Application of Ecological Niche Models in Wildlife Disease Surveillance

Wildlife populations serve as primary reservoirs for many emerging and endemic infectious agents, yet surveillance of pathogens in natural systems remains notoriously difficult, posing significant challenges for traditional statistical frameworks. Conventional wildlife health surveillance is often constrained by irregular sampling, remote geography, and sparse data. To overcome these limitations, Ecological Niche Modeling (ENM) has emerged as an essential computational framework. By integrating wildlife occurrence and infection data with high-resolution bioclimatic, landscape, and anthropogenic variables, ENM can elucidate the environmental drivers that constrain pathogen transmission, providing a rigorous basis for mapping disease risk and predicting its geographic shifts in wild reservoirs.This Research Topic invites high-impact original research, methodological frameworks, and systematic reviews that advance the application of ENM to wildlife disease surveillance. We welcome submissions that leverage advanced spatial ecology and machine learning to move beyond descriptive mapping toward predictive models capable of guiding the allocation of intervention resources. Key priority areas include:Host-pathogen-environment interactions: deploying models that disentangle how environmental gradients alter wildlife reservoir densities, behavioural ecology, and pathogen shedding dynamics.Methodological rigour in wildlife disease ecology: novel analytical approaches designed to mitigate systemic spatial sampling biases, spatial autocorrelation, and the severe data gaps inherent in wildlife disease data.Algorithmic innovation: leveraging interpretable machine learning architectures to decipher complex, non-linear interactions between multi-scale environmental predictors and wildlife hosts.Operational field translation: demonstrating how ENM analytical frameworks and their predictive spatial outputs can be optimised to guide active wildlife sampling designs, early-detection strategies, and conservation-aligned biosecurity policies.By merging advanced spatial ecology with wildlife disease epidemiology, this collection aims to catalyse a paradigm shift toward structured, predictive sampling in natural ecosystems. Consolidating these computational insights establishes the quantitative foundation needed to detect, map, and manage infectious threats within wildlife populations before they cross ecological boundaries into naïve populations. Topic editors: Moh A. Alkhamis, Nick Fountain-Jones Source: https://www.frontiersin.org/research-topics/83114/application-of-ecological-niche-models-in-wildlife-disease-surveillance