanol consumption had begun increasing before the RFS program
came into existence. The Energy Policy Act which created the RFS program was enacted in
August of 2005, and the first applicable regulatory requirements for the use of renewable fuel
under the then-new RFS program did not go into effect until 2007. Yet by the end of 2006,
ethanol consumption had already increased by about 80% relative to the 2000 level.
Second, actual consumption of ethanol exceeded the requirements of the RFS program
between 2007 and 2011, often by a wide margin. Since the RFS program was intended drive
increases in the use of renewable fuel in the transportation sector, one would expect that actual
use of renewable fuel would be close to the applicable standards if the RFS program was
operating in this way. The fact that actual consumption exceeded the applicable standards means
that the RFS program was not driving consumption. Instead, it was factors other than the RFS
program that were driving consumption.
-------
Third, the price of RINs, the currency of the RFS program and the means through which
refiners demonstrate compliance with the RFS standards, were very low until 2013.
Figure VI.A-3: Historical weekly RIN prices for conventional renewable fuel3
(predominantly corn ethanol in S/gal)
$2.50
$2.00
ai
~ $1.50
D_
en
<£> $1.00
o
$0.50
A
rfy
(V
<\*
rSy
cO
S>
r?>
Jr oF oF
-------
function of the total renewable fuel standard being met with renewable fuel that also qualifies
advanced biofuel. These advanced biofuels have generally be used to meet the total renewable
fuel standard due to limited ability for market factors (including high RIN prices) to incentivize
the use of ethanol blends containing greater than 10% ethanol. Because greater incentives are
needed to increase the use of advanced biofuels as well as ethanol blends containing greater than
10% ethanol, the RIN prices associated with increasing volumes of these fuels are higher. Thus,
the higher RIN prices are primarily due to the higher RIN prices for the marginal gallon of
conventional renewable fuel.
Finally, ethanol production capacity in the early years of the RFS program far exceeded
what would have been needed to meet the original RFS volume requirements, often referred to as
RFS1.8
Figure VI.A-4: Ethanol Production Capacity Though the End of 2007
16,000
¦ Operating capacity
14,000
12,000
£ 10,000
o
J? 8,000
o
| 6,000
4,000
2,000
0
^ ^ ^ ^ $ ¦$' ^ ^ ^
The Energy Independence and Security Act (EISA), which established the RFS2
Program, was enacted in December of 2007. Thus prior to this date, investors would only have
had the RFS1 volume requirements on which to base their investment decisions, and could not
have based decisions to invest in new ethanol production facilities on the RFS2 volume
requirements as they did not yet exist. Nevertheless, new construction rose dramatically in the
years prior to and including 2007 to levels far above the highest level promised under RFS1; the
2012 requirement under RFS1 was 7.5 billion gallons, while the sum of operating and under
construction capacity at the end of 2007 was 13.4 billion gallons. This suggests that investors
were responding to future outlooks for ethanol demand that were based on factors beyond the
RFS1 standards.
The remainder of this section discusses the various market factors other than the RFS
program that likely contributed to the increased use of ethanol after 2006, the factors we believe
will drive ethanol use in the near future, and the associated impacts of all factors on corn growth.
8 That is, the RFS volume targets for 2006 - 2012 established in the Energy Policy Act of 2005.
79
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I, Factors Contributing to Increased Ethanol Production in the Past
While there is a clear correlation between the applicable RFS standards and historical
ethanol production as shown in Figure VI. A-2, this correlation does not indicate that the RFS
standards were entirely responsible for the increase in ethanol. There were also multiple other
factors that contributed to the increase in ethanol production, particularly in the early years of the
RFS program when growth in ethanol was highest, i.e. 2006-2010. These factors include:
• The phaseout of methyl tertiary butyl ether (MTBE) as a gasoline additive
• Other federal programs which required or otherwise incentivized the use of ethanol in
gasoline, such as the reformulated gasoline (RFG) program and the oxygenated fuels
(Oxyfuels) program
• Increases in crude oil prices
• The federal excise tax credit for ethanol
• State ethanol mandates and programs that incentivized the use of ethanol in gasoline
• State tax incentives for ethanol and grants for constructing new ethanol facilities
• The value of ethanol as a low-cost contributor to the octane rating of gasoline
What follows is a summary of the most important of these factors. A more detailed
discussion of each of these factors and how they have affected renewable fuel production can be
found in the draft Third Biofuels Report to Congress, released on December 15, 2022, and in the
Draft Regulatory Impact Analysis associated with the proposed standards under the RFS
program for 2023-2025 (US EPA Center for Public Health & Environmental Assessment &
Clark, 2023) (EPA, 2022a).
MTBE phaseout
In the years leading up to enactment of the Energy Policy Act (EPAct) of 2005, which
established the RFS program, MTBE was the preferred oxygenate because it was less expensive
than ethanol on a volumetric basis, could be shipped in pipelines, and had no impact on the Reid
Vapor Pressure (RVP) of gasoline.9 However, concern at the time was growing about the
environmental effects of MTBE, specifically groundwater contamination resulting from leaking
underground storage tanks. The California Air Resources Board (CARB) made a formal request
to EPA in 1999 for a waiver from the requirement to use oxygenates in reformulated gasoline.
The governor of California issued an executive order in March 1999 to ban MTBE in the state's
gasoline by the end of 2002; and, by 2000, the replacement of MTBE by ethanol was underway
in California. By the end of July 2005, before the passage of the Energy Policy Act in August, 17
states had some form of partial or complete ban on MTBE use. These states represented 41% of
the domestic gasoline consumption in 2005. From 2001-2005, domestic ethanol production
increased from 1.8 to 3.9 billion gallons per year. This rate was over five times the annual
average rate from the previous two decades and was driven in large part by the move away from
MTBE.
9 Oxygenates are any hydrocarbon fuels which also contain oxygen as part of the molecular structure, and which can
be blended into gasoline. The most common oxygenates are alcohols and ethers. They generally help fuels to
combust more thoroughly.
80
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At the federal level, multiple bills banning MTBE were considered by Congress, but none
were ultimately adopted. At the same time, Congress also considered providing liability
protection for refiners using MTBE under the premise that refiners had no choice but to use an
oxygenate in the RFG and Oxyfuels Programs, and that the EPA had implicitly approved of the
use of MTBE as an oxygenate given that MTBE was the most widely available and often the
least expensive oxygenate available when the RFG program was originally implemented. The
potential for some sort of liability protection, as well as the lack of sufficient infrastructure for
distributing and blending ethanol to coastal urban areas during this period, may have encouraged
refiners to continue using MTBE despite state bans and concerns expressed by the EPA and the
public.
The Energy Policy Act of 2005, which included the RFS program along with many other
provisions, was signed into law on August 8, 2005, and effectively (though not by mandate)
ended all use of MTBE in gasoline. Although the EPAct did not include a nationwide ban on the
use of MTBE as had previous bills that Congress considered, neither did it include any form of
liability protection that had been sought after by refiners who blended MTBE into gasoline.
Instead, EPAct eliminated the oxygen requirement for federal RFG and created the RFS
Program. Although the oxygen requirement for RFG was removed, the emission standards were
neither eliminated nor modified, and the use of an oxygenate continued to be the most
economical way to meet those emission standards. The combination of these changes in the
EPAct, in addition to the lack of any explicit or implicit liability protection, meant that refiners
had little incentive to continue using MTBE. Alternatives to MTBE existed in the form of
different ethers and alcohols, but many such alternatives also raised water quality concerns. As a
result, refiners eliminated their use of MTBE and instead began using ethanol to meet their
various obligations under the RFG, oxyfuels, and other fuels programs. The result was that
MTBE use in federal RFG areas outside California dropped by nearly 80% between 2005 and
2006, while the use of ethanol increased dramatically in the same timeframe.
Figure VI.A-5: Consumption of MTBE and ethanol in all gasoline outside of California
12,000
10,000
g 8,000
™ 6,000
O
^ 4,000
2,000
0
C>T-irMr0'=ti-ni£>r^00O*H
OOOOOOOOOOt—It—It—It—It—It—It—I
OOOOOOOOOOOOOOOOO
NNlNlNlNlSlNlNlNNNNlNNlNNN
Source: EPA batch report data (required under 40 CFR 80.75 and 80.105).
Notably, the first year in which a regulatory requirement for the use of biofuel existed
under the RFS program was 2007, after most of the transition from MTBE to ethanol had
81
-------
occurred. Thus the increase in ethanol consumption which occurred between 2000 and 2006,
shown in Figure VI.A-2, can be attributed primarily to the phaseout of MTBE.
Crude oil prices
Oil prices have complex and important associations with many kinds of economic
activity, including gasoline, ethanol, and corn production. This dynamic has implications for the
economics of ethanol as an additive to gasoline: it becomes cheaper to make gasoline with
ethanol than gasoline without ethanol as crude oil prices, and thus gasoline prices, increase
relative to ethanol. Thus, as crude oil prices increased, ethanol as E10 became more attractive by
comparison.
Figure VI.A-6: Historical Prices of Crude Oil and Gasoline
140
120
100
CD
-Q
5
4.5
k
jU
j\
4 _
"ro
, I
rt
m
n
00
3.5 vy
0)
(_>
-------
Table VI.A-1: State Mandates for the Use of Ethanol"
State
Blend
Requirement
First Applicable
Year
Last Applicable
Year
Minnesota
10%b
1997
Still in effect
Hawaii
10%c
2006
2015
Oregon
10%
2007
Still in effect
Missouri
10%
2008
Still in effect
Washington
2%
2009d
Still in effect
Florida
10%
2011
2013
a Does not include biodiesel mandates or mandates for ethanol use in state vehicle fleets.
b Between 1997 and 2002, the Minnesota requirement was 2.7wt% oxygen and was not
specific to ethanol. Nevertheless, ethanol was the primary oxygenate used. Between 2003
and 2012, the requirement was for 10vol% ethanol. For 2013 and thereafter, the
requirement was for 10vol% "conventional biofuel," of which ethanol was the primary
option available.
0 This requirement applied to 85% of gasoline sold in Hawaii.
d Actual start date was 12/1/2008.
Most of these state ethanol requirements included some exemptions such as for aviation
gasoline, gasoline used in nonroad and marine engines, and/or premium gasoline.
Additionally, a variety of state programs provided some form of economic incentive to
build or expand corn ethanol production facilities between 2005 and 2018. These programs
included grants, loans, tax credits, and rebates of varying sizes and applicability, with various
beginning and ending dates. These state programs were legally independent of the RFS Program
and may have been implemented even if the RFS Program had not existed. Thus, they may have
helped to expand ethanol production capacity.
83
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Table VI.A-2: State incentives for corn ethanol production
State
Title
Type
AL
Agriculturally Based Fuel Production Wage and
Salary Tax Credit
Tax credit/exemption
AR
Biofuels Industry Development Grants
Grant
AR
Biofuels Production Incentive
Rebate
CA
Alternative Fuel Production Tax Credits
Tax credit/exemption
FL
Ethanol and Biodiesel Fuel Production Grant
Grant
GA
Ethanol Motor Fuel Production Tax Credit
Tax credit/exemption
GA
Ethanol Production Investment Tax Credits
Tax credit/exemption
IA
Ethanol Production Incentive
Tax credit/exemption
IA
Biofuel Production Facility Tax Credit
Tax credit/exemption
IA
Ethanol Production Incentive
Tax credit/exemption
IL
Alternative Fuel Grants and Rebates
Grant/rebate
IL
Alternative Fuel Loan Program
Loan
IL
Alternative Fuel Production Tax Credit
Tax credit/exemption
IN
Alternative Fuel Production Facility Tax Exemption
Tax credit/exemption
KS
Biofuels Production Tax Credit
Tax credit/exemption
KY
Ethanol Production Tax Credit
Tax credit/exemption
ME
Ethanol Production Tax Credit
Tax credit/exemption
MN
Alternative Fuel Production Loans
Loan
MN
Biofuel Production Facility Tax Credit
Tax credit/exemption
MS
Renewable Fuel Production Facility Tax Credit
Tax credit/exemption
NC
Biofuels Production Tax Exemption
Tax credit/exemption
NC
Biofuels Production Incentive
Grant
ND
Ethanol Production Incentive
Rebate
OH
Alternative Fuel Development and Deployment
Grants
Grant
OR
Biofuels Production Tax Credit
Tax credit/exemption
PA
Renewable Energy Property Tax Credit
Tax credit/exemption
PA
Biofuels Investment Tax Credit
Tax credit/exemption
TN
Alternative Fuel Production Tax Incentives
Tax credit/exemption
TX
Biofuels Production Facility Grants
Grant
TX
Biofuels Business Planning Grants
Grant
TX
Ethanol Production Incentive
Rebate
WA
Renewable Fuel Production Grants
Grant
WA
Biofuels Production Incentive Fund
Loan
Source: U.S. Department of Energy (DOE), Alternative Fuels Data Center
Finally, California's Low Carbon Fuel Standard (LCFS) program was legislated in 2007
but did not go into effect until 2011. Beginning in 2011 the LCFS requires that the average
carbon intensity of gasoline decrease each year. Ethanol is one means of meeting the applicable
requirements, and thus the LCFS provides an additional incentive to use ethanol.
In summary, the vast majority of ethanol consumption has been driven by factors other
than the RFS program in past years. Many of these factors are expected to continue into the
2023-2025 timeframe for which standards will be established through the RFS Set Rule and
which are the focus of this Biological Evaluation. As a result, the vast majority of ethanol
84
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consumption in the 2023-2025 timeframe is expected to continue to be driven by these other
factors rather than by the Set Rule.
2. Potential Impacts of the RFS Set Rule on Ethanol Production
Our estimation of the impacts of future ethanol production attributable to the standards
established through the Set rule on species and habitat is complicated by a multitude of factors as
discussed in detail in Appendix A. Our assessment of the impact of potential increased corn
ethanol consumption attributable to the Set Rule begins with estimating the total increase in
cropland attributable to increased corn ethanol consumption (See Figure ES.l). Figure VLA-7
provides and illustration of the steps we have taken to estimate the increase in total cropland due
to the increase ethanol consumption attributable to the Set Rule.10
Figure VLA-7: Process for Estimating Impacts of Corn Ethanol on Cropland
Note that the end result of this process is an estimate of national-level cropland changes that may
occur as a result of the Set Rule. Following this process we estimate the potential overlap with
critical habitats and species ranges, and then finally make a determination as to whether that
potential overlap may have any negative impacts.
This Section VI.A.2 addresses Steps 1 through 3. Section VIA.3 provides some
additional historical context about the relationship between corn production and ethanol
production prior to addressing Step 4 in Section VI.A.4. Section VI.A.5 addresses some of the
uncertainties associated with estimating the amount of corn production that can be attributed to
the RFS program.
10 Figure VLA-7 differs from Figure 1 in Appendix A because they represent two different perspectives on the
connection between the applicable standards under the RFS program and impacts on species and habitat. Figure
VI.A-7 provides an overview of the methodology that was used to make quantified estimates of the impact of the
RFS standards in the Set Rule on cropland for the purposes of this Biological Evaluation. Figure 1 in Appendix A, in
contrast, provides a more comprehensive picture of all the factors that affect ethanol consumption in addition to the
applicable standards under the RFS program, and is used only in a qualitative fashion.
85
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The proposed volume requirements for 2023 through 2025 include an implied volume
requirement for conventional renewable fuel that is slightly higher than in previous years. For
2017-2022, EPA established an implied volume requirement of 15 billion gallons.11'12 For 2023-
2025, the implied volume requirement is proposed at 15.25 billion gallons (US EPA, 2022a).
However, as for previous years, not all of this volume is expected to be comprised of corn
ethanol. Actual corn ethanol consumption in the near future is expected to be a function not of
the implied volume requirement for conventional renewable fuel, but rather of total gasoline
consumption and trends in the number of retail stations offering El 5 and E85; in general terms,
ethanol consumption is closely tied to the El0 blendwall. As a result, actual corn ethanol
consumption will very likely be considerably lower than 15.25 billion gallons, with other
renewable fuels, primarily biodiesel, making up the difference between that 15.25 billion gallon
target and the El0 blendwall.
As discussed above, growth in the use of E15 and E85 has been fairly slow. Higher
consumption of E15 has been limited in the past by availability of terminals where it can be
blended and legal concerns on the part of retail station owner regarding liability for misfueling
(the use of higher ethanol blends in vehicles or engines not designed for them), as well as retail
infrastructure limitations. Higher consumption of E85 has been limited in the past by limited
sales of flex fuel vehicles (FFVs) and consumers consistently choosing to refuel with E10 rather
than E85. But the most significant constraint for both E15 and E85 sales has been the relatively
small number of retail stations that offer them.
As shown in Figure VI.A-1, growth in ethanol consumption slowed dramatically as it
approached the E10 blendwall. The nationwide average ethanol concentration, based on the total
volumes of ethanol and gasoline consumption from EIA, makes this effect even more evident.
11 2021 was an exception. Since the applicable standards were set in 2022, the implied volume requirement for 2021
was set at the level that was actually consumed in 2021. Also, while the 2020 implied volume requirement was
originally set at 15 billion gallons [see 85 FR 7016 (February 6, 2020)] it was revised downward in 2022 to the level
of actual consumption [see 87 FR 39600 (July 1, 2022)].
12 For 2022, the applicable standards included a "supplemental standard" of 250 million gallons intended to address
a court remand of the 2016 standards. While the implied conventional renewable fuel volume requirement was
technically 15 billion gallons for 2022, the inclusion of the supplemental standard meant that the volume
requirement was effectively 15.25 billion gallons for 2022. This is also true for the 2023 standards.
86
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Figure VI.A-8: Nationwide Average Concentration of Ethanol in Gasoline
12%
OJ
= 10%
O
10
ro
GO
c 8%
9
.659
6 9-839
6 10.02% 10.08% 10.23%
9.
33 °A
7 (
3.61% 9
.759
6 9
.919
c -
.0.13%
:
.0.20%
"o
c
7
.019
6 j
/ 8
1.00%
j— o/o
O)
C A%
3.86% .
^4.;
B4%
O) H/0
u
.06%
2000
2001
zooz
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
Source: EIA's Monthly Energy Review
The annual increase in the average ethanol concentration slowed considerably after 2010
as it approached 10.00%. Ongoing increases in the use of E15 and E85 have brought the average
ethanol concentration above 10.00% in recent years, but their use continues to be constrained by
limited offerings at retail. In the near future, we expect the annual rate of increase in the
nationwide average ethanol concentration would be similar to what it has been in recent years,
and thus might reach about 10.5% by 2025.
For the purposes of a Biological Evaluation, the impact of a federal action is determined
in part by assessing what would occur in the absence of that action as a means to identify the
consequences that would not occur but for the proposed federal action. Or, as expressed by 50
C.F.R. §§ 402.2 and 402.17, a particular consequence must be "reasonably certain to occur" for
it to be caused by the agency action under review. As illustrated above, the absence of the RFS
program or the Set Rule would not mean the absence of ethanol in our nation's fuel. Most of the
factors contributing to the historical use of ethanol (as detailed in Section V.A.I) would continue
in the future, even were the RFS program to cease to exist or EPA not to adopt the Set Rule. In
addition, market inertia would likely drive the continued use of ethanol at nearly current levels
absent the RFS program, at least in the short term. In the absence of the RFS program, any
attempt to reduce the use of ethanol would incur additional costs to switch back to producing
finished gasoline (E0) rather than blendstocks for oxygenate blending (BOBs) for E10.
Furthermore, refiners would have to not only replace the lost volume but also adjust their
refining operations to produce gasoline that meets the minimum octane and emissions
requirements, without the addition of ethanol. While refiners could likely produce some quantity
of E0 gasoline using existing equipment, recent refinery modeling conducted by MathPro on
behalf of EPA concluded that if ethanol were removed from the entire conventional gasoline
pool, refiners would have to invest significant capital in some combination of alkylation,
isomerization, and reforming units to meet the minimum octane requirements for gasoline (US
EPA, 2018). There would also be costs associated with making the necessary adjustment to the
distribution system to accommodate larger volumes of E0 in a system that is currently oriented
towards E10. Given such economic dynamics, we expect that most parties would seek to avoid
these additional costs and instead continue supplying E10.
87
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Modeling conducted by MathPro and internal EPA analyses have confirmed that the
market would continue supplying E10 in the future even if the RFS program were to cease to
exist (US EPA, 2022b). Sales and consumption of higher level ethanol blends, such as El 5 and
E85, would fall significantly, most likely to levels dictated by the incentives and requirements of
other state and local requirements. Small volumes of E0 would continue to be produced to meet
the demand from, for instance, owners of recreational marine engines whose concerns about
engine damage compel them to seek out and pay a premium for ethanol-free gasoline. This small
volume of E0 would mean that the nationwide average ethanol concentration in gasoline would
be slightly less than 10%; in the Set Rule proposal analysis of the "No RFS" baseline we
estimate it to be 9.95% (US EPA, 2022e).13
As shown in the table below, in comparison to projected future ethanol concentrations
under the RFS program, we estimate that the RFS program would be responsible for 5-6% of
ethanol consumption in the 2023-2025 timeframe.
Table VI.A-3: Fraction of Ethanol Consumption Attributable to the RFS Program
Average ethanol concentration of all
gasoline
Approximate fraction
of ethanol
consumption
attributable to the
RFS program51
Under the RFS
program
If the RFS
program ceased to
exist
2023
10.44% b
9.95%
4.82%
2024
10.49% b
9.95%
5.30%
2025
10.53% b
9.95%
5.76%
a This approximation is based on the simple difference between the % ethanol in the previous two columns.
A more accurate value would include the influence of the change in total gasoline consumption if some
ethanol is removed from the gasoline pool. However, the difference is very small.
b EPA projection provided in the Set Rule proposal
Note that a scenario in which the RFS program ceased to exist in the future is not the
same as a scenario in which the RFS program had never existed. If the RFS program had not
been instituted by EPAct of 2005 it is possible but not reasonably certain that ethanol
consumption would have risen more slowly or may not have reached a poolwide average ethanol
concentration of 10%, other factors driving ethanol consumption notwithstanding.
The fraction of ethanol consumption attributable to the Set Rule shown in Table VI.A-3
can be translated into volumes of ethanol consumption. The corresponding volumes of ethanol
are shown below. The estimation of these ethanol consumption volumes complete Step 2 of
Figure VI.A-7.
13 Specifically, see Table VI.G-1, footnote b, on page 80629. The presence of 2,128 mill gal of E0 brings the
poolwide average denatured ethanol concentration down from 10.1% to 9.95%.
88
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Table VI.A-4: Volume of Ethanol Consumption Potentially Attributable to the Set Rule14
Approximate fraction of
ethanol consumption
potentially attributable to
the RFS program
Total estimated
ethanol consumption
(mill gal)
Volume of ethanol
consumption potentially
attributable to the RFS
program (mill gal)
2023
4.82%
13,974 a
673
2024
5.30%
14,128 a
748
2025
5.76%
13,978 a
805
a EPA projection provided in the Set Rule proposal
We note that though the total volumes of ethanol consumption potentially attributable to
the RFS program range from 673-805 million gallons from 2023-2025 the total estimated
ethanol consumption volumes for 2023-2025 are approximately equal to total ethanol
consumption in 2022, and significantly less than total ethanol production in 2022. According to
the EIA Monthly Energy Review, U.S. ethanol production in 2022 was 15.37 billion gallons, and
ethanol consumption was 13.98 billion gallons. This suggests that the renewable fuel volumes
we are finalizing for 2023-2025 could likely be met with little or no additional ethanol
production (and thus little to no conversion to cropland resulting from additional ethanol
production) relative to 2022 levels.
While the EPA's actions to set standards for 2023-2025 would likely result in some
consumption of ethanol higher than what would occur in the absence of the RFS program, those
incremental volumes of consumption do not necessarily translate into equivalent volumes of
production. Domestic ethanol production serves both domestic and foreign markets, and ethanol
exports were considerably higher in 2021 than they were when the RFS program began in 2006.
14 The top-down methodology followed to determine these values likely results in a over-estimate of the volumes of
ethanol consumed as E15 and E85. For example, a bottom-up estimate provided in Chapter 10.4.2 of the RIA for
the final Set Rule yields an estimate of 383 million gallons of ethanol for 2025.
89
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Figure VI.A-9: Imports and Exports of Ethanol
2,000
1,500
1,000
£ 500
_o
"to
£ 0
o
^ -500
-1,000
-1,500
-2,000
Source: EI A
Ethanol exports are driven by two factors: foreign demand and domestic production in
excess of domestic demand. Foreign demand is primarily a function of biofuel mandates and
incentives in other countries and the relative economics of ethanol as a source of octane, while
excess domestic production is a function of domestic production capacity and domestic
consumption of ethanol. Insofar as domestic consumption of ethanol falls below production
capacity, as has been the case historically and is expected to be the case through 2025, there is an
incentive to find alternative markets in which to sell ethanol. This incentive increases if domestic
demand falls. Thus, we estimate if the RFS program were to cease to exist, domestic
consumption of ethanol would fall by the amounts shown in Table VI.A-4 and ethanol producers
could be expected to seek to increase exports to offset the loss of domestic sales.
Exports of ethanol are difficult to project. However, it seems likely that at least a
portion—and possibly all—of the ethanol volumes attributable to the Set Rule (Table VI.A-4)
would continue to be produced and exported even if the RFS program were to cease to exist. As
a result, ethanol production would most certainly change by an amount lower than the volumes
in Table VI.A-4, and may possibly not change at all. Nevertheless, for the purposes of
quantifying potential impacts in this Biological Evaluation, we have chosen to use the
conservative assumption that the change in ethanol production that is potentially attributable to
the Set Rule is identical to the change in ethanol consumption shown in Table VI.A-4. This
assumption very likely overestimates the impact of the Set Rule on ethanol production. The
estimation of these ethanol production volumes complete Step 3 of Figure VI.A-7.
3. Historical Corn Production and Ethanol Production
As the production of ethanol from corn increased over the last 20 years, production of
corn also increased.
90
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Figure VI.A-10: U.S. Corn Production and Portion Used for Fuel Ethanol, Feed, and Other
Uses
14 -i
12 --
¦ Ethanol Use
¦ Feed and Residual Us
¦ Other Uses
d)
* 6
1
o
5
4 --
1
1
-
:
<5? ^ £> g ^ <#• <5?
-------
Figure VI.A-11: Ethanol and Corn Production in Recent Years
15,500
15500
15,000
14,500
/
15000
ro
on
O)
l/>
3
-Q
J
t
14500
I
c
o
I
/
/
14000
u
3
c
o
\ ^
V /
/
T3
O
o
3
14,000
\
V* J
f
13500
Q.
"O
o
CL
c
V
V
13000
"o
c
03
-C
q5
o
13,500
c
u
Corn production
^^»Ethanol production
12500
o
u
13,000
12000
2016 2017 2018 2019
2020
2021
Source for corn production: USDA Economic Research Service
Source for ethanol production: EP"s EPA Moderated Transaction System
If changes in ethanol production were directly correlated with corn production, we would
expect to see them rise and fall in tandem. While this did seem to occur in 2019 and 2021, it did
not occur in 2017, 2018, or 2020. These counterintuitive interactions are very likely the result of
multiple factors affecting corn production in addition to ethanol production, and highlight the
difficulty in determining whether and to what degree the ethanol volume changes shown in Table
VI.A-4 would lead to changes in corn production. Notably, a number of researchers have
attempted to estimate the impact that a change in ethanol production would have on corn
production. As noted in Thompson et al., the range of outcomes is broad: one billion gallons of
additional ethanol production would lead to between 0 and 110 million bushels of corn
production (Food and Agricultural Policy Research Institute at University of Missouri, 2016).
4. Potential Impacts of Future RFS Standards on Corn Production and Land
Use
This Section addresses Step 4 in Figure VI.A-7.
To the degree that the Set Rule, especially the annual required volumes for 2023-2025
analyzed by this Biological Evaluation may be responsible for a portion of total ethanol
production, the next steps in the causation analysis are whether and to what degree corn
production and the associated land use would also be affected.
As a bounding exercise prior to the investigation of a more accurate approach as
discussed later, we start by estimating the area that would be impacted if the volumes shown in
Table VI.A-4 are produced from corn grown entirely on newly planted acres in the U.S.
Million acres = (X mill gallons) x ( bushel x ( acre ") Eq. 1
\Y gallons) \Z bushelj
92
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This approach likely generates an unreasonably high estimate as there is substantial evidence
indicating that a large share of the ethanol is produced from corn grown on existing cropland,
and it does not account for changes in ethanol exports or diversion of corn from food/feed to
ethanol production.
The values for X, Y, and Z are specific to each calendar year. The values for X are
provided in Table VI. A-4. Moreover, the values for Y vary by ethanol production facility and the
values for Z vary by county, but for the purposes of this assessment we have used the nationwide
averages. The derivation of the values for Y and Z are provided in the two tables below.
Table VI.A-5: Ethanol Production Yield Estimates (term Y in Equation 1)
Corn used to make
ethanol (mill bushels)
Ethanol production
(mill gal)
Ethanol yield
(gal/bushel)
2023
5,462
15,822
2.90
2024
5,473
15,867
2.90
2025
5,493
15,910
2.90
Source: U.S. Agricultural Market Outlook, University of Missouri (March 2022)
Table VI.A-6: Crop Yield Estimates (term Z in Equation 1)
Corn production
(mill bushels)
Acres harvested51
(mill acres)
Crop yield
(bushel/acre)
2023
15,377
84.7
181.4
2024
15,739
85.7
183.6
2025
15,858
85.3
185.8
a Acres harvested are used in this calculation rather than acres planted to be consistent with the
approach taken in the source data.
Source: U.S. Agricultural Market Outlook, University of Missouri (March 2022)
Using the values from Tables VI.A-4, VI.A-5, and VI.A-6, the maximum possible harvested corn
land use impact of the action addressed in this Biological Evaluation can be estimated as shown
below.
93
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Table VI.A-7: Maximum Possible Direct Impact on Corn Acreage
Volume of
ethanol (X)
(mill gal)
Ethanol
production yield
(Y) (gal/bushel)
Crop yield (Z)
(bushel/acre)
Million acres
2023
706
2.90
181.4
1.3
2024
776
2.90
183.6
1.5
2025
840
2.90
185.8
1.6
This approach yields the maximum possible direct impact of ethanol production volumes
on corn cropland that could possibly be attributable to the RFS program's Set Rule. At the same
time, it does not take into account the complex interactions between different commodities and
markets, nor does it include the indirect effects of cropland changes that may be driven by
changes in corn prices. Some studies have investigated these other interactions, and they provide
a more accurate way to estimate the impacts that the ethanol volumes shown in Table VI. A-4
would have on cropland. Rather than using the values estimated in Table VI.A-7 based on the
simple but intuitive Equation 1 above, we have chosen to use the results from these more
comprehensive studies for purposes of this Biological Evaluation.
As reviewed in the Draft Third Triennial Report to Congress on Biofuels, the most robust
estimate available to date for the effect on corn ethanol production on corn acreage and cropland
is from Li et al. (2019). This is the only study available to date using empirical data that
explicitly separated the price effect from the ethanol effect on the estimates of land use change.
Li et al. (2019) analyzed historical data to estimate the impact that a change in ethanol
production volume might have on acres of corn or total crops planted (Li et al., 2018). That study
separately investigated the impacts of increases in ethanol production volume on the need for
more corn cropland, and also the impacts of changes in corn price on changes in total cropland
(i.e., changes in cropland for other crops caused by the changes in corn). For the nation as a
whole, that study concluded with the factors shown below.
94
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Table VI.A-8: Effects of Changes in Cropland per Li et al. (2018)
Corn
cropland
Total
cropland
Impacts of increases in ethanol
production volumes
Acres per mill gal
ethanol
884
599
Impacts of changes in corn prices
Acres per dollar
2,532
n/a
Impacts of changes in crop price
index
Acres per index
point
n/a
4,484
However, the impacts of corn price were modeled separately from, and thus are independent of,
estimated impacts from changes in ethanol production volumes.
The 2018 Li study used data from 2003—2014. Subsequently, the same research team
repeated the analysis with additional data covering 2003-2018 (RTI International & Kahanna,
2021). Kahanna et al. found that the addition of more recent data resulted in a smaller impact of
ethanol production on corn acreage and resulted in the impact of ethanol production on total
cropland being not statistically different from zero. Thus the Kahanna analysis appears to
conclude that increases in corn cropland have not resulted in increases in total cropland in the
past. This could occur if farmers produced more corn by using their existing cropland differently,
for instance reducing acres devoted to wheat or cotton while increasing acres devoted to corn.
Kahanna also investigated the impacts of corn price changes on cropland, but as with Li
did not associate those price changes directly with changes in ethanol production volume. In its
draft Third Triennial Biofuels Report to Congress, EPA extended the Li et al. (2019) analysis to
associate the changes in corn and crop price with changes in ethanol volume (US EPA Center for
Public Health & Environmental Assessment & Clark, 2023). Doing so enabled EPA to estimate
the indirect impact of increases in ethanol production volume on cropland as mediated through
the influence that those ethanol volumes have on corn and crop prices. The result is a more
comprehensive representation of land use changes resulting from increases in ethanol production,
with the direct impacts of ethanol volumes and the indirect impacts of corn prices being additive.
The resulting factors are shown below, with the indirect effects of corn price converted into the
same units as the direct ethanol production volume effects.
95
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Table VI.A-9: Effects of Changes in Cropland per Kahanna et al. as Modified by EPA
Corn
cropland
Total
cropland
Direct impact of increases in ethanol
production volumes
Acres per mill gal
ethanol
730
0
Indirect impact of increases in ethanol
production volumes as mediated
through changes in corn prices (for
corn cropland) or crop price index
(for total cropland)
Acres per mill gal
ethanol
360
570
Based on this analysis, the ethanol volumes potentially attributable to the RFS program would
have the cropland impacts shown below.
Table VI.A-10: Estimated Impact on Corn Acreage and Cropland Using Factors Derived
by EPA (million acres)
Volume of ethanol
consumption
attributable to the RFS
program (mill gal)a
Corn cropland
Total cropland
Through
ethanol
volumes
Through
corn
prices
Through
ethanol
volumes
Through
crop
prices
2023
706
0.52
0.27
0
0.39
2024
776
0.57
0.27
0
0.44
2025
840
0.62
0.27
0
0.46
a From Table V.A-4
The estimated corn cropland impacts from Khanna (as modified by EPA) represent 1% or less of
all corn acres planted as shown below.
96
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Table VLA-11: Fraction of Corn Acres Planted that Could Be Attributed to the RFS
Program
Total planted
(million acres)a
Sum of direct and indirect
corn land attributable to RFS
(million acres)
Fraction
2023
92.9
0.79
0.9%
2024
93.9
0.84
0.9%
2025
93.5
0.89
1.0%
a Source: U.S. Agricultural Market Outlook, University of Missouri (March 2022)
As shown in Table VI.A-10, the estimates based on Khanna are that each billion gallons
of corn ethanol increases cropland by 0.57 million acres in 2023-2025. As a point of
comparison, we also considered estimates of land use change caused by increasing biofuel
production to estimates form a recent study (Lark et al. 2022). This paper considered observed
land use changes from 2008 - 2016, a time period of significant expansion of corn ethanol
production. Based on this data, the paper estimated that an increase of 5.5 billion gallons of corn
ethanol production per year was responsible for an increase of 6.1 million acres of total cropland.
Using these values, we can calculate an expected increase of 1.1 million acres per billion gallons
of ethanol produced. While these numbers are higher than the estimates based on Khanna (0.57
million acres per billion gallons of corn ethanol), we note that the Khanna estimates include a
consideration of more recent data. Previous estimates of land use change using the same
methodology as Khanna, but with a data set more comparable to that considered in Lark et al.
(2022) estimated total cropland increases of 0.92 million acres per billion gallons of corn
ethanol.16 Thus, after accounting for the updated data set we believe the acreage estimates from
Khanna are generally consistent with those estimated by Lark et al. (2022).
Despite the corn and total cropland acreage that may be attributable to the RFS program
as shown in Table VI.A-10, we note that future increases in total corn production are likely to be
driven primarily by demand for corn used for non-ethanol purposes. The University of
Missouri's "U.S. Agricultural Market Outlook" projects that total corn production will increase
through 2025 (University of Missouri, 2022). However, that increase is projected to be
predominately the result in increases in demand for corn for non-ethanol purposes. Increases in
the use of corn to produce ethanol is projected to be a considerably smaller portion of the total
increases in corn production.
16 These estimates are summarized in Table 6.10 of the External Review Draft of the Biofuels and the Environment
Third Triennial Report to Congress.
97
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Figure VI.A-12: Corn projections from University of Missouri (million bushels)
16,000
7,500
15,500
7,000
c
o
15,000
6,500
o
c
<_>
¦O
f
j=
ciJ
Q.
14,500
/
6,000
£
O
<_>
\ /
"S
in
3
"ro
14,000
5,500
C
o
1—
/ y.
O
u
13,500
^ Total corn production
5,000
• Corn used for ethanol
13,000
4,500
2018 2019 2020 2021 2022 2023 2024 2025
Total corn production would increase by about 170 million bushels per year on average
between 2022 and 2025, while corn production for ethanol would increase by only 18 million
bushels per year on average over the same timeframe. This result indicates that the demand for
non-ethanol uses of corn are anticipated to increase at a considerably faster rate than the demand
for corn used to produce ethanol. While this result does not change the fact that some corn
production can be attributed to the RFS program, it does indicate that any incremental impact on
cropland used to grow corn for the purposes of producing ethanol would be small in comparison
with increases in corn cropland for non-ethanol purposes. Consequently, corn production that
may be attributable to the RFS program may not be meaningfully measurable or observable.
Finally, corn crop yields have generally increased over time, and this increase reduces the
amount of new corn cropland that would be needed to grow the corn used to produce the ethanol
volumes shown in Table VI.A-4. Between 1990 and 2020 corn crop yields have increased by an
average of about 2 bushels per acre per year.
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Figure VI.A-13: Trends in Corn Crop Yields
190.00
Crop year
Source: USD"s Economic Research Service
We can approximate the impact that this annual increase in corn crop yields could have
on corn used to produce ethanol by combining it with other average factors derived from the
Tables VI.A-5 and VIA-6 above.
493 million gallons = (85 mill acres) x (2 9gal") x f2 bushel\ gq \
\bushelj V acre )
Thus, each year the additional corn that can be produced through higher crop yields is equivalent
to about 500 million gallons of ethanol. Over the period 2023-2025 that is the focus of this
Biological Evaluation, the annual average increase in the volume of ethanol projected to be
consumed is about 320 million gallons (US EPA, 2022d). Thus the increase in ethanol-
equivalent corn production due to increases in crop yields exceeds the incremental amounts
potentially consumed under the influence of the RFS program by a substantial margin.
We recognize that this simple comparison of corn crop yields to the demands of the RFS
program ignores the use of corn for non-ethanol purposes. Indeed, as shown in Figure VI. A-5,
demand for non-ethanol uses of corn is expected to increase through 2025, and annual increases
in corn crop yields will also help to meet this increasing demand. Nevertheless, the fact that corn
crop yields are expected to continue to increase in the future, and those increases are independent
of demand created by the RFS program, we can expect that the need for additional corn cropland
to meet the needs of the RFS program will be consequently diminished in the future.
5. Uncertainty in Estimating the Land Use Impacts of RFS-Driven Ethanol
Consumption
While we estimate that the Set Rule may be responsible for as much as 5-6% of ethanol
consumption in the 2023-2025 timeframe as shown in Table VI.A-3, this ethanol volume would
correspond to about 1% of the land devoted to growing corn as shown in Table VI. A-11. The
ultimate impacts on species and habitat of ethanol consumption that can be attributed to the Set
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Rule is very likely to be even smaller than these estimates imply, and there is reason to believe
that they could be zero, as discussed below.
If the RFS program were to cease to exist, it is unlikely that ethanol production would
decrease by the same amount as the reduction in domestic ethanol consumption. As discussed in
Section VI. A.2, domestic ethanol producers could be expected to seek to increase exports to
offset the loss of domestic sales. While domestic sales may be more profitable than exports,
exports would nevertheless be expected to be profitable given the already high level of exports.
This incentive alone has the potential to result in no change in domestic ethanol production at all
despite the fact that domestic ethanol consumption could decrease by up to 6% if the RFS
program were to cease to exist.
If there were some decrease in domestic ethanol production in the absence of the RFS
program, there may nevertheless be no decrease in total corn production. This could occur in two
ways. First, farmers may seek to increase exports of corn to offset the loss of a portion of their
ethanol market. As with ethanol, while domestic sales of corn may be more profitable, exports
would nevertheless be expected to be profitable given the already high level of corn exports.
Second, the corn that would otherwise have been used for ethanol might be diverted to the
domestic food and feed markets. Corn used for non-ethanol purposes in the U.S. has steadily
increased since the 2012 drought.
Figure VI.A-14: Corn Consumed Domestically for Non-Ethanol Purposes
Source: USD"s Economic Research Service
Such a shift between the use of corn for food and feed and the use of corn for ethanol
appears to have occurred between 2007 and 2012 as shown in Figure VI.A-10 and thus could
occur again in the 2023-2025 timeframe. While we cannot quantify the impacts of these two
factors, it seems likely that together they would reduce the impact of the RFS program on acres
devoted to corn production, already less than 1%, to significantly lower levels. Combined with
the likelihood that ethanol production may in fact not change at all, the impact on corn
production is likely to effectively be zero.
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In conclusion, after analyzing all of the steps connecting the standards established
through the RFS Set Rule and changes in land use as shown in Figure VI. A-7, and after a
consideration of all factors that have impacted the production and consumption of corn and corn
ethanol historically and which could be expected to also apply in the 2023-2025 timeframe as
discussed in this Section VI.A, EPA has determined that the impacts of the Set Rule on corn
cropland and the indirect effects on other cropland is highly likely to be very small. By
extension, EPA has determined that while some impacts on listed species or their habitats could
potentially occur as a result of the Set Rule, the consequences to the listed species or critical
habitats could not be meaningfully measured, detected, or evaluated. Further, we note that as
discussed in Section VI.A, ethanol consumption in 2023 - 2025 is projected to be approximately
equal to ethanol consumption in 2022, which suggest that little to no additional cropland would
be needed for ethanol production in 2023-2025. Nevertheless, in order to be thorough in the
assessment of potential impacts on species and habitats, EPA made an effort to quantify those
impacts using very conservative (i.e., worst-case) estimates of land use changes associated with
corn ethanol production potentially attributed to the Set Rule. These efforts are discussed in
Section VILA below.
B. Soybean Production Potentially Attributable to the RFS Set Mule
After ethanol, the fuels produced in the largest quantities to satisfy RFS obligations are
biodiesel, which displaces petroleum-based diesel fuel. Biodiesel is currently produced from a
wide variety of feedstocks, including waste fats, oils, and greases (FOG), distillers corn oil, and
virgin vegetable oils (see Table II.C-2). In the U.S., soybean oil is the vegetable oil used in the
largest quantities for biodiesel production, while smaller amounts of these fuels are produced
from canola oil. This section of the Biological Evaluation discusses the potential impact of the
RFS volume requirements in 2023-2025 on domestic soybean production. The methodology
used to project the impact of the Set Rule on soybean acreage in the U.S. is illustrated in Figure
VI.B-1.
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Figure VI.B-1: Process for Estimating Impacts of Soybean Biodiesel on Soybean Planting
Sections VLB 1 and VLB.2 provide some historical context for the production of
biodiesel and an overview of soybean markets and end uses. Section VI.B.3 discusses the impact
of the RFS program on the use of soybean oil for biofuel production. Section IV.B.4 discusses
interactions between biofuel production and soybean planting. Section VI.B.5 addresses Steps 1
through 5 of Figure VI.B-1.
1. Historical Biodiesel Production and Use
As with ethanol, there are many different factors that influence the domestic production
and use of biodiesel in any given year. These factors include, but are not limited to, the relative
pricing of vegetable oils and other feedstocks used to produce biodiesel and crude oil, federal tax
credits, state and local mandates and incentives, and the RFS volume requirements. Due in large
part to the federal and state incenti ves, including the RFS program, the production and use of
biodiesel has increased significantly since 2011.
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Figure VI.B-2: Historical Biodiesel Production and Imports (2012-2021)
3,000
2012 2013 2014 2015 2016 2017 2018 2019 2020 2021
(500)
~ Domestic Biodiesel ¦ Domestic Renewable Diesel
~ Net Biodiesel Imports ¦ Net Renewable Diesel Imports
Since 2016, about 53% of all biodiesel has been produced from crop-based feedstocks,
the majority of which was soybean oil. Fats, oils, and greases (FOG) are waste products collected
primarily from restaurants, animal processing facilities, and wastewater treatment plants, while
corn oil is a byproduct of corn ethanol production.
Table VI.B-1: Proportions of Feedstocks Used to Produce Domestic Biodiesel Between 2016
and 2021
2016
2017
2018
2019
2020
2021
Average
Soy oil
46%
45%
49%
50%
56%
50%
50%
FOG
28%
29%
27%
28%
25%
29%
28%
Corn oil
13%
15%
15%
14%
11%
14%
14%
Canola oil
12%
11%
8%
8%
8%
8%
9%
Source: EPA-Moderated Transaction System (EMTS)
Since we do not anticipate any adverse impacts on species or their habitats for non-crop
feedstocks such as FOG, and the production of such is not attributable to the RFS program, we
have focused on the production of soybeans and canola for the purposes of this Biological
Evaluation. However, in order to project the volumes of soy oil that might be used in the future
to produce biodiesel, we have also projected potential volumes of FOG and corn oil.
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2. Overview of Soybean Markets
Projecting the impact of the RFS program on soybean planting is complicated by the fact
that there are multiple markets, and therefore multiple economic factors, that impact the demand
for soybeans and soybean oil in the U.S. Therefore, before addressing our assessment of the
impacts of the RFS program on soybean oil consumption and ultimately soybean plantings, we
provide here an overview of the soybean market to provide context for the later discussion.
In the U.S., soybeans are grown for two primary purposes: export and crush, in roughly
equal proportions (See Figure VI.B-3). Soybeans that are crushed yield meal and oil, with the oil
comprising about 20% of the total mass crushed. Parties that use the soybean oil and meal
produced by soybean crushers generally purchase these products from crushing facilities, rather
than purchasing whole soybeans directly from soybean farmers.
Figure VI.B-3: Typical Uses of Soybeans (Average 2016—2021)
Soybeans used for seed
and animal feed (3%)
I
Soybean exports (48%)
Soybeans crushed (49%)
^ J
V
1
Meal (80%)
Oil
(20%)
Source: USD"s Economic Research Service
Like whole soybeans, some portion of both the meal and the oil are exported to meet
foreign demand. In terms of domestic use, the remaining meal is used primarily for animal feed
while the remaining oil is used for food, industrial purposes, and biofuel. Figure VI.B-4 shows
the proportional uses of meal and oil on average for the years 2016-2021.
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Figure VI.B-4: Markets for Soybeans that are Crushed (Average 2016—2021)
Source: USD"s Economic Research Service
The use of soybean oil to produce biodiesel has accounted for an average of about 32% of
all soy oil produced in the 2016-2021 timeframe. Since soy oil was produced from 49% of
soybeans over the same timeframe, we could conclude that about 16% (32% x 49%) of total
domestic soybean production was used for biofuel production. However, this calculation
obscures an important aspect of the soybean market. With rare exceptions, the meal has had a
higher market value than the oil over the last 30 years. This strongly implies that demand for
soybean meal in the animal feed market has driven the amount of soybeans crushed, rather than
demand for soybean oil. Historically soybean oil production has been a byproduct of the
crushing process whose primary purpose was to produce meal. This fact has implications for
whether and to what degree the demand for biofuel can be said to influence the production of soy
oil and, consequently, the production of soybeans. For example, soybean production and soybean
crush could increase in future years in response to increased demand for soybean meal for
livestock feed. This increased soybean crush would result in increased production of soybean oil
that could be used to produce biodiesel or renewable diesel, but in this case the increase in
soybean production and soybean crush would be attributable to increased demand for soybean
meal rather than to increased demand for biodiesel to meet the RFS volume requirements.
3. Potential Impact of the RFS program on Soybean Oil Use for Biofuel
Production
To project the potential impacts of the proposed RFS volume requirements for 2023-
2025 on soybean production, we first estimated the quantity of soybean oil that would be used
for biofuel production in the absence of the RFS program. We then compared the quantities of
soybean oil estimated to be used in the absence of RFS volume requirements in 2023-2025 to the
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quantity of soybean oil projected to be used to produce biofuels in the U.S. during these years
with the RFS volume requirements in place.
While historically biodiesel has generally been more expensive to produce than
petroleum-based diesel, in some situations the combination of available federal (non-RFS) and
state incentives are sufficient to make the blending of biodiesel profitable despite its higher
production costs. In other cases, states have enacted biodiesel use mandates so that minimum
quantities of biodiesel must be used regardless of its higher cost.
To estimate the quantity of biodiesel that would likely be used in a given year absent the
RFS volume requirements, we created a spreadsheet tool that compared the delivered cost of
biodiesel and the cost of diesel in each state. This tool considered both the production cost of
biodiesel produced from different feedstocks and the cost to distribute these fuels to each state,
as well as the incentives available for their use in that state. Where the cost of biodiesel was
projected to be lower than the reported cost of diesel fuel, or where there was a state mandate for
the use of biodiesel, we projected that biodiesel would be used even in the absence of the RFS
volume requirements. In situations where biodiesel cost less to supply than diesel, we projected
that consumption of biodiesel in the absence of RFS volume requirements would have been
equal to the volume of these fuels used actually used in that state in previous years. In these
projections we used price projections from the Energy Information Administration and USDA.
The methodology used to project biodiesel use in the absence of the RFS volume requirements is
described in more detail in Chapter 2.1.3 of the draft regulatory impact analysis for the rule
proposing RFS volume requirements for 2023-2025, and the results of that analysis are
summarized in Table VI.B-2.
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Table VI.B-2: Estimated Use of Biodiesel and Renewable Diesel without RFS Incentives
Feedstock
2023
2024
2025
Biodiesel (Million Gallons)
Soybean Oil
199
199
199
FOG
147
147
147
Distillers Corn Oil
86
86
86
Canola Oil
0
0
0
Renewable Diesel (Million Gallons)
Soybean Oil
0
0
0
FOG
390
390
390
Distillers Corn Oil
34
34
34
Canola Oil
0
0
0
After estimating the use of biodiesel in the U.S. in the absence of the RFS volume
requirements, we next compared these values to the quantity of soybean oil projected to be used
to produce biofuels in the U.S. in 2023-2025. Our projections of soybean oil used for biofuel
production in this Biological Evaluation are taken from the projection of fuels used to meet the
proposed RFS volume requirements in these years in the Set NPRM. These volumes are
summarized in Table VI.B-3. Finally, we estimated the impact of the RFS volume requirements
from 2023-2025 on the use of soybean oil for biofuel production by taking the difference
between the quantity of soybean oil projected to be used for biofuel production and the volumes
we estimate would have been or would be used for biofuel production in the absence of the RFS
volume requirements. These volumes are summarized in Table VI.B-4.
Table VI.B-3: Domestic Biodiesel and Renewable Diesel Projected to Be Produced from
Soybean Oil (million gallons)
2023
2024
2025
Biodiesel
927
893
860
Renewable Diesel
1,026
1,026
1,032
Total
1,953
1,919
1,892
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Table VI.B-4: Projected Impact of the RFS Volume Requirements on Use of Soybean Oil
for Biofuel Production (million gallons)
2023
2024
2025
Estimated Soybean Oil Use
Without RFS Requirements
199
199
199
Actual/Projected Soybean Oil
Use With RFS Requirements
1,953
1,919
1,892
Difference
1,754
1,720
1,693
4. Interactions Between Biofuel Production and Domestic Soybean
Production
Estimating the impact of the RFS program on domestic soybean production is
complicated by a number of factors. First, both soybeans and soybean oil have a number of
different markets, each of which could potentially impact soybean production in the U.S. The
primary uses for soybeans in the U.S. are crushing to produce soybean oil (used in a wide variety
of domestic markets, including fuel, food, and industrial) and soybean meal (for domestic use as
animal feed) and exports (for similar uses abroad), with a very small quantity of soybeans used
for seed, feed, and other uses (See Figure VI.B-5). From the 2000/2001 crop year to the
2020/2021 crop year, domestic soybean crush has increased by approximately 500 million
bushels, or approximately 30%. Soybean exports increased by nearly 1.3 billion bushels during
this time period, an increase of over 120%. Soybeans used for seed, feed, and other uses have
remained fairly consistent, decreasing slightly from 2000/2001 to 2020/2021 (see Figure VI.B-
5). Because there are multiple demand drivers for increased soybean production, we cannot
simply assume that the RFS program is responsible for the increase in soybean production since
the inception of the RFS program. In fact, these data indicate that much of the increase in
soybean production over the past 20 years has been driven by increased exports to meet demand
in foreign markets, while a relatively small portion (approximately 30%) of the increase in
soybean production has been due to increases in demand for soybean crushing to produce
soybean meal and soybean oil.
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Figure VI.B-5: U.S. Soybean End Uses
5,000
4,500
4,000
3,500
M
jr 3,000
in
u
^ 2,500
o
= 2,000
5
1,500
1,000
500
0
vO* \0V ^ vO6 vC? vN* & O? ^ V-^ vNfe A*^ /v1 o?
-------
Figure VLB-6: Soybean Oil End Uses
30,000
25,000
u, 20,000
"O
c
o
^ 15,000
o
10,000
5,000
>pN xO°> & vO^ jS? /y> A0 s? XT>
/ #X / #<°X ^ <$£ <$? ^
f\r 'XT V IT V "k iP V
¦ Non-Biofuel Domestic ¦ Biofuel ¦ Exports
Historically, the amount of soybeans crushed has been driven by the demand for meal,
with the oil being a byproduct. Consequently, the majority of the value of soybeans (historically
approximately 70%) has come from the meal. Soybean oil has historically been a relatively low
value byproduct of producing soybean meal for livestock feed. In fact, in the early years of the
biodiesel industry one of the perceived benefits of biodiesel production was to provide another
market for excess soy oil resulting from meal production. It provided a higher value market for
excess soybean oil than the existing markets for food, cosmetics, and industrial applications,
which in turn could reduce the prices for soybean-based animal feed and/or increase the price
farmers received for soybeans (Schmidt, 2007). The fact that soybean oil is a minority
component of the soybean, both by mass and value, suggests that it is highly unlikely that there
is a direct relationship between demand for soybean oil for biofuel production and domestic
soybean production.
Additionally, biodiesel and renewable diesel producers generally do not purchase
soybeans directly from farmers. Instead, soybeans must first be processed at a crushing facility to
separate the oil (which can be used as a feedstock for biofuel production) from the meal. While
some biodiesel is produced at large integrated soybean processing facilities that also crush
soybeans and produce a variety of end products, most biodiesel and renewable diesel producers
purchase soybean oil from soybean crushing facilities. The need to crush soybeans before the
soybean oil can be used to produce biofuels introduces another potentially limiting factor
because existing crushing facilities are currently operating at or near capacity. Even in a scenario
where production of biodiesel and renewable diesel were to increase significantly, the capacity of
existing soybean crushing facilities could limit the quantity of soybean oil that can be produced
domestically and used to produce biofuel. In such circumstances, increasing biodiesel and
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renewable diesel production would require sourcing feedstock from non-crop sources (FOG,
distillers corn oil, etc.) or diverting soybean oil from other markets. In either case, because
soybean crushing facilities are already operating at or near capacity there would be little or no
increase in soybean demand from crushing facilities and thus little to no market signal for U.S.
farmers to increase soybean production. Increasing soybean crushing capacity is therefore
necessary for any increase in demand for soybean oil or meal to result in an increase in soybean
planting. As discussed further in the following section, we have used USDA projections to
estimate increases in soybean crushing capacity, and the resulting increase in soybean planting
through 2025.
Notably, while the quantity of soybean oil produced in the U.S. has steadily increased
since 2000/2001, these increases have been more modest than the increase in soybean oil that has
been used for biofuel production during this same time period (see Figure VI.B-7). Since
2000/2001 approximately two-thirds of the soybean oil used to produce biofuels has come from
increased soybean oil production, while approximately one third has come from diverting
soybean oil from other markets to biofuel production. While some of the decrease in the use of
soybean oil for non-biofuel uses may have been due to increased demand for biofuel production,
other factors, such as the Food and Drug Administration's prohibition on the use of partially
hydrogenated oils in food products, also impacted demand for soybean oil in non-biofuel markets
(Center for Food Safety and Applied Nutrition, 2018).17
Figure VI.B-7: Domestic Soybean Oil Production and End Use
30,000
25,000
-g 20,000
C
13
O
15,000
c
o
if 10,000
Total US Soybean Oil Production Biofuel Use Non-BiofuelUse
In summary, it is difficult to project with any degree of precision the likely impact of the
RFS program on soybean planting in 2023-2025. This task is complicated by a variety of factors,
including a wide variety of feedstocks that can be used to produce biodiesel and renewable diesel
17 In 2013 FDA made a preliminary determination that partially hydrogenated oils are not generally recognized as
safe for use in food. This determination was finalized in 2015. .
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under the RFS program, the different markets for soybeans and soybean oil, and potential
limitations in soybean crush capacity. These confounding factors make it difficult to project the
degree to which any increase in demand for soybean oil attributable to the RFS program would
induce farmers to increase soybean production in 2023-2025. This inherent uncertainty means
that any projections of the potential impact of the RFS program on soybean plantings discussed
next lack any ability to say, with certainty whether the soybean planting changes will occur, and
secondarily, where they would occur.
5. Projecting the Potential Impact of the RFS program on Soybean Planting
Notwithstanding the challenges and uncertainties discussed in the preceding section, in
order to complete the analyses required under the Endangered Species Act we have made an
attempt to estimate the increase on soybean planting in 2023-2025 potentially attributable to the
RFS program using the best available data. As discussed throughout this section, there is
uncertainty associated with many of the inputs used to project the increase in soybean planting.
Where possible we have identified the level of uncertainty associated with various elements of
our projection, however in some cases (such as when we use projections from USD A) we are not
able to quantify the uncertainty.
To estimate the potential impact of the RFS program on soybean planting we compared
projected soybean planting in the U.S. in 2023-2025 with the RFS volume requirements in place
to estimates of what soybean planting would have been in each year in the absence of the RFS
volume requirements. To project data on soybean planting and other relevant factors such as
soybean yields and soybean oil yields in future years with the RFS program in place we used
USDA projections from their Long Term Agricultural Projections to 2031 (LTAP). Since it is a
projection into the future for years for which EPA had not yet set the standards, there is some
question as to the degree to which LTAP reflect the RFS program being in place. To verify that
the LTAP was appropriate to use as a projection for soybean plantings and other relevant factors
with the RFS volume requirements in place we compared the quantity of soybean oil projected to
be used for biofuel production in the LTAP in 2023-2025 to our own estimates of the quantity of
soybean oil that would be used to produce biofuels during this same time period from the
proposed Set Rule. These quantities are shown in Table VI.B-5.
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Table VI.B-5: Biofuel Projected to be Produced from Soybean Oil 2023-2025 (Million
Gallons)
2023
2024
2025
USDA LTAPa
1,510
1,540
1,550
EPA Set NPRM
1,950
1,920
1,890
Difference
440
380
340
a USDA's LTAP projects the pounds of soybean oil used to produce biofuel on an agricultural year basis. We have
converted their agricultural year projections to calendar years, and converted pounds of soybean oil to gallons of
biofuel assuming 7.6 pounds of soybean oil is used to produce one gallon of biofuel
While the quantities of biofuel that EPA projects will be produced from soybean oil from
2023-2025 are higher than the projections in USDA's LTAP, we note that there are important
differences between these projections that can account for the higher EPA projections. EPA's
projections include all biofuel produced from soybean oil, including imported biofuels.
Conversely USDA's LTAP projects only the quantity of biofuels produced from soybean oil in
the U.S. In previous years significant quantities of biofuels produced from soybean oil have
imported. The maximum quantity of imported biofuel produced from soybean oil was
approximately 425 million gallons in 2016, representing over 35% of all biofuel produced from
soybean oil used in the U.S. in that year. This suggests that the USDA LTAP projections are
consistent with our projections in the Set proposal. Therefore, we believe that it is appropriate to
use the projections of soybean planting in the USDA LTAP as a valid projection of soybean
plantings in 2023-2025 with the RFS volume requirements in place. Table VI.B-6. shows
projected soybean plantings from 2023-2025 from USDA's LTAP.
Table VI.B-6: Estimated Soybean Planting with the RFS Volume Requirements
2023
2024
2025
Million Acres
88.0
88.0
88.0
Next, we assessed the available data to project what soybean plantings would be in 2023-
2025 in the absence of the RFS volume requirements. The total number of acres of soybeans
projected to be planted in 2023-2025 in the absence of the RFS program can then be compared
to the number of acres of soybeans projected to be planted in these years in USDA's LTAP to
project the impact of the RFS volume requirements on soybean planting in these years.
Determining what soybean plantings would be in the absence of RFS volume requirements,
however, is not simple. We approached the task of estimating the impact of the RFS volume
requirements on soybean planting by considering the mechanisms by which increased demand
for biofuels could influence soybean planting. In general, increased demand for biofuels can
increase demand for soybean oil as a feedstock for biofuel production, which could in turn result
in higher soybean prices and incentivize farmers to increase soybean planting. However, as
discussed above, historically the majority of the value of soybeans has been derived from the
soybean meal, with the soybean oil representing an important but less valuable byproduct. In
more recent years export markets have also played an increasing role in the demand for, and thus
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the price of, soybeans. Both of these factors (demand for soybean meal for livestock feed and
demand for soybeans in the export) are expected to continue to influence demand for soybeans in
future years. To increase soybean planting, increased biofuel demand would have to increase
demand for soybean oil for biofuel production above and beyond what would already be
produced as a byproduct of soybean meal production. A key element of our projection of what
soybean planting would be in future years in the absence of the RFS program is projected meat
production, since the soybean meal is used almost exclusively as livestock feed.
To estimate what soybean planting would be from 2023-2025 without the RFS volume
requirements we considered actual and projected meat production in the U.S. in these years. We
found that soybean planting, and especially soybean crush, has historically been well correlated
with domestic meat production. This makes sense since, as noted above, historically the majority
of the value of soybeans has been derived from the soybean meal that is sold as livestock feed.
Thus, as meat production has increased, soybean crush and soybean planting have also increased.
Using correlations between domestic meat production and soybean plantings and crush to inform
our estimates of what soybean planting would have been or would be in the absence of RFS
volume requirements also benefits from the fact that we do not expect the RFS volume
requirements to appreciably impact domestic meat production.
USDA reports domestic meat production (red meat and poultry) starting in 1921.
However, some of the data sets appear missing or incomplete prior to 1983. To estimate soybean
plantings in the absence of RFS volume requirements we first considered the correlation between
total red meat and poultry production18 and soybean plantings as reported by USDA from 1983—
2020 (USDA, 2022b). While data for 2021 is available, we chose not to include these data in our
assessment of the relationship between meat production and soybean planting. This is because,
as discussed further below, the price for soybean oil and the value of the soybean oil relative to
the soybean meal produced when soybeans are crushed increased significantly in 2021 relative to
historical norms (see Figure VI.B-9). This indicates that starting in 2021 demand for soybean oil,
whether for biofuels or other markets, may be a bigger factor in the demand for soybeans from
soybean crushing facilities and ultimately overall soybean demand relative to previous years.
We used the linear least squares regression function in Microsoft Excel to determine an
equation to define the correlation between domestic meat production and soybean planting
between 1983 and 2020 and to assess the strength of this correlation. The equation describing the
correlation was used to project what soybean planting would have been in the absence of the
RFS volume requirements based on projected meat production from 2023-2025.19 The data
described in this paragraph, including the linear regression, the equation used to estimate
soybean planting from 2023-2025 in the absence of the RFS volume requirements using this
methodology, and the strength of the correlation (the R2 value), and lines representing the 95th
18 Data on domestic meat production from USDA ERS Livestock and Meat Data, Meat Statistics Tables, Historical.
The correlation was based on total red meat and poultry production. Red meat includes beef, veal, pork, and lamb
and mutton. Poultry includes broilers, other chicken, turkey, and other poultry.
19 Data on projected meat production obtained from the USDA LTAP to 2031. As discussed above, we believe it is
reasonable to use the LTAP as a projection of soybean plantings with the RFS volume requirements, but since we do
not expect the RFS volume requirements will appreciably impact meat production we also believe the LTAP is a
reasonable projection of meat production in the absence of the RFS volume requirements.
114
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confidence interval are shown in Figure VI.B-7. Soybean planting estimates for 2023-2025 using
this equation and a comparison of these values and the estimated soybean planting with the RJFS
volume requirements in place (from USDA's LTAP) are shown in Table VI.B-8.
Figure VI.B-8: Domestic Meat Production vs. Soybean Planting
_ 100,000
£ 95,000
o 90,000
o
° 85,000
g? 80,000
| 75,000
c 70,000
J 65,000
£ 60,000
B 55,000
S
£ 50,000
q 40,000 60,000 80,000 100,000 120,000
Domestic Meat Production (Million Pounds)
• 1983-2020 • 2021-2025 95% CI Linear (1983-2020)
Table VI.B-7: Estimates of Soybean Planting With and Without RFS Volume
Requirements Based on Soybean Planting Correlation (Million Acres)
2023
2024
2025
No RFS Volume Requirements3
84.9
85.3
85.8
With RFS Volume Requirements'3
88.0
88.0
88.0
Difference
3.1
2.7
2.2
a Based on correlation between domestic meat production and soybean planting
b From USDA's LTAP, shown in Table VI.B-6
While there appears to be a reasonably strong correlation between domestic meat
production and soybean planting there also appear to be some shortcomings in using this method
to estimating soybean planting. The largest problem with this correlation appears to be the fact
that it does not accurately account for the impact of changes to soybean plantings to supply
foreign markets. As shown in Figure VI.B-4, exports have become an increasingly important
market for soybeans over the past 10-15 years. Through 2010 soybean exports, while not
insignificant, were a relati vely small portion of the domestic soybean market. From
approximately 2010 through 2021 soybean exports increased significantly. The fact that this
correlation does not account for changes in exports negatively impacts the strength of the
correlation. This is particularly apparent when there are factors such as China's ban on U.S.
y =
0.503x + 30864
i _
R2 = 0.7543
_jrit
•• •
•
•
••
#
115
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soybeans in 2019 that have a dramatic short-term impact on exports as seen in Figure VI.B-4.
Despite these shortcomings, projecting soybean plantings based on a correlation with domestic
meat consumption results in acreage estimates that, with the exception of 2019 and 2020, are at
least directionally consistent with what would be expected.
As an alternative means of assessing soybean plantings in the absence of RFS volume
requirements that is less impacted by external market factors such as soybean exports, we next
considered the correlation between domestic meat production and soybean crushing. This
correlation is of interest because both the livestock industry and the biofuel industry use soybean
products that are produced when soybeans are crushed, soybean meal and soybean oil
respectively, rather than whole soybeans. Because historically most of the value of the soybean
comes from the soybean meal when soybeans are crushed, we would expect to see a strong
correlation between domestic meat production and soybean crushing. If demand for soybean oil
in recent and future years is increasing soybean crushing rates above and beyond what would be
expected based on the historical correlation with meat production, we can likely attribute the
increased crushing of soybeans to increased demand for biofuels.
As with the data on domestic meat production and soybean planting, we considered the
correlation between total red meat and poultry production20 and soybean crushing as reported by
USD A from 1983-2020 (USD A, 2022b). During these years the value of the soybean oil was
generally small relative to the value of the soybean meal produced when soybeans are crushed.
As a result, we believe that it is reasonable to assume that during these years the quantity of
soybean crushed was determined by demand for soybean meal for livestock feed rather than
soybean oil for food or biofuel production. A correlation based on this data therefore can be used
to project likely future soybean crushing if soybean crushing continues to be determined by
demand for soybean meal, as we expect would be the case in the absence of the RFS volume
requirements.
We used the linear least squares regression function in Microsoft Excel to determine an
equation to define the correlation between domestic meat production and soybean crushing
between 1983 and 2020 and to assess the strength of this correlation. The equation describing the
correlation was used to project what soybean crushing would have been in the absence of the
RFS volume requirements based on projected meat production from 2023-2025 in the absence of
the RFS volume requirements using this methodology.21 The data described in this paragraph,
including the linear regression, the equation used to estimate soybean crushing from 2023-2025,
the strength of the correlation (the R2 value), and lines representing the 95th confidence interval
are shown in Figure VI.B-8. Soybean crushing estimates for 2023-2025 using this equation and a
comparison of these values and the projected soybean crushing with the RFS volume
requirements in place (from USDA's LTAP) are shown in Table VI.B-9.
20 Data on domestic meat production from USD A ERS Livestock and Meat Data, Meat Statistics Tables, Historical.
The correlation was based on total red meat and poultry production.
21 Data on projected meat production obtained from the USDA LTAP to 2031.
116
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Figure VI.B-9: Domestic Meat Production Vs. Soybean Crushing
in
13
00
c
o
lyi
3
o
c
ro
(Li
_Q
>•
0
u
'71
01
E
o
Q
2,500
2,300
2,100
1,900
1,700
1,500
1,300
1,100
900
700
500
40,000
V =
0.02x -58.773
i
~3
7
R2 = 0.9682
«¦
120,000
1983-2020
60,000 80,000 100,000
Domestic Meat Production (Million Pounds)
• 2021-2025 95% CI Linear (1983-2020)
117
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Table VI.B-8: Estimates of Soybean Crush With and Without RFS Volume Requirements
Based on Correlation with Meat Production (Million Bushels)
2023
2024
2025
No RFS Volume Requirements®
2,088
2,105
2,125
With RFS Volume Requirements'5
2,250
2,290
2,328
Difference
162
185
203
" Based on correlation between domestic meat production and soybean crushing
b From USDA's LTAP
As expected, the correlation between domestic meat production and soybean crush is
stronger than the correlation between domestic meat production and soybean planting. Unlike the
correlation with soybean planting, external factors such as changes in trade policies by foreign
countries do not appear to have an appreciable impact on the correlation with soybean crush. As
anticipated, we see that in recent years as demand for biofuels has increased and the market has
responded by crushing a greater quantity of soybeans than would have been expected based on
the historical relationship between domestic meat production and soybean crushing. This same
effect can also be seen in the actual/projected relative values of soybean meal and soybean oil.
USD A data through 2020/2021 shows that the percent value from soybean oil in 2020/2021 was
notably higher than in previous years, and projections of soybean meal and oil yields and prices
from USDA's LTAP show that this trend is expected to continue in future years (See Figure
VI.B-10).
Figure VI.B-10: Relative Value of Soybean Meal and Soybean Oil
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
¦4—'
ro
Q
,o>"v VC?> v«£ & , .
-------
We note, however, that during this time period demand for soybean oil in other markets
is also expected to increase. According to USDA's LTAP, soybean oil used for food, feed, and
other industrial uses is projected to increase by 400 million pounds, or approximately 130
million pounds per year, from the agricultural marketing year 2022/23 to 2025/26. The increase
in demand for soybean oil in non-biofuel markets could also be responsible for a portion of the
projected increase in soybean crushing beyond what would be expected based on the historical
relationship between domestic meat production and soybean crushing. Because we are unable to
determine the portion of increase in soybean crushing (relative to the historical observed
relationship) attributable to the projected increase in demand for soybean oil in non-biofuel
markets we have assumed that the entire increase is attributable to biofuel production. This
assumption very likely over-estimates the impact of the RFS program on soybean crushing.
While the correlation between domestic meat production and soybean crushing gives us a
reasonably robust way to project the impact of the RFS volume requirements on soybean
crushing, estimating the impact that increased soybean crushing has on soybean planting presents
another challenge. In one extreme case, we could assume that all of the additional soybeans that
are crushed as a result of the RFS volume requirements are from acres that would not otherwise
be planted. In the other extreme, we could assume that soybean yields increase and soybean
exports decrease in response to additional demand from soybean crushing facilities, and that
soybean planting does not change at all.
The most likely scenario lies between these two extremes. It is likely the RFS volume
requirements will cause an increase in demand for soybean oil, and ultimately an increase in the
price of both soybean oil and whole soybeans. This increase in the price of soybeans could result
in a marginal decrease in the quantity of soybeans demanded in the export market relative to a
scenario without the RFS volume requirements in place. Increasing soybean yields will likely
result in greater soybean production from existing soybean acres, reducing (and potentially even
eliminating) the need for an increase in soybean acreage to meet the increased demand for
soybeans. At this time, we are unable to determine the degree to which increased demand for
soybeans from crushing facilities would result in increased soybean planting vs. reduced soybean
exports. Table VI.B-9 shows the expected impact on soybean planting under three different
scenarios; one scenario where the entire increase in soybean demand is met by increased soybean
planting, a scenario where 50% of the increased demand is met by increased soybean planting
and 50% is met by reduced soybean exports, and a scenario where the entire increase in soybean
demand results in reduced exports. We project that the scenario where 50% of the increased
demand for soybeans from crush facilities is met via increased soybean planting and 50% is met
by increased soybean yields and/or reduced exports is the most reasonable scenario to assume for
further analysis, and have used the expected soybean planting increases from this scenario to
inform the expected impact on listed species in this Biological Evaluation. This estimate is
consistent with the USDA Agricultural Projections to 2031, which project a relatively small
increase in soybean planting (from 87.2 million acres in the 2021/22 agricultural marketing year
to 88.0 million acres in the 2025/26 marketing year) despite a projected increase in soybean
crushing (from 2,190 million bushels in the 2021/22 agricultural marketing year to 2,350 million
bushels in the 2025/26 agricultural marketing year).
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Another important factor that must be considered when estimating changes in soybean
planting attributable to increased demand for soybean oil for biofuel production is the degree to
which new soybean acres are planted on land that is not currently being used to produce crops
(extensification) or land that is currently being used to produce non-soybean crops
(intensification). If new soybean acres are grown on land not currently being used to produce
crops, we would expect total cropland in the U.S. to increase proportionally to the increase in
soybean acres. That is, total U.S. cropland would be expected to increase by one acre for every
new acre of soybeans that are planted on land that is not currently being used to produce crops.
If, however, new soybean acres are planted on land currently being used to produce other crops,
the situation is more complicated. In this case it is possible that the crops displaced by new
soybean acres would instead be grown on land that is not currently being used to produce crops.
Alternatively, it is possible that total production of the crops displaced by soybeans decreases. In
the case where soybean acres are planted on land currently being used to produce other crops
total U.S. cropland would be expected to increase by less than one acre for every new acre of
soybeans that are planted. The analysis of cropland changes associated with corn ethanol
production discussed in Section VI.A.4 found that total cropland increases were less than
increases in corn planting. This suggests that in response to increasing corn ethanol production
corn was planted on land previously used for other crops, and the amount of land used to produce
these other crops decreased rather than moving to areas that were not previously cropland. While
no such analysis has been conducted for soybean biodiesel, we believe a similar effect is likely.
At this time, we do not have sufficient information to estimate the quantity of new
soybean acres that would be grown on land that is not currently being used to produce crops vs.
land that is currently being used to produce non-soybean crops, nor do we have sufficient
information to project whether production of non-soybean crops displaced by new soybean acres
would decrease or shift to land not currently being used to produce crops. In the absence of this
information, we have assumed a worst-case scenario; that every additional acre of soybeans
attributable to the RFS volume requirements increases total U.S. cropland by one acre.
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Table VI.B-9: Potential Soybean Planting Increases from RFS Volume Requirements
(Million Acres)
Scenario
2023
2024
2025
100% New Planting
3.14
3.55
3.86
50% New Planting/ 50% Reduced Exports
1.57
1.78
1.93
100%) Reduced Exports
0.00
0.00
0.00
The projections in Table VI.B-9 represent our best efforts to project the increase in
soybean acreage in 2023-2025 that might possibly be attributable to the RFS volume
requirements using the best data currently available. Nevertheless, there is significant uncertainty
inherent in these projections. The projected acreage increases are based on an increase in
soybean crushing calculated as the difference between a historical correlation between domestic
meat production (for the scenario that represents soybean crushing in the absence of the RFS
volume requirement) and a USD A projection of soybean crushing in future years (for the
scenario that represents soybean crushing with the RFS volume requirements in place). While we
believe both of these projections are reasonable, projecting future activity based on a correlation
from historical data or projections that do not explicitly consider the RFS volume requirements
introduces uncertainty to our projections. This uncertainty is compounded by the fact that we
have no reliable data to inform our estimates of whether the increase in the quantity of soybeans
processed at a crushing facility would result in reduced soybean exports or increased soybean
production, and if it were to result in increased soybean production what effect this increase in
production would have on total U.S. cropland. In general we have tended to make assumptions
that would tend to over-estimate the impact of the RFS volume requirements on soybean planting
and cropland expansion, and as such the acreage increases we have projected are likely over-
estimates.
C. Canola Production Potentially Attributable to the RFS Set Mule
In the context of a recent final rulemaking (FRM) in response to an RFS pathway petition
from the US Canola Association (USCA) to add canola oil-based pathways22 to the program,
EPA conducted an analysis of the impacts of consuming more canola oil-based biofuels in the
United States (U.S.). This analysis was described in detail in the notice of proposed rulemaking
(NPRM) (87 FR 22823, 2022) and summarized in the subsequent FRM (87 FR 73956, 2022).
This analysis was primarily comprised of agricultural economic modeling and included
estimates of the U.S. cropland and other land cover changes which might result from an increase
in U.S. consumption of canola oil-based fuels. We combine this analysis with recent estimates of
the increase in canola oil-based fuels though 2025 associated with the RFS program. This
estimate was produced for the 2023-2025 Set Proposed Rulemaking which is the focus of this
22 As described in Table 1 of 40 CFR 80.1426, a renewable fuel pathway is defined for the purposes of the RFS
program as a unique combination of three essential characteristics: a feedstock (e.g., corn starch, soybean oil, canola
oil), a fuel production process (e.g., fermentation, transesterification, hydrotreating), and a finished fuel (e.g.,
ethanol, biodiesel, renewable diesel).
121
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Biological Evaluation. Through the combination of these two analyses, we derive an estimate of
the cropland impact of the use of canola oil-based fuels potentially attributable to the RFS
program. Figure VI.C-1 illustrates the steps we used to estimate the increase in total cropland
due to the increased consumption of canola oil-based fuels potentially attributable to the Set
Rule.
Figure VI.C-1: Process for Estimating Impacts of Canola Oil-Based Fuels on Total
Cropland in the U.S.
We first provide a summaiy of the agricultural economic modeling conducted for the
canola oil pathways rulemaking to estimate the U.S. cropland and other land cover impacts of
canola oil-based fuels (Sections VI.C.l and VI.C.2). Following this, we describe the estimated
volume of canola oil-based fuel production in the United States potentially attributable to the
RFS program estimated for the Set Rulemaking (Section VI.C.3). Finally, we combine these two
analyses to derive the total quantity of U.S. canola crop area potentially associated with this
increase in fuel production (Section VI.C.4).
X. Description of EPA Agricultural Economic Modeling of Canola Oil-Based
Fuels
In the recent canola oil-based fuel pathways FRM described above, EPA used the same
biofuel lifecycle analysis methodology and modeling framework developed for the March 2010
RFS2 rule (75 FR 14670, 2010) and that was subsequently used for the September 2010 Canola
Oil Rule (75 FR 59622, 2010).2 , The components of this methodology relevant to the present BE
involve the use of domestic agricultural modeling to estimate emissions from land use change,
crop production, and livestock in the U.S. This methodology was developed to estimate
"lifecycle greenhouse gas emissions" as defined at section 2ri(o)(l)(H) of the Clean Air Act. It
was used for the March 2010 RFS2 rule after an extensive peer review and public comment
process.
23 For information about our 2010 methodology' and analysis see Section 2 of the regulatory impact analysis (RIA)
for the March 2010 RFS2 rule and the associated lifecycle results (Docket Item No. EPA-HQ-OAR-2005-0161-
3173).
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This domestic agricultural modeling methodology uses the Forest and Agricultural Sector
Optimization Model with Greenhouse Gases model (FASOM). Using this methodology, we
modeled and evaluated a hypothetical canola oil demand shock scenario to estimate changes in
domestic agricultural production, trade, and land use and associated GHG emissions associated
with the biofuel pathway under consideration. In this demand shock scenario, U.S. domestic
consumption of a specific biofuel pathway is assumed to increase by some amount relative to the
volume of U.S. domestic consumption in a reference scenario.
EPA conducted two modeling scenarios in FASOM for this analysis.24 The difference in
GHG emissions between these two scenarios represents our estimate of the emissions from land
use change, agricultural input, livestock, and other agricultural sector impacts associated with
using canola oil as a biofuel feedstock. First, we ran an updated Control Case that reflected the
updated assumptions for global canola oil production, yields, and trade.25 In this Control Case,
we assumed no canola oil-based biofuels were consumed in the U.S. Second, we conducted a
shock scenario that assumed a 1.53 billion pound increase in canola oil production for use as
feedstock to produce approximately 200 million gallons of canola oil-based fuels for U.S.
consumption of in 2022 (hereafter the "Canola Case"), which was assumed to ramp up linearly
from 2012 to 2022 (see Table VI.C-1).26 According to USDA historical data, annual U.S.
consumption of canola oil ranged from about 5.3 to 6.4 billion pounds over the period between
2015 and 2020 (USDA, 2022b). In addition, global canola/rapeseed seed annual exports ranged
from approximately 32 to 38 billion pounds between 2015 and 2020 and canola/rapeseed oil
exports ranged from about 9 to 13 billion pounds over the same period; this suggests substantial
quantities of additional feedstock may be available for import to the U.S. market (USDA, n.d.).
Based on data from the EPA Moderated Transaction System (EMTS), the U.S. produced
approximately 160 million gallons of canola oil biodiesel in 2020, and another 123 million
gallons of biodiesel produced from a mix of feedstocks were imported from Canada, which
likely included a portion from canola oil. Thus, the volume of hydrotreated canola oil-based fuels
in the modeled shock is a similar order of magnitude as the volume of biodiesel currently
produced from canola oil. Finally, according to EPA's administrative data from the RFS
program, about 1.5 billion RINs were generated for renewable diesel in 2019, equivalent to about
900 million gallons (US EPA, 2018a). Based on these data, we believe the magnitude of the
assumed shock in the Canola Case is reasonable and appropriate.
All other assumptions were held constant between the Control Case and the Canola Case.
The structure of this shock was designed to be consistent with the shock methodology approach
used for EPA's previous lifecycle GHG analyses of agricultural feedstocks under the RFS
program.
24 Complete sets of results for these FASOM modeling scenarios are available on the docket for the rulemaking they
were conducted for: EPA-HQ-OAR-2021-0845.
25 A memorandum describing these updates and referencing their sources is available on the docket EPA-HQ-OAR-
2021-0845.
26 Depending on the source of hydrotreating process data used, the size of the shock ranges from 187 million gallons
of hydrotreated renewable fuel (based on GREET-2021) to 220 million gallons (based on data in petitions submitted
pursuant to 40 CFR 80.1416 claimed as confidential business information).
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Table VI.C-1 - Canola Oil Shock Scenario27
Year
Assumed Increase in USA Canola Oil Consumption for Biodiesel
Production
(Billion Pounds of Canola Oil)
2012
0.25
2017
0.9
2022 through 2057
1.53
EPA used FASOM to estimate, among other impacts, domestic land use change
associated with using canola oil as a biofuel feedstock. The differences in modeled biofuel
consumption outcomes between the Control Case and the Canola Case are described in Table
VI.C-1. Unless otherwise stated, the data presented in the tables below are the calculated
differences between the Control Case and the Canola Case (i.e., the model output value for a
variable reported in the Canola Case minus the output value for that same variable reported in the
Control Case). In this summary, we first describe the ways in which FASOM estimates the
canola oil feedstock used to supply the biofuel shock would be sourced. We then describe the
market adjustments in canola oil prices, supply, demand, and trade which FASOM estimates
would be necessary to facilitate this sourcing of canola oil for fuel use. Following this, we
describe the shifts in production of other crops, cropland use, and land use which FASOM
estimates would occur as a result of the sourcing of canola oil for fuel use.
2. Results of the Economic Modeling for Canola Oil Bio fuels
The total quantity of canola oil required to produce the assumed marginal volume shock
in the Canola Case was assumed to be approximately 1.53 billion pounds. To supply this
quantity of canola oil to the biofuel production sector, FASOM made several market
adjustments. Of the total 1.53 billion pounds required, FASOM estimated approximately 1.28
billion pounds would be supplied by increasing the total U.S. supply of canola oil via a
combination of increased imports and increased domestic production. These 1.28 billion pounds
would represent an approximately 28 percent increase in total domestic supplies of canola oil.
FASOM estimates canola oil imports would increase by about 1.18 billion pounds. Domestic
crushing of canola seed into meal and oil would produce about 0.1 billion pounds of additional
canola oil. Domestic demand for non-fuel uses of canola oil, inclusive of all food uses (e.g.,
cooking, baking, salad dressings) and non-fuel industrial uses (e.g., industrial lubricants,
cleaning products, cosmetics), would decrease by approximately 0.25 billion pounds to provide
the remaining canola oil required to meet the 1.53-billion-pound shock. These shares of biofuel
feedstock are summarized in Table VI.C-2.
27 Note that, consistent with our existing methodology, the volume shock is implemented slightly differently in
FASOM and FAPRI. For FASOM, which operates in 5-year time steps, the values in this table fully represent the
assumptions used to implement the shock. For FAPRI, which operates in annual time steps, interim year assumption
values are interpolated linearly to create a smooth "ramp-up" path for the volume shock. Further description of this
methodology can be found in Chapter 2 of the Final Regulatory Impact Analysis associated with the March 2010
RFS2 rule (EPA-420-R-10-006).
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Table VI.C-2 - Sources of Canola Oil for Biofuel Feedstock in the Canola Case
Quantity
Percent of Total
Feedstock Source
(Billion Pounds)
Volume Shock
Increased Imports
1.18
77%
Reduced Domestic Demand
for Non-Fuel Uses
0.25
16%
Increased Domestic
Production
0.1
7%
Total Volume Shock
1.53
100%
FASOM estimates canola oil imports would increase by approximately 40 percent in
2022 in response to the shock. Because modeled non-fuel uses of canola oil are not drawn on as
significantly to provide feedstock for this shock, FASOM does not estimate there would be a
significant need to backfill the domestic U.S. vegetable oil market. Domestic consumption of
other vegetable oils therefore does not change significantly in these results. Following this,
FASOM estimates virtually no changes in imports of other vegetable oils in these results.
Increased demand for canola oil in response to the volume shock is estimated to cause the
average price of canola oil for all uses to increase by approximately 24 percent in the Canola
Case. This price increase would put downward pressure on other uses of canola oil, and non-
biofuel domestic demand for canola oil is estimated to decrease by approximately 5.6 percent.
FASOM estimates these higher prices would also induce domestic U.S. production of canola oil
to increase by about 7 percent. Table VI.C-3 reports changes in supply, demand, and prices for
canola oil in the Canola Case relative to the Control case. Changes for other modeled vegetable
oils, specifically soybean oil and corn oil, are estimated to be in the range of 0.03 percent or less
and are not presented here, though these results are available in the docket of the rulemaking for
which this analysis was originally conducted.28
Table VI.C-3 - Canola Oil Market Responses in 2022 (in percentage changes)
Percent
Change from
Control Case
Total Domestic Demand
-5.6%
U.S. Imports
38.9%
U.S. Production
7.0%
U.S. Price
24.1%
FASOM estimates the increase in canola oil production would result in an increase in
canola seed crushing of approximately 253.5 million pounds, an increase in domestic canola oil
production of about 7 percent compared to the Control Case. Most of this increase in canola
crushing would be supplied through increased imports of whole canola seed. Of the total increase
28 Further information is available in the documents, "CanolaFASOM results" and "FASOM HTML (full results)"
available in docket EPA-HQ-OAR-2021-0845.
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in canola seed supply to the crushing market, 87 percent is estimated to come from increased
imports and 13 percent is estimated to come from increased domestic U.S. production. As
observed above, the U.S. canola product markets are historically import-dependent. Based on
this, we believe the response in FASOM is consistent with historical market patterns. However,
FASOM estimates the increase in domestic crushing would also induce a response from
domestic canola seed demands. FASOM estimates direct domestic uses of canola seed other than
crushing would decrease by approximately 16 percent. Domestic canola seed production also
responds, and FASOM estimates domestic production would increase by approximately 1
percent. These impacts are summarized in Table VI.C-4. This increase in U.S. canola seed
production would be facilitated in part by a modeled expansion in canola harvested crop area of
about 17,600 acres, or about 1.2 percent, in the U.S. in 2022 (see Table VI.C-5).
Table VI.C-4 - Canola Seed Market Responses in 2022 (in Million Pounds)
Change from Control Case
Total Domestic Demand
-5.8 (-16%)
U.S. Imports
216.5 (20%)
U.S. Production
31.3 (1%)
U.S. Canola Seed Crushing
253.5 (7%)
These shifts in canola supply, demand, and trade would also have implications for
production and consumption of other crops. The modeled increase in canola crushing also
produces an additional 156 million pounds of canola meal, all of which FASOM estimates would
be supplied to the domestic livestock market. This influx of meal would primarily displace corn
in livestock diets. Corn consumption in the domestic feed market is estimated to decrease by
about 306 million pounds (about 0.08 percent). This same dynamic can be observed in the
FASOM results for commodity trade. As international trade partners increase exports of canola
oil to the U.S., these exporters crush additional canola seed. This creates additional supplies of
meal for these canola-producing nations, reducing their demands for corn as well. As a result,
corn exports from the U.S. are estimated to decrease by about 271 million pounds (about 0.28
percent). On net, FASOM estimates that U.S. corn production would decline by about 589
million pounds and that corn harvested area would decline by about 49,100 acres, or about 0.06
percent (see Table VI.C-5).
Note that, as described further below in this section, we did not consider any of
FASOM's estimated decreases in crop area when estimating the impacts on endangered species.
This is a very conservative assumption which likely leads to an overstatement in the nationwide
crop area impact of canola oil-based fuels on endangered species. However, as described earlier
in this evaluation, we canola oil-based fuels and corn-based fuels may both expand production
under Set rule volume standards. We believe ignoring the decline in corn area projected by
FASOM improves alignment between our canola analysis and our corn analysis, where corn area
increases within the study area. Therefore, while it leads to a very conservative estimate of
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nationwide crop area expansion attributable to canola oil, we believe it improves the overall
scientific robustness of our findings with respect to potential impacts on endangered species.
Canola and wheat can be produced on the same type of land in high latitude agricultural
systems like Canada and North Dakota, and many farmers rotate the two crops. In response to an
increase in production of canola, farmers are likely to respond in one of two ways. One option is
that total acres in wheat/canola rotation could increase. The other option is for canola to displace
wheat area to some extent as farmers tilt rotations more heavily towards the former (e.g., canola-
canola rotations rather than canola-wheat rotations). We observe these complex dynamics in the
FASOM results for the Canola Case. To increase canola exports to the U.S. market, FASOM
estimates the international market would decrease production of wheat, creating an opportunity
for U.S. wheat producers to increase their exports. This impact is relatively marginal in
comparison to the shock. However, FASOM estimates U.S. wheat exports would increase by
about 174 million pounds, or about 0.18 percent. Domestic wheat production would increase by
about 169 million pounds and the harvested area in wheat production (excluding wheat used for
grazing) would expand by about 63,000 acres, or about 0.02 percent (see Table VI.C-5).
The modeling results also show some minor net shifts in other cropland as markets re-
equilibrate in response to the shock, totaling about 28,100 harvested acres, or about 0.01 percent.
Harvested crop area impacts are summarized in Table VI.C-5. The shock results in modeled net
increase in total domestic harvested crop area of approximately 60,600 acres. This increase
would require some shifting of land use from other uses to cropland; as discussed later in this
section this land is shifted into cropland from pasture and cropland pasture on net.
Table VI.C-5 - Harvested Crop Area Responses in 2022 (in Thousand Acres)
Change from Control
Canola
17.6(1.2%)
Wheat
63 (0.02%)
Corn
-49.1 (-0.06%)
All Else
28.1 (0.01%)
Total
60.6 (0.02%)
Geographically, the modeled domestic response to the shock is concentrated in North
Dakota. Canola production is estimated to increase in North Dakota by about 28.9 million
pounds (about 1.4 percent) and canola crop area is estimated to expand by 16,300 acres (as
discussed later in this section, this acreage comes from a mix of existing and new agricultural
land). This accounts for about 92 percent of the total estimated increase in U.S. domestic canola
production in the Canola Case. As North Dakota is the dominant producer of canola in the U.S.,
this modeled impact appears to be consistent with historical agricultural patterns. North Dakota
is also a significant producer of wheat. As canola production is estimated to expand in North
Dakota, FASOM estimated wheat production would shift to North Dakota region by about 218
million pounds, decreasing on net in all other regions by about 50 million pounds.
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Canola is generally crushed near areas of cultivation and a majority of U.S. facilities that
process canola seed are located in North Dakota (NOPA, 2022). Following this, as North Dakota
canola production is estimated to expand to supply the canola shock, FASOM estimates the
additional seed would be crushed into oil and meal in this region as well. This would expand
regional supply of livestock feed and would decrease regional feed prices, relative to other
regions of the U.S. FASOM estimates that this, in turn, would create incentives to shift livestock
production to North Dakota and nearby states. Since livestock feed mixes require several
different components, FASOM estimates this shift in livestock production towards North Dakota
would also shift production of other feed crops (e.g., corn, soybeans, hay) into North Dakota.
Production of these feed crops are estimated to increase by a total of 115,000 acres in 2022. The
modeled changes in North Dakota crop area are summarized in Table VI.C-6. FASOM estimates
net cropland in North Dakota would increase by 218,300 acres.29
Table VI.C-6 - Changes in North Dakota Crop Area in 2022 (in Thousand Acres)
Change from Control Case
Canola
16.3 (1.39%)
Wheat
86.8 (1.42%)
All Else
115.2(1.38%)
Total
218.3 (1.39%)
Within North Dakota, FASOM estimates that most this additional cropland (212,000
acres) would be taken from USD A Conservation Reserve Program (CRP) land and a smaller
amount (7,000 acres) would be taken from cropland pasture. CRP land is essentially cropland
which has been allowed to lie fallow to improve environmental health and quality (USDA,
2013). Cropland pasture is a USDA-defined category, describing land on which crops are planted
but not harvested and on which animals are allowed to feed or graze (USDA, 2019).
As crop area expands in North Dakota in response to the shock and livestock production
shifts to this region, FASOM estimates total crop area would decrease in the rest of the U.S.
FASOM estimates this dynamic would primarily shift production from Iowa and Kansas to
North Dakota, suggesting a relatively modest northwesterly shift overall. On net, national crop
area is estimated to expand by 60,600 acres in 2022. The modeled state-level changes in total
harvested crop area are summarized in Table VI.C-7.
29 Note that FASOM does not track conversion of other land types to cropland by crop. This modeled expansion in
North Dakota cropland is best understood as an increase in total cropland at the expense of other land uses rather
than an expansion cropland for canola, wheat, or any other specific crop into previously uncropped area.
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Table VI.C-7 - Changes in Regional Harvested Crop Area in 2022 (in Thousand Acres)
Change from Control Case
North Dakota
218.3 (1.4%)
Iowa
-82.7 (-0.3%)
Kansas
-60.5 (-0.5%)
All Other Regions
-14.5 (-0.01%)
Total
60.6 (0.02%)
There are two important observations that can be drawn for comparing the projected
changes in harvested crop areas in the U.S. (Table VI.C-5) and in North Dakota (Table VI.C-6).
The first is that the projected change in total crop area in North Dakota is greater than the
projected change in total crop area in the U.S. This indicates that there is a projected decrease in
total crop area in the U.S. in states other than North Dakota. The FASOM estimates project that
significant quantities of cropland shift from Iowa and Kansas to North Dakota in response to the
shock in canola oil demand (see Table VI.C-7). By focusing our analyses on the projected
change in harvested crop area we are assessing something akin to a worst-case scenario, as it is
possible that rather than increasing harvested crop area in North Dakota and decreasing
harvested crop area in Iowa, Kansas, and others states, farmers could instead respond by keeping
this cropland in other states in production, thus decreasing the demand for new cropland in North
Dakota. This alternative outcome seems even more plausible when considering the types of
cropland expected to increase in the U.S. in response to the Canola shock. In the FASOM
estimates both North Dakota and the U.S. as a whole saw larger increases in acreage for wheat
and other crops than acreage for canola production. Because both wheat and other crops have
greater ranges in the U.S. than canola, there is likely greater uncertainty in the geographic
locations for increases in these crops. Taken together, these two observations suggest that the
FASOM results may over-estimate land use changes in North Dakota because the estimated
acreage increases could occur on cropland that is estimated to cease production in other states
and because the estimated land use changes may occur in a broader geographic area than
estimated, lessening the intensity of the estimated land use changes in North Dakota.
As FASOM estimates cropland would expand in North Dakota, the majority, about
212,000 acres, is estimated to shift into cropland status from land that is placed in CRP in the
Control Case. The remaining area shifting into cropland status is estimated to shift from cropland
pasture. As modeled crop production shifts on the margin out of Iowa and Kansas, FASOM
estimates CRP area would increase in these regions to compensate for the decrease in North
Dakota CRP area; nationwide CRP area does not change on net in our results. FASOM estimates
pasture area would decrease nationwide as greater availability of livestock feed would slightly
reduce demand for grazing. In some regions, FASOM estimates this previously grazed
pastureland would be forested instead, leading to a modeled increase in forestland. The changes
in total regional crop area are summarized in Table VI.C-8.
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Table VI.C-8 - Changes in National Land Area in 2022 (in Thousand Acres)
Change from Control Case
Cropland30
61 (0.02%)
Cropland Pasture
-57 (-0.07%)
Pasture
-36 (-0.04%)
Forest
32 (0.01%)
In summary, our FASOM results suggest that the production of 200 million gallons of
canola oil-based fuels in the U.S. would result in an increase in North Dakota crop area of
218,300 acres, a decrease across all other regions of approximately 157,700 acres, and a net U.S.
cropland increase of 60,600 acres. These results are summarized in Table VI.C-9. This results in
an impact of approximately 1,092 acres per million gallons of fuel in North Dakota, and 305
acres per million gallons nationwide. This impact is substantially smaller than the estimates
discussed for corn ethanol and soybean oil biodiesel discussed elsewhere in this Biological
Evaluation. This difference is largely attributable to the significant U.S. reliance on imported
canola oil to meet an increase in canola oil-fuel demand.
Table VI.C-9 - Change in Cropland Area per Million Gallons (in Thousand Acres)
Area
North Dakota
218.3
Rest of U.S.
-158.7
National
60.6
3. Estimated Volume of Canola-Oil Based Fuels Attributable to the RFS Set
Mule
In the context of the Set Rulemaking NPRM, EPA produced estimates of the volume of
canola-based fuel that might be expected to be produced with and without the proposed volume
regulations. These estimates are shown in Table VI.C-11 below. The quantities of canola oil
feedstock expected to be used to produce these fuels were also estimated in the Set NPRM and
are shown in Table VI.C-10 below.
30 Note that cropland reported in national land area includes land that is planted but intentionally not harvested, e.g.,
crops grown for grazing. Land area totals will therefore differ slightly from the harvested crop area data discussed
above.
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Table VI.C-10: Domestic Biodiesel and Renewable Diesel
Projected to Be Produced from Canola Oil (million gallons)
2023
2024
2025
Biodiesel
240
240
240
Renewable Diesel
0
0
0
Total
240
240
240
Table VI.C-11 Potential Impact of the RFS Volume Requirements on Use of Canola Oil for
Biofuel Production (million lbs)
2023
2024
2025
Estimated Canola Oil Use
Without RFS Requirements
0
0
0
Actual/Projected Canola Oil
Use With RFS Requirements
1,824
1,824
1,824
Difference
1,824
1,824
1,824
EPA estimates that, with proposed RFS standards for 2023 through 2025 in place,
approximately 240 million gallons per year biodiesel-equivalent (MGY) of canola oil-based fuels
would be consumed in the U.S. Canola oil use to produce biofuel has been relatively constant in
previous years, and as a result we project it will be relatively constant in the near future as well.
EPA estimates that, without these standards in place, the volume of U.S. canola oil-based fuel
consumption would be virtually zero. Therefore, we estimate that this full 240 MGY would be
attributable to the RFS program. A more detailed discussion of our assessment of the projected
volume of canola oil used to produce biofuel for 2023-2025 can be found in the RFS Set Rule
proposal (87 FR 80582, 2022).
We acknowledge that there is significant uncertainty in this estimate and the attribution
of it to the RFS program. Recently enacted incentives under the Inflation Reduction Act (IRA)
are one source of this uncertainty. The IRA includes tax incentives for sustainable aviation fuel
(SAF) which may create significant demand pull for these types of fuels even absent RFS
standards. Like other vegetable oils, canola oil can be transformed into jet fuel via hydrotreating
processes. Therefore, with these IRA SAF incentives in place in future years, financial incentives
to consume canola oil-based biofuels may exist over the time frame of this analysis, even if the
RFS program itself were to cease. At this time we do not have the tools that would allow us to
incorporate a consideration of these uncertainties in our assessment of future consumption of
canola oil for biofuel use. But we do acknowledge that their existence makes it appropriate to
characterize the estimated impact of the RFS program on canola oil-based fuels as a relatively
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conservative estimate, probably closer to the upper bound of expected fuel demand pull than it is
to the lower bound of expectations.
4. Estimated Potential Impact of Increased ( anola-Based Fuels on U.S.
Cropland
Using the estimated canola oil-based fuel volume potentially attributable to the RFS
program and the estimated cropland cover impact of canola derived from our FASOM analysis,
it is possible to derive an estimate of the quantity of cropland needed to produce the volume of
canola oil attributable to the RFS program. This estimate is relative to a baseline representing a
hypothetical scenario where the RFS program does not exist.
Assuming, firstly, that 240 MGY of canola oil-based biofuels can be attributed to the
RFS program and, secondly, that the average U.S. cropland impact of canola oil-based fuels is
1,092 acres per million gallons of fuel in North Dakota, and 305 acres per million gallons
nationwide, we estimate a total impact of 262,080 acres of cropland in North Dakota and 73,200
net acres nationwide are attributable to canola oil-based fuels produced due the RFS program.
These results are summarized in Table VI.C-12.
Table VI.C-12 - Estimated change in total crop area attributable to canola oil-based fuels
under the RFS program (in Million Acres)
Area
North Dakota
0.26
National
0.07
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YH. Land Use Change Potential Impacts on I isteel Species
A. Potential Impacts from Increased Corn Production
1. Identifying Potential Locations of Acres Impacted (FWS Species)
Given the acreage of new cropland and corn estimated in Table VI.A-10 potentially
attributable to the Set Rule, we next needed to identify the areas where those land conversion
changes may occur in order to assess the potential impact on listed species and habitat (Step 2 of
the process outlined in Figure ES.l). As discussed in more detail in Section VLA.4, the total
cropland impacts of increased ethanol demand are expected to be induced through market
mediated effects and thus are not any more (or less) likely to occur near ethanol production
facilities. Furthermore, there is no modeling tool that we are aware of at this time that can predict
with any certainty the precise location of these likely very small land use changes.
Though we may not have been able to estimate the precise location of land conversions to
corn or any cropland due to ethanol production that may be attributable to the Set Rule, we were
able to use a probabilistic approach to estimate the potential overlap between cropland changes
and critical habitats or listed species ranges. In essence, a probabilistic approach randomly
selects lands for conversion from a defined set of available land that add up to the amounts in
Table VIA-10, which can subsequently be used to assess whether the species in those habitats or
the habitats themselves could potentially be affected by those land conversions. If we repeat that
random land-selection process a large number of times, we generate an estimated probability of
impact for the land use change estimates potentially attributable to the Set Rule in Table VI. A-
10. This overlap could indicate a modification to the geographical area that represents the
species' critical habitat and, as such, could impact essential PBFs present in critical habitat. Not
all land within the boundary of a critical habitat unit will have PCEs or PBFs. Additionally,
given the uncertainty described below, such an overlap does not indicate that such a result is
likely to occur.
As shown in the right two columns in Table VIA-10, we estimated that 390,000-460,000
acres of noncropland may be converted to cropland between 2023 and 2025. Assuming the same
trends found in Lark et al. 2015, most of this noncropland that is converted would likely be
grassland including both native and planted grasslands, as well as lands that may have been
previously used for pasture or hay or retired croplands planted to permanent vegetative cover
through the Conservation Reserve Program. The conversions of such lands may involve new
tillage and application of fertilizers and pesticides for the new crop. This may occur inside
species' critical habitat and/or range, the former which may be detrimental to the species if PBFs
within the critical habitat are affected, or it may be outside the critical habitat and/or range. It is
possible that grassland within a critical habitat unit does not contain any PCEs or PBFs; on the
other hand, such grasslands could have the potential to provide those PCEs/PBFs in the future if
they were not converted. The same could be the case within other land cover types (not just
grasslands).
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Conversion outside the critical habitat and/or range may also affect the species through
things such as pesticide drift or overland flow of nutrients or pesticides. A common buffer used
to capture potential effects nearby to habitat is 2600 feet.31 The effect of the conversion of one
crop to another (e.g., wheat to corn, soy to corn), depends on the specific conversion considered,
and the species under consideration (because some species may be more sensitive to the
pesticides for one crop over another). Corn uses more fertilizers than most other crops, but the
mixture of pesticides used may differ from crop to crop, with differing effects depending on the
crop switch and species under consideration. For example, the rise of genetically engineered corn
(e.g. Roundup Ready) facilitated greater adoption of glyphosate-based pesticides, which are less
toxic to many non-plant species than many of the pesticides used earlier. Because of these
complexities, and because we anticipate the largest potential effect on species to be from
conversion of non-cropland habitat to any cropland, we focus on the increase in total cropland in
Table VI.A-10 of 390,00-460,000 acres (rather than the increase in corn planting) as the effect to
examine in terms of the impacts of land use change on listed species. Impacts on species from
water quality impacts are discussed in Section VIII. Much of the increase in corn acreage in
Table VI.A-10 is likely from cultivation of new cropland, and much of the new cropland is likely
corn.
To assess the potential land use impacts on listed species from increased demand for corn
ethanol using a probabilistic approach, we began with the area of potential land use change in
Figure III.B-2 and overlayed that with the critical habitat and the range data provided by the
Services. We first discuss results for FWS species and then NMFS species separately.
The largest estimated land conversion in Table VI.A-10 is for 2025, with an estimated
increase of 460,000 acres of total cropland. To be conservative, we estimate the potential effect
of conversion of 500,000 acres of available land to cropland in the area of potential land use
change. Not all land in the area of potential land use change is likely to be converted to
agriculture (e.g., urban areas, water, etc.). We used land cover classes from the National Land
Cover Dataset (NLCD) to identify areas for potential conversion from non-cropland to cropland.
Lark et al. (2015) found that the most common land cover type converted to crop production was
grassland (77%), shrubland (8%), and idle land (8%). We used as the summation of four land
cover classes and the most likely set of land for potential conversion:
• shrub (NLCD class #52)
• grassland/herbaceous (#71)
• pasture/hay (#81)
• emergent herbaceous wetlands (#95)
Idle land is not a land cover class in the NLCD and is likely pasture/hay. Wetlands have a high
conservation value for the ecosystem services that they provide and as habitat for many species,
and emergent herbaceous wetlands are easier to convert to agriculture than wooded wetlands due
31 From the EPA Revised Method for National Level Listed Species Biological Evaluations of Conventional
Pesticides (US EPA, 2020): "The endpoint that results in the farthest distance from the treated field where any effect
to the listed species or it's Prey, Pollination, Habitat, and/or Dispersal (PPHD) may occur relative to a specific listed
species will be used to determine the off-site transport distance for that species. This distance is capped at 2600 feet
(the area limit of the AgDRIFT model)."
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to the absence of trees. Thus, to be conservative we included this land cover class. Other land
cover classes are unlikely to be converted to cropland in the area of potential land use change.
Forested areas are uncommon, more difficult to convert to agricultural production, and were
found in Lark et al. (2015) to account for a small percentage (3%) of lands converted to
agriculture. We therefore focus on the total of these four land cover classes as the source of land
for potential conversion to cropland.
Figure VII.A-1: NLCD land cover classes for potential conversion.
NLCD Land Cover Classification Legend
11 Open Water
12 Perennial Ice/ Snow
21 Developed, Open Space
22 Developed, Low Intensity
| 23 Developed, Medium Intensity
I 24 Developed, High Intensity
31 Barren Land (Rock/Sand/Clay)
41 Deciduous Forest
| 42 Evergreen Forest
43 Mixed Forest
51 Dwarf Scrub*
I 152 Shrub/Scrub
71 Grassland/Herbaceous
72 Sedge/Herbaceous*
I 73 Lichens*
74 Moss*
81 Pasture/Hay
82 Cultivated Crops
90 Woody Wetlands
95 Emergent Herbaceous Wetlands
* Alaska only
In order to determine how the 500,000 acres of conversion were to be distributed, we
considered the factors used to estimate the national-level acreage impacts shown in Table VI. A-
10. These factors were derived from analyses originally completed by Li et al. (2019) and were
expanded in the context of the draft Third Biofuels Report to Congress (Li et al., 2018) (US EPA
Center for Public Health & Environmental Assessment & Clark, 2023). These effects manifest
either through ethanol production or through price effects. Effects through ethanol production are
simulated to occur closer to biorefmeries (i.e., within 25 miles in Li et al (2019), while effects
through price may occur anywhere in the action area. Since the effects were estimated to be
dominated indirectly by price effects rather than directly through ethanol production, we
simulated the conversion of 500,000 acres of available non-cropland to cropland randomly
across the area of potential land use change.
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We overlayed a 30-acre grid over the continental United States (CONUS) and randomly
sampled 500,000 acres of available land in the area of potential land use change for conversion.32
We then compared this with critical habitat to estimate the area of critical habitat estimated to be
converted to cropland for each species. We repeated the 500,000 acre-conversion simulations
500 times to generate a distribution of probabilistic effects. We also ran simulations for
conversion of 500,000 acres that included a 2600-foot buffer around critical habitat to account
for pesticide drift or other potential effects nearby to species' critical habitat. Finally, conversion
to cropland may impact a species even if it is well outside the critical habitat if it is within the
range that the species occupies. Thus we repeated the above analyses using the range of species
instead of the critical habitat. Because the ranges of species are much larger than the critical
habitat, the simulations were much more computationally intensive. Thus we reduced the number
of replicates for the range simulations to 100. In total, there were four scenarios run to assess
effects on species critical habitat and range (Table VII.A-1) The results are summarized
separately by critical habitat (CH) and range), and by the absence or presence of a buffer.
Table VII.A-1. List of scenarios for FWS species-level effects from increases in cropland
Scenario
#
Total acres
converted (acres)
Critical habitat
(CH) or range (R)
Buffer
Replicate
iterations
SI
500,000
CH
None
500
S2
500,000
CH
2600'
500
S3
500,000
R
None
100
S4
500,000
R
2600'
100
2. Potential Impacts on Listed Species and Critical Habitat (FWS species)
The probability analysis provides some indication about the likelihood that a change in
land use that is attributable to the Set rule might occur within or near the geographical
boundaries of a species' critical habitat or range. For instance, if the probability analysis found
that, out of 500 iterations, 50 of them included land use changes on critical habitat, one might
conclude that there is a 10% chance (50/500) of this occurring in actuality. Such conclusions
necessarily include uncertainty, since a repeat of the 500 iterations might result in more or less
than 50 occasions of overlap between land use change and critical habitat, and a larger or small
number of total iterations can also affect the outcomes. Nevertheless, the probability analysis
provides some indication of what one might expect to see once the standards in the Set Rule are
put into effect.
For the analysis of critical habitat with no buffer (SI), we found that roughly 112 unique
species that were impacted at least once across all 500 iterations of the 500,000-acre conversion
32 The vast majority of U.S. farms are 10-49 acres or larger (USD A, 2019 Census, Figure 2). Thus, using a 30-acre
grid size to capture areas for potential conversion is consistent with U.S. farms. We ran sensitivity analyses using a
15-acre grid and the results were not affected.
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simulations. (Table VII. A-2). We found conversion of 4024 acres of critical habitat on average
across all iterations (10th - 90th percentiles: 3480 - 4560 acres). Some species were impacted in
only a few simulations, while others were impacted more frequently. To illustrate, although there
were 112 species impacted in at least one simulation, there were 23 species impacted in 50% of
the simulations and 2 species impacted in every simulation. There were 46 species that had some
critical habitat converted in 5% or more of iterations. In addition to the frequency of impact
across simulations, the magnitude of impact is relevant. We also tabulated the amount and
percent of critical habitat impacted by species. We found that zero species had one percent or
more on average of its critical habitat impacted (i.e., conversion within critical habitat), while 28
species had greater than 0.05% of critical habitat impacted on average. The Clay-Loving Wild
Buckwheat saw the largest potential impacts to its critical habitat at an average of 0.4%.
For the analysis of critical habitat with a 2600' buffer (S2), we found that 145 unique
species were impacted at least once across all 500 iterations, and 121 species had some critical
habitat converted in 5% or more of iterations (Table VII. A-2). We found conversion of 7926
acres of critical habitat plus the 2600' buffer on average across all iterations (10th-90th
percentiles: 7110-8730 acres). We found that 32 species had one percent or more of its critical
habitat plus buffer potentially impacted (i.e., conversion within critical habitat or within 2600' of
critical habitat). The Fleshy-Fruit Gladecress saw the largest potential impacts to its critical
habitat plus buffer at an average of 13.6%.
For simulations with the buffer added, we deliberated what to use in the denominator
when calculating % of species critical habitat or range impacted, whether the total area (i.e.,
critical habitat and buffer) or only the critical habitat or range area. We examined both
approaches and chose the more conservative approach. We found that in these simulations the
total area affected for a species often increased due to the inclusion of the buffer, but when the
denominator includes the total area with buffer, the percent area affected decreased compared to
the simulations without the buffer. This occurs because the addition of buffer in the denominator
adds a large amount of non-critical habitat area. But when we keep the denominator the same as
the simulations without the buffer (just the area of a species critical habitat or range) then we
find that the percent area affected increased. Because the notion is that conversion near critical
habitat (but not inside) actually affects critical habitat (e.g., through pesticide drift), we opted to
use the version of the simulations that had only critical habitat in the denominator. This is a more
conservative approach. This means that we may interpret these simulations to represent the
acreage and percent of critical habitat affected by conversion in (no buffer) or near (with buffer)
critical habitat. We believe that this is the best approach but recognize that others may be
reasonable as well.
For the analysis of species range with no buffer (S3), we found that 582 unique species
were impacted at least once across all 100 iterations (Table VII. A-2). Because nearly the entire
CONUS is covered by the range of at least one listed species, it is expected that much of the
projected land conversion would occur in the range of at least one listed species. Despite these
estimated conversions of range, we found only four species had one percent or more of its range
converted (Table VII. A-2). One of these species, the Scioto madtom, is listed as extinct by the
International Union for Conservation of Nature and is currently proposed to be delisted by the
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FWS (USDA, 2013). This species was also identified in scenario 4. The White catspaw saw the
largest potential impacts to its range at an average of 7.4%.
For the analysis of species range with a 2600' buffer (S4), we found that 581 unique
species were impacted at least once across all 100 iterations. We found only three species had
one percent or more of its range converted (Table VII. A-2), including the Scioto Madtom. The
White catspaw again saw the largest potential impacts to its range at an average of 6.9%.
The top 20 species impacted as assessed by percent of critical habitat with a 2600' buffer
(S2) are shown in Table VII. A-3 (full results of all species are included as an excel sheet
attached to this Biological Evaluation). We provide more information on the potentially
impacted species further below, including information on species that have the largest potential
impacts based on these acreage impact results alone (and not on other important information
including PBFs for critical habitat).
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Table VII.A-2. Summary of effects across scenarios from the corn ethanol probabilistic analysis (FWS species)
Scenario
#
# Spp. impacted at least
once
# spp. impacted in
5% or more of
iterations
Average
acreage of CH
or range
conversion
(10th -90th
range)
Number of spp.
with 1% or more
of CH or range
impacted on
average
Common name of sp. with >1% of CH or range
converted.
SI
112
46
4024 (3480 -
4560)
0
None
S2
145
121
7926 (7110 -
8730)
32
Fleshy-fruit gladecress, Slenderclaw crayfish, Devils
River minnow, Slackwater darter, False spike,
Roswell springsnail, Texas fawnsfoot, Guadalupe
Orb, Noel's Amphipod, Koster's springsnail, Amber
darter, Niangua darter, Texas pimpleback, Conasauga
logperch, Rush Darter, Clay-Loving wild buckwheat,
Yellow lance, Maryland darter, St. Francis River
Crayfish, diamond Darter, Finelined pocketbook, San
Marcos gambusia, Salt Creek Tiger beetle, Texas
wild-rice, Carolina heelsplitter, Big Creek Crayfish,
Frecklebelly madtom, Short's bladderpod, Canoe
Creek Clubshell, Umtanum desert buckwheat,
Peppered chub, Topeka shiner
S3
582
N/A*
N/A*
4
White catspaw (pearlymussel), Virginia round-leaf
birch, Scioto madtom, San Marcos salamander
S4
581
N/A*
N/A*
3
White catspaw (pearlymussel), Virginia round-leaf
birch, Scioto madtom
* These summarizing statistic results for the range scenarios (S3) and (S4) include 200+ species that are not considered in this Biological
Evaluation because they have a listing status of resolved, under review, recovery, or undefined. As such, the results are not presented here as they
provide a skewed assessment with the inclusion of those species.
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Table VII.A-3. Summary of probabilistic results for the top 20 FWS species affected (ranked by the percent effect from S2,
which depicts results from simulations for conversion of 500,000 acres that included a 2600-foot buffer around critical
Acres affected of critical habitat (SI and
S2) or range (S3 and S4)
Percent
anc
affected of critical habitat (S1
S2) or range (S3 and S4)
Common name
Scientific name
SI
S2
S3
S4
SI
S2
S3
S4
Fleshy-fruit gladecress
Leavenworthia crass a
0
4.1
258.3
261.6
0
13.631
0.076
0.077
Slenderclaw crayfish
Cambarus cracens
0.1
41.5
246.3
252.6
0.028
9.767
0.112
0.115
Devils River minnow
Dionda diaboli
0
11.3
438.9
458.1
0
7.51
0.015
0.015
Slackwater darter
Etheostoma boschungi
0.9
56.3
520.5
528.3
0.105
6.588
0.072
0.073
false spike
Fusconaia mitchelli
0.5
116.6
3764.4
3823.8
0.018
4.388
0.047
0.047
Roswell springsnail
Pyrgulopsis roswellensis
0.1
3.1
49.2
40.8
0.17
4.336
0.048
0.04
Texas fawnsfoot
Truncilla macrodon
0.6
315.2
8411.7
8496.3
0.008
4.259
0.043
0.043
Guadalupe Orb
Cyclonaias necki
0.1
96.4
2589.3
2567.4
0.005
4.034
0.067
0.06'
Koster's springsnail
Juturnia kosteri
0.1
2.3
52.5
42.9
0.17
3.23
0.052
0.042
Amber darter
Percina antesella
0.2
12.9
153.6
159.6
0.044
3.154
0.024
0.025
Niangua darter
Etheostoma nianguae
0
37
3311.1
3319.8
0
2.823
0.071
0.071
Texas pimpleback
Cyclonaias petrina
0.4
104.3
2644.8
2614.8
0.011
2.611
0.03
0.03
Conasauga logperch
Percina ienkinsi
0
5.3
98.4
97.5
0
2.488
0.047
0.047
Rush Darter
Etheostoma phytophilum
0
0.7
531.9
550.5
0
2.428
0.037
0.039
Clay-Loving wild
buckwheat
Eriogonum pelinophilum
0.5
2.9
339.3
332.1
0.397
2.383
0.102
0.1
Yellow lance
Elliptio lanceolata
0.5
65.5
2208
2184
0.017
2.318
0.036
0.036
Maryland darter
Etheostoma sellare
0
0.5
25.2
26.7
0
1.681
0.06
0.063
St. Francis River
Crayfish
Faxonius quadruncus
2
133.9
239.4
225.6
0.025
1.616
0.029
0.027
diamond Darter
Crystallaria cincotta
0.2
30.5
232.8
234.6
0.012
1.574
0.019
0.019
140
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Plants
Based on our analysis, the Fleshy-fruit gladecress could be impacted by pesticide drift
from cropland expansion due to increases in corn ethanol from the RFS Set Rule. The S2
scenario suggests that, on average, 13.4% of the area representing this species' critical habitat
plus a 2600-foot buffer could be affected. Found only in the state of Alabama, this endangered
species has been primarily affected by habitat destruction with off-road vehicle use and
agricultural conversion as threats to their growth. They tend to thrive in limestone outcroppings
near forest edges as shade plants will inhibit their growth. The existing populations reside
primarily on residential lands and in rocky outcrops in pasture fields (US FWS, 2020). While
13.4% of the Fleshy-fruit gladecress' critical habitat with a buffer may be affected, our analyses
show that none (0%) of its critical habitat itself may be converted. Further, only a small
percentage of its range (0.08%) may be converted.
The Clay-loving buckwheat is primarily affected by recreational vehicle traffic and
livestock grazing. It is also impacted by other stressors including commercial and residential
development, invasive species, and climate change. This endangered species is predominantly
located in Colorado and the land they thrive on is described as barren and inhospitable to most
vegetation. Because of this specific terrain, this plant has limited habitat and is highly
fragmented. Currently there are numerous conservation efforts in place to mitigate stressors and
enhance habitat conditions (US FWS, 2022). About 0.4 and 0.1 percent of this species' critical
habitat and range, respectively, could be converted to agriculture based on our analysis. This
could potentially contribute to more livestock grazing. However, it is unknown if any potential
agricultural production would actually occur in many of these locations, since the lands where
these species are found are barren with clay-rich soils which may present challenges for farming.
Aquatic Species
Many of the FWS species in Tables VII. A-2 and VII. A-3 are aquatic species. Although
the probabilistic analysis we conducted was a land use change exercise, it is possible that these
species were picked up if their critical habitat or range includes riparian or other surrounding
lands. If land use changes were to occur in such locations, they could still impact the species by
pesticide drift (demonstrated with scenarios 2 and 4) or localized water quality impacts.
However, it is possible that land use changes may not impact the aquatic species at all if, for
instance, a farmer uses best management conservation practices on the new cropland, or if the
unique geomorphology of the land directs runoff to another location where the species is not
present. At this time, we are unable to assess these factors fully, and therefore our interpretation
of the results is likely more conservative than what may occur in reality.
Based on our results, one aquatic species that may be impacted is the Slenderclaw
crayfish. Located in Alabama, this endangered species' critical habitat could be impacted by
pesticide drift based on scenario two. A very small percentage of its critical habitat (0.03%) and
range (0.1 %) could be impacted directly. Historically, the largest impact to their critical habitat
occurred with the construction of the dam for the Tennessee River. This dam created Lake
Guntersville in 1939 which destroyed several habitats and isolated the two remaining populations
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from each other. The remaining two habitats have also experienced impacts from invasive
species and water pollution, including pollution from upstream animal farming (US FWS, 2019).
The Noel's amphipod is another type of crustacean. Their habitat, located in New
Mexico, has similarly been historically affected by water quality impacts. Their population is
thought to exist only in the Bitter Lake National Wildlife Refuge. Because this endangered
species is located entirely in a wildlife refuge, impacts from nearby land changes have been
greatly reduced (US FWS, 2019a). Any land converted to corn cropland would likely be located
a significant distance from their habitat.
Based on our analysis, the Devils river minnow may be impacted by pesticide drift from
newly converted lands. None of its critical habitat, and only a small percentage of its range,
however, could be directly impacted. This threatened fish is found in a small area of the Del Rio
in southern Texas. Its range was at some point considerably larger but has shrunk due to
pollutants entering the local aquifer and drought which impacts their reproductive grounds. A
recovery plan has been implemented by several governmental agencies. This plan primarily
revolves around ensuring land management by local ranges to prevent pollutants from reaching
the river where these fish and other species are located (US FWS, n.d.-b).
Darters are small freshwater fish which tend to live along the bottom of rivers. Because
of this bottom dwelling tendency, they are more susceptible to impacts on water quality than
other species. They use the rocky or sandy bottom of waterways for protection and foraging. Any
addition of silt due to soil erosion could impact their habitats. The Amber darter for example
lives in two rivers in Georgia and Tennessee. This endangered species has been affected by water
quality impacts including land change, urbanization, and extreme weather events due to climate
change. These weather events cause changes in the flow of the rivers potentially disrupting their
reproductive habitats. Recovery plans are in place. However, construction of a reservoir near one
of their habitats has the potential to disrupt waterflow and further alter their existing habitat (US
FWS, 2020a). The Amber Darter could see very small direct impacts to its critical habitat and
range, based on our analyses.
Freshwater mussels are an important species in all ecosystems. They are filter feeders
which means that they siphon their food from the water, eating mostly small organisms and
organic materials. Similar to the discussion of darters above, freshwater mussels live along the
bottom of waterways. This makes them susceptible to the impacts of poor water quality and
pollution. The False spike, Guadalupe orb, and Texas Pimpleback are all proposed endangered
freshwater mussel species with critical habitat found mostly in Texas. Habitat change for these
mussels has been attributed to water flow inconsistency, with rivers being prone to both flood
and drought. Inconsistent water flow can change the terrain on the river bottom where they tend
to dwell in the gravel and cracks of rocks. The rivers also flow through areas of high agriculture
and pastoral lands which both pump water from the habitats and contribute to water pollution.
Recent conservation efforts have increased their focus on freshwater mussel populations.
Recovery efforts with the Texas pimpleback for example, have been working to breed and
redistribute these mussels into the waterways (Aubry & US FWS, 2021).
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3. Identifying Potential Locations of Acres Impacted (NMFS Species)
We used the same general procedures for identifying potential locations of acres impacts
from increased canola production as was used for identifying locations of acres impacted from
increased corn production. The details of that analysis are described in section VILA and
relevant differences are summarized here.
The same analysis as described in section VILA was applied. For convenience, we use
the same scenario names (Table VII.A-4).
Table VII.A-4. List of scenarios for NMFS species-level effects from potential increases in
cropland
Scenario
#
Total acres
converted (acres)
Critical habitat
(CH) range (R)
Buffer
Replicate
iterations
SI
500,000
CH
None
500
S2
500,000
CH
2600'
500
S3
500,000
R
None
100
S4
500,000
R
2600'
100
4. Potential Impacts on Listed Species and Critical Habitat (NMFS Species)
For the analysis of critical habitat with no buffer (SI), we found that 31 unique species
populations were potentially impacted at least once across all 500 iterations, and the same 31
populations had some critical habitat potentially converted in 5% or more of iterations (Table
VII. A-5). We found conversion of 12,686 total acres of critical habitat on average across all
iterations (10th - 90th percentiles: 11,700 - 13,710 acres). We found that zero species had 0.1
percent or more of its critical habitat potentially impacted (i.e., conversion within critical
habitat). The species with the greatest potential impact was the Chinook Salmon (Snake River
fall-run) with 0.031% of its critical habitat potentially affected.
For the analysis of critical habitat with a 2600' buffer (S2), we found that 31 unique
species populations (the same as above) were potentially impacted at least once across all 500
iterations, and the same 31 populations had some critical habitat potentially converted in 5% or
more of iterations (Table VII. A-5). We found conversion of 13,828 acres of critical habitat plus
the 2600' buffer on average across all iterations (10th - 90th percentiles: 12,780 - 14,850 acres).
We found that zero species had 0.1 percent or more of its critical habitat potentially impacted
(i.e., conversion within critical habitat or within 2600' of critical habitat). The species with the
greatest potential impact was the Chinook Salmon (Snake River fall-run population) with
0.034% of its critical habitat potentially affected.
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For the analysis of species range with no buffer (S3), we found that 36 unique species
were potentially impacted at least once across all 500 iterations, and 36 species had some of its
range potentially converted in 5% or more of iterations (Table VII. A-5). We found conversion of
16,181 acres of range on average across all iterations (10th - 90th percentiles: 15,090 -17,250
acres). We found that zero species had 0.1 percent or more of its range potentially impacted. The
species with the greatest potential impact was the Chinook salmon (Snake River fall-run) with
0.03 percent of its range potentially affected.
For the analysis of species range with a 2600' buffer (S4), we found that 36 unique
species were potentially impacted at least once across all 500 iterations, and 36 species had some
of its range potentially converted in 5% or more of iterations (Table VII. A-5). We found
conversion of 17,210 acres of range plus the 2600' buffer on average across all iterations (10th-
90th percentiles: 16,050-18,330 acres). We found that zero species had 0.1 percent or more of its
range potentially impacted. The species with the greatest potential impact was the Chinook
salmon (Snake River fall-run) with 0.033 percent of its range potentially affected.
The top 20 species impacted as assessed by percent of critical habitat with a 2600' buffer
(S2) are shown in Table VII. A-6 (full results of all species are included as an excel sheet
attached to this Biological Evaluation). We provide more information on the potentially
impacted species further below, including information on species that have the largest potential
impacts based on these acreage impact results alone (and not on other important information
including PBFs for critical habitat).
Table VII.A-5. Summary of effects across scenarios
Scenario
#
# populations
impacted at
least once
# populations
impacted in 5%
or more of
iterations
Average acreage
of CH or range
conversion (10th -
90th range)
Number of
populations
with 0.1% or
more of CH or
range impacted
on average
Common
name of sp.
with >0.1% of
CH or range
converted.
SI
31
31
12,686 (11,700-
13,710)
0
None
S2
31
31
13,828 (12,780-
14,850)
0
None
S3
36
36
16,181 (15,090-
17,250)
0
None
S4
36
36
17,210 (16,050-
18,330)
0
None
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Table VII.A-6. Summary of probabilistic results for the top 20 NMFS populations affected (ranked by the percent effect from
S2, which depicts results from simulations for conversion of 500,000 acres that included a 2600-foot buffer around critical
habitat to account for pesticide drift or other potential effects nearby to species' critical habitat)
Acres affected of critical habitat (SI and S2) or
range (S3 and S4)
Common name
Scientific name
Population name
SI
S2
S3
S4
Chinook salmon
Oncorhynchus tshawytscha
Snake River fall-run
0.031
0.034
0.03
0.033
Steelhead
Oncorhynchus mykiss
Upper Columbia
River
0.03
0.032
0.028
0.031
Chinook salmon
Oncorhynchus tshawytscha
Upper Columbia
River spring-run
0.024
0.026
0.029
0.032
Atlantic sturgeon
Acipenser oxyrinchus
oxyrinchus
South Atlantic
0.023
0.025
0.019
0.021
Steelhead
Oncorhynchus mykiss
Upper Willamette
River
0.021
0.023
0.022
0.023
Sockeye salmon
Oncorhynchus (=Salmo)
nerka
Snake River
0.02
0.021
0.023
0.026
Atlantic sturgeon
(Gulf subspecies)
Acipenser oxyrinchus desotoi
None
0.016
0.02
0.002
0.003
Chinook salmon
Oncorhynchus tshawytscha
Upper Willamette
River
0.017
0.019
0.017
0.018
Atlantic sturgeon
Acipenser oxyrinchus
oxyrinchus
Carolina
0.016
0.018
0.012
0.013
Steelhead
Oncorhynchus mykiss
Snake River Basin
0.014
0.015
0.014
0.015
Atlantic Sturgeon
Acipenser oxyrinchus
oxyrinchus
Gulf of Maine
0.012
0.014
0.002
0.002
Steelhead
Oncorhynchus mykiss
Middle Columbia
River
0.012
0.013
0.013
0.014
Atlantic sturgeon
Acipenser oxyrinchus
oxyrinchus
Chesapeake Bay
0.011
0.013
0.011
0.011
Atlantic sturgeon
Acipenser oxyrinchus
New York Bight
0.011
0.013
0.008
0.008
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oxyrinchus
Chinook salmon
Oncorhynchus tshawytscha
Snake River
spring/summer-run
0.011
0.012
0.013
0.014
Chinook salmon
Oncorhynchus tshawytscha
Sacramento River
winter-run
0.009
0.011
0.009
0.009
Chum salmon
Oncorhynchus keta
Columbia River
0.009
0.01
0.006
0.007
Yelloweye rockfish
Sebastes ruberrimus
Puget Sound/
Georgia Basin
0.008
0.01
0.01
0.013
Steelhead
Oncorhynchus mykiss
California Central
Valley
0.008
0.009
0.01
0.01
Chinook salmon
Oncorhynchus tshawytscha
Puget Sound
0.008
0.009
0.008
0.008
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Fish (Salmonids)
Sturgeon are one of the most endangered species in the United States. There are currently
29 species, most live in the ocean and travel upriver during the spring and summer to spawn. In
the corn probabilistic analysis, three species of sturgeon were identified with a low potential
impact from the RFS rule: The Atlantic sturgeon (Gulf sturgeon), Green sturgeon and the
Shortnose sturgeon.
The Gulf sturgeon is a sub-species of the Atlantic sturgeon. As the name suggest, this
species resides primarily in the gulf region of Louisiana, Mississippi, and Florida, however, the
larger Atlantic sturgeon species can range as far north as Canada. Populations of Gulf sturgeon
have been impacted over the years from factor such a dam construction to water degradation. As
sturgeon spawn in the rivers during the summer, dams can impede their cycle and have been
known in some cases to separate some species above and below the obstacle. The remainder of
the year, they live in the estuary, or mixed salt and freshwater region of the river and fully in the
ocean during the winter months. Pollutants pose a significant effect to the gulf sturgeon. Impacts
may be caused directly by impacting their organs and reproductive systems or indirectly by
becoming incorporated into the food they eat (NOAA, 2022; U.S. Fish and Wildlife Service,
n.d.-a).
The shortnose and green sturgeon share many characteristics of the gulf sturgeon
discussed in the NMFS soybean section of this BE. This includes their living situations
throughout the year (river and ocean migrations) and species stressors such as spawning route
obstructions and sensitivity to water quality.
The shortnose sturgeon resides in Atlantic waters between Canada and Florida. Three
large populations exist but there is a large gap between the southern and northern populations
which keeps them completely separated. The shortnose sturgeon was overfished alongside the
Atlantic sturgeon which impacted their numbers in the early 1900s. Unlike the Atlantic sturgeon,
they tend to remain in their freshwater territories and spend a very short time in the ocean.
Similar to other sturgeon species, their habitat is often disturbed by obstructions such as dams
which prevent them from reaching their spawning grounds or reaching their food sources
(NOAA, 2023a).
The green sturgeon differs from those we've discussed as they reside on the west coast
and are one of only two that do. Although they can range from Alaska to Mexico, they are
commonly found in the San Francisco area. Unlike their east coast relatives, this species spends
the majority of their time in the estuaries or out in oceanic waters (NOAA, 2023d).
Cool, turbulent waters are needed for this subspecies to spawn. This has become a
problem in recent years as access to these habitats needed for spawning have become harder to
access. These issues include dams and altered water flows. Insufficient water availability has
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also affected this species. Once again, we refer to the sturgeon section as the steelhead trout has
very similar habitat impacts.
Several subspecies of steelhead trout, most of which are either endangered or threated.
The species we have evaluated for this Biological Evaluation are designated as threatened,
o Snake river basin
o Upper Columbia river
o Middle Columbia river
o Upper Willamette river
o California central valley
o Puget sound
Like the sturgeon, steelhead trout live in cold oceanic waters and migrate into rivers or
streams in order to spawn. A gravel base is required spawning as the female steelhead will dig a
nest into the rocky material. Young steelhead remain in the rivers for a few years to feed on
zooplankton before moving in the river estuary (NOAA, 2023b; NOAA, 2023c).
The bocaccio is a large Pacific coast rockfish commonly found in Punta Blanca, Baja
California, and the Gulf of Alaska off Krozoff and the Kodiak Islands, yet most populations are
found between Oregon and northern Baja California. The bocaccio, like most rockfish species,
are an integral part of the aquatic food web. Larval bocaccio, for example, are a food source for
juvenile salmon and other marine fish and seabirds. Since the bocaccio do not begin to produce
offspring until they are 5 to 20 years old, their populations are largely dependent on the how
many sexually mature fish were caught that season. As such, certain populations can be
susceptible to overfishing. Their most significant stressors are from bycatch and bottom trawling
gear, which destroys their sensitive rocky, cold-water coral and sponge habitats (NOAA
Fisheries, 2022a; NOAA Fisheries, 2023a). Our analysis suggests that up to 0.009% and 0.013%
of the Bocaccio's critical habitat and range, respectively, could potentially be impacted by the
projected corn expansion area.
The Yelloweye rockfish is found along the western coast of North America from the
Aleutian Islands to the Baja Peninsula. They are often solitary and inhabit steep rocky areas
where they may shelter in nooks and crannies. This species is the longest living rockfish species,
with some fish living as long as 150 years. Similar to the bocaccio, they grow very slowly and
are late to mature. Due to this, their species depends on maintaining an extended population age
structure, leaving them susceptible to habitat degradation and overfishing. In 2002, the National
Marine Fisheries Service declared that the west coast yelloweye rockfish was being overfished.
Since then, major recovery efforts have been undertaken, yet their population has been slow to
rebound due to their slow maturation rate (NOAA Fisheries, 2023e). Our analysis suggests that
up to 0.01%) and 0.013%> of the Yelloweye rockfish's critical habitat and range, respectively,
could potentially be impacted by the projected corn expansion area.
The Chinook salmon is found in North America, ranging from the Monterey Bay area of
California to the Chukchi Sea area of Alaska. The subpopulations covered as part of our regional
assessment are the Central Valley spring-run, Puget Sound, Snake River fall-run, Snake River
spring/summer-run, Upper Columbia River spring-run, and the Upper Willamette River. The
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Chinook salmon is an anadromous fish, meaning they can live in both fresh and saltwater
environments. Their early life is spent growing and feeding in freshwater streams, estuaries, and
associated wetlands. The remainder of their life is spent foraging in the ocean before their return
to the streams and tributaries where they spawn. Other than the Klamath River Fall stocks, the
Chinook Salmon is not subject to overfishing, and since fishing gear used to catch Chinook
salmon rarely contacts the ocean floor, fishing of this species rarely impacts other aquatic
habitats. The main habitat issue for salmon recovery is restoring quality salmon habitat that once
supported thriving and robust salmon runs (NOAA Fisheries, 2023b; NOAA Fisheries, 2023c).
Our analysis suggests that up to 0.034% and 0.033% of the Chinook salmon's critical habitat and
range, respectively, could potentially be impacted by the projected corn expansion area.
The Sockeye salmon is found along the west coast of North America, ranging from the
Klamath River in Oregon to Point Hope in northwestern Alaska. The largest sockeye salmon
populations are found in Kvichak, Naknek, Ugashik, Egegik, and Nushagak Rivers that flow in
Alaska's Bristol Bray, as well as the Fraser River system in Canada. Like the Chinook salmon,
this species is anadromous, where the youth are spawned and raised in rivers, followed by a
migration to saltwater to feed, grow and mature before returning back to spawning fresh waters.
Sockeye salmon are particularly vulnerable to habitat and migratory disruptions from blocked
access to spawning grounds caused by dams and culverts (NOAA Fisheries, 2023d). Our
analysis suggests that up to 0.021% and 0.026% of the sockeye salmon's critical habitat and
range, respectively, could potentially be impacted by the projected corn expansion area.
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B. Potential Impacts from Increased Soybean Production
1. Identifying Potential Locations of Acres Impacted (FWS and NMFS
species)
In order to assess the potential impact on listed species and critical habitat for the changes
in soy planting related to increased use of biomass-based diesel (BBD), EPA adapted the
analysis of its contractor ICF to estimate where additional soy acres are likely to occur and
overlay those results with species and habitat data using a quantitative GIS-based approach. EPA
staff then performed additional analysis using the ICF results and other information to assess
impacts on specific species.33
The ICF work evaluated a range of soy oil volume scenarios in two contexts: an
extensification case where all additional acres were sited on land not previously cultivated, and
an intensification case where the additional acres were made up through higher yields on existing
soy fields or displacement of other crops. These scenario parameters were meant to bracket the
potential scale and impacts of the 2023-25 standards. The acreage estimates we developed
earlier in Section VI.B.5 align well with the extensification results of ICF's 100 and 250-million-
gallon scenarios, and thus we believe those land use changes are relevant and useful in
determining species impacts.
This section starts with a summary of the ICF work and its results, and then proceeds into
the EPA assessment of potential species impacts. Copies of the ICF reports are available as an
attachment to this Biological Evaluation.
Overview of ICF Workflow
The first step in determining the potential location and extent of soybean expansion areas
was to compute a total acreage target by dividing the additional soybean demand by the
projected crop yield in bushels per acre for the scenario year of 2025. A land selection model
was then devised that assigned a rank to potential new acres based on a number of factors, and
then added them to the expansion area according to their rank until the total acreage target was
met. Figure VII.B-1 summarizes this workflow. The rest of Section VII.B will summarize key
aspects of the analysis.
33 Differing approaches were taken to assess habitat and species impacts of soy, corn, and canola expansions because
of the types of information available and the timelines for different parts of the work.
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Figure VII.B-1: Soy expansion workflow summary
source I exclusion I influence
Developed,
Existing
Soybean
Cropland
Data Layer
(CDL)
Excluded Areas
El. Census Urban
E2. Public Lands
Fl, Conservation
Protection Status
F2. Forest/Wetland
Conversion
F3. Soybean Distance
F4. Soil-based Yield
F5. Historic Soybean
Growth Rate
F6. Cash Rents
Available
Lands
Ranking
(expansion
potential)
Processing Workflow
Target Expansion Acreage
The first step in computing the target expansion acreage is converting the incremental
biomass-based diesel (BBD) volume scenarios into bushels. This was done using a factor of 1.5
gallons BBD per bushel of soybeans, a factor derived from the crushing and conversion
processes that is used throughout the RFS analyses. This factor means that 100 million and 250
million gallons will require 67 million and 167 million bushels, respectively. Note that this 1.5
value is not related to climate, soil quality, or other agricultural parameters.
The second piece of information is the soybean yield in bushels per acre. Using a
regression of historical yield data from USDA for years 2000 to 2020, ICF projected a yield of
55 bushels/acre in the scenario year of 2025 (USDA, 2022b). Combining this yield with the
required bushels above gives target expansion areas of 1.2 and 3 .0 million acres for the 100-
million and 250-million-gallon scenarios, respectively. Note that the final expansion area is
somewhat larger than these figures due to downward adjustment of yields for new acres relative
to the historical average for currently-producing acres. This is reasonable if we assume that the
most suitable land for crop production is already in use. This adjustment is discussed further
below.
Note also that the extensification scenario stipulated that the expansion acres must be
accommodated in addition to all existing soybean producing farmland. Therefore, increased
yields from existing soybean acres that might occur prior to 2025 were not considered available
in the extensification scenarios. Thus, these acreage values could be considered upper-end
estimates.
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Location-Specific Crop Yields
USDA's Natural Resources Conservation Service (NRCS) publishes a national soil
quality database that incorporates information from field studies and satellite surveys (NRCS,
2019). In addition, USDA's National Agricultural Statistics Service (NASS) publishes crop yield
data for counties where crop production occurs (USDANASS, n.d.). These data sources were
combined to estimate soybean yields for potential expansion acres, including areas where no soy
may have been grown historically. This information was used in the ranking of suitability of
parcels for new soy planting, as discussed further in the ICF report. It was also used to adjust the
weight of a particular acre of new soy, relative to the national average 55 bushels per acre noted
above, before subtraction from the total acreage target.
Geographic Scope of Analysis
Before assessing specific locations where additional soy acres are likely to be sited, a
review of recent soybean expansion at the state level was used to guide projections of where
future soybean expansion would be expected to occur. Soybean acreage planting data from
USD A NASS were used to produce average year-over-year increases in planted soybean acreage
for each US state since 2007. The results are shown in Figure VII.B-2. States with a positive
average year-over-year change and located within the Plains, Midwest, and Mississippi regions
were selected as the geographic scope of potential new parcels for soy planing. This set of states
roughly corresponds to those to the left of the line in Figure VII.B-2. A map of this area is shown
in Figure VII.B-3, with included states in green and excluded states in red.
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Figure VII.B-2: Average Year-Over-Year Change in Soybean Planting Acreage
Average Year Over Year Change in Soybean Planting Acreage, 2008-2021
lllhi
ND IL SD KS MO MN Wl KY NE Ml OH IN MS PA OK TN IA NY LA VA WV NJ OT MD FL DE AL TX NC SC AR GA
State
Source: USDA NASS
Figure VII.B-3: Map Showing Potential Soy Expansion Areas in Green
MilJ:
Wyom*ig
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In addition to considering states with year-over-year increases in soybean planting, we
also considered total soybean planting by state to confirm the geographic scope of our analysis.
Total soybean planting by state is a relevant consideration since states that currently plant
significant acres of soybeans are likely to be states that would see increased soybean planting in
response to increased demand for soybean oil for biofuel production. There are several reasons
we expect this would be the case. First, the fact that these states already dedicate significant
acreage to soybean production strongly suggests that these states have the appropriate climates
for soybean production. Second, these areas already have the necessary equipment and expertise
required to plant, cultivate, and harvest soybeans. We expect that the marginal cost of soybean
production would be much lower in areas that already produce significant quantities of soybeans
and do not have to make the significant capital investments required to purchase the appropriate
machinery for soybean production. Finally, areas that currently produce soybeans are much more
likely to have the necessary infrastructure to bring additional soybeans to market. This
infrastructure could include things such as access to soybean crushing facilities, access to
established markets for soybeans, soybean oil, and soybean meal, and access to rail or barge
terminals to transport soybeans to distant markets domestically or internationally.
To assess where soybeans are currently grown, we accessed the most recent NASS data
for soybean acres harvested annually from 2018 - 2022. During this time approximately 94%
percent of all acres of soybeans harvested were from the geographic region identified by ICF.
This percentage we very consistent, ranging from a low of 93.81% in 2021 to a high of 94.44%
in 2018. In 2022, the most recent year for which data are available, these states accounted for
94.23%) of the total acres of soybeans harvested. Only one state outside of the geographic scope
identified by ICF (North Carolina) accounted for more than 1% of the total acres of soybeans
harvested in any year from 2018 - 2022. North Carolina accounted for a high of 2.03% of all
soybeans harvested in the U.S. in 2019 and a low of 1.79% of all soybeans harvested in the U.S.
in 2018. This analysis supports the geographic scope selected by ICF, as the vast majority of
soybeans harvested annually within the U.S. (as well as nearly all the states that saw increasing
soybean acreage, as shown in Figure VII.B-2) are within this geographic scope. The results of
this state-by-state assessment are shown in Tables VII.B-1 and VII.B-2.
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Table VII.B-1: Soybean Acres Harvested Acres by State
State(s)
2018
2019
2020
2021
2022
All States in the Geographic Scope
82,720,000
74,939,000
78,050,000
80,970,000
81,355,000
NORTH CAROLINA
1,570,000
1,520,000
1,570,000
1,640,000
1,690,000
PENNSYLVANIA
630,000
610,000
630,000
595,000
590,000
VIRGINIA
590,000
560,000
560,000
590,000
610,000
MARYLAND
515,000
475,000
465,000
485,000
510,000
ALABAMA
335,000
315,000
275,000
305,000
355,000
SOUTH CAROLINA
330,000
260,000
295,000
385,000
390,000
NEW YORK
325,000
225,000
312,000
320,000
325,000
DELAWARE
168,000
153,000
148,000
153,000
158,000
TEXAS
135,000
73,000
110,000
100,000
85,000
GEORGIA
130,000
86,000
95,000
135,000
160,000
NEW JERSEY
107,000
92,000
93,000
99,000
108,000
WEST VIRGINIA
27,000
0
0
0
N/A*
FLORIDA
12,000
0
0
0
N/A*
OTHER STATES
0
0
0
0
N/A*
*For 2022 the NASS database did not list a total for "Other States"
All data in Table VII.B-1 from USDA NASS database
Table VII.B-2: Percent of U.S. Soybean Acres Harvested Acres by State
State(s)
2018
2019
2020
2021
2022
All States in the Geographic Scope
94.44%
94.17%
93.83%
93.81%
94.23%
NORTH CAROLINA
1.79%
2.03%
1.90%
1.90%
1.96%
PENNSYLVANIA
0.72%
0.81%
0.76%
0.69%
0.68%
VIRGINIA
0.67%
0.75%
0.68%
0.68%
0.71%
MARYLAND
0.59%
0.63%
0.56%
0.56%
0.59%
ALABAMA
0.38%
0.35%
0.33%
0.35%
0.41%
SOUTH CAROLINA
0.38%
0.42%
0.36%
0.45%
0.45%
NEW YORK
0.37%
0.30%
0.38%
0.37%
0.38%
DELAWARE
0.19%
0.20%
0.18%
0.18%
0.18%
TEXAS
0.15%
0.10%
0.13%
0.12%
0.10%
GEORGIA
0.15%
0.11%
0.12%
0.16%
0.19%
NEW JERSEY
0.12%
0.12%
0.11%
0.11%
0.13%
WEST VIRGINIA
0.03%
0.00%
0.00%
0.00%
N/A*
FLORIDA
0.01%
0.00%
0.00%
0.00%
N/A*
OTHER STATES
0.00%
0.00%
0.00%
0.00%
N/A*
*For 2022 the NASS database did not list a total for "Other States"
All data in Table VII.B-2 from USDA NASS database
Cropland Data Layer Status
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As this analysis focuses on cropland expansion, only lands that are in the Cropland Data
Layer as uncultivated land cover categories were identified as potential suitable lands. Categories
that are either currently in cropland production, idle lands, developed, or other land covers not
suitable for conversion to agricultural production (e.g., open water) were not included as suitable
land under this approach.
In addition, other areas that were fully excluded as potentially suitable land included core
urban areas and federal public lands. In some instances, the Croplands Data Layer may show
small pockets of uncultivated land cover within core urban areas, which would be unlikely areas
for soybean expansion. A separate layer of urban cores from census data was used to exclude
these areas as suitable habitat for extensification.34 Federal public lands, available through a
national data set were also excluded.35
This approach used the same National Land Cover Dataset (NLCD) as the corn analysis
in Section VII. A. 1, but slightly different logic. While the corn analysis selected four specific land
categories to include (shrub, grassland/herbaceous, hay/pasture, emergent herbaceous wetlands),
this analysis started with all categories, and then excluded several as unsuitable. In addition,
forest and wetland categories, which were not excluded up front, were given low rankings such
that they would not be added to expansion acreage until other types had been consumed
(discussed more below).
Other Ranking Factors
The land selection model included additional ranking factors including conservation
protection status, forest and wetlands constraint, distance to existing soybean fields, soils-based
yield, historic soybean growth rates, and cash rents. These are described in more detail in the
Appendices.
Land Selection Results
Table VII.B-1 summarizes the model results by NLCD land cover type, and Figure
VII.B-4 shows the modeled soybean planting expansion areas that correspond to the 250-million-
gallon scenario. A more detailed presentation of the results are included in the ICF reports.
34 See ICF (2021), Supplemental Figure 1-3
35 See ICF (2021), Supplemental Figure 1-4
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Table VII.B-1: Summary of Land Selection Results by Land Cover Type (Extensification)36
Land Cover
Types
Total Area
(Acres)
Acres and percent of total
100-million-gallon BBD
scenario
250-million-gallon BBD
scenario
Grassland /
Pasture
142,969,840
1,420,758
(0.99%)
3,720,691
(2.60%)
Shrubland
11,700,016
24,019
(0.21%)
45,475
(0.39%)
Forest
163,761,230
48,966
(0.03%)
48,966
(0.03%)
Wetlands
60,134,738
9,905
(0.02%)
9,905
(0.02%)
Barren / Other
62,432,375
20,158
(0.03%)
37,464
(0.06%)
Totals
440,998,199
1,523,806
(0.35%)
3,862,501
(0.88%)
36 See ICF (2022).
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Figure VII.B-4: Modeled Soybean Expansion Areas (Red Color) for 250- Mil I io n-G a I Ion
Scenario 37
Potential Impacts of
Expanded Biodiesel
and Renewable Diesel
Production on
Endangered Species
and Critical Habitats
Legend
I I Limits of Analysis
(ZD Soybean
¦¦ Other Cultivated
H Non-Cultivated
ED Developed
¦I Water
~ Modeled Soybean
Expansion Area -
Extensification
(expand onto
previously
uncultivated lands)
Soybean Expansion Target
2,994.307 acres
2. Potential Impacts on Listed Species and Critical Habitat (FWS species)
To evaluate potential impacts on listed species and critical habitat, ICF then calculated
the area of overlap between the modeled soybean expansion areas with FWS listed species and
critical habitat. We evaluated the potential species impacts based on two scenarios from ICF's
work: the 100-million-gallon scenario (1,523,806 acres converted) and 250-million-gallon
scenario (3,862,501 acres converted) which were discussed previously. We chose to focus on
these two scenarios as they most closely match our maximum potential land use impact of 1.9
million acres from increases in soybean biodiesel.
ICF found that 203 ranges or critical habitat layers overlapped with the proposed affected
area for soybean crop expansion under the 100-million-gallon (-1.5 million acres) scenario.
They found 212 ranges or critical habitat layers that overlapped with the proposed affected area
for soybean crop expansion in the 250-million-gallon (-3.8 million acres) scenario. The tables
below list the top 10 species with the biggest direct impacts on their critical habitat, along with
their range (on a percentage basis) for each of the two scenarios. The full results can be found in
a supplemental Excel document attached with this Biological Evaluation. We provide more
33 See ICF (2022)
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information on the potentially impacted species further below, including information on species
that have the largest potential impacts.
Table VII.B-2. Results from 100-Million-Gallon Scenario (~1.5 million acres) Critical
Habitat Overlap with Potential Soybean Land Expansion
Direct Impacts
Direct Impact (Percent
Common Name
Scientific Name
(Acres)
of Critical Habitat)
Salt Creek Tiger beetle
Cicindela nevadica lincolniana
50
4.51%
Kentucky glade cress
Leavenworthia exigua laciniata
37
1.80%
Poweshiek skipperling
Oarisma poweshiek
412
1.56%
Dakota Skipper
Hesperia dacotae
199
0.98%
Piping Plover
Charadrius melodus
5036
0.35%
St. Francis River Crayfish
Faxonius quadruncus
16
0.19%
Big Creek Crayfish
Faxonius peruncus
14
0.16%
diamond Darter
Crystal 1 aria cincotta
3
0.15%
Topeka shiner
Notropis topeka (=tristis)
24
0.15%
Cumberlandian combshell
Epioblasma brevidens
13
0.11%
Table VII.B-3. Results from 100-Million-Gallon Scenario (~1.5 million acres) Range
Overlap with Potential Soybean Land Expansion
Common Name
Scientific Name
Direct Impacts
(Acres)
Direct Impact
(Percent of Range)
Neosho madtom
Noturus placidus
204,861
3.69%
Scioto madtom
Noturus trautmani
20
2.59%
Kentucky glade cress
Leavenworthia exigua laciniata
1,837
2.37%
Neosho Mucket
Lampsilis rafinesqueana
271,830
2.03%
Dakota Skipper
Hesperia dacotae
307,682
1.83%
Salt CreekTiger beetle
Cicindela nevadica lincolniana
496
1.76%
Mead's milkweed
Asclepias meadii
279,319
1.36%
Lakeside daisy
Hymenoxys herbacea
11,879
0.61%
Short's bladderpod
Physaria globosa
24,515
0.57%
Rabbitsfoot
Quadrula cylindrica cylindrica
230,051
0.54%
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Table VII.B-4. Results from the 250Million-Gallon Scenario (~3.8 million acres) Critical
Habitat Overlap with Potential Soybean Land Expansion
Direct Impacts
Direct Impact (Percent
Common Name
Scientific Name
(Acres)
of Critical Habitat)
Salt Creek Tiger beetle
Cicindela nevadica lincolniana
50
4.51%
Kentucky glade cress
Leavenworthia exigua laciniata
65
3.16%
Poweshiek skipperling
Oarisma poweshiek
575
2.18%
Dakota Skipper
Hesperia dacotae
362
1.78%
Slender chub
Erimystax cahni
67
1.56%
Braun's rock-cress
Arabis perstellata
16
1.34%
Topeka shiner
Notropis topeka (=tristis)
168
1.02%
St. Francis River Crayfish
Faxonius quadruncus
79
0.95%
Big Creek Crayfish
Faxonius peruncus
68
0.80%
Piping Plover
Charadrius melodus
7,319
0.50%
Table VII.B-5. Results from the 250-Million-Gallon Scenario (~3.8 million acres) Range
Overlap with Potential Soybean Land Expansion
Common Name
Scientific Name
Direct Impacts
(Acres)
Direct Impact
(Percent of Range)
Neosho madtom
Noturus placidus
751,709
13.55%
Neosho Mucket
Lampsilis rafinesqueana
926,403
6.91%
Kentucky glade cress
Leavenworthia exigua laciniata
4,609
5.96%
Illinois cave amphipod
Gammarus acherondytes
3,033
5.39%
Scioto madtom
Noturus trautmani
36
4.74%
Dakota Skipper
Hesperia dacotae
769,914
4.58%
Mead's milkweed
Asclepias meadii
933,947
4.56%
Rabbitsfoot
Quadrula cylindrica cylindrica
832,003
1.95%
Salt Creek Tiger beetle
Cicindela nevadica lincolniana
532
1.89%
Topeka shiner
Notropis topeka (=tristis)
443,951
1.50%
In assessing potential impacts to the species identified in this ICF analysis, it is important
to recognize that the overlap percentage numbers represent the highest maximum impact that
could occur due to the numerous conservative assumptions made in determining the number of
acres potentially impacted by increases in soybean biodiesel (discussed in Section VI.B). We
conservatively estimated that up to 1.9 million acres could be impacted from increases in
soybean biodiesel from the RFS Set Rule. However, it is possible that the RFS Set rule will lead
to zero acres being converted. For instance, as described in more detail in their report, ICF found
that based on historical yield data, future projected soybean yield increases on existing soybean
acres would be sufficient to meet the biofuel demands in both the 100-milliongallon and 250-
million-gallon scenarios, as well as in another scenario with even larger acreage impacts (6
million acres) that were not assessed for this Biological Evaluation. There is a lengthy causal
chain that influences on-the-ground soybean plantings, including economic drivers. Soybean
biodiesel demands could also be met by reducing exports (Table VI.B-9). As such, we cannot say
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with reasonable certainty that species identified in this analysis will be impacted. Additionally, it
is important to note that this assessment is based on the potential acreage impact results alone
(and not on other important information including PBFs for critical habitat as discussed in more
detail in Section IX).
Mussels and Water-dwelling Species
In this analysis completed by ICF, the Neosho mucket was identified as a species that
could be affected. For instance, future soybean expansion could overlap with up to 6.91% with
its range based on the 250-million-gallon scenario. The Neosho mucket is a type of freshwater
mussel. Mussels are filter feeders and live on the bottom of waterways, where sediment and
pollutants may accumulate in freshwater rivers and streams. In addition to the Neosho mucket,
other species of freshwater mussels such as the madtom and pearly mussel were among the list of
species that overlapped with the potential soybean expansion area.
The Neosho mucket is found in Illinois, Arkansas, Kansas, Missouri, and Oklahoma.
Although they were historically found in rivers much more widespread, they are found primarily
in 10 different river systems across the previously named states. They are often found in areas
with swift current, though there are locations where it is found in areas that are near-shore or
away from the main current. The species is threatened by impoundments, sedimentation,
chemical contaminants, mining, invasive species, and changes in temperature, among other
factors (US FWS, 2018).
In addition to mussels, many other species found in the ICF analysis are affected by
changes in their habitats adjacent to water ways. Bottom dwelling animals are particularly
susceptible to habitat changes. This includes crayfish and small fish such as the Neosho madtom
which has the largest overlap in the 250-million-gallon scenario at 13.55% overlap with its
range. Madtoms live on the rock covered bottoms of riverbeds and use this terrain as a feeding
ground and as protection. Madtoms often bury themselves during the day to protect from
predators and then forage at night. Water dwelling species can be affected when natural habitats
near waterways are changed for agricultural purposes. Removal of grassland and trees allow soil
erosion to increase. This can lead to changes in stream morphology which can impact creatures
who use the river bottom for protection such as the madtom. Additional soil gain in rivers can
also lead to flooding as there is less space available during heavy water flow.
The ICF analysis suggests that increases in soybean plantings near some water dwelling
species' ranges and/or critical habitats could occur. This could contribute further stressors to the
species (e.g., in the case of the Neosho mucket and Neosho madtom). However, as explained
previously, these overlap numbers likely represent maximum potential impacts and due to
various uncertainties, we cannot say with certainty if impacts will occur.
Plants
Two plants listed as endangered are within the potential affected area of soybean crop
expansion: Kentucky glade cress and Mead's Milkweed. The Kentucky glade cress is currently
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found in only a few locations in Kentucky. This plant has mostly been impacted by residential
expansion and tends to grow in rocky outcrops. This type of land is unlikely to be subject to
cultivation for soybean production because it is not suitable for conversion to agricultural
production.
Mead's Milkweed is an herb found predominantly in Midwest prairie habitats. Its habitat
has been fragmented which threatens diversity and reproduction ability. Due to the location of its
prime habitat, it has been heavily affected by agricultural conversion and other land
development. This plant has the potential for overlap with additional soybean land expansion
based on the two scenarios from ICF's work. Despite this potential, we cannot say for certain
whether this species will be negatively impacted due to the high uncertainties and conservative
assumptions made in our analyses. Additionally, in most states where mead's milkweed is found,
strategic recovery efforts are in place. Steps have been taken to reintroduce this species in
locations of its former habitat. Indiana and Wisconsin have successfully re-established habitats
near cities such and Chicago and Madison (US FWS, 2003).
Other flowering plant species such as Braun's Rockcress and Short's Bladderpod were
found to have a relatively smaller percentage of potential overlap with their critical habitat or
range. The primary threats to these species, however, are not from agriculture. Braun's rockcress
is threatened by development (home and road construction), competition from other plant
species, grazing, as well as timber harvesting (US FWS, 2004). The Short's Bladderpod is
threatened by habitat degradation from construction for transportation and utility rights-of ways,
soil erosion, overstory shading, and invasive plants. Further, the Short's Bladderpod is often
found on rocky and wooded slopes in wet forested areas of Kentucky and Tennessee which are
not likely to be converted for agricultural purposes (84 FR 33962, 2019).
Insects
The spread and intensification of agriculture following World War II was a great boon to
the US, helping feed a growing population and reinvigorating its post-war economy. But with the
spread and intensification of agriculture also came major declines in insect biomass and
diversity. Increased application of pesticides and fertilizers, great expansions of scale, and the
increased fragmentation of wildlife habitats all proved to be prime drivers of insect population
decline (Raven & Wagner, 2021). Insects, and the plants in which they depend on for sustenance,
protection and hosting, might be more vulnerable to the expansion of soybean crop production
relative to other types of organisms.
There are three insect species listed as endangered within the potential affected area of
soybean crop expansion: the Poweshiek Skipperling, the Dakota Skipper, and the Salt Creek
Tiger Beetle. There is also one threatened species that might be impacted: the American Burying
Beetle.
The Poweshiek Skipperling inhabits prairie fens, grassy lake and stream margins, moist
meadows, sedge meadows, and wet-to dry native prairies. Major stressors of this species include
habitat loss and degradation of native prairies and prairie fens; flooding; and groundwater
depletion, alteration, and contamination with pesticides and herbicides. Our analysis suggests
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that up to 2.18% of the Poweshiek Skipperling's critical habitat could be impacted by the
projected soybean expansion area based on the 250-million-gallon scenario. Due to this species'
susceptibility to land changes resulting from agricultural development, this species could
potentially be impacted by the expansion of soybean croplands.
The Dakota Skipper is native to tallgrass and mixed grass prairies of the northern Great
Plains. Once ranging from northeast Illinois to southern Saskatchewan, today Dakota Skippers
are found only in small, scattered prairies in the Dakotas, Minnesota and southern Canada. This
species' main stressor is habitat loss due to anthropogenic factors, primarily cultivation for
agriculture. Our analysis suggests that up to 1.78% and 4.58% of the Dakota Skipper's critical
habitat and range, respectively, could potentially be impacted by the 250-million-gallon
scenario's projected soybean expansion area. The Dakota Skipper has been impacted by past
cropland expansion and therefore any additional agricultural development has the potential to
further impact their habitat. Again, however, due to the relatively low potential impacts and
various uncertainties in our analyses, we cannot say for certain that they will be impacted.
The Salt Creek Tiger Beetle inhabits the salty muddy banks of the Little Salt Creek near
Lincoln, NE. This beetle requires saline mud flats and exposed mud banks with salt deposits
within saline wetlands and along stream edges for foraging, feeding, reproduction, and
overwintering. Salt Creek Tiger Beetles depend on the presence of moist, muddy areas and are
most often found within a few feet of a stream or wetland edge. Major stressors of the Salt Creek
Tiger Beetle include habitat loss due to urbanization, bank stabilization, and agricultural
development. Our analysis suggests that up to 4.51% and 1.89% of the Salt Creek Tiger Beetle's
critical habitat and range, respectively, could potentially be impacted by the projected soybean
expansion area under the 250-million-gallon analysis.
The American Burying Beetle can be found in various habitats, including open fields to
grasslands to different types of forests. It is particularly vulnerable to habitat loss and
fragmentation. Habitat fragmentation and deforestation especially have reduced populations of
species that become carrion in which this species broods. Our analysis suggests that up to 0.78%
of the American Burying Beetle's range could potentially be impacted by the projected soybean
expansion area.
There are also various species of butterflies, bees, and dragonflies that may also be
impacted by this proposal.
The Monarch Butterfly, the Mitchell's Satyr Butterfly, and the Karner Blue Butterfly are
all threatened species that may be susceptible to population decline as a result of further
agricultural expansion. The monarch butterfly may be impacted due to its reliance on Mead's
Milkweed. Plants in the milkweed family are the sole host plant to the monarch butterfly's
caterpillar, and as such are vitally important for the monarch butterfly's life cycle and overall
species survival. Should the mead's milkweed be severely impacted by the potential cropland
expansion, this could resultantly impact the populations of monarch butterfly and other
pollinators that may inhabit those areas or make use of them on their migratory journeys (US
FWS, 2021b). Our analysis suggests that up to 0.20% of the Monarch Butterfly's range could be
impacted by the projected soybean expansion area. As for the Mitchell's Satyr Butterfly and the
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Karner Blue Butterfly, the range that could potentially be impacted by projected soybean
expansion is 0.14% and 0.14%, respectively.
The two other insect species, the Rusty Patched Bumble Bee and Hine's Emerald
Dragonfly, are susceptible to habitat destruction or alteration, as well as increased pesticide
usage. Our analysis suggests that up to 0.56% of the Rusty Patched Bumble Bee's range could be
impacted by the projected soybean expansion area, and that up to 0.02% and 0.35% of Hine's
Emerald Dragonfly's critical habitat and range, respectively, could potentially be impacted by
the projected soybean expansion area.
Mammals
Since the rise of industrial agriculture, terrestrial mammalian populations have decreased
significantly. While disease, climactic changes, and decreases in biological diversity as a result
of increases in inbreeding have plagued many mammals, the loss of wild habitat due to
agricultural development has also been a driver of mammalian population declines for nearly a
century (Our World in Data, 2021).
There are three species listed as endangered within the potential affected area of soybean
crop expansion: the Black-footed Ferret, the Northern Long-Eared Bat, and the Indiana Bat.
There is also one threatened species that may be impacted: the Gray Bat. Other greater
mammalian species, like the Canada Lynx and the Gray Wolf, are even less likely to be
impacted.
The Black-footed Ferret inhabits intermountain prairies and grasslands wherever prairie
dogs may be found. This is because ferrets are obligate associates of prairie dogs, using prairie
dog burrows instead of their own. Resultantly, ferrets can typically be found in areas within high
burrow density prairie dog colonies. The Black-footed ferret's main stressors include disease,
drought, declining genetic fitness due to increased inbreeding and a reduction in genetic
diversity, and prairie dog shooting and poisoning (US FWS, 2021b). Our analysis suggests that
up to 0.09% of the Black-footed Ferret's range could potentially be impacted by the projected
soybean expansion area.
The Northern Long-Eared Bat, the Indiana Bat, and the Gray Bat spend their winters
hibernating in caves and mines, and during the summer and portions of fall and spring, they may
be found roosting in colonies or singly underneath bark, in crevices or in cavities of both live and
dead trees (US FWS, 2023). Our analysis suggests that up to 0.50% of the Northern Long-Eared
Bat's range could potentially be impacted by the projected soybean expansion area; up to 0.25%
and 0.26% of the Indiana Bat's critical habitat and range, respectively, could potentially be
impacted by the projected soybean expansion area; and up to 0.46% of the Gray Bat's range
could potentially be impacted by the projected soybean expansion. While there are many
stressors to these bat species, the main threat is white-nose syndrome, caused by the fungus
Pseudogymnoascus destructans. It is likely that had this disease not emerged, these three bats
may not have been experiencing such large population declines.
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Birds
Potential land use change may also mean habitat loss for birds. Especially for birds which
have large migratory pathways, habitat changes lead to biodiversity implications and resource
availability. Species such as the piping plover and whooping crane both have migratory
pathways which overlap with potential expansion of soybean cropland mostly in the Midwest.
The piping plover is a small bird which tends to live along waterways such as lakes and
wetlands. They are known to inhabit the Great Plains and the Great Lakes regions for part of the
year. One of their populations that nests in great plains and has struggled with reproduction due
to habitat changes. Although their populations have struggled, recovery plans have been enacted
to protect their populations (US FWS, 2016). The analysis from ICF suggests that up to 0.5% of
its critical impact could be impacted.
Similarly, whooping cranes have two separate populations which migrate long distances
for the winter. One population breeds in Wisconsin with wintering areas spanning into Kentucky
and Tennessee down into Florida. Their habitat consists primarily of wetland habitat which has a
history of being converted to farmland. However, whooping crane population have primarily
been impacted by hunting and egg collection. Recovery has been successful with crane
reintroduction and wetland preservation actions (Smith et al., 2019).
3. Potential Impacts on Listed Species and Critical Habitat (NMFS species)
ICF did not include NMFS species in their overlap analyses. However, EPA was able to
complete this by using ICF's modeled soybean expansion areas and GIS species data from
NMFS. As was done for the FWS species, we used the two scenarios from ICF's work: the 100-
million-gallon scenario (1,523,806 acres converted) and 250-million-gallon scenario (3,862,501
acres converted). We chose to focus on these two scenarios as they most closely match our
maximum potential land use impact of 1.9 million acres from increases in soybean biodiesel.
The critical habitat layer of the Atlantic sturgeon (Gulf subspecies) was the only polygon
found to overlap with the two expansion areas. We found that 0.003% and 0.007% of the
Atlantic sturgeon (Gulf subspecies)'s critical habitat overlapped with the 100 million and 250-
million-gallon scenarios, respectively.
The Gulf sturgeon as the name suggests, resides primarily in the gulf region of Louisiana,
Mississippi, and Florida. This is a smaller area when compared to the larger Atlantic sturgeon
species which can range as far north as Canada. Populations of Gulf sturgeon have been
impacted over the years from factors such a dam construction to water degradation. As sturgeons
spawn in the rivers during the spring and summer, dams can impede their cycle and have been
known in some cases to separate some populations above and below the obstacle. After
spawning they move to the estuary, or mixed salt and freshwater region of the river where it is
cooler and they have better food sources. They tend to move fully in the ocean during the winter
months (NOAA, 2022).
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Pollutants pose a significant effect to the gulf sturgeon. Impacts may be caused directly
by impacting their organs and reproductive systems or indirectly by becoming incorporated into
the food they eat. These pollutants stem from industrial and agricultural activities which can be
washed or discharged into the rivers and oceans. Climate change has also caused some changes
to their environment. Changes in water temperature and levels can affect their habits and
habitats. Weather events such as hurricanes are impactful especially with their increased
frequency due to climate changes.
In addition to pollutants, physical barriers and impacts can affect the Gulf sturgeon. As
mentioned above, dams are a significant threat to all sturgeons. This can also change the flow of
the river where spawning occurs. Changes in water flow can alter habitat needed for
reproduction and can destroy feeding areas. This is consequence of river dredging as well which
is common activity with dam construction and on industrial use rivers near the Gulf of Mexico.
( . Potential Impacts from Increased Canola Production
1. Identifying Potential Locations of Acres Impacted
We used the same general procedures for identifying potential locations of acres impacts
from increased canola production as was used for identifying locations of acres impacted from
increased corn production. The details of that analysis are described in section VILA and
relevant differences are summarized here.
There were four key differences between the analysis of impacts from increased corn
versus canola: (1) the geographic scope of the analysis, (2) the total acres of conversion
simulated, and (3) the number of iterations. For the canola analysis, the geographic scope of the
analysis was constrained to North Dakota as opposed to the entire area of potential land use
change, as that is where the vast majority of new canola in the U.S. is expected to be cultivated
(Table VI.C-12). The total acres of conversion simulated was 0.26 million acres, as opposed to
0.5 million acres for the impacts from corn production. The number of replicates for all scenarios
was 500 because the geographic scope was smaller and thus allowed greater computational
output. Other than those differences, the same analysis as described in section VILA was applied.
For convenience, we use the same scenario names (Table VII.A-1) but with "ND" at the end
(e.g., Sl-ND, Table VII.C-1).
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Table VII.C-1. List of scenarios for species-level effects from potential increases in
cropland in North Dakota
Scenario
#
Total acres
converted (acres)
Critical habitat
(CH) range (R)
Buffer
Replicate
iterations
Sl-ND
260,000
CH
None
500
S2-ND
260,000
CH
2600'
500
S3-ND
260,000
R
None
500
S4-ND
260,000
R
2600'
500
2. Potential Impacts on Listed Species and Critical Habitat
For the analysis of critical habitat with no buffer (Sl-ND), we found that 3 species were
potentially impacted at least once across all 500 iterations, and the same species had some
critical habitat potentially converted in 5% or more of iterations (Table VII.C-2). These three
species are all FWS species and include: Piping plover (Atlantic Coast and Northern Great Plains
population), Dakota skipper, and Poweshiek skipperling. We found conversion of 845 total acres
of critical habitat on average across all iterations (10th - 90th percentiles: 600 - 1110 acres). We
found that zero species had one percent or more of its critical habitat potentially impacted (i.e.,
conversion within critical habitat), with 1 species (Dakota skipper) with 0.33% of its critical
habitat potentially impacted on average. The Piping plover (Atlantic Coast and Northern Great
Plains population) and Poweshiek skipperling had 0.05 and 0.01 percent of their critical habitats
potentially impacted, respectively.
For the analysis of critical habitat with a 2600' buffer (S2-ND), we found that 3 species
(the same as above) were potentially impacted at least once across all 500 iterations, and these 3
species had some critical habitat potentially converted in 5% or more of iterations (Table VII. C-
2). We found conversion of 6535 acres of critical habitat plus the 2600' buffer on average across
all iterations (10th - 90th percentiles: 5850 - 7260 acres). We found that the Dakota skipper had
1.83% of its critical habitat with buffer potentially impacted (i.e., conversion within critical
habitat or within 2600' of critical habitat). On average, the Piping plover (Atlantic Coast and
Northern Great Plains population) had 0.42% of its critical habitat with buffer potentially
impacted; the Poweshiek skipperling had 0.09% of its critical habitat with buffer potentially
impacted on average.
For the analysis of species range with no buffer (S3-ND), we found that 10 FWS species
were potentially impacted at least once across all 500 iterations. We found that zero species had
one percent or more of its range potentially impacted; all 10 species had between zero and 0.19%
of their respective range potentially impacted on average.
For the analysis of species range with a 2600' buffer (S4-ND), we found that 11 FWS
species were potentially impacted at least once across all 500 iterations. We found that zero
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species had one percent or more of its range plus buffer potentially impacted; all 11 species had
between zero and 0.88% of their respective range plus buffer potentially impacted on average.
Out of the 11 species identified in the two range analyses (i.e., in the S3-ND and/or in the
S4-ND scenarios), only 8 had greater than zero percent of their range or range plus buffer
impacted. These include: the Monarch butterfly, Northern Long-Eared Bat, Whooping crane
(endangered population), Piping Plover (Atlantic Coast and Northern Great Plains population),
Red knot, Dakota Skipper, Western prairie fringed Orchid, and Pallid sturgeon. The full results
are included as an excel sheet attached to this Biological Evaluation.
Table VII.C-2. Summary of effects across scenarios for the North Dakota simulations
Scenario
#
# spp.
impacted at
least once
# spp. impacted
in 5% or more of
iterations
Average
acreage of CH
conversion
(10th-90th
range)
Number of
spp. with 1%
or more of CH
or range
impacted on
average
Common
name of sp.
with >1% of
CH or range
converted.
Sl-ND
3
3
845 (600-
1110)
0
None
S2-ND
3
3
6535 (5850-
7260)
1
Dakota
skipper
S3-ND
10
N/A*
N/A*
0
None
S4-ND
11
N/A*
N/A*
0
None
* These summarizing statistic results for the range scenarios (S3-ND) and (S4-ND) include 5-8 species
that are not considered in this Biological Evaluation because they have a listing status of resolved, under
review, recovery, or undefined. As such, the results are not presented here as they provide a skewed
assessment with the inclusion of those species.
On average, our analyses suggest that 0.33 and 0.79 percent of the Dakota skipper's
critical habitat and range, respectively, could be directly impacted by increased cropland driven
by increases in canola biodiesel. When a 2500-foot buffer is added, these numbers increase to
1.83 and 0.88 percent. The Dakota skipper was also identified in our soybean analysis and is
discussed in more detail in that section (VII.B).
I). Total Potential Impacts of Increased Bio fuel Crop Production
After individually estimating the impact of potential land use changes from non-cropland
to cropland that could result from increased demand for each of the three biofuel feedstocks—
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corn, soybean oil, and canola oil—we then considered the potential cumulative impacts for all
three combined. It is these cumulative impacts, rather than the expected impacts from increased
demand for corn, soybean oil, and canola oil individually (discussed in Sections VII.A-VII.C),
along with the water quality impacts (discussed in Section VIII), that must be considered to
understand potential impacts on listed species. These potential cumulative impacts, however, do
not fully account for how the individual acreage assessments from increases corn ethanol,
soybean oil, and canola oil described in Sections VI.A-VI.C interact with one another. For
example, as described in Section VI.C, the results from economic modeling suggests that total
acreage for corn cropland could actually decrease due to increases in canola oil. We do not
account for this potential decrease in assessing the cumulative impacts; as such, the cumulative
impacts include yet another level of conservative (and worst-case) estimating.
Tables VII.D-1 through VII.D-4 show the expected impacts for the 10 FWS species with
the greatest expected impact (on a percentage basis) for each of the four scenarios we
considered.38 The full lists with the expected impacts on all of the FWS species with critical
habitat or range in the action area has been provided as a separate file.
The only NMFS species that was present in more than one of the individual analyses was
the Atlantic sturgeon (Gulf subspecies)' critical habitat. Based on the probabilistic results from
the corn ethanol analysis and the conservative 250-million-gallon (-3.8 million acre) scenario
from the ICF analysis, the cumulative impact for this species, in terms of maximum potential
overlap, is 0.023% of its total critical habitat.
Table VII.D-1: FWS Species with the Highest Impact on Critical Habitat Based on Land
Use Impact Analyses Alone
Species Common Name
Species Scientific Name
Percent Critical Habitat/Range Converted
Critical Habitat
Critical Habitat + Buffer
Range
Range + Buffer
Salt CreekTiger beetle
Cicindela nevadica lincolniana
4.62%
5.82%
1.99%
1.99%
Kentucky glade cress
Leavenworthia exigua laciniata
3.16%
3.47%
6.00%
6.01%
Poweshiek skipperling
Oarisma poweshiek
2.34%
2.73%
0.68%
0.68%
Dakota Skipper
Hesperia dacotae
2.27%
4.21%
5.51%
5.59%
Slender chub
Erimystax cahni
1.56%
1.84%
0.02%
0.02%
Braun's rock-cress
Arabis perstellata
1.35%
2.16%
0.56%
0.56%
Topeka shiner
Notropis topeka (=tristis)
1.13%
2.07%
1.56%
1.56%
St. Francis River Crayfish
Faxonius quadruncus
0.98%
2.57%
0.68%
0.68%
Big Creek Crayfish
Faxonius peruncus
0.82%
2.02%
0.67%
0.67%
Piping Plover
Charadrius melodus
0.57%
1.11%
0.48%
0.48%
TO
The four scenarios are the impacts on critical habitat, the impacts on critical habitat with a 2600-foot buffer, the
impacts on range, and the impacts on range with a 2600 foot-buffer. For the impacts on increased demand for
soybean oil we do not have results for scenarios with the 2600-foot buffer. For the total expected impacts from all
biofuel feedstocks we used the soybean results from the estimates on the critical habitat and range for the expected
impacts on the critical habitat and range with the 2600-foot buffer.
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Table VII.D-2: FWS Species with the Highest Impact on Critical Habitat (with 2600-Foot
Buffer) Based on Land Use Impact Analyses Alone
Species Common Name
Species Scientific Name
Percent Critical Habitat/Range Converted
Critical Habitat
Critical Habitat + Buffer
Range
Range + Buffer
Fleshy-fruit gladecress
Leavenworthia crassa
0.00%
13.50%
0.08%
0.08%
Slenderclaw crayfish
Cambarus cracens
0.02%
9.78%
0.11%
0.11%
Devils River minnow
Dionda diaboli
0.00%
7.48%
0.02%
0.01%
Slackwater darter
Etheostoma boschungi
0.11%
6.58%
0.08%
0.08%
Salt CreekTiger beetle
Cicindela nevadica lincolniana
4.62%
5.82%
1.99%
1.99%
Roswell springsnail
Pyrgulopsis roswellensis
0.14%
4.39%
0.04%
0.05%
false spike
Fusconaia mitche 11 i
0.02%
4.39%
0.05%
0.05%
Texas fawnsfoot
Truncilla macrodon
0.01%
4.26%
0.04%
0.04%
Dakota Skipper
Hesperia dacotae
2.27%
4.21%
5.51%
5.59%
Guadalupe Orb
Cyclonaias necki
0.00%
4.03%
0.07%
0.07%
Table VII.D-3: FWS Species with the Highest Impact on Range Based on Land Use Impact
Analyses Alone
Species Common Name
Species Scientific Name
Percent Critical Habitat/Range Converted
Critical Habitat
Critical Habitat + Buffer
Range
Range + Buffer
Neosho madtom
Noturus placidus
#N/A
#N/A
13.67%
13.67%
White catspaw (pearlymussel)
Epioblasma perobliqua
#N/A
#N/A
7.44%
6.87%
Neosho Mucket
Lampsilis rafinesqueana
#N/A
#N/A
7.00%
7.00%
Scioto madtom
Noturus trautmani
#N/A
#N/A
6.35%
6.03%
Kentucky glade cress
Leavenworthia exigua laciniata
3.16%
3.47%
6.00%
6.01%
Dakota Skipper
Hesperia dacotae
2.27%
4.21%
5.51%
5.59%
Illinois cave amphipod
Gammarus acherondytes
#N/A
#N/A
5.43%
5.44%
Mead's milkweed
Asclepias meadii
#N/A
#N/A
4.62%
4.62%
Virginia round-leaf birch
Betula uber
#N/A
#N/A
2.14%
1.36%
Salt CreekTiger beetle
Cicindela nevadica lincolniana
4.62%
5.82%
1.99%
1.99%
Table VII.D-4: FWS Species with the Highest Impact on Range (with 2600-Foot Buffer)
Based on Land Use Impact Analyses Alone
Species Common Name
Species Scientific Name
Percent Critical Habitat/Range Converted
Critical Habitat
Critical Habitat + Buffer
Range
Range + Buffer
Neosho madtom
Noturus placidus
#N/A
#N/A
13.67%
13.67%
Neosho Mucket
Lampsilis rafinesqueana
#N/A
#N/A
7.00%
7.00%
White catspaw (pearlymussel)
Epioblasma perobliqua
#N/A
#N/A
7.44%
6.87%
Scioto madtom
Noturus trautmani
#N/A
#N/A
6.35%
6.03%
Kentucky glade cress
Leavenworthia exigua laciniata
3.16%
3.47%
6.00%
6.01%
Dakota Skipper
Hesperia dacotae
2.27%
4.21%
5.51%
5.59%
Illinois cave amphipod
Gammarus acherondytes
#N/A
#N/A
5.43%
5.44%
Mead's milkweed
Asclepias meadii
#N/A
#N/A
4.62%
4.62%
Rabbitsfoot
Quadrula cylindrica cylindrica
#N/A
#N/A
1.99%
1.99%
Salt CreekTiger beetle
Cicindela nevadica lincolniana
4.62%
5.82%
1.99%
1.99%
Many of the species above are discussed in more detail in Sections VII. A-VII. C. These
results linking potential land use changes and overlap with listed species is one piece of the
puzzle in understanding potential effects attributed to the RFS Set Rule. Though they represent a
worst-case scenario, and most species overall saw relatively small percentage impacts, it is also
important to consider the potential consequences of these land use changes and the endpoints
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relevant to listed species in making effect determinations for this Biological Evaluation. For
aquatic species, one important potential effect would be exposure to changes in water quality.
We discuss potential water quality impacts in the next section, Section VIII. However, it is also
important to consider other factors such as species' life histories and specific PBFs or PCEs
present in their critical habitats. As was stated previously, not all land within a species'
designated critical habitat contains PBFs or PCEs. We examine these factors more closely in
Section IX where we make our final effect determinations for this Biological Evaluation.
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VIM. Potential Impacts on Listed Species from Changes in Water
Qiv\Ut\
In addition to contributing to agricultural intensification or the conversion of non-
cropland to cropland within species' critical habitat and ranges, increased demand for biofuels
can also negatively impact water quality. These water quality impacts are generally related to the
land use changes to produce biofuel crops discussed in the previous sections. Water quality can
be adversely impacted by the production of biofuel feedstocks, primarily due to the sediment,
nutrients, and pesticides directly or indirectly released during the production of biofuel
feedstocks. Increased production of crops used to produce biofuels can impact water quality at
nearby edge-of-field streams and rivers as well as at a significant distance from the location of
the land use change as contaminants associated with crop production travel downstream and into
major waterways. This is particularly true for contaminants with greater mobility and
contaminants that persist for longer time periods in soil and aquatic environments. This section
begins by discussing the water quality impacts associated with crop production, and then uses the
best available data to project potential impacts of the RFS volume requirements on water quality.
A. Potential Impact of Increased Crop Production on Water Quality
Increased crop production and expansion of new cropland leads to changes and increases
in fertilizers and pesticides used to grow and protect these crops. Water quality assessments have
often suggested that agriculture is a leading source of water quality problems. Although not
intended by farmers, pollutants such as sediments, nutrients and pesticides travel from fields to
sources of water such as rivers and streams. The most recent USGS SPARROW model
(SPAtially Referenced Regression On Watershed attributes) found that fertilizers contributed to
25% of the total nitrogen (TN) and 39% of the total phosphorus (TP) to the Mississippi River
drainage basin. For TN, an additional 18% was from N fixing crops such as soybean, alfalfa,
clover, and other crops (Robertson & Saad, 2019). USGS has also studied the effects of
pesticides in water through the National Water-Quality Assessment Project (NAWQA), which
has indicated that the mere existence of agricultural land alone in a watershed is a reasonable
predictor for the existence of surface level water contamination of common pesticides. In a
sampling study testing sites in the Mississippi River Basin, USGS found that deethylatrazine,
atrazine, and metolachlor alone were present in 100% of stream samples and a majority of all
groundwater samples as well (Stark et al., 2000).
In addition to effects from fertilizers and pesticides, impacts may occur from soil
disruption such as erosion and sedimentation. Soil erosion is often increased due to tillage and
cultivation of land. Additionally, if previously vegetative land such as grassland is left
uncovered, this may increase the amount of erosion leading to sedimentation in nearby water
systems or municipal drainage. Increases in sediment in streams and rivers can raise streambeds
which could lead to an increase in flooding. This also impacts the habitats of aquatic life
especially those that are considered bottom dwellers such as freshwater mussels.
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It is important to recognize that the existing land that may be converted or used to
produce more cropland (e.g., through intensification of existing cropland) already affects water
quality. Pasture or grazing land, for example, have effects on water quality as herbicides may be
used on these lands and sediment transport could still be an issue. For example, based on
California Pesticide Use Report database, in 2021 Amador County pasture land applied a variety
of pesticides including glysophate as a weed control to their acreage. It can be noted that
glysophate is a product listed for pesticide use on corn and soy cropland. Like other kinds of
pesticides, these chemicals are known to drift from their place of application into local bodies of
water.
Unless managed correctly, pasture and rangelands may experience poor forage coverage
and heavy traffic from animals. This can also cause soil erosion and sedimentation into local
waterways and affect habitats of aquatic species downstream such as bottom feeding mollusks
and fish such as the steelhead trout which spawns in rocky river bottoms. Additionally, urine and
feces from animals on such lands can contribute to nutrient deposition in local waterways
(Hubbard et al., 2004).
1. Estimated Potential Impacts of Increased Fertilizer Use
Estimating the impact of increasing crop production is inherently complex. The impact of
crop production on water quality is impacted by a wide variety of different factors including
agricultural practices, soil type, rainfall, and many others, which can vary widely depending on
where biofuel crops are produced. Further, individual contaminants such as sediments, nitrogen,
phosphorus, and various pesticides have differing characteristics that impact their mobility and
persistence in soil and aquatic environments. To address the complexities associated with
estimating changes in water quality various models have been developed, such as the Soil and
Water Assessment Tool (SWAT). SWAT is a small watershed to river basin-scale model used to
simulate the quality and quantity of surface and ground water and predict the environmental
impact of land use, land management practices, and climate change. SWAT has been used in
numerous peer reviewed publications to assess the water quality impacts in various regions of the
United States and around the world (Wang et al., 2019).
In support of the upcoming Third Triennial Biofuels Report to Congress (RtC3) Chen et
al applied the SWAT to the Missouri river basin to estimate the water quality changes resulting
from land use changes due to all causes in recent years (Chen et al., 2021). The Missouri river
basin (Figure VIII.A-l) was chosen as the geographic area for this analysis because this is the
region in which some of the highest rates of grassland conversion to cropland occurred from
2008 - 2016 when the domestic production of biofuels, particularly ethanol, expanded greatly
(Lark et al., 2020). Much of the observed increase in cropland during this time period was the
result of the conversion of grassland to cropland to produce corn and soybeans.
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Figure VI11. A-1: Modeling Domain in Chen et al. (2021) and the Land Cover Types
Montana
Minnesota
Wisconsin
SontiriLMkofa
Nebraska
Illinois
CX5
•4 AWfc
C3 c<"
' , Corn xtbean rotation
C3 Corn ring tr be at rotation i Other
voter wheat rotation
For«t
Grav.land Pasture
Hav
Shrub land
Sovran
Urban
Water
Wetland.
L
C3
\ __ \ Spin g u bent
' , Sprngnbeat wrbtao rotation \ 1 Winter ubeat
\ ^ \ Spragvbeat corn rotatioo \ 1 \ Wintembeat *.o>t)ean rotation
n_n_r
0 70 140
280 420
I Kilometers
560
The SWAT modeling conducted in support of the RtC3 estimated the water quality
changes associated with land use change in the Missouri river basin documented by Lark et al.
from 2008 to 2016. The modeling considered three different scenarios where grassland39 was
converted to continuous corn, a corn/soybean rotation, and a corn/wheat rotation. The total
quantity of land converted to cropland in the Missouri river basin during this time period was
approximately 2.51 million acres. For the purposes of the SWAT modeling, all of the converted
land was assumed to previously be grassland. The SWAT modeling considered the impact of this
conversion to cropland on total suspended sediments, total nitrogen (including both dissolved
and organic nitrogen) and total phosphorus (including both dissolved and organic phosphorus).
Here "grassland" merely means land covered with grass. Lark et al. (2020) attempted to isolate lands that had
been covered in grass for 25 years and assumed to be uncultivated for that entire period. There is uncertainty in how
long these areas were covered in grass, and methods for estimating grassland and whether these lands represent long
term grassland or areas that are intermittently utilized for pasture, hay, or other lightly managed areas (Dunn et al.
2018, Copenhaver et al. 2021).
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While the SWAT modeling conducted in support of the RtC3 does not exactly match the
land use changes we projected could result from the RFS volume requirements in 2023-2025, we
believe it is a reasonable proxy and that it provides the best information available on the types of
water quality impacts we may see from this action. The three scenarios considered in the SWAT
analysis (conversion of grassland to continuous corn, corn/soybean rotation, and corn/wheat
rotation) are consistent with the types of land conversion we expect would result from increasing
biofuel demand. The total area converted from non-cropland to cropland in the SWAT modeling
(2.51 million acres) is also similar to the total conversion we are expecting from this action (see
Table VIII.A-1). We note, however, that in the SWAT analysis all 2.51 million acres of land
conversion occurred in the Missouri river basin, while the projections of conversion to cropland
attributable to the RFS volume requirements could occur on any available land in the action area,
which is substantially larger than the Missouri river basin. By concentrating the entire quantity of
cropland conversion in the Missouri river basin the use of the SWAT analysis for our purposes
here is expected to estimate greater water quality impacts than if the cropland conversion
occurred over a larger geographic area in multiple river basins. It is therefore reasonable to
expect that the water quality impacts of the RFS volume requirements for 2023-2025 may be
less than those estimated in the SWAT modeling conducted for the RtC3.
Table VIII.A-1: Projected Conversion to Cropland by Year (million acres)
Land Use
Change
Attributable To:
2023
2024
2025
Corn Ethanol
0.39
0.44
0.46
Soybean Biodiesel/RD
1.57
1.78
1.93
Canola Biodiesel/RDa
0.26
0.26
0.26
Total
2.22
2.48
2.65
a In this table We are using the projected land use change in North Dakota, rather than the lower national projected
land use change, to represent a worst-case scenario.
As noted above, the model in Chen et al. (2021) was used to simulate three crop scenarios
representing conversion of grassland (G): continuous corn (C), corn/soybean rotation (C/S), and
corn/wheat rotation (C7W). Conversion to different types of cropland is important because there
are significant differences in the average application rates of nitrogen and phosphorus between
crops (See Figure VIII. A-2); for example, conversion to soybean would require less nitrogen
application relative to corn, wheat, and cotton. Conversion was simulated only in locations of
observed land use changes from Lark et al. (2020) and was summarized for two periods, 2008-
2012 and 2008-2016. The SWAT model then estimated stream flow and riverine sediment and
nutrient loads throughout HUC-8 watersheds in the Missouri River Basin (MORB). Changes
observed in water quality continued to increase over the two periods, consistent with the
magnitude of increased cropland conversion, as seen in Figure VIII.A-1. Historical cropland
conversion from non-cropland during 2008-2012 was 0.77%, and 1.18% from 2008-2016 (Lark
et al., 2020). The water quality changes observed with these associated numbers were of the
same magnitude, as increases in nutrient loads increased by 1.5 times from 2008-2016 compared
to 2008-201240.
40 For more information on these results and the baseline scenario used in comparison to the different biofuel
scenarios, see Chen et al. (2021)
175
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Figure VIII.A-2: Fertilizer Application Rates for Different Crops
160
Nitrogen Phosphorus
~ Corn H Soy ^ Wheat n Cotton
176
-------
Figure VIII.A-3: Summary of the Results at the MORB Outlet. Shown are the mean annual
changes in flow, total suspended sediment, organic nitrogen, dissolved nitrogen, total
nitrogen, organic phosphorus, dissolved phosphorus, and total phosphorus between SI and
the different biofuel/cropland conversion scenarios.
2008-2012
IS2 DS3 ~ S4
10.0
2
ai
c 8.0
ai
6.0
E
o
4=
n
dc 4.0
U
2.0
2008-2016
IS2 a S3 U1S4
CL
Flow
TSS OrgN DisN TN
OrgP DisP TP
At the outlet of the MORB, SWAT modeling showed that G to C conversion resulted in
the greatest increase in TN and TP loads (6.4% and 8.7%, respectively), followed by G to C/S
conversion (6.0% increase in TN and 6.5% increase in TP), and then G to C/W (2.5% increase in
TN and 3.9% increase in TP). The greatest percentage increase in TN and TP occurred in the
Dakotas, coinciding with the highest amount of grassland conversion. However, because of the
relatively low percentage of cropland in these areas, they contributed relatively little TN and TP
to total basin loads. Rather, "hotspots" of change predominantly in downstream collecting areas
that are also cropland heavy states like Iowa, Missouri, Nebraska, and Kansas, contributed the
bulk of TN and TP to basin-wide loads. Loading from existing cropland, grassland conversion,
and precipitation are major factors in the contribution of land to nutrient loads. These results
have implications for streams within the MORB as well as endangered and threatened species.
177
-------
While Chen et al. (2021) provides a reasonable explanation for how nitrogen and
phosphorus respond to increasing cropland conversion, it does not address how upstream
tributaries, including small rivers and streams, may be affected by nearby cropland conversion.
While absolute quantities of TN, TP, sediment, and some fertilizers may be higher at river
junctions downstream, there is still some concern surrounding waterbodies that are directly
adjacent to cropland. Concentrations in these small streams and tributaries can be higher despite
lower total loading, as there is less dilution and degradation.
Another area of concern from increasing cropland for biofuel production is the
contribution of fertilizers used on this new cropland to hypoxia in the Mississippi River and the
Gulf of Mexico. Hypoxia is caused by excess nutrients, most especially nitrogen and phosphorus,
entering the water from agricultural runoff and other point sources. These excess nutrients
stimulate the growth of algae, which decomposes, consuming oxygen, and leading to fish die-
offs and harm to aquatic life. Apart from the SWAT modeling discussed above, we are not aware
of any modeling efforts or published efforts that have attempted to estimate the impact of the
specific potential cropland increases we estimate could result from the RFS volume requirements
in the Set Rule. In the absence of more specific data, we have used the results from the SWAT
modeling discussed above to estimate the potential impact of increased crop production on the
amount of nitrogen and phosphorus entering the Gulf of Mexico.
The SWAT modeling conducted for the RtC3 estimated the impact of increased cropland
in the Missouri River basin observed from 2008-2016, which is similar in magnitude to the
increase in cropland we project could be attributable to the RFS volume requirements in the Set
Rule, on the nitrogen and phosphorus loads at the mouth of the Missouri River for several
scenarios. To inform our understanding of the potential magnitude of this impact, we compared
the results of these scenarios to the total nitrogen and phosphorus loads at the Mississippi River
outlet. The total nitrogen load has been relatively stable since 1995 at approximately 1.5 million
metric tons and the total phosphorus load has been relatively consistent since 2005 at
approximately 0.15 million metric tons (Stackpoole et al., 2021). Thus, the modeled increases in
total nitrogen (5,400-13,800 tons per year) and total phosphorus (1,500-3,400 tons per year) at
the mouth of the Missouri River would represent an increase of 0.3%-0.8% and 0.9%-2.1% of
total nitrogen and phosphorus respectively at the outlet of the Mississippi River. This would
represent a minor increase in the total hypoxic area expected in the Gulf of Mexico- literature
estimates range from a 56 to 80 percent reduction in nutrient loading to achieve a 5000 km2
reduction in hypoxic area. A 20 percent total load reduction is estimated to reduce hypoxic area
between 124,000 km2 and 156,000 km2(Scavia, 2017).
These estimates of the percent increase in total nitrogen and phosphorus at the
Mississippi River outlet that could potentially result from an increase in cropland to produce
biofuels attributable to the RFS volume requirements make several key assumptions. First, they
are based on worst-case scenario maximum land use acreage impacts that could be attributed to
the RFS Set Rule. Second, the high end of the ranges presented represent a scenario where all of
the new cropland is used to produce continuous corn. As discussed in Section VI, most of the
increase in cropland is expected to be used for soybean production, with a smaller area expected
to be used for wheat production. Lastly, as mentioned above, while Chen et al. (2021) provides a
reasonable explanation for how nitrogen and phosphorus respond to increasing cropland
178
-------
conversion, it does not address how upstream tributaries, including small rivers and streams, may
be affected by nearby cropland conversion, and how species that occur in such freshwater
ecosystems may be impacted by potential effects. EPA completed a qualitative analysis in
collaboration with NMFS, as described in Section IX, that examines this more closely for NMFS
species.
2. Estimated Potential Impacts of Increased Pesticide Use
Listed species can also be impacted by a variety of pesticides that are commonly used in
the production of biofuel crops such as corn and soybeans. Any increase in crop production that
is attributable to the RFS volume requirements could therefore potentially impact listed species if
the pesticides applied to this cropland was transported to local waterways and then downstream
to larger streams and rivers. To consider the potential impact of increased use of pesticides we
first identified the 15 most commonly used pesticides applied to corn, soybeans, and wheat
(based on percent of crop acres treated). While wheat is not commonly used to produce biofuel
in the US, the modeling conducted in support of the canola renewable diesel pathway indicated
that an increase in wheat production is likely as increasing demand for canola renewable diesel
resulted in canola displacing wheat in Canada causing additional wheat production in North
Dakota.
After identifying the 15 pesticides most likely to be applied to corn, soybeans, and wheat
we identified a movement rating, soil half-life, and aquatic half-life for each of the pesticides that
was one of the top 15 most widely used pesticides for at least one of the three crops (Table
VIII.A-2).41 These characteristics inform the likelihood that the pesticides will transport from the
field on which they are applied and end up on local waterways or streams and rivers downstream
of new cropland. The movement rating of a pesticide indicates how likely the pesticide is to
move in a solution with water below the root zone or to a field edge. Pesticides with higher
movement ratings are more likely to move from the field on which they are applied to local
waterways and are more likely to move from these local waterways to downstream rivers. Half-
life is the time it takes for certain quantity of a pesticide to be reduced by half as it breaks down
in the environment—whether that is in soil (soil half-life) or in water (aquatic half-life).
Pesticides with higher movement ratings and/or longer half-lives are generally of greater
concern, as these pesticides are more likely to be transported from the fields on which they are
applied to waterways and will remain in waterways for more time when they reach the waterway.
41 Information on the mobility rating, soil half-life, and aquatic half-life are summarized in the table below.
Information in this table was collected from the University of Hertfordshire Pesticide Properties Database
(http://sitem.herts.ac.uk/aeru/ppdb/), Oregon State University Pesticide Properties Database
(http://npic.orst.edu/ingred/ppdmove.htm) and Pesticideinfo.org (https://pesticideinfo.org/).
179
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Table VIII.A-2: Physical Properties of Pesticides Used for Corn, Soybeans, and Wheat
Hdf-Life Da~-, i
2016 - 2020 A"eraz*
?er:e:/
A-engs Qmznfr Apphei
.lb-. a:rei
C -lie £r
j "r "cir.ent P.r.ir.2
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j Inderal?
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60
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78
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BIC VC LCPYF.OXE
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213
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!
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0.00
BROMOXiXIL
Heibicide
Ver" Exci erne b Low
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0.03
CHLCF.E il'P.CN
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Hid;
40
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.
CHLOr.iU.RT.OX
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Hi«h
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CLE7K0BIM
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55,5
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V-IFEXTP-AZOXE
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0.00
The SWAT modeling conducted in support of the RtC3 considered the impact of
increased cropland on several contaminants that could affect listed species such as total
suspended sediment, nitrogen, and phosphorus; the SWAT modeling did not consider the impact
of increased use of pesticides. While the SWAT model is capable of modeling changes in
pesticide concentrations expected to result from increased cropland for some pesticides, we are
not aware of any existing SWAT modeling that explored the changes in pesticide concentrations
that would be expected to result from the renewable fuel volume production increases or the land
use changes we have estimated to potentially result from the Set Rule.
Nevertheless, we believe we can leverage the results of the SWAT modeling conducted
for the RtC3 in the Missouri River Basin to inform our understanding of potential impacts of the
RFS volume requirements in the Set Rule. As discussed previously, the SWAT modeling
conducted for the RtC3 modeled changes in nitrogen, phosphorus, and total suspended solids
from an observed increase in cropland that is comparable to what we have estimated could
potentially result from the RFS volume requirements in the Set Rule. Nitrogen, phosphorus, and
suspended solids are not perfect analogs to the pesticides we think will be used in increasing
quantities as the result of the Set Rule. However, they do share some important similarities.
180
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Nitrogen and phosphorus application rates vary by crop type and the characteristics of the
cropland, but on average we would expect that the total application of nitrogen and phosphorus
in the Missouri River basin is proportional to the increase in total cropland (e.g., a 10% increase
in cropland would result in a 10% increase in the quantity of nitrogen and phosphorus applied to
cropland across the entire area). Similarly, we would expect that, while pesticides are not
universally applied to all new cropland, the increase in pesticide applications would be
proportional to the increase in new cropland.
Further, nitrogen and phosphorus have differing mobilities. Nitrogen has a very high
mobility and is therefore a reasonable surrogate for pesticides with similarly high mobilities.
Phosphorus has a much lower mobility and is therefore a reasonable surrogate for pesticides with
lower mobilities. With regard to aquatic half-life (i.e., how mobile they are in the water as
opposed to on land), some pesticides may have aquatic half-lives that are comparable to the
aquatic half-life of nitrogen or phosphorus.
Using the SWAT modeling results for nitrogen and phosphorus as surrogates for the
impact of pesticides on water quality, we would expect any modeling to show similar changes in
pesticide concentrations as a result of the RFS volume requirements. As with nitrogen and
phosphorus, we would expect to see the greatest increases in pesticide total load at the mouth of
the Missouri River downstream of the expected areas of conversion to cropland. Tributaries
would see smaller overall increases, but greater overall changes in concentration.
Small, upland tributaries within the MORB and the Mississippi/Atchafalaya River Basin
(MARB) (for example in North and South Dakota) are of concern, despite the relatively low
absolute total increase in nitrogen and phosphorus seen in them. These areas, according to Chen
et al., saw the greatest percentage increase in TN and TP due to grassland conversion into
cropland. However, this also coincided with the largest amount of new cultivation in the entire
MORB, much of which is not attributable to the RFS. As discussed in more detail in EPA's
external review draft of the Biofuels and the Environment: Third Triennial Report to Congress,
subbasins still maintained TSS, TP, and TN levels well below what is considered impactful to
species, and well below that of the outlet of the MORB and other hotspots (US EPA Center for
Public Health & Environmental Assessment & Clark, 2023). Further, as noted previously, these
waterways are already impacted by pesticide use from existing cropland and other land uses.
Beyond the Missouri River basin, we may also expect to see new cropland in other
watersheds, but likely to lesser extent relative to the MORB. This is because both because the
Missouri River basin is the region in which we have seen the greatest conversion of non-
cropland to cropland in recent years and because it contains a greater number of acres within the
area of potential land use change (depicted in Figure IV.B-2) than any other HUC2 region in the
U.S. (Table VIII.A-3). This means that, in the probabilistic analyses we conducted to assess
potential land use changes from increases in corn ethanol from the Set Rule, there would be a
higher chance of lands being randomly selected for conversion in the MORB relative to other
regions as there is more land for the model to choose from within the MORB. We may also
expect some potential land use changes and subsequent water quality effects to occur primarily
in the broader Mississippi region, as depicted with the relatively higher area of potential land use
181
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change as a percent of total area for several HUC2 watersheds in that region (Table VIII. A-3) as
well as the potential soybean expansion areas analyzed by ICF and explained in other sections of
this Biological Evaluation. However, it is important to recognize that potential land use changes
from the RFS Set Rule, especially due to increases from corn ethanol, could still occur in the
other watersheds (e.g., Chesapeake Bay region), and understanding species' occurrence in those
regions and potential responses to water quality-related effects is key to making species
determinations for this Biological Evaluation. To explore this further, EPA worked with NMFS
on a qualitative analysis that was based on the corn ethanol probabilistic results for NMFS
species. This additional analysis is explained in more detail in Section IX.
Table VIII.A-3: Area of Potential Land Use Change as a Percent of Total Area in HUC2
Regions
HUC 2
Code
States
Name
Area of Potential
Land Use Change
in the Region
(acres)
Area of Potential
Land Use Change
as a Percent of
Total Area
10
CN,CO, IA, 1D, KS, M N, MO, MT, N D, N E,S D, WY
Missouri Region
205,320,132
62%
07
IA,IL,IN,KY,MI,MN,MO,ND,SD,WI
Upper Mississippi Region
113,560,314
93%
03
AL,FL,GA,LA,MS,NC,SC,TN,VA
South Atlantic-Gulf Region
101,954,573
56%
05
IL,IN,KY,MD,NC,NY,OH,PA,TN,VA,WV
Ohio Region
81,614,228
78%
11
AR,CO, KS, LA, M O, N M, O K,TX
Arkansas-White-Red Region
76,366,961
48%
04
CN,IL,IN,ME,MI,MN,NH,NY,OH,PA,VT,WI
Great Lakes Region
62,402,626
30%
02
CT, DC, D E, M A, M D, NJ, NY, PA, Rl, VA, VT, WV
Mid Atlantic Region
52,207,998
76%
08
AR,IL,KY,LA,MO,MS,TN
Lower Mississippi Region
42,779,650
63%
12
LA,NM,TX
Texas-Gulf Region
37,659,287
32%
09
CN,MN,MT,ND,SD
Souris-Red-Rainy Region
32,184,253
50%
17
CA,CN,ID,MT,NV,OR,UT,WA,WY
Pacific Northwest Region
29,781,861
14%
06
AL,GA,KY,MS,NC,SC,TN,VA,WV
Tennessee Region
18,829,385
72%
01
CN,CT,MA,ME,NH,NY,RI,VT
New England Region
12,852,085
26%
18
CA,MX,NV,OR
California Region
7,991,830
7%
16
CA,ID,NV,OR,UT,WY
Great Basin Region
6,174,304
7%
15
AZ,CA,MX,NM,NV,UT
Lower Colorado Region
4,381,121
4%
13
CO,MX,NM,TX
Rio Grande Region
3,805,981
3%
14
AZ,CO,NM,UT,WY
Upper Colorado Region
2,868,964
4%
B. Ongoing Mitigation Efforts
While the RFS volumes could potentially result in increases in the quantities of fertilizers
and pesticides present in waterways, particularly in waterways located near the expected areas of
cropland expansion, EPA is currently implementing a number of programs intended to reduce
these types of impacts and to improve water quality. These efforts are not directly related to the
182
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RFS program, but the ongoing efforts to reduce the impact of agriculture on water quality and to
generally improve overall water quality are expected to reduce the impacts on water quality
discussed in the preceding sections.
One of the programs EPA implements to improve water quality is the establishment of
Total Maximum Daily Loads (TMDLs) for impaired waters. Under Section 303(d) of the Clean
Water Act, each state must develop TMDLs for all the waters identified on their Section 303(d)
list of impaired waters, according to their priority ranking on that list. TMDLs are the calculation
of the maximum amount of a pollutant allowed to enter a waterbody, including from both point
sources and non-point sources, so that the waterbody will meet and continue to meet water
quality standards for that particular pollutant. TMDLs determine a pollutant target, allocate loads
to the source(s) of the pollutant, and identify reductions needed to meet those loads. The TMDL
process is important for improving water quality because it serves as a link in the chain between
water quality standards and implementation of control actions designed to attain those standards.
Non-point source load reduction actions are implemented through a wide variety of programs at
the state, local and federal level. These programs may be regulatory, non-regulatory or incentive-
based e.g., a cost-share program. In addition, waterbody restoration can be assisted by voluntary
actions on the part of citizen and/or environmental groups.
EPA also provides funding for water quality improvement through a number of
programs. The EPA Section 319 program provides grant money to the states, tribes, and
territories to fund specific projects aimed at reducing the nonpoint source pollution. This
program addresses the need for greater federal leadership to help focus state and local nonpoint
source efforts. Under Section 319 of the Clean Water Act, states, territories and tribes receive
grant money that supports a wide variety of activities including technical assistance, financial
assistance, education, training, technology transfer, demonstration projects and monitoring to
assess the success of specific nonpoint source implementation projects. EPA also offers
communities low-cost financing for a wide range of water quality infrastructure projects through
the Clean Water State Revolving Fund. The recently passed Bipartisan Infrastructure Law also
provided funding to improve water quality in a number of watersheds, including the Great Lakes,
Chesapeake Bay, National estuary Program, Long Island Sound, Puget Sound, Columbia River
Basin, and the Gulf of Mexico.
With regard to the Mississippi River Basin, the EPA has been working to combat hypoxia
in the northern Gulf of Mexico for several decades. To address this issue, the EPA has
implemented a number of programs and initiatives, most prominently the establishment of the
Hypoxia Task Force (HTF), a partnership of federal, state, and tribal agencies that work together
to reduce hypoxia in the Gulf of Mexico and improve water quality in the Mississippi River
Basin. The HTF member states have developed nutrient reduction strategies that aim to meet the
HTF goals, including reducing size of the zone by 2035.
The HTF members are USEPA (co-chair), USD A, DOI, USACE, NOAA, twelve states:
Iowa (co-chair), Arkansas, Illinois, Indiana, Kentucky, Louisiana, Minnesota, Mississippi,
Missouri, Ohio, Tennessee, and Wisconsin, and a tribal representative. Established in 1997, it
collaboratively works towards nutrient reduction goals, with funding from the Bipartisan
Infrastructure Law most recently to help HTF states, tribes, and other partners support the 2008
183
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action plan. This action plan affirmed six overarching principles; to encourage voluntary,
incentive-based actions; utilize existing state and federal regulatory mechanisms; follow adaptive
management; identify additional funding needs and sources during annual budget processes;
identify barriers to market-based solutions; and to provide measurable outcomes. These goals are
focused on reducing the size of the Gulf of Mexico hypoxic zone to less than 5000 square miles
by 2035 with an interim target of reducing nutrient loads to the Gulf of Mexico by 20% by 2025
and to increase the quality of life of communities within the Mississippi/Atchafalaya river basin,
especially those relying on agriculture, fisheries, and recreation sectors.
With the help of EPA funding, states and tribes have adopted a variety of remedial
actions and programs to fight hypoxia. Among these are state cost share programs to advance
conservation implementation, nutrient credit trading programs, implementation of TMDLs, and
long-term monitoring programs. In its 2019/2021 Report to Congress, the HTF reported that
more monitoring support was a priority for the task force in order to track how the HTF is
meeting the interim 2025 and 2035 goals. In FY17 and FY18, EPA provided $94.9 million in
grants to HTF states. The EPA Gulf of Mexico Division awarded $2 million for two grants in
FY18 and $7.5 million for seven grants in FY 19 to fund projects that improved water quality
and environmental education in the Gulf watershed. In FY 19 and FY20, EPA provided $2.4
million for direct grants to the 12 member states and starting in FY22, EPA is providing $60
million through FY26 to states, tribes and partners towards actions to reach the HTF goals.
Monitoring programs already in place show a decline in total nitrogen from the MARB to the
Gulf.
EPA is also working to improve the current ESA-FIFRA process for pesticides. This
involves collaborations with the Services and U.S. Department of Agriculture (USDA). EPA has
provided workplan entitled "Balancing Wildlife Protection and Responsible Pesticide Use: How
EPA's Pesticide Program Will Meet its Endangered Species Act Obligations" which reflects
EPA's experiences, assesses its future ESA workload, and describes administrative and other
improvements that EPA will pursue or consider pursuing. This is a difficult task, considering that
there are thousands of pesticide products and amendments and over 1,600 listed species in the
United States. EPA's workplan and the associated update in 2022 reflects the Agency's most
comprehensive thinking to date on how to create a sustainable ESA program for pesticides. The
workplan involves several strategies relevant to registration and registration review activities and
includes possible ESA programmatic initiatives. The registration and registration review efforts
include proposed label language to expand the use of online endangered species protection
bulletins. This online system allows EPA to implement geographically specific mitigation
measures for listed species that are designed to focus protections in specific areas of need,
thereby minimizing impacts of the mitigations to agriculture.
EPA is beginning registration review ESA pilots to incorporate early ESA mitigation
measures into registration review decisions for carbaryl, methomyl, rodenticides and
neonicotinoids. Some of the ESA strategies include identifying ESA mitigation measures for
vulnerable species, incorporating early ESA mitigation measures for groups of pesticides rather
than single pesticides, developing region-specific strategies, and exploring broad mitigation
strategies for nonagricultural uses. Taken together, these efforts are intended to reduce the
impacts of pesticide use on listed species.
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I\. Framework antl Spccies\Critical Habitat Determinations
The goal of the analyses presented herein is to link RFS Set Rule (the action) to estimates
of the potential increases in biofuel demand that may be a result of the action and the
corresponding potential increases in biofuel feedstock crop production necessitating land use
changes to meet that demand. By using the maximum potential acreage impacts, we then
assessed the potential locations of land use changes and linked that to potential species impacts
by examining the areas that may be affected (on a percentage basis) relative to the total area of
each species' critical habitat and/or range. In Section VII we present the analyses related to land
use changes, and in Section VIII we present information related to potential changes in water
quality associated with any land changes at both the edge-of-field and downstream levels. This
Section IX takes our analyses a step further by assessing the potential effects of the action as
they relate to endpoints specifically relevant to species, such as PBFs and PCEs of critical habitat
and other pertinent life history information, to make final species determinations for this
Biological Evaluation.
As an initial matter, we describe our interpretation of the thresholds for the various
determinations we are making in this action following our analysis.
A. Framework for Spedes\€ritkal Habitat Determinations
Under Services' implementing regulations, effects of the action are all consequences to
listed species or critical habitat that are caused by the proposed action, including the
consequences of other activities that are caused by the proposed action. A consequence is caused
by the proposed action if it would not occur but for the proposed action and it is reasonably
certain to occur. In other words, if the agency fails to take the proposed action and the activity
would still occur, there is no "but for" causation. Given the significant uncertainty associated
with land use change impacts as a result of the set rule, particularly the inability to know what, if
any, parcels of land which will actually be converted as a result of the Set Rule, it is
correspondingly difficult to say, with certainty, whether the set rule may affect terrestrial listed
species and critical habitat through this mechanism. Given this high degree of uncertainty, a "no
effect" determination may be justified for some species and critical habitat.
However, in a worst-case scenario, some land may be converted as a result of the Set
Rule, and this land conversion may impact listed species or critical habitat. We, therefore, in the
alternative, conclude that the set rule may affect listed species or critical habitat. However, these
effects are not likely to adversely affect listed species or critical habitat due to the limited nature
of changes that may be attributable to the RFS Set Rule.
As to species and critical habitat at risk of potential impacts of the Set Rule as a result of
water quality degredation, we determine that the RFS standard may affect some listed species
and critical habitat. This is because certainty in the location of any such effects is not necessary
to make such an effects determination. The RFS Set Rule may result in increases in pesticide and
fertilizer applications, and these increases may affect some listed species and critical habitat as
185
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they transport through runoff to progressively larger bodies of water. However, we anticipate
that such effects are not likely to adversely affect species and critical habitat given the limited
nature of any such increases in pesticide and fertilizer quantities in the endpoint bodies of water.
B. FWS Species and Critical Habitats
The 672 FWS species (737 populations total) found within our action area were grouped
into 14 taxonomic groupings that are commonly used by the FWS (US FWS, n.d.-c). Species
within each of these taxonomic groups can be assessed together as they typically share similar
patterns of survival and reproduction (i.e., life histories) and critical habitat PBFs/PCEs.
Insects
A total of 41 insect populations were found within the action area (Table IX.B-1). Based
on the potential land use impact analyses described in previous sections, which were akin to a
worst-case scenario, the Salt Creek Tiger Beetle, Poweshiek skipperling, Dakota Skipper, and
Guadalupe Orb were some species that could see impacts from increases in corn ethanol, soy oil,
and canola oil from the RFS Set Rule alone (Tables VII.D-1 to VII.D-4).
Out of the 41 insect populations found within the action area, 12 have critical habitat with
associated PBFs. PBFs for listed insects may include natural plant communities and herbaceous,
woody, and aquatic vegetation for refugia, resting, reproduction, and prey avoidance; wet-mesic,
moist meadows river floodplain, depression wetlands or other aquatic or semi-aquatic
environments for refugia and shelter, reproduction, and prey avoidance; specific soil types, such
as loam, sandy loam, loamy sand, gravel, organic soils, other types of soils conducive to larval
survival and specific vegetation; and specific host or nectar plants required for reproduction,
feeding, and growth. Other insects, such as butterflies, may require dynamic habitats with trees
and/or understory plants with a specific range of elevations, densities, and canopy cover.
It is well known that insects in general are vulnerable to pesticide exposure and toxicity
effects. Additionally, insect species in the action area that have PBFs more closely associated
with specific grassland habitats (e.g., the Poweshiek skipperling) may be more likely to be
affected. However, this does not necessarily mean that PBFs will be affected; lands that were
once used for pasture, hay, or were retired could serve as more ideal locations for conversion,
and these lands are not likely to have essential PBFs. Due to this and due to the limited nature of
changes attributable to the RFS Set Rule, potential effects on listed insects (as well as critical
habitat PBFs) are insignificant or discountable.
Table IX.B-1. The 41 FWS insects and those with designated critical habitat found within the
action area that receive a NLAA finding due to insignificant or discountable effects.
CH,
Range,
or Both
Common Name
Scientific Name
DPS (if applicable)
Listing Status
Range
Island marble
Butterfly
Euchloe ausonides
insulanus
Endangered
Range
Smith's blue butterfly
Euphilotes enoptes smithi
Endangered
186
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Both
Taylor's (=whulge)
Checkerspot
Euphydryas editha taylori
Endangered
Range
Northeastern beach
tiger beetle
Habroscelimorpha
dorsalis dorsalis
Threatened
Both
Dakota Skipper
Hesperia dacotae
Threatened
Both
Comal Springs riffle
beetle
Heterelmis comalensis
Endangered
Both
Fender's blue
butterfly
Icaricia icarioides fenderi
Endangered
Range
Mission blue
butterfly
Icaricia icarioides
missionensis
Endangered
Range
Lotis blue butterfly
Lycaeides argyrognomon
lotis
Endangered
Range
Karner blue butterfly
Lycaeides melissa
samuelis
Endangered
Range
Saint Francis' satyr
butterfly
Neonympha mitchellii
francisci
Endangered
Range
Mitchell's satyr
Butterfly
Neonympha mitchellii
mitchellii
Endangered
Range
American burying
beetle
Nicrophorus americanus
Wherever found; Except
where listed as
Experimental Populations
Threatened
Range
American burying
beetle
Nicrophorus americanus
In southwestern Missouri,
the counties of Cedar, St.
Clair, Bates, and Vernon.
Experimental
Population,
Non-Essential
Both
Poweshiek
skipperling
Oarisma poweshiek
Endangered
Range
Mount Hermon June
beetle
Polyphylla barbata
Endangered
Both
[no common name]
Beetle
Rhadine exilis
Endangered
Both
[no common name]
Beetle
Rhadine infernalis
Endangered
Range
Tooth Cave ground
beetle
Rhadine persephone
Endangered
Both
Hine's emerald
dragonfly
Somatochlora hineana
Endangered
Range
Behren's silverspot
butterfly
Speyeria zerene behrensii
Endangered
Range
Oregon silverspot
butterfly
Speyeria zerene hippolyta
Threatened
Range
Myrtle's silverspot
butterfly
Speyeria zerene myrtleae
Endangered
Both
Comal Springs
dryopid beetle
Stygoparnus comalensis
Endangered
Range
Kretschmarr Cave
mold beetle
Texamaurops reddelli
Endangered
Range
Zayante band-
winged grasshopper
Trimerotropis infantilis
Endangered
Both
Lange's metalmark
butterfly
Apodemia mormo langei
Endangered
Range
Coffin Cave mold
beetle
Batrisodes texanus
Endangered
187
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Range
Helotes mold beetle
Batrisodes venyivi
Endangered
Range
Rusty patched
bumble bee
Bombus affinis
Endangered
Range
Franklin's bumble
bee
Bombus franklini
Endangered
Both
Hungerford's
crawling water
Beetle
Brychius hungerfordi
Endangered
Range
San Bruno elfin
butterfly
Callophrys mossii
bayensis
Endangered
Range
Salt Creek Tiger
beetle
Cicindela nevadica
lincolniana
Endangered
Both
Ohlone tiger beetle
Cicindela ohlone
Endangered
Range
Monarch butterfly
Danaus plexippus
Endangered
Range
Valley elderberry
longhorn beetle
Desmocerus californicus
dimorphus
Threatened
Range
Delta green ground
beetle
Elaphrus viridis
Threatened
Range
Puritan tiger beetle
Ellipsoptera puritana
Threatened
Range
bog buck moth
Hemileuca maia
menyanthevora
Proposed
Endangered
Range
Silverspot
Speyeria nokomis nokomis
Proposed
Threatened
Flowering Plants
The taxonomic group with the largest number of listed populations found within the
action area is the flowering plants group. A total of 242 flowering plants were found within the
action area (Table IX.B-2). Several species—including the Braun's rockcress, Fleshy-fruit
gladecress, Mead's milkweed, and Virginia round-leaf birch—saw some of the relatively higher
cumulative impacts (Tables VII.D-1 to VII.D-4) based on the potential land use change impact
analyses.
Potential effects of the action that could harm flowering plants include, but are not
limited to, effects on pollinators and seed dispersers, toxicity from pesticide exposure (e.g.
herbicides), and loss of habitat. While some flowering plants may rely on abiotic pollination such
as wind, others rely on biotic (e.g., insect or bat) pollination as their propagation strategy. The
species that require biotic pollination may be indirectly affected if their pollinator populations
are harmed by pesticide drift and/or habitat loss attributed to the RFS Set Rule. This may occur
near lands that are newly cultivated for agriculture, as well as near lands that apply higher
amounts of pesticides to increase crop yields. While insecticide drift could harm some
pollinators, it is unlikely that it would significantly reduce the population. It is possible that
herbicide drift to a certain distance may harm listed plants. However, it is challenging to say with
a high degree of confidence where potential effects of the RFS Set Rule may occur at the local
level, if at all. Converted fields are likely to be widely distributed across suitable farming land or
in areas that are not suitable for listed species, even inf in a species' range, or areas that don't
meet the definition of PBFs/PCEs. As such, EPA anticipates that effects to pollinators would be
insignificant or discountable.
188
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Another potential effect is habitat loss. Many listed flowering plants rely on certain
landscape features that are essential for their conservation. For example, the Short's Bladderpod
relies on the following PBFs within its critical habitat: (1) bedrock formations and outcrops of
calcareous limestone on steeply sloped hillsides near the mainstream and tributary areas of the
Kentucky and Cumberland rivers; (2) well-drained soils that are undisturbed and shallow or
rocky; (3) low-level canopy forest communities with some openings that provide sufficient
sunlight (US FWS Region 3, n.d.). Other species, such as the Braun's rockcress, are also found
in similar environments as discussed in Section VII.B. While it is possible that such areas could
be affected by erosion exacerbated by agriculture, these or other nearby areas would very likely
not be suitable for agriculture conversion and therefore not be affected.
Other species that rely on PBFs that are present in pasture and grassland ecosystems (or
comparable ecosystems—e.g., the Fleshy-fruit gladecress' cedar glade habitat) may be more
likely to have PBFs that are affected. But as discussed previously, new conversion of agricultural
lands could occur in areas that are already not very suitable for PBFs. For instance, areas that
were once used for pasture, hay, or retired croplands planted to permanent vegetative cover
through the Conservation Reserve Program could be some of the lands that would be converted
for agriculture. Such lands are not likely to have the essential PBFs that those species require.
For this and the other reasons stated above, and because we cannot say for certain that effects
will occur, any potential effects to flowering plants (as well as critical habitat PBFs) are likely
discountable or insignificant.
Table IX.B-2. The 242 FWS flowering plants and those with designated critical habitat found
within the action area that receive a NLAA finding due to insignificant or discountable effects.
CH,
Common Name
Scientific Name
Listing Status
Range,
or Both
Range
Leafy prairie-clover
Dalea foliosa
Endangered
Range
Beautiful pawpaw
Deeringothamnus pulchellus
Endangered
Range
Rugel's pawpaw
Deeringothamnus rugelii
Endangered
Range
Baker's larkspur
Delphinium bakeri
Endangered
Range
Yellow larkspur
Delphinium luteum
Endangered
Range
Longspurred mint
Dicerandra cornutissima
Endangered
Range
Lakela's mint
Dicerandra immaculata
Endangered
Range
Smooth coneflower
Echinacea laevigata
Threatened
Both
Black lace cactus
Echinocereus reichenbachii var.
albertii
Endangered
Both
Acuna Cactus
Echinomastus erectocentrus var.
acunensis
Endangered
Range
Kern mallow
Eremalche kernensis
Endangered
Range
Willamette daisy
Erigeron decumbens
Endangered
Range
Umtanum desert
buckwheat
Eriogonum codium
Threatened
Both
Gypsum wild-
buckwheat
Eriogonum gypsophilum
Threatened
189
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Range
Clay-Loving wild
buckwheat
Eriogonum pelinophilum
Endangered
Both
San Mateo woolly
sunflower
Eriophyllum latilobum
Endangered
Range
Arizona eryngo
Eryngium sparganophyllum
Endangered
Range
Contra Costa
wallflower
Erysimum capitatum var.
angustatum
Endangered
Range
Menzies' wallflower
Erysimum menziesii
Endangered
Range
Ben Lomond
wallflower
Erysimum teretifolium
Endangered
Range
Minnesota dwarf trout
lily
Erythronium propullans
Endangered
Range
Telephus spurge
Euphorbia telephioides
Threatened
Range
Gentner's Fritillary
Fritillaria gentneri
Endangered
Range
No common name
Geocarpon minimum
Threatened
Range
Spreading avens
Geum radiatum
Endangered
Range
Monterey gilia
Gilia tenuiflora ssp. arenaria
Endangered
Range
Bartram's stonecrop
Graptopetalum bartramii
Threatened
Range
Showy stickseed
Hackelia venusta
Endangered
Range
Harper's beauty
Harperocallis flava
Endangered
Range
Todsen's pennyroyal
Hedeoma todsenii
Endangered
Range
Roan Mountain bluet
Hedyotis purpurea var. montana
Endangered
Range
Virginia sneezeweed
Helenium virginicum
Threatened
Both
Pecos (=puzzle,
=paradox) sunflower
Helianthus paradoxus
Threatened
Range
Schweinitz's sunflower
Helianthus schweinitzii
Endangered
Both
Whorled Sunflower
Helianthus verticillatus
Endangered
Range
Swamp pink
Helonias bullata
Threatened
Range
Marin dwarf-flax
Hesperolinon congestum
Threatened
Range
Dwarf-flowered
heartleaf
Hexastylis naniflora
Threatened
Range
Neches River rose-
mallow
Hibiscus dasycalyx
Threatened
Range
Slender rush-pea
Hoffmannseggia tenella
Endangered
Range
Santa Cruz tarplant
Holocarpha macradenia
Threatened
Both
Mountain golden
heather
Hudsonia montana
Threatened
Range
Lakeside daisy
Hymenoxys herbacea
Threatened
Range
Texas prairie dawn-
flower
Hymenoxys texana
Endangered
Range
Peter's Mountain
mallow
Iliamna corei
Endangered
Range
Holy Ghost ipomopsis
Ipomopsis sancti-spiritus
Endangered
Range
Dwarf lake iris
Iris lacustris
Threatened
Range
Small whorled pogonia
Isotria medeoloides
Threatened
Range
Burke's goldfields
Lasthenia burkei
Endangered
Both
Contra Costa goldfields
Lasthenia conjugens
Endangered
-------
Range
Beach layia
Layia carnosa
Threatened
Both
Fleshy-fruit gladecress
Leavenworthia crassa
Endangered
Both
Kentucky glade cress
Leavenworthia exigua laciniata
Threatened
Range
Barneby ridge-cress
Lepidium barnebyanum
Endangered
Both
Slickspot peppergrass
Lepidium papilliferum
Threatened
Range
Prairie bush-clover
Lespedeza leptostachya
Threatened
Range
Lyrate bladderpod
Lesquerella lyrata
Threatened
Range
Spring Creek
bladderpod
Lesquerella perforata
Endangered
Range
San Francisco lessingia
Lessingia germanorum (=L.g. var.
germanorum)
Endangered
Range
Heller's blazingstar
Liatris helleri
Threatened
Range
Huachuca water-umbel
Lilaeopsis schaffneriana var.
recurva
Endangered
Range
Western lily
Lilium occidentale
Endangered
Range
Butte County
meadowfoam
Limnanthes floccosa ssp. californica
Endangered
Range
Pondberry
Lindera melissifolia
Endangered
Both
Kincaid's Lupine
Lupinus sulphureus ssp. kincaidii
Threatened
Range
Clover (Tidestrom"s)
lupine
Lupinus tidestromii
Endangered
Range
Rough-leaved
loosestrife
Lysimachia asperulaefolia
Endangered
Range
White birds-in-a-nest
Macbridea alba
Threatened
Range
Walker's manioc
Manihot walker ae
Endangered
Range
Mohr's Barbara's
buttons
Marshallia mohrii
Threatened
Range
Michigan monkey-
flower
Mimulus michiganensis
Endangered
Range
MacFarlane's four-
o'clock
Mirabilis macfarlanei
Threatened
Range
San Joaquin wooly-
threads
Monolopia (=Lembertia) congdonii
Endangered
Range
Spreading navarretia
Navarretia fossalis
Threatened
Both
Colusa grass
Neostapfia colusana
Threatened
Range
Britton's beargrass
Nolina brittoniana
Endangered
Both
Antioch Dunes
evening-primro se
Oenothera deltoides ssp. howellii
Endangered
Range
Bakersfield cactus
Opuntia treleasei
Endangered
Both
San Joaquin Orcutt
grass
Orcuttia inaequalis
Threatened
Both
Hairy Orcutt grass
Orcuttia pilosa
Endangered
Range
Slender Orcutt grass
Orcuttia tenuis
Threatened
Both
Sacramento Orcutt
grass
Orcuttia viscida
Endangered
Range
Canby's dropwort
Oxypolis canbyi
Endangered
Range
Fassett's locoweed
Oxytropis campestris var. chartacea
Threatened
Range
Papery whitlow-wort
Paronychia chartacea
Threatened
-------
Range
Brady pincushion
cactus
Pediocactus bradyi
Endangered
Range
San Rafael cactus
Pediocactus despainii
Endangered
Range
Knowlton's cactus
Pediocactus knowltonii
Endangered
Both
Fickeisen plains cactus
Pediocactus peeblesianus ssp.
fickeiseniae
Endangered
Range
Blowout penstemon
Penstemon haydenii
Endangered
Range
White-rayed
pentachaeta
Pentachaeta bellidiflora
Endangered
Range
Yreka phlox
Phlox hirsuta
Endangered
Both
White Bluffs
bladderpod
Physaria douglasii ssp. tuplashensis
Threatened
Range
Missouri bladderpod
Physaria filiformis
Threatened
Both
Short's bladderpod
Physaria globosa
Endangered
Both
Zapata bladderpod
Physaria thamnophila
Endangered
Range
Godfrey's butterwort
Pinguicula ionantha
Threatened
Both
Yadon's piperia
Piperia yadonii
Endangered
Range
Ruth's golden aster
Pityopsis ruthii
Endangered
Range
White fringeless orchid
Platanthera integrilabia
Threatened
Range
Eastern prairie fringed
orchid
Platanthera leucophaea
Threatened
Range
Western prairie fringed
Orchid
Platanthera praeclara
Threatened
Range
Lewton's poly gala
Polygala lewtonii
Endangered
Range
Tiny poly gala
Polygala smallii
Endangered
Range
Sandlace
Polygonella myriophylla
Endangered
Range
Scotts Valley
Polygonum
Polygonum hickmanii
Endangered
Range
Hickman's potentilla
Potentilla hickmanii
Endangered
Range
Maguire primrose
Primula maguirei
Threatened
Range
Hartweg's golden
sunburst
Pseudobahia bahiifolia
Endangered
Range
San Joaquin adobe
sunburst
Pseudobahia peirsonii
Threatened
Range
Harperella
Ptilimnium nodosum
Endangered
Range
Arizona Cliffrose
Purshia (=Cowania) subintegra
Endangered
Range
Autumn Buttercup
Ranunculus aestivalis (=acriformis)
Endangered
Range
Leedy's roseroot
Rhodiola integrifolia ssp. leedyi
Threatened
Range
Chapman rhododendron
Rhododendron chapmanii
Endangered
Range
Michaux's sumac
Rhus michauxii
Endangered
Range
Knieskern's Beaked-
rush
Rhynchospora knieskernii
Threatened
Range
Miccosukee gooseberry
Ribes echinellum
Threatened
Range
Bunched arrowhead
Sagittaria fasciculata
Endangered
Range
Krai's water-plantain
Sagittaria secundifolia
Threatened
Range
Green pitcher-plant
Sarracenia oreophila
Endangered
-------
Range
Alabama canebrake
pitcher-plant
Sarracenia rubra ssp. alabamensis
Endangered
Range
Mountain sweet
pitcher-plant
Sarracenia rubra ssp. jonesii
Endangered
Range
Clay reed-mustard
Schoenocrambe argillacea
Threatened
Range
Barneby reed-mustard
Schoenocrambe barnebyi
Endangered
Range
Shrubby reed-mustard
Schoenocrambe suffrutescens
Endangered
Range
American chaffseed
Schwalbea americana
Endangered
Range
Northeastern bulrush
Scirpus ancistrochaetus
Endangered
Range
Tobusch fishhook
cactus
Sclerocactus brevihamatus ssp.
tobuschii
Threatened
Range
Pariette cactus
Sclerocactus brevispinus
Threatened
Range
Colorado hookless
Cactus
Sclerocactus glaucus
Threatened
Range
Mesa Verde cactus
Sclerocactus mesae-verdae
Threatened
Range
Uinta Basin hookless
cactus
Sclerocactus wetlandicus
Threatened
Range
Florida skullcap
Scutellaria floridana
Threatened
Range
Large-flowered
skullcap
Scutellaria montana
Threatened
Range
Keek's Checker-mallow
Sidalcea keckii
Endangered
Range
Nelson's checker-
mallow
Sidalcea nelsoniana
Threatened
Range
Wenatchee Mountains
checkermallow
Sidalcea oregana var. calva
Endangered
Range
Fringed campion
Silene polypetala
Endangered
Range
Spalding's Catchfly
Silene spaldingii
Threatened
Range
White irisette
Sisyrinchium dichotomum
Endangered
Range
Houghton's goldenrod
Solidago houghtonii
Threatened
Range
Short's goldenrod
Solidago shortii
Endangered
Range
Blue Ridge goldenrod
Solidago spithamaea
Threatened
Both
Gierisch mallow
Sphaeralcea gierischii
Endangered
Range
Gentian pinkroot
Spigelia gentianoides
Endangered
Range
Virginia spiraea
Spiraea virginiana
Threatened
Range
Ute ladies'-tresses
Spiranthes diluvialis
Threatened
Range
Navasota ladies-tresses
Spiranthes parksii
Endangered
Both
Bracted twistflower
Streptanthus bracteatus
Proposed
Threatened
Range
Tiburon j ewelflower
Streptanthus niger
Endangered
Range
California seablite
Suaeda californica
Endangered
Range
Cooley's meadowrue
Thalictrum cooleyi
Endangered
Range
Howell"s spectacular
thelypody
Thelypodium howellii ssp.
spectabilis
Threatened
Range
Ashy dogweed
Thymophylla tephroleuca
Endangered
Range
Last Chance
townsendia
Townsendia aprica
Threatened
Range
Showy Indian clover
Trifolium amoenum
Endangered
-------
Range
Monterey clover
Trifolium trichocalyx
Endangered
Range
Persistent trillium
Trillium persistens
Endangered
Range
Relict trillium
Trillium reliquum
Endangered
Both
Greene's tuctoria
Tuctoria greenei
Endangered
Both
Solano grass
Tuctoria mucronata
Endangered
Range
Carter's mustard
Warea carteri
Endangered
Range
Tennessee yellow-eyed
grass
Xyris tennesseensis
Endangered
Both
Texas wild-rice
Zizania texana
Endangered
Range
Large-fruited sand-
verbena
Abronia macrocarpa
Endangered
Both
San Mateo thornmint
Acanthomintha obovata ssp.
duttonii
Endangered
Range
Northern wild
monkshood
Aconitum noveboracense
Threatened
Range
Sensitive j oint-vetch
Aeschynomene virginica
Threatened
Range
Sandplain gerardia
Agalinis acuta
Endangered
Range
Sonoma alopecurus
Alopecurus aequalis var.
sonomensis
Endangered
Range
Seabeach amaranth
Amaranthus pumilus
Threatened
Range
South Texas ambrosia
Ambrosia cheiranthifolia
Endangered
Range
Little amphianthus
Amphianthus pusillus
Threatened
Range
Large-flowered
fiddleneck
Amsinckia grandiflora
Endangered
Range
Price"s potato-bean
Apios priceana
Threatened
Range
Georgia rockcress
Arabis georgiana
Threatened
Both
McDonald's rock-cress
Arabis macdonaldiana
Endangered
Range
Braun's rock-cress
Arabis perstellata
Endangered
Range
Dwarf Bear-poppy
Arctomecon humilis
Endangered
Range
Franciscan manzanita
Arctostaphylos franciscana
Endangered
Range
Presidio Manzanita
Arctostaphylos hookeri var. ravenii
Endangered
Range
Pallid manzanita
Arctostaphylos pallida
Threatened
Range
Marsh Sandwort
Arenaria paludicola
Endangered
Range
Sacramento prickly
poppy
Argemone pleiacantha ssp.
pinnatisecta
Endangered
Range
Mead's milkweed
Asclepias meadii
Threatened
Range
Prostrate milkweed
Asclepias prostrata
Proposed
Endangered
Both
Welsh's milkweed
Asclepias welshii
Threatened
Range
Shivwits milk-vetch
Astragalus ampullarioides
Endangered
Range
Guthrie's (=Pyne's)
ground-plum
Astragalus bibullatus
Endangered
Range
Sentry milk-vetch
Astragalus cremnophylax var.
cremnophylax
Endangered
Both
Holmgren milk-vetch
Astragalus holmgreniorum
Endangered
Range
Mancos milk-vetch
Astragalus humillimus
Endangered
-------
Range
Peirson's milk-vetch
Astragalus magdalenae var.
peirsonii
Threatened
Range
Jesup"s milk-vetch
Astragalus robbinsii var. jesupii
Endangered
Range
Coastal dunes milk-
vetch
Astragalus tener var. titi
Endangered
Range
Star cactus
Astrophytum asterias
Endangered
Range
Texas ayenia
Ayenia limitaris
Endangered
Range
Hairy rattleweed
Baptisia arachnifera
Endangered
Range
Virginia round-leaf
birch
Betula uber
Threatened
Range
Sonoma sunshine
Blennosperma bakeri
Endangered
Range
Shale barren rock cress
Boechera serotina
Endangered
Range
Decurrent false aster
Boltonia decurrens
Threatened
Range
Texas poppy-mallow
Callirhoe scabriuscula
Endangered
Range
Tiburon mariposa lily
Calochortus tiburonensis
Threatened
Range
Small-anthered
bittercress
Cardamine micranthera
Endangered
Range
Golden sedge
Carex lutea
Endangered
Both
Navajo sedge
Carex specuicola
Threatened
Range
Tiburon paintbrush
Castilleja affinis ssp. neglecta
Endangered
Range
Fleshy owl's-clover
Castilleja campestris ssp.
succulenta
Threatened
Both
golden paintbrush
Castilleja levisecta
Threatened
Range
California jewelflower
Caulanthus californicus
Endangered
Range
Fragrant prickly-apple
Cereus eriophorus var. fragrans
Endangered
Both
Hoover's spurge
Chamaesyce hooveri
Threatened
Both
Pygmy fringe-tree
Chionanthus pygmaeus
Endangered
Range
Ben Lomond
spineflower
Chorizanthe pungens var.
hartwegiana
Endangered
Both
Monterey spineflower
Chorizanthe pungens var. pungens
Threatened
Range
Scotts Valley
spineflower
Chorizanthe robusta var. hartwegii
Endangered
Range
Robust spineflower
Chorizanthe robusta var. robusta
Endangered
Range
Sonoma spineflower
Chorizanthe valida
Endangered
Range
Florida golden aster
Chrysopsis floridana
Endangered
Range
Fountain thistle
Cirsium fontinale var. fontinale
Endangered
Range
Pitcher's thistle
Cirsium pitcheri
Threatened
Range
Sacramento Mountains
thistle
Cirsium vinaceum
Threatened
Range
Wright's marsh thistle
Cirsium wrightii
Proposed
Threatened
Range
Presidio clarkia
Clarkia franciscana
Endangered
Range
Springville clarkia
Clarkia springvillensis
Threatened
Range
Morefield"s leather
flower
Clematis morefieldii
Endangered
Range
Alabama leather flower
Clematis socialis
Endangered
195
-------
Range
Pigeon wings
Clitoria fragrans
Threatened
Range
Etonia rosemary
Conradina etonia
Endangered
Range
Apalachicola rosemary
Conradina glabra
Endangered
Range
Cumberland rosemary
Conradina verticillata
Threatened
Range
Salt marsh bird's-beak
Cordylanthus maritimus ssp.
maritimus
Endangered
Range
Soft bird's-beak
Cordylanthus mollis ssp. mollis
Endangered
Range
Palmate-bracted bird's
beak
Cordylanthus palmatus
Endangered
Range
Lee pincushion cactus
Coryphantha sneedii var. leei
Threatened
Range
Sneed pincushion
cactus
Coryphantha sneedii var. sneedii
Endangered
Range
Okeechobee gourd
Cucurbita okeechobeensis ssp.
okeechobeensis
Endangered
Range
Jones Cycladenia
Cycladenia humilis var. jonesii
Threatened
Range
Siler pincushion cactus
Pediocactus
(=Echinocactus,=Utahia) sileri
Threatened
Both
Ocmulgee skullcap
Scutellaria ocmulgee
Proposed
Threatened
Fishes, Clams, and Crustaceans
We found 104 fishes, 129 clams, and 21 crustaceans in the action area (Table IX.B-3,
IX.B-4, IX.B-5). The Slender chub, Topeka shiner, St. Francis River crayfish, Texas fawnsfoot,
Slackwater darter, Big Creek crayfish, Neosho madtom, False spike, and Neosho mucket were
some species within these three taxonomic groups that saw the relatively larger percentage
overlap results from the potential land use impact analyses (Tables VII.D-1 to VII.D-4).
The PBFs for many fish include creeks and streams with low turbidity, well oxygenated
and moderately clean water, and riffles, pools, and runs with differing substrates of gravel,
pebble, sand, and silt. Other essential features may include riparian cover and cooler
temperature of waters, an abundant source of food, absence of invasive species, geomorphically
stable river channels and banks, and sufficient water depth.
Clams have very similar PBFs, but also rely on the occurrence of certain fish
assemblages and community compositions. For many clams, like the Neosho mucket, the
presence of specific fish hosts is also necessary. In the case of the Neosho mucket, hosts include
smallmouth bass, largemouth bass, and spotted bass (US FWS Region 3, n.d.).
Finally, crustaceans have similar PBFs as well; many require small rocks or shallow
burrows in gravel for shelter in small to medium flowing streams with boulder and pebble
substrates. For the fish, clam, and crustacean taxonomic groups combined, the RFS Set Rule
could alter habitats and their essential features by contributing to water quality impairments.
Species could also be exposed to pesticide runoff. However, EPA anticipates that potential
effects of the RFS Set Rule would be insignificant or discountable for fish, clam, and crustacean
populations as well as their critical habitats due to the uncertainties associated with the location
196
-------
of potential localized effects and due to the limited nature of changes that may be attributable to
the RFS Set Rule.
Table IX.B-3. The 104 FWS fish populations and those with designated critical habitat found
within the action area that receive a NLAA finding due to insignificant or discountable effects.
CH,
Range,
or Both
Common Name
Scientific Name
DPS (if applicable)
Listing Status
Range
Laurel dace
Chrosomus saylori
Endangered
Range
Pygmy Sculpin
Cottus paulus (=pygmaeus)
Threatened
Range
Grotto Sculpin
Cottus specus
Endangered
Range
diamond Darter
Crystallaria cincotta
Endangered
Range
Blue shiner
Cyprinella caerulea
Threatened
Range
Desert pupfish
Cyprinodon macularius
Endangered
Range
Lost River sucker
Deltistes luxatus
Endangered
Range
Devils River
minnow
Dionda diaboli
Threatened
Range
Spring pygmy
sunfish
Elassoma alabamae
Threatened
Both
Spotfin Chub
Erimonax monachus
Wherever found; Except
where listed as
Experimental
Populations
Threatened
Range
Spotfin Chub
Erimonax monachus
U.S.A. (TN-specified
portions of the Tellico
River
Experimental
Population, Non-
Essential
Range
Spotfin Chub
Erimonax monachus
U.S.A. (AL, TN-
specified portions of
Shoal Creek
Experimental
Population, Non-
Essential
Range
Spotfin Chub
Erimonax monachus
U.S.A. (TN-specified
portions of the French
Broad and Holston
Rivers
Experimental
Population, Non-
Essential
Range
Slender chub
Erimystax cahni
Threatened
Range
bluemask darter
Etheostoma akatulo
Endangered
Range
Slackwater darter
Etheostoma boschungi
Threatened
Range
Vermilion darter
Etheostoma chermocki
Endangered
Range
Relict darter
Etheostoma chienense
Endangered
Range
Etowah darter
Etheostoma etowahae
Endangered
Range
Fountain darter
Etheostoma fonticola
Endangered
Both
Yellowcheek Darter
Etheostoma moorei
Endangered
Range
Niangua darter
Etheostoma nianguae
Threatened
Range
Candy darter
Etheostoma osburni
Endangered
Range
Duskytail darter
Etheostoma percnurum
Wherever found
Endangered
Range
Duskytail darter
Etheostoma percnurum
The Tellico River,
between the backwaters
of the Tellico Reservoir
(approximately Tellico
Experimental
Population, Non-
Essential
197
-------
River mile 19 (30.4
kilometers) and Tellico
River mile 33 (52.8
kilometers), near the
Tellico Ranger Station,
Monroe County,
Tennessee.
Range
Duskytail darter
Etheostoma percnurum
U.S.A. (TN - specified
portions of the French
Broad and Holston
Rivers
Experimental
Population, Non-
Essential
Both
Rush Darter
Etheostoma phytophilum
Endangered
Range
Bayou darter
Etheostoma rubrum
Threatened
Range
Cherokee darter
Etheostoma scotti
Threatened
Both
Maryland darter
Etheostoma sellare
Endangered
Range
Kentucky arrow
darter
Etheostoma spilotum
Threatened
Range
Cumberland darter
Etheostoma susanae
Endangered
Both
Trispot darter
Etheostoma trisella
Threatened
Range
Boulder darter
Etheostoma wapiti
Wherever found
Endangered
Range
Boulder darter
Etheostoma wapiti
Shoal Creek (from Shoal
Creek mile 41.7 (66.7
km)) at the mouth of
Long Branch, Lawrence
County, TN, downstream
to the backwaters of
Wilson Reservoir (Shoal
Creek mile 14 (22 km))
at Goose Shoals,
Lauderdale County, AL,
including the lower 5
miles (8 km) of all
tributaries that enter this
reach
Experimental
Population, Non-
Essential
Both
Tidewater goby
Eucyclogobius newberryi
Endangered
Both
San Marcos
gambusia
Gambusia georgei
Endangered
Range
Pecos gambusia
Gambusia nobilis
Endangered
Both
Humpback chub
Gila cypha
Threatened
Both
Bonytail
Gila elegans
Endangered
Range
Gila chub
Gila intermedia
Endangered
Range
Chihuahua chub
Gila nigrescens
Threatened
Range
Yaqui chub
Gila purpurea
Endangered
Both
Virgin River Chub
Gila seminuda (=robusta)
Endangered
Both
Rio Grande Silvery
Minnow
Hybognathus amarus
Wherever found; Except
where listed as
Experimental
Populations
Endangered
198
-------
Range
Rio Grande Silvery
Minnow
Hybognathus amarus
Rio Grande, from Little
Box Canyon
(approximately 10.4 river
miles downstream of
Fort Quitman, TX) to
Amistad Dam; and on
the Pecos River, from its
confluence with
Independence Creek to
its confluence with the
Rio Grande
Experimental
Population, Non-
Essential
Both
Delta smelt
Hypomesus transpacificus
Threatened
Range
Yaqui catfish
Ictalurus pricei
Threatened
Both
Peppered chub
Macrhybopsis tetranema
Endangered
Range
Spikedace
Meda fulgida
Endangered
Both
Waccamaw
silverside
Menidia extensa
Threatened
Range
Palezone shiner
Notropis albizonatus
Endangered
Range
Smalleye Shiner
Notropis buccula
Endangered
Range
Cahaba shiner
Notropis cahabae
Endangered
Both
Arkansas River
shiner
Notropis girardi
Threatened
Both
Cape Fear shiner
Notropis mekistocholas
Endangered
Range
Sharpnose Shiner
Notropis oxyrhynchus
Endangered
Both
Pecos bluntnose
shiner
Notropis simus pecosensis
Threatened
Both
Topeka shiner
Notropis topeka (=tristis)
Wherever found; Except
where listed as
Experimental
Populations
Endangered
Range
Topeka shiner
Notropis topeka (=tristis)
U.S.A. (MO-specified
portions of Little Creek,
Big Muddy Creek, and
Spring Creek watersheds
in Adair, Gentry,
Harrison, Putnam,
Sullivan, and Worth
Counties
Experimental
Population, Non-
Essential
Both
Smoky madtom
Noturus baileyi
Wherever found
Endangered
Range
Smoky madtom
Noturus baileyi
The Tellico River,
between the backwaters
of the Tellico Reservoir
(approximately Tellico
River mile 19 (30.4
kilometers) and Tellico
River mile 33 (52.8
kilometers), near the
Tellico Ranger Station,
Monroe County,
Tennessee
Experimental
Population, Non-
Essential
Range
Chucky Madtom
Noturus crypticus
Endangered
199
-------
Both
Yellowfin madtom
Noturus flavipinnis
Wherever found; Except
where listed as
Experimental
Populations
Threatened
Range
Yellowfin madtom
Noturus flavipinnis
U.S.A. (TN-specified
portions of the Tellico
River
Experimental
Population, Non-
Essential
Range
Yellowfin madtom
Noturus flavipinnis
U.S.A. (TN, VA-
specified portions of the
Holston River and
watershed
Experimental
Population, Non-
Essential
Range
Yellowfin madtom
Noturus flavipinnis
U.S.A. (TN - specified
portions of the French
Broad and Holston
Rivers
Experimental
Population, Non-
Essential
Both
Carolina madtom
Noturus furiosus
Endangered
Both
Frecklebelly
madtom
Noturus munitus
Proposed
Threatened
Range
Neosho madtom
Noturus placidus
Threatened
Range
Pygmy madtom
Noturus stanauli
Wherever found
Endangered
Range
Pygmy madtom
Noturus stanauli
U.S.A. (TN - specified
portions of the French
Broad and Holston
Rivers)
Experimental
Population, Non-
Essential
Range
Scioto madtom
Noturus trautmani
Endangered
Range
Apache trout
Oncorhynchus apache
Threatened
Range
Lahontan cutthroat
trout
Oncorhynchus clarkii
henshawi
Threatened
Range
Greenback
Cutthroat trout
Oncorhynchus clarkii stomias
Threatened
Range
Gila trout
Oncorhynchus gilae
Threatened
Both
Amber darter
Percina antesella
Endangered
Range
Goldline darter
Percina aurolineata
Threatened
Range
Pearl darter
Percina aurora
Threatened
Both
Conasauga logperch
Percina jenkinsi
Endangered
Range
Leopard darter
Percina pantherina
Threatened
Range
Roanoke logperch
Percina rex
Endangered
Range
Blackside dace
Phoxinus cumberlandensis
Threatened
Both
Woundfin
Plagopterus argentissimus
Wherever found; Except
where listed as
Experimental
Populations
Endangered
Range
Woundfin
Plagopterus argentissimus
Gila R. drainage, AZ,
NM
Experimental
Population, Non-
Essential
Range
Gila topminnow
(incl. Yaqui)
Poeciliopsis occidentalis
Endangered
Both
Colorado
pikeminnow
Ptychocheilus lucius
Wherever found; Except
where listed as
Experimental
Populations
Endangered
200
-------
Range
Colorado
pikeminnow
Ptychocheilus lucius
Salt and Verde R.
drainages, AZ
Experimental
Population, Non-
Essential
Both
Atlantic salmon
Salmo salar
Endangered
Both
Bull Trout
Salvelinus confluentus
Threatened
Range
Pallid sturgeon
Scaphirhynchus albus
Endangered
Range
Alabama sturgeon
Scaphirhynchus suttkusi
Endangered
Both
Alabama cavefish
Speoplatyrhinus poulsoni
Endangered
Range
Loach minnow
Tiaroga cobitis
Endangered
Both
Razorback sucker
Xyrauchen texanus
Endangered
Range
Gulf sturgeon
Acipenser oxyrinchus
(=oxyrhynchus) desotoi
Threatened
Range
White sturgeon
Acipenser transmontanus
Endangered
Both
Ozark cavefish
Amblyopsis rosae
Threatened
Range
Shortnose Sucker
Chasmistes brevirostris
Endangered
Range
June sucker
Chasmistes liorus
Threatened
Range
Rio Grande
cutthroat trout
Oncorhynchus clarkii
virginalis
Candidate
Range
Mexican blindcat
(catfish)
Prietella phreatophila
Endangered
Range
Longfin Smelt
Spirinchus thaleichthys
Candidate
Table IX.B-4. The 129 FWS clam populations and those with designated critical habitat found
within the action area that receive a NLAA finding due to insignificant or discounta
)le effects.
CH,
Range,
or Both
Common Name
Scientific Name
DPS (if applicable)
Listing
Status
Range
Spectaclecase
(mussel)
Cumberlandia monodonta
Endangered
Range
Fanshell
Cyprogenia stegaria
Endangered
Range
Dromedary
pearlymussel
Dromus dromas
Wherever found; Except where
listed as Experimental
Populations
Endangered
Range
Dromedary
pearlymussel
Dromus dromas
U.S.A. (AL;The free-flowing
reach of the Tennessee R. from
the base of Wilson Dam
downstream to the backwaters of
Pickwick Reservoir [about 12
RM (19 km)] and the lower 5
RM [8 km] of all tributaries to
this reach in Colbert and
Lauderdale Cos.
Experimenta
1 Population,
Non-
Essential
201
-------
Range
Dromedary
pearlymussel
Dromus dromas
U.S.A. (TN - specified portions
of the French Broad and Holston
Rivers
Experimenta
1 Population,
Non-
Essential
Both
Chipola slabshell
Elliptic) chipolaensis
Threatened
Range
Yellow lance
Elliptic) lanceolata
Threatened
Range
Altamaha
Spinymussel
Elliptio spinosa
Endangered
Range
Purple bankclimber
(mussel)
Elliptoideus sloatianus
Threatened
Both
Cumberlandian
combshell
Epioblasma brevidens
Wherever found; Except where
listed as Experimental
Populations
Endangered
Range
Cumberlandian
combshell
Epioblasma brevidens
U.S.A. (AL;The free-flowing
reach of the Tennessee R. from
the base of Wilson Dam
downstream to the backwaters of
Pickwick Reservoir [about 12
RM (19 km)] and the lower 5
RM [8 km] of all tributaries to
this reach in Colbert and
Lauderdale Cos.
Experimenta
1 Population,
Non-
Essential
Range
Cumberlandian
combshell
Epioblasma brevidens
U.S.A. (TN - specified portions
of the French Broad and Holston
Rivers
Experimenta
1 Population,
Non-
Essential
Both
Oyster mussel
Epioblasma capsaeformis
Wherever found; Except where
listed as Experimental
Populations
Endangered
Range
Oyster mussel
Epioblasma capsaeformis
U.S.A. (AL;The free-flowing
reach of the Tennessee R. from
the base of Wilson Dam
downstream to the backwaters of
Pickwick Reservoir [about 12
RM (19 km)] and the lower 5
RM [8 km] of all tributaries to
this reach in Colbert and
Lauderdale Cos.
Experimenta
1 Population,
Non-
Essential
Range
Oyster mussel
Epioblasma capsaeformis
U.S.A. (TN - specified portions
of the French Broad and Holston
Rivers
Experimenta
1 Population,
Non-
Essential
202
-------
Range
Curtis pearlymussel
Epioblasma florentina
curtisii
Endangered
Range
Yellow blossom
(pearlymussel)
Epioblasma florentina
florentina
Wherever found; Except where
listed as Experimental
Populations
Endangered
Range
Yellow blossom
(pearlymussel)
Epioblasma florentina
florentina
U.S.A. (AL;The free-flowing
reach of the Tennessee R. from
the base of Wilson Dam
downstream to the backwaters of
Pickwick Reservoir [about 12
RM (19 km)] and the lower 5
RM [8 km] of all tributaries to
this reach in Colbert and
Lauderdale Cos.
Experimenta
1 Population,
Non-
Essential
Range
Tan riffle shell
Epioblasma florentina
walkeri (=E. walkeri)
Endangered
Range
Upland combshell
Epioblasma metastriata
Endangered
Range
Purple Cat"s paw
(=Purple Cat"s paw
pearlymussel)
Epioblasma obliquata
Wherever found; Except where
listed as Experimental
Populations
Endangered
Range
Purple Cat"s paw
(=Purple Cat"s paw
pearlymussel)
Epioblasma obliquata
U.S.A. (AL;The free-flowing
reach of the Tennessee R. from
the base of Wilson Dam
downstream to the backwaters of
Pickwick Reservoir [about 12
RM (19 km)] and the lower 5
RM [8 km] of all tributaries to
this reach in Colbert and
Lauderdale Cos.
Experimenta
1 Population,
Non-
Essential
Range
Southern acornshell
Epioblasma
othcaloogensis
Endangered
Range
Southern combshell
Epioblasma penita
Endangered
Range
White catspaw
(pearlymussel)
Epioblasma perobliqua
Endangered
Range
Northern riffleshell
Epioblasma rangiana
Endangered
Range
Green blossom
(pearlymussel)
Epioblasma torulosa
gubernaculum
Endangered
Range
Tubercled blossom
(pearlymussel)
Epioblasma torulosa
torulosa
Wherever found; Except where
listed as Experimental
Populations
Endangered
203
-------
Range
Tubercled blossom
(pearlymussel)
Epioblasma torulosa
torulosa
U.S.A. (AL;The free-flowing
reach of the Tennessee R. from
the base of Wilson Dam
downstream to the backwaters of
Pickwick Reservoir [about 12
RM (19 km)] and the lower 5
RM [8 km] of all tributaries to
this reach in Colbert and
Lauderdale Cos.
Experimenta
1 Population,
Non-
Essential
Both
Snuffbox mussel
Epioblasma triquetra
Endangered
Range
Turgid blossom
(pearlymussel)
Epioblasma turgidula
Wherever found; Except where
listed as Experimental
Populations
Endangered
Range
Turgid blossom
(pearlymussel)
Epioblasma turgidula
U.S.A. (AL;The free-flowing
reach of the Tennessee R. from
the base of Wilson Dam
downstream to the backwaters of
Pickwick Reservoir [about 12
RM (19 km)] and the lower 5
RM [8 km] of all tributaries to
this reach in Colbert and
Lauderdale Cos.
Experimenta
1 Population,
Non-
Essential
Range
Tapered pigtoe
Fusconaia burkei
Threatened
Range
Shiny pigtoe
Fusconaia cor
Wherever found; Except where
listed as Experimental
Populations
Endangered
Range
Shiny pigtoe
Fusconaia cor
U.S.A. (AL;The free-flowing
reach of the Tennessee R. from
the base of Wilson Dam
downstream to the backwaters of
Pickwick Reservoir [about 12
RM (19 km)] and the lower 5
RM [8 km] of all tributaries to
this reach in Colbert and
Lauderdale Cos.
Experimenta
1 Population,
Non-
Essential
Range
Shiny pigtoe
Fusconaia cor
U.S.A. (TN - specified portions
of the French Broad and Holston
Rivers
Experimenta
1 Population,
Non-
Essential
Range
Finerayed pigtoe
Fusconaia cuneolus
Wherever found; Except where
listed as Experimental
Populations
Endangered
Range
Finerayed pigtoe
Fusconaia cuneolus
U.S.A. (AL;The free-flowing
reach of the Tennessee R. from
the base of Wilson Dam
Experimenta
1 Population,
204
-------
downstream to the backwaters of
Pickwick Reservoir [about 12
RM (19 km)] and the lower 5
RM [8 km] of all tributaries to
this reach in Colbert and
Lauderdale Cos.
Non-
Essential
Range
Finerayed pigtoe
Fusconaia cuneolus
U.S.A. (TN - specified portions
of the French Broad and Holston
Rivers
Experimenta
1 Population,
Non-
Essential
Both
Narrow pigtoe
Fusconaia escambia
Threatened
Range
Atlantic pigtoe
Fusconaia masoni
Threatened
Range
Longsolid
Fusconaia subrotunda
Proposed
Threatened
Both
Finelined
pocketbook
Hamiota altilis
Threatened
Range
Southern Sandshell
Hamiota australis
Threatened
Range
Orangenacre mucket
Hamiota perovalis
Threatened
Range
Shiny rayed
pocketbook
Hamiota subangulata
Endangered
Range
Cracking
pearlymussel
Hemistena lata
Wherever found; Except where
listed as Experimental
Populations
Endangered
Range
Cracking
pearlymussel
Hemistena lata
U.S.A. (AL;The free-flowing
reach of the Tennessee R. from
the base of Wilson Dam
downstream to the backwaters of
Pickwick Reservoir [about 12
RM (19 km)] and the lower 5
RM [8 km] of all tributaries to
this reach in Colbert and
Lauderdale Cos.
Experimenta
1 Population,
Non-
Essential
Range
Cracking
pearlymussel
Hemistena lata
U.S.A. (TN - specified portions
of the French Broad and Holston
Rivers
Experimenta
1 Population,
Non-
Essential
Range
Pink mucket
(pearlymussel)
Lampsilis abrupta
Endangered
Range
Higgins eye
(pearlymussel)
Lampsilis higginsii
Endangered
Range
Arkansas fatmucket
Lampsilis powellii
Threatened
205
-------
Range
Neosho Mucket
Lampsilis rafinesqueana
Endangered
Range
Speckled
pocketbook
Lampsilis streckeri
Endangered
Range
Alabama
lampmussel
Lampsilis virescens
Wherever found; Except where
listed as Experimental
Populations
Endangered
Range
Alabama
lampmussel
Lampsilis virescens
U.S.A. (AL;The free-flowing
reach of the Tennessee R. from
the base of Wilson Dam
downstream to the backwaters of
Pickwick Reservoir [about 12
RM (19 km)] and the lower 5
RM [8 km] of all tributaries to
this reach in Colbert and
Lauderdale Cos.
Experimenta
1 Population,
Non-
Essential
Both
Carolina heelsplitter
Lasmigona decorata
Endangered
Range
Birdwing
pearlymussel
Lemiox rimosus
Wherever found; Except where
listed as Experimental
Populations
Endangered
Range
Birdwing
pearlymussel
Lemiox rimosus
U.S.A. (AL;The free-flowing
reach of the Tennessee R. from
the base of Wilson Dam
downstream to the backwaters of
Pickwick Reservoir [about 12
RM (19 km)] and the lower 5
RM [8 km] of all tributaries to
this reach in Colbert and
Lauderdale Cos.
Experimenta
1 Population,
Non-
Essential
Range
Birdwing
pearlymussel
Lemiox rimosus
U.S.A. (TN - specified portions
of the French Broad and Holston
Rivers
Experimenta
1 Population,
Non-
Essential
Range
Scaleshell mussel
Leptodea leptodon
Endangered
Range
Louisiana pearlshell
Margaritifera hembeli
Threatened
Range
Alabama pearlshell
Margaritifera marrianae
Endangered
Range
Alabama
moccasinshell
Medionidus acutissimus
Threatened
Range
Coosa
moccasinshell
Medionidus parvulus
Endangered
Range
Gulf moccasinshell
Medionidus penicillatus
Endangered
206
-------
Range
Ochlockonee
moccasinshell
Medionidus simpsonianus
Endangered
Range
Suwannee
moccasinshell
Medionidus walkeri
Threatened
Range
Choctaw bean
Obovaria choctawensis
Endangered
Range
Ring pink (mussel)
Obovaria retusa
Endangered
Range
Round hickorynut
Obovaria subrotunda
Proposed
Threatened
Range
James spinymussel
Parvaspina collina
Endangered
Range
Tar River
spinymussel
Parvaspina steinstansana
Endangered
Range
Littlewing
pearlymussel
Pegias fabula
Endangered
Range
White wartyback
(pearlymussel)
Plethobasus cicatricosus
Endangered
Range
Orangefoot
pimpleback
(pearlymussel)
Plethobasus cooperianus
Wherever found
Endangered
Range
Orangefoot
pimpleback
(pearlymussel)
Plethobasus cooperianus
U.S.A. (TN - specified portions
of the French Broad and Holston
Rivers
Experimenta
1 Population,
Non-
Essential
Range
Sheepnose Mussel
Plethobasus cyphyus
Endangered
Both
Canoe Creek
Clubshell
Pleurobema athearni
Endangered
Range
Clubshell
Pleurobema clava
Wherever found; Except where
listed as Experimental
Populations
Endangered
Range
Clubshell
Pleurobema clava
U.S.A. (AL;The free-flowing
reach of the Tennessee R. from
the base of Wilson Dam
downstream to the backwaters of
Pickwick Reservoir [about 12
RM (19 km)] and the lower 5
RM [8 km] of all tributaries to
this reach in Colbert and
Lauderdale Cos.
Experimenta
1 Population,
Non-
Essential
Range
Black clubshell
Pleurobema curtum
Endangered
Range
Southern clubshell
Pleurobema decisum
Endangered
207
-------
Range
Dark pigtoe
Pleurobema furvum
Endangered
Range
Southern pigtoe
Pleurobema georgianum
Endangered
Range
Georgia pigtoe
Pleurobema hanleyianum
Endangered
Range
Flat pigtoe
Pleurobema marshalli
Endangered
Range
Ovate clubshell
Pleurobema perovatum
Endangered
Range
Rough pigtoe
Pleurobema plenum
Endangered
Range
Oval pigtoe
Pleurobema pyriforme
Endangered
Range
Fuzzy pigtoe
Pleurobema strodeanum
Threatened
Range
Heavy pigtoe
Pleurobema taitianum
Endangered
Range
Slabside
Pearlymussel
Pleuronaia dolabelloides
Endangered
Range
Cumberland pigtoe
Pleuronaia gibber
Endangered
Both
Texas Hornshell
Popenaias popeii
Endangered
Range
Fat pocketbook
Potamilus capax
Endangered
Range
Inflated heelsplitter
Potamilus inflatus
Threatened
Range
Triangular
Kidneyshell
Ptychobranchus greenii
Endangered
Range
Southern
kidneyshell
Ptychobranchus jonesi
Endangered
Range
Fluted kidneyshell
Ptychobranchus subtentus
Endangered
Range
Rabbitsfoot
Quadrula cylindrica
cylindrica
Threatened
Both
Rough rabbitsfoot
Quadrula cylindrica
strigillata
Endangered
Range
Winged Mapleleaf
Quadrula fragosa
Wherever found; Except where
listed as Experimental
Populations
Endangered
Range
Winged Mapleleaf
Quadrula fragosa
U.S.A. (AL-specified portions of
the Tennessee River
Experimenta
1 Population,
Non-
Essential
Range
Stirrupshell
Quadrula stapes
Endangered
Range
Round Ebonyshell
Reginaia rotulata
Endangered
208
-------
Range
Cumberland
monkeyface
(pearlymussel)
Theliderma intermedia
Wherever found; Except where
listed as Experimental
Populations
Endangered
Range
Cumberland
monkeyface
(pearlymussel)
Theliderma intermedia
U.S.A. (TN - specified portions
of the French Broad and Holston
Rivers
Experimenta
1 Population,
Non-
Essential
Range
Cumberland
monkeyface
(pearlymussel)
Theliderma intermedia
U.S.A. (AL;The free-flowing
reach of the Tennessee R. from
the base of Wilson Dam
downstream to the backwaters of
Pickwick Reservoir [about 12
RM (19 km)] and the lower 5
RM [8 km] of all tributaries to
this reach in Colbert and
Lauderdale Cos.
Experimenta
1 Population,
Non-
Essential
Range
Appalachian
monkeyface
(pearlymussel)
Theliderma sparsa
Wherever found
Endangered
Range
Appalachian
monkeyface
(pearlymussel)
Theliderma sparsa
USA (TN - specified portions of
the French Broad and Holston
Rivers)
Experimenta
1 Population,
Non-
Essential
Range
Pale lilliput
(pearlymussel)
Toxolasma cylindrellus
Endangered
Range
Rayed Bean
Villosa fabalis
Endangered
Both
Purple bean
Villosa perpurpurea
Endangered
Range
Cumberland bean
(pearlymussel)
Villosa trabalis
Wherever found; Except where
listed as Experimental
Populations
Endangered
Range
Cumberland bean
(pearlymussel)
Villosa trabalis
U.S.A. (AL;The free-flowing
reach of the Tennessee R. from
the base of Wilson Dam
downstream to the backwaters of
Pickwick Reservoir [about 12
RM (19 km)] and the lower 5
RM [8 km] of all tributaries to
this reach in Colbert and
Lauderdale Cos.
Experimenta
1 Population,
Non-
Essential
Range
Cumberland bean
(pearlymussel)
Villosa trabalis
U.S.A. (TN - specified portions
of the French Broad and Holston
Rivers
Experimenta
1 Population,
Non-
Essential
209
-------
Both
Cumberland elktoe
Alasmidonta
atropurpurea
Endangered
Range
Dwarf wedgemussel
Alasmidonta heterodon
Endangered
Range
Appalachian elktoe
Alasmidonta raveneliana
Endangered
Range
Fat threeridge
(mussel)
Amblema neislerii
Endangered
Both
Ouachita rock
pocketbook
Arcidens wheeleri
Endangered
Range
Guadalupe Orb
Cyclonaias necki
Proposed
Endangered
Range
Texas pimpleback
Cyclonaias petrina
Proposed
Endangered
Range
Western fanshell
Cyprogenia aberti
Proposed
Threatened
Both
false spike
Fusconaia mitchelli
Proposed
Endangered
Range
Guadalupe
Fatmucket
Lampsilis bergmanni
Proposed
Endangered
Both
Texas fatmucket
Lampsilis bracteata
Proposed
Endangered
Both
Texas fawnsfoot
Truncilla macrodon
Proposed
Threatened
Table IX.B-5. The 21 FWS crustacean species and those with designated critical habitat found
within the action area that receive a NLAA finding due to insignificant or discountable effects.
\lone of these species have separate
DPSs.
CH,
Range,
or Both
Common Name
Scientific Name
Listing Status
Both
Big Creek Crayfish
Faxonius peruncus
Proposed
Threatened
Both
St. Francis River Crayfish
Faxonius quadruncus
Proposed
Threatened
Range
Illinois cave amphipod
Gammarus acherondytes
Endangered
Both
Noel's Amphipod
Gammarus desperatus
Endangered
Both
Vernal pool tadpole shrimp
Lepidurus packardi
Endangered
Range
Lee County cave isopod
Lirceus usdagalun
Endangered
Range
Nashville crayfish
Orconectes shoupi
Endangered
Range
Squirrel Chimney Cave
shrimp
Palaemonetes cummingi
Threatened
Range
Alabama cave shrimp
Palaemonias alabamae
Endangered
210
-------
Both
Kentucky cave shrimp
Palaemonias ganteri
Endangered
Both
Peck's cave amphipod
Stygobromus
(=Stygonectes) pecki
Endangered
Range
California freshwater shrimp
Syncaris pacifica
Endangered
Range
Socorro isopod
Thermosphaeroma
thermophilus
Endangered
Range
Madison Cave isopod
Antrolana lira
Threatened
Both
Conservancy fairy shrimp
Branchinecta conservatio
Endangered
Range
Longhorn fairy shrimp
Branchinecta
longiantenna
Endangered
Range
Vernal pool fairy shrimp
Branchinecta lynchi
Threatened
Range
Benton County cave
crayfish
Cambarus aculabrum
Endangered
Both
Big Sandy crayfish
Cambarus callainus
Threatened
Range
Slenderclaw crayfish
Cambarus cracens
Endangered
Range
Hell Creek Cave crayfish
Cambarus zophonastes
Endangered
Mammals
Fifty-five mammals were found to be present in the action area (Table XI.A-6). Among
the mammals, potential effects to most carnivores are very unlikely to occur. Many listed
mammalian carnivores are nocturnal or crepuscular for most or part of the year and are also
found within larger ranges. Depending on the species and its mobility, we anticipate that
potential impacts would be discountable or insignificant as many carnivores are able to move to
another region of their range if they experience some sort of localized disturbance. It is also
unlikely that their prey base would be reduced to a degree that would harm the carnivorous
species; again, they could move to another part of their range if there were any impacts to
localized prey populations. Further, many carnivores such as the Canada lynx depend on certain
types of vegetative cover such as dense understories and forests. As discussed previously, it is
unlikely that forests would be converted to agriculture to meet biofuel demand attributable to the
RFS Set Rule.
For other types of mammals, such as rodents, bats, and ungulates, potential effects are
also likely to be insignificant or discountable. Habitat types for many, but not all, of these
species within these groups are unlikely to be affected by agriculture. Many ungulates, for
example, are found in steep, high elevation, and rocky habitat; many squirrel and chipmunk
populations occur in mature forest stands in protected areas; and bats are typically shelter in
trees, under bark, or inside caves. Many of these mammal groups are also mobile and able to
travel to other areas to forage if their food sources are impacted.
Other mammals may be more likely to be impacted by agricultural practices. For
example, rodents that rely on more riparian areas (e.g., the New Mexico meadow jumping
mouse) or grassland ecosystems for PBFs such as insects for food. As another example, the West
Indian Manatee could be affected by water pollution from agricultural runoff. However, we
anticipate that potential effects to these species would also be insignificant and discountable for
the same reasons previously stated for flowering plants and other taxa (due to the limited nature
and uncertainty of changes attributed to the RFS Set Rule).
211
-------
Table IX.B-6. The 55 FWS mammal populations and those with designated critical habitat
found within the action area that receive a NLAA finding due to insignificant or discountable
effects.
CH,
Range,
or Both
Common Name
Scientific Name
DPS (if applicable)
Listing Status
Range
Carolina northern
flying squirrel
Glaucomys sabrinus
coloratus
Endangered
Range
Ocelot
Leopardus (=Felis)
pardalis
Endangered
Range
Mexican long-nosed
bat
Leptonycteris nivalis
Endangered
Both
Canada Lynx
Lynx canadensis
Threatened
Both
Pacific Marten,
Coastal Distinct
Population Segment
Martes caurina
Threatened
Range
Florida salt marsh vole
Microtus pennsylvanicus
dukecampbelli
Endangered
Range
Black-footed ferret
Mustela nigripes
Wherever found; Except
where listed as
Experimental Populations
Endangered
Range
Black-footed ferret
Mustela nigripes
U.S.A. (WY and specified
portions of AZ, CO, MT,
SD, and UT,
Experimental
Population, Non-
Essential
Range
Gray bat
Myotis grisescens
Endangered
Range
Northern Long-Eared
Bat
Myotis septentrionalis
Endangered
Both
Indiana bat
Myotis sodalis
Endangered
Range
Riparian woodrat
(=San Joaquin Valley)
Neotoma fuscipes riparia
Endangered
Range
Columbian white-
tailed deer
Odocoileus virginianus
leucurus
Threatened
Range
Jaguar
Panthera onca
Endangered
Range
Fisher
Pekania pennanti
Endangered
Both
Choctawhatchee beach
mouse
Peromyscus polionotus
allophrys
Endangered
Both
Alabama beach mouse
Peromyscus polionotus
ammobates
Endangered
Range
Southeastern beach
mouse
Peromyscus polionotus
niveiventris
Threatened
Both
St. Andrew beach
mouse
Peromyscus polionotus
peninsularis
Endangered
Both
Perdido Key beach
mouse
Peromyscus polionotus
trissyllepsis
Endangered
Range
Florida panther
Puma (=Felis) concolor
coryi
Endangered
Range
Gulf Coast jaguarundi
Puma yagouaroundi
cacomitli
Endangered
Range
Southern Mountain
Caribou DPS
Rangifer tarandus ssp.
caribou
Endangered
212
-------
Range
Salt marsh harvest
mouse
Reithrodontomys
raviventris
Endangered
Both
Buena Vista Lake
ornate Shrew
Sorex ornatus relictus
Endangered
Range
Riparian brush rabbit
Sylvilagus bachmani
riparius
Endangered
Both
Olympia pocket
gopher
Thomomys mazama
pugetensis
Threatened
Both
Tenino pocket gopher
Thomomys mazama
tumuli
Threatened
Range
Yelm pocket gopher
Thomomys mazama
yelmensis
Threatened
Both
West Indian Manatee
Trichechus manatus
Threatened
Range
Grizzly bear
Ursus arctos horribilis
Threatened
Range
San Joaquin kit fox
Vulpes macrotis mutica
Endangered
Both
New Mexico meadow
jumping mouse
Zapus hudsonius luteus
Endangered
Both
Preble's meadow
jumping mouse
Zapus hudsonius preblei
Threatened
Range
Sonoran pronghorn
Antilocapra americana
sonoriensis
Endangered
Range
Point Arena mountain
beaver
Aplodontia rufa nigra
Endangered
Range
red tree vole
Arborimus longicaudus
Candidate
Both
Columbia Basin
Pygmy Rabbit
Brachylagus idahoensis
Endangered
Range
Gray wolf
Canis lupus
U.S. - multiple states
Endangered
Both
Gray wolf
Canis lupus
Minnesota
Threatened
Range
Mexican wolf
Canis lupus baileyi
Wherever found; Except
where listed as
Experimental Populations
Endangered
Range
Mexican wolf
Canis lupus baileyi
U.S.A. (portions of AZ
andNM)
Experimental
Population, Non-
Essential
Range
Red wolf
Canis rufus
Wherever found; Except
where listed as
Experimental Populations
Endangered
Range
Red wolf
Canis rufus
U.S.A. (portions of NC
and TN)
Experimental
Population, Non-
Essential
Range
Ozark big-eared bat
Corynorhinus (=Plecotus)
townsendii ingens
Endangered
Range
Virginia big-eared bat
Corynorhinus (=Plecotus)
townsendii virginianus
Endangered
Range
Utah prairie dog
Cynomys parvidens
Threatened
Range
Giant kangaroo rat
Dipodomys ingens
Endangered
Range
Fresno kangaroo rat
Dipodomys nitratoides
exilis
Endangered
Range
Tipton kangaroo rat
Dipodomys nitratoides
nitratoides
Endangered
Range
Southern sea otter
Enhydra lutris nereis
Threatened
213
-------
Both
Florida bonneted bat
Eumops floridanus
Endangered
Range
North American
wolverine
Gulo gulo luscus
Proposed
Threatened
Range
Tricolored bat
Perimyotis subflavus
Proposed
Endangered
Range
Penasco least
chipmunk
Tamias minimus
atristriatus
Proposed
Endangered
Birds
Among the 41 birds found within the action area (Table XI.A-7), the Piping plover was
one species that saw relatively higher potential impacts based on the land use impacts alone
(Tables VII.D-1 to VII.D-4). The Piping plover Great Lakes breeding population have PBFs that
include shorelines and islands of the Great Lakes with sparsely vegetated and sandy landscapes
dunes and wetlands. This population also relies on the complex and dynamic ecology of the
Great Lakes shoreline, which is in a constant change with natural disturbances from storms and
sediment transportation (US FWS Region 3, n.d.).
The Norther Great Plains Piping plover population also depends on dynamic ecological
processes and landscape features including permanently flooded wetlands, and sparsely
vegetated sandbars, islands, and peninsulas on rivers, reservoirs, and inland lakes (US FWS
Region 3, n.d.). These features could be potentially affected by erosion and runoff from
agricultural fields. However, for reasons stated in IX. A, we anticipate discountable or
insignificant effects.
This is the case for other listed birds as well. PBFs for many birds include access to forest
and/or riparian areas with certain tree and understory species and diversity for roosting, nesting,
and shelter. They also rely on such ecosystems for foraging of insects and other food sources.
Birds typically have high mobility and are able to forage widely if they encounter localized
threats or disturbances. As such, potential impacts from the RFS Rule, if any, are expected to be
insignificant or discountable.
Table IX.B-7. The 41 FWS bird populations found within the action area that receive a NLAA
Inding due to insignificant or discountable effects.
CH,
Range,
or Both
Common Name
Scientific Name
DPS (if applicable)
Listing
Status
Range
Florida scrub-jay
Aphelocoma coerulescens
Threatened
Both
Marbled murrelet
Brachyramphus marmoratus
Threatened
Range
Red knot
Calidris canutus rufa
Threatened
Range
Ivory-billed
woodpecker
Campephilus principalis
Endangered
Range
Gunnison sage-grouse
Centrocercus minimus
Threatened
Both
Piping Plover
Charadrius melodus
Great Lakes Watershed
Endangered
Both
Piping Plover
Charadrius melodus
Atlantic Coast and
Northern Great Plains
Threatened
Both
Western snowy plover
Charadrius nivosus nivosus
Threatened
Range
Yellow-billed Cuckoo
Coccyzus americanus
Threatened
214
-------
Range
Southwestern willow
flycatcher
Empidonax traillii extimus
Endangered
Both
Streaked Horned lark
Eremophila alpestris strigata
Threatened
Range
Northern Aplomado
Falcon
Falco femoralis
septentrionalis
Endangered
Both
Whooping crane
Grus americana
Wherever found; Except
where listed as
Experimental Populations
Endangered
Range
Whooping crane
Grus americana
U.S.A. (CO, ID, FL, NM,
UT, and the western half
of Wyoming)
Experimental
Population,
Non-
Essential
Range
Whooping crane
Grus americana
U.S.A. (AL, AR, CO, FL,
GA, ID, IL, IN, IA, KY,
LA, MI, MN, MS, MO,
NC, NM, OH, SC, TN,
UT, VA, WI, WV,
western half of WY)
Experimental
Population,
Non-
Essential
Range
Whooping crane
Grus americana
U.S.A (Southwestern
Louisiana)
Experimental
Population,
Non-
Essential
Both
Mississippi sandhill
crane
Grus canadensis pulla
Endangered
Both
California condor
Gymnogyps californianus
Wherever found; Except
where listed as
Experimental Populations
Endangered
Range
California condor
Gymnogyps californianus
U.S.A. (specific portions
of Arizona, Nevada, and
Utah)
Experimental
Population,
Non-
Essential
Range
Eastern Black rail
Laterallus jamaicensis ssp.
jamaicensis
Threatened
Range
Wood stork
Mycteria americana
Threatened
Range
Eskimo curlew
Numenius borealis
Endangered
Range
Short-tailed albatross
Phoebastria (=Diomedea)
albatrus
Endangered
Range
Red-cockaded
woodpecker
Picoides borealis
Endangered
Range
Audubon's crested
caracara
Polyborus plancus audubonii
Threatened
Range
Hawaiian petrel
Pterodroma sandwichensis
Endangered
Range
California clapper rail
Rallus longirostris obsoletus
Endangered
Range
Yuma Ridgway"s rail
Rallus obsoletus yumanensis
Endangered
Both
Everglade snail kite
Rostrhamus sociabilis
plumbeus
Endangered
Range
golden-cheeked
warbler
Setophaga chrysoparia
Endangered
Range
California least tern
Sterna antillarum browni
Endangered
Range
Roseate tern
Sterna dougallii dougallii
Atlantic Coast south to
NC, Canada, Bermuda
Endangered
215
-------
Range
Roseate tern
Sterna dougallii dougallii
Western Hemisphere and
adjacent oceans, including
USA (FL, PR, VI), where
not listed as endangered
Endangered
Both
Northern spotted owl
Strix occidentalis caurina
Threatened
Both
Mexican spotted owl
Strix occidentalis lucida
Threatened
Range
Attwater's greater
prairie-chicken
Tympanuchus cupido
attwateri
Endangered
Range
Bachman's warbler
(=wood)
Vermivora bachmanii
Endangered
Range
Least Bell's vireo
Vireo bellii pusillus
Endangered
Range
Florida grasshopper
sparrow
Ammodramus savannarum
floridanus
Endangered
Range
Cactus ferruginous
pygmy-owl
Glaucidium brasilianum
cactorum
Proposed
Threatened
Range
Black-capped petrel
Pterodroma hasitata
Proposed
Threatened
Snails
Twenty-nine snails were present in the action area for the RFS Set Rule (Table IX. A-8).
Snails are found on a variety of terrestrial and aquatic ecosystems. The PBFs for snails that live
in aquatic environments are very similar to the PBFs for fish, clams, and crustaceans described
previously (e.g., clean, well-oxygenated water with gravely beds and riffles). One with critical
habitat found within the action area includes the Koster's springsnail which is found in New
Mexico. The Roswell springsnail also has critical habitat in New Mexico, but in it lives in
wetland sinkholes and spring-fed caves (US FWS, n.d.-d). It is unlikely that any effects of the
RFS Rule would be detrimental to Roswell springsnail in particular because of where it lives.
Nonetheless, EPA anticipates insignificant or discountable effects for all snails and their critical
habitats due to the limited and uncertain nature of changes from the RFS Set Rule.
Table IX.B-8. The 27 FWS snail populations and those with designated critical habitat found
within the action area that receive a NLAA finding due to insignificant or discounta
)le effects.
CH,
Range,
or Both
Common Name
Scientific Name
DPS (if applicable)
Listing
Status
Both
Koster's
springsnail
Juturnia kosteri
Endangered
Range
Round rocksnail
Leptoxis ampla
Threatened
Range
Interrupted
(=Georgia)
Rocksnail
Leptoxis foremani
Endangered
Range
Plicate rocksnail
Leptoxis plicata
Endangered
Range
Painted rocksnail
Leptoxis taeniata
Threatened
Range
Cylindrical
lioplax (snail)
Lioplax cyclostomaformis
Endangered
Range
Royal marstonia
(snail)
Marstonia ogmorhaphe
Endangered
Range
Armored snail
Marstonia pachyta
Endangered
216
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Range
Chittenango
ovate amber snail
Novisuccinea chittenangoensis
Threatened
Range
Rough hornsnail
Pleurocera foremani
Endangered
Range
Virginia fringed
mountain snail
Polygyriscus virginianus
Endangered
Range
Bruneau Hot
springsnail
Pyrgulopsis bruneauensis
Endangered
Range
Chupadera
springsnail
Pyrgulopsis chupaderae
Endangered
Range
Socorro
springsnail
Pyrgulopsis neomexicana
Endangered
Both
Roswell
springsnail
Pyrgulopsis roswellensis
Endangered
Range
Bliss Rapids
snail
Taylorconcha serpenticola
Threatened
Range
Flat-spired three-
toothed Snail
Triodopsis platysayoides
Threatened
Range
Tulotoma snail
Tulotoma magnifica
Threatened
Range
Painted snake
coiled forest snail
Anguispira picta
Threatened
Range
Pecos assiminea
snail
Assiminea pecos
Endangered
Range
Anthony's
riversnail
Athearnia anthonyi
Wherever found; Except
where listed as Experimental
Populations
Endangered
Range
Anthony's
riversnail
Athearnia anthonyi
U.S.A. (AL;The free-flowing
reach of the Tennessee R.
from the base of Wilson Dam
downstream to the backwaters
of Pickwick Reservoir [about
12 RM (19 km)] and the
lower 5 RM [8 km] of all
tributaries to this reach in
Colbert and Lauderdale Cos.
Experimenta
1 Population,
Non-
Essential
Range
Anthony's
riversnail
Athearnia anthonyi
U.S.A. (TN - specified
portions of the French Broad
and Holston Rivers
Experimenta
1 Population,
Non-
Essential
Range
Slender
campeloma
Campeloma decampi
Endangered
Range
Iowa Pleistocene
snail
Discus macclintocki
Endangered
Range
Lacy elimia
(snail)
Elimia crenatella
Threatened
Range
Banbury Springs
limpet
Lanx sp.
Endangered
Range
Snake River
physa snail
Physa natricina
Endangered
Both
Magnificent
ramshorn
Planorbella magnifica
Proposed
Endangered
217
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Arachnids
Many of the 10 arachnids in the action area (Table IX.A-8) are spiders that occur in cave
and forest habitats. It is not very likely that such areas would be affected by the RFS Set Rule as
they are areas that not favorable for agriculture conversion. PBFs for arachnids may be particular
to the caves or other environments in which they are found, but in general PBFs would be similar
to those described under the insects section previously (e.g., vegetation and other features needed
for refugia and foraging). As is the case with the insects taxonomic group, EPA anticipates
discountable and insignificant effects for arachnids. Agriculture conversion or intensification
caused by the Set Rule, if any, would likely occur in areas that are already impacted and not
suitable for habitat or in areas that meet criteria for arachnids' PBFs/PCEs.
Table IX.B-9. The 10 FWS arachnid populations and those with designated critical habitat found
within the action area that receive a NLAA finding due to insignificant or discountable effects.
\lone of these species have separate D
PSs.
CH,
Common Name
Scientific Name
Listing
Range,
or Both
Status
Both
Robber Baron Cave
Meshweaver
Cicurina baronia
Endangered
Both
Madia Cave Meshweaver
Cicurina madia
Endangered
Range
Government Canyon Bat
Cave meshweaver
Cicurina vespera
Endangered
Range
Spruce-fir moss spider
Microhexura montivaga
Endangered
Range
Tooth Cave pseudoscorpion
Tartarocreagris texana
Endangered
Both
Government Canyon Bat
Cave spider
Tayshaneta microps
Endangered
Range
Tooth Cave spider
Tayshaneta myopica
Endangered
Range
Cokendolpher Cave
Harvestman
Texella cokendolpheri
Endangered
Range
Bee Creek Cave harvestman
Texella reddelli
Endangered
Range
Bone Cave harvestman
Texella reyesi
Endangered
Reptiles
Twenty-nine reptiles were found within the action area (Table IX.A-10). Many reptiles
can be found in a variety of habitats, including but not limited to forests, open fields, and near
water. Some reptiles are fossorial and spend time underground, such as the Blunt-nose leopard
lizard and narrow-headed garter snake. Habitat for shelter and protection, hibernation,
thermoregulation, foraging, and gestation are important, as is the absence of invasive species and
access to prey and other food sources. Critical habitat PBFs may include woody debris and
riparian vegetation, streams and ponds, and presence of small mammal burrows. It is possible
that RFS rule may impact some of these features and habitats through conversion of lands and
increases in sediment and pollution from agricultural runoff. EPA determines, however, that
potential effects are discountable or insignificant because of the limited nature and uncertainty of
changes especially at the local level.
Runoff from agriculture attributed to the RFS Rule may also affect other reptiles such as
sea turtles that occur in marine and coastal regions. Some of these FWS marine reptiles are also
managed by NMFS and, as discussed in Section V, EPA anticipates discountable and
218
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insignificant potential effects to these species. Further analyses in Section VIII affirm that
potential effects in downstream areas would be limited relative to baseline conditions, and
therefore would not contribute to any measurable adverse effects.
Table IX.B-10. The 29 FWS reptile populations and those with designated critical habitat found
within the action area that receive a NLAA finding due to insignificant or discountable effects.
\lone of these species have separate
DPSs.
CH,
Range,
or Both
Common Name
Scientific Name
Listing
Status
Range
bog turtle
Glyptemys muhlenbergii
Threatened
Both
Desert tortoise
Gopherus agassizii
Threatened
Range
Gopher tortoise
Gopherus polyphemus
Threatened
Range
Yellow-blotched map turtle
Graptemys flavimaculata
Threatened
Range
Ringed map turtle
Graptemys oculifera
Threatened
Range
Kemp's ridley sea turtle
Lepidochelys kempii
Endangered
Range
Olive ridley sea turtle
Lepidochelys olivacea
Threatened
Range
Alameda whipsnake
(=striped racer)
Masticophis lateralis
euryxanthus
Threatened
Range
Sand skink
Neoseps reynoldsi
Threatened
Range
Atlantic salt marsh snake
Nerodia clarkii taeniata
Threatened
Range
Copperbelly water snake
Nerodia erythrogaster
neglecta
Threatened
Both
Black pinesnake
Pituophis melanoleucus
lodingi
Threatened
Both
Louisiana pinesnake
Pituophis ruthveni
Threatened
Range
Alabama red-bellied turtle
Pseudemys alabamensis
Endangered
Range
Plymouth Redbelly Turtle
Pseudemys rubriventris
bangsi
Endangered
Range
Eastern Massasauga
(=rattlesnake)
Sistrurus catenatus
Threatened
Range
Flattened musk turtle
Sternotherus depressus
Threatened
Both
Northern Mexican
gartersnake
Thamnophis eques
megalops
Threatened
Range
Giant garter snake
Thamnophis gigas
Threatened
Both
Narrow-headed gartersnake
Thamnophis rufipunctatus
Threatened
Range
San Francisco garter snake
Thamnophis sirtalis
tetrataenia
Endangered
Both
Loggerhead sea turtle
Caretta caretta
Threatened
Range
Green sea turtle
Chelonia mydas
Threatened
Range
Leatherback sea turtle
Dermochelys coriacea
Endangered
Range
Eastern indigo snake
Drymarchon couperi
Threatened
Range
Hawksbill sea turtle
Eretmochelys imbricata
Endangered
Range
Blunt-nosed leopard lizard
Gambelia silus
Endangered
Range
Suwannee alligator snapping
turtle
Macrochelys suwanniensis
Proposed
Threatened
219
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Range
Alligator snapping turtle
Macrochelys temminckii
Proposed
Threatened
Amphibians
This Biological Evaluation identified 25 amphibians that may be impacted by the action
(Table IX.A-11). As is the case for reptiles, many amphibians are fossorial and remain in
underground burrows for long periods at a time. Others are semi or fully aquatic. Amphibians
rely on food such as insects, crayfish, snails, and earthworms. Some essential features found in
critical habitat may include the following: depressions in land that create ephemeral bodies of
fresh water; tree and plant communities encompassing specific plant species or types; large
shelter rocks in rivers and other habitat for refugia; wetlands with herbaceous vegetation. While
it is possible that such features may be affected by agricultural conversion or runoff, EPA
anticipates that potential effects would be discountable or insignificant for amphibians as well.
Table IX.B-11. The 25 FWS amphibian populations and those with designated critical habitat
found within the action area that receive a NLAA finding due to insignificant or discountable
effects.
CH,
Range,
or Both
Common Name
Scientific Name
DPS (if applicable)
Listing
Status
Range
Reticulated flatwoods
salamander
Ambystoma bishopi
Endangered
Both
California tiger salamander
Ambystoma californiense
Central California
Threatened
Both
California tiger salamander
Ambystoma californiense
Santa Barbara County
Endangered
Both
California tiger salamander
Ambystoma californiense
Sonoma County
Endangered
Range
Frosted Flatwoods
salamander
Ambystoma cingulatum
Threatened
Range
Santa Cruz long-toed
salamander
Ambystoma
macrodactylum croceum
Endangered
Range
Dixie Valley Toad
Anaxyrus williamsi
Endangered
Range
Houston toad
Bufo houstonensis
Endangered
Range
Ozark Hellbender
Cryptobranchus
alleganiensis bishopi
Endangered
Both
Salado Salamander
Eurycea chisholmensis
Threatened
Both
San Marcos salamander
Eurycea nana
Threatened
Both
Georgetown Salamander
Eurycea naufragia
Threatened
Range
Texas blind salamander
Eurycea rathbuni
Endangered
Range
Barton Springs salamander
Eurycea sosorum
Endangered
Both
Jollyville Plateau
Salamander
Eurycea tonkawae
Threatened
Both
Austin blind Salamander
Eurycea waterlooensis
Endangered
Range
Black warrior (=Sipsey
Fork) Waterdog
Necturus alabamensis
Endangered
Both
Neuse River waterdog
Necturus lewisi
Threatened
Range
Red Hills salamander
Phaeognathus hubrichti
Threatened
Range
Cheat Mountain salamander
Plethodon nettingi
Threatened
Range
Shenandoah salamander
Plethodon shenandoah
Endangered
220
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Range
Chiricahua leopard frog
Rana chiricahuensis
Threatened
Both
California red-legged frog
Rana draytonii
Threatened
Both
Oregon spotted frog
Rana pretiosa
Threatened
Both
dusky gopher frog
Rana sevosa
Endangered
Ferns and Allies; Conifers and Cycads; Lichens
Five ferns and allies, four conifers and cycads, and two lichens are present within the
action area. The species within these three taxonomic groups are found within a variety of
habitats, including forests of varying tree densities and species, ephemeral pools and aquatic
ecosystems, vertical rock faces (in the case of the Rock Gnome lichen), and landscapes with
particular soil types and/or moisture levels that are suitable for the species. Like flowering plants,
some species rely on pollinators for reproduction. None of the species within the three groups
have critical habitat. EPA anticipates that potential effects would be discountable or
insignificant.
Table IX.B-12. The 5 FWS ferns and allies found within the action area that receive a NLAA
lave separate DPSs.
inding due to insignificant or discountable effects. None of these species
CH,
Range,
or Both
Common Name
Scientific Name
Listing
Status
Range
Louisiana quillwort
Isoetes louisianensis
Endangered
Range
Black spored quillwort
Isoetes melanospora
Endangered
Range
Mat-forming quillwort
Isoetes tegetiformans
Endangered
Range
Alabama streak-sorus fern
Thelypteris pilosa var.
alabamensis
Threatened
Range
American hart's-tongue fern
Asplenium scolopendrium
var. americanum
Threatened
Table IX.B-13. The 4 FWS conifers and cycads found within the action area that receive a
NLAA finding due to insignificant or discountable effects. None of these species have separate
DPSs.
CH,
Range,
or Both
Common Name
Scientific Name
Listing
Status
Range
Santa Cruz cypress
Cupressus abramsiana
Threatened
Range
Gowen cypress
Cupressus goveniana ssp.
goveniana
Threatened
Range
Whitebark pine
Pinus albicaulis
Threatened
Range
Florida torreya
Torreya taxifolia
Endangered
Table IX.B-14. The 2 FWS lichens found within the action area that receive a NLAA finding
CH,
Range, or
Both
Common Name
Scientific Name
Listing
Status
Range
Rock gnome lichen
Gymnoderma lineare
Endangered
Range
Florida perforate cladonia
Cladonia perforata
Endangered
221
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C. N\1FS Species and Critical Habitats
Seventy-three NMFS populations were found in the action area. Out of the 73
populations, four are corals; 44 are fishes; 14 are mammals; nine are reptiles; one is a snail; and
one is an echinoderm (Table IX.C-1).
Table IX.C-1. The 73 NMFS populations found within the action area and their associated
taxonomic group.
CH,
Range,
or Both
Common Name
Scientific Name
DPS or ESU (if
applicable)
Listing
Status
Taxonomic
Group
Range
Shortnose sturgeon
Acipenser
brevirostrum
Endangered
Fishes
Both
Sturgeon, Green
Acipenser meclirostris
Southern
Threatened
Fishes
Both
Sturgeon, Atlantic
(Gulf subspecies)
Acipenser oxyrinchus
(=oxyrhynchus)
desotoi
Threatened
Fishes
Both
Sturgeon, Atlantic
Acipenser oxyrinchus
oxyrinchus
Carolina
Endangered
Fishes
Both
Sturgeon, Atlantic
Acipenser oxyrinchus
oxyrinchus
Chesapeake Bay
Endangered
Fishes
Both
Sturgeon, Atlantic
Acipenser oxyrinchus
oxyrinchus
Gulf of Maine
Threatened
Fishes
Both
Sturgeon, Atlantic
Acipenser oxyrinchus
oxyrinchus
New York Bight
Endangered
Fishes
Both
Sturgeon, Atlantic
Acipenser oxyrinchus
oxyrinchus
South Atlantic
Endangered
Fishes
Both
Elkhorn coral
Acropora palmata
Threatened
Corals
Range
Guadalupe fur seal
Arctocephalus
townsendi
Threatened
Mammals
Range
Sei Whale
Balaenoptera
borealis
Endangered
Mammals
Range
Blue Whale
Balaenoptera
musculus
Endangered
Mammals
Range
Fin Whale
Balaenoptera
physalus
Endangered
Mammals
Range
Rice's Whale
Balaenoptera ricei
Gulf of Mexico
Endangered
Mammals
Range
Oceanic Whitetip
Shark
Carcharhinus
longimanus
Threatened
Fishes
Both
Loggerhead Sea
Turtle
Caretta caretta
Northwest Atlantic
Ocean
Endangered
Reptiles
Range
Loggerhead Sea
Turtle
Caretta caretta
North Pacific Ocean
Endangered
Reptiles
Range
Green Sea Turtle
Chelonia mydas
North Atlantic
Threatened
Reptiles
Range
Green Sea Turtle
Chelonia mydas
East Pacific
Threatened
Reptiles
222
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Both
Leatherback Sea
Turtle
Dermochelys
coriacea
Endangered
Reptiles
Range
Hawskbill Sea
Turtle
Eretmochelys
imbricata
Endangered
Reptiles
Both
North Atlantic Right
Whale
Eubalaena glacialis
Endangered
Mammals
Range
North Pacific Right
Whale
Eubalaena japonica
Endangered
Mammals
Both
Steller Sea Lion
Eumetopias jubatus
Western
Endangered
Mammals
Both
Abalone, black
Haliotis cracherodii
Endangered
Snails
Range
Kemp's Ridley Sea
Turtle
Lepidochelys kempii
Endangered
Reptiles
Range
Olive Ridley Sea
Turtle
Lepidochelys olivacea
All other areas
Threatened
Reptiles
Range
Olive Ridley Sea
Turtle
Lepidochelys olivacea
Mexico's Pacific
coast breeding
colonies
Endangered
Reptiles
Range
Giant Manta Ray
Manta birostris
Threatened
Fishes
Both
Humpback Whale
Megaptera
novaeangliae
Central America
Endangered
Mammals
Both
Humpback Whale
Megaptera
novaeangliae
Mexico
Threatened
Mammals
Range
Humpback Whale
Megaptera
novaeangliae
Western North
Pacific
Endangered
Mammals
Both
Coho Salmon
Oncorhynchus
(=Salmo) kisutch
Central California
coast
Endangered
Fishes
Both
Coho Salmon
Oncorhynchus
(=Salmo) kisutch
Lower Columbia
River
Threatened
Fishes
Both
Coho Salmon
Oncorhynchus
(=Salmo) kisutch
Oregon coast
Threatened
Fishes
Both
Coho Salmon
Oncorhynchus
(=Salmo) kisutch
Southern Oregon &
Northern California
coasts (SONCC)
Threatened
Fishes
Both
Steelhead
Oncorhynchus
(=Salmo) mykiss
California Central
Valley
Threatened
Fishes
Both
Steelhead
Oncorhynchus
(=Salmo) mykiss
Central California
coast
Threatened
Fishes
Both
Steelhead
Oncorhynchus
(=Salmo) mykiss
Lower Columbia
River
Threatened
Fishes
223
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Both
Steelhead
Oncorhynchus
(=Salmo) mykiss
Middle Columbia
River
Threatened
Fishes
Both
Steelhead
Oncorhynchus
(=Salmo) mykiss
Northern California
Threatened
Fishes
Both
Steelhead
Oncorhynchus
(=Salmo) mykiss
Puget Sound
Threatened
Fishes
Both
Steelhead
Oncorhynchus
(=Salmo) mykiss
Snake River Basin
Threatened
Fishes
Both
Steelhead
Oncorhynchus
(=Salmo) mykiss
South-Central
California coast
Threatened
Fishes
Both
Steelhead
Oncorhynchus
(=Salmo) mykiss
Southern California
Endangered
Fishes
Both
Steelhead
Oncorhynchus
(=Salmo) mykiss
Upper Columbia
River
Threatened
Fishes
Both
Steelhead
Oncorhynchus
(=Salmo) mykiss
Upper Willamette
River
Threatened
Fishes
Both
Salmon, sockeye
Oncorhynchus
(=Salmo) nerka
Ozette Lake
Threatened
Fishes
Both
Salmon, sockeye
Oncorhynchus
(=Salmo) nerka
Snake River
Endangered
Fishes
Both
Chinook Salmon
Oncorhynchus
(=Salmo)
tshawytscha
California coastal
Threatened
Fishes
Both
Chinook Salmon
Oncorhynchus
(=Salmo)
tshawytscha
Central Valley
spring-run
Threatened
Fishes
Both
Chinook Salmon
Oncorhynchus
(=Salmo)
tshawytscha
Lower Columbia
River
Threatened
Fishes
Both
Chinook Salmon
Oncorhynchus
(=Salmo)
tshawytscha
Puget Sound
Threatened
Fishes
Both
Chinook Salmon
Oncorhynchus
(=Salmo)
tshawytscha
Sacramento River
winter-run
Endangered
Fishes
Both
Chinook Salmon
Oncorhynchus
(=Salmo)
tshawytscha
Snake River fall-run
Threatened
Fishes
Both
Chinook Salmon
Oncorhynchus
(=Salmo)
tshawytscha
Snake River
spring/summer-run
Threatened
Fishes
Both
Chinook Salmon
Oncorhynchus
(=Salmo)
tshawytscha
Upper Columbia
River spring-run
Endangered
Fishes
224
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Both
Chinook Salmon
Oncorhynchus
(=Salmo)
tshawytscha
Upper Willamette
River
Threatened
Fishes
Both
Chum Salmon
Oncorhynchus keta
Columbia River
Threatened
Fishes
Both
Chum Salmon
Oncorhynchus keta
Hood Canal summer-
run
Threatened
Fishes
Both
Coral, lobed star
Orbicella annularis
Threatened
Corals
Both
Coral, mountainous
star
Orbicella faveolata
Threatened
Corals
Both
Boulder star coral
Orbicella franksi
Threatened
Corals
Both
Whale, killer
Orcinus orca
Southern Resident
Endangered
Mammals
Range
Sperm Whale
Physeter
macrocephalus (=
catodon)
Endangered
Mammals
Range
Smalltooth sawfish
Pristis pectinata
U.S. portion of range
Endangered
Fishes
Range
False Killer Whale
Pseudorca crassidens
Main Hawaiian
Islands Insular
Endangered
Mammals
Range
Sunflower sea star
Pycnopodia
helianthoides
Proposed
Threatened
Echinoderms
Both
Salmon, Atlantic
Salmo salar
Gulf of Maine
Endangered
Fishes
Both
Bocaccio
Sebastes paucispinis
Puget Sound/ Georgia
Basin
Endangered
Fishes
Both
Rockfish, yelloweye
Sebastes ruberrimus
Puget Sound/ Georgia
Basin
Threatened
Fishes
Range
Scalloped
Hammerhead
Sphyrna lewini
Central & Southwest
Atlantic
Threatened
Fishes
Both
Eulachon
Thaleichthys
pacificus
Southern
Threatened
Fishes
Another way to group species is by type of aquatic ecosystem(s) in which they reside.
NMFS populations can be found in one or more of the following: offshore marine ecosystems,
coastal ecosystems, and inland aquatic ecosystems (e.g., in the case of migratory salmonid
species that spawn in headwaters of rivers and streams). As discussed in Section V of this
Biological Evaluation, EPA concludes that potential effects would be insignificant or
discountable for most of the offshore and/or coastal NMFS populations which include the
225
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following: Sei Whale, Rice's Whale Gulf of Mexico population, Blue Whale, Finback Whale, all
listed Humpback Whale DPSs, Sperm Whale, the North Atlantic Right Whale, North Pacific
Right Whale, False Killer Whale Main Hawaiian Islands Insular population). Olive Ridley Sea
Turtle (Mexico's Pacific coast breeding colonies and all other areas populations), Leatherback
Sea Turtle, Loggerhead Sea Turtle (Northwest Atlantic Ocean and North Pacific Ocean
populations), Green Sea Turtle (North Atlantic and East Pacific populations), Hawksbill Sea
Turtle, Kemp's Ridley Sea Turtle, Elkhorn Coral, Lobed Star Coral, Mountainous Star Coral,
Boulder Star Coral, Steller Sea lion (Western population), Guadalupe Fur Seal, Oceanic Whitetip
Shark, Scalloped Hammerhead Shark (Central & Southwest Atlantic population), and Giant
Manta Ray. These species were not assessed in the worst-case scenario potential land use
impacts analyses described in Section VII. The Southern Resident Killer Whale was also not
included in those analyses but must be considered separately as it depends on Chinook and other
salmon populations that may be affected by the action. We discuss this in more detail in later
paragraphs.
After making these conclusions for the above 40 populations, in Sections VILA and
VII.B of this Biological Evaluation we evaluated the potential land use impacts that could occur
within ranges and critical habitats of the remaining 43 NMFS populations. These assessments
represented a worst-case scenario due to a variety of conservative assumptions we made in
attributing potential land use impacts to the RFS Set Rule alone. In the same sections, we
provided detailed information on many of these listed NMFS populations, including information
on where they are found, what they are threatened by, and life history traits.
38 of the 40 populations belong to the fish taxonomic group. The remaining two species
are an echinoderm (Sunflower sea star) and a snail (Black Abalone). The mechanism through
which the RFS Rule may impact these species is through water quality effects from
intensification and extensification of agriculture. Pesticide exposure can lead to toxicity effects
in species and increases in nutrient and sediment deposition can alter their ecosystems and
habitat as well as PBFs. PBFs for these aquatic species would be similar to the PBFs for FWS
fish, clams, and crustaceans as discussed previously including clean and well-oxygenated water;
creeks and streams with low turbidity and riffles, pools, and runs; riparian tree cover to provide
shade and protect species from the heat; an abundant source of food and absence of invasive
species; in addition to other essential physical, geomorphological, and biological features.
Additionally, as discussed in the water quality section (Section VIII), potential concentrations of
pollutants would be highest nearest to edge of field. While it is not feasible to determine the
magnitude of localized pollutant concentrations associated with potential land use changes
resulting from the RFS Set Rule, it is important to assess the potential impacts on these
populations which may occur in areas that are near or within watersheds where agricultural
impacts could occur.
Recognizing that these 40 NMFS populations are already exposed to pollution from
existing cropland within their critical habitat and/or range, EPA worked with NMFS on an
additional analysis to better understand the potential effects. Using the conservative 90%
percentile acreage increase results from the probabilistic analysis described in Section VILA, in
addition to the number of acres of corn already existing within species' critical habitats and
226
-------
ranges, we calculated the potential percent increase in corn acreage within species' critical
habitat or range. It is important to note that there are several assumptions in this analysis. First,
the corn acreage numbers that were used as the existing baseline for comparison came from a
Biological Opinion (BiOP) thatNMFS developed for another federal action; the corn acreage
numbers were acquired from CDL data from years 2013-2017 which likely do not accurately
represent current conditions as they were from 6+ years ago. In addition, since the release of the
BiOp, the critical habitat and range GIS layers for many NMFS species may have been updated
and therefore it is likely that the total corn acreage numbers changed as well with the change of
those boundaries. Nonetheless, we believe that the BiOp corn acreage numbers can serve as a
ballpark estimate for the purposes of this analysis. Another assumption is that the acreage
increases from the probabilistic analysis would represent corn cropland only, when in reality as
discussed in Section VILA they could represent other crops. Finally, it is important to recognize
that the baseline corn acreage numbers came from CDL data which, as was also discussed
previously in this Biological Evaluation, does not always accurately capture land cover data. The
overall accuracy for CDL cropland classification is around 90% for corn (USDA-NASS, 2021).
The results of this additional analysis help to provide some understanding of potential impacts,
but due to such assumptions and uncertainties they they are used qualitatively. For example, to
assess the extent to which the range of a species will see expansion of corn. This informs how 1)
the potential for an individual to be in close proximity to new corn cropland and 2) the potential
magnitude of changes in pollutant concentrations at a larger scale (e.g. away from the converted
cropland).
The results from this additional analysis are shown in Table IX.C-2 and Table IX.C-3.
Table IX.C-2 shows the results for critical habitat without a buffer (SI or scenario 1) and critical
habitat with a buffer (S2 or scenario 2). Table IX.C-3 shows the results based on the probabilistic
analysis for range without a buffer (S3 or scenario 3) and with a buffer (S4 or scenario 4).
Overall, across all scenarios, the increase in percentage of corn cropland was very small relative
to the baseline, ranging from an increase of 0.001 to 0.04 percent. On average, the change in
percentage before and after the land use probabilistic analysis was 0.016 percent.
Table IX.C-2. Critical habitat results from the additional qualitative analysis. For each
NMFS population with critical habitat, the percent increase in corn acreage was calculated for
scenario 1 (without a buffer) and scenario 2 (with a buffer) based on total acres of critical habitat,
an estimate number for existing corn acres in critical habitat, and 90th percentile acreage impact
results from the corn ethanol probabilistic analysis.
Common
Population
Scientific Name
Total CH
Corn
Scenario 1
Scenario 2
Scenario 1
Scenario
Name
Acres
Acres
Acres
Acres
Percent
2 Percent
inCH
Impacted
(90th
percentile)
Impacted
(90th
percentile)
Increase
Increase
Atlantic
None
Acipenser
7843992.54
106034
1620
1950
0.021
0.025
sturgeon
(Gulf
subspecies)
oxyrinchus
(=oxyrhynchus)
desotoi
Chum
Hood Canal
Oncorhynchus
610912.59
578
60
60
0.010
0.010
salmon
summer-ran
keta
Chinook
Central
Oncorhynchus
3486524.67
136407
420
420
0.012
0.012
salmon
Valley
spring-run
(=Salmo)
tshawytscha
227
-------
Steelhead
Upper
Columbia
River
Oncorhynchus
(=Salmo) mykiss
7051473.74
180157
2490
2700
0.035
0.038
Chinook
salmon
Snake River
spring/
summer-run
Oncorhynchus
(=Salmo)
tshawytscha
13821589.59
20923
1920
2010
0.014
0.015
green
sturgeon
Southern
Acipenser
medirostris
13042187.39
271003
630
660
0.005
0.005
Steelhead
Upper
Willamette
River
Oncorhynchus
(=Salmo) mykiss
3301904.46
46196
930
1020
0.028
0.031
Steelhead
California
Central
Valley
Oncorhynchus
(=Salmo) mykiss
5837579.79
388644
660
750
0.011
0.013
Chinook
salmon
Snake River
fall-run
Oncorhynchus
(=Salmo)
tshawytscha
5653843.90
144469
2160
2280
0.038
0.040
Chinook
salmon
Puget Sound
Oncorhynchus
(=Salmo)
tshawytscha
4328340.70
56470
540
570
0.012
0.013
Chum
salmon
Columbia
River
Oncorhynchus
keta
1954501.64
11773
300
330
0.015
0.017
Coho salmon
Oregon
coast
Oncorhynchus
(=Salmo) kisutch
6213617.80
2644
150
150
0.002
0.002
Steelhead
Middle
Columbia
River
Oncorhynchus
(=Salmo) mykiss
14566302.72
210326
2130
2280
0.015
0.016
Steelhead
Lower
Columbia
River
Oncorhynchus
(=Salmo) mykiss
4158668.06
11796
330
330
0.008
0.008
Chinook
salmon
Upper
Columbia
River
spring-run
Oncorhynchus
(=Salmo)
tshawytscha
5976906.82
163153
1800
1920
0.030
0.032
Chinook
salmon
Lower
Columbia
River
Oncorhynchus
(=Salmo)
tshawytscha
3642262.09
11898
330
330
0.009
0.009
Chinook
salmon
Sacramento
River
winter-run
Oncorhynchus
(=Salmo)
tshawytscha
1551693.99
106009
240
300
0.015
0.019
Steelhead
Snake River
Basin
Oncorhynchus
(=Salmo) mykiss
20160639.13
145663
3300
3450
0.016
0.017
Chinook
salmon
Upper
Willamette
River
Oncorhynchus
(=Salmo)
tshawytscha
4581901.04
46092
1050
1110
0.023
0.024
Sockeye
salmon
Snake River
Oncorhynchus
(=Salmo) nerka
6528151.82
26006
1680
1710
0.026
0.026
Coho salmon
Lower
Columbia
River
Oncorhynchus
(=Salmo) kisutch
4574604.36
12067
360
330
0.008
0.007
Steelhead
Puget Sound
Oncorhynchus
(=Salmo) mykiss
6010259.79
70182
690
720
0.011
0.012
Atlantic
salmon
Gulf of
Maine
Salmo salar
513271.64
1059
30
30
0.006
0.006
Bocaccio
Puget
Sound/
Georgia
Basin
Sebastes
paucispinis
1373482.37
10615
180
240
0.013
0.017
Eulachon
Southern
Thaleichthys
pacificus
1555543.65
10922
240
240
0.015
0.015
yelloweye
rockfish
Puget
Sound/
Georgia
Basin
Sebastes
ruberrimus
1242811.97
10584
210
240
0.017
0.019
Atlantic
sturgeon
Gulf of
Maine
Acipenser
oxyrinchus
oxyrinchus
925163.49
5785
210
240
0.023
0.026
228
-------
Atlantic
sturgeon
New York
Bight
Acipenser
oxyrinchus
oxyrinchus
2639906.10
108053
450
510
0.017
0.019
Atlantic
sturgeon
Chesapeake
Bay
Acipenser
oxyrinchus
oxyrinchus
2925765.34
288926
480
540
0.016
0.018
Atlantic
sturgeon
Carolina
Acipenser
oxyrinchus
oxyrinchus
5892363.45
534326
1200
1350
0.020
0.023
Atlantic
sturgeon
South
Atlantic
Acipenser
oxyrinchus
oxyrinchus
9789946.26
312303
2640
2910
0.027
0.030
Table IX.C-3. Range results from the additional qualitative analysis. For each NMFS
population with range, the percent increase in corn acreage was calculated for scenario 3
(without a buffer) and scenario 4 (with a buffer) based on total acres of range, an estimate
number for existing corn acres in critical habitat, and 90th percentile acreage impact results from
the corn et
ianol probabi
istic analysis.
Common
Name
Population
Scientific Name
Total Range
Acres
Corn
Acres in
Range
Scenario 3
Acres
Impacted
(90th
percentile)
Scenario 4
Acres
Impacted
(90th
percentile)
Scenario
3 Percent
Increase
Scenario
4
Percent
Increase
Atlantic
sturgeon
(Gulf
subspecies)
None
Acipenser
oxyrinchus
(=oxyrhynchus)
desotoi
8562606.84
31018
300
360
0.004
0.004
Chum
salmon
Hood Canal
summer-run
Oncorhynchus keta
687019.44
580
90
90
0.013
0.013
Chinook
salmon
Central Valley
spring-run
Oncorhynchus
(=Salmo)
tshawytscha
12522233.53
664302
1440
1470
0.011
0.012
Steelhead
Upper Columbia
River
Oncorhynchus
(=Salmo) mykiss
7514261.89
187957
2550
2820
0.034
0.038
Chinook
salmon
Snake River
spring/ summer-
run
Oncorhynchus
(=Salmo)
tshawytscha
16568922.70
139960
2640
2790
0.016
0.017
Steelhead
Central
California coast
Oncorhynchus
(=Salmo) mykiss
3806847.33
1502
30
30
0.001
0.001
green
sturgeon
Southern
Acipenser
medirostris
18132188.78
299167
870
930
0.005
0.005
Steelhead
Upper
Willamette
River
Oncorhynchus
(=Salmo) mykiss
4294283.77
54429
1230
1320
0.029
0.031
Steelhead
California
Central Valley
Oncorhynchus
(=Salmo) mykiss
14686561.35
711933
1770
1770
0.012
0.012
Chinook
salmon
Snake River
fall-run
Oncorhynchus
(=Salmo)
tshawytscha
6388833.65
144474
2340
2550
0.037
0.040
Shortnose
sturgeon
None
Acipenser
brevirostrum
32552627.77
1598998
4290
4830
0.013
0.015
Chinook
salmon
Puget Sound
Oncorhynchus
(=Salmo)
tshawytscha
6024710.96
76940
660
690
0.011
0.011
Smalltooth
sawfish
U.S. portion of
range
Pristis pectinata
13284212.53
4295
90
90
0.001
0.001
Chum
salmon
Columbia River
Oncorhynchus keta
3079569.86
12002
330
330
0.011
0.011
Coho
salmon
Oregon coast
Oncorhynchus
(=Salmo) kisutch
6464676.43
2645
150
150
0.002
0.002
Steelhead
Middle
Columbia River
Oncorhynchus
(=Salmo) mykiss
17284614.97
247425
2760
2850
0.016
0.016
Steelhead
Lower
Columbia River
Oncorhynchus
(=Salmo) mykiss
4412124.01
12051
330
360
0.007
0.008
229
-------
Coho
salmon
Southern
Oregon &
Northern
California coasts
(SONCC)
Oncorhynchus
(=Salmo) kisutch
11816475.29
1803
120
120
0.001
0.001
Chinook
salmon
Upper Columbia
River spring-run
Oncorhynchus
(=Salmo)
tshawytscha
6941669.66
165202
2460
2640
0.035
0.038
Chinook
salmon
Lower
Columbia River
Oncorhynchus
(=Salmo)
tshawytscha
4764245.77
12074
360
330
0.008
0.007
Chinook
salmon
Sacramento
River winter-run
Oncorhynchus
(=Salmo)
tshawytscha
3489726.96
144686
480
480
0.014
0.014
Steelhead
Snake River
Basin
Oncorhynchus
(=Salmo) mykiss
20970139.13
145729
3510
3630
0.017
0.017
Chinook
salmon
Upper
Willamette
River
Oncorhynchus
(=Salmo)
tshawytscha
5669747.42
51384
1230
1320
0.022
0.023
Sockeye
salmon
Snake River
Oncorhynchus
(=Salmo) nerka
6561935.30
144671
1890
2070
0.029
0.032
Coho
salmon
Lower
Columbia River
Oncorhynchus
(=Salmo) kisutch
4666311.14
12073
360
360
0.008
0.008
Steelhead
Puget Sound
Oncorhynchus
(=Salmo) mykiss
6834219.44
77558
720
720
0.011
0.011
Atlantic
salmon
Gulf of Maine
Salmo salar
11295748.00
2090
60
330
0.002
0.003
Bocaccio
Puget Sound/
Georgia Basin
Sebastes paucispinis
3162389.10
33648
300
60
0.020
0.002
Eulachon
Southern
Thaleichthys
pacificus
1512022.74
10922
240
330
0.015
0.022
yelloweye
rockfish
Puget Sound/
Georgia Basin
Sebastes ruberrimus
1555543.65
33648
270
270
0.018
0.017
Atlantic
sturgeon
Gulf of Maine
Acipenser
oxyrinchus
oxyrinchus
1512022.74
12199
300
330
0.004
0.022
Atlantic
sturgeon
New York Bight
Acipenser
oxyrinchus
oxyrinchus
8353481.66
292589
1080
330
0.010
0.004
Atlantic
sturgeon
Chesapeake Bay
Acipenser
oxyrinchus
oxyrinchus
10926367.48
1067249
1380
1170
0.014
0.011
Atlantic
sturgeon
Carolina
Acipenser
oxyrinchus
oxyrinchus
10171166.55
851265
1710
1470
0.015
0.014
Atlantic
sturgeon
South Atlantic
Acipenser
oxyrinchus
oxyrinchus
11511799.75
321427
2850
1890
0.023
0.016
Sunflower
sea star
Pychnopodia
helianthoides
12323258.02
39189
300
3030
0.003
0.025
Because these numbers are very small, and they represent potential land use effects of the
action based on a worst-case scenario, EPA anticipates that potential effects of the RFS Set Rule
on all these species are discountable. Since this list of species includes the salmon that the
Southern resident killer whale depends on, we also conclude that the Southern resident killer
whale would experience discountable effects in regards to prey availability and otherwise
insignificant effects as described in Section X for some marine species that reside in coastal
regions.
230
-------
\. ( oiuiusions
Based on the analyses presented in this Biological Evaluation, we find that the
Renewable Fuel Standard (RFS) Program: Standards for 2023-2025 and Other Changes, or the
"Set Rule," is not likely to adversely affect listed species or critical habitat. The primary
mechanism through which this rule is expected to impact listed species is through establishing
volume requirements for the use of various types of renewable fuels, thus increasing demand for
these renewable fuels. For non-crop-based biofuels, such as CNG/LNG derived from biogas and
biodiesel and renewable diesel produced from waste fats, oils and greases, we determined there
would be no effect on listed species and critical habitat. In addition, this action implements
regulatory changes that will not impact the volumes of renewable fuel and will also not impact
listed species or critical habitat because they are administrative in nature.
For crop-based biofuels such as corn ethanol, soybean biodiesel, and canola biodiesel, we
determined that the increased consumption and production may affect listed species. Ultimately,
the increase in demand for feedstocks used to produce renewable fuels could potentially lead to
an increase in the amount of land used to produce these crops. Listed species could potentially be
impacted by loss of PBFs in critical habitat or loss of range to cropland, or by water quality
impacts from increased loads of fertilizers and pesticides. In this Biological Evaluation, we
identified the action area where these impacts could occur and found that 810 unique populations
may be impacted by the action.
However, our analyses conclude that impacts from the RFS Set Rule, if any, would be
insignificant and/or discountable. First and foremost, we reach this conclusion because of various
uncertainties and complex causal chain of steps that occur in-between EPA setting the RFS
volume requirements to potential on-the-ground land use changes (Figure 1 in Appendix A).
In this Biological Evaluation, we first projected the degree to which the Set Rule might
increase the consumption of renewable fuels in 2023-2025 relative to a scenario where there
were no RFS volume requirements for these three years.42 We assumed (conservatively) that the
entire increase in renewable fuel consumption attributable to the Set Rule would result in a
corresponding increase in domestic biofuel production.
After projecting the potential increase in biofuel production, we next projected the
potential impact of the increased demand for feedstocks used to produce these biofuels on crop
production. Where possible, we relied upon the best available science and data (e.g., published
literature or assessments completed in the context of other RFS actions) to inform our estimates.
The changes in land use potentially attributable to the Set Rule, briefly described above
and presented in more detail in Section VI, represent our best estimates using the available data.
There is, however, a considerable degree of uncertainty associated with these estimates. For
example, there is uncertainty associated with estimating the volume biofuel consumption that can
be attributed to the RFS program generally, and to the Set Rule in particular. We cannot predict
42
While the analysis was initially performed based on the proposed applicable volumes, for the reasons described in
Section IV.A.2, the analysis is still appropriate.
231
-------
with certainty which biofuels will be used to meet the broad RFS volume requirements in 2023-
2025, and it is even more difficult to project the quantity of these fuels that would be used in the
absence of the RFS program. The use of compliance flexibilities under the RFS program, in
particular carryover RINs and deficit carryovers, also introduce uncertainty into the volume of
renewable fuel that will be consumed in the future.
There is also considerable uncertainty in the relationship between biofuel consumption
and biofuel production in the U.S. The RFS program is designed to ensure a minimum volume of
biofuel consumption, but it does not directly regulate domestic biofuel production or limit it to
U.S. production. For example, ethanol producers can, and typically do, produce higher volumes
than can be consumed domestically, with the excess ethanol being exported. While we have
estimated the volume of biofuel consumption that can potentially be attributed to the Set Rule, it
is considerably more difficult to determine how this volume of biofuel consumption influences
domestic biofuel production since an increase in biofuel consumption can also be met with a
decrease in biofuel exports and/or an increase in biofuel imports. For the purposes of our
analyses, we have made the conservative assumption that every gallon of biofuel consumption
attributable to the Set Rule corresponds to one gallon of additional domestic production of
biofuel, but in fact the actual impact on biofuel production is likely to be smaller, and could in
fact be zero, as the market adjusts import and export volumes in response to changes in domestic
biofuel demand.
A similar dynamic is at play in the relationship between domestic biofuel production and
domestic production of the crops used to produce biofuels, adding even more uncertainty to the
analyses. The corn, soybean oil, and canola oil used to produce the volumes of renewable fuel
that we estimate are potentially attributable to the Set Rule can come from several sources. If the
necessary feedstocks result in increased corn, soybean, and canola plantings, this could have
direct implications for listed species or critical habitat. However, the necessary feedstocks could
also derive from a reduction in exports or a diversion of these feedstocks away from food and
feed markets. In both cases, there may be no change in crop plantings and thus no direct impact
on species or habitat, though there might still be some indirect effects on total cropland as
markets shift to accommodate the change in the use of these feedstocks.
In general, we have made conservative assumptions in our projection of the amount of
land use change potentially attributable to the Set Rule (e.g., assumptions that would lead to
higher projections of land use change). We believe this is appropriate in the context of this
Biological Evaluation, as we consider the potential impacts of this rule on listed species.
However, we note that the consistent use of these conservative assumptions in the many steps of
this analysis compound on each other to likely result in an over-projection of land use change
potentially attributable to the Set Rule.
In order to assess the potential impacts on listed species and critical habitat, it is
important to know geographically where land use changes from the RFS Set Rule, if any, could
occur. Potential land use changes from the RFS Set rule can affect the PBFs of listed species
found within their critical habitat (e.g., by affecting their prey or pollinators). Land use changes
can also contribute to species' exposure to pollution from nutrient, sediment, and pesticide
runoff. However, any potential land use changes from the Set Rule would occur at a very small
232
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scale and local level across a very large geographical area that represents available land for
conversion within the action area. As there are many factors that drive agricultural growth, the
location of such changes attributable to the RFS Set Rule alone is very challenging to assess and
determine with any certainty.
Nevertheless, we attempted to identify potential locations of land use change. To assess
potential impacts from soybean biodiesel, a contractor to EPA developed a soybean-specific land
selection model. We used the two scenarios closest to the maximum projected acreage changes
from increases in soybean biodiesel from the RFS Set Rule (1.9 million acres). Interestingly, the
contractor found the biofuel demand for these two scenarios could be met by projected soybean
yields on existing soybean acres, highlighting again the complexity of various factors that
influence biofuel production, and that the RFS Set Rule could in fact lead to zero acres being
converted for agricultural purposes. Still, we conservatively assumed that demands would be met
by newly converted soybean acres and used their modeled expansion areas to assess potential
impacts to species.
We used a different approach to assess impacts from increases in corn ethanol and canola
biodiesel. Unlike the case for soybean oil which is expected to result in increased soybean
plantings, the increase in demand for corn and canola oil suggest that the new cropland will not
be limited to these crops. We developed a probabilistic approach to select available lands for
conversion within the action area and repeated the process 100 to 500 times to generate an
estimated probability that any given acre of land would be converted. For corn ethanol this was
applied to the area of potential land use change within the action area and for canola biodiesel we
limited the analysis to North Dakota since previous modeling work suggests that most changes
could occur in that state.
Although we separately assessed impacts on listed species and designated critical habitat
from potential increases in corn ethanol (Section VILA), soybean biodiesel (Section VII.B), and
canola biodiesel (Section VII.C), in Section VII.D we show the total potential impacts from all
three analyses combined. In no particular order, the following FWS species were found to
experience higher potential acreage impacts to their critical habitat and/or range relative to all
other species: the Salt Creek Tiger beetle, Kentuck glade cress, Poweshiek skipperling, Dakota
Skipper, Slender chub, Braun's rock-cress, Topeka shiner, St. Francis River Crayfish, Big Creek
Crayfish, Piping Plover, Fleshy-fruit gladecress, Slenderclaw crayfish, Devils River minnow,
Slackwater darter, Roswell springsnail, False spike, Texas fawnsfoot, Guadalupe Orb, Neosho
madtom, White catspaw, Neosho mucket, Illinois cave amphipod, Mead's milkweed, Virginia
round-leaf birch, and Rabbitsfoot.
With regard to critical habitat alone (i.e., no buffer), the maximum potential impacts
occurred to the Salt Creek Tiger beetle at 4.62% overlap between the critical habitat and land
potentially converted due to the Set Rule. With regard to range alone (i.e., no buffer), the
maximum potential impacts occurred to the Neosho madtom at 13.67% overlap. We estimated
that only 7 species would have greater than 1% of their critical habitat converted to cropland (38
species had greater than 1% of critical habitat plus buffer converted) and 15 species would have
greater than 1% of their range converted (14 species had greater than 1% of their range plus
buffer converted).
233
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Additionally, we considered essential PBFs/PCEs of critical habitat and species taxa
information such as feeding, survival, and reproduction needs and strategies in Section IX. In the
case of the Salt Creek Tiger beetle's critical habitat as an example, the species is found in a very
small area (1,100 acres) north of Lincoln, Nebraska. It includes saline wetlands and streams that
are fed by groundwater discharge originating from Pennsylvanian and/or Permian formations as
it passes through a salt source (79 FR 26014, 2014). Although this area is classified as available
land in our analyses, we cannot determine with reasonable certainty that agricultural growth
attributable to the Set Rule would occur in or near this critical habitat. Again, there are many
other factors, beyond the RFS program, that influence biofuel production and land use change.
Therefore, it is possible that the RFS Set Rule alone won't contribute to any future land use or
water quality changes in or around Lincoln, Nebraska, or indeed anywhere at all. We therefore
determine that effects, if any, would not likely adversely affect listed species or critical habitat.
We also assessed the potential water quality impacts that may occur at larger regional
scales due to smaller cumulative water quality impacts across the action area. We primarily
relied on published literature that used the SWAT to estimate the water quality impacts from
observed increases in cropland in the Missouri River basin from 2008-2016 (Chen et al., 2021).
We found that modeled increases in total nitrogen and phosphorus would represent increases of
approximately 0.8% and 2.1% respectively at the Mississippi River outlet if we conservatively
assume that the modeled increase in nitrogen and phosphorus at the mouth of the Missouri River
is equal to the increase in nitrogen and phosphorus at the Mississippi River outlet. We expect
that the increases in nitrogen and phosphorus from new cropland potentially attributable to the
Set Rule would be similar to these SWAT results. We also estimated that any increase in
pesticides in aquatic environments would be approximately equal to the potential increases in
nitrogen and phosphorus projected using SWAT.
With regard to coastal and marine species, such as the NMFS species identified in the
action area, the potential water quality impacts from the RFS Set Rule would be either
discountable (for offshore species) or undetectable and not measurable relative to baseline
conditions and would not rise to the level of take. The latter would likely be the case for potential
effects in estuarine and coastal regions found in the action area, though we expect that
downstream impacts, if they were to occur, would mostly take place in the Gulf of Mexico
region. Potential effects from the action would be discountable or insignificant for species that
live offshore as well as species that occur along the coast in more shallow waters. Potential
effects on NMFS species that migrate to headwaters of streams and rivers for spawning (e.g.,
salmonids) would also be discountable, as supported by an additional analysis EPA completed
that demonstrated very small percentage increases in total corn acreage within those species'
critical habitats and ranges.
Furthermore, we note that EPA currently has several programs and funding opportunities
designed to improve water quality. We would therefore expect that these ongoing efforts,
discussed further in Section VIII.B, would reduce any water quality impacts of increased
cropland potentially attributable to the Set Rule.
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In summary, our Biological Evaluation finds that the 810 populations with critical
habitats and/or ranges within the action area may be affected. The potential impacts, however,
would be insignificant or discountable and therefore we determine that the effects from the RFS
Set Rule are not likely to adversely affect species and critical habitats.
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Appendix A. Overview of how the RFS program could affect listed
species and critical habitat via land use change
The RFS program does not directly affect land use, listed species, or critical habitat.
Instead, there is a multi-step causal chain between the standards and potential land use changes
resulting from production of crops that involves several layers of third parties who are not
subject to the RFS standards. The diagram below shows this causal chain.
Figure A-l: Causal chain between RFS standards and impacts on species and habitat
RFS standards
Refiner access to renew ablefuel
Relatwe costs between different renew able fuels
Refiner decisions about the m ix of biofueltypes and/or RINs
needed to meet the RFS standards
Avalabiity.type, and price of RINs
Relative retail price of fossil-based gasoline
and diesel versusrenewablefuel
Inf rastructur eto su pp or t d istr ibut ion, b le nd ing,
dispensing, and consumption of renew able fuel
T otal co nsu m pt ion of r enewab lef u el in th e U. S
Size of carryover R IN bank
Other state and federal programs
th at r equ ire ren ew abtef uels
Federal and state tax incentives and grants
Statutory and rqjulatory constraints on renewable
fuels blended hto transportation fuels
Consumer atttudes and preferences
Importsand exportsof re new able fuel
Total production of renew able fuel in the U.S.
Production of non-crop-basedfeed stocks
for r enewab lef u el production
Production of crop-based feedstocks
for r enew ab lef u el pr od uct bn
Importsand exportsof crops
Total production of crops
Domestic ren ew able fuel production capacity
Crop production for human
consumption andanimalfeed
Extenstficatbn vs intensification
Suitability of land for grow ing crops
Alternative usesfor land
Land used to grow crops
Conservation Research Program
8
Impactson species and habitat
Stage 1: RFS standards
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Stage 1: RFS standards
The applicable percentage standards under the RFS program provide the means through
which each individual refiner determines its Renewable Volume Obligations (RVO). The RVOs
represent the unique volume of renewable fuel that each refiner is responsible for ensuring is
blended into gasoline and diesel. There are four separate RVOs as shown in Table II.A-1.
Refiners can either blend renewable fuel into the gasoline and diesel they produce, or can
purchase credits (Renewable Identification Numbers or RINs) from other parties who have done
such blending.
Stage 2: Refiner decisions
While refiners must ensure that the renewable fuel volumes identified by their individual
RVOs are used as transportation fuel, they are not required to use any particular type of
renewable fuel. Insofar as a refiner is blending renewable fuel into its own gasoline and
diesel, it can choose the mix of renewable fuel types it uses. Generally, refiners would be
expected to do so in a way that minimizes overall costs, and this in turn is a function of the
renewable fuels available to it and their relative costs, the relative amounts of gasoline and diesel
that it produces, the equipment it has available to manage the production, storage, and blending
of renewable fuel, and the demand for (or tolerance of) renewable fuels in the refiner's marketing
area.
To the degree that a refiner chooses to purchase RINs instead of blending renewable fuel
into its own gasoline and diesel (many refiners are in this position, as some or even all of the fuel
they produce is sold to others for subsequent blending), however, the refiner has little control
over the mix of renewable fuels used as transportation fuel. RINs are not specific to fuel type and
feedstock, but instead are designated only as qualifying for one (or more) of the four categories
shown in Table II.A-1. Renewable fuel producers decide what renewable fuels to produce and
from what feedstocks based on market demand. Parties downstream of the refiner make
decisions about what specific types of renewable fuels are blended into gasoline or diesel or are
otherwise used as transportation fuel, and make the RINs associated with that renewable fuel
available for sale to refiners. Consumers ultimately make the fuel purchase decisions for the
fuels and the renewable fuels they contain. As for refiners, all parties would be expected to make
decisions that maximize profit potential and/or minimize cost.
Stage 3: Total consumption of renewable fuel
While the RFS program requires minimum volumes of renewable fuel to be used in the
transportation sector, actual total consumption of renewable fuel can and in some cases has been
higher under appropriate economic circumstances. The total volume of renewable fuel
consumed in the U.S. includes some that is used outside of the transportation sector and which,
as a result, does not qualify under the RFS program. Finally, the total volume of renewable fuel
produced in the U.S. includes volumes that are exported and consumed outside of the U.S.,
which again does not qualify under the RFS program.
There are a number of other state and federal programs that also require or incentivize the
use of renewable fuel confounding attempts to assess the impacts of the RFS program alone. For
instance, Minnesota requires that diesel fuel contain an average of 11% biodiesel,
while California's Low Carbon Fuel Standard (LCFS) creates a demand for various advanced
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biofuels. Other states have similar requirements. The federal reformulated gasoline program does
not require the use of an oxygenate, but the applicable emission standards are generally more
difficult (i.e., more costly) to meet without the use of ethanol. A biodiesel tax credit of $1 per
gallon was originally established by the Energy Policy Act of 2005 and has temporarily expired
and been retroactively reinstated multiple times since then. A number of states offer tax subsidies
that offset the production costs of renewable fuels, making them more attractive to consumers.
And some vehicle fleets owned by state or federal agencies are required to refuel on renewable
fuel when it is available.
At the retail level, the consumption of renewable fuel is driven primarily by their price in
comparison to petroleum-based gasoline and diesel. Retail prices are a function of the cost
of production which in the case of both renewable fuels and petroleum-based gasoline and
diesel is in turn driven primarily by the costs of the feedstocks. Thus, to a large degree the
economic attractiveness of renewable fuel to consumers is a function of crude oil prices and crop
prices. Consumer choices about whether, how much, and what type of renewable fuel to
consume can also be influenced by other factors such as perceptions of the impacts that
renewable fuels may have on vehicles or engines, the impact that renewable fuels have on the
environment, or the benefits of renewable fuels to rural economic development and farmers.
Certain constraints on renewable fuel use can also affect the mix of fuel types that are
consumed. For instance, gasoline that can be used in all vehicles can contain no more than 10%
ethanol (E10). While higher ethanol blends such as E15 and E85 are also possible, they can only
be used in certain vehicles and the fraction of retail service stations offering such blends is very
small. As a result, most ethanol blending occurs as E10 with limited volumes of higher level
ethanol blends. Higher volumes of renewable fuel consumption typically comes in the form of
non-ethanol renewable fuels such as biodiesel, renewable diesel, and biogas. For biogas used in
CNG vehicles, the number of CNG vehicles in the current fleet places an upper bound on the
total volume of biogas that can be consumed.
Thus, in addition to the RFS program standards, there are a wide variety of factors that
can influence the actual consumption of renewable fuel, both in terms of total volumes as well as
the mix of types of renewable fuel. These consumption-side factors strongly influence the
choices that upstream parties make in terms of which renewable fuels to produce and blend into
gasoline and diesel.
Stage 4: Total production of renewable fuel
While domestic production of renewable fuel is largely a function of domestic demand,
other factors also influence what is produced and how much. Domestic renewable fuel
production capacity places a limit on how much of each type of renewable fuel can be produced.
As the production of one type of renewable fuel approaches its production capacity limit,
additional volumes must come from other types of renewable fuel. For instance, the production
capacity of liquid cellulosic biofuels remains very low, and cellulosic biogas for use in CNG
engines has proliferated.
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Imports and exports of renewable fuel also influence domestic production volumes. In
recent years, the primary fuel types that have been imported are biodiesel, renewable diesel, and
ethanol, in total representing about 5% of domestic consumption. Smaller amounts of biogas
have also been imported. Greater import volumes generally mean that there is less need for
domestic production in order to meet the RFS standards. Exports of renewable fuel, in contrast,
generally mean that domestic production is higher than what is needed to meet the RFS
standards. However, volumes that are produced domestically and then exported cannot be
attributed to the RFS program since they are being used to meet foreign demand. Over the last
several years, the primary type of renewable fuel exported has been ethanol, though not
insignificant volumes of biodiesel have also been exported. In total, these exports represent
about 10% of domestic consumption over the last several years.
Stage 5: Production of crop-basedfeedstocks
The fraction of total renewable fuel production that is derived from crop-based
feedstocks is a function of their cost, availability, and ease with which they can be converted into
renewable fuel in comparison to non-crop-based feedstocks. Each renewable fuel producer
decides which feedstocks they will use to produce renewable fuel, and those decisions determine
the renewable fuel category into which that renewable fuel falls. The choice of feedstock also
likely impacts the selling price of that fuel. Downstream parties such as blenders and distributors
will make their own choices about which biofuels to purchase, and can be expected to make
choices based primarily on price. Few downstream parties have an incentive to make fuel
purchasing choices based directly on feedstock, and more importantly they rarely
have sufficient access to information about feedstocks to enable them to do so.
The driving factors for competing feedstocks have different outcomes for each of the
renewable fuel categories shown in Figure II.A-1. As described previously, essentially all
cellulosic biofuel has been derived from the non-crop feedstock biogas, while essentially all
conventional renewable fuel has been derived from the crop-based feedstock corn. For non-
cellulosic advanced biofuel, composed predominately of biodiesel, crop-based feedstocks have
represented on average 56% of total domestic production over the last several years.
Stage 6: Total production of crops
Individual farmers choose what crops they will grow based primarily on projected grain
and oilseed market prices, but their choices also depend on the land available to them and
its suitability for growing certain crops. They do not grow particular crops for the purposes of
meeting demand for renewable fuel or any other particular end use. Moreover, their choices can
and often do change from year to year. Actual crop production is also-affected by climate, the
availability of irrigation water, and a host of other factors.
Crops are grown for a variety of purposes in addition to renewable fuel. These include
food, animal feed, and various industrial and manufacturing processes. Between 2016 and 2020,
an average of about 37% of domestic corn production was used for fuel ethanol, while an
average of about 29% of domestic soybean oil (representing about 14% of domestic soybean
production) was used for biodiesel (USDA Economic Research Reserve, 2022). Figures III.B.4-2
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and III.B.4-3 show the yearly corn and soybean acreage used for biofuel production relative to
total corn and soybean planted acreage in the United States.
Figure A-2: U.S. Corn Cropland used for Ethanol
120,000
100,000
£ 80,000
u
ro
1 60,000
¦Total planted corn acres
¦Acres harvested for ethanol
Source: USDA's Economic Research Sendee
Figure A-3: U.S. Soybean Cropland used for Oil for Biodiesel Production
100,000
90,000
80,000
70,000
60,000
50,000
40,000
30,000
20,000
10,000
-Total planted soybean acres
-Acres harvested for soy oil used for biofuel
(N (N (N (N
Source: USDA's Economic Research Service
Taken together, soybeans and corn used for biofuel production represent a small but not
insignificant portion of total cropland as shown below.
240
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Figure A-4: U.S. Soybean and Corn Cropland used for Biofuel Production versus Total
Cropland3
300,000
250,000
£ 200,000
u
ro
= 150,000
i/>
o
i— 100,000
50,000
0
a For purposes of tliis assessment, "total cropland" includes
corn, soybeans, wheat, cotton, sorghum, barley, and oats
As for domestic renewable fuel production, domestic crop production is affected by
imports and exports of crops for their many other uses including food and feed. Imported crops
reduce the need for domestic production, while exported crops cannot be attributed to the RFS
program since they meet foreign demand. Between 2016 and 2020, almost no corn has been
imported, but 15% of corn grown in the U.S. has been exported. Similarly, almost no soybeans
have been imported, but on average 49% of domestically grown soybeans have been exported.
Attempts to model where biofuel feedstocks might be grown in the future, even at a
coarse level, rely on a range of assumptions that result in widely different conclusions. One
analysis used two types of models—GTAP-BIO and GLOBIOM—to predict land use change
effects that may occur in the United States from increased biofuel production from soy oil. While
the GTAP-BIO model predicted that crop switching (i.e., a decrease in the use of cropland for
non-biofuel crops accompanied by an increase in the use of cropland for soybeans for biofuel) on
existing croplands would be the dominant change to supply the additional soy oil feedstock, the
GLOBIOM model predicted crop-switching to be low and instead showed major changes to
natural and abandoned lands (CORSIA, 2019). Another study by Zhao et al. (2021) used GTAP-
BIO to estimate the land use impacts from increasing jet fuel and renewable diesel production
from soy oil in the U.S. by about two billion gallons. They projected a fair amount of crop
switching in the U.S. and small increases in total cropland (Zhao et al., 2021).
In the vast majority of cases, farmers do not know which bushels they produce will be
used to produce renewable fuel. Instead, farmers sell their crops to distributors
(e.g., grain elevators) who meet the regional demand for the crops they collect. Bushels can
change hands multiple times before they reach their final destination, and as fungible
commodities those bushels are often mixed together without regard for their farm of origin.
Nevertheless, in very general terms it is likely that crops used to produce renewable fuel are
more likely to be grown near a renewable fuel production facility than further away. A study by
Wright et al. (2017) assessed land use changes from 2008 to 2012 and found that the rate of
grassland conversion to cropland increased with proximity to a bioreftnery location. Other
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studies that quantitatively correlate crop production with distance to a production facility have
shown similar results (Austin et al., 2022).
Stage 7: Land used to grow crops
Not only do individual farmers choose what crops they will grow, they also choose what
land they will use to grow those crops based upon the availability of land, their rights to grow
crops on that land, and its suitability for particular crop types. If a farmer chooses to increase
production of a particular crop, he can do so through conversion of non-cropland to crop
production if there is suitable land available to him to do so ("extensification"). But he can also
increase production of a particular crop without increasing total land used through one of several
different "intensification" methods:
• Increase the density of rows, plants, or plant closer to the edges of fields.
• This is one of the most common forms of intensification.
• Reduce production of one crop type and increase production of another
crop type.
• Increase the yield of an existing crop through increased use of fertilizer,
herbicides, pesticides, and/or fungicides.
• Harvest two crops in a single year from the same plot of land (so-called double-
cropping or multi-cropping).
• This is not common in the U.S.
In these intensification cases, total land used to produce crops remains unchanged, but
farming activities may change (e.g. application rates for fertilizer or pesticides, frequency of
equipment use, irrigation needs). Since farmers make decisions about extensification versus
intensification based on their particular circumstances, there is no straightforward way to predict
what those choices will be for total cropland writ large.
Stage 8: Impacts on species and habitat
Changes in the way that land is used to grow crops can impact species and habitat in
several ways. Non-cropland that is converted to cropland can result in adverse effects to habitats
within the range of listed species, and nearby habitats can indirectly be affected by the noise,
dust, or runoff created during the land conversion. After conversion, the new cropland can also
affect listed species or habitat on both the land in question and nearby areas through sediment,
pesticide, herbicide, or fertilizer runoff. Similarly, in cases where additional crops are grown on
existing cropland through various intensification measures such as double-cropping or increased
fertilizer or pesticide use, there can be impacts on flora and fauna for that land and nearby areas.
242
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254
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Phillips, Tuana (she/her/hers)
Subject:
Attachments:
From:
Sent:
To:
Phillips, Tuana (she/her/hers)
Wednesday, May 31, 2023 1:55 PM
David Baldwin - NOAA Federal; Miller, Meredith; Michaels, Lauren (she/her/hers);
Lisamarie Carrubba - NOAA Federal; jennifer.douglas
RE: One piece of new analysis/info needed for RFS consultation
Soybean Analysis Comment _with EPA response.docx
Hi David
As we discussed yesterday, see attached for our response to your comment for recordkeeping purposes. We decided to
just add to the document you provided. You can find our response towards the bottom of page 3.
Thank you and let us know if you have any questions,
Tuana
From: David Baldwin - NOAA Federal
Sent: Friday, May 26, 2023 3:44 PM
To: Phillips, Tuana (she/her/hers) ; Miller, Meredith ; Michaels,
Lauren (she/her/hers) ; Lisamarie Carrubba - NOAA Federal
; jennifer.douglas
Subject: One piece of new analysis/info needed for RFS consultation
I've finished reviewing the BE. I identified just one piece of analyses/info that is lacking in the BE. Basically, we want to
adequately capture the risks posed by soybean expansion to all of NMFS species. Based on the info in the BE, the
potential for soybean expansion in several Atlantic states does appear to be a concern that is not addressed. I've
attached a file with more details and options for addressing this assessment need.
EPA does not need to provide a new BE or redo the ICF model. Instead, EPA can provide the additional analysis or info as
a separate file.
Let me know if there are any questions.
David
David H. Baldwin, Ph.D.
Biologist (Endangered Species)
NOAA Fisheries
Office of Protected Resources
email: David. Baldwinffinoaa.gov
phone: (301)427-8412
l
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Exchange between EPA and NMFS
NMFS's request for addressing potential soybean expansion in states such as NY and PA
Comment #27 (NMFS): The potential for soybean expansion outside the modelled area should
be addressed qualitatively, at least. Based on Figure VII.B-2 that would include PA, NY, and
VA.
EPA Response (05/19/23): We added the following text to Section VII.B.l, page 154:
To assess where soybeans are currently grown, we accessed the most recent NASS data for soybean acres harvested
annually from 2018 - 2022. During this time approximately 94% of all acres of soybeans harvested were from the
geographic region identified by ICF. This percentage we very consistent, ranging from a low of 93.81% in 2021 to a
high of 94.44% in 2018. In 2022, the most recent year for which data are available, these states accounted for
94.23% of the total acres of soybeans harvested. Only one state outside of the geographic scope identified by ICF
(North Carolina) accounted for more than 1% of the total acres of soybeans harvested in any year from 2018 - 2022.
North Carolina accounted for a high of 2.03% of all soybeans harvested in the U.S. in 2019 and a low of 1.79% of
all soybeans harvested in the U.S. in 2018. This analysis supports the geographic scope selected by ICF, as the vast
majority of soybeans harvested annually within the U.S. (as well as nearly all the states that saw increasing soybean
acreage, as shown in Figure VII.B-2) are within this geographic scope. The results of this state-by-state assessment
are shown in Tables VII.B-l and VII.B-2.
NMFS Reply (05/26/2023):
The BE excludes consideration of any effects due to soybean expansion to species located in
states outside the modelled region. While LA and MS were included in the modelled region, NY
and PA were excluded despite showing greater changes in soybean acres than LA with PA
showing almost as much increase as MS (Figure VII.B.2). Both LA and MS were the basis of the
ICF overlap analyses for NMFS species. However, NMFS species will overlap with Atlantic
coast states such as NY, PA, and NC. Although soybean acres in a state may represent a small
percent of the national total, the state should not be excluded from the overall assessment of
soybean expansion. For example, the 169,0000 acres grown in NC or 510,000 acres grown in
MD (Table VII.B.l) indicate that soybean expansion might pose a risk to species located in those
states. Excluding states such as NC and MD effectively assumes no expansion will occur there
and, therefore, no risk to NMFS species located in those areas. The available data doesn't appear
to support that conclusion. Importantly, we want to adequately capture the risks posed by
soybean expansion to all NMFS species.
To that end, while redoing the ICF is not necessary, some assessment of the potential extent of
soybean expansion in other states is needed. While the existing ICF modeling won't provide
quantitative info, could it provide qualitative extrapolations to states not included in the model?
Or are other sources of info available for this assessment? One option is that EPA provide some
additional analyses for these states. Alternatively, NMFS is willing to perform analyses similar
to those being done for corn expansion provided EPA provides some additional info.
That info should be readily available and consists of:
1) the GIS raster data for Figure VII.B-4,
2) which categories of non-soybean pixels were considered suitable for soybean expansion, and
3) the average probability of a non-soybean pixel being converted to soybean (this might be able
to come from the ICF modeling).
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Figure VII.B-2: Average Year-Over-Year Change in Soybean Planting Acreage
Average Year Over Year Change in Soybean Planting Acreage. 2008-2021
llllllii...—.
J NJ or MD FV. DE Al T> NC SC AR GA
Sou.^ tJSDA NASS
Figure VII.B-3: Map Showing Potential Soy Expansion Areas in Green
Figure VII.B-4: Modeled Soybean Expansion Areas (Red Color) for 250-Million-Gallon
Scenario 37
n
Potential Impacts of
Expanded Biodiesel
and Renewable Diesel
Production on
Endangered Species
and Critical Habitats
Legend
¦ L ¦- "" \
1 1 Limits of Analysis
Hp [a]
CD Soybean
1 ' , ' r' ' j
m Other Cultivated
1 v " ' C Xl
Si Non-Cultivated
OJ Developed
EH Water
f \
IU Modeled Soybean
Expansion Area -
Extensification
previously
uncultivated lands)
Soybean Expansion Target
2.994.307 acres
-------
Eafek.VinMiSoybran AcreiHa
^estedAc
State(s)
2018
2019
2020
2021
2022
All States in the GeographicScope
82,720,000
74,939,000
78,050,000
80,970,000
81,355,000
NORTH CAROLINA
1,570,000
1,520,000
1,570,000
1,640,000
1,690,000
PENNSYLVANIA
630,000
610,000
630,000
595,000
590,000
VIRGINIA
590,000
560,000
560,000
590,000
610,000
MARYLAND
515,000
475,000
465,000
485,000
510,000
ALABAMA
335,000
315,000
275,000
305,000
355,000
SOUTH CAROLINA
330,000
260,000
295,000
385,000
390,000
NEW YORK
325,000
225,000
312,000
320,000
325,000
DELAWARE
168,000
153,000
148,000
153,000
158,000
TEXAS
135,000
73,000
110,000
100,000
85,000
GEORGIA
130,000
86,000
95,000
135,000
160,000
NEW JERSEY
107,000
92,000
93,000
99,000
108,000
WEST VIRGINIA
27,000
0
0
0
N/A*
FLORIDA
12,000
0
0
0
N/A*
OTHER STATES
0
0
0
0
N/A*
Table VII.B~2r Percent of U.S. Sot
States"
''bean Acres Harvested Acres bv State
State(s)
2018
2019
2020
2021
2022
All States in the GeographicScope
94.44%
94.17%
93.83%
93.81%
94.23%
NORTH CAROLINA
1.79%
2.03%
1.90%
1.90%
1.96%
PENNSYLVANIA
0.72%
0.81%
0.76%
0.69%
0.68%
VIRGINIA
0.67%
0.75%
0.68%
0.68%
0.71%
MARYLAND
0.59%
0.63%
0.56%
0.56%
0.59%
ALABAMA
0.38%
0.35%
0.33%
0.35%
0.41%
SOUTH CAROLINA
0.38%
0.42%
0.36%
0.45%
0.45%
NEW YORK
0.37%
0.30%
0.38%
0.37%
0.38%
DELAWARE
0.19%
0.20%
0.18%
0.18%
0.18%
TEXAS
0.15%
0.10%
0.13%
0.12%
0.10%
GEORGIA
0.15%
0.11%
0.12%
0.16%
0.19%
NEW JERSEY
0.12%
0.12%
0.11%
0.11%
0.13%
WEST VIRGINIA
0.03%
0.00%
0.00%
0.00%
N/A*
FLORIDA
0.01%
0.00%
0.00%
0.00%
N/A*
OTHER STATES
0.00%
0.00%
0.00%
0.00%
N/A*
EPA Response (5/31/2023)
In response to NMFS' comments, we have provided the GIS raster file for Figure VII.B-4 in the
BE's SharePoint folder that is shared by EPA and the Services. To complete the qualitative
extrapolation that NMFS describes above, we think it would be appropriate to use the
information presented in Table VII.B-1 which shows the ICF land selection results by land cover
type. For example, the table shows that for the 250-million-gallon scenario approximately 3.7
million acres or 2.6% of grassland may be converted within the potential soy expansion area
shown in Figure VII.B-3 of the BE. These same numbers could be used to make conservative
assumptions about potential land use changes in states like North Carolina (e.g., 2.6% of
grasslands may be impacted there as well). We believe using these numbers to extrapolate in the
additional Atlantic coast states would be conservative because it assumes the same percentage of
conversion from non-cropland to soybean planting in states outside of the ICF study without
-------
consideration of the other weighting factors such as proximity to existing soybean planting. Most
of the soybean is grown in the Midwest, and expansion would largely occur in that region for
reasons discussed in more detail in the BE.
EPA discussed the above plan with NMFS during the May 30, 2023 EPA-Services ESA call and
NMFS agreed this would be appropriate. NMFS agreed to do some more analysis and thinking to
assess potential increases in soybean growth within NMFS species' critical habitat or range in
these Atlantic coast states. EPA is available to support and discuss more as needed.
NMFS Reply (06/04/2023)
NMFS appreciates EPA's response and subsequent feedback. A summary of the current state of
the specific information needs identified by NMFS follows.
1) In a subsequent email, EPA recognized that the GIS raster data provided via the
SharePoint folder did not extend to areas outside the ICF modelled states as needed for
NMFS additional analyses. However, NMFS identified GIS data from previous NMFS
assessments that can be applied to the RFS analysis. These include Soybean and
Rangeland raster data.
2) The Rangeland and Soybean GIS raster data were provided by EPA as part of their recent
carbaryl and methomyl BEs. While they are not identical to that used in the ICF
modelling, they are suitable for the purposes of assessing the potential for soybean
expansion due to the RFS Rule. In particular, the Rangeland GIS raster encompasses the
majority of land uses considered suitable by the ICF modelling.
3) EPA has identified the ICF model results as the best available information on the extent
of soybean expansion in states outside the modeled region (e.g. North Carolina). NMFS
agrees that these estimates are conservative and the actual extent is likely less due to a
variety of factors. For example, NMFS will consider proximity to existing soybean acres
similar to that done for corn expansion (i.e. 15 mile buffer).
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