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.

The Cross River Gorillas (Gorilla gorilla diehli) at Mawambi Hills, South-West Cameroon: Habitat Suitability and Vulnerability to Anthropogenic Disturbance

“…However, anthropogenic pressures on the remaining habitats driven by population growth and lack of alternative livelihood choices of communities living adjacent to CRG habitat continue unabated and represent a serious threat to the survival of these gorillas. While CRG remain relatively understudied when compared to other gorilla subspecies, new data are continually becoming available on population history, nesting ecology, habitat requirements and suitability, and on the spatial relationship between CRG and its habitat Mcfarland, 2007;Bergl et al, 2011;de Vere et al, 2011;Thalman et al, 2011;Sawyer, 2012]. The survival of CRG depends on how this valuable information can be quickly assimilated to reduce forest dependence of local communities, maintain habitat connectivity and eliminate the threat from hunting.…”

“…This information is needed by conservationists and wildlife officials to design site-specific strategies e.g. delineation of core areas [Gils and Kayijamahe, 2009;Sawyer 2012]. The MaxEnt modelling method provides a powerful analytical tool capable of predicting the potential distribution of ecological phenomena, such as suitable gorilla habitats, based on geo-referenced occurrence data [Phillips et al, 2006].…”

Evaluating methods for estimating home ranges using GPS collars: A comparison using proboscis monkeys (Nasalis larvatus)

“…The grid-cell method (GCM) is the simplest approach to estimating the utilisation distribution, in which a grid is superimposed over the area, and the number of times an animal enters each cell counted [17,18]. Other approaches based upon parametric kernel density estimators are also used (e.g.…”

“…Although GCM is useful in showing hot spots in utilisation patterns, its main disadvantage is in measuring overall home range size, as well as estimating range boundaries, i.e. barriers or ranges with complex boundaries [18]. Both GCM and parametric kernels are widely used throughout ecological studies, but the disadvantage of these approaches is that they are sensitive to the degree of smoothing (e.g.…”

“…In primatology, however, this issue has received scant consideration as home range studies are still in their infancy, with few GPS-based studies and the analysis often relying upon the GCM (e.g. [12,17,18,35]). Here, using a high quality GPS collaring data set collected from 10 proboscis monkeys ( Nasalis larvatus ) in northern Borneo, we aimed to: 1) compare home range estimates generated by the most commonly used estimator in primatology, the GCM, with three alternative methods designed for large and/or temporally correlated data sets (adaptive LoCoH, time LoCoH and BRB); 2) evaluate model performance with known physical barriers for a species which recurrently utilises forest edges; and 3) test which of the models is the most versatile and robust by simulating less intense sampling regimes resulting from technological limitations or failures.…”