Development of Conservation
Focus Area Models
for EPA Region 7
David D. Diamond1
Walter E. Foster2
Scott P. Sowa1
Tasia M. Gordon1
C. Diane True1
Jason M. Boyer3
Casey J. McLaughlin3
'Missouri Resource Assessment Partnership, University of Missouri, 4200 New Haven Road,
Columbia, MO 65203, U.S.A.
2United States Environmental Protection Agency, 901 North 5th Street, Kansas City, KS 66101,
U.S.A.
3JMA Information Technology, 10551 Barkley Street, Suite 400, Overland Park, KS 66212, U.S.A.
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Table of Contents
Table of Contents 2
List of Contributors 4
List of Figures 4
List of Tables 7
List of Appendices 7
Executive Summary 9
I. Introduction 11
A. Goal and Objectives 11
II. Terrestrial Assessment 12
A. Assessment of Ecological Risk 14
1. Creation of Significance Surface 15
a. Abiotic Site Type Modeling 15
b. Percent Conversion by Abiotic Site Type 20
c. Opportunity Area Data Layer 20
d. Final Ecological Significance Data Layer: Percent Conversion and
Opportunity Area Representation 22
2. Creation of Threats Surface 25
a. Development Land Demand 25
b. Agricultural Threat 26
c. Toxics Index 27
d. Creation of Final Threats Surface 27
3. Creation of Ecological Risk Surface: A Combination of Significance and Threat
28
B. Irreplaceability Analysis 30
1. Overall methodology 30
2. EPA Region 7 Results 31
C. Identification of Conservation Focus Areas: A Combination of Risk and
Irreplaceability 33
III. Aquatic Assessment.... 36
A. Aquatic Conservation Assessment for Missouri 37
1. Aquatic Classification 38
a. Levels 1 - 3: Zone, Subzone, and Region 40
b. Level 4: Aquatic Subregions 41
c. Level 5: Ecological Drainage Units 42
d. Level 6: Aquatic Ecological System Types 43
e. Level 7: Valley Segment Types 45
f. Level 8: Habitat Types 46
2. Biological Data 47
3. Human Stressors 49
4. Public Ownership and Stewardship Statistics 51
5. Conservation Strategy 52
6. Results for the Pilot Area 55
7. Statewide Results for Missouri 57
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B. Regional Conservation Assessment 60
1. Aquatic Classification 61
a. Level 4: Aquatic Subregions 61
b. Level 5: Ecological Drainage Units 62
c. Level 6: Aquatic Ecological System Types 63
d. Level 7: Valley Segment Types 64
2. Biological Data 65
3. Human Stressors 69
4. Public Ownership 72
5. Conservation Assessment Strategy 74
6. Results of the Regional Aquatic Assessment 76
IV. Discussion and Future Needs 78
A. Terrestrial Assessment 78
B. Aquatic Assessment 78
V. References 80
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List of Contributors
David Diamond, Scott Sowa, Tasia M. Gordon, C. Diane True, Gust Annis and Mike
Morey - Missouri Resource Assessment Partnership
Walt Foster and Holly Mehl - EPA Region 7
Jason Boyer and Casey McLaughlin - JMA Information Technology
List of Figures
Figure 1. Flow chart showing variables used to reach the final conservation focus area data
layer. Please note that intermediate layers, such as ecological risk, significance, and
development land demand may prove as useful for planning and management as the final
conservation focus area layer.
Figure 2. Terrestrial ecoregions intersecting the boundary of EPA Region 7 states that
were used as planning regions for the terrestrial conservation focus area assessment.
Figure 3a. Abiotic site type modeling procedures. Site types were modeled using values for
solar insolation and land position, as well as modeled river floodplains and well-defined
stream valleys.
Figure 3b. Abiotic site types for EPA Region 7.
Figure 4. Example of the final river floodplain and well-defined stream vailey data layer,
which was incorporated into the modeled site types for EPA Region 7.
Figure 5. Ecological significance modeling procedures. Significance was determined
from evaluation of percent conversion of site types and opportunity area representation
(see Table 2).
Figure 6. Ecological risk modeling procedures. Risk was determined by evaluating
significance and threat (see Table 3).
Figure 7. Irreplaceabi 1 ity values attached to 40 square kilometer hexagons (assessment
units) for EPA Region 7. Irreplaceabi 1 ity scores for each hexagon was determined by
evaluation of biotic (opportunity area representation, vertebrate species diversity) and
abiotic (site type representation) targets.
Figure 8. Conservation focus areas for EPA Region 7.
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Figure 9. Maps showing Levels 4-7 of the MoRAP Aquatic Ecological Classification
hierarchy.
Figure 10. Map showing the boundaries of the three Aquatic Subregions of Missouri.
Figure 11. Map of Ecological Drainage Units (EDUs) for Missouri.
Figure 12. Map of the thirty-nine distinct Aquatic Ecological System Types (AES-
Types) for Missouri.
Figure 13. Map showing streams classified in to distinct stream Valley Segment Types for
Missouri.
Figure 14. Map of species richness for Missouri, which is based upon predicted distribution
models for 315 fish, mussel, and crayfish species. Users can also individually select stream
segments within a G1S to obtain a list of the species predicted to occur within each segment
of interest.
Figure 15. Map showing the composite Human Stressor Index (HSI) values for each Aquatic
Ecological System in Missouri. The first number represents the highest value received
across all 11 metrics included in the HSI, while the last two digits represent the sum of the
scores received for each of the 11 metrics.
Figure 16. Map of 11 Conservation Focus Areas, within the Ozark/Meramec EDU, that were
selected to meet all elements of the basic conservation strategy developed for the freshwater
biodiversity conservation planning process in Missouri. The figure also shows the Aquatic
Ecological System Types for context. Lower and Upper types differ in terms of their position
within the larger drainage network. Specifically, a "Lower AES Type" contains streams
classified as Large River and associated headwater and creek tributaries, while Upper types
contain streams classified as Small River and these smaller tributaries.
Figure 17. Map showing all 158 freshwater Conservation Focus Areas that were selected for
Missouri. Taking measures to conserve all of these locations represents an efficient approach
to representing multiple examples of all the distinct species, stream types, and watershed
types that exist within the state.
Figure 18. Map showing the overall irreplaceabi lity values for each of the 158 focus areas
identified in Missouri. These values generated by summing the individual values obtained
from separate analyses performed for fish, mussels, and crayfish.
Figure 19. Aquatic Subregions within EPA Region 7.
Figure 20. Ecological Drainage Units within EPA Region 7.
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Figure 21. Map showing the 95 distinct Aquatic Ecological System Types that occur
throughout EPA Region 7. Red lines show Aquatic Subregion boundaries and thick
black lines show Ecological Drainage Unit boundaries.
Figure 22. Map of the 1:100,000 Valley Segment Coverage for EPA Region 7 displayed
according to the five general stream size classes.
Figure 23. Fish collection records compiled for Aquatic GAP projects throughout Iowa,
Kansas, and Nebraska.
Figure 24. Scatter plot showing the number of native fish species documented to occur-
within each AES polygon versus the number of fish collections within AES polygon
throughout Iowa, Kansas, and Nebraska. This plot shows that anywhere from 50 to 100
collections are needed to accurately document the species composition of a given AES
throughout this region.
Figure 25. Number of fish collection records for each AES polygon in Iowa, Kansas, and
Nebraska.
Figure 26. Native fish species richness by AES polygon. The patterns displayed on this
map reflect both real and perceived patterns of biodiversity due to geographic variations in
sampling effort.
Figure 27. Map of federally licensed dams throughout EPA Region 7.
Figure 28. Map of lead and coal mines within EPA Region 7.
Figure 29. Map showing the percentage of urban area occurring within each AES
polygons throughout EPA Region 7.
Figure 30. Graduated color map of the cumulative stressor index that was used to rank
AESs across EPA Region 7.
Figure 31. Map showing the distribution of the public lands within EPA Region 7.
Figure 32. Graduated color inap showing the percentage of public lands within each AES
polygon.
Figure 33. Map of the 200 aquatic focus areas identified throughout Iowa, Kansas, and
Nebraska.
Figure 34. Map of the 358 aquatic focus areas identified throughout EPA Region 7.
Figure 35. Map showing the 200 aquatic focus areas for Iowa, Kansas, and Nebraska
(highlighted in both red and green). The focus areas highlighted in red were those that had
both the lowest relative cumulative stressor index and highest relative percentage of public
land (70% of the total.
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List of Tables
Table I. Abiotic site types for EPA Region 7. Solar insolation I to 4 is wet to dry, while
land position 1 to 4 is low to high. These modeled site types were intersected with soils and
geological data to define geolandforms for some sections (see text).
Table 2. Ecological significance ranking scheme combining percent conversion and
opportunity area representation.
Table 3. Algorithm for assigning ecological risk values based on significance and threat.
Table 4. Summary of conservation focus areas by area and percent for ecological
planning regions in EPA Region 7. Only areas >= 2 hectares were selected.
Table 5. List of the GIS coverages, and their sources, that were obtained or created in
order to account for existing and potential future threats to freshwater biodiversity in
Missouri.
Table 6. The 11 stressor metrics included in the Human Stressor Index (HSI) and the
specific criteria used to define the four relative ranking categories for each metric that
were used to calculate the HSI for each Aquatic Ecological System.
Table 7. Individual human stressor statistics that were generated for each AES polygon
across EPA Region 7.
List of Appendices
Appendix 1. Abiotic site types in EPA Region 7.
Appendix 2. Summary of ecological significance ranks by area and percent for
ecological planning regions in EPA Region 7. Table and Figures.
Appendix 3. Summary of ecological risk by area and percent for ecological planning
regions in EPA Region 7. Table and Figures.
Appendix 4. Summary of irreplaceability by area and percent for ecological planning
regions in EPA Region 7. Table and Figures.
Appendix 5. Summary of conservation focus areas by area and percent for ecological
planning regions in EPA Region 7. Table and Figures.
Appendix 6. List of the fundamental principals, theories, and assumptions identified by the
team of aquatic resource professionals that must be adhered to during the conservation
assessment in order to meet the overall goal of the assessment.
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Appendix 7. Results of the irreplaceability analyses performed on the 158 aquatic focus
areas for Missouri using native fish, mussel, and crayfish species as conservation targets.
Appendix 8. Maps of the aquatic focus areas for each Ecological Drainage Unit within
EPA Region 7.
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Executive Summary
We used current scientific techniques and uniform, transparent methods to identify
conservation focus areas as an aid to identification of critical ecosystems, to provide a
basis for permit and project review, to aid in funds allocation, and for other uses by EPA
Region 7 and its partners. We designed an approach to ensure locally arid ecologically
relevant results. Key elements include:
1. Separate terrestrial and aquatic assessments.
2. Assessments completed within ecologically-based planning regions
(ecoregions for terrestrial ecosystems and evolutionarily significant
watersheds for aquatic ecosystems).
3. Use of relatively uniform, region-wide data sets to ensure consistent
regional coverage to the maximum extent possible.
4. Evaluation of both biological and abiotic (representation) targets in
determining ecological significance whenever possible.
5. Evaluation of both significance/importance and threat/stressors to
assign final priorities whenever possible.
6. Assignment of spatially specific results at as fine of resolution as
allowed by the data sets.
Terrestrial and aquatic assessments were conducted separately because different stressors
operate on aquatic versus terrestrial ecosystems differently, and because watershed
boundaries need to be used as aquatic planning regions, since they circumscribe
evolutionary significant sub-divisions of riverine ecosystems. Ecologically-based
planning regions were used in order to make results both more locally and ecologically
relevant.
Terrestrial conservation focus areas were defined based on an algorithm combining a risk
data layer (defined by a combination of ecological significance and threat) and an
Irreplaceability data layer (based on the ranking of 40 sq km hexagons using abiotic and
biotic targets; see Figure I). Since assessments were specific to ecological planning
units, conservation focus areas are identified in all parts of EPA Region 7, with an
average of 8.3% of all planning regions identified as conservation focus areas. More
natural planning regions such as the Ozark Highlands, Nebraska Sand Hills, Flint Hills,
and Cross Timbers and Prairies had more focus areas, whereas areas that are heavily
agricultural had fewer (see Appendix 4). Because of inherent differences in land use
practices and some input data, notably roads, results are most valid on a planning region
by planning region (usually section by section) basis.
Aquatic conservation focus areas were defined at two resolutions based on the
availability of data. Watersheds were ranked using human stressors and the distribution
of public lands for the region (see Figures 17, 33, 34), and groups of connected stream
valley segments were identified as conservation focus areas within Missouri. The 358
aquatic focus areas that were identified and mapped across the EPA Region 7 provide a
blueprint for holistic conservation of the freshwater ecosystems within the region, as
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opposed to the largely random and patchwork approach used in the past. These areas can
be, and in Missouri are already, used to guide protection efforts sucli as land acquisitions,
restoration efforts, and regulatory activities like the permit review process administered
under the Clean Water Act. These areas also provide an ideal template for research
designed to elucidate fundamental ecological processes within riverine ecosystems.
Data development, especially the modeling of aquatic species distribution by stream
valley segment type, and efforts of partners, particularly the Missouri Department of
Conservation, made a finer resolution assessment possible in Missouri. Hence, 158
conservation focus areas are identified by targeting representation of distinct watershed
(aquatic ecological system) types, distinct stream valley segment types, and aquatic
species within aquatic planning regions (ecological drainage units, which are
evolutionarily significant larger watersheds). In every instance, this initial strategy of
ensuring the representation of abiotic targets successfully represented 95-100% of the
biotic targets (species) within the initially-selected set of conservation focus areas. This is
especially surprising in the Ozark Aquatic Subregion, which contains numerous local
endemics with restricted and patchy distributions. These results suggest that our
classification units do a good job of capturing the range of variation in stream and
watershed characteristics that are partly responsible for the patchy distribution of these
species. These results also illustrate the utility of abiotic targets for freshwater
conservation planning, which can prove critical for regions lacking sufficient biological
data. This is especially encouraging in terms of the regional results considering the fact
that we were unable to include biological targets in the regional assessment.
Results of this project are meant to be used, along with other data and considerations, to
help EPA R7 and state and local partners define priorities at multiple scales. The
example of how these data were refined in Missouri to define conservation focus areas
should be repeated across the region for both terrestrial and aquatic assessments.
Whereas information provided can be combined with existing analyses to suggest the top
few regional conservation focus areas, we also provide several uniform, continuous,
relatively fine-resolution data layers ranking ecological significance, risk, and threat that
can be used for refined priority setting and individual project and permit review
throughout the region.
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f. Introduction
EPA Region 7 set the identification of critical ecosystems as one of three strategic
priorities (see http://www.epa.gov/region7/priorities/index.htm). According to the web
site, "The mission of the Critical Ecosystems Team is to facilitate the protection and/or
restoration of the ecosystems in EPA Region 7 which are critical to biodiversity, human
quality of life, and/or landscape functions." The guiding principles include the definition
of critical ecosystems and development of criteria for selection, integration of protection
into EPA programs, and enhancement of ecosystem protection via better communication
about Region 7 ecosystem protection strategies and initiatives.
The conservation focus area results provide spatially-specific, scientifically based input
data toward identification and selection of critical ecosystems. The idea was to build on,
and to move past, previous efforts. Past work continues to provide valuable insights, but
was based largely on methods that were not uniformly applied across the region, were not
transparent, relied too heavily on professional judgment, and failed to adequately
consider aquatic resources. What sets the current effort apart from past effort is (i) the
rigorous application of current scientific methods, (2) the more careful documentation of
logic and methodology, (3) the application of newly available, digital data sets, (4) the
uniform use of ecologically based planning regions, (5) the assignment of ecological
value at a relatively fine level of resolution to the entire region, and (6) the increased
attention paid to aquatic resource assessment.
A. Goal and Objectives
Our overall goal is to effectively conserve ecosystem structure and function and protect
human health and quality of life in EPA R7. The objectives are to (1) assign terrestrial
ecological risk scores to the entire region at relatively fine resolution based on
significance and threat, (2) assign terrestrial irreplaceability scores to 40 sq km hexagons
based on the distribution and abundance of abiotic and biotic conservation targets, (3)
combine terrestrial irreplaceability and risk scores to identify terrestrial conservation
focus areas, (4) rank watersheds throughout the region based on stressor variables
important to aquatic ecosystem function and the distribution of public lands by
watershed, and (5) identify and rank aquatic conservation focus areas for Missouri by
building on work already completed at the state level. We followed guidelines for
conservation assessments and planning outlined in Noss and Cooperrider (1994),
Margules and Pressey (2000), Noss et al. 2002, and Groves (2003).
To ensure better buy-in from key partners, we formed an interagency expert group to help
formulate basic methods. EPA Region 7 staff, MoRAP staff, and key state partners
formed this group, and we started with basic, accepted principles of conservation
planning (see Margules and Pressey 2000, Groves 2003). This group settled on the
following principles: (I) assessments need to be based on rigorous, transparent
methodologies so that planners and managers can understand, and embrace, results, (2)
assessments must be based on the best available data, (3) insofar as possible, a uniform,
region-wide assessment should be provided, but given that data are not uniform across
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R7, we should provide examples of better assessments using better data where
appropriate, (4) assessments need to be conducted within ecologically defined subunits,
so as to be representative of the biogeographic conditions across the region and therefore
both scientifically sound (assessments compare apples to apples) and locally applicable
(the subunits are small enough to make results locally relevant), (5) since assessments
identify conservation focus areas within ecologically based planning regions, whole
planning regions, extending beyond state borders, must be analyzed whenever
appropriate data are available (e.g. we did not conduct the assessment only with state
boundaries), and (6) assessments need to be as fine-resolution as possible to ensure
maximum practical utility at the regional, state, and local level.
Separate terrestrial and aquatic natural resource assessment are warranted because
different stressors impact terrestrial and aquatic resources in different ways, and because
we can identify watershed divides across which the biotic composition (e.g. ecosystems)
of similar stream types change dramatically due to the impact of isolation (e.g.
evolutionary history), even within a single terrestrial ecoregion (Sowa et al. 2005).
Therefore, our aquatic assessment used a hierarchical, watershed-based classification
system to define planning regions (Sowa et al. 2005), whereas our terrestrial planning
regions were based on a hierarchical ecoregion classification (see Bailey 1996, Cleland et
al. 2005).
In fiscal year 2004, we analyzed the Ozark Highlands and Chariton River Hills as pilots
for conservation focus area identification. The current effort builds on those results. The
following text is divided into major sections detailing the separate terrestrial and aquatic
assessments. For clarity, we organized the presentation such that methods and results are
grouped together within a single section for each of the several data layers developed.
II. Terrestrial Assessment
We developed a series of data layers and combined them in ecologically meaningful
ways to produce the final conservation focus area result (Figure 1). Ecological
significance and threat were combined to define risk, and then risk was combined with
irreplaceability to define conservation focus areas. Significance and threat are, in turn,
each developed from intermediate data layers. To ensure that results were locally
relevant and ecologically based, all analyses were conducted within ecological planning
regions based on ecological sections (Cleland et al. 2005) oil a planning region by
planning region basis (Figure 2; see Margules and Pressey 2000, Noss et al. 2002). Each
data layer developed, and the variables and methods used to create the layers, are
described in the following sections.
For large ecoregions at the edge of EPA Region 7 states, we did not choose full
ecological sections as planning regions, but rather combinations of subsections. These
modifications were as follows: the inclusion of only the Cross Timbers-Cherokee Prairies
and Central Tall Grass Prairie subsections within the Cross Timbers and Prairies section
(255A, Figure 2), only the Red Prairie within the Canadian-Cimarron Breaks within the
Northern Texas High Plains (315F), only the Sand Hill-Ogolla Plateau, Sandy-Smooth
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High Plains, and Western Arkansas River Lowlands within the Southern High Plains
(331B), and only the Oak Savannah Till and Loess Plains within the Minnesota and
Northeast Jowa Morainal-Oak Savannah section (222M). In addition, we excluded the
Hartsville Uplift subsection and subsections west and north of the Shale Scablands, Pine
Ridge Escarpment, and Keya Paha Tablelands within the Western Great Plains section
(331F). To gain complete coverage of western Kansas, we included the Lower Arkansas-
Big Sandy Valley subsection (part of the Arkansas Tablelands section) together with the
Central High Tablelands section (33 IC). Finally, the Boston Mountains section was
added as a southern extension of the Ozark Highlands section (223A).
Risk
Threat
Significance
Irreplaceability
Abiotic Site
Type Target
Development Land
Demand
Agriculture Land
Demand
Opportunity Area
Representation
Vertebrate Richness
Index Target
Percent Conversion
by Abiotic Site Type
Toxic Release
Potential
Conservation Focus
Areas
Opportunity Area
Representation Target
Figure I. Flow chart showing variables used to reach the final Conservation Focus Area
data layer. Please note that intermediate layers, such as ecological risk, significance, and
development land demand may prove as useful for planning and management as the final
conservation focus area layer.
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Figure 2. Terrestrial eeoregions intersecting the boundary of EPA Region 7 stales that
were used as planning regions for the terrestrial conservation focus area assessment.
25 IB
!51G
251H
251C
251E
ttie
315F
150
Terrestrial Ecoregions
2221 North Central U S Dnftless and Escarpment
222M Minnesota and Northeast low a Moramal-Oak Savannah
223AOzartc Highland
223S Missouri Rivet Loess
234B Northern Mississippi Alluvial Plain
234D While and Black River Alluvial Plains
2510 North Central Glaciated Plains
251C Central Dissected Till Plains
251E Osage Plains
251F Ffcnt HtNs
251G Missouri Loess Hills
N
251H Nebraska Rolling Hills
255ACross Timbers and Prairies
315F Northern Texas High Plains
3318 Scutnern Hlgn Plains
331C Central Htgh Tablelands
331F Western Great Plains
331H Central High Rams
332C Nebraska Sand Hills
332D North-Central Great Rains
332E South Central Great Plains
332F South Central and Red Bed Plans
M334A Black Hills
A. Assessment of Ecological Risk
The ecological risk data layer is derived from significance and threats data layers. Those
data, in turn, were developed from other layers. The following sections describe the
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creation of the significance and threats data, and how those were combined to define an
ecological risk layer.
1. Creation of Significance Surface
Ecological significance is an indicator of the relative importance of an area to
conservation of the biota and maintenance of ecological processes based on evaluation of
relevant, surrogate characteristics (Margules and Pressey 2000, Moss etal. 2002).
Significance values were attached to each 30m pixel based on two separate variables: (1)
values representing percent conversion of a given abiotic site type from natural or semi-
natural land cover to non-natural land cover, which is a surrogate for importance based
on the loss of major habitat types iri the landscape, and (2) values representing terrestrial
opportunity areas representation, which is a surrogate for viability and functionality of
existing extant vegetation patches across all landscape types (see section d., Final
Ecological Significance Data Layer, below). Opportunity areas are also places on the
landscape where development land demand is relatively low, so the opportunity to pursue
conservation management extends farther into the future. These two variables were in
turn combined into a single value and pixels were ranked from one (high significance) to
five (low ecological significance), with areas of non-natural vegetation ranked six.
a. Abiotic Site Type Modeling
To model abiotic site types, we used neighborhood analyses of 30-m resolution digital
elevation models (DEMs). The key variables assigned to each pixel included solar
insolation, which integrates slope percent, shading, and exposure, and relative land
position. We used a program called Shortwave to calculate solar insolation, and a
program developed initially by Frank Biasi of The Nature Conservancy to calculate
relative land position within a 9-cell neighborhood. Finally, we placed the pixels into
classes (one to four) for solar insolation and land position, and then combined these to
identify seven different abiotic site types (Table 1, Figure 3a, Figure 3b, Appendix I).
Flat uplands were modeled as an eighth site type when local relief within a 9-cell
neighborhood was less than 15m, and the pixel was not identified as a floodplain or well-
defined river valley bottom, which is the ninth abiotic site type. Finally, we identified all
sandy soil types from the digital version of the state soil geographic (STATSGO) soils
data layer from the National Resource Conservation Service (NRCS; download available
at http://www.ncgc.nrcs.usda.gov/products/datasets/statsgo/fact-slieet.html) and, within
the Ozark Highlands planning region, sedimentary rocks versus granitic parent materials
based on a digital version of the 1979 geologic map of Missouri (down load available at
http://msdisweb.missouri.edu/metadata/sgeol.html).
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Solar Insolation
Land Position
Flats
Solar Insolation
1 Less Light
4 More Light
Land Position
fc - J 1 Low
y.
s si'fvp'
- Site Type
ilS ฆ'
ฃ ti
Flat Areas
| Stream Valeys
I I Upper Flats
Abiotic Site types
; 1 low to mid wet slopes
Mi 2 mid to high wet slopes
[ ] 3 valleys and toe slopes
14 gentle uplands and gentle slopes
5 well-drained uplands and ridges
j 6 low to mid dry slopes
SS|; 7 mid to high dry slopes
; 8 floodplains and well-defined stream valleys
[ ] 9 flat uplands
Figure 3a. Abiotic site type modeling procedures. Site types were modeled using values
for solar insolation and landposition, as well as modeled river floodplains and well-
defined stream valleys.
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Figure 3b. Abiotic site types for EPA Region 7.
C~] 1 low to mid wet slopes ~
Ml 2 mid to high wet slopes
ฆI 3 valleys and toe slopes
~ 4 gentle uplands and gentle slopes
7.1 5 wflll-dratfied uplands and ridges
~ 6 low to mid dry slopes
Hm 7 mid to high dry slopes CD
1 1 8 floodplams and well-defined stream valleys j 1
1 1 9 flat uplands
I 1 11 low to mid wet slopes (granitic) ฆi
]ง 12 mid to high wet slopes (granitic)
H 13 valleys and toe slopes (gran(ic)
14 gentle uplands and gentle slopes (grantic) I 1
j| 15 wel-draned uplands and ndges (granitic)
16 low to mid dry slopes (granitic)
17 mid to high dry slopes (granitic)
18 floodplains and well-defined stream valleys (granitic)
19 flat uplands (granitic)
101 low to mid wet slopes (sandy)
102 mid to high wet slopes (sancty)
103 valleys and toe slopes (sandy)
104 gentle uplands and gentle slopes (sandy)
105 well-drained uplands and ndges (sandy)
106 low to mid dry slopes (sand/)
107 mid to high dry slopes (sandy)
108 floodplains and well-defined stream valleys (sand/)
109 flat uplands (sand/)
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Table 1. Abiotic Site Types for EPA Region 7 (based on Solar Insolation and Land Position)*
Solar
Insolation1
Land
Position2
Site Description/ Examples of Site Types
Abiotic Site Type
Site Type
Code
1
1
moderately to poorly drained with low light
(mainly toe slopes and low slopes)
low to mid wet slopes
1
1
2
moderately drained with low light
(mainly low and mid slopes)
low to mid wet slopes
1
1
3
well drained with low light
(mid and high slopes)
mid to high wet slopes
2
1
4
very well drained with low light
(high slopes and slope crests)
mid to high wet slopes
2
2
1
poorly drained with moderately low light
(relatively moist valleys)
valleys and toe slopes
3
2
2
moderately drained with moderate light
(gentle uplands and lower gentle slopes)
gentle uplands and
gentle slopes
4
2
3
moderately drained with moderate light
(gentle uplands and higher gentle slopes)
gentle uplands and
gentle slopes
4
2
4
very well drained with moderate light
(high uplands and ridges)
well-drained uplands and
ridges
5
3
1
poorly drained with moderately low light
(relatively moist valleys)
valleys and toe slopes
3
3
2
moderately drained with moderate light
(gentle uplands and higher gentle slopes)
gentle uplands and
gentle slopes
4
3
3
well drained with moderately high light
(typical uplands, high gentle slopes)
gentle uplands and
gentle slopes
4
3
4
very well drained with moderate light
(high uplands and ridges)
well-drained uplands
and ridges
5
4
1
moderately to poorly drained with high light
(toe slopes and low slopes)
low to mid dry slopes
6
4
2
moderately drained with low light
(low slopes to mid slopes)
low to mid dry slopes
6
4
3
well drained with low light
(mid slopes to high slopes)
mid to high dry slopes
7
4
very well drained with high light
(high slopes and slope crests)
mid to high dry slopes
7
Other Modeled Site Types **
Modeled floodplains and well-defined stream valleys
floodplains and well-
defined stream valleys
8
Modeled flat and gentle uplands with local relief less than 15 meters
flat uplands
9
1 Solar
Insolation 2 Land position
1 to 4 = wet to dry 1 to 4 = low to high
* Modeled site types were intersected with soils and geologic data to define geolandforms for some sections
** See text for description of other modeled site types
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Floodplain and well-defined river valley modeling required a separate and time-
consuming procedure. Modeled floodplains were a combination of five different
datasets: I) Missouri Alluvium, 2) Missouri River valley bottom, 3) floodplains created
using digital elevation models, 4) FEMA floodplains data, and 5) buffered streams.
Missouri Alluvium
This dataset was acquired from the Missouri Department of Natural Resources. It
represents areas within the state that have an alluvium surficial geology. This dataset was
used as a surrogate for floodplains within the state of Missouri.
Missouri River Valley Bottom
This dataset was acquired from the River Studies Unit at USGS's Columbia
Environmental Research Center. The dataset represents the valley bottom of the
Missouri River.
Floodplains delineated by MoRAP using Digital Elevation Models
For the creation of this dataset we used NED elevation data and selected all 30m pixels
with less than 8% slope. The study area was then divided into 40 square kilometer
hexagons. Flat areas within each hexagon were placed into one of nine classes
corresponding to different elevations. These classes included 10% of the highest
elevation within the hexagon, 20%, 30%, and so on to 90%. We then color-coded each
hexagon by these percent values for on-screen analysis using a backdrop of a topographic
hi I Ishade and a 1:100,000 stream network. This procedure included zooming to each
hexagon within a section and making a decision as to the best cut-off value (10%, 20%,
etc) for floodplain representation. These cut-off values were used to create grids of
potential floodplains for each section. As a general rule, floodplains were only delineated
for streams with Strahler stream order of two or greater. These grids were then converted
into shapefiles for on-screen digitizing of any necessary corrections. Once again using a
backdrop of a topographic hillshade and a 1:100,000 stream network, we edited these
shapefiles to better represent the potential floodplain. These shapefiles were then
converted into grids for final representation of floodplains and flat stream valleys.
FEMA Floodplains
Of the 769 counties within or partially, within the study area, 115 had floodplain data
delineated by the Federal Emergency Management Agency (FEMA). We ordered these
data and in places where FEMA floodplains existed, we used those delineations instead
of modeling them from DEMs. Most counties had complete coverage, however some
had only partial coverage around large cities and towns. Because of this intermittent
coverage, the FEMA data were used in these counties to augment the floodplains created
from DEMS.
Buffered Streams
In an effort to ensure that all primary waterways were included in the floodplains data
layer, all 1:100,000 streams with a Strahler stream order of 3 or higher were incorporated
into the final floodplains for each section. Streams were converted into 30m grids and
then buffered by one 30m grid cell on either side.
19
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For the final floodplains data layer for each section, these five datasets were merged
together in the order they are listed above (Figure 4). In this way, datasets at the
beginning of the list were treated as the most important.
31 Final floodplain grid
~ 40 sq km hexagon
Figure 4. Example of the final river floodplain and well-defined stream valley data layer,
which was incorporated into the modeled site types for EPA Region 7.
b. Percent Conversion by Abiotic Site Type
Percent conversion is based on the amount of natural or semi-natural land cover (from the
National Land Cover Dataset, NLCD, see Vogelmann et al. 2001) remaining within each
abiotic site type, and was calculated by ecological section. Hence, for each section, each
site type or geolandform was summarized by the amount of non-natural land cover it
supported. Land cover types considered non-natural were urban, cropland, water, and
bare ground. The area of non-natural land cover was divided by the total area of the site
type within that section and the result was multiplied by 100 to represent percent
conversion.
c. Opportunity Area Data Layer
Opportunity areas are natural and semi-natural land cover patches that are away from
roads and away from habitat patch edges. They are ranked based on size by landscape,
20
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from one (most important) to five (least important) within each ecological subsection.
Following are brief" methods; a complete outline of methods is found in (Diamond et al.
2003).
Base Data Creation
Land Cover. We used the NLCD, derived from 30-meter resolution classified Landsat 7
Thematic Mapper satellite data, to calculate land cover metrics (Vogelmann et al. 2001).
We reclassified the NLCD from 21 land cover classes for the study area to seven major
classes: forest, shrubland, grassland, cropland, urban, barren or sparsely vegetated, and
water.
Creation of Distance Grids for Land Cover Patches and Roads. Each 30-m pixel in a
grid was assigned a value from zero to nine for distance into the interior of a forest,
grassland, shrubland, or 'mosaic' (see below) land cover patch, and distance away from a
road. Many studies have shown that the impacts of edge and habitat fragmentation vary
among species and land cover types (see Noss and Csuti 1997, Villard et al. 1999).
Likewise, the impacts of roads, and of different road types, vary by species and habitat
(see Trombulak and Frissell 2000). Therefore, we selected a mathematical rule for
assigning cell values to create the distance grids for land cover and roads. The interval
between high and low values for each category, is 1.5 times the distance between high
and low for the category below it. A cell value of one corresponds with all cells zero to
30 meters from the edge of a land cover patch or a road right-of-way, and a two is
assigned to cells 30 to 75 meters from the edge, and so on. Interstate highways with
limited access (see TIGER roads data files at http:/www.census.gov/geo.maps/) were
assigned zeros for three pixels that represent the road and right-of-way, whereas a zero
was assigned to the single centerline pixel for all others.
We created a 'mosaic' land cover class to recognize areas of natural and semi-natural
vegetation with high interspersion but no large patches of any one land cover type.
Ninety-meter edges between forest, grassland, and shrubland were collectively defined
and modeled as 'mosaic' land cover. Ninety-meter edges were selected after iterative
modeling trials were run with wider and narrower edges; wider edges had more and more
overlap with large patches of a single land cover type, whereas results using narrower
edges did not capture significant mosaics of interspersion of different classes of natural
and semi-natural vegetation.
Creation of Landscape Type Coverage. We modeled landscape types by calculating
neighborhood statistics from original 30-meter DEM input data. Model results were
initially classified following Hammond (1954, 1964), who used slope, relief, and profile
to define landforms for the United States based on examination of 1:250,000 USGS
quadrangles. We modified his definitions in an iterative way using more than 20
modeling trials. For the models, we grouped all pixels into landscape type classes based
on analysis of slope and relief within circular neighborhoods ranging from 0.25-square
kilometers to five-square kilometers. We selected a model in which slope was broken
into two categories: more than 50% of the neighborhood on >8% slope or less than 50%,
and relief was broken into seven categories; < 15 meters, 15 to 30 meters, 30 to 90
21
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meters, 90 to 150 meters, 150 to 300 meters, 300 to 900 meters, and >900 meters.
Results fit the recognizable landforms of the study area. Hence, 14 landscape types are
possible (two slope categories multiplied by seven relief categories). We selected a one-
kilometer neighborhood size base on visual examination of on-screen overlays of the
DEMs with results using smaller and larger neighborhood windows, and overlays of the
results from different trials themselves. Smaller neighborhoods did not identify
important, larger-resolution land form variations such as gently sloping hills, whereas
larger neighborhoods failed to accurately define the spatial location of features such as
break-points where plateaus and hills come together on the landscape. Nigh and
Schroeder (2002) also selected a one-square kilometer neighborhood roughness grid to
delineate ecological subsection lines for Missouri.
Defining and Ranking Opportunity Areas
We intersected each land cover distance grid with the road distance grid to identify
opportunity areas. We selected all distance grid cell values of three or more for any land
cover class and for roads. The result is a coverage that represents areas more than 75m
into the interior of a land cover patch and 75m away from any road. We then ranked all
conservation opportunity areas based on size by landscape type within each ecological
subsection. Each opportunity area was assigned a single, ordinal value from one (highest
value) to N (lowest value; where N is the total number of conservation opportunity areas
within the subsection). The value was equal to the highest value (lowest ordinal rank) for
any landscape type patch comprising a portion of the opportunity area. The largest
opportunity area polygons for each landscape type were considered the most important,
and the smallest patches were considered least important.
d. Final Ecological Significance Data Layer: Percent Conversion and
Opportunity Area Representation
We combined scores for percent conversion and opportunity area representation to create
final ecological significance scores (Table 2, Figure 5). Natural and semi-natural land
cover on abiotic site types that have been largely converted to cultural uses were
considered more significant, because they represent habitats that were once more
common but have become relatively rare in the modern landscape. For example, extant
forests on large river floodplains, which have largely been converted to cropland, were
considered more important than forest on slopes, since the present-day forests on slopes
are relatively intact. Opportunity areas are relatively large patches of natural and semi-
natural vegetation that are away from roads and habitat patch edges, and therefore are
relatively more likely to be viable and functional, and less likely to be lost to urban
development, in the near future (Fahrig 1997, Noss and Csuti 1997, Villard et al. 1999,
Trombulak and Frissell 2000). They are ranked based on size by landscape
representation. Therefore, they capture the most viable land cover patches across all
representative landscape types within each subsection.
22
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Opportunity Area Rank
Percent Conversion
Opportunity Area Rank
HHI 1
3
4
5
Significance Rank
1
Figure 5. Ecological significance modeling procedures. Significance was determined
from evalution of percent conversion of site sipes and opportunity area representation
(see Table 2).
Percent Conversion
10-20%
20 - 30%
j | 30 - 40%
40 - 50%
50 - 60%
~
~
60 - 70%
I I 70 - 80%
!=~ Non-natural
= Significance
-------
Table 2. Ecological Significance Ranking Scheme Combining
Percent Conversion and Opportunity Area Representation
Percent High (70-100)
Conversion Medium (40-60)
Low (10-30)
Opportunity Area Rank
1 2,3,4.5 none non-natural
1
2
2
6
1
2
4
6
1
3
5
6
We set out to combine individual pixel scores for percent conversion and opportunity
area representation uniformly across all ecological sections in an ecologically meaningful
and logical way. Thus, we did not use the product or sum of ranked values for percent
conversion and opportunity area representation. Some assumptions included (1) all
natural and semi-natural land cover has some ecological significance in terms of
ecosystem function, biological conservation, and human health, and non-natural land
cover is generally much less important, especially to biological conservation, (2) natural
and semi-natural land cover on abiotic site types that have largely been converted to non-
natural uses is more significant versus that on site types that are relatively intact, (3) all
opportunity areas have at least a medium level of significance, since they represent areas
that are away from roads and away from habitat patch edges, and are assumed to be both
more functional and less subject to immediate future disturbance, and (4) high ranked
opportunity areas have the most significance, since they are the largest, representative
land cover patches of all landscape types (Table 2).
We considered a number of different ranking schemes that corresponded to our
assumptions, and also looked at the additive and multiplicative models. Each resulted in
a different percent of the ecological sections being identified as highly significant or
significant (a rank of one or two on a scale of one to six, as outlined in Table 2). We
settled on the first ranking scheme, since it corresponded most closely with our
assumptions and logic, and no alternate scheme was meaningful across all sections. The
top two ranks pulled out a mean of 14.04% of the area of each planning region with a
standard deviation of 6.27% (Appendix 2).
The results of this assessment should be viewed as having most meaning on a planning
region by planning region basis, and comparisons across the entire region should be
avoided. Our algorithms for assigning significance were uniform across the region, but
inherent differences among planning regions in terms of land use, land cover, internal
landscape variability, and road density influence the results. The total area identified in
the top two ranks for three planning regions was more than one standard deviation higher
than the mean, whereas this value was more than one standard deviation less than the
mean for three planning regions (Appendix 1). These latter two planning regions, the
North Central U.S. Driftless and Escarpment section (222L), the Cross Timbers and
24
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Prairies (225A) and the Osage Plains (251 E, Figure 2), each had more than 70% natural
and semi-natural vegetation and a relatively high density of roads. Therefore, the
opportunity areas were small due to road density and the percent conversion values were
low, which combined resulted in a low area represented in significance scores one and
two. In the case of the three sections where relatively more area was identified in
significance class one and two, the values for percent conversion were high, and thus
much of the remaining natural and semi-natural vegetation in these largely agricultural
regions fell within significance class two (see the North Missouri Alluvial Plain section,
234B; Southern High Plains 331B; Central High Plains 331 H; Figure 2).
2. Creation of Threats Surface
The primary threats to ecological integrity in EPA Region 7 result from habitat alteration
or destruction due to development of urban infrastructure or conversion of natural
vegetation to row crops. For terrestrial ecosystems, there is a lesser threat from toxic
releases. The threat index was constructed to reflect these three sources of stress by
combining indices constructed from widely available medium to large scale data sets.
a. Development Land Demand
Development land demand is a surface of 30-meter pixels that represents the base desire
for land (Wickman et al., 2000) based on proximity to urban areas (cities greater than
10,000 people) and population density change from 1990 to 2000. Previous work
completed by Wickman et al. (2000) modeled land demand by splining quotients of
population over distance and tested several weighted results using an inverse distance
weighted surface. We adapted the analysis through expanded roles for the two primary
variables^ urban area and population density.
The proximity portion of the development land demand index weighted combinations of
buffers around urban areas, roads, and metropolitan statistical areas (MSAs). We made
the basic assumption that growth is more likely to occur within urban areas and we
filtered the weights along roads (1 km buffer) and within MSA boundaries (25 km).
Pixels are weighted from one (not within a buffer) to five (within an urban atea> 10,000
people) and are summarized below:
1 - not within I km of any road and not near a city or metropolitan statistical area
(MSA)
2 - within 1km of a road but not within 25km of a metropolitan MSA or 10km of
city
3 - within I km of a road and within 25km of an (MSA) but not within 10km of a
city
4 - within 1 km of a road and within 10km of a city
5 - within the boundary of a city limit
Proximity data sources are summarized as follows:
Cities larger than 10,000
National Atlas of the United
ESRI Data & Maps, 2003
25
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States and the United States
Geological Survey, ESRI
Roads
U.S. Census Tiger/Line
U.S. Census, 2004
Metropolitan Statistical
Areas (MSAs)
U.S. Office of Management
and Budget
ESRI Data & Maps, 2003
Weighted change in population density from 1990 to 2000 reflects the population demand
portion of the Development Land Demand index. We reasoned that areas where the
human population expanded would be subjected to a higher development land demand
versus those that were stable or declined in population. Population density change was
calculated using U.S. Census Blocks. Geolytics software rectified spatial changes in
block boundaries by weighting data across the 1990 or 2000 block and than re-
apportioned the data to the "corresponding" block (A complete technical description of
the area weighting methodology can be found online at:
http:/Avww.geolvtics.coiri/USCcnsus.Census-1990-Long-Form-2000-
Boundaries.Data.Methodology.Products.aspl. The density change equals the 1990
population per km2 subtracted from the 2000 population per km and then normalized by
the 2000 population per km2. The resulting percentages received a weight from one to
five.
1 = large population loss (less than -1.0)
2 = population loss (-1 to -0.25)
3 = stable population (-0.25 to 0.25)
4 = population growth (0.25 to 0.50)
5 = large positive growth (0.50 to 1.0)
Proximity and population density change analyses occurred within vector polygon
shapefiles and then were converted into 30-meter grid datasets. The proximity weight
grid summed with the population change weight grid resulted in the Development Land
Demand (Dp) index with a value range from one (low demand) to ten (high demand).
b. Agricultural Threat
An agricultural threat index was created froin the USGS GIRAS land cover data and
NLCD data. Both data sets were reclassified to reflect only agricultural and non-
agricultural land uses. The historic data used was the USGS GIRAS landcover data with
dates ranging from the mid 1970s to the early 1980s (Environmental Protection Agency's
Office of Information Resources Management (OIRM). The data set was re-classified to
reflect agricultural land coded as 1 and all other classes coded to zero as follows:
21 Cropland and pasture
22 Orchards, groves, vineyards, nurseries, and ornamental horticultural
23 Confined feeding operations
26
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24 Other agricultural land
31 Herbaceous rangeland
33 Mixed rangeland
The existing 100 m grid was re-sampled to a 30m grid to conform to the NLCD grid
structure. Agricultural density was then calculated using ArcGIS 9.1 rectangular
neighborhood analysis with a 33x33 cell local window. The "'current" data used was the
NLCD land cover classification product based primarily on 1992 Landsat
Thematic Mapper (TM) data. This data set was also re-classified to reflect agricultural
land coded as 1 and all other classes coded to zero as follows:
6! Orchards/Vineyards/Other
71 Grasslands/Herbaceous
81 Pasture/Hay
82 Row Crops
83 Small Grains
84 Fallow
Agricultural density was calculated as for the GIRAS grid. The change in density was
then calculated by subtracting the GJRAS density grid from the NLCD density grid and
the result was reclassified into five classes using Jenk's natural breaks.
The NLCD density grid was then multiplied by the change weighting factor and the
results reclassified into a final five class agricultural threat index, where one is the lowest
threat and five the highest. Natural breaks were again used to derive the classes.
c. Toxics Index
The toxics index was derived from the EPA Toxic Release Inventory (TRI) of 2002 and
the location of Missouri lead mines and smelters. Only air releases in the TRI were
considered to have a potentially significant impact on terrestrial systems. Lead is a
significant ecological problem in the historic and current lead mining areas of Missouri,
not all of which are represented in the TRI, hence the addition of these data into the index
calculations. Buffers were created for the TRI facilities based on the amount of the
annual release. Total air releases were categorized into a five tier classification and
buffers were created from 1-5000 m based on the class for each facility. Lead mines and
smelters were also buffered, 1000 m for mine sites and 3000 m for smelters. Only those
sites not included in the TRI were used. The three shapefiles were then combined and the
combined shapefile converted to a 30m grid with each grid cell having a value equal to
the total number of buffers that overlay it. This grid was then reclassified from 0-5 for the
final toxics index.
d. Creation of Final Threats Surface
The final threats surface was calculated as the sum of development land demand,
agriculture land demand, and potential toxic release impacts. The final grid was ranked
from 1-6 based on standard deviations, where one is the lowest threat and six the highest.
27
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3. Creation of Ecological Risk Surface: A Combination of Significance and
Threat
By our definition, ecological risk is high when there is a high risk of loosing a highly
significant patch of natural or semi-natural vegetation. Our approach to combining
ecological significance and threat data to create a risk surface was based on the
assumption that ecological significance should be weighted more than threat. We also
assumed that areas of non-natural vegetation are of low risk, because they are of low
functional ecological value. Areas of high significance are important regardless of the
threat level, and areas of low significance are low risk regardless of threat. Areas of
intermediate significance are more important if the threat is higher (Figure 6, Appendix
3)
The mean percent of area within the highest two risk categories by planning region was
32.4%, with a standard deviation of 16.7%. These results are most relevant at on a
planning region by planning region basis.
28
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Significance +
Threat
Figure 6. Ecological risk modeling procedures, Risk was determined by evaluating
significance and threat (see Table 3).
29
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Table 3. Risk Assessment Methods
Significance
high
low
non-
natural
1
2
3
4
5
6
low
10
11
12
13
14
15
16
20
21
22
23
24
25
26
30
31
32
33
34
35
36
40
41
42
43
44
45
46
50
51
52
53
54
55
56
high
60
61
62
63
64
65
66
Risk
1
2
3
4
5
high
low
non-
natural
B. Irreplaceability Analysis
Several algorithms and software programs have been recently designed to attach values to
assessment units, such as hexagons, parcels, or a regular grid, within assessment regions,
such as ecoregions or states (see Ferrier et al. 200, Noss 2004). Such assessments require
a combination of biotic and abiotic conservation targets that represent ecological
structure, function, and processes (Margules and Pressey 2000). Planners and managers
must also set quantitative goals for representing the targets, such as hectares or percent
representation within the planning region (see Noss et al. 2002). Noss (2004) points out
that appropriate, even coverage of digital data is required for all targets, and that different
assessments and assessment regions may require a different set of surrogate targets.
1. Overall methodology
We selected the software package C-Plan to attach irreplaceability values to 40 square
kilometer hexagons, our assessment units, within each planning region. The definition of
irreplaceability is "the likelihood that a given site will need to be protected to achieve a
specified set of targets or, conversely, the extent to which options for achieving these
targets are reduced if the site is not protected" (Pressey et al. 1994). A highly
irreplaceable hexagon has few or no replacements in the scheme of selected sets of
hexagons that achieve the conservation goals within the section.
30
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The irreplaceability of hexagon X is based 011 the proportion of sets of hexagons that
meet the quantitative target goals ("representative sets," R) that must include hexagon X
versus those that meet the target goals without hexagon X:
Irreplaceability = R(x included") - Rfx removed)
R(x included) + R (x removed)
When multiple targets are assessed, the site irreplaceability is equal to the highest
irreplaceability value for a given hexagon across all targets, whereas the summed
irreplaceability is the sum of all irreplaceability values for all targets for a given hexagon.
We were interested in site irreplaceability, so each 40 sq km hexagon was assigned a
value between 0 and 1.
2. EPA Region 7 Results
For EPA Region 7, we selected targets and set thresholds for capture of targets in EPA
R7 as follows:
Abiotic Site Types: 25% of each within the section
Opportunity Areas Ranked #1: 40%
Areas of High Vertebrate Richness: 25% of the top 20% richest areas
Abiotic site type targets ensure representation of habitats, whereas high vertebrate
richness is a biotic target Opportunity areas ate both a biotic and abiotic target, since
they are the largest, most functional patches of extant semi-natural vegetation of each
landscape type by section.
Vertebrate richness was assigned to 30 m grid cells based on state by state results of Gap
Analysis projects. Since different states used different methods to model species
distribution, we first clipped each state grid with the section boundaries, and then selected
the top 20% richest grid cells for each state. We then merged the section pieces together
and selected the top 25% richest cells in each section. This process served to smooth
differences among results across state lines. No results were available for the states of
Minnesota and Wisconsin, so we ran separate Irreplaceability analyses for sections that
intersected those states excluding vertebrate richness as a target (sections 251B, the North
Central Glaciated Plains; 222M, the Minnesota and Northeast Iowa Morainal-Oak
Savanna; and section 222L, the North Central U. S. Driftless and Escarpment section).
We assigned each 40 square kilometer hexagon into one of five Irreplaceability classes
based on the raw scores. Raw scores ranged from 0 (most replaceable) to 1 (highly
irreplaceable) and were assigned to classes one to five as follows:
Raw Irreplaceability Score: Irreplaceability Class
0-0.2 5
0.2 - 0.4 4
31
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0.4 - 0.6 3
0.6-0.8 2
0.8-1.0 I
The mean area by section within IrreplaceabiIity categories one was 4.3% with a standard
deviation of 5.9% (see Figure 7, Appendix 3). The mean area within categories one plus
two combined was 8.7% with a standard deviation of 9.2%. Again, results are most
relevant on a planning region by region (section by section) basis.
32
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Figure 7. Irreplaceability values attached to 40 square kilometer hexagons (assessment
units) for EPA Region 7. Irreplaceability scores for each hexagon were determined by
evaluation of biotic (opportunity area representation, vertebrate species diversity) and
abiotic (site type representation) taigets.
t'22L
3320
222M
332C
25 IB
251G
33 tH
251C
251E
234D,
150
Irreplaceability
I >0 6 - 0.8
I I >0.4 - 0.6
I I >0.2 - 0.4
I I >0-0.2
i IIRREPL = 0
C. Identification of Conservation Focus Areas: A Combination of Risk and
Irreplaceability
We used the ecological risk and irreplaceability results to identify conservation focus
areas (Figure 8). We used logic similar to that used to combine significance and threat to
33
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define risk. Areas of highest risk or high irreplaceability and high risk or at least
moderate risk and highest irreplaceability were identified as conservation focus areas:
Conservation Focus Area Identification:
Case 1: highest risk (ranked I) and any irreplaceability
Case 2: high risk (>=2) and high irreplaceability (>=2)
Case 3: at least moderate risk (>=3) and moderate irreplaceability (>=3)
We eliminated all conservation focus area patches that were less than two hectares. An
average of 8.3% of each planning region was within conservation focus areas, with a
standard deviation of 4.3% (Table 4, Figure 8). Planning regions that are relatively
natural had higher percentages of conservation focus areas. These planning regions
included the Nebraska Sand Hills (332C), Flint Hills (25IE) and adjacent Cross Timbers
and Prairies, and Ozark Highlands (223A) had relatively large patches of natural and
semi-natural vegetation that are away from roads and habitat patch edges, which are
considered conservation focus areas (Figure 8, Appendix 5). Planning regions that are
largely cultural such as the North Central Glaciated Plains (251C) and the Central
Dissected Till Plains (251B) had relatively small percentages of conservation focus areas.
However, due to the scale at which the figures are produced herein, they appear to have
more conservation focus areas than they do, because many of the conservation focus
areas are small patches of semi-natural vegetation within a sea of row crop agriculture.
34
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Figure 8. Conservation locus areas Ibr L-TA Region 7.
300 Miles
Hi Conservation Focus Areas
~ State 9
... ] Terrestrial Ecoregions
-------
Table 4. Summary of Conservation Focus Areas by Area and Percent for
Ecological Planning Regions in EPA Region 7 *
Section
Number
Section Name
Focus
Areas
Area (ha)
Percent
222 L
North Central U. S. Driftless and Escarpment
275,130
5 5%
222M
Minnesota and Northeast Iowa Morainal-Oak Savanna
90,730
3.2%
223A
Ozark Highlands
1,567,500
13.5%
223S
Missouri River Loess
209,920
8.7%
234B
North Mississippi Alluvial Plain
165,940
4 8%
234D
White and Black River Alluvial Plains
162,210
6 9%
251B
North Central Glaciated Plains
383,980
3.0%
251C
Central Dissected Till Plains
528,020
4.3%
251E
Osage Plains
121,440
2.8%
251F
Flint Hills
226,850
8 6%
251G
Missouri Loess Hills
195,290
4.2%
251H
Nebraska Rolling Hills
151,690
2.9%
255A
Cross Timbers and Prairies
229,540
8.5%
315F
Northern Texas High Plains
333,340
12.9%
331B
Southern High Plains
690,790
11.4%
331C
Central High Tablelands
701,820
9.1%
331F
Western Great Plains
978,720
15.1%
331H
Central High Plains
501,710
11.4%
332C
Nebraska Sand Hills
1,441,900
15.4%
332D
North-Central Great Plains
223,810
10.5%
332 E
South Central Great Plains
419,810
4.4%
332F
South Central and Red Bed Plains
429,320
7.5%
M334A
Black Hills
195,170
15.1%
* Only areas >= 2 hectares were selected
III. Aquatic Assessment
The methods used to identify aquatic conservation focus areas throughout EPA Region 7
were developed by a EPA staff, MoRAP staff, and a team of aquatic resource
professional from around Missouri. At a series of meetings this team was instructed on
the general goal of the project and was provided detailed overviews on the geospatial and
tabular data available for the assessment process. The first task set before the team was
to develop a narrative goal for the aquatic assessment that would provide a common
baseline for all those involved. The team formulated the following goal; "Ensure the
long-term persistence of native aquatic plant and animal communities, by conserving the
conditions and processes that sustain them, so people may benefit from their values in the
future. " The team then identified a list of principles, theories, and assumptions they
believed had to be considered or adhered to in order to achieve this goal. These mainly
related to basic principles of stream ecology, landscape ecology, and conservation
36
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biology (Appendix 6). However, some reflected the personal experiences of leam
members and the challenges the)' face when conserving natural resources in regions with
limited public land holdings. For instance, one of the assumptions identified by the team
was: "Success will often hinge upon the participation of local stakeholders, which will
often be private landowners." In fact, the importance of private lands management for
-aquatic biodiversity conservation was a topic that permeated throughout the initial
meetings of the team. Next, the team drafted a more specific tactical objective for
meeting the overall goal; "Identify and map a set of aquatic conservation focus areas that
holistically represent the full breadth of distinct riverine ecosystems and multiple
populations of all native aquatic species. "
Once the goal, fundamental principals and assumptions, and tactical objective were
established, we worked to develop a customized GIS-based decision support system for
the Meramec Ecological Drainage Unit, which served as the pilot area for the assessment.
The team developed a specific assessment strategy that identified/adopted the, a)
geographic framework for the assessment, b) abiotic and biotic targets, and c)
quantitative and qualitative assessment criteria for selecting priority locations for
conservation. The pilot decision support system and assessment strategy were slightly
modified based on the collective input of all individuals participating in the assessment.
Decision support systems were then developed for all of the other EDUs across Missouri.
Regional teams of experts were established and conservation assessments were then
conducted for each EDU. Based on these assessments, a total of 158 conservation focus
areas were identified across Missouri. We then used a conservation planning software
(C-Plan) to assess the complimentarity of species capture across all of the focus areas and
provide one means of prioritizing all 158 areas.
Only a subset of the data used to identify aquatic conservation focus areas in Missouri
were available for the other three states within EPA Region 7. Consequently, we
developed a more general and coarser-scale conservation assessment strategy to identify
conservation focus areas throughout Iowa, Kansas, and Nebraska. Yet, the resulting
focus areas across these three states still provide a very useful blueprint for conserving
the diversity of freshwater ecosystems that occur within this part of EPA Region 7.
A. Aquatic Conservation Assessment for Missouri
The decision support systems that were used to conduct the aquatic conservation
assessments across Missouri included all of the data compiled or created for the Missouri
Aquatic GAP Project, as well as other pertinent geospatial data developed for this project.
In particular, four geospatial datasets served as the core information sources used to
identify conservation focus areas across the state. In the next four sections we provide
overviews of these primary geospatial datasets in order to provide the reader an
understanding of the utility and limitations of these data. Following these overviews are
sections outlining the conservation assessment strategy developed by the team of aquatic
resource professionals and the results of the assessment for the pilot area and the state.
37
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1. Aquatic Classification
Conservation planning and assessment are geographical exercises and thus require the
selection of a suitable geographic framework. More specifically, this involves selecting,
defining, and mapping planning regions and assessment units. A planning region refers
to the area for which the conservation assessment is conducted. It defines the spatial
extent of the assessment or conservation plan. Assessment units are geographic subunits
of the planning region. These units define the spatial grain of analysis and represent
those units among which relative quantitative or qualitative comparisons will be made in
order to select specific geographic locations as priorities for conservation. Planning
regions and assessment units can be variously defined and should be hierarchical in
nature to allow for multiscale assessment and planning (Wiens 1989). Boundaries could
be based on sociopolitical boundaries (e.g., nations, states, counties, townships), regular
grids (e.g., UTM zones or EPA EMAP hexagons), or ecologically defined units (e.g.,
watersheds or ecoregions).
Since ecosystems or patterns of biodiversity do not follow sociopolitical boundaries or
regular grids, whenever possible, planning regions and assessment units should be based
on ecologically defined boundaries since these boundaries provide a more informative
ecological context (Bailey I995;0mernik 1995; Leslie et al. 1996; Higgins 2003).
Agreeing with this premise, the team of aquatic resource professionals selected the
MoRAP aquatic ecological classification hierarchy as the geographic framework for the
conservation assessment. This classification hierarchy is briefly described below.
It is widely accepted that to conserve biodiversity we must conserve ecosystems
(Franklin 1993; Grumbine 1994). It is also widely accepted that ecosystems can be
defined at multiple spatial scales (Noss 1990; Orians 1993). Consequently, a key
objective was to define and map distinct riverine ecosystems (often termed ecological
units) at multiple levels. Yet, before distinct riverine ecosystems could be classified and
mapped, the question "What factors make an ecosystem distinct?" needed to be
answered. Ecosystems can be distinct with regard to their structure, function, or
composition (Noss 1990).
Structural features in riverine ecosystems include factors such as depth, velocity,
substrate, or the presence and relative abundance of habitat types. Functional properties
include factors such as flow regime, thermal regime, sediment budgets, energy sources,
and energy budgets. Composition can refer to either abiotic (e.g., habitat types) or biotic
factors (e.g., species). While both are important, our focus here will be on biological
composition, which can be further subdivided into ecological composition (e.g.,
physiological tolerances, reproductive strategies, foraging strategies, etc...) ortaxonomic
composition (e.g., distinct species or phylogenies) (Angermeier and Schlosser 1995).
Geographic variation in ecological composition is generally closely associated with
geographic variation in ecosystem structure and function. For instance, fish species
found in streams draining the Central Plains of northern Missouri generally have higher
physiological tolerances for low dissolved oxygen and high temperatures than species
restricted to the Ozarks, which corresponds to the prevalence of such conditions within
38
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the Central Plains (Pflieger 1971; Matthews 1987; Smale and Rabeni 1995a, 1995b).
Differences in taxonomic composition, not related to differences in ecological
composition, are typically the result of differences in evolutionary history between
locations (Mayr 1963). For instance, differences among biological assemblages found on
islands despite the physiographic similarity of the islands.
Considering the above, a more specific objective was to identify and map riverine
ecosystems that are relatively distinct with regard to ecosystem structure, function, and
evolutionary history at multiple levels. To accomplish this, an eight-level classification
hierarchy was developed in conjunction with The Nature Conservancy's Freshwater
Initiative (Higgins 2003, Figure 9). These eight geographically-dependent and
hierarchically-nested levels (described next) were either empirically delineated using
biological data or delineated in a top-down fashion. For the top-down approach we used
landscape and stream features (e.g., drainage boundaries, geology, soils, landform, stream
size, gradient, etc.) that have consistently been shown to be associated with or ultimately
control structural, functional, and compositional variation in riverine ecosystems (Hynes
1975; Dunne and Leopold 1978; Matthews 1998). More specifically, levels 1-3 and 5
account for geographic variation in taxonomic or genetic-level composition resulting
from distinct evolutionary histories, while levels 4 and 6-8 account for geographic
variation in ecosystem structure, function, and ecological composition of riverine
assemblages. The most succinct way to think about the hierarchy is that it represents a
merger between the different approaches taken by biogeographers and physical scientists
for tesselating the landscape into distinct geographic units.
39
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Aquatic Ecological System
Types
Ecological Drainage Units
Figure 9. Maps
hierarchy.
a. Levels I - 3: Zone, Subzone, and Region
The upper three levels of the hierarchy are largely zoogeographic strata representing
geographic variation in taxonomic (family and species-level) composition of aquatic
assemblages across the landscape resulting from distinct evolutionary histories (e.g.,
Pacific versus Atlantic drainages). For these three levels we adopted the ecological units
delineated by Maxwell et al. (1995) who used existing literature and data, expert opinion,
and maps of North American aquatic zoogeography (primarily broad family-level
patterns for fish and also unique aquatic communities) to delineate each of the geographic
units in their hierarchy. More recent quantitative analyses of family-level faunal
Valley Segment Types
showing Levels 4-7 of the MoRAP Aquatic Ecological Classification
40
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similarities for fishes conducted by Matthews (1998) provide additional empirical support
for the upper levels of the Maxwell et al. (1995) hierarchy. The ecological context
provided by these first three levels may seem of little value; however, such global or
subcontinental perspectives are critically important for research and conservation (see pp.
261-262 in Matthews 1998). For instance, the physiographic similarities along the
boundary of the Mississippi and Atlantic drainages often produce ecologically similar
(i.e., functional composition) riverine assemblages within the smaller streams draining
either side of this boundary, as Angermeier and Winston (1998) and Angermeier et al.
(2000) found in Virginia. However, from a species composition or phylogenetic
standpoint, these ecologically similar assemblages are quite different as a result of their
distinct evolutionary histories (Angermeier and Winston 1998; Angermeier et al. 2000).
Such information is especially important for those states that straddle these two
drainages, such as Georgia, Maryland, New York, North Carolina, Pennsylvania,
Tennessee, Virginia, and West Virginia, since simple richness or diversity measures not
placed within this broad ecological context would fail to identify, separate, and thus
conserve distinctive components of biodiversity. The importance of this broader context
also holds for those states that straddle the continental divide or any of the major drainage
systems of the United States (e.g., Mississippi Drainage vs. Great Lakes or Rio Grande
Drainage).
b. Level 4: Aquatic Subregions
Aquatic Subregions are physiographic or ecoregional substrata of Regions and thus
account for differences in the ecological composition of riverine assemblages resulting
from geographic variation in ecosystem structure and function (Figure 10). However, the
boundaries between Subregions follow major drainage divides to account for drainage-
specific evolutionary histories in subsequent levels of the hierarchy. The three Aquatic
Subregions that cover Missouri (i.e., Central Plains, Ozarks, and Mississippi Alluvial
Basin) largely correspond with the three major aquatic faunal regions of Missouri
described by Pflieger (1989). Pflieger used a species distributional limit analysis and
multivariate analyses of fish community data to empirically define these three major
faunal regions. Subsequent studies examining macroinvertebrate assemblages have
provided additional empirical evidence that these Subregions are necessary strata to
account for biophysical variation in Missouri's riverine ecosystems (Pflieger 1996;
Rabeni et al. 1997; Rabeni and Doisy 2000). Each Subregion contains streams with
relatively distinct structural features, functional processes, and aquatic assemblages in
terms of both taxonomic and ecological composition.
41
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*
J
Central Plains '}
Ozarks
Mississippi Alluvial Basin
amr
Figure 10. Map showing the boundaries of the three Aquatic Subregions of Missouri.
c. Level 5: Ecological Drainage Units
Embedded within Aquatic Subregions are geographic variations in taxonomic
composition (species- and genetic-level) resulting from the geographically distinct
evolutionary histories of the major drainages within each Subregion (Pflieger 1971;
Mayden 1987; Mayden 1988; Crandall 1998; Matthews and Robison 1998). Level 5 of
the hierarchy, Ecological Drainage Units (EDUs), account for these differences (Figure
11). An initial set of EDUs was empirically defined by grouping USGS 8-digit
hydrologic units (HUs) with relatively similar fish assemblages based on the results of
multivariate analyses of fish community data (Nonmetric Multidimensional Scaling,
Principal Components Analysis, and Cluster Analysis). We then used collection records
for three other taxa (crayfish, mussels, and snails) to further examine faunal similarities
among the major drainages within each Subregion and refined the boundaries of this draft
set of EDUs when necessary. Spatial biases and other problems with the data prohibited
including these taxa in the multivariate analyses. In only one instance were the draft
boundaries altered. Within the Ozark Aquatic Subregion the subdrainages of the Osage
and Gasconade basins consistently grouped together using the methods described above.
However, a more general assessment using Jacaard similarity coefficients suggested the
need to separate these two drainages. Using just fish community data, the Jacaard
similarity coefficient among these two drainages is 86, while when using combined data
for crayfish, mussels, and snails the similarity coefficient drops to only 56.
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Figure 11. Map of Ecological Drainage Units (EDUs) for Missouri.
03 Acnafc Subregion Boundary
Central P lains/BI ackv^at er A.arni ne
Centra! Plains/Cuivre/Salt
v'. Central Plains/Des Moines
Central PlainsA3rand/Chariton
Central Plans/Kansas
Central Plains/NishnabotnaJPIatte
Central Plains/Osags/South Grand
(XiMS Alluvid BasnBlack/Cache
MS flluMd Basin/St FrancisAJttle
MS AJIu\4d Basin/St Johns Bayou
;; Ozark /Appt e/JoachirTi
CS Ozark ฉlack*: urent
Ozark Gasconade
C;3 Ozark Meramec
Ozark Moreau/lout re
CZ Ozark Neosho
Ozark/Osage
Ozark AJpper St. FrandsCastcr
C3 Ozark AMIte
EDUs are very much analogous to "islands" when viewed within the context of the
surrounding Aquatic Subregion, which is analogous to the "sea" in which the EDUs
reside. Our analyses show that the relative similarity (based on centroid distance) of
EDUs, within an Aquatic Subregion, is negatively related to the number of river miles
separating their respective outlets. Matthews and Robison (1998) found this same
relationship for a similar analysis conducted in Arkansas. These results also directly
correspond with the relative similarity of assemblages on two or more islands, which is
generally negatively related to the distance between the islands (Mayr 1963).
Consequently, within a given Aquatic Subregion, all of the EDUs have assemblages with
relatively similar ecological composition (e.g., physiological tolerances, reproductive and
foraging strategies). However, the taxonomic composition (species and genetic level) of
the assemblage of any given EDU is relatively distinct due to evolutionary processes such
as adaptive radiation, differences in colonization history, random genetic mutation, etc.
cl. Level 6: Aquatic Ecological System Types
While Aquatic Subregions are relatively distinct in terms of their climatic, geologic, soil,
landform, and stream character, they are by no means homogeneous. These finer-
resolution variations in physiography also influence the ecological composition of local
assemblages (Pflieger 1971; Hynes 1975; Richards et al. 1996; Panfil and Jacobson 2001;
43
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Wang et al. 2003). To account for this finer-resolution variation in ecological
composition we used multivariate cluster analysis of quantitative landscape data to group
small- and large-river watersheds into distinct Aquatic Ecological System Types (AES-
Types). AES-Types represent watersheds or subdrainages (that are approximately 100 to
600 mi2 with relatively distinct (local and overall watershed) combinations of geology,
soils, landform, and groundwater influence (Figure 12). We determined the number of
distinct types by examining relativized overlay plots of the cubic clustering criterion,
pseudo F-statistic, and the overall R-square as the number of clusters was increased
(Calinski and Harabasz 1974; Sarle 1983). Plotting these criteria against the number of
clusters and then determining where these three criteria are simultaneously maximized
provides a good indication of the number of distinct clusters within the overall data set
(Calinski and Harabasz 1974; Sarle 1983; Milligan and Cooper 1985; SAS 1990;
Salvador and Chan 2003). Thirty-eight AES-Types were identified for Missouri with this
method.
AES-Types often initially generate confusion simply because the words or acronym used
to name them are unfamiliar. In reality, AES-Types are just "habitat types" at a much
broader scale than most aquatic ecologists are familiar with. We have no problem
recognizing lake types or wetland types; AES-Types are no different except that they
apply specifically to riverine ecosystems. And, just like any habitat classification, there
can be multiple instances of the same habitat type. For example, a riffle is a habitat type,
yet there are literally millions of individual riffles that occupy the landscape. Each riffle
is a spatially distinct habitat, however, they all fall under the same habitat type with
relatively similar structural features, functional processes, and ecologically-defined
assemblages. The same holds true for AES-Types. Each individual AES is a spatially
distinct macrohabitat, however, all individual AESs that are structurally and functionally
similar fall under the same AES-Type.
One assumption for this level of the hierarchy is that under natural conditions individual
AESs of the same Type will contain streams having relatively similar hydrologic
regimes, physical habitat, water chemistries, energy sources, energy and sediment
budgets, and ultimately aquatic assemblages. Another assumption is that each AES-Type
has a relatively distinct land use potential and vulnerability to a given land use. The
reason biological data were not used to empirically define and map AES-Types is that the
available data was not suited to the task at hand. At this level of the hierarchy we are
interested in differences in the relative abundance of various physiological and functional
guilds, not the mere presence or absence of species and existing data are not suited to this
more detailed quantification. We are also interested in defining assemblages in a
pluralistic context at this level, meaning we are trying to identify relatively distinct
complexes of multiple local assemblages (e.g., distinct interacting complexes of
headwater, creek, small, and/or large river assemblages).
44
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t.v
EDU Boundary
Beaver Creek
Big Creek of the St. Francis
Boeuf Creek
Bull Creek
Cane Creek
Crowley's Ridge
City of Chaffee
City of Charleston
City of Gideon
'
CityofHayti
, City of Senath
Clear Creek
. Dry Fork of the Meramec
AES Type Names
East Locust Creek
FWeV Creek
Honey Creek
Indian Creek
Jacks Fork
L,ck Cfeek
Little River
Little St. Francis River
l& Lower Meramec
Middle River
t Middle Upper Big River
Middle Upper Little Sac
Moniteau Creek
'
Ramsey Creek
Rock Creek
Sampson Creek
^0^ South Deepwater Creek
{ Spring Creek of the Eleven Point
Spring River of the Eleven Point
' ' \ St Johns Diversion Ditch
Tavern Creek
Upper Big Piney
Upper Cuivre River
f ; Upper Spring River of the Neosho
West Ditch
Wilkerson Ditch
Figure 12. Map of the thirty-nine distinct Aquatic Ecological System Types (AES-Types) for
Missouri.
e. Level 7: Valley Segment Types
In Level 7 of the hierarchy Valley Segment Types (VSTs) are defined and mapped to
account for longitudinal and other linear variation in ecosystem structure and function
that is so prevalent in lotic environments (Figure 13). Stream segments within the
45
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1:100.000 USGS/EPA National Hydrography Dataset were attributed according to
various categories of stream size, flow, gradient, temperature, and geology through which
they flow, and also the position of the segment within the larger drainage network. These
variables have been consistently shown to be associated with geographic variation in
assemblage composition (Moyle and Cech 1988; Pflieger 1989, Osborne and Wiley 1992;
Allan 1995; Seelbach et al. 1997; Matthews 1998). Each distinct combination of variable
attributes represents a distinct VST. Stream size classes (i.e., headwater, creek, small
river, large river, and great river) are based on those of Pflieger (1989), which were
empirically derived with multivariate analyses and prevalence indices. As in the level 6
AESs, VSTs may seem foreign to some, yet if they are simply viewed as habitat types the
confusion is removed. Each individual valley segment is a spatially distinct habitat, but
valley segments of the same size, temperature, flow, gradient, etc. all fall under the same
VST.
Figure 13. Map showing streams classified in to distinct stream Valley Segment Types
for Missouri.
f Level 8: Habitat Types
Units of the final level of the hierarchy, Habitat Types (e.g., high-gradient riffle, lateral
scour pool), are simply too small and temporally dynamic to map within a GIS across
broad regions or at a scale of 1:100,000. However, we believe it is important to
recognize this level of the hierarchy since it is a widely recognized component of natural
46
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variation in riverine assemblages (Bisson et a], 1982; Frissell et al. 1986; Peterson 1996;
Peterson and Rabeni 2001).
2. Biological Data
For the Missouri Aquatic GAP Project, MoRAP compiled nearly 7,000 collection records
for fish, mussels, and crayfish. Despite this relatively high number of samples, these data
reveal that 99.7% of the stream miles in Missouri have never been sampled. In addition,
several of the USGS/NRCS 10 and 12-digit Hydrologic Units have either no samples or
only a handful of samples for any of these three taxa. Analyses, performed by MoRAP
also showed that approximately 30 to 40 samples are required to accurately document
fish species composition within a 10-digit HU alone. These analyses reveal that
conservation assessments that utilize existing collection records to calculate various
biological metrics (e.g., species richness) to identify geographic priorities will, in all
likelihood, generate priorities that are more a reflection of sampling effort than true
patterns of biodiversity. Consequently, to overcome this problem MoRAP developed
predicted distribution models for 315 fish, mussel, and crayfish species that occur within
Missouri. The team of aquatic resource professionals agreed that the biological metrics
used to identify conservation focus areas should be primarily based on the data provided
by these predicted models. However, they also agreed that, when necessary and
appropriate, actual collection records should be used as a supplemental information
source in the decision making process. The following paragraphs provide a brief
description of the methods used to generate predicted distribution models and maps for
riverine biota in Missouri. More detailed methods can be found in Sowa et al. (2005).
To construct our predictive distribution models we compiled nearly 7,000 collection
records for fish, mussels, and crayfish and spatially linked these records to the 12-digit
USGS/NRCS Hydrologic Unit coverage for Missouri and also to the Valley Segment GIS
coverage, described above. Range maps were produced for each of the 315 species, sent
out for professional review, and modified as needed. Then we used Decision Tree
Analyses to construct predictive distribution models for each species. Ultimately, a total
of 571 models were developed to construct reach-specific predictive distribution maps for
the 315 species. The resulting maps were merged into a single hyperdistribution (Figure
(4), which is related to a database containing information on the conservation status,
ecological character, and endemism level of each species.
47
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(Crayfish.
Species Richness
(Crayfish, Fish, Mussel)
EDU Boundary
61 - 89
90 -146
1-20
21 -37
38-60
II
Figure 14. Map of species richness for Missouri, which is based upon predicted
distribution models for 315 fish, mussel, and crayfish species. Users can also
individually select stream segments within a GIS to obtain a list of the species predicted
to occur within each segment of interest.
Users can select an individual stream segment within the Valley Segment coverage and
generate a list of those species (and associated information) predicted to occur in that
segment under relatively undisturbed conditions (anthropogenic stressors were not or
could not be accounted for). In addition, the data from these predictive models were
spatially linked to the upper levels of the MoRAP aquatic classification hierarchy so that
species lists can be generated for any or all of the spatial units at any given level of the
hierarchy. An accuracy assessment was conducted for each taxonomic group using
independent data. Commission errors, averaged across all three taxa, were relatively high
(55%), while omission errors were relatively low (9%). We believe these accuracy
statistics can be improved by incorporating watershed variables as predictors as well as
by getting more detailed temperature data for valley segments. However, it must be
pointed out that this accuracy assessment is fraught with problems mainly related to the
inadequacy of the independent data used to evaluate the accuracy of our models (e.g.,
insufficient length of stream sampled, only a single sample at a single point in time,
inefficient gear, and many of the sampling sites were degraded to some degree while our
models predict composition under relatively undisturbed conditions). An assessment of a
handful of relatively high-quality, intensively-sampled, streams revealed a much lower
commission error rate (35%), but also a higher omission error rate (18%).
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3. Human Stressors
Another fundamental principal or assumption identified by the team of aquatic resource
professionals was that, proactive protective measures are less costly and more likely to
succeed than intensive restoration measures with regard to the conservation of freshwater
ecosystems. Based on this assumption the team agreed that the conservation assessment
should take account for human stressors affecting the ecological integrity of freshwater
ecosystems. When all other elements being assessed were equal, then the geographic
location (i.e., AES polygon or VST complex) determined to have the lowest degree of
human disturbance was selected. To make these determinations the team agreed to use
the Human Stressor Index developed by MoRAP to account for human stressors at the
AES-level of assessment and to use a more subjective visual assessment of human
stressors and professional knowledge for the VST-level of assessment. The methods we
used to quantify human stressors at the AES and VST level of the aquatic classification
hierarchy are briefly described in the following paragraphs. More detailed descriptions
of the methods can be found in Sowa et al. (2005).
Working in consultation with a team of aquatic resource professionals, we generated a list
of the principal human activities known to negatively affect the ecological integrity of
Missouri streams. We then assembled the best available (i.e., highest resolution and most
recent) geospatial data that could be found for each of these stressors. Next, we generated
statistics on 65 individual human stressors (e.g., percent urban, lead mine density, degree
of fragmentation) for each of the 542 Aquatic Ecological System (AES) polygons in
Missouri (Table 5). We then used correlation analysis to reduce this overall set of metrics
into a final set of 11, relatively uncorrelated, measures of human disturbance (Table 6).
Relativized rankings (range 1 to 4) were then developed for each of these 11 metrics. A
rank of 1 is indicative of relatively low disturbance for that particular metric, while a rank
of 4 indicates a relatively high level of disturbance. The relativized rankings for each of
these 11 metrics were then combined into a three number Human Stressor Index (HSI)
(Figure 15). The first number reflects the highest ranking across all 11 metrics (range 1 to
4). The last two numbers reflect the sum of the 11 metrics (range 11 to 44). This index
allows you to evaluate both individual and cumulative effects of the various human
stressors. For instance, a value of 418, indicates relatively low cumulative impacts (i.e.,
last two digits = 18 out of a possible 44), however, the first number is a 4, which
indicates that one of the stressors is relatively high and potentially acting as a major
human disturbance within that particular ecological unit.
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Table 5. List of the GIS coverages, and their sources, that were obtained or created in
order to account for existing and potential future threats to freshwater biodiversity in
Missouri.
Data layer
Source
303d Listed Streams
Missouri Department of Natural Resources (MoDNR)
Confined Animal Feeding Operations
MoDNR
Dam Locations
U.S. Army Corps of Engineers (1996)
Drinking Water Supply (DWS) Sites
U.S. Environmental Protection Agency (USEPA)
High Pool Reservoir Boundaries
Elevations from U.S. Army Corps of Engineers
Industrial Facilities Discharge (IFD)
Sites
USEPA
Land Cover
1992-93 MoF?AP Landcover Classification
Landfills
Missouri Department of Natural Resources, Air and Land
Protection Division, Solid Waste Management Program
Mines - Coal
U.S. Bureau of Mines
Mines - Instream Gravel
Missouri Department of Conservation (MDC)
Mines - Lead
U.S. Bureau of Mines
Mines-All other
U.S. Bureau of Mines
Nonnative Species
Missouri Aquatic Gap Project - Predicted Species
Distributions; Missouri Resource Assessment Partnership
(MoRAP)
Permit Compliance System (PCS)
Sites
USEPA; Ref: http://www/epa.gov/enviro
Resource Conservation and Recovery
Information System (RCRIS) Sites
USEPA; Ref: http://www.epa.gov/enviro
Riparian Land Cover
MDC
Superfund National Priority List Sites
USEPA; Ref: http://www.epa.gov/enviro
TIGER Road Files
United States Department of Commerce, Bureau of the
Census
Toxic Release Inventory (TRI) Sites
USEPA; Ref: http://www.epa.gov/enviro
Table 6. The 11 stressor metrics included in the Human Stressor Index (HSI) and the
specific criteria used to define the four relative ranking categories for each metric that
were used to calculate the HSI for each Aquatic Ecological System.
Relative Ranks
Metric
1
2
3
4
Number of Introduced Species
1
2
3
4-5
Percent Urban
0-5
5-10
11-20
>20
Percent Agriculture
0-25
26-50
51-75
>75
Density of Road-Stream Crossings (#/mi2)
0-0.24
0.25-0.49
0.5-0.9
>1
Population Change 1990-2000 (#/miz)
-42-0
0.1-14
15-45
>45
Degree of Hydrologic Modification and/or
Fragmentation by Major Impoundments
1
2 or 3
4 or 5
6
Number of Federally Licensed Dams
0
1-9
10-20
>20
Density of Coal Mines (#/miz)
0
1-5
6-20
>20
Density of Lead Mines (#/mi2)
0
1-5
6-20
>20
Density of Permitted Discharges (#/mi2)
0
1-5
6-20
>20
Density of Confined Animal Feeding Operations
(#/mi2)
0
1-5
5-10
>10
50
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Aquatic Subregion
EDU Boundary
C3 111*220
C3 221 " 3i9
Figure 15. Map showing the composite Human Stressor Index (HSI) values for each
Aquatic Ecological System in Missouri. The first number represents the highest value
received across all 11 metrics included in the HSI, while the last two digits represent the
sum of the scores received for each of the 11 metrics.
4. Public Ownership and Stewardship Statistics
Two of the fundamental principals or assumptions identified by the team of aquatic
resource professionals were; a) public lands or protected areas are critical to ecosystem
conservation and the long-term maintenance of biodiversity and b) it is easier to
implement on-the-ground conservation measures on public lands. Based on these
assumptions the team agreed that the conservation assessment should take into
consideration the amount of public land when decisions between two or more locations
were being made. More specifically, when all other elements being assessed were equal,
then the geographic location (i.e., AES polygon or VST complex) containing the highest
percentage of public land would be selected. The methods we used to quantify public
ownership at the AES and VST level of the aquatic classification hierarchy are briefly
described in the following paragraphs. Again, more detailed descriptions of the methods
can be found in Sowa et al. (2005).
To quantify public ownership for each AES polygon we simply quantified the percentage
of public lands within each polygon based on the Missouri GAP Stewardship coverage.
During the assessment process each AES polygon was labeled with this percentage so
that the assessment team could easily compare the ownership percentages among two or
more AES polygons.
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The GAP stewardship coverage for Missouri was used in conjunction with the Valley
Segment coverage to identify stream segments flowing through public lands. A
customized ArcView tool was used to first identify and attribute stream segments that
had the majority of their length (> 51%) within public lands. These segments were then
further attributed with the agency responsible for the management of the surrounding
tract of land. Another Arc Marco Language algorithm was then used to calculate the
percentage of each stream segment's watershed and upstream drainage network that is
within public lands. Since the watersheds of many of the stream segments within
Missouri extend beyond the state boundary, the GAP stewardship coverages for the
neighboring states of Arkansas, Iowa, and Kansas were merged with that of Missouri.
This collection of attributes allowed the assessment teams to select any, of the
approximately 154,000 individual, stream segments within Missouri and see which
segments are flowing through public lands and also the percentage of the overall
watershed and upstream drainage network that is within public lands.
5. Conservation Strategy
Once all of the data were assembled into GIS-based decision support systems the team
crafted a general conservation strategy that would be used to identify and map a statewide
portfolio of Conservation Focus Areas (COAs) that collectively and holistically represent
all of the distinct riverine ecosystems within Missouri and multiple populations of all
fish, mussel, and crayfish species. The reasoning behind each component of this strategy
is best illustrated by discussing what conservation objectives the team hoped to achieve
with each component. These reasons are provided in Box 1, below.
Basic Elements of the Conservation Strategy:
Separate conservation plans must be developed for each EDU,
whenever possible, represent two distinct spatial occurrences/populations of each
target species within each EDU;
AES-Types should be further stratified according to the size of mainstem stream
flowing within its boundary (i.e., small, large, or great river)
represent one example of each AES-Type within each EDU;
within each selected AES, represent at least 1 km of the dominant VSTs for each
size class (headwater, creek, small river, and large river) as an interconnected
complex; and
represent a least three separate headwater VSTs within each of the Conservation
Focus Areas.
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Box 1. Explanation of what we were attempting to achieve with each component of the
general conservation strategy that was used to select aquatic conservation focus
areas.
By attempting to conserve every EDU
Provide a holistic ecosystem approach to conservation, since each EDU represents an
interacting biophysical system
Represent al! of the characteristic species and species of concern within the broader
Aquatic Subregion and the entire state, since no single EDU contains the full range of
species found within the upper levels of the classification hierarchy
Represent multiple distinct spatial occurrences ("populations") or phylogenies for
large-river or wide-ranging species (e.g., sturgeon, catfish, paddlefish), which, from a
population standpoint, can only be captured once in any given EDU
By attempting to conserve two distinct occurrences of each Target Species within each EDU
Provide redundancy in the representation of those species that collectively determine
the distinctive biological composition of each EDU in order to provide a safeguard for
the long term persistence of these species
By attempting to conserve an individual example of each AES-Type within each EDU
Represent a wide spectrum of the diversity of maciohabitats (distinct watershed types)
within each EDU
Account for successional pathways and safeguard against long-term changes in
environmental conditions caused by factors like Global Climate Change.
o For instance, gross climatic or land use changes may make conditions in one
AES-Type unsuitable for a certain species, but at the same time make
conditions in another AES-Type more favorable for that species
Represent multiple distinct spatial occurrences ("populations") for species with
moderate (e.g., bass or sucker species) and limited dispersal capabilities (e.g., darters,
sculpins, certain minnow species, most crayfish and mussels)
Account for metapopulation dynamics (source/sink dynamics)
By attempting to conserve the dominant VSTs for each size class within a single AES
Represent the dominant physicochemical conditions within each AES, which we
assume represent the environmental conditions to which most species in the
assemblage have evolved adaptations for maximizing growth, reproduction and
survival {sensu Southwood 1977)
Represent a wide spectrum of the diversity of mesohabitats (i.e., stream types) within
each EDU since the dominant stream types vary among AES-Types
Promote an ecosystem approach to conservation by representing VSTs within a single
watershed
Account for metapopulation dynamics (source/sink dynamics)
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Box 1. Continued.
By attempting to conserve an interconnected complex of dominant VSTs
ฎ Account for seasonal and ontogenetic changes in habitat use or changes in habitat use
brought about by disturbance (floods and droughts)
o For instance, during periods of severe drought many headwater species may
have to seek refuge in larger streams in order to find any form of suitable
habitat due to the lack of water or flow in the headwaters
Account for metapopulation dynamics (source/sink dynamics)
Further promote an ecosystem approach to conservation by conserving an
interconnected/interacting system
By attempting to conserve at least 3 headwater VSTs within each COA
Represent multiple distinct spatial occurrences ("populations") for headwater species
with limited dispersal capabilities (e.g., darters, sculpins, certain minnow species,
most crayfish and mussels)
Represent multiple high-quality examples of key reproductive or nursery habitats for
many species
By attempting to conserve at least a 1 km of each priority VST
Represent a wide spectrum of the diversity of Habitat Types (e.g., riffles, pools, runs,
backwaters, etc.) within each VST and ensure connectivity of these habitats
Account for seasonal and ontogenetic changes in local habitat use or changes in
habitat use brought about by disturbance (e.g., floods and droughts)
o For instance, many species require different habitats for foraging (deep
habitats with high amounts of cover), reproduction (high gradient riffles),
over-wintering (extremely deep habitats with flow refugia or thermally stable
habitats like spring branches), or disturbance avoidance (deep or shallow
habitats with flow refugia).
Account for metapopulation dynamics (source/sink dynamics)
Again, further promote an ecosystem approach to conservation by representing an
interacting system of Habitat Types
The team then established quantitative and qualitative assessment criteria for making
relative comparisons among the assessment units. Since the assessment was conducted at
two spatial grains (AES and VST), there exist two different assessment units with
assessment criteria developed separately for each.
AES level criteria (listed in order of importance)
Highest target species richness (based on predicted models)
ฎ Lowest Human Stressor Index value, further supported by a qualitative
examination of threats posed by the individual human stressors
Highest percentage of public ownership
Degree of overlap with existing conservation initiatives
Ability to achieve connectivity among dominant VSTs across size classes
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When necessary, incorporate professional knowledge of opportunities,
constraints, or human stressors not captured within the GIS projects to guide the
above decisions.
VST level criteria (listed in order of importance)
If possible, select a complex of valley segments that contains known viable
populations of species of special concern.
If possible, select the highest quality complex of valley segments by qualitatively
evaluating the relative local and watershed conditions using the full breadth of
available human stressor data.
If possible, select a complex of valley segments that is already within the existing
matrix of public lands.
If possible, select a complex valley segments that overlaps with existing
conservation initiatives or where local support for conservation is high.
When necessary, incorporate professional knowledge of opportunities,
constraints, or human stressors not captured within the GIS projects to guide
above decisions.
The conservation strategy and assessment boils down to a five-step process:
Use the AES selection criteria to identify one priority AES for each AES-Type
within the EDU.
Within each priority AES, use the VST selection criteria, to identify a priority
complex of the dominant VSTs.
For each complex of VSTs create a map of the localized subdrainage, termed
"Conservation Focus Area", that specifically contains the entire interconnected
complex.
Evaluate the capture of target species.
If necessary, select additional focus areas to capture underrepresented target
species.
Since conservation efforts cannot be initiated immediately within all of the Focus Areas,
priorities must be established among the Focus Areas in order to develop a schedule of
conservation action (Margules and Pressey 2000). For Missouri, we generated statewide
priorities by calculating irreplaceability values for each Focus Area using all of the native
fish, mussel, and crayfish species as conservation targets. We used a target capture
threshold of three for each species in order to represent three distinct populations of each
species across the state. Due to data management limitations of C-Plan, the
irreplaceability analyses had to be performed separately for each taxonomic group. To
get an overall picture of irreplaceability we simply summed that resulting values across
all three taxa.
6. Results for the Pilot Area
The team then used the conservation strategy and assessment process to develop a
conservation plan for the Meramec EDU, which served as the initial pilot area for the
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statewide conservation plan. By using the above process all elements of the conservation
strategy were met with 11 conservation focus areas (Focus Areas) (Figure 16). With the
initial assessment process and selection criteria, which focus on abiotic targets (AESs and
VSTs), 10 separate focus areas were selected. These 10 areas represent the broad
diversity of watershed and stream types that occur throughout the Meramec EDU. Within
this initial set of 10 focus areas, all but five of the 103 target species were captured. The
distribution of all five of these species overlapped within the same general area of the
EDU, near the confluence of the Meramec and Dry Fork Rivers. Consequently, all five of
these species were captured by adding a single focus area (Dry Fork/Upper Meramec)
(see Figure 16).
Ozark/ Meramec Ecological Drainage Unit
Figure 16. Map of 11 Conservation Focus Areas, within the Ozark/Meramec EDU, that
were selected to meet all elements of the basic conservation strategy developed for the
freshwater biodiversity conservation planning process in Missouri. The figure also shows
the Aquatic Ecological System Types for context. Lower and Upper types differ in terms
COA Streams
Major Streams
COA
Upper AES Types
Boeuf Creek
Dry Fork of the Meramec
Indian Creek
Jacks Fork
Little St Francis River
Middle Upper Big River
Lower AES Types
Boeuf Creek
Jacks Fork
Lower Meramec
Middle Upper Big River
Conservation Opportunity Areas
1 Bootleg Access
2 Dry Fork Upper Meramec
3 Huuah Creek
4 La Barque Cteek
5. Lower Bourbeu&e
6. Maupm Creek
7 Middle Meramec
8 Mill Creek
9 Miner# Fork
!0 Rockwoods
11 Wa3en Creek
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of their position within the larger drainage network. Specifically, a "Lower AES Type"
contains streams classified as Large River and associated headwater and creek tributaries,
while Upper types contain streams classified as Small River and these smaller tributaries.
The final set of priority valley segments, within the 11 Focus Areas, constitutes 299 km
of stream. This represents 2.8% of the total length of stream within the Meramec EDU.
The Focus Areas themselves represent an overall area of 552 km2, which is just 5% of the
nearly 10,360 km2 contained within the EDU. Obviously, efforts to conserve the overall
ecological integrity of the Meramec EDU cannot be strictly limited to the land area and
stream segments within these Focus Areas. In some instances, the most important initial
conservation action will have to occur outside of a given Focus Area, yet the intent of
those actions will be to conserve the integrity of the streams within that particular Focus
Area. All of the team members agreed that specific attention to, and more intensive
conservation efforts within, these 11 Focus Areas will provide an efficient and effective
strategy for the long-term maintenance of relatively high quality, examples of the various
ecosystem and community types that exist within the Meramec River watershed.
In addition to devising the conservation strategy for identifying and mapping Focus
Areas, the team also identified other information that needed to be documented during the
conservation planning process. This information was captured within a database that can
be spatially related to the resulting GIS coverage of the Focus Areas. Specifically, each
Focus Area was given a name that generally corresponds with the name of the largest
tributary stream, and then each of the following items was documented:
all of the agencies or organizations that own stream segments within the Focus
Area and own portions of the overall watershed or upstream riparian area,
the specific details of why each AES and VST complex was selected,
any uncertainties pertaining to the selection of the AES or VST complex and if
there are any alternative selections that should be further investigated,
how these uncertainties might be overcome, such as conducting field sampling to
evaluate the accuracy of the predictive models or doing site visits to determine the
relative influence of a particular human stressor,
all of the management concerns within each Focus and the overall watershed,
any critical structural features, functional processes, or natural disturbances,
what fish, mussel, and crayfish species exist within the Focus Area for each
stream size class, and
any potential opportunities for cooperative management or working in
conjunction with existing conservation efforts
All of this information is critical to the remaining logistical aspects of conservation
planning that must be addressed once geographic priorities have been established.
7. Statewide Results for Missouri
Once the core team finalized the conservation strategy and had completed the
conservation plan for the pilot area, the state was partitioned into four "regions" with
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each of these regions containing four EDUs. Regional teams of aquatic resource
professionals were then established for each region. Each team consisted of six or more
resource managers/biologists with detailed and extensive knowledge of the stream
resources within the region they were assigned. Three-day conservation planning sessions
were held in each region during summer and early fall of 2004. During these three-day
sessions, the regional team used the overall conservation strategy to develop conservation
plans for each of the EDUs within their region.
Conservation plans were completed for all 17 EDUs in Missouri. Statewide, a total of 158
Focus Areas were identified through the above assessment and planning process (Figure
17). These Focus Areas represent the broad diversity of stream ecosystems and riverine
assemblages within Missouri and cover a relatively small percentage of the landscape.
Specifically, the Focus Areas contain 10,915 km of stream, which represents 6.3% of the
174,059 km of stream within Missouri. In terms of land area, the Focus Areas cover
11,331 km2 (2.8 million acres), or just 6.6% of the state. Collectively, these 158 Focus
Areas represent multiple distinct occurrences of all native fish, mussel, and crayfish
species in the state. They also represent the best opportunity for successful conservation
since they represent the highest quality examples of each ecosystem unit and in many
instances those having the highest percentage of public land within the immediate
drainage and overall watershed. This relatively high percentage of public ground with
facilitate on the ground conservation action and provide flexibility in long-term strategies
for conservation.
Area
Figure 17. Map showing all 158 freshwater Conservation Focus Areas that were selected
for Missouri. Taking measures to conserve all of these locations represents an efficient
C3 AES Boundary
C3 EDU Boundary
C i Conservation Focus
-------
approach to representing multiple examples of all the distinct species, stream types, and
watershed types that exist within the state.
Results of the statewide irreplaceability analyses identified mainly four regions of the
state as critical initial priorities for the long-term maintenance of freshwater biodiversity
and ecosystem conservation (Appendix 7 and Figure 18). These regions included
virtually all of the Mississippi Alluvial Basin, the southern Ozarks (particularly the
Neosho and White River EDUs), the Meramec River watershed, and to a lesser extent the
Focus Areas containing the mainstems of both the Missouri and Mississippi Rivers. The
relatively high irreplaceability values for the Focus Areas in the MAB are likely a
reflection of the fact that Missouri is situated at the northern edge of this ecoregion which
contains many unique species that are otherwise more extensively distributed throughout
this region to the south. The high values along the southern Ozarks and the Meramec
EDU are a reflection of the many local endemic fish, mussel, and crayfish species that
occur within these two regions. Finally, the moderately high values for those focus areas
that contain the Missouri and Mississippi Rivers mainly reflect the distinctive great river
fish species that occur exclusively within these rivers within the state; many of which are
wide-ranging species with distinctive life-history strategies.
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influenced both the resolution at which the regional assessment could be conducted and
the finality of the resulting focus areas.
1. Aquatic Classification
Through funding provided by EPA Region 7 and the USGS National Gap Analysis
Program, MoRAP is currently classifying and mapping distinct aquatic ecosystem units
throughout Iowa, Kansas, and Nebraska, following the same methods that were
developed and used in Missouri (Sowa et al. 2005). The classification units are not
finalized and may change based on professional review or further analyses. Based on our
experience with generating classification units for Missouri, however, it is likely that only
minor revisions will be made to these draft units, which would have only a minor
influence on the final results. Consequently, since we believe that the existing draft units
for the classification hierarchy provide a more appropriate ecological context than either
ecoregions or USGS Hydrologic Units, we elected to use the draft classification units as
the geographic framework for our regional aquatic assessment.
a. Level 4: Aquatic Subregions
Following the methods of Pflieger (1971), a range-limit analysis for fishes was conducted
throughout Iowa, Kansas, and Nebraska in order to identify and map relatively distinct
Aquatic Subregions. Based on these analyses a total of four Aquatic Subregions were
identified, which, when added to those already identified for Missouri results in a total of
seven distinct Subregions throughout EPA Region 7 (Figure 19). As we described above,
Aquatic Subregions are physiographic or ecoregional substrata of regions and thus
account for differences in the ecological composition of riverine assemblages resulting
from geographic variation in ecosystem structure and function. However, the boundaries
between Subregions follow major drainage divides to account for drainage-specific
evolutionary histories in subsequent levels of the hierarchy.
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Figure 19. Aquatic Subregions within EPA Region 7.
b. Level 5: Ecological Drainage Units
A total of 38 Ecological Drainage Units were identified and mapped throughout EPA
Region 7 (Figure 20). EDUs represent islands in the landscape. Each EDU has a
relatively distinct aquatic assemblage with a relatively distinct evolutionary history.
These ecological units served as our primary planning units for both the Missouri and
regional aquatic assessments since each EDU circumscribes a functionally distinct
ecosystem unit that plays an important role in defining the overall ecological character of
upper levels of the classification hierarchy.
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YMiccrisin>:ttf*f-v>ซ
Upper Dei Moire J Cetlar
Efchcyn
North flatte/Souh Platte
Det Mules
Kansas
wtferjLamine
Moreau/Loutre
Nfeidi^is/Neosho
Figure 20. Ecological Drainage Units within EPA Region 7.
c. Level 6: Aquatic Ecological System Types
Thirty nine Aquatic Ecological System Types (AES-Types) were identified in Missouri.
Seven of these overlap with the 63 AES-Types that were identified throughout Iowa,
Kansas, and Nebraska. Consequently, based on our multivariate analyses of watershed
landscape data there are a total of 95 distinct AES-Types throughout EPA Region 7
(Figure 21). These distinct watershed types served as our principal conservation target in
the regional aquatic assessment.
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Figure 21. Map showing the 95 distinct Aquatic Ecological System Types that occur
throughout EPA Region 7. Red lines show Aquatic Subregion boundaries and thick
black lines show Ecological Drainage Unit boundaries.
cl. Level 7: Valley Segment Types
Valley Segment Types (VSTs) have been mapped throughout EPA Region 7 (Figure 22).
However, the lack of biological and human stressor data for these geographic units,
coupled with our inability to incorporate professional judgment into the assessment
process, precluded the use of these finer-grained spatial units in our regional aquatic
assessment. These VST data should be incorporated into future assessments that seek to
identify more spatially-explicit conservation priorities within the each of the AES
polygons that were identified as conservation focus areas throughout Iowa, Kansas, and
Nebraska.
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Figure 22. Map of the 1:100,000 Valley Segment Coverage for EPA Region 7 displayed
according to the five general stream size classes.
2. Biological Data
Biological data played an important role in the identification of aquatic focus areas
throughout Missouri. Specifically, richness statistics, based on the predictive distribution
models for 315 fish, mussel, and crayfish species, were used to rank AES polygons
within each EDU, while actual collection records and professional knowledge were used
to aid the selection of specific VST complexes. Predicted distribution data were used to
avoid the many biases and limitations of the existing collection data (Sowa et al. 2005).
Unfortunately, these same biases and limitations exist within Iowa, Kansas, and Nebraska
despite the fact that 16,529 distinct fish collection records have been compiled for these
three states (Figure 23).
CX3> Aquatic Subregion
C3 Ecological Drainage Unit
Headwater
Creek
Small River
Large River
-/v- Great River
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Legend
Sample Location Points
Major Streams
1 I EDU Boundaries
I I State Boundaries
Figure 23. Fish collection records compiled for Aquatic GAP projects throughout Iowa,
Kansas, and Nebraska.
While 16,529 collection records may seem more than adequate for accurately
characterizing the fish assemblages occurring within the watersheds or hydrologic units
across these states, a closer examination of these data reveals that this is not the case. A
simple plot of native species richness versus the number of samples occurring within
each AES polygon reveals that a staggering 100 or more samples are needed in order to
accurately characterize the fish assemblage of these geographic units (Figure 24). When
you consider that only 22 (1.4%) of the 1,603 individual AES polygons within Iowa,
Kansas, and Nebraska have more than 100 samples and many have only a handful or no
samples at all (Figure 25), it becomes readily apparent that any priorities based on these
existing collection records would be more a reflection of disparities in sampling effort
rather than true biogeographic patterns (Figure 26). Although predictive distribution
models are being developed for the fish species across these three states, these data were
not available at the time of this project. As a result, we decided not to use any biological
data for the regional aquatic assessment and to rather focus on abiotic conservation
targets based on the aquatic classification hierarchy.
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&
Number of Samples
Figure 24. Scatter plot showing the number of native fish species documented to occur
within each AES polygon versus the number of fish collections within AES polygon
throughout Iowa, Kansas, and Nebraska. This plot shows that anywhere from 50 to 100
collections are needed to accurately document the species composition of a given AES
throughout this region.
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Legend
Count of Native Species by AES
Figure 26. Native fish species richness by AES polygon. The patterns displayed on this
map reflect both real and perceived patterns of biodiversity due to geographic variations
in sampling effort.
Figure 25. Number of fish collection records for each AES polygon in Iowa, Kansas, and
Nebraska.
Legend
Counts of Fish Collections
by AES
mi*
CZJi.ซ
r i ซ to
ฆ 11-20
71-20
T Stale Boundaries
68
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3. Human Stressors
Most of the geospatial data used to account for human stressors in the statewide aquatic
assessment for Missouri were also available for Iowa, Kansas, and Nebraska (Figures 27
and 28). Lacking were spatial data on confined animal feeding operations and predictive
distributions for nonnative aquatic species. Using the available data we generated
statistics for nine human stressors for each of the 2,244 AES polygons that occur in EPA
Region 7 (Table 7).
Leซjenซl
0 Dams
~ EDU Boundaries
Major Highway*
~ Stat* Boundams
Figure 27. Map of federally licensed dams throughout EPA Region 7.
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Leijeml
Coal Mines
Lead Mines
~ EDU Boundaries
Major Streams
= Major Highways
~ State Boundaries
Figure 28. Map of lead and coal mines within EPA Region 7.
Table 7. Individual human stressor statistics that were generated
for each AES polygon across EPA Region 7.
Human Stressor Statistic
Percent Urban
Percent Agriculture
Density of Road-Stream Crossings (#/mi2)
Population Change 1990-2000 (#/mi2)
Degree of Hydrologic Modification and/or
Fragmentation by Major Impoundments
Density of Federally Licensed Dams
Density of Coal Mines (#/mi2)
Density of Lead Mines (#/mi2)
Density of Permitted Discharges (#/mi2)
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In an effort to more accurately quantify the degree of human disturbance within a given
AES, we elected to use slightly different methods for ranking both within and among
these nine human stressors throughout the region. Each of the 2,244 AES polygons
within EPA Region 7 were ranked from 1 to N for each of the human stressors, where N
equals the total number of AES polygons in Region 7. The lowest values were given a
rank of 1 and the highest values were given a rank of 2,244. Ties were all given the next
lowest value in the ranking sequence. Figure 29 provides an example of the resulting
rankings for the percentage of urban area within each AES across EPA Region 7.
Figure 29. Map showing the percentage of urban area occurring within each AES
polygons throughout EPA Region 7.
After the rankings were completed for each of the human stressors we generated a
cumulative stressor index by summing the ranks across all nine stressors for each AES
polygon. During this summing process the ranks for the percentage of urban area were
weighted by a factor of three to account for the fact that urbanization of a watershed
generally results in severe and irreparable disturbance to freshwater ecosystems (Klein
1979; Osborne and Wiley 1988; Limburg et al. .1990; Booth 1991; Weaver and Garmen
1994; Booth and Jackson 1997; Wang et al. 2000). The resulting index provides a
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relative measure of the degree of cumulative human disturbance within each individual
AES throughout EPA Region 7 (Figure 30).
Figure 30. Graduated color map of the cumulative stressor index that was used to rank
AESs across EPA Region 7.
The lowest value for the cumulative stressor index was 470 and occurred within the AES
containing the upper reaches of the White River between Willow and Grass Creeks, just
north of the Nebraska state line. The highest value was 21,252 and occurred within the
AES containing the Missouri River between Cedar Creek and the Moreau River, which
falls mainly within the boundaries of Jefferson City, Missouri.
4. Public Ownership
We assembled the GAP Stewardship coverages from each the four states in EPA Region
7 (Figure 31). Reservoirs coded as public land were removed from these coverages. We
used these coverages to calculate the percent of public ownership within each AES
polygon. No distinctions were made among owners or the gap stewardship codes. The
percentage of public ownership was used as another means of ranking AESs across EPA
470 - 5772
5780 - 8732
8755 - 10788
10795 - 13033
13048 - 21252
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Region 7 Figure 32 shows the AES polygons within EPA Region 7 displayed aeeording
to the percentage of public lands within the.r boundaries.
Legend
| Public Lands
EDU Boundaries
Maior Streams
Maior Highways
~ Slate Boundaries
Figure 31. Map showing the
distribution of the public lands within EPA Region 7.
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Figure 32. Graduated color map showing the percentage of public lands within each AES
polygon.
5. Conservation Assessment Strategy
We wanted to ensure that the conservation strategy used to select aquatic conservation
focus areas for Iowa, Kansas, and Nebraska was consistent with the more detailed
assessment carried out for Missouri. However, the lack of biological data (predicted
distribution data) and expert input for these three states dictated that a coarser-grained
and more general conservation strategy be used.
Basic elements of the regional conservation strategy:
Separate conservation plans must be developed for each EDU,
Select one example of each AES-Type within each EDU,
Prior to the ranking process all AES-Types should be further stratified according to the
size of the largest stream flowing within its boundary (i.e., small, large, or great river)
Like the assessment for Missouri, EDUs served as the primary planning unit and AES-
Types were a principal conservation target. However, again due to the lack of biological
data, we were unable to use biological targets, and the lack of expert review prevented us
from using VSTs as targets. Yet, if the results of the Missouri assessment hold for these
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other three states, then this more general strategy should still provide a set of focus areas
that represent the full breadth of freshwater ecosystems and multiple populations of 95%
or more of the native aquatic species that occur in these three states (Sowa et a). 2005).
The reason we further stratified AES-Types according to the size of the largest stream
was to account for the fact that drainage area plays such a critical role in the structural
and functional character of riverine ecosystems and associated wetland complexes and
their biotic communities (Vannote et al. 1980). All AES polygons contain streams
classified as headwater and creek, but in addition only contain segments falling into one
of the three larger size classes (small, large, or great river). Therefore, those AESs that
contain small river were differentiated from those containing large river and these were
further differentiated from those containing great river stream segments. AESs that
contain complexes of headwater, creek, and small river are termed "upper" units since
they are generally situated in the uppermost positions of the larger drainage network. By
extension, those containing headwater, creek, and large river complexes are termed
"middle" units and those containing headwater, creek, and great river are termed "lower"
units.
Another major difference between the assessment conducted in Missouri and what could
be achieved in the other three states pertains to the integration of multiple assessment
criteria. In Missouri, the human stressor index, percentage of public ownership, and
target species richness were the three principle assessment criteria that were used to
collectively identify and rank AESs and VST complexes. The integration of these criteria
was subjectively carried out by the team of aquatic resource professionals since there is
no clear way to automate the integration of such criteria in the computer based on simple
ranking criteria. Lacking this professional input we decided to generate separate rankings
based on the cumulative stressor index and the percentage of public ownership. Due to
the limited amount of public land in these three states we determined that the cumulative
stressor index should serve as the primary ranking criteria for selecting aquatic focus
areas. Separate rankings were also done based on the percentage of public land,
however, these rankings were not used in the selection of focus areas, but were integrated
with the rankings based on the cumulative stressor index in order to provide information
that could be used to possibly refine the initial selection. Consequently, the focus areas
that we identified across Iowa, Kansas, and Nebraska represent the AES polygons
of a given Type with the lowest rank for the cumulative stressor index value within a
given EDU. Specifically, within each EDU we ranked each AES polygon from 1 to N
based on the cumulative stressor index, where N equals the number of AES polygons of a
given Type within that EDU. The AES polygon with the lowest cumulative stressor
index rank was given a value of 1. We also separately ranked each AES polygon from I
to N, using the same stratification, based on the percentage of public ownership within
the AES. The AES polygon with the highest percentage of public land was given a value
of 1. These two rankings were then integrated in order to identify those AESs that had
both the lowest relative cumulative stressor index and the highest relative percentage of
public land.
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6. Results of the Regional Aquatic Assessment
A total of 200 aquatic focus areas were identified throughout Iowa, Kansas, and Nebraska
(Figure 33). The highest concentration of focus areas occurs in those regions with the
greatest variability of watershed conditions, which tend to correspond with the areas of
highest species diversity. When combined with the 158 focus areas identified for
Missouri, a total of 358 focus areas were identified throughout EPA Region 7 (Figure
34). The relatively high number of AESs selected within the Ozarks is again reflective of
the relatively high abiotic and biotic diversity that occurs within this Aquatic Subregion.
However, since the assessment for Missouri also focused on target species capture there
were several instances in which additional AES polygons were selected in order to
capture underrepresented species. This finer-filter assessment was not done for Iowa,
Kansas, and Nebraska. It is likely that a similar, more-detailed, assessment for these
three states would add more AESs to the existing portfolio of aquatic focus areas. When
we integrated the rankings based on the cumulative stressor index with those based on the
percentage of public land, an amazing 139 (70%) of the 200 focus areas within Kansas,
Iowa, and Nebraska had both the lowest relative stressor index and highest relative
percentage of public land (Figure 35).
Legend
3 Focus Areas
~ AES Boundaries
~ EDU Boundaries
~ State Boundanes
Figure 33. Map of the 200 aquatic focus areas identified throughout Iowa, Kansas, and
Nebraska.
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Legend
OB FocusAreas
| AES Boundaries
~~j EDU Boundaries
~ Slate Boundaries
Figure 34. Map of the 358 aquatic focus areas identified throughout EPA Region 7.
Legend
FocusAreas
~ AES Boundaries
~ EDU Boundaries
~ State Boundaries
Figure 35. Map showing the 200 aquatic focus areas for Iowa, Kansas, and Nebraska
(highlighted in both red and green). The focus areas highlighted in red were those that
had both the lowest relative cumulative stressor index and highest relative percentage of
public land (70% of the total).
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IV. Discussion and Future Needs
A. Terrestrial Assessment
Terrestrial conservation focus areas were identified on a planning region by planning
region basis across EPA Region 7 using relatively uniform methods and data sets. Even
though we used regionally available data sets, inconsistencies in input data and in land
use among the regions do exist. For example, roads are developed and mapped
differently across EPA Region 7, even in rural areas, and differences in road density have
profound impacts on the significance, threats, and risk results. Because of inherent
differences among regions, we believe that it is most appropriate to view results on a
planning region by planning region (essentially section by section) basis, rather than
comparing results across sections. Results within a planning region are both locally
relevant and ecologically most meaningful.
The terrestrial conservation focus areas we identified are not ranked within section, so
local priorities cannot be discerned. Likewise, they are only polygons of various sizes
without names, so local managers and planners will have trouble relating to the results in
that regard. Local, finer-resolution input needs to be used to rank conservation focus
areas for conservation action, the polygon boundaries will need to be re-drafted based on
finer resolution data, and the most important areas will need to be provided with locally-
identifiable names. These actions need to take place at the state and local level.
B. Aquatic Assessment
During the conservation assessment process for Missouri we found that the local experts
are often humbled by the G1S data. Often, what appear to be the best places to conserve
are those places that the local managers know little or nothing about. This exemplifies
that the world is a big place, and we cannot expect a handful of experts to know every
square inch of an Ecological Drainage Unite (i.e., 10,000+ km2). At the same time we
found that the G1S data are often insufficient and, if solely relied upon, may lead to poor
decisions. In several cases, GIS data identified a particular location, while the local
experts quickly pointed out problems. For example, in one case the sewage treatment
facility just upstream from one potential focus area had one of the worst spill records in
the state, and fish kills occur almost on an annual basis. While the GIS data show the
location of the sewage treatment facility, they do not contain this more detailed
information. Capturing this type of information within a GIS must become a priority.
In Missouri, we were pleasantly surprised to find that even in the most highly altered and
severely degraded landscapes we were able to identify "hidden jewels" that have
somehow escaped the massive landscape transformations and other insults in neighboring
watersheds. Yet, in many instances these relatively high quality locations were quite
small and therefore highly susceptible to any future changes in local or watershed
conditions. Those locations facing any potential immediate threats must be identified and
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the necessary conservation actions must be put into action quickly, otherwise these
"hidden jewels" could be lost forever.
Another surprising result was that we were able to represent all of the abiotic and biotic
targets within a relatively small fraction of the overall resource base in Missouri (-6%).
Unfortunately, the area that must be managed in order to protect/restore the ecological
integrity of any given focus area is often substantially larger and much more daunting
than the boundaries we delineated. However, the spatially-explicit nature of the focal
point areas provides a focal point for resource managers, because even when on-the-
ground management is far removed from one of these priority locations, the streams and
assemblages within each focus area are the ultimate focus of conservation action.
When we began our project we recognized the fact that, whenever possible, priorities
should be established at a scale that managers can understand and use (e.g., individual
stream segments) in order to apply spatially-explicit conservation actions. Each team of
local experts found the conservation planning process much more useful than previous
planning efforts they were involved in, which identified relatively large areas as priorities
for conservation. The managers stated that, because we selected localized complexes of
specific stream segments, much of the guesswork on where conservation action should be
focused has been taken "out of the-equation," which will expedite conservation action.
This same level of geographic precision is not provided with the focus areas identified for
Iowa, Kansas, and Nebraska. Identifying more spatially-explicit conservation priorities
within the focus areas of these three states must become a priority.
Since conservation efforts cannot be initiated immediately within all of the focus areas,
priorities must be established among these areas in order to develop a schedule of
conservation action (Margules and Pressey 2000). For Missouri, this was accomplished
by conducting an irreplaceabiIity analysis based on the representation of native fish,
mussel, and crayfish species. While all of the focus areas are important to the long-term
conservation of freshwater ecosystems in Missouri, the results of these analyses identified
several critical locations in the state where conservation action wilt provide the greatest
initial return for the effort expended. Once predictive distribution models are completed
for the fish species in Iowa, Kansas, and "Nebraska, the regional focus areas should be
reexamined and an irreplaceability analysis should be performed in order to rank the
priority AES polygons across the entire region.
A surprisingly high percentage (70%) of the aquatic focus areas had both the lowest
relative stressor rank and the highest percentage of public land. These results illustrate
two important points. First, public lands are critical to minimizing human disturbance to
freshwater ecosystems as well as in terrestrial ecosystems. Second, state and federal
resource management agencies have a critical role to piay in the long-term conservation
of many of these focus areas, even in states with a relatively low percentage of public
land. One of the more difficult tasks will be getting these many agencies to work
together in order to develop holistic management strategies for each of these focus areas.
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Data development and analysis needs beyond species modeling and the incorporation of
local knowledge in the priority-setting process also exist. These include the development
of data on critical stress such as water withdrawals and channelization. We also need to
and evaluate the accuracy of the inputs that have already been used. We need to generate
quantitative date for the inputs (e.g. ranking one mine versus others or one point source
versus another. We need to calculate each human stressor for each individual stream
segment, rather than simply for larger watersheds. Finally, we need to provide for
validation of GIS-based human stressor metrics with field data.
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