Evidence map›Paper›PMID 40439992›Full record

ArticleTropical animal health and production2025

Do foraging hotspots defined by key browse species facilitate interaction between livestock and wildlife?

Clarice Princess Mudzengi, Amon Murwira, Fadzai Zengeya, Tinyiko Halimani, Herve Fritz, Chrispen Murungweni

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Article in Tropical animal health and production, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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2 · The registry

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Clarice Princess MudzengiDepartment of Livestock, Wildlife and Fisheries, Great Zimbabwe University, P. Bag 1235, Masvingo, Zimbabwe. clarice.mudzengi@gmail.com.
Amon MurwiraDepartment of Geography, Geospatial Science and Earth Observation, University of Zimbabwe, Mount Pleasant, P. O Box MP 167, Harare, Zimbabwe.
Fadzai ZengeyaDepartment of Geography, Geospatial Science and Earth Observation, University of Zimbabwe, Mount Pleasant, P. O Box MP 167, Harare, Zimbabwe.
Tinyiko HalimaniDepartment of Animal Science, University of Zimbabwe, Mount Pleasant, P. O Box MP 167, Harare, Zimbabwe.
Herve FritzSustainability Research Unit, Nelson Mandela Metropolitan University, George, South Africa.
Chrispen MurungweniDepartment of Animal Production and Technology, School of Agricultural Sciences and Technology, Chinhoyi University of Technology, Pvt. Bag 7724, Chinhoyi, Zimbabwe.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Livestock based rural livelihoods in close proximity to conservation areas are often associated with human-wildlife conflict. A deeper comprehension of factors contributing to this can help develop strategies to resolve the impact of human-wildlife conflict on livestock production. We tested whether dry season livestock foraging hotspots were linked to key browse species (KBS), and whether they facilitated livestock-wildlife interactions. We detected livestock browsing hotspots, determined their patch characteristics, and established factors influencing their occurrence. We used kernel density estimation (KDE) and nearest neighbor hierarchical clustering (Nnh). The KDE is a non-parametric method that uses a density estimation technique, while the Nnh is a method used to group similar data points based on their distances. Both methods detected three hotspots comprising Colophospermum mopane and Dichrostachys cinerea mixed woodland, and D. cinerea vegetation. Spatial overlap between detected hotspots was relatively high (Szymkiewicz-Simpson coefficient (Overlap Coefficient) = 0.7; Minta Index = 0.6). Fine-scale habitat patch quantifications indicated that connectivity between D. cinerea patches was higher than D. cinerea and C. mopane patches (Connectivity Index = 60.0%; Patch Cohesion Index = 98.0%). Distance from water was the most significant factor (P < 0.05; AUC = 0.9; AICc = 59.1), explaining probability of occurrence of hotspots. Multiple-distance scale spatial cluster analysis showed significant spatial associations between elephants and livestock. Results suggest that KBS are concentrated around browsing hotspots, which could facilitate livestock-wildlife interactions. Improving livestock feed availability outside conservation areas, and spatial planning for grazing management and wildlife corridors could mitigate human-wildlife conflict. Overall, this study provides valuable insights into foraging behaviours at the livestock-wildlife interface, and underscores the need for sustainable management strategies to balance conservation and rural livelihoods, thereby improving coexistence.

Indexed as

Animals, WildFeeding BehaviorHerbivoryLivestockAnimalsConservation of Natural ResourcesEcosystemSeasonsForage resource gradientsLivestock productionLivestock-wildlife interactionsSustainabilityWildlife conservation

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.