Predictive Model of Mountain Goat Summer Habitat Suitability in Glacier National Park, Montana, USA...
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Predictive Model of Mountain Goat Summer Habitat Suitability in Glacier National Park, Montana, USA Don White, Jr. 1 and Steve Gniadek 2 1 University of Arkansas Agricultural Experiment Station, 110 University Court, University of Ar Monticello, AR 2 Glacier National Park, West Glacier, MT Introduction Habitat suitability modeling using resource selection functions (RSF) are useful tools in assessing and understanding wildlife-habitat relationships (Manley et al. 2010), especially if used to evaluate potential effects of land management and habitat changes on species over large areas where sampling is difficult due to logistical or financial constraints. We developed a landscape-scale mountain goat habitat suitability model using location data collected during aerial survey flights in Glacier National Park (GNP), Montana, during August 2008 and 2009. Methods We used logistic regression, summer individual or group mountain goat locations (N = 107), and random coordinates (N = 39,815) to model the habitat suitability of mountain goats as a function of 8 statistically independent map variables in GNP. We calculated RSF for used (aerial flight locations) and available (random) resources using the exponential function, w(x) = exp(β 1 X 1 +…+ β p X p ), where w(x) was the RSF and β 1 X 1 +…+ β p X p represented the relative probability of an individual or group of mountain goats using each unique variable combination. Probability of species presence was used as an index of habitat suitability. Eight map variables were developed: slope, slope 2 , elevation, elevation 2 , relative slope position, distance to escape cover, % canopy cover, and fractal dimension. A total of 259 RSF were calculated and ranked using AIC. As no single model was clearly superior (i.e., the top 7 models were separated by <2 AIC values), we used model averaging, which allowed inference to be based on multiple models (Burnham and Anderson 2010). A model average was calculated for the coefficients of each parameter in each of the top 7 models using the equation, where was the model-averaged estimate and was the coefficient for model i. The model- averaged RSF was then spatially extrapolated in the GIS. Model validation was performed using k- fold cross validation (Boyce et al. 2002). We used the log-likelihood ratio (G) to test goodness of fit between observed and expected Results Predictive performance of the averaged-model was high (Spearman rank correlation, r = 0.81), indicating a good fit of the model to the data. Figure 1. Relative probability of use by mountain goats in Glacier National Park, Montana, based on resource selection functions estimated from mountain goat locations obtained from aerial flights in August 2008 and 2009. Acknowledgments We thank Jamie Belt and John Waller for conducting the aerial flights and providing us with the mountain goat location data. Conclusions The most important habitat features for mountain goats in GNP appeared to be slope, relative slope position, elevation, fractal dimension, and % canopy cover. Assuming that the predicted probability of mountain goat occurrence is an index of habitat suitability, the majority of GNP was predicted as either low (31% of Park area, 33,364 ha) or medium low (51%, 54,962 ha) predicted probability of use. Only a small proportion of the Park was predicted as medium high (17%, 18,741 ha) or high probability of use (0.65%, 705 ha). Habitat suitability maps not only provide baseline information about the spatial arrangement of potentially suitable habitat for a species, but they also help to refine the search for individuals, making them an important tool in population monitoring. Literature cited Boyce, M.S., S.E. Nielsen, P.R. Vernier, and F.K.A. Schmiegelow. 2002. Evaluation resource selection functions. Ecological Modelling 157:281-300. Burnham, K.P., and D.R. Anderson. 2010. Model selection and multimodel inference: a practical information-theoretic approach, 2 nd edition. Springer-Verlag, New York, New York, USA. Manley, B.F.J., L.L. McDonald, D.L. Thomas, T.L. McDonald, and W.P. Erickson. 2010. Resource selection by animals: statistical design and analysis for field studies, 2 nd edition. Kluwer Academic Publishers, Boston, Massachusetts, USA. Zar, J.H. 1999. Biostatistcal analysis, 4 th edition. Prentice Hall, Upper Saddle River, New Jersey, USA. For further information Please contact Don White, Jr., Ph.D. Voice: 870.460.1490 Email: [email protected]. A PDF-version of this poster can be obtained at http://www.afrc.uamont.edu/whited/presentations.htm. Low Medium - low Medium - high High Figure 3. Observed and expected frequencies of individual mountain goats or groups during summer in Glacier National Park, Montana, by slope, relative slope position, elevation, fractal dimension, and canopy cover. A plus or minus sign denotes observed frequencies significantly greater or less than expected, respectively (P<0.05). Figure 2. Influence of slope, relative slope position, elevation, fractal dimension, and canopy cover on the relative probability of occurrence for mountain goats during summer in Glacier National Park, Montana. Covari ate Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model Average β SE β SE β SE β SE β SE β SE β SE β SE SLOPE 0.1691 0.0432 0.1648 0.0431 0.1976 0.0524 0.1935 0.0521 0.1934 0.0524 0.1892 0.0522 0.1705 0.0438 0.1825 9 0.0484 6 SLOPE 2 - 0.0019 2 0.0005 7 - 0.0019 0.0005 7 - 0.0021 4 0.0006 2 - 0.0021 2 0.0006 2 - 0.0020 7 0.0006 2 - 0.0020 5 0.0006 2 - 0.0019 9 0.0006 6 - 0.0020 3 0.0006 1 ELEV 0.0334 0.0070 6 0.0337 0.0070 7 0.0335 0.0070 6 0.0338 0.0070 8 0.0324 0.0071 0.0327 0.0071 1 0.0334 0.0070 6 0.0332 7 0.0070 8 ELEV 2 - 7.19E- 6 1.62E- 6 - 7.29E- 6 1.63E- 6 - 7.21E- 6 1.62E- 6 - 7.32E- 6 1.63E- 6 - 7.03E- 6 1.63E- 6 - 7.13E- 6 1.63E- 6 - 7.19E- 6 1.62E- 6 - 7.19E- 6 1.63E- 6 RELSLO - 0.0053 1 0.0035 - 0.0052 8 0.0035 - 0.0050 8 0.0035 - 0.0053 5 0.0035 - 0.0052 6 0.0035 ESCAPE - 0.3672 0.3356 - 0.3713 0.3356 -0.361 0.3356 - 0.3652 0.3355 - 0.3661 8 0.3355 8 CANCOV - 0.0058 6 0.0046 - 0.0061 0.0045 8 - 0.0059 8 0.0045 9 FRACT 0.6953 3.3343 0.6953 3.3343 AIC 1269.71 1269.93 1270.52 1270.71 1270.83 1270.86 1271.67 N 107 107 107 107 107 107 107 Table 1. Top 7 habitat suitability models (lowest AIC) and a model average for mountain goats in Glacier National Park, Montana. The model-averaged RSF was spatially extrapolated in the GIS in Figure 1.
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Transcript of Predictive Model of Mountain Goat Summer Habitat Suitability in Glacier National Park, Montana, USA...
Predictive Model of Mountain Goat Summer Habitat Suitabilityin Glacier National Park, Montana, USA
Don White, Jr.1 and Steve Gniadek2
1University of Arkansas Agricultural Experiment Station, 110 University Court, University of Arkansas, Monticello, AR 2Glacier National Park, West Glacier, MT
IntroductionHabitat suitability modeling using resource selection functions (RSF) are useful tools in assessing and understanding wildlife-habitat relationships (Manley et al. 2010), especially if used to evaluate potential effects of land management and habitat changes on species over large areas where sampling is difficult due to logistical or financial constraints. We developed a landscape-scale mountain goat habitat suitability model using location data collected during aerial survey flights in Glacier National Park (GNP), Montana, during August 2008 and 2009.
MethodsWe used logistic regression, summer individual or group mountain goat locations (N = 107), and random coordinates (N = 39,815) to model the habitat suitability of mountain goats as a function of 8 statistically independent map variables in GNP. We calculated RSF for used (aerial flight locations) and available (random) resources using the exponential function, w(x) = exp(β1X1+…+ βpXp), where w(x) was the RSF and β1X1+…+ βpXp represented the relative probability of an individual or group of mountain goats using each unique variable combination. Probability of species presence was used as an index of habitat suitability.
Eight map variables were developed: slope, slope2, elevation, elevation2, relative slope position, distance to escape cover, % canopy cover, and fractal dimension. A total of 259 RSF were calculated and ranked using AIC. As no single model was clearly superior (i.e., the top 7 models were separated by <2 AIC values), we used model averaging, which allowed inference to be based on multiple models (Burnham and Anderson 2010). A model average was calculated for the coefficients of each parameter in each of the top 7 models using the equation,
where was the model-averaged estimate and was the coefficient for model i. The model-averaged RSF was then spatially extrapolated in the GIS. Model validation was performed using k-fold cross validation (Boyce et al. 2002).
We used the log-likelihood ratio (G) to test goodness of fit between observed and expected frequencies (Zar 1999) of mountain goat sightings among slope, relative slope position, elevation, fractal dimension, and canopy cover classes.
ResultsPredictive performance of the averaged-model was high (Spearman rank correlation, r = 0.81), indicating a good fit of the model to the data.
Figure 1. Relative probability of use by mountain goats in Glacier National Park, Montana, based on resource selection functions estimated from mountain goat
locations obtained from aerial flights in August 2008 and 2009.
AcknowledgmentsWe thank Jamie Belt and John Waller for conducting the aerial flights and providing us with the mountain goat location data.
ConclusionsThe most important habitat features for mountain goats in GNP appeared to be slope, relative slope position, elevation, fractal dimension, and % canopy cover. Assuming that the predicted probability of mountain goat occurrence is an index of habitat suitability, the majority of GNP was predicted as either low (31% of Park area, 33,364 ha) or medium low (51%, 54,962 ha) predicted probability of use. Only a small proportion of the Park was predicted as medium high (17%, 18,741 ha) or high probability of use (0.65%, 705 ha).
Habitat suitability maps not only provide baseline information about the spatial arrangement of potentially suitable habitat for a species, but they also help to refine the search for individuals, making them an important tool in population monitoring.
Literature citedBoyce, M.S., S.E. Nielsen, P.R. Vernier, and F.K.A. Schmiegelow.
2002. Evaluation resource selection functions. Ecological Modelling 157:281-300.
Burnham, K.P., and D.R. Anderson. 2010. Model selection and multimodel inference: a practical information-theoretic approach, 2nd edition. Springer-Verlag, New York, New York, USA.
Manley, B.F.J., L.L. McDonald, D.L. Thomas, T.L. McDonald, and W.P. Erickson. 2010. Resource selection by animals: statistical design and analysis for field studies, 2nd edition. Kluwer Academic Publishers, Boston, Massachusetts, USA.
Zar, J.H. 1999. Biostatistcal analysis, 4th edition. Prentice Hall, Upper Saddle River, New Jersey, USA.
For further informationPlease contact Don White, Jr., Ph.D. Voice: 870.460.1490Email: [email protected]. A PDF-version of this poster can be obtained at http://www.afrc.uamont.edu/whited/presentations.htm.
LowMedium - lowMedium - high
High
Figure 3. Observed and expectedfrequencies of individual mountaingoats or groups during summer inGlacier National Park, Montana, by slope, relative slope position, elevation, fractal dimension, and canopy cover. A plus or minus sign denotes observed frequencies significantly greater or less than expected, respectively (P<0.05).
Figure 2. Influence of slope, relative slope position, elevation, fractal dimension, and canopy cover on the relative probability of occurrence for mountain goats during summer in Glacier National Park, Montana.
Covariate Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model Average
β SE β SE β SE β SE β SE β SE β SE β SE
SLOPE 0.1691 0.0432 0.1648 0.0431 0.1976 0.0524 0.1935 0.0521 0.1934 0.0524 0.1892 0.0522 0.1705 0.0438 0.18259 0.04846
SLOPE2 -0.00192 0.00057 -0.0019 0.00057 -0.00214 0.00062 -0.00212 0.00062 -0.00207 0.00062 -0.00205 0.00062 -0.00199 0.00066 -0.00203 0.00061
ELEV 0.0334 0.00706 0.0337 0.00707 0.0335 0.00706 0.0338 0.00708 0.0324 0.0071 0.0327 0.00711 0.0334 0.00706 0.03327 0.00708
ELEV2 -7.19E-6 1.62E-6 -7.29E-6 1.63E-6 -7.21E-6 1.62E-6 -7.32E-6 1.63E-6 -7.03E-6 1.63E-6 -7.13E-6 1.63E-6 -7.19E-6 1.62E-6 -7.19E-6 1.63E-6
RELSLO -0.00531 0.0035 -0.00528 0.0035 -0.00508 0.0035 -0.00535 0.0035 -0.00526 0.0035
ESCAPE -0.3672 0.3356 -0.3713 0.3356 -0.361 0.3356 -0.3652 0.3355 -0.36618 0.33558
CANCOV -0.00586 0.0046 -0.0061 0.00458 -0.00598 0.00459
FRACT 0.6953 3.3343 0.6953 3.3343
AIC 1269.71 1269.93 1270.52 1270.71 1270.83 1270.86 1271.67
N 107 107 107 107 107 107 107
Table 1. Top 7 habitat suitability models (lowest AIC) and a model average for mountain goats in Glacier National Park, Montana. The model-averaged RSF was spatially extrapolated in the GIS in Figure 1.
