Ecology

1.Menotti, F. & O’Sullivan, A. The Oxford Handbook of Wetland Archaeology. (Oxford University Press, 2013)2.O’Sullivan, A. The Archaeology of Lake Settlement in Ireland. (Discovery Programme, 1998)3.Crone, A. Forging a chronological framework for Scottish crannogs; the radiocarbon and dendrochronological evidence. In Lake Dwellings After Robert Munro. Proceedings from the Munro International Seminar: The Lake Dwellings of Europe (Midgley, M.S., Sanders, J. eds.) (University of Edinburgh, 2012).4.Lane, A., M. & Redknap, M. Llangorse Crannog: Excavation of an Early Medieval Royal Site in the Kingdom of Brycheiniog. (Oxbow Books, 2019).5.Hertz, J. The excavation of Solvig a Danish crannog in southern Denmark. Further excavation at Solvig. Chateau Gaillard 6, 84–105 (1974).
Google Scholar 
6.Barber, J. & Crone, B. A. Crannogs: A diminishing resource? A survey of the crannogs of south-west Scotland and excavations at Buiston crannog. Antiquity 67, 520–533 (1993).Article 

Google Scholar 
7.Henderson, J. & Cavers, G. Proc. Soc. An Iron age crannog in south-west Scotland: Underwater survey and excavation at Lough Arthur. Antiq. Scot. 141, 103–124 (2011).8.Garrow, D. & Sturt, F. Neolithic crannogs: Rethinking settlement, monumentality and deposition in the Outer Hebrides and beyond. Antiquity 93, 664–684 (2019).Article 

Google Scholar 
9.O’Brien, C.E., Selby, K.A., Ruiz, Z., Brown, A.G., Dinnin, M Caseldine, C., Langdon, P.G. & Stuijts, I. Sediment-based multi-proxy approach to the archaeology of crannogs: A case study from Central Ireland. Holocene 15, 707–719 (2005).10.O’Sullivan, A. Crannogs Lake Dwellings of Early Ireland. (Country House, 2000).11.Wood-Martin, W. G. The Lake Dwellings of Ireland (Figgis and Co., 1886).
Google Scholar 
12.Fredengren, C. Crannogs: A study of people’s interaction with lakes, with particular reference to Lough Gara in the north-west of Ireland (Wordwell Press, 2002).
Google Scholar 
13.Stratigos, M. J. & Noble, G. Crannogs, castles and lordly residences: New research and dating of crannogs in north-east Scotland. Proc. Soc. Antiq. Scot. 144, 205–222 (2014).
Google Scholar 
14.Tóth, M., Hardenbroek, M. van., Bleicher, N. & Heiri, O. Pronounced early human impact on lakeshore environments documented by aquatic invertebrate remains in waterlogged Neolithic settlement deposits. Q. Sci. Rev. 205, 126–142. https://doi.org/10.1016/j.quascirev.2018.12.015 (2019).15.Chen, W. & Ficetola, G. F. Conditionally autoregressive models improve occupancy analyses of autocorrelated data: An example with environmental DNA. Mol. Ecol. Resour. 19, 163–175 (2019).Article 

Google Scholar 
16.Prost, K., Birk, J.J,. Lehndorff, E., Gerlach, R. & Amelung, W. Steroid biomarkers revisited—Improved source identification of faecal remains in archaeological soil material. PLoS ONE 12(1), 1/30 (2017).17.Selby, K. A. & Brown, A. G. The holocene development, spatial variations and anthropogenic record of a shallow lake system in central Ireland as recorded by diatom stratigraphy. J. Palaeolimnol. 38, 419–440 (2007).ADS 
Article 

Google Scholar 
18.Hawksworth, D. L., Van Geel, B. & Wiltshire, E. J. The enigma of the Diporotheca palynomorph. Rev. Palaeobot. Palynol. 235, 94–98 (2016).Article 

Google Scholar 
19.Alsos, I. G. et al. Plant DNA metabarcoding of lake sediments: How does it represent the contemporary vegetation. PLoS ONE 13(4), e0195403. https://doi.org/10.1371/journal.pone.0195403 (2018).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
20.Giguet-Covex, C., Pansu, J., Arnaud, F., Rey, P-J., Griggo, C., Gielly, L. Domaizon, I., Coissac, E. David, F., Choler P., Poulenard, J. & Taberlet, P. Long livestock farming history and human landscape shaping revealed by lake sediment DNA.Nat. Commun. 5, 1–7 (2014).21.Sabatier, P. et al. Long-term relationships among pesticide applications, mobility, and soil erosion in a vineyard watershed. Proc. Natl. Acad. Sci. U.S.A. 111, 15647–15652 (2014).ADS 
CAS 
Article 

Google Scholar 
22.Crone, A. Crannogs and chronologies. Procs. Antiq. Soc. Scotland 123, 245–254. http://journals.socantscot.org/index.php/psas/article/view/9462 (1993).23.Lammers, Y., Clark, C., Erséus, C., Brown, A.G., Edwards, M. E., Gielly, L., Haflidason, H., Mangerud, J., Rota, E., Svendsen, J-I., & Alsos, I.G. Earthworms in late glacial and holocene records discovered in DNA bycatch. Boreas 48, 317–329 (2018).24.McClatchie, M., Mccormick, F. M., Kerr, T. R. & O’Sullivan, A. Early medieval farming and food production: A review of the archaeobotanical evidence from archaeological excavations in Ireland. Veg. Hist. Archaeobotany 24, 179–186 (2015).Article 

Google Scholar 
25.McCormick, F., & Murray, E. Knowth and the Zooarchaeology of Early Christian Ireland, Excavations at Knowth 3. (Royal Irish Academy Monographs in Archaeology, 2007).26.Soderberg, J. Wild cattle: Red deer in the religious texts, iconography, and archaeology of early Medieval Ireland. Int. J. Hist. Arch. 8, 167–183 (2004).Article 

Google Scholar 
27.Rymer, L. The history and ethnobotany of bracken. J. Linn. Soc. 76, 151–176 (1976).Article 

Google Scholar 
28.Stuart, J. Notice of a group of artificial islands in the Lough of Dowalton, Wigtownshire and other artificial islands or crannogs throughout Scotland. Procs. Soc. Antiq. Scot. 10, 31–34 (1866).
Google Scholar 
29.Dixon, N. The history of crannog survey and excavation in Scotland. Int. J. Nautical Arch. 20, 1–8 (1991).Article 

Google Scholar 
30.Jones, D. & Haggar, R. J. Impact of nitrogen and organic manures on yield, botanical composition and herbage quality of two contrasting grassland field margins. Biol. Agric. Hortic. 14, 107–123 (1997).Article 

Google Scholar 
31.O’Sullivan, A. The Social and Idealogical Role of Crannogs in Early Medieval Ireland. PhD Thesis. (UCD, 2004).32.Bourke, C. The Excavations of an Early Medieval Crannog at Newtownlow, County Westmeath (December Publications, 2015).
Google Scholar 
33.Jones, M. Feast: Why Humans Share Food. (Oxford University Press).34.Bronk Ramsey, C. Deposition models for chronological records. Q. Sci. Rev. 27, 42–60 (2008).ADS 
Article 

Google Scholar 
35.Reimer, P. J. et al. The IntCal20 Northern hemisphere radiocarbon age calibratin curve. Radiocarbon 1, 1–33 (2020).
Google Scholar 
36.Kotov S, Pälike H. QAnalySeries—A cross-platform time series tuning and analysis tool. In AGU Fall Meeting. https://doi.org/10.1002/essoar.10500226.1 (2018)37.Croudace, I. W., Rindby, A. & Rothwell, R. G. ITRAX: Description and evaluation of a new multi-function X-ray core scanner. Geol. Soc. Lond. Spec. Public. 267, 51–63 (2006).ADS 
CAS 
Article 

Google Scholar 
38.Pirrie, D. & Rollinson. G. K. Unlocking the application of automated mineralogy. Geol. Today 27, 226–235 (2011).39.Pirrie, D., Rollinson, G. K., Andersen, J. C., Wootton, D. & Moorhead, S. Soil forensics as a tool to test reported artefact find sites. J. Archaeol. Sci. 41, 461–473 (2014).CAS 
Article 

Google Scholar 
40.Taberlet, P., Coissac, E., Pompanon, F., Gielly, L., Miquel, C., Valentini, A., Vermat, T., Gérard, C., Brochmann, C. & Willerslev E. Power and limitations of the chloroplast trnL (UAA) intron for plant DNA barcoding. Nucl. Acids Res. e14 (2007).41.Boyer, F., Mercier, C., Bonin, A,. Le Bras, Y., Taberlet, P. & Coissac E. Obitools: A unix-inspired software package for DNA metabarcoding. Mol. Ecol. Resour. 16, 176–182 (2016).42.De Barba, M. et al. DNA metabarcoding multiplexing and validation of data accuracy for diet assessment: Application to omnivorous diet. Mol. Ecol. Resour. 14, 306–323 (2014).Article 

Google Scholar 
43.Ficetola, G. F., Taberlet, P. & Coissac, E. How to limit false positives in environmental DNA and metabarcoding?. Mol. Ecol. Resour. 16, 604–607 (2016).CAS 
Article 

Google Scholar 
44.Lahoz-Monfort, J.J., Guillera-Arroita, G. & Tingley, G., R. Statistical approaches to account for false positive errors in environmental DNA samples. Mol. Ecol. Resour. 16, 673–685 (2016).45.Bull, I., Lockheart, M., Elhmmali, M. Roberts, D. E. & Evershed, R. The origin of faeces by means of biomarker detection. Environ. Int. 27, 647–654 (2002).46.Mackay, H., Davies, K.L., Robertson, J., Roy, L., Bull, I.D. Whitehouse, N.J., Crone, A., Cavers, G., McCormick, F., Brown, A.G. & Henderson, A.C.G. Characterising life in settlements and structures: Incorporating faecal lipid biomarkers within a multiproxy case study of a wetland village. J. Archaeol. Sci. 121 (2020).47.Battarbee, R. W. Diatom analysis. In Handbook of Holocene Palaeoecology and Palaeohydrology (ed. Berglund, B. E.) 527–570 (Wiley, 1986).
Google Scholar 
48.Krammer, K., Lange-Bertalot, H. Bacillariophyceae. 1–4. Süßwasserflora von Mitteleuropa, Band 2/1–2/3. (Gustav Fischer, 1999–2004).49.Brooks, S.J., Langdon, P.G. & Heiri, O. The Identification and Use of Palaearctic Chironomidae Larvae in Palaeoecolgy. QRA Technical Guide No. 10. (Quaternary Research Association, 2007).50.Tóthm, M., van Hardenbroek, M., Bleicher, N. & Heiri, O. Pronounced early human impact on lakeshore environments documented by aquatic invertebrate remains in waterlogged Neolithic settlement deposits. Q. Sci. Rev. 205, 126–142 (2019).ADS 
Article 

Google Scholar 
51.Moog, O. Fauna Aquatica Austriaca. Wasserwirtshcaftskataster, Bundesministerium für Land- und Forstwirtschaft, Umwelt und Wasserwirtschaft, Vienna (2002)52.Bennion, H. A diatom-total phosphorus transfer function for shallow, eutrophic ponds in southeast England. Hydrobiologia 275(276), 391–410 (1994).Article 

Google Scholar  More

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This study demonstrated that several anuran genera (Xenopus, Lithobates [Rana], Hyla, and Anaxyrus [Bufo]), as well as Nile monitor lizards, green anoles, and featherfin catfish, are susceptible to infection with D. insignis and/or D. medinensis L3s. We also found that D. insignis and D. medinensis larvae can persist in anuran tissues for at least eight and two months, respectively, although the number of L3s recovered from each infected animal was generally low. Regardless, these data show that these animals could serve as paratenic hosts if they ingest infected copepods in nature and are subsequently ingested by an appropriate definitive host.We exposed animals using two different methods (group batch or by mouth [PO]), but aimed to primarily batch expose animals as that better mimics natural exposure. A few anuran species (i.e., American toads, Cope’s gray treefrogs, and adult African clawed frogs) were exposed to D. medinensis-infected copepods PO, as they had metamorphosed into adults before D. medinensis larvae became available for use and would be unlikely to ingest all copepods autonomously. Our primary goal in this study was to determine susceptibility to Dracunculus infection.Six anurans that were exposed as tadpoles underwent metamorphosis to froglets before being necropsied. Dracunculus L3s were recovered from two of these animals, supporting previous findings that D. insignis larvae can persist in anuran tissues through metamorphosis14. The persistence of larvae in the tissues through metamorphosis may facilitate Dracunculus transmission from aquatic to terrestrial food chains. This could be an important factor in transmission, as the majority of definitive hosts of Dracunculus nematodes are terrestrial. This study found that, in addition to X. laevis and Lithobates spp. (which have previously been infected with Dracunculus spp. larvae), Anaxyrus sp. and Hyla sp. can also become infected with Dracunculus L3s14,21. The infection of Anaxyrus sp. and Hyla sp. is particularly interesting, as members of these genera transition to a terrestrial or arboreal existence as adults, compared to Xenopus sp. and Lithobates spp. which remain completely or predominantly aquatic, even as adults. This transition to a terrestrial habitat could carry infectious larvae further from water sources, making them available to definitive hosts more widely across the landscape. However, the role of these animals in Dracunculus transmission would still depend on many other factors, including the natural history of these amphibian species, diets of definitive hosts, and how long Dracunculus L3s persist in paratenic hosts, as terrestrial anurans would be unlikely to acquire new infections after metamorphosis.During a previous experimental study, D. insignis L3s persisted in amphibian paratenic hosts for up to 37 DPI, at which time the animals were necropsied16. In this long-term infection trial, we found that D. insignis larvae persisted for at least 244 days (approximately eight months), while D. medinensis larvae persisted for at least 58 days (approximately two months). These results demonstrate that infection of a paratenic host can extend the time that L3s may persist in the environment well beyond the lifespan of a copepod21. As we had a limited supply of D. medinensis L3s, we were unable to conduct sufficient trials to determine whether D. insignis may persist longer in paratenic hosts than D. medinensis. If this difference was found to exist, it could contribute to the higher proportion of wild-caught adult frogs found to be infected with D. insignis than with D. medinensis during field surveys18,19. Further testing with an increased sample size would be required to determine whether the persistence of larvae actually differs between Dracunculus species or paratenic host species.No Dracunculus larvae were recovered from the two adult African clawed frogs that were fed D. medinensis L3s that had been recovered from other paratenic hosts. It is likely that our very small sample size (two animals) and the prolonged period before necropsy (4 months) explain these negative results. In our persistence trials, there was attrition over time so these animals should have been examined earlier after exposure. Future efforts to investigate transmission of Dracunculus between different paratenic hosts should use larger sample sizes and shorter infection periods. It would also be interesting to know if predatory animals, such as Nile monitor lizards, which can experimentally become infected with Dracunculus sp. larvae could become infected by ingesting other paratenic hosts.Fish were investigated for their potential role in Dracunculus transmission, as many fish species consume copepods as part of a natural diet25,26. Despite this, Dracunculus larvae have not been recovered during multiple studies screening wild-caught fish17,19. Dracunculus insignis L3s have rarely been recovered from previous experimental trials with fish16. When larvae were recovered from fish, larval recovery rates were very low (0.6–2.0% recovery; 1–2 larvae per fish) and only 3/43 (7.0%) of the fish harbored Dracunculus larvae upon necropsy16. In a separate trial, fish experimentally functioned as short-term transport hosts of D. medinensis and D. insignis to infect domestic ferrets7. Our findings from this trial were surprising, as we recovered up to 6 D. medinensis L3s from the tissues of three out of four (75%) exposed featherfin catfish. This fish species is common in the Chari River Basin area in Chad, Africa where high numbers of D. medinensis infections are reported in domestic dogs living in fishing villages, and is consumed by both people and dogs17. Dogs in these villages often eat discarded small fish or fish viscera4. Although our sample size was small, our current findings are evidence that some fish species may be more capable of serving as paratenic hosts for Dracunculus than those that have been previously tested. This finding further supports the continuation of the screening of wild fish muscle tissues for Dracunculus larvae.Lizards were included in this study because large, subcutaneous nematodes (believed to be Dracunculus sp.) were historically reported from Nile monitor lizards and these lizards are consumed by people20,22. However, a lack of contemporary reports and recent work in Chad, Africa, determining that large, subcutaneous nematodes recovered from wild Nile monitor lizards were not Dracunculus sp. but actually most similar to Ochoterenella sp., suggest that monitor lizards in this region are not definitive hosts for D. medinensis17. This current study confirms that Nile monitor and green anole lizards could become infected with Dracunculus larvae. As the diet of Nile monitor lizards can include amphibians and fish, were those prey to contain Dracunculus larvae, it is possible that monitors could serve as paratenic hosts, either by ingestion of larvae in fish intestines or in tissues of amphibians or fish, although these modes of transmission to paratenic hosts have not been confirmed23,24. It is unlikely that green anoles would become naturally infected with Dracunculus spp. due to their diet and primarily arboreal habitat; however, their infection demonstrates that multiple, distantly related lizard species are susceptible to experimental infection.Although anoles were exposed to both D. insignis and D. medinensis larvae, it is most likely that the recovered larvae were D. insignis, as only two D. medinensis-infected copepods were administered (in addition to 23 D. insignis-infected copepods). Species identity of these larvae could not be confirmed, however, as Dracunculus larvae can only be identified to species using molecular diagnostic techniques, which would destroy the sample, and these larvae were used in an experimental infection trial after recovery. Exposure of a ferret PO to the four larvae recovered from this anole (as part of a separate study) did not yield an infection, which is unsurprising given the low dose of larvae used. A previous study has shown that as few as 10 Dracunculus larvae may lead to infection of a ferret when administered interperitoneally (IP) (which was a more effective infection route than PO inoculation), therefore, four larvae administered PO would be unlikely to yield infection of a ferret27,28.In all trials, infection occurred only in those animals that were inoculated with or exposed to at least 20 copepods per individual, suggesting an impact of parasite dose-dependent infection probability for Dracunculus infection in paratenic hosts. As copepod infection rate during this study was estimated to be (ge) 25%, it is likely that animals ingesting 20 copepods would consume at least 5 Dracunculus sp. larvae. Previous studies demonstrated that 10 larvae (administered IP) were sufficient to infect a ferret, but that percent recovery was higher with IP infection than PO27,28. It is likely that a similar minimum infectious dose also exists for paratenic hosts and may differ by paratenic host species and mode of infection. Parasite dose-dependent infection probability of Dracunculus spp. merits further investigation, as understanding this relationship could help researchers to more effectively study transmission in the laboratory by performing experimental infection trials with greater reliability.Despite the variable sample sizes and exposure routes in this study, we demonstrated that a wide range of animals (anurans, fish, and lizards) were susceptible to infection with D. insignis and/or D. medinensis L3s. Importantly, one exposed fish species (Synodontis eupterus) was susceptible, opening up further concerns that certain fish species could serve as transport and paratenic hosts of Dracunculus species. Nile monitor lizards and anoles were successfully infected with L3s, demonstrating the first experimental infection of lizards with Dracunculus larvae. Dracunculus larvae remained L3s in the tissues of tested anurans for up to 244 days, extending the known persistence time of infectious larvae. Although no larvae were recovered from frogs that were fed L3s recovered from other paratenic hosts, continued investigation into the possibility of paratenic host to paratenic host transmission would be particularly interesting in determining if some predatory frogs (tadpoles or adults), fish, or lizards may concentrate higher numbers of L3s over time through predation of other infected paratenic hosts. Despite this study not determining how infectious larvae recovered from each of these paratenic hosts would be to another host, our findings contribute to a better understanding of the ability of these paratenic hosts to harbor Dracunculus L3s. This information is valuable to understanding how transmission to animal definitive hosts may be occurring, in addition to informing GWEP management decisions aiming to decrease transmission of D. medinensis to humans and animals. More

1.Hillebrand, H. et al. Thresholds for ecological responses to global change do not emerge from empirical data. Nat. Ecol. Evol. 4, 1502–1509 (2020).Article 

Google Scholar 
2.Biggs, R. O., Peterson, G. D. & Rocha, J. C. C. The Regime Shifts Database: a framework for analyzing regime shifts in social-ecological systems. Ecol. Soc. 23, 9 (2018).Article 

Google Scholar 
3.Diaz, R. J. & Rosenberg, R. Spreading dead zones and consequences for marine ecosystems. Science 321, 926–929 (2008).CAS 
Article 

Google Scholar 
4.Walker, B. & Meyers, J. A. Thresholds in ecological and social–ecological systems: a developing database. Ecol. Soc. 9, 3 (2004).Article 

Google Scholar 
5.Hirota, M., Holmgren, M., Van Nes, E. H. & Scheffer, M. Global resilience of tropical forest and savanna to critical transitions. Science 334, 232–235 (2011).CAS 
Article 

Google Scholar 
6.Scheffer, M. et al. Creating a safe operating space for iconic ecosystems. Science 347, 1317–1319 (2015).CAS 
Article 

Google Scholar 
7.Eppinga, M. B. et al. Long-term transients help explain regime shifts in consumer-renewable resource systems. Commun. Earth Environ. 2, 42 (2021).8.Hughes, T. P., Linares, C., Dakos, V., van de Leemput, I. A. & van Nes, E. H. Living dangerously on borrowed time during slow, unrecognized regime shifts. Trends Ecol. Evol. 28, 149–155 (2013).Article 

Google Scholar 
9.Dakos, V., Carpenter, S. R., van Nes, E. H. & Scheffer, M. Resilience indicators: prospects and limitations for early warnings of regime shifts. Philos. Trans. R. Soc. Lond. B Biol. Sci. 370, 20130263 (2015).Article 

Google Scholar 
10.Santos, F. C. & Pacheco, J. M. Risk of collective failure provides an escape from the tragedy of the commons. Proc. Natl Acad. Sci. USA 108, 10421–10425 (2011).CAS 
Article 

Google Scholar 
11.Rocha, J. C., Schill, C., Saavedra-Díaz, L. M., Del Pilar Moreno, R. & Maldonado, J. H. Cooperation in the face of thresholds, risk, and uncertainty: experimental evidence in fisher communities from Colombia. PLoS ONE 15, e0242363 (2020).CAS 
Article 

Google Scholar 
12.Barrett, S. & Dannenberg, A. Climate negotiations under scientific uncertainty. Proc. Natl Acad. Sci. USA 109, 17372–17376 (2012).CAS 
Article 

Google Scholar 
13.Tengö, M., Brondizio, E. S., Elmqvist, T., Malmer, P. & Spierenburg, M. Connecting diverse knowledge systems for enhanced ecosystem governance: the multiple evidence base approach. Ambio 43, 579–591 (2014).Article 

Google Scholar 
14.Peterson, G. D., Carpenter, S. R. & Brock, W. A. Uncertainty and the management of multistate ecosystems: an apparently rational route to collapse. Ecology 84, 1403–1411 (2003).Article 

Google Scholar 
15.Vea, E. B., Ryberg, M., Richardson, K. & Hauschild, M. Z. Framework to define environmental sustainability boundaries and a review of current approaches. Environ. Res. Lett. 15, 103003 (2020).Article 

Google Scholar 
16.McCool, S. F. Planning for sustainable nature dependent tourism development. Tour. Recreat. Res. 19, 51–55 (1994).
Google Scholar 
17.Bruckner, T., Petschel-Held, G., Leimbach, M. & Toth, F. L. Methodological aspects of the tolerable windows approach. Clim. Change 56, 73–89 (2003).Article 

Google Scholar 
18.UN Environment Programme. Convention on Biological Diversity. Aichi Biodiversity Targets https://www.cbd.int/sp/targets/ (2010).19.Dearing, J. A. et al. Safe and just operating spaces for regional social-ecological systems. Glob. Environ. Change 28, 227–238 (2014).Article 

Google Scholar 
20.Strassburg, B. B. N. et al. Global priority areas for ecosystem restoration. Nature 586, 724–729 (2020).CAS 
Article 

Google Scholar  More

1.UNEP. Global Environment Outlook – GEO6: Healthy Planet, Healthy People (Cambridge Univ. Press, 2019); https://www.unep.org/resources/global-environment-outlook-62.Dinerstein, E. et al. A global deal for nature: guiding principles, milestones, and targets. Sci. Adv. 5, eaaw2869 (2019).CAS 
Article 

Google Scholar 
3.Mori, A. S., Spies, T. A., Sudmeier-Rieux, K. & Andrade, A. Reframing ecosystem management in the era of climate change: issues and knowledge from forests. Biol. Conserv. 165, 115–127 (2013).Article 

Google Scholar 
4.Warren, R., Price, J., Graham, E., Forstenhaeusler, N. & VanDerWal, J. The projected effect on insects, vertebrates, and plants of limiting global warming to 1.5° C rather than 2° C. Science 360, 791–795 (2018).CAS 
Article 

Google Scholar 
5.Garcia, R. A., Cabeza, M., Rahbek, C. & Araujo, M. B. Multiple dimensions of climate change and their implications for biodiversity. Science 344, 1247579 (2014).Article 
CAS 

Google Scholar 
6.Urban, M. C. Accelerating extinction risk from climate change. Science 348, 571–573 (2015).CAS 
Article 

Google Scholar 
7.IPBES secretariat. Summary for Policymakers of the Global Assessment Report on Biodiversity and Ecosystem Services of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (eds. Diaz, S. et al.) (IPBES, 2019); https://ipbes.net/global-assessment8.Midgley, G. F. et al. Terrestrial carbon stocks and biodiversity: key knowledge gaps and some policy implications. Curr. Opin. Environ. Sustain. 2, 264–270 (2010).Article 

Google Scholar 
9.Jones, A. D., Calvin, K. V., Collins, W. D. & Edmonds, J. Accounting for radiative forcing from albedo change in future global land-use scenarios. Clim. Change 131, 691–703 (2015).Article 

Google Scholar 
10.Griscom, B. W. et al. Natural climate solutions. Proc. Natl Acad. Sci. USA 114, 11645–11650 (2017).CAS 
Article 

Google Scholar 
11.Seddon, N., Turner, B., Berry, P., Chausson, A. & Girardin, C. A. J. Grounding nature-based climate solutions in sound biodiversity science. Nat. Clim. Change 9, 84–87 (2019).Article 

Google Scholar 
12.Morecroft, M. D. et al. Measuring the success of climate change adaptation and mitigation in terrestrial ecosystems. Science 366, eaaw9256 (2019).CAS 
Article 

Google Scholar 
13.Mori, A. S. Advancing nature-based approaches to address the biodiversity and climate emergency. Ecol. Lett. 23, 1729–1732 (2020).14.Lewis, S. L., Wheeler, C. E., Mitchard, E. T. A. & Koch, A. Restoring natural forests is the best way to remove atmospheric carbon. Nature 568, 25–28 (2019).CAS 
Article 

Google Scholar 
15.Holl, K. D. & Brancalion, P. H. S. Tree planting is not a simple solution. Science 368, 580–581 (2020).CAS 
Article 

Google Scholar 
16.Hisano, M., Searle, E. B. & Chen, H. Y. H. Biodiversity as a solution to mitigate climate change impacts on the functioning of forest ecosystems. Biol. Rev. 93, 439–456 (2018).Article 

Google Scholar 
17.Liang, J. et al. Positive biodiversity-productivity relationship predominant in global forests. Science 354, aaf8957 (2016).Article 
CAS 

Google Scholar 
18.Mori, A. S. Environmental controls on the causes and functional consequences of tree species diversity. J. Ecol. 106, 113–125 (2018).Article 

Google Scholar 
19.Hulvey, K. B. et al. Benefits of tree mixes in carbon plantings. Nat. Clim. Change 3, 869–874 (2013).CAS 
Article 

Google Scholar 
20.World Economic Forum. The Global Risks Report 2020 https://www.weforum.org/reports/the-global-risks-report-2020 (2020).21.Tilman, D., Isbell, F. & Cowles, J. M. Biodiversity and ecosystem functioning. Ann. Rev. Ecol. Evol. Syst. 45, 471–493 (2014).Article 

Google Scholar 
22.Isbell, F., Tilman, D., Polasky, S. & Loreau, M. The biodiversity-dependent ecosystem service debt. Ecol. Lett. 18, 119–134 (2015).Article 

Google Scholar 
23.Gonzalez, A. et al. Scaling-up biodiversity-ecosystem functioning research. Ecol. Lett. 23, 757–776 (2020).Article 

Google Scholar 
24.Mokany, K. et al. Integrating modelling of biodiversity composition and ecosystem function. Oikos 125, 10–19 (2016).Article 

Google Scholar 
25.Isbell, F. et al. Linking the influence and dependence of people on biodiversity across scales. Nature 546, 65–72 (2017).CAS 
Article 

Google Scholar 
26.Running, S., Mu, Q., Zhao, M. & MODAPS-SIPS. MOD17A3 MODIS/Terra Gross Primary Productivity Yearly L4 Global 1km SIN Grid (NASA, 2015); https://doi.org/10.5067/MODIS/MOD17A3.00627.Fujimori, S., Hasegawa, T., Ito, A., Takahashi, K. & Masui, T. Gridded emissions and land-use data for 2005-2100 under diverse socioeconomic and climate mitigation scenarios. Sci. Data 5, 180210 (2018).Article 

Google Scholar 
28.Ohashi, H. et al. Biodiversity can benefit from climate stabilization despite adverse side effects of land-based mitigation. Nat. Commun. 10, 5240 (2019).Article 
CAS 

Google Scholar 
29.Bellard, C., Bertelsmeier, C., Leadley, P., Thuiller, W. & Courchamp, F. Impacts of climate change on the future of biodiversity. Ecol. Lett. 15, 365–377 (2012).Article 

Google Scholar 
30.Fadrique, B. et al. Widespread but heterogeneous responses of Andean forests to climate change. Nature 564, 207–212 (2018).CAS 
Article 

Google Scholar 
31.Ammer, C. Diversity and forest productivity in a changing climate. New Phytol. 221, 50–66 (2019).Article 

Google Scholar 
32.Hasegawa, T. et al. Risk of increased food insecurity under stringent global climate change mitigation policy. Nat. Clim. Change 8, 699–703 (2018).Article 

Google Scholar 
33.Ricke, K., Drouet, L., Caldeira, K. & Tavoni, M. Country-level social cost of carbon. Nat. Clim. Change 8, 895–900 (2018).CAS 
Article 

Google Scholar 
34.Anderson, C. M. et al. Natural climate solutions are not enough. Science 363, 933–934 (2019).CAS 
Article 

Google Scholar 
35.Potapov, P. et al. The last frontiers of wilderness: tracking loss of intact forest landscapes from 2000 to 2013. Sci. Adv. 3, e1600821 (2017).Article 

Google Scholar 
36.Mori, A. S., Lertzman, K. P. & Gustafsson, L. Biodiversity and ecosystem services in forest ecosystems: a research agenda for applied forest ecology. J. Appl. Ecol. 54, 12–27 (2017).Article 

Google Scholar 
37.Bastin, J. F. et al. The global tree restoration potential. Science 365, 76–79 (2019).CAS 
Article 

Google Scholar 
38.Quine, C. P., Bailey, S. A., Watts, K. & Hulme, P. Sustainable forest management in a time of ecosystem services frameworks: common ground and consequences. J. Appl. Ecol. 50, 863–867 (2013).Article 

Google Scholar 
39.Climate Change for Forest Policy-Makers: An Approach for Integrating Climate Change into National Forest Policy in Support of Sustainable Forest Management Version 2.0. FAO Forestry Paper No. 181 (FAO, 2018); http://www.fao.org/3/CA2309EN/ca2309en.pdf40.The Future We Want: Biodiversity and Ecosystems—Driving Sustainable Development. United Nations Development Programme Biodiversity and Ecosystems Global Framework 2012-2020 (UNDP, 2012); https://www.cbd.int/financial/mainstream/undp-globalframework2012-2020.pdf41.Thompson, I., Mackey, B., McNulty, S. & Mosseler, A. Forest Resilience, Biodiversity, and Climate Change. A Synthesis of the Biodiversity/Resilience/Stability Relationship in Forest Ecosystems. Technical Series No. 43 (Convention on Biological Diversity, 2009); https://www.cbd.int/doc/publications/cbd-ts-43-en.pdf42.CBD secretariat. Connecting Biodiversity and Climate Change Mitigation and Adaptation: Report of the Second ad hoc Technical Expert Group on Biodiversity and Climate Change. Technical Series No. 41 (Convention on Biological Diversity, 2009); https://www.cbd.int/doc/publications/cbd-ts-41-en.pdf43.Pimm, S. L. et al. The biodiversity of species and their rates of extinction, distribution, and protection. Science 344, 1246752 (2014).CAS 
Article 

Google Scholar 
44.Dee, L. E. et al. When do ecosystem services depend on rare species? Trends Ecol. Evol. 34, 746–758 (2019).Article 

Google Scholar 
45.Fois, M., Cuena-Lombraña, A., Fenu, G. & Bacchetta, G. Using species distribution models at local scale to guide the search of poorly known species: review, methodological issues and future directions. Ecol. Model. 385, 124–132 (2018).Article 

Google Scholar 
46.Jordano, P. & Rees, M. What is long-distance dispersal? And a taxonomy of dispersal events. J. Ecol. 105, 75–84 (2017).Article 

Google Scholar 
47.Veldman, J. W. et al. Comment on ‘The global tree restoration potential’. Science 366, eaay7976 (2019).Article 

Google Scholar 
48.Naudts, K. et al. Europe’s forest management did not mitigate climate warming. Science 351, 597–600 (2016).CAS 
Article 

Google Scholar 
49.Luyssaert, S. et al. Trade-offs in using European forests to meet climate objectives. Nature 562, 259–262 (2018).CAS 
Article 

Google Scholar 
50.Crowther, T. W. et al. Quantifying global soil carbon losses in response to warming. Nature 540, 104–108 (2016).CAS 
Article 

Google Scholar 
51.Cook-Patton, S. C. et al. Mapping carbon accumulation potential from global natural forest regrowth. Nature 585, 545–550 (2020).CAS 
Article 

Google Scholar 
52.Bellamy, R. & Osaka, S. Unnatural climate solutions? Nat. Clim. Change 10, 98–99 (2020).Article 

Google Scholar 
53.Wisz, M. S. et al. Effects of sample size on the performance of species distribution models. Divers. Distrib. 14, 763–773 (2008).Article 

Google Scholar 
54.Hijmans, R. J., Cameron, S. E., Parra, J. L., Jones, P. G. & Jarvis, A. Very high resolution interpolated climate surfaces for global land areas. Int. J. Climatol. 25, 1965–1978 (2005).Article 

Google Scholar 
55.Watanabe, S. et al. MIROC-ESM 2010: model description and basic results of CMIP5-20c3m experiments. Geosci. Model Dev. 4, 845–872 (2011).Article 

Google Scholar 
56.Collins, W. J. et al. Development and evaluation of an Earth-System model – HadGEM2. Geosci. Model Dev. 4, 1051–1075 (2011).Article 

Google Scholar 
57.Jones, C. D. et al. The HadGEM2-ES implementation of CMIP5 centennial simulations. Geosci. Model Dev. 4, 543–570 (2011).Article 

Google Scholar 
58.Griffies, S. M. et al. The GFDL CM3 coupled climate model: characteristics of the ocean and sea ice simulations. J. Clim. 24, 3520–3544 (2011).Article 

Google Scholar 
59.Fujimori, S., Hasegawa, T. & Masui, T. In Post-2020 Climate Action (eds Fujimori, S., Kainuma, M. & Masui, T.) 305–328 (Springer, 2017).60.Hasegawa, T., Fujimori, S., Ito, A., Takahashi, K. & Masui, T. Global land-use allocation model linked to an integrated assessment model. Sci. Total Environ. 580, 787–796 (2017).CAS 
Article 

Google Scholar 
61.Riahi, K. et al. The Shared Socioeconomic Pathways and their energy, land use, and greenhouse gas emissions implications: an overview. Glob. Environ. Change 42, 153–168 (2017).Article 

Google Scholar 
62.Phillips, S. J., Anderson, R. P. & Schapire, R. E. Maximum entropy modeling of species geographic distributions. Ecol. Model. 190, 231–259 (2006).Article 

Google Scholar 
63.Warren, D. L. & Seifert, S. N. Ecological niche modeling in Maxent: the importance of model complexity and the performance of model selection criteria. Ecol. Appl. 21, 335–342 (2011).Article 

Google Scholar 
64.Boyce, M. S., Vernier, P. R., Nielsen, S. E. & Schmiegelow, F. K. A. Evaluating resource selection functions. Ecol. Model. 157, 281–300 (2002).Article 

Google Scholar 
65.Pearson, R. G., Dawson, T. P. & Liu, C. Modelling species distributions in Britain: a hierarchical integration of climate and land-cover data. Ecography 27, 285–298 (2004).Article 

Google Scholar 
66.Tamme, R. et al. Predicting species’ maximum dispersal distances from simple plant traits. Ecology 95, 505–513 (2014).Article 

Google Scholar 
67.Engen, S., Lande, R., Walla, T. & DeVries, P. J. Analyzing spatial structure of communities using the two-dimensional Poisson lognormal species abundance model. Am. Nat. 160, 60–73 (2002).Article 

Google Scholar 
68.He, F. & Gaston, K. J. Occupancy, spatial variance, and the abundance of species. Am. Nat. 162, 366–375 (2003).Article 

Google Scholar 
69.Magurran, A. E. & McGill, B. J. Biological Diversity (Oxford Univ. Press, 2011).70.Chen, T. & Guestrin, C. XGBoost: a scalable tree boosting system. In Proc. 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 785–794 (KDD, 2016); https://doi.org/10.1145/2939672.293978571.He, F. & Hubbell, S. P. Species–area relationships always overestimate extinction rates from habitat loss. Nature 473, 368–371 (2011).CAS 
Article 

Google Scholar 
72.Neigel, J. E. Species–area relationships and marine conservation. Ecol. Appl 13, 138–145 (2003).Article 

Google Scholar 
73.Rogan, J. E. & Lacher, T. E. Impacts of Habitat Loss and Fragmentation on Terrestrial Biodiversity. in Earth Systems and Environmental Sciences. https://doi.org/10.1016/b978-0-12-409548-9.10913-3 (Elsevier, 2018).74.Chase, J. M. & Leibold, M. A. Spatial scale dictates the productivity–biodiversity relationship. Nature 416, 427–430 (2002).CAS 
Article 

Google Scholar 
75.Botanic Gardens Conservation International. Global Tree Search Database. Version 1.3 (Botanic Gardens Conservation International, 2019); https://tools.bgci.org/global_tree_search.php More

1.Harvell, D., Altizer, S., Cattadori, I. M., Harrington, L. & Weil, E. Climate change and wildlife diseases: when does the host matter the most? Ecology 90, 912–920 (2009).2.Wood, C. L. & Johnson, P. T. J. A world without parasites: exploring the hidden ecology of infection. Front. Ecol. Environ. 13, 425–434 (2015).3.Randall, C. J. & Van Woesik, R. Contemporary white-band disease in Caribbean corals driven by climate change. Nat. Clim. Chang. 5, 375–379 (2015).Article 

Google Scholar 
4.Bruno, J. F. et al. Thermal stress and coral cover as drivers of coral disease outbreaks. PLoS Biol. 5, 1220–1227 (2007).Article 
CAS 

Google Scholar 
5.Pollock, F. J. et al. Sediment and turbidity associated with offshore dredging increase coral disease prevalence on nearby reefs. PLoS ONE 9, e102498 (2014).6.Harvell, C. D. et al. Climate warming and disease risks for terrestrial and marine biota. Science 296, 2158–2162 (2002).7.Lafferty, K. D. & Kuris, A. M. Mass mortality of abalone Haliotis cracherodii on the California Channel Islands: tests of epidemiological hypotheses. Mar. Ecol. Prog. Ser. 96, 239–239 (1993).8.Miner, C. M. et al. Large-scale impacts of sea star wasting disease (SSWD) on intertidal sea stars and implications for recovery. PLoS ONE 13, e0192870 (2018).9.Patterson, K. L. et al. The etiology of white pox, a lethal disease of the Caribbean elkhorn coral, Acropora palmata. Proc. Natl Acad. Sci. USA 99, 8725–8730 (2002).10.Aronson, R. B. & Precht, W. F. White-band disease and the changing face of Caribbean coral reefs. Hydrobiologia. 460, 25–38 (2001).Article 

Google Scholar 
11.Weil, E., Croquer, A. & Urreiztieta, I. Temporal variability and impact of coral diseases and bleaching in La Parguera, Puerto Rico from 2003-2007. Caribb. J. Sci. 45, 221–246 (2009).12.Muller, E. et al. Coral disease following massive bleaching in 2005 causes 60% decline in coral cover on reefs in the US Virgin Islands. Coral Reefs. 28, 925–937 (2009).Article 

Google Scholar 
13.Jones, G. P., McCormick, M. I., Srinivasan, M. & Eagle, J. V. Coral decline threatens fish biodiversity in marine reserves. Proc. Natl Acad. Sci. USA 101, 8251–8253 (2004).14.Jones, D. O. B. et al. Global reductions in seafloor biomass in response to climate change. Glob Chang Biol. 20, 1861–1872 (2014).15.Descombes, P. et al. Forecasted coral reef decline in marine biodiversity hotspots under climate change. Glob. Chang Biol. 21, 2479–2487 (2015).16.Weil, E., Smith, G. & Gil-Agudelo, D. L. Status and progress in coral reef disease research. Dis. Aquatic Organ. 69, 1–7 (2006).17.Sutherland, K. P., Porter, J. W. & Torres, C. Disease and immunity in Caribbean and Indo-Pacific zooxanthellate corals. Marine Ecol. Progress Ser. 266, 273–302 (2004).18.Pollock, F. J., Morris, P. J., Willis, B. L. & Bourne, D. G. The urgent need for robust coral disease diagnostics. PLoS Pathog. 7, e1002183 (2011).19.Williams, L., Smith, T. B., Burge, C. A. & Brandt, M. E. Species-specific susceptibility to white plague disease in three common Caribbean corals. Coral Reefs 39, 27–31 (2020).20.Velthuis, A. G. J., Bouma, A., Katsma, W. E. A., Nodelijk, G. & De Jong, M. C. M. Design and analysis of small-scale transmission experiments with animals. Epidemiol. Infect. 135, 202–217 (2007).21.Richardson, L. L., Goldberg, W. M., Carlton, R. G. & Halas, J. C. Coral disease outbreak in the Florida keys: Plague type II. Rev. Biol. Trop. 46, 187–198 (1998).
Google Scholar 
22.Frias-Lopez, J., Klaus, J. S., Bonheyo, G. T. & Fouke, B. W. Bacterial community associated with black band disease in corals. Appl. Environ. Microbiol. 70, 5955–5962 (2004).23.Soffer, N., Brandt, M. E., Correa, A. M. S., Smith, T. B. & Thurber, R. V. Potential role of viruses in white plague coral disease. ISME J. 8, 271–283 (2014).24.Sweet, M. et al. Compositional homogeneity in the pathobiome of a new, slow-spreading coral disease. Microbiome 7, 1–14 (2019).25.Sweet, M. J. & Bulling, M. T. On the importance of the microbiome and pathobiome in coral health and disease. Front. Marine Sci. 4, 9 (2017).26.Egan, S. & Gardiner, M. Microbial dysbiosis: rethinking disease in marine ecosystems. Front. Microbiol. 7, 991 (2016).27.Ezzat, L. et al. Parrotfish predation drives distinct microbial communities in reef-building corals. Anim. Microbiome 2, 5 (2020).28.Ezzat, L. et al. Surgeonfish feces increase microbial opportunism in reef-building corals. Mar. Ecol. Prog. Ser. 631, 81–97 (2019).29.Meyer, J. L. et al. Microbial community shifts associated with the ongoing stony coral tissue loss disease outbreak on the Florida reef tract. Front. Microbiol. 10, 2244 (2019).30.Lima, L. F. O. et al. Modeling of the coral microbiome: the influence of temperature and microbial network. MBio. 11, e02691–19 (2020).31.Thurber, R. V. et al. Deciphering coral disease dynamics: integrating host, microbiome, and the changing environment. Front. Ecol. Evol. 8, 402 (2020).
Google Scholar 
32.Cárdenas, A., Rodriguez-R, L. M., Pizarro, V., Cadavid, L. F. & Arévalo-Ferro, C. Shifts in bacterial communities of two Caribbean reef-building coral species affected by white plague disease. ISME J. 6, 502–512 (2012).Article 
CAS 

Google Scholar 
33.Meyer, J. L., Gunasekera, S. P., Scott, R. M., Paul, V. J. & Teplitski M. Microbiome shifts and the inhibition of quorum sensing by Black Band Disease cyanobacteria. ISME J. 10, 1204–1216 (2016).34.Sweet M. J., Burian A., Bulling M. Corals as canaries in the coalmine: towards the incorporation of marine ecosystems into the ‘One Health’ concept. OSF Prepr. https://doi.org/10.31219/osf.io/gv6s7 (2020).35.Glasl, B. et al. Microbial indicators of environmental perturbations in coral reef ecosystems. Microbiome 7, 1–13 (2019).36.Zaneveld, J. R., McMinds, R. & Thurber, R. V. Stress and stability: applying the Anna Karenina principle to animal microbiomes. Nat. Microbiol. 2, 1–8 (2017).37.Fan, L., Liu, M., Simister, R., Webster, N. S. & Thomas T. Marine microbial symbiosis heats up: the phylogenetic and functional response of a sponge holobiont to thermal stress. ISME J. 7, 991–1002 (2013).38.Darling, E. S., Alvarez-Filip, L., Oliver, T. A., Mcclanahan, T. R. & Côté, I. M. Evaluating life-history strategies of reef corals from species traits. Ecol. Lett. 15, 1378–1386 (2012).39.Calnan, J. M., Smith, T. B., Nemeth, R. S., Kadison, E. & Blondeau, J. Coral disease prevalence and host susceptibility on mid-depth and deep reefs in the United States Virgin Islands. Rev. Biol. Trop. 56, 223–224 (2008).40.Perry, C. T. et al. Regional-scale dominance of non-framework building corals on Caribbean reefs affects carbonate production and future reef growth. Glob Chang Biol. 21, 1153–1164 (2015).41.Okazaki, R. R. et al. Species-specific responses to climate change and community composition determine future calcification rates of Florida Keys reefs. Glob. Chang Biol. 23, 1023–1035 (2017).42.Green, D. H., Edmunds, P. J. & Carpenter, R. C. Increasing relative abundance of Porites astreoides on Caribbean reefs mediated by an overall decline in coral cover. Mar. Ecol. Prog. Ser. 359, 1–10 (2008).43.Pinzón, C. J. H., Beach-Letendre, J., Weil, E. & Mydlarz, L. D. Relationship between phylogeny and immunity suggests older caribbean coral lineages are more resistant to disease. PLoS ONE 9, e104787 (2014).44.Smith, T. B. et al. Convergent mortality responses of Caribbean coral species to seawater warming. Ecosphere 4, 1–40 (2013).45.Jolles, A. E., Sullivan, P., Alker, A. P., Harvell, C. D. Disease transmission of aspergillosis in sea fans: Inferring process from spatial pattern. Ecology 83, 2373–2378 (2002).46.Shore, A. & Caldwell, J. M. Modes of coral disease transmission: how do diseases spread between individuals and among populations? Mar. Biol. 166, 45 (2019).Article 

Google Scholar 
47.Glasl, B., Herndl, G. J., Frade, P. R. The microbiome of coral surface mucus has a key role in mediating holobiont health and survival upon disturbance. ISME J. 10, 2280–2292 (2016).48.Lesser, M. P., Bythell, J. C., Gates, R. D. & Johnstone, R. W., Hoegh-Guldberg, O. Are infectious diseases really killing corals? Alternative interpretations of the experimental and ecological data. J. Exp. Mar. Bio. Ecol. 346, 36–44 (2007).49.Muller, E. M. & Van Woesik, R. Caribbean coral diseases: primary transmission or secondary infection? Glob. Chang Biol. 18, 3529–3535 (2012).50.Fernandes, N. et al. Genomes and virulence factors of novel bacterial pathogens causing bleaching disease in the marine red alga Delisea pulchra. PLoS ONE 6, e27387 (2011).51.Vandecandelaere, I. et al. Nautella italica gen. nov., sp. nov., isolated from a marine electroactive biofilm. Int. J. Syst. Evol. Microbiol. 59, 811–817 (2009).52.Dang, H. & Lovell, C. R. Bacterial primary colonization and early succession on surfaces in marine waters as determined by amplified rRNA gene restriction analysis and sequence analysis of 16S rRNA genes. Appl. Environ. Microbiol. 66, 467–475 (2000).53.Kviatkovski, I. & Minz, D. A member of the Rhodobacteraceae promotes initial biofilm formation via the secretion of extracellular factor(s). Aquat. Microb. Ecol. 75, 155–167 (2015).54.Rosales, S. M., Clark, A. S., Huebner, L. K., Ruzicka, R. R. & Muller E. M. Rhodobacterales and Rhizobiales are associated with stony coral tissue loss disease and its suspected sources of transmission. Front. Microbiol. 11, 681 (2020).55.Campbell, A. H., Harder, T., Nielsen, S., Kjelleberg, S. & Steinberg, P. D. Climate change and disease: bleaching of a chemically defended seaweed. Glob. Chang. Biol. 17, 2958–2970 (2011).56.Kumar, V., Zozaya-Valdes, E., Kjelleberg, S., Thomas, T. & Egan, S. Multiple opportunistic pathogens can cause a bleaching disease in the red seaweed Delisea pulchra. Environ. Microbiol. 18, 3962–3975 (2016).57.Brandt, M. E. & Mcmanus, J. W. Disease incidence is related to bleaching extent in reef-building corals. Ecology 90, 2859–2867 (2009).58.Brandt, M. E., Smith, T. B., Correa, A. M. S. & Vega-Thurber, R. Disturbance driven colony fragmentation as a driver of a coral disease outbreak. PLoS ONE 8, e57164 (2013).59.Godwin, S., Bent, E., Borneman, J. & Pereg, L. The role of coral-associated bacterial communities in Australian subtropical white Syndrome of Turbinaria mesenterina. PLoS ONE 7, e44243 (2012).60.Ranson, H. J. et al. Draft Genome Sequence of the Putative Marine Pathogen Thalassobius sp. I31.1. Microbiol. Resour. Announc. 8, e01431–18 (2019).61.Miller A. W., Richardson L. L. Fine structure analysis of black band disease (BBD) infected coral and coral exposed to the BBD toxins microcystin and sulfide. J. Invertebr. Pathol. 109, 27–33 (2012).62.Geffen, Y., Ron, E. Z. & Rosenberg, E. Regulation of release of antibacterials from stressed scleractinian corals. FEMS Microbiol. Lett. 295, 103–109 (2009).63.Beurmann, S. et al. Pseudoalteromonas piratica strain OCN003 is a coral pathogen that causes a switch from chronic to acute Montipora white syndrome in Montipora capitata. PLoS ONE (2017).64.Apprill, A., Marlow, H. Q., Martindale, M. Q., Rappé, M. S. Specificity of associations between bacteria and the coral Pocillopora meandrina during early development. Appl. Environ. Microbiol. 78, 7467–7475 (2012).65.Shnit-Orland, M., Sivan, A. & Kushmaro, A. Antibacterial activity of Pseudoalteromonas in the Coral Holobiont. Microb. Ecol. 64, 851–859 (2012).66.Sunagawa, S. et al. Bacterial diversity and white Plague disease-associated community changes in the caribbean coral montastraea faveolata. ISME J. 3, 512–521 (2009).Article 
CAS 

Google Scholar 
67.Bettarel, Y. et al. Corallivory and the microbial debacle in two branching scleractinians. ISME J. 12, 1109–1126 (2018).68.Ritchie, K. B. Regulation of microbial populations by coral surface mucus and mucus-associated bacteria. Mar. Ecol. Prog. Ser. 322, 1–14 (2006).69.Bayer, T. et al. The microbiome of the red sea coral stylophora pistillata is dominated by tissue-associated endozoicomonas bacteria. Appl. Environ. Microbiol. 79, 4759–4762 (2013).70.Lesser, M. P. & Jarett, J. K. Culture-dependent and culture-independent analyses reveal no prokaryotic community shifts or recovery of Serratia marcescens in Acropora palmata with white pox disease. FEMS Microbiol. Ecol. 88, 457–467 (2014).Article 
CAS 

Google Scholar 
71.Morrow, K. M., Muller, E. & Lesser, M. P. How does the coral microbiome cause, respond to, or modulate the bleaching process? Coral Bleaching 233, 153–188 (2018).72.Pollock, F. J. et al. Coral-associated bacteria demonstrate phylosymbiosis and cophylogeny. Nat. Commun. 9, 1–13 (2018).73.Price, E. P. et al. Accurate and rapid identification of the Burkholderia pseudomallei near-neighbour, Burkholderia ubonensis, using real-time PCR. PLoS ONE 8, e71647 (2013).74.Price, E. P. et al. Phylogeographic, genomic, and meropenem susceptibility analysis of Burkholderia ubonensis. PLoS Negl. Trop. Dis. 11, e0005928 (2017).75.Therneau, T. Package Survival: A Package for Survival Analysis in R. R Package version 238. (2015).76.Oksanen, J. et al. Vegan: community ecology. R package version 2.2-1. (2015).77.Andres, B., David, O., Sebastien, V., Julien, De B. & Fabien, L. betapart: partitioning Beta Diversity into Turnover and Nestedness Components. R Packag. (1.5.1). https://cran.r-project.org/package=betapart (2018).78.nmacknight. nmacknight/16sCommunityAnalysis: First Release. 2021 Mar 24 [cited 2021 Mar 24]: https://doi.org/10.5281/zenodo.4635319#.YFvOI-zLDNs.mendeley. (2021). More

1.Levin, S. A. The problem of pattern and scale in ecology. Ecology 73, 1943–1967 (1992).Article 

Google Scholar 
2.Brown, J. H. & Kodric-Brown, A. Turnover rates in insular biogeography: Effect of immigration on extinction. Ecology 58, 445–449 (1977).Article 

Google Scholar 
3.Leibold, M. A. et al. The metacommunity concept: A framework for multi-scale community ecology. Ecol. Lett. 7, 601–613 (2004).Article 

Google Scholar 
4.Costello, E. K., Stagaman, K., Dethlefsen, L., Bohannan, B. J. & Relman, D. A. The application of ecological theory toward an understanding of the human microbiome. Science 336, 1255–1262 (2012).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
5.Presley, S. J., Higgins, C. L. & Willig, M. R. A comprehensive framework for the evaluation of metacommunity structure. Oikos 119, 908–917 (2010).Article 

Google Scholar 
6.Leibold, M. A. & Mikkelson, G. M. Coherence, species turnover, and boundary clumping: Elements of metacommunity structure. Oikos 97, 237–250 (2002).Article 

Google Scholar 
7.Clements, F. E. Plant Succession: An Analysis of the Development of Vegetation (Carnegie Institution of Washington, Washington, DC, 1916).Book 

Google Scholar 
8.Patterson, B. D. & Atmar, W. Nested subsets and the structure of insular mammalian faunas and archipelagos. Biol. J. Linn. Soc. 28, 65–82 (1986).Article 

Google Scholar 
9.Nekola, J. C. & White, P. S. The distance decay of similarity in biogeography and ecology. J. Biogeogr. 26, 867–878 (1999).Article 

Google Scholar 
10.Tornero, I. et al. Dispersal mode and spatial extent influence distance-decay patterns in pond metacommunities. PLOS ONE 13, e0203119. https://doi.org/10.1371/journal.pone.0203119 (2018).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
11.Heino, J. The importance of metacommunity ecology for environmental assessment research in the freshwater realm. Biol. Rev. 88, 166–178 (2013).PubMed 
Article 

Google Scholar 
12.Walker, D. M. et al. Variability in snake skin microbial assemblages across spatial scales and disease states. ISME J. 13, 2209–2222 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
13.Presley, S. J., Cisneros, L. M., Patterson, B. D. & Willig, M. R. Vertebrate metacommunity structure along an extensive elevational gradient in the tropics: A comparison of bats, rodents and birds. Glob. Ecol. Biogeogr. 21, 968–976 (2012).Article 

Google Scholar 
14.Heino, J. et al. Elements of metacommunity structure and community-environment relationships in stream organisms. Freshw. Biol. 60, 973–988 (2015).Article 

Google Scholar 
15.Hernández-Gómez, O., Hoverman, J. T. & Williams, R. N. Cutaneous microbial community variation across populations of eastern hellbenders (Cryptobranchus alleganiensis alleganiensis). Front. Microbiol. 8, 1379. https://doi.org/10.3389/fmicb.2017.01379 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
16.Wilber, M. Q., Jani, A. J., Mihaljevic, J. R. & Briggs, C. J. Fungal infection alters the selection, dispersal and drift processes structuring the amphibian skin microbiome. Ecol. Lett. 23, 88–98 (2020).PubMed 
Article 

Google Scholar 
17.Brown, J. J. et al. Metacommunity theory for transmission of heritable symbionts within insect communities. Ecol. Evol. 10, 1703–1721 (2020).PubMed 
Article 

Google Scholar 
18.Belden, L. K. & Harris, R. N. Infectious diseases in wildlife: The community ecology context. Front. Ecol. Environ. 5, 533–539 (2007).Article 

Google Scholar 
19.Grice, E. A. & Segre, J. A. The skin microbiome. Nat. Rev. Microbiol. 9, 244–253 (2011).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
20.Blehert, D. S. et al. Bat white-nose syndrome: An emerging fungal pathogen?. Science 323, 227 (2009).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
21.Frick, W. F., Puechmaille, S. J. & Willis, C. K. R. White-nose syndrome in bats. In Bats in the Anthropocene: Conservation of Bats in a Changing World (eds Voigt, C. C. & Kingston, T.) 245–262 (Springer, New York, 2016). https://doi.org/10.1007/978-3-319-25220-9_9
Google Scholar 
22.Langwig, K. E. et al. Resistance in persisting bat populations after white-nose syndrome invasion. Philos. Trans. R. Soc. B. 372, 20160044. (2017).Article 

Google Scholar 
23.Langwig, K. E. et al. Sociality, density-dependence and microclimates determine the persistence of populations suffering from a novel fungal disease, white-nose syndrome. Ecol. Lett. 15, 1050–1057 (2012).PubMed 
Article 

Google Scholar 
24.Grisnik, M. et al. The cutaneous microbiota of bats has in vitro antifungal activity against the white nose pathogen. FEMS Microbiol. Ecol. 96, fiz193. https://doi.org/10.1093/femsex/fitz193 (2020).CAS 
Article 
PubMed 

Google Scholar 
25.Wickham H. ggplot2: Elegant Graphics for Data Analysis. R package version 3.2.2. https://CRAN.R-project.org/package=ggplot2 (2020).26.Dallas, T. metacom: An R package for the analysis of metacommunity structure. Ecography 37, 402–405 (2014).Article 

Google Scholar 
27.Alves, A. T., Petsch, D. K. & Barros, F. Drivers of benthic metacommunity structure along tropical estuaries. Sci. Rep. 10, 1–12 (2020).Article 
CAS 

Google Scholar 
28.Risely, A. Applying the core microbiome to understand host–microbe systems. J Anim. Ecol. 89, 1549–1558 (2020).PubMed 
Article 

Google Scholar 
29.Harris, R. N. et al. Skin microbes on frogs prevent morbidity and mortality caused by a lethal skin fungus. ISME J. 3, 818–824 (2009).CAS 
PubMed 
Article 

Google Scholar 
30.Lemieux-Labonté, V., Simard, A., Willis, C. K. & Lapointe, F. J. Enrichment of beneficial bacteria in the skin microbiota of bats persisting with white-nose syndrome. Microbiome 5, 115 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
31.Buckley, D. H., Huangyutitham, V., Nelson, T. A., Rumberger, A. & Thies, J. E. Diversity of Planctomycetes in soil in relation to soil history and environmental heterogeneity. Appl Environ Microbiol 72, 4522–4531 (2006).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
32.Zimmermann, J., Gonzalez, J. M., Saiz-Jimenez, C. & Ludwig, W. Detection and phylogenetic relationships of highly diverse uncultured acidobacterial communities in altamira cave using 23s rRNA sequence analysis. Geomicrobiol. J. 22, 379–388 (2005).CAS 
Article 

Google Scholar 
33.Wilder, A. P., Kunz, T. H. & Sorenson, M. D. Population genetic structure of a common host predicts the spread of white-nose syndrome, an emerging infectious disease in bats. Mol. Ecol. 24, 5495–5506 (2015).PubMed 
Article 

Google Scholar 
34.Martin, A. M. Historical Demography and Dispersal Patterns in the Eastern Pipistrelle Bat (Perimyotis subflavus). MS Thesis Grand Valley State University (2014).35.Kolodny, O. et al. Coordinated change at the colony level in fruit bat fur microbiomes through time. Nat Ecol. Evol. 3, 116–124 (2019).PubMed 
Article 

Google Scholar 
36.Fierer, N. & Jackson, R. B. The diversity and biogeography of soil bacterial communities. Proc. Natl. Acad. Sci. U.S.A. 103, 626–631 (2006).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
37.Liu, L., Yang, J., Yu, Z. & Wilkinson, D. M. The biogeography of abundant and rare bacterioplankton in the lakes and reservoirs of China. ISME J. 9, 2068–2077 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
38.Reche, I., Pulido-Villena, E., Morales-Baquero, R. & Casamayor, E. O. Does ecosystem size determine aquatic bacterial richness?. Ecology 86, 1715–1722 (2005).Article 

Google Scholar 
39.Hillebrand, H., Watermann, F., Karez, R. & Berninger, U. G. Differences in species richness patterns between unicellular and multicellular organisms. Oecologia 126, 114–124 (2001).ADS 
PubMed 
Article 
PubMed Central 

Google Scholar 
40.Avena, C. V. et al. Deconstructing the bat skin microbiome: Influences of the host and the environment. Front. Microbiol. 7, 1–14 (2016).MathSciNet 
Article 

Google Scholar 
41.Lemieux-Labonté, V., Tromas, N., Shapiro, B. J. & Lapointe, F. J. Environment and host species shape the skin microbiome of captive neotropical bats. PeerJ 4, e2430 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
42.Goldenberg Vilar, A. et al. Eutrophication decreases distance decay of similarity in diatom communities. Freshw. Biol. 59, 1522–1531 (2014).Article 

Google Scholar 
43.Chase, J. M. Drought mediates the importance of stochastic community assembly. Proc. Natl. Acad. Sci. U.S.A. 104, 17430–17434 (2007).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
44.Muletz-Wolz, C. R., Fleischer, R. C. & Lips, K. R. Fungal disease and temperature alter skin microbiome structure in an experimental salamander system. Mol. Ecol. 2, 2917–3293 (2019).
Google Scholar 
45.Minich, J. J. et al. Quantifying and understanding well-to-well contamination in microbiome research. MSystems 4, e00186-e219 (2019).PubMed 
PubMed Central 
Article 

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

Google Scholar 
47.Muller, L. K. et al. Bat white-nose syndrome: A real-time TaqMan polymerase chain reaction test targeting the intergenic spacer region of Geomyces destructans. Mycologia 105, 253–259 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
48.Janicki, A. F. et al. Efficacy of visual surveys for white-nose syndrome at bat hibernacula. PLoS ONE 10, e01333902015 (2015).Article 
CAS 

Google Scholar 
49.Ellison, S. L., English, C. A., Burns, M. J. & Keer, J. T. Routes to improving the reliability of low level DNA analysis using real-time PCR. BMC Biotechnol. 6, 33 (2006).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
50.Schloss, P. D. et al. Introducing mothur: Open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl. Environ. Microbiol. 75, 7537–7541 (2009).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
51.Quast, C. et al. The SILVA ribosomal RNA gene database project: Improved data processing and web-based tools. Nucleic Acids Res. 41, 590–596 (2012).Article 
CAS 

Google Scholar 
52.Rognes, T., Flouri, T., Nichols, B., Quince, C. & Mahé, F. VSEARCH: A versatile open source tool for metagenomics. PeerJ 4, e2584 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
53.Schloss, P. D. & Westcott, S. L. Assessing and improving methods used in operational taxonomic unit-based approaches for 16S rRNA gene sequence analysis. Appl. Environ. Microbiol. 77, 3219–3226 (2011).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
54.Glassman, S.I., & Martiny, J.B. Broadscale ecological patterns are robust to use of exact sequence variants versus operational taxonomic units. MSphere, 3, (2018).
55.Weiss, S. et al. Normalization and microbial differential abundance strategies depend upon data characteristics. Microbiome 5, 27 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
56.R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. http://www.R-project.org/ (2020).57.De Caceres, M., Jansen, F. & De Caceres, M.M. ‘indicspecies’. R package version 1.7.9. https://CRAN.R-project.org/package=indicspecies (2020).58.Bates, D., Sarkar, D., Bates, M.D. & Matrix, L. The lme4 package. R package version 1–1.26. https://CRAN.R-project.org/package=lme4 (2020).59.Gloor, G. B., Macklaim, J. M., Pawlowsky-Glahn, V. & Egozcue, J. J. Microbiome datasets are compositional: And this is not optional. Front Microbiol. 8, 2224. https://doi.org/10.3389/fmicb.2017.02224 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
60.Baselga, A. & Orme, C. D. L. betapart: An R package for the study of beta diversity. Methods Ecol. Evol. 3, 808–812 (2012).Article 

Google Scholar 
61.Oksanen, J. et al. vegan: Community ecology package. R package version 2.5–2. https://CRAN.R-project.org/package=vegan (2019).62.Fox, J. et al. ‘car’. R package version 2.1-4. https://CRAN.R-project.org/package=car (2016).63.Anderson, M. J. & Walsh, D. C. PERMANOVA, ANOSIM, and the mantel test in the face of heterogeneous dispersions: What null hypothesis are you testing?. Ecol. Monogr. 83, 557–574 (2013).Article 

Google Scholar  More

Exp 1: chemo-tactile conditioning of the proboscis extension response (PER)Bee foragers may assess the quality of floral nectars through chemo-sensilla located on their antennae47. In this first experiment, we asked whether nectar-relevant concentrations of GABA, β-alanine, taurine, citrulline and ornithine can be detected by bees through their antennae. To this aim, we used a chemo-tactile differential conditioning of PER protocol48 in which different groups of bees were trained to discriminate one of the five NPAAs from water. Briefly, tethered bees experienced five pairings of a neutral stimulus (either NPAA-laced water or water) (CS+) with a 30% sucrose solution reinforcement (US) and five pairings (either water or NPAA-laced water) (CS−) with a saturated NaCl solution (US) used as punishment. The results showed that bees increased their response to both the rewarded (CS+) and the punished (CS−) stimuli over the ten conditioning trials (GLMM, trial: GABA: n = 76, χ2 = 65.75, df = 1, p  More