Philippa Kaur

Climate dataThermal profiles showed considerable diel variation at each field location (Fig. 3). Soil temperatures on the ground surface fluctuated the most, reaching maximum temperature (Mt Ginini, 45.5 °C; Piccadilly Circus first season, 46.5 °C; Piccadilly Circus second season, 46.0 °C; Cooma, 42.1 °C; Dartmouth, 47.5 °C) during the day (1330–1730 h) and dropping to low level (Mt Ginini, 4.0 °C; Piccadilly Circus first season, 8.5 °C; Piccadilly Circus second season, 6.5 °C; Cooma, 7 °C; Dartmouth 12.5 °C) at night (2330–0400 h) (ANOVA, F4, 6521 = 279.4, P  More

1.Stearns, S. C. The Evolution of Life Histories (Oxford Univ. Press, 1992).
Google Scholar 
2.Churchill, E. R., Dytham, C. & Thom, M. D. F. Differing effects of age and starvation on reproductive performance in Drosophila melanogaster. Sci. Rep. 9, 2167. https://doi.org/10.1038/s41598-019-38843-w (2019).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
3.Price, P. W. Strategies for egg production. Evolution 28(1), 76–84 (1974).PubMed 
Article 

Google Scholar 
4.Roff, D. A. The Evolution of Life Histories. Theory and analysis (Chapman and Hall, 1992).
Google Scholar 
5.Smith, C. C. & Fretwell, S. D. The optimal balance between size and number of offspring. Am. Nat. 108(962), 499–506 (1974).Article 

Google Scholar 
6.Durrant, K. et al. Comparative morphological trade-offs between pre- and post-copulatory sexual selection in Giant hissing cockroaches (Tribe: Gromphadorhini). Sci. Rep. 6, 36755. https://doi.org/10.1038/srep36755 (2016).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
7.Timi, J. T., Lanfranchi, A. L. & Poulin, R. Is there a trade-off between fecundity and egg volume in the parasitic copepod Lernanthropus cynoscicola?. Parasitol. Res. 95, 1–4 (2005).PubMed 
Article 

Google Scholar 
8.Cavaleiro, F. I. & Santos, M. J. Egg number–egg size: An important trade-off in parasite life history strategies. Int. J. Parasitol. 44, 173–182 (2014).PubMed 
Article 

Google Scholar 
9.Poulin, R. Clutch size and egg size in free-living and parasitic copepods: A comparative analysis. Evolution 49(2), 325–336 (1995).PubMed 
Article 

Google Scholar 
10.Caley, M. J., Schwarzkoff, L. & Shine, R. Does total reproductive effort evolve independently of offspring size?. Evolution 55(6), 1245–1248 (2001).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
11.McGinty, N. et al. Anthropogenic climate change impacts on copepod trait biogeography. Glob. Change Biol. 27, 1431–1442 (2021).ADS 
Article 

Google Scholar 
12.Ianora, A., Miralto, A. & Halsband-Lenk, C. Reproduction, hatching success, and early naupliar survival in Centropages typicus. Prog. Oceanogr. 72, 195–213 (2007).ADS 
Article 

Google Scholar 
13.Uye, S. & Sano, K. Seasonal reproductive biology of the small cyclopoid copepod Oithona davisae in a temperate eutrophic inlet. Mar. Ecol. Prog. Ser. 118, 121–128 (1995).ADS 
Article 

Google Scholar 
14.Guisande, C., Sanchez, J., Maneiro, I. & Miranda, A. Trade-off between offspring number and offspring size in the marine copepod Euterpina acutifrons at different food concentrations. Mar. Ecol. Prog. Ser. 143, 37–44 (1996).ADS 
Article 

Google Scholar 
15.Liang, D. & Uye, S. Seasonal reproductive biology of the egg-carrying calanoid copepod Pseudocalanus marinus in a eutrophic inlet of the Inland Sea of Japan. Mar. Biol. 128, 409–414 (1997).Article 

Google Scholar 
16.Hämäläinen, A. et al. Fitness consequences of peak reproductive effort in a resource pulse system. Sci. Rep. 7, 9335 (2017).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
17.Souissi, S. & Souissi, A. Promotion of the development of sentinel species in the water column: Example using body size and fecundity of the egg-bearing calanoid copepod Eurytemora affinis. Water 13, 1442. https://doi.org/10.3390/w13111442 (2021).Article 

Google Scholar 
18.Madhupratap, M., Nehring, S. & Lenz, J. Resting eggs of zooplankton (Copepoda and Cladocera) from the Kiel Bay and adjacent waters (southwestern Baltic). Mar. Biol. 125, 77–87 (1996).Article 

Google Scholar 
19.Katajisto, T., Viitasalo, M. & Koski, M. Seasonal occurrence and hatching of calanoid eggs in sediments of the northern Baltic Sea. Mar. Ecol. Prog. Ser. 163, 133–143 (1998).ADS 
Article 

Google Scholar 
20.Walsh, M. R. The link between environmental variation and evolutionary shifts in dormancy in zooplankton. Integr. Comp. Biol. 53(4), 713–722 (2013).PubMed 
Article 

Google Scholar 
21.Glippa, O., Denis, L., Lesourd, S. & Souissi, S. Seasonal fluctuations of the copepod resting egg bank in the middle Seine estuary, France: Impact on the nauplii recruitment. Estuar. Coast. Shelf Sci. 142, 60–67 (2014).ADS 
Article 

Google Scholar 
22.Jamieson, C. D. & Santer, B. Maternal aging in the univoltine freshwater copepod Cyclops kolensis: variation in egg sizes, egg development times, and naupliar development times. Hydrobiologia 510, 75–81 (2003).Article 

Google Scholar 
23.Kiørboe, T. & Sabatini, M. Reproduction and life cycle strategies in egg-carrying cyclopoid and free-spawning calanoid copepods. J. Plankton Res. 16(10), 1353–1366 (1994).Article 

Google Scholar 
24.Hirst, A. G. & Kiørboe, T. Mortality of marine planktonic copepods: global rates and patterns. Mar. Ecol. Prog. Ser. 230, 195–209 (2002).ADS 
Article 

Google Scholar 
25.Andersen, M. C. & Nielson, T. G. Hatching rate of the egg carrying estuarine copepod Eurytemora affinis. Mar. Ecol. Prog. Ser. 160, 283–289 (1997).ADS 
Article 

Google Scholar 
26.Winkler, G., Dodson, J. J. & Lee, C. E. Heterogeneity within the native range: population genetic analyses of sympatric invasive and noninvasive clades of the freshwater invading copepod Eurytemora affinis. Mol. Ecol. 17, 415–430 (2008).PubMed 
Article 

Google Scholar 
27.Devreker, D. et al. Tidal and annual variability of the population structure of Eurytemora affinis in the middle part of the Seine Estuary during 2005. Estuar. Coast. Shelf Sci. 89, 245–255 (2010).ADS 
CAS 
Article 

Google Scholar 
28.Ban, S. Effect of temperature and food concentration on post-embryonic development, egg production and adult body size of calanoid copepod Eurytemora affinis. J. Plankton Res. 16, 721–735 (1994).Article 

Google Scholar 
29.Devreker, D., Souissi, S., Winkler, G., Forget-Leray, J. & Leboulenger, F. Effects of salinity and temperature on the reproduction of Eurytemora affinis (Copepoda; Calanoida) from the Seine estuary: a laboratory study. J. Exp. Mar. Biol. Ecol. 368, 113–123 (2009).Article 

Google Scholar 
30.Dur, G. et al. An individual based model to study the reproduction of egg bearing copepods: application to Eurytemora affinis (Copepoda Calanoida) from the Seine estuary, France. Ecol. Model. 220, 1073–1089 (2009).Article 

Google Scholar 
31.Michalec, F.-G. et al. Differences in behavioral responses of Eurytemora affinis (Copepoda, Calanoida) reproductive stages to salinity variations. J. Plankton Res. 32(6), 805–813 (2010).Article 

Google Scholar 
32.Michalec, F.-G., Holzner, M., Menu, D., Hwang, J.-S. & Souissi, S. Behavioral responses of the estuarine calanoid copepod Eurytemora affinis to sub-lethal concentrations of waterborne pollutants. Aquat. Toxicol. 138–139, 129–138 (2013).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
33.Souissi, A., Souissi, S. & Hwang, J.-S. Evaluation of the copepod Eurytemora affinis life history response to temperature and salinity increases. Zool. Stud. 55, e4. https://doi.org/10.6620/ZS.2016.55-04 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
34.Frölicher, T. L., Fischer, E. M. & Gruber, N. Marine heatwaves under global warming. Nature 560, 360–364 (2018).ADS 
PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
35.Souissi, A., Souissi, S. & Hansen, B. W. Physiological improvement in the copepod Eurytemora affinis through thermal and multigenerational selection. Aquac. Res. 47, 2227–2242 (2016).Article 

Google Scholar 
36.Souissi, A., Souissi, S., Devreker, D. & Hwang, J.-S. Occurence of intersexuality in a laboratory culture of the copepod Eurytemora affinis from the Seine estuary (France). Mar. Biol. 157, 851–861 (2010).Article 

Google Scholar 
37.Heinle, D. R. & Flemer, D. A. Carbon requirements of a population of the estuarine copepod Eurytemora affinis. Mar. Biol. 31, 235–247 (1975).Article 

Google Scholar 
38.Hirche, H.-J. Egg production of Eurytemora affinis—effect of K-strategy. Estuar. Coast. Shelf Sci. 35, 395–407 (1992).ADS 
Article 

Google Scholar 
39.Crawford, P. & Daborn, G. R. Seasonal variations in body size and fecundity in a copepod of turbid estuaries. Estuaries 9(2), 133–141 (1986).Article 

Google Scholar 
40.IPCC Climate change The physical science basis. In Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change (eds Solomon, S. et al.) 2007 (Cambridge University Press, 2007).
Google Scholar 
41.Guisande, C. & Gliwicz, Z. M. Egg size and clutch size in 2 Daphnia species grown at different food levels. J. Plankton Res. 14, 997–1007 (1992).Article 

Google Scholar 
42.Carrière, Y. & Roff, D. A. The evolution of offspring size and number: a test of the Smith-Fretwell model in three species of crickets. Oecologia 102, 389–396 (1995).ADS 
PubMed 
Article 

Google Scholar 
43.Beyrend-Dur, D., Souissi, S., Devreker, D., Winklerd, G. & Hwang, J.-S. Life cycle traits of two transatlantic populations of Eurytemora affinis (Copepoda: Calanoida): Salinity effects. J. Plankton Res. 31(7), 713–728 (2009).Article 

Google Scholar 
44.Dur, G. & Souissi, S. Ontogenetic optimal temperature and salinity envelops of the copepod Eurytemora affinis in the Seine estuary (France). Est. Coast Shelf Sci. 200, 311–323 (2018).ADS 
Article 

Google Scholar 
45.Mouny, P. & Dauvin, J. C. Environmental control of mesozooplankton communities in the Seine estuary (English Channel). Oceanol. Acta 25, 13–22 (2002).Article 

Google Scholar 
46.Mouny, P., Dauvin, J. C., Bessineton, C., Elkaim, B. & Simon, S. Biological components from the Seine estuary: first results. Hydrobiologia 373(374), 333–347 (1998).Article 

Google Scholar 
47.Dur, G., Jimenez-Melero, R., Beyrend-Dur, D., Hwang, J.-S. & Souissi, S. Individual-based model of the phenology of egg-bearing copepods application to Eurytemora affinis from the Seine estuary, France. Ecol. Model. 269, 21–36 (2013).Article 

Google Scholar 
48.Cailleaud, K. et al. Changes in the swimming behavior of Eurytemora affinis (Copepoda, Calanoida) in response to a sub-lethal exposure to nonylphenols. Aquat. Tox. 112, 228–231 (2011).Article 
CAS 

Google Scholar 
49.Mahjoub, M.-S., Souissi, S., Michalec, F.-G., Schmitt, F. G. & Hwang, J.-S. Swimming kinematics of Eurytemora affinis (Copepoda, Calanoida) reproductive stages and differential vulnerability to predation of larval Dicentrarchus labrax (Teleostei, Perciformes). J. Plankton Res. 33(7), 1095–1103 (2011).Article 

Google Scholar 
50.Lee, C. E. Rapid and repeated invasion of freshwater by the copepod Eurytemora affinis. Evolution 53(5), 1423–1434 (1999).PubMed 
Article 
PubMed Central 

Google Scholar  More

Alfaro-Nunez A, Bertelsen MF, Bojesen AM, Rasmussen I, Zepeda-Mendoza L, Olsen MT et al. (2014) Global distribution of Chelonid fibropapilloma-associated herpesvirus among clinically healthy sea turtles. BMC Evol Biol 14:206PubMed 
PubMed Central 
Article 

Google Scholar 
Ali OA, O’Rourke SM, Amish SJ, Meek MH, Luikart G, Jeffres C et al. (2016) RAD capture (Rapture): flexible and efficient sequence-based genotyping. Genetics 202:389–400CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Amos B, Hoelzel AR (1991). Long-term preservation of whale skin for DNA analysis. Rep Int Whal Comm Spec Issue 13:99–103Baird NA, Etter PD, Atwood TS, Currey MC, Shiver AL, Lewis ZA et al. (2008) Rapid SNP discovery and genetic mapping using sequenced RAD markers. PLoS ONE 3:e3376–8PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Ball RM, Neigel JE, Avise JC (1990) Gene genealogies within the organismal pedigrees of random-mating populations. Evolution 44:360–370PubMed 
PubMed Central 

Google Scholar 
Beerli P (2006) Comparison of Bayesian and maximum-likelihood inference of population genetic parameters. Bioinformatics 22:341–345CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Beerli P, Palczewski M (2010) Unified framework to evaluate panmixia and migration direction among multiple sampling locations. Genetics 185:313–326PubMed 
PubMed Central 
Article 

Google Scholar 
Bell CDL, Parsons J, Austin TJ, Broderick AC, Ebanks-Petrie G, Godley BJ (2005) Some of them came home: the Cayman Turtle Farm headstarting project for the green turtle Chelonia mydas. Oryx 39:137–148Article 

Google Scholar 
Bhatia G, Patterson N, Sankararaman S, Price AL (2013) Estimating and interpreting FST: The impact of rare variants. Genome Res 23:1514–1521CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Bintanja R, van de Wal RSW, Oerlemans J (2005) Modelled atmospheric temperatures and global sea levels over the past million years. Nature 437:125–128CAS 
PubMed 
Article 

Google Scholar 
Bjorndal KA, Bolten AB, Chaloupka MY (2000) Green turtle somatic growth model: evidence for density dependence. Ecol Appl 10:269–282
Google Scholar 
Bjorndal KA, Bolten AB, Chaloupka M, Saba VS, Bellini C, Marcovaldi MAG et al. (2017) Ecological regime shift drives declining growth rates of sea turtles throughout the West Atlantic. Glob Change Biol 23:4556–4568Article 

Google Scholar 
Bourjea J, Lapegue S, Gagnevin L, Broderick D, Mortimer JA, Ciccione S et al. (2007) Phylogeography of the green turtle, Chelonia mydas, in the Southwest Indian Ocean. Mol Ecol 16:175–186CAS 
PubMed 
Article 

Google Scholar 
Bowen BW, Abreu-Grobois FA, Balazs GH, Kamezaki N, Limpus CJ, Ferl RJ (1995) Trans-Pacific migrations of the loggerhead turtle (Caretta caretta) demonstrated with mitochondrial DNA markers. Proc Natl Acad Sci 92:3731–3734CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Bowen BW, Clark AM, Abreu-Grobois FA, Chaves A, Reichart HA, Ferl RJ (1997) Global phylogeography of the ridley sea turtles (Lepidochelys spp.) as inferred from mitochondrial DNA sequences. Genetica 101:179–189CAS 
PubMed 
Article 

Google Scholar 
Bowen BW, Gaither MR, DiBattista JD, Iacchei M, Andrews KR, Grant WS et al. (2016) Comparative phylogeography of the ocean planet. Proc Natl Acad Sci 113:7962–7969CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Boyle MC, FitzSimmons NN, Limpus CJ, Kelez S, Velez-Zuazo X, Waycott M (2009) Evidence for transoceanic migrations by loggerhead sea turtles in the southern Pacific Ocean. Proc R Soc B 276:1993–1999CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Bradshaw PJ, Broderick AC, Carreras C, Fuller W, Snape RTE, Wright LI et al. (2018) Defining conservation units with enhanced molecular tools to reveal fine scale structuring among Mediterranean green turtle rookeries. Biol Conserv 222:253–260Article 

Google Scholar 
Carr AF, Carr MH, Meylan AB (1978). The ecology and migrations of sea turtles, 7. The West Caribbean green turtle colony. Bull Am Mus Nat Hist 162:1–46Catchen JM, Amores A, Hohenlohe P, Cresko W, Postlethwait JH (2011) Stacks: building and genotyping loci de novo from short-read sequences. G3 Genes Genomes Genet 1:171–182CAS 

Google Scholar 
Clark PU, Dyke AS, Shakun JD, Carlson AE, Clark J, Wohlfarth B et al. (2009) The last glacial maximum. Science 325:710–714CAS 
PubMed 
Article 

Google Scholar 
Coates AG, Jackson JBC, Collins LS, Cronin TM, Dowsett HJ, Bybell LM et al. (1992) Closure of the Isthmus of Panama: the near-shore marine record of Costa Rica and western Panama. GSA Bull 104:814–828Article 

Google Scholar 
Dahl-Jensen D, Albert MR, Aldahan A, Azuma N, Balslev-Clausen D, Baumgartner M et al. (2013) Eemian interglacial reconstructed from a Greenland folded ice core. Nature 493:489–494CAS 
Article 

Google Scholar 
Danecek P, Auton A, Abecasis G, Albers CA, Banks E, DePristo MA et al. (2011) The variant call format and VCFtools. Bioinformatics 27:2156–2158CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Domingues RR, Hilsdorf AWS, Shivji MM, Hazin FVH, Gadig OBF (2018) Effects of the Pleistocene on the mitochondrial population genetic structure and demographic history of the silky shark (Carcharhinus falciformis) in the western Atlantic Ocean. Rev Fish Biol Fish 28:213–227Article 

Google Scholar 
Dray S, Dufour A-B (2007) The ade4 package: Implementing the duality diagram for ecologists. J Stat Softw 22:1–20.Article 

Google Scholar 
Duncan KM, Martin AP, Bowen BW, Couet HGD (2006) Global phylogeography of the scalloped hammerhead shark (Sphyrna lewini). Mol Ecol 15:2239–2251CAS 
PubMed 
Article 

Google Scholar 
Edwards S, Beerli P (2000) Perspective: gene divergence, population divergence, and the variance in coalescence time in phylogeographic studies. Evolution 54:1839–1854CAS 
PubMed 

Google Scholar 
Encalada SE, Lahanas PN, Bjorndal KA, Bolten AB, Miyamoto MM, Bowen BW (1996) Phylogeography and population structure of the Atlantic and Mediterranean green turtle Chelonia mydas: a mitochondrial DNA control region sequence assessment. Mol Ecol 5:473–483CAS 
PubMed 
Article 

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

Google Scholar 
Falush D, Stephens M, Pritchard JK (2003) Inference of population structure using multilocus genotype data: linked loci and correlated allele frequencies. Genetics 164:1567–1587CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Felsenstein J (2006) Accuracy of coalescent likelihood estimates: do we need more sites, more sequences, or more loci? Mol Biol Evol 23:691–700CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Felsenstein J, Churchill GA (1996) A Hidden Markov Model approach to variation among sites in rate of evolution. Mol Biol Evol 13:93–104CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
FitzSimmons NN, Limpus CJ, Norman JA, Goldizen AR, Miller JD, Moritz C (1997) Philopatry of male marine turtles inferred from mitochondrial DNA markers. Proc Natl Acad Sci 94:8912–8917CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Francis RM (2017) pophelper: an R package and web app to analyse and visualize population structure. Mol Ecol Resour 17:27–32CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Gaither MR, Bernal MA, Fernandez-Silva I, Mwale M, Jones SA, Rocha C et al. (2015) Two deep evolutionary lineages in the circumtropical glasseye Heteropriacanthus cruentatus (Teleostei, Priacanthidae) with admixture in the south-western Indian Ocean. J Fish Biol 87:715–727CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Gaither MR, Bowen BW, Bordenave T-R, Rocha LA, Newman SJ, Gomez JA et al. (2011). Phylogeography of the reef fish Cephalopholis argus (Epinephelidae) indicates Pleistocene isolation across the Indo-Pacific barrier with contemporary overlap in the coral triangle. BMC Evol Biol 11:189Gnirke A, Melnikov A, Maguire J, Rogov P, LeProust EM, Brockman W et al. (2009) Solution hybrid selection with ultra-long oligonucleotides for massively parallel targeted sequencing. Nat Biotechnol 27:182–189CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Goshe LR, Avens L, Scharf FS, Southwood AL (2010) Estimation of age at maturation and growth of Atlantic green turtles (Chelonia mydas) using skeletochronology. Mar Biol 157:1725–1740Article 

Google Scholar 
Green RE, Braun EL, Armstrong J, Earl D, Nguyen N, Hickey G et al. (2014) Three crocodilian genomes reveal ancestral patterns of evolution among archosaurs. Science 346:1254449PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Hamann M, Godfrey MH, Seminoff JA, Arthur K, Barata PCR, Bjorndal KA et al. (2010) Global research priorities for sea turtles: informing management and conservation in the 21st century. Endanger Species Res 11:245–269Article 

Google Scholar 
Hewitt G (2000) The genetic legacy of the Quaternary ice ages. Nature 405:907–913CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Ho SYW, Phillips MJ, Cooper A, Drummond AJ (2005) Time dependency of molecular rate estimates and systematic overestimation of recent divergence times. Mol Biol Evol 22:1561–1568CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Hudson RR, Slatkin M, Maddison WP (1992) Estimation of levels of gene flow from DNA sequence data. Genetics 132:583–589CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Jensen MP, Dalleau M, Gaspar P, Lalire M, Jean C, Ciccione S et al. (2020) Seascape genetics and the spatial ecology of juvenile green turtles. Genes 11:278CAS 
PubMed Central 
Article 

Google Scholar 
Jensen MP, FitzSimmons NN, Bourjea J, Hamabata T, Reece J, Dutton PH (2019) The evolutionary history and global phylogeography of the green turtle (Chelonia mydas). J Biogeogr 46:1–11.Article 

Google Scholar 
Jombart T, Devillard S, Balloux F (2010) Discriminant analysis of principal components: a new method for the analysis of genetically structured populations. BMC Genet 11:94PubMed 
PubMed Central 
Article 

Google Scholar 
Kalinowski ST (2011) The computer program STRUCTURE does not reliably identify the main genetic clusters within species: simulations and implications for human population structure. Heredity 106:625–632CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Karl SA, Bowen BW, Avise JC (1992) Global population genetic structure and male-mediated gene flow in the green turtle (Chelonia mydas): RFLP analyses of anonymous nuclear loci. Genetics 131:163–173CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Kelleher J, Etheridge AM, McVean G (2016) Efficient coalescent simulation and genealogical analysis for large sample sizes. PLOS Comput Biol 12:e1004842PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Kiessling W, Simpson C, Beck B, Mewis H, Pandolfi JM (2012) Equatorial decline of reef corals during the last Pleistocene interglacial. Proc Natl Acad Sci 109:21378–21383CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Komoroske LM, Miller MR, O’Rourke SM, Stewart KR, Jensen MP, Dutton PH (2019) A versatile Rapture (RAD-Capture) platform for genotyping marine turtles. Mol Ecol Resour 19:497–511PubMed 
Article 

Google Scholar 
Kong A, Frigge ML, Masson G, Besenbacher S, Sulem P, Magnusson G et al. (2012) Rate of de novo mutations and the importance of father’s age to disease risk. Nature 488:471–475CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Kukla GJ, Bender ML, de Beaulieu J-L, Bond G, Broecker WS, Cleveringa P et al. (2002) Last interglacial climates. Quat Res 58:2–13Article 

Google Scholar 
Lahanas PN, Bjorndal KA, Bolten AB, Encalada SE, Miyamoto MM, Valverde RA et al. (1998) Genetic composition of a green turtle (Chelonia mydas) feeding ground population: evidence for multiple origins. Mar Biol 130:345–352Article 

Google Scholar 
Langmead B, Trapnell C, Pop M, Salzberg SL (2009) Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol 10:R25PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Latch EK, Dharmarajan G, Glaubitz JC, Rhodes Jr OE (2006) Relative performance of Bayesian clustering software for inferring population substructure and individual assignment at low levels of population differentiation. Conserv Genet 7:295–302Article 

Google Scholar 
Lessios HA (2008) The great American schism: divergence of marine organisms after the rise of the Central American Isthmus. Annu Rev Ecol Evol Syst 39:63–91Article 

Google Scholar 
Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N et al. (2009) The Sequence Alignment/Map format and SAMtools. Bioinformatics 25:2078–2079PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Linck EB, Battey CJ (2019) Minor allele frequency thresholds strongly affect population structure inference with genomic datasets. Mol Ecol Resour 19:639–647CAS 
PubMed 
Article 

Google Scholar 
Ludt WB, Rocha LA (2015) Shifting seas: the impacts of Pleistocene sea-level fluctuations on the evolution of tropical marine taxa. J Biogeogr 42:25–38Article 

Google Scholar 
Malinsky M, Trucchi E, Lawson DJ, Falush D (2018) RADpainter and fineRADstructure: population inference from RADseq data. Mol Biol Evol 35:1284–1290CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Maruki T, Lynch M (2017) Genotype calling from population-genomic sequencing data. G3 Genes Genomes Genet 7:1393–1404CAS 

Google Scholar 
Meylan AB, Bowen BW, Avise JC (1990) A genetic test of the natal homing versus social facilitation models for green turtle migration. Science 248:724–727CAS 
PubMed 
Article 

Google Scholar 
Miles A, Murillo R, Ralph P, Harding N, Pisupati R, Rae S et al. (2017). cggh/scikit-allel: v1.3.2. ZenodoMonzón-Argüello C, López-Jurado LF, Rico C, Marco A, López P, Hays GC et al. (2010) Evidence from genetic and Lagrangian drifter data for transatlantic transport of small juvenile green turtles. J Biogeogr 37:1752–1766Article 

Google Scholar 
Naro-Maciel E, Reid BN, Alter SE, Amato G, Bjorndal KA, Bolten AB et al. (2014) From refugia to rookeries: phylogeography of Atlantic green turtles. J Exp Mar Biol Ecol 461:306–316Article 

Google Scholar 
Patrício AR, Formia A, Barbosa C, Broderick AC, Bruford M, Carreras C et al. (2017) Dispersal of green turtles from Africa’s largest rookery assessed through genetic markers. Mar Ecol Prog Ser 569:215–225Article 

Google Scholar 
Peeters FJC, Acheson R, Brummer G-JA, De Ruijter WPM, Schneider RR, Ganssen GM et al. (2004) Vigorous exchange between the Indian and Atlantic oceans at the end of the past five glacial periods. Nature 430:661–665CAS 
PubMed 
Article 

Google Scholar 
Penven P, Lutjeharms JRE, Marchesiello P, Roy C, Weeks SJ (2001) Generation of cyclonic eddies by the Agulhas Current in the lee of the Agulhas Bank. Geophys Res Lett 28:1055–1058Article 

Google Scholar 
Peterson BK, Weber JN, Kay EH, Fisher HS, Hoekstra HE (2012) Double digest RADseq: an inexpensive method for de novo SNP discovery and genotyping in model and non-model species. PLoS ONE 7:e37135CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Pillans B, Chappell J, Naish TR (1998) A review of the Milankovitch climatic beat: template for Plio–Pleistocene sea-level changes and sequence stratigraphy. Sediment Geol 122:5–21CAS 
Article 

Google Scholar 
Pritchard JK, Stephens M, Donnelly P (2000) Inference of population structure using multilocus genotype data. Genetics 155:945–959CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
R Core Team (2021) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria
Google Scholar 
Reece JS, Castoe TA, Parkinson CL (2005) Historical perspectives on population genetics and conservation of three marine turtle species. Conserv Genet 6:235–251Article 

Google Scholar 
Reid BN, Naro‐Maciel E, Hahn AT, FitzSimmons NN, Gehara M (2019) Geography best explains global patterns of genetic diversity and post-glacial co-expansion in marine turtles. Mol Ecol 28:3358–3370PubMed 
PubMed Central 

Google Scholar 
Roberts MA, Schwartz TS, Karl SA (2004) Global population genetic structure and male-mediated gene flow in the green sea turtle (Chelonia mydas): analysis of microsatellite loci. Genetics 166:1857–1870CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Rocha LA (2003) Patterns of distribution and processes of speciation in Brazilian reef fishes. J Biogeogr 30:1161–1171Article 

Google Scholar 
Rocha LA, Robertson DR, Rocha CR, Van Tassell JL, Craig MT, Bowen BW (2005) Recent invasion of the tropical Atlantic by an Indo-Pacific coral reef fish. Mol Ecol 14:3921–3928PubMed 
Article 
PubMed Central 

Google Scholar 
Scally A, Durbin R (2012) Revising the human mutation rate: implications for understanding human evolution. Nat Rev Genet 13:745–753CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Seminoff JA (2004). Chelonia mydas. IUCN Red List Threat Species 2004: e.T4615A11037468Seminoff JA, Allen CD, Balazs GH, Dutton PH, Eguchi T, Haas HL et al. (2015). Status review of the green turtle (Chelonia mydas) under the U.S. Endangered Species Act. NOAA Technical Memorandum, NOAA-NMFS-SWFSC-539. 571ppStrasburg JL, Rieseberg LH (2010) How robust are ‘isolation with migration’ analyses to violations of the im model? A simulation study. Mol Biol Evol 27:297–310CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Taberlet P, Fumagalli L, Wust‐Saucy A-G, Cosson J-F (1998) Comparative phylogeography and postglacial colonization routes in Europe. Mol Ecol 7:453–464CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Teske PR, Von Der Heyden S, McQuaid CD, Barker NP (2011) A review of marine phylogeography in southern. Afr South Afr J Sci 107:43–53
Google Scholar 
Van der Zee JP, Christianen MJA, Nava M, Velez-Zuazo X, Hao W, Bérubé M et al. (2019) Population recovery changes population composition at a major southern Caribbean juvenile developmental habitat for the green turtle, Chelonia mydas. Sci Rep 9:14392Wallace BP, DiMatteo AD, Hurley BJ, Finkbeiner EM, Bolten AB, Chaloupka MY et al. (2010) Regional management units for marine turtles: a novel framework for prioritizing conservation and research across multiple scales. PLoS ONE 5:e15465PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Wang J (2017) The computer program structure for assigning individuals to populations: easy to use but easier to misuse. Mol Ecol Resour 17:981–990CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Wang Z, Pascual-Anaya J, Zadissa A, Li W, Niimura Y, Huang Z et al. (2013) The draft genomes of soft-shell turtle and green sea turtle yield insights into the development and evolution of the turtle-specific body plan. Nat Genet 45:701–706CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Waples RS (1998) Separating the wheat from the chaff: patterns of genetic differentiation in high gene flow species. J Hered 89:438–450Article 

Google Scholar 
Weir BS, Cockerham CC (1984) Estimating F-Statistics for the Analysis of Population Structure. Evolution 38:1358–1370CAS 
PubMed 

Google Scholar 
Willing E-M, Dreyer C, van Oosterhout C (2012) Estimates of genetic differentiation measured by FST do not necessarily require large sample sizes when using many SNP markers. PLoS ONE 7:e42649–7CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Wilson AC, Cann RL, Carr SM, George M, Gyllensten UB, Helm-Bychowski KM et al. (1985) Mitochondrial DNA and two perspectives on evolutionary genetics. Biol J Linn Soc 26:375–400Article 

Google Scholar 
Woodruff DS (2010) Biogeography and conservation in Southeast Asia: how 2.7 million years of repeated environmental fluctuations affect today’s patterns and the future of the remaining refugial-phase biodiversity. Biodivers Conserv 19:919–941Article 

Google Scholar 
Wright S (1951) The genetical structure of populations. Ann Eugen 15:323–354CAS 
PubMed 
Article 

Google Scholar  More

Step-pools are natural geomorphologic forms developed under the action of extreme floods1 in mountain streams with bed slopes ranging from 3 to 20%2,3. The step-pools are characterized by poorly graded bed materials intricately packed to form a step and pool sequence, generating high energy tumbling and tranquil mountain flows. The typical bed morphology is irregular and results in spatially and temporally varied hydrodynamics over various functional units within the step-pools such as step, tread, base of step, and pool region (Fig. 1b). The step denotes the portion of bed comprising boulders or bedrock outcrops jam-packed across the width of the channel. The pool consists of finer bed materials and deeper cross-sections as a result of scour due to submerged or unsubmerged hydraulic jumps generated in the pool. The base of step is the section immediately downstream of step unit where the flow over the step impinges into the pool with high amounts of turbulence and self-aeration. The tread is the region extending from the downstream end of the scour pool up to the step unit. Step-pool systems can also exist without the presence of a tread region. In that case, the pool region directly ends in a step unit4.Figure 1Details of step-pool systems in the present study: (a) Longitudinal section of the field site, (b) Longitudinal section of the laboratory model, (c) Photograph of the field site, (d) Photograph of the experimental setup.Full size imageThe evaluation of flow parameters in step-pool streams does not follow the general criteria recommended for lowland rivers. The commonly used flow friction factors such as Manning’s n and Chezy’s C cannot be applied here due to the non-uniform nature of flow at meso-scale. In step-pool mountain streams, the rational frictional coefficient to define flow resistance is the non-dimensional Darcy Weisbach friction factor5,6,7. Dedicated field and laboratory investigations of the step-pools are necessary to create a sufficient database for the development of accurate hydraulic models.In addition, the design of step-pools is adopted for stream restorations8,9, storm water conveyance systems10, and for creating close-to-nature step-pool fish passes11,12,13. Primal research on step-pools has been largely limited to the analysis of bed morphology14,15,16,17, flow resistance18,19,20,21,22 and sediment transport23,24 by considering the step-pool reach as a single system. A detailed review on the hydrodynamics of step-pools in mountain streams is available in Kalathil and Chandra7.The variations in the flow characteristics imposed by the various morphological units within the step-pool system (SPS) were not studied until the 2000s. Adverse pressure gradients in the pools and upstream of steps lead to increased turbulence, while favourable pressure gradients on steps suppress turbulence. Accordingly, pools are dominated by wake turbulence and the steps, treads and runs are governed by form or bed-generated turbulence. The wake turbulence in pools is characterized by recirculation eddies and its strength diminishes with increase in distance from the impingement point25,26. Incidentally, the variations in hydrodynamics within step-pool systems do not furnish considerable differences in the sediment transport estimation since the measured and computed magnitudes differ up to an order of three because of the limited sediment availability in mountain streams27,28. Nevertheless, updated knowledge on the flow dynamics at different regions within the step-pool reach will aid in providing guidelines for designing close-to-nature fish passes to enable target species to pass through the fluvial system29,30. In recent times, with increased demands to implement and maintain environmental flow schemes, cost-effective and eco-friendly structures such as step-pools provide a promising tool to facilitate economic development together with ecological conservation.The presence of a wide range of substrates (fine sand to boulders) and varying flow conditions in step-pools facilitate the inhabitation, migration, and dispersal of diverse aquatic species31. The productive range of water depth and flow velocity for inhabitation lies between 0.16 m to 0.5 m and 0.3 m/s to 1.2 m/s, respectively11. In addition to the range of flow depth and velocity requirements, hydraulic shear stress and turbulence characteristics also affect fish behaviour and locomotion32. Depending on the turbulence scale and intensities, various damages on the fish body or disorientation of the species may occur. Therefore, it is important to consider fish behaviour, life stage, swimming ability, and hydraulic conditions including velocity and turbulence characteristics in step-pool structures prior to its ecological applications. Adequate design guidelines for the construction of close to nature fish passes are not available due to lack of studies31,33.Limited research addresses the influence of bed morphology on the turbulence characteristics in step-pools. Wohl and Thompson4 studied the variations in flow profiles at different locations in a step-pool with the use of an electromagnetic current meter of sampling frequency 0.5 Hz. The study was limited to the analysis of mean velocity and coefficient of variation in velocity which is sometimes used synonymous to turbulence intensities. Although flow profiles showed variations in pattern, ANOVA and ANCOVA results were rather inconclusive regarding the dependence of flow parameters on the bed form types. Later on, Wilcox and Wohl34 and Wilcox et al.35 conducted three-dimensional velocity measurements using SonTek FlowTracker operating at 1 Hz sampling frequency to study the spatial variation of velocity and turbulence intensities in step-pools. The pools exhibit increased levels of turbulence intensities and less velocity reduction in cases where the upstream step-units do not span the entire width of the channel and effects as leaky or porous steps35. The turbulence characteristics in terms of the root mean square of the fluctuating velocities showed considerable differences between step, tread and pool. However, due to the low sampling frequency of the velocity measuring instrument, the accuracy of turbulence analysis is questionable. Considering the complex terrain in step-pool streams and practical difficulties in the use of high frequency instruments that require proper stationing and continuous power supply, it is arduous to produce good quality data of the fluctuating velocities. To bridge the gap in research on the fluctuating velocity components in step-pools, extensive laboratory studies are required.In this context, to shed light upon the variation of hydraulic parameters with the morphological units, we discuss the results from a physical model downscaled according to field measurements conducted in a step-pool stream in Erumakolli, Wayanad, India. Figure 1 shows the longitudinal sections and photographs of the field site and the laboratory experimental setup. The field investigation comprised the measurements of bed material size, bed topography and flow velocity measurements. The physical model study discusses the variation in the turbulence characteristics across steps, treads and pools. The analysis is limited to the vertical distribution of velocity magnitude and turbulence intensities across the morphological units, the propagation of velocity magnitude and Reynolds shear stress in the flow direction, relationship between the turbulent kinetic energy and velocity magnitude, and the evaluation of energy dissipation factor in the step-pools. The present study is the foremost attempt in the analysis and discussions of turbulence fluctuations, Reynolds shear stresses and energy dissipations in self-formed step-pool systems.Experimental setup validationThe laboratory experimental setup was created by establishing dynamic similarity between the field and the laboratory model through Froude’s Model Law. Model scales less than 10:1 can successfully simulate field conditions in the case of turbulent self-aerated flows36. A length scale ratio of 3.3: 1 was chosen for creating the physical model. The corresponding velocity scale and discharge scale are (3.3)1/2: 1 and (3.3)5/2: 1, respectively. The laboratory step-pool system is self-formed under a formative discharge and is not expected to generate the exact bed topography as observed in the field. However, effect of the influencing parameters such as D84, step-height, bed slope, and discharge on the velocity and turbulence characteristics would be adequately simulated. A comparison of the thalweg velocity and Froude number over step, tread and pool at d = 0.6 H between the field and laboratory data is presented in Table 1, where d is the depth of measurement and H is the total flow depth at that point. Since the measured data is location specific and due to the limitations in the number of data points available, only the reach scale average values of velocity and Froude number was used to estimate the error. An absolute error of 0.04 m/s (6.3%) and 0.02 (4.9%) was observed between the field and laboratory up scaled data for thalweg reach average velocity and Froude number, respectively.Table 1 Comparison of the thalweg velocity and Froude number for step, tread and pool at d = 0.6 H between the field and laboratory data.Full size tableVelocity and turbulence intensitiesThe velocity and turbulence characteristics pertinent to accurate design and model development of step-pools are velocity magnitude (VR), turbulence intensities (TI), normalized turbulent kinetic energy (K), and energy dissipation factor (EDF). We obtained velocity data in the physical model using Nortek Vectrino 3-D Acoustic Doppler Velocimeter. A total of 16 thalweg velocity data at d = 0.6H and 24 vertical velocity profiles at 1 cm intervals have been retained after velocity filtering and processing (see “Methods”), where d is the depth of measurement and H is the total flow depth at that point. The velocity measurements were confined in the range of 0.003 m/s to 0.796 m/s, bounding the productive range for aquatic species inhabitation in field scale (0.005 m/s to 1.446 m/s).The propagation of flow in a step-pool system is illustrated in Fig. 2. The x-axis shows the measurement sections along the longitudinal direction (X) for step-pool system 2 (see “Methods”). The variable on the y-axis z + H − d denotes the elevation of the measurement point above the datum which is set at the deepest scour point of X = 2.60 m. Where, z is the vertical distance from the datum to the bed surface, H is the total flow depth at the point, and d is the depth of measurement with respect to the free surface. The first vertical corresponding to X = 2.40 m is at a distance of 0.15 m downstream of a step unit. Any data collected closer to the steps were removed in data filtration. The average velocity at d = 0.6H is shown in the plot legend. The lowest velocity is observed at the deepest scour section of X = 2.60 m.Figure 2Variation of resultant velocity magnitude VR in the longitudinal direction of the SPS 2.Full size imageTo examine the statistical differences in the distribution of velocity and turbulence intensities across the morphological units, the 24 vertical velocity measurements comprising longitudinal and cross-stream points have been subjected to Kruskal–Wallis ANOVA. The earlier studies that sought to distinguish the morphological units on the basis of velocity components performed one-way ANOVA on the datasets. Although the measurements on steps, treads and pools are independent of each other and randomly sampled, the available data fails to uniformly conform to normal distribution, which is a prerequisite for ANOVA test. Therefore, the present work revisits this analysis for velocity components, velocity magnitude and turbulence intensities using Kruskal–Wallis ANOVA which is a non-parametric test that does not assume a normally distributed dataset. The analysis is conducted on the ranks of the data values rather than the data values, and tests whether the median values are significantly different from each other. A resultant p value of 0 from the analysis indicates that there is significant difference between the groups, while a p value of 1 indicates vice-versa. The null hypothesis of Kruskal–Wallis ANOVA is that the sample groups come from the same population. Closeness of the p value to 0 is a measure of the confidence in rejecting the null hypothesis. In the present study, data points were categorized with respect to d/H values to normalize the effect of depth on the velocity variations, and the data points confined in the range d/H = 0.50–0.70 were considered for the test (Case I). The d/H is thus selected to obtain a wider range of data points pertaining to the average velocity which is typically at d/H = 0.6. The non-parametric test was also repeated for depth averaged values (Case II) to produce similar results. Except in the case of cross-stream velocity v, all other groupings showed significant difference between the median values for step, tread and pool data points. The p values of 0.24 and 0.41 were obtained for the hypothesis test on v for Case I and Case II, respectively. This shows that the variation in the cross-stream velocity is independent of the morphological type and is not a characteristic feature of step-pool system in a straight channel. The step-pool system that encounters bends within the reach may have an influence on the cross-stream velocity component. The results of the statistical analysis and box-plots of the velocity magnitude and turbulence intensities for both Cases I and II are given in Table 2 and Fig. 3, respectively. A negligible absolute error of 0.027 m/s and 0.031 m/s in velocity magnitude was obtained between the mean and median of Cases I and II, respectively. Whereas, a maximum absolute error of 0.134 and 0.087 was observed in the respective turbulence intensities. However, the differences in the methods are not substantial enough to alter the results of the hypothesis testing.Table 2 Comparison and analysis results of Kruskal–Wallis ANOVA for data points in the range of d/H = 0.50–0.70 and depth-averaged values at various verticals across the morphological units.Full size tableFigure 3Distribution of resultant velocity magnitude VR and turbulence intensities TI of the fluctuating velocities, u′, v′, and w′ for different morphological unit: (a) Case I: data points confined to d/H = 0.50–0.70. (b) Case II: depth-averaged values at each vertical.Full size imageThe average values of TIu′, TIv′, and TIw′ combining the 24 verticals (d/H ranging from 0.20 to 1.00) are 0.065,0.055 and 0.097 for steps, 0.146, 0.110, and 0.165 for treads, and 0.453, 0.265, and 0.523 for pools, respectively. The values show an increase of 55%, 50% and 41% for TIv′ with respect to TIu′ for step, tread and pool, respectively, while a sizeable increase of 597%, 382% and 439% for TIw′ with respect to TIu′, which evidently indicates the dominance of vertical fluctuations in the pools. The pattern of variation of turbulence intensities at step, tread and pools can be better understood with the help of vertical profiles. Figure 4 shows the vertical profiles of velocity magnitude and turbulence intensity profiles corresponding to step, tread and pool regions in different step-pool systems, namely, SPS 1, SPS 2, and SPS 3 (see “Methods”). Compared to the velocity profiles in step and tread, a visible mid-profile shear layer can be seen in the pool. Previous researchers have identified the presence of mid-profile shear in regions of wake turbulence. Thompson and Wohl4 illustrated the shear layer downstream of steps with the help of velocity profiles in step-pool systems. Baki et al.37 illustrated the presence of shear layer in the wake turbulence regions of a rock-ramp fish pass. A staggered arrangement of natural boulders of equivalent diameter 14 cm was used to prepare the rock-ramp bed. The wake area downstream of the boulders is similar to the downstream of steps in step-pool systems. Fang et al.38 illustrated the shift in the vertical profile of Reynolds shear stress due to near-bed and boulder-induced shear stresses. In the present study, the shear layer is prominent in SPS 3, milder in SPS 1 and fairly non-existent in SPS 2 which corresponds to the profile at X = 2.40 m in Fig. 2. The shear layer is generated due to the momentum exchange occurring in the pools consequent to flow impingement. The occurrence of the shear layer and the magnitude of velocity shift depend on the characteristics of the upstream step unit and spacing between the upstream step and point of interest. In the case of leaky step units where some portion of the step cross section is devoid of elevated step units, the flow passes through without causing consideration impingement to the downstream pool. Hence, the flow does not produce a downstream wake region resulting in the absence of shear layers in the vertical profile. The same can be observed from Fig. 4e, where the vertical section in SPS 2 existed downstream of a leaky step unit, which also lead to lower levels of energy dissipation and less velocity reduction.Figure 4Variation of resultant velocity magnitude VR and turbulence intensities TI of the fluctuating velocities, u′, v′, and w′ along the depth: (a) VR at Step, (b) TI at Step, (c) VR at Tread, (d) TI at Tread, (e) VR at Pool, and (f) TI at Pool.Full size imageThe magnitude of turbulence intensities is lower on step and tread, and maximum in the pools as can be observed in Fig. 4b, d and f, respectively. The fluctuations are higher in the pools due to the varied velocity distribution and wake turbulence characteristics in the pools.Reynolds shear stressReynolds shear stress is the stress generated due to the momentum exchange between the fluctuating velocity components. The range of shear stress in the flow medium has implications in the suitability of a flow body to various aquatic lives since high levels of shear stress may even lead to major injuries or mortality to the species. The vertical profiles of the time averaged and normalized Reynolds shear stress in the x–z plane in the longitudinal direction is shown in Fig. 5. The x-axis shows the normalized Reynolds shear stress (- overline{{u^{{prime }} w^{{prime }} }} /V_{max }^{2}) for each section, where Vmax = 0.796 m/s is the maximum velocity measured during the experimental runs. The variable on the y-axis follows the same convention as described in Fig. 2. The fluctuations in the profile are more in the deeper locations in the pool (X = 2.40 m to 2.80 m) due to the increased turbulence at the bottom as a result of flow impingement. The error bar shown for X = 2.50, 2.90 and 3.05 is calculated from the additional 4 verticals measured in the cross-stream direction, for each of the sections. The maximum Reynolds shear stress variation is observed in x–z plane compared to x–y and y–z planes, with normalized values ranging from − 19.477 to 13.729. The absolute maximum value of 19.477 amounts to 12.34 N/m2 in model scale and 40.73 N/m2 in prototype scale. Reynolds shear stress as low as 30 N/m2 can cause reduction in startle response in some species39. Therefore, ensuring acceptable limits of turbulence fluctuations is essential in the design for artificial constructions of the step-pool morphology. While recreating the morphology for a fish pass design, control can be placed on the pool volume, characteristic grain size (equivalent to step height) or allowable discharge to reduce the turbulence levels in pools. However, this entails detailed study into the cause and effect of these parameters on the hydrodynamics.Figure 5Variation of time averaged and normalized Reynolds shear stress in the x–z plane (( – overline{{u^{{prime }} w^{{prime }} }} /V_{max }^{2} )) in the longitudinal direction of SPS 2 (Vmax = 0.796 m/s).Full size imageTurbulent kinetic energyAnother indicator of turbulence characteristic to the morphological units in the present study is Turbulent Kinetic Energy (TKE) which is a measure of kinetic energy per unit mass of the turbulent flow. It is an important parameter that determines the locomotive characteristics of various species40 and key to evaluating the energy loss to fishes41. In the present study, normalized form of turbulent kinetic energy (K) follows an inverse power relation to the velocity magnitude as shown in Fig. 6. The x-axis is normalized using Vmax and TKE is normalized by the transformation (K = sqrt {{text{TKE}}} /V_{R}). The data is inclusive of all the depth-wise data points measured over step, tread and pool regions along the thalweg. Larger values of K occurred in pools followed by tread and step regions. The pattern is comprehensible from visual observation of the flow field, where the flow occurs as high-velocity sheet with limited agitation over tread and step, resulting in plunging flow with recirculating eddies in the pools. A non-linear curve fitting of type Power-Allometric 1 was used to generate the empirical equation (K = aleft( {{{V_{R} } mathord{left/ {vphantom {{V_{R} } {V_{max } }}} right. kern-nulldelimiterspace} {V_{max } }}} right)^{b}), where a = 0.12398 and b = − 0.89947 with a standard error of ± 0.01018 and ± 0.03398, respectively. The coefficient of determination of the plot is 0.93.Figure 6Variation of normalized turbulent kinetic energy K with the time-averaged thalweg velocity ratio VR/Vmax (Vmax = 0.796 m/s).Full size imageEnergy dissipation factorThe energy dissipation factor (EDF) is an engineering design parameter that checks the turbulence level in the fish pathways. The flow energy must be sufficiently dissipated to ensure velocity levels less than 2 m/s. The EDF is also representative of the eddies and turbulence generated due to flow impingement into pools. Contrary to the conventional pool fish passes and slot fish passes, the pool cross-sections in natural step-pools are not uniform33. The pool dimensions vary along the reach and accordingly reflects in the EDF values. Hence, calculation of EDF using the equation (EDF=gamma QS/A) would result in considerable errors since A is not a constant within and across step-pool systems, where γ is the specific weight of water, Q is the discharge, S is the bed slope and A is the cross-sectional area of the pool. Here, the EDF calculations were based on the basic equation (EDF = gamma QDelta H/forall), where ΔH is the drop in water elevation level per pool and (forall) is the pool volume. The step-pool systems 1, 2 and 3 have been evaluated for EDF. The pool volume was calculated applying the trapezoidal rule to the wetted areas of cross-sections in pool spaced at 10 cm apart. The wetted area was calculated from the measured bed and water elevation levels. The region starting immediately downstream of the steps up to the exit slope of the scour pool is considered for calculating the pool volume. Table 3 presents the pool dimensions and EDF values obtained in the present study. The EDF values obtained for step-pool systems 1, 2, and 3 were 321, 207, and 123 W/m3 in model scale, respectively. The results corresponds to 590, 380, and 226 W/m3 in prototype scale. However, a value of 150 W/m3 should not be exceeded to ensure acceptable levels of turbulence in the pools33. Specific EDF criteria for various fish species are available to design fish passes accommodating the requirements of the predominant fish population42.Table 3 Computation of energy dissipation factor in step-pool systems 1, 2 and 3.Full size tableConsidering the range of shear stresses and energy dissipation factors obtained in the present study, it can be inferred that construction of step-pool fish passes simulating the field parameters may not provide adequate flow conditions for a step-pool type fish pass. For the design of step-pool type fish passes, the pool volumes should be back calculated from the EDF equation for specific species. The translation of the pool volume in terms of the width of channel, pool length and pool depth will ensure lower levels of turbulence intensities and shear stresses in the passage. Since the interest towards close-to nature fish passes have been developed only in the recent years, specific guidelines for step-pool type fish passes are yet to be formulated. This calls for research in artificial step-pool constructions based on the pool and turbulence requirements of the dominant target species. Nevertheless, the nature of hydrodynamics in the self-formed and artificially constructed would coincide since the step-pool bed morphology has an inherent tendency to attain a state of maximum resistance. The concept of creating artificial structures is limited to providing and placing the bed materials into required bed slopes and approximate design dimensions. The ultimate bed morphology of the structure will be modelled over time by the hydraulic force of the flowing water. More

1.Filkins LM, O’Toole GA. Cystic fibrosis lung infections: polymicrobial, complex, and hard to treat. PLoS Pathog. 2015;11:e1005258.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
2.Paterson IK, Hoyle A, Ochoa G, Baker-Austin C, Taylor NGH. Optimising antibiotic usage to treat bacterial infections. Sci Rep. 2016;6:37853.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
3.Andrews JM. Determination of minimum inhibitory concentrations. J Antimicrob Chemother. 2001;48:5–16.CAS 
PubMed 
Article 

Google Scholar 
4.Brook I. Inoculum effect. Rev Infect Dis. 1989;11:361–8.CAS 
PubMed 
Article 

Google Scholar 
5.Karslake J, Maltas J, Brumm P, Wood KB. Population density modulates drug inhibition and gives rise to potential bistability of treatment outcomes for bacterial infections. PLOS Comput Biol. 2016;12:e1005098.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
6.Udekwu KI, Parrish N, Ankomah P, Baquero F, Levin BR. Functional relationship between bacterial cell density and the efficacy of antibiotics. J Antimicrob Chemother. 2009;63:745–57.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
7.Sweeney E, Sabnis A, Edwards AM, Harrison F. Effect of host-mimicking medium and biofilm growth on the ability of colistin to kill Pseudomonas aeruginosa. Microbiology. 2020;166:1171–80.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
8.Walters MC, Roe F, Bugnicourt A, Franklin MJ, Stewart PS. Contributions of antibiotic penetration, oxygen limitation, and low metabolic activity to tolerance of Pseudomonas aeruginosa biofilms to ciprofloxacin and tobramycin. Antimicrob Agents Chemother. 2003;47:317–23.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
9.Nguyen D, Joshi-Datar A, Lepine F, Bauerle E, Olakanmi O, Beer K, et al. Active starvation responses mediate antibiotic tolerance in biofilms and nutrient-limited bacteria. Science. 2011;334:982–6.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
10.Høiby N, Bjarnsholt T, Givskov M, Molin S, Ciofu O. Antibiotic resistance of bacterial biofilms. Int J Antimicrob Agents. 2010;35:322–32.PubMed 
Article 
CAS 

Google Scholar 
11.Olsen I. Biofilm-specific antibiotic tolerance and resistance. Eur J Clin Microbiol Infect Dis. 2015;34:877–86.CAS 
PubMed 
Article 

Google Scholar 
12.Macia MD, Rojo-Molinero E, Oliver A. Antimicrobial susceptibility testing in biofilm-growing bacteria. Clin Microbiol Infect. 2014;20:981–90.CAS 
PubMed 
Article 

Google Scholar 
13.Thieme L, Hartung A, Tramm K, Klinger-Strobel M, Jandt KD, Makarewicz O, et al. MBEC versus MBIC: the lack of differentiation between biofilm reducing and inhibitory effects as a current problem in biofilm methodology. Biol Proced Online. 2019;21:18.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
14.Bottery MJ, Pitchford JW, Friman V-P. Ecology and evolution of antimicrobial resistance in bacterial communities. ISME J. 2021;15:939–48.PubMed 
Article 

Google Scholar 
15.Smith AL, Fiel SB, Mayer-Hamblett N, Ramsey B, Burns JL. Susceptibility testing of Pseudomonas aeruginosa isolates and clinical response to parenteral antibiotic administration: lack of association in cystic fibrosis. Chest. 2003;123:1495–502.CAS 
PubMed 
Article 

Google Scholar 
16.Radlinski L, Conlon B. Antibiotic efficacy in the complex infection environment. Curr Opin MicrobioL 2018;42:19–24.CAS 
PubMed 
Article 

Google Scholar 
17.Vos MGJ, de, Zagorski M, McNally A, Bollenbach T. Interaction networks, ecological stability, and collective antibiotic tolerance in polymicrobial infections. PNAS. 2017;114:10666–71.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
18.Adamowicz EM, Flynn J, Hunter RC, Harcombe WR. Cross-feeding modulates antibiotic tolerance in bacterial communities. ISME J. 2018;12:2723–35.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
19.Aranda-Díaz A, Obadia B, Dodge R, Thomsen T, Hallberg ZF, Güvener ZT, et al. Bacterial interspecies interactions modulate pH-mediated antibiotic tolerance. eLife. 2020;9:e51493.PubMed 
PubMed Central 
Article 

Google Scholar 
20.Vega NM, Gore J. Collective antibiotic resistance: mechanisms and implications. Curr Opin Microbiol. 2014;21:28–34.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
21.Beaudoin T, Yau YCW, Stapleton PJ, Gong Y, Wang PW, Guttman DS, et al. Staphylococcus aureus interaction with Pseudomonas aeruginosa biofilm enhances tobramycin resistance. NPJ Biofilms Microbiomes. 2017;3:25.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
22.Orazi G, O’Toole GA. Pseudomonas aeruginosa alters Staphylococcus aureus sensitivity to vancomycin in a biofilm model of cystic fibrosis infection. mBio. 2017;8:e00873–17.PubMed 
PubMed Central 
Article 

Google Scholar 
23.Sorg RA, Lin L, Doorn GS, van, Sorg M, Olson J, Nizet V, et al. Collective resistance in microbial communities by intracellular antibiotic deactivation. PLOS Biol. 2016;14:e2000631.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
24.Perlin MH, Clark DR, McKenzie C, Patel H, Jackson N, Kormanik C, et al. Protection of Salmonella by ampicillin-resistant Escherichia coli in the presence of otherwise lethal drug concentrations. Proc R Soc B. 2009;276:3759–68.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
25.Flynn JM, Cameron LC, Wiggen TD, Dunitz JM, Harcombe WR, Hunter RC. Disruption of cross-feeding inhibits pathogen growth in the sputa of patients with cystic fibrosis. mSphere. 2020;5:e00343–20.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
26.Gurney J, Brown SP, Kaltz O, Hochberg ME. Steering phages to combat bacterial pathogens. Trends Microbiol. 2020;28:85–94.CAS 
PubMed 
Article 

Google Scholar 
27.Waters VJ, Kidd TJ, Canton R, Ekkelenkamp MB, Johansen HK, LiPuma JJ, et al. Reconciling antimicrobial susceptibility testing and clinical response in antimicrobial treatment of chronic cystic fibrosis lung infections. Clin Infect Dis. 2019;69:1812–6.CAS 
PubMed 
Article 

Google Scholar 
28.Somayaji R, Parkins MD, Shah A, Martiniano SL, Tunney MM, Kahle JS, et al. Antimicrobial susceptibility testing (AST) and associated clinical outcomes in individuals with cystic fibrosis: a systematic review. J Cyst Fibros. 2019;18:236–43.CAS 
PubMed 
Article 

Google Scholar 
29.Raghuvanshi R, Vasco K, Vázquez-Baeza Y, Jiang L, Morton JT, Li D, et al. High-resolution longitudinal dynamics of the cystic fibrosis sputum microbiome and metabolome through antibiotic therapy. mSystems. 2020;5:e00292–20.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
30.Cystic Fibrosis Trust. UK cystic fibrosis registry annual data report 2019. 2020. [online] Available at: https://www.cysticfibrosis.org.uk/sites/default/files/2020-12/2019%20Registry%20Annual%20Data%20report_Sep%202020.pdf [Accessed 5 June 2021].31.Nixon GM, Armstrong DS, Carzino R, Carlin JB, Olinsky A, Robertson CF, et al. Clinical outcome after early Pseudomonas aeruginosa infection in cystic fibrosis. J Pediatr. 2001;138:699–704.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
32.Sánchez MB. Antibiotic resistance in the opportunistic pathogen Stenotrophomonas maltophilia. Front Microbiol. 2015;6:658.PubMed 
PubMed Central 
Article 

Google Scholar 
33.Salsgiver EL, Fink AK, Knapp EA, LiPuma JJ, Olivier KN, Marshall BC, et al. Changing epidemiology of the respiratory bacteriology of patients with cystic fibrosis. Chest. 2016;149:390–400.PubMed 
PubMed Central 
Article 

Google Scholar 
34.Cystic Fibrosis Trust. Antibiotic treatment for cystic fibrosis. 2009. [online] Available at: https://www.cysticfibrosis.org.uk/sites/default/files/2020-11/Anitbiotic%20Treatment.pdf [Accessed 7 June 2021].35.Denton M, Todd NJ, Littlewood JM. Role of anti-pseudomonal antibiotics in the emergence of Stenotrophomonas maltophilia in cystic fibrosis patients. Eur J Clin Microbiol Infect Dis. 1996;15:402–5.CAS 
PubMed 
Article 

Google Scholar 
36.Esposito A, Pompilio A, Bettua C, Crocetta V, Giacobazzi E, Fiscarelli E, et al. Evolution of Stenotrophomonas maltophilia in cystic fibrosis lung over chronic infection: a genomic and phenotypic population study. Front Microbiol. 2017;8:1590.PubMed 
PubMed Central 
Article 

Google Scholar 
37.Pompilio A, Crocetta V, De Nicola S, Verginelli F, Fiscarelli E, Di Bonaventura G. Cooperative pathogenicity in cystic fibrosis: Stenotrophomonas maltophilia modulates Pseudomonas aeruginosa virulence in mixed biofilm. Front Microbiol. 2015;6:951.PubMed 
PubMed Central 
Article 

Google Scholar 
38.Dalbøge CS, Hansen CR, Pressler T, Høiby N, Johansen HK. Chronic pulmonary infection with Stenotrophomonas maltophilia and lung function in patients with cystic fibrosis. J Cyst Fibros. 2011;10:318–25.PubMed 
Article 

Google Scholar 
39.Okazaki A, Avison MB. Induction of L1 and L2 β-lactamase production in Stenotrophomonas maltophilia is dependent on an AmpR-type regulator. Antimicrob Agents Chemother. 2008;52:1525–8.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
40.Yurtsev EA, Chao HX, Datta MS, Artemova T, Gore J. Bacterial cheating drives the population dynamics of cooperative antibiotic resistance plasmids. Mol Syst Biol. 2013;9:683.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
41.Bottery MJ, Wood AJ, Brockhurst MA. Selective conditions for a multidrug resistance plasmid depend on the sociality of antibiotic resistance. Antimicrob Agents Chemother. 2016;60:2524–7.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
42.Palmer KL, Aye LM, Whiteley M. Nutritional cues control Pseudomonas aeruginosa multicellular behavior in cystic fibrosis sputum. J Bacteriol. 2007;189:8079–87.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
43.Artemova T, Gerardin Y, Dudley C, Vega NM, Gore J. Isolated cell behavior drives the evolution of antibiotic resistance. Mol Syst Biol. 2015;11:822.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
44.Harrison E, Wood AJ, Dytham C, Pitchford JW, Truman J, Spiers A, et al. Bacteriophages limit the existence conditions for conjugative plasmids. mBio. 2015;6:e00586–15.PubMed 
PubMed Central 

Google Scholar 
45.Hall JPJ, Wood AJ, Harrison E, Brockhurst MA. Source–sink plasmid transfer dynamics maintain gene mobility in soil bacterial communities. PNAS. 2016;113:8260–5.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
46.Regoes RR, Wiuff C, Zappala RM, Garner KN, Baquero F, Levin BR. Pharmacodynamic functions: a multiparameter approach to the design of antibiotic treatment regimens. Antimicrob Agents Chemother. 2004;48:3670–6.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
47.Yu G, Baeder DY, Regoes RR, Rolff J. Predicting drug resistance evolution: insights from antimicrobial peptides and antibiotics. Proc R Soc B. 2018;285:20172687.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
48.Zhanel GG, Simor AE, Vercaigne L, Mandell L. Imipenem and meropenem: comparison of in vitro activity, pharmacokinetics, clinical trials and adverse effects. Can J Infect Dis. 1998;9:215–28.CAS 
PubMed 
PubMed Central 

Google Scholar 
49.Gould VC, Okazaki A, Avison MB. β-Lactam resistance and β-lactamase expression in clinical Stenotrophomonas maltophilia isolates having defined phylogenetic relationships. J Antimicrob Chemother. 2006;57:199–203.CAS 
PubMed 
Article 

Google Scholar 
50.European Cystic Fibrosis Society Patient Registry. ECFS patient registry annual data report 2018. 2020. [online] Available at: https://www.ecfs.eu/sites/default/files/general-content-files/working-groups/ecfs-patient-registry/ECFSPR_Report_2018_v1.4.pdf [Accessed 7 June 2021].51.Radlinski L, Rowe SE, Kartchner LB, Maile R, Cairns BA, Vitko NP, et al. Pseudomonas aeruginosa exoproducts determine antibiotic efficacy against Staphylococcus aureus. PLoS Biol. 2017;15:e2003981.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
52.Harrison FY. Microbial ecology of the cystic fibrosis lung. Microbiology. 2007;153:917–23.CAS 
PubMed 
Article 

Google Scholar 
53.Keel RA, Sutherland CA, Crandon JL, Nicolau DP. Stability of doripenem, imipenem and meropenem at elevated room temperatures. Int J Antimicrob Agents. 2011;37:184–5.CAS 
PubMed 
Article 

Google Scholar 
54.Okazaki A, Avison MB. Aph(3′)-IIc, an Aminoglycoside resistance determinant from Stenotrophomonas maltophilia. Antimicrob Agents Chemother. 2007;51:359–60.CAS 
PubMed 
Article 

Google Scholar 
55.Li X-Z, Zhang L, McKay GA, Poole K. Role of the acetyltransferase AAC(6′)-Iz modifying enzyme in aminoglycoside resistance in Stenotrophomonas maltophilia. J Antimicrob Chemother. 2003;51:803–11.CAS 
PubMed 
Article 

Google Scholar 
56.Frost I, Smith WPJ, Mitri S, Millan AS, Davit Y, Osborne JM, et al. Cooperation, competition and antibiotic resistance in bacterial colonies. ISME J. 2018;12:1582–93.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
57.Yin C, Yang W, Meng J, Lv Y, Wang J, Huang B. Co-infection of Pseudomonas aeruginosa and Stenotrophomonas maltophilia in hospitalised pneumonia patients has a synergic and significant impact on clinical outcomes. Eur J Clin Microbiol Infect Dis. 2017;36:2231–5.CAS 
PubMed 
Article 

Google Scholar 
58.Waters V, Yau Y, Prasad S, Lu A, Atenafu E, Crandall I, et al. Stenotrophomonas maltophilia in cystic fibrosis: serologic response and effect on lung disease. Am J Respir Crit Care Med. 2011;183:635–40.PubMed 
Article 

Google Scholar 
59.Goss CH, Mayer-Hamblett N, Aitken ML, Rubenfeld GD, Ramsey BW. Association between Stenotrophomonas maltophilia and lung function in cystic fibrosis. Thorax. 2004;59:955–9.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
60.Mojica MF, Ouellette CP, Leber A, Becknell MB, Ardura MI, Perez F, et al. Successful treatment of bloodstream infection due to metallo-β-lactamase-producing Stenotrophomonas maltophilia in a renal transplant patient. Antimicrob Agents Chemother. 2016;60:5130–4.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
61.McCutcheon JG, Dennis JJ. The potential of phage therapy against the emerging opportunistic pathogen Stenotrophomonas maltophilia. Viruses. 2021;13:1057.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
62.Rossi E, La Rosa R, Bartell JA, Marvig RL, Haagensen JAJ, Sommer LM, et al. Pseudomonas aeruginosa adaptation and evolution in patients with cystic fibrosis. Nat Rev Microbiol. 2021;19:331–42.CAS 
PubMed 
Article 

Google Scholar 
63.Davies EV, James CE, Brockhurst MA, Winstanley C. Evolutionary diversification of Pseudomonas aeruginosa in an artificial sputum model. BMC Microbiol. 2017;17:3.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
64.Bara JJ, Matson Z, Remold SK. Life in the cystic fibrosis upper respiratory tract influences competitive ability of the opportunistic pathogen Pseudomonas aeruginosa. R Soc Open Sci. 2018;5:180623.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
65.Bartell JA, Sommer LM, Haagensen JAJ, Loch A, Espinosa R, Molin S, et al. Evolutionary highways to persistent bacterial infection. Nat Commun. 2019;10:629.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
66.Estrela S, Brown SP. Community interactions and spatial structure shape selection on antibiotic resistant lineages. PLOS Comput Biol. 2018;14:e1006179.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
67.McNally L, Bernardy E, Thomas J, Kalziqi A, Pentz J, Brown SP, et al. Killing by Type VI secretion drives genetic phase separation and correlates with increased cooperation. Nat Commun. 2017;8:14371.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
68.Burmølle M, Webb JS, Rao D, Hansen LH, Sørensen SJ, Kjelleberg S. Enhanced biofilm formation and increased resistance to antimicrobial agents and bacterial invasion are caused by synergistic interactions in multispecies biofilms. Appl Environ Microbiol. 2006;72:3916–23.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
69.Willner D, Haynes MR, Furlan M, Schmieder R, Lim YW, Rainey PB, et al. Spatial distribution of microbial communities in the cystic fibrosis lung. ISME J. 2012;6:471–4.CAS 
PubMed 
Article 

Google Scholar 
70.Turner KH, Wessel AK, Palmer GC, Murray JL, Whiteley M. Essential genome of Pseudomonas aeruginosa in cystic fibrosis sputum. PNAS. 2015;112:4110–5.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
71.Kirchner S, Fothergill JL, Wright EA, James CE, Mowat E, Winstanley C. Use of artificial sputum medium to test antibiotic efficacy against Pseudomonas aeruginosa in conditions more relevant to the cystic fibrosis lung. J Vis Exp. 2012;64:e3857.
Google Scholar 
72.Harrison F, Diggle SP. An ex vivo lung model to study bronchioles infected with Pseudomonas aeruginosa biofilms. Microbiology. 2016;162:1755–60.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
73.Harrington NE, Sweeney E, Harrison F. Building a better biofilm – formation of in vivo-like biofilm structures by Pseudomonas aeruginosa in a porcine model of cystic fibrosis lung infection. Biofilm. 2020;2:100024.PubMed 
PubMed Central 
Article 

Google Scholar 
74.Bricio-Moreno L, Sheridan VH, Goodhead I, Armstrong S, Wong JKL, Waters EM, et al. Evolutionary trade-offs associated with loss of PmrB function in host-adapted Pseudomonas aeruginosa. Nat Commun. 2018;9:2635.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
75.Castellani S, Di Gioia S, di Toma L, Conese M. Human cellular models for the investigation of lung inflammation and mucus production in cystic fibrosis. Anal Cell Pathol. 2018;2018:3839803.Article 
CAS 

Google Scholar 
76.Levin-Reisman I, Ronin I, Gefen O, Braniss I, Shoresh N, Balaban NQ. Antibiotic tolerance facilitates the evolution of resistance. Science. 2017;355:826–30.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
77.Wistrand-Yuen E, Knopp M, Hjort K, Koskiniemi S, Berg OG, Andersson DI. Evolution of high-level resistance during low-level antibiotic exposure. Nat Commun. 2018;9:1599.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
78.Choi K-H, Schweizer HP. mini-Tn7 insertion in bacteria with single attTn7 sites: example Pseudomonas aeruginosa. Nat Protoc. 2006;1:153–61.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
79.Bankevich A, Nurk S, Antipov D, Gurevich AA, Dvorkin M, Kulikov AS, et al. SPAdes: a new genome assembly algorithm and Its applications to single-cell Sequencing. J Comput Biol. 2012;19:455–77.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
80.Seemann T. Prokka: rapid prokaryotic genome annotation. Bioinformatics. 2014;30:2068–9.CAS 
Article 

Google Scholar 
81.Onoue Y, Mori M. Amino acid requirements for the growth and enterotoxin production by Staphylococcus aureus in chemically defined media. Int J Food Microbiol. 1997;36:77–82.CAS 
PubMed 
Article 

Google Scholar 
82.Kumar S, Stecher G, Li M, Knyaz C, Tamura K. MEGA X: molecular evolutionary genetics analysis across computing platforms. Mol Biol Evol. 2018;35:1547–9.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
83.Guindon S, Dufayard J-F, Lefort V, Anisimova M, Hordijk W, Gascuel O. New algorithms and methods to estimate maximum-likelihood phylogenies: assessing the performance of PhyML 3.0. Syst Biol. 2010;59:307–21.CAS 
PubMed 
Article 

Google Scholar 
84.Lefort V, Longueville J-E, Gascuel O. SMS: smart model selection in PhyML. Mol Biol Evol. 2017;34:2422–4.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
85.Kuznetsov A, Bollin CJ. NCBI Genome Workbench: desktop software for comparative genomics, visualization, and GenBank data submission. Methods Mol Biol. 2021;2231:261–95.CAS 
PubMed 
Article 

Google Scholar 
86.Li H, Durbin R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics. 2009;25:1754–60.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar  More

1.Andersson, M. Sexual Selection (Princeton University Press, 1996).
Google Scholar 
2.Clutton-Brock, T. H. The Evolution of Parental Care (Princeton University Press, 1991).Book 

Google Scholar 
3.Olsson, M., Shine, R., Wapstra, E., Ujvari, B. & Madsen, T. Sexual dimorphism in lizard body shape: The roles of sexual selection and fecundity selection. Evolution 56, 1538–1542 (2002).Article 

Google Scholar 
4.McPherson, F. J. & Chenoweth, P. J. Mammalian sexual dimorphism. Anim. Reprod. Sci. 131, 109–122 (2012).Article 
CAS 

Google Scholar 
5.Shine, R. The evolution of large body size in females: A critique of Darwin’s “fecundity advantage” model. Am. Nat. 131, 124–131 (1988).Article 

Google Scholar 
6.Fairbairn, D. J. et al. (eds) Sex, Size and Gender Roles: Evolutionary Studies of Sexual Size Dimorphism (Oxford University Press, 2007).
Google Scholar 
7.Slatkin, M. Ecological causes of sexual dimorphism. Evolution 38, 622–630 (1984).Article 

Google Scholar 
8.Shine, R. Ecological causes for the evolution of sexual dimorphism: A review of the evidence. Q. Rev. Biol. 64, 419–461 (1989).Article 
CAS 

Google Scholar 
9.Herrel, A., Spithoven, L., Van Damme, R. & De Vree, F. Sexual dimorphism of head size in Gallotia galloti: Testing the niche divergence hypothesis by functional analyses. Funct. Ecol. 13, 289–297 (1999).Article 

Google Scholar 
10.Pearson, D., Shine, R. & How, R. Sex-specific niche partitioning and sexual size dimorphism in Australian pythons (Morelia spilota imbricata). Biol. J. Linn. Soc. 77, 113–125 (2002).Article 

Google Scholar 
11.Hierlihy, C. A., Garcia-Collazo, R., Chavez Tapia, C. B. & Mallory, F. F. Sexual dimorphism in the lizard Sceloporus siniferus: Support for the intraspecific niche divergence and sexual selection hypotheses. Salamandra 49, 1–6 (2013).
Google Scholar 
12.Vitt, L. J. & Cooper, W. E. Jr. The evolution of sexual dimorphism in the skink Eumeces laticeps: An example of sexual selection. Can. J. Zool. 63, 995–1002 (1985).Article 

Google Scholar 
13.Shine, R. Intersexual dietary divergence and the evolution of sexual dimorphism in snakes. Am. Nat. 138, 103–122 (1991).Article 

Google Scholar 
14.Fitzgerald, M. & Shine, R. Mate-guarding in free-ranging Carpet Pythons (Morelia spilota). Aust. Zool. 39, 434–439 (2018).Article 

Google Scholar 
15.Cundall, D. & Greene, H. W. Feeding in snakes. In Feeding: Form, Function, and Evolution in Tetrapod Vertebrates (ed. Schwenk, K.) 293–333 (Academic Press, 2000).Chapter 

Google Scholar 
16.Goiran, C., Dubey, S. & Shine, R. Effects of season, sex and body size on the feeding ecology of turtle-headed sea snakes (Emydocephalus annulatus) on IndoPacific inshore coral reefs. Coral Reefs 32, 527–538 (2013).ADS 
Article 

Google Scholar 
17.Shine, R., Bonnet, X., Elphick, M. J. & Barrott, E. G. A novel foraging mode in snakes: Browsing by the sea snake Emydocephalus annulatus (Serpentes, Hydrophiidae). Funct. Ecol. 18, 16–24 (2004).Article 

Google Scholar 
18.Lynch, T. P. The Behavioural Ecology of the Olive Sea Snake, Aipysurus laevis. PhD thesis, James Cook University (2000).19.Borczyk, B., Paśko, Ł, Kusznierz, J. & Bury, S. Sexual dimorphism and skull size and shape in the highly specialized snake species, Aipysurus eydouxii (Elapidae: Hydrophiinae). PeerJ 9, e11311 (2021).Article 

Google Scholar 
20.Queral-Regil, A. & King, R. B. Evidence for phenotypic plasticity in snake body size and relative head dimensions in response to amount and size of prey. Copeia 1998, 423–429 (1998).Article 

Google Scholar 
21.Bonnet, X., Shine, R., Naulleau, G. & Thiburce, C. Plastic vipers: influence of food intake on the size and shape of Gaboon vipers (Bitis gabonica). J. Zool. 255, 341–351 (2001).Article 

Google Scholar 
22.Sanders, K. L., Lee, M. S., Leys, R., Foster, R. & Keogh, J. S. Molecular phylogeny and divergence dates for Australasian elapids and sea snakes (Hydrophiinae): Evidence from seven genes for rapid evolutionary radiations. J. Evol. Biol. 21, 682–695 (2008).Article 
CAS 

Google Scholar 
23.Aubret, F. & Shine, R. Genetic assimilation and the postcolonization erosion of phenotypic plasticity in island tiger snakes. Curr. Biol. 19, 1932–1936 (2009).Article 
CAS 

Google Scholar 
24.McCarthy, C. J. Adaptations of sea snakes that eat fish eggs; with a note on the throat musculature of Aipysurus eydouxi (Gray, 1849). J. Nat. Hist. 21, 1119–1128 (1987).Article 

Google Scholar 
25.Shine, R., Shine, T. G., Brown, G. P. & Goiran, C. Life history traits of the sea snake Emydocephalus annulatus, based on a 17-yr study. Coral Reefs 39, 1407–1414 (2020).Article 

Google Scholar 
26.Segall, M., Cornette, R., Fabre, A. C., Godoy-Diana, R. & Herrel, A. Does aquatic foraging impact head shape evolution in snakes? Proc. R. Soc. B 283, 20161645 (2016).Article 

Google Scholar 
27.Avolio, C., Shine, R. & Pile, A. J. The adaptive significance of sexually dimorphic scale rugosity in sea snakes. Am. Nat. 167, 728–738 (2006).Article 

Google Scholar 
28.Sherratt, E., Rasmussen, A. R. & Sanders, K. L. Trophic specialization drives morphological evolution in sea snakes. R. Soc. Open Sci. 5, 172141 (2018).ADS 
Article 

Google Scholar 
29.Frédérich, B. & Parmentier, E. (eds) Biology of Damselfishes (CRC Press, 2016).
Google Scholar 
30.Heatwole, H. Sea Snakes 2nd edn. (Krieger Publishing Company, 1999).
Google Scholar 
31.Lukoschek, V. & Shine, R. Sea snakes rarely venture far from home. Ecol. Evol. 2, 1113–1121 (2012).Article 

Google Scholar 
32.Shine, R., Shine, T. & Shine, B. Intraspecific habitat partitioning by the sea snake Emydocephalus annulatus (Serpentes, Hydrophiidae): The effects of sex, body size, and colour pattern. Biol. J. Linn. Soc. 80, 1–10 (2003).Article 

Google Scholar 
33.Goiran, C., Brown, G. P. & Shine, R. Niche partitioning within a population of sea snakes is constrained by ambient thermal homogeneity and small prey size. Biol. J. Linn. Soc. 129, 644–651 (2020).Article 

Google Scholar  More

Seasonal changes in weather, food availability and mice body conditionThe weather was hot and dry during summer (temperature: 24.42 ± 0.36 °C; total rainfall: 0.60 mm) and temperatures were lower and rainfall was higher during the winter months (temperature: 13.47 ± 0.45 °C; total rainfall: 39.60 mm; LM: N = 138, F = 368.4, P  More

1.Amat, J. A. & Masero, J. A. How Kentish plovers, Charadrius alexandrinus, cope with heat stress during incubation. Behav. Ecol. Sociobiol. 56, 26–33 (2004).Article 

Google Scholar 
2.du Plessis, K. L., Martin, R. O., Hockey, P. A. R., Cunningham, S. J. & Ridley, A. R. The costs of keeping cool in a warming world: Implications of high temperatures for foraging, thermoregulation and body condition of an arid-zone bird. Glob. Chang. Biol. 18, 3063–3070 (2012).ADS 
PubMed 
Article 
PubMed Central 

Google Scholar 
3.Cunningham, S. J., Martin, R. O. & Hockey, P. A. R. Can behaviour buffer the impacts of climate change on an arid-zone bird?. Ostrich 86, 119–126 (2015).Article 

Google Scholar 
4.Smit, B. et al. Behavioural responses to heat in desert birds: implications for predicting vulnerability to climate warming. Clim. Chang. Responses 3, 1–14 (2016).Article 

Google Scholar 
5.McNab, B.K. The Physiological Ecology of Vertebrates: A View from Energetics (Cornell University Press, 2002).6.Cunningham, S. J., Gardner, J. L. & Martin, R. O. Opportunity costs and the response of birds and mammals to climate warming. Front. Ecol. Environ. 1, 1–8. https://doi.org/10.1002/fee.2324 (2021).Article 

Google Scholar 
7.Wolf, B. O., Wooden, K. M. & Walsberg, G. E. The use of thermal refugia by two small desert birds. Condor 98(2), 424–428 (1996).Article 

Google Scholar 
8.Cook, T. R. et al. Parenting in a warming world: Thermoregulatory responses to heat stress in an endangered seabird. Conserv. Physiol. 8, 1–13 (2020).Article 

Google Scholar 
9.Speakman, J. R. & Król, E. Maximal heat dissipation capacity and hyperthermia risk: Neglected key factors in the ecology of endotherms. J. Anim. Ecol. 79, 726–746 (2010).PubMed 
PubMed Central 

Google Scholar 
10.Nilsson, J. Å. & Nord, A. Testing the heat dissipation limit theory in a breeding passerine. Proc. R. Soc. B Biol. Sci. 285, 1 (2018).
Google Scholar 
11.Nord, A. & Nilsson, J. Å. Heat dissipation rate constrains reproductive investment in a wild bird. Funct. Ecol. 33, 250–259 (2019).Article 

Google Scholar 
12.Tapper, S., Nocera, J. J. & Burness, G. Heat dissipation capacity influences reproductive performance in an aerial insectivore. J. Exp. Biol. 223, 1 (2020).
Google Scholar 
13.Buckley, L. B., Ehrenberger, J. C. & Angilletta, M. J. Thermoregulatory behaviour limits local adaptation of thermal niches and confers sensitivity to climate change. Funct. Ecol. 29, 1038–1047 (2015).Article 

Google Scholar 
14.Edwards, E. K., Mitchell, N. J. & Ridley, A. R. The impact of high temperatures on foraging behaviour and body condition in the Western Australian Magpie Cracticus tibicen dorsalis. Ostrich 86, 137–144 (2015).Article 

Google Scholar 
15.Thompson, M. L., Cunningham, S. J. & McKechnie, A. E. Interspecific variation in avian thermoregulatory patterns and heat dissipation behaviours in a subtropical desert. Physiol. Behav. 188, 311–323 (2018).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
16.Kemp, R. et al. Sublethal fitness costs of chronic exposure to hot weather vary between sexes in a threatened desert lark. Emu 120, 216–229 (2020).Article 

Google Scholar 
17.Funghi, C., McCowan, L. S. C., Schuett, W. & Griffith, S. C. High air temperatures induce temporal, spatial and social changes in the foraging behaviour of wild zebra finches. Anim. Behav. 149, 33–43 (2019).Article 

Google Scholar 
18.Pattinson, N. B. et al. Heat dissipation behaviour of birds in seasonally hot arid-zones: are there global patterns?. J. Avian Biol. 51, 1–11 (2020).Article 

Google Scholar 
19.Moyer-Horner, L., Mathewson, P. D., Jones, G. M., Kearney, M. R. & Porter, W. P. Modeling behavioral thermoregulation in a climate change sentinel. Ecol. Evol. 5, 5810–5822 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
20.Moore, D., Stow, A. & Kearney, M. R. Under the weather?—The direct effects of climate warming on a threatened desert lizard are mediated by their activity phase and burrow system. J. Anim. Ecol. 87, 660–671 (2018).PubMed 
Article 
PubMed Central 

Google Scholar 
21.Bladon, A. J. et al. Behavioural thermoregulation and climatic range restriction in the globally threatened ethiopian bush-crow Zavattariornis stresemanni. Ibis 161(3), 546–558. https://doi.org/10.1111/ibi.12660 (2019).Article 

Google Scholar 
22.Conradie, S. R., Woodborne, S. M., Cunningham, S. J. & McKechnie, A. E. Chronic, sublethal effects of high temperatures will cause severe declines in southern African arid-zone birds during the 21st century. Proc. Natl. Acad. Sci. USA 116, 14065–14070 (2019).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
23.Enriquez-Urzelai, U. et al. The roles of acclimation and behaviour in buffering climate change impacts along elevational gradients. J. Anim. Ecol. 89, 1722–1734 (2020).PubMed 
Article 
PubMed Central 

Google Scholar 
24.Albright, T. P. et al. Mapping evaporative water loss in desert passerines reveals an expanding threat of lethal dehydration. Proc. Natl. Acad. Sci. USA 114, 2283–2288 (2017).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
25.Dawson, W. R. Evaporative losses of water by birds. Comp. Biochem. Physiol. Part A Physiol. 71, 495–509 (1982).Article 
CAS 

Google Scholar 
26.Wolf, B. O. & Walsberg, G. E. Respiratory and cutaneous evaporative water loss at high environmental temperatures in a small bird. J. Exp. Biol. 199, 451–457 (1996).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
27.Calder, W. A. & Smichdt-Nielsen, K. Evaporative cooling and respiratory alkalosis in the pigeon. Proc. Natl. Acad. Sci. USA 55(4), 750–756. https://doi.org/10.1073/pnas.55.4.750 (1966).ADS 
Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
28.Bartholomew, G. A. The role of behavior in the temperature regulation of the masked booby. Condor 68, 523–535. https://doi.org/10.2307/1366261 (1966).Article 

Google Scholar 
29.Bryant, D. M. Heat stress in tropical birds: behavioural thermoregulation during flight. Ibis (Lond. 1859). 125, 313–323 (1983).30.Tattersall, G. J., Andrade, D. V. & Abe, A. S. Heat exchange from the toucan bill reveals a controllable vascular thermal radiator. Science (80-. ). 325, 468–470 (2009).31.Van De Ven, T. M. F. N., Martin, R. O., Vink, T. J. F., McKechnie, A. E. & Cunningham, S. J. Regulation of heat exchange across the hornbill beak: Functional similarities with toucans?. PLoS ONE 11, 1–14 (2016).
Google Scholar 
32.Van Vuuren, A. K., Kemp, L. V. & McKechnie, A. E. The beak and unfeathered skin as heat radiators in the southern ground-hornbill. J. Avian Biol. 51, 1–7 (2020).
Google Scholar 
33.Winkler, D.W., Billerman, S.M. & Lovette, I.J. Storks (Ciconiidae), version 1.0. In Birds of the World (S. M. Billerman, B. K. Keeney, P. G. Rodewald, and T. S. Schulenberg, Editors). Cornell Lab of Ornithology (2020) https://doi.org/10.2173/bow.ciconi2.0134.Kahl, P. M. Thermoregulation in the wood stork, with special reference to the role of the legs. Physiol Zool. 36(2), 141–151 (1963).Article 

Google Scholar 
35.Steen, I. & Steen, J. B. The Importance of the Legs in the Thermoregulation of Birds. Acta Physiol. Scand. 63, 285–291 (1965).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
36.Hainsworth, F. R. Saliva spreading, activity and body temperature regulation in the rat. Am J Physiol. 212, 1288–1292 (1967).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
37.Gentry, R. L. Thermoregulatory behavior of eared seals. Behaviour 46(2), 73–93. https://doi.org/10.1163/156853973×00175 (1973).Article 
PubMed 
CAS 
PubMed Central 

Google Scholar 
38.Sturbaum, B. A. & Riedesel, M. L. Dissipation of stored body heat by the ornate box turtle, Terrapene ornata. Comp. Biochem. Physiol. Part A Physiol. 58, 93–97 (1977).Article 

Google Scholar 
39.Marder, J., Porat, I., Raber, P. & Adler, J. Acid-base balance and body temperature regulation of heat stressed Psammomys obesus (Gerbillinae): The effect of bicarbonate loss via saliva spreading. Physiol Zool. 56(3), 389–396. https://doi.org/10.1086/physzool.56.3.30152603 (1983).Article 

Google Scholar 
40.Hatch, D. E. Energy conserving and heat dissipating mechanisms of the turkey vulture. Auk 87(1), 111–124. https://doi.org/10.2307/4083662 (1970).Article 

Google Scholar 
41.Cooper, J. & Siegfried, W. R. Behavioural responses of young cape gannets Sula capensis to high ambient temperatures. Mar. Behav. Physiol. 3, 211–220 (1976).Article 

Google Scholar 
42.Thomas, B. T. Maguari Stork Nesting: Juvenile Growth and Behavior. Auk 101, 812–823 (1984).Article 

Google Scholar 
43.Hancock, J.A., Kushlan, J.A. & Kahl, M.P. Storks, Ibises and Spoonbills of the World (Academic Press, 1992).44.Townsend, H., Huyvaert, K. P., Hodum, P. J. & Anderson, D. J. Nesting distributions of Galapagos boobies (Aves: Sulidae): an apparent case of amensalism. Oecologia 132, 419–427. https://doi.org/10.1007/s00442-002-0992-7 (2002).ADS 
Article 
PubMed 
PubMed Central 

Google Scholar 
45.Finkelstein, M., Kuspa, Z., Snyder, N.F. & Schmitt, N.J. California condor (Gymnogyps californianus), version 2.0. In The Birds of North America (P. G. Rodewald, Editor). Cornell Lab of Ornithology (2015). https://doi.org/10.2173/bna.61046.Czenze, Z. J. et al. Regularly drinking desert birds have greater evaporative cooling capacity and higher heat tolerance limits than non-drinking species. Funct. Ecol. 34, 1589–1600 (2020).Article 

Google Scholar 
47.Nudds, R. L. & Oswald, S. A. An interspecific test of Allen’s rule: Evolutionary implications for endothermic species. Evolution (N. Y). 61, 2839–2848 (2007).48.Symonds, M. R. E. & Tattersall, G. J. Geographical variation in bill size across bird species provides evidence for Allen’s rule. Am. Nat. 176, 188–197 (2010).PubMed 
Article 
PubMed Central 

Google Scholar 
49.Galván, I., Rodríguez-Martínez, S. & Carrascal, L. M. Dark pigmentation limits thermal niche position in birds. Funct. Ecol. 32, 1531–1540 (2018).Article 

Google Scholar 
50.Wilman, H. et al. EltonTraits 1 . 0 : Species-level foraging attributes of the world ’ s birds and mammals. Ecology 95, 2027 (2014).51.Brooke, M. D. L. Ecological factors influencing the occurrence of ‘flash marks’ in wading birds. Funct. Ecol. 12, 339–346 (1998).Article 

Google Scholar 
52.Maclean, I. M. D., Mosedale, J. R. & Bennie, J. J. Microclima: An r package for modelling meso- and microclimate. Methods Ecol Evol. 10(2), 280–290. https://doi.org/10.1111/2041-210X.13093 (2019).Article 

Google Scholar 
53.Hadfield, A. J. Package ‘ MCMCglmm ’. https://cran.r-project.org/web/packages/MCMCglmm/ (2019)54.Jetz, W., Thomas, G.H., Joy, J.B., Hartmann, K. & Mooers, A.O. 2012. The global diversity of birds in space and time. Nature. 491(7424): 444–448 (2012). https://doi.org/10.1038/nature1163155.Revell, M.L.J. Package ‘ phytools ’ https://cran.r-project.org/web/packages/phytools/ (2020)56.Freckleton, R. P., Harvey, P. H. & Pagel, M. Phylogenetic analysis and comparative data: A test and review of evidence. Am. Nat. 160, 712–726 (2002).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
57.Bates, D., Mächler, M., Bolker, B. M. & Walker, S. C. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, (2015).58.Crawley, M.J. The R Book (John Wiley & Sons, 2013).59.Barton, K. Package MuMin: Multi-model Inference https://cran.r-project.org/web/packages/MuMIn/index.html (2020).60.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 (2011).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
61.Symonds, M. R. E. & Moussalli, A. A brief guide to model selection, multimodel inference and model averaging in behavioural ecology using Akaike’s information criterion. Behav. Ecol. Sociobiol. 65, 13–21 (2011).Article 

Google Scholar 
62.R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/ (2020).63.Bakken, G. S. & Angilletta, M. J. How to avoid errors when quantifying thermal environments. Funct. Ecol. 28, 96–107 (2014).Article 

Google Scholar 
64.van Dyk, M., Noakes, M. J. & McKechnie, A. E. Interactions between humidity and evaporative heat dissipation in a passerine bird. J. Comp. Physiol. B. 189, 299–308. https://doi.org/10.1007/s00360-019-01210-2 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
65.Webster, M.D., Campbell, G.S. & King, J.R. Cutaneous resistance to water-vapor diffusion in pigeons and the role of the plumage. Physiol. Zool. 58(1): 58–70 (1985). http://www.jstor.org/stable/30161220.66.Battley, P. F., Rogers, D. I., Piersma, T. & Koolhaas, A. Behavioural evidence for heat-load problems in Great Knots in tropical Australia fuelling for long-distance flight. Emu 103, 97–103 (2003).Article 

Google Scholar 
67.Piersma, T. & van Gils, J.A. The Flexible Phenotype: A Body-Centered Integration of Ecology, Physiology, and Behavior (Oxford University Press, 2011).68.Fitzpatrick, M. J., Mathewson, P. D. & Porter, W. P. Validation of a mechanistic model for non-invasive study of ecological energetics in an endangered wading bird with counter-current heat exchange in its legs. PLoS ONE 10, 1–34 (2015).Article 
CAS 

Google Scholar 
69.Lustick, S., Battersby, B. & Kelty, M. Effects of insolation on juvenile herring gull energetics and behavior. Ecologia. 60(4), 673–678. https://doi.org/10.2307/1936603 (1979).Article 

Google Scholar 
70.Ward, J. M., Blount, J. D., Ruxton, G. D. & Houston, D. C. The adaptive significance of dark plumage for birds in desert environments. Ardea 90, 311–323 (2002).
Google Scholar 
71.Nicolaï, M. P. J., Shawkey, M. D., Porchetta, S., Claus, R. & D’Alba, L. Exposure to UV radiance predicts repeated evolution of concealed black skin in birds. Nat. Commun. 11, (2020).72.Mitchell, D. et al. Revisiting concepts of thermal physiology: Predicting responses of mammals to climate change. J. Anim. Ecol. 87, 956–973 (2018).PubMed 
Article 
PubMed Central 

Google Scholar 
73.Walsberg, G. E., Campbell, G. S. & King, J. R. Animal coat color and radiative heat gain: A re-evaluation. J. Comp. Physiol. B 126, 211–222 (1978).Article 

Google Scholar 
74.McFarland, D. J. & Baher, E. Factors affecting feather posture in the barbary dove. Anim. Behav. 16, 171–177 (1968).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
75.Hohtola, E., Rintamäki, H. & Hissa, R. Shivering and ptiloerection as complementary cold defense responses in the pigeon during sleep and wakefulness. J Comp Physiol. 136, 77–81. https://doi.org/10.1007/BF00688626 (1980).Article 

Google Scholar 
76.Kahl, P. M. Spread-wing postures and their possible functions in the Ciconiidae. Auk 88(4), 715–722. https://doi.org/10.2307/4083833 (1971).Article 

Google Scholar 
77.Dawson, T. J., Robertshaw, D. & Taylor, C. R. Sweating in the kangaroo: A cooling mechanism during exercise, but not in the heat. Am J Physiol. 227(2), 494–498. https://doi.org/10.1152/ajplegacy.1974.227.2.494 (1974).Article 
PubMed 
CAS 
PubMed Central 

Google Scholar 
78.Hoffman, T. C. M., Walsberg, G. E. & DeNardo, D. F. Cloacal evaporation: an important and previously undescribed mechanism for avian thermoregulation. J. Exp. Biol. 210, 741–749 (2007).PubMed 
Article 
PubMed Central 

Google Scholar 
79.Graves, G. R. Urohidrosis and tarsal color in Cathartes vultures (Aves: Cathartidae). Proc. Biol. Soc. Washingt. 132, 56–64 (2019).Article 

Google Scholar 
80.Torres, R. & Velando, A. Male preference for female foot colour in the socially monogamous blue-footed booby, Sula nebouxii.. Anim. Behav. 69, 59–65 (2005).Article 

Google Scholar 
81.López-Rull, I., Lifshitz, N., Macías Garcia, C., Graves, J. A. & Torres, R. Females of a polymorphic seabird dislike foreign-looking males. Anim. Behav. 113, 31–38 (2016).82.Gutiérrez, J. S. & Soriano-Redondo, A. Laterality in foraging phalaropes promotes phenotypically assorted groups. Behav. Ecol. 31, 1429–1435 (2021).Article 

Google Scholar 
83.Jarić, I. et al. iEcology: Harnessing large online resources to generate ecological insights. Trends Ecol. Evol. 35, 630–639 (2020).PubMed 
Article 
PubMed Central 

Google Scholar 
84.Vrettos, M., Reynolds, C. & Amar, A. Malar stripe size and prominence in peregrine falcons vary positively with solar radiation: support for the solar glare hypothesis. Biol. Lett. 17, 20210116. https://doi.org/10.1098/rsbl.2021.0116 (2021).Article 
PubMed 
PubMed Central 

Google Scholar  More