Integrated species distribution models: combining presence-background data and site-occupany data with imperfect detection

Two main sources of data for species distribution models (SDMs) are site-occupancy (SO) data from planned surveys, and presence-background (PB) data from opportunistic surveys and other sources. SO surveys give high quality data about presences and absences of the species in a particular area. However, due to their high cost, they often cover a smaller area relative to PB data, and are usually not representative of the geographic range of a species. In contrast, PB data is plentiful, covers a larger area, but is less reliable due to the lack of information on species absences, and is usually characterised by biased sampling. Here we present a new approach for species distribution modelling that integrates these two data types. We have used an inhomogeneous Poisson point process as the basis for constructing an integrated SDM that fits both PB and SO data simultaneously. It is the first implementation of an Integrated SO–PB Model which uses repeated survey occupancy data and also incorp...物种分布模型（Species Distribution Models, SDMs）的两大核心数据来源，分别为源自计划性调查的样地占用（Site-occupancy, SO）数据，以及源自机会性调查与其他渠道的存在-背景（Presence-background, PB）数据。样地占用调查可获取特定区域内物种种群存在与缺失的高质量观测数据。然而，受限于高昂的调查成本，相较存在-背景数据，其覆盖范围通常更为有限，且往往无法完整代表物种的地理分布范围。与之相对，存在-背景数据存量充沛、覆盖范围更广，但因缺乏物种缺失的相关信息，可靠性相对不足，且通常存在采样偏差问题。 本研究提出一种可整合上述两类数据的物种分布建模新方法。我们采用非齐次泊松点过程（inhomogeneous Poisson point process）作为构建集成模型的核心基础，该集成物种分布模型可同时适配存在-背景与样地占用两类数据。本模型是首个采用重复调查占用数据的集成样地占用-存在-背景模型（Integrated SO–PB Model）的实现方案，且可整合……