Accurate predictions of the populations and spatial distributions of wild animal species is critical from a species management and conservation perspective. Culling is a measure taken for various reasons, including when overpopulation of a species is observed or suspected. Thus accurate estimates of population numbers are essential for specifying, monitoring, and evaluating the impact of such programmes. Population data for wild animals is generally collated from various sources and at differing spatial resolutions. Citizen science projects typically provide point referenced data, whereas site surveys, hunter reports, and official government data may be aggregated and released at a small area or regional level. Jointly modelling these data resources involves overcoming challenges of spatial misalignment. In this article, we develop an N mixture modelling methodology for joint modelling of species populations in the presence of spatially misaligned data, motivated by the three main species of wild deer in the Republic of Ireland; fallow, red and sika. Previous studies of deer populations investigated the distribution and abundance on a species by species basis, failing to account for possible correlation between individual species and the impact of ecological covariates on their distributions.

翻译：准确预测野生动物物种的种群数量及其空间分布，对于物种管理与保护至关重要。当观察到或怀疑某物种数量过剩时，常采取猎杀措施。因此，精确的种群数量估计对于制定、监测和评估此类计划的影响必不可少。野生动物种群数据通常来源于不同渠道且空间分辨率各异。公民科学项目多提供点参考数据，而实地调查、猎人报告及官方政府数据可能以小区或区域层面汇总发布。联合建模这些数据资源需克服空间错位带来的挑战。本文以爱尔兰共和国的三种主要野生鹿——黇鹿、马鹿和梅花鹿——为研究对象，开发了一种在空间错位数据存在条件下联合建模物种种群的N-混合模型方法。既往鹿类种群研究仅从单一物种角度分析其分布与丰度，未能考虑物种间潜在相关性及生态协变量对分布的影响。