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Identifying hotspots of chimpanzee group activity from transect surveys in Taï National Park, Côte d'Ivoire

Published online by Cambridge University Press:  30 September 2011

Célestin Yao Kouakou*
Affiliation:
Unité de Formation et Recherches des Sciences de la Nature, Université d'Abobo-Adjamé, Abidjan Côte-d'Ivoire Centre Suisse de Recherches Scientifiques en Côte-d'Ivoire, Abidjan Côte-d'Ivoire Department of Primatology, Max Planck Institute for Evolutionary Anthropology, LeipzigGermany Wild Chimpanzee Foundation, Abidjan Côte-d'Ivoire
Christophe Boesch
Affiliation:
Department of Primatology, Max Planck Institute for Evolutionary Anthropology, LeipzigGermany Wild Chimpanzee Foundation, Abidjan Côte-d'Ivoire
Hjalmar S. Kuehl
Affiliation:
Department of Primatology, Max Planck Institute for Evolutionary Anthropology, LeipzigGermany
*
1Corresponding author. Email: [email protected] or [email protected]

Abstract:

Locating activity hotspots – areas of higher density, more intense use, or distinct social units – is a prerequisite for answering many questions in animal ecology. However, for many species, carrying out such research from direct observations in tropical habitat is time-consuming and unrealistic for non-habituated animals. This study aimed to locate chimpanzee home ranges from transect nest counts. For validation purposes, 233 line transects were sampled within the home ranges of four habituated social groups of chimpanzees in Taï National Park, Côte d'Ivoire. In total, 373 km of transects were surveyed over 188 days and 683 nests of chimpanzee were recorded. First, we characterized heterogeneity of nest distribution patterns, including variation in density and group size within the area. Second, we used scan statistics, a likelihood-based cluster technique to locate chimpanzee social groups and compared them with the known home range boundaries. Chimpanzee nest distribution was characterized by a positive density and group-size gradient away from the range periphery. Furthermore, nest distribution clusters corresponding to the four groups could be successfully identified, although additional clusters for, for example, low-density areas between social groups seem to be an unavoidable by-product. The approach taken can be extended to a wide spectrum of data stemming from direct observations, camera traps, acoustic or genetic sampling to derive information about structure and patchiness of wild animal populations.

Type
Research Article
Copyright
Copyright © Cambridge University Press 2011

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