United States
Environmental Protection
Agency
Office of Research and
Development
Washington DC 20460
EPA/600/R-95/018
April 1995
svEPA
Stressor Data Sets for
Studying Species
Diversity at Large
Spatial Scales
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EPA/600/R-95/018
April 1995
Stressor Data Sets for Studying Species Diversity at Large Spatial Scales
by
James D. Wickham
Jianguo Wu
Desert Research Institute
Reno, NV
and
David F. Bradford
Environmental Monitoring Systems Laboratory
U.S. Environmental Protection Agency
Las Vegas, NV
Environmental Monitoring Systems Laboratory
Office of Research and Development
U.S. Environmental Protection Agency
Las Vegas, NV 89193-3478
Printed on Recycled Paper
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NOTICE
The U. S. Environmental Protection Agency, through its Office of Research and Development (ORD),
funded the extramural research described here under Cooperative Agreement CR-816385-01-0 with the
Desert Research Institute. The research undertaken for this report was conducted in collaboration with
partners in the Biodiversity Research Consortium, which is comprised of the U.S. EPA, U.S. Forest
Service, U.S. Geological Survey, U.S. Fish and Wildlife Service, and The Nature Conservancy. It has
been reviewed by the Agency and approved as an EPA publication. Mention of trade names or
commercial products does not constitute endorsement or recommendation for use.
11
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CONTENTS
Page
NOTICE ii
EXECUTIVE SUMMARY v
ACKNOWLEDGEMENTS vi
1.0 INTRODUCTION 1
1.1 Overview of the Biodiversity Research Consortium (BRC) 1
1.2 Objectives of the Report 3
1.3 Definitions of Stress and Stressors 3
2.0 PATTERNS AND HYPOTHESES OF SPECIES RICHNESS 5
2.1 Distributional Patterns of Species Richness 5
2.2 Hypotheses Explaining Distributional Patterns of
Species Richness 7
3.0 FRAMEWORK FOR IDENTIFYING AND EVALUATING POTENTIAL STRESSORS ... 12
3.1 Stressor Categories 12
3.2 Scales at Which Stressors Operate 12
3.3 Criteria for Selecting Stressor Data Sets 14
3.4 A Consideration in Studying the Correlation Between
Stressors and Species Richness 14
4.0 EXISTING DATA FOR IDENTIFYING STRESSORS 16
4.1 Habitat Fragmentation Data 16
4.1.1 Land Cover 16
4.1.2 Digital Line Graphs (Roads) 18
4.1.3 Wetland and Riparian Habitat Loss 19
4.1.4 Census Data 20
4.2 Pollution Data 21
4.3 Exotic Species Data 21
4.4 Data Sets for Non-Anthropogenic Factors 24
4.4.1 Historical Climatology Network (HCN) 24
4.4.2 USGS Digital Elevation Model (DEM) 25
4.4.3 Federal Insect and Disease Conditions
and Wildland Fire Statistics 25
5.0 DISCUSSION AND SUMMARY 26
5.1 Importance of Scale 26
5.2 Proposed Data Sets and Considerations in Examining
Stressor-Species Relationships 26
6.0 LIST OF ABBREVIATIONS 28
7.0 REFERENCES 29
iii
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FIGURES
rage
Figure 1: BRC Hexagonal Sampling Units 2
Figure 2: Species Richness Versus Latitude 6
Figure 3: Species Richness Versus PET 9
Figure 4: Proposed Spatial Scales at which Species Richness Patterns Are
Evident (A) and Associated Hypotheses (B) 11
TABLES
Page
Table 1: Definitions of Stress 4
Table 2: Data for Land Use and Habitat Fragmentation 17
Table 3: Population Change (1980 to 1990) for Selected High Growth
Counties in Pennsylvania and Oregon 20
Table 4: Pollution Data Sets 22
Table 5: Data Sets for Exotic Species, Including Managed Livestock
and Grazing Practices 23
Table 6: Data Sets for Non-Anthropogenic Factors 25
Table 7: Data Proposed for Examination of Stressors-Species Relationships 27
IV
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EXECUTIVE SUMMARY
There is increasing scientific and societal concern over the impact of anthropogenic activities (e.g.,
habitat destruction, pollution) on biodiversity. The impact of anthropogenic activities on biodiversity is
generally recognized as a global phenomenon. At large spatial scales, several studies have shown
geographic patterns in species diversity, and these patterns have been most strongly linked to aspects
of climate and topography, not anthropogenic activities. What is known about the impact of
anthropogenic activities on species diversity (and loss) is mostly from studies at much smaller spatial
scales (e.g., up to a few U.S. counties). This gap in knowledge poses a challenge to the study of the
influence of stressors on species diversity patterns at large spatial scales (e.g., regions and continents).
Based on the literature, stress is defined as the response of a system to a disturbance, and stressor is
the disturbance. The purpose of this report is to review what is currently known about factors that
influence distributional patterns of species richness, and to identify the appropriate stressor data sets to
evaluate the influence of stressors on patterns of species richness at large spatial scales. This
research is being done for the interagency Biodiversity Research Consortium (BBC), which has been
formed to study species diversity patterns at large spatial scales.
Spatial patterns of species richness are found across a range of spatial scales. These include
latitudinal gradients, elevation gradients, aridity gradients, species-area relationships, and more complex
microenvironmental patterns. Several hypotheses of mechanisms controlling species richness patterns
are summarized here, including (1) time, (2) origination-extinction dynamics, (3) available
energy/productivity (4) habitat heterogeneity, (5) disturbance, and (6) niche theory/species interaction.
Particular patterns of species diversity usually occur over a range of spatial scales. Therefore, different
patterns and mechanisms should be expected on disparate scales. Based on this scale distinction, we
have organized the hypotheses according to scales at which they are likely to operate. Time,
origination-extinction dynamics, and available energy/productivity are likely to operate over the entire
range of spatial scales from local to global. Habitat heterogeneity and disturbance operate
predominantly at local to regional scales. Niche theory/species interaction operates primarily at local
scales.
Anthropogenic stressors are most relevant to the habitat heterogeneity and disturbance hypotheses.
Stressors (both anthropogenic and non-anthropogenic) are likely to predominate at local and regional
scales. In addition, it is proposed that stressors can be separated into four basic categories: habitat
fragmentation, pollution, exotic species, and non-anthropogenic factors. Inclusion of data representing
each of these categories is necessary to gain a more complete understanding of the relationship
between stressors and species diversity. For instance, several studies have shown that habitat
fragmentation can lead to species extirpation, but none of these studies has included other stressor
data (e.g., drought, extreme temperatures, pollution) to determine the relative contribution of each in
explaining species diversity patterns. For each category, two data sets are proposed for evaluation of
the influence of stressors on species richness for the conterminous U.S. For habitat fragmentation,
road density and wetland loss are proposed. Other metrics of habitat fragmentation, such as patch
size, are being developed elsewhere within the BRC. For pollution, data on tissue concentration in
selected species are proposed, as well as stream and lake pH measurements. For exotic species, data
from The Nature Conservancy's (TNC) Heritage Data Base are proposed. Data on livestock grazing
intensity are also recommended. For non-anthropogenic factors, climate data are proposed for the
development of departures from averages. Use of topographic data are also proposed. Topography
does not represent a stressor per se, but information on it is needed to establish baseline conditions.
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ACKNOWLEDGEMENTS
Earlier drafts of this report benefitted from the comments of Drs. Dennis Jelinski of University of
Nebraska (Lincoln, Nebraska), Orie Loucks of Miami University (Oxford, Ohio), and Raymond O'Connor
of University of Maine (Orono, Maine), and discussions with Drs. Ross Kiester, Raymond O'Connor,
Eric Preston, and Denis White. Timothy Wade provided GIS support and valuable advice on many
technical points.
Linda Piehl provided administrative support.
VI
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1.0 INTRODUCTION
1.1 OVERVIEW OF THE BIODIVERSITY
RESEARCH CONSORTIUM (BRC)
The U.S. Environmental Protection
Agency's (USEPA) Science Advisory Board
(SAB) has identified habitat and species loss as
serious ecological problems in the United States
(USEPA 1990). In response to the SAB's finding,
the USEPA formed the Biodiversity Research
Consortium (BRC) in 1993 in cooperation with
several federal and non-governmental agencies,
including the U.S. Fish and Wildlife Service
(USFWS), the U.S. Forest Service (USFS), the
U.S. Geological Survey (USGS), and The Nature
Conservancy (TNC). BRC has four primary
objectives (sensu Kiester et al. 1993):
1) To analyze biodiversity nationwide;
2) To search for correlations between species
diversity, environmental diversity, and
stressor information;
3) To evaluate the comparative risk to
biodiversity using species diversity,
environmental diversity, and stressor
information; and
4) To begin developing approaches to
managing environmental diversity in order to
achieve species diversity goals.
To accomplish these objectives, BRC will
compile species occurrence using TNC's heritage
data base and other sources, and use USEPA's
hexagonal grid, originally developed for the
Environmental Monitoring and Assessment
Program (EMAP), as the basis for data collection
and analysis (Kiester et al. 1993). The EMAP
grid is made up of 640 km2 hexagons that
completely cover the conterminous United States
(Figure 1). Vertebrate species richness will be
used as the measure of biodiversity by the BRC.
A generally accepted definition of biodiversity
(Noss 1990) is "the variety and variability of life
and the ecological complexes in which they
occur" (U.S. Congress Office of Technology
Assessment (OTA) 1987). Species richness is a
measure of the variety of different organisms.
Species diversity includes both the variety and
variability of different organisms. Species,
environmental, and stressor data will be compiled
for each hexagon and analyzed using multivariate
statistical techniques to meet objectives 1, 2, and
3. The results of the multivariate analyses will be
used to address the fourth objective.
BRC's focus on nationwide patterns of
biodiversity is based partly on previous work that
has shown the existence of continental scale
patterns of species richness (Simpson 1964,
Kiester 1971, Currie 1991). These studies
highlighted patterns that are not observable at
smaller spatial scales (Kiester et al. 1993). The
national level focus also helps to link the first
three analysis-based objectives with the fourth,
management-oriented objective.
BRC is organized into four functional groups:
species, landscape, stressors, and analysis. The
species team is responsible for compiling the
species data for each hexagon. The landscape
team is responsible for development of
environmental diversity measures from remotely
sensed data. Many of these metrics will come
from studies of landscape pattern and dynamics
(O'Neill et al. 1988, Turner et al. 1991). AVHRR
satellite data will be used at the outset to derive
these measures. The stressors team is
responsible for the development of measures,
outside those developed by the landscape team,
which are likely to influence geographic patterns
of species richness. Once completed, the three
teams will provide their data to the analysis team,
who will conduct the multivariate analyses using
species, landscape, and stressor data.
This report reviews what is currently known
about the relationships between stressors and
species diversity. Information on the relationship
between stressors and species diversity patterns
is important to achieving BRC's objectives 2 and
3. BRC has initiated pilot studies in Pennsylvania
and Oregon to evaluate data quality and test
proposed methods. The results of the pilot
studies will be used to guide the national effort.
Where possible, examples will make reference to
Pennsylvania and Oregon.
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ro
Figure 1: BRC Hexagonal Sampling Units
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1.2 OBJECTIVES OF THE REPORT
The objectives of this report are four-fold.
The first is to review the extensive body of
literature on anthropogenic and natural factors
that influence geographic patterns of species
richness. A review and synthesis of this literature
provides a starting point to develop a framework
for selecting stressor data sets. The second is to
develop a comprehensive framework for the
selection of appropriate stressor data sets. The
third objective is to survey and select existing
stressor data, based on the framework
developed. The fourth objective is to examine,
to the degree possible, the quality of the data.
Since the analyses proposed by BRC are
correlative, information on data quality is critically
important.
Error propagation is an important component
of data quality analysis. Error propagation, in
regard to stressor data sets and BRC, is the
impact of error in collection and compilation of
data on measurements derived from the data
and, subsequently, on interpretation of results
showing relationships between stressors and
species diversity. However, error propagation is
an empirical phenomenon that must be
investigated on a case by case basis, since the
collection methods are different for each data set.
The impact of error in any given data set on
derived measurements and interpretation of
results is more appropriately addressed during
data analysis. Discussion of data quality will not
extend to error propagation in this report.
1.3 DEFINITIONS OF STRESS AND
STRESSORS
One of the goals of BRC is to evaluate the
comparative risk to biodiversity using species
diversity, environmental diversity, and stressor
information (sensu Kiester et al. 1993).
Unfortunately, the term stress has not been used
consistently in the ecological literature (Rykiel
1985). For example, stress has been defined as
a prevailing, unfavorable condition to which
organisms respond (e.g., Larcher 1980), or the
response of an organism to prevailing
environmental conditions, either favorable or
unfavorable (e.g., Odum et al. 1979, Barrett
1981, Rykiel 1985). A list of definitions by
different authors is given in Table 1.
The definitions by Odum et. al. (1979),
Barrett (1981), and Rykiel (1985) are consistent
in that each defines stress as an effect, not a
prevailing condition (e.g., Larcher 1980). For
example, stress is the effect of a disturbance
(e.g., pollution, fragmentation) on a biological
entity or process, not disturbance itself. The
definition of stress as an effect is most
appropriate for BRC because of its goal to
evaluate comparative risk to biodiversity. USEPA
defined risk assessment as the evaluation of the
likelihood of adverse ecological effects as a result
of exposure to stressors (Norton et al. 1992). In
the context of BRC, stress may manifest itself as
a change in species richness.
The definition of stress by Barrett (1981) is
the most clear and explicitly uses the term
stressor. Barrett (1981) defined stressor as the
agent (e.g., prolonged drought) which causes a
physical or functional effect. Barrett's (1981)
definition is also valuable because it (1)
distinguishes anthropogenic from natural
stressors and (2) identified natural factors as
stressors only when applied at excessive levels
(e.g., prolonged drought). Rykiel's (1985)
definition of disturbance - a physical force,
agent, or process, either abiotic or biotic, causing
a stress in an ecological component or system
is consistent with Barrett's (1981) definiton of
stressor. For BRC, stressors are quantitative
measures of disturbance which can be used to
explain geographic patterns in species richness.
Odum et al. (1979) distinguished stress as
having only negative effects and reserves the
term "subsidy" to connote positive effects. We do
not necessarily recommend making a distinction
between stress and subsidy, and stress will be
used herein to include both positive and negative
effects. The reason is not to introduce additional
terminology.
The definitions of stress by Odum et al.
(1979), Barrett (1981), and Rykiel (1985) all
indicate that stress must be measured against a
reference condition. An example of using a
reference condition is that species and stressor
data are measured over time so that changes in
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the species and stressors data can be used to
determine the impact of stressors on species
richness. Although this method seems valid and
feasible, it requires time series data. An
alternative (or complimentary) approach is to use
natural spatial patterns of species richness that
are found in relatively undisturbed environments
or predicted by validated theories as reference
conditions. When appreciable differences in
species distributional patterns in similar
environments and at similar scales are detected,
stressor data may be used to evaluate the effect
of disturbances on species richness. This is
essentially a space-for-time method, which has
long been used in ecological studies (see Pickett
1989).
Table 1: Definitions of Stress
Author
Larcher(1980)
Odumetal. (1979)
Barrett (1981)
Rykiel (1985)
Definition
Stress: the exposure to extraordinarily unfavorable conditions; they need
not necessarily represent a threat to life, but they do trigger an "alarm"
response (e.g., defensive and adaptive reactions) in the organism if it is
not in a dormant state.
Stress: deviation from nominal; unfavorable deflections.
Stress: a perturbation that is applied to a system by a stressor which is
foreign to that system or which may be natural to it but, in the instance
concerned, is applied at an excessive level.
Stress: an effect; a physiological or functional effect; the physiological
response of an individual, or the functional response of a system caused
by disturbance or other ecological process; relative to a reference
condition; characterized by direction, magnitude, and persistence; a type
of perturbation.
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2.0 PATTERNS AND HYPOTHESES OF SPECIES RICHNESS
The study of the distributional patterns of
species richness is an important step to
understanding the relationship between biological
diversity and underlying ecological factors and
processes. This relationship is fundamental to
conservation biology in general and the
identification of stressors in particular. The
distributional patterns of species richness found
in natural or semi-natural ecological systems on
different scales may serve as one kind of
baseline condition, against which changes in
species richness can be detected in relation to
anthropogenic stressors. In this section, we shall
discuss several major geographic patterns of
species richness and hypotheses of mechanisms,
which serves as a conceptual basis for later
sections.
2.1 DISTRIBUTIONAL PATTERNS OF
SPECIES RICHNESS
The pattern of species richness distribution in
time and space has been a major theme in both
ecology and biogeography for several decades
(e.g., Pianka 1966, MacArthur 1965, MacArthur
and Wilson 1967, Loucks 1970, Brown and
Gibson 1983, Begon et al. 1986, Hall et al. 1992,
Currie 1991, Wu and Vankat 1991, 1994, Tilman
and Pacala 1993). Several geographical
patterns have been documented across a range
of spatial scales. For terrestrial systems, the
patterns include gradual, sometimes monotonic,
changes in species richness along physical
environmental gradients (e.g., latitude, elevation)
on large scales, as well as more complex
patterns that are determined by both local
physical factors and biotic interactions on smaller
scales. There have been several recent reviews
of the patterns of species richness for plants,
animals, and microbial organisms (e.g., Krebs
1985, Begon et al. 1986, Brown 1988, Ricklefs
and Schluter 1993, Orians 1994). Here, we
compile a list of the major spatial patterns of
species richness with related hypotheses
pertinent to explaining the patterns (cf. MacArthur
1965, Pianka 1966, MacArthur and Wilson 1967,
Brown and Gibson 1983, Brown 1988, Orians
1994, Wu and Vankat 1994).
Latitudinal gradient. Species richness tends to
decrease with increasing latitude from the
equator to the poles, though the relationship is
not monotonic for most taxonomic groups of
organisms (Figure 2). This pattern appears
equally general for plants, animals, and microbes,
and has been well documented in the literature
(e.g., Fischer 1960, Simpson 1964, Cook 1969,
Kiester 1971, MacArthur 1972, Currie 1991).
Elevation gradient. Species richness decreases
with increasing elevation for most organisms.
The elevation gradient of species richness is also
rather general, and has been fairly well
documented (Yoda 1967, Glenn-Lewin 1977,
Brown 1988).
Aridity gradient. Species richness decreases
with increasing aridity across a geographic region
or a continent. A striking example is in the
temperate Eurasian continent where the number
of plant species declines dramatically as one
moves westward, across the north-south oriented
vegetation zones, from the deciduous forest, to
forest steppe, to typical steppe, and to desert.
An aridity gradient often interacts with other
gradients like elevation, which complicates the
species richness patterns and their interpretation
(Brown 1973, Whittaker and Niering 1965,
Glenn-Lewin 1977, Brown 1988).
Species-Area relationship. Species richness
tends to increase monotonically with habitat area.
A widely cited mathematical expression is of the
form: S = cAz, or, log S = z log A + log c, where
S is species richness, A is area, and c and z
are positive constants, c usually reflects the
effect of geographical variation on species
richness, and z usually varies between 0.18 and
0.35. This relationship has long been found in
numerous studies of terrestrial community
ecology and island biogeography (e.g., Preston
1962, MacArthur and Wilson 1967, Williamson
1988, Wu 1989). The spatial scales on which
this relationship is most likely to hold are from
local communities (e.g., 10 - 10s unit area) to
landscapes (104 109 unit area) (Auerbach and
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Rgure 2: Species richness versus latitude. Reproduced with permission from Currie (1991),
American Naturalist, 137(1):27-49, University of Chicago Press.
and Shmida 1987). The effect of area on
species richness seems to disappear at larger
spatial scales, such as continents (see Currie
1991).
Microenvironmental pattern. Species richness
exhibits gradient-like changes or more complex
patterns on local, small scales in response to
variations in abiotic and biotic environments.
Numerous studies have demonstrated the
importance of biological processes (e.g.,
competition, predation, mutualism) on species
richness through mechanisms such as niche
differentiation and competitive exclusion (e.g.,
Schoener 1974, 1988, Shmida and Wilson 1985,
Auerbach and Shmida 1987, Tilman 1993).
Spatial patterns of species richness correlated
with local-scale physical environmental conditions
(e.g., soil properties, micrometerological
conditions) have been well documented for plant
species (e.g., Qoodall 1970, Tilman 1982,
Qreig-Smith 1983, Wu 1992), but much less for
animals and microbes.
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2.2 Hypotheses Explaining Distributional
Patterns of Species Richness
Several hypotheses that invoke various
mechanisms have been proposed to explain the
observed spatial patterns of species richness on
different scales (see Pianka 1966, Loucks 1970,
Brown and Gibson 1983, Krebs 1985, Brown
1988, Currie 1991). These include time,
origination-extinction dynamics, available
energy/productivity, habitat heterogeneity,
disturbance, and niche theory/species interaction.
In the following sections, we shall summarize
these hypotheses by providing a concise
description of their essential elements with a few
relevant references. Then, a synthesis will be
given based on a hierarchical and scale
perspective.
Time hypothesis. Species richness increases
with time because longer time allows both
colonization and speciation to operate which in
turn result in more diverse biotas. This
hypothesis has been applied on both ecological
(102 years) and evolutionary (106 years) time
scales. Time was first proposed as a hypothesis
to explain the decrease in species richness with
increasing latitude, in which tropical areas were
thought to have more time to diversify because of
the absence of the glaciation that took place at
temperate latitudes (e.g., Fischer 1960).
However, there is little direct evidence available,
and it is extremely difficult to test the hypothesis
for evolutionary or geological time scales. At
local spatio-temporal scales, it seems generally
acceptable that the longer the time since last
disturbance, the more species are likely to
colonize (Pickett and White 1985, Pickett et al.
1987).
Origination-extinction dynamics hypothesis.
Species richness is a result of the balance
between species origination (colonization and
speciation) and extinction, and, therefore, the
patterns of species richness may be explained by
the differences in these processes. This
hypothesis also involves both ecological and
evolutionary time-scales, and has been used to
explain species richness patterns from the local
community to the continental and global levels
(MacArthur and Wilson 1967, Pielou 1979,
Benton 1987, Brown 1988). On ecological time
scales, the theory of island biogeography
(Munroe 1948, 1953, MacArthur and Wilson
1967) asserts that the number of species in
insular habitats is determined primarily by
colonization and extinction rates which in turn are
affected by habitat area and distance to the
colonizing source. While the theory has been
criticized on several grounds (Wu and Vankat
1994), there have been many studies of oceanic
and terrestrial habitat islands supporting the basic
idea of colonization-extinction dynamics (e.g.,
Pickett and Thompson 1978, Burgess and
Sharpe 1981, Harris 1984, Wu 1989). There has
been little direct evidence, however, for the
applicability of this hypothesis at continental and
global scales or on evolutionary and geological
times (Benton 1987, Brown 1988).
Available energy/productivity hypothesis.
Species richness proliferates with increasing
energy availability in the environment. At
regional (10s - 1013 m2 cf. Auerbach and Shmida
1987) and continental scales, it has been shown
that species richness is a function of available
energy or primary productivity (Wright 1983,
Currie 1991, Hall et al. 1992). Based on the
species-area relationship and the equilibrium
theory of island biogeography, Wright (1983)
developed a "species-energy theory." The core
of the theory is represented by the mathematical
formulation, S = kEz, where S is the species
richness, E is the total production of available
energy, and k and z are constants. Wright
(1983) found that available energy, measured as
total actual evapotranspiration (AET) for
angiosperms and total net primary production
(NPP) for breeding land and freshwater birds,
was able to account for 70-80% of the variation
in species richness on islands ranging from
Greenland and Spitsbergen to New Guinea and
Jamaica. Currie (1991) demonstrated that, for
the four vertebrate classes in North America he
studied (birds, mammals, amphibians, and
reptiles), annual potential evapotranspiration
(PET) alone accounted for 80-90% of the
variability in species richness (Figure 3). At
smaller scales, on the other hand, empirical
studies (especially in plant community ecology)
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have shown that species richness decreases with
increasing community productivity (e.g.,
Whteside and Harmsworth 1967, Rosenzweig
1968, Tilman 1982, 1993). Therefore, the
interpretation of, and mechanisms involved in this
hypothesis is scale-dependent.
Habitat heterogeneity hypothesis. Species
richness increases with habitat heterogeneity
which reflects the diversity and variability in the
structure, function and dynamics of the
environment organisms live in. Among the first,
Williams (1964) asserted that the species-area
relationship results from a positive correlation
existing between area and habitat diversity and
between habitat diversity and species diversity.
High habitat heterogeneity usually supports more
species by providing more habitat types and
reducing local extinctions caused by adverse
biotic interactions such as competition and
predation. There has been ample supporting
evidence for the habitat heterogeneity-species
richness relationship (Cody 1974, Tilman 1982,
1993, Shmida and Wilson 1985, Boecklen 1986).
Recent studies of patch dynamics at the local
and landscape scales have shown the effects of
spatial heterogeneity on species richness by
emphasizing the interactions between pattern and
process (Picket! and Thompson 1978, Steele
1978, Pickett and White 1985, Auerbach and
Shmida 1987, Wu et al. 1992, Levin et al. 1993).
Moderate disturbance/stress hypothesis. The
highest species richness usually occurs where
disturbance or stress is intermediate (Connell
1978). Disturbance often creates structural and
functional heterogeneity in time and space, and
promotes the coexistence of species by directly
suppressing destabilizing biotic interactions (e.g.,
intense interspecific competition and predation) or
by providing regeneration niches. A number of
empirical and theoretical studies on scales from
local communities to regional landscapes support
this notion (Loucks 1970, Grime 1973, Connell
1978, Huston 1979, Suffling et al. 1988).
However, the terms "moderate" and
"intermediate" seem ambiguous and, therefore,
have been used largely in a qualitative sense.
The patch dynamics perspective in ecology
(Pickett and White 1985, Wu and Loucks 1992)
has provided a new and comprehensive
framework for studying the effects of disturbance
on species richness at population and community
levels.
Niche theory/species interaction hypothesis.
Species richness in a biotic community is a
function of the number of niches; interspecific
interactions such as competition, predation, and
mutualism may promote species proliferation
through modifying niche relations (Brown 1988,
Schoener 1988). The study of interactions
between species diversity and niche relations has
been a central theme in both theoretical and field
community ecology for the past several decades
(e.g., Schoener 1974, 1988, Cody and Diamond
1975, Krebs 1985). In particular, it has been
suggested that interspecific competition facilitates
niche differentiation, while predation reduces
competition among prey species which in turn
reduces the probability of competitive exclusion
(e.g., Paine 1966, Connell 1978, Hubbell 1980,
Brown 1988).
The above hypotheses are not mutually
exclusive, but are complimentary to each other.
Evidence supporting one hypothesis does not
imply that others are not valid. Indeed, multiple
hypotheses are usually necessary to better
account for an observed species distribution
pattern. In most cases, each hypothesis
represents only one of several explanations
(Brown 1988). On the other hand, overlap
among some of the hypotheses is evident. For
example, time, be it ecological or evolutionary, is
critical in the origination-extinction dynamics
hypothesis since dispersal and speciation are
considered essential. Also, the hypotheses about
habitat heterogeneity, disturbance, and niche
theory/species interactions are interrelated
conceptually and practically. Disturbance is
important in both creation and maintenance of
habitat heterogeneity, which in turn affects the
disturbance regime itself (Pickett and White 1985,
Kolasa and Pickett 1991). Both habitat
heterogeneity and disturbance interact with
species and population dynamics. In concert,
these three hypotheses may account for many, if
not most, species richness patterns on small
scales. The interaction among the three
hypotheses is best exemplified by the
regeneration niche theory which has been
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50-
10-1
5-
1-
0
REFT1UA
500 1000 1500 2000
500 1000 1500 2000
POTENTIAL EVAPOTOANSPIRAT1ON
(mm • yr -
Figure 3: Species Richness versus PET. Reproduced with permission from Currie (1991), American
Naturalist, 137(1):27-49, University of Chicago Press.
evidenced by numerous studies at the community
level (Grubb 1977, Pickett and White 1985, Wu
and Levin 1994).
One of the problems in the study of biological
diversity has been the lack of a conceptual
framework to integrate information obtained from
numerous ecological and biogeographical
observations on a wide range of scales.
Available energy and its allocation across
different organizational levels may serve as a
basis for developing a general theory of species
richness (see Brown 1981, Wright 1983, Currie
1991, Hall et al. 1992). Towards this end, Hall et
al. (1992) has recently developed an integrative
framework for the distribution and abundance of
organisms using energy cost and gain analysis.
Importantly, any general theory must take a
hierarchical and scale perspective that explicitly
considers the multiplicity of the various
mechanisms that operate on different temporal,
spatial and organizational levels. Based on
previous studies, we propose a hierarchical
structure to relate both the patterns and
hypotheses of species richness to spatial scales
(Figure 4). We divide spatial scales into
continental, regional, and local domains. Based
-------
on the research design adopted by BRC, the
continental scale corresponds to all the hexagons
that make up the conterminous U.S. (see Figure
1). The regional scale corresponds to some
logical subset of the hexagons, such as those of
an ecoregion (e.g., Omernik 1987). The local
scale denotes a range in spatial dimension from
a single hexagon to a small subset, such as
those that comprise the Delaware-Maryland-
Virginia (DelMarVA) Peninsula.
It is important to notice that a particular
pattern of species richness usually occurs over a
certain range of spatial scales, and, therefore,
different patterns and mechanisms should be
expected on disparate scales. In other words,
the hypotheses of mechanisms regarding species
richness patterns have to be scale-dependent. In
particular, at very large (e.g., continental or
global) scales, the available energy in the
environment seems to be the ultimate
determinant of the number of species, although
the specific measure of available energy may
vary with taxonomic group (see Wright 1983,
Currie 1991). That is, the maximum or potential
species richness is constrained by energetics
on large scales. Brown (1981) conveyed
essentially the same idea using the term
"capacity rules." However, to fully explain the
large-scale patterns of species richness, other
hypotheses such as time and
origination-extinction dynamics must be invoked,
because processes like colonization and
speciation take time and history can be just as
important as anything else. The number of
species on smaller scales, on the other hand, is
primarily determined by energy partitioning and
balance. This is consistent with Brown's (1981)
concept of "allocation rules." Any factors and
processes that affect the energy allocation among
species thus may influence the distribution of
species richness. On landscape or regional
scales, spatial heterogeneity and disturbance are
important, whereas on local scales species
interactions, disturbances, and
microenvironmental factors most likely become
the determinants of species richness. Indeed,
the pluralism in mechanisms and multiplicity in
scale may have been significant sources of
controversy and confusion in the study of species
richness in particular and biodiversity in general.
A hierarchical approach as illustrated here may
facilitate the examination of the relationships
among different patterns and mechanisms and,
therefore, the development of a more
comprehensive understanding of species
richness.
10
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Latitudinal gradient
Aridity gradient
Blevational gradient
Species-area relationships
Ifioroenvlronment pattern*
(A)
Local
Regional
Contto6ntal/Global
Time
Origination-extinction dynamics
Available energy/Productivity
Habitat heterogeneity
Disturbance
Flche theory/
special interaction
OB)
Local
Regional
Spatial Scale
Figure 4: Proposed spatial scales at which species richness patterns are
evident (40 and associated hypotheses OB).
11
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3.0 FRAMEWORK FOR IDENTIFYING AND EVALUATING POTENTIAL STRESSORS
The previous section summarized major
empirical patterns and mechanisms of species
richness. However, there has not been a parallel
conceptualization of the influence of stressors on
species richness patterns. For example, there
are no studies known to the authors that
simultaneously examined the influence of natural
factors (e.g., climate and topography) in
conjuction with habitat fragmentation data over a
range of spatial scales. In this section we shall
propose a conceptual framework for identifying
and evaluating stressor data. The framework
includes the following elements: categories of
stressors, spatial scales at which stressors
operate, criteria for selection of stressor data
sets, and methodological considerations for
studying relationships between stressors and
species richness and among stressors.
3.1 STRESSOR CATEGORIES
Based on literature and communication with
experts, Finch (1992) compiled a list of threats to
56 bird and mammal species across the five
states of Wyoming, Colorado, South Dakota,
Nebraska, and Kansas. The threats identified
were: (1) agricultural conversion, (2) timber
harvesting, (3) livestock industry, overgrazing, (4)
fire suppression, (5) accidental pesticide
poisoning, (6) pest and predator control, (7)
overharvesting (trapping and hunting), (8) human
disturbance, (9) mining, energy development,
(10) competition (brood parasitism), (11) loss of
specialized habitat (wetland/riparian) (12) habitat
fragmentation, and (13) causes undetermined.
Other researchers have also identified
urbanization, desertification, ozone depletion, acid
rain, global warming, and introduced species
(Hodges 1977, Ehrlich and Ehrlich 1981, Soul6
and Kohm 1989, Barker and Tingey 1992). All of
these can be broadly categorized as human
disturbance.
Stressors affecting species diversity can be
grouped into four categories: habitat
fragmentation, pollution, introduction of exotic
species, and non-anthropogenic disturbances.
Agricultural conversion, timber harvesting, and
loss of wetlands and riparian habitats,
urbanization, and desertification all can be
included in a more general category of habitat
fragmentation. Pollution includes the threats
posed by pesticide use (herbicides, insecticides
and rodenticides), acid rain, ozone depletion, and
global warming. Mining and energy development
include aspects of both habitat fragmentation and
pollution. Livestock grazing can be included in
the exotic species category (see Section 4.3).
Overlaid on these human-induced
disturbances is a fourth category, non-
anthropogenic stressors, that contribute to
geographic patterns in species diversity. Using
Barrett's (1981) definition of a stressor as a
disturbance foreign to the system or natural to
the system but applied at unusual levels,
examples of non-anthropogenic stress include
severe weather or climatic events, man-
precipitated or suppressed fires, and some pest
outbreaks. In contrast, prevailing climatic and
topographic conditions are part of the system
where organisms live.
3.2 SCALES AT WHICH STRESSORS
OPERATE
Three scales for examination of species-
stressors relationships were identified in Section
2.0: the scale of one or just a few hexagons
(local), many hexagons covering an entire region
(regional); and the entire conterminous United
States (continental). The identification of scales
on which stressors operate is important to
understanding the scope of stressors and their
effects on species diversity.
(1) Habitat Fragmentation. Habitat
fragmentation has been cited by many as posing
the most serious threat to biodiversity (NRC
1982, Noss 1983,1987. Harris 1984, Wilcoxand
Murphy 1985, Wilcove et al. 1986, Wilcove 1987,
Soul<§ and Kohm 1989, Dobkin 1992). Wilcove et
al. (1986) have defined habitat fragmentation as
"transformation of a large expanse of habitat into
a number of smaller patches of smaller total
area, isolated from each other by a matrix of
habitat unlike the original." Do measures of
habitat fragmentation show correlations with
species diversity at continental, regional and local
12
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scales? Many of the empirical studies on the
effects of habitat fragmentation have been at
what we refer to here as a local scale. The study
by Lynch and Whigham (1984) of the effects of
forest fragmentation on breeding birds covered
roughly a 100x100 km area over six counties in
Maryland. The studies by Bolger et al. (1991)
and Soul6 et. al. (1992) on the effect of habitat
fragmentation of chaparral species was restricted
to coastal San Diego County. Studies of the
effect of fragmentation on red squirrel (Verboom
and van Apeldoorn 1990) and other small
mammals (Bennett 1990) covered 150 km2 and
105 km2, respectively. Lauga and Joachim's
(1992) study of forest fragmentation on breeding
birds covered 2327 km2. Lyon's (1979, 1983)
study of the impact on road density on elk
covered about 205 km2, and a similar study by
Thiel (1985) on gray wolf covered about 13
counties in northern Wisconsin. Empirical studies
have largely been restricted to local scales.
Given that habitat conversion has occurred over
larger regions (e.g., agricultural conversion in the
American Midwest), it is also logical that there is
a relationship between species diversity and
fragmentation at a regional scale.
(2) Pollution. Newman et al. (1992) have
suggested that animal species diversity is
affected by pollution at local and regional levels.
The range of scales at which pollution is likely to
impact species diversity is the result of the
combination of the extent of the release of the
pollutant, the effect the pollutant has on a
particular species, and the geographic range of
the species. For example, blindness in big horn
sheep populations of the San Bernardino
Mountains has been attributed to ozone transport
from the Los Angeles Basin (Light 1973). This is
an example of a local effect resulting from a
more regionally distributed stressor. In contrast,
region-wide pollution from acidic deposition has
caused region-wide decline in aquatic diversity
(Dickson 1986). Pesticides are released over
large regions of the United States. The
contiguous states of Nebraska, Minnesota,.Iowa,
Illinois, Indiana, and Ohio are in the top ten in
application of pesticides (Waddell et al. 1988).
Thus, pesticide use could affect species diversity
patterns at regional scales.
(3) Exotic Species. The work on modeling the
rate of spread of an invading organism provides
some insight into scale characteristics of species
introductions. Much of the work has been based
on diffusion models (see Skellam 1951,
Roughgarden 1986, Hengeveld 1989, Andow et
al. 1990). These models predict species spread
by assuming biological movement to be similar to
random brownian motion. The rate of spread is
a function of the species intrinsic growth rate and
a diffusion coefficient. The models seem to work
well (see Andow et al. 1990) when applied to
species for which habitat and abiotic
requirements can be relaxed (e.g. cabbage
butterfly, muskrat).
The models have not been applied to a suite
of species representing a range habitat and
abiotic requirements and a range of possible
outcomes of introductions (i.e., failure to establish
widespread distribution). Most introduced
species do not become established. Lindroth
(1957) observed that only a minority of European
insects have invaded North America and few
have spread from their liberation point. Mayr
(1965) has observed the same pattern for birds;
and Mitchell (1978) notes that no European
conifers that have been planted as ornamentals
in North America have established populations
outside their points of introduction. Other
examples also show the importance of habitat
and the physical environment in restricting the
spread of invaders (Hengeveld 1989). Melaleuca
and Brazilian pepper, though widespread in
south Florida, are restricted in their northward
migration by cold temperature (Ewell 1986).
Many of the escaped cage birds in southerly U.S.
urban environments (e.g., Miami, Los Angeles)
have not spread beyond urban boundaries
because they require the planted, tropical
ornamental trees for food and shelter (Orians
1986). Gray squirrels, introduced into California
from the east, are successful only in urban
environments where xeric conditions are
overridden (Mooney et al. 1986).
Modeling studies highlight that biological
invasions can spread over continents when
habitat and/or constraints imposed by the
physical environment can be ignored. These
cases seem to be the exception rather than the
13
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rule. Examples such as European starling,
house sparrow, and cattle egret are human
commensals. These species have continental
(and global) ranges because suitable habitat has
been expanded across entire continents.
(4) Non-anthropogenic Stressors. Climate is
the primary data set for which non-anthropogenic
stressors can be developed (see Section 4.4).
Characteristic climates (e.g., the Koeppen
classification) are regionally distributed.
Trewartha (1961), for example, divides North
America into six climatic regions, and describes
several subtypes within these regions. The
severe winter of 1993/1994 in the eastern United
States highlights that extreme weather events are
also distributed regionally. Record snowfalls
were recorded in several locations in the mid-
Atlantic States, for example, southeastern
Pennsylvania. However, only as far south as
Washington, DC, the cold was not as severe and
ice storms were the prevailing form of
precipitation.
3.3 CRITERIA FOR SELECTION OF
STRESSOR DATA SETS
There are several sources of stressor data
available. Abramovitz et al. (1990) have
published a guide to over 30 environmental data
sets that are available through the federal
government. It would not be feasible in terms of
time or expense to examine each of them.
Criteria must be established for selecting data
sets to develop stressor measurements.
The first criterion is to select at least one
data set from each of the stressor categories
identified (habitat fragmentation, pollution,
introduced and exotic species, and non-
anthropogenic). Hall et al. (1992) noted that at
least one example could be found to support a
particular hypothesis for explaining species
richness diversity, but little work has been done
to integrate the numerous hypotheses for
explaining species diversity. To gain a more
complete understanding of the relationship
between stressors and species diversity would
seem to require data sets representing stressors
from each category. Also data sets within each
category should be selected such that as many
aspects of that stressor category as possible are
represented. For example, land cover data can
be used to develop many stressor metrics of
habitat fragmentation. However, land cover data
is often deficient in its representation of roads
and wetlands. Separate data sets of roads and
wetlands might be necessary to develop habitat
fragmentation measures that cannot be obtained
from land cover data alone. Second, the data
sets selected should cover the entire
conterminous United States or at least the
stressor's region of influence. Third, available
reference information (not necessarily part of the
data set itself) is needed to assess the quality of
the data set. Assessment of data quality is
important for the correlative analyses proposed
by BRC. Without knowing the quality of the data
used, it is impossible to determine whether or not
correlations (or lack thereof) are an artifact of
poor data quality.
3.4 A CONSIDERATION IN STUDYING
CORRELATIONS BETWEEN
STRESSORS AND SPECIES RICHNESS
Once the stressor data sets have been
selected, the question arises as to how the data
sets should be used to determine their effects on
species richness. The problem of scale is central
to this issue. Many of the continental-scale
studies have shown strong correlations with
aspects of climate and topography, but there has
been little empirical work at similar spatial scales
showing strong correlations between species
richness and stressors of habitat fragmentation,
pollution, or exotic species. The effect of these
disturbances appears to be most prominent on
local and regional scales (see Section 3.2).
Scaling-up is recommended here. By
scaling-up, correlations between stressors and
species richness, as well as among stressors,
can be studied progressively. The spatial scale
can be increased by continually adding more
hexagons. A pattern of increasing correlation
with increasing geographic extent (more
hexagons) followed by a drop in correlation may
indicate the scale at which that stressor
influences species richness patterns. This
approach is conceptually similar to that used by
Krummel et al. (1987). The authors examined
the change in fractal dimension as a function of
14
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forest patch size. To start, the 200 smallest
forest patches were selected and their fractal
dimension calculated. This process was
repeated, using progressively larger 200-patch
subsets. Other examples using a conceptually
similar approach can be found in O'Neill et al.
(1991) and Turner et al. (1991).
The same approach could be examined in
the Pennsylvania and Oregon Pilot Studies by
starting with a small subset of hexagons,
examining the results of multivariate analyses,
adding additional hexagons to increase the
spatial extent, followed by a re-examination of the
results of the multivariate analyses. There are
several logical starting points in Pennsylvania and
Oregon. In Pennsylvania, examples are the
Great Valley, Coastal Plain, and Piedmont, all of
which would be comprised of only a small
number of hexagons. In Oregon, examples
include the Willamette Valley and the portion of
Malheur County south of the Owyhee River and
west of the Owyhee Mountains.
15
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4.0 EXISTING DATA FOR IDENTIFYING STRESSORS
4.1 HABITAT FRAGMENTATION DATA
Habitat fragmentation alters the physical
environment in numerous ways (Wilcove et al.
1986, Wilcove 1987, Saunders et al. 1991).
These include loss of habitat area, loss of habitat
heterogeneity, isolation, edge creation and
changes in edge-to-area ratio, and modification of
microclimate in remaining patches. Many
biological responses are incumbent with these
changes, such as density dependent extinctions
(Wilcove et al. 1986, Pimm et al. 1988, Soul6 et
al. 1988, Bolger et al. 1991), dysfunctional
behavior (e.g., inability to avoid nest predation)
(Wilcove 1987), and secondary extinctions
(Wilcove et al. 1986).
Loss of habitat heterogeneity may be one of
the most pervasive but least recognized aspects
of fragmentation. Habitat heterogeneity is
typically considered a within-stand or community-
level phenomenon, such as snags and downed
logs in old-growth forests (e.g., Franklin 1993).
However, habitat fragmentation may also
eliminate juxtaposition of different habitat types
(e.g., removal of forest patches near riparian
habitat), which can make remnant habitats
unusable or less than optimal. Of the 67 bird,
mammal, and herptile species studied by Finch
(1992), 59 used more than one habitat type.
Thomas et al. (1976), in a study of 13 bird
species in the Blue Mountains of Oregon, found
that all 13 species required at least two habitat
types for reproduction and/or feeding. A complex
of habitat types has also been demonstrated as
a requisite for beaver (Castor canadensis)
(Genoways 1986) and the desert bighorn sheep
(Ovis canadensis) (Leslie and Douglas 1979).
Existing data that can be used to measure
aspects of habitat fragmentation are listed in
Table 2. The data are in four groups: land
cover, roads, population, and wetland and
riparian habitats.
4.1.1 Land Cover
Land cover data (Table 2) are the primary
source for generating metrics of habitat
fragmentation (e.g., shape, size, interspersion,
connectivity, and diversity). The developing
discipline of landscape ecology (Naveh and
Lieberman 1984, Risser et al. 1984, Forman and
Godron 1986) provides both a conceptual basis
and a practical framework for such studies. Land
cover data are available from several sources, at
several scales, and in several formats.
The USGS land cover regionalization
(Loveland et al.1991) is a nationwide data base
that has 159 classes mapped from the Advanced
Very High Resolution Radiometer (AVHRR)
satellite at about 1 km2 resolution. Use of
AVHRR-derived land cover data to generate
patch-based measures of habitat fragmentation
should be undertaken with caution. The AVHRR
land cover regionalization data provides a map of
the probability of a particular land cover type
occurrence (Brown et al. 1993). It is not a
traditional land cover map in the sense of
providing data with specified geographic and
thematic accuracy. Therefore, measures such as
patch size, distance between patches of the
same type, and patch shape are, too, only
probabilities. Also, Gervin et al. (1985) found
that thematic accuracy of AVHRR land cover data
was poor in heterogeneous areas when
compared to those mapped from Landsat MSS.
The USGS Land Use Data Analysis (LUDA)
and USFWS GAP land cover have specified
thematic and geographic accuracy requirements.
These data are more appropriate for developing
metrics of habitat fragmentation than AVHRR.
USGS LUDA data were compiled from ca. 1975
high altitude aerial photography. The USFWS
GAP program (Scott et al. 1993) uses Landsat
TM to map land cover. Several state agencies
are also mapping land cover using Landsat
satellite data (e.g., Florida, Georgia, South
Carolina). The Department of Defense (DOD)
has published a Digital Chart of the World
(DCW), which provides digital, polygon-based
data of urban areas for the United States
(Loveland, pers. comm.). Urban categories are
typically included in land cover maps.
16
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Table 2: Data for Land Use and Habitat Fragmentation.
Data
Land Cover
Roads
Population
Wetlands &
Riparian
Name
Land Cover
Regionalization
LUDA
GAP
MLU
PSU
NRI
DCW
DLQ
CENDATA
NWI
Collection
Method
AVHRR
Aerial
Photography
Landsat TM
Survey
Landsat TM
Field Sample
Satellite
Aerial
Photography
Survey
Aerial
Photography
Source
USGS
USGS
USFWS
USDA
USDA
NRI
DOD
USGS
Census
USFWS
Trends
No
No
No
Yes
Yes
Yes
?
No
Yes
Yes
NCSS
(soils)
DLG
(streams)
Survey
Aerial
Photography
USDA
USGS
No
No
The U.S. Department of Agriculture (USDA)
provides at least three sources of land cover data
through its various branches. These are Major
Land Uses (MLU) from the Economic Research
Service (ERS), National Agriculture Statistical
Service (NASS) Primary Sampling Units (PSU),
and the National Resources Inventory (NRI) of
the Soil Conservation Service (SCS). Data from
these sources are updated every five years.
MLU provides 5-year trend analysis in 11
land use categories. These data are tabular
statistics by state, compiled by correspondence
with other federal inventory programs. For
example, forest area is taken from USDA Forest
Service estimates. The estimates are adjusted to
a total of 100 percent.
NASS collects agricultural and land cover
data using a stratified sampling design. Primary
Sampling Units (PSU) are the most basic strata.
These units vary in size from 40 to 640 acres,
and are classified according to the intensity of
agricultural land use. An example of a PSU
class is an area with greater than 50 percent
agriculture. These data are not land cover per
se. They are a land cover regionalization based
on intensity of agriculture. Over the long term
17
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(20 to 30 years or more), NASS PSU data may
have potential for monitoring the trend in
agriculture toward fewer but larger farms
projected by NRC(1982).
NRI data were first collected in 1982, and
repeated in 1987 and 1992. NRI is primarily
intended to provide an inventory of status and
condition of soil and water resources, but also
provides information on land cover. Data are
collected using PSUs that are similar in concept
to NASS PSUs. These units are about 160 acres
in size. Land cover data are collected using 3
sample points within each PSU. Area estimates
of land cover are statistically reliable using USGS
8-digit watersheds (cataloguing units) and Major
Land Resource Areas (MLRA, an "ecoregion"
map based on soils and land use). The 1982
and 1992 data also have information on land
cover surrounding each point within 0.25 miles.
The point data are available as a CIS data set
with attribute coding. A polygon-based land
cover data set for the watersheds codes each
unit by proportion of land cover classes.
Land cover data mapped from satellites with
higher resolution than AVHRR or aerial
photography should be used to generate habitat
fragmentation metrics. These data are typically
compiled with specified thematic and geographic
accuracy standards. Use of NRI land cover data
should be explored as a substitute when satellite-
based land cover is not available. NRI data are
compiled from a design that provides statistically
reliable estimates of land cover for small
watersheds, and can provide change and trend
information. Their primary shortcoming is that
land cover is represented as points instead of
polygons, but habitat measurements (e.g.,
interspersion) should still be possible. The other
land use data generated by USDA is not
recommended because measurement of habitat
fragmentation would not be possible with them.
4.1.2 Digital Line Graphs (Roads)
Roads are discussed separately from land
cover because land cover data do not typically
include roads unless they are of sufficient width
(e.g., interstate highways). Moreover, metrics
such as road density can capture aspects of
fragmentation that cannot be measured from land
cover alone. Several studies have shown that
roads have fragmented habitats for larger
animals. Elk (Cervus elaphus) habitat was
reduced by 75 percent at a road density of 2
km/km2 (Lyon 1979, 1983). If the average road
width in this study was 20 meters, the area
occupied by roads was only 4 percent of a 1 km2
unit. Thiel (1985), studying gray wolf (Canis
lupus) in Wisconsin, found that populations failed
to survive at road densities greater than 0.93
mi/mi2. Storm et al. (1967) found that-red fox
(Canis vulpes) avoided roads. These findings
illustrate that for a given species, habitats (e.g.,
forests) which seem suitable in terms of size,
shape, and connectivity with other patches of the
same type, might actually not be suitable
because of the existing road network. Metrics
such as patch size alone would not be able to
detect this problem.
Road data are available from various
sources, including USGS Digital Line Graphs
(DLG), Census TIGER files, EtakMap Corp.,
Wessex First Street, and Delorme (Street Atlas)
Inc. USGS DLG data is probably the most widely
recognized source. The other data sets are
based on USGS DLGs. For example, Census
TIGER files are essentially USGS DLG road data
with address geocoding (e.g., zip codes and
street addresses). EtakMap Data are updates of
the USGS 1:100,000-scale USGS data.
DLG scale options are: 1:24,000, 1:100,000
and 1:2,000,000. These data are attribute-coded,
giving information on the types of roads (e.g.,
interstate highways, limited access/divided
highways, other U.S. highways, state secondary
highways, improved roads, and unimproved
roads). USGS DLG 1:24,000-scale data is not
available nationwide. The 1:100,000-scale data
is the largest scale data that is available on a
nationwide basis. USGS 1:100,000-scale DLGs
are created by photomosaicking and then
photoreducing the 1:24,000-scale USGS
topographic maps. The photoreductions are then
scribed and these are scanned to create the
digital 1:100,000-scale data (pers. comm., USGS,
Reston, VA).
USGS DLG is the most accessible (via
INTERNET) road data available, and all other
available road data are compiled from DLGs.
18
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Thus, there is no independent data source for
comparison. The amount of error in the USGS
DLG data is unknown. Three analyses can be
performed to assess the quality of DLQ road
data. First, the spatial pattern in road density
can be examined by map sheet. This has been
done for Pennsylvania using the 1:100,000-scale
DLG data. The highest densities are in the
southeastern portion of the state, followed by the
southwestern portion. The lowest densities are
in the central portion of the state, starting north of
Harrisburg and spreading east and west in a "T"
along the border with New York. The rank order
pattern follows the population distribution in the
state. Second, graphs and correlations scores
between road and population density can be
examined. Since these data should be positively
correlated, a finding to the contrary would
suggest error in the road data. Third, DLGs can
be compared to digital orthophoto quads.
4.1.3 Wetland and Riparian Habitat Loss
There is increasing recognition that wetland
and riparian habitats support greater numbers of
species than surrounding uplands (Odum 1979a,
1979b; Thomas et al. 1979; Kauffman and
Kreuger 1984; Gregory et al. 1990; Williams
1991; Finch 1992; Mitsch and Gosselink 1993;
Naiman et al. 1993). Eighty (80) percent of the
United States' breeding bird populations rely on
wetlands (Wharton et al. 1982). Wetland and
riparian habitats also support a disproportionately
greater number of the endangered and
threatened species in the United States (Mitsch
and Gosselink 1993). In a survey by Finch
(1992), loss of wetland and riparian habitat was
the most often cited cause of species decline.
Loss of wetland and riparian habitat is likely to be
an important measure for explaining geographic
patterns of species diversity.
The ability of wetland and riparian habitat to
support high numbers of species may be in part
due to their greater productivity. In comparing
net primary productivity (NPP) for temperate
wetlands from Richardson (1979) with NPP
estimates of temperate uplands from Lerth
(1975), Williams (1991) found that temperate
wetlands were 2 to 5 times more productive than
surrounding upland communities.
There are two reasons why wetland and
riparian habitat loss should not be developed
from land cover data alone. First, satellite data
(even Landsat TM) are not generally capable of
detecting wetlands accurately (see Dottavio and
Dottavio 1984). A multistage approach (use of
several remote platforms simultaneously - air
photos, airborne and satellite spectral data) for
accurate detection of wetlands is advocated by
Jensen et al. (1986,1991). Second, much of the
wetland loss that has occurred in the lower 48
states predates the advent of satellite remote
sensing (Williams 1991). Temporal land cover
mapping could not be used to determine wetland
loss between the time periods represented by the
land cover data.
To determine where wetlands have been lost
requires three data sets: land cover, streams,
and soils. Soils data are critical. Soils data can
be used to determine where wetlands would be
supported independent of the present land cover
and, since soils change slowly relative to land
cover, wetland loss estimates can be generated
that reflect a longer history than would be
possible by comparison of temporal land cover
data.
Soil Conservation Service (SCS) has mapped
soils nationwide using the soil series taxonomic
unit (USDA 1987). The soils series is "the basic
unit of soil classification,... consisting of soils that
are essentially alike in all major profile
characteristics except texture of the A horizon"
(Steila 1976). There is an extensive list of
attribute information that accompanies the data,
such as length of flooding. By combining land
cover and stream data with soil attributes such as
length of flooding, wetland and riparian habitat
loss could be measured as the sum (by hexagon)
of anthropogenic land use (e.g., agriculture,
urban) on wetland soils.
Data for streams is available as part of the
USGS DLG series. DLG characteristics
previously described for roads also apply to
streams (except for classification). Land cover
data would be taken from sources previously
described. The stream data would be utilized to
distinguish riparian wetlands from those that are
not.
19
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4.1.4 Census Data
Population growth is the driving force behind
the threats to biodiversity. The Bureau of Census
provides several data sets covering various
aspects of population through its CENDATA
system. The County Statistics File 3 contains
time-series data for over 1600 items. Population
change (1980-1990) for selected high growth
counties in Pennsylvania and Oregon are shown
in Table 3.
Population growth in both Pennsylvania and
Oregon follow urbanization patterns. In
Pennsylvania dramatic growth has occurred in
the counties that form its eastern and southern
borders with New Jersey and Maryland. These
include Pike (FIPS 42103) Monroe (42089),
Wayne (42127), Adams (42001), York (42133),
Lancaster (42071), and Chester (42029)
Counties. The improvement of US15 in
Pennsylvania is at least partly responsible for
increased population growth in Adams County,
making Gettysburg, PA a distant but feasible
commute into northern Washington, DC and
other Interstate 270 markets. Several new
residential developments are evident along the
Interstate 270/US15 corridor. High growth
counties in Oregon are Washington (41067),
Clackamas (41005), Marion (41047), Jackson
(41029), Deschutes (41017), and Yamhill
(41071). These counties are all in the vicinity of
Portland and the Interstate 5 corridor, except for
Deschutes which includes the city of Bend.
Use of population data is not recommended
because it is an indirect influence. It is not
population per se, but the resultant human
activities that act as stressors to biodiversity.
The danger of using indirect measures such as
population in correlative studies was illustrated
well by Cole et al. (1993). The authors showed
a R2 of 0.76 between population density and
nitrogen concentration in rivers. However, this
relationship does not indicate the processes by
which human population causes the increase in
nitrate concentration. Further investigation
showed that sewage discharge, automobile
emissions, agriculture, and forest cutting were the
true culprits. Use of population density will likely
show strong (negative) correlations with species
diversity, but finding such a relationship will not
provide insight into the activities reducing
species diversity. Knowledge of the actual
mechanisms that cause a reduction in species
diversity is needed to develop management
options from analysis of geographic patterns of
stressors and species diversity (i.e., linking BRC
objectives 1 and 4; Section 1.0).
Table 3: Population change (1980-1990) for selected high growth counties in Pennsylvania and
Oregon (from ArcUSAm ESR11992). FIPS is the county identification code.
Pennsylvania
FIPS
42001
42029
42071
42089
42103
42127
42133
Oregon
Population By County
1980 1986
68292
316660
362346
69409
18271
35237
312963
71200
339100
393500
82700
22300
38700
326600
1990
78274
376396
422822
95709
27966
39944
339574
Population Change
80 to 86 86 to 90
4.2
7.1
8.6
19.1
22.2
9.9
4.3
9.9
11.0
7.5
15.7
25.4
3.2
4.0
41005
41017
41029
41047
41067
41071
241911
62142
132456
204692
245860
55332
256900
68700
140000
215400
271400
57500
278850
74958
146389
228483
311554
65551
6.2
10.5
5.7
5.2
10.4
3.9
8.5
9.1
4.6
6.1
14.8
14.0
20
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4.2 POLLUTION DATA
Widespread awareness of the potential
impact of pollution on species arose after World
War II, following the dramatic increase in
pesticide use (Moriarty 1983, Peterle 1993). The
field of ecotoxicology began to emerge in the
1970s as a discipline focused on the study of the
ecological effects of pollutants (Truhaut 1977).
Pesticides use is probably the most serious
pollution problem relative to wildlife populations.
Land in farms comprises about 50 percent of the
conterminous United States (USDA 1989, ESRI
1992), and pesticides are used throughout.
There are approximately 50,000 different
chemical compounds that are used as pesticides
(Waddell et al. 1988) out of approximately 63,000
chemical compounds that are commonly used by
industry worldwide (Moriarty 1983).
Another pollution problem that has impacted
species diversity is acidification of lakes and
streams in the northeastern United States.
Schindler et al. (1989) have modeled the
potential loss of taxonomic groups of fish, based
on their documented sensitivity to pH values less
than 5.0. Fish kills have been reported at pH
values of 5.0, apparently because of increased
aluminum toxicrty (Moriarty 1983).
Outside of these two examples much of what
is known about the impact of pollution on wildlife
is from case studies. Newman et al. (1992)
discussed several case studies of impacts of
pollutants on wildlife populations. In another
recent case study, high concentrations of mercury
have been found in the endangered populations
of Florida panther (George Taylor, pers. comm.).
Table 4 is a list of available pollution data
sets. The data from the USFWS National
Contaminant Biomonitoring Program (NCBP) and
the USGS National Stream Quality Accounting
Network (NASQAN) appear to be the two data
sets that are most likely to show a relationship
with species diversity patterns at large spatial
scales. The others listed in Table 4 provide only
ambient concentrations in the environment.
Actual concentrations in species are more
valuable than concentrations in the environment
because of species differential response to the
presence of pollutants (Loucks, pers. comm.).
The USFWS NCBP program provides data
on tissue concentrations of agricultural pesticides
in fish, waterfowl and starlings. There are over
100 fish and starling collection sites;
concentrations in waterfowl are collected from
hunters. The program was initiated in 1964 with
the objective of providing geographic and
temporal trends. NCBP data will be stored in the
Environmental Contaminant Data Management
System (ECDMS). Fish data are already in the
EPA STORET system.
Although NASQAN does not provide
information on presence of toxics (e.g., Al"*) in
aquatic biota, the well documented susceptibility
of fish species to pH values less than 5.0
(Dickson 1986, Schindler et al. 1989) suggests
that use of pH data for lakes and streams should
be a useful stressor metric for correlation with
aquatic diversity. Because of the relationship
between acid deposition and the buffering
capacity of soils (Moriarty 1983), it is probably
better to restrict such correlation studies to areas
with inherently low Acid Neutralizing Capacity
(ANC), such as the northeast United States. It is
not likely that the other data listed in Table 4 will
provide useful information for correlation with
geographic patterns of species diversity. Each
provides only information on release of pollutants
into or concentration in the environment.
4.3 EXOTIC SPECIES DATA
The data available for introduced and exotic
species are listed in Table 5. The primary source
of data is TNC's Heritage Data Base, which is
being used to develop the species richness data
for each hexagon (see Figure 1). The vertebrate
species data set being developed for each
hexagon will classify each species according to
its probability of occurrence (1: >95%; 2:80-95%;
3: 10-80%; 4: <10%) and its origin (1: native; 2:
introduced; 3: reintroduced; 4: unknown). The
quality of these data, relative to whether a
species is native or introduced, is probably high,
given that distinction between native and
introduced is easier in countries that have been
explored and settled by Europeans only in the
last three hundred or so years (Usher 1988).
These data are being developed by the Species
Team. Development of specific metrics will likely
21
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Table 4: Pollution Data Sets (from Abramovitz et al. 1990).
Agency
DOI, USFWS
EPA
Commerce,
Census
DOI, USGS
DOI, USGS
Commerce,
NOAA
DOE
EPA
EPA
EPA
DOE, ORNL
Data
NCBP
NPS
Census
of Ag.
NASQAN
Trends
Yes
NCDPI
MSCET
AIRS
CERCLIS
TRI
CDIAC
Yes
Yes
NADP/NTN Yes
No
Yes
Yes
Yes
Yes
Yes
Description
National Contaminant Biomonitoring Program
(NCBP). Tissue examination of fish,
waterfowl, and starlings.
National Pesticide Survey (NPS). Data on
126 pesticides in water supply wells.
Census of Agriculture. Data on chemical
applications by county.
National Stream Quality Accounting Network
(NASQAN). Data on H2O quality and quantity.
Monitoring was established in 1972.
National Atmospheric Deposition
Program/National Trends Network
(NADP/NTN). Data on precipitation chemistry
for -200 sites nationwide.
National Coastal Pollutant Discharge Inventory
(NCDPI). Inventory of 9 categories of
pollutants for coastal areas.
Month & State Current Emission Trends
(MSCET). NOX, SOX, and VOC emissions by
state.
Aerometric Information Retrieval System
(AIRS). Data on air quality and pollution
collected from state and local agencies.
Comprehensive Environmental Response,
Compensation and Liability Information
System (CERCLIS). Data on the location of
-30,000 hazardous waste sites.
Toxic Release Inventory (TRI). Toxic release
inventory of over 17,000 manufacturing sites.
Carbon Dioxide Information Analysis Center
(CDIAC). Data on CO2 parameters.
22
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Table 5: Data Sets for Exotic Species, Including Managed Livestock and Grazing Practices.
Agency
The Nature
Conservancy
USFS, FS
Data
Species
occurrence
FSRAMIS
Trends
Yes
DOI, BLM
PLS & ESI
DOC, Census
Census of
Agriculture
Yes
Description
Species occurrence by hexagon.
Species include mammals, reptiles,
amphibians, and fish.
Forest Service Range Management
Information Service (FSRAMIS). Data
on grazing use in national forests and
grasslands.
Public Land Statistics (PLS) and
Range (Ecological) Site Inventory
(ESI). Provides data on grazing use
and ecological condition, respectively.
Data on livestock numbers by county.
be a collaborative effort among the Species,
Analysis, and Stressors Team.
Data for grazing is presented here under the
category of exotic species, instead of creating a
separate category. This is because herbivory by
large ungulates in the intermountain west was not
an ecosystem component prior to their
introduction by man (Mack and Thompson 1982).
Livestock grazing is the most extensive land use
in the interior Pacific Northwest (Kauffman and
Kreuger 1984). Demand for rangeland is
projected to increase 38 percent by 2030 in the
Pacific Northwest (NRC 1982). Data on grazing
use seems necessary to help explain species
diversity patterns because of the extent and
projected increases in grazing use throughout the
western United States (NRC 1982), and also
because of the potential for domestic livestock
grazing to alter competitive relationships with
other organisms, transmit disease, accelerate
erosion, change plant community composition,
and alter riparian habitat (Cooperrider 1990,
Kauffman and Kreuger 1984).
Grazing data is available from three federal
sources: BLM, the U.S. Forest Service, and the
Department of Census (Census of Agriculture).
BLM's Public Land Statistics (PLS) provides
grazing use data, and the Ecological (Range)
Site Index data have been used to determine
past grazing use. These data are not stored
digitally, but instead are kept as paper records in
each BLM state office. Moreover, these data
are in Animal Unit Months (AUM), which is the
amount of forage necessary to sustain one cow
and calf for one month. Since, the amount of
forage concept of an AUM is based on the
vegetation present, an AUM is unitless in terms
of area. It might take only forty acres to make up
one AUM in one allotment versus 60 acres per
AUM in another. The use of AUM makes it
difficult to determine grazing use in terms of
number of livestock per unit area. BLM's ESI
data classifies allotments into low, mid-, and high
serai, and potential natural community (PNC),
based on vegetation composition. Some have
suggested that each stage reflects past grazing
use (L. Walker, pers. comm.). For example, low
serai would reflect past heavy grazing pressure.
However, there is not complete agreement on the
23
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relationship between grazing use and BLM serai
stages. The Forest Service Range Management
Information Service (FSRAMIS) stores grazing
use in a computerized format. Information
collected includes size, state, and county of
allotment, and grazing use. Geographic
coordinates are optional. The data base is not
proprietary. Also, the Census of Agriculture,
conducted every five years by the Department of
Commerce, provides livestock inventories by
county.
The FSRAMIS data base appears to have
the most appropriate data (allotment
characteristics and actual grazing use in a
computerized format with geographic
coordinates), but the data only cover Forest
Service lands. It is disappointing that BLM does
not appear to have this type of data available to
the public. At present, the Census of Agriculture
data on livestock inventory by county seems to
be the only nationwide data.
A per hexagon livestock density data set with
known data quality can be developed through
boolean modeling using the Census of
Agriculture livestock estimates followed by
comparison to livestock data from FSRAMIS (for
counties of co-occurrence). Livestock distribution
is not spatially uniform (Senft et al. 1985, Smith
1988). Studies have shown that cattle avoid
steep slopes and exposed aspects (Roath and
Kreuger 1982) and tend to congregate near water
(Roath and Kreuger 1982, Senft et al. 1983,
1985). These factors combined with land cover
data (e.g., presence of urban development) and
land ownership permit bollean modeling of the
presence of cattle in individual hexagons.
The boolean model would be county based.
The model would query each hexagon to
establish the proportions of the hexagons with
steep slopes, exposed aspects, cropland and
urban land use, and land ownership that
excluded grazing (e.g., National Park). The
result of these queries would eliminate some
portion of the area of some or all hexagons in the
county from grazing use. For example, assume
that livestock density for a county is 100,000
animals, and a hexagon comprises 5 percent of
the area of that county. If the modeling
eliminated 50 percent of the area of that
hexagon, then 2.5 percent of 100,000 (2.500
animals) would be the density of livestock in that
hexagon. For hexagons straddling one or more
county boundaries, the starting proportion for the
hexagon would be the area in the county being
modeled. FSRAMIS data could be used to
validate and refine the model, because it contains
information on size, state, and county of
allotment, grazing use, and geographical
coordinates for Forest Service lands.
4.4 DATA SETS FOR NON-
ANTHROPOGENIC FACTORS
Data on climate are the primary source to
develop stressor metrics of non-anthropogenic
stress - a disturbance which is part of the
system but occurs at an excessive level at a
particular point in time (Barrett 1981). There are
also data on fire and pest outbreaks. The data
sets are listed in Table 6.
4.4.1 Historical Climatology Network
(HCN)
Climatic data have been widely used to study
geographic patterns in species richness (e.g.,
Wright 1983; Owen 1990a, 1990b; Currie 1991).
Climate is noted to have a particular influence on
the distribution of bird species (Temple and
Wiens 1989). The National Oceanic and
Atmospheric Administration's (NOAA) Historical
Climatology Network (HCN) contains 1219
stations of serially complete monthly temperature
(mean, minimum, and maximum) and total
precipitation throughout the United States. The
data represent the best available out of more
than 5000 cooperative weather stations, and
"probably represents the best monthly
temperature and precipitation data set available
for the contiguous United States" (Karl et al.
1990).
There are about 22 stations in Oregon; 19 of
these stations are along Interstates 5 and 84
corridors. There are three stations east and
south of Bend. One is near Oregon's border with
California and Nevada, another in the
northeastern corner of Malheur County, and a
third near Bend. There are approximately 12
stations in Pennsylvania. These stations are
24
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more evenly distributed. Neilson et al. (1992)
noted that stations tend to be at low elevations.
Notwithstanding the apparent elevational
bias, the data completeness (both within and
across years) and quality control to remove urban
effects and station changes (see Quinlan et al.
1987) indicate that HCN data would be able to
provide relatively error free measurements of
both seasonal, annual, and longer term (e.g, five
years) departures from average conditions.
Averages can be calculated from 65 or more
years of data.
4.4.2 USGS Digital Elevation Model (DEM)
Topography has been a primary source of
data to test the hypotheses of spatial
heterogeneity, elevation, and even aridity as
mechanisms influencing spatial patterns (e.g.,
Whittaker and Niering 1965, Glenn-Lewin 1977,
Owen 1990a, 1990b). Although topography is
not a stressor as we have defined the term here,
such data are essential in establishing a baseline
(see Section 2.0). Therefore, we have included
topography in the category of non-anthropogenic
data sets.
4.4.3 Federal Insect and Disease
Conditions (FIDC) and Wildland Fire
Statistics (WFS)
Fire and pest infestations are natural
components of many ecosystems that in part
determine the dynamics of species occurrence
(see Loucks 1970, Hengeveld 1989, Romme
annight 1982). Like topographic data, fire and
pest outbreaks could be useful in distinguishing
between natural fluctuations in species
occurrence and the impact of stressors. Also,
these data could be useful as stressors, to the
extent that fire and pest outbreaks can be
determined to be man-precipitated (see Blais
1985, regarding pest outbreaks). Use of these
data as stressors, though, would assume that
man's hand in causing a fire or pest infestation
was out of phase with the normal cycle for that
ecosystem, which may be difficult to establish.
The Forest Service publishes annual reports
on forest pest conditions (FIDC) and wildland
fires on federal and non-federal lands (WFS).
FIDC data are compiled from Forest Service
regional and district offices. The information in
the annual reports is typically in the form of
summary statistics. Detailed information would
need to be acquired from regional and local
offices. The Fire and Aviation Management Staff
of the Forest Service collects annual statistics on
wildland fires on federal and non-federal land.
Statistics include total area burned, source of fire
(on Forest Service land only), and year-to-date
and annual statistics.
No digital, geographically referenced data exists
for either FIDC or WFS. Also, to our knowledge,
these data have only been used for reporting.
There is no information on the quality of the data.
Table 6: Data Sets for Non-Anthropogenic Factors (from Abramovitz et ai. 1990).
Agency
NOAA
Data
HCN
Trends
Yes
Description
Historical Climatology Network (HCN). U.S.
temperature and precipitation for =1200 stations.
DOI, USQS
USD/i,, FS
USDA, FS
DEM
FIDC
WFS
No
Yes
Yes
Digital Elevation Model (DEM). Elevation, slope,
and aspect at 1 :24K, 1 :250K, and 1 :2M scales.
Forest Insect & Disease Conditions (FIDC) across
all forest and ownership classes.
Wildland Fire Statistics (WFS). Wildland files on
public and private land. Origin on public land only.
25
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5.0 DISCUSSION
5.1 IMPORTANCE OF SCALE
The relationship between geographic patterns
of species richness and environmental factors are
scale-dependent (see Section 2.0). At
continental and global scales, available energy,
as measured from climatic data, and species
richness are strongly correlated (see Currie
1991). Early studies, such as Simpson (1964)
and Kiester (1971), showed that species richness
patterns were correlated with latitude and
longitude. Available energy has also been shown
to be the best predictor of decomposition
(Meentemeyer 1984) and productivity
(Rosenzwieg 1968, Leith and Box 1972) at
continental and global scales. Little evidence can
be found to support hypotheses of time,
origination-extinction dynamics, habitat
heterogeneity, disturbance, and niche theory to
explain species richness patterns at continental
and global scales (Rhode 1992).
At regional and smaller spatial scales, the
influence of climate on species richness is less
universal. While Owen (1990a), studying species
richness patterns in Texas, found a strong
correlation between aspects of temperature and
precipitation, variance in elevation was also
signifcant. Glenn-Lewin (1977) and Whittaker
and Niering (1965) also found a relationship
between species richness and elevation. Pianka
(1967) found that the variety of vegetation life
forms was the best predictor of the number of
herptile species along a gradient from the
Sonoran through Great Basin Deserts.
Based on the review of the literature on the
factors influencing patterns of species richness,
there seems to be a lack of integration of natural
and anthropogenic factors. None of the studies
discussed in Section 2.0 included anthropogenic
factors. We have proposed that anthropogenic
and non-anthropogenic stressors operate
primarily at local and regional scales, and,
therefore, stronger correlations between species
richness and stressors should be more likely
found on these scales.
5.2 PROPOSED DATA SETS AND
CONSIDERATIONS IN EXAMINING
STRESSOR-SPECIES RELATIONSHIPS
The data sets proposed for study are listed in
Table 7. For habitat fragmentation, these include
road density and wetland/riparian habitat loss.
Road density would be measured directly using
existing digital data. Wetland and riparian habitat
loss would be measured by combining land
cover, streams, and soils data in a GIS. Other
measures of habitat fragmentation, such as patch
size and isolation are being developed by the
Landscape Team, and therefore are not
discussed here. For pollution, use of the USFWS
NCBP data and USGS NASQAN water quality
data (pH and Acid Nuetralizing Capacity (ANC))
are proposed. The USFWS NCBP provides data
on pesticide pollutant loading for fish, waterfowl
and starling. Samples are collected across the
conterminous United States. Decreased pH
(below 5.0) in lakes has been shown to result in
a decrease in aquatic biota (Schindler et. al.
1989). USGS, NASQAN data can be used to
derive pH and ANC measures. Data on exotic
species are part of the TNC Heritage data, which
is being used to create the hexagon species list
for BRC. Livestock density by hexagon, included
under the category of exotic species because
herbivory by large, congregating ungulates was
not an ecosystem component in the
intermountain west prior to European introduction
(Mack and Thompson 1982), can be modeled
using Census of Agriculture livestock density
estimates, topography, land cover, and land
ownership data. For non-anthropogenic factors,
climate and topography data are proposed. The
NOAA, HCN data can be used to measure
departures from long-term normals (both
temperature and precipitation). Topographic
data, though not a stressor, should be included
because of its importance in establishing baseline
conditions.
In examining the relationships between these
stressors and species diversity, consideration of
scale-dependence and data quality are important.
Because the majority of evidence shows that
stressors influence species diversity at local and
regional scales, and that climate appears to have
26
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Table 7: Data Proposed for Examination of Stressors-Species Relationships
Stressor Category
Fragmentation
Pollution
Exotic Species
Non-Anthropogenic Factors
Data
Wetland loss: Land cover, streams, soils
Road density
Loading: National Contaminant Biomonitoring
Program data
Water quality (pH, ANC) in streams and lakes
Heritage (species occurrence) Data
Livestock grazing
Climate (departures from normals)
Topography
an overriding influence at the continental scale,
we have suggested a hierarchical approach to
the study of the relationship between stressors
and species richness. Also, an assessment of
quality of the stressor data must be made prior to
analyzing species stressor relationships. Poor
data quality can lead to failure to identify a
relationship when one exists or identification of a
relationship when none exists. A description of
data quality analysis procedures have been
presented for many but not all of the data sets
proposed. This is because data quality
evaluation for some of the data sets proposed is
problematic. For example, there is no
independent information that can be used to
assess the quality of the USGS stream data.
One possible approach to evaluation of the
quality of such data is to compare the sum of
squares (or logical counterpart) using the entire
data set with a subset with which one is most
confident. If the two sums of squares are not
very different it would be fair to conclude that
either data quality is good overall or the error in
the data is not affecting the statistical results.
27
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6.0 LIST OF ABBREVIATIONS
Federal Agencies
DOC Department of Commerce
Census
NOAA - National Oceanic and Atmospheric Administration
DOD Department of Defense
DOE Department of Energy
DOI Department of Interior
BLM - Bureau of Land Management
USQS - United States Geological Survey
USFWS - United States Fish & Wildlife Service
EPA Environmental Protection Agency
NOAA National Oceanic and Atmospheric Administration
USDA United States Department of Agriculture
ERS - Economic Research Service
SCS - Soil Conservation Service
NASS - National Agricultural Statistics Service
FS - Forest Service (USFS)
Data
AVHRR Advanced Very High Resolution Radiometer
(Remotely sensed data acquired from NOAA satellites) (USGS)
LUDA Land Use Data Analysis (USGS)
DLG Digital Line Graph (USGS)
NADP/NTN National Acid Deposition Program/National Trends Network (USGS)
NASQAN National Stream Quality Accounting Network (USGS)
DEM Digital Elevation Model (USGS)
MLU Major Land Uses (USDA, ERS)
NRI National Resources Inventory (USDA, SCS)
NCSS National Cartographic Soil Survey (USDA, SCS)
PSU Primary Sampling Unit (USDA, NASS)
FSRAMIS Forest Service Range Management Information Service (USFS)
FIDC Forest Insect and Disease Conditions (USFS)
WFS Wildland Fire Statistics (USFS)
NWI National Wetlands Inventory (USFWS)
NCBP National Contaminant Biomonitoring Program (USFWS)
DCW Digital Chart of the World (DOD)
CENDATA Census data (Census)
Census/Ag. Census of Agriculture (Census)
HCN Historical Climatology Network (NOAA)
NCDPI National Coastal Pollutant Discharge Inventory (NOAA)
PLS & ESI Public Land Statistics & Ecological Site Inventory (BLM)
CDIAC Carbon Dioxide Information Analysis Center (POE)
MSCET Month and State Current Emission Trends (DOE)
NPS National Pesticide Survey (NPS)
AIRS Aerometric Information Retrieval System (EPA)
CERCLIS Comprehensive Environmental Response, Compensation and Liability
Information System (EPA)
TRI Toxic Release Inventory (TRI)
28
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7.0 REFERENCES
Abramovrtz, J.N., Baker, D.S., Tunstall, D.B.
1990. Guide to key environmental statistics
in the U.S. Government. World Resources
Institute, Washington, DC, USA.
Andow, D.A., Kareiva, P.M., Levin, S.A., and
Okubo, A. 1990. Spread of invading
organisms. Landscape Ecology, 4(2/3) :171-
188.
Auerbach, M. and Shmida, A. 1987. Spatial
scale and the determinants of plant species
richness. Trends in Ecology and Evolution
(TREE), 2:238-242.
Barker, J.R. and Tingey, D.T. (Eds.) 1992.
Air Pollution Effects On Biodiversity. Van
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