期刊
ECOLOGY AND EVOLUTION
卷 9, 期 24, 页码 13764-13775出版社
WILEY
DOI: 10.1002/ece3.5795
关键词
beta diversity; climate; insects; remote sensing
资金
- Strategic Priority Research Program of the Chinese Academy of Sciences [XDB310304]
- National Science Fund for Distinguished Young Scholars [31625024]
- National Natural Science Foundation of China [31772495, 31850410464]
- CAS President's International Fellowship Initiative [2015VBC058, 2018PB0003]
Aim We construct a framework for mapping pattern and drivers of insect diversity at the continental scale and use it to test whether and which environmental gradients drive insect beta diversity. Location Global; North and Central America; Western Europe. Time period 21st century. Major taxa studied Insects. Methods An informatics system was developed to integrate terrestrial data on insects with environmental parameters. We mined repositories of data for distribution, climatic data were retrieved (WorldClim), and vegetation parameters inferred from remote sensing analysis (MODIS Vegetation Continuous Fields). Beta diversity between sites was calculated and then modeled with two methods, Mantel test with multiple regression and generalized dissimilarity modeling. Results Geographic distance was the main driver of insect beta diversity. Independent of geographic distance, bioclimate variables explained more variance in dissimilarity than vegetation variables, although the particular variables found to be significant were more consistent in the latter, particularly, tree cover. Tree cover gradients drove compositional dissimilarity at denser coverages, in both continental case studies. For climate, gradients in temperature parameters were significant in driving beta diversity more so than gradients in precipitation parameters. Main conclusions Although environmental gradients drive insect beta diversity independently of geography, the relative contribution of different climatic and vegetational parameters is not expected to be consistent in different study systems. With further incorporation of additional temporal information and variables, this approach will enable the development of a predictive framework for conserving insect biodiversity at the global scale.
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