Modeling the Potential Distribution of Two Species of Shrews (Chodsigoa hypsibia and Anourosorex squamipes) under Climate Change in China

Wenhao Hu,Hailong Wu,Xuelong Jiang,Zhongzheng Chen,Kenneth Otieno Onditi

doi:10.3390/d14020087

Abstract

Understanding how the direct and indirect effects of climate change may affect species distributions is a key topic in ecology. We used maximum entropy models to explore the distribution of two species of shrews (Chodsigoa hypsibia and Anourosorex squamipes) in China and analyzed the main environmental factors affecting their current distribution and potential distribution changes under two future climate scenarios. The results showed that the major environmental factors determining the current distribution of C. hypsibia were the mean temperature of the coldest quarter (contributing 47.4%), annual mean temperature (contributing 24.7%), precipitation of the driest quarter (contributing 21.1%) and isothermality (contributing 6%). Annual precipitation (contributing 42.9%), precipitation of the driest month (contributing 28.1%), annual mean temperature (contributing 14.8%) and temperature seasonality (contributing 12.6%) had the highest contributions to the distribution of A. squamipes. Under future climate scenarios, the suitable habitat range of C. hypsibia increased while that of A. squamipes decreased. These findings demonstrate that different small mammal species respond differently to climate change.

Highlights

Natural populations respond to global climate change by changing their geographic distribution and timing of growth and reproduction, which, in turn, change the composition of communities and the nature of species interactions
We evaluated model performance using the area under curve (AUC)—the area under the receiver operating characteristic curve (ROC) [29]
The results showed that the maximum entropy approach (MaxEnt) model simulated the relationship between the geographical distribution and environmental variables of C. hypsibia and A. squamipes well (Table 2)

Summary

Introduction

Natural populations respond to global climate change by changing their geographic distribution and timing of growth and reproduction, which, in turn, change the composition of communities and the nature of species interactions. Many populations’ reactions, are likely to be insufficient to deal with the speed and extent of climate change, leaving them vulnerable to population decline and extinction [1]. Climate change could result in the extinction of a quarter of all plants and animals if greenhouse gas emissions are not curbed [2]. Future distributions are usually estimated by relating climatic conditions with the current distribution ranges of key species and projecting future conditions [4]. These species distribution models (SDMs) are useful tools for deciphering the elements that influence the prospective distributions of several organisms at various scales [5]. The maximum entropy approach (MaxEnt) [6] has been widely used in biological invasion predictions [7], habitat prediction for endangered animals [8], forest fire prediction [9], responses of species to climate change [10] and prediction of potential threats from pest species [11], due to its good simulation accuracy [12]

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