Ecology

1.Foster, S. A. & Endler, J. A. Geographic Variation in Behavior: Perspectives on Evolutionary Mechanisms 1–336 (Oxford University Press, 1999).Book 

Google Scholar 
2.Mundiger, P. C. Microgeographic and macrogeographic variation in the acquired vocalizations of birds. In Acoustic Communication in Birds 147–208 (Academic Press, 1982).
Google Scholar 
3.Green, S. Dialects in Japanese monkeys: Vocal learning and cultural transmission of locale-specific vocal behavior?. Z. Tierpsychol. J. Comp. Ethol. 38(3), 304–314 (1975).CAS 
Article 

Google Scholar 
4.Hodun, A., Snowdon, C. T. & Soini, P. Subspecific variation in the long calls of the tamarin, Saguinus fusckollis. Z. Tierpsychol. 57, 97–110 (1981).Article 

Google Scholar 
5.Ford, J. K. B. & Fisher, H. D. Group-specific dialects of killer whales (Orcinus orca) in British Columbia. In Communication and Behavior of Whales 129–161 (Westview Press for the American Association for the Advancement of Science, 1983).
Google Scholar 
6.Filatova, O. A. et al. Call diversity in the North Pacific killer whale populations: Implications for dialect evolution and population history. Anim. Behav. 83, 595–603 (2012).Article 

Google Scholar 
7.Rendell, L. E. & Whitehead, H. Vocal clans in sperm whales (Physeter macrocephalus). Proc. Biol. Sci. R. Soc. 270, 225–231 (2003).CAS 
Article 

Google Scholar 
8.Gero, S., Whitehead, H. & Rendell, L. Individual, unit and vocal clan level identity cues in sperm whale codas. R. Soc. Open Sci. 3, 1–12 (2016).
Google Scholar 
9.Cise, A. M., Van Mahaffy, S. D., Baird, R. W., Mooney, T. A. & Barlow, J. Song of my people: Dialect differences among sympatric social groups of short-finned pilot whales in Hawai’i. Behav. Ecol. Sociobiol. 72, 1–13 (2018).Article 

Google Scholar 
10.Podos, J. & Warren, P. S. The evolution of geographic variation in birdsong. Adv. Study Behav. 37, 403–458 (2007).Article 

Google Scholar 
11.Walker, T. J. Factors responsible for intraspecific variation in the calling songs of crickets. Evolution 16, 407–428 (1962).Article 

Google Scholar 
12.Velásquez, N. A. Geographic variation in acoustic communication in anurans and its neuroethological implications. J. Physiol. 108, 167–173 (2014).
Google Scholar 
13.Amorim, T. O. S., Andriolo, A., Reis, S. S. & dos Santos, M. E. Vocalizations of Amazon river dolphins (Inia geoffrensis): Characterization, effect of physical environment and differences between populations. J. Acoust. Soc. Am. 139, 1285–1293 (2016).ADS 
PubMed 
Article 

Google Scholar 
14.Moron, J. R. et al. Spinner dolphin whistle in the Southwest Atlantic Ocean: Is there a geographic variation?. J. Acoust. Soc. Am. 138, 2495–2498 (2015).ADS 
PubMed 
Article 

Google Scholar 
15.Bjørgesæter, A., Ugland, K. I. & Bjørge, A. Geographic variation and acoustic structure of the underwater vocalization of harbor seal (Phoca vitulina) in Norway, Sweden and Scotland. J. Acoust. Soc. Am. 116, 2459–2468 (2004).ADS 
PubMed 
Article 

Google Scholar 
16.Janik, V. & Slater, P. The different roles of social learning in vocal communication. Anim. Behav. 60, 1–11 (2000).CAS 
PubMed 
Article 

Google Scholar 
17.Lameira, A. R., Delgado, R. A. & Wich, S. A. Review of geographic variation in terrestrial mammalian acoustic signals: Human speech variation in a comparative perspective. J. Evol. Psychol. 8, 309–332 (2010).Article 

Google Scholar 
18.Janik, V. Acoustic communication networks in marine mammals. In Animal Communication Networks 390–415 (University Press, 2005).
Google Scholar 
19.Deecke, V. B., Ford, J. K. B. & Spong, P. Dialect change in resident killer whales: Implications for vocal learning and cultural transmission. Anim. Behav. 60, 629–638 (2000).CAS 
PubMed 
Article 

Google Scholar 
20.Weilgart, L. & Whitehead, H. Group-specific dialects and geographical variation in coda repertoire in South Pacific sperm whales. Behav. Ecol. Sociobiol. 40, 277–285 (1997).Article 

Google Scholar 
21.Azevedo, A. F. & Van Sluys, M. Whistles of tucuxi dolphins (Sotalia fluviatilis) in Brazil: Comparisons among populations. J. Acoust. Soc. Am. 117, 1456–1464 (2005).ADS 
PubMed 
Article 

Google Scholar 
22.Bazúa-Durán, C. & Au, W. W. L. Geographic variations in the whistles of spinner dolphins (Stenella longirostris) of the Main Hawaiian Islands. J. Acoust. Soc. Am. 116, 3757–3769 (2004).ADS 
PubMed 
Article 

Google Scholar 
23.Hawkins, E. R. Geographic variations in the whistles of bottlenose dolphins (Tursiops aduncus) along the east and west coasts of Australia. J. Acoust. Soc. Am. 128, 924–935 (2010).ADS 
CAS 
PubMed 
Article 

Google Scholar 
24.Wang, D., Würsig, B. & Evans, W. Whistles of bottlenose dolphins: Comparisons among populations. Aquat. Mamm. 21, 65–77 (1995).
Google Scholar 
25.Connor, R. C., Wells, R. S., Mann, J. & Read, A. J. The bottlenose dolphin: Social relationships in a fission–fusion society. In Cetacean Societies: Field Studies of Dolphins and Whales 91–126 (The University of Chicago Press, 2000).
Google Scholar 
26.Costa, A. P. B. et al. Ecological divergence and speciation in common bottlenose dolphins in the western South Atlantic. J. Evol. Biol. 34, 16–32 (2021).PubMed 
Article 

Google Scholar 
27.Hoelzel, A. R., Potter, C. W. & Best, P. B. Genetic differentiation between parapatric “nearshore” and “offshore” populations of the bottlenose dolphin. Proc. R. Soc. Lond. B 265, 1177–1183 (1998).CAS 
Article 

Google Scholar 
28.Louis, M. et al. Habitat-driven population structure of bottlenose dolphins, Tursiops truncatus, in the North-East Atlantic. Mol. Ecol. 23, 857–874 (2014).PubMed 
Article 

Google Scholar 
29.Wells, R. S., Natoli, A. & Braulik, G. Tursiops truncatus. The IUCN Red List of Threatened Species (2019).30.Marino, L. et al. Cetaceans have complex brains for complex cognition. PLoS Biol. 5, 966–972 (2007).CAS 
Article 

Google Scholar 
31.Janik, V. & Slater, P. Context-specific use suggests that bottlenose dolphin signature whistles are cohesion calls. Anim. Behav. 56, 829–838 (1998).CAS 
PubMed 
Article 

Google Scholar 
32.Sayigh, L. et al. Individual recognition in wild bottlenose dolphins: a field test using playback experiments. Anim. Behav. 57, 41–50 (1999).CAS 
PubMed 
Article 

Google Scholar 
33.Au, W. W. L. Echolocation signals of wild dolphins. Acoust. Phys. 50, 454–462 (2004).ADS 
Article 

Google Scholar 
34.Herzing, D. & dos Santos, M. E. Functional aspects of echolocation in dolphins. In Echolocation in Bats and Dolphins 386–393 (The University of Chicago Press, 2004).
Google Scholar 
35.Jensen, F. H., Bejder, L., Wahlberg, M. & Madsen, P. T. Biosonar adjustments to target range of echolocating bottlenose dolphins (Tursiops sp.) in the wild. J. Exp. Biol. 212, 1078–1086 (2009).CAS 
PubMed 
Article 

Google Scholar 
36.Diáz-López, B. & Shirai, J. Mediterranean common bottlenose dolphin’s repertoire and communication use. In Dolphins: Anatomy, Behavior, and Threats 129–148 (Nova Science Publishers, 2009).
Google Scholar 
37.Herzing, D. L. Acoustics and social behavior of wild dolphins: Implications for a sound society. In Hearing by Whales and Dolphins Springer Handbook of Auditory Research 225–272 (Springer, 2000).
Google Scholar 
38.dos Santos, M. E., Ferreira, A. J. & Harzen, S. Rhythmic sound sequences emitted by aroused bottlenose dolphins in the Sado estuary, Portugal. In Sensory Systems of Aquatic Mammals 325–334 (De Spil Publishers, 1995).
Google Scholar 
39.Luís, A. R., Alves, I. S., Sobreira, F. V., Couchinho, M. N. & dos Santos, M. E. Brays and bits: Information theory applied to acoustic communication sequences of bottlenose dolphins. Bioacoustics 28, 286–296 (2019).Article 

Google Scholar 
40.Jones, B., Zapetis, M., Samuelson, M. M. & Ridgway, S. Sounds produced by bottlenose dolphins (Tursiops): A review of the defining characteristics and acoustic criteria of the dolphin vocal repertoire. Bioacoustics 29(4), 399–440 (2020).Article 

Google Scholar 
41.May-Collado, L. J. & Wartzok, D. A. comparison of bottlenose dolphin whistles in the Atlantic ocean: Factors promoting whistle variation. J. Mammal. 89, 1229–1240 (2008).Article 

Google Scholar 
42.Jones, G. J. & Sayigh, L. S. Geographic variation in rates of vocal production of free-ranging bottlenose dolphins. Mar. Mamm. Sci. 18, 374–393 (2002).Article 

Google Scholar 
43.La Manna, G. et al. Assessing geographical variation on whistle acoustic structure of three Mediterranean populations of common bottlenose dolphin (Tursiops truncatus). Behaviour 154, 583–607 (2017).Article 

Google Scholar 
44.Papale, E. et al. Acoustic divergence between bottlenose dolphin whistles from the Central-Eastern North Atlantic and Mediterranean Sea. Acta Ethologica 17, 155–165 (2014).Article 

Google Scholar 
45.R Development Core Team. R: A Language and Environment for Statistical Computing (2018).46.Wickham, H. ggplot2: Elegant Graphics for Data Analysis. https://ggplot2.tidyverse.org (Springer, 2016).47.Mccomb, K. & Semple, S. Coevolution of vocal communication and sociality in primates. Biol. Lett. 1, 381–385 (2005).PubMed 
PubMed Central 
Article 

Google Scholar 
48.Leighton, G. M. Cooperative breeding influences the number and type of vocalizations in avian lineages. Proc. R. Soc. B Biol. Sci. 284, 1–9 (2017).
Google Scholar 
49.Freeberg, T. M., Dunbar, R. I. M. & Ord, T. J. Social complexity as a proximate and ultimate factor in communicative complexity. Philos. Trans. R. Soc. B Biol. Sci. 367, 1785–1801 (2012).Article 

Google Scholar 
50.Pollard, K. A. & Blumstein, D. T. Evolving communicative complexity: insights from rodents and beyond. Philos. Trans. R. Soc. B Biol. Sci. 367, 1869–1878 (2012).Article 

Google Scholar 
51.Gustison, M. L., Le Roux, A. & Bergman, T. J. Derived vocalizations of geladas (Theropithecus gelada) and the evolution of vocal complexity in primates. Philos. Trans. R. Soc. B Biol. Sci. 367, 1847–1859 (2012).Article 

Google Scholar 
52.Augusto, J. F., Rachinas-Lopes, P. & dos Santos, M. E. Social structure of the declining resident community of common bottlenose dolphins in the Sado Estuary, Portugal. J. Mar. Biol. Assoc. U. K. 92, 1773–1782 (2012).Article 

Google Scholar 
53.Luís, A. R., Couchinho, M. N. & dos Santos, M. E. Changes in the acoustic behavior of resident bottlenose dolphins near operating vessels. Mar. Mamm. Sci. 30, 1417–1426 (2014).Article 

Google Scholar 
54.Ridgway, S. H., Moore, P. W., Carder, D. A. & Romano, T. A. Forward shift of feeding buzz components of dolphins and belugas during associative learning reveals a likely connection to reward expectation, pleasure and brain dopamine activation. J. Exp. Biol. 217, 2910–2919 (2014).CAS 
PubMed 
Article 

Google Scholar 
55.Luís, A. R., Couchinho, M. N. & dos Santos, M. E. A quantitative analysis of pulsed signals emitted by wild bottlenose dolphins. PLoS ONE 11, 1–11 (2016).
Google Scholar 
56.Nowacek, D. P. Acoustic ecology of foraging bottlenose dolphins (Tursiops truncatus) habitat-specific use of three sound types. Mar. Mamm. Sci. 21, 587–602 (2005).Article 

Google Scholar 
57.Caldwell, M. C., Caldwell, D. K. & Tyack, P. L. Review of the signature-whistle-hypothesis for the Atlantic bottlenose dolphin, Tursiops truncatus. In The Bottlenose Dolphin 199–234 (Academic Press, 1990).
Google Scholar 
58.Laland, K. N. & Janik, V. M. The animal cultures debate. Evolution 21, 542–547 (2006).
Google Scholar 
59.Kershenbaum, A., Sayigh, L. S. & Janik, V. M. The encoding of individual identity in dolphin signature whistles: How much information is needed?. PLoS ONE 8, e77671 (2013).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
60.King, S. L. & Janik, V. M. Bottlenose dolphins can use learned vocal labels to address each other. Proc. Natl. Acad. Sci. U.S.A. 110, 13216–13221 (2013).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
61.Sayigh, L., Esch, H., Wells, R. & Janik, V. Facts about signature whistles of bottlenose dolphins, Tursiops truncatus. Anim. Behav. 74, 1631–1642 (2007).Article 

Google Scholar 
62.Buckstaff, K. C. Effects of watercraft noise on the acoustic behavior of bottlenose dolphins, Tursiops truncatus, in Sarasota Bay, Florida. Mar. Mamm. Sci. 20, 709–725 (2004).Article 

Google Scholar 
63.Morisaka, T., Shinohara, M., Nakahara, F. & Akamatsu, T. Geographic variations in the whistles among three Indo-Pacific bottlenose dolphin. Fish. Sci. 71, 568–576 (2005).CAS 
Article 

Google Scholar 
64.May-Collado, L. J. & Quiñones-Lebrón, S. G. Dolphin changes in whistle structure with watercraft activity depends on their behavioral state. J. Soc. Am. 135, EL193–EL198 (2014).ADS 

Google Scholar 
65.Garland, E. C. et al. Report dynamic horizontal cultural transmission of humpback whale song at the ocean basin scale. Curr. Biol. 21, 687–691 (2011).CAS 
PubMed 
Article 

Google Scholar 
66.Whitehead, H. & Rendell, L. The Cultural Lives of Whales and Dolphins (The University of Chicago Press, 2015).
Google Scholar 
67.Herzing, D. L. Vocalizations and associated underwater behavior of free-ranging Atlantic spotted dolphins, Stenella frontalis and bottlenose dolphins, Tursiops truncatus. Aquat. Mamm. 22, 61–79 (1996).
Google Scholar 
68.May-Collado, L. J. Changes in whistle structure of two dolphin species during interspecific associations. Ethology 116, 1065–742010 (2010).Article 

Google Scholar 
69.Catchpole, C. K. The evolution of bird sounds in relation to mating and spacing behavior. In Acoustic Communication in Birds 297–319 (Academic Press, 1982).
Google Scholar 
70.Herman, L. M. The multiple functions of male song within the humpback whale (Megaptera novaeangliae) mating system: Review, evaluation, and synthesis. Biol. Rev. 92, 1795–1818 (2017).PubMed 
Article 

Google Scholar 
71.Janik, V. M. Food-related bray calls in wild bottlenose dolphins (Tursiops truncatus). Proc. R. Soc. B Biol. Sci. 267, 923–927 (2000).CAS 
Article 

Google Scholar 
72.King, S. L. & Janik, V. M. Come dine with me: food-associated social signalling in wild bottlenose dolphins (Tursiops truncatus). Anim. Cogn. 18, 969–974 (2015).PubMed 
Article 

Google Scholar 
73.Herzing, D. L. Synchronous and rhythmic vocalizations and correlated underwater behavior of free-ranging Atlantic Spotted Dolphins (Stenella frontalis) and Bottlenose Dolphins (Tursiops truncatus) in the Bahamas. Anim. Behav. Cogn. 2, 14–29 (2015).Article 

Google Scholar 
74.Pleslić, G. et al. The abundance of common bottlenose dolphins (Tursiops truncatus) in the former special marine reserve of the Cres-Lošinj Archipelago, Croatia. Aquat. Conserv. Mar. Freshwat. Ecosyst. 25, 125–137 (2015).Article 

Google Scholar 
75.Rako-Gospic, N., Radulovi, M., Vu, T., Plesli, G. & Mackelworth, P. Factor associated variations in the home range of a resident Adriatic common bottlenose dolphin population. Mar. Pollut. Bull. 124, 234–244 (2017).CAS 
PubMed 
Article 

Google Scholar 
76.Rako, N. et al. Leisure boating noise as a trigger for the displacement of the bottlenose dolphins of the Cres-Lošinj archipelago (northern Adriatic Sea, Croatia). Mar. Pollut. Bull. 68, 77–84 (2013).CAS 
PubMed 
Article 

Google Scholar 
77.Barragán-Barrera, D. C. et al. High genetic structure and low mitochondrial diversity in bottlenose dolphins of the Archipelago of Bocas del Toro, Panama: A population at risk?. PLoS ONE 12, 1–22 (2017).Article 
CAS 

Google Scholar 
78.Ey, E. & Fischer, J. The, “Acoustic Adaptation Hypothesis”—A review of the evidence from birds, anurans and mammals. Bioacoustics 19, 21–48 (2009).Article 

Google Scholar 
79.Papale, E., Azzolin, M. & Giacoma, C. Vessel traffic affects bottlenose dolphin (Tursiops truncatus) behaviour in waters surrounding Lampedusa Island, south Italy. J. Mar. Biol. Assoc. U.K. 92, 1877–1885 (2012).Article 

Google Scholar 
80.Gridley, T., Nastasi, A., Kriesell, H. J. & Elwen, S. H. The acoustic repertoire of wild common bottlenose dolphins (Tursiops truncatus) in Walvis Bay, Namibia. Bioacoustics 24, 153–174 (2015).Article 

Google Scholar 
81.Au, W. W. L. & Hastings, M. C. Emission of social sounds by marine animals. In Principles of Marine Bioacoustics 401–499 (Springer, 2008).
Google Scholar 
82.Bázua-Duran, C. & Bazúa-Durán, C. Differences in the whistle characteristics and repertoire of Bottlenose and Spinner Dolphins. An. Acad. Bras. Ciênc. 76, 386–392 (2004).PubMed 
Article 

Google Scholar 
83.Lammers, M. O., Au, W. W. L. & Herzing, D. L. The broadband social acoustic signaling behavior of spinner and spotted dolphins. J. Acoust. Soc. Am. 114, 1629–1639 (2003).ADS 
PubMed 
Article 

Google Scholar 
84.Simard, P. et al. Low frequency narrow-band calls in bottlenose dolphins (Tursiops truncatus): Signal properties, function, and conservation implications. J. Acoust. Soc. Am. 130, 3068–3076 (2011).ADS 
PubMed 
Article 

Google Scholar 
85.Luís, A. R., Couchinho, M. N. & dos Santos, M. E. Signature whistles in wild bottlenose dolphins: Long-term stability and emission rates. Acta Ethologica 19, 113–122 (2016).Article 

Google Scholar 
86.Ford, J. K. B. Vocal traditions among resident killer whales (Orcinus orca) in coastal waters of British Columbia. Can. J. Zool. 69, 1454–1483 (1991).Article 

Google Scholar 
87.Papale, E. et al. Biphonic calls as signature whistles in a free-ranging bottlenose dolphin. Bioacoustics 24, 223–231 (2015).Article 

Google Scholar 
88.Elliser, C. R. & Herzing, D. L. Long-term interspecies association patterns of Atlantic bottlenose dolphins, Tursiops truncatus, and Atlantic spotted dolphins, Stenella frontalis, in the Bahamas. Mar. Mamm. Sci. 32, 38–56 (2015).Article 

Google Scholar 
89.Hoffmann-Kuhnt, M., Herzing, D. L., Ho, A. & Chitre, M. A. Whose line sound is it anyway? Identifying the vocalizer on underwater video by localizing with a hydrophone array. Anim. Behav. Cogn. 3, 288–298 (2016).Article 

Google Scholar 
90.Lima, I. M. S. et al. Whistle comparison of four delphinid species in Southeastern Brazil. J. Acoust. Soc. Am. 139, EL124 (2016).ADS 
PubMed 
Article 

Google Scholar  More

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

1.Lade, S. J., Wang-Erlandsson, L., Staal, A. & Rocha, J. C. Empirical pressure-response relations can benefit assessment of safe operating spaces. Nat. Ecol. Evol. https://doi.org/10.1038/s41559-021-01481-5 (2021).2.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 
3.Borenstein, M., Hedges, L. V., Higgins, J. P. T. & Rothstein, H. R. in Introduction to Meta-Analysis (eds Borenstein M. et al.) 277–292 (John Wiley & Sons, 2009).4.Barto, E. K. & Rillig, M. C. Dissemination biases in ecology: effect sizes matter more than quality. Oikos 121, 228–235 (2012).Article 

Google Scholar 
5.Carpenter, G., Kleinjans, R., Villasante, S. & O’Leary, B. C. Landing the blame: the influence of EU Member States on quota setting. Mar. Policy 64, 9–15 (2016).Article 

Google Scholar 
6.Galland, G. R., Nickson, A. E. M., Hopkins, R. & Miller, S. K. On the importance of clarity in scientific advice for fisheries management. Mar. Policy 87, 250–254 (2018).Article 

Google Scholar 
7.Lechenet, M., Dessaint, F., Py, G., Makowski, D. & Munier-Jolain, N. Reducing pesticide use while preserving crop productivity and profitability on arable farms. Nat. Plants 3, 17008 (2017).Article 

Google Scholar 
8.Gaba, S., Gabriel, E., Chadœuf, J., Bonneu, F. & Bretagnolle, V. Herbicides do not ensure for higher wheat yield, but eliminate rare plant species. Sci. Rep. 6, 30112 (2016).CAS 
Article 

Google Scholar 
9.Hillebrand, H. & Kunze, C. Meta-analysis on pulse disturbances reveals differences in functional and compositional recovery across ecosystems. Ecol. Lett. 23, 575–585 (2020).Article 

Google Scholar 
10.Elahi, R. et al. Recent trends in local-scale marine biodiversity reflect community structure and human impacts. Curr. Biol. 25, 1938–1943 (2015).CAS 
Article 

Google Scholar 
11.Hillebrand, H. et al. Biodiversity change is uncoupled from species richness trends: consequences for conservation and monitoring. J. Appl. Ecol. 55, 169–184 (2018).Article 

Google Scholar 
12.Dornelas, M. et al. Assemblage time series reveal biodiversity change but not systematic loss. Science 344, 296–299 (2014).CAS 
Article 

Google Scholar 
13.Blowes, S. A. et al. The geography of biodiversity change in marine and terrestrial assemblages. Science 366, 339–345 (2019).CAS 
Article 

Google Scholar 
14.Gurevitch, J., Koricheva, J., Nakagawa, S. & Stewart, G. Meta-analysis and the science of research synthesis. Nature 555, 175–182 (2018).CAS 
Article 

Google Scholar  More