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Inferring ecological processes from taxonomic, phylogenetic and functional trait β-diversity.

Stegen JC, Hurlbert AH - PLoS ONE (2011)

Bottom Line: After developing the framework we compared the model-generated expectations to the commonly used "intuitive" expectation that the variance explained by the environment or by space will, respectively, increase monotonically with the strength of environmental filtering or dispersal limitation.We show that by combining the three types of β-diversity with model-generated expectations our framework is able to provide rigorous inferences of the relative and absolute strengths of dispersal limitation and environmental filtering.Phylogenetic, functional and taxonomic β-diversity can therefore be used simultaneously to infer processes by comparing their empirical patterns to the expectations generated by frameworks similar to the one developed here.

View Article: PubMed Central - PubMed

Affiliation: Department of Biology, University of North Carolina, Chapel Hill, North Carolina, United States of America. stegen@email.unc.edu

ABSTRACT
Understanding the influences of dispersal limitation and environmental filtering on the structure of ecological communities is a major challenge in ecology. Insight may be gained by combining phylogenetic, functional and taxonomic data to characterize spatial turnover in community structure (β-diversity). We develop a framework that allows rigorous inference of the strengths of dispersal limitation and environmental filtering by combining these three types of β-diversity. Our framework provides model-generated expectations for patterns of taxonomic, phylogenetic and functional β-diversity across biologically relevant combinations of dispersal limitation and environmental filtering. After developing the framework we compared the model-generated expectations to the commonly used "intuitive" expectation that the variance explained by the environment or by space will, respectively, increase monotonically with the strength of environmental filtering or dispersal limitation. The model-generated expectations strongly departed from these intuitive expectations: the variance explained by the environment or by space was often a unimodal function of the strength of environmental filtering or dispersal limitation, respectively. Therefore, although it is commonly done in the literature, one cannot assume that the strength of an underlying process is a monotonic function of explained variance. To infer the strength of underlying processes, one must instead compare explained variances to model-generated expectations. Our framework provides these expectations. We show that by combining the three types of β-diversity with model-generated expectations our framework is able to provide rigorous inferences of the relative and absolute strengths of dispersal limitation and environmental filtering. Phylogenetic, functional and taxonomic β-diversity can therefore be used simultaneously to infer processes by comparing their empirical patterns to the expectations generated by frameworks similar to the one developed here.

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Effects of assembly processes on the relationship between environmental optima and range centroids.The strengths of environmental filtering, dispersal limitation, and environmental spatial structure constrain the evolutionary-time-scale relationship between species' intrinsic environmental optima (functional trait value) and the spatial position where their abundance is maximized (the range centroid). Simulation output when both processes are strong (n = d = 0.0001; panels A,C) or weak (n = d = 10; panels B,D). The environment has either weak (≈0.3; panels A,B) or strong (≈0.95; panels C,D) spatial structure. All axes are normalized as standard normal deviates, with mean zero and standard deviation of one. Solid red lines represent the one to one line and solid blue lines are linear regressions. (A) When both processes are strong but there is little environmental spatial structure, a moderately tight relationship emerges between species' trait values and the positions of their range centroids. (C) Increasing the degree of environmental spatial structure leads to a much tighter, one to one relationship. (B,D) Irrespective of how spatially structured the environment is, when both processes are weak there is no relationship between species' trait values and the positions of their range centroid.
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pone-0020906-g002: Effects of assembly processes on the relationship between environmental optima and range centroids.The strengths of environmental filtering, dispersal limitation, and environmental spatial structure constrain the evolutionary-time-scale relationship between species' intrinsic environmental optima (functional trait value) and the spatial position where their abundance is maximized (the range centroid). Simulation output when both processes are strong (n = d = 0.0001; panels A,C) or weak (n = d = 10; panels B,D). The environment has either weak (≈0.3; panels A,B) or strong (≈0.95; panels C,D) spatial structure. All axes are normalized as standard normal deviates, with mean zero and standard deviation of one. Solid red lines represent the one to one line and solid blue lines are linear regressions. (A) When both processes are strong but there is little environmental spatial structure, a moderately tight relationship emerges between species' trait values and the positions of their range centroids. (C) Increasing the degree of environmental spatial structure leads to a much tighter, one to one relationship. (B,D) Irrespective of how spatially structured the environment is, when both processes are weak there is no relationship between species' trait values and the positions of their range centroid.

Mentions: Evolve species environmental optima [traits] across the regional species pool phylogeny. At the same time that traits are evolving, species geographic ranges are allowed to move through space. For simplicity the spatial position of a species range is summarized by its center of abundance, referred to here as the ‘range centroid.’ For any given species, trait evolution and changes in the position of its range centroid are not independent. The covariance between them is determined by the strength of dispersal limitation, the strength of environmental filtering, and the degree of environmental spatial structure (see Fig. 2 and the following section for details).


Inferring ecological processes from taxonomic, phylogenetic and functional trait β-diversity.

Stegen JC, Hurlbert AH - PLoS ONE (2011)

Effects of assembly processes on the relationship between environmental optima and range centroids.The strengths of environmental filtering, dispersal limitation, and environmental spatial structure constrain the evolutionary-time-scale relationship between species' intrinsic environmental optima (functional trait value) and the spatial position where their abundance is maximized (the range centroid). Simulation output when both processes are strong (n = d = 0.0001; panels A,C) or weak (n = d = 10; panels B,D). The environment has either weak (≈0.3; panels A,B) or strong (≈0.95; panels C,D) spatial structure. All axes are normalized as standard normal deviates, with mean zero and standard deviation of one. Solid red lines represent the one to one line and solid blue lines are linear regressions. (A) When both processes are strong but there is little environmental spatial structure, a moderately tight relationship emerges between species' trait values and the positions of their range centroids. (C) Increasing the degree of environmental spatial structure leads to a much tighter, one to one relationship. (B,D) Irrespective of how spatially structured the environment is, when both processes are weak there is no relationship between species' trait values and the positions of their range centroid.
© Copyright Policy
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC3117851&req=5

pone-0020906-g002: Effects of assembly processes on the relationship between environmental optima and range centroids.The strengths of environmental filtering, dispersal limitation, and environmental spatial structure constrain the evolutionary-time-scale relationship between species' intrinsic environmental optima (functional trait value) and the spatial position where their abundance is maximized (the range centroid). Simulation output when both processes are strong (n = d = 0.0001; panels A,C) or weak (n = d = 10; panels B,D). The environment has either weak (≈0.3; panels A,B) or strong (≈0.95; panels C,D) spatial structure. All axes are normalized as standard normal deviates, with mean zero and standard deviation of one. Solid red lines represent the one to one line and solid blue lines are linear regressions. (A) When both processes are strong but there is little environmental spatial structure, a moderately tight relationship emerges between species' trait values and the positions of their range centroids. (C) Increasing the degree of environmental spatial structure leads to a much tighter, one to one relationship. (B,D) Irrespective of how spatially structured the environment is, when both processes are weak there is no relationship between species' trait values and the positions of their range centroid.
Mentions: Evolve species environmental optima [traits] across the regional species pool phylogeny. At the same time that traits are evolving, species geographic ranges are allowed to move through space. For simplicity the spatial position of a species range is summarized by its center of abundance, referred to here as the ‘range centroid.’ For any given species, trait evolution and changes in the position of its range centroid are not independent. The covariance between them is determined by the strength of dispersal limitation, the strength of environmental filtering, and the degree of environmental spatial structure (see Fig. 2 and the following section for details).

Bottom Line: After developing the framework we compared the model-generated expectations to the commonly used "intuitive" expectation that the variance explained by the environment or by space will, respectively, increase monotonically with the strength of environmental filtering or dispersal limitation.We show that by combining the three types of β-diversity with model-generated expectations our framework is able to provide rigorous inferences of the relative and absolute strengths of dispersal limitation and environmental filtering.Phylogenetic, functional and taxonomic β-diversity can therefore be used simultaneously to infer processes by comparing their empirical patterns to the expectations generated by frameworks similar to the one developed here.

View Article: PubMed Central - PubMed

Affiliation: Department of Biology, University of North Carolina, Chapel Hill, North Carolina, United States of America. stegen@email.unc.edu

ABSTRACT
Understanding the influences of dispersal limitation and environmental filtering on the structure of ecological communities is a major challenge in ecology. Insight may be gained by combining phylogenetic, functional and taxonomic data to characterize spatial turnover in community structure (β-diversity). We develop a framework that allows rigorous inference of the strengths of dispersal limitation and environmental filtering by combining these three types of β-diversity. Our framework provides model-generated expectations for patterns of taxonomic, phylogenetic and functional β-diversity across biologically relevant combinations of dispersal limitation and environmental filtering. After developing the framework we compared the model-generated expectations to the commonly used "intuitive" expectation that the variance explained by the environment or by space will, respectively, increase monotonically with the strength of environmental filtering or dispersal limitation. The model-generated expectations strongly departed from these intuitive expectations: the variance explained by the environment or by space was often a unimodal function of the strength of environmental filtering or dispersal limitation, respectively. Therefore, although it is commonly done in the literature, one cannot assume that the strength of an underlying process is a monotonic function of explained variance. To infer the strength of underlying processes, one must instead compare explained variances to model-generated expectations. Our framework provides these expectations. We show that by combining the three types of β-diversity with model-generated expectations our framework is able to provide rigorous inferences of the relative and absolute strengths of dispersal limitation and environmental filtering. Phylogenetic, functional and taxonomic β-diversity can therefore be used simultaneously to infer processes by comparing their empirical patterns to the expectations generated by frameworks similar to the one developed here.

Show MeSH
Related in: MedlinePlus