Biology subjects

Warton, D. I.

Publications and source records attributed to Warton, D. I..

3 recordsLinked to original sources

bioRxiv2025

Fast phylogenetic generalised linear mixed-effects modelling using the glmmTMB R package

Phylogenetic generalised linear mixed models (PGLMMs) help ecologists to distinguish ecological drivers from other processes shaping evolutionary patterns, yet existing implementations are often limited in distributional scope or computational speed. We compare five R packages for fitting PGLMMs and highlight the new covariance structure propto in the general-purpose GLMM package glmmTMB. Simulations show that glmmTMB fits PGLMMs faster overall than brms, MCMCglmm, INLA, and phyr, while producing similar model estimates. We present the first practical application of glmmTMB for fitting phylogenetic random effects using likelihood-based models that accommodate repeated measures, demonstrated through case studies of evolutionary trait data. By improving both speed and flexibility, glmmTMB broadens access to PGLMM and supports deeper insights into trait evolution and diversification.

evolutionary biology↗

bioRxiv2021

Fast model-based ordination with copulas

O_LIVisualising data is a vital part of analysis, allowing researchers to find patterns, and assess and communicate the results of statistical modeling. In ecology, visualisation is often challenging when there are many variables (often for different species or other taxonomic groups) and they are not normally distributed (often counts or presence-absence data). Ordination is a common and powerful way to overcome this hurdle by reducing data from many response variables to just two or three, to be easily plotted. C_LIO_LIOrdination is traditionally done using dissimilarity-based methods, most commonly non-metric multidimensional scaling (nMDS). In the last decade however, model-based methods for unconstrained ordination have gained popularity. These are primarily based on latent variable models, with latent variables estimating the underlying, unobserved ecological gradients. C_LIO_LIDespite some major benefits, a major drawback of model-based ordination methods is their speed, as they typically taking much longer to return a result than dissimilarity-based methods, especially for large sample sizes. C_LIO_LIWe introduce copula ordination, a new, scalable model-based approach to unconstrained ordination. This method has all the desirable properties of model-based ordination methods, with the added advantage that it is computationally far more efficient. In particular, simulations show copula ordination is an order of magnitude faster than current model-based methods, and can even be faster than nMDS for large sample sizes, while being able to produce similar ordination plots and trends as these methods. C_LI

ecology↗

bioRxiv2018

Untangling direct species associations from mediator species effects with graphical models

Ecologists often investigate co-occurrence patterns in multi-species data in order to gain insight into the ecological causes of observed co-occurrences. Apart from direct associations between two species, two species may co-occur because they both respond in similar ways to environmental variables, or due to the presence of other (mediator) species.\n\nA wide variety of methods are now available for modelling how environmental filtering drives species distributions. In contrast, methods for studying other causes of co-occurence are much more limited. \"Graphical\" methods, which can be used to study how mediator species impact co-occurrence patterns, have recently been proposed for use in ecology. However, available methods are limited to presence/absence data and methods assuming multivariate normality, which is problematic when analysing abundances.\n\nWe propose Gaussian copula graphical models (GCGMs) for studying the effect of mediator species on co-occurence patterns. GCGMs are a flexible type of graphical model which naturally accommodates all data types - binary (presence/absence), counts, as well as ordinal data and biomass, in a unified framework. Simulations for count data demonstrate that GCGMs are better able to distinguish effects of mediator species from direct associations than using existing methods designed for multivariate normal data.\n\nWe apply GCGMs to counts of hunting spiders, in order to visualise associations between species. We then analyze abundance data of New Zealand native forest cover (on an ordinal scale) to show how GCGMs can be used analyze large and complex datasets. In these data, we were able to reproduce known species relationships as well as generate new ecological hypotheses about species associations.

ecology↗