PT  - JOURNAL ARTICLE
AU  - Sean W McHugh
AU  - Anahí Espíndola
AU  - Emma White
AU  - Josef Uyeda
TI  - Jointly Modeling Species Niche and Phylogenetic Model in a Bayesian Hierarchical Framework
AID  - 10.1101/2022.07.06.499056
DP  - 2022 Jan 01
TA  - bioRxiv
PG  - 2022.07.06.499056
4099  - http://biorxiv.org/content/early/2022/07/11/2022.07.06.499056.short
4100  - http://biorxiv.org/content/early/2022/07/11/2022.07.06.499056.full
AB  - When studying how species will respond to climatic change, a common goal is to predict how species distributions change through time. Environmental niche models (ENMs) are commonly used to estimate a species’ environmental niche from observed patterns of occurrence and environmental predictors. However, species distributions are often shaped by non-environmental factors–including biotic interactions and dispersal barriers—truncating niche estimates. Though a truncated niche estimate may accurately predict present-day species distribution within the sampled area, this accuracy decreases when predicting occurrence at different places and under different environmental conditions. Modeling niche in a phylogenetic framework leverages a clade’s shared evolutionary history to pull species estimates closer towards phylogenetic conserved values and farther away from species specific biases. We propose a new Bayesian model of phylogenetic niche estimation implemented in R called BePhyNE (Bayesian environmental Phylogenetic Niche Estimation). Under our model, species ENM parameters are transformed into biologically interpretable continuous parameters of environmental niche optimum, breadth, and tolerance evolving as a multivariate Brownian motion. Through simulation analyses, we demonstrate model accuracy and precision that improve as phylogeny size increases. We also demonstrate our model on eastern United States Plethodontid salamanders and recover accurate estimates of species niche, even when species occurrence data is lacking and entirely informed by the evolutionary model. Our model demonstrates a novel framework where niche changes can be studied forwards and backwards through time to understand ancestral ranges, patterns of environmental specialization, and estimate niches of data-deficient species.Competing Interest StatementThe authors have declared no competing interest.