A hierarchical modeling approach to predict the distribution and density of Sierra Nevada Red Fox (Vulpes vulpes necator)

JOURNAL OF MAMMALOGY(2023)

引用 0|浏览8
暂无评分
摘要
Carnivores play critical roles in ecosystems, yet many species are declining worldwide. The Sierra Nevada Red Fox (Vulpes vulpes necator; SNRF) is a rare and endangered subspecies of red fox limited to upper montane forests, subalpine, and alpine environments of California and Oregon, United States. Having experienced significant distribution contractions and population declines in the last century, the subspecies is listed as at-risk by relevant federal and state agencies. Updated information on its contemporary distribution and density is needed to guide and evaluate conservation and management actions. We combined 12 years (2009-2020) of detection and nondetection data collected throughout California and Oregon to model the potential distribution and density of SNRFs throughout their historical and contemporary ranges. We used an integrated species distribution and density modeling approach, which predicted SNRF density in sampled locations based on observed relationships between environmental covariates and detection frequencies, and then projected those predictions to unsampled locations based on the estimated correlations with environmental covariates. This approach provided predictions that serve as density estimates in sampled regions and projections in unsampled areas. Our model predicted a density of 1.06 (95% credible interval = 0.8-1.36) foxes per 100 km(2) distributed throughout 22,926 km(2) in three distinct regions of California and Oregon-Sierra Nevada, Lassen Peak, and Oregon Cascades. SNRFs were most likely to be found in areas with low minimum temperatures and high snow water equivalent. Our results provide a contemporary baseline to inform the development and evaluation of conservation and management actions, and guide future survey efforts.
更多
查看译文
关键词
sierra nevada predict fox,hierarchical modeling approach,sierra nevada,distribution
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要