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      Genotypes selected for early and late avian lay date differ in their phenotype, but not fitness, in the wild

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          Abstract

          Global warming has shifted phenological traits in many species, but whether species are able to track further increasing temperatures depends on the fitness consequences of additional shifts in phenological traits. To test this, we measured phenology and fitness of great tits ( Parus major) with genotypes for extremely early and late egg lay dates, obtained from a genomic selection experiment. Females with early genotypes advanced lay dates relative to females with late genotypes, but not relative to nonselected females. Females with early and late genotypes did not differ in the number of fledglings produced, in line with the weak effect of lay date on the number of fledglings produced by nonselected females in the years of the experiment. Our study is the first application of genomic selection in the wild and led to an asymmetric phenotypic response that indicates the presence of constraints toward early, but not late, lay dates.

          Abstract

          Genomic selection for avian lay dates in the wild led to a phenotypic response without any detectable effect on fitness.

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          Most cited references49

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          A globally coherent fingerprint of climate change impacts across natural systems.

          Causal attribution of recent biological trends to climate change is complicated because non-climatic influences dominate local, short-term biological changes. Any underlying signal from climate change is likely to be revealed by analyses that seek systematic trends across diverse species and geographic regions; however, debates within the Intergovernmental Panel on Climate Change (IPCC) reveal several definitions of a 'systematic trend'. Here, we explore these differences, apply diverse analyses to more than 1,700 species, and show that recent biological trends match climate change predictions. Global meta-analyses documented significant range shifts averaging 6.1 km per decade towards the poles (or metres per decade upward), and significant mean advancement of spring events by 2.3 days per decade. We define a diagnostic fingerprint of temporal and spatial 'sign-switching' responses uniquely predicted by twentieth century climate trends. Among appropriate long-term/large-scale/multi-species data sets, this diagnostic fingerprint was found for 279 species. This suite of analyses generates 'very high confidence' (as laid down by the IPCC) that climate change is already affecting living systems.
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            Mapping genes for complex traits in domestic animals and their use in breeding programmes.

            Genome-wide panels of SNPs have recently been used in domestic animal species to map and identify genes for many traits and to select genetically desirable livestock. This has led to the discovery of the causal genes and mutations for several single-gene traits but not for complex traits. However, the genetic merit of animals can still be estimated by genomic selection, which uses genome-wide SNP panels as markers and statistical methods that capture the effects of large numbers of SNPs simultaneously. This approach is expected to double the rate of genetic improvement per year in many livestock systems.
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              An ecologist's guide to the animal model.

              1. Efforts to understand the links between evolutionary and ecological dynamics hinge on our ability to measure and understand how genes influence phenotypes, fitness and population dynamics. Quantitative genetics provides a range of theoretical and empirical tools with which to achieve this when the relatedness between individuals within a population is known. 2. A number of recent studies have used a type of mixed-effects model, known as the animal model, to estimate the genetic component of phenotypic variation using data collected in the field. Here, we provide a practical guide for ecologists interested in exploring the potential to apply this quantitative genetic method in their research. 3. We begin by outlining, in simple terms, key concepts in quantitative genetics and how an animal model estimates relevant quantitative genetic parameters, such as heritabilities or genetic correlations. 4. We then provide three detailed example tutorials, for implementation in a variety of software packages, for some basic applications of the animal model. We discuss several important statistical issues relating to best practice when fitting different kinds of mixed models. 5. We conclude by briefly summarizing more complex applications of the animal model, and by highlighting key pitfalls and dangers for the researcher wanting to begin using quantitative genetic tools to address ecological and evolutionary questions.
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                Author and article information

                Contributors
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: Project administrationRole: SoftwareRole: VisualizationRole: Writing - original draftRole: Writing - review & editing
                Role: ConceptualizationRole: Data curationRole: InvestigationRole: MethodologyRole: Project administrationRole: ValidationRole: Writing - original draft
                Role: Writing - review & editing
                Role: Data curationRole: InvestigationRole: Writing - review & editing
                Role: Resources
                Role: Writing - review & editing
                Role: ConceptualizationRole: Funding acquisitionRole: InvestigationRole: MethodologyRole: Project administrationRole: SupervisionRole: ValidationRole: Writing - original draftRole: Writing - review & editing
                Journal
                Sci Adv
                Sci Adv
                sciadv
                advances
                Science Advances
                American Association for the Advancement of Science
                2375-2548
                June 2023
                07 June 2023
                : 9
                : 23
                : eade6350
                Affiliations
                [ 1 ]Department of Animal Ecology, Netherlands Institute of Ecology (NIOO-KNAW), Wageningen, Netherlands.
                [ 2 ]Chronobiology Unit, Groningen Institute for Evolutionary Life Sciences (GELIFES), University of Groningen, Groningen, Netherlands.
                [ 3 ]Mathematical and Statistical Methods–Biometris, Wageningen University & Research (WUR), Wageningen, Netherlands.
                [ 4 ]Wageningen University & Research (WUR) Library, Wageningen, Netherlands.
                [ 5 ]Department of Arctic and Marine Biology, UiT The Arctic University of Norway, Tromsø, Norway.
                [ 6 ]Michael-Otto-Institut, NABU, Bergenhusen, Germany.
                Author notes
                [* ]Corresponding author. Email: m.lindner@ 123456nioo.knaw.nl (M.L.); m.visser@ 123456nioo.knaw.nl (M.E.V.)
                Author information
                https://orcid.org/0000-0003-2931-265X
                https://orcid.org/0000-0002-0617-930X
                https://orcid.org/0000-0001-5588-1333
                https://orcid.org/0000-0002-8855-4803
                https://orcid.org/0000-0002-9832-5780
                https://orcid.org/0000-0002-9368-8769
                https://orcid.org/0000-0002-1456-1939
                Article
                ade6350
                10.1126/sciadv.ade6350
                10246905
                c77382e3-4150-4506-a57b-4a56c9ff8ae9
                Copyright © 2023 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC).

                This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial license, which permits use, distribution, and reproduction in any medium, so long as the resultant use is not for commercial advantage and provided the original work is properly cited.

                History
                : 29 August 2022
                : 01 May 2023
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100000781, European Research Council;
                Award ID: 339092 – E-Response
                Categories
                Research Article
                Earth, Environmental, Ecological, and Space Sciences
                SciAdv r-articles
                Applied Ecology
                Evolutionary Biology
                Evolutionary Biology
                Custom metadata
                Eunice Diego

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