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Biodiversity patterns along ecological gradients: unifying β-diversity indices.

Szava-Kovats RC, Pärtel M - PLoS ONE (2014)

Bottom Line: Second, despite being a function of the same measured α- and γ-diversity, the resultant biodiversity pattern depends on the choice of β-diversity index.Moreover, this transformation results in identical biodiversity patterns for three commonly used classical beta-diversity indices.As a result, this transformation eliminates the difficulties of both shortcomings, while allowing the researcher to use whichever beta-diversity index deemed most appropriate.

View Article: PubMed Central - PubMed

Affiliation: Institute of Ecology and Earth Sciences, University of Tartu, Tartu, Estonia.

ABSTRACT
Ecologists have developed an abundance of conceptions and mathematical expressions to define β-diversity, the link between local (α) and regional-scale (γ) richness, in order to characterize patterns of biodiversity along ecological (i.e., spatial and environmental) gradients. These patterns are often realized by regression of β-diversity indices against one or more ecological gradients. This practice, however, is subject to two shortcomings that can undermine the validity of the biodiversity patterns. First, many β-diversity indices are constrained to range between fixed lower and upper limits. As such, regression analysis of β-diversity indices against ecological gradients can result in regression curves that extend beyond these mathematical constraints, thus creating an interpretational dilemma. Second, despite being a function of the same measured α- and γ-diversity, the resultant biodiversity pattern depends on the choice of β-diversity index. We propose a simple logistic transformation that rids beta-diversity indices of their mathematical constraints, thus eliminating the possibility of an uninterpretable regression curve. Moreover, this transformation results in identical biodiversity patterns for three commonly used classical beta-diversity indices. As a result, this transformation eliminates the difficulties of both shortcomings, while allowing the researcher to use whichever beta-diversity index deemed most appropriate. We believe this method can help unify the study of biodiversity patterns along ecological gradients.

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Scatterplots of beta-diversity indices against hypothetical ecological gradient.(a) Scenario B: βMd (circles, left axis) and βPt (squares, right axis); linear regression trends, for βMd (), for βPt (). (b) Scenario C: βMd (circles, left axis) and βPt (squares, right axis); linear regression trends, for βMd (), for βPt (). (c) Scenario B: β*Md (circles) and β*Pt (squares); linear regression trends, for β*Md, for β*Pt ( for both). (d) Scenario C: β*Md (circles) and β*Pt (squares); linear regression trends, for β*Md, for β*Pt ( for both). See Table 1 for description of beta-diversity indices and Table 2 for data for Scenario A.
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pone-0110485-g002: Scatterplots of beta-diversity indices against hypothetical ecological gradient.(a) Scenario B: βMd (circles, left axis) and βPt (squares, right axis); linear regression trends, for βMd (), for βPt (). (b) Scenario C: βMd (circles, left axis) and βPt (squares, right axis); linear regression trends, for βMd (), for βPt (). (c) Scenario B: β*Md (circles) and β*Pt (squares); linear regression trends, for β*Md, for β*Pt ( for both). (d) Scenario C: β*Md (circles) and β*Pt (squares); linear regression trends, for β*Md, for β*Pt ( for both). See Table 1 for description of beta-diversity indices and Table 2 for data for Scenario A.

Mentions: The linear regression curves of βMd, βMd-1, and βPt in Scenarios B and C all lie within the upper and lower limits. However, the relationship between βPt and the gradient in Scenario B is statistically significant at 95% confidence (p = 0.026), whereas those between βMd and βMd-1 are non-significant (p = 0.097) (Figure 2a). This result is opposite in Scenario C: the relationship between βPt and the gradient is statistically non-significant (p = 0.121), whereas those between βMd and βMd-1 are significant (p = 0.036) (Figure 2b). In addition, the magnitude of the residuals in Scenarios B and C differ between βMd (or βMd-1) and βPt. By contrast, linear regression of β*Md, β*Md-1, and β*Pt against the gradient results in identical models for both Scenario B and C with the exception of their respective intercepts. The relationship is significant for both Scenario B (p = 0.023) and Scenario C (p = 0.039) and both relationships are independent of the choice of beta-diversity index. In addition, this choice has no effect on the resultant residual patterns (Figure 2c,d).


Biodiversity patterns along ecological gradients: unifying β-diversity indices.

Szava-Kovats RC, Pärtel M - PLoS ONE (2014)

Scatterplots of beta-diversity indices against hypothetical ecological gradient.(a) Scenario B: βMd (circles, left axis) and βPt (squares, right axis); linear regression trends, for βMd (), for βPt (). (b) Scenario C: βMd (circles, left axis) and βPt (squares, right axis); linear regression trends, for βMd (), for βPt (). (c) Scenario B: β*Md (circles) and β*Pt (squares); linear regression trends, for β*Md, for β*Pt ( for both). (d) Scenario C: β*Md (circles) and β*Pt (squares); linear regression trends, for β*Md, for β*Pt ( for both). See Table 1 for description of beta-diversity indices and Table 2 for data for Scenario A.
© Copyright Policy
Related In: Results  -  Collection

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Show All Figures
getmorefigures.php?uid=PMC4201547&req=5

pone-0110485-g002: Scatterplots of beta-diversity indices against hypothetical ecological gradient.(a) Scenario B: βMd (circles, left axis) and βPt (squares, right axis); linear regression trends, for βMd (), for βPt (). (b) Scenario C: βMd (circles, left axis) and βPt (squares, right axis); linear regression trends, for βMd (), for βPt (). (c) Scenario B: β*Md (circles) and β*Pt (squares); linear regression trends, for β*Md, for β*Pt ( for both). (d) Scenario C: β*Md (circles) and β*Pt (squares); linear regression trends, for β*Md, for β*Pt ( for both). See Table 1 for description of beta-diversity indices and Table 2 for data for Scenario A.
Mentions: The linear regression curves of βMd, βMd-1, and βPt in Scenarios B and C all lie within the upper and lower limits. However, the relationship between βPt and the gradient in Scenario B is statistically significant at 95% confidence (p = 0.026), whereas those between βMd and βMd-1 are non-significant (p = 0.097) (Figure 2a). This result is opposite in Scenario C: the relationship between βPt and the gradient is statistically non-significant (p = 0.121), whereas those between βMd and βMd-1 are significant (p = 0.036) (Figure 2b). In addition, the magnitude of the residuals in Scenarios B and C differ between βMd (or βMd-1) and βPt. By contrast, linear regression of β*Md, β*Md-1, and β*Pt against the gradient results in identical models for both Scenario B and C with the exception of their respective intercepts. The relationship is significant for both Scenario B (p = 0.023) and Scenario C (p = 0.039) and both relationships are independent of the choice of beta-diversity index. In addition, this choice has no effect on the resultant residual patterns (Figure 2c,d).

Bottom Line: Second, despite being a function of the same measured α- and γ-diversity, the resultant biodiversity pattern depends on the choice of β-diversity index.Moreover, this transformation results in identical biodiversity patterns for three commonly used classical beta-diversity indices.As a result, this transformation eliminates the difficulties of both shortcomings, while allowing the researcher to use whichever beta-diversity index deemed most appropriate.

View Article: PubMed Central - PubMed

Affiliation: Institute of Ecology and Earth Sciences, University of Tartu, Tartu, Estonia.

ABSTRACT
Ecologists have developed an abundance of conceptions and mathematical expressions to define β-diversity, the link between local (α) and regional-scale (γ) richness, in order to characterize patterns of biodiversity along ecological (i.e., spatial and environmental) gradients. These patterns are often realized by regression of β-diversity indices against one or more ecological gradients. This practice, however, is subject to two shortcomings that can undermine the validity of the biodiversity patterns. First, many β-diversity indices are constrained to range between fixed lower and upper limits. As such, regression analysis of β-diversity indices against ecological gradients can result in regression curves that extend beyond these mathematical constraints, thus creating an interpretational dilemma. Second, despite being a function of the same measured α- and γ-diversity, the resultant biodiversity pattern depends on the choice of β-diversity index. We propose a simple logistic transformation that rids beta-diversity indices of their mathematical constraints, thus eliminating the possibility of an uninterpretable regression curve. Moreover, this transformation results in identical biodiversity patterns for three commonly used classical beta-diversity indices. As a result, this transformation eliminates the difficulties of both shortcomings, while allowing the researcher to use whichever beta-diversity index deemed most appropriate. We believe this method can help unify the study of biodiversity patterns along ecological gradients.

Show MeSH
Related in: MedlinePlus