Search results for "presence-only data"

showing 4 items of 4 documents

Antarctic and Sub-Antarctic Asteroidea database

2018

The present dataset is a compilation of georeferenced occurrences of asteroids (Echinodermata: Asteroidea) in the Southern Ocean. Occurrence data south of 45°S latitude were mined from various sources together with information regarding the taxonomy, the sampling source and sampling sites when available. Records from 1872 to 2016 were thoroughly checked to ensure the quality of a dataset that reaches a total of 13,840 occurrences from 4,580 unique sampling events. Information regarding the reproductive strategy (brooders vs. broadcasters) of 63 species is also made available. This dataset represents the most exhaustive occurrence database on Antarctic and Sub-Antarctic asteroids.

0106 biological sciencesPresence-only dataSciences et médecine vétérinairesReproductive strategyOccurrence data[SDV.BID]Life Sciences [q-bio]/BiodiversityEvolution des espècescomputer.software_genre010603 evolutionary biology01 natural sciencesLatitudeAsteroideaData analysis & Modellinglcsh:ZoologyAnimalia14. Life underwaterlcsh:QL1-991Southern OceanEcology Evolution Behavior and SystematicsInvertebrata[ SDV.BID ] Life Sciences [q-bio]/Biodiversity[ SDE.BE ] Environmental Sciences/Biodiversity and EcologySub-AntarcticDatabaseEcologie010604 marine biology & hydrobiologySampling (statistics)Sub antarcticGeographyBiogeographyAntarctic Asteroidea Presence-only data Southern Ocean Sub-AntarcticGeoreferenceAnimal Science and ZoologyAntarctic[SDE.BE]Environmental Sciences/Biodiversity and Ecologypresence-only dataPolarcomputerData PaperEchinodermataZooKeys
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Data Augmentation Approach in Bayesian Modelling of Presence-only Data

2011

Abstract Ecologists are interested in prediction of potential distribution of species in suitable areas, essential for planning conservation and management strategies. Unfortunately, often the only available information in such studies is the true presence of the species at few locations of the study area and the associated environmental covariates over the entire area, referred as presence-only data. We propose a Bayesian approach to estimate logistic linear regressions adapted to presence-only data through the introduction of a random approximation of the correction factor in the adjusted logistic model that allows us to overcome the need to know a priori the prevalence of the species.

Data augmentationPresence-only dataComputer scienceBayesian probabilityLogistic regressionBayesian inferencePseudo-absence approachBayesian statisticsBayesian model; Data augmentation; MCMC algorithm; Potential distribution; Presence-only data; Pseudo-absence approachBayesian model Data augmentation MCMC algorithm Presence-only data Pseudo-absence approach Potential distributionpotentialdistributionBayesian modelBayesian multivariate linear regressionPotential distributionStatisticsCovariateEconometricsGeneral Earth and Planetary Sciencespseudo-absence approach; potentialdistribution.; data augmentation; presence-only data; potential distribution; mcmc algorithm; bayesian modelBayesian linear regressionBayesian averageMCMC algorithmGeneral Environmental ScienceProcedia Environmental Sciences
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Spatial Bayesian Modeling of Presence-only Data

2011

Settore ING-IND/09 - Sistemi per l'Energia e L'AmbienteData augmentationMCMCPresence-only dataBayesian modelSpatial distributionBayesian model Data augmentation MCMC Presence-only data Spatial distribution.Bayesian model; Data augmentation; MCMC; Presence-only data; Spatial distribution.
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Identifying territories using presence-only citizen science data : An application to the Finnish wolf population

2022

Citizens, community groups and local institutions participate in voluntary biological monitoring of population status and trends by providing species data e.g. for regulations and conservation. Sophisticated statistical methods are required to unlock the potential of such data in the assessment of wildlife populations. We develop a statistical modelling framework for identifying territories based on presence-only citizen science data. The framework can be used to jointly estimate the number of active animal territories and their locations in time. Our approach is based on a data generating model which consists of a dynamic submodel for the appearance/removal of territories and an observatio…

reviiritEcological Modelingbayesilainen menetelmäcitizen science datasusipaikkatietoanalyysisequential Monte CarloeläinkannatBayesian statisticsterritory identificationMonte Carlo -menetelmätpopulaatiotkansalaishavainnotkansalaistiedepresence-only dataspatio-temporal model
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