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      Annual aboveground carbon uptake enhancements from assisted gene flow in boreal black spruce forests are not long-lasting

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          Abstract

          Assisted gene flow between populations has been proposed as an adaptive forest management strategy that could contribute to the sequestration of carbon. Here we provide an assessment of the mitigation potential of assisted gene flow in 46 populations of the widespread boreal conifer Picea mariana, grown in two 42-year-old common garden experiments and established in contrasting Canadian boreal regions. We use a dendroecological approach taking into account phylogeographic structure to retrospectively analyse population phenotypic variability in annual aboveground net primary productivity (NPP). We compare population NPP phenotypes to detect signals of adaptive variation and/or the presence of phenotypic clines across tree lifespans, and assess genotype‐by‐environment interactions by evaluating climate and NPP relationships. Our results show a positive effect of assisted gene flow for a period of approximately 15 years following planting, after which there was little to no effect. Although not long lasting, well-informed assisted gene flow could accelerate the transition from carbon source to carbon sink after disturbance.

          Abstract

          The long-term effectiveness of assisted gene flow of trees could be jeopardised by rapid climate change. Here the authors analyse a large dataset of relocated black spruce populations in Canada, finding that local adaptation to climate of origin improved NPP responses, but only for up to ~15 years after planting.

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          Detecting the number of clusters of individuals using the software structure: a simulation study

          The identification of genetically homogeneous groups of individuals is a long standing issue in population genetics. A recent Bayesian algorithm implemented in the software STRUCTURE allows the identification of such groups. However, the ability of this algorithm to detect the true number of clusters (K) in a sample of individuals when patterns of dispersal among populations are not homogeneous has not been tested. The goal of this study is to carry out such tests, using various dispersal scenarios from data generated with an individual-based model. We found that in most cases the estimated 'log probability of data' does not provide a correct estimation of the number of clusters, K. However, using an ad hoc statistic DeltaK based on the rate of change in the log probability of data between successive K values, we found that STRUCTURE accurately detects the uppermost hierarchical level of structure for the scenarios we tested. As might be expected, the results are sensitive to the type of genetic marker used (AFLP vs. microsatellite), the number of loci scored, the number of populations sampled, and the number of individuals typed in each sample.
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            Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models

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              Inference of population structure using multilocus genotype data.

              We describe a model-based clustering method for using multilocus genotype data to infer population structure and assign individuals to populations. We assume a model in which there are K populations (where K may be unknown), each of which is characterized by a set of allele frequencies at each locus. Individuals in the sample are assigned (probabilistically) to populations, or jointly to two or more populations if their genotypes indicate that they are admixed. Our model does not assume a particular mutation process, and it can be applied to most of the commonly used genetic markers, provided that they are not closely linked. Applications of our method include demonstrating the presence of population structure, assigning individuals to populations, studying hybrid zones, and identifying migrants and admixed individuals. We show that the method can produce highly accurate assignments using modest numbers of loci-e.g. , seven microsatellite loci in an example using genotype data from an endangered bird species. The software used for this article is available from http://www.stats.ox.ac.uk/ approximately pritch/home. html.
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                Author and article information

                Contributors
                martin.girardin@canada.ca
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                19 February 2021
                19 February 2021
                2021
                : 12
                : 1169
                Affiliations
                [1 ]GRID grid.146611.5, ISNI 0000 0001 0775 5922, Natural Resources Canada, Canadian Forest Service, , Laurentian Forestry Centre, ; Québec, QC Canada
                [2 ]GRID grid.38678.32, ISNI 0000 0001 2181 0211, Centre d’étude de la forêt, , Université du Québec à Montréal, ; Montréal, QC Canada
                [3 ]GRID grid.23856.3a, ISNI 0000 0004 1936 8390, Canada Research Chair in Forest Genomics, Faculté de Foresterie, de Géographie et de Géomatique, , Université Laval, ; Québec, QC Canada
                [4 ]GRID grid.202033.0, ISNI 0000 0001 2295 5236, Natural Resources Canada, , Canadian Wood Fibre Centre, ; Québec, QC Canada
                Author information
                http://orcid.org/0000-0002-5947-533X
                Article
                21222
                10.1038/s41467-021-21222-3
                7895975
                33608515
                a6114c36-721c-41e8-b35c-9450e20a6a61
                © Crown 2021

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 23 July 2020
                : 12 January 2021
                Categories
                Article
                Custom metadata
                © The Author(s) 2021

                Uncategorized
                plant breeding,climate-change mitigation,ecological genetics,forest ecology
                Uncategorized
                plant breeding, climate-change mitigation, ecological genetics, forest ecology

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