Document: Functional trait diversity - DP Faith talk at INTECOL 2013

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"Using databases of observed traits to infer more general trait patterns and produce useful indices based on functional trait diversity"

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There is increasing interest in aspects of functional trait diversity, both in geographic space (within and among ecosystems) and in phylogenetic tree space (e.g. within a given taxonomic group). This level of biodiversity deserves conservation attention because it represents not only current benefits (such as ecosystem functioning) but also insurance or option values. Functional trait diversity measures typically focus on recognised important traits. This information increasingly is organised into databases for selected traits. However, an inclusive biodiversity measure must somehow provide information about more general trait diversity within the given set of species. Recent work highlights the need to include a broader array of traits and has explored surrogates strategies. One strategy has used phylogeny. The phylogenetic diversity measure, PD, provides one surrogate for a broader array of functional traits. However, PD‘s assumption that shared ancestry explains shared features naturally will not account for all traits. An alternative functional diversity index has considered convergent evolution, and has assumed that shared habitat explains shared traits among species. This EDf surrogates approach (Faith 1996) applies the well-known environmental diversity (ED) indices to a functional space – an ordination in which the points are species. Many functional traits methods use dissimilarity among species in a traits space, the T-dimensional space defined by the T traits. In contrast, the space for EDf calculations has dimensions that reflect environmental or habitat gradients. The method assumes a unimodal response model of traits to habitat gradients. The model in this way reflects the core idea that shared habitat explains shared traits among species. These functional trait diversity analyses can use a functional space derived use general traits/morphology databases, such as those based on museum collections for a given taxonomic group. I illustrate this approach for plant and animal taxa, with some detailed analyses for Anseriformes characters/traits. These case studies are the basis for exploring useful extensions of the basic EDf calculations for management and biodiversity conservation planning. In parallel with the PD approach, we use a pattern among species (here, the functional space of species) to mimic the calculations we would make at trait-level if we had observed all traits. Overall EDf values will indicate trait-based functional diversity for communities or regions. For priority setting among species, the weighted EDf distinctiveness index indicates the extent to which a species has traits shared by few others. Probabilistic EDf indices integrate estimated probabilities of species‘ extinctions. Among species, the probabilistic functional diversity indices may support priority setting for threatened species. EDf dissimilarities among localities measure functional beta diversity. Among localities, the EDf endemism indices also may highlight functional trait hotspots. EDf complementarity indices will be fundamental tools for spatial planning and monitoring. This broad EDf “calculus” supports functional-diversity conservation planning that effectively balances conservation of traits of known importance with conservation of more general functional trait diversity.

Faith DP. 2013 "Using databases of observed traits to infer more general trait patterns and produce useful indices based on functional trait diversity" Abstract. The 11th INTECOL Congress. Ecology: Into the next 100 years. London, 18-23 August 2013,


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