A Trait-Based Test for Habitat Filtering: Convex Hull Volume

Cornwell, William K.; Schwilk, L Dylan W; Ackerly, David D.

doi:10.1890/0012-9658(2006)87[1465:attfhf]2.0.co;2

Cited by 1,095 publications

(1,047 citation statements)

References 36 publications

(44 reference statements)

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“…(2008)FDivFunctional divergence: mean abundance‐weighted deviance from an absolute abundance‐weighted devianceVilleger et al. (2008)FRicFunctional richness: convex hull volume of the trait spaceCornwell, Schwilk, & Ackerly, 2006; Villeger et al. (2008)RaoQQuadratic entropyBotta‐Dukat (2005)

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Section: Table A1mentioning

confidence: 99%

Biodiversity explains maximum variation in productivity under experimental warming, nitrogen addition, and grazing in mountain grasslands

Liu

et al. 2018

Ecology and Evolution

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Anthropogenic global warming, nitrogen addition, and overgrazing alter plant communities and threaten plant biodiversity, potentially impacting community productivity, especially in sensitive mountain grassland ecosystems. However, it still remains unknown whether the relationship between plant biodiversity and community productivity varies across different anthropogenic influences, and especially how changes in multiple biodiversity facets drive these impacts on productivity. Here, we measured different facets of biodiversity including functional and phylogenetic richness and evenness in mountain grasslands along an environmental gradient of elevation in Yulong Mountain, Yunnan, China. We combined biodiversity metrics in a series of linear mixed‐effect models to determine the most parsimonious predictors for productivity, which was estimated by aboveground biomass in community. We examined how biodiversity–productivity relationships were affected by experimental warming, nitrogen addition, and livestock‐grazing. Species richness, phylogenetic diversity, and single functional traits (leaf nitrogen content, mg/g) represented the most parsimonious combination in these scenarios, supporting a consensus that single‐biodiversity metrics alone cannot fully explain ecosystem function. The biodiversity–productivity relationships were positive and strong, but the effects of treatment on biodiversity–productivity relationship were negligible. Our findings indicate that the strong biodiversity–productivity relationships are consistent in various anthropogenic drivers of environmental change.

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Section: Table A1mentioning

confidence: 99%

Biodiversity explains maximum variation in productivity under experimental warming, nitrogen addition, and grazing in mountain grasslands

Liu

et al. 2018

Ecology and Evolution

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“…Pakeman (2014) showed that five common metrics used to quantify FD, namely functional divergence and dispersion, Rao's quadratic entropy, and particularly functional richness and evenness, were sensitive to missing values. There has been limited discussion of how correlations among multiple traits might impact indices of FD (Cornwell et al 2006, Villéger et al 2008. To explore this phenomenon, we simulated values for the above indices of FD while varying the degree of correlation, the number of traits, and species richness (see supplementary material).…”

Section: More Is Not Always Bettermentioning

confidence: 99%

Choosing and using multiple traits in functional diversity research

2014

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SUMMARYSpecies are different, but they are not equally different. Yet many indices of biodiversity assume species vary to identical degrees. This notion does not meet with intuition: some species vary greatly in terms of their morphology, behaviour and ecology, while others vary only a little. One way to reconcile the dissimilarity between species is by collecting information on their functional traits (FTs), descriptors of how organisms interact with their environment and each other. Functional diversity (FD) is the total variation in one or more FTs across all species within a community, and provides a powerful complement to species diversity. There are several challenges facing the application of FD to conservation science, including lack of rigorous trait data for many organisms, and sparse details on how to select available traits to generate meaningful inferences for the various summary metrics of FD. This Comment provides a brief discussion on choosing and using FTs, and recommendations for best practice. Ultimately, researchers need to consider using a variety of traits when hypotheses are multifaceted or could potentially evolve, at the same time thinking critically about trait selection to avoid redundant information.

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“…Prior to analysis, continuous traits (all but internode; Table 2) were z ‐score‐transformed (e.g., Cornwell et al., 2006). We then created 100 balanced datasets, each time by randomly selecting a single population per each of the seven species, including nine individuals from each chosen population.…”

Section: Methodsmentioning

confidence: 99%

Trait dimensionality and population choice alter estimates of phenotypic dissimilarity

Carscadden

Cadotte

Gilbert

2017

Ecology and Evolution

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The ecological niche is a multi‐dimensional concept including aspects of resource use, environmental tolerance, and interspecific interactions, and the degree to which niches overlap is central to many ecological questions. Plant phenotypic traits are increasingly used as surrogates of species niches, but we lack an understanding of how key sampling decisions affect our ability to capture phenotypic differences among species. Using trait data of ecologically distinct monkeyflower (Mimulus) congeners, we employed linear discriminant analysis to determine how (1) dimensionality (the number and type of traits) and (2) variation within species influence how well measured traits reflect phenotypic differences among species. We conducted analyses using vegetative and floral traits in different combinations of up to 13 traits and compared the performance of commonly used functional traits such as specific leaf area against other morphological traits. We tested the importance of intraspecific variation by assessing how population choice changed our ability to discriminate species. Neither using key functional traits nor sampling across plant functions and organs maximized species discrimination. When using few traits, vegetative traits performed better than combinations of vegetative and floral traits or floral traits alone. Overall, including more traits increased our ability to detect phenotypic differences among species. Population choice and the number of traits used had comparable impacts on discriminating species. We addressed methodological challenges that have undermined cross‐study comparability of trait‐based approaches. Our results emphasize the importance of sampling among‐population trait variation and suggest that a high‐dimensional approach may best capture phenotypic variation among species with distinct niches.

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A Trait-Based Test for Habitat Filtering: Convex Hull Volume

Cited by 1,095 publications

References 36 publications

Biodiversity explains maximum variation in productivity under experimental warming, nitrogen addition, and grazing in mountain grasslands

Biodiversity explains maximum variation in productivity under experimental warming, nitrogen addition, and grazing in mountain grasslands

Choosing and using multiple traits in functional diversity research

Trait dimensionality and population choice alter estimates of phenotypic dissimilarity

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