Search results for "Bayesian modelling"

showing 7 items of 7 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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Modelling the presence of disease under spatial misalignment using Bayesian latent Gaussian models.

2015

Modelling patterns of the spatial incidence of diseases using local environmental factors has been a growing problem in the last few years. Geostatistical models have become popular lately because they allow estimating and predicting the underlying disease risk and relating it with possible risk factors. Our approach to these models is based on the fact that the presence/absence of a disease can be expressed with a hierarchical Bayesian spatial model that incorporates the information provided by the geographical and environmental characteristics of the region of interest. Nevertheless, our main interest here is to tackle the misalignment problem arising when information about possible covar…

Health (social science)Computer scienceEpidemiologyGaussian030231 tropical medicineGeography Planning and DevelopmentBayesian probabilityNormal Distributionlcsh:G1-922Medicine (miscellaneous)Bayesian inference01 natural sciencesNormal distribution010104 statistics & probability03 medical and health sciencessymbols.namesakeBayes' theorem0302 clinical medicineCovariateStatisticsINLAHierarchical Bayesian modellingEconometricsHumansGeostatistics0101 mathematicsSpatial AnalysisStochastic ProcessesModels StatisticalHealth PolicyBayes TheoremFasciola hepaticaLaplace's methodsymbolsGaussian network modelBayesian Kriginglcsh:Geography (General)Geospatial health
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Statistical models and inference for spatial point patterns with intensity-dependent marks

2009

MCMCGaussian excursion setbayesilainen menetelmätilastomenetelmätsademetsätBitterlich samplinglog Gaussian Cox processpine samplingsdensity-dependenceMonte Carlo -menetelmätmark-dependent thinningalgoritmitmarked point processrandom set marked Cox processtropical rainforestBayesian modelling
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Bayesian Modeling of caries onset and progression: the Belo Horizonte Caries Prevention Study

2011

bayesian modelling zero-inflated binomial dmft count dataSettore MED/01 - Statistica Medica
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Cluster priors in the Bayesian modelling of fMRI data

2001

bildanalysmarked point processesMonte Carlo -menetelmätMarkov chain Monte Carloimage analysiskuva-analyysiMarkovin ketjutmagneettitutkimusaivotfunctional magnetic resonance imaginghuman brainBayesian modellingMarkovkedjor
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High‐resolution 3D forest structure explains ecomorphological trait variation in assemblages of saproxylic beetles

2022

1. Climate, topography and the 3D structure of forests are major drivers affecting local species communities. However, little is known about how the specific functional traits of saproxylic (wood-living) beetles, involved in the recycling of wood, might be affected by those environmental characteristics. 2. Here, we combine ecological and morphological traits available for saproxylic beetles and airborne laser scanning (ALS) data in Bayesian trait-based joint species distribution models to study how traits drive the distributions of more than 230 species in temperate forests of Europe. 3. We found that elevation (as a proxy for temperature and precipitation) and the proportion of conifers p…

environmental gradientLiDARairborne laser scanning; Bayesian modelling; Coleoptera; environmental gradient; functional traits; HMSC; LiDAR; phylogenyDIVERSITYMELANISMairborne laser scanningECOLOGYphylogenyfunctional traits3D-mallinnusPREDICTORSEcology Evolution Behavior and SystematicskovakuoriaisetlajistokartoitusfylogeniaEcologybayesilainen menetelmäeliöyhteisötilmastonmuutoksetHMSCmetsätEVOLUTIONColeopteraMORPHOLOGICAL TRAITSFUNCTIONAL TRAITS1181 Ecology evolutionary biologyBIODIVERSITYABUNDANCEBayesian modellingympäristönmuutoksetlaserkeilausRESPONSESFunctional Ecology
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Spatial patterns of, and environmental controls on, soil properties at a riparianepaddock interface

2012

Abstract Riparian zones are prominent features of agricultural landscapes because they are the last point to intercept nutrients and sediments before they enter water bodies. We investigated the soil properties, nutrient dynamics and vegetation composition at the riparian–agriculture interface. Soil physicochemical and vegetation properties were spatially heterogeneous along the transition from the grazed paddock into the un-grazed and revegetated riparian zone. Soil C stocks varied considerably across the site, with values ranging from 2% in the paddock to 5% in the riparian zone. Using Bayesian model selection, a predictive model for total soil carbon was developed. By including soil mois…

geographygeography.geographical_feature_categorySoil nutrientSettore AGR/13 - Chimica AgrariaSoil ScienceSoil scienceVegetationSoil carbonSoil respirationSoil typeMicrobiologySoil carbonSoil respirationRiparian restorationNutrientSoil waterEnvironmental scienceEcosystemNuclear magnetic resonance spectroscopy (NMR)Bayesian modellingRiparian zoneNitrogen cycling
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