Ecology

In a paper in Nature, Terrer et al.1 reveal an unexpected trade-off between the effects of rising atmospheric carbon dioxide levels on plant biomass and on stocks of soil carbon. Contrary to the assumptions encoded in most computational models of terrestrial ecosystems, the accrual of soil carbon is not positively related to the amount of carbon taken up by plants for biomass growth when CO2 concentrations increase. Instead, the authors show that carbon accumulates in soils when there is a small boost in plant biomass growth in response to CO2, and declines when the growth of biomass is high. Terrer et al. propose that associations of plants with mycorrhizal soil fungi are a key factor in this relationship between the above- and below-ground responses to elevated CO2 levels.
Read the paper: A trade-off between plant and soil carbon storage under elevated CO2
Rising levels of atmospheric CO2 are thought to have driven an increase in the amount of carbon absorbed globally by land ecosystems over the past few decades, a phenomenon known as the CO2 fertilization effect2. This occurs because, at the scale of leaves, higher CO2 levels enhance photosynthesis and the efficiency with which resources (water, light and nutrients such as nitrogen) are used to assimilate CO2 and support biomass growth3. Evidence supporting the existence of the CO2 fertilization effect has been observed in experiments in which the atmosphere around plants or plant communities is enriched with CO2. But at the level of whole ecosystems, responses to CO2 enrichment are more difficult to track, because the effects are diluted throughout a chain of connected processes. Constraining estimates of the response of the global land carbon sink to rising CO2 levels therefore remains a major challenge (see go.nature.com/3vgvhj).Changes in soil carbon are inherently difficult to detect, and studies that assess the effects of elevated CO2 levels on soil-carbon stocks have been equivocal4. Terrer and colleagues set out to investigate these effects by carrying out a meta-analysis of 108 CO2-enrichment experiments. The authors estimate that, in these studies, soil-carbon stocks increased in non-forest sites but remained almost unchanged in forests. By evaluating the effects of multiple environmental variables, the authors found that, surprisingly, the best explanation for the observed patterns is that the changes in soil carbon stocks are inversely related to the changes in above-ground plant biomass: high accumulation of carbon in biomass was associated with soil-carbon loss, whereas low biomass accumulation was associated with soil-carbon gain. This relationship was evident only in experiments in which no nutrients had been added to the studied systems, leading the authors to propose that plant nutrient-acquisition strategies are responsible — which, in turn, depend on the mycorrhizal soil fungi associated with the plants.
Soils linked to climate change
A previous study reported5 that only a small increase in above-ground biomass occurs in CO2-enriched plants that associate with a particular family of mycorrhizae (arbuscular mycorrhizae; AM). AM-associated plants benefit from the fungi’s extensive network of hyphae (branching filaments that aid vegetative growth), which support the plants’ uptake of nitrogen from the soil solution. However, AM have only a limited ability to ‘mine’ nitrogen from organic matter in the soil. The availability of soil nitrogen therefore limits the increase of biomass growth of AM-associated plants in response to elevated CO2 levels. By contrast, plant species that associate with a different group of soil fungi (the ectomycorrhizae; ECM) exhibit a greater increase in above-ground biomass in CO2-enrichment studies, because some of their carbon is allocated to ECM that can mine for nitrogen5. Mining for nutrients by ECM is, however, thought to accelerate the decomposition of organic matter in soil.Terrer et al. now find that AM-associated plants produce a bigger increase in soil-carbon stocks in CO2-enrichment experiments than do ECM-associated plants. The authors suggest that this is because AM-associated plants allocate more carbon to fine roots and to compounds exuded by the roots, resulting in soil-carbon accrual (Fig. 1a). By contrast, nutrient acquisition by ECM-associated plants results in increased turnover — and therefore loss — of soil organic matter (Fig. 1b). Overall, this would lead to an ecosystem-scale trade-off between the amount of carbon sequestered in plants and that sequestered in soil, in a CO2-enriched atmosphere.

Figure 1 | Proposed effects of elevation of atmospheric carbon dioxide levels. Terrer et al.1 suggest that associations of plants with different types of mycorrhizal soil fungi affect plant and soil responses to increases in atmospheric carbon dioxide levels. a, Plants that associate with arbuscular mycorrhizal fungi (grasses and some trees, in this study) do not ‘mine’ nitrogen (N, a nutrient) from the soil, and therefore do not produce much extra above-ground biomass when CO2 levels rise. Instead, they allocate carbon to fine roots and to root-exuded substances, resulting in soil-carbon accrual. Carbon dioxide produced from the respiration of soil microorganisms returns carbon to the atmosphere. b, Plants that associate with ectomycorrhizal fungi (only trees in this study) mine the soil for nitrogen, the uptake of which supports a bigger increase in biomass growth than in a. However, nutrient mining increases the rate of decomposition of organic matter in soil. The amount of carbon in the soil therefore decreases in response to elevated CO2 levels; microbial soil respiration is greater than in a.

Most Earth-system models that account for land carbon-cycling processes assume that rising levels of atmospheric CO2 will increase plant growth, thus producing more plant litter and thereby increasing stocks of soil carbon6. The authors compared the changes in soil carbon and above-ground plant biomass predicted by various models, both in simulations of six open-air CO2-enrichment experiments, and in global simulations of historical and future increases in atmospheric CO2. None of the models reproduced the negative relationship between carbon sequestration by soil and growth in plant biomass that was observed in the current study.Terrer and co-workers’ findings thus provide another urgent warning that current climate models overestimate the amount of carbon that will be sequestered by land ecosystems as atmospheric CO2 levels increase — not only because the models largely ignore the effects of nutrient limitations, but also because they overestimate the amount of carbon that could be sequestered in soil, particularly in forest ecosystems7. But the new study also reveals that grasslands, shrublands and other ecosystems that already have high soil-carbon stocks have great potential to accumulate more soil carbon as CO2 levels increase. These results thus add weight to previous calls to protect existing soil-carbon stocks to mitigate the effects of climate change8.
Carbon dioxide loss from tropical soils increases on warming
There are some limitations to the set of CO2-enrichment experiments included in Terrer and colleagues’ meta-analysis. The experiments are biased towards temperate systems, and most of the forests studied are associated with ECM, whereas the grasslands are all AM-associated. The authors did not find that the type of ecosystem had a substantial effect on the observed responses to CO2, but it remains to be seen whether the reported trade-off between above- and below-ground carbon sequestration for AM- compared with ECM-associated plants applies to forests alone9. Further experiments, especially in tropical ecosystems, are now needed to address these issues.Tropical ecosystems are large contributors to the global terrestrial carbon sink10, but they are notoriously under-studied. Field observations are scarce and few manipulation experiments — such as CO2 enrichment or nutrient additions — have been carried out in these ecosystems11,12. Below-ground processes are particularly challenging to assess in the tropics, where the effects of multiple nutrient scarcities often come into play12. Terrer and colleagues’ study provides a promising framework that can be elaborated to describe diverse plant–soil interactions in various terrestrial ecosystems in the future.CO2-enrichment experiments generally last for just a few years, or just over a decade at most13. Such timescales are unlikely to capture the effects of elevated CO2 levels on plant mortality, plant-species composition and soil-carbon turnover time, all of which can affect the sequestration of carbon by ecosystems in different ways in the longer term. Mechanistic understanding gained from experiments about the coupling between carbon and nutrient cycling can, however, be integrated into computational models. And this will allow us to constrain estimates of the size of the terrestrial carbon sink in the coming decades. The interactions between plants and their associated soil fungi, as well as other crucial below-ground agents and processes such as microbial communities, are already stirring up modelling efforts14,15. Terrer and colleagues’ study now invites researchers to test hypotheses about the processes that drive coordinated above- and below-ground responses to rising CO2 levels. Such studies could be a real step forwards in our understanding of the fate of the terrestrial carbon sink. More

1.Luyssaert, S. et al. Old-growth forests as global carbon sinks. Nature 455, 213–215 (2008).ADS 
CAS 
Article 

Google Scholar 
2.Odum, E. P. The strategy of ecosystem development. Science 164, 262–270 (1969).ADS 
CAS 
Article 

Google Scholar 
3.Pan, Y. D. et al. A large and persistent carbon sink in the world’s forests. Science 333, 988–993 (2011).ADS 
CAS 
Article 

Google Scholar 
4.Ciais, P. et al. Carbon and other biogeochemical cycles. In Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (eds Stocker, T. F. et al.) (Cambridge Univ. Press, 2013).5.Baccini, A. et al. Tropical forests are a net carbon source based on aboveground measurements of gain and loss. Science 358, 230–234 (2017).ADS 
MathSciNet 
CAS 
Article 

Google Scholar 
6.Global Soil Organic Carbon Map (GSOCmap) Technical Report http://www.fao.org/3/I8891EN/i8891en.pdf (FAO/ITPS, 2018).7.Belyea, L. R. & Malmer, N. Carbon sequestration in peatland: patterns and mechanisms of response to climate change. Glob. Change Biol. 10, 1043–1052 (2004).ADS 
Article 

Google Scholar 
8.Zhang, J. et al. C:N:P stoichiometry in China’s forests: from organs to ecosystems. Funct. Ecol. 32, 50–60 (2018).Article 

Google Scholar 
9.Fang, Y. et al. Atmospheric deposition and leaching of nitrogen in Chinese forest ecosystems. J. For. Res. 16, 341–350 (2011).CAS 
Article 

Google Scholar 
10.Fenn, M. E. et al. Nitrogen excess in North American ecosystems: predisposing factors, ecosystem responses, and management strategies. Ecol. Appl. 8, 706–733 (1998).Article 

Google Scholar 
11.MacDonald, J. A. et al. Nitrogen input together with ecosystem nitrogen enrichment predict nitrate leaching from European forests. Glob. Change Biol. 8, 1028–1033 (2002).ADS 
Article 

Google Scholar 
12.Dentener, F. et al. Nitrogen and sulfur deposition on regional and global scales: a multimodel evaluation. Glob. Biogeochem. Cycles 20, GB4003 (2006).ADS 
Article 

Google Scholar 
13.Yang, Y., Luo, Y. & Finzi, A. C. Carbon and nitrogen dynamics during forest stand development: a global synthesis. New Phytol. 190, 977–989 (2011).CAS 
Article 

Google Scholar 
14.Moffat, A. M. et al. Comprehensive comparison of gap-filling techniques for eddy covariance net carbon fluxes. Agric. For. Meteorol. 147, 209–232 (2007).ADS 
Article 

Google Scholar 
15.Wu, J. et al. Synthesis on the carbon budget and cycling in a Danish, temperate deciduous forest. Agric. For. Meteorol. 181, 94–107 (2013).ADS 
Article 

Google Scholar 
16.Soloway, A. D., Amiro, B. D., Dunn, A. L. & Wofsy, S. C. Carbon neutral or a sink? Uncertainty caused by gap-filling long-term flux measurements for an old-growth boreal black spruce forest. Agric. For. Meteorol. 233, 110–121 (2017).ADS 
Article 

Google Scholar 
17.McHugh, I. D. et al. Interactions between nocturnal turbulent flux, storage and advection at an “ideal” eucalypt woodland site. Biogeosciences 14, 3027–3050 (2017).ADS 
CAS 
Article 

Google Scholar 
18.Campioli, M. et al. Evaluating the convergence between eddy-covariance and biometric methods for assessing carbon budgets of forests. Nat. Commun. 7, 13717 (2016).ADS 
CAS 
Article 

Google Scholar 
19.Kang, M. et al. New gap-filling strategies for long-period flux data gaps using a data-driven approach. Atmosphere 10, 568 (2019).ADS 
Article 

Google Scholar 
20.Hayek, M. N. et al. A novel correction for biases in forest eddy covariance carbon balance. Agric. For. Meteorol. 250–251, 90–101 (2018).ADS 
Article 

Google Scholar 
21.Wirth, C., Messier, C., Bergeron, Y., Frank, D. & Fankhänel, A. Old-growth forest definitions: a pragmatic view. In Old‐Growth Forests (eds Wirth, C. et al.) Ecological Studies Vol. 207, 1–33 (Springer, 2009).22.Luyssaert, S., Inglima, I. & Jung, M. Global Forest Ecosystem Structure and Function Data for Carbon Balance Research https://doi.org/10.3334/ORNLDAAC/949 (Oak Ridge National Laboratory Distributed Active Archive Center, 2009). More

1.Hurka, H. et al. The Eurasian steppe belt: Status quo, origin and evolutionary history. Turczaninowia 22, 5–71 (2019).
Google Scholar 
2.Walter, H. Die Vegetation Osteuropas (Gustav Fischer Verlag, 1974).
Google Scholar 
3.Walter, H. Die Vegetation der Erde in öko-physiologischer Betrachtung , Band II : Die gemäßigten und arktischen Zonen, in ökologischer Betrachtung (Gustav Fischer Verlag, 1968).
Google Scholar 
4.Cohen, K. M. & Gibbard, P. L. Global chronostratigraphical correlation table for the last 2.7 million years, version 2019 QI-500. Quat. Int. 500, 20–31 (2019).Article 

Google Scholar 
5.Frenzel, B. Grundzüge der Pleistozänen Vegetationsgeschichte Nord-Euroasiens. Geogr. J. 136, 291 (1968).
Google Scholar 
6.Tarasov, P. E. et al. Last glacial maximum biomes reconstructed from pollen and plant macrofossil data from northern Eurasia. J. Biogeogr. 27, 609–620 (2000).Article 

Google Scholar 
7.Caves Rugenstein, J., Sjostrom, D., Mix, H., Winnick, M. & Chamberlain, C. Aridification of Central Asia and uplift of the Altai and Hangay Mountains, Mongolia: Stable isotope evidence. Am. J. Sci. 314, 1171–1201 (2014).ADS 
Article 
CAS 

Google Scholar 
8.Yanina, T., Sorokin, V., Bezrodnykh, Y. & Romanyuk, B. Late Pleistocene climatic events reflected in the Caspian Sea geological history (based on drilling data). Quat. Int. 465, 130–141 (2018).Article 

Google Scholar 
9.Dolukhanov, P. M., Chepalyga, A. L., Shkatova, V. K. & Lavrentiev, N. V. Late Quaternary Caspian: Sea-levels, environments and human settlement. Open Geogr. J. 2, 1–15 (2009).Article 

Google Scholar 
10.Tudryn, A. et al. Late Quaternary Caspian Sea environment: Late Khazarian and Early Khvalynian transgressions from the lower reaches of the Volga River. Quat. Int. 292, 193–204 (2013).Article 

Google Scholar 
11.Dengler, J., Janišová, M., Török, P. & Wellstein, C. Biodiversity of Palaearctic grasslands: A synthesis. Agric. Ecosyst. Environ. 182, 1–14 (2014).Article 

Google Scholar 
12.Hejcman, M., Hejcmanová, P., Pavlů, V. & Beneš, J. Origin and history of grasslands in Central Europe—a review. Grass Forage Sci. 68, 345–363 (2013).Article 

Google Scholar 
13.Franzke, A. et al. Molecular signals for Late Tertiary/Early Quaternary range splits of an Eurasian steppe plant: Clausia aprica (Brassicaceae). Mol. Ecol. 13, 2789–2795 (2004).CAS 
PubMed 
Article 

Google Scholar 
14.Hurka, H., Friesen, N., German, D. A., Franzke, A. & Neuffer, B. ‘Missing link’ species Capsella orientalis and Capsella thracicaelucidate evolution of model plant genus Capsella (Brassicaceae). Mol. Ecol. 21, 1223–1238 (2012).PubMed 
Article 

Google Scholar 
15.Seregin, A. P., Anačkov, G. & Friesen, N. Molecular and morphological revision of the Allium saxatile group (Amaryllidaceae): Geographical isolation as the driving force of underestimated speciation. Bot. J. Linn. Soc. 178, 67–101 (2015).Article 

Google Scholar 
16.Friesen, N. et al. Dated phylogenies and historical biogeography of Dontostemon and Clausia (Brassicaceae) mirror the palaeogeographical history of the Eurasian steppe. J. Biogeogr. 43, 738–749 (2015).Article 

Google Scholar 
17.Friesen, N. et al. Allium species of section Rhizomatosa, early members of the Central Asian steppe vegetation. Flora 263, 151536 (2020).Article 

Google Scholar 
18.Friesen, N. et al. Evolutionary history of the Eurasian steppe plant Schivereckia podolica (Brassicaceae) and its close relatives. Flora 268, 151602 (2020).Article 

Google Scholar 
19.Volkova, P. A., Herden, T. & Friesen, N. Genetic variation in Goniolimon speciosum (Plumbaginaceae) reveals a complex history of steppe vegetation. Bot. J. Linn. Soc. 184, 113–121 (2017).
Google Scholar 
20.Žerdoner Čalasan, A., Seregin, A. P., Hurka, H., Hofford, N. P. & Neuffer, B. The Eurasian steppe belt in time and space: Phylogeny and historical biogeography of the false flax (Camelina Crantz, Camelineae, Brassicaceae). Flora 260, 151477 (2019).Article 

Google Scholar 
21.Kirschner, P. et al. Long-term isolation of European steppe outposts boosts the biome’s conservation value. Nat. Commun. 11, 1968 (2020).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
22.Heklau, H. & von Wehrden, H. Wood anatomy reflects the distribution of Krascheninnikovia ceratoides (Chenopodiaceae). Flora Morphol. Distrib. Funct. Ecol. Plants 206, 300–309 (2011).Article 

Google Scholar 
23.Heklau, H. & Röser, M. Delineation, taxonomy and phylogenetic relationships of the genus Krascheninnikovia (Amaranthaceae subtribe Axyridinae). Taxon 57, 563–576 (2008).
Google Scholar 
24.Takhtajan, A. Floristic Regions of the World (University of California Press, 1986).
Google Scholar 
25.Manafzadeh, S., Staedler, Y. M. & Conti, E. Visions of the past and dreams of the future in the Orient: The Irano-Turanian region from classical botany to evolutionary studies. Biol. Rev. Camb. Philos. Soc. 92, 1365–1388 (2017).PubMed 
Article 

Google Scholar 
26.Walter, H. & Breckle, S.-W. Ecological systems of the geobiosphere. 2 Tropical and subtropical zonobiomes (Springer, 1986). https://doi.org/10.1007/978-3-662-06812-0.
Google Scholar 
27.Hartmann, H. Zur Flora und Vegetation der Halbwüsten, Steppen und Rasengesellschaften im südöstlichen Ladakh (Indien). in Jahrbuch des Vereins zum Schutz der Bergwelt 129–188 (1997).28.Kraudzun, T., Vanselow, K. A. & Samimi, C. Realities and myths of the Teresken syndrome—An evaluation of the exploitation of dwarf shrub resources in the Eastern Pamirs of Tajikistan. J. Environ. Manag. 132, 49–59 (2014).Article 

Google Scholar 
29.Vanselow, K. & Samimi, C. Predictive mapping of dwarf shrub vegetation in an arid high mountain ecosystem using remote sensing and random forests. Remote Sens. 6, 6709–6726 (2014).ADS 
Article 

Google Scholar 
30.Smoliak, S. & Bezeau, L. M. Chemical composition and in vitro digestibility of range forage plants of the Stipa-Bouteloua prairie. Can. J. Plant Sci. 47, 161–167 (1967).CAS 
Article 

Google Scholar 
31.Waldron, B. L., Eun, J.-S., ZoBell, D. R. & Olson, K. C. Forage kochia (Kochia prostrata) for fall and winter grazing. Small Rumin. Res. 91, 47–55 (2010).Article 

Google Scholar 
32.Steshenko, A. P. Formation of the semi-shrub structure in the high mountains of Pamir. Trans Akad Nauk Tadzhik SSR 50, 2 (1956).
Google Scholar 
33.Zalenski, O. V. & Steshenko, A. P. On the special features of the main species of the vegetation of the Pamir mountains. Proc. Bot. Soc. 7, 9–12 (1957).
Google Scholar 
34.Barnes, M. The Effect of Plant Source Location on Restoration Success: A Reciprocal Transplant Experiment with Winterfat (Krascheninnikovia lanata) (University of New Mexico, 2009).
Google Scholar 
35.Seidl, A. et al. Phylogeny and biogeography of the Pleistocene Holarctic steppe and semi-desert goosefoot plant Krascheninnikovia ceratoides. Flora 262, 151504 (2020).Article 

Google Scholar 
36.Yang, J. Y., Fu, X. Q., Yan, G. X. & Zhang, S. Z. Analysis of three species of the genus Ceratoides. Grassl. China 1, 67–71 (1996).
Google Scholar 
37.Rubtsov, M., Sagimbaev, R., Shakhanov, E., Tiran, T. & Balyan, G. Natural polyploids of prostrate summer cypress and winterfat as initial material for breeding. Sov. Agric. Sci. 4, 20–24 (1989).
Google Scholar 
38.Yan, G., Zhang, S., Yan, J., Fu, X. & Wang, L. Chromosome numbers and geographical distribution of 68 species of forage plants. Grassl. China 4, 53–60 (1989).
Google Scholar 
39.Kurban, N. Karyotype analysis of three species of Ceratoides (Chenopodiaceae). J. Syst. Evol. 22, 466–468 (1984).
Google Scholar 
40.Zakharjeva, O. I. & Soskov, Y. D. Chromosome numbers in desert herbage plants. Bulleten VNII Rastenievod. Im. N.I. Vavilova 108, 57–60 (1981).
Google Scholar 
41.Domínguez, F. et al. Krascheninnikovia ceratoides (L.) Gueldenst (Chenopodiaceae) en Aragón (España): Algunos resultados para su conservación. Bol. R. Soc. Esp. Hist. Nat. (Sec. Biol.) 96, 15–26 (2001).
Google Scholar 
42.Zakirova, R. Chromosome numbers of some Alliaceae, Salicaceae, Polygonaceae, and Chenopodiaceae of the South Balkhash territory. Citologija 41, 1064 (1999).
Google Scholar 
43.Dobes, C. H., Hahn, B. & Morawetz, W. Chromosomenzahlen zur Gefässpflanzenflora Österreichs. Linzer Biol. Beitr 29, 5–43 (1997).
Google Scholar 
44.Sainz Ollero, H., Múgica, F. & Arias Torcal, J. Estrategias para la conservación de la flora amenazada de Aragón (Consejo de Protección de la Naturaleza de Aragón, 1996).
Google Scholar 
45.Lomonosova, M. N. & Krasnikov, A. A. Chromosome numbers in some members of the Chenopodiaceae. Bot. Zurn. (Moscow Leningrad) 78, 158–159 (1993).
Google Scholar 
46.Castroviejo, S. & Soriano, C. Krascheninnikovia ceratoides Gueldenst (Publicaciones del CSIC, 1990).
Google Scholar 
47.Takhtajan, A. Numeri chromosomatum magnoliophytorum florae URSS. Aceraceae–Menyanthaceae. (Academis Scientiarum Rossica, Institutum Botanicum nomine VL Komarovii;” Nauka”, 1990).48.Ghaffari, S. M., Balaei, Z., Chatrenoor, T. & Akhani, H. Cytology of SW Asian Chenopodiaceae: New data from Iran and a review of previous records and correlations with life forms and C4 photosynthesis. Plant Syst. Evol. 301, 501–521 (2014).Article 

Google Scholar 
49.eFloras. Published on the Internet http://www.efloras.org. (2008).50.Kadereit, G., Mavrodiev, E. V., Zacharias, E. H. & Sukhorukov, A. P. Molecular phylogeny of Atripliceae (Chenopodioideae, Chenopodiaceae): Implications for systematics, biogeography, flower and fruit evolution, and the origin of C4 photosynthesis. Am. J. Bot. 97, 1664–1687 (2010).PubMed 
Article 

Google Scholar 
51.Di Vincenzo, V. et al. Evolutionary diversification of the African achyranthoid clade (Amaranthaceae) in the context of sterile flower evolution and epizoochory. Ann. Bot. 122, 69–85 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
52.Janis, C. M. Tertiary mammal evolution in the context of changing climates, vegetation, and tectonic events. Annu. Rev. Ecol. Syst. 24, 467–500 (1993).Article 

Google Scholar 
53.Doležel, J. & Greilhuber, J. Nuclear genome size: Are we getting closer?. Cytom. Part A 77, 635–642 (2010).Article 
CAS 

Google Scholar 
54.Yokoya, K., Roberts, A. V., Mottley, J., Lewis, R. & Brandham, P. E. Nuclear DNA amounts in roses. Ann. Bot. 85, 557–561 (2000).CAS 
Article 

Google Scholar 
55.Poland, J. A., Brown, P. J., Sorrells, M. E. & Jannink, J.-L. Development of high-density genetic maps for barley and wheat using a novel two-enzyme genotyping-by-sequencing approach. PLoS ONE 7, e32253 (2012).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
56.Catchen, J., Hohenlohe, P. A., Bassham, S., Amores, A. & Cresko, W. A. Stacks: An analysis tool set for population genomics. Mol. Ecol. 22, 3124–3140 (2013).PubMed 
PubMed Central 
Article 

Google Scholar 
57.Weiß, C. L., Pais, M., Cano, L. M., Kamoun, S. & Burbano, H. A. nQuire: A statistical framework for ploidy estimation using next generation sequencing. BMC Bioinform. 19, 122 (2018).Article 
CAS 

Google Scholar 
58.Corrêa, A., dos Santos, R., Goldman, G. H. & Riaño-Pachón, D. M. ploidyNGS: Visually exploring ploidy with next generation sequencing data. Bioinformatics 33, 2575–2576 (2017).Article 
CAS 

Google Scholar 
59.Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25, 1754–1760 (2009).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
60.Li, H. A statistical framework for SNP calling, mutation discovery, association mapping and population genetical parameter estimation from sequencing data. Bioinformatics 27, 2987–2993 (2011).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
61.R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing (2013).62.Frichot, E. & François, O. LEA: An R package for landscape and ecological association studies. Methods Ecol. Evol. 6, 925–929 (2015).Article 

Google Scholar 
63.Jombart, T. adegenet: A R package for the multivariate analysis of genetic markers. Bioinformatics 24, 1403–1405 (2008).CAS 
PubMed 
Article 

Google Scholar 
64.Gruber, B., Unmack, P. J., Berry, O. F. & Georges, A. dartr: An R package to facilitate analysis of SNP data generated from reduced representation genome sequencing. Mol. Ecol. Resour. 18, 691–699 (2018).PubMed 
Article 

Google Scholar 
65.Bradley, M. raxml_ascbias. GitHub https://github.com/btmartin721/raxml_ascbias (2018).66.Stamatakis, A. RAxML version 8: A tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics 30, 1312–1313 (2014).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
67.Minh, B. Q. et al. IQ-TREE 2: New models and efficient methods for phylogenetic inference in the genomic era. Mol. Biol. Evol. 37, 1530–1534 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
68.Lewis, P. O. A likelihood approach to estimating phylogeny from discrete morphological character data. Syst. Biol. 50, 913–925 (2001).CAS 
PubMed 
Article 

Google Scholar 
69.Guindon, S. et al. New algorithms and methods to estimate maximum-likelihood phylogenies: Assessing the performance of PhyML 3.0. Syst. Biol. 59, 307–321 (2010).CAS 
PubMed 
Article 

Google Scholar 
70.Minh, B. Q., Nguyen, M. A. T. & von Haeseler, A. Ultrafast approximation for phylogenetic bootstrap. Mol. Biol. Evol. 30, 1188–1195 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
71.Huson, D. H. & Bryant, D. Application of phylogenetic networks in evolutionary studies. Mol. Biol. Evol. 23, 254–267 (2005).PubMed 
Article 
CAS 

Google Scholar 
72.Rambaut, A. FigTree v1.3.1. (2010).73.Kalinowski, S. T. hp-rare 1.0: A computer program for performing rarefaction on measures of allelic richness. Mol. Ecol. Notes 5, 187–189 (2005).CAS 
Article 

Google Scholar 
74.Brummitt, R. World geographical scheme for recording plant distributions. (2001).75.Britton, T., Anderson, C. L., Jacquet, D., Lundqvist, S. & Bremer, K. Estimating divergence times in large phylogenetic trees. Syst. Biol. 56, 741–752 (2007).PubMed 
Article 

Google Scholar 
76.Matzke, N. J. BioGeoBEARS: BioGeography with Bayesian (and likelihood) evolutionary analysis with R scripts. Version 1.1. 1, published on GitHub on 6 November 2018. (2018).77.Matzke, N. J. Model selection in historical biogeography reveals that founder-event speciation is a crucial process in island clades. Syst. Biol. 63, 951–970 (2014).PubMed 
Article 

Google Scholar 
78.Matzke, N. J. Probabilistic historical biogeography: New models for founder-event speciation, imperfect detection, and fossils allow improved accuracy and model-testing. Front. Biogeogr. 5, 2 (2013).Article 

Google Scholar 
79.Ronquist, F. Dispersal-vicariance analysis: A new approach to the quantification of historical biogeography. Syst. Biol. 46, 195–203 (1997).Article 

Google Scholar 
80.Strömberg, C. A. E. Evolution of grasses and grassland ecosystems. Annu. Rev. Earth Planet. Sci. 39, 517–544 (2011).ADS 
Article 
CAS 

Google Scholar 
81.Linder, H. P., Lehmann, C. E. R., Archibald, S., Osborne, C. P. & Richardson, D. M. Global grass (Poaceae) success underpinned by traits facilitating colonization, persistence and habitat transformation. Biol. Rev. 93, 1125–1144 (2017).PubMed 
Article 

Google Scholar 
82.Devyatkin, E. V. Meridional distribution of Pleistocene ecosystems in Asia: Basic problems. Stratigr. Geol. Correl. 1, 77–83 (1993).
Google Scholar 
83.Arkhipov, S. A. & Volkova, V. S. Geological history of Pleistocene landscapes and climate in West Siberia. (1994).84.Akhmetyev, M. A. et al. Chapter 8: Kazakhstan and Central Asia (plains and foothills). In Cenozoic Climatic and Environmental Changes in Russia (Geological Society of America, 2005). https://doi.org/10.1130/0-8137-2382-5.139.
Google Scholar 
85.Arkhipov, S. A. et al. Chapter 4: West Siberia. In Cenozoic Climatic and Environmental Changes in Russia (Geological Society of America, 2005). https://doi.org/10.1130/0-8137-2382-5.67.
Google Scholar 
86.Li, Q. Q. et al. Phylogeny and biogeography of Allium (Amaryllidaceae: Allieae) based on nuclear ribosomal internal transcribed spacer and chloroplast rps16 sequences, focusing on the inclusion of species endemic to China. Ann. Bot. 106, 709–733 (2010).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
87.Hais, M., Komprdová, K., Ermakov, N. & Chytrý, M. Modelling the last glacial maximum environments for a refugium of Pleistocene biota in the Russian Altai mountains Siberia. Palaeogeogr. Palaeoclimatol. Palaeoecol. 438, 135–145 (2015).Article 

Google Scholar 
88.Fedeneva, I. N. & Dergacheva, M. I. Paleosols as the basis of environmental reconstruction in Altai mountainous areas. Quat. Int. 106–107, 89–101 (2003).Article 

Google Scholar 
89.Braun-Blanquet, J. & Bolòs i Capdevila, O. de. Les groupements végétaux du bassin moyen de l’Ebre et leur dynamisme. An. la Estac. Exp. Aula Dei 5, 1–266 (1957).
Google Scholar 
90.Tutin, T., Webb, D., Heywood, V., Walters, S. & Moore, D. Flora Europaea (Cambridge University Press, 1993).
Google Scholar 
91.Heklau, H. Proposal to conserve the name Krascheninnikovia against Ceratoides (Chenopodiaceae. Taxon 55, 1044–1045 (2006).Article 

Google Scholar 
92.Davis, P. H. Flora of Turkey and the east Aegean islands (Edinburgh University Press, 1988).
Google Scholar 
93.Welsh, S., Atwood, N., Higgins, L. & Goodrich, S. A Utah Flora. Gt. Basin Nat. 9, 123 (1987).
Google Scholar 
94.Täckholm, V. Students’ Flora of Egypt (Cairo University Publishing, 1974).
Google Scholar 
95.Komarov, V. Flora of the U.R.S.S (Academiae Sciencitarum U.R.S.S, 1964).
Google Scholar 
96.Rechinger, K. Flora Iranica (Akademische Druck- und Verlagsanstalt, 1963).
Google Scholar 
97.Crawford, K. M. & Whitney, K. D. Population genetic diversity influences colonization success. Mol. Ecol. 19, 1253–1263 (2010).CAS 
PubMed 
Article 

Google Scholar 
98.Hilbig, W. Vegetation of Mongolia (SPB Academic Pubishing, 1995).
Google Scholar 
99.Briggs, J. C. Chapter 7 Neogene. In Global Biogeography Vol. 14 (ed. Briggs, J. C.) 147–189 (Elsevier, Amsterdam, 1995).
Google Scholar 
100.Yurtsev, B. A. The Pleistocene ‘Tundra-steppe’ and the productivity paradox: The landscape approach. Quat. Sci. Rev. 20, 165–174 (2001).ADS 
Article 

Google Scholar 
101.Stewart, J. R., Lister, A. M., Barnes, I. & Dalén, L. Refugia revisited: Individualistic responses of species in space and time. Proc. Biol. Sci. 277, 661–671 (2010).PubMed 

Google Scholar 
102.Varga, Z. Extra-Mediterranean refugia, post-glacial vegetation history and area dynamics in eastern Central Europe. Relict Species https://doi.org/10.1007/978-3-540-92160-8_3 (2009).Article 

Google Scholar 
103.Willis, K. J. & Vanandel, T. Trees or no trees? The environments of central and eastern Europe during the Last Glaciation. Quat. Sci. Rev. 23, 2369–2387 (2004).ADS 
Article 

Google Scholar 
104.Tremetsberger, K. et al. Pleistocene refugia and polytopic replacement of diploids by tetraploids in the Patagonian and Subantarctic plant Hypochaeris incana (Asteraceae, Cichorieae). Mol. Ecol. 18, 3668–3682 (2009).CAS 
PubMed 
Article 

Google Scholar  More

1.Tsukamoto, K., Nakai, I. & Tesch, W.-V. Do all freshwater eels migrate?. Nature 396, 635–636 (1998).ADS 
CAS 
Article 

Google Scholar 
2.Fromentin, J.-M. & Powers, J. E. Atlantic bluefin tuna: population dynamics, ecology, fisheries and management. Fish. Fish. 6, 281–306 (2005).Article 

Google Scholar 
3.Kerr, L. A. & Secor, D. H. Bioenergetic trajectories underlying partial migration in Patuxent River (Chesapeake Bay) white perch (Morone americana). Can. J. Fish. Aquat. Sci. 66, 602–612 (2009).Article 

Google Scholar 
4.Cadrin, S. X. et al. Population structure of beaked redfish, Sebastes mentella: evidence of divergence associated with different habitats. ICES J. Mar. Sci. 67, 1617–1630 (2010).Article 

Google Scholar 
5.Doak, D. F. et al. The statistical inevitability of stability-diversity relationships in community ecology. Am. Nat. 151, 264–276 (1998).CAS 
PubMed 
Article 

Google Scholar 
6.Tilman, D., Lehman, C. L. & Bristow, C. E. Diversity-stability relationships: statistical inevitability or ecological consequence?. Am. Nat. 151, 277–282 (1998).CAS 
PubMed 
Article 

Google Scholar 
7.Secor, D. H., Kerr, L. A. & Cadrin, S. X. Connectivity effects on productivity, stability, and persistence in a herring metapopulation model. ICES J. Mar. Sci. 66, 1726–1732 (2009).Article 

Google Scholar 
8.Cadrin, S. X. & Secor, D. H. Accounting for spatial population structure in stock assessment: past, present, and future. In The Future of Fisheries Science in North America (eds Beamish, R. J. & Rothschild, B. J.) 405–426 (Springer, 2009).
Google Scholar 
9.Secor, D. H. The unit stock concept: bounded fish and fisheries. In Stock Identification Methods: Applications in Fishery Science 2nd edn (eds Cadrin, S. X. et al.) 7–28 (Elsevier, 2014).
Google Scholar 
10.Ricker, W. E. Maximum sustained yields from fluctuating environments and mixed stocks. J. Fish. Res. Board Can. 15, 991–1006 (1958).Article 

Google Scholar 
11.Kerr, L. A. et al. Lessons learned from practical approaches to reconcile mismatches between biological population structure and stock units of marine fish. ICES J. Mar. Sci. 74, 1708–1722 (2017).Article 

Google Scholar 
12.Kerr, L. A., Cadrin, S. X. & Kovach, A. I. Consequences of a mismatch between biological and management units on our perception of Atlantic cod off New England. ICES J. Mar. Sci. 71, 1366–1381 (2014).Article 

Google Scholar 
13.Goethel, D. R. & Berger, A. M. Accounting for spatial complexities in the calculation of biological reference points: effects of misdiagnosing population structure for stock status indicators. Can. J. Fish. Aquat. Sci. 74, 1878–1894 (2017).Article 

Google Scholar 
14.Van Beveren, E., Duplisea, D. E., Brosset, P. & Castonguay, M. Assessment modelling approaches for stocks with spawning components, seasonal and spatial dynamics, and limited resources for data collection. PLoS ONE 14, e0222472 (2019).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
15.Cadrin, S. X. Defining spatial structure for fishery stock assessment. Fish. Res. 221, 105397 (2020).Article 

Google Scholar 
16.Sette, O. E. Biology of the Atlantic mackerel (Scomber scombrus) of North America. Part II:migration and habits. Fish. Bull. 51, 251–358 (1950).
Google Scholar 
17.Moores, J. A., Winters, G. H. & Parsons, L. S. Migrations and biological characteristics of Atlantic mackerel (Scomber scombrus) occurring in Newfoundland waters. J. Fish. Res. Board Can. 32, 1347–1357 (1975).Article 

Google Scholar 
18.Redding, S. G., Cooper, L. W., Castonguay, M., Wiernicki, C. & Secor, D. H. Northwest Atlantic mackerel population structure evaluated using otolith δ18O composition. ICES J. Mar. Sci. 77, 2582–2589 (2020).Article 

Google Scholar 
19.Overholtz, W. J., Link, J. S. & Suslowicz, L. E. Consumption of important pelagic fish and squid by predatory fish in the northeastern USA shelf ecosystem with some fishery comparisons. ICES J. Mar. Sci. 57, 1147–1159 (2000).Article 

Google Scholar 
20.Tyrrell, M. C., Link, J. S., Moustahfid, H. & Overholtz, W. J. Evaluating the effect of predation mortality on forage species population dynamics in the Northeast US continental shelf ecosystem using multispecies virtual population analysis. ICES J. Mar. Sci. 65, 1689–1700 (2008).Article 

Google Scholar 
21.Jansen, T. & Gislason, H. Population structure of Atlantic mackerel (Scomber scombrus). PLoS ONE 8, e64744 (2013).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
22.Nøttestad, L. et al. Quantifying changes in abundance, biomass, and spatial distribution of Northeast Atlantic mackerel (Scomber scombrus) in the Nordic seas from 2007 to 2014. ICES J. Mar. Sci. 73, 359–373 (2016).Article 

Google Scholar 
23.Olafsdottir, A. H. et al. Geographical expansion of Northeast Atlantic mackerel (Scomber scombrus) in the Nordic Seas from 2007 to 2016 was primarily driven by stock size and constrained by low temperatures. Deep-Sea. Res. Part II 159, 152–168 (2019).Article 

Google Scholar 
24.FAO. The state of world fisheries and aquaculture 2020. Sustainability in action. 244 http://www.fao.org/documents/card/en/c/ca9229en (2020). Accessed on 23 July 2020.25.NEFSC. 64th Northeast Regional Stock Assessment Workshop (64th SAW) Assessment Report. 536 (2018).26.DFO. Assessment of the Atlantic mackerel stock for the Northwest Atlantic (Subareas 3 and 4) in 2018. DFO Can. Sci. Advis. Sec. Sci. Advis. Rep. 2019/035: 14 (2019).27.Secor, D. H. Specifying divergent migrations in the concept of stock: the contingent hypothesis. Fish. Res. 43, 13–34 (1999).Article 

Google Scholar 
28.Sette, O. E. Biology of the Atlantic mackerel (Scomber scombrus) of North America. Part I: early life history, including the growth, drift, and mortality of the egg and larval populations. Fish. Bull. 50, 149–237 (1943).
Google Scholar 
29.Berrien, P. L. Eggs and larvae of Scomber scombrus and Scomber japonicus in continental shelf waters between Massachusetts and Florida. Fish. Bull. 76, 95–115 (1978).
Google Scholar 
30.Overholtz, W. J., Hare, J. A. & Keith, C. M. Impacts of interannual environmental forcing and climate change on the distribution of Atlantic mackerel on the U.S. Northeast continental shelf. Mar. Coast. Fish. 3, 219–232 (2011).Article 

Google Scholar 
31.McManus, M. C., Hare, J. A., Richardson, D. E. & Collie, J. S. Tracking shifts in Atlantic mackerel (Scomber scombrus) larval habitat suitability on the Northeast U.S. Continental Shelf. Fish. Oceanogr. 27, 49–62 (2018).Article 

Google Scholar 
32.Richardson, D. E., Carter, L., Curti, K. L., Marancik, K. E. & Castonguay, M. Changes in the spawning distribution and biomass of Atlantic mackerel (Scomber scombrus) in the western Atlantic Ocean over 4 decades. Fish. Bull. 118, 120–134 (2020).Article 

Google Scholar 
33.Moura, A. et al. Population structure and dynamics of the Atlantic mackerel (Scomber scombrus) in the North Atlantic inferred from otolith chemical and shape signatures. Fish. Res. 230, 105621 (2020).Article 

Google Scholar 
34.Rooker, J. et al. Evidence of trans-Atlantic movement and natal homing of bluefin tuna from stable isotopes in otoliths. Mar. Ecol. Prog. Ser. 368, 231–239 (2008).ADS 
Article 

Google Scholar 
35.Clarke, L. M., Munch, S. B., Thorrold, S. R. & Conover, D. O. High connectivity among locally adapted populations of a marine fish (Menidia menidia). Ecology 91, 3526–3537 (2010).PubMed 
Article 
PubMed Central 

Google Scholar 
36.Wells, R. J. D. et al. Natural tracers reveal population structure of albacore (Thunnus alalunga) in the eastern North Pacific. ICES J. Mar. Sci. 72, 2118–2127 (2015).Article 

Google Scholar 
37.Moreira, C. et al. Population structure of the blue jack mackerel (Trachurus picturatus) in the NE Atlantic inferred from otolith microchemistry. Fish. Res. 197, 113–122 (2018).Article 

Google Scholar 
38.Trueman, C. N., MacKenzie, K. M. & Palmer, M. R. Identifying migrations in marine fishes through stable-isotope analysis. J. Fish. Biol. 81, 826–847 (2012).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
39.McMahon, K. W., Hamady, L. L. & Thorrold, S. R. A review of ecogeochemistry approaches to estimating movements of marine animals. Limnol. Oceanogr. 58, 697–714 (2013).ADS 
CAS 
Article 

Google Scholar 
40.Kalish, J. M. 13C and 18O isotopic disequilibria in fish otoliths: metabolic and kinetic effects. Mar. Ecol. Prog. Ser. 75, 191–203 (1991).ADS 
Article 

Google Scholar 
41.Solomon, C. T. et al. Experimental determination of the sources of otolith carbon and associated isotopic fractionation. Can. J. Fish. Aquat. Sci. 63, 79–89 (2006).CAS 
Article 

Google Scholar 
42.Tohse, H. & Mugiya, Y. Sources of otolith carbonate: experimental determination of carbon incorporation rates from water and metabolic CO2, and their diel variations. Aquat. Biol. 1, 259–268 (2008).Article 

Google Scholar 
43.Chung, M.-T., Trueman, C. N., Godiksen, J. A., Holmstrup, M. E. & Grønkjær, P. Field metabolic rates of teleost fishes are recorded in otolith carbonate. Commun. Biol. 2, 24 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
44.Rooker, J. R. & Secor, D. H. Microchemistry: migration and ecology of Atlantic bluefin tuna. In The Future of Bluefin Tunas: Ecology, Fisheries Management, and Conservation (ed. Block, B. A.) (Johns Hopkins University Press, 2019).
Google Scholar 
45.Uriarte, A. et al. Spatial pattern of migration and recruitment of North East Atlantic mackerel. ICES CM 2001/O:17 (2001).46.Mendiola, D., Alvarez, P., Cotano, U. & Martínez de Murguía, A. Early development and growth of the laboratory reared north-east Atlantic mackerel (Scomber scombrus) L. J. Fish. Biol. 70, 911–933 (2007).Article 

Google Scholar 
47.Bates, D., Maechler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).Article 

Google Scholar 
48.Harrison, X. A. et al. A brief introduction to mixed effects modelling and multi-model inference in ecology. PeerJ 6, e4794 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
49.Hartig, F. DHARMa: residual diagnostics for hierarchical (multi-level/mixed) regression models (2020).50.Kerr, L. A. et al. Mixed stock origin of Atlantic bluefin tuna in the U.S. rod and reel fishery (Gulf of Maine) and implications for fisheries management. Fish. Res. 224, 105461 (2020).Article 

Google Scholar 
51.R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2020).
Google Scholar 
52.Smith, A. D. et al. Atlantic mackerel (Scomber scombrus L.) in NAFO Subareas 3 and 4 in 2018. DFO Can. Sci. Advis. Sec. Res. Doc. 2020/013. iv + 37 p. (2020).53.Lambrey de Souza, J., Sévigny, J.-M., Chanut, J.-P., Barry, W. F. & Grégoire, F. High genetic variability in the mtDNA control region of a Northwestern Atlantic teleost, Scomber scombrus L. Can. Tech. Rep. Fish. Aquat. Sci. 2625, vi+25 (2006).
Google Scholar 
54.Radlinski, M. K., Sundermeyer, M. A., Bisagni, J. J. & Cadrin, S. X. Spatial and temporal distribution of Atlantic mackerel (Scomber scombrus) along the northeast coast of the United States, 1985–1999. ICES J. Mar. Sci. 70, 1151–1161 (2013).Article 

Google Scholar 
55.Castonguay, M., Plourde, S., Robert, D., Runge, J. A. & Fortier, L. Copepod production drives recruitment in a marine fish. Can. J. Fish. Aquat. Sci. 65, 1528–1531 (2008).Article 

Google Scholar 
56.McManus, M. C. Atlantic Mackerel (Scomber scombrus) Population and Habitat Trends in the Northwest Atlantic (University of Rhode Island, 2017).
Google Scholar 
57.Schloesser, R. W., Rooker, J. R., Louchuoarn, P., Neilson, J. D. & Secord, D. H. Interdecadal variation in seawater δ13C and δ18O recorded in fish otoliths. Limnol. Oceanogr. 54, 1665–1668 (2009).ADS 
CAS 
Article 

Google Scholar 
58.Schloesser, R. W., Neilson, J. D., Secor, D. H. & Rooker, J. R. Natal origin of Atlantic bluefin tuna (Thunnus thynnus) from Canadian waters based on otolith δ13C and δ18O. Can. J. Fish. Aquat. Sci. 67, 563–569 (2010).CAS 
Article 

Google Scholar 
59.Thorrold, S. R., Campana, S. E., Jones, C. M. & Swart, P. K. Factors determining δ13C and δ18O fractionation in aragonitic otoliths of marine fish. Geochim. Cosmochim. Acta. 61, 2909–2919 (1997).ADS 
CAS 
Article 

Google Scholar 
60.Campana, S. E. Chemistry and composition of fish otoliths: pathways, mechanisms and applications. Mar. Ecol. Prog. Ser. 188, 263–297 (1999).ADS 
CAS 
Article 

Google Scholar 
61.Caesar, L., Rahmstorf, S., Robinson, A., Feulner, G. & Saba, V. Observed fingerprint of a weakening Atlantic Ocean overturning circulation. Nature 556, 191–196 (2018).ADS 
CAS 
PubMed 
Article 

Google Scholar 
62.Saba, V. S. et al. Enhanced warming of the Northwest Atlantic Ocean under climate change. J. Geophys. Res. Oceans 121, 118–132 (2016).ADS 
Article 

Google Scholar 
63.Pershing, A. J. et al. Slow adaptation in the face of rapid warming leads to collapse of the Gulf of Maine cod fishery. Science 350, 809–812 (2015).ADS 
CAS 
PubMed 
Article 

Google Scholar 
64.Brickman, D., Hebert, D. & Wang, Z. Mechanism for the recent ocean warming events on the Scotian Shelf of eastern Canada. Cont. Shelf. Res. 156, 11–22 (2018).ADS 
Article 

Google Scholar 
65.Thorrold, S. R., Latkoczy, C., Swart, P. K. & Jones, C. M. Natal homing in a marine fish metapopulation. Science 291, 297–299 (2001).ADS 
CAS 
PubMed 
Article 

Google Scholar 
66.Gillanders, B. M. Using elemental chemistry of fish otoliths to determine connectivity between estuarine and coastal habitats. Estuar. Coast. Shelf. Sci. 64, 47–57 (2005).ADS 
Article 

Google Scholar 
67.Høie, H., Andersson, C., Folkvord, A. & Karlsen, Ø. Precision and accuracy of stable isotope signals in otoliths of pen-reared cod (Gadus morhua) when sampled with a high-resolution micromill. Mar. Biol. 144, 1039–1049 (2004).Article 

Google Scholar 
68.Martino, J. C., Doubleday, Z. A., Chung, M.-T. & Gillanders, B. M. Experimental support towards a metabolic proxy in fish using otolith carbon isotopes. J. Exp. Biol. 223, jeb217091 (2020).PubMed 
Article 

Google Scholar 
69.Manel, S., Gaggiotti, O. E. & Waples, R. S. Assignment methods: matching biological questions with appropriate techniques. Trends Ecol. Evol. 20, 136–142 (2005).PubMed 
Article 

Google Scholar 
70.Siskey, M. R., Wilberg, M. J., Allman, R. J., Barnett, B. K. & Secor, D. H. Forty years of fishing: changes in age structure and stock mixing in northwestern Atlantic bluefin tuna (Thunnus thynnus) associated with size-selective and long-term exploitation. ICES J. Mar. Sci. 73, 2518–2528 (2016).Article 

Google Scholar 
71.Kerr, L. A., Cadrin, S. X. & Secor, D. H. The role of spatial dynamics in the stability, resilience, and productivity of an estuarine fish population. Ecol. Appl. 20, 497–507 (2010).CAS 
PubMed 
Article 

Google Scholar 
72.Goethel, D. R., Quinn, T. J. & Cadrin, S. X. Incorporating spatial structure in stock assessment: movement modeling in marine fish population dynamics. Rev. Fish. Sci. 19, 119–136 (2011).Article 

Google Scholar 
73.Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer, 2016).
Google Scholar  More

1.Firth, L. B. et al. Ocean sprawl: Challenges and opportunities for biodiversity management in a changing world. Oceanogr. Mar. Biol. Annu. Rev. 54, 193–269 (2016).
Google Scholar 
2.Forman, R. T. T. et al. Road Ecology: Science and Solutions (Island Press, 2003).
Google Scholar 
3.Bishop, M. J. et al. Effects of ocean sprawl on ecological connectivity: Impacts and solutions. J. Exp. Mar. Biol. Ecol. 492, 7–30. https://doi.org/10.1016/j.jembe.2017.01.021 (2017).Article 

Google Scholar 
4.Sobocinski, K. L., Cordell, J. R. & Simenstad, C. A. Effects of shoreline modifications on supratidal macroinvertebrate fauna on Puget Sound, Washington beaches. Estuar. Coast 33, 699–711. https://doi.org/10.1007/s12237-009-9262-9 (2010).CAS 
Article 

Google Scholar 
5.Carlton, J. T. & Hodder, J. Maritime mammals: Terrestrial mammals as consumers in marine intertidal communities. Mar. Ecol. Prog. Ser. 256, 271–286. https://doi.org/10.3354/meps256271 (2003).ADS 
Article 

Google Scholar 
6.Levin, L. A. et al. The function of marine critical transition zones and the importance of sediment biodiversity. Ecosystems 4, 430–451. https://doi.org/10.1007/s10021-001-0021-4 (2001).CAS 
Article 

Google Scholar 
7.Lee, Y. Study on Changes in the Coastal Environment Due to Human Interference: A Case Study of Sand Beach Coast in Gangneung. Master’s Thesis. Korea National University of Education, Cheongju (2011).8.Son, S. et al. Analysis of Influential factors of roadkill occurrence—A case study of Seorak national park. J. Korean Inst. Landsc. Arch. 44, 1–12. https://doi.org/10.9715/KILA.2016.44.3.001 (2016).ADS 
Article 

Google Scholar 
9.Carr, L. W. & Fahrig, L. Effect of road traffic on two amphibian species of differing vagility. Conserv. Biol. 15, 1071–1078. https://doi.org/10.1046/j.1523-1739.2001.0150041071.x (2001).Article 

Google Scholar 
10.Coffin, A. W. From roadkill to road ecology: A review of the ecological effects of roads. J. Transp. Geogr. 15, 396–406. https://doi.org/10.1016/j.jtrangeo.2006.11.006 (2007).Article 

Google Scholar 
11.Zielin, S. B., Littlejohn, J., de Rivera, C. E., Smith, W. P. & Jacobson, S. L. Ecological investigations to select mitigation options to reduce vehicle-caused mortality of a threatened butterfly. J. Insect Conserv. 20, 845–854. https://doi.org/10.1007/s10841-016-9916-4 (2016).Article 

Google Scholar 
12.Bonnet, X., Naulleau, G. & Shine, R. The dangers of leaving home: Dispersal and mortality in snakes. Biol. Conserv. 89, 39–50. https://doi.org/10.1016/S0006-3207(98)00140-2 (1999).Article 

Google Scholar 
13.Fahrig, L., Pedlar, J. H., Pope, S. E., Taylor, P. D. & Wegner, J. F. Effect of road traffic on amphibian density. Biol. Conserv. 73, 177–182. https://doi.org/10.1016/0006-3207(94)00102-V (1995).Article 

Google Scholar 
14.Hobday, A. J. & Minstrell, M. L. Distribution and abundance of roadkill on Tasmanian highways: Human management options. Wildl. Res. 35, 712–726. https://doi.org/10.1080/15627020.2015.1021161 (2008).Article 

Google Scholar 
15.Finder, R. A., Roseberry, J. L. & Woolf, A. Site and landscape conditions at white-tailed deer/vehicle collision locations in Illinois. Landsc. Urban Plan. 44, 77–85. https://doi.org/10.1016/S0169-2046(99)00006-7 (1999).Article 

Google Scholar 
16.Glista, D. J., DeVault, T. L. & DeWoody, J. A. Vertebrate road mortality predominantly impacts amphibians. Herpetol. Conserv. Biol. 3, 77–87 (2008).
Google Scholar 
17.Grilo, C., Bissonette, J. A. & Cramer, P. C. Mitigation measures to reduce impacts on biodiversity. In Highways: Construction (ed. Jones, S. R.) 73–114 (Management and Maintenance. Nova Science Publishers, 2010).
Google Scholar 
18.Baine, M. et al. The development of management options for the black land crab (Gecarcinus ruricola) catchery in the San Andres Archipelago, Colombia. Ocean Coast Manage. 50, 564–589. https://doi.org/10.1016/j.ocecoaman.2007.02.007 (2007).Article 

Google Scholar 
19.Kantola, T., Tracy, J. L., Baum, K. A., Quinn, M. A. & Coulson, R. N. Spatial risk assessment of eastern monarch butterfly road mortality during autumn migration within the southern corridor. Biol. Conserv. 231, 150–160. https://doi.org/10.1016/j.biocon.2019.01.008 (2019).Article 

Google Scholar 
20.Koivula, M. J. & Vermeulen, H. J. W. Highways and forest fragmentation—Effects on carabid beetles (Coleoptera, Carabidae). Landsc. Ecol. 20, 911–926. https://doi.org/10.1007/s10980-005-7301-x (2005).Article 

Google Scholar 
21.Costa, L. L., Mothé, N. A. & Zalmon, I. R. Light pollution and ghost crab road-kill on coastal habitats. Reg. Stud. Mar. Sci. 39, 101457. https://doi.org/10.1016/j.rsma.2020.101457 (2020).Article 

Google Scholar 
22.Hübner, L., Pennings, S. C. & Zimmer, M. Sex- and habitat-specific movement of an omnivorous semi-terrestrial crab controls habitat connectivity and subsidies: A multi-parameter approach. Oecologia 178, 999–1015. https://doi.org/10.1007/s00442-015-3271-0 (2015).ADS 
Article 
PubMed 

Google Scholar 
23.Burggren, W. W. & McMahon, B. R. Biology of the Terrestrial Crabs (Cambridge University Press, 1988).
Google Scholar 
24.Micheli, F., Gherardi, F. & Vannini, M. Feeding and burrowing ecology of two East African mangrove crabs. Mar. Biol. 111, 247–254. https://doi.org/10.1007/BF01319706 (1991).Article 

Google Scholar 
25.Green, P. T., O’Dowd, D. J. & Lake, P. S. Recruitment dynamics in a rainforest seedling community: Context independent impact of a keystone consumer. Oecologia 156, 373–385. https://doi.org/10.1007/s00442-008-0992-3 (2008).ADS 
Article 
PubMed 

Google Scholar 
26.Suzuki, S. The life history of Sesarma haematocheirin the Miura peninsula. Res. Crust 11, 51–65. https://doi.org/10.18353/rcustacea.11.0_51 (1981).Article 

Google Scholar 
27.Adamczewska, A. M. & Morris, S. Ecology and behavior of Gecarcoideanatalis, the Christmas Island red crab, during the annual breeding migration. Biol. Bull. 200, 305–320. https://doi.org/10.2307/1543512 (2001).Article 

Google Scholar 
28.Le Galliard, J.-F., Fitze, P. S., Ferriere, R. & Clobert, J. Sex ratio bias, male aggression, and population collapse in lizards. Proc. Natl. Acad. Sci. 102, 18231–18236. https://doi.org/10.1073/pnas.0505172102 (2005).ADS 
CAS 
Article 
PubMed 

Google Scholar 
29.Caswell, H. Matrix Population Models: Construction, Analysis, and Interpretation (Sinauer Associates, 2001).
Google Scholar 
30.Aresco, M. J. The effect of sex-specific terrestrial movements and roads on the sex ratio of freshwater turtles. Biol. Conserv. 123, 37–44. https://doi.org/10.1016/j.biocon.2004.10.006 (2005).Article 

Google Scholar 
31.Mumme, R. L., Schoech, S. J., Woolfenden, G. E. & Fitzpatrick, J. W. Life and death in the fast lane: Demographic consequences of road mortality in the Florida scrub-jay. Conserv. Biol. 14, 501–512. https://doi.org/10.1046/j.1523-1739.2000.98370.x (2000).Article 

Google Scholar 
32.Kioko, J., Kiffner, C., Jenkins, N. & Collinson, W. J. Wildlife roadkill patterns on a major highway in northern Tanzania. Afr. Zool. 50, 17–22. https://doi.org/10.1080/15627020.2015.1021161 (2015).Article 

Google Scholar 
33.Seo, C., Thorne, J. H., Choi, T., Kwon, H. & Park, C. H. Disentangling roadkill: The influence of landscape and season on cumulative vertebrate mortality in South Korea. Landsc. Ecol. Eng. 11, 87–99. https://doi.org/10.1007/s11355-013-0239-2 (2015).Article 

Google Scholar 
34.Beebee, T. J. C. Effects of road mortality and mitigation measures on amphibian populations. Conserv. Biol. 27, 657–668. https://doi.org/10.1111/cobi.12063 (2013).Article 
PubMed 

Google Scholar 
35.Zhang, W. et al. Daytime driving decreases amphibian roadkill. PeerJ 6, e5385. https://doi.org/10.7717/peerj.5385 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
36.Saigusa, M. & Hidaka, T. Semilunar rhythm in the zoea-release activity of the terrestrial crabs Sesarma. Oecologia 37, 163–176. https://doi.org/10.1007/BF00344988 (1978).ADS 
Article 
PubMed 

Google Scholar 
37.Hartnoll, R. G. et al. Reproduction in the land crab Johngarthialagostoma on Ascension Island. J. Crust. Biol. 30, 83–92. https://doi.org/10.1651/09-3143.1 (2010).Article 

Google Scholar 
38.Schmidt, A. J., Bemvenutia, C. E. & Dieleet, K. Effects of geophysical cycles on the rhythm of mass mate searching of a harvested mangrove crab. Anim. Behav. 84, 333–340. https://doi.org/10.1016/j.anbehav.2012.04.023 (2012).Article 

Google Scholar 
39.Saigusa, M. Ecological distribution of three species of the genus Sesarma in winter season. Zool. Mag. 87, 142–150 (1978).
Google Scholar 
40.Saigusa, M. Adaptive significance of a semilunar rhythm in the terrestrial crab Sesarma. Biol. Bull. 160, 311–321. https://doi.org/10.2307/1540891 (1981).Article 

Google Scholar 
41.Saigusa, M., Terajima, M. & Yamamoto, M. Structure, formation, mechanical properties, and disposal of the embryo attachment system of an estuarine crab, Sesarma haematocheir. Biol. Bull. 203, 289–306. https://doi.org/10.2307/1543572 (2002).CAS 
Article 
PubMed 

Google Scholar 
42.Saigusa, M. Hatching of an estuarine crab, sesarma haematochier: Factors affecting the timing of hatching in detached embryos, and enhancement of hatching synchrony by the female. J. Oceanogr. 56, 93–102. https://doi.org/10.1023/A:1011118726283 (2000).Article 

Google Scholar 
43.Saigusa, M. Larval release rhythm coinciding with solar day and tidal cycles in the terrestrial crab Sesarma-harmony with the semilunar timing and its adaptive significance. Biol. Bull. 162, 371–386. https://doi.org/10.2307/1540990 (1982).Article 

Google Scholar 
44.Forward, R. B. Larval release rhythms of decapod crustaceans: An overview. Bull. Mar. Sci. 41, 165–176 (1987).
Google Scholar 
45.Hicks, J. W. The breeding behaviour and migrations of the terrestrial crab Gecarcoideanatalis (Decapoda: Brachyura). Aust. J. Zool. 33, 127–142. https://doi.org/10.1071/ZO9850127 (1985).Article 

Google Scholar 
46.Morgan, S. G. & Christy, J. H. Adaptive significance of the timing of larval release by crabs. Am. Nat. 145, 457–479. https://doi.org/10.1086/285749 (1995).Article 

Google Scholar 
47.Paula, J. Rhythms of larval release of decapod crustaceans in the Mira Estuary, Portugal. Mar. Biol. 100, 309–312. https://doi.org/10.1007/BF00391144 (1989).Article 

Google Scholar 
48.Bergin, M. E. Hatching rhythms in Ucapugilator (Decapoda: Brachyura). Mar. Biol. 63, 151–158. https://doi.org/10.1007/BF00406823 (1981).Article 

Google Scholar 
49.Christy, J. H. Adaptive significance of semilunar cycles of larval release in fiddler crabs (Genus Uca): Test of a hypothesis. Biol. Bull. 163, 251–263. https://doi.org/10.2307/1541264 (1982).Article 

Google Scholar 
50.Quintero-Angel, A., Osorio-Dominguez, D., Vargas-Salinas, F. & Saavedra-Rodriguez, C. A. Roadkill rate of snakes in a disturbed landscape of central Andes of Columbia. Herpetol. Notes 5, 99–105 (2012).
Google Scholar 
51.Orłowski, G. Roadside hedgerows and trees as factors increasing road mortality of birds: Implications for management of roadside vegetation in rural landscapes. Landsc. Urban Plan. 86, 153–161. https://doi.org/10.1016/j.landurbplan.2008.02.003 (2008).Article 

Google Scholar 
52.Saeki, M. & Macdonald, D. W. The effects of traffic on the raccoon dog (Nyctereutes procyonoides viverrinus) and other mammals in Japan. Biol. Conserv. 118, 559–571. https://doi.org/10.1016/j.biocon.2003.10.004 (2004).Article 

Google Scholar 
53.Costa, L. L., Secco, H., Arueira, V. F. & Zalmon, I. R. Mortality of the Atlantic ghost crab Ocypode quadrata (Fabricius, 1787) due to vehicle traffic on sandy beaches: A road ecology approach. J. Environ. Manage. 260, 110168. https://doi.org/10.1016/j.jenvman.2020.110168 (2020).Article 
PubMed 

Google Scholar 
54.Tsai, J. R., Hsieh, Y. T., Lin & H. C. The effect of dike types on terrestrial crab passage through the access road: The predicament of terrestrial crab conservation in Gaomei Wetland. In Proceedings of the 39th Oceans Engineering Conference in Taiwan Hungkuang University, November (2017)55.Bellis, M. A., Jackson, S. D., Griffin, C. R., Warren, P. S. & Thompson, A. O. Utilizing a multi-technique, multi-taxa approach to monitoring wildlife passageways in southern Vermont. Oecol. Aust. 17, 111–128. https://doi.org/10.4257/oeco.2013.1701.10 (2007).Article 

Google Scholar 
56.Song, J. et al. Roadkill of amphibians in the Korea national park. Korean J. Environ. Ecol. 23, 187–193 (2009).
Google Scholar 
57.Ryu, M. & Kim, J. G. Influence of roadkill during breeding migration on the sex ratio of land crab (Sesarma haematoche). J. Environ. Ecol. 44, 23. https://doi.org/10.1186/s41610-020-00167-6 (2020).Article 

Google Scholar 
58.Mizuta, T. Moonlight-related mortality: Lunar conditions and roadkill occurrence in the Amami woodcock Scolopax mira. Wilson J. Ornithol. 126, 544–552. https://doi.org/10.1676/13-159.1 (2014).Article 

Google Scholar 
59.Gibbs, J. P. & Steen, D. A. Trends in sex ratios of turtles in the United States: Implications of road mortality. Conserv. Biol. 19, 552–556. https://doi.org/10.1111/j.1523-1739.2005.000155.x (2005).Article 

Google Scholar 
60.Rytwinski, T. & Fahrig, L. Do species life history traits explain population responses to roads? A meta-analysis. Biol. Conserv. 147, 87–98. https://doi.org/10.1016/j.biocon.2011.11.023 (2012).Article 

Google Scholar 
61.Korea Astronomy and Space Science Institute. Korean Astronomical Almanac (Korea Astronomy and Space Science Institute, 2017).
Google Scholar  More

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1.Holzner, W. Concepts, categories and characteristics of weeds. Biol. Ecol. Weeds https://doi.org/10.1007/978-94-017-0916-3_1 (1982).Article 

Google Scholar 
2.Randall, J. M. Weed control for the preservation of biological diversity. Weed Technol. 10, 370–383 (1996).Article 

Google Scholar 
3.Atkinson, I. A. E. Problem Weeds on New Zealand Islands. (Dept. of Conservation, 1997).4.Goslee, S. C., Peters, D. P. C. & Beck, K. G. Modeling invasive weeds in grasslands: the role of allelopathy in Acroptilon repens invasion. Ecological Modelling (2001). https://www.sciencedirect.com/science/article/pii/S0304380001002319. Accessed 2 Oct 2020.5.Dawson, W., Burslem, D. F. R. P. & Hulme, P. E. Factors explaining alien plant invasion success in a tropical ecosystem differ at each stage of invasion. J. Ecol. 97, 657–665 (2009).Article 

Google Scholar 
6.Baker, H. G. The evolution of weeds, annual review of ecology, evolution, and systematics. DeepDyve (1974). https://www.deepdyve.com/lp/annual-reviews/the-evolution-of-weeds-YxSFG7LI8J. Accessed 2 Oct 2020.7.Perrins, J., Williamson, M. & Fitter, A. A survey of differing views of weed classification: Implications for regulation of introductions. Biol. Conserv. 60, 47–56 (1992).Article 

Google Scholar 
8.Mack, R. N. Predicting the identity and fate of plant invaders: Emergent and emerging approaches. Biol. Conserv. 78, 107–121 (1996).Article 

Google Scholar 
9.Sutherland, S. What Makes a Weed a Weed: Life History Traits of Native (2004). https://www.jstor.org/stable/pdf/40005745.pdf. Accessed 2 Oct 2020.10.Leather, G. R. Weed control using allelopathic crop plants. J. Chem. Ecol. 9, 983–989 (1983).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
11.Mersie, W. & Singh, M. Allelopathic effect of parthenium (Parthenium hysterophorus L.) extract and residue on some agronomic crops and weeds. J. Chem. Ecol. 13, 1739–1747 (1987).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
12.Derya, E., yildiz, O. & Nelson, E. T. (PDF) Ecology, Competitive Advantages, and Integrated (2006). https://www.researchgate.net/publication/287491753_Ecology_Competitive_Advantages_and_Integrated_Control_of_Rhododendron_An_Old_Ornamental_yet_Emerging_Invasive_Weed_Around_the_Globe. Accessed 2 Oct 2020.13.Clements, D. R. & Ditommaso, A. Climate change and weed adaptation: Can evolution of invasive plants lead to greater range expansion than forecasted?. Weed Res. 51, 227–240 (2011).Article 

Google Scholar 
14.Sebasky, M. E., Keller, S. R. & Taylor, D. R. Investigating past range dynamics for a weed of cultivation, Silene vulgaris. Ecol. Evol. 6, 4800–4811 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
15.Hodgins, K. Unearthing the impact of human disturbance on a notorious weed. Mol. Ecol. 23, 2141–2143 (2014).PubMed 
Article 
PubMed Central 

Google Scholar 
16.Hobbs, R. J. & Huenneke, L. F. Disturbance, diversity, and invasion: Implications for conservation. Ecosyst. Manag. https://doi.org/10.1007/978-1-4612-4018-1_16 (1992).Article 

Google Scholar 
17.Lozon, J. D. & Macisaac, H. J. Biological invasions: Are they dependent on disturbance?. Environ. Rev. 5, 131–144 (1997).Article 

Google Scholar 
18.Ditomaso, J. M. Invasive weeds in rangelands: Species, impacts, and management. Weed Sci. 48, 255–265 (2000).CAS 
Article 

Google Scholar 
19.Larson, D. L., Anderson, P. J. & Newton, W. Alien plant invasion in mixed-grass prairie: Effects of vegetation type and anthropogenic disturbance. Ecol. Appl. 11, 128–141 (2001).Article 

Google Scholar 
20.Chiuffo, M. C., Cock, M. C., Prina, A. O. & Hierro, J. L. Response of native and non-native ruderals to natural and human disturbance. Biol. Invasions 20, 2915–2925 (2018).Article 

Google Scholar 
21.Kariyat, R. R., Scanlon, S. R., Mescher, M. C., De Moraes, C. M. & Stephenson, A. G. Inbreeding depression in Solanum carolinense (Solanaceae) under field conditions and implications for mating system evolution. PLoS ONE (2011). https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3236180/. Accessed 2 Oct 2020.22.Li, B., Shibuya, T., Yogo, Y. & Hara, T. Effects of ramet clipping and nutrient availability on growth and biomass allocation of yellow nutsedge. Ecol. Res. 19, 603–612 (2004).Article 

Google Scholar 
23.Jia, X., Pan, X. Y., Li, B., Chen, J. K. & Yang, X. Z. Allometric growth, disturbance regime, and dilemmas of controlling invasive plants: A model analysis. Biol. Invasions 11, 743–752 (2008).Article 

Google Scholar 
24.Ramula, S. Annual mowing has the potential to reduce the invasion of herbaceous Lupinus polyphyllus. Biol. Invasions 22, 3163–3173 (2020).Article 

Google Scholar 
25.Liu, X. & Huang, B. Mowing effects on root production, growth, and mortality of creeping bentgrass. Crop Sci. 42, 1241–1250 (2002).Article 

Google Scholar 
26.Biazzo, J. & Milbrath, L. R. Response of pale swallowwort (Vincetoxicum rossicum) to multiple years of mowing. Invasive Plant Sci. Manag. 12, 169–175 (2019).Article 

Google Scholar 
27.Yong, X.-H. et al. Maternal Mowing Effect on Seed Traits of an Invasive Weed, Erigeron annus in farmland. Sains Malay. 44, 347–354 (2015).Article 

Google Scholar 
28.Mithöfer, A., Wanner, G. & Boland, W. Effects of feeding spodoptera littoralis on lima bean leaves. II. Continuous mechanical wounding resembling insect feeding is sufficient to elicit herbivory-related volatile emission. Plant Physiol. 137, 1160–1168 (2005).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
29.Engelberth, J. & Engelberth, M. The Costs of Green Leaf Volatile-Induced Defense Priming: Temporal Diversity in Growth Responses to Mechanical Wounding and Insect Herbivory. Plants 8, 23 (2019).CAS 
PubMed Central 
Article 

Google Scholar 
30.Erfmeier, A. & Bruelheide, H. Invasive and nativeRhododendron ponticumpopulations: Is there evidence for genotypic differences in germination and growth?. Ecography 28, 417–428 (2005).Article 

Google Scholar 
31.Milbau, A., Nijs, I., Van Peer, L., Reheul, D. & De Cauwer, B. Disentangling invasiveness and invasibility during invasion in synthesized grassland communities. New Phytol. 159, 657–667 (2003).Article 

Google Scholar 
32.Etten, M. L. V., Conner, J. K., Chang, S.-M. & Baucom, R. S. Not all weeds are created equal: A database approach uncovers differences in the sexual system of native and introduced weeds. Ecol. Evol. 7, 2636–2642 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
33.Baker, H. G. Self-compatibility and establishment after “long-distance” dispersal. Evolution 9, 347 (1955).
Google Scholar 
34.Tabassum, S. & Leishman, M. R. It doesn’t take two to tango: Increased capacity for self-fertilization towards range edges of two coastal invasive plant species in eastern Australia. Biol. Invasions 21, 2489–2501 (2019).Article 

Google Scholar 
35.Pannell, J. R. & Barrett, S. C. H. Baker’s law revisited: reproductive assurance in a metapopulation. Evolution 52, 657–668 (1998).PubMed 
Article 
PubMed Central 

Google Scholar 
36.Pannell, J. R. Evolution of the mating system in colonizing plants. Mol. Ecol. 24, 2018–2037 (2015).PubMed 
Article 
PubMed Central 

Google Scholar 
37.Mena-Ali, J. I., Keser, L. H. & Stephenson, A. G. Inbreeding depression in Solanum carolinense (Solanaceae), a species with a plastic self-incompatibility response. BMC Evol. Biol. 8, 10 (2008).PubMed 
PubMed Central 
Article 

Google Scholar 
38.Chauhan, B. S., Migo, T., Westerman, P. R. & Johnson, D. E. Post-dispersal predation of weed seeds in rice fields. Weed Res. 50, 553–560 (2010).Article 

Google Scholar 
39.Muniappan, R. & Viraktamath, C. A. Invasive alien weeds in the Western Ghats. Curr. Sci. 64, 555–558 (1993).
Google Scholar 
40.Ziller S. R. A Estepe Gramineo-Lenhosa no Segundo Plan-alto do Paraná: Diagnóstico Ambiental com Enfoque à Contami-nacão Biológica (PhD Thesis). Universidade Federal doParaná (2000).41.Javaid, A. & Riaz, T. Parthenium hysterophorus L., an alien invasive weed threatening natural vegetations in Punjab, Pakistan. Pak. J. Bot. 44, 123–126 (2012).
Google Scholar 
42.Alves, M. T. & Hilker, F. M. Hunting cooperation and Allee effects in predators. J. Theor. Biol. 419, 13–22 (2017).MathSciNet 
MATH 
Article 

Google Scholar 
43.Kariyat, R. R., Mauck, K. E., Moraes, C. M. D., Stephenson, A. G. & Mescher, M. C. Inbreeding alters volatile signalling phenotypes and influences tri-trophic interactions in horsenettle (Solanum carolinense L..). Ecol. Lett. 15, 301–309 (2012).PubMed 
Article 
PubMed Central 

Google Scholar 
44.Nihranz, C. T. et al. Herbivory and inbreeding affect growth, reproduction, and resistance in the rhizomatous offshoots of Solanum carolinense (Solanaceae). Evol. Ecol. 33, 499–520 (2019).Article 

Google Scholar 
45.Nihranz, C. T. et al. Transgenerational impacts of herbivory and inbreeding on reproductive output in Solanum carolinense. Am. J. Bot. 107, 286–297 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
46.Wilkens, R. T., Shea, G. O., Halbreich, S. & Stamp, N. E. Resource availability and the trichome defenses of tomato plants. Oecologia 106, 181–191 (1996).ADS 
PubMed 
Article 
PubMed Central 

Google Scholar 
47.Zaynab, M. et al. Role of secondary metabolites in plant defense against pathogens. Microb. Pathog. 124, 198–202 (2018).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
48.Neilson, E. H., Goodger, J. Q., Woodrow, I. E. & Møller, B. L. Plant chemical defense: at what cost?. Trends Plant Sci. 18, 250–258 (2013).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
49.Boyd, J. W., Murray, D. S. & Tyrl, R. J. Silverleaf nightshade, Solarium elaeagnifolium, origin, distribution, and relation to man. Econ. Bot. 38, 210–217 (1984).Article 

Google Scholar 
50.EPPO Global Database. Solanum elaeagnifolium (SOLEL)[Documents]| EPPO Global Database. https://gd.eppo.int/taxon/SOLEL/documents. Accessed 5th Nov 2020.51.Travlos, I. S. Responses of invasive silverleaf nightshade (Solanum elaeagnifolium) populations to varying soil water availability. Phytoparasitica 41, 41–48 (2012).Article 

Google Scholar 
52.Mekki, M. Biology, distribution and impacts of silverleaf nightshade (Solanum elaeagnifolium Cav.). EPPO Bull. 37, 114–118 (2007).Article 

Google Scholar 
53.Cuthbertson, E.G. Morphology of the underground parts of silverleaf nightshade. 5th Australian Weeds Conference (1976).54.Heap, J., Honan, I. & Smith, E. Silverleaf nigthshade: A Technical Handbook for Animal and Plant Control Boards in South Australia (Adelaide, 1997).
Google Scholar 
55.Petanidou, T. et al. Self-compatibility and plant invasiveness: Comparing species in native and invasive ranges. Perspect. Plant Ecol. Evol. Syst. 14, 3–12 (2012).Article 

Google Scholar 
56.Kariyat, R. R. & Chavana, J. Field data on plant growth and insect damage on the noxious weed Solanum eleaegnifolium in an unexplored native range. Data Brief 19, 2348–2351 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
57.Centibas, M. & Koyuncu, F. The ripening and fruit quality of ‘Monroe’ peaches in response to pre-harvest application gibberellic acid. Akdeniz Üniv. Ziraat Fakült. Dergisi 26, 73–80 (2013).
Google Scholar 
58.Pornaro, C., Macolino, S., Menegon, A. & Richardson, M. WinRHIZO technology for measuring morphological traits of Bermudagrass Stolons. Agron. J. 109, 3007–3010 (2017).CAS 
Article 

Google Scholar 
59.Kariyat, R. R. et al. Inbreeding, herbivory, and the transcriptome of Solanum carolinense. Entomol. Exp. Appl. 144, 134–144 (2012).Article 

Google Scholar 
60.Kariyat, R. R. et al. Feeding on glandular and non-glandular leaf trichomes negatively affect growth and development in tobacco hornworm (Manduca sexta) caterpillars. Arthropod Plant Interact. 13, 321–333 (2019).Article 

Google Scholar 
61.Tayal, M., Chavana, J. & Kariyat, R. R. Efficiency of using electric toothbrush as an alternative to a tuning fork for artificial buzz pollination is independent of instrument buzzing frequency. BMC Ecol. 20, 1 (2020).Article 

Google Scholar 
62.Singh, S. & Kariyat, R. R. Exposure to polyphenol-rich purple corn pericarp extract restricts fall armyworm (Spodoptera frugiperda) growth. Plant Signal. Behav. 15, 1784545 (2020).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
63.Kariyat, R. R. et al. Constitutive and herbivore-induced structural defenses are compromised by inbreeding in Solanum carolinense (Solanaceae). Am. J. Bot. 100, 1014–1021 (2013).PubMed 
Article 
PubMed Central 

Google Scholar 
64.Paez-Garcia, A. et al. Root traits and phenotyping strategies for plant improvement. Plants 4, 334–355 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
65.Pinke, G., Pál, R. & Botta-Dukát, Z. Effects of environmental factors on weed species composition of cereal and stubble fields in western Hungary. Open Life Sci. 5, 283–292 (2010).Article 

Google Scholar 
66.Tremayne, M. A. & Richards, A. J. Seed weight and seed number affect subsequent fitness in outcrossing and selfing Primula species. New Phytol. 148, 127–142 (2000).Article 

Google Scholar 
67.Ramesh, K., Matloob, A., Aslam, F., Florentine, S. K. & Chauhan, B. S. Weeds in a changing climate: Vulnerabilities, consequences, and implications for future weed management. Front. Plant Sci. 8, 1 (2017).CAS 
Article 

Google Scholar 
68.Rha, E. S. & Jamil, M. Gibberellic acid (GA3) enhance seed water uptake, germination and early seedling growth in sugar beet under salt stress. Pak. J. Biol. Sci. 10, 654–658 (2007).PubMed 
Article 
PubMed Central 

Google Scholar 
69.Stoller, E. W. & Wax, L. M. Periodicity of germination and emergence of some annual weeds. Weed Sci. 21, 574–580 (1973).Article 

Google Scholar 
70.Meyer, S. E. & Pendleton, B. K. Factors affecting seed germination and seedling establishment of a long-lived desert shrub (Coleogyne ramosissima: Rosaceae). Plant Ecol. 178, 171–187 (2005).Article 

Google Scholar 
71.Milbau, A., Scheerlinck, L., Reheul, D., De Cauwer, B. & Nijs, I. Ecophysiological and morphological parameters related to survival in grass species exposed to an extreme climatic event. Physiol. Plant. 125, 500–512 (2005).CAS 
Article 

Google Scholar 
72.Gioria, M. & Pyšek, P. Early bird catches the worm: Germination as a critical step in plant invasion. Biol. Invasions 19, 1055–1080 (2016).Article 

Google Scholar 
73.Mahmood, A. H. et al. Influence of various environmental factors on seed germination and seedling emergence of a noxious environmental weed: Green galenia (Galenia pubescens). Weed Sci. 64, 486–494 (2016).Article 

Google Scholar 
74.Mcnaughton, S. J. Grazing lawns: On domesticated and wild grazers. Am. Nat. 128, 937–939 (1986).Article 

Google Scholar 
75.McNaughton, S. J. Adaptation of herbivores to seasonal changes in nutrient supply. Nutr. Herb. 1, 391–408 (1987).
Google Scholar 
76.Laliberté, E., Lambers, H., Burgess, T. I. & Wright, S. J. Phosphorus limitation, soil-borne pathogens and the coexistence of plant species in hyperdiverse forests and shrublands. New Phytol. 206, 507–521 (2014).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
77.Kramer-Walter, K. R. et al. Root traits are multidimensional: Specific root length is independent from root tissue density and the plant economic spectrum. J. Ecol. 104, 1299–1310 (2016).Article 

Google Scholar 
78.Losapio, G. et al. An invasive plant species enhances biodiversity in overgrazed pastures but inhibits its recovery in protected areas. J. Ecol. https://doi.org/10.1101/2020.08.16.227066 (2020).Article 

Google Scholar 
79.Onen, H., Farooq, S., Gunal, H., Ozaslan, C. & Erdem, H. Higher tolerance to abiotic stresses and soil types may accelerate common ragweed (Ambrosia artemisiifolia) invasion. Weed Sci. 65, 115–127 (2016).Article 

Google Scholar 
80.Wittstock, U. & Gershenzon, J. Constitutive plant toxins and their role in defense against herbivores and pathogens. Curr. Opin. Plant Biol. 5, 300–307 (2002).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
81.Mooney, E. H., Tiedeken, E. J., Muth, N. Z. & Niesenbaum, R. A. Differential induced response to generalist and specialist herbivores by Lindera benzoin (Lauraceae) in sun and shade. Oikos 118, 1181–1189 (2009).Article 

Google Scholar 
82.Baldwin, I. T. Plant volatiles. Curr. Biol. 20, 392–397 (2011).Article 
CAS 

Google Scholar 
83.Coley, P. D., Bryant, J. P. & Chapin, F. S. Resource availability and plant antiherbivore defense. Science 230, 895–899 (1985).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
84.Fine, P. V. A. Herbivores promote habitat specialization by trees in amazonian forests. Science 305, 663–665 (2004).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
85.Zandt, P. A. V. Plant defense, growth, and habitat: A comparative assessment of constitutive and induced resistance. Ecology 88, 1984–1993 (2007).PubMed 
Article 
PubMed Central 

Google Scholar 
86.Salminen, S. O. & Grewal, P. S. Does decreased mowing frequency enhance alkaloid production in endophytic tall fescue and perennial ryegrass?. J. Chem. Ecol. 28, 939–950 (2002).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
87.Freeman. An Overview of Plant Defenses against Pathogens and Herbivores. The Plant Health Instructor (2008). https://doi.org/10.1094/phi-i-2008-0226-01.88.Davis, H. N. et al. Review of Major Crop and Animal Arthropod Pests of South Texas. Subtropical Agriculture and Environments (2020).89.Traw, M. B., Kim, J., Enright, S., Cipollini, D. F. & Bergelson, J. Negative cross-talk between salicylate- and jasmonate-mediated pathways in the Wassilewskija ecotype of Arabidopsis thaliana. Mol. Ecol. 12, 1125–1135 (2003).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
90.Bostock, R. M. Signal crosstalk and induced resistance: Straddling the line between cost and benefit. Annu. Rev. Phytopathol. 43, 545–580 (2005).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
91.Lefoe, G. et al. Assessing the fundamental host-range of Leptinotarsa texana Schaeffer as an essential precursor to biological control risk analysis. Biol. Control 143, 104165 (2020).CAS 
Article 

Google Scholar 
92.Chung, S. H. & Felton, G. W. Specificity of induced resistance in tomato against specialist lepidopteran and coleopteran species. J. Chem. Ecol. 37, 378–386 (2011).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
93.Korpita, T., Gómez, S. & Orians, C. M. Cues from a specialist herbivore increase tolerance to defoliation in tomato. Funct. Ecol. 28, 395–401 (2013).Article 

Google Scholar 
94.Yang, Q. et al. Plant–soil biota interactions of an invasive species in its native and introduced ranges: Implications for invasion success. Soil Biol. Biochem. 65, 78–85 (2013).CAS 
Article 

Google Scholar 
95.Blair, A. C. & Wolfe, L. M. The evolution of an invasive plant: An experimental study with Silene latifolia. Ecology 85, 3035–3042 (2004).Article 

Google Scholar 
96.Kariyat, R. R., Smith, J. D., Stephenson, A. G., Moraes, C. M. D. & Mescher, M. C. Non-glandular trichomes of Solanum carolinense deter feeding by Manduca sexta caterpillars and cause damage to the gut peritrophic matrix. Proc. R. Soc. B 284, 20162323 (2017).PubMed 
Article 
PubMed Central 

Google Scholar 
97.Kariyat, R. R. et al. Leaf trichomes affect caterpillar feeding in an instar-specific manner. Commun. Integr. Biol. 11, 1–6 (2018).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
98.Karabourniotis, G., Liakopoulos, G., Nikolopoulos, D. & Bresta, P. Protective and defensive roles of non-glandular trichomes against multiple stresses: Structure–function coordination. J. For. Res. 31, 1–12 (2019).Article 
CAS 

Google Scholar 
99.Kang, J.-H., Shi, F., Jones, A. D., Marks, M. D. & Howe, G. A. Distortion of trichome morphology by the hairless mutation of tomato affects leaf surface chemistry. J. Exp. Bot. 61, 1053–1064 (2009).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
100.Tian, D., Tooker, J., Peiffer, M., Chung, S. H. & Felton, G. W. Role of trichomes in defense against herbivores: Comparison of herbivore response to woolly and hairless trichome mutants in tomato (Solanum lycopersicum). Planta 236, 1053–1066 (2012).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
101.An, F. et al. Ethylene-induced stabilization of ETHYLENE INSENSITIVE3 and EIN3-LIKE1 is mediated by proteasomal degradation of EIN3 binding F-Box 1 and 2 That requires EIN2 in arabidopsis. Plant Cell 22, 2384–2401 (2010).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
102.Lämke, J. & Bäurle, I. Epigenetic and chromatin-based mechanisms in environmental stress adaptation and stress memory in plants. Genome Biol. 18, 1 (2017).Article 
CAS 

Google Scholar 
103.Weinhold, A. Transgenerational stress-adaption: an opportunity for ecological epigenetics. Plant Cell Rep. 37, 3–9 (2017).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
104.Miryeganeh, M. & Saze, H. Epigenetic inheritance and plant evolution. Popul. Ecol. 62, 17–27 (2019).Article 

Google Scholar  More

Demographic dataTo address our hypotheses, we used matrix population models (MPMs) or integral projection models (IPMs) from the COMPADRE Plant Matrix Database (v. 5.0.156) and the PADRINO IPM Database57, which we amended with a systematic literature search. First, we selected density-independent models from COMPADRE and PADRINO which described the transition of a population from 1 year to the next. Among these, we selected studies with at least six annual transition matrices, to balance the needs of adequate yearly temporal replicates and sufficient sample size for a quantitative synthesis. This yielded data from 48 species and 144 populations.We then performed a systematic literature search for studies linking climate drivers to structured population models in the form of either MPMs or IPMs. We performed this search on ISI Web of Science for studies published between 1997 and 2017. We used a Boolean expression containing keywords related to plant form, structured demographic models, and environmental drivers (Supplementary Methods). We only considered studies linking macro-climatic drivers to natural populations (e.g., transplant experiments and studies focused on local climatic factors such as soil moisture, light due to treefall gaps, etc. were excluded). Finally, we used the same criteria used to filter studies in COMPARE and PARDINO, by selecting studies with at least six, density-independent, annual projection models. This search brought two additional species, belonging to three additional populations, which we entered in the COMPADRE database.One of the studies we excluded from the literature search because it contained density-dependent IPMs, also provided raw data with high temporal replication (14–32 years of sampling) for 12 species from 15 populations58. Therefore, we re-analyzed these freely available data to produce density-independent MPMs that were directly comparable to the other studies in our dataset (Supplementary Methods).The resulting dataset consisted of 46 studies, 62 species, 162 populations, and a total of 3761 MPMs and 52 IPMs (Supplementary Data 1). The analyzed plant populations were tracked for a mean of 16 (median of 12) annual transitions. To our knowledge, this is the largest open-access dataset of long-term structured population projection models. However, this dataset is taxonomically and geographically biased. Specifically, among our 62 species, this dataset contains 54 herbaceous perennials (11 of which graminoids), and eight woody species: five shrubs, two trees, and one woody succulent (Opuntia imbricata). Moreover, almost all of these studies were conducted in North America and Europe (Supplementary Fig. 1), in temperate biomes that are cold, dry, or both cold and dry (Supplementary Fig. 1, inset). Our geographic and taxonomic bias reflects the rarity of long-term plant demographic data in general. This dearth of long-term demographic data is particularly evident in the tropics. The ForestGEO network59 is an exception to this rule, but to date, no matrix population models or integral projection models using these data have been published.We used the MPMs and IPMs in this dataset to calculate the response variable of our analyses: the yearly asymptotic population growth rate (λ). This measure is one of the most widely used summary statistics in population ecology60, as it integrates the response of multiple interacting vital rates. Specifically, λ reflects the population growth rate that a population would attain if its vital rates remained constant through time61. This metric therefore distills the effect of underlying vital rates on population dynamics, free of other confounding factors (e.g., transient dynamics arising from population structure62). We calculated λ of each MPM or IPM with standard methods61,63. Because our MPMs and IPMs described the demography of a population transitioning from one year to the next, our λ values were comparable in time units. Finally, we identified and categorized any non-climatic driver associated with these MPMs and IPMs. Data associated with 21 of our 62 species explicitly quantified a non-climatic driver (e.g., grazing, neighbor competition), for a total of 60 of our 162 populations. Of the datasets associated with these species, 19 included discrete drivers, and only three included a continuous driver.Climatic dataTo test the effect of temporal climatic variation on demography, we gathered global climatic data. We downloaded 1 km2 gridded monthly values for maximum temperature, minimum temperature, and total precipitation between 1901 and 2016 from CHELSAcruts64, which combines the CRU TS 4.0165, and CHELSA66 datasets. Gridded climatic data are especially suited to estimate annual climatic means45. These datasets include values from 1901 to 2016, which are necessary to cover the temporal extent of all 162 plant populations considered in our analysis. For our temperature analyses, we calculated the mean monthly temperature as the mean of the minimum and maximum monthly temperatures. We used monthly values to calculate the time series of mean annual temperature and total annual precipitation at each site. We then used this dataset to calculate our annual anomalies for each census year, defined as the 12 months preceding a population census. Our annual anomalies are standardized z-scores. For example, if X is a vector of 40 yearly precipitation or temperature values, E() calculates the mean, and σ() calculates the standard deviation, we compute annual anomalies as A = [X − E(X)]/σ(X). Therefore, an anomaly of one refers to a year where precipitation or temperature was one standard deviation above the 40-year mean. In other words, anomalies represent how infrequent annual climatic conditions are at a site. Specifically, if we assume that A values are normally distributed, values exceeding one and two should occur every 6 and 44 years, respectively. We used 40-year means because the minimum number of years suggested to calculate climate averages is 3067.Z-scores are commonly used in global studies on vegetation responses to climate8,68, and they reflect the null hypothesis that species are adapted to the climatic variation at their respective sites. Across our populations, the standard deviations of annual precipitation and temperature anomalies change by 300% and 60%, respectively (Supplementary Fig. 2). Thus, a z-score of one refers to a precipitation anomaly of 50 or 160 mm and to a temperature anomaly of 0.5 or 0.8 °C. Our null hypothesis posits that species are adapted to these conditions, regardless of the absolute magnitude of the standard deviation in annual climatic anomalies. If this null hypothesis were true, each species would respond similarly to z-scores. Z-scores are more easily interpreted when calculated on normally distributed variables. We found our temperature and precipitation z-scores were highly skewed (skewness above 1) only in, respectively, 2 (for temperature) and three (for precipitation) of our 162 populations. We concluded that this degree of skewness should not bias our z-scores substantially.To test how the response of plant populations to climate changes based on biome we used two proxies of water and temperature limitation. For each study population, we computed a proxy for water limitation, water availability index (WAI), and temperature limitation using mean annual temperature. To compute these metrics, we downloaded data at 1 km2 resolution for mean annual potential evapotranspiration, mean annual precipitation, and mean annual temperature referred to the 1970–2000 period. We obtained potential evapotranspiration data from the CGIAR-CSI consortium (http://www.cgiar-csi.org/). This dataset calculates potential evapotranspiration using the Hargreaves method69. We obtained mean annual precipitation and mean annual temperature from Worldclim70. Here, we used WorldClim rather than CHELSA climatic data because the CGIAR-CSI potential evapotranspiration data were computed from the former. We calculated the WAI values at each of our sites by subtracting mean annual potential evapotranspiration from the mean annual precipitation. Such proxy is a coarse measure of plant water availability that ignores information such as soil characteristics and plant rooting depth. However, WAI is useful to compare water availability among disparate environments, so that it is often employed in global analyses68,71. As our proxy of temperature limitation, we use mean annual temperature. While growing degree days would be a more mechanistic measure of temperature limitation48, this requires daily weather data. However, we could not find a global, downscaled, daily gridded weather dataset to calculate this metric.The overall effect of climate on plant population growth rateTo test H1, we estimated the overall effect sizes of responses to anomalies in temperature, precipitation, and their interaction with a linear mixed-effect model.$${mathrm{log}}left( lambda right) = alpha + beta P + eta T + theta P{mathrm{x}}T + varepsilon$$
(1)
where log(λ) is the log of the asymptotic population growth rate of plant population P is precipitation, T is temperature. We included random population effects on the intercept and the slopes to account for the nonindependence of measurements within populations. We then compared the mean absolute effect size of precipitation, temperature, and their interaction. This final model did not include a quadratic term of temperature and precipitation because these additional terms led to convergence issues. This likely occurred because single data sets did not include enough years of data.Population-level effect of climate on plant population growth ratesTo test our remaining three hypotheses, we carried out meta-regressions where the response variable was the slope (henceforth “effect size”) of climatic anomalies on the population growth rate for each of our populations. Before carrying out our meta-regression, we first estimated the effect size of our two climatic anomalies on the population growth rate of each population separately. We initially fit population-level and meta-regression simultaneously, in a hierarchical Bayesian framework. However, these Bayesian models shrunk the uncertainty of the noisiest population–level relationships, resulting in unrealistically strong meta-regressions. We, therefore, chose to fit population models separately, resulting in more conservative results.For each population, we fit multiple regressions with an autoregressive error term, and we evaluated the potential for nonlinear effects in the datasets longer than 14 years. We fit multiple regressions because temperature and precipitation anomalies were negatively correlated, so that fitting separate models for temperature and precipitation would yield biased results72. We fit an autoregressive error term because density dependence and autocorrelated climate anomalies can produce autocorrelated plant population growth rates. The form of our baseline model was$${rm{log}}(lambda )_y = alpha + beta _pP_y + beta _tT_y + varepsilon _y$$
(2)
$$varepsilon _y = rho varepsilon _{y – 1} + eta _y$$
(3)
The model in Eq. 2 is a linear regression relating each log(λ) data point observed in year y, to the corresponding precipitation (P) and temperature (T) anomalies observed in year y, via the intercept α, the effect sizes, β, and an error term, εy, which depends on white noise, ηy, and on the correlation with the error term of the previous year, ρ. When multiple spatial replicates per each population were available each year, we estimated the ρ autocorrelation value separately for each replicate. This happened in the few cases when a study contained contiguous populations, with no ecologically meaningful (e.g., habitat) differences.We compared the baseline model in Eqs. 2 and 3 to models including a quadratic climatic effect and non-climatic covariates. We estimated quadratic climatic effects only for time series longer than 14 years. We choose this threshold because when using a model selection approach to select a quadratic or linear regression model, the recommended minimum sample size is between 8 and 25 data points73. We fit models including a quadratic effect of temperature, precipitation, or both (Supplementary Table 1).Finally, we also tested whether non-climatic covariates could bias the effects of climate on log(λ) estimated in our analysis. Such bias, either upwards or downwards, could result in the case non-climatic co-variates interacted with climate. For example, harvest can have multiplicative, rather than additive effects on the climate responses of forest understory herbs74. We tested for an interaction between a covariate and climate anomaly in 17 of the 21 studies that included a non-climatic covariate. In the remaining three studies, discrete covariates corresponded with the single populations. Because Eqs. 2 and 3 is fit on separate populations, it implicitly accounted for these covariates. For the 17 studies above, we fit a linear effect of the non-climatic covariate and its interaction with one of the two linear climatic anomalies. Thus, including the linear model in Eqs. 2 and 3, the nonlinear models, and the covariate interaction models, we tested up to six alternative models for each one of our populations (Supplementary Table 1). We selected the best model according to the Akaike Information Criterion corrected for small sample sizes (AICc75). We carried out these and subsequent analyses in R version 3.6.176.In the populations for which AICc selected one of the model alternatives to the baseline in Eqs. 2 and 3, we calculated the effect size of climate by adding the effect of the new terms to the linear climatic terms. For example, when a quadratic precipitation model was selected, we calculated the effect size of precipitation as β = βp + βp2. For models including an interaction between temperature and a non-climatic covariate, we evaluated the effect of the interaction at the mean value of the covariate. Therefore, we calculated the effect size as β = βt + βxE(Ci) for continuous covariates. For categorical variables, we calculated the effect size as βp + βx0.5: that is, we calculated the mean effect size between the two categories. We quantified the standard error of the resulting effect sizes by adding the standard errors of the two terms.The effect of biome on the response of plants to climateWe used a simulation procedure to run two meta-regressions to test for the correlation between the effect size of climate drivers on λ, and our measures of water or temperature limitation. These meta-regressions accounted for the uncertainty, measured as the standard error, in the effect sizes of climate drivers. We represented the effect of biome using a proxy of water (WAI) and temperature (mean annual temperature) limitation. For each of our 162 populations, the response data of this analysis were the effect sizes (βp or βt values) estimated by Eqs. 2 and 3 or their modifications in case a quadratic or non-climatic covariate model were selected. In these meta-regressions, the weight of each effect size was inversely proportional to its standard error. To test H2 and H3 on how water and temperature limitation should affect the response of populations to climate, we used linear meta-regressions. These two hypotheses tested both the sign and magnitude of the effect of climate. Therefore, we used the effect sizes as a response variable which could take negative or positive values. As predictors, we used population-specific WAI (H2, only for effect sizes quantifying the effect of precipitation), and mean annual temperature (H3, only for effect sizes quantifying the effect of temperature). The null hypothesis of these meta-regressions is that plant species are adapted to the climatic variation at their respective sites. Such an adaptation implies that a precipitation z-score of one should produce effects on log(λ) of similar magnitude and sign across different climates. This should happen across average climatic values that are connected to substantially different absolute climatic anomalies (Supplementary Fig. 2). On the other hand, our hypotheses posit that at low WAI and MAT values, species are more responsive to z-scores than expected under the null hypothesis.We performed these two meta-regressions by exploiting the standard error of each effect size. We simulated 1000 separate datasets where each effect size was independently drawn from a normal distribution whose mean was the estimated β value, and the standard deviation was the standard error of this β. These simulated datasets accounted for the uncertainty in the β values. We fit 1000 linear models, extracting for each its slope, βmeta. Each one of these slopes had in turn an uncertainty, quantified by its standard error, σmeta. For each βmeta, we then drew 1000 values from a normal distribution with mean βmeta and standard deviation σmeta. We used the resulting 1 × 106 values to estimate the confidence intervals of βmeta. This procedure assumes that the distribution of βmeta values is normally distributed. We performed one-tailed hypothesis tests, considering meta-regression slopes significant when over 95% of simulated values were below zero.The effect of generation time on the response of plants to climateTo test H4 on how the generation time of a species should mediate its responses to climate, we used a gamma meta-regression. We fitted gamma meta-regressions because our response variables were the absolute effect sizes of precipitation and temperature anomalies, |β|, which are bounded between 0 and infinity. To test H4, we therefore fit gamma meta-regressions with a log link, using |β| values as response variable and generation time (T) as predictor. We calculated T directly from the MPMs and IPMs (Supplementary Methods). We log-transformed T to improve model fit. We carried out these meta-regressions using the same simulation procedure described for testing H2 and H3. We also carried out one-tailed hypothesis tests, by verifying whether 95% of βmeta values were below zero.The effect of plant types on estimates of climate effectsWe verified whether certain plant types could bias our results by subdividing our species as graminoids, herbaceous perennials, ferns, woody species (shrubs and trees), and succulents. We ran ANOVA tests to verify whether the effect sizes of precipitation and temperature anomalies differed between plant types. We then tested for significant differences in pairwise contrasts between plants types by running Tukey’s honestly significant difference tests. We carried out these tests on the average effects of climate, without accounting for differences in parameter uncertainty. If Tukey’s test identified significant differences among plant types, we ran additional tests of H2–H4 excluding the plant type, or plant types, whose response to climate differed.Reporting summaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. More