Modelling seasonal habitat suitability for wide-ranging species: Invasive wild pigs in northern Australia

Invasive wildlife often causes serious damage to the economy and agriculture as well as environmental, human and animal health. Habitat models can fill knowledge gaps about species distributions and assist planning to mitigate impacts. Yet, model accuracy and utility may be compromised by small study areas and limited integration of species ecology or temporal variability. Here we modelled seasonal habitat suitability for wild pigs, a widespread and harmful invader, in northern Australia. We developed a resource-based, spatially-explicit and regional-scale approach using Bayesian networks and spatial pattern suitability analysis. We integrated important ecological factors such as variability in environmental conditions, breeding requirements and home range movements. The habitat model was parameterized during a structured, iterative expert elicitation process and applied to a wet season and a dry season scenario. Model performance and uncertainty was evaluated against independent distributional data sets. Validation results showed that an expert-averaged model accurately predicted empirical wild pig presences in northern Australia for both seasonal scenarios. Model uncertainty was largely associated with different expert assumptions about wild pigs’ resource-seeking home range movements. Habitat suitability varied considerably between seasons, retracting to resource-abundant rainforest, wetland and agricultural refuge areas during the dry season and expanding widely into surrounding grassland floodplains, savanna woodlands and coastal shrubs during the wet season. Overall, our model suggested that suitable wild pig habitat is less widely available in northern Australia than previously thought. Mapped results may be used to quantify impacts, assess risks, justify management investments and target control activities. Our methods are applicable to other wide-ranging species, especially in data-poor situations.

入侵野生动物往往会对经济、农业以及生态环境、人类与动物健康造成严重损害。生境模型（habitat models）可填补物种分布相关的知识空白，辅助制定减缓其危害的规划方案。然而，研究区域过小、对物种生态学或时间变异性的整合不足，可能会降低模型的准确性与实用性。本研究针对澳大利亚北部分布广泛且危害严重的入侵物种——野猪，构建了季节性生境适宜性模型。本研究采用贝叶斯网络（Bayesian networks）与空间格局适宜性分析方法，构建了基于资源的、空间显式且区域尺度的建模框架，整合了多项关键生态学因素，包括环境条件变异、繁殖需求以及家域移动规律。该生境模型通过结构化、迭代式的专家征询流程完成参数化，并分别应用于湿季与干季的情景模拟。研究基于独立分布数据集对模型性能与不确定性进行了评估。验证结果表明，经专家平均后的模型可准确预测澳大利亚北部两种季节情景下野猪的实际分布点位。模型不确定性主要源于不同专家对野猪觅食型家域移动规律的假设差异。生境适宜性在季节间差异显著：干季时适宜区收缩至资源丰沛的雨林、湿地与农业庇护区域，湿季时则大幅扩张至周边的草原泛滥平原、稀树草原林地与沿海灌丛。总体而言，本模型显示澳大利亚北部适宜野猪生存的生境范围较此前认知更为狭窄。该制图结果可用于量化危害、评估风险、论证管理投入的合理性，并精准定位防控行动的实施区域。本研究的建模方法可推广应用于其他广布物种，尤其适用于数据匮乏的研究场景。