Subpopulation range estimation for conservation planning: a case study of the critically endangered Cross River gorilla

Abstract

Measuring and characterizing the area utilized by a population or species is essential for assessment of conservation status and for effective allocation of habitat to ensure population persistence. Yet population-level range delineation is complicated by the variety of available techniques coupled with a lack of empirical methods to compare the relative value of these techniques. This study assesses the effect of model choice on resulting subpopulation range estimation for the critically endangered and patchily distributed Cross River gorilla, and evaluates the conservation conclusions that can be drawn from each model. Models considered range from basic traditional approaches (e.g. minimum convex polygon) to newer home range techniques such as local convex hull (LoCoH). Overlap analysis comparing sub-sampled to complete data sets are used to evaluate the robustness of various modeling techniques to data limitations. Likelihood cross validation criterion is employed to compare core range model performance. Results suggest that differing LoCoH models produce similar range estimates, are robust to data requirements, provide a good fit for core habitat estimation, and are best able to detect unused habitat within the subpopulation range. LoCoH methods may thus be useful for studies into habitat selection and factors limiting endangered species distributions. However, LoCoH models tend to over-fit data, and kernel methods may provide similar information about animal space use while supporting protection of larger swaths of critical habitat. Subpopulation range analyses for conservation/management planning should therefore explore multiple modeling techniques, and employ both qualitative and quantitative assessments to select the best models to inform decision making for species of conservation concern.