Search results for " mathématiques et statistiques"

showing 10 items of 13 documents

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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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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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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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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GeneSys-Beet: A model of the effects of cropping systems on gene flow between sugar beet and weed beet

2008

A weedy form of the genus Beta, i.e. Beta vulgaris ssp. vulgaris (hence ''weed beet'') frequently found in sugar beet is impossible to eliminate with herbicides because of its genetic proximity to the crop. It is presumed to be the progeny of accidental hybrids between sugar beet (ssp. vulgaris) and wild beet (ssp. maritima), or of sugar beet varieties sensitive to vernalization and sown early in years with late cold spells. In this context, genetically modified (GM) sugar beet varieties tolerant to non-selective herbicides would be interesting to manage weed beet. However, because of the proximity of the weed to the crop, it is highly probable that the herbicide-tolerance transgene would b…

0106 biological scienceshttp://aims.fao.org/aos/agrovoc/c_890PopulationSoil ScienceContext (language use)H60 - Mauvaises herbes et désherbageFlux de gènesGenetically modified01 natural sciencesF30 - Génétique et amélioration des planteshttp://aims.fao.org/aos/agrovoc/c_9000024Crophttp://aims.fao.org/aos/agrovoc/c_37331http://aims.fao.org/aos/agrovoc/c_34285[SDV.BV]Life Sciences [q-bio]/Vegetal Biologyhttp://aims.fao.org/aos/agrovoc/c_2018Cropping systemeducation2. Zero hungereducation.field_of_studybiologyU10 - Informatique mathématiques et statistiquesModélisation des culturesfungifood and beverages04 agricultural and veterinary sciences15. Life on landbiology.organism_classificationWeed controlGene flowTillagePratique culturalehttp://aims.fao.org/aos/agrovoc/c_8347AgronomyOrganisme génétiquement modifié040103 agronomy & agriculture0401 agriculture forestry and fisheriesSugar beetBeta vulgarisWeedAgronomy and Crop ScienceMauvaise herbeModelCropping system010606 plant biology & botanyField Crops Research
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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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Morphological characterisation of soil structure in tilled fields: from a diagnosis method to the modelling of structural changes over time

2004

Characterisation of soit structure within the tilled layer of cultivated fields is crucial because the importance of this soil characteristic on the biological, chemical and physical properties of the soil and its repercussions on water cycle, root growth and functioning. We present in this paper a method for field characterisation of soil structure. This method, practised since the 1970s, was designed for field diagnosis of the effects of cropping systems on soil structure. It is based on a stratification of the observation face of a pit dug perpendicular to the direction of tillage and traffic: spatial compartments are distinguished, according to the nature of the mechanical stresses they…

P33 - Chimie et physique du solhttp://aims.fao.org/aos/agrovoc/c_24242Travail du solCompactionSoil ScienceSoil scienceTrait morphologique du sol010501 environmental sciences[SDV.SA.SDS]Life Sciences [q-bio]/Agricultural sciences/Soil study01 natural sciencesSoil surveyMouvement de l'eau dans le solhttp://aims.fao.org/aos/agrovoc/c_7209http://aims.fao.org/aos/agrovoc/c_7163http://aims.fao.org/aos/agrovoc/c_2018AGRONOMIEPropriété physicochimique du solPorosity[SDV.SA.SDS] Life Sciences [q-bio]/Agricultural sciences/Soil studyhttp://aims.fao.org/aos/agrovoc/c_34900ComputingMilieux_MISCELLANEOUS0105 earth and related environmental sciencesEarth-Surface Processes2. Zero hungerStructure du solU10 - Informatique mathématiques et statistiqueshttp://aims.fao.org/aos/agrovoc/c_7182Soil morphologyModèle de simulation04 agricultural and veterinary sciences15. Life on landProctor compaction testMotte de terreCompactage du solSoil gradationTillagePratique culturaleSoil structureMécanique du sol040103 agronomy & agriculture0401 agriculture forestry and fisheriesEnvironmental sciencehttp://aims.fao.org/aos/agrovoc/c_7196Agronomy and Crop Sciencehttp://aims.fao.org/aos/agrovoc/c_7771http://aims.fao.org/aos/agrovoc/c_7177http://aims.fao.org/aos/agrovoc/c_7179
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Adapter localement les prévisions climatiques saisonnières : désagrégation stochastique et interpolation spatiale.

2013

6 pages; International audience; Un panorama est fait des méthodes de descente d’échelles permettant de passer de prévisions climatiques saisonnières de large-échelle à des séries locales journalières. L’exemple des générateurs stochastiques de temps est appliqué à la prévision des récoltes de sorgho au Kenya, dans le cadre du programme ANR PICREVAT. Une méthode d’interpolation spatiale des paramètres des générateurs est testée, pour obtenir des séries journalières de précipitations en tout point du territoire. Les séries générées sont utilisées en entrée du modèle agronomique SARRA-H.

P40 - Météorologie et climatologieF01 - Culture des plantesU10 - Informatique mathématiques et statistiques[SDU.STU.GC]Sciences of the Universe [physics]/Earth Sciences/Geochemistry[SDU.STU.GC] Sciences of the Universe [physics]/Earth Sciences/Geochemistrydésagrégationgénérateur stochastiqueprécipitationsrendementsprévision saisonnière[ SDU.STU.GC ] Sciences of the Universe [physics]/Earth Sciences/Geochemistry
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Climatic gradients along the windward slopes of Mount Kenya and their implication for crop risks. Part 2 : crop sensitivity.

2016

16 pages; International audience; Mount Kenya is an equatorial mountain whose climatic setting is fairly simple (two rainy seasons in March–May, the Long Rains, and October–December, the Short Rains) though concealing significant spatial variations related to elevation and aspect (part I, Camberlin et al., 2014). This part II is dedicated to the sensitivity of sorghum yields to climate variability in space and time, with a focus on the intra-seasonal characteristics of the rainy seasons. To that aim we use the crop model SARRA-H calibrated for the region and fed with rainfall, temperature, wind speed, humidity and solar radiation data over the period 1973–2001 at three stations located on t…

P40 - Météorologie et climatologie[SDV.SA.AGRO]Life Sciences [q-bio]/Agricultural sciences/AgronomySARRA-Hintra-seasonal componentsrainy seasonhttp://aims.fao.org/aos/agrovoc/c_9000024http://aims.fao.org/aos/agrovoc/c_10176[SDU.STU.CL] Sciences of the Universe [physics]/Earth Sciences/Climatology[ SDV.SA.AGRO ] Life Sciences [q-bio]/Agricultural sciences/AgronomyF01 - Culture des planteshttp://aims.fao.org/aos/agrovoc/c_7244ComputingMilieux_MISCELLANEOUSPrécipitationhttp://aims.fao.org/aos/agrovoc/c_24894rainfall variabilityU10 - Informatique mathématiques et statistiquesModélisation des culturescrop modelKenyaVariation saisonnièreRendement des cultureselevation gradientshttp://aims.fao.org/aos/agrovoc/c_4086[SDU.STU.CL]Sciences of the Universe [physics]/Earth Sciences/Climatologyhttp://aims.fao.org/aos/agrovoc/c_6161sorghum[ SDU.STU.CL ] Sciences of the Universe [physics]/Earth Sciences/Climatology
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Timing and patterns of the ENSO signal in Africa over the last 30 years: insights from normalized difference vegetation index data.

2014

Abstract A more complete picture of the timing and patterns of the ENSO signal during the seasonal cycle for the whole of Africa over the three last decades is provided using the normalized difference vegetation index (NDVI). Indeed, NDVI has a higher spatial resolution and is more frequently updated than in situ climate databases, and highlights the impact of ENSO on vegetation dynamics as a combined result of ENSO on rainfall, solar radiation, and temperature. The month-by-month NDVI–Niño-3.4 correlation patterns evolve as follows. From July to September, negative correlations are observed over the Sahel, the Gulf of Guinea coast, and regions from the northern Democratic Republic of Congo…

RainfallSaisonAtmospheric ScienceEquatorhttp://aims.fao.org/aos/agrovoc/c_50098F62 - Physiologie végétale - Croissance et développementhttp://aims.fao.org/aos/agrovoc/c_6734http://aims.fao.org/aos/agrovoc/c_8516http://aims.fao.org/aos/agrovoc/c_7222http://aims.fao.org/aos/agrovoc/c_8038http://aims.fao.org/aos/agrovoc/c_6498http://aims.fao.org/aos/agrovoc/c_24199U10 - Informatique mathématiques et statistiquesIndice de surface foliairehttp://aims.fao.org/aos/agrovoc/c_165VegetationRemote sensing[ SDE.MCG ] Environmental Sciences/Global Changeshttp://aims.fao.org/aos/agrovoc/c_7657El Niño Southern OscillationGeography[SDU.STU.CL]Sciences of the Universe [physics]/Earth Sciences/ClimatologyClimatologyhttp://aims.fao.org/aos/agrovoc/c_6161P01 - Conservation de la nature et ressources foncières[ SDU.STU.CL ] Sciences of the Universe [physics]/Earth Sciences/Climatologyhttp://aims.fao.org/aos/agrovoc/c_7252http://aims.fao.org/aos/agrovoc/c_7497ENSOModèle mathématiquehttp://aims.fao.org/aos/agrovoc/c_8500http://aims.fao.org/aos/agrovoc/c_1671P40 - Météorologie et climatologieTélédétectionhttp://aims.fao.org/aos/agrovoc/c_29553[SDE.MCG]Environmental Sciences/Global ChangesNormalized Difference Vegetation Indexhttp://aims.fao.org/aos/agrovoc/c_35196Interannual variabilityhttp://aims.fao.org/aos/agrovoc/c_6911Donnée climatiquePrecipitationCombined resulthttp://aims.fao.org/aos/agrovoc/c_8176http://aims.fao.org/aos/agrovoc/c_2676PrécipitationWinter rainfallIntertropical Convergence ZoneVégétation15. Life on landTempérature13. Climate actionVegetation-atmosphere interactionsAfricaClimatologiehttp://aims.fao.org/aos/agrovoc/c_4964Énergie solaire
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