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Including intraspecific trait variability to avoid distortion of functional diversity and ecological inference: lessons from natural assemblages

Citation

Carmona, Carlos; Wong, Mark K. L. (2020), Including intraspecific trait variability to avoid distortion of functional diversity and ecological inference: lessons from natural assemblages, Dryad, Dataset, https://doi.org/10.5061/dryad.612jm642j

Abstract

1. Functional diversity assessments are crucial and increasingly used for understanding ecological processes and managing ecosystems. The functional diversity of a community is assessed by sampling traits at one or more scales (individuals, populations, species) and calculating a summary index of the variation in trait values. However, it remains unclear how the scale at which traits are sampled and the indices used to estimate functional diversity may alter the patterns observed and inferences about ecological processes.

2. For 40 plant and 61 ant communities, we assess functional diversity using six methods – encompassing various mean-based and probabilistic methods – chosen to reflect common scenarios where different levels of detail are available in trait data. We test whether including trait variability at different scales (from individuals to species) alter functional diversity values calculated using volume-based and dissimilarity-based indices, Functional Richness (FRic) and Rao, respectively. We further test whether such effects alter the functional diversity patterns observed across communities and their relationships with environmental drivers such as abiotic gradients and occurrences of invasive species.

3. Intraspecific trait variability strongly determined FRic and Rao. Methods using only species’ mean trait values to calculate FRic (convex hulls) and Rao (Gower-based dissimilarity) distorted the patterns observed when intraspecific trait variability was considered. These distortions generated Type I and Type II errors for the effects of environmental factors structuring the plant and ant communities.

4. The high sensitivity of FRic to individuals with extreme trait values was revealed in comparisons of different probabilistic methods including among-individual and among-population trait variability in functional diversity. By contrast, values and ecological patterns in Rao were consistent among methods including different scales of intraspecific trait variability.

5. Decisions about where traits are sampled and how trait variability is included in functional diversity can drastically change the patterns observed and conclusions about ecological processes. We recommend sampling the traits of multiple individuals per species and capturing their intraspecific trait variability using probabilistic methods. We discuss how intraspecific trait variability can be reasonably estimated and included in functional diversity in the common circumstance where only limited trait data are available.

Methods

The dataset includes:

  1. Functional traits measurements for ants (described in Wong et al. 2020 Oikos, 129, 585-597)
  2. Functional traits measurements for vascular plants (described in Carmona et al. 2015 Functional Ecology, 29, 579-588)
  3. Community composition (sites x species matrices) both for plants and ants
  4. Environmental information associated to each site, both for plants (water availability) and ants (invasion status and percentage ground cover)
  5. Scripts to perform all analyses and figures contained in the paper

Funding

National Geographic Society, Award: 60-16

University of Oxford, Award: Clarendon Scholarship

Estonian Research Competency Council, Award: PSG293

European Regional Development Fund, Award: Center of Excellence EcolChange