Coastal oceanographic connectivity at the global scale: a dataset of pairwise probabilities and travel times derived from biophysical modeling
Abstract Ocean currents are fundamental drivers of marine biodiversity distribution, mediating the exchange of genetic material and individuals between populations. Their effect ranges from creating barriers that foster isolation to facilitating long-distance dispersal, which is crucial for species...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-05-01
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| Series: | Scientific Data |
| Online Access: | https://doi.org/10.1038/s41597-025-05060-2 |
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| author | Jorge Assis Eliza Fragkopoulou Ester A. Serrão Miguel B. Araújo |
| author_facet | Jorge Assis Eliza Fragkopoulou Ester A. Serrão Miguel B. Araújo |
| author_sort | Jorge Assis |
| collection | DOAJ |
| description | Abstract Ocean currents are fundamental drivers of marine biodiversity distribution, mediating the exchange of genetic material and individuals between populations. Their effect ranges from creating barriers that foster isolation to facilitating long-distance dispersal, which is crucial for species expansion and resilience in the face of climate change. Despite the significance of oceanographic connectivity, comprehensive global estimates remain elusive, hindering our understanding of species’ dispersal ecology and limiting the development of effective conservation strategies. We present the first dataset of connectivity estimates (including probability of connectivity and travel time) along the world’s coastlines. The dataset is derived from Lagrangian simulations of passive dispersal driven by 21 years of ocean current data and can be combined with species’ biological traits, including seasonality and duration of planktonic dispersal stages. Alongside, we provide coastalNet, an R package designed to streamline access, analysis, and visualization of connectivity estimates. The dataset provides a new benchmark for research in oceanographic connectivity, enabling a deeper exploration of the complex dynamics of coastal marine ecosystems and informing more effective conservation strategies. |
| format | Article |
| id | doaj-art-83897cc86638447faefc483f5b6cc2cb |
| institution | OA Journals |
| issn | 2052-4463 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Data |
| spelling | doaj-art-83897cc86638447faefc483f5b6cc2cb2025-08-20T01:47:33ZengNature PortfolioScientific Data2052-44632025-05-011211810.1038/s41597-025-05060-2Coastal oceanographic connectivity at the global scale: a dataset of pairwise probabilities and travel times derived from biophysical modelingJorge Assis0Eliza Fragkopoulou1Ester A. Serrão2Miguel B. Araújo3Centre of Marine Sciences (CCMAR/CIMAR LA), Universidade do AlgarveCentre of Marine Sciences (CCMAR/CIMAR LA), Universidade do AlgarveCentre of Marine Sciences (CCMAR/CIMAR LA), Universidade do AlgarveDepartment of Biogeography and Global Change, National Museum of Natural Sciences, Consejo Superior de Investigaciones Científicas (CSIC)Abstract Ocean currents are fundamental drivers of marine biodiversity distribution, mediating the exchange of genetic material and individuals between populations. Their effect ranges from creating barriers that foster isolation to facilitating long-distance dispersal, which is crucial for species expansion and resilience in the face of climate change. Despite the significance of oceanographic connectivity, comprehensive global estimates remain elusive, hindering our understanding of species’ dispersal ecology and limiting the development of effective conservation strategies. We present the first dataset of connectivity estimates (including probability of connectivity and travel time) along the world’s coastlines. The dataset is derived from Lagrangian simulations of passive dispersal driven by 21 years of ocean current data and can be combined with species’ biological traits, including seasonality and duration of planktonic dispersal stages. Alongside, we provide coastalNet, an R package designed to streamline access, analysis, and visualization of connectivity estimates. The dataset provides a new benchmark for research in oceanographic connectivity, enabling a deeper exploration of the complex dynamics of coastal marine ecosystems and informing more effective conservation strategies.https://doi.org/10.1038/s41597-025-05060-2 |
| spellingShingle | Jorge Assis Eliza Fragkopoulou Ester A. Serrão Miguel B. Araújo Coastal oceanographic connectivity at the global scale: a dataset of pairwise probabilities and travel times derived from biophysical modeling Scientific Data |
| title | Coastal oceanographic connectivity at the global scale: a dataset of pairwise probabilities and travel times derived from biophysical modeling |
| title_full | Coastal oceanographic connectivity at the global scale: a dataset of pairwise probabilities and travel times derived from biophysical modeling |
| title_fullStr | Coastal oceanographic connectivity at the global scale: a dataset of pairwise probabilities and travel times derived from biophysical modeling |
| title_full_unstemmed | Coastal oceanographic connectivity at the global scale: a dataset of pairwise probabilities and travel times derived from biophysical modeling |
| title_short | Coastal oceanographic connectivity at the global scale: a dataset of pairwise probabilities and travel times derived from biophysical modeling |
| title_sort | coastal oceanographic connectivity at the global scale a dataset of pairwise probabilities and travel times derived from biophysical modeling |
| url | https://doi.org/10.1038/s41597-025-05060-2 |
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