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      The cycle of seagrass life: From flowers to new meadows

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          Abstract

          Understanding sexual reproduction and recruitment in seagrasses is crucial to their conservation and restoration. Flowering, seed production, seed recruitment, and seedling establishment data for the seagrass Posidonia australis was collected annually between 2013 and 2018 in meadows at six locations around Rottnest Island, Western Australia. Variable annual rates of flowering and seed production were observed among meadows between northern and southern sides of the island and among years. Meadows on the northern shore consistently flowered more intensely and produced more seeds across the years of the survey. Inter‐site variation in clonal diversity and size of clones, seed production, wind and surface currents during pollen and seed release, and the large, but variable, impact of seed predation are likely the principal drivers of successful recruitment into established meadows and in colonizing unvegetated sands. The prolific but variable annual reproductive investment increases the probability of low levels of continuous recruitment from seed in this seagrass, despite high rates of abiotic and biotic disturbance at seedling, shoot, and patch scales. This strategy also imparts a level of ecological resilience to this long‐lived and persistent species.

          Abstract

          Flowering, seed production, seed recruitment, and seedling establishment for the seagrass Posidonia australis was collected annually between 2013 and 2018. Meadows on the northern shore consistently flowered more intensely and produced more seeds across the years of the survey. Variation in clonal diversity and size of clones, seed production, island wakes developed by wind and surface currents, and the large, but variable, impact of seed predation are likely the principal drivers of successful recruitment from seeds.

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          Fitting Linear Mixed-Effects Models Usinglme4

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            GenAlEx 6.5: genetic analysis in Excel. Population genetic software for teaching and research—an update

            Summary: GenAlEx: Genetic Analysis in Excel is a cross-platform package for population genetic analyses that runs within Microsoft Excel. GenAlEx offers analysis of diploid codominant, haploid and binary genetic loci and DNA sequences. Both frequency-based (F-statistics, heterozygosity, HWE, population assignment, relatedness) and distance-based (AMOVA, PCoA, Mantel tests, multivariate spatial autocorrelation) analyses are provided. New features include calculation of new estimators of population structure: G′ST, G′′ST, Jost’s D est and F′ST through AMOVA, Shannon Information analysis, linkage disequilibrium analysis for biallelic data and novel heterogeneity tests for spatial autocorrelation analysis. Export to more than 30 other data formats is provided. Teaching tutorials and expanded step-by-step output options are included. The comprehensive guide has been fully revised. Availability and implementation: GenAlEx is written in VBA and provided as a Microsoft Excel Add-in (compatible with Excel 2003, 2007, 2010 on PC; Excel 2004, 2011 on Macintosh). GenAlEx, and supporting documentation and tutorials are freely available at: http://biology.anu.edu.au/GenAlEx. Contact: rod.peakall@anu.edu.au
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              What is a population? An empirical evaluation of some genetic methods for identifying the number of gene pools and their degree of connectivity.

              We review commonly used population definitions under both the ecological paradigm (which emphasizes demographic cohesion) and the evolutionary paradigm (which emphasizes reproductive cohesion) and find that none are truly operational. We suggest several quantitative criteria that might be used to determine when groups of individuals are different enough to be considered 'populations'. Units for these criteria are migration rate (m) for the ecological paradigm and migrants per generation (Nm) for the evolutionary paradigm. These criteria are then evaluated by applying analytical methods to simulated genetic data for a finite island model. Under the standard parameter set that includes L = 20 High mutation (microsatellite-like) loci and samples of S = 50 individuals from each of n = 4 subpopulations, power to detect departures from panmixia was very high ( approximately 100%; P < 0.001) even with high gene flow (Nm = 25). A new method, comparing the number of correct population assignments with the random expectation, performed as well as a multilocus contingency test and warrants further consideration. Use of Low mutation (allozyme-like) markers reduced power more than did halving S or L. Under the standard parameter set, power to detect restricted gene flow below a certain level X (H(0): Nm < X) can also be high, provided that true Nm < or = 0.5X. Developing the appropriate test criterion, however, requires assumptions about several key parameters that are difficult to estimate in most natural populations. Methods that cluster individuals without using a priori sampling information detected the true number of populations only under conditions of moderate or low gene flow (Nm < or = 5), and power dropped sharply with smaller samples of loci and individuals. A simple algorithm based on a multilocus contingency test of allele frequencies in pairs of samples has high power to detect the true number of populations even with Nm = 25 but requires more rigorous statistical evaluation. The ecological paradigm remains challenging for evaluations using genetic markers, because the transition from demographic dependence to independence occurs in a region of high migration where genetic methods have relatively little power. Some recent theoretical developments and continued advances in computational power provide hope that this situation may change in the future.
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                Author and article information

                Contributors
                gary.kendrick@uwa.edu.au
                Journal
                Ecol Evol
                Ecol Evol
                10.1002/(ISSN)2045-7758
                ECE3
                Ecology and Evolution
                John Wiley and Sons Inc. (Hoboken )
                2045-7758
                31 August 2023
                September 2023
                : 13
                : 9 ( doiID: 10.1002/ece3.v13.9 )
                : e10456
                Affiliations
                [ 1 ] School of Biological Sciences and UWA Oceans Institute The University of Western Australia Western Australia Crawley Australia
                [ 2 ] Virginia Institute of Marine Science College of William and Mary Gloucester Point Virginia USA
                [ 3 ] Oceans Graduate School and UWA Oceans Institute The University of Western Australia Crawley Western Australia Australia
                Author notes
                [*] [* ] Correspondence

                Gary A. Kendrick, School of Biological Sciences and UWA Oceans Institute, The University of Western Australia, 35 Stirling Hwy, Crawley, 6009 WA, Australia.

                Email: gary.kendrick@ 123456uwa.edu.au

                Author information
                https://orcid.org/0000-0002-0276-6064
                https://orcid.org/0000-0003-2781-4429
                https://orcid.org/0000-0003-2491-7430
                https://orcid.org/0000-0003-2309-8074
                https://orcid.org/0000-0003-0308-3609
                https://orcid.org/0000-0002-1956-9382
                https://orcid.org/0000-0003-2229-6183
                https://orcid.org/0000-0002-5789-8945
                Article
                ECE310456 ECE-2023-05-00903.R1
                10.1002/ece3.10456
                10469021
                37664509
                3f947d43-c0b4-4822-8aea-c60ee602a96b
                © 2023 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
                : 01 August 2023
                : 02 June 2023
                : 11 August 2023
                Page count
                Figures: 7, Tables: 4, Pages: 14, Words: 8338
                Funding
                Funded by: Australian Research Council , doi 10.13039/501100000923;
                Award ID: DP180100668
                Award ID: DP210101932
                Award ID: LP130100198
                Award ID: LP160101011
                Categories
                Botany
                Research Article
                Research Articles
                Custom metadata
                2.0
                September 2023
                Converter:WILEY_ML3GV2_TO_JATSPMC version:6.3.3 mode:remove_FC converted:31.08.2023

                Evolutionary Biology
                clonal diversity,flowering,microsatellite dna,posidonia australis,recruitment,reproductive effort,seed predation

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