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      Integrative behavioral ecotoxicology: bringing together fields to establish new insight to behavioral ecology, toxicology, and conservation

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          Abstract

          The fields of behavioral ecology, conservation science, and environmental toxicology individually aim to protect and manage the conservation of wildlife in response to anthropogenic stressors, including widespread anthropogenic pollution. Although great emphasis in the field of toxicology has been placed on understanding how single pollutants affect survival, a comprehensive, interdisciplinary approach that includes behavioral ecology is essential to address how anthropogenic compounds are a risk for the survival of species and populations in an increasingly polluted world. We provide an integrative framework for behavioral ecotoxicology using Tinbergen’s four postulates (causation and mechanism, development and ontogeny, function and fitness, and evolutionary history and phylogenetic patterns). The aims of this review are: 1) to promote an integrative view and re-define the field of integrative behavioral ecotoxicology; 2) to demonstrate how studying ecotoxicology can promote behavior research; and 3) to identify areas of behavioral ecotoxicology that require further attention to promote the integration and growth of the field.

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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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              Adverse outcome pathways: a conceptual framework to support ecotoxicology research and risk assessment.

              Ecological risk assessors face increasing demands to assess more chemicals, with greater speed and accuracy, and to do so using fewer resources and experimental animals. New approaches in biological and computational sciences may be able to generate mechanistic information that could help in meeting these challenges. However, to use mechanistic data to support chemical assessments, there is a need for effective translation of this information into endpoints meaningful to ecological risk-effects on survival, development, and reproduction in individual organisms and, by extension, impacts on populations. Here we discuss a framework designed for this purpose, the adverse outcome pathway (AOP). An AOP is a conceptual construct that portrays existing knowledge concerning the linkage between a direct molecular initiating event and an adverse outcome at a biological level of organization relevant to risk assessment. The practical utility of AOPs for ecological risk assessment of chemicals is illustrated using five case examples. The examples demonstrate how the AOP concept can focus toxicity testing in terms of species and endpoint selection, enhance across-chemical extrapolation, and support prediction of mixture effects. The examples also show how AOPs facilitate use of molecular or biochemical endpoints (sometimes referred to as biomarkers) for forecasting chemical impacts on individuals and populations. In the concluding sections of the paper, we discuss how AOPs can help to guide research that supports chemical risk assessments and advocate for the incorporation of this approach into a broader systems biology framework.
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                Author and article information

                Journal
                Curr Zool
                Curr Zool
                czoolo
                Current Zoology
                Oxford University Press
                1674-5507
                April 2017
                22 February 2017
                22 February 2017
                : 63
                : 2
                : 185-194
                Affiliations
                [a ]Department of Biological Sciences, State University of New York-Albany, Albany, NY 12222, USA
                [b ]Department of Biological Sciences, North Carolina State University, Raleigh, NC 27695, USA
                [c ]Department of Biology, University of South Dakota, Vermillion, SD 57069, USA
                [d ]Department of Biological Sciences, George Washington University, Washington, DC 20052, USA
                [e ]Department of Biology, Colorado State University-Pueblo, Pueblo, CO 81001, USA
                [f ]Biology Department, Institute for Integrative Bird Behavior Studies, College of William & Mary, Williamsburg, VA 23187-8795, USA
                Author notes
                [* ]Address correspondence to John P. Swaddle. E-mail: jpswad@ 123456wm.edu .
                Article
                zox010
                10.1093/cz/zox010
                5804166
                29491976
                2e062a63-02dd-4ded-a455-7ac935a102db
                © The Author (2017). Published by Oxford University Press.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com

                History
                : 31 October 2016
                : 8 February 2017
                Page count
                Pages: 10
                Funding
                Funded by: National Science Foundation
                Award ID: IOS1257590
                Funded by: Animal Behavior Society
                Categories
                Special Column: Conservation Concerns in Behavioral Toxicology

                animal behavior,behavioral ecology,conservation,phylogenetic,pollution,toxicology.

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