Philippa Kaur

One of the striking features of the Anthropocene is a rapid degradation of natural ecosystems1,2, and an alarming decline of many species, which ultimately may lead to extinctions3,4,5. Whereas conserving ecosystem functions is increasingly recognised as a vital need for humans6,7,8, the interspecific interactions underpinning these functions are poorly understood9,10. However, conserving such interactions can be particularly important when taxa providing high-value ecosystem services are involved10,11.Ground beetles (Coleoptera: Carabidae) have been long known for their benefits in agroecosystems12,13. They play an important role in suppressing pests14, but several carabid species also consume seeds of herbaceous plants, making them a valuable asset for weed control as well15.Fungi are also of vital significance in most of the world’s terrestrial ecosystems16. Mycorrhizal fungi improve nutrient uptake by a large range of plant species through intimate and specialised associations17, other fungi play a crucial role in decomposition18, and yet others are pathogens of both crops and pests in agroecosystems19. Fungal parasitism is one of the crucial agents of evolution20.Fungi and carabids often co-occur, and they can potentially interact in many ways. The soil environment carabids often inhabit is a reservoir of fungal propagules where the beetles can feed on spores, hyphae or fruiting bodies21. They may also be responsible for dispersal of spores of certain fungi22. Several parasitic or entomopathogenic fungi are in an obligatory relationship with their beetle hosts23, therefore, the population decline of a ground beetle species could potentially lead to overlooked extinction cascades24. However, our knowledge of the fungal-carabid interactions is still limited concerning the frequency of these interactions and on how their exact nature affect the parties involved. Indeed, we do not even have a catalogue of the carabid-fungi interactions, and they have not yet been organized into a comprehensive database. Such a database would be of particular importance from an integrated pest management point of view because both fungi and carabids can deliver ecosystem services, but how their interactions, and potential synergies or antagonisms, influence the delivery of these services is poorly understood.In order to have a detailed overview of the interactions between Carabidae and the fungal kingdom, we collated a database containing previously reported associations between these taxa. Carabid and fungal species involved in the interaction, the type of the interaction (e. g. parasitic, pathogenic, mutualistic, or trophic interactions), the location (country) the interaction was reported from, and the publication source combined with detailed notes to each questionable entry comprised one record. Publications available in printed formats only were either digitized and data were extracted using semi-automatic text-mining processes, or they were manually screened. We aimed at possible completeness, using a wide range of databases and search engines and several languages to cover most of the published literature.Both ground beetle and fungal names were validated and their higher taxonomical classifications were also extracted. When it was possible, historical localities were converted to their current country names. The full bibliographical details were also stored in the database.The database covers a time-period from 1793 to 2020, spans over all geographic sub-regions defined by the United Nations (“UNSD — Methodology”, unstats.un.org. Retrieved 2020–10–11) with recorded associations from 129 countries. Our effort yielded 3,378 unique associations in 5,564 records between 1,776 carabid and 676 fungal species. Although rapidly developing molecular methods have largely facilitated the mapping of complex interaction networks in ecological studies25,26,27, due to the historic nature of our dataset, most of the records rely on traditional taxonomical identification. Yet, 16 records were based purely on metabarcoding studies; comments linked to these associations clearly identify them.Whilst we found relatively few pathogenic interactions, a great diversity between ectoparasitic Laboulbeniales fungi and carabids was revealed (Fig. 1). Soft bodied, cave-dwelling members of the Trechinae subfamily were particularly prone to these parasitic infections. Little information was available on mutualistic relationships but the presence of Yarrowia yeast reported from the gut of several carabid species28 is probably beneficial for both parties. The data show two distinct peaks in publications registering new associations, in the early 19th century and in the late 20th century (Fig. 2a) but the steady increase in the cumulative number of associations (Fig. 2b) suggests that further research is required to fully resolve this association network. Although we believe that most of the data published so far were collected, data submission will remain open to researchers wishing to contribute.Fig. 1The number of unique associations between Carabidae subfamilies and fungal classes. Side bar plots show the number of species in each subfamily/class recorded in our dataset.Full size imageFig. 2The number of recorded unique associations over time. Changes in the number of new records (a) and in the cumulative number (b) per year. Dark green lines indicate smoothed trends.Full size image More

1.Christie, K. S., Gilbert, S. L., Brown, C. L., Hatfield, M. & Hanson, L. Unmanned aircraft systems in wildlife research: Current and future applications of a transformative technology. Front. Ecol. Environ. 14, 241–251 (2016).Article 

Google Scholar 
2.Anderson, K. & Gaston, K. J. Lightweight unmanned aerial vehicles will revolutionize spatial ecology. Front. Ecol. Environ. 11, 138–146 (2013).Article 

Google Scholar 
3.Chabot, D. & Bird, D. M. Wildlife research and management methods in the 21st century: Where do unmanned aircraft fit in?. J. Unmanned Veh. Syst. 3, 137–155 (2015).Article 

Google Scholar 
4.Sasse, D. B. Job-related mortality of wildlife workers in the United States, 1937–2000. Wildl. Soc. Bull. 4, 1015–1020 (2003).
Google Scholar 
5.Wiegmann, D. A. & Taneja, N. Analysis of injuries among pilots involved in fatal general aviation airplane accidents. Accid. Anal. Prev. 35, 571–577 (2003).PubMed 
Article 
PubMed Central 

Google Scholar 
6.Vas, E., Lescroël, A., Duriez, O., Boguszewski, G. & Grémillet, D. Approaching birds with drones: first experiments and ethical guidelines. Biol. Lett. 11, 20140754 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
7.Hodgson, J. C., Baylis, S. M., Mott, R., Herrod, A. & Clarke, R. H. Precision wildlife monitoring using unmanned aerial vehicles. Sci. Rep. 6, 22574. https://doi.org/10.1038/srep22574 (2016).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
8.Egan, C. C., Blackwell, B. F., Fernández-Juricic, E. & Klug, P. E. Testing a key assumption of using drones as frightening devices: Do birds perceive drones as risky?. The Condor 122, 1–15. https://doi.org/10.1093/condor/duaa014 (2020).Article 

Google Scholar 
9.Hahn, N. et al. Unmanned aerial vehicles mitigate human–elephant conflict on the borders of Tanzanian Parks: A case study. Oryx 51, 513–516 (2017).Article 

Google Scholar 
10.FAA. Protocol for the Conduct and Review of Wildlife Hazard Site Visits, Wildlife Hazard Assessments, and Wildlife Hazard Management Plan. (2018).11.Dolbeer, R. A., Begier, M. J., Miller, P. R., Weller, J. R. & Anderson, A. L. Wildlife strikes to civil aircraft in the United States 1990–2019. 124 (Federal Aviation Administration, Washington, D.C., USA, 2021).12.Bivings, A. in Bird Strike Committee Europe. 481–487.13.Wandrie, L. J., Klug, P. E. & Clark, M. E. Evaluation of two unmanned aircraft systems as tools for protecting crops from blackbird damage. Crop Prot. 117, 15–19 (2019).Article 

Google Scholar 
14.Ydenberg, R. C. & Dill, L. M. The economics of fleeing from predators. Adv. Study Behav. 16, 229–249 (1986).Article 

Google Scholar 
15.Cooper, W. E., Samia, D. S. & Blumstein, D. T. Chapter five-FEAR, spontaneity, and artifact in economic escape theory: A review and prospectus. Adv. Study Behav. 47, 147–179 (2015).Article 

Google Scholar 
16.Lima, S. L., Blackwell, B. F., DeVault, T. L. & Fernandez-Juricic, E. Animal reactions to oncoming vehicles: A conceptual review. Biol. Rev. Camb. Philos. Soc. 90, 60–76. https://doi.org/10.1111/brv.12093 (2015).Article 
PubMed 

Google Scholar 
17.Bernhardt, G. E., Blackwell, B. F., DeVault, T. L. & Kutschbach-Brohl, L. Fatal injuries to birds from collisions with aircraft reveal anti-predator behaviours. Ibis https://doi.org/10.1111/j.1474-919X.2010.01043.x (2010).Article 

Google Scholar 
18.McEvoy, J. F., Hall, G. P. & McDonald, P. G. Evaluation of unmanned aerial vehicle shape, flight path and camera type for waterfowl surveys: Disturbance effects and species recognition. PeerJ 4, e1831 (2016).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
19.Mulero-Pázmány, M. et al. Unmanned aircraft systems as a new source of disturbance for wildlife: A systematic review. PLoS ONE 12, e0178448 (2017).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
20.Tinbergen, N. Social releasers and the experimental method required for their study. Wilson Bull., 6–51 (1948).21.Kirk, D. A. & Mossman, M. J. in Bird of the World (ed Cornell Lab of Ornithology) (Poole, A.F.,Gill, F.B., Ithaca, NY, USA, 2020).22.FAA. Wildlife Strike Database, wildlife.faa.gov (2020).23.DeVault, T. L. et al. Estimating interspecific economic risk of bird strikes with aircraft. Wildl. Soc. Bull. 42, 94–101 (2018).Article 

Google Scholar 
24.DeVault, T. L., Blackwell, B. F., Seamans, T. W. & Belant, J. L. Identification of off airport interspecific avian hazards to aircraft. J. Wildl. Manag. 80, 746–752 (2016).Article 

Google Scholar 
25.Kluever, B. M., Pfeiffer, M. B., Barras, S. C., Dunlap, B. G. & Humberg, L. A. Black vulture conflict and management in the United States: Damage trends, management overview, and research needs. Hum. Wildl. Interact. 14, 8 (2020).
Google Scholar 
26.Walters, J. R. Anti-predatory behavior of lapwings: field evidence of discriminative abilities. Wilson Bull., 49–70 (1990).27.Septon, G. Peregrine falcon strikes turkey vulture. Passenger Pigeon 53, 192 (1991).
Google Scholar 
28.Coleman, J. S. & Fraser, J. D. Predation on black and Turkey vultures. Wilson Bull. 98, 600–601 (1986).
Google Scholar 
29.Rush, G. P., Clarke, L. E., Stone, M. & Wood, M. J. Can drones count gulls? Minimal disturbance and semiautomated image processing with an unmanned aerial vehicle for colony-nesting seabirds. Ecol. Evol. 8, 12322–12334 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
30.Bennitt, E., Bartlam-Brooks, H. L. A., Hubel, T. Y. & Wilson, A. M. Terrestrial mammalian wildlife responses to unmanned aerial systems approaches. Sci. Rep. 9, 2142. https://doi.org/10.1038/s41598-019-38610-x (2019).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
31.Weston, M. A., O’Brien, C., Kostoglou, K. N. & Symonds, M. R. Escape responses of terrestrial and aquatic birds to drones: Towards a code of practice to minimize disturbance. J. Appl. Ecol. 57, 777–785 (2020).Article 

Google Scholar 
32.Belant, J. L., Seamans, T. W., Gabrey, S. W. & Dolbeer, R. A. Abundance of gulls and other birds at landfills in northern Ohio. Am. Midl. Nat. 134, 30–40 (1995).Article 

Google Scholar 
33.Barnas, A. F. et al. A standardized protocol for reporting methods when using drones for wildlife research. J. Unmanned Veh. Syst. 8, 89–98 (2020).Article 

Google Scholar 
34.DeVault, T. L., Blackwell, B. F., Seamans, T. W., Lima, S. L. & Fernández-Juricic, E. Effects of vehicle speed on flight initiation by turkey vultures: implications for bird-vehicle collisions. PLoS ONE 9, e87944 (2014).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
35.Doppler, M. S., Blackwell, B. F., DeVault, T. L. & Fernández-Juricic, E. Cowbird responses to aircraft with lights tuned to their eyes: Implications for bird–aircraft collisions. The Condor 117, 165–177 (2015).Article 

Google Scholar 
36.Blackwell, B. F., Fernandez-Juricic, E., Seamans, T. W. & Dolan, T. Avian visual system configuration and behavioural response to object approach. Anim. Behav. 77, 673–684 (2009).Article 

Google Scholar 
37.DeVault, T. L., Reinhart, B. D., Brisbin, I. L., Rhodes, O. E. & Bechard. Flight Behavior of Black and Turkey Vultures: Implications for reducing bird–aircraft collisions. J. Wildl. Manag. 69, 601–608. https://doi.org/10.2193/0022-541X(2005)069[0601:FBOBAT]2.0.CO;2 (2005).38.Runyan, A. M. & Blumstein, D. T. Do individual differences influence flight initiation distance?. J. Wildl. Manag. 68, 1124–1129 (2004).Article 

Google Scholar 
39.Rebolo-Ifrán, N., Grilli, M. G. & Lambertucci, S. A. Drones as a threat to wildlife: YouTube complements science in providing evidence about their effect. Environ. Conserv. 46, 205–210 (2019).Article 

Google Scholar 
40.Fernández-Juricic, E., Deisher, M., Stark, A. C. & Randolet, J. Predator detection is limited in microhabitats with high light intensity: An experiment with Brown-headed Cowbirds. Ethology 118, 341–350 (2012).Article 

Google Scholar 
41.Koch, D. D. Glare and contrast sensitivity testing in cataract patients. J. Cataract Refract. Surg. 15, 158–164 (1989).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
42.Vorobyev, M. & Osorio, D. Receptor noise as a determinant of colour thresholds. Proc. Royal Soc. B. 265, 351–358 (1998).CAS 
Article 

Google Scholar 
43.Ödeen, A. & Håstad, O. The phylogenetic distribution of ultraviolet sensitivity in birds. BMC Evol. Biol. 13, 36 (2013).PubMed 
PubMed Central 
Article 

Google Scholar 
44.Hill, G. E., Hill, G. E., McGraw, K. J. & Kevin, J. Bird coloration: mechanisms and measurements. Vol. 1 (Harvard University Press, 2006).45.Maia, R., Eliason, C. M., Bitton, P. P., Doucet, S. M. & Shawkey, M. D. pavo: Asn R package for the analysis, visualization and organization of spectral data. Methods Ecol. Evol. 4, 906–913 (2013).
Google Scholar 
46.Lakens, D. Sample Size Justification. (2021).47.Nakagawa, S. & Cuthill, I. C. Effect size, confidence interval and statistical significance: a practical guide for biologists. Biol. Rev. 82, 591–605 (2007).PubMed 
PubMed Central 
Article 

Google Scholar 
48.Hurlbert, S. H. Pseudoreplication and the design of ecological field experiments. Ecol. Monogr. 54, 187–211. https://doi.org/10.2307/1942661 (1984).Article 

Google Scholar 
49.Garamszegi, L. Z. A simple statistical guide for the analysis of behaviour when data are constrained due to practical or ethical reasons. Anim. Behav. 120, 223–234 (2016).Article 

Google Scholar 
50.Burnham, K. P. & Anderson, D. R. Model selection and multimodel inference: a practical information-theoretic approach. (Springer Science & Business Media, 2002).51.Nauman, L. E. Spatial distribution in a turkey vulture roost, The Ohio State University, (1965).52.Bertram, B. C. Living in groups: predators and prey. Behavioural ecology: an evolutionary approach, 221–248 (1978).53.Blackwell, B. F. et al. Social information affects Canada goose alert and escape responses to vehicle approach: Implications for animal–vehicle collisions. PeerJ 7, e8164. https://doi.org/10.7717/peerj.8164 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
54.Blackwell, B. F., Seamans, T. W., Fernández-Juricic, E., Devault, T. L. & Outward, R. J. Avian responses to aircraft in an airport environment. J. Wildl. Manag. 83, 893–901 (2019).Article 

Google Scholar 
55.Beauchamp, G. Social predation: how group living benefits predators and prey. (Elsevier, 2013).56.Fox, J., Friendly, M. & Weisberg, S. Hypothesis tests for multivariate linear models using the car package. The R Journal 5, 39–52 (2013).Article 

Google Scholar 
57.Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. arXiv preprint arXiv:1406.5823(2014).58.DeVault, T. L., Blackwell, B. F., Seamans, T. W., Lima, S. L. & Fernandez-Juricic, E. Speed kills: Ineffective avian escape responses to oncoming vehicles. Proc. R. Soc. B. 282, 20142188. https://doi.org/10.1098/rspb.2014.2188 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
59.DeVault, T. L. et al. Can experience reduce collisions between birds and vehicles?. J. Zool. 301, 17–22. https://doi.org/10.1111/jzo.12385 (2016).Article 

Google Scholar 
60.Rhoades, E. & Blumstein, D. T. Predicted fitness consequences of threat-sensitive hiding behavior. Behav. Ecol. 18, 937–943 (2007).Article 

Google Scholar 
61.Cooper Jr, W. E. Factors affecting risk and cost of escape by the broad-headed skink (Eumeces laticeps): predator speed, directness of approach, and female presence. Herpetologica, 464–474 (1997).62.Cooper, W. E. Jr., Hawlena, D. & Pérez-Mellado, V. Interactive effect of starting distance and approach speed on escape behavior challenges theory. Behav. Ecol. 20, 542–546 (2009).Article 

Google Scholar 
63.Fernández-Juricic, E., Jimenez, M. D. & Lucas, E. Alert distance as an alternative measure of bird tolerance to human disturbance: Implications for park design. Environ. Conserv. 28, 263–269. https://doi.org/10.1017/S0376892901000273 (2001).Article 

Google Scholar 
64.Dill, L. M. The escape response of the zebra danio (Brachydanio rerio) I. The stimulus for escape. Anim. Behav. 22, 711–722 (1974).Article 

Google Scholar 
65.Sun, H. & Frost, B. J. Computation of different optical variables of looming objects in pigeon nucleus rotundus neurons. Nat. Neurosci. 1, 296–303 (1998).CAS 
PubMed 
Article 

Google Scholar 
66.Pfeiffer, M. B., Iglay, R. B., Seamans, T. W., Blackwell, B. F. & DeVault, T. L. Deciphering interactions between white-tailed deer and approaching vehicles. Transp. Res. D Transp. Environ. 79, 102251. https://doi.org/10.1016/j.trd.2020.102251 (2020).Article 

Google Scholar 
67.Collins, S. A., Giffin, G. J. & Strong, W. T. Using flight initiation distance to evaluate responses of colonial-nesting Great Egrets to the approach of an unmanned aerial vehicle. J. Field. Ornithol. 90, 382–390 (2019).Article 

Google Scholar 
68.Kane, S. A., Fulton, A. H. & Rosenthal, L. J. When hawks attack: Animal-borne video studies of goshawk pursuit and prey-evasion strategies. J. Exp. Biol. 218, 212–222 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
69.Frid, A. & Dill, L. Human-caused disturbance stimuli as a form of predation risk. Conserv. Ecol. 6 (2002).70.Lambertucci, S. A., Shepard, E. L. & Wilson, R. P. Human-wildlife conflicts in a crowded airspace. Science 348, 502–504 (2015).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
71.Ballejo, F., Plaza, P., Speziale, K. L., Lambertucci, A. P. & Lambertucci, S. A. Plastic ingestion and dispersion by vultures may produce plastic islands in natural areas. Sci. Total Environ. 755, 142421. https://doi.org/10.1016/j.scitotenv.2020.142421 (2021).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
72.Conover, M. R. Resolving human-wildlife conflicts: the science of wildlife damage management. (CRC press, 2001).73.Pfeiffer, M. B., Blackwell, B. F. & DeVault, T. L. Collective effect of landfills and landscape composition on bird–aircraft collisions. Hum.–Wildl. Interact. 14, 43–54 (2020).74.Dolbeer, R. A. Aerodrome bird hazard prevention: case study at John F. Kennedy International Airport. (1999).75.Blackwell, B. F. et al. Exploiting avian vision with aircraft lighting to reduce bird strikes. J. Appl. Ecol. 49, 758–766 (2012).Article 

Google Scholar 
76.Goller, B., Blackwell, B. F., DeVault, T. L., Baumhardt, P. E. & Fernández-Juricic, E. Assessing bird avoidance of high-contrast lights using a choice test approach: Implications for reducing human-induced avian mortality. PeerJ 6, e5404 (2018).PubMed 
PubMed Central 
Article 

Google Scholar  More

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

Google Scholar 
2.Soininen, J., McDonald, R. & Hillebrand, H. The distance decay of similarity in ecological communities. Ecography 30, 3–12 (2007).Article 

Google Scholar 
3.Whittaker, R. H. Communities and Ecosystems (MacMillan Publishing, 1975).
Google Scholar 
4.Pulliam, H. R. On the relationship between niche and distribution. Ecol. Lett. 3, 349–361 (2000).Article 

Google Scholar 
5.Pulliam, H. Sources, sinks, and population regulation. Am. Nat. 132, 652–661 (1988).Article 

Google Scholar 
6.Hanski, I. & Gilpin, M. Metapopulation dynamics: Brief history and conceptual domain. Biol. J. Linn. Soc. 42, 3–16 (1991).Article 

Google Scholar 
7.MacArthur, R. H. & Wilson, E. O. The Theory of Island Biogeography (Princeton University Press, 2001).Book 

Google Scholar 
8.Tuomisto, H. & Ruokolainen, K. Analyzing or explaining beta diversity? Understanding the targets of different methods of analysis. Ecology 87, 2697–2708 (2006).PubMed 
Article 
PubMed Central 

Google Scholar 
9.Astorga, A. et al. Distance decay of similarity in freshwater communities: Do macro- and microorganisms follow the same rules?: Decay of similarity in freshwater communities. Glob. Ecol. Biogeogr. 21, 365–375 (2012).Article 

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

Google Scholar 
11.Nekola, J. C. & Brown, J. H. The wealth of species: Ecological communities, complex systems and the legacy of Frank Preston. Ecol. Lett. 10, 188–196 (2007).PubMed 
Article 
PubMed Central 

Google Scholar 
12.Hubbell, S. The Unified Neutral Theory of Biodiversity and Biogeography (MPB-32) (Princeton University Press, 2001).
Google Scholar 
13.Fodelianakis, S., Valenzuela-Cuevas, A., Barozzi, A. & Daffonchio, D. Direct quantification of ecological drift at the population level in synthetic bacterial communities. ISME J. https://doi.org/10.1038/s41396-020-00754-4 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
14.Gravel, D., Canham, C. D., Beaudet, M. & Messier, C. Reconciling niche and neutrality: The continuum hypothesis: Reconciling niche and neutrality. Ecol. Lett. 9, 399–409 (2006).PubMed 
Article 
PubMed Central 

Google Scholar 
15.Legendre, P., Borcard, D. & Peres-Neto, P. R. Analyzing beta diversity: Partitioning the spatial variation of community composition data. Ecol. Monogr. 75, 435–450 (2005).Article 

Google Scholar 
16.Wilson, K. A., Cabeza, M. & Klein, C. J. Fundamental concepts of spatial conservation prioritization. In Spatial Conservation Prioritization: Quantitative Methods & Computational Tools (eds Moilanen, A. et al.) 16–27 (Oxford University Press, 2009).
Google Scholar 
17.Morlon, H. et al. A general framework for the distance-decay of similarity in ecological communities. Ecol. Lett. 11, 904–917 (2008).PubMed 
PubMed Central 
Article 

Google Scholar 
18.Tuomisto, H. Dispersal, environment, and floristic variation of western Amazonian forests. Science 299, 241–244 (2003).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
19.Gómez-Rodríguez, C. & Baselga, A. Variation among European beetle taxa in patterns of distance decay of similarity suggests a major role of dispersal processes. Ecography 41, 1825–1834 (2018).Article 

Google Scholar 
20.Stella, J. C., Rodríguez-González, P. M., Dufour, S. & Bendix, J. Riparian vegetation research in Mediterranean-climate regions: Common patterns, ecological processes, and considerations for management. Hydrobiologia 719(1), 291–315 (2013).Article 

Google Scholar 
21.Vannote, R. L., Minshall, G. W., Cummins, K. W., Sedell, J. R. & Cushing, C. E. The river continuum concept. Can. J. Fish. Aquat. Sci. 37, 130–137 (1980).Article 

Google Scholar 
22.Rouquette, J. R. et al. Species turnover and geographic distance in an urban river network. Divers. Distrib. 19, 1429–1439 (2013).Article 

Google Scholar 
23.Kuglerová, L., Jansson, R., Sponseller, R. A., Laudon, H. & Malm-Renöfält, B. Local and regional processes determine plant species richness in a river-network metacommunity. Ecology 96, 381–391 (2015).PubMed 
Article 
PubMed Central 

Google Scholar 
24.Zhang, Z., Gao, J. & Cai, Y. The effects of environmental factors and geographic distance on species turnover in an agriculturally dominated river network. Environ. Monit. Assess. 191, 201 (2019).PubMed 
Article 
PubMed Central 

Google Scholar 
25.Jost, L., Chao, A. & Chazdon, R. Compositional similarity and beta diversity. In Biological Diversity: Frontiers in Measurement and Assessment (eds Magurran, A. & McGill, B.) 66–84 (Oxford University Press, 2011).
Google Scholar 
26.Olson, D. M. et al. Terrestrial ecoregions of the world: A new map of life on earth. Bioscience 51, 933 (2001).Article 

Google Scholar 
27.Miranda, P., Coelho, F., Tomé, A. & Valente, M. Climate Change in Portugal. Scenarios, Impacts and Adaptation Measures—SIAM Project (Gradiva, 2002).
Google Scholar 
28.CIS-WFD. River and lakes—Typology, reference conditions and classification systems, Common Implementation Strategy for the Water Framework Directive (2000/60/EC), Guidance document no 10. 94 (2003).29.INAG. Manual para a avaliação biológica da qualidade da água em sistemas fluviais segundo a DQA—Protocolo de amostragem e análise para os macrófitos (2008).30.Agência Portuguesa do Ambiente. Plano de Gestão da Região Hidrográfica do Tejo, Relatório técnico, Versão Extensa Parte 2—Caracterização e Diagnóstico da Região Hidrográfica. (2012).31.Oksanen, J. et al. vegan: Community Ecology Package—Version 2.7-7. https://CRAN.R-project.org/package=vegan (2021).32.R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2021).33.Peterson, E. E., Theobald, D. M. & Ver Hoef, J. M. Geostatistical modelling on stream networks: Developing valid covariance matrices based on hydrologic distance and stream flow. Freshw. Biol. 52, 267–279 (2007).Article 

Google Scholar 
34.Csardi, G. & Nepusz, T. The Igraph software package for complex network research. InterJournal Complex Syst. 1695, 1–9 (2005).
Google Scholar 
35.Lu, B., Sun, H., Harris, P., Xu, M. & Charlton, M. Shp2graph: Tools to convert a spatial network into an Igraph graph in R. ISPRS Int. J. Geo-Inf. 7, 293 (2018).Article 

Google Scholar 
36.Vogt, J. & Foisneau, S. CCM River and Catchment Database—Version 2.0 Analysis Tools. (2007).37.Monteiro-Henriques, T. et al. Bioclimatological mapping tackling uncertainty propagation: Application to mainland Portugal. Int. J. Climatol. 36, 400–411 (2016).Article 

Google Scholar 
38.Ward, J. V. & Stanford, J. A. The serial discontinuity concept: Extending the model to floodplain rivers. Regul. Rivers Res. Manag. 10, 159–168 (1995).Article 

Google Scholar 
39.Dias, F. S., Betancourt, M., Rodríguez-González, P. M. & Borda-de-Água, L. A Bayesian Approach for Analysing Pairwise Comparisons: A Case Study Using Species Composition Similarity (2021) https://doi.org/10.32942/osf.io/sn5jr.40.Stan Development Team. Stan Functions Reference Version 2.25. (2020).41.McElreath, R. Statistical Rethinking: A Bayesian Course with Examples in R and Stan (Chapman and Hall/CRC, 2020).Book 

Google Scholar 
42.Rodríguez-González, P. M., Ferreira, M. T., Albuquerque, A., Santo, D. E. & Rego, P. R. Spatial variation of wetland woods in the latitudinal transition to arid regions: A multiscale approach. J. Biogeogr. 35, 1498–1511 (2008).Article 

Google Scholar 
43.Stan Development Team. RStan: the R interface to Stan Version 2.21. https://github.com/stan-dev/rstan/wiki/RStan-Getting-Started (2020).44.Betancourt, M. Hierarchical Modeling (2020).45.Muneepeerakul, R., Weitz, J. S., Levin, S. A., Rinaldo, A. & Rodriguez-Iturbe, I. A neutral metapopulation model of biodiversity in river networks. J. Theor. Biol. 245, 351–363 (2007).ADS 
MathSciNet 
PubMed 
MATH 
Article 
PubMed Central 

Google Scholar 
46.Thompson, R. & Townsend, C. A truce with neutral theory: Local deterministic factors, species traits and dispersal limitation together determine patterns of diversity in stream invertebrates: Neutral theory and local determinism. J. Anim. Ecol. 75, 476–484 (2006).PubMed 
Article 
PubMed Central 

Google Scholar 
47.Steinitz, O., Heller, J., Tsoar, A., Rotem, D. & Kadmon, R. Environment, dispersal and patterns of species similarity. J. Biogeogr. 33, 1044–1054 (2006).Article 

Google Scholar 
48.Nilsson, C., Brown, R. L., Jansson, R. & Merritt, D. M. The role of hydrochory in structuring riparian and wetland vegetation. Biol. Rev. Camb. Philos. Soc. 85, 837–858 (2010).PubMed 
PubMed Central 

Google Scholar 
49.Gelmi-Candusso, T. A. et al. Estimating seed dispersal distance: A comparison of methods using animal movement and plant genetic data on two primate-dispersed Neotropical plant species. Ecol. Evol. 9, 8965–8977 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
50.Rodríguez-González, P. M. et al. A spatial stream-network approach assists in managing the remnant genetic diversity of riparian forests. Sci. Rep. 9, 6741 (2019).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
51.Ward, J. V., Tockner, K., Arscott, D. B. & Claret, C. Riverine landscape diversity. Freshw. Biol. 47, 517–539 (2002).Article 

Google Scholar 
52.Fraaije, R. G. A. et al. Spatial patterns of water-dispersed seed deposition along stream riparian gradients. PLoS ONE 12, e0185247 (2017).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
53.Bendix, J. Flood disturbance and the distribution of riparian species diversity. Geogr. Rev. 87, 468–483 (1997).Article 

Google Scholar 
54.Kuglerová, L., Dynesius, M., Laudon, H. & Jansson, R. Relationships between plant assemblages and water flow across a boreal forest landscape: A comparison of liverworts, mosses, and vascular plants. Ecosystems 19, 170–184 (2016).Article 
CAS 

Google Scholar 
55.Wubs, E. R. J. et al. Going against the flow: A case for upstream dispersal and detection of uncommon dispersal events. Freshw. Biol. 61, 580–595 (2016).CAS 
Article 

Google Scholar 
56.Carrera, M., Gyakum, J. & Lin, C. Observational study of wind channeling within the St. Lawrence river valley. J. Appl. Meteorol. Climatol. 48, 2341–2361 (2009).ADS 
Article 

Google Scholar 
57.Kuparinen, A., Katul, G., Nathan, R. & Schurr, F. M. Increases in air temperature can promote wind-driven dispersal and spread of plants. Proc. R. Soc. B Biol. Sci. 276, 3081–3087 (2009).Article 

Google Scholar 
58.Soomers, H. et al. Wind and water dispersal of wetland plants across fragmented landscapes. Ecosystems 16, 434–451 (2013).Article 

Google Scholar 
59.Jones, K. N. Analysis of pollinator foraging: Tests for non-random behaviour. Funct. Ecol. 11, 255–259 (1997).Article 

Google Scholar 
60.Ferreira, M. T. & Aguiar, F. Riparian and aquatic vegetation in Mediterranean-type streams (western Iberia). Limnetica 25, 411–424 (2005).
Google Scholar 
61.Petts, G. E. & Amoros, C. Fluvial hydrosystems: a management perspective. In The Fluvial Hydrosystems (eds Petts, G. E. & Amoros, C.) 263–278 (Springer Netherlands, 1996) https://doi.org/10.1007/978-94-009-1491-9_12.Chapter 

Google Scholar 
62.Benda, L. et al. The network dynamics hypothesis: How channel networks structure riverine habitats. Bioscience 54, 413–427 (2004).Article 

Google Scholar 
63.QGIS Development Team. QGIS Geographic Information System-Version 3.20.3. (2021). More

Design of species assemblages with contrasting species and functional diversitiesWe designed eight assemblages of native and perennial plants differing in terms of species richness (three levels), functional diversity of the traits involved in plant–arthropod interactions (two levels) and species identity (two sets of species). We combined these first two factors to define four categories of plant assemblages for further study:

Low functional diversity and medium species richness (14 species), LFMS;

High functional diversity and low species richness (9 species), HFLS;

High functional diversity and medium species richness (14 species), HFMS;

High functional diversity and high species richness (29 species), HFHS.

For each of these four categories, we designed two assemblages with different species identities, as described in the Supplementary information, resulting in eight plant assemblages in total. Functional characterization was based on a rough classification of plant species into functional groups (Supplementary Table S1), according to the mains traits involved in plant–species interactions easily accessible from databases: (1) flower resources, i.e. floral and extrafloral nectar or pollen, (2) accessibility of the resource, depending on flower shape, (3) availability of the resource, i.e. the flowering period and (4) flowering height.We generated the seed mixtures from commercial seeds, using ecotypes of local origin wherever possible (northern part of the Parisian basin, France). All applicable international, national, and institutional guidelines relevant for the use of plants were followed.Experimental designThe experiment was conducted between 2013 and 2017 in a 6.5-ha field at Grignon, France (N 48.837, E 1.956), on a deep loamy clay soil, in which soil depth decreased along a gradient from north to south. The field was divided in three blocks running from north to south to take this soil heterogeneity into account.Each assemblage was sown on a 6 × 44 m2 strip, with three replicates (Supplementary Fig. S2), with each assemblage represented once per block. A control treatment, sown with the same crop species as the rest of the field, was also included in the experimental design, resulting in nine experimental treatments in total. From the autumn of 2013 to the 2017 harvest, a winter barley–maize–faba bean–oilseed rape rotation was grown in the field. Crops were managed without insecticide treatment, but with a mean of 0.75 fungicide and 1.25 herbicide treatments per year. The observations were made in faba bean in 2016 and in oilseed rape in 2017.Botanical assessments and functional characterization of the plant communitiesBotanical assessments were conducted in April and June, in 2016 and 2017. In each treatment, the vegetation was assessed in 3 × 15 m2 plots at a position representative of the whole strip, generally in the center of the strip, to prevent edge effects. The percentage of the ground covered by each sown or spontaneously growing plant species was estimated by eye, by the same observer in each case. We noted the phenological development stage of each species in each treatment on an 11-point scale, to ensure an accurate assessment of flowering phenology. In the control plots (sown with the crop species only), we took into account the resources provided by weed species.The functional characterization of plant communities was based on the plant traits assumed to be involved in plant–parasitoid interactions6 (Supplementary Table S3). These traits were related to (1) the provision of trophic resources (presence of floral and extrafloral nectar, qualitative estimation of floral nectar), (2) the temporal availability of the resource (date of flowering onset and duration of flowering), (3) flower attractiveness (flower or inflorescence diameter, color, UV reflectance pattern), (4) nectar accessibility (flower opening diameter, corolla height, nectar depth and nectar tube diameter) and (5) the provision of physical habitats (leaf distribution, vegetative and flowering height). We measured most of these traits, particularly all those relating to flower morphology, phenology and nectar provision (see more detailed methods in the Supplementary information). Only a few were retrieved from previous publications and online databases: flower color and UV reflectance pattern, leaf distribution, vegetative and flower height.These traits were used (1) to determine the accessibility of nectar to each parasitoid (see below) and (2) to calculate the functional diversity of the plant assemblages. We calculated functional dispersion as the abundance-weighted mean distance of individual species from the centroid of all species in the trait space50 and Rao quadratic entropy51. Since these two parameters were highly correlated (Supplementary information), we considered only functional dispersion a measurement of functional diversity. The traits associated with the provision, availability and accessibility of nectar resources were measured for all the dicotyledonous species sown and for all spontaneous species occurring in the plant communities and flowering during parasitoid activity. Overall, considering the traits we measured and those retrieved from databases, the trait matrix was complete for more than 95% of the species, accounting for 99.6% of total plant cover.Assessment of the levels of parasitism on five herbivorous pests of faba bean and oilseed rapeIn the adjacent crop, 5 and 20 m from the wildflower strip, we measured the level of parasitism in one herbivorous pest of faba bean (2016) and four herbivorous pests of oilseed rape (2017). We chose a distance close to the strip (5 m) to prevent confounding effects with the other adjacent strips, knowing that their effect is the strongest in the first few meters from the strip52. A further distance was also chosen (20 m) to determine whether the strips promoted biological control at field level, while taking into account the spatial constraint of the distance between strips (50 m between opposing strips).All the protocols are detailed in the Supplementary information. Parasitism was assessed in Bruchus rufimanus larvae after the visual examination of faba bean seeds after harvest. For oilseed rape, we collected and reared Ceutorhynchus pallidactylus and Psylliodes chrysocephala larvae until the adult stage or parasitoid emergence. In Brassicogethes aeneus larvae, parasitism was assessed by observing the eggs of Tersilochus heterocerus in the host larvae in oilseed rape flowers. Finally, after oilseed rape harvest, we retrieved cocoons of Dasineura brassicae from the soil, which we dissected, recording the number of cocoons occupied by parasitoids.Measurement of parasitoid traitsWe carried out morphological measurements on parasitoids (Supplementary Table S4), to determine their degree of access to the nectar provided by plants, as a function of the size of their mouthparts and head, which limit corolla penetration, using an approach analogous to that of van Rijn and Wäckers16. Parasitoid individuals, preserved in 70% ethanol, were obtained (1) from our rearing experiments (for Bruchus rufimanus, Psylliodes chrysocephala and Ceutorhynchus pallidactylus), (2) from the dissection of cocoons for Dasineura brassicae or (3) by field sampling in the flower strips with a sweep net in April 2017 to collect Tersilochus heterocerus, parasitoids of Brassicogethes aeneus identified with53. For each parasitoid species or morphospecies, we measured, on at least 10 individuals, proboscis length, proboscis width (at mid-length)54 and the maximum dorsal head width, including the eyes. Observations were carried out under a binocular microscope (Leica M80, 60 ×) linked to a video camera (Moticam 10, Motic), and measurements were made with ImageJ v1.50i digital image analysis software (National Institute of Health, Bethesda, http://imagej.nih.gov/ij).Nectar resources for parasitoidsWe estimated the amount of nectar provided by the plants by summing, for each flower strip corresponding to a treatment, the percent cover of plants providing available and accessible nectar, as assessed in vegetation surveys. Separate estimates were obtained for each parasitoid species or morphospecies.Plant species producing floral or extrafloral nectar were first selected on the basis of the observations detailed in the botanical assessment section. Nectar was considered to be available when it was produced during the period of parasitoid activity (Supplementary Table S4), by selecting species at the flowering stage or producing extrafloral nectar based on the phenological observations carried out during the botanical assessments. Nectar accessibility depended on morphological matching between plants and insects. Extrafloral nectar, which is not enclosed in a perianth, but produced on bracts or stipules, was considered to be accessible. We determined the accessibility of floral nectar with a mechanistic trait-based approach (Supplementary Information), by adapting the geometric model proposed by van Rijn and Wäckers16. A decision tree was built (Fig. 2) to take into account the three constraints limiting nectar accessibility: (1) ability of the insect to penetrate the flower, which is dependent on head size and flower opening, (2) ability to reach the nectar, which depends on proboscis length, nectar depth and corolla height, and (3) proboscis width and nectar tube diameter in the presence of nectar.Statistical analysesWe investigated the effects of the different plant assemblages on the rates of parasitism for the five herbivorous species, at 5 and 20 m from the flower strip, considered separately as individual response variables. We first tested the effect of each assemblage (nine treatments as factors) on parasitism rates. We used generalized linear mixed models in the lme package55, with a binomial error distribution. The models included plot (n = 9 flower strips × 3 replicates = 27), strip (1–3) or block (1–3) as a random effect. All models were run three times with each random effect variable, and the model giving the lowest AIC was retained. Strips consistently yielded the lowest AIC. This factor was therefore introduced as a random effect variable for all statistical analyses. The significance of the fixed effects was evaluated by type II analyses of deviance with Wald chi-squared tests from the Anova function from the car package56. If a significant effect (p value  More

1.Sih, A., Crowley, P., McPeek, M., Petranka, J. & Strohmeier, K. Predation, competition, and prey communities: A review of field experiments. Annu. Rev. Ecol. Syst. 16, 269–311 (1985).Article 

Google Scholar 
2.Werner, E. E. & Peacor, S. D. A review of trait-mediated indirect interactions in ecological communities. Ecology 84, 1083–1100 (2003).Article 

Google Scholar 
3.Abrams, P. A. The evolution of predator–prey interactions: theory and evidence. Annu. Rev. Ecol. Syst. 31, 79–105 (2000).Article 

Google Scholar 
4.Lima, S. L. & Bednekoff, P. A. Temporal variation in danger drives antipredator behavior: The predation risk allocation hypothesis. Am. Nat. 153, 649–659 (1999).PubMed 
Article 
PubMed Central 

Google Scholar 
5.Peacor, S. D. & Werner, E. E. Nonconsumptive effects of predators and trait-mediated indirect effects. Encycl. Life Sci. https://doi.org/10.1002/9780470015902.a0021216 (2008).Article 

Google Scholar 
6.Schmitz, O. J., Krivan, V. & Ovadia, O. Trophic cascades: The primacy of trait-mediated indirect interactions. Ecol. Lett. 7, 153–163 (2004).Article 

Google Scholar 
7.Mittelbach, G. G. Fish foraging and habitat choice: a theoretical perspective. In Handbook of Fish Biology and Fisheries, Volume 1 Fish Biology (eds Hart, P. J. B. & Reynolds, J. D.) 251–266 (Blackwell, 2002).Chapter 

Google Scholar 
8.Mittelbach, G. G. & McGill, B. J. Community Ecology (Oxford University Press, 2019) https://doi.org/10.1017/CBO9781107415324.004.Book 

Google Scholar 
9.Lima, S. L. Nonlethal effects in the ecology of predator-prey interactions. Bioscience 48, 25–34 (1998).Article 

Google Scholar 
10.Jeschke, J. M., Laforsch, C. & Tollrian, R. Animal prey defenses. In Encyclopedia of Ecology 189–194 (2008).11.Harvell, C. D. The ecology and evolution of inducible defenses. Q. Rev. Biol. 65, 323–340 (1990).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
12.Peckarsky, B. L. et al. Revisiting the classics: Considering nonconsumptive effects in textbook examples of predator prey interactions. Ecology 89, 2416–2425 (2008).PubMed 
Article 
PubMed Central 

Google Scholar 
13.Goricki, Š et al. Environmental DNA in subterranean biology: Range extension and taxonomic implications for Proteus. Sci. Rep. 7, 91–93 (2017).Article 
CAS 

Google Scholar 
14.Sket, B. Distribution of Proteus (Amphibia: Urodela: Proteidae) and its possible explanation. J. Biogeogr. 24, 263–280 (1997).Article 

Google Scholar 
15.Jugovic, J., Prevorčnik, S., Aljančič, G. & Sketa, B. The atyid shrimp (Crustacea: Decapoda: Atyidae) rostrum: Phylogeny versus adaptation, taxonomy versus trophic ecology. J. Nat. Hist. 44, 2509–2533 (2010).Article 

Google Scholar 
16.Aljančič, M. Prehrana močerila. Proteus 23, 224–225 (1961).
Google Scholar 
17.Parzefall, J., Durand, J. P. & Sket, B. Prouteus anguinus Laurenti, 1768—Grottenolm. In Handbuch der Reptilien und Amphibien Europas (ed. Böhme, W.) 59–76 (Aula-Verlag, 1999).
Google Scholar 
18.Trontelj, P., Blejec, A. & Fišer, C. Ecomorphological convergence of cave communities. Evolution 66, 3852–3865 (2012).PubMed 
Article 
PubMed Central 

Google Scholar 
19.Karaman, S. Podrod Orniphargus u Jugoslaviji I. & II. in O nekim amfipodima—izopodima Balkana i o njihovoj sistematici 119–159 (Srpska akademija nauka-Posebna izdanja CLXIII, 1950).20.Fišer, C., Trontelj, P. & Sket, B. Phylogenetic analysis of the Niphargus orcinus species-aggregate (Crustacea: Amphipoda: Niphargidae) with description of new taxa. J. Nat. Hist. 40, 2265–2315 (2006).Article 

Google Scholar 
21.Bollache, L. Ï., Kaldonski, N., Troussard, J. P., Lagrue, C. & Rigaud, T. Spines and behaviour as defences against fish predators in an invasive freshwater amphipod. Anim. Behav. 72, 627–633 (2006).Article 

Google Scholar 
22.Copilaş-Ciocianu, D., Borza, P. & Petrusek, A. Extensive variation in the morphological anti-predator defense mechanism of Gammarus roeselii Gervais, 1835 (Crustacea:Amphipoda). Freshw. Sci. 39, 47–55 (2020).Article 

Google Scholar 
23.Veech, J. A. A probabilistic model for analysing species co-occurrence. Glob. Ecol. Biogeogr. 22, 252–260 (2013).Article 

Google Scholar 
24.Borko, Š, Trontelj, P., Seehausen, O., Moškrič, A. & Fišer, C. A subterranean adaptive radiation of amphipods in Europe. Nat. Commun. 12, 1–12 (2021).Article 
CAS 

Google Scholar 
25.SubBioDB. Subterranean Fauna Database. Research group for speleobiology, Biotechnical faculty, University of Ljubljana. https://db.subbio.net/ (2021).26.Culver, D. C., Fong, D. W. & Jernigan, R. W. Species interactions in cave stream communities: Experimental results and microdistribution effects. Am. Midl. Nat. 126, 364 (1991).Article 

Google Scholar 
27.Lavoie, K. H., Helf, K. L. & Poulson, T. L. The biology and ecology of North American cave crickets. J. Cave Karst Stud. 69, 114–134 (2007).
Google Scholar 
28.Ercoli, F. et al. Differing trophic niches of three French stygobionts and their implications for conservation of endemic stygofauna. Aquat. Conserv. Mar. Freshw. Ecosyst. 29, 2193–2203 (2019).Article 

Google Scholar 
29.Pacioglu, O. et al. Ecophysiological and life-history adaptations of Gammarus balcanicus (Schäferna, 1922) in a sinking-cave stream from Western Carpathians (Romania). Zoology 139, 125754 (2020).PubMed 
Article 
PubMed Central 

Google Scholar 
30.Parimuchová, A., Dušátková, L. P., Kováč, Ľ & Macháčková, T. The food web in a subterranean ecosystem is driven by intraguild predation. Sci. Rep. https://doi.org/10.1038/s41598-021-84521-1 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
31.Premate, E. et al. Cave amphipods reveal co-variation between morphology and trophic niche in a low-productivity environment. Freshw. Biol. 66, 1876–1888 (2021).Article 

Google Scholar 
32.Sacco, M. et al. Elucidating stygofaunal trophic web interactions via isotopic ecology. PLoS ONE 14, 1–25 (2019).MathSciNet 
Article 
CAS 

Google Scholar 
33.Pohlman, J. W., Iliffe, T. M. & Cifuentes, L. A. A stable isotope study of organic cycling and the ecology of an anchialine cave ecosystem. Mar. Ecol. Prog. Ser. 155, 17–27 (1997).ADS 
CAS 
Article 

Google Scholar 
34.Graening, G. O. & Brown, A. V. Ecosystem dynamics and pollution effects in an Ozark cave stream. J. Am. Water Resour. Assoc. 39, 1497–1507 (2003).ADS 
CAS 
Article 

Google Scholar 
35.Manenti, R., Melotto, A., Guillaume, O., Ficetola, G. F. & Lunghi, E. Switching from mesopredator to apex predator: How do responses vary in amphibians adapted to cave living?. Behav. Ecol. Sociobiol. 74, 1–13 (2020).Article 

Google Scholar 
36.Uiblein, F. & Juberthie, C. Predation in caves: the effects of prey immobility and darkness on the foraging behaviour of two salamanders, Euproctus asper and Proteus anguinus. Behav. Process. 28, 33–40 (1992).CAS 
Article 

Google Scholar 
37.Prevorčnik, S., Verovnik, R., Zagmajster, M. & Sket, B. Biogeography and phylogenetic relations within the Dinaric subgenus Monolistra (Microlistra) (Crustacea: Isopoda: Sphaeromatidae), with a description of two new species. Zool. J. Linn. Soc. 159, 1–21 (2010).Article 

Google Scholar 
38.Mammola, S. Finding answers in the dark: Caves as models in ecology fifty years after Poulson and White. Ecography 42, 1331–1351 (2019).Article 

Google Scholar 
39.Culver, D. C. & Pipan, T. The Biology of Caves and Other Subterranean Habitats (Oxford University Press, 2009).
Google Scholar 
40.Kellner, K. F. & Swihart, R. K. Accounting for imperfect detection in ecology: A quantitative review. PLoS ONE 9, e111436 (2014).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
41.Mackenzie, D. I., Bailey, L. L. & Nichols, J. D. Investigating species co-occurrence patterns when species are detected imperfectly. J. Anim. Ecol. 73, 546–555 (2004).Article 

Google Scholar 
42.Vörös, J., Márton, O., Schmidt, B. R., Tünde Gál, J. & Jelić, D. Surveying Europe’s only cave-dwelling chordate species (Proteus anguinus) using environmental DNA. PLoS ONE 12, e0170945 (2017).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
43.Niemiller, M. L. et al. Evaluation of eDNA for groundwater invertebrate detection and monitoring: A case study with endangered Stygobromus (Amphipoda: Crangonyctidae). Conserv. Genet. Resour. 10, 247–257 (2018).Article 

Google Scholar 
44.Yonezawa, S., Nakano, T., Nakahama, N., Tomikawa, K. & Isagi, Y. Environmental DNA reveals cryptic diversity within the subterranean amphipod genus Pseudocrangonyx Akatsuka & Komai, 1922 (Amphipoda: Crangonyctoidea: Pseudocrangonyctidae) from Central Japan. J. Crustac. Biol. 40, 479–483 (2020).Article 

Google Scholar 
45.Arntzen, J. W. et al. Proteus anguinus. IUCN Red List Threat. Species (2009).46.Communities, T. C. of E. Council Directive 92/43/EEC of 21 May 1992 on the conservation of natural habitats and of wild fauna and flora. Official J. Eur. Communities 35, 8–51 (1992).
Google Scholar 
47.Vörös, J., Ursenbacher, S. & Jelić, D. Population genetic analyses using 10 new polymorphic microsatellite loci confirms genetic subdivision within the olm, Proteus anguinus. J. Hered. 110, 211–218 (2019).PubMed 
Article 
PubMed Central 

Google Scholar 
48.Gorički, Š & Trontelj, P. Structure and evolution of the mitochondrial control region and flanking sequences in the European cave salamander Proteus anguinus. Gene 378, 31–41 (2006).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
49.Gravel, D., Albouy, C. & Thuiller, W. The meaning of functional trait composition of food webs for ecosystem functioning. Philos. Trans. R. Soc. B Biol. Sci. 371, 20150268 (2016).Article 

Google Scholar 
50.Schmitz, O. Predator and prey functional traits: Understanding the adaptive machinery driving predator-prey interactions. F1000Research 6, 1767 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
51.R Development Core Team. A language and environment for statistical computing. (2020).52.R Studio Team. RStudio: Integrated Development for R. (2020).53.Wickham, H. & Bryan, J. readxl: Read Excel Files. R package version 1.3.1. (2019).54.Dragulescu, A. A. & Arendt, C. xlsx: Read, Write, Format Excel 2007 and Excel 97/2000/XP/2003 Files. R package version 0.6.1. (2018).55.Wickham, H., Francois, R., Henry, L. & Müller, K. dplyr: A Grammar of Data Manipulation. R package version 0.8.3. (2019).56.Wickham, H. ggplot2: Elegant Graphics for Data Analysis. (Springer-Verlag, 2016).57.Kong, D. Ipaper: Collection of personal practical R functions. (2021).58.Pebesma, E. Simple features for R: Standardized support for spatial vector data. R J. 10, 439–446 (2018).Article 

Google Scholar 
59.Hijmas, R. J. raster: Geographic Data Analysis and Modeling. (2020).60.Baddeley, A., Rubak, E. & Turner, R. Spatial Point Patterns: Methodology and Applications with R (Chapman and Hall/CRC Press, 2015).MATH 
Book 

Google Scholar 
61.Kassambara, A. rstatix: Pipe-Friendly Framework for Basic Statistical Tests. R package version 0.5.0. (2020).62.Griffith, D. M., Veech, J. A. & Marsh, C. J. Cooccur: Probabilistic species co-occurrence analysis in R. J. Stat. Softw. 69, 1–17 (2016).Article 

Google Scholar 
63.Revell, L. J. phytools: An R package for phylogenetic comparative biology (and other things). Methods Ecol. Evol. 3, 217–223 (2012).Article 

Google Scholar 
64.Meade, A. & Pagel, M. Bayes Traits V3. (2017).65.Griffin, R. H. btw: Run BayesTraitsV3 from R. (2018). More

1.McFall-Ngai, M. et al. Animals in a bacterial world, a new imperative for the life sciences. Proc. Natl Acad. Sci. USA 110, 3229–3236 (2013).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
2.Bang, C. et al. Metaorganisms in extreme environments: do microbes play a role in organismal adaptation? Zoology 127, 1–9 (2018).PubMed 
Article 
PubMed Central 

Google Scholar 
3.Mueller, U. G. & Sachs, J. L. Engineering microbiomes to improve plant and animal health. Trends Microbiol. 23, 606–617 (2015).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
4.Theis, K. R., Whittaker, D. J. & Rojas, C. A. A hologenomic approach to animal behavior. In Evolution in Action: Past, Present and Future 247–263 (Springer, 2020).5.Foster, K. R., Schluter, J., Coyte, K. Z. & Rakoff-Nahoum, S. The evolution of the host microbiome as an ecosystem on a leash. Nature 548, 43–51 (2017).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
6.Ziegler, M., Seneca, F. O., Yum, L. K., Palumbi, S. R. & Voolstra, C. R. Bacterial community dynamics are linked to patterns of coral heat tolerance. Nat. Commun. 8, 1–8 (2017).Article 
CAS 

Google Scholar 
7.Robbins, S. J. et al. A genomic view of the reef-building coral Porites lutea and its microbial symbionts. Nat. Microbiol. 4, 2090–2100 (2019).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
8.Berendsen, R. L., Pieterse, C. M. & Bakker, P. A. The rhizosphere microbiome and plant health. Trends Plant Sci. 17, 478–486 (2012).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
9.Voolstra, C. R. & Ziegler, M. Adapting with microbial help: Microbiome flexibility facilitates rapid responses to environmental change. BioEssays 2, 2000004 (2020).Article 

Google Scholar 
10.Cárdenas, C. A., Bell, J. J., Davy, S. K., Hoggard, M. & Taylor, M. W. Influence of environmental variation on symbiotic bacterial communities of two temperate sponges. FEMS Microbiol. Ecol. 88, 516–527 (2014).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
11.Pantos, O., Bongaerts, P., Dennis, P. G., Tyson, G. W. & Hoegh-Guldberg, O. Habitat-specific environmental conditions primarily control the microbiomes of the coral Seriatopora hystrix. ISME J. 9, 1916–1927 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
12.Roder, C., Bayer, T., Aranda, M., Kruse, M. & Voolstra, C. R. Microbiome structure of the fungid coral Ctenactis echinata aligns with environmental differences. Mol. Ecol. 24, 3501–3511 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
13.Neave, M. J. et al. Differential specificity between closely related corals and abundant Endozoicomonas endosymbionts across global scales. ISME J. 11, 186–200 (2017).PubMed 
Article 
PubMed Central 

Google Scholar 
14.Carrier, T. J. & Reitzel, A. M. Convergent shifts in host-associated microbial communities across environmentally elicited phenotypes. Nat. Commun. 9, 1–9 (2018).CAS 
Article 

Google Scholar 
15.Pollock, F. J. et al. Coral-associated bacteria demonstrate phylosymbiosis and cophylogeny. Nat. Commun. 9, 1–13 (2018).CAS 
Article 

Google Scholar 
16.Glasl, B., Smith, C. E., Bourne, D. G. & Webster, N. S. Disentangling the effect of host-genotype and environment on the microbiome of the coral Acropora tenuis. PeerJ 7, e6377 (2019).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
17.Macke, E., Callens, M., De Meester, L. & Decaestecker, E. Host-genotype dependent gut microbiota drives zooplankton tolerance to toxic cyanobacteria. Nat. Commun. 8, 1–13 (2017).CAS 
Article 

Google Scholar 
18.Casey, J. M., Connolly, S. R. & Ainsworth, T. D. Coral transplantation triggers shift in microbiome and promotion of coral disease associated potential pathogens. Sci. Rep. 5, 11903 (2015).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
19.Ziegler, M. et al. Coral bacterial community structure responds to environmental change in a host-specific manner. Nat. Commun. 10, 1–11 (2019).ADS 
CAS 
Article 

Google Scholar 
20.Turnbaugh, P. J. et al. A core gut microbiome in obese and lean twins. Nature 457, 480–484 (2009).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
21.Spor, A., Koren, O. & Ley, R. Unravelling the effects of the environment and host genotype on the gut microbiome. Nat. Rev. Microbiol. 9, 279–290 (2011).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
22.Rothschild, D. et al. Environment dominates over host genetics in shaping human gut microbiota. Nature 555, 210–215 (2018).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
23.Jaspers, C. et al. Resolving structure and function of metaorganisms through a holistic framework combining reductionist and integrative approaches. Zoology 113, 81–87 (2019).Article 

Google Scholar 
24.Blackall, L. L., Wilson, B. & van Oppen, M. J. H. Coral—the world’s most diverse symbiotic ecosystem. Mol. Ecol. 24, 5330–5347 (2015).PubMed 
Article 
PubMed Central 

Google Scholar 
25.Hernandez-Agreda, A., Gates, R. D. & Ainsworth, T. D. Defining the core microbiome in corals’ microbial soup. Trends Microbiol. 25, 125–140 (2017).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
26.LaJeunesse, T. C. et al. Systematic revision of Symbiodiniaceae highlights the antiquity and diversity of coral endosymbionts. Curr. Biol. 28, 2570–2580 (2018).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
27.Rohwer, F., Seguritan, V., Azam, F. & Knowlton, N. Diversity and distribution of coral-associated bacteria. Mar. Ecol. Prog. Ser. 243, 1–10 (2002).ADS 
Article 

Google Scholar 
28.Rosenberg, E., Koren, O., Reshef, L., Efrony, R. & Zilber-Rosenberg, I. The role of microorganisms in coral health, disease and evolution. Nat. Rev. Microbiol. 5, 355–362 (2007).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
29.Bourne, D. G., Morrow, K. M. & Webster, N. S. Insights into the coral microbiome: underpinning the health and resilience of reef ecosystems. Annu. Rev. Microbiol. 70, 317–340 (2016).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
30.Muscatine, L., Porter, J. W. & Kaplan, I. R. Resource partitioning by reef corals as determined from stable isotope composition. Mar. Biol. 100, 185–193 (1989).Article 

Google Scholar 
31.Rädecker, N., Pogoreutz, C., Voolstra, C. R., Wiedenmann, J. & Wild, C. Nitrogen cycling in corals: the key to understanding holobiont functioning? Trends Microbiol. 23, 490–497 (2015).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
32.Wegley, L., Edwards, R., Rodriguez‐Brito, B., Liu, H. & Rohwer, F. Metagenomic analysis of the microbial community associated with the coral Porites astreoides. Environ. Microbiol. 9, 2707–2719 (2007).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
33.Raina, J. B., Tapiolas, D., Willis, B. L. & Bourne, D. G. Coral-associated bacteria and their role in the biogeochemical cycling of sulfur. Appl. Environ. Microbiol. 75, 3492–3501 (2009).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
34.Lema, K. A., Willis, B. L. & Bourne, D. G. Corals form characteristic associations with symbiotic nitrogen-fixing bacteria. Appl. Environ. Microbiol. 78, 3136–3144 (2012).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
35.Krediet, C. J., Ritchie, K. B., Paul, V. J. & Teplitski, M. Coral-associated micro-organisms and their roles in promoting coral health and thwarting diseases. Proc. R. Soc. B 280, 20122328 (2013).PubMed 
PubMed Central 
Article 

Google Scholar 
36.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).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
37.Boilard, A. et al. Defining coral bleaching as a microbial dysbiosis within the coral holobiont. Microorganisms 8, 1682 (2020).CAS 
PubMed Central 
Article 

Google Scholar 
38.Apprill, A., Weber, L. G. & Santoro, A. E. Distinguishing between microbial habitats unravels ecological complexity in coral microbiomes. mSystems 1, e00143–16 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
39.Glasl, E.B., B. et al. Microbial indicators of environmental perturbations in coral reef ecosystems. Microbiome 7, 1–13 (2019).Article 

Google Scholar 
40.Damjanovic, K., Blackall, L. L., Peplow, L. M. & van Oppen, M. J. H. Assessment of bacterial community composition within and among Acropora loripes colonies in the wild and in captivity. Coral Reefs 39, 1245–1255 (2020).Article 

Google Scholar 
41.Dubé, E. B. et al. Ecology, biology and genetics of Millepora hydrocorals on coral reefs. In Invertebrates – Ecophysiology and Management (eds. Ray, S., Diarte-Plata, G. &  Escamilla-Montes, R.), (IntechOpen, 2019).42.Rodríguez, L. et al. Genetic relationships of the hydrocoral Millepora alcicornis and its symbionts within and between locations across the Atlantic. Coral Reefs 38, 255–268 (2019).ADS 
Article 

Google Scholar 
43.Lewis, J. B. Biology and ecology of the hydrocoral Millepora on coral reefs. Adv. Mar. Biol. 50, 1–55 (2006).PubMed 
Article 
PubMed Central 

Google Scholar 
44.Arrigoni, R. et al. An integrated morpho-molecular approach to delineate species boundaries of Millepora from the Red Sea. Coral Reefs 37, 967–984 (2018).ADS 
Article 

Google Scholar 
45.Boissin, E., Leung, J. K., Denis, V., Bourmaud, C. A. & Gravier-Bonnet, N. Morpho-molecular delineation of structurally important reef species, the fire corals, Millepora spp., at Réunion Island, Southwestern Indian Ocean. Hydrobiologia 847, 1237–1255 (2020).Article 

Google Scholar 
46.Dubé, C. E., Boissin, E., Maynard, J. A. & Planes, S. Fire coral clones demonstrate phenotypic plasticity among reef habitats. Mol. Ecol. 26, 3860–3869 (2017).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
47.Schwartzman, J. A. & Ruby, E. G. Stress as a normal cue in the symbiotic environment. Trends Microbiol. 24, 414–424 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
48.van Oppen, M. J. H. et al. Adaptation to reef habitats through selection on the coral animal and its associated microbiome. Mol. Ecol. 27, 2956–2971 (2018).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
49.Sunagawa, S. et al. Structure and function of the global ocean microbiome. Science 348, 6237 (2015).Article 
CAS 

Google Scholar 
50.Douglas, G. M. et al. PICRUSt2 for prediction of metagenome functions. Nat. Biotechnol. 38, 685–688 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
51.Hernandez-Agreda, A., Leggat, W., Bongaerts, P., Herrera, C. & Ainsworth, T. D. Rethinking the coral microbiome: simplicity exists within a diverse microbial biosphere. MBio 9, e00812–18 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
52.Bongaerts, P. et al. Adaptive divergence in a scleractinian coral: physiological adaptation of Seriatopora hystrix to shallow and deep reef habitats. BMC Evol. Biol. 11, 303 (2011).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
53.Albright, R., Benthuysen, J., Cantin, N., Caldeira, K. & Anthony, K. Coral reef metabolism and carbon chemistry dynamics of a coral reef flat. Geophys. Res. Lett. 42, 3980–3988 (2015).ADS 
CAS 
Article 

Google Scholar 
54.Pootakham, W. et al. Dynamics of coral‐associated microbiomes during a thermal bleaching event. MicrobiologyOpen 7, e00604 (2018).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
55.Neave, M. J., Apprill, A., Ferrier-Pagès, C. & Voolstra, C. R. Diversity and function of prevalent symbiotic marine bacteria in the genus Endozoicomonas. Appl. Microbiol. Biotechnol. 100, 8315–8324 (2016).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
56.Meyer, J. L., Paul, V. J. & Teplitski, M. Community shifts in the surface microbiomes of the coral Porites astreoides with unusual lesions. PLoS ONE 9, e100316 (2014).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
57.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).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
58.Jessen, C. et al. In-situ effects of eutrophication and overfishing on physiology and bacterial diversity of the Red Sea coral Acropora hemprichii. PLoS ONE 8, e62091 (2013).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
59.Morrow, K. M. et al. Natural volcanic CO2 seeps reveal future trajectories for host–microbial associations in corals and sponges. ISME J. 9, 894–908 (2015).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
60.Dubé, C. E., Ky, C. L. & Planes, S. Microbiome of the black-lipped pearl oyster Pinctada margaritifera, a multi-tissue description with functional profiling. Front. Microbiol. 10, 1548 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
61.Neave, M. J., Michell, C. T., Apprill, A. & Voolstra, C. R. Endozoicomonas genomes reveal functional adaptation and plasticity in bacterial strains symbiotically associated with diverse marine hosts. Sci. Rep. 7, 40579 (2017).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
62.Tandon, K. et al. Comparative genomics: dominant coral-bacterium Endozoicomonas acroporae metabolizes dimethylsulfoniopropionate (DMSP). ISME J. 14, 1290–1303 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
63.Ngugi, D. K., Ziegler, M., Duarte, C. M. & Voolstra, C. R. Genomic blueprint of glycine betaine metabolism in coral metaorganisms and their contribution to reef nitrogen budgets. iScience 23, 101120 (2020).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
64.González, J. M., Kiene, R. P. & Moran, M. A. Transformation of sulfur compounds by an abundant lineage of marine bacteria in the α-subclass of the class Proteobacteria. Appl. Environ. Microbiol. 65, 3810–3819 (1999).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
65.Curson, A. R. J., Rogers, R., Todd, J. D., Brearley, C. A. & Johnston, A. W. B. Molecular genetic analysis of a dimethylsulfoniopropionate lyase that liberates the climate-changing gas dimethylsulfide in several marine α-proteobacteria and Rhodobacter spharoides. Environ. Microbiol. 10, 757–767 (2008).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
66.Reisch, C. R., Moran, M. A. & Whitman, W. B. Bacterial catabolism of dimethylsulfoniopropionate (DMSP). Front. Microbiol. 2, 172 (2011).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
67.Thompson, J. R., Rivera, H. E., Closek, C. J. & Medina, M. Microbes in the coral holobiont: partners through evolution, development, and ecological interactions. Front. Cell. Infect. Microbiol. 4, 176 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
68.Durante, M. K., Baums, I. B., Williams, D. E., Vohsen, S. & Kemp, D. W. What drives phenotypic divergence among coral clonemates of Acropora palmata? Mol. Ecol. 28, 3208–3224 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
69.Wagner, M. R. et al. Host genotype and age shape the leaf and root microbiomes of a wild perennial plant. Nat. Commun. 7, 1–5 (2016).ADS 
CAS 
Article 

Google Scholar 
70.Fuerst, J. & Sagulenko, E. Beyond the bacterium: planctomycetes challenge our concepts of microbial structure and function. Nat. Rev. Microbiol. 9, 403–413 (2011).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
71.Forquin-Gomez, M. P. et al. The family Brevibacteriaceae. In Prokaryotes Actinobacteria. 4th edn., (eds. Rosenberg E. et al.), 141–153 (Springer, 2014).72.Baker, B. J., Lazar, C. S., Teske, A. P. & Dick, G. J. Genomic resolution of linkages in carbon, nitrogen, and sulfur cycling among widespread estuary sediment bacteria. Microbiome 3, 14 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
73.Tian, R. M. et al. Genomic analysis reveals versatile heterotrophic capacity of a potentially symbiotic sulfur‐oxidizing bacterium in sponge. Environ. Microbiol. 16, 3548–3561 (2014).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
74.Gauthier, M. E., Watson, J. R. & Degnan, S. M. Draft genomes shed light on the dual bacterial symbiosis that dominates the microbiome of the coral reef sponge Amphimedon queenslandica. Front. Mar. Sci. 3, 196 (2016).Article 

Google Scholar 
75.Dyksma, S. et al. Ubiquitous Gammaproteo-bacteria dominate dark carbon fixation in coastal sediments. ISME J. 8, 1939–1953 (2016).Article 
CAS 

Google Scholar 
76.Raina, J. B., Dinsdale, E. A., Willis, B. L. & Bourne, D. G. Do the organic sulfur compounds DMSP and DMS drive coral microbial associations? Trends Microbiol. 18, 101–108 (2010).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
77.Morrow, K. M., Moss, A. G., Chadwick, N. E. & Liles, M. R. Bacterial associates of two Caribbean coral species reveal species-specific distribution and geographic variability. Appl. Environ. Microbiol. 78, 6438–6449 (2012).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
78.Sabdono, A. & Radjasa, O. K. Phylogenetic diversity of organophosphorous pesticide-degrading coral bacteria from mid-west coast of Indonesia. Biotechnology 7, 694–701 (2008).CAS 
Article 

Google Scholar 
79.Kannapiran, E. & Ravindran, J. Dynamics and diversity of phosphate mineralizing bacteria in the coral reefs of Gulf of Mannar. J. Basic Microbiol. 52, 91–98 (2012).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
80.Mahmoud, H. M. & Kalendar, A. A. Coral-associated actinobacteria: diversity, abundance, and biotechnological potentials. Front. Microbiol. 7, 204 (2016).PubMed 
PubMed Central 

Google Scholar 
81.Probandt, D. et al. Permeability shapes bacterial communities in sublittoral surface sediments. Environ. Microbiol. 19, 1584–1599 (2017).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
82.Doolittle, W. F. & Booth, A. It’s the song, not the singer: an exploration of holobiosis and evolutionary theory. Biol. Philos. 32, 5–24 (2017).Article 

Google Scholar 
83.Louca, S. et al. Function and functional redundancy in microbial systems. Nat. Ecol. Evol. 2, 936–943 (2018).PubMed 
Article 
PubMed Central 

Google Scholar 
84.Kelly, L. W. et al. Local genomic adaptation of coral reef-associated microbiomes to gradients of natural variability and anthropogenic stressors. Proc. Natl Acad. Sci. USA 111, 10227–10232 (2014).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
85.Peixoto, R. S., Rosado, P. M., Leite, D. C. D. A., Rosado, A. S. & Bourne, D. G. Beneficial microorganisms for corals (BMC): proposed mechanisms for coral health and resilience. Front. Microbiol. 8, 341 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
86.Peixoto, R. S. et al. Coral probiotics: premise, promise, prospects. Annu. Rev. Anim. Biosci. 9, 265–288 (2021).PubMed 
Article 
PubMed Central 

Google Scholar 
87.Voolstra, C. R. et al. Extending the natural adaptive capacity of coral holobionts. Nat Rev Earth Environ. 1–16 (2021). https://doi.org/10.1038/s43017-021-00214-3.88.Santoro, E. P. et al. Coral microbiome manipulation elicits metabolic and genetic restructuring to mitigate heat stress and evade mortality. Sci Adv. 7 (2021). https://doi.org/10.1126/sciadv.abg3088.89.Adam, T. C. et al. Landscape‐scale patterns of nutrient enrichment in a coral reef ecosystem: implications for coral to algae phase shifts. Ecol. Appl. 31, e2227 (2021).PubMed 
Article 
PubMed Central 

Google Scholar 
90.Buckling, A., Kassen, R., Bell, G. & Rainey, P. B. Disturbance and diversity in experimental microcosms. Nature 408, 961–964 (2000).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
91.Berga, M., Szekely, A. J. & Langenheder, S. Effects of disturbance intensity and frequency on bacterial community composition and function. PLoS ONE 7, e36959 (2012).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
92.Neulinger, S. C., Järnegren, J., Ludvigsen, M., Lochte, K. & Dullo, W. C. Phenotype-specific bacterial communities in the cold-water coral Lophelia pertusa (Scleractinia) and their implications for the coral’s nutrition, health, and distribution. Appl. Environ. Microbiol. 74, 7272–7285 (2008).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
93.Kanukollu, S. et al. Distinct compositions of free-living, particle-associated and benthic communities of the Roseobacter group in the North Sea. FEMS Microbiol. Ecol. 92, 1 (2016).Article 
CAS 

Google Scholar 
94.Santos, H. F. et al. Climate change affects key nitrogen-fixing bacterial populations on coral reefs. ISME J. 8, 2272–2279 (2014).PubMed 
PubMed Central 
Article 

Google Scholar 
95.Sorokin, D. Y., Tourova, T. P. & Muyzer, G. Citreicella thiooxidans gen. nov., sp. nov., a novel lithoheterotrophic sulfur-oxidizing bacterium from the Black Sea. Syst. Appl. Microbiol. 28, 679–687 (2005).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
96.Chen, Y. J. et al. Metabolic flexibility allows generalist bacteria to become dominant in a frequently disturbed ecosystem. bioRxiv (2020). Preprint at https://doi.org/10.1101/2020.02.12.94522097.Spring, S., Scheuner, C., Göker, M. & Klenk, H. P. A taxonomic framework for emerging groups of ecologically important marine gammaproteobacteria based on the reconstruction of evolutionary relationships using genome-scale data. Front. Microbiol. 9, 281 (2015).
Google Scholar 
98.Preston, G. M. Metropolitan microbes: type III secretion in multi-host symbionts. Cell Host Microbe 2, 291–294 (2007).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
99.Lutz, A., Raina, J.-B., Motti, C. A., Miller, D. J. & van Oppen, M. J. H. Host coenzyme Q redox state is an early biomarker of thermal stress in the coral Acropora millepora. PLoS ONE 10, e0139290 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
100.Smith, D. J., Suggett, D. J. & Baker, N. R. Is photoinhibition of zooxanthellae photosynthesis the primary cause of thermal bleaching in corals? Glob. Chang. Biol. 11, 1–11 (2005).ADS 
Article 

Google Scholar 
101.Gardner, S. G. et al. A multi-trait systems approach reveals a response cascade to bleaching in corals. BMC Biol. 15, 1–14 (2017).Article 
CAS 

Google Scholar 
102.Lema, K. A., Bourne, D. G. & Willis, B. L. Onset and establishment of diazotrophs and other bacterial associates in the early life history stages of the coral Acropora millepora. Mol. Ecol. 23, 4682–4695 (2014).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
103.Pogoreutz, C. et al. Nitrogen fixation aligns with nifH abundance and expression in two coral trophic functional groups. Front. Microbiol. 8, 1187 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
104.Marangoni, L. F. et al. Peroxynitrite generation and increased heterotrophic capacity are linked to the disruption of the coral–dinoflagellate symbiosis in a scleractinian and hydrocoral species. Microorganisms 7, 426 (2019).PubMed Central 
Article 
CAS 

Google Scholar 
105.Quigley, K. M., Alvarez Roa, C., Torda, G., Bourne, D. G. & Willis, B. L. Co‐dynamics of Symbiodiniaceae and bacterial populations during the first year of symbiosis with Acropora tenuis juveniles. MicrobiologyOpen 9, e959 (2020).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
106.Dubé, C. E., Mercière, A., Vermeij, M. J. A. & Planes, S. Population structure of the hydrocoral Millepora platyphylla in habitats experiencing different flow regimes in Moorea, French Polynesia. PLoS ONE 12, e0173513 (2017).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
107.Agostini, S. et al. Biological and chemical characteristics of the coral gastric cavity. Coral Reefs 31, 147–156 (2012).ADS 
Article 

Google Scholar 
108.Williams, A. D., Brown, B. E., Putchim, L. & Sweet, M. J. Age-related shifts in bacterial diversity in a reef coral. PLoS ONE 10, e0144902 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
109.Sweet, M. J., Brown, B. E., Dunne, R. P., Singleton, I. & Bulling, M. Evidence for rapid, tide-related shifts in the microbiome of the coral Coelastrea aspera. Coral Reefs 36, 815–828 (2017).ADS 
Article 

Google Scholar 
110.Dubé, C. E., Boissin, E., Mercière, A. & Planes, S. Parentage analyses identify local dispersal events and sibling aggregations in a natural population of Millepora hydrocorals, a free‐spawning marine invertebrate. Mol. Ecol. 29, 1508–1522 (2020).PubMed 
Article 
PubMed Central 

Google Scholar 
111.Abràmoff, M. D., Magalhães, P. J. & Ram, S. J. Image processing with ImageJ. Biophotonics Int. 11, 36–42 (2004).
Google Scholar 
112.Dubé, C. E., Planes, S., Zhou, Y., Berteaux-Lecellier, V. & Boissin, E. Genetic diversity and differentiation in reef-building Millepora species, as revealed by cross-species amplification of fifteen novel microsatellite loci. PeerJ 5, e2936 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
113.Arnaud-Haond, S. & Belkhir, K. GENCLONE: A computer pro- gram to analyze genotypic data, test for clonality and describe spatial clonal organization. Mol. Ecol. Notes 7, 15–17 (2007).CAS 
Article 

Google Scholar 
114.Peakall, R. & Smouse, P. E. GENALEX 6: Genetic analysis in Excel. Population genetic software for teaching and research. Mol. Ecol. Notes 6, 288–295 (2006).Article 

Google Scholar 
115.Wickham, H. ggplot2: Elegant Graphics for Data Analysis. (Springer, 2016).116.R Development Core Team. R: A language and environment for statistical computing (ISBN 3-900051-07-0, http://www.R-project.org/ (R Foundation for Statistical Computing, 2020).117.Andersson, A. F. et al. Comparative analysis of human gut microbiota by barcoded pyrosequencing. PloS ONE 3, e2836 (2008).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
118.Bolyen, E. et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat. Biotechnol. 37, 852–857 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
119.Callahan, B. J. et al. DADA2: high-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581–583 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
120.Kozich, J. J., Westcott, S. L., Baxter, N. T., Highlander, S. K. & Schloss, P. D. Development of a dual-index sequencing strategy and curation pipeline for analyzing amplicon sequence data on the MiSeq Illumina sequencing platform. Appl. Environ. Microbiol. 79, 5112–5120 (2013).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
121.Pedregosa, F. et al. Scikit-learn: Machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011).MathSciNet 
MATH 

Google Scholar 
122.Bokulich, N. A. et al. Optimizing taxonomic classification of marker-gene amplicon sequences with QIIME 2’s q2-feature-classifier plugin. Microbiome 6, 1–17 (2018).Article 

Google Scholar 
123.Yilmaz, P. et al. The SILVA and “All-species Living Tree Project (LTP)” taxonomic frameworks. Nucl. Acids Res. 42, D643–D648 (2014).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
124.Oksanen, J. et al. vegan: Community Ecology Package (2018).125.Weerdt, W. H. Transplantation experiments with Caribbean Millepora species (Hydrozoa, Coelenterata), including some ecological observations on growth forms. Bijdr. Dierkd. 51, 1–19 (1981).Article 

Google Scholar 
126.Cáceres, M. D. & Legendre, P. Associations between species and groups of sites: indices and statistical inference. Ecology 90, 3566–3574 (2009).PubMed 
Article 
PubMed Central 

Google Scholar 
127.Langille, M. G. et al. Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences. Nat. Biotechnol. 31, 814–821 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
128.Segata, N. et al. Metagenomic biomarker discovery and explanation. Genome Biol. 12, R60 (2011).PubMed 
PubMed Central 
Article 

Google Scholar  More

1.Parmesan, C. Ecological and evolutionary responses to recent climate change. Annu. Rev. Ecol. Evol. Syst. 37, 637–669 (2006).Article 

Google Scholar 
2.IPCC. Climate change 2014: Synthesis report. Contribution of working groups I, II and III to the fifth assessment report of the Intergovernmental Panel on Climate Change. (2014).3.Inouye, D. W., Barr, B., Armitage, K. B. & Inouye, B. D. Climate change is affecting altitudinal migrants and hibernating species. Proc. Natl. Acad. Sci. 97, 1630–1633 (2000).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
4.Adamík, P. & Král, M. Climate- and resource-driven long-term changes in dormice populations negatively affect hole-nesting songbirds. J. Zool. 275, 209–215 (2008).Article 

Google Scholar 
5.Ozgul, A. et al. Coupled dynamics of body mass and population growth in response to environmental change. Nature 466, 482–485 (2010).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
6.Moyes, K. et al. Advancing breeding phenology in response to environmental change in a wild red deer population. Glob. Chang. Biol. 17, 2455–2469 (2011).ADS 
Article 

Google Scholar 
7.Both, C., Van Asch, M., Bijlsma, R. G., Van Den Burg, A. B. & Visser, M. E. Climate change and unequal phenological changes across four trophic levels: Constraints or adaptations?. J. Anim. Ecol. 78, 73–83 (2009).PubMed 
Article 

Google Scholar 
8.Visser, M. E., Van Noordwijk, A. J., Tinbergen, J. M. & Lessells, C. M. Warmer springs lead to mistimed reproduction in great tits (Parus major). Proc. R. Soc. B Biol. Sci. 265, 1867–1870 (1998).Article 

Google Scholar 
9.Thackeray, S. J. et al. Trophic level asynchrony in rates of phenological change for marine, freshwater and terrestrial environments. Glob. Chang. Biol. 16, 3304–3313 (2010).ADS 
Article 

Google Scholar 
10.Spooner, F. E. B., Pearson, R. G. & Freeman, R. Rapid warming is associated with population decline among terrestrial birds and mammals globally. Glob. Chang. Biol. 24, 4521–4531 (2018).ADS 
PubMed 
Article 
PubMed Central 

Google Scholar 
11.Sheriff, M. J., Boonstra, R., Palme, R., Loren Buck, C. & Barnes, B. M. Coping with differences in snow cover: The impact on the condition, physiology and fitness of an arctic hibernator. Conserv. Physiol. 5, 1–12 (2017).Article 

Google Scholar 
12.Easterling, D. R. et al. Climate extremes: Observations, modeling, and impacts. Science 289, 2068–2075 (2000).ADS 
CAS 
Article 

Google Scholar 
13.IPCC. Managing the risks of extreme events and disasters to advance climate change adaptation: Special report of the Intergovernmental Panel on Climate Change. (2012).14.Krause, J. S. et al. The effect of extreme spring weather on body condition and stress physiology in Lapland longspurs and white-crowned sparrows breeding in the Arctic. Gen. Comp. Endocrinol. 237, 10–18 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
15.Latimer, C. E. & Zuckerberg, B. How extreme is extreme? Demographic approaches inform the occurrence and ecological relevance of extreme events. Ecol. Monogr. 89, 1–15 (2019).Article 

Google Scholar 
16.Gutschick, V. P. & BassiriRad, H. Extreme events as shaping physiology, ecology, and evolution of plants: Toward a unified definition and evaluation of their consequences. New Phytol. 160, 21–42 (2003).PubMed 
Article 
PubMed Central 

Google Scholar 
17.Bailey, L. D. & van de Pol, M. Tackling extremes: Challenges for ecological and evolutionary research on extreme climatic events. J. Anim. Ecol. 85, 85–96 (2016).PubMed 
Article 
PubMed Central 

Google Scholar 
18.Welbergen, J. A., Klose, S. M., Markus, N. & Eby, P. Climate change and the effects of temperature extremes on Australian flying-foxes. Proc. R. Soc. B Biol. Sci. 275, 419–425 (2008).Article 

Google Scholar 
19.Boucek, R. E. & Rehage, J. S. Climate extremes drive changes in functional community structure. Glob. Chang. Biol. 20, 1821–1831 (2014).ADS 
PubMed 
Article 
PubMed Central 

Google Scholar 
20.Hale, S. et al. Fire and climatic extremes shape mammal distributions in a fire-prone landscape. Divers. Distrib. 22, 1127–1138 (2016).Article 

Google Scholar 
21.Frederiksen, M., Daunt, F., Harris, M. P. & Wanless, S. The demographic impact of extreme events: Stochastic weather drives survival and population dynamics in a long-lived seabird. J. Anim. Ecol. 77, 1020–1029 (2008).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
22.Wingfield, J. C., Kelley, J. P. & Angelier, F. What are extreme environmental conditions and how do organisms cope with them?. Curr. Zool. 57, 363–374 (2011).Article 

Google Scholar 
23.Helm, B. et al. Annual rhythms that underlie phenology: Biological time-keeping meets environmental change. Proc. R. Soc. B Biol. Sci. 280, 1–10 (2013).
Google Scholar 
24.Sheriff, M. J., Richter, M. M., Buck, C. L. & Barnes, B. M. Changing seasonality and phenological responses of free-living male Arctic ground squirrels: The importance of sex. Philos. Trans. R. Soc. B Biol. Sci. 368, (2013).25.Michener, G. R. & Locklear, L. Differential costs of reproductive effort for male and female Richardson’s ground squirrels. Ecology 71, 855–868 (1990).Article 

Google Scholar 
26.Williams, C. T., Barnes, B. M., Kenagy, G. J. & Buck, C. L. Phenology of hibernation and reproduction in ground squirrels: Integration of environmental cues with endogenous programming. J. Zool. 292, 112–124 (2014).Article 

Google Scholar 
27.Michener, G. R. Age, sex, and species differences in the annual cycles of ground-dwelling sciurids: Implications for sociality. in The biology of ground-dwelling squirrels: annual cycles, behavioral ecology, and sociality (eds. Murie, J. O. & Michener, G. R.) 81–107 (University of Nebraska Press, Lincoln, 1984).28.Kenagy, G. J., Sharbaugh, S. M. & Nagy, K. A. Annual cycle of energy and time expenditure in a golden-mantled ground squirrel population. Oecologia 78, 269–282 (1989).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
29.Michener, G. R. Sexual Differences in over-winter torpor patterns of Richardson’s ground squirrels in natural hibernacula. Oecologia 89, 397–406 (1992).ADS 
PubMed 
Article 
PubMed Central 

Google Scholar 
30.Michener, G. R. Effect of climatic conditions on the annual activity and hibernation cycle of Richardson’s ground squirrels and Columbian ground squirrels. Can. J. Zool. 55, 693–703 (1977).Article 

Google Scholar 
31.Michener, G. R. The circannual cycle of Richardson’s ground squirrels in southern Alberta. J. Mammal. 60, 760–768 (1979).Article 

Google Scholar 
32.Sheriff, M. J., Buck, C. L. & Barnes, B. M. Autumn conditions as a driver of spring phenology in a free-living arctic mammal. Clim. Chang. Responses 2, 1–7 (2015).Article 

Google Scholar 
33.Edic, M. N., Martin, J. G. A. & Blumstein, D. T. Heritable variation in the timing of emergence from hibernation. Evol. Ecol. 34, 763–776 (2020).Article 

Google Scholar 
34.Lane, J. E., Kruuk, L. E. B., Charmantier, A., Murie, J. O. & Dobson, F. S. Delayed phenology and reduced fitness associated with climate change in a wild hibernator. Nature 489, 554–557 (2012).ADS 
CAS 
PubMed 
Article 

Google Scholar 
35.Dobson, F. S., Lane, J. E., Low, M. & Murie, J. O. Fitness implications of seasonal climate variation in Columbian ground squirrels. Ecol. Evol. 6, 5614–5622 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
36.Armitage, K. B. Climate change and the conservation of marmots. Nat. Sci. 05, 36–43 (2013).
Google Scholar 
37.Neuhaus, P., Bennett, R. & Hubbs, A. Effects of a late snowstorm and rain on survival and reproductive success in Columbian ground squirrels (Spermophilus columbianus). Can. J. Zool. 77, 879–884 (1999).Article 

Google Scholar 
38.Williams, C. T. et al. Sex-dependent phenological plasticity in an arctic hibernator. Am. Nat. 190, 854–859 (2017).PubMed 
Article 

Google Scholar 
39.Barnes, B. M. Relationship between hibernation and reproduction in male ground squirrels. in Adaptations to the Cold: Tenth International Hibernation Symposium (eds. Geiser, F., Hulbert, A. J. & Nicol, S. C.) 71–80 (University of New England Press, 1996).40.Lee, T. M., Pelz, K., Licht, P. & Zucker, I. Testosterone influences hibernation in golden-mantled ground squirrels. Am. J. Physiol. Regul. Integr. Comput. Physiol. 259, 760–767 (1990).Article 

Google Scholar 
41.Richter, M. M., Barnes, B. M., Reilly, K. M. O., Fenn, A. M. & Buck, C. L. The influence of androgens on hibernation phenology of free-livingmale arctic ground squirrels. Horm. Behav. 89, 92–97 (2017).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
42.Michener, G. R. Spring emergence schedules and vernal behavior of Richardson’s ground squirrels: Why do males emerge from hibernation before females?. Behav. Ecol. Sociobiol. 14, 29–38 (1983).Article 

Google Scholar 
43.Wells, L. J. Seasonal sexual Rhythm and its experimental modification in the male of the thirteen-lined ground squirrel (Citellus tridecemlineatus). Anat. Rec. 62, 409–447 (1935).Article 

Google Scholar 
44.Michener, G. R. & Locklear, L. Over-winter weight loss by Richardson’s ground squirrels in relation to sexual differences in mating effort. J. Mammal. 71, 489–499 (1990).Article 

Google Scholar 
45.Poiani, A. Complexity of seminal fluid: A review. Behav. Ecol. Sociobiol. 60, 289–310 (2006).Article 

Google Scholar 
46.Michener, G. R. Estrous and gestation periods in Richardson’s ground squirrels. J. Mammal. 61, 531–534 (1980).Article 

Google Scholar 
47.Michener, G. R. Chronology of reproductive events for female Richardson’s ground aquirrels. J. Mammal. 66, 280–288 (1985).Article 

Google Scholar 
48.Michener, G. R. & McLean, I. G. Reproductive behaviour and operational sex ratio in Richardson’s ground squirrels. Anim. Behav. 52, 743–758 (1996).Article 

Google Scholar 
49.Hare, J. F., Todd, G. & Untereiner, W. A. Multiple mating results in multiple paternity in Richardson’s Ground Squirrels Spermophilus richardsonii. Can. Field Nat. 118, 90–94 (2004).Article 

Google Scholar 
50.Grumm, R., Arnott, J. & Halblaub, J. The epic eastern North American warm episode of March 2012. J. Oper. Meteorol. 2, 36–50 (2014).Article 

Google Scholar 
51.Environment and Climate Change Canada (ECCC). Top ten weather stories for 2012: story four—March’s meteorological mildness. (2017). Available at: https://www.ec.gc.ca/meteo-weather/default.asp?lang=En&n=70B4A3E9-1. (Accessed: 20th May 2020)52.Wilson, D. F. & Hare, J. F. Ground squirrel uses ultrasonic alarms. Nature 430, 523 (2004).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
53.Waterman, J. M., Macklin, G. F. & Enright, C. Sex-biased parasitism in Richardson’s ground squirrels (Urocitellus richardsonii) depends on the parasite examined. Can. J. Zool. 92, 73–79 (2014).Article 

Google Scholar 
54.Murie, J. O. & Harris, M. A. Annual variation of spring emergence and breeding in Columbian ground squirrels (Spermophilus columbianus). J. Mammal. 63, 431–439 (1982).Article 

Google Scholar 
55.Sikes, R. S. & Gannon, W. L. Guidelines of the American Society of Mammalogists for the use of wild mammals in research. J. Mammal. 92, 235–253 (2011).Article 

Google Scholar 
56.Gannon, W. L. & Sikes, R. S. Guidelines of the American society of mammalogists for the use of wild mammals in research. J. Mammal. 88, 809–823 (2007).Article 

Google Scholar 
57.Zucker, I. & Boshes, M. Circannual body weight rhythms of ground squirrels: Role of gonadal hormones. Am. J. Physiol. Regul. Int. Comput. Physiol. 12, 546–551 (1982).Article 

Google Scholar 
58.Boonstra, R., Hubbs, A. H., Lacey, E. A. & McColl, C. J. Seasonal changes in glucocorticoid and testosterone concentrations in free-living arctic ground squirrels from the boreal forest of the Yukon. Can. J. Zool. 79, 49–58 (2001).Article 

Google Scholar 
59.Bottini Luzardo, M., Centurion Castro, F., Alfaro Gamboa, M., Lopez, A. & Ake Lopez, A. Osmolarity of coconut water (Cocos nucifera) based diluents and their effect over viability of frozen boar semen. Am. J. Anim. Vet. Sci. 5, 187–191 (2010).Article 

Google Scholar 
60.Mollineau, W. M., Adogwa, A. O. & Garcia, G. W. Liquid and frozen storage of agouti (Dasyprocta leporina) semen extended with UHT milk, unpasteurized coconut water, and pasteurized coconut water. Vet. Med. Int. 2011, 1–5 (2011).Article 

Google Scholar 
61.Schulte-Hostedde, A. I., Millar, J. S. & Hickling, G. J. Evaluating body condition in small mammals. Can. J. Zool. 79, 1021–1029 (2001).Article 

Google Scholar 
62.Møller, A. P. & Birkhead, T. R. Copulation behaviour in mammals: Evidence that sperm competition is widespread. Biol. J. Linn. Soc. 38, 119–131 (1989).Article 

Google Scholar 
63.Sugg, D. W. & Chesser, R. K. Effective population sizes with multiple paternity. Genetics 137, 1147–1155 (1994).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
64.Murie, J. O. & Harris, M. A. Territoriality and dominance in male Columbian ground squirrels (Spermophilus columbianus). Can. J. Zool. 56, 2402–2412 (1978).Article 

Google Scholar 
65.Morton, M. L. & Gallup, J. S. Reproductive cycle of the Belding ground squirrel (Spermophilus beldingi beldingi): Seasonal and age differences. Gt. Basin Nat. 35, 427–433 (1975).
Google Scholar 
66.Barnes, B. M., Kretzmann, M., Licht, P. & Zucker, I. The influence of hibernation on testis growth and spermatogenesis in the golden-mantled ground squirrel Spermophilus lateralis. Biol. Reprod. 35, 1289–1297 (1986).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar  More

1.Eckstein, F. Phosphorothioation of DNA in bacteria. Nat. Chem. Biol. 3, 689–690 (2007).CAS 
PubMed 
Article 

Google Scholar 
2.Wang, L. et al. Phosphorothioation of DNA in bacteria by dnd genes. Nat. Chem. Biol. 3, 709–710 (2007).CAS 
PubMed 
Article 

Google Scholar 
3.Zhou, X. et al. A novel DNA modification by sulphur. Mol. Microbiol. 57, 1428–1438 (2005).CAS 
PubMed 
Article 

Google Scholar 
4.Chen, S., Wang, L. & Deng, Z. Twenty years hunting for sulfur in DNA. Protein cell 1, 14–21 (2010).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
5.Xu, T. et al. DNA phosphorothioation in Streptomyces lividans: mutational analysis of the dnd locus. BMC Microbiol. 9, 41 (2009).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
6.You, D., Wang, L., Yao, F., Zhou, X. & Deng, Z. A novel DNA modification by sulfur: DndA is a NifS-like cysteine desulfurase capable of assembling DndC as an iron-sulfur cluster protein in Streptomyces liVidans. Biochemistry 46, 6126–6133 (2007).CAS 
PubMed 
Article 

Google Scholar 
7.Chen, F. et al. Crystal structure of the cysteine desulfurase DndA from Streptomyces lividans which is involved in DNA phosphorothioation. PLoS ONE 7, e36635 (2012).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
8.An, X. et al. A novel target of IscS in Escherichia coli: participating in DNA phosphorothioation. PLoS ONE 7, e51265 (2012).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
9.Wang, L., Jiang, S., Deng, Z., Dedon, P. C. & Chen, S. DNA phosphorothioate modification-a new multi-functional epigenetic system in bacteria. FEMS Microbiol. Rev. 43, 109–122 (2019).CAS 
PubMed 
Article 

Google Scholar 
10.Yao, F., Xu, T., Zhou, X., Deng, Z. & You, D. Functional analysis of spfD gene involved in DNA phosphorothioation in Pseudomonas fluorescens Pf0-1. FEBS Lett. 583, 729–733 (2009).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
11.Hu, W. et al. Structural insights into DndE from Escherichia coli B7A involved in DNA phosphorothioation modification. Cell Res. 22, 1203–1206 (2012).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
12.Cheng, Q. et al. Regulation of DNA phosphorothioate modifications by the transcriptional regulator DptB in Salmonella. Mol. Microbiol. 97, 1186–1194 (2015).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
13.Xiong, W., Zhao, G., Yu, H. & He, X. Interactions of Dnd proteins involved in bacterial DNA phosphorothioate modification. Front. Microbiol. 6, 1139 (2015).PubMed 
PubMed Central 

Google Scholar 
14.Dai, D. et al. DNA phosphorothioate modification plays a role in peroxides resistance in Streptomyces lividans. Front. Microbiol. 7, 1380 (2016).PubMed 
PubMed Central 

Google Scholar 
15.Xie, X. et al. Phosphorothioate DNA as an antioxidant in bacteria. Nucleic Acids Res. 40, 9115–9124 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
16.Yang, Y. et al. DNA backbone sulfur-modification expands microbial growth range under multiple stresses by its anti-oxidation function. Sci. Rep. 7 (2017).17.Xu, T., Yao, F., Zhou, X., Deng, Z. & You, D. A novel host-specific restriction system associated with DNA backbone S-modification in Salmonella. Nucleic Acids Res. 38, 7133–7141 (2010).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
18.Liu, G. et al. Cleavage of phosphorothioated DNA and methylated DNA by the Type IV restriction endonuclease ScoMcrA. PLoS Genet. 6, e1001253 (2010).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
19.Tong, T. et al. Occurrence, evolution, and functions of DNA phosphorothioate epigenetics in bacteria. Proc. Natl Acad. Sci. USA 115, E2988–E2996 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
20.Xiong, L. et al. A new type of DNA phosphorothioation-based antiviral system in archaea. Nat. Commun. 10 (2019).21.Xiong, X. et al. SspABCD-SspE is a phosphorothioation-sensing bacterial defence system with broad anti-phage activities. Nat. Microbiol. 5, 917–928 (2020).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
22.Dai, D., Pu, T., Liang, J., Wang, Z. & Tang, A. Regulation of dndB gene expression in Streptomyces lividans. Front. Microbiol. 9, 2387 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
23.Zhou, X., Deng, Z., Firmin, J. L., Hopwood, D. A. & Kieser, T. Site-specific degradation of Streptomyces lividans DNA during electrophoresis in buffers contaminated with ferrous iron. Nucleic Acids Res. 16, 4341–4352 (1988).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
24.Sun, Y. et al. DNA phosphorothioate modifications are widely distributed in the human microbiome. Biomolecules 10, 1175 (2020).CAS 
PubMed Central 
Article 

Google Scholar 
25.Khan, H. et al. DNA phosphorothioate modification facilitates the dissemination of mcr-1 and blaNDM-1 in drinking water supply systems. Environ. Pollut. 268, 115799 (2021).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
26.Wang, L. et al. DNA phosphorothioation is widespread and quantized in bacterial genomes. Proc. Natl Acad. Sci. USA 108, 2963–2968 (2011).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
27.Blow, M. J. et al. The epigenomic landscape of prokaryotes. PLoS Genet. 12, e1005854 (2016).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
28.Yang, X., Jian, H. & Wang, F. pSW2, a novel low-temperature-inducible gene expression vector based on a filamentous phage of the deep-sea bacterium Shewanella piezotolerans WP3. Appl. Environ. Microbiol. 81, 5519–5526 (2015).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
29.Cao, B. et al. Genomic mapping of phosphorothioates reveals partial modification of short consensus sequences. Nat. Commun. 5, 3951 (2014).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
30.Jian, H. et al. Multiple mechanisms are involved in repression of filamentous phage SW1 transcription by the DNA-binding protein FpsR. J. Mol. Biol. 431, 1113–1126 (2019).CAS 
PubMed 
Article 

Google Scholar 
31.Lai, C. et al. In vivo mutational characterization of DndE involved in DNA phosphorothioate modification. PLoS ONE 9, e107981 (2014).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
32.Schoemaker, J. M., Gayda, R. C. & Markovitz, A. Regulation of cell division in Escherichia coli: SOS induction and cellular location of the SulA protein, a key to lon-associated filamentation and death. J. Bacteriol. 158, 551–561 (1984).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
33.Jian, H., Xiong, L., Xu, G., Xiao, X. & Wang, F. Long 5′ untranslated regions regulate the RNA stability of the deep-sea filamentous phage SW1. Sci. Rep. 6, 21908 (2016).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
34.Chen, C. et al. Convergence of DNA methylation and phosphorothioation epigenetics in bacterial genomes. Proc. Natl Acad. Sci. USA 114, 4501–4506 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
35.Maleki, F., Khosravi, A., Nasser, A., Taghinejad, H. & Azizian, M. Bacterial heat shock protein activity. J. Clin. Diagnostic Res. 10, BE01–BE03 (2016).CAS 

Google Scholar 
36.Knoll, A. H. Paleobiological perspectives on early microbial evolution. Cold Spring Harb. Perspect. Biol. 7, a018093 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
37.Schirrmeister, B. E., Gugger, M. & Donoghue, P. C. Cyanobacteria and the great oxidation event: evidence from genes and fossils. Palaeontology 58, 769–785 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
38.Luo, G. et al. Rapid oxygenation of Earth’s atmosphere 2.33 billion years ago. Sci. Adv. 2, e1600134 (2016).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
39.Wacey, D., Kilburn, M. R., Saunders, M., Cliff, J. & Brasier, M. D. Microfossils of sulphur-metabolizing cells in 3.4-billion-year-old rocks of Western Australia. Nat. Geosci. 4, 698–702 (2011).ADS 
CAS 
Article 

Google Scholar 
40.Bontognali, T. R. R. et al. Sulfur isotopes of organic matter preserved in 3.45-billion-year-old stromatolites reveal microbial metabolism. Proc. Natl Acad. Sci. USA 109, 15146–15151 (2012).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
41.Schirrmeister, B. E., Vos, J. M. D., Antonelli, A. & Bagheri, H. C. Evolution of multicellularity coincided with increased diversification of cyanobacteria and the great oxidation event. Proc. Natl Acad. Sci. USA 110, 1791–1796 (2013).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
42.Pang, K. et al. Nitrogen-fixing heterocystous Cyanobacteria in the tonian period. Curr. Biol. 28, 616–622 (2018).CAS 
PubMed 
Article 

Google Scholar 
43.Demoulin, C. F. et al. Cyanobacteria evolution: Insight from the fossil record. Free Radic. Biol. Med. in press (2021).44.Soo, R. M., Hemp, J., Parks, D. H., Fischer, W. W. & Hugenholtz, P. On the origins of oxygenic photosynthesis and aerobic respiration in Cyanobacteria. Science 355, 1436–1440 (2017).ADS 
CAS 
PubMed 
Article 

Google Scholar 
45.Ou, H.-Y. et al. dndDB: a database focused on phosphorothioation of the DNA backbone. PLoS ONE 4, e5132 (2009).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
46.Janda, J. M. & Abbott, S. L. The genus Shewanella: from the briny depths below to human pathogen. Crit. Rev. Microbiol. 40, 293–312 (2014).PubMed 
Article 

Google Scholar 
47.Fredrickson, J. K. et al. Towards environmental systems biology of Shewanella. Nat. Rev. Microbiol. 6, 592–603 (2008).CAS 
PubMed 
Article 

Google Scholar 
48.Hau, H. H. & Gralnick, J. A. Ecology and biotechnology of the genus Shewanella. Annu. Rev. Microbiol. 61, 237–258 (2007).CAS 
PubMed 
Article 

Google Scholar 
49.Nealson, K. H. & Scott, J. Ecophysiology of the Genus Shewanella. Prokaryotes 6, 1133–1151 (2006).Article 

Google Scholar 
50.Roux, S. et al. Cryptic inoviruses revealed as pervasive in bacteria and archaea across Earth’s biomes. Nat. Microbiol. 4, 1895–1906 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
51.Hay, I. D. & Lithgow, T. Filamentous phages: masters of a microbial sharing economy. EMBO Rep. 20, e47427 (2019).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
52.Mai-Prochnow, A. et al. ‘Big things in small packages: the genetics of filamentous phage and effects on fitness of their host’. FEMS Microbiol. Rev. 39, 465–487 (2015).PubMed 
Article 
PubMed Central 

Google Scholar 
53.Middelboe, M., Glud, R. N. & Finster, K. Distribution of viruses and bacteria in relation to diagenetic activity in an estuarine sediment. Limnol. Oceanogr. 48, 1447–1456 (2003).ADS 
Article 

Google Scholar 
54.Engelhardt, T., Orsi, W. D. & Jørgensen, B. B. Viral activities and life cycles in deep subseafloor sediments. Environ. Microbiol. Rep. 7, 868–873 (2015).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
55.Dell’Anno, A., Corinaldesi, C. & Danovaro, R. Virus decomposition provides an important contribution to benthic deep-sea ecosystem functioning. Proc. Natl Acad. Sci. USA 112, E2014–E2019 (2015).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
56.Rakonjac, J. Filamentous Bacteriophages: Biology and Applications. eLS (2012).57.Güemes, A. G. C. et al. Viruses as winners in the game of life. Annu. Rev. Virol. 3, 197–214 (2016).Article 
CAS 

Google Scholar 
58.Breitbart, M. Marine viruses: truth or dare. Annu. Rev. Mar. Sci. 4, 425–448 (2012).ADS 
Article 

Google Scholar 
59.Danovaro, R. et al. Marine viruses and global climate change. FEMS Microbiol. Rev. 35, 993–1034 (2011).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
60.Rohwer, F. & Thurber, R. V. Viruses manipulate the marine environment. Nature 459, 207–212 (2009).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
61.Touchon, M., Bernheim, A. & Rocha, E. P. Genetic and life-history traits associated with the distribution of prophages in bacteria. ISME J. 10, 2744–2754 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
62.Harrison, E. & Brockhurst, M. A. Ecological and evolutionary benefits of temperate phage: what does or doesn’t kill you makes you stronger. Bioessays 39, 201700112 (2017).Article 

Google Scholar 
63.Paul, J. H. Prophages in marine bacteria: dangerous molecular time bombs or the key to survival in the seas? ISME J. 2, 579–589 (2008).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
64.Wu, X. et al. Epigenetic competition reveals density-dependent regulation and target site plasticity of phosphorothioate epigenetics in bacteria. PNAS 117, 14322–14330 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
65.Willbanks, A. et al. The evolution of epigenetics: from prokaryotes to humans and its biological consequences. Genet. Epigenet. 8, 25–36 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
66.Razin, A. & Cedar, H. DNA methylation and gene expression. Microbiol. Rev. 55, 451–458 (1991).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
67.Casadesús, J. & Low, D. Epigenetic gene regulation in the bacterial world. Microbiol. Mol. Biol. Rev. 70, 830–856 (2006).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
68.Iyer, L. M., Abhiman, S. & Aravind, L. Natural history of eukaryotic DNA methylation systems. Prog. Mol. Biol. Transl. Sci. 101, 25–104 (2011).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
69.Weiss, M. C. et al. The physiology and habitat of the last universal common ancestor. Nat. Microbiol. 1, 16116 (2016).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
70.Gan, R. et al. DNA phosphorothioate modifications influence the global transcriptional response and protect DNA from double-stranded breaks. Sci. Rep. 4, 6642 (2014).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
71.Chen, L. et al. Theoretical study on the relationship between Rp-phosphorothioation and base-step in S-DNA: based on energetic and structural analysis. J. Phys. Chem. B 119, 474–481 (2015).CAS 
PubMed 
Article 

Google Scholar 
72.Kellner, S. et al. Oxidation of phosphorothioate DNA modifications leads to lethal genomic instability. Nat. Chem. Biol. 13, 888–894 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
73.Ślesak, I., Kula, M., Ślesak, H., Miszalski, Z. & Strzałka, K. How to define obligatory anaerobiosis? An evolutionary view on the antioxidant response system and the early stages of the evolution of life on Earth. Free Radic. Biol. Med. 140, 61–73 (2019).PubMed 
Article 
CAS 

Google Scholar 
74.Brioukhanov, A. L., Thauer, R. K. & Netrusov, A. I. Catalase and superoxide dismutase in the cells of strictly anaerobic microorganisms. Microbiol. (Russ. Acad. Sci.) 71, 330–335 (2002).
Google Scholar 
75.Sebaihia, M. et al. The multidrug-resistant human pathogen Clostridium difficile has a highly mobile, mosaic genome. Nat. Genet. 38, 779–786 (2006).PubMed 
Article 
CAS 

Google Scholar 
76.Kanehisa, M. et al. Data, information, knowledge and principle: back to metabolism in KEGG. Nucleic Acids Res. 42, D199–D205 (2014).CAS 
PubMed 
Article 

Google Scholar 
77.Buchfink, B., Xie, C. & Huson, D. H. Fast and sensitive protein alignment using DIAMOND. Nat. Methods 12, 59–60 (2015).CAS 
PubMed 
Article 

Google Scholar 
78.Kieft, K., Zhou, Z. & Anantharaman, K. VIBRANT: automated recovery, annotation and curation of microbial viruses, and evaluation of viral community function from genomic sequences. Microbiome 8, 90 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
79.Gregory, A. C. et al. Marine DNA viral macro- and microdiversity from pole to pole. Cell 177, 1109–1123 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
80.Katoh, K. & Standley, D. M. MAFFT multiple sequence alignment software version 7: improvements in performance and usability. Mol. Biol. Evol. 30, 772–780 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
81.Capella-Gutiérrez, S., Silla-Martínez, J. M. & Gabaldón, T. trimAl: a tool for automated alignment trimming in large-scale phylogenetic analyses. Bioinformatics 25, 1972–1973 (2009).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
82.Stamatakis, A. RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics 30, 1312–1313 (2014).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
83.Chaumeil, P.-A., Mussig, A. J., Hugenholtz, P. & Parks, D. H. GTDB-Tk: a toolkit to classify genomes with the genome taxonomy database. Bioinformatics 36, 1925–1927 (2020).CAS 

Google Scholar 
84.Price, M. N., Dehal, P. S. & Arkin, A. P. FastTree 2–approximately maximum-likelihood treesfor large alignments. PLoS ONE 5, e9490 (2010).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
85.Kumar, S., Stecher, G., Li, M., Knyaz, C. & Tamura, K. MEGA X: molecular evolutionary genetics analysis across computing platforms. Mol. Biol. Evol. 35, 1547–1549 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
86.Letunic, I. & Bork, P. Interactive Tree Of Life (iTOL) v4: recent updates and new developments. Nucleic Acids Res. 47, W256–W259 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
87.Kwak, S. G. & Kim, J. H. Central limit theorem: the cornerstone of modern statistics. Korean J. Anesthesiol. 70, 144–156 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
88.Virtanen, P. et al. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nat. Methods 17, 261–272 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
89.R: A language and environment for statistical computing (R Foundation for Statistical Computing, Vienna, Austria, 2013).90.Chok, N. S. Pearson’s versus Spearman’s and Kendall’s correlation coefficients for continuous data Master of Science thesis, University of Pittsburgh, (2010).91.Jian, H., Xu, G., Gai, Y., Xu, J. & Xiao, X. The histone-like nucleoid structuring protein (H-NS) is a negative regulator of the lateral flagellar system in the deep-sea bacterium Shewanella piezotolerans WP3. Appl. Environ. Microbiol. 82, 2388–2398 (2016).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
92.Wang, F. et al. Environmental adaptation: genomic analysis of the piezotolerant and psychrotolerant deep-sea iron reducing bacterium Shewanella piezotolerans WP3. PLoS ONE 3, e1937 (2008).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
93.Jian, H., Xu, J., Xiao, X. & Wang, F. Dynamic modulation of DNA replication and gene transcription in deep-sea filamentous phage SW1 in response to changes of host growth and temperature. PLoS ONE 7, e41578 (2012).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
94.Chin, C.-S. et al. Nonhybrid, finished microbial genome assemblies from long-read SMRT sequencing data. Nat. Methods 10, 563–569 (2016).Article 
CAS 

Google Scholar 
95.Chen, S., Zhou, Y., Chen, Y. & Gu, J. fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics 34, i884–i890 (2018).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
96.Kim, D., Langmead, B. & Salzberg, S. L. HISAT: a fast spliced aligner with low memory requirements. Nat. Methods 12, 357–360 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
97.Li, B. & Dewey, C. N. RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinform. 12, 323 (2011).CAS 
Article 

Google Scholar 
98.Wang, L., Feng, Z., Wang, X., Wang, X. & Zhang, X. DEGseq: an R package for identifying differentially expressed genes from RNA-seq data. Bioinformatics 26, 136–138 (2010).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
99.Trapnell, C. et al. Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nat. Biotechnol. 28, 511–515 (2010).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
100.Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
101.Gao, H. et al. Reduction of nitrate in Shewanella oneidensis depends on atypical NAP and NRF systems with NapB as a preferred electron transport protein from CymA to NapA. ISME J. 3, 966–976 (2009).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
102.Lenski, R. E., Rose, M. R., Simpson, S. C. & Tadler, S. C. Long-term experimental evolution in Escherichia coli. I. Adaptation and divergence during 2000 generations. Am. Naturalist 138, 1315–1341 (1991).Article 

Google Scholar  More