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Qualitative Data on 60 Small-Scale Fisheries: A socio-ecological rapid appraisal applied to cases from America (Colombia, Ecuador, and Mexico), Africa (Kenya, Madagascar, and Nigeria), and Europe (France and Spain)
<h2><span lang="EN-US">Dataset name</span></h2> <p><span lang="EN-US">Small_Scale_Fishery_Data_2023_v2 </span><span lang="EN-US"> </span></p> <h2><span lang="EN-US">Title</span></h2> <p><span lang="EN-US">Qualitative Data on 60 Small-Scale Fisheries: A socio-ecological rapid appraisal applied to cases from America (Colombia, Ecuador, and Mexico), Africa (Kenya, Madagascar, and Nigeria), and Europe (France and Spain). </span><span lang="EN-US"> </span></p> <h2><span lang="EN-US">Description </span></h2> <p><span lang="EN-US"> This dataset was created for the Fish2Sustainability research project, which aims to evaluate how small-scale fisheries (SSF) contribute to Sustainable Development Goals (SDGs). The dataset includes 60 case studies across eight countries and was developed using a rapid appraisal framework. The framework includes a four-step process: </span></p> <p><span lang="EN-US"> 1. Identifying specific SDG targets influenced by SSF;</span></p> <p><span lang="EN-US"> 2. Extracting relevant variables from UN indicators;</span></p> <p><span lang="EN-US"> 3. Gathering expert input via a questionnaire to score these variables;</span></p> <p><span lang="EN-US"> 4. Creating composite indicators to measure SSF performance against SDGs.</span></p> <p><span lang="EN-US"> The dataset contains raw data from step 3, case study details, variable scores, and comments from data collectors (contributing authors). The dataset is valuable for researchers interested in small-scale fisheries and socio-ecological systems. By incorporating expert judgments from individuals with expertise in SSF, particularly in data-poor contexts, the dataset offers a wealth of knowledge for conducting comparative analyses across different contexts.</span><span lang="EN-US"> </span></p> <h2><span lang="EN-US">Authors </span></h2> <p><span lang="EN-US">Léopold, M.1, Bitoun, R.E.2, Beckensteiner, J.3, Chuenpagdee, R.4, Fondo, E.N.5, Akintola, S.L.6, Bach, P.7, Frangoudes, K.8, Gaibor, N.9, Gutierrez-Cala, L.10, Massey, Y.7, Randrianandrasana, R.11, Razanakoto, T.11, Saavedra-Díaz, L.M.10, Schreiber Arias, M.12,13, Salas, S.14, Devillers, R.2,4 </span></p> <h3><span lang="EN-US">Affiliations </span></h3> <p><span lang="EN-US">1 ENTROPIE (IRD, University of La Reunion, CNRS, University of New Caledonia, Ifremer), c/o IUEM, Plouzané, France </span></p> <p><span lang="EN-US">2 Espace-Dev (IRD, Univ. </span>Montpellier, Univ. Guyane, Univ. La Réunion, Univ. Antilles, Univ. Nouvelle Calédonie), Montpellier, France</p> <p>3 AMURE (Ifremer, UBO, CNRS), Plouzané, France</p> <p><span lang="EN-US">4 Department of Geography, Memorial University of Newfoundland, St. John’s, NL, Canada</span></p> <p><span lang="EN-US">5 Kenya Marine and Fisheries Research Institute, Mombasa, Kenya</span></p> <p><span lang="EN-US">6 Department of Fisheries, Faculty of Science, Lagos State University, Nigeria</span></p> <p><span lang="EN-US">7 MARBEC, University of Montpellier, CNRS, Ifremer, IRD, Sète, France</span></p> <p>8 Université de Bretagne Occidentale: Brest, France</p> <p>9 Instituto Público de Investigación de Acuicultura y Pesca (IPIAP), Universidad del Pacifico (UPAC), Guayaquil, Ecuador</p> <p>10 Grupo de Investigación en Sistemas Socioecológicos para el Bienestar Humano (GISSBH), Programa de Biología, Universidad del Magdalena, Colombia</p> <p>11 Centre d’Etudes et de Recherches Economiques pour le Développement (CERED), Université d’Antananarivo, Madagascar</p> <p><span lang="EN-US">12 EqualSea Lab, Universidad Santiago de Compostela, A Coruña, Spain</span></p> <p><span lang="EN-US">13 School of Global Studies, University of Gothenburg, Gothenburg, Sweden</span></p> <p>14 Centro de Investigación y de Estudios Avanzados (CINVESTAV), IPN, Unidad Mérida, Mexico </p> <h2><span lang="EN-US">Method </span></h2> <p><span lang="EN-US">Case studies were selected in eight countries by national SSF experts, based on specific criteria and research priorities. Case studies were not selected to represent the full diversity of SSF globally or even nationally. Instead, they were chosen to capture a range of fisheries that could showcase different contributions to SDGs. SSF were defined based on various characteristics, such as resources harvested, gear used, and location of the fishery. </span><span lang="EN-US"> </span></p> <h3><span lang="EN-US">Geographical Coverage </span></h3> <p><span lang="EN-US">60 small-scale fisheries located in seven countries are documented in the data:</span></p> <ul> <li><span lang="EN-US">Colombia (4 case studies) – Pacifico: La Guajira, San Andrés y Providencia; Caribe: Chocó, Cauca, Valle del Cauca, Nariño.</span></li> <li><span lang="EN-US">Ecuador (3) – Region: Esmeraldas, Manabi, Guayas, El Oro.</span></li> <li><span lang="EN-US">France (2) – Region: Bretagne, Occitanie.</span></li> <li><span lang="EN-US">Kenya (22) – County: Kilifi, Kwale, Lamu, Mombasa, Tana River.</span></li> <li><span lang="EN-US">Madagascar (20) – Region: Analanjirofo, Anosy, Atsimo Andrefana, Boeny, Diana, Menabe, Vatovavy Fitovinany.</span></li> <li><span lang="EN-US">Mexico (2) – State: Baja California Sur, Campeche, Yucatan.</span></li> <li><span lang="EN-US">Nigeria (6) – State: Bayelsa, Cross River, Lagos, Ondo, Ogun. </span></li> <li><span lang="EN-US">Spain (1) – State: Galicia.</span> </li> </ul> <h3><span lang="EN-US">Data Collection </span></h3> <p><span lang="EN-US">Data collection took place from November 30, 2022, to July 3, 2023, spanning approximately seven months. The data presented serve as a snapshot of the conditions within a specific small-scale fishery during the assessment period. To consider the evolution of trends such as exports, economic growth, and income, we considered any relevant variables over the past decade. </span></p> <p><span lang="EN-US">Data collection approaches varied depending on the context, and data collectors received training to ensure survey consistency. We used primary data sources such as interviews, observations, and measurements whenever possible. In cases where resources were limited, we preferred secondary sources such as existing datasets and literature. Our methods were standardized, but data collectors could adjust them based on their resources. We primarily used direct observation, focus groups, and interviews to collect data. Scoring in interviews and focus groups was done directly or through group analysis by interviewers. Disagreements were resolved through additional interviews or group discussions, with secondary data used if needed. Please refer to the methods in : </span></p> <p><strong><span lang="EN-US">Bitoun et al., (2024). A methodological framework for capturing marine small-scale fisheries’ contributions to the sustainable development goals. Sustainability Science, 19(4), 1119–1137. https://doi.org/10.1007/s11625-024-01470-0. </span></strong><span lang="EN-US"><strong> </strong> </span></p> <h3><span lang="EN-US">Ethics </span></h3> <p><span lang="EN-US">Participants had the option to join of their own accord, were fully briefed on the research goals, and were given the opportunity to review interview guidelines before proceeding. Depending on the circumstances, interviews could last 45 minutes to 4.5 hours. Participants were guaranteed confidentiality and anonymity in the handling and reporting of their data.</span><span lang="EN-US"> </span></p> <h3><span lang="EN-US">Suggested citation</span></h3> <p><span lang="EN-US">Léopold, M., Bitoun, R., & Devillers, R. (2023). Qualitative Data on 61 Small-Scale Fisheries: A socio-ecological rapid appraisal applied to cases from America (Colombia, Ecuador, and Mexico), Africa (Kenya, Madagascar, and Nigeria), and Europe (France and Spain) (Version 2) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.16077739</span></p> <h2><span lang="EN-US">Data Files</span></h2> <p><span lang="EN-US">The dataset includes the following:</span></p> <ul> <li><span lang="EN-US">The raw dataset (.xls format).</span></li> <li><span lang="EN-US">A data dictionary describing and defining each dataset column (.xls format).</span></li> </ul>
Raw data and code for "Addressing gaps in small-scale fisheries: a low-cost tracking system"
<p>This repository contains the raw data and code to reproduce results and plots presented in: "Addressing gaps in small-scale fisheries: a low-cost tracking system". The release contains:</p> <ul> <li>ssf_function.R. The R function developed for the analysis</li> <li>ssf_workflow.R. The R scripts to reproduce the analysis and the results.</li> <li>gps_data.csv. Raw data used in the paper</li> </ul>
Fisheries independent trawl survey data of fish biomass on North American and European oceanic shelves.
<p>Publicly available scientific bottom trawl survey data, primarily sampling demersal commercial species, were obtained from the Northeast Pacific and North Atlantic shelf regions in 2021. The final dataset contains approx. 197,000 unique tows and includes data from 1970 to 2019 (166,000 tows between 1980-2015). For each tow in each survey, we selected all teleost and elasmobranch species and obtained species weight. We corrected these weights for differences in sampling area (in km2) and trawl gear catchability.</p> <p>The data processing scripts and individual survey data can be found on Github (DOI: 10.5281/zenodo.7992482). The data processing scripts are modified based on earlier work from Pinsky et al. (2013) and Maureaud et al. (2019).</p> <p>If the correction for gear catchability is not important, it is recommended to use the FishGlob database (DOI: 10.5281/zenodo.7484547).</p> <p>Please contact Daniel van Denderen (pdvd@aqua.dtu.dk) for questions.</p> <p><strong>Column names</strong><br> Haul_id: unique haul identifyer<br> Survey_Region: survey name or name of ecoregion (depending on survey)<br> Gear: gear information (only included for northeast Atlantic region). Gear information is available for other regions. See original survey description (sources in manuscript).<br> Year: sampling year<br> Month: sampling month<br> Longitude: longitude (EPSG:4326)<br> Latitude: latitude (EPSG:4326)<br> Swept_area: estimate of swept area of survey gear (only included for northeast Atlantic region)<br> Bottom_depth: bottom depth in meters (as recorded in the survey data)<br> Family: taxonomic family of the teleost/elasmobranch<br> Name: species name (or higher taxonomic grouping)<br> kg_km2: wet weight (kilogram) per unit of swept area (km2)<br> kg_km2_corrected: wet weight (kilogram) per unit of swept area (km2) corrected for trawl gear catchability<br> F_type: fish type (demersal or pelagic)<br> Trophic_lev: Species-specific trophic level information</p> <p><br> <strong>Data uncertainties</strong><br> Data have predominantly been analysed at the community level and between 1980 and 2015. Any species-specific inferences may need further checking.</p> <p>To reduce the effect of potential outlying biomass estimates, it is recommended to remove all individual observations 1.5 times less/greater than the interquantile range per survey and year based on log10-transformed biomass values.</p> <p>Please contact Daniel van Denderen (pdvd@aqua.dtu.dk) for any comments/questions.</p>
Data from: Identifying priority areas for spatial management of mixed fisheries using ensemble of multi-species distribution models. Panzeri D. et al., 2023, Fish and Fisheries
<p>Panzeri D.<sup>1</sup>, Russo T., Arneri E., Carlucci R., Cossarini G., Isajlović I., Krstulović Šifner S., Manfredi C., Masnadi F., Reale M., Scarcella G., Solidoro C., Spedicato M.T., Vrgoč N., W. Zupa, Libralato S<sup>2</sup>.</p> <p><sup>1 </sup>dpanzeri@ogs.it<br> <sup>2 </sup>slibralato@ogs.it</p> <p>Spatial fisheries management is widely used to reduce overfishing, rebuild stocks, and protect biodiversity. However, the effectiveness and optimization of spatial measures depend on accurately identifying ecologically meaningful areas, which can be difficult in mixed fisheries. To apply a method generally to a range of target species, we developed an ensemble of species distribution models (e-SDM) that combines general additive models, generalized linear mixed models, random forest, and gradient-boosting machine methods in a training and testing protocol. The e-SDM was used to integrate density indices from two scientific bottom trawl surveys with the geopositional data, relevant oceanographic variables from the three-dimensional physical-biogeochemical operational model, and fishing effort from the vessel monitoring system. The determined best distributions for juveniles and adults are used to determine hot spots of aggregation based on single or multiple target species. We applied e-SDM to juvenile and adult stages of 10 marine demersal species representing 60% of the total demersal landings in the central areas of the Mediterranean Sea. Using the e-SDM results, hot spots of aggregation and grounds potentially more selective were identified for each species and for the target species group of otter trawl and beam trawl fisheries. The results confirm the ecological appropriateness of existing fishery restriction areas and support the identification of locations for new spatial management measures.</p> <p>Data (csv) for Panzeri et al. 2023</p> <p>1. <a href="https://zenodo.org/api/files/0b1b7af4-6a3b-481d-8d5f-57cf02d20eaa/Ensemble_density_F%26F_D.Panzeri_et_al_2023.csv">Ensemble_density_F&F_D.Panzeri_et_al_2023.csv: CSV file with density values (column pred) in terms of number of individuals (log N/km2) for each species (column sp) and life stage (column age) for each grid cell (X = longitude and Y = latitude).</a> </p> <p>2. <a href="https://zenodo.org/api/files/0b1b7af4-6a3b-481d-8d5f-57cf02d20eaa/Ensemble_density_F%26F_D.Panzeri_et_al_2023.csv">Getis_hotspot_F&F_D.Panzeri_et_al_2023.csv: CSV file with Getis ord Gi* values (column Gi) derived from the previous file 1, developed for each species and life stage for each grid cell (X = longitude and Y = latitude).</a></p> <p>3. <a href="https://zenodo.org/api/files/0b1b7af4-6a3b-481d-8d5f-57cf02d20eaa/Ensemble_density_F%26F_D.Panzeri_et_al_2023.csv">Multispecies_HotSpot_F&F_D.Panzeri_et_al_2023.csv: Frequency map expressed as the number of species for each grid cell (column freq) that has the hotspot (previous file 2) above the third quartile.</a></p> <p> </p> <p> </p>
Size data for Chinook salmon caught in the Tengu Derby and Puget Sound commercial purse seine fisheries
<p>The tengu_derby_size.csv file contains information on the following fields (columns):</p> <ol> <li>year</li> <li>members (number of anglers who participated in the derby; not all anglers fished each day the derby was open)</li> <li>n_over_10 (total number of Chinok salmon greater than 10 pounds)</li> <li>n_over_5 (total number of Chinok salmon greater than 5 pounds)</li> <li>size_1 (mass in kg of the largest fish landed)</li> <li>size_2 (mass in kg of the second largest fish landed)</li> <li>size_3 (mass in kg of the third largest fish landed)</li> <li>size_4 (mass in kg of the fourth largest fish landed)</li> <li>size_5 (mass in kg of the fifth largest fish landed) </li> </ol> <p>The wdfw_size.csv file contains the following fields (columns):</p> <ol> <li>year</li> <li>mass (mean mass in kg of natural- and hatchery-origin Chinook salmon combined)</li> </ol>
Data and code from: Recreational fisheries selectively capture and harvest large predators
<p>Size and species selective harvest, inevitably alters the composition of targeted populations and communities. This can potentially harm fish stocks, ecosystem functionality, and related services, as evidenced in numerous commercial fisheries. The high popularity of rod-and-reel recreational fishing, practiced by hundreds of millions globally, raises concerns about similar deteriorating effects. Despite its prevalence, the species and size selectivity of recreational fisheries remain largely unquantified due to a lack of combined catch data and fisheries-independent surveys. This study addresses this gap by using standardised monitoring data and over 60,000 digital angling catch reports from 62 distinct fisheries. The findings demonstrate a pronounced selectivity in recreational fisheries, targeting top-predators and large individuals. Catch-and-release practices reduced the overall harvest by 60 % but did not substantially alter this selectivity. The strong species- and size-specific selectivity mirror patterns observed in other fisheries, emphasising the importance of managing the potential adverse effects of recreational fisheries selective mortality and overfishing.</p>
Mediterranean risk assessment data based on the concurrency between climate change, fisheries, stocks, and biodiversity
<p>Data associated to the paper "Detecting Ecosystem Risk Hotspots: A Mediterranean Case Study" by G. Coro, L. Pavirani, A. Ellenbroek.</p>
Data for: Spatial and temporal genetic stock composition of river herring bycatch in southern New England Atlantic herring and mackerel fisheries
<p>Anadromous river herring (alewife and blueback herring) persist at historically low abundances and are caught as bycatch in commercial fisheries, potentially preventing recovery despite conservation efforts. We used newly established single-nucleotide polymorphism genetic baselines for alewife and blueback herring to define fine-scale reporting groups for each species. We then determined the occurrence of fish from these reporting groups in bycatch samples from a Northwest Atlantic fishery over four years.Within sampled bycatch events, the highest proportions of alewife were from the Block Island (34%) and Long Island Sound (22%) reporting groups, while for blueback herring the highest proportions were from the Mid-Atlantic (47%) and Northern New England (24%) reporting groups. We then quantified stock-specific mortality in a focal geographic area (~3500 km<sup>2</sup> including Block Island Sound) of high bycatch incidence and sampling effort, where the most accurate estimates of mortality could be made. During this period, we estimate that bycatch took about 4.6 million alewife and 1.2 million blueback herring, highlighting the need to reduce bycatch mortality for the most depleted river herring stocks.</p>
Data for: Coupling dynamic energy budget and population dynamic models to inform stock enhancement in fisheries management
<p><span>Extensive applications of fishery stock enhancement worldwide bring up broad concerns about its negative effects, creating a pivotal need for science-based assessment and planning of enhancement strategies. However, the lack of mechanistic understanding of enhanced population dynamics, particularly the density-dependent processes, leads to compromise in model development and limits the capacity in predicting enhancement effects. Here, we developed an individual-based model based on dynamic energy budget theory and full life history processes, to understand the mechanism of density dependence in population dynamics that emerge from individual-level processes. We demonstrated the utility of the model framework by applying it </span><span>to an extensively enhanced species, Chinese prawn (<em>Fenneropenaeus chinensis</em></span><span>, Penaeidae</span><span>). The model could yield projections reflecting the observed trajectory of population biomass and yields. The model also delineated the key effects of density dependence on the vital rates of growth, fecundity, and starvation mortality. Regarding the manifold effects of stock enhancement, we demonstrated a dampened shape in population biomass and yields with increasing magnitude of enhancement, and trade-offs between the ecological and economic objectives, i.e., pursuing high benefit might compromise the wild population without proper management. Furthermore, we illustrated the possibility of combining stock enhancement and harvest regulation in promoting population recovery while maintaining fisheries yields. We highlight the potential of the proposed model for understanding density dependence in enhancement program, and for designing integrated management strategies. The approach developed herein may serve as a general approach to assess the population dynamics in stock enhancement and inform enhancement management.</span><span> </span></p>
Data and code: Assessing fish-fishery dynamics from a spatially explicit metapopulation perspective reveals winners and losers in fisheries management
<ol> <li><span>Sustainable management of living resources must reconcile biodiversity conservation and socioeconomic viability of human activities. In the case of fisheries, sustainable management design is made challenging by the complex spatiotemporal interactions between fish and fisheries.</span></li> <li><span>We develop a comprehensive metapopulation framework integrating data on species life-history traits, connectivity and habitat distribution to identify priority areas for fishing regulation and assess how management impacts are spatially distributed. We trial this approach on European hake fisheries in the north-western Mediterranean, where we assess area-based management scenarios in terms of stock status and fishery productivity to prioritize areas for protection. </span></li> <li><span>Model simulations show that local fishery closures have the potential to enhance both spawning stock biomass and landings on a regional scale compared to a status quo scenario, but that improving protection is easier than increasing productivity. Moreover, the interaction between metapopulation dynamics and the redistribution of fishing effort following local closures implies that benefits and drawbacks are heterogeneously distributed in space, the former being concentrated in the proximity of the protected site. </span></li> <li><span>A network analysis shows that priority areas for protection are those with the highest connectivity (as expressed by network metrics) if the objective is to improve the spawning stock, while no significant relationship emerges between connectivity and potential for increased landings.</span></li> <li> <span><em>Synthesis and applications</em> – </span><span>Our framework provides a tool for 1) assessing area-based management measures aimed at improving fisheries outcomes in terms of both conservation and socioeconomic viability and 2) describing the spatial distribution of costs and benefits, which can help guide effective management and gain stakeholder support. Adult dispersal remains the main source of uncertainty that needs to be investigated to effectively apply our model to fisheries regulation.</span> </li> </ol>
The CALFISH database: A century of California's non-confidential fisheries landings and participation data
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Data for: Spatial and temporal genetic stock composition of river herring bycatch in southern New England Atlantic herring and mackerel fisheries
Open the record for dataset details and reuse information.
Data for: Coupling dynamic energy budget and population dynamic models to inform stock enhancement in fisheries management
Open the record for dataset details and reuse information.
Data and code from: Recreational fisheries selectively capture and harvest large predators
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Data and code: Assessing fish-fishery dynamics from a spatially explicit metapopulation perspective reveals winners and losers in fisheries management
Open the record for dataset details and reuse information.
Data from: Rescue or murder? The effect of prey adaptation to the predator subjected to fisheries
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Data from: Extending full protection inside existing marine protected areas or reducing fishing effort outside can reconcile conservation and fisheries goals
<p>1. Most fish stocks worldwide are fished at maximum sustainable yield (MSY) or overfished, as many fisheries management strategies have failed to achieve sustainable fishing. Identifying effective fisheries management strategies has now become urgent.</p> <p>2. Here, we developed a spatially-explicit metapopulation model accounting for population connectivity in the north-western Mediterranean Sea, and parameterized it for three ecologically and economically important coastal fish species: the white seabream <i>Diplodus sargus</i>, the two-banded seabream <i>Diplodus vulgaris</i> and the dusky grouper <i>Epinephelus marginatus</i>.</p> <p>3. We used the model to assess how stock biomass and catches respond to changes in fishing mortality rate (<i>F</i>) and in the size of fully protected areas within the existing system of multiple-use marine protected areas (MPAs). For each species, we estimated MSY and the corresponding values of stock biomass (<i>B</i><sub>MSY</sub>) and fishing mortality rate (<i>F</i><sub>MSY</sub>), providing crucial reference points for the assessment of fisheries management.</p> <p>4. <i>D. sargus</i> is currently in low overfishing, while <i>D. vulgaris</i> and <i>E. marginatus</i> are in high overfishing. Stock recovery to <i>B</i><sub>MSY</sub> for the last two species requires a reduction of current <i>F</i> around 50%. This would guarantee an increase in both stock biomass (around 50 and 75% for <i>D. vulgaris</i> and <i>E. marginatus</i>, respectively) and catch (around 15 and 30%) after a transient time of ~15–30 years. Alternatively, doubling the size of fully protected areas over fishable areas within the existing network of MPAs would lead to positive conservation effects for all three species without substantially affecting the overall productivity of the fishery and the total economic value of the catch.</p> <p>5. <i>Synthesis and applications.</i> We provide the first assessment of stock status for three coastal species in the north-western Mediterranean and evaluate the ecological and fisheries outcomes of different management strategies. Extending full protection inside existing multiple-use marine protected areas or reducing fishing effort outside can deliver both conservation and fisheries benefits.</p>
Data from: Using a participatory impact assessment framework to evaluate a community-led mangrove and fisheries conservation approach in West Kalimantan, Indonesia
<ol> <li>Community-based conservation (CBC) has been identified as a solution to biodiversity loss, climate change, and the reduction of rural poverty. The heterogeneity in social and economic inequalities often acts as a barrier to community engagement in resource management and further inhibits the distributional equity of social and ecological outcomes.</li> <li>This study presents a participatory impact assessment (PIA) framework that evaluated the outcomes of a cross-sector community-led conservation initiative. Community members involved in the program identified activities and outcomes for the Conservation Cooperative (CC), ranking the influence of the former on the latter as well as their daily life through multiple focus group discussions (FGDs). Participants were asked to rank the impact of activities on outcomes and the scale of the outcome which was totaled to identify the most impactful program activities and outcomes during the project period.</li> <li>Community members reported improved income, health, education and the creation of a locally-led natural resource management system. Members also reported improved crab harvest rates and reduced mangrove deforestation. Environmental outcomes identified by community members through the PIA were verified through a secondary spatial analysis and mud-crab independent fisheries monitoring.</li> <li>The results support the hypothesis that environmental NGOs need to consider a multi-dimensional view of human well-being, and that cross-sector integrated interventions may be effective at improving multiple outcomes.</li> <li>Future steps should focus on spatial replication of the CC program which will provide further insights by testing for differences in outcomes between villages, how those are impacted by preexisting social and ecological systems, and comparing outcomes between control sites that did not receive interventions.</li> </ol>
Model output data for Marine Wild-Capture Fisheries after Nuclear War
<p>This is the model data material for Scherrer at al., PNAS. [Scherrer K. J. N., et al. Marine wild-capture fisheries after nuclear war, PNAS in press]. Input files include time series of gridded global oceanic Sea Surface Temperature and Net Primary Productivity (output from the CESM model) and socioeconomic input for the global fisheries model (BOATS). Output files include globally integrated time series (used in Figs. 1-3 and 6) and global gridded model output (used in Figs. 4-5) for each of the five ensemble member runs.</p>
Data from: Making Brexit work for the environment and livelihoods: delivering a stakeholder informed vision for agriculture and fisheries
1. The UK's decision to leave the EU has far-reaching implications for agriculture and fisheries. To ensure the future sustainability of UK agricultural and fisheries systems, we argue that it is essential to grasp the opportunity that Brexit is providing to develop integrated policies that improve the management and protection of the natural environments, upon which these industries rely. 2. This article advances a stakeholder informed vision of the future design of UK agriculture and fisheries policies. We assess how currently emerging UK policy will need to be adapted in order to implement this vision. Our starting point is that Brexit provides the opportunity to redesign current unsustainable practices and can in principle deliver a sustainable future for agriculture and fisheries. 3. Underpinning policies with an ecosystem approach, with explicit inclusion of public goods provision, and social welfare equity were found to be central to the success of this endeavour. Recognition of the needs of, and innovative practices in, the devolved UK nations is also required as the new policy and regulatory landscape is established. 4. Achieving our proposed vision will necessitate drawing on best practice and creating more coherent and integrated food, environment and rural and coastal economy policies. Sustainable prosperity should and can form the core of future post-Brexit environmental policy thinking and our findings demonstrate the "bottom-up" and co-production approaches that will be key to the development of more environmentally sustainable agricultural and fisheries policies.
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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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.