Abstract
We modeled summer and winter habitat suitability of Marco Polo argali in the Pamir Mountains in southeastern Tajikistan using these statistical algorithms: Generalized Linear Model, Random Forest, Boosted Regression Tree, Maxent, and Multivariate Adaptive Regression Splines. Using sheep occurrence data collected from 2009 to 2015 and a set of selected habitat predictors, we produced summer and winter habitat suitability maps and determined the important habitat suitability predictors for both seasons. Our results demonstrated that argali selected proximity to riparian areas and greenness as the two most relevant variables for summer, and the degree of slope (gentler slopes between 0° to 20°) and Landsat temperature band for winter. The terrain roughness was also among the most important variables in summer and winter models. Aspect was only significant for winter habitat, with argali preferring south-facing mountain slopes. We evaluated various measures of model performance such as the Area Under the Curve (AUC) and the True Skill Statistic (TSS). Comparing the five algorithms, the AUC scored highest for Boosted Regression Tree in summer (AUC = 0.94) and winter model runs (AUC = 0.94). In contrast, Random Forest underperformed in both model runs.
Highlights
One effective approach to guide conservation efforts for terrestrial species is the establishment of habitat suitability models
While these models performed well in areas for which they were intended, they may be poor predictors when applied to predict habitat suitability in other areas (Cunningham, 1989; Wenger and Olden, 2012). Probable reasons for this are the selection of model predictors (Zeigenfuss et al, 2000), inaccurate processing of variables (e.g., Normalized Difference Vegetation Index, NDVI) from remotely-sensed data (Borowik et al, 2013; Wen et al, 2016), and incomplete coverage of speciesenvironment response curves by the presence data used in the model
To further evaluate the performance of the modeling algorithms, we evaluated various measures of model performance, including the Area Under the Receiver Operating Characteristic (ROC) Curve (AUC) for the test data and correct classification rate (Fielding and Bell, 1997) and the True Skill Statistic (TSS) (Allouche et al, 2006)
Summary
One effective approach to guide conservation efforts for terrestrial species is the establishment of habitat suitability models These species distribution models (SDMs) are normally used for predicting suitable habitats (Gionfriddo and Krausman, 1986; Smith et al, 1991; Andrew et al, 1999; Bangs et al, 2005), animal abundance (Bristow and Crabb, 2008), separation of habitats between species (Kissell et al, 1996), restoration of large mammals (Johnson, 1995), and habitat connectivity (Gagnon et al, 2013). A model that used expert-opinion by Rubin et al (2010) in the peninsular ranges of southern California, showed vulnerability of bighorn sheep to lack of habitat connectivity While these models performed well in areas for which they were intended, they may be poor predictors when applied to predict habitat suitability in other areas (Cunningham, 1989; Wenger and Olden, 2012). Probable reasons for this are the selection of model predictors (Zeigenfuss et al, 2000), inaccurate processing of variables (e.g., Normalized Difference Vegetation Index, NDVI) from remotely-sensed data (Borowik et al, 2013; Wen et al, 2016), and incomplete coverage of speciesenvironment response curves by the presence data used in the model
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