Department of Geo Informatics and Environment conservation

Wildlife persists in the Midlands Black Rhino Conservancy, Zimbabwe, but requires an emergency conservation plan

2026-06-16T10:50:37Z

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 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.

A comparison of home range estimates using the time local convex hull (T‐LoCoH) and minimum convex polygon (MCP) methods for African savannah elephants in a semi‐arid protected area

2026-05-12T10:02:13Z

A comparison of home range estimates using the time local convex hull (T‐LoCoH) and minimum convex polygon (MCP) methods for African savannah elephants in a semi‐arid protected area Mukomberanwa, Nobert T.; Taru, Phillip; Utete, Beaven; Ngorima, Patmore Knowledge of home ranges (HRs) helps conservationists understand movement patterns and can aid management including avoidance of human‐wildlife conflicts. This study examined the African savannah elephant seasonal HRs and space use using telemetry data in Mana Pools National Park, Zimbabwe. The objectives were to (i) compare the HR sizes and (ii) construct utilization distribution of African savannah elephants using the minimum convex polygon (MCP) method and the time‐local convex hull (T‐LoCoH). The results revealed that the dry, transitional, and wet season HR sizes estimated by the MCP method were significantly larger than those of the T‐LoCoH method. Significant differences were observed between core T‐LoCoH home‐range distributions for the wet, transition, and dry seasons. T‐LoCoH more accurately represented the HR size and nuances of repeated movements and internal spaces than the MCP method. The findings show larger‐scale movements in the transition season, which would enhance the potential for human–elephant conflicts.

Modelling Tree Allometries to Understand the Impact of African Savannah Elephant Herbivory Dynamics on the Vegetation Structure and Tree Cover Change in a Protected Area

2026-05-12T08:09:32Z

Modelling Tree Allometries to Understand the Impact of African Savannah Elephant Herbivory Dynamics on the Vegetation Structure and Tree Cover Change in a Protected Area Mukomberanwa, Nobert Tafadzwa; Taru, Phillip; Utete, Beaven; Ngorima, Patmore In landscapes with high elephant density, trees often exhibit more open canopies with fewer branches and foliage due to browsing pressure. This can result in altered tree morphology, with trees exhibiting stunted growth, multiple stems or unusual branching patterns in response to repeated damage from browsing. The objectives of this research were to (i) model the vegetation structure allometries, (ii) assess the impact of African savannah elephant (Loxodonta africana) herbivory on the vegetation structure and (iii) assess tree cover change and vegetation performance over time in Mana Pools National Park in Zimbabwe. We established 26 plots of 30 × 30 m size. Selection of sampling plots was done following several steps. First, a fish net grid with 30 × 30 m polygons was created and projected on the polygon of Mana Pools National Park. The polygons for exclusion zones were then clipped from the fish net grid using the clip tool in ArcGIS Pro 3.0. Then, selection of sampling plots was done initially by stratified random sampling using the Sampling Design Tool add in for ArcGIS Pro 3.0. Landsat images for the years 2003, 2013 and 2023 were used to assess land use land cover (LULC) time series and to calculate Normalised Difference Vegetation Index (NDVI) and Soil Adjusted Vegetation Index (SAVI) for the period. A generalised linear model (GLM) was used to analyse tree allometries. Further statistical investigations were performed using Bayesian piecewise regression (BPR) and Bayesian regression modelling (BRM). Basal area, number of stems, height, long canopy, diameter and basal circumference were all significantly different (p < 0.05) across all sampled plots. The change in growing conditions occurring as a tree grows beyond the reach of the African savannah elephant browsing indicates a natural system breakpoint. The best-fitting models were a simple linear model and a two breakpoint model for the plant population exposed to elephant herbivory. LULC, NDVI and SAVI confirm evidence of high tree regeneration over 2 decades. Understanding the dynamics in vegetation and LULC changes is critical for effective conservation and management of the habitats for African savannah elephants, as well as for maintaining the health and resilience of forest ecosystems.

Innovative Geographic Information Science (GIS) and Remote Sensing Tools for Modelling the Ranging Behaviour and Habitat Dynamics of the African Savannah Elephant (Loxodonta africana) in Mesic Protected Areas

2026-05-12T07:57:31Z

Innovative Geographic Information Science (GIS) and Remote Sensing Tools for Modelling the Ranging Behaviour and Habitat Dynamics of the African Savannah Elephant (Loxodonta africana) in Mesic Protected Areas Mukomberanwa, Nobert Tafadzwa; Taru, Phillip; Utete, Beaven; Ngorima, Patmore Transboundary wildlife species like the African savannah elephant (Loxodonta africana) requires a comprehensive regional approach to monitoring and effective conservation. This requires a thorough understanding of their ecology, ranging behaviour and the distribution of suitable habitats. In diverse landscapes, the management and conservation of the African savannah elephant are critical, particularly in dry protected areas where water and food resources are limited. The use of innovative Geographic Information Science (GIS) and remote sensing tools is revolutionising the understanding of the ranging behaviour and habitat dynamics of the African savannah elephant. When adopting GIS and remote sensing tools, park managers and conservationists must remember that: (i) the African savannah elephant has a determinate movement pattern and clusters around dominant vegetation types, (ii) the soil-adjusted vegetation index (SAVI) performs better relative to other indices in modelling the distribution of the African savannah elephant in arid areas, (iii) cellular automata–artificial neural network (CA-ANN) is a robust technique in modelling future landscapes, (iv) landscapes or environments near water points are significantly utilised by the African savannah elephant and vegetation performance is usually better far from the piosphere, (v) significant difference in the size of the home ranges and habitat selection by the African savannah elephant is mostly influenced by vegetation type and seasonal variations of resources, (vi) hyperslender stems in forest gaps confirms minimal damage in African savannah elephant dominated landscapes (satellite data confirms evidence of high tree regeneration) and (vii) the dynamic Brownian Bridge Movement Model (dBBMM) is a smart technique for home range and utilisation distribution construction in different protected zones.