Ecology

Brondizio, E. S., Settele, J., Díaz, S. & Ngo, H. T. (eds.) Global Assessment Report on Biodiversity and Ecosystem Services of the Intergovernmental Science–Policy Platform on Biodiversity and Ecosystem Services (IPBES Secretariat, 2019).Weiskopf, S. R. et al. Climate change effects on biodiversity, ecosystems, ecosystem services, and natural resource management in the United States. Sci. Total Environ. 733, 137782. https://doi.org/10.1016/j.scitotenv.2020.137782 (2020).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Turner, J. & Marshall, G. J. Climate Change in the Polar Regions (Cambridge University Press, 2011).Book 

Google Scholar 
Meredith, M. et al. Polar Regions. Chapter 3, IPCC Special Report on the Ocean and Cryosphere in a Changing Climate. https://www.ipcc.ch/srocc/chapter/chapter-3-2/ (2019).Rignot, E. et al. Four decades of Antarctic Ice Sheet mass balance from 1979–2017. Proc. Natl. Acad. Sci. USA 116, 1095–1103. https://doi.org/10.1073/pnas.1812883116 (2019).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Siegert, M. et al. The Antarctic Peninsula under a 1.5°C global warming scenario. Front. Environ. Sci. 7, 102. https://doi.org/10.3389/fenvs.2019.00102 (2019).Article 

Google Scholar 
Iz, H. B. Is the global sea surface temperature rise accelerating?. Geod. Geodyn. 9, 432–438. https://doi.org/10.1016/j.geog.2018.04.002 (2018).Article 

Google Scholar 
Qiu, Z. et al. Future climate change is predicted to affect the microbiome and condition of habitat-forming kelp. Proc. R. Soc. B. 286, 20181887. https://doi.org/10.1098/rspb.2018.1887 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Burge, C. A., Kim, C. J., Lyles, J. M. & Harvell, C. D. Special issue Oceans and Humans Health: The ecology of marine opportunists. Microb. Ecol. 65, 869–879. https://doi.org/10.1007/s00248-013-0190-7 (2013).Article 
PubMed 

Google Scholar 
Cavicchioli, R. et al. Scientists’ warning to humanity: Microorganisms and climate change. Nat. Rev. Microbiol. 17, 569–586. https://doi.org/10.1038/s41579-019-0222-5 (2019).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Harvell, C. D. et al. Emerging marine diseases–climate links and anthropogenic factors. Science 285, 1505–1510. https://doi.org/10.1126/science.285.5433.1505 (1999).CAS 
Article 
PubMed 

Google Scholar 
Egan, S. & Gardiner, M. Microbial dysbiosis: Rethinking disease in marine ecosystems. Front. Microbiol. 7, 991. https://doi.org/10.3389/fmicb.2016.00991 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Wilkins, L. G. E. et al. Host-associated microbiomes drive structure and function of marine ecosystems. PLoS Biol. 17, e3000533. https://doi.org/10.1371/journal.pbio.3000533 (2019).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Seuront, L., Nicastro, K. R., Zardi, G. I. & Goberville, E. Decreased thermal tolerance under recurrent heat stress conditions explains summer mass mortality of the blue mussel Mytilus edulis. Sci. Rep. 9, 17498. https://doi.org/10.1038/s41598-019-53580-w (2019).ADS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Tsuchiya, M. Mass mortality in a population of the mussel Mytilus edulis L. caused by high temperature on rocky shores. J. Exp. Mar. Biol. Ecol. 66, 101–111. https://doi.org/10.1016/0022-0981(83)90032-1 (1983).Article 

Google Scholar 
Malham, S. K. et al. Summer mortality of the Pacific oyster, Crassostrea gigas, in the Irish Sea: The influence of temperature and nutrients on health and survival. Aquaculture 287, 128–138. https://doi.org/10.1016/j.aquaculture.2008.10.006 (2009).CAS 
Article 

Google Scholar 
Beyer, J. et al. Blue mussels (Mytilus edulis spp.) as sentinel organisms in coastal pollution monitoring: A review. Mar. Environ. Res. 130, 338–365. https://doi.org/10.1016/j.marenvres.2017.07.024 (2017).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Ladeiro, M. P. et al. Mussel as a tool to define continental watershed quality. In Organismal and Molecular Malacology (ed Ray, S.), IntechOpen. https://doi.org/10.5772/67995 (2017).Bonacci, S. et al. Esterase activities in the bivalve mollusc Adamussium colbecki as a biomarker for pollution monitoring in the Antarctic marine environment. Mar. Pollut. Bull. 49, 445–455. https://doi.org/10.1016/j.marpolbul.2004.02.033 (2004).CAS 
Article 
PubMed 

Google Scholar 
Storhaug, E. et al. Seasonal and spatial variations in biomarker baseline levels within Arctic populations of mussels (Mytilus spp.). Sci. Total Environ. 656, 921–936. https://doi.org/10.1016/j.scitotenv.2018.11.397 (2019).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Caza, F. et al. Liquid biopsies for omics-based analysis in sentinel mussels. PLoS ONE 14, e0223525. https://doi.org/10.1371/journal.pone.0225359 (2019).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Ignatiadis, M., Sledge, G. W. & Jeffrey, S. S. Liquid biopsy enters the clinic – implementation issues and future challenges. Nat. Rev. Clin. Oncol. 18, 297–312. https://doi.org/10.1038/s41571-020-00457-x (2021).Article 
PubMed 

Google Scholar 
Kowarsky, M. et al. Numerous uncharacterized and highly divergent microbes which colonize humans are revealed by circulating cell-free DNA. Proc. Natl. Acad. Sci. USA 114, 9623–9628. https://doi.org/10.1073/pnas.1707009114 (2017).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Chen, H. et al. Circulating microbiome DNA: An emerging paradigm for cancer liquid biopsy. Cancer Lett. 521, 82–87. https://doi.org/10.1016/j.canlet.2021.08.036 (2021).CAS 
Article 
PubMed 

Google Scholar 
Lokmer, A. et al. Spatial and temporal dynamics of Pacific oyster hemolymph microbiota across multiple scales. Front. Microbiol. 7, 1367. https://doi.org/10.3389/fmicb.2016.01367 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Lokmer, A. & Wegner, M. K. Hemolymph microbiome of Pacific oysters in response to temperature, temperature stress and infection. ISME J. 9, 670–682. https://doi.org/10.1038/ismej.2014.160 (2015).CAS 
Article 
PubMed 

Google Scholar 
Auguste, M. et al. Exposure to TiO2 nanoparticles induces shifts in the microbiota composition of Mytilus galloprovincialis hemolymph. Sci. Total Environ. 670, 129–137. https://doi.org/10.1016/j.scitotenv.2019.03.133 (2019).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Vezzulli, L. et al. Climate influence on Vibrio and associated human diseases during the past half-century in the coastal North Atlantic. Proc. Natl. Acad. Sci. USA 113, E5062–E5071. https://doi.org/10.1073/pnas.1609157113 (2016).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Musella, M. et al. Tissue-scale microbiota of the Mediterranean mussel (Mytilus galloprovincialis) and its relationship with the environment. Sci. Total Environ. 717, 137209. https://doi.org/10.1016/j.scitotenv.2020.137209 (2020).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Féral, J.-P. et al. PROTEKER: Implementation of a submarine observatory at the Kerguelen islands (Southern Ocean). Underw. Technol. 34, 3–10. https://doi.org/10.3723/ut.34.003 (2016).Article 

Google Scholar 
Spain, E. A. et al. Shallow seafloor gas emissions near Heard and McDonald Islands on the Kerguelen Plateau, southern Indian Ocean. Earth Space Sci. 7, e2019EA000695. https://doi.org/10.1029/2019EA000695 (2020).ADS 
Article 

Google Scholar 
Cao, S. et al. Structure and function of the Arctic and Antarctic marine microbiota as revealed by metagenomics. Microbiome. 8, 47. https://doi.org/10.1186/s40168-020-00826-9 (2020).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Wang, L.-Y. et al. Comparison of bacterial community in aqueous and oil phases of water-flooded petroleum reservoirs using pyrosequencing and clone library approaches. Appl. Microbiol. Biotechnol. 98, 4209–4221. https://doi.org/10.1007/s00253-013-5472-y (2014).CAS 
Article 
PubMed 

Google Scholar 
Gutierrez, T., Berry, D., Teske, A. & Aitken, M. D. Enrichment of Fusobacteria in sea surface oil slicks from the Deepwater Horizon oil spill. Microorganisms. 4, 24. https://doi.org/10.3390/microorganisms4030024 (2016).CAS 
Article 
PubMed Central 

Google Scholar 
Michelou, V. K., Caporaso, J. G., Knight, R. & Palumbi, S. R. The ecology of microbial communities associated with Macrocystis pyrifera. PLoS ONE 8, e67480. https://doi.org/10.1371/annotation/48e29578-a073-42e7-bca4-2f96a5998374 (2013).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Florez, J. Z. et al. Structure of the epiphytic bacterial communities of Macrocystis pyrifera in localities with contrasting nitrogen concentrations and temperature. Algal Res. 44, 101706. https://doi.org/10.1016/j.algal.2019.101706 (2019).Article 

Google Scholar 
Minich, J. J. et al. Elevated temperature drives kelp microbiome dysbiosis, while elevated carbon dioxide induces water microbiome disruption. PLoS ONE 13, e0192772. https://doi.org/10.1371/journal.pone.0192772 (2018).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Lin, J. D., Lemay, M. A. & Parfrey, L. W. Diverse bacteria utilize alginate within the microbiome of the giant kelp Macrocystis pyrifera. Front. Microbiol. 9, 1914. https://doi.org/10.3389/fmicb.2018.01914 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Pierce, M. L. & Ward, J. E. Microbial ecology of the Bivalvia, with an emphasis on the family Ostreidae. J. Shellfish Res. 37, 793–806. https://doi.org/10.2983/035.037.0410 (2018).Article 

Google Scholar 
Pierce, M. L. & Ward, J. E. Gut Microbiomes of the Eastern Oyster (Crassostrea virginica) and the Blue Mussel (Mytilus edulis): Temporal variation and the influence of marine aggregate-associated microbial communities. mSphere. 4, e00730-19. https://doi.org/10.1128/mSphere.00730-19 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Delille, D. & Gleizon, F. Distribution of enteric bacteria in Antarctic seawater surrounding the Port-aux-Francais permanent station (Kerguelen Island). Mar. Pollut. Bull. 46, 1179–1183. https://doi.org/10.1016/S0025-326X(03)00164-4 (2003).CAS 
Article 
PubMed 

Google Scholar 
Nguyen, T. V. & Alfaro, A. C. Metabolomics investigation of summer mortality in New Zealand Greenshell mussels (Perna canaliculus). Fish Shellfish Immunol. 106, 783–791. https://doi.org/10.1016/j.fsi.2020.08.022 (2020).CAS 
Article 
PubMed 

Google Scholar 
Vezzulli, L. et al. Comparative 16SrDNA gene-based microbiota profiles of the Pacific oyster (Crassostrea gigas) and the Mediterranean Mussel (Mytilus galloprovincialis) from a shellfish farm (Ligurian Sea, Italy). Microb. Ecol. 75, 495–504. https://doi.org/10.1007/s00248-017-1051-6 (2018).CAS 
Article 
PubMed 

Google Scholar 
Romalde, J. L., Diéguez, A. L., Lasa, A. & Balboa, S. New Vibrio species associated to molluscan microbiota: A review. Front. Microbiol. 4, 413. https://doi.org/10.3389/fmicb.2013.00413 (2014).Article 
PubMed 
PubMed Central 

Google Scholar 
Narayan, N. R. et al. Piphillin predicts metagenomic composition and dynamics from DADA2-corrected 16S rDNA sequences. BMC Genom. 21, 56. https://doi.org/10.1186/s12864-019-6427-1 (2020).CAS 
Article 

Google Scholar 
Peng, W. et al. Integrated 16S rRNA sequencing, metagenomics, and metabolomics to characterize gut microbial composition, function, and fecal metabolic phenotype in non-obese type 2 diabetic Goto-Kakizaki rats. Front. Microbiol. 10, 3141. https://doi.org/10.3389/fmicb.2019.03141 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Koner, S. et al. Assessment of carbon substrate catabolism pattern and functional metabolic pathway for microbiota of limestone caves. Microorganisms 9, 1789. https://doi.org/10.21203/rs.3.rs-549787/v1 (2021).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Li, Y. F. et al. Temperature elevation and Vibrio cyclitrophicus infection reduce the diversity of haemolymph microbiome of the mussel Mytilus coruscus. Sci. Rep. 9, 16391. https://doi.org/10.1038/s41598-019-52752-y (2019).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Scanes, E. et al. Climate change alters the haemolymph microbiome of oysters. Mar. Pollut. Bull. 164, 111991. https://doi.org/10.1016/j.marpolbul.2021.111991 (2021).CAS 
Article 
PubMed 

Google Scholar 
Hylander, B. L. & Repasky, E. A. Temperature as a modulator of the gut microbiome: What are the implications and opportunities for thermal medicine?. Int. J. Hyperth. 36, 83–89. https://doi.org/10.1080/02656736.2019.1647356 (2019).CAS 
Article 

Google Scholar 
Lo Giudice, A. et al. Marine bacterioplankton diversity and community composition in an antarctic coastal environment. Microb. Ecol. 63, 210–223. https://doi.org/10.1007/s00248-011-9904-x (2012).Article 
PubMed 

Google Scholar 
Yumoto, I. et al. Temperature and nutrient availability control growth rate and fatty acid composition of facultatively psychrophilic Cobetia marina strain L-2. Arch. Microbiol. 181, 345–351. https://doi.org/10.1007/s00203-004-0662-8 (2004).CAS 
Article 
PubMed 

Google Scholar 
Weingarten, E. A., Atkinson, C. L. & Jackson, C. R. The gut microbiome of freshwater Unionidae mussels is determined by host species and is selectively retained from filtered seston. PLoS ONE 14, e0224796. https://doi.org/10.1371/journal.pone.0224796 (2019).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Rosa, M., Ward, J. E. & Shumway, S. E. Selective capture and ingestion of particles by suspension-feeding bivalve molluscs: A review. J. Shellfish Res. 37, 727–746. https://doi.org/10.2983/035.037.0405 (2018).Article 

Google Scholar 
Griffiths, C. L. & King, J. A. Some relationships between size, food availability and energy balance in the ribbed mussel Aulacomya ater. Mar. Biol. 51, 141–149. https://doi.org/10.1007/BF00555193 (1979).Article 

Google Scholar 
Riisgård, H. U. Filtration rate and growth in the blue mussel, Mytilus edulis Linneaus, 1758: Dependence on algal concentration. J. Shellfish Res. 10, 29–36 (1991).
Google Scholar 
Sonier, R. et al. Picophytoplankton contribution to Mytilus edulis growth in an intensive culture environment. Mar. Biol. 163, 73. https://doi.org/10.1007/s00227-016-2845-7 (2016).Article 

Google Scholar 
Jacobs, P., Troost, K., Riegman, R. & Van der Meer, J. Length-and weight-dependent clearance rates of juvenile mussels (Mytilus edulis) on various planktonic prey items. Helgol. Mar. Res. 69, 101–112. https://doi.org/10.1007/s10152-014-0419-y (2015).ADS 
Article 

Google Scholar 
Ward, J. E. & Shumway, S. E. Separating the grain from the chaff: Particle selection in suspension- and deposit-feeding bivalves. J. Exp. Mar. 300, 83–130. https://doi.org/10.1016/j.jembe.2004.03.002 (2004).Article 

Google Scholar 
Waite, A. M., Safi, K. A., Hall, J. A. & Nodder, S. D. Mass sedimentation of picoplankton embedded in organic aggregates. Limnol. Oceanogr. 45, 87–97. https://doi.org/10.4319/lo.2000.45.1.0087 (2000).ADS 
Article 

Google Scholar 
Ward, J. E. & Kach, D. J. Marine aggregates facilitate ingestion of nanoparticles by suspension-feeding bivalves. Mar. Environ. Res. 68, 137–142. https://doi.org/10.1016/j.marenvres.2009.05.002 (2009).CAS 
Article 
PubMed 

Google Scholar 
Ward, J. E. Biodynamics of suspension-feeding in adult bivalve molluscs: Particle capture, processing, and fate. Invertebr. Biol. 115, 218–231. https://doi.org/10.2307/3226932 (1996).Article 

Google Scholar 
Rosa, M. et al. Physicochemical surface properties of microalgae and their combined effects on particle selection by suspension-feeding bivalve molluscs. J. Exp. Mar. 486, 59–68. https://doi.org/10.1016/j.jembe.2016.09.007 (2017).CAS 
Article 

Google Scholar 
Allam, B. & Espinosa, E. P. Bivalve immunity and response to infections: Are we looking at the right place?. Fish Shellfish Immunol. 53, 4–12. https://doi.org/10.1016/j.fsi.2016.03.037 (2016).CAS 
Article 
PubMed 

Google Scholar 
Barr, J. J. et al. Bacteriophage adhering to mucus provide a non-host-derived immunity. Proc. Natl. Acad. Sci. USA 110, 10771–10776. https://doi.org/10.1073/pnas.1305923110 (2013).ADS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Allam, B. & Espinosa, E. P. Mucosal immunity in mollusks. In Mucosal Health in Aquaculture (eds Beck, B. H. & Peatman, E.) 325–370 (Academic Press, 2015).Chapter 

Google Scholar 
Huang, J. et al. Hemocytes in the extrapallial space of Pinctada fucata are involved in immunity and biomineralization. Sci. Rep. 8, 4657. https://doi.org/10.1038/s41598-018-22961-y (2018).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Kim, H. J. et al. Isolation and characterization of two bacteriophages and their preventive effects against pathogenic Vibrio coralliilyticus causing mortality of Pacific oyster (Crassostrea gigas) larvae. Microorganisms. 8, 926. https://doi.org/10.3390/microorganisms8060926 (2020).CAS 
Article 
PubMed Central 

Google Scholar 
Ihara, H. et al. Sulfur-oxidizing bacteria mediate microbial community succession and element cycling in launched marine sediment. Front. Microbiol. 8, 152. https://doi.org/10.3389/fmicb.2017.00152 (2017).ADS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Jørgensen, B. B. & Nelson, D. C. Sulfide oxidation in marine sediments: Geochemistry meets microbiology. Geol. S. Am. S. 379, 63–81. https://doi.org/10.1130/0-8137-2379-5.63 (2004).Article 

Google Scholar 
Zhou, M. et al. Surface currents and upwelling in Kerguelen Plateau regions. Biogeosci. Discuss. 11, 6845–6876. https://doi.org/10.5194/bgd-11-6845-2014 (2014).ADS 
Article 

Google Scholar 
Gille, S. T., Carranza, M. M., Cambra, R. & Morrow, R. Wind-induced upwelling in the Kerguelen Plateau region. Biogeosciences 11, 6389–6400. https://doi.org/10.5194/bg-11-6389-2014 (2014).ADS 
Article 

Google Scholar 
Park, Y. H., Roquet, F., Durand, I. & Fuda, J. L. Large-scale circulation over and around the Northern Kerguelen Plateau. Deep Sea Res. II(55), 566–581. https://doi.org/10.1016/j.dsr2.2007.12.030 (2008).ADS 
Article 

Google Scholar 
Renac, C. et al. Hydrothermal fluid interaction in basaltic lava units, Kerguelen Archipelago (SW Indian Ocean). Eur. J. 22, 215–234. https://doi.org/10.1127/0935-1221/2009/0022-1993 (2010).CAS 
Article 

Google Scholar 
Vancanneyt, M. et al. Sphingomonas alaskensis sp. nov., a dominant bacterium from a marine oligotrophic environment. Int. J. Syst. Evol. 51, 73–79. https://doi.org/10.1099/00207713-51-1-73 (2001).CAS 
Article 

Google Scholar 
Helmuth, B. S. & Hofmann, G. E. Microhabitats, thermal heterogeneity, and patterns of physiological stress in the rocky intertidal zone. Biol. Bull. 201, 374–384. https://doi.org/10.2307/1543615 (2001).CAS 
Article 
PubMed 

Google Scholar 
Testut, L., Wöppelmann, G., Simon, B. & Téchiné, P. The sea level at Port-aux-Français, Kerguelen Island, from 1949 to the present. Ocean Dyn. 56, 464–472. https://doi.org/10.1007/s10236-005-0056-8 (2006).ADS 
Article 

Google Scholar 
Pohl, B. et al. Recent climate variability around the Kerguelen Islands (Southern Ocean) seen through weather regimes. J. Appl. Meteorol. Climatol. 60, 711–731. https://doi.org/10.1175/JAMC-D-20-0255.1 (2021).ADS 
Article 

Google Scholar 
PROTEKER. Ilôt Channer (Passe Royale)—Sea water temperature at 5 and 13 m depth (T°C) daily average 2014–2019. https://www.proteker.net/swt-ilot-channer-passe-royale/ (2021).Caza, F. et al. Comparative analysis of hemocyte properties from Mytilus edulis desolationis and Aulacomya ater in the Kerguelen Islands. Mar. Environ. Res. 110, 174–182. https://doi.org/10.1016/j.marenvres.2015.09.003 (2015).CAS 
Article 
PubMed 

Google Scholar 
Caza, F., Cledon, M. & St-Pierre, Y. Biomonitoring climate change and pollution in marine ecosystems: A review on Aulacomya ater. J. Mar. Biol. 2016, 7183813. https://doi.org/10.1155/2016/7183813 (2016).Article 

Google Scholar 
Rey-Campos, M. et al. High individual variability in the transcriptomic response of Mediterranean mussels to Vibrio reveals the involvement of myticins in tissue injury. Sci. Rep. 9, 3569. https://doi.org/10.1038/s41598-019-39870-3 (2019).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Caza, F. et al. Hemocytes released in seawater act as Trojan horses for spreading of bacterial infections in mussels. Sci. Rep. 10, 19696. https://doi.org/10.1038/s41598-020-76677-z (2020).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Yao, C. L. & Somero, G. N. Thermal stress and cellular signaling processes in hemocytes of native (Mytilus californianus) and invasive (M. galloprovincialis) mussels: Cell cycle regulation and DNA repair. Comp. Biochem. Physiol. 165, 159–168. https://doi.org/10.1016/j.cbpa.2013.02.024 (2013).CAS 
Article 

Google Scholar 
Lockwood, B. L., Sanders, J. G. & Somero, G. N. Transcriptomic responses to heat stress in invasive and native blue mussels (genus Mytilus): Molecular correlates of invasive success. J. Exp. Biol. 213, 3548–3558. https://doi.org/10.1242/jeb.046094 (2010).CAS 
Article 
PubMed 

Google Scholar 
Klindworth, A. et al. Evaluation of general 16S ribosomal RNA gene PCR primers for classical and next-generation sequencing-based diversity studies. Nucleic Acids Res. 41, e1. https://doi.org/10.1093/nar/gks808 (2013).CAS 
Article 
PubMed 

Google Scholar 
Callahan, B. J. et al. DADA2: High-resolution sample inference from Illumina amplicon data. Nat. Methods. 13, 581–583. https://doi.org/10.1038/nmeth.3869 (2016).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2021).
Google Scholar 
McMurdie, P. J. & Holmes, S. phyloseq: An R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE 8, e61217. https://doi.org/10.1371/journal.pone.0061217 (2013).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Oksanen, J. & Blanchet, F. G. Vegan: Community Ecology Package. 2. 3-0 (2015).Ssekagiri, A., Sloan, W. & Ijaz, U. Z. microbiomeSeq: an R package for analysis of microbial communities in an environmental context, In ISCB Africa ASBCB Conference (Kumasi, Ghana, 2017).Cao, Y. Microbiome marker: Microbiome Biomarker Analysis Toolkit. R package version 0.99.0 (2020). https://github.com/yiluheihei/microbiomeMarker. Accessed March 2022.Kanehisa, M. et al. KEGG for integration and interpretation of large-scale molecular data sets. Nucleic Acids Res. 40, D109–D114. https://doi.org/10.1093/nar/gkr988 (2012).CAS 
Article 
PubMed 

Google Scholar 
Iwai, S. et al. Piphillin: Improved prediction of metagenomic content by direct inference from human microbiomes. PLoS ONE 11, e0166104. https://doi.org/10.1371/journal.pone.0166104 (2016).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Dhariwal, A. et al. MicrobiomeAnalyst: A web-based tool for comprehensive statistical, visual and meta-analysis of microbiome data. Nucleic Acids Res. 45, W180–W188. https://doi.org/10.1093/nar/gkx295 (2017).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar  More

Alexandratos, N. & Bruinsma, J. World Agriculture Towards 2030/2050. The 2012 Revision (FAO, 2012).Tilman, D., Balzer, C., Hill, J. & Befort, B. L. Global food demand and the sustainable intensification of agriculture. Proc. Natl Acad. Sci. USA 108, 20260–20264 (2011).CAS 
Article 

Google Scholar 
Mueller, N. D. et al. Closing yield gaps through nutrient and water management. Nature 490, 254–257 (2012).CAS 
Article 

Google Scholar 
Chen, X. et al. Producing more grain with lower environmental costs. Nature 514, 486–489 (2014).CAS 
Article 

Google Scholar 
Fan, M. S. et al. Improving crop productivity and resource use efficiency to ensure food security and environmental quality in China. J. Exp. Bot. 63, 13–24 (2012).CAS 
Article 

Google Scholar 
Godfray, H. C. J. et al. Food security: the challenge of feeding 9 billion people. Science 327, 812–818 (2010).CAS 
Article 

Google Scholar 
Foley, J. A. et al. Solutions for a cultivated planet. Nature 478, 337–342 (2011).CAS 
Article 

Google Scholar 
Porter, J. R. et al. Food Security and Food Production Systems (Cambridge Univ. Press, 2014).Ray, D. K. & Foley, J. A. Increasing global crop harvest frequency: recent trends and future directions. Environ. Res. Lett. 8, 044041 (2013).Article 

Google Scholar 
Lal, R. Restoring soil quality to mitigate soil degradation. Sustainability 7, 5875–5895 (2015).Article 

Google Scholar 
Wall, D. & Six, J. Give soils their due. Science 347, 695 (2015).CAS 
Article 

Google Scholar 
Ray, D. K. et al. Climate variation explains a third of global crop yield variability. Nat. Commun. 6, 5989 (2015).CAS 
Article 

Google Scholar 
Battisti, D. S. & Naylor, R. L. Historical warnings of future food insecurity with unprecedented seasonal heat. Science 323, 240–244 (2009).CAS 
Article 

Google Scholar 
Nelson, G. C. et al. Climate Change: Impact on Agriculture and Costs of Adaptation (International Food Policy Research Institute, 2009).Challinor, A. J., Koehler, A. K., Ramirez-Villegas, J., Whitfield, S. & Das, B. Current warming will reduce yields unless maize breeding and seed systems adapt immediately. Nat. Clim. Change 6, 954–958 (2016).Article 

Google Scholar 
Zhao, C. et al. Temperature increase reduces global yields of major crops in four independent estimates. Proc. Natl Acad. Sci. USA 114, 9326–9331 (2017).CAS 
Article 

Google Scholar 
Schlenker, W., Hanemann, M. & Fisher, A. Will US agriculture really benefit from global warming? Accounting for irrigation in the hedonic approach. Am. Econ. Rev. 95, 395–406 (2005).Article 

Google Scholar 
Piao, S. L. et al. The impacts of climate change on water resources and agriculture in China. Nature 467, 43–51 (2010).CAS 
Article 

Google Scholar 
Ray, D. K. et al. Climate change has likely already affected global food production. PLoS ONE 14, e0217148 (2019).CAS 
Article 

Google Scholar 
Ramankutty, N. et al. The global distribution of cultivable lands: current patterns and sensitivity to possible climate change. Glob. Ecol. Biogeogr. 11, 377–392 (2002).Article 

Google Scholar 
Rosenzweig, C. et al. Assessing agricultural risks of climate change in the 21st century in a global gridded crop model intercomparison. Proc. Natl Acad. Sci. USA 111, 3268–3273 (2014).CAS 
Article 

Google Scholar 
Lobell, D. B. & Burke, M. B. On the use of statistical models to predict crop yield responses to climate change. Agr. For. Meteorol. 150, 1443–1452 (2010).Article 

Google Scholar 
Auffhammer, M. & Schlenker, W. Empirical studies on agricultural impacts and adaptation. Energy Econ. 46, 555–561 (2014).Article 

Google Scholar 
Folberth, C. et al. Uncertainty in soil data can outweigh climate impact signals in global crop yield simulations. Nat. Commun. 7, 11872 (2016).CAS 
Article 

Google Scholar 
Asseng, S. et al. Uncertainty in simulating wheat yields under climate change. Nat. Clim. Change 3, 827–832 (2013).CAS 
Article 

Google Scholar 
Basso, B. et al. Soil organic carbon and nitrogen feedbacks on crop yields under climate change. Agr. Environ. Lett. 3, 180026 (2018).Mϋller, C. et al. Implication of climate mitigation for future agricultural production. Environ. Res. Lett. 10, 125004 (2015).Article 

Google Scholar 
IPCC Climate Change 2022: Impacts, Adaptation, and Vulnerability (eds Pörtner, H. O. et al.) (Cambridge Univ. Press, 2022).Zhang, W. et al. Closing yield gaps in China by empowering smallholder farmers. Nature 537, 671–674 (2016).CAS 
Article 

Google Scholar 
Cui, Z. L. et al. Pursuing sustainable productivity with millions of smallholder farmers. Nature 555, 363–368 (2018).CAS 
Article 

Google Scholar 
Knapp, S. & van der Heijden, M. G. A. A global meta-analysis of yield stability in organic and conservation agriculture. Nat. Commun. 9, 3632 (2018).Article 
CAS 

Google Scholar 
Müller, C. et al. Global Gridded Crop Model evaluation: benchmarking, skills, deficiencies and implications. Geosci. Model Dev. 10, 1403–1422 (2017).Jamieson, P. D., Porter, J. R. & Wilson, D. R. A test of the computer simulation model ARC-WHEAT on wheat crops grown in New Zealand. Field Crops Res. 27, 337–350 (1991).Article 

Google Scholar 
Warszawski, L. et al. The inter-sectoral impact model intercomparison project (ISI–MIP): project framework. Proc. Natl Acad. Sci. USA 111, 3228–3232 (2014).CAS 
Article 

Google Scholar 
Xiong, W. et al. The Impacts of Climate Change on Chinese Agriculture—Phase II National Level Study Final Report (AEA Group, 2008).Liu, B. et al. Similar estimates of temperature impacts on global wheat yield by three independent methods. Nat. Clim. Change 6, 1130–1136 (2016).Article 

Google Scholar 
Tao, F. et al. Global warming, rice production, and water use in China: developing a probabilistic assessment. Agr. For. Meteorol. 148, 94–110 (2008).Article 

Google Scholar 
Xiong, W. et al. Different uncertainty distribution between high and low latitudes in modelling warming impacts on wheat. Nat. Food 1, 63–69 (2020).Article 

Google Scholar 
Fernandez-Illescas, C. P., Porporato, A., Laio, F. & Rodriguez-Iturbe, I. The ecohydrological role of soil texture in a water-limited ecosystem. Water Resour. Res. 37, 2863–2872 (2001).Article 

Google Scholar 
Wang, E. L. et al. Capacity of soils to buffer impact of climate variability and value of seasonal forecasts. Agr. For. Meteorol. 149, 38–50 (2009).Article 

Google Scholar 
Vereecken, H. et al. Modeling soil processes: review, key challenges, and new perspectives. Vadose Zone J. 15, 1–57 (2016).Myers, R. J. K. et al. in The Biological Management of Tropical Soil Fertility (eds Woomer, P.I. & Swift, M.J.) Ch. 4 (Wiley, 1994).Smith, P. & Gregory, P. J. Climate change and sustainable food production. P. Nutr. Soc. 72, 21–28 (2013).Article 

Google Scholar 
Khasawneh, F. E., Sample, E. C. & Kamprath, E. J. The Role of Phosphorus in Agriculture (American Society of Agronomy, 1980).FAOSTAT (Statistics Division of the Food and Agriculture Organization of the United Nations, 2006); http://www.fao.org/faostat/en/#homeFan, M. S. et al. Plant-based assessment of inherent soil productivity and contributions to China’s cereal crop yield increase since 1980. PLoS ONE 8, e74617 (2013).CAS 
Article 

Google Scholar 
Liu, X. & Chen, F. Farming System in China (China Agriculture Press, 2005).Chen, X. P. in Fertilization Technology Highlights, (ed. Zhang, F. S) Ch. 6 (Chinese Agricultural Univ. Press, 2006).Zhang, F. et al. Integrated nutrient management for food security and environmental quality in China. Adv. Agron. 116, 1–40 (2012).CAS 
Article 

Google Scholar 
Bünemann, E. K. et al. Soil quality—a critical review. Soil Biol. Biochem. 120, 105–125 (2018).Article 
CAS 

Google Scholar 
National Soil Survey Office. Chinese Soil (China Agriculture Press, 1998) .Jiang, R. F. & Cui, J. Y. in Fertilization Technology Highlights, (ed. Zhang, F. S.) Ch. 5 (China Agricultural Univ. Press, 2006).Cramer, W. P. & Solomon, A. M. Climatic classification and future global redistribution of agricultural land. Clim. Res. 3, 97–110 (1993).Article 

Google Scholar 
Elith, J., Leathwick, J. R. & Hastie, T. A working guide to boosted regression trees. J. Anim. Ecol. 77, 802–813 (2008).CAS 
Article 

Google Scholar 
Friedman, J. H. Stochastic gradient boosting. Comput. Stat. Data 38, 367–378 (2002).Article 

Google Scholar 
Buston, P. M. & Elith, J. Determinants of reproductive success in dominant pairs of clownfish: a boosted regression tree analysis. J. Anim. Ecol. 80, 528–538 (2011).Article 

Google Scholar 
Friedman, J. H. & Meulman, J. J. Multiple additive regression trees with application in epidemiology. Stat. Med. 22, 1365–1381 (2003).Article 

Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2018).Kuhn, M. & Johnson, K. Applied Predictive Modeling (Springer, 2013).Yang, J. M., Yang, J. Y., Liu, S. & Hoogenboom, G. An evaluation of the statistical methods for testing the performance of crop models with observed data. Agric. Syst. 127, 81–89 (2014).Article 

Google Scholar 
Loague, K. & Green, R. E. Statistical and graphical methods for evaluating solute transport models: overview and application. J. Contamin. Hydro. 7, 51–73 (1991).CAS 
Article 

Google Scholar 
Akinremi, O. O. et al. Evaluation of LEACHMN under Dryland conditions. I. Simulation of water and solute transport. Can. J. Soil Sci. 85, 223–232 (2005).Article 

Google Scholar 
Palosuo, T. et al. Simulation of winter wheat yield and its variability in different climates of Europe: a comparison of eight crop growth models. Eur. J. Agron. 35, 103–114 (2011).Article 

Google Scholar 
Deng, N. et al. Closing yield gaps for rice self-sufficiency in China. Nat. Commun. 10, 1725 (2019).Article 
CAS 

Google Scholar 
Correndo, A. A. et al. Assessing the uncertainty of maize yield without nitrogen fertilization. Field Crops Res. 260, 107985 (2021).Article 

Google Scholar 
Rattalino Edreira, J. I. et al. Spatial frameworks for robust estimation of yield gaps. Nat. Food 2, 773–779 (2021).Article 

Google Scholar 
Tilman, D., Reich, P. B. & Knops, J. M. H. Biodiversity and ecosystem stability in a decade-long grassland experiment. Nature 441, 629–632 (2006).CAS 
Article 

Google Scholar 
Kuhn, M. Building predictive models in R using the caret package. J. Stat. Softw. 28, 1–26 (2008).Article 

Google Scholar 
IPCC Climate Change 2014: Climate Change: Synthesis Report (eds Core Writing Team, Pachauri, R. K. & Meyer L. A.) (IPCC, 2014).van Vuuren, D. P. et al. The representative concentration pathways: an overview. Clim. Change 109, 5–31 (2011).Article 

Google Scholar 
Hempel, S., Frieler, K., Warszawski, L., Schewe, J. & Piontek, F. A trend-preserving bias correction—the ISI-MIP approach. Earth Syst. Dynam. 4, 219–236 (2013).Article 

Google Scholar 
Chen, H., Sun, J., Lin, W. & Xu, H. Comparison of CMIP6 and CMIP5 models in simulating climate extremes. Sci. Bull. 65, 1415–1418 (2020).Article 

Google Scholar 
China Agriculture Yearbook (China Agriculture Press, 2005). More

Chloroplast and repetitive nuclear DNA enrichment in the sedaDNA extractsTo the best of our knowledge, we generated the first large-scale target enriched dataset using sedaDNA extracted from sediments of multiple lakes. Sequencing of two datasets produced 325.5 million (M) quality-filtered paired-end DNA sequences. The first target enriched dataset, targeting both the chloroplast and a set of nuclear genes of Larix on 64 sedaDNA extracts and 19 negative controls from seven lake sediment records resulted in 324 M quality-filtered paired-end sequences. The second target enriched dataset, targeting only the set of nuclear genes of Larix on four samples and two negative controls from an additional lake (Lake CH12) resulted in 1.5 M sequences. Quality-filtering of an additional published target enriched dataset29, targeting the Larix chloroplast genome on the same CH12 samples as applied for the second dataset, added another 54 M sequences.For the chloroplast enrichment, 390 thousand (K) sequences (1%) were classified as Larix at the genus or species level. The average coverage of bait regions was 19% at a mean sequence depth of 0.8. Sequencing of 19 library and extraction blank (negative control) samples resulted in 597 K paired-end sequences, of which 58% quality-filtered and deduplicated sequences remained. Of these, 38% were classified, with 0.03% of them (463 sequences) corresponding to the genus Larix. Negative controls from library preparation resulted in no to very few (0 to 5) sequences mapping to the Larix chloroplast reference genome. Negative controls from DNA extractions, which were in several cases pooled to one library, showed a low number of sequences mapped to Larix (0 to 94 sequences, except 237 sequences in one case). Excluding all sequences in negative controls from the sample analysis had no impact on the patterns resulting from the analysis of sample data. Detailed results and evaluation of negative controls are included in the Supplementary Information (Fig. S5) and Supplementary Data 1 and 2. Samples of all lake records with sufficient sequence coverage showed damage patterns typical of ancient DNA (see Supplementary Data 3).These results are comparable to the results obtained by Schulte et al.29, where 36% of quality-filtered sequences were classified as Viridiplantae with 9% assigned to Larix. In contrast to29, we raised the confidence threshold of taxonomic classification (a parameter defining the number of k-mers needed to produce a match against a taxon in the database), which drastically reduced the number of classified sequences, but increased the confidence in the analysis36.To analyze the enrichment obtained by the nuclear gene bait set, taxonomic classification was repeated using a plant genome database including available Pinaceae genomes. The classification resulted in 716 K sequences assigned to Larix, increasing the previous results by 325 K sequences. However, almost no sequences were mapped against the targeting baits (a maximum of five sequences for some samples). A closer inspection of unmapped sequences assigned to Larix revealed a high content of repetitive DNAs. More specifically, taxonomically classified Larix sequences could be assembled to EulaSat1, the most abundant satellite repeat in the nuclear genome of Larix32,37. This short repeat with a 173 bp long motif is arranged in large arrays of tandemly repeated motifs and is exclusively present in larches32. Analysis of modern L. sibirica, and L. gmelinii (western and eastern range) genomes reveals that EulaSat1 occurs in all species, contributing to 0.62% (L. sibirica), 2.52% (western range L. gmelinii), and 2.39% (eastern range L. gmelinii), of the genomes, respectively (Fig. S2). A comparison of the sequence proportions mapping to the repeat motif in the different datasets of Lake CH12 showed a specific enrichment of the repeat motif by the nuclear gene hybridization probe set (Fig. S3).In total, 17 K sequences mapped to the repeat motif of EulaSat1. The abundance of all sequences mapped per sample is in agreement with the abundance of sequences mapped to the chloroplast genome, confirming the general history of forest development (Fig. 2). Analysis of the nucleotide frequencies in the repeat motif showed a high constancy over all samples (Fig. S4). This suggests high conservation of the EulaSat1 motif in Siberian larches over time and space. Although satellite repeats are reported to have a high sequence turnover, for larches it has been shown that repeat profiles between two geographically well-separated species—the European larch (L. decidua) and the Japanese larch (L. kaempferi)—are very similar32. The main satellite in all larches, EulaSat1, is believed to have greatly multiplied after the split of Larix from Pseudotsuga32. Given the ongoing hybridization between the three Siberian larch species, it is not surprising to find a consistent pattern of nucleotide frequencies in all samples.Fig. 2: Comparison of target enrichment with available DNA metabarcoding and pollen datasets.From left to right: Larix-classified sequence counts mapping to (1) the Larix chloroplast and (2) the EulaSat1 satellite repeat motif, (3) percentage of Larix counts in metabarcoding data, (4) percentage of Larix pollen in pollen assemblages. All data from this study, except metabarcoding data from lakes CH1213 and Bolshoye Shchuchye55 and all pollen data except for several samples of Lake Kyutyunda which were produced in this study56,57,71. Pollen data of Lake Lama and the Holocene part of Lake Kyutyunda are based on parallel sediment cores PG1111 and PG2022, respectively. No available data are marked with crosses, asterisk marks a single Larix pollen grain found in the Bolshoye Shchuchye sediments.Full size imageOff-target sequences in target enriched datasets have already been demonstrated to be useful for the analysis of high-copy DNA such as ribosomal DNA or plastomes34,38,39. A recent study on five modern sedges showed that target enriched sequencing data originally targeting a set of gene exons can also be used to study the repetitive sequence fraction and even infer phylogenetic relationships based on repetitive sequence abundance35. Another study showed that also sequence similarities between homologous repeat motifs can be used to reconstruct phylogenetic relationships among closely related taxa40,41. In the case of Larix satellite EuLaSat1 in our study, no change in nucleotide frequencies, neither related to locations nor in time, could be detected. However, our results show that the off-target fraction in target enriched sedaDNA datasets can hold valuable information and that repeat motifs in more diverse taxon groups could even be a target for enrichment. Specifically enriching for repeat motifs in sedaDNA extracts could enable the study of satellite repeat evolution as well as giving additional information on species abundance and phylogeography.In the two target enriched datasets, sequences taxonomically classified to the genus Larix and mapping to the chloroplast and to the repeat sequence, respectively, show similar patterns of abundance (see Fig. 2). Compared with published metabarcoding and pollen data from the same locations, the Larix abundance patterns can be globally reproduced, underpinning the notion that sequence abundances in target enriched data can be used as good estimates of plant abundances. For older parts of the lake records, target enriched data show Larix where metabarcoding data were unable to detect a clear signal (see Fig. 2, lakes Billyakh, Bolshoye Shchuchye, Kyutyunda, and Lama). This shows that target enrichment is superior to metabarcoding when analyzing one taxonomic group in-depth, as it is less prone to errors by DNA degradation, which can impede primer binding if the molecule becomes too short. Also, independent of age, rare taxa mostly need multiple PCR replicates to be detected by metabarcoding42,43. Target enrichment, however, is more sensitive in identifying one focal taxon group, as the total target length can be much larger (e.g., a complete organellar genome) than for metabarcoding, and the DNA damage patterns are put to use to authenticate ancient DNA. Also, it is limited by molecule length only by the applied threshold in the bioinformatic analysis, for which we used 30 base pairs (bp) as opposed to a minimum of 85 bp molecule length for the Larix metabarcoding marker (for the plant-specific trnL g/h marker44). Similarly, compared to traditional pollen analysis, target enrichment is more accurate at tracing a specific target group, as it is not dependent on pollen productivity. Especially in the case of Larix, pollen productivity is low and preservation poor, resulting in rare findings of its pollen in the sediments22,45. This could explain why for Lake Bolshoye Shchuchye, only a single Larix pollen grain was retrieved throughout the core, whereas target enrichment and metabarcoding show a strong signal in the Holocene sediments (last ~12 ka BP). Target-enriched data also records signals in MIS 2 sediments, however, sequence counts are extremely low, and as it is the only record, where both of the other proxies fail to report a signal, it should be interpreted with caution.A wider pre-glacial distribution of L. sibirica
Chloroplast genomes of L. gmelinii and L. sibirica differ at 157 positions, which can be used to differentiate species in target enriched sedaDNA29. Here, we applied this approach to lake sediment records, which are distributed across Siberia (Fig. 1) and have time ranges back to MIS3, and thereby were able to track species composition in space and time for wide parts of the species ranges.In lakes Billyakh and Kyutyunda, ca. 1500 km east of L. sibirica current range (Fig. 1), we found evidence for a wider distribution of L. sibirica around 32 and 34 ka BP in MIS3 (Fig. 3). Billyakh is situated in the western part of the Verkhoyansk Mountains, and Kyutyunda on the Central Siberian Plateau. Both lakes have low counts of Larix DNA sequences in their oldest samples dated to 51 ka BP (Billyakh) and 38 ka BP (Kyutyunda) with variants of L. gmelinii, but there is a sudden rise in variants attributed to L. sibirica at 34 ka BP (Billyakh) and 32 ka BP (Kyutyunda), which persists in the following samples, but strongly decreases in younger samples (Fig. 3). The rise in the L. sibirica DNA sequence variants coincides with a peak in sequence counts for Lake Kyutyunda. These signals suggest a rapid invasion of L. sibirica into the ranges of L. gmelinii in climatically favorable times and a local depletion or extinction of L. sibirica during the following harsher climates. Lake Billyakh pollen data suggest a moister and warmer climate around 50–30 ka BP than in the latter part of the Last Glacial associated with the MIS3 Interstadial in Siberia46.Fig. 3: Percentage and sequence counts at variable positions along Larix chloroplast genome assigned to species.Left: Alignment of Larix-classified DNA sequences against the chloroplast genome at the 157 variable positions between the species. For each position, the percentage of sequences assigned to a single species is displayed. Each row represents one sample named according to the calibrated age before present. Gray background indicates no coverage at the respective position. Right: Total number of sequences assigned to each of the species per sample.Full size imageStrong support for a wider pre-glacial distribution of L. sibirica comes from genetic analyses which show that it is genetically close to L. olgensis, today occurring on the Korean Peninsula and adjacent areas of China and Russia27,47. It is assumed that the L. sibirica-L. olgensis complex used to share a common range, which was disrupted and displaced when the better cold-adapted L. gmelinii expanded south and southwest during the more continental climatic conditions of the Pleistocene47,48. Furthermore, modern and ancient genetic studies suggest that the L. sibirica zone was recently invaded by L. gmelinii from the east in the hybridization zone of the species, as the climate cooled after the mid-Holocene thermal maximum13,23. Today, pure stands of L. sibirica do not form a continuous habitat, but occur in netted islands5 and morphological features of L. sibirica can be found in populations of L. gmelinii located at least a hundred kilometers east of the closest L. sibirica populations49. Macrofossil findings of L. sibirica in Scandinavia dated to the early Holocene, point to the capability of rapid long-distance jump dispersal of this species50. Fossil L. sibirica cones dated to the end of the Pliocene and in the Pleistocene have also been found far east of its current range in several river valleys including Kolyma, Aldan, and Omolon, and even in the basin of the Sea of Okhotsk9. These indicate long-distance seed dispersal by rivers which may also have assisted in successful establishment since the active-layer depth is deeper close to rivers51,52. As mentioned earlier, L. sibirica is sensitive to permafrost and waterlogged soils. A warmer phase with a deeper thawed layer above the permafrost could have enabled L. sibirica to spread and establish in regions that today are part of the geographic range of L. gmelinii, as L. sibirica is reported to have higher growth rates than L. gmelinii13.
Larix gmelinii formed northern LGM refugia across SiberiaThe possible survival of Larix in high latitude glacial refugia during the LGM is still under discussion4,53 although more and more evidence is reported in favor of the existence of such refugia17,20,21. The question of which of the Larix species formed these populations has hitherto been unanswered, as both pollen and established metabarcoding markers are not able to distinguish between species in the genus Larix, and findings of fossilized cones identifiable to species are rare. By enriching sedaDNA extracts for chloroplast genome sequences, we are, to the best of our knowledge, for the first time, able to distinguish between L. sibirica and L. gmelinii in glacial refugial populations.From Lake Lama, located at the western margin of the Putorana Plateau (Taymyr Peninsula), we obtained a continuous record extending from 23 ka BP to today with varying sequence counts with minima around 18–17 ka BP and 13 ka BP. All samples prior to the Holocene show variations predominantly assigned to L. gmelinii (Fig. 3). Our results suggest a local survival of L. gmelinii in the vicinity of Lake Lama throughout the LGM, which is supported by low numbers of Larix pollen detected through this period. Both target enriched sequence data and pollen indicate an increase from ca. 11 ka BP54. Sparse Larix pollen in the bottom part of the record could be an indication of a possible refugial population (Fig. 2; ref. 54).In Bolshoye Shchuchye, the westernmost lake of the study, situated in the Polar Ural Mountains, all Pleistocene samples show similarly a dominance of L. gmelinii sequence variations (Fig. 3). However, sequence counts for some samples are extremely low and samples from 18 and 10 ka BP had so low counts of mapped DNA sequences that none of the variable positions between the species was covered. Although sequences mapped to the satellite repeat of Larix also showed a Pleistocene signal, this was not repeated in pollen or metabarcoding (Fig. 2) which instead indicates a treeless arctic-alpine flora for the late Pleistocene55,56. Especially for the sample of 20.4 ka, Larix sequence counts are extremely low and new investigations would be needed to confirm a local presence of Larix during the LGM.The record of Lake Billyakh situated in the western Verkhoyansk Mountain Range, likewise shows extremely low counts of sequences mapped to the reference for a range of samples with no sequences covering the studied variable sites (45, 42, and 15 ka BP, 11–56 sequences mapped to non-variable sites). However, the pollen record for the same core shows a quasi-continuous record of Larix with a gap only occurring during the early LGM46 (25–22 ka BP, Fig. 2). Considering the known short-distance dispersal ability and poor preservation of Larix pollen, this strongly supports the presumed existence of a local glacial refugium at Lake Billyakh during that time20. Our samples also show a low but steady presence of Larix throughout the rest of the record, thus making glacial survival probable. The sample closest to the LGM (24 ka BP) indicates a clear dominance of L. gmelinii type variations.The only exception to this general pattern is the record from Lake Kyutyunda, which is located on the Central Siberian Plateau west of the Verkhoyansk Mountain Range. In this record, LGM samples have extremely low counts but show variations assigned to L. sibirica and not to L. gmelinii as in the other lakes. In addition, the preceding sample dated to the MIS3 interstadial shows L. sibirica variation. A possible explanation is that relics of L. sibirica survived during the LGM, but were unable to spread after climate warming, possibly due to genetic depletion or later local extinction. The presence of reworked sediment material can also not be excluded, as suggested by reworked pollen in the record57.In conclusion, our data show almost exclusively L. gmelinii variation for samples covering the most severe LGM climate conditions. This is in agreement with the ecological characteristics describing the species as adapted to extreme cold. In contrast to L. sibirica, it can grow in dwarf forms and propagate clonally and potentially survive thousands of years of adverse climatic conditions58.Postglacial colonization history—differences among larch speciesOf great interest in the Larix history is not only the location and extent of possible high latitude glacial refugia but also if and to what extent these refugia contributed to the recolonization of Siberia after the LGM. Northern refugial populations could have functioned as kernels of postglacial population spread and recolonization, or spreading could have been driven by populations that survived in southern refugia. There are only a few studies on modern populations that report evidence for possible recolonization scenarios of Larix23,27,28. Here, we show that patterns differ between L. sibirica and L. gmelinii.In the western part of our study region, two lakes are situated in the current distribution range of L. sibirica (Figs. 1, 4): Lake Bolshoye Shchuchye in the Polar Ural Mountains and Lake Lama on the Taymyr Peninsula. Despite this, both lakes show L. gmelinii for all Pleistocene samples, and a strong signal of L. sibirica variants only in the Holocene samples, with ages of 5.1 ka BP in Lake Bolshoye Shchuchye and 9.7 ka BP in Lake Lama (Fig. 3). The peak in L. sibirica also coincides with a peak of sequence counts in the respective sample, with a Larix pollen peak in Lake Lama sediments54, and metabarcoding for Lake Bolshoye Shchuchye55. This points to a migration of L. sibirica in its current northern area of northern distribution in the course of climate warming during the early Holocene, whereas glacial refugial populations were consisting of L. gmelinii. Although the local survival of L. gmelinii around Lake Bolshoye Shchuchye remains uncertain due to extremely low sequence counts, it is clear that L. sibirica did not form a refugial population at this site.Fig. 4: Percentage of DNA sequences assigned to references displayed on the geographical locations of the lakes investigated.Samples in the same time frame are averaged. Lake names and current species ranges are annotated in the middle plots. Colors indicate current species distribution (adapted from Semerikov and Lascoux72). The base map is done with ggmap73, map tiles by Stamen Design, under CC BY 3.0. Data by OpenStreetMap, under ODbL.Full size imageA range-wide genetic study of L. sibirica analyzing chlorotypes and mitotypes of individuals23 found strong indications for rapid colonization of the West Siberian Plains from populations originating from the foothills of the Sayan Mountains in the south, close to the border of Mongolia, with only limited contribution from local populations. According to our results, the local populations could have been L. gmelinii populations, while the rapid invasion could have been L. sibirica.In the eastern range of the study region, in the current range of L. gmelinii, namely at lakes Emanda, Satagay, and Malaya Chabyda, genetic variations throughout the records are less pronounced. Of the three lake records, only that from Lake Emanda reaches back beyond the LGM, but with a sampling gap for the time of the LGM. Therefore, it remains uncertain whether populations survived the LGM locally, or whether they were invaded or replaced by populations coming from the south with Holocene warming. The restricted variations throughout the record, however, hint at stable populations, which is supported by scarce pollen findings (Fig. 2).Our data suggest that postglacial recolonization of L. sibirica was not started from high latitude glacial refugia, but from southern populations. In contrast, northern glacial populations of L. gmelinii could have potentially enhanced rapid dispersal after the LGM in their current area of distribution.Environment likely plays a more important role than historical factorsThe current boundaries of boreal Larix species arranged from west to east suggest a possible strong influence of the historical species distribution on the current distribution, whereas the gradient of increasing continental climate towards the east assumes a strong influence on the environment. By tracking species distribution in the past, spanning the time of the strongly adverse climate of the LGM, we can give hitherto unprecedented insights into species distribution history.Several lines of evidence suggest a strong influence of the environment on species distribution: (1) Signals for L. sibirica appeared in its current area of distribution as late as the Holocene warming, whereas cold Pleistocene samples are dominated by L. gmelinii type variation; (2) in lakes far east of its modern range, signals of variation typical for L. sibirica coincide with peaks in sequence counts (29 ka BP, Lake Billyakh; 32 ka BP Lake Kyutyunda), which point to more forested vegetation around the lakes and consequently a more favorable climate at that time; and (3) samples dated to the severely cold LGM are dominated by variations of the L. gmelinii type.This is in accordance with the different ecological characteristics described for the species. L. sibirica is sensitive to permafrost and only occurs outside of the zone of continuous permafrost5. In addition, L. sibirica achieves substantially higher growth rates and longer growth periods than L. gmelinii9,13 and can also produce more than twice as many seeds5. This potentially gives L. sibirica the ability to quickly react to climate change and outcompete the other species when the climate becomes more favorable.In contrast, L. gmelinii is adapted to extremely low soil and air temperatures and is able to grow on permafrost with very shallow thaw depths. It’s distribution almost completely coincides with continuous permafrost5, and even a restriction to permafrost areas is discussed as it does not grow well in field trials on warmer soils or where there is a small temperature gradient between air and soil9. Due to this ecology, L. gmelinii is more likely to survive in a high latitude refugium, even during the severe continental climate of the LGM, which was most probably connected to continuous permafrost of low active-layer depths.A study combining mitochondrial barcoding on sedaDNA and a modeling approach on Larix distribution in the Taymyr region around Lake CH12 concluded that the distributions of L. gmelinii and L. sibirica are most strongly influenced by stand density and thus by competition between the species, with L. gmelinii outcompeting L. sibirica at high stand densities13. As our study includes sediment cores reaching further back in time, we see a different trend. Instead of L. gmelinii, it was L. sibirica, which dominated samples with high sequence counts, suggesting high stand density and a more favorable climate. A possible explanation for the different outcomes is the use of different organelle genomes. Epp et al.13 used a marker representing the mitochondrial genome, which is known to introgress more rapidly and as a consequence might show a long past species history59,60.Our findings have potentially important implications for the projections of vegetation-climate feedback. A warming climate in conjunction with a greater permafrost thaw depth could enable the replacement of L. gmelinii by L. sibirica. In contrast to L. gmelinii, L. sibirica is not known to stabilize permafrost thus potentially further promoting permafrost thaw and with it the release of greenhouse gases, creating positive feedback on global warming11. On the other hand, the substantially higher growth rates of L. sibirica in comparison to L. gmelinii would increase carbon sequestration, thus mitigating global warming13. This shows the importance of understanding species-specific reactions to climate change, which can result in great shifts in distribution. Target enrichment applied on sedaDNA is able to reveal the impact of past climate change on populations and the increasing availability of modern reference genomes will further enhance its value of information. More

TRY meta-­analysisWe used the TRY plant trait database27 to identify traits that show systematic differences between the tree and liana growth forms, as a way to narrow the scope of the rest of the analysis. We chose traits to represent major trade­offs within the “economic spectrum” framework, which places plants along a spectrum of strategies from acquisitive, fast return on investment to conservative, slow return on investment according to key functional trait values30. We narrowed traits to those that had observations for at least four tree and liana species. We then compiled our dataset using the following steps during November and December 2019. For each trait, we downloaded the dataset for all species available globally and averaged the observations of the trait to the species level to avoid statistical biases introduced in our growth form comparison due to a high density of observations in a few commercially valuable species. We matched the species ID number with the most frequently used growth form identifier using the TRY “growth form” trait and kept the species with the most frequent identifier of “tree,” “liana,” or “woody vine.” We subsetted the data to keep only species with a majority of observations ascribed to the tree and liana growth forms (i.e., no herbaceous species, ferns, etc.), resulting in observations for 44,222 total species. Finally, we filtered the dataset of 44,222 species by hand to remove species misclassified as trees or lianas; species occurring entirely in temperate to boreal biomes; species from all gymnosperm lineages except the order Gnetales; and entries for taxonomic classifications broader than the genus level (e.g., taxonomic families). We found that hydraulic functional traits in the TRY database (i.e., Ks,max and P50) show systematic differences between growth forms (Supplementary Fig. 1; Supplementary Tables 3 and 4), while there is mixed evidence for differences in the acquisitiveness of trees and lianas in terms of stem anatomical traits (Supplementary Fig. 1; Supplementary Tables 3 and 4) and leaf functional traits (Supplementary Fig. 6; Supplementary Tables 3 and 4), and no evidence of differences between tropical trees and lianas with respect to root functional traits (Supplementary Fig. 7; Supplementary Tables 3 and 4).Extended meta­-analysisWe conducted an additional literature search to supplement the hydraulic trait observations from the TRY database. The additional literature search served two purposes: (1) to fill a major gap identified during our TRY analysis in terms of liana trait observations, and (2) to address the methodological inconsistency of measuring Ks,max and P50 on liana branches shorter than the longest vessel, which incorrectly measures Ks,max and P50 without accounting for end wall resistivity59,60.We conducted a literature search using Web of Science and Google Scholar. We searched the following phrases in combination with “liana:” “hydraulic conductivity,” “hydraulic trait,” “hydraulic efficiency,” and “hydraulic K.” Of the literature we found, we kept only the studies that met the following criteria: (1) reported Ks,max measurements for lianas, (2) measured Ks,max instead of computing Ks,max from xylem conduit dimensions, (3) measured Ks,max on sunlit, terminal branches of mature individuals or saplings, and (4) measured Ks,max on a branch longer than the longest vessel. We considered the authors to have used a branch length longer than maximum vessel length if the authors measured or reported maximum vessel length for the species and a longer branch was used. Because the best methodological practice for measuring P50, especially in species with long vessels, is currently a matter of debate, we additionally removed all observations of P50  > ­0.75. This filtering was performed to reduce the probability that falsely high (i.e., less negative) P50 values were retained in our analysis because of improper measurement technique and is consistent with the P50 filtering performed by Trugman et al.61. Improper measurement technique is a particular concern for lianas, whose wide and long vessels require cautious implementation of the traditional measurement techniques developed for trees. We note that retaining all liana P50 observations (i.e., not filtering out observations  > −0.75) results in a significant difference between trees and lianas (Mann­–Whitney test statistic = 1029, ntree = 61, nliana = 46, p  More

Experimental designTo test whether an 8-year treatment of recurrent summer droughts would change biodiversity effects and species interactions of grassland plants when facing a subsequent drought event, we grew ambient- vs. drought-selected plants of 12 species in a glasshouse. The plants were grown from seeds collected from 40 plots (Supplementary Data 2) under 8-year treatments of yearly summer droughts vs. ambient precipitation in a biodiversity field experiment in Jena, Germany11,41.The Jena Experiment was established in 2002 using a common seed pool of 60 grassland species, with 80 (20times 20,{{{{{rm{m}}}}}}) large plots of species richness levels of 1, 2, 4, 8, 16, and 60 species40. Most of the species are perennial and capable of outcrossing (Supplementary Table 1). The Jena Drought Experiment11,41 was initiated in 2008. Two (1times 1,{{{{{rm{m}}}}}}) subplots were set within each large plot, designated as either drought treatment or ambient control. For the drought treatment, rainout shelters were set up to exclude natural rainfall in mid-summer for 6 weeks. The ambient control treatment got the same shelter construction but rain water was reapplied to not confound the results with artifacts from the shelter60. We repeatedly harvested the aboveground biomass per year, once before and once after the summer drought treatment11,41. The design of the Jena (Drought) Experiment did not allow the exclusion of cross pollination or gene flow between subplots or large plots in the field. Such gene flow may have reduced the possibility for genetic differentiation and for the observed effect sizes of the selection treatment23. We collected seeds from drought and control subplots throughout the 2016 growing season (Fig. 1). We obtained seeds of 17 species, but only used 12 of them, because the other five species had either few seeds or low germination rates. Seeds per species per selection treatment were collected from 4 to 23 (interquartile range: [8.50, 17.00]) maternal plants distributed across 2–10 (interquartile range: [4.75, 9.00]) large plots in Jena Experiment, in which the functional group richness ranged from 1 to 4 (Supplementary Data 2). The 12 plant species represented four functional groups (grass, small herb, tall herb, and legume) (Supplementary Table 1). The detailed classifications of the functional grouping can be found in the design of the Jena Experiment40. Eleven of the 12 species were perennial, and one was annual (Trifolium dubium). The average longevity of the perennial species in the Jena Experiment has been estimated at 3–4 years61, so that multiple generations and sexual reproduction cycles could occur during the 8-year drought treatment. Although each subplot was small, population sizes of each species were estimated to range from 100 to 1000 individuals m−2 in ambient and drought subplots at the beginning of the drought treatment in the field62.We germinated the seeds in Petri dishes and transplanted the seedlings into pots in February 2017 in a glasshouse (day temperature range 20–25 °C, night temperature range 15–21 °C, and humidity range 60–80%) at the University of Zurich, Switzerland. Seedlings were planted individually, in monocultures, or in 2-species mixtures in the glasshouse (Fig. 1). In the glasshouse experiment, both monocultures and mixtures contained four plants within a pot. The pots were (11times 11times 11.5) cm in size and filled with soil composed of 50% collected from a sugar-beet field, 25% sand and 25% perlite. We randomly assigned the pots into four blocks in the glasshouse. To test the effects of drought-induced selection on plant traits, we planted individual seedlings of the 12 species in a fifth block. Within the first 2 weeks, dead individuals were replaced, thereafter dead individuals were not replaced anymore. In total, we established 958 pots: 257 pots of mixtures, 217 pots of monocultures, and 484 pots of individual plants (244 pots of individuals in blocks 1–4, and 240 pots of individuals in block 5; Supplementary Methods). For mixtures, there were 21 species pairs (Supplementary Table 1). Species pairs composed of Crepis biennis or Lotus corniculatus had low numbers of replicates (Supplementary Table 1). However, including or excluding these communities produced qualitatively similar results. Thus, we present the results including these two species in this paper. We provide detailed explanations on the choices of species pairs and regarding the biodiversity treatment history in the Jena Experiment in Supplementary Methods.During a first phase of 3 months in the glasshouse (Fig. 1), pots were watered regularly (“before drought”). After 14–16 weeks, when most of the species had reached peak aboveground biomass, we harvested all individuals in each pot by cutting them 3 cm above the ground, allowing regrowth from the left plant bases (first harvest, “before drought”). The time span for the first harvest included both the time for trait measurements (section “Plant traits” below) and for the immediately following biomass harvest. We completed the biomass harvest of each block within 1–2 days. This allowed us to account for the larger time differences between blocks by fitting block effects in the statistical analyses. After the first harvest of each block, plants were watered regularly and allowed to regrow until the 18th week from planting. This was followed by a second phase of 2 weeks without watering. Soil moisture decreased from more than 40% to less than 10% after 10 days since drought initiation. At the end of the second phase, that is after 20 weeks from planting, we made a second aboveground harvest at 3 cm above the ground (second harvest, “during drought”). During a third phase of 7 weeks, pots were watered regularly again for recovery until most plants reached a new aboveground biomass peak again. At the end of the third phase, that is after 27 weeks from planting, we harvested both above- and belowground plant biomass (third harvest, “after drought”). We checked and confirmed that most plants had reached the full-grown state and peak biomass before each harvest by monitoring their flowering. After each harvest, we cleaned and dried the harvested plant material at 70 °C for 48 h to obtain the dry biomass. We used the aboveground biomass as a proxy for productivity. Although clipping may affect plant responses to the experimental drought in the glasshouse, clipping had the advantage that all plants were “standardized” in height before the experimental drought, thus reducing carry-over effects of differential growth before the experimental drought.Additive partitioningWe used the additive partitioning approach (Eq. 1)17 to decompose the net biodiversity effect (NE) on aboveground biomass into the complementarity effect (CE) and the sampling effect (SE):$$triangle Y={Y}_{O}-{Y}_{E}=N,overline{triangle {RY}},{bar{M}}+N,{{{{{{rm{cov}}}}}}}left({{triangle }}{{{{{bf{RY}}}}}},,{{{{{bf{M}}}}}}right),$$
(1)
where (triangle Y) is the NE; ({Y}_{O}) is the observed yield (productivity) in a mixture; ({Y}_{E}) is the expected yield in the mixture, calculated from the observed yield in monocultures and their corresponding species proportions planted in the mixture, here 0.5; the two additive terms at the right side of the equation represent CE and SE, respectively; N is the number of species in the mixture, here 2. The partitioning is based on the observed and expected relative yield (RY) of species in the mixture. The expected RY of species in the mixture is the proportion planted. ∆({{{{{bf{RY}}}}}}) is the difference between observed and expected RY of species in the mixture; (overline{triangle {RY}}) is the average of ∆({{{{{bf{RY}}}}}}). A positive (overline{triangle {RY}}) indicates a positive CE; a positive covariation between monoculture yield (M), and ∆({{{{{bf{RY}}}}}}) indicates a positive SE. More details about the calculation can be found in Loreau and Hector17. We conducted the partitioning separately for each harvest, selection treatment, and block. We did not perform the partitioning for mixtures with zero biomass63. For monocultures with zero biomass in the second or third harvest, we kept the ones which had positive biomass in the previous harvest but excluded the ones which had zero biomass in the previous harvest. For example, when performing the partitioning for the second harvest, we kept the monocultures that had zero biomass in the second harvest but non-zero biomass in the first harvest; we excluded the monocultures that had zero biomass already in the first harvest. This was to assure that communities that died before the drought could not reappear during or after the drought, and communities that had died during the drought could not reappear after the drought.We used mixed-effects models to assess the influences of drought vs. ambient-selection treatments on biodiversity effects (NEs, CEs, and SEs) separately for each harvest (Fig. 2; Table 1). Block and selection treatment were set as fixed-effects terms, while species composition (identity of species pair) and its interaction with selection treatment were set as random-effects terms. This conservative approach was used to allow for generalizations across all possible species compositions, although a more liberal approach with species composition and its interactions as fixed-effects terms could also have been applied (see Schmid et al.64 for a discussion of defining terms as fixed- vs. random-effects terms, including a justification of preference for treating block as a fixed-effects term). We square-root transformed the CEs and SEs with sign reconstruction (({{{{{{rm{sign}}}}}}}(y)sqrt{y})) prior to analysis to improve the normality of residuals17. The mixed-effects model did not converge in the analysis with CE after the drought event. In this case, we used a general linear model, in which we fitted block, species composition, selection treatment, and species composition by selection treatment interaction in this order. Then we tested the significance of selection treatment using its interaction with species composition as an error term. This procedure is an alternative to mixed-effects models that estimate variance components for random-effects terms with maximum likelihood64.To test whether biodiversity effects on productivity differed from zero, we additionally tested the significance of NEs, CEs, and SEs separately for each selection treatment and harvest (Supplementary Table 3). We set block and species composition as fixed- and random-effects terms, respectively. The model corresponding to CE for ambient-selected plants during the drought event did not converge so that we fitted it with a general linear model, in which we tested the significance of the overall mean (intercept) using species composition as an error term. All statistical analyses were conducted in R 3.6.365. The mixed-effects models were conducted with asreml-R package 4.1.0.11066.Finally, we also tested whether the effects of drought selection on biodiversity effects (NEs, CEs, and SEs) in the glasshouse depended on the history of biodiversity treatment in the Jena Experiment. Most plants in the 2-species communities in the glasshouse originated from mixtures in the Jena Experiment (Supplementary Data 2; whether mixtures in the glasshouse composed of plants originating from monoculture field plots did not affect the effects of drought-selection on biodiversity effects on productivity (Supplementary Data 3)). To increase statistical power, we used functional group richness, ranging from 1 to 4, instead of species richness of the field plots as explanatory variable (Supplementary Methods). We fitted functional group richness either in linear (Supplementary Data 4) or log-linear (Supplementary Data 5) form. We did not detect significant effects of field treatment of functional group richness nor significant interactions between field treatment of functional group richness and the drought-selection history. Therefore, we excluded the history of biodiversity treatments in the field from further analyses.Biomass stability to the drought event in the glasshouseTo assess the temporal responses of community aboveground biomass to the drought event, we calculated three indices representing different facets of stability: biomass resistance, recovery, and resilience (see van Moorsel et al.43 for an example). We calculated resistance as the biomass ratio during vs. before the drought, recovery as the ratio after vs. during the drought and resilience as the ratio after vs. before the drought (see also Isbell et al.9). We log-transformed the indices (plus a half of the minimum positive value to allow taking logs of indices that were originally zero) prior to statistical analyses to improve the normality of residuals. Excluding index values that were originally zero produced qualitatively similar results.To assess the effects of drought-selection on biomass stability, we fitted mixed-effects models with block and selection treatment as fixed-effects terms, and species composition and its interaction with selection treatment as random-effects terms (Supplementary Fig. 3; Supplementary Table 4). We fitted the models separately for mixtures and monocultures. We included the log-transformed biomass at the first harvest as a covariate because biomass stability in response to droughts often depends on plant performance under ambient conditions.In the same way as net biodiversity effects on productivity were calculated for additive partitioning, we calculated biodiversity effects on biomass stability as the difference between each mixture and its corresponding monocultures. Then, we tested the influence of selection treatment on the biodiversity effects on biomass stability. Block and selection treatment were set as fixed-effects terms; species composition and its interaction with selection treatment were set as random-effects terms (Fig. 3; Supplementary Table 5). The log-transformed biomass at the first harvest was also included as a covariate43. To assess the significance of biodiversity effects on biomass stability for each selection treatment, we fitted another set of simplified models, with block and log-transformed biomass as fixed-effects terms, and species composition as random-effects term (Fig. 3).Neighbor interactionsWe assessed interactions between neighboring plants within pots using the metrics of neighbor interaction intensity with multiplicative symmetry (NIntM)44:$${NIn}{t}_{M}=2frac{triangle P}{{P}_{-N}+{P}_{+N}+left|triangle Pright|},$$
(2)
where ({P}_{-N}) and ({P}_{+N}) are the productivities without (individual plant) and with neighbors (monocultures or mixtures), respectively; (triangle P={P}_{+N}-{P}_{-N}). Negative values of NIntM indicate competition and positive values indicate facilitation. NIntM is bounded between –1 (competitive exclusion) and 1 (“obligate” facilitation). For monocultures, we first calculated the per-plant biomass as the ratio between total biomass and planting density, and then used the per-plant value to compare with the corresponding individuals (without neighbor) of the same species with the same selection treatment in the same block. Note that under the reciprocal yield law45, an individual grown alone in a pot should be four times larger than an individual grown with three others in a pot, resulting in a NIntM of –0.75. For 2-species mixtures, we calculated the per-plant biomass separately for each species and took the average NIntM of the two species to measure the interaction intensity of the mixture. We set zero biomass for dead plants in the calculation. Again, if mixtures would also follow the reciprocal yield law independent of species identity, then NIntM = –0.75 would be expected. Values greater than –0.75 indicate some sort of overyielding due to higher density or higher density and higher diversity.To assess how selection treatment modified interactions between plants, we tested the effects of selection treatment on neighbor interaction intensity separately for monocultures and mixtures. We included block and selection treatment as fixed-effects terms, species composition and its interaction with selection treatment as random-effects terms (Supplementary Fig. 4; Supplementary Table 6).We calculated the difference between the heterospecific interaction in a mixture and the conspecific interactions in its two corresponding monocultures. A positive value of this difference indicates a weaker heterospecific than conspecific competition (i.e., niche differentiation) or stronger heterospecific than conspecific facilitation, which may lead to a positive complementarity effect. We tested the effects of selection treatment on interaction difference for each harvest by fitting block and selection treatment as fixed-effects terms, and species composition and its interaction with selection treatment as random-effects terms (Fig. 4; Supplementary Table 8). We also tested the significance of the interaction difference for each selection treatment by fitting block and species composition as fixed- and random-effects term, respectively (Fig. 4; Supplementary Table 7).Plant traitsTo assess whether drought selection would change plant traits, we measured six traits (Supplementary Table 9) closely related to plant usages of water or carbon on plants in pots with one individual from blocks 1–5. We focused on the traits on individual plants without neighbor to evaluate the influence of selection treatment on traits without the impacts of plasticity induced by plant interactions. We measured leaf relative chlorophyll content, leaf area (LA), leaf mass per area (LMA) and leaf osmometric pressure before the drought; leaf stomatal conductance both before and during the drought; and dry biomass ratio between root and shoot after the drought (in the third harvest). Leaf relative chlorophyll content was measured for three mature, fully expanded leaves per plant by using a SPAD-502 Plus chlorophyll meter from Konica Minolta. LA was obtained by scanning 3–4 mature, fully expanded leaves per plant with a LI-3100C Area Meter from LI-COR. LMA was calculated as the ratio between leaf dry mass (oven-dried at 70 °C for 48 h, using the same leaves that for LA) and LA. Leaf osmotic potential at full hydration was considered as an important trait associated with plant tolerance to drought30. We measured leaf osmotic potential with freeze-thaw leaf pieces cut from 1 to 2 mature, fully expanded leaves per plant by using a Wescor vapor pressure osmometer VAPRO (Model 5520) according to the method by Bartlett, et al.30. Plants were fully hydrated 1 day before the leaf sampling for osmotic potential measurement. Leaf stomatal conductance is a measure of exchange rate of carbon dioxide and water vapor through the stomata67. It was measured for 3–5 healthy mature leaves per plant by using a SC-1 Leaf Porometer from Decagon Devices. For grass species, 3 blades were placed adjacent to each other to have a large enough area for the measurement of stomatal conductance. For stomatal conductance during the drought event, we measured the individual plants from block 5 only due to limited time during the drought phase. We harvested aboveground and belowground plant biomass separately for alive individuals at the end of the experiment (after the complete recovery from the drought). The oven-dried (70 °C for 48 h) aboveground and belowground biomass were used to calculate the biomass ratio between root and shoot. We took the average value of each trait of each plant for statistical analyses. Each trait was measured for each block in turn.We used linear mixed-effects models to assess the influence (generalized across species) of selection treatment on trait values (red lines in Supplementary Figs. 5–7). Block and selection treatment were set as fixed-effects terms; species and its interaction with selection treatment were set as random-effects terms. Alternatively, we set species, selection treatment and their interaction as fixed-effects terms to assess whether species responded differently to the selection treatment (Supplementary Table 9). To test whether effects of selection treatment on traits differed between the five trait groups (leaf relative chlorophyll content, leaf area, leaf mass per area, leaf osmometric pressure, and leaf stomatal conductance) measured before the drought event in the glasshouse, we conducted two alternative analyses. First, we performed a principal component analysis with all traits and retained the first two principal axes (PC1 and PC2), which accounted for 39.06% and 22.3% of the total variation, respectively. Then we used PC1 and PC2 as response variables in mixed-effect models, separately. We fitted the models with the same fixed- and random-effects terms as those using each separate trait as the response variable. Effects of selection treatment on PC1 or PC2 were not significant. Second, we pooled the five traits as a single response variable in a mixed-effect model (corresponding to multivariate analysis of variance). Block, trait group (a factor with five levels), selection treatment, and the interaction between trait group and selection treatment were set as fixed-effects terms; species and its interactions with trait group and selection treatment and their three-way interaction were set as random-effects terms. We did not detect significant effects of selection treatment nor its interaction with trait group. Therefore, we did not present the results associated with these multivariate analyses in this paper. LMA, LA, leaf osmotic potential, leaf stomatal conductance, and root-shoot biomass ratio were log-transformed to improve normality of residuals.We also measured leaf relative chlorophyll content, LA and LMA in mixtures before the drought event (Supplementary Table 10) to evaluate the influence of selection treatment on trait dissimilarity between interacting species within communities. We calculated the absolute trait distance between two species in each mixture both separately for each trait and jointly with the three traits. For multi-trait-based dissimilarity, we standardized each trait to mean zero and unit standard deviation and calculated the Euclidean trait distance in standardized three-dimensional trait space.We used linear mixed-effects models to assess the effects of selection treatment on trait dissimilarity in mixtures (Supplementary Table 10). Block and selection treatment were set as fixed-effects terms; species composition and its interaction with selection treatment were set as random-effects terms. The model for LA dissimilarity did not converge so that we fit it with a general linear model, in which we tested the significance of selection treatment using its interaction with species composition as an error term. For the models with LA, LMA, and the joint three traits as dependent variables, we removed one pot (B1P674) because the LA value of Alopecurus pratensis in this pot was extremely small (about 1/3 of the second minimum value of the same species in mixtures). However, including or excluding this pot produced qualitatively similar results.Reporting summaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. More

The CAN-SAR22 database was created to provide access to publicly available data on species at risk in Canada in a standardized format that can be used in a wide range of applied research contexts. The variables included in the database were chosen to provide a range of information available for species at risk with a particular focus on climate change to support the first publication using the database6. The database includes numerous data fields including extinction risk status, various biological and geographical attributes, threat assessments, date of listing, recovery actions, and a set of climate change impact and adaptation variables. CAN-SAR is a living database that can be updated as new information and reports become available, or as other targeted data extraction efforts become available23.In Canada, the listing process begins with an assessment of a wildlife species’ risk of extinction by the Committee on the Status of Endangered Wildlife in Canada (COSEWIC). A wildlife species can be either a species or a ‘designatable unit’, which includes subspecies, varieties, or other geographically or genetically distinct populations. Herein these are referred to collectively as ‘species’. COSEWIC is an independent body of experts who synthesize the best available information to date into a status report containing elements such as population size and trends, habitat availability, and threat assessments (Fig. 1)17. This report is then used as the basis for a status recommendation that is passed on to the Government of Canada, who makes the final decision on whether to legally list the species under Schedule 1 of SARA24. The species can be listed as ‘Special concern’, ‘Threatened’, ‘Endangered’, or ‘Extirpated’. If a species is listed as ‘Threatened’, ‘Endangered’ or ‘Extirpated’ then a recovery strategy is required, while for species listed as ‘Special concern’ a management plan must be created24. Recovery strategies must provide a description of the species’ needs, address identified threats, identify critical habitat (where applicable and to the extent possible), and include population and distribution objectives for the species’ recovery. Management plans include conservation measures for the species and its habitat24. Hereafter, we refer to recovery strategies and management plans collectively as ‘recovery documents’.Information included in the database was extracted from various sources and documents that are available from the online SAR Public Registry, including COSEWIC status reports and status appraisal summaries, and recovery documents (Fig. 1). A COSEWIC status appraisal summary is produced instead of a new status report when a species has been previously assessed and COSEWIC experts are confident that its status will not change (https://www.cosewic.ca/index.php/en-ca/assessment-process/status-appraisal-summary-process.html). It is considered an addendum to the existing status report; thus, we use ‘status report’ to refer to either a status report or a status appraisal summary and the previous status report. From the SAR Public Registry website we accessed information from 1146 documents for all 594 species listed under SARA Schedule 1 as of March 23, 2021, that were classified with the status of ‘Special concern’, ‘Threatened’, or ‘Endangered’. Some species have multiple documents of the same type because COSEWIC reassesses at risk species every 10 years or less and recovery strategies and management plans are reviewed every 5 years and updated as needed. As new documents have become available they have been added to the CAN-SAR database without overriding the previously existing document, which allows for tracking of changes in various data fields over time. Only documents between 2018 and 2021, inclusive, have an updated version due to our updating schedule.Data extractionVariables included in the CAN-SAR database were categorised as either directly transcribed or derived. Directly transcribed variables reflect information extracted from documents that require limited interpretation, such as scientific name or date of legal listing (Online-only Table 1). Derived variables reflect species’ attributes that required interpretation of text by data recorders (Online-only Table 1). The data dictionary (CAN-SAR_data_dictionary.xlsx) contains a description of each variable, including details of their extraction and synthesis22.Several derived variables were extracted from the status report technical summary section, including whether the species is endemic to Canada or North America, and whether the species’ range is continuous with the United States. Endemism was determined for each species at two spatial extents, Canada and North America, based on descriptions of their global distributions from status reports. Whether a Canadian species’ range is continuous with its conspecifics in the United States was interpreted from descriptions of geographic isolation in the distribution and rescue effect sections of the status reports.Variables related to species’ threats were derived from information in the status reports, recovery strategies and management plans. In 2012, COSEWIC initiated use of the IUCN threats classification system in status reports for some species; a ‘threats calculator’25. Threats calculators may also be included in recovery strategies and management plans. A threats calculator is a table included in the document that classifies threats into 11 general ‘level one’ classes and, more specific ‘level two’ subclasses (Table 1)26. Four variables (impact, severity, scope, and timing) for each level one and level two threats were scored independently and then combined into an overall impact score for each species. Impact is defined as the degree to which the species is threatened by the threat class; severity is the level of damage to the species from the threat class that is expected within ten years or three generations, whichever is longer; scope is the proportion of the species that is expected to be affected within ten years; and timing is the immediacy of the threat25. Threat-related variables were either transcribed directly from the threats calculator, or from the derived description of threats in the document if a threats calculator was not included.Table 1 Definitions of level one threat classes and names of level two threat classes following Version 1.1 of the IUCN threats classification system.Full size tableFor species where a threats calculator was included, we recorded whether each of the level one and level two threat classes were identified (i.e., considered a threat), and transcribed the scores for each of impact, scope, severity, and timing. Threat classes were considered identified if the impact was negligible, low, moderate, high, very high, unknown, or not calculated (outside assessment timeframe). Impact, scope, severity, and timing values were coded as ranked values of ‘0’: not a threat; ‘1’: neglible; ‘2’: low; ‘3’: moderate; ‘4’: high; ‘5’: very high; ‘-1’: unknown; ‘-2’: not calculated; or ‘NA’ where there were blank values. For exact ranking interpretations see CAN-SAR_data_dictionary22. For some species, the threats calculator was available from the COSEWIC Secretariat as a Microsoft Excel file, in which case threats information was extracted directly from the spreadsheet using R v 3.6.227. For species where a Microsoft Excel file was not available, threats calculator information was manually extracted from the status report.For species where a threats calculator was not included in the document, threats described in the text were classified into threat classes based on version 1.1 of the IUCN threats classification system (Table 1)26. Although a more recent version of the threats calculator exists, we applied version 1.1 classification to reflect the approach applied across the majority of species. Threats were considered identified if the threat was discussed as having any negative or potentially negative impact on the species. In cases where no threat calculator was available, the threat attributes of impact, scope, severity, and timing were scored as not applicable; ‘NA’.Several variables were derived to determine how climate change was addressed in status reports and recovery documents. Whether climate change was mentioned anywhere in the status report was determined by searching the document for the words climat*, warm, temperat*, and drought. If a document contained any of these search terms, we assessed the context for description of anthropogenic climate change impacts. In cases where the terms were not found, the threats section was checked for any other descriptions that were related to climate change; if none were found, climate change was recorded as not mentioned. When climate change was mentioned, we then determined if it was identified as a threat by interpreting whether it was described as having a negative or potentially negative impact on the species. If a threats calculator was included in the status report, climate change was considered a threat if the ‘Climate change and severe weather’ threat class had an impact that was more than negligible or if climate change was described outside the threats calculator as a threat or potential threat. We recorded whether the threat of climate change was unknown. This included instances where climate change was described as having unknown effects on the species, if ‘unknown’ was assigned to impact, scope, severity, or timing in the threats calculator, or if knowledge gaps related to climate change were identified. Finally, the impact of climate change relative to other threats was classified based on descriptions of threats in the status report. The relative impact of climate change was classified as ‘0’ if it was not a threat; ‘1’ if it was described as a minor, potential, possible, or other threat; ‘2’ if it was a significant threat but not the most important or if it was among the list of threats with no indication of relative importance; or ‘3’ if it was among the most important threats described.Additional derived variables extracted from recovery documents available on the SAR Public Registry included those related to critical habitat identification and recovery actions. For species with recovery strategies, we recorded whether critical habitat was described as identified, partially identified, or not identified. In cases where critical habitat was described as “identified to the extent possible”, it was marked as identified. We extracted information from recovery documents on what types of actions were recommended and whether the actions addressed the threat of climate change. Actions were categorized into four categories: outreach and stewardship, research and monitoring, habitat management, and population management (Table 2). Within each of the four categories, a set of 16 sub-types were recorded if any actions of that type were recommended or already completed. We also recorded action types and sub-types that specifically addressed climate change threats if climate change was listed as the threat addressed or the reason the action was necessary6.Table 2 Categories of actions specified in Recovery Strategies.Full size tableFive data recorders conducted the initial data extraction, synthesis, and interpretation. All recorders were trained on the definitions, interpretation, and general process of data extraction to ensure consistent extraction of all variables. Data extraction occurred in multiple stages and included an iterative set of verifications and assessments of the same species among recorders to ensure consistent and standardized interpretations. Once convergence of interpretations was achieved, each recorder was assigned a set of species/reports from which to extract information.Next stepsThe CAN-SAR database is intended to be a living database that can be updated by adding information from new documents or species as they become available, adding more historical documents, or extracting new information from all documents. The current set of species and associated information includes those listed on Schedule 1 of SARA (as of March 23rd 2021) as ‘Special concern’, ‘Threatened’, or ‘Endangered’. Examples of future data additions include integration of data from species assessed by COSEWIC that are not listed under Schedule 1 of SARA, adding fields that specify the criteria used to arrive at a risk status designation, and integration of data from action plans. We anticipate updating the database periodically, as time and resources allow, and we also encourage anyone interested in extending or expanding on the CAN-SAR database to communicate to discuss a collaboration. Integration of new datasets will require screening and validation to ensure adherence to data standards and consistent interpretations. In the longer term, we foresee the implementation of automatic updating of the CAN-SAR database for variables that do not require interpretation by using machine-readable formatted status and recovery documents.ApplicationsApplications of the CAN-SAR database reflect both opportunities to synthesise the data in novel ways and to expand the scope of the current database to include new data fields representing information contained in status assessments and recovery documents. The CAN-SAR database facilitates independent data analysis and synthesis efforts ranging from trend analysis of threats, identifying research and monitoring gaps, and assessing the effectiveness of recovery actions, which target various steps of the listing and recovery process. For example, the database provides a platform to extend existing climate change focused work6 to assess the prevalence of recommended climate change targeted recovery actions, such as translocations. With recent adoption of the ‘Pan-Canadian approach to transforming Species at Risk conservation in Canada’28, which emphasizes multi-species recovery planning approaches, there is an opportunity to assess patterns in key sectors, which include agriculture, forestry, and urban development, over time and by taxa and how they map to threats.With the integration of additional variables through future data extraction or integration efforts, the CAN-SAR database can be used to assess novel questions. For example, broadening recovery action categories to include those that reflect natural climate solutions can highlight where recovery efforts may provide co-benefits, thus achieving biodiversity conservation and climate change mitigation goals29. Specifically, habitat restoration actions for a forest-dependent species primarily threatened by habitat loss may lead to improved recovery outcomes while also resulting in carbon sequestration and improved climate change mitigation efforts. Tracking these types of actions in CAN-SAR could highlight both opportunities and gaps for the integration of climate smart conservation principles30 into species at risk recovery planning and the adoption of climate change adaption measures for species directly considered climate change threatened and those that are not6. More

Newton, I. The Migration Ecology of Birds (Academic Press, 2008).Dingle, H. Migration: The Biology of Life on the Move (Oxford Univ. Press, 1996).Chapman, J. W. et al. Animal orientation strategies for movement in flows. Curr. Biol. 21, R861–R870 (2011).CAS 
PubMed 
Article 

Google Scholar 
Alerstam, T. Wind as a selective agent in bird migration. Ornis Scand. 10, 76–93 (1979).Article 

Google Scholar 
Berthold, P. Bird Migration: A General Survey (Oxford Univ. Press, 2001).Alerstam, T. & Lindstrom, A. in Bird Migration: Physiology and Ecophysiology (ed. Gwinner, E.) 331–351 (Springer, 1990).Chapman, J. W. et al. Wind selection and drift compensation optimize migratory pathways in a high-flying moth. Curr. Biol. 18, 514–518 (2008).CAS 
PubMed 
Article 

Google Scholar 
Hays, G. C. et al. Route optimisation and solving Zermelo’s navigation problem during long distance migration in cross flows. Ecol. Lett. 17, 137–143 (2014).PubMed 
Article 

Google Scholar 
Chapman, J. W. et al. Adaptive strategies in nocturnally migrating insects and songbirds: contrasting responses to wind. J. Anim. Ecol. 85, 115–124 (2016).PubMed 
Article 

Google Scholar 
Alerstam, T. Optimal bird migration revisited. J. Ornithol. 152, 5–23 (2011).Article 

Google Scholar 
Alerstam, T. & Hedenström, A. The development of bird migration theory. J. Avian Biol. 29, 343–369 (1998).Article 

Google Scholar 
Shamoun, J., Felix, B. & Wouter, L. Atmospheric conditions create freeways, detours and tailbacks for migrating birds. J. Comp. Physiol. A 203, 509–529 (2017).Article 
CAS 

Google Scholar 
Liechti, F. Birds: Blowin’ by the wind? J. Ornithol. 147, 202–211 (2006).Article 

Google Scholar 
Thorup, K., Alerstam, T., Hake, M. & Kjellén, N. Bird orientation: compensation for wind drift in migrating raptors is age dependent. Proc. R. Soc. B 270, 8–11 (2003).Article 

Google Scholar 
Sergio, F. et al. Migration by breeders and floaters of a long-lived raptor: implications for recruitment and territory quality. Anim. Behav. 131, 59–72 (2017).Article 

Google Scholar 
Sergio, F. et al. Individual improvements and selective mortality shape lifelong migratory performance. Nature 515, 410–413 (2014).CAS 
PubMed 
Article 

Google Scholar 
Sergio, F., Blas, J. & Hiraldo, F. Predictors of floater status in a long-lived bird: a cross-sectional and longitudinal test of hypotheses. J. Anim. Ecol. 78, 109–118 (2009).PubMed 
Article 

Google Scholar 
Bildstein, K. L. Migrating Raptors of the World: Their Ecology and Conservation (Cornell Univ. Press, 2006).Zalles, J. I. & Bildstein, K. L. Raptor Watch: A Global Directory of Raptor Migration Sites (Birdlife International, 2000).Kerlinger, P. Flight Strategies of Migrating Hawks (University of Chicago Press, 1989).Sergio, F. et al. When and where mortality occurs throughout the annual cycle changes with age in a migratory bird: individual vs population implications. Sci. Rep. 9, 17352 (2019).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Sergio, F. et al. Raptor nest decorations are a reliable threat against conspecifics. Science 331, 327–330 (2011).CAS 
PubMed 
Article 

Google Scholar 
Parker, D. & Diop-Kane, M. Meteorology of Tropical West Africa: The Forecaster’s Handbook (2017).Liechti, F., Hedenström, A. & Alerstam, T. Effects of sidewinds on optimal flight speed of birds. J. Theor. Biol. 170, 219–225 (1994).Article 

Google Scholar 
Liechti, F. & Bruderer, B. The relevance of wind for optimal migration theory. J. Avian Biol. 29, 561–568 (1998).Article 

Google Scholar 
Cresswell, W. Migratory connectivity of Palaearctic–African migratory birds and their responses to environmental change: the serial residency hypothesis. Ibis 156, 493–510 (2014).Article 

Google Scholar 
Alerstam, T., Hedenström, A. & Åkesson, S. Long-distance migration: evolution and determinants. Oikos 103, 247–260 (2003).Article 

Google Scholar 
Bowlin, M. S. et al. Grand challenges in migration biology. Integr. Comp. Biol. 50, 261–279 (2010).PubMed 
Article 

Google Scholar 
Mitchell, G. W., Woodworth, B. K., Taylor, P. D. & Norris, D. R. Automated telemetry reveals age specific differences in flight duration and speed are driven by wind conditions in a migratory songbird. Mov. Ecol. https://doi.org/10.1186/s40462-015-0046-5 (2015).Rotics, S. et al. The challenges of the first migration: movement and behaviour of juvenile vs. adult white storks with insights regarding juvenile mortality. J. Anim. Ecol. 85, 938–947 (2016).Horvitz, N. et al. The gliding speed of migrating birds: slow and safe or fast and risky? Ecol. Lett. 17, 670–679 (2014).PubMed 
Article 

Google Scholar 
Reichler, T. Changes in the Atmospheric Circulation as Indicator of Climate Change (Elsevier, 2009).Kling, M. M. & Ackerly, D. D. Global wind patterns and the vulnerability of wind-dispersed species to climate change. Nat. Clim. Change 10, 868–875 (2020).Article 

Google Scholar 
Drake, A., Rock, C. A., Quinlan, S. P., Martin, M. & Green, D. J. Wind speed during migration influences the survival, timing of breeding, and productivity of a neotropical migrant, Setophaga petechia. PLoS ONE 9, e97152 (2014).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Newton, I. Can conditions experienced during migration limit the population levels of birds? J. Ornithol. 147, 146–166 (2006).Article 

Google Scholar 
Loonstra, A. H. J., Verhoeven, M. A., Senner, N. R., Both, C. & Piersma, T. Adverse wind conditions during northward Sahara crossings increase the in-flight mortality of black-tailed godwits. Ecol. Lett. 22, 2060–2066 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Blas, J., Sergio, F. & Hiraldo, F. Age-related improvement in reproductive performance in a long-lived raptor: a cross-sectional and longitudinal study. Ecography 32, 647–657 (2009).Article 

Google Scholar 
Sergio, F. et al. No effect of satellite tagging on survival, recruitment, longevity, productivity and social dominance of a raptor, and the provisioning and condition of its offspring. J. Appl. Ecol. 52, 1665–1675 (2015).Article 

Google Scholar 
Kenward, R. A Manual for Wildlife Radio Tagging (Academic Press, 2001).Hersbach, H., et al. ERA5 hourly data on pressure levels from 1979 to present. Copernicus Climate Change Service Climate Data Store https://doi.org/10.24381/cds.bd0915c6 (2018).Klaassen, R. H. G., Hake, M., Strandberg, R. & Alerstam, T. Geographical and temporal flexibility in the response to crosswinds by migrating raptors. Proc. R. Soc. B 278, 1339–1346 (2011).PubMed 
Article 

Google Scholar 
Bohrer, G. et al. Estimating updraft velocity components over large spatial scales: contrasting migration strategies of golden eagles and turkey vultures. Ecol. Lett. 15, 96–103 (2012).PubMed 
Article 

Google Scholar 
Shannon, H. D., Young, G. S., Yates, M. A., Fuller, M. R. & Seegar, W. S. Measurements of thermal updraft intensity over complex terrain using American white pelicans and a simple boundary-layer forecast model. Bound. Layer Meteorol. 104, 167–199 (2002).Article 

Google Scholar 
Stull, R. B. An Introduction to Boundary Layer Meteorology (Springer, 1988).Safi, K. et al. Flying with the wind: scale dependency of speed and direction measurements in modelling wind support in avian flight. Mov. Ecol. 1, 1–13 (2013).Article 

Google Scholar 
Batschelet, E. Circular Statistics in Biology (Academic Press, 1981).R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2019).O’Neill, P. Magnetoreception and baroreception in birds. Dev. Growth Differ. 55, 188–197 (2013).PubMed 
Article 

Google Scholar 
Bingman, V.P. and Moore, P. in Aeroecology (eds. Chilson, P. B. et al.) 119–143 (Springer International Publishing, 2017).Liechti, F. and McGuire, L. P. in Aeroecology (eds. Chilson, P. B. et al.) 179–198 (Springer International Publishing, 2017).Richardson, W. J. Wind and orientation of migrating birds: a review. EXS 60, 226–249 (1991).CAS 
PubMed 

Google Scholar 
Pettorelli, N. et al. Using the satellite-derived NDVI to assess ecological responses to environmental change. Trends Ecol. Evol. 20, 503–510 (2005).PubMed 
Article 

Google Scholar 
Dodge, S. et al. Environmental drivers of variability in the movement ecology of turkey vultures (Cathartes aura) in North and South America. Philos. Trans. R. Soc. B 369, 20130195 (2014).Article 

Google Scholar 
Schaub, M., Kania, W. & Köppen, U. Variation of primary production during winter induces synchrony in survival rates in migratory white storks Ciconia ciconia. J. Anim. Ecol. 74, 656–666 (2005).Article 

Google Scholar 
Despland, E., Rosenberg, J. & Simpson, S. J. Landscape structure and locust swarming: a satellite’s eye view. Ecography 27, 381–391 (2004).Article 

Google Scholar 
Trierweiler, C. et al. A Palaearctic migratory raptor species tracks shifting prey availability within its wintering range in the Sahel. J. Anim. Ecol. 82, 107–120 (2013).PubMed 
Article 

Google Scholar 
Zuur, A. F., Ieno, E. N., Walker, N. J., Saveliev, A. A. & Smith, G. M. Mixed Effects Models and Extensions in Ecology with R (Springer, 2009).Sapir, N., Horvitz, N., Dechmann, D. K. N., Fahr, J. & Wikelski, M. Commuting fruit bats beneficially modulate their flight in relation to wind. Proc. R. Soc. B 281, 20140018 (2014).PubMed 
PubMed Central 
Article 

Google Scholar 
Becciu, P., Panuccio, M., Catoni, C., Dell’omo, G. & Sapir, N. Contrasting aspects of tailwinds and asymmetrical response to crosswinds in soaring migrants. Behav. Ecol. Sociobiol. 72, 28 (2018).Article 

Google Scholar 
Klaassen, R. H. G. et al. Loop migration in adult marsh harriers Circus aeruginosus, as revealed by satellite telemetry. J. Avian Biol. 41, 200–207 (2010).Article 

Google Scholar 
Strandberg, R., Klaassen, R. H. G., Hake, M. & Alerstam, T. How hazardous is the Sahara Desert crossing for migratory birds? Indications from satellite tracking of raptors. Biol. Lett. 6, 297–300 (2010).PubMed 
Article 

Google Scholar 
Pennycuick, D. J. Modelling the Flying Bird (Academic Press, 2008).Shepard, E. L. C., Ross, A. N. & Portugal, S. J. Moving in a moving medium: new perspectives on flight. Phil. Trans. R. Soc. B 371, 20150382 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
Van Doren, B. M., Horton, K. G., Stepanian, P. M., Mizrahi, D. S. & Farnsworth, A. Wind drift explains the reoriented morning flights of songbirds. Behav. Ecol. 27, 1122–1131 (2016).Article 

Google Scholar 
Sergio, F., Tanferna, A., Blas, J., Blanco, G. & Hiraldo, F. Reliable methods for identifying animal deaths in GPS- and satellite-tracking data: review, testing, and calibration. J. Appl. Ecol. 56, 562–572 (2019).Article 

Google Scholar 
Crawley, M. J. The R Book (Wiley, 2013).Nakagawa, S. & Schielzeth, H. A general and simple method for obtaining R2 from generalized linear mixed-effects models. Methods Ecol. Evol. 4, 133–142 (2013).Article 

Google Scholar  More

Dungan, M. L., Miller, T. E. & Thomson, D. A. Catastrophic decline of a top carnivore in the Gulf of California rocky intertidal zone. Science 216, 989–991 (1982).CAS 
PubMed 
Article 

Google Scholar 
Pounds, J. A. et al. Widespread amphibian extinctions from epidemic disease driven by global warming. Nature 439, 161–167 (2006).CAS 
PubMed 
Article 

Google Scholar 
Nicholls, H. Mysterious die-off sparks race to save saiga antelope. Nature 1–2. https://doi.org/10.1038/nature.2015.17675 (2015).Daszak, P., Cunningham, A. A. & Hyatt, A. D. Emerging infectious diseases of wildlife – Threats to biodiversity and human health. Science 287, 443–449 (2000).CAS 
PubMed 
Article 

Google Scholar 
Peters, E. C. Diseases of Coral Reef Organisms. in Coral Reefs in the Anthropocene (ed. Birkeland, C.) 147–178 (Springer Netherlands, 2015). https://doi.org/10.1007/978-94-017-7249-5_8.Weil, E. Coral Reef Diseases in the Wider Caribbean. in Coral Health and Disease (eds. Rosenberg, E. & Loya, Y.) 35–68 (Springer, 2004). https://doi.org/10.1007/978-3-662-06414-6_2.Harvell, C. D. et al. Coral diseases, Environmental drivers and the balance between corals and microbial associates. Oceanography 20, 172–195 (2007).Article 

Google Scholar 
Lessios, H. A., Robertson, D. R. & Cubit, J. D. Spread of Diadema mass mortality through the Caribbean. Science 226, 335–337 (1984).CAS 
PubMed 
Article 

Google Scholar 
Perry, C. T. & Alvarez-Filip, L. Changing geo-ecological functions of coral reefs in the Anthropocene. Funct. Ecol. 33, 976–988 (2019).
Google Scholar 
Aronson, R. B. & Precht, W. F. White-band disease and the changing face of Caribbean coral reefs. in. Hydrobiologia 460, 25–38 (2001).Article 

Google Scholar 
Alvarez-Filip, L., Dulvy, N. K., Gill, J. a, Côté, I. M. & Watkinson, A. R. Flattening of Caribbean coral reefs: region-wide declines in architectural complexity. Proceedings. Biological sciences / The Royal Society 276, 3019–3025 https://doi.org/10.1098/rspb.2009.0339 (2009).Estrada-Saldívar, N., Jordán-Dahlgren, E., Rodriguez-Martinez, R. E., Perry, C. T. & Alvarez-Filip, L. Functional consequences of the long-term decline of reef-building corals in the Caribbean: evidence of across-reef functional convergence. R. Soc. Open Sci. 6, 1–15 (2019).Article 

Google Scholar 
Cramer, K. L. et al. Widespread loss of Caribbean acroporid corals was underway before coral bleaching and disease outbreaks. Sci. Adv. 6 https://doi.org/10.1126/sciadv.aax9395 (2020).Bruno, J. F. et al. Thermal stress and coral cover as drivers of coral disease outbreaks. PLoS Biol. 5, 1220–1227 (2007).CAS 
Article 

Google Scholar 
Vega Thurber, R. et al. Chronic nutrient enrichment increases prevalence and severity of coral disease and bleaching. Glob. Change Biol. 20, 544–554 (2014).Article 

Google Scholar 
Wear, S. L. & Thurber, R. V. Sewage pollution: Mitigation is key for coral reef stewardship. Ann. N. Y. Acad. Sci. 1355, 15–30 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
Randall, C. J. & Van Woesik, R. Some coral diseases track climate oscillations in the Caribbean. Sci. Rep. 7, 1–8 (2017).CAS 
Article 

Google Scholar 
Lapointe, B. E., Brewton, R. A., Herren, L. W., Porter, J. W. & Hu, C. Nitrogen enrichment, altered stoichiometry, and coral reef decline at Looe Key, Florida Keys, USA: a 3 ‑ decade study. Marine Biology 166, (Springer Berlin Heidelberg, 2019) https://doi.org/10.1007/s00227-019-3538-9.Precht, W. F., Gintert, B. E., Robbart, M. L., Fura, R. & van Woesik, R. Unprecedented disease-related coral mortality in Southeastern Florida. Sci. Rep. 6, 1–11 (2016).Article 
CAS 

Google Scholar 
Kramer, P. R., Roth, L. & Lang, J. Map of Coral Cover of Susceptible Coral Species to SCTLD. (2020). Available at: www.agrra.org. ArcGIS Online.Aeby, G. S. et al. Pathogenesis of a tissue loss disease affecting multiple species of corals along the Florida Reef Tract. Front. Mar. Sci. 6, 1–18 (2019).Article 

Google Scholar 
Landsberg, J. H. et al. Stony coral tissue loss disease in Florida is associated with disruption of Host–Zooxanthellae Physiology. Front. Mar. Sci. 7, 1–24 (2020).Article 

Google Scholar 
Work, T. M. et al. Viral-like particles are associated with endosymbiont pathology in Florida Corals affected by stony coral tissue loss disease. Front. Mar. Sci. 8, 1–18 (2021).Article 

Google Scholar 
Alvarez-Filip, L., Estrada-Saldívar, N., Pérez-Cervantes, E., Molina-Hernández, A. & González-Barrios, F. J. A rapid spread of the Stony Coral Tissue Loss Disease outbreak in the Mexican Caribbean. PeerJ https://doi.org/10.7717/peerj.8069 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Gintert, B. E. et al. Regional coral disease outbreak overwhelms impacts from local dredge project. Environ. Monit. Assess. 191, 1–39 (2019).Article 

Google Scholar 
Muller, E. M., Sartor, C., Alcaraz, N. I. & van Woesik, R. Spatial epidemiology of the stony-coral-tissue-loss disease in Florida. Front. Mar. Sci. 7, 11 (2020).Article 

Google Scholar 
Estrada-Saldívar, N., Quiroga-García, B. A., Pérez-Cervantes, E., Rivera-Garibay, O. O. & Alvarez-Filip, L. Effects of the stony coral tissue loss disease outbreak on coral communities and the benthic composition of cozumel reefs. Front. Mar. Sci. 8, 1–13 (2021).Article 

Google Scholar 
Neely, K. L., Lewis, C. L., Lunz, K. & Kabay, L. Rapid population decline of the Pillar coral Dendrogyra cylindrus along the Florida Reef Tract. Front. Mar. Sci. 8 https://doi.org/10.3389/fmars.2021.656515 (2021).Rippe, J. P., Kriefall, N. G., Davies, S. W. & Castillo, K. D. Differential disease incidence and mortality of inner and outer reef corals of the upper Florida Keys in association with a white syndrome outbreak. Bull. Mar. Sci. 95, 305–316 (2019).Article 

Google Scholar 
Sharp, W. C., Shea, C. P., Maxwell, K. E., Muller, E. M. & Hunt, J. H. Evaluating the small-scale epidemiology of the stony-coral -tissue-loss-disease in the middle. PLOS ONE 15, 1–25 (2020).Article 
CAS 

Google Scholar 
Estrada-Saldívar, N. et al. Reef-scale impacts of the stony coral tissue loss disease outbreak. Coral Reefs https://doi.org/10.1007/s00338-020-01949-z (2020).Article 

Google Scholar 
González-Barrios, F. J., Cabral-Tena, R. A. & Alvarez-Filip, L. Recovery disparity between coral cover and the physical functionality of reefs with impaired coral assemblages. Glob. Change Biol. 27, 640–651 (2021).Article 

Google Scholar 
McWilliam, M. et al. Biogeographical disparity in the functional diversity and redundancy of corals. Proc. Natl Acad. Sci. 115, 3084–3089 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Suchley, A. & Alvarez-Filip, L. Local human activities limit marine protection efficacy on Caribbean coral reefs. Conserv. Lett. 11, 1–9 (2018).Article 

Google Scholar 
Hernández-Terrones, L. et al. Groundwater Pollution in a Karstic Region (NE Yucatan): baseline nutrient content and flux to coastal ecosystems. Water Air Soil Pollut. 218, 517–528 (2011).Article 
CAS 

Google Scholar 
Cejudo, E., Acosta-González, G., Ortega-Camacho, D. & Ventura-Sanchez, K. Water quality in natural protected areas in Cancun, Mexico: A historic perspective for decision makers. Reg Stud Mar Sci 48 https://doi.org/10.1016/j.rsma.2021.102035 (2021).Iwanowicz, D. D. et al. Explor. Stony Coral Tissue Loss Dis. Bact. Pathobiome https://doi.org/10.1017/CBO9781107415324.004 (2020).Article 

Google Scholar 
Studivan, M. S. et al. Reef sediments can act as a stony coral tissue loss disease vector. Front. Mar. Sci. 8, 1–15 (2022).Article 

Google Scholar 
Bruno, J. F., Petes, L. E., Harvell, C. D. & Hettinger, A. Nutrient enrichment can increase the severity of coral diseases. Ecol. Lett. 6, 1056–1061 (2003).Article 

Google Scholar 
Aeby, G. S. et al. Changing stony coral tissue loss disease dynamics through time in Montastraea cavernosa. Front. Mar. Sci. 8, 1–13 (2021).Article 

Google Scholar 
Perry, C. T. et al. Regional-scale dominance of non-framework building corals on Caribbean reefs affects carbonate production and future reef growth. Glob. Change Biol. 21, 1153–1164 (2015).Article 

Google Scholar 
Toth, L. T. et al. The unprecedented loss of Florida’s reef‐building corals and the emergence of a novel coral‐reef assemblage. Ecology e02781. https://doi.org/10.1002/ecy.2781 (2019).Alves, C. et al. Twenty years of change in benthic communities across the Belizean Barrier Reef. PLoS ONE 17, 1–36 (2022).
Google Scholar 
Bruno, J. F. Implications for reef restoration efforts. Science 345, 879–880 (2014).CAS 
PubMed 
Article 

Google Scholar 
Rodríguez-Martínez, R. E., Banaszak, A. T., McField, M. D., Beltrán-Torres, A. & Alvarez-Filip, L. Assessment of Acropora palmata in the mesoamerican reef system. PLoS ONE 9, 1–7 (2014).
Google Scholar 
Mudge, L., Alves, C., Figueroa-Zavala, B. & Bruno, J. F. Assessment of Elkhorn coral populations and associated herbivores in Akumal, Mexico. Front. Mar. Sci. 6, 1–12 (2019).Article 

Google Scholar 
Baums, I. B., Miller, M. W. & Hellberg, M. E. Regionally isolated populations of an imperiled Caribbean coral, Acropora palmata. Mol. Ecol. 14, 1377–1390 (2005).CAS 
PubMed 
Article 

Google Scholar 
Perry, C. T. et al. Changing dynamics of Caribbean reef carbonate budgets: emergence of reef bioeroders as critical controls on present and future reef growth potential. Proc. R. Soc. B: Biol. Sci. 281, 20142018–20142018 (2014).Article 

Google Scholar 
Molina-Hernández, A., González-Barrios, F. J., Perry, C. T. & Álvarez-Filip, L. Two decades of carbonate budget change on shifted coral reef assemblages: Are these reefs being locked into low net budget states?: Caribbean reefs carbonate budget trends. Proc. Royal Soc. B: Biol. Sci. 287. https://doi.org/10.1098/rspb.2020.2305rspb20202305 (2020).Perry, C. T. et al. Loss of coral reef growth capacity to track future increases in sea level. Nature 558, 396–400 (2018).CAS 
PubMed 
Article 

Google Scholar 
Enochs, I. C. et al. Ocean acidification enhances the bioerosion of a common coral reef sponge: Implications for the persistence of the Florida Reef Tract. Bull. Mar. Sci. 91, 271–290 (2015).Article 

Google Scholar 
Veron, J., Stafford-Smith, M., DeVantier, L. & Turak, E. Overview of distribution patterns of zooxanthellate Scleractinia. Front. Mar. Sci. 2, 1–19 (2015).
Google Scholar 
Miller, M. W., Lohr, K. E., Cameron, C. M., Williams, D. E. & Peters, E. C. Disease dynamics and potential mitigation among restored and wild staghorn coral, Acropora cervicornis. PeerJ 2014, 1–30 (2014).
Google Scholar 
Hughes, A. R. & Stachowicz, J. J. Genetic diversity enhances the resistance of a seagrass ecosystem to disturbance. Proc. Natl Acad. Sci. USA 101, 8998–9002 (2004).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Sokolow, S. H. Effects of a changing climate on the dynamics of coral infectious disease: A review of the evidence. Dis. Aquat. Org. 87, 5–18 (2009).Article 

Google Scholar 
Bellwood, D. R. et al. Coral reef conservation in the Anthropocene: Confronting spatial mismatches and prioritizing functions. Biol. Conserv. 236, 604–615 (2019).Article 

Google Scholar 
Grosso-Becerra, M. V., Mendoza-Quiroz, S., Maldonado, E. & Banaszak, A. T. Cryopreservation of sperm from the brain coral Diploria labyrinthiformis as a strategy to face the loss of corals in the Caribbean. Coral Reefs 40, 937–950 (2021).Article 

Google Scholar 
Edmunds, P. J. Long-term dynamics of coral reefs in St. John, US Virgin Islands. Coral Reefs 21, 357–367 (2002).Article 

Google Scholar 
Vermeij, M. J. A. Early life-history dynamics of Caribbean coral species on artificial substratum: The importance of competition, growth and variation in life-history strategy. Coral Reefs 25, 59–71 (2006).Article 

Google Scholar 
Webster, F. J., Babcock, R. C., Van Keulen, M. & Loneragan, N. R. Macroalgae inhibits larval settlement and increases recruit mortality at Ningaloo Reef, Western Australia. PLoS ONE 10, 1–14 (2015).CAS 

Google Scholar 
Suchley, A., McField, M. D. & Alvarez-Filip, L. Rapidly increasing macroalgal cover not related to herbivorous fishes on Mesoamerican reefs. PeerJ 4, e2084 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
Contreras-Silva, A. et al. A meta-analysis to assess long-term spatiotemporal changes of benthic coral and macroalgae cover in the Mexican caribbean. 1–12. https://doi.org/10.1038/s41598-020-65801-8 (2020).Meiling, S. S., Muller, E. M., Smith, T. B. & Brandt, M. E. 3D photogrammetry reveals dynamics of stony coral tissue loss disease (SCTLD) Lesion progression across a thermal stress event. Front. Mar. Sci. 7, 1–12 (2020).Article 

Google Scholar 
Espinosa-Andrade, N., Suchley, A., Reyes-Bonilla, H. & Alvarez-Filip, L. The no-take zone network of the Mexican Caribbean: assessing design and management for the protection of coral reef fish communities. Biodivers. Conserv. https://doi.org/10.1007/s10531-020-01966-y (2020).Lang, J. C., Marks, K. W., Kramer, P. R., Kramer, P. A. & Ginsburg, R. N. AGRRA Protocols. Version 5.5. The Atlantic and Gulf Rapid Reef Assessment (AGRRA) Program. (2012).Burke, L., Reytar, K., Spalding, M. & Perry, A. Reefs risk. Natl Geographic https://doi.org/10.1016/0022-0981(79)90136-9 (2011).Article 

Google Scholar 
Chollett, I., Müller-Karger, F. E., Heron, S. F., Skirving, W. & Mumby, P. J. Seasonal and spatial heterogeneity of recent sea surface temperature trends in the Caribbean Sea and southeast Gulf of Mexico. Mar. Pollut. Bull. 64, 956–965 (2012).CAS 
PubMed 
Article 

Google Scholar 
Cox, C., Valdivia, A., McField, M., Castillo, K. & Bruno, J. F. Establishment of marine protected areas alone does not restore coral reef communities in Belize. Mar. Ecol. Prog. Ser. 563, 65–79 (2017).Article 

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

Google Scholar 
Core Team, R. R: A language and environment for statistical computing. R Foundation for Statistical Computing. Vienna, Austria: URL https://www.R-project.org/ (2020).Hughes, T. P. et al. Spatial and temporal patterns of mass bleaching of corals in the Anthropocene. Science 359, 80–83 (2018).CAS 
PubMed 
Article 

Google Scholar 
Riddle, D. Coral reproduction, part one: A natural coral spawning in Hawaii, The cauliflower coral (Pocillopora meandrina). Adv. Aquarist’s Online Mag. 7, 10–16 (2008).
Google Scholar 
Madin, J. S. et al. The Coral Trait Database, a curated database of trait information for coral species from the global oceans. Sci. Data 3, 160017 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
McWilliam, M., Pratchett, M. S., Hoogenboom, M. O. & Hughes, T. P. Deficits in functional trait diversity following recovery on coral reefs. Proc. Royal Soc. B: Biol. Sci. 287. https://doi.org/10.1098/rspb.2019.2628 (2020).Hughes, T. P. et al. Global warming transforms coral reef assemblages. Nature https://doi.org/10.1038/s41586-018-0041-2 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Oksanen, J. Vegan: community ecology package version 1.8-6. http://cran.r-project.org (2007).Maechler, M. et al. Cluster: Cluster Analysis Basics and Extensions. R package version 2.1.3. https://CRAN.R-project.org/package=cluster (2022).CLARKE, K. R. Non‐parametric multivariate analyses of changes in community structure. Aust. J. Ecol. 18, 117–143 (1993).Article 

Google Scholar 
Clarke, K. R. & Warwick, R. M. Change in marine communities: an approach to statistical analysis and interpretation. 2nd edition. Primer-E, Plymouth. Plymouth, United Kingsom: PRIMER-E 172 (2001).Lavorel, S. et al. Assessing functional diversity in the field – Methodology matters! Funct. Ecol. 22, 134–147 (2008).
Google Scholar 
Ricotta, C. & Moretti, M. CWM and Rao’s quadratic diversity: A unified framework for functional ecology. Oecologia 167, 181–188 (2011).PubMed 
Article 

Google Scholar 
Villéger, S., Mason, N. W. H. & Mouillot, D. New multidimensional functional diversity indices for a multifaceted framework in functional ecology. Ecology 89, 2290–2301 (2008).PubMed 
Article 

Google Scholar 
Denis, V., Ribas-Deulofeu, L., Sturaro, N., Kuo, C. Y. & Chen, C. A. A functional approach to the structural complexity of coral assemblages based on colony morphological features. Sci. Rep. 7, 1–11 (2017).Article 

Google Scholar 
Teixidó, N. et al. Functional biodiversity loss along natural CO2 gradients. Nat. Commun. 9, 1–9 (2018).Article 
CAS 

Google Scholar 
Laliberté, E. et al. FD: measuring functional diversity from multiple traits, and other tools for functional ecology. R package version 1.0-12. (2014).González-Barrios, F. J. & Alvarez-Filip, L. A framework for measuring coral species-specific contribution to reef functioning in the Caribbean. Ecol. Indic. 95, 877–886 (2018).Article 

Google Scholar 
Patterson, K. L. et al. The etiology of white pox, a lethal disease of the Caribbean elkhorn coral, Acropora palmata. Proc. Natl Acad. Sci. 99, 8725–8730 (2002).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Edmunds, P. J. & Elahi, R. The demographics of a 15-year decline in covers of the Caribbean reef coral Montastraea annularis. Ecol. Monogr. 77, 3–18 (2007).Article 

Google Scholar 
Eakin, C. M. et al. Caribbean corals in crisis: Record thermal stress, bleaching, and mortality in 2005. PLoS ONE 5. https://doi.org/10.1371/journal.pone.0013969 (2010). More