Continuous space species distribution models (SDMs) have a long-standing history as a valuable tool in ecological statistical analysis. Geostatistical and preferential models are both common models in ecology. Geostatistical models are employed when the process under study is independent of the sampling locations, while preferential models are employed when sampling locations are dependent on the process under study. But, what if we have both types of data collectd over the same process? Can we combine them? If so, how should we combine them? This study investigated the suitability of both geostatistical and preferential models, as well as a mixture model that accounts for the different sampling schemes. Results suggest that in general the preferential and mixture models have satisfactory and close results in most cases, while the geostatistical models presents systematically worse estimates at higher spatial complexity, smaller number of samples and lower proportion of completely random samples.

翻译：连续空间物种分布模型作为生态统计分析中的重要工具已有悠久历史。地质统计模型与优先模型均为生态学中的常见模型：当所研究的过程独立于采样位置时，采用地质统计模型；而当采样位置依赖于所研究过程时，则采用优先模型。然而，若在同一过程中同时收集到两种类型的数据，我们能否将其整合？如能整合，应如何实施？本研究探讨了地质统计模型、优先模型及一种考虑不同采样方案的混合模型的适用性。结果表明，在多数情况下优先模型与混合模型表现令人满意且结果相近，而地质统计模型在空间复杂度较高、样本量较小及完全随机样本比例较低时，系统性地呈现出更差的估计效果。