What factors affect species richness and distribution dynamics within two Afromontane protected areas?
Eustrate Uzabaho
A * , Charles Birasa Kayijamahe A , Abel Musana B , Prosper Uwingeli B , Christopher Masaba C , Madeleine Nyiratuza D and Jennifer Frances Moore E
+ Author Affiliations
- Author Affiliations
A International Gorilla Conservation Programme, P.O. Box 931, Kigali, Rwanda.
B Volcanoes National Park, Rwanda Development Board, P.O. Box 6239, Kigali, Rwanda.
C Mgahinga Gorilla National Park, Uganda Wildlife Authority, P.O. Box 3530, Kampala, Uganda.
D United Nations Development Programme, P.O. Box 445, Kigali, Rwanda.
E Moore Ecological Analysis and Management, LLC, 3142 NW 12th Street, Gainesville, FL 32609, USA.
Wildlife Research - https://doi.org/10.1071/WR21171
Submitted: 7 December 2021 Accepted: 13 September 2022 Published online: 21 October 2022
© 2022 The Author(s) (or their employer(s)). Published by CSIRO Publishing
Abstract
Context: Biodiversity monitoring programs provide information on the status and trends in wildlife populations. These trends are unknown for most mammals within African montane forests, which harbour many endemic and threatened species. Camera traps are useful for assessing mammal populations, because they allow for the estimation of species richness, occupancy, and activity patterns.
Aims: We sought to explore the richness and distribution of small- to large-sized mammals by using occupancy models while accounting for imperfect detection in Volcanoes and Mgahinga Gorilla National Parks, in Rwanda and Uganda.
Methods: We used camera-trap data collected from 2014 to 2017 by the Tropical Ecology Assessment and Monitoring (TEAM) network and multi-season occupancy models with multispecies data to assess the dynamics of species richness and distribution in the Virunga Massif and the influence of site covariates on species detection probability, occupancy, colonisation and extinction.
Key results: We identified 17 species from 7047 trap-days, with most of them showing an uneven distributional pattern throughout the park. We found that average species richness per site increased from five to seven species in 2017. Average local colonisation was estimated at 0.13 (s.e. 0.014), but the probability of local extinction was 0.17 (s.e. 0.028) and negatively influenced by distance from the park boundary. Detection probability was highest for medium-sized species. For species distribution, we found that black-fronted duiker, Cephalophus nigrifrons, and bushbuck, Tragelaphus scriptus, declined in distribution but remained widespread in our study area, while all other species showed an increase in distribution over the study period.
Conclusions: Our approach allowed us to draw inferences on rare species, such as African golden cat, Caracal aurata, by estimating detection probability on the basis of shared covariate information with more common, widespread species. As such, we were able to estimate all occupancy parameters across the terrestrial mammal community.
Implications: The results of this study on the distribution of terrestrial mammal species can be used by park management to inform optimal ranger patrolling efforts for mitigating threats in areas of high species presence. Additionally, the results can highlight locations of potential human–wildlife conflicts on the basis of species commonly found along the park boundary.
Keywords: camera-trapping, occupancy models, Rwanda, species distribution, species richness, terrestrial mammals, Uganda, Virunga Massif.
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