Impacts of vegetation properties and temperature characteristics on species richness patterns in drylands: Case study from Xinjiang. (December 2021)

• Record Type: Journal Article
• Authors: Zhang, Chunyan; Li, Liping; Guan, Yanning; Cai, Danlu; Chen, Hong; Bian, Xiaolin; Guo, Shan
• Is Part Of: Ecological indicators. Volume 133(2021)
• Publication Date: 2021-12
• Subjects: Drylands -- Dynamic habitat indices -- Indices of land surface temperature -- Random forest -- Species richness -- Energy hypothesis; Environmental monitoring -- Periodicals; Environmental management -- Periodicals; Environmental impact analysis -- Periodicals; Environmental risk assessment -- Periodicals; Sustainable development -- Periodicals; 333.71405
• DOI: 10.1016/j.ecolind.2021.108417
• Languages: English
• ISSNs: 1470-160X

Abstract

Graphical abstract: Highlights: ILST is a good surrogate of species richness across drylands. LST std is the most important variable affecting species richness of birds and plants. High LST std is related to areas covered by sparse vegetation. DHI cum is the most important variable affecting species richness of mammals. Abstract: Energy availability at trophic and hydrologic level dominates species richness gradients by constraining food resources, and regulating population sizes and extinction rates. Remote sensing datasets have mapped vegetation productivities as a proxy for energy availability, for example, using Dynamic Habitat Indices (DHIs). Considering the sparse vegetation across drylands, we developed indices of Land surface temperature (ILST) based on daytime land surface temperature from Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS), which has three components: (1) annual mean temperature ( LST mean ), (2) annual maximum temperature ( LST max ), and (3) standard deviation of temperature ( LST std ). We hypothesized that the temperature variables, such as ILST, would predict species richness better than productivity proxies across drylands. Thus, our objective was to determine how well they would predict the richness of plants, mammals and birds across the Xinjiang Uygur Autonomous Region. We calculated the DHIs and ILST from the MODIS vegetation and temperature products from 2001 to 2015. We found that: (1) ILST could capture more additive information compared with DHIs in terms of the relatively high variance explanation of species richness and high variable importance, and the combination of ILST and DHIs gave better predictions than single metrics for species richness patterns. (2) Plants and birds were more sensitive to temperature than vegetation productivity, probably due to physiological tolerance and evolutionary processes. (3) LST std was the most important variable affecting species richness, except on mammals. High LST std was related to more food resources and habitats, and low LST std represented extreme environment and environmental stress. Combined vegetation properties and temperature variabilities are good determinants of species richness, and should be carefully considered in future research.