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      Excess forest mortality is consistently linked to drought across Europe

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          Abstract

          Pulses of tree mortality caused by drought have been reported recently in forests around the globe, but large-scale quantitative evidence is lacking for Europe. Analyzing high-resolution annual satellite-based canopy mortality maps from 1987 to 2016 we here show that excess forest mortality (i.e., canopy mortality exceeding the long-term mortality trend) is significantly related to drought across continental Europe. The relationship between water availability and mortality showed threshold behavior, with excess mortality increasing steeply when the integrated climatic water balance from March to July fell below −1.6 standard deviations of its long-term average. For −3.0 standard deviations the probability of excess canopy mortality was 91.6% (83.8–97.5%). Overall, drought caused approximately 500,000 ha of excess forest mortality between 1987 and 2016 in Europe. We here provide evidence that drought is an important driver of tree mortality at the continental scale, and suggest that a future increase in drought could trigger widespread tree mortality in Europe.

          Abstract

          Droughts pose an increasingly important threat to forests. Here the authors analyse a high-resolution Landsat-based dataset of forest canopy mortality in Europe over 1987–2016 to show that drought is already a major driver of tree mortality.

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          Fitting Linear Mixed-Effects Models Usinglme4

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            Stan: A Probabilistic Programming Language

            Stan is a probabilistic programming language for specifying statistical models. A Stan program imperatively defines a log probability function over parameters conditioned on specified data and constants. As of version 2.14.0, Stan provides full Bayesian inference for continuous-variable models through Markov chain Monte Carlo methods such as the No-U-Turn sampler, an adaptive form of Hamiltonian Monte Carlo sampling. Penalized maximum likelihood estimates are calculated using optimization methods such as the limited memory Broyden-Fletcher-Goldfarb-Shanno algorithm. Stan is also a platform for computing log densities and their gradients and Hessians, which can be used in alternative algorithms such as variational Bayes, expectation propagation, and marginal inference using approximate integration. To this end, Stan is set up so that the densities, gradients, and Hessians, along with intermediate quantities of the algorithm such as acceptance probabilities, are easily accessible. Stan can be called from the command line using the cmdstan package, through R using the rstan package, and through Python using the pystan package. All three interfaces support sampling and optimization-based inference with diagnostics and posterior analysis. rstan and pystan also provide access to log probabilities, gradients, Hessians, parameter transforms, and specialized plotting.
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              Terrestrial Ecoregions of the World: A New Map of Life on Earth

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                Author and article information

                Contributors
                cornelius.senf@tum.de
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                3 December 2020
                3 December 2020
                2020
                : 11
                : 6200
                Affiliations
                [1 ]GRID grid.6936.a, ISNI 0000000123222966, Ecosystem Dynamics and Forest Management Group, , Technical University of Munich, ; Hans-Carl-von-Carlowitz-Platz 2, 85354 Freising, Germany
                [2 ]GRID grid.6936.a, ISNI 0000000123222966, Land Surface-Atmosphere Interactions, , Technical University of Munich, ; Hans-Carl-von-Carlowitz-Platz 2, 85354 Freising, Germany
                [3 ]Berchtesgaden National Park, Doktorberg 6, 83471 Berchtesgaden, Germany
                Author information
                http://orcid.org/0000-0002-3338-3402
                Article
                19924
                10.1038/s41467-020-19924-1
                7713373
                33273460
                c28b5b02-5dd0-40dd-aa58-b8bbddfe6723
                © The Author(s) 2020

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 15 July 2020
                : 4 November 2020
                Categories
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                © The Author(s) 2020

                Uncategorized
                fire ecology,forest ecology,forestry,climate-change ecology
                Uncategorized
                fire ecology, forest ecology, forestry, climate-change ecology

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