0000000000172439

AUTHOR

Facundo Muñoz

0000-0002-5061-4241

showing 8 related works from this author

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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Bovine paramphistomosis in Galicia (Spain): Prevalence, intensity, aetiology and geospatial distribution of the infection

2013

12 páginas, 5 figuras, 4 tablas.

Veterinary medicinehttp://aims.fao.org/aos/agrovoc/c_2615EpidemiologyBayesian geostatistical modelCattle DiseasesL73 - Maladies des animauxFecesÉtiologieRisk FactorsPrevalenceParasite Egg CountParasite hostingParamphistomatidaebiologyCalicophoron daubneyiGeneral Medicinehttp://aims.fao.org/aos/agrovoc/c_ded17449ÉpidémiologieRoe deerMaladie des animauxParasitosehttp://aims.fao.org/aos/agrovoc/c_1038Géostatistiqueshttp://aims.fao.org/aos/agrovoc/c_3852InfectionL72 - Organismes nuisibles des animauxCalicophoron daubneyiRumenhttp://aims.fao.org/aos/agrovoc/c_4936Cattle DiseasesTrematode InfectionsBovidaeParamphistomidaehttp://aims.fao.org/aos/agrovoc/c_15588Paramphistomosisbiology.animalAnimalsParasite Egg CountDairy cattleFeceshttp://aims.fao.org/aos/agrovoc/c_31996Roe deerGeneral VeterinaryBayes TheoremL70 - Sciences et hygiène vétérinaires - Considérations généralesDistribution spatialeMorbiditébiology.organism_classificationhttp://aims.fao.org/aos/agrovoc/c_426Spainhttp://aims.fao.org/aos/agrovoc/c_5579http://aims.fao.org/aos/agrovoc/c_36230Bovidaehttp://aims.fao.org/aos/agrovoc/c_7273CattleParasitologyVeterinary Parasitology
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Bayesian spatio-temporal approach to identifying fish nurseries by validating persistence areas

2015

Spatial and temporal closures of fish nursery areas to fishing have recently been recognized as useful tools for efficient fisheries management, as they preserve the reproductive potential of populations and increase the recruitment of target species. In order to identify and locate potential nursery areas for spatio-temporal closures, a solid understanding of species− environment relationships is needed, as well as spatial identification of fish nurseries through the application of robust analyses. One way to achieve knowledge of fish nurseries is to analyse the persistence of recruitment hotspots. In this study, we propose the comparison of different spatiotemporal model structures to ass…

0106 biological sciencesMediterranean climatehttp://aims.fao.org/aos/agrovoc/c_28840[SDV]Life Sciences [q-bio]01 natural sciencesMediterranean seaAbundance (ecology)Ecosystem approachEcologybiologyEcologyU10 - Informatique mathématiques et statistiquesinteraction élevage environnementmodèle de distributionMerluccius merlucciushttp://aims.fao.org/aos/agrovoc/c_41529zone de pêcheNursery areasSpatio temporal analysisanalyse bayésienneGeographyGestion des pêchesgestion spatialealevinageFisheries managementFishinganalyse spatiotemporellegestion des ressources naturellesAquatic Science010603 evolutionary biologyhttp://aims.fao.org/aos/agrovoc/c_24026étude comparativeHakeMerluccius merluccius14. Life underwaterhttp://aims.fao.org/aos/agrovoc/c_4699Ecology Evolution Behavior and Systematicshttp://aims.fao.org/aos/agrovoc/c_12399Distribution patternapproche ecosystémiqueÉcologie marinehttp://aims.fao.org/aos/agrovoc/c_4609010604 marine biology & hydrobiologybiology.organism_classificationBiologie marineFisheryThéorie bayésiennehttp://aims.fao.org/aos/agrovoc/c_9000115M40 - Écologie aquatiqueBayesian hierarchical modellingMarine protected areaSpatial fisheries managementNursery areas;Distribution pattern;Ecosystem approach;Spatial fisheries management;Spatio temporal analysis;Bayesian hierarchical modelling;Merluccius merluccius
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Modelling sensitive elasmobranchs habitat

2013

Basic information on the distribution and habitat preferences of ecologically important species is essential for their management and protection. In the Mediterranean Sea there is increasing concern over elasmobranch species because their biological (ecological) characteristics make them highly vulnerable to fishing pressure. Their removal could affect the structure and function of marine ecosystems, inducing changes in trophic interactions at the community level due to the selective elimination of predators or prey species, competitors and species replacement. In this study Bayesian hierarchical spatial models are used to map the sensitive habitats of the three most caught elasmobranch spe…

0106 biological scienceshttp://aims.fao.org/aos/agrovoc/c_28840Etmopterus spinaxhabitatAquatic ScienceDistribution des populationshttp://aims.fao.org/aos/agrovoc/c_38371OceanographyGaleus melastomus010603 evolutionary biology01 natural sciencesElasmobranch habitatPredationMediterranean seahttp://aims.fao.org/aos/agrovoc/c_38127http://aims.fao.org/aos/agrovoc/c_3041Scyliorhinus caniculaMediterranean SeaVulnerable speciesMarine ecosystem14. Life underwaterhttp://aims.fao.org/aos/agrovoc/c_4699Ecology Evolution Behavior and Systematicshttp://aims.fao.org/aos/agrovoc/c_12399Trophic levelhttp://aims.fao.org/aos/agrovoc/c_6113biologyEcologyU10 - Informatique mathématiques et statistiques010604 marine biology & hydrobiologyScyliorhinus caniculabiology.organism_classificationBiologie marinetechnique de prévisionBayesian hierarchical spatial modelSpecies distribution modelingFisheryHabitatThéorie bayésienneGaleus melastomusM40 - Écologie aquatiquehttp://aims.fao.org/aos/agrovoc/c_10566http://aims.fao.org/aos/agrovoc/c_3456http://aims.fao.org/aos/agrovoc/c_38117Elasmobranchii
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Optimizing the level of service quality of a bike-sharing system

2016

Public bike-sharing programs have been deployed in hundreds of cities worldwide, improving mobility in a socially equitable and environmentally sustainable way. However, the quality of the service is drastically affected by imbalances in the distribution of bicycles among stations. We address this problem in two stages. First, we estimate the unsatisfied demand (lack of free lockers or lack of bicycles) at each station for a given time period in the future and for each possible number of bicycles at the beginning of the period. In a second stage, we use these estimates to guide our redistribution algorithms. Computational results using real data from the bike-sharing system in Palma de Mall…

Information Systems and ManagementOperations researchStrategy and Managementmedia_common.quotation_subject0211 other engineering and technologiesDistribution (economics)02 engineering and technologyManagement Science and Operations Researchhttp://aims.fao.org/aos/agrovoc/c_63329Transport engineeringhttp://aims.fao.org/aos/agrovoc/c_3041http://aims.fao.org/aos/agrovoc/c_7524http://aims.fao.org/aos/agrovoc/c_353320502 economics and businessserviceQuality (business)media_common050210 logistics & transportation021103 operations researchU10 - Informatique mathématiques et statistiquesLevel of servicebusiness.industry05 social sciencesRedistribution (cultural anthropology)Demand forecastingtechnique de prévisionhttp://aims.fao.org/aos/agrovoc/c_9000074BicyclettesOffre et demandehttp://aims.fao.org/aos/agrovoc/c_dda00d10Développement durableService (economics)http://aims.fao.org/aos/agrovoc/c_6989http://aims.fao.org/aos/agrovoc/c_7273Bike sharingapproches communautairesBusinessHeuristicsOmega
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Geostatistical computing of acoustic maps in the presence of barriers

2009

Acoustic maps are the main diagnostic tools used by authorities for addressing the growing problem of urban acoustic contamination. Geostatistics models phenomena with spatial variation, but restricted to homogeneous prediction regions. The presence of barriers such as buildings introduces discontinuities in prediction areas. In this paper we investigate how to incorporate information of a geographical nature into the process of geostatistical prediction. In addition, we study the use of a Cost-Based distance to quantify the correlation between locations.

acoustic mapsComputer sciencehttp://aims.fao.org/aos/agrovoc/c_8085non_euclidean geostatisticsClassification of discontinuities010502 geochemistry & geophysicsDiagnostic toolscomputer.software_genre01 natural scienceshttp://aims.fao.org/aos/agrovoc/c_35131acousticcomputational methods010104 statistics & probabilitySystème d'information géographique11. Sustainability[STAT.CO]Statistics [stat]/Computation [stat.CO]http://aims.fao.org/aos/agrovoc/c_98[STAT.AP]Statistics [stat]/Applications [stat.AP]mapshttp://aims.fao.org/aos/agrovoc/c_49911Propriété acoustiquetechnique de prévisionhttp://aims.fao.org/aos/agrovoc/c_ded17449B10 - GéographieComputer Science Applicationscost-Based distanceHomogeneousModeling and Simulationhttp://aims.fao.org/aos/agrovoc/c_7251Data miningGéostatistiques[STAT.ME]Statistics [stat]/Methodology [stat.ME]Zone urbaineSon (acoustique)Process (engineering)Geostatisticscost_surfacegiscomputinghttp://aims.fao.org/aos/agrovoc/c_3041Modelling and Simulationacoustiquegeostatistics0101 mathematics0105 earth and related environmental sciencesbusiness.industry[SDE.ES]Environmental Sciences/Environmental and SocietySpatial variabilityArtificial intelligenceU30 - Méthodes de recherchebusinesscomputerMathematical and Computer Modelling
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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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Accounting for preferential sampling in species distribution models

2019

D. C., A. L. Q. and F. M. would like to thank the Ministerio de Educación y Ciencia (Spain) for financial support (jointly financed by the European Regional Development Fund) via Research Grants MTM2013‐42323‐P and MTM2016‐77501‐P, and ACOMP/2015/202 from Generalitat Valenciana (Spain). Species distribution models (SDMs) are now being widely used in ecology for management and conservation purposes across terrestrial, freshwater, and marine realms. The increasing interest in SDMs has drawn the attention of ecologists to spatial models and, in particular, to geostatistical models, which are used to associate observations of species occurrence or abundance with environmental covariates in a fi…

0106 biological sciencesComputer scienceQH301 BiologySpecies distributionPoint processesStochastic partial differential equation01 natural scienceshttp://aims.fao.org/aos/agrovoc/c_6774EspèceAbundance (ecology)StatisticsPesqueríasQAOriginal Researchhttp://aims.fao.org/aos/agrovoc/c_241990303 health sciencesEcologyU10 - Informatique mathématiques et statistiquesSampling (statistics)Integrated nested Laplace approximationstochastic partial differential equationVariable (computer science)symbolsÉchantillonnageSpecies Distribution Models (SDMs)Modèle mathématiqueBayesian probabilityNDASDistribution des populations010603 evolutionary biologyQH30103 medical and health sciencessymbols.namesakeCovariateQA MathematicsSDG 14 - Life Below WaterCentro Oceanográfico de Murciaspecies distribution modelsRelative species abundanceEcology Evolution Behavior and Systematicspoint processes030304 developmental biologyNature and Landscape Conservationhttp://aims.fao.org/aos/agrovoc/c_6113http://aims.fao.org/aos/agrovoc/c_7280Markov chain Monte Carlointegrated nested Laplace approximationU30 - Méthodes de rechercheBayesian modelling
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