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      Time and habitat structure shape insect acoustic activity in the Amazon

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          Abstract

          Insects are the most diverse animal taxon on Earth and play a key role in ecosystem functioning. However, they are often neglected by ecological surveys owing to the difficulties involved in monitoring this small and hyper-diverse taxon. With technological advances in biomonitoring and analytical methods, these shortcomings may finally be addressed. Here, we performed passive acoustic monitoring at 141 sites (eight habitats) to investigate insect acoustic activity in the Viruá National Park, Brazil. We first describe the frequency range occupied by three soniferous insect groups (cicadas, crickets and katydids) to calculate the acoustic evenness index (AEI). Then, we assess how AEI varies spatially and temporally among habitat types, and finally we investigate the relationship between vegetation structure variables and AEI for each insect category. Overall, crickets occupied lower and narrower frequency bands than cicadas and katydids. AEI values varied among insect categories and across space and time. The highest acoustic activity occurred before sunrise and the lowest acoustic activity was recorded in pastures. Canopy cover was positively associated with cricket acoustic activity but not with katydids. Our findings contribute to a better understanding of the role of time, habitat and vegetation structure in shaping insect activity within diverse Amazonian ecosystems.

          This article is part of the theme issue ‘Towards a toolkit for global insect biodiversity monitoring’.

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          lmerTest Package: Tests in Linear Mixed Effects Models

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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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              A general and simple method for obtainingR2from generalized linear mixed-effects models

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                Author and article information

                Contributors
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                Journal
                Philosophical Transactions of the Royal Society B: Biological Sciences
                Phil. Trans. R. Soc. B
                The Royal Society
                0962-8436
                1471-2970
                June 24 2024
                May 06 2024
                June 24 2024
                : 379
                : 1904
                Affiliations
                [1 ]Science Department, Biometrio.Earth, 66123 Saarbrücken, Germany
                [2 ]Ecology Department, Alicante University, 03690 Alicante, Spain
                [3 ]Instituto Nacional de Pesquisas da Amazônia, Programa de Pós-Graduação em Ciências Biológicas (Entomologia), 69067-375 Manaus, Amazonas, Brazil
                [4 ]Department of Wildland Resources and Ecology Center, Utah State University, 84322-5230 Logan, UT, USA
                Article
                10.1098/rstb.2023.0112
                0810f82c-fa63-475e-bed0-e788f62b5549
                © 2024

                https://royalsociety.org/-/media/journals/author/Licence-to-Publish-20062019-final.pdf

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