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Rosenberger KJ, Hoban S. Simulating pollen flow and field sampling constraints helps revise seed sampling recommendations for conserving genetic diversity. APPLICATIONS IN PLANT SCIENCES 2024; 12:e11561. [PMID: 38912130 PMCID: PMC11192154 DOI: 10.1002/aps3.11561] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Revised: 11/19/2023] [Accepted: 12/01/2023] [Indexed: 06/25/2024]
Abstract
Premise In this study, we use simulations to determine how pollen flow and sampling constraints can influence the genetic conservation found in seed collections. Methods We simulated genotypes of parental individuals and crossed the parentals based on three different ranges of pollen flow (panmictic, limited, and highly limited) to create new seed sets for sampling. We tested a variety of sampling scenarios modeled on those occurring in nature and calculated the proportion of alleles conserved in each scenario. Results We found that pollen flow greatly influences collection outcomes, with panmictic pollen flow resulting in seed sets containing 21.6% more alleles than limited pollen flow and 48.6% more alleles than highly limited pollen flow, although this impact diminishes when large numbers of maternal plants are sampled. Simulations of realistic seed sampling (sampling more seed from some plants and fewer from others) showed a relatively minor impact (<2.5%) on genetic diversity conserved compared to ideal sampling (uniform sampling across all maternal plants). Discussion We conclude that future work must consider limited pollen flow, but collectors can be flexible with their sampling in the field as long as many unique maternal plants are sampled. Simulations remain a fruitful method to advance ex situ sampling guidelines.
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Affiliation(s)
- Kaylee J. Rosenberger
- The Morton Arboretum4100 IL‐53LisleIllinois60532USA
- Department of Ecology and Evolutionary BiologyUniversity of Colorado Boulder1900 Pleasant St.BoulderColorado80302USA
| | - Sean Hoban
- Department of Ecology and Evolutionary BiologyUniversity of Colorado Boulder1900 Pleasant St.BoulderColorado80302USA
- Committee on Evolutionary BiologyThe University of Chicago1025 E. 57th St.ChicagoIllinois60637USA
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2
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Johnson OL, Tobler R, Schmidt JM, Huber CD. Population genetic simulation: Benchmarking frameworks for non-standard models of natural selection. Mol Ecol Resour 2024; 24:e13930. [PMID: 38247258 DOI: 10.1111/1755-0998.13930] [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: 10/17/2023] [Revised: 12/21/2023] [Accepted: 01/09/2024] [Indexed: 01/23/2024]
Abstract
Population genetic simulation has emerged as a common tool for investigating increasingly complex evolutionary and demographic models. Software capable of handling high-level model complexity has recently been developed, and the advancement of tree sequence recording now allows simulations to merge the efficiency and genealogical insight of coalescent simulations with the flexibility of forward simulations. However, frameworks utilizing these features have not yet been compared and benchmarked. Here, we evaluate various simulation workflows using the coalescent simulator msprime and the forward simulator SLiM, to assess resource efficiency and determine an optimal simulation framework. Three aspects were evaluated: (1) the burn-in, to establish an equilibrium level of neutral diversity in the population; (2) the forward simulation, in which temporally fluctuating selection is acting; and (3) the final computation of summary statistics. We provide typical memory and computation time requirements for each step. We find that the fastest framework, a combination of coalescent and forward simulation with tree sequence recording, increases simulation speed by over twenty times compared to classical forward simulations without tree sequence recording, although it does require six times more memory. Overall, using efficient simulation workflows can lead to a substantial improvement when modelling complex evolutionary scenarios-although the optimal framework ultimately depends on the available computational resources.
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Affiliation(s)
- Olivia L Johnson
- School of Biological Sciences, University of Adelaide, Adelaide, South Australia, Australia
| | - Raymond Tobler
- Evolution of Cultural Diversity Initiative, The Australian National University, Canberra, Australian Capital Territory, Australia
| | - Joshua M Schmidt
- Department of Ophthalmology, College of Medicine and Public Health, Flinders University, Adelaide, South Australia, Australia
| | - Christian D Huber
- School of Biological Sciences, University of Adelaide, Adelaide, South Australia, Australia
- Department of Biology, Pennsylvania State University, University Park, Pennsylvania, USA
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3
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Ferreiro D, Branco C, Arenas M. Selection among site-dependent structurally constrained substitution models of protein evolution by approximate Bayesian computation. Bioinformatics 2024; 40:btae096. [PMID: 38374231 PMCID: PMC10914458 DOI: 10.1093/bioinformatics/btae096] [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: 03/22/2023] [Revised: 01/15/2024] [Accepted: 02/16/2024] [Indexed: 02/21/2024] Open
Abstract
MOTIVATION The selection among substitution models of molecular evolution is fundamental for obtaining accurate phylogenetic inferences. At the protein level, evolutionary analyses are traditionally based on empirical substitution models but these models make unrealistic assumptions and are being surpassed by structurally constrained substitution (SCS) models. The SCS models often consider site-dependent evolution, a process that provides realism but complicates their implementation into likelihood functions that are commonly used for substitution model selection. RESULTS We present a method to perform selection among site-dependent SCS models, also among empirical and site-dependent SCS models, based on the approximate Bayesian computation (ABC) approach and its implementation into the computational framework ProteinModelerABC. The framework implements ABC with and without regression adjustments and includes diverse empirical and site-dependent SCS models of protein evolution. Using extensive simulated data, we found that it provides selection among SCS and empirical models with acceptable accuracy. As illustrative examples, we applied the framework to analyze a variety of protein families observing that SCS models fit them better than the corresponding best-fitting empirical substitution models. AVAILABILITY AND IMPLEMENTATION ProteinModelerABC is freely available from https://github.com/DavidFerreiro/ProteinModelerABC, can run in parallel and includes a graphical user interface. The framework is distributed with detailed documentation and ready-to-use examples.
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Affiliation(s)
- David Ferreiro
- CINBIO, Universidade de Vigo, 36310 Vigo, Spain
- Department of Biochemistry, Genetics and Immunology, Universidade de Vigo, 36310 Vigo, Spain
| | - Catarina Branco
- CINBIO, Universidade de Vigo, 36310 Vigo, Spain
- Department of Biochemistry, Genetics and Immunology, Universidade de Vigo, 36310 Vigo, Spain
| | - Miguel Arenas
- CINBIO, Universidade de Vigo, 36310 Vigo, Spain
- Department of Biochemistry, Genetics and Immunology, Universidade de Vigo, 36310 Vigo, Spain
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4
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Ali HAA, Coulson T, Clegg SM, Quilodrán CS. The effect of divergent and parallel selection on the genomic landscape of divergence. Mol Ecol 2024; 33:e17225. [PMID: 38063473 DOI: 10.1111/mec.17225] [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: 07/19/2023] [Revised: 10/25/2023] [Accepted: 11/16/2023] [Indexed: 01/25/2024]
Abstract
While the role of selection in divergence along the speciation continuum is theoretically well understood, defining specific signatures of selection in the genomic landscape of divergence is empirically challenging. Modelling approaches can provide insight into the potential role of selection on the emergence of a heterogenous genomic landscape of divergence. Here, we extend and apply an individual-based approach that simulates the phenotypic and genotypic distributions of two populations under a variety of selection regimes, genotype-phenotype maps, modes of migration, and genotype-environment interactions. We show that genomic islands of high differentiation and genomic valleys of similarity may respectively form under divergent and parallel selection between populations. For both types of between-population selection, negative and positive frequency-dependent selection within populations generated genomic islands of higher magnitude and genomic valleys of similarity, respectively. Divergence rates decreased under strong dominance with divergent selection, as well as in models including genotype-environment interactions under parallel selection. For both divergent and parallel selection models, divergence rate was higher under an intermittent migration regime between populations, in contrast to a constant level of migration across generations, despite an equal number of total migrants. We highlight that interpreting a particular evolutionary history from an observed genomic pattern must be done cautiously, as similar patterns may be obtained from different combinations of evolutionary processes. Modelling approaches such as ours provide an opportunity to narrow the potential routes that generate the genomic patterns of specific evolutionary histories.
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Affiliation(s)
- Hisham A A Ali
- Department of Biology, Edward Grey Institute of Field Ornithology, University of Oxford, Oxford, UK
| | - Tim Coulson
- Department of Biology, Edward Grey Institute of Field Ornithology, University of Oxford, Oxford, UK
| | - Sonya M Clegg
- Department of Biology, Edward Grey Institute of Field Ornithology, University of Oxford, Oxford, UK
| | - Claudio S Quilodrán
- Department of Biology, Edward Grey Institute of Field Ornithology, University of Oxford, Oxford, UK
- Department of Genetics and Evolution, University of Geneva, Geneva, Switzerland
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5
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Schiebelhut LM, Guillaume AS, Kuhn A, Schweizer RM, Armstrong EE, Beaumont MA, Byrne M, Cosart T, Hand BK, Howard L, Mussmann SM, Narum SR, Rasteiro R, Rivera-Colón AG, Saarman N, Sethuraman A, Taylor HR, Thomas GWC, Wellenreuther M, Luikart G. Genomics and conservation: Guidance from training to analyses and applications. Mol Ecol Resour 2024; 24:e13893. [PMID: 37966259 DOI: 10.1111/1755-0998.13893] [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: 06/10/2022] [Revised: 10/25/2023] [Accepted: 10/30/2023] [Indexed: 11/16/2023]
Abstract
Environmental change is intensifying the biodiversity crisis and threatening species across the tree of life. Conservation genomics can help inform conservation actions and slow biodiversity loss. However, more training, appropriate use of novel genomic methods and communication with managers are needed. Here, we review practical guidance to improve applied conservation genomics. We share insights aimed at ensuring effectiveness of conservation actions around three themes: (1) improving pedagogy and training in conservation genomics including for online global audiences, (2) conducting rigorous population genomic analyses properly considering theory, marker types and data interpretation and (3) facilitating communication and collaboration between managers and researchers. We aim to update students and professionals and expand their conservation toolkit with genomic principles and recent approaches for conserving and managing biodiversity. The biodiversity crisis is a global problem and, as such, requires international involvement, training, collaboration and frequent reviews of the literature and workshops as we do here.
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Affiliation(s)
- Lauren M Schiebelhut
- Life and Environmental Sciences, University of California, Merced, California, USA
| | - Annie S Guillaume
- Geospatial Molecular Epidemiology group (GEOME), Laboratory for Biological Geochemistry (LGB), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Arianna Kuhn
- Department of Biological Sciences, University of Lethbridge, Lethbridge, Alberta, Canada
- Virginia Museum of Natural History, Martinsville, Virginia, USA
| | - Rena M Schweizer
- Division of Biological Sciences, University of Montana, Missoula, Montana, USA
| | | | - Mark A Beaumont
- School of Biological Sciences, University of Bristol, Bristol, UK
| | - Margaret Byrne
- Department of Biodiversity, Conservation and Attractions, Biodiversity and Conservation Science, Perth, Western Australia, Australia
| | - Ted Cosart
- Flathead Lake Biology Station, University of Montana, Missoula, Montana, USA
| | - Brian K Hand
- Flathead Lake Biological Station, University of Montana, Polson, Montana, USA
| | - Leif Howard
- Flathead Lake Biology Station, University of Montana, Missoula, Montana, USA
| | - Steven M Mussmann
- Southwestern Native Aquatic Resources and Recovery Center, U.S. Fish & Wildlife Service, Dexter, New Mexico, USA
| | - Shawn R Narum
- Hagerman Genetics Lab, University of Idaho, Hagerman, Idaho, USA
| | - Rita Rasteiro
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
| | - Angel G Rivera-Colón
- Department of Evolution, Ecology, and Behavior, University of Illinois at Urbana-Champaign, Champaign, Illinois, USA
| | - Norah Saarman
- Department of Biology and Ecology Center, Utah State University, Logan, Utah, USA
| | - Arun Sethuraman
- Department of Biology, San Diego State University, San Diego, California, USA
| | - Helen R Taylor
- Royal Zoological Society of Scotland, Edinburgh, Scotland
| | - Gregg W C Thomas
- Informatics Group, Harvard University, Cambridge, Massachusetts, USA
| | - Maren Wellenreuther
- Plant and Food Research, Nelson, New Zealand
- University of Auckland, Auckland, New Zealand
| | - Gordon Luikart
- Division of Biological Sciences, University of Montana, Missoula, Montana, USA
- Flathead Lake Biology Station, University of Montana, Missoula, Montana, USA
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6
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Vera-Escalona I, Brante A. A simulation study evaluating how population survival and genetic diversity in a newly established population can be affected by propagule size, extinction rates, and initial heterozygosity. PeerJ 2024; 12:e16628. [PMID: 38239294 PMCID: PMC10795529 DOI: 10.7717/peerj.16628] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Accepted: 11/16/2023] [Indexed: 01/22/2024] Open
Abstract
The introduction and establishment of invasive species in regions outside their native range, is one of the major threats for the conservation of ecosystems, affecting native organisms and the habitat where they live in, causing substantial biological and monetary losses worldwide. Due to the impact of invasive species, it is important to understand what makes some species more invasive than others. Here, by simulating populations using a forward-in-time approach combining ecological and single polymorphic nucleotides (SNPs) we evaluated the relation between propagule size (number of individuals = 2, 10, 100, and 1,000), extinction rate (with values 2%, 5%, 10%, and 20%), and initial heterozygosity (0.1, 0.3, and 0.5) on the population survival and maintenance of the heterozygosity of a simulated invasive crab species over 30 generations assuming a single introduction. Our results revealed that simulated invasive populations with initial propagule sizes of 2-1,000 individuals experiencing a high extinction rate (10-20% per generation) were able to maintain over 50% of their initial heterozygosity during the first generations and that under scenarios with lower extinction rates invasive populations with initial propagule sizes of 10-1,000 individuals can survive up to 30 generations and maintain 60-100% of their initial heterozygosity. Our results can help other researchers better understand, how species with small propagule sizes and low heterozygosities can become successful invaders.
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Affiliation(s)
- Iván Vera-Escalona
- Departamento de Ecología, Facultad de Ciencias, Universidad Católica de la Santísima Concepción, Concepción, BioBío, Chile
- Centro de Investigación en Biodiversidad y Ambientes Sustentables (CIBAS), Universidad Católica de la Santísima Concepción, Concepción, BioBío, Chile
| | - Antonio Brante
- Departamento de Ecología, Facultad de Ciencias, Universidad Católica de la Santísima Concepción, Concepción, BioBío, Chile
- Centro de Investigación en Biodiversidad y Ambientes Sustentables (CIBAS), Universidad Católica de la Santísima Concepción, Concepción, BioBío, Chile
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Kyriazis CC, Robinson JA, Lohmueller KE. Using Computational Simulations to Model Deleterious Variation and Genetic Load in Natural Populations. Am Nat 2023; 202:737-752. [PMID: 38033186 PMCID: PMC10897732 DOI: 10.1086/726736] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/02/2023]
Abstract
AbstractDeleterious genetic variation is abundant in wild populations, and understanding the ecological and conservation implications of such variation is an area of active research. Genomic methods are increasingly used to quantify the impacts of deleterious variation in natural populations; however, these approaches remain limited by an inability to accurately predict the selective and dominance effects of mutations. Computational simulations of deleterious variation offer a complementary tool that can help overcome these limitations, although such approaches have yet to be widely employed. In this perspective article, we aim to encourage ecological and conservation genomics researchers to adopt greater use of computational simulations to aid in deepening our understanding of deleterious variation in natural populations. We first provide an overview of the components of a simulation of deleterious variation, describing the key parameters involved in such models. Next, we discuss several approaches for validating simulation models. Finally, we compare and validate several recently proposed deleterious mutation models, demonstrating that models based on estimates of selection parameters from experimental systems are biased toward highly deleterious mutations. We describe a new model that is supported by multiple orthogonal lines of evidence and provide example scripts for implementing this model (https://github.com/ckyriazis/simulations_review).
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8
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Sena LS, Lemes RB, Furtado GV, Saraiva-Pereira ML, Jardim LB. A model for the dynamics of expanded CAG repeat alleles: ATXN2 and ATXN3 as prototypes. Front Genet 2023; 14:1296614. [PMID: 38034492 PMCID: PMC10682950 DOI: 10.3389/fgene.2023.1296614] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Accepted: 10/27/2023] [Indexed: 12/02/2023] Open
Abstract
Background: Spinocerebellar ataxia types 2 (SCA2) and 3 (SCA3/MJD) are diseases due to dominant unstable expansions of CAG repeats (CAGexp). Age of onset of symptoms (AO) correlates with the CAGexp length. Repeat instability leads to increases in the expanded repeats, to important AO anticipations and to the eventual extinction of lineages. Because of that, compensatory forces are expected to act on the maintenance of expanded alleles, but they are poorly understood. Objectives: we described the CAGexp dynamics, adapting a classical equation and aiming to estimate for how many generations will the descendants of a de novo expansion last. Methods: A mathematical model was adapted to encompass anticipation, fitness, and allelic segregation; and empirical data fed the model. The arbitrated ancestral mutations included in the model had the lowest CAGexp and the highest AO described in the literature. One thousand generations were simulated until the alleles were eliminated, fixed, or 650 generations had passed. Results: All SCA2 lineages were eliminated in a median of 10 generations. In SCA3/MJD lineages, 593 were eliminated in a median of 29 generations. The other ones were eliminated due to anticipation after the 650th generation or remained indefinitely with CAG repeats transitioning between expanded and unexpanded ranges. Discussion: the model predicted outcomes compatible with empirical data - the very old ancestral SCA3/MJD haplotype, and the de novo SCA2 expansions -, which previously seemed to be contradictory. This model accommodates these data into understandable dynamics and might be useful for other CAGexp disorders.
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Affiliation(s)
- Lucas Schenatto Sena
- Programa de Pós-Graduação em Genética e Biologia Molecular, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
- Centros de Pesquisa Clínica e Experimental, Hospital de Clínicas de Porto Alegre, Porto Alegre, Brazil
| | | | - Gabriel Vasata Furtado
- Centros de Pesquisa Clínica e Experimental, Hospital de Clínicas de Porto Alegre, Porto Alegre, Brazil
| | - Maria Luiza Saraiva-Pereira
- Programa de Pós-Graduação em Genética e Biologia Molecular, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
- Centros de Pesquisa Clínica e Experimental, Hospital de Clínicas de Porto Alegre, Porto Alegre, Brazil
- Serviço de Genética Médica, Hospital de Clínicas de Porto Alegre, Porto Alegre, Brazil
- Departamento de Bioquímica, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
| | - Laura Bannach Jardim
- Programa de Pós-Graduação em Genética e Biologia Molecular, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
- Centros de Pesquisa Clínica e Experimental, Hospital de Clínicas de Porto Alegre, Porto Alegre, Brazil
- Serviço de Genética Médica, Hospital de Clínicas de Porto Alegre, Porto Alegre, Brazil
- Departamento de Medicina Interna, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
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9
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Medina-Muñoz SG, Ortega-Del Vecchyo D, Cruz-Hervert LP, Ferreyra-Reyes L, García-García L, Moreno-Estrada A, Ragsdale AP. Demographic modeling of admixed Latin American populations from whole genomes. Am J Hum Genet 2023; 110:1804-1816. [PMID: 37725976 PMCID: PMC10577084 DOI: 10.1016/j.ajhg.2023.08.015] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Revised: 08/17/2023] [Accepted: 08/23/2023] [Indexed: 09/21/2023] Open
Abstract
Demographic models of Latin American populations often fail to fully capture their complex evolutionary history, which has been shaped by both recent admixture and deeper-in-time demographic events. To address this gap, we used high-coverage whole-genome data from Indigenous American ancestries in present-day Mexico and existing genomes from across Latin America to infer multiple demographic models that capture the impact of different timescales on genetic diversity. Our approach, which combines analyses of allele frequencies and ancestry tract length distributions, represents a significant improvement over current models in predicting patterns of genetic variation in admixed Latin American populations. We jointly modeled the contribution of European, African, East Asian, and Indigenous American ancestries into present-day Latin American populations. We infer that the ancestors of Indigenous Americans and East Asians diverged ∼30 thousand years ago, and we characterize genetic contributions of recent migrations from East and Southeast Asia to Peru and Mexico. Our inferred demographic histories are consistent across different genomic regions and annotations, suggesting that our inferences are robust to the potential effects of linked selection. In conjunction with published distributions of fitness effects for new nonsynonymous mutations in humans, we show in large-scale simulations that our models recover important features of both neutral and deleterious variation. By providing a more realistic framework for understanding the evolutionary history of Latin American populations, our models can help address the historical under-representation of admixed groups in genomics research and can be a valuable resource for future studies of populations with complex admixture and demographic histories.
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Affiliation(s)
- Santiago G Medina-Muñoz
- National Laboratory of Genomics for Biodiversity (LANGEBIO), Advanced Genomics Unit (UGA), CINVESTAV, Irapuato, Guanajuato 36824, Mexico
| | - Diego Ortega-Del Vecchyo
- Laboratorio Internacional de Investigación sobre el Genoma Humano, Universidad Nacional Autónoma de Mexico, Juriquilla, Querétaro 76230, Mexico
| | | | | | | | - Andrés Moreno-Estrada
- National Laboratory of Genomics for Biodiversity (LANGEBIO), Advanced Genomics Unit (UGA), CINVESTAV, Irapuato, Guanajuato 36824, Mexico.
| | - Aaron P Ragsdale
- National Laboratory of Genomics for Biodiversity (LANGEBIO), Advanced Genomics Unit (UGA), CINVESTAV, Irapuato, Guanajuato 36824, Mexico; Department of Integrative Biology, University of Wisconsin-Madison, Madison, WI 53706, USA.
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10
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Tchounke B, Sanchez L, Bell JM, Cros D. Mate selection: A useful approach to maximize genetic gain and control inbreeding in genomic and conventional oil palm (Elaeis guineensis Jacq.) hybrid breeding. PLoS Comput Biol 2023; 19:e1010290. [PMID: 37695766 PMCID: PMC10513302 DOI: 10.1371/journal.pcbi.1010290] [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: 06/09/2022] [Revised: 09/21/2023] [Accepted: 07/31/2023] [Indexed: 09/13/2023] Open
Abstract
Genomic selection (GS) is an effective method for the genetic improvement of complex traits in plants and animals. Optimization approaches could be used in conjunction with GS to further increase its efficiency and to limit inbreeding, which can increase faster with GS. Mate selection (MS) typically uses a metaheuristic optimization algorithm, simulated annealing, to optimize the selection of individuals and their matings. However, in species with long breeding cycles, this cannot be studied empirically. Here, we investigated this aspect with forward genetic simulations on a high-performance computing cluster and massively parallel computing, considering the oil palm hybrid breeding example. We compared MS and simple methods of inbreeding management (limitation of the number of individuals selected per family, prohibition of self-fertilization and combination of these two methods), in terms of parental inbreeding and genetic progress over four generations of genomic selection and phenotypic selection. The results showed that, compared to the conventional method without optimization, MS could lead to significant decreases in inbreeding and increases in annual genetic progress, with the magnitude of the effect depending on MS parameters and breeding scenarios. The optimal solution retained by MS differed by five breeding characteristics from the conventional solution: selected individuals covering a broader range of genetic values, fewer individuals selected per full-sib family, decreased percentage of selfings, selfings preferentially made on the best individuals and unbalanced number of crosses among selected individuals, with the better an individual, the higher the number of times he is mated. Stronger slowing-down in inbreeding could be achieved with other methods but they were associated with a decreased genetic progress. We recommend that breeders use MS, with preliminary analyses to identify the proper parameters to reach the goals of the breeding program in terms of inbreeding and genetic gain.
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Affiliation(s)
- Billy Tchounke
- Department of Plant Biology, Faculty of Science, University of Yaoundé I, Yaoundé, Cameroon
| | | | - Joseph Martin Bell
- Department of Plant Biology, Faculty of Science, University of Yaoundé I, Yaoundé, Cameroon
| | - David Cros
- CIRAD, UMR AGAP Institut, Montpellier, France
- UMR AGAP Institut, Univ. Montpellier, CIRAD, INRAE, Institut Agro, Montpellier, France
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11
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Teterina AA, Willis JH, Lukac M, Jovelin R, Cutter AD, Phillips PC. Genomic diversity landscapes in outcrossing and selfing Caenorhabditis nematodes. PLoS Genet 2023; 19:e1010879. [PMID: 37585484 PMCID: PMC10461856 DOI: 10.1371/journal.pgen.1010879] [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: 12/28/2022] [Revised: 08/28/2023] [Accepted: 07/21/2023] [Indexed: 08/18/2023] Open
Abstract
Caenorhabditis nematodes form an excellent model for studying how the mode of reproduction affects genetic diversity, as some species reproduce via outcrossing whereas others can self-fertilize. Currently, chromosome-level patterns of diversity and recombination are only available for self-reproducing Caenorhabditis, making the generality of genomic patterns across the genus unclear given the profound potential influence of reproductive mode. Here we present a whole-genome diversity landscape, coupled with a new genetic map, for the outcrossing nematode C. remanei. We demonstrate that the genomic distribution of recombination in C. remanei, like the model nematode C. elegans, shows high recombination rates on chromosome arms and low rates toward the central regions. Patterns of genetic variation across the genome are also similar between these species, but differ dramatically in scale, being tenfold greater for C. remanei. Historical reconstructions of variation in effective population size over the past million generations echo this difference in polymorphism. Evolutionary simulations demonstrate how selection, recombination, mutation, and selfing shape variation along the genome, and that multiple drivers can produce patterns similar to those observed in natural populations. The results illustrate how genome organization and selection play a crucial role in shaping the genomic pattern of diversity whereas demographic processes scale the level of diversity across the genome as a whole.
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Affiliation(s)
- Anastasia A. Teterina
- Institute of Ecology and Evolution, University of Oregon, Eugene, Oregon, United States of America
- Center of Parasitology, Severtsov Institute of Ecology and Evolution RAS, Moscow, Russia
| | - John H. Willis
- Institute of Ecology and Evolution, University of Oregon, Eugene, Oregon, United States of America
| | - Matt Lukac
- Institute of Ecology and Evolution, University of Oregon, Eugene, Oregon, United States of America
| | - Richard Jovelin
- Department of Ecology and Evolutionary Biology, University of Toronto, Toronto, Ontario, Canada
| | - Asher D. Cutter
- Department of Ecology and Evolutionary Biology, University of Toronto, Toronto, Ontario, Canada
| | - Patrick C. Phillips
- Institute of Ecology and Evolution, University of Oregon, Eugene, Oregon, United States of America
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12
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Seaborn T, Landguth EL, Caudill CC. Simulating plasticity as a framework for understanding habitat selection and its role in adaptive capacity and extinction risk through an expansion of CDMetaPOP. Mol Ecol Resour 2023; 23:1458-1472. [PMID: 37081173 PMCID: PMC11081408 DOI: 10.1111/1755-0998.13799] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Revised: 03/29/2023] [Accepted: 03/31/2023] [Indexed: 04/22/2023]
Abstract
Adaptive capacity can present challenges for modelling as it encompasses multiple ecological and evolutionary processes such as natural selection, genetic drift, gene flow and phenotypic plasticity. Spatially explicit, individual-based models provide an outlet for simulating these complex interacting eco-evolutionary processes. We expanded the existing Cost-Distance Meta-POPulation (CDMetaPOP) framework with inducible plasticity modelled as a habitat selection behaviour, using temperature or habitat quality variables, with a genetically based selection threshold conditioned on past individual experience. To demonstrate expected results in the new module, we simulated hypothetical populations and then evaluated model performance in populations of redband trout (Oncorhynchus mykiss gairdneri) across three watersheds where temperatures induce physiological stress in parts of the stream network. We ran simulations using projected warming stream temperature data under four scenarios for alleles that: (1) confer thermal tolerance, (2) bestow plastic habitat selection, (3) give both thermal tolerance and habitat selection preference and (4) do not provide either thermal tolerance or habitat selection. Inclusion of an adaptive allele decreased declines in population sizes, but this impact was greatly reduced in the relatively cool stream networks. As anticipated with the new module, high-temperature patches remained unoccupied by individuals with the allele operating plastically after exposure to warm temperatures. Using complete habitat avoidance above the stressful temperature threshold, habitat selection reduced the overall population size due to the opportunity cost of avoiding areas with increased, but not guaranteed, mortality. Inclusion of plasticity within CDMetaPOP will provide the potential for genetic or plastic traits and 'rescue' to affect eco-evolutionary dynamics for research questions and conservation applications.
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Affiliation(s)
- Travis Seaborn
- Department of Fish and Wildlife Sciences, University of Idaho, Moscow, Idaho, USA
- School of Natural Resource Sciences, North Dakota State University, Fargo, North Dakota, USA
| | - Erin L. Landguth
- Computational Ecology Laboratory & Center for Population Health Research, University of Montana, Missoula, Montana, USA
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13
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Cavedon M, Neufeld L, Finnegan L, Hervieux D, Michalak A, Pelletier A, Polfus J, Schwantje H, Skinner G, Steenweg R, Thacker C, Poissant J, Musiani M. Genomics of founders for conservation breeding: the Jasper caribou case. CONSERV GENET 2023; 24:855-867. [PMID: 37969360 PMCID: PMC10638200 DOI: 10.1007/s10592-023-01540-3] [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: 12/05/2022] [Accepted: 06/07/2023] [Indexed: 11/17/2023]
Abstract
Conservation breeding programs are increasingly used as recovery actions for wild animals; bringing founders into captivity to rear captive populations for future reintroduction into the wild. The International Union for the Conservation of Nature recommends that founders should come from genetically close populations and should have sufficient genetic diversity to avoid mating among relatives. Genomic data are highly informative for evaluating founders due to their high resolution and ability to capture adaptive divergence, yet, their application in that context remains limited. Woodland caribou are federally listed as a Species at Risk in Canada, with several populations facing extirpation, such as those in the Rocky Mountains of Alberta and British Columbia (BC). To prevent local extirpation, Jasper National Park (JNP) is proposing a conservation breeding program. We examined single nucleotide polymorphisms for 144 caribou from 11 populations encompassing a 200,0002 km area surrounding JNP to provide information useful for identifying appropriate founders for this program. We found that this area likely hosts a caribou metapopulation historically characterized by high levels of gene flow, which indicates that multiple sources of founders would be appropriate for initiating a breeding program. However, population structure and adaptive divergence analyses indicate that JNP caribou are closest to populations in the BC Columbia range, which also have suitable genetic diversity for conservation breeding. We suggest that collaboration among jurisdictions would be beneficial to implement the program to promote recovery of JNP caribou and possibly other caribou populations in the surrounding area, which is strategically at the periphery of the distribution of this endangered species. Supplementary Information The online version contains supplementary material available at 10.1007/s10592-023-01540-3.
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Affiliation(s)
- Maria Cavedon
- Deparment of Biological Sciences, University of Calgary, Calgary, AB T2N 1N4 Canada
| | - Lalenia Neufeld
- Jasper National Park of Canada, Parks Canada, Jasper, Canada
| | - Laura Finnegan
- fRI Research, 1176 Switzer Drive, Hinton, AB T7V 1V3 Canada
| | - Dave Hervieux
- Fish and Wildlife Stewardship Branch, Alberta Environment and Protected Areas, Grande Prairie, AB T8V 6J4 Canada
| | - Anita Michalak
- Faculty of Veterinary Medicine, University of Calgary, Calgary, AB T2N 1N4 Canada
| | - Agnes Pelletier
- Ministry of Land, Water and Resource Stewardship Northeast Region, 400-10003-110Th Avenue, Fort St. John, BC V1J 6M7 Canada
| | - Jean Polfus
- Canadian Wildlife Service – Pacific Region, Environment and Climate Change Canada, 1238 Discovery Ave, Kelowna, BC V1V 1V9 Canada
| | - Helen Schwantje
- Wildlife and Habitat Branch, Ministry of Forests, Lands, Natural Resource Operations and Rural Development, Government of British Columbia, 2080 Labieux Road, Nanaimo, BC V9T 6J 9 Canada
| | - Geoff Skinner
- Jasper National Park of Canada, Parks Canada, Jasper, Canada
| | - Robin Steenweg
- Canadian Wildlife Service – Pacific Region, Environment and Climate Change Canada, 1238 Discovery Ave, Kelowna, BC V1V 1V9 Canada
| | - Caeley Thacker
- Wildlife and Habitat Branch, Ministry of Forests, Lands, Natural Resource Operations and Rural Development, Government of British Columbia, 2080 Labieux Road, Nanaimo, BC V9T 6J 9 Canada
| | - Jocelyn Poissant
- Faculty of Veterinary Medicine, University of Calgary, Calgary, AB T2N 1N4 Canada
| | - Marco Musiani
- Dipartimento Scienze Biologiche Geologiche Ambientali, Università Di Bologna, Via Zamboni, 33 - 40126 Bologna, Italia
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14
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Del Amparo R, Arenas M. Influence of substitution model selection on protein phylogenetic tree reconstruction. Gene 2023; 865:147336. [PMID: 36871672 DOI: 10.1016/j.gene.2023.147336] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Revised: 02/22/2023] [Accepted: 02/28/2023] [Indexed: 03/06/2023]
Abstract
Probabilistic phylogenetic tree reconstruction is traditionally performed under a best-fitting substitution model of molecular evolution previously selected according to diverse statistical criteria. Interestingly, some recent studies proposed that this procedure is unnecessary for phylogenetic tree reconstruction leading to a debate in the field. In contrast to DNA sequences, phylogenetic tree reconstruction from protein sequences is traditionally based on empirical exchangeability matrices that can differ among taxonomic groups and protein families. Considering this aspect, here we investigated the influence of selecting a substitution model of protein evolution on phylogenetic tree reconstruction by the analyses of real and simulated data. We found that phylogenetic tree reconstructions based on a selected best-fitting substitution model of protein evolution are the most accurate, in terms of topology and branch lengths, compared with those derived from substitution models with amino acid replacement matrices far from the selected best-fitting model, especially when the data has large genetic diversity. Indeed, we found that substitution models with similar amino acid replacement matrices produce similar reconstructed phylogenetic trees, suggesting the use of substitution models as similar as possible to a selected best-fitting model when the latter cannot be used. Therefore, we recommend the use of the traditional protocol of selection among substitution models of evolution for protein phylogenetic tree reconstruction.
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Affiliation(s)
- Roberto Del Amparo
- CINBIO, Universidade de Vigo, 36310 Vigo, Spain; Department of Biochemistry, Genetics and Immunology, Universidade de Vigo, 36310 Vigo, Spain.
| | - Miguel Arenas
- CINBIO, Universidade de Vigo, 36310 Vigo, Spain; Department of Biochemistry, Genetics and Immunology, Universidade de Vigo, 36310 Vigo, Spain; Galicia Sur Health Research Institute (IIS Galicia Sur), 36310 Vigo, Spain.
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15
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Heino MT, Nyman T, Palo JU, Harmoinen J, Valtonen M, Pilot M, Översti S, Salmela E, Kunnasranta M, Väinölä R, Hoelzel AR, Aspi J. Museum specimens of a landlocked pinniped reveal recent loss of genetic diversity and unexpected population connections. Ecol Evol 2023; 13:e9720. [PMID: 36699566 PMCID: PMC9849707 DOI: 10.1002/ece3.9720] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Revised: 12/14/2022] [Accepted: 12/19/2022] [Indexed: 01/20/2023] Open
Abstract
The Saimaa ringed seal (Pusa hispida saimensis) is endemic to Lake Saimaa in Finland. The subspecies is thought to have originated when parts of the ringed seal population of the Baltic region were trapped in lakes emerging due to postglacial bedrock rebound around 9000 years ago. During the 20th century, the population experienced a drastic human-induced bottleneck. Today encompassing a little over 400 seals with extremely low genetic diversity, it is classified as endangered. We sequenced sections of the mitochondrial control region from 60 up to 125-years-old museum specimens of the Saimaa ringed seal. The generated dataset was combined with publicly available sequences. We studied how genetic variation has changed through time in this subspecies and how it is phylogenetically related to other ringed seal populations from the Baltic Sea, Lake Ladoga, North America, Svalbard, and the White Sea. We observed temporal fluctuations in haplotype frequencies and loss of haplotypes accompanied by a recent reduction in female effective population size. In apparent contrast with the traditionally held view of the Baltic origin of the population, the Saimaa ringed seal mtDNA variation also shows affinities to North American ringed seals. Our results suggest that the Saimaa ringed seal has experienced recent genetic drift associated with small population size. The results further suggest that extant Baltic ringed seal is not representative of the ancestral population of the Saimaa ringed seal, which calls for re-evaluation of the deep history of this subspecies.
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Affiliation(s)
- Matti T. Heino
- Ecology and Genetics Research UnitUniversity of OuluOuluFinland,Department of Forensic MedicineUniversity of HelsinkiHelsinkiFinland
| | - Tommi Nyman
- Department of Ecosystems in the Barents Region, Svanhovd Research StationNorwegian Institute of Bioeconomy ResearchSvanvikNorway
| | - Jukka U. Palo
- Department of Forensic MedicineUniversity of HelsinkiHelsinkiFinland,Forensic Chemistry Unit/Forensic GeneticsFinnish Institute for Health and WelfareHelsinkiFinland
| | - Jenni Harmoinen
- Ecology and Genetics Research UnitUniversity of OuluOuluFinland,Wildlife Ecology GroupNatural Resources Institute FinlandHelsinkiFinland
| | - Mia Valtonen
- Wildlife Ecology GroupNatural Resources Institute FinlandHelsinkiFinland,Department of Environmental and Biological SciencesUniversity of Eastern FinlandJoensuuFinland,Institute of BiotechnologyUniversity of HelsinkiHelsinkiFinland
| | - Małgorzata Pilot
- School of Biological and Biomedical SciencesDurham UniversityDurhamUK,Museum and Institute of ZoologyPolish Academy of SciencesGdańskPoland,Faculty of BiologyUniversity of GdańskGdańskPoland
| | - Sanni Översti
- Transmission, Infection, Diversification and Evolution GroupMax‐Planck Institute for the Science of Human HistoryJenaGermany,Organismal and Evolutionary Biology Research Programme, Faculty of Biological and Environmental SciencesUniversity of HelsinkiHelsinkiFinland
| | - Elina Salmela
- Organismal and Evolutionary Biology Research Programme, Faculty of Biological and Environmental SciencesUniversity of HelsinkiHelsinkiFinland,Department of Biology, Faculty of ScienceUniversity of TurkuTurkuFinland
| | - Mervi Kunnasranta
- University of Eastern FinlandJoensuuFinland,Natural Resources Institute FinlandJoensuuFinland
| | - Risto Väinölä
- Finnish Museum of Natural HistoryUniversity of HelsinkiHelsinkiFinland
| | | | - Jouni Aspi
- Ecology and Genetics Research UnitUniversity of OuluOuluFinland
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16
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Del Amparo R, González-Vázquez LD, Rodríguez-Moure L, Bastolla U, Arenas M. Consequences of Genetic Recombination on Protein Folding Stability. J Mol Evol 2023; 91:33-45. [PMID: 36463317 PMCID: PMC9849154 DOI: 10.1007/s00239-022-10080-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2022] [Accepted: 11/25/2022] [Indexed: 12/05/2022]
Abstract
Genetic recombination is a common evolutionary mechanism that produces molecular diversity. However, its consequences on protein folding stability have not attracted the same attention as in the case of point mutations. Here, we studied the effects of homologous recombination on the computationally predicted protein folding stability for several protein families, finding less detrimental effects than we previously expected. Although recombination can affect multiple protein sites, we found that the fraction of recombined proteins that are eliminated by negative selection because of insufficient stability is not significantly larger than the corresponding fraction of proteins produced by mutation events. Indeed, although recombination disrupts epistatic interactions, the mean stability of recombinant proteins is not lower than that of their parents. On the other hand, the difference of stability between recombined proteins is amplified with respect to the parents, promoting phenotypic diversity. As a result, at least one third of recombined proteins present stability between those of their parents, and a substantial fraction have higher or lower stability than those of both parents. As expected, we found that parents with similar sequences tend to produce recombined proteins with stability close to that of the parents. Finally, the simulation of protein evolution along the ancestral recombination graph with empirical substitution models commonly used in phylogenetics, which ignore constraints on protein folding stability, showed that recombination favors the decrease of folding stability, supporting the convenience of adopting structurally constrained models when possible for inferences of protein evolutionary histories with recombination.
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Affiliation(s)
- Roberto Del Amparo
- CINBIO, Universidade de Vigo, 36310 Vigo, Spain ,Departamento de Bioquímica, Genética e Inmunología, Universidade de Vigo, 36310 Vigo, Spain
| | - Luis Daniel González-Vázquez
- CINBIO, Universidade de Vigo, 36310 Vigo, Spain ,Departamento de Bioquímica, Genética e Inmunología, Universidade de Vigo, 36310 Vigo, Spain
| | - Laura Rodríguez-Moure
- CINBIO, Universidade de Vigo, 36310 Vigo, Spain ,Departamento de Bioquímica, Genética e Inmunología, Universidade de Vigo, 36310 Vigo, Spain
| | - Ugo Bastolla
- Centre for Molecular Biology Severo Ochoa (CSIC-UAM), 28049 Madrid, Spain
| | - Miguel Arenas
- CINBIO, Universidade de Vigo, 36310 Vigo, Spain ,Departamento de Bioquímica, Genética e Inmunología, Universidade de Vigo, 36310 Vigo, Spain ,Galicia Sur Health Research Institute (IIS Galicia Sur), 36310 Vigo, Spain
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17
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White JM, Schumaker NH, Chock RY, Watkins SM. Adding pattern and process to eco-evo theory and applications. PLoS One 2023; 18:e0282535. [PMID: 36893082 PMCID: PMC9997922 DOI: 10.1371/journal.pone.0282535] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Accepted: 02/17/2023] [Indexed: 03/10/2023] Open
Abstract
Eco-evolutionary dynamics result when interacting biological forces simultaneously produce demographic and genetic population responses. Eco-evolutionary simulators traditionally manage complexity by minimizing the influence of spatial pattern on process. However, such simplifications can limit their utility in real-world applications. We present a novel simulation modeling approach for investigating eco-evolutionary dynamics, centered on the driving role of landscape pattern. Our spatially-explicit, individual-based mechanistic simulation approach overcomes existing methodological challenges, generates new insights, and paves the way for future investigations in four focal disciplines: Landscape Genetics, Population Genetics, Conservation Biology, and Evolutionary Ecology. We developed a simple individual-based model to illustrate how spatial structure drives eco-evo dynamics. By making minor changes to our landscape's structure, we simulated continuous, isolated, and semi-connected landscapes, and simultaneously tested several classical assumptions of the focal disciplines. Our results exhibit expected patterns of isolation, drift, and extinction. By imposing landscape change on otherwise functionally-static eco-evolutionary models, we altered key emergent properties such as gene-flow and adaptive selection. We observed demo-genetic responses to these landscape manipulations, including changes in population size, probability of extinction, and allele frequencies. Our model also demonstrated how demo-genetic traits, including generation time and migration rate, can arise from a mechanistic model, rather than being specified a priori. We identify simplifying assumptions common to four focal disciplines, and illustrate how new insights might be developed in eco-evolutionary theory and applications by better linking biological processes to landscape patterns that we know influence them, but that have understandably been left out of many past modeling studies.
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Affiliation(s)
- Jennifer M. White
- Center for Conservation Biology, Department of Biology, University of Washington, Seattle, Washington, United States of America
| | - Nathan H. Schumaker
- U.S. Environmental Protection Agency, Pacific Ecological Systems Division, Corvallis, Oregon, United States of America
- * E-mail:
| | - Rachel Y. Chock
- San Diego Zoo Wildlife Alliance, Conservation Science, Escondido, California, United States of America
| | - Sydney M. Watkins
- Oak Ridge Institute for Science and Education, Oak Ridge, Tennessee, United States of America
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18
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Mijangos JL, Gruber B, Berry O, Pacioni C, Georges A.
dartR
v2: an accessible genetic analysis platform for conservation, ecology, and agriculture. Methods Ecol Evol 2022. [DOI: 10.1111/2041-210x.13918] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
Affiliation(s)
- Jose Luis Mijangos
- Centre for Conservation Ecology and Genomics, Institute for Applied Ecology University of Canberra Bruce ACT Australia
| | - Bernd Gruber
- Centre for Conservation Ecology and Genomics, Institute for Applied Ecology University of Canberra Bruce ACT Australia
| | - Oliver Berry
- Environomics Future Science Platform, Indian Ocean Marine Research Centre, Commonwealth Scientific and Industrial Research Organisation (CSIRO) Crawley WA Australia
| | - Carlo Pacioni
- Department of Environment, Land, Water, and Planning Arthur Rylah Institute for Environmental Research Heidelberg VIC Australia
- Environmental and Conservation Sciences School of Veterinary and Life Sciences, Murdoch University Murdoch WA Australia
| | - Arthur Georges
- Centre for Conservation Ecology and Genomics, Institute for Applied Ecology University of Canberra Bruce ACT Australia
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19
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Lunn RB, Blackwell BF, DeVault TL, Fernández-Juricic E. Can we use antipredator behavior theory to predict wildlife responses to high-speed vehicles? PLoS One 2022; 17:e0267774. [PMID: 35551549 PMCID: PMC9098083 DOI: 10.1371/journal.pone.0267774] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Accepted: 04/14/2022] [Indexed: 11/18/2022] Open
Abstract
Animals seem to rely on antipredator behavior to avoid vehicle collisions. There is an extensive body of antipredator behavior theory that have been used to predict the distance/time animals should escape from predators. These models have also been used to guide empirical research on escape behavior from vehicles. However, little is known as to whether antipredator behavior models are appropriate to apply to an approaching high-speed vehicle scenario. We addressed this gap by (a) providing an overview of the main hypotheses and predictions of different antipredator behavior models via a literature review, (b) exploring whether these models can generate quantitative predictions on escape distance when parameterized with empirical data from the literature, and (c) evaluating their sensitivity to vehicle approach speed using a simulation approach wherein we assessed model performance based on changes in effect size with variations in the slope of the flight initiation distance (FID) vs. approach speed relationship. The slope of the FID vs. approach speed relationship was then related back to three different behavioral rules animals may rely on to avoid approaching threats: the spatial, temporal, or delayed margin of safety. We used literature on birds for goals (b) and (c). Our review considered the following eight models: the economic escape model, Blumstein's economic escape model, the optimal escape model, the perceptual limit hypothesis, the visual cue model, the flush early and avoid the rush (FEAR) hypothesis, the looming stimulus hypothesis, and the Bayesian model of escape behavior. We were able to generate quantitative predictions about escape distance with the last five models. However, we were only able to assess sensitivity to vehicle approach speed for the last three models. The FEAR hypothesis is most sensitive to high-speed vehicles when the species follows the spatial (FID remains constant as speed increases) and the temporal margin of safety (FID increases with an increase in speed) rules of escape. The looming stimulus effect hypothesis reached small to intermediate levels of sensitivity to high-speed vehicles when a species follows the delayed margin of safety (FID decreases with an increase in speed). The Bayesian optimal escape model reached intermediate levels of sensitivity to approach speed across all escape rules (spatial, temporal, delayed margins of safety) but only for larger (> 1 kg) species, but was not sensitive to speed for smaller species. Overall, no single antipredator behavior model could characterize all different types of escape responses relative to vehicle approach speed but some models showed some levels of sensitivity for certain rules of escape behavior. We derive some applied applications of our findings by suggesting the estimation of critical vehicle approach speeds for managing populations that are especially susceptible to road mortality. Overall, we recommend that new escape behavior models specifically tailored to high-speeds vehicles should be developed to better predict quantitatively the responses of animals to an increase in the frequency of cars, airplanes, drones, etc. they will face in the next decade.
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Affiliation(s)
- Ryan B. Lunn
- Department of Biological Sciences, Purdue University, West Lafayette, IN, United States of America
| | - Bradley F. Blackwell
- USDA, APHIS, Wildlife Services, National Wildlife Research Center, Sandusky, OH, United States of America
| | - Travis L. DeVault
- Savannah River Ecology Laboratory, University of Georgia, Jackson, SC, United States of America
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20
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Weiss M, Weigand H, Leese F. Individual small in‐stream barriers contribute little to strong local population genetic structure five strictly aquatic macroinvertebrate taxa. Ecol Evol 2022; 12:e8807. [PMID: 35432929 PMCID: PMC9006233 DOI: 10.1002/ece3.8807] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2022] [Revised: 03/15/2022] [Accepted: 03/17/2022] [Indexed: 11/24/2022] Open
Abstract
Water flow in river networks is frequently regulated by man‐made in‐stream barriers. These obstacles can hinder dispersal of aquatic organisms and isolate populations leading to the loss of genetic diversity. Although millions of small in‐stream barriers exist worldwide, their impact on dispersal of macroinvertebrates remains unclear. Therefore, we, therefore, assessed the effects of such barriers on the population structure and effective dispersal of five macroinvertebrate species with strictly aquatic life cycles: the amphipod crustacean Gammarus fossarum (clade 11), three snail species of the Ancylus fluviatilis species complex and the flatworm Dugesia gonocephala. We studied populations at nine weirs and eight culverts (3 pipes, 5 tunnels), built 33–109 years ago, mainly in the heavily fragmented catchment of the river Ruhr (Sauerland, Germany). To assess fragmentation and barrier effects, we generated genome‐wide SNP data using ddRAD sequencing and evaluated clustering, differentiation between populations up‐ and downstream of each barrier and effective migration rates among sites and across barriers. Additionally, we applied population genomic simulations to assess expected differentiation patterns under different gene flow scenarios. Our data show that populations of all species are highly isolated at regional and local scales within few kilometers. While the regional population structure likely results from historical processes, the strong local differentiation suggests that contemporary dispersal barriers exist. However, we identified significant barrier effects only for pipes (for A. fluviatilis II and III) and few larger weirs (>1.3 m; for D. gonocephala). Therefore, our data suggest that most small in‐stream barriers can probably be overcome by all studied taxa frequently enough to prevent fragmentation. However, it remains to be tested if the strong local differentiation is a result of a cumulative effect of small barriers, or if larger in‐stream barriers, land use, chemical pollution, urbanization, or a combination of these factors impede gene flow.
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Affiliation(s)
- Martina Weiss
- Aquatic Ecosystem Research University of Duisburg‐Essen Essen Germany
- Centre for Water and Environmental Research (ZWU) University of Duisburg‐Essen Essen Germany
| | - Hannah Weigand
- Aquatic Ecosystem Research University of Duisburg‐Essen Essen Germany
- Musée National d'Histoire Naturelle Luxembourg City Luxembourg
| | - Florian Leese
- Aquatic Ecosystem Research University of Duisburg‐Essen Essen Germany
- Centre for Water and Environmental Research (ZWU) University of Duisburg‐Essen Essen Germany
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21
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Bendall EE, Bagley RK, Sousa VC, Linnen CR. Faster-haplodiploid evolution under divergence-with-gene-flow: simulations and empirical data from pine-feeding hymenopterans. Mol Ecol 2022; 31:2348-2366. [PMID: 35231148 DOI: 10.1111/mec.16410] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2021] [Revised: 02/10/2022] [Accepted: 02/21/2022] [Indexed: 11/28/2022]
Abstract
Although haplodiploidy is widespread in nature, the evolutionary consequences of this mode of reproduction are not well characterized. Here, we examine how genome-wide hemizygosity and a lack of recombination in haploid males affects genomic differentiation in populations that diverge via natural selection while experiencing gene flow. First, we simulated diploid and haplodiploid "genomes" (500-kb loci) evolving under an isolation-with-migration model with mutation, drift, selection, migration, and recombination; and examined differentiation at neutral sites both tightly and loosely linked to a divergently selected site. So long as there is divergent selection and migration, sex-limited hemizygosity and recombination cause elevated differentiation (i.e., produce a "faster-haplodiploid effect") in haplodiploid populations relative to otherwise equivalent diploid populations, for both recessive and codominant mutations. Second, we used genome-wide SNP data to model divergence history and describe patterns of genomic differentiation between sympatric populations of Neodiprion lecontei and N. pinetum, a pair of pine sawfly species (order: Hymenoptera; family: Diprionidae) that are specialized on different pine hosts. These analyses support a history of continuous gene exchange throughout divergence and reveal a pattern of heterogeneous genomic differentiation that is consistent with divergent selection on many unlinked loci. Third, using simulations of haplodiploid and diploid populations evolving according to the estimated divergence history of N. lecontei and N. pinetum, we found that divergent selection would lead to higher differentiation in haplodiploids. Based on these results, we hypothesize that haplodiploids undergo divergence-with-gene-flow and sympatric speciation more readily than diploids.
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Affiliation(s)
- Emily E Bendall
- Department of Biology, University of Kentucky, Lexington, Kentucky, 40506, USA.,Department of Microbiology and Immunology, University of Michigan, Ann Arbor, Michigan, 48109, USA
| | - Robin K Bagley
- Department of Biology, University of Kentucky, Lexington, Kentucky, 40506, USA.,Department of Evolution, Ecology, and Organismal Biology, The Ohio State University at Lima, Lima, OH, 45804, USA
| | - Vitor C Sousa
- CE3C - Centre for Ecology, Evolution and Environmental Changes, Department of Animal Biology, Faculdade de Ciências da Universidade de Lisboa, University of Lisbon, Campo Grande 1749-016, Lisboa, Portugal
| | - Catherine R Linnen
- Department of Biology, University of Kentucky, Lexington, Kentucky, 40506, USA
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22
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Sorojsrisom ES, Haller BC, Ambrose BA, Eaton DAR. Selection on the gametophyte: Modeling alternation of generations in plants. APPLICATIONS IN PLANT SCIENCES 2022; 10:e11472. [PMID: 35495198 PMCID: PMC9039789 DOI: 10.1002/aps3.11472] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/19/2021] [Accepted: 03/14/2022] [Indexed: 05/12/2023]
Abstract
PREMISE The degree of gametophyte dependence on the sporophyte life stage is a major feature that differentiates the life cycles of land plants, yet the evolutionary consequences of this difference remain poorly understood. Most evolutionary models assume organisms are either haploid or diploid for their entire lifespan, which is not appropriate for simulating plant life cycles. Here, we introduce shadie (Simulating Haploid-Diploid Evolution), a new, simple Python program for implementing simulations with biphasic life cycles and analyzing their results, using SLiM 3 as a simulation back end. METHODS We implemented evolutionary simulations under three realistic plant life cycle models supported in shadie, using either standardized or biologically realistic parameter settings to test how variation in plant life cycles and sexual systems affects patterns of genome diversity. RESULTS The dynamics of single beneficial mutation fixation did not vary dramatically between different models, but the patterns of spatial variation did differ, demonstrating that different life histories and model parameters affect both genetic diversity and linkage disequilibrium. The rate of linkage disequilibrium decay away from selected sites varied depending on model parameters such as cloning and selfing rates, through their impact on effective population sizes. DISCUSSION Evolutionary simulations are an exciting, underutilized approach in evolutionary research and education. shadie can aid plant researchers in developing null hypotheses, examining theory, and designing empirical studies, in order to investigate the role of the gametophyte life stage, and the effects of variation in plant life cycles, on plant genome evolution.
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Affiliation(s)
- Elissa S. Sorojsrisom
- Department of Ecology, Evolution, and Environmental BiologyColumbia University1200 Amsterdam Ave., Schermerhorn Ext.New York10027USA
- New York Botanical Garden2900 Southern Blvd.BronxNew York10458USA
| | - Benjamin C. Haller
- Department of Computational BiologyCornell University, IthacaNew York14853USA
| | | | - Deren A. R. Eaton
- Department of Ecology, Evolution, and Environmental BiologyColumbia University1200 Amsterdam Ave., Schermerhorn Ext.New York10027USA
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23
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Passo MDS, Carvalho GGCD. In silico evaluation of potential drugs for the treatment of Colorectal Carcinoma. BRAZ J PHARM SCI 2022. [DOI: 10.1590/s2175-97902022e20343] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023] Open
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24
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Baumdicker F, Bisschop G, Goldstein D, Gower G, Ragsdale AP, Tsambos G, Zhu S, Eldon B, Ellerman EC, Galloway JG, Gladstein AL, Gorjanc G, Guo B, Jeffery B, Kretzschmar WW, Lohse K, Matschiner M, Nelson D, Pope NS, Quinto-Cortés CD, Rodrigues MF, Saunack K, Sellinger T, Thornton K, van Kemenade H, Wohns AW, Wong Y, Gravel S, Kern AD, Koskela J, Ralph PL, Kelleher J. Efficient ancestry and mutation simulation with msprime 1.0. Genetics 2021; 220:6460344. [PMID: 34897427 PMCID: PMC9176297 DOI: 10.1093/genetics/iyab229] [Citation(s) in RCA: 90] [Impact Index Per Article: 30.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Accepted: 12/03/2021] [Indexed: 11/13/2022] Open
Abstract
Stochastic simulation is a key tool in population genetics, since the models involved are often analytically intractable and simulation is usually the only way of obtaining ground-truth data to evaluate inferences. Because of this, a large number of specialized simulation programs have been developed, each filling a particular niche, but with largely overlapping functionality and a substantial duplication of effort. Here, we introduce msprime version 1.0, which efficiently implements ancestry and mutation simulations based on the succinct tree sequence data structure and the tskit library. We summarize msprime’s many features, and show that its performance is excellent, often many times faster and more memory efficient than specialized alternatives. These high-performance features have been thoroughly tested and validated, and built using a collaborative, open source development model, which reduces duplication of effort and promotes software quality via community engagement.
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Affiliation(s)
- Franz Baumdicker
- Cluster of Excellence "Controlling Microbes to Fight Infections", Mathematical and Computational Population Genetics, University of Tübingen, 72076 Tübingen, Germany
| | - Gertjan Bisschop
- Institute of Evolutionary Biology,The University of Edinburgh, EH9 3FL, UK
| | - Daniel Goldstein
- Khoury College of Computer Sciences, Northeastern University, MA 02115, USA.,No affiliation
| | - Graham Gower
- Lundbeck GeoGenetics Centre, Globe Institute, University of Copenhagen, 1350 Copenhagen K, Denmark
| | - Aaron P Ragsdale
- Department of Integrative Biology, University of Wisconsin-Madison, WI 53706, USA
| | - Georgia Tsambos
- Melbourne Integrative Genomics, School of Mathematics and Statistics, University of Melbourne, Victoria, 3010, Australia
| | - Sha Zhu
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, OX3 7LF, UK
| | - Bjarki Eldon
- Leibniz Institute for Evolution and Biodiversity Science,Museum für Naturkunde Berlin, 10115, Germany
| | | | - Jared G Galloway
- Institute of Ecology and Evolution, Department of Biology, University of Oregon, OR 97403-5289, USA.,Computational Biology Program, Fred Hutchinson Cancer Research Center, Seattle, WA 98102, USA
| | - Ariella L Gladstein
- Department of Genetics, University of North Carolina at Chapel Hill, NC 27599-7264, USA.,Embark Veterinary, Inc., Boston, MA 02111, USA
| | - Gregor Gorjanc
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, EH25 9RG, UK
| | - Bing Guo
- Institute for Genome Sciences,University of Maryland School of Medicine, Baltimore, MD, 21201, USA
| | - Ben Jeffery
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, OX3 7LF, UK
| | - Warren W Kretzschmar
- Center for Hematology and Regenerative Medicine, Karolinska Institute, 141 83 Huddinge, Sweden
| | - Konrad Lohse
- Institute of Evolutionary Biology,The University of Edinburgh, EH9 3FL, UK
| | | | - Dominic Nelson
- Department of Human Genetics, McGill University, Montréal, QC H3A 0C7, Canada
| | - Nathaniel S Pope
- Department of Entomology, Pennsylvania State University, PA 16802, USA
| | - Consuelo D Quinto-Cortés
- National Laboratory of Genomics for Biodiversity (LANGEBIO), Unit of Advanced Genomics, CINVESTAV, Irapuato, Mexico
| | - Murillo F Rodrigues
- Institute of Ecology and Evolution, Department of Biology, University of Oregon, OR 97403-5289, USA
| | - Kumar Saunack
- IIT Bombay, Powai, Mumbai 400 076, Maharashtra, India
| | - Thibaut Sellinger
- Professorship for Population Genetics, Department of Life Science Systems, Technical University of Munich, 85354 Freising, Germany
| | - Kevin Thornton
- Ecology and Evolutionary Biology, University of California, Irvine, CA 92697, USA
| | | | - Anthony W Wohns
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, OX3 7LF, UK.,Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Yan Wong
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, OX3 7LF, UK
| | - Simon Gravel
- Department of Human Genetics, McGill University, Montréal, QC H3A 0C7, Canada
| | - Andrew D Kern
- Institute of Ecology and Evolution, Department of Biology, University of Oregon, OR 97403-5289, USA
| | - Jere Koskela
- Department of Statistics, University of Warwick, CV4 7AL, UK
| | - Peter L Ralph
- Institute of Ecology and Evolution, Department of Biology, University of Oregon, OR 97403-5289, USA.,Department of Mathematics, University of Oregon, OR 97403-5289 USA
| | - Jerome Kelleher
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, OX3 7LF, UK
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25
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Ekanayake-Weber M, Swedell L. An agent-based model of coercive female transfer in a multilevel society. Anim Behav 2021. [DOI: 10.1016/j.anbehav.2021.10.004] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
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26
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Urban MC, Travis JMJ, Zurell D, Thompson PL, Synes NW, Scarpa A, Peres-Neto PR, Malchow AK, James PMA, Gravel D, De Meester L, Brown C, Bocedi G, Albert CH, Gonzalez A, Hendry AP. Coding for Life: Designing a Platform for Projecting and Protecting Global Biodiversity. Bioscience 2021. [DOI: 10.1093/biosci/biab099] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Abstract
Time is running out to limit further devastating losses of biodiversity and nature's contributions to humans. Addressing this crisis requires accurate predictions about which species and ecosystems are most at risk to ensure efficient use of limited conservation and management resources. We review existing biodiversity projection models and discover problematic gaps. Current models usually cannot easily be reconfigured for other species or systems, omit key biological processes, and cannot accommodate feedbacks with Earth system dynamics. To fill these gaps, we envision an adaptable, accessible, and universal biodiversity modeling platform that can project essential biodiversity variables, explore the implications of divergent socioeconomic scenarios, and compare conservation and management strategies. We design a roadmap for implementing this vision and demonstrate that building this biodiversity forecasting platform is possible and practical.
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Affiliation(s)
- Mark C Urban
- University of Connecticut, Storrs, Connecticut, United States
| | | | | | | | | | - Alice Scarpa
- University of Aberdeen, Aberdeen, Scotland, United Kingdom
| | | | | | | | | | - Luc De Meester
- Laboratory of Aquatic Ecology, Evolution, and Conservation, KU Leuven, Leuven, Belgium, with the Leibniz-Institut für Gewässerökologie und Binnenfischerei, Berlin, Germany, and with the Institute of Biology, Freie Universität Berlin, Berlin, Germany
| | - Calum Brown
- IMK-IFU, Karlsruhe Institute of Technology, Garmisch-Partenkirchen, Germany
| | - Greta Bocedi
- University of Aberdeen, Aberdeen, Scotland, United Kingdom
| | - Cécile H Albert
- Aix Marseille Univ, CNRS, Univ Avignon, IRD, IMBE, Marseille, France
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27
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Zhang R, Liu C, Yuan K, Ni X, Pan Y, Xu S. AdmixSim 2: a forward-time simulator for modeling complex population admixture. BMC Bioinformatics 2021; 22:506. [PMID: 34663213 PMCID: PMC8522168 DOI: 10.1186/s12859-021-04415-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2020] [Accepted: 09/30/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Computer simulations have been widely applied in population genetics and evolutionary studies. A great deal of effort has been made over the past two decades in developing simulation tools. However, there are not many simulation tools suitable for studying population admixture. RESULTS We here developed a forward-time simulator, AdmixSim 2, an individual-based tool that can flexibly and efficiently simulate population genomics data under complex evolutionary scenarios. Unlike its previous version, AdmixSim 2 is based on the extended Wright-Fisher model, and it implements many common evolutionary parameters to involve gene flow, natural selection, recombination, and mutation, which allow users to freely design and simulate any complex scenario involving population admixture. AdmixSim 2 can be used to simulate data of dioecious or monoecious populations, autosomes, or sex chromosomes. To our best knowledge, there are no similar tools available for the purpose of simulation of complex population admixture. Using empirical or previously simulated genomic data as input, AdmixSim 2 provides phased haplotype data for the convenience of further admixture-related analyses such as local ancestry inference, association studies, and other applications. We here evaluate the performance of AdmixSim 2 based on simulated data and validated functions via comparative analysis of simulated data and empirical data of African American, Mexican, and Uyghur populations. CONCLUSIONS AdmixSim 2 is a flexible simulation tool expected to facilitate the study of complex population admixture in various situations.
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Affiliation(s)
- Rui Zhang
- Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Chang Liu
- Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Kai Yuan
- Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Xumin Ni
- Department of Mathematics, School of Science, Beijing Jiaotong University, Beijing, 100044, China
| | - Yuwen Pan
- Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Shuhua Xu
- Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, China. .,State Key Laboratory of Genetic Engineering, Collaborative Innovation Center of Genetics and Development, School of Life Sciences, Fudan University, Shanghai, 200438, China. .,Human Phenome Institute, Fudan University, Shanghai, 201203, China. .,School of Life Science and Technology, ShanghaiTech University, Shanghai, 201210, China. .,Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, 650223, China. .,Henan Institute of Medical and Pharmaceutical Sciences, Zhengzhou University, Zhengzhou, 450052, China.
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28
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Stankiewicz KH, Vasquez Kuntz KL, Baums IB. The impact of estimator choice: Disagreement in clustering solutions across K estimators for Bayesian analysis of population genetic structure across a wide range of empirical data sets. Mol Ecol Resour 2021; 22:1135-1148. [PMID: 34597471 DOI: 10.1111/1755-0998.13522] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2019] [Revised: 08/21/2021] [Accepted: 09/07/2021] [Indexed: 11/29/2022]
Abstract
The software program STRUCTURE is one of the most cited tools for determining population structure. To infer the optimal number of clusters from STRUCTURE output, the ΔK method is often applied. However, a recent study relying on simulated microsatellite data suggested that this method has a downward bias in its estimation of K and is sensitive to uneven sampling. If this finding holds for empirical data sets, conclusions about the scale of gene flow may have to be revised for a large number of studies. To determine the impact of method choice, we applied recently described estimators of K to re-estimate genetic structure in 41 empirical microsatellite data sets; 15 from a broad range of taxa and 26 from one phylogenetic group, coral. We compared alternative estimates of K (Puechmaille statistics) with traditional (ΔK and posterior probability) estimates and found widespread disagreement of estimators across data sets. Thus, one estimator alone is insufficient for determining the optimal number of clusters; this was regardless of study organism or evenness of sampling scheme. Subsequent analysis of molecular variance (AMOVA) did not necessarily clarify which clustering solution was best. To better infer population structure, we suggest a combination of visual inspection of STRUCTURE plots and calculation of the alternative estimators at various thresholds in addition to ΔK. Disagreement between traditional and recent estimators may have important biological implications, such as previously unrecognized population structure, as was the case for many studies reanalysed here.
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Affiliation(s)
- Kathryn H Stankiewicz
- Department of Biology, Pennsylvania State University, University Park, Pennsylvania, USA
| | - Kate L Vasquez Kuntz
- Department of Biology, Pennsylvania State University, University Park, Pennsylvania, USA
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29
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Applying Population Viability Analysis to Inform Genetic Rescue That Preserves Locally Unique Genetic Variation in a Critically Endangered Mammal. DIVERSITY 2021. [DOI: 10.3390/d13080382] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
Genetic rescue can reduce the extinction risk of inbred populations, but it has the poorly understood risk of ‘genetic swamping’—the replacement of the distinctive variation of the target population. We applied population viability analysis (PVA) to identify translocation rates into the inbred lowland population of Leadbeater’s possum from an outbred highland population that would alleviate inbreeding depression and rapidly reach a target population size (N) while maximising the retention of locally unique neutral genetic variation. Using genomic kinship coefficients to model inbreeding in Vortex, we simulated genetic rescue scenarios that included gene pool mixing with genetically diverse highland possums and increased the N from 35 to 110 within ten years. The PVA predicted that the last remaining population of lowland Leadbeater’s possum will be extinct within 23 years without genetic rescue, and that the carrying capacity at its current range is insufficient to enable recovery, even with genetic rescue. Supplementation rates that rapidly increased population size resulted in higher retention (as opposed to complete loss) of local alleles through alleviation of genetic drift but reduced the frequency of locally unique alleles. Ongoing gene flow and a higher N will facilitate natural selection. Accordingly, we recommend founding a new population of lowland possums in a high-quality habitat, where population growth and natural gene exchange with highland populations are possible. We also recommend ensuring gene flow into the population through natural dispersal and/or frequent translocations of highland individuals. Genetic rescue should be implemented within an adaptive management framework, with post-translocation monitoring data incorporated into the models to make updated predictions.
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30
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Larsson DJ, Pan D, Schneeweiss GM. Addressing alpine plant phylogeography using integrative distributional, demographic and coalescent modeling. ALPINE BOTANY 2021; 132:5-19. [PMID: 35368907 PMCID: PMC8933363 DOI: 10.1007/s00035-021-00263-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/07/2021] [Accepted: 07/05/2021] [Indexed: 06/14/2023]
Abstract
Phylogeographic studies of alpine plants have evolved considerably in the last two decades from ad hoc interpretations of genetic data to statistical model-based approaches. In this review we outline the developments in alpine plant phylogeography focusing on the recent approach of integrative distributional, demographic and coalescent (iDDC) modeling. By integrating distributional data with spatially explicit demographic modeling and subsequent coalescent simulations, the history of alpine species can be inferred and long-standing hypotheses, such as species-specific responses to climate change or survival on nunataks during the last glacial maximum, can be efficiently tested as exemplified by available case studies. We also discuss future prospects and improvements of iDDC.
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Affiliation(s)
- Dennis J. Larsson
- Department of Botany and Biodiversity Research, University of Vienna, Vienna, Austria
| | - Da Pan
- Jiangsu Key Laboratory for Biodiversity and Biotechnology, College of Life Sciences, Nanjing Normal University, Nanjing, China
| | - Gerald M. Schneeweiss
- Department of Botany and Biodiversity Research, University of Vienna, Vienna, Austria
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31
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Terasaki Hart DE, Bishop AP, Wang IJ. Geonomics: forward-time, spatially explicit, and arbitrarily complex landscape genomic simulations. Mol Biol Evol 2021; 38:4634-4646. [PMID: 34117771 PMCID: PMC8476160 DOI: 10.1093/molbev/msab175] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022] Open
Abstract
Understanding the drivers of spatial patterns of genomic diversity has emerged as a major
goal of evolutionary genetics. The flexibility of forward-time simulation makes it
especially valuable for these efforts, allowing for the simulation of arbitrarily complex
scenarios in a way that mimics how real populations evolve. Here, we present Geonomics, a
Python package for performing complex, spatially explicit, landscape genomic simulations
with full spatial pedigrees that dramatically reduces user workload yet remains
customizable and extensible because it is embedded within a popular, general-purpose
language. We show that Geonomics results are consistent with expectations for a variety of
validation tests based on classic models in population genetics and then demonstrate its
utility and flexibility with a trio of more complex simulation scenarios that feature
polygenic selection, selection on multiple traits, simulation on complex landscapes, and
nonstationary environmental change. We then discuss runtime, which is primarily sensitive
to landscape raster size, memory usage, which is primarily sensitive to maximum population
size and recombination rate, and other caveats related to the model’s methods for
approximating recombination and movement. Taken together, our tests and demonstrations
show that Geonomics provides an efficient and robust platform for population genomic
simulations that capture complex spatial and evolutionary dynamics.
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Affiliation(s)
- Drew E Terasaki Hart
- Department of Environmental Science, Policy, and Management, College of Natural Resources, University of California, Berkeley, CA, 94720, USA
| | - Anusha P Bishop
- Department of Environmental Science, Policy, and Management, College of Natural Resources, University of California, Berkeley, CA, 94720, USA
| | - Ian J Wang
- Department of Environmental Science, Policy, and Management, College of Natural Resources, University of California, Berkeley, CA, 94720, USA
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32
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Clemente F, Unterländer M, Dolgova O, Amorim CEG, Coroado-Santos F, Neuenschwander S, Ganiatsou E, Cruz Dávalos DI, Anchieri L, Michaud F, Winkelbach L, Blöcher J, Arizmendi Cárdenas YO, Sousa da Mota B, Kalliga E, Souleles A, Kontopoulos I, Karamitrou-Mentessidi G, Philaniotou O, Sampson A, Theodorou D, Tsipopoulou M, Akamatis I, Halstead P, Kotsakis K, Urem-Kotsou D, Panagiotopoulos D, Ziota C, Triantaphyllou S, Delaneau O, Jensen JD, Moreno-Mayar JV, Burger J, Sousa VC, Lao O, Malaspinas AS, Papageorgopoulou C. The genomic history of the Aegean palatial civilizations. Cell 2021; 184:2565-2586.e21. [PMID: 33930288 PMCID: PMC8127963 DOI: 10.1016/j.cell.2021.03.039] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2020] [Revised: 09/17/2020] [Accepted: 03/18/2021] [Indexed: 12/30/2022]
Abstract
The Cycladic, the Minoan, and the Helladic (Mycenaean) cultures define the Bronze Age (BA) of Greece. Urbanism, complex social structures, craft and agricultural specialization, and the earliest forms of writing characterize this iconic period. We sequenced six Early to Middle BA whole genomes, along with 11 mitochondrial genomes, sampled from the three BA cultures of the Aegean Sea. The Early BA (EBA) genomes are homogeneous and derive most of their ancestry from Neolithic Aegeans, contrary to earlier hypotheses that the Neolithic-EBA cultural transition was due to massive population turnover. EBA Aegeans were shaped by relatively small-scale migration from East of the Aegean, as evidenced by the Caucasus-related ancestry also detected in Anatolians. In contrast, Middle BA (MBA) individuals of northern Greece differ from EBA populations in showing ∼50% Pontic-Caspian Steppe-related ancestry, dated at ca. 2,600-2,000 BCE. Such gene flow events during the MBA contributed toward shaping present-day Greek genomes.
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Affiliation(s)
- Florian Clemente
- Department of Computational Biology, University of Lausanne, 1015 Lausanne, Switzerland; Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
| | - Martina Unterländer
- Laboratory of Physical Anthropology, Department of History and Ethnology, Democritus University of Thrace, 69100 Komotini, Greece; Palaeogenetics Group, Institute of Organismic and Molecular Evolution, Johannes Gutenberg University of Mainz, 55099 Mainz, Germany
| | - Olga Dolgova
- CNAG-CRG, Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Baldiri Reixac 4, 08028 Barcelona, Spain
| | - Carlos Eduardo G Amorim
- Department of Computational Biology, University of Lausanne, 1015 Lausanne, Switzerland; Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
| | - Francisco Coroado-Santos
- CE3C, Centre for Ecology, Evolution and Environmental Changes, Faculty of Sciences of the University of Lisbon, 1749-016 Lisbon, Portugal
| | - Samuel Neuenschwander
- Department of Computational Biology, University of Lausanne, 1015 Lausanne, Switzerland; Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland; Vital-IT, Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
| | - Elissavet Ganiatsou
- Laboratory of Physical Anthropology, Department of History and Ethnology, Democritus University of Thrace, 69100 Komotini, Greece
| | - Diana I Cruz Dávalos
- Department of Computational Biology, University of Lausanne, 1015 Lausanne, Switzerland; Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
| | - Lucas Anchieri
- Department of Computational Biology, University of Lausanne, 1015 Lausanne, Switzerland; Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
| | - Frédéric Michaud
- Department of Computational Biology, University of Lausanne, 1015 Lausanne, Switzerland; Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
| | - Laura Winkelbach
- Palaeogenetics Group, Institute of Organismic and Molecular Evolution, Johannes Gutenberg University of Mainz, 55099 Mainz, Germany
| | - Jens Blöcher
- Palaeogenetics Group, Institute of Organismic and Molecular Evolution, Johannes Gutenberg University of Mainz, 55099 Mainz, Germany
| | - Yami Ommar Arizmendi Cárdenas
- Department of Computational Biology, University of Lausanne, 1015 Lausanne, Switzerland; Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
| | - Bárbara Sousa da Mota
- Department of Computational Biology, University of Lausanne, 1015 Lausanne, Switzerland; Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
| | - Eleni Kalliga
- Laboratory of Physical Anthropology, Department of History and Ethnology, Democritus University of Thrace, 69100 Komotini, Greece
| | - Angelos Souleles
- Laboratory of Physical Anthropology, Department of History and Ethnology, Democritus University of Thrace, 69100 Komotini, Greece
| | - Ioannis Kontopoulos
- Center for GeoGenetics, GLOBE Institute, University of Copenhagen, 1350 Copenhagen, Denmark
| | | | - Olga Philaniotou
- Ephor Emerita of Antiquities, Hellenic Ministry of Culture and Sports, 10682 Athens, Greece
| | - Adamantios Sampson
- Department of Mediterranean Studies, University of the Aegean, 85132 Rhodes, Greece
| | - Dimitra Theodorou
- Ephorate of Antiquities of Kozani, Hellenic Ministry of Culture and Sports, 50004 Kozani, Greece
| | - Metaxia Tsipopoulou
- Ephor Emerita of Antiquities, Hellenic Ministry of Culture and Sports, 10682 Athens, Greece
| | - Ioannis Akamatis
- Department of History and Archaeology, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
| | - Paul Halstead
- Department of Archaeology, University of Sheffield, Minalloy House, 10-16 Regent St., Sheffield S1 3NJ, UK
| | - Kostas Kotsakis
- Department of History and Archaeology, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
| | - Dushka Urem-Kotsou
- Department of History and Ethnology, Democritus University of Thrace, 69100 Komotini, Greece
| | - Diamantis Panagiotopoulos
- Institute of Classical Archaeology, University of Heidelberg, Marstallhof 4, 69117 Heidelberg, Germany
| | - Christina Ziota
- Ephorate of Antiquities of Florina, Hellenic Ministry of Culture and Sports, 53100 Florina, Greece
| | - Sevasti Triantaphyllou
- Department of History and Archaeology, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
| | - Olivier Delaneau
- Department of Computational Biology, University of Lausanne, 1015 Lausanne, Switzerland; Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
| | - Jeffrey D Jensen
- School of Life Sciences, Arizona State University, Tempe, AZ 85287, USA
| | - J Víctor Moreno-Mayar
- Department of Computational Biology, University of Lausanne, 1015 Lausanne, Switzerland; Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland; Center for GeoGenetics, GLOBE Institute, University of Copenhagen, 1350 Copenhagen, Denmark; National Institute of Genomic Medicine (INMEGEN), 14610 Mexico City, Mexico
| | - Joachim Burger
- Palaeogenetics Group, Institute of Organismic and Molecular Evolution, Johannes Gutenberg University of Mainz, 55099 Mainz, Germany
| | - Vitor C Sousa
- CE3C, Centre for Ecology, Evolution and Environmental Changes, Faculty of Sciences of the University of Lisbon, 1749-016 Lisbon, Portugal
| | - Oscar Lao
- CNAG-CRG, Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Baldiri Reixac 4, 08028 Barcelona, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain
| | - Anna-Sapfo Malaspinas
- Department of Computational Biology, University of Lausanne, 1015 Lausanne, Switzerland; Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland.
| | - Christina Papageorgopoulou
- Laboratory of Physical Anthropology, Department of History and Ethnology, Democritus University of Thrace, 69100 Komotini, Greece.
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33
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Flanagan BA, Krueger-Hadfield SA, Murren CJ, Nice CC, Strand AE, Sotka EE. Founder effects shape linkage disequilibrium and genomic diversity of a partially clonal invader. Mol Ecol 2021; 30:1962-1978. [PMID: 33604965 DOI: 10.1111/mec.15854] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2020] [Revised: 01/18/2021] [Accepted: 02/01/2021] [Indexed: 12/20/2022]
Abstract
The genomic variation of an invasive species may be affected by complex demographic histories and evolutionary changes during the invasion. Here, we describe the relative influence of bottlenecks, clonality, and population expansion in determining genomic variability of the widespread red macroalga Agarophyton vermiculophyllum. Its introduction from mainland Japan to the estuaries of North America and Europe coincided with shifts from predominantly sexual to partially clonal reproduction and rapid adaptive evolution. A survey of 62,285 SNPs for 351 individuals from 35 populations, aligned to 24 chromosome-length scaffolds indicate that linkage disequilibrium (LD), observed heterozygosity (Ho ), Tajima's D, and nucleotide diversity (Pi) were greater among non-native than native populations. Evolutionary simulations indicate LD and Tajima's D were consistent with a severe population bottleneck. Also, the increased rate of clonal reproduction in the non-native range could not have produced the observed patterns by itself but may have magnified the bottleneck effect on LD. Elevated marker diversity in the genetic source populations could have contributed to the increased Ho and Pi observed in the non-native range. We refined the previous invasion source region to a ~50 km section of northeastern Honshu Island. Outlier detection methods failed to reveal any consistently differentiated loci shared among invaded regions, probably because of the complex A. vermiculophyllum demographic history. Our results reinforce the importance of demographic history, specifically founder effects, in driving genomic variation of invasive populations, even when localized adaptive evolution and reproductive system shifts are observed.
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Affiliation(s)
- Ben A Flanagan
- Department of Biology, College of Charleston, Charleston, SC, USA.,Department of Biological Sciences, University of Southern California, Los Angeles, CA, USA
| | - Stacy A Krueger-Hadfield
- Department of Biology, College of Charleston, Charleston, SC, USA.,Department of Biology, University of Alabama at Birmingham, Birmingham, AL, USA
| | | | - Chris C Nice
- Department of Biology, Population and Conservation Biology Program, Texas State University, San Marcos, TX, USA
| | - Allan E Strand
- Department of Biology, College of Charleston, Charleston, SC, USA
| | - Erik E Sotka
- Department of Biology, College of Charleston, Charleston, SC, USA
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Matthey-Doret R. SimBit: A high performance, flexible and easy-to-use population genetic simulator. Mol Ecol Resour 2021; 21:1745-1754. [PMID: 33713044 DOI: 10.1111/1755-0998.13372] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2020] [Revised: 02/11/2021] [Accepted: 02/17/2021] [Indexed: 11/28/2022]
Abstract
SimBit is a general purpose, high performance forward-in-time population genetics simulator. SimBit can simulate a wide variety of selection scenarios (any selection and dominance coefficients variation, any epistatic interaction, any spatial and temporal changes of selection scenario, etc.), demographic scenarios (any changes in patch sizes, migration rates, realistic demography dependent on fecundity, hard vs. soft selection, exponential vs. logistic growth, gametic or zygotic dispersion, etc.) and mating systems (cloning and selfing rates, hermaphrodites or males and females). SimBit can also track QTLs (with hyperdimensional phenotypes, explicit fitness landscape, plasticity, developmental noise, etc.). Finally, SimBit can simulate multiple species with their ecological relationships. SimBit comes with a R wrapper that simplifies the management of an entire research project from the creation of a grid of parameters and corresponding inputs, running simulations and gathering outputs for analysis. SimBit's performance was extensively benchmarked in comparison to SLiM, Nemo and SFS_CODE, varying population size, recombination rate, mutation rate, and the number of loci. I also reproduced simulations from previous studies, benchmarked QTLs and coalescent tree recording techniques. SimBit was most often the highest performing program with the only notable exception of SLiM outperforming SimBit in scenarios with few loci and low genetic diversity.
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Affiliation(s)
- Remi Matthey-Doret
- Department of Zoology and Biodiversity Research Centre, University of British Columbia, Vancouver, BC, Canada.,Institute of Ecology and Evolution, University of Bern, Bern, Switzerland
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35
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Kunz F, Kohnen A, Nopp-Mayr U, Coppes J. Past, present, future: tracking and simulating genetic differentiation over time in a closed metapopulation system. CONSERV GENET 2021. [DOI: 10.1007/s10592-021-01342-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
AbstractGenetic differentiation plays an essential role in the assessment of metapopulation systems of conservation concern. Migration rates affect the degree of genetic differentiation between subpopulations, with increasing genetic differentiation leading to increasing extinction risk. Analyses of genetic differentiation repeated over time together with projections into the future are therefore important to inform conservation. We investigated genetic differentiation in a closed metapopulation system of an obligate forest grouse, the Western capercaillie Tetrao urogallus, by comparing microsatellite population structure between a historic and a recent time period. We found an increase in genetic differentiation over a period of approximately 15 years. Making use of forward simulations accounting for population dynamics and genetics from both time periods, we explored future genetic differentiation by implementing scenarios of differing migration rates. Using migration rates derived from the recent dataset, simulations predicted further increase of genetic differentiation by 2050. We then examined effects of two realistic yet hypothetical migration scenarios on genetic differentiation. While isolation of a subpopulation led to overall increased genetic differentiation, the re-establishment of connectivity between two subpopulations maintained genetic differentiation at recent levels. Our results emphasize the importance of maintaining connectivity between subpopulations in order to prevent further genetic differentiation and loss of genetic variation. The simulation set-up we developed is highly adaptable and will aid researchers and conservationists alike in anticipating consequences of conservation strategies for metapopulation systems.
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36
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Tahir M, Sardaraz M, Mehmood Z, Khan MS. ESREEM: Efficient Short Reads Error Estimation Computational Model for Next-generation Genome Sequencing. Curr Bioinform 2021. [DOI: 10.2174/1574893615999200614171832] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Aims:
To assess the error profile in NGS data, generated from high throughput
sequencing machines.
Background:
Short-read sequencing data from Next Generation Sequencing (NGS) are currently
being generated by a number of research projects. Depicting the errors produced by NGS
platforms and expressing accurate genetic variation from reads are two inter-dependent phases. It
has high significance in various analyses, such as genome sequence assembly, SNPs calling,
evolutionary studies, and haplotype inference. The systematic and random errors show incidence
profile for each of the sequencing platforms i.e. Illumina sequencing, Pacific Biosciences, 454
pyrosequencing, Complete Genomics DNA nanoball sequencing, Ion Torrent sequencing, and
Oxford Nanopore sequencing. Advances in NGS deliver galactic data with the addition of errors.
Some ratio of these errors may emulate genuine true biological signals i.e., mutation, and may
subsequently negate the results. Various independent applications have been proposed to correct
the sequencing errors. Systematic analysis of these algorithms shows that state-of-the-art models
are missing.
Objective:
In this paper, an effcient error estimation computational model called ESREEM is
proposed to assess the error rates in NGS data.
Methods:
The proposed model prospects the analysis that there exists a true linear regression
association between the number of reads containing errors and the number of reads sequenced. The
model is based on a probabilistic error model integrated with the Hidden Markov Model (HMM).
Result:
The proposed model is evaluated on several benchmark datasets and the results obtained are
compared with state-of-the-art algorithms.
Conclusions:
Experimental results analyses show that the proposed model efficiently estimates errors
and runs in less time as compared to others.
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Affiliation(s)
- Muhammad Tahir
- Department of Computer Science, COMSATS University Islamabad, Attock Campus, Attock,Pakistan
| | - Muhammad Sardaraz
- Department of Computer Science, COMSATS University Islamabad, Attock Campus, Attock,Pakistan
| | - Zahid Mehmood
- Department of Software Engineering, University of Engineering and Technology, Taxila,Pakistan
| | - Muhammad Saud Khan
- Department of Computer Science, COMSATS University Islamabad, Attock Campus, Attock,Pakistan
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Yang X, Yuan K, Ni X, Zhou Y, Guo W, Xu S. AdmixSim: A Forward-Time Simulator for Various Complex Scenarios of Population Admixture. Front Genet 2020; 11:601439. [PMID: 33343638 PMCID: PMC7744625 DOI: 10.3389/fgene.2020.601439] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2020] [Accepted: 10/29/2020] [Indexed: 11/15/2022] Open
Abstract
Background: Population admixture is a common phenomenon in humans, animals, and plants, and it plays a very important role in shaping individual genetic architecture and population genetic diversity. Inference of population admixture, however, is very challenging and typically relies on in silico simulation. We are aware of the lack of a computerized tool for such a purpose. A simulator capable of generating data under various complex admixture scenarios would facilitate the study of recombination, linkage disequilibrium, ancestry tracing, and admixture dynamics in admixed populations. We described such a simulator here. Results: We developed a forward-time simulator (AdmixSim) under the standard Wright Fisher model. It can simulate the following admixed populations: (1) multiple ancestral populations; (2) multiple waves of admixture events; (3) fluctuating population size; and (4) admixtures of fluctuating proportions. Analysis of the simulated data by AdmixSim showed that our simulator can quickly and accurately generate data resembling real-world values. We included in AdmixSim all possible parameters that would allow users to modify and simulate any kind of admixture scenario easily, so it is very flexible. AdmixSim records recombination break points and traces of each chromosomal segment from different ancestral populations, with which users can easily perform further analysis and comparative studies with empirical data. Conclusions:AdmixSim facilitates the study of population admixture by providing a simulation framework with the flexible implementation of various admixture models and parameters.
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Affiliation(s)
- Xiong Yang
- Key Laboratory of Computational Biology, Chinese Academy of Sciences (CAS) and Max Planck Society (MPG) Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Kai Yuan
- Key Laboratory of Computational Biology, Chinese Academy of Sciences (CAS) and Max Planck Society (MPG) Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Xumin Ni
- Department of Mathematics, School of Science, Beijing Jiaotong University, Beijing, China
| | - Ying Zhou
- Key Laboratory of Computational Biology, Chinese Academy of Sciences (CAS) and Max Planck Society (MPG) Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Wei Guo
- Institute of Applied Mathematics, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China
| | - Shuhua Xu
- Key Laboratory of Computational Biology, Chinese Academy of Sciences (CAS) and Max Planck Society (MPG) Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China.,School of Life Science and Technology, ShanghaiTech University, Shanghai, China.,Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, China.,Henan Institute of Medical and Pharmaceutical Sciences, Zhengzhou University, Zhengzhou, China.,Collaborative Innovation Center of Genetics and Development, Fudan University, Shanghai, China
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38
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Brauer CJ, Beheregaray LB. Recent and rapid anthropogenic habitat fragmentation increases extinction risk for freshwater biodiversity. Evol Appl 2020; 13:2857-2869. [PMID: 33294027 PMCID: PMC7691462 DOI: 10.1111/eva.13128] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2020] [Revised: 08/31/2020] [Accepted: 09/01/2020] [Indexed: 12/16/2022] Open
Abstract
Anthropogenic habitat fragmentation is often implicated as driving the current global extinction crisis, particularly in freshwater ecosystems. The genetic signal of recent population isolation can be confounded by the complex spatial arrangement of dendritic river systems. Consequently, many populations may presently be managed separately based on an incorrect assumption that they have evolved in isolation. Integrating landscape genomics data with models of connectivity that account for landscape structure, we show that the cumulative effects of multiple in-stream barriers have contributed to the recent decline of a freshwater fish from the Murray-Darling Basin, Australia. In addition, individual-based eco-evolutionary simulations further demonstrate that contemporary inferences about population isolation are consistent with the 160-year time frame since construction of in-stream barriers began in the region. Our findings suggest that the impact of very recent fragmentation may be often underestimated for freshwater biodiversity. We argue that proactive conservation measures to reconnect many riverine populations are urgently needed.
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Affiliation(s)
- Chris J. Brauer
- Molecular Ecology Laboratory, College of Science and EngineeringFlinders UniversityAdelaideSAAustralia
| | - Luciano B. Beheregaray
- Molecular Ecology Laboratory, College of Science and EngineeringFlinders UniversityAdelaideSAAustralia
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39
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Andrello M, Noirot C, Débarre F, Manel S. MetaPopGen 2.0: A multilocus genetic simulator to model populations of large size. Mol Ecol Resour 2020; 21:596-608. [PMID: 33030758 DOI: 10.1111/1755-0998.13270] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2019] [Revised: 08/05/2020] [Accepted: 09/23/2020] [Indexed: 11/27/2022]
Abstract
Multilocus genetic processes in subdivided populations can be complex and difficult to interpret using theoretical population genetics models. Genetic simulators offer a valid alternative to study multilocus genetic processes in arbitrarily complex scenarios. However, the use of forward-in-time simulators in realistic scenarios involving high numbers of individuals distributed in multiple local populations is limited by computation time and memory requirements. These limitations increase with the number of simulated individuals. We developed a genetic simulator, MetaPopGen 2.0, to model multilocus population genetic processes in subdivided populations of arbitrarily large size. It allows for spatial and temporal variation in demographic parameters, age structure, adult and propagule dispersal, variable mutation rates and selection on survival and fecundity. We developed MetaPopGen 2.0 in the R environment to facilitate its use by non-modeler ecologists and evolutionary biologists. We illustrate the capabilities of MetaPopGen 2.0 for studying adaptation to water salinity in the striped red mullet Mullus surmuletus.
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Affiliation(s)
- Marco Andrello
- MARBEC, Univ Montpellier, CNRS, Ifremer, IRD, Sète, France
| | - Christelle Noirot
- CEFE, Univ Montpellier, CNRS, EPHE-PSL University, IRD, Univ Paul Valéry Montpellier 3, Montpellier, France
| | - Florence Débarre
- Sorbonne Université, CNRS, INRAE, IRD, UPEC, Institut d'Ecologie et des Sciences de l'Environnement de Paris (iEES-Paris), UMR 7618, Paris, France
| | - Stéphanie Manel
- CEFE, Univ Montpellier, CNRS, EPHE-PSL University, IRD, Univ Paul Valéry Montpellier 3, Montpellier, France
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40
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Riggs K, Chen HS, Rotunno M, Li B, Simonds NI, Mechanic LE, Peng B. On the application, reporting, and sharing of in silico simulations for genetic studies. Genet Epidemiol 2020; 45:131-141. [PMID: 33063887 PMCID: PMC7984380 DOI: 10.1002/gepi.22362] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2020] [Revised: 09/11/2020] [Accepted: 09/14/2020] [Indexed: 12/31/2022]
Abstract
In silico simulations play an indispensable role in the development and application of statistical models and methods for genetic studies. Simulation tools allow for the evaluation of methods and investigation of models in a controlled manner. With the growing popularity of evolutionary models and simulation‐based statistical methods, genetic simulations have been applied to a wide variety of research disciplines such as population genetics, evolutionary genetics, genetic epidemiology, ecology, and conservation biology. In this review, we surveyed 1409 articles from five journals that publish on major application areas of genetic simulations. We identified 432 papers in which genetic simulations were used and examined the targets and applications of simulation studies and how these simulation methods and simulated data sets are reported and shared. Whereas a large proportion (30%) of the surveyed articles reported the use of genetic simulations, only 28% of these genetic simulation studies used existing simulation software, 2% used existing simulated data sets, and 19% and 12% made source code and simulated data sets publicly available, respectively. Moreover, 15% of articles provided no information on how simulation studies were performed. These findings suggest a need to encourage sharing and reuse of existing simulation software and data sets, as well as providing more information regarding the performance of simulations.
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Affiliation(s)
- Kaleigh Riggs
- Department of Statistics, Rice University, Houston, Texas, USA
| | - Huann-Sheng Chen
- Division of Cancer Control and Population Sciences, Statistical Research and Applications Branch, Surveillance Research Program, National Cancer Institute (NCI), National Institutes of Health (NIH), Bethesda, Maryland, USA
| | - Melissa Rotunno
- Division of Cancer Control and Population Sciences, Genomic Epidemiology Branch, Epidemiology and Genomics Research Program, NCI, NIH, Bethesda, Maryland, USA
| | - Bing Li
- Department of Biostatistics, Brown University, Providence, Rhode Island, USA
| | | | - Leah E Mechanic
- Division of Cancer Control and Population Sciences, Genomic Epidemiology Branch, Epidemiology and Genomics Research Program, NCI, NIH, Bethesda, Maryland, USA
| | - Bo Peng
- Department of Medicine, Baylor College of Medicine, Houston, Texas, USA
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41
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Dai QL, Li JW, Yang Y, Li M, Zhang K, He LY, Zhang J, Tang B, Liu HP, Li YX, Zhu LF, Yang ZS, Dai Q. Genetic Diversity and Prediction Analysis of Small Isolated Giant Panda Populations After Release of Individuals. Evol Bioinform Online 2020; 16:1176934320939945. [PMID: 32699496 PMCID: PMC7357131 DOI: 10.1177/1176934320939945] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2020] [Accepted: 06/15/2020] [Indexed: 11/16/2022] Open
Abstract
Release of individuals is an effective conservation approach to protect endangered species. To save this small isolated giant panda population in Liziping Nature Reserve, a few giant pandas have been released to this population. Here we assess genetic diversity and future changes in the population using noninvasive genetic sampling after releasing giant pandas. In this study, a total of 28 giant pandas (including 4 released individuals) were identified in the Liziping, China. Compared with other giant panda populations, this population has medium-level genetic diversity; however, a Bayesian-coalescent method clearly detected, quantified, and dated a recent decrease in population size. The predictions for genetic diversity and survival of the population in the next 100 years indicate that this population has a high risk of extinction. We show that released giant pandas can preserve genetic diversity and improve the probability of survival in this small isolated giant panda population. To promote the recovery of this population, we suggest that panda release should be continued and this population will need to release 10 males and 20 females in the future.
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Affiliation(s)
- Qin-Long Dai
- Key Laboratory of Southwest China Wildlife Resources Conservation (Ministry of Education), China West Normal University, Nanchong, Sichuan, China
- Liziping National Nature Reserve, Shimian, Sichuan, China
- Shimian Research Center of Giant Panda Small Populaion Conservation and Rejuvenation, Shimian, Sichuan, China
| | - Jian-Wei Li
- Key Laboratory of Southwest China Wildlife Resources Conservation (Ministry of Education), China West Normal University, Nanchong, Sichuan, China
- Liziping National Nature Reserve, Shimian, Sichuan, China
- Shimian Research Center of Giant Panda Small Populaion Conservation and Rejuvenation, Shimian, Sichuan, China
| | - Yi Yang
- Liziping National Nature Reserve, Shimian, Sichuan, China
- Shimian Research Center of Giant Panda Small Populaion Conservation and Rejuvenation, Shimian, Sichuan, China
| | - Min Li
- Key Laboratory of Southwest China Wildlife Resources Conservation (Ministry of Education), China West Normal University, Nanchong, Sichuan, China
- Liziping National Nature Reserve, Shimian, Sichuan, China
- Shimian Research Center of Giant Panda Small Populaion Conservation and Rejuvenation, Shimian, Sichuan, China
| | - Kan Zhang
- Liziping National Nature Reserve, Shimian, Sichuan, China
- Shimian Research Center of Giant Panda Small Populaion Conservation and Rejuvenation, Shimian, Sichuan, China
| | - Liu-Yang He
- Gonggashan National Nature Reserve, Shimian, Sichuan, China
| | - Jun Zhang
- Liziping National Nature Reserve, Shimian, Sichuan, China
- Shimian Research Center of Giant Panda Small Populaion Conservation and Rejuvenation, Shimian, Sichuan, China
| | - Bo Tang
- Liziping National Nature Reserve, Shimian, Sichuan, China
- Shimian Research Center of Giant Panda Small Populaion Conservation and Rejuvenation, Shimian, Sichuan, China
| | - Hui-Ping Liu
- Liziping National Nature Reserve, Shimian, Sichuan, China
- Shimian Research Center of Giant Panda Small Populaion Conservation and Rejuvenation, Shimian, Sichuan, China
| | - Yu-Xia Li
- Shimian Agricultural and Rural Bureau, Shimian, Sichuan, China
| | - Li-Feng Zhu
- Nanjing Normal University, Nanjing, Jiangsu, China
| | - Zhi-Song Yang
- Key Laboratory of Southwest China Wildlife Resources Conservation (Ministry of Education), China West Normal University, Nanchong, Sichuan, China
- Sichuan Academy of Giant Panda, Chengdu, Sichuan, China
| | - Qiang Dai
- Chengdu Institute of Biology, Chinese Academy of Sciences, Chengdu, Sichuan, China
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42
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Blanchet S, Prunier JG, Paz‐Vinas I, Saint‐Pé K, Rey O, Raffard A, Mathieu‐Bégné E, Loot G, Fourtune L, Dubut V. A river runs through it: The causes, consequences, and management of intraspecific diversity in river networks. Evol Appl 2020; 13:1195-1213. [PMID: 32684955 PMCID: PMC7359825 DOI: 10.1111/eva.12941] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2019] [Revised: 02/14/2020] [Accepted: 02/19/2020] [Indexed: 01/01/2023] Open
Abstract
Rivers are fascinating ecosystems in which the eco-evolutionary dynamics of organisms are constrained by particular features, and biologists have developed a wealth of knowledge about freshwater biodiversity patterns. Over the last 10 years, our group used a holistic approach to contribute to this knowledge by focusing on the causes and consequences of intraspecific diversity in rivers. We conducted empirical works on temperate permanent rivers from southern France, and we broadened the scope of our findings using experiments, meta-analyses, and simulations. We demonstrated that intraspecific (genetic) diversity follows a spatial pattern (downstream increase in diversity) that is repeatable across taxa (from plants to vertebrates) and river systems. This pattern can result from interactive processes that we teased apart using appropriate simulation approaches. We further experimentally showed that intraspecific diversity matters for the functioning of river ecosystems. It indeed affects not only community dynamics, but also key ecosystem functions such as litter degradation. This means that losing intraspecific diversity in rivers can yield major ecological effects. Our work on the impact of multiple human stressors on intraspecific diversity revealed that-in the studied river systems-stocking of domestic (fish) strains strongly and consistently alters natural spatial patterns of diversity. It also highlighted the need for specific analytical tools to tease apart spurious from actual relationships in the wild. Finally, we developed original conservation strategies at the basin scale based on the systematic conservation planning framework that appeared pertinent for preserving intraspecific diversity in rivers. We identified several important research avenues that should further facilitate our understanding of patterns of local adaptation in rivers, the identification of processes sustaining intraspecific biodiversity-ecosystem function relationships, and the setting of reliable conservation plans.
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Affiliation(s)
- Simon Blanchet
- Centre National pour la Recherche ScientifiqueStation d'Écologie Théorique et Expérimentale du CNRS à MoulisUniversité Toulouse III Paul SabatierUMR‐5321MoulisFrance
- Centre National pour la Recherche ScientifiqueLaboratoire Evolution & Diversité BiologiqueInstitut de Recherche pour le DéveloppementUniversité Toulouse III Paul SabatierUMR‐5174 EDBToulouseFrance
| | - Jérôme G. Prunier
- Centre National pour la Recherche ScientifiqueStation d'Écologie Théorique et Expérimentale du CNRS à MoulisUniversité Toulouse III Paul SabatierUMR‐5321MoulisFrance
| | - Ivan Paz‐Vinas
- Centre National pour la Recherche ScientifiqueLaboratoire Evolution & Diversité BiologiqueInstitut de Recherche pour le DéveloppementUniversité Toulouse III Paul SabatierUMR‐5174 EDBToulouseFrance
- Laboratoire Ecologie Fonctionnelle et EnvironnementUniversité de ToulouseUPSCNRSINPUMR‐5245 ECOLABToulouseFrance
| | - Keoni Saint‐Pé
- Centre National pour la Recherche ScientifiqueLaboratoire Evolution & Diversité BiologiqueInstitut de Recherche pour le DéveloppementUniversité Toulouse III Paul SabatierUMR‐5174 EDBToulouseFrance
| | - Olivier Rey
- IHPEUniv. MontpellierCNRSIfremerUniv. Perpignan Via DomitiaPerpignanFrance
| | - Allan Raffard
- Centre National pour la Recherche ScientifiqueStation d'Écologie Théorique et Expérimentale du CNRS à MoulisUniversité Toulouse III Paul SabatierUMR‐5321MoulisFrance
| | - Eglantine Mathieu‐Bégné
- Centre National pour la Recherche ScientifiqueLaboratoire Evolution & Diversité BiologiqueInstitut de Recherche pour le DéveloppementUniversité Toulouse III Paul SabatierUMR‐5174 EDBToulouseFrance
- IHPEUniv. MontpellierCNRSIfremerUniv. Perpignan Via DomitiaPerpignanFrance
| | - Géraldine Loot
- Centre National pour la Recherche ScientifiqueLaboratoire Evolution & Diversité BiologiqueInstitut de Recherche pour le DéveloppementUniversité Toulouse III Paul SabatierUMR‐5174 EDBToulouseFrance
| | - Lisa Fourtune
- Centre National pour la Recherche ScientifiqueLaboratoire Evolution & Diversité BiologiqueInstitut de Recherche pour le DéveloppementUniversité Toulouse III Paul SabatierUMR‐5174 EDBToulouseFrance
- PEIRENEEA 7500Université de LimogesLimogesFrance
| | - Vincent Dubut
- Aix Marseille UniversitéCNRSIRDAvignon UniversitéIMBEMarseilleFrance
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43
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Yannic G, Hagen O, Leugger F, Karger DN, Pellissier L. Harnessing paleo-environmental modeling and genetic data to predict intraspecific genetic structure. Evol Appl 2020; 13:1526-1542. [PMID: 32684974 PMCID: PMC7359836 DOI: 10.1111/eva.12986] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2019] [Revised: 04/15/2020] [Accepted: 04/21/2020] [Indexed: 12/18/2022] Open
Abstract
Spatially explicit simulations of gene flow within complex landscapes could help forecast the responses of populations to global and anthropological changes. Simulating how past climate change shaped intraspecific genetic variation can provide a validation of models in anticipation of their use to predict future changes. We review simulation models that provide inferences on population genetic structure. Existing simulation models generally integrate complex demographic and genetic processes but are less focused on the landscape dynamics. In contrast to previous approaches integrating detailed demographic and genetic processes and only secondarily landscape dynamics, we present a model based on parsimonious biological mechanisms combining habitat suitability and cellular processes, applicable to complex landscapes. The simulation model takes as input (a) the species dispersal capacities as the main biological parameter, (b) the species habitat suitability, and (c) the landscape structure, modulating dispersal. Our model emphasizes the role of landscape features and their temporal dynamics in generating genetic differentiation among populations within species. We illustrate our model on caribou/reindeer populations sampled across the entire species distribution range in the Northern Hemisphere. We show that simulations over the past 21 kyr predict a population genetic structure that matches empirical data. This approach looking at the impact of historical landscape dynamics on intraspecific structure can be used to forecast population structure under climate change scenarios and evaluate how species range shifts might induce erosion of genetic variation within species.
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Affiliation(s)
- Glenn Yannic
- Univ. Grenoble Alpes Univ. Savoie Mont Blanc CNRS LECA Grenoble France
| | - Oskar Hagen
- Landscape Ecology Department of Environmental Systems Sciensce Institute of Terrestrial Ecosystems ETH Zürich Zürich Switzerland.,Swiss Federal Institute for Forest, Snow and Landscape Research Birmensdorf Switzerland
| | - Flurin Leugger
- Landscape Ecology Department of Environmental Systems Sciensce Institute of Terrestrial Ecosystems ETH Zürich Zürich Switzerland.,Swiss Federal Institute for Forest, Snow and Landscape Research Birmensdorf Switzerland
| | - Dirk N Karger
- Swiss Federal Institute for Forest, Snow and Landscape Research Birmensdorf Switzerland
| | - Loïc Pellissier
- Landscape Ecology Department of Environmental Systems Sciensce Institute of Terrestrial Ecosystems ETH Zürich Zürich Switzerland.,Swiss Federal Institute for Forest, Snow and Landscape Research Birmensdorf Switzerland
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44
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Xue AT, Hickerson MJ. Comparative phylogeographic inference with genome‐wide data from aggregated population pairs. Evolution 2020; 74:808-830. [DOI: 10.1111/evo.13945] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2017] [Revised: 01/24/2020] [Accepted: 01/29/2020] [Indexed: 12/20/2022]
Affiliation(s)
- Alexander T. Xue
- Subprogram in Ecology, Evolutionary Biology, and Behavior, Department of BiologyGraduate Center of City University of New York New York NY 10016
- Subprogram in Ecology, Evolutionary Biology, and Behavior, Department of BiologyCity College of City University of New York New York NY 10031
- Human Genetics Institute of New Jersey and Department of GeneticsRutgers University Piscataway NJ 08854
- Simons Center for Quantitative BiologyCold Spring Harbor Laboratory Cold Spring Harbor NY 11724
| | - Michael J. Hickerson
- Subprogram in Ecology, Evolutionary Biology, and Behavior, Department of BiologyGraduate Center of City University of New York New York NY 10016
- Subprogram in Ecology, Evolutionary Biology, and Behavior, Department of BiologyCity College of City University of New York New York NY 10031
- Division of Invertebrate ZoologyAmerican Museum of Natural History New York NY 10024
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45
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Del Amparo R, Vicens A, Arenas M. The influence of heterogeneous codon frequencies along sequences on the estimation of molecular adaptation. Bioinformatics 2020; 36:430-436. [PMID: 31304972 DOI: 10.1093/bioinformatics/btz558] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2019] [Revised: 07/08/2019] [Accepted: 07/11/2019] [Indexed: 11/12/2022] Open
Abstract
MOTIVATION The nonsynonymous/synonymous substitution rate ratio (dN/dS) is a commonly used parameter to quantify molecular adaptation in protein-coding data. It is known that the estimation of dN/dS can be biased if some evolutionary processes are ignored. In this concern, common ML methods to estimate dN/dS assume invariable codon frequencies among sites, despite this characteristic is rare in nature, and it could bias the estimation of this parameter. RESULTS Here we studied the influence of variable codon frequencies among genetic regions on the estimation of dN/dS. We explored scenarios varying the number of genetic regions that differ in codon frequencies, the amount of variability of codon frequencies among regions and the nucleotide frequencies at each codon position among regions. We found that ignoring heterogeneous codon frequencies among regions overall leads to underestimation of dN/dS and the bias increases with the level of heterogeneity of codon frequencies. Interestingly, we also found that varying nucleotide frequencies among regions at the first or second codon position leads to underestimation of dN/dS while variation at the third codon position leads to overestimation of dN/dS. Next, we present a methodology to reduce this bias based on the analysis of partitions presenting similar codon frequencies and we applied it to analyze four real datasets. We conclude that accounting for heterogeneous codon frequencies along sequences is required to obtain realistic estimates of molecular adaptation through this relevant evolutionary parameter. AVAILABILITY AND IMPLEMENTATION The applied frameworks for the computer simulations of protein-coding data and estimation of molecular adaptation are SGWE and PAML, respectively. Both are publicly available and referenced in the study. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Roberto Del Amparo
- Department of Biochemistry, Genetics and Immunology.,Biomedical Research Center (CINBIO), University of Vigo, 36310 Vigo, Spain
| | - Alberto Vicens
- Department of Biochemistry, Genetics and Immunology.,Biomedical Research Center (CINBIO), University of Vigo, 36310 Vigo, Spain
| | - Miguel Arenas
- Department of Biochemistry, Genetics and Immunology.,Biomedical Research Center (CINBIO), University of Vigo, 36310 Vigo, Spain
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Abstract
Coalescent simulation is a fundamental tool in modern population genetics. The msprime library provides unprecedented scalability in terms of both the simulations that can be performed and the efficiency with which the results can be processed. We show how coalescent models for population structure and demography can be constructed using a simple Python API, as well as how we can process the results of such simulations to efficiently calculate statistics of interest. We illustrate msprime's flexibility by implementing a simple (but functional) approximate Bayesian computation inference method in just a few tens of lines of code.
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Affiliation(s)
- Jerome Kelleher
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK.
| | - Konrad Lohse
- Institute of Evolutionary Biology, University of Edinburgh, Edinburgh, UK
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47
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Neuenschwander S, Michaud F, Goudet J. QuantiNemo 2: a Swiss knife to simulate complex demographic and genetic scenarios, forward and backward in time. Bioinformatics 2019; 35:886-888. [PMID: 30816926 PMCID: PMC6394393 DOI: 10.1093/bioinformatics/bty737] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2018] [Revised: 06/28/2018] [Accepted: 08/22/2018] [Indexed: 01/25/2023] Open
Abstract
Summary QuantiNemo 2 is a stochastic simulation program for quantitative population genetics. It was developed to investigate the effects of selection, mutation, recombination and drift on quantitative traits and neutral markers in structured populations connected by migration and located in heterogeneous habitats. A specific feature is that it allows to switch between an individual-based full-featured mode and a population-based faster mode. Several demographic, genetic and selective parameters can be fine-tuned in QuantiNemo 2: population, selection, trait(s) architecture, genetic map for QTL and/or markers, environment, demography and mating system are the main features. Availability and implementation QuantiNemo 2 is a C++ program with a source code available under the GNU General Public License version 3. Executables are provided for Windows, MacOS and Linux platforms, together with a comprehensive manual and tutorials illustrating its flexibility. The executable, manual and tutorial can be found on the website www2.unil.ch/popgen/softwares/quantinemo/, while the source code and user support are given through GitHub: github.com/jgx65/quantinemo. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Samuel Neuenschwander
- Vital-IT, Swiss Institute of Bioinformatics, Lausanne, Switzerland.,Department of Ecology and Evolution, University of Lausanne, Lausanne, Switzerland
| | - Frédéric Michaud
- Department of Ecology and Evolution, University of Lausanne, Lausanne, Switzerland.,Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Jérôme Goudet
- Department of Ecology and Evolution, University of Lausanne, Lausanne, Switzerland.,Swiss Institute of Bioinformatics, Lausanne, Switzerland
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48
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Cherry SG, Merkle JA, Sigaud M, Fortin D, Wilson GA. Managing Genetic Diversity and Extinction Risk for a Rare Plains Bison (Bison bison bison) Population. ENVIRONMENTAL MANAGEMENT 2019; 64:553-563. [PMID: 31578626 DOI: 10.1007/s00267-019-01206-2] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/18/2019] [Accepted: 09/03/2019] [Indexed: 06/10/2023]
Abstract
Unfenced plains bison are rare and only occur in a small number of locations throughout Canada and the United States. We examined management guidelines for maintenance of genetic health and population persistence for a small and isolated population of plains bison that occupy the interface between a protected national park and private agricultural lands. To address genetic health concerns, we measured genetic diversity relative to other populations and assessed the potential effects of genetic augmentation. We then used individual-based population viability analyses (PVA) to determine the minimum abundance likely to prevent genetic diversity declines. We assessed this minimum relative to a proposed maximum social carrying capacity related to bison use of human agricultural lands. We also used the PVA to assess the probability of population persistence given the limiting factors of predation, hunting, and disease. Our results indicate that genetic augmentation will likely be required to achieve genetic diversity similar to that of other plains bison populations. We also found that a minimum population of 420 bison yields low probability of additional genetic loss while staying within society-based maxima. Population estimates based on aerial surveys indicated that the population has been below this minimum since 2007. Our PVA simulations indicate that current hunting practices will result in undesirable levels of population extinction risk and further declines in genetic variability. Our study demonstrates that PVA can be used to evaluate potential management scenarios as they relate to long-term genetic conservation and population persistence for rare species.
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Affiliation(s)
- Seth G Cherry
- Parks Canada Agency, Box 100, Waskesiu, SK, S0J 2Y0, Canada.
- Parks Canada Agency, Box 220, Radium Hot Springs, BC, V0A 1M0, Canada.
| | - Jerod A Merkle
- Département de Biologie and Centre d'Étude de la Forêt, Université Laval, 1045 avenue de la Médecine, Québec, Québec, G1V 0A6, Canada
- Department of Zoology and Physiology, University of Wyoming, Dept. 3166, 1000 East University Avenue, Laramie, WY, 82071, USA
| | - Marie Sigaud
- Département de Biologie and Centre d'Étude de la Forêt, Université Laval, 1045 avenue de la Médecine, Québec, Québec, G1V 0A6, Canada
| | - Daniel Fortin
- Département de Biologie and Centre d'Étude de la Forêt, Université Laval, 1045 avenue de la Médecine, Québec, Québec, G1V 0A6, Canada
| | - Greg A Wilson
- Parks Canada Agency, 1-55401 R.R. 203, Fort Saskatchewan, T8L 0V3, AB, Canada
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Zhou Y, Tian X, Browning BL, Browning SR. POPdemog: visualizing population demographic history from simulation scripts. Bioinformatics 2019; 34:2854-2855. [PMID: 29590339 DOI: 10.1093/bioinformatics/bty184] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2017] [Accepted: 03/23/2018] [Indexed: 11/12/2022] Open
Abstract
Summary We present POPdemog, an R package which converts coalescent simulation program input parameters into a visual representation of the demographic model. This package is useful for preparing figures, for checking that demographic simulation parameters have been correctly specified, and for understanding demographic models that other researchers have used to simulate genetic data. The POPdemog package supports the ms, msa, msHot, MaCS, msprime, scrm and Cosi2 programs, and includes options for customizing the output figures. Availability and implementation The POPdemog package and its tutorial can be freely downloaded from https://github.com/YingZhou001/POPdemog. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Ying Zhou
- Department of Biostatistics, University of Washington, WA, USA
| | - Xiaowen Tian
- Department of Biostatistics, University of Washington, WA, USA
| | - Brian L Browning
- Department of Biostatistics, University of Washington, WA, USA.,Division of Medical Genetics, Department of Medicine, University of Washington, WA, USA
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Sipola A, Marttinen P, Corander J. Bacmeta: simulator for genomic evolution in bacterial metapopulations. Bioinformatics 2019; 34:2308-2310. [PMID: 29474733 DOI: 10.1093/bioinformatics/bty093] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2017] [Accepted: 02/20/2018] [Indexed: 12/25/2022] Open
Abstract
Summary The advent of genomic data from densely sampled bacterial populations has created a need for flexible simulators by which models and hypotheses can be efficiently investigated in the light of empirical observations. Bacmeta provides fast stochastic simulation of neutral evolution within a large collection of interconnected bacterial populations with completely adjustable connectivity network. Stochastic events of mutations, recombinations, insertions/deletions, migrations and micro-epidemics can be simulated in discrete non-overlapping generations with a Wright-Fisher model that operates on explicit sequence data of any desired genome length. Each model component, including locus, bacterial strain, population and ultimately the whole metapopulation, is efficiently simulated using C++ objects and detailed metadata from each level can be acquired. The software can be executed in a cluster environment using simple textual input files, enabling, e.g. large-scale simulations and likelihood-free inference. Availability and implementation Bacmeta is implemented with C++ for Linux, Mac and Windows. It is available at https://bitbucket.org/aleksisipola/bacmeta under the BSD 3-clause license. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Aleksi Sipola
- Department of Mathematics and Statistics, University of Helsinki, Finland.,Department of Computer Science, Helsinki Institute for Information Technology HIIT, Aalto University, Finland
| | - Pekka Marttinen
- Department of Computer Science, Helsinki Institute for Information Technology HIIT, Aalto University, Finland
| | - Jukka Corander
- Department of Mathematics and Statistics, University of Helsinki, Finland.,Department of Biostatistics, University of Oslo, Norway
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