Search results for " mac"

showing 10 items of 3066 documents

Fish introductions and light modulate food web fluxes in tropical streams: a whole-ecosystem experimental approach.

2016

Decades of ecological study have demonstrated the importance of top-down and bottom-up controls on food webs, yet few studies within this context have quantified the magnitude of energy and material fluxes at the whole-ecosystem scale. We examined top-down and bottom-up effects on food web fluxes using a field experiment that manipulated the presence of a consumer, the Trinidadian guppy Poecilia reticulata, and the production of basal resources by thinning the riparian forest canopy to increase incident light. To gauge the effects of these reach-scale manipulations on food web fluxes, we used a nitrogen (15 N) stable isotope tracer to compare basal resource treatments (thinned canopy vs. co…

0106 biological sciencesCanopyNeotropicsFood ChainLightPopulation DynamicsContext (language use)010603 evolutionary biology01 natural sciencesRiverstrophic linkagesAnimalsEcosystemTrinidad guppyBiomassEcology Evolution Behavior and Systematicstop-down and bottom-up effectsTrophic levelTropical ClimateDetritusbiologyEcology010604 marine biology & hydrobiologynitrogen fluxFishesWaterbiology.organism_classificationFood webGuppyreach-scale experimentstable isotope tracersTrinidad and TobagoBenthic zoneta1181stream food webbenthic macroinvertebratesprimary productionEcology
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An assessment of the floristic composition, structure and possible origin of a liana forest in the Guayana Shield

2015

Liana is a life form that possess high importance in many neotropical forests. Density of climbers apparently increases with the intervention rate (eg. logging). The aim of this work is to characterize the structure, floristic composition and soils of a sector classified as Liana Forest (LF). We identified a LF sector in a not-logged area; three 1 ha square plots were measured (individuals ≥ 10 cm dbh, “diameter at breast height”). In each plot we evaluate four 100 m2 square understory sub-plots (all spermatophyta individuals < 10 cm dbh). LF has a low canopy (< 15 m) and is dominated by Alexa imperatricis and Pentaclethra macroloba. Basal area (20.4 m2ha-1) and diversity (H´= 2.6) ar…

0106 biological sciencesCanopyfood.ingredientEcologyForest managementDiameter at breast heightForestryEdaphicPlant ScienceUnderstoryBiology010603 evolutionary biology01 natural sciencesBasal areafoodLianaPentaclethra macrolobaEcology Evolution Behavior and Systematics010606 plant biology & botany
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Benchmark database for fine-grained image classification of benthic macroinvertebrates

2018

Managing the water quality of freshwaters is a crucial task worldwide. One of the most used methods to biomonitor water quality is to sample benthic macroinvertebrate communities, in particular to examine the presence and proportion of certain species. This paper presents a benchmark database for automatic visual classification methods to evaluate their ability for distinguishing visually similar categories of aquatic macroinvertebrate taxa. We make publicly available a new database, containing 64 types of freshwater macroinvertebrates, ranging in number of images per category from 7 to 577. The database is divided into three datasets, varying in number of categories (64, 29, and 9 categori…

0106 biological sciencesComputer scienceta1172Sample (statistics)monitorointi02 engineering and technologyneuroverkot01 natural sciencesConvolutional neural network0202 electrical engineering electronic engineering information engineeringkonenäköfine-grained classification14. Life underwaterFine-grained classificationInvertebrateta113ta112Contextual image classificationbusiness.industry010604 marine biology & hydrobiologyDeep learningConvolutional Neural NetworksBenchmark databasedeep learningPattern recognitionDeep learningselkärangattomatvedenlaatu6. Clean waterkoneoppiminenBenthic zoneBenthic macroinvertebratesbiomonitoringSignal ProcessingBiomonitoringta1181lajinmääritys020201 artificial intelligence & image processingComputer Vision and Pattern RecognitionArtificial intelligenceWater qualitybusinessbenthic macroinvertebrates
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Fine‐grain beta diversity of Palaearctic grassland vegetation

2021

QUESTIONS: Which environmental factors influence fine-grain beta diversity of vegetation and do they vary among taxonomic groups? LOCATION: Palaearctic biogeographic realm. METHODS: We extracted 4,654 nested-plot series with at least four different grain sizes between 0.0001 m² and 1,024 m² from the GrassPlot database, covering a wide range of different grassland and other open habitat types. We derived extensive environmental and structural information for these series. For each series and four taxonomic groups (vascular plants, bryophytes, lichens, all), we calculated the slope parameter (z-value) of the power law species–area relationship (SAR), as a beta diversity measure. We tested whe…

0106 biological sciencesCzechAgriculture and Food SciencesFine grainelevation333.7: Landflächen NaturerholungsgebietehabitatPlant ScienceMaster planFine-grain beta diversity01 natural sciencesScale dependenceevolutionaryRICHNESSvascular plantsHABITATMacroecologyComputingMilieux_MISCELLANEOUSmedia_commonMean occupancyProductivity2. Zero hungerdisturbance0303 health sciencesEcologySettore BIO/02 - Botanica SistematicaEnvironmental researchPalaearctic grasslanddifferentiationenvironmental heterogeneityspecies-area relationship (SAR)gradientDIFFERENTIATION580: Pflanzen (Botanik)disturbance; elevation; fine-grain beta diversity; heterogeneity; land use; macroecology; mean occupancy; Palaearctic grassland; productivity; scale dependence; species–area relationship (SAR); z-valuescale dependencelanguagemacroecologyproductivitymedia_common.quotation_subjectmean occupancyLibrary scienceSpecies–area relationship (SAR)Environmental drivers Grasslands Lichens Mosses Species-area relationship SAR Vascular Plands010603 evolutionary biologySpecies-area curve03 medical and health sciencesspecies–area relationship (SAR)ExcellencePolitical scienceGRADIENTSlovak030304 developmental biologyspatial scalefine-grain beta diversityBiology and Life Sciencesland useDisturbance15. Life on landZ-valuelanguage.human_languageENVIRONMENTAL HETEROGENEITYEarth and Environmental Sciencesz-valueElevationLand useEVOLUTIONARYSPATIAL SCALESPECIES-AREA RELATIONSHIPSVASCULAR PLANTS[SDE.BE]Environmental Sciences/Biodiversity and EcologyheterogeneityHeterogeneityrichness
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Macrophyte assessment in European lakes: Diverse approaches but convergent views of ‘good’ ecological status

2018

Graphical abstract

0106 biological sciencesEvolution/dk/atira/pure/thematic/inbo_th_00006/dk/atira/pure/policy/kaderrichtlijn_water_krw_General Decision SciencesZannichellia palustrisSpecies and biotopes010501 environmental sciences01 natural sciencesArticle/dk/atira/pure/thematic/inbo_th_00044Water Framework DirectiveAbundance (ecology)Restoration ecologyEcology Evolution Behavior and SystematicsComputingMethodologies_COMPUTERGRAPHICS0105 earth and related environmental sciencesB003-ecologyEcologybiologyEcologyEcological status010604 marine biology & hydrobiologyPhosphorusEcological assessmentNutrientsVegetationEutrophication15. Life on landbiology.organism_classificationBehaviour and SystematicsMacrophytemacrophytes (aquatic plants)PolicyGeographyWater Framework DirectiveIndicator species13. Climate action/dk/atira/pure/discipline/B000/B003articlesSpecies richness/dk/atira/pure/taxonomic/macrofytenAquatic macrophytesSpecies richnessEcological Indicators
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A Methodology to Derive Global Maps of Leaf Traits Using Remote Sensing and Climate Data

2018

This paper introduces a modular processing chain to derive global high-resolution maps of leaf traits. In particular, we present global maps at 500 m resolution of specific leaf area, leaf dry matter content, leaf nitrogen and phosphorus content per dry mass, and leaf nitrogen/phosphorus ratio. The processing chain exploits machine learning techniques along with optical remote sensing data (MODIS/Landsat) and climate data for gap filling and up-scaling of in-situ measured leaf traits. The chain first uses random forests regression with surrogates to fill gaps in the database (> 45% of missing entries) and maximizes the global representativeness of the trait dataset. Plant species are then a…

0106 biological sciencesFOS: Computer and information sciences010504 meteorology & atmospheric sciencesSpecific leaf areaClimateBos- en LandschapsecologieSoil ScienceFOS: Physical sciencesApplied Physics (physics.app-ph)010603 evolutionary biology01 natural sciencesStatistics - ApplicationsGoodness of fitAbundance (ecology)Machine learningForest and Landscape EcologyApplications (stat.AP)Computers in Earth SciencesPlant ecologyVegetatie0105 earth and related environmental sciencesRemote sensingMathematics2. Zero hungerPlant traitsVegetationData stream miningClimate; Landsat; Machine learning; MODIS; Plant ecology; Plant traits; Random forests; Remote sensing; Soil Science; Geology; Computers in Earth SciencesGlobal MapRegression analysisGeologyPhysics - Applied Physics15. Life on landRandom forestsRemote sensingPE&RCRandom forestMODISTraitVegetatie Bos- en LandschapsecologieVegetation Forest and Landscape EcologyLandsat
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Temperate Fish Detection and Classification: a Deep Learning based Approach

2021

A wide range of applications in marine ecology extensively uses underwater cameras. Still, to efficiently process the vast amount of data generated, we need to develop tools that can automatically detect and recognize species captured on film. Classifying fish species from videos and images in natural environments can be challenging because of noise and variation in illumination and the surrounding habitat. In this paper, we propose a two-step deep learning approach for the detection and classification of temperate fishes without pre-filtering. The first step is to detect each single fish in an image, independent of species and sex. For this purpose, we employ the You Only Look Once (YOLO) …

0106 biological sciencesFOS: Computer and information sciencesComputer Science - Machine LearningComputer scienceComputer Vision and Pattern Recognition (cs.CV)Computer Science - Computer Vision and Pattern Recognition010603 evolutionary biology01 natural sciencesConvolutional neural networkVDP::Matematikk og Naturvitenskap: 400::Informasjons- og kommunikasjonsvitenskap: 420Machine Learning (cs.LG)Artificial IntelligenceClassifier (linguistics)FOS: Electrical engineering electronic engineering information engineeringbusiness.industry010604 marine biology & hydrobiologyDeep learningImage and Video Processing (eess.IV)Process (computing)Pattern recognitionElectrical Engineering and Systems Science - Image and Video ProcessingObject detectionA priori and a posterioriNoise (video)Artificial intelligenceTransfer of learningbusiness
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Vitality and growth of the threatened lichen Lobaria pulmonaria (L.) Hoffm. in response to logging and implications for its conservation in mediterra…

2020

Forest logging can be detrimental for non-vascular epiphytes, determining the loss of key components for ecosystem functioning. Legal logging in a Mediterranean mixed oak forest (Tuscany, Central Italy) in 2016 heavily impacted sensitive non-vascular epiphytes, including a large population of the threatened forest lichen Lobaria pulmonaria (L.) Hoffm. This event offered the background for this experiment, where the potential effects of logging in oak forests are simulated by means of L. pulmonaria micro-transplants (thallus fragments &lt

0106 biological sciencesForest managementBiodiversity conservation010603 evolutionary biology01 natural sciencesGrowth ratesPulmonariaEpiphytic macrolichenEpiphytic macrolichensLobaria pulmonariabiologyGrowth rateForest managementLoggingQuercus cerrisForestryForestrylcsh:QK900-989biology.organism_classificationBiodiversity conservation; Chlorophyll fluorescence; Epiphytic macrolichens; Forest management; Growth rates; Indicator speciesIndicator speciesIndicator speciesThreatened specieslcsh:Plant ecologyEpiphyteChlorophyll fluorescence010606 plant biology & botany
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Plot - A new tool for global vegetation analyses

2019

23Biodiversity Conservation Department, ISPRA – Italian National Institute for Environmental Protection and Research, Rome, Italy

0106 biological sciencesGender and DiversityBos- en LandschapsecologieBiomeэкология сообществPlant Scienceэкоинформатика[SDV.BID.SPT]Life Sciences [q-bio]/Biodiversity/Systematics Phylogenetics and taxonomy01 natural sciencesAbundance (ecology)EcoinformaticsForest and Landscape EcologyMacroecologyResearch methodology and philosophyрастительностьbiodiversityEcologyvascular plantPlot база данныхVegetation[SDV.BV.BOT]Life Sciences [q-bio]/Vegetal Biology/BotanicsPE&RCfunctional diversityGeography580: Pflanzen (Botanik)Ecosystems Researchvegetation relevémacroecologyPlantenecologie en Natuurbeheerphylogenetic diversityVegetatie Bos- en Landschapsecologievegetation relevebiodiversity ; community ecology ; ecoinformatics ; functional diversity ; global scale ; macroecology ; phylogenetic diversity ; plot database ; staxonomic ; diversity ; vascular plant ; vegetation relevé/dk/atira/pure/core/keywords/nachhaltigkeitswissenschaftContext (language use)Plant Ecology and Nature Conservationecoinformatic010603 evolutionary biologySustainability Scienceecoinformatics[SDV.EE.ECO]Life Sciences [q-bio]/Ecology environment/EcosystemsEcoinformaticsbiodiversity; community ecology; ecoinformatics; functional diversity; global scale; macroecology; phylogenetic diversity; plot database; sPlot; taxonomic diversity; vascular plant; vegetation relevé577: Ökologieplot database/dk/atira/pure/core/keywords/biologyVegetatieVegetationsPlotPlant communityMacroecology Phylogenetic diversitybiodiversity; community ecology; ecoinformatics; functional diversity; global scale; macroecology; phylogenetic diversity; plot database; sPlot; taxonomic diversity; vascular plant; vegetation releve15. Life on landтаксономическое разнообразиеtaxonomic diversityglobal scaleбиоразнообразиеCrop husbandrySpecies richnessPhysical geographyVegetation Forest and Landscape Ecology[SDE.BE]Environmental Sciences/Biodiversity and Ecology/dk/atira/pure/core/keywords/gender_and diversityфилогенетическое разнообразиемакроэкологияcommunity ecology010606 plant biology & botany
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Does catchment geodiversity foster stream biodiversity?

2019

Abstract Context One approach to maintain the resilience of biotic communities is to protect the variability of abiotic characteristics of Earth’s surface, i.e. geodiversity. In terrestrial environments, the relationship between geodiversity and biodiversity is well recognized. In streams, the abiotic properties of upstream catchments influence stream communities, but the relationships between catchment geodiversity and aquatic biodiversity have not been previously tested. Objectives The aim was to compare the effects of local environmental and catchment variables on stream biodiversity. We specifically explored the usefulness of catchment geodiversity in explaining the species richness on …

0106 biological sciencesGeography Planning and DevelopmentDrainage basinBiodiversity01 natural sciencesbakteeritfreshwatersspecies richnessbacteriaSCALEAbiotic componentFreshwatersgeography.geographical_feature_categoryCLIMATE-CHANGEEcologyMacroinvertebratesEcologyenvironmental heterogeneityselkärangattomatgeodiversiteettiHabitatCatchment featuresvirtavedet1181 Ecology evolutionary biologyBENTHIC MACROINVERTEBRATE ASSEMBLAGESvaluma-alueetmacroinvertebratesCONTEXT DEPENDENCY010603 evolutionary biologydiatomsPLANT-SPECIES RICHNESSpiilevätcatchment features1172 Environmental sciencesNature and Landscape ConservationDiatomsgeographyLand useBacteriaFRESH-WATER BIODIVERSITYLAND-USELANDSCAPE010604 marine biology & hydrobiologyEnvironmental heterogeneity15. Life on landCOMMUNITY-ENVIRONMENT RELATIONSHIPSluonnon monimuotoisuusbiodiversiteettiGeodiversity13. Climate actionSpecies richnessLandscape ecologySpecies richnessMICROBIAL DIVERSITY
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