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      Demography–environment relationships improve mechanistic understanding of range dynamics under climate change

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          Abstract

          Species respond to climate change with range and abundance dynamics. To better explain and predict them, we need a mechanistic understanding of how the underlying demographic processes are shaped by climatic conditions. Here, we aim to infer demography–climate relationships from distribution and abundance data. For this, we developed spatially explicit, process-based models for eight Swiss breeding bird populations. These jointly consider dispersal, population dynamics and the climate-dependence of three demographic processes—juvenile survival, adult survival and fecundity. The models were calibrated to 267 nationwide abundance time series in a Bayesian framework. The fitted models showed moderate to excellent goodness-of-fit and discriminatory power. The most influential climatic predictors for population performance were the mean breeding-season temperature and the total winter precipitation. Contemporary climate change benefitted the population trends of typical mountain birds leading to lower population losses or even slight increases, whereas lowland birds were adversely affected. Our results emphasize that generic process-based models embedded in a robust statistical framework can improve our predictions of range dynamics and may allow disentangling of the underlying processes. For future research, we advocate a stronger integration of experimental and empirical studies in order to gain more precise insights into the mechanisms by which climate affects populations.

          This article is part of the theme issue ‘Detecting and attributing the causes of biodiversity change: needs, gaps and solutions’.

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          A globally coherent fingerprint of climate change impacts across natural systems.

          Causal attribution of recent biological trends to climate change is complicated because non-climatic influences dominate local, short-term biological changes. Any underlying signal from climate change is likely to be revealed by analyses that seek systematic trends across diverse species and geographic regions; however, debates within the Intergovernmental Panel on Climate Change (IPCC) reveal several definitions of a 'systematic trend'. Here, we explore these differences, apply diverse analyses to more than 1,700 species, and show that recent biological trends match climate change predictions. Global meta-analyses documented significant range shifts averaging 6.1 km per decade towards the poles (or metres per decade upward), and significant mean advancement of spring events by 2.3 days per decade. We define a diagnostic fingerprint of temporal and spatial 'sign-switching' responses uniquely predicted by twentieth century climate trends. Among appropriate long-term/large-scale/multi-species data sets, this diagnostic fingerprint was found for 279 species. This suite of analyses generates 'very high confidence' (as laid down by the IPCC) that climate change is already affecting living systems.
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            Climatologies at high resolution for the earth’s land surface areas

            High-resolution information on climatic conditions is essential to many applications in environmental and ecological sciences. Here we present the CHELSA (Climatologies at high resolution for the earth’s land surface areas) data of downscaled model output temperature and precipitation estimates of the ERA-Interim climatic reanalysis to a high resolution of 30 arc sec. The temperature algorithm is based on statistical downscaling of atmospheric temperatures. The precipitation algorithm incorporates orographic predictors including wind fields, valley exposition, and boundary layer height, with a subsequent bias correction. The resulting data consist of a monthly temperature and precipitation climatology for the years 1979–2013. We compare the data derived from the CHELSA algorithm with other standard gridded products and station data from the Global Historical Climate Network. We compare the performance of the new climatologies in species distribution modelling and show that we can increase the accuracy of species range predictions. We further show that CHELSA climatological data has a similar accuracy as other products for temperature, but that its predictions of precipitation patterns are better.
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              Rapid range shifts of species associated with high levels of climate warming.

              The distributions of many terrestrial organisms are currently shifting in latitude or elevation in response to changing climate. Using a meta-analysis, we estimated that the distributions of species have recently shifted to higher elevations at a median rate of 11.0 meters per decade, and to higher latitudes at a median rate of 16.9 kilometers per decade. These rates are approximately two and three times faster than previously reported. The distances moved by species are greatest in studies showing the highest levels of warming, with average latitudinal shifts being generally sufficient to track temperature changes. However, individual species vary greatly in their rates of change, suggesting that the range shift of each species depends on multiple internal species traits and external drivers of change. Rapid average shifts derive from a wide diversity of responses by individual species.
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                Author and article information

                Contributors
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: MethodologyRole: SoftwareRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: Formal analysisRole: MethodologyRole: SupervisionRole: Writing – review & editing
                Role: SoftwareRole: VisualizationRole: Writing – review & editing
                Role: Data curationRole: Writing – review & editing
                Role: ConceptualizationRole: Funding acquisitionRole: MethodologyRole: SupervisionRole: Writing – review & editing
                Journal
                Philos Trans R Soc Lond B Biol Sci
                Philos Trans R Soc Lond B Biol Sci
                RSTB
                royptb
                Philosophical Transactions of the Royal Society B: Biological Sciences
                The Royal Society
                0962-8436
                1471-2970
                July 17, 2023
                May 29, 2023
                May 29, 2023
                : 378
                : 1881 , Theme issue ‘Detecting and attributing the causes of biodiversity change: needs, gaps and solutions’ compiled and edited by Eden Tekwa, Andrew Gonzalez, Damaris Zurell and Mary O’Connor
                : 20220194
                Affiliations
                [ 1 ] Institute for Biochemistry and Biology, University of Potsdam, , 14469 Potsdam, Germany
                [ 2 ] Theoretical Ecology Lab, Faculty of Biology and Pre-Clinical Medicine, University of Regensburg, , 93053 Regensburg, Germany
                [ 3 ] Swiss Ornithological Institute, , 6204 Sempach, Switzerland
                Author notes

                Electronic supplementary material is available online at https://doi.org/10.6084/m9.figshare.c.6620248.

                Author information
                http://orcid.org/0000-0003-1446-6365
                http://orcid.org/0000-0002-6255-9059
                http://orcid.org/0000-0003-3269-1929
                http://orcid.org/0000-0001-7476-7616
                http://orcid.org/0000-0002-4628-3558
                Article
                rstb20220194
                10.1098/rstb.2022.0194
                10225853
                37246385
                3a335ee1-0607-413a-97f7-f59180134f49
                © 2023 The Authors.

                Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, provided the original author and source are credited.

                History
                : September 27, 2022
                : April 15, 2023
                Funding
                Funded by: Deutsche Forschungsgemeinschaft, http://dx.doi.org/10.13039/501100001659;
                Award ID: ZU 361/1-1
                Categories
                1001
                60
                203
                Articles
                Research Articles
                Custom metadata
                July 17, 2023

                Philosophy of science
                attribution science,individual-based model,bayesian calibration,spatially explicit process-based model,range shifts

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