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

Abstract

Comparative models used to predict species threat status can help identify the 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, 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.

Geographic range size and speciation in honeyeaters

“…For the phylogenetic effect, INLA requires a phylogenetic precision matrix which is the inverse of a phylogenetic covariance matrix. Prior to inverting, the phylogenetic covariance matrix was standardised by dividing by its determinant raised to the power of 1/N species [ 112 ]. To model the spatial effect across the entire landscape, we used a spatial mesh, which was constructed over the point occurrence records and averaged across the spatial random fields of each occurrence record for each species.…”

Correlates of extinction risk in Australian squamate reptiles

“…Though not technically threatened, these species provide the most data to correlate with extinction risk. Using a binary threat status variable rather than an ordinal response removes the effects of skewed distributions typical of the ordinal scales used in the IUCN Red List(Dinnage et al, 2020).…”

sdmTMB: An R Package for Fast, Flexible, and User-Friendly Generalized Linear Mixed Effects Models with Spatial and Spatiotemporal Random Fields

“…Spatially and spatiotemporally explicit data are increasingly collected in ecology and have the power to reveal new ecological processes (e.g., Dinnage et al . 2020; English et al .…”

“…Speed-wise, sdmTMB (and by association VAST) were fastest up to at least 1000 mesh nodes at approximately Spatially and spatiotemporally explicit data are increasingly collected in ecology and have the power to reveal new ecological processes (e.g., Dinnage et al 2020;English et al 2022) and improve ecological management (Sofaer et al 2019). These data present statistical challenges to modelling them effectively and efficiently since appropriate models such as GLMMs with random fields are often computationally intensive and challenging to implement, interpret, and evaluate.…”