Etard, A; Morrill, S; Newbold, T; (2020) Global gaps in trait data for terrestrial vertebrates. Global Ecology and Biogeography 10.1111/geb.13184. (In press).

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Abstract

AIM: Trait data are increasingly being used in studies investigating the impacts of global changes on the structure and functioning of ecological communities. Despite a growing number of trait data collations for terrestrial vertebrates, there is to date no global assessment of the gaps and biases the data present. Here, we assess whether terrestrial vertebrate trait data are taxonomically, spatially and phylogenetically biased. LOCATION: Global. TIME PERIOD: Present. MAJOR TAXA STUDIED: Terrestrial vertebrates. METHODS: We compile seven ecological traits and quantify coverage as the proportion of species for which an estimate is available. For a species, we define completeness as the proportion of non‐missing values across traits. We assess whether coverage and completeness differ across classes and examine phylogenetic biases in trait data. To investigate spatial biases, we test whether wider‐ranging species have more complete trait data than narrow‐ranging species. Additionally, we test whether species‐rich regions, which are of most concern for conservation, are less well sampled than species‐poor regions. RESULTS: Mammals and birds are well sampled even in species‐rich regions. For reptiles and amphibians (herptiles), only body size presents a high coverage (>80%), in addition to habitat‐related variables (amphibians). Herptiles are poorly sampled for other traits. The shortfalls are particularly acute in some species‐rich regions and for certain clades. Across all classes, geographically rarer species have less complete trait information. MAIN CONCLUSIONS: Trait information is less available on average in some of the most diverse areas and in geographically rarer species, both of which crucial for biodiversity conservation. Gaps in trait data might impede our ability to conduct large‐scale analyses, whereas biases can impact the validity of extrapolations. A short‐term solution to the problem is to estimate missing trait data using imputation techniques, whereas a longer‐term and more robust filling of existing gaps requires continued data‐collection efforts.

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