Functional traits predict relationship between plant abundance dynamic and long-term climate warming.

N.A. Soudzilovskaia, T.G. Elumeeva, V.G. Onipchenko, I.I. Shidakov, F.S. Salpagarova, A.B. Khubiev, D.K. Tekeev, J.H.C. Cornelissen

Research output: Contribution to JournalArticleAcademicpeer-review

Abstract

Predicting climate change impact on ecosystem structure and services is one of the most important challenges in ecology. Until now, plant species response to climate change has been described at the level of fixed plant functional types, an approach limited by its inflexibility as there is much interspecific functional variation within plant functional types. Considering a plant species as a set of functional traits greatly increases our possibilities for analysis of ecosystem functioning and carbon and nutrient fluxes associated therewith. Moreover, recently assembled large-scale databases hold comprehensive per-species data on plant functional traits, allowing a detailed functional description of many plant communities on Earth. Here, we show that plant functional traits can be used as predictors of vegetation response to climate warming, accounting in our test ecosystem (the species-rich alpine belt of Caucasus mountains, Russia) for 59% of variability in the per-species abundance relation to temperature. In this mountain belt, traits that promote conservative leaf water economy (higher leaf mass per area, thicker leaves) and large investments in belowground reserves to support next year's shoot buds (root carbon content) were the best predictors of the species increase in abundance along with temperature increase. This finding demonstrates that plant functional traits constitute a highly useful concept for forecasting changes in plant communities, and their associated ecosystem services, in response to climate change.
Original languageEnglish
Pages (from-to)18180-18184
JournalProceedings of the National Academy of Sciences of the United States of America
Volume110
DOIs
Publication statusPublished - 2013

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