1
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Zhang E, Wong SY, Czechowski P, Terauds A, Ray AE, Benaud N, Chelliah DS, Wilkins D, Montgomery K, Ferrari BC. Effects of increasing soil moisture on Antarctic desert microbial ecosystems. CONSERVATION BIOLOGY : THE JOURNAL OF THE SOCIETY FOR CONSERVATION BIOLOGY 2024; 38:e14268. [PMID: 38622950 DOI: 10.1111/cobi.14268] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/29/2023] [Revised: 01/28/2024] [Accepted: 02/02/2024] [Indexed: 04/17/2024]
Abstract
Overgeneralization and a lack of baseline data for microorganisms in high-latitude environments have restricted the understanding of the microbial response to climate change, which is needed to establish Antarctic conservation frameworks. To bridge this gap, we examined over 17,000 sequence variants of bacteria and microeukarya across the hyperarid Vestfold Hills and Windmill Islands regions of eastern Antarctica. Using an extended gradient forest model, we quantified multispecies response to variations along 79 edaphic gradients to explore the effects of change and wind-driven dispersal on community dynamics under projected warming trends. We also analyzed a second set of soil community data from the Windmill Islands to test our predictions of major environmental tipping points. Soil moisture was the most robust predictor for shaping the regional soil microbiome; the highest rates of compositional turnover occurred at 10-12% soil moisture threshold for photoautotrophs, such as Cyanobacteria, Chlorophyta, and Ochrophyta. Dust profiles revealed a high dispersal propensity for Chlamydomonas, a microalga, and higher biomass was detected at trafficked research stations. This could signal the potential for algal blooms and increased nonendemic species dispersal as human activities increase in the region. Predicted increases in moisture availability on the Windmill Islands were accompanied by high photoautotroph abundances. Abundances of rare oligotrophic taxa, such as Eremiobacterota and Candidatus Dormibacterota, which play a crucial role in atmospheric chemosynthesis, declined over time. That photosynthetic taxa increased as soil moisture increased under a warming scenario suggests the potential for competition between primary production strategies and thus a more biotically driven ecosystem should the climate become milder. Better understanding of environmental triggers will aid conservation efforts, and it is crucial that long-term monitoring of our study sites be established for the protection of Antarctic desert ecosystems. Furthermore, the successful implementation of an improved gradient forest model presents an exciting opportunity to broaden its use on microbial systems globally.
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Affiliation(s)
- Eden Zhang
- School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney, New South Wales, Australia
- Evolution and Ecology Research Centre, University of New South Wales, Sydney, New South Wales, Australia
| | - Sin Yin Wong
- School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney, New South Wales, Australia
- Evolution and Ecology Research Centre, University of New South Wales, Sydney, New South Wales, Australia
| | - Paul Czechowski
- Helmholtz Institute for Metabolic, Obesity and Vascular Research, Leipzig, Germany
| | - Aleks Terauds
- Environmental Stewardship Program, Australian Antarctic Division, Department of Climate Change, Energy, the Environment and Water, Kingston, Tasmania, Australia
| | - Angelique E Ray
- School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney, New South Wales, Australia
| | - Nicole Benaud
- School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney, New South Wales, Australia
- Evolution and Ecology Research Centre, University of New South Wales, Sydney, New South Wales, Australia
| | - Devan S Chelliah
- School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney, New South Wales, Australia
- Evolution and Ecology Research Centre, University of New South Wales, Sydney, New South Wales, Australia
| | - Daniel Wilkins
- Environmental Stewardship Program, Australian Antarctic Division, Department of Climate Change, Energy, the Environment and Water, Kingston, Tasmania, Australia
| | - Kate Montgomery
- School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney, New South Wales, Australia
| | - Belinda C Ferrari
- School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney, New South Wales, Australia
- Evolution and Ecology Research Centre, University of New South Wales, Sydney, New South Wales, Australia
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2
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Aitken SN, Jordan R, Tumas HR. Conserving Evolutionary Potential: Combining Landscape Genomics with Established Methods to Inform Plant Conservation. ANNUAL REVIEW OF PLANT BIOLOGY 2024; 75:707-736. [PMID: 38594931 DOI: 10.1146/annurev-arplant-070523-044239] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/11/2024]
Abstract
Biodiversity conservation requires conserving evolutionary potential-the capacity for wild populations to adapt. Understanding genetic diversity and evolutionary dynamics is critical for informing conservation decisions that enhance adaptability and persistence under environmental change. We review how emerging landscape genomic methods provide plant conservation programs with insights into evolutionary dynamics, including local adaptation and its environmental drivers. Landscape genomic approaches that explore relationships between genomic variation and environments complement rather than replace established population genomic and common garden approaches for assessing adaptive phenotypic variation, population structure, gene flow, and demography. Collectively, these approaches inform conservation actions, including genetic rescue, maladaptation prediction, and assisted gene flow. The greatest on-the-ground impacts from such studies will be realized when conservation practitioners are actively engaged in research and monitoring. Understanding the evolutionary dynamics shaping the genetic diversity of wild plant populations will inform plant conservation decisions that enhance the adaptability and persistence of species in an uncertain future.
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Affiliation(s)
- Sally N Aitken
- Department of Forest and Conservation Sciences, University of British Columbia, Vancouver, Canada; ,
| | | | - Hayley R Tumas
- Department of Forest and Conservation Sciences, University of British Columbia, Vancouver, Canada; ,
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3
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Izaguirre-Toriz V, Aguirre-Liguori JA, Latorre-Cárdenas MC, Arima EY, González-Rodríguez A. Local adaptation of Pinus leiophylla under climate and land use change models in the Avocado Belt of Michoacán. Mol Ecol 2024; 33:e17424. [PMID: 38813851 DOI: 10.1111/mec.17424] [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: 02/27/2024] [Revised: 05/01/2024] [Accepted: 05/17/2024] [Indexed: 05/31/2024]
Abstract
Climate change and land use change are two main drivers of global biodiversity decline, decreasing the genetic diversity that populations harbour and altering patterns of local adaptation. Landscape genomics allows measuring the effect of these anthropogenic disturbances on the adaptation of populations. However, both factors have rarely been considered simultaneously. Based on a set of 3660 SNPs from which 130 were identified as outliers by a genome-environment association analysis (LFMM), we modelled the spatial turnover of allele frequencies in 19 localities of Pinus leiophylla across the Avocado Belt in Michoacán state, Mexico. Then, we evaluated the effect of climate change and land use change scenarios, in addition to evaluating assisted gene flow strategies and connectivity metrics across the landscape to identify priority conservation areas for the species. We found that localities in the centre-east of the Avocado Belt would be more vulnerable to climate change, while localities in the western area are more threatened by land conversion to avocado orchards. Assisted gene flow actions could aid in mitigating both threats. Connectivity patterns among forest patches will also be modified by future habitat loss, with central and eastern parts of the Avocado Belt maintaining the highest connectivity. These results suggest that areas with the highest priority for conservation are in the eastern part of the Avocado Belt, including the Monarch Butterfly Biosphere Reserve. This work is useful as a framework that incorporates distinct layers of information to provide a more robust representation of the response of tree populations to anthropogenic disturbances.
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Affiliation(s)
- Vanessa Izaguirre-Toriz
- Instituto de Investigaciones en Ecosistemas y Sustentabilidad, Universidad Nacional Autónoma de México, Morelia, Mexico
- Posgrado en Ciencias Biológicas, Universidad Nacional Autónoma de México (Posgrado en Ciencias Biológicas, Unidad de Posgrado, Edificio D, 1° Piso, Circuito de Posgrados, Ciudad Universitaria), Coyoacán, Mexico
| | - Jonás A Aguirre-Liguori
- Departamento de Ecología Tropical, Campus de Ciencias Biológicas y Agropecuarias, Universidad Autónoma de Yucatán, Mérida, Mexico
| | - María Camila Latorre-Cárdenas
- Instituto de Investigaciones en Ecosistemas y Sustentabilidad, Universidad Nacional Autónoma de México, Morelia, Mexico
| | - Eugenio Y Arima
- Department of Geography and the Environment, University of Texas at Austin, Austin, Texas, USA
| | - Antonio González-Rodríguez
- Laboratorio Nacional de Innovación Ecotecnológica Para la Sustentabilidad (LANIES), Instituto de Investigaciones en Ecosistemas y Sustentabilidad, UNAM Campus Morelia, Morelia, Mexico
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4
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Camus L, Gautier M, Boitard S. Predicting species invasiveness with genomic data: Is genomic offset related to establishment probability? Evol Appl 2024; 17:e13709. [PMID: 38884022 PMCID: PMC11178484 DOI: 10.1111/eva.13709] [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: 02/19/2024] [Revised: 04/30/2024] [Accepted: 05/04/2024] [Indexed: 06/18/2024] Open
Abstract
Predicting the risk of establishment and spread of populations outside their native range represents a major challenge in evolutionary biology. Various methods have recently been developed to estimate population (mal)adaptation to a new environment with genomic data via so-called Genomic Offset (GO) statistics. These approaches are particularly promising for studying invasive species but have still rarely been used in this context. Here, we evaluated the relationship between GO and the establishment probability of a population in a new environment using both in silico and empirical data. First, we designed invasion simulations to evaluate the ability to predict establishment probability of two GO computation methods (Geometric GO and Gradient Forest) under several conditions. Additionally, we aimed to evaluate the interpretability of absolute Geometric GO values, which theoretically represent the adaptive genetic distance between populations from distinct environments. Second, utilizing public empirical data from the crop pest species Bactrocera tryoni, a fruit fly native from Northern Australia, we computed GO between "source" populations and a diverse range of locations within invaded areas. This practical application of GO within the context of a biological invasion underscores its potential in providing insights and guiding recommendations for future invasion risk assessment. Overall, our results suggest that GO statistics represent good predictors of the establishment probability and may thus inform invasion risk, although the influence of several factors on prediction performance (e.g., propagule pressure or admixture) will need further investigation.
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Affiliation(s)
- Louise Camus
- CBGP, INRAE, CIRAD, IRD, L'institut Agro, Université de Montpellier Montpellier France
| | - Mathieu Gautier
- CBGP, INRAE, CIRAD, IRD, L'institut Agro, Université de Montpellier Montpellier France
| | - Simon Boitard
- CBGP, INRAE, CIRAD, IRD, L'institut Agro, Université de Montpellier Montpellier France
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5
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Lind BM, Candido-Ribeiro R, Singh P, Lu M, Obreht Vidakovic D, Booker TR, Whitlock MC, Yeaman S, Isabel N, Aitken SN. How useful are genomic data for predicting maladaptation to future climate? GLOBAL CHANGE BIOLOGY 2024; 30:e17227. [PMID: 38558300 DOI: 10.1111/gcb.17227] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Revised: 12/18/2023] [Accepted: 12/27/2023] [Indexed: 04/04/2024]
Abstract
Methods using genomic information to forecast potential population maladaptation to climate change or new environments are becoming increasingly common, yet the lack of model validation poses serious hurdles toward their incorporation into management and policy. Here, we compare the validation of maladaptation estimates derived from two methods-Gradient Forests (GFoffset) and the risk of non-adaptedness (RONA)-using exome capture pool-seq data from 35 to 39 populations across three conifer taxa: two Douglas-fir varieties and jack pine. We evaluate sensitivity of these algorithms to the source of input loci (markers selected from genotype-environment associations [GEA] or those selected at random). We validate these methods against 2- and 52-year growth and mortality measured in independent transplant experiments. Overall, we find that both methods often better predict transplant performance than climatic or geographic distances. We also find that GFoffset and RONA models are surprisingly not improved using GEA candidates. Even with promising validation results, variation in model projections to future climates makes it difficult to identify the most maladapted populations using either method. Our work advances understanding of the sensitivity and applicability of these approaches, and we discuss recommendations for their future use.
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Affiliation(s)
- Brandon M Lind
- Centre for Forest Conservation Genetics and Department of Forest and Conservation Sciences, University of British Columbia, Vancouver, British Columbia, Canada
| | - Rafael Candido-Ribeiro
- Centre for Forest Conservation Genetics and Department of Forest and Conservation Sciences, University of British Columbia, Vancouver, British Columbia, Canada
| | - Pooja Singh
- Department of Biological Sciences, University of Calgary, Calgary, Alberta, Canada
| | - Mengmeng Lu
- Department of Biological Sciences, University of Calgary, Calgary, Alberta, Canada
| | - Dragana Obreht Vidakovic
- Centre for Forest Conservation Genetics and Department of Forest and Conservation Sciences, University of British Columbia, Vancouver, British Columbia, Canada
| | - Tom R Booker
- Department of Zoology, University of British Columbia, Vancouver, British Columbia, Canada
| | - Michael C Whitlock
- Department of Zoology, University of British Columbia, Vancouver, British Columbia, Canada
| | - Sam Yeaman
- Department of Biological Sciences, University of Calgary, Calgary, Alberta, Canada
| | - Nathalie Isabel
- Canada Research Chair in Forest Genomics, Centre for Forest Research and Institute for Systems and Integrative Biology, Université Laval, Québec, Quebec, Canada
- Natural Resources Canada, Canadian Forest Service, Laurentian Forestry Centre, Québec, Quebec, Canada
| | - Sally N Aitken
- Centre for Forest Conservation Genetics and Department of Forest and Conservation Sciences, University of British Columbia, Vancouver, British Columbia, Canada
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6
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Krieg CP, Smith DD, Adams MA, Berger J, Layegh Nikravesh N, von Wettberg EJ. Greater ecophysiological stress tolerance in the core environment than in extreme environments of wild chickpea (Cicer reticulatum). Sci Rep 2024; 14:5744. [PMID: 38459248 PMCID: PMC10923935 DOI: 10.1038/s41598-024-56457-9] [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: 10/31/2023] [Accepted: 03/05/2024] [Indexed: 03/10/2024] Open
Abstract
Global climate change and land use change underlie a need to develop new crop breeding strategies, and crop wild relatives (CWR) have become an important potential source of new genetic material to improve breeding efforts. Many recent approaches assume adaptive trait variation increases towards the relative environmental extremes of a species range, potentially missing valuable trait variation in more moderate or typical climates. Here, we leveraged distinct genotypes of wild chickpea (Cicer reticulatum) that differ in their relative climates from moderate to more extreme and perform targeted assessments of drought and heat tolerance. We found significance variation in ecophysiological function and stress tolerance between genotypes but contrary to expectations and current paradigms, it was individuals from more moderate climates that exhibited greater capacity for stress tolerance than individuals from warmer and drier climates. These results indicate that wild germplasm collection efforts to identify adaptive variation should include the full range of environmental conditions and habitats instead of only environmental extremes, and that doing so may significantly enhance the success of breeding programs broadly.
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Affiliation(s)
| | | | - Mark A Adams
- Swinburne University of Technology, Hawthorn, VIC, Australia
| | - Jens Berger
- CSIRO, Agriculture and Food, Perth, WA, Australia
| | | | - Eric J von Wettberg
- Department of Plant and Soil Science, University of Vermont, Burlington, VT, USA
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7
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Layton KKS, Brieuc MSO, Castilho R, Diaz-Arce N, Estévez-Barcia D, Fonseca VG, Fuentes-Pardo AP, Jeffery NW, Jiménez-Mena B, Junge C, Kaufmann J, Leinonen T, Maes SM, McGinnity P, Reed TE, Reisser CMO, Silva G, Vasemägi A, Bradbury IR. Predicting the future of our oceans-Evaluating genomic forecasting approaches in marine species. GLOBAL CHANGE BIOLOGY 2024; 30:e17236. [PMID: 38519845 DOI: 10.1111/gcb.17236] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Revised: 02/21/2024] [Accepted: 02/25/2024] [Indexed: 03/25/2024]
Abstract
Climate change is restructuring biodiversity on multiple scales and there is a pressing need to understand the downstream ecological and genomic consequences of this change. Recent advancements in the field of eco-evolutionary genomics have sought to include evolutionary processes in forecasting species' responses to climate change (e.g., genomic offset), but to date, much of this work has focused on terrestrial species. Coastal and offshore species, and the fisheries they support, may be even more vulnerable to climate change than their terrestrial counterparts, warranting a critical appraisal of these approaches in marine systems. First, we synthesize knowledge about the genomic basis of adaptation in marine species, and then we discuss the few examples where genomic forecasting has been applied in marine systems. Next, we identify the key challenges in validating genomic offset estimates in marine species, and we advocate for the inclusion of historical sampling data and hindcasting in the validation phase. Lastly, we describe a workflow to guide marine managers in incorporating these predictions into the decision-making process.
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Affiliation(s)
- K K S Layton
- Department of Biology, University of Toronto Mississauga, Mississauga, Ontario, Canada
- School of Biological Sciences, University of Aberdeen, Aberdeen, UK
| | | | - R Castilho
- University of the Algarve, Faro, Portugal
- Centre for Marine Sciences, University of the Algarve, Faro, Portugal
- Pattern Institute, Faro, Portugal
| | - N Diaz-Arce
- AZTI Marine Research, Basque Research and Technology Alliance (BRTA), Sukarrieta, Spain
| | - D Estévez-Barcia
- Department of Fish and Shellfish, Greenland Institute of Natural Resources, Nuuk, Greenland
| | - V G Fonseca
- Centre for Environment, Fisheries and Aquaculture Science, Weymouth, UK
| | - A P Fuentes-Pardo
- Department of Immunology, Genetics and Pathology, SciLifeLab Data Centre, Uppsala University, Uppsala, Sweden
| | - N W Jeffery
- Bedford Institute of Oceanography, Fisheries and Oceans Canada, Dartmouth, Nova Scotia, Canada
| | - B Jiménez-Mena
- Section for Marine Living Resources, National Institute of Aquatic Resources, Technical University of Denmark, Silkeborg, Denmark
| | - C Junge
- Institute of Marine Research, Tromso, Norway
| | | | - T Leinonen
- Natural Resources Institute Finland, Helsinki, Finland
| | - S M Maes
- Flanders Research Institute for Agriculture, Fisheries and Food, Ostend, Belgium
| | - P McGinnity
- School of Biological, Earth & Environmental Sciences, University College Cork, Cork, Ireland
| | - T E Reed
- School of Biological, Earth & Environmental Sciences, University College Cork, Cork, Ireland
| | - C M O Reisser
- MARBEC, University of Montpellier, CNRS, IFREMER, IRD, Montpellier, France
| | - G Silva
- MARE-Marine and Environmental Sciences Centre/ARNET-Aquatic Research Network, ISPA-Instituto Universitário, Lisbon, Portugal
| | - A Vasemägi
- Swedish University of Agricultural Sciences, Drottningholm, Sweden
- Estonian University of Life Sciences, Tartu, Estonia
| | - I R Bradbury
- Northwest Atlantic Fisheries Centre, Fisheries and Oceans Canada, St. John's, Newfoundland and Labrador, Canada
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8
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Miller CV, Bossu CM, Sarraco JF, Toews DPL, Rushing CS, Roberto-Charron A, Tremblay JA, Chandler RB, DeSaix MG, Fiss CJ, Larkin JL, Haché S, Nebel S, Ruegg KC. Genomics-informed conservation units reveal spatial variation in climate vulnerability in a migratory bird. Mol Ecol 2024; 33:e17199. [PMID: 38018020 DOI: 10.1111/mec.17199] [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: 08/02/2023] [Revised: 10/18/2023] [Accepted: 10/27/2023] [Indexed: 11/30/2023]
Abstract
Identifying genetic conservation units (CUs) in threatened species is critical for the preservation of adaptive capacity and evolutionary potential in the face of climate change. However, delineating CUs in highly mobile species remains a challenge due to high rates of gene flow and genetic signatures of isolation by distance. Even when CUs are delineated in highly mobile species, the CUs often lack key biological information about what populations have the most conservation need to guide management decisions. Here we implement a framework for CU identification in the Canada Warbler (Cardellina canadensis), a migratory bird species of conservation concern, and then integrate demographic modelling and genomic offset to guide conservation decisions. We find that patterns of whole genome genetic variation in this highly mobile species are primarily driven by putative adaptive variation. Identification of CUs across the breeding range revealed that Canada Warblers fall into two evolutionarily significant units (ESU), and three putative adaptive units (AUs) in the South, East, and Northwest. Quantification of genomic offset, a metric of genetic changes necessary to maintain current gene-environment relationships, revealed significant spatial variation in climate vulnerability, with the Northwestern AU being identified as the most vulnerable to future climate change. Alternatively, quantification of past population trends within each AU revealed the steepest population declines have occurred within the Eastern AU. Overall, we illustrate that genomics-informed CUs provide a strong foundation for identifying current and future regional threats that can be used to inform management strategies for a highly mobile species in a rapidly changing world.
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Affiliation(s)
- Caitlin V Miller
- Department of Biology, Colorado State University, Fort Collins, Colorado, USA
| | - Christen M Bossu
- Department of Biology, Colorado State University, Fort Collins, Colorado, USA
| | - James F Sarraco
- The Institute for Bird Populations, Petaluma, California, USA
| | - David P L Toews
- Department of Biology, Pennsylvania State University, State College, Pennsylvania, USA
| | - Clark S Rushing
- Warnell School of Forestry and Natural Resources, University of Georgia, Athens, Georgia, USA
| | | | - Junior A Tremblay
- Wildlife Research Division, Environment and Climate Change Canada, Québec, Quebec, Canada
| | - Richard B Chandler
- Warnell School of Forestry and Natural Resources, University of Georgia, Athens, Georgia, USA
| | - Matthew G DeSaix
- Department of Biology, Colorado State University, Fort Collins, Colorado, USA
| | - Cameron J Fiss
- Department of Biology, Indiana University of Pennsylvania, Indiana, Pennsylvania, USA
| | - Jeff L Larkin
- Department of Biology, Indiana University of Pennsylvania, Indiana, Pennsylvania, USA
| | - Samuel Haché
- Canadian Wildlife Service, Environment Climate Change Canada, Yellowknife, Northwest Territories, Canada
| | | | - Kristen C Ruegg
- Department of Biology, Colorado State University, Fort Collins, Colorado, USA
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9
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Lovell RSL, Collins S, Martin SH, Pigot AL, Phillimore AB. Space-for-time substitutions in climate change ecology and evolution. Biol Rev Camb Philos Soc 2023; 98:2243-2270. [PMID: 37558208 DOI: 10.1111/brv.13004] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Revised: 07/20/2023] [Accepted: 07/24/2023] [Indexed: 08/11/2023]
Abstract
In an epoch of rapid environmental change, understanding and predicting how biodiversity will respond to a changing climate is an urgent challenge. Since we seldom have sufficient long-term biological data to use the past to anticipate the future, spatial climate-biotic relationships are often used as a proxy for predicting biotic responses to climate change over time. These 'space-for-time substitutions' (SFTS) have become near ubiquitous in global change biology, but with different subfields largely developing methods in isolation. We review how climate-focussed SFTS are used in four subfields of ecology and evolution, each focussed on a different type of biotic variable - population phenotypes, population genotypes, species' distributions, and ecological communities. We then examine the similarities and differences between subfields in terms of methods, limitations and opportunities. While SFTS are used for a wide range of applications, two main approaches are applied across the four subfields: spatial in situ gradient methods and transplant experiments. We find that SFTS methods share common limitations relating to (i) the causality of identified spatial climate-biotic relationships and (ii) the transferability of these relationships, i.e. whether climate-biotic relationships observed over space are equivalent to those occurring over time. Moreover, despite widespread application of SFTS in climate change research, key assumptions remain largely untested. We highlight opportunities to enhance the robustness of SFTS by addressing key assumptions and limitations, with a particular emphasis on where approaches could be shared between the four subfields.
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Affiliation(s)
- Rebecca S L Lovell
- Ashworth Laboratories, Institute of Ecology and Evolution, The University of Edinburgh, Charlotte Auerbach Road, Edinburgh, EH9 3FL, UK
| | - Sinead Collins
- Ashworth Laboratories, Institute of Ecology and Evolution, The University of Edinburgh, Charlotte Auerbach Road, Edinburgh, EH9 3FL, UK
| | - Simon H Martin
- Ashworth Laboratories, Institute of Ecology and Evolution, The University of Edinburgh, Charlotte Auerbach Road, Edinburgh, EH9 3FL, UK
| | - Alex L Pigot
- Centre for Biodiversity and Environment Research, Department of Genetics, Evolution and Environment, University College London, Gower Street, London, WC1E 6BT, UK
| | - Albert B Phillimore
- Ashworth Laboratories, Institute of Ecology and Evolution, The University of Edinburgh, Charlotte Auerbach Road, Edinburgh, EH9 3FL, UK
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10
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Wu R, Qi J, Li W, Wang L, Shen Y, Liu J, Teng Y, Roos C, Li M. Landscape genomics analysis provides insights into future climate change-driven risk in rhesus macaque. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 899:165746. [PMID: 37495138 DOI: 10.1016/j.scitotenv.2023.165746] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Revised: 07/01/2023] [Accepted: 07/21/2023] [Indexed: 07/28/2023]
Abstract
Climate change significantly affects the suitability of wildlife habitats. Thus, understanding how animals adapt ecologically and genetically to climate change is important for targeted species protection. Rhesus macaques (Macaca mulatta) are widely distributed and multi-climatically adapted primates. This study explored how rhesus macaques adapt to climate change by integrating ecological and genetic methods and applying species distribution models (SDMs) and a gradient forest (GF) model. The findings suggested that temperature seasonality primarily affects habitat suitability and indicated that climate change will have a dramatic impact on macaque populations in the future. We also applied genotype-environment association (GEA) analyses and selection signature analyses to identify genes associated with climate change and provide possible explanations for the adaptation of rhesus macaques to climatic environments. The population genomics analyses suggested that the Taihang population has the highest genomic vulnerability with inbreeding and low heterozygosity. Combined with the higher ecological vulnerability, additional conservation strategies are required for this population under higher risk of climate change. Our work measured the impact of climate change and enabled the identification of populations that exhibit high vulnerability to severe climate change. Such information is useful for selecting populations of rhesus macaques as subject of long-term monitoring or evolutionary rescue under future climate change.
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Affiliation(s)
- Ruifeng Wu
- CAS Key Laboratory of Animal Ecology and Conservation Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing 100101, China; College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Jiwei Qi
- CAS Key Laboratory of Animal Ecology and Conservation Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing 100101, China
| | - Wenbo Li
- CAS Key Laboratory of Animal Ecology and Conservation Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing 100101, China
| | - Ling Wang
- CAS Key Laboratory of Animal Ecology and Conservation Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing 100101, China; College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Ying Shen
- CAS Key Laboratory of Animal Ecology and Conservation Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing 100101, China; College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Jiawen Liu
- CAS Key Laboratory of Animal Ecology and Conservation Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing 100101, China; College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yang Teng
- CAS Key Laboratory of Animal Ecology and Conservation Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing 100101, China; College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Christian Roos
- Primate Genetics Laboratory, German Primate Center, Leibniz Institute for Primate Research, Kellnerweg 4, 37077 Göttingen, Germany
| | - Ming Li
- CAS Key Laboratory of Animal Ecology and Conservation Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing 100101, China; Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming 650223, China.
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11
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Lotterhos KE. The paradox of adaptive trait clines with nonclinal patterns in the underlying genes. Proc Natl Acad Sci U S A 2023; 120:e2220313120. [PMID: 36917658 PMCID: PMC10041142 DOI: 10.1073/pnas.2220313120] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Accepted: 02/03/2023] [Indexed: 03/16/2023] Open
Abstract
Multivariate climate change presents an urgent need to understand how species adapt to complex environments. Population genetic theory predicts that loci under selection will form monotonic allele frequency clines with their selective environment, which has led to the wide use of genotype-environment associations (GEAs). This study used a set of simulations to elucidate the conditions under which allele frequency clines are more or less likely to evolve as multiple quantitative traits adapt to multivariate environments. Phenotypic clines evolved with nonmonotonic (i.e., nonclinal) patterns in allele frequencies under conditions that promoted unique combinations of mutations to achieve the multivariate optimum in different parts of the landscape. Such conditions resulted from interactions among landscape, demography, pleiotropy, and genetic architecture. GEA methods failed to accurately infer the genetic basis of adaptation under a range of scenarios due to first principles (clinal patterns did not evolve) or statistical issues (clinal patterns evolved but were not detected due to overcorrection for structure). Despite the limitations of GEAs, this study shows that a back-transformation of multivariate ordination can accurately predict individual multivariate traits from genotype and environmental data regardless of whether inference from GEAs was accurate. In addition, frameworks are introduced that can be used by empiricists to quantify the importance of clinal alleles in adaptation. This research highlights that multivariate trait prediction from genotype and environmental data can lead to accurate inference regardless of whether the underlying loci display clinal or nonmonotonic patterns.
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Affiliation(s)
- Katie E. Lotterhos
- Department of Marine and Environmental Sciences, Northeastern University Marine Science Center, Nahant, MA01908
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12
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Korfmann K, Gaggiotti OE, Fumagalli M. Deep Learning in Population Genetics. Genome Biol Evol 2023; 15:6997869. [PMID: 36683406 PMCID: PMC9897193 DOI: 10.1093/gbe/evad008] [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: 10/13/2022] [Revised: 12/19/2022] [Accepted: 01/16/2023] [Indexed: 01/24/2023] Open
Abstract
Population genetics is transitioning into a data-driven discipline thanks to the availability of large-scale genomic data and the need to study increasingly complex evolutionary scenarios. With likelihood and Bayesian approaches becoming either intractable or computationally unfeasible, machine learning, and in particular deep learning, algorithms are emerging as popular techniques for population genetic inferences. These approaches rely on algorithms that learn non-linear relationships between the input data and the model parameters being estimated through representation learning from training data sets. Deep learning algorithms currently employed in the field comprise discriminative and generative models with fully connected, convolutional, or recurrent layers. Additionally, a wide range of powerful simulators to generate training data under complex scenarios are now available. The application of deep learning to empirical data sets mostly replicates previous findings of demography reconstruction and signals of natural selection in model organisms. To showcase the feasibility of deep learning to tackle new challenges, we designed a branched architecture to detect signals of recent balancing selection from temporal haplotypic data, which exhibited good predictive performance on simulated data. Investigations on the interpretability of neural networks, their robustness to uncertain training data, and creative representation of population genetic data, will provide further opportunities for technological advancements in the field.
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Affiliation(s)
- Kevin Korfmann
- Professorship for Population Genetics, Department of Life Science Systems, Technical University of Munich, Germany
| | - Oscar E Gaggiotti
- Centre for Biological Diversity, Sir Harold Mitchell Building, University of St Andrews, Fife KY16 9TF, UK
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13
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Gallegos C, Hodgins KA, Monro K. Climate adaptation and vulnerability of foundation species in a global change hotspot. Mol Ecol 2023; 32:1990-2004. [PMID: 36645732 DOI: 10.1111/mec.16848] [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: 04/05/2022] [Accepted: 01/05/2023] [Indexed: 01/17/2023]
Abstract
Climate change is altering species ranges, and relative abundances within ranges, as populations become differentially adapted and vulnerable to the climates they face. Understanding present species ranges, whether species harbour and exchange adaptive variants, and how variants are distributed across landscapes undergoing rapid change, is therefore crucial to predicting responses to future climates and informing conservation strategies. Such insights are nonetheless lacking for most species of conservation concern. We assess genomic patterns of neutral variation, climate adaptation and climate vulnerability (offsets in predicted distributions of putatively adaptive variants across present and future landscapes) for sister foundation species, the marine tubeworms Galeolaria caespitosa and Galeolaria gemineoa, in a sentinel region for climate change impacts. We find that species are genetically isolated despite uncovering sympatry in their ranges, show parallel and nonparallel signals of thermal adaptation on spatial scales smaller than gene flow across their ranges, and are predicted to face different risks of maladaptation under future temperatures across their ranges. Our findings have implications for understanding local adaptation in the face of gene flow, and generate spatially explicit predictions for climatic disruption of adaptation and species distributions in coastal ecosystems that could guide experimental validation and conservation planning.
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Affiliation(s)
- Cristóbal Gallegos
- School of Biological Sciences, Monash University, Melbourne, Victoria, Australia
| | - Kathryn A Hodgins
- School of Biological Sciences, Monash University, Melbourne, Victoria, Australia
| | - Keyne Monro
- School of Biological Sciences, Monash University, Melbourne, Victoria, Australia
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14
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Lasky JR, Josephs EB, Morris GP. Genotype-environment associations to reveal the molecular basis of environmental adaptation. THE PLANT CELL 2023; 35:125-138. [PMID: 36005926 PMCID: PMC9806588 DOI: 10.1093/plcell/koac267] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/12/2022] [Accepted: 08/23/2022] [Indexed: 06/14/2023]
Abstract
A fundamental goal in plant biology is to identify and understand the variation underlying plants' adaptation to their environment. Climate change has given new urgency to this goal, as society aims to accelerate adaptation of ecologically important plant species, endangered plant species, and crops to hotter, less predictable climates. In the pre-genomic era, identifying adaptive alleles was painstaking work, leveraging genetics, molecular biology, physiology, and ecology. Now, the rise of genomics and new computational approaches may facilitate this research. Genotype-environment associations (GEAs) use statistical associations between allele frequency and environment of origin to test the hypothesis that allelic variation at a given gene is adapted to local environments. Researchers may scan the genome for GEAs to generate hypotheses on adaptive genetic variants (environmental genome-wide association studies). Despite the rapid adoption of these methods, many important questions remain about the interpretation of GEA findings, which arise from fundamental unanswered questions on the genetic architecture of adaptation and limitations inherent to association-based analyses. We outline strategies to ground GEAs in the underlying hypotheses of genetic architecture and better test GEA-generated hypotheses using genetics and ecophysiology. We provide recommendations for new users who seek to learn about the molecular basis of adaptation. When combined with a rigorous hypothesis testing framework, GEAs may facilitate our understanding of the molecular basis of climate adaptation for plant improvement.
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Affiliation(s)
- Jesse R Lasky
- Department of Biology, Pennsylvania State University, University Park, Pennsylvania 16802, USA
| | - Emily B Josephs
- Department of Plant Biology; Ecology, Evolution, and Behavior Program, Michigan State University, East Lansing, Michigan 48824, USA
| | - Geoffrey P Morris
- Department of Soil and Crop Sciences; Cell and Molecular Biology Program, Colorado State University, Fort Collins, Colorado 80526, USA
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15
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Aguirre-Liguori JA, Morales-Cruz A, Gaut BS. Evaluating the persistence and utility of five wild Vitis species in the context of climate change. Mol Ecol 2022; 31:6457-6472. [PMID: 36197804 PMCID: PMC10092629 DOI: 10.1111/mec.16715] [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: 02/02/2022] [Revised: 09/23/2022] [Accepted: 09/29/2022] [Indexed: 01/13/2023]
Abstract
Crop wild relatives (CWRs) have the capacity to contribute novel traits to agriculture. Given climate change, these contributions may be especially vital for the persistence of perennial crops, because perennials are often clonally propagated and consequently do not evolve rapidly. By studying the landscape genomics of samples from five Vitis CWRs (V. arizonica, V. mustangensis, V. riparia, V. berlandieri and V. girdiana) in the context of projected climate change, we addressed two goals. The first was to assess the relative potential of different CWR accessions to persist in the face of climate change. By integrating species distribution models with adaptive genetic variation, additional genetic features such as genomic load and a phenotype (resistance to Pierce's Disease), we predicted that accessions from one species (V. mustangensis) are particularly well-suited to persist in future climates. The second goal was to identify which CWR accessions may contribute to bioclimatic adaptation for grapevine (V. vinifera) cultivation. To do so, we evaluated whether CWR accessions have the allelic capacity to persist if moved to locations where grapevines are cultivated in the United States. We identified six candidates from V. mustangensis and hypothesized that they may prove useful for contributing alleles that can mitigate climate impacts on viticulture. By identifying candidate germplasm, this study takes a conceptual step toward assessing the genomic and bioclimatic characteristics of CWRs.
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Affiliation(s)
- Jonas A Aguirre-Liguori
- Department of Ecology and Evolutionary Biology, University of California, Irvine, California, USA
| | - Abraham Morales-Cruz
- Department of Ecology and Evolutionary Biology, University of California, Irvine, California, USA
| | - Brandon S Gaut
- Department of Ecology and Evolutionary Biology, University of California, Irvine, California, USA
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16
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Bellis ES, Lucardi RD, Saltonstall K, Marsico TD. Predicting invasion risk of grasses in novel environments requires improved genomic understanding of adaptive potential. AMERICAN JOURNAL OF BOTANY 2022; 109:1965-1968. [PMID: 36200340 PMCID: PMC10100010 DOI: 10.1002/ajb2.16079] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/05/2022] [Revised: 09/29/2022] [Accepted: 09/29/2022] [Indexed: 06/16/2023]
Affiliation(s)
- Emily S. Bellis
- Department of Computer Science, Arkansas State UniversityState UniversityARUSA
- Center for No‐Boundary Thinking, Arkansas State UniversityState UniversityARUSA
| | - Rima D. Lucardi
- Southern Research StationUnited States Department of Agriculture Forest ServiceAthensGAUSA
| | | | - Travis D. Marsico
- Department of Biological Sciences, Arkansas State UniversityState UniversityARUSA
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17
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Lotterhos KE, Fitzpatrick MC, Blackmon H. Simulation Tests of Methods in Evolution, Ecology, and Systematics: Pitfalls, Progress, and Principles. ANNUAL REVIEW OF ECOLOGY, EVOLUTION, AND SYSTEMATICS 2022; 53:113-136. [PMID: 38107485 PMCID: PMC10723108 DOI: 10.1146/annurev-ecolsys-102320-093722] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
Complex statistical methods are continuously developed across the fields of ecology, evolution, and systematics (EES). These fields, however, lack standardized principles for evaluating methods, which has led to high variability in the rigor with which methods are tested, a lack of clarity regarding their limitations, and the potential for misapplication. In this review, we illustrate the common pitfalls of method evaluations in EES, the advantages of testing methods with simulated data, and best practices for method evaluations. We highlight the difference between method evaluation and validation and review how simulations, when appropriately designed, can refine the domain in which a method can be reliably applied. We also discuss the strengths and limitations of different evaluation metrics. The potential for misapplication of methods would be greatly reduced if funding agencies, reviewers, and journals required principled method evaluation.
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Affiliation(s)
- Katie E Lotterhos
- Department of Marine and Environmental Sciences, Northeastern University, Nahant, Massachusetts, USA
| | - Matthew C Fitzpatrick
- Appalachian Lab, University of Maryland Center for Environmental Science, Frostburg, Maryland, USA
| | - Heath Blackmon
- Department of Biology, Texas A&M University, College Station, Texas, USA
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18
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Van Daele F, Honnay O, De Kort H. Genomic analyses point to a low evolutionary potential of prospective source populations for assisted migration in a forest herb. Evol Appl 2022; 15:1859-1874. [DOI: 10.1111/eva.13485] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Revised: 08/13/2022] [Accepted: 09/17/2022] [Indexed: 11/26/2022] Open
Affiliation(s)
- Frederik Van Daele
- Department of Biology, Plant Conservation and Population Biology KU Leuven Leuven Belgium
| | - Olivier Honnay
- Department of Biology, Plant Conservation and Population Biology KU Leuven Leuven Belgium
| | - Hanne De Kort
- Department of Biology, Plant Conservation and Population Biology KU Leuven Leuven Belgium
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19
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Campagna L, Toews DP. The genomics of adaptation in birds. Curr Biol 2022; 32:R1173-R1186. [DOI: 10.1016/j.cub.2022.07.076] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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20
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DeSaix MG, George TL, Seglund AE, Spellman GM, Zavaleta ES, Ruegg KC. Forecasting climate change response in an alpine specialist songbird reveals the importance of considering novel climate. DIVERS DISTRIB 2022. [DOI: 10.1111/ddi.13628] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022] Open
Affiliation(s)
- Matthew G. DeSaix
- Department of Biology Colorado State University Fort Collins Colorado USA
| | - T. Luke George
- Department of Fish, Wildlife, and Conservation Biology Colorado State University Fort Collins Colorado USA
| | | | - Garth M. Spellman
- Department of Zoology Denver Museum of Nature and Science Denver Colorado USA
| | - Erika S. Zavaleta
- Department of Ecology and Evolutionary Biology University of California Santa Cruz California USA
| | - Kristen C. Ruegg
- Department of Biology Colorado State University Fort Collins Colorado USA
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