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Gargiulo R, Decroocq V, González‐Martínez SC, Paz‐Vinas I, Aury J, Lesur Kupin I, Plomion C, Schmitt S, Scotti I, Heuertz M. Estimation of contemporary effective population size in plant populations: Limitations of genomic datasets. Evol Appl 2024; 17:e13691. [PMID: 38707994 PMCID: PMC11069024 DOI: 10.1111/eva.13691] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Revised: 03/22/2024] [Accepted: 04/03/2024] [Indexed: 05/07/2024] Open
Abstract
Effective population size (N e) is a pivotal evolutionary parameter with crucial implications in conservation practice and policy. Genetic methods to estimate N e have been preferred over demographic methods because they rely on genetic data rather than time-consuming ecological monitoring. Methods based on linkage disequilibrium (LD), in particular, have become popular in conservation as they require a single sampling and provide estimates that refer to recent generations. A software program based on the LD method, GONE, looks particularly promising to estimate contemporary and recent-historical N e (up to 200 generations in the past). Genomic datasets from non-model species, especially plants, may present some constraints to the use of GONE, as linkage maps and reference genomes are seldom available, and SNP genotyping is usually based on reduced-representation methods. In this study, we use empirical datasets from four plant species to explore the limitations of plant genomic datasets when estimating N e using the algorithm implemented in GONE, in addition to exploring some typical biological limitations that may affect N e estimation using the LD method, such as the occurrence of population structure. We show how accuracy and precision of N e estimates potentially change with the following factors: occurrence of missing data, limited number of SNPs/individuals sampled, and lack of information about the location of SNPs on chromosomes, with the latter producing a significant bias, previously unexplored with empirical data. We finally compare the N e estimates obtained with GONE for the last generations with the contemporary N e estimates obtained with the programs currentNe and NeEstimator.
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Affiliation(s)
| | | | | | - Ivan Paz‐Vinas
- Department of BiologyColorado State UniversityFort CollinsColoradoUSA
- CNRS, ENTPE, UMR5023 LEHNAUniversité Claude Bernard Lyon 1VilleurbanneFrance
| | - Jean‐Marc Aury
- Génomique Métabolique, Genoscope, Institut François Jacob, CEA, CNRS, Univ EvryUniversité Paris‐SaclayEvryFrance
| | | | | | - Sylvain Schmitt
- AMAPUniv. Montpellier, CIRAD, CNRS, INRAE, IRDMontpellierFrance
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vonHoldt BM, Stahler DR, Brzeski KE, Musiani M, Peterson R, Phillips M, Stephenson J, Laudon K, Meredith E, Vucetich JA, Leonard JA, Wayne RK. Demographic history shapes North American gray wolf genomic diversity and informs species' conservation. Mol Ecol 2024; 33:e17231. [PMID: 38054561 DOI: 10.1111/mec.17231] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Revised: 11/19/2023] [Accepted: 11/21/2023] [Indexed: 12/07/2023]
Abstract
Effective population size estimates are critical information needed for evolutionary predictions and conservation decisions. This is particularly true for species with social factors that restrict access to breeding or experience repeated fluctuations in population size across generations. We investigated the genomic estimates of effective population size along with diversity, subdivision, and inbreeding from 162,109 minimally filtered and 81,595 statistically neutral and unlinked SNPs genotyped in 437 grey wolf samples from North America collected between 1986 and 2021. We found genetic structure across North America, represented by three distinct demographic histories of western, central, and eastern regions of the continent. Further, grey wolves in the northern Rocky Mountains have lower genomic diversity than wolves of the western Great Lakes and have declined over time. Effective population size estimates revealed the historical signatures of continental efforts of predator extermination, despite a quarter century of recovery efforts. We are the first to provide molecular estimates of effective population size across distinct grey wolf populations in North America, which ranged between Ne ~ 275 and 3050 since early 1980s. We provide data that inform managers regarding the status and importance of effective population size estimates for grey wolf conservation, which are on average 5.2-9.3% of census estimates for this species. We show that while grey wolves fall above minimum effective population sizes needed to avoid extinction due to inbreeding depression in the short term, they are below sizes predicted to be necessary to avoid long-term risk of extinction.
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Affiliation(s)
- Bridgett M vonHoldt
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, New Jersey, USA
| | - Daniel R Stahler
- Yellowstone Center for Resources, Yellowstone National Park, Wyoming, USA
| | - Kristin E Brzeski
- College of Forest Resources and Environmental Science, Michigan Technological University, Houghton, Michigan, USA
| | - Marco Musiani
- Dipartimento di Scienze Biologiche, Geologiche e Ambientali (BiGeA), Università di Bologna, Bologna, Italy
| | - Rolf Peterson
- College of Forest Resources and Environmental Science, Michigan Technological University, Houghton, Michigan, USA
| | | | | | - Kent Laudon
- California Department of Fish and Wildlife, Northern Region, Redding, California, USA
| | - Erin Meredith
- California Department of Fish and Wildlife, Wildlife Forensic Laboratory, Sacramento, California, USA
| | - John A Vucetich
- College of Forest Resources and Environmental Science, Michigan Technological University, Houghton, Michigan, USA
| | - Jennifer A Leonard
- Conservation and Evolutionary Genetics Group, Estación Biológica de Doñana (EBD-CSIC), Seville, Spain
| | - Robert K Wayne
- Department of Ecology and Evolutionary Biology, University of California, Los Angeles, Los Angeles, California, USA
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Novo I, Ordás P, Moraga N, Santiago E, Quesada H, Caballero A. Impact of population structure in the estimation of recent historical effective population size by the software GONE. Genet Sel Evol 2023; 55:86. [PMID: 38049712 PMCID: PMC10694967 DOI: 10.1186/s12711-023-00859-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Accepted: 11/20/2023] [Indexed: 12/06/2023] Open
Abstract
BACKGROUND Effective population size (Ne) is a crucial parameter in conservation genetics and animal breeding. A recent method, implemented by the software GONE, has been shown to be rather accurate in estimating recent historical changes in Ne from a single sample of individuals. However, GONE estimations assume that the population being studied has remained isolated for a period of time, that is, without migration or confluence of other populations. If this occurs, the estimates of Ne can be heavily biased. In this paper, we evaluate the impact of migration and admixture on the estimates of historical Ne provided by GONE through a series of computer simulations considering several scenarios: (a) the mixture of two or more ancestral populations; (b) subpopulations that continuously exchange individuals through migration; (c) populations receiving migrants from a large source; and (d) populations with balanced systems of chromosomal inversions, which also generate genetic structure. RESULTS Our results indicate that the estimates of historical Ne provided by GONE may be substantially biased when there has been a recent mixture of populations that were previously separated for a long period of time. Similarly, biases may occur when the rate of continued migration between populations is low, or when chromosomal inversions are present at high frequencies. However, some biases due to population structuring can be eliminated by conducting population structure analyses and restricting the estimation to the differentiated groups. In addition, disregarding the genomic regions that are involved in inversions can also remove biases in the estimates of Ne. CONCLUSIONS Different kinds of deviations from isolation and panmixia of the populations can generate biases in the recent historical estimates of Ne. Therefore, estimation of past demography could benefit from performing population structure analyses beforehand, by mitigating the impact of these biases on historical Ne estimates.
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Affiliation(s)
- Irene Novo
- Centro de Investigación Mariña, Universidade de Vigo, Facultade de Bioloxía, 36310, Vigo, Spain.
| | - Pilar Ordás
- Centro de Investigación Mariña, Universidade de Vigo, Facultade de Bioloxía, 36310, Vigo, Spain
| | - Natalia Moraga
- Centro de Investigación Mariña, Universidade de Vigo, Facultade de Bioloxía, 36310, Vigo, Spain
| | - Enrique Santiago
- Departamento de Biología Funcional, Facultad de Biología, Universidad de Oviedo, 33006, Oviedo, Spain
| | - Humberto Quesada
- Centro de Investigación Mariña, Universidade de Vigo, Facultade de Bioloxía, 36310, Vigo, Spain
| | - Armando Caballero
- Centro de Investigación Mariña, Universidade de Vigo, Facultade de Bioloxía, 36310, Vigo, Spain
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