Is the protected area coverage still relevant in protecting the Southern Ground-hornbill (Bucorvus leadbeateri) biological niche in Zimbabwe? Perspectives from ecological predictions
Examining the suitability of landscape patches for endangered species enhances critical insights and indicators into the processes of population structure, community dynamics, and functioning in ecosystems particularly in protected areas (PAs). While PAs are the cornerstone in biodiversity conservat...
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Bellwether Publishing
2021
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| Online Access: | https://www-tandfonline-com.access.library.msu.ac.zw/doi/pdf/10.1080/15481603.2021.1883947?needAccess=true http://hdl.handle.net/11408/4149 |
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| author | Mudereri, Bester Tawona Chitata, Tavengwa Chemura, Abel Makaure, Joseph Mukanga, Concilia Abdel-Rahman, Elfatih M. |
| author_facet | Mudereri, Bester Tawona Chitata, Tavengwa Chemura, Abel Makaure, Joseph Mukanga, Concilia Abdel-Rahman, Elfatih M. |
| author_sort | Mudereri, Bester Tawona |
| collection | DSpace |
| description | Examining the suitability of landscape patches for endangered species enhances critical insights and indicators into the processes of population structure, community dynamics, and functioning in ecosystems particularly in protected areas (PAs). While PAs are the cornerstone in biodiversity conservation, there is debate on their efficacy to retain their conservation superiority over unprotected areas under climate change. In the present study, we examined the spatial and temporal effectiveness of PAs at maintaining suitable habitat for the “vulnerable” Southern Ground-hornbill (SGH), Bucorvus leadbeateri compared with the unprotected areas in Zimbabwe. We used a landscape-scale analysis of 182 PAs, their surrounding buffer zones, and unprotected areas coupled with three machine learning models (maximum entropy: MaxEnt, random forest, and support vector machines) to simulate SGH habitat suitability. Bioclimatic, vegetation seasonality
and terrain variables were used as predictors against SGH “presence-only” observations and the models were projected for 2050 as future climatic scenarios (i.e. representative concentration pathways: RCP2.6 and RCP8.5). The true skill statistic (TSS) and area under the curve (AUC) were used to evaluate the performance of the modeling framework. Our results show that the PAs
network in Zimbabwe is extremely relevant for the conservation of SGH, with 8% of the suitable habitat within PAs projected to become unsuitable by 2050. Higher levels of protection status resulted in higher levels of suitable habitat for the SGH while the suitability of eastern-based PAs showed a decrease and the western-based PAs will potentially increase in suitability. Thus, conservation strategies should take the eastern PAs range contraction and associated westward shift
into account. The established potential increase in suitability outside the PAs network (23%–31%) might increase conflicts between agriculture and conservation. We, therefore, suggest an expanded cross-boundary institutional alliance and policy development with all stakeholders to implement a holistic conservation plan. Our work demonstrates the importance of combining
multi-source remotely sensed data in predicting habitat suitability for endangered species such as the SGH as key indicators of biological conservation and PAs’ effectiveness. |
| format | Article |
| id | ir-11408-4149 |
| institution | My University |
| language | English |
| publishDate | 2021 |
| publisher | Bellwether Publishing |
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| spelling | ir-11408-41492022-06-27T13:49:06Z Is the protected area coverage still relevant in protecting the Southern Ground-hornbill (Bucorvus leadbeateri) biological niche in Zimbabwe? Perspectives from ecological predictions Mudereri, Bester Tawona Chitata, Tavengwa Chemura, Abel Makaure, Joseph Mukanga, Concilia Abdel-Rahman, Elfatih M. Biogeography Climate change Ecological niche Landscape conservation Machine learning Protected area Examining the suitability of landscape patches for endangered species enhances critical insights and indicators into the processes of population structure, community dynamics, and functioning in ecosystems particularly in protected areas (PAs). While PAs are the cornerstone in biodiversity conservation, there is debate on their efficacy to retain their conservation superiority over unprotected areas under climate change. In the present study, we examined the spatial and temporal effectiveness of PAs at maintaining suitable habitat for the “vulnerable” Southern Ground-hornbill (SGH), Bucorvus leadbeateri compared with the unprotected areas in Zimbabwe. We used a landscape-scale analysis of 182 PAs, their surrounding buffer zones, and unprotected areas coupled with three machine learning models (maximum entropy: MaxEnt, random forest, and support vector machines) to simulate SGH habitat suitability. Bioclimatic, vegetation seasonality and terrain variables were used as predictors against SGH “presence-only” observations and the models were projected for 2050 as future climatic scenarios (i.e. representative concentration pathways: RCP2.6 and RCP8.5). The true skill statistic (TSS) and area under the curve (AUC) were used to evaluate the performance of the modeling framework. Our results show that the PAs network in Zimbabwe is extremely relevant for the conservation of SGH, with 8% of the suitable habitat within PAs projected to become unsuitable by 2050. Higher levels of protection status resulted in higher levels of suitable habitat for the SGH while the suitability of eastern-based PAs showed a decrease and the western-based PAs will potentially increase in suitability. Thus, conservation strategies should take the eastern PAs range contraction and associated westward shift into account. The established potential increase in suitability outside the PAs network (23%–31%) might increase conflicts between agriculture and conservation. We, therefore, suggest an expanded cross-boundary institutional alliance and policy development with all stakeholders to implement a holistic conservation plan. Our work demonstrates the importance of combining multi-source remotely sensed data in predicting habitat suitability for endangered species such as the SGH as key indicators of biological conservation and PAs’ effectiveness. 2021-05-10T11:38:42Z 2021-05-10T11:38:42Z 2021 Article 1548-1603 https://www-tandfonline-com.access.library.msu.ac.zw/doi/pdf/10.1080/15481603.2021.1883947?needAccess=true http://hdl.handle.net/11408/4149 en GIScience and Remote Sensing, Vol. 58, No. 3: p. 405-424; open Bellwether Publishing |
| spellingShingle | Biogeography Climate change Ecological niche Landscape conservation Machine learning Protected area Mudereri, Bester Tawona Chitata, Tavengwa Chemura, Abel Makaure, Joseph Mukanga, Concilia Abdel-Rahman, Elfatih M. Is the protected area coverage still relevant in protecting the Southern Ground-hornbill (Bucorvus leadbeateri) biological niche in Zimbabwe? Perspectives from ecological predictions |
| title | Is the protected area coverage still relevant in protecting the Southern Ground-hornbill (Bucorvus leadbeateri) biological niche in Zimbabwe? Perspectives from ecological predictions |
| title_full | Is the protected area coverage still relevant in protecting the Southern Ground-hornbill (Bucorvus leadbeateri) biological niche in Zimbabwe? Perspectives from ecological predictions |
| title_fullStr | Is the protected area coverage still relevant in protecting the Southern Ground-hornbill (Bucorvus leadbeateri) biological niche in Zimbabwe? Perspectives from ecological predictions |
| title_full_unstemmed | Is the protected area coverage still relevant in protecting the Southern Ground-hornbill (Bucorvus leadbeateri) biological niche in Zimbabwe? Perspectives from ecological predictions |
| title_short | Is the protected area coverage still relevant in protecting the Southern Ground-hornbill (Bucorvus leadbeateri) biological niche in Zimbabwe? Perspectives from ecological predictions |
| title_sort | is the protected area coverage still relevant in protecting the southern ground-hornbill (bucorvus leadbeateri) biological niche in zimbabwe? perspectives from ecological predictions |
| topic | Biogeography Climate change Ecological niche Landscape conservation Machine learning Protected area |
| url | https://www-tandfonline-com.access.library.msu.ac.zw/doi/pdf/10.1080/15481603.2021.1883947?needAccess=true http://hdl.handle.net/11408/4149 |
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