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Is There a Temperate Bias in Our Understanding of How Climate Change Will Alter Plant-Herbivore Interactions? A Meta-analysis of Experimental Studies

食草动物 气候变化 生物群落 温带气候 生态学 生物 生态系统
作者
Fabiane M. Mundim,Emilio M. Bruna
出处
期刊:The American Naturalist [University of Chicago Press]
卷期号:188 (S1): S74-S89 被引量:20
标识
DOI:10.1086/687530
摘要

Climate change can drive major shifts in community composition and interactions between resident species. However, the magnitude of these changes depends on the type of interactions and the biome in which they take place. We review the existing conceptual framework for how climate change will influence tropical plant-herbivore interactions and formalize a similar framework for the temperate zone. We then conduct the first biome-specific tests of how plant-herbivore interactions change in response to climate-driven changes in temperature, precipitation, ambient CO2, and ozone. We used quantitative meta-analysis to compare predicted and observed changes in experimental studies. Empirical studies were heavily biased toward temperate systems, so testing predicted changes in tropical plant-herbivore interactions was virtually impossible. Furthermore, most studies investigated the effects of CO2 with limited plant and herbivore species. Irrespective of location, most studies manipulated only one climate change factor despite the fact that different factors can act in synergy to alter responses of plants and herbivores. Finally, studies of belowground plant-herbivore interactions were also rare; those conducted suggest that climate change could have major effects on belowground subsystems. Our results suggest that there is a disconnection between the growing literature proposing how climate change will influence plant-herbivore interactions and the studies testing these predictions. General conclusions will also be hampered without better integration of above- and belowground systems, assessing the effects of multiple climate change factors simultaneously, and using greater diversity of species in experiments.
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