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      When phylogeny and ecology meet: Modeling the occurrence of Trichoptera with environmental and phylogenetic data

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          Abstract

          Ecological studies are increasingly considering phylogenetic relationships among species. The phylogeny is used as a proxy or filter to improve statistical tests and retain evolutionary elements, such as niche conservation. We used the phylogenetic topology to improve the model for occurrence of Trichoptera genera in Cerrado (Brazilian Savanna) streams. We tested whether parameters generated by logistic models of occurrence, using phylogenetic signals, are better than models generated without phylogenetic information. We used a model with Bayesian updating to examine the influence of stream water pH and phylogenetic relationship among genera on the occurrence of Trichoptera genera. Then, we compared this model with the logistic model for each Trichoptera genus. The probability of occurrence of most genera increased with water pH, and the phylogeny‐based explicit logistic model improved the parameters estimated for observed genera. The inferred relationship between genera occurrence and stream pH improved, indicating that phylogeny adds relevant information when estimating ecological responses of organisms. Water with elevated acidity (low pH values) may be restrictive for the occurrence of Trichoptera larvae, especially if the regional streams exhibit neutral to alkaline water, as is observed in the Cerrado region. Using phylogeny‐based modeling to predict species occurrence is a prominent opportunity to extend our current statistical framework based on environmental conditions, as it enables a more precise estimation of ecological parameters.

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          Testing for phylogenetic signal in comparative data: behavioral traits are more labile.

          The primary rationale for the use of phylogenetically based statistical methods is that phylogenetic signal, the tendency for related species to resemble each other, is ubiquitous. Whether this assertion is true for a given trait in a given lineage is an empirical question, but general tools for detecting and quantifying phylogenetic signal are inadequately developed. We present new methods for continuous-valued characters that can be implemented with either phylogenetically independent contrasts or generalized least-squares models. First, a simple randomization procedure allows one to test the null hypothesis of no pattern of similarity among relatives. The test demonstrates correct Type I error rate at a nominal alpha = 0.05 and good power (0.8) for simulated datasets with 20 or more species. Second, we derive a descriptive statistic, K, which allows valid comparisons of the amount of phylogenetic signal across traits and trees. Third, we provide two biologically motivated branch-length transformations, one based on the Ornstein-Uhlenbeck (OU) model of stabilizing selection, the other based on a new model in which character evolution can accelerate or decelerate (ACDC) in rate (e.g., as may occur during or after an adaptive radiation). Maximum likelihood estimation of the OU (d) and ACDC (g) parameters can serve as tests for phylogenetic signal because an estimate of d or g near zero implies that a phylogeny with little hierarchical structure (a star) offers a good fit to the data. Transformations that improve the fit of a tree to comparative data will increase power to detect phylogenetic signal and may also be preferable for further comparative analyses, such as of correlated character evolution. Application of the methods to data from the literature revealed that, for trees with 20 or more species, 92% of traits exhibited significant phylogenetic signal (randomization test), including behavioral and ecological ones that are thought to be relatively evolutionarily malleable (e.g., highly adaptive) and/or subject to relatively strong environmental (nongenetic) effects or high levels of measurement error. Irrespective of sample size, most traits (but not body size, on average) showed less signal than expected given the topology, branch lengths, and a Brownian motion model of evolution (i.e., K was less than one), which may be attributed to adaptation and/or measurement error in the broad sense (including errors in estimates of phenotypes, branch lengths, and topology). Analysis of variance of log K for all 121 traits (from 35 trees) indicated that behavioral traits exhibit lower signal than body size, morphological, life-history, or physiological traits. In addition, physiological traits (corrected for body size) showed less signal than did body size itself. For trees with 20 or more species, the estimated OU (25% of traits) and/or ACDC (40%) transformation parameter differed significantly from both zero and unity, indicating that a hierarchical tree with less (or occasionally more) structure than the original better fit the data and so could be preferred for comparative analyses.
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            Phylogenetic niche conservatism, phylogenetic signal and the relationship between phylogenetic relatedness and ecological similarity among species.

            Ecologists are increasingly adopting an evolutionary perspective, and in recent years, the idea that closely related species are ecologically similar has become widespread. In this regard, phylogenetic signal must be distinguished from phylogenetic niche conservatism. Phylogenetic niche conservatism results when closely related species are more ecologically similar that would be expected based on their phylogenetic relationships; its occurrence suggests that some process is constraining divergence among closely related species. In contrast, phylogenetic signal refers to the situation in which ecological similarity between species is related to phylogenetic relatedness; this is the expected outcome of Brownian motion divergence and thus is necessary, but not sufficient, evidence for the existence of phylogenetic niche conservatism. Although many workers consider phylogenetic niche conservatism to be common, a review of case studies indicates that ecological and phylogenetic similarities often are not related. Consequently, ecologists should not assume that phylogenetic niche conservatism exists, but rather should empirically examine the extent to which it occurs.
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              Early bursts of body size and shape evolution are rare in comparative data.

              George Gaylord Simpson famously postulated that much of life's diversity originated as adaptive radiations-more or less simultaneous divergences of numerous lines from a single ancestral adaptive type. However, identifying adaptive radiations has proven difficult due to a lack of broad-scale comparative datasets. Here, we use phylogenetic comparative data on body size and shape in a diversity of animal clades to test a key model of adaptive radiation, in which initially rapid morphological evolution is followed by relative stasis. We compared the fit of this model to both single selective peak and random walk models. We found little support for the early-burst model of adaptive radiation, whereas both other models, particularly that of selective peaks, were commonly supported. In addition, we found that the net rate of morphological evolution varied inversely with clade age. The youngest clades appear to evolve most rapidly because long-term change typically does not attain the amount of divergence predicted from rates measured over short time scales. Across our entire analysis, the dominant pattern was one of constraints shaping evolution continually through time rather than rapid evolution followed by stasis. We suggest that the classical model of adaptive radiation, where morphological evolution is initially rapid and slows through time, may be rare in comparative data.
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                Author and article information

                Contributors
                bspacek@gmail.com
                Journal
                Ecol Evol
                Ecol Evol
                10.1002/(ISSN)2045-7758
                ECE3
                Ecology and Evolution
                John Wiley and Sons Inc. (Hoboken )
                2045-7758
                08 May 2018
                June 2018
                : 8
                : 11 ( doiID: 10.1002/ece3.2018.8.issue-11 )
                : 5313-5322
                Affiliations
                [ 1 ] Núcleo de Ciências Agrárias e Desenvolvimento Rural Univ Federal do Pará Belém Brazil
                [ 2 ] Departament of Entomology Univ of Minnesota Saint Paul MN USA
                [ 3 ] Programa de Pós‐Graduação em Ecologia e Evolução Univ Federal de Goiás Goiânia Brazil
                Author notes
                [*] [* ] Correspondence

                Bruno Spacek Godoy, Núcleo de Ciências Agrárias e Desenvolvimento Rural, Universidade Federal do Pará, Belém, Brazil.

                Email: bspacek@ 123456gmail.com

                Author information
                http://orcid.org/0000-0001-9751-9885
                Article
                ECE34031
                10.1002/ece3.4031
                6010749
                d1459cbe-3433-4393-a7d9-f866f4c10ba4
                © 2018 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd.

                This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

                History
                : 16 November 2016
                : 16 February 2018
                : 28 February 2018
                Page count
                Figures: 5, Tables: 1, Pages: 10, Words: 7921
                Funding
                Funded by: Universidade Federal do Pará
                Award ID: 02/2017
                Funded by: Coordenação de Aperfeiçoamento de Pessoal de Nível Superior
                Award ID: 10075/2013‐05
                Funded by: Conselho Nacional de Desenvolvimento Científico e Tecnológico
                Award ID: 303835/2009‐5
                Award ID: 475355/2007‐5
                Categories
                Original Research
                Original Research
                Custom metadata
                2.0
                ece34031
                June 2018
                Converter:WILEY_ML3GV2_TO_NLMPMC version:version=5.4.1.1 mode:remove_FC converted:20.06.2018

                Evolutionary Biology
                aquatic insects,bayesian updating model,evolutionary ecology,predictive modeling

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