Spatial conservation planning: Proposing clustering methods to improve connectivity protection
Abstract Spatial conservation prioritization is traditionally focusing on ensuring the representation of species populations and habitats within protected areas. Recently there has been an increased interest in incorporating connectivity into planning, with higher priority given to areas exhibiting...
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| Format: | Article |
| Language: | English |
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Wiley
2025-02-01
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| Series: | Methods in Ecology and Evolution |
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| Online Access: | https://doi.org/10.1111/2041-210X.14459 |
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| author | Nikolaos Nagkoulis Maria Papazekou Stelios Katsanevakis Antonios Mazaris |
| author_facet | Nikolaos Nagkoulis Maria Papazekou Stelios Katsanevakis Antonios Mazaris |
| author_sort | Nikolaos Nagkoulis |
| collection | DOAJ |
| description | Abstract Spatial conservation prioritization is traditionally focusing on ensuring the representation of species populations and habitats within protected areas. Recently there has been an increased interest in incorporating connectivity into planning, with higher priority given to areas exhibiting strong ecological linkages. We introduce three metrics (s‐core, Louvain clustering, walktrap clustering) that allow us to improve the spatial prioritization process by protecting areas that present high connectivity values. Instead of prioritizing unique planning units (PUs), by incorporating these metrics into spatial prioritization process we manage to identify clusters of PUs that collectively exhibit high connectivity values. This way we account for properties of connectivity structure (i.e. densely connected sites) into final detection of areas of high conservation interest. We evaluated the efficacy of these metrics in safeguarding ecological connectivity. The proposed metrics result in up to 25% higher connectivity values compared with the scenario in which no connectivity metrics are used. The results of the connectivity metrics were compared to the results obtained from other classic graph‐theoretic centrality metrics (degree, betweenness centrality, Eigenvector centrality, page rank) highlighting their potential to enhance performance across various spatial contexts. The proposed metrics can utilize existing connectivity data, such as edge lists, and their application can be tailored to address diverse conservation priorities. Overall, by illustrating the clustering properties of the connectivity datasets, the proposed metrics can introduce new approaches to improve the integration of ecological connectivity in conservation prioritization. |
| format | Article |
| id | doaj-art-ac12adf273d94248818e49a1b69d1007 |
| institution | Kabale University |
| issn | 2041-210X |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Wiley |
| record_format | Article |
| series | Methods in Ecology and Evolution |
| spelling | doaj-art-ac12adf273d94248818e49a1b69d10072025-02-05T05:43:20ZengWileyMethods in Ecology and Evolution2041-210X2025-02-0116237738710.1111/2041-210X.14459Spatial conservation planning: Proposing clustering methods to improve connectivity protectionNikolaos Nagkoulis0Maria Papazekou1Stelios Katsanevakis2Antonios Mazaris3Department of Marine Sciences University of the Aegean Mytilene GreeceDepartment of Ecology, School of Biology Aristotle University of Thessaloniki Thessaloniki GreeceDepartment of Marine Sciences University of the Aegean Mytilene GreeceDepartment of Ecology, School of Biology Aristotle University of Thessaloniki Thessaloniki GreeceAbstract Spatial conservation prioritization is traditionally focusing on ensuring the representation of species populations and habitats within protected areas. Recently there has been an increased interest in incorporating connectivity into planning, with higher priority given to areas exhibiting strong ecological linkages. We introduce three metrics (s‐core, Louvain clustering, walktrap clustering) that allow us to improve the spatial prioritization process by protecting areas that present high connectivity values. Instead of prioritizing unique planning units (PUs), by incorporating these metrics into spatial prioritization process we manage to identify clusters of PUs that collectively exhibit high connectivity values. This way we account for properties of connectivity structure (i.e. densely connected sites) into final detection of areas of high conservation interest. We evaluated the efficacy of these metrics in safeguarding ecological connectivity. The proposed metrics result in up to 25% higher connectivity values compared with the scenario in which no connectivity metrics are used. The results of the connectivity metrics were compared to the results obtained from other classic graph‐theoretic centrality metrics (degree, betweenness centrality, Eigenvector centrality, page rank) highlighting their potential to enhance performance across various spatial contexts. The proposed metrics can utilize existing connectivity data, such as edge lists, and their application can be tailored to address diverse conservation priorities. Overall, by illustrating the clustering properties of the connectivity datasets, the proposed metrics can introduce new approaches to improve the integration of ecological connectivity in conservation prioritization.https://doi.org/10.1111/2041-210X.14459clusteringMarxanneighbourhoodsprioritizRspatial optimization |
| spellingShingle | Nikolaos Nagkoulis Maria Papazekou Stelios Katsanevakis Antonios Mazaris Spatial conservation planning: Proposing clustering methods to improve connectivity protection Methods in Ecology and Evolution clustering Marxan neighbourhoods prioritizR spatial optimization |
| title | Spatial conservation planning: Proposing clustering methods to improve connectivity protection |
| title_full | Spatial conservation planning: Proposing clustering methods to improve connectivity protection |
| title_fullStr | Spatial conservation planning: Proposing clustering methods to improve connectivity protection |
| title_full_unstemmed | Spatial conservation planning: Proposing clustering methods to improve connectivity protection |
| title_short | Spatial conservation planning: Proposing clustering methods to improve connectivity protection |
| title_sort | spatial conservation planning proposing clustering methods to improve connectivity protection |
| topic | clustering Marxan neighbourhoods prioritizR spatial optimization |
| url | https://doi.org/10.1111/2041-210X.14459 |
| work_keys_str_mv | AT nikolaosnagkoulis spatialconservationplanningproposingclusteringmethodstoimproveconnectivityprotection AT mariapapazekou spatialconservationplanningproposingclusteringmethodstoimproveconnectivityprotection AT stelioskatsanevakis spatialconservationplanningproposingclusteringmethodstoimproveconnectivityprotection AT antoniosmazaris spatialconservationplanningproposingclusteringmethodstoimproveconnectivityprotection |