A predictive model of community assembly that incorporates intraspecific trait variation

被引:217
作者
Laughlin, Daniel C. [1 ]
Joshi, Chaitanya [2 ]
van Bodegom, Peter M. [3 ]
Bastow, Zachary A. [4 ]
Fule, Peter Z. [4 ]
机构
[1] Univ Waikato, Dept Biol Sci, Hamilton 3240, New Zealand
[2] Univ Waikato, Dept Stat, Hamilton 3240, New Zealand
[3] Vrije Univ Amsterdam, Dept Syst Ecol, NL-1081 HV Amsterdam, Netherlands
[4] No Arizona Univ, Sch Forestry, Flagstaff, AZ 86011 USA
关键词
Assembly rules; bark thickness; environmental filtering; hierarchical Bayesian model; limiting similarity; maximum entropy; specific leaf area; trait convergence; trait divergence; wood density; PLANT TRAITS; ENTROPY MAXIMIZATION; SCALE DIFFERENCES; VARIABILITY; LIMITATIONS; ECOLOGY;
D O I
10.1111/j.1461-0248.2012.01852.x
中图分类号
Q14 [生态学(生物生态学)];
学科分类号
071012 ; 0713 ;
摘要
Community assembly involves two antagonistic processes that select functional traits in opposite directions. Environmental filtering tends to increase the functional similarity of species within communities leading to trait convergence, whereas competition tends to limit the functional similarity of species within communities leading to trait divergence. Here, we introduce a new hierarchical Bayesian model that incorporates intraspecific trait variation into a predictive framework to unify classic coexistence theory and evolutionary biology with recent trait-based approaches. Model predictions exhibited a significant positive correlation (r = 0.66) with observed relative abundances along a 10 degrees C gradient in mean annual temperature. The model predicted the correct dominant species in half of the plots, and accurately reproduced species' temperature optimums. The framework is generalizable to any ecosystem as it can accommodate any species pool, any set of functional traits and multiple environmental gradients, and it eliminates some of the criticisms associated with recent trait-based community assembly models.
引用
收藏
页码:1291 / 1299
页数:9
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