Addressing biased occurrence data in predicting potential Sierra Nevada red fox habitat for survey prioritization

[摘要] ABSTRACT: The Sierra Nevada red fox Vulpes vulpes necator is listed as a threatened species under the California Endangered Species Act. It originally occurred throughout California’s Cascade and Sierra Nevada mountain regions. Its current distribution is unknown but should be determined in order to guide management actions. We used occurrence data from the only known population, in the Lassen Peak region of northern California, combined with climatic and remotely sensed variables, to predict the species’ potential distribution throughout its historic range. These model predictions can guide future surveys to locate additional fox populations. Moreover, they allow us to compare the relative performances of presence-absence (logistic regression) and presence-only (maximum entropy, or Maxent) modeling approaches using occurrence data with potential false absences and geographical biases. We also evaluated the recently revised Maxent algorithm that reduces the effect of geographically biased occurrence data by subsetting background pixels to match biases in the occurrence data. Within the Lassen Peak region, all models had good fit to the test data, with high values for the true skill statistic (76–83%), percent correctly classified (86–92%), and area under the curve (0.94–0.96), with Maxent models yielding slightly higher values. Outside the Lassen Peak region, the logistic regression model yielded the highest predictive performance, providing the closest match to the fox’s historic range and also predicting a site where red foxes were subsequently detected in autumn 2010. Subsetting background pixels in Maxent reduced but did not eliminate the effect that geographically biased occurrence data had on prediction results relative to the Maxent model using full background pixels.

[学科分类] 动物科学

[关键词] California;GIS;Logistic regression;Maxent;Species distribution model;Vulpes vulpes necator