Ecology

1.Friedlingstein, P. et al. Global carbon budget 2020. Earth Syst. Sci. Data 12, 3269–3340 (2020).ADS 

Google Scholar 
2.Schimel, D., Stephens, B. B. & Fisher, J. B. Effect of increasing CO2 on the terrestrial carbon cycle. Proc. Natl Acad. Sci. USA 112, 436–441 (2015).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
3.Keenan, T. et al. Recent pause in the growth rate of atmospheric CO2 due to enhanced terrestrial carbon uptake. Nat. Commun. 7, 13428 (2016).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
4.Baig, S., Medlyn, B. E., Mercado, L. M. & Zaehle, S. Does the growth response of woody plants to elevated CO2 increase with temperature? A model-oriented meta-analysis. Glob. Change Biol. 21, 4303–4319 (2015).ADS 

Google Scholar 
5.Drake, J. E. et al. Increases in the flux of carbon belowground stimulate nitrogen uptake and sustain the long‐term enhancement of forest productivity under elevated CO2. Ecol. Lett. 14, 349–357 (2011).PubMed 
PubMed Central 

Google Scholar 
6.Norby, R. J. et al. Forest response to elevated CO2 is conserved across a broad range of productivity. Proc. Natl Acad. Sci. USA 102, 18052–18056 (2005).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
7.van Groenigen, K. J., Qi, X., Osenberg, C. W., Luo, Y. & Hungate, B. A. Faster decomposition under increased atmospheric CO2 limits soil carbon storage. Science 344, 508 (2014).ADS 
PubMed 
PubMed Central 

Google Scholar 
8.Friedlingstein, P. et al. Uncertainties in CMIP5 climate projections due to carbon cycle feedbacks. J. Clim. 27, 511–526 (2014).ADS 

Google Scholar 
9.Todd-Brown, K. E. O. et al. Changes in soil organic carbon storage predicted by Earth system models during the 21st century. Biogeosciences 11, 2341–2356 (2014).ADS 
CAS 

Google Scholar 
10.Heimann, M. & Reichstein, M. Terrestrial ecosystem carbon dynamics and climate feedbacks. Nature 451, 289–292 (2008).ADS 
CAS 

Google Scholar 
11.Bradford, M. A. et al. Managing uncertainty in soil carbon feedbacks to climate change. Nat. Clim. Chang. 6, 751–758 (2016).ADS 

Google Scholar 
12.Terrer, C. et al. Nitrogen and phosphorus constrain the CO2 fertilization of global plant biomass. Nat. Clim. Chang. 9, 684–689 (2019).ADS 
CAS 

Google Scholar 
13.Reich, P. B., Hungate, B. A. & Luo, Y. Carbon-nitrogen interactions in terrestrial ecosystems in response to rising atmospheric carbon dioxide. Annu. Rev. Ecol. Evol. Syst. 37, 611–636 (2006).
Google Scholar 
14.Norby, R. J. & Zak, D. R. Ecological lessons from free-air CO2 enrichment (FACE) experiments. Annu. Rev. Ecol. 42, 181–203 (2011).
Google Scholar 
15.Terrer, C. et al. Ecosystem responses to elevated CO2 governed by plant–soil interactions and the cost of nitrogen acquisition. New Phytol. 217, 507–522 (2018).CAS 
PubMed 
PubMed Central 

Google Scholar 
16.Olson, J. S. Energy storage and the balance of producers and decomposers in ecological systems. Ecology 44, 322–331 (1963).
Google Scholar 
17.Hungate, B. A. et al. Assessing the effect of elevated carbon dioxide on soil carbon: a comparison of four meta‐analyses. Glob. Change Biol. 15, 2020–2034 (2009).ADS 

Google Scholar 
18.Kuzyakov, Y., Horwath, W. R., Dorodnikov, M. & Blagodatskaya, E. Review and synthesis of the effects of elevated atmospheric CO2 on soil processes: no changes in pools, but increased fluxes and accelerated cycles. Soil Biol. Biochem. 128, 66–78 (2019).CAS 

Google Scholar 
19.Tian, H. et al. Global patterns and controls of soil organic carbon dynamics as simulated by multiple terrestrial biosphere models: current status and future directions. Glob. Biogeochem. Cycles 29, 775–792 (2015).ADS 
CAS 

Google Scholar 
20.Todd-Brown, K. E. O. et al. Causes of variation in soil carbon simulations from CMIP5 Earth system models and comparison with observations. Biogeosciences 10, 1717–1736 (2013).ADS 

Google Scholar 
21.Nie, M., Lu, M., Bell, J., Raut, S. & Pendall, E. Altered root traits due to elevated CO2: a meta‐analysis. Glob. Ecol. Biogeogr. 22, 1095–1105 (2013).
Google Scholar 
22.Kuzyakov, Y. Priming effects: interactions between living and dead organic matter. Soil Biol. Biochem. 42, 1363–1371 (2010).CAS 

Google Scholar 
23.Treseder, K. K. A meta‐analysis of mycorrhizal responses to nitrogen, phosphorus, and atmospheric CO2 in field studies. New Phytol. 164, 347–355 (2004).
Google Scholar 
24.Jastrow, J. D. et al. Elevated atmospheric carbon dioxide increases soil carbon. Glob. Change Biol. 11, 2057–2064 (2005).ADS 

Google Scholar 
25.Carrillo, Y., Dijkstra, F. A., LeCain, D. & Pendall, E. Mediation of soil C decomposition by arbuscular mycorrizhal fungi in grass rhizospheres under elevated CO2. Biogeochemistry 127, 45–55 (2016).CAS 

Google Scholar 
26.Averill, C., Bhatnagar, J. M., Dietze, M. C., Pearse, W. D. & Kivlin, S. N. Global imprint of mycorrhizal fungi on whole-plant nutrient economics. Proc. Natl Acad. Sci. USA 116, 23163–23168 (2019).CAS 

Google Scholar 
27.Cotrufo, M. F., Wallenstein, M. D., Boot, C. M., Denef, K. & Paul, E. The Microbial Efficiency-Matrix Stabilization (MEMS) framework integrates plant litter decomposition with soil organic matter stabilization: do labile plant inputs form stable soil organic matter? Glob. Change Biol. 19, 988–995 (2013).ADS 

Google Scholar 
28.Cotrufo, M. F., Ranalli, M. G., Haddix, M. L., Six, J. & Lugato, E. Soil carbon storage informed by particulate and mineral-associated organic matter. Nat. Geosci. 12, 989–994 (2019).ADS 
CAS 

Google Scholar 
29.Craig, M. E. et al. Tree mycorrhizal type predicts within-site variability in the storage and distribution of soil organic matter. Glob. Change Biol. 24, 3317–3330 (2018).ADS 

Google Scholar 
30.Schmidt, M. W. I. et al. Persistence of soil organic matter as an ecosystem property. Nature 478, 49–56 (2011).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
31.Jobbágy, E. G. & Jackson, R. B. The vertical distribution of soil organic carbon and its relation to climate and vegetation. Ecol. Appl. 10, 423–436 (2000).
Google Scholar 
32.Sokol, N. W., Kuebbing, S. E., Karlsen‐Ayala, E. & Bradford, M. A. Evidence for the primacy of living root inputs, not root or shoot litter, in forming soil organic carbon. New Phytol. 221, 233–246 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
33.Evans, R. D. et al. Greater ecosystem carbon in the Mojave Desert after ten years exposure to elevated CO2. Nat. Clim. Chang. 4, 394–397 (2014).ADS 
CAS 

Google Scholar 
34.Walker, A. P. et al. FACE-MDS Phase 2: Model Output https://www.osti.gov/dataexplorer/biblio/dataset/1480327 (2018).35.Wieder, W. R. et al. Carbon cycle confidence and uncertainty: exploring variation among soil biogeochemical models. Glob. Change Biol. 24, 1563–1579 (2018).ADS 

Google Scholar 
36.Sulman, B. N. et al. Diverse mycorrhizal associations enhance terrestrial C storage in a global model. Glob. Biogeochem. Cycles 33, 501–523 (2019).ADS 
CAS 

Google Scholar 
37.Shi, M., Fisher, J. B., Brzostek, E. R. & Phillips, R. P. Carbon cost of plant nitrogen acquisition: global carbon cycle impact from an improved plant nitrogen cycle in the Community Land Model. Glob. Change Biol. 22, 1299–1314 (2016).ADS 

Google Scholar 
38.Norby, R. J., Warren, J. M., Iversen, C. M., Medlyn, B. E. & McMurtrie, R. E. CO2 enhancement of forest productivity constrained by limited nitrogen availability. Proc. Natl Acad. Sci. USA 107, 19368–19373 (2010).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
39.Jiang, M. et al. The fate of carbon in a mature forest under carbon dioxide enrichment. Nature 580, 227–231 (2020).ADS 
CAS 
PubMed 

Google Scholar 
40.Wieder, W. R., Bonan, G. B. & Allison, S. D. Global soil carbon projections are improved by modelling microbial processes. Nat. Clim. Chang. 3, 909–912 (2013).ADS 
CAS 

Google Scholar 
41.Terrer, C. Report of Mutualistic Associations, Nutrients, and Carbon Under eCO2 (ROMANCE) v1.0 Dataset. https://doi.org/10.6084/m9.figshare.11704491.v7 (2020).42.Dieleman, W. I. J. et al. Simple additive effects are rare: a quantitative review of plant biomass and soil process responses to combined manipulations of CO2 and temperature. Glob. Change Biol. 18, 2681–2693 (2012).ADS 

Google Scholar 
43.Borenstein, M., Hedges, L. V., Higgins, J. P. T. & Rothstein, H. R. in Introduction to Meta‐Analysis 225–238 (John Wiley & Sons, 2009).44.Del Re, A. C. & Hoyt, W. T. MAd: meta-analysis with mean differences. R Package Version 08-2 https://cran.r-project.org/package=MAd (2014).45.Song, J. & Wan, S. A Global Database Of Plant Production And Carbon Exchange From Global Change Manipulative Experiments https://doi.org/10.6084/m9.figshare.7442915.v9 (2020).46.Viechtbauer, W. Conducting meta-analyses in R with the metafor Package. J. Stat. Softw. 36, https://doi.org/10.18637/jss.v036.i03 (2010).47.Osenberg, C. W., Sarnelle, O., Cooper, S. D. & Holt, R. D. Resolving ecological questions through meta-analysis: goals, metrics, and models. Ecology 80, 1105–1117 (1999).
Google Scholar 
48.Rubin, D. B. & Schenker, N. Multiple imputation in health‐are databases: an overview and some applications. Stat. Med. 10, 585–598 (1991).CAS 
PubMed 
PubMed Central 

Google Scholar 
49.Lajeunesse, M. J. Facilitating systematic reviews, data extraction and meta‐analysis with the METAGEAR package for R. Methods Ecol. Evol. 7, 323–330 (2016).
Google Scholar 
50.Van Lissa, C. J. MetaForest: exploring heterogeneity in meta-analysis using random forests. Preprint at https://psyarxiv.com/myg6s/ (2017).51.Kuhn, M. Building predictive models in R using the caret package. J. Stat. Softw. 28, https://doi.org/10.18637/jss.v028.i05 (2008).52.Calcagno, V. & de Mazancourt, C. glmulti: an R package for easy automated model selection with (generalized) linear models. J. Stat. Softw. 34, https://doi.org/10.18637/jss.v034.i12 (2010).53.van Groenigen, K. J. et al. Element interactions limit soil carbon storage. Proc. Natl Acad. Sci. USA 103, 6571–6574 (2006).ADS 

Google Scholar 
54.Wang, B. & Qiu, Y. L. Phylogenetic distribution and evolution of mycorrhizas in land plants. Mycorrhiza 16, 299–363 (2006).CAS 

Google Scholar 
55.Maherali, H., Oberle, B., Stevens, P. F., Cornwell, W. K. & McGlinn, D. J. Mutualism persistence and abandonment during the evolution of the mycorrhizal symbiosis. Am. Nat. 188, E113–E125 (2016).
Google Scholar 
56.Terrer, C., Vicca, S., Hungate, B. A., Phillips, R. P. & Prentice, I. C. Mycorrhizal association as a primary control of the CO2 fertilization effect. Science 353, 72–74 (2016).ADS 
CAS 

Google Scholar 
57.Medlyn, B. E. et al. Using ecosystem experiments to improve vegetation models. Nat. Clim. Chang. 5, 528–534 (2015).ADS 

Google Scholar 
58.Zaehle, S. et al. Evaluation of 11 terrestrial carbon–nitrogen cycle models against observations from two temperate Free‐Air CO2 Enrichment studies. New Phytol. 202, 803–822 (2014).CAS 
PubMed 
PubMed Central 

Google Scholar 
59.De Kauwe, M. G. et al. Where does the carbon go? A model-data intercomparison of vegetation carbon allocation and turnover processes at two temperate forest free-air CO2 enrichment sites. New Phytol. 203, 883–899 (2014).PubMed 
PubMed Central 

Google Scholar 
60.Walker, A. P. et al. Comprehensive ecosystem model‐data synthesis using multiple data sets at two temperate forest free‐air CO2 enrichment experiments: model performance at ambient CO2 concentration. J. Geophys. Res. Biogeosci. 119, 937–964 (2014).ADS 
CAS 

Google Scholar 
61.Walker, A. P. et al. Decadal biomass increment in early secondary succession woody ecosystems is increased by CO2 enrichment. Nat. Commun. 10, 454 (2019).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
62.Schlesinger, W. et al. in Managed Ecosystems and CO2 197–212 (2006).63.Hungate, B. A. et al. Cumulative response of ecosystem carbon and nitrogen stocks to chronic CO2 exposure in a subtropical oak woodland. New Phytol. 200, 753–766 (2013).CAS 
PubMed 
PubMed Central 

Google Scholar 
64.Jordan, D. N. et al. Biotic, abiotic and performance aspects of the Nevada Desert Free-Air CO2 Enrichment (FACE) Facility. Glob. Change Biol. 5, 659–668 (1999).ADS 

Google Scholar 
65.Carrillo, Y., Dijkstra, F., LeCain, D., Blumenthal, D. & Pendall, E. Elevated CO2 and warming cause interactive effects on soil carbon and shifts in carbon use by bacteria. Ecol. Lett. 21, 1639–1648 (2018).
Google Scholar 
66.Mueller, K. E. et al. Impacts of warming and elevated CO2 on a semi‐arid grassland are non‐additive, shift with precipitation, and reverse over time. Ecol. Lett. 19, 956–966 (2016).CAS 

Google Scholar 
67.Zak, D. R., Pregitzer, K. S., Kubiske, M. E. & Burton, A. J. Forest productivity under elevated CO2 and O3: positive feedbacks to soil N cycling sustain decade‐long net primary productivity enhancement by CO2. Ecol. Lett. 14, 1220–1226 (2011).
Google Scholar 
68.Oleson, K. et al. Technical Description of Version 4.5 of the Community Land Model (CLM) Report NCAR/TN-503+STR, https://doi.org/10.5065/D6RR1W7M (2013).69.Clark, D. B. et al. The Joint UK Land Environment Simulator (JULES), model description—Part 2: Carbon fluxes and vegetation dynamics. Geosci. Model Dev. 4, 701–722 (2011).ADS 

Google Scholar 
70.Krinner, G. et al. A dynamic global vegetation model for studies of the coupled atmosphere-biosphere system. Glob. Biogeochem. Cycles 19, https://doi.org/10.1029/2003GB002199 (2005).71.Haverd, V. et al. A new version of the CABLE land surface model (subversion revision r4601) incorporating land use and land cover change, woody vegetation demography, and a novel optimisation-based approach to plant coordination of photosynthesis. Geosci. Model Dev. 11, 2995–3026 (2018).ADS 
CAS 

Google Scholar 
72.Lawrence, D. M. et al. The Community Land Model Version 5: description of new features, benchmarking, and impact of forcing uncertainty. J. Adv. Model. Earth Syst. 11, 4245–4287 (2019).ADS 

Google Scholar 
73.Meiyappan, P., Jain, A. K. & House, J. I. Increased influence of nitrogen limitation on CO2 emissions from future land use and land use change. Glob. Biogeochem. Cycles 29, 1524–1548 (2015).ADS 
CAS 

Google Scholar 
74.Smith, B. et al. Implications of incorporating N cycling and N limitations on primary production in an individual-based dynamic vegetation model. Biogeosciences 11, 2027–2054 (2014).ADS 

Google Scholar 
75.Goll, D. S. et al. A representation of the phosphorus cycle for ORCHIDEE (revision 4520). Geosci. Model Dev. 10, 3745–3770 (2017).ADS 
CAS 

Google Scholar 
76.Friedlingstein, P. et al. Global carbon budget 2019. Earth Syst. Sci. Data 11, 1783–1838 (2019).ADS 

Google Scholar 
77.Harris, I., Jones, P. D., Osborn, T. J. & Lister, D. H. Updated high‐resolution grids of monthly climatic observations—the CRU TS3.10 dataset. Int. J. Climatol. 34, 623–642 (2014).
Google Scholar 
78.Soudzilovskaia, N. A. et al. Global mycorrhizal plant distribution linked to terrestrial carbon stocks. Nat. Commun. 10, 5077 (2019).ADS 
PubMed 
PubMed Central 

Google Scholar 
79.Hengl, T. et al. SoilGrids250m: global gridded soil information based on machine learning. PLoS One 12, e0169748 (2017).PubMed 
PubMed Central 

Google Scholar 
80.Batjes, N. H. Harmonized soil property values for broad-scale modelling (WISE30sec) with estimates of global soil carbon stocks. Geoderma 269, 61–68 (2016).ADS 
CAS 

Google Scholar 
81.Shangguan, W., Dai, Y., Duan, Q., Liu, B. & Yuan, H. A global soil data set for earth system modeling. J. Adv. Model. Earth Syst. 6, 249–263 (2014).ADS 

Google Scholar  More

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.Bonnet, S. I., Binetruy, F., Hernández-Jarguín, A. M. & Duron, O. The tick microbiome: Why non-pathogenic microorganisms matter in tick biology and pathogen transmission. Front. Cell. Infect. Microbiol. 7, 236. https://doi.org/10.3389/fcimb.2017.00236 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
2.Burgdorfer, W., Hayes, S. & Mavros, A. Non-pathogenic rickettsiae in Dermacentor andersoni: A limiting factor for the distribution of Rickettsia rickettsii. In Rickettsia and Rickettsial Disease (eds Burgdorfer, A. A. & Anacker, R. L.) 585–594 (Academic, 1981).
Google Scholar 
3.Chauvin, A., Moreau, E., Bonnet, S., Plantard, O. & Malandrin, L. Babesia and its hosts: Adaptation to long-lasting interactions as a way to achieve efficient transmission. Vet. Res. 40, 37. https://doi.org/10.1051/vetres/2009020 (2009).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
4.Ravi, A. et al. Metagenomic profiling of ticks: Identification of novel rickettsial genomes and detection of tick-borne canine parvovirus. PLoS Negl. Trop. Dis. 13(1), 1–19 (2019).CAS 
Article 

Google Scholar 
5.Greay, T. L. et al. Recent insights into the tick microbiome gained through next-generation sequencing. Parasites Vectors 11(1), 1–14 (2018).Article 

Google Scholar 
6.Rar, V. et al. Detection and genetic characterization of a wide range of infectious agents in Ixodes pavlovskyi ticks in Western Siberia, Russia. Parasites Vectors 10(1), 1–24 (2017).Article 

Google Scholar 
7.Filippova, N. A. Ixodid Ticks of the Subfamily Ixodinae (Publishing House Nauka, 1977).
Google Scholar 
8.Bouquet, J. et al. Metagenomic-based surveillance of pacific coast tick dermacentor occidentalis identifies two novel bunyaviruses and an emerging human Ricksettsial pathogen. Sci. Rep. 7(1), 1–10. https://doi.org/10.1038/s41598-017-12047-6 (2017).CAS 
Article 

Google Scholar 
9.Andreotti, R. et al. Assessment of bacterial diversity in the cattle tick Rhipicephalus (Boophilus) microplus through tag-encoded pyrosequencing. BMC Microbiol. 11(6), 1–11 (2011).
Google Scholar 
10.Nakao, R. et al. A novel approach, based on BLSOMs (batch learning self-organizing maps), to the microbiome analysis of ticks. ISME J. 7(5), 1003–1015. https://doi.org/10.1038/ismej.2012.171 (2013).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
11.Xia, H. et al. Metagenomic profile of the viral communities in Rhipicephalus spp. ticks from Yunnan, China. PLoS ONE 10(3), 1–16. https://doi.org/10.1371/journal.pone.0121609 (2015).CAS 
Article 

Google Scholar 
12.Barros-Battesti, D., Arzua, M. & Bechara, H. Carrapato de Importância Medico-Veterinaria da Região Neotropical: Um Guia Ilustrado para Identificação de Espécies (Ticks of Medical-Veterinary Importance in the Neotropical Region: An Illustrated Guide for Species Identification). 10ma edição 223 (Butantan Publicação, 2006).
Google Scholar 
13.QIAGEN. Gentra, Puregene (QIAGEN GROUP), 2007–2010 (accessed 9 June 2017); https://www.qiagen.com/us/shop/sample-technologies/dna/genomic-dna/gentra-puregene-tissue-kit/#orderinginformation.14.Sperling, J. L. et al. Comparison of bacterial 16S rRNA variable regions for microbiome surveys of ticks. Ticks Tick Borne Dis. 8, 453–461 (2017).Article 

Google Scholar 
15.Caporaso, J. G. et al. Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample. Proc. Natl. Acad. Sci. 108(Supplement 1), 4516–4522 (2011).ADS 
CAS 
Article 

Google Scholar 
16.Edgar, R. C., Haas, B. J., Clemente, J. C., Quince, C. & Knight, R. UCHIME improves sensitivity and speed of chimera detection. Bioinformatics 27(16), 2194–2200 (2011).CAS 
Article 

Google Scholar 
17.Glassing, A. et al. Changes in 16S RNA gene microbial community profiling by concentration of prokaryotic DNA. J. Microbiol. Methods 119, 239242 (2015).Article 

Google Scholar 
18.Bokulich, N. A. et al. Quality-filtering vastly improves diversity estimates from Illumina amplicon sequencing. Nat. Methods 10(1), 57–59 (2013).CAS 
Article 

Google Scholar 
19.Andersen, K. S., Kirkegaard, R. H., Karst, S. M. & Albertsen, M. ampvis2: An R package to analyse and visualise 16S rRNA amplicon data. BioRxiv. https://doi.org/10.1101/299537 (2018).Article 

Google Scholar 
20.McMurdie, P. J. & Holmes, S. phyloseq: An R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE 8(4), 1–11 (2013).Article 

Google Scholar 
21.DeSantis, T. Z. et al. Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB. Appl. Environ. Microbiol. 72(7), 5069–5072 (2006).CAS 
Article 

Google Scholar 
22.Obregón, D., Bard, E., Abrial, D., Estrada-Peña, A. & Cabezas-Cruz, A. Sex-specific linkages between taxonomic and functional profiles of tick gut microbiomes. Front. Cell. Infect. Microbiol. 9, 298. https://doi.org/10.3389/fcimb.2019.00298 (2019).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
23.Qiu, Y., Nakao, R., Ohnuma, A., Kawamori, F. & Sugimoto, C. Microbial population analysis of the salivary glands of ticks; a possible strategy for the surveillance of bacterial pathogens. PLoS ONE 9(8), e103961 (2014).ADS 
Article 

Google Scholar 
24.Van Treuren, W. et al. Variation in the microbiota of Ixodes ticks with regard to geography, species, and sex. Appl. Environ. Microbiol. 81, 6200–6209 (2015).Article 

Google Scholar 
25.Carpi, G. et al. Metagenomic profile of the bacterial communities associated with Ixodes ricinus ticks. PLoS ONE 6(10), e25604 (2011).ADS 
CAS 
Article 

Google Scholar 
26.Zhang, X.-C., Yang, Z.-N., Lu, B., Ma, X.-F. & Zhang, C.-X. The composition and transmission of microbiome in hard tick, Ixodes persulcatus, during blood meal. Ticks Tick Borne Dis. 5, 864–870 (2014).Article 

Google Scholar 
27.Menchaca, A. C. et al. Preliminary assessment of microbiome changes following blood-feeding and survivorship in the Amblyomma americanum nymph-to-adult transition using semiconductor sequencing. PLoS ONE 8, 1–10 (2013).Article 

Google Scholar 
28.Clayton, K. A., Gall, C. A., Mason, K. L., Scoles, G. A. & Brayton, K. A. The characterization and manipulation of the bacterial microbiome of the Rocky Mountain wood tick, Dermacentor andersoni. Parasites Vectors 8, 1–5 (2018).CAS 

Google Scholar 
29.Crump, J. A., Sjölund-Karlsson, M., Gordon, M. A. & Parry, C. M. Epidemiology, clinical presentation, laboratory diagnosis, antimicrobial resistance, and antimicrobial management of invasive Salmonella infections. Clin. Microbiol. Rev. 1, 901–937. https://doi.org/10.1128/CMR.00002-15 (2015).Article 

Google Scholar 
30.Jesser, K. J. & Noble, R. T. Vibrio ecology in the Neuse River Estuary, North Carolina, characterized by next-generation amplicon sequencing of the gene encoding heat shock protein 60 (hsp60). Appl. Environ. Microbiol. 84, 1–21. https://doi.org/10.1128/AEM.00333-18 (2018).Article 

Google Scholar 
31.Payne, S. M., Mey, A. R. & Wyckoff, E. E. Vibrio iron transport: Evolutionary adaptation to life in multiple environments. Microbiol. Mol. Biol. Rev. 80, 69–90. https://doi.org/10.1128/MMBR.00046-15 (2016).CAS 
Article 
PubMed 

Google Scholar 
32.Boyd, E. F. et al. Post genomic analysis of the evolutionary history and innovations of the family Vibrionaceae. Microbiol. Spectr. 3(5), 1–43. https://doi.org/10.1128/microbiolspec.VE-0009-2014 (2015).ADS 
CAS 
Article 

Google Scholar 
33.Maj, A. et al. Plasmids of carotenoid-producing Paracoccus spp. (Alphaproteobacteria)—Structure, diversity and evolution. PLoS ONE 8(11), 1–27. https://doi.org/10.1371/journal.pone.0080258 (2013).CAS 
Article 

Google Scholar 
34.Patro, L. P. P. & Rathinavelan, T. Targeting the sugary armor of Klebsiella species. Front. Cell. Infect. Microbiol. 9, 1–23. https://doi.org/10.3389/fcimb.2019.00367 (2019).CAS 
Article 

Google Scholar 
35.Folkesson, A. et al. Adaptation of Pseudomonas aeruginosa to the cystic fibrosis airway: An evolutionary perspective. Nat. Rev. Microbiol. 10, 841–851. https://doi.org/10.1038/nrmicro2907 (2019).CAS 
Article 

Google Scholar 
36.Wong, J. S. J. et al. Corynebacterium accolens-associated pelvic osteomyelitis. J. Clin. Microbiol. 48(2), 654–655 (2010).Article 

Google Scholar 
37.Gay, N. R., Fleming, E. & Oh, J. Draft genome sequence of Cloacibacterium normanense NRS-1 isolated from municipal wastewater. Genome Announc. 4(6), 1–2. https://doi.org/10.1128/genomeA.01397-16 (2016).Article 

Google Scholar 
38.Kurilshikov, A. et al. Comparative metagenomic profiling of symbiotic bacterial communities associated with ixodes persulcatus, ixodes pavlovskyi and dermacentor reticulatus ticks. PLoS ONE 10(7), 1–13 (2015).CAS 
Article 

Google Scholar 
39.Martínez, M. A. Retrato microbiológico. J. Microbiol. Immunol. Infect. 44(1), 289–295 (2011).
Google Scholar 
40.Moreno-Forero, S. K. & Van-Der-Meer, J. R. Genome-wide analysis of Sphingomonas wittichii RW1 behaviour during inoculation and growth in contaminated sand. ISME J. 9(1), 150–165 (2015).CAS 
Article 

Google Scholar 
41.Giron, S. Diversidad bacteriana de la garrapata Rhipicephalus (Boophilus) microplus en el ganado bovino del estado de Tamaulipas (Bacterial diversity of Rhipicephalus (Boophilus) microplus tick in cattle of the state of Tamaulipas). (2015). [Thesis]. Thesis to obtain the title of Master of Science in Genomic Biotechnology viable (accessed 14 October 2019); https://tesis.ipn.mx/handle/123456789/24552.42.Jimemez, M., Gasper, M., Carmona, M. & Terio, K. Suidae and Tayassuidae. Pathol. Wildl. Zoo Anim. 1, 207–228 (2018).
Google Scholar 
43.Sutherland-Smith, M. Suidae and Tayassuidae (Wild Pigs, Peccaries). Fowler’s Zoo Wild Anim. Med. 1(8), 568–584 (2015).Article 

Google Scholar 
44.Bermúdez, S., Meyer, N., Moreno, R. & Artavia, A. NOTAS SOBRE Pecari tajacu (L., Y Tayassu peccari (LINK, 1795) (ARTIODACTYLA: TAYASSUIDAE) COMO HOSPEDEROS DE GARRAPATAS DURAS (ACARI: IXODIDAE) EN PANAMÁ. Tecnociencia 20(1), 61–70 (2008).
Google Scholar 
45.Rodríguez-Vivas, R. I., Quiñones, A. F. & Fragoso, S. H. Epidemiología y control de la garrapata Boophilus en México (Epidemiology and control of Boophilus tick in Mexico). In Enfermedades de Importancia Económica en Producción Animal (Diseases of Economic Importance in Animal Production) (ed. Rodríguez-Vivas, R. I.) 571–592 (McGraw-Hill-UADY, 2005).
Google Scholar 
46.Duron, O. et al. Evolutionary changes in symbiont community structure in ticks. Mol. Ecol. 26, 2905–2921. https://doi.org/10.1111/mec.14094 (2017).CAS 
Article 
PubMed 

Google Scholar 
47.Zhong, J., Jasinskas, A. & Barbour, A. G. Antibiotic treatment of the tick vector Amblyomma americanum reduced reproductive fitness. PLoS ONE 2, 1–7. https://doi.org/10.1371/journal.pone.0000405 (2017).CAS 
Article 

Google Scholar 
48.Gottlieb, Y., Lalzar, I. & Klasson, L. Distinctive genome reduction rates revealed by genomic analyses of two Coxiella-like endosymbionts in ticks. Genome Biol. Evol. 7, 1779–1796. https://doi.org/10.1093/gbe/evv108 (2015).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
49.Gerhart, J. G., Moses, A. S. & Raghavan, R. A. Francisella-like endosymbiont in the Gulf Coast tick evolved from a mammalian pathogen. Sci. Rep. 6, 1–6. https://doi.org/10.1038/srep33670 (2016).CAS 
Article 

Google Scholar 
50.Sjodin, A. et al. Genome characterisation of the genus Francisella reveals insight into similar evolutionary paths in pathogens of mammals and fish. BMC Genomics 13, 1–13. https://doi.org/10.1186/1471-2164-13-268 (2012).Article 

Google Scholar 
51.Machado-Ferreira, E. et al. Coxiella symbionts are widespread into hard ticks. Parasitol. Res. 115(12), 4691–4699. https://doi.org/10.1007/s00436-016-5230-z (2016).Article 
PubMed 

Google Scholar 
52.Duron, O. The IS1111 insertion sequence used for detection of Coxiella burnetii is widespread in Coxiella-like endosymbionts of ticks. FEMS Microbiol. Lett. 362(17), 1–8. https://doi.org/10.1093/femsle/fnv132 (2015).CAS 
Article 

Google Scholar  More

1.Walker, M. et al. Formal ratification of the subdivision of the Holocene Series/Epoch (Quaternary System/Period): two new Global Boundary Stratotype Sections and Points (GSSPs) and three new stages/subseries. Episodes [online]. https://doi.org/10.18814/epiiugs/2018/018016 (2018).2.Liu, T., Lu, Y. & Zheng, H. Loess and the Environment. (China Ocean Press, 1985).3.An, Z. et al. Asynchronous Holocene optimum of the East Asian monsoon. Quat. Sci. Rev. 19, 743–762 (2000).ADS 
Article 

Google Scholar 
4.Dai, A. G. & Zhao, T. Uncertainties in historical changes and future projections of drought. Part I: estimates of historical drought changes. Clim. Chang. 144, 519–533 (2017).ADS 
Article 

Google Scholar 
5.Zastrow, M. China’s tree-planting could falter in a warming world. Nature 573, 474–475 (2019).ADS 
CAS 
PubMed 
Article 

Google Scholar 
6.Huang, J., Yu, H., Guan, X., Wang, G. & Guo, R. Accelerated dryland expansion under climate change. Nat. Clim. Change 6, 166–172 (2016).ADS 
Article 

Google Scholar 
7.Jin, G. & Liu, T. Mid-Holocene climate change in North China, and the effect on cultural development. Chin. Sci. Bull. 47, 408–413 (2002).Article 

Google Scholar 
8.Wang, Y. et al. The Holocene Asian monsoon: links to solar changes and North Atlantic climate. Science 308, 854–857 (2005).ADS 
CAS 
PubMed 
Article 

Google Scholar 
9.Cheng, H. et al. The Asian monsoon over the past 640,000 years and ice age terminations. Nature 534, 640–646 (2016).ADS 
CAS 
PubMed 
Article 

Google Scholar 
10.Dong, J. et al. A high-resolution stalagmite record of the Holocene East Asian monsoon from Mt Shennongjia, central China. Holocene 20, 257–264 (2010).ADS 
Article 

Google Scholar 
11.Beck, J. W. et al. A 550,000-year record of East Asian monsoon rainfall from Be-10 in loess. Science 360, 877–881 (2018).ADS 
CAS 
PubMed 
Article 

Google Scholar 
12.Goldsmith, Y. et al. Northward extent of East Asian monsoon covaries with intensity on orbital and millennial timescales. Proc. Natl Acad. Sci. U. S. A. 114, 1817–1821 (2017).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
13.Chen, F. et al. East Asian summer monsoon precipitation variability since the last deglaciation. Sci. Rep. 5, 11186 (2015).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
14.Li, Q. et al. Reconstructed moisture evolution of the deserts in northern China since the Last Glacial Maximum and its implications for the East Asian Summer Monsoon. Glob. Planet. Change 121, 101–112 (2014).ADS 
Article 

Google Scholar 
15.Xu, Z. et al. Critical transitions in Chinese dunes during the past 12,000 years. Sci. Adv. 6, eaay8020 (2020).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
16.Lu, H. et al. Late Quaternary aeolian activity in the Mu Us and Otindag dune fields (north China) and lagged response to insolation forcing. Geophys. Res. Lett. 32, L21716 (2005).ADS 
Article 

Google Scholar 
17.Peterse, F. et al. Decoupled warming and monsoon precipitation in East Asia over the last deglaciation. Earth Planet. Sci. Lett. 301, 256–264 (2011).ADS 
CAS 
Article 

Google Scholar 
18.Yang, X. et al. Early-Holocene monsoon instability and climatic optimum recorded by Chinese stalagmites. Holocene 29, 1059–1067 (2019).ADS 
Article 

Google Scholar 
19.Wei, Y. et al. Holocene and deglaciation hydroclimate changes in northern China as inferred from stalagmite growth frequency. Glob. Planet. Change 195, 103360 (2020).Article 

Google Scholar 
20.Wang, B. et al. How to measure the strength of the East Asian Summer Monsoon. J. Clim. 21, 4449–4463 (2008).ADS 
Article 

Google Scholar 
21.Liu, J. et al. Holocene East Asian summer monsoon records in northern China and their inconsistency with Chinese stalagmite delta O-18 records. Earth-Sci. Rev. 148, 194–208 (2015).ADS 
CAS 
Article 

Google Scholar 
22.Kutzbach, J. E. & Street-Perrott, F. A. Milankovitch forcing of fluctuations in the level of tropical lakes from 18 to 0 kyr BP. Nature 317, 130–134 (1985).ADS 
Article 

Google Scholar 
23.Liu, Z. et al. Chinese cave records and the East Asia Summer Monsoon. Quat. Sci. Rev. 83, 115–128 (2014).ADS 
Article 

Google Scholar 
24.Wen, X., Liu, Z., Wang, S., Cheng, J. & Zhu, J. Correlation and anti-correlation of the East Asian summer and winter monsoons during the last 21,000 years. Nat. Commun. 7, 11999 (2016).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
25.Liu, Z. et al. Transient simulation of last deglaciation with a new mechanism for Bolling-Allerod warming. Science 325, 310–314 (2009).ADS 
CAS 
PubMed 
Article 

Google Scholar 
26.LeGrande, A. N. & Schmidt, G. A. Sources of Holocene variability of oxygen isotopes in paleoclimate archives. Clim. Past 5, 441–455 (2009).Article 

Google Scholar 
27.Zhang, H. et al. East Asian hydroclimate modulated by the position of the westerlies during Termination I. Science 362, 580–583 (2018).ADS 
CAS 
PubMed 
Article 

Google Scholar 
28.Chiang, J. C. H. et al. Role of seasonal transitions and westerly jets in East Asian paleoclimate. Quat. Sci. Rev. 108, 111–129 (2015).ADS 
Article 

Google Scholar 
29.Waelbroeck, C. et al. Sea-level and deep water temperature changes derived from benthic foraminifera isotopic records. Quat. Sci. Rev. 21, 295–305 (2002).ADS 
Article 

Google Scholar 
30.Joos, F. & Spahni, R. Rates of change in natural and anthropogenic radiative forcing over the past 20,000 years. Proc. Natl Acad. Sci. U.S.A. 105, 1425–1430 (2008).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
31.He, C. et al. The hydroclimate footprint accompanying pan-Asian monsoon water isotope evolution during the last deglaciation. Sci. Adv. 7, eabe2611 (2021).PubMed 
Article 

Google Scholar 
32.Wu, D. et al. Decoupled early Holocene summer temperature and monsoon precipitation in southwest China. Quat. Sci. Rev. 193, 54–67 (2018).ADS 
Article 

Google Scholar 
33.Xiao, X., Yao, A., Hillman, A., Shen, J. & Haberle, S. G. Vegetation, climate and human impact since 20 ka in central Yunnan Province based on high-resolution pollen and charcoal records from Dianchi, southwestern China. Quat. Sci. Rev. 236, 106297 (2020).Article 

Google Scholar 
34.Ding, Q. & Wang, B. Circumglobal teleconnection in the Northern Hemisphere summer. J. Clim. 18, 3483–3505 (2005).ADS 
Article 

Google Scholar 
35.Seneviratne, S. I. et al. Investigating soil moisture-climate interactions in a changing climate: a review. Earth-Sci. Rev. 99, 125–161 (2010).ADS 
CAS 
Article 

Google Scholar 
36.Bonan, G. B., Levis, S., Sitch, S., Vertenstein, M. & Oleson, K. W. A dynamic global vegetation model for use with climate models: concepts and description of simulated vegetation dynamics. Glob. Chang. Biol. 9, 1543–1566 (2003).ADS 
Article 

Google Scholar 
37.Yamazaki, T., Yabuki, H., Ishii, Y., Ohta, T. & Ohata, T. Water and energy exchanges at forests and a grassland in Eastern Siberia evaluated using a one-dimensional land surface model. J. Hydrometeor. 5, 504–515 (2004).ADS 
Article 

Google Scholar 
38.Overpeck, J., Anderson, D., Trumbore, S. & Prell, W. The southwest Indian Monsoon over the last 18 000 years. Clim. Dyn. 12, 213–225 (1996).Article 

Google Scholar 
39.Ruddiman, W. F. What is the timing of orbital-scale monsoon changes? Quat. Sci. Rev. 25, 657–658 (2006).ADS 
Article 

Google Scholar 
40.Clemens, S. C. & Prell, W. L. A 350,000 year summer-monsoon multi-proxy stack from the Owen Ridge, Northern Arabian Sea. Mar. Geol. 201, 35–51 (2003).ADS 
Article 

Google Scholar 
41.Xie, P. & Arkin, P. A. Global precipitation: a 17-year monthly analysis based on gauge observations, satellite estimates, and numerical model outputs. Bull. Am. Meteorol. Soc. 78, 2539–2558 (1997).ADS 
Article 

Google Scholar 
42.Xu, Q. et al. Vegetation succession and East Asian Summer Monsoon Changes since the last deglaciation inferred from high-resolution pollen record in Gonghai Lake, Shanxi Province, China. Holocene 27, 835–846 (2017).ADS 
Article 

Google Scholar 
43.Xiao, J. et al. Holocene vegetation variation in the Daihai Lake region of north-central China: a direct indication of the Asian monsoon climatic history. Quat. Sci. Rev. 23, 1669–1679 (2004).ADS 
Article 

Google Scholar 
44.Blaauw, M. Methods and code for ‘classical’ age-modelling of radiocarbon sequences. Quat. Geochronol. 5, 512–518 (2010).Article 

Google Scholar 
45.Zheng, Z. et al. East Asian pollen database: modern pollen distribution and its quantitative relationship with vegetation and climate. J. Biogeogr. 41, 1819–1832 (2014).Article 

Google Scholar 
46.Song, C. et al. Simulation of China Biome reconstruction based on pollen data from surface sediment samples. Acta Botanica. Sin. 43, 201–209 (2001).
Google Scholar 
47.Fick, S. E. & Hijmans, R. J. WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37, 4302–4315 (2017).Article 

Google Scholar 
48.Overpeck, J. T., Webb, T. & Prentice, I. C. Quantitative interpretation of fossil pollen spectra: dissimilarity coefficients and the method of modern analogs. Quat. Res. 23, 87–108 (1985).Article 

Google Scholar 
49.Guiot, J. Methodology of the last climatic cycle reconstruction in France from pollen data. Palaeogeogr. Palaeoclimatol. Palaeoecol. 80, 49–69 (1990).Article 

Google Scholar 
50.Peyron, O. et al. Climatic reconstruction in Europe for 18,000 YR B.P. from pollen data. Quat. Res. 49, 183–196 (1998).Article 

Google Scholar 
51.Davis, A. S., Brewer, S., Stevenson, A. C. & Guiot, J. The temperature of Europe during the Holocene reconstructed from pollen data. Quat. Sci. Rev. 22, 1701–1716 (2003).ADS 
Article 

Google Scholar 
52.Marsicek, J., Shuman, B. N., Bartlein, P. J., Shafer, S. L. & Brewer, S. Reconciling divergent trends and millennial variations in Holocene temperatures. Nature 554, 92–96 (2018).ADS 
CAS 
PubMed 
Article 

Google Scholar 
53.Juggins, S. Rioja: analysis of quaternary science data. https://cran.r-project.org/package=rioja (2017).54.Prentice, C. et al. Special paper: a global biome model based on plant physiology and dominance, soil properties and climate. J. Biogeogr. 19, 117–134 (1992).Article 

Google Scholar 
55.Williams, W. & Shuman, B. Obtaining accurate and precise environmental reconstructions from the modern analog technique and North American surface pollen dataset. Quat. Sci. Rev. 27, 669–687 (2008).ADS 
Article 

Google Scholar 
56.Simpson, G. L. “Analogue methods in palaeolimnology” in Tracking Environmental Change Using Lake Sediments: Data Handling and Numerical Techniques, (eds Birks, H. J. B., Lotter, A. F., Juggins, S. & Smol, J. P.) 495–522 (Springer, 2012).57.Birks, H. J. B., Line, J. M., Juggins, S., Stevenson, A. C. & ter Braak, C. J. F. Diatoms and pH reconstruction. Philos. Trans. R. Soc. B 327, 263–278 (1990).ADS 

Google Scholar 
58.ter Braak, C. J. F. & Juggins, S. Weighted averaging partial least squares regression (WA-PLS): an improved method for reconstructing environmental variables from species assemblages. Hydrobiologia 269, 485–502 (1993).Article 

Google Scholar 
59.Collins, W. D. et al. The Community Climate System Model version 3 (CCSM3). J. Clim. 19, 2122–2143 (2006).ADS 
Article 

Google Scholar 
60.Berger, A. Long-term variations of daily insolation and quaternary climatic changes. J. Atmos. Sci. 35, 2362–2367 (1978).ADS 
Article 

Google Scholar 
61.Peltier, W. R. Global glacial isostasy and the surface of the ice-age earth: the ICE-5G (VM2) model and GRACE. Annu. Rev. Earth Planet. Sci. 32, 111–149 (2004).ADS 
CAS 
Article 

Google Scholar 
62.He, F. Simulating transient climate evolution of the last deglaciation with CCSM3. PhD thesis, University of Wisconsin-Madison (2011).63.Shakun, J. D. et al. Global warming preceded by increasing carbon dioxide concentrations during the last deglaciation. Nature 484, 49–54 (2012).ADS 
CAS 
PubMed 
Article 

Google Scholar 
64.He, F. et al. Northern hemisphere forcing of southern hemisphere climate during the last deglaciation. Nature 494, 81–85 (2013).ADS 
CAS 
PubMed 
Article 

Google Scholar 
65.Liu, Z. et al. Younger Dryas cooling and the Greenland climate response to CO2. Proc. Natl Acad. Sci. U.S.A. 109, 11101–11104 (2012).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
66.Liu, Z. et al. Evolution and forcing mechanisms of El Nino over the past 21,000 years. Nature 515, 550–553 (2014).ADS 
CAS 
PubMed 
Article 

Google Scholar 
67.Liu, Z. et al. The Holocene temperature conundrum. Proc. Natl Acad. Sci. U.S.A. 111, E3501–E3505 (2014).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
68.Otto-Bliesner, B. L. et al. Coherent changes of southeastern equatorial and northern African rainfall during the last deglaciation. Science 346, 1223–1227 (2014).ADS 
CAS 
PubMed 
Article 

Google Scholar  More

Altogether nine polished pieces of the lower Cenomanian Kachin amber from northern Myanmar (Figs. 1A–D, 2A–E) were examined in this study (depository: Russian Museum of Biodiversity Hotspots, N. Laverov Federal Center for Integrated Arctic Research of the Ural Branch of the Russian Academy of Sciences, Arkhangelsk, Russia). A brief description of each amber piece is given below.Figure 1Lower Cenomanian Kachin amber samples with specimens and borings of †Palaeolignopholas kachinensis gen. & sp. nov. from northern Myanmar used in this study. (A) RMBH biv1115 (frontal view with the holotype). (B) RMBH biv1101 (lateral view with two paratypes and a shell fragment). (C) RMBH biv1116 (frontal view with the fossilized paratype). (D) RMBH biv1100 (frontal view with borings). The red frames indicate position of the type specimens (holotype and some paratypes). The red arrows indicate bivalve borings. Scale bars = 5 mm. (Photos: Ilya V. Vikhrev).Full size imageFigure 2Lower Cenomanian Kachin amber samples with borings of †Palaeolignopholas kachinensis gen. & sp. nov. from northern Myanmar used in this study. (A) RMBH biv1102 (frontal view). (B) RMBH biv1103 (frontal view). (C) RMBH biv1114 (frontal view). (D) RMBH biv1118 (frontal view). (E) RMBH biv1117 (frontal view). The red arrows indicate bivalve borings. Scale bars = 5 mm. (Photos: Ilya V. Vikhrev).Full size imageRMBH biv1115: Size 8.5 × 5.8 × 8.1 mm (Fig. 1A). Inclusions: articulated shell of †Palaeolignopholas kachinensis gen. & sp. nov., “floating” in the resin (the holotype).RMBH biv1101: Size 15.6 × 6.4 × 11.5 mm (Fig. 1B). Inclusions: two complete articulated shells (paratypes) and a shell fragment of †Palaeolignopholas kachinensis gen. & sp. nov., “floating” in the resin.RMBH biv1116: Size 22.5 × 8.3 × 16.5 mm (Fig. 1C). Inclusions: fossilized shell of †Palaeolignopholas kachinensis gen. & sp. nov. (paratype), borings of this species (filled with fine gray sand), unidentified fly specimens (Insecta: Diptera), and unidentified organic fragments (probably, plant debris).RMBH biv1100: Size 17.5 × 4.9 × 12.0 mm (Fig. 1D). Inclusions: borings of †Palaeolignopholas kachinensis gen. & sp. nov. (filled with fine gray sand), and an unidentified caddisfly specimen (Insecta: Trichoptera).RMBH biv1102: Size 15.6 × 5.1 × 12.7 mm (Fig. 2A). Inclusions: borings of †Palaeolignopholas kachinensis gen. & sp. nov. (filled with fine gray sand), and unidentified organic fragments (probably, plant debris).RMBH biv1103: Size 19.6 × 4.7 × 14.3 mm (Fig. 2B). Inclusions: borings of †Palaeolignopholas kachinensis gen. & sp. nov. (filled with fine gray sand), an unidentified beetle specimen (Insecta: Coleoptera), and unidentified organic fragments (probably, plant debris).RMBH biv1114: Size 33.1 × 7.8 × 21.7 mm (Fig. 2C). Inclusions: multiple borings of †Palaeolignopholas kachinensis gen. & sp. nov. (filled with fine gray sand), and unidentified plant remains.RMBH biv1118: Size 25.1 × 8.4 × 14.3 mm (Fig. 2D). Inclusions: separate borings of †Palaeolignopholas kachinensis gen. & sp. nov. (filled with fine gray sand), a plant fragment with a cluster of borings around, and an unidentified insect specimen.RMBH biv1117: Size 15.5 × 3.9 × 10.7 mm (Fig. 2E). Inclusions: borings of †Palaeolignopholas kachinensis gen. & sp. nov. (filled with fine gray sand), and an unidentified insect specimen.Additionally, six amber samples containing adult and sub-adult specimens of †Palaeolignopholas kachinensis gen. & sp. nov. were examined using photographs in published works as follows: BMNH 20205 (Department of Palaeontology, Natural History Museum, London, UK)15, NIGP 169623 and NIGP 169624 (Nanjing Institute of Geology and Palaeontology, Chinese Academy of Sciences, Nanjing, China)20, RS.P1450 (Ru D. A. Smith collection, Kuala Lumpur, Malaysia)19, and AMNH (Division of Invertebrates, American Museum of Natural History, New York, NY, United States of America)16.Based on morphological analyses of the fossil piddock shells, it was found to be a genus and species new to science, which is described here.Systematic paleontologyPhylum Mollusca Linnaeus, 1758Class Bivalvia Linnaeus, 1758Family Pholadidae Lamarck, 1809Subfamily Martesiinae Grant & Gale, 1931†Palaeolignopholas gen. novLSID: http://zoobank.org/urn:lsid:zoobank.org:act:1D686DCE-A5E9-41DA-9504-2EC58C93D988Type species: †Palaeolignopholas kachinensis gen. & sp. nov.Etymology. This name is derived from the prefix ‘Palaeo-’ (ancient), and ‘-lignopholas’, the name of a recent genus of estuarine and freshwater piddocks boring into wood, mudstone rocks, brickwork, laterites, etc.11,13. Masculine in gender.Diagnosis. The new monotypic genus is conchologically similar to several other piddock genera such as Lignopholas, Martesia, and Diplothyra Tryon 1862 but can be distinguished from these taxa by the following combination of characters: mesoplax relatively small, triangular, divided longitudinally, posterior slope without concentric sculpture, sculptured valve with concave parallel ridges (Martesia-like “rasping teeth”) curved anteriorly, periostracal lamellae dense, fine, hair-like. The fossil genus †Opertochasma Stephenson, 1952 shares a divided mesoplax but it clearly differs from both †Palaeolignopholas gen. nov. and Lignopholas by having two radial grooves on the shell surface21.Distribution. Kachin State, northern Myanmar; Upper Cretaceous (lower Cenomanian)15,19,22.Comments. Both †Palaeolignopholas gen. nov. and Lignopholas appear to be closely related to each other because they share a longitudinally divided mesoplax and periostracal lamellae, which are considered diagnostic features distinguishing this clade from Martesia + Diplothyra. Based on available conchological characters, we assume that †Palaeolignopholas gen. nov. might be placed on the ancestral stem lineage of the Lignopholas clade, although a possibility of homeomorphy could not entirely be excluded.†Palaeolignopholas kachinensis gen. & sp. nov = Plant Antheridia or Fungal Sporangia indet. sensu Grimaldi et al. (2002): 9, fig. 2a,b (bivalve specimens), fig. 3 (borings), fig. 5 (shell reconstruction of an immature specimen), figs. 6 and 7 (SEMs of borings surface showing rasped ornament at different magnifications)16. = Palaeoclavaria burmitis Poinar & Brown (2003): 765, figs. 1–4 (borings) [this fungal taxon was introduced using a trace fossil (boring) as the holotype]17; Poinar (2016): 2, figs. 10, 15, 16 (borings)18. = Martesiinae indet. sensu Smith & Ross (2018): 4, figs. 1a–c, 2a,b, 3a–d (borings), 4a,b, 5a–e (bivalve specimens)19. = Pholadidae indet. sensu Mao et al. (2018): 99, figs. 8a–f (borings), 8g,h (bivalve specimens)20. = Martesia sp. 2 sensu Mayoral et al. (2020): 10, figs. 4a (borings), 7b, 8a–l (bivalve specimens)15. = Pholadidae indet. sensu Balashov (2020): 623.Figures 1, 2, 3, 4, 5, 6 and 7.Figure 3Holotype and a paratype of †Palaeolignopholas kachinensis gen. & sp. nov. from lower Cenomanian Kachin amber, northern Myanmar. (A) Holotype: ventro-lateral view of articulated shell. (B) Paratype: anterio-lateral view of fossilized shell. VN ventral margin; DR dorsal margin; AN anterior margin; PS posterior margin; d disc; rs rasping surface of the valve; uvs umbonal ventral sulcus; pg pedal gape; pl periostracal lamellae. Scale bars = 500 µm. (Photos: Ilya V. Vikhrev).Full size imageFigure 4Paratypes of †Palaeolignopholas kachinensis gen. & sp. nov. from lower Cenomanian Kachin amber, northern Myanmar. (A) Paratype: dorsal view of articulated shell. Scale bar = 500 µm. (B) Paratype: dorsal view of articulated shell. The detached and deflected umbonal paired fragment of the valves is framed by red square. The blue contour indicates the lifetime position of this fragment. The blue arrows show the shell breakages. Scale bar = 200 µm. (C) Umbonal paired fragment of the holotype valves (inner view). The blue arrows show the shell breakage. Scale bar = 200 µm.  VN ventral margin; DR dorsal margin; AN anterior margin; PS posterior margin; ms longitudinally divided mesoplax (inner view); pr prora; d disc; rs rasping surface of the valve; uvs umbonal ventral sulcus; pg pedal gape; pl periostracal lamellae; sb shell breakage. (Photos: Ilya V. Vikhrev).Full size imageFigure 5Rasping surface of †Palaeolignopholas kachinensis gen. & sp. nov. shell. (A) Holotype shell. The red frame marks position of the enlarged area. (B) Undulated micro-sculpture of the rasping surface. Scale bar = 100 µm. (Photos: Ilya V. Vikhrev).Full size imageFigure 6Schematic reconstruction of †Palaeolignopholas kachinensis gen. & sp. nov. from lower Cenomanian Kachin amber, northern Myanmar based on the type series and other fossil material15,16,19,20. (A) Lateral view of adult specimen. (B) Dorsal view of adult specimen. (C) Ventral view of adult specimen (based on a paratype BMNH 2020515). (D) Anterio-ventral view of immature specimen. (E) Dorsal view of immature specimen. (F) Mesoplax of adult specimen. (G) Mesoplax of immature specimen. d disc; mt metaplax; ms mesoplax; hp hypoplax; ca callum; uvs umbonal ventral sulcus; pg pedal gape; pl periostracal lamellae. Scale bars = 1 mm (A–C). (Line graphics: Yulia E. Chapurina).Full size imageFigure 7Clavate borings of †Palaeolignopholas kachinensis gen. & sp. nov. from lower Cenomanian Kachin amber, northern Myanmar. (A) Cluster of borings. It marks drilling of immature piddocks into soft resin from the unidentified plant (wood?) fragment. (B–D) Clavate borings of adult piddocks. Scale bars = 1 mm. Abbreviation: bg a characteristic bioglyph indicating the shell rotation inside hardening resin. (Photos: Ilya V. Vikhrev).Full size imageLSID: http://zoobank.org/urn:lsid:zoobank.org:act:F6659EBF-B0A4-4B21-A99B-2C56BDB7EC9B.Common name. Kachin Amber Piddock.Holotype. RMBH biv1115, the adult shell with length 3.07 mm and width 1.13 mm “floating” in the resin (Figs. 1A, 3A, 5A,B), local collector leg., Russian Museum of Biodiversity Hotspots, N. Laverov Federal Center for Integrated Arctic Research of the Ural Branch of the Russian Academy of Sciences, Arkhangelsk, Russia.Paratypes. RMBH biv1116, the fossilized adult shell with length 4.05 mm and width 1.83 mm (Figs. 1C, 3B); RMBH biv1101, the immature specimen with articulated shell (width 1.86 mm) sharing a detached and deflected umbonal paired fragment of the valves due to the shell breakage (Figs. 1B, 4B,C); RMBH biv1101, the other immature specimen with shell length 2.68 mm and shell width 2.52 mm in this amber piece (Figs. 1B, 4A); BMNH 20205, adult specimen [illustrated in Mayoral et al. (2020): fig. 7B15], Department of Palaeontology, Natural History Museum, London, UK; NIGP 169623, adult specimen [illustrated in Mao et al. (2018): 100, fig. 8G20], and NIGP 169624, two adult specimens [illustrated in Mao et al. (2018): 100, fig. 8H20], Nanjing Institute of Geology and Palaeontology, Chinese Academy of Sciences, Nanjing, China; RS.P1450, two sub-adult specimens [illustrated in Smith & Ross (2018): 5, fig. 4A,B19], Ru D. A. Smith collection, Kuala Lumpur, Malaysia.Type locality and strata. The Noije Bum Hill mines, Hukawng Valley, near Tanai (26.3593°N, 96.7200°E), Kachin State, northern Myanmar; Upper Cretaceous (lower Cenomanian; absolute age of youngest zircons in enclosing marine sediment: 98.79 ± 0.62 Ma)19,22.Etymology. The name of this species reflects its type locality, which is situated in the Kachin State of Myanmar.Diagnosis. As for the genus.Description. Shell small (up to 9.3 mm in length15,19,20), conical, with a rounded anterior margin, tapering posteriorly (Figs. 3A,B, 4A–C, 6A–E); its shape is similar to those in the recent Lignopholas, Martesia, and Diplothyra. Valve sculptured, with concave parallel ridges (Martesia-like “rasping teeth”) curved anteriorly (Fig. 5A,B). The ridges share a characteristic wave-like micro-sculpture (Fig. 5B). Sulcus deep (Figs. 3A, 4C, 6A–C). Mesoplax longitudinally divided, relatively small, triangular, tapering or lobed anteriorly (Fig. 3A, 6B,F), in immature specimens sometimes with lateral lobes (Figs. 4C, 6E,G). Metaplax and hypoplax long, narrow, not longitudinally divided but sometimes slightly bifurcated posteriorly (Fig. 6A–C). Periostracum densely covered by fine, hair-like lamellae (Figs. 4B,C and 6D). Umbonal reflection with large flattened ridge. Pedal gape presents in immature (Figs. 4A,B, 6D) and some adult specimens (Fig. 3A) but it is covered by callum in older specimens (Figs. 3B, 6C). Morphological details of the new species were also presented in a series of micro-CT images published Mayoral et al. (see Fig. 8 in that paper15) and in the reconstruction of Grimaldy et al. (see Fig. 5 in that work16).Figure 8Recent freshwater piddock Lignopholas fluminalis (Blanford, 1867) in the middle reaches of the Kaladan River, Rakhine State, Myanmar13. (A) Habitat of the freshwater piddock: river pool with siltstone rocks at the bottom, a possible modern analogue of the Mesozoic riverine ecosystem with †Palaeolignopholas. (B) Siltstone rock fragment with living freshwater piddocks inside their clavate borings. (C) Ethanol-preserved piddock (dorsal view). (D) Living piddock with fully developed callum (ventral view). (E) Living piddock with pedal gape (ventral view). Abbreviations: d disc; mt metaplax; ms mesoplax; ca callum; uvs umbonal ventral sulcus; pg pedal gape; pl periostracal lamellae. Scale bar = 2 mm. (Photos: Olga V. Aksenova).Full size imageBorings and corresponding ichnotaxon. The borings produced by †Palaeolignopholas kachinensis gen. & sp. nov. represent club-shaped (clavate) structures (Figs. 1C,D, 2A–E, 7A–D), sometimes with a characteristic bioglyph revealing the shell rotation in hardening resin (Fig. 7C). These borings were illustrated in detail15,16,17,19,20, and were considered belonging to Teredolites clavatus Leymerie, 184215. Initially, the trace fossils produced by the Kachin amber piddock were described as sporocarps of Palaeoclavaria burmitis Poinar & Brown, 2003, a non-gilled hymenomycete taxon17. The holotype of this taxon represents a club-shaped piddock crypt labelled as follows: “Amber from the Hukawng Valley in Burma; specimen (in piece B with accession number B-P-1) deposited in the Poinar amber collection maintained at Oregon State University (holotype)17”. Hence, Palaeoclavaria Poinar & Brown, 2003 and P. burmitis Poinar & Brown, 2003 must be considered ichnogenus and ichnospecies, respectively. New ichnotaxonomic synonymies are formally proposed here as follows: Teredolites Leymerie, 1842 (= Palaeoclavaria Poinar & Brown, 2003 syn. nov.), and Teredolites clavatus Leymerie, 1842 (= Palaeoclavaria burmitis Poinar & Brown, 2003 syn. nov.). 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