David I. Warton

Among the many pathways through which human activity can shape biodiversity patterns, modification of biotic interactions tends to be overlooked. Here we show how interacting anthropogenic, biotic and abiotic factors drive the continental-scale expansion of a highly adaptive mesocarnivore, the golden jackal. Using a dataset of playback surveys conducted across Europe, we find that the presence of an apex predator, the grey wolf, is the primary factor limiting jackal occurrence. However, a human presence acts as a shield that considerably reduces the strength of wolf suppression and provides jackals refuge near people. Our fitted distribution model predicts that jackals have the potential to expand until they occupy 75% of the continent, almost six times larger than their current range. The ongoing recovery of wolves in Europe could reduce suitable areas for expanding jackals by up to 18% in the near future, but a persistent human-shield effect, coupled with climate warming and landscape modification, is expected to sustain continued jackal expansion across the continent. Our results illustrate how cascading anthropogenic impacts can hamper the ecological benefits provided by apex predator recovery.

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.

In ecology and evolution, meta‐analysis is an important tool to synthesise findings across separate studies and identify sources of heterogeneity. However, ecological and evolutionary data often exhibit complex dependence structures, such as shared sources of variation within studies, phylogenetic relationships and hierarchical sampling designs. Recent statistical advancements offer approaches for handling such complexities in dependence, yet these methods remain under‐utilised or unfamiliar to ecologists and evolutionary biologists. We conducted extensive simulations to evaluate modelling approaches for handling dependence in effect sizes and sampling errors in ecological and evolutionary meta‐analyses. We assessed the performance of multilevel models, incorporating an assumed sampling error variance–covariance (VCV) matrix (which account for within‐study correlation), cluster robust variance estimation (CRVE) methods and their combination across different true within‐study correlations. Finally, we showcased the applications of these models in two case studies of published meta‐analyses. Multilevel models produced unbiased regression coefficient estimates, and when a sampling VCV matrix was used, it provided accurate random effect variance components estimates within and among studies. However, the latter had no impact on regression coefficient estimates if the model was misspecified. In simulations involving phylogenetic multilevel meta‐analysis, models using CRVE methods generated narrower confidence intervals and lower coverage rates than the nominal expectations. The case study results showed the importance of considering a sampling error VCV matrix to improve the model fit. Our results provide clear modelling recommendations for ecologists and evolutionary biologists conducting meta‐analyses. To improve the precision of variance component estimates, we recommend constructing a VCV matrix that accounts for dependencies in sampling errors within studies. Although CRVE methods provide robust inference under certain conditions, we caution against their use with crossed random effects, such as phylogenetic multilevel meta‐analyses, as CRVE methods currently do not account for multi‐way clustering and may inflate Type I error rates. Finally, we recommend using multilevel meta‐analytic models to account for heterogeneity at all relevant hierarchical levels and to follow guidance on inference methods to ensure accurate coverage of the overall mean.

: Repeat observations of size are a common tool for understanding growth across taxa, and are used to estimate parameters for functions that describe growth rates. Recent advances in estimating differential equation parameters with a hierarchical longitudinal model have gone beyond available software. Custom implementation of such models is a barrier to use particularly for people who are not familiar with statistical programming. We introduce a new R package implementing a hierarchical Bayesian longitudinal model for repeat observation data with three growth models from ecological case studies. The package provides tools for model fitting and estimate extraction, example data, and case studies to demonstrate the use-case for each of the example models.

Repeat measurement surveys of tree size are used in forests to estimate growth behaviour, biomass and population dynamics. Although size is measured with error and individuals vary in their growth trajectories, current size‐based growth modelling approaches do not usually or fully account for both of these features, and therefore under‐utilise available data. We present a new method that leverages the auto‐correlation structure of repeat surveys into a hierarchical Bayesian longitudinal growth model. This new structure allows users to correct for measurement error and capture individual‐level variation in growth trajectories and parameters. To demonstrate the new method we applied it to a sample of tropical tree survey data from long‐term monitoring sites at Barro Colorado Island. We were able to reduce estimated error in size and growth, and extract individual‐ and population‐level growth parameter estimates. We used simulation to evaluate the ability of the new method to improve estimates of growth rate and size, and estimate individual and species‐level parameters. Our method substantially improved the root mean squared error (RMSE) for growth by an average of 61% compared to existing approaches using pairwise differences and reduced RMSE in estimated size RMSE compared to ‘observed’ values in simulated data. Better numerical integration methods (Runge–Kutta fourth order in comparison to Euler and midpoint) provided better estimates of parameters, but did not improve the estimation of size and growth. The choice of a positive growth function eliminated all negative increments without data exclusion. Overall, this study shows how we can gain new and improved insights on growth, using repeat forest surveys. Our new method offers improved biomass dynamics estimation through reduced error in sizes over time, coupled with novel information about within‐species variation in growth behaviour that is inaccessible with species average models, such as individual parameters for the growth function which allows for relationships between parameters to be considered for the first time.

Simulation studies are essential tools to assess statistical methods. Functioning as controlled experiments, simulations generate data from known underlying processes. However, unclear or incomplete reporting of simulation studies can impact their interpretability and reproducibility, potentially leading to the misuse of statistical methods. While Morris et al. (2019, Stat Med, 38, p. 2074) recently provided guidance on the planning and conduct of simulation studies for statistical method evaluation, there is currently no comprehensive set of reporting guidelines in ecology and evolutionary biology. Here, we propose 11 reporting items for statistical simulation studies extending on Morris and colleagues' guidance. These items span across three stages: planning, coding and analysis. We also clarify the terminology related to statistical components and the broad purposes of statistical simulation studies. To highlight our proposed reporting items with current practices, we surveyed 100 articles in ecology and evolution journals that included a simulation study evaluating a statistical method. Our survey found room for improvement in more transparent reporting to ensure clear evaluation of statistical methods. Most notably, only a small proportion of articles reported a Monte Carlo uncertainty (17%; 17 out of 98), and 32% (32 out of 100) articles did not provide code. Beyond the proposed reporting items, we discuss the benefits of open science tools to enhance the reproducibility of simulation studies. Specifically, we propose the registration of statistical simulation studies to enhance planning, reporting and collaboration. We aim to instigate discussions to improve the reporting of simulation studies for statistical method research. The reporting items we propose, along with open science tools, serve as a template for developing standards and guidelines for simulation studies evaluating statistical methods. These reporting guidelines will help enhance reproducibility and indirectly encourage more consideration in the design and conduct of these simulation studies.

: Repeat measurement surveys of tree size are used in forests to estimate growth be-haviour, biomass, and population dynamics. Although size is measured with error, and individuals vary in their growth trajectories, current size-based growth modelling approaches do not usually or fully account for both of these features, and therefore under-utilise available data. We present a new method that leverages the auto-correlation structure of repeat surveys into a hierarchical Bayesian longitudinal growth model. This new structure allows users to correct for measurement error and capture individual-level variation in growth trajectories and parameters. To demonstrate the new method we applied it to a sample of tropical tree survey data from long-term monitoring sites at Barro Colorado Island. We were able to reduce estimated error in size and growth, and extract individual-and population-level growth parameter estimates. We used simulation to evaluate the ability of the new method to improve estimates of growth rate and size, and estimate individual and species-level parameters. Our method substantially improved RMSE for growth by an average of 61% compared to existing approaches using pairwise differences; and reduced RMSE in estimated size RMSE compared to ‘observed’ values in simulated data. Better numerical integration methods (Runge-Kutta 4th order in comparison to Euler and midpoint) provided better estimates of parameters, but did not improve the estimation of size and growth. The choice of a positive growth function eliminated all negative increments without data exclusion. Overall, this study shows how we can gain new and improved insights on growth, using repeat forest surveys. Our new method offers improved biomass dynamics estimation through reduced error in sizes over time, coupled with novel information about within-species variation in growth behaviour that is inaccessible with species average models, such as individual parameters for the growth function which allows for relationships between parameters to be considered for the first time.

Integrated distribution models (IDMs) predict where species might occur using data from multiple sources, a technique thought to be especially useful when data from any individual source are scarce. Recent advances allow us to fit such models with latent terms to account for dependence within and between data sources, but they are computationally challenging to fit. We propose a fast new methodology for fitting integrated distribution models using presence/absence and presence‐only data, via a spatial random effects approach combined with automatic differentiation. We have written an R package (called scampr) for straightforward implementation of our approach. We use simulation to demonstrate that our approach has comparable performance to INLA—a common framework for fitting IDMs—but with computation times up to an order of magnitude faster. We also use simulation to look at when IDMs can be expected to outperform models fitted to a single data source, and find that the amount of benefit gained from using an IDM is a function of the relative amount of additional information available from incorporating a second data source into the model. We apply our method to predict 29 plant species in NSW, Australia, and find particular benefit in predictive performance when data from a single source are scarce and when compared to models for presence‐only data. Our faster methods of fitting IDMs make it feasible to more deeply explore the model space (e.g. comparing different ways to model latent terms), and in future work, to consider extensions to more complex models, for example the multi‐species setting.