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RANGELAND BIOCOMPLEXITY AND CATTLE STOCKING RATES IN KANSAS
Jonathan B. Thayn, Graduate Research Assistant
Kevin P. Price, Associate Director
Kansas Applied Remote Sensing (KARS) Program
University of Kansas
Lawrence KS 66047
[email protected]
[email protected]
Randall B. Boone, Research Associate
Natural Resource Ecology Laboratory
Colorado State University
Fort Collins, CO 80523-1499
[email protected]
ABSTRACT
A long-held theory is that increased plant biocomplexity increases the number of animals that can be sustained
within an ecosystem. Typically this theory has been applied to wildlife. In this study, however, we examine
relationships between landscape scale measurements of biocomplexity and stocking rates of cattle on the rangelands
of Kansas. Biocomplexity was characterized at the landscape scale using Advanced Very High Resolution
Radiometer (AVHRR) maximum Normalized Difference Vegetation Index (NDVI) biweekly time-series composites
and the USGS National Land Cover Dataset (NLCD). The NDVI values were accumulated for rangeland areas over
the 2002-growing season and then summarized by county. FRAGSTATS spatial pattern analysis software and the
NDVI data were used to derive 45 metrics of landscape biocomplexity within each county. The metrics used were
of two types: spatial pattern metrics such as fractal dimension and perimeter to area ratio (based on the NLCD), and
spectral diversity metrics such as Shannon’s Diversity Index and Patch Richness (based on the NDVI values). A
principle components stepwise regression model revealed a strong correlation with the county cattle-stocking rate
(R2 = .53, p-value = .000). Bivariate regression models based on the significant components indicated that spatial
pattern metrics are better predictors of rangeland stocking rates than spectral diversity metrics.
INTRODUCTION
The effects of habitat biocomplexity on animal populations have been studied with small birds (Clark et al.,
1999; Derleth et al., 1989; Keller and Anderson, 1992; McGarigal and McComb, 1995; Penhollow and Stauffer,
2000; Seto et al., 2004; Welsh and Healy, 1993), turkeys (Glennon and Porter, 1999), chipmunks (Henderson et al.,
1985), and stingless honey bees (Brown and Albrecht, 2001). Assessments of biocomplexity have been used
effectively to predict plant species richness in agricultural matrices (Luoto et al., 2002) and to determine the
effectiveness of governmental prairie land conservation reserve programs (Egbert et al., 2002). However, there is a
paucity of research dealing with large herbivore populations. Some large mammal studies have examined the
relationships between species richness and reduced vegetation biodiversity (Oindo and Skidmore, 2002; Oindo et
al., 2003; Wallace et al., 1995) and some conservation efforts have found that cattle grazing actually supports
increased vegetation biodiversity in grasslands (Jensen, 2001; Pykälä, 2003). However, very little has been done to
model the effects of habitat biocomplexity on domestic rangeland animals. The present study addresses this issue.
Biocomplexity is commonly defined using to the United Nations Environment Program (UNEP) definition,
which includes variation within ecosystems and within species, although other definitions exist (Magurran, 2004). In
general, these definitions rely on extensive fieldwork and species censuses for their quantification, limiting the
geographic scope of analyses. As natural and anthropogenic threats to biocomplexity increase throughout the world,
remotely sensed imagery represents the best method for assessing biocomplexity at a landscape scale.
For the purposes of this study, biocomplexity is defined in two parts, (i) the extent of habitat fragmentation, i.e
spatial pattern; and (ii) the variation in spectral response of rangeland vegetation, i.e. spectral reflectance diversity.
Pecora 16 “Global Priorities in Land Remote Sensing
October 23-27 * Sioux Falls, South Dakota
Fragmentation of Habitat
Fragmentation is the breaking apart of habitat resulting in the reduced ability or inability of large herbivores to
access natural vegetation complexity—it does not imply habitat loss, although it is usually an accompanying
phenomenon (Fahrig, 1997). Although fragmentation may be less impactful on populations than actual habitat loss
(Fahrig, 1997), it is nonetheless a significant condition (Laurance et al., 2001). Tilman and Lehman (1997)
document three distinct factors of habitat area than effect diversity. First, larger areas contain more individuals.
Species have minimum population sizes below which stochastic disturbances cause extinction; large populations are
more likely to survive demographic fluctuations (Walker, 1992, 1995). Second, some species simply require more
area. Small rodents, for example, can survive in small habitat patches, but large migratory herbivores need more
space in which to gather resources. Larger habitat areas support greater diversity in part because they meet or
exceed the minimum area requirements of a greater number of species. Third, large areas include greater climatic
and environmental variation, thereby encompassing the optimal habitat conditions of more species. These factors
alone, however, are not sufficient to explain all of the
STUDY AREA
variation in the biodiversity of a region. Tilman and
Lehman (1997) also explain that “more explicitly
spatial considerations of fragment size, fragment
locations, the patterning of corridors that link
fragments, and movement patterns among fragments”
are necessary for a more complete understanding of the
drivers of biodiversity. In other words, the spatial
arrangement of habitat is as important as the extent of
habitat. It has been determined, as might be expected,
that more habitat destruction is required to cause a
species’ extinction when larger blocks of habitat were
left intact than when the same undestroyed area was
dispersed among many smaller blocks (Chapin et al.,
2002; Tilman and Lehman, 1997). Species that are
poor dispersers are more susceptible to fragmentationcaused extinction (Cornell and Karlson, 1997; Foster et
al., 2004).
Although contrary examples exist (Brown and
Albrecht, 2001; Derleth et al., 1989; McGarigal and
McComb, 1995; Thompson et al., 1992; Welsh and
Healy, 1993), increasing fragmentation generally
decreases biocomplexity. Fragmentation results in
isolated homogeneous habitat patches that stop floral
and faunal migration and reduce species’ ability to
rebound from local extinction (Henderson et al., 1985;
Laurance et al., 2001). O’Neill et al (1988b) used
Figure 1: The light counties were included in the
neutral landscapes to demonstrate that when habitat
analysis, the blue were excluded either because there
occupies less than 59.28 percent of the landscape,
was insufficient rangeland or no cattle in the county,
organisms cannot move from one end of the landscape
and the orange were excluded because they contain
to the other without passing through undesirable
feedlots.
landcover types. When fragmentation is slight, these
boundaries halt the spread of plants and thereby create
areas that can be utilized by non-dominant competitors (Levin and Paine, 1974); however, when fragmentation
becomes excessive, these boundaries limit the spread of new vegetation types and decrease natural vegetation
diversity (Foster et al., 2004; Watt, 1947; Wu and Levin, 1994). High levels of biocomplexity imply redundancy or
equivalency within functional groups in ecosystems, which mitigates the effects of environmental fluctuations and
helps stabilize populations. (Chapin et al., 2002; Chapin et al., 1997; McNaughton, 1977; Roff, 1974, 1975; Walker,
1992, 1995).
Variation in Spectral Reflectance
Typically, measures of vegetation diversity and evenness are calculated from plant species census data collected
in the field (Magurran, 2004). However, several studies have applied the same measures of species diversity and
Pecora 16 “Global Priorities in Land Remote Sensing
October 23-27 * Sioux Falls, South Dakota
evenness, e.g. Shannon’s Diversity Index, Simpon’s Diversity Index, etc., to remotely sensed data by substituting
pixel values for species counts. Specifically, normalized difference vegetation index (NDVI), which captures
vegetation green-leaf biomass, has been used successfully. This approach has been successful in part because plant
biomass or production (rather than plant density or area cover) is usually the best discriminate variable (Guo and
Rundel, 1997). In Spain, Ortega et al. (2004) found that Shannon’s Diversity Index, combined with a satellite
derived landcover map, was an accurate estimator of plant diversity at the landscape level. The species richness of
vascular plants in Finland were predicted using environmental variables derived from Landsat TM imagery and the
model explained 71 percent of the variation in ground-based species richness measures (Luoto et al., 2002). Gould
(2000) discovered a positive correlation (R2 = 0.79) between spatial variation in NDVI values and measured plant
species richness in the Canadian Artic. Honnay et al. (2003) used FRAGSTATS spatial pattern analysis software to
calculate 17 landscape structure metrics from remotely sensed imagery that were then submitted to a principal
components regression (PCR) model. The model explained 54 percent of the variation in plant species richness.
High vegetation biocomplexity generally translates into high consumer biocomplexity (Chapin et al., 2002).
The correlation between remotely sensed estimates of
vegetation biocomplexity is strong enough that many
RANGELANDS OF KANSAS
researchers have been able to use satellite-derived data
to predict animal species diversity. Seto and his coresearchers (2004) successfully used the standard
deviation of single-date Landsat TM NDVI values to
predict bird (r2=0.55) and butterfly (r2=0.18) species
richness in the Great Basin region of the United States
of America. The mean, standard deviation, and
coefficient of variance of inter-annual NDVI values
have been found to correlate strongly with large
animal species richness in Kenya, Africa (Oindo and
Skidmore, 2002; Oindo et al., 2003). Jørgensen and
Nøhr (1996) used Shannon’s and Simpson’s diversity
indices, calculated for both NDVI values and
landcover classes, to explain 40-50 percent of
ornithological species richness in the Ferlo region of
Senegal on the west coast of Africa. Oindo (2002)
found not only that he could predict 57 percent of the
species richness of large non-migrating mammals in
Africa, but that the mean and standard deviation of
multi-year NDVI were more strongly correlated to
species richness than to the number of individuals in
Figure 2: Rangelands as identified using classes
the study site.
51 and 71 of the USDA National Land Cover
The current study addresses two hypothesis: (1)
Dataset. Scott, Stafford and Chase counties
that there is no relationship between cattle stocking
represent highly, moderately, and slightly
rates in Kansas and rangeland biocomplexity, and (2)
fragmented counties respectively.
that landscape biocomplexity metrics calculated from
remotely sensed data cannot be used to reliably predict
cattle stocking rates.
STUDY AREA
This study was conducted for the entire state of Kansas, which is part of the central Great Plains area of North
America (Figure 1). Annual precipitation ranges from less than 450 mm in the west to more than 1200 mm in the
southeast while mean annual temperature ranges from less than 11 C in the north-west to more than 15 C in the
south-east (Wang et al., 2001; Wang et al., 2003). Interannual variation in precipitation is high, with precipitation in
wet years often four times that of dry years, and the region is highly susceptible to drought (Bark, 1978; Reed, 1993;
Warrick, 1975). Natural vegetation ranges from shortgrass prairie in the west to a mosaic of tallgrass prairie and
oak-hickory forest in the east (Abrams, 1986; Kuchler, 1974; Loehle et al., 1996). Over half of the land area is
occupied by croplands (KARS, 2002; Whistler et al., 1996) and another 44 percent of the land area is rangeland.
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DATA COLLECTION
Biocomplexity Metrics
The rangeland areas of Kansas were isolated using the National Land Cover Data set (NLCD) provided by
Categories 51 and 71 (“shrublands” and
EROS Data Center (EDC) in Sioux Falls, South Dakota.
“grasslands/herbaceous”, respectively) were aggregated as rangeland and all other categories were deemed nonrangeland (Figure 2). The spatial pattern of rangeland in each county was quantified using FRAGSTATS spatial
pattern analysis software (McGarigal and Marks, 1995). Many landscape pattern metrics have been developed in
the last decades (Bulla, 1994; McGarigal and Marks, 1995; Molinaryi, 1996; O'Neill et al., 1988a; Riitters et al.,
1995), but most of them are highly autocorrelated (O'Neill et al., 1988a; Riitters et al., 1995) and not all of them are
empirically useful (Tischendorf, 2001). Several authors (Gustafson and Parker, 1992; Hargis et al., 1998) report that
many common landscape pattern metrics are only useful within a narrow range of fragmentation conditions. This
inutility is indicated by a severely non-normal distribution. The landscape pattern metrics were submitted to
exploratory Kolmogorov-Smirnov tests of normality and scatter plot comparisons. Two of the pattern variables
(ENN and SPLIT) were eliminated in this way. Twenty-nine landscape pattern metrics were calculated for
rangelands in each county (Table 1). Equations and definitions of the landscape pattern metrics can be found in the
FRAGSTATS Spatial Pattern Analysis Software user guide (McGarigal and Marks, 1995).
Sixteen spectral diversity metrics were also calculated using FRAGSTATS spatial pattern analysis software.
These metrics included the standard deviation, coefficient of variation, Shannon’s diversity index (Shannon and
Weaver, 1949), Simpson’s diversity index (Simpson, 1949), modified Simpson’s diversity index (Pielou, 1975;
Rosenzweig, 1995), Shannon’s evenness index (Pielou, 1969, 1975), Simpson’s evenness index (Krebs, 1999; Smith
and Wilson, 1996) and the modified Simpson’s evenness index (McGarigal and Marks, 1995). Magurran (2004)
provides an excellent discussion of these measures.
Diversity indices, in ecology, are based on species richness measures—in remote sensing the indices are based
on pixel richness measures. When diversity indices are applied to 8-bit data there are 256 potential species or pixel
values that determine the index value. When the remotely sensed data are themselves an index (such as NDVI) the
potential range of pixel values varies based on the precision of the calculations. For example, if the NDVI were
calculated to the thousandths decimal place, the diversity indices would indicate a very diverse study area where
each pixel was unique. On the other hand,
the same NDVI values calculated to the
nearest tens value, would produce low
diversity index values where many pixels
share the same value. In the current study,
NDVI values were calculated using three
levels of categorization: (1) the whole
integer, (2) categories of 5 (i.e. the 8-bit
data were categorized in bins of 5: 0-5, 6Table 2: The p-values range from .000 to .040 except for
10, etc.), and (3) categories of 10 (i.e. the
those of Patch Richness in the G5 and G10 groups where
data were categorized in bins of 10: 0-10,
they were not significant. The NDVI values grouped into
11-20, etc). The three groups were labeled
categories of 5 (05, 6-10, etc.) exhibit the strongest
G1, G5, and G10. FRAGSTATS software
correlations with the cattle stocking rate.
was used to calculate patch richness, patch
richness density, Shannon’s diversity
index, and modified Simpson’s diversity index for each of the 3 groups of NDVI values. Pearson Correlation values
were calculated for each of the metrics and the Cattle Stocking rates by counties in Kansas (Table 2). The patch
richness values for the G5 and G10 groups did not have significant correlations with the cattle stocking rates (the pvalues were 0.404 and 0.849 respectively). The patch richness density for the G1 group was slightly stronger than
that of the other groups. The Shannon’s and modified Simpson’s diversity indices for the G5 group were
considerably stronger than those of the G1 group and slightly stronger than those of the G10 group. Accordingly,
the diversity metrics based on the G5 NDVI data were used in the subsequent analysis.
Cattle Data
The USDA Census of Agricultural is performed every five years for every county in the United States. Some
data (like cattle in feedlots, dairy cattle, etc.) are reported at the states’ discretion; however, all states report total
number of cattle per county. The data is readily accessed at http://www.nass.usda.gov/census/. The rangeland cattle
Pecora 16 “Global Priorities in Land Remote Sensing
October 23-27 * Sioux Falls, South Dakota
stocking rate for each county in the study was calculated by dividing the All Cattle data listed in the 2002 USDA
Agriculture Census by the hectares of rangeland in each county as calculated by FRAGSTATS spatial pattern
analysis software.
ANALYSIS
Several counties in the southwest of Kansas are known to contain feedlots, which produced cattle to rangeland
area ratios (cattle stocking rates) up to 10.28. The median cattle stocking rate was 0.63. Tukey’s 1.5-hinge (Tukey,
1977) for detecting outliers was applied to the data—counties with cattle stocking rates greater than 2.15 were
eliminated from further analysis, effectively excluding counties with feedlots. This also eliminated 13 eastern
counties because they had very little rangeland. It is safe to conclude that grazing cattle in these counties either
receive supplemental hay or they graze in non-rangeland areas. Four other counties (Gove, Grant, Seward, and
Wichita) were not included in the analysis because no cattle were reported for them in the 2002 Census of
Agriculture (Figure 1).
Principal components were calculated since landscape pattern metrics are highly intercorrelated (O'Neill et al.,
1988a; Riitters et al., 1995). Several authors suggest that the first few components may not capture the variation in
the dataset that explains the behavior of the dependent variable (Hadi and Ling, 1998; Jackson, 1991). The pitfalls
mentioned by Hadi and Ling (1998) were absent in this data set. Accordingly, the principal components regression
methodology was used in this study.
The six significant components were relatively easy to name (Table 1). The first component was loaded heavily
with the metrics that focused on patch perimeter lengths and patch dispersion. Accordingly, it was named “Pattern”.
Several metrics were calculated for the non-rangeland patches as well. These were then included with those of the
rangeland patches in area-weighted means for the entire landscape. These metrics loaded heavily in the second
component, so it was named “Landscape”. The Shannon’s and Simpson’s biodiversity indices loaded heavily in the
third component so it was named “Biodiversity”. The fourth component was named “Disjunct”. It was loaded with
metrics that focused on disjunct core area density. Core area metrics deal with the interior of the patch (a 100
metered distance from the edge was specified). Skole and Tucker (1993) report that these boundary zones are
subject to micrometeorological differences, increased livestock herbivory, invasion by non-native species, and a
general decrease in plant and animal species diversity. The core area metrics that dealt with the patch’s shape and
spread were loaded in the first component,
Area and Cattle Stocking Rates
but the two metrics that measure disjunct
0.4
core area density (which changes when
removing the outer buffer separates the
0.2
patch into two or more patches) were loaded
0.0
in the fourth component.
The fifth
0
50,000
100,000
150,000
200,000
250,000
300,000
-0.2
component was named “Patch Richness” as
it was loaded heavily with the patch richness
-0.4
and relative patch richness variables. The
-0.6
sixth component was called “Variation”. It
r = 0.40
p-value=0.000
-0.8
was loaded with metrics based on the
standard deviation and coefficient of
-1.0
Rangeland Area
variation of the spectral data.
Each of the six significant principal
components was used in a bivariate
Figure 4: Correlation between the area of rangeland per county
regression analysis to determine the effects
and the cattle stocking rate.
of each on cattle stocking rates. They were
also entered into a step-wise ordinary least squares multiple regression model with the cattle stocking rates as the
dependent variable. The entry criterion was set at 0.05 and the removal criterion was set at 0.1. Components 1, 6,
and 3 (“Pattern”, “Variation”, and “Biodiversity”) were accepted into the multiple regression model. SPSS
statistical software was used for the regression analyzes.
2
Pecora 16 “Global Priorities in Land Remote Sensing
October 23-27 * Sioux Falls, South Dakota
RESULTS and DISCUSSION
Bivariate Regression Models
As discussed earlier, area strongly affects the biocomplexity of a region. In this study, the rangeland area per
county explained 40 percent (p-value=0.000) of the variation in the cattle stocking rates; however, the relationship
was negative (Figure 4). Those counties with the largest amounts of rangeland area are located in the west, more
arid, side of the state. In those counties, more area is required to support equal numbers of cattle because vegetation
is sparser. Figure 5, an accumulated biweekly NDVI map of 2002, clearly illustrates the greater aridity of western
Kansas.
The results from the six bivariate correlations performed on the significant principal components and the cattle
stocking rates reveal a lot about the relationship between rangeland fragmentation and the corresponding domestic
livestock productivity (Figure 3). The first principal component, which encompasses the pattern of fragmentation of
habitat, accounts for 45 percent
(p=0.000) of the variation in the cattle
CLIMATE VARIABILTIY IN KANSAS
stocking rate (the rangeland area
metric mentioned earlier was loaded
heavily in this component).
The
stocking rates decrease as the extent of
rangeland fragmentation increases. In
other words, cattle prefer large,
compact patches of habitat. Previous
studies have found that fragmentation
has the opposite effect on bird
populations (Derleth et al., 1989;
Laurance et al., 2001; Welsh and
Healy, 1993) and other wild species.
The results presented here are likely
different for at least two reasons. First,
many wild species are edge species
that utilize edge zones to their
Figure 5: Accumulated bi-weekly NDVI for 2002.
advantage and therefore prefer highly
fragmented areas.
Cattle are not.
Second, cattle are not a true migratory grazing population, in the sense that they are restrained by fences and other
effects of human manipulation. Also, many of the pattern dispersion metrics that contributed heavily to this
component have been shown to be associated with patch size (Gustafson and Parker, 1992; Hargis et al., 1998). The
correlation between this component and area weighted mean patch size is significant (r2 = 0.850, p = 0.000). In
other words, a high score in these metrices indicates that large habitat patches are relatively unfragmented, but that
they occur some distance from each other. As these larger patches are fragmented, the mean distance to the nearest
patch decreases. Therefore this component indicates that a county’s cattle stocking rate declines as rangeland is
increasingly fragmented.
The third component, “Biodiversity”, accounted for only 4 percent of the variation in cattle stocking rates (pvalue = .076). The metrics that loaded heavily in this component were Shannon’s and Simpson’s diversity indices
and their associated evenness indices (Magurran, 2004). The sixth component, “Variation”, was loaded with
variables based on the standard deviation of NDVI values per county and it accounted for 4% of the variation in
cattle stocking rates (p-value = .070). The regression models based on these two components indicate that the cattle
stocking rate increases with increasing biodiversity as measured by variation in NDVI values. Other studies have
found that these metrics are able to predict between 75 and 86 percent of variation in mammal species richness
(Oindo and Skidmore, 2002; Oindo et al., 2003). Several factors may have resulted in the lower coefficients of
determination in this study. First, the earlier studies calculated the diversity metrics for landcover maps rather than
the NDVI values of a single landcover type. Limiting the study to just one landcover type, rangeland, limits floral
variation and biodiversity. These components were low predictors of cattle stocking rates because the biodiversity
within rangelands is generally consistent between counties. Second, the metrics are calculated on AVHRR data,
which has a spatial resolution of one kilometer. At this resolution pure pixels of homogenous rangeland vegetation
are unlikely so any spectral variation caused by species composition has been averaged out of the data.
Pecora 16 “Global Priorities in Land Remote Sensing
October 23-27 * Sioux Falls, South Dakota
The
significant
predictor
components indicate that the cattle
carrying capacity of unfragmented,
biologically diverse areas are greater
than
those
of
fragmented,
homogenous regions.
Cattle
stocking
rates
increase
with
increased biocomplexity.
Useful
information can be gleaned from the
component models with insignificant
correlations as well. The model
based on the second component,
“Landscape”, is loaded heavily by
those metrics that included measures
of the non-rangeland patches as well
as the rangeland patches. The very
low predictive capability of this
component means that the nonrangeland areas have no discernible
Figure 6: Results of the stepwise multiple regression. Biodiversity
impact on grazing cattle.
This
metrics explain 53 percent of the variation in cattle stocking rates
component
would
have
a
much
in Kansas.
stronger predictive capability if the
cattle migrated through non-rangeland patches in search of better forage. In more mobile animal populations this
metric would be more significant.
The fourth component, “Disjunct”, had insignificant predictive ability (p-value = .304). The metrics that loaded
heavily in this component were calculated for the interior of rangeland patches by ignoring the outermost 100 meters
of each patch. This means that patches with diameters less than 100 meters were not included in the calculations,
and that, at times, one patch may have become two or more for the purposes of these metrics (McGarigal and Marks,
1995). These metrics increase as patches become more fragmented and are theoretically useful for differentiating
between highly fragmented landscapes. Rangelands in Kansas generally consist of large intact patches that are
separated from one another, so this component was not a useful predictor in this study.
The fifth component, “Patch Richness”, was also an insignificant predictor variable. This component was based
on the number of different pixel values in each county. The metrics do not quantify the distribution of pixel values
like the variables that loaded heavily in the “Biodiversity” component and therefore have much less predictive
capability.
Multiple Regression Model
When the six significant components are entered into a step-wise multiple regression model, they account for
over half of the variation in the cattle stocking rates (R2 = 0.53, p=0.000) (Figure 6). The “Pattern”, “Variation”,
and “Biodiversity” components were admitted to the multiple regression model. In a previous study based solely on
the fragmentation metrics, the multiple regression model was validated by predicting the cattle stocking rates in
Nebraska (Thayn et al., 2005). The correlation between the predicted and the actual cattle stocking rates in
Nebraska was high (r2 = 0.54, p-value = 0.000) compared to the models’ results in Kansas (R2 = 0.48, p-value =
0.000). We can expect similar robustness in the current model, as the only alteration has been the addition of the
biodiversity indices and measures of spectral variation.
The strength of our multiple regression model compares favorably with previous fragmentation studies.
McGarigal and McComb (1995) found that landscape structure generally explained >50 percent of variation in bird
species abundance and that abundances were generally greater in more fragmented landscapes. Landscape pattern
metrics were able to predict between 43 and 59 percent (adjusted R2, p=0.01) of the variation in bird assemblages
and 17 to 77 percent (adjusted R2, p=0.05) of the variation in bird species abundance models in Quantico Marine
Base, Virginia. In southwestern New York, between 28 and 56 percent (p=0.057) of the variation in wild turkey
abundances was explained by several landscape pattern metrics (Glennon and Porter, 1999). In a northern hardwood
forest in New Hampshire, Welsh and Healy (1993) discovered that sites managed for lumber extraction were more
fragmented and had 61 percent more bird species than the sites that were not managed and were less fragmented. In
Missouri Ozark forest sites, three bird species had lower mean densities in clear cut sites than in non-harvested sites
(p=0.06) while six species were more common in the clear cut sites (p=0.03) (Thompson et al., 1992). These studies
Pecora 16 “Global Priorities in Land Remote Sensing
October 23-27 * Sioux Falls, South Dakota
found an increase in bird populations in fragmented environments, while the present study found a decrease in cattle
population in fragmented areas, indicating a difference among birds and large animals in their response to
fragmentation. Derleth et al. (1989) reports that, in general, bird species abundance and richness is greater within 50
meters of the forest edge than the forest interior, primarily because many bird species prefer the open, more
accessible habitat that results from fragmentation. On the other hand, cattle prefer large homogenous patches of
habitat.
Conclusion
This study begins to address the paucity of ecological landscape pattern studies of large animals. Censuses of
larger animals are more costly and difficult than those of smaller animals such as birds. Nevertheless, such
information is critical to our understanding of anthropogenic and natural fragmentation disturbances on largemammal populations. This understanding becomes more and more crucial as urbanization increases and expands
into animals’ habitat. The results indicate that cattle, and likely other large herbivores, are able to maintain larger,
more stable populations when their habitat is not fragmented and when their habitat exhibits increased levels of
biocomplexity. Intact, heterogeneous rangeland areas can support greater numbers of grazing livestock than
homogenous fragmented regions.
The success of this study attests that remote sensing based empirical models may be used to assess the effects of
future development projects by quantifying the resultant increase in fragmentation and determining the effects on
local cattle stocking rates. Similar methodologies may be used to create empirical models in other geographic
locations and with other large mammals of interest.
ACKNOWLEDGEMENTS
The authors are indebted to the National Science Foundation for funding and to the Kansas Applied Remote
Sensing Program at the University of Kansas and the Natural Resource Ecology Laboratory at Colorado State
University for their academic support.
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Pecora 16 “Global Priorities in Land Remote Sensing
October 23-27 * Sioux Falls, South Dakota