Land use drives differential resource selection by African elephants in the Greater Mara Ecosystem, Kenya

Jake Wall; Nathan Hahn; Sarah Carroll; Stephen Mwiu; Marc Goss; Wilson Sairowua; Kate Tiedeman; Sospeter Kiambi; Patrick Omondi; Iain Douglas-Hamilton; George Wittemyer

doi:10.1186/s40462-023-00436-8

Land use drives differential resource selection by African elephants in the Greater Mara Ecosystem, Kenya

Mov Ecol. 2024 Feb 1;12(1):11. doi: 10.1186/s40462-023-00436-8.

Authors

Jake Wall^{1

2}, Nathan Hahn³, Sarah Carroll³, Stephen Mwiu⁴, Marc Goss⁵, Wilson Sairowua⁵, Kate Tiedeman⁶, Sospeter Kiambi⁴, Patrick Omondi⁴, Iain Douglas-Hamilton⁷, George Wittemyer^{3

7}

Affiliations

¹ Mara Elephant Project, Nairobi, Kenya. jake@maraelephantproject.org.
² Colorado State University, Fort Collins, USA. jake@maraelephantproject.org.
³ Colorado State University, Fort Collins, USA.
⁴ Kenya Wildlife Research and Training Institute, Naivasha, Kenya.
⁵ Mara Elephant Project, Nairobi, Kenya.
⁶ Max Planck Institute of Animal Behavior, Constance, Germany.
⁷ Save the Elephants, Nairobi, Kenya.

Abstract

Understanding drivers of space use by African elephants is critical to their conservation and management, particularly given their large home-ranges, extensive resource requirements, ecological role as ecosystem engineers, involvement in human-elephant conflict and as a target species for ivory poaching. In this study we investigated resource selection by elephants inhabiting the Greater Mara Ecosystem in Southwestern Kenya in relation to three distinct but spatially contiguous management zones: (i) the government protected Maasai Mara National Reserve (ii) community-owned wildlife conservancies, and (iii) elephant range outside any formal wildlife protected area. We combined GPS tracking data from 49 elephants with spatial covariate information to compare elephant selection across these management zones using a hierarchical Bayesian framework, providing insight regarding how human activities structure elephant spatial behavior. We also contrasted differences in selection by zone across several data strata: sex, season and time-of-day. Our results showed that the strongest selection by elephants was for closed-canopy forest and the strongest avoidance was for open-cover, but that selection behavior varied significantly by management zone and selection for cover was accentuated in human-dominated areas. When contrasting selection parameters according to strata, variability in selection parameter values reduced along a protection gradient whereby elephants tended to behave more similarly (limited plasticity) in the human dominated, unprotected zone and more variably (greater plasticity) in the protected reserve. However, avoidance of slope was consistent across all zones. Differences in selection behavior was greatest between sexes, followed by time-of-day, then management zone and finally season (where seasonal selection showed the least differentiation of the contrasts assessed). By contrasting selection coefficients across strata, our analysis quantifies behavioural switching related to human presence and impact displayed by a cognitively advanced megaherbivore. Our study broadens the knowledge base about the movement ecology of African elephants and builds our capacity for both management and conservation.

Keywords: Bayesian; EarthRanger; Ecoscope; GPS tracking; Home-range; INLA; Land cover; Landscape dynamics; Loxodonta africana; Resource selection.