Biology subjects

Redding, D. W.

Publications and source records attributed to Redding, D. W..

5 recordsLinked to original sources

bioRxiv2026

Towards the automation of Species Distribution Modelling: The AutoMaxEnt routine

Species distribution models are widely used for the evaluation and analysis of species spatial distributions and responses to environmental changes. Due to this, there's a wide diversity of tools and algorithms to perform these types of analyses. However, despite being a mature field of study, SDM still requires a large amount of research intervention and decision-making to produce reliable models. This is a natural consequence of each species unique response to environmental factors, but in practice it hinders the potential of SDMs to be used effectively and with confidence across large sets of species. Here we present the AutoMaxEnt repository, a collection of functions and tools for the automation of maximum entropy species distribution models. AutoMaxEnt offers a flexible environment to model species distribution models, automating most of the data pre-processing and preparation and giving the user the possibility of exploring multiple model scenarios automatically or with control over how those scenarios are generated. AutoMaxent offers model fit control, multiple background points generation algorithms, automatic model and variable selection, and study area configuration, among other parameters. AutoMaxEnt allows for the automation of model fitting, evaluation and model selection across different species, study areas and time periods.

ecology↗

bioRxiv2026

Viral reservoir status in small mammals emerges as a predictable life-history trait after correcting for surveillance bias

Small mammals, particularly rodents and shrews, act as primary reservoirs for Arenaviruses and Hantaviruses, zoonotic pathogens causing substantial global morbidity. However, our understanding of reservoir ecology is obscured by biased surveillance efforts, where sampling preferentially targets synanthropic species and high-income regions. It remains unclear whether observed patterns of reservoir competence, such as the association with synanthropy, are biological realities or artefacts of surveillance bias. We conducted a systematic review and data synthesis of global surveillance efforts (1960-2023), creating a harmonised database of over 590,000 recorded small mammals contributing 716,000 assay results. We then integrated this with macroecological trait data and phylogenetics to model reservoir probability using Bayesian phylogenetic dyadic generalised linear mixed models. We identified substantial taxonomic and geographic biases; surveillance is heavily skewed towards the Palearctic and widespread, large-bodied species, while 46% of host genera remain entirely unsampled. Geographically, surveillance intensity correlates strongly with accessibility and night-light intensity rather than host biodiversity. After statistically correcting for historical sampling volume, we demonstrate that reservoir status is a predictable biological trait. A fast pace of life (e.g., early maturity, large litters) is associated with an increased probability of reservoir status, independent of sampling effort. Synanthropy also remains a strong, independent predictor, indicating that commensal species act as genuine biological amplifiers in modified landscapes. Evolutionary analyses reveal a mosaic of broad lineage-level co-divergence punctuated by frequent, reactive host-switching. By projecting these models globally, we demonstrate that anthropogenic disturbance acts as an ecological filter, fundamentally challenging the assumption that pristine tropical ecosystems represent the highest intrinsic hazard for viral emergence.

ecology↗

bioRxiv2025

Protocol for the production of an Arenavirus and Hantavirus host-pathogen database: Project ArHa.

1Arenaviruses and Hantaviruses, primarily hosted by rodents and shrews, represent significant public health threats due to their potential for zoonotic spillover into human populations. Despite their global distribution, the full impact of these viruses on human health remains poorly understood, particularly in regions like Africa, where data is sparse. Both virus families continue to emerge, with pathogen evolution and spillover driven by anthropogenic factors such as land use change, climate change, and biodiversity loss. Recent research highlights the complex interactions between ecological dynamics, host species, and environmental factors in shaping the risk of pathogen transmission and spillover. This underscores the need for integrated ecological and genomic approaches to better understand these zoonotic diseases. A comprehensive, spatially and temporally explicit dataset, incorporating host-pathogen dynamics and human disease data, is crucial for improving risk assessments, enhancing disease surveillance, and guiding public health interventions. Such a dataset (ArHa) would also support predictive modelling efforts aimed at mitigating future spillover events. This paper proposes the development of this unified database for small-mammal hosts of Arenaviruses and Hantaviruses, identifying gaps in current research and promoting a more comprehensive understanding of pathogen prevalence, spillover risk, and viral evolution. 2 Strengths and Limitations of this studyO_LIThis dataset combines detailed spatial and temporal information, providing a unique resource for understanding geographic and temporal trends in Arenavirus and Hantavirus host-pathogen relationships. C_LIO_LIBy explicitly quantifying sampling biases and detection efforts, the dataset allows more robust and accurate asssessments of pathogen prevalence and distribution. C_LIO_LIThe dataset offers a platform for linking ecological data with human health outcomes, supporting the identification of spillover hotspots. C_LIO_LIThe dataset relies on published material, which may vary in terms of detail, accuracy and completeness. Missing or imprecise information may limit the reliability of subsequent analyses. C_LIO_LIThe dataset will be produced as a static resource which could limit its relevance over time as emerging data will not be added. C_LI

microbiology↗

bioRxiv2017

Evaluating Bayesian spatial methods for modelling species distributions with clumped and restricted occurrence data

1. Statistical approaches for inferring the spatial distribution of taxa (Species Distribution Models, SDMs) commonly rely on available occurrence data, which is often non-randomly distributed and geographically restricted. Although available SDM methods address some of these problems, the errors could be more directly and accurately modelled using a spatially-explicit approach. Software to implement spatial autocorrelation terms into SDMs are now widely available, but whether such approaches for inferring SDMs are an improvement over existing methodologies is unknown.\n\n2. Here, within a simulated environment using 1000 generated species ranges, we compared the performance of two commonly used non-spatial SDM methods (Maximum Entropy Modelling, MAXENT and Boosted Regression Trees, BRT) to a spatially-explicit Bayesian SDM method (Integrated Laplace Approximation, INLA), when the underlying data exhibit varying combinations of clumping and geographic restriction. Finally, we tested whether any recommended methodological settings for all methods were further impacted by spatially non-random patterns in these data.\n\n3. Spatially-explicit INLA was the most consistently accurate method, being most or equal most accurate in 5 out of 8 data sampling scenarios. Within high-coverage sample datasets, all methods performed fairly similarly, but when sampling points were randomly spread BRT had a 1-3% greater accuracy over the other methods and when samples were clumped, spatial-INLA had a 4%-8% better in AUC score. Alternatively, when sampling points were restricted to a small section of the true range, all methods were on average 10-12% less accurate, with higher variation among the methods. None of the recommended settings for the different methods were found to be sensitive to clumping or restriction of data, except the complexity of the INLA spatial term.\n\n4. INLA-based modelling approaches can be successfully used to account for spatial autocorrelation in an SDM context and, by taking account of random effects, produce outputs that can better elucidate the role of covariates in predicting species occurrence. Given that it is often unclear what the drivers are behind data clumping in an empirical occurrence dataset, or indeed how geographically restricted these data are, spatially-explicit INLA-based SDMs may be the better choice when modelling the spatial distribution of target species.

ecology↗

bioRxiv2016

The Global Avian Invasions Atlas - A database of alien bird distributions worldwide

The introduction of species to locations where they do not naturally occur (termed aliens) can have far-reaching and unpredictable environmental and economic consequences. Therefore there is a strong incentive to stem the tide of alien species introduction and spread. In order to identify broad patterns and processes of alien invasions, a spatially referenced, global dataset on the historical introductions and alien distributions of a complete taxonomic group is required.\n\nHere we present the Global Avian Invasions Atlas (GAVIA) - a new spatial and temporal dataset comprising 27,723 distribution records for 971 alien bird species introduced to 230 countries and administrative areas spanning the period 6000BCE - AD2014. GAVIA was initiated to provide a unified database of records on alien bird introductions, incorporating records from all stages of invasion, including introductions that have failed as well as those that have succeeded. GAVIA represents the most comprehensive resource on the global distribution of alien species in any major taxon, allowing the spatial and temporal dynamics of alien bird distributions to be examined.

ecology↗