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Spatiophylogenetic modelling of extinction risk reveals evolutionary distinctiveness and brief flowering period as threats in a hotspot plant genus

Citation

Dinnage, Russell; Skeels, Alexander; Cardillo, Marcel (2020), Spatiophylogenetic modelling of extinction risk reveals evolutionary distinctiveness and brief flowering period as threats in a hotspot plant genus, Dryad, Dataset, https://doi.org/10.5061/dryad.jh9w0vt73

Abstract

Comparative models used to predict species threat status can help to identify diagnostic features of species at risk. Such models often combine variables measured at the species level with spatial variables, causing multiple statistical challenges, including phylogenetic and spatial non-independence. We present a novel Bayesian approach for modelling threat status that simultaneously deals with both forms of non-independence and estimates their relative contribution, and we apply the approach to modelling threat status in the Australian plant genus Hakea. We find that after phylogenetic and spatial effects are accounted for, species with greater evolutionary distinctiveness and a shorter annual flowering period are more likely to be threatened. The model allows us to combine information on evolutionary history, species biology, and spatial data, to calculate latent extinction risk (potential for non-threatened species to become threatened), estimate the most important drivers of risk for individual species, and map spatial patterns in the effects of different predictors on extinction risk. This could be of value for proactive conservation decision-making based on the early identification of species and regions of potential conservation concern.

Methods

Details on data collection methods can be found in the accompanying article.

Usage Notes

Metadata on the dataset can be found in metadata.html, produced using the R package dataspice (https://github.com/ropenscilabs/dataspice).

Dinnage_Skeels_Cardillo_tutorial_and_sims.zip contains data necessary to run the tutorial code included in the paper's supplement, as well as simulations designed to test some aspects of the method presented in the paper. It also includes the .Rmd source file used to generate the supplementary tutorial.

Funding

Australian Research Council, Award: DP160103942