Linking ecological niche models and common garden experiments to predict phenotypic differentiation in stressful environments: Assessing the adaptive value of marginal populations in an alpine plant

Global Change Biology - Tập 28 Số 13 - Trang 4143-4162 - 2022
Javier Morente‐López1,2, Jamie M. Kass3,4,5, Carlos Lara‐Romero2, Josep M. Serra‐Diaz6, José C. Soto-Correa7, Robert P. Anderson4,8,5, José María Iriondo2
1Island Ecology and Evolution Research Group Institute of Natural Products and Agrobiology, Consejo Superior de Investigaciones Científicas (IPNA‐CSIC) San Cristóbal de La Laguna, Tenerife Spain
2Área de Biodiversidad y Conservación Depto. de Biología, Geología Física y Química Inorgánica ESCET Universidad Rey Juan Carlos (URJC) Madrid Móstoles Spain
3Biodiversity and Biocomplexity Unit Okinawa Institute of Science and Technology Graduate University Kunigami-gun Okinawa Japan
4Department of Biology City College of New York City University of New York New York New York USA
5Ph.D. Program in Biology Graduate Center City University of New York New York New York USA
6Université de Lorraine, AgroParisTech, INRAE, Nancy, France
7Facultad de Ciencias Naturales Universidad Autónoma de Querétaro (FCN‐UAQ) Santa Rosa Jáuregui, Querétaro Mexico
8Division of Vertebrate Zoology (Mammalogy) American Museum of Natural History New York New York USA

Tóm tắt

AbstractEnvironmental variation within a species’ range can create contrasting selective pressures, leading to divergent selection and novel adaptations. The conservation value of populations inhabiting environmentally marginal areas remains in debate and is closely related to the adaptive potential in changing environments. Strong selection caused by stressful conditions may generate novel adaptations, conferring these populations distinct evolutionary potential and high conservation value under climate change. On the other hand, environmentally marginal populations may be genetically depauperate, with little potential for new adaptations to emerge. Here, we explored the use of ecological niche models (ENMs) linked with common garden experiments to predict and test for genetically determined phenotypic differentiation related to contrasting environmental conditions. To do so, we built an ENM for the alpine plant Silene ciliata in central Spain and conducted common garden experiments, assessing flowering phenology changes and differences in leaf cell resistance to extreme temperatures. The suitability patterns and response curves of the ENM led to the predictions that: (1) the environmentally marginal populations experiencing less snowpack and higher minimum temperatures would have delayed flowering to avoid risks of late‐spring frosts and (2) those with higher minimum temperatures and greater potential evapotranspiration would show enhanced cell resistance to high temperatures to deal with physiological stress related to desiccation and heat. The common garden experiments revealed the expected genetically based phenotypic differentiation in flowering phenology. In contrast, they did not show the expected differentiation for cell resistance, but these latter experiments had high variance and hence lower statistical power. The results highlight ENMs as useful tools to identify contrasting putative selective pressures across species ranges. Linking ENMs with common garden experiments provides a theoretically justified and practical way to study adaptive processes, including insights regarding the conservation value of populations inhabiting environmentally marginal areas under ongoing climate change.

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Tài liệu tham khảo

10.1111/jbi.12215

10.1111/jbi.13196

10.1111/ecog.01132

10.1111/ele.13427

10.1111/nyas.12264

10.1016/j.ecolmodel.2011.04.011

10.1111/j.1365‐2699.2010.02290.x

10.1890/0012-9658(2006)87[2014:DOCAMP]2.0.CO;2

10.1126/sciadv.aat4858

10.1111/j.1365‐2699.2006.01584.x

10.1111/j.1461‐0248.2012.01796.x

Baskin C. C., 1998, Seeds: Ecology, biogeography, and evolution of dormancy and germination

Bates D. Mächler M. Bolker B. &Walker S.(2014).Fitting linear mixed‐effects models using lme4. ArXiv Preprint ArXiv:1406.5823.

10.1111/j.1466‐8238.2010.00646.x

10.1111/nph.15716

10.1111/ele.12150

10.1111/ele.13396

10.1111/evo.14231

10.1016/j.ecolmodel.2013.12.012

10.1111/j.1365‐294X.2007.03509.x

10.1086/284267

10.1111/ele.12696

10.1146/annurev‐ecolsys‐020720‐042553

Channell R.(2004).The conservation value of peripheral populations: the supporting science. InProceedings of the species at risk 2004 pathways to recovery conference(pp. 1–17). Species at Risk 2004 Pathways to Recovery Conference Organizing Committee.

10.1111/ecog.04630

10.1126/science.1206432

10.1098/rstb.2010.0142

10.1111/ele.12794

10.1111/geb.12820

10.1002/hyp

10.1007/BF00363833

10.3732/ajb.1600414

Drennan P. M., 2009, Perspectives in biophysical plant ecophysiology, 57

10.1890/0012-9615(2003)073[0069:SMFPRT]2.0.CO;2

10.1111/j.1365‐294X.2007.03659.x

10.1146/annurev.ecolsys.110308.120159

10.1038/s41586‐019‐1520‐9

10.1038/s41559‐017‐0423‐0

10.1111/ele.12376

10.1126/science.1209271

Fox G. A., 2003, Assortative mating and plant phenology: Evolutionary and practical consequences, Evolutionary Ecology Research, 5, 1

10.1073/pnas.0608379104

10.1111/ecog.02909

10.1111/plb.12226

10.1111/j.1438‐8677.2012.00638.x

10.1093/aob/mcs195

10.2307/2410956

10.1098/rspb.2008.1480

10.1111/j.1600‐0587.1984.tb01098.x

10.1111/j.1600‐0587.2010.06250.x

10.1111/plb.12643

10.1093/aob/mcm007

10.1111/j.1469‐8137.2006.01932.x

10.1111/j.0906‐7590.2008.05509.x

10.1007/s11284‐005‐0059‐4

10.1007/s10682‐010‐9440‐z

10.2307/4072271

10.1111/gcb.13992

10.1111/j.1461‐0248.2005.00792.x

10.1890/15‐0926

10.1139/A09‐014

10.1007/s00442‐008‐1112‐0

10.2307/2844717

10.1525/bio.2012.62.2.8

10.3732/ajb.92.4.744

10.1111/j.1365‐294X.2008.04004.x

Hijmans R. J. &vanEtten J.(2012).raster: Geographic analysis and modeling with raster data. R Package Version 2.

10.1016/0169‐5347(94)90248‐8

10.1038/nature09670

10.1073/pnas.0905137106

10.1111/ecog.04828

10.1111/j.0030-1299.2005.13145.x

10.1046/j.1523‐1739.1994.08041163.x

Hutchinson G. E., 1957, Concluding remarks cold spring harbor symposia on quantitative biology, GS SEARCH, 22, 415

10.1111/gcb.13470

10.1890/06‐2128.1

10.1111/nyas.14104

10.1007/s00442‐001‐0835‐y

10.1029/2007JG000680

10.1111/j.1365‐294X.2011.05105.x

10.1146/annurev.ecolsys.38.091206.095622

10.1111/j.1461‐0248.2004.00684.x

10.1111/j.1461‐0248.2008.01277.x

Körner C., 2003, Alpine plant life. Functional plant ecology of high mountain ecosystems

Körner C. &Larcher W.(1988).Plant life in cold climates. InSymposia of the society for experimental biology(Vol. 42 pp. 25–57).

10.1890/09‐1160.1

10.7717/peerj.1193

10.1111/boj.12208

10.1038/hdy.2015.102

10.1371/journal.pone.0087189

10.1111/ele.12604

10.1046/j.1523‐1739.1995.09040753.x

10.1016/j.foreco.2009.06.020

10.1600/036364406777585928

10.1111/evo.12343

10.3354/cr033257

10.1007/s11258-018-0855-x

10.1111/j.1365‐294X.2012.05656.x

10.1002/ece3.2010

10.1111/nph.12082

10.1111/ecog.00845

Millar C. I., 1991, Strategies for conserving clinal, ecotypic, and disjunct population diversity in widespread species, Genetics and Conservation of Rare Plants, 149, 170

10.2307/1551922

10.1111/1365‐2745.13455

10.1016/j.envexpbot.2019.103894

10.1111/j.1461‐0248.2011.01601.x

10.1111/2041‐210X.12261

10.1055/s-2000-16635

Ninyerola M., 2005, Atlas climático digital de la Península Ibérica: metodología y aplicaciones en bioclimatología y geobotánica

Nobel P. S., 2002, Temperature limitations for cultivation of edible cacti in California, California Botanical Society, 49, 228

10.1111/mec.12773

10.1111/ecog.04442

10.1111/j.1466‐8238.2011.00663.x

10.1073/pnas.1908684117

10.1111/ecog.03331

10.2307/annurev.ecolsys.37.091305.30000024

10.1073/pnas.0900284106

10.1111/evo.14292

10.1371/journal.pone.0118876

10.23943/princeton/9780691136868.001.0001

Phillips S. J., 2005, A brief tutorial on Maxent, AT&T Research, 190, 231

10.1016/j.ecolmodel.2005.03.026

10.1111/j.2007.0906‐7590.05203.x

10.1111/brv.12313

10.1111/geb.12263

10.1111/ecog.03414

R Core Team, 2013, R: A language and environment for statistical computing

10.1111/jbi.12227

10.1073/pnas.1820663116

10.1016/j.ppees.2015.07.005

10.1038/nature01333

10.1017/S1479262119000157

10.1016/j.ecolmodel.2012.04.001

10.1111/j.1365‐2486.2008.01577.x

10.1086/685387

10.1111/jbi.12142

10.1146/annurev.ecolsys.110308.120317

10.1086/665388

10.1111/j.1461‐0248.2007.01107.x

10.1111/jbi.12297

10.1111/j.1438‐8677.2012.00716.x

10.1146/annurev.es.04.110173.001121

10.1111/ecog.00585

Therneau T. M.(2018).coxme: Mixed effects cox models. R Package Version 2 2–7.

10.1098/rsbl.2009.0669

10.1111/ecog.02880

Tutin T. G., 1964, Flora europaea

10.1111/ele.12348

10.1111/j.1600‐0587.2012.07425.x

10.1111/j.1469‐8137.2011.03799.x

10.1111/j.1600‐0587.2010.06658.x

10.1038/416389a

10.1890/10‐1171.1

10.1007/s00442‐013‐2872‐8

10.1371/journal.pone.0034470

10.1002/ece3.1898

10.1111/j.1365‐294X.2007.03488.x

10.1111/ppl.12540