Ecology

1.Hope, P. R. & Jones, G. Warming up for dinner: Torpor and arousal in hibernating Natterer’s bats (Myotis nattereri) studied by radio telemetry. J. Comp. Physiol. B Biochem. Syst. Environ. Physiol. 182, 569–578. https://doi.org/10.1007/s00360-011-0631-x (2012).Article 

Google Scholar 
2.Czenze, Z. J., Jonasson, K. A. & Willis, C. K. R. Thrifty females, frisky males: Winter energetics of hibernating bats from a cold climate. Physiol. Biochem. Zool. 90, 502–511. https://doi.org/10.1086/692623 (2017).Article 
PubMed 

Google Scholar 
3.Reynolds, D. S., Shoemaker, K., von Oettingen, S. & Najjar, S. High rates of winter activity and arousals in two New England bat species: Implications for a reduced white-nose syndrome impact?. Northeast. Nat. 24, B188–B208 (2017).Article 

Google Scholar 
4.Kunz, T. H. & Martin, R. A. Plecotus townsendii. Mamm. Species 175, 1–6 (1982).
Google Scholar 
5.Twente, J. W. Aspects of a population study of cavern-dwelling bats. J. Mamm. 36, 379–390 (1955).Article 

Google Scholar 
6.Humphrey, S. R. & Kunz, T. H. Ecology of a Pleistocene relict, the western big-eared bat (Plecotus townsendii), in the southern Great Plains. J. Mamm. 57, 470–494. https://doi.org/10.2307/1379297 (1976).Article 

Google Scholar 
7.Czenze, Z. J., Park, A. D. & Willis, C. K. R. Staying cold through dinner: Cold-climate bats rewarm with conspecifics but not sunset during hibernation. J. Comp. Physiol. B Biochem. Syst. Environ. Physiol. 183, 859–866. https://doi.org/10.1007/s00360-013-0753-4 (2013).Article 

Google Scholar 
8.Pearson, O. P., Koford, M. R. & Pearson, A. K. Reproduction of the lump-nosed bat (Corynorhinus rafinesquei) in California. J. Mamm. 33, 273–320 (1952).Article 

Google Scholar 
9.Johnson, J. S., Lacki, M. J., Thomas, S. C. & Grider, J. F. Frequent arousals from winter torpor in Rafinesque’s big-eared bat (Corynorhinus rafinesquii). PLoS ONE 7, e49754. https://doi.org/10.1371/journal.pone.0049754 (2012).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
10.Lausen, C. L. & Barclay, R. M. R. Winter bat activity in the Canadian prairies. Can. J. Zool.-Rev. Can. Zool. 84, 1079–1086. https://doi.org/10.1139/z06-093 (2006).Article 

Google Scholar 
11.Thomas, D. W. & Cloutier, D. Evaporative water-loss by hibernating little brown bats, Myotis lucifugus. Physiol. Zool. 65, 443–456 (1992).Article 

Google Scholar 
12.Ben-Hamo, M., Munoz-Garcia, A., Williams, J. B., Korine, C. & Pinshow, B. Waking to drink: Rates of evaporative water loss determine arousal frequency in hibernating bats. J. Exp. Biol. 216, 573–577. https://doi.org/10.1242/jeb.078790 (2013).Article 
PubMed 

Google Scholar 
13.Czenze, Z. J. & Willis, C. K. R. Warming up and shipping out: Arousal and emergence timing in hibernating little brown bats (Myotis lucifugus). J. Comp. Physiol. B-Biochem. Syst. Environ. Physiol. 185, 575–586. https://doi.org/10.1007/s00360-015-0900-1 (2015).Article 

Google Scholar 
14.Choate, J. R. & Anderson, J. M. Bats of jewel cave national monument, South Dakota. Prairie Nat. 29, 39–47 (1997).
Google Scholar 
15.Klüg-Baerwald, B. J., Gower, L. E., Lausen, C. L. & Brigham, R. M. Environmental correlates and energetics of winter flight by bats in southern Alberta, Canada. Can. J. Zool. 94, 829–836. https://doi.org/10.1139/cjz-2016-0055 (2016).Article 

Google Scholar 
16.Johnson, J. S. et al. Migratory and winter activity of bats in Yellowstone National Park. J. Mamm. 98, 211–221. https://doi.org/10.1093/jmammal/gyw175 (2017).Article 

Google Scholar 
17.Norquay, K. & Willis, C. Hibernation phenology of Myotis lucifugus. J. Zool. 294, 85–92 (2014).Article 

Google Scholar 
18.Barclay, R. M. et al. Variation in the reproductive rate of bats. Can. J. Zool. 82, 688–693 (2004).Article 

Google Scholar 
19.Jonasson, K. A. & Willis, C. K. Changes in body condition of hibernating bats support the thrifty female hypothesis and predict consequences for populations with white-nose syndrome. PLoS ONE 6, e21061. https://doi.org/10.1371/journal.pone.0021061 (2011).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
20.Speakman, J. R., Webb, P. I. & Racey, P. A. Effects of disturbance on the energy expenditure of hibernating bats. J. Appl. Ecol. 28, 1087–1104. https://doi.org/10.2307/2404227 (1991).Article 

Google Scholar 
21.Reeder, D. M., Field, K. A. & Slater, M. H. Balancing the costs of wildlife research with the benefits of understanding a panzootic disease, white-nose syndrome. ILAR J. 56, 275–282. https://doi.org/10.1093/ilar/ilv035 (2015).CAS 
Article 

Google Scholar 
22.Boyles, J. G. Benefits of knowing the costs of disturbance to hibernating bats. Wildl. Soc. Bull. 41, 388–392. https://doi.org/10.1002/wsb.755 (2017).Article 

Google Scholar 
23.Thomas, D. W. Hibernating bats are sensitive to nontactile human disturbance. J. Mamm. 76, 940–946. https://doi.org/10.2307/1382764 (1995).Article 

Google Scholar 
24.Furey, N. M. & Racey, P. A. Bats in the Anthropocene: Conservation of Bats in a Changing World 463–500 (Springer, 2016).
Google Scholar 
25.Sheffield, S. R., Shaw, J. H., Heidt, G. A. & McClenaghan, L. R. Guidelines for the protection of bat roosts. J. Mamm. 73, 707–710 (1992).
Google Scholar 
26.Jones, G., Jacobs, D. S., Kunz, T. H., Willig, M. R. & Racey, P. A. Carpe noctem: The importance of bats as bioindicators. Endang. Species Res. 8, 93–115 (2009).Article 

Google Scholar 
27.Blehert, D. S. et al. Bat white-nose syndrome: An emerging fungal pathogen?. Science 323, 227. https://doi.org/10.1126/science.1163874 (2009).CAS 
Article 
PubMed 

Google Scholar 
28.Foley, J., Clifford, D., Castle, K., Cryan, P. & Ostfeld, R. S. Investigating and managing the rapid emergence of white-nose syndrome, a novel, fatal, infectious disease of hibernating bats. Conserv. Biol. 25, 223–231. https://doi.org/10.1111/j.1523-1739.2010.01638.x (2011).Article 
PubMed 

Google Scholar 
29.Ingersoll, T. E., Sewall, B. J. & Amelon, S. K. Effects of white-nose syndrome on regional population patterns of 3 hibernating bat species. Conserv. Biol. 30, 1048–1059. https://doi.org/10.1111/cobi.12690 (2016).Article 
PubMed 

Google Scholar 
30.Minnis, A. M. & Lindner, D. L. Phylogenetic evaluation of Geomyces and allies reveals no close relatives of Pseudogymnoascus destructans, comb. nov., in bat hibernacula of eastern North America. Fungal Biol. 117, 638–649. https://doi.org/10.1016/j.funbio.2013.07.001 (2013).Article 
PubMed 

Google Scholar 
31.Lorch, J. M. et al. Experimental infection of bats with Geomyces destructans causes white-nose syndrome. Nature 480, 376 (2011).ADS 
CAS 
Article 

Google Scholar 
32.Verant, M. L. et al. White-nose syndrome initiates a cascade of physiologic disturbances in the hibernating bat host. BMC Physiol. 14, 10 (2014).Article 

Google Scholar 
33.Warnecke, L. et al. Inoculation of bats with European Geomyces destructans supports the novel pathogen hypothesis for the origin of white-nose syndrome. Proc. Natl. Acad. Sci. U.S.A. 109, 6999–7003. https://doi.org/10.1073/pnas.1200374109 (2012).ADS 
Article 
PubMed 
PubMed Central 

Google Scholar 
34.Lilley, T. M. et al. White-nose syndrome survivors do not exhibit frequent arousals associated with Pseudogymnoascus destructans infection. Front. Zool. https://doi.org/10.1186/s12983-016-0143-3 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
35.McGuire, L. P., Mayberry, H. W. & Willis, C. K. R. White-nose syndrome increases torpid metabolic rate and evaporative water loss in hibernating bats. Am. J. Physiol.-Regulat. Integr. Compar. Physiol. 313, R680–R686. https://doi.org/10.1152/ajpregu.00058.2017 (2017).CAS 
Article 

Google Scholar 
36.Knudsen, G. R., Dixon, R. D. & Amelon, S. K. Potential spread of white-nose syndrome of bats to the Northwest: Epidemiological considerations. Northwest Sci. 87, 292–306. https://doi.org/10.3955/046.087.0401 (2013).Article 

Google Scholar 
37.Bernard, R. F. & McCracken, G. F. Winter behavior of bats and the progression of white-nose syndrome in the southeastern United States. Ecol. Evol. 7, 1487–1496. https://doi.org/10.1002/ece3.2772 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
38.Cheng, T. L. et al. Higher fat stores contribute to persistence of little brown bat populations with white-nose syndrome. J. Anim. Ecol. 88, 591–600 (2019).Article 

Google Scholar 
39.Turner, J. M. et al. Conspecific disturbance contributes to altered hibernation patterns in bats with white-nose syndrome. Physiol. Behav. 140, 71–78 (2015).CAS 
Article 

Google Scholar 
40.Blazek, J. et al. Numerous cold arousals and rare arousal cascades as a hibernation strategy in European Myotis bats. J. Therm. Biol 82, 150–156. https://doi.org/10.1016/j.jtherbio.2019.04.002 (2019).Article 
PubMed 

Google Scholar 
41.Lorch, J. M. et al. First detection of bat white-nose syndrome in Western North America. mSphere 1(4), e00148. https://doi.org/10.1128/mSphere.00148-16 (2016).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
42.Weller, T. J. et al. A review of bat hibernacula across the western United States: Implications for white-nose syndrome surveillance and management. PLoS ONE https://doi.org/10.1371/journal.pone.0205647 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
43.Whiting, J. C. et al. Bat hibernacula in caves of southern Idaho: Implications for monitoring and management. West. N. Am. Nat. 78, 165–173 (2018).Article 

Google Scholar 
44.Whiting, J. C. et al. Long-term bat abundance in sagebrush steppe. Sci. Rep. 8, 12288 (2018).ADS 
Article 

Google Scholar 
45.Call, R. S. et al. Maternity roosts of Townsend’s big-eared bats in lava tube caves of southern Idaho. Northwest Sci. 92, 158–165 (2018).ADS 
Article 

Google Scholar 
46.Clark, B. S., Clark, B. K. & Leslie, D. M. Seasonal variation in activity patterns of the endangered Ozark big-eared bat (Corynorhinus townsendii ingens). J. Mamm. 83, 590–598. https://doi.org/10.1644/1545-1542(2002)083%3c0590:sviapo%3e2.0.co;2 (2002).Article 

Google Scholar 
47.French, A. R. The patterns of mammalian hibernation. Am. Sci. 76, 568–575 (1988).ADS 

Google Scholar 
48.Reynolds, T. D., Connelly, J. W., Halford, D. K. & Arthur, W. J. Vertebrate fauna of the Idaho National Environmental Research Park. Gt. Basin Nat. 46, 513–527 (1986).
Google Scholar 
49.Genter, D. L. Wintering bats of the upper Snake River Plain: Occurrence in lava-tube caves. Gt. Basin Nat. 46, 241–244 (1986).
Google Scholar 
50.Gillies, K. E., Murphy, P. J. & Matocq, M. D. Hibernacula characteristics of Townsend’s big-eared bats in southeastern Idaho. Nat. Areas J. 34, 24–30 (2014).Article 

Google Scholar 
51.Sikes, R. S. et al. Guidelines of the American Society of Mammalogists for the use of wild mammals in research and education. J. Mamm. 97(663–688), 2016. https://doi.org/10.1093/jmammal/gyw078 (2016).Article 

Google Scholar 
52.Schwab, N. A. & Mabee, T. J. Winter acoustic activity of bats in Montana. Northwest. Nat. 95, 13–27 (2014).Article 

Google Scholar 
53.Britzke, E. R., Slack, B. A., Armstrong, M. P. & Loeb, S. C. Effects of orientation and weatherproofing on the detection of bat echolocation calls. J. Fish Wildl. Manage. 1, 136–141. https://doi.org/10.3996/072010-jfwm-025 (2010).Article 

Google Scholar 
54.Skalak, S. L., Sherwin, R. E. & Brigham, R. M. Sampling period, size and duration influence measures of bat species richness from acoustic surveys. Methods Ecol. Evol. 3, 490–502. https://doi.org/10.1111/j.2041-210X.2011.00177.x (2012).Article 

Google Scholar 
55.Miller, B. W. A method for determining relative activity of free flying bats using a new activity index for acoustic monitoring. Acta Chiropt. 3, 93–105 (2001).
Google Scholar 
56.Nocera, T., Ford, W. M., Silvis, A. & Dobony, C. A. Patterns of acoustical activity of bats prior to and 10 years after WNS on Fort drum army installation, New York. Glob. Ecol. Conserv. https://doi.org/10.1016/j.gecco.2019.e00633 (2019).Article 

Google Scholar 
57.Britzke, E. R., Gillam, E. H. & Murray, K. L. Current state of understanding of ultrasonic detectors for the study of bat ecology. Acta Theriol. 58, 109–117. https://doi.org/10.1007/s13364-013-0131-3 (2013).Article 

Google Scholar 
58.O’Farrell, M. J., Miller, B. W. & Gannon, W. L. Qualitative identification of free-flying bats using the Anabat detector. J. Mamm. 80, 11–23. https://doi.org/10.2307/1383203 (1999).Article 

Google Scholar 
59.Whiting, J. C., Doering, B. & Pennock, D. Acoustic surveys for local, free-flying bats in zoos: An engaging approach for bat education and conservation. J. Bat Res. Conserv. 12, 94–99. https://doi.org/10.14709/BarbJ.12.1.2019.12 (2019).Article 

Google Scholar 
60.O’Farrell, M. J. & Gannon, W. L. A comparison of acoustic versus capture techniques for the inventory of bats. J. Mamm. 80, 24–30. https://doi.org/10.2307/1383204 (1999).Article 

Google Scholar 
61.Stahlschmidt, P. & Bruhl, C. A. Bats as bioindicators—The need of a standardized method for acoustic bat activity surveys. Methods Ecol. Evol. 3, 503–508. https://doi.org/10.1111/j.2041-210X.2012.00188.x (2012).Article 

Google Scholar 
62.Avery, M. I. Winter activity of pipistrelle bats. J. Anim. Ecol. 54, 721–738. https://doi.org/10.2307/4374 (1985).Article 

Google Scholar 
63.McCulloch, C. E. & Neuhaus, J. M. Generalized linear mixed models. In Encyclopedia of Biostatistics (eds Armitage, P. & Colton, T.) (Wiley, 2005).
Google Scholar 
64.Nelder, J. A. & Wedderburn, R. W. Generalized linear models. J. R. Stat. Soc. Ser. A (Gen.) 135, 370–384 (1972).Article 

Google Scholar 
65.Hardin, J. W. & Hilbe, J. M. Generalized Linear Models and Extensions (Stata Press, 2007).
Google Scholar 
66.Consul, P. & Famoye, F. Generalized Poisson regression model. Commun. Stat. Theory Methods 21, 89–109 (1992).Article 

Google Scholar 
67.Aho, K. A. Foundational and Applied Statistics for Biologists using R (CRC Press, 2013).
Google Scholar 
68.Akaike, H. Selected Papers of Hirotugu Akaike 199–213 (Springer, 1998).
Google Scholar 
69.Burnham, K. P. & Anderson, D. A. Model Selection and Multimodel Inference: A practical Information-Theoretic Approach 2nd edn. (Springer, 2002).
Google Scholar 
70.RCoreTeam. R: A Language and Environment for Statistical Computing (2020).71.Venables, W. N. & Ripley, B. D. Modern Applied Statistics with S-PLUS (Springer, 2013).
Google Scholar 
72.Brooks, M. E. et al. glmmTMB balances speed and flexibility among packages for zero-inflated generalized linear mixed modeling. R J. 9, 378–400 (2017).Article 

Google Scholar 
73.Perkins, J. M., Barss, J. M. & Peterson, J. Winter records of bats in Oregon and Washington. Northwest. Nat. 71, 59–62. https://doi.org/10.2307/3536594 (1990).Article 

Google Scholar 
74.Nagorsen, D. W. et al. Winter bat records for British Columbia. Northwest Nat. 74, 61–66 (1993).Article 

Google Scholar 
75.Hayman, D. T., Cryan, P. M., Fricker, P. D. & Dannemiller, N. G. Long-term video surveillance and automated analyses reveal arousal patterns in groups of hibernating bats. Methods Ecol. Evol. 8, 1813–1821 (2017).Article 

Google Scholar 
76.Boyles, J. G., Dunbar, M. B. & Whitaker, J. O. Activity following arousal in winter in North American vespertilionid bats. Mamm. Rev. 36, 267–280. https://doi.org/10.1111/j.1365-2907.2006.00095.x (2006).Article 

Google Scholar 
77.Speakman, J. R. & Racey, P. A. Hibernal ecology of the pipistrelle bat: Energy expenditure, water requirements and mass-loss, implications for survial and the function of winter emergence flights. J. Anim. Ecol. 58, 797–813. https://doi.org/10.2307/5125 (1989).Article 

Google Scholar 
78.Lawrence, B. D. & Simmons, J. A. Measurements of atmospheric attenuation at ultrasonic frequencies and the significance for echolocation by bats. J. Acoust. Soc. Am. 71, 585–590 (1982).ADS 
CAS 
Article 

Google Scholar 
79.Dunbar, M. B. & Tomasi, T. E. Arousal patterns, metabolic rate, and an energy budget of eastern red bats (Lasiurus borealis) in winter. J. Mamm. 87, 1096–1102. https://doi.org/10.1644/05-mamm-a-254r3.1 (2006).Article 

Google Scholar 
80.Ford, W. M., Britzke, E. R., Dobony, C. A., Rodrigue, J. L. & Johnson, J. B. Patterns of acoustical activity of bats prior to and following white-nose syndrome occurrence. J. Fish Wildl. Manage. 2, 125–134. https://doi.org/10.3996/042011-jfwm-027 (2011).Article 

Google Scholar 
81.Bernard, R. F., Foster, J. T., Willcox, E. V., Parise, K. L. & McCracken, G. F. Molecular detection of the causative agent of white-nose syndrome on Rafinesque’s big-eared bats (Corynorhinus rafinesquii) and two species of migratory bats in the southeastern USA. J. Wildl. Dis. 51, 519–522. https://doi.org/10.7589/2014-08-202 (2015).Article 
PubMed 

Google Scholar 
82.Dzal, Y., McGuire, L. P., Veselka, N. & Fenton, M. B. Going, going, gone: the impact of white-nose syndrome on the summer activity of the little brown bat (Myotis lucifugus). Biol. Lett. 7, 392–394 (2010).Article 

Google Scholar 
83.Brooks, R. T. Declines in summer bat activity in central New England 4 years following the initial detection of white-nose syndrome. Biodivers. Conserv. 20, 2537–2541. https://doi.org/10.1007/s10531-011-9996-0 (2011).Article 

Google Scholar 
84.Holloway, G. L. & Barclay, R. M. R. Myotis ciliolabrum. Mamm. Species 670, 1–5. https://doi.org/10.1644/1545-1410(2001)670%3c0001:mc%3e2.0.co;2 (2001).Article 

Google Scholar 
85.Halsall, A. L., Boyles, J. G. & Whitaker, J. O. Jr. Body temperature patterns of big brown bats during winter in a building hibernaculum. J. Mamm. 93, 497–503 (2012).Article 

Google Scholar 
86.Paige, K. N. Bats and barometric pressure: conserving limited energy and tracking insects from the roost. Funct. Ecol. 9, 463–467 (1995).Article 

Google Scholar 
87.Frick, W. F. Acoustic monitoring of bats, considerations of options for long-term monitoring. Therya 4, 69–78 (2013).ADS 
Article 

Google Scholar 
88.Whitaker, J. O. & Rissler, L. J. Winter activity of bats at a mine entrance in Vermillion County, Indiana. Am. Midl. Nat. 127, 52–59. https://doi.org/10.2307/2426321 (1992).Article 

Google Scholar  More

1.Carlson, A. K., Phelps, Q. E. & Graeb, B. D. S. Chemistry to conservation: Using otoliths to advance recreational and commercial fisheries management. J. Fish Biol. 90, 505–527 (2017).CAS 
PubMed 
Article 

Google Scholar 
2.Ward, R. D. Genetics in fisheries management. Hydrobiologia 420, 191–201 (2000).CAS 
Article 

Google Scholar 
3.Tracey, S. R., Lyle, J. M. & Duhamel, G. Application of elliptical Fourier analysis of otolith form as a tool for stock identification. Fish. Res. 77, 138–147 (2006).Article 

Google Scholar 
4.Ferguson, G. J., Ward, T. M. & Gillanders, B. M. Otolith shape and elemental composition: Complementary tools for stock discrimination of mulloway (Argyrosomus japonicus) in southern Australia. Fish. Res. 110, 75–83 (2011).Article 

Google Scholar 
5.Campana, S. E. & Casselman, J. M. Stock discrimination using otolith shape analysis. Can. J. Fish. Aquat. Sci. 50(5), 1062-1083 (1993).Article 

Google Scholar 
6.Begg, G. A., Overholtz, W. J. & Munroe, N. J. The use of internal otolith morphometrics for identification of haddock (Melanogrammus aeglefinus) stocks on Georges Bank. Fish. Bull. 99, 1–1 (2001).
Google Scholar 
7.Miyan, K., Khan, M. A., Patel, D. K., Khan, S. & Ansari, N. G. Truss morphometry and otolith microchemistry reveal stock discrimination in Clarias batrachus (Linnaeus, 1758) inhabiting the Gangetic river system. Fish. Res. 173, 294–302 (2016).Article 

Google Scholar 
8.Nazir, A. & Khan, M. A. Spatial and temporal variation in otolith chemistry and its relationship with water chemistry: Stock discrimination of Sperata aor. Ecol. Freshw. Fish 28, 499–511 (2019).Article 

Google Scholar 
9.Bird, J. L., Eppler, D. T. & Checkley, D. M. Jr. Comparisons of herring otoliths using Fourier series shape analysis. Can. J. Fish. Aquat. Sci. 43(6), 1228-1234 (1986).Article 

Google Scholar 
10.Castonguay, M., Simard, P. & Gagnon, P. Usefulness of Fourier analysis of otolith shape for Atlantic Mackerel (Scomber scombrus) stock discrimination. Can. J. Fish. Aquat. Sci. 48(2), 296-302 (1991).Article 

Google Scholar 
11.Friedland, K. D. & Reddin, D. G. Use of otolith morphology in stock discriminations of Atlantic Salmon (Salmo salar). Can. J. Fish. Aquat. Sci. 51(1), 91-98 (1994).Article 

Google Scholar 
12.Vignon, M. & Morat, F. Environmental and genetic determinant of otolith shape revealed by a non-indigenous tropical fish. Mar. Ecol. Prog. Ser. 411, 231–241 (2010).ADS 
Article 

Google Scholar 
13.Campana, S. E., Chouinard, G. A., Hanson, J. M., Fréchet, A. & Brattey, J. Otolith elemental fingerprints as biological tracers of fish stocks. Fish. Res. 46, 343–357 (2000).Article 

Google Scholar 
14.Elsdon, T. S. & Gillanders, B. M. Reconstructing migratory patterns of fish based on environmental influences on otolith chemistry. Rev. Fish Biol. Fish. 13, 217–235 (2003).Article 

Google Scholar 
15.Stransky, C. Geographic variation of golden redfish (Sebastes marinus) and deep-sea redfish (S. mentella) in the North Atlantic based on otolith shape analysis. ICES J. Mar. Sci. 62, 1691–1698 (2005).Article 

Google Scholar 
16.Grammer, G. L. et al. Coupling biogeochemical tracers with fish growth reveals physiological and environmental controls on otolith chemistry. Ecol. Monogr. 87, 487–507 (2017).Article 

Google Scholar 
17.Izzo, C., Reis-Santos, P. & Gillanders, B. M. Otolith chemistry does not just reflect environmental conditions: A meta-analytic evaluation. Fish Fish. 19, 441–454 (2018).Article 

Google Scholar 
18.Elsdon, T. S. & Gillanders, B. M. Fish otolith chemistry influenced by exposure to multiple environmental variables. J. Exp. Mar. Biol. Ecol. 313, 269–284 (2004).CAS 
Article 

Google Scholar 
19.Khan, M. A., Miyan, K., Khan, S., Patel, D. K. & Ansari, G. Studies on the elemental profile of otoliths and truss network analysis for stock discrimination of the threatened stinging catfish Heteropneustes fossilis (Bloch 1794) from the Ganga river and its tributaries. Zool. Stud. 51, 1195–1206 (2012).
Google Scholar 
20.Miyan, K., Khan, M. A. & Khan, S. Stock structure delineation using variation in otolith chemistry of snakehead, Channa punctata (Bloch, 1793), from three Indian rivers. J. Appl. Ichthyol. 30, 881–886 (2014).CAS 
Article 

Google Scholar 
21.Miyan, K., Khan, M. A., Patel, D. K., Khan, S. & Prasad, S. Otolith fingerprints reveal stock discrimination of Sperata seenghala inhabiting the Gangetic river system. Ichthyol. Res. 63, 294–301 (2016).Article 

Google Scholar 
22.Fowler, A. M., Macreadie, P. I., Bishop, D. P. & Booth, D. J. Using otolith microchemistry and shape to assess the habitat value of oil structures for reef fish. Mar. Environ. Res. 106, 103–113 (2015).CAS 
PubMed 
Article 

Google Scholar 
23.Schilling, H. T. et al. Evaluating estuarine nursery use and life history patterns of Pomatomus saltatrix in eastern Australia. Mar. Ecol. Prog. Ser. 598, 187–199 (2018).ADS 
Article 

Google Scholar 
24.Biolé, F. G. et al. Fish stocks of Urophycis brasiliensis revealed by otolith fingerprint and shape in the Southwestern Atlantic Ocean. Estuar. Coast. Shelf Sci. 229, 106406 (2019).Article 
CAS 

Google Scholar 
25.Maguffee, A. C., Reilly, R., Clark, R. & Jones, M. L. Examining the potential of otolith chemistry to determine natal origins of wild Lake Michigan Chinook salmon. Can. J. Fish. Aquat. Sci. 76(11), 2035-2044 (2019).Article 

Google Scholar 
26.Tanner, S. E., Vasconcelos, R. P., Cabral, H. N. & Thorrold, S. R. Testing an otolith geochemistry approach to determine population structure and movements of European hake in the northeast Atlantic Ocean and Mediterranean Sea. Fish. Res. 125–126, 198–205 (2012).Article 

Google Scholar 
27.Andrade, H. et al. Ontogenetic movements of cod in Arctic fjords and the Barents Sea as revealed by otolith microchemistry. Polar Biol. 43, 409–421 (2020).Article 

Google Scholar 
28.Warton, D. I. Why you cannot transform your way out of trouble for small counts. Biometrics 74, 362–368 (2018).MathSciNet 
PubMed 
MATH 
Article 

Google Scholar 
29.Foster, S. D. & Bravington, M. V. A Poisson-Gamma model for analysis of ecological non-negative continuous data. Environ. Ecol. Stat. 20, 533–552 (2013).MathSciNet 
Article 

Google Scholar 
30.Taylor, L. R. Aggregation, variance and the mean. Nature 189, 732–735 (1961).ADS 
Article 

Google Scholar 
31.Kendal, R. L., Coolen, I. & Laland, K. N. The role of conformity in foraging when personal and social information conflict. Behav. Ecol. 15, 269–277 (2004).Article 

Google Scholar 
32.Warton, D. I., Wright, S. T. & Wang, Y. Distance-based multivariate analyses confound location and dispersion effects. Methods Ecol. Evol. 3, 89–101 (2012).Article 

Google Scholar 
33.Warton, D. I., Foster, S. D., De’ath, G., Stoklosa, J. & Dunstan, P. K. Model-based thinking for community ecology. Plant Ecol. 216, 669–682 (2015).Article 

Google Scholar 
34.Wang, Y., Naumann, U., Wright, S. T. & Warton, D. I. mvabund– an R package for model-based analysis of multivariate abundance data. Methods Ecol. Evol. 3, 471–474 (2012).Article 

Google Scholar 
35.Niku, J., Warton, D. I., Hui, F. K. C. & Taskinen, S. Generalized linear latent variable models for multivariate count and biomass data in ecology. J. Agric. Biol. Environ. Stat. 22, 498–522 (2017).MathSciNet 
MATH 
Article 

Google Scholar 
36.Dunn, P. K. & Smyth, G. K. Randomized quantile residuals. J. Comput. Graph. Stat. 5, 236–244 (1996).
Google Scholar 
37.Dunn, P. K. & Smyth, G. K. Chapter 8: generalized linear models: Diagnostics. In Generalized Linear Models With Examples in R (eds. Dunn, P. K. & Smyth, G. K.) 297–331 (Springer, 2018). https://doi.org/10.1007/978-1-4419-0118-7_8.38.Hui, F. K. C., Taskinen, S., Pledger, S., Foster, S. D. & Warton, D. I. Model-based approaches to unconstrained ordination. Methods Ecol. Evol. 6, 399–411 (2015).Article 

Google Scholar 
39.Hui, F. K. C. Boral–Bayesian ordination and regression analysis of multivariate abundance Data in r. Methods Ecol. Evol. 7, 744–750 (2016).Article 

Google Scholar 
40.Popovic, G. C., Warton, D. I., Thomson, F. J., Hui, F. K. C. & Moles, A. T. Untangling direct species associations from indirect mediator species effects with graphical models. Methods Ecol. Evol. 10, 1571–1583 (2019).Article 

Google Scholar 
41.Jones, C. M., Palmer, M. & Schaffler, J. J. Beyond Zar: The use and abuse of classification statistics for otolith chemistry. J. Fish Biol. 90, 492–504 (2017).CAS 
PubMed 
Article 

Google Scholar 
42.Rahman, M. A. & Awal, S. Development of captive breeding, seed production and culture techniques of snakehead fish for species conservation and sustainable aquaculture. Int. J. Adv. Agric. Environ. Eng. 3, 117–120 (2016).
Google Scholar 
43.Khan, M. A., Khan, S. & Miyan, K. Stock identification of the Channa striata inhabiting the Gangetic River System using Truss Morphometry. Russ. J. Ecol. 50, 391–396 (2019).Article 

Google Scholar 
44.Phen, C., Thang, T. B., Baran, E. & Vann, L. S. Biological reviews of important Cambodian fish species, based on FishBase 2004. Volume 1: Channa striata; Channa micropeltes; Barbonymus altus; Barbonymus gonionotus; Cyclocheilichthys apogon; Cyclocheilichthys enoplos; Henicorhynchus lineatus; Henicorhynchus siamensis; Pangasius hypophthalmus; Pangasius djambal. (WorldFish Center and Inland Fisheries Research and Development Institute, 2005).45.War, M. & Haniffa, M. A. Growth and survival of larval snakehead Channa striatus (Bloch, 1793) fed different live feed organisms. Turk. J. Fish. Aquat. Sci. 11, 523–528 (2011).
Google Scholar 
46.Cagauan, A. G. Exotic aquatic species introduction in the Philippines for aquaculture—A threat to biodiversity or a boon to the economy?. J. Environ. Sci. Manag. 10, 48–62 (2007).
Google Scholar 
47.Jayaram, K. C. The Freshwater Fishes of the Indian Region (Narendra Publishing House, 1999).
Google Scholar 
48.Talwar, P. K. & Jhingran, A. G. Inland fishes of India and adjacent countries Vol. 2 (CRC Press, 1991).
Google Scholar 
49.R Core Team. R: A Language and Environment for Statistical Computing. (R Foundation for Statistical Computing, 2019).50.Libungan, L. A. & Pálsson, S. ShapeR: An R package to study otolith shape variation among fish populations. PLoS ONE 10, e0121102 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
51.Graps, A. An introduction to wavelets. IEEE Comput. Sci. Eng. 2, 50–61 (1995).Article 

Google Scholar 
52.Turan, C. The use of otolith shape and chemistry to determine stock structure of Mediterranean horse mackerel Trachurus mediterraneus (Steindachner). J. Fish Biol. 69, 165–180 (2006).CAS 
Article 

Google Scholar 
53.Oksanen, J. vegan: Community Ecology Package. (2019).54.Venables, W. N. & Ripley, B. D. Modern applied statistics with S-PLUS (Springer Science & Business Media, 2013).
Google Scholar 
55.Warton, D. I. Raw data graphing: An informative but under-utilized tool for the analysis of multivariate abundances. Austral. Ecol. 33, 290–300 (2008).Article 

Google Scholar 
56.Begg, G. A., Friedland, K. D. & Pearce, J. B. Stock identification and its role in stock assessment and fisheries management: An overview. Fish. Res. 43, 1–8 (1999).Article 

Google Scholar 
57.Sengupta, B. Water Quality Status of Yamuna River (1999-2005), Assessment and Development of River Basin Series: ADSORBS/41/2006-07. Cent. Pollut. Control Board Delhi (2006).58.Bhardwaj, R., Gupta, A. & Garg, J. K. Evaluation of heavy metal contamination using environmetrics and indexing approach for River Yamuna, Delhi stretch, India. Water Sci. 31, 52–66 (2017).Article 

Google Scholar  More

1.MacArthur, R. H., Wilson, E. O. The theory of island biogeography. in Monographs in Population Biology (Princeton University Press, Princeton, NJ, 1967)2.Hubbell, S. P. The unified neutral theory of biodiversity and biogeography. in Monographs in Population Biology, Vol. 32 (Princeton University Press, Princeton, NJ, 2001).3.Brown, J. H., Gillooly, J. F., Allen, A. P., Savage, V. M. & West, G. B. Toward a metabolic theory of ecology. Ecology 85, 1771–1789 (2004).
Google Scholar 
4.Ryther, J. Photosynthesis and fish production in the sea. Science 166, 72–76 (1969).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
5.Cushing, D. A difference in structure between ecosystems in strongly stratified waters and in those that are only weakly stratified. J. Plankton Res. 11, 1–13 (1989).Article 

Google Scholar 
6.Barber, R. T. & Hiscock, M. R. A rising tide lifts all phytoplankton: growth response of other phytoplankton taxa in diatom‐dominated blooms. Glob. Biogeoch. Cycl. 20, GB4S03 (2006).
Google Scholar 
7.Siegel, D. A. et al. Global assessment of ocean carbon export by combining satellite observations and food-web models. Global Biogeochem. Cycl. 28, 181–196 (2014).CAS 
Article 

Google Scholar 
8.Buesseler, K. O., Boyd, P. W., Black, E. E. & Siegel, D. A. Metrics that matter for assessing the ocean biological carbon pump. Proc. Natl Acad. Sci. USA 117, 9679–9687 (2020).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
9.Irwin, A. J., Finkel, Z. V., Schofield, O. M. & Falkowski, P. G. Scaling-up from nutrient physiology to the size-structure of phytoplankton communities. J. Plankt. Res. 28, 459–471 (2006).Article 

Google Scholar 
10.Litchman, E., Klausmeier, C. A. & Yoshiyama, K. Contrasting size evolution in marine and freshwater diatoms. Proc. Natl Acad. Sci. USA 106, 2665–2670 (2009).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
11.Tozzi, S., Schofield, O. & Falkowski, P. Historical climate change and ocean turbulence as selective agents for two key phytoplankton functional groups. Mar. Ecol. Prog. Ser. 274, 123–132 (2004).Article 

Google Scholar 
12.Follows, M. J., Dutkiewicz, S., Grant, S. & Chisholm, S. W. Emergent biogeography of microbial communities in a model ocean. Science 315, 1843–1846 (2007).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
13.Gregg, W. W., Casey, N. W. & Rousseaux, C. S. Global surface ocean carbon estimates in a model forced by MERRA NASA Technical Report Series on Global Modeling and Data Assimilation. NASA TM-2013-104606, Vol. 31, 39 (2013).14.Hulburt, E. M. Competition for nutrients by marine phytoplankton in oceanic, coastal, and estuarine regions. Ecology 51, 475–484 (1970).Article 

Google Scholar 
15.Siegel, D. A. Resource competition in a discrete environment: why are plankton distributions paradoxical? Limnol. Oceanogr. 43, 1133–1146 (1998).Article 

Google Scholar 
16.Cyr, H., Peters, R. H. & Downing, J. A. Population density and community size structure: comparison of aquatic and terrestrial systems. Oikos 80, 139–149 (1997).Article 

Google Scholar 
17.White, E. P., Ernest, S. M., Kerkhoff, A. J. & Enquist, B. J. Relationships between body size and abundance in ecology. Trends Ecol. Evol. 22, 323–330 (2007).PubMed 
Article 
PubMed Central 

Google Scholar 
18.McCauley, D. J. et al. On the prevalence and dynamics of inverted trophic pyramids and otherwise top-heavy communities. Ecol. Lett. 21, 439–454 (2018).PubMed 
Article 
PubMed Central 

Google Scholar 
19.West, G. B., Brown, J. H. & Enquist, B. J. A general model for the origin of allometric scaling laws in biology. Science 276, 122–126 (1997).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
20.West, G. B., Brown, J. H. & Enquist, B. J. The fourth dimension of life: fractal geometry and allometric scaling of organisms. Science 284, 1677–1679 (1999).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
21.Sheldon, R. W., Prakash, A. & Sutcliffe, W. Jr The size distribution of particles in the Ocean 1. Limnol. Oceanogr. 17, 327–340 (1972).Article 

Google Scholar 
22.Jonasz, M. & Fournier, G. Light Scattering by Particles in Water: Theoretical and Experimental Foundations. (Elsevier, 2011).23.Huete-Ortega, M., Cermeno, P., Calvo-Díaz, A. & Maranon, E. Isometric size-scaling of metabolic rate and the size abundance distribution of phytoplankton. Proc. Royal Soc. B 279, 1815–1823 (2012).Article 

Google Scholar 
24.Marañón, E. Cell size as a key determinant of phytoplankton metabolism and community structure. Annu. Rev. Mar. Sci. 7, 241–264 (2015).Article 

Google Scholar 
25.Riley, G. A., Stommel, H. M., Bumpus, D. F. Quantitative ecology of the plankton of the western North Atlantic. Bulletin of the Bingham Oceanographic Collection 12 (Yale Univ., New Haven, CT, 1949)26.Evans, G. T. & Parslow, J. S. A model of annual plankton cycles. Biol. Oceanogr. 3, 327–347 (1985).
Google Scholar 
27.Margalef, R. Perspectives in Ecological Theory. 111 pp (Univ. Chicago Press, Chicago, Ill, 1968).28.Behrenfeld, M. J. & Boss, E. S. Resurrecting the ecological underpinnings of ocean plankton blooms. Ann. Rev. Mar. Sci. 6, 167–194 (2014).PubMed 
Article 
PubMed Central 

Google Scholar 
29.Behrenfeld, M. J. & Boss, E. S. Student’s tutorial on bloom hypotheses in the context of phytoplankton annual cycles. Glob. Change Biol. 24, 55–77 (2018).Article 

Google Scholar 
30.Strom, S. L. & Buskey, E. J. Feeding, growth, and behavior of the thecate heterotrophic dinoflagellate Oblea rotunda. Limnol. Oceanogr. 38, 965–977 (1993).Article 

Google Scholar 
31.Strom, S. L., Macri, E. L. & Olson, M. B. Microzooplankton grazing in the coastal Gulf of Alaska: Variations in top-down control of phytoplankton. Limnol. Oceanogr. 52, 1480–1494 (2007).Article 

Google Scholar 
32.Wirtz, K. W. Who is eating whom? Morphology and feeding type determine the size relation between planktonic predators and their ideal prey. Mar. Ecol. Progr. Ser. 445, 1–12 (2012).Article 

Google Scholar 
33.Kiørboe, T. How zooplankton feed: mechanisms, traits and trade-offs. Biol. Rev. 86, 311–339 (2011).PubMed 
Article 
PubMed Central 

Google Scholar 
34.Hansen, B., Bjornsen, P. K. & Hansen, P. J. The size ratio between planktonic predators and their prey. Limnol. Oceanogr. 39, 395–403 (1994).Article 

Google Scholar 
35.Sommer, U. & Sommer, F. Cladocerans versus copepods: the cause of contrasting top–down controls on freshwater and marine phytoplankton. Oecologia 147, 183–194 (2006).PubMed 
Article 

Google Scholar 
36.Hébert, M.-P., Beisner, B. E. & Maranger, R. Linking zooplankton communities to ecosystem functioning: Toward an effect-trait framework. J. Plankton Res. 39, 3–12 (2017).Article 
CAS 

Google Scholar 
37.Fuchs, H. L. & Franks, P. J. Plankton community properties determined by nutrients and size-selective feeding. Mar. Ecol. Progr. Ser. 413, 1–15 (2010).Article 

Google Scholar 
38.Sutherland, K. R., Madin, L. P. & Stocker, R. Filtration of submicrometer particles by pelagic tunicates. Proc. Natl Acad. Sci. USA 107, 15129–15134 (2010).CAS 
PubMed 
Article 

Google Scholar 
39.Dadon-Pilosof, A., Lombard, F., Genin, A., Sutherland, K. R. & Yahel, G. Prey taxonomy rather than size determines salp diets. Limnol. Oceanogr. 64, 1996–2010 (2019).Article 

Google Scholar 
40.Antoine, D., Andre, J. M. & Morel, A. Oceanic primary production 2. Estimation at global scale from satellite (coastal zone color scanner) chlorophyll. Global Biogeochem. Cycl. 10, 57–69 (1996).CAS 
Article 

Google Scholar 
41.Brewin, R. J. W. et al. A three-component model of phytoplankton size class for the Atlantic Ocean. Ecol. Model. 221, 1472–1483 (2010).CAS 
Article 

Google Scholar 
42.Marañón, E., Cermeño, P., Latasa, M. & Tadonléké, R. D. Temperature, resources, and phytoplankton size structure in the ocean. Limnol. Oceanogr. 5, 1266–1278 (2012).Article 

Google Scholar 
43.Kerr, S. R., Dickie, L. M. The Biomass Spectrum: a Predator-prey Theory of Aquatic Production (Columbia University Press, 2001).44.Behrenfeld, M. J., et al. Annual boom-bust cycles of polar phytoplankton biomass revealed by space-based lidar. Nat. Geosci. 2017; https://doi.org/10.1038/NGEO2861.45.Kiorboe, T. Turbulence, phytoplankton cell size, and the structure of pelagic food-webs. Adv. Mar. Biol. 29, 1–72 (1993).Article 

Google Scholar 
46.DeLong, J. P. & Vasseur, D. A. Size-density scaling in protists and the links between consumer–resource interaction parameters. J. Animal Ecol. 81, 1193–1201 (2012).Article 

Google Scholar 
47.Smetacek, V. Diatoms and the ocean carbon cycle. Protist 150, 25–32 (1999).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
48.Smetacek, V., Assmy, P. & Henjes, J. The role of grazing in structuring Southern Ocean pelagic ecosystems and biogeochemical cycles. Antarct. Sci. 16, 541–558 (2004).Article 

Google Scholar 
49.Behrenfeld, M. J., Halsey, K. H., Boss, E., Karp-Boss, L., Milligan, A. J. & Peers, G. Thoughts on the evolution and ecological niche of diatoms. Ecol. Monogr. 2021; in press.50.Glibert, P. M. Margalef revisited: a new phytoplankton mandala incorporating twelve dimensions, including nutritional physiology. Harmful Algae 55, 25–30 (2016).PubMed 
Article 
PubMed Central 

Google Scholar 
51.Margalef, R. Life-forms of phytoplankton as survival alternatives in an unstable environment. Oceanolog. Acta 1, 493–509 (1978).
Google Scholar 
52.Cullen, J. J. & MacIntyre, J. G. Behavior, physiology and the niche of depth-regulating phytoplankton. Nato ASI Ser. G Ecol. Sci. 41, 559–580 (1998).53.Kemp, A. E. & Villareal, T. A. The case of the diatoms and the muddled mandalas: Time to recognize diatom adaptations to stratified waters. Prog. Oceanogr. 167, 138–149 (2018).Article 

Google Scholar 
54.Kudela, R. M. Does horizontal mixing explain phytoplankton dynamics? Proc. Natl Acad. Sci. USA 107, 18235–18236 (2010).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
55.Wyatt, T. Margalef’s mandala and phytoplankton bloom strategies. Deep Sea Res. II 101, 32–49 (2014).Article 

Google Scholar 
56.Waite, A., Fisher, A., Thompson, P. A. & Harrison, P. J. Sinking rate versus cell volume relationships illuminate sinking rate control mechanisms in marine diatoms. Mar. Ecol. Prog. Ser. 157, 97–108 (1997).Article 

Google Scholar 
57.Moore, J. K. & Villareal, T. A. Size-ascent rate relationships in positively buoyant marine diatoms. Limnol. Oceanogr. 41, 1514–1520 (1996).Article 

Google Scholar 
58.Bienfang, P. & Szyper, J. Effects of temperature and salinity on sinking rates of the centric diatom Ditylum brightwellii. Biol. Oceanogr. 1, 211–223 (1982).
Google Scholar 
59.Bienfang, P., Szyper, J. & Laws, E. Sinking rate and pigment responses to light-limitation of a marine diatom – implications to dynamics of chlorophyll maximum layers. Oceanolog. Acta 6, 55–62 (1983).CAS 

Google Scholar 
60.Villareal, T. A., Pilskaln, C. H., Montoya, J. P. & Dennett, M. Upward nitrate transport by phytoplankton in oceanic waters: balancing nutrient budgets in oligotrophic seas. PeerJ 2, e302 (2014).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
61.Irigoien, X., Flynn, K. J. & Harris, R. P. Phytoplankton blooms: a “loophole” in micozooplankton grazing impact? J. Plankton Res. 27, 313–321 (2005).Article 

Google Scholar 
62.Bolaños, L. M., et al. Small phytoplankton dominate western North Atlantic biomass. ISME J: 1–12, https://doi.org/10.1038/s41396-020-0636-0 (2020).63.Guillard, R., Kilham, P. The ecology of marine planktonic diatoms. in The Biology of Diatoms, Vol. 13, 372–469 (Blackwell Oxford, 1977).64.Malviya, S. et al. Insights into global diatom distribution and diversity in the world’s ocean. Proc. Natl Acad. Sci. USA 113, E1516–E1525 (2016).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
65.Barton, A. D., Finkel, Z. V., Ward, B. A., Johns, D. G. & Follows, M. J. On the roles of cell size and trophic strategy in North Atlantic diatom and dinoflagellate communities. Limnol. Oceanogr. 58, 254–266 (2013).Article 

Google Scholar 
66.Edwards, K. F. Mixotrophy in nanoflagellates across environmental gradients in the ocean. Proc. Natl Acad. Sci. USA 116, 6211–6220 (2019).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
67.Boyd, P. W. Environmental factors controlling phytoplankton processes in the Southern Ocean. J. Phycol. 38, 844–861 (2002).Article 

Google Scholar 
68.Fauchereau, N., Tagliabue, A., Bopp, L. & Monteiro, P. M. The response of phytoplankton biomass to transient mixing events in the Southern Ocean. Geophys. Res. Lett. 38, L17601 (2011).Article 

Google Scholar 
69.Wolfe, G. V., Steinke, M. & Kirst, G. O. Grazing-activated chemical defence in a unicellular marine alga. Nature 387, 894–897 (1997).CAS 
Article 

Google Scholar 
70.Colin, S. P. & Dam, H. G. Effects of the toxic dinoflagellate Alexandrium fundyense on the copepod Acartia hudsonica: a test of the mechanisms that reduce ingestion rates. Mar. Ecol. Prog. Ser. 248, 55–65 (2003).Article 

Google Scholar 
71.Van Donk, E., Ianora, A. & Vos, M. Induced defences in marine and freshwater phytoplankton: a review. Hydrobiol. 668, 3–19 (2011).Article 
CAS 

Google Scholar 
72.Pohnert, G., Steinke, M. & Tollrian, R. Chemical cues, defense metabolites and the shaping of pelagic interspecific interactions. Trends Ecol. Evol. 22, 198–204 (2007).PubMed 
Article 
PubMed Central 

Google Scholar 
73.DeMott, W. R. & Moxter, F. Foraging cyanobacteria by copepods: responses to chemical defense and resource abundance. Ecology 72, 1820–1834 (1991).Article 

Google Scholar 
74.Ger, K. A., Naus-Wiezer, S., De Meester, L. & Lürling, M. Zooplankton grazing selectivity regulates herbivory and dominance of toxic phytoplankton over multiple prey generations. Limnol. Oceanogr. 64, 1214–1227 (2019).Article 

Google Scholar 
75.Smayda, T. J. & Reynolds, C. S. Community assembly in marine phytoplankton: application of recent models to harmful dinoflagellate blooms. J. Plankt. Res. 23, 447–461 (2001).Article 

Google Scholar 
76.Acevedo-Trejos, E., Brandt, G., Bruggeman, J. & Merico, A. Mechanisms shaping size structure and functional diversity of phytoplankton communities in the ocean. Sci. Rep 5, 8918 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
77.Cuesta, J. A., Delius, G. W. & Law, R. Sheldon spectrum and the plankton paradox: two sides of the same coin—a trait-based plankton size-spectrum model. J. Math. Biol. 76, 67–96 (2018).PubMed 
Article 
PubMed Central 

Google Scholar 
78.Hutchinson, G. E. Ecological aspects of succession in natural populations. Amer. Nat. 75, 406–418 (1941).Article 

Google Scholar 
79.Tilman, D. Resource competition between plankton algae: an experimental and theoretical approach. Ecology 58, 338–348 (1977).CAS 
Article 

Google Scholar 
80.Tilman, D., Mattson, M. & Langer, S. Competition and nutrient kinetics along a temperature gradient: An experimental test of a mechanistic approach to niche theory 1. Limnol. Oceanogr. 26, 1020–1033 (1981).Article 

Google Scholar 
81.Sommer, U. Nutrient competition between phytoplankton species in multispecies chemostat experiments. Archiv hydrobiol. 96, 399–416 (1983).
Google Scholar 
82.Sommer, U. Comparison between steady state and non-steady state competition: experiments with natural phytoplankton. Limnol. Oceanogr. 30, 335–346 (1985).CAS 
Article 

Google Scholar 
83.Tilman, D. Resource Competition and Community Structure (Princeton University Press, 1982).84.Sommer, U. The role of competition for resources in phytoplankton succession. in Plankton Ecology. Berlin, Heidelberg: Springer. 1989, pp. 57-106.85.Burd, A. B. & Jackson, G. A. Particle aggregation. Annu. Rev. Mar. Sci. 1, 65–90 (2009).Article 

Google Scholar 
86.Kahl, L. A., Vardi, A. & Schofield, O. Effects of phytoplankton physiology on export flux. Mar. Ecol. Prog. Ser. 354, 3–19 (2008).CAS 
Article 

Google Scholar 
87.Guidi, L. et al. Effects of phytoplankton community on production, size and export of large aggregates: a world-ocean analysis. Limnol. Oceanogr. 54, 1951–1963 (2009).Article 

Google Scholar 
88.Kiørboe, T., Lundsgaard, C., Olesen, M. & Hansen, J. L. S. Aggregation and sedimentation processes during a spring phytoplankton bloom: a field experiment to test coagulation theory. J. Mar. Res. 52, 297–323 (1994).Article 

Google Scholar 
89.Prairie, J. C., Montgomery, Q. W., Proctor, K. W. & Ghiorso, K. S. Effects of phytoplankton growth phase on settling properties of marine aggregates. J. Mar. Sci. Engineer. 7, 265 (2019).Article 

Google Scholar 
90.Lima-Mendez, G. et al. Determinants of community structure in the global plankton interactome. Science 348, 6237 (2015).Article 
CAS 

Google Scholar 
91.Sañudo-Wilhelmy, S. A., Gómez-Consarnau, L., Suffridge, C. & Webb, E. A. The role of B vitamins in marine biogeochemistry. Ann. Rev. Mar. Sci. 6, 339–367 (2014).PubMed 
Article 
PubMed Central 

Google Scholar 
92.Helliwell, K. E. The roles of B vitamins in phytoplankton nutrition: new perspectives and prospects. New Phytol. 216, 62–68 (2017).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
93.Chisholm, S. W. et al. A novel free-living prochlorophyte abundant in the oceanic euphotic zone. Nature 334, 340–343 (1988).Article 

Google Scholar 
94.Caputo, A., Nylander, J. A. & Foster, R. A. The genetic diversity and evolution of diatom-diazotroph associations highlights traits favoring symbiont integration. FEMS Microbiol. Lett. 366, fny297 (2019).CAS 
PubMed Central 
Article 

Google Scholar 
95.Decelle, J. et al. An original mode of symbiosis in open ocean plankton. Proc. Natl Acad. Sci. USA 109, 18000–18005 (2012).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
96.Decelle, J. et al. Algal remodeling in a ubiquitous planktonic photosymbiosis. Curr. Biol. 29, 968–978 (2019).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
97.Behrenfeld, M. J. et al. The North Atlantic aerosol and marine ecosystem study (NAAMES): science motive and mission overview. Front. Mar. Sci. 6, 122 (2019).Article 

Google Scholar 
98.Menden-Deuer, S. & Lessard, E. J. Carbon to volume relationships for dinoflagellates, diatoms, and other protist plankton. Limnol. Oceanogr. 45, 569–579 (2000).CAS 
Article 

Google Scholar  More

1.Perissinotto, R., Mayzaud, P., Nichols, P. D. & Labat, J. P. Grazing by Pyrosoma atlanticum (Tunicata, Thaliacea) in the south Indian Ocean. Mar. Ecol. Prog. Ser. 330, 1–11 (2007).CAS 
Article 

Google Scholar 
2.Drits, A. V., Arashkevich, E. G. & Semenova, T. N. Pyrosoma atlanticum (Tunicata, Thaliacea): grazing impact on phytoplankton standing stock and role in organic carbon flux. J. Plankton Res. 14, 799–809 (1992).Article 

Google Scholar 
3.Henschke, N. et al. Large vertical migrations of Pyrosoma atlanticum play an important role in active carbon transport. J. Geophys. Res. Biogeosci. 124, 1056–1070 (2019).Article 

Google Scholar 
4.Schram, J. B., Sorensen, H. L., Brodeur, R. D., Galloway, A. W. E. & Sutherland, K. R. Abundance, distribution, and feeding ecology of Pyrosoma atlanticum in the Northern California Current. Mar. Ecol. Prog. Ser. 651, 97–110 (2020).5.O’Loughlin, J. H. et al. Implications of Pyrosoma atlanticum range expansion on phytoplankton standing stocks in the Northern California Current. Prog. Oceanogr. 188, 102424 (2020).6.Hobson, E. S. & Chess, J. Trophic relations of the blue rockfish, Sebastes mystinus, in a coastal upwelling system off northern California. in Fishery Bulletin, Vol. 86, 715–743 (National Marine Fisheries Service, 1988).7.Bulman, C. M., He, X. & Koslow, J. A. Trophic ecology of the mid-slope demersal fish community off Southern Tasmania, Australia. Mar. Freshw. Res. 53, 59–72 (2002).Article 

Google Scholar 
8.Harbison, G. R. The parasites and predators of Thaliacea. in The Biology of Pelagic Tunicates (Oxford University Press, 1998).9.James, G. D. & Stahl, J. -C. Diet of southern Buller’s albatross (Diomedea bulleri bulleri) and the importance of fishery discards during chick rearing. N. Z. J. Mar. Freshw. Res. 34, 435–454 (2000).Article 

Google Scholar 
10.Hedd, A. & Gales, R. The diet of shy albatrosses (Thalassarche cauta) at Albatross Island, Tasmania. J. Zool. 253, 69–90 (2001).Article 

Google Scholar 
11.Childerhouse, S., Dix, B. & Gales, N. Diet of New Zealand sea lions (Phocarctos hookeri) at the Auckland Islands. Wildl. Res. 28, 291–298 (2001).Article 

Google Scholar 
12.Lindley, J. A., Hernández, F., Scatllar, J. & Docoito, J. Funchalia sp. (Crustacea: Penaeidae) associated with Pyrosoma atlanticum (Thaliacea: Pyrosomidae) off the Canary Islands. J. Mar. Biol. Assoc. UK 81, 173–174 (2001).Article 

Google Scholar 
13.Lebrato, M. & Jones, D. O. B. Mass deposition event of Pyrosoma atlanticum carcasses off Ivory Coast (West Africa). Limnol. Oceanogr. 54, 1197–1209 (2009).CAS 
Article 

Google Scholar 
14.Archer, S. K. et al. Pyrosome consumption by benthic organisms during blooms in the northeast Pacific and Gulf of Mexico. Ecology 99, 981–984 (2018).PubMed 
Article 
PubMed Central 

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

Google Scholar 
16.Sherr E. & Sherr B. Understanding roles of microbes in marine pelagic food webs: a brief history. in Microbial Ecology of the Oceans 27–44 (John Wiley & Sons Ltd, 2008).17.Falkowski, P. G., Fenchel, T. & Delong, E. F. The microbial engines that drive earth’s biogeochemical cycles. Science 320, 1034–1039 (2008).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
18.Décima, M., Stukel, M. R., López-López, L. & Landry, M. R. The unique ecological role of pyrosomes in the Eastern Tropical Pacific. Limnol. Oceanogr. 64, 728–743 (2019).Article 

Google Scholar 
19.Gauns, M., Mochemadkar, S., Pratihary, A., Roy, R. & Naqvi, S. W. A. Biogeochemistry and ecology of Pyrosoma spinosum from the Central Arabian Sea. Zool. Stud. 54, 3 (2015).Article 
CAS 

Google Scholar 
20.Bowlby, M. R., Widder, E. A. & Case, J. F. Patterns of stimulated bioluminescence in two pyrosomes (Tunicata: Pyrosomatidae). Biol. Bull. 179, 340–350 (1990).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
21.Haddock, S. H. D., Moline, M. A. & Case, J. F. Bioluminescence in the sea. Annu. Rev. Mar. Sci. 2, 443–493 (2010).Article 

Google Scholar 
22.Swift, E., Biggley, W. H. & Napora, T. A. The bioluminescence emission spectra of Pyrosoma atlanticum, P. spinosum (Tunicata), Euphausia tenera (Crustacea) and Gonostoma sp. (Pisces). J. Mar. Biol. Assoc. UK 57, 817–823 (1977).23.Martínez‐García, M. et al. Ammonia-oxidizing Crenarchaeota and nitrification inside the tissue of a colonial ascidian. Environ. Microbiol. 10, 2991–3001 (2008).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
24.Donia, M. S. et al. Complex microbiome underlying secondary and primary metabolism in the tunicate-Prochloron symbiosis. Proc. Natl Acad. Sci. 108, E1423–E1432 (2011).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
25.Kwan, J. C. et al. Host control of symbiont natural product chemistry in cryptic populations of the tunicate Lissoclinum patella. PLoS ONE 9, e95850 (2014).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
26.Purcell, J. E. & Arai, M. N. Interactions of pelagic cnidarians and ctenophores with fish: a review. Hydrobiologia. 451, 27–44 (2001).Article 

Google Scholar 
27.Delannoy, C. M. J., Houghton, J. D. R., Fleming, N. E. C. & Ferguson, H. W. Mauve stingers (Pelagia noctiluca) as carriers of the bacterial fish pathogen Tenacibaculum maritimum. Aquaculture. 311, 255–257 (2011).Article 

Google Scholar 
28.Lee, M. D., Kling, J. D., Araya, R. & Ceh, J. Jellyfish life stages shape associated microbial communities, while a core microbiome is maintained across all. Front. Microbiol. 9, 1534 (2018).29.Troussellier, M., Escalas, A., Bouvier, T. & Mouillot, D. Sustaining rare marine microorganisms: macroorganisms as repositories and dispersal agents of microbial diversity. Front. Microbiol. 8 (2017).30.Brodeur, R. et al. An unusual gelatinous plankton event in the NE Pacific: the Great Pyrosome Bloom of 2017. PICES Press; Sidney Vol. 26, 22–27 (Winter, 2018).31.Sutherland, K. R., Sorensen, H. L., Blondheim, O. N., Brodeur, R. D. & Galloway, A. W. E. Range expansion of tropical pyrosomes in the northeast Pacific Ocean. Ecology 99, 2397–2399 (2018).PubMed 
Article 
PubMed Central 

Google Scholar 
32.Miller, R. R. et al. Distribution of pelagic Thaliaceans, Thetys vagina and Pyrosoma Atlanticum, during a period of mass occurrence within the California current. CalCOFI Rep. 60, (2019).33.Guigand, C. M., Cowen, R. K., Llopiz, J. K. & Richardson, D. E. A coupled asymmetrical multiple opening closing net with environmental sampling system. Mar. Technol. Soc. J. 39, 22–24 (2005).34.Parada, A. E., Needham, D. M. & Fuhrman, J. A. Every base matters: assessing small subunit rRNA primers for marine microbiomes with mock communities, time series and global field samples. Environ. Microbiol. 18, 1403–1414 (2016).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
35.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 
36.Cole, J. R. et al. Ribosomal Database Project: data and tools for high throughput rRNA analysis. Nucleic Acids Res. 42, D633–D642 (2014).CAS 
Article 

Google Scholar 
37.Wang, Q., Garrity, G. M., Tiedje, J. M. & Cole, J. R. Naïve Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl. Environ. Microbiol. 73, 5261–5267 (2007).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
38.O’Leary, N. A. et al. Reference sequence (RefSeq) database at NCBI: current status, taxonomic expansion, and functional annotation. Nucleic Acids Res. 44, D733–D745 (2016).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
39.Johnson, M. et al. NCBI BLAST: a better web interface. Nucleic Acids Res. 36, W5–W9 (2008).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
40.McMurdie, P. J. & Holmes, S. phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE 8, e61217 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
41.Clarke, K. R. Non-parametric multivariate analyses of changes in community structure. Aust. J. Ecol. 18, 117–143 (1993).Article 

Google Scholar 
42.Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
43.McMurdie, P. J. & Holmes, S. Waste not, want not: why rarefying microbiome data is inadmissible. PLoS Comput. Biol. 10, e1003531 (2014).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
44.Duperron, S. Microbial Symbioses 168 p. (Elsevier, 2016).45.Schmitt, S. et al. Assessing the complex sponge microbiota: core, variable and species-specific bacterial communities in marine sponges. ISME J. 6, 564–576 (2012).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
46.Gloor, G. B., Macklaim, J. M., Pawlowsky-Glahn, V. & Egozcue, J. J. Microbiome datasets are compositional: and this is not optional. Front. Microbiol. 8 (2017).47.Urbanczyk, H., Ast, J. C., Higgins, M. J., Carson, J. & Dunlap, P. V. Reclassification of Vibrio fischeri, Vibrio logei, Vibrio salmonicida and Vibrio wodanis as Aliivibrio fischeri gen. nov., comb. nov., Aliivibrio logei comb. nov., Aliivibrio salmonicida comb. nov. and Aliivibrio wodanis comb. nov. Int. J. Syst. Evol. Microbiol. 57, 2823–2829 (2007).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
48.Stecher, G., Tamura, K. & Kumar, S. Molecular Evolutionary Genetics Analysis (MEGA) for macOS. Mol. Biol. Evol. 37, 1237–1239 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
49.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 
50.Booth, B. C. Marine phytoplankton. A guide to naked flagellates and coccolithophorids (C. R. Tomas [ed.]). Limnol. Oceanogr. 39, 982–983 (1994).Article 

Google Scholar 
51.Halse, G. R. & Syvertsen, E. E. Chapter 2—marine diatoms. in Identifying Marine Diatoms and Dinoflagellates (ed. Tomas C. R.) 5–385 (Academic Press, 1996).52.Steidinger, K. A. & Tangen, K. Chapter 3—dinoflagellates. in Identifying Marine Diatoms and Dinoflagellates (ed. Tomas C. R.) 387–584 (Academic Press, 1996).53.Daniels, C. & Breitbart, M. Bacterial communities associated with the ctenophores Mnemiopsis leidyi and Beroe ovata. FEMS Microbiol. Ecol. 82, 90–101 (2012).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
54.Kramar, M. K., Tinta, T., Lučić, D., Malej, A. & Turk, V. Bacteria associated with moon jellyfish during bloom and post-bloom periods in the Gulf of Trieste (northern Adriatic). PLoS ONE 14, e0198056 (2019).Article 
CAS 

Google Scholar 
55.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).56.Webster, N. S. & Bourne, D. Bacterial community structure associated with the Antarctic soft coral, Alcyonium antarcticum. FEMS Microbiol. Ecol. 59, 81–94 (2007).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
57.Rodrigues, C. F., Hilário, A., Cunha, M. R., Weightman, A. J. & Webster, G. Microbial diversity in Frenulata (Siboglinidae, Polychaeta) species from mud volcanoes in the Gulf of Cadiz (NE Atlantic). Antonie Van Leeuwenhoek 100, 83–98 (2011).PubMed 
Article 
PubMed Central 

Google Scholar 
58.McCann, J., Stabb, E. V., Millikan, D. S. & Ruby, E. G. Population dynamics of Vibrio fischeri during Infection of Euprymna scolopes. Appl. Environ. Microbiol. 69, 5928–5934 (2003).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
59.Hammann, S., Moss, A. & Zimmer, M. Sterile surfaces of Mnemiopsis leidyi; (Ctenophora) in bacterial suspension—a key to invasion success? Open J. Mar. Sci. 05, 237–246 (2015).Article 

Google Scholar 
60.Hammer, T. J., Sanders, J. G. & Fierer, N. Not all animals need a microbiome. FEMS Microbiol. Lett. 366, fnz117 https://doi.org/10.1093/femsle/fnz117 (2019).61.Nedashkovskaya, O. I., Kukhlevskiy, A. D., Zhukova, N. V. & Kim, S. B. Amylibacter ulvae sp. nov., a new alphaproteobacterium isolated from the Pacific green alga Ulva fenestrata. Arch. Microbiol. 198, 251–256 (2016).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
62.Burke, C., Thomas, T., Lewis, M., Steinberg, P. & Kjelleberg, S. Composition, uniqueness and variability of the epiphytic bacterial community of the green alga Ulva australis. ISME J. 5, 590–600 (2011).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
63.Catão, E. C. P. et al. Shear stress as a major driver of marine biofilm communities in the NW Mediterranean Sea. Front. Microbiol. 10 (2019).64.Chafee, M. et al. Recurrent patterns of microdiversity in a temperate coastal marine environment. ISME J. 12, 237–252 (2018).PubMed 
Article 
PubMed Central 

Google Scholar 
65.Bondoso, J. et al. Roseimaritima ulvae gen. nov., sp. nov. and Rubripirellula obstinata gen. nov., sp. nov. two novel planctomycetes isolated from the epiphytic community of macroalgae. Syst. Appl. Microbiol. 38, 8–15 (2015).PubMed 
Article 
PubMed Central 

Google Scholar 
66.Zhu, P., Li, Q. & Wang, G. Unique microbial signatures of the Alien Hawaiian marine sponge Suberites zeteki. Microb. Ecol. 55, 406–414 (2008).PubMed 
Article 
PubMed Central 

Google Scholar 
67.Pimentel-Elardo, S., Wehrl, M., Friedrich, A. B., Jensen, P. R. & Hentschel, U. Isolation of planctomycetes from Aplysina sponges. Aquat. Microb. Ecol. 33, 239–245 (2003).Article 

Google Scholar 
68.da Silva Oliveira, F. A. et al. Microbial epibionts of the colonial ascidians Didemnum galacteum and Cystodytes sp. Symbiosis 59, 57–63 (2013).Article 

Google Scholar 
69.Yakimov, M. M. et al. Phylogenetic survey of metabolically active microbial communities associated with the deep-sea coral Lophelia pertusa from the Apulian plateau, Central Mediterranean Sea. Deep Sea Res. A Oceanogr. Res. Pap. 53, 62–75 (2006).Article 

Google Scholar 
70.Duque-Alarcón, A., Santiago-Vázquez, L. Z. & Kerr, R. G. A microbial community analysis of the octocoral Eunicea fusca. Electron. J. Biotechnol. 15, 15–15 (2012).
Google Scholar 
71.Wiegand, S., Jogler, M. & Jogler, C. On the maverick Planctomycetes. FEMS Microbiol. Rev. 42, 739–760 (2018).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
72.Lage, O. M. & Bondoso, J. Planctomycetes and macroalgae, a striking association. Front. Microbiol. 5 (2014).73.Ward, A. C. & Bora, N. Diversity and biogeography of marine Actinobacteria. Curr. Opin. Microbiol. 9, 279–286 (2006).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
74.Hahn, M. W. Description of seven candidate species affiliated with the phylum Actinobacteria, representing planktonic freshwater bacteria. Int. J. Syst. Evol. Microbiol. 59, 112–117 (2009).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
75.Gandhimathi, R. et al. Antimicrobial potential of sponge associated marine actinomycetes. J. Mycol. Méd. 18, 16–22 (2008).Article 

Google Scholar 
76.Abdelmohsen, U. R., Bayer, K. & Hentschel, U. Diversity, abundance and natural products of marine sponge-associated actinomycetes. Nat. Prod. Rep. 31, 381–399 (2014).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
77.Wu, Z. et al. A new tetrodotoxin-producing actinomycete, Nocardiopsis dassonvillei, isolated from the ovaries of puffer fish Fugu rubripes. Toxicon. 45, 851–859 (2005).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
78.Reichenbach, H. The ecology of the myxobacteria. Environ. Microbiol. 1, 15–21 (1999).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
79.Marshall, R. C. & Whitworth, D. E. Is “Wolf-Pack” predation by antimicrobial bacteria cooperative? Cell behaviour and predatory mechanisms indicate profound selfishness, even when working alongside Kin. BioEssays 41, 1800247 (2019).Article 

Google Scholar 
80.Welsh, R. M. et al. Bacterial predation in a marine host-associated microbiome. ISME J. 10, 1540–1544 (2016).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
81.Wang, Z., Kadouri, D. E. & Wu, M. Genomic insights into an obligate epibiotic bacterial predator: Micavibrio aeruginosavorus ARL-13. BMC Genomics 12, 453 (2011).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
82.Garcia, G. D. et al. Metagenomic analysis of healthy and white plague-affected Mussismilia braziliensis corals. Microb. Ecol. 65, 1076–1086 (2013).PubMed 
Article 
PubMed Central 

Google Scholar 
83.Rosales, S. M. et al. Microbiome differences in disease-resistant vs. susceptible Acropora corals subjected to disease challenge assays. Sci. Rep. 9, 18279 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
84.Evans, A. G. L. et al. Predatory activity of Myxococcus xanthus outer-membrane vesicles and properties of their hydrolase cargo. Microbiology 158, 2742–2752 (2012).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
85.Sudo, S. & Dworkin, M. Bacteriolytic enzymes produced by Myxococcus xanthus. J. Bacteriol. 110, 236–245 (1972).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
86.Tessler, M. et al. A putative chordate luciferase from a cosmopolitan tunicate indicates convergent bioluminescence evolution across phyla. Sci. Rep. 10, 17724 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
87.Berger, A. et al. Microscopic and Genetic Characterization of Bacterial Symbionts With Bioluminescent Potential in Pyrosoma Atlanticum. Frontiers in Marine Science. 8 https://doi.org/10.3389/fmars.2021.606818 (2021).88.Leisman, G., Cohn, D. H. & Nealson, K. H. Bacterial origin of luminescence in marine animals. Science 208, 1271–1273 (1980).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
89.Mackie, G. O. & Bone, Q. Luminescence and associated effector activity in Pyrosoma (Tunicata: Pyrosomida). Proc. R. Soc. Lond. B Biol. Sci. 202, 483–495 (1978).Article 

Google Scholar 
90.Nyholm, S. V. & McFall-Ngai, M. The winnowing: establishing the squid–vibrio symbiosis. Nat. Rev. Microbiol. 2, 632–642 (2004).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
91.Takemura, A. F., Chien, D. M. & Polz M. F. Associations and dynamics of Vibrionaceae in the environment, from the genus to the population level. Front. Microbiol. 5 (2014).92.Barnes, E. M., Carter, E. L. & Lewis, J. D. Predicting microbiome function across space is confounded by strain-level differences and functional redundancy across taxa. Front. Microbiol. 11 (2020).93.Tian, L. et al. Deciphering functional redundancy in the human microbiome. bioRxiv 176313 https://doi.org/10.1101/176313 (2017).94.Kaeding, A. J. et al. Phylogenetic diversity and cosymbiosis in the bioluminescent symbioses of “Photobacterium mandapamensis”. Appl. Environ. Microbiol. 73, 3173–3182 (2007).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
95.Baker, L. J. et al. Diverse deep-sea anglerfishes share a genetically reduced luminous symbiont that is acquired from the environment. eLife 8 e47606 (2019).96.Godeaux, J. E. A., Bone, Q. & Braconnot, J. C. Anatomy of Thaliacea. in The Biology of Pelagic Tunicates (Oxford University Press, 1998).97.Alldredge, A. L. & Madin, L. P. Pelagic tunicates: unique herbivores in the marine plankton. BioScience. 32, 655–663 (1982).Article 

Google Scholar 
98.Bone, Q., Carre, C. & Ryan, K. P. The endostyle and the feeding filter in salps (Tunicata). J. Mar. Biol. Assoc. UK 80, 523–534 (2000).Article 

Google Scholar 
99.Sutherland, K. R., Madin, L. P. & Stocker, R. Filtration of submicrometer particles by pelagic tunicates. Proc. Natl Acad. Sci. 107, 15129–15134 (2010).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
100.Dadon-Pilosof, A. et al. Surface properties of SAR11 bacteria facilitate grazing avoidance. Nat. Microbiol. 2, 1608–1615 (2017).PubMed 
Article 
PubMed Central 

Google Scholar 
101.Larson, R. J. Daily ration and predation by medusae and ctenophores in Saanich Inlet, B.C., Canada. Neth. J. Sea Res. 21, 35–44 (1987).Article 

Google Scholar 
102.Suchman, C. L., Daly, E. A., Keister, J. E., Peterson, W. T. & Brodeur, R. D. Feeding patterns and predation potential of scyphomedusae in a highly productive upwelling region. Mar. Ecol. Prog. Ser. 358, 161–172 (2008).Article 

Google Scholar 
103.Bennke, C. M. et al. The distribution of phytoplankton in the Baltic Sea assessed by a prokaryotic 16S rRNA gene primer system. J. Plankton Res. 40, 244–254 (2018).CAS 
Article 

Google Scholar 
104.Green, B. R. Chloroplast genomes of photosynthetic eukaryotes. Plant J. 66, 34–44 (2011).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
105.Luo, J. Y. et al. Gelatinous zooplankton-mediated carbon flows in the global oceans: a data-driven modeling study. Glob. Biogeochem. Cycles. 34, e2020GB006704 (2020).106.Dadon‐Pilosof, A., Lombard, F., Genin, A., Sutherland, K. R. & Yahel, G. Prey taxonomy rather than size determines salp diets. Limnol. Oceanogr. 64, 1996–2010 (2019).Article 

Google Scholar 
107.Brand, A., Liz, A., Micah, A., Marjorie, H. & Jo, S. Beyond Authorship: Attribution, Contribution, Collaboration, and Credit. Learned Publishing. 28, 151–155 (2015).Article 

Google Scholar  More

1.Stanley, D. & Stout, J. Pollinator sharing between mass-flowering oilseed rape and co-flowering wild plants: implications for wild plant pollination. Plant Ecol. 215, 315–325. https://doi.org/10.1007/s11258-014-0301-7 (2014).Article 

Google Scholar 
2.Kovacs-Hostyanszki, A. et al. Contrasting effects of mass-flowering crops on bee pollination of hedge plants at different spatial and temporal scales. Ecol. Appl. 23, 1938–1946. https://doi.org/10.1890/12-2012.1 (2013).Article 
PubMed 

Google Scholar 
3.Holzschuh, A. et al. Mass-flowering crops dilute pollinator abundance in agricultural landscapes across Europe. Ecol. Lett. 19, 1228–1236. https://doi.org/10.1111/ele.12657 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
4.Potts, S. G. et al. Global pollinator declines: trends, impacts and drivers. Trends Ecol. Evol. 25, 345–353. https://doi.org/10.1016/j.tree.2010.01.007 (2010).Article 
PubMed 

Google Scholar 
5.Kremen, C., Williams, N. M. & Thorp, R. W. Crop pollination from native bees at risk from agricultural intensification. Proc Natl Acad Sci U S A 99, 16812–16816. https://doi.org/10.1073/pnas.262413599 (2002).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
6.Ollerton, J., Erenler, H., Edwards, M. & Crockett, R. Pollinator declines: extinctions of aculeate pollinators in Britain and the role of large-scale agricultural changes. Science 346, 1360–1362. https://doi.org/10.1126/science.1257259 (2014).ADS 
CAS 
Article 
PubMed 

Google Scholar 
7.Kovacs-Hostyanszki, A. et al. Ecological intensification to mitigate impacts of conventional intensive land use on pollinators and pollination. Ecol. Lett. 20, 673–689. https://doi.org/10.1111/ele.12762 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
8.Tscharntke, T., Klein, A. M., Kruess, A., Steffan-Dewenter, I. & Thies, C. Landscape perspectives on agricultural intensification and biodiversity: ecosystem service management. Ecol. Lett. 8, 857–874. https://doi.org/10.1111/j.1461-0248.2005.00782.x (2005).Article 

Google Scholar 
9.Holland, J. M. et al. Semi-natural habitats support biological control, pollination and soil conservation in Europe: a review. Agron. Sustain. Dev. https://doi.org/10.1007/s13593-017-0434-x (2017).Article 

Google Scholar 
10.Garibaldi, L. A. et al. Stability of pollination services decreases with isolation from natural areas despite honey bee visits. Ecol. Lett. 14, 1062–1072. https://doi.org/10.1111/j.1461-0248.2011.01669.x (2011).Article 
PubMed 

Google Scholar 
11.Bartomeus, I. et al. Contribution of insect pollinators to crop yield and quality varies with agricultural intensification. PeerJ 2, e328. https://doi.org/10.7717/peerj.328 (2014).Article 
PubMed 
PubMed Central 

Google Scholar 
12.Holzschuh, A., Dudenhoffer, J. H. & Tscharntke, T. Landscapes with wild bee habitats enhance pollination, fruit set and yield of sweet cherry. Biol. Conserv. 153, 101–107. https://doi.org/10.1016/j.biocon.2012.04.032 (2012).Article 

Google Scholar 
13.Marini, L. et al. Crop management modifies the benefits of insect pollination in oilseed rape. Agric. Ecosyst. Environ. 207, 61–66. https://doi.org/10.1016/j.agee.2015.03.027 (2015).Article 

Google Scholar 
14.Persson, A. S. & Smith, H. G. Seasonal persistence of bumblebee populations is affected by landscape context. Agric. Ecosyst. Environ. 165, 201–209. https://doi.org/10.1016/j.agee.2012.12.008 (2013).Article 

Google Scholar 
15.Rundlof, M., Persson, A. S., Smith, H. G. & Bommarco, R. Late-season mass-flowering red clover increases bumble bee queen and male densities. Biol. Conserv. 172, 138–145. https://doi.org/10.1016/j.biocon.2014.02.027 (2014).Article 

Google Scholar 
16.Westphal, C., Steffan-Dewenter, I. & Tscharntke, T. Mass flowering oilseed rape improves early colony growth but not sexual reproduction of bumblebees. J. Appl. Ecol. 46, 187–193. https://doi.org/10.1111/j.1365-2664.2008.01580.x (2009).Article 

Google Scholar 
17.Williams, N. M., Regetz, J. & Kremen, C. Landscape-scale resources promote colony growth but not reproductive performance of bumble bees. Ecology 93, 1049–1058. https://doi.org/10.1890/11-1006.1 (2012).Article 
PubMed 

Google Scholar 
18.Steffan-Dewenter, I., Munzenberg, U., Burger, C., Thies, C. & Tscharntke, T. Scale-dependent effects of landscape context on three pollinator guilds. Ecology 83, 1421–1432. https://doi.org/10.2307/3071954 (2002).Article 

Google Scholar 
19.Steffan-Dewenter, I., Münzenberg, U. & Tscharntke, T. Pollination, seed set and seed predation on a landscape scale. Proc. Natl. Acad. Sci. USA 268, 1685–1690. https://doi.org/10.1098/rspb.2001.1737 (2001).CAS 
Article 

Google Scholar 
20.Bartual, A. et al. The potential of different semi-natural habitats to sustain pollinators and natural enemies in European agricultural landscapes. Agric. Ecosyst. Environ. 279, 43–52. https://doi.org/10.1016/j.agee.2019.04.009 (2019).Article 

Google Scholar 
21.Ewers, R. M. & Didham, R. K. Confounding factors in the detection of species responses to habitat fragmentation. Biol. Rev. Camb. Philos. Soc. 81, 117–142. https://doi.org/10.1017/s1464793105006949 (2006).Article 
PubMed 

Google Scholar 
22.Blaauw, B. R. & Isaacs, R. Larger patches of diverse floral resources increase insect pollinator density, diversity, and their pollination of native wild flowers. Basic Appl. Ecol. 15, 701–711. https://doi.org/10.1016/j.baae.2014.10.001 (2014).Article 

Google Scholar 
23.Martin, E. A. et al. The interplay of landscape composition and configuration: new pathways to manage functional biodiversity and agroecosystem services across Europe. Ecol. Lett. 22, 1083–1094. https://doi.org/10.1111/ele.13265 (2019).Article 
PubMed 

Google Scholar 
24.Bihaly, Á., Dóra, V., Lajos, K. & Sárospataki, M. Effect of semi-natural habitat patches on the pollinator assemblages of sunflower in an intensive agricultural landscape. Tájökológiai Lapok 16, 45–52 (2018).
Google Scholar 
25.Foldesi, R. et al. Relationships between wild bees, hoverflies and pollination success in apple orchards with different landscape contexts. Agric. For. Entomol. 18, 68–75. https://doi.org/10.1111/afe.12135 (2016).Article 

Google Scholar 
26.Sárospataki, M. et al. The role of local and landscape level factors in determining bumblebee abundance and richness. Acta Zool. Acad. Sci. Hung. 62, 387–407. https://doi.org/10.17109/AZH.62.4.387.2016 (2016).Article 

Google Scholar 
27.Schellhorn, N. A., Gagic, V. & Bommarco, R. Time will tell: resource continuity bolsters ecosystem services. Trends Ecol. Evol. 30, 524–530. https://doi.org/10.1016/j.tree.2015.06.007 (2015).Article 
PubMed 

Google Scholar 
28.Tscharntke, T. et al. Landscape moderation of biodiversity patterns and processes: eight hypotheses. Biol. Rev. Camb. Philos. Soc. 87, 661–685. https://doi.org/10.1111/j.1469-185X.2011.00216.x (2012).Article 
PubMed 

Google Scholar 
29.Stephens, A. E. A. & Myers, J. H. Resource concentration by insects and implications for plant populations. J. Ecol. 100, 923–931. https://doi.org/10.1111/j.1365-2745.2012.01971.x (2012).Article 

Google Scholar 
30.Tscheulin, T., Neokosmidis, L., Petanidou, T. & Settele, J. Influence of landscape context on the abundance and diversity of bees in Mediterranean olive groves. Bull. Entomol. Res. 101, 557–564. https://doi.org/10.1017/S0007485311000149 (2011).CAS 
Article 
PubMed 

Google Scholar 
31.Kennedy, C. M. et al. A global quantitative synthesis of local and landscape effects on wild bee pollinators in agroecosystems. Ecol. Lett. 16, 584–599. https://doi.org/10.1111/ele.12082 (2013).Article 
PubMed 

Google Scholar 
32.Eurostat. Archive: Main annual crop statistics, https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Main_annual_crop_statistics&oldid=389868#Oilseeds (2018).33.KSH. STADAT tables – Agriculture. http://www.ksh.hu/docs/hun/xstadat/xstadat_eves/i_omn007b.html. (KSH, 2019).34.Hevia, V. et al. Bee diversity and abundance in a livestock drove road and its impact on pollination and seed set in adjacent sunflower fields. Agric. Ecosyst. Environ. 232, 336–344. https://doi.org/10.1016/j.agee.2016.08.021 (2016).Article 

Google Scholar 
35.Silva, C. et al. Bee pollination highly improves oil quality in sunflower. Sociobiology 65, 583–590. https://doi.org/10.13102/sociobiology.v65i4.3367 (2018).Article 

Google Scholar 
36.Terzić, S., Miklič, V. & Čanak, P. Review of 40 years of research carried out in Serbia on sunflower pollination. OCL 24, D608 (2017).Article 

Google Scholar 
37.Perrot, T. et al. Experimental quantification of insect pollination on sunflower yield, reconciling plant and field scale estimates. Basic Appl. Ecol. 34, 75–84. https://doi.org/10.1016/j.baae.2018.09.005 (2019).Article 

Google Scholar 
38.Martin, C. S. & Farina, W. M. Honeybee floral constancy and pollination efficiency in sunflower (Helianthus annuus) crops for hybrid seed production. Apidologie 47, 161–170 (2016).Article 

Google Scholar 
39.DeGrandi-Hoffman, G. & Watkins, J. C. The foraging activity of honey bees Apis mellifera and non—Apis bees on hybrid sunflowers (Helianthus annuus) and its influence on cross—pollination and seed set. J. Apic. Res. 39, 37–45. https://doi.org/10.1080/00218839.2000.11101019 (2000).Article 

Google Scholar 
40.Cerrutti, N. & Pontet, C. Differential attractiveness of sunflower cultivars to the honeybee Apis mellifera L. OCL 23, D204 (2016).Article 

Google Scholar 
41.Chambó, E. D., Garcia, R. C., Oliveira, N. T. E. D. & Duarte-Júnior, J. B. Honey bee visitation to sunflower: effects on pollination and plant genotype. Sci. Agric. 68, 647–651 (2011).Article 

Google Scholar 
42.Oz, M., Karasu, A., Cakmak, I., Goksoy, A. T. & Turan, Z. M. Effects of honeybee (Apis mellifera) pollination on seed set in hybrid sunflower (Helianthus annuus L.). Afr. J. Biotechnol. 8 (2009).43.Puškadija, Z. et al. Influence of weather conditions on honey bee visits (Apis mellifera carnica) during sunflower (Helianthus annuus L.) blooming period. Poljoprivreda 13, 230–233 (2007).
Google Scholar 
44.Greenleaf, S. S. & Kremen, C. Wild bees enhance honey bees’ pollination of hybrid sunflower. Proc. Natl. Acad. Sci. USA 103, 13890–13895 (2006).ADS 
CAS 
Article 

Google Scholar 
45.Nderitu, J., Nyamasyo, G., Kasina, M. & Oronje, M. Diversity of sunflower pollinators and their effect on seed yield in Makueni District, Eastern Kenya. Span. J. Agric. Res. 6, 271–278 (2008).Article 

Google Scholar 
46.Carvalheiro, L. G. et al. Natural and within-farmland biodiversity enhances crop productivity. Ecol. Lett. 14, 251–259. https://doi.org/10.1111/j.1461-0248.2010.01579.x (2011).Article 
PubMed 

Google Scholar 
47.Sardiñas, H. S. & Kremen, C. Pollination services from field-scale agricultural diversification may be context-dependent. Agric. Ecosyst. Environ. 207, 17–25 (2015).Article 

Google Scholar 
48.Riedinger, V., Renner, M., Rundlof, M., Steffan-Dewenter, I. & Holzschuh, A. Early mass-flowering crops mitigate pollinator dilution in late-flowering crops. Landscape Ecol. 29, 425–435. https://doi.org/10.1007/s10980-013-9973-y (2014).Article 

Google Scholar 
49.Bennett, A. B. & Isaacs, R. Landscape composition influences pollinators and pollination services in perennial biofuel plantings. Agric. Ecosyst. Environ. 193, 1–8. https://doi.org/10.1016/j.agee.2014.04.016 (2014).Article 

Google Scholar 
50.Lowenstein, D. M., Huseth, A. S. & Groves, R. L. Response of wild bees (Hymenoptera: Apoidea: Anthophila) to surrounding land cover in Wisconsin pickling cucumber. Environ. Entomol. 41, 532–540. https://doi.org/10.1603/EN11241 (2012).CAS 
Article 
PubMed 

Google Scholar 
51.Pfister, S. C. et al. Dominance of cropland reduces the pollen deposition from bumble bees. Sci. Rep. 8, 13873. https://doi.org/10.1038/s41598-018-31826-3 (2018).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
52.Gathmann, A. & Tscharntke, T. Foraging ranges of solitary bees. J. Anim. Ecol. 71, 757–764. https://doi.org/10.1046/j.1365-2656.2002.00641.x (2002).Article 

Google Scholar 
53.Greenleaf, S. S., Williams, N. M., Winfree, R. & Kremen, C. Bee foraging ranges and their relationship to body size. Oecologia 153, 589–596. https://doi.org/10.1007/s00442-007-0752-9 (2007).ADS 
Article 
PubMed 

Google Scholar 
54.Lihoreau, M., Chittka, L., Le Comber, S. C. & Raine, N. E. Bees do not use nearest-neighbour rules for optimization of multi-location routes. Biol. Lett. 8, 13–16. https://doi.org/10.1098/rsbl.2011.0661 (2012).Article 
PubMed 

Google Scholar 
55.Berger-Tal, O. & Bar-David, S. Recursive movement patterns: review and synthesis across species. Ecosphere 6, 149. https://doi.org/10.1890/es15-00106.1 (2015).Article 

Google Scholar 
56.Wesserling, J. Habitatwahl und Ausbreitungsverhalten von Stechimmen (Hymenoptera: Aculeata) in Sandgebieten unterschiedlicher Sukzessionsstadien, University of Karlsruhe, (1996).57.Hagler, J. R., Mueller, S., Teuber, L. R., Machtley, S. A. & Van Deynze, A. Foraging range of honey bees, Apis mellifera, in alfalfa seed production fields. J. Insect Sci. 11, 144 (2011).PubMed 
PubMed Central 

Google Scholar 
58.Couvillon, M. J. et al. Honey bee foraging distance depends on month and forage type. Apidologie 46, 61–70. https://doi.org/10.1007/s13592-014-0302-5 (2015).Article 

Google Scholar 
59.Beekman, M. & Ratnieks, F. L. W. Long-range foraging by the honey-bee, Apis mellifera L.. Funct. Ecol. 14, 490–496. https://doi.org/10.1046/j.1365-2435.2000.00443.x (2000).Article 

Google Scholar 
60.Gary, N. E., Witherell, P. C. & Lorenzen, K. Effect of age on honey bee foraging distance and pollen collection. Environ. Entomol. 10, 950–952 (1981).Article 

Google Scholar 
61.Walther-Hellwig, K. & Frankl, R. Foraging habitats and foraging distances of bumblebees, Bombus spp. (Hym., Apidae), in an agricultural landscape. J. Appl. Entomol. 124, 299–306. https://doi.org/10.1046/j.1439-0418.2000.00484.x (2000).Article 

Google Scholar 
62.Dramstad, W. E. Do bumblebees (Hymenoptera: Apidae) really forage close to their nests?. J. Insect Behav. 9, 163–182. https://doi.org/10.1007/bf02213863 (1996).Article 

Google Scholar 
63.Knight, M. E. et al. An interspecific comparison of foraging range and nest density of four bumblebee (Bombus) species. Mol. Ecol. 14, 1811–1820 (2005).CAS 
Article 

Google Scholar 
64.Wolf, S. & Moritz, R. F. Foraging distance in Bombus terrestris L. (Hymenoptera: Apidae). Apidologie 39, 419–427 (2008).Article 

Google Scholar 
65.Osborne, J. L. et al. Bumblebee flight distances in relation to the forage landscape. J. Anim. Ecol. 77, 406–415 (2008).Article 

Google Scholar 
66.Zurbuchen, A. et al. Maximum foraging ranges in solitary bees: only few individuals have the capability to cover long foraging distances. Biol. Conserv. 143, 669–676 (2010).Article 

Google Scholar 
67.Hopfenmuller, S., Steffan-Dewenter, I. & Holzschuh, A. Trait-specific responses of wild bee communities to landscape composition, configuration and local factors. PLoS ONE 9, e104439. https://doi.org/10.1371/journal.pone.0104439 (2014).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
68.Hung, K. J., Kingston, J. M., Albrecht, M., Holway, D. A. & Kohn, J. R. The worldwide importance of honey bees as pollinators in natural habitats. Proc R Soc Biol Sci Ser B 285, 20172140. https://doi.org/10.1098/rspb.2017.2140 (2018).Article 

Google Scholar 
69.Requier, F. et al. Honey bee diet in intensive farmland habitats reveals an unexpectedly high flower richness and a major role of weeds. Ecol. Appl. 25, 881–890. https://doi.org/10.1890/14-1011.1 (2015).Article 
PubMed 

Google Scholar 
70.Bonoan, R. E., Gonzalez, J. & Starks, P. T. The perils of forcing a generalist to be a specialist: lack of dietary essential amino acids impacts honey bee pollen foraging and colony growth. J. Apic. Res. 59, 95–103. https://doi.org/10.1080/00218839.2019.1656702 (2020).Article 

Google Scholar 
71.Di Pasquale, G. et al. Influence of pollen nutrition on honey bee health: Do pollen quality and diversity matter?. PLoS ONE 8, e72016. https://doi.org/10.1371/journal.pone.0072016 (2013).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
72.Di Pasquale, G. et al. Variations in the availability of pollen resources affect honey bee health. PLoS ONE 11, e0162818. https://doi.org/10.1371/journal.pone.0162818 (2016).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
73.Alaux, C., Ducloz, F., Crauser, D. & Le Conte, Y. Diet effects on honeybee immunocompetence. Biol. Lett. 6, 562–565. https://doi.org/10.1098/rsbl.2009.0986 (2010).Article 
PubMed 
PubMed Central 

Google Scholar 
74.Colwell, M. J., Williams, G. R., Evans, R. C. & Shutler, D. Honey bee-collected pollen in agro-ecosystems reveals diet diversity, diet quality, and pesticide exposure. Ecol. Evol. 7, 7243–7253. https://doi.org/10.1002/ece3.3178 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
75.Zhang, G., St. Clair, A. L., Dolezal, A., Toth, A. L. & O’Neal, M. Honey Bee (Hymenoptera: Apidea) pollen forage in a highly cultivated agroecosystem: limited diet diversity and its relationship to virus resistance. J. Econ. Entomol. 113, 1062–1072 (2020).76.QGIS Development Team. QGIS Geographic Information System. Open Source Geospatial Foundation. http://qgis.osgeo.org. (2009).77.FÖMI. MePAR, the Hungarian Agricultural Land Parcel Identification System, accessed 22 November 2019 http://www.mepar.hu/ (2016).78.McGarigal, K., Cushman, S. & Ene, E. Spatial Pattern Analysis Program for Categorical and Continuous Maps. available from http://www.umass.edu/landeco/research/fragstats/fragstats.html. (University of Massachusetts, 2012).79.McGarigal, K. FRAGSTATS help. Documentation for FRAGSTATS, 4. (2014).80.McGarigal, K. (2017). Landscape metrics for categorical map patterns. Lecture Notes. Available online: accessed 28 Feb 2021 http://www.umass.edu/landeco/teaching/landscape_ecology/schedule/chapter9_metrics.pdf.81.R Core Team. R: A Language and Environment for Statistical Computing. version 3.6.0. https://www.R-project.org. (R Foundation for Statistical Computing, 2020).82.Paradis, E. & Schliep, K. ape 5.0: an environment for modern phylogenetics and evolutionary analyses in R. Bioinformatics 35, 526–528. https://doi.org/10.1093/bioinformatics/bty633 (2019).CAS 
Article 
PubMed 

Google Scholar 
83.Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting Linear Mixed-Effects Models Usinglme4. Journal of Statistical Software 67, https://doi.org/10.18637/jss.v067.i01 (2015).84.Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer, 2016).
Google Scholar 
85.DHARMa: Residual Diagnostics for Hierarchical (Multi-Level / Mixed) Regression Models. v. 0.3.3.0. (2020).86.Fox, J. & Weisberg, S. An R Companion to Applied Regression, Third edition. Sage, Thousand Oaks CA. https://socialsciences.mcmaster.ca/jfox/Books/Companion/. (2019). More

1.Marlon, J. R. et al. Climate and human influences on global biomass burning over the past two millennia. Nat. Geosci. 1, 697–702 (2008).CAS 
Article 

Google Scholar 
2.Pausas, J. G. & Keeley, J. E. A burning story: the role of fire in the history of life. BioScience 59, 593–601 (2009).Article 

Google Scholar 
3.Dennison, P. E., Brewer, S. C., Arnold, J. D. & Mortiz, M. A. Large wildfire trends in the western United States, 1984–2011. Geophys. Res. Lett. 41, 2928–2933 (2014).Article 

Google Scholar 
4.Abatzoglou, J. T. & Williams, A. P. Impact of anthropogenic climate change on wildfire across western US forests. Proc. Natl Acad. Sci. USA 113, 11770–11775 (2016).CAS 
Article 

Google Scholar 
5.Westerling, A. L., Hidalgo, H. G., Cayan, D. R. & Swetnam, T. W. Warming and earlier spring increase western U.S. forest wildfire activity. Science 313, 940–943 (2006).CAS 
Article 

Google Scholar 
6.Westerling, A. L. R. Increasing western US forest wildfire activity: Sensitivity to changes in the timing of spring. Phil. Trans. R. Soc. B Biol. Sci. 371, 1–10 (2016).
Google Scholar 
7.Schwartz, M. W. et al. Increasing elevation of fire in the Sierra Nevada and implications for forest change. Ecosphere 6, 1–10 (2015).Article 

Google Scholar 
8.Trujillo, E., Molotch, N. P., Goulden, M. L., Kelly, A. E. & Bales, R. C. Elevation-dependent influence of snow accumulation on forest greening. Nat. Geosci. 5, 705–709 (2012).CAS 
Article 

Google Scholar 
9.Trouet, V., Taylor, A. H., Wahl, E. R., Skinner, C. N. & Stephens, S. L. Fire-climate interactions in the American West since 1400 CE. Geophys. Res. Lett. 37, 1–5 (2010).Article 

Google Scholar 
10.Kitchen, S. G. Climate and human influences on historical fire regimes (AD 1400–1900) in the eastern Great Basin (USA). Holocene 26, 397–407 (2016).Article 

Google Scholar 
11.Klimaszewski-Patterson, A., Weisberg, P. J., Mensing, S. A. & Scheller, R. M. Using paleolandscape modeling to investigate the impact of native American–set fires on pre-Columbian forests in the Southern Sierra Nevada, California, USA. Ann. Am. Assoc. Geographers 108, 1635–1654 (2018).
Google Scholar 
12.Taylor, A. H., Trouet, V., Skinner, C. N. & Stephens, S. Socioecological transitions trigger fire regime shifts and modulate fire-climate interactions in the Sierra Nevada, USA, 1600-2015 CE. Proc. Natl Acad. Sci. USA 113, 13684–13689 (2016).CAS 
Article 

Google Scholar 
13.Ryan, K. C., Knapp, E. E. & Varner, J. M. Prescribed fire in North American forests and woodlands: history, current practice, and challenges. Front. Ecol. Environ. 11, e15–e24 (2013).14.Herring, E. M., Anderson, R. S. & San Miguel, G. L. Fire, vegetation, and Ancestral Puebloans: a sediment record from Prater Canyon in Mesa Verde National Park, Colorado, USA. Holocene 24, 853–863 (2014).Article 

Google Scholar 
15.Liebmann, M. J. et al. Native American depopulation, reforestation, and fire regimes in the Southwest United States, 1492-1900 CE. Proc. Natl Acad. Sci. USA 113, E696–E704 (2016).CAS 
Article 

Google Scholar 
16.Swetnam, T. W. et al. Multiscale perspectives of fire, climate and humans in Western North America and the Jemez Mountains, USA. Phil. Trans. R. Soc. B Biol. Sci. 371, (2016).17.Levis, C. et al. Persistent effects of pre-Columbian plant domestication on Amazonian forest composition. Science 358, 925–931 (2017).Article 
CAS 

Google Scholar 
18.Maezumi, S. Y. et al. The legacy of 4,500 years of polyculture agroforestry in the eastern Amazon. Nat. Plants 4, 540–547 (2018).Article 

Google Scholar 
19.Vale, T. R. The Pre-European landscape of the United States: Pristine or Humanized? in Fire, Native Peoples, and the Natural Landscape 1–39 (Island Press, 2002).20.Lightfoot, K. G. & Lopez, V. The study of indigenous management practices in California: an introduction. California Archaeol. 5, 209–219 (2013).Article 

Google Scholar 
21.Oswald, W. W. et al. Conservation implications of limited Native American impacts in pre-contact New England. Nat. Sustain. 3, 241–246 (2020).Article 

Google Scholar 
22.Vachula, R. S., Russell, J. M. & Huang, Y. Climate exceeded human management as the dominant control of fire at the regional scale in California’s Sierra Nevada. Environ. Res. Lett. 14, 104011 (2019).CAS 
Article 

Google Scholar 
23.Baker, W. L. Indians and Fire in the Rocky Mountains: The Wilderness Hypothesis Renewed. in Fire, Native Peoples, and the Natural Landscape 41–76 (2002).24.Kimmerer, R. W. & Lake, F. K. Maintaining the Mosaic: the role of indigenous burning in land management. J. Forestry 99, 36–41 (2001).
Google Scholar 
25.Power, M. J. et al. Human fire legacies on ecological landscapes. Front. Earth Sci. 6, 1–6 (2018).Article 

Google Scholar 
26.Keeley, J. E. Native American impacts on fire regimes of the California coastal ranges. J. Biogeogr. 29, 303–320 (2002).Article 

Google Scholar 
27.Lightfoot, K. G., Parrish, O., Panich, L. M. & Schneider, T. D. California Indians and Their Environment: An Introduction (Univ. California Press, 2009).28.Ryan, K. C., Jones, A. T., Koerner, C. L. & Lee, K. M. Wildland Fire in Ecosystems: Effects of Fire on Cultural Resources and Archaeology. Vol. 3., 224. Rocky Mountain Research Station General Technical Report RMRS-GTR-42 (US Department of Agriculture, Forest Service, 2012).29.Roos, C. I., Zedeño, M. N., Hollenback, K. L. & Erlick, M. M. H. Indigenous impacts on North American Great Plains fire regimes of the past millennium. Proc. Natl Acad. Sci. USA 115, 8143–8148 (2018).CAS 
Article 

Google Scholar 
30.Thomas, D. H. The 1981 Alta Toquima Village project: A Preliminary Report. Desert Research Institute Social Sciences and Humanities Publications Technical Report 27, 1–202 (Desert Research Institute Social Sciences and Humanities Publications, 1982).31.Benedict, J. B. Footprints in the snow: high-altitude cultural ecology of the Colorado Front Range, USA. Arctic Alpine Res. 24, 1–16 (1992).Article 

Google Scholar 
32.Stevens, N. E. Changes in prehistoric land use in the Alpine Sierra Nevada: a regional exploration using temperature-adjusted obsidian hydration rates. J. California Great Basin Anthropol. 25, 187–205 (2005).
Google Scholar 
33.Klimaszewski-Patterson, A. & Mensing, S. Paleoecological and paleolandscape modeling support for pre-Columbian burning by Native Americans in the Golden Trout Wilderness Area, California, USA. Landscape Ecol. https://doi.org/10.1007/s10980-020-01081-x (2020).34.Swetnam, T. W., Allen, C. D. & Betancourt, J. L. Applied historical ecology: using the past to manage for the future. Ecol. Appl. 9, 1189–1206 (1999).Article 

Google Scholar 
35.Roos, C. I., Williamson, G. J. & Bowman, D. M. Is anthropogenic pyrodiversity invisible in paleofire records? Fire 2, 42 (2019).Article 

Google Scholar 
36.Marlon, J. R. et al. Global biomass burning: a synthesis and review of Holocene paleofire records and their controls. Quat. Sci. Rev. 65, 5–25 (2013).Article 

Google Scholar 
37.Bowman, D. M. et al. The human dimension of fire regimes on Earth. J. Biogeogr. 38, 2223–2236 (2011).Article 

Google Scholar 
38.Adolf, C. et al. The sedimentary and remote-sensing reflection of biomass burning in Europe. Global Ecol. Biogeogr. 27, 199–212 (2018).Article 

Google Scholar 
39.Vachula, R. S. A meta-analytical approach to understanding the charcoal source area problem. Palaeogeogr. Palaeoclimatol. Palaeoecol. 562, 110111 https://doi.org/10.1016/j.palaeo.2020.110111 (2021).40.Munoz, S. E., Gajewski, K. & Peros, M. C. Synchronous environmental and cultural change in the prehistory of the northeastern United States. Proc. Natl Acad. Sci. USA 107, 22008–22013 (2010).CAS 
Article 

Google Scholar 
41.Peros, M. C., Munoz, S. E., Gajewski, K. & Viau, A. E. Prehistoric demography of North America inferred from radiocarbon data. J. Archaeol. Sci. 37, 656–664 (2010).Article 

Google Scholar 
42.Brown, P. M., Heyerdahl, E. K., Kitchen, S. G. & Weber, M. H. Climate effects on historical fires (1630-1900) in Utah. Int. J. Wildland Fire 17, 28–39 (2008).Article 

Google Scholar 
43.Li, J. et al. Interdecadal modulation of El Niño amplitude during the past millennium. Nat. Clim. Change 1, 114–118 (2011).CAS 
Article 

Google Scholar 
44.Gedalof, Z. & Peterson, D. L. & Mantua, N. J. Atmospheric, climatic, and ecological controls on extreme wildfire years in the Northwestern United States. Ecol. Appl. 15, 154–174 (2005).45.Morgan, P., Hardy, C. C., Swetnam, T. W., Rollins, M. G. & Long, D. G. Mapping fire regimes across time and space: Understanding coarse and fine-scale fire patterns. Int. J. Wildland Fire 10, 329–342 (2001).Article 

Google Scholar 
46.Marchetti, D. W., Harris, M. S., Bailey, C. M., Cerling, T. E. & Bergman, S. Timing of glaciation and last glacial maximum paleoclimate estimates from the Fish Lake Plateau, Utah. Quat. Res. 75, 183–195 (2011).CAS 
Article 

Google Scholar 
47.Kemperman, J. A. & Barnes, B. V. Clone size in American aspens. Can. J. Botany 54, 2603–2607 (1976).Article 

Google Scholar 
48.Mitton, J. B. & Grant, M. C. Genetic variation and the natural history of quaking Aspen. BioScience 46, 25–31 (1996).Article 

Google Scholar 
49.Wood, S. N. Generalized Additive Models: an Introduction with R (Chapman and Hall, 2006).50.Hastie, T. & Tibshirani, R. Generalized additive models. Stat. Sci. 1, 297–318 (1992).
Google Scholar 
51.Madsen, D. B. & Simms, S. R. The Fremont complex: a behavioral perspective. J. World Prehistory 12, 255–336 (1998).Article 

Google Scholar 
52.Massimino, J. & Metcalfe, D. New form for the formative. Utah Archaeol. 12, 1–16 (1999).
Google Scholar 
53.Coltrain, J. B. & Leavitt, S. W. Climate and diet in Fremont prehistory: economic variability and abandonment of maize agriculture in the Great Salt Lake Basin. Am. Antiquity 67, 453–485 (2002).Article 

Google Scholar 
54.Magargal, K. E., Parker, A. K., Vernon, K. B., Rath, W. & Codding, B. F. The ecology of population dispersal: modeling alternative basin-plateau foraging strategies to explain the Numic expansion. Am. J. Hum. Biol. 29, 1–14 (2017).
Google Scholar 
55.Thomson, M. J., Balkovič, J., Krisztin, T. & MacDonald, G. M. Simulated impact of paleoclimate change on Fremont Native American maize farming in Utah, 850–1449 CE, using crop and climate models. Quat. Int. 507, 95–107 (2019).Article 

Google Scholar 
56.Finley, J. B., Robinson, E., Derose, R. J. & Hora, E. Multidecadal climate variability and the florescence of Fremont societies in Eastern Utah. American Antiquity 85, 93–112 (2020).Article 

Google Scholar 
57.Janetski, J. C. Archaeology and Native American history at Fish Lake, Central Utah. vol. 16 (Museum of Peoples and Cultures, Brigham Young University, 2010).58.Fowler, C. S. in Handbook of North American Indians (eds. Sturtevant, W. C. & D’Azevedo, W. L.) vol. 11, 64–97 (Smithsonian Institution, 1986).59.Sullivan, A. P. & Mink, P. B. Theoretical and socioecological consequences of fire foodways. Am. Antiquity 83, 619–638 (2018).Article 

Google Scholar 
60.Mann, M. E. et al. Global signatures and dynamical origins of the Little Ice Age and Medieval Climate Anomaly. Science 326, 1256–1260 (2009).CAS 
Article 

Google Scholar 
61.Woodhouse, C. A., Meko, D. M., MacDonald, G. M., Stahle, D. W. & Cook, E. R. A 1,200-year perspective of 21st century drought in southwestern North America. Proc. Natl Acad. Sci. USA 107, 21283–21288 (2010).CAS 
Article 

Google Scholar 
62.Meko, D. M. et al. Medieval drought in the upper Colorado River Basin. Geophys. Res. Lett. 34, 1–5 (2007).Article 

Google Scholar 
63.Salzer, M. W. & Kipfmueller, K. F. Reconstructed temperature and precipitation on a millennial timescale from tree-rings in the southern Colorado Plateau, U.S.A. Clim. Change 70, 465–487 (2005).CAS 
Article 

Google Scholar 
64.Knight, T. A., Meko, D. M. & Baisan, C. H. A bimillennial-length tree-ring reconstruction of precipitation for the Tavaputs Plateau, Northeastern Utah. Quat. Res. 73, 107–117 (2010).Article 

Google Scholar 
65.Margolis, E. Q. & Swetnam, T. W. Historical fire-climate relationships of upper elevation fire regimes in the south-western United States. Int. J. Wildland Fire 22, 588–598 (2013).Article 

Google Scholar 
66.Calder, W. J., Parker, D., Stopka, C. J., Jiménez-Moreno, G. & Shuman, B. N. Medieval warming initiated exceptionally large wildfire outbreaks in the Rocky Mountains. Proc. Natl Acad. Sci. USA 112, 13261–13266 (2015).CAS 
Article 

Google Scholar 
67.Bliege, R. B., Codding, B. F., Kauhanen, P. G. & Bird, D. W. Aboriginal hunting buffers climate-driven fire-size variability in Australia’s spinifex grasslands. Proc. Natl Acad. Sci. USA 109, 10287–10292 (2012).Article 

Google Scholar 
68.Parisien, M. A. et al. The spatially varying influence of humans on fire probability in North America. Environ. Res. Lett. 11, 075005 (2016).Article 

Google Scholar 
69.Codding, B. F. et al. Socioecological dynamics structuring the spread of farming in the North American Basin-Plateau Region. Environ. Archaeol. (in review).70.Robinson, E., Nicholson, C. & Kelly, R. L. The importance of spatial data to open-access national archaeological databases and the development of paleodemography research. Adv. Archaeol. Pract. 7, 395–408 (2019).Article 

Google Scholar 
71.Marlon, J. R. et al. Long-term perspective on wildfires in the western USA. Proc. Natl Acad. Sci. USA 109, 535–543 (2012).Article 

Google Scholar 
72.Kent McAdoo, J., Schultz, B. W. & Swanson, S. R. Aboriginal precedent for active management of sagebrush-perennial grass communities in the Great Basin. Rangeland Ecol. Manag. 66, 241–253 (2013).Article 

Google Scholar 
73.Heyerdahl, E. K., Brown, P. M., Kitchen, S. G. & Weber, M. H. Multicentury Fire and Forest Histories at 19 sites in Utah and Eastern Nevada. Rocky Mountain Research Station General Technical Report RMRS-GTR-261WWW, 192 (US Department of Agriculture, Forest Service, 2011).74.Charles, K. Long-term Vegetation Change on Utah’s Fishlake National Forest: A Study in Repeat Photography (Utah State Univ., 2003).75.USDA Forest Service. Fishlake National Forest (N.F.), Salina Planning Unit: Environmental Impact Statement. 1–125 (USDA Forest Service, 1976).76.Morris, J. L., Brunelle, A., Munson, A. S., Spencer, J. & Power, M. J. Holocene vegetation and fire reconstructions from the Aquarius Plateau, Utah, USA. Quat. Int. 310, 111–123 (2013).Article 

Google Scholar 
77.MTBS Data Access: Fire Level Geospatial Data. (2020, November – last revised). MTBS Project (USDA Forest Service/U.S. Geological Survey). Available online: http://mtbs.gov/direct-download [2020, December 15].78.Kitzberger, T., Falk, D. A., Westerling, A. L. & Swetnam, T. W. Direct and indirect climate controls predict heterogeneous early-mid 21st century wildfire burned area across western and boreal North America. PLoS ONE 12, e0188486 (2017).Article 
CAS 

Google Scholar 
79.Dean, W. E. Jr. Determination of carbonate and organic matter in calcareous sediments and sedimentary rocks by loss on ignition: comparision with other methods. J. Sediment. Petrol. 44, 242–248 (1974).CAS 

Google Scholar 
80.Reimer, P. J. et al. Intcal13 and Marine13 radiocarbon age calibration curves 0–50,000 years cal Bp. Radiocarbon 55, 1869–1887 (2013).CAS 
Article 

Google Scholar 
81.Blaauw, M. & Christen, J. A. Flexible paleoclimate age-depth models using an autoregressive gamma process. Bayesian Anal. 6, 457–474 (2011).Article 

Google Scholar 
82.Higuera, P. E., Brubaker, L. B., Anderson, P. M., Hu, F. S. & Brown, T. A. Vegetation mediated the impacts of postglacial climate change on fire regimes in the south-central Brooks Range, Alaska. Ecol. Monographs 79, 201–219 (2009).Article 

Google Scholar 
83.Crema, E. R., Bevan, A. & Shennan, S. Spatio-temporal approaches to archaeological radiocarbon dates. J. Archaeol. Sci. 87, 1–9 (2017).CAS 
Article 

Google Scholar 
84.Kelly, R. L., Surovell, T. A., Shuman, B. N. & Smith, G. M. A continuous climatic impact on Holocene human population in the Rocky Mountains. Proc. Natl Ac. Sci. USA 110, 443–447 (2013).CAS 
Article 

Google Scholar 
85.Shennan, S. et al. Regional population collapse followed initial agriculture booms in mid-Holocene Europe. Nat Commun. 4, 31–34 (2013).Article 
CAS 

Google Scholar 
86.Bevan, A. & Crema, E. rcarbon v1. 2.0: Methods for calibrating and analysing radiocarbon dates, https://cran.r-project.org/web/packages/rcarbon/index.html (2018).87.Contreras, D. A. & Meadows, J. Summed radiocarbon calibrations as a population proxy: A critical evaluation using a realistic simulation approach. J. Archaeol. Sci. 52, 591–608 (2014).Article 

Google Scholar 
88.Wood, S. N. Package ‘mgvc,’ https://cran.r-project.org/web/packages/mgcv/mgcv.pdf (2017). More

1.Fragkias, M., Güneralp, B., Seto, K. C. & Goodness, J. In Urbanization, Biodiversity and Ecosystem Services: Challenges and Opportunities: A Global Assessment (eds Thomas, E. et al.) 409–435 (Springer, 2013).
Google Scholar 
2.Seto, K. C., Woodcock, C. E. & Kaufmann, R. K. In Land Change Science: Observing, Monitoring and Understanding Trajectories of Change on the Earth’s Surface (eds Garik, G. et al.) 223–236 (Springer, 2004).
Google Scholar 
3.McKinney, M. L. Urbanization, biodiversity, and conservation: The impacts of urbanization on native species are poorly studied, but educating a highly urbanized human population about these impacts can greatly improve species conservation in all ecosystems. Bioscience 52, 883–890. https://doi.org/10.1641/0006-3568(2002)052[0883:Ubac]2.0.Co;2 (2002).Article 

Google Scholar 
4.Müller, N., Ignatieva, M., Nilon, C. H., Werner, P. & Zipperer, W. C. In Urbanization, Biodiversity and Ecosystem Services: Challenges and Opportunities: A Global Assessment (eds Thomas, E. et al.) 123–174 (Springer, 2013).
Google Scholar 
5.Kaufmann, R. K. et al. Climate response to rapid urban growth: Evidence of a human-induced precipitation deficit. J. Clim. 20, 2299–2306. https://doi.org/10.1175/jcli4109.1 (2007).ADS 
MathSciNet 
Article 

Google Scholar 
6.Reisinger, A. J., Groffman, P. M. & Rosi-Marshall, E. J. Nitrogen-cycling process rates across urban ecosystems. FEMS Microbiol. Ecol. https://doi.org/10.1093/femsec/fiw198 (2016).Article 
PubMed 

Google Scholar 
7.Kaye, J. P., Groffman, P. M., Grimm, N. B., Baker, L. A. & Pouyat, R. V. A distinct urban biogeochemistry?. Trends Ecol. Evol. 21, 192–199. https://doi.org/10.1016/j.tree.2005.12.006 (2006).Article 
PubMed 

Google Scholar 
8.Xu, H.-J. et al. Does urbanization shape bacterial community composition in urban park soils? A case study in 16 representative Chinese cities based on the pyrosequencing method. FEMS Microbiol. Ecol. 87, 182–192. https://doi.org/10.1111/1574-6941.12215 (2014).CAS 
Article 
PubMed 

Google Scholar 
9.Godefroid, S. & Koedam, N. The impact of forest paths upon adjacent vegetation: Effects of the path surfacing material on the species composition and soil compaction. Biol. Cons. 119, 405–419. https://doi.org/10.1016/j.biocon.2004.01.003 (2004).Article 

Google Scholar 
10.Zhu, W.-X., Hope, D., Gries, C. & Grimm, N. B. Soil characteristics and the accumulation of inorganic nitrogen in an arid urban ecosystem. Ecosystems 9, 711–724. https://doi.org/10.1007/s10021-006-0078-1 (2006).CAS 
Article 

Google Scholar 
11.Tenenbaum, D. E., Band, L. E., Kenworthy, S. T. & Tague, C. L. Analysis of soil moisture patterns in forested and suburban catchments in Baltimore, Maryland, using high-resolution photogrammetric and LIDAR digital elevation datasets. Hydrol. Process. 20, 219–240. https://doi.org/10.1002/hyp.5895 (2006).ADS 
Article 

Google Scholar 
12.Chen, W., Lu, S., Pan, N., Wang, Y. & Wu, L. Impact of reclaimed water irrigation on soil health in urban green areas. Chemosphere 119, 654–661. https://doi.org/10.1016/j.chemosphere.2014.07.035 (2015).ADS 
CAS 
Article 
PubMed 

Google Scholar 
13.Mehmood, K., Ahmad, H. R., Abbas, R., Saifullah, A. & Murtaza, G. Heavy metals in urban and peri-urban soils of a heavily-populated and industrialized city: Assessment of ecological risks and human health repercussions. Hum. Ecol. Risk Assess. https://doi.org/10.1080/10807039.2019.1601004 (2019).Article 

Google Scholar 
14.Decina, S. M., Templer, P. H., Hutyra, L. R., Gately, C. K. & Rao, P. Variability, drivers, and effects of atmospheric nitrogen inputs across an urban area: Emerging patterns among human activities, the atmosphere, and soils. Sci. Total Environ. 609, 1524–1534. https://doi.org/10.1016/j.scitotenv.2017.07.166 (2017).ADS 
CAS 
Article 
PubMed 

Google Scholar 
15.Decina, S. M., Templer, P. H. & Hutyra, L. R. Atmospheric inputs of nitrogen, carbon, and phosphorus across an urban area: Unaccounted fluxes and canopy influences. Earth’s Fut. 6, 134–148. https://doi.org/10.1002/2017ef000653 (2018).ADS 
CAS 
Article 

Google Scholar 
16.Tresch, S. et al. Direct and indirect effects of urban gardening on aboveground and belowground diversity influencing soil multifunctionality. Sci. Rep. 9, 9769. https://doi.org/10.1038/s41598-019-46024-y (2019).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
17.Epp Schmidt, D. J. et al. Metagenomics reveals bacterial and archaeal adaptation to urban land-use: N catabolism, methanogenesis, and nutrient acquisition. Front. Microbiol. https://doi.org/10.3389/fmicb.2019.02330 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
18.Wang, H. et al. Soil bacterial diversity is associated with human population density in urban greenspaces. Environ. Sci. Technol. 52, 5115–5124. https://doi.org/10.1021/acs.est.7b06417 (2018).ADS 
CAS 
Article 
PubMed 

Google Scholar 
19.Wang, H. et al. Changes in land use driven by urbanization impact nitrogen cycling and the microbial community composition in soils. Sci. Rep. 7, 44049. https://doi.org/10.1038/srep44049 (2017).ADS 
Article 
PubMed 
PubMed Central 

Google Scholar 
20.Pouyat, R. V. et al. A global comparison of surface soil characteristics across five cities: A test of the urban ecosystem convergence hypothesis. Soil Sci. 180, 136–145. https://doi.org/10.1097/ss.0000000000000125 (2015).CAS 
Article 

Google Scholar 
21.Yan, B. et al. Urban-development-induced changes in the diversity and composition of the soil bacterial community in Beijing. Sci. Rep. 6, 38811. https://doi.org/10.1038/srep38811 (2016).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
22.Yan, Z. Z., Chen, Q. L., Zhang, Y. J., He, J. Z. & Hu, H. W. Industrial development as a key factor explaining variances in soil and grass phyllosphere microbiomes in urban green spaces. Environ. Pollut. 261, 114201. https://doi.org/10.1016/j.envpol.2020.114201 (2020).CAS 
Article 
PubMed 

Google Scholar 
23.Joyner, J. L. et al. Green infrastructure design influences communities of urban soil bacteria. Front. Microbiol. https://doi.org/10.3389/fmicb.2019.00982 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
24.Deeb, M. et al. Soil and microbial properties of green infrastructure stormwater management systems. Ecol. Eng. 125, 68–75. https://doi.org/10.1016/j.ecoleng.2018.10.017 (2018).Article 

Google Scholar 
25.Ramirez, K. S. et al. Biogeographic patterns in below-ground diversity in New York City’s Central Park are similar to those observed globally. Proc. R. Soc. B Biol. Sci. 281, 20141988. https://doi.org/10.1098/rspb.2014.1988 (2014).Article 

Google Scholar 
26.Zissimos, A. M., Cohen, D. R. & Christoforou, I. C. Land use influences on soil geochemistry in Lefkosia (Nicosia) Cyprus. J. Geochem. Explor. 187, 6–20. https://doi.org/10.1016/j.gexplo.2017.03.005 (2018).CAS 
Article 

Google Scholar 
27.Henry, S., Bru, D., Stres, B., Hallet, S. & Philippot, L. Quantitative detection of the nosZ gene, encoding nitrous oxide reductase, and comparison of the abundances of 16S rRNA, narGnirK and nosZ genes in soils. Appl. Environ. Microbiol. 72, 5181. https://doi.org/10.1128/AEM.00231-06 (2006).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
28.Bru, D., Sarr, A. & Philippot, L. Relative abundances of proteobacterial membrane-bound and periplasmic nitrate reductases in selected environments. Appl. Environ. Microbiol. 73, 5971. https://doi.org/10.1128/AEM.00643-07 (2007).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
29.Jones, C. M., Graf, D. R. H., Bru, D., Philippot, L. & Hallin, S. The unaccounted yet abundant nitrous oxide-reducing microbial community: a potential nitrous oxide sink. ISME J. 7, 417–426. https://doi.org/10.1038/ismej.2012.125 (2013).CAS 
Article 
PubMed 

Google Scholar 
30.Bustin, S. A. et al. The MIQE guidelines: Minimum information for publication of quantitative real-time PCR experiments. Clin. Chem. 55, 611–622. https://doi.org/10.1373/clinchem.2008.112797 (2009).CAS 
Article 
PubMed 

Google Scholar 
31.Jalili, V. et al. The galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2020 update. Nucleic Acids Res. 48, W395–W402. https://doi.org/10.1093/nar/gkaa434 (2020).CAS 
Article 
PubMed 
PubMed Central 

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

Google Scholar 
33.Quast, C. et al. The SILVA ribosomal RNA gene database project: Improved data processing and web-based tools. Nucleic Acids Res. 41, D590–D596. https://doi.org/10.1093/nar/gks1219 (2012).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
34.Price, M. N., Dehal, P. S. & Arkin, A. P. FastTree: Computing large minimum evolution trees with profiles instead of a distance matrix. Mol. Biol. Evol. 26, 1641–1650. https://doi.org/10.1093/molbev/msp077 (2009).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
35.Katoh, K. & Standley, D. M. MAFFT multiple sequence alignment software version 7: Improvements in performance and usability. Mol. Biol. Evol. 30, 772–780. https://doi.org/10.1093/molbev/mst010 (2013).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
36.Roller, B. R. K., Stoddard, S. F. & Schmidt, T. M. Exploiting rRNA operon copy number to investigate bacterial reproductive strategies. Nat. Microbiol. 1, 16160. https://doi.org/10.1038/nmicrobiol.2016.160 (2016).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
37.Stoddard, S. F., Smith, B. J., Hein, R., Roller, B. R. K. & Schmidt, T. M. rrnDB: improved tools for interpreting rRNA gene abundance in bacteria and archaea and a new foundation for future development. Nucleic Acids Res. 43, D593–D598. https://doi.org/10.1093/nar/gku1201 (2014).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
38.Starke, R., Pylro, V. S. & Morais, D. K. 16S rRNA gene copy number normalization does not provide more reliable conclusions in metataxonomic surveys. Microb. Ecol. 81, 535–539. https://doi.org/10.1007/s00248-020-01586-7 (2021).CAS 
Article 
PubMed 

Google Scholar 
39.Cáceres, M. D. & Legendre, P. Associations between species and groups of sites: Indices and statistical inference. Ecology 90, 3566–3574. https://doi.org/10.1890/08-1823.1 (2009).Article 
PubMed 

Google Scholar 
40.Banerjee, S. et al. Agricultural intensification reduces microbial network complexity and the abundance of keystone taxa in roots. ISME J. 13, 1722–1736. https://doi.org/10.1038/s41396-019-0383-2 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
41.Oka, M. & Uchida, Y. Heavy metals in slag affect inorganic N dynamics and soil bacterial community structure and function. Environ. Pollut. 243, 713–722. https://doi.org/10.1016/j.envpol.2018.09.024 (2018).CAS 
Article 
PubMed 

Google Scholar 
42.Guo, H., Nasir, M., Lv, J., Dai, Y. & Gao, J. Understanding the variation of microbial community in heavy metals contaminated soil using high throughput sequencing. Ecotoxicol. Environ. Saf. 144, 300–306. https://doi.org/10.1016/j.ecoenv.2017.06.048 (2017).CAS 
Article 
PubMed 

Google Scholar 
43.Singh, B. K. et al. Loss of microbial diversity in soils is coincident with reductions in some specialized functions. Environ. Microbiol. 16, 2408–2420. https://doi.org/10.1111/1462-2920.12353 (2014).Article 
PubMed 

Google Scholar 
44.Haferburg, G. & Kothe, E. Microbes and metals: Interactions in the environment. J. Basic Microbiol. 47, 453–467. https://doi.org/10.1002/jobm.200700275 (2007).CAS 
Article 
PubMed 

Google Scholar 
45.Normand, P., Daffonchio, D. & Gtari, M. In The Prokaryotes: Actinobacteria (eds Eugene, R. et al.) 361–379 (Springer, 2014).
Google Scholar 
46.Kelly, D. P., Rainey, F. A. & Wood, A. P. In The Prokaryotes: Volume 5: Proteobacteria: Alpha and Beta Subclasses (eds Martin, D. et al.) 232–249 (Springer, 2006).
Google Scholar 
47.Peters, M. K. et al. Climate–land-use interactions shape tropical mountain biodiversity and ecosystem functions. Nature 568, 88–92. https://doi.org/10.1038/s41586-019-1048-z (2019).ADS 
CAS 
Article 
PubMed 

Google Scholar 
48.Paula, F. S. et al. Land use change alters functional gene diversity, composition and abundance in Amazon forest soil microbial communities. Mol. Ecol. 23, 2988–2999. https://doi.org/10.1111/mec.12786 (2014).Article 
PubMed 

Google Scholar 
49.Zhang, X. et al. Effect of intermediate disturbance on soil microbial functional diversity depends on the amount of effective resources. Environ. Microbiol. 20, 3862–3875. https://doi.org/10.1111/1462-2920.14407 (2018).CAS 
Article 
PubMed 

Google Scholar 
50.Szoboszlay, M., Dohrmann, A. B., Poeplau, C., Don, A. & Tebbe, C. C. Impact of land-use change and soil organic carbon quality on microbial diversity in soils across Europe. FEMS Microbiol. Ecol. https://doi.org/10.1093/femsec/fix146 (2017).Article 
PubMed 

Google Scholar 
51.Huot, H. et al. Characterizing urban soils in New York City: profile properties and bacterial communities. J. Soils Sediments 17, 393–407 (2017).Article 

Google Scholar 
52.Nehls, T., Rokia, S., Mekiffer, B., Schwartz, C. & Wessolek, G. Contribution of bricks to urban soil properties. J. Soils Sediments 13, 575–584 (2013).CAS 
Article 

Google Scholar 
53.Hemmat-Jou, M. H., Safari-Sinegani, A. A., Mirzaie-Asl, A. & Tahmourespour, A. Analysis of microbial communities in heavy metals-contaminated soils using the metagenomic approach. Ecotoxicology 27, 1281–1291. https://doi.org/10.1007/s10646-018-1981-x (2018).CAS 
Article 
PubMed 

Google Scholar 
54.Zaborowska, M., Kucharski, J. & Wyszkowska, J. Biological activity of soil contaminated with cobalt, tin, and molybdenum. Environ. Monit. Assess 188, 398–398. https://doi.org/10.1007/s10661-016-5399-8 (2016).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
55.Drenovsky, R., Vo, D., Graham, K. & Scow, K. Soil water content and organic carbon availability are major determinants of soil microbial community composition. Microb. Ecol. 48, 424–430 (2004).CAS 
Article 

Google Scholar 
56.Fierer, N. & Jackson, R. B. The diversity and biogeography of soil bacterial communities. Proc. Natl. Acad. Sci. U.S.A. 103, 626. https://doi.org/10.1073/pnas.0507535103 (2006).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
57.Bru, D. et al. Determinants of the distribution of nitrogen-cycling microbial communities at the landscape scale. ISME J. 5, 532–542. https://doi.org/10.1038/ismej.2010.130 (2011).CAS 
Article 
PubMed 

Google Scholar 
58.Vos, M., Wolf, A. B., Jennings, S. J. & Kowalchuk, G. A. Micro-scale determinants of bacterial diversity in soil. FEMS Microbiol. Rev. 37, 936–954. https://doi.org/10.1111/1574-6976.12023 (2013).CAS 
Article 
PubMed 

Google Scholar 
59.Rath, K. M., Fierer, N., Murphy, D. V. & Rousk, J. Linking bacterial community composition to soil salinity along environmental gradients. ISME J. 13, 836–846. https://doi.org/10.1038/s41396-018-0313-8 (2019).CAS 
Article 
PubMed 

Google Scholar 
60.Jones, C. M. & Hallin, S. Ecological and evolutionary factors underlying global and local assembly of denitrifier communities. ISME J. 4, 633–641. https://doi.org/10.1038/ismej.2009.152 (2010).Article 
PubMed 

Google Scholar 
61.Philippot, L. et al. Mapping field-scale spatial patterns of size and activity of the denitrifier community. Environ. Microbiol. 11, 1518–1526. https://doi.org/10.1111/j.1462-2920.2009.01879.x (2009).Article 
PubMed 

Google Scholar 
62.Graf, D. R. H., Jones, C. M. & Hallin, S. Intergenomic comparisons highlight modularity of the denitrification pathway and underpin the importance of community structure for N2O emissions. PLoS ONE 9, e114118. https://doi.org/10.1371/journal.pone.0114118 (2014).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
63.Domeignoz-Horta, L. et al. The diversity of the N2O reducers matters for the N2O:N2 denitrification end-product ratio across an annual and a perennial cropping system. Front. Microbiol. https://doi.org/10.3389/fmicb.2015.00971 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
64.Hallin, S., Philippot, L., Löffler, F. E., Sanford, R. A. & Jones, C. M. Genomics and ecology of novel N2O-reducing microorganisms. Trends Microbiol. 26, 43–55. https://doi.org/10.1016/j.tim.2017.07.003 (2018).CAS 
Article 
PubMed 

Google Scholar 
65.Xu, X. et al. NosZ clade II rather than clade I determine in situ N2O emissions with different fertilizer types under simulated climate change and its legacy. Soil Biol. Biochem. 150, 107974. https://doi.org/10.1016/j.soilbio.2020.107974 (2020).CAS 
Article 

Google Scholar  More

1.Tyack, P. L. et al. Beaked whales respond to simulated and actual navy sonar. PLoS ONE 6, e17009 (2011).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
2.Lusseau, D. Effects of tour boats on the behavior of bottlenose dolphins: using Markov chains to model anthropogenic impacts. Conserv. Biol. 17, 1785–1793 (2003).Article 

Google Scholar 
3.Currey, R. J. C. et al. Survival rates for a declining population of bottlenose dolphins in Doubtful Sound, New Zealand: an information theoretic approach to assessing the role of human impacts. Aquat. Conserv. Mar. Freshwat. Ecosyst. 19, 658–670 (2009).MathSciNet 
Article 

Google Scholar 
4.Caswell, H., Fujiwara, M. & Brault, S. Declining survival probability threatens the North Atlantic right whale. Proc. Natl. Acad. Sci. 96, 3308–3313 (1999).ADS 
CAS 
PubMed 
Article 

Google Scholar 
5.Bejder, L. et al. Decline in relative abundance of bottlenose dolphins exposed to long-term disturbance. Conserv. Biol. 20, 1791–1798 (2006).PubMed 
Article 

Google Scholar 
6.Pirotta, E. et al. Understanding the population consequences of disturbance. Ecol. Evol. 8, 9934–9946 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
7.New, L. F., Moretti, D. J., Hooker, S. K., Costa, D. P. & Simmons, S. E. Using energetic models to investigate the survival and reproduction of beaked whales (family Ziphiidae). PLoS ONE 8, e68725 (2013).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
8.Fleishman, E. et al. Monitoring population-level responses of marine mammals to human activities. Mar. Mamm. Sci. 32, 1004–1021 (2016).Article 

Google Scholar 
9.Pirotta, E., Merchant, N. D., Thompson, P. M., Barton, T. R. & Lusseau, D. Quantifying the effect of boat disturbance on bottlenose dolphin foraging activity. Biol. Cons. 181, 82–89 (2015).Article 

Google Scholar 
10.Benoit-Bird, K. J. Prey caloric value and predator energy needs: foraging predictions for wild spinner dolphins. Mar. Biol. 145, 435–444 (2004).Article 

Google Scholar 
11.Wisniewska, D. M. et al. Ultra-high foraging rates of harbor porpoises make them vulnerable to anthropogenic disturbance. Curr. Biol. 26, 1441–1446 (2016).CAS 
PubMed 
Article 

Google Scholar 
12.Farmer, N. A., Noren, D. P., Fougères, E. M., Machernis, A. & Baker, K. Resilience of the endangered sperm whale Physeter macrocephalus to foraging disturbance in the Gulf of Mexico, USA: a bioenergetics approach. Mar. Ecol. Prog. Ser. 589, 241–261 (2018).ADS 
CAS 
Article 

Google Scholar 
13.New, L. F. et al. Modelling the biological significance of behavioural change in coastal bottlenose dolphins in response to disturbance. Funct. Ecol. 27, 314–322 (2013).Article 

Google Scholar 
14.Christiansen, F. et al. Poor body condition associated with an unusual mortality event in gray whales. Mar. Ecol. Prog. Ser. 658, 237–252 (2021).ADS 
Article 

Google Scholar 
15.Christiansen, F., Dujon, A. M., Sprogis, K. R., Arnould, J. P. Y. & Bejder, L. Noninvasive unmanned aerial vehicle provides estimates of the energetic cost of reproduction in humpback whales. Ecosphere 7, e01468 (2016).Article 

Google Scholar 
16.Christiansen, F. et al. Maternal body size and condition determine calf growth rates in southern right whales. Mar. Ecol. Prog. Ser. 592, 267–281 (2018).ADS 
Article 

Google Scholar 
17.Kastelein, R. A., Helder-Hoek, L., Jennings, N., van Kester, R. & Huisman, R. Reduction in body mass and blubber thickness of harbor porpoises (Phocoena phocoena) due to near-fasting for 24 hours in four seasons. Aquat. Mamm. 45, 37–47 (2019).Article 

Google Scholar 
18.Marine mammal populations and ocean noise: determining when noise causes biologically significant effects. In (eds. National Research Council (U.S.) & National Academies Press (U.S.)) 142 (National Academies Press, 2005).19.Booth, C. G., Sinclair, R. R. & Harwood, J. Methods for monitoring for the population consequences of disturbance in marine mammals: a review. Front. Mar. Sci. 7, 115 (2020).ADS 
Article 

Google Scholar 
20.Villegas-Amtmann, S., Schwarz, L. K., Sumich, J. L. & Costa, D. P. A bioenergetics model to evaluate demographic consequences of disturbance in marine mammals applied to gray whales. Ecosphere 6, art183 (2015).21.Wikelski, M. & Cooke, S. J. Conservation physiology. Trends Ecol. Evol. 21, 38–46 (2006).PubMed 
Article 

Google Scholar 
22.Fearnbach, H., Durban, J., Ellifrit, D. & Balcomb, K. Using aerial photogrammetry to detect changes in body condition of endangered southern resident killer whales. Endanger. Spec. Res. 35, 175–180 (2018).Article 

Google Scholar 
23.Harwood, J. et al. Understanding the Population Consequences of Acoustic Disturbance for Marine Mammals. In The Effects of Noise on Aquatic Life II (eds. Popper, A. N. & Hawkins, A.) 417–423 (Springer, 2016). https://doi.org/10.1007/978-1-4939-2981-8_49.24.Christiansen, F., Rojano-Doñate, L., Madsen, P. T. & Bejder, L. Noise levels of multi-rotor unmanned aerial vehicles with implications for potential underwater impacts on marine mammals. Front. Mar. Sci. 3, 277 (2016).Article 

Google Scholar 
25.Christiansen, F., Nielsen, M. L. K., Charlton, C., Bejder, L. & Madsen, P. T. Southern right whales show no behavioral response to low noise levels from a nearby unmanned aerial vehicle. Mar. Mam. Sci. mms.12699 (2020) https://doi.org/10.1111/mms.12699.26.Castrillon, J. & Nash, S. B. Evaluating cetacean body condition; a review of traditional approaches and new developments. Ecol. Evol. 10, 6144–6162 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
27.Durban, J. W. et al. Photogrammetry of blue whales with an unmanned hexacopter. Mar. Mamm. Sci. 32, 1510–1515 (2016).Article 

Google Scholar 
28.Christiansen, F. et al. Estimating body mass of free‐living whales using aerial photogrammetry and 3D volumetrics. Methods Ecol. Evol. 2041–210X.13298 (2019). https://doi.org/10.1111/2041-210X.13298.29.Krause, D. J., Hinke, J. T., Perryman, W. L., Goebel, M. E. & LeRoi, D. J. An accurate and adaptable photogrammetric approach for estimating the mass and body condition of pinnipeds using an unmanned aerial system. PLoS ONE 12, e0187465 (2017).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
30.Fiori, L., Doshi, A., Martinez, E., Orams, M. B. & Bollard-Breen, B. The use of unmanned aerial systems in marine mammal research. Remote Sensing 9, 543 (2017).ADS 
Article 

Google Scholar 
31.Lusseau, D. The hidden cost of tourism: detecting long-term effects of tourism using behavioral information. Ecol. Soc. 9, 2 (2004).Article 

Google Scholar 
32.Bejder, L., Samuels, A., Whitehead, H. & Gales, N. Interpreting short-term behavioural responses to disturbance within a longitudinal perspective. Anim. Behav. 72, 1149–1158 (2006).Article 

Google Scholar 
33.Baird, R. Odontocete cetaceans around the main Hawaiian Islands: habitat use and relative abundance from small-boat sighting surveys. Aquat. Mamm. 39, 253–269 (2013).Article 

Google Scholar 
34.Hawaii Tourism Authority. Monthly Visitor Statistics. https://www.hawaiitourismauthority.org/research/monthly-visitor-statistics/ (2020).35.Tyne, J. A., Johnston, D. W., Rankin, R., Loneragan, N. R. & Bejder, L. The importance of spinner dolphin (Stenella longirostris) resting habitat: implications for management. J. Appl. Ecol. 52, 621–630 (2015).Article 

Google Scholar 
36.Wiener, C., Bejder, L., Johnston, D., Fawcett, L. & Wilkinson, P. Cashing in on spinners: revenue estimates of wild Dolphin-Swim Tourism in the Hawaiian Islands. Front. Mar. Sci. 7, (2020).37.Stack, S. H. et al. Identifying spinner dolphin Stenella longirostris longirostris movement and behavioral patterns to inform conservation strategies in Maui Nui, Hawai‘i. Mar. Ecol. Prog. Ser. 644, 187–197 (2020).ADS 
Article 

Google Scholar 
38.Tyne, J. A., Christiansen, F., Heenehan, H. L., Johnston, D. W. & Bejder, L. Chronic exposure of Hawaii Island spinner dolphins (Stenella longirostris) to human activities. R. Soc. Open Sci. 5, 171506 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
39.Heenehan, H. et al. Using Ostrom’s common-pool resource theory to build toward an integrated ecosystem-based sustainable cetacean tourism system in Hawai`i. J. Sustain. Tour. 23, 536–556 (2015).Article 

Google Scholar 
40.Baird, R. W. et al. Movements of two satellite-tagged pygmy killer whales (Feresa attenuata) off the island of Hawai‘i. Mar. Mamm. Sci. 27, E332–E337 (2011).Article 

Google Scholar 
41.Baird, R. W. et al. Movements and Spatial Use of Odontocetes in the Western Main Hawaiian Islands: Results of a Three-Year Study Off O’ahu and Kaua’i: http://www.dtic.mil/docs/citations/ADA602078 (2013). https://doi.org/10.21236/ADA602078.42.Forrester, D. J., Odell, D. K., Thompson, N. P. & White, J. R. Morphometrics, parasites, and chlorinated hydrocarbon residues of pygmy killer whales from Florida. J. Mammal. 61, 356–360 (1980).Article 

Google Scholar 
43.Kastelein, R. A., Mosterd, J., Schooneman, N. M. & Wiepkema, P. R. Food consumption, growth, body dimensions, and respiration rates of captive false killer whales (Pseudorca crassidens). Aquat. Mamm. 26, 33–44 (2000).
Google Scholar 
44.Elorriaga-Verplancken, F. R. et al. First record of pygmy killer whales (Feresa attenuata) in the Gulf of California, Mexico: diet inferences and probable relation with warm conditions during 2014. Aquat. Mamm. 42, 20–26 (2016).Article 

Google Scholar 
45.Castrillon, J., Huston, W. & Nash, S. B. The blubber adipocyte index: a nondestructive biomarker of adiposity in humpback whales (Megaptera novaeangliae). Ecol. Evol. 7, 5131–5139 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
46.Fahlman, A. et al. Field energetics and lung function in wild bottlenose dolphins, Tursiops truncatus, in Sarasota Bay Florida. R. Soc. Open Sci. 5, 171280 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
47.Nowacek, D. P., Christiansen, F., Bejder, L., Goldbogen, J. A. & Friedlaender, A. S. Studying cetacean behaviour: new technological approaches and conservation applications. Anim. Behav. 120, 235–244 (2016).Article 

Google Scholar 
48.Adamczak, S. K., Pabst, A., McLellan, W. A. & Thorne, L. H. Using 3D models to improve estimates of marine mammal size and external morphology. Front. Mar. Sci. 6, 334 (2019).Article 

Google Scholar 
49.Lindstedt, S. L. & Boyce, M. S. Seasonality, fasting endurance, and body size in mammals. Am. Nat. 125, 873–878 (1985).Article 

Google Scholar 
50.Blueweiss, L. et al. Relationships between body size and some life history parameters. Oecologia 37, 257–272 (1978).ADS 
CAS 
PubMed 
Article 

Google Scholar 
51.Senigaglia, V. et al. Meta-analyses of whale-watching impact studies: comparisons of cetacean responses to disturbance. Mar. Ecol. Prog. Ser. 542, 251–263 (2016).ADS 
CAS 
Article 

Google Scholar 
52.Sprogis, K. R., Christiansen, F., Wandres, M. & Bejder, L. E. Niño Southern Oscillation influences the abundance and movements of a marine top predator in coastal waters. Glob. Change Biol. 24, 1085–1096 (2018).ADS 
Article 

Google Scholar 
53.Henderson, E. E. et al. Delphinid behavioral responses to incidental mid-frequency active sonar. J. Acoust. Soc. America 136, 2003–2014 (2014).ADS 
Article 

Google Scholar 
54.Schwacke, L. H. et al. Quantifying injury to common bottlenose dolphins from the Deepwater Horizon oil spill using an age-, sex- and class-structured population model. Endanger. Spec. Res. 33, 265–279 (2017).Article 

Google Scholar 
55.Rolland, R. M. et al. Health of North Atlantic right whales Eubalaena glacialis over three decades: from individual health to demographic and population health trends. Mar. Ecol. Prog. Ser. 542, 265–282 (2016).ADS 
CAS 
Article 

Google Scholar 
56.Dawson, S., Bowmwn, H., Leunissen, E. & Sirguey, P. Inexpensive aerial photogrammetry for studies of whales and large marine animals. Front. Mar. Sci. 4, 366 (2017).Article 

Google Scholar 
57.Karns, B. L., Ewing, R. Y. & Schaefer, A. M. Evaluation of body mass index as a prognostic indicator from two rough-toothed dolphin (Steno bredanensis) mass strandings in Florida. Ecol. Evol. 9, 10544–10552 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
58.Stevenson, R. D. & Woods, W. A. Condition indices for conservation: new uses for evolving tools. Integr. Comp. Biol. 46, 1169–1190 (2006).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
59.Kershaw, J. L., Sherrill, M., Davison, N. J., Brownlow, A. & Hall, A. J. Evaluating morphometric and metabolic markers of body condition in a small cetacean, the harbor porpoise (Phocoena phocoena). Ecol. Evol. 7, 3494–3506 (2017).PubMed 
PubMed Central 
Article 

Google Scholar  More