Distinguishing between the nests of sympatric chimpanzees and gorillas.
Abstract
Our current inability to estimate precisely the population sizes of chimpanzees and gorillas across much of the Congo Basin has been detrimental to the development of conservation strategies for the preservation of these endangered apes. Systematic counts of nests are currently the most commonly used method to estimate ape abundance, but distinguishing between the nests of sympatric chimpanzees and gorillas has proven to be an enduring obstacle to estimating species-specific abundance. In general, the builder of more than 75% of nests recorded during surveys is undetermined. We hypothesized that sleeping habits and nest building patterns would allow us to differentiate between the nests of these apes. We constructed a predictive model using stepwise discriminant function analysis to determine characteristics that accurately distinguished between chimpanzee and gorilla nests. We analysed 13 variables associated with 3425 ape nests from three independent surveys conducted in the Goualougo Triangle of the Nouabalé-Ndoki National Park, Republic of Congo. The model correctly classified more than 90% of nests in our validation subsample. Nest height, nest type, forest type and understorey closure were identified as important variables for distinguishing between chimpanzee and gorilla nests at this site. Attributing nests to either species increased the precision of resulting density estimates, which enhanced the statistical power to detect trends in population fluctuation. Although specific variables may differ between study sites, we have demonstrated that predictive models to distinguish between the nests of sympatric chimpanzee and gorillas provide a promising approach to improving the quality of ape survey data. Synthesis and applications. Our study introduces an innovative solution to the dilemma of discriminating between the nests of sympatric chimpanzees and gorillas, which increases the specificity and precision of resulting ape abundance estimates. There is an urgent need to improve methods to evaluate and monitor remaining ape populations across western and central Africa that are experiencing the imminent threats of emergent diseases, poaching and expanding human development. Increasing the quality of density estimates from field survey data will aid in the development of local conservation initiatives, national strategies and international policies on behalf of remaining ape populations.