MtnGoatHabModel_SawNRA
To aid in development of the IDFG Mountain Goat Management Plan 2019-2024, we created a preliminary model of mountain goat distribution using maximum entropy methods (Maxent 3.4.1; Phillips et al. 2006, Phillips and Dudík 2008). Given a set of environmental variables and species presence locations, Maxent identifies the correlations between each variable and the presence data, compares that with the range of environmental conditions available in the modeled region, and develops a continuous model of the relative likelihood, or probability, of suitable habitat across the study area based on environmental similarity to known occupied sites. Our modeling process incorporated all available occurrence data and several environmental variables hypothesized to influence the distributions of mountain goats in previous modeling efforts. Conducting all spatial analyses in ArcGIS 10.5.1 (ESRI 2017), we ensured spatial data were in a common geographic coordinate system, spatial resolution (30mX30m) and extent, then exported as ASCII files for input into R and Maxent.All known observations of mountain goats in Idaho as of October 12, 2018, were compiled for this modeling effort. We carefully evaluated all data for use in the distribution model to ensure observational, spatial and temporal accuracy. Of the 25,222 observations compiled, we categorized 25,005 as verified (e.g., specimen, DNA, or photograph) or trusted (e.g., documented by a biologist, researcher, or taxonomic expert) and 23,776 of these as having sufficient spatial accuracy (≤1000m) for our modeling purposes. We reduced the locally dense sampling of mountain goats by randomly subsampling with a minimum distance of 270 m. These filtering procedures (verified or trusted, ≤1,000 m accuracy, within Idaho, and >270 m separation) resulted in a total of 4,250 observations available for use in our modeling effort. Previous modeling efforts have focused on topographic, vegetative, and heat-related suites of environmental covariates at a variety of spatial scales (White et al. 2018, White and Gregovich 2017, Lowrey et al. 2017, DeVoe et al. 2015, Gross et al. 2002). Given that topographic measures were by far the most significant variables in these efforts, and limited time constraints for our effort, we used a subset of fine-scale (30m resolution) topographic and climatic covariates that were already developed for use in other statewide modeling projects. Following recommended approaches, we calculated species-specific Maxent model parameters with regard to collinearity, regularization multiplier and feature types.Maxent accurately predicted mountain goat distribution with AUC = 0.857. Averaged over replicate runs, the most important variables were precipitation of the driest month (bio14), roughness, VRM, temperature seasonality (bio4), and elevation (in order of permutation importance). We identified areas of suitable and unsuitable habitat based on the 10 percentile training presence threshold calculated by Maxent (0.3625). This threshold identifies the model value that excludes 10% of training locations having the lowest predicted value. For comparative purposes, we further binned the suitable habitat using other Maxent calculated thresholds to identify low, medium, and high suitability. To separate low and medium suitable habitat we used the ‘balance training omission, predicted area and threshold value’ threshold (0.4739), which uses weighting constants to provide a balance between over-fitting and over-estimating. To separate medium and high suitable habitat we used the ‘equal training sensitivity and specificity’ threshold (0.5174), which equalizes omission and commission errors. To visually display these thresholds, use the MtnGoat_2018.lyr file.For more modeling details, please see Appendix C in the Mountain Goat Management Plan 2019-2024 or contact Leona Svancara (leona.svancara@idfg.idaho.gov).
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