Search results for "spatial"

showing 10 items of 2121 documents

Comparative analysis of abundance–occupancy relationships for species at risk at both broad taxonomic and spatial scales

2015

The abundance–occupancy relationship is one of the most well-examined relationships in ecology. At the species level, a positive association has been widely documented. However, until recently, research on the nature of this relationship at broad taxonomic and spatial scales has been limited. Here, we perform a comparative analysis of 12 taxonomic groups across a large spatial scale (Canada), using data on Canadian species at risk: amphibians, arthropods, birds, freshwater fishes, lichens, marine fishes, marine mammals, molluscs, mosses, reptiles, terrestrial mammals, and vascular plants. We find a significantly positive relationship in all taxonomic groups with the exception of freshwater…

0106 biological scienceseducation.field_of_studyOccupancyEcology010604 marine biology & hydrobiologyEcology (disciplines)PopulationZoology15. Life on landBiology010603 evolutionary biology01 natural sciencesAbundance (ecology)Spatial ecologyAnimal Science and Zoology14. Life underwaterTaxonomic rankLicheneducationSpecies at riskEcology Evolution Behavior and SystematicsCanadian Journal of Zoology
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2017

When foraging in a social group, individuals are faced with the choice of sampling their environment directly or exploiting the discoveries of others. The evolutionary dynamics of this trade-off have been explored mathematically through the producer-scrounger game, which has highlighted socially exploitative behaviours as a major potential cost of group living. However, our understanding of the tight interplay that can exist between social dominance and scrounging behaviour is limited. To date, only two theoretical studies have explored this relationship systematically, demonstrating that because scrounging requires joining a competitor at a resource, it should become exclusive to high-rank…

0106 biological scienceseducation.field_of_studyResource (biology)EcologyGeneral Neuroscience05 social sciencesPopulationForagingGeneral MedicineBiology010603 evolutionary biology01 natural sciencesGeneral Biochemistry Genetics and Molecular BiologySocial groupDominance (ethology)RankingSpatial ecology0501 psychology and cognitive sciences050102 behavioral science & comparative psychologyGeneral Agricultural and Biological ScienceseducationEvolutionary dynamicsCognitive psychologyPeerJ
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A global analysis of complexity–biodiversity relationships on marine artificial structures

2020

Aim Topographic complexity is widely accepted as a key driver of biodiversity, but at the patch‐scale, complexity–biodiversity relationships may vary spatially and temporally according to the environmental stressors complexity mitigates, and the species richness and identity of potential colonists. Using a manipulative experiment, we assessed spatial variation in patch‐scale effects of complexity on intertidal biodiversity. Location 27 sites within 14 estuaries/bays distributed globally. Time period 2015–2017. Major taxa studied Functional groups of algae, sessile and mobile invertebrates. Methods Concrete tiles of differing complexity (flat; 2.5‐cm or 5‐cm complex) were affixed at low–high…

0106 biological sciencesestuariebays benthic biodiversity breakwaters eco-engineering estuaries intertidal sea- walls tile urbanBiodiversityIntertidal zone010603 evolutionary biology01 natural sciencesAbundance (ecology)bayseawallintertidalEcology Evolution Behavior and SystematicsInvertebratebiodiversityAbiotic componentGlobal and Planetary ChangebaysbenthicEcologyEcology010604 marine biology & hydrobiologyeco-engineeringseawallsestuariesGeographyHabitatbreakwatersbreakwaterbays; benthic; biodiversity; breakwaters; eco-engineering; estuaries; intertidal; seawalls; tile; urbanSpatial variabilitySpecies richnessurbantile
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Mixed company : a framework for understanding the composition and organization of mixed‐species animal groups

2020

Mixed‐species animal groups (MSGs) are widely acknowledged to increase predator avoidance and foraging efficiency, among other benefits, and thereby increase participants' fitness. Diversity in MSG composition ranges from two to 70 species of very similar or completely different phenotypes. Yet consistency in organization is also observable in that one or a few species usually have disproportionate importance for MSG formation and/or maintenance. We propose a two‐dimensional framework for understanding this diversity and consistency, concentrating on the types of interactions possible between two individuals, usually of different species. One axis represents the similarity of benefit types …

0106 biological sciencesevolution of socialityTime Factorsmutualismspecies networksForagingSpatial Behavior010603 evolutionary biology01 natural sciencesGeneral Biochemistry Genetics and Molecular BiologyBirdsMicroeconomicsinterspecific communicationEating03 medical and health sciencesMixed speciesddc:570Animalsco‐evolutionSocial informationKeystone species030304 developmental biologyMammalsMutualism (biology)0303 health sciencesBehavior AnimalFishesReptilesGroup compositionOriginal ArticlesBiodiversityFeeding BehaviorBiological EvolutionAnimal groupsPredatory BehaviorMimicrypublic informationOriginal ArticleBusinessGeneral Agricultural and Biological SciencesBehavior Observation Techniquesmimicrykeystone species
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Regional and Supra-Regional Coherence in Limnological Variabler

2009

Limnologists and water resources managers have traditionally perceived lakes as discrete geographical entities. This has resulted in a tendency for scientific lake studies to concentrate on lakes as individuals, with little connection either to each other or to large-scale driving forces. Since the 1990s, however, a shift in the prevailing paradigm has occurred, with lakes increasingly being seen as responding to regional, rather than local, driving forces. The seminal work on regional coherence in lake behaviour was that of Magnuson et al. (1990), who showed that many features of lakes within the same region respond coherently to drivers such as climate forcing and catchment processes. Fro…

0106 biological sciencesgeographygeography.geographical_feature_category010504 meteorology & atmospheric sciencesCatchment ModellingEcology010604 marine biology & hydrobiologyClimate ChangeDrainage basinClimate changeCoherence (statistics)Lake ModellingRadiative forcing01 natural sciencesWater resourcesSpatial coherence13. Climate actionNorth Atlantic oscillationBiological propertyddc:570Water QualityPhysical geography0105 earth and related environmental sciences
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Spatial Variation of Soil Seed Bank under Cushion Plants in a Subalpine Degraded Grassland

2017

Cushion plants can affect wind speed and sediment movement patterns which probably modify the water and sediment redistribution along slopes and increase the accumulation of seeds under and around their canopies. This study was carried out to assess the spatial variability of soil seed bank (SSB) and seed bank composition around cushion plants to estimate the SSB potential for restoration of degraded area. Twenty cushions of Onobrychis cornuta were selected in a mountainous rangelands in northern Alborz in Iran, measuring density, richness and composition of SSB at four locations of each cushion (upslope edge, downslope edge, center and outside). SSB composition and density were determined …

0106 biological sciencesgeographygeography.geographical_feature_categorybiologySoil seed bankEcologySoil ScienceSediment04 agricultural and veterinary sciencesDevelopmentbiology.organism_classification010603 evolutionary biology01 natural sciencesGrasslandAgronomySeedlingCushion040103 agronomy & agriculture0401 agriculture forestry and fisheriesEnvironmental ChemistryEnvironmental scienceSpatial variabilitySpecies richnessRangelandGeneral Environmental ScienceLand Degradation & Development
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Selection on fish personality differs between a no-take marine reserve and fished areas

2021

9 pages, 2 tables, 3 figures.-- This is an open access article under the terms of the Creative Commons Attribution License

0106 biological sciencesharvest selectionEvolutionmedia_common.quotation_subjectHome rangeMovementFishinghome rangeBiology010603 evolutionary biology01 natural sciencesAbundance (ecology)salmonidsQH359-425GeneticsPersonalitySpatial ecology14. Life underwaterRepeatabilityrepeatabilityDiel vertical migrationacoustic telemetryVDP::Landbruks- og Fiskerifag: 900::Fiskerifag: 920Ecology Evolution Behavior and Systematicsmedia_common010604 marine biology & hydrobiologyMarine reservespatial ecologyMarine habitatsSalmonidsOriginal ArticlesFisheryHome rangeHabitatpersonalityOriginal ArticleAcoustic telemetrymovementGeneral Agricultural and Biological SciencesHarvest selectionPersonalityEvolutionary Applications
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Vegetation structure and greenness in Central Africa from Modis multi-temporal data.

2013

African forests within the Congo Basin are generally mapped at regional scale as broad-leaved evergreen forests, with a main distinction between terra-firme and swamp forests types. At the same time, commercial forest inventories, as well as national maps, have highlighted a strong spatial heterogeneity of forest types. A detailed vegetation map generated using consistent methods is needed to inform decision makers about spatial forest organisation and theirs relationships with environmental drivers in the context of global change. We propose a multi-temporal remotely sensed data approach to characterize vegetation types using vegetation index annual profiles. The classifications identified…

0106 biological scienceshttp://aims.fao.org/aos/agrovoc/c_28568Time Factors010504 meteorology & atmospheric sciencesDatabases FactualRainEcological Parameter Monitoringhttp://aims.fao.org/aos/agrovoc/c_900018001 natural sciencesTrees[ SDE ] Environmental Sciencesremote sensinghttp://aims.fao.org/aos/agrovoc/c_3062K01 - Foresterie - Considérations généralesDynamique des populationsForêt tropicale humidehttp://aims.fao.org/aos/agrovoc/c_6498http://aims.fao.org/aos/agrovoc/c_29008geography.geographical_feature_categoryCentral AfricaEcologyInventaire forestierVegetationArticlesClassificationSpatial heterogeneity[ SDE.MCG ] Environmental Sciences/Global ChangesDeciduoushttp://aims.fao.org/aos/agrovoc/c_7976CongoP31 - Levés et cartographie des solsForêt[SDE]Environmental SciencesSeasonshttp://aims.fao.org/aos/agrovoc/c_1432General Agricultural and Biological Scienceshttp://aims.fao.org/aos/agrovoc/c_34911Research ArticleF40 - Écologie végétaleTélédétectionClimate Change[SDE.MCG]Environmental Sciences/Global ChangesSpectroscopie infrarougeContext (language use)69Typologie010603 evolutionary biologySwampGeneral Biochemistry Genetics and Molecular BiologyCarbon Cycle[ SDU.ENVI ] Sciences of the Universe [physics]/Continental interfaces environmentHumansAfrica Centralhttp://aims.fao.org/aos/agrovoc/c_1666http://aims.fao.org/aos/agrovoc/c_1344http://aims.fao.org/aos/agrovoc/c_8176[SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces environmenthttp://aims.fao.org/aos/agrovoc/c_6111Ecosystem0105 earth and related environmental sciencesChangement climatiquegeographyCartographiehttp://aims.fao.org/aos/agrovoc/c_24174Enhanced vegetation index15. Life on landEvergreenVégétationStructure du peuplement13. Climate actionCouvert forestierPhysical geographyU30 - Méthodes de recherchehttp://aims.fao.org/aos/agrovoc/c_1653tropical rainforestTropical rainforest
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Fishery-dependent and -independent data lead to consistent estimations of essential habitats

2016

AbstractSpecies mapping is an essential tool for conservation programmes as it provides clear pictures of the distribution of marine resources. However, in fishery ecology, the amount of objective scientific information is limited and data may not always be directly comparable. Information about the distribution of marine species can be derived from two main sources: fishery-independent data (scientific surveys at sea) and fishery-dependent data (collection and sampling by observers in commercial vessels). The aim of this paper is to compare whether these two different sources produce similar, complementary, or different results. We compare them in the specific context of identifying the Es…

0106 biological scienceshttp://aims.fao.org/aos/agrovoc/c_28840Biodiversité et Ecologiehabitatmodélisation spatialehttp://aims.fao.org/aos/agrovoc/c_38371OceanographyGaleus melastomus01 natural sciencesRessource halieutiquehttp://aims.fao.org/aos/agrovoc/c_38127Scyliorhinus caniculamodèle hiérarchiqueSpatial statisticsEcologymodèle de distributionSampling (statistics)Contrast (statistics)Cross-validationModélisation et simulationGeographyHabitatGestion des pêchesModeling and Simulationhttp://aims.fao.org/aos/agrovoc/c_10566http://aims.fao.org/aos/agrovoc/c_3456http://aims.fao.org/aos/agrovoc/c_38117survey designMarine conservationSpecies Distribution ModelsEcology (disciplines)Bayesian probabilityEtmopterus spinaxenquête statistiqueDonnée sur les pêchesmodèle spatiotemporelSede Central IEOContext (language use)Aquatic ScienceDistribution des populationsBayesian hierarchical models010603 evolutionary biologyhttp://aims.fao.org/aos/agrovoc/c_24026elasmobranchsBiodiversity and Ecologyélasmobrancheétude comparativeBayesian hierarchical models;Cross-validation;Species Distribution Models;Spatial statistics;INLA;elasmobranchs ; survey designINLA14. Life underwaterspecies distribution modelsEcology Evolution Behavior and Systematicshttp://aims.fao.org/aos/agrovoc/c_6113collecte des donnéesÉcologie marinehttp://aims.fao.org/aos/agrovoc/c_29788http://aims.fao.org/aos/agrovoc/c_4609010604 marine biology & hydrobiologyGestion et conservation des pêchescross validation[INFO.INFO-MO]Computer Science [cs]/Modeling and Simulationmodèle bayésienFisheryM01 - Pêche et aquaculture - Considérations généraleshttp://aims.fao.org/aos/agrovoc/c_2a75d27eThéorie bayésienneM40 - Écologie aquatiqueSpatial ecologyhttp://aims.fao.org/aos/agrovoc/c_2942[SDE.BE]Environmental Sciences/Biodiversity and Ecologyvalidation croiséeElasmobranchii
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Modelling sensitive elasmobranchs habitat

2013

Basic information on the distribution and habitat preferences of ecologically important species is essential for their management and protection. In the Mediterranean Sea there is increasing concern over elasmobranch species because their biological (ecological) characteristics make them highly vulnerable to fishing pressure. Their removal could affect the structure and function of marine ecosystems, inducing changes in trophic interactions at the community level due to the selective elimination of predators or prey species, competitors and species replacement. In this study Bayesian hierarchical spatial models are used to map the sensitive habitats of the three most caught elasmobranch spe…

0106 biological scienceshttp://aims.fao.org/aos/agrovoc/c_28840Etmopterus spinaxhabitatAquatic ScienceDistribution des populationshttp://aims.fao.org/aos/agrovoc/c_38371OceanographyGaleus melastomus010603 evolutionary biology01 natural sciencesElasmobranch habitatPredationMediterranean seahttp://aims.fao.org/aos/agrovoc/c_38127http://aims.fao.org/aos/agrovoc/c_3041Scyliorhinus caniculaMediterranean SeaVulnerable speciesMarine ecosystem14. Life underwaterhttp://aims.fao.org/aos/agrovoc/c_4699Ecology Evolution Behavior and Systematicshttp://aims.fao.org/aos/agrovoc/c_12399Trophic levelhttp://aims.fao.org/aos/agrovoc/c_6113biologyEcologyU10 - Informatique mathématiques et statistiques010604 marine biology & hydrobiologyScyliorhinus caniculabiology.organism_classificationBiologie marinetechnique de prévisionBayesian hierarchical spatial modelSpecies distribution modelingFisheryHabitatThéorie bayésienneGaleus melastomusM40 - Écologie aquatiquehttp://aims.fao.org/aos/agrovoc/c_10566http://aims.fao.org/aos/agrovoc/c_3456http://aims.fao.org/aos/agrovoc/c_38117Elasmobranchii
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