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      Relationship of insect biomass and richness with land use along a climate gradient

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          Abstract

          Recently reported insect declines have raised both political and social concern. Although the declines have been attributed to land use and climate change, supporting evidence suffers from low taxonomic resolution, short time series, a focus on local scales, and the collinearity of the identified drivers. In this study, we conducted a systematic assessment of insect populations in southern Germany, which showed that differences in insect biomass and richness are highly context dependent. We found the largest difference in biomass between semi-natural and urban environments (−42%), whereas differences in total richness (−29%) and the richness of threatened species (−56%) were largest from semi-natural to agricultural environments. These results point to urbanization and agriculture as major drivers of decline. We also found that richness and biomass increase monotonously with increasing temperature, independent of habitat. The contrasting patterns of insect biomass and richness question the use of these indicators as mutual surrogates. Our study provides support for the implementation of more comprehensive measures aimed at habitat restoration in order to halt insect declines.

          Abstract

          Land use is a key control of insect communities. Here the authors investigate relationships of insect biomass and richness with land use along a climate gradient, finding evidence of urbanisation and agriculture as drivers of decline, and of biomass and species richness not being suitable as mutual surrogates.

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          Fitting Linear Mixed-Effects Models Usinglme4

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            Simultaneous inference in general parametric models.

            Simultaneous inference is a common problem in many areas of application. If multiple null hypotheses are tested simultaneously, the probability of rejecting erroneously at least one of them increases beyond the pre-specified significance level. Simultaneous inference procedures have to be used which adjust for multiplicity and thus control the overall type I error rate. In this paper we describe simultaneous inference procedures in general parametric models, where the experimental questions are specified through a linear combination of elemental model parameters. The framework described here is quite general and extends the canonical theory of multiple comparison procedures in ANOVA models to linear regression problems, generalized linear models, linear mixed effects models, the Cox model, robust linear models, etc. Several examples using a variety of different statistical models illustrate the breadth of the results. For the analyses we use the R add-on package multcomp, which provides a convenient interface to the general approach adopted here. Copyright 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim
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              The value of the world's ecosystem services and natural capital

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                Author and article information

                Contributors
                joerg.mueller@npv-bw.bayern.de
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                12 October 2021
                12 October 2021
                2021
                : 12
                : 5946
                Affiliations
                [1 ]GRID grid.8379.5, ISNI 0000 0001 1958 8658, Field Station Fabrikschleichach, Department of Animal Ecology and Tropical Biology, , Julius-Maximilians-University Würzburg, ; Würzburg, Germany
                [2 ]GRID grid.8379.5, ISNI 0000 0001 1958 8658, Department of Animal Ecology and Tropical Biology, , Julius-Maximilians-University Würzburg, ; Würzburg, Germany
                [3 ]GRID grid.7400.3, ISNI 0000 0004 1937 0650, Epidemiology, Biostatistics and Prevention Institute, , University Zürich, ; Zürich, Switzerland
                [4 ]GRID grid.4819.4, ISNI 0000 0001 0704 7467, Institute of Ecology and Landscape, , Weihenstephan-Triesdorf University of Applied Sciences, ; Freising, Germany
                [5 ]GRID grid.4819.4, ISNI 0000 0001 0704 7467, Botany & Vegetation Science, Faculty of Forestry, , Weihenstephan-Triesdorf University of Applied Sciences, ; Freising, Germany
                [6 ]GRID grid.6936.a, ISNI 0000000123222966, Ecosystem Dynamics and Forest management Group, , Technical University of Munich, ; Freising, Germany
                [7 ]Berchtesgaden National Park, Berchtesgaden, Germany
                [8 ]AIM – Advanced Identification Methods GmbH, Leipzig, Germany
                [9 ]GRID grid.6936.a, ISNI 0000000123222966, TUM School of Life Sciences, Ecoclimatology, , Technical University of Munich, ; Freising, Germany
                [10 ]GRID grid.7384.8, ISNI 0000 0004 0467 6972, Professorship of Ecological Services, Bayreuth Centre of Ecology and Environmental Research (BayCEER), , University of Bayreuth, ; Bayreuth, Germany
                [11 ]GRID grid.6936.a, ISNI 0000000123222966, Chair of Restoration Ecology, , Technical University of Munich, ; Freising, Germany
                [12 ]GRID grid.7307.3, ISNI 0000 0001 2108 9006, Institute of Geography, , University of Augsburg, ; Augsburg, Germany
                [13 ]GRID grid.452781.d, ISNI 0000 0001 2203 6205, SNSB-Zoologische Staatssammlung Muenchen, ; Munich, Germany
                [14 ]GRID grid.452215.5, Bavarian Forest National Park, ; Grafenau, Germany
                Author information
                http://orcid.org/0000-0001-5609-0576
                http://orcid.org/0000-0003-2599-5983
                http://orcid.org/0000-0002-7968-4489
                http://orcid.org/0000-0002-5284-4518
                http://orcid.org/0000-0003-4530-8968
                http://orcid.org/0000-0002-8218-4717
                http://orcid.org/0000-0002-0366-7383
                http://orcid.org/0000-0001-5751-8706
                http://orcid.org/0000-0002-1409-1586
                Article
                26181
                10.1038/s41467-021-26181-3
                8511018
                34642336
                84a69c9e-9b2e-4f86-9a4c-211f8efe1f89
                © The Author(s) 2021

                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
                : 29 March 2021
                : 17 September 2021
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                © The Author(s) 2021

                Uncategorized
                ecology,biodiversity
                Uncategorized
                ecology, biodiversity

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