1. Lake productivity and waterbird functional diversity across geographic and environmental gradients in temperate China.
- Author
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Zhang, Yamian, Tan, Wenzhuo, Zeng, Qing, Tian, Haitao, Jia, Yifei, Lei, Guangchun, and Wen, Li
- Subjects
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WATER birds , *PLANT productivity , *STRUCTURAL equation modeling , *LAKES , *SPECIES diversity - Abstract
Geographical gradients in species diversity have long fascinated biogeographers and ecologists. However, the extent and generality of the effects of the important factors governing functional diversity (FD) patterns are still debated, especially for the freshwater domain. We examined the relationship between lake productivity and functional diversity of waterbirds sampled from 35 lakes and reservoirs in northern China with a geographic coverage of over 5 million km2. We used structural equation modeling (SEM) to explore the causal relationships between geographic position, climate, lake productivity, and waterbird FD. We found unambiguous altitudinal and longitudinal gradients in lake productivity and waterbird FD, which were strongly mediated by local environmental factors. Specifically, we found (a) lake productivity increased northeast and decreased with altitude. The observed geographic and altitudinal gradients were driven by climatic conditions and nutrient availability, which collectively explained 93% of the variations in lake productivity; (b) waterbird FD showed similar geographic and altitudinal gradients; the environmental factors which had direct and/or indirect effects on these gradients included climate and lake area, which collectively explained more than 39% of the variation in waterbird FD; and 3) a significant (p =.029) causality between lake productivity and waterbird FD was confirmed. Nevertheless, the causality link was relatively weak in comparison with climate and lake area (the standardized path coefficient was 0.55, 0.23, and 0.03 for climate, lake area, and productivity, respectively). Our study demonstrates how the application of multivariate technique (e.g., SEM) enables the illustration of complex causal paths in ecosystems, enhancing mechanistic explanations that underlie the observed broadscale biodiversity gradients. [ABSTRACT FROM AUTHOR]
- Published
- 2020
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