Библиографическое описание:Proximal microclimate: Moving beyond spatiotemporal resolution improves ecological predictions : научное издание / David H. Klinges [et al.]. - Текст : непосредственный // Global Ecology and Biogeography. - 2024. - № . - ISSN 1466-822X. - ISSN 1466-8328, DOI 10.1111/geb.13884.
Аннотация:<jats:title>Abstract</jats:title><jats:sec><jats:title>Aim</jats:title><jats:p>The scale of environmental data is often defined by their extent (spatial area, temporal duration) and resolution (grain size, temporal interval). Although describing climate data scale via these terms is appropriate for most meteorological applications, for ecology and biogeography, climate data of the same spatiotemporal resolution and extent may differ in their relevance to an organism. Here, we propose that climate proximity, or how well climate data represent the actual conditions that an organism is exposed to, is more important for ecological realism than the spatiotemporal resolution of the climate data.</jats:p></jats:sec><jats:sec><jats:title>Location</jats:title><jats:p>Temperature comparison in nine countries across four continents; ecological case studies in Alberta (Canada), Sabah (Malaysia) and North Carolina/Tennessee (USA).</jats:p></jats:sec><jats:sec><jats:title>Time Period</jats:title><jats:p>1960–2018.</jats:p></jats:sec><jats:sec><jats:title>Major Taxa Studied</jats:title><jats:p>Case studies with flies, mosquitoes and salamanders, but concepts relevant to all life on earth.</jats:p></jats:sec><jats:sec><jats:title>Methods</jats:title><jats:p>We compare the accuracy of two macroclimate data sources (ERA5 and WorldClim) and a novel microclimate model (<jats:italic>microclimf</jats:italic>) in predicting soil temperatures. We then use ERA5, WorldClim and <jats:italic>microclimf</jats:italic> to drive ecological models in three case studies: temporal (fly phenology), spatial (mosquito thermal suitability) and spatiotemporal (salamander range shifts) ecological responses.</jats:p></jats:sec><jats:sec><jats:title>Results</jats:title><jats:p>For predicting soil temperatures, <jats:italic>microclimf</jats:italic> had 24.9% and 16.4% lower absolute bias than ERA5 and WorldClim respectively. Across the case studies, we find that increasing proximity (from macroclimate to microclimate) yields a 247% improvement in performance of ecological models on average, compared to 18% and 9% improvements from increasing spatial resolution 20-fold, and temporal resolution 30-fold respectively.</jats:p></jats:sec><jats:sec><jats:title>Main Conclusions</jats:title><jats:p>We propose that increasing climate proximity, even if at the sacrifice of finer climate spatiotemporal resolution, may improve ecological predictions. We emphasize biophysically informed approaches, rather than generic formulations, when quantifying ecoclimatic relationships. Redefining the scale of climate through the lens of the organism itself helps reveal mechanisms underlying how climate shapes ecological systems.</jats:p></jats:sec>
Держатель оригинала документа:Centre for Sustainable Ecosystem Solutions University of Wollongong Wollongong New South Wales Australia, CIRAD, UMR Eco&Sols Montpellier France, Department of Biology Aarhus University Aarhus Denmark, Department of Biology University of Antwerp Antwerp Belgium, Department of Biology Utrecht University Utrecht The Netherlands, Department of Biosciences, Environment and Sustainability Institute University of Exeter Exeter UK, Department of Ecology, Environment and Plant Science Stockholm University Stockholm Sweden, Department of Forest Botany, Dendrology and Geobiocenology, Faculty of Forestry and Wood Technology Mendel University in Brno Brno Czech Republic, Department of Forest Ecology and Management Swedish University of Agricultural Sciences Uppsala Sweden, Department of Geoecology Institute of Botany of the Czech Academy of Sciences Průhonice Czech Republic, Department of Geosciences and Geography University of Helsinki Helsinki Finland, Department of Geosciences University of Tübingen Tübingen Germany, Department of Physical Geography Stockholm University Stockholm Sweden, Department of Wildlife Ecology and Conservation University of Florida Gainesville Florida USA, Eco&Sols University Montpellier, CIRAD, INRAe, Institut Agro, IRD Montpellier France, ECOBIOSIS, Department of Botany University of Concepción Concepción Chile, Facultad de Ciencias Agropecuarias Cátedra de Climatología Agrícola (UNER) Oro Verde Argentina, Facultad de Ciencias Biológicas Universidad Nacional de San Antonio Abad del Cusco Cusco Peru, Faculty of Environmental and Forest Sciences Agricultural University of Iceland Reykjavík Iceland, Faculty of Forestry and Wood Sciences University of Life Sciences Prague Praha 6-Suchdol Czech Republic, Forest & Nature Lab, Department of Environment Ghent University Ghent Belgium, Forest Science Department University of São Paulo/ESALQ Piracicaba Brazil, Geography Department Humboldt-Universität zu Berlin Berlin Germany, Grupo de Estudios Ambientales Instituto de Matemática Aplicada San Luis (UNSL & CONICET) San Luis Argentina, iDiv German Centre for Integrative Biodiversity Research Halle-Jena-Leipzig Leipzig Germany, Institute of Biology Martin Luther University Halle-Wittenberg Halle (Saale) Germany, Institute of Ecology and Geography Siberian Federal University Krasnoyarsk Russia, Permafrost Research Section Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research Potsdam Germany, Plant Ecology Group, Department of Biology University of Tübingen Tübingen Germany, School of Biological Sciences University of Bristol Bristol UK, School of BioSciences The University of Melbourne Melbourne Victoria Australia, School of Natural Resources and Environment University of Florida Gainesville Florida USA, UMR CNRS 7058, Ecologie et Dynamique Des Systèmes Anthropisés (EDYSAN) Université de Picardie Jules Verne Amiens France, Weather and Climate Change Impact Research Finnish Meteorological Institute Helsinki Finland
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