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Landscape Connectivity Modelling for the African Savannah Elephant With Spatial Absorbing Markov Chain and Predicting the Regenerative Power of the Range in a Mesic Protected Area

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dc.contributor.author Mukomberanwa, Nobert Tafadzwa
dc.contributor.author Taru, Phillip
dc.contributor.author Utete, Beaven
dc.contributor.author Ngorima, Patmore
dc.contributor.author Madamombe, Honest Komborero
dc.date.accessioned 2026-05-07T07:39:25Z
dc.date.available 2026-05-07T07:39:25Z
dc.date.issued 2025-02-19
dc.identifier.citation Mukomberanwa, N. T., Taru, P., Utete, B., Ngorima, P., & Madamombe, H. K. (2025). Landscape connectivity modelling for the African Savannah elephant with Spatial absorbing Markov chain and predicting the regenerative power of the range in a mesic protected area. African Journal of Ecology, 63(2), e70034. en_US
dc.identifier.uri https://doi.org/10.1111/aje.70034
dc.identifier.uri https://ir.cut.ac.zw:8080/xmlui/handle/123456789/723
dc.description.abstract Landscape connectivity is a critical factor influencing the survival and ecological roles of large terrestrial herbivores within dynamic ecosystems. Yet, the increasing fragmentation of habitats due to human activities, such as agricultural expansion and infrastructure development, disrupts natural movement patterns and limits access to essential resources. This is particularly concerning in mesic protected areas, where moderate rainfall supports diverse vegetation but is often bordered by human-dominated landscapes. To address this challenge, the use of Spatial Absorbing Markov Chain (SAMC) provides a robust framework to simulate the African savannah elephant (Loxodonta africana) dispersal and identify critical connectivity nodes within fragmented landscapes. Additionally, assessing and understanding the regenerative potential of these landscapes is vital for evaluating their capacity to sustain wildlife populations and maintain ecological balance. The objectives of this study were to (i) model the ecological connectivity of Mana Pools National Park (MPNP) by assessing spatial and functional linkages among African savannah elephant herds and (ii) predict the regenerative potential of the park's range. We used multi-temporal satellite data (2003, 2013, and 2023), GPS collar data, road transects, and plot-based surveys. The study employed a cellular automata artificial neural network (CA-ANN) to forecast the regenerative potential of the range. Connectivity maps illuminated vital pathways that sustain the elephants' migratory and foraging behaviours, underscoring the holistic interplay of land cover, slope, and terrain in shaping movement patterns. The study identified core micro-corridors and broader sub-landscape linkages essential for maintaining the park's ecological vitality. This interconnectedness serves as a testament to the resilience and regenerative power of the semi-arid savannah. CA-ANN projections predicted a high landscape regenerative capacity by the year 2083. Highlighting diverse geographical priorities for connectivity conservation, the research advocates for integrated, multi-scale actions to preserve these vital linkages. Such insights are pivotal in nurturing the relational integrity of MPNP, ensuring its long-term viability as a sanctuary for elephants and other coexisting life forms. By integrating connectivity modelling and habitat regeneration predictions, this study advances conservation strategies. It highlights the importance of maintaining functional landscapes to preserve ecosystem resilience, enhance biodiversity, and mitigate human-wildlife conflicts in increasingly fragmented ecosystems. en_US
dc.language.iso en en_US
dc.publisher Willey en_US
dc.subject connectivity en_US
dc.subject corridors en_US
dc.subject Markov chain en_US
dc.subject resource selection functions en_US
dc.title Landscape Connectivity Modelling for the African Savannah Elephant With Spatial Absorbing Markov Chain and Predicting the Regenerative Power of the Range in a Mesic Protected Area en_US
dc.type Article en_US
dc.identifier.orcid 0009-0003-1896-9813 en_US
dc.identifier.orcid 0000-0001-5493-4421 en_US
dc.identifier.orcid 0000-0002-3135-8270 en_US
dc.identifier.orcid 0000-0003-2465-7720 en_US


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