Ecology

1.
Sanderson, E. W. et al. The human footprint and the last of the wild. Bioscience 52, 891–904 (2002).
Article  Google Scholar 
2.
Millenium Ecosystem Assessment. Ecosystems and Human Wellbeing: Wetlands and Water. World Resources Institute, Washington, DC. https://www.millenniumassessment.org/documents/document.358.aspx.pdf (2005).

3.
Waters, C. N. et al. The Anthropocene is functionally and stratigraphically distinct from the Holocene. Science 351, aad2622. https://doi.org/10.1126/science.aad2622 (2016).

4.
Halpern, B. S. et al. Recent pace of change in human impact on the world’s ocean. Sci. Rep. 9, 11609 (2019).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

5.
Vinebrooke, R. D. et al. Impacts of multiple stressors on biodiversity and ecosystem functioning: The role of species co-tolerance. Oikos 104, 451–457 (2004).
Article  Google Scholar 

6.
Hewitt, J. E., Ellis, J. I. & Thrush, S. F. Multiple stressors, nonlinear effects and the implications of climate change impacts on marine coastal ecosystems. Glob. Chang. Biol. 22, 2665–2675 (2016).
ADS  PubMed  Article  Google Scholar 

7.
Côté, I., Darling, E. & Brown, C. Interactions among ecosystem stressors and their importance in conservation. Proc. R. Soc. Lond. B Biol. Sci. 283, 20152592. Doi: https://doi.org/10.1098/rspb.2015.2592 (2016).

8.
Vörösmarty, C. J. et al. Global threats to human water security and river biodiversity. Nature 467, 555–561 (2010).
ADS  PubMed  Article  CAS  Google Scholar 

9.
Séguin, A., Gravel, D. & Archambault, P. Effect of disturbance regime on Alpha and Beta diversity of rock pools. Biodivers. J. 6, 1–17 (2014).
Google Scholar 

10.
Halpern, B. S. et al. Spatial and temporal changes in cumulative human impacts on the world’s ocean. Nat. Commun. 6, 1–7 (2015).
Article  CAS  Google Scholar 

11.
Folt, C. L., Chen, C. Y., Moore, M. V. & Burnaford, J. Synergism and antagonism among multiple stressors. Limnol. Oceanogr. 44, 864–877 (1999).
ADS  Article  Google Scholar 

12.
Brook, B. W., Sodhi, N. S. & Bradshaw, C. J. A. Synergies among extinction drivers under global change. Trends Ecol. Evol. 23, 453–460 (2008).
PubMed  Article  Google Scholar 

13.
Crain, C. M., Kroeker, K. & Halpern, B. S. Interactive and cumulative effects of multiple human stressors in marine systems. Ecol. Lett. 11, 1304–1315 (2008).
PubMed  Article  Google Scholar 

14.
Piggott, J. J., Townsend, C. R. & Matthaei, C. D. Reconceptualizing synergism and antagonism among multiple stressors. Ecol. Evol. 5, 1538–1547 (2015).
PubMed  PubMed Central  Article  Google Scholar 

15.
Galic, N., Sullivan, L. L., Grimm, V. & Forbes, V. E. When things don’t add up: quantifying impacts of multiple stressors from individual metabolism to ecosystem processing. Ecol. Lett. 21, 568–577 (2018).
PubMed  Article  Google Scholar 

16.
Brown, C. J., Saunders, M. I., Possingham, H. P. & Richardson, A. J. Managing for interactions between local and global stressors of ecosystems. PLoS ONE 8, e65765. https://doi.org/10.1371/journal.pone.0065765 (2013).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

17.
Brown, C. J., Saunders, M. I., Possingham, H. P. & Richardson, A. J. Interactions between global and local stressors of ecosystems determine management effectiveness in cumulative impact mapping. Divers. Distrib. 20, 538–546 (2014).
Article  Google Scholar 

18.
Kaplan, I. C., Levin, P. S., Burden, M. & Fulton, E. A. Fishing catch shares in the face of global change: A framework for integrating cumulative impacts and single species management. Can. J. Fish. Aquat. Sci. 67, 1968–1982 (2010).
Article  Google Scholar 

19.
Ghedini, G., Russell, B. D. & Connell, S. D. Managing local coastal stressors to reduce the ecological effects of ocean acidification and warming. Water (Switzerland) 5, 1653–1661 (2013).
Google Scholar 

20.
Hodgson, E. E., Halpern, B. S. & Essington, T. E. Moving beyond silos in cumulative effects assessment. Front. Ecol. Evol. 7, 1–8 (2019).
Article  Google Scholar 

21.
Schindler, D. E. & Hilborn, R. Prediction, precaution, and policy under global change. Science 347, 953–954 (2015).
ADS  CAS  PubMed  Article  Google Scholar 

22.
Ling, S. D., Johnson, C. R., Frusher, S. D. & Ridgway, K. R. Overfishing reduces resilience of kelp beds to climate-driven catastrophic phase shift. Proc. Natl. Acad. Sci. USA 106, 22341–22345 (2009).
ADS  CAS  PubMed  Article  Google Scholar 

23.
Munday, P. L. et al. Ocean acidification impairs olfactory discrimination and homing ability of a marine fish. Proc. Natl. Acad. Sci. USA 106, 1848–1852 (2009).
ADS  CAS  PubMed  Article  Google Scholar 

24.
Power, M. Assessing the effects of environmental stressors on fish populations. Aquat. Toxicol. 39, 151–169 (1997).
CAS  Article  Google Scholar 

25.
Hodgson, E. E., Essington, T. E. & Halpern, B. S. Density dependence governs when population responses to multiple stressors are magnified or mitigated. Ecology 98, 2673–2683 (2017).
PubMed  Article  Google Scholar 

26.
Griffith, G. P. & Fulton, E. A. New approaches to simulating the complex interaction effects of multiple human impacts on the marine environment. ICES J. Mar. Sci. 71, 764–774 (2014).
Article  Google Scholar 

27.
Harvey, E., Séguin, A., Nozais, C., Archambault, P. & Gravel, D. Identify effects dominate the impacts of multiple species extinctions on the functioning of complex food webs. Ecology 94, 169–179 (2013).
PubMed  Article  Google Scholar 

28.
Schmolke, A., Brain, R., Thorbek, P., Perkins, D. & Forbes, V. Population modeling for pesticide risk assessment of threatened species—A case study of a terrestrial plant Boltonia decurrens. Environ. Toxicol. Chem. 36, 480–491 (2017).
CAS  PubMed  Article  Google Scholar 

29.
Calosi, P. et al. Adaptation and acclimatization to ocean acidification in marine ectotherms: An in situ transplant experiment with polychaetes at a shallow CO2 vent system. Philos. Trans. R. Soc. B, Biol. Sci. 368, (2013).

30.
Alsterberg, C., Sundbäck, K. & Hulth, S. Functioning of a shallow-water sediment system during experimental warming and nutrient enrichment. PLoS One 7, (2012).

31.
Rosenberg, R. Eutrophication – The future marine coastal nuisance?. Mar. Pollut. Bull. 16, 227–231 (1985).
CAS  Article  Google Scholar 

32.
Levin, L. A. et al. Effects of natural and human-induced hypoxia on coastal benthos. Biogeosciences 6, 2063–2098 (2009).
ADS  CAS  Article  Google Scholar 

33.
McGlathery, K. J., Sundbäck, K. & Anderson, I. C. Eutrophication in shallow coastal bays and lagoons: The role of plants in the coastal filter. Mar. Ecol. Prog. Ser. 348, 1–18 (2007).
ADS  CAS  Article  Google Scholar 

34.
Attrill, M. J. & Power, M. Effects on invertebrate populations of drought-induced changes in estuarine water quality. Mar. Ecol. Prog. Ser. 203, 133–143 (2000).
ADS  CAS  Article  Google Scholar 

35.
McLusky, D. S., Hull, S. C. & Elliott, M. Variations in the intertidal and subtidal macrofauna and sediments along a salinity gradient in the upper Forth estuary. Netherlands J. Aquat. Ecol. 27, 101–109 (1993).
Article  Google Scholar 

36.
Levinton, J., Doall, M., Ralston, D., Starke, A. & Allam, B. Climate change, precipitation and impacts on an estuarine refuge from disease. PLoS ONE 6(4), e18849 (2011).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

37.
Greimel, F. et al. Hydropeaking impacts and mitigation in Riverine ecosystem management: Science for governing towards a sustainable future (ed. Schmutz, S. & Sendzimir, J.) 91–110 (Aquatic Ecology Series 8, 2018).

38.
Parmesan, C. & Yohe, G. A globally coherent fingerprint of climate change. Nature 421, 37–42 (2003).
ADS  CAS  PubMed  Article  Google Scholar 

39.
Jordà, G., Marbà, N. & Duarte, C. M. Mediterranean seagrass vulnerable to regional climate warming. Nat. Clim. Chang. 2, 821–824 (2012).
ADS  Article  Google Scholar 

40.
Lotzel, H. K. & Worm, B. Complex interactions of climatic and ecological controls on macroalgal recruitment. Limnol. Oceanogr. 47, 1734–1741 (2002).
ADS  Article  Google Scholar 

41.
Paerl, H. W. & Scott, J. T. Throwing fuel on the fire: Synergistic effects of excessive nitrogen inputs and global warming on harmful algal blooms. Environ. Sci. Technol. 44, 7756–7758 (2010).
ADS  CAS  PubMed  Article  Google Scholar 

42.
Drejou, E. et al. Biodiversity and habitat assessment of coastal benthic communities in a sub-Arctic industrial harbour area. Water J. 12, 2424. https://doi.org/10.3390/w12092424 (2020).
Article  Google Scholar 

43.
Romero, F., Acuña, V., Font, C., Freixa, A. & Sabater, S. Effects of multiple stressors on river biofilms depend on the time scale. Sci. Rep. 9, 15810. https://doi.org/10.1038/s41598-019-52320-42 (2019).
ADS  Article  PubMed  PubMed Central  Google Scholar 

44.
Borja, A., Franco, J. & Pérez, V. A. A marine biotic index to establish the ecological quality of soft-bottom benthos within European estuarine and coastal environmentls. Mar. Pollut. Bull. 40, 1100–1114 (2000).
CAS  Article  Google Scholar 

45.
Bourget, E., Ardisson, P.-L., Lapointe, L. & Daigle, G. Environmental factors as predictors of epibenthic assemblage biomass in the St Lawrence system. Estuar. Coast. Shelf. Sci. 57, 641–652 (2003).
ADS  CAS  Article  Google Scholar 

46.
McLusky, D.S. & Allan, D.G. Aspects of the biology of Macoma balthica (L.) from estuarine Firth of Forth. J. Molluscan Stud. 42, 31–45 (1976).

47.
Cottrell, R. S., Kenny, D. B., Hutchison, Z. L. & Last, K. S. The influence of organic material and temperature on the burial tolerance of the blue mussel, Mytilus edulis: Considerations for the management of marine aggregate dredging. PLoS ONE 11, 1. https://doi.org/10.1371/journal.pone.0147534 (2020).
CAS  Article  Google Scholar 

48.
Pearson, T. H. & Rosenberg, R. Macrobenthic succession in relation to organic enrichment and pollution of the marine environment. Oceanogr. Mar. Biol. 16, 229–311 (1978).
Google Scholar 

49.
Ratcliffe, P.J., Jones, N.V. & Walters, N.J. The survival of Macoma balthica (L.) in mobile sediments. In Feeding and Survival Strategies of Estuarine Organisms (ed. Jones, N.V & Wolff, W.J.) 91–108 (Plenum Press, 1981).

50.
Riaux-Gobin, C. & Klein, B. Microphytobenthic biomass measurement using HPLC and conventional pigment analysis. In Handbooks of Methods in Aquatic Microbial Ecology, (ed. Kemp, P.F., Sherr, B.F., Sherr, E.B. & Cole, J.J.) 369–376 (Lewis Publishers, 1993).

51.
Davies, B. E. Loss-on-ignition as an estimate of soil organic matter. Soil Sci. Soc. Am. J. 38, 150–151 (1974).
ADS  Article  Google Scholar 

52.
Wentworth, C. K. A scale of grade and class terms for clastic sediments. J. Geol. 30, 377–392 (1922).
ADS  Article  Google Scholar 

53.
Folk, R. L. & Ward, W. C. Brazos River Bar: a study in the significance of grain size parameters. J. Sediment. Petrol. 27, 3–26 (1957).
ADS  Article  Google Scholar 

54.
Galbraith, P. et al. Physical oceanographic conditions in the Gulf of St. Lawrence during 2018. DFO Can. Sci. Advis. Sec. Res. Doc. 2019/046, iv + 69 p. (2019).

55.
Baden, S., Boström, C., Tobiasson, S., Arponen, H. & Moksnes, P. O. Relative importance of trophic interactions and nutrient enrichment in seagrass ecosystems: A broad-scale field experiment in the Baltic-Skagerrak area. Limnol. Oceanogr. 55, 1435–1448 (2010).
ADS  CAS  Article  Google Scholar 

56.
Moksnes, P.-O., Gullström, M., Tryman, K. & Baden, S. Trophic cascades in a temperature seagrass community. Oikos 117, 763–777 (2008).
Article  Google Scholar 

57.
Bonsdorff, E. Establisment, growth and dynamics of a Macoma balthica (L.) population. Limnologica. 15, 403–405 (1984)

58.
Castañeda, R. A., Cvetanovska, E., Hamelin, K. M., Simard, M. A. & Ricciardi, A. Distribution, abundance and condition of an invasive bivalve (Corbicula fluminea) along an artificial thermal gradient in the St Lawrence River. Aquat. Invasions. 13, 379–392 (2018).
Article  Google Scholar 

59.
Baden, S. P. & Eriksson, S. P. Role, routes and effects of manganese in crustaceans. Oceanogr. Mar. Biol. Ann. Rev. 44, 61–83 (2006).
Google Scholar 

60.
Page, T. M., Worthington, S., Calosi, P. & Stillman, J. H. Effects of elevated pCO2 on crab survival and exoskeleton composition depend on shell function and species distribution: A comparative analysis of carapace and claw mineralogy across four porcelain crab species from different habitats. ICES J. Mar. Sci. 74, 1021–1032 (2017).
Article  Google Scholar 

61.
Small, D., Calosi, P., White, D., Spicer, J. I. & Widdicombe, S. Impact of medium-term exposure to CO2 enriched seawater on the physiological functions of the velvet swimming crab Necora puber. Aquat. Biol. 10, 11–21 (2010).
Article  Google Scholar 

62.
Marchant, H. K., Calosi, P. & Spicer, J. I. Short-term exposure to hypercapnia does not compromise feeding, acid-base balance or respiration of Patella vulgata but surprisingly is accompanied by radula damage. J. Mar. Biol. Assoc. UK 90, 1379–1384 (2010).
Article  Google Scholar 

63.
Horne, F.R. & Tarsitano, S. The mineralization and biomechanics of the exoskeleton. In The Biology and Fisheries of the Slipper Lobster (ed. Lavalli, K.L & Spanier, E.) 183–189 (CRC Press, 2007).

64.
Tao, J., Zhou, D., Zhang, Z., Xu, X. & Tang, R. Magnesium-aspartate-based crystallization switch inspired from shell molt of crustacean. Proc. Natl. Acad. Sci. USA 106, 22096–22101 (2009).
ADS  CAS  PubMed  Article  Google Scholar 

65.
Menu-Courey, K. et al. Energy metabolism and survival of the juvenile recruits of the American lobster (Homarus americanus) exposed to a gradient of elevated seawater pCO2. Mar. Environ. Res. 143, 111–123 (2019).
CAS  PubMed  Article  Google Scholar 

66.
Siddon, E. C., Heintz, R. A. & Mueter, F. J. Conceptual model of energy allocation in walleye pollock (Theragra chalcogramma) from age-0 to age-1 in the southeastern Bering Sea. Deep Sea Res. Part II Top. Stud. Oceanogr. 94, 140–149 (2013).

67.
Anderson, M. J. Permanova: A fortran computer program for permutational multivariate analysis of variance (University of Auckland, Auckland, Department of Statistics, 2005).
Google Scholar 

68.
Clarke, K.R & Gorley, R.N. PRIMER v6: User Manual/Tutorial (Plymouth Routines in Multivariate Ecological Research). PRIMER-E, Plymouth (2006).

69.
Sih, A., Englund, G. & Wooster, D. Emergent impacts of multiple predators on prey. Trends Ecol. Evol. 13, 350–355 (1998).
CAS  PubMed  Article  Google Scholar 

70.
Thornton, D. C. O., Dong, L. F., Underwood, G. J. C. & Nedwell, D. B. Factors affecting microphytobenthic biomass, species composition and production in the Colne Estuary (UK). Aquat. Microb. Ecol. 27, 285–300 (2002).
Article  Google Scholar 

71.
Pinckney, J., Paerl, H. W. & Fitzpatrick, M. Impacts of seasonality and nutrients on microbial mat community structure and function. Mar. Ecol. Prog. Ser. 123, 207–216 (1995).
ADS  Article  Google Scholar 

72.
Lin, J. & Hines, A. H. Effects of suspended food availability on the feeding mode and burial depth of the Baltic clam Macoma balthica. Oikos 69, 28–36 (1994).
Article  Google Scholar 

73.
Bougrier, S., Hawkins, A. J. S. & Héral, M. Preingestive selection of different microalgal mixtures in Crassostrea gigas and Mytilus edulis, analyzed by flow cytometry. Aquaculture 150, 123–134 (1997).
Article  Google Scholar 

74.
Cognie, B., Barillé, L. & Rincé, Y. Selective feeding of the oyster Crassostrea gigas fed on a natural microphytobenthos assemblage. Estuaries Coast. 24, 126–131 (2001).
Article  Google Scholar 

75.
Camargo, J. A. & Alonso, Á. Ecological and toxicological effects of inorganic nitrogen pollution in aquatic ecosystems: A global assessment. Environ. Int. 32, 831–849 (2006).
CAS  PubMed  Article  Google Scholar 

76.
Davenport, J. & Redpath, K.J. Copper and the mussel Mytilus edulis (L.) in Toxins, drugs and pollutants in marine animals (ed. Bolis, L., Zadunaisky, J. & Gilles, R.) 176–189 (Springler-Verlag, 1984).

77.
Gosling, E. Bivalve Molluscs: Biology, Ecology and Culture (ed. Blackwell Publishing) 95–96 (Wiley-Blackwell, 2003).

78.
Hauton, C. Physiological responses: Effects of salinity as a stressor to aquatic in- vertebrates. In Stressors in the Marine Environment: Physiological and Ecological Responses; Societal Implications (ed. Solan, M & Whiteley, N.M.) 3–24 (Oxford University Press, 2016)

79.
Almada-Villela, P. C. The effects of reduced salinity on the shell growth of small Mytilus edulis. J. Mar. Biol. Assoc. U.K. 64, 171–182 (1984).

80.
Kautsky, N., Johannesson, K. & Tedengren, M. Genotypic and phenotypic differences between Baltic and North Sea populations of Mytilus edulis evaluated through reciprocal transplantations. I. Growth and morphology. Mar. Ecol. Prog. Ser. 59, 203–210 (1990).

81.
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 

82.
Qiu, J., Tremblay, R. & Bourget, E. Ontogenetic changes in hyposaline tolerance in the mussels Mytilus edulis and M. trossulus: implications for distribution. Mar. Ecol. Prog. Ser. 228, 143–152 (2002).

83.
Cederwal, H. & Elmgren, R. Biomass increase of benthic macro- fauna demonstrates eutrophication of the Baltic Sea. Ophelia Suppl. 1, 287–304 (1980).
Google Scholar 

84.
Josefson, A. B. & Rasmussen, B. Nutrient retention by benthic macrofaunal biomass of Danish estuaries: Importance of nutrient load and residence time. Estuar. Coast. Shelf Sci. 50, 205–216 (2000).
ADS  CAS  Article  Google Scholar 

85.
Carmichael, R. H., Shriver, A. C. & Valiela, I. Bivalve response to estuarine eutrophication: The balance between enhanced food supply and habitat alterations. J. Shellfish Res. 31, 1–11 (2012).
Article  Google Scholar 

86.
Lin, J. & Hines, A. Effects of suspended food availability on the feeding mode and burial depth of the Baltic clam. Macoma balthica. Oiko 69, 28–36 (1994).
Article  Google Scholar 

87.
Findlay, H. S. et al. Comparing the impact of high CO2 on calcium carbonate structures in different marine organisms. Mar. Biol. Res. 7, 565–575 (2011).
Article  Google Scholar 

88.
Ries, J.B., Cohen. A.L. & McCorkle, D.C. Marine calcifiers exhibit mixed responses to CO2-induced ocean acidification. Geology 37, 1131−1134 (2009).

89.
Michaelidis, B., Ouzounis, C., Paleras, A. & Pörtner, H. O. Effects of long-term moderate hypercapnia on acid-base balance and growth rate in marine mussels Mytilus galloprovincialis. Mar. Ecol. Prog. Ser. 293, 109–118 (2005).
ADS  Article  Google Scholar 

90.
Whiteley, N. M., Scott, J. L., Breeze, S. J. & McCann, L. Effects of water salinity on acid-base balance in decapod crustaceans. J. Exp. Biol. 204, 1003–1011 (2001).
CAS  PubMed  Google Scholar 

91.
Darling, E. S. & Côté, I. M. Quantifying the evidence for ecological synergies. Ecol. Lett. 11, 1278–1286 (2008).
PubMed  Article  Google Scholar 

92.
Withey, J. C. et al. Maximizing return on conservation investment in the conterminous USA. Ecol. Lett. 15, 1249–1256 (2012).
PubMed  Article  Google Scholar  More

1.
Nicola, G. G., Elvira, B., Jonsson, B., Ayllon, D. & Almodovar, A. Local and global climatic drivers of Atlantic salmon decline in southern Europe. Fish. Res. 198, 78–85 (2018).
Article  Google Scholar 
2.
Peterman, R. M. & Dorner, B. A widespread decrease in productivity of Sockeye Salmon (Oncorhynchus nerka) populations in western North America. Canadian J. Fisheries Aquatic Sci. 69, 1255–1260 (2012).

3.
Chaput, G. Overview of the status of Atlantic salmon (Salmo salar) in the North Atlantic and trends in marine mortality. ICES J. Mar. Sci. 69, 1538–1548 (2012).
Article  Google Scholar 

4.
Mills, K. E., Pershing, A. J., Sheehan, T. F. & Mountain, D. Climate and ecosystem linkages explain widespread declines in North American Atlantic salmon populations. Glob. Chang Biol. 19, 3046–3061 (2013).
PubMed  Article  PubMed Central  Google Scholar 

5.
Ward, E. J., Anderson, J. H., Beechie, T. J., Pess, G. R. & Ford, M. J. Increasing hydrologic variability threatens depleted anadromous fish populations. Glob. Chang Biol. 21, 2500–2509 (2015).
PubMed  Article  PubMed Central  Google Scholar 

6.
Galbreath, P. F., Bisbee, M. A., Dompier, D. W., Kamphaus, C. M. & Newsome, T. H. Extirpation and tribal reintroduction of Coho salmon to the interior Columbia River basin. Fisheries 39, 77–87 (2014).
Article  Google Scholar 

7.
Wasser, S. K. et al. Population growth is limited by nutritional impacts on pregnancy success in endangered Southern Resident killer whales (Orcinus orca). PLoS ONE 12, 0179824 (2017).
Article  CAS  Google Scholar 

8.
NMFS, National Marine Fisheries Service. West Coast salmon & steelhead listings. NOAA Fisheries West Coast Region. www.westcoast.fisheries.noaa.gov/protected_species/salmon_steelhead/salmon_and_steelhead_listings/salmon_and_steelhead_listings.html (2014).

9.
NRC, Committee on Protection and Management of Pacific Northwest Anadromous Salmonids. Upstream: salmon and society in the Pacific Northwest. Vol. Board on Environmental Studies and Toxicology. Commission on Life Sciences (National Academies Press, 1996).

10.
Lehnert, S. J. et al. Genomic signatures and correlates of widespread population declines in salmon. Nat. Commun. 10, 10 (2019).
Article  CAS  Google Scholar 

11.
Cunningham, C. J., Westley, P. A. H. & Adkison, M. D. Signals of large scale climate drivers, hatchery enhancement, and marine factors in Yukon River Chinook salmon survival revealed with a Bayesian life history model. Glob. Change Biol. 24, 4399–4416 (2018).
Article  Google Scholar 

12.
Abdul-Aziz, O. I., Mantua, N. J. & Myers, K. W. Potential climate change impacts on thermal habitats of Pacific salmon (Oncorhynchus spp.) in the North Pacific Ocean and adjacent seas. Can. J. Fish. Aquat. Sci. 68, 1660–1680 (2011).
Article  Google Scholar 

13.
Hare, J. A. et al. A vulnerability assessment of fish and invertebrates to climate change on the northeast U.S. continental shelf. PLoS ONE 11, e0146756 (2016).
PubMed  PubMed Central  Article  CAS  Google Scholar 

14.
Crozier, L. G. et al. Climate vulnerability assessment for Pacific salmon and steelhead in the California Current Large Marine Ecosystem. PLoS ONE 14, e0217711 (2019).
CAS  PubMed  PubMed Central  Article  Google Scholar 

15.
Healey, M. The cumulative impacts of climate change on Fraser River sockeye salmon (Oncorhynchus nerka) and implications for management. Can. J. Fish. Aquat. Sci. 68, 718–737 (2011).
Article  Google Scholar 

16.
Honea, J. M., McClure, M. M., Jorgensen, J. C. & Scheuerell, M. D. Assessing freshwater life-stage vulnerability of an endangered Chinook salmon population to climate change influences on stream habitat. Clim. Res. 71, 127–137 (2017).
Article  Google Scholar 

17.
Battin, J. et al. Projected impacts of climate change on salmon habitat restoration. Proc. Natl Acad. Sci. USA 104, 6720–6725 (2007).
CAS  PubMed  Article  PubMed Central  Google Scholar 

18.
Thompson, L. C. et al. Water management adaptations to prevent loss of spring-run Chinook Salmon in California under climate change. J. Water Resour. Plan. Manag. 138, 465–478 (2012).
Article  Google Scholar 

19.
Cheung, W. W. L., Brodeur, R. D., Okey, T. A. & Pauly, D. Projecting future changes in distributions of pelagic fish species of Northeast Pacific shelf seas. Prog. Oceanogr. 130, 19–31 (2015).
Article  Google Scholar 

20.
Morley, J. W. et al. Projecting shifts in thermal habitat for 686 species on the North American continental shelf. PLoS ONE 13, e0196127 (2018).
PubMed  PubMed Central  Article  CAS  Google Scholar 

21.
Burke, B. J. et al. Multivariate models of adult Pacific salmon returns. PLoS ONE 8, e54134 (2013).
CAS  PubMed  PubMed Central  Article  Google Scholar 

22.
Newman, M. et al. The Pacific decadal oscillation, revisited. J. Clim. 29, 4399–4427 (2016).
Article  Google Scholar 

23.
Achuthavarier, D., Schubert, S. D. & Vikhliaev, Y. V. North Pacific decadal variability: insights from a biennial ENSO environment. Clim. Dyn. 49, 1379–1397 (2017).
Article  Google Scholar 

24.
Crozier, L. G. et al. Potential responses to climate change in organisms with complex life histories: evolution and plasticity in Pacific salmon. Evolut. Appl. 1, 252–270 (2008).
CAS  Article  Google Scholar 

25.
Litzow, M. A. et al. Non-stationary climate-salmon relationships in the Gulf of Alaska. Proc. R. Soc. B-Biol. Sci. 285, 9 (2018).
Google Scholar 

26.
O’Connor, C. M., Norris, D. R., Crossin, G. T. & Cooke, S. J. Biological carryover effects: linking common concepts and mechanisms in ecology and evolution. Ecosphere 5, 1–11 (2014).

27.
Carlson, S. M. & Seamons, T. R. A review of quantitative genetic components of fitness in salmonids: implications for adaptation to future change. Evol. Appl. 1, 222–238 (2008).
PubMed  PubMed Central  Article  Google Scholar 

28.
Munsch, S. H. et al. Warm, dry winters truncate timing and size distribution of seaward-migrating salmon across a large, regulated watershed. Ecol. Appl. 29, 14 (2019).
Article  Google Scholar 

29.
Otero, J. et al. Basin-scale phenology and effects of climate variability on global timing of initial seaward migration of Atlantic salmon (Salmo salar). Glob. Change Biol. 20, 61–75 (2014).
Article  Google Scholar 

30.
Crozier, L. G., Scheuerell, M. D. & Zabel, R. W. Using time series analysis to characterize evolutionary and plastic responses to environmental change: A case study of a shift toward earlier migration date in sockeye salmon. Am. Naturalist 178, 755–773 (2011).
Article  Google Scholar 

31.
Gosselin, J. L. et al. Conservation planning for freshwater-marine carryover effects on Chinook salmon survival. Ecol. Evolution 8, 319–332 (2018).
Article  Google Scholar 

32.
United States v. Oregon. 2018-2027 United States v. Oregon Management Agreement. Case 3:68-cv-00513-MO Document 2607-1. (2018).

33.
IPCC. Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. (IPCC, 2014).

34.
IPCC. The Ocean and Cryosphere in a Changing Climate. A Special Report of the Intergovernmental Panel on Climate Change. https://www.ipcc.ch/srocc/ (2019).

35.
USGCRP, U.S. Global Change Research Program. Climate Science Special Report: Fourth National Climate Assessment, Volume I. (U.S. Global Change Research Program, 2017).

36.
Gosselin, J. L., Crozier, L. G. & Burke, B. J. Shifting signals: Correlations among freshwater, marine and climatic indices often investigated in Pacific salmon studies. Ecological Indicators, 121, 107167 https://doi.org/10.1016/j.ecolind.2020.107167 (2021).
Article  Google Scholar 

37.
Crozier, L. G., Zabel, R. W. & Hamlett, A. F. Predicting differential effects of climate change at the population level with life-cycle models of spring Chinook salmon. Glob. Change Biol. 14, 236–249 (2008).
Article  Google Scholar 

38.
Zabel, R. W., Scheuerell, M. D., McClure, M. M. & Williams, J. G. The interplay between climate variability and density dependence in the population viability of Chinook salmon. Conserv. Biol. 20, 190–200 (2006).
PubMed  Article  Google Scholar 

39.
Ford, M. J. et al. 2015 Status Review Update for Pacific Salmon and Steelhead Listed under the Endangered Species Act: Pacific Northwest. National Marine Fisheries Service, Northwest Fisheries Science Center. https://www.nwfsc.noaa.gov/publications/scipubs/display_doctrack_allinfo.cfm?doctrackmetadataid=8623 (2016).

40.
NMFS, National Marine Fisheries Service. Endangered Species Act Section 7(a)(2) Biological Opinion and Magnuson-Stevens Fishery Conservation and Management Act Essential Fish Habitat Consultation. Consultation for the Continued Operation and Maintenance of the Columbia River System. NMFS, Portland, Oregon. https://doi.org/10.25923/3tce-8p07. Report No. Consultation number: WCRO-2020-00113 (2020).

41.
Doney, S. C. et al. Climate Change Impacts on Marine Ecosystems. Annual Review of Marine Science, 4, 11–37 (2012).
PubMed  Article  PubMed Central  Google Scholar 

42.
Carlson, S. M., Cunningham, C. J. & Westley, P. A. H. Evolutionary rescue in a changing world. Trends Ecol. Evol. 29, 521–530 (2014).
PubMed  Article  PubMed Central  Google Scholar 

43.
Waples, R. S. Pacific salmon, Oncorhynchus spp., and the definition of “species” under the endangered species act. Marine Fisheries Rev. 53, 11–22 (1991).

44.
Waples, R. S. Life-history traits and effective population size in species with overlapping generations revisited: the importance of adult mortality. Heredity 117, 241–250 (2016).
CAS  PubMed  PubMed Central  Article  Google Scholar 

45.
ICTRT & Zabel, R. W. Required survival rate changes to meet Technical Recovery Team abundance and productivity viability criteria for interior Columbia River basin salmon and steelhead populations. http://www.nwfsc.noaa.gov/trt/col_docs/ictrt_gaps_report_nov_2007_final.pdf (NWFSC, Seattle, Washington, 2007).

46.
Scheuerell, M. D., Zabel, R. W. & Sandford, B. P. Relating juvenile migration timing and survival to adulthood in two species of threatened Pacific salmon (Oncorhynchus spp.). J. Appl. Ecol. 46, 983–990 (2009).
Article  Google Scholar 

47.
Crozier, L. G. et al. Snake River sockeye and Chinook salmon in a changing climate: implications for upstream migration survival during recent extreme and future climates. PLoS ONE 15, e0238886 (2020).
CAS  PubMed  PubMed Central  Article  Google Scholar 

48.
McElhany, P., Ruckelshaus, M. H., Ford, M. J., Wainwright, T. C. & Bjorkstedt, E. P. Viable Salmonid Populations and the recovery of Evolutionarily Significant Units. Report No. Technical Memorandum NMFS-NWFSC 42, 156 (National Marine Fisheries Service, Northwest Fisheries Science Center, Seattle, WA, 2000).

49.
Jorgensen, J. C., Ward, E. J., Scheuerell, M. D. & Zabel, R. W. Assessing spatial covariance among time series of abundance. Ecol. Evol. 6, 2472–2485 (2016).
PubMed  PubMed Central  Article  Google Scholar 

50.
Ohlberger, J., Scheuerell, M. D. & Schindler, D. E. Population coherence and environmental impacts across spatial scales: a case study of Chinook salmon. Ecosphere 7, e01333 (2016).

51.
Zimmerman, M. S. et al. Spatial and temporal patterns in smolt survival of wild and hatchery coho salmon in the Salish Sea. Mar. Coast. Fish. 7, 116–134 (2015).
Article  Google Scholar 

52.
Welch, D. W., Porter, A. D. & Rechisky, E. L. A synthesis of the coast‐wide decline in survival of West Coast Chinook Salmon (Oncorhynchus tshawytscha, Salmonidae). Fish Fish. 22, 194–211 (2021).

53.
Dorner, B., Catalano, M. J. & Peterman, R. M. Spatial and temporal patterns of covariation in productivity of Chinook salmon populations of the northeastern Pacific Ocean. Can. J. Fish. Aquat. Sci. 75, 1082–1095 (2018).
Article  Google Scholar 

54.
Black, B. A. et al. Rising synchrony controls western North American ecosystems. Glob. Change Biol. 24, 2305–2314 (2018).
Article  Google Scholar 

55.
Jones, L. A. et al. Watershed-scale climate influences productivity of Chinook salmon populations across southcentral Alaska. Glob. Change Biol. 26, 4919–4936 (2020).

56.
Cline, T. J., Ohlberger, J. & Schindler, D. E. Effects of warming climate and competition in the ocean for life-histories of Pacific salmon. Nat. Ecol. Evol. 3, 935–942 (2019).
PubMed  Article  PubMed Central  Google Scholar 

57.
Litzow, M. A., Ciannelli, L., Cunningham, C. J., Johnson, B. & Puerta, P. Nonstationary effects of ocean temperature on Pacific salmon productivity. Can. J. Fish. Aquat. Sci. 76, 1923–1928 (2019).
Article  Google Scholar 

58.
Johnstone, J. A. & Mantua, N. J. Atmospheric controls on northeast Pacific temperature variability and change, 1900-2012. Proc. Natl Acad. Sci. USA 111, 14360–14365 (2014).
CAS  PubMed  Article  PubMed Central  Google Scholar 

59.
Lindenmayer, D. B., Likens, G. E., Krebs, C. J. & Hobbs, R. J. Improved probability of detection of ecological “surprises”. Proc. Natl Acad. Sci. USA 107, 21957–21962 (2010).
CAS  PubMed  Article  PubMed Central  Google Scholar 

60.
Ottersen, G., Kim, S., Huse, G., Polovina, J. J. & Stenseth, N. C. Major pathways by which climate may force marine fish populations. J. Mar. Syst. 79, 343–360 (2010).
Article  Google Scholar 

61.
Chasco, B. E. et al. Competing tradeoffs between increasing marine mammal predation and fisheries harvest of Chinook salmon. Sci. Rep. 7, 15439 (2017).
PubMed  PubMed Central  Article  CAS  Google Scholar 

62.
Ruzicka, J. J., Daly, E. A. & Brodeur, R. D. Evidence that summer jellyfish blooms impact Pacific Northwest salmon production. Ecosphere 7, https://doi.org/10.1002/ecs2.1324 (2016).

63.
Morgan, C. A., Beckman, B. R., Weitkamp, L. A. & Fresh, K. L. Recent ecosystem disturbance in the Northern California current. Fisheries 44, 465–474 (2019).
Article  Google Scholar 

64.
Auth, T. D., Daly, E. A., Brodeur, R. D. & Fisher, J. L. Phenological and distributional shifts in ichthyoplankton associated with recent warming in the northeast Pacific Ocean. Glob. Change Biol. 24, 259–272 (2018).
Article  Google Scholar 

65.
Zeidberg, L. D. & Robison, B. H. Invasive range expansion by the Humboldt squid, Dosidicus gigas, in the eastern North Pacific. Proc. Natl Acad. Sci. USA 104, 12948–12950 (2007).
CAS  PubMed  Article  PubMed Central  Google Scholar 

66.
Wells, B. K. et al. Environmental conditions and prey-switching by a seabird predator impact juvenile salmon survival. J. Mar. Syst. 174, 54–63 (2017).
Article  Google Scholar 

67.
Marshall, K. N. et al. Risks of ocean acidification in the California Current food web and fisheries: Ecosystem model projections. Glob. Change Biol. 23, 1525–1539 (2017).
Article  Google Scholar 

68.
Ou, M. et al. Responses of pink salmon to CO2-induced aquatic acidification. Nat. Clim. Change 5, 950–955 (2015).

69.
Williams, C. R. et al. Elevated CO2 impairs olfactory-mediated neural and behavioral responses and gene expression in ocean-phase coho salmon (Oncorhynchus kisutch). Glob. Change Biol. 25, 963–977 (2019).

70.
Chegwidden, O. S. et al. How do modeling decisions affect the spread among hydrologic climate change projections? Exploring a large ensemble of simulations across a diversity of hydroclimates. Earth’s Future 7, 623–637 (2019).

71.
Paulsen, C. M. & Fisher, T. R. Statistical relationship between parr-to-smolt survival of Snake River spring-summer Chinook salmon and indices of land use. Trans. Am. Fish. Soc. 130, 347–358 (2001).
Article  Google Scholar 

72.
Justice, C., White, S. M., McCullough, D. A., Graves, D. S. & Blanchard, M. R. Can stream and riparian restoration offset climate change impacts to salmon populations? J. Environ. Manag. 188, 212–227 (2017).
Article  Google Scholar 

73.
Andrews, K. S. et al. The legacy of a crowded ocean: indicators, status, and trends of anthropogenic pressures in the California Current ecosystem. Environ. Conserv. 42, 139–151 (2015).
Article  Google Scholar 

74.
Harvey, C. J. et al. Ecosystem status report of the California current for 2019: a summary of Ecosystem indicators compiled by the california current integrated ecosystem assessment team (CCIEA). NOAA Institutional Repository: https://doi.org/10.25923/mvhf-yk36, https://doi.org/10.25923/p0ed-ke21 (2019).

75.
Harvey, C. J., Reum, J. C. P., Poe, M. R., Williams, G. D. & Kim, S. J. Using conceptual models and qualitative network models to advance integrative assessments of marine ecosystems. Coast. Manag. 44, 486–503 (2016).
Article  Google Scholar 

76.
Wells, B. K. et al. Implementing ecosystem-based management principles in the design of a Salmon ocean ecology program. Front. Marine Sci. 7, https://doi.org/10.3389/fmars.2020.00342 (2020).

77.
Adams, J. et al. A century of Chinook salmon consumption by marine mammal predators in the Northeast Pacific Ocean. Ecol. Inform. 34, 44–51 (2016).
Article  Google Scholar 

78.
Thorne, K. et al. U.S. Pacific coastal wetland resilience and vulnerability to sea-level rise. Sci. Adv. 4, eaao3270 (2018).
PubMed  PubMed Central  Article  Google Scholar 

79.
Weitkamp, L. A., Bentley, P. J. & Litz, M. N. C. Seasonal and interannual variation in juvenile salmonids and associated fish assemblage in open waters of the lower Columbia River estuary. Fish. Bull. 110, 426–450 (2012).
Google Scholar 

80.
Diefenderfer, H. L. et al. Evidence-based evaluation of the cumulative effects of ecosystem restoration. Ecosphere 7, https://doi.org/10.1002/ecs2.1242 (2016).

81.
Kaplan, I. C. et al. Impacts of depleting forage species in the California current. Environ. Conserv. 40, 380–393 (2013).
Article  Google Scholar 

82.
Collie, J. S. et al. Ecosystem models for fisheries management: finding the sweet spot. Fish. Fish. 17, 101–125 (2016).
Article  Google Scholar 

83.
Skalski, J. R. et al. Status after 5 years of survival compliance testing in the federal Columbia river power system (FCRPS). North Am. J. Fish. Manag. 36, 720–730 (2016).
Article  Google Scholar 

84.
Welch, D. W. et al. Survival of migrating salmon smolts in large rivers with and without dams. PLoS Biol. 6, 2101–2108 (2008).
CAS  Google Scholar 

85.
Environmental Protection Agency U.S.A. Region 10. Total Maximum Daily Load (TMDL) for Temperature in the Columbia and Lower Snake Rivers, May 18, 2020 TMDL for Public Comment. Available at: https://www.epa.gov/columbiariver/tmdl-temperature-columbia-and-lower-snake-rivers (2020).

86.
Gosselin, J. L. & Anderson, J. J. Combining migration history, river conditions, and fish condition to examine cross-life-stage effects on marine survival in Chinook Salmon. Trans. Am. Fish. Soc. 146, 408–421 (2017).
Article  Google Scholar 

87.
Zabel, R. W. & Williams, J. G. Selective mortality in chinook salmon: what is the role of human disturbance? Ecol. Appl. 12, 173–183 (2002).
Article  Google Scholar 

88.
Bond, M. H., Nodine, T. G., Beechie, T. J. & Zabel, R. W. Estimating the benefits of widespread floodplain reconnection for Columbia River Chinook salmon. Can. J. Fish. Aquat. Sci. 76, 1212–1226 (2019).
Article  Google Scholar 

89.
National Marine Fisheries Service, W. C. R. ESA recovery plan for Snake river sockeye salmon (Oncorhynchus nerka). https://repository.library.noaa.gov/view/noaa/16001 (2015).

90.
Hinrichsen, R. A., Hasselman, D. J., Ebbesmeyer, C. C. & Shields, B. A. The role of impoundments, temperature, and discharge on colonization of the Columbia River basin, USA, by nonindigenous American Shad. Trans. Am. Fish. Soc. 142, 887–900 (2013).
Article  Google Scholar 

91.
Herbold, B. et al. Managing for salmon resilience in California’s variable and changing climate. San Franc. Estuary Watershed Sci. 16, https://escholarship.org/uc/item/8rb3z3nj (2018).

92.
Chittaro, P. et al. Variability in the performance of juvenile Chinook salmon is explained primarily by when and where they resided in estuarine habitats. Ecol. Freshw. Fish. 27, 857–873 (2018).
Article  Google Scholar 

93.
Beechie, T. et al. Restoring salmon habitat for a changing climate. River Res. Appl. 29, 939–960 (2013).
Article  Google Scholar 

94.
Idaho Department of Fish and Game, Oregon Department of Fish and Wildlife & Washington Department of Fish and Wildlife. Snake River ESU Spring Summer Chinook Natural Origin Spawner Abundance Dataset (1949-2017) (2018).

95.
Nez Perce Tribe East Fork South Fork Salmon River summer Chinook and Secesh River summer Chinook, Natural Origin Spawner Abundance Dataset (1957-2017). (Protocol and methods available at https://www.cbfish.org/Document.mvc/Viewer/P165414. Personal communication with Mari Williams, NOAAF NWFSC/OAI 2019, 2019).

96.
StreamNet. Fish data for the Northwest. http://www.streamnet.org/ (2018).

97.
Faulkner, J. R., Widener, D. L., Smith, S. G., Marsh, T. M. & Zabel, R. W. Survival estimates for the passage of spring migrating juvenile salmonids through Snake and Columbia River dams and reservoirs, 2017. (Draft report of the National Marine Fisheries Service to the Bonneville Power Administration. Portland, Oregon). https://www.nwfsc.noaa.gov/contact/display_staffprofilepubs.cfm?staffid=1524 (2018).

98.
Lamb, J. J. et al. Monitoring the migrations of wild Snake River spring/summer Chinook salmon juveniles: survival and timing, 2017. (Report of the National Marine Fisheries Service to the Bonneville Power Administration. Portland, Oregon. https://www.nwfsc.noaa.gov/contact/display_staffprofilepubs.cfm?staffid=550 (2018).

99.
DART. Columbia river data access in real time. http://www.cbr.washington.edu/dart/dart.html (2019).

100.
NOAA Fisheries. Salmon population summary. https://catalog.data.gov/dataset/sps-abundance-salmon-population-summary-database (2019).

101.
Kareiva, P., Marvier, M. & McClure, M. Recovery and management options for spring/summer Chinook salmon in the Columbia River basin. Science 290, 977–979 (2000).
CAS  PubMed  Article  PubMed Central  Google Scholar 

102.
Hartig, F., Calabrese, J. M., Reineking, B., Wiegand, T. & Huth, A. Statistical inference for stochastic simulation models—theory and application. Ecol. Letts. 14, 816–827 (2011).

103.
Csillery, K., Blum, M. G. B., Gaggiotti, O. E. & Francois, O. Approximate Bayesian computation (ABC). Pract. Trends Ecol. Evolution 25, 410–418 (2010).
Article  Google Scholar 

104.
R. Core Team. R version 3.6.2: A Language and Environmental for Statistical Computing (R Foundation for Statistical Computing, Vienna, Austria, 2019).

105.
Gompertz, B. On the nature of the function expressive of the law of human mortality, and on a new mode of determining the value of life contingencies. Philos. Trans. R. Soc. Lond. B: Biol. Sci. 182, 513–585 (1825).
Google Scholar 

106.
Gelman, A., Carlin, J. B. & Rubin, D. B. Bayesian Data Analysis (Chapman & Hall, 2004).

107.
Vehtari, A., Gelman, A. & Gabry, J. Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC. Stat. Comput. 27, 1413–1432 (2017).
Article  Google Scholar 

108.
Zabel, R. W., Burke, B. J., Moser, M. L. & Peery, C. Relating dam passage of adult salmon to varying river conditions using time-to-event analysis. Am. Fish. Soc. Symp. 61, 153–163 (2008).
Google Scholar 

109.
Chasco, B. E., Burke, B. J., Crozier, L. G. & Zabel, R. W. In press. Differential impacts of freshwater and marine covariates on wild and hatchery Chinook salmon marine survival. Plos One.

110.
U.S. Army Corps of Engineers (ACOE), Northwestern Division Bureau of Reclamation & Administration, P. N. R. B. P. Columbia River System Operations Draft Environmental Impact Statement, February 2020. DOE/EIS-0529 (2020).

111.
Kristensen, K., Nielsen, A., Berg, C. W., Skaug, H. & Bell, B. M. TMB: automatic differentiation and laplace approximation. J. Stat. Softw. 70, 1–21 (2016).
Article  Google Scholar 

112.
Brady, R. X., Alexander, M. A., Lovenduski, N. S. & Rykaczewski, R. R. Emergent anthropogenic trends in California Current upwelling. Geophys. Res. Lett. 44, 5044–5052 (2017).
Article  Google Scholar 

113.
National Oceanic and Atmospheric Administration. NOAA Earth System Research Laboratory, Climate Change web portal, CMIP5 maps. Available at https://psl.noaa.gov/ipcc/ocn/ccwp.html (2018). (Accessed November 2018).

114.
Yearsley, J. R. A semi-Lagrangian water temperature model for advection-dominated river systems. Water Resour. Res. 45, W12405 (2009).
Article  Google Scholar 

115.
Brekke, L., Kuepper, B. & Vaddey, S. Climate and hydrology datasets for use in the RMJOC agencies’ longer-term planning studies: Part 1 – Future Climate and Hydrology Datasets. https://www.usbr.gov/pn/climate/planning/reports/index.html (2010). More

1.
Eckert, K. D., Keiter, D. A. & Beasley, J. C. Animal visitation to wild pig (Sus scrofa) wallows and implications for disease transmission. J. Wildl. Dis. 55, 488–493. https://doi.org/10.7589/2018-05-143 (2019).
Article  PubMed  Google Scholar 
2.
Fenaux, H. et al. Transmission of hepatitis E virus by water: an issue still pending in industrialized countries. Water Res. 151, 144–157 (2019).
CAS  Article  Google Scholar 

3.
Atwill, E. R. et al. Prevalence of and associated risk factors for shedding Cryptosporidium parvum oocysts and Giardia cysts within feral pig populations in California. Appl. Environ. Microbiol. 63, 3946–3949 (1997).
CAS  Article  Google Scholar 

4.
Hampton, J., Spencer, P. B. S., Elliot, A. D. & Thompson, R. C. A. Prevalence of zoonotic pathogens from feral pigs in major public drinking water catchments in Western Australia. EcoHealth 3, 103–108. https://doi.org/10.1007/s10393-006-0018-8 (2006).
Article  Google Scholar 

5.
McClure, M. L. et al. Modeling and mapping the probability of occurrence of invasive wild pigs across the contiguous United States. PLoS ONE 10, e0133771. https://doi.org/10.1371/journal.pone.0133771 (2015).
CAS  Article  PubMed  PubMed Central  Google Scholar 

6.
Mayer, J. J. & Beasley, J. C. Ecology and management of terrestrial vertebrate invasive species in the United States 221–250 (CRC Press, Boca Raton, 2017).
Google Scholar 

7.
Boughton, E. H. & Boughton, R. K. Modification by an invasive ecosystem engineer shifts a wet prairie to a monotypic stand. Biol. Invas. 16, 2105–2114. https://doi.org/10.1007/s10530-014-0650-0 (2014).
Article  Google Scholar 

8.
Hone, J. Feral pig rooting in a mountain forest and woodland: distribution, abundance and relationships with environmental variables. Aust. J. Ecol. 13, 393–400 (1988).
Article  Google Scholar 

9.
Belden, R. C. & Pelton, M. R. Wallows of the European Wild Hog in the Mountains of East Tennessee. J. Tenn. Acad. Sci. 51, 91–93 (1976).
Google Scholar 

10.
Kaller, M. & Kelso, W. in Proceedings of the Annual Conference of the southeastern Association of Fish and Wildlife Agencies. 291–298.

11.
Kaller, M. D. & Kelso, W. E. Swine activity alters invertebrate and microbial communities in a coastal plain watershed. Am. Midl. Nat. 156, 163–178 (2006).
Article  Google Scholar 

12.
Kaller, M. D., Hudson, J. D. III., Achberger, E. C. & Kelso, W. E. Feral hog research in western Louisiana: expanding populations and unforeseen consequences. Hum. Wildl. Confl. 1, 168–177 (2007).
Google Scholar 

13.
U.S. Environmental Protection Agency. National Summary of Impaired Waters and TMDL Information, accessed Nov. 20, 2020 at https://iaspub.epa.gov/waters10/attains_nation_cy.control?p_report_type=T (2020).

14.
U.S. Environmental Protection Agency. Recreational Water Quality Criteria. (Health and Ecological Criteria Division, Office of Science and Technology, U.S. Environmental Protection Agency. EPA 820-F-12-058, 2012).

15.
Hutton, T., DeLiberto, T. J., Owen, S. & Morrison, B. Disease risks associated with increasing feral swine numbers and distribution in the United States. Mich. Bovine Tubercul. Bibliogr. Database 59, 1–15 (2006).
Google Scholar 

16.
Ruiz-Fons, F. A review of the current status of relevant zoonotic pathogens in wild swine (Sus scrofa) populations: changes modulating the risk of transmission to humans. Transb. Emerg. Dis. 64, 68–88 (2017).
CAS  Article  Google Scholar 

17.
Shwiff, S., Shwiff, S., Holderieath, J., Haden-Chomphosy, W. & Anderson, A. Ecology and management of terrestrial vertebrate invasive species in the United States 35–60 (CRC Press, Boca Raton, 2017).
Google Scholar 

18.
Brown, V., Bowen, R. & Bosco-Lauth, A. Zoonotic pathogens from feral swine that pose a significant threat to public health. Transb. Emerg. Dis. 65, 649–659 (2018).
CAS  Article  Google Scholar 

19.
Jay, M. T. et al. Escherichia coli O157:H7 in feral swine near spinach fields and cattle, central California coast. Emerg. Infect. Dis. 13, 1908–1911. https://doi.org/10.3201/eid1312.070763 (2007).
Article  PubMed  PubMed Central  Google Scholar 

20.
Friebel, A. & Jodice, P. Home range and habitat use of feral hogs in Congaree National Park, South Carolina. Hum. Wildl. Confl. 3, 49–63 (2009).
Google Scholar 

21.
Zengel, S. Wild pig habitat use, substrate disturbance, and understory vegetation at Congaree National Park Ph. D. thesis, Clemson University (2008).

22.
U.S. National Park Service. Final management plan for non-native wild pigs within Congaree National Park (CONG), with Environmental Assessment. (2014).

23.
U.S. National Park Service. National Park Service Visitor Use Statistics, 2020).

24.
McCarthy, S. Bacterial Water Quality Monitoring as Citizen Science in Congaree National Park, South Carolina MSc thesis, University of South Carolina, (2020).

25.
DHEC. The State of South Carolina’s 2016 Integrated Report Part I: Listing of Impaired Waters, accessed Nov. 20, 2020 at https://scdhec.gov/sites/default/files/docs/HomeAndEnvironment/Docs/tmdl_16-303d.pdf (2016).

26.
Meays, C., Broersma, K., Nordin, R. & Mazumder, A. Source tracking fecal bacteria in water: a critical review of current methods. J. Environ. Manag. 73, 71–79 (2004).
Article  Google Scholar 

27.
Bernhard, A. & Field, K. A PCR assay to discriminate human and ruminant feces on the basis of host differences in Bacteroides-Prevotella genes encoding 16S rRNA. Appl. Environ. Microbiol. 66, 4571–4574 (2000).
CAS  Article  Google Scholar 

28.
Beversdorf, L. J., Bornstein-Forst, S. M. & McLellan, S. L. The potential for beach sand to serve as a reservoir for Escherichia coli and the physical influences on cell die-off. J. Appl. Microbiol. 102, 1372–1381. https://doi.org/10.1111/j.1365-2672.2006.03177.x (2007).
CAS  Article  PubMed  Google Scholar 

29.
Byappanahalli, M. N., Nevers, M. B., Korajkic, A., Staley, Z. R. & Harwood, V. J. Enterococci in the Environment. Microbiol. Mol. Biol. Rev. 76, 685–706. https://doi.org/10.1128/MMBR.00023-12 (2012).
CAS  Article  PubMed  PubMed Central  Google Scholar 

30.
Byappanahalli, M. N., Roll, B. M. & Fujioka, R. S. Evidence for occurrence, persistence, and growth potential of Escherichia coli and enterococci in Hawaii’s soil environments. Microbes Environ. 27, 164–170. https://doi.org/10.1264/jsme2.ME11305 (2012).
Article  PubMed  Google Scholar 

31.
Bradshaw, J. K. et al. Characterizing relationships among fecal indicator bacteria, microbial source tracking markers, and associated waterborne pathogen occurrence in stream water and sediments in a mixed land use watershed. Water Res https://doi.org/10.1016/j.watres.2016.05.014 (2016).
Article  PubMed  Google Scholar 

32.
Leray, M. et al. A new versatile primer set targeting a short fragment of the mitochondrial COI region for metabarcoding metazoan diversity: application for characterizing coral reef fish gut contents. Front. Zool. 10, 1 (2013).
Article  Google Scholar 

33.
McKee, B. A., Molina, M., Cyterski, M. & Couch, A. Microbial source tracking (MST) in chattahoochee river national recreation area: seasonal and precipitation trends in MST marker concentrations, and associations with E. coli levels, pathogenic marker presence, and land use. Water Res. 171, 115435. https://doi.org/10.1016/j.watres.2019.115435 (2020).
CAS  Article  PubMed  Google Scholar 

34.
Allsop, K. & Stickler, J. An assessment fo Bacteroides fragilis group organisms as indicators of human faecal pollution. J. Appl. Bacteriol. 58, 95–99 (1985).
CAS  Article  Google Scholar 

35.
Kreader, C. A. Persistence of PCR-detectable Bacteroides distasonis from human feces in river water. Appl. Environ. Microbiol. 64, 4103–4105. https://doi.org/10.1128/aem.64.10.4103-4105.1998 (1998).
CAS  Article  PubMed  PubMed Central  Google Scholar 

36.
Ballesté, E. & Blanch, A. R. Persistence of Bacteroides Species populations in a river as measured by molecular and culture techniques. Appl. Environ. Microbiol. 76, 7608–7616. https://doi.org/10.1128/aem.00883-10 (2010).
Article  PubMed  PubMed Central  Google Scholar 

37.
USNPS. Foundation Document Congaree National Park South Carolina. 76 (U.S. National Park Servic (USNPS), 2014).

38.
Congaree River Keeper. Lower Richland Sewer Project (2014).

39.
McKee, A. M., Bradley, P. M. & Romanok, K. M. Microbial Source Tracking Marker Concentrations in Congaree National Park in 2017–2019, South Carolina, USA: U.S. Geological Survey data release. https://doi.org/10.5066/P9GFT8M7 (2020).

40.
U.S. Environmental Protection Agency. Method 1603: Escherichia coli (E. coli) in Water by Membrane Filtration Using Modified membrane-Thermotolerant Escherichia coli Agar (Modified mTEC). 42 (Washington, D.C., 2009).

41.
Bauer, L. & Alm, E. Escherichia coli toxin and attachment genes in sand at Great Lakes recreational beaches. J. Great Lakes Res. 38, 129–133. https://doi.org/10.1016/j.jglr.2011.10.004 (2012).
CAS  Article  Google Scholar 

42.
Duris, J. W., Haack, S. K. & Fogarty, L. R. Gene and antigen markers of shiga-toxin producing E. coli from Michigan and Indiana River water: occurrence and relation to recreational water quality criteria. J. Environ. Qual. 38, 1878–1886 (2009).
CAS  Article  Google Scholar 

43.
Myers, D. N., Stoeckel, D. M., Bushon, R. N., Francy, D. S. & Brady, A. M. G. in Book 9, chap. A7, section 7.1, May 2014 (2014).

44.
Videnska, P. et al. Stool sampling and DNA isolation kits affect DNA quality and bacterial composition following 16S rRNA gene sequencing using MiSeq Illumina platform. Sci. Rep. 9, 1–14 (2019).
CAS  Article  Google Scholar 

45.
Claassen, S. et al. A comparison of the efficiency of five different commercial DNA extraction kits for extraction of DNA from faecal samples. J. Microbiol. Methods 94, 103–110. https://doi.org/10.1016/j.mimet.2013.05.008 (2013).
CAS  Article  PubMed  PubMed Central  Google Scholar 

46.
Bradshaw, J. K. et al. Characterizing relationships among fecal indicator bacteria, microbial source tracking markers, and associated waterborne pathogen occurrence in stream water and sediments in a mixed land use watershed. Water Res. 101, 498–509 (2016).
CAS  Article  Google Scholar 

47.
Haugland, R. A. et al. Evaluation of genetic markers from the 16S rRNA gene V2 region for use in quantitative detection of selected Bacteroidales species and human fecal waste by qPCR. Syst. Appl. Microbiol. 33, 348–357. https://doi.org/10.1016/j.syapm.2010.06.001 (2010).
CAS  Article  PubMed  Google Scholar 

48.
Green, H. C. et al. Improved HF183 quantitative real-time PCR assay for characterization of human fecal pollution in ambient surface water samples. Appl. Environ. Microbiol. 80, 3086–3094 (2014).
Article  Google Scholar 

49.
Mieszkin, S., Yala, J. F., Joubrel, R. & Gourmelon, M. Phylogenetic analysis of Bacteroidales 16S rRNA gene sequences from human and animal effluents and assessment of ruminant faecal pollution by real-time PCR. J. Appl. Microbiol. 108, 974–984. https://doi.org/10.1111/j.1365-2672.2009.04499.x (2010).
CAS  Article  PubMed  Google Scholar 

50.
Ryu, H. et al. Comparison of two poultry litter qPCR assays targeting the 16S rRNA gene of Brevibacterium sp. Water Res. 48, 613–621 (2014).
CAS  Article  Google Scholar 

51.
Shanks, O. C. et al. Quantitative PCR for detection and enumeration of genetic markers of bovine fecal pollution. Appl. Environ. Microbiol. 74, 745–752 (2008).
CAS  Article  Google Scholar 

52.
Imamovic, L., Serra-Moreno, R., Jofre, J. & Muniesa, M. Quantification of Shiga toxin 2-encoding bacteriophages, by real-time PCR and correlation with phage infectivity. J. Appl. Microbiol. 108, 1105–1114 (2010).
CAS  Article  Google Scholar 

53.
Mieszkin, S., Furet, J.-P., Corthier, G. & Gourmelon, M. Estimation of pig fecal contamination in a river catchment by real-time PCR using two pig-specific Bacteroidales 16S rRNA genetic markers. Appl. Environ. Microbiol. 75, 3045–3054 (2009).
CAS  Article  Google Scholar 

54.
Field, J. A. & Lettinga, G. in Plant Polyphenols. Basic Life Sciences, vol 59 (eds Hemingway R.W. & Laks P.E.) (Springer, 1992).

55.
Bradley, P. M. et al. Widespread occurrence and potential for biodegradation of bioactive contaminants in Congaree National Park, USA. Environ. Toxicol. Chem. 36, 3045–3056 (2017).
CAS  Article  Google Scholar 

56.
Harestad, A. S. & Bunnel, F. Home range and body weight—a reevaluation. Ecology 60, 389–402 (1979).
Article  Google Scholar 

57.
Sanderson, G. C. The study of mammal movements: a review. J. Wildl. Manag. 30, 215–235 (1966).
Article  Google Scholar 

58.
Wood, G. W. & Brenneman, R. E. Feral hog movements and habitat use in coastal South Carolina. J. Wildl. Manag. 44, 420–427. https://doi.org/10.2307/3807973 (1980).
Article  Google Scholar 

59.
Kurz, J. C. & Marchinton, R. L. Radiotelemetry studies of feral hogs in South Carolina. J. Wildl. Manag. 36, 1240–1248 (1972).
Article  Google Scholar 

60.
Johnston, M. A., Porter, D. E., Scott, G. I., Rhodes, W. E. & Webster, L. F. Isolation of faecal coliform bacteria from the American alligator (Alligator mississippiensis). J. Appl. Microbiol. 108, 965–973. https://doi.org/10.1111/j.1365-2672.2009.04498.x (2010).
CAS  Article  PubMed  Google Scholar 

61.
Ramos, C. P. et al. Identification and characterization of Escherichia coli, Salmonella spp., Clostridium perfringens, and C. difficile Isolates from reptiles in Brazil. BioMed Res. Int. 2019, 9530732. https://doi.org/10.1155/2019/9530732 (2019).
CAS  Article  PubMed  PubMed Central  Google Scholar 

62.
Ballesté, E., García-Aljaro, C. & Blanch, A. R. Assessment of the decay rates of microbial source tracking molecular markers and faecal indicator bacteria from different sources. J. Appl. Microbiol. 125, 1938–1949. https://doi.org/10.1111/jam.14058 (2018).
Article  Google Scholar 

63.
Hadler, J. L. et al. Ten-year trends and risk factors for non-O157 Shiga Toxin-producing Escherichia coli found through Shiga Toxin testing, Connecticut, 2000–2009. Clin. Infect. Dis. 53, 269–276. https://doi.org/10.1093/cid/cir377 (2011).
Article  PubMed  Google Scholar 

64.
Doyle, M. P. & Schoeni, J. L. Survival and growth characteristics of Escherichia coli associated with hemorrhagic colitis. Appl. Environ. Microbiol. 48, 855–856 (1984).
CAS  Article  Google Scholar 

65.
Raghubeer, E. V. & Matches, J. R. Temperature range for growth of Escherichia coli serotype O157:H7 and selected coliforms in E. coli medium. J. Clin. Microbiol. 28, 803–805 (1990).
CAS  Article  Google Scholar 

66.
Soller, J. A., Schoen, M. E., Bartrand, T., Ravenscroft, J. E. & Ashbolt, N. J. Estimated human health risks from exposure to recreational waters impacted by human and non-human sources of faecal contamination. Water Res. 44, 4674–4691 (2010).
CAS  Article  Google Scholar 

67.
Schoen, M. E. & Ashbolt, N. J. Assessing pathogen risk to swimmers at non-sewage impacted recreational beaches. Environ. Sci. Technol. 44, 2286–2291 (2010).
ADS  CAS  Article  Google Scholar 

68.
Sedmak, G., Bina, D., MacDonald, J. & Couillard, L. Nine-year study of the occurrence of culturable viruses in source water for two drinking water treatment plants and the influent and effluent of a wastewater treatment plant in Milwaukee, Wisconsin (August 1994 through July 2003). Appl. Environ. Microbiol. 71, 1042–1050 (2005).
CAS  Article  Google Scholar 

69.
Medina, R. A. & García-Sastre, A. Influenza A viruses: new research developments. Nat. Rev. Microbiol. 9, 590–603. https://doi.org/10.1038/nrmicro2613 (2011).
CAS  Article  PubMed  Google Scholar 

70.
Soller, J. A., Bartrand, T., Ashbolt, N. J., Ravenscroft, J. & Wade, T. J. Estimating the primary etiologic agents in recreational freshwaters impacted by human sources of faecal contamination. Water Res. 44, 4736–4747 (2010).
CAS  Article  Google Scholar 

71.
U.S. Environmental Protection Agency. in EPA 822-R-10-005. (US EPA Office of Water, Washington, DC, 2010).

72.
Fredriksson-Ahomaa, M., Wacheck, S., Bonke, R. & Stephan, R. Different enteropathogenic Yersinia strains found in wild boars and domestic pigs. Foodborne Pathogens Dis. 8, 733–737 (2011).
CAS  Article  Google Scholar 

73.
Lara, G. H. B. et al. Occurrence of Mycobacterium spp. and other pathogens in lymph nodes of slaughtered swine and wild boars (Sus scrofa). Res. Vet. Sci. 90, 185–188. https://doi.org/10.1016/j.rvsc.2010.06.009 (2011).
Article  PubMed  Google Scholar 

74.
Pond, K. Water recreation and disease, plausibility of associated infections: acute effects, sequelae, and mortality (World Health Organization, London, 2005).
Google Scholar 

75.
Soller, J. et al. Estimated human health risks from recreational exposures to stormwater runoff containing animal faecal material. Environ. Model. Softw. 72, 21–32. https://doi.org/10.1016/j.envsoft.2015.05.018 (2015).
Article  Google Scholar 

76.
Nguyen, K. et al. Determination of wild animal sources of fecal indicator bacteria by microbial source tracking (MST) influences regulatory decisions. Water Res. 144, 424–434 (2018).
CAS  Article  Google Scholar 

77.
Balanson, S. Holding nature responsible: the natural conditions exception to water quality standards of the clean water act. Clev. St. L. Rev. 56, 1057 (2008).
Google Scholar  More

Present study aimed to develop a risk model to identify the risk localities in the dengue high risk areas. Kernel density and Euclidean distance based approaches are widely used in raster development of GIS modelling. Kernel density was used to fit a smoothly tapered surface to point layers while Euclidean distance was used to identify close exposures of polygon layers17. The risk values were ranked for each layer depending on their contribution to the transmission of dengue incidences. Based on the ILWIS Applications Guide18, the maximum risk value for the developed model was assigned as 10. Previous study conducted on mathematical modelling of dengue incidences in the Gampaha District have stated exponential influence of previous month cases on current month disease transmission in the district19. Further, investigation on adult and immature stages of dengue vector mosquitoes indicated that DENV are present in adult dengue vector mosquitoes and significant correlations of entomological indices with patient cases in the same district20,21. Therefore, patient locations and positive breeding container layers were selected as maximum risk variables and assigned the risk value of 10 as these variables are directly involved in disease transmission. In the modelling, dispersed risk distance patient cases and breeding places were selected less than average flight distance of dengue vector mosquitoes which is 400m22. Further, these study areas are considered to be highly congested areas and therefore, total building and home garden layers were given second highest ranking. Previous study conducted in Indonesia reported that consistent high number of dengue cases in larger areas of buildings even though the correlation is weak23. Further, higher dengue vector population densities were reported around home gardens from many countries24,25,26. Therefore, moderate risk level, risk value of 6, was assigned to total buildings and home garden layers. Recent study conducted in the Gampaha District demonstrated the contribution of daily commutes of people for transmission of dengue in the district27. When people visited to urban areas, there is higher probability of acquiring of dengue as these urban and suburban areas may act as dengue hot spots and artificial reservoirs which has been documented previously in Sri Lanka as well as other countries28,29,30,31. Therefore, land use layer for urban areas was given third highest ranking in the risk model. Another study conducted in Sri Lanka reported roads are important aspects for transmission of dengue25 and households in the present study areas located along main roads or have access roads. Further, previous study in Sri Lanka reported the potentials of public places play as artificial reservoirs dengue32 because of higher prevalence of breeding places around public places. It is a well-known fact that distribution of dengue vector mosquitoes varies with the elevation depending on geographical areas. Therefore, roads, public places and elevation layers were ranked in the third position with risk level of 4. However, the lower risk distances were assigned to road and contour layers as the layers are not related directly for transmission of the disease even though they play important role. Previous study conducted in Kenya and Uganda has reported higher dengue vector mosquito populations close to vegetation and marshy lands33 which may provide resting places of dengue vectors, especially for male mosquitos. When considering the study areas, with the exception of the 3rd Kurana study area, all other study areas have close proximity to marshy areas and therefore these areas were included as a variable in the present study. Since it is not directly involving in mosquito population increase or disease transmission, the lowest rank was assigned to marshy areas of land use layers.
When comparing the generated risk maps with satellite imageries, vegetation covers were observed in high risk localities in all study areas. The reason could be the vegetation covers make better resting places for dengue vector mosquitoes. Even though the Ae. aegypti mosquitoes, the main vector of DENV, rest indoors34, previous studies conducted in Malaysia and Kenya reported the preference of Ae. aegypti to rest and breed outdoors due to increased breeding opportunities without affecting lifespan or gonotrophic activity35,36. Meanwhile, it is well-known fact that Ae. albopictus, the subsidiary vector of DENV, prefers vegetation to rest and breeding in both natural and man-made containers37,38.
When comparing the intensity maps generated from the Poisson point process model with generated risk maps, differences in localization of intensities were observed specially in Eriyawetiya and Welikadamulla study areas. In the risk map of Eriyawetiya study area, risk localities were located mainly along the roads in the area and this observation was even statistically significant in Pearson correlation analysis. However, when considering the intensity map from the Poisson point process model, lower predicted intensity was observed in most of the locations in the study area and high intensities were observed around the southern border along the Devasumithrarama road and in central area. When considering the Welikadamulla study area, even though risk map indicates that dengue is high virtually all over the area, the predicted intensity map illustrates that dengue may high in central and northern border of the area along the Welikadamulla road. Interestingly, while the dengue high intensity localities in both Eriyawetiya and Welikadamulla study areas are mainly used as home gardens, these localities have close proximity to crowded public places, such as schools, temples, community halls, etc. Perhaps, these public places may have acted as artificial reservoirs of dengue. This is further observation in the high density localities in Akbar Town and 3rd Kurana study areas. In the Akbar Town area, high intensities were observed around mosques. In the 3rd Kurana study area, many public places, such as schools and churches, are located in the central and southern area where intensities were high. However, the lowest dengue intensities were observed from the 3rd Kurana study area.
In the Poisson point process model, highest intensity range was observed in the Eriyawetiya study area while the lowest was observed from 3rd Kurana. Eriyawetiya study area is located close to the northern border of Colombo, the commercial capital in Sri Lanka, where highest number of dengue cases are reported in the country39. Recent study reported that human commutes to risk areas in Colombo and transportations may play significant role transmission of dengue in the nearby areas, such as Eriyawetiya study area, leading to higher intensities21. However, the overall lowest intensities reported from 3rd Kurana study area may be due to continuous encouragement of dwellers in the area to remove dengue vector mosquito breeding places and use of protective measures by the churches and clergies.
The results of Pearson correlation analysis and Poisson multivariate point process model were also different especially with respect to positive breeding locations and roads layers. Positive correlation was observed between breeding places and patient locations in Pearson correlation analysis, which can be expected as dengue vector mosquitoes are anthropophilic mosquitoes with low flying ranges, were different from the results of Poisson point process model. In the model, no or negative correlation was observed between patient locations and breeding places. In a multivariate model, all explanatory variables are modelled to capture the true variation of the response variable while in Pearson correlation only one explanatory variable is considered at a time. The negative correlation in Poisson model with breeding places may be due to the hidden breeding places. These breeding places may be unidentified due to level of personal expertise, restrictions of accessibility to household, limitations due to inadequate resources, etc. which lead to differences between actual adult population and larval indices21. Further, even though road layers were shown similar behaviours for 3rd Kurana and Welikadamulla study areas both in Pearson correlations and Poisson modelling, differences were observed in Eriyawetiya and Akbar Town. The positive correlations observed between patient locations and road layers could probably be because of high congestion of households alongside the roads and therefore, even single DENV infected mosquitos can spread the disease to all households as these mosquitoes probe many humans during blood feeding. Similar observation has been reported in previous study conducted in West Indies40. The study further states that more dengue cases being found within 1–3 km away from various types of roads. This may be the reason for the observed negative estimates from multivariate Poisson model in Eriyawetiya and Akbar Town study areas as the patient locations are very close to access roads.
When analysing the observed (K) -functions of the developed Poisson multivariate models for the study areas, both clustering and dispersions were observed for Eriyawetiya and 3rd Kurana study areas while only clustering was observed in the Akbar Town and Welikadamulla areas. Interestingly, in Eriyawetiya and 3rd Kurana study areas, clustering was observed a radius of approximately 150 m. This is comparable to the general flying range of dengue vector mosquitoes, especially with regards to the Ae. aegypti41, the main dengue vector mosquito. Further, this may be an indicative of that patients in a small areal cluster are prompted due to a single infected dengue vector mosquito. During the analysis, both isotropic42 and translation43 edge correction methods were considered, therefore, edge effects arising from the unobserved patient locations outside study area can be hampered when estimating the (K)-functions. The estimations of (K)-functions were within the upper and lower envelopes of simulated functions in Akbar Town, 3rd Kurana and Welikadamulla study areas, that is, given particular distance, the data and simulated patterns were statistically equivalent. This indicates that dengue patient locations in the study areas were undergone a complete random pattern or CSR except for Eriyawetiya study area. This observation is further confirmed by the results of Maximum Absolute Deviation (MAD) and the Diggle-Cressie-Loosmore-Ford (DCLF) non-graphical tests44.
Among four monsoon seasons, the first inter-monsoon season occurs during March and April months. The Southwest monsoon period starts in May and it lasts till September. During the October and November, the second inter-monsoon period occurs and the Northeast monsoon lasts for three months from December to February. When analysing the distribution of dengue incidences in the monsoon periods, the highest number of dengue incidences were reported from the Southwest monsoon period in all study areas. The Gampaha District is located in the western part of Sri Lanka and during the monsoon period, the district experiences a rainfall of 750–2000 mm. In other monsoon periods, rainfall of the Gampaha District is less than 1000 mm45. The reason for higher precipitation in the Southwest monsoon period includes the presence of abundant water bodies, such as Arabian Sea and Indian Ocean, leading to higher accumulation of moisture in Southwest monsoon winds46. The higher rainfalls increase not only the availability of the breeding containers for dengue vector mosquitoes, but also favourable environmental conditions, viz. humidity and temperature, for its development. This will lead to increased disease transmission during the Southwest monsoon season compared to other monsoon seasons.
The developed models can be used to identify risk localities easily for healthcare workers and decision makers. The Poisson point process models can be developed using freely available software and packages. Further, road maps can be easily obtained for freely available sources and modified easily using freely available GIS software. With the advantages of technology, correct GPS locations of positive dengue vector mosquito breeding places and patients can be easily obtained using mobile devices with minimum wage during vector control programmes and export directly into GIS software. Since roads, land use, buildings and contour being not changing frequently in a particular area, with the aid of available data on patient locations as well as positive breeding places, it is possible to develop risk maps monthly or biannually to assess the risk levels of high risk areas. Further, when health authorities have risk map of particular area over few years, then it is possible to identify risk localities and transmission of dengue in an area in advance. This is particularly important in outbreaks and epidemic progression, so that they can have a better scenario of undergone situation to use scarce health resources effectively to control disease transmission. Meantime, the model can be further enhanced by incorporating serotype data which may lead identify index cases and initial clusters. A combined approach of predictive mathematical models19 and genetic approaches to identify the virulence of circulating dengue viruses21 will provide sufficient information for health authorities to take timely actions, such as intensive source reduction programmes, targeted intervention programmes or deploy vector reduction tools such as ovitraps9, to manage the situation to prevent propagation of outbreaks and epidemics. More

Study area
The study took place in the south-western Kalahari region of Botswana, known as the Bakgalagadi Schwelle (S 24.35°, E 20.62°), including the Botswana side of the Kgalagadi Transfrontier Park. The vegetation forms an open savanna, overlying deep sandy substrate with limited free-standing water. There is an intermittent river, Nossob river, in the south, ~ 80 km from the centre of the study area. A characteristic of this area is the highly mineralized, clay-rich depressions called pans, which retain water for variable periods after rain6. Air temperatures exceed 40 °C in summer and fall below 1 °C in winter6. Rainfall is seasonal but erratic, falling primarily during short-duration, high-intensity thunderstorms between November and April6. Mean annual rainfall in the Schwelle region ranges between 250 and 350 mm13.
Climatic variables
A free-standing miniature black globe thermometer (“miniglobe”), identical to the collar miniglobe thermometer, was placed within the area used by the animals in direct sun, 1 m aboveground, and recorded temperature (°C) every hour (S 24.307°, E 20.745°; reference miniglobe). Dry-bulb air temperature (°C), wind speed (ms−1), and solar radiation (Wm−2) data were obtained from the Agricultural Research Council (ARC) weather station located at the Nossob campsite (S 25.4°, E 20.6°). Normalised Difference Vegetation Index (NDVI) (MODIS Terra 16-day) and local rainfall (mm; CHIRPS) data covering the study area (S 24.434°, E 20.293°) were obtained from Google Earth Engine14.
Study species and data collection
In August 2013, eight individual female gemsbok and eight individual female wildebeest, each from separate herds, were darted by a veterinarian from a helicopter. Each dart consisted of Thiafentanil (gemsbok: 7–8 mg, wildebeest: 4–6 mg, Thianil, Kyron Laboratories, Johannesburg, South Africa), medetomidine hydrochloride (gemsbok: 3–6 mg, wildebeest: 2–4 mg, Kyron Laboratories, Johannesburg, South Africa) and ketamine (gemsbok: 75–150 mg, wildebeest: 50–150 mg Pfizer Animal Health, Sandton, South Africa). Each individual was fitted with a GPS collar (African Wildlife Tracking, Pretoria, South Africa) that supported a miniglobe attached to the top to record the thermal environment that the individual bearing it occupied15. Miniglobe temperatures and GPS locations were recorded hourly. In addition, each individual underwent surgery to implant miniature temperature-sensitive data loggers in the retroperitoneal space and had a motion-sensitive data logger tethered to the abdominal muscle wall (see7 for details). The data loggers were covered with biologically and chemically inert wax (Sasol, South Africa) and sterilised in instant sterilant (F10 Sterilant with rust inhibitor, Health and Hygiene (Pty) Ltd., Roodepoort, South Africa) before implantation. Once the individual animal was immobile, it was placed in sternal recumbency with its head elevated and supported by sandbags. Following intubation, anaesthesia was maintained with 2–5% isoflurane (Aerrane, Astra Zeneca, Johannesburg, South Africa), administered in 100% oxygen. Incision sites were shaved and sterilised with chlorhexidine gluconate (Hibitane, Zeneca, Johannesburg, South Africa). A local anaesthetic (3 ml subcutaneously (S.C.); lignocaine hydrochloride, Bayer Animal Health (Pty) Ltd., Isando, South Africa) was administered to the incision site. After placement of the loggers, the incision was sutured closed. Respiratory rate, heart rate, arterial oxygen saturation, and rectal temperature were monitored throughout the surgery, which lasted approximately 30–45 min. Each individual animal also received an antibiotic (~ 0.04 ml kg−1, intra muscularly (I.M.), Duplocillin, Schering-Plough Animal Health Ltd., New Zealand), and anti-inflammatory (~ 0.5 mg kg−1 I.M., Metacam, Meloxicam injectable solution, Boehringer Ingelheim Vetmedica, Inc, St. Joseph, U.S.A.) medication. Following surgery and termination of inhalation anaesthesia, the immobilization drugs were completely reversed by a combination of naltrexone (gemsbok: 75–120 mg, wildebeest: 60–100 mg, I.M. Naltrexone, Kyron Laboratories, Johannesburg, South Africa) and atipamezole (gemsbok: 10–20 mg; wildebeest: 10–15 mg, I.M. Antisedan, Orion Corporation, Orion Pharma, Finland).
The temperature-sensitive data loggers (DST centi-T, Star-Oddi, Iceland) recorded body temperature at 10-min intervals (Fig. 1a,b) and the motion-sensitive data logger recorded whole body movements (i.e., motion changes) as activity counts within the first 10 s of each 5-min interval. The motion-sensitive logger had a triaxial accelerometer (ADXL345, Sigma Delta Technologies, Australia) with equal sensitivity across three planes (resolution one-fourth 4 mg/least significant bit). We adjusted the activity units to be relative to the maximum activity count for the entire study period per logger, to account for differences in the sensitivity of the individual motion-sensitive loggers. The data loggers and the collar weighed less than 1% of the individual animal’s body mass and is unlikely to have adversely affected their behaviour.
Figure 1

Ten-min recordings of body temperature from a representative female wildebeest (a) and female gemsbok (b) over the study period (September 2013 to November 2014); and the monthly dry-bulb air temperature (solid black line), rainfall (grey bars) and monthly composited vegetation greenness (NDVI; dashed grey line) over two years (c) highlighting drought conditions in the first year. The light grey boxes represent the two hot-dry seasons compared in the current study.

Full size image

Two wildebeest were never relocated, possibly as a result of collar failure or predation. Three gemsbok died in October 2013. The remaining 11 animals were recaptured in May 2015, and data loggers and collars were removed. Thereafter the animals were released. Because of the inability to relocate all animals, animal deaths, and technological failures, we recovered a sample of 11 internal body temperature loggers (five gemsbok and six wildebeest); eight internal motion-sensitive loggers (four gemsboks and four wildebeest); nine GPS units (five gemsboks and four wildebeest) and nine miniglobe temperature sensors from the collars (five gemsboks and four wildebeest).
All procedures were approved by the Animal Ethics Screening Committee of the University of the Witwatersrand (protocol no. 2012/24/04) and all experiment procedures were performed in accordance with relevant guidelines and regulations as well as the ARRIVE guidelines (https://arriveguidelines.org/). The Government of Botswana via the Ministry of Environment, Wildlife and Tourism and Department of Wildlife and National Parks granted approvals and permits [numbers EWT 8/36/4 XX (32), EWT 8/36/4 XXVII (15), EWT 8/36/4 XXIV (102)] to conduct the study.
Data analysis
During the study period, the first hot-dry season (September to November 2013, ‘drought’) occurred at the end of a prolonged dry period, whereas the second hot-dry season (September to November 2014, ‘non-drought’) followed more typical rainfall conditions (Fig. 1c). Miniglobe temperature (24 h mean, minimum and maximum) and dry-bulb air temperature (24 h minimum and maximum), as well as mean 24 h wind speed and solar radiation were similar between the two hot-dry periods (Table 1). We averaged 16-day NDVI composites per season as an index of vegetation greenness in response to prior rainfall. Rainfall during the wet season prior to the commencement of the study (December to May 2013) was less than 40% ( More

1.
Qian, H. & Ricklefs, R. E. Large-scale processes and the Asian bias in species diversity of temperate plants. Nature 407, 180–182 (2000).
ADS  CAS  PubMed  Article  Google Scholar 
2.
Wu, Z. Y., Sun, H., Zhou, Z. K., Li, D. Z. & Peng, H. Floristics of Seed Plants From China (Science Press, Beijing, 2010).
Google Scholar 

3.
Ying, T. S. & Chen, M. L. Plant Geography of China (Shanghai Scientific and Technical Publishers, Shanghai, 2011).
Google Scholar 

4.
Ye, J. W., Zhang, Y. & Wang, X. J. Phylogeographic breaks and their forming mechanisms in Sino-Japanese Floristic Region. Chin. J. Plant Ecol. 41, 1003–1019 (2017).
Article  Google Scholar 

5.
Guo, X. D. et al. Evolutionary history of a widespread tree species Acer mono in East Asia. Ecol. Evol. 4, 4332–4345 (2014).
PubMed  PubMed Central  Article  Google Scholar 

6.
Liu, C. P. et al. Genetic structure and hierarchical population divergence history of Acer mono var. mono in south and northeast china. PLoS ONE 9, e87187 (2014).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

7.
Bai, W. N., Wang, W. T. & Zhang, D. Y. Phylogeographic breaks within Asian butternuts indicate the existence of a phytogeographic divide in East Asia. New Phytol. 209, 1757–1772 (2016).
CAS  PubMed  Article  Google Scholar 

8.
Ye, J. W., Bai, W. N., Bao, L., Wang, H. F. & Ge, J. P. Sharp genetic discontinuity in the arid-sensitive species Lindera obtusiloba (Lauraceae): Solid evidence supporting the Tertiary floral subdivision in East Asia. J. Biogeogr. 44, 2082–2095 (2017).
Article  Google Scholar 

9.
Cao, Y. N., Comes, H. P., Sakaguchi, S., Chen, L. Y. & Qiu, Y. X. Evolution of East Asia’s Arcto-Tertiary relict Euptelea (Eupteleaceae) shaped by Late Neogene vicariance and Quaternary climate change. BMC Evol. Biol. 16, 1–17 (2016).
Article  CAS  Google Scholar 

10.
Qi, X. S., Yuan, N., Comes, H. P., Sakaguchi, S. & Qiu, Y. X. A strong “filter” effect of the East China Sea land bridge for East Asia’s temperate plant species: Inferences from molecular phylogeography and ecological niche modelling of Platycrater arguta (Hydrangeaceae). BMC Evol. Biol. 14, 14–41 (2014).
Article  Google Scholar 

11.
Ye, J. W., Zhang, Y. & Wang, X. J. Phylogeographic history of broad-leaved forest plants in subtropical China. Acta Ecol. Sin. 37, 5894–5904 (2017).
Google Scholar 

12.
Wang, Y. H. et al. Molecular phylogeography and ecological niche modelling of a widespread herbaceous climber, Tetrastigma hemsleyanum (Vitaceae): Insights into Plio-Pleistocene range dynamics of evergreen forest in subtropical China. New Phytolt. 206, 852–867 (2015).
Article  Google Scholar 

13.
Fan, D. M. et al. Idiosyncratic responses of evergreen broad-leaved forest constituents in China to the late Quaternary climate changes. Sci. Rep.-U.K. 6, 31044 (2016).
ADS  CAS  Article  Google Scholar 

14.
Mu, H. P. et al. Genetic variation of Ardisia crenata in south China revealed by nuclear microsatellite. J. Syst. Evol. 48, 279–285 (2010).
Article  Google Scholar 

15.
Shi, M. M., Michalski, S. G., Welk, E., Chen, X. Y. & Durka, W. Phylogeography of a widespread Asian subtropical tree: Genetic east-west differentiation and climate envelope modelling suggest multiple glacial refugia. J. Biogeogr. 41, 1710–1720 (2014).
Article  Google Scholar 

16.
Zheng, J. Y., Yin, Y. H. & Li, B. Y. A new scheme for climate regionalization in China. Acta Geogr. Sin. 65, 3–12 (2010).
ADS  Google Scholar 

17.
Bai, W. N. & Zhang, D. Y. Current status and future direction in plant phylogeography. Chin. Bull. Life Sci. 26, 125–137 (2014).
Google Scholar 

18.
Wang, X. H., Kent, M. & Fang, X. F. Evergreen broad-leaved forest in Eastern China: Its ecology and conservation and the importance of resprouting in forest restoration. For. Ecol. Manag. 245, 76–87 (2007).
Article  Google Scholar 

19.
Hirayama, D., Itoh, A. & Yamakura, T. Implications from seed traps for reproductive success, allocation and cost in a tall tree species Lindera erythrocarpa. Plant Spec. Biol. 19, 185–196 (2004).
Article  Google Scholar 

20.
Ye, J. W., Li, D. Z. & Hampe, A. Differential Quaternary dynamics of evergreen broadleaved forests in subtropical China revealed by phylogeography of Lindera aggregata (Lauraceae). J. Biogeogr. 46, 1112–1123 (2019).
Article  Google Scholar 

21.
Wright, S. Isolation by distance. Genetics 28, 114 (1943).
CAS  PubMed  PubMed Central  Google Scholar 

22.
Wang, I. J. & Bradburd, G. S. Isolation by environment. Mol. Ecol. 23, 5649–5662 (2014).
PubMed  Article  Google Scholar 

23.
McRae, B. H. & Beier, P. Circuit theory predicts gene flow in plant and animal populations. Proc. Natl. Acad. Sci. U. S. A. 104, 19885–19890 (2007).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

24.
Drouin, G., Daoud, H. & Xia, J. Relative rates of synonymous substitutions in the mitochondrial, chloroplast and nuclear genomes of seed plants. Mol. Phylogenet. Evol. 49, 827–831 (2008).
CAS  PubMed  Article  Google Scholar 

25.
Meirmans, P. G. The trouble with isolation by distance. Mol. Ecol. 21, 2839–2846 (2012).
PubMed  Article  Google Scholar 

26.
Meirmans, P. G. Seven common mistakes in population genetics and how to avoid them. Mol. Ecol. 24, 3223–3231 (2015).
PubMed  Article  Google Scholar 

27.
Gong, W. et al. From glacial refugia to wide distribution range: Demographic expansion of Loropetalum chinense (Hamamelidaceae) in Chinese subtropical evergreen broadleaved forest. Org. Divers. Evol. 16, 23–38 (2016).
Article  Google Scholar 

28.
Li, X. H., Shao, J. W., Lu, C., Zhang, X. P. & Qiu, Y. X. Chloroplast phylogeography of a temperate tree Pteroceltis tatarinowii (Ulmaceae) in China. J. Syst. Evol. 50, 325–333 (2012).
Article  Google Scholar 

29.
Tian, S. et al. Phylogeography of Eomecon chionantha in subtropical China: The dual roles of the Nanling Mountains as a glacial refugium and a dispersal corridor. BMC Evol. Biol. 18, 20 (2018).
PubMed  PubMed Central  Article  Google Scholar 

30.
Waters, J. M., Fraser, C. I. & Hewitt, G. M. Founder takes all: Density-dependent processes structure biodiversity. Trends Ecol. Evol. 28, 78–85 (2013).
PubMed  Article  Google Scholar 

31.
Smith, C. G. III., Hamel, P. B., Devall, M. S. & Schiff, N. M. Hermit thrush is the first observed dispersal agent for pondberry (Lindera melissifolia). Castanea 69, 1–8 (2004).
Article  Google Scholar 

32.
Excoffier, L., Foll, M. & Petit, R. J. Genetic consequences of range expansions. Annu. Rev. Ecol. Evol. S 40, 481–501 (2009).
Article  Google Scholar 

33.
Ge, X. J. et al. Inferring multiple refugia and phylogeographical patterns in Pinus massoniana based on nucleotide sequence variation and DNA fingerprinting. PLoS ONE 7, e43717 (2012).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

34.
Chen, Y. et al. Genetic diversity and variation of Chinese fir from Fujian province and Taiwan, China, based on ISSR markers. PLoS ONE 12, e0175571 (2017).
PubMed  PubMed Central  Article  CAS  Google Scholar 

35.
Jiang, X. L., Gardner, E. M., Meng, H. H., Deng, M. & Xu, G. B. Land bridges in the Pleistocene contributed to flora assembly on the continental islands of South China: Insights from the evolutionary history of Quercus championii. Mol. Phylogenet. Evol. 132, 36–45 (2019).
PubMed  Article  Google Scholar 

36.
Hewitt, G. M. Genetic consequences of climatic oscillations in the Quaternary. Philos. Trans. R. Soc. B 359, 183–195 (2004).
CAS  Article  Google Scholar 

37.
Miller, K. G., Mountain, G. S., Wright, J. D. & Browning, J. V. A 180-million-year record of sea level and ice volume variations from continental margin and deep-sea isotopic records. Oceanography 24, 40–53 (2011).
Article  Google Scholar 

38.
Voris, H. K. Maps of Pleistocene sea levels in Southeast Asia: Shorelines, river systems and time durations. J. Biogeogr. 27, 1153–1167 (2000).
Article  Google Scholar 

39.
Yao, Y. T., Harff, J., Meyer, M. & Zhan, W. H. Reconstruction of paleocoastlines for the northwestern South China Sea since the Last Glacial Maximum. Sci. China Ser. D Earth Sci. 52, 1127–1136 (2009).
ADS  CAS  Article  Google Scholar 

40.
He, J. K., Gao, Z. F., Su, Y. Y., Lin, S. L. & Jiang, H. S. Geographical and temporal origins of terrestrial vertebrates endemic to Taiwan. J. Biogeogr. 45, 2458–2470 (2018).
Article  Google Scholar 

41.
Li, H. W. Parallel evolution in Litsea and Lindera of lauraceae. Acta Bot. Yunnanica 7, 129–135 (1985).
Google Scholar 

42.
Tian, X. Y., Ye, J. W. & Song, Y. Plastome sequences help to improve the systematic position of trinerved Lindera species in the family Lauraceae. Peerj 7, e7662 (2019).
PubMed  PubMed Central  Article  Google Scholar 

43.
Rozas, J., Sánchez-DelBarrio, J. C., Messeguer, X. & Rozas, R. DnaSP, DNA polymorphism analyses by the coalescent and other methods. Bioinformatics 19, 2496–2497 (2003).
CAS  PubMed  Article  Google Scholar 

44.
Falush, D., Stephens, M. & Pritchard, J. K. Inference of population structure using multilocus genotype data: Dominant markers and null alleles. Mol. Ecol. Notes 7, 574–578 (2007).
CAS  PubMed  PubMed Central  Article  Google Scholar 

45.
Dellicour, S. & Mardulyn, P. spads 1.0: A toolbox to perform spatial analyses on DNA sequence data sets. Mol. Ecol. Resour. 14, 647–651 (2014).
PubMed  Article  Google Scholar 

46.
Heled, J. & Drummond, A. J. Bayesian inference of species trees from multilocus data. Mol. Biol. Evol. 27, 570–580 (2010).
CAS  PubMed  Article  Google Scholar 

47.
Drummond, A. J. & Rambaut, A. BEAST: Bayesian evolutionary analysis by sampling trees. BMC Evol. Biol. 7, 214 (2007).
PubMed  PubMed Central  Article  CAS  Google Scholar 

48.
Cornuet, J. M. et al. DIYABC v2.0: A software to make approximate Bayesian computation inferences about population history using single nucleotide polymorphism, DNA Sequence and microsatellite data. Bioinformatics 30, 1187–1189 (2014).
CAS  PubMed  Article  Google Scholar 

49.
Yu, Y., Harris, A. J., Blair, C. & He, X. RASP (Reconstruct Ancestral State in Phylogenies): A tool for historical biogeography. Mol. Phylogenet. Evol. 87, 46–49 (2015).
PubMed  Article  Google Scholar 

50.
R Core Team R: a language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing. http://www.Rproject.org/. Accessed 24 May 2014. (2013).

51.
Hijmans, R. J., Cameron, S. E., Parra, J. L., Jones, P. G. & Jarvis, A. Very high resolution interpolated climate surfaces for global land areas. Int. J. Climatol. 25, 1965–1978 (2005).
Article  Google Scholar 

52.
Phillips, S. J., Anderson, R. P. & Schapire, R. E. Maximum entropy modeling of species geographic distributions. Ecol. Model 190, 231–259 (2006).
Article  Google Scholar 

53.
Wang, Y. H., Yang, K. C., Bridgman, C. L. & Lin, L. K. Habitat suitability modelling to correlate gene flow with landscape connectivity. Landsc. Ecol. 23, 989–1000 (2008).
Google Scholar 

54.
Wang, I. J. Examining the full effects of landscape heterogeneity on spatial genetic variation: A multiple matrix regression approach for quantifying geographic and ecological isolation. Evolution 67, 3403–3411 (2013).
PubMed  Article  Google Scholar  More

1.
Blanchoud, S., Rutherford, K., Zondag, L., Gemmell, N. J. & De Wilson, M. J. De novo draft assembly of the Botrylloides leachii genome provides further insight into tunicate evolution. Sci. Rep. 8, 5518 (2018).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 
2.
Lambert, G. Invasive sea squirts: A growing global problem. J. Exp. Mar. Biol. Ecol. 342, 3–4 (2007).
Article  Google Scholar 

3.
Manni, L., Zaniolo, G., Cima, F., Burighel, P. & Ballarin, L. Botryllus schlosseri: A model ascidian for the study of asexual reproduction. Dev. Dyn. 236, 335–352 (2007).
CAS  PubMed  Article  PubMed Central  Google Scholar 

4.
McKitrick, T. R., Muscat, C. C., Pierce, J. D., Bhattacharya, D. & De Tomaso, A. W. Allorecognition in a basal chordate consists of independent activating and inhibitory pathways. Immunity 34, 616–626 (2011).
CAS  PubMed  Article  PubMed Central  Google Scholar 

5.
Rosengarten, R. D. & Nicotra, M. L. Model systems of invertebrate allorecognition. Curr. Biol. 21, R82-92 (2011).
CAS  PubMed  Article  PubMed Central  Google Scholar 

6.
Voskoboynik, A. & Weissman, I. L. Botryllus schlosseri, an emerging model for the study of aging, stem cells, and mechanisms of regeneration. Invertebr. Reprod. Dev. 59, 33–38 (2015).
PubMed  Article  PubMed Central  Google Scholar 

7.
Gasparini, F. et al. Sexual and asexual reproduction in the colonial ascidian Botryllus schlosseri. Genes. N. Y. N 2000(53), 105–120 (2015).
Google Scholar 

8.
Manni, L. et al. Sixty years of experimental studies on the blastogenesis of the colonial tunicate Botryllus schlosseri. Curr. Dir. Tunicate Dev. 448, 293–308 (2019).
CAS  Google Scholar 

9.
Bock, D. G., MacIsaac, H. J. & Cristescu, M. E. Multilocus genetic analyses differentiate between widespread and spatially restricted cryptic species in a model ascidian. Proc. R. Soc. B Biol. Sci. 279, 2377–2385 (2012).
Article  Google Scholar 

10.
Brunetti, R. Botryllid species (Tunicata, Ascidiacea) from the Mediterranean coast of Israel, with some considerations on the systematics of Botryllinae. Zootaxa 2289, 18–32 (2009).
Article  Google Scholar 

11.
Monniot, C. & Monniot, F. Les ascidies de Polynésie francaise. Mem Mus Nat Hist Nat Paris 136, 1–154 (1987).
Google Scholar 

12.
Milne Edwards, H. Observation sur les Ascidies composées des côtes de la Manche. Mém. Académie Sci. Inst. Fr. 18, 217–326 (1841).
Google Scholar 

13.
Saito, Y., Shirae, M., Okuyama, M. & Cohen, S. Phylogeny of botryllid ascidians. in The Biology of Ascidians (eds. Sawada, H., Yokosawa, H. & Lambert, C. C.) 315–320 (Springer-Verlag, 2001).

14.
Saito, Y. & Okuyama, M. Studies on Japanese botryllid ascidians. IV. A new species of the genus Botryllus with a unique colony shape, from the vicinity of Shimoda. Zoolog. Sci. 20, 1153–61 (2003).

15.
Saito, Y., Mukai, H. & Watanabe, H. Studies of Japanese compound styelid ascidians I. Two new species of Botryllus from the vicinity of Shimoda. Publ. Seto Mar. Biol. Lab. 26, 347–355 (1981).

16.
Saito, Y., Mukai, H. & Watanabe, H. Studies on Japanese compound styelid ascidians. II. A new species of the genus Botrylloides and redescription of B. violaceus Oka. Publ. Seto Mar. Biol. Lab. 26, 357–368 (1981).

17.
Saito, Y. & Watanabe, H. Studies on Japanese compound styelid ascidians IV. Three new species of the genus Botrylloides from the vicinity of Shimoda. Publ. Seto Mar. Biol. Lab. 30, 227–240 (1985).

18.
Lopez-Legentil, S., Turon, X. & Planes, S. Genetic structure of the star sea squirt, Botryllus schlosseri, introduced in southern European harbours. Mol. Ecol. 15, 3957–3967 (2006).
CAS  PubMed  Article  PubMed Central  Google Scholar 

19.
Pérez-Portela, R., Bishop, J. D., Davis, A. R. & Turon, X. Phylogeny of the families Pyuridae and Styelidae (Stolidobranchiata, Ascidiacea) inferred from mitochondrial and nuclear DNA sequences. Mol. Phylogenet. Evol. 50, 560–570 (2009).
PubMed  Article  CAS  PubMed Central  Google Scholar 

20.
Yund, P. O., Collins, C. & Johnson, S. L. Evidence of a native Northwest Atlantic COI haplotype clade in the cryptogenic colonial ascidian Botryllus schlosseri. Biol. Bull. 228, 201–216 (2015).
CAS  PubMed  Article  PubMed Central  Google Scholar 

21.
Hebert, P. D., Cywinska, A., Ball, S. L. & deWaard, J. R. Biological identifications through DNA barcodes. Proc. R. Soc. B Biol. Sci. 270, 313–21 (2003).

22.
Folmer, O., Black, M., Hoeh, W., Lutz, R. & Vrijenhoek, R. DNA primers for amplification of mitochondrial cytochrome c oxidase subunit I from diverse metazoan invertebrates. Mol. Mar. Biol. Biotechnol. 3, 294–299 (1994).
CAS  PubMed  PubMed Central  Google Scholar 

23.
Haydar, D., Hoarau, G., Olsen, J. L., Stam, W. T. & Wolff, W. J. Introduced or glacial relict? Phylogeography of the cryptogenic tunicate Molgula manhattensis (Ascidiacea, Pleurogona). Divers. Distrib. 17, 68–80 (2011).
Article  Google Scholar 

24.
Monniot, F., Dettaï, A., Eléaume, M., Cruaud, C. & Améziane, N. Antarctic Ascidians (Tunicata) of the French-Australian survey CEAMARC in Terre Adélie. Zootaxa 2817, 1–54 (2011).
Article  Google Scholar 

25.
Nydam, M. L. & Harrison, R. G. Genealogical relationships within and among shallow-water Ciona species (Ascidiacea). Mar. Biol. 151, 1839–1847 (2007).
Article  Google Scholar 

26.
Pérez-Portela, R., Duran, S., Palacín, C. & Turon, X. The genus Pycnoclavella (Ascidiacea) in the Atlanto-Mediterranean region: a combined molecular and morphological approach. Invertertebrate Syst. 21, 187–205 (2007).
Article  Google Scholar 

27.
Rubinstein, N. D. et al. Deep sequencing of mixed total DNA without barcodes allows efficient assembly of highly plastic ascidian mitochondrial genomes. Genome Biol. Evol. 5, 1185–1199 (2013).
PubMed  PubMed Central  Article  CAS  Google Scholar 

28.
Stefaniak, L. et al. Genetic conspecificity of the worldwide populations of Didemnum vexillum Kott, 2002. Aquat. Invasions 4, 29–44 (2009).
Article  Google Scholar 

29.
Cohen, C. S., Saito, Y. & Weissman, I. L. Evolution of allorecognition in Botryllid ascidians inferred from a molecular phylogeny. Evolution 52, 746–756 (1998).
PubMed  Article  PubMed Central  Google Scholar 

30.
Atsumi, M. O. & Saito, Y. Studies on Japanese botryllid ascidians. V. A New species of the genus Botrylloides very similar to Botrylloides simodensis in morphology. Zoolog. Sci. 28, 532–542 (2011).

31.
Griggio, F. et al. Ascidian mitogenomics: comparison of evolutionary rates in closely related taxa provides evidence of ongoing speciation events. Genome Biol. Evol. 6, 591–605 (2014).
CAS  PubMed  PubMed Central  Article  Google Scholar 

32.
Nydam, M. L., Giesbrecht, K. B. & Stephenson, E. E. Origin and dispersal history of two colonial ascidian clades in the Botryllus schlosseri species complex. PLoS ONE 12, e0169944 (2017).
PubMed  PubMed Central  Article  CAS  Google Scholar 

33.
Reem, E., Douek, J., Paz, G., Katzir, G. & Rinkevich, B. Phylogenetics, biogeography and population genetics of the ascidian Botryllus schlosseri in the Mediterranean Sea and beyond. Mol. Phylogenetic Evol. 107, 221–231 (2017).
Article  Google Scholar 

34.
Berrill, N. J. The Tunicata with an account of the British species. vol. 133 (1950).

35.
Ben-Shlomo, R., Reem, E., Douek, J. & Rinkevich, B. Population genetics of the invasive ascidian Botryllus schlosseri from South American coasts. Mar. Ecol. Prog. Ser. 412, 85–92 (2010).
ADS  Article  Google Scholar 

36.
Lord, J. Temperature, space availability, and species assemblages impact competition in global fouling communities. Biol. Invasions 19, 43–55 (2017).
Article  Google Scholar 

37.
Rocha, R. M. et al. The power of combined molecular and morphological analyses for the genus Botrylloides: identification of a potentially global invasive ascidian and description of a new species. Syst. Biodivers. 17, 509–526 (2019).
Article  Google Scholar 

38.
Brunetti, R., Griggio, F., Mastrototaro, F., Gasparini, F. & Gissi, C. Toward a resolution of the cosmopolitan Botryllus schlosseri species complex (Ascidiacea, Styelidae): mitogenomics and morphology of clade E (Botryllus gaiae). Zool. J. Linn. Soc. 190, 1175–1192 (2020).
Google Scholar 

39.
Brunetti, R., Manni, L., Mastrototaro, F., Gissi, C. & Gasparini, F. Fixation, description and DNA barcode of a neotype for Botryllus schlosseri (Pallas, 1766) (Tunicata, Ascidiacea). Zootaxa 4353, 29–50 (2017).
PubMed  Article  PubMed Central  Google Scholar 

40.
Bay-Nouailhat, A., Bay-Nouailhat, W., Gasparini, F. & Brunetti, R. Botrylloides crystallinus n. sp., a new Botryllinae Adams & Adams, 1858 (Ascidiacea) from Mediterranean Sea. Zoosystema 42, 131–138 (2020).

41.
Shenkar, N. & Monniot, F. A new species of the genus Botryllus (Ascidiacea) from the Red Sea. Zootaxa 1256, 11–19 (2006).
Google Scholar 

42.
Brunetti, R. Fixation and redescription of a neotype for Polyclinus renierii Lamarck, 1815 (Tunicata, Ascidiacea, Styelidae, Botryllinae). Bolletino Mus. Storia Nat. Venezia 62, 105–113 (2011).
Google Scholar 

43.
Sigovini, M., Keppel, E. & Tagliapietra, D. Open Nomenclature in the biodiversity era. Methods Ecol. Evol. 7, 1217–1225 (2016).
Article  Google Scholar 

44.
Kearse, M. et al. Geneious Basic: an integrated and extendable desktop software platform for the organization and analysis of sequence data. Bioinformatics 28, 1647–1649 (2012).
PubMed  PubMed Central  Article  Google Scholar 

45.
Crooks, G. E., Hon, G., Chandonia, J.-M. & Brenner, S. E. WebLogo: a sequence logo generator. Genome Res. 14, 1188–1190 (2004).
CAS  PubMed  PubMed Central  Article  Google Scholar 

46.
Gouy, M., Guindon, S. & Gascuel, O. SeaView version 4: A multiplatform graphical user interface for sequence alignment and phylogenetic tree building. Mol. Biol. Evol. 27, 221–224 (2010).
CAS  PubMed  Article  PubMed Central  Google Scholar 

47.
Durrheim, G. A., Corfield, V. A., Harley, E. H. & Ricketts, M. H. Nucleotide sequence of cytochrome oxidase (subunit III) from the mitochondrion of the tunicate Pyura stolonifera: evidence that AGR encodes glycine. Nucleic Acids Res. 21, 3587–3588 (1993).
CAS  PubMed  PubMed Central  Article  Google Scholar 

48.
Yokobori, S., Ueda, T. & Watanabe, K. Codons AGA and AGG are read as glycine in ascidian mitochondria. J. Mol. Evol. 36, 1–8 (1993).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

49.
Iannelli, F., Griggio, F., Pesole, G. & Gissi, C. The mitochondrial genome of Phallusia mammillata and Phallusia fumigata (Tunicata, Ascidiacea): high genome plasticity at intra-genus level. BMC Evol. Biol. 7, 155 (2007).
PubMed  PubMed Central  Article  CAS  Google Scholar 

50.
Hirose, M. & Hirose, E. DNA barcoding in photosymbiotic species of Diplosoma (Ascidiacea: Didemnidae), with the description of a new species from the southern Ryukyus Japan. Zool. Sci. 26, 564–568 (2009).
CAS  Article  Google Scholar 

51.
Fulton, T. M., Chunwongse, J. & Tanksley, S. D. Microprep protocol for extraction of DNA from tomato and other herbaceous plants. Plant Mol. Biol. Report. 13, 207–209 (1995).
CAS  Article  Google Scholar 

52.
Viard, F., Roby, C., Turon, X., Bouchemousse, S. & Bishop, J. Cryptic diversity and database errors challenge non-indigenous species surveys: an illustration with Botrylloides spp. in the English channel and Mediterranean Sea. Front. Mar. Sci. 6, (2019).

53.
Brunetti, R. & Mastrototaro, F. Ascidiacea of the European waters. (Edagricole – Edizioni Agricole di New Business Media Srl, 2017).

54.
Zeng, L., Jacobs, M. W. & Swalla, B. J. Coloniality has evolved once in Stolidobranch ascidians. Integr. Comp. Biol. 46, 255–268 (2006).
CAS  PubMed  Article  PubMed Central  Google Scholar 

55.
Katoh, K. & Toh, H. Recent developments in the MAFFT multiple sequence alignment program. Brief. Bioinform. 9, 286–298 (2008).
CAS  PubMed  Article  PubMed Central  Google Scholar 

56.
Lacoursiere-Roussel, A. et al. Disentangling invasion processes in a dynamic shipping-boating network. Mol. Ecol. 21, 4227–4241 (2012).
PubMed  Article  PubMed Central  Google Scholar 

57.
Lejeusne, C., Bock, D. G., Therriault, T. W., MacIsaac, H. J. & Cristescu, M. E. Comparative phylogeography of two colonial ascidians reveals contrasting invasion histories in North America. Biol. Invasions 13, 635–650 (2011).
Article  Google Scholar 

58.
Guindon, S. & Gascuel, O. A simple, fast, and accurate algorithm to estimate large phylogenies by maximum likelihood. Syst. Biol. 52, 696–704 (2003).
PubMed  PubMed Central  Article  Google Scholar 

59.
Lefort, V., Longueville, J. E. & Gascuel, O. SMS: Smart Model Selection in PhyML. Mol. Biol. Evol. 34, 2422–2424 (2017).
CAS  PubMed  PubMed Central  Article  Google Scholar 

60.
Ronquist, F. et al. MrBayes 3.2: efficient Bayesian phylogenetic inference and model choice across a large model space. Syst. Biol. 61, 539–542 (2012).

61.
Puillandre, N., Lambert, A., Brouillet, S. & Achaz, G. ABGD, automatic barcode gap discovery for primary species delimitation. Mol. Ecol. 21, 1864–1877 (2012).
CAS  PubMed  Article  PubMed Central  Google Scholar 

62.
Zhang, J., Kapli, P., Pavlidis, P. & Stamatakis, A. A general species delimitation method with applications to phylogenetic placements. Bioinformatics 29, 2869–2876 (2013).
CAS  PubMed  PubMed Central  Article  Google Scholar 

63.
Jukes, T. H. & Cantor, C. R. Evolution of protein molecules. (Academic Press, 1969).

64.
Kimura, M. A simple method for estimating evolutionary rates of base substitutions through comparative studies of nucleotide sequences. J. Mol. Evol. 16, 111–120 (1980).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

65.
Tang, C. Q., Humphreys, A. M., Fontaneto, D. & Barraclough, T. G. Effects of phylogenetic reconstruction method on the robustness of species delimitation using single-locus data. Methods Ecol. Evol. 5, 1086–1094 (2014).
PubMed  PubMed Central  Article  Google Scholar 

66.
Voskoboynik, A. et al. The genome sequence of the colonial chordate Botryllus schlosseri. eLife 2, e00569 (2013).
PubMed  PubMed Central  Article  Google Scholar 

67.
Stach, T. & Turbeville, J. M. Phylogeny of Tunicata inferred from molecular and morphological characters. Mol. Phylogenet. Evol. 25, 408–428 (2002).
CAS  PubMed  Article  PubMed Central  Google Scholar 

68.
Webb, K. E., Barnes, D. K. A., Clark, M. S. & Bowden, D. A. DNA barcoding: A molecular tool to identify Antarctic marine larvae. Deep Sea Res. Part II Top. Stud. Oceanogr. 53, 1053–1060 (2006).

69.
Bock, D. G., Zhan, A., Lejeusne, C., MacIsaac, H. J. & Cristescu, M. E. Looking at both sides of the invasion: Patterns of colonization in the violet tunicate Botrylloides violaceus. Mol. Ecol. 20, 503–516 (2011).
CAS  PubMed  Article  PubMed Central  Google Scholar 

70.
Bishop, J. D. D. et al. The Southern Hemisphere ascidian Asterocarpa humilis is unrecognised but widely established in NW France and Great Britain. Biol. Invasions 15, 253–260 (2013).
Article  Google Scholar 

71.
Geller, J., Meyer, C., Parker, M. & Hawk, H. Redesign of PCR primers for mitochondrial cytochrome c oxidase subunit I for marine invertebrates and application in all-taxa biotic surveys. Mol. Ecol. Resour. 13, 851–861 (2013).
CAS  PubMed  Article  PubMed Central  Google Scholar 

72.
Erpenbeck, D., Hooper, J. N. A. & Wörheide, G. CO1 phylogenies in diploblasts and the ‘Barcoding of Life’— are we sequencing a suboptimal partition?. Mol. Ecol. Notes 6, 550–553 (2006).
CAS  Article  Google Scholar 

73.
Herdman, W. A. Descriptive catalogue of the Tunicata in the Australian Museum Sydney N.S.W. Aust. Mus. Syd. Cat. 17, 1–139 (1899).

74.
Herdman, W. A. A revised classification of the Tunicata, with definitions of the orders, suborders, families, subfamilies, and genera, and analytical keys to the species. J. Linn. Soc. Lond. Zool. 23, 558–652 (1891).
Article  Google Scholar 

75.
Kott, P. Catalogue of Tunicata in Australian waters. Australian Biological Resources Study. (2005).

76.
Kott, P. The Australian Ascidiacea. Part I, Phlebobranchia and Stolidobranchia. Mem. Qld. Mus. 23, 1–440 (1985).

77.
Tsagkogeorga, G. et al. An updated 18S rRNA phylogeny of tunicates based on mixture and secondary structure models. BMC Evol. Biol. 9, 187 (2009).
PubMed  PubMed Central  Article  CAS  Google Scholar 

78.
Turon, X. & Lopez-Legentil, S. Ascidian molecular phylogeny inferred from mtDNA data with emphasis on the Aplousobranchiata. Mol. Phylogenet. Evol. 33, 309–320 (2004).
CAS  PubMed  Article  PubMed Central  Google Scholar 

79.
Swalla, B. J., Cameron, C. B., Corley, L. S. & Garey, J. R. Urochordates are monophyletic within the deuterostomes. Syst. Biol. 49, 52–64 (2000).
CAS  PubMed  Article  PubMed Central  Google Scholar 

80.
Delsuc, F., Brinkmann, H., Chourrout, D. & Philippe, H. Tunicates and not cephalochordates are the closest living relatives of vertebrates. Nature 439, 965–968 (2006).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

81.
Tsagkogeorga, G., Turon, X., Galtier, N., Douzery, E. J. P. & Delsuc, F. Accelerated evolutionary rate of housekeeping genes in tunicates. J. Mol. Evol. 71, 153–167 (2010).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

82.
Yokobori, S. i et al. Complete DNA sequence of the mitochondrial genome of the ascidian Halocynthia roretzi (Chordata, Urochordata). Genetics 153, 1851–1862 (1999).

83.
Haye, P. A. & Muñoz-Herrera, N. C. Isolation with differentiation followed by expansion with admixture in the tunicate Pyura chilensis. BMC Evol. Biol. 13, 252 (2013).
PubMed  PubMed Central  Article  Google Scholar 

84.
Pérez-Portela, R. & Turon, X. Cryptic divergence and strong population structure in the colonial invertebrate Pycnoclavella communis (Ascidiacea) inferred from molecular data. Zool. Jena 111, 163–178 (2008).
Article  Google Scholar 

85.
Sheets, E. A., Cohen, C. S., Ruiz, G. M. & Rocha, R. M. Investigating the widespread introduction of a tropical marine fouling species. Ecol. Evol. 6, 2453–2471 (2016).
PubMed  PubMed Central  Article  Google Scholar 

86.
Smith, K. F. et al. Increased inter-colony fusion rates are associated with reduced COI haplotype diversity in an invasive colonial ascidian Didemnum vexillum. PLoS ONE 7, e30473 (2012).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

87.
Tarjuelo, I., Posada, D., Crandall, K. A., Pascual, M. & Turon, X. Phylogeography and speciation of colour morphs in the colonial ascidian Pseudodistoma crucigaster. Mol. Ecol. 13, 3125–3136 (2004).
CAS  PubMed  Article  PubMed Central  Google Scholar 

88.
Tarjuelo, I., Posada, D., Crandall, K., Pascual, M. & Turon, X. Cryptic species of Clavelina (Ascidiacea) in two different habitats: Harbours and rocky littoral zones in the northwestern Mediterranean. Mar. Biol. 139, 455–462 (2001).
Article  Google Scholar 

89.
de France, F. Harant, H. & Vernieres, P. Tuniciers. Fasc. 1. Ascidies. in. In. Paris 27, 1–101 (1933).
Google Scholar 

90.
Lambert, G. Ecology and natural history of the protochordates. Can. J. Zool. 83, 34–50 (2005).
Article  Google Scholar 

91.
Millar, R. H. The biology of ascidians. Adv. Mar. Biol. 9, 1–100 (1971).
ADS  Article  Google Scholar 

92.
da Silva Oliveira, F. A., Michonneau, F. & da Cruz Lotufo, T. M. Molecular phylogeny of Didemnidae (Ascidiacea: Tunicata). Zool. J. Linn. Soc. 180, 603–612 (2017).

93.
Tabudravu, J. N. et al. LC-HRMS-Database screening metrics for rapid prioritization of samples to accelerate the discovery of structurally new natural products. J. Nat. Prod. 82, 211–220 (2019).
CAS  PubMed  Article  PubMed Central  Google Scholar 

94.
Mastrototaro, F. et al. An integrative taxonomic framework for the study of the genus Ciona (Ascidiacea) and description of a new species Ciona intermedia. Zool. J. Linn. Soc. 190, 1193–1216 (2020).
Google Scholar 

95.
Mastrototaro, F. et al. Hitch-hikers of the sea: concurrent morphological and molecular identification of Symplegma brakenhielmi (Tunicata: Ascidiacea) in the western Mediterranean Sea. Mediterr. Mar. Sci. 20, 197–207 (2019).
Google Scholar  More

1.
Zhao, H. et al. The evolution of color vision in nocturnal mammals. PNAS 106, 8980–8985 (2009).
ADS  CAS  Article  Google Scholar 
2.
Douglas, R. H. & Jeffery, G. The spectral transmission of ocular media suggests ultraviolet sensitivity is widespread among mammals. Proc. R. Soc. B. 281, 1471–2954. https://doi.org/10.1098/rspb.2013.2995 (2014).
Article  Google Scholar 

3.
Pearn, S. M., Bennett, A. T. & Cuthill, I. C. Ultraviolet vision, fluorescence and mate choice in a parrot, the budgerigar Melopsittacus undulates. Proc. R. Soc. B. 268, 2273–2279. https://doi.org/10.1098/rspb.2001.1813 (2001).
CAS  Article  PubMed  Google Scholar 

4.
Olofsson, M., Vallin, A., Jakobsson, S. & Wiklund, C. Marginal eyespots on butterfly wings deflect bird attacks under low light intensities with UV wavelengths. PLoS ONE 5, e10798. https://doi.org/10.1371/journal.pone.0010798 (2010).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

5.
Honkavaara, J., Koivula, M., Korpimaki, E., Siitari, H. & Viitala, J. Ultraviolet vision and foraging in terrestrial vertebrates. Oikos 98, 505–511. https://doi.org/10.1034/j.1600-0706.2002.980315.x (2008).
Article  Google Scholar 

6.
McDonald, B., Geiger, B. & Vrla, S. Ultraviolet vision in Ord’s kangaroo rat (Dipodomys ordii). J. Mammal. https://doi.org/10.1093/jmammal/gyaa083 (2020).
Article  Google Scholar 

7.
Hunt, D. M., Carvalho, L. S., Cowing, J. A. & Davies, W. L. Evolution and spectral tuning of visual pigments in birds and mammals. Phil. Trans. R. Soc. B. 364, 2941–2955. https://doi.org/10.1098/rstb.2009.0044 (2009).
CAS  Article  PubMed  Google Scholar 

8.
Davies, W. L. et al. Visual pigments of the platypus: a novel route to mammalian colour vision. Curr. Biol. 17, R161–R163. https://doi.org/10.1016/j.cub.2007.01.037 (2007).
CAS  Article  PubMed  Google Scholar 

9.
Jeng, M.-L. Biofluorescence in terrestrial animals, with emphasis on fireflies: A review and field observation. In Bioluminescence – analytical applications and basic biology (ed. Suzuki, H.) Ch. 6, https://doi.org/10.5772/intechopen.86029 (IntechOpen, 2019).

10.
Sparks, J. S. et al. The covert world of fish biofluorescence: A phylogenetically widespread and phenotypically variable phenomenon. PLoS ONE https://doi.org/10.1371/journal.pone.0083259 (2014).
Article  PubMed  PubMed Central  Google Scholar 

11.
Park, H. B. et al. Bright green biofluorescence in sharks derives from Bromo-kynurenine metabolism. iScience 19, 1277–1286. https://doi.org/10.1016/j.isci.2019.07.019 (2019).
CAS  Article  Google Scholar 

12.
Gruber, D. F. & Sparks, J. S. First observation of fluorescence in marine turtles. Am. Mus. Novit. 3845, 1–8. https://doi.org/10.1206/3845.1 (2015).
Article  Google Scholar 

13.
Taboada, C. et al. Naturally occurring fluorescence in frogs. PNAS 114, 3672–3677. https://doi.org/10.1073/pnas.1701053114 (2017).
CAS  Article  PubMed  Google Scholar 

14.
Prötzel, D. et al. Widespread bone-based fluorescence in chameleons. Sci. Rep. 8, 698. https://doi.org/10.1038/s41598-017-19070-7 (2018).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

15.
Goutte, S. et al. Intense bone fluorescence reveals hidden patterns in pumpkin toadlets. Sci. Rep. 9, 5388. https://doi.org/10.1038/s41598-019-41959-8 (2019).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

16.
Lamb, J. Y. & Davis, M. P. Salamanders and other amphibians are aglow with biofluorescence. Sci. Rep. 10, 2821. https://doi.org/10.1038/s41598-020-58528-9 (2020).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

17.
Weidensaul, C. S., Colvin, B. A., Brinker, D. F. & Huy, J. S. Use of ultraviolet light as an aid in age classification of owls. Wilson J Ornithol. 123, 373–377. https://doi.org/10.1676/09-125.1 (2011).
Article  Google Scholar 

18.
Camacho, C., Negro, J. J., Redondo, I., Palacios, S. & Sáez-Gómez, P. Correlates of individual variation in the porphyrin-based fluorescence of red-necked nightjars. Sci. Rep. 9, 19115. https://doi.org/10.1038/s41598-019-55522-y (2019).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

19.
Kohler, A. M., Olson, E. R., Martin, J. G. & Anich, P. S. Ultraviolet fluorescence discovered in New World flying squirrels (Glaucomys). J. Mammal. 100, 21–30. https://doi.org/10.1093/jmammal/gyy177 (2019).
Article  Google Scholar 

20.
Meisner, D. H. Psychedelic opossums: fluorescence of the skin and fur of Didelphis virginiana Kerr. Ohio J. Sci. 83, 4 (1983).
Google Scholar 

21.
Pine, R. H., Rice, J. E., Bucher, J. E., Tank, D. J. Jr. & Greenhall, A. M. Labile pigments and fluorescent pelage in Didelphid marsupials. Mammalia 49, 249–256 (1985).
Article  Google Scholar 

22.
Anich, P. S. et al. Biofluorescence in the platypus (Orinthorhynchus anatinus). Mammalia https://doi.org/10.1515/mammalia-2020-0027 (2020).
Article  Google Scholar 

23.
Matthee, C. A. & Robinson, T. J. Mitochondrial DNA phylogeography and comparative cytogenetics of the springhare, Pedetes capensis (Mammalia: Reodentia). J. Mammal. Evol. 4, 53–73. https://doi.org/10.1023/A:1027331727034 (1997).
Article  Google Scholar 

24.
Augustine, D. J., Manzon, A., Klopp, C. & Elter, J. Habitat selection and group foraging of the springhare, Pedetes capensis larvalis Hollister, East Africa. Afr. J. Ecol. 33, 347–357 (1995).
Article  Google Scholar 

25.
Peinke, D. M. & Brown, C. R. Habitat use by the southern springhare (Pedetes capensis) in the Eastern Cape Province, South Africa. S. Afr. J. Wildl. Res. 36(2), 103–111 (2006).
Google Scholar 

26.
Kennedy, G. Y. & Vevers, H. G. The occurrence of porphyrins in certain marine invertebrates. J. Mar. Biol. Ass. UK 33, 663–576 (1954).
CAS  Article  Google Scholar 

27.
Comfort, A. The pigmentation of molluscan shells. Biol. Rev. 26, 285–301. https://doi.org/10.1111/j.1469-185X.1951.tb01358.x (1951).
CAS  Article  Google Scholar 

28.
Thomas, D. B., McGoverin, C. M., McGraw, K. J., James, H. F. & Madden, O. Vibrational spectroscopic analyses of unique yellow feather pigments (spheniscins) in penguins. J. R. Soc. Interface 10, 20121065. https://doi.org/10.1098/rsif.2012.1065 (2012).
Article  Google Scholar 

29.
With, T. K. On porphyrins in feathers of owls and bustards. Int. J. Biochem. 9, 893–895 (1978).
CAS  Article  Google Scholar 

30.
With, T. K. Pure unequivocal uroporphyrin III simplified method of preparation from turaco feathers. J. Clin. Lab Invest. 9, 398–401 (1957).
CAS  Article  Google Scholar 

31.
Dooley, A. C. Jr. & Moncrief, N. D. Fluorescence provides evidence of congenital erythropoietic porphyria in 7000-year-old specimens of the eastern fox squirrel (Sciurus niger) from the Devil’s Den. J. Vert. Paleontol. 32, 495–497 (2012).
Article  Google Scholar 

32.
Ajioka, R. S., Phillips, J. D. & Kushner, J. P. Biosynthesis of heme in mammals. Biochem. Biophys. Acta. 1763, 723–736. https://doi.org/10.1016/j.bbamcr.2006.05.005 (2006).
CAS  Article  PubMed  Google Scholar 

33.
Seo, I., Tseng, S. H., Cula, G. O., Bargo, P. R. & Kollias, N. Fluorescence spectroscopy for endogenous porphyrins in human facial skin. Proc. SPIE. https://doi.org/10.1117/12.811913 (2009).
Article  Google Scholar 

34.
Heckl, C. et al. Rapid spectrophotometric quantification of urinary porphyrins and porphobilinogen as screening tool for attacks of acute porphyria. Proc. SPIE. https://doi.org/10.1117/12.2527105 (2019).
Article  Google Scholar 

35.
Levin, E. Y. & Flyger, V. Erythropoietic Porphyria of Fox Squirrel Sciurus niger. J. Clin. Invest. 52, 96–105 (1973).
CAS  Article  Google Scholar 

36.
Turner, W. J. Studies on porphyria. I. Observations on the fox squirrel, Sciurus niger. J. Biol. Chem. 118, 519–530 (1937).
CAS  Article  Google Scholar 

37.
Rivera, D. F. & Leung, L.K.-P. A rare autosomal recessive condition, congenital erythropoietic porphyria, found in canefield rat Rattus sordidus Gould 1858. Integative Zool. 216–218, 2008. https://doi.org/10.1111/j.1749-4877.2008.00088.x (2008).
Article  Google Scholar 

38.
Bickers, D. R., Keogh, L., Rifkind, A. B., Harber, L. C. & Kappas, A. Studies in porphyria VI. Biosynthesis of porphyrins in mammalian skin and in the skin of porphyric patients. J. Invest. Dermatol. 68(1), 5–9. https://doi.org/10.1111/1523-1747.ep12485121 (1977).
CAS  Article  PubMed  Google Scholar 

39.
Yolton, R. L., Yolton, D. P., Renz, J. & Jacobs, G. H. Preretinal absorbance in sciurid eyes. J. Mammal. 55, 14–20 (1974).
CAS  Article  Google Scholar 

40.
Friedmann, H. C. & Baldwin, E. T. Reverse-phase purification and silica gel thin-layer chromatography of porphyrin carboxylic acids. Anal. Biochem 137, 473–480 (1984).
CAS  Article  Google Scholar 

41.
Lim, C. K. & Peters, T. J. Urine and faecal porphyrin profiles by reversed-phase high performance liquid chromatography in the porphyrias. Clin. Chim. Acta. 139, 55–63 (1984).
CAS  Article  Google Scholar 

42.
To-Figueras, J., Ozalla, D. & Mateu, C. H. Long-standing changes in the urinary profile of porphyrin isomers after clinical remission of porphyria cutanea tarda. Ann. Clin. Lab. Sci. 33, 251–256 (2003).
CAS  PubMed  Google Scholar  More