Philippa Kaur

Darling, E. S., Alvarez-Filip, L., Oliver, T. A., McClanahan, T. R. & Côté, I. M. Evaluating life-history strategies of reef corals from species traits. Ecol. Lett. 15, 1378–1386 (2012).PubMed 

Google Scholar 
Bridge, T. C. L. et al. Incongruence between life-history traits and conservation status in reef corals. Coral Reefs 39, 271–279 (2020).
Google Scholar 
Raja, N. B. et al. Morphological traits of reef corals predict extinction risk but not conservation status. Glob. Ecol. Biogeogr. 30, 1597–1608 (2021).
Google Scholar 
Orzechowski, E. A. et al. Marine extinction risk shaped by trait–environment interactions over 500 million years. Glob. Change Biol. 21, 3595–3607 (2015).ADS 

Google Scholar 
Pietsch, C., Mata, S. A. & Bottjer, D. J. High temperature and low oxygen perturbations drive contrasting benthic recovery dynamics following the end-Permian mass extinction. Palaeogeogr. Palaeoclimatol. Palaeoecol. 399, 98–113 (2014).
Google Scholar 
Wagner, P. J. & Estabrook, G. F. Trait-based diversification shifts reflect differential extinction among fossil taxa. Proc. Natl. Acad. Sci. 111, 16419–16424 (2014).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Kiessling, W. Geologic and Biologic Controls on the Evolution of Reefs. Annu. Rev. Ecol. Evol. Syst. 40, 173–192 (2009).
Google Scholar 
Kiessling, W. Reef expansion during the Triassic: Spread of photosymbiosis balancing climatic cooling. Palaeogeogr. Palaeoclimatol. Palaeoecol. 290, 11–19 (2010).
Google Scholar 
Foden, W. B. et al. Identifying the World’s Most Climate Change Vulnerable Species: A Systematic Trait-Based Assessment of all Birds, Amphibians and Corals. PLOS ONE 8, e65427 (2013).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Hughes, A. D. & Grottoli, A. G. Heterotrophic Compensation: A Possible Mechanism for Resilience of Coral Reefs to Global Warming or a Sign of Prolonged Stress? PLOS ONE 8, e81172 (2013).ADS 
PubMed 
PubMed Central 

Google Scholar 
Stanley, G. D. Jr & Helmle, K. P. Middle Triassic Coral Growth Bands and Their Implication for Photosymbiosis. PALAIOS 25, 754–763 (2010).ADS 

Google Scholar 
van Woesik, R. et al. Hosts of the Plio-Pleistocene past reflect modern-day coral vulnerability. Proc. R. Soc. B Biol. Sci. 279, 2448–2456 (2012).
Google Scholar 
Madin, J. S. et al. The Coral Trait Database, a curated database of trait information for coral species from the global oceans. Sci. Data 3, 160017 (2016).PubMed 
PubMed Central 

Google Scholar 
Madin, J. S. et al. A Trait-Based Approach to Advance Coral Reef Science. Trends Ecol. Evol. 31, 419–428 (2016).PubMed 

Google Scholar 
Riedel, P. Korallen in der Trias der Tethys:. Stratigraphische Reichweiten, Diversitätsmuster, Entwicklungstrends und Bedeutung als Rifforganismen. Mitteilungen Ges. Geol.- Bergbaustud. Österr. 37, 97–118 (1991).
Google Scholar 
Budd, A. F., Adrain, T. S., Park, J. W., Klaus, J. S. & Johnson, K. G. The Neogene Marine Biota of Tropical America (“NMITA”) Database: Integrating Data from the Dominican Republic Project. in Evolutionary Stasis and Change in the Dominican Republic Neogene (eds. Nehm, R. H. & Budd, A. F.) 301–310, https://doi.org/10.1007/978-1-4020-8215-3_13 (Springer Netherlands, 2008).Budd, A. F., Foster, C. T., Dawson, J. P. & Johnson, K. G. The Neogene Marine Biota of Tropical America (“NMITA”) database: Accounting for biodiversity in paleontology. J. Paleontol. 75, 743–751 (2001).
Google Scholar 
Scotese, C. R. PALEOMAP PaleoAtlas for GPlates and the PaleoData Plotter Program. https://www.earthbyte.org/paleomap-paleoatlas-for-gplates/ (2016).Johnson, K. G., Budd, A. F. & Stemann, T. A. Extinction selectivity and ecology of Neogene Caribbean reef corals. Paleobiology 21, 52–73 (1995).
Google Scholar 
Pinzón, J. H. et al. Blind to morphology: genetics identifies several widespread ecologically common species and few endemics among Indo-Pacific cauliflower corals (Pocillopora, Scleractinia). J. Biogeogr. 40, 1595–1608 (2013).
Google Scholar 
Lathuilière, B. Coraux constructeurs du Bajocien inférieur de France: 2ème partie. Geobios 33, 153–181 (2000).
Google Scholar 
Kiessling, W. & Kocsis, Á. T. Biodiversity dynamics and environmental occupancy of fossil azooxanthellate and zooxanthellate scleractinian corals. Paleobiology 41, 402–414 (2015).
Google Scholar 
Raja, N. B., Dimitrijević, D., Krause, M. C. & Kiessling, W. Ancient Reef Traits Database. Zenodo https://doi.org/10.5281/zenodo.5717611 (2022).Mannani, M. Late Triassic scleractinian corals from Nayband Formation, southwest Ardestan, Central Iran. Bol. Soc. Geológica Mex. 72, A090619 (2020).
Google Scholar 
Löser, H., Stemann, T. A. & Mitchell, S. Oldest scleractinian fauna from Jamaica (Hauterivian, Benbow Inlier). J. Paleontol. 83, 333–349 (2009).
Google Scholar 
Löser, H. Morphology, Taxonomy and Distribution of the Cretaceous coral genus Aulastraeopora (Late Barremian-Early Cenomanian; Scleractinia). Riv. Ital. Paleontol. E Stratigr. 114, (2008).Löser, H. Revision of Actinastrea, the most common Cretaceous coral genus. Paläontol. Z. 86, 15–22 (2012).
Google Scholar 
Löser, H., Werner, W. & Darga, R. A Middle Cenomanian coral fauna from the Northern Calcareous Alps (Bavaria, Southern Germany) – new insights into the evolution of Mid-Cretaceous corals. Zitteliana 53, 37–76 (2013).
Google Scholar 
Löser, H. & Bilotte, M. Taxonomy of a platy coral association from the Late Cenomanian of the southern Corbières (Aude, France). Ann. Paléontol. 103, 3–17 (2017).
Google Scholar 
Löser, H., Steuber, T. & Löser, C. Early Cenomanian coral faunas from Nea Nikopoli (Kozani, Greece; Cretaceous). Carnets Géologie Noteb. Geol. 18, 23–121 (2018).
Google Scholar 
Löser, H. Early evolution of the family Siderastraeidae (Scleractinia; Cretaceous-extant). Paläontol. Z. 90, 1–17 (2016).
Google Scholar 
Kiessling, W. et al. Massive corals in Paleocene siliciclastic sediments of Chubut (Argentina). Facies 51, 233–241 (2005).
Google Scholar 
Stolarski, J. & Vertino, A. First Mesozoic record of the scleractinian Madrepora from the Maastrichtian siliceous limestones of Poland. Facies 53, 67–78 (2007).
Google Scholar 
Yabe, H. & Sugiyama, T. 5. Younger Cenozoic Reef-corals from the Nabire Beds of Nabire, Dutch New Guinea. Proc. Imp. Acad. 18, 16–23 (1942).
Google Scholar 
Wilson, M. A., Vinn, O. & Palmer, T. J. Bivalve borings, bioclaustrations and symbiosis in corals from the Upper Cretaceous (Cenomanian) of southern Israel. Palaeogeogr. Palaeoclimatol. Palaeoecol. 414, 243–245 (2014).
Google Scholar 
Tomás, S., Löser, H. & Salas, R. Low-light and nutrient-rich coral assemblages in an Upper Aptian carbonate platform of the southern Maestrat Basin (Iberian Chain, eastern Spain). Cretac. Res. 29, 509–534 (2008).
Google Scholar 
Baron-Szabo, R. C. Scleractinian corals from the upper Berriasian of central Europe and comparison with contemporaneous coral assemblages. Zootaxa 4383, 1 (2018).Kiessling, W., Roniewicz, E., Villier, L., Leonide, P. & Struck, U. An early Hettangian coral reef in southern France: Implications for the end-Triassic reef crisis. PALAIOS 24, 657–671 (2009).ADS 

Google Scholar 
Stanley, G. D. & Beauvais, L. Middle Jurassic corals from the Wallowa terrane, west-central Idaho. J. Paleontol. 64, 352–362 (1990).
Google Scholar 
Gretz, M., Lathuilière, B., Martini, R. & Bartolini, A. The Hettangian corals of the Isle of Skye (Scotland): An opportunity to better understand the palaeoenvironmental conditions during the aftermath of the Triassic–Jurassic boundary crisis. Palaeogeogr. Palaeoclimatol. Palaeoecol. 376, 132–148 (2013).
Google Scholar 
Reolid, M., Molina, J. M., Löser, H., Navarro, V. & Ruiz-Ortiz, P. A. Coral biostromes of the Middle Jurassic from the Subbetic (Betic Cordillera, southern Spain): facies, coral taxonomy, taphonomy, and palaeoecology. Facies 55, 575–593 (2009).
Google Scholar 
Pandey, D. K., Lathuilière, B., Fürsich, F. T. & Kuldeep, S. The oldest Jurassic cyathophorid coral (Scleractinia) from siliciclastic environments of the Kachchh Basin, western India. Paläontol. Z. 76, 347–356 (2002).
Google Scholar 
Löser, H. & Heinrich, M. New coral genera and species from the Rußbach and Gosau area (Upper Cretaceous; Austria). Palaeodiversity 11, 127–149 (2018).
Google Scholar 
Stanley, G. D. & Whalen, M. T. Triassic corals and spongiomorphs from Hells Canyon, Wallowa terrane, Oregon. J. Paleontol. 63, 800–819 (1989).
Google Scholar 
Gill, G. A., Santantonio, M. & Lathuilière, B. The depth of pelagic deposits in the Tethyan Jurassic and the use of corals: an example from the Apennines. Sediment. Geol. 166, 311–334 (2004).ADS 

Google Scholar 
Baron-Szabo, R. C., Hamedani, A. & Senowbari-Daryan, B. Scleractinian corals from lower cretaceous deposits north of Esfahan (central Iran). Facies 48, 199–215 (2003).
Google Scholar 
Lathuilière, B., Baron-Szabo, R. C., Charbonnier, S. & Pacaud, J.-M. The Mesozoic scleractinian genus Adelocoenia (Stylinidae) and its Jurassic species. Carnets Géologie Noteb. Geol. 20, 367–406 (2020).
Google Scholar 
Roniewicz, E. & Stanley, G. D. Middle Triassic cnidarians from the New Pass Range, Central Nevada. J. Paleontol. 72, 246–256 (1998).
Google Scholar 
Shepherd, H. M. E., Stanley, G. D. & Amirhassankhani, F. Norian to Rhaetian scleractinian corals in the Ferdows Patch Reef (Nayband Formation, east central Iran). J. Paleontol. 86, 801–812 (2012).
Google Scholar 
Budd, A. F. & Wallace, C. C. First record of the Indo-Pacific reef coral genus Isopora in the Caribbean Region: two new species from the Neogene of Curaçao, Netherlands Antilles. Palaeontology 51, 1387–1401 (2008).
Google Scholar 
Pandolfi, J. M. A new, extinct pleistocene reef coral from the Montastraea “annularis” species complex. J. Paleontol. 81, 472–482 (2007).
Google Scholar 
El-Asa’ad, G. M. A. Oxfordian hermatypic corals from Central Saudi Arabia. Geobios 24, 267–287 (1991).
Google Scholar 
Masse, J.-P., Morycowa, E. & Fenerci-Masse, M. Valanginian-Hauterivian scleractinian coral communities from the Marseille region (SE France). Cretac. Res. 30, 178–192 (2009).
Google Scholar 
El-Sorogy, A. S. & Al-Kahtany, K. M. Contribution to the scleractinian corals of Hanifa Formation, Upper Jurassic, Jabal Al-Abakkayn, central Saudi Arabia. Hist. Biol. 27, 90–102 (2015).
Google Scholar 
Beauvais, L. & Stump, T. E. Corals, molluscs, and paleogeography of late Jurassic strata of the Cerro Pozo Serna, Sonora, Mexico. Palaeogeogr. Palaeoclimatol. Palaeoecol. 19, 275–301 (1976).
Google Scholar 
Roniewicz, E., Stanley, G. D., da Costa Monteiro, F. & Grant-Mackie, J. A. Late Triassic (Carnian) corals from Timor-Leste (East Timor): their identity, setting, and biogeography. Alcheringa Australas. J. Palaeontol. 29, 287–303 (2005).
Google Scholar 
Stanley, G. D. & Onoue, T. Upper Triassic reef corals from the Sambosan Accretionary Complex, Kyushu, Japan. Facies 61, 1 (2015).
Google Scholar 
Melnikova, G. K. & Roniewicz, E. Early Jurassic corals with dominating solitary growth forms from the Kasamurg Mountains, Central Asia. Palaeoworld 26, 124–148 (2017).
Google Scholar 
Stanley, G. D. & Beauvais, L. Corals from an Early Jurassic coral reef in British Columbia: refuge on an oceanic island reef. Lethaia 27, 35–47 (1994).
Google Scholar 
Caruthers, A. H. & Stanley, G. D. Systematic analysis of Upper Triassic silicified scleractinian corals from Wrangellia and the Alexander Terrane, Alaska and British Columbia. J. Paleontol. 82, 470–491 (2008).
Google Scholar 
Roniewicz, E. & Stanley, G. D. Upper Triassic corals from Nevada, western North America, and the implications for paleoecology and paleogeography. J. Paleontol. 87, 934–964 (2013).
Google Scholar 
Lathuilière, B. Coraux constructeurs du Bajocien inférieur de France. 1ere partie. Geobios 33, 51–72 (2000).
Google Scholar 
Morycowa, E. Supplemental data on Triassic (Anisian) corals from Upper Silesia (Poland). Ann. Soc. Geol. Pol. https://doi.org/10.14241/asgp.2018.001 (2018).Budd, A. F. & Bosellini, F. R. Revision of Oligocene Mediterranean meandroid corals in the scleractinian families Mussidae, Merulinidae and Lobophylliidae. J. Syst. Palaeontol. 14, 771–798 (2016).
Google Scholar 
Roniewicz, E. Early Norian (Triassic) Corals from the Northern Calcareous Alps, Austria, and the Intra-Norian Faunal Turnover. Acta Palaeontol. Pol. 56, 401–428 (2011).
Google Scholar 
Budd, A. F., Adrain, T. S., Park, J. W., Klaus, J. S. & Johnson, K. G. The Neogene Marine Biota of Tropical America (“NMITA”) Database: Integrating Data from the Dominican Republic Project. in Evolutionary Stasis and Change in the Dominican Republic Neogene (eds. Nehm, R. H. & Budd, A. F.) vol. 30 301–310 (Springer Netherlands, 2008).Mielnikova, G. Monstroseris, a new Upper Triassic scleractinian coral from Iran. Acta Palaeontol. Pol. 34, 71–74 (1989).
Google Scholar 
Löser, H. Taxonomy, stratigraphic distribution and palaeobiogeography of the Early Cretaceous coral genus Holocystis. Rev. Mex. Cienc. Geológicas 23, 288–301 (2006).
Google Scholar 
Löser, H. Corals from the Maastrichtian Ocozocoautla Formation (Chiapas, Mexico)-a closer look. Rev. Mex. Cienc. Geológicas 29, 534–550 (2012).
Google Scholar 
Löser, H. The Barremian coral fauna of the Serre de Bleyton mountain range (Drôme, SE France). Ann. Naturhistorischen Mus. Wien Ser. Für Mineral. Petrogr. Geol. Paläontol. Anthropol. Prähistorie 112, 575–612 (2010).
Google Scholar 
Löser, H., García-Barrera, P., Mendoza-Rosales, C. C. & Ortega-Hernández, J. Corals from the Early Cretaceous (Barremian – Early Albian) of Puebla (Mexico) – Introduction and Family Stylinidae. Rev. Mex. Cienc. Geológicas 30, 385–403 (2013).
Google Scholar 
Morycowa, E., Masse, J.-P., Arias, C. & Minondo, L. V. Montlivaltia multiformis Toula (Scleractinia) from the Aptian of the Prebetic domain (SE Spain). Span. J. Palaeontol. 16, 131–144 (2001).
Google Scholar 
Morycowa, E. & Masse, J.-P. Actinaraeopsis ventosiana, a new scleractinian species from the Lower Cretaceous of Provence (SE France). Ann. Soc. Geol. Pol. 77, 141–145 (2007).
Google Scholar 
Stolarski, J. & Taviani, M. Oligocene scleractinian corals from CRP- 3 drillhole, McMurdo Sound (Victoria Land Basin, Antarctica). Terra Antarct. 8, 1–4 (2001).
Google Scholar 
Morycowa, E. & Marcopoulou-Diacantoni, A. Albian corals from the Subpelagonian zone of Central Greece (Agrostylia, Parnassos region). Ann. Soc. Geol. Pol. 72, 1–65 (2002).
Google Scholar 
Morycowa, E. & Roniewicz, E. Revision of the genus Cladophyllia and description of Apocladophyllia gen. n.(Cladophylliidae fam. n., Scleractinia). Acta Palaeontol. Pol. 35, 165–190 (1990).
Google Scholar 
Morycowa, E. & Masse, J.-P. Lower Cretaceous Microsolenina (Scleractinia) from Provence (southern France). Ann. Soc. Geol. Pol. 79, 97–140 (2009).
Google Scholar 
Squires, R. L. & Demetrion, R. A. Paleontology of the Eocene Bateque Formation, Baja California Sur, Mexico. Contrib. Sci. 434, 1–55 (1992).
Google Scholar 
Wells, J. W. Cretaceous, Tertiary, and Recent Corals, a Sponge, and an Alga from Venezuela. J. Paleontol. 18, 429–447 (1944).
Google Scholar 
Morycowa, E. & Decrouez, D. Early Aptian scleractinian corals from the Upper Schrattenkalk of Hergiswil (Lucerne region, Helvetic Zone of central Switzerland). Rev. Paléobiol. 25, 791 (2006).
Google Scholar 
Stolarski, J. Paleogene corals from Seymour Island, Antarctic Peninsula. Palaeontol. Pol. 55, 1–63 (1996).
Google Scholar 
Vaughan, T. W. New Corals: One Recent, Alaska; Three Eocene, Alabama and Louisiana. J. Paleontol. 15, 280–284 (1941).
Google Scholar 
Stolarski, J. & Russo, A. Microstructural diversity of the stylophyllid [Scleractinia] skeleton. Acta Palaeontol. Pol. 47, (2002).Roniewicz, E. Jurassic scleractinian coral Thamnoseris Etallon, 1864 (Scleractinia), and its homeomorphs. Acta Palaeontol. Pol. 24, 51–70 (1979).
Google Scholar 
Lathuilière, B., Charbonnier, S. & Pacaud, J.-M. Nomenclatural and taxonomic acts and remarks for the revision of Jurassic corals. Zitteliana 89, 133–150 (2017).
Google Scholar 
Roniewicz, E. Upper Kimmeridgian Scleractinia of Pomerania (Poland). Ann. Soc. Geol. Pol. 47, 613–622 (1977).
Google Scholar 
Roniewicz, E. Scleractinia from the Upper Portlandian of Tisbury, Wiltshire, England. Acta Palaeontol. Pol. 15, 519–541 (1970).
Google Scholar 
Roniewicz, E. Kimmeridgian-Valanginian reef corals from the Moesian platform from Bulgaria. Ann. Soc. Geol. Pol. 78, 91–134 (2008).
Google Scholar 
Ricci, C., Lathuiliere, B. & Rusciadelli, G. Coral communities, zonation and paleoecology of an Upper Jurassic reef complex (Ellipsactinia Limestones, Central Apennines, Italy). Riv. Ital. Paleontol. E Stratigr. 124, 433–508 (2018).
Google Scholar 
Pandey, D. K. et al. Jurassic corals from southern Tunisia. Zitteliana A45, 3–34 (2005).
Google Scholar 
Pandey, D. K. et al. Jurassic corals from the Shemshak Formation of the Alborz Mountains, Iran. Zitteliana A46, 41–74 (2006).
Google Scholar 
Pandey, D. K. & Fürsich, F. T. Contributions to the Jurassic of Kachchh, Western India I. The coral fauna. Beringeria 8, 3–69.Morycowa, E. & Mišík, M. Upper Jurassic shallow-water scleractinian corals from the Pieniny Klippen Belt (Western Carpathians, Slovakia). Geol. Carpathica 56, (2005).Pandey, D. K. et al. Lower Cretaceous corals from the Koppeh Dagh, NE-Iran. Zitteliana A47, 3–52 (2007).
Google Scholar 
Morycowa, E. Corals from the Tithonian carbonate complex in the Dąbrowa Tarnowska–Szczucin area (Polish Carpathian Foreland). Ann. Soc. Geol. Pol. 82, 1–38 (2012).
Google Scholar 
Baron-Szabo, R. Corals of the Theresienstein reef (Upper Turonian-Coniacian, Salzburg, Austria). Proc. Biol. Soc. Wash. 10, 257–268 (2001).
Google Scholar 
Morycova, E. Middle Triassic Scleractinia from the Cracow-Silesia region, Poland. Acta Palaeontol. Pol. 33, 91–121 (1988).
Google Scholar 
El-Asa’ad, G. M. A. Callovian colonial corals from the Tuwaiq Mountain Limestone of Saudi Arabia. Paleontology 32, 675–684 (1989).
Google Scholar 
Roniewicz, E. & Michalik, J. Rhaetian scleractinian corals in the Western Carpathians. Geol. Carpathica 49, 391–399 (1998).
Google Scholar 
Roniewicz, E. & Michalik, J. Carnian corals from the Male Karpaty Mountains, Western Carpathians, Slovakia. Geol. Carpathica 53, 149–157 (2002).
Google Scholar 
Roniewicz, E. Rhaetian corals of the Tatra Mts. Acta Geol. Pol. 24, 97–116 (1974).
Google Scholar 
Turnšek, D. et al. Contributions to the fauna (corals, brachiopods) and stable isotopes of the Late Triassic Steinplatte reef/basin-complex, Northern Calcareous Alps, Austria. Abh. Geol. Bundensanstalt 56, 121–142 (1999).
Google Scholar 
Roniewicz, E. Upper Triassic Solitary Corals from the Gosaukamm and other North Alpine Regions. Sitzungsberichte Biol. Wiss. Erdwissenschaften 3–41 (1995).Wells, J. W. & Jenks, W. F. Mesozoic invertebrate faunas of Peru. Part 3, Lower Jurassic corals from the Arequipa region. Am. Mus. Novit. 1631 (1953).Turnšek, D. & Senowbari-Daryan, B. Upper Triassic (Carnian-Lowermost Norian) Corals from the Pantokrator Limestone of Hydra (Greece). AbhGeolB-A 50, (1994).Wells, J. W. Jurassic Corals from the Smackover Limestone, Arkansas. J. Paleontol. 16, 126–129 (1942).
Google Scholar 
Turnšek, D., Buser, S. & Debeljak, I. Liassic coral patch reef above the” Lithiotid limestone” on Trnovski gozd plateau, west Slovenia: Liasni koralni kopasti greben na” litiotidnem apnencu” v Trnovskem gozdu, zahodna Slovenija. Razpr. IV Razreda SAZU XLIV–1, 285–331 (2003).
Google Scholar 
Turnšek, D. & Košir, A. Early Jurassic corals from Krim Mountain, Slovenia. Razpr. IV Razreda SAZU XLI–1, 81–113 (2000).
Google Scholar 
Roniewicz, E. Triassic scleractinian corals of the Zlambach Beds, Northern Calcareous Alps, Austria. Denkschr Osterr Akad Wiss Math Nat K1 126, 1–152 (1989).
Google Scholar 
Roniewicz, E. Les scléractiniaires du Jurassique supérieur de la Dobrogea centrale, Roumanie. Palaeontol. Pol. 34, 17–121 (1976).
Google Scholar 
Kiessling, W., Kumar Pandey, D., Schemm-Gregory, M., Mewis, H. & Aberhan, M. Marine benthic invertebrates from the Upper Jurassic of northern Ethiopia and their biogeographic affinities. J. Afr. Earth Sci. 59, 195–214 (2011).ADS 

Google Scholar 
Lathuilière, B. Coraux constructeurs du Bajocien inférieur de France: 2ème partie. Geobios 33, 153–181 (2000).
Google Scholar 
Baron‐Szabo, R. C. Corals of the K/T‐boundary: Scleractinian corals of the suborders Astrocoeniina, Faviina, Rhipidogyrina and Amphiastraeina. J. Syst. Palaeontol. 4, 1–108 (2006).
Google Scholar 
Filkorn, H. F. & Pantoja-Alor, J. NOMENCLATURAL NOTES Mexican Cretaceous coral species (Cnidaria, Anthozoa, Scleractinia) described as new by Filkorn & Pantoja-Alor (2009), but deemed ‘unpublished’ under the International Code of Zoological Nomenclature: republication of data necessary for nomenclatural availability. Bull. Zool. Nomencl. 72, 93–101 (2015).
Google Scholar 
Olden, J. D., Poff, N. L. & Bestgen, K. R. Trait Synergisms and the Rarity, Extirpation, and Extinction Risk of Desert Fishes. Ecology 89, 847–856 (2008).PubMed 

Google Scholar 
Schleuning, M. et al. Trait-Based Assessments of Climate-Change Impacts on Interacting Species. Trends Ecol. Evol. 35, 319–328 (2020).PubMed 

Google Scholar 
Solan, M., Aspden, R. J. & Paterson, D. M. Marine Biodiversity and Ecosystem Functioning: Frameworks, Methodologies, and Integration. (OUP Oxford, 2012).Suding, K. N. et al. Scaling environmental change through the community-level: a trait-based response-and-effect framework for plants. Glob. Change Biol. 14, 1125–1140 (2008).ADS 

Google Scholar 
Finnegan, S. et al. Paleontological baselines for evaluating extinction risk in the modern oceans. Science https://doi.org/10.1126/science.aaa6635 (2015).Yasuhara, M. & Deutsch, C. A. Paleobiology provides glimpses of future ocean. Science https://doi.org/10.1126/science.abn2384 (2022).Cooley, S. et al. Ocean and coastal ecosystems and their services. in Climate change 2022: Impacts, adaptation and vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel of Climate Change (IPCC) (eds. Pörtner, H.-O. et al.) (Cambridge University Press, 2022). More

Xie, Y., Fang, M. & Shauman, K. STEM education. Annu. Rev. Sociol. 41, 331–357 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
Chen, X. STEM attrition: College students’ paths into and out of STEM fields. National Center for Education Statistics. Retrieved from http://ies.ed.gov/pubsearch/pubsinfo.asp?pubid=2014001rev. Accessed 22 September 2021.Huang G, Taddese N, Walter E (2000) Entry and persistence of women and minorities in college science and engineering education. National Center for Education Statistics. Retrieved from https://eric.ed.gov/?id=ED566411. Accessed 22 September 2021.Hurtado, S., Eagan, K., & Chang, M. Degrees of Success: Bachelor’s Degree Completion Rates among Initial STEM Majors (Higher Education Research Institute, Los Angeles, CA) (2010).National Science Foundation, Broadening Participation Working Group (2014) Pathways to broadening participation in response to the CEOSE 2011–2012 recommendation. National Science Foundation. Retrieved from https://www.nsf.gov/pubs/2015/nsf15037/nsf15037.pdf. Accessed 22 Sep 2021.James, S. M. & Singer, S. R. From the NSF: The National Science Foundation’s investments in broadening participation in science, technology, engineering, and mathematics education through research and capacity building. CBE Life Sci. Educ. 15(3), 1–8 (2016).Article 

Google Scholar 
Smith, B. L., MacGregor, J., Matthews, R. & Gabelnick, F. Learning communities: Reforming undergraduate education (Jossey-Bass, 2004).
Google Scholar 
Andrade, M. S. Learning communities: Examining positive outcomes. J. Coll. Stud. Ret. 9(1), 1–20 (2007).Article 

Google Scholar 
Maton, K. I., Pollard, S. A., McDougall Weise, T. V. & Hrabowski, F. A. Meyerhoff Scholars Program: A strengths-based, institution-wide approach to increasing diversity in science, technology, engineering, and mathematics. Mt Sinai J. Med. 79(5), 610–623 (2012).PubMed 
PubMed Central 
Article 

Google Scholar 
Dagley, M., Georgiopoulos, M., Reece, A. & Young, C. Increasing retention and graduation rates through a STEM learning community. J. Coll. Stud. Ret. 18(2), 167–182 (2016).Article 

Google Scholar 
National Survey of Student Engagement (2015) Engagement Insights: Survey Findings on the Quality of Undergraduate Education—Annual Results 2015 (Bloomington, IN).Tinto, V. Leaving college: Rethinking the causes and cures of student attrition (University of Chicago Press, 1987).
Google Scholar 
Tinto, V. Learning better together: The impact of learning communities on student success. Higher Educ. Monogr. Ser. 1(8), 1–8 (2003).
Google Scholar 
Otto, S., Evins, M. A., Boyer-Pennington, M. & Brinthaupt, T. M. Learning communities in higher education: Best practices. Journal of Student Success and Retention 2(1), 1–20 (2015).
Google Scholar 
Boda, Z., Elmer, T., Vörös, A. & Stadtfeld, C. Short-term and long-term effects of a social network intervention on friendships among university students. Sci. Rep. 10(1), 1–2 (2020).Article 
CAS 

Google Scholar 
Hotchkiss, J. L., Moore, R. E. & Pitts, M. M. Freshman learning communities, college performance, and retention. Educ. Econ. 14(2), 197–210 (2006).Article 

Google Scholar 
Whalen, D. F. & Shelley, M. C. Academic success for STEM and non-STEM majors. J. STEM Educ. 11(1), 45–60 (2010).
Google Scholar 
Xu, D., Solanki, S., McPartlan, P. & Sato, B. EASEing students into college: The impact of multidimensional support for underprepared students. Educ. Res. 47(7), 435–450 (2018).Article 

Google Scholar 
Jaffee, D., Carle, A., Phillips, R. & Paltoo, L. Intended and unintended consequences of first-year learning communities: An initial investigation. J. First-Year Exp. Stud. Trans. 20(1), 53–70 (2008).
Google Scholar 
Tinto, V. & Goodsell, A. Freshman interest groups and the first-year experience: Constructing student communities in a large university. J. First Year Exp. Stud. Trans. 6(1), 7–28 (1994).
Google Scholar 
Domizi, D. Student perceptions about their informal learning experiences in a first-year residential learning community. J. First Year Exp. Stud. Transit. 20(1), 97–110 (2008).
Google Scholar 
Lee, D. S. & Lemieux, T. Regression discontinuity designs in economics. J. Econ. Lit. 2, 281–355 (2010).Article 

Google Scholar 
Jacob, R., Zhu, P., Somers, M.A., & Bloom, H. A Practical Guide to Regression Discontinuity (MDRC, New York, NY, 2012).Hays, R. B. & Oxley, D. Social network development and functioning during a life transition. J. Pers. Soc. Psychol. 50(2), 305–313 (1986).CAS 
PubMed 
Article 

Google Scholar 
Freeman, T. M., Anderman, L. H. & Jensen, J. M. Sense of belonging in college freshmen at the classroom and campus levels. J. Exp. Educ. 75(3), 203–220 (2007).Article 

Google Scholar 
Zumbrunn, S., McKim, C., Buhs, E. & Hawley, L. R. Support, belonging, motivation, and engagement in the college classroom: A mixed method study. Instr. Sci. 42(5), 661–684 (2014).Article 

Google Scholar 
Hasan, S. & Bagde, S. The mechanics of social capital and academic performance in an Indian college. Am. Sociol. Rev. 78(6), 1009–1032 (2013).Article 

Google Scholar 
Stadtfeld, C., Vörös, A., Elmer, T., Boda, Z. & Raabe, I. J. Integration in emerging social networks explains academic failure and success. Proc. Natl. Acad. Sci. USA 116(3), 792–797 (2019).CAS 
PubMed 
Article 

Google Scholar 
Kraemer, B. A. The academic and social integration of Hispanic students into college. Rev. High Educ. 20(2), 163–179 (1997).Article 

Google Scholar 
Nora, A. Two-year colleges and minority students’ educational aspirations: Help or hindrance. Higher Educ. Handb. Theory Res. 9(3), 212–247 (1993).
Google Scholar 
McCabe, J.M. Connecting in College: How Friendship Networks Matter for Academic and Social Success (University of Chicago Press, Chicago, IL, 2016).Felten, P., & Lambert, L. M. Relationship-rich Education: How Human Connections Drive Success in College (Johns Hopkins University Press, Baltimore, MD, 2020).Hallinan, M. T. The peer influence process. Stud. Educ. Eval. 7(3), 285–306 (1981).Article 

Google Scholar 
Thomas, S. L. Ties that bind: A social network approach to understanding student integration and persistence. J. Higher Educ. 71(5), 591–615 (2000).
Google Scholar 
Turetsky, K. M., Purdie-Greenaway, V., Cook, J. E., Curley, J. P. & Cohen, G. L. A psychological intervention strengthens students’ peer social networks and promotes persistence in STEM. Sci. Adv. 6(45), 1–10 (2020).Article 

Google Scholar 
Dokuka, S., Valeeva, D. & Yudkevich, M. How academic achievement spreads: The role of distinct social networks in academic performance diffusion. PLoS ONE 15(7), 1–16 (2020).Article 
CAS 

Google Scholar 
Epstein, J. L. & Karweit, N. (eds) Friends in school: Patterns of selection and influence in secondary schools (Academic Press, 1983).
Google Scholar 
Feld, S. L. The focused organization of social ties. AJS 86(5), 1015–1035 (1981).
Google Scholar 
Rivera, M. T., Soderstrom, S. B. & Uzzi, B. Dynamics of dyads in social networks: Assortative, relational, and proximity mechanisms. Annu. Rev. Sociol. 36, 91–115 (2010).Article 

Google Scholar 
Mollenhorst, G., Volker, B. & Flap, H. Changes in personal relationships: How social contexts affect the emergence and discontinuation of relationships. Soc. Netw. 37, 65–80 (2014).Article 

Google Scholar 
Thomas, R. J. Sources of friendship and structurally induced homophily across the life course. Sociol Perspect 62(6), 822–843 (2019).Article 

Google Scholar 
Kubitschek, W. N. & Hallinan, M. T. Tracking and students’ friendships. Soc. Psychol. Q 46, 1–5 (1998).Article 

Google Scholar 
Frank, K. A., Muller, C. & Mueller, A. S. The embeddedness of adolescent friendship nominations: The formation of social capital in emergent network structures. AJS 119(1), 216–253 (2013).PubMed 
PubMed Central 

Google Scholar 
Kossinets, G. & Watts, D. J. Origins of homophily in an evolving social network. AJS 115(2), 405–450 (2009).
Google Scholar 
Wimmer, A. & Lewis, K. Beyond and below racial homophily: ERG models of a friendship network documented on Facebook. AJS 116(2), 583–642 (2010).PubMed 

Google Scholar 
Hallinan, M. T. & Sørensen, A. B. Ability grouping and student friendships. Am. Educ. Res. J. 51, 485–499 (1985).Article 

Google Scholar 
Leszczensky, L. & Pink, S. Ethnic segregation of friendship networks in school: Testing a rational-choice argument of differences in ethnic homophily between classroom-and grade-level networks. Soc. Netw. 42, 18–26 (2015).Article 

Google Scholar 
DiMaggio, P. & Garip, F. Network effects and social inequality. Annu. Rev. Sociol. 54, 93–118 (2012).Article 

Google Scholar 
Johnson, A. M. ‘“I can turn it on when i need to”’: Pre-college Integration, culture, and peer academic engagement among black and Latino/a engineering Students. Sociol. Educ. 56, 1–20 (2019).Article 

Google Scholar 
Perry, B. L., Pescosolido, B. A. & Borgatti, S. P. Egocentric network analysis: Foundations, methods, and models (Cambridge University Press, 2018).Book 

Google Scholar 
Wasserman, S. & Faust, K. Social network analysis: Methods and applications (Cambridge University Press, 1994).MATH 
Book 

Google Scholar 
Hartup, W. W. & Stevens, N. Friendships and adaptation in the life course. Psychol. Bull. 121(3), 355 (1997).Article 

Google Scholar 
Vaquera, E. & Kao, G. Do you like me as much as I like you? Friendship reciprocity and its effects on school outcomes among adolescents. Soc. Sci. Res. 37(1), 55–72 (2008).PubMed 
PubMed Central 
Article 

Google Scholar 
Imbens, G. W. & Lemieux, T. Regression discontinuity designs: A guide to practice. J. Econom. 142(2), 615–635 (2008).MathSciNet 
MATH 
Article 

Google Scholar 
Imbens, G. W. & Angrist, J. D. Identification and estimation of local average treatment effects. Econometrica 62(2), 467–475 (1994).MATH 
Article 

Google Scholar 
Robins, G., Pattison, P., Kalish, Y. & Lusher, D. An introduction to exponential random graph (p*) models for social networks. Soc. Netw. 29(2), 173–191 (2007).Article 

Google Scholar 
Handcock, M. S., Hunter, D. R., Butts, C. T., Goodreau, S. M. & Morris, M. Statnet: Software tools for the representation, visualization, analysis and simulation of network data. J. Stat. Softw. 24(1), 1548–7660 (2008).PubMed 
PubMed Central 
Article 

Google Scholar 
Calonico, S., Cattaneo, M. D. & Titiunik, R. Optimal data-driven regression discontinuity plots. J. Am. Stat. Assoc. 110(512), 1753–1769 (2015).MathSciNet 
CAS 
MATH 
Article 

Google Scholar 
Duxbury, S. W. The problem of scaling in exponential random graph models. Sociol. Methods Res. https://doi.org/10.1177/0049124120986178:1-39 (2021).MathSciNet 
Article 

Google Scholar 
McPherson, M., Smith-Lovin, L. & Cook, J. M. Birds of a feather: Homophily in social networks. Annu. Rev. Sociol. 27(1), 415–444 (2001).Article 

Google Scholar 
Kadushin, C. Understanding social networks: Theories, concepts, and findings (Oxford University Press, 2012).
Google Scholar 
Flashman, J. Academic achievement and its impact on friend dynamics. Sociol. Educ. 85(1), 61–80 (2012).PubMed 
PubMed Central 
Article 

Google Scholar 
Carrell, S. E., Sacerdote, B. I. & West, J. E. From natural variation to optimal policy? The importance of endogenous peer group formation. Econometrica 81(3), 855–882 (2013).MathSciNet 
MATH 
Article 

Google Scholar 
Cox, A. B. Cohorts, ‘“siblings”,’ and mentors: Organizational structures and the creation of social capital. Sociol. Educ. 90(1), 47–63 (2017).Article 

Google Scholar 
Valente, T. W. Network interventions. Science 337(6090), 49–53 (2012).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Nunn, L. M. College belonging: How first-year and first-generation students navigate campus life (Rutgers University Press, 2021).Book 

Google Scholar 
Garlick, R. Academic peer effects with different group assignment policies: Residential tracking versus random assignment. Am. Econ. J. Appl. Econ. 10(3), 345–369 (2018).Article 

Google Scholar 
Carrell, S. E., Fullerton, R. L. & West, J. E. Does your cohort matter? Measuring peer effects in college achievement. J. Labor. Econ. 27(3), 439–464 (2009).Article 

Google Scholar 
Lomi, A., Snijders, T. A., Steglich, C. E. & Torló, V. J. Why are some more peer than others? Evidence from a longitudinal study of social networks and individual academic performance. Soc. Sci. Res. 40(6), 1506–1520 (2011).PubMed 
PubMed Central 
Article 

Google Scholar 
Poldin, O., Valeeva, D. & Yudkevich, M. Which peers matter: How social ties affect peer-group effects. Res. High Educ. 57(4), 448–468 (2016).Article 

Google Scholar 
Raabe, I. J., Boda, Z. & Stadtfeld, C. The social pipeline: How friend influence and peer exposure widen the STEM gender gap. Sociol. Educ. 92(2), 105–123 (2019).Article 

Google Scholar 
Burt, R. S. Structural holes and good ideas. AJS 110(2), 349–399 (2004).
Google Scholar 
Oakes, J. Keeping track: How schools structure inequality (Yale University Press, 2005).
Google Scholar 
Park JJ et al. (2021) Who are you studying with? The role of diverse friendships in STEM and corresponding inequality. Res. High Educ. https://doi.org/10.1007/s11162-021-09638-8.Marsden, P. V. & Campbell, K. E. Measuring tie strength. Soc. Forces 63(2), 482–501 (1984).Article 

Google Scholar 
Mattie, H., Engø-Monsen, K., Ling, R. & Onnela, J. P. Understanding tie strength in social networks using a local “bow tie” framework. Sci. Rep. 8(1), 1–9 (2018).CAS 
Article 

Google Scholar 
Sørensen, A. B. Organizational differentiation of students and educational opportunity. Sociol. Educ. 43(4), 355–376 (1970).Article 

Google Scholar  More

All figures were produced using the R package ggplot2 v3.3.534.eBird and covariate dataeBird data are structured as follows. Birders submit observations as species checklists with counts of each species they identify. They report associated metadata, such as location, date and time, duration of the observation period, number of observers, and sampling protocol25,26,31. The birder indicates whether their checklist is “complete”; complete checklists yield inferred zeroes for all species not reported on a checklist.We retrieved the eBird Basic Dataset containing all eBird observations and sampling metadata. We extracted all complete checklists that occurred within the U.S. state of California between April 1 and June 30, 2019. Four survey-level covariates were retrieved from eBird checklist metadata as detection covariates: number of observers, checklist duration, date of year, and time of day; any checklist that failed to report one or more of these variables was dropped. Corresponding to best practices for use of eBird data, we filtered the data for quality according to the following criteria: we discarded checklists other than those following the “Stationary” survey protocol (observations made at a single spatial location) with duration shorter than 4 hours and at most 10 observers in the group31,35.We selected twenty circular regions of high sampling intensity with 10 km radii across California (Supplemental Fig. 1). These spanned the state’s many habitats including coastal, agricultural, wetland, and mountain areas, and contained active birding areas such as parks and human population centers. In each subregion, we selected 10 species with the highest reporting rate (proportion of checklists including that species) and 10 representing an intermediate reporting rate. An additional 10 species were selected that were detected in many regions to enable cross-region comparisons, yielding 407 species-subregion (SSR) datasets (with overlaps between the two species selection protocols; see Supplemental Section 2 for the full algorithm). Across 20 subregions, we accepted 6094 eBird checklists for analysis, each with an associated count (potentially zero) for each species. Observations were aggregated to sampling sites defined by a 50 m spatial grid. The 50 m grid was chosen to conservatively identify related surveys and was not motivated by biological processes, nor does it represent the sampling area of each survey. In this context, the concept of “closure” in the latent state is already suspect due to the fact that eBird checklist sampling areas are inconsistent. Data were processed in R using the ‘auk’ package36,37.An elevation surface for the state of California was retrieved from WorldClim at (8.3 times 10^{-3}) decimal degrees resolution using the R package raster38,39. This commonly used covariate was included as a baseline spatial covariate to enable comparison of estimation properties across sites, but its biological relevance to abundance is not crucial to our analysis31. Land cover data were retrieved from the LandFire GIS database’s Existing Vegetation Type layer40. For each unique survey location, a 500 m buffer was calculated around the reported location, and the percent of the buffer which was water, tree cover, agriculture or other vegetation (shrub or grassland) was calculated. We used the following five site-level covariates: elevation, and percent of the landscape within a 500 m buffer of the site that was water, trees, agricultural land, or other vegetation. We included six checklist-level covariates: duration, number of observers, time of day, time of day squared, Julian date, and Julian date squared. Covariates were dropped in datasets where only a single unique value was observed for that covariate.Model implementation and selectionWe considered four variants of the N-mixture model and two variants of the GLMM comprising a total of 6 distinct models, defined by the distributions used in the model or sub-model.The GLMM for count data that we considered is defined as$$begin{array}{*{20}l} {y_{{ij}} sim D(mu _{{ij}} ,[theta ])} hfill \ {log (mu _{{ij}} ) = beta _{0} + {mathbf{x}}_{{ij}}^{T} user2{beta } + alpha _{i} } hfill \ {alpha _{i} sim {mathcal{N}}(0,sigma _{alpha } )} hfill \ end{array}$$where (y_{ij}) is th jth observation at site i, D is a probability distribution (which may contain an extra parameter (theta) to account for overdispersion), (mu _{ij}) represents the mean expected count and is a logit-linear combination of observed site- and observation-level covariates (x_{ij}), (beta) are coefficients representing the effect of those covariates, (beta _0) is a log-scale intercept corresponding to the expected log count at the mean site (i.e. with all centered covariates set to 0), and (alpha _i) is the random effect of site i following a normal distribution. Due to the right skew of (exp (y_{ij})), by log-normal distribution theory the log of the expected count at the mean site is (beta _0 + 0.5 sigma _{alpha }^2). We considered two forms of this model, where D was either a Poisson or a negative binomial distribution, in the latter case with the extra parameter (theta).The N-mixture model is defined as$$begin{array}{*{20}l} {y_{{ij}} sim D_{w} (N_{i} ,p_{{ij}} ,[theta _{w} ])} hfill \ {N_{i} sim D_{b} (lambda _{i} ,[theta _{b} ])} hfill \ {{text{logit}}(p_{{ij}} ) = {text{}}{text{logit}}(p_{0} ) + {mathbf{x}}_{{ij(w)}} {mathbf{beta }}_{w} } hfill \ {log (lambda _{i} ) = log (lambda _{0} ) + {mathbf{x}}_{{i(b)}} {mathbf{beta }}_{b} } hfill \ {p_{0} = e^{{frac{{phi _{1} + phi _{2} }}{2}}} } hfill \ {lambda _{0} = e^{{frac{{phi _{1} – phi _{2} }}{2}}} } hfill \ end{array}$$where (D_b) and (D_w) are probability distributions representing between- and within-site variation, respectively; (N_i) is a site-level latent variable normally representing the “true” abundance at site i; (p_{ij}) is the detection probability of each individual on the jth observation event at site i; (lambda _i) is the mean abundance at site i; and (x_{(w)}) and (x_{(b)}) are covariate vectors for detection and abundance, respectively, with corresponding coefficients (beta _w) and (beta _b). For reasons described below, we reparameterize the intercept parameters of the N-mixture submodels, (log (lambda _0)) and (text{ logit }(p_0)), in terms of two orthogonal parameters (phi _1 = log (lambda _0 p_0)) and (phi _2=log (p_0 / lambda _0)). Now (phi _1) and (phi _2) represent the expected log count and the contrast between detection and abundance, respectively, at the mean site. This parameterization allows us to investigate stability of parameter estimation. The log-scale expected count of the N-mixture model is (phi _1 = log (lambda _0 p_0)), analogous to (beta _0 + 0.5 sigma _{alpha }^2) in the GLMM (see Supplemental Section 6). Each submodel distribution D could include or not include an overdispersion parameter ((theta _w) and (theta _b)), yielding four possible N-mixture model variants: binomial-Poisson (B-P), binomial-negative binomial (B-NB), beta-binomial-Poisson (BB-P), and beta-binomial-negative binomial (BB-NB)8,11.We chose to fit models with maximum likelihood estimation (MLE) for computational feasibility and because key diagnostic tools, such as AIC and methods for checking goodness-of-fit and autocorrelation, were best suited to MLE estimation15. We fit N-mixture models with the nimble and nimbleEcology R packages starting with a conservatively large choice of K, the truncation value of the infinite sum in the N-mixture likelihood calculation33,41 (see Supplemental Section 4 for a discussion of maximum likelihood estimation with NIMBLE). We fit GLMMs with the R package glmmTMB42. We applied forward AIC selection to choose the best covariates for each model with each dataset (illustrated in Fig. S1). One spatial covariate (elevation) and two checklist metadata covariates (duration and number of observers) were treated as a priori important and were included in all models. In the N-mixture model, checklist-specific sampling metadata were only allowed in the detection submodel, while land cover covariates and the interactions between them were allowed in both the detection and abundance submodels. Interactions were dropped in datasets when interaction values showed a correlation of > 0.8 with one of their first-order terms. In N-mixture models, additions to both submodels were considered simultaneously during forward AIC selection.For comparisons between models, we selected a heuristic threshold of (Delta text {AIC} > 2) to say that one model is supported over another30.Fit, estimation, and computationGoodness-of-fitWe used the Kolmogorov-Smirnov (KS) test, a p-value based metric, to evaluate goodness-of-fit on each selected model. For GLMMs, residuals were obtained using the DHARMa R package’s ‘simulateResiduals’ and the KS test was applied using the ‘testUniformity’ function43. For N-mixture models, we considered the site-sum randomized quantile (SSRQ) residuals described by Knape et al.15, computing these for each N-mixture model and running a KS test against the normal CDF. We assumed that covariate effects did not vary by space within subregions and chose not to use spatially explicit models31,44. To test this assumption, we applied Moran’s I test to the SSRQ or DHARMa-generated residuals for each site or observation.Parameter estimationWe compared two abundance parameters of interest across models: coefficients for elevation and log expected count at a standard site (in the GLMM, (beta _0 + 0.5 sigma _alpha ^2); in the N-mixture model, (log (lambda _0 p_0))). We examined absolute differences in point estimates and the log-scale ratios between their standard errors.Stability of estimated parametersAttempting to decompose the expected value of observed data into within- and between-site components can lead to ridged likelihood surfaces with difficult-to-estimate optima. Kéry found that instability of model estimates with increasing K occurred when there was a likelihood tradeoff between detection and abundance, resulting in a tendency in abundance toward positive infinity restrained only by K10. Dennis et al. showed that N-mixture models could in fact yield estimates of absolute abundance at infinity18. We interpreted this as a case of a boundary parameter estimate rather than non-identifiability and explored it by reparametrizing as follows. We estimated the intercepts for detection and abundance with two orthogonal parameters (rotated in log space) (phi _1 = log (lambda _0 p_0)) and (phi _2 = log (p_0 / lambda _0)), where (lambda _0) and (p_0) are real-scale abundance and detection probability at the mean site. We hypothesized that in unstable cases, (phi _1), log expected count, is well-informed by the data, but (phi _2), the contrast between abundance and detection, is not well-informed, corresponding to a likelihood ridge as (phi _2 rightarrow -infty) due to detection probability approaching 0 and abundance approaching infinity. This reparameterization isolates the likelihood ridge to one parameter direction, similar to a boundary estimate as (exp (phi _2) rightarrow 0). Boundary estimates occur in many models and are distinct from non-identifiability in that they result from particular datasets. Confidence regions extending from a boundary estimate may include reasonable parameters, reflecting that there is information in the data. We defined a practical lower bound for (phi _2). When (phi _2) was estimated very near that bound, we conditioned on that boundary for (phi _2) when estimating confidence regions for other parameters.In the N-mixture case, diagnosing a boundary estimate for (phi _2) is made more difficult by the need to increase K for large negative (phi _2) to calculate the likelihood accurately. We used an approach like that of Dennis et al.18 to numerically diagnose unstable cases. For each N-mixture variant in each SSR, the final model was refitted twice, using values of K 2000 and 4000 greater than the initial choice. Estimates were considered unstable if the absolute value of the difference in AIC between these two large-K refits was above a tolerance of 0.1. We monitored whether MLE estimates of (phi _1) and (phi _2) also varied with increasing K.Evaluating the fast N-mixture calculationWe extended previous work by Meehan et al. to drastically improve the efficiency of N-mixture models using negative binomial or beta-binomial distributions in submodels45 (see Supplemental Section 3).We ran benchmarks of this likelihood calculation for a single site against the traditional algorithm, which involves iterating over values of N to compute a truncated infinite sum. We calculated the N-mixture likelihood at 5,000 sites and compared the computation time between the two methods for all four N-mixture model variations. We ran benchmarks along gradients of (text {length}(y_i)) (number of replicate observations at the simulated site) and K (the upper bound of the truncated infinite sum) for each variant. More

Berthold, P. Bird Migration: A General Survey. (Oxford University Press, 2001).Harrison, X. A., Blount, J. D., Inger, R., Norris, D. R. & Bearhop, S. Carry-over effects as drivers of fitness differences in animals. J. Anim. Ecol. 80, 4–18 (2011).PubMed 
Article 

Google Scholar 
Webster, M. S., Marra, P. P., Haig, S. M., Bensch, S. & Holmes, R. T. Links between worlds: Unraveling migratory connectivity. Trends Ecol. Evol. 17, 76–83 (2002).Article 

Google Scholar 
Saurola, P., Valkama, J. & Velmala, W. Suomen rengastusatlas Osa I/The Finnish Bird Ringing Atlas Vol. I. (Finnish Museum of Natural History and Ministry of Environment, 2013).Bergman, G. Allin ja mustalinnun muuttokannat keväällä 1960 (in Finnish). Suomen Riista 14, 69–74 (1961).
Google Scholar 
Skov, H. et al. Waterbird Populations and Pressures in the Baltic Sea. (TemaNord 550, 2011).Grenquist, P. Öljytuhoista Suomen aluevesillä v. 1948–1955. Suomen Riista 10, 105–116 (1956).Hario, M., Rintala, J. & Nordenswan, G. Dynamics of wintering long-tailed ducks in the Baltic Sea–the connection with lemming cycles, oil disasters, and hunting. Suomen Riista 55, 83–96 (2009).
Google Scholar 
Ellermaa, M. & Pettay, T. Põõsaspean niemen arktinen muutto syksyllä 2004. Linnut Vuosik. 2005, 99–112 (2005).
Google Scholar 
Delany, S. & Scott, D. Waterbird Population Estimates. (Wetlands International, 2006).Nolet, B. A. et al. Faltering lemming cycles reduce productivity and population size of a migratory Arctic goose species. J. Anim. Ecol. 82, 804–813 (2013).PubMed 
PubMed Central 
Article 

Google Scholar 
Sokolov, V., Vardeh, S. & Quillfeldt, P. Long-tailed Duck (Clangula hyemalis) ecology: Insights from the Russian literature. Part 1: Asian part of the Russian breeding range. Polar Biol. 42, 2259–2276 (2019).Article 

Google Scholar 
Summers, R. W. & Underhill, L. G. Factors related to breeding production of Brent Geese Branta b. bernicla and waders (Charadrii) on the Taimyr Peninsula. Bird Study 34(161), 171 (1987).
Google Scholar 
Summers, R. W., Underhill, L. G. & Syroechkovski, J. The breeding productivity of dark-bellied brent geese and curlew sandpipers in relation to changes in the numbers of arctic foxes and lemmings on the Taimyr Peninsula Siberia. Ecography 21, 573–580 (1998).Article 

Google Scholar 
Underhill, L. G. et al. Breeding of waders (Charadrii) and Brent Geese Branta bernicla bernicla at Pronchishcheva Lake, northeastern Taimyr, Russia, in a peak and a decreasing lemming year. Ibis 135, 277–292 (1993).Article 

Google Scholar 
Gauthier, G., Bëty, J., Giroux, J.-F. & Rochefort, L. Trophic interactions in a High Arctic snow goose colony. Integr. Comp. Biol. 44, 119–129 (2004).PubMed 
Article 

Google Scholar 
Elton, C. Voles, Mice and Lemmings: Problems in Population Dynamics. (Clarendon Press, 1942).Ehrich, D. et al. Documenting lemming population change in the Arctic: Can we detect trends?. Ambio https://doi.org/10.1007/s13280-019-01198-7 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Kokorev, Y. I. & Kuksov, V. A. Population dynamics of lemmings, Lemmus sibirica and Dicrostonyx torquatus, and Arctic Fox Alopex lagopus on the Taimyr peninsula, Siberia, 1960–2001. Ornis Svecica 12, 139–145 (2002).
Google Scholar 
Angerbjörn, A., Tannerfeldt, M. & Erlinge, S. Predator-prey relationships: Arctic foxes and lemmings. J. Anim. Ecol. 68, 34–49 (1999).Article 

Google Scholar 
Fauteux, D., Gauthier, G. & Berteaux, D. Seasonal demography of a cyclic lemming population in the Canadian Arctic. J. Anim. Ecol. 84, 1412–1422 (2015).PubMed 
Article 

Google Scholar 
Gilg, O., Sittler, B. & Hanski, I. Climate change and cyclic predator–prey population dynamics in the high Arctic. Glob. Chang. Biol. 15, 2634–2652 (2009).ADS 
Article 

Google Scholar 
Berryman, A. A. The orgins and evolution of predator-prey theory. Ecology 73, 1530–1535 (1992).Article 

Google Scholar 
Framstad, E., Stenseth, N. C., Bjørnstad, O. N. & Falck, W. Limit cycles in Norwegian lemmings: Tensions between phase-dependence and density-dependence. Proc. R Soc. London. Ser. B Biol. Sci. 264, 31–38 (1997).ADS 
Article 

Google Scholar 
Hanski, I. & Korpimaki, E. Microtine rodent dynamics in northern Europe: Parameterized models for the predator-prey interaction. Ecology 76, 840–850 (1995).Article 

Google Scholar 
May, R. M. Limit cycles in predator-prey communities. Science 177, 900–902 (1972).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Gilg, O., Hanski, I. & Sittler, B. Cyclic dynamics in a simple vertebrate predator-prey community. Science 302, 866–868 (2003).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Juhasz, C. C., Shipley, B., Gauthier, G., Berteaux, D. & Lecomte, N. Direct and indirect effects of regional and local climatic factors on trophic interactions in the Arctic tundra. J. Anim. Ecol. 89, 704–715 (2020).PubMed 
Article 

Google Scholar 
McKinnon, L., Berteaux, D., Gauthier, G. & Bêty, J. Predator-mediated interactions between preferred, alternative and incidental prey in the arctic tundra. Oikos 122, 1042–1048 (2013).Article 

Google Scholar 
Angelstam, P., Lindström, E. & Widén, P. Role of predation in short-term population fluctuations of some birds and mammals in Fennoscandia. Oecologia 62, 199–208 (1984).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Ehrich, D. et al. Vole abundance and reindeer carcasses determine breeding activity of Arctic foxes in low Arctic Yamal Russia. BMC Ecol. 17, 1–13 (2017).Article 

Google Scholar 
Brook, R. W., Duncan, D. C., Hines, J. E., Carrière, S. & Clark, R. G. Effects of small mammal cycles on productivity of boreal ducks. Wildlife Biol. 11, 3–11 (2005).Article 

Google Scholar 
Guillemain, M. et al. Effects of climate change on European ducks: what do we know and what do we need to know?. Wildlife Biol. 19, 404–419 (2013).Article 

Google Scholar 
Pehrsson, O. Duckling production of the Oldsquaw in relation to spring weather and small-rodent fluctuations. Can. J. Zool. 64, 1835–1841 (1986).Article 

Google Scholar 
ACIA. Impacts of a Warming Arctic: Arctic Climate Impact Assessment. (Cambridge University Press, 2004).Høye, T. T., Post, E., Meltofte, H., Schmidt, N. M. & Forchhammer, M. C. Rapid advancement of spring in the High Arctic. Curr. Biol. 17, R449–R451 (2007).PubMed 
Article 
CAS 

Google Scholar 
Post, E. et al. Ecological dynamics across the Arctic associated with recent climate change. Science 325, 1355–1358 (2009).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Kausrud, K. L. et al. Linking climate change to lemming cycles. Nature 456, 93–97 (2008).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Berteaux, D. et al. Effects of changing permafrost and snow conditions on tundra wildlife: Critical places and times. Arct. Sci. 3, 65–90 (2017).Article 

Google Scholar 
Bilodeau, F., Gauthier, G. & Berteaux, D. The effect of snow cover on lemming population cycles in the Canadian High Arctic. Oecologia 172, 1007–1016 (2013).ADS 
PubMed 
Article 

Google Scholar 
Madsen, F. J. On the food habits of the diving ducks in Denmark. Danish Rev. Game Biol. 3, 2–83 (1954).
Google Scholar 
Nilsson, L. Habitat selection, food choice, and feeding habits of diving ducks in coastal waters of South Sweden during the non-breeding season. Ornis Scand. 3, 55–78 (1972).Article 

Google Scholar 
Žydelis, R. & Ruškytė, D. Winter foraging of long-tailed ducks (Clangula hyemalis) exploiting different benthic communities in the Baltic Sea. Wilson Bull. 117, 133–141 (2005).Article 

Google Scholar 
Skabeikis, A. et al. Effect of round goby (Neogobius melanostomus) invasion on blue mussel (Mytilus edulis trossulus) population and winter diet of the long-tailed duck (Clangula hyemalis). Biol. Invasions 21, 911–923 (2019).Article 

Google Scholar 
Laursen, K. & Møller, A. P. Long-Term changes in nutrients and mussel stocks are related to numbers of breeding eiders Somateria mollissima at a large Baltic colony. PLoS ONE 9, e95851 (2014).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Carstensen, J., Andersen, J. H., Gustafsson, B. G. & Conley, D. J. Deoxygenation of the baltic sea during the last century. Proc. Natl. Acad. Sci. USA 111, 5628–5633 (2014).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Savchuk, O. P. Large-scale nutrient dynamics in the Baltic Sea, 1970–2016. Front. Mar. Sci. 5, 95 (2018).Article 

Google Scholar 
Møller, A. P., Flensted-Jensen, E. & Mardal, W. Agriculture, fertilizers and life history of a coastal seabird. J. Anim. Ecol. 76, 515–525 (2007).PubMed 
Article 

Google Scholar 
Møller, A. P., Thorup, O. & Laursen, K. Predation and nutrients drive population declines in breeding waders. Ecol. Appl. 28, 1292–1301 (2018).PubMed 
Article 

Google Scholar 
Gelman, A., Carlin, J. B., Stern, H. S. & Rubin, D. B. Bayesian Data Analysis. (Chapman & Hall/CRC, 2004).Lebreton, J.-D. & Gimenez, O. Detecting and estimating density dependence in wildlife populations. J. Wildl. Manage. 77, 12–23 (2013).Article 

Google Scholar 
Bergman, G. The spring migration of the Long-tailed Duck and the Common Scoter in western Finland. Ornis Fenn. 51, 129–145 (1974).
Google Scholar 
Richardson, W. J. Timing and amount of bird migration in relation to weather: A Review. Oikos 30, 224–272 (1978).Article 

Google Scholar 
Alerstam, T. Bird flight and optimal migration. Trends Ecol. Evol. 6, 210–215 (1991).CAS 
PubMed 
Article 

Google Scholar 
Richardson, W. J. Wind and Orientation of Migrating Birds: A Review. in Orientation in Birds (ed. Berthold, P.) 226–249 (Birkhäuser, 1991). https://doi.org/10.1007/978-3-0348-7208-9_11.Christensen, T. K. & Fox, A. D. Changes in age and sex ratios amongst samples of hunter-shot wings from common duck species in Denmark 1982–2010. Eur. J. Wildl. Res. 60, 303–312 (2014).Article 

Google Scholar 
Fox, A. D., Clausen, K. K., Dalby, L., Christensen, T. K. & Sunde, P. Age-ratio bias among hunter-based surveys of Eurasian Wigeon Anas penelope based on wing vs. field samples. Ibis 157, 391–395 (2015).Article 

Google Scholar 
Møller, A. P., Flensted-Jensen, E., Laursen, K. & Mardal, W. Fertilizer leakage to the marine environment, ecosystem effects and population trends of waterbirds in Denmark. Ecosystems 18, 30–44 (2015).Article 
CAS 

Google Scholar 
Scott, D. A. & Rose, P. M. Atlas of Anatidae Populations in Africa and Western Eurasia. Wetlands International Publication 41 (Wetlands International, 1996).Fick, S. E. & Hijmans, R. J. WorldClim 2: New 1-km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37, 4302–4315 (2017).Article 

Google Scholar 
Harris, I., Jones, P. D., Osborn, T. J. & Lister, D. H. Updated high-resolution grids of monthly climatic observations–the CRU TS3.10 Dataset. Int. J. Climatol. 34, 623–642 (2014).Article 

Google Scholar 
Hijmans, R. J. Introduction to the ’raster’ package (version 3.0–12). https://rspatial.org/raster/pkg/index.html (2020).National Center for Atmospheric Research Staff. The climate data guide: Hurrell North Atlantic Oscillation (NAO) index (PC-based). https://climatedataguide.ucar.edu/climate-data/hurrell-north-atlantic-oscillation-nao-index-pc-based (2019).Hurrell, J. W. Decadal trends in the north atlantic oscillation: Regional temperatures and precipitation. Science 269, 676–679 (1995).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Büttger, H., Nehls, G. & Stoddard, P. The history of intertidal blue mussel beds in the North Frisian Wadden Sea in the 20th century: Can we define reference conditions for conservation targets by analysing aerial photographs?. J. Sea Res. 87, 91–102 (2014).ADS 
Article 

Google Scholar 
Kristensen, P. S. & Borgstrøm, R. The Danish Wadden Sea: Fishery of mussels (Mytilus edulis L.) in a wildlife reserve? in Proceedings from the 11. Scientific Wadden Sea Symposium, Esbjerg, Denmark, 4.-8. April 2005. NERI technical report (ed. Laursen, K.) vol. 573 107–111 (National Environmental Research Institute. Department of Wildlife Ecology and Biodiversity, 2006).Baird, R. H. Measurement of condition in mussels and oysters. ICES J. Mar. Sci. 23, 249–257 (1958).Article 

Google Scholar 
Waldeck, P. & Larsson, K. Effects of winter water temperature on mass loss in Baltic blue mussels: Implications for foraging sea ducks. J. Exp. Mar. Bio. Ecol. 444, 24–30 (2013).Article 

Google Scholar 
Nehls, G. et al. Beds of blue mussels and Pacific oysters. Quality Status Report, Thematic Report; No. 11. Wadden Sea Ecosystem; No. 25 (2009).Laursen, K., Møller, A. P., Haugaard, L., Öst, M. & Vainio, J. Allocation of body reserves during winter in eider Somateria mollissima as preparation for spring migration and reproduction. J. Sea Res. 144, 49–56 (2019).ADS 
Article 

Google Scholar 
Morelli, F., Laursen, K., Svitok, M., Benedetti, Y. & Møller, A. P. Eiders, nutrients and eagles: Bottom-up and top-down population dynamics in a marine bird. J. Anim. Ecol. https://doi.org/10.1111/1365-2656.13498 (2021).Article 
PubMed 

Google Scholar 
Westerbom, M., Kilpi, M. & Mustonen, O. Blue mussels, Mytilus edulis, at the edge of the range: population structure, growth and biomass along a salinity gradient in the north-eastern Baltic Sea. Mar. Biol. 140, 991–999 (2002).Article 

Google Scholar 
Kery, M. & Schaub, M. Bayesian Population Analysis Using WinBUGS: A Hierarchical Perspective. (Elsevier, 2012).Kerman, J. Neutral noninformative and informative conjugate beta and gamma prior distributions. Electron. J. Stat. 5, 1450–1470 (2011).MathSciNet 
MATH 
Article 

Google Scholar 
Crainiceanu, C. M., Ruppert, D. & Wand, M. P. Bayesian analysis for penalized spline regression using WinBUGS. J. Stat. Softw. 14, (2005).Saha, K. & Paul, S. Bias-corrected maximum likelihood estimator of the negative binomial dispersion parameter. Biometrics 61, 179–185 (2005).MathSciNet 
PubMed 
MATH 
Article 

Google Scholar 
Mutshinda, C. M., O’Hara, R. B. & Woiwod, I. P. A multispecies perspective on ecological impacts of climatic forcing. J. Anim. Ecol. 80, 101–107 (2011).PubMed 
Article 

Google Scholar 
Pöysä, H. et al. Environmental variability and population dynamics: Do European and North American ducks play by the same rules?. Ecol. Evol. 6, 7004–7014 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
Almaraz, P., Green, A. J., Aguilera, E., Rendón, M. A. & Bustamante, J. Estimating partial observability and nonlinear climate effects on stochastic community dynamics of migratory waterfowl. J. Anim. Ecol. https://doi.org/10.1111/j.1365-2656.2012.01972.x (2012).Article 
PubMed 

Google Scholar 
Schmidt, N. M. et al. Response of an arctic predator guild to collapsing lemming cycles. Proc. R. Soc. B Biol. Sci. 279, 4417–4422 (2012).Article 

Google Scholar 
Ebbinge, B. S., Heesterbeek, H. J. A. P., Ens, B. J. & Goedhart, P. W. Density dependent population limitation in dark-bellied brent geese Branta b. bernicla. Avian Sci. 2, 63–75 (2002).
Google Scholar 
Domine, F. et al. Snow physical properties may be a significant determinant of lemming population dynamics in the high Arctic. Arct. Sci. 4, 813–826 (2018).Article 

Google Scholar 
Ims, R. A., Henden, J.-A. & Killengreen, S. T. Collapsing population cycles. Trends Ecol. Evol. 23, 79–86 (2008).PubMed 
Article 

Google Scholar 
Korslund, L. & Steen, H. Small rodent winter survival: Snow conditions limit access to food resources. J. Anim. Ecol. 75, 156–166 (2006).PubMed 
Article 

Google Scholar 
Callaghan, T. V. et al. The changing face of Arctic snow cover: A synthesis of observed and projected changes. Ambio 40, 17–31 (2011).Article 

Google Scholar 
Machín, P. et al. The role of ecological and environmental conditions on the nesting success of waders in sub-Arctic Sweden. Polar Biol. 42, 1571–1579 (2019).Article 

Google Scholar 
Koneff, M. D. et al. Evaluation of harvest and information needs for North American sea ducks. PLoS ONE 12, e0175411 (2017).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Benton, T. G. & Grant, A. Elasticity analysis as an important tool in evolutionary and population ecology. Trends Ecol. Evol. 14, 467–471 (1999).CAS 
PubMed 
Article 

Google Scholar 
Heppell, S. S., Caswell, H. & Crowder, L. B. Life histories and elasticity patterns: Perturbation analysis for species with minimal demographic data. Ecology 81, 654–665 (2000).Article 

Google Scholar 
Sæther, B.-E. & Bakke, O. Avian life history variation and contribution of demographic traits to the population growth rate. Ecology 81, 642–653 (2000).Article 

Google Scholar 
Öst, M., Ramula, S., Lindén, A., Karell, P. & Kilpi, M. Small-scale spatial and temporal variation in the demographic processes underlying the large-scale decline of eiders in the Baltic Sea. Popul. Ecol. 58, 121–133 (2016).Article 

Google Scholar 
Holopainen, S. & Fox, A. D. Associations between duck harvest, hunting wing ratios and measures of reproductive output in Northern Europe. Eur. J. Wildl. Res. 64, (2018).Conley, D. J., Humborg, C., Rahm, L., Savchuk, O. P. & Wulff, F. Hypoxia in the Baltic Sea and basin-scale changes in phosphorus biogeochemistry. Environ. Sci. Technol. 36, 5315–5320 (2002).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Carstensen, J. et al. Hypoxia in the Baltic Sea: Biogeochemical cycles, benthic fauna, and management. Ambio 43, 26–36 (2014).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Conley, D. J. et al. Hypoxia-related processes in the Baltic Sea. Environ. Sci. Technol. 43, 3412–3420 (2009).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Conley, D. J. et al. Long-term changes and impacts of hypoxia in Danish coastal waters. Ecol. Appl. 17, 165–184 (2007).Article 

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

Google Scholar 
Fox, A. D. et al. Current and potential threats to Nordic duck populations–a horizon scanning exercise. Ann. Zool. Fennici 52, 193–220 (2015).Article 

Google Scholar 
Møller, A. P. Biological consequences of global change for birds. Integr. Zool. 8, 136–144 (2013).PubMed 
Article 

Google Scholar  More

After the successful protection of Himalayan areas on the border of China and Nepal, we propose that the two nations should create the world’s highest international peace park by combining the Qomolangma and Sagarmatha national parks. This would align with United Nations Sustainable Development Goal 17, to achieve sustainable development through international cooperation (see go.nature.com/3ixmini).
Competing Interests
The authors declare no competing interests. More

Climate change drove evolution, biogeography, and demographyPhylogenetic results (Fig. 1 and Supplementary Fig. 2) confirm previous findings, recovering Aptenodytes (king and emperor penguins) as the sister clade to all other crown penguins, with brush-tailed (Pygoscelis) penguins in turn sister to two clades uniting the banded (Spheniscus) and little (Eudyptula) penguins and the yellow-eyed (Megadyptes) and crested (Eudyptes) penguins6,7,9. Biogeographical reconstructions (Fig. 1, Supplementary Figs. 3–4 and Supplementary Data 1) support a Zealandian origin for penguins6,7. Stem penguins radiated extensively in Zealandia before dispersing to South America and Antarctica multiple times, following the eastward-flowing direction of the Antarctic Circumpolar Current (ACC) (Fig. 1). Crown penguins most likely arose from descendant lineages in South America, before dispersing back to Zealandia at least three times. Interestingly, at least two such dispersals occurred before the inferred onset of the ACC system, suggesting that early stem penguins were not dependent on currents to disperse over long distances. A second pulse of speciation coincides with the onset of the ACC, though understanding whether this pattern is real or an artifact of fossil sampling requires more collecting from early Eocene localities. We infer an age of ~14 Ma for the origin of crown penguins, which is more recent than the ~24 Ma age recovered in genomic analyses, not including fossil taxa7 (Supplementary Fig. 2b) and coincides with the onset of global cooling during the middle Miocene climate transition4,10 (Supplementary Fig. 3a). This young age suggests that expansion of Antarctic ice sheets and the onset of dispersal vectors such as the Benguela Current11 during the middle to late Miocene facilitated crown penguin dispersal and speciation, as hinted at by fossil evidence12.Incongruences between species trees and gene trees were identified, e.g., alternate topologies occurred at high frequencies ( >10%) for several internal branches (Fig. 1c; Supplementary Fig. 5). These patterns indicate that gene tree discordance may be caused by incomplete lineage sorting (ILS) or introgression events. By quantifying ILS and introgression via branch lengths from over 10,000 gene trees, we found that the rapid speciation within crown penguins was accompanied by >5% ILS content within the ancestors of Spheniscus, Eudyptula, Eudyptes, and several subgroups within Eudyptes (Fig. 2a). Our dated tree provides a temporal framework for this rapid radiation: the four extant Spheniscus taxa are all inferred to have split from one another within the last ~3 Ma, and likewise the nine extant Eudyptes taxa likely split from one another in that same time (Fig. 1b). Many closely related penguin species/lineages are known to hybridize in the wild (see supplementary methods). Consistent with this, multiple analyses suggest that introgression also contributes to species tree—gene tree incongruence (Supplementary Figs. 6–9 and Supplementary Data 2; also see Supplementary Methods for further details). This could explain the most notable conflict in previous phylogenetic results, which showed inconsistency over whether Aptenodytes alone7 or Aptenodytes and Pygoscelis together4,5 represent the sister clade to all other extant penguins. Introgression was detected between the ancestor of Aptenodytes and the ancestor of other extant penguins, and is inferred to have occurred when the range of these ancestors overlapped in South America (Fig. 2a and Supplementary Data 2). Introgression ( >9%) was also detected between Eudyptula novaehollandiae and Eudyptula minor, and several introgression events were especially pervasive in Eudyptes (Fig. 2a and Supplementary Fig. 6).Fig. 2: Incomplete lineage sorting, introgression events, and demographic history among penguins.a Model of incomplete lineage sorting (ILS) and introgression events estimated from QuIBL and hybrid pairwise sequentially Markovian coalescent (hPSMC) results. hPSMC was only run for 20 species pairs (see b). Numbers on branches represent the proportion (%) of ILS (orange branches) or introgression (blue lines, blue dashed lines, and blue dotted lines) detected by QuIBL. Proportions 50 km; see Supplementary Data 117), while taxa that decreased towards the end of the LGP (e.g., S. humboldti, S. demersus, M. a. antipodes and likely M. a. richdalei) tend to be residential, and forage inshore; see Supplementary Data 1. Taxa that disperse farther may have overcome local impacts of global climate cooling during the LGP (e.g., changes in sea-ice extent, prey abundance and terrestrial glaciation, however see18) largely by relocating to lower latitudes (e.g.,14), whereas locally-restricted taxa may have been more prone to sudden population collapses.Penguins have the slowest evolutionary rates among birdsThe integrated evolutionary speed hypothesis (IESH) proposes that temperature, water availability, population size, and spatial heterogeneity influence evolutionary rate19. Life history traits also impact the evolutionary rate, but such relationships remain incompletely understood in birds20. Penguins are long-lived, large-bodied, and produce few offspring, thus providing an ideal case study in how life history may impact evolutionary rate. We tested the IESH using three proxies for evolutionary rate: substitution rate, P and K2P distances between lineages and their ancestors (Supplementary Fig. 12 and Supplementary Data 3). We found that penguins and their sister group (Procellariiformes) had the lowest evolutionary rates of the 17 avian orders sampled by21 (Fig. 3a, Supplementary Fig. 13, and Supplementary Data 3). Because other aquatic orders also show slow rates (e.g., the aquatic Anseriformes show a significantly slower rate than their terrestrial sister group Galliformes), we hypothesize that the rate in penguins represents the culmination of a gradual slowdown associated with increasingly aquatic ecology. Intriguingly, we detected a trend toward decreasing rate over the first ~10 Ma of crown penguin evolution, followed by a marked uptick ~2 Ma, which suggests the onset of glacial-interglacial cycles contributed to a recent increase in evolutionary rates in penguins (Fig. 3b).Fig. 3: Evolutionary rates in birds.a Evolutionary rate in avian orders based on a ~19 Mbp alignment of highly conserved genome regions. Sphenisciformes and Procellariiformes have the lowest evolutionary rate among modern bird orders (One-sided Wilcoxon Rank sum test, P values  0.1)). Numbers at the tips represent the sample size in each group. Numbers at nodes represent the divergence times (Ma) between each order and its sister taxon and red dots within the boxplots indicate average values. We did not attempt to estimate the evolutionary rates for orders containing less than three sampled species (gray font; Musophagiformes, Mesitornithiformes, and Struthioniformes). Boxplots show the median with hinges at the 25th and 75th percentile and whiskers extending 1.5 times the interquartile range. Some bird images were downloaded from phylopic.org and were licensed under the Creative Commons (CC0) 1.0 Universal Public Domain Dedication. b Evolutionary rates inferred for extant penguin lineages at internal nodes from the maximum clade credibility tree, calculated using a 500 Mbp genome alignment. Gray shadows represent the 95% credible intervals. c–e Correlations between c, body mass and generation time (P value  More

Fisher, M. C. et al. Emerging fungal threats to animal, plant and ecosystem health. Nature 484, 186–194 (2012).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Fisher, M. C., Gow, N. A. & Gurr, S. J. Tackling emerging fungal threats to animal health, food security and ecosystem resilience. Philos. Trans. R. Soc. B 371, 20160332. https://doi.org/10.1098/rstb.2016.0332 (2016).Article 

Google Scholar 
Lips, K. R. Overview of chytrid emergence and impacts on amphibians. Philos. Trans. R. Soc. B 371, 20150465. https://doi.org/10.1098/rstb.2015.0465 (2016).Article 

Google Scholar 
Lips, K. R., Diffendorfer, J., Mendelson, J. R. III. & Sears, M. W. Riding the wave: Reconciling the roles of disease and climate change in amphibian declines. PLoS Biol. 6, e72 (2008).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Caruso, N. M. & Lips, K. R. Truly enigmatic declines in terrestrial salamander populations in great smoky mountains national park. Divers. Distrib. 19, 38–48 (2013).Article 

Google Scholar 
Martel, A. et al. Recent introduction of a chytrid fungus endangers western palearctic salamanders. Science 346, 630–631 (2014).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
van der Spitzen Sluijs, A. et al. Rapid enigmatic decline drives the fire salamander (Salamandra salamandra) to the edge of extinction in the Netherlands. Amphib.-Reptil. 34, 233–239 (2013).Article 

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

Google Scholar 
Thogmartin, W. E., King, R. A., McKann, P. C., Szymanski, J. A. & Pruitt, L. Population-level impact of white-nose syndrome on the endangered Indiana bat. J. Mammal. 93, 1086–1098 (2012).Article 

Google Scholar 
Fisher, M. C., Garner, T. W. & Walker, S. F. Global emergence of Batrachochytrium dendrobatidis and amphibian chytridiomycosis in space, time, and host. Annu. Rev. Microbiol. 63, 291–310 (2009).CAS 
PubMed 
Article 

Google Scholar 
Martel, A. et al. Batrachochytrium salamandrivorans sp. Nov. causes lethal chytridiomycosis in amphibians. Proc. Natl. Acad. Sci. 110, 15325–15329 (2013).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Allender, M. C., Raudabaugh, D. B., Gleason, F. H. & Miller, A. N. The natural history, ecology, and epidemiology of Ophidiomyces ophiodiicola and its potential impact on free-ranging snake populations. Fungal Ecol. 17, 187–196. https://doi.org/10.1016/j.funeco.2015.05.003 (2015).Article 

Google Scholar 
Grioni, A. et al. Detection of Ophidiomyces ophidiicola in a wild Burmese python (Python bivittatus) in Hong Kong SAR, China. J. Herpetol. Med. Surg. 31, 283–291 (2021).Article 

Google Scholar 
Allender, M. C. et al. Chrysosporium sp. infection in eastern massasauga rattlesnakes. Emerg. Infect. Dis. 17, 2383–2384. https://doi.org/10.1136/vr.b4816 (2011).Article 
PubMed 
PubMed Central 

Google Scholar 
Franklinos, L. H. V. et al. Emerging fungal pathogen Ophidiomyces ophiodiicola in wild European snakes. Sci. Rep. 7, 1–7. https://doi.org/10.1038/s41598-017-03352-1 (2017).CAS 
Article 

Google Scholar 
Lorch, J. M. et al. Experimental infection of snakes with Ophidiomyces ophiodiicola causes pathological changes that typify snake fungal disease. mBio 6, 1–9. https://doi.org/10.1128/mBio.01534-15 (2015).CAS 
Article 

Google Scholar 
Clark, R. W., Marchand, M. N., Clifford, B. J., Stechert, R. & Stephens, S. Decline of an isolated timber rattlesnake (Crotalus horridus) population: Interactions between climate change, disease, and loss of genetic diversity. Biol. Cons. 144, 886–891. https://doi.org/10.1016/j.biocon.2010.12.001 (2011).Article 

Google Scholar 
Chandler, H. C. et al. Ophidiomycosis prevalence in Georgia’s eastern indigo snake (Drymarchon couperi) populations. PLoS ONE 14, e0218351 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Guthrie, A. L., Knowles, S., Ballmann, A. E. & Lorch, J. M. Detection of snake fungal disease due to Ophidiomyces ophiodiicola in Virginia, USA. J. Wildl. Dis. 52, 143–149. https://doi.org/10.7589/2015-01-007 (2016).CAS 
Article 
PubMed 

Google Scholar 
Last, L. A., Fenton, H., Gonyor-McGuire, J., Moore, M. & Yabsley, M. J. Snake fungal disease caused by Ophidiomyces ophiodiicola in a free-ranging mud snake (Farancia abacura). J. Vet. Diagn. Invest. 28, 709–713. https://doi.org/10.1177/1040638716663250 (2016).CAS 
Article 
PubMed 

Google Scholar 
Lorch, J. M. et al. Snake fungal disease: An emerging threat to wild snakes. Philos. Trans. R. Soc. B 371, 20150457. https://doi.org/10.1098/rstb.2015.0457 (2016).Article 

Google Scholar 
Haynes, E. et al. First report of ophidiomycosis in a free-ranging California Kingsnake (Lampropeltis californiae) in California, USA. J. Wildl. Dis. 57, 246–249 (2021).CAS 
PubMed 
Article 

Google Scholar 
Burbrink, F. T., Lorch, J. M. & Lips, K. R. Host susceptibility to snake fungal disease is highly dispersed across phylogenetic and functional trait space. Sci. Adv. 3, 1–10. https://doi.org/10.1126/sciadv.1701387 (2017).Article 

Google Scholar 
Dixon, J. R. Amphibians and Reptiles of Texas: With Keys, Taxonomic synopses, Bibliography, and Distribution Maps 3rd edn. (Texas A&M University Press, 2000).
Google Scholar 
McKeown, S. A Field Guide to Reptiles and Amphibians in the Hawaiian Islands (Diamond Head Publishing, 1996).
Google Scholar 
Powell, R., Conant, R. & Collins, J. T. Peterson Field Guide to Reptiles and Amphibians of Eastern and Central NORTH AMERICA (Houghton Mifflin Harcourt, 2016).
Google Scholar 
Stebbins, R. C. & McGinnis, S. M. Peterson Field Guide to Western Reptiles and Amphibians (Houghton Mifflin Harcourt, 2018).
Google Scholar 
Texas Administrative Code. State‐listed threatened species in Texas. 31 TAC §65.175. (2020).Dixon, J. R., Werler, J. E. & Forstner, M. R. J. Texas Snakes: A Field Guide Revised. (University of Texas Press, 2020).Book 

Google Scholar 
Rodriguez, D., Forstner, M. R. J., McBride, D. L., Densmore, L. D. III. & Dixon, J. R. Low genetic diversity and evidence of population structure among subspecies of Nerodia harteri, a threatened water snake endemic to Texas. Conserv. Genet. 13, 977–986 (2012).Article 

Google Scholar 
Scott, N. J., Maxwell, T. C., Thornton, O. W., Fitzgerald, L. A. & Flury, J. W. Distribution, habitat, and future of Harter’s water snake, Nerodia harteri Texas. J. Herpetol. 23, 373–389 (1989).Article 

Google Scholar 
Whiting, M. J., Dixon, J. R. & Greene, B. D. Spatial ecology of the Concho water snake (Nerodia harteri paucimaculata) in a large lake system. J. Herpetol. 31, 327–335 (1997).Article 

Google Scholar 
McBride, D. L. Distribution and status of the Brazos water snake (Nerodia harteri harteri) Master of Science thesis, Tarleton State University (2009).United States Office of the Federal Register. Endangered and threatened wildlife and plants; determination of Nerodia harteri paucimaculata (Concho water snake) to be a threatened species Final rule. Fed. Regist. 51, 31412–31422 (1986).
Google Scholar 
United States Office of the Federal Register. Endangered and threatened wildlife and plants; removal of the Concho water snake from the federallist of endangered and threatened wildlife and removal of designated critical habitat. Fed. Reg. 76, 66779–66804 (2011).
Google Scholar 
United States Office of the Federal Register. Endangered and threatened wildlife and plants; findings on petitions and initiation of status review. Fed. Reg. 50, 29238–29239 (1985).
Google Scholar 
United States Office of the Federal Register. Endangered and threatened wildlife and plants; animal candidate review for listing as endangered or threatened species. Fed. Reg. 59, 58982–59028 (1994).
Google Scholar 
Gibbons, J. W. & Dorcas, M. E. North American Watersnakes: A Natural History (University of Oklahoma Press, 2004).
Google Scholar 
Werler, J. E. & Dixon, J. R. Texas Snakes: Identification, Distribution, and Natural History (University of Texas Press, 2000).
Google Scholar 
Lind, C. M., McCoy, C. M. & Farrell, T. M. Tracking outcomes of snake fungal disease in free-ranging pigmy rattlesnakes (Sistrurus miliarius). J. Wildl. Dis. 54, 352–356. https://doi.org/10.7589/2017-05-109 (2018).Article 
PubMed 

Google Scholar 
McBride, M. P. et al. Ophidiomyces ophiodiicola dermatitis in eight free-ranging timber rattlesnakes (Crotalus horridus) from Massachusetts. J. Zoo Wildl. Med. 46, 86–94. https://doi.org/10.1638/2012-0248R2.1 (2015).Article 
PubMed 

Google Scholar 
McCoy, C. M., Lind, C. M. & Farrell, T. M. Environmental and physiological correlates of the severity of clinical signs of snake fungal disease in a population of pigmy rattlesnakes Sistrurus miliarius. Conserv. Physiol. 5, cow077. https://doi.org/10.1093/conphys/cow077 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Haynes, E. et al. Ophidiomycosis surveillance of snakes in Georgia, USA reveals new host species and taxonomic associations with disease. Sci. Rep. 10, 1–15 (2020).Article 
CAS 

Google Scholar 
Stengle, A. G. et al. Evidence of vertical transmission of the snake fungal pathogen Ophidiomyces ophiodiicola. J. Wildl. Dis. 55, 961–964 (2019).PubMed 
Article 

Google Scholar 
Britton, M., Allender, M. C., Hsiao, S.-H. & Baker, S. J. Postnatal mortality in neonate rattlesnakes associated with Ophidiomyces ophiodiicola. J. Zoo Wildl. Med. 50, 672–677 (2019).PubMed 
Article 

Google Scholar 
Allender, M. C., Hileman, E., Moore, J. & Tetzlaff, S. Detection of Ophidiomyces, the caustive agent of snake fungal disease, in the eastern massasauga (Sistrurus catenatus) in Michigan, USA, 2014. J. Wildl. Dis. 52, 694–698. https://doi.org/10.7589/2015-12-333 (2016).CAS 
Article 
PubMed 

Google Scholar 
Hileman, E. T. et al. Estimation of Ophidiomyces prevalence to evaluate snake fungal disease risk. J. Wildl. Manag. 82, 173–181. https://doi.org/10.1002/jwmg.21345 (2018).Article 

Google Scholar 
McKenzie, J. M. et al. Field diagnostics and seasonality of Ophidiomyces ophiodiicola in wild snake populations. EcoHealth 16, 141–150 (2019).PubMed 
Article 

Google Scholar 
Snyder, S. D., Sutton, W. B. & Walker, D. M. Prevalence of Ophidiomyces ophiodiicola, the causative agent of Snake Fungal Disease, in the Interior Plateau Ecoregion of Tennessee, USA. J. Wildl. Dis. 56, 907–911 (2020).PubMed 
Article 

Google Scholar 
Tetzlaff, S. J. et al. Snake fungal disease affects behavior of free-ranging massasauga rattlesnakes (Sistrurus catenatus). Herpetol. Conserv. Biol. 12, 624–634 (2017).
Google Scholar 
Aldridge, R. D., Flanagan, W. P. & Swarthout, J. T. Reproductive biology of the water snake Nerodia rhombifer from Veracruz, Mexico, with comparisons of tropical and temperate snakes. Herpetologica 51, 182–192 (1995).
Google Scholar 
Greene, B. D., Dixon, J. R., Whiting, M. J. & Mueller, J. M. Reproductive ecology of the Concho water snake Nerodia harteri paucimaculata. Copeia 1999, 701–709 (1999).Article 

Google Scholar 
Kofron, C. P. Reproduction of aquatic snakes in south-central Louisiana. Herpetologica 35, 44–50 (1979).
Google Scholar 
Green, B. D. Life History and Ecology of the Concho Water Snake, Nerodia harteri paucimaculata. Dissertation (Texas A&M University, 1993).
Google Scholar 
McKenzie, C. M. et al. Ophidiomycosis in red cornsnakes (Pantherophis guttatus): potential roles of brumation and temperature on pathogenesis and transmission. Vet. Pathol. 57, 825–837 (2020).CAS 
PubMed 
Article 

Google Scholar 
Gregoire, D. R. Nerodia rhombifer (Hallowell, 1852): U.S. geological survey, nonindigenous aquatic species database, Gainesville, FL, Retrieved from 27 Oct 2009 https://nas.er.usgs.gov/queries/FactSheet.aspx?SpeciesID=2577.Janecka, M. J., Janecka, J. E., Haines, A. M., Michaels, A. & Criscione, C. D. Post-delisting genetic monitoring reveals population subdivision along river and reservoir localities of the endemic Concho water snake (Nerodia harteri paucimaculata). Conserv. Genet. 22, 1005–1021 (2021).CAS 
Article 

Google Scholar 
Madsen, T., Stille, B. & Shine, R. Inbreeding depression in an isolated population of adders Vipera berus. Biol. Cons. 75, 113–118 (1996).Article 

Google Scholar 
Carter, J. et al. Variation in pathogenicity associated with the genetic diversity of Fusarium graminearum. Eur. J. Plant Pathol. 18, 573–583 (2002).Article 

Google Scholar 
Charlesworth, D. & Willis, J. H. The genetics of inbreeding depression. Nat. Rev. Genet. 10, 783 (2009).CAS 
PubMed 
Article 

Google Scholar 
Keller, L. F. & Waller, D. M. Inbreeding effects in wild populations. Trends Ecol. Evol. 17, 230–241 (2002).Article 

Google Scholar 
Nieminen, M., Singer, M. C., Fortelius, W., Schöps, K. & Hanski, I. Experimental confirmation that inbreeding depression increases extinction risk in butterfly populations. Am. Nat. 157, 237–244 (2001).CAS 
PubMed 
Article 

Google Scholar 
Roelke, M. E., Martenson, J. S. & O’Brien, S. J. The consequences of demographic reduction and genetic depletion in the endangered Florida panther. Curr. Biol. 3, 340–350. https://doi.org/10.1016/0960-9822(93)90197-v (1993).CAS 
Article 
PubMed 

Google Scholar 
United States Office of the Federal Register. Endangered and threatened wildlife and plants: findings on petitions involving the Yacare Caiman and Harter’s water snake. Fed. Reg. 49, 21089–21090 (1984).
Google Scholar 
NatureServe. NatureServe Explorer: An Online Encyclopedia of Life [web application]. Version 7.0. NatureServe, Arlington, Virginia., http://www.natureserve.org/explorer (2020).Hammerson, G. A. Nerodia harteri (Trapido, 1941). The IUCN red list of threatened species 2007. https://doi.org/10.2305/IUCN.UK.2007.RLTS.T62238A12583490.en (2007).Allender, M. C. et al. Hematology in an eastern massasauga (Sistrurus catenatus) population and the emergence of Ophidiomyces in Illinois, USA. J. Wildl. Dis. 52, 258–269 (2016).CAS 
PubMed 
Article 

Google Scholar 
Becker, C. G., Rodriguez, D., Lambertini, C., Toledo, L. F. & Haddad, C. F. Historical dynamics of Batrachochytrium dendrobatidis in Amazonia. Ecography 39, 954–960 (2016).Article 

Google Scholar 
Rodriguez, D., Becker, C. G., Pupin, N. C., Haddad, C. F. B. & Zamudio, K. R. Long-term endemism of two highly divergent lineages of the amphibian-killing fungus in the Atlantic forest of Brazil. Mol. Ecol. 23, 774–787. https://doi.org/10.1111/mec.12615 (2014).CAS 
Article 
PubMed 

Google Scholar 
Fitch, H. S. Collecting and Life-History Techniques. In Snakes: Ecology and Evolutionary Biology (eds Seigel, Richard A. et al.) 143–164 (Macmillan, 1987).
Google Scholar 
Winne, C. T., Willson, J. D., Andrews, K. M. & Reed, R. N. Efficacy of marking snakes with disposable medical cautery units. Herpetol. Rev. 37, 52–54 (2006).
Google Scholar 
Greene, B. D., Dixon, J. R., Mueller, J. M., Whiting, M. J. & Thornton, O. W. Jr. Feeding ecology of the Concho water snake, Nerodia harteri paucimaculata. J. Herpetol. 28, 165–172 (1994).Article 

Google Scholar 
Lacki, M. J., Hummer, J. W. & Fitzgerald, J. L. Population patterns of copperbelly water snakes (Nerodia erythrogaster neglecta) in a riparian corridor impacted by mining and reclamation. Am. Midl. Nat. 153, 357–369 (2005).Article 

Google Scholar 
Hyatt, A. D. et al. Diagnostic assays and sampling protocols for the detection of Batrachochytrium dendrobatidis. Dis. Aquat. Org. 73, 175–192 (2007).CAS 
Article 

Google Scholar 
Allender, M. C., Bunick, D., Dzhaman, E., Burrus, L. & Maddox, C. Development and use of a real-time polymerase chain reaction assay for the detection of Ophidiomyces ophiodiicola in snakes. J. Vet. Diagn. Invest. 27, 217–220. https://doi.org/10.1177/1040638715573983 (2015).CAS 
Article 
PubMed 

Google Scholar 
Bohuski, E., Lorch, J. M., Griffin, K. M. & Blehert, D. S. TaqMan real-time polymerase chain reaction for detection of Ophidiomyces ophiodiicola, the fungus associated with snake fungal disease. BMC Vet. Res. 11, 1–10. https://doi.org/10.1186/s12917-015-0407-8 (2015).CAS 
Article 

Google Scholar 
Longo, A. V. et al. ITS1 copy number varies among Batrachochytrium dendrobatidis strains: Implications for qPCR estimates of infection intensity from field-collected amphibian skin swabs. PLoS ONE 8, e59499 (2013).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Ohkura, M. et al. Genome sequence of Ophidiomyces ophiodiicola, an emerging fungal pathogen of snakes. Genome Announc. 5, 1–2 (2017).Article 

Google Scholar 
Falk, B. G., Snow, R. W. & Reed, R. N. A validation of 11 body-condition indices in a giant snake species that exhibits positive allometry. PLoS ONE 12, e0180791 (2017).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Garrow, J. S. & Webster, J. Quetelet’s index (W/H2) as a measure of fatness. Int. J. Obes. 9, 147–153 (1985).CAS 
PubMed 

Google Scholar 
Dorai-Raj, S. binom: Binomial confidence intervals for several parameterizations. https://CRAN.R-project.org/package=binom (2014).Thiele, C. & Hirschfeld, G. cutpointr: Improved estimation and validation of optimal cutpoints in R. J. Stat. Softw. 98, 1–27 (2021).Article 

Google Scholar 
Diggle, P. J. Estimating prevalence using an imperfect test. Epidemiol. Res. Int. 2011, 1–5 (2011).Article 

Google Scholar 
Bender, R. & Lange, S. Adjusting for multiple testing: When and how?. J. Clin. Epidemiol. 54, 343–349 (2001).CAS 
PubMed 
Article 

Google Scholar 
Brooks, M. et al. glmmTMB balances speed and flexibility among packages for zero-inflated generalized linear mixed modeling. R J. 9, 378–400 (2017).Article 

Google Scholar 
Barton, K. MuMIn: Multi-model inference. R package version 1.43.6 (2019).Lenth, R., Singmann, H., Love, J., Buerkner, P. & Herve, M. emmeans: Estimated marginal means, aka least-squares means, R package version 1.4.8. https://CRAN.R-project.org/package=emmeans (2020). More

Temporal differentiation of wild populations of T. hispidus and climatic parametersComparison of morphometric features of T. hispidus shells collected in different years in two geographic regions, i.e., Wrocław and Lubawka, showed significant differences depending on the year of collection. The largest number of differences was revealed in shells from Wrocław (Figs. 1 and 2A; Additional file 2: Table S1). Out of 210 comparisons (15 pairs of collection years × 14 features), 84 were statistically significant (Additional file 2: Table S2), e.g., shell diameter (D) was significantly different in 11 cases, shell height (H) and shell width (W) in 10 cases, body whorl height (bwH), the number of whorls (whl), umbilicus major (U) and minor (u) diameters in 9 cases and aperture height/width ratio (h/w) in 7 cases. Nine features obtained more than 10% difference between shells in at least one comparison of mean values, e.g., U 24%, u 19%, H 16% and D 15% (Additional file 2: Table S2). Umbilicus major (U) and minor (u) diameters showed the largest average percentage difference, i.e., 12% and 10%, respectively, in comparisons of all years.Figure 1Shells of Trochulus hispidus collected in different years in Wrocław.Full size imageFigure 2Changes in: mean values of selected morphometric features of shells collected in various years in Wrocław (A) as well as the mean temperature (B) and the relative humidity (C) recorded in four seasons in Wrocław in eight-year period. Abbreviations: D—shell diameter (in mm), H—shell height (in mm), h/w—aperture height/width ratio, whl—number of whorls. The summary statistics for A is included in Table S1 and original data in Table S10 in Additional file 2.Full size imageFor snails from Lubawka, out of 84 comparisons (6 pairs of collection years × 14 features) only 8 were statistically significant (Additional file 2: Table S3). The shells differed significantly in their aperture height (h) and width (w) in 3 comparisons. The h feature showed the percentage difference up to 9% (Additional file 2: Table S3) and the largest average difference was 4.5%.Besides the phenotypic variation, climatic parameters also showed high fluctuations in the studied period (Fig. 2B,C, Additional file 2: Table S4). The maximum difference reported between temperature parameters in some years prior to sample collection in Wrocław was up to 3.7 °C for the maximum winter temperature, while the maximum difference in the relative humidity was up to 11% for autumn. The maximum temperature difference in Jelenia Góra close to Lubawka was up to 3.5 °C for the minimum winter temperature, while the relative humidity differed at most by up to 8% in summer.Differences in shell morphometry under various climatic conditionsThe distinction between shells collected in individual years and changes in climatic parameters along the same period suggest that these differences can be associated with the climate. Therefore, we calculated the average value of a given climatic parameter for each season and studied region and next divided the collected shell data into two groups according to this value. The first group included the shells that developed in conditions above this average and the second below this average (Additional file 2: Table S5). The differences between these groups were statistically significant for 15 out of 16 considered climatic parameters for at least two shell features (Fig. 3). Similarly, each of 14 features significantly separated the groups based on at least two climatic conditions. The results demonstrated that the mean winter temperature substantially influenced nine morphometric shell features, whereas eight characters were changed due to the maximum winter temperature as well as the mean and minimum temperatures in spring, summer and autumn. Umbilicus major (U) and minor (u) diameters as well as umbilicus relative diameter (U/D) were significantly different in 14 pairs of groups characterized by various climatic parameters. In 11 pairs, the height/width ratio (H/W) was significantly different and shell height (H) in 10 pairs.Figure 3Mean percentage differences in morphometric features between shells that were grown in different conditions. The shells were divided into two groups according to the average value of a given climatic parameter for each season and studied region. The first group included the shells that developed in conditions above this average and the second below this average. Positive values indicate that the given feature was greater in the first group, whereas negative values indicate that this feature was greater in the second group. Dendrograms cluster the features and the parameters according to their similarity in the percentage differences. Values marked in bold indicate statistically significant differences between the compared groups of shells. Values at the dendrogram nodes indicate significance assessed according to approximately unbiased test (au) and bootstrap resampling (bp).Full size imageThe umbilicus diameters (u and U) as well as umbilicus relative diameter (U/D) clustered together in the dendrogram based on the mean percentage difference, which indicates that they similarly responded to climatic conditions (Fig. 3). The features u and U revealed the strongest average increase of all features, from 4.1 to 10.5% in shells developed in higher temperatures in all seasons. The largest percentage difference exceeding 10% was recorded for groups separated according to the mean summer and autumn temperatures as well as the maximum summer and minimum autumn temperatures. The U/D ratio was also significantly greater with the mean percentage difference of 2.8–7.6% in shells grown under high temperatures for all seasons and almost all temperature types. On the other hand, the u and U diameters as well as the U/D ratio were on average by 3.7–6.0% significantly smaller in shells developed under higher humidity in summer and winter.The height/width shell ratio (H/W) was grouped with H and bwH features in the dendrogram and was on average by up to 3.6% significantly smaller in shells grown under higher temperatures in all seasons for almost all types of parameters. The maximum winter temperature caused a significant increase, on average by ca. 3%, in shell height (H) and body whorl height (bwH), whereas higher temperatures in other seasons led to their decrease by up to 3.4% (Fig. 3).The shells that were grown in autumn with a relatively high maximum temperature were characterized by ca. 3% significantly smaller aperture height (h) and aperture height/width ratio (h/w), which were clustered together in the dendrogram (Fig. 3).Other four features, shell diameter (D), number of whorls (whl) as well as shell (W) and aperture width (w), formed an additional cluster in the dendrogram (Fig. 3). All of them were on average significantly greater in shells collected one year after winter that was characterized by relatively higher mean and maximum temperatures. The percentage difference was greater, with 3.6–3.9% for W and D.In the dendrogram, the climatic parameters were clustered in several groups indicating their similar influence on the morphometric features of shells (Fig. 3). There are separate clusters for temperature and humidity parameters with the exception of the autumn maximum temperature and autumn humidity, which are grouped together. The other temperature parameters for warmer seasons are separated from those for winter, which indicates that they differently influenced the shell morphometry.Correlations between morphometric shell features and climatic parametersThe influence of climatic conditions on the shells collected in individual years was also assessed using Spearman’s correlation coefficient between the morphometric features and climatic parameters (Fig. 4). Of 224 potential relationships 113 were statistically significant. The spring mean temperature was significantly correlated with 10 morphometric features. Summer humidity and six temperature parameters, i.e., the minimum temperatures as well as the spring and winter maximum temperatures, significantly correlated with eight shell features. Minor umbilicus diameter (u) and umbilicus relative diameter (U/D) were significantly correlated with almost all climatic parameters, i.e., 15, umbilicus major diameter (U) and height/width ratio (H/W) with 13 and the ratio of umbilicus minor to its major diameter (u/U) with 11.Figure 4Spearman’s correlation coefficients between morphometric features of shells with climatic parameters under which the snails were grown. Dendrograms cluster the features and the parameters according to their similarity in the coefficients. Values marked in bold are statistically significant. Values at the dendrogram nodes indicate significance assessed according to approximately unbiased test (au) and bootstrap resampling (bp).Full size imageAs in the case of percentage difference, we can also recognize groups of morphometric features that were similarly correlated with climatic parameters (Fig. 4). Features U/D, U and u were significantly positively correlated with all or almost all temperature parameters for four seasons with the coefficients up to 0.34, 0.30 and 0.36, respectively. On the other hand, the significant correlation coefficients between these features and the humidity in spring, summer and winter were negative and reached − 0.34.Another group of features included shell height/width ratio (H/W), shell height (H) and body whorl height (bwH) (Fig. 4). All of them showed significant negative correlations with all temperature parameters for spring and summer as well as the minimum autumn temperature, and H/W also with the mean and maximum autumn temperatures as well as the mean and minimum winter temperatures. The correlation coefficients reached − 0.28, − 0.27 and − 0.28, respectively. These three features significantly correlated with summer and spring humidity, at up to 0.23.The number of whorls (whl), shell width (W), shell diameter (D), demonstrated a similar correlation with climatic parameters (Fig. 4). They showed the largest and significant correlation coefficients with winter temperatures: up to 0.24, 0.22 and 0.22, respectively. The ratio of umbilicus minor to its major diameter (u/U) showed significant positive correlation up to 0.22 with temperature of warmer seasons.The climatic parameters were grouped into several clusters indicating their similar relationships with morphometric features (Fig. 4). Humidity parameters of warmer seasons formed a separate cluster and temperature parameters were grouped according to seasons. The winter parameters were connected with autumn humidity and separated from temperatures for warmer seasons.Modelling relationships between morphometric shell features and climatic parametersThe joint influence of many climatic parameters on morphometry of shells collected in individual years was studied using a linear mixed-effects (LME) model after exclusion of correlated parameters and a linear ridge regression (LRR) model including all climatic parameters. The latter allows for the inclusion of correlated variables. We separately investigated the seasonal maximum, mean and minimum temperature parameters in combination with seasonal humidity parameters (Additional file 2: Table S6) because they are obviously correlated.Umbilicus minor (u) and major (U) diameters as well as umbilicus relative diameter (U/D) turned out best explained by the climatic parameters (with R2  > 0.15) in two models (Additional file 2: Table S6). Moreover, u, U and U/D were described in LME models by the largest number of significant climatic parameters, i.e., 15. The features u and U had also the largest number of significant parameters in LRR models, i.e., 18 out of 24 possibilities. The largest average values of temperature coefficients for the LRR models were 0.66 for D, 0.58 for W, 0.32 for H, 0.26 for U and 0.22 for u. Thus, all the above-mentioned features were under the strongest influence of the climatic conditions.In the case of LRR models, the coefficients at the winter mean temperature were most often selected as significant, in 12 out of 14 possibilities (Additional file 2: Table S6). The humidity coefficients for autumn were significant in 30 cases of 42 possibilities. The highest average absolute values of coefficients in climatic variables were those for the summer (0.63), spring (0.31) and autumn (0.24) minimum temperatures as well as the summer mean temperature (0.31). Thus, the temperatures of warmer seasons were more important for developing shell morphology. Seasonal humidity coefficients showed similar values compared to each other.Comparison of shell morphometry of T. hispidus and T. sericeus kept under various conditionsIn order to verify the influence of different climatic parameters on Trochulus shell morphometry in selected conditions, we compared shells from three groups of T. hispidus, which represented several subsequent generations: (1) parental snails collected in the wild in Wrocław-Jarnołtów, (2) their offspring bred in the laboratory for two generations and (3) offspring of the second laboratory-bred generation transplanted again into a garden in Wrocław (Fig. 5A–C). The comparison of the group 2 and 1 was to verify if laboratory conditions with controlled temperature and humidity can influence the shell morphometry within only one generation, whereas including the group 3 in the comparison, we wanted to check if snails raised in wild garden conditions can recover the original phenotype. Furthermore, we transplanted into the same garden conditions T. sericeus, which was collected in the wild in Muszkowice (Fig. 5D,E). In this case, we verified if two originally different ecophenotypes T. hispidus and T. sericeus, develop the same shell morphometry under the same conditions.Figure 5Shells of two Trochulus ecophenotypes: parental T. hispidus from wild habitat in Wrocław (A), the first generation of T. hispidus raised in laboratory (B); T. hispidus reared in garden in Wrocław (C); T. sericeus from wild habitat in Muszkowice (D); T. sericeus reared in garden in Wrocław (E).Full size imageConditions in which these snails developed were different. According to WorldClim, the wild environment of T. hispidus in Wrocław was generally warmer than that of T. sericeus in Muszkowice (Additional file 2: Table S7). The largest difference was 1.4 °C for the maximum summer temperature. Relative humidity was lower in Wrocław by up to 2% for warmer seasons but was higher in winter by 1.6%. The difference between the wild and garden localities in Wrocław was much smaller and did not exceed 0.41 °C. The garden conditions were less humid, by up to 2%. However, data from WorldClim are generalized over a longer period and wider regions, so may not well reflect local conditions in the studied places. Actually, the Wrocław site was an open habitat covered with a nettle community like a garden patch, while the Muszkowice site was overgrown by a beech forest, which most likely maintained a higher humidity and a more stable temperature.Laboratory temperatures were substantially different from those in the field, especially for winter (by 18–19.7 °C) as well as for spring and autumn (by 8.2–12 °C). Laboratory humidity was by up to 4.5% lower compared to winter and 5.9–9.9% higher than in spring and summer.A discriminant function analysis (DFA) for the defined groups of snails provided their interesting grouping and separation (Fig. 6). The analysis identified three significant discriminant functions (p  More