Advances in modeling trait-based plant community assembly

被引:94
作者
Laughlin, Daniel C. [1 ]
Laughlin, David E. [2 ]
机构
[1] Univ Waikato, Dept Biol Sci, Hamilton 3240, New Zealand
[2] Carnegie Mellon Univ, Dept Mat Sci & Engn, Pittsburgh, PA 15213 USA
关键词
environmental filtering; intraspecific trait variation; hierarchical Bayesian model; limiting similarity; maximum entropy; natural range of variability; niche differentiation; species distribution modeling; trait convergence; trait divergence; STATISTICAL MECHANISTIC APPROACH; MAXIMUM-ENTROPY; FUNCTIONAL DIVERSITY; INTRASPECIFIC VARIABILITY; NONTRIVIAL APPLICATIONS; SPECIES DISTRIBUTIONS; SCALE DIFFERENCES; ECOLOGY; LEAF; MAXIMIZATION;
D O I
10.1016/j.tplants.2013.04.012
中图分类号
Q94 [植物学];
学科分类号
071001 ;
摘要
In this review, we examine two new trait-based models of community assembly that predict the relative abundance of species from a regional species pool. The models use fundamentally different mathematical approaches and the predictions can differ considerably. Maxent obtains the most even probability distribution subject to community-weighted mean trait constraints. Traitspace predicts low probabilities for any species whose trait distribution does not pass through the environmental filter. Neither model maximizes functional diversity because of the emphasis on environmental filtering over limiting similarity. Traitspace can test for the effects of limiting similarity by explicitly incorporating intraspecific trait variation. The range of solutions in both models could be used to define the range of natural variability of community composition in restoration projects.
引用
收藏
页码:584 / 593
页数:10
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