Species distribution models (SDMs) are usually based on a single data type, such as presence-only (PO), presence-absence (PA) or abundance (AA). Results from SDMs using single sources of data will suffer from inherent biases and limitations to that data type. For example, PO data contain sampling-bias and PA/AA data are often less expansive and more sparse. Integrated SDMs (ISDMs) combine multiple data types and have recently emerged as a way to leverage strengths and minimise weaknesses of the different data types. They pose a common (distribution) model and separate observation models for each of the data types. The ‘RISDM' package for the R environment (www.r-project.org) provides access to this modelling framework using functions for preparation, fitting, interpreting and diagnosing models. The functionality of the package is demonstrated here using synthetic data sets.

中文翻译：

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“RISDM”：R 中多个数据源的物种分布建模

物种分布模型 (SDM) 通常基于单一数据类型，例如仅存在 (PO)、存在-不存在 (PA) 或丰度 (AA)。使用单一数据源的 SDM 得出的结果将受到该数据类型固有的偏差和限制的影响。例如，PO 数据包含采样偏差，而 PA/AA 数据通常范围较小且更加稀疏。集成 SDM (ISDM) 结合了多种数据类型，最近作为一种利用不同数据类型的优势并最大限度地减少其劣势的方法而出现。他们为每种数据类型提出了一个通用（分布）模型和单独的观察模型。R 环境的“RISDM”包 (www.r-project.org) 提供了对该建模框架的访问，使用了准备、拟合、解释和诊断模型的功能。此处使用合成数据集演示了该包的功能。