Search results for "Sede Central IEO"

showing 10 items of 15 documents

Seasonality of spatial patterns of abundance, biomass and biodiversity in a demersal community of the NW Mediterranean Sea

2020

14 pages, 5 figures, 4 tables

0106 biological sciencesBiodiversityBayesian analysisSede Central IEOAquatic ScienceOceanography010603 evolutionary biology01 natural sciencesDemersal zoneMediterranean seaAbundance (ecology)medicineMediterranean SeaPesqueríasspecies distribution modelsEcology Evolution Behavior and SystematicsBiomass (ecology)TemperaturesEcologyEcology010604 marine biology & hydrobiologyseasonal patternsspatial ecologytemperatureSeasonalitymedicine.diseaseGeographySpatial ecology
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Discard ban: A simulation-based approach combining hierarchical Bayesian and food web spatial models

2020

12 pages, 6 figures, 6 tables, 2 appendixes, supplementary data https://doi.org/10.1016/j.marpol.2019.103703

0106 biological sciencesEconomics and EconometricsComputer scienceFishingSede Central IEOContext (language use)Management Monitoring Policy and LawAquatic ScienceBayesian inference01 natural sciencesEnvironmental datamedia_common.cataloged_instanceEcoSimSpatial ecologyPesquerías14. Life underwaterEuropean unionGeneral Environmental Sciencemedia_commonEcospacebusiness.industry010604 marine biology & hydrobiologyEnvironmental resource management04 agricultural and veterinary sciencesFood web modelDiscardsDiscards13. Climate actionBayesian modelLanding obligationMediterranean sea040102 fisheries0401 agriculture forestry and fisheriesFisheries managementbusinessLaw
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The analysis of convergence in ecological indicators: An application to the Mediterranean fisheries

2017

9 pages, 4 figures, 3 tables

0106 biological sciencesMediterranean climateIndex (economics)[SDE.MCG]Environmental Sciences/Global ChangesFishingGeneral Decision SciencesTransition probability matrix;Sede Central IEOtMediterranean sea010603 evolutionary biology01 natural sciencesEcological indicatorsMediterranean sea[SDV.EE.ECO]Life Sciences [q-bio]/Ecology environment/EcosystemsEcosystem approach to fisheries managemenConvergence analysisMediterranean SeaEcosystemEcosystem approach to fisheries management14. Life underwaterEcology Evolution Behavior and SystematicsTrophic levelEstimationEcologybusiness.industry010604 marine biology & hydrobiologyEnvironmental resource managementTransition probability matrixFisheryEcological indicatorGeographyNon-parametric density estimation[SDV.SA.STP]Life Sciences [q-bio]/Agricultural sciences/Sciences and technics of fishery[SDE.BE]Environmental Sciences/Biodiversity and Ecologybusiness
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Searching for a compromise between biological and economic demands to protect vulnerable habitats

2018

AbstractIdentifying vulnerable habitats is necessary to designing and prioritizing efficient marine protected areas (MPAs) to sustain the renewal of living marine resources. However, vulnerable habitats rarely become MPAs due to conflicting interests such as fishing. We propose a spatial framework to help researchers and managers determine optimal conservation areas in a multi-species fishery, while also considering the economic relevance these species may have in a given society, even in data poor situations. We first set different ecological criteria (i.e. species resilience, vulnerability and trophic level) to identify optimal areas for conservation and restoration efforts, which was bas…

0106 biological sciencesNature reserveMarine conservationFlexibility (engineering)Multidisciplinary010604 marine biology & hydrobiologylcsh:RFishingVulnerabilitylcsh:MedicineSede Central IEO15. Life on land010603 evolutionary biology01 natural sciencesArticle/dk/atira/pure/sustainabledevelopmentgoals/life_below_waterHabitatlcsh:QMarine protected areaSDG 14 - Life Below Water14. Life underwaterBusinesslcsh:ScienceResilience (network)Environmental planningScientific Reports
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Bayesian spatio-temporal discard model in a demersal trawl fishery

2014

Spatial management of discards has recently been proposed as a useful tool for the protection of juveniles, by reducing discard rates and can be used as a buffer against management errors and recruitment failure. In this study Bayesian hierarchical spatial models have been used to analyze about 440 trawl fishing operations of two different metiers, sampled between 2009 and 2012, in order to improve our understanding of factors that influence the quantity of discards and to identify their spatio-temporal distribution in the study area. Our analysis showed that the relative importance of each variable was different for each metier, with a few similarities. In particular, the random vessel eff…

0106 biological sciencesPerteSpatial correlationhttp://aims.fao.org/aos/agrovoc/c_28840Computer scienceProcess (engineering)Bayesian probabilitySede Central IEOAquatic ScienceOceanography01 natural sciencesRessource halieutiquehttp://aims.fao.org/aos/agrovoc/c_2173Abundance (ecology)Component (UML)http://aims.fao.org/aos/agrovoc/c_4438Pesquerías14. Life underwaterM11 - Production de la pêchehttp://aims.fao.org/aos/agrovoc/c_7881Ecology Evolution Behavior and SystematicsChalutageU10 - Informatique mathématiques et statistiques010604 marine biology & hydrobiologyhttp://aims.fao.org/aos/agrovoc/c_2801204 agricultural and veterinary sciencesDiscardsFisheryRessource marineVariable (computer science)Théorie bayésienneM40 - Écologie aquatique040102 fisheries0401 agriculture forestry and fisherieshttp://aims.fao.org/aos/agrovoc/c_2942Fisheries managementPêche démersale
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Dealing with physical barriers in bottlenose dolphin (Tursiops truncatus) distribution

2019

Abstract Worldwide, cetacean species have started to be protected, but they are still very vulnerable to accidental damage from an expanding range of human activities at sea. To properly manage these potential threats we need a detailed understanding of the seasonal distributions of these highly mobile populations. To achieve this goal, a growing effort has been underway to develop species distribution models (SDMs) that correctly describe and predict preferred species areas. However, accuracy is not always easy to achieve when physical barriers, such as islands, are present. Indeed, SDMs assume, if only implicitly, that the spatial effect is stationary, and that correlation is only depende…

0106 biological sciencesRange (biology)Bayesian probabilitySpecies distributionDistribution (economics)Sede Central IEO010603 evolutionary biology01 natural sciencesINLAPesqueríasArchipelago de La MaddalenaSPDEgeographyCetaceansgeography.geographical_feature_categorybiologybusiness.industry010604 marine biology & hydrobiologyEcological ModelingEnvironmental resource managementBottlenose dolphinbiology.organism_classificationPhysical BarrierHabitatArchipelagoHierarchical Bayesian spatial modelsbusinessEcological Modelling
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Shift in Tuna Catches due to Ocean Warming.

2017

Ocean warming is already affecting global fisheries with an increasing dominance of catches of warmer water species at higher latitudes and lower catches of tropical and subtropical species in the tropics. Tuna distributions are highly conditioned by sea temperature, for this reason and their worldwide distribution, their populations may be a good indicator of the effect of climate change on global fisheries. This study shows the shift of tuna catches in subtropical latitudes on a global scale. From 1965 to 2011, the percentage of tropical tuna in longliner catches exhibited a significantly increasing trend in a study area that included subtropical regions of the Atlantic and western Pacifi…

0106 biological sciencesTime Factors010504 meteorology & atmospheric sciencesEffects of global warming on oceanslcsh:MedicineMarine and Aquatic SciencesOceanographyGlobal Warming01 natural sciencesOceansTropical climateClimate changeZoologíaPesqueríaslcsh:ScienceIndian OceanNorthern HemisphereLatitudeMultidisciplinaryGeographyFishesTemperatureAgricultureOsteichthyesVertebratesResearch ArticleCartographyOceans and SeasFisheriesClimate changeSede Central IEOSubtropicsAnimalsVulnerability of tropical countries to climate change14. Life underwaterOcean TemperatureTropical tuna distribution0105 earth and related environmental sciencesTropical ClimatePacific OceanTuna010604 marine biology & hydrobiologylcsh:RGlobal warmingOrganismsBiology and Life SciencesTropicsNumerical Analysis Computer-AssistedBodies of WaterModels TheoreticalFisherySea surface temperatureEffect on fisheries13. Climate actionEarth SciencesEnvironmental sciencelcsh:QTunaGeographic areas
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Fishery-dependent and -independent data lead to consistent estimations of essential habitats

2016

AbstractSpecies mapping is an essential tool for conservation programmes as it provides clear pictures of the distribution of marine resources. However, in fishery ecology, the amount of objective scientific information is limited and data may not always be directly comparable. Information about the distribution of marine species can be derived from two main sources: fishery-independent data (scientific surveys at sea) and fishery-dependent data (collection and sampling by observers in commercial vessels). The aim of this paper is to compare whether these two different sources produce similar, complementary, or different results. We compare them in the specific context of identifying the Es…

0106 biological scienceshttp://aims.fao.org/aos/agrovoc/c_28840Biodiversité et Ecologiehabitatmodélisation spatialehttp://aims.fao.org/aos/agrovoc/c_38371OceanographyGaleus melastomus01 natural sciencesRessource halieutiquehttp://aims.fao.org/aos/agrovoc/c_38127Scyliorhinus caniculamodèle hiérarchiqueSpatial statisticsEcologymodèle de distributionSampling (statistics)Contrast (statistics)Cross-validationModélisation et simulationGeographyHabitatGestion des pêchesModeling and Simulationhttp://aims.fao.org/aos/agrovoc/c_10566http://aims.fao.org/aos/agrovoc/c_3456http://aims.fao.org/aos/agrovoc/c_38117survey designMarine conservationSpecies Distribution ModelsEcology (disciplines)Bayesian probabilityEtmopterus spinaxenquête statistiqueDonnée sur les pêchesmodèle spatiotemporelSede Central IEOContext (language use)Aquatic ScienceDistribution des populationsBayesian hierarchical models010603 evolutionary biologyhttp://aims.fao.org/aos/agrovoc/c_24026elasmobranchsBiodiversity and Ecologyélasmobrancheétude comparativeBayesian hierarchical models;Cross-validation;Species Distribution Models;Spatial statistics;INLA;elasmobranchs ; survey designINLA14. Life underwaterspecies distribution modelsEcology Evolution Behavior and Systematicshttp://aims.fao.org/aos/agrovoc/c_6113collecte des donnéesÉcologie marinehttp://aims.fao.org/aos/agrovoc/c_29788http://aims.fao.org/aos/agrovoc/c_4609010604 marine biology & hydrobiologyGestion et conservation des pêchescross validation[INFO.INFO-MO]Computer Science [cs]/Modeling and Simulationmodèle bayésienFisheryM01 - Pêche et aquaculture - Considérations généraleshttp://aims.fao.org/aos/agrovoc/c_2a75d27eThéorie bayésienneM40 - Écologie aquatiqueSpatial ecologyhttp://aims.fao.org/aos/agrovoc/c_2942[SDE.BE]Environmental Sciences/Biodiversity and Ecologyvalidation croiséeElasmobranchii
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A spatially explicit risk assessment approach: Cetaceans and marine traffic in the Pelagos Sanctuary (Mediterranean Sea).

2017

15 páginas, 5 figuras, 2 tablas.-- This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

0106 biological scienceslcsh:MedicineMarine and Aquatic SciencesTransportationStenella coeruleoalba01 natural sciencesMediterranean seaStenellaOceanslcsh:ScienceConservation ScienceMammalsMultidisciplinarybiologyFin WhaleMarine reserveFin WhalesHabitatsBottle-Nosed DolphinGeographyHabitatVertebratesEngineering and TechnologyResearch ArticleConservation of Natural ResourcesDolphinsMarine BiologySede Central IEO010603 evolutionary biologyRisk AssessmentBodies of waterbiology.animalMediterranean SeaAnimals14. Life underwaterMarine MammalsEcosystemShipsModels StatisticalBalaenoptera010604 marine biology & hydrobiologylcsh:REcology and Environmental SciencesOrganismsWhalesBiology and Life SciencesAquatic EnvironmentsPelagic zoneMarine spatial planningBayes Theorembiology.organism_classificationMarine EnvironmentsBoatsFisheryAmniotesEarth Scienceslcsh:QMarine protected area
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Global variability in seawater Mg:Ca and Sr:Ca ratios in the modern ocean

2020

12 pages, 5 figures, supporting information https://doi.org/10.1073/pnas.1918943117.-- Data Availability. Our published databases are publicly accessible for readers, and they are deposited at the NOAA NCEI at https://data.nodc.noaa.gov/cgi-bin/iso?id=gov.noaa.nodc:0171017.-- Correction for Lebrato et al., Global variability in seawater Mg:Ca and Sr:Ca ratios in the modern ocean; Proceedings of the National Academy of Sciences of the USA 118(49): e2119099118 (2021); doi: 10.1073/pnas.2119099118; http://hdl.handle.net/10261/258054.-- This is Pacific Marine Environmental Laboratory contribution number 5046

Biogeochemical cycleMedio Marino y Protección Ambiental010504 meteorology & atmospheric sciencesHigh variabilityAlkalinitySede Central IEO010502 geochemistry & geophysics01 natural sciencesCA [MG]CA [SR]//purl.org/becyt/ford/1 [https]//purl.org/becyt/ford/1.5 [https]14. Life underwater0105 earth and related environmental sciencesMultidisciplinarySEAWATERCorrectionBiogeochemistryBIOGEOCHEMISTRYEnvironmental effect13. Climate actionEnvironmental chemistry[SDE]Environmental SciencesUpwellingSeawaterEarth (classical element)GLOBAL
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