Search results for "Data"

showing 10 items of 12992 documents

A novel method to predict dark diversity using unconstrained ordination analysis

2019

[Questions] Species pools are the product of complex ecological and evolutionary mechanisms, operating over a range of spatial scales. Here, we focus on species absent from local sites but with the potential to establish within communities — known as dark diversity. Methods for estimating dark diversity are still being developed and need to be compared, as well as tested for the type, and amount, of reference data needed to calibrate these methods. [Location] South Bohemia (48°58′ N, 14°28′ E) and Železné Hory (49°52′ N, 15°34′ E), Czech Republic. [Method] We compared a widely accepted algorithm to estimate species pools (Beals smoothing index, based on species co-occurrence) against a nove…

0106 biological sciencesEcologyReference data (financial markets)Species poolCommunity structureBeals smoothing indexPlant Science010603 evolutionary biology01 natural sciencesCommunity structureEllenberg valuesUnconstrained ordinationCommon speciesDark diversityStatisticsRange (statistics)OrdinationScale (map)Nested sampling algorithmSmoothing010606 plant biology & botanyMathematics
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A new algorithm for the identification of dives reveals the foraging ecology of a shallow-diving seabird using accelerometer data

2017

International audience; The identification of feeding events is crucial to our understanding of the foraging ecology of seabirds. Technology has made small devices, such as time-depth recorders (TDRs) and accelerometers available. However, TDRs might not be sensitive enough to identify shallow dives, whereas accelerometers might reveal more subtle behaviours at a smaller temporal scale. Due to the limitations of TDRs, the foraging ecology of many shallow-diving seabirds has been poorly investigated to date. We thus developed an algorithm to identify dive events in a shallow-diving seabird species, the Scopoli’s shearwater, using only accelerometer data. The accuracy in the identification of…

0106 biological sciencesEcologybiologyEcology010604 marine biology & hydrobiologyEcology (disciplines)ForagingAquatic Sciencebiology.organism_classification010603 evolutionary biology01 natural sciencesShearwaterCalonectris diomedea foraging divingSettore AGR/11 - Entomologia Generale E Applicatabiology.animal[SDE]Environmental Sciences14. Life underwaterAccelerometer dataSeabirdAlgorithmEcology Evolution Behavior and Systematics
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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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Assessing multiple sources of data to detect illegal fishing, trade and mislabelling of elasmobranchs in Greek markets

2020

Abstract Elasmobranchs, extremely charismatic and threatened animals, still are an important economic source for fishers in many parts of the world, providing significant income through trade. Even though Greek seas host at least 67 elasmobranch species, our knowledge about their biology and ecology is to a large extent unknown. In the present study the integration of conventional (legislation, official data from fisheries landings and fish market value and import/export data) and unconventional (social media) sources of data, accompanied with the use of genetics, aim at outlining the elasmobranch fisheries and trade in Greece and identifying “weak spots” that sabotage their conservation. R…

0106 biological sciencesEconomics and EconometricsEastern MediterraneanSettore BIO/05 - ZoologiaLegislationContext (language use)Management Monitoring Policy and LawAquatic Science01 natural sciencesseafood fraudsharkdata qualitymedia_common.cataloged_instanceEuropean unionGeneral Environmental Sciencemedia_commonbiologyrayskate010604 marine biology & hydrobiologyPrionace glaucaLegislature04 agricultural and veterinary sciencesbiology.organism_classificationIllegal fishingFisherytraceabilityThreatened species040102 fisheries0401 agriculture forestry and fisheriesFisheries managementLawMarine Policy
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Eliciting expert knowledge to inform stock status for data-limited stock assessments

2019

Data-limited fisheries are a major challenge for stock assessment analysts, as many traditional data-rich models cannot be implemented. Approaches based on stock reduction analysis offer simple ways to handle low data availability, but are particularly sensitive to assumptions on relative stock status (i.e., current biomass compared to unperturbed biomass). For the vast majority of data-limited stocks, stock status is unmeasured. The present study presents a method to elicit expert knowledge to inform stock status and a novel, user-friendly on-line application for expert elicitation. Expert opinions are compared to stock status derived from data-rich models. Here, it is evaluated how expert…

0106 biological sciencesEconomics and EconometricsStock assessmentstock statusstock-assessmentManagement Monitoring Policy and LawAquatic Sciencekalastuksenhoito01 natural sciencesRisk neutralasiantuntijatExperience levelStock (geology)General Environmental ScienceData limitedActuarial sciencetietovarannotkalakannat010604 marine biology & hydrobiologydata-limitedExpert elicitation04 agricultural and veterinary sciencesData availabilitykalastusexpert elicitationdatafisheries management040102 fisheriesta11810401 agriculture forestry and fisheriesFisheries managementPsychologyLawMarine Policy
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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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Multiple‐batch spawning as a bet‐hedging strategy in highly stochastic environments: An exploratory analysis of Atlantic cod

2021

Stochastic environments shape life‐history traits and can promote selection for risk‐spreading strategies, such as bet‐hedging. Although the strategy has often been hypothesised to exist for various species, empirical tests providing firm evidence have been rare, mainly due to the challenge in tracking fitness across generations. Here, we take a ‘proof of principle’ approach to explore whether the reproductive strategy of multiple‐batch spawning constitutes a bet‐hedging. We used Atlantic cod (Gadus morhua) as the study species and parameterised an eco‐evolutionary model, using empirical data on size‐related reproductive and survival traits. To evaluate the fitness benefits of multiple‐batc…

0106 biological sciencesEmpirical dataEvolutionReproductive strategyBiology010603 evolutionary biology01 natural sciencesrisk‐spreadingturskaEnvironmental riskGeneticsQH359-425Gadus14. Life underwaterEcology Evolution Behavior and SystematicsSelection (genetic algorithm)VDP::Landbruks- og Fiskerifag: 900::Fiskerifag: 920kuntosopeutuminenlisääntymiskäyttäytyminenEcology010604 marine biology & hydrobiologyOriginal ArticlesExploratory analysisbiology.organism_classificationlisääntyminenfitnesselinkiertomultiple‐batch spawningAtlantic codTraitOriginal Articlebet‐hedgingGeneral Agricultural and Biological SciencesAtlantic codenvironmental stochasticityympäristönmuutoksetEvolutionary Applications
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Mechanisms of reciprocity and diversity in social networks: a modeling and comparative approach

2018

Individual-based computer models show that different mechanisms, proximity-based or emotional bookkeeping, can lead to reciprocation. By comparing social networks from different computer models with those of empirical data, we show that the models’ social networks bear limited resemblance with some features of the observed social networks. This indicates that additional social processes (third-party awareness) may be needed in these models to represent more accurately the social behavior and interaction patterns observed in group-living animals.

0106 biological sciencesEmpirical dataSocial networkbusiness.industryComparative method[SDV]Life Sciences [q-bio]05 social sciencesGroup livingBiology010603 evolutionary biology01 natural sciencesBookkeepingSocial processesReciprocity (social psychology)[SDE]Environmental Sciences[SDV.BA.ZV]Life Sciences [q-bio]/Animal biology/Vertebrate Zoology0501 psychology and cognitive sciencesAnimal Science and Zoology050102 behavioral science & comparative psychologybusinessEcology Evolution Behavior and SystematicsComputingMilieux_MISCELLANEOUSCognitive psychologyDiversity (business)
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Notulae to the Italian native vascular flora: 8

2019

In this contribution, new data concerning the distribution of native vascular flora in Italy are presented. It includes new records, confirmations, exclusions, and status changes to the Italian administrative regions for taxa in the genera Ajuga, Chamaemelum, Clematis, Convolvulus, Cytisus, Deschampsia, Eleocharis, Epipactis, Euphorbia, Groenlandia, Hedera, Hieracium, Hydrocharis, Jacobaea, Juncus, Klasea, Lagurus, Leersia, Linum, Nerium, Onopordum, Persicaria, Phlomis, Polypogon, Potamogeton, Securigera, Sedum, Soleirolia, Stachys, Umbilicus, Valerianella, and Vinca. Nomenclatural and distribution updates, published elsewhere, and corrigenda are provided as Suppl. material 1.

0106 biological sciencesEndemic Floristic data Italy NomenclatureFloraNomenclatureZoologyFloristic dataPlant ScienceBiology010603 evolutionary biology01 natural scienceslcsh:QK1-989EndemicItalylcsh:BotanySettore BIO/03 - Botanica Ambientale E ApplicataNomenclatureEcology Evolution Behavior and SystematicsEndemic; Floristic data; Italy; Nomenclature010606 plant biology & botany
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