Spatial Joint Species N-Mixture Models for Multi-Source Observational Data with Application to Wild Deer Population Abundance
arxiv(2023)
摘要
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.
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