Conclusion and Future
Directions
Plant
response to climate change in the field is complex and can run contrary
to empirical expectations (Parmesan & Hanley, 2015). This complexity
has made predicting ‘real world’ patterns of climate response
challenging and is a significant barrier to successful,
climate-resilient revegetation. Our work addresses this gap by
presenting a straightforward way of incorporating genetic variation,
environmental variation, and their interaction into a single predictive
model. Using A. thaliana as an example, we show the capacity for
the model to accurately predict non-linear responses to climate change.
We demonstrate its potential use in seed provenancing through a worked
example involving the selection of relevant traits to quantify climate
response, gathering of multi-environment multi-genotype trait data,
generation of landscape predictions under different climate change
scenarios, and identification of suitable genotypes for revegetation
based on available genotypic data. Although the model was developed
using a well-characterised species, our framework shows potential for
use in non-model species due to its simple data requirements and minimal
biological assumptions.
Data Availability
All the experimental data used in the analysis are publicly available at
Figshare
(https://melbourne.figshare.com/articles/dataset/FIBR_project_data/12824765).
All the scripts used to run the analysis are accessible through a Github
repository
(https://github.com/andhikarp/AraCast).
The outcome of the analysis can be further visualised using the AraCast
shiny application available at
(https://adaptive-evolution.biosciences.unimelb.edu.au/shiny/AraCast2/.
Author Contributions
AFL designed the study; ARP performed modelling work and analysed the
results. JDLY and AFL provided feedback and suggestions throughout the
project. ARP wrote the initial draft of the manuscript; all three
authors provided edits and revisions.
Acknowledgements
The authors would like to thank Mark Taylor for providing the
temperature data, Daniel Runcie for helping clarify the maths of the
models, Moises Exposito-Alonso for sharing germination time models and
Johanna Schmitt for providing feedback on the manuscript.
References
Alexander, D. H., & Lange, K. (2011). Enhancements to the ADMIXTURE
algorithm for individual ancestry estimation. BMC Bioinformatics ,12 (1), 246. https://doi.org/10.1186/1471-2105-12-246
Amasino, R. (2010). Seasonal and developmental timing of flowering.The Plant Journal , 61 (6), 1001–1013.
https://doi.org/10.1111/j.1365-313X.2010.04148.x
Anderson, J. T., Inouye, D. W., McKinney, A. M., Colautti, R. I., &
Mitchell-Olds, T. (2012). Phenotypic plasticity and adaptive evolution
contribute to advancing flowering phenology in response to climate
change. Proceedings of the Royal Society B: Biological Sciences ,279 (1743), 3843–3852. https://doi.org/10.1098/rspb.2012.1051
Arft, A. M., Walker, M. D., Gurevitch, J., Alatalo, J. M., Bret-Harte,
M. S., Dale, M., Diemer, M., Gugerli, F., Henry, G. H. R., Jones, M. H.,
Hollister, R. D., Jónsdóttir, I. S., Laine, K., Lévesque, E., Marion, G.
M., Molau, U., Mølgaard, P., Nordenhäll, U., Raszhivin, V., …
Wookey, P. A. (1999). Responses of Tundra Plants to Experimental
Warming:meta-Analysis of the International Tundra Experiment.Ecological Monographs , 69 (4), 491–511.
https://doi.org/10.1890/0012-9615(1999)069[0491:ROTPTE]2.0.CO;2
Arouisse, B., Korte, A., Eeuwijk, F. van, & Kruijer, W. (2020).
Imputation of 3 million SNPs in the Arabidopsis regional mapping
population. The Plant Journal , 102 (4), 872–882.
https://doi.org/10.1111/tpj.14659
Bathiany, S., Dakos, V., Scheffer, M., & Lenton, T. M. (2018). Climate
models predict increasing temperature variability in poor countries.Science Advances , 4 (5), eaar5809.
https://doi.org/10.1126/sciadv.aar5809
Bay, R. A., Rose, N., Barrett, R., Bernatchez, L., Ghalambor, C. K.,
Lasky, J. R., Brem, R. B., Palumbi, S. R., & Ralph, P. (2017).
Predicting Responses to Contemporary Environmental Change Using
Evolutionary Response Architectures. The American Naturalist ,189 (5), 463–473. https://doi.org/10.1086/691233
Benito Garzón, M., Robson, T. M., & Hampe, A. (2019). ΔTraitSDMs:
Species distribution models that account for local adaptation and
phenotypic plasticity. New Phytologist , 222 (4),
1757–1765. https://doi.org/10.1111/nph.15716
Brachi, B., Faure, N., Horton, M., Flahauw, E., Vazquez, A., Nordborg,
M., Bergelson, J., Cuguen, J., & Roux, F. (2010). Linkage and
Association Mapping of Arabidopsis thaliana Flowering Time in Nature.PLoS Genetics , 6 (5).
https://doi.org/10.1371/journal.pgen.1000940
Breed, M. F., Stead, M. G., Ottewell, K. M., Gardner, M. G., & Lowe, A.
J. (2013). Which provenance and where? Seed sourcing strategies for
revegetation in a changing environment. Conservation Genetics ,14 (1), 1–10. https://doi.org/10.1007/s10592-012-0425-z
Broadhurst, L. M., Lowe, A., Coates, D. J., Cunningham, S. A., McDonald,
M., Vesk, P. A., & Yates, C. (2008). Seed supply for broadscale
restoration: Maximizing evolutionary potential. Evolutionary
Applications , 1 (4), 587–597.
https://doi.org/10.1111/j.1752-4571.2008.00045.x
Buisson, E., Alvarado, S. T., Le Stradic, S., & Morellato, L. P. C.
(2017). Plant phenological research enhances ecological restoration.Restoration Ecology , 25 (2), 164–171.
https://doi.org/10.1111/rec.12471
Burghardt, L. T., Metcalf, C. J. E., & Donohue, K. (2016). A cline in
seed dormancy helps conserve the environment experienced during
reproduction across the range of Arabidopsis thaliana. American
Journal of Botany , 103 (1), 47–59.
https://doi.org/10.3732/ajb.1500286
Capblancq, T., Fitzpatrick, M. C., Bay, R. A., Exposito-Alonso, M., &
Keller, S. R. (2020). Genomic Prediction of (Mal)Adaptation Across
Current and Future Climatic Landscapes. Annual Review of Ecology,
Evolution, and Systematics , 51 (1), null.
https://doi.org/10.1146/annurev-ecolsys-020720-042553
Ceballos, G., Ehrlich, P. R., Barnosky, A. D., García, A., Pringle, R.
M., & Palmer, T. M. (2015). Accelerated modern human–induced species
losses: Entering the sixth mass extinction. Science Advances .
https://www.science.org/doi/abs/10.1126/sciadv.1400253
Chew, Y. H., Wilczek, A. M., Williams, M., Welch, S. M., Schmitt, J., &
Halliday, K. J. (2012). An augmented Arabidopsis phenology model reveals
seasonal temperature control of flowering time. New Phytologist ,194 (3), 654–665.
https://doi.org/10.1111/j.1469-8137.2012.04069.x
Choe, S., Fujioka, S., Noguchi, T., Takatsuto, S., Yoshida, S., &
Feldmann, K. A. (2001). Overexpression of DWARF4 in the brassinosteroid
biosynthetic pathway results in increased vegetative growth and seed
yield in Arabidopsis. The Plant Journal , 26 (6), 573–582.
https://doi.org/10.1046/j.1365-313x.2001.01055.x
Cook, B. I., Wolkovich, E. M., & Parmesan, C. (2012). Divergent
responses to spring and winter warming drive community level flowering
trends. Proceedings of the National Academy of Sciences of the
United States of America , 109 (23), 9000–9005.
https://doi.org/10.1073/pnas.1118364109
Cornes, R. C., Schrier, G. van der, Besselaar, E. J. M. van den, &
Jones, P. D. (2018). An Ensemble Version of the E-OBS Temperature and
Precipitation Data Sets. Journal of Geophysical Research:
Atmospheres , 123 (17), 9391–9409.
https://doi.org/10.1029/2017JD028200
Daele, I. V., Gonzalez, N., Vercauteren, I., Smet, L. de, Inzé, D.,
Roldán‐Ruiz, I., & Vuylsteke, M. (2012). A comparative study of seed
yield parameters in Arabidopsis thaliana mutants and transgenics.Plant Biotechnology Journal , 10 (4), 488–500.
https://doi.org/10.1111/j.1467-7652.2012.00687.x
Daetwyler, H. D., Calus, M. P. L., Pong-Wong, R., Campos, G. de los, &
Hickey, J. M. (2013). Genomic Prediction in Animals and Plants:
Simulation of Data, Validation, Reporting, and Benchmarking.Genetics , 193 (2), 347–365.
https://doi.org/10.1534/genetics.112.147983
Dale, V. H. (1997). The Relationship Between Land-Use Change and Climate
Change. Ecological Applications , 7 (3), 753–769.
https://doi.org/10.1890/1051-0761(1997)007[0753:TRBLUC]2.0.CO;2
DeLeo, V. L., Menge, D. N. L., Hanks, E. M., Juenger, T. E., & Lasky,
J. R. (2020). Effects of two centuries of global environmental variation
on phenology and physiology of Arabidopsis thaliana. Global Change
Biology , 26 (2), 523–538. https://doi.org/10.1111/gcb.14880
de Oliveira, A.
A., Resende, M. F. R., Ferrão, L. F. V., Amadeu, R. R., Guimarães, L. J.
M., Guimarães, C. T., Pastina, M. M., & Margarido, G. R. A. (2020).
Genomic prediction applied to multiple traits and environments in second
season maize hybrids. Heredity, 125(1), 60–72.
https://doi.org/10.1038/s41437-020-0321-0
Deomano, E., Jackson, P., Wei, X., Aitken, K., Kota, R., &
Pérez-Rodríguez, P. (2020). Genomic prediction of sugar content and cane
yield in sugar cane clones in different stages of selection in a
breeding program, with and without pedigree information. Molecular
Breeding , 40 (4), 38. https://doi.org/10.1007/s11032-020-01120-0
Donohue, K. (2002). Germination Timing Influences Natural Selection on
Life-History Characters in Arabidopsis Thaliana. Ecology ,83 (4), 1006–1016.
https://doi.org/10.1890/0012-9658(2002)083[1006:GTINSO]2.0.CO;2
Donohue, K., Dorn, L., Griffith, C., Kim, E., Aguilera, A., Polisetty,
C. R., & Schmitt, J. (2005). Niche Construction Through Germination
Cueing: Life-History Responses to Timing of Germination in Arabidopsis
Thaliana. Evolution , 59 (4), 771–785.
https://doi.org/10.1111/j.0014-3820.2005.tb01752.x
Durvasula, A., Fulgione, A., Gutaker, R. M., Alacakaptan, S. I., Flood,
P. J., Neto, C., Tsuchimatsu, T., Burbano, H. A., Picó, F. X.,
Alonso-Blanco, C., & Hancock, A. M. (2017). African genomes illuminate
the early history and transition to selfing in Arabidopsis thaliana.Proceedings of the National Academy of Sciences , 114 (20),
5213–5218. https://doi.org/10.1073/pnas.1616736114
El-Soda, M., Malosetti, M., Zwaan, B. J., Koornneef, M., & Aarts, M. G.
M. (2014). Genotype × environment interaction QTL mapping in plants:
Lessons from Arabidopsis. Trends in Plant Science , 19 (6),
390–398. https://doi.org/10.1016/j.tplants.2014.01.001
Eu-ahsunthornwattana, J., Miller, E. N., Fakiola, M., Jeronimo, S. M.
B., Blackwell, J. M., & Cordell, H. J. (2014). Comparison of Methods to
Account for Relatedness in Genome-Wide Association Studies with
Family-Based Data. PLoS Genetics , 10 (7).
https://doi.org/10.1371/journal.pgen.1004445
Exposito-Alonso, M. (2020). Seasonal timing adaptation across the
geographic range of Arabidopsis thaliana. Proceedings of the
National Academy of Sciences , 117 (18), 9665–9667.
https://doi.org/10.1073/pnas.1921798117
Fedriani, J. M., Garrote, P. J., Calvo, G., Delibes, M., Castilla, A.
R., & Żywiec, M. (2019). Combined effects of seed provenance, plant
facilitation and restoration site on revegetation success. Journal
of Applied Ecology , 56 (4), 996–1006.t
https://doi.org/10.1111/1365-2664.13343
Fick, S. E., & Hijmans, R. J. (2017). WorldClim 2: New 1-km spatial
resolution climate surfaces for global land areas. International
Journal of Climatology , 37 (12), 4302–4315.
https://doi.org/10.1002/joc.5086
Finch‐Savage, W. E., & Leubner‐Metzger, G. (2006). Seed dormancy and
the control of germination. New Phytologist , 171 (3),
501–523. https://doi.org/10.1111/j.1469-8137.2006.01787.x
Fitzpatrick, C. R., Mustafa, Z., & Viliunas, J. (2019). Soil microbes
alter plant fitness under competition and drought. Journal of
Evolutionary Biology , 32 (5), 438–450.
https://doi.org/10.1111/jeb.13426
Fitzpatrick, M. C., & Keller, S. R. (2015). Ecological genomics meets
community-level modelling of biodiversity: Mapping the genomic landscape
of current and future environmental adaptation. Ecology Letters ,18 (1), 1–16. https://doi.org/10.1111/ele.12376
Foden, W., Midgley, G. F., Hughes, G., Bond, W. J., Thuiller, W.,
Hoffman, M. T., Kaleme, P., Underhill, L. G., Rebelo, A., & Hannah, L.
(2007). A changing climate is eroding the geographical range of the
Namib Desert tree Aloe through population declines and dispersal lags.Diversity and Distributions , 13 (5), 645–653.
https://doi.org/10.1111/j.1472-4642.2007.00391.x
Fournier-Level, A., Korte, A., Cooper, M. D., Nordborg, M., Schmitt, J.,
& Wilczek, A. M. (2011). A Map of Local Adaptation in Arabidopsis
thaliana. Science , 334 (6052), 86–89.
https://doi.org/10.1126/science.1209271
Fournier-Level A, Wilczek AM, Cooper MD, Roe JL, Anderson J, Eaton D,
Moyers BT, Petipas RH, Schaeffer RN, Pieper B et al . 2013. Paths
to selection on life history loci in different natural environments
across the native range of Arabidopsis thaliana . Molecular
Ecology 22 (13), 3552– 3566. https://doi.org/10.1111/mec.12285
Fournier-Level, A., Perry, E. O., Wang, J. A., Braun, P. T., Migneault,
A., Cooper, M. D., Metcalf, C. J. E., & Schmitt, J. (2016). Predicting
the evolutionary dynamics of seasonal adaptation to novel climates in
Arabidopsis thaliana. Proceedings of the National Academy of
Sciences , 113 (20), E2812–E2821.
https://doi.org/10.1073/pnas.1517456113
Gao, N., Teng, J., Ye, S., Yuan, X., Huang, S., Zhang, H., Zhang, X.,
Li, J., & Zhang, Z. (2018). Genomic Prediction of Complex Phenotypes
Using Genic Similarity Based Relatedness Matrix. Frontiers in
Genetics , 9 . https://doi.org/10.3389/fgene.2018.00364
Gent, P. R., Danabasoglu, G., Donner, L. J., Holland, M. M., Hunke, E.
C., Jayne, S. R., Lawrence, D. M., Neale, R. B., Rasch, P. J.,
Vertenstein, M., Worley, P. H., Yang, Z.-L., & Zhang, M. (2011). The
Community Climate System Model Version 4. Journal of Climate ,24 (19), 4973–4991. https://doi.org/10.1175/2011JCLI4083.1
Ghalambor, C. K.,
McKAY, J. K., Carroll, S. P., & Reznick, D. N. (2007). Adaptive versus
non-adaptive phenotypic plasticity and the potential for contemporary
adaptation in new environments. Functional Ecology, 21(3),
394–407. https://doi.org/10.1111/j.1365-2435.2007.01283.x
Gnan, S., Priest, A., & Kover, P. X. (2014). The Genetic Basis of
Natural Variation in Seed Size and Seed Number and Their Trade-Off Using
Arabidopsis thaliana MAGIC Lines. Genetics , 198 (4),
1751–1758. https://doi.org/10.1534/genetics.114.170746
Gougherty, A. V., Keller, S. R., & Fitzpatrick, M. C. (2021).
Maladaptation, migration and extirpation fuel climate change risk in a
forest tree species. Nature Climate Change , 11 (2),
166–171. https://doi.org/10.1038/s41558-020-00968-6
Granier, C., Massonnet, C., Turc, O., Muller, B., Chenu, K., & Tardieu,
F. (2002). Individual leaf development in Arabidopsis thaliana: A stable
thermal-time-based programme. Annals of Botany , 89 (5),
595–604. https://doi.org/10.1093/aob/mcf085
Gray, S. B., & Brady, S. M. (2016). Plant developmental responses to
climate change. Developmental Biology , 419 (1), 64–77.
https://doi.org/10.1016/j.ydbio.2016.07.023
Guerin, G. R., Wen, H., & Lowe, A. J. (2012). Leaf morphology shift
linked to climate change. Biology Letters , 8 (5), 882–886.
https://doi.org/10.1098/rsbl.2012.0458
Hancock, A. M., Brachi, B., Faure, N., Horton, M. W., Jarymowycz, L. B.,
Sperone, F. G., Toomajian, C., Roux, F., & Bergelson, J. (2011).
Adaptation to Climate Across the Arabidopsis thaliana Genome.Science , 334 (6052), 83–86.
https://doi.org/10.1126/science.1209244
Harris, J. A., Hobbs, R. J., Higgs, E., & Aronson, J. (2006).
Ecological Restoration and Global Climate Change. Restoration
Ecology , 14 (2), 170–176.
https://doi.org/10.1111/j.1526-100X.2006.00136.x
Hiemstra, P. (2013). automap: Automatic interpolation package(1.0-14) [Computer software].
https://CRAN.R-project.org/package=automap
Hijmans, R. J., Etten, J. van, Sumner, M., Cheng, J., Baston, D., Bevan,
A., Bivand, R., Busetto, L., Canty, M., Forrest, D., Ghosh, A.,
Golicher, D., Gray, J., Greenberg, J. A., Hiemstra, P., Hingee, K.,
Geosciences, I. for M. A., Karney, C., Mattiuzzi, M., … Wueest,
R. (2020). raster: Geographic Data Analysis and Modeling (3.3-13)
[Computer software]. https://CRAN.R-project.org/package=raster
Koornneef, M., Alonso-Blanco, C., Peeters, A. J. M., & Soppe, W.
(1998). Genetic control of flowering time in arabidopsis. Annual
Review of Plant Physiology and Plant Molecular Biology , 49 (1),
345–370. https://doi.org/10.1146/annurev.arplant.49.1.345
Koornneef, M., & Meinke, D. (2010). The development of Arabidopsis as a
model plant. The Plant Journal , 61 (6), 909–921.
https://doi.org/10.1111/j.1365-313X.2009.04086.x
Korves, T. M., Schmid, K. J., Caicedo, A. L., Mays, C., Stinchcombe, J.
R., Purugganan, M. D., & Schmitt, J. (2007). Fitness effects associated
with the major flowering time gene FRIGIDA in Arabidopsis thaliana in
the field. The American Naturalist , 169 (5), E141-157.
https://doi.org/10.1086/513111
Krannitz, P. G., Aarssen, L. W., & Dow, J. M. (1991). The Effect of
Genetically Based Differences in Seed Size on Seedling Survival in
Arabidopsis thaliana (Brassicaceae). American Journal of Botany ,78 (3), 446–450. JSTOR. https://doi.org/10.2307/2444967
Laughlin, D. C. (2014). Applying trait-based models to achieve
functional targets for theory-driven ecological restoration.Ecology Letters , 17 (7), 771–784.
https://doi.org/10.1111/ele.12288
Li, Y., Cheng, R., Spokas, K. A., Palmer, A. A., & Borevitz, J. O.
(2014). Genetic variation for life history sensitivity to seasonal
warming in Arabidopsis thaliana. Genetics , 196 (2),
569–577. https://doi.org/10.1534/genetics.113.157628
Linde, M., Hattendorf, A., Kaufmann, H., & Debener, Th. (2006). Powdery
mildew resistance in roses: QTL mapping in different environments using
selective genotyping. Theoretical and Applied Genetics ,113 (6), 1081–1092. https://doi.org/10.1007/s00122-006-0367-2
Lu, P., Yu, Q., Liu, J., & Lee, X. (2006). Advance of tree-flowering
dates in response to urban climate change. Agricultural and Forest
Meteorology , 138 (1), 120–131.
https://doi.org/10.1016/j.agrformet.2006.04.002
Martínez-Berdeja, A., Stitzer, M. C., Taylor, M. A., Okada, M., Ezcurra,
E., Runcie, D. E., & Schmitt, J. (2020). Functional variants of DOG1
control seed chilling responses and variation in seasonal life-history
strategies in Arabidopsis thaliana. Proceedings of the National
Academy of Sciences , 117 (5), 2526–2534.
https://doi.org/10.1073/pnas.1912451117
Mátyás, C. (1996). Climatic adaptation of trees: Rediscovering
provenance tests. Euphytica , 92 (1), 45–54.
https://doi.org/10.1007/BF00022827
Millet, E. J., Kruijer, W., Coupel-Ledru, A., Alvarez Prado, S.,
Cabrera-Bosquet, L., Lacube, S., Charcosset, A., Welcker, C., van
Eeuwijk, F., & Tardieu, F. (2019). Genomic prediction of maize yield
across European environmental conditions. Nature Genetics ,51 (6), 952–956. https://doi.org/10.1038/s41588-019-0414-y
Mondoni, A., Rossi, G., Orsenigo, S., & Probert, R. J. (2012). Climate
warming could shift the timing of seed germination in alpine plants.Annals of Botany , 110 (1), 155–164.
https://doi.org/10.1093/aob/mcs097
Montesinos-López, A., Montesinos-López, O. A., Gianola, D., Crossa, J.,
& Hernández-Suárez, C. M. (2018). Multi-environment Genomic Prediction
of Plant Traits Using Deep Learners With Dense Architecture. G3:
Genes, Genomes, Genetics , 8 (12), 3813–3828.
https://doi.org/10.1534/g3.118.200740
Oliver, M. A., & Webster, R. (1990). Kriging: A method of interpolation
for geographical information systems. International Journal of
Geographical Information Systems , 4 (3), 313–332.
https://doi.org/10.1080/02693799008941549
Paril, J. F., Balding, D. J., & Fournier-Level, A. (2021). Optimizing
sampling design and sequencing strategy for the genomic analysis of
quantitative traits in natural populations. Molecular Ecology
Resources .
https://onlinelibrary.wiley.com/doi/abs/10.1111/1755-0998.13458
Parmesan, C., & Hanley, M. E. (2015). Plants and climate change:
Complexities and surprises. Annals of Botany , 116 (6),
849–864. https://doi.org/10.1093/aob/mcv169
Platt, A., Horton, M., Huang, Y. S., Li, Y., Anastasio, A. E., Mulyati,
N. W., Ågren, J., Bossdorf, O., Byers, D., Donohue, K., Dunning, M.,
Holub, E. B., Hudson, A., Corre, V. L., Loudet, O., Roux, F., Warthmann,
N., Weigel, D., Rivero, L., … Borevitz, J. O. (2010). The Scale
of Population Structure in Arabidopsis thaliana. PLOS Genetics ,6 (2), e1000843. https://doi.org/10.1371/journal.pgen.1000843
Post, E. S., Pedersen, C., Wilmers, C. C., & Forchhammer, M. C. (2008).
Phenological Sequences Reveal Aggregate Life History Response to
Climatic Warming. Ecology , 89 (2), 363–370.
https://doi.org/10.1890/06-2138.1
Post, E., Steinman, B. A., & Mann, M. E. (2018). Acceleration of
phenological advance and warming with latitude over the past century.Scientific Reports , 8 (1), 3927.
https://doi.org/10.1038/s41598-018-22258-0
Pouteau, S., & Albertini, C. (2009). The significance of bolting and
floral transitions as indicators of reproductive phase change in
Arabidopsis. Journal of Experimental Botany , 60 (12),
3367–3377. https://doi.org/10.1093/jxb/erp173
Primack, D., Imbres, C., Primack, R. B., Miller‐Rushing, A. J., &
Tredici, P. D. (2004). Herbarium specimens demonstrate earlier flowering
times in response to warming in Boston. American Journal of
Botany , 91 (8), 1260–1264. https://doi.org/10.3732/ajb.91.8.1260
Prober, S., Byrne, M., McLean, E., Steane, D., Potts, B., Vaillancourt,
R., & Stock, W. (2015). Climate-adjusted provenancing: A strategy for
climate-resilient ecological restoration. Frontiers in Ecology and
Evolution , 3 , 65. https://doi.org/10.3389/fevo.2015.00065
Purcell, S., Neale, B., Todd-Brown, K., Thomas, L., Ferreira, M. A. R.,
Bender, D., Maller, J., Sklar, P., de Bakker, P. I. W., Daly, M. J., &
Sham, P. C. (2007). PLINK: A Tool Set for Whole-Genome Association and
Population-Based Linkage Analyses. American Journal of Human
Genetics , 81 (3), 559–575.
Rakitsch, B., Lippert, C., Stegle, O., & Borgwardt, K. (2013). A Lasso
multi-marker mixed model for association mapping with population
structure correction. Bioinformatics , 29 (2), 206–214.
https://doi.org/10.1093/bioinformatics/bts669
Ramalho, C. E., Byrne, M., & Yates, C. J. (2017). A Climate-Oriented
Approach to Support Decision-Making for Seed Provenance in Ecological
Restoration. Frontiers in Ecology and Evolution , 5 , 95.
https://doi.org/10.3389/fevo.2017.00095
Ramstein, G. P., Evans, J., Kaeppler, S. M., Mitchell, R. B., Vogel, K.
P., Buell, C. R., & Casler, M. D. (2016). Accuracy of Genomic
Prediction in Switchgrass (Panicum virgatum L.) Improved by Accounting
for Linkage Disequilibrium. G3: Genes, Genomes, Genetics ,6 (4), 1049–1062. https://doi.org/10.1534/g3.115.024950
Roberts, D. R., Bahn, V., Ciuti, S., Boyce, M. S., Elith, J.,
Guillera‐Arroita, G., Hauenstein, S., Lahoz‐Monfort, J. J., Schröder,
B., Thuiller, W., Warton, D. I., Wintle, B. A., Hartig, F., & Dormann,
C. F. (2017). Cross-validation strategies for data with temporal,
spatial, hierarchical, or phylogenetic structure. Ecography ,40 (8), 913–929. https://doi.org/10.1111/ecog.02881
Rosloski, S. M., Singh, A., Jali, S. S., Balasubramanian, S., Weigel,
D., & Grbic, V. (2013). Functional analysis of splice variant
expression of MADS AFFECTING FLOWERING 2 of Arabidopsis thaliana.Plant Molecular Biology , 81 (1–2), 57–69.
https://doi.org/10.1007/s11103-012-9982-2
Salomé, P. A., Bomblies, K., Laitinen, R. A. E., Yant, L., Mott, R., &
Weigel, D. (2011). Genetic Architecture of Flowering-Time Variation in
Arabidopsis thaliana. Genetics , 188 (2), 421–433.
https://doi.org/10.1534/genetics.111.126607
Sasaki, E., Zhang, P., Atwell, S., Meng, D., & Nordborg, M. (2015).
“Missing” G x E Variation Controls Flowering Time in Arabidopsis
thaliana. PLOS Genetics , 11 (10), e1005597.
https://doi.org/10.1371/journal.pgen.1005597
Schär, C., Vidale, P. L., Lüthi, D., Frei, C., Häberli, C., Liniger, M.
A., & Appenzeller, C. (2004). The role of increasing temperature
variability in European summer heatwaves. Nature ,427 (6972), 332–336. https://doi.org/10.1038/nature02300
Scheepens, J. F., Deng, Y., & Bossdorf, O. (2018). Phenotypic
plasticity in response to temperature fluctuations is genetically
variable, and relates to climatic variability of origin, in Arabidopsis
thaliana. AoB PLANTS , 10 (4).
https://doi.org/10.1093/aobpla/ply043
Scheepens, J. F., & Stöcklin, J. (2013). Flowering phenology and
reproductive fitness along a mountain slope: Maladaptive responses to
transplantation to a warmer climate in Campanula thyrsoides.Oecologia , 171 (3), 679–691.
https://doi.org/10.1007/s00442-012-2582-7
Schwartz, M. D., & Hanes, J. M. (2010). Continental-scale phenology:
Warming and chilling. International Journal of Climatology ,30 (11), 1595–1598. https://doi.org/10.1002/joc.2014
Scoccimarro, E., Gualdi, S., Bellucci, A., Sanna, A., Giuseppe Fogli,
P., Manzini, E., Vichi, M., Oddo, P., & Navarra, A. (2011). Effects of
Tropical Cyclones on Ocean Heat Transport in a High-Resolution Coupled
General Circulation Model. Journal of Climate , 24 (16),
4368–4384. https://doi.org/10.1175/2011JCLI4104.1
Screen, J. A. (2014). Arctic amplification decreases temperature
variance in northern mid- to high-latitudes. Nature Climate
Change , 4 (7), 577–582. https://doi.org/10.1038/nclimate2268
Seymour, D. K., Chae, E., Grimm, D. G., Pizarro, C. M., Habring-Müller,
A., Vasseur, F., Rakitsch, B., Borgwardt, K. M., Koenig, D., & Weigel,
D. (2016). Genetic architecture of nonadditive inheritance in
Arabidopsis thaliana hybrids. Proceedings of the National Academy
of Sciences . https://doi.org/10.1073/pnas.1615268113
Sharbel, T. F., Haubold, B., & Mitchell-Olds, T. (2000). Genetic
isolation by distance in Arabidopsis thaliana: Biogeography and
postglacial colonization of Europe. Molecular Ecology ,9 (12), 2109–2118.
https://doi.org/10.1046/j.1365-294x.2000.01122.x
Sherry, R. A., Zhou, X., Gu, S., Arnone, J. A., Schimel, D. S., Verburg,
P. S., Wallace, L. L., & Luo, Y. (2007). Divergence of reproductive
phenology under climate warming. Proceedings of the National
Academy of Sciences , 104 (1), 198–202.
https://doi.org/10.1073/pnas.0605642104
Sills, G. R., & Nienhuis, J. (1995). Maternal phenotypic effects due to
soil nutrient levels and sink removal in Arabidopsis thaliana
(Brassicaceae). American Journal of Botany , 82 (4),
491–495. https://doi.org/10.1002/j.1537-2197.1995.tb15669.x
Speed, D., & Balding, D. J. (2015). Relatedness in the post-genomic
era: Is it still useful? Nature Reviews Genetics , 16 (1),
33–44. https://doi.org/10.1038/nrg3821
Springate, D. A., & Kover, P. X. (2014). Plant responses to elevated
temperatures: A field study on phenological sensitivity and fitness
responses to simulated climate warming. Global Change Biology ,20 (2), 456–465. https://doi.org/10.1111/gcb.12430
Suding, K., Higgs, E., Palmer, M., Callicott, J. B., Anderson, C. B.,
Baker, M., Gutrich, J. J., Hondula, K. L., LaFevor, M. C., Larson, B. M.
H., Randall, A., Ruhl, J. B., & Schwartz, K. Z. S. (2015). Committing
to ecological restoration. Science , 348 (6235), 638–640.
https://doi.org/10.1126/science.aaa4216
Sun, Y., Bossdorf, O., Grados, R. D., Liao, Z., & Müller‐Schärer, H.
(2020). Rapid genomic and phenotypic change in response to climate
warming in a widespread plant invader. Global Change Biology ,26 (11), 6511–6522. https://doi.org/10.1111/gcb.15291
Supple, M. A., Bragg, J. G., Broadhurst, L. M., Nicotra, A. B., Byrne,
M., Andrew, R. L., Widdup, A., Aitken, N. C., & Borevitz, J. O. (2018).
Landscape genomic prediction for restoration of a Eucalyptus foundation
species under climate change. ELife , 7 , e31835.
https://doi.org/10.7554/eLife.31835
Thornton, P. E., Thornton, M. M., Mayer, B. W., Wei, Y., Devarakonda,
R., Vose, R. S., & Cook, R. B. (2016). Daymet: Daily Surface Weather
Data on a 1-km Grid for North America, Version 3. ORNL DAAC .
https://doi.org/10.3334/ORNLDAAC/1328
Tibshirani, R. (1996). Regression Shrinkage and Selection via the Lasso.Journal of the Royal Statistical Society. Series B
(Methodological) , 58 (1), 267–288.
Tienderen, P. H. van, Hammad, I., & Zwaal, F. C. (1996). Pleiotropic
effects of flowering time genes in the annual crucifer Arabidopsis
thaliana (Brassicaceae). American Journal of Botany ,83 (2), 169–174.
https://doi.org/10.1002/j.1537-2197.1996.tb12693.x
Tilman, D., Clark, M., Williams, D. R., Kimmel, K., Polasky, S., &
Packer, C. (2017). Future threats to biodiversity and pathways to their
prevention. Nature , 546 (7656), 73–81.
https://doi.org/10.1038/nature22900
van Vuuren, D. P., Edmonds, J., Kainuma, M., Riahi, K., Thomson, A.,
Hibbard, K., Hurtt, G. C., Kram, T., Krey, V., Lamarque, J.-F., Masui,
T., Meinshausen, M., Nakicenovic, N., Smith, S. J., & Rose, S. K.
(2011). The representative concentration pathways: An overview.Climatic Change , 109 (1), 5.
https://doi.org/10.1007/s10584-011-0148-z
Velazco, J. G., Jordan, D. R., Mace, E. S., Hunt, C. H., Malosetti, M.,
& van Eeuwijk, F. A. (2019). Genomic Prediction of Grain Yield and
Drought-Adaptation Capacity in Sorghum Is Enhanced by Multi-Trait
Analysis. Frontiers in Plant Science , 10 .
https://www.frontiersin.org/article/10.3389/fpls.2019.00997
Weinig, C., Stinchcombe, J. R., & Schmitt, J. (2003). Evolutionary
Genetics of Resistance and Tolerance to Natural Herbivory in Arabidopsis
thaliana. Evolution , 57 (6), 1270–1280. JSTOR.
Wheeler, T. R., Craufurd, P. Q., Ellis, R. H., Porter, J. R., & Vara
Prasad, P. V. (2000). Temperature variability and the yield of annual
crops. Agriculture, Ecosystems & Environment , 82 (1),
159–167. https://doi.org/10.1016/S0167-8809(00)00224-3
Wilczek, A. M., Burghardt, L. T., Cobb, A. R., Cooper, M. D., Welch, S.
M., & Schmitt, J. (2010). Genetic and physiological bases for
phenological responses to current and predicted climates.Philosophical Transactions of the Royal Society B: Biological
Sciences , 365 (1555), 3129–3147.
https://doi.org/10.1098/rstb.2010.0128
Wilczek, A. M., Cooper, M. D., Korves, T. M., & Schmitt, J. (2014).
Lagging adaptation to warming climate in Arabidopsis thaliana.Proceedings of the National Academy of Sciences , 111 (22),
7906–7913. https://doi.org/10.1073/pnas.1406314111
Wilczek, A. M., Roe, J. L., Knapp, M. C., Cooper, M. D., Lopez-Gallego,
C., Martin, L. J., Muir, C. D., Sim, S., Walker, A., Anderson, J., Egan,
J. F., Moyers, B. T., Petipas, R., Giakountis, A., Charbit, E.,
Coupland, G., Welch, S. M., & Schmitt, J. (2009). Effects of Genetic
Perturbation on Seasonal Life History Plasticity. Science ,323 (5916), 930–934. https://doi.org/10.1126/science.1165826
Wilson, A. J., Réale, D., Clements, M. N., Morrissey, M. M., Postma, E.,
Walling, C. A., Kruuk, L. E. B., & Nussey, D. H. (2010). An ecologist’s
guide to the animal model. Journal of Animal Ecology ,79 (1), 13–26. https://doi.org/10.1111/j.1365-2656.2009.01639.x
Windhausen, V. S., Atlin, G. N., Hickey, J. M., Crossa, J., Jannink,
J.-L., Sorrells, M. E., Raman, B., Cairns, J. E., Tarekegne, A., Semagn,
K., Beyene, Y., Grudloyma, P., Technow, F., Riedelsheimer, C., &
Melchinger, A. E. (2012). Effectiveness of Genomic Prediction of Maize
Hybrid Performance in Different Breeding Populations and Environments.G3: Genes, Genomes, Genetics , 2 (11), 1427–1436.
https://doi.org/10.1534/g3.112.003699
Wortley, L., Hero, J.-M., & Howes, M. (2013). Evaluating Ecological
Restoration Success: A Review of the Literature. Restoration
Ecology , 21 (5), 537–543. https://doi.org/10.1111/rec.12028
Wu, X., Liu, H., Li, X., Tian, Y., & Mahecha, M. D. (2017). Responses
of Winter Wheat Yields to Warming-Mediated Vernalization Variations
Across Temperate Europe. Frontiers in Ecology and Evolution ,5 . https://doi.org/10.3389/fevo.2017.00126
Yu, H., Luedeling, E., & Xu, J. (2010). Winter and spring warming
result in delayed spring phenology on the Tibetan Plateau.Proceedings of the National Academy of Sciences , 107 (51),
22151–22156. https://doi.org/10.1073/pnas.1012490107
Zhang, J., Song, Q., Cregan, P. B., & Jiang, G.-L. (2016). Genome-wide
association study, genomic prediction and marker-assisted selection for
seed weight in soybean (Glycine max). TAG. Theoretical and Applied
Genetics. Theoretische Und Angewandte Genetik , 129 (1), 117–130.
https://doi.org/10.1007/s00122-015-2614-x
Zhang, X., Tarpley, D., & Sullivan, J. T. (2007). Diverse responses of
vegetation phenology to a warming climate. Geophysical Research
Letters , 34 (19). https://doi.org/10.1029/2007GL031447
Zhao, C., Liu, B., Piao, S., Wang, X., Lobell, D. B., Huang, Y., Huang,
M., Yao, Y., Bassu, S., Ciais, P., Durand, J.-L., Elliott, J., Ewert,
F., Janssens, I. A., Li, T., Lin, E., Liu, Q., Martre, P., Müller, C.,
… Asseng, S. (2017). Temperature increase reduces global yields
of major crops in four independent estimates. Proceedings of the
National Academy of Sciences , 114 (35), 9326–9331.
https://doi.org/10.1073/pnas.1701762114
Tables and Figures
Table 1. Summary of model performance