Biases in comparative analyses of extinction risk: mind the gap

Abstract

1. Comparative analyses are used to address the key question of what makes a species more prone to extinction by exploring the links between vulnerability and intrinsic species' traits and/or extrinsic factors. This approach requires comprehensive species data but information is rarely available for all species of interest. As a result comparative analyses often rely on subsets of relatively few species that are assumed to be representative samples of the overall studied group. 2. Our study challenges this assumption and quantifies the taxonomic, spatial, and data type biases associated with the quantity of data available for 5415 mammalian species using the freely available life-history database PanTHERIA. 3. Moreover, we explore how existing biases influence results of comparative analyses of extinction risk by using subsets of data that attempt to correct for detected biases. In particular, we focus on links between four species' traits commonly linked to vulnerability (distribution range area, adult body mass, population density and gestation length) and conduct univariate and multivariate analyses to understand how biases affect model predictions. 4. Our results show important biases in data availability with c.22% of mammals completely lacking data. Missing data, which appear to be not missing at random, occur frequently in all traits (14-99% of cases missing). Data availability is explained by intrinsic traits, with larger mammals occupying bigger range areas being the best studied. Importantly, we find that existing biases affect the results of comparative analyses by overestimating the risk of extinction and changing which traits are identified as important predictors. 5. Our results raise concerns over our ability to draw general conclusions regarding what makes a species more prone to extinction. Missing data represent a prevalent problem in comparative analyses, and unfortunately, because data are not missing at random, conventional approaches to fill data gaps, are not valid or present important challenges. These results show the importance of making appropriate inferences from comparative analyses by focusing on the subset of species for which data are available. Ultimately, addressing the data bias problem requires greater investment in data collection and dissemination, as well as the development of methodological approaches to effectively correct existing biases.

Intraspecific Trait Variation Is Correlated with Establishment Success of Alien Mammals

“…Future work is necessary to determine whether our results can be generalized to other taxonomic groups and to understand how different processes leading to observed variation influence population dynamics. To achieve this goal, it is critical to assemble data sets that capture intraspecific variation-if possible at the population level-instead of providing only mean trait descriptors, while trying to avoid taxonomic and spatial biases that can influence results from comparative analyses (González-Suárez et al 2012). In addition, future research should aim to identify the mechanisms responsible for the observed patterns, differentiating genetic and phenotypic sources of variation, since they may play different roles in population dynamics.…”

“…Unfortunately, missing trait data are prevalent in our database (table 1) and present a challenge for the analyses (González-Suárez et al 2012). Although imputation techniques may be used to fill missing data, we did not follow this approach because missing values are not missing at random in these data (González-Suárez et al 2012) and because of the complexity of imputing intraspecific variation.…”

“…Although imputation techniques may be used to fill missing data, we did not follow this approach because missing values are not missing at random in these data (González-Suárez et al 2012) and because of the complexity of imputing intraspecific variation. Instead, we used a two-step approach based on variable selection using the Akaike information criterion (AIC c ) and variable weights (Burnham and Anderson 2002) to maximize the use of available data.…”

Which intrinsic traits predict vulnerability to extinction depends on the actual threatening processes

“…PanTHERIA provides median estimates for 30 variables describing morphology, development, reproduction, ecology and spatial data for 5415 mammals, but data are not available for all species and some traits are particularly data-poor with a pattern of data not missing at random (González-Suárez et al 2012). To address some of these limitations we only considered traits with data for .1000 species, among which we selected five variables that describe the reproductive and life-cycle speed and one describing diet breadth (Table 2).…”

Four barriers to the global understanding of biodiversity conservation: wealth, language, geographical location and security

“…Gaps and heterogeneity in the geographical and taxonomical coverage of existing information on biodiversity have also been recognized as critical problems in other global efforts to assess the status of global biodiversity [5,8,9]. A recent study demonstrated that existing biases in available information can lead to inaccurate inferences in ecological studies [10].…”