Wildlife persists in the Midlands Black Rhino Conservancy, Zimbabwe, but requires an emergency conservation plan
Mukomberanwa, Nobert Tafadzwa; Chibura, Briliant Makuwe; Madamombe, Honest Komborero; Keche, Last; Muchenjekwa, Trevor; Tsuro, Diarson Ishmael; Murwadzi, Takudzwa Praisegod; Moyo, Blessings; Kadzere, Munyaradzi; Muredzi, Kelvin Charles; Gwara, Tadiwanashe Blessed; Diwa, Amon; Mutasa, Melody; Mukume, Triumph Mugove; Mudzimiri, Nichol; Muzari, Mutsawashe Tadiwanashe; Mudzanirwa, Osley; Mandonye, Wesley Tanatswa; Alison, Akasha Alice; Maradza, Innocent; Boys, Ellen; Madanhe, Nyasha Chelsea; Chinyanga, Brian; Nyamahumba, Tafadzwa; Mharakurwa, Brendon; Mugaviri, Mitchell Chido; Nyamadzawo, Active Farai Moses; Mukinya, Tinotenda Nyasha; Chigumira, Dexter Farai; Chikowero, Sarah Mudiwa; Chipfu, Trevor Tinashe; Mbarami, Mercy Joyline; Gwenzi, Michael; Guchu, Florence; Manyika, Kudakwashe; Zimunya, Mitchell Tendesai; Manyika, Munashe; Sopuka, Thembeka; Bangwayo, Andrew Takunda; Taruvinga, Charlotte Tadiwa; Mboto, Zvikomborero Samuel; Muchepa, Tsitsi Tamia; Muradzi, Makanaka; Gatsi, Isheanopa Pasco; Mapuranga, Courtney; Chirova, Munyaradzi; Bisenti, Olinda Samantha; Takaindisa, Archiford; Muradzikwa, Wellington; Dzambo, Fidelis Duncan; Parirewa, Maxwell; Taru, Taurai Allan; Thabeti, Jeremiah; Mandiyanike, Delight Panashe; Chirambasadza, Takudzwa
Date:
2026-05-02
Abstract:
The persistence of wildlife in the Midlands Black Rhino Conservancy (MBRC), Zimbabwe,
highlights both species resilience and landscape value, yet escalating anthropogenic
pressures demand urgent conservation action. This study aimed to: (i) model land use/
land cover (LULC) changes from 1985–2055 using multi-decadal Landsat imagery; (ii)
assess the frequency, distribution and impact of fires between 2010–2025; (iii) evaluate
vegetation disturbance from mining through a Bayesian framework; and (iv) determine
the status and abundance of key wildlife via systematic transect surveys. Future
scenarios were predicted using cellular automata–artificial neural networks (CA-ANN).
Fire regimes were analysed using Landsat, FIRMS and dNBR indices, while Bayesian
regression models quantified mining impacts. Species distribution was modelled with
MaxEnt. Results show shrinking suitable habitats, with many species increasingly
confined to fragmented populations. Despite these challenges, the findings underscore
opportunities for proactive biodiversity management through robust local and international
conservation policies.
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