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Individual and combined effects of multiple global change drivers on terrestrial phosphorus pools: A meta-analysis.

Individual and combined effects of multiple global change drivers on terrestrial phosphorus pools: A meta-analysis.
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Yue K, Yang W, Peng Y, Peng C, Tan B, Xu Z, Zhang L, Ni X, Zhou W, Wu F,


Yue K, Yang W, Peng Y, Peng C, Tan B, Xu Z, Zhang L, Ni X, Zhou W, Wu F, (click to view)

Yue K, Yang W, Peng Y, Peng C, Tan B, Xu Z, Zhang L, Ni X, Zhou W, Wu F,

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The Science of the total environment 2018 02 21630() 181-188 pii S0048-9697(18)30612-0
Abstract

Human activity-induced global change drivers have dramatically changed terrestrial phosphorus (P) dynamics. However, our understanding of the interactive effects of multiple global change drivers on terrestrial P pools remains elusive, limiting their incorporation into ecological and biogeochemical models. We conducted a meta-analysis using 1751 observations extracted from 283 published articles to evaluate the individual, combined, and interactive effects of elevated CO, warming, N addition, P addition, increased rainfall, and drought on P pools of plant (at both single-plant and plant-community levels), soil and microbial biomass. Our results suggested that (1) terrestrial P pools showed the most sensitive responses to the individual effects of warming and P addition; (2) P pools were consistently stimulated by P addition alone or in combination with simultaneous N addition; (3) environmental and experimental setting factors such as ecosystem type, climate, and latitude could significantly influence both the individual and combined effects; and (4) the interactive effects of two-driver pairs across multiple global change drivers are more likely to be additive rather than synergistic or antagonistic. Our findings highlighting the importance of additive interactive effects among multiple global change drivers on terrestrial P pools would be useful for incorporating P as controls on ecological processes such as photosynthesis and plant growth into ecosystem models used to analyze effects of multiple drivers under future global change.

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