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Operationalising connectivity in the spatial management of mangroves

James Cook University
Thompson Saud, Gabriela
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ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rfr_id=info%3Asid%2FANDS&rft_id=info:doi10.25903/a8y1-v626&rft.title=Operationalising connectivity in the spatial management of mangroves &rft.identifier=10.25903/a8y1-v626&rft.publisher=James Cook University&rft.description=Background: Mangroves form highly interconnected ecosystems, where natural rehabilitation often relies on propagule dispersal to recolonise degraded or fragmented areas where suitable hydrological and substrate conditions exist. Yet, dispersal and connectivity are rarely integrated in the spatial planning of mangroves due to limited data and a lack of operational guidance. Objective and methods: To address this gap, we developed and tested a decision support framework that integrates mangrove connectivity and condition to guide spatial management prioritisation. Using the central Great Barrier Reef, Queensland (Australia) as a case study, we simulated dispersal of two mangrove species: Rhizophora stylosa and Bruguiera gymnorhiza, using a biophysical model driven by oceanographic currents (SLIM) and parameterised using experimentally derived propagule viability and floating duration data (IBM). Network analysis was applied to the dispersal outputs to identify mangrove sub-population importance (centrality) and key dispersal pathways that sustain connectivity processes. Habitat patches defined by five connectivity-driven management objectives were then mapped and assessed alongside satellite-derived mangrove condition (NDVI) to generate an urgency index for spatial management prioritisation. Results: Our results identified critical areas of replenishment sources or connectivity corridors that overlapped with degraded mangrove habitats, highlighting these areas for targeted management. This framework demonstrates how explicitly incorporating connectivity into spatial planning can improve the identification of areas that may contribute to mangrove recovery and persistence under accelerating climate change and coastal development. This dataset consists of: - Two sets of .shp, including their associated files, containing the release locations of particles for Bruguiera and Rhizophora - Two .RData files containing adjacency matrices for each species Software/equipment used to manipulate/analyse the data: - R - Phyton - ArcGIS pro &rft.creator=Thompson Saud, Gabriela &rft.date=2026&rft.coverage=145.594153,-20.128155 145.594153,-16.804541 148.339978,-16.804541 148.339978,-20.128155 145.594153,-20.128155&rft.coverage=&rft_rights=&rft_rights=CC BY 4.0: Attribution 4.0 International http://creativecommons.org/licenses/by/4.0&rft_subject=Blue Carbon&rft_subject=Great Barrier Reef (GBR)&rft_subject=urgency index&rft_subject=spatial management&rft_subject=connectivity&rft_subject= Rhizophora stylosa &rft_subject=Bruguiera gymnorhiza&rft_subject=Plant biology not elsewhere classified&rft_subject=Plant biology&rft_subject=BIOLOGICAL SCIENCES&rft_subject=Environmental rehabilitation and restoration&rft_subject=Environmental management&rft_subject=ENVIRONMENTAL SCIENCES&rft_subject=Conservation and biodiversity&rft_subject=Assessment and management of coastal and estuarine ecosystems&rft_subject=Coastal and estuarine systems and management&rft_subject=ENVIRONMENTAL MANAGEMENT&rft.type=dataset&rft.language=English Access the data
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CC BY 4.0: Attribution 4.0 International
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Background: Mangroves form highly interconnected ecosystems, where natural rehabilitation often relies on propagule dispersal to recolonise degraded or fragmented areas where suitable hydrological and substrate conditions exist. Yet, dispersal and connectivity are rarely integrated in the spatial planning of mangroves due to limited data and a lack of operational guidance. Objective and methods: To address this gap, we developed and tested a decision support framework that integrates mangrove connectivity and condition to guide spatial management prioritisation. Using the central Great Barrier Reef, Queensland (Australia) as a case study, we simulated dispersal of two mangrove species: Rhizophora stylosa and Bruguiera gymnorhiza, using a biophysical model driven by oceanographic currents (SLIM) and parameterised using experimentally derived propagule viability and floating duration data (IBM). Network analysis was applied to the dispersal outputs to identify mangrove sub-population importance (centrality) and key dispersal pathways that sustain connectivity processes. Habitat patches defined by five connectivity-driven management objectives were then mapped and assessed alongside satellite-derived mangrove condition (NDVI) to generate an urgency index for spatial management prioritisation. Results: Our results identified critical areas of replenishment sources or connectivity corridors that overlapped with degraded mangrove habitats, highlighting these areas for targeted management. This framework demonstrates how explicitly incorporating connectivity into spatial planning can improve the identification of areas that may contribute to mangrove recovery and persistence under accelerating climate change and coastal development. This dataset consists of: - Two sets of .shp, including their associated files, containing the release locations of particles for Bruguiera and Rhizophora - Two .RData files containing adjacency matrices for each species Software/equipment used to manipulate/analyse the data: - R - Phyton - ArcGIS pro

Created: 03 11 2026

Data time period: 2015 to 31 12 2016

Data time period: 2015-2016

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145.59415,-20.12816 148.33998,-20.12816 148.33998,-16.80454 145.59415,-16.80454 145.59415,-20.12816

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Subjects
Rhizophora stylosa | Assessment and Management of Coastal and Estuarine Ecosystems | Biological Sciences | Blue Carbon | Bruguiera gymnorhiza | Coastal and Estuarine Systems and Management | Conservation and Biodiversity | Environmental Management | Environmental Sciences | Environmental Management | Environmental Rehabilitation and Restoration | Great Barrier Reef (GBR) | Plant Biology | Plant biology not elsewhere classified | connectivity | spatial management | urgency index |

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Identifiers
  • DOI : 10.25903/A8Y1-V626
  • Local : researchdata.jcu.edu.au//published/d6382050b06611efb083b9e4ac874acf
ACN 633 798 857