Philippa Kaur

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain
the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in
Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles
and JavaScript. More

1.Mayr, E. Geographical character gradients and climatic adaptation. Evolution 10, 105–108 (1956).Article 

Google Scholar 
2.Sand, H., Cederlund, G. & Danell, K. Geographical and latitudinal variation in growth patterns and adult body size of Swedish moose (Alces alces). Oecologia 102, 433–442 (1995).ADS 
PubMed 
Article 

Google Scholar 
3.Gigliotti, L. C. et al. Latitudinal variation in snowshoe hare (Lepus americanus) body mass: a test of Bergmann’s rule. Can. J. Zool. 98, 88–95 (2020).Article 

Google Scholar 
4.Best, T. L. Intraspecific Variation in the Agile Kangaroo Rat (Dipodomys agilis). J. Mammal. 64, 426–436. https://doi.org/10.2307/1380355 (1983).Article 

Google Scholar 
5.Terada, C., Tatsuzawa, S. & Saitoh, T. Ecological correlates and determinants in the geographical variation of deer morphology. Oecologia 169, 981–994 (2012).ADS 
PubMed 
Article 

Google Scholar 
6.Gigliotti, L. C., Diefenbach, D. R. & Sheriff, M. J. Geographic variation in winter adaptations of snowshoe hares (Lepus americanus). Can. J. Zool. 95, 539–545 (2017).Article 

Google Scholar 
7.Singaravelan, N. et al. Adaptation of pelage color and pigment variations in Israeli subterranean blind mole rats, Spalax ehrenbergi. PloS ONE 8, 119 (2013).Article 

Google Scholar 
8.Price, T., Ndiaye, O., Hammerschmidt, K. & Fischer, J. Limited geographic variation in the acoustic structure of and responses to adult male alarm barks of African green monkeys. Behav. Ecol. Sociobiol. 68, 815–825 (2014).PubMed 
PubMed Central 
Article 

Google Scholar 
9.Lagos, L. & Bárcena, F. Spatial variability in wolf diet and prey selection in Galicia (NW Spain). Mammal Res. 63, 125–139. https://doi.org/10.1007/s13364-018-0352-6 (2018).Article 

Google Scholar 
10.Ashton, K. G., Tracy, M. C. & Queiroz, A. D. Is Bergmann’s rule valid for mammals?. Am. Nat. 156, 390–415 (2000).PubMed 
Article 

Google Scholar 
11.Watt, C., Mitchell, S. & Salewski, V. Bergmann’s rule; a concept cluster?. Oikos 119, 89–100 (2010).Article 

Google Scholar 
12.Yom-Tov, Y. & Geffen, E. Recent spatial and temporal changes in body size of terrestrial vertebrates: probable causes and pitfalls. Biol. Rev. 86, 531–541 (2011).PubMed 
Article 

Google Scholar 
13.Basuony, M., Mohamed, W. & Shalabi, M. Food and feeding ecology of the Egyptian Mongoose, Herpestes ichneumon (Linnaeus, 1758) in Egypt. J. Appl. Sci. Res. 9, 5811–5816 (2013).
Google Scholar 
14.McNab, B. K. Geographic and temporal correlations of mammalian size reconsidered: a resource rule. Oecologia 164, 13–23 (2010).ADS 
PubMed 
Article 

Google Scholar 
15.Wang, M. et al. Ambient temperature correlates with geographic variation in body size of least horseshoe bats. Curr. Zool. 2, 19 (2020).
Google Scholar 
16.Taggart, D. A. et al. Environmental factors influencing hairy-nosed wombat abundance in semi-arid rangelands. J. Wildl. Manag. 84, 921–929 (2020).Article 

Google Scholar 
17.Brandimarti, M. E. et al. Reference intervals for parameters of health of eastern grey kangaroos Macropus giganteus and management implications across their geographic range. Wildl. Biol. 2020 (2020).18.Fancourt, B. A., Hawkins, C. E. & Nicol, S. C. Mechanisms of climate-change-induced species decline: spatial, temporal and long-term variation in the diet of an endangered marsupial carnivore, the eastern quoll. Wildl. Res. 45, 737–750 (2019).Article 

Google Scholar 
19.Phillips, B. L. & Shine, R. Adapting to an invasive species: toxic cane toads induce morphological change in Australian snakes. Proc. Natl. Acad. Sci. 101, 17150–17155 (2004).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
20.Both, C. & Visser, M. E. The effect of climate change on the correlation between avian life-history traits. Global Change Biol. 11, 1606–1613 (2005).ADS 
Article 

Google Scholar 
21.Borg, C., Majolo, B., Qarro, M. & Semple, S. A comparison of body size, coat condition and endoparasite diversity of wild Barbary macaques exposed to different levels of tourism. Anthrozoös 27, 49–63 (2014).Article 

Google Scholar 
22.Maceda-Veiga, A., Green, A. J. & De Sostoa, A. Scaled body-mass index shows how habitat quality influences the condition of four fish taxa in north-eastern Spain and provides a novel indicator of ecosystem health. Freshwat. Biol. 59, 1145–1160 (2014).Article 

Google Scholar 
23.Thatcher, H. R., Downs, C. T. & Koyama, N. F. Using parasitic load to measure the effect of anthropogenic disturbance on vervet monkeys. EcoHealth 15, 676–681 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
24.Boyce, M. S. Population viability analysis. Annu. Rev. Ecol. Syst. 23, 481–497 (1992).Article 

Google Scholar 
25.Gaillard, J.-M., Festa-Bianchet, M., Yoccoz, N., Loison, A. & Toigo, C. Temporal variation in fitness components and population dynamics of large herbivores. Annu. Rev. Ecol. Syst. 31, 367–393 (2000).Article 

Google Scholar 
26.Reed, D. H., O’Grady, J. J., Brook, B. W., Ballou, J. D. & Frankham, R. Estimates of minimum viable population sizes for vertebrates and factors influencing those estimates. Biol. Conserv. 113, 23–34 (2003).Article 

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

Google Scholar 
28.Schulte-Hostedde, A. I., Zinner, B., Millar, J. S. & Hickling, G. J. Restitution of mass–size residuals: validating body condition indices. Ecology 86, 155–163 (2005).Article 

Google Scholar 
29.Weiss, D. J. & Wardrop, K. J. Schalm’s Veterinary Hematology (Wiley, 2011).
Google Scholar 
30.Hanks, J., Fowler, C. & Smith, T. Dynamics of large mammal populations. Dyn. Large Mamm. Popul. 2, 47–73 (1981).
Google Scholar 
31.Mapfumo, L., Muchenje, V., Mupangwa, J. F. & Scholtz, M. M. Changes in biochemical proxy indicators for nutritional stress resilience from Boran and Nguni cows reared in dry arid rangeland. Trop. Anim. Health Prod. 49, 1383–1392 (2017).PubMed 
Article 
PubMed Central 

Google Scholar 
32.Miller, D. S. et al. Biomedical evaluation of free-ranging ring-tailed lemurs (Lemur catta) in three habitats at the Beza Mahafaly Special Reserve, Madagascar. J. Zoo Wildl. Med. 38, 201–216 (2007).ADS 
PubMed 
Article 
PubMed Central 

Google Scholar 
33.Pérez, J. M. et al. Distinguishing disease effects from environmental effects in a mountain ungulate: seasonal variation in body weight, hematology, and serum chemistry among Iberian ibex (Capra pyrenaica) affected by sarcoptic mange. J. Wildl. Dis. 51, 148–156 (2015).PubMed 
Article 
PubMed Central 

Google Scholar 
34.Webster, K. N., Hill, N. J., Burnett, L. & Deane, E. M. Ectoparasite infestation patterns, haematology and serum biochemistry of urban-dwelling common brushtail possums. Wildl. Biol. 20, 206–216 (2014).Article 

Google Scholar 
35.Perrault, J. R. & Stacy, N. I. Note on the unique physiologic state of loggerhead sea turtles (Caretta caretta) during nesting season as evidenced by a suite of health variables. Mar. Biol. 165, 71 (2018).Article 

Google Scholar 
36.O’Brien, J., Schmitt, T., Nollens, H., Dubach, J. & Robeck, T. Reproductive physiology of the female Magellanic penguin (Spheniscus magellanicus): insights from the study of a zoological colony. Gen. Comp. Endocrinol. 225, 81–94 (2016).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
37.Robert, K. A. & Schwanz, L. E. Monitoring the health status of free-ranging tammar wallabies using hematology, serum biochemistry, and parasite loads. J. Wildl. Manag. 77, 1232–1243 (2013).Article 

Google Scholar 
38.Portas, T. J. et al. Beyond morbidity and mortality in reintroduction programmes: changing health parameters in reintroduced eastern bettongs Bettongia gaimardi. Oryx 50, 674–683 (2016).Article 

Google Scholar 
39.Lücker, A., Secomb, T. W., Weber, B. & Jenny, P. The relative influence of hematocrit and red blood cell velocity on oxygen transport from capillaries to tissue. Microcirculation 24, e12337. https://doi.org/10.1111/micc.12337 (2017).CAS 
Article 

Google Scholar 
40.Shield, J. A seasonal change in blood cell volume of the Rottnest Island quokka, Setonix brachyurus. J. Zool. 165, 343–354 (1971).Article 

Google Scholar 
41.Sealander, J. A. Seasonal changes in blood values of deer mice and other small mammals. Ecology 12, 107–119 (1962).Article 

Google Scholar 
42.Trumble, S. J., Castellini, M. A., Mau, T. L. & Castellini, J. M. Dietary and seasonal influences on blood chemistry and hematology in captive harbor seals. Mar. Mamm. Sci. 22, 104–123 (2006).Article 

Google Scholar 
43.Boonstra, R., McColl, C. J. & Karels, T. J. Reproduction at all costs: The adaptive stress response of male Arctic ground squirrels. Ecology 82, 1930–1946. (2001).Article 

Google Scholar 
44.Stockham, S. L. & Scott, M. A. Fundamentals of Veterinary Clinical Pathology (Wiley, 2013).
Google Scholar 
45.Thrall, M. A., Weiser, G., Allison, R. W. & Campbell, T. W. Veterinary Hematology and Clinical Chemistry (Wiley, 2012).
Google Scholar 
46.Gruys, E., Toussaint, M., Niewold, T. & Koopmans, S. Acute phase reaction and acute phase proteins. J. Zhejiang Univ. Sci. B 6, 1045 (2005).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
47.Serrano, E. et al. The use of total serum proteins and triglycerides for monitoring body condition in the Iberian wild goat (Capra pyrenaica). J. Zoo Wildl. Med. 39, 646–649 (2008).PubMed 
Article 
PubMed Central 

Google Scholar 
48.Stevens, L. A. & Levey, A. S. Measurement of kidney function. . Med. Clin. 89, 457–473 (2005).
Google Scholar 
49.Vanholder, R., Glorieux, G., De Smet, R. & Lameire, N. New insights in uremic toxins. Kidney Int. 63, S6–S10 (2003).Article 

Google Scholar 
50.Caldeira, R., Belo, A., Santos, C., Vazques, M. & Portugal, A. The effect of body condition score on blood metabolites and hormonal profiles in ewes. Small Rumin. Res. 68, 233–241 (2007).Article 

Google Scholar 
51.Schutte, J. E., Longhurst, J. C., Gaffney, F. A., Bastian, B. C. & Blomqvist, C. G. Total plasma creatinine: an accurate measure of total striated muscle mass. J. Appl. Physiol. 51, 762–766 (1981).CAS 
PubMed 
Article 

Google Scholar 
52.Kaneko, J. J., Harvey, J. W. & Bruss, M. L. Clinical Biochemistry of Domestic Animals. (Academic Press, 2008).53.Stirrat, S. C. Body condition and blood chemistry of agile wallabies (Macropus agilis) in the wet–dry tropics. Wildl. Res. 30, 59–67 (2003).CAS 
Article 

Google Scholar 
54.Lassen, E. Perspectives in data interpretation. Vet. Hematol. Clini. Chem. 5, 45–49 (2004).
Google Scholar 
55.Maceda-Veiga, A. et al. Inside the Redbox: applications of haematology in wildlife monitoring and ecosystem health assessment. Sci. Total Environ. 514, 322–332 (2015).ADS 
CAS 
PubMed 
Article 

Google Scholar 
56.Brandimarti, M. E., Gray, R., Silva, F. R. & Herbert, C. A. Kangaroos at maximum capacity: health assessment of free-ranging eastern grey kangaroos on a coastal headland. J. Mamm. 2, 96 (2021).
Google Scholar 
57.Clark, P. Haematology of Australian Mammals. (CSIRO Publishing, 2004).58.Solberg, H. A guide to IFCC recommendations on reference values. J. Int. Fed. Clin. Chem. 5, 162–165 (1993).CAS 
PubMed 

Google Scholar 
59.Gongora, J. et al. Genetic structure and phylogeography of platypuses revealed by mitochondrial DNA. J. Zool. 286, 110–119 (2012).Article 

Google Scholar 
60.Grant, T. & Fanning, D. The Platypus: A Unique Mammal. (University of New South Wales Press, 1995).61.Furlan, E. et al. Is body size variation in the platypus (Ornithorhynchus anatinus) associated with environmental variables?. Aust. J. Zool. 59, 201–215 (2012).Article 

Google Scholar 
62.Allen, A. Allens rule. The influence of Physical conditions in the genesis of species. Rad. Rev. 1, 108–140 (1877).
Google Scholar 
63.Bergmann, C. Uber die Verhaltnisse der warmeokonomie der Thiere zu uber Grosso. Gottinger Studien 3, 595–708 (1847).
Google Scholar 
64.Grant, T., Griffiths, M. & Temple-Smith, P. in Proc. Linn. Soc. N.S.W. 227 (Linnean Society of New South Wales).65.Munks, S., Otley, H., Bethge, P. & Jackson, J. Reproduction, diet and daily energy expenditure of the platypus in a sub-alpine Tasmanian lake. Aust. Mamm. 21, 260–261 (2000).
Google Scholar 
66.Temple-Smith, P. & Grant, T. Uncertain breeding: a short history of reproduction in monotremes. Reprod. Fertil. Dev. 13, 487–497 (2001).CAS 
PubMed 
Article 

Google Scholar 
67.Chessman, B. C. & Williams, S. A. Biodiversity and conservation of river macroinvertebrates on an expanding urban fringe: western Sydney, New South Wales, Australia. Pac. Conserv. Biol. 5, 36–55 (1999).Article 

Google Scholar 
68.Magierowski, R. H., Davies, P. E., Read, S. M. & Horrigan, N. Impacts of land use on the structure of river macroinvertebrate communities across Tasmania, Australia: spatial scales and thresholds. Mar. Freshw. Res. 63, 762–776 (2012).Article 

Google Scholar 
69.Verkaik, I., Prat, N., Rieradevall, M., Reich, P. & Lake, P. S. Effects of bushfire on macroinvertebrate communities in south-east Australian streams affected by a megadrought. Mar. Freshw. Res. 65, 359–369 (2014).Article 

Google Scholar 
70.Stitz, L., Fabbro, L. & Kinnear, S. Response of macroinvertebrate communities to seasonal hydrologic changes in three sub-tropical Australian streams. Environ. Monit. Assess. 189, 254 (2017).PubMed 
Article 
CAS 

Google Scholar 
71.McLachlan-Troup, T., Dickman, C. & Grant, T. Diet and dietary selectivity of the platypus in relation to season, sex and macroinvertebrate assemblages. J. Zool. 280, 237–246 (2010).Article 

Google Scholar 
72.Bino, G. et al. The platypus: evolutionary history, biology, and an uncertain future. J. Mamm. 100, 308–327 (2019).Article 

Google Scholar 
73.Grant, T. & Temple-Smith, P. Conservation of the platypus, Ornithorhynchus anatinus: threats and challenges. Aquat. Ecosyst. Health Manag. 6, 5–18 (2003).Article 

Google Scholar 
74.Gust, N. et al. Distribution, prevalence and persistence of mucormycosis in Tasmanian platypuses (Ornithorhynchus anatinus). Aust. J. Zool. 57, 245–254 (2009).Article 

Google Scholar 
75.Klamt, M., Thompson, R. & Davis, J. Early response of the platypus to climate warming. Global Change Biol. 17, 3011–3018 (2011).ADS 
Article 

Google Scholar 
76.Richmond, E. K. et al. A diverse suite of pharmaceuticals contaminates stream and riparian food webs. Nat. Commun. 9, 4491 (2018).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
77.Scheelings, T. Morbidity and mortality of monotremes admitted to the Australian Wildlife Health Centre, Healesville Sanctuary, Australia, 2000–2014. Aust. Vet. J. 94, 121–124 (2016).CAS 
PubMed 
Article 

Google Scholar 
78.Hawke, T., Bino, G. & Kingsford, R. T. A silent demise: historical insights into population changes of the iconic platypus (Ornithorhynchus anatinus). Global Ecol. Conserv. 20, 720 (2019).
Google Scholar 
79.Connolly, J., Obendorf, D. & Whittington, R. Haematological, serum biochemical and serological features of platypuses with and without mycotic granulomatous dermatitis. Aust. Vet. J. 77, 809–813 (1999).CAS 
PubMed 
Article 

Google Scholar 
80.Geraghty, D. P., Griffiths, J., Stewart, N., Robertson, I. K. & Gust, N. Hematologic, plasma biochemical, and other indicators of the health of Tasmanian platypuses (Ornithorhynchus anatinus): predictors of mucormycosis. J. Wildl. Dis. 47, 483–493 (2011).CAS 
PubMed 
Article 

Google Scholar 
81.Macgregor, J. W. et al. A need for dynamic hematology and serum biochemistry reference tools: Novel use of sine wave functions to produce seasonally varying reference curves in platypuses (Ornithorhynchus anatinus). J. Wildl. Dis. 53, 235–247. https://doi.org/10.7589/2015-12-336 (2017).Article 
PubMed 

Google Scholar 
82.Booth, R. & Connolly, J. in Medicine in Australian Mammals 103–132 (CSIRO Publishing, 2008).83.Whittington, R. & Grant, T. Haematology and blood chemistry of the free-living platypus, Ornithorhynchus anatinus (Shaw) (Monotremata: Ornithorhynchidae). Aust. J. Zool. 31, 475–482 (1983).CAS 
Article 

Google Scholar 
84.Whittington, R. & Grant, T. Haematology and Blood Chemistry of the Conscious Platypus, Ornithorhynchus anatinus (Shaw) (Monotremata: Ornithorhynchidae). Aust. J. Zool. 32, 631–635. https://doi.org/10.1071/ZO9840631 (1984).CAS 
Article 

Google Scholar 
85.Grant, T. & Carrick, F. Some aspects of the ecology of the platypus, Ornithorhynchus anatinus, in the upper Shoalhaven River. New South Wales. Australian Zool. 20, 181–199 (1978).
Google Scholar 
86.Bino, G., Kingsford, R. T., Grant, T., Taylor, M. D. & Vogelnest, L. Use of implanted acoustic tags to assess platypus movement behaviour across spatial and temporal scales. Sci. Rep. 8, 1–12 (2018).CAS 
Article 

Google Scholar 
87.Hawke, T., Bino, G. & Kingsford, R. T. Damming insights: impacts and implications of river regulation on platypus populations. Aquatic Conservation in press (2020).88.Gallant, J. & Read, A. A near-global bare-Earth DEM from SRTM. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 41, B4 (2016).
Google Scholar 
89.Temple-Smith, P. D. M. Seasonal breeding biology of the platypus, Ornithorhynchus anatinus (Shaw, 1799), with special reference to the male. (1973).90.Williams, G., Serena, M. & Grant, T. Age-related change in spurs and spur sheaths of the platypus (Ornithorhynchus anatinus). Australian Mammalogy 35, 107–114 (2013).Article 

Google Scholar 
91.Grueber, C., Nakagawa, S., Laws, R. & Jamieson, I. Multimodel inference in ecology and evolution: challenges and solutions. J. Evol. Biol. 24, 699–711 (2011).CAS 
PubMed 
Article 

Google Scholar 
92.Burnham, K. P. & Anderson, D. R. Multimodel inference: understanding AIC and BIC in model selection. Sociol. Methods Res. 33, 261–304 (2004).MathSciNet 
Article 

Google Scholar 
93.R Development Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing., (R Foundation for Statistical Computing., 2020).94.Wickham, H. ggplot2-Elegant Graphics for Data Analysis (Springer International Publishing, 2016).MATH 

Google Scholar 
95.Wood, S. Mixed GAM computation vehicle with GCV/AIC/REML smoothness estimation and GAMMs by REML/PQL. R Package Version, 1.8–23 (2018).96.Wood, S. & Wood, M. S. Package ‘mgcv’. R Package Ver. 1, 29 (2015).
Google Scholar 
97.Breheny, P. & Burchett, W. Visualization of regression models using visreg. R J. 9, 56 (2017).Article 

Google Scholar 
98.Geffré, A., Concordet, D., Braun, J. P. & Trumel, C. Reference Value Advisor: a new freeware set of macroinstructions to calculate reference intervals with Microsoft Excel. Vet. Clin. Pathol. 40, 107–112 (2011).PubMed 
Article 

Google Scholar 
99.Friedrichs, K. R. et al. ASVCP reference interval guidelines: determination of de novo reference intervals in veterinary species and other related topics. Vet. Clin. Pathol. 41, 441–453 (2012).PubMed 
Article 

Google Scholar 
100.Calver, M. C., Goldman, B., Hutchings, P. A. & Kingsford, R. T. Why discrepancies in searching the conservation biology literature matter. Biol. Conserv. 213, 19–26 (2017).Article 

Google Scholar 
101.Pfeffermann, D. The role of sampling weights when modeling survey data. International Statistical Review/Revue Internationale de Statistique, 317–337 (1993).102.Deem, S. L., Karesh, W. B. & Weisman, W. Putting theory into practice: wildlife health in conservation. Conserv. Biol. 15, 1224–1233 (2001).Article 

Google Scholar 
103.Isaksson, C. Urbanization, oxidative stress and inflammation: a question of evolving, acclimatizing or coping with urban environmental stress. Funct. Ecol. 29, 913–923 (2015).Article 

Google Scholar 
104.Karesh, W. B. & Cook, R. A. Applications of veterinary medicine to in situ conservation efforts. Oryx 29, 244–252 (1995).Article 

Google Scholar 
105.Cahill, A. E. et al. Causes of warm-edge range limits: systematic review, proximate factors and implications for climate change. J. Biogeogr. 41, 429–442 (2014).Article 

Google Scholar 
106.Elmore, R. D. et al. Implications of the thermal environment for terrestrial wildlife management. Wildl. Soc. Bull. 41, 183–193 (2017).Article 

Google Scholar 
107.Todgham, A. E. & Stillman, J. H. Physiological responses to shifts in multiple environmental stressors: relevance in a changing world. Integr. Comput. Biol. 53, 539–544 (2013).Article 

Google Scholar 
108.Brice, P. H. Thermoregulation in monotremes: riddles in a mosaic. Aust. J. Zool. 57, 255–263 (2009).Article 

Google Scholar 
109.Grant, T. Body temperatures of free-ranging platypuses, Ornithorhynchus anatinus (Monotremata), with observations on their use of burrows. Aust. J. Zool. 31, 117–122 (1983).Article 

Google Scholar 
110.Grant, T. & Dawson, T. Temperature regulation in the platypus, Ornithorhynchus anatinus: maintenance of body temperature in air and water. Physiol. Zool. 51, 1–6 (1978).Article 

Google Scholar 
111.Grant, T. & Dawson, T. J. Temperature regulation in the platypus, Ornithorhynchus anatinus: production and loss of metabolic heat in air and water. Physiol. Zool. 51, 315–332 (1978).Article 

Google Scholar 
112.Connolly, J. H., Claridge, T., Cordell, S. M., Nielsen, S. & Dutton, G. J. Distribution and characteristics of the platypus (Ornithorhynchus anatinus) in the Murrumbidgee catchment. Aust. Mamm. 38, 58–67 (2016).Article 

Google Scholar 
113.Grant, T. Historical and current distribution of the platypus, Ornithorhynchus anatinus. Australia. In Platypus and echidnas (ed. ML Augee), 232–254 (1992).114.Grant, T., Gehrke, P., Harris, J. & Hartley, S. Distribution of the platypus (Ornithorhynchus anatinus) in NSW: results of the 1994–96 NSW Rivers Survey. Aust. Mamm. 21, 177–184 (2000).Article 

Google Scholar 
115.Nazifi, S., Gheisari, H. & Poorabbas, H. The influences of thermal stress on serum biochemical parameters of dromedary camels and their correlation with thyroid activity. Comp. Haematol. Int. 9, 49–54 (1999).Article 

Google Scholar 
116.Singh, K. M. et al. Evaluation of Indian sheep breeds of arid zone under heat stress condition. Small Rumin. Res. 141, 113–117 (2016).Article 

Google Scholar 
117.Zhang, Y. & Kieffer, J. D. Critical thermal maximum (CTmax) and hematology of shortnose sturgeons (Acipenser brevirostrum) acclimated to three temperatures. Can. J. Zool. 92, 215–221 (2014).CAS 
Article 

Google Scholar 
118.Burgmer, T., Hillebrand, H. & Pfenninger, M. Effects of climate-driven temperature changes on the diversity of freshwater macroinvertebrates. Oecologia 151, 93–103 (2007).ADS 
CAS 
PubMed 
Article 

Google Scholar 
119.Carr, M., Li, L., Sadeghian, A., Phillips, I. D. & Lindenschmidt, K. E. Modelling the possible impacts of climate change on the thermal regime and macroinvertebrate species of a regulated prairie river. Ecohydrology 12, e2102 (2019).Article 

Google Scholar 
120.Daufresne, M., Bady, P. & Fruget, J.-F. Impacts of global changes and extreme hydroclimatic events on macroinvertebrate community structures in the French Rhône River. Oecologia 151, 544–559 (2007).ADS 
PubMed 
Article 

Google Scholar 
121.Durance, I. & Ormerod, S. J. Climate change effects on upland stream macroinvertebrates over a 25-year period. Global Change Biol. 13, 942–957 (2007).ADS 
Article 

Google Scholar 
122.Walsh, C. J. Biological indicators of stream health using macroinvertebrate assemblage composition: a comparison of sensitivity to an urban gradient. Mar. Freshw. Res. 57, 37–47 (2006).Article 

Google Scholar 
123.Marchant, R. & Grant, T. The productivity of the macroinvertebrate prey of the platypus in the upper Shoalhaven River, New South Wales. Mar. Freshw. Res. 66, 1128–1137 (2015).Article 

Google Scholar 
124.Bino, G., Kingsford, R. T. & Wintle, B. A. A stitch in time–Synergistic impacts to platypus metapopulation extinction risk. Biol. Conserv. 242, 108399 (2020).125.Ambrosio, A. M. et al. Significant hematocrit decrease in healthy horses during clinical anesthesia. Braz. j. vet. Res. Anim. Sci. 49, 139–145 (2012).Article 

Google Scholar 
126.Dhumeaux, M. P. et al. Effects of a standardized anesthetic protocol on hematologic variables in healthy cats. J. Feline Med. Surg. 14, 701–705 (2012).PubMed 
Article 

Google Scholar 
127.Marini, R. et al. Effect of isoflurane on hematologic variables in ferrets. Am. J. Vet. Res. 55, 1479–1483 (1994).CAS 
PubMed 

Google Scholar 
128.Bejaei, M. & Cheng, K. Effects of pretransport handling stress on physiological and behavioral response of ostriches. Poult. Sci. 93, 1137–1148 (2014).CAS 
PubMed 
Article 

Google Scholar 
129.Delgiudice, G. D., Kunkel, K. E., Mech, L. D. & Seal, U. S. Minimizing capture-related stress on white-tailed deer with a capture collar. J. Wildl. Manag. 11, 299–303 (1990).Article 

Google Scholar 
130.Harvey, J. W. Veterinary Hematology-E-Book: A Diagnostic Guide and Color Atlas. (Elsevier Health Sciences, 2011).131.Raskin, R. E. Hematologic disorders 6. Clinical medicine of the dog and cat, Schaer M, editor. Manson Publishing, London, UK, 227–288 (2009).132.Mayer, J. & Donnelly, T. M. Clinical Veterinary Advisor-E-Book: Birds and Exotic Pets. (Elsevier Health Sciences, 2012).133.Bino, G., Grant, T. R. & Kingsford, R. T. Life history and dynamics of a platypus (Ornithorhynchus anatinus) population: four decades of mark-recapture surveys. Sci. Rep. 5, 16073 (2015).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
134.Gust, N. & Handasyde, K. Seasonal-variation in the ranging behavior of the platypus (Ornithorhynchus-anatinus) on the Goulburn River, Victoria. Aust. J. Zool. 43, 193–208 (1995).Article 

Google Scholar 
135.Handasyde, K., McDonald, I. & Evans, B. Plasma glucocorticoid concentrations in free-ranging platypuses (Ornithorhynchus anatinus): response to capture and patterns in relation to reproduction. Comp. Biochem. Physiol. A: Mol. Integr. Physiol. 136, 895–902 (2003).CAS 
Article 

Google Scholar 
136.Wang, J.-C., Gray, N. E., Kuo, T. & Harris, C. A. Regulation of triglyceride metabolism by glucocorticoid receptor. Cell Biosci. 2, 19–19. https://doi.org/10.1186/2045-3701-2-19 (2012).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
137.Griffiths, M. Reproduction and embryology. Biol. Monotremes, 209–254 (1978).138.Hawkins, M. & Battaglia, A. Breeding behaviour of the platypus (Ornithorhynchus anatinus) in captivity. Aust. J. Zool. 57, 283–293 (2009).Article 

Google Scholar 
139.Thomas, J., Handasyde, K., Parrott, M. & Temple-Smith, P. The platypus nest: burrow structure and nesting behaviour in captivity. Aust. J. Zool. 65, 347–356 (2018).Article 

Google Scholar 
140.Holland, N. & Jackson, S. M. Reproductive behaviour and food consumption associated with the captive breeding of platypus (Ornithorhynchus anatinus). J. Zool. 256, 279–288 (2002).Article 

Google Scholar 
141.Thomas, J. L., Handasyde, K. A., Temple-Smith, P. & Parrott, M. L. Seasonal changes in food selection and nutrition of captive platypuses (Ornithorhynchus anatinus). Aust. J. Zool. 65, 319–327. https://doi.org/10.1071/ZO18004 (2017).Article 

Google Scholar 
142.Kruger, B., Hunter, S. & Serena, M. Husbandry, diet and behaviour of platypus Ornithorhynchus anatinus at Healesville Sanctuary. International Zoo Yearbook 31, 64–71 (1992).Article 

Google Scholar 
143.El-Sherif, M. & Assad, F. Changes in some blood constituents of Barki ewes during pregnancy and lactation under semi arid conditions. Small Rumin. Res. 40, 269–277 (2001).PubMed 
Article 

Google Scholar 
144.Hõrak, P., Jenni-Eiermann, S., Ots, I. & Tegelmann, L. Health and reproduction: the sex-specific clinical profile of great tits (Parus major) in relation to breeding. Can. J. Zool. 76, 2235–2244 (1998).Article 

Google Scholar 
145.dos Santos Schmidt, E. M. et al. Serum biochemical parameters of female bronze turkeys (Meleagris gallopavo) during egg-laying season. Int J Poult Sci 9, 177–179 (2010).146.Lumeij, J. in Clinical biochemistry of domestic animals 857–883 (Elsevier, 1997).147.Whittington, C. M. & Belov, K. Tracing monotreme venom evolution in the genomics era. Toxins 6, 1260–1273 (2014).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
148.Grant, T. & Temple–Smith, P. Field biology of the platypus (Ornithorhynchus anatinus): historical and current perspectives. Philos. Trans. R. Soc. London. Ser. B Biol. Sci. 353, 1081–1091 (1998).149.Handasyde, K. & McDonald, I. Reproductive hormones and reproduction in the platypus. Progress Comp. Endocrinol., 184–185 (1993).150.Wikelski, M., Lynn, S., Breuner, J., Wingfield, J. & Kenagy, G. Energy metabolism, testosterone and corticosterone in white-crowned sparrows. J. Comp. Physiol. A. 185, 463–470 (1999).CAS 
Article 

Google Scholar 
151.Thomas, J. L., Parrott, M. L., Handasyde, K. A. & Temple-Smith, P. Female control of reproductive behaviour in the platypus (Ornithorhynchus anatinus), with notes on female competition for mating. Behaviour 155, 27–53 (2018).Article 

Google Scholar 
152.Hawke, T. et al. Long term movements and activity patterns of platypus on regulated rivers. Scientific Reports in press (2020).153.Andersen, N. A., Mesch, U., Lovell, D. J. & Nicol, S. C. The effects of sex, season, and hibernation on haematology and blood viscosity of free-ranging echidnas (Tachyglossus aculeatus). Can. J. Zool. 78, 174–181 (2000).Article 

Google Scholar 
154.Barnett, J., How, R. & Humphreys, W. Blood parameters in natural populations of Trichosurus species (Marsupialia: Phalangeridae). I. Age, sex and seasonal variation in T. caninus and T. vulpecula. II. Influence of habitat and population strategies of T. caninus and T. vulpecula. Aust. J. Zool. 27, 913–926 (1979).155.Fancourt, B. A. & Nicol, S. C. Hematologic and serum biochemical reference intervals for wild eastern quolls (Dasyurus viverrinus): variation by age, sex, and season. Vet. Clin. Pathol. 48, 114–124 (2019).PubMed 
Article 
PubMed Central 

Google Scholar 
156.McKenzie, S., Deane, E. & Burnett, L. Haematology and serum biochemistry of the tammar wallaby, Macropus eugenii. Comp. Clin. Pathol. 11, 229–237 (2002).CAS 
Article 

Google Scholar 
157.Schultz, D. J. et al. Investigations into the health of brush-tailed rock-wallabies (Petrogale penicillata) before and after reintroduction. Aust. Mamm. 33, 235–244 (2011).Article 

Google Scholar 
158.Warren, K. S., Holyoake, C. S., Friend, T. J., Yeap, L. & McConnell, M. Hematologic and serum biochemical reference intervals for the bilby (Macrotis lagotis). J. Wildl. Dis. 51, 889–895 (2015).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
159.Woolford, L. et al. Serum biochemistry of free-ranging southern hairy-nosed wombats (Lasiorhinus latifrons). J. Zool. Wildl. Med. 50, 937–946 (2020).Article 

Google Scholar 
160.Sidman, C. L. et al. Increased expression of major histocompatibility complex antigens on lymphocytes from aged mice. Proc. Natl. Acad. Sci. 84, 7624–7628 (1987).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
161.Gust, N. & Griffiths, J. Platypus mucormycosis and its conservation implications. Australasian Mycol. 28, 1–8 (2009).
Google Scholar 
162.MacGregor, J. W. et al. Assessing body condition in the platypus (Ornithorhynchus anatinus): A comparison of new and old methods. Aust. J. Zool. 64, 421–429. https://doi.org/10.1071/ZO16071 (2016).Article 

Google Scholar 
163.Peig, J. & Green, A. J. The paradigm of body condition: a critical reappraisal of current methods based on mass and length. Funct. Ecol. 24, 1323–1332 (2010).Article 

Google Scholar 
164.Woinarski, J. C., Burbidge, A. A. & Harrison, P. L. The action plan for Australian mammals 2012. (2014).165.Parer, J. & Metcalfe, J. Respiratory studies of monotremes. I. Blood of the platypus (Ornithorynchus anatinus). Respir. Physiol. 3, 136–142 (1967).CAS 
PubMed 
Article 

Google Scholar 
166.Isaacks, R., Nicol, S., Sallis, J., Zeidler, R. & Kim, H. D. Erythrocyte phosphates and hemoglobin function in monotremes and some marsupials. Am. J. Physiol. Regul. Integr. Comp. Physiol. 246, R236–R241 (1984).CAS 
Article 

Google Scholar  More

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain
the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in
Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles
and JavaScript. More

1.Feynman, J. & Ruzmaikin, R. in Climate Change and Agriculture (Ed. Hussain, S.) (IntechOpen, 2018); https://doi.org/10.5772/intechopen.833442.Ferrio, J. P., Voltas, J. & Araus, J. L. in Crop Stress Management and Global Climate Change (eds Araus, J. L. & Slafer, G. A.) 1–14 (CABI Publishing, 2011).3.Chaloner, T. M., Gurr, S. J. & Bebber, P. Nat. Clim. Change https://doi.org/10.1038/s41558-021-01104-8 (2021).Article 

Google Scholar 
4.Saunders, D. G. O., Pretorius, Z. A. & Hovmoller, M. S. Commun. Biol. 2, 51 (2019).Article 

Google Scholar 
5.Fisher, M. C. et al. mBio 11, e00449-20 (2020).
Google Scholar 
6.Bebber, D. P., Ramotowski, M. A. T. & Gurr, S. J. Nat. Clim. Change 3, 985–988 (2013).Article 

Google Scholar 
7.Turner, R. S. Hist. Stud. Phys. Biol. 35, 341–370 (2005).Article 

Google Scholar 
8.Sen, A. in Poverty and Famines: An Essay on Entitlement and Deprivation (Clarendon Press, 1981).9.Gottwald, T., Luo, W., Posny, D., Riley, T. & Louws, F. Philos. Trans. R. Soc. Lond. B Biol. Sci. 374, 20180260 (2019).Article 

Google Scholar 
10.Fisher, M. C. et al. Nature 484, 186–194 (2012).CAS 
Article 

Google Scholar 
11.Islam, M. T. et al. BMC Biol. 14, 84 (2016).Article 

Google Scholar 
12.Gregory, P. J., Johnson, S. N., Newton, A. C. & Ingram, J. S. I. J. Exp. Bot. 60, 2827–2838 (2009).CAS 
Article 

Google Scholar 
13.Lehmann, P. et al. Front. Ecol. Environ. 18, 141–150 (2020).Article 

Google Scholar 
14.Plumpton, H. & Wentworth, J. Climate Change and Agriculture (The Parliamentary Office of Science and Technology, 2019).15.Orton, E. S., Lewis, C. M., Davey, P. E., Radhakrishnan, G. V. & Saunders, D. G. O. New Dis. Rep. 40, 11 (2019).Article 

Google Scholar  More

1.Holden, C. Report warns of looming pollination crisis in North America. Science 314, 397 (2006).CAS 
PubMed 
Article 

Google Scholar 
2.Aizen, M. A. & Harder, L. D. The global stock of domesticated honey bees is growing slower than agricultural demand for pollination. Curr. Biol. 19, 915–918 (2009).CAS 
PubMed 
Article 

Google Scholar 
3.Goulson, D., Nicholls, E., Botías, C. & Rotheray, E. L. Bee declines driven by combined stress from parasites, pesticides, and lack of flowers. Science 347, 1255957 (2015).PubMed 
Article 
CAS 

Google Scholar 
4.Woodcock, B. A. et al. Impacts of neonicotinoid use on long-term population changes in wild bees in England. Nat. Commun. 7, 12459 (2016).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
5.Siviter, H., Brown, M. J. F. & Leadbeater, E. Sulfoxaflor exposure reduces bumblebee reproductive success. Nature 561, 109–112 (2018).ADS 
CAS 
PubMed 
Article 

Google Scholar 
6.Cameron, S. A. et al. Patterns of widespread decline in North American bumble bees. Proc. Natl Acad. Sci. USA 108, 662–667 (2011).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
7.Powney, G. D. et al. Widespread losses of pollinating insects in Britain. Nat. Commun. 10, 1018 (2019).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
8.Vanbergen, A. J. & The Insect Pollinators Initiative. Threats to an ecosystem service: pressures on pollinators. Front. Ecol. Environ. 11, 251–259 (2013).Article 

Google Scholar 
9.EFSA. Bee health. https://www.efsa.europa.eu/en/topics/topic/bee-health (2019).10.Foley, J. A. et al. Global consequences of land use. Science 309, 570–574 (2005).ADS 
CAS 
PubMed 
Article 

Google Scholar 
11.Tilman, D., Cassman, K. G., Matson, P. A., Naylor, R. & Polasky, S. Agricultural sustainability and intensive production practices. Nature 418, 671–677 (2002).ADS 
CAS 
Article 

Google Scholar 
12.Potts, S. G. et al. Safeguarding pollinators and their values to human well-being. Nature 540, 220–229 (2016).ADS 
CAS 
PubMed 
Article 

Google Scholar 
13.Pettis, J. S. et al. Crop pollination exposes honey bees to pesticides which alters their susceptibility to the gut pathogen Nosema ceranae. PLoS ONE 8, e70182 (2013).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
14.Siviter, H., Folly, A. J., Brown, M. J. F. & Leadbeater, E. Individual and combined impacts of sulfoxaflor and Nosema bombi on bumblebee (Bombus terrestris) larval growth. Proc. R. Soc. B 287, 20200935 (2020).CAS 
PubMed 
Article 

Google Scholar 
15.Retschnig, G. et al. Effects, but no interactions, of ubiquitous pesticide and parasite stressors on honey bee (Apis mellifera) lifespan and behaviour in a colony environment. Environ. Microbiol. 17, 4322–4331 (2015).CAS 
PubMed 
Article 

Google Scholar 
16.Doublet, V., Labarussias, M., de Miranda, J. R., Moritz, R. F. A. & Paxton, R. J. Bees under stress: sublethal doses of a neonicotinoid pesticide and pathogens interact to elevate honey bee mortality across the life cycle. Environ. Microbiol. 17, 969–983 (2015).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
17.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 
18.Di Prisco, G. et al. Neonicotinoid clothianidin adversely affects insect immunity and promotes replication of a viral pathogen in honey bees. Proc. Natl Acad. Sci. USA 110, 18466–18471 (2013).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
19.Collison, E., Hird, H., Cresswell, J. & Tyler, C. Interactive effects of pesticide exposure and pathogen infection on bee health – a critical analysis. Biol. Rev. Camb. Philos. Soc. 91, 1006–1019 (2016).PubMed 
Article 

Google Scholar 
20.Tsvetkov, N. et al. Chronic exposure to neonicotinoids reduces honey bee health near corn crops. Science 356, 1395–1397 (2017).ADS 
CAS 
PubMed 
Article 

Google Scholar 
21.Carnesecchi, E. et al. Investigating combined toxicity of binary mixtures in bees: meta-analysis of laboratory tests, modelling, mechanistic basis and implications for risk assessment. Environ. Int. 133 (Pt B), 105256 (2019).CAS 
PubMed 
Article 

Google Scholar 
22.Jackson, M. C., Loewen, C. J. G., Vinebrooke, R. D. & Chimimba, C. T. Net effects of multiple stressors in freshwater ecosystems: a meta-analysis. Glob. Change Biol. 22, 180–189 (2016).ADS 
Article 

Google Scholar 
23.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 
24.Ascher, J. S. & Pickering, J. Discover life: bee species guide and world checklist (Hymenoptera: Apoidea: Anthophila). https://www.discoverlife.org/mp/20q?guide=Apoidea_species&flags=HAS (2012).25.Gill, R. J., Ramos-Rodriguez, O. & Raine, N. E. Combined pesticide exposure severely affects individual- and colony-level traits in bees. Nature 491, 105–108 (2012).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
26.Schmid-Hempel, P. Evolutionary Parasitology (Oxford Univ. Press, 2011).27.Sánchez-Bayo, F. et al. Are bee diseases linked to pesticides? — A brief review. Environ. Int. 89–90, 7–11 (2016).PubMed 
Article 
CAS 

Google Scholar 
28.Brandt, A., Gorenflo, A., Siede, R., Meixner, M. & Büchler, R. The neonicotinoids thiacloprid, imidacloprid, and clothianidin affect the immunocompetence of honey bees (Apis mellifera L.). J. Insect Physiol. 86, 40–47 (2016).CAS 
PubMed 
Article 

Google Scholar 
29.Vaudo, A. D., Patch, H. M., Mortensen, D. A., Tooker, J. F. & Grozinger, C. M. Macronutrient ratios in pollen shape bumble bee (Bombus impatiens) foraging strategies and floral preferences. Proc. Natl Acad. Sci. USA 113, E4035–E4042 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
30.Fürst, M. A., McMahon, D. P., Osborne, J. L., Paxton, R. J. & Brown, M. J. F. Disease associations between honeybees and bumblebees as a threat to wild pollinators. Nature 506, 364–366 (2014).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
31.Cedergreen, N. Quantifying synergy: a systematic review of mixture toxicity studies within environmental toxicology. PLoS ONE 9, e96580 (2014).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
32.Carvell, C. et al. Declines in forage availability for bumblebees at a national scale. Biol. Conserv. 132, 481–489 (2006).Article 

Google Scholar 
33.Baude, M. et al. Historical nectar assessment reveals the fall and rise of floral resources in Britain. Nature 530, 85–88 (2016).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
34.Ovaskainen, O. et al. Community-level phenological response to climate change. Proc. Natl Acad. Sci. USA 110, 13434–13439 (2013).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
35.Carvell, C. et al. Bumblebee family lineage survival is enhanced in high-quality landscapes. Nature 543, 547–549 (2017).ADS 
CAS 
PubMed 
Article 

Google Scholar 
36.Siviter, H. & Muth, F. Do novel insecticides pose a threat to beneficial insects? Proc. R. Soc. B 287, 20201265 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
37.Topping, C. J., Aldrich, A. & Berny, P. Overhaul environmental risk assessment for pesticides. Science 367, 360–363 (2020).ADS 
CAS 
PubMed 
Article 

Google Scholar 
38.Sgolastra, F. et al. Bees and pesticide regulation: lessons from the neonicotinoid experience. Biol. Conserv. 241, 108356 (2020).Article 

Google Scholar 
39.Mullin, C. A. Effects of ‘inactive’ ingredients on bees. Curr. Opin. Insect Sci. 10, 194–200 (2015).PubMed 
Article 
PubMed Central 

Google Scholar 
40.Colin, T., Monchanin, C., Lihoreau, M. & Barron, A. B. Pesticide dosing must be guided by ecological principles. Nat. Ecol. Evol. 4, 1575–1577 (2020).PubMed 
Article 

Google Scholar 
41.Milner, A. M. & Boyd, I. L. Toward pesticidovigilance. Science 357, 1232–1234 (2017).ADS 
CAS 
PubMed 
Article 

Google Scholar 
42.Franklin, E. L. & Raine, N. E. Moving beyond honeybee-centric pesticide risk assessments to protect all pollinators. Nat. Ecol. Evol. 3, 1373–1375 (2019PubMed 
Article 

Google Scholar 
43.Brühl, C. A. & Zaller, J. G. Biodiversity decline as a consequence of an inappropriate environmental risk assessment of pesticides. Front. Environ. Sci. 7, 177 (2019).Article 

Google Scholar 
44.OECD. Test No. 245: Honey Bee (Apis Mellifera L.), Chronic Oral Toxicity Test (10-Day Feeding) (OECD, 2017).45.Viechtbauer, W. Conducting meta-analyses in R with the metafor package. J. Stat. Softw. 36, 1–48 (2010).Article 

Google Scholar 
46.Duval, S. & Tweedie, R. Trim and fill: a simple funnel-plot-based method of testing and adjusting for publication bias in meta-analysis. Biometrics 56, 455–463 (2000).CAS 
PubMed 
MATH 
Article 

Google Scholar 
47.Woodcock, B. A. et al. Meta-analysis reveals that pollinator functional diversity and abundance enhance crop pollination and yield. Nat. Commun. 10, 1481 (2019).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
48.Siviter, H., Koricheva, J., Brown, M. J. F. & Leadbeater, E. Quantifying the impact of pesticides on learning and memory in bees. J. Appl. Ecol. 55, 2812–2821 (2018).PubMed 
PubMed Central 
Article 

Google Scholar  More

1.Hutchinson, G. E. Cold Spring Harbor Symposia on Quantitative Biology. Concluding Remarks 22 415–427 (1957).2.Smith, E. P. Niche breadth, resource availability, and inference. Ecology 63, 1675–1681. https://doi.org/10.2307/1940109 (1982).Article 

Google Scholar 
3.Leibold, M. A. The niche concept revisited: Mechanistic models and community context. Ecology 76, 1371–1382. https://doi.org/10.2307/1938141 (1995).Article 

Google Scholar 
4.Sexton, J. P., Montiel, J., Shay, J. E., Stephens, M. R. & Slatyer, R. A. Evolution of ecological niche breadth. Annu. Rev. Ecol. Evol. Syst. 48, 183–206. https://doi.org/10.1146/annurev-ecolsys-110316-023003 (2017).Article 

Google Scholar 
5.Jaenike, J. Host specialization in phytophagous insects. Annu. Rev. Ecol. Syst. 21, 243–273. https://doi.org/10.1146/annurev.es.21.110190.001331 (1990).Article 

Google Scholar 
6.Thompson, J. N. The Coevolutionary Process (University of Chicago Press, 1994).Book 

Google Scholar 
7.Krasnov, B. R., Mouillot, D., Shenbrot, G. I., Khokhlova, I. S. & Poulin, R. Geographical variation in host specificity of fleas (Siphonaptera) parasitic on small mammals: The influence of phylogeny and local environmental conditions. Ecography 27, 787–797. https://doi.org/10.1111/j.0906-7590.2004.04015.x (2004).Article 

Google Scholar 
8.Poullain, V., Gandon, S., Brockhurst, M. A., Buckling, A. & Hochberg, M. E. The evolution of specificity in evolving and coevolving antagonistic interactions between bacteria and its phage. Evolution 62, 1–11. https://doi.org/10.1111/j.1558-5646.2007.00260.x (2008).Article 
PubMed 

Google Scholar 
9.Whitlock, M. C. The Red Queen beats the Jack-Of-All-Trades: The limitations on the evolution of phenotypic plasticity and niche breadth. Am. Nat. 148, S65–S77. https://doi.org/10.1086/285902 (1996).Article 

Google Scholar 
10.Gandon, S. Local adaptation and the geometry of host–parasite coevolution. Ecol. Lett. 5, 246–256. https://doi.org/10.1046/j.1461-0248.2002.00305.x (2002).Article 

Google Scholar 
11.Alizon, S. & Michalakis, Y. Adaptive virulence evolution: The good old fitness-based approach. Trends Ecol. Evol. 30, 248–254. https://doi.org/10.1016/j.tree.2015.02.009 (2015).Article 
PubMed 

Google Scholar 
12.Frank, S. A. & Schmid-Hempel, P. Mechanisms of pathogenesis and the evolution of parasite virulence. J. Evol. Biol. 21, 396–404. https://doi.org/10.1111/j.1420-9101.2007.01480.x (2008).CAS 
Article 
PubMed 

Google Scholar 
13.Beadell, J. S. et al. Global phylogeographic limits of Hawaii’s avian malaria. Proc. R. Soc. B: Biol. Sci. 273, 2935–2944. https://doi.org/10.1098/rspb.2006.3671 (2006).Article 

Google Scholar 
14.Krasnov, B. R. Functional and Evolutionary Ecology of Fleas: A Model for Ecological Parasitology (Cambridge University Press, 2008).Book 

Google Scholar 
15.Poulin, R. Evolutionary Ecology of Parasites (Princeton University Press, 2011).Book 

Google Scholar 
16.Välimäki, P. et al. Geographical variation in host use of a blood-feeding ectoparasitic fly: Implications for population invasiveness. Oecologia 166, 985–995. https://doi.org/10.1007/s00442-011-1951-y (2011).ADS 
Article 
PubMed 

Google Scholar 
17.Theodosopoulos, A. N., Hund, A. K. & Taylor, S. A. Parasites and host species barriers in animal hybrid zones. Trends Ecol. Evol. 34, 19–30. https://doi.org/10.1016/j.tree.2018.09.011 (2019).Article 
PubMed 

Google Scholar 
18.Mackenzie, A. A trade-off for host plant utilization in the black bean aphid, Aphis fabae. Evolution 50, 155–162. https://doi.org/10.1111/j.1558-5646.1996.tb04482.x (1996).Article 
PubMed 

Google Scholar 
19.Harrington, L. C., Edman, J. D. & Scott, T. W. Why do female Aedes aegypti (Diptera: Culicidae) feed preferentially and frequently on human blood?. J. Med. Entomol. 38, 411–422. https://doi.org/10.1603/0022-2585-38.3.411 (2001).CAS 
Article 
PubMed 

Google Scholar 
20.Dick, C. W. & Patterson, B. D. Against all odds: Explaining high host specificity in dispersal-prone parasites. Int. J. Parasitol. 37, 871–876. https://doi.org/10.1016/j.ijpara.2007.02.004 (2007).Article 
PubMed 

Google Scholar 
21.Torchin, M. E. & Mitchell, C. E. Parasites, pathogens, and invasions by plants and animals. Front. Ecol. Environ. 2, 183–190. https://doi.org/10.1890/1540-9295(2004)002[0183:PPAIBP]2.0.CO;2 (2004).Article 

Google Scholar 
22.Clark, N. J. & Clegg, S. M. The influence of vagrant hosts and weather patterns on the colonization and persistence of blood parasites in an island bird. J. Biogeogr. 42, 641–651. https://doi.org/10.1111/jbi.12454 (2015).Article 

Google Scholar 
23.Kawecki, T. J. Red Queen meets Santa Rosalia: Arms races and the evolution of host specialization in organisms with parasitic lifestyles. Am. Nat. 152, 635–651. https://doi.org/10.1086/286195 (1998).CAS 
Article 
PubMed 

Google Scholar 
24.Egas, M., Dieckmann, U. & Sabelis, M. W. Evolution restricts the coexistence of specialists and generalists: The role of trade-off structure. Am. Nat. 163, 518–531. https://doi.org/10.1086/382599 (2004).Article 
PubMed 

Google Scholar 
25.Poulin, R. & Keeney, D. B. Host specificity under molecular and experimental scrutiny. Trends Parasitol. 24, 24–28. https://doi.org/10.1016/j.pt.2007.10.002 (2008).CAS 
Article 
PubMed 

Google Scholar 
26.Lyimo, I. N. & Ferguson, H. M. Ecological and evolutionary determinants of host species choice in mosquito vectors. Trends Parasitol. 25, 189–196. https://doi.org/10.1016/j.pt.2009.01.005 (2009).Article 
PubMed 

Google Scholar 
27.Visher, E. & Boots, M. The problem of mediocre generalists: Population genetics and eco-evolutionary perspectives on host breadth evolution in pathogens. Proc. R. Soc. B: Biol. Sci. 287, 20201230. https://doi.org/10.1098/rspb.2020.1230 (2020).Article 

Google Scholar 
28.Sarfati, M. et al. Energy costs of blood digestion in a host-specific haematophagous parasite. J. Exp. Biol. 208, 2489. https://doi.org/10.1242/jeb.01676 (2005).Article 
PubMed 

Google Scholar 
29.Fry, J. D. The evolution of host specialization: Are trade-offs overrated?. Am. Nat. 148, S84–S107. https://doi.org/10.1086/285904 (1996).Article 

Google Scholar 
30.Fessl, B. et al. Galápagos landbirds (passerines, cuckoos, and doves): Status, threats, and knowledge gaps. Galápagos Rep. 2016, 149 (2015).
Google Scholar 
31.Fessl, B., Heimpel, G. E. & Causton, C. E. Invasion of an avian nest parasite, Philornis downsi, to the Galapagos Islands: colonization history, adaptations to novel ecosystems, and conservation challenges. In Disease Ecology: Galapagos Birds and their Parasites (ed Patricia G. Parker) 213–266 (Springer International Publishing, 2018).32.Frankham, R. Do island populations have less genetic variation than mainland populations?. Heredity 78, 311–327. https://doi.org/10.1038/hdy.1997.46 (1997).Article 
PubMed 

Google Scholar 
33.Reichard, M. et al. The bitterling–mussel coevolutionary relationship in areas of recent and ancient sympatry. Evolution 64, 3047–3056. https://doi.org/10.1111/j.1558-5646.2010.01032.x (2010).Article 
PubMed 

Google Scholar 
34.Wiedenfeld, D. A., Jiménez, G. U., Fessl, B., Kleindorfer, S. & Carlos Valarezo, J. Distribution of the introduced parasitic fly Philornis downsi (Diptera, Muscidae) in the Galápagos Islands. Pacific Conserv. Biol. 13, 14–19. https://doi.org/10.1071/PC070014 (2007).Article 

Google Scholar 
35.Fessl, B., Sinclair, B. J. & Kleindorfer, S. The life-cycle of Philornis downsi (Diptera: Muscidae) parasitizing Darwin’s finches and its impacts on nestling survival. Parasitology 133, 739–747. https://doi.org/10.1017/S0031182006001089 (2006).CAS 
Article 
PubMed 

Google Scholar 
36.Kleindorfer, S. & Dudaniec, R. Y. Host-parasite ecology, behavior and genetics: A review of the introduced fly parasite Philornis downsi and its Darwin’s finch hosts. BMC Zool. 1, 1. https://doi.org/10.1186/s40850-016-0003-9 (2016).Article 

Google Scholar 
37.Galligan, T. H. & Kleindorfer, S. Naris and beak malformation caused by the parasitic fly, Philornis downsi (Diptera: Muscidae), in Darwin’s small ground finch, Geospiza fuliginosa (Passeriformes: Emberizidae). Biol. J. Lin. Soc. 98, 577–585. https://doi.org/10.1111/j.1095-8312.2009.01309.x (2009).Article 

Google Scholar 
38.Kleindorfer, S., Custance, G., Peters Katharina, J. & Sulloway Frank, J. Introduced parasite changes host phenotype, mating signal and hybridization risk: Philornis downsi effects on Darwin’s finch song. Proc. R. Soc. B: Biol. Sci. 286, 20190461. https://doi.org/10.1098/rspb.2019.0461 (2019).Article 

Google Scholar 
39.Kleindorfer, S., Peters, K. J., Custance, G., Dudaniec, R. Y. & O’Connor, J. A. Changes in Philornis infestation behavior threaten Darwin’s finch survival. Curr. Zool. 60, 542–550. https://doi.org/10.1093/czoolo/60.4.542 (2014).Article 

Google Scholar 
40.O’Connor, J. A., Sulloway, F. J., Robertson, J. & Kleindorfer, S. Philornis downsi parasitism is the primary cause of nestling mortality in the critically endangered Darwin’s medium tree finch (Camarhynchus pauper). Biodivers. Conserv. 19, 853–866. https://doi.org/10.1007/s10531-009-9740-1 (2010).Article 

Google Scholar 
41.Knutie, S. A. et al. Galápagos mockingbirds tolerate introduced parasites that affect Darwin’s finches. Ecology https://doi.org/10.1890/15-0119 (2016).Article 
PubMed 

Google Scholar 
42.Peters, K. J., Evans, C., Aguirre, J. D. & Kleindorfer, S. Genetic admixture predicts parasite intensity: Evidence for increased hybrid performance in Darwin’s tree finches. R. Soc. Open Sci. 6, 181616. https://doi.org/10.1098/rsos.181616 (2019).ADS 
Article 
PubMed 
PubMed Central 

Google Scholar 
43.Kleindorfer, S. The ecology of clutch size variation in Darwin’s Small Ground Finch Geospiza fuliginosa: Comparison between lowland and highland habitats. Ibis 149, 730–741. https://doi.org/10.1111/j.1474-919X.2007.00694.x (2007).Article 

Google Scholar 
44.Fessl, B. & Tebbich, S. Philornis downsi– a recently discovered parasite on the Galápagos archipelago: A threat for Darwin’s finches?. Ibis 144, 445–451. https://doi.org/10.1046/j.1474-919X.2002.00076.x (2002).Article 

Google Scholar 
45.Dudaniec, R. Y., Fessl, B. & Kleindorfer, S. Interannual and interspecific variation in intensity of the parasitic fly, Philornis downsi, Darwin’s finches. Biol. Cons. 139, 325–332. https://doi.org/10.1016/j.biocon.2007.07.006 (2007).Article 

Google Scholar 
46.Cimadom, A. et al. Invasive parasites, habitat change and heavy rainfall reduce breeding success in Darwin’s Finches. PLoS ONE 9, e107518. https://doi.org/10.1371/journal.pone.0107518 (2014).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
47.Cimadom, A. et al. Weed management increases the detrimental effect of an invasive parasite on arboreal Darwin’s finches. Biol. Cons. 233, 93–101. https://doi.org/10.1016/j.biocon.2019.02.025 (2019).Article 

Google Scholar 
48.Kleindorfer, S. & Dudaniec, R. Y. Love thy neighbour? Social nesting pattern, host mass and nest size affect ectoparasite intensity in Darwin’s tree finches. Behav. Ecol. Sociobiol. 63, 731–739. https://doi.org/10.1007/s00265-008-0706-1 (2009).Article 

Google Scholar 
49.Common, L. K., Dudaniec, R. Y., Colombelli-Négrel, D. & Kleindorfer, S. Taxonomic shifts in Philornis larval behaviour and rapid changes in Philornis downsi Dodge & Aitken (Diptera: Muscidae): An invasive avian parasite on the Galápagos Islands. in Life Cycle and Development of Diptera (ed Muhammad Sarwar) (IntechOpen, 2019).50.McNew, S. M. et al. Annual environmental variation influences host tolerance to parasites. Proc. R. Soc. B: Biol. Sci. 286, 20190049. https://doi.org/10.1098/rspb.2019.0049 (2019).CAS 
Article 

Google Scholar 
51.McNew, S. M. & Clayton, D. H. Alien invasion: Biology of Philornis flies highlighting Philornis downsi, an introduced parasite of Galápagos birds. Annu. Rev. Entomol. 63, 369–387. https://doi.org/10.1146/annurev-ento-020117-043103 (2018).CAS 
Article 
PubMed 

Google Scholar 
52.Kleindorfer, S. & Dudaniec, R. Y. Hybridization fluctuates with rainfall in Darwin’s tree finches. Biol. J. Lin. Soc. 130, 79–88. https://doi.org/10.1093/biolinnean/blaa029 (2020).Article 

Google Scholar 
53.Peters, K. J., Myers, S. A., Dudaniec, R. Y., O’Connor, J. A. & Kleindorfer, S. Females drive asymmetrical introgression from rare to common species in Darwin’s tree finches. J. Evol. Biol. 30, 1940–1952. https://doi.org/10.1111/jeb.13167 (2017).CAS 
Article 
PubMed 

Google Scholar 
54.Kleindorfer, S. et al. Species collapse via hybridization in Darwin’s Tree Finches. Am. Nat. 183, 325–341. https://doi.org/10.1086/674899 (2014).Article 
PubMed 

Google Scholar 
55.Loo, W. T., Dudaniec, R. Y., Kleindorfer, S. & Cavanaugh, C. M. An inter-island comparison of Darwin’s finches reveals the impact of habitat, host phylogeny, and island on the gut microbiome. PLoS ONE 14, e0226432. https://doi.org/10.1371/journal.pone.0226432 (2019).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
56.Galapagos Conservancy. Galapagos Vital Signs: A satellite-based environmental monitoring system for the Galapagos Archipelago, https://galapagosvitalsigns.org (2021).57.Couri, M. Considerações sobre as relações ecológicas das larvas de Philornis Meinert, 1890 (Diptera, Muscidae) com aves. Revista Brasileira de Entomologia 29, 17–20. https://doi.org/10.1017/S0031182006001089 (1985).Article 

Google Scholar 
58.Skidmore, P. The Biology of the Muscidae of the World Vol. 29 (Springer, 1985).
Google Scholar 
59.O’Connor, J. A., Robertson, J. & Kleindorfer, S. Video analysis of host–parasite interactions in nests of Darwin’s finches. Oryx 44, 588–594. https://doi.org/10.1017/S0030605310000086 (2010).Article 

Google Scholar 
60.O’Connor, J. A., Robertson, J. & Kleindorfer, S. Darwin’s finch begging intensity does not honestly signal need in parasitised nests. Ethology 120, 228–237. https://doi.org/10.1111/eth.12196 (2014).Article 

Google Scholar 
61.Kleindorfer, S. & Sulloway, F. J. Naris deformation in Darwin’s finches: Experimental and historical evidence for a post-1960s arrival of the parasite Philornis downsi. Glob. Ecol. Conserv. 7, 122–131. https://doi.org/10.1016/j.gecco.2016.05.006 (2016).Article 

Google Scholar 
62.Lahuatte, P. F., Lincango, M. P., Heimpel, G. E. & Causton, C. E. Rearing larvae of the avian nest parasite, Philornis downsi (Diptera: Muscidae), on chicken blood-based diets. J. Insect Sci. https://doi.org/10.1093/jisesa/iew064 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
63.Kleindorfer, S. Nesting success in Darwin’s small tree finch, Camarhynchus parvulus: Evidence of female preference for older males and more concealed nests. Anim. Behav. 74, 795–804. https://doi.org/10.1016/j.anbehav.2007.01.020 (2007).Article 

Google Scholar 
64.Nijhout, H. F. & Callier, V. Developmental mechanisms of body size and wing-body scaling in insects. Annu. Rev. Entomol. 60, 141–156. https://doi.org/10.1146/annurev-ento-010814-020841 (2015).CAS 
Article 
PubMed 

Google Scholar 
65.Singh, D. & Bala, M. The effect of starvation on the larval behavior of two forensically important species of blow flies (Diptera: Calliphoridae). For. Sci. Int. 193, 118–121. https://doi.org/10.1016/j.forsciint.2009.09.022 (2009).Article 

Google Scholar 
66.Coulson, S. J. & Bale, J. S. Characterisation and limitations of the rapid cold-hardening response in the housefly Musca domestica (Diptera: Muscidae). J. Insect Physiol. 36, 207–211. https://doi.org/10.1016/0022-1910(90)90124-X (1990).Article 

Google Scholar 
67.R Core Team. R: A language and environment for statistical computing. R version 4.0.3 (R Foundation for Statistical Computing, Vienna, Austria, 2020).68.Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. https://doi.org/10.18637/jss.v067.i01 (2015).Article 

Google Scholar 
69.Venables, B. & Ripley, B. Modern Applied Statistics with S-PLUS (Springer Science & Business Media, 2002).70.Fox, J. & Weisberg, S. An R Companion to Applied Regression (Sage publications, 2011).
Google Scholar 
71.Sarkar, D. Lattice: Multivariate Data Visualization with R (Springer, 2008).Book 

Google Scholar 
72.Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer, 2009).Book 

Google Scholar 
73.Fox, J. Effect displays in R for generalised linear models. J. Stat. Softw. https://doi.org/10.18637/jss.v008.i15 (2003).Article 

Google Scholar 
74.Burnham, K. P. & Anderson, D. R. Model Selection and Multimodal Inference: A Practical Information-Theoretic Approach (eds Kenneth P. Burnham & David R. Anderson) 75–117 (Springer New York, 1998).75.Grueber, C. E., Nakagawa, S., Laws, R. J. & Jamieson, I. G. Multimodel inference in ecology and evolution: Challenges and solutions. J. Evol. Biol. 24, 699–711. https://doi.org/10.1111/j.1420-9101.2010.02210.x (2011).CAS 
Article 
PubMed 

Google Scholar 
76.Haaland, T. R., Wright, J. & Ratikainen, I. I. Generalists versus specialists in fluctuating environments: A bet-hedging perspective. Oikos 129, 879–890. https://doi.org/10.1111/oik.07109 (2020).Article 

Google Scholar 
77.Davies, N. Cuckoos, Cowbirds and Other Cheats (Bloomsbury Publishing, 2010).
Google Scholar 
78.Dudaniec, R. Y., Gardner, M. G. & Kleindorfer, S. Offspring genetic structure reveals mating and nest infestation behaviour of an invasive parasitic fly (Philornis downsi) of Galápagos birds. Biol. Invas. 12, 581–592. https://doi.org/10.1007/s10530-009-9464-x (2010).Article 

Google Scholar 
79.Fredensborg, B. L. & Poulin, R. Larval helminths in intermediate hosts: Does competition early in life determine the fitness of adult parasites?. Int. J. Parasitol. 35, 1061–1070. https://doi.org/10.1016/j.ijpara.2005.05.005 (2005).CAS 
Article 
PubMed 

Google Scholar 
80.Begon, M., Harper, J. L. & Townsend, C. R. Ecology: Individuals, Populations and Communities (Blackwell Scientific Publications, 1986).
Google Scholar 
81.Fraik, A. K. et al. Disease swamps molecular signatures of genetic-environmental associations to abiotic factors in Tasmanian devil (Sarcophilus harrisii) populations. Evolution 74, 1392–1408. https://doi.org/10.1111/evo.14023 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
82.Dvorak, M. et al. Conservation status of landbirds on Floreana: The smallest inhabited Galápagos Island. J. Field Ornithol. 88, 132–145. https://doi.org/10.1111/jofo.12197 (2017).Article 

Google Scholar 
83.Hedrick, P. W., Kim, T. J. & Parker, K. M. Parasite resistance and genetic variation in the endangered Gila topminnow. Anim. Conserv. 4, 103–109. https://doi.org/10.1017/S1367943001001135 (2001).Article 

Google Scholar 
84.Lewontin, R. C. & Birch, L. C. Hybridization as a source of variation for adaptation to new environments. Evolution 20, 315–336. https://doi.org/10.2307/2406633 (1966).CAS 
Article 
PubMed 

Google Scholar 
85.Wolinska, J., Lively, C. M. & Spaak, P. Parasites in hybridizing communities: The Red Queen again?. Trends Parasitol. 24, 121–126. https://doi.org/10.1016/j.pt.2007.11.010 (2008).Article 
PubMed 

Google Scholar 
86.Floate, K. D. & Whitham, T. G. The, “Hybrid Bridge” Hypothesis: Host shifting via plant hybrid swarms. Am. Nat. 141, 651–662. https://doi.org/10.1086/285497 (1993).CAS 
Article 
PubMed 

Google Scholar 
87.Le Brun, N., Renaud, F., Berrebi, P. & Lambert, A. Hybrid zones and host-parasite relationships: Effect on the evolution of parasitic specificity. Evolution 46, 56–61. https://doi.org/10.1111/j.1558-5646.1992.tb01984.x (1992).Article 
PubMed 

Google Scholar 
88.Fritz, R. S., Moulia, C. & Newcombe, G. Resistance of hybrid plants and animals to herbivores, pathogens, and parasites. Annu. Rev. Ecol. Syst. 30, 565–591. https://doi.org/10.1146/annurev.ecolsys.30.1.565 (1999).Article 

Google Scholar 
89.Moulia, C., Brun, N. L., Loubes, C., Marin, R. & Renaud, F. Hybrid vigour against parasites in interspecific crosses between two mice species. Heredity 74, 48–52. https://doi.org/10.1038/hdy.1995.6 (1995).Article 
PubMed 

Google Scholar 
90.Gibson, A. K., Refrégier, G., Hood, M. E. & Giraud, T. Performance of a hybrid fungal pathogen on pure-species and hybrid host plants. Int. J. Plant Sci. 175, 724–730. https://doi.org/10.1086/676621 (2014).Article 

Google Scholar 
91.Arnold, M. L. & Martin, N. H. Hybrid fitness across time and habitats. Trends Ecol. Evol. 25, 530–536. https://doi.org/10.1016/j.tree.2010.06.005 (2010).Article 
PubMed 

Google Scholar 
92.Ben-Yosef, M. et al. Host-specific associations affect the microbiome of Philornis downsi, an introduced parasite to the Galápagos Islands. Mol. Ecol. 26, 4644–4656. https://doi.org/10.1111/mec.14219 (2017).Article 
PubMed 

Google Scholar 
93.Loo, W. T., García-Loor, J., Dudaniec, R. Y., Kleindorfer, S. & Cavanaugh, C. M. Host phylogeny, diet, and habitat differentiate the gut microbiomes of Darwin’s finches on Santa Cruz Island. Sci. Rep. 9, 18781. https://doi.org/10.1038/s41598-019-54869-6 (2019).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
94.Knutie, S. A. Relationships among introduced parasites, host defenses, and gut microbiota of Galapagos birds. Ecosphere 9, e02286. https://doi.org/10.1002/ecs2.2286 (2018).Article 

Google Scholar 
95.Knutie, S. A., Chaves, J. A. & Gotanda, K. M. Human activity can influence the gut microbiota of Darwin’s finches in the Galapagos Islands. Mol. Ecol. 28, 2441–2450. https://doi.org/10.1111/mec.15088 (2019).Article 
PubMed 

Google Scholar  More

1.Barringer, L. E., Donovall, L. R., Spichiger, S. E., Lynch, D. & Henry, D. The first New World record of Lycorma delicatula (Insecta: Hemiptera: Fulgoridae). Entomol. News 125, 20–23 (2015).Article 

Google Scholar 
2.Dara, S. K., Barringer, L. & Arthurs, S. P. Lycorma delicatula (Hemiptera: Fulgoridae): A new invasive pest in the United States. J. Integr. Pest Manag. 6, 20 (2015).Article 

Google Scholar 
3.Jung, J. M., Jung, S., Byeon, D. & Lee, W. Model-based prediction of potential distribution of the invasive insect pest, spotted lanternfly Lycorma delicatula (Hemiptera: Fulgoridae), by using CLIMEX. J. Asia-Pac. Biodivers. 10, 532–538 (2017).Article 

Google Scholar 
4.Lee, D.-H., Park, Y.-L. & Leskey, T. C. A review of biology and management of Lycorma delicatula (Hemiptera: Fulgoridae), an emerging global invasive species. J. Asia-Pac. Entomol. 22, 589–596 (2019).Article 

Google Scholar 
5.(NYSIPM) New York State Integrated Pest Management. 2020. Spotted lanternfly. https://nysipm.cornell.edu/environment/invasive-species-exotic-pests/spotted-lanternfly/. Accessed 18 January 2021.6.Park, M., Kim, S. M. & Lee, J. H. Genetic structure of Lycorma delicatula (Hemiptera: Fulgoridae) populations in Korea: implication for invasion processes in heterogeneous landscapes. Bull. Entomol. Res. 103, 414–424 (2013).CAS 
Article 

Google Scholar 
7.Keller, J. A. et al. Dispersal of Lycorma delicatula (Hemiptera: Fulgoridae) nymphs through contiguous, deciduous forest. Environ. Entomol. 49, 1012–1018 (2020).Article 

Google Scholar 
8.Smyers, E. C. et al. Spatio-temporal model for predicting spring hatch of the spotted lanternfly (Hemiptera: Fulgoridae). Environ. Entomol. 50, 126–137 (2020).Article 

Google Scholar 
9.Wakie, T. T., Neven, L. G., Yee, W. L. & Lu, Z. The establishment risk of Lycorma delicatula (Hemiptera: Fulgoridae) in the United States and globally. J. Econ. Entomol. 113, 306–314 (2019).
Google Scholar 
10.Urban, J. M. Perspective: shedding light on spotted lanternfly impacts in the USA. Pest Manag. Sci. 76, 10–17 (2020).CAS 
Article 

Google Scholar 
11.Harper, J. K., Stone, W., Kelsey, T. W. & Kime, L. F. Potential Economic Impact of the Spotted Lanternfly on Agriculture and Forestry in Pennsylvania (The Center for Rural Pennsylvania, 2019).
Google Scholar 
12.Song, M. K. Damage by Lycorma delicatula and chemical control in vineyards. M.S. thesis. Chunbuk National University, Korea (2010).13.Tedders, W. L. & Smith, J. S. Shading effect on pecan by sooty mold growth. J. Econ. Entomol. 69, 551–553 (1976).Article 

Google Scholar 
14.Lemos-Filho, J. P. D. & Paiva, É. A. S. The effects of sooty mold on photosynthesis and mesophyll structure of mahogany (Swietenia macrophylla King., Meliaceae). Bragantia 65, 11–17 (2006).Article 

Google Scholar 
15.Han, J. M. et al. Lycorma delicatula (Hemiptera: Auchenorrhyncha: Fulgoridae: Aphaeninae) finally, but suddenly arrived in Korea. Entomol. Res. 38, 281–286 (2008).Article 

Google Scholar 
16.Park, J. D. et al. Biological characteristics of Lycorma delicatula and the control effects of some insecticides. Korean J. Appl. Entomol. 48, 53–57 (2009).Article 

Google Scholar 
17.Liu, H. Oviposition substrate selection, egg mass characteristics, host preference, and life history of the spotted lanternfly (Hemiptera: Fulgoridae) in North America. Environ. Entomol. 48, 1452–1468 (2019).Article 

Google Scholar 
18.Barringer, L. E. & Ciafré, C. M. Worldwide feeding host plants of spotted lanternfly, with significant additions from North America. Environ. Entomol. 49, 999–1011 (2020).Article 

Google Scholar 
19.Uyi, O. et al. Spotted lanternfly (Hemiptera: Fulgoridae) can complete development and reproduce without access to the preferred host, Ailanthus altissima. Environ. Entomol. 49, 1185–1190 (2020).Article 

Google Scholar 
20.Murman, K. Distribution, survival, and development of spotted lanternfly on host plants found in north America. Environ. Entomol. 49, 1270–1281 (2020).Article 

Google Scholar 
21.Magnusson, A. glmmTMB: Generalized linear mixed models using template model builder. R package v. 0.1.3. https://github.com/glmmTMB (2017).22.R Development Core Team. R: A Language and Environment for Statistical Computing Computer Program, Version 3.6.3 (R Development Core Team, 2020).
Google Scholar 
23.Kariyat, R. R. & Portman, S. L. Plant–herbivore interactions: Thinking beyond larval growth and mortality. Am. J. Bot. 103, 1–3 (2016).Article 

Google Scholar 
24.Fordyce, J. A. & Shapiro, A. M. Another perspective on the slow-growth/high-mortality hypothesis: chilling effects on swallowtail larvae. Ecology 84, 263–268 (2003).Article 

Google Scholar 
25.Uesugi, A. The slow-growth high-mortality hypothesis: direct experimental support in a leaf mining fly. Ecol. Entomol. 40, 221–228 (2015).Article 

Google Scholar 
26.Song, S., Kim, S., Kwon, S. W., Lee, S.-I. & Jablonski, P. G. Defense sequestration associated with narrowing of diet and ontogenetic change to aposematic colours in the spotted lanternfly. Sci. Rep. 8, 16831 (2018).ADS 
Article 

Google Scholar 
27.Domingue, M. J. & Baker, T. C. Orientation of flight for physically disturbed spotted lanternflies, Lycorma delicatula, (Hemiptera, Fulgoridae). J. Asia Pac. Entomol. 22, 117–120 (2019).Article 

Google Scholar 
28.Lee, J. E. et al. Feeding behavior of Lycorma delicatula (Hemiptera: Fulgoridae) and response on feeding stimulants of some plants. Korean J. Appl. Entomol. 48, 467–477 (2009).Article 

Google Scholar 
29.Liu, H. Seasonal development, cumulative growing degree-days, and population density of spotted lanternfly (Hemiptera: Fulgoridae) on selected hosts and substrates. Environ. Entomol. 49, 1171–1184 (2020).Article 

Google Scholar 
30.Jaenike, J. On optimal oviposition behavior in phytophagous insects. Theor. Popul. Biol. 14, 350–356 (1978).CAS 
Article 

Google Scholar 
31.Gripenberg, S., Mayhew, P. J., Parnell, M. & Roslin, T. A meta-analysis of preference–performance relationships in phytophagous insects. Ecol. Lett. 13, 383–393 (2010).Article 

Google Scholar 
32.Fujiyama, N., Torii, C., Akabane, M. & Katakura, H. Oviposition site selection by herbivorous beetles: a comparison of two thistle feeders: Cassida rubiginosa and Henosepilachnniponica. Entomol. Exp. Appl. 128, 41–48 (2008).Article 

Google Scholar 
33.Wolfin, M. S., Myrick, A. J. & Baker, T. C. Flight duration capabilities of dispersing adult spotted lanternflies, Lycorma delicatula. J. Insect Behav. 33, 125–137 (2020).Article 

Google Scholar 
34.Faraji, F., Janssen, A. & Sabelis, M. W. Oviposition patterns in a predatory mite reduce the risk of egg predation caused by prey. Ecol. Entomol. 27, 660–664 (2002).Article 

Google Scholar 
35.Behmer, S. T. & Joern, A. Coexisting generalist herbivores occupy unique nutritional feeding niches. Proc. Natl. Acad. Sci. USA 105, 1977–1982 (2008).ADS 
CAS 
Article 

Google Scholar 
36.Behmer, S. T. Insect herbivore nutrient regulation. Annu. Rev. Entomol. 54, 165–187 (2009).CAS 
Article 

Google Scholar  More

Acoustic measurementsA set of acoustic echo sounder data was used to analyze fish density. This was collected within the Mycto-3D-MAP program using split-beam echo sounders at 38 and 120 kHz. The Mycto-3D-MAP program included multiple large-scale oceanographic surveys during 2 years and a dedicated cruise in the Kerguelen area. The dataset was collected during 4 large-scale surveys in 2013 and 2014, both in summer (including both northward and southward transects) and in winter, corresponding to 6 acoustic transects of 2860 linear kilometers (see Table 1 for more details). Note that all legs except summer 2014 (MYCTO-3D-Map cruise) were logistic operations, during which environmental in situ data (such as temperature or salinity profiles) could not be collected. The data were then treated with a bi-frequency algorithm, applied to the 38 and 120 kHz frequencies (details of data collection and processing are provided in37). This provides a quantitative estimation of the concentration of gas-bearing organisms, mostly attributed to fish containing a gas-filled swimbladder in the water column38. Most mesopelagic fish present swimbladders and several works pointed out that myctophids are the dominant mesopelagic fish in the region39. Therefore, we considered the acoustic signal as mainly representative of myctophids concentration. Data were organized in acoustic units, averaging acoustic data over 1.1 km along the ship trajectory on average. Myctophid school length is in the order of tens of meters40. For this reason, acoustic units were considered as not autocorrelated. Every acoustic unit is composed of 30 layers, ranging from 10 to 300 meters (30 layers in total).The dataset was used to infer the Acoustic Fish Concentration (AFC) in the water column. We considered as AFC of the point ((x_i), (y_i)) of the ship trajectory the average of the bifrequency acoustic backscattering of 29 out of 30 layers (the first layer, 0-10 m, was excluded due to surface noise) AFC quantity is dimensionless.As the previous measurements were performed through acoustic measurements, a non-invasive technique, fishes were not handled for this study.Table 1 Details of the acoustic transects analyzed.Full size tableRegional data selectionThe geographic area of interest of the present study is the Southern Ocean. To select the ship transects belonging to this region, we used the ecopartition of41. Only points falling in the Antarctic Southern Ocean region were considered. We highlight that this choice is consistent with the ecopartition of42 (group 5), which is specifically designed for myctophids, the reference fish family (Myctophidae) of this study. Furthermore, this choice allowed us to exclude large scale fronts (i.e., fronts that are visible on monthly or yearly averaged maps) which have been the subject of past research works43,44. In this way our analysis is focused specifically on fine-scale fronts.Day-night data separationSeveral species of myctophids present a diel vertical migration. They live at great depths during the day (between 500 and 1000 m), ascending at dusk in the upper euphotic layer, where they spend the night. Since the maximal depth reached by the echo sounder we used is 300 m, in the analysis reported in Figs. 2 and 3 we excluded data collected during the day. However, their analysis is reported in SI.1. A restriction of our acoustic analyses to the ocean upper layer is also consistent with the fact that the fine-scale features we computed are derived in this work by satellite altimetry, thus representative of the upper part of the water column ((sim 50) m). Finally, we note that the choice of considering the echo sounder data in the first 300 m of the water columns is coherent with the fact that LCS may extend almost vertically in depth even at 600 m depth45,46 and with the fact that altimetry-derived velocity fields are consistent with subsurface currents in our region of interest down to 500 m20.Satellite dataVelocity current data and Finite-Size Lyapunov Exponent (FSLE) processing. Velocity currents are obtained from Sea Surface Height (SSH), which is measured by satellite altimetry, through geostrophic approximation. Data, which were downloaded from E.U. Copernicus Marine Environment Monitoring Service (CMEMS, http://marine.copernicus.eu/), were obtained from DUACS (Data Unification and Altimeter Combination System) delayed-time multi-mission altimeter, and displaced over a regular grid with spatial resolution of (frac{1}{4}times frac{1}{4}^circ) and a temporal resolution of 1 day.Trajectories were computed with a Runge-Kutta scheme of the 4th order with an integration time of 6 hours. Finite-size Lyapunov Exponents (FSLE) were computed following the methods in14, with initial and final separation of (0.04^circ) and (0.4^circ) respectively. This Lagrangian diagnostic is commonly used to identify Lagrangian Coherent Structures. This method determines the location of barriers to transport, and it is usually associated with oceanic fronts9. Details on the Lagrangian techniques applied to altimetry data, including a discussion of its limitation, can be found in10.Temperature field and gradient computation The Sea Surface Temperature (SST) field was produced from the OSTIA global foundation Sea Surface Temperature (product id: SST_GLO_ SST_L4_NRT_OBSERVATIONS_010_001) from both infrared and microwave radiometers, and downloaded from CMEMS website. The data are represented over a regular grid with spatial resolution of (0.05times 0.05^circ) and daily-mean maps. The SST gradient was obtained from:$$begin{aligned} Grad SST=sqrt{g_x^2+g_y^2} end{aligned}$$where (g_x) and (g_y) are the gradients along the west-east and the north-south direction, respectively. To compute (g_x), the following expression was used:$$begin{aligned} g_x=frac{1}{2 dx}cdot (SST_{i+1}-SST_{i-1}) end{aligned}$$where the SST values of the adjacent grid cells (along the west-east direction: cells (i+1) and (i-1)) were employed. dx identifies the kilometric distance between two grid points along the longitude (which varies with latitude). The definition is analog for (g_y), considering this time the north-south direction and (dysimeq 5) km (0.05(^circ)).Chlorophyll field Chlorophyll estimations were obtained from the Global Ocean Color product (OCEANCOLOUR_ GLO_CHL_L4_REP_OBSERVATIONS_009_082-TDS) produced by ACRI-ST. This was downloaded from CMEMS website. Daily observations were used, in order to match the temporal resolution of the acoustic and satellite observations. The spatial resolution of the product is 1/24(^{circ }).Estimation of satellite data along ship trajectory For each point ((x_i), (y_i)) of the ship trajectory, we extracted a local value of FSLE, SST gradient, and chlorophyll concentration. These were obtained by considering the respective average value in a circular around of radius (sigma) of each point ((x_i), (y_i)) . Different (sigma) were tested (ranging from 0.1(^circ) to 0.6(^circ)), and the best results were obtained with (sigma =0.2^circ), reference value reported in the present work. This value is consistent with the resolution of the altimetry data.Statistical processingFront identification FSLE and SST gradient were used as diagnostics to detect frontal features, since they are typically associated with front intensity and Lagrangian Coherent Structures10. Note that the two diagnostics provide similar but not identical information. FSLEs are used to analyze the horizontal transport and to identify material lines along which a confluence of waters with different origins occur. These lines typically display an enhanced SST gradient because water masses of different origin have often contrasted SST signature. However, this is not a necessary condition. FSLE ridges may be invisible in SST maps if transport occurs in a region of homogeneous SST. Conversely, SST gradient unveils structures separating waters of different temperatures, whose contrast is often – but not always – associated with horizontal transport. Therefore, even if they usually detect the same structures, these two metrics are complementary. Frontal features were identified by considering a local FSLE (or SST gradient, respectively) value larger than a given threshold. The threshold values have been chosen heuristically but consistently with the ones found in previous works. For the FSLEs, we used 0.08 days(^{-1}), a threshold value in the range of the ones chosen in18 and47. For the SST gradient, we considered representative of thermal front values greater than 0.009({^circ })C/km, which is about half the value chosen in47. However, in that work, the SST gradient was obtained from the advection of the SST field with satellite-derived currents for the previous 3 days, a procedure which is known to enhance tracer gradients.Bootstrap method The threshold value splits the AFC into two groups: AFC co-located with FSLE or SST gradient values over the threshold are considered as measured in proximity of a front (i.e., statistically associated with a front), while AFC values below the threshold are considered as not associated with a frontal structure. The statistical independence of the two groups was tested using a Mann-Whitney or U test. Finally, bootstrap analysis is applied following the methodologies used in47. This allows estimating the probability that the difference in the mean AFC values, over and under the threshold, is significant, and not the result of statistical fluctuations. Other diagnostics tested are reported in SI.1.Linear quantile regression Linear quantile regression method48 was employed as no significant correlation was found between the explanatory and the response variables. This can be due to the fact that multiple factors (such as prey or predator distributions) influence the fish distribution other than the frontal activity considered. The presence of these other factors can shadow the relationship of the explanatory variables (in this case, the FSLE and the SST gradient) with the mean value of the response variable (the AFC). A common method to address this problem is the use of the quantile regression48,49, that explores the influence of the explanatory variables on other parts of the response variable distribution. Previous studies, adopting this methodology, revealed the limiting role played by the explanatory variables in the processes considered50. The percentiles values used are 75th, 90th, 95th, and 99th. The analysis is performed using the statistical package QUANTREG in R (https://CRAN.R-project.org/package=quantreg, v.5.3848,51), using the default settings.Chlorophyll-rich waters selection The AFC observations were considered in chlorophyll-rich waters if the corresponding chlorophyll concentration was higher than a given threshold. All the other AFC measurements were excluded, and a linear regression performed between the remaining AFC and FSLE (or SST gradient) values. The corresponding thresholds (one for FSLE and one for SST gradient case) were selected though a sensitivity test reported in SI.1. These resulted in 0.22 and 0.17 mg/m(^3) for FSLE and for SST gradient, respectively. These values are consistent among them and, in addition, they are in coherence with previous estimates of chlorophyll concentration used to characterise productive waters in the Southern Ocean (0.26mg/m(^3)52).Gradient climbing modelAn individual-based mechanistic model is built to describe how fish could move along frontal features. We assume that the direction of fish movement along a frontal gradient is influenced by the noise of the prey field (SI. 2). Specifically, we consider a Markovian process along the (one dimensional) cross-front direction. Time is discretized in timesteps of length (varDelta tau). We presuppose that, at each timestep, the fish chooses between swimming in one of the two opposite cross-front directions (“left” and “right”). This decision depends on the comparison between the quantity of a tracer (a cue) present at its actual position and the one perceived at a distance (p_R) from it, where (p_R) is the perceptual range of the fish. We consider the fish immersed in a tracer whose concentration is described by the function T(x).An expression for the average velocity of the fish, (U_F(x)), can now be derived. We assume that the fish is able to observe simultaneously the tracer to its right and its left. Fish sensorial capacities are discussed in SI.2. The tracer observed is affected by noise. Noise distribution is considered uniform, with (-xi _{MAX}{tilde{T}}(x_0-varDelta x)), the fish will move to the right, and, vice versa, to the left. We hypothesize that the observational range is small enough to consider the tracer variation as linear, as illustrated in Fig. S7 (SI. 3). In this way:$$begin{aligned}&{tilde{T}}(x_0+varDelta x)=T(x_0)+ p_R,frac{partial T}{partial x}+xi _1 \&{tilde{T}}(x_0-varDelta x)=T(x_0)- p_R,frac{partial T}{partial x}+xi _2 ;. end{aligned}$$In case of absence of noise, or with (xi _{MAX}p_R,frac{partial T}{partial x}). If (T(x_0+varDelta x) >T(x_0-varDelta x)) (as in Fig. S7), and the fish will swim leftward if$$begin{aligned} xi _1-xi _2 >2p_R,frac{partial T}{partial x}; . end{aligned}$$Since (xi _1) and (xi _2) range both between (-xi _{MAX}) and (xi _{MAX}), we can obtain the probability of leftward moving P(L). This will be the probability that the difference between (xi _1) and (xi _2) is greater than (2p_R,frac{partial T}{partial x})$$begin{aligned} P(L)&=frac{1}{8xi _{MAX}^2} bigg (2 xi _{MAX} – 2 p_R,frac{partial T}{partial x}bigg )^2\&=frac{1}{2} bigg (1-frac{p_R}{xi _{MAX}},frac{partial T}{partial x}bigg )^2 end{aligned}$$.The probability of moving right will be$$begin{aligned} P(R)&=1-P(L) end{aligned}$$and their difference gives the frequency of rightward moving$$begin{aligned} P(R)-P(L)&=1-2P(L)=1-bigg (1-frac{p_R}{xi _{MAX}},frac{partial T}{partial x}bigg )^2\&=frac{p_R}{xi _{MAX}}frac{partial T}{partial x}bigg (2-frac{p_R}{xi _{MAX}}bigg |frac{partial T}{partial x}bigg |bigg ); , end{aligned}$$where the absolute value of (frac{partial T}{partial x}) has been added to preserve the correct climbing direction in case of negative gradient. The above expression leads to:$$begin{aligned} U_F(x)=frac{V p_R}{xi _{MAX}}frac{partial T}{partial x}bigg (2-frac{p_R}{xi _{MAX}}bigg |frac{partial T}{partial x}bigg |bigg );. end{aligned}$$
(1)
We then assume that, over a certain value of tracer gradient (frac{partial T}{partial x}_{MAX}), the fish are able to climb the gradient without being affected by the noise. This assumption, from a biological perspective, means that the fish does not live in a completely noisy environment, but that under favorable circumstances it is able to correctly identify the swimming direction that leads to higher prey availability. This means that$$begin{aligned} p_R*frac{partial T}{partial x}_{MAX}=xi _{MAX},. end{aligned}$$
(2)
Substituting then (2) into (1) gives:$$begin{aligned} U_F(x)=V frac{frac{partial T}{partial x}}{frac{partial T}{partial x}_{MAX}}bigg (2-frac{big |frac{partial T}{partial x}big |}{frac{partial T}{partial x}_{MAX}}bigg );. end{aligned}$$
(3)
Finally, we can include an eventual effect of transport by the ocean currents, considering that the tracer is advected passively by them, simply adding the current speed (U_C) to Expr. (3).The representations provided are individual based, with each individual representing a single fish. However, we note that all the considerations done are also valid if we consider an individual representing a fish school. (U_F) will then simply represent the average velocity of the fish schools. This aspect should be stressed since many fish species live and feed in groups, especially myctophids (see SI.2 for further details).Continuity equation in one dimension The solution of this model will now be discussed. The continuity equation is first considered in one dimension. The starting scenario is simply an initially homogeneous distribution of fish, that are moving in a one dimensional space with a velocity given by (U_{F}(x)).We assume that in the time scales considered (few days to some weeks), the fish biomass is conserved, so for instance fishing mortality or growing rates are neglected. In that case, we can express the evolution of the concentration of the fish (rho) by the continuity equation$$begin{aligned} frac{partial rho }{partial t}+nabla cdot mathbf{j },=,0 end{aligned}$$
(4)
in which (mathbf{j }=rho ;U_{F}(x)), so that Eq. (4) becomes$$begin{aligned} frac{partial rho }{partial t}+nabla cdot big (rho ;U_{F}(x)big ),=,0;. end{aligned}$$
(5)
In one dimension, the divergence is simply the partial derivate along the x-axis. Eq. (5) becomes$$begin{aligned} frac{partial rho }{partial t}=-frac{partial }{partial x} bigg (rho ;U_{F}bigg ) end{aligned}$$
(6)
Now, we decompose the fish concentration (rho) in two parts, a constant one and a variable one (rho ,=,rho _0+{tilde{rho }}). Eq. (6) will then become$$begin{aligned} frac{partial rho }{partial t}=-U_Ffrac{partial {tilde{rho }}}{partial x}-rho frac{partial U_F}{partial x};. end{aligned}$$
(7)
Using Expr. (3), Eq. (7) is numerically simulated with the Lax method. In Expr. (3) we impose that (U_F(x)=V) when (U_F(x) >V). This biological assumption means that fish maximal velocity is limited by a physiological constraint rather than by gradient steepness. Details of the physical and biological parameters are provided in SI.6. More