Morelle, K. | Search bioRxiv

bioRxiv2025

How big is enough? Movement-informed zoning for African swine fever mitigation

O_LIAfrican swine fever (ASF) poses a serious threat to domestic pigs and wild boar populations. Wild boar can disperse the virus, making effective containment crucial. One of the main control strategies involves establishing restricted zones around detected cases, i.e., areas with temporary restrictions on access and hunting; however, determining the appropriate size of these zones remains a major challenge. C_LIO_LITo inform the size of restricted zones, we analyzed GPS data from 527 wild boar across 46 European study sites using a two-step approach combining first-passage time analysis and survival modelling to quantify the risk of wild boar leaving areas of different radii (i.e., spatial scales). We investigated how the risk of leaving varied over time and across environmental gradients. To go further, we used our model findings to develop an online application that generates predictive maps of optimal buffer sizes for ASF management at the European scale, based on a given risk threshold (the maximum acceptable probability that a wild boar leaves the area). C_LIO_LIWe found that the relationship between radius and the risk of leaving is negative exponential, and the risk of leaving increased over time, with a more rapid increase for smaller radii. Landscape homogeneity, terrain ruggedness and human impact increased the risk of leaving, with stronger effects at small scales. Contrary to other predictors, agricultural cover exerted a strong effect on risk of leaving over large spatial scales, especially when it was abundant. C_LIO_LIAcross Europe, a buffer radius of [~]8 km is likely sufficient around high-risk infection zones in most areas (considering an infectious period of 14 days and a risk threshold of 5%); however, in certain areas, a radius of up to 20 km may be needed to effectively limit wild boar movement. C_LIO_LISynthesis and applications: Our results highlight the need for adaptive, context-specific restricted zones. Buffers of 8 km around ASF-affected areas can limit the risk of infected wild boar dispersal, but they may be reduced to 5 km in highly heterogeneous landscapes or high-human impacted areas. Larger buffers may be required in agricultural landscapes. We provide spatially explicit outputs (optimal buffer sizes) that can directly inform policy and wildlife disease response strategies. The approach can be adapted to any other infectious disease. C_LI

ecology↗

bioRxiv2022

Bayesian species distribution models integrate presence-only and presence-absence data to predict deer distribution and relative abundance.

The use of georeferenced information on the presence of a species to predict its distribution across a geographic area is one of the most common tools in management and conservation. The collection of high-quality presence-absence data through structured surveys is, however, expensive, and managers usually have more abundant low-quality presence-only data collected by citizen scientists, opportunistic observations, and culling returns for game species. Integrated Species Distribution Models (ISDMs) have been developed to make the most of the data available by combining the higher-quality, but usually less abundant and more spatially restricted presence-absence data, with the lower quality, unstructured, but usually more extensive and abundant presence-only data. Joint-likelihood ISDMs can be run in a Bayesian context using INLA (Integrated Nested Laplace Approximation) methods that allow the addition of a spatially structured random effect to account for data spatial autocorrelation. These models, however, have only been applied to simulated data so far. Here, for the first time, we apply this approach to empirical data, using presence-absence and presence-only data for the three main deer species in Ireland: red, fallow and sika deer. We collated all deer data available for the past 15 years and fitted models predicting distribution and relative abundance at a 25 km2 resolution across the island. Models predictions were associated to spatial estimate of uncertainty, allowing us to assess the quality of the model and the effect that data scarcity has on the certainty of predictions. Furthermore, we validated the three species-specific models using independent deer hunting returns. Our work clearly demonstrates the applicability of spatially-explicit ISDMs to empirical data in a Bayesian context, providing a blueprint for managers to exploit unused and seemingly unusable data that can, when modelled with the proper tools, serve to inform management and conservation policies.

ecology↗