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603 results for “fishery”
Social-Ecological Dynamics of Recreational Fishery Landscapes: Recreational angler catch and satisfaction in the Midwest, 2018 to 2024
The goal of this dataset is to understand angler information gathering and sharing, technology use, expectation-setting, site choice, and the factors influencing angler satisfaction in rural and urban environments. These data were collected through on-site creel surveys and instantaneous counts of fishing effort and associated angler vehicles. Traditional creel surveys are completed only at the end of an angler's fishing trip. These dual-intercept creel survey data are novel because anglers were intercepted both before and after their fishing trip. This survey design allowed us to collect unbiased estimates of anglers' expected catch. These data can be used to understand the role of angler expectations in angler satisfaction and behavior. Data collection were conducted in two counties in Wisconsin, USA. Vilas County is a primarily rural, forested, and glaciated region with many relatively small lakes. Dane County has a larger urban center (Madison, WI) that is surrounded by agricultural areas. Most of the Dane County lakes surveyed were part of the Yahara chain of lakes: Lakes Mendota, Monona, Wingra, Waubesa, and Kegonsa. Dane county has fewer, larger lakes with greater connectivity to each other. Vilas County was sampled in the summer of 2018, the summer of 2019, and the summer and winter of 2022. Dane County was sampled in the winter and summer of 2022.
Wisconsin creel dataset as well as predictor variables for lakes from 1990 to 2017 to estimate statewide recreational fisheries harvest
Recreational fisheries have high economic worth, valued at $190B globally. An important, but underappreciated, secondary value of recreational catch is its role as a source of food. This contribution is poorly understood due to difficulty in estimating recreational harvest at spatial scales beyond an individual system, as traditionally estimated from angler creel surveys. Here, we address this gap using a 28-year creel survey of ~300 Wisconsin inland lakes. We develop a statistical model of recreational harvest for individual lakes and then scale-up to unsurveyed lakes (3769 lakes; 73% of statewide lake surface area) to generate a statewide estimate of recreational lake harvest of ~4200 t and an estimated annual angler consumption rate of ~3 kg, nearly double estimated United States per capita freshwater fish consumption. Recreational fishing harvest makes significant contributions to human diets, is critical for discussions on food security, and the multiple ecosystem services of freshwater systems.
Global monthly catches from tuna surface fisheries by 1° grid (1958-2023) (FIRMS level 0)
<p>We compiled a comprehensive dataset of geo-referenced catches from global tuna fisheries that use fishing gears set at the water's surface. This dataset was created by harmonizing public domain data from the five tuna Regional Fisheries Management Organizations (t-RFMOs) for the period 1958-2023. Under the auspices of the Fisheries and Resources Monitoring System (FIRMS) of the United Nations Food and Agriculture Organization (FAO), we developed a systematic data flow process in collaboration with the t-RFMO Secretariats. This process involved the implementation of a data exchange format adhering to the standards of the FAO Coordinating Working Party on Fishery Statistics (CWP), facilitating the seamless integration of data into the dataset.</p> <p>Geo-referenced catch data from tuna surface fisheries are reported in either the number of fish or live-weight equivalent (metric tonnes), with some strata providing catches in both units. The catches primarily represent the quantities of retained fish either landed or transhipped at sea and in ports. The data are stratified by year, month, fishing fleet, fishing gear, fishing mode, 1° grid area of longitude and latitude, and taxon.</p> <p>The dataset encompasses 42 medium- and large-sized pelagic species found in both neritic and oceanic habitats of the world's oceans. This includes 14 species of tunas, 9 species of billfish, 4 species of Spanish mackerels, 2 species of bonitos, and wahoo. Despite uncertainties and incomplete data due to under-reporting, the dataset also includes reported catches for 12 species of pelagic sharks and rays that may be either targeted or incidentally caught in tuna and tuna-like fisheries.</p> <p>The dataset serves as a benchmark for the monitoring and assessment of both artisanal and industrial fisheries using surrounding nets, gillnets, entangling nets, and pole-and-lines from over 70 fishing fleets across 69 countries that have exploited tuna and tuna-like species for subsistence and commercial purposes over more than six decades.</p>
Global annual catches from tuna fisheries (1918-2023) (FIRMS level 0)
<p>We constructed the most comprehensive dataset of nominal catches from global tuna fisheries by compiling and harmonizing public domain data from the five tuna Regional Fisheries Management Organizations (t-RFMOs) for the period 1918-2023. Under the auspices of the Fisheries and Resources Monitoring System (FIRMS) of the United Nations Food and Agriculture Organization (FAO),we developed a systematic data flow process in collaboration with the t-RFMO Secretariats. This process involved the implementation of a data exchange format adhering to the standards of the FAO Coordinating Working Party on Fishery Statistics (CWP),facilitating the seamless integration of data into the dataset.<br><br>Nominal catch data are expressed in live-weight equivalent (metric tonnes) and primarily represent the quantities of retained fish either landed or transhipped at sea and in ports. In recent years,data from fisheries in the Atlantic and Western-Central Pacific Oceans have partially included amounts of fish discarded dead. The data are stratified by year,fishing fleet,fishing gear,large spatial area,and taxon.<br><br>The dataset encompasses 50 medium- and large-sized pelagic species found in both neritic and oceanic habitats of the world's oceans. This includes 15 species of tunas,10 species of billfish,8 species of Spanish mackerels,2 species of bonitos,and wahoo. In 2023,the global catch for these species was estimated to exceed 6.4 million metric tonnes. Despite uncertainties and incomplete data due to under-reporting,the dataset also includes reported catches for 14 species of pelagic sharks and rays that may be either targeted or incidentally caught in tuna and tuna-like fisheries. The total reported catch of these elasmobranch species was approximately 154,000 metric tonnes in 2023.<br><br>The dataset serves as a benchmark for the monitoring and assessment of both artisanal and industrial fisheries from over 161 fishing fleets across 159 countries that have exploited tuna and tuna-like species for subsistence and commercial purposes over more than seven decades.</p>
WDNR Yahara Lakes Fisheries: Fish Lengths and Weights 1987-1998
These data were collected by the Wisconsin Department of Natural Resources (WDNR) from 1987-1998. Most of these data (1987-1993) precede 1995, the year that the University of Wisconsin NTL-LTER program took over sampling of the Yahara Lakes. However, WDNR data collected from 1997-1998 (unrelated to LTER sampling) is also included. In 1987 a joint project by the WDNR and the University of Wisconsin-Madison, Center for Limnology (CFL) was initiated on Lake Mendota. The project involved biomanipulation of fish communities within the lake, which was acheived by stocking game fish species (northern pike and walleye). The goal was to induce a trophic cascade that would improve the water clarity of Lake Mendota. See Lathrop et al. 2002. Stocking piscivores to improve fishing and water clarity: a synthesis of the Lake Mendota biomanipulation project. Freshwater Biology 47, 2410-2424. In collecting these data, the objective was to gather population data and monitor populations to track the progress of the biomanipulation. The data is dominated by an assesssment of the game fishery in Lake Mendota, however other Yahara Lakes and non-game fish species are also represented. A combination of gear types was used to gather the population data including boom shocking, fyke netting, mini-fyke netting, seining, and gill netting. Not every sampling year includes length and weight data from all gear types. The WDNR also carried out randomized, access-point creel surveys to estimate fishing pressure, catch rates, harvest, and exploitation rates. Five data files each include length-weight data, and are organized by the type of gear or method which was used to collect the data: 1) fyke, mini-fyke, and seine netting 2) boom shocking 3) gill netting (1993 only) 4)walleye age as determined by scale and spine analysis (1987 only), and 5) creel survey. The final data file contains creel survey information: number of anglers fishing the shoreline, and number of anglers that started and complete
Small-scale fisheries adaptations understudied in climate change hotspots - database
<p>Using a systematic review approach, we identified a global dataset of 301 reported adaptation responses of small-scale fishers to climate change. The adaptations were extracted from academic publications and grey literature (reports and Ph.D. theses) published from 2008 to 2020. The database provides coordinates and/or location, climate change hazard identified as motivating the response, small-scale fisher adaptation response, and any other stressor related to the response.</p>
Dataset of paper "Bioelectrochemically-improved anaerobic digestion of fishery processing industrial wastewater"
<p>Dataset of operation of a bioelectrochemically-improved anaerobic digester (AD-BES), treating real fishery processing wastewater.<br>This dataset was used to publish the paper "Bioelectrochemically-improved anaerobic digestion of fishery processing industrial wastewater" in Journal of Water Process Engineering (DOI: 10.1016/j.jwpe.2024.105848).</p>
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>
Fishery impacts - CA commercial lobster catch and effort, and trap distribution around marine reserves
These data describe landings and fishing effort for the CA commercial spiny lobster fishery. Data are contained in two tables: 1) a time series collected by the California Fish and Wildlife (CDFW) of lobster catch (kg) and effort (lobster trap pulls) by year (1998-2020) and location (geographic fishery blocks). Catch is recorded and reported to CDFW by fish processors (buyers) with “fish tickets”. Trap pulls are recorded and reported by fishermen with logbooks. 2) The number of traps counted in the water at increasing distances from the boundary of marine reserves, three reserves located in the northern region of the fishery and three from the southern region of the fishery, in November 2022.
Global monthly catches from tuna fisheries by 1° and 5° grids (1950-2023) (FIRMS level 0)
<p>We compiled a comprehensive dataset of geo-referenced catches from global tuna fisheries available at a spatial resolution of 1° and 5° grid areas. This dataset was created by harmonizing public domain data from the five tuna Regional Fisheries Management Organizations (t-RFMOs) for the period 1950-2023. Under the auspices of the Fisheries and Resources Monitoring System (FIRMS) of the United Nations Food and Agriculture Organization (FAO), we developed a systematic data flow process in collaboration with the t-RFMO Secretariats. This process involved the implementation of a data exchange format adhering to the standards of the FAO Coordinating Working Party on Fishery Statistics (CWP), facilitating the seamless integration of data into the dataset.<br><br>Geo-referenced catch data from tuna fisheries are reported in either the number of fish or live-weight equivalent (metric tonnes), with some strata providing catches in both units. The catches primarily represent the quantities of retained fish either landed or transhipped at sea and in ports. The data are stratified by year, month, fishing fleet, fishing gear, fishing mode, 1° or 5° grid area of longitude and latitude, and taxon.<br><br>The dataset encompasses 49 medium- and large-sized pelagic species found in both neritic and oceanic habitats of the world's oceans. This includes 15 species of tunas, 10 species of billfish, 7 species of Spanish mackerels, 2 species of bonitos, and wahoo. Despite uncertainties and incomplete data due to under-reporting, the dataset also includes reported catches for 14 species of pelagic sharks and rays that may be either targeted or incidentally caught in tuna and tuna-like fisheries.<br><br>The dataset serves as a benchmark for the monitoring and assessment of both artisanal and industrial fisheries from over 115 fishing fleets across 114 countries that have exploited tuna and tuna-like species for subsistence and commercial purposes over more than seven decades.</p>
Global monthly catches from tuna fisheries by 5° grid (1950-2023) (FIRMS level 0)
We compiled a comprehensive dataset of geo-referenced catches from global tuna fisheries available on a spatial resolution of 5° grid areas. This dataset was created by harmonizing public domain data from the five tuna Regional Fisheries Management Organizations (t-RFMOs) for the period 1950-2023. Under the auspices of the Fisheries and Resources Monitoring System (FIRMS) of the United Nations Food and Agriculture Organization (FAO), we developed a systematic data flow process in collaboration with the t-RFMO Secretariats. This process involved the implementation of a data exchange format adhering to the standards of the FAO Coordinating Working Party on Fishery Statistics (CWP), facilitating the seamless integration of data into the dataset.<br><br>Geo-referenced catch data from tuna fisheries are reported in either the number of fish or live-weight equivalent (metric tonnes), with some strata providing catches in both units. The catches primarily represent the quantities of retained fish either landed or transhipped at sea and in ports. The data are stratified by year, month, fishing fleet, fishing gear, fishing mode, 5° grid area of longitude and latitude, and taxon.<br><br>The dataset encompasses 49 medium- and large-sized pelagic species found in both neritic and oceanic habitats of the world's oceans. This includes 15 species of tunas, 10 species of billfish, 7 species of Spanish mackerels, 2 species of bonitos, and wahoo. Despite uncertainties and incomplete data due to under-reporting, the dataset also includes reported catches for 14 species of pelagic sharks and rays that may be either targeted or incidentally caught in tuna and tuna-like fisheries.<br><br>The dataset serves as a benchmark for the monitoring and assessment of both artisanal and industrial fisheries from over 115 fishing fleets across 114 countries that have exploited tuna and tuna-like species for subsistence and commercial purposes over more than seven decades.
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 dataset on moulting patterns and shell quality of American lobsters H. americanus in Atlantic Canada
<p>This survey collated data on lobster moult indicators and on life-history traits (sex, size) during a twelve-year monitoring program (2004 – 2015) in six lobster fishing areas in Atlantic Canada. A standardized sampling protocol was followed to collect data from a total of 141,659 lobsters over 1,195 sampling events using commercial lobster fishing traps. Data on pleopod stages, hemolymph protein levels (˚Brix values) and shell hardness can be used for moult stage determination. Evaluation of sex ratio dynamics is also possible but existing biases in sampling males and females need to be noted. This dataset is valuable in terms of inferring spatio-temporal trends in the life history of lobsters, as well as in the analysis of their moult cycle, and hence more generally for fisheries science and marine ecology.</p>
GO-FISH: Geolocated Ocean-Fishery Identified Spawning Habitats
<p>This dataset represents geocoded spawning regions for 1,045 marine fish species described in the Fishbase (https://www.fishbase.se/) and Science and Conservation of Fish Aggregations (SCRFA, <a href="https://www.scrfa.org/database/">https://www.scrfa.org/database/</a>) datasets. These global databases have painstakingly aggregated the fieldwork of countless biologists and ecologists to summarize our knowledge of fish species. We further constrained geographic locations using AquaMaps (<a href="https://www.aquamaps.org/">https://www.aquamaps.org</a>) to produce 2,931 polygons or groups of polygons, which we call "spawning regions".</p> <p>Reproduction code for the dataset is available at <a href="https://github.com/openmodels/spawning-dataset">https://github.com/openmodels/spawning-dataset</a>, archived at <a href="../records/11098955">https://zenodo.org/records/11098955</a>.</p>
Models simulating abrupt changes in the Chilika lagoon fishery, the Easter Island community, forest dieback and lake water quality
<p>This deposit is in support of Willcock et al "Earlier collapse of Anthropocene ecosystems driven by multiple faster and noisier drivers". It covers the following items: (i) A list of the files contained within this data deposit; (ii) How to access and download the specialist software required to view and simulate the system dynamics models (STELLA ‘isee Player’); (iii) How to run isee Player to simulate the models; (iv) How to access and download the standard statistical software ‘R’ to run the R scripts; (v) How to load ‘R’ and modify the standard R script to analyse a subset of the model runs. This file will also details the ‘required content’ (e.g. software versions), as specified in the ‘nr-software-policy.pdf’ document.</p> <p>The full descriptions of each of the four system dynamics models used in this manuscript can be read in the following papers:</p> <ol> <li>Lake Chilika – Cooper, G. S. & Dearing, J. A. Modelling future safe and just operating spaces in regional social-ecological systems. <em>Sci. Total Environ.</em> <strong>651</strong>, 2105–2117 (2019), <a href="https://doi.org/10.1016/j.scitotenv.2018.10.118">https://doi.org/10.1016/j.scitotenv.2018.10.118</a></li> <li>Easter Island – Brandt, G. & Merico, A. The slow demise of Easter Island: Insights from a modeling investigation. <em>Front. Ecol. Evol.</em> <strong>3</strong>, 13 (2015), <a href="https://www.frontiersin.org/article/10.3389/fevo.2015.00013">https://www.frontiersin.org/article/10.3389/fevo.2015.00013</a></li> <li>Lake phosphorus – Wang, R. <em>et al.</em> Flickering gives early warning signals of a critical transition to a eutrophic lake state. <em>Nature</em> <strong>492</strong>, 419–22 (2012), <a href="http://dx.doi.org/10.1038/nature11655">http://dx.doi.org/10.1038/nature11655</a></li> <li>TRIFFID - Ritchie, P. D. L., Clarke, J. J., Cox, P. M. & Huntingford, C. Overshooting tipping point thresholds in a changing climate. <em>Nat. 2021 5927855</em> <strong>592</strong>, 517–523 (2021), <a href="http://dx.doi.org/10.1038/nature11655">http://dx.doi.org/10.1038/nature11655</a></li> </ol>
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>
Marine Stewardship Council (MSC) Fisheries Standard v2.0 assessment scores
<p>MSC fishery assessment scores manually collated by Marine Stewardship Council (MSC). See about file for complete details.</p> <p>This data has been manually collated by MSC from reports prepared by third party assessors. This dataset shows, for each Unit of Assessment (UoA), scoring data from the Public Certification Report of the UoA's most recent initial assessment or re-assessment conducted against the Default Tree v2.0 (including Default Tree v2.01) of the MSC Fisheries Standard as of 26 February 2021. A Unit of Assessment is a unique combination of target stock, fishing method or gear, geographic area, and operators or vessels being assessed which may eventually carry the MSC certification status. This excludes UoA which failed their assessment because failing scores are not consistently reported (they are often simply considered a ‘fail’).</p> <p>The following constraints and limitations apply to this dataset:<br> 1. This data has been manually collated by MSC from fishery reports prepared by third party assessors. MSC carries out data assurance to a standard that is fit for the purpose the information is used for, including being complete, accurate and as up to date as possible. If accuracy is paramount to a finite resolution, receivers are asked to validate data against assessment reports which can all be found published on fisheries.msc.org. The MSC is not responsible for any issues arising to any parties as a result of any information provided therein.<br> 2. This dataset shows, for each UoA, scoring data from the certification report of the UoA's most recent initial assessment or re-assessment conducted against v2.01 of the Standard as of 26 February 2021. This excludes UoA which failed their assessment.<br> 3. UoA details (uoa_details) can be joined to scoring data (scores) by the Event_unitbk. This will join the details of the scored UoA with the score that was awarded to the UoA.</p> <p>This dataset consists of two tables:</p> <p>1. uoa_details<br> This table contains details describing UoA included in the dataset. UoA descriptions include the name of fishery as published on Track a Fishery (fisheries.msc.org), species, gear, and other description of the UoA that was assessed.<br> <br> 2. scores<br> Scoring results to the lowest level (scoring guidepost of the scoring issue) for each UoA, as presented in the scoring tables of the report. One row represents a single guidepost result of a single scoring issue. However, the performance indicator (PI) ‘Score’ will be duplicated for all scoring issues in the same PI. For example, if a UoA received a score of 80 for a performance indicator, and in this performance indicator there are 2 scoring issues each scored at 3 guideposts, there will be 6 total rows showing the results for each guidepost of each scoring issue, but all rows will show 80 for the overall 'Score' of the PI. One row also exists for each Principle score of each UoA, in which the principle will be noted in the 'PI' column, and the Principle score in 'Score'. Data has been copied from the scoring tables in the Public Certification Report.</p>
Figures 10–17. Cyrtinus fisheri. 10–13 in Taxonomic notes on Western Hemisphere Cyrtinini (Coleoptera: Cerambycidae: Lamiinae) including description of two new Cyrtinus LeConte species
Figures 10–17. Cyrtinus fisheri. 10–13) Holotype male. 10) Dorsal habitus. 11) Ventral habitus. 12) Head, frontal view. 13) Lateral habitus. 14–17) Paratype female. 14) Lateral habitus. 15) Head, frontal view. 16) Dorsal habitus. 17) Ventral habitus.
Fig. 18. Monostaechas fisheri Nutting, 1905 in Plumularioid hydroids (Cnidaria: Hydrozoa) from off New Caledonia collected during KANACONO and KANADEEP expeditions of the French Tropical Deep-Sea Benthos Program
Fig. 18. Monostaechas fisheri Nutting, 1905, morphotype with shallower hydrothecae and longer lateral nematothecae: patterns of branching. Slender lines represent cladia, thicker lines show primary, secondary or tertiary stems; dots correspond to the hydrothecae.
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Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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OpenNeuro
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