Proximal microclimate: Moving beyond spatiotemporal resolution improves ecological predictions
2024
Klinges, David | Baecher, J. Alex | Lembrechts, Jonas | Maclean, Ilya | Lenoir, Jonathan | Greiser, Caroline | Ashcroft, Michael | Evans, Luke | Kearney, Michael | Aalto, Juha | Barrio, Isabel | de Frenne, Pieter | Guillemot, Joannès | Hylander, Kristoffer | Jucker, Tommaso | Kopecký, Martin | Luoto, Miska | Macek, Martin | Nijs, Ivan | Urban, Josef | van den Brink, Liesbeth | Vangansbeke, Pieter | von Oppen, Jonathan | Wild, Jan | Boike, Julia | Canessa, Rafaella | Nosetto, Marcelo | Rubtsov, Alexey | Sallo-Bravo, Jhonatan | Scheffers, Brett | University of Florida [Gainesville] (UF) | Universiteit Utrecht / Utrecht University [Utrecht] | University of Antwerp (UA) | University of Exeter | Ecologie et Dynamique des Systèmes Anthropisés - UMR CNRS 7058 UPJV (EDYSAN) ; Université de Picardie Jules Verne (UPJV)-Centre National de la Recherche Scientifique (CNRS) | Department of Ecology, Environment and Plant Sciences [Stockholm] ; Stockholm University | Swedish University of Agricultural Sciences = Sveriges lantbruksuniversitet (SLU) | University of Wollongong [Australia] | University of Melbourne | Finnish Meteorological Institute (FMI) | Agricultural University of Iceland | Universiteit Gent = Ghent University = Université de Gand (UGENT) | Ecologie fonctionnelle et biogéochimie des sols et des agro-écosystèmes (UMR Eco&Sols) ; Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut de Recherche pour le Développement (IRD)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut Agro Montpellier ; Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro) | Escola Superior de Agricultura "Luiz de Queiroz" (ESALQ) ; Universidade de São Paulo = University of São Paulo (USP) | Stockholm University | University of Bristol [Bristol] | Institute of Botany of the Czech Academy of Sciences (IB / CAS) ; Czech Academy of Sciences [Prague] (CAS) | Czech University of Life Sciences Prague (CZU) | Helsingin yliopisto = Helsingfors universitet = University of Helsinki | Mendel University in Brno (MENDELU) | Eberhard Karls Universität Tübingen = University of Tübingen | Universidad de Concepción = University of Concepción [Chile] (UdeC) | Magnel Laboratory [Ghent University] ; Universiteit Gent = Ghent University = Université de Gand (UGENT) | Aarhus University [Aarhus] | Alfred-Wegener-Institut, Helmholtz-Zentrum für Polar- und Meeresforschung = Alfred Wegener Institute for Polar and Marine Research = Institut Alfred-Wegener pour la recherche polaire et marine (AWI) ; Helmholtz-Gemeinschaft = Helmholtz Association | German Centre for Integrative Biodiversity Research (iDiv) | Martin-Luther-Universität Halle Wittenberg - Martin-Luther-University Halle Wittenberg (MLU) | Universidad Nacional de San Luis [San Luis] (UNSL) | Consejo Nacional de Investigaciones Científicas y Técnicas [Buenos Aires] (CONICET) | Universidad Nacional de Entre Ríos [Argentine] (UNER) | Siberian Federal University (SibFU) | Universidad Nacional de San Antonio Abad del Cusco (UNSAAC) | Danmarks Frie Forskningsfond, Grant/Award Number: 7027- 00133B; AgenciaNacional de Promoción Científica yTecnológica, Grant/Award Number:PICT504/2020; RVO, Grant/AwardNumber: 67985939; Natural EnvironmentResearch Council, Grant/Award Number:NE/S01537X/1; National ScienceFoundation, Grant/Award Number:1842473; Fonds WetenschappelijkOnderzoek, Grant/Award Number:12P1819N, 1512720N, G018919N andW001919N; European Research Council,Grant/Award Number: FORMICA-757833;Grantová Agentura České Republiky,Grant/Award Number: 23- 06614S;Agence Nationale de la Recherche, Grant/Award Number: JCJC-N°ANR-19- CE32-0005- 01 IMPRINT and PRC-N°ANR-21- CE32- 0012- 03 MaCCMic; ResearchCouncil of Finland, Grant/Award Number:342890; Swedish research councilFORMAS, Grant/Award Number: 2021-01993 | ANR-19-CE32-0005,IMPRINT,IMpacts des PRocessus mIcroclimatiques sur la redistributioN de la biodiversiTé forestière en contexte de réchauffement du macroclimat(2019) | ANR-21-CE32-0012,MaCCMic,Impact de la gestion forestière et du changement climatique sur le microclimat en sous-bois(2021)
International audience
显示更多 [+] 显示较少 [-]英语. Aim 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. Location Temperature comparison in nine countries across four continents; ecological case studies in Alberta (Canada), Sabah (Malaysia) and North Carolina/Tennessee (USA). Time Period 1960–2018. Major Taxa Studied Case studies with flies, mosquitoes and salamanders, but concepts relevant to all life on earth. Methods We compare the accuracy of two macroclimate data sources (ERA5 and WorldClim) and a novel microclimate model ( microclimf ) in predicting soil temperatures. We then use ERA5, WorldClim and microclimf to drive ecological models in three case studies: temporal (fly phenology), spatial (mosquito thermal suitability) and spatiotemporal (salamander range shifts) ecological responses. Results For predicting soil temperatures, microclimf 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. Main Conclusions 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.
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