Philippa Kaur

Rice, D. W. Marine mammals of the world: systematics and distribution. In The Society for Marine Mammalogy (ed. Rice, D. W.) 231 (Allen Press, 1998).
Google Scholar 
Best, P. B. External characters of southern minke whales and the existence of a diminutive form. Sci. Rep. Whales Res. Inst. 36, 1–33 (1985).
Google Scholar 
Acevedo, J. et al. Occurrence of dwarf minke whales (Balaenoptera acutorostrata subsp.) around the Antarctic Peninsula. Polar Biol. 34, 313–318 (2011).Article 

Google Scholar 
Risch, D., Norris, T., Curnock, M. & Friedlaender, A. Common and Antarctic minke whales: Conservation status and future research directions. Front. Mar. Sci. 6, 247. https://doi.org/10.3389/fmars.2019.00247 (2019).Article 

Google Scholar 
International Whaling Commission (IWC). Report of the scientific committee. J. Cetacean Res. Manag. 14, 102 (2013).
Google Scholar 
Matsuoka, K. et al. Overview of minke whale sightings surveys conducted on IWC/IDCR and SOWER Antarctic cruises from 1978/79 to 2000/01. J. Cetacean Res. Manag. 5, 173–201 (2003).
Google Scholar 
Glover, K. A. et al. Migration of Antarctic minke whales to the Arctic. PLoS One 5, e15197. https://doi.org/10.1371/journal.pone.0015197 (2010).Article 
ADS 
CAS 

Google Scholar 
Williams, R., Brierley, A., Friedlaender, A. & Scheidat, M. Densitiy of Antarctic minke whales in Weddell Sea from helicopter survey data. Ecology 63, IA14 (2011).
Google Scholar 
Williams, R. et al. Counting whales in a challenging, changing environment. Sci. Rep. 4, 4170. https://doi.org/10.1038/srep04170 (2014).Article 
CAS 

Google Scholar 
Shabangu, F. W., Findlay, K. & Stafford, K. M. Seasonal acoustic occurrence, diel vocalizing patterns and bioduck call-type composition of Antarctic minke whales off the west coast of South Africa and the Maud Rise Antarctica. Mar. Mamm. Sci. 36, 658–675 (2019).Article 

Google Scholar 
Kasamatsu, F., Nishiwaki, S. & Ishikawa, H. Breeding areas and southbound migrations of southern minke whales Balaenoptera acutorostrata. Mar. Ecol. Prog. Ser. 119, 1–10 (1995).Article 
ADS 

Google Scholar 
Tamura, T. & Konishi, K. Food habit and prey consumption of Antarctic minke whale Balaenoptera bonaerensis in the JARPA research area. J. Northwest Atl. Fish. Sci. 42, 13–25 (2009).Article 

Google Scholar 
Perrin, W. F., Mallette, S. D. & Brownell, R. L. Minke whales. In Encyclopedia of Marine Mammals (eds Perrin, W. F. et al.) 608–613 (Academic Press, 2018).Chapter 

Google Scholar 
Taylor, R. J. F. An unusual record of three species of whale being restricted to pools in Antarctic sea-ice. Proc. R. Soc. Lond. 129, 325–331 (1957).
Google Scholar 
Ensor, P. H. Minke whales in the pack ice zone, East Antarctica, during the period of maximum annual ice extent. Rep. Int. Whal. Commn 39, 219–225 (1989).
Google Scholar 
Scheidat, M. et al. Cetacean surveys in the Southern Ocean using icebreaker-supported helicopters. Polar Biol. 34, 1513–1522 (2011).Article 

Google Scholar 
Meirelles, A. C. O. & Furtado-Neto, M. A. A. Stranding of an Antarctic minke whale, Balaenoptera bonaerensis Burmeister, 1867, on the northern coast of South America. Lat. Am. J. Aquat. Mamm. 3, 81–82 (2004).Article 

Google Scholar 
Juri, E., Valdivia, M., Simoes-Lopes, P. C. & Le Bas, A. A note on minke whales (Cetacea: Balaenopteridae) in Uruguay: Strandings review. JCRM 21, 135–140 (2020).Article 

Google Scholar 
Williamson, G. R. Minke whales off Brazil. Sci. Rep. Whales Res. Inst. 27, 37–59 (1975).
Google Scholar 
Pastene, L. A. & Goto, M. Genetic characterization and population genetic structure of the Antarctic minke whale Balaenoptera bonaerensis in the Indo-Pacific region of the Southern Ocean. Fish Sci. 82, 873–886 (2016).Article 
CAS 

Google Scholar 
Balbuena, J. A., Aznar, F. J., Fernández, M. & Raga, J. A. Parasites as indicators of social structure and stock identity of marine mammals. Dev. Mar. Biol. 4, 133–139 (1995).
Google Scholar 
Kuramochi, T., Araki, J., Uchida, Moriyama, N., Takeda, Y., Hayashi, N., Wakao, H., Machida, M. & Nagasawa, K. Summary of parasite and epizoit investigations during JARPN surveys 1994–1999, with reference to stock structure analysis for the western North Pacific minke whales. IWC Scientific Committee Workshop to Review the Japanese Whaling Programme under Special Permit for North Pacific Minke Whales (JARPN) SC/F2K/J19 (2000).Kaliszewska, Z. A. et al. Population histories of right whales (Cetacea: Eubalaena) inferred from mitochondrial sequence diversities and divergences of their whale lice (Amphipoda: Cyamus). Mol. Ecol. 14, 3439–3456 (2005).Article 
CAS 

Google Scholar 
Ólafsdóttir, D. & Shinn, A. P. Epibiotic macrofauna on common minke whales, Balaenoptera acutorostrata Lacépède, 1804 Icelandic waters. Parasit. Vectors 6, 1–10 (2013).Article 

Google Scholar 
Matthews, C. J., Ghazal, M., Lefort, K. J. & Inuarak, E. Epizoic barnacles on Arctic killer whales indicate residency in warm waters. Mar. Mamm. Sci. 36, 1010–1014 (2020).Article 

Google Scholar 
Flach, L., Van Bressem, M. F., Pitombo, F. & Aznar, F. J. Emergence of the epibiotic barnacle Xenobalanus globicipitis in Guiana dolphins after a morbillivirus outbreak in Sepetiba Bay Brazil. Estuar. Coast. Shelf Sci. 263, 107632. https://doi.org/10.1016/j.ecss.2021.107632 (2021).Article 

Google Scholar 
Ten, S., Raga, J. A. & Aznar, F. J. Epibiotic fauna on cetaceans worldwide: A systematic review of records and indicator potential. Front. Mar. Sci. 9, 846558. https://doi.org/10.3389/fmars.2022.846558 (2022).Article 

Google Scholar 
Liouville, J. Cétacés de l’Antarctique. Paris: Deuxième Expédition Antarctique Française (1908–1910) (1913).Ohsumi, S., Masaki, Y. & Kawamura, A. Stock of the Antarctic minke whale. Sci. Rep. Whales Res. Inst. 22, 75–125 (1970).
Google Scholar 
Ohsumi, S. Find of marlin spear from the Antarctic minke whales. Sci. Rep. Whales Res. Inst. 25, 237–239 (1973).
Google Scholar 
Ivashin, M. V. External Parasites on Lesser Rorquals in the Antarctic 125–127 (Naukova Dumka, 1975).
Google Scholar 
Berzin, A. A. & Vlasova, L. P. Fauna of the Cetacea Cyamidae (Amphipoda) of the world ocean. Investig. Cet. 13, 149–164 (1982).
Google Scholar 
Best, P. B. Seasonal abundance, feeding, reproduction, age and growth in minke whales off Durban (with incidental observations from the Antarctic). Rep. Int. Whal. Commn 32, 759–786 (1982).
Google Scholar 
Avdeev, V. V. Parasitic amphipods of the family Cyamidae and the problem of Cetacea origin. Biol. Morja 4, 27–33 (1989).
Google Scholar 
Bushuev, S. G. A study of the population structure of the southern minke whale (Balaenoptera acutorostrata Lacepede) based on morphological and ecological variability. Rep. Int. Whal. Commn 40, 317–324 (1990).
Google Scholar 
Sedlak-Weinstein, E. Preliminary report of parasitic infestation of the minke whale Balaenoptera acutorostrata taken during the 1988/89 Antarctic expedition. Unpublished paper (1990).Dailey, M. D. & Vogelbein, W. Parasite fauna of 3 species of Antarctic whales with reference to their use as potential stock indicators. Fish. Bull. 89, 355–365 (1991).
Google Scholar 
Nemoto, T., Best, P. B., Ishimaru, K. & Takano, H. Diatom films on whales in South African waters. Sci. Rep. Whales Res. Inst. 32, 97–103 (1980).
Google Scholar 
Donovan, G. A review of IWC stock boundaries. Rep. Int. Whal. Commn 13, 39–68 (1991).
Google Scholar 
Lester, R. J. G. & MacKenzie, K. The use and abuse of parasites as stock markers for fish. Fish. Res. 97, 1–2 (2009).Article 

Google Scholar 
Ten, S. et al. Epibiotic barnacles of sea turtles as indicators of habitat use and fishery interactions: an analysis of juvenile loggerhead sea turtles, Caretta caretta, in the western Mediterranean. Ecol. Indic. 107, 105672. https://doi.org/10.1016/j.ecolind.2019.105672 (2019).Article 

Google Scholar 
Calman, W. T. A whale-barnacle of the genus Xenobalanus from Antarctic Seas. Ann. Mag. Nat. Hist. 6, 165–166 (1920).Article 

Google Scholar 
Kato, H., Hiroyama, H., Fujise, Y. & Ono, K. Preliminary report of the 1987/88 Japanese feasibility study of the special permit proposal for Southern Hemisphere Minke Whales. Rep. int. Whal. Commn 39, 235–248 (1989).
Google Scholar 
International Whaling Commission (IWC). Report of the Intersessional Workshop to review data and results from special permit research on minke whales in the Antarctic, Tokyo, 7–8 December 2006. J. Cetacean Res. Manag. 10, 411–445 (2008).
Google Scholar 
Bush, A. O., Lafferty, K. D., Lotz, J. M. & Shostak, A. W. Parasitology meets ecology on its own terms: Margolis et al. revisited. J. Parasitol. 83, 575–583 (1997).Article 
CAS 

Google Scholar 
Kim, H., Chan, B., Kang, C., Kim, H. & Kim, W. How do whale barnacles live on their hosts? Functional morphology and mating-group sizes of Coronula diadema (Linnaeus, 1767) and Conchoderma auritum (Linnaeus, 1767) (Cirripedia: Thoracicalcarea). J. Crustac. Biol. 40, 808–824 (2020).Article 

Google Scholar 
Reiczigel, J. Confidence intervals for the binomial parameter: Some new considerations. Stat. Med. 22, 611–621 (2003).Article 

Google Scholar 
Kato, H. Migration strategy of southern minke whales to maintain high reproductive rate. Dev. Mar. Biol. 4, 465–480 (1995).
Google Scholar 
Zuur, A. F., Ieno, E. N., Walker, N. J., Saveliev, A. A. & Smith, G. M. Mixed effects models and extensions in ecology with R. In Statistics for Biology and Health (ed. Gail, M.) (Springer, 2009).MATH 

Google Scholar 
Fransen, C. H. J. M. & Smeenk, C. Whale-lice (Amphipoda: Cyamidae) recorded from The Netherlands. Zool. Meded. 65, 393–405 (1991).
Google Scholar 
Barton, N. A., Farewell, T. S. & Hallett, S. H. Using generalized additive models to investigate the environmental effects on pipe failure in clean water networks. NPJ Clean Water 3, 31. https://doi.org/10.1038/s41545-020-0077-3 (2020).Article 

Google Scholar 
Schindelin, J. et al. Fiji: An open-source platform for biological-image analysis. Nat. Methods 9, 676–682. https://doi.org/10.1038/nmeth.2019 (2012).Article 
CAS 

Google Scholar 
Kane, E. A., Olson, P. A., Gerrodette, T. & Fiedler, P. Prevalence of the commensal barnacle Xenobalanus globicipitis on cetacean species in the eastern tropical Pacific Ocean, and a review of global occurrence. Fish. Bull. 106, 395–404 (2008).
Google Scholar 
Aznar, F. J., Balbuena, J. A. & Raga, J. A. Are epizoites biological indicators of a western Mediterranean striped dolphin die-off?. Dis. Aquat. Organ. 18, 159–163 (1994).Article 

Google Scholar 
Carrillo, J. M., Overstreet, R. M., Raga, J. A. & Aznar, F. J. Living on the edge: Settlement patterns by the symbiotic barnacle Xenobalanus globicipitis on small cetaceans. PLoS One 10, e0127367. https://doi.org/10.1371/journal.pone.0127367 (2015).Article 
CAS 

Google Scholar 
Moreno-Colom, P., Ten, S., Raga, J. A. & Aznar, F. J. Spatial distribution and aggregation of Xenobalanus globicipitis on the flukes of striped dolphins, Stenella coeruleoalba: An indicator of host hydrodynamics?. Mar. Mamm. Sci. 36, 897–914 (2020).Article 

Google Scholar 
Aznar, F. J. et al. Changes in epizoic crustacean infestations during cetacean die-offs: The mass mortality of Mediterranean striped dolphins Stenella coeruleoalba revisited. Dis. Aquat. Org. 67, 239–247 (2005).Article 
CAS 

Google Scholar 
Wood, S. N. & Augustin, N. H. GAMs with integrated model selection using penalized regression splines and applications to environmental modelling. Ecol. Modell. 157, 157–177 (2002).Article 

Google Scholar 
Wood, S. N. Generalized Additive Models: An Introduction with R (Chapman and Hall/CRC, 2017).Book 
MATH 

Google Scholar 
Bloch, D. et al. Short-term movements of long-finned pilot whales Globicephala melas around the Faroe Islands. Wildl. Biol. 9, 47–58 (2003).Article 

Google Scholar 
Beasley, I. et al. Stomach contents of long-finned pilot whales, Globicephala melas mass-stranded in Tasmania. PLoS One 14, e0206747. https://doi.org/10.1371/journal.pone.0206747 (2019).Article 
CAS 

Google Scholar 
Ohno, M. & Fujino, K. Biological investigation on the whales caught by the Japanese Antarctic whaling fleets, season 1950/51. Sci. Rep. Whales Res. Inst. 7, 125–188 (1952).
Google Scholar 
Clarke, R. The stalked barnacle Conchoderma, ectoparasitic on whales. Norsk Hvalfangst-Tidende 55, 153–168 (1966).
Google Scholar 
Christensen, I. First record of gooseneck barnacles (Conchoderma auritum) on a minke whale (Balaenoptera acutorostrata). ICES C. M. 1985/N:9 (1985).Bertulli, C. G., Cecchetti, A., Van Bressem, M. F. & Van Waerebeek, K. Skin disorders in common minke whales and white-beaked dolphins off Iceland, a photographic assessment. J. Mar. Anim. Ecol. 5, 29–40 (2012).
Google Scholar 
Knowlton, N. Sibling species in the sea. Annu. Rev. Ecol. Evol. Syst. 24, 189–216 (1993).Article 

Google Scholar 
Trontelj, P. & Fišer, C. Perspectives: Cryptic species diversity should not be trivialised. Syst. Biodivers. 7, 1–3 (2009).Article 

Google Scholar 
Norris, R. & Hull, P. The temporal dimension of marine speciation. Evol. Ecol. 26, 393–415 (2011).Article 

Google Scholar 
Rawson, P., Macnamee, R., Frick, M. & Williams, K. Phylogeography of the coronulid barnacle, Chelonibia testudinaria, from loggerhead sea turtles Caretta caretta. Mol. Ecol. 12, 2697–2706 (2003).Article 
CAS 

Google Scholar 
Cabezas, M. P., Cabezas, P., Machordom, A. & Guerra-García, J. M. Hidden diversity and cryptic speciation refute cosmopolitan distribution in Caprella penantis (Crustacea: Amphipoda: Caprellidae). J. Zool. Syst. Evol. 51, 85–99 (2013).Article 

Google Scholar 
Boyd, L. L., Zardus, J. D., Knauer, C. M. & Wood, L. D. Evidence for host selectivity and specialization by epizoic Chelonibia barnacles between hawksbill and green sea turtles. Front. Ecol. Evol. 9, 807237. https://doi.org/10.3389/fevo.2021.807237 (2021).Article 

Google Scholar 
Schell, D., Rowntree, V. & Pfeiffer, C. Stable-isotope and electron-microscopic evidence that cyamids (Crustacea: Amphipoda) feed on whale skin. Can. J. Zool. 78, 721–727 (2000).Article 

Google Scholar 
Iwasa-Arai, T. & Serejo, C. S. Phylogenetic analysis of the family Cyamidae (Crustacea: Amphipoda): A review based on morphological characters. Zool. J. Linn. Soc. 184, 66–94 (2018).Article 

Google Scholar 
Fraija-Fernández, N. et al. Living in a harsh habitat: Epidemiology of the whale louse, Syncyamus aequus (Cyamidae), infecting striped dolphins in the Western Mediterranean. J. Zool. 303, 199–206 (2017).Article 

Google Scholar 
Angot, M. Rapport scientifique sur les expeditions baleinieres autour de Madagascar (saisons 1949 et 1950). Mem. Inst. Sci. Madag. Ser. A 6, 439–486 (1951).
Google Scholar 
Newman, W. A. & Abbott, D. P. Cirripedia: The barnacles. In Intertidal Invertebrates of California (eds Morris, R. H. et al.) 504–535 (Stanford University Press, 1980).
Google Scholar 
Nogata, Y. & Matsumura, K. Larval development and settlement of a whale barnacle. Biol Lett. 2, 92–93 (2006).Article 

Google Scholar 
Hiro, F. The fauna of Akkeshi Bay. II. Cirripedia. J. Fac. Sci. Hokkaido Univ. 4, 213–229 (1935).
Google Scholar 
Rice, D. W. Progress report on biological studies of the larger Cetacea in the waters off California. Norsk Hvalfangst-Tid 52, 181–187 (1963).
Google Scholar 
Klinkhart, E. G. The beluga whale in Alaska. State Alsk. Dep. Fish 7, 11 (1966).
Google Scholar 
Nilsson-Cantell, C. A. Cirripedia Thoracica and Acrothoracica. MIOS 5, 1–133 (1978).
Google Scholar 
Scarff, J. E. Occurrence of the barnacles Coronula diadema, C. reginae and Cetopirus complanatus (Cirripedia) on right whales. Sci. Rep. Whales Res. Inst. 37, 129–153 (1986).
Google Scholar 
Kakuwa, Z., Kawakami, T. & Iguchi, K. Biological investigation on the whales caught by the Japanese Antarctic whaling fleets in the 1951–52 season. Sci. Rep. Whales Res. Inst. 8, 147–213 (1953).
Google Scholar 
Nishiwaki, M. Humpback whales in Ryukyuan waters. Sci. Rep. Whales Res. Inst. 14, 49–87 (1959).
Google Scholar 
Best, P. B. The presence of coronuline barnacles on a southern right whale Eubalaena australis. S. Afr. J. Mar. Sci. 11, 585–587 (1991).Article 

Google Scholar 
Mackintosh, N. A. & Wheeler, J. F. G. Southern blue and fin whales. Disc. Rep. 1, 257–540 (1929).
Google Scholar 
Nilsson-Cantell, C. A. Thoracic cirripedes collected in 1925–1927. Disc. Rep. 2, 223–260 (1930).
Google Scholar 
Nishiwaki, M. & Hayashi, K. Biological survey of fin and blue whales taken in the Antarctic season 1947–48 by the Japanese fleet. Sci. Rep. Whales Res. Inst. 3, 132–190 (1950).
Google Scholar 
Mizue, K. & Murata, T. Biological investigation on the whales caught by the Japanese Antarctic whaling fleets season 1949–50. Sci. Rep. Whales Res. Inst. 6, 73–131 (1951).
Google Scholar 
Nishiwaki, M. & Oye, T. Biological investigation on blue whales (Balaenoptera musculus) and Fin Whales (Balaenoptera physalus) caught by the Japanese Antarctic Whaling Fleets. Sci. Rep. Whales Res. Inst. 5, 91–167 (1951).
Google Scholar 
Tomilin, A. G. Cetacea. In Mammals of the U.S.S.R. and Adjacent Countries Vol. 9 (ed. Tomilin, A. G.) 717 (Akademii Nauk SSSR, 1957).
Google Scholar 
Cockrill, W. R. Pathology of the cetacea. A veterinary study on whales. Br. Vet. J. 116, 1–28 (1960).
Google Scholar 
Kawamura, A. Some consideration on the stock unit of sei whales by the aspect of ectoparasitic organisms on the body. Bull. Jpn. Soc. Fish. Oceanogr. 14, 38–43 (1969).
Google Scholar 
Fraija-Fernández, N., Hernández-Hortelano, A., Ahuir-Baraja, A. E., Raga, J. A. & Aznar, F. J. Taxonomic status and epidemiology of the mesoparasitic copepod Pennella balaenoptera in cetaceans from the western Mediterranean. Dis. Aquat. Org. 128, 249–258 (2018).Article 

Google Scholar 
Foster, B. A. & Willan, R. C. Foreign barnacles transported to New Zealand on an oil platform. N. Z. J. Mar. Freshw. Res. 13, 143–149 (1979).Article 

Google Scholar 
González, J. et al. Cirripedia of the Canary islands: Distribution and ecological notes. J. Mar. Biol. Assoc. U.K. 92, 129–141 (2012).Article 

Google Scholar 
Zettler, M. L. An example for transatlantic hitchhiking by macrozoobenthic organisms with a research vessel. Helgol. Mar. Res. 75, 4. https://doi.org/10.1186/s10152-021-00549-w (2021).Article 

Google Scholar 
Matthews, L. H. The humpback whale Megaptera novaeangliae. Disc. Rep. 17, 7–92 (1937).
Google Scholar 
Scheffer, V. B. Organisms collected from whales in the Aleutian Islands. Murrelet 20, 67–69 (1939).Article 

Google Scholar 
Symons, H. W. & Weston, R. D. Studies on the humpback whale (Megaptera nodosa) in the Bellinghausen Sea. Norsk Hvalfangsttid 47, 53–81 (1958).
Google Scholar 
Van Waerebeek, K., Reyes, J. C. & Alfaro, J. Helminth parasites and phoronts of dusky dolphins Lagenorhynchus obscurus (Gray, 1828) from Peru. Aquat. Mamm. 19, 159–169 (1993).
Google Scholar 
Fertl, D. Barnacles. In Encyclopedia of Marine Mammals (eds Perrin, W. F. et al.) 75–78 (Academic Press, 2002).
Google Scholar 
Cornwall, I. E. The barnacles of british Columbia. Br. Col. Prov. Mus. Dept. 7, 5–69 (1955).
Google Scholar 
Abaunza, P., Arroyo, N. L. & Preciado, I. A contribution to the knowledge on the morphometry and the anatomical characters of Pennella balaenopterae (Copepoda, Ciphonostomatoida, Pennellidae), with special reference to the buccal complex. Crustaceana 74, 193–210 (2001).Article 

Google Scholar 
Marcer, F. et al. Parasitological and pathological findings in fin whales Balaenoptera physalus stranded along Italian coastlines. Dis. Aquat. Org. 133, 25–37 (2019).Article 
CAS 

Google Scholar 
Turner, W. On Pennella balænopteræ: A crustacean, parasitic on a finner whale, Balaenoptera musculus. Earth. Environ. Sci. Trans. R. Soc. Edinb. 41, 409–434 (1905).Article 

Google Scholar 
Walker, W. A. & Hanson, M. B. Biological observations on Stejneger’s beaked whale, Mesoplodon stejnegeri, from strandings on Adak Alaska. Mar. Mamm. Sci. 15, 1314–1329 (1999).Article 

Google Scholar 
Delaney, M. A., Ford, J. K. B., Tang, K. & Gaydos, J. K. Mesoparasitic copepod (Pennella balaenopterae) infestation of a stranded offshore orca (Orcinus orca) in Southeast Alaska: Review of significance as a health indicator in cetaceans. In IAAAM 21–26 (2016).Suyama, S., Kakehi, S., Yanagimoto, T. & Chow, S. Infection of the pacific saury Cololabis saira (Brevoort, 1856) (Teleostei: Beloniformes: Scomberesocidae) by Pennella sp. (Copepoda: Siphonostomatoida: Pennellidae) south of the Subarctic Front. J. Crust. Biol. 40, 384–389 (2020).Article 

Google Scholar 
Rowntree, V. J. Feeding, distribution and reproductive behavior of cyamids (Crustacea: Amphipoda) living on humpback and right whales. Can. J. Zool. 74, 103–109 (1996).Article 

Google Scholar 
Leung, Y. M. Life cycle of Cyamus scammoni (Amphipoda: Cyamidae), ectoparasite of gray whale, with a remark on the associated species. Sci. Rep. Whales Res. Inst. 28, 153–160 (1976).
Google Scholar 
MacIntyre, R. J. Rapid growth in stalked barnacles. Nature 212, 637–638 (1966).Article 
ADS 

Google Scholar 
Rasmussen, T. Notes on the biology of the shipfouling gooseneck barnacle Conchoderma auritum Linnaeus, 1776 (Cirripedia; Lepadomorpha). Biol. Mar. 2, 37–44 (1980).
Google Scholar 
Dalley, R. & Crisp, D. J. Conchoderma: A fouling hazard to ships underway. Mar. Biol. Lett. 2, 141–152 (1981).
Google Scholar 
Dalley, R. The larval stages of the oceanic, pedunculate barnacle Conchoderma auritum (L) (Cirripedia, Thoracica). Crustaceana 46, 39–54 (1984).Article 

Google Scholar 
Foskolos, I., Provata, M. T. & Frantzis, A. First record of Conchoderma auritum (Cirripedia: Lepadidae) on Ziphius cavirostris (Cetacea: Ziphiidae) in Greece. Ann. Ser. Hist. 27, 29–34 (2017).
Google Scholar 
Lee, J. F., Friedlaender, A. S., Oliver, M. J. & DeLiberty, T. L. Behavior of satellite-tracked Antarctic minke whales (Balaenoptera bonaerensis) in relation to environmental factors around the western Antarctic Peninsula. Anim. Biotelem. 5, 23. https://doi.org/10.1186/s40317-017-0138-7 (2017).Article 

Google Scholar 
Darwin, C. A Monograph on the Subclass Cirripedia Vol. 1 (The Ray Society, 1851).
Google Scholar 
Tsikhon-Lukanina, V. A., Soldatova, I. N., Kuznetsova, I. A. & Il’in, I. I. Macrofouling community in the Strait of Tunisia (Sicily). Oceanology 16, 519–522 (1977).
Google Scholar 
Nilsson-Cantell, C. A. Cirripedien von der Stewart Insel und von Südgeorgien. Senckenbergiana 12, 210–213 (1930).
Google Scholar 
Slijper, E. J. Whales (Hutchinson, 1962).
Google Scholar 
Kaufman, G. D. & Forestell, P. H. Hawaii’s humpback whales, a complete whalewatching guide (Pacific Whale Foundation Press, 1986).
Google Scholar 
Dawbin, W. H. Baleen whales. In Whales, Dolphins and Porpoises (eds Harrison, R. & Bryden, M.) 44–65 (Facts on File, 1988).
Google Scholar 
Félix, F., Bearson, B. & Falconí, J. Epizoic barnacles removed from the skin of a humpback whale after a period of intense surface activity. Mar. Mamm. Sci. 22, 979–984 (2006).Article 

Google Scholar 
Towers, J. R. et al. Seasonal movements and ecological markers as evidence for migration of common minke whales photo-identified in the eastern North Pacific. J. Cetacean Res. Manag. 13, 221–229 (2013).
Google Scholar 
Iwasa-Arai, T. et al. The host-specific whale louse (Cyamus boopis) as a potential tool for interpreting humpback whale (Megaptera novaeangliae) migratory routes. J. Exp. Mar. Biol. Ecol. 505, 45–51 (2018).Article 

Google Scholar 
Lehnert, K. et al. Whale lice (Isocyamus deltobranchium & Isocyamus delphinii; Cyamidae) prevalence in odontocetes off the German and Dutch coasts – Morphological and molecular characterization and health implications. Int. J. Parasitol. 15, 22–30 (2021).
Google Scholar 
Dreyer, N. et al. How whale and dolphin barnacles attach to their hosts and the paradox of remarkably versatile attachment structures in cypris larvae. Org. Divers. Evol. 20, 233–249 (2020).Article 

Google Scholar 
Visser, I. N., Cooper, T. E. & Grimm, H. Duration of pseudo-stalked barnacles (Xenobalanus globicipitis) on a New Zealand Pelagic ecotype orca (Orcinus orca), with comments on cookie cutter shark bite marks (Isistius sp.); can they be used as biological tags?. Biol. Divers. 11, 1067–1086 (2020).
Google Scholar 
Van Waerebeek, K. & Reyes, J. C. A note on incidental fishery mortality of southern minke whales off western South America. Rep. Int. Whal. Commn 15, 521–523 (1994).
Google Scholar 
Félix, F. & Haase, B. A note on the northernmost record of the Antarctic minke whale (Balaenoptera bonaerensis) in the Eastern Pacific. J. Cetacean Res. Manag. 13, 191–194 (2013).
Google Scholar 
Esposito, C., Bichet, O. & Petit, M. First sightings of Antarctic minke whale (Balaenoptera bonaerensis) mother–calf pairs in French Polynesia. Aquat. Mamm. 47, 175–180 (2021).Article 

Google Scholar 
Karaa, S., Insacco, G., Bradai, M. N. & Scaravelli, D. Records of Xenobalanus globicipitis on Balaenoptera physalus and Stenella coeruleoalba in Tunisian and Sicilian waters. Nat. Rerum 1, 55–59 (2011).
Google Scholar 
Oliveira, J. B., Morales, J. A., González-Barrientos, R. C., Hernández-Gamboa, J. & Hernández-Mora, G. Parasites of cetaceans stranded on the Pacific Coast of Costa Rica. Vet. Parasitol. 182, 319–328. https://doi.org/10.1016/j.vetpar.2011.05.014 (2011).Article 
CAS 

Google Scholar 
Dı́az-Gamboa, R. E. Varamiento de orcas pigmeas (Feresa attenuata Gray 1874) en Yucatán: Reporte de caso. Bioagrociencias 8, 36–43 (2015).
Google Scholar 
IJsseldijk, L. L. et al. Beached bachelors: An extensive study on the largest recorded sperm whale Physeter macrocephalus mortality event in the north sea. PloS One 13, e0201221. https://doi.org/10.1371/journal.pone.0201221 (2018).Article 
CAS 

Google Scholar 
Guerrero-Ruiz, M. & Urbán, J. R. First report of remoras on two killer whales (Orcinus orca) in the Gulf of California Mexico. Aquat. Mamm. 26, 148–150 (2000).
Google Scholar 
Kautek, G., Van Bressem, M. F. & Ritter, F. External body conditions in cetaceans from La Gomera, Canary Islands Spain. J. Marine Anim. Ecol. 11, 4–17 (2008).
Google Scholar 
Bearzi, M. & Patonai, K. Occurrence of the barnacle (Xenobalanus globicipitis) on coastal and offshore common bottlenose dolphins (Tursiops truncatus) in Santa Monica Bay and adjacent areas California. Bull. S. Calif. Acad. Sci. 109, 37–44. https://doi.org/10.3160/0038-3872-109.2.37 (2010).Article 

Google Scholar 
Foote, A. D. et al. Genetic differentiation among North Atlantic killer whale populations. Mol. Ecol. 20, 629–641. https://doi.org/10.1111/j.1365-294X.2010.04957.x (2011).Article 

Google Scholar 
Toth, J. L., Hohn, A. A., Able, K. W. & Gorgone, A. M. Defining bottlenose dolphin (Tursiops truncatus) stocks based on environmental, physical and behavioral characteristics. Mar. Mamm. Sci. 28, 461–478. https://doi.org/10.1111/j.1748-7692.2011.00497.x (2012).Article 

Google Scholar 
Urian, K. W., Kaufmann, R., Waples, D. M. & Read, A. J. The prevalence of ectoparasitic barnacles discriminates stocks of Atlantic common bottlenose dolphins (Tursiops truncatus) at risk of entanglement in coastal gill net fisheries. Mar. Mamm. Sci. 35, 290–299. https://doi.org/10.1111/mms.12522 (2019).Article 

Google Scholar 
Siciliano, S. et al. Epizoic barnacle (Xenobalanus globicipitis) infestations in several cetacean species in South-Eastern Brazil. Mar. Biol. Res. 16, 1–13. https://doi.org/10.1080/17451000.2020.1783450 (2020).Article 

Google Scholar 
Whitehead, T. O., Rollinson, D. P. & Reisinger, R. R. Pseudostalked barnacles Xenobalanus globicipitis attached to killer whales Orcinus orca in South African waters. Mar. Biodivers. Rec. 45, 873–876. https://doi.org/10.1007/s12526-014-0296-2 (2014).Article 

Google Scholar 
Methion, S. & Dı́az López, B. First record of atypical pigmentation pattern in fin whale Balaenoptera physalus in the Atlantic ocean. Dis. Aquat. Org. 135, 121–125. https://doi.org/10.3354/dao03385 (2019).Article 

Google Scholar 
Herr, H., Burkhardt-Holm, P., Heyer, K., Siebert, U. & Selling, J. Injuries, malformations and epidermal conditions in cetaceans of the strait of Gibraltar. Aquat. Mamm. 46, 215–235. https://doi.org/10.1578/AM.46.2.2020.215 (2020).Article 

Google Scholar 
Herr, H. et al. Return of large fin whale feeding aggregations to historical whaling grounds in the southern ocean. Sci. Rep. 12, 9458. https://doi.org/10.1038/s41598-022-13798-7 (2022).Article 
ADS 
CAS 

Google Scholar 
Gruvel, J. A. Cirrhipèdes Provenant Des Campagnes Scientifiques De S.A.S. Le Prince De Monaco, (1885– 1913). In Résultas Des Campagnes Scientifiques Accomplies Sur Son Yacht Par Albert Ler (Monaco: Prince Souverain de Monaco) 1-88 (1920).Annandale, N. The rate of growth in Conchoderma and Lepas. Rec. Indian Mus. 3, 295 (1909).
Google Scholar 
Il’in, I. I., Kuznetsova, L. A. & Starostin, I. V. Oceanic fouling in the equatorial Atlantic. Oceanology 18, 597–599 (1978).
Google Scholar 
Eckert, K. L. & Eckert, S. A. Growth rate and reproductive condition of the barnacle Conchoderma virgatum on gravid leatherback sea turtles in Caribbean waters. J. Crust. Biol. 7, 682–690. https://doi.org/10.2307/1548651 (1987).Article 

Google Scholar 
Arroyo, N. L., Abaunza, P. & Preciado, I. The first naupliar stage of Pennella balaenopterae Koren and Danielssen 1877 (Copepoda: Siphonostomatoida, Pennellidae). Sarsia 87, 333–337. https://doi.org/10.1080/0036482021000155785 (2002).Article 

Google Scholar  More

Waples, R. S. & Gaggiotti, O. INVITED REVIEW: What is a population? An empirical evaluation of some genetic methods for identifying the number of gene pools and their degree of connectivity. Mol. Ecol. 15, 1419–1439 (2006).Article 
CAS 

Google Scholar 
Mendez, M., Rosenbaum, H. C., Subramaniam, A., Yackulic, C. & Bordino, P. Isolation by environmental distance in mobile marine species: Molecular ecology of franciscana dolphins at their southern range. Mol. Ecol. 19, 2212–2228 (2010).Article 
CAS 

Google Scholar 
De Meeûs, T. et al. Population genetics and molecular epidemiology or how to “débusquer la bête”. Infect. Genet. Evol. 7, 308–332 (2007).Article 

Google Scholar 
Durigan, M. et al. Population genetic analysis of Giardia duodenalis: Genetic diversity and haplotype sharing between clinical and environmental sources. MicrobiologyOpen 6, e00424 (2017).Article 

Google Scholar 
Amaral, A. R. et al. Seascape genetics of a globally distributed, highly mobile marine mammal: The short-beaked common dolphin (genus Delphinus). PLoS ONE 7, e31482 (2012).Article 
ADS 
CAS 

Google Scholar 
Mendez, M. et al. Molecular ecology meets remote sensing: Environmental drivers to population structure of humpback dolphins in the Western Indian Ocean. Heredity 107, 349–361 (2011).Article 
CAS 

Google Scholar 
de los Angeles Bayas-Rea, R., Félix, F. & Montufar, R. Genetic divergence and fine scale population structure of the common bottlenose dolphin (Tursiops truncatus, Montagu) found in the Gulf of Guayaquil. Ecuador. PeerJ 6, e4589 (2018).Article 

Google Scholar 
Natoli, A., Peddemors, V. M. & Rus Hoelzel, A. Population structure and speciation in the genus Tursiops based on microsatellite and mitochondrial DNA analyses. J. Evol. Biol. 17, 363–375 (2004).Article 
CAS 

Google Scholar 
Oliveira, L. R., Loizaga De Castro, R., Cárdenas-Alayza, S. & Bonatto, S. L. Conservation genetics of South American aquatic mammals: An overview of gene diversity, population structure, phylogeography, non-invasive methods and forensics. Mammal Rev. 42, 275–303 (2012).Article 

Google Scholar 
Vollmer, N. L. & Rosel, P. E. Fine-scale population structure of common bottlenose dolphins (Tursiops truncatus) in offshore and coastal waters of the US Gulf of Mexico. Mar. Biol. 164, 1–15 (2017).Article 

Google Scholar 
MacLeod, C. D. Global climate change, range changes and potential implications for the conservation of marine cetaceans: A review and synthesis. Endanger. Species Res. 7, 125–136 (2009).Article 

Google Scholar 
Hartl, D. L., Clark, A. G. & Clark, A. G. Principles of Population Genetics, Vol. 116 (Sinauer associates Sunderland, 1997).Thomas, C. D. et al. Extinction risk from climate change. Nature 427, 145 (2004).Article 
ADS 
CAS 

Google Scholar 
Reeves, R. R., Smith, B. D., Crespo, E. A. & Notarbartolo di Sciara, G. Dolphins, whales and porpoises: 2002–2010 conservation action plan for the world’s cetaceans, Vol. 58 (IUCN, 2003).Crespo, E. A. & Hall, M. A. In Marine Mammals, 463–490 (Springer, 2002).Crespo, E. A. et al. Direct and indirect effects of highseas fisheries on the marine mammal populations in the northern and central Patagonian coast. J. Northwest Atl. Fish. Sci. 22, 189–207 (1997).Article 

Google Scholar 
Harlin-Cognato, A. D., Markowitz, T., Würsig, B. & Honeycutt, R. L. Multi-locus phylogeography of the dusky dolphin (Lagenorhynchus obscurus): Passive dispersal via the west-wind drift or response to prey species and climate change?. BMC Evol. Biol. 7, 1–17 (2007).Article 

Google Scholar 
Hoelzel, A. Evolution of population genetic structure in marine mammal species. In Population genetics for animal conservation, 294–318 (Cambridge University Press, Cambridge, 2009).Fraser, C. I., Nikula, R., Ruzzante, D. E. & Waters, J. M. Poleward bound: Biological impacts of Southern Hemisphere glaciation. Trends Ecol. Evol. 27, 462–471 (2012).Article 

Google Scholar 
Louis, M. et al. Influence of past climate change on phylogeography and demographic history of narwhals, Monodon monoceros. Proc. R. Soc. B 287, 20192964 (2020).Article 
CAS 

Google Scholar 
Skovrind, M. et al. Circumpolar phylogeography and demographic history of beluga whales reflect past climatic fluctuations. Mol. Ecol. 30, 2543–2559 (2021).Article 

Google Scholar 
Foote, A. D. et al. Ancient DNA reveals that bowhead whale lineages survived Late Pleistocene climate change and habitat shifts. Nat. Commun. 4, 1–7 (2013).Article 

Google Scholar 
Crespo, E. A. et al. Status, population trend and genetic structure of South American fur seals, Arctocephalus australis, in southwestern Atlantic waters. Mar. Mamm. Sci. 31, 866–890 (2015).Article 

Google Scholar 
Feijoo, M., Lessa, E. P., De Castro, R. L. & Crespo, E. A. Mitochondrial and microsatellite assessment of population structure of South American sea lion (Otaria flavescens) in the Southwestern Atlantic Ocean. Mar. Biol. 158, 1857–1867 (2011).Article 

Google Scholar 
Túnez, J. I., Cappozzo, H. L., Nardelli, M. & Cassini, M. H. Population genetic structure and historical population dynamics of the South American sea lion, Otaria flavescens, in north-central Patagonia. Genetica 138, 831–841 (2010).Article 

Google Scholar 
Oliveira, L., Ott, P. H., Grazziotin, F. G., White, B. & Bonatto, S. In Paper (SC/S11/RW26) presented to the Southern Right Whale Assessment Workshop (Commission International Whaling).Loizaga de Castro, R., Dans, S. L. & Crespo, E. A. Spatial genetic structure of dusky dolphin, Lagenorhynchus obscurus, along the argentine coast: Preserve what scale?. Aquat. Conserv. Mar. Freshw. Ecosyst. 26, 173–183 (2016).Article 

Google Scholar 
Pimper, L. E., Goodall, R. N. P. & Remis, M. I. First mitochondrial DNA analysis of the spectacled porpoise (Phocoena dioptrica) from Tierra del Fuego, Argentina. Mamm. Biol. 77, 459–462 (2012).Article 

Google Scholar 
Pichler, F. B. et al. Origin and radiation of Southern Hemisphere coastal dolphins (genus Cephalorhynchus). Mol. Ecol. 10, 2215–2223 (2001).Article 
CAS 

Google Scholar 
Dawson, S. M. In Encyclopedia of Marine Mammals, 166–172 (Elsevier, 2018).Robineau, D., Goodall, R. N. P., Pichler, F. & Baker, C. S. Description of a new subspecies of Commerson’s dolphin, Cephalorhynchus commersonii (Lacépède, 1804), inhabiting the coastal waters of the Kerguelen Islands. Mammalia 71, 172–180 (2007).Article 

Google Scholar 
Crespo, E. A. et al. Cephalorhynchus commersonii, Commerson’s Dolphin. IUCN; The IUCN Red List of Threatened Species; 10-2017; 1-14 (2017).Goodall, R. Commerson’s dolphin Cephalorhynchus commersonii (Lacépède 1804). Handb. Mar. Mamm. 5, 241–267 (1994).
Google Scholar 
Coscarella, M. A. Ecologıa, comportamiento y evaluación del impacto de embarcaciones sobre manadas de tonina overa Cephalorhynchus commersonii en Bahıa Engano, Chubut (Universidad de Buenos Aires, Buenos Aires, 2005).Dellabianca, N. A. et al. Spatial models of abundance and habitat preferences of commerson’s and peale’s dolphin in southern patagonian waters. PLoS ONE 11, e0163441 (2016).Article 

Google Scholar 
Goodall, R. et al. Studies of Commerson’s dolphins, Cephalorhynchus commersonii, off Tierra del Fuego, 1976–1984. Report of the International Whaling Commission (Special Issue 9), 143–160 (1988).White, R. The Distribution of Seabirds and Marine Mammals in Falkland Islands Waters (Joint Nature Conservation Committee, 2002).Loizaga de Castro, R., Dans, S. L., Coscarella, M. A. & Crespo, E. A. Living in an estuary: Commerson’s dolphin (Cephalorhynchus commersonii (Lacépède, 1804)), habitat use and behavioural pattern at the Santa Cruz River, Patagonia, Argentina. Latin Am. J. Aquat. Res. 41, 985–991 (2013).Article 

Google Scholar 
Pedraza, S. Ecología poblacional de la tonina overa, Cephalorhynchus commersonii, (Lacépède, 1804) en el litoral patagónico. Unpublished PhD thesis, Universidad de Buenos Aires, Buenos Aires, Argentina (2008).Garaffo, G. V. et al. Modeling habitat use for dusky dolphin and Commerson’s dolphin in Patagonia. Mar. Ecol. Prog. Ser. 421, 217–227 (2011).Article 
ADS 

Google Scholar 
Cipriano, F., Hevia, M. & Iñíguez, M. Genetic divergence over small geographic scales and conservation implications for Commerson’s dolphins (Cephalorhynchus commersonii) in southern Argentina. Mar. Mamm. Sci. 27, 701–718 (2011).Article 
CAS 

Google Scholar 
Pimper, L. E., Baker, C. S., Goodall, R. N. P., Olavarría, C. & Remis, M. I. Mitochondrial DNA variation and population structure of Commerson’s dolphins (Cephalorhynchus commersonii) in their southernmost distribution. Conserv. Genet. 11, 2157–2168 (2010).Article 

Google Scholar 
O’Brien, S. J. A role for molecular genetics in biological conservation. Proc. Natl. Acad. Sci. 91, 5748–5755 (1994).Article 
ADS 
CAS 

Google Scholar 
Loizaga de Castro, R., Hoelzel, A. & Crespo, E. Behavioural responses of Argentine coastal dusky dolphins (Lagenorhynchus obscurus) to a biopsy pole system. Anim. Welf. 22, 13–23 (2013).Article 
CAS 

Google Scholar 
Elphinstone, M. S., Hinten, G. N., Anderson, M. J. & Nock, C. J. An inexpensive and high-throughput procedure to extract and purify total genomic DNA for population studies. Mol. Ecol. Notes 3, 317–320 (2003).Article 
CAS 

Google Scholar 
Bérubé, M. & Palsbøll, P. Identification of sex in cetaceans by multiplexing with three ZFX and ZFY specific primers. Mol. Ecol. 5, 283–287 (1996).Article 

Google Scholar 
Hoelzel, A., Hancock, J. & Dover, G. Evolution of the cetacean mitochondrial D-loop region. Mol. Biol. Evol. 8, 475–493 (1991).CAS 

Google Scholar 
Kumar, S., Stecher, G. & Tamura, K. MEGA7: Molecular evolutionary genetics analysis version 7.0 for bigger datasets. Mol. Biol. Evol. 33, 1870–1874 (2016).Article 
CAS 

Google Scholar 
Ruzzante, D. E. et al. Validation of close-kin mark–recapture (CKMR) methods for estimating population abundance. Methods Ecol. Evol. 10, 1445–1453 (2019).Article 

Google Scholar 
Faircloth, B. C., Branstetter, M. G., White, N. D. & Brady, S. G. Target enrichment of ultraconserved elements from arthropods provides a genomic perspective on relationships among H ymenoptera. Mol. Ecol. Resour. 15, 489–501 (2015).Article 
CAS 

Google Scholar 
Faircloth, B. C. MSATCOMMANDER: Detection of microsatellite repeat arrays and automated, locus-specific primer design. Mol. Ecol. Resour. 8, 92–94 (2008).Article 
CAS 

Google Scholar 
Zhan, L. et al. MEGASAT: Automated inference of microsatellite genotypes from sequence data. Mol. Ecol. Resour. 17, 247–256 (2017).Article 
CAS 

Google Scholar 
Nei, M. Molecular Evolutionary Genetics (Columbia University Press, 1987).Librado, P. & Rozas, J. DnaSP v5: A software for comprehensive analysis of DNA polymorphism data. Bioinformatics 25, 1451–1452 (2009).Article 
CAS 

Google Scholar 
Schneider, S., Roessli, D. & Excoffier, L. Arlequin: A software for population genetics data analysis, version 2.000. Genetics Biometry Laboratory, Department of Anthropology, University of Geneva, Switzerland (2000).Excoffier, L., Smouse, P. E. & Quattro, J. M. Analysis of molecular variance inferred from metric distances among DNA haplotypes: Application to human mitochondrial DNA restriction data. Genetics 131, 479–491 (1992).Article 
CAS 

Google Scholar 
Darriba, D., Taboada, G. L., Doallo, R. & Posada, D. jModelTest 2: More models, new heuristics and parallel computing. Nat. Methods 9, 772 (2012).Article 
CAS 

Google Scholar 
Excoffier, L. & Lischer, H. E. Arlequin suite ver 3.5: A new series of programs to perform population genetics analyses under Linux and Windows. Mol. Ecol. Resour 10, 564–567 (2010).Article 

Google Scholar 
Bandelt, H.-J., Forster, P. & Röhl, A. Median-joining networks for inferring intraspecific phylogenies. Mol. Biol. Evol. 16, 37–48 (1999).Article 
CAS 

Google Scholar 
Fu, Y.-X. Statistical tests of neutrality of mutations against population growth, hitchhiking and background selection. Genetics 147, 915–925 (1997).Article 
CAS 

Google Scholar 
Rogers, A. R. & Harpending, H. Population growth makes waves in the distribution of pairwise genetic differences. Mol. Biol. Evol. 9, 552–569 (1992).CAS 

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

Google Scholar 
Mantel, N. The detection of disease clustering and a generalized regression approach. Can. Res. 27, 209–220 (1967).CAS 

Google Scholar 
Drummond, A. J. & Rambaut, A. BEAST: Bayesian evolutionary analysis by sampling trees. BMC Evol. Biol. 7, 214 (2007).Article 

Google Scholar 
Harlin, A. D., Markowitz, T., Baker, C. S., Würsig, B. & Honeycutt, R. L. Genetic structure, diversity, and historical demography of New Zealand’s dusky dolphin (Lagenorhynchus obscurus). J. Mammal. 84, 702–717 (2003).Article 

Google Scholar 
Rambaut, A., Suchard, M., Xie, D. & Drummond, A. Tracer v1. 6. http://beast.bio.ed.ac.uk/Tracer (2014).Van Oosterhout, C., Hutchinson, W. F., Wills, D. P. & Shipley, P. MICRO-CHECKER: Software for identifying and correcting genotyping errors in microsatellite data. Mol. Ecol. Notes 4, 535–538 (2004).Article 

Google Scholar 
Rice, W. R. Analyzing tables of statistical tests. Evolution 43, 223–225 (1989).
Google Scholar 
Goudet, J. FSTAT, a program to estimate and test gene diversities and fixation indices, version 2.9. 3. http://www2.unil.ch/popgen/softwares/fstat.htm (2001).Waples, R. S. & Do, C. LDNE: A program for estimating effective population size from data on linkage disequilibrium. Mol. Ecol. Resour. 8, 753–756 (2008).Article 

Google Scholar 
Pritchard, J. K., Stephens, M. & Donnelly, P. Inference of population structure using multilocus genotype data. Genetics 155, 945–959 (2000).Article 
CAS 

Google Scholar 
Evanno, G., Regnaut, S. & Goudet, J. Detecting the number of clusters of individuals using the software STRUCTURE: A simulation study. Mol. Ecol. 14, 2611–2620 (2005).Article 
CAS 

Google Scholar 
Earl, D. A. STRUCTURE HARVESTER: A website and program for visualizing STRUCTURE output and implementing the Evanno method. Conserv. Genet. Resour. 4, 359–361 (2012).Article 

Google Scholar 
Queller, D. C. & Goodnight, K. F. Estimating relatedness using genetic markers. Evolution 43, 258–275 (1989).
Google Scholar 
Wilson, G. A. & Rannala, B. Bayesian inference of recent migration rates using multilocus genotypes. Genetics 163, 1177–1191 (2003).Article 

Google Scholar 
Milinkovitch, M. C., Leduc, R., Tiedemann, R. & Dizon, A. In Marine Mammals: Biology and Conservation (ed Evans, P. G. H. & Raga, J. A.) 325–359 (Springer, 2002).Pichler, F. Population structure and genetic variation in Hector’s dolphin (Cephalorhynchus hectori), ResearchSpace@ Auckland (2001).Pichler, F. & Baker, C. Loss of genetic diversity in the endemic Hector’s dolphin due to fisheries-related mortality. Proc. R. Soc. Lond. Ser. B Biol. Sci. 267, 97–102 (2000).Article 
CAS 

Google Scholar 
Greenwood, P. J. Mating systems, philopatry and dispersal in birds and mammals. Anim. Behav. 28, 1140–1162 (1980).Article 

Google Scholar 
Chilvers, B. L. & Wilkinson, I. S. Philopatry and site fidelity of New Zealand sea lions (Phocarctos hookeri). Wildl. Res. 35, 463–470 (2008).Article 

Google Scholar 
Engelhaupt, D. et al. Female philopatry in coastal basins and male dispersion across the North Atlantic in a highly mobile marine species, the sperm whale (Physeter macrocephalus). Mol. Ecol. 18, 4193–4205 (2009).Article 
CAS 

Google Scholar 
Möller, L. M. & Beheregaray, L. B. Genetic evidence for sex-biased dispersal in resident bottlenose dolphins (Tursiops aduncus). Mol. Ecol. 13, 1607–1612 (2004).Article 

Google Scholar 
Jansen van Vuuren, B., Best, P., Roux, J. P. & Robinson, T. Phylogeographic population structure in the Heaviside’s dolphin (Cephalorhynchus heavisidii): Conservation implications. Anim. Conserv. 5, 303–307 (2002).Article 

Google Scholar 
Pérez-Alvarez, M. J. et al. Microsatellite markers reveal strong genetic structure in the endemic Chilean dolphin. PLoS ONE 10, e0123956 (2015).Article 

Google Scholar 
Hamner, R. M., Pichler, F. B., Heimeier, D., Constantine, R. & Baker, C. S. Genetic differentiation and limited gene flow among fragmented populations of New Zealand endemic Hector’s and Maui’s dolphins. Conserv. Genet. 13, 987–1002 (2012).Article 

Google Scholar 
Pichler, F., Dawson, S., Slooten, E. & Baker, C. Geographic isolation of Hector’s dolphin populations described by mitochondrial DNA sequences. Conserv. Biol. 12, 676–682 (1998).Article 

Google Scholar 
Kraft, S. et al. From settlers to subspecies: Genetic differentiation in commerson’s Dolphins between South America and the Kerguelen Islands. Front. Mar. Sci. 8, 782512 (2021).Article 

Google Scholar 
Grant, W. & Bowen, B. W. Shallow population histories in deep evolutionary lineages of marine fishes: Insights from sardines and anchovies and lessons for conservation. J. Hered. 89, 415–426 (1998).Article 

Google Scholar 
Ponce, J. F., Rabassa, J., Coronato, A. & Borromei, A. M. Palaeogeographical evolution of the Atlantic coast of Pampa and Patagonia from the last glacial maximum to the Middle Holocene. Biol. J. Lin. Soc. 103, 363–379 (2011).Article 

Google Scholar 
Wright, S. Isolation by distance. Genetics 28, 114 (1943).Article 
CAS 

Google Scholar 
Meirmans, P. G. Nonconvergence in B ayesian estimation of migration rates. Mol. Ecol. Resour. 14, 726–733 (2014).Article 

Google Scholar  More

Correction to: Communications Biology https://doi.org/10.1038/s42003-020-1022-1, published online 11 June 2020.In the original version of the Perspective, a unit conversion error affected calculations for cereal rye, triticale, barley, and oats. Further, berseem clover yield estimates were mistranscribed from the original source. These mistakes led to errors in Supplementary Data 1, Figure 2 and in the presentation of the data in the text.Supplementary Data 1 has now been replaced with a file containing the correct numbers.Figure 2 has been corrected:Original figure 2New figure 2The Abstract stated: “In this Perspective, we estimate land use requirements to supply the United States maize production area with cover crop seed, finding that across 18 cover crops, on average 3.8% (median 2.0%) of current production area would be required, with the popular cover crops rye and hairy vetch requiring as much as 4.5% and 11.9%, respectively”.The text should read: “In this Perspective, we estimate land use requirements to supply the United States maize production area with cover crop seed, finding that across 18 cover crops, on average 2.4% (median 2.1%) of current production area would be required, with the popular cover crops rye and hairy vetch requiring as much as 4.8% and 11.9%, respectively”.In the 1st paragraph of the right hand column on page 2, the text said: “(…), we find that the land requirements for production of cover crop seed would be on average 1.4 million hectares (median 746,000 ha), which is equivalent to 3.8% (median 2.0%) of the U.S. maize farmland. Rye (Secale cereale L.) – a midrange seed yielding cover crop and one of the most commonly used in the corn belt, would require as much as 1,661,000 hectares (4.5% of maize farmland), (…)”The text should read: “(…) we find that the land requirements for production of cover crop seed would be on average 892,526 hectares (median 774,417 ha), which is equivalent to 2.4% (median 2.1%) of the U.S. maize farmland. Rye (Secale cereale L.) – a midrange seed yielding cover crop and one of the most commonly used in the corn belt, would require as much as 1,779,770 hectares (4.8% of maize farmland), (…)”On page 3, second paragraph the text said: “Cover cropping the entire U.S. maize area would require the equivalent of as much as 18% (rye) to 49% (hairy vetch) (…)”The text should read: “Cover cropping the entire U.S. maize area would require the equivalent of as much as 19% (rye) to 49% (hairy vetch) (…)”This errors have now been corrected in the Perspective Article. More

Aerial imagesWe use publicly available aerial images of Rwanda at 0.25 × 0.25 m2 resolution, collected in June–August of 2008 and 2009. The images were acquired from 3,000 m altitude above ground level, originally with a mean ground resolution of 0.22 × 0.22 m2 pixel size then resampled to 0.25 × 0.25 m2, using a Vexcel UltraCam-X aerial digital photography camera34. The images exhibit a red, green and blue band stored under 8 bit unsigned integer format. The aerial images cover 96% of the country and the remaining 4% was filled with satellite images from WorldView-2, Ikonos, Spot and QuickBird satellite sensors which are part of the publicly available dataset.Environmental dataWe use locally available climate data: mean annual rainfall, mean annual temperature and elevation data (10 × 10 m2 resolution) to assess relationships between tree density, crown cover and environmental gradients. We also use land cover data to extract the spatial extent of plantations, forest, farmland, and urban and built-up areas for our landscape stratification. Climate data were obtained from the Rwanda Meteorological Agency as daily records from 1971 to 2017. The national forest map was manually created in 2012 using on-screen digitizing techniques over the 2008 aerial images35. A forest was defined as ‘a group of trees higher than 7 m and a tree cover of more than 10% or trees able to reach these thresholds in situ on a land of about 0.25 ha or more’51. A shrub was defined as ‘a group of perennial trees smaller than 7 m at maturity and a canopy cover of more than 10% on a land of about 0.25 ha or more’. The forest dataset was composed of 105,690 forest polygons, classified as either natural forest (closed natural forest, degraded natural forest, bamboo stand, wooded savanna and shrubland) or ‘forest plantations’ (Eucalyptus spp., eucalyptus; Pinus spp., pine; Callitris spp., callitris; Cupressus spp., cypress; Acacia mearnsii, black wattle; Acacia melanoxylon, melanoxylon; Grevillea robusta, grevillea; Maesopsis eminii, maesopsis; Alnus acuminata, alnus; Jacaranda mimosifolia, jacaranda; mixed species, mixed; and others) (Extended Data Fig. 7i). We separate shrubland from natural forest and merged it with savanna into the class ‘savannas and shrublands’. We further separated tree plantations and grouped them into Eucalyptus and non-Eucalyptus plantations. Then, a farmland map was acquired from the Rwanda Land Management and Use Authority (RLMUA)52 and overlaid with the 2012 forest cover map as a reference to clean the overlapping parts, under an assumption that the overlap is due to land use dynamics. Finally, a layer marking urban and built-up areas was acquired from RLMUA as well and the same preprocessing step as done for farmlands was applied. The combination of the land cover datasets resulted in our stratification scheme with six classes: natural forests, savannas and shrublands, Eucalyptus plantations, non-Eucalyptus plantations, farmland and urban and built-up.Mapping of individual trees using deep learningWe used the open-source framework developed by ref. 17 to map individual tree crowns. The framework uses a deep neural network based on the U-Net architecture53,54. We trained the network using 97,574 manually delineated tree crowns spread over 103 areas/bounding boxes representing the full range of biogeographical conditions found across Rwanda. To cope with the challenge of separating touching tree crowns, we used a higher weight for boundary areas between crowns, as suggested in refs. 17,53. Crown sizes in the predictions were found to be 27% smaller as compared to the manual delineations within the 103 training areas, due to the applied boundary weight that emphasizes gaps between tree crowns. Therefore, to calculate the real canopy cover, we extended each predicted tree crown by 27% and dissolved the touching crowns into continuous features. We counted single tree crowns for each hectare presented here as tree density and the percentage of each hectare covered by the extended tree crowns as canopy cover.We developed a postprocessing method that separates clumped tree crowns and fills any gap inside a single crown (Extended Data Fig. 2). Our postprocessing method, which we refer to as detect centre and relabel (DCR), determines the crown centres in the model predictions assuming that tree crowns have a round shape and then relabels the model predictions on the basis of weighted distances to the identified crown centres. First, DCR performs a distance transform, computing for each pixel the Euclidean distance to the nearest pixel predicted as background. Let the transformed image be distance-transformed (DT). Then an m × m maximum filter is applied to DT, where m depends on the size of the smallest object to be separated. We store all pixels for which the original DT value is the same before and after max-filtering. These pixels are the instance centres as they are furthest away from the boundary and have the highest distance values within the area defined by m. In the case of several connected instance centres in regions where multiple connected pixels have the same distance from the background, only a single instance centre is kept. Finally, each pixel x predicted as a crown in the original image is assigned to its nearest instance centre, where the distance function penalizes background pixels on the connecting line between the instance centre and x.Allometry for biomass and carbon stock estimationGenerally, allometric equations define a statistical relationship between structural properties of a tree and its biomass55,56. In our case, we assume a relationship between the crown area and aboveground biomass (AGB), which varies between biomes36. Since destructive AGB measurements are rare, we established biome-specific relationships between crown diameter (CD) derived from the crown area (CD = 2√(crown area/π)) and stem diameter at breast height (DBH) (equations (3) and (6)). DBH has been shown to be highly correlated with AGB36,37,38,39,40. We then used established relationships from literature to derive AGB from DBH for savannas and shrublands (equation (4)), tree plantations (equation (5)) and natural forests (equation (7)). AGB was predicted for each tree and summed for 1 ha grids to derive AGB in the unit Mg per ha. Values were multiplied by 0.47 (refs. 57,58) to derive aboveground carbon (AGC). Summed numbers over land cover classes are considered as carbon stocks. The bias as reported here was calculated following the approach from ref. 36 reporting the relative systematic error in per cent:$$mathrm {bias} = frac{1}{N}mathop {sum}limits_{i = 1}^N {frac{{(Y_{mathrm {obs}} – Y_{mathrm {pred}})}}{{Y_{mathrm {obs}}}}}times 100$$
(1)
The error for the evaluation with NFI data was defined by:$$mathrm{bias} = frac{{left| {mathop {sum}nolimits_N {(Y_{mathrm{obs}} – Y_{mathrm{pred}})} } right|}}{{left| {mathop {sum}nolimits_N {Y_{mathrm{obs}}} } right|}}$$
(2)
For trees outside natural forests, we used the database from ref. 36 including 10,591 field-measured trees from woodlands and savanna plus 952 samples from agroforestry landscapes in Kenya37 to establish a linear relationship between CD and DBH (Extended Data Fig. 3a). The Kenyan dataset is compatible with the trees in Rwanda. To ensure compatibility, the Kenya data contained open-grown trees most of which are of the same families or genus as in Rwanda grown under the same conditions, the latter factor shown to be important for generalizing37.A major axis regression (average of four runs each 50% of the data) led to equation (3):$${{{mathrm{DBH}}}}_{{{{mathrm{predicted}}}}},{{{mathrm{in}}}},{{{mathrm{cm}}}} = – 4.665 + 5.102 times {{{mathrm{CD}}}}$$
(3)
Equation (3) showed a reasonable performance with a very low bias (average of four runs on the 50% not used to establish the equation (3)): r² = 0.71; slope = 0.95; root mean square error (RMSE) = 6.2 cm; relative RMSE (rRMSE) = 42%; bias = 1%). We tested equation (3) on an independent dataset from Kenya consisting of 93 trees where AGB was destructively measured (Fig. 3b). The Kenyan database provides an uncommon opportunity to use destructive samples in which the carbon mass is not estimated indirectly and the relationship between crown area and carbon is direct: we do not need to invoke a second allometry to derive the dependent variable. All trees were open-grown trees in the same growing conditions as the agricultural areas of Rwanda. On these 93 trees, DBH can be predicted reasonably well from CD using equation (3) (r² = 0.84; slope = 0.86; RMSE = 8 cm; rRMSE = 25%; bias = 6%). We then applied an allometric equation from literature37 established for non-forest trees in East Africa to estimate AGB from DBHpredicted and compared the predicted AGB with the destructively measured AGB (r² = 0.81; RMSE = 511 kg; rRMSE = 55%; bias = 25%) showing an acceptable performance (Extended Data Fig. 3c) but indicating a systematic bias, which will be further tested with biome-specific field data (next section). We apply equation (4) to estimate AGB for trees outside forests in Rwanda in savannas and shrublands:$${{{mathrm{AGB}}}}_{{{{mathrm{predicted}}}}},{{{mathrm{in}}}},{{{mathrm{kg}}}} = 0.091 times {{mathrm{DBH}}_{{mathrm{predicted}}}}^{2.472}$$
(4)
Given the different structure of trees in farmlands, urban and built-up areas and plantations as compared to trees in natural forests and in natural non-forest areas, we used a different equation for trees in these areas. It was established in Rwanda using destructive samples from tree plantations39:$${{{mathrm{AGB}}}}_{{{{mathrm{predicted}}}}},{{{mathrm{in}}}},{{{mathrm{kg}}}} = 0.202 times {{mathrm{DBH}}_{{mathrm{predicted}}}}^{2.447}$$
(5)
A different CD–DBH relationship was established for natural forests. Here, we conducted a field campaign in December 2021 sampling 793 overstory trees in Rwanda’s protected natural forest. We measured both CD and DBH and established a logarithmic major axis regression model with a Baskerville correction59 between the two variables to predict DBH from CD (Extended Data Fig. 3d). We did four runs each using 50% of the data to establish equation (6) (average of the four runs) and the other 50% to test the performance also averaged over the four runs (r² = 0.71; slope = 0.99; RMSE = 13 cm; rRMSE = 45%; bias = 19%). Note that CD is extended by 27% to account for underestimations of touching crowns in dense forests (see previous section):$$begin{array}{l}{mathrm{DBH}}_{{mathrm{predicted}}},{mathrm{in}},{mathrm{cm}} = left({mathrm{exp}}left(1.154 + 1.248 times {mathrm{ln}}({mathrm{CD}} times 1.27) right)right.\left. times left({mathrm{exp}}(0.3315^2/2) right) right)end{array}$$
(6)
We then used a state-of-the-art allometric equation established for tropical forests38 to predict AGB from DBH for natural forests in Rwanda:$$begin{array}{l}{{{mathrm{AGB}}}}_{{{{mathrm{predicted}}}}},{{{mathrm{in}}}},{{{mathrm{kg}}}} = {{{mathrm{exp}}}}Big[ {1.803 – 0.976{{{E}}} + 0.976,{{{mathrm{ln}}}}left( rho right)}\+ 2.673;{{{mathrm{ln}}}}left( {{{{mathrm{DBH}}}}} right) – 0.0299left[ {{{{mathrm{ln}}}}left( {{{mathrm{DBH}}}} right)} right]^2 Big]end{array}$$
(7)
where E measures the environmental stress38 (a gridded layer is accessible via https://chave.ups-tlse.fr/pantropical_allometry.htm) and ρ is the wood density. Here, we used a fixed number (0.54), which is the average wood density for 6,161 trees from ref. 40, weighted according to the abundance of the species in the plots. The relative error was calculated by the quadratic mean of the intraplot and interplot variations, which is 18.2% (Extended Data Table 1b). No destructive AGB measurements were found that showed a similar CD–DBH relationship as we measured during the field trip in Rwanda’s forest. We could thus not evaluate the performance for natural forests at tree level but had to rely on plot-level comparisons (next section).Evaluation and uncertainties of the allometryBiomass estimations without direct measurements of height or DBH inevitably include a relatively high level of uncertainty at tree level38,60. Uncertainty does not only originate from the CD to DBH conversion but also the equation converting DBH to AGB. As shown in the previous section, no strong systematic bias could be detected for the CD to DBH conversion but the evaluation of the CD-based AGB prediction with an independent dataset from destructively measured AGB revealed a bias of 25%. However, this comparison (Extended Data Fig. 3c) may not be representative for an entire country having a variety of landscapes and tree species, so a systematic propagation is unlikely. We also did not have sufficient field data to evaluate the conversions in natural forests. Here, we used data from 15 natural forest plots with 6,161 trees published by ref. 40 and ref. 41 and directly compared the summed biomass of the trees we predicted over their plots. The median measured biomass for the plots is 121 MgC ha−1 and we predict a median biomass of 81 MgC ha−1 (plot-based rRMSE = 54%; bias = 11%; bias on summed plots = 26%). The overall underestimation by our prediction is not necessarily a model bias but may be partly explained by the contribution of the understory trees, which cannot be captured by aerial images. Interestingly, our C stock estimates are in the same range of magnitude as global biomass products43,44,45,61 (Extended Data Fig. 4), indicating that overstory tree-level carbon stock assessments are possible from optical very high resolution images, even in tropical forests. Several global products overestimated biomass for non-forest areas like savannas or croplands, which is probably because they are calibrated in denser forests. The most recent products of ref. 42 and ref. 61 are much closer to the estimates from our results and the NFI. This is also seen in the grid-based correlation matrix where ref. 42 correlates best with our map, followed by ref. 61.We further use NFI data from 2014 to measure the uncertainty of the final carbon stock estimates and evaluate if systematic differences between AGB predictions and field assessments can be found for different land cover classes (Extended Data Table 1). For the NFI data, a total of 373 plots with 2,415 trees were measured and species-specific allometric equations applied62. To identify systematic errors at landscape scale, we extracted averaged values for areas around the plots from our predictions and calculated statistics on averages over all plots. Interestingly, our predictions for farmlands only show a bias of 5.9%: we estimate on average 2.46 MgC ha−1 and the inventories measure 2.37 MgC ha−1 on their 150 plots. For savanna and shrublands, we estimate 4.16 MgC ha−1 while inventories measure 3.31 MgC ha−1 (bias = 18.9%). For plantations, we estimate lower values (8.16 compared to 16.79 MgC ha−1; bias = 52.6%). To calculate the total uncertainty on country-wide C stock estimates, we weighted the bias from the different classes according to their relative area. We estimate a total uncertainty on the carbon stock predictions of 16.9% at the national scale (Extended Data Table 1).We found a very low bias for estimated C density in farmlands (5.9% bias) which make up most of the areas outside natural forests in Rwanda (Extended Data Table 1, Extended Data Fig. 6). The high bias for plantations can be explained by three factors: large bare areas considered part of plantations by the manual delineation of plantation areas (Extended Data Fig. 1); regular harvesting and continual thinning which keep many plantation trees young and small; and the fact that our aerial images are from 2008 while plantation trees have grown until 2014 with a few new NFI plots initiated after 2008. The bias in savannas and shrublands can be explained by the following factors: the presence of multistemed trees with large crowns such as Acacia spp. and Ficus spp. among others; the fact that a crown-based method overestimates C stocks of shrubs with a small height; and presence of shrub trees with both small height and small (multiple) stems. If tree-level based carbon stock assessments derived from crown diameter as presented here should become standard to complement national inventories, a database with sufficient samples to evaluate for systematic errors needs to be established for each biome and inventory and satellite/aerial image-based methods need to be further harmonized.To further quantify the error propagation of the CD to DBH conversion for our application, we established four equations each randomly using 50% of the dataset and predicted the carbon stock for each tree in Rwanda with each equation. We did this separately for natural forests and trees outside natural forests. We calculated the rRMSE between the aggregated carbon stocks for each hectare. We averaged the rRMSE for each land cover class and show that the uncertainty for all classes does not exceed 5% (Extended Data Table 2a).Evaluation and uncertainties of tree crown mappingWe created an independent test dataset, which was never seen during training and was also not used to optimize hyperparameters. The test set consists of 6,591 manually labelled trees located in 15 random 1 ha plots (Extended Data Fig. 5). Thanks to the size of the country, the plots represent all rainfall zones and three major landscapes of the country. The plot-level comparison yielded very high correlations between the predictions and the labels and is shown in Extended Data Fig. 5. We also calculated a confusion matrix showing an overall per pixel accuracy of 96.2%, a true positive rate of 79.6% and a false positive rate of 6.8% (Extended Data Table 2b). Trees outside natural forests are easy to spot and count for the human eye, so we have confidence in the plot-based evaluation. However, it is often challenging in natural forests. Here, we used again the field measurements from 15 plots with 6,161 trees40,41. We find that we underestimate the total tree count by 22.6%, which may, at least partly, be explained by understory trees hidden by overstory trees and which are, therefore, not visible in our images. New field campaigns are needed to better understand and calibrate our results and possibly correct for systematic bias.Application and evaluation beyond RwandaWe acquired 83 Skysat scenes at 80 cm for Tanzania, Burundi, Uganda, Rwanda and Kenya. The model trained on the 25 cm resolution aerial images of Rwanda from 2008 was directly applied on the Skysat images. Forest and non-forest areas were manually delineated to decide which allometric equation to use for the carbon stock conversion. We randomly selected 150 1 × 1 km2 patches and aggregated the predicted carbon density per patch and compared the results with previously published maps42,43,44,45. Results show that the model can directly be applied to comparable landscapes on different datasets. Note, however, that accurate carbon stock predictions need local adjustments with field data. We then tested the tree crown model transferability on aerial images from California (NAIP; 60 cm) and France (20 cm) and found that the model delivers realistic results without any local training or calibration (Extended Data Figure 8).Reporting summaryFurther information on research design is available in the Nature Portfolio Reporting Summary linked to this article. More

FAO. Global forest resources assessment. www.fao.org/publications (2015).Finlayson, M. et al. A Report of the Millennium Ecosystem Assessment. (The Cropper Foundation, 2005).Lal, R., & Lorenz, K. In Recarbonization of the Biosphere: Ecosystems and the Global Carbon Cycle (eds Lal, R., Lorenz, K., Hüttl, R. F., Schneider, B. U. & von Braun, J.) Ch. 9 (Springer, 2012).Gilliam, F. S. Forest ecosystems of temperate climatic regions: from ancient use to climate change. N. Phytologist 212, 871–887 (2016).Article 

Google Scholar 
de Gouvenain, R. C. & Silander, J. A. Temperate forests in Reference Module in Life Sciences (Elsevier, 2017).Keith, S. A., Newton, A. C., Morecroft, M. D., Bealey, C. E. & Bullock, J. M. Taxonomic homogenization of woodland plant communities over 70 years. Proc. R. Soc. B: Biol. Sci. 276, 3539–3544 (2009).Article 

Google Scholar 
Rackham, O. Ancient woodlands: modern threats. N. Phytologist 180, 571–586 (2008).Article 

Google Scholar 
Bernhardt-Römermann, M. et al. Drivers of temporal changes in temperate forest plant diversity vary across spatial scales. Glob. Chang. Biol. 21, 3726–3737 (2015).Article 
ADS 

Google Scholar 
Waller, D. M. & Alverson, W. S. The white-tailed deer: a keystone herbivore. Wildl. Soc. Bull. 25, 217–226 (1997).
Google Scholar 
Ramirez, J. I. Uncovering the different scales in deer–forest interactions. Ecol. Evol. 11, 5017–5024 (2021).Article 

Google Scholar 
Rooney, T. P., Wiegmann, S. M., Rogers, D. A. & Waller, D. M. Biotic impoverishment and homogenization in unfragmented forest understory communities. Conserv. Biol. 18, 787–798 (2004).Stockton, S. A., Allombert, S., Gaston, A. J. & Martin, J. L. A natural experiment on the effects of high deer densities on the native flora of coastal temperate rain forests. Biol. Conserv 126, 118–128 (2005).Article 

Google Scholar 
Hegland, S. J., Lilleeng, M. S. & Moe, S. R. Old-growth forest floor richness increases with red deer herbivory intensity. Ecol. Manag. 310, 267–274 (2013).Article 

Google Scholar 
Simončič, T., Bončina, A., Jarni, K. & Klopčič, M. Assessment of the long-term impact of deer on understory vegetation in mixed temperate forests. J. Veg. Sci. 30, 108–120 (2019).Article 

Google Scholar 
Vild, O. et al. The paradox of long-term ungulate impact: increase of plant species richness in a temperate forest. Appl. Veg. Sci. 20, 282–292 (2017).Article 

Google Scholar 
Russell, F. L., Zippin, D. B. & Fowler, N. L. Effects of white-tailed deer (Odocoileus virginianus) on plants, plant populations and communities: a review. Am. Midl. Nat. 146, 1–26 (2001).Article 

Google Scholar 
Öllerer, K. et al. Beyond the obvious impact of domestic livestock grazing on temperate forest vegetation–A global review. Biol. Conserv. 237, 209–219 (2019).Article 

Google Scholar 
Borer, E. T. et al. Nutrients cause grassland biomass to outpace herbivory. Nat. Commun. 11, 1–8 (2020).Article 
ADS 

Google Scholar 
Kaarlejärvi, E., Eskelinen, A. & Olofsson, J. Herbivores rescue diversity in warming tundra by modulating trait-dependent species losses and gains. Nat. Commun. 8, 1–8 (2017).
Google Scholar 
Simkin, S. M. et al. Conditional vulnerability of plant diversity to atmospheric nitrogen deposition across the United States. Proc. Natl Acad. Sci. USA 113, 4086–4091 (2016).Article 
ADS 
CAS 

Google Scholar 
Bobbink, R. et al. Global assessment of nitrogen deposition effects on terrestrial plant diversity: A synthesis. Ecol. Appl. 20, 30–59 (2010).Article 
CAS 

Google Scholar 
Reinecke, J., Klemm, G. & Heinken, T. Vegetation change and homogenization of species composition in temperate nutrient deficient Scots pine forests after 45 yr. J. Veg. Sci. 25, 113–121 (2014).Article 

Google Scholar 
Speed, J. D. M., Austrheim, G., Kolstad, A. L. & Solberg, E. J. Long-term changes in northern large-herbivore communities reveal differential rewilding rates in space and time. PLoS ONE 14, e0217166 (2019).Article 
CAS 

Google Scholar 
Valente, A. M., Acevedo, P., Figueiredo, A. M., Fonseca, C. & Torres, R. T. Overabundant wild ungulate populations in Europe: management with consideration of socio-ecological consequences. Mamm. Rev. 50, 353–366 (2020).Article 

Google Scholar 
Linnell, J. D. C. et al. The challenges and opportunities of coexisting with wild ungulates in the human-dominated landscapes of Europe’s Anthropocene. Biol. Conserv. 244, 108500 (2020).Waller, D. M. The Herbaceous Layer in Forests of Eastern North America (ed. Gilliam, F.) Ch. 16 (Oxford Univ. Press, 2014).Kerley, G. I. H., Kowalczyk, R. & Cromsigt, J. P. G. M. Conservation implications of the refugee species concept and the European bison: king of the forest or refugee in a marginal habitat? Ecography 35, 519–529 (2011).Svenning, J. C. A review of natural vegetation openness in north-western Europe. Biol. Conserv 104, 133–148 (2002).Article 

Google Scholar 
Sandom, C. J., Ejrnaes, R., Hansen, M. D. D. & Svenning, J. C. High herbivore density associated with vegetation diversity in interglacial ecosystems. Proc. Natl Acad. Sci. USA 111, 4162–4167 (2014).Article 
ADS 
CAS 

Google Scholar 
Ramirez, J. I., Jansen, P. A., den Ouden, J., Goudzwaard, L. & Poorter, L. Long-term effects of wild ungulates on the structure, composition and succession of temperate forests. Ecol. Manag. 432, 478–488 (2019).Article 

Google Scholar 
Ramirez, J. I., Jansen, P. A. & Poorter, L. Effects of wild ungulates on the regeneration, structure and functioning of temperate forests: A semi-quantitative review. Ecol. Manag. 424, 406–419 (2018).Article 

Google Scholar 
Albert, A. et al. Seed dispersal by ungulates as an ecological filter: a trait-based meta-analysis. Oikos 124, 1109–1120 (2015).Article 

Google Scholar 
McNaughton, S. J. Grazing lawns: on domesticated and wild grazers. Am. Nat. 128, 937–939 (1986).Article 

Google Scholar 
Cromsigt, J. P. G. M. & Kuijper, D. P. J. Revisiting the browsing lawn concept: evolutionary Interactions or pruning herbivores? Perspect. Plant Ecol. 13, 207–215 (2011).Article 

Google Scholar 
Ramirez, J. I. et al. Temperate forests respond in a non-linear way to a population gradient of wild deer. Forestry 94, 502–511 (2021).Article 

Google Scholar 
Boulanger, V. et al. Ungulates increase forest plant species richness to the benefit of non‐forest specialists. Glob. Chang. Biol. 24, e485–e495 (2018).Article 

Google Scholar 
Kirby, K. J. The impact of deer on the ground flora of British broadleaved woodland. Forestry 74, 219–229 (2001).Article 

Google Scholar 
Royo, A. A., Collins, R., Adams, M. B., Kirschbaum, C. & Carson, W. P. Pervasive interactions between ungulate browsers and disturbance regimes promote temperate forest herbaceous diversity. Ecology 91, 93–105 (2010).Happonen, K. et al. Trait-based responses to land use and canopy dynamics modify long-term diversity changes in forest understories. Glob. Ecol. Biogeogr. 30, 1863–1875 (2021).Article 

Google Scholar 
Peñuelas, J. & Sardans, J. The global nitrogen-phosphorus imbalance. Science 375, 266–267 (2022).Article 
ADS 

Google Scholar 
Staude, I. R. et al. Replacements of small- by large-ranged species scale up to diversity loss in Europe’s temperate forest biome. Nat. Ecol. Evol. 4, 802–808 (2020).Article 

Google Scholar 
Newbold, T. et al. Widespread winners and narrow-ranged losers: Land use homogenizes biodiversity in local assemblages worldwide. PLoS Biol. 16, e2006841 (2018).Article 

Google Scholar 
Verheyen, K. et al. Driving factors behind the eutrophication signal in understorey plant communities of deciduous temperate forests. Br. Ecol. Soc. J. Ecol. 100, 352–365 (2012).
Google Scholar 
Gilliam, F. S. Response of the herbaceous layer of forest ecosystems to excess nitrogen deposition. J. Ecol. 94, 1176–1191 (2006).Article 
CAS 

Google Scholar 
de Schrijver, A. et al. Cumulative nitrogen input drives species loss in terrestrial ecosystems. Glob. Ecol. Biogeogr. 652, 803–816 (2011).Article 

Google Scholar 
de Frenne, P. et al. Light accelerates plant responses to warming. Nat. Plants 1, 15110 (2015).Article 

Google Scholar 
Baeten, L. et al. Herb layer changes (1954-2000) related to the conversion of coppice-with-standards forest and soil acidification. Appl. Veg. Sci. 12, 187–197 (2009).Article 

Google Scholar 
Becker, T., Spanka, J., Schröder, L. & Leuschner, C. Forty years of vegetation change in former coppice-with-standards woodlands as a result of management change and N deposition. Appl. Veg. Sci. 20, 304–313 (2017).Article 

Google Scholar 
van Calster, H. et al. Diverging effects of overstorey conversion scenarios on the understorey vegetation in a former coppice-with-standards forest. Ecol. Manag. 256, 519–528 (2008).Article 

Google Scholar 
Luyssaert, S. et al. The European carbon balance. Part 3: forests. Glob. Chang. Biol. 16, 1429–1450 (2010).Article 
ADS 

Google Scholar 
Kirby, K. J. et al. Five decades of ground flora changes in a temperate forest: the good, the bad and the ambiguous in biodiversity terms. Ecol. Manag. 505, 119896 (2022).Article 

Google Scholar 
Hautier, Y., Niklaus, P. A. & Hector, A. Competition for light causes plant biodiversity loss after eutrophication. Science 324, 636–638 (2009).Article 
ADS 
CAS 

Google Scholar 
Kowalczyk, R., Kamiński, T. & Borowik, T. Do large herbivores maintain open habitats in temperate forests? For. Ecol. Manag. 494, 119310 (2021).Dormann, C. F. et al. Plant species richness increases with light availability, but not variability, in temperate forests understorey. BMC Ecol. 20, 1–9 (2020).Article 

Google Scholar 
Dirnböck, T. et al. Forest floor vegetation response to nitrogen deposition in Europe. Glob. Chang. Biol. 20, 429–440 (2014).Article 
ADS 

Google Scholar 
Perring, M. P. et al. Understanding context dependency in the response of forest understorey plant communities to nitrogen deposition. Environ. Pollut. 242, 1787–1799 (2018).Article 
CAS 

Google Scholar 
Anderson, T. M. et al. Herbivory and eutrophication mediate grassland plant nutrient responses across a global climatic gradient. Ecology 99, 822–831 (2018).Article 

Google Scholar 
Gough, L. & Grace, J. B. Herbivore effects on plant species density at varying productivity levels. Ecology 79, 1586–1594 (1998).Article 

Google Scholar 
Eskelinen, A., Harpole, W. S., Jessen, M.-T., Virtanen, R. & Hautier, Y. Light competition drives herbivore and nutrient effects on plant diversity. Nature 611, 301–305 (2022).Knight, T. M., Dunn, J. L., Smith, L. A., Davis, J. A. & Kalisz, S. Deer facilitate invasive plant success in a Pennsylvania forest understory. Nat. Areas 29, 110–116 (2009).Article 

Google Scholar 
Beguin, J., Pothier, D. & Côté, S. D. Deer browsing and soil disturbance induce cascading effects on plant communities: a multilevel path analysis. Ecol. Appl. 21, 439–451 (2011).Gilliam, F. S. et al. Twenty-five-year response of the herbaceous layer of a temperate hardwood forest to elevated nitrogen deposition. Ecosphere 7, e01250 (2016).Article 

Google Scholar 
de Frenne, P. et al. Microclimate moderates plant responses to macroclimate warming. Proc. Natl Acad. Sci. USA 110, 18561–18565 (2013).Article 
ADS 

Google Scholar 
Hedwall, P. O. et al. Half a century of multiple anthropogenic stressors has altered northern forest understory plant communities. Ecol. Appl. 29, e01874 (2019).Perring, M. P. et al. Global environmental change effects on plant community composition trajectories depend upon management legacies. Glob. Chang. Biol. 24, 1722–1740 (2018).Article 
ADS 

Google Scholar 
Boulanger, V. et al. Decreasing deer browsing pressure influenced understory vegetation dynamics over 30 years. Ann. Sci. 72, 367–378 (2015).Article 

Google Scholar 
Bernes, C. et al. Manipulating ungulate herbivory in temperate and boreal forests: effects on vegetation and invertebrates. A systematic review. Environ. Evid. 7, 1–32 (2018).Article 

Google Scholar 
Reimoser, F. Steering the impacts of ungulates on temperate forests. J. Nat. Conserv. 10, 243–252 (2003).Article 

Google Scholar 
Vavra, M., Parks, C. G. & Wisdom, M. J. Biodiversity, exotic plant species, and herbivory: the good, the bad, and the ungulate. Ecol. Manag. 246, 66–72 (2007).Article 

Google Scholar 
Depauw, L. et al. Light availability and land-use history drive biodiversity and functional changes in forest herb layer communities. J. Ecol. 108, 1411–1425 (2020).Article 
CAS 

Google Scholar 
Chevaux, L. et al. Effects of stand structure and ungulates on understory vegetation in managed and unmanaged forests. Ecol. Appl. 32, e01874 (2022).Gordon, I. J. Browsing and grazing ruminants: are they different beasts? Ecol. Manag. 181, 13–21 (2003).Article 

Google Scholar 
Brasseur, B. et al. What deep‐soil profiles can teach us on deep‐time pH dynamics after land use change? Land Degrad. Dev. 29, 2951–2961 (2018).Article 

Google Scholar 
Schmitz, A. et al. Responses of forest ecosystems in Europe to decreasing nitrogen deposition. Environ. Pollut. 244, 980–994 (2019).Article 
CAS 

Google Scholar 
Dirnböck, T. et al. Currently legislated decreases in nitrogen deposition will yield only limited plant species recovery in European forests. Environ. Res. Lett. 13, 125010 (2018).Article 

Google Scholar 
Peterken, G. F. Natural Woodland: Ecology and Conservation in Northern Temperate Regions (Cambridge Univ. Press, 1996).Chamberlain, S. A. & Boettiger, C. R Python, and Ruby clients for GBIF species occurrence data. preprint. PeerJ Preprints 5, e3304v1 (2017).Chamberlain, S. A. & Szöcs, E. taxize: taxonomic search and retrieval in R. F1000Res 2, 191 (2013).Article 

Google Scholar 
Hédl, R., Kopecký, M. & Komárek, J. Half a century of succession in a temperate oakwood: from species-rich community to mesic forest. Divers Distrib. 16, 267–276 (2010).Article 

Google Scholar 
Giménez-Anaya, A., Herrero, J., Rosell, C., Couto, S. & García-Serrano, A. Food habits of wild boars (Sus scrofa) in a Mediterranean coastal wetland. Wetlands 28, 197–203 (2008).Article 

Google Scholar 
Barrios-Garcia, M. N. & Ballari, S. A. Impact of wild boar (Sus scrofa) in its introduced and native range: a review. Biol. Invasions 14, 2283–2300 (2012).Article 

Google Scholar 
Andersen, R. et al. An overview of the progress and challenges of peatland restoration in Western Europe. Restor. Ecol. 25, 271–282 (2017).Article 

Google Scholar 
Faurby, S. et al. PHYLACINE 1.2: the phylogenetic atlas of mammal macroecology. Ecology 99, 2626 (2018).Article 

Google Scholar 
van den Berg, L. J. L. et al. Evidence for differential effects of reduced and oxidised nitrogen deposition on vegetation independent of nitrogen load. Environ. Pollut. 208, 890–897 (2016).Article 

Google Scholar 
McNaughton, S. J., Oesterheld, M., Frank, D. A. & Williams, K. J. Ecosystem-level patterns of primary productivity and herbivory in terrestrial habitats. Nature 341, 142–144 (1989).Article 
ADS 
CAS 

Google Scholar 
Koerner, S. E. et al. Change in dominance determines herbivore effects on plant biodiversity. Nat. Ecol. Evol. 2, 1925–1932 (2018).Article 

Google Scholar 
Fréjaville, T. & Garzón, M. B. The EuMedClim database: yearly climate data (1901-2014) of 1 km resolution grids for Europe and the Mediterranean Basin. Front. Ecol. Evol. 6, 1–5 (2018).Article 

Google Scholar 
Al‐Yaari, A. et al. Asymmetric responses of ecosystem productivity to rainfall anomalies vary inversely with mean annual rainfall over the conterminous United States. Glob. Chang. Biol. 26, 6959–6973 (2020).Article 
ADS 

Google Scholar 
Szabó, P. & Hédl, R. Advancing the integration of history and ecology for conservation. Conserv. Biol. 25, 680–687 (2011).Article 

Google Scholar 
Hedges, L. V., Gurevitch, J. & Curtis, P. S. The meta-analysis of response ratios in experimental ecology. Spec. Feature Ecol. 80, 1150–1156 (1999).
Google Scholar 
Hillebrand, H. et al. Biodiversity change is uncoupled from species richness trends: consequences for conservation and monitoring. J. Appl. Ecol. 55, 169–184 (2018).Article 

Google Scholar 
Holz, H., Segar, J., Valdez, J. & Staude, I. R. Assessing extinction risk across the geographic ranges of plant species in Europe. Plants People Planet 4, 303–311 (2022).Article 

Google Scholar 
Staude, I. R. et al. Directional turnover towards larger‐ranged plants over time and across habitats. Ecol. Lett. 25, 466–482 (2021).Article 

Google Scholar 
Ellenberg, H., Weber, H. E., Düll, R., Wirth, V. & Werner, W. Zeigerwerte von Pflanzen in Mitteleuropa (Verlag Wrich Goltze, 2001).Chytrý, M., Tichý, L., Dřevojan, P., Sádlo, J. & Zelený, D. Ellenbergtype indicator values for the Czech flora. Preslia 90, 83–103 (2018).Bürkner, P.-C. brms: an R package for Bayesian multilevel models using Stan. J. Stat. Softw. 80, 1–28 (2017).Article 

Google Scholar 
Brooks, S. P. & Gelman, A. General methods for monitoring convergence of iterative simulations. J. Comput. Graph. Stat. 7, 434–455 (1998).MathSciNet 

Google Scholar 
Dushoff, J., Kain, M. P. & Bolker, B. M. I can see clearly now: reinterpreting statistical significance. Methods Ecol. Evol. 10, 756–759 (2019).Article 

Google Scholar 
Bradshaw, L. & Waller, D. M. Impacts of white-tailed deer on regional patterns of forest tree recruitment. Ecol. Manag. 375, 1–11 (2016).Article 

Google Scholar 
McGarvey, J. C., Bourg, N. A., Thompson, J. R., McShea, W. J. & Shen, X. Effects of twenty years of deer exclusion on woody vegetation at three life-history stages in a mid-atlantic temperate deciduous forest. Northeast. Nat. 20, 451–468 (2013).Nuttle, T., Ristau, T. E. & Royo, A. A. Long-term biological legacies of herbivore density in a landscape-scale experiment: forest understoreys reflect past deer density treatments for at least 20 years. J. Ecol. 102, 221–228 (2013). More

Seebens, H. et al. No saturation in the accumulation of alien species worldwide. Nat. Commun. 8, 14435 (2017).Article 
CAS 

Google Scholar 
Pimentel, D., Zuniga, R. & Morrison, D. Update on the environmental and economic costs associated with alien-invasive species in the United States. Ecol. Econ. 52, 273–288 (2005).Article 

Google Scholar 
Simberloff, D. & Von Holle, B. Positive interactions of nonindigenous species: invadsional meltdown? Biol. Invasions 1, 21–32 (1999).Article 

Google Scholar 
Montgomery, W. I., Lundy, M. G. & Reid, N. ‘Invasional meltdown’: evidence for unexpected conseuences and cumulative impacts of multispecies invasions. Biol. Invasions 14, 1111–1115 (2012).Article 

Google Scholar 
Jackson, M. C. Interactions among multiple invasive animals. Ecology 96, 2035–2041 (2015).Article 
CAS 

Google Scholar 
Braga, R. R. et al. Invasional meltdown: an experimental test and a framework to distinguish synergistic, additive, and antagonistic effects. Hydrobiologia 847, 1603–1618 (2020).Article 

Google Scholar 
Crooks, K. R. & Soulé, M. E. Mesopredator release and avifaunal extinctions in a fragmented system. Nature 400, 563–566 (1999).Article 
CAS 

Google Scholar 
Klemmer, A. J., Wissinger, S. A., Greig, H. S. & Ostrofsky, M. L. Nonlinear effects of consumer density on multiple ecosystem processes. J. Anim. Ecol. 81, 779–780 (2012).Article 

Google Scholar 
De Meester, L., Vanoverbeke, J., Kilsdonk, L. J. & Urban, M. C. Evolving perspectives on monopolization and priority effects. Trends Ecol. Evol. 31, 136–146 (2016).Article 

Google Scholar 
Vitousek, P. M., D’Antonio, C. M., Loope, L. L. & Westbrooks, R. Biological invasions as global environmental change. Am. Sci. 84, 468–478 (1996).
Google Scholar 
McKinney, M. L. & Lockwood, J. L. Biotic homogenization: a few winners replacing many losers in the next mass extinction. Trends Ecol. Evol. 14, 450–453 (1999).Article 
CAS 

Google Scholar 
Liebig, J. et al. Bythotrephes longimanus: U.S. Geological Survey, Nonindigenous Aquatic Species Database (2021). Available at: https://nas.er.usgs.gov/queries/factsheet.aspx?SpeciesID=162.Benson, A.J. et al. Dreissena polymorpha (Pallas, 1771): U.S. Geological Survey, Nonindigenous Aquatic Species Database (2021). Available at: https://nas.er.usgs.gov/queries/FactSheet.aspx?speciesID=5Stewart, T. J., Johannsson, O. E., Holeck, K., Sprules, W. G. & O’Gorman, R. The Lake Ontario zooplankton community before (1987-1991) and after (2001-2005) invasion-induced ecosystem change. J. Gt. Lakes Res. 36, 596–605 (2010).Article 

Google Scholar 
Strecker, A. L. et al. Direct and indirect effects of an invasive planktonic predator on pelagic food webs. Limnol. Oceanogr. 56, 179–192 (2011).Article 

Google Scholar 
Karatayev, A. Y., Burlakova, L. E. & Padilla, D. K. Zebra versus quagga mussels: a review of their spread, population dynamics, and ecosystem impacts. Hydrobiologia 746, 97–112 (2015).Article 
CAS 

Google Scholar 
Kerfoot, W. C. et al. A plague of waterfleas (Bythotrephes): impacts on microcrustacean community structure, seasonal biomass, and secondary production in a large inland-lake complex. Biol. Invasions 18, 1121–1145 (2016).Article 

Google Scholar 
Strayer, D. et al. Long-term variability and density dependence in Hudson River Dreissena populations. Freshw. Biol. 65, 474–489 (2019).Article 

Google Scholar 
Fang, X., Stefan, H. G., Jiang, L., Jacobson, P. C. & Pereira D. L. Projected impacts of climatic changes on cisco oxythermal habitat in Minnesota lakes and management strategies in Handbook of Climate Change mitigation and Adaptation (eds. Chen, W.-Y., Suzuki, T. & Lackner, M.) 657-722 (Springer, 2015).Stefan, H. G., Hondzo, M., Fang, X., Eaton, J. G. & McCormick, J. H. Simulated long-term temperature and dissolved oxygen characteristics of lakes in the north-central United States and associated fish habitat limits. Limnol. Oceanogr. 41, 1124–1135 (1996).Article 

Google Scholar 
Jacobson, P. C., Jones, T. S., Rivers, P. & Pereira, D. L. Field estimation of a lethal oxythermal niche boundary for adult ciscoes in Minnesota lakes. T. Am. Fish. Soc. 137, 1464–1474 (2008).Article 

Google Scholar 
Hecky, R. E. et al. The nearshore phosphorus shunt: a consequence of ecosystem engineering by dreissenids in the Laurentian Great Lakes. Can. J. Fish. Aquat. Sci. 61, 1285–1293 (2004).Article 
CAS 

Google Scholar 
Sousa, R., Gutiérrez, J. L. & Aldridge, D. C. Non-indigenous invasive bivalves as ecosystem engineers. Biol. Invasions 11, 2367–2385 (2009).Article 

Google Scholar 
Higgins, S. N. & Vander Zanden, M. J. What a difference a species makes: a meta-analysis of dreissenid mussel impacts on freshwater ecosystems. Ecol. Monogr. 80, 179–196 (2010).Article 

Google Scholar 
Mayer, C. M. et al. Benthification of Freshwater Lakes: Exotic Mussels Turning Ecosystems Upside Down in Quagga and Zebra Mussels: Biology, Impacts, and Control, 2nd ed. (eds. Nalepa, T. F. & Schloesser D. W.) 575-586 (CRC Press, 2014).Lehman, J. T. & Cárcres, C. E. Food-web responses to species invasion by a predatory invertebrate: Bythotrephes in Lake Michigan. Limnol. Oceanogr. 38, 879–891 (1993).Article 

Google Scholar 
Bunnell, D. B., Keeler, K. M., Puchala, E. A., Davis, B. M. & Pothoven, S. A. Comparing seasonal dynamics of the Lake Huron zooplankton community between 1983-1984 and 2007 and revisiting the impact of Bythotrephes planktivory. J. Gt. Lakes Res. 38, 451–462 (2012).Article 

Google Scholar 
Pawlowski, M. B., Branstrator, D. K., Hrabik, T. R. & Sterner, R. W. Changes in the cladoceran community of Lake Superior and thee role of Bythotrephes longimanus. J. Gt. Lakes Res. 43, 1101–1110 (2017).Article 

Google Scholar 
Hoffman, J. C., Smith, M. E. & Lehman, J. T. Perch or plankton: top-down control of Daphnia by yellow perch (Perca flavescens) or Bythotrephes cederstroemi in an inland lake? Freshw. Biol. 46, 759–775 (2001).Article 

Google Scholar 
Bunnell, D. B., Davis, B. M., Warner, D. M., Chriscinske, M. A. & Roseman, E. F. Planktivory in the changing Lake Huron zooplankton community: Bythotrephes consumption exceeds that of Mysis and fish. Freshw. Biol. 56, 1281–1296 (2011).Article 

Google Scholar 
Merkle, C. & De Stasio, B. Bythotrephes longimanus in shallow, nearshore waters: interactions with Leptodora kindtii, impacts on zooplankton, and implications for secondary dispersal from southern Green Bay, Lake Michigan. J. Gt. Lakes Res. 44, 934–942 (2018).Article 

Google Scholar 
Walsh, J. R., Carpenter, S. R. & Vander Zanden, M. J. Invasive species triggers a massive loss of ecosystem services through a trophic cascade. Proc. Natl. Acad. Sci. USA 113, 4081–4085 (2016).Article 
CAS 

Google Scholar 
Lehman, J. T. Algal biomass unaltered by food-web changes in Lake Michigan. Nature 332, 537–538 (1988).Article 

Google Scholar 
Wahlström, E. & Westman, E. Planktivory by the predacious cladoceran Bythotrephes longimanus: effects on zooplankton size structure and density. Can. J. Fish. Aquat. Sci. 56, 1865–1872 (1999).Article 

Google Scholar 
Strecker, A. L. & Arnott, S. E. Invasive predator, Bythotrephes, has varied effects on ecosystem function in freshwater lakes. Ecosystems 11, 490–503 (2008).Article 

Google Scholar 
Benke, A. C. Concepts and patterns of invertebrate production in running waters. Verh. Int. Theor. Angew. Limnol. 25, 15–38 (1993).
Google Scholar 
Jones, T. & Montz, G. Population increase and associated effects of zebra mussels Dreissena polymorpha in Lake Mille Lacs, Minnesota, U.S.A. Bioinvasion Rec. 9, 772–792 (2020).Article 

Google Scholar 
Strayer, D. L. & Malcom, H. M. Long-term demography of a zebra mussel (Dreissena polymorpha) population. Freshw. Biol. 51, 117–130 (2006).Article 

Google Scholar 
Geisler, M. E., Rennie, M. D., Gillis, D. M. & Higgins, S. N. A predictive model for water clarity following dreissenid invasion. Biol. Invasions 18, 1989–2006 (2016).Article 

Google Scholar 
Barbiero, R. P. & Tuchman, M. L. Long-term dreissenid impacts on water clarity in Lake Erie. J. Gt. Lakes Res. 30, 557–565 (2004).Article 

Google Scholar 
Fishman, D. B., Adlerstein, S. A., Vanderploeg, H. A., Fahnenstiel, G. L. & Scavia, D. Causes of phytoplankton changes in Saginaw Bay, Lake Huron, during the zebra mussel invasion. J. Gt. Lakes Res. 35, 482–495 (2009).Article 

Google Scholar 
Zhang, H., Culver, D. A. & Boegman, L. Dreissenids in Lake Erie: an algal filter or a fertilizer? Aquat. Invasions 6, 175–194 (2011).Article 

Google Scholar 
Higgins, S. N., Vander Zanden, M. J., Joppa, L. N. & Vadeboncoeur, Y. The effect of dreissenid invasions on chlorophyll and the chlorophyll: total phosphorus ration in north-temperate lakes. Can. J. Fish. Aquat. Sci. 68, 319–329 (2011).Article 
CAS 

Google Scholar 
Lehman, J. T. & Branstrator, D. K. A model for growth, development, and diet selection by the invertebrate predator Bythotrephes cederstroemi. J. Gt. Lakes Res. 21, 610–619 (1995).Article 

Google Scholar 
Azan, S. S. E., Arnott, S. E. & Yan, N. D. A review of the effects of Bythotrephes longimanus and calcium decline on zooplankton communities – can interactive effects be predicted? Environ. Rev. 23, 395–413 (2015).Article 
CAS 

Google Scholar 
Pangle, K. L., Peacor, S. D. & Johannsson, O. E. Large nonlethal effects of an invasive invertebrate predator on zooplankton population growth rate. Ecology 88, 402–412 (2007).Article 

Google Scholar 
Cross, T. K. & Jacobson, P. C. Landscape factors influencing lake phosphorus concentrations across Minnesota. Lake Reserv Manag 29, 1–12 (2013).Article 
CAS 

Google Scholar 
McQueen, D. J., Johannes, M. R. S., Post, J. R., Stewart, T. J. & Lean, D. R. S. Bottom-up and top-down impacts on freshwater pelagic community structure. Ecol. Monogr. 59, 289–309 (1989).Article 

Google Scholar 
Mills, E. L. et al. Lake Ontario: food web dynamics in a changing ecosystem (1970-2000). Can. J. Fish. Aquat. Sci. 60, 471–490 (2003).Article 

Google Scholar 
Lehman, J. T. Causes and consequences of cladoceran dynamics in Lake Michigan: implications of species invasion by. Bythotrephes. J. Gt. Lakes Res. 17, 437–445 (1991).Article 

Google Scholar 
Yan, N. D. et al. Long-term trends in zooplankton of Dorset, Ontario, lakes: the probable interactive effects of changes in pH, total phosphorus, dissolved organic carbon, and predators. Can. J. Fish. Aquat. Sci. 65, 862–877 (2008).Article 
CAS 

Google Scholar 
Brooks, J. L. & Dodson, S. I. Predation, body size, and composition of plankton. Science 150, 28–35 (1965).Article 
CAS 

Google Scholar 
Rennie, M. D., Evans, D. O. & Young, J. D. Increased dependence on nearshore benthic resources in the Lake Simcoe ecosystem after dreissenid invasion. Inland Waters 3, 297–310 (2013).Article 

Google Scholar 
Goto, D., Dunlop, E. S., Young, J. D. & Jackson, D. A. Shifting trophic control of fishery-ecosystem dynamics following biological invasions. Ecol. Appl. 30, e02190 (2020).Article 

Google Scholar 
Hansen, G. J. A. et al. Walleye growth declines following zebra mussel and Bythotrephes invasion. Biol. Invasions 22, 1481–1495 (2020).Article 

Google Scholar 
Yan, N. & Pawson, T. Changes in the crustacean zooplankton community of Harp Lake, Canada, following invasion by. Bythotrephes cederstrœmi. Freshw. Biol. 37, 409–425 (1997).Article 

Google Scholar 
Bourdeau, P. E., Bach, M. T. & Peacor, S. D. Predator presence dramatically reduces copepod abundance through condition-mediated non-consumptive effects. Freshw. Biol. 61, 1020–1031 (2016).Article 
CAS 

Google Scholar 
Lehman, J. R. Ecological principles affecting community structure and secondary production by zooplankton in marine and freshwater environments. Limnol. Oceanogr. 33, 931–945 (1988).
Google Scholar 
Walsh, J. R., Lathrop, R. C., & Vander Zanden, M.J.Invasive invertebrate predator, Bythotrephes longimanus, reverses trophic cascade in a north-temperate lake. Limnol. Oceanogr. 62, 2498–2509 (2017).Article 

Google Scholar 
Underwood, A. J. On beyond BACI: sampling designs that might reliably detect environmental disturbances. Ecol. Appl. 4, 3–15 (1994).Article 

Google Scholar 
Sala, O. E. et al. Global biodiversity scenarios for the year 2100. Science 287, 1770–1774 (2000).Article 
CAS 

Google Scholar 
Strayer, D. L., Eviner, V. T., Jeschke, J. M. & Pace, M. L. Understanding the long-term effects of species invasions. Trends Ecol. Evol. 21, 645–651 (2006).Article 

Google Scholar 
Magnuson, J. J. Long-term ecological research and the invisible present. BioScience 40, 495–501 (1990).Article 

Google Scholar 
Doak, D. F. et al. Understanding and predicting ecological dynamics: are major surprises inevitable? Ecology 89, 952–961 (2008).Article 

Google Scholar 
Hansen, G. J. A., Gaeta, J. W., Hansen, J. F. & Carpenter, S. R. Learning to manage and managing to learn: sustaining freshwater recreational fisheries in a changing environment. Fisheries 40, 56–64 (2015).Article 

Google Scholar 
Dumont, H. J., Van De Velde, I. & Dumont, S. The dry weight estimate of biomass in a selection of Cladocera, Copepoda and Rotifera from the plankton, periphyton and benthos of continental waters. Oecologia 19, 75–97 (1975).Article 

Google Scholar 
Culver, D. A., Boucherle, M. M., Bean, D. J. & Fletcher, J. W. Biomass of freshwater crustacean zooplankton from length-weight regressions. Can. J. Fish. Aquat. Sci. 42, 1380–1390 (1985).Article 

Google Scholar 
Manly, B. F. J. Randomization, bootstrap and Monet Carlo methods in biology, 3rd ed. (Chapman and Hall/CRC, 2007).Arar, E. J. Method 446.0. In vitro determination of chlorophylls a, b, c1 + c2 and pheopigments in marine and freshwater algae by visible spectrophotometry, revision 1.2. (U.S. Environmental Protection Agency, 1997).O’Dell, J. W. Method 365.1 Determination of phosphorus by semi-automated colorimetry, revision 2.0. (U.S. Environmental Agency, 1993).Helsel, D. R. & Hirsch, R. M. Statistical methods in water resources (U. S. Geological Survey, 2002).Minnesota Pollution Control Agency (MPCA). Surface water data. https://webapp.pca.state.mn.us/wqd/surface-water (MPCA, 2021).Read, J. S. et al. Data release: Process-based predictions of lake water temperature in the Midwest US: U.S. Geological Survey data release, https://doi.org/10.5066/P9CA6XP8 (USGS, 2021).Hothorn, T., Hornik, K., van de Wiel, M. A. & Zeileis, A. Lego system for conditional inference. Am. Stat. 60, 257–263 (2006).Article 

Google Scholar 
Dewitz, J. National Land Cover Database (NLCD) 2016 Products: U.S. Geological Survey data release, https://doi.org/10.5066/P96HHBIE (USGS, 2019).Use of Fishes in Research Committee. Guidelines for the use of fishes in research. (American Fisheries Society, 2014)Pedersen, E. J., Miller, D. L., Simpson, G. L. & Ross, N. Hierarchical generalized additive models in ecology: an introduction with mgcv. PeerJ 7, e6876 (2019).Article 

Google Scholar 
Minnesota Geospatial Commons. DNR Hydrology Dataset. (2022). Available at: https://gisdata.mn.gov/dataset/water-dnr-hydrography.Minnesota Geospatial Commons. Lake Bathymetric Outlines, Contours, and DEM. (2021). Available at: https://gisdata.mn.gov/dataset/water-lake-bathymetry.ESRI ArcGIS Desktop: Release 10.6. Redlands, CA: Environmental Systems Research Institute (2018).Wood, S. N. Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models. J. R. Stat. Soc. 73, 3–36 (2011).Article 

Google Scholar 
Guerrero, F. & Rodríguez, V. Secondary production of a congeneric species assemblage of Acartia (Copepoda: Calanoida): a calculation based on the size-frequency distribution. Sci. Mar. 58, 161–167 (1994).
Google Scholar 
Cross, W. F. et al. Ecosystem ecology meets adaptive management: food web response to a controlled flood on the Colorado River, Glen Canyon. Ecol. Appl. 21, 2016–2033 (2011).Article 

Google Scholar 
Gillooly, J. F. Effect of body size and temperature on generation time in zooplankton. J. Plankton Res. 22, 241–251 (2000).Article 

Google Scholar 
Benke, A. C. & Huryn, A. D. Secondary production and quantitative food webs in Methods in Stream Ecology, Volume 2: ecosystem function (eds. Lamberti, G.A. & Hauer, F. R.) 235-254 (Academic Press, 2017).Wu, L. & Culver, D. A. Zooplankton grazing and phytoplankton abundance: an assessment before and after invasion of Dreissena polymorpha. J. Gt. Lakes Res. 17, 425–436 (1991).Article 

Google Scholar 
R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing (Vienna, Austria, 2020). Accessible at: https://www.R-project.org/Minnesota Geospatial Commons. State Boundary. (2013). Available at: https://gisdata.mn.gov/dataset/bdry-state-of-minnesota.United States Geological Survey. North America Political Boundaries. (2006). Available at: https://www.sciencebase.gov/catalog/item/4fb555ebe4b04cb937751db9. More

Bovet, D. & Vauclair, J. Picture recognition in animals and humans. Behav. Brain. Res. 109, 143–165 (2000).Article 
CAS 

Google Scholar 
Anonymous. Tinder for Orangutans. Dublin Zoo. https://www.dublinzoo.ie/news/tinder-for-orangutans (2020).Henley, J. “Tinder for Orangutans”: Dutch zoo to let female choose mate on a tablet. The Guardian. https://www.theguardian.com/environment/2017/jan/31/tinder-for-orangutans-dutch-zoo-to-let-female-choose-mate-on-a-tablet (2017).Gierszewski, S. et al. The virtual lover: variable and easily guided 3D fish animations as an innovative tool in mate-choice experiments with sailfin mollies-II validation. Curr. Zool. 6, 65–74 (2017).Article 

Google Scholar 
Dolins, F. L., Klimowicz, C., Kelley, J. & Menzel, C. R. Using virtual reality to investigate comparative spatial cognitive abilities in chimpanzees and humans. Am. J. Primat. 76, 496–513 (2014).Article 

Google Scholar 
Faria, J. J. et al. A novel method for investigating the collective behaviour of fish: Introducing ‘Robofish’. Behav. Ecol. Sociobiol. 64, 1211–1218 (2010).Article 

Google Scholar 
Kozak, E. C. & Uetz, G. W. Male courtship signal modality and female mate preference in the wolf spider Schizocosa ocreata: results of digital multimodal playback studies. Curr. Zool. 65, 705–711 (2019).Article 

Google Scholar 
Loukola, O. J., Perry, C. J., Coscos, L. & Chittka, L. Bumblebees show cognitive flexibility by improving on an observed complex behavior. Science 355, 833–836 (2017).Article 
ADS 
CAS 

Google Scholar 
MacLaren, R. D. Evidence of an emerging female preference for an artificial male trait and the potential for spread via mate choice copying in Poecilia latipinna. Ethology 125, 575–586 (2019).
Google Scholar 
Rönkä, K. et al. Geographic mosaic of selection by avian predators on hindwing warning colour in a polymorphic aposematic moth. Ecol. Lett. 23, 1654–1663 (2020).Article 

Google Scholar 
Rosenthal, G. G., Rand, A. S. & Ryan, M. J. The vocal sac as a visual cue in anuran communication: An experimental analysis using video playback. Anim. Behav. 68, 55–58 (2004).Article 

Google Scholar 
Thurley, K. & Ayaz, A. Virtual reality systems for rodents. Curr. Zool. 63, 109–119 (2017).Article 

Google Scholar 
Ware, E. L., Saunders, D. R. & Troje, N. F. Social interactivity in pigeon courtship behavior. Curr. Zool. 63, 85–95 (2017).Article 

Google Scholar 
Wang, D. et al. The influence of model quality on self-other mate choice copying. Pers. Ind. Diff. 17, 110481 (2021).Article 

Google Scholar 
Gray, J. R., Pawlowski, V. & Willis, M. A. A method for recording behavior and multineuronal CNS activity from tethered insects flying in virtual space. J. Neurosci. Meth. 120, 211–223 (2002).Article 

Google Scholar 
Strauss, R., Schuster, S. & Götz, K. G. Processing of artificial visual feedback in the walking fruit fly Drosophila melanogaster. J. Exp. Biol. 200, 1281–1296 (1997).Article 
CAS 

Google Scholar 
Kemppainen, J. et al. Binocular mirror-symmetric microsaccadic sampling enables Drosophila hyperacute 3D vision. PNAS 119, e2109717119 (2022).Article 
CAS 

Google Scholar 
Bowers, R. I., Place, S. S., Todd, P. M., Penke, L. & Asendorpf, J. B. Generalization in mate-choice copying in humans. Behav. Ecol. 23, 112–124 (2012).Article 

Google Scholar 
Pruett-Jones, S. Independent versus nonindependent mate choice: do females copy each other? Am. Nat. 140, 1000–1006 (1992).Article 
CAS 

Google Scholar 
Dagaeff, A.-C., Pocheville, A., Nöbel, S., Isabel, G. & Danchin, E. Drosophila mate copying correlates with atmospheric pressure in a speed learning situation. Anim. Behav. 121, 163–174 (2016).Article 

Google Scholar 
Mery, F. et al. Public versus personal information for mate copying in an invertebrate. Curr. Biol. 19, 730–734 (2009).Article 
CAS 

Google Scholar 
Danchin, E. et al. Cultural flies: Conformist social learning in fruitflies predicts long-lasting mate-choice traditions. Science 362, 1025–1030 (2018).Article 
ADS 
CAS 

Google Scholar 
Monier, M., Nöbel, S., Isabel, G. & Danchin, E. Effects of a sex ratio gradient on female mate-copying and choosiness in Drosophila melanogaster. Curr. Zool. 64, 251–258 (2018).Article 

Google Scholar 
Monier, M., Nöbel, S., Danchin, E. & Isabel, G. Dopamine and serotonin are both required for mate-copying in Drosophila melanogaster. Front. Behav. Neurosci. 12, 334 (2019).Article 

Google Scholar 
Nöbel, S., Allain, M., Isabel, G. & Danchin, E. Mate copying in Drosophila melanogaster males. Anim. Behav. 141, 9–15 (2018).Article 

Google Scholar 
Nöbel, S., Danchin, E. & Isabel, G. Mate-copying for a costly variant in Drosophila melanogaster females. Behav. Ecol. 29, 1150–1156 (2018).Article 

Google Scholar 
Dukas, R. Natural history of social and sexual behavior in fruit flies. Sci. rep. 10, 1–11 (2020).Article 

Google Scholar 
Chouinard-Thuly, L. et al. Technical and conceptual considerations for using animated stimuli in studies of animal behavior. Curr. Zool. 63, 5–19 (2017).Article 

Google Scholar 
Nöbel, S. et al. Female fruit flies copy the acceptance, but not the rejection, of a mate. Behav. Ecol. 33, 1018–1024 (2022)Article 

Google Scholar 
Bretman, A., Westmancoat, J. D., Gage, M. J. G. & Chapman, T. Males use multiple, redundant cues to detect mating rivals. Curr. Biol. 21, 617–622 (2011).Article 
CAS 

Google Scholar 
Greenspan, R. J. & Ferveur, J. F. Courtship in drosophila. Ann. Rev. Gen. 34, 205 (2000).Article 
CAS 

Google Scholar 
Grillet, M., Dartevelle, L. & Ferveur, J. F. A Drosophila male pheromone affects female sexual receptivity. Proc. Roy. Soc. B. 273, 315–323 (2006).Article 
CAS 

Google Scholar 
Borst, A. Drosophila’s view on insect vision. Curr. Biol. 19, R36–R47 (2009).Article 
CAS 

Google Scholar 
Paulk, A., Millard, S. & van Swinderen, B. Vision in Drosophila: Seeing the world through a model´s eye. Ann. Rev. Entomol. 58, 313–332 (2013).Article 
CAS 

Google Scholar 
Antony, C. & Jallon, J. M. The chemical basis for sex recognition in Drosophila melanogaster. J. Insect. Physiol. 28, 873–880 (1982).Article 
CAS 

Google Scholar 
Keesey, I. W. et al. Adult frass provides a pheromone signature for Drosophila feeding and aggregation. J. Chem. Ecol. 42, 739–747 (2016).Article 
CAS 

Google Scholar 
Talyn, B. C. & Bowse, H. B. The role of courtship song in sexual selection and species recognition by female Drosophila melanogaster. Anim. Behav. 68, 1165–1180 (2004).Article 

Google Scholar 
von Schilcher, F. The function of pulse song and sine song in the courtship of Drosophila melanogaster. Anim. Behav. 24, 622–6251976 (1976).Article 

Google Scholar 
McGregor, P. K. et al. Design of playback experiments: The Thornbridge hall NATO ARW consensus. In Playback and Studies of Animal Communication (ed. McGregor, P.) 1–9 (Plenum Press, New York, 1992).Chapter 

Google Scholar 
Richmond, J. The three Rs. In The UFAW Handbook on the Care and Management of Laboratory and Other Research Animals (eds Hubrecht, R. & Kirkwood, J.) 5–22 (Wiley-Blackwell, Hoboken, 2002).
Google Scholar 
Russell, W. M. S. & Burch, R. L. The Principles of Humane Experimental Technique (Methuen & Co Ltd, 1959).
Google Scholar 
Schlupp, I., Ryan, M. & Waschulewski, M. Female preferences for naturally-occurring novel male traits. Behaviour 136, 519–527 (1999).Article 

Google Scholar 
Witte, K. & Klink, K. No pre-existing bias in sailfin molly females, Poecilia latipinna, for a sword in males. Behaviour 142, 283–303 (2005).Article 

Google Scholar 
Gerlai, R. Animated images in the analysis of zebrafish behavior. Curr. Zool. 63, 35–44 (2017).Article 

Google Scholar 
Ioannou, C. C., Guttal, V. & Couzin, I. D. Predatory fish select for coordinated collective motion in virtual prey. Science 337, 1212–1215 (2012).Article 
ADS 
CAS 

Google Scholar 
Little, A. C., Jones, B. C. & DeBruine, L. M. Preferences for variation in masculinity in real male faces change across the menstrual cycle: Women prefer more masculine faces when they are more fertile. Pers. Ind. Diff. 45, 478–482 (2008).Article 

Google Scholar 
Little, A. C., Jones, B. C. & DeBruine, L. M. Facial attractiveness: Evolutionary based research. Phil. Trans. R. Soc. B. 366, 1638–1659 (2011).Article 

Google Scholar 
Morrison, E. R., Clark, A. P., Tiddeman, B. P. & Penton-Voak, I. S. Manipulating shape cues in dynamic human faces: Sexual dimorphism is preferred in female but not male faces. Ethology 116, 1234–1243 (2010).Article 

Google Scholar 
Kacsoh, B. Z., Bozler, J., Ramaswami, M. & Bosco, G. Social communication of predator-induced changes in Drosophila behavior and germ line physiology. eLife. 4, e07423 (2015).Article 

Google Scholar 
Caruana, N. & Seymour, K. Objects that induce face pareidolia are prioritized by the visual system. Brit. J. Psychol. 113, 496–507 (2022).Article 

Google Scholar 
Agrawal, S., Safarik, S. & Dickinson, M. The relative roles of vision and chemosensation in mate recognition of Drosophila melanogaster. J. Exp. Biol. 217, 2796–2805 (2014).
Google Scholar 
R Development Core Team. R: A Language and Environment for Statistical Computing (Austria, Vienna, 2021).
Google Scholar 
Bates, D., Maechler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).Article 

Google Scholar 
Fox, J. & Weisberg, S. An {R} Companion to Applied Regression 2nd edn. (Sage Publishing, London, 2001).
Google Scholar  More

Zhang, Z.-Q. Phylum Athropoda. Zootaxa 3703, 17 (2013).Article 

Google Scholar 
Christenhusz, M. J. M. & Byng, J. W. The number of known plants species in the world and its annual increase. Phytotaxa 261, 201 (2016).Article 

Google Scholar 
Forbes, A. A., Bagley, R. K., Beer, M. A., Hippee, A. C. & Widmayer, H. A. Quantifying the unquantifiable: why Hymenoptera, not Coleoptera, is the most speciose animal order. BMC Ecol 18, 21 (2018).Article 

Google Scholar 
Glor, R. E. Phylogenetic Insights on Adaptive Radiation. Annu. Rev. Ecol. Evol. Syst. 41, 251–270 (2010).Article 

Google Scholar 
Schluter, D. The ecology of adaptive radiation. (Oxford Univ Press, 2000).Simpson, G. G. Tempo and mode in evolution. (Columbia Univ Press, 1944).Gavrilets, S. & Losos, J. B. Adaptive Radiation: Contrasting Theory with Data. Science 323, 732–737 (2009).Article 
CAS 

Google Scholar 
Losos, J. B. Adaptive Radiation, Ecological Opportunity, and Evolutionary Determinism: American Society of Naturalists E. O. Wilson Award Address. Am. Nat. 175, 623–639 (2010).Article 

Google Scholar 
Hodges, S. A. & Arnold, M. L. Spurring plant diversification: are floral nectar spurs a key innovation? Proc. R Soc. B: Biol. Sci. 262, 343–348 (1995).Article 

Google Scholar 
Hunter, J. P. & Jernvall, J. The hypocone as a key innovation in mammalian evolution. Proc. Natl. Acad. Sci. U.S.A. 92, 10718–10722 (1995).Article 
CAS 

Google Scholar 
Grant, P. R. Ecology and Evolution of Darwin’s Finches. (Princeton Univ Press, 1986).Erwin, D. H. The end and the beginning: recoveries from mass extinctions. Trend. Ecol. Evol. 13, 344–349 (1998).Article 
CAS 

Google Scholar 
Jablonski, D. Lessons from the past: Evolutionary impacts of mass extinctions. Proc. Natl. Acad. Sci. U.S.A. 98, 5393–5398 (2001).Article 
CAS 

Google Scholar 
Donoghue, M. J. & Sanderson, M. J. Confluence, synnovation, and depauperons in plant diversification. New Phytol. 207, 260–274 (2015).Article 

Google Scholar 
Pie, M. R. & Feitosa, R. S. M. Relictual ant lineages and their evolutionary implications. Myrmecol News 22, 55–58 (2016).
Google Scholar 
Eldredge, N. & Stanley, S. M. Living Fossils (Casebooks in Earth Sciences). (Springer, 1984).Alfaro, M. E. et al. Nine exceptional radiations plus high turnover explain species diversity in jawed vertebrates. Proc. Natl. Acad. Sci. 106, 13410–13414 (2009).Article 
CAS 

Google Scholar 
Magallón, S., Sánchez-Reyes, L. L. & Gómez-Acevedo, S. L. Thirty clues to the exceptional diversification of flowering plants. Ann. Botany 123, 491–503 (2019).Article 

Google Scholar 
Hunter, J. P. Key innovations and the ecology of macroevolution. Trend. Ecol. Evol. 13, 31–36 (1998).Article 
CAS 

Google Scholar 
Wellborn, G. A. & Langerhans, R. B. Ecological opportunity and the adaptive diversification of lineages. Ecol. Evol. 5, 176–195 (2015).Article 

Google Scholar 
Gould, S. J. & Vrba, E. S. Exaptation—a Missing Term in the Science of Form. Paleobiology 8, 4–15 (1982).Article 

Google Scholar 
Eldredge, N. Simpson’s Inverse: Bradytely and the Phenomenon of Living Fossils. in Living Fossils (eds. Eldredge, N. & Stanley, S. M.) 272–277 (Springer New York, 1984). https://doi.org/10.1007/978-1-4613-8271-3_34.Nagalingum, N. S. et al. Recent Synchronous Radiation of a Living Fossil. Science 334, 796–799 (2011).Article 
CAS 

Google Scholar 
Schopf, T. J. M. Rates of Evolution and the Notion of ‘Living Fossils’. Annu. Rev. Earth Planet. Sci. 12, 245–292 (1984).Article 

Google Scholar 
Czekanski-Moir, J. E. & Rundell, R. J. The Ecology of Nonecological Speciation and Nonadaptive Radiations. Trend. Ecol. Evol. 34, 400–415 (2019).Article 

Google Scholar 
Olson, M. E. & Arroyo-Santos, A. Thinking in continua: beyond the “adaptive radiation” metaphor. BioEssays 31, 1337–1346 (2009).Article 

Google Scholar 
Barnes, B. D., Sclafani, J. A. & Zaffos, A. Dead clades walking are a pervasive macroevolutionary pattern. Proc. Natl. Acad. Sci. USA 118, e2019208118 (2021).Article 
CAS 

Google Scholar 
Quental, T. B. & Marshall, C. R. How the Red Queen Drives Terrestrial Mammals to Extinction. Science 341, 290–292 (2013).Article 
CAS 

Google Scholar 
Strathmann, R. R. & Slatkin, M. The improbability of animal phyla with few species. Paleobiology 9, 97–106 (1983).Article 

Google Scholar 
Darwin, C. On the origin of species by means of natural selection. (J. Murray, 1859).Kase, T. & Hayami, I. Unique submarine cave mollusc fauna: composition, origin and adaptation. J. Mollus Stud. 58, 446–449 (1992).Article 

Google Scholar 
Tunnicliffe, V. The Nature and Origin of the Modern Hydrothermal Vent Fauna. PALAIOS 7, 338 (1992).Article 

Google Scholar 
Oji, T. Is predation intensity reduced with increasing depth? Evidence from the west Atlantic stalked crinoid Endoxocrinus parrae (Gervais) and implications for the Mesozoic marine revolution. Paleobiology 22, 339–351 (1996).Article 

Google Scholar 
Oji, T. & Okamoto, T. Arm autotomy and arm branching pattern as anti-predatory adaptations in stalked and stalkless crinoids. Paleobiology 20, 27–39 (1994).Article 

Google Scholar 
Rest, J. S. et al. Molecular systematics of primary reptilian lineages and the tuatara mitochondrial genome. Mol. Phyl. Evol. 29, 289–297 (2003).Article 
CAS 

Google Scholar 
Rabosky, D. L. & Benson, R. B. J. Ecological and biogeographic drivers of biodiversity cannot be resolved using clade age-richness data. Nat. Commun. 12, 2945 (2021).Article 
CAS 

Google Scholar 
Louca, S., Henao‐Diaz, L. F. & Pennell, M. The scaling of diversification rates with age is likely explained by sampling bias. Evolution 76, 1625–1637 (2022).Article 

Google Scholar 
Qian, H. & Zhang, J. Using an updated time-calibrated family-level phylogeny of seed plants to test for non-random patterns of life forms across the phylogeny: Phylogeny of seed plant families. J. Syst. Evol. 52, 423–430 (2014).Article 

Google Scholar 
The Plant List. Version 1.1. http://www.theplantlist.org/.Jetz, W. & Pyron, R. A. The interplay of past diversification and evolutionary isolation with present imperilment across the amphibian tree of life. Nat. Ecol. Evol. 2, 850–858 (2018).Article 

Google Scholar 
Tonini, J. F. R., Beard, K. H., Ferreira, R. B., Jetz, W. & Pyron, R. A. Fully-sampled phylogenies of squamates reveal evolutionary patterns in threat status. Biol. Conservation 204, 23–31 (2016).Article 

Google Scholar 
Faurby, S. et al. PHYLACINE 1.2: The Phylogenetic Atlas of Mammal Macroecology. Ecology 99, 2626–2626 (2018).Article 

Google Scholar 
IUCN. IUCN red List of Threatened Species. Version 2017.3. Retrieved from https://www.iucnredlist.org. Downloaded on May 14, 2020. (2017).Magallon, S. & Sanderson, M. J. Absolute diversification rates in angiosperm clades. Evolution 55, 1762–1780 (2001).CAS 

Google Scholar 
Raup, D. M. Mathematical models of cladogenesis. Paleobiology 11, 42–52 (1985).Article 

Google Scholar 
Kembel, S. W. et al. Picante: R tools for integrating phylogenies and ecology. Bioinformatics 26, 1463–1464 (2010).Article 
CAS 

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

Google Scholar 
Vilela, B. & Villalobos, F. letsR: a new R package for data handling and analysis in macroecology. Methods Ecol. Evol. 6, 1229–1234 (2015).Article 

Google Scholar 
R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. Retrieved from https://www.R-project.org/. (2020).Garnier, S. viridis: Default Color Maps from ‘matplotlib’. Version 0.5.1. Available in https://CRAN.R-project.org/package=viridis (2018).Bivand, R. et al. rgdal: Bindings for the ‘Geospatial’ Data Abstraction Library. Version 1.5.32. Available in https://CRAN.R-project.org/package=rgdal (2020). More