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      Hot and dry conditions predict shorter nestling telomeres in an endangered songbird: Implications for population persistence

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          Abstract

          Climate warming is increasingly exposing wildlife to sublethal high temperatures, which may lead to chronic impacts and reduced fitness. Telomere length (TL) may link heat exposure to fitness, particularly at early-life stages, because developing organisms are especially vulnerable to adverse conditions, adversity can shorten telomeres, and TL predicts fitness. Here, we quantify how climatic and environmental conditions during early life are associated with TL in nestlings of wild purple-crowned fairy-wrens ( Malurus coronatus ), endangered songbirds of the monsoonal tropics. We found that higher average maximum air temperature (range 31 to 45 °C) during the nestling period was associated with shorter early-life TL. This effect was mitigated by water availability (i.e., during the wet season, with rainfall), but independent of other pertinent environmental conditions, implicating a direct effect of heat exposure. Models incorporating existing information that shorter early-life TL predicts shorter lifespan and reduced fitness showed that shorter TL under projected warming scenarios could lead to population decline across plausible future water availability scenarios. However, if TL is assumed to be an adaptive trait, population viability could be maintained through evolution. These results are concerning because the capacity to change breeding phenology to coincide with increased water availability appears limited, and the evolutionary potential of TL is unknown. Thus, sublethal climate warming effects early in life may have repercussions beyond individual fitness, extending to population persistence. Incorporating the delayed reproductive costs associated with sublethal heat exposure early in life is necessary for understanding future population dynamics with climate change.

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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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              Ecological responses to recent climate change.

              There is now ample evidence of the ecological impacts of recent climate change, from polar terrestrial to tropical marine environments. The responses of both flora and fauna span an array of ecosystems and organizational hierarchies, from the species to the community levels. Despite continued uncertainty as to community and ecosystem trajectories under global change, our review exposes a coherent pattern of ecological change across systems. Although we are only at an early stage in the projected trends of global warming, ecological responses to recent climate change are already clearly visible.
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                Author and article information

                Contributors
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                Journal
                Proceedings of the National Academy of Sciences
                Proc. Natl. Acad. Sci. U.S.A.
                Proceedings of the National Academy of Sciences
                0027-8424
                1091-6490
                June 21 2022
                June 13 2022
                June 21 2022
                : 119
                : 25
                Affiliations
                [1 ]School of Biological Sciences, Monash University, Clayton, VIC 3800, Australia
                [2 ]Department Behavioural Ecology & Evolutionary Genetics, Max Planck Institute for Ornithology, 82319 Seewiesen, Germany
                [3 ]Bush Heritage Australia, Melbourne, VIC 3000, Australia
                [4 ]School of BioSciences, University of Melbourne, Melbourne, Parkville, VIC 3010, Australia
                [5 ]School of Biological Sciences, University of Western Australia, Crawley, WA 6009, Australia
                [6 ]Max Planck Institute for Ornithology, Vogelwarte Radolfzell, 78315 Radolfzell, Germany
                [7 ]Behavioural Ecology Group, Department of Animal Sciences, Wageningen University and Research, 6708 WD Wageningen, The Netherlands
                [8 ]Groningen Institute for Evolutionary Life Sciences, University of Groningen, 9747AG Groningen, The Netherlands
                Article
                10.1073/pnas.2122944119
                4d1185ad-4552-46d4-81de-f3a6325842c5
                © 2022

                https://creativecommons.org/licenses/by-nc-nd/4.0/

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