Search results for "Bayesian"

showing 10 items of 604 documents

Does the genetic diversity among pubescent white oaks in southern Italy, Sicily and Sardinia islands support the current taxonomic classification?

2020

AbstractMolecular diversity analysis of deciduous pubescent oaks was conducted for populations from Calabria, Sicily and Sardinia. The aims of this study were twofold. First, to provide data on the genetic diversity of pubescent oaks from an understudied area which currently exhibits one of the highest concentrations of pubescent oak species in Europe. Second, to verify if these groups of oaks are genetically distinct and if their identification is in accordance with the current taxonomic classification. Molecular analyses of leaf material of 480 trees from seventeen populations belonging to putatively different pubescent oak species (Quercus amplifolia,Q. congesta,Q. dalechampii,Q. ichnusa…

0106 biological sciencesBiogeographyBayesian analysisZoologyPlant ScienceBiology010603 evolutionary biology01 natural sciences03 medical and health sciencesBiogeography Bayesian analysis Genetic variation Nuclear microsatellites EST-SSRs Pubescent oaks TaxonomyGenetic variationGenetic variation030304 developmental biologyBayesian analysis; Biogeography; EST-SSRs; Genetic variation; Nuclear microsatellites; Pubescent oaks; TaxonomyTaxonomy0303 health sciencesGenetic diversityNuclear microsatellitesForestrylanguage.human_languageEST-SSRsPubescent oaksDeciduousGenetic distanceBiogeographySettore BIO/03 - Botanica Ambientale E ApplicatalanguageTaxonomy (biology)Species richnessSicilian
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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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Calibrating Expert Assessments Using Hierarchical Gaussian Process Models

2020

Expert assessments are routinely used to inform management and other decision making. However, often these assessments contain considerable biases and uncertainties for which reason they should be calibrated if possible. Moreover, coherently combining multiple expert assessments into one estimate poses a long-standing problem in statistics since modeling expert knowledge is often difficult. Here, we present a hierarchical Bayesian model for expert calibration in a task of estimating a continuous univariate parameter. The model allows experts' biases to vary as a function of the true value of the parameter and according to the expert's background. We follow the fully Bayesian approach (the s…

0106 biological sciencesComputer sciencepäätöksentekoRECONCILIATIONInferencecomputer.software_genre01 natural sciencesSTOCK ASSESSMENTenvironmental management010104 statistics & probabilityJUDGMENTSELICITATIONkalakantojen hoito111 Mathematicstilastolliset mallitReliability (statistics)Applied Mathematicsgaussiset prosessitfisheries sciencebias correctionexpert elicitationPROBABILITY62P1260G15symbols62F15Statistics and ProbabilityarviointimenetelmätBayesian probabilityenvironmental management.Bayesian inferenceMachine learningHEURISTICSsymbols.namesakeasiantuntijatMANAGEMENT0101 mathematicsGaussian processGaussian processCATCH LIMITSbusiness.industrybayesilainen menetelmä010604 marine biology & hydrobiologyUnivariateExpert elicitationOPINIONSupra BayesArtificial intelligenceHeuristicsbusinessFISHERIEScomputerBayesian Analysis
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Integrating spatial management measures into fisheries: The Lepidorhombus spp. case study

2020

Most fisheries management systems rely on a set of regulatory measures to achieve desired objectives. Controls on catch and effort are usually supplemented with gear restrictions, minimum landing sizes, and in the framework of the new common fisheries policy, limitation of discards and by-catch. However, the increasing use of spatial management measures such as conservation areas or spatial and temporal area closures faces new challenges for fishery managers. Here we present an integrated spatial framework to identify areas in which undersized commercial species are more abundant. Once these areas are identified they could be avoided by fishers, minimizing the fishing impact over the immatu…

0106 biological sciencesEconomics and EconometricsCentro Oceanográfico de SantanderFishingManagement Monitoring Policy and LawAquatic Science01 natural sciencesEnvironmental dataIntegrated fishery managementMedio MarinoUndersized fishBayesian modelsGeneral Environmental Sciencefishbiology010604 marine biology & hydrobiologySensitive areas04 agricultural and veterinary sciencesbiology.organism_classificationDiscardsFisheryLepidorhombusGeographyDiscardsocean policySpatial managementLanding obligation040102 fisheriesSpatial ecology0401 agriculture forestry and fisheriesFisheries managementMegrimecologyLaw
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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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Small-scale shrimp fisheries bycatch: a multi-criteria approach for data-poor situations

2020

Abstract Bycatch and discards from small-scale fisheries (SSF) are usually ignored when compared with industrial fisheries, not only by policy-makers, but also by scientists. Therefore, SSF social, economic and ecological impacts are poorly known and especially in the context of incidental catches, regardless of whether they become bycatch or discards. Such neglect is worrisome due to the role that SSF play in food security and poverty alleviation, particularly in coastal and rural communities in developing countries. In this study, a combination of sampling data and the fishers' behavior (specifically the basis of their decision on where to fish) were used. Bayesian models were applied to …

0106 biological sciencesEconomics and EconometricsFishingFishers' behaviorContext (language use)Management Monitoring Policy and LawAquatic Science01 natural sciencesCentro Oceanográfico de MurciaPesqueríasBayesian modelsGeneral Environmental ScienceFood security010604 marine biology & hydrobiologyShrimp fisherySubsistence agriculture04 agricultural and veterinary sciencesLivelihoodDiscardsBycatchFisheryGeographyEconomic incentives040102 fisheries0401 agriculture forestry and fisheriesLaw
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Examining nonstationarity in the recruitment dynamics of fishes using Bayesian change point analysis

2017

Marine ecosystems can undergo regime shifts, which result in nonstationarity in the dynamics of the fish populations inhabiting them. The assumption of time-invariant parameters in stock–recruitment models can lead to severe errors when forecasting renewal ability of stocks that experience shifts in their recruitment dynamics. We present a novel method for fitting stock–recruitment models using the Bayesian online change point detection algorithm, which is able to cope with sudden changes in the model parameters. We validate our method using simulations and apply it to empirical data of four demersal fishes in the southern Gulf of St. Lawrence. We show that all of the stocks have experience…

0106 biological sciencesEmpirical dataEcology010604 marine biology & hydrobiologyBayesian probabilityModel parametersAquatic Science010603 evolutionary biology01 natural sciencesChange-Point AnalysisEconometricsEnvironmental scienceFish <Actinopterygii>Marine ecosystem14. Life underwaterEcology Evolution Behavior and SystematicsChange detectionCanadian Journal of Fisheries and Aquatic Sciences
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Thompson Sampling Based Active Learning in Probabilistic Programs with Application to Travel Time Estimation

2019

The pertinent problem of Traveling Time Estimation (TTE) is to estimate the travel time, given a start location and a destination, solely based on the coordinates of the points under consideration. This is typically solved by fitting a function based on a sequence of observations. However, it can be expensive or slow to obtain labeled data or measurements to calibrate the estimation function. Active Learning tries to alleviate this problem by actively selecting samples that minimize the total number of samples needed to do accurate inference. Probabilistic Programming Languages (PPL) give us the opportunities to apply powerful Bayesian inference to model problems that involve uncertainties.…

0106 biological sciencesEstimation0303 health sciencesSequenceActive learning (machine learning)business.industryComputer scienceProbabilistic logicInferenceFunction (mathematics)Bayesian inferenceMachine learningcomputer.software_genre010603 evolutionary biology01 natural sciences03 medical and health sciencesArtificial intelligencebusinesscomputerThompson sampling030304 developmental biology
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The past and the present in decision-making: the use of conspecific and heterospecific cues in nest site selection

2014

International audience; Nest site selection significantly affects fitness, so adaptations for assessment of the qualities of available sites are expected. The assessment may be based on personal or social information, the latter referring to the observed location and performance of both conspecific and heterospecific individuals. Contrary to large-scale breeding habitat selection, small-scale nest site selection within habitat patches is insufficiently understood. We analyzed nest site selection in the migratory Collared Flycatcher Ficedula albicollis in relation to present and past cues provided by conspecifics and by resident tits within habitat patches by using long-term data. Collared F…

0106 biological sciencesFicedula albicollismedia_common.quotation_subject[SDV]Life Sciences [q-bio]Bayesian statistics010603 evolutionary biology01 natural sciencesCompetition (biology)Nest0501 psychology and cognitive sciences050102 behavioral science & comparative psychologyEcology Evolution Behavior and SystematicsSelection (genetic algorithm)media_commonParusbiologyReproductive successEcologyprospecting05 social sciencesheterospecific attractionInterspecific competitionbiology.organism_classificationsocial informationconspecific attractionHabitatcompetition
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Comparison of Bayesian and numerical optimization-based diet estimation on herbivorous zooplankton

2020

Consumer diet estimation with biotracer-based mixing models provides valuable information about trophic interactions and the dynamics of complex ecosystems. Here, we assessed the performance of four Bayesian and three numerical optimization-based diet estimation methods for estimating the diet composition of herbivorous zooplankton using consumer fatty acid (FA) profiles and resource library consisting of the results of homogeneous diet feeding experiments. The method performance was evaluated in terms of absolute errors, central probability interval checks, the success in identifying the primary resource in the diet, and the ability to detect the absence of resources in the diet. Despite …

0106 biological sciencesFood ChainBayesian probability010603 evolutionary biology01 natural sciencesZooplanktonGeneral Biochemistry Genetics and Molecular BiologyDistance measuresZooplanktonFASTARStatisticsAnimalsravintoaineetMixSIARHerbivoryMathematicsTrophic levelestimointi2. Zero hungerEstimationHerbivorefood web010604 marine biology & hydrobiologybayesilainen menetelmäplanktonFatty AcidsBayes TheorembiotracersArticlesFood webDietDaphniaQFASAvesikirputGeneral Agricultural and Biological SciencesEstimation methodsravintoverkotFood Analysis
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