期刊
METHODS IN ECOLOGY AND EVOLUTION
卷 8, 期 10, 页码 1200-1211出版社
WILEY
DOI: 10.1111/2041-210X.12735
关键词
biodiversity; coexistence; ecosystem function; environmental filtering; limiting similarity; multivariate analyses; neutral theory; null models; phylogenetic corrections
类别
资金
- Czech Science Foundation [GA16-15012S]
- Brazilian CAPES PVE grant [88881.068053/2014-01]
- European Union's FP7 [GA-2010-267243 - PLANT FELLOWS]
- Center of Excellence PLADIAS [GACR-14-36079G]
- Marie Curie Intra-European Fellowship within European Union's FP7 (TANDEM) [626392]
1. Functional traits and phylogeny offer different, and often complementary, information about ecological differences between species, an essential step to uncover biodiversity assembly mechanisms and their feedbacks to ecosystem functions. However, traits and phylogeny are often related due to underlying trait evolution. Consequently, when combined, their shared information can be overemphasized, hindering their complementarity. It is therefore desirable to decouple their unique and overlapping contributions. 2. We propose a conceptual and mathematical framework that produces a set of meaningful measures of ecological differences between species. 3. We test the properties of these measures and the validity of the approach with extensive simulated data to show (i) the information provided by decoupling traits from phylogeny and vice versa, and (ii) that decoupling trait and phylogenetic information can uncover otherwise hidden signals underlying species coexistence and turnover. The application of the approach is further illustrated using a large dataset of Central European meadows as a case study. 4. Decoupling traits and phylogeny particularly reveals the importance of differentiation between phylogenetically related species, which can be essential to understand species replacements along environmental gradients and the combined action of environmental filtering and limiting similarity within communities. 5. Decoupling traits and phylogeny provides an avenue for connecting macro-evolutionary and local factors affecting coexistence and for understanding how complex species differences affect multiple ecosystem functions. We present an R function called 'decouple', which allows a simple and wide application of the framework.
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