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      BEETLE ASSEMBLAGE COMPOSITION (COLEOPTERA) ACROSS THE BORBOREMA PLATEAU IN NORTHEASTERN BRAZIL Translated title: COMPOSIÇÃO DA ASSEMBLEIA DE BESOUROS (COLEOPTERA) NO PLANALTO DA BORBOREMA, NORDESTE DO BRASIL

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          Abstract

          ABSTRACT The objective of this work was to compare soil beetle (Coleoptera) species composition between two sites on the Borborema Plateau in Northeastern Brazil. We collected 483 individuals belonging to 23 Coleoptera families and 75 species/morphospecies. On the east face of the Borborema Plateau (Areia-PB) we collected a total of 332 individuals representing 58 species/morphospecies and 21 families. On the west face (Cabaceiras-PB) we collected 151 individuals representing 24 species/morphospecies and eight families. Among the 75 total species/morphospecies collected, 51 occurred exclusively on the eastern face of the plateau and 17 occurred exclusively on the west face. Only seven morphospecies were sampled in both areas. The marked difference in beetle assemblage species composition between the west and east sides of the Borborema Plateau suggests that the positioning and climatic variations maintain and promote high levels of beetle diversity in northeastern Brazil.

          Translated abstract

          RESUMO O objetivo deste trabalho foi comparar as assembleias de besouros de solo entre dois locais no Planalto da Borborema. Dados de georreferenciamento e climáticos foram usados para comparar as condições locais com as assembleias de besouros. Foram coletados 483 indivíduos, classificados em 75 espécies/ morfoespécies, pertencentes a 23 famílias de Coleoptera. As coletas na face leste do Planalto da Borborema (Areia-PB) totalizaram 332 indivíduos (58 espécies/morfoespécies) pertencentes a 21 famílias. No lado oeste (Cabaceiras-PB) foram coletados 151 indivíduos, 24 espécies/morfoespécies pertencentes a oito famílias. Entre as 75 morfótipos/espécies coletadas, 51 ocorreram exclusivamente na face oriental do Planalto e 17 ocorreram exclusivamente na face oeste. Entre eles, apenas sete morfoespécies foram amostradas em ambas as áreas. A diferença de composição de espécies de besouros entre os lados oeste e leste do Planalto da Borborema sugere que o posicionamento e variações climáticas mantêm e promovem altos níveis de diversidade de besouros no nordeste do Brasil.

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          Fitting Linear Mixed-Effects Models Using lme4

          Maximum likelihood or restricted maximum likelihood (REML) estimates of the parameters in linear mixed-effects models can be determined using the lmer function in the lme4 package for R. As for most model-fitting functions in R, the model is described in an lmer call by a formula, in this case including both fixed- and random-effects terms. The formula and data together determine a numerical representation of the model from which the profiled deviance or the profiled REML criterion can be evaluated as a function of some of the model parameters. The appropriate criterion is optimized, using one of the constrained optimization functions in R, to provide the parameter estimates. We describe the structure of the model, the steps in evaluating the profiled deviance or REML criterion, and the structure of classes or types that represents such a model. Sufficient detail is included to allow specialization of these structures by users who wish to write functions to fit specialized linear mixed models, such as models incorporating pedigrees or smoothing splines, that are not easily expressible in the formula language used by lmer. Journal of Statistical Software, 67 (1) ISSN:1548-7660
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            A general and simple method for obtainingR2from generalized linear mixed-effects models

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              Generalized linear mixed models: a practical guide for ecology and evolution.

              How should ecologists and evolutionary biologists analyze nonnormal data that involve random effects? Nonnormal data such as counts or proportions often defy classical statistical procedures. Generalized linear mixed models (GLMMs) provide a more flexible approach for analyzing nonnormal data when random effects are present. The explosion of research on GLMMs in the last decade has generated considerable uncertainty for practitioners in ecology and evolution. Despite the availability of accurate techniques for estimating GLMM parameters in simple cases, complex GLMMs are challenging to fit and statistical inference such as hypothesis testing remains difficult. We review the use (and misuse) of GLMMs in ecology and evolution, discuss estimation and inference and summarize 'best-practice' data analysis procedures for scientists facing this challenge.
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                Author and article information

                Journal
                rcaat
                Revista Caatinga
                Rev. Caatinga
                Universidade Federal Rural do Semi-Árido (Mossoró, RN, Brazil )
                0100-316X
                1983-2125
                March 2022
                : 35
                : 1
                : 148-159
                Affiliations
                [2] Manaus Amazonas orgnameInstituto Nacional de Pesquisas da Amazônia orgdiv1Department of Entomology Brazil jandersonrn@ 123456gmail.com
                [4] Manaus AM orgnameUniversadade Federal do Amazonas orgdiv1Departament of Biological Sciences Brazil fbaccaro.ecolab@ 123456gmail.com
                [5] Campina Grande Paraíba orgnameUniversidade Estadual da Paraíba orgdiv1Departament of Biological Sciences Brazil clbicho@ 123456gmail.com
                [3] Areia Paraíba orgnameUniversidade Federal da Paraíba orgdiv1Departament of Biological Sciences Brazil carlos@ 123456cca.ufpb.br
                Article
                S1983-21252022000100148 S1983-2125(22)03500100148
                10.1590/1983-21252022v35n115rc
                c1a5e11b-d47c-4adb-b0ed-cf33130dc976

                This work is licensed under a Creative Commons Attribution 4.0 International License.

                History
                : 11 August 2020
                : 06 September 2021
                Page count
                Figures: 0, Tables: 0, Equations: 0, References: 56, Pages: 12
                Product

                SciELO Brazil

                Categories
                Forest Science

                Northeastern Brazil,Orographic effect,Beetle distribution,Altitudinal gradientes,Efeito orográfico,Distribuição de besouros,Clima,Nordeste

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