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145 results for “marine protected areas”
FESOM-REcoM model data: Severe 21st-century ocean acidification in Antarctic Marine Protected Areas
<p>This repository contains all post-processed model output used in the paper "Severe 21st-century ocean acidification in Antarctic Marine Protected Areas". It contains the data underlying the figures in the paper, such as regional averages, as well as masks for the marine protected areas and the grid information file of the original model output.</p><p>The data were created using python scripts provided at <a href="https://doi.org/10.5281/zenodo.10295920">https://doi.org/10.5281/zenodo.10295920</a>. </p><p>Original model output, including full fields of computed pH and saturation states with respect to aragonite and calcite, is available at the World Data Center for Climate (WDCC) under the following DOIs:</p><ul><li>simA, historical: <a href="https://doi.org/10.26050/WDCC/FESOM14-REcoM2_A_hist_vA_vC">https://doi.org/10.26050/WDCC/FESOM14-REcoM2_A_hist_vA_vC</a></li><li>simA, ssp126: <a href="https://doi.org/10.26050/WDCC/FESOM14-REcoM2_A_s126_vA_vC">https://doi.org/10.26050/WDCC/FESOM14-REcoM2_A_s126_vA_vC</a></li><li>simA, ssp245: <a href="https://doi.org/10.26050/WDCC/FESOM14-REcoM2_A_s245_vA_vC">https://doi.org/10.26050/WDCC/FESOM14-REcoM2_A_s245_vA_vC</a></li><li>simA, ssp370: <a href="https://doi.org/10.26050/WDCC/FESOM14-REcoM2_A_s370_vA_vC">https://doi.org/10.26050/WDCC/FESOM14-REcoM2_A_s370_vA_vC</a></li><li>simA, ssp585: <a href="https://doi.org/10.26050/WDCC/FESOM14-REcoM2_A_s585_vA_vC">https://doi.org/10.26050/WDCC/FESOM14-REcoM2_A_s585_vA_vC</a></li><li>simB: <a href="https://doi.org/10.26050/WDCC/FESOM14-REcoM2_B_1921_cA_cC">https://doi.org/10.26050/WDCC/FESOM14-REcoM2_B_1921_cA_cC</a></li><li>simC, historical: <a href="https://doi.org/10.26050/WDCC/FESOM14-REcoM2_C_hist_vA_cC">https://doi.org/10.26050/WDCC/FESOM14-REcoM2_C_hist_vA_cC</a></li><li>simC, ssp245: <a href="https://doi.org/10.26050/WDCC/FESOM14-REcoM2_C_s245_vA_cC">https://doi.org/10.26050/WDCC/FESOM14-REcoM2_C_s245_vA_cC</a></li><li>simC, ssp585: <a href="https://doi.org/10.26050/WDCC/FESOM14-REcoM2_C_s585_vA_cC">https://doi.org/10.26050/WDCC/FESOM14-REcoM2_C_s585_vA_cC</a></li><li>simC, historical: <a href="https://doi.org/10.26050/WDCC/FESOM14-REcoM2_D_hist_cA_vC">https://doi.org/10.26050/WDCC/FESOM14-REcoM2_D_hist_cA_vC</a></li><li>simC, ssp585: <a href="https://doi.org/10.26050/WDCC/FESOM14-REcoM2_D_s585_cA_vC">https://doi.org/10.26050/WDCC/FESOM14-REcoM2_D_s585_cA_vC</a></li></ul><p> </p>
Shapefiles showing the locations of long-term climate change refugia and hotspots identified in the FairSeas report "A Climate Resilient Path for Ireland's Marine Protected Areas Network"
<p>Shapefiles created for the report "A Climate Resilient Path for Ireland’s Marine Protected Areas Network", an addendum chapter to "Revitalising Our Seas report: Identifying<br>Areas of Interest for Marine Protected Area Designation in Irish Waters"</p> <p>These shapefiles summarise long-term patterns that emerge from the spatial-meta analysis of physical-biogeochemical and species distribution modelling data, providing an overview of the distribution of climate change refugia and climate change hotspots across Ireland's National Marine Planning Framework between 2026 - 2069, and across the two emissions scenarios considered in the report (RCP4.5 and RCP8.5). </p> <p>Filenames refer to the specific analysis each set of shapefiles belong to: Benthic habitats, benthic megafauna, pelagic habitats, pelagic megafauna and forage fish. Details of the modelling datasets used in each of these analyses, the meta-analysis method and shapefile creation can be found in Annex A1 in the report "A Climate Resilient Path for Ireland’s Marine Protected Areas Network".</p>
Identifying South African Marine Protected Areas at risk from marine heatwaves and cold spells
<p>This data reflects information on marine heatwaves (MHWs) and marine cold spells (MCSs) that occurred along the South African coast from January 1982 to April 2022, with special focus on Marine Protected Areas. Thermal metrics for MHW and MCS events were obtained using the HeatwaveR package (Schlegel and Smit, 2018) and the associated Marine Heatwave Tracker (Schlegel, 2020). </p> <p> </p> <p>THis data stems from Courtailac et al (in review) Indentifying South AFrican Marine Protected Areas at risk of marine heatwaves and cold-spells </p>
First Three-dimensional Quantification of Planktic Food Chain lower levels (Copepods) for the Ross Sea region Marine Protected Area (RSRMPA), Antarctica: Using FAIR-inspired legacy data with Machine Learning, and Open Source GIS
<p>This dataset is relative to the paper entitled: "First Three-dimensional Quantification of Planktic Food Chain lower levels (Copepods) for the Ross Sea region Marine Protected Area (RSRMPA), Antarctica: Using FAIR-inspired legacy data with Machine Learning, and Open Source GIS" publishing in journal Diversity (MPDI).</p> <p>Abstract:</p> <p>Zooplankton is a fundamental group in all aquatic ecosystems located the base of the food chain. It forms a link between the lower trophic levels with secondary consumers and shows marked fluctuations of populations with environmental change, especially reacting to heating and water acidification. At sea copepod crustaceans account for app. 70% in abundance of zooplankton and are a target of monitoring activities in key areas such as the Southern Ocean. In this study we have used FAIR-inspired legacy data (dating back to the ‘80s) collected in the Ross Sea by the Italian National Antarctic Program in GBIF.org. Together with other open-access GIS data sources and tools it allows generating, for the first time, three-dimensional predictive distribution maps for twenty-six copepod species. These predictive maps were obtained by applying machine learning techniques to grey literature data, which were visualized in open-source GIS platforms. In a Species Distribution Modeling (SDM) framework we used machine learning with three types of algorithms (TreeNet, RandomForest and Ensemble) to analyze the presence and absence of copepods at different areas and depth classes in function of environmental descriptors obtained from the Polar Macroscope Layers present in Quantartica. The models allow for the first time to map-predict the food chain in quantitative terms showing the relative index of occurrence (RIO) and identified the presence for each copepod species analyzed in the Ross Sea. Our results show marked geographical preferences that vary with species and trophic strategy. This study demonstrates that machine learning is a successful method in accurately predicting Antarctic copepod presence, also providing useful data to orient future sampling and management of wildlife and conservation.</p>
Short-term Monitoring of Coral Reef Marine Protected Areas (MPAs) in the Municipality of Liloan, Central Visayas, Philippines
<p>This is a sampling-event dataset of the short-term monitoring of Poblacion and Kadurong Reefs, two of the marine protected areas Municipality of Liloan, Cebu, Philippines. Water quality and ecological assessments were carried out to monitor the status and trends of biological and physical parameters associated with coral reefs using the standard protocols for surveying tropical marine resources. Specifically, the following measurements were conducted: (1) physico-chemical parameters, (2) phytoplankton and zooplankton occurrence and abundance, (3) fish occurrence and density, and (4) percent cover of benthic components of coral reef. The data can serve as the basis for the formulation and implementation of relevant measures for conservation and protection management of the Poblacion and Kadurong Reefs in Liloan, Cebu, Philippines.</p> <p>In this version, occurrence.csv was revised as described below:</p> <ul> <li>taxonID for <em>Abudefduf vaigiensis</em> (Quoy & Gaimard, 1825) and <em>Hemiaulus</em> P.A.C. Heiberg, 1863 were corrected.</li> <li>Author names with corrupted characters/symbols were corrected. </li> </ul>
List and date of establishment of Marine Protected Areas and Key Biodiversity Areas of the Alboran Sea
<p>List of Marine Protected Areas and Key Biodiversity Areas for the Alboran Sea (Abbreviation in Spanish, French and English with lenguage among brackets- Fr: French; S: Spanish), indicating its figure of conservation, year of establishment for each figure of protection and national or regional management body (in brackets). IBA: Importante Bird Area; IMMA: Important Marine Mammals Area; MR: Marine Reserve; MR/FR: Marine and Fishing Reserve; NA: Natural Area; NM: Natural monument; NP: Natural Park; SPAMI: Specially Protected Areas of Mediterranean Importance; RAMSAR: Wetlands of International Importance (Ramsar Sites); SCI: Site of Community Importance of Natura 2000; SAC: Special Area of Conservation of Natura 2000; SPA: Special Protection Area of Natura 2000; ZEPA: Zona de Especial Protección para las Aves; ZEPIM: Zonas Especialmente Protegidas de Importancia para el Mediterráneo; LIC: Lugar de Importancia Comunitaria de Natura 2000; ZEC: Zona de Especial Conservación de Natura 2000; ASPIM: Aire Spécialement Protégée d'Importance Méditerranéenne; SIC: Site d'Importance Communautaire; ZPS: Zones de Protection Spéciale.</p>
Data from: Shark movement strategies influence poaching risk and can guide enforcement decisions in a large, remote Marine Protected Area
<ol> <li>Large, remote marine protected areas (MPAs) containing both reef and pelagic habitats, have been shown to offer considerable refuge to populations of reef-associated sharks. Many large MPAs are, however, impacted by illegal fishing activity conducted by unlicensed vessels. While enforcement of these reserves is often expensive, it would likely benefit from the integration of ecological data on the mobile animals they are designed to protect. Consequently, shark populations in some protected areas continue to decline, as they remain a prime target for illegal fishers.</li> <li>To understand shark movements and their vulnerability to illegal fishing, three years of acoustic tracking data, from 101 reef-associated sharks, were analysed as movement networks to explore the predictability of movement patterns and identify key movement corridors within the British Indian Ocean Territory (BIOT) MPA. We examined how space use and connectivity overlap with spatially-explicit risk of illegal fishing, through data obtained from the management consultancy enforcing the MPA.</li> <li>Using individual-based models, the movement networks of two sympatric shark species were efficiently predicted with distance-decay functions (>95% movements accurately predicted). Model outliers were used to highlight the locations with unexpectedly high movement rates where MPA enforcement patrols might most efficiently mitigate predator removal.</li> <li>Activity space estimates and network metrics illustrate that silvertip sharks were more dynamic, less resident and link larger components of the MPA than grey reef sharks. However, we show that this behaviour potentially enhances their exposure to illegal fishing activity.</li> <li> <i>Synthesis and applications. </i>Marine protected area (MPA) enforcement strategies are often limited by resources. The British Indian Ocean Territory MPA, one of the world's largest 'no take' MPAs, has a single patrol vessel to enforce 640,000 km<sup>2</sup> of open ocean, atoll and reef ecosystems. We argue that to optimise the patrol vessel search strategy and thus enhance their protective capacity, ecological data on the space use and movements of desirable species, such as large-bodied reef predators, must be incorporated into management plans. Here, we use electronic tracking data to evaluate how shark movement dynamics influence species mortality trajectories in exploited reef ecosystems. In doing so we discuss how network analyses of such data might be applied for protected area enforcement.</li> </ol>
Fig. 8 in Meiofaunal Biodiversity In A Marine Protected Area: A Case Study In The Rocky And Sedimentary Shores Of The Snake Island (North-Western Black Sea)
Fig. 8. Plot of the non-metric multidimensional scaling (nMDS) based on the by Bray–Curtis similarity index for logarithmic values of meiobenthos taxa density in the recognized habitats of the Snake Island MPA (Black Sea).
Fig. 7 in Meiofaunal Biodiversity In A Marine Protected Area: A Case Study In The Rocky And Sedimentary Shores Of The Snake Island (North-Western Black Sea)
Fig. 7. Cluster analysis dendrogram based on meiobenthos density on the different habitats in MPA of the Snake Island (Black Sea).
Fig. 4 in Meiofaunal Biodiversity In A Marine Protected Area: A Case Study In The Rocky And Sedimentary Shores Of The Snake Island (North-Western Black Sea)
Fig. 4. The average density (N, means ± SE ind.·m–2) and biomass (B, means ± SE mg·m–2) of the total meiobenthos with contribution permanent and temporary taxa in the different habitats of the Snake Island MPA (Black Sea).
Fig. 3 in Meiofaunal Biodiversity In A Marine Protected Area: A Case Study In The Rocky And Sedimentary Shores Of The Snake Island (North-Western Black Sea)
Fig. 3. Meiobenthic community structure of different substrate types in the MB143 habitat of the Snake Island MPA (Black Sea).
Fig. 2 in Meiofaunal Biodiversity In A Marine Protected Area: A Case Study In The Rocky And Sedimentary Shores Of The Snake Island (North-Western Black Sea)
Fig. 2. The average density (N, means ± SE ind.·m–2) and biomass (B, means ± SE mg·m–2) of the total meiobenthos of different substrate types in the MB143 habitat of the Snake Island MPA (Black Sea).
Fig. 1 in Meiofaunal Biodiversity In A Marine Protected Area: A Case Study In The Rocky And Sedimentary Shores Of The Snake Island (North-Western Black Sea)
Fig. 1. The map-scheme of the study area near the Snake Island (north-western Ukrainian shelf of the Black Sea).
Fig. 6 in Meiofaunal Biodiversity In A Marine Protected Area: A Case Study In The Rocky And Sedimentary Shores Of The Snake Island (North-Western Black Sea)
Fig. 6. The contribution (%) of each meiobenthic taxon to the average density and biomass in the different habitats of the Snake Island MPA (Black Sea).
Data and scripts for: Green turtles highlight connectivity across a regional marine protected area network in West Africa
<p>Data derivates and analysis scripts (in R) used for the paper on analyzing green turtle MPA coverage and connectivity in West Africa.</p>
Fig. 4 in Benthic hydrozoan assemblages as potential indicators of environmental health in a mediterranean marine protected area Abstract
Fig. 4: Two-dimensional nMDS representation of the similarity (Bray-Curtis) of hydrozoan assemblages among samples in the winter campaign. Samples displayed according to stations (numbers) and sampling depth (a), anthropogenic impact (b) and substrate type (c).
Fig. 2 in Benthic hydrozoan assemblages as potential indicators of environmental health in a mediterranean marine protected area Abstract
Fig. 2: Species numbers according to depth strata and total depth integrated species numbers in the study area.
Fig. 5 in Fish assemblages along the coasts of Tunisia: a baseline study to assess the effectiveness of future Marine Protected Areas
Fig. 5: Size-class (S: small, M: medium and L: large) frequency distribution (%) of relevant target fishes in Unprotected (UP) and Future Protected (FP) zones at the three studied locations (KU: Kuriat Islands, CNCS: Cap Negro-Cap Serrat, TA: Tabarka), (Number of individuals used to calculate percentages is given in Supp. Mat. 2).
Fig. 3 in Fish assemblages along the coasts of Tunisia: a baseline study to assess the effectiveness of future Marine Protected Areas
Fig. 3: Mean density (±standard error) per trophic category at the sampling locations (KU: Kuriat Islands, CNCS: Cap Negro-Cap Serrat, TA: Tabarka) and per protection level (UP: Unprotected, FP: Future Protected).
Fig. 1 in Fish assemblages along the coasts of Tunisia: a baseline study to assess the effectiveness of future Marine Protected Areas
Fig. 1: Locations where MPAs will be established along the Tunisian coast. Location of future protected sites (FP) and those outside (that will remain unprotected) (UP) (separated with dotted lines indicating borders of future MPAs as they are proposed in management plans).
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.