Greater Galagos near Mt. Kasigau, Kenya: Population Density Estimates

<p>This study examined population density and habitat use of a species of greater galago, genus <em>Otolemur</em>, around Mt. Kasigau, Kenya. Mt. Kasigau has a unique regional microclimate, a cloud forest, which provides many different flora and fauna a home. To examine population density, two different methods were used. The first method was using line transects and analyzing results using Distance 6.0. These surveys were conducted during both dry and wet season and results were compared after the study. The second method was to trap individuals and use mark-recapture to determine population density. When caught in a trap, individuals were also weighed and measurements were taken. Accurate weight was taken for 17 individuals; ear height, tail length, and hind foot length were recorded for 21 individuals. Mark-recapture data were analyzed using Krebs/WIN 3.0. Population density estimates using both methods were compared and provided similar results; Distance 6.0 estimated 0.62 individuals per hectare and Krebs/WIN 3.0 estimated 0.51 individuals per hectare. Morphological measurements were not consistent with published data of known species of <em>Otolemur garnettii</em>, which is the most common greater galago in East Africa. It is possible the Mt. Kasigau population is reproductively isolated and DNA analysis should be conducted in the future. Habitat usage of greater and lesser galagos and diurnal primates was examined to determine which tree species are commonly used by these individuals.</p>

Name thesis:degree_name

Master of Science

Discipline thesis:degree_discipline

Department of Biology

Year

2012

Author dc:creator

• Falcetto, Andrea

Contributors dc:contributor

• Dr. Michael Stokes (Director), Dr. Bruce Schulte, Dr. Jerry Daday

• otolemur
• bushbaby
• distance sampling
• diurnal primates
• Biology
• Population Biology

Repository record dc:identifier

https://digitalcommons.wku.edu/theses/1187

OAI identifier oai:identifier

oai:digitalcommons.wku.edu:theses-2189