Bootstrapping outperforms community‐weighted approaches for estimating the shapes of phenotypic distributions
Abstract Estimating phenotypic distributions of populations and communities is central to many questions in ecology and evolution. These distributions can be characterized by their moments (mean, variance, skewness and kurtosis) or diversity metrics (e.g. functional richness). Typically, such moment...
| Main Authors: | , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2023-10-01
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| Series: | Methods in Ecology and Evolution |
| Subjects: | |
| Online Access: | https://doi.org/10.1111/2041-210X.14160 |
| _version_ | 1827800187147386880 |
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| author | Brian S. Maitner Aud H. Halbritter Richard J. Telford Tanya Strydom Julia Chacon Christine Lamanna Lindsey L. Sloat Andrew J. Kerkhoff Julie Messier Nick Rasmussen Francesco Pomati Ewa Merz Vigdis Vandvik Brian J. Enquist |
| author_facet | Brian S. Maitner Aud H. Halbritter Richard J. Telford Tanya Strydom Julia Chacon Christine Lamanna Lindsey L. Sloat Andrew J. Kerkhoff Julie Messier Nick Rasmussen Francesco Pomati Ewa Merz Vigdis Vandvik Brian J. Enquist |
| author_sort | Brian S. Maitner |
| collection | DOAJ |
| description | Abstract Estimating phenotypic distributions of populations and communities is central to many questions in ecology and evolution. These distributions can be characterized by their moments (mean, variance, skewness and kurtosis) or diversity metrics (e.g. functional richness). Typically, such moments and metrics are calculated using community‐weighted approaches (e.g. abundance‐weighted mean). We propose an alternative bootstrapping approach that allows flexibility in trait sampling and explicit incorporation of intraspecific variation, and show that this approach significantly improves estimation while allowing us to quantify uncertainty. We assess the performance of different approaches for estimating the moments of trait distributions across various sampling scenarios, taxa and datasets by comparing estimates derived from simulated samples with the true values calculated from full datasets. Simulations differ in sampling intensity (individuals per species), sampling biases (abundance, size), trait data source (local vs. global) and estimation method (two types of community‐weighting, two types of bootstrapping). We introduce the traitstrap R package, which contains a modular and extensible set of bootstrapping and weighted‐averaging functions that use community composition and trait data to estimate the moments of community trait distributions with their uncertainty. Importantly, the first function in the workflow, trait_fill, allows the user to specify hierarchical structures (e.g. plot within site, experiment vs. control, species within genus) to assign trait values to each taxon in each community sample. Across all taxa, simulations and metrics, bootstrapping approaches were more accurate and less biased than community‐weighted approaches. With bootstrapping, a sample size of 9 or more measurements per species per trait generally included the true mean within the 95% CI. It reduced average percent errors by 26%–74% relative to community‐weighting. Random sampling across all species outperformed both size‐ and abundance‐biased sampling. Our results suggest randomly sampling ~9 individuals per sampling unit and species, covering all species in the community and analysing the data using nonparametric bootstrapping generally enable reliable inference on trait distributions, including the central moments, of communities. By providing better estimates of community trait distributions, bootstrapping approaches can improve our ability to link traits to both the processes that generate them and their effects on ecosystems. |
| first_indexed | 2024-03-11T20:04:43Z |
| format | Article |
| id | doaj.art-abe9df0088314d6d833f93a886ff2e02 |
| institution | Directory Open Access Journal |
| issn | 2041-210X |
| language | English |
| last_indexed | 2024-03-11T20:04:43Z |
| publishDate | 2023-10-01 |
| publisher | Wiley |
| record_format | Article |
| series | Methods in Ecology and Evolution |
| spelling | doaj.art-abe9df0088314d6d833f93a886ff2e022023-10-04T06:42:59ZengWileyMethods in Ecology and Evolution2041-210X2023-10-0114102592261010.1111/2041-210X.14160Bootstrapping outperforms community‐weighted approaches for estimating the shapes of phenotypic distributionsBrian S. Maitner0Aud H. Halbritter1Richard J. Telford2Tanya Strydom3Julia Chacon4Christine Lamanna5Lindsey L. Sloat6Andrew J. Kerkhoff7Julie Messier8Nick Rasmussen9Francesco Pomati10Ewa Merz11Vigdis Vandvik12Brian J. Enquist13Department of Geography University at Buffalo Buffalo New York USADepartment of Biological Sciences and Bjerknes Centre for Climate Research University of Bergen Bergen NorwayDepartment of Biological Sciences and Bjerknes Centre for Climate Research University of Bergen Bergen NorwayDépartement de Sciences Biologiques Université de Montréal Montréal Quebec CanadaDepartment of Ecology and Evolutionary Biology University of Arizona Tucson Arizona USAWorld Agroforestry Centre (ICRAF) Nairobi KenyaWorld Resources Institute Washington District of Columbia USADepartment of Biology Kenyon College Gambier Ohio USADepartment of Biology University of Waterloo Waterloo Ontario CanadaCalifornia Department of Water Resources West Sacramento California USASwiss Federal Institute of Aquatic Science and Technology (Eawag) Dübendorf SwitzerlandSwiss Federal Institute of Aquatic Science and Technology (Eawag) Dübendorf SwitzerlandDepartment of Biological Sciences and Bjerknes Centre for Climate Research University of Bergen Bergen NorwayDepartment of Ecology and Evolutionary Biology University of Arizona Tucson Arizona USAAbstract Estimating phenotypic distributions of populations and communities is central to many questions in ecology and evolution. These distributions can be characterized by their moments (mean, variance, skewness and kurtosis) or diversity metrics (e.g. functional richness). Typically, such moments and metrics are calculated using community‐weighted approaches (e.g. abundance‐weighted mean). We propose an alternative bootstrapping approach that allows flexibility in trait sampling and explicit incorporation of intraspecific variation, and show that this approach significantly improves estimation while allowing us to quantify uncertainty. We assess the performance of different approaches for estimating the moments of trait distributions across various sampling scenarios, taxa and datasets by comparing estimates derived from simulated samples with the true values calculated from full datasets. Simulations differ in sampling intensity (individuals per species), sampling biases (abundance, size), trait data source (local vs. global) and estimation method (two types of community‐weighting, two types of bootstrapping). We introduce the traitstrap R package, which contains a modular and extensible set of bootstrapping and weighted‐averaging functions that use community composition and trait data to estimate the moments of community trait distributions with their uncertainty. Importantly, the first function in the workflow, trait_fill, allows the user to specify hierarchical structures (e.g. plot within site, experiment vs. control, species within genus) to assign trait values to each taxon in each community sample. Across all taxa, simulations and metrics, bootstrapping approaches were more accurate and less biased than community‐weighted approaches. With bootstrapping, a sample size of 9 or more measurements per species per trait generally included the true mean within the 95% CI. It reduced average percent errors by 26%–74% relative to community‐weighting. Random sampling across all species outperformed both size‐ and abundance‐biased sampling. Our results suggest randomly sampling ~9 individuals per sampling unit and species, covering all species in the community and analysing the data using nonparametric bootstrapping generally enable reliable inference on trait distributions, including the central moments, of communities. By providing better estimates of community trait distributions, bootstrapping approaches can improve our ability to link traits to both the processes that generate them and their effects on ecosystems.https://doi.org/10.1111/2041-210X.14160body sizecommunity ecologycommunity‐weighted meanfunctional ecologyfunctional traitsnonparametric bootstrapping |
| spellingShingle | Brian S. Maitner Aud H. Halbritter Richard J. Telford Tanya Strydom Julia Chacon Christine Lamanna Lindsey L. Sloat Andrew J. Kerkhoff Julie Messier Nick Rasmussen Francesco Pomati Ewa Merz Vigdis Vandvik Brian J. Enquist Bootstrapping outperforms community‐weighted approaches for estimating the shapes of phenotypic distributions Methods in Ecology and Evolution body size community ecology community‐weighted mean functional ecology functional traits nonparametric bootstrapping |
| title | Bootstrapping outperforms community‐weighted approaches for estimating the shapes of phenotypic distributions |
| title_full | Bootstrapping outperforms community‐weighted approaches for estimating the shapes of phenotypic distributions |
| title_fullStr | Bootstrapping outperforms community‐weighted approaches for estimating the shapes of phenotypic distributions |
| title_full_unstemmed | Bootstrapping outperforms community‐weighted approaches for estimating the shapes of phenotypic distributions |
| title_short | Bootstrapping outperforms community‐weighted approaches for estimating the shapes of phenotypic distributions |
| title_sort | bootstrapping outperforms community weighted approaches for estimating the shapes of phenotypic distributions |
| topic | body size community ecology community‐weighted mean functional ecology functional traits nonparametric bootstrapping |
| url | https://doi.org/10.1111/2041-210X.14160 |
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