Philippa Kaur

Jean Combes’s love of nature as a child led her to note the signs of starting spring. Her long-term records are now part of a vital growing citizen science dataset that starkly shows how climate change is shifting the timing of the natural world.For people living in colder parts of the world, watching for the first signs of spring — from the opening of snowdrops and daffodils, to birds building their nests, to the return of bees and butterflies — is a common winter pastime. Jean Combes has not just been watching out for these signs, but also recording them, ever since she was a child. Taking note of the earliest emergence of leaves in springtime — first as a child of 11 years, and then continuously from the age of 20 years — Jean has now collected one of the longest continuous datasets of spring leaf-out time in the UK (see also Correspondence by Vitasse et al.). These almost 75 years of data show a clear shift that corroborates shifts now acknowledged for diverse species around the world: springtime is coming earlier, and the patterns of advance match the global trends in the changing climate. Jean’s naturalist endeavours have already earned her high honours in the form of an OBE (Order of the British Empire), and recognition of her own work is mirrored in a growing recognition of the vital role of citizen scientists in tracking the signs of our rapidly changing world.
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Meng, L. et al. Proc. Natl Acad. Sci. USA 117, 4228 (2020).CAS 
Article 

Google Scholar 
Wohlfahrt, G., Tomelleri, E. & Hammerle, A. Nat. Ecol. Evol. 3, 1668–1674 (2019).Article 

Google Scholar 
Wortman, S. E. & Lovell, S. T. J. Environ. Qual. 42, 1283–1294 (2013).CAS 
Article 

Google Scholar 
Su, Y. et al. Agri. For. Meterol. 280, 107765 (2020).Article 

Google Scholar 
Smith, I. A., Dearborn, V. K. & Hutyra, L. R. PLoS ONE 14, e0215846 (2019).Article 

Google Scholar 
Richardson, A. D. et al. Nature 560, 368–371 (2018).CAS 
Article 

Google Scholar 
Meineke, E. K., Dunn, R. R. & Frank, S. D. Biol. Lett. 10, 20140586 (2014).Article 

Google Scholar 
Liu, J. et al. Tour. Manag. 70, 262–272 (2019).Article 

Google Scholar 
Li, X. et al. Remote Sens. Environ. 222, 267–274 (2019).CAS 
Article 

Google Scholar 
Wang, S. et al. Nat. Ecol. Evol. 3, 1076–1085 (2019).Article 

Google Scholar 
Feeley, K. J. et al. Nat. Clim. Change 10, 965–970 (2020).CAS 
Article 

Google Scholar 
Li, D. et al. Nat. Ecol. Evol. 3, 1661–1667 (2019).Article 

Google Scholar 
Li, X. et al. Earth Syst. Sci. Data 11, 881–894 (2019).Article 

Google Scholar 
Román, M. O. et al. Remote Sens. Environ. 210, 113–143 (2018).Article 

Google Scholar 
Li, X. et al. Remote Sens. Environ. 215, 74–84 (2018).Article 

Google Scholar  More

Andersen EC, Bloom JS, Gerke JP, Kruglyak L (2014) A variant in the neuropeptide receptor npr-1 is a major determinant of Caenorhabditis elegans growth and physiology. PLoS Genet 10(2):e1004156. https://doi.org/10.1371/journal.pgen.1004156Andersen EC, Shimko TC, Crissman JR, Ghosh R, Bloom JS, Seidel HS et al. (2015) A powerful new quantitative genetics platform, combining caenorhabditis elegans high-throughput fitness assays with a large collection of recombinant strains. G3 5(5):911–920. https://doi.org/10.1534/g3.115.017178Article 
PubMed 
PubMed Central 

Google Scholar 
Angilletta MJ, Dunham AE (2003) The temperature-size rule in ectotherms: simple evolutionary explanations may not be general. Am Naturalist 162:3
Google Scholar 
Atkinson D (1994) Temperature and organism size–a biological law for ectotherms? Adv Ecol Res 25:1–58Azevedo RBR, French V, Partridge L (2002) Temperature modulates epidermal cell size in Drosophila melanogaster. J Insect Physiol 48:231–237CAS 
Article 

Google Scholar 
Azevedo RBR, James AC, McCabe J, Partridge L (1998) Latitudinal variation of wing: thorax size and wing-aspect ration in Drosophila melanogaster. Evolution 52(5):1353–1362PubMed 

Google Scholar 
Bates D, Mächler M, Bolker BM, Walker SC (2015) Fitting linear mixed-effects models using lme4. J Stat Softw 67(1):1–48. https://doi.org/10.18637/jss.v067.i01Beldade P, Mateus ARA, Keller RA (2011) Evolution and molecular mechanisms of adaptive developmental plasticity. Mol Ecol 20:1347–1363. https://doi.org/10.1111/j.1365-294X.2011.05016.xArticle 
PubMed 

Google Scholar 
Bochdanovits Z, Van Der Klis H, De Jong G (2003) Covariation of larval gene expression and adult body size in natural populations of Drosophila melanogaster. Mol Biol Evolution 20(11):1760–1766. https://doi.org/10.1093/molbev/msg179CAS 
Article 

Google Scholar 
Brenner S (1974) Genetics of the Caenorhabditis elegans. ChemBioChem 4(8):683–687. https://doi.org/10.1002/cbic.200300625CAS 
Article 

Google Scholar 
Callahan HS, Dhanoolal N, Ungerer MC (2005) Plasticity genes and plasticity costs: a new approach using an Arabidopsis recombinant inbred population. N Phytologist 166(1):129–140. https://doi.org/10.1111/j.1469-8137.2005.01368.xCAS 
Article 

Google Scholar 
Carta D, Villanova L, Costacurta S, Patelli A, Poli I, Vezzù S et al. (2011) Method for optimizing coating properties based on an evolutionary algorithm approach. Anal Chem 83(16):6373–6380. https://doi.org/10.1021/ac201337eCAS 
Article 
PubMed 

Google Scholar 
Czarnoleski M, Kramarz P, Malek D, Drobniak SM (2017) Genetic components in a thermal developmental plasticity of the beetle Tribolium castaneum. J Therm Biol 68:55–62. https://doi.org/10.1016/j.jtherbio.2017.01.015Article 
PubMed 

Google Scholar 
Dupuis J, Siegmund D (1999) Statistical methods for mapping quantitative trait loci from a dense set of markers. Genetics 151(1):373–386CAS 
Article 

Google Scholar 
Ellers J, Driessen G (2011) Genetic correlation between temperature-induced plasticity of life-history traits in a soil arthropod. Evolut Ecol 25:473–484. https://doi.org/10.1007/s10682-010-9414-1Article 

Google Scholar 
Fischer K, Bauerfeind SS, Fiedler K (2006) Temperature-mediated plasticity in egg and body size in egg size-selected lines of a butterfly. J Therm Biol 31:347–354. https://doi.org/10.1016/j.jtherbio.2006.01.006Article 

Google Scholar 
Gaertner BE, Phillips PC (2010) Caenorhabditis elegans as a platform for molecular quantitative genetics and the systems biology of natural variation. Genet Res 92(5–6):331–348. https://doi.org/10.1017/S0016672310000601CAS 
Article 

Google Scholar 
Gao AW, Sterken MG, uit de Bos J, van Creij J, Kamble R, Snoek BL et al. (2018) Natural genetic variation in C. elegans identified genomic loci controlling metabolite levels. Genome Res 28(9):1296–1308. https://doi.org/10.1101/gr.232322.117CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Ghosh SM, Testa ND, Shingleton AW (2013) Temperature-size rule is mediated by thermal plasticity of critical size in Drosophila melanogaster. Proc Biol Sci 280(1760):20130174. https://doi.org/10.1098/rspb.2013.0174Gutteling EW, Doroszuk A, Riksen JAG, Prokop Z, Reszka J, Kammenga JE (2007a) Environmental influence on the genetic correlations between life-history traits in Caenorhabditis elegans. Heredity 98:206–213. https://doi.org/10.1038/sj.hdy.6800929CAS 
Article 
PubMed 

Google Scholar 
Gutteling EW, Riksen JAG, Bakker J, Kammenga JE (2007b) Mapping phenotypic plasticity and genotype – environment interactions affecting life-history traits in Caenorhabditis elegans. Heredity 98:28–37. https://doi.org/10.1038/sj.hdy.6800894CAS 
Article 
PubMed 

Google Scholar 
Jovic K, Sterken MG, Grilli J, Bevers RPJ, Rodriguez M, Riksen JAG, et al. (2017) Temporal dynamics of gene expression in heat-stressed Caenorhabditis elegans. PLoS One 12:e0189445Kammenga JE, Doroszuk A, Riksen JAG, Hazendonk E, Spiridon L, Petrescu AJ et al. (2007) A Caenorhabditis elegans wild type defies the temperature-size rule owing to a single nucleotide polymorphism in tra-3. PLoS Genet 3(3):0358–0366. https://doi.org/10.1371/journal.pgen.0030034CAS 
Article 

Google Scholar 
Kang MH, Zaitlen NA, Wade CM, Kirby A, Heckerman D, Daly MJ et al. (2008) Efficient control of population structure in model organism association mapping. Genetics 178(3):1709–1723. https://doi.org/10.1534/genetics.107.080101Article 
PubMed 
PubMed Central 

Google Scholar 
Klok CJ, Harrison JF (2013) The temperature size rule in Arthropods: independent of macro-environmental variables but size dependent. Integr Comp Biol 53(4):557–570. https://doi.org/10.1093/icb/ict075Article 
PubMed 

Google Scholar 
Kruijer W, Boer MP, Malosetti M, Flood PJ, Engel B, Kooke R et al. (2014) Marker-based estimation of heritability in immortal populations. Genetics 199(2):379–398. https://doi.org/10.1534/genetics.114.167916Article 
PubMed 
PubMed Central 

Google Scholar 
Lafuente E, Beldade P (2019) Genomics of developmental plasticity in animals. Front Genet 10:1–18. https://doi.org/10.3389/fgene.2019.00720CAS 
Article 

Google Scholar 
Lafuente E, Duneau D, Beldade P (2018) Genetic basis of thermal plasticity variation in Drosophila melanogaster body size. PLoS Genet 14(9):1–24. https://doi.org/10.1371/journal.pgen.1007686CAS 
Article 

Google Scholar 
Li Y, Álvarez OA, Gutteling EW, Tijsterman M, Fu J, Riksen JAG et al. (2006) Mapping determinants of gene expression plasticity by genetical genomics in C. elegans. PLoS Genet 2(12):2155–2161. https://doi.org/10.1371/journal.pgen.0020222CAS 
Article 

Google Scholar 
Nagashima T, Ishiura S, Suo S (2017) Regulation of body size in Caenorhabditis elegans: effects of environmental factors and the nervous system. Int J Developmental Biol 61(6–7):367–374. https://doi.org/10.1387/ijdb.160352ssCAS 
Article 

Google Scholar 
Nakad R, Snoek LB, Yang W, Ellendt S, Schneider F, Mohr TG et al. (2016) Contrasting invertebrate immune defense behaviors caused by a single gene, the Caenorhabditis elegans neuropeptide receptor gene npr-1. BMC Genomics 17(1):1–20. https://doi.org/10.1186/s12864-016-2603-8CAS 
Article 

Google Scholar 
Norry FM, Loeschcke VR (2002) Longevity and resistance to cold stress in cold-stress selected lines and their controls in Drosophila melanogaster. J Evolut Biol 15:775–783Article 

Google Scholar 
Paaby AB, Rockman MV (2014) Cryptic genetic variation: evolution’ s hidden substrate. Nat Rev Genet 15(4):247–258. https://doi.org/10.1038/nrg3688CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Peng IF, Berke BA, Zhu Y, Lee WH, Chen W, Wu CF (2007) Temperature-dependent developmental plasticity of drosophila neurons: cell-autonomous roles of membrane excitability, Ca2+ influx, and cAMP signaling. J Neurosci 27(46):12611–12622. https://doi.org/10.1523/JNEUROSCI.2179-07.2007CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Petersen C, Dirksen P, Schulenburg H (2015) Why we need more ecology for genetic models such as C. elegans. Trends Genet 31(3):120–127. https://doi.org/10.1016/j.tig.2014.12.001CAS 
Article 
PubMed 

Google Scholar 
R Core Team (2021) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/Reddy KC, Andersen EC, Kruglyak L, Kim DH (2009) A polymorphism in npr-1 is a behavioral determinant of pathogen susceptibility in C. elegans. Science 323(5912):382–384. https://doi.org/10.1126/science.1166527CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Rieseberg LH, Archer MA, Wayne RK (1999) Transgressive segregation, adaptation and speciation. Heredity 83:363–372Rockman MV, Skrovanek SM, Kruglyak L (2010) Selection at linked sites shapes. Science 330:372–376. https://doi.org/10.1126/science.1194208Rodriguez M, Snoek LB, Riksen JAG, Bevers RP, Kammenga JE (2012) Genetic variation for stress-response hormesis in C. elegans lifespan. Exp Gerontol 47(8):581–587. https://doi.org/10.1016/j.exger.2012.05.005CAS 
Article 
PubMed 

Google Scholar 
Saltz JB, Bell AM, Flint J, Gomulkiewicz R, Hughes KA, Keagy J (2018) Why does the magnitude of genotype-by-environment interaction vary? Ecol Evolution 8(12):6342–6353. https://doi.org/10.1002/ece3.4128Article 

Google Scholar 
Scheiner S (1993) Plasticity as a selectable trait: reply to via. Am Soc Naturalist 142(2):371–373Article 

Google Scholar 
Sgro C, Hoffmann A (2004) Genetic correlations, tradeoffs and environmental variation. Heredity 93:241–248. https://doi.org/10.1038/sj.hdy.6800532Snoek BL, Sterken MG, Bevers RPJ, Volkers RJM, van Hof A, Brenchley R, et al. (2017) Contribution of trans regulatory eQTL to cryptic genetic variation in C. elegans. BMC Genomics 18:500. https://doi.org/10.1186/s12864-017-3899-8Snoek BL, Volkers RJM, Nijveen H, Petersen C, Dirksen P, Sterken MG, et al. (2019) A multi-parent recombinant inbred line population of C. elegans allows identification of novel QTLs for complex life history traits. BMC Biol 17:24Snoek LB, Orbidans HE, Stastna JJ, Aartse A, Rodriguez M, Riksen JAG et al. (2014) Widespread genomic incompatibilities in Caenorhabditis elegans. G3: Genes, Genomes, Genet 4(10):1813–1823. https://doi.org/10.1534/g3.114.013151Article 

Google Scholar 
Snoek LB, Sterken MG, Hartanto M, van Zuilichem AJ, Kammenga JE, de Ridder D et al. (2020) WormQTL2: an interactive platform for systems genetics in Caenorhabditis elegans. Database 2020:baz149. https://doi.org/10.1093/database/baz149Steigenga MJ, Zwaan BJ, Brakefield PM, Fischer K (2005) The evolutionary genetics of egg size plasticity in a butterfly. J Evolut Biol 18:281–289. https://doi.org/10.1111/j.1420-9101.2004.00855.xCAS 
Article 

Google Scholar 
Sterken MG, Bevers RPJ, Volkers RJM, Riksen JAG, Kammenga JE, Snoek BL (2020) Dissecting the eQTL micro-architecture in Caenorhabditis elegans. Front Genet 11(Nov):1–15. https://doi.org/10.3389/fgene.2020.501376CAS 
Article 

Google Scholar 
Sterken MG, Plaat LVB, Van Der Riksen JAG, Rodriguez M, Schmid T, Hajnal A, et al. (2017) Ras/MAPK modifier loci revealed by eQTL in Caenorhabditis elegans. G3 (Bethesda) 7:3185–3193. https://doi.org/10.1534/g3.117.1120Sterken MG, Snoek LB, Kammenga JE, Andersen EC (2015) The laboratory domestication of Caenorhabditis elegans. Trends Genet 31(5):224–231. https://doi.org/10.1016/j.tig.2015.02.009CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Têtard-jones C, Kertesz M, Preziosi R (2011) Quantitative trait loci mapping of phenotypic plasticity and genotype-environment interactions in plant and insect performance. Philos Trans R Soc B: Biol Sci 366:1569Article 

Google Scholar 
Thompson OA, Snoek LB, Nijveen H, Sterken MG, Volkers RJM, Brenchley R et al. (2015) Remarkably divergent regions punctuate the genome assembly of the Caenorhabditis elegans Hawaiian strain CB4856. Genetics 200(3):975–989. https://doi.org/10.1534/genetics.115.175950CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Van Voorhies WA (1996) Bergmann size clines: a simple explanation for their occurrence in ectotherms. Evolution 50(3):1259–1264. https://doi.org/10.1111/j.1558-5646.1996.tb02366.xArticle 
PubMed 

Google Scholar 
Via S, Gomulkiewicz R, de Jong G, Scheiner SM, Schlichting CD, van Tienderen PH (1995) Adaptive phenotypic plasticity: consensus and controversy. Trends Ecol Evolution 10:5Article 

Google Scholar 
Viñuela A, Snoek LB, Riksen JAG, Kammenga JE (2010) Genome-wide gene expression regulation as a function of genotype and age in C. elegans. Genome Res 20(7):929–937. https://doi.org/10.1101/gr.102160.109CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Viñuela A, Snoek LB, Riksen JAG, Kammenga JE (2011) Gene expression modifications by temperature-toxicants interactions in Caenorhabditis elegans. PLoS One 6(9):e24676. https://doi.org/10.1371/journal.pone.0024676Wickham H (2011) Ggplot2. Wiley Interdiscip Rev: Computational Stat 3(2):180–185. https://doi.org/10.1002/wics.147Article 

Google Scholar 
Wickham H, Averick M, Bryan J, Chang W, McGowan L, François R et al. (2019) Welcome to the Tidyverse. J Open Source Softw 4(43):1686. https://doi.org/10.21105/joss.01686Article 

Google Scholar  More

Polyzos, S. & Tsiotas, D. The contribution of transport infrastructures to the economic and regional development: A review of the conceptual framework. Theor. Empir. Res. Urban Manag. 15, 5–23 (2020).
Google Scholar 
Ledec, G. & Posas, P. J. Biodiversity conservation in road projects: Lessons from World Bank experience in Latin America. Transp. Res. Rec. 1819, 198–202 (2003).Article 

Google Scholar 
Hughes, A. C. Understanding and minimizing environmental impacts of the Belt and Road Initiative. Conserv. Biol. 33, 883–894 (2019).Article 

Google Scholar 
Seiler, A. in COST 341—habitat fragmentation due to transportation infrastructure: the European review (eds Trocmé, M. et al.) Ch. 3, 31–50 (Office for Official Publications of the European Communities, 2002).Marcantonio, M., Rocchini, D., Geri, F., Bacaro, G. & Amici, V. Biodiversity, roads, & landscape fragmentation: Two Mediterranean cases. Appl. Geogr. 42, 63–72. https://doi.org/10.1016/j.apgeog.2013.05.001 (2013).Article 

Google Scholar 
Plămădeal, V. & Slobodeaniuc, S. Negative impact of railway transport on the ambient environment. J. Eng. Sci. https://doi.org/10.5281/zenodo.2640044 (2019).Lala, F. et al. Wildlife roadkill in the Tsavo Ecosystem, Kenya: Identifying hotspots, potential drivers, and affected species. Heliyon 7, e06364 (2021).Article 

Google Scholar 
Laurance, W. F. et al. A global strategy for road building. Nature 513, 229–232. https://doi.org/10.1038/nature13717 (2014).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Laurance, W. F., Goosem, M. & Laurance, S. G. W. Impacts of roads and linear clearings on tropical forests. Trends Ecol. Evol. 24, 659–669. https://doi.org/10.1016/j.tree.2009.06.009 (2009).Article 
PubMed 

Google Scholar 
Clair, C. C. S., Whittington, J., Forshner, A., Gangadharan, A. & Laskin, D. N. Railway mortality for several mammal species increases with train speed, proximity to water, and track curvature. Sci. Rep. 10, 20476. https://doi.org/10.1038/s41598-020-77321-6 (2020).CAS 
Article 

Google Scholar 
Kušta, T., Ježek, M. & Keken, Z. Mortality of large mammals on railway tracks. Sci. Agric. Bohem. 42, 12–18 (2011).
Google Scholar 
Dorsey, B. & Olsson, M. Handbook of Road Ecology (eds van der Ree, R. et al.) Ch. 26, 219–227 (Wiley, 2015).Barrientos, R. & Borda-de-Água, L. Railway Ecology (eds Borda-de-Água, L. et al.) Ch. 4, 43–64 (Springer Open, 2017).Lucas, P. S., de Carvalho, R. G. & Grilo, C. Railway Ecology Ch. Chapter 6, 81–99 (2017).Barrientos, R., Ascensão, F., Beja, P., Pereira, H. M. & Borda-de-Água, L. Railway ecology vs. road ecology: Similarities and differences. Eur. J. Wildl. Res. 65, 1–9. https://doi.org/10.1007/s10344-018-1248-0 (2019).Article 

Google Scholar 
Jasińska, K. D. et al. Linking habitat composition, local population densities and traffic characteristics to spatial patterns of ungulate-train collisions. J. Appl. Ecol. 56, 2630–2640. https://doi.org/10.1111/1365-2664.13495 (2019).Article 

Google Scholar 
Smith, D. J., Ree, R. v. d. & Rosell, C. Handbook of Road Ecology (eds van der Ree, R. et al.) Ch. 21, 172–183 (Wiley, 2015).Gilhooly, P. S., Nielsen, S. E., Whittington, J. & Clair, C. C. S. Wildlife mortality on roads and railways following highway mitigation. Ecosphere 10, e02597 (2019).Article 

Google Scholar 
Clevenger, A. P., Chruszcz, B. & Gunson, K. E. Highway mitigation fencing reduces wildlife-vehicle collisions. Wildl. Soc. Bull. 29, 646–653 (2001).
Google Scholar 
Simpson, N. O. et al. Overpasses and underpasses: Effectiveness of crossing structures for migratory ungulates. J. Wildl. Manag. 80, 1370–1378. https://doi.org/10.1002/jwmg.21132 (2016).Article 

Google Scholar 
Seidler, R. G., Green, D. S. & Beckmann, J. P. Highways, crossing structures and risk: Behaviors of Greater Yellowstone pronghorn elucidate efficacy of road mitigation. Glob. Ecol. Conserv. 15, e00416. https://doi.org/10.1016/j.gecco.2018.e00416 (2018).Article 

Google Scholar 
Huijser, M. P. et al. Effectiveness of short sections of wildlife fencing and crossing structures along highways in reducing wildlife–vehicle collisions and providing safe crossing opportunities for large mammals. Biol. Conserv. 197, 61–68. https://doi.org/10.1016/j.biocon.2016.02.002 (2016).Article 

Google Scholar 
Olsson, M. P. O. & Widen, P. Effects of highway fencing and wildlife crossings on moose Alces alces movements and space use in southwestern Sweden. Wildl. Biol. 14, 111–117 (2008).Article 

Google Scholar 
Donaldson, B. Use of highway underpasses by large mammals and other wildlife in Virginia: Factors influencing their effectiveness. Transp. Res. Rec. 157–164, 2007. https://doi.org/10.3141/2011-17 (2011).Article 

Google Scholar 
Foster, M. L. & Humphrey, S. R. Use of highway underpasses by Florida panthers and other wildlife. Wildl. Soc. Bull. 23, 95–100 (1995).
Google Scholar 
Caldwell, M. R. & Klip, J. M. K. Wildlife interactions within highway underpasses. J. Wildl. Manag. 84, 227–236. https://doi.org/10.1002/jwmg.21801 (2019).Article 

Google Scholar 
Clevenger, A. P. & Waltho, N. Performance indices to identify attributes of highway crossing structures facilitating movement of large mammals. Biol. Conserv. 121, 453–464. https://doi.org/10.1016/j.biocon.2004.04.025 (2005).Article 

Google Scholar 
Mcdonald, W. & Clair, C. C. S. Elements that promote highway crossing structure use by small mammals in Banff National Park. J. Appl. Ecol. 41, 82–93 (2004).Article 

Google Scholar 
Mata Estacio, C., Hervás Bengoechea, I., Herranz Barrera, J., Suárez Cardona, F. & Arrazola, J. E. M. International Conference on Ecology and Transportation (ICOET 2003) Federal Highway Administration.Sawyer, H., Lebeau, C. & Hart, T. Mitigating roadway impacts to migratory mule deer—A case study with underpasses and continuous fencing. Wildl. Soc. Bull. 36, 492–498. https://doi.org/10.1002/wsb.166 (2012).Article 

Google Scholar 
Rodriguez, A., Crema, G. & Delibes, M. Use of non-wildlife passages across a high speed railway by terrestrial vertebrates. J. Appl. Ecol. 33, 1527–1540 (1996).Article 

Google Scholar 
Yanes, M., Velasco, J. M. & Sufirez, F. Permeability of roads and railways to vertebrates: The importance of culverts. Biol. Conserv. 71, 217–222 (1995).Article 

Google Scholar 
Rodriguez, A., Crema, G. & Delibes, M. Factors affecting crossing of red foxes and wildcats through non-wildlife passages across a high-speed railway. Ecography 2, 287–294 (1997).Article 

Google Scholar 
Weeks, S. Handbook of Road Ecology (eds van der Ree, R. et al.) Ch. 43, 353–356 (Wiley, 2015).Okita-Ouma, B. et al. Effectiveness of wildlife underpasses and culverts in connecting elephant habitats: A case study of new railway through Kenya’s Tsavo National Parks. Afr. J. Ecol. 59(3), 624–640 (2021).Article 

Google Scholar 
Collinson, W., Davies-Mostert, H., Roxburgh, L. & van der Ree, R. Status of road ecology research in Africa: Do we understand the impacts of roads, and how to successfully mitigate them?. Front. Ecol. Evol. 7, 479. https://doi.org/10.3389/fevo.2019.00479 (2019).ADS 
Article 

Google Scholar 
Wang, Y., Guan, L., Chen, J. & Kong, Y. Influences on mammals frequency of use of small bridges and culverts along the Qinghai-Tibet railway, China. Ecol. Res. 33, 879–887. https://doi.org/10.1007/s11284-018-1578-0 (2018).Article 

Google Scholar 
Ng, S. J., Dole, J. W., Sauvajot, R. M., Riley, S. P. D. & Valone, T. J. Use of highway undercrossings by wildlife in southern California. Biol. Conserv. 115, 499–507. https://doi.org/10.1016/s0006-3207(03)00166-6 (2004).Article 

Google Scholar 
Mata, C., Hervas, I., Herranz, J., Suarez, F. & Malo, J. E. Are motorway wildlife passages worth building? Vertebrate use of road-crossing structures on a Spanish motorway. J. Environ. Manag. 88, 407–415. https://doi.org/10.1016/j.jenvman.2007.03.014 (2008).CAS 
Article 

Google Scholar 
Mata, C., Herranz, J. & Malo, J. E. Attraction and avoidance between predators and prey at wildlife crossings on roads. Diversity 12, 166. https://doi.org/10.3390/d12040166 (2020).Article 

Google Scholar 
Stewart, L., Russell, B., Zelig, E., Patel, G. & Whitney, K. S. Wildlife crossing design influences effectiveness for small and large mammals in Banff National Park. Case Stud. Environ. 4, 1231752. https://doi.org/10.1525/cse.2020.1231752 (2020).Article 

Google Scholar 
Mysłajek, R. W., Nowak, S., Kurek, K., Tołkacz, K. & Gewartowska, O. Utilisation of a wide underpass by mammals on an expressway in the Western Carpathians, S Poland. Folia Zool. 65, 225–232. https://doi.org/10.25225/fozo.v65.i3.a8.2016 (2016).Article 

Google Scholar 
Clevenger, A. P. & Waltho, N. factors influencing the effectiveness of wildlife underpasses in Banff National Park, Alberta, Canada. Conserv. Biol. 14, 47–56 (2000).Article 

Google Scholar 
Laurance, W. F., Sloan, S., Weng, L. & Sayer, J. A. Estimating the environmental costs of Africa’s massive “development corridors”. Curr. Biol. 25, 3202–3208. https://doi.org/10.1016/j.cub.2015.10.046 (2015).CAS 
Article 
PubMed 

Google Scholar 
van der Ree, R., Gagnon, J. W. & Smith, D. J. Handbook of Road Ecology (eds van der Ree, R. et al.) Ch. 20, 159–171 (Wiley, 2015).Ascensão, F. & Mira, A. Factors affecting culvert use by vertebrates along two stretches of road in southern Portugal. Ecol. Res. 22, 57–66. https://doi.org/10.1007/s11284-006-0004-1 (2006).Article 

Google Scholar 
Hepenstrick, D., Thiel, D., Holderegger, R. & Gugerli, F. Genetic discontinuities in roe deer (Capreolus capreolus) coincide with fenced transportation infrastructure. Basic Appl. Ecol. 13, 631–638. https://doi.org/10.1016/j.baae.2012.08.009 (2012).Article 

Google Scholar 
Wilson, R. E., Farley, S. D., McDonough, T. J., Talbot, S. L. & Barboza, P. S. A genetic discontinuity in moose (Alces alces) in Alaska corresponds with fenced transportation infrastructure. Conserv. Genet. 16, 791–800. https://doi.org/10.1007/s10592-015-0700-x (2015).Article 

Google Scholar 
Jaeger, J. A. G. & Fahrig, L. Effects of road fencing on population persistence. Conserv. Biol. 18, 1651–1657 (2004).Article 

Google Scholar 
Ngene, S., Lala, F., Nzisa, M., Kimitei, K., Mukeka, J., Kiambi, S., Davidson, Z., Bakari, S., Lyimo, E. & Khayale, C. (eds Arusha Kenya Wildlife Service (KWS) and Tanzania Wildlife Research Institute (TAWIRI)) (2017).World Resources Institute, Department of Resource Surveys and Remote Sensing Ministry of Environment and Natural Resources Kenya, Central Bureau of Statistics Ministry of Planning and National Development Kenya & International Livestock Research Institute. Nature’s Benefits in Kenya, An Atlas of Ecosystems and Human Well-Being (World Resources Institute, 2007).Wijngaarden, W. V. Elephants, trees, grass, grazers: relationships between climate, soils, vegetation, and large herbivores in a semi-arid savanna ecosystem (Tsavo, Kenya) Doctor of Philosophy thesis, Landbouwhogeschool te Wageningen (1985).Stuart, C. Field Guide to Tracks & Signs of Southern, Central & East African Wildlife (Penguin Random House South Africa, 2013).
Google Scholar 
Murie, O. J. & Elbroch, M. A Field Guide to Animal Tracks Vol. 3 (Houghton Mifflin Harcourt, 2005).
Google Scholar 
Kerley, G. I. H., Pressey, R. L., Cowling, R. M., Boshoff, A. F. & Sims-Castley, R. Options for the conservation of large and medium-sized mammals in the Cape Floristic Region hotspot, South Africa. Biol. Conserv. 112, 169–190. https://doi.org/10.1016/S0006-3207(02)00426-3 (2003).Article 

Google Scholar 
R: A language and environment for statistical computing. R Foundation for Statistical Computing. https://www.R-project.org/ (2021).Hayward, M. W., Hayward, G. J., Tambling, C. J. & Kerley, G. I. Do lions Panthera leo actively select prey or do prey preferences simply reflect chance responses via evolutionary adaptations to optimal foraging?. PLoS ONE 6, e23607 (2011).ADS 
CAS 
Article 

Google Scholar 
De Boer, W. F. et al. Spatial distribution of lion kills determined by the water dependency of prey species. J. Mammal. 91, 1280–1286 (2010).Article 

Google Scholar 
Hayward, M. W. & Kerley, G. I. H. Prey preferences of the lion (Panthera leo). J. Zool. 267, 309–322. https://doi.org/10.1017/S0952836905007508 (2005).Article 

Google Scholar 
Davidson, Z. et al. Seasonal diet and prey preference of the African lion in a waterhole-driven semi-arid Savanna. PLoS ONE 8, e55182. https://doi.org/10.1371/journal.pone.0055182 (2013).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Patterson, B. D., Kasiki, S. M., Selempo, E. & Kays, R. W. Livestock predation by lions (Panthera leo) and other carnivores on ranches neighboring Tsavo National ParkS, Kenya. Biol. Conserv. 119, 507–516. https://doi.org/10.1016/j.biocon.2004.01.013 (2004).Article 

Google Scholar 
Hayward, M. W. et al. Prey preferences of the leopard (Panthera pardus). J. Zool. 270, 298–313. https://doi.org/10.1111/j.1469-7998.2006.00139.x (2006).Article 

Google Scholar 
Ogara, W. O. et al. Determination of carnivores prey base by scat analysis in Samburu community group ranches in Kenya. Afr. J. Environ. Sci. Technol. 4, 540–546 (2010).
Google Scholar 
Hayward, M. W. Prey preferences of the spotted hyaena (Crocuta crocuta) and degree of dietary overlap with the lion (Panthera leo). J. Zool. 270, 606–614. https://doi.org/10.1111/j.1469-7998.2006.00183.x (2006).Article 

Google Scholar 
Brooks, M. E. et al. glmmTMB balances speed and flexibility among packages for zero-inflated generalized linear mixed modeling. R J. 9, 378–400 (2017).Article 

Google Scholar 
Barton, K. & Barton, M. K. Package ‘MuMIn’. Version 1, 18 (2015).
Google Scholar 
Williams, E. M. Giraffe stature and neck elongation: Vigilance as an evolutionary mechanism. Biology 5, 35 (2016).Article 

Google Scholar 
Shorrocks, B. The Giraffe: Biology, Ecology, Evolution and Behaviour (Wiley, 2016).Book 

Google Scholar 
Mata, C., Bencini, R., Chambers, B. K. & Malo, J. E. Handbook of Road Ecology (eds Smith, D. J. & van der Ree, C. G. R.) Ch. 23, 190–197 (Wiley, 2015).Harris, I. M., Mills, H. R. & Bencini, R. Multiple individual southern brown bandicoots (Isoodonobesulus fusciventer) and foxes (Vulpes vulpes) use underpasses installed at a new highway in Perth, Western Australia. Wildl. Res. 37, 127–133 (2010).Article 

Google Scholar 
Fehlmann, G. et al. Extreme behavioural shifts by baboons exploiting risky, resource-rich, human-modified environments. Sci. Rep. 7, 1–8 (2017).CAS 
Article 

Google Scholar 
McLennan, M. R., Spagnoletti, N. & Hockings, K. J. The implications of primate behavioral flexibility for sustainable human-primate coexistence in anthropogenic habitats. Int. J. Primatol. 38, 105–121. https://doi.org/10.1007/s10764-017-9962-0 (2017).Article 

Google Scholar 
Riley, E. P. Flexibility in diet and activity patterns of Macaca tonkeana in response to anthropogenic habitat alteration. Int. J. Primatol. 28, 107–133. https://doi.org/10.1007/s10764-006-9104-6 (2007).Article 

Google Scholar 
Johnson-Ulrich, L., Yirga, G., Strong, R. L. & Holekamp, K. E. The effect of urbanization on innovation in spotted hyenas. Anim. Cogn. 24, 1027–1038. https://doi.org/10.1007/s10071-021-01494-4 (2021).Article 
PubMed 

Google Scholar 
Holekamp, K. E. & Dloniak, S. M. Intraspecific variation in the behavioral ecology of a tropical carnivore, the spotted hyena. Adv. Study Behav. 42, 189–229 (2010).Article 

Google Scholar 
Devens, C. H. et al. Estimating leopard density across the highly modified human-dominated landscape of the Western Cape, South Africa. Oryx 55, 34–45. https://doi.org/10.1017/S0030605318001473 (2021).Article 

Google Scholar 
Van Cleave, E. K. et al. Diel patterns of movement activity and habitat use by leopards (Panthera pardus pardus) living in a human-dominated landscape in central Kenya. Biol. Conserv. 226, 224–237. https://doi.org/10.1016/j.biocon.2018.08.003 (2018).Article 

Google Scholar 
Odden, M., Athreya, V., Rattan, S. & Linnell, J. D. C. Adaptable neighbours: Movement patterns of GPS-collared leopards in human dominated landscapes in India. PLoS ONE 9, e112044. https://doi.org/10.1371/journal.pone.0112044 (2014).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Athreya, V., Odden, M., Linnell, J. D. C., Krishnaswamy, J. & Karanth, K. U. A cat among the dogs: Leopard Panthera pardus diet in a human-dominated landscape in western Maharashtra, India. Oryx 50, 156–162. https://doi.org/10.1017/S0030605314000106 (2016).Article 

Google Scholar 
Suraci, J. P. et al. Behavior-specific habitat selection by African lions may promote their persistence in a human-dominated landscape. Ecology 100, e02644. https://doi.org/10.1002/ecy.2644 (2019).Article 
PubMed 

Google Scholar 
Daniels, S. E., Fanelli, R. E., Gilbert, A. & Benson-Amram, S. Behavioral flexibility of a generalist carnivore. Anim. Cogn. 22, 387–396 (2019).Article 

Google Scholar 
Murray, M. H. & St. Clair, C. C. Individual flexibility in nocturnal activity reduces risk of road mortality for an urban carnivore. Behav. Ecol. 26, 1520–1527. https://doi.org/10.1093/beheco/arv102 (2015).Article 

Google Scholar 
Galanti, V., Preatoni, D., Martinoli, A., Wauter, L. A. & Tosi, G. Space and habitat use of the African elephant in the Tarangire-Manyara ecosystem, Tanzania: Implications for conservation. Mamm. Biol. 71, 99–114. https://doi.org/10.1016/j.mambio.2005.10.001 (2006).Article 

Google Scholar 
Douglas-Hamilton, I., Krink, T. & Vollrath, F. Movements and corridors of African elephants in relation to protected areas. Naturwissenschaften 92, 158–163. https://doi.org/10.1007/s00114-004-0606-9 (2005).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Coe, P. K. et al. Identifying migration corridors of mule deer threatened by highway development. Wildl. Soc. Bull. 39, 256–267. https://doi.org/10.1002/wsb.544 (2015).Article 

Google Scholar 
Spinage, C. A. Territoriality and social organization of the Uganda defassa waterbuck Kobus defassa ugandae. J. Zool. Lond. 159, 329–361 (1969).Article 

Google Scholar 
Mizutani, F. & Jewell, P. A. Home-range and movements of leopards (Panthera pardus) on a livestock ranch in Kenya. J. Zool. Lond. 244, 269–286 (1998).Article 

Google Scholar 
Riley, S. P. et al. A southern California freeway is a physical and social barrier to gene flow in carnivores. Mol. Ecol. 15, 1733–1741. https://doi.org/10.1111/j.1365-294X.2006.02907.x (2006).CAS 
Article 
PubMed 

Google Scholar 
Sells, S. N. & Mitchell, M. S. The economics of territory selection. Ecol. Model. 438, 109329. https://doi.org/10.1016/j.ecolmodel.2020.109329 (2020).Article 

Google Scholar 
Valls-Fox, H. et al. Water and cattle shape habitat selection by wild herbivores at the edge of a protected area. Anim. Conserv. 21, 365–375. https://doi.org/10.1111/acv.12403 (2018).Article 

Google Scholar 
Hibert, F. et al. Spatial avoidance of invading pastoral cattle by wild ungulates: Insights from using point process statistics. Biodivers. Conserv. 19, 2003–2024 (2010).Article 

Google Scholar 
Stewart, K. M., Bowyer, R. T., Kie, J. G., Cimon, N. J. & Johnson, B. K. Temporospatial distributions of elk, mule deer, and cattle: Resource partitioning and competitive displacement. J. Mammal. 83, 229–244. https://doi.org/10.1644/1545-1542(2002)083%3c0229:Tdoemd%3e2.0.Co;2 (2002).Article 

Google Scholar 
Leeuw, J. D. et al. Distribution and diversity of wildlife in northern Kenya in relation to livestock and permanent water points. Biol. Conserv. 100, 297–306 (2001).Article 

Google Scholar 
Donaldson, B. Use of highway underpasses by large mammals and other wildlife in Virginia. Transp. Res. Rec 157–164, 2007. https://doi.org/10.3141/2011-17 (2011).Article 

Google Scholar 
Dodd, N. L., Gagnon, J. W., Manzo, A. L. & Schweinsburg, R. E. Video surveillance to assess highway underpass use by elk in Arizona. J. Wildl. Manag. 71, 637–645. https://doi.org/10.2193/2006-340 (2007).Article 

Google Scholar 
Gordon, K. M. & Anderson, S. H. International Conference on Ecology and Transportation https://escholarship.org/uc/item/2wv1v6dz.Bond, A. R. & Jones, D. N. Temporal trends in use of fauna-friendly underpasses and overpasses. Wildl. Res. 35, 103–112. https://doi.org/10.1071/WR07027 (2008).Article 

Google Scholar 
Altmann, J., Schoeller, D., Altmann, S. A., Muruthi, P. & Sapolsky, R. M. Body size and fatness of free-living baboons reflect food availability and activity levels. Am. J. Primatol. 30, 149–161. https://doi.org/10.1002/ajp.1350300207 (1993).Article 
PubMed 

Google Scholar 
Kiffner, C. et al. Road-based line distance surveys overestimate densities of olive baboons. PLoS ONE 17, e0263314. https://doi.org/10.1371/journal.pone.0263314 (2022).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Strandburg-Peshkin, A., Farine, D. R., Crofoot, M. C. & Couzin, I. D. Habitat and social factors shape individual decisions and emergent group structure during baboon collective movement. Elife 6, e19505. https://doi.org/10.7554/eLife.19505 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Bohrer, G., Beck, P. S., Ngene, S. M., Skidmore, A. K. & Douglas-Hamilton, I. Elephant movement closely tracks precipitation driven vegetation dynamics in a Kenyan forest-savanna landscape. Mov. Ecol. 2, 2 (2014).Article 

Google Scholar 
Merkle, J. A. et al. Large herbivores surf waves of green-up during spring. Proc. Biol. Sci. 283, 20160456. https://doi.org/10.1098/rspb.2016.0456 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Middleton, A. D. et al. Green-wave surfing increases fat gain in a migratory ungulate. Oikos 127, 1060–1068. https://doi.org/10.1111/oik.05227 (2018).Article 

Google Scholar 
Bartlam-Brooks, H. L. A., Beck, P. S. A., Bohrer, G. & Harris, S. In search of greener pastures: Using satellite images to predict the effects of environmental change on zebra migration. J. Geophys. Res. Biogeosci. 118, 1427–1437. https://doi.org/10.1002/jgrg.20096 (2013).Article 

Google Scholar 
Bischof, R. et al. A migratory northern ungulate in the pursuit of spring: Jumping or surfing the green wave?. Am. Nat. 180, 407–424. https://doi.org/10.1086/667590 (2012).Article 
PubMed 

Google Scholar 
Aikens, E. O. et al. The greenscape shapes surfing of resource waves in a large migratory herbivore. Ecol. Lett. 20, 741–750. https://doi.org/10.1111/ele.12772 (2017).Article 
PubMed 

Google Scholar 
Mandinyenya, B., Monks, N., Mundy, P. J., Sebata, A. & Chirima, A. Habitat choices of African buffalo (Syncerus caffer) and plains zebra (Equus quagga) in a heterogeneous protected area. Wildl. Res. 47, 106–113. https://doi.org/10.1071/WR18201 (2020).Article 

Google Scholar  More

The DarkCideS database was initially conceptualised and developed by KCT, JAG, and ACH as part of the “Global Bat Cave Vulnerability and Conservation Mapping Initiative” in 2014, and later with the “Mapping Karst Biodiversity in Yunnan” and the “Southeast Asian Atlas of Biodiversity” projects. The initiative includes developing tools and methods (e.g., the Bat Cave Vulnerability Index14) and synthesis (e.g., the global bat cave vulnerability assessment11) to identify conservation priorities and important bat caves in the tropics. Since 2019, the initiative has expanded and potential collaborators and contributors were invited through scientific conferences (Association for Tropical Biology and Conservation 2018, International Bat Research Conference 2019), social media platforms, and personal correspondences. At present, the database has 36 collaborators from twenty countries on six continents with expertise and research interests in bat conservation. Four main datasets for all known cave-dwelling bats were built for the DarkCideS database version 1.0.Datasets and compilation for species checklistThe first dataset contains taxonomic checklists for all extant cave-dwelling bats species extracted from the expert-based International Union for the Conservation Union (IUCN) Red List database version 2020-1 (Table 1). We screened and included all bat species that were reported to use, roost in, or aggregate in “Caves”, “Underground”, and “Karsts” habitats in any part of their life histories. We also scanned major publicly available bat cave databases from expeditions such as “Bats in China” (http://www.bio.bris.ac.uk/research/bats/China/) and UNEP-EUROBATS (https://www.eurobats.org/) for European bats24 for additional information and datasets. In addition, the first dataset contains species ecological traits, distribution range, and threatening processes (Table 1).Table 1 DarkCideS 1.0 includes key traits for all living cave-dwelling bat species (N = 679). General metadata for traits included in the current version of the database: habitat preference, ecological status, feeding groups, geographical range, island endemism, geopolitical endemism, distribution range, biogeographical breadth, generation length, body mass, and threatening process.Full size tableInformation per species was pooled from the IUCN Red List versions 2020-125. Species taxonomy was then curated and updated (e.g., synonyms or merged species) using the nomenclature from Simmons and Cirranello12. The “checklist for global cave-dwelling bats” derived from the IUCN Red List includes 679 species. Meanwhile, the DarkCideS 1.0 dataset contains occurrence data for 402 species from 16 families representing 59% of all cave-dwelling species11 (Fig. 2). We found a marginally significant relationship between the species richness and proportion of threatened species between the IUCN-based global cave-dwelling bat and DarkCideS datasets (Kendall’s τ b = 0.60, P = 0.07). The highest completeness of sampled species is in the Neotropics (67.38%) and Indomalayan region (66.08%), and the greatest gaps are in Austral-Oceania (40.28%). Highest endemism was recorded in Austral-Oceania (58.62%) (χ2 = 227.32, df = 5, P  More

Earth’s forests imperiled by further warmingWe quantified a global-scale hotter-drought fingerprint, representing a global climate signal for years with documented site-specific tree mortality. Climate-induced tree mortality in recent decades under hotter-drought conditions has been documented across forests from a diverse array of boundary conditions, spanning from the tropics to the boreal, from sea level to 3,500 m, and across a four-meter precipitation gradient and 30 °C of mean annual temperature. One reason that the hotter-drought fingerprint is similarly evident in the year prior to reported mortality onset (Fig. 3), as well as largely echoed in the year after, may be due to the imprecise nature of identifying the “onset” and duration of mortality (e.g., visual indications of mortality may lag significantly behind environmental drivers16). In addition, chronic drought conditions commonly span multiple years, cumulatively predisposing eventual, lagged mortality events13,26,27—consistent with our observed “3-year hotter-drier window,” centered on the nominal mortality year (Fig. 3).Our global-scale hotter-drought fingerprint, focused on acute hotter-drought extremes, represents a cohesive signal for climatic drivers of tree die-off in many of Earth’s forests. Other approaches could consider other temporal dimensions of climate signals (e.g., shorter-term heat-wave stress, longer-term chronic drought, changes in seasonal drought duration or timing), which may further improve our understanding of climatic drivers of tree mortality. Ideally, future efforts to harmonize global forest inventory and monitoring methodologies, including their currently-disparate documentation of tree mortality, will reduce the inherent sampling biases (typically favoring northern hemisphere and/or areas adjacent to well-funded research institutions) and presence-only limitations of our present database11.Additionally, we found that many of Earth’s forests may become increasingly imperiled by further warming and drought, as the frequency of lethal climate conditions observed with recently documented global mortality events will accelerate with further warming (Fig. 6d). Although our approach does not reveal the particular detailed mechanistic ecophysiological responses to the hotter drought that are driving mortality for each specific site, it exemplifies the powerful utility and practical potential of empirical approaches that link direct observations of tree mortality from diverse precisely georeferenced locations to observed climate drivers. While multiple emerging lines of evidence indicate that warming puts trees at greater risk under drought conditions9,14,15,19,24,35, the quantitative hotter-drought fingerprint we identified here suggests that further warming may accelerate global forest die-off across many biomes. The impact of this hotter-drought fingerprint is acting on Earth’s forests already, with nearly half a billion trees having died from hotter-drought events in Texas and California alone since 201036,37. In central Europe, hotter drought starting in 2018 has led to extensive dieback of forests that is ongoing—and of yet undetermined magnitude and extent—which could lead to significant ecological transitions38. Other notable global tree mortality events documented during hotter-drought episodes include three pulses of large-tree mortality since 2005 across Amazon basin tropical moist forests39,40, and historically unprecedented hotter-drought-triggered dieback in Jarrah forests of southwest Australia in 20118,19.Individual trees and forest ecosystems may benefit in various ways (e.g., increased water-use efficiency, stored non-structural carbon, etc.) from productivity gains under elevated atmospheric CO222—when soil nutrients and water are not limiting. However, the net effects of increasing CO2 in combination with a changing climate on the mortality of global forests during hotter drought are uncertain4,9,35. In particular, during hotter-drought events, plant uptake of CO2 is limited by the initial closing of stomata—with CO2 uptake eventually blocked as leaves lose turgor, followed by failure of the coupled plant water-and-carbon transport system which may ultimately result in death16,28. Thus, potential amelioration of tree mortality risk by the ~85 ppm atmospheric CO2 increase during the timeframe in our database (1970–2018) might have been overwhelmed by the concurrent increases in temperature during mortality-event years (Fig. 5). This warming presents a triple threat to tree survival in the form of amplified soil drought, atmospheric drought, and heat stress, and our results are consistent with experimental findings that drought and warming can negate or overcome the effects of elevated CO217,18.Earth’s historical forests are especially vulnerableAs the longest-lived organisms on Earth, trees routinely are imbued with historical and cultural significance by human societies, while also persistently sequestering carbon and amplifying local biodiversity for centuries, sometimes millennia. In contrast, extreme climate stress events occur on the scale of days to months to a few years, and in these relatively brief periods, large old trees—exemplars of Earth’s historical forests6—can be especially susceptible to mortality5,41,42,43,44. Forests will certainly persist and thrive over large areas into Earth’s future, but increasingly they will have to rapidly shift in physiological function, morphology, genetics, species composition, structure, and geographic distribution in response to anticipated climate changes. Where the pace of climate change outruns the adaptive or acclimation capacities of historically-dominant tree individuals and species, additional die-off events are likely to occur and some forests may even cease to exist. In particular, the current tree communities of Earth’s historical old-growth forests—which took centuries, sometimes millennia, to grow to structural dominance under now locally-shifted climate conditions—may continue to often be most negatively affected by continued warming and drying4,43, as novel hotter-drought extremes increasingly exceed their range of survivable climate across diverse forested biomes. The expected near-term outcome is simplified tree communities, where more drought- and heat-tolerant species survive, and less tolerant species diminish or perish. In many cases, this may lead to lasting changes in vegetation composition, stature, and spacing, where surviving woody plants in these communities do not maintain or develop the complex canopy structure typical of historical old-growth forests4,9,35,45.Underestimation of tree mortality from hotter droughtsWhile our projections for an increase by up to 140% in the frequency of climate conditions associated with recent forest die-off under +4 °C may seem severe, they are modest in comparison to some current empirical and mechanistic process-based model predictions for catastrophic forest die-off at continental scales under hotter droughts12,14. Our projections for increasing die-offs under further warming are consistent with projections showing the potential for large increases in mortality under future hotter drought12,14,46, although these projections are often limited to single species or single biomes. Even continental-scale projections for up to 40% increases in the frequency of mortality-inducing hotter droughts under ~+2.5 °C since pre-industrial20 are in general agreement with our global analysis’s 20% under +2 °C (Fig. 6d). Further, our projections of increasingly frequent, historically lethal climate conditions for Earth’s forests may be conservative for several reasons:

(1)

Requiring that all six climate variables meet or exceed mortality year conditions, concurrently in the same year, is a strong filter. For example, TMAX, VPD, and PDSI all exceed mortality-year conditions under +4 °C in about 4 out of every 5 years (Supplementary Fig. S3), whereas under the same warming scenario, all six metrics exceeded the hotter-drought fingerprint only half as often.

(2)

Tree mortality involves diverse disturbance processes that amplify forest die-off in the presence of global warming and hotter droughts4,24,35 but these were excluded in our analysis, including insects44,47, pathogens48, wind40,49, and lightning50. Additionally, anthropogenic warming promotes greater wildfire activity, particularly fire extent and severity in many forests worldwide7,51, driving further declines in some of Earth’s forests. We also have not considered disturbance interactions among these many amplifying and synergistic agents of tree mortality49,52—but conversely, we also acknowledge that thinning from either climate-triggered mortality or these associated synergistic agents, may partially buffer against future losses35,45.

(3)

Our findings indicate that climate anomalies of tree mortality event years are trending towards ever hotter and drier conditions (Fig. 5, Supplementary Fig. S7), concurrent with any potential ongoing forest acclimation to temperature and/or CO2 fertilization15,22. Yet the potential for tree species to acclimate to ongoing climate warming, even with increasing atmospheric CO2 concentrations, is not unlimited—and when exhausted—forest die-off may rapidly accelerate9,35,53. Since projected warmer climate conditions include unprecedented extremes of hotter drought for which there are no observed analogs, the potential for crossing historically unknown tipping-point climatic stress thresholds may increase, further amplifying tree mortality35.

(4)

Our analysis of mortality-year frequency uses monthly climate data, yet important drivers can occur on longer (e.g., drought26), and shorter (e.g., heatwave8,19) timescales. For example, the 4-year-prior signal of cooler/wetter climate (Fig. 3) may reflect favorable pre-drought conditions promoting structural overshoot of trees, which could amplify dieback and mortality risk during subsequent years of hotter drought45.

Roadmap for research enabled by a quantitative ground-based global databaseThe widespread global coherence of our empirically quantified hotter-drought fingerprint may provide immediate opportunities to validate projections of tree mortality in existing models of the Earth system, while also enabling diverse future analyses. Although global in geographic extent, our database is limited by the availability of peer-reviewed, ground-based empirical studies of climate-induced tree mortality, and thus only sparsely covers some regions, particularly large portions of boreal and tropical forests. For example, our hotter-drought fingerprint was consistent across all biomes except the tropical rainforest (Fig. 4)—despite published direct observations of hotter drought as a driver of tree mortality at these tropical rainforest sites39,40. Additionally, this biome may experience pulses of tree mortality in response to different climate fingerprints, particularly involving longer-duration dry seasons—not just intensified single monthly extremes.Despite this and some other limitations, our database represents a globally-distributed dataset with precisely geo-referenced sites where ground-based heat- and drought-induced tree mortality has been documented. Our use of this database to quantify a global hotter-drought fingerprint of tree mortality illustrates the potential for rapid progress in empirical modeling of forest mortality drivers and thresholds at spatial scales from local to global, where direct observations of forest responses to climate stress can help identify and quantify mortality drivers. Toward the goal of fostering further rapid community development of many more such direct observational records of climate-induced forest stress and tree mortality worldwide—with methods ranging from local ground-based sites to synoptic remote-sensing—this database immediately will be served as an open-access resource at the International Tree Mortality Network (https://www.tree-mortality.net), an academic networking initiative associated with the International Union of Forest Research Organizations’ (IUFRO) task force on monitoring global tree mortality patterns and trends (https://www.iufro.org/science/task-forces/tree-mortality-patterns). The complete database—along with an interactive version of Fig. 1 from this paper—will allow users to zoom in on dense plot networks, with direct links to the supporting literature for each point. This online database includes the reference for each plot, its precise coordinates, dominant species, associated biotic agents, and the year of mortality onset. To further update and rapidly increase the quantity and spatial representativeness of global tree mortality observations, ongoing online contributions from diverse observer groups, ranging from practicing foresters and field ecologists to remote-sensing scientists, can be integrated into the website in near-real-time via a user-friendly entry form.As the only global set of ground-truthed observations of drought- and heat-induced tree mortality, this database can immediately aid in validating remote-sensing technologies for eventual synoptic monitoring in near-real-time of tree mortality (which could then feedback into the database). Additional groups to benefit from the database are those interested in climate and physiological mechanisms of tree mortality, including the connected fates of all forest-dependent life5,19, with an aim toward improving the representation of climate-induced tree mortality representations in Earth system models. Related future research opportunities associated with this initial online database include:

(1)

Identify additional chronic (e.g., seasonal to decadal) and acute (daily to weekly) climatic signals of tree mortality, including thorough analyses that quantitatively consider antecedent and lagging factors, and duration and seasonality of drought stress;

(2)

Synthesize mortality observations from extensive forestry plot inventory networks, to increase spatial representation for the global climate signal of tree mortality, and to identify where during these events trees did not die-off;

(3)

Apply remote-sensing approaches to mortality detection using this spatially precise (and in some places plot-dense) database for ground-truthing, to determine the full spatial extent of known mortality events, and aid in ongoing monitoring of forest stress and tree mortality events in near-real-time;

(4)

Benchmark state-of-the-art Earth system models via hindcasting, to assess the accuracy of tree mortality event representation—and to do so across spatial resolutions (as in Supplementary Fig. S4) at which these planetary models operate;

(5)

Develop approaches to understand potentially unique features and drivers of hotter-drought mortality in tropical rainforests (differing climate signals, e.g., extended dry seasons, where warming/drying of typically moderate shoulder seasons may matter more than intensified single-month extremes), the single biome in which our global approach did not reveal a strong hotter-drought fingerprint;

(6)

Investigate how the severity of forest die-off events will respond to further warming; and

(7)

Invest in monitoring, documenting, and gathering mortality data for forests under-represented in this initial global database—especially in the extensive critical carbon sinks of boreal forests and tropical rainforests.

Future challenges for Earth’s forests and societies under hotter droughtIn conclusion, our findings reveal the emergence of a global acceleration of lethal climate conditions, associated with recent forest mortality events, under further warming. Earth’s historical forests in particular face a challenging future, including dramatic changes in the extent, composition, age, and structure of these unique and irreplaceable forests, with planetary-scale consequences for biodiversity and the cycling of water and carbon. Our findings both corroborate earlier studies of hotter-drought driven mortality at local to regional scales8,13,19,20,24,36,38 and extend these findings by quantifying the commonality in climate anomalies across this planetary-scale observation-based database of tree die-off. Although forests often are invoked as an important part of the solution to the present global climate crisis, their role as reliable carbon sinks in mitigating climate change depends upon their ability to survive further warming10,22,52—which our global hotter-drought fingerprint identifies as an imminent threat. Our findings show that limiting warming to +2 °C over pre-industrial levels could reduce the frequency of these climate conditions associated with observed tree mortality events to less than half that predicted at +4 °C. Efforts to protect the world’s climate from excessive warming likely will be decisive in determining the future persistence of many of Earth’s forests. More

Collection of wolf hair samplesHair samples were collected by researchers from opportunistically found-dead wolves upon standard necropsy (all the Alpine and part of the Iberian samples) or in the field (all the Dinaric-Balkan and most of the Iberian samples), or from legally harvested wolves (only in the Scandinavian population). At the time of sample collection, wolves were legally harvested in Sweden, Slovenia, and Spain, and under total protection in Portugal and Italy. Hair samples were collected from four body regions, when possible: lumbar (n = 133), dorsal cervical (n = 66), tail (n = 33) and ventral thorax (n = 27) (Tables S1 and S2). The hair was cut as close as possible to the skin with scissors to avoid collecting hair follicles, but in some samples, hairs were pulled from the carcass. Samples were stored at room temperature in paper envelopes. Age, sex, date, and cause of death/capture, geographical location, body mass, and total length were obtained for most of the wolves.Age was estimated by the dental eruption and wear or cementum age analysis and classified as ‘juveniles’ ( 2 years)40, or ‘unknown’. Sex was assessed by inspection of genitalia. Causes of death were classified as ‘acute’, likely lasting minutes to hours (vehicle accident and legal or illegal shooting); ‘subacute’, likely lasting hours to days (drowning, poisoning, trapping and intraspecific aggression); ‘chronic’, likely lasting several weeks (infectious diseases—canine distemper, canine parvovirosis, leptospirosis; sarcoptic mange; or neoplastic diseases) or ‘unknown’. Total length was obtained by measuring with metric tape (1 mm precision) the distance from snout to the distal end of the last tail vertebrae. The body mass was measured with 100 g precision with scales.The detailed protocol for the handling of wolves live trapped in the scope of ecological and conservation studies (n = 7, all from the Iberian population) has been previously described5. Traps were monitored twice every day, in the early morning and late afternoon, hence the duration of restraint after capture was unknown for 8 wolves, potentially up to 12 h. Trap-alarms were deployed in the capture of 2 wolves, with 41 and 70 min intervals between activation of the alarm and administration of the drugs. Live trapping was conducted under permits issued by the nature conservation authorities of Portugal (Instituto de Conservação da Natureza e das Florestas: 338/2007/CAPT, 258/2008/CAPT, 286/2008/CAPT, 260/2009/CAPT, 332/2010/MANU, 333/2010/CAPT, 336/2010/MANU, 26/2012/MANU, and 72/2014/CAPT) and Spain (Dirección Xeral de Conservación da Naturaleza, Xunta de Galicia: E-0020/13-PNPE, 095/2013; Consejería de Medio Ambiente, Principado de Asturias: 31/08/2017-BOPA 05/09/17) and according to European Union directives on the protection of animals used for scientific purposes (Directive 2010/63/EU) and international wildlife standards41,42. The study was undertaken in compliance with the ARRIVE guidelines43.Cortisol extractionThe protocol for the extraction of cortisol from the hair was adapted from previously described procedures15,27. Forty mg of guard hairs were separated from the undercoat and placed in 15 ml falcon tubes. Hair follicles were cut whenever found in the sample. For each sample, the length of three intact hairs was recorded. The samples were washed twice with 40 µl of distilled water/mg hair and three times with the same amount of isopropanol. In each washing step, the samples and washing solution were vortexed, the supernatant discarded, and the hair dried using clean paper towels. After the final wash, samples were dried overnight at room temperature and 30 mg of hair cut into a 2 ml polypropylene screw cap plastic tube with five 4 mm steel beads added to each tube.The hair was ground to a fine powder in a FastPrep sample homogenizer (MP Biomedicals, USA) for four times 1 min at 6.0 m/s. 50 µl methanol/mg hair were added to each sample and sonicated for 30 min at 50 Hz at 50 °C. The samples were incubated for 18 h at 50 °C in an orbital shaker at 160 rpm, centrifuged for 15 min at 14,000g at 20 °C, and 1000 µl of supernatant was collected to a screw cap glass chromatography vial and dried at room temperature in a gentle stream of nitrogen gas. Due to restrictions on laboratory use during the SARS-Cov-2 pandemic, some batches of samples were instead evaporated overnight on a suction hood. This unexpected change in the methanol evaporation protocol was recorded and accounted for in the statistical analysis.Cortisol quantificationA commercial competitive ELISA kit (Cortisol free in Saliva ELISA, Demeditec, Germany) was used to quantify the concentration of cortisol, following the manufacturer’s instructions. The kit plate wells are provided coated with polyclonal rabbit antibody against cortisol, and cortisol-horseradish peroxidase was used as conjugate. According to the manufacturer, the cross-reactivity of the test to selected steroids is low (Table S3), the intra-assay variation is 3.8–5.8% and the inter-assay variation is 6.2–6.4%. Samples, standards, and controls were tested in duplicate.The 4-parameter standard curve was calculated from the log-transformed cortisol concentration of the standard solutions and their measured OD45044. Standard curves were estimated using the software GraphPad Prism 6.04 (GraphPad Software, La Jolla, California USA), and yielded an average R2adjusted = 0.991 (range 0.968–0.999). The cortisol concentration of the reconstituted samples was estimated from the standard curve and converted to cortisol concentration as picograms (pg) of cortisol/mg of guard hair.Intra and inter-assay coefficients of variation were estimated for six ELISA assays of 37–40 samples each. The low and high controls included in the kit were used to estimate the inter-assay coefficient of variation and the duplicate runs of each sample were used to estimate the intra-assay coefficient of variation. Linearity was assessed by two-fold dilutions (1:1, 1:2, 1:4 and 1:8) of 4 extracted samples, comparing the expected and observed concentrations. Recovery was assessed by spiking 6 ground hair samples with known concentrations of cortisol (50, 25, 12.5, 6.25 pg/mg, and no spiking), comparing the expected and observed concentrations.The intra-assay coefficient of variation of the ELISA assays ranged from 6.50 to 9.97% (average 7.66%). The inter-assay coefficient of variation was 11.54% for the low concentration controls and 9.08% for the high concentration controls (average 10.31%). Assay linearity was 91% for the 1:2 dilution, 103% for 1:4, and 117% for 1:8 (average 103%). The recovery of cortisol averaged 94%, being 73% for the 50 pg/mg spiked samples, 74% for 25 pg/mg, 95% for 12.5 pg/mg, and 113% for 6.25 pg/mg.Determinants of hair cortisol concentrationThe potential determinants of HCC investigated included wolf intrinsic variables: sex, age, body condition, body structural size, month of death/capture, and wolf population. The scaled mass index was selected as a measure of body condition45 and estimated from the log-transformed body weight (g) and total length (mm). Log-transformed total length was used as an indicator of body structural size46. Samples were assigned to the Iberian, Alpine, Dinaric-Balkan, or Scandinavian wolf populations16 from the geographical location of the death or live-trapping sites (Fig. 1).The relationship between HCC and additional variables related to the sampling procedure or to the work conducted in the laboratory (length of hair used for cortisol extraction, sample storage time, body region, cause of death/capture, and methanol evaporation protocol), herein referred to as methodological variables, was also investigated as potential confounding variables. Sample storage time was the period in months between death/capture and cortisol extraction. In those samples for which only the year of death was available, 30 June was assigned as the date of death, solely to estimate storage time. All continuous variables were standardized to their z-scores.Statistical analysisFirst, the effect of body region was investigated by a linear mixed model with HCC as the dependent variable, and the independent variables body region, as a categorical fixed effect, and individual wolf, as a random effect. The lumbar region was set as the reference class as it was the most represented in our sample (Table S1). Data from 27 wolves for which samples were available from all 4 body regions were used in this analysis. Four outliers in the dataset violated the assumption of normality in the residuals of the model comparing HCC across body regions (Fig. S1A) and were excluded from this model’s dataset (Fig. S1B).Second, the effect of intrinsic and methodological variables on HCC from the lumbar body region was investigated by another linear mixed model with sex, age, body condition, body structural size (standardized log-transformed total length), cause of death/capture, wolf population, hair length, sample storage time, and methanol evaporation protocol as fixed effect independent variables. The month of death/capture was included as a random effect. Reference classes for the categorical variables were set as female, adult, acute death, Iberian population, and methanol evaporation by nitrogen gas stream. Two outliers in the dataset violated the assumption of normality in the residuals of the model (Full model, Table S4) and were excluded from this analysis (Fig. S1C,D).The goal of this analysis was to assess the relationship between HCC and wolf intrinsic variables, controlling for the potential confounding effect of the methodological variables. Starting from the full model (Table S4), models including all possible combinations of variables were ranked by their AICc using the package “MuMIn”47 in R 3.6.148. The most supported model was selected for inference and models with ΔAICc  More

Lozupone CA, Knight R. Global patterns in bacterial diversity. Proc Natl Acad Sci USA. 2007;104:11436–40.CAS 
PubMed 
PubMed Central 

Google Scholar 
Logares R, Bråte J, Bertilsson S, Clasen JL, Shalchian-Tabrizi K, Rengefors K. Infrequent marine-freshwater transitions in the microbial world. Trends Microbiol. 2009;17:414–22.CAS 
PubMed 

Google Scholar 
Cavalier-Smith T. Megaphylogeny, cell body plans, adaptive zones: causes and timing of eukaryote basal radiations. J Eukaryot Microbiol. 2009;56:26–33.PubMed 

Google Scholar 
Nakov T, Beaulieu JM, Alverson AJ. Diatoms diversify and turn over faster in freshwater than marine environments. Evolution. 2019;73:2497–511.PubMed 

Google Scholar 
Dittami SM, Heesch S, Olsen JL, Collén J. Transitions between marine and freshwater environments provide new clues about the origins of multicellular plants and algae. J Phycol. 2017;53:731–45.PubMed 

Google Scholar 
Dickson B, Yashayaev I, Meincke J, Turrell B, Dye S, Holfort J. Rapid freshening of the deep North Atlantic Ocean over the past four decades. Nature. 2002;416:832–7.CAS 
PubMed 

Google Scholar 
Aretxabaleta AL, Smith KW, Kalra TS. Regime changes in global sea surface salinity trend. J Mar Sci Eng. 2017;5:57.
Google Scholar 
López-Maury L, Marguerat S, Bähler J. Tuning gene expression to changing environments: from rapid responses to evolutionary adaptation. Nat Rev Genet. 2008;9:583–93.PubMed 

Google Scholar 
Björck S. A review of the history of the Baltic Sea, 13.0-8.0 ka BP. Quat Int. 1995;27:19–40.
Google Scholar 
Krauss W. Baltic sea circulation. In: Steele JH, editor. Encyclopedia of ocean sciences. Oxford: Academic Press; 2001. p. 236–44.Telesh I, Schubert H, Skarlato S. Life in the salinity gradient: discovering mechanisms behind a new biodiversity pattern. Estuar Coast Shelf Sci. 2013;135:317–27.
Google Scholar 
Johannesson K, Le Moan A, Perini S, André C. A Darwinian laboratory of multiple contact zones. Trends Ecol Evol. 2020;35:1021–36.PubMed 

Google Scholar 
Olofsson M, Hagan JG, Karlson B, Gamfeldt L. Large seasonal and spatial variation in nano- and microphytoplankton diversity along a Baltic Sea-North Sea salinity gradient. Sci Rep. 2020;10:17666.CAS 
PubMed 
PubMed Central 

Google Scholar 
Sjöqvist C, Godhe A, Jonsson PR, Sundqvist L, Kremp A. Local adaptation and oceanographic connectivity patterns explain genetic differentiation of a marine diatom across the North Sea-Baltic Sea salinity gradient. Mol Ecol. 2015;24:2871–85.PubMed 
PubMed Central 

Google Scholar 
Jochem F. Distribution and importance of autotrophic ultraplankton in a boreal inshore area (Kiel Bight, Western Baltic). Mar Ecol Prog Ser. 1989;53:153–68.
Google Scholar 
Wasmund N, Nausch G, Gerth M, Busch S, Burmeister C, Hansen R, et al. Extension of the growing season of phytoplankton in the western Baltic Sea in response to climate change. Mar Ecol Prog Ser. 2019;622:1–16.CAS 

Google Scholar 
van Wirdum F, Andrén E, Wienholz D, Kotthoff U, Moros M, Fanget A-S, et al. Middle to Late Holocene variations in salinity and primary productivity in the Central Baltic Sea: a multiproxy study from the Landsort Deep. Front Mar Sci. 2019;6:51.
Google Scholar 
Alverson AJ. Timing marine–freshwater transitions in the diatom order Thalassiosirales. Paleobiology. 2014;40:91–101.
Google Scholar 
Nakov T, Beaulieu JM, Alverson AJ. Insights into global planktonic diatom diversity: the importance of comparisons between phylogenetically equivalent units that account for time. ISME J. 2018;12:2807–10.PubMed 
PubMed Central 

Google Scholar 
Kremp A, Godhe A, Egardt J, Dupont S, Suikkanen S, Casabianca S, et al. Intraspecific variability in the response of bloom-forming marine microalgae to changed climate conditions. Ecol Evol. 2012;2:1195–207.PubMed 
PubMed Central 

Google Scholar 
Olofsson M, Kourtchenko O, Zetsche E-M, Marchant HK, Whitehouse MJ, Godhe A, et al. High single-cell diversity in carbon and nitrogen assimilations by a chain-forming diatom across a century. Environ Microbiol. 2019;21:142–51.CAS 
PubMed 

Google Scholar 
Olofsson M, Almén A-K, Jaatinen K, Scheinin M. Temporal escape – adaptation to eutrophication by Skeletonema marinoi. FEMS Microbiol Lett. 2022;fnac011. https://pubmed.ncbi.nlm.nih.gov/35137038/.Godhe A, Härnström K. Linking the planktonic and benthic habitat: genetic structure of the marine diatom Skeletonema marinoi. Mol Ecol. 2010;19:4478–90.PubMed 

Google Scholar 
Dobin A, Gingeras TR. Mapping RNA-seq reads with STAR. Curr Protoc Bioinform. 2015;51:11.14.1–11.14.19.
Google Scholar 
Anders S, Pyl PT, Huber W. HTSeq-a Python framework to work with high-throughput sequencing data. Bioinformatics. 2015;31:166–9.CAS 
PubMed 
PubMed Central 

Google Scholar 
Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ. Basic local alignment search tool. J Mol Biol. 1990;215:403–10.CAS 
PubMed 
PubMed Central 

Google Scholar 
Jones P, Binns D, Chang H-Y, Fraser M, Li W, McAnulla C, et al. InterProScan 5: genome-scale protein function classification. Bioinformatics. 2014;30:1236–40.CAS 
PubMed 
PubMed Central 

Google Scholar 
Aramaki T, Blanc-Mathieu R, Endo H, Ohkubo K, Kanehisa M, Goto S, et al. KofamKOALA: KEGG Ortholog assignment based on profile HMM and adaptive score threshold. Bioinformatics. 2020;36:2251–2.CAS 
PubMed 

Google Scholar 
Emms DM, Kelly S. OrthoFinder: phylogenetic orthology inference for comparative genomics. Genome Biol. 2019;20:238.PubMed 
PubMed Central 

Google Scholar 
Almagro Armenteros JJ, Salvatore M, Emanuelsson O, Winther O, von Heijne G, Elofsson A, et al. Detecting sequence signals in targeting peptides using deep learning. Life Sci Alliance. 2019;2:e201900429.PubMed 
PubMed Central 

Google Scholar 
Gruber A, Rocap G, Kroth PG, Armbrust EV, Mock T. Plastid proteome prediction for diatoms and other algae with secondary plastids of the red lineage. Plant J. 2015;81:519–28.CAS 
PubMed 
PubMed Central 

Google Scholar 
Bendtsen JD, Nielsen H, von Heijne G, Brunak S. Improved prediction of signal peptides: SignalP 3.0. J Mol Biol. 2004;340:783–95.PubMed 

Google Scholar 
Gschloessl B, Guermeur Y, Cock JM. HECTAR: a method to predict subcellular targeting in heterokonts. BMC Bioinforma. 2008;9:393.
Google Scholar 
Claros MG. MitoProt, a Macintosh application for studying mitochondrial proteins. Comput Appl Biosci. 1995;11:441–7.CAS 
PubMed 

Google Scholar 
Robinson MD, McCarthy DJ, Smyth GK. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics. 2010;26:139–40.CAS 

Google Scholar 
Van den Berge K, Soneson C, Robinson MD, Clement L. stageR: a general stage-wise method for controlling the gene-level false discovery rate in differential expression and differential transcript usage. Genome Biol. 2017;18:151.PubMed 
PubMed Central 

Google Scholar 
Heller R, Manduchi E, Grant GR, Ewens WJ. A flexible two-stage procedure for identifying gene sets that are differentially expressed. Bioinformatics. 2009;25:1019–25.CAS 
PubMed 

Google Scholar 
Alexa A, and Rahnenfuhrer J. topGO: Enrichment Analysis for GeneOntology. R package version 2.44.0. 2021. https://bioconductor.org/packages/release/bioc/html/topGO.html.Wu D, Smyth GK. Camera: a competitive gene set test accounting for inter-gene correlation. Nucleic Acids Res. 2012;40:e133.CAS 
PubMed 
PubMed Central 

Google Scholar 
Supek F, Bošnjak M, Škunca N, Šmuc T. REVIGO summarizes and visualizes long lists of gene ontology terms. PLoS ONE. 2011;6:e21800.CAS 
PubMed 
PubMed Central 

Google Scholar 
Bussard A, Corre E, Hubas C, Duvernois-Berthet E, Le Corguillé G, Jourdren L, et al. Physiological adjustments and transcriptome reprogramming are involved in the acclimation to salinity gradients in diatoms. Environ Microbiol. 2017;19:909–25.CAS 
PubMed 

Google Scholar 
Matthijs M, Fabris M, Obata T, Foubert I, Franco-Zorrilla JM, Solano R, et al. The transcription factor bZIP14 regulates the TCA cycle in the diatom Phaeodactylum tricornutum. EMBO J. 2017;36:1559–76.CAS 
PubMed 
PubMed Central 

Google Scholar 
Kong L, Price NM. Transcriptomes of an oceanic diatom reveal the initial and final stages of acclimation to copper deficiency. Environ Microbiol. 2021;24:951–66.Amato A, Sabatino V, Nylund GM, Bergkvist J, Basu S, Andersson MX, et al. Grazer-induced transcriptomic and metabolomic response of the chain-forming diatom Skeletonema marinoi. ISME J. 2018;12:1594–604.CAS 
PubMed 
PubMed Central 

Google Scholar 
Maumus F, Allen AE, Mhiri C, Hu H, Jabbari K, Vardi A, et al. Potential impact of stress activated retrotransposons on genome evolution in a marine diatom. BMC Genomics. 2009;10:624.PubMed 
PubMed Central 

Google Scholar 
Pargana A, Musacchia F, Sanges R, Russo MT, Ferrante MI, Bowler C, et al. Intraspecific diversity in the cold stress response of transposable elements in the diatom Leptocylindrus aporus. Genes. 2019;11:9.PubMed Central 

Google Scholar 
Smith SR, Dupont CL, McCarthy JK, Broddrick JT, Oborník M, Horák A, et al. Evolution and regulation of nitrogen flux through compartmentalized metabolic networks in a marine diatom. Nat Commun. 2019;10:4552.PubMed 
PubMed Central 

Google Scholar 
Kageyama H, Tanaka Y, Shibata A, Waditee-Sirisattha R, Takabe T. Dimethylsulfoniopropionate biosynthesis in a diatom Thalassiosira pseudonana: Identification of a gene encoding MTHB-methyltransferase. Arch Biochem Biophys. 2018;645:100–6.CAS 
PubMed 

Google Scholar 
Nakov T, Judy KJ, Downey KM, Ruck EC, Alverson AJ. Transcriptional response of osmolyte synthetic pathways and membrane transporters in a euryhaline diatom during long-term acclimation to a salinity gradient. J Phycol. 2020;56:1712–28.CAS 
PubMed 

Google Scholar 
Kageyama H, Tanaka Y, Takabe T. Biosynthetic pathways of glycinebetaine in Thalassiosira pseudonana; functional characterization of enzyme catalyzing three-step methylation of glycine. Plant Physiol Biochem. 2018;127:248–55.CAS 
PubMed 

Google Scholar 
Krell A, Funck D, Plettner I, John U, Dieckmann G. Regulation of proline metabolism under salt stress in the psychrophilic diatom Fragilariopsis cylindrus (Bacillariophyceae). J Phycol. 2007;43:753–62.CAS 

Google Scholar 
Latta LC, Weider LJ, Colbourne JK, Pfrender ME. The evolution of salinity tolerance in Daphnia: a functional genomics approach. Ecol Lett. 2012;15:794–802.PubMed 

Google Scholar 
Ferrante MI, Entrambasaguas L, Johansson M, Töpel M, Kremp A, Montresor M, et al. Exploring molecular signs of sex in the marine diatom Skeletonema marinoi. Genes. 2019;10:494.Kroth PG. The biodiversity of carbon assimilation. J Plant Physiol. 2015;172:76–81.CAS 
PubMed 

Google Scholar 
Obata T, Fernie AR, Nunes-Nesi A. The central carbon and energy metabolism of marine diatoms. Metabolites. 2013;3:325–46.CAS 
PubMed 
PubMed Central 

Google Scholar 
Smith SR, Abbriano RM, Hildebrand M. Comparative analysis of diatom genomes reveals substantial differences in the organization of carbon partitioning pathways. Algal Res. 2012;1:2–16.CAS 

Google Scholar 
Kroth PG, Chiovitti A, Gruber A, Martin-Jezequel V, Mock T, Parker MS, et al. A model for carbohydrate metabolism in the diatom Phaeodactylum tricornutum deduced from comparative whole genome analysis. PLoS ONE. 2008;3:e1426.PubMed 
PubMed Central 

Google Scholar 
Furumoto T, Yamaguchi T, Ohshima-Ichie Y, Nakamura M, Tsuchida-Iwata Y, Shimamura M, et al. A plastidial sodium-dependent pyruvate transporter. Nature. 2011;476:472–5.CAS 
PubMed 

Google Scholar 
Chen G-Q, Jiang Y, Chen F. Salt-induced alterations in lipid composition of diatom Nitzschia laevis (Bacillariophyceae) under heterotrophic culture condition. J Phycol. 2008;44:1309–14.CAS 
PubMed 

Google Scholar 
Sayanova O, Mimouni V, Ulmann L, Morant-Manceau A, Pasquet V, Schoefs B, et al. Modulation of lipid biosynthesis by stress in diatoms. Philos Trans R Soc Lond B Biol Sci. 2017;372:20160407.PubMed 
PubMed Central 

Google Scholar 
Vårum KM, Myklestad S. Effects of light, salinity and nutrient limitation on the production of β-1,3-d-glucan and exo-d-glucanase activity in Skeletonema costatum (Grev.) Cleve. J Exp Mar Bio Ecol. 1984;83:13–25.
Google Scholar 
Radchenko IG, Il’yash LV. Growth and photosynthetic activity of diatom Thalassiosira weissflogii at decreasing salinity. Biol Bull. 2006;33:242–7.CAS 

Google Scholar 
Adams C, Bugbee B. Enhancing lipid production of the marine diatom Chaetoceros gracilis: synergistic interactions of sodium chloride and silicon. J Appl Phycol. 2014;26:1351–7.CAS 

Google Scholar 
Shetty P, Gitau MM, Maróti G. Salinity stress responses and adaptation mechanisms in eukaryotic green microalgae. Cells. 2019;8:1657.Jacob A, Kirst GO, Wiencke C, Lehmann H. Physiological responses of the Antarctic green alga Prasiola crispa ssp. antarctica to salinity stress. J Plant Physiol. 1991;139:57–62.CAS 

Google Scholar 
Bazzani E, Lauritano C, Mangoni O, Bolinesi F, Saggiomo M. Chlamydomonas responses to salinity stress and possible biotechnological exploitation. J Mar Sci Eng. 2021;9:1242.
Google Scholar 
Cheng R-L, Feng J, Zhang B-X, Huang Y, Cheng J, Zhang C-X. Transcriptome and gene expression analysis of an oleaginous diatom under different salinity conditions. Bioenergy Res. 2014;7:192–205.CAS 

Google Scholar 
Stock W, Blommaert L, Daveloose I, Vyverman W, Sabbe K. Assessing the suitability of imaging-PAM fluorometry for monitoring growth of benthic diatoms. J Exp Mar Bio Ecol. 2019;513:35–41.
Google Scholar 
Reichmann D, Voth W, Jakob U. Maintaining a healthy proteome during oxidative stress. Mol Cell. 2018;69:203–13.CAS 
PubMed 
PubMed Central 

Google Scholar 
Latowski D, Kuczyńska P, Strzałka K. Xanthophyll cycle-a mechanism protecting plants against oxidative stress. Redox Rep. 2011;16:78–90.CAS 
PubMed 

Google Scholar 
Chen D, Shao Q, Yin L, Younis A, Zheng B. Polyamine function in plants: metabolism, regulation on development, and roles in abiotic stress responses. Front Plant Sci. 2018;9:1945.PubMed 

Google Scholar 
Liu Q, Nishibori N, Imai I, Hollibaugh JT. Response of polyamine pools in marine phytoplankton to nutrient limitation and variation in temperature and salinity. Mar Ecol Prog Ser. 2016;544:93–105.CAS 

Google Scholar 
Scoccianti V, Penna A, Penna N, Magnani M. Effect of heat stress on polyamine content and protein pattern in Skeletonema costatum. Mar Biol. 1995;121:549–54.CAS 

Google Scholar 
Alscher RG, Erturk N, Heath LS. Role of superoxide dismutases (SODs) in controlling oxidative stress in plants. J Exp Bot. 2002;53:1331–41.CAS 
PubMed 

Google Scholar 
Kumar M, Kumari P, Gupta V, Reddy CRK, Jha B. Biochemical responses of red alga Gracilaria corticata (Gracilariales, Rhodophyta) to salinity induced oxidative stress. J Exp Mar Bio Ecol. 2010;391:27–34.CAS 

Google Scholar 
von Alvensleben N, Magnusson M, Heimann K. Salinity tolerance of four freshwater microalgal species and the effects of salinity and nutrient limitation on biochemical profiles. J Appl Phycol. 2016;28:861–76.
Google Scholar 
Rijstenbil JW, Wijnholds JA, Sinke JJ. Implications of salinity fluctuation for growth and nitrogen metabolism of the marine diatom Ditylum brightwellii in comparison with Skeletonema costatum. Mar Biol. 1989;101:131–41.CAS 

Google Scholar 
Mansour MMF. Nitrogen containing compounds and adaptation of plants to salinity stress. Biol Plant. 2000;43:491–500.CAS 

Google Scholar 
Garcia N, Lopez Elias JA, Miranda A, Martinez Porchas M, Huerta N, Garcia A. Effect of salinity on growth and chemical composition of the diatom Thalassiosira weissflogii at three culture phases. Lat Am J Aquat Res. 2012;40:435–40.
Google Scholar 
Van den Berge K, Hembach KM, Soneson C, Tiberi S, Clement L, Love MI, et al. RNA sequencing data: Hitchhiker’s guide to expression analysis. Annu Rev Biomed Data Sci. 2019;2:139–73.
Google Scholar 
Kremp A. Effects of cyst resuspension on germination and seeding of two bloom-forming dinoflagellates in the Baltic Sea. Mar Ecol Prog Ser. 2001;216:57–66.
Google Scholar 
Juneau P, Barnett A, Méléder V, Dupuy C, Lavaud J. Combined effect of high light and high salinity on the regulation of photosynthesis in three diatom species belonging to the main growth forms of intertidal flat inhabiting microphytobenthos. J Exp Mar Bio Ecol. 2015;463:95–104.CAS 

Google Scholar 
Vargas C, Argandoña M, Reina-Bueno M, Rodríguez-Moya J, Fernández-Aunión C, Nieto JJ. Unravelling the adaptation responses to osmotic and temperature stress in Chromohalobacter salexigens, a bacterium with broad salinity tolerance. Saline Syst. 2008;4:14.PubMed 
PubMed Central 

Google Scholar 
Khmelenina VN, Sakharovskii VG, Reshetnikov AS, Trotsenko YA. Synthesis of osmoprotectants by halophilic and alkaliphilic methanotrophs. Microbiology. 2000;69:381–6.CAS 

Google Scholar 
Fenizia S, Thume K, Wirgenings M, Pohnert G. Ectoine from bacterial and algal origin is a compatible solute in microalgae. Mar Drugs. 2020;18:42.CAS 
PubMed Central 

Google Scholar 
Amin SA, Hmelo LR, van Tol HM, Durham BP, Carlson LT, Heal KR, et al. Interaction and signalling between a cosmopolitan phytoplankton and associated bacteria. Nature. 2015;522:98–101.CAS 
PubMed 

Google Scholar 
Krell A, Beszteri B, Dieckmann G, Glöckner G, Valentin K, Mock T. A new class of ice-binding proteins discovered in a salt-stress-induced cDNA library of the psychrophilic diatom Fragilariopsis cylindrus (Bacillariophyceae). Eur J Phycol. 2008;43:423–33.CAS 

Google Scholar 
Helliwell KE, Kleiner FH, Hardstaff H, Chrachri A, Gaikwad T, Salmon D, et al. Spatiotemporal patterns of intracellular Ca2+ signalling govern hypo-osmotic stress resilience in marine diatoms. N Phytol. 2021;230:155–70.CAS 

Google Scholar 
Kaczmarska I, Poulíčková A, Sato S, Edlund MB, Idei M, Watanabe T, et al. Proposals for a terminology for diatom sexual reproduction, auxospores and resting stages. Diatom Res. 2013;28:263–94.
Google Scholar 
Godhe A, Kremp A, Montresor M. Genetic and microscopic evidence for sexual reproduction in the centric diatom Skeletonema marinoi. Protist. 2014;165:401–16.PubMed 

Google Scholar 
Annunziata R, Mele BH, Marotta P, Volpe M, Entrambasaguas L, Mager S, et al. Trade-off between sex and growth in diatoms: Molecular mechanisms and demographic implications. Sci Adv. 2022;8:eabj9466.CAS 
PubMed 
PubMed Central 

Google Scholar 
Ajani PA, Petrou K, Larsson ME, Nielsen DA, Burke J, Murray SA. Phenotypic trait variability as an indication of adaptive capacity in a cosmopolitan marine diatom. Environ Microbiol. 2021;23:207–23.CAS 
PubMed 

Google Scholar 
Sjöqvist CO, Kremp A. Genetic diversity affects ecological performance and stress response of marine diatom populations. ISME J. 2016;10:2755–66.PubMed 
PubMed Central 

Google Scholar 
Godhe A, Rynearson T. The role of intraspecific variation in the ecological and evolutionary success of diatoms in changing environments. Philos Trans R Soc Lond B Biol Sci. 2017;372:20160399.PubMed 
PubMed Central 

Google Scholar 
Bulankova P, Sekulić M, Jallet D, Nef C, van Oosterhout C, Delmont TO, et al. Mitotic recombination between homologous chromosomes drives genomic diversity in diatoms. Curr Biol. 2021;31:3221–32. e9CAS 
PubMed 

Google Scholar 
Pinseel E, Janssens SB, Verleyen E, Vanormelingen P, Kohler TJ, Biersma EM, et al. Global radiation in a rare biosphere soil diatom. Nat Commun. 2020;11:2382.CAS 
PubMed 
PubMed Central 

Google Scholar 
Savchuk OP. Large-scale nutrient dynamics in the Baltic sea, 1970–2016. Front Mar Sci. 2018;5:95.
Google Scholar 
Gomez-Mestre I, Jovani R. A heuristic model on the role of plasticity in adaptive evolution: plasticity increases adaptation, population viability and genetic variation. Proc Biol Sci. 2013;280:20131869.PubMed 
PubMed Central 

Google Scholar 
Lambert BS, Groussman RD, Schatz MJ, Coesel SN, Durham BP, Alverson AJ, et al. The dynamic trophic architecture of open-ocean protist communities revealed through machine-guided metatranscriptomics. Proc Natl Acad Sci USA. 2022;119:e2100916119.Harrison PF, Pattison AD, Powell DR, Beilharz TH. Topconfects: a package for confident effect sizes in differential expression analysis provides a more biologically useful ranked gene list. Genome Biol. 2019;20:67.PubMed 
PubMed Central 

Google Scholar  More