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Title: Estimating abundance of African great apes
Author: Howe, Eric J.
ISNI:       0000 0004 8502 4692
Awarding Body: University of St Andrews
Current Institution: University of St Andrews
Date of Award: 2019
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All species and subspecies of African great apes are listed by the International Union for the Conservation of Nature as endangered or critically endangered, and populations continue to decline. As human populations and industry expand into great ape habitat, efficient, reliable estimators of great ape abundance are needed to inform conservation status and land-use planning, to assess adverse and beneficial effects of human activities, and to help funding agencies and donors make informed and efficient contributions. Fortunately, technological advances have improved our ability to sample great apes remotely, and new statistical methods for estimating abundance are constantly in development. Following a brief general introduction, this thesis reviews established and emerging approaches to estimating great ape abundance, then describes new methods for estimating animal density from photographic data by distance sampling with camera traps, and for selecting among models of the distance sampling detection function when distance data are overdispersed. Subsequent chapters quantify the effect of violating the assumption of demographic closure when estimating abundance using spatially explicit capture-recapture models for closed populations, and describe the design and implementation of a camera trapping survey of chimpanzees at the landscape scale in Kibale National Park, Uganda. The new methods developed have generated considerable interest, and allow abundances of multiple species, including great apes, to be estimated from data collected during a single photographic survey. Spatially explicit capture-recapture analyses of photographic data from small study areas yielded accurate and precise estimates of chimpanzee abundance, and this combination of methods could be used to enumerate great apes over large areas and in dense forests more reliably and efficiently than previously possible.
Supervisor: Buckland, Stephen T. ; Borchers, D. L. ; Kühl, Hjalmar Sponsor: Max-Planck-Institut für Evolutionäre Anthropologie ; University of St Andrews
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
Keywords: Camera trapping ; Distance sampling ; Spatially explicit capture-recapture ; Model selection ; Chimpanzee ; Pan troglodytes ; QL737.P94H7 ; Mammal populations--Estimates ; Hominids--Africa ; Sampling (Statistics)