Philippa Kaur

1.
Dreyer, D. et al. The Earth’s magnetic field and visual landmarks steer migratory flight behavior in the nocturnal Australian Bogong Moth. Curr. Biol. 28, 2160–2166 (2018).
CAS  Article  Google Scholar 
2.
Guerra, P. A., Gegear, R. J. & Reppert, S. M. A magnetic compass aids monarch butterfly migration. Nat. Commun. 5, 4164 (2014).
ADS  CAS  Article  Google Scholar 

3.
Mouritsen, H. Long-distance navigation and magnetoreception in migratory animals. Nature 558, 50–59 (2018).
ADS  CAS  Article  Google Scholar 

4.
Uebe, R. & Schuler, D. Magnetosome biogenesis in magnetotactic bacteria. Nat. Rev. Microbiol. 14, 621–637 (2016).
CAS  Article  Google Scholar 

5.
Hore, P. J. & Mouritsen, H. The radical-pair mechanism of magnetoreception. Annu. Rev. Biophys. 45, 299–344 (2016).
CAS  Article  Google Scholar 

6.
Ritz, T., Adem, S. & Schulten, K. A model for photoreceptor-based magnetoreception in birds. Biophys. J. 78, 707–718 (2000).
CAS  Article  Google Scholar 

7.
Schulten, K., Swenberg, C. E. & Weller, A. A biomagnetic sensory mechanism based on magnetic field modulated coherent electron spin motion. Z. Phys. Chem. 111, 1–5 (1978).
Article  Google Scholar 

8.
Rodgers, C. T. & Hore, P. J. Chemical magnetoreception in birds: the radical pair mechanism. Proc. Natl Acad. Sci. USA 106, 353–360 (2009).
ADS  CAS  Article  Google Scholar 

9.
Kerpal, C. et al. Chemical compass behaviour at microtesla magnetic fields strengthens the radical pair hypothesis of avian magnetoreception. Nat. Commun. 10, 3707 (2019).
ADS  Article  Google Scholar 

10.
Maeda, K. et al. Magnetically sensitive light-induced reactions in cryptochrome are consistent with its proposed role as a magnetoreceptor. Proc. Natl Acad. Sci. USA 109, 4774–4779 (2012).
ADS  CAS  Article  Google Scholar 

11.
Emery, P. et al. Drosophila CRY is a deep brain circadian photoreceptor. Neuron 26, 493–504 (2000).
CAS  Article  Google Scholar 

12.
Zhu, H. et al. The two CRYs of the butterfly. Curr. Biol. 15, R953–R954 (2005).
CAS  Article  Google Scholar 

13.
Zoltowski, B. D. et al. Chemical and structural analysis of a photoactive vertebrate cryptochrome from pigeon. Proc. Natl Acad. Sci. USA 116, 19449–19457 (2019).
CAS  Article  Google Scholar 

14.
Merlin, C., Beaver, L. E., Taylor, O. R., Wolfe, S. A. & Reppert, S. M. Efficient targeted mutagenesis in the monarch butterfly using zinc-finger nucleases. Genome Res. 23, 159–168 (2013).
CAS  Article  Google Scholar 

15.
Michael, A. K., Fribourgh, J. L., Van Gelder, R. N. & Partch, C. L. Animal cryptochromes: divergent roles in light perception, circadian timekeeping and beyond. Photochem. Photobiol. 93, 128–140 (2017).
CAS  Article  Google Scholar 

16.
Yuan, Q., Metterville, D., Briscoe, A. D. & Reppert, S. M. Insect cryptochromes: gene duplication and loss define diverse ways to construct insect circadian clocks. Mol. Biol. Evol. 24, 948–955 (2007).
CAS  Article  Google Scholar 

17.
Zhang, Y., Markert, M. J., Groves, S. C., Hardin, P. E. & Merlin, C. Vertebrate-like CRYPTOCHROME 2 from monarch regulates circadian transcription via independent repression of CLOCK and BMAL1 activity. Proc. Natl Acad. Sci. USA 114, E7516–E7525 (2017).
CAS  Article  Google Scholar 

18.
Fedele, G. et al. Genetic analysis of circadian responses to low frequency electromagnetic fields in Drosophila melanogaster. PLoS Genet. 10, e1004804 (2014).
Article  Google Scholar 

19.
Fedele, G., Green, E. W., Rosato, E. & Kyriacou, C. P. An electromagnetic field disrupts negative geotaxis in Drosophila via a CRY-dependent pathway. Nat. Commun. 5, 4391 (2014).
ADS  CAS  Article  Google Scholar 

20.
Gegear, R. J., Casselman, A., Waddell, S. & Reppert, S. M. Cryptochrome mediates light-dependent magnetosensitivity in Drosophila. Nature 454, 1014–1018 (2008).
ADS  CAS  Article  Google Scholar 

21.
Gegear, R. J., Foley, L. E., Casselman, A. & Reppert, S. M. Animal cryptochromes mediate magnetoreception by an unconventional photochemical mechanism. Nature 463, 804–807 (2010).
ADS  CAS  Article  Google Scholar 

22.
Foley, L. E., Gegear, R. J. & Reppert, S. M. Human cryptochrome exhibits light-dependent magnetosensitivity. Nat. Commun. 2, 356 (2011).
ADS  Article  Google Scholar 

23.
Kutta, R. J., Archipowa, N., Johannissen, L. O., Jones, A. R. & Scrutton, N. S. Vertebrate cryptochromes are vestigial flavoproteins. Sci. Rep. 7, 44906 (2017).
ADS  CAS  Article  Google Scholar 

24.
Zhu, H., Gegear, R. J., Casselman, A., Kanginakudru, S. & Reppert, S. M. Defining behavioral and molecular differences between summer and migratory monarch butterflies. BMC Biol. 7, 14 (2009).
Article  Google Scholar 

25.
Lin, C., Top, D., Manahan, C. C., Young, M. W. & Crane, B. R. Circadian clock activity of cryptochrome relies on tryptophan-mediated photoreduction. Proc. Natl Acad. Sci. USA 115, 3822–3827 (2018).
CAS  Article  Google Scholar 

26.
Nohr, D. et al. Extended electron-transfer in animal cryptochromes mediated by a tetrad of aromatic amino acids. Biophys. J. 111, 301–311 (2016).
ADS  CAS  Article  Google Scholar 

27.
Nohr, D. et al. Determination of radical-radical distances in light-active proteins and their implication for biological magnetoreception. Angew. Chem. Int. Ed. Engl. 56, 8550–8554 (2017).
CAS  Article  Google Scholar 

28.
Palomares, L. A., Joosten, C. E., Hughes, P. R., Granados, R. R. & Shuler, M. L. Novel insect cell line capable of complex N-glycosylation and sialylation of recombinant proteins. Biotechnol. Prog. 19, 185–192 (2003).
CAS  Article  Google Scholar 

29.
Bazalova, O. et al. Cryptochrome 2 mediates directional magnetoreception in cockroaches. Proc. Natl Acad. Sci. USA 113, 1660–1665 (2016).
ADS  CAS  Article  Google Scholar 

30.
Merlin, C., Gegear, R. J. & Reppert, S. M. Antennal circadian clocks coordinate sun compass orientation in migratory monarch butterflies. Science 325, 1700–1704 (2009).
ADS  CAS  Article  Google Scholar 

31.
Yoshii, T., Ahmad, M. & Helfrich-Forster, C. Cryptochrome mediates light-dependent magnetosensitivity of Drosophila’s circadian clock. PLoS Biol. 7, e1000086 (2009).
Article  Google Scholar 

32.
Worster, S., Mouritsen, H. & Hore, P. J. A light-dependent magnetoreception mechanism insensitive to light intensity and polarization. J. R. Soc. Interface 14, (2017).

33.
Oztürk, N., Song, S.-H., Selby, C. P. & Sancar, A. Animal type 1 cryptochromes. Analysis of the redox state of the flavin cofactor by site-directed mutagenesis. J. Biol. Chem. 283, 3256–3263 (2008).
Article  Google Scholar 

34.
Wu, H., Scholten, A., Einwich, A., Mouritsen, H. & Koch, K.-W. Protein-protein interaction of the putative magnetoreceptor cryptochrome 4 expressed in the avian retina. Sci. Rep. 10, 7364 (2020).
ADS  CAS  Article  Google Scholar 

35.
Wan, G.-J. et al. Reduced geomagnetic field may affect positive phototaxis and flight capacity of a migratory rice planthopper. Anim. Behav. 121, 107–116 (2016).
Article  Google Scholar 

36.
Hwang, W. Y. et al. Efficient genome editing in zebrafish using a CRISPR-Cas system. Nat. Biotechnol. 31, 227–229 (2013).
CAS  Article  Google Scholar 

37.
Iiams, S. E., Lugena, A. B., Zhang, Y., Hayden, A. N. & Merlin, C. Photoperiodic and clock regulation of the vitamin A pathway in the brain mediates seasonal responsiveness in the monarch butterfly. Proc. Natl Acad. Sci. USA 116, 25214–25221 (2019).
CAS  Article  Google Scholar 

38.
Markert, M. J. et al. Genomic access to monarch migration using TALEN and CRISPR/Cas9-mediated targeted mutagenesis. G3 (Bethesda) 6, 905–915 (2016).
CAS  Article  Google Scholar 

39.
Jao, L. E., Wente, S. R. & Chen, W. Efficient multiplex biallelic zebrafish genome editing using a CRISPR nuclease system. Proc. Natl Acad. Sci. USA 110, 13904–13909 (2013).
ADS  CAS  Article  Google Scholar 

40.
Kim, J. M., Kim, D., Kim, S. & Kim, J. S. Genotyping with CRISPR-Cas-derived RNA-guided endonucleases. Nat. Commun. 5, 3157 (2014).
ADS  Article  Google Scholar 

41.
Zhu, H. et al. Cryptochromes define a novel circadian clock mechanism in monarch butterflies that may underlie sun compass navigation. PLoS Biol. 6, e4 (2008).
Article  Google Scholar  More

Abellán P, Svenning JC (2014) Refugia within refugia–patterns in endemism and genetic divergence are linked to Late Quaternary climate stability in the Iberian Peninsula. Biol J Linn Soc 113:13–28
Article  Google Scholar 

Alarcón-Ríos L, Nicieza AG, Kaliontzopoulou A, Buckley D, Velo-Antón G (2020) Evolutionary history and not heterochronic modifications associated with viviparity drive head shape differentiation in a reproductive polymorphic species, Salamandra salamandra. Evol Biol 47:43–55
Article  Google Scholar 

Alcobendas M, Castanet J (2000) Bone growth plasticity among populations of Salamandra salamandra: interactions between internal and external factors. Herpetologica 56:14–26
Google Scholar 

Alcobendas M, Buckley D, Tejedo M (2004) Variability in survival, growth and metamorphosis in the larval fire salamander (Salamandra salamandra): effects of larval birth size, sibship and environment. Herpetologica 60:232–245
Article  Google Scholar 

Antunes B, Lourenço A, Caeiro-Dias G, Dinis M, Gonçalves H, Martínez-Solano I et al. (2018) Combining phylogeography and landscape genetics to infer the evolutionary history of a short-range Mediterranean relict, Salamandra salamandra longirostris. Conserv Genet 19:1411–1424
CAS  Article  Google Scholar 

Arntzen JW, van Belkom J (2020) ‘Mainland-island’ population structure of a terrestrial salamander in a forest-bocage landscape with little evidence for in situ ecological speciation. Sci Rep 10:1–15
Article  CAS  Google Scholar 

Balkenhol N, Cushman SA, Waits LP, Storfer A (2016) Current status, future opportunities, and remaining challenges in landscape genetics. In: Balkenhol N, Cushman SA, Storfer AT, Waits LP (eds) Landscape genetics: concepts, methods, applications, John Wiley and Sons Ltd, Chichester, pp 247–255.

Barton NH, Gale KS (1993) Genetic analysis of hybrid zones. Hybrid zones and the evolutionary process. In: Harrison RG (ed) Hybrid zones and the evolutionary process. Oxford University Press, Oxford, p 13–45
Google Scholar 

Benjamini Y, Hochberg Y (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Ser B Stat Methodol 57:289–300
Google Scholar 

Beukema W, Nicieza AG, Lourenço A, Velo‐Antón G (2016) Colour polymorphism in Salamandra salamandra (Amphibia: Urodela), revealed by a lack of genetic and environmental differentiation between distinct phenotypes. J Zool Syst Evol 54:127–136
Article  Google Scholar 

Bisconti R, Porretta D, Arduino P, Nascetti G, Canestrelli D (2018) Hybridization and extensive mitochondrial introgression among fire salamanders in peninsular Italy. Sci Rep 8:13187
PubMed  PubMed Central  Article  CAS  Google Scholar 

Bosch J, López-Bueis I (1994) Comparative study of the dorsal pattern in Salamandra salamandra bejarae (Wolterstorff, 1934) and S. s. almanzoris (Müller & Hellmich, 1935). Herpetol J 4:46–48
Google Scholar 

Burgon JD, Vieites DR, Jacobs A, Weidt SK, Gunter HM, Steinfartz S et al. (2020) Functional colour genes and signals of selection in colour polymorphic salamanders. Mol Ecol 29:1284–1299
CAS  PubMed  Article  Google Scholar 

Burgon JD, Vences M, Steinfartz S, Bogaerts S, Bonato L, Donaire-Barroso D, Martínez-Solano I, Velo-Antón G, Vieites DR, Mable BK, Elmer KR (2021) Phylogenomic inference of species and subspecies diversity in the Pal earctic salamander genus Salamandra. Molecular Phylogenetics and Evolution 157:107063

Chybicki IJ, Burczyk J (2009) Simultaneous estimation of null alleles and inbreeding coefficients. J Hered 100:106–113
CAS  PubMed  Article  Google Scholar 

Clarke RT, Rothery P, Raybould AF (2002) Confidence limits for regression relationships between distance matrices: estimating gene flow with distance. JABES 7:361
Google Scholar 

Cushman SA (2006) Effects of habitat loss and fragmentation on amphibians: a review and prospectus. Biol Conserv 128:231–240
Article  Google Scholar 

Czypionka T, Goedbloed DJ, Steinfartz S, Nolte AW (2018) Plasticity and evolutionary divergence in gene expression associated with alternative habitat use in larvae of the European Fire Salamander. Mol Ecol 27:2698–2713
PubMed  Article  Google Scholar 

Dinis M, Joger U, Slimani T, Martínez-Freiría F, Merabet K, Donaire D et al. (2018) Allopatric diversification and evolutionary melting pot in a North African Palearctic relict: the biogeographic history of Salamandra algira. Mol Phylogenet Evol 130:81–91
PubMed  Article  Google Scholar 

Domínguez-Villar D, Carrasco RM, Pedraza J, Cheng H, Edwards R, Willenbring JK (2013) Early maximum extent of paleoglaciers from Mediterranean mountains during the last glaciation. Sci Rep. 3:2034
PubMed  PubMed Central  Article  Google Scholar 

Dufresnes C, Pribille M, Alard B, Dubey S, Perrin N, Gonçalves H et al. (2020) Integrating hybrid zone analyses in species delimitation: lessons from two anuran radiations of the Western Mediterranean. Heredity 124:423–438
CAS  PubMed  Article  Google Scholar 

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

Emel SL, Olson DH, Knowles LL, Storfer A (2019) Comparative landscape genetics of two endemic torrent salamander species, Rhyacotriton kezeri and R. variegatus: implications for forest management and species conservation. Conserv Genet 20:801–815
CAS  Article  Google Scholar 

Epps CW, Keyghobadi N (2015) Landscape genetics in a changing world: disentangling historical and contemporary influences and inferring change. Mol Ecol 24:6021–6040
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 14:2611–2620
CAS  PubMed  PubMed Central  Article  Google Scholar 

Ficetola GF, Colleoni E, Renaud J, Scali S, Padoa‐Schioppa E, Thuiller W (2016) Morphological variation in salamanders and their potential response to climate change. Glob Chang Biol 22:2013–2024
PubMed  PubMed Central  Article  Google Scholar 

Fletcher R, Fortin M (2018) Spatial ecology and conservation modeling. Springer International Publishing, Cham
Google Scholar 

Fourcade Y, Engler JO, Rödder D, Secondi J (2014) Mapping species distributions with MAXENT using a geographically biased sample of presence data: a performance assessment of methods for correcting sampling bias. PLoS ONE 9:e97122
PubMed  PubMed Central  Article  CAS  Google Scholar 

Francis RM (2016) pophelper: An r package and web app to analyse and visualize population structure. Mol Ecol Resour 17:27–32
PubMed  Article  CAS  Google Scholar 

García-París M, Alcobendas M, Alberch P (1998) Influence of the Guadalquivir river basin on mitochondrial DNA evolution of Salamandra salamandra (Caudata: Salamandridae) from southern Spain. Copeia 1998:173–176
Article  Google Scholar 

García-París M, Alcobendas M, Buckley D, Wake DB (2003) Dispersal of viviparity across contact zones in iberian populations of fire salamanders (Salamandra) inferred from discordance of genetic and morphological traits. Evolution 57:129–143

Gomez A, Lunt DH (2007) Refugia within refugia: patterns of phylogeographic concordance in the Iberian Peninsula. In: Weiss S, Ferrand N (eds). Phylogeography of Southern European Refugia. Springer: Dordrecht. pp 155–188

Gray LN, Barley AJ, Poe S, Thomson RC, Nieto‐Montes de Oca A, Wang IJ (2019) Phylogeography of a widespread lizard complex reflects patterns of both geographic and ecological isolation. Mol Ecol 28:644–657
PubMed  Article  Google Scholar 

Gutiérrez-Rodríguez J, Barbosa AM, Martínez-Solano I (2017a) Present and past climatic effects on the current distribution and genetic diversity of the Iberian spadefoot toad (Pelobates cultripes): an integrative approach. J Biogeogr 44:245–258
Article  Google Scholar 

Gutiérrez-Rodríguez J, Barbosa AM, Martínez-Solano I (2017b) Integrative inference of population history in the Ibero-Maghrebian endemic Pleurodeles waltl (Salamandridae). Mol Phylogenet Evol 112:122–137
PubMed  Article  Google Scholar 

Hendrix R, Schmidt BR, Schaub M, Krause ET, Steinfartz S (2017) Differentiation of movement behaviour in an adaptively diverging salamander population. Mol Ecol 26:6400–6413
PubMed  Article  Google Scholar 

Hendrix R, Susanne Hauswaldt J, Veith M, Steinfartz S (2010) Strong correlation between cross-amplification success and genetic distance across all members of “True Salamanders” (Amphibia: Salamandridae) revealed by Salamandra salamandra-specific microsatellite loci. Mol Ecol Resour 10:1038–1047
CAS  PubMed  Article  Google Scholar 

Hendry AP (2017) Eco-evolutionary dynamics. Princeton University Press, Princeton
Google Scholar 

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

Hijmans RJ, Van Etten J (2016) raster: Geographic Data Analysis and Modeling. R package version 2.5-8. Available from: http://CRAN.R-project.org/package=raster

Jombart T, Devillard S, Balloux F (2010) Discriminant analysis of principal components: a new method for the analysis of genetically structured populations. BMC Genet 11:1–94
Article  Google Scholar 

Kalinowski ST (2005) HP-RARE 10: a computer program for performing rarefaction on measures of allelic richness. Mol Ecol Notes 5:187–189
CAS  Article  Google Scholar 

Keenan K, Mcginnity P, Cross TF, Crozier WW, Prodöhl PA (2013) DiveRsity: an R package for the estimation and exploration of population genetics parameters and their associated errors. Methods Ecol Evol 4:782–788
Article  Google Scholar 

Linnaeus C (1758) Systema Naturae per Regna Tria Naturae, Secundum Classes, Ordines, Genera, Species, cum Characteribus, Differentiis, Synonymis, Locis. 10th Edition. Volume 1. Stockholm, Sweden: L. Salvii

Lourenço A, Gonçalves J, Carvalho F, Wang IJ, Velo-Antón G (2019) Comparative landscape genetics reveals the evolution of viviparity reduces genetic connectivity in fire salamanders. Mol Ecol 28:4573–4591
PubMed  Article  CAS  Google Scholar 

Maia-Carvalho B, Vale CG, Sequeira F, Ferrand N, Martínez-Solano I, Gonçalves H (2018) The roles of allopatric fragmentation and niche divergence in intraspecific lineage diversification in the common midwife toad (Alytes obstetricans). J Biogeogr 45:2146–2158
Article  Google Scholar 

Martínez-Freiría F, Freitas I, Zuffi MAL, Golay P, Ursenbacher S, Velo-Antón G (2020) Climatic refugia boosted allopatric diversification in Western Mediterranean vipers. J Biogeogr 47:1698–1713
Article  Google Scholar 

Martínez-Solano I (2006) Atlas de distribución y estado de conservación de los anfibios de la Comunidad de Madrid. Graellsia 62:253–291
Article  Google Scholar 

Martínez-Solano I, Alcobendas M, Buckley D, García-París M (2005) Molecular characterisation of the endangered Salamandra salamandra almanzoris (Caudata, Salamandridae). Ann Zool Fenn 42:57–68
Google Scholar 

McRae BH (2006) Isolation by resistance. Evolution 60:1551–1561
PubMed  PubMed Central  Article  Google Scholar 

McRae BH, Dickson BG, Keitt TH, Shah VB (2008) Concepts and synthesis emphasizing new ideas to stimulate research in ecology using circuit theory to model connectivity in ecology, evolution, and conservation. Ecology 89:2712–2724
PubMed  Article  Google Scholar 

Méndez L, Perdices A, Machordom A (2019) Genetic structure and diversity of the Iberian populations of the freshwater blenny Salaria fluviatilis (Asso, 1801) and its conservation implications. Conserv Genet 20:1223–1236
Article  Google Scholar 

Miraldo A, Hewitt GM, Paulo OS, Emerson BC (2011) Phylogeography and demographic history of Lacerta lepida in the Iberian Peninsula: multiple refugia, range expansions and secondary contact zones. BMC Evol Biol 11:170
PubMed  PubMed Central  Article  Google Scholar 

Mulder KP, Rodriguez NC, Grant EHC, Brand A, Fleischer RC (2019) North ‐ facing slopes and elevation shape asymmetric genetic structure in the range ‐ restricted salamander Plethodon shenandoah. Ecol Evol 9:5094–5105
PubMed  PubMed Central  Article  Google Scholar 

Noguerales V, Cordero PJ, Ortego J (2017) Testing the role of ancient and contemporary landscapes on structuring genetic variation in a specialist grasshopper. Ecol Evol 7:3110–3122
PubMed  PubMed Central  Article  Google Scholar 

Nosil P (2012) Ecological speciation. Oxford University Press, New York
Google Scholar 

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

Pereira RJ, Martínez-Solano I, Buckley D (2016) Hybridization during altitudinal range shifts: nuclear introgression leads to extensive cyto-nuclear discordance in the fire salamander. Mol Ecol 25:1551–1565
CAS  PubMed  Article  Google Scholar 

Peterman WE (2018) ResistanceGA: an R package for the optimization of resistance surfaces using genetic algorithms. Methods Ecol Evol 9:1638–1647
Article  Google Scholar 

Phillips SB, Aneja VP, Kang D, Arya SP (2006) Modelling and analysis of the atmospheric nitrogen deposition in North Carolina. IJGEI 6:231–252
Google Scholar 

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

Prunier JG, Colyn M, Legendre X, Nimon KF, Flamand MC (2015) Multicollinearity in spatial genetics: Separating the wheat from the chaff using commonality analyses. Mol Ecol 24:263–283
CAS  Article  Google Scholar 

R Core Team (2018) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria, https://wwwR-project.org/
Google Scholar 

Rousset F (2008) GENEPOP’007: a complete re-implementation of the GENEPOP software for Windows and Linux. Mol Ecol Resour 8:103–106
PubMed  PubMed Central  Article  Google Scholar 

Sánchez‐Montes G, Wang J, Ariño AH, Martínez‐Solano I (2018) Mountains as barriers to gene flow in amphibians: quantifying the differential effect of a major mountain ridge on the genetic structure of four sympatric species with different life history traits. J Biogeogr 45:318–331
Article  Google Scholar 

Sánchez-Montes G, Recuero E, Barbosa AM, Martínez-Solano I (2019) Complementing the Pleistocene biogeography of European amphibians: testimony from a southern Atlantic species. J Biogeogr 46:568–583
Article  Google Scholar 

Sexton JP, Hangartner SB, Hoffmann AA (2014) Genetic isolation by environment or distance: which pattern of gene flow is most common? Evolution 68:1–15
CAS  Article  Google Scholar 

Silva P, López-Bao JV, Llaneza L, Álvares F, Lopes S, Blanco JC et al. (2018) Cryptic population structure reveals low dispersal in Iberian wolves. Sci Rep 8:14108
PubMed  PubMed Central  Article  CAS  Google Scholar 

Steinfartz S, Küsters D, Tautz D (2004) Isolation and characterization of polymorphic tetranucleotide microsatellite loci in the fire salamander Salamandra salamandra (Amphibia: Caudata). Mol Ecol Notes 4:626–628
CAS  Article  Google Scholar 

Velo-Antón G, García-París M, Galán P, Cordero Rivera A (2007) The evolution of viviparity in Holocene islands: ecological adaptation versus phylogenetic descent along the transition from aquatic to terrestrial environments. J Zool Syst Evol 45:345–352
Article  Google Scholar 

Velo‐Antón G, Parra JL, Parra‐Olea G, Zamudio KR (2013) Tracking climate change in a dispersal‐limited species: reduced spatial and genetic connectivity in a montane salamander. Mol Ecol 22:3261–3278
PubMed  Article  Google Scholar 

Velo-Antón G, Buckley D (2015) Salamandra común—Salamandra salamandra. In: Salvador A, Martínez-Solano I (eds), Enciclopedia Virtual de los Vertebrados Españoles, Museo Nacional de Ciencias Naturales, CSIC www.vertebradosibericos.org

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

Wang IJ, Bradburd GS (2014) Isolation by environment. Mol Ecol 23:5649–5662
PubMed  PubMed Central  Article  Google Scholar 

Wang IJ, Glor RE, Losos JB (2013) Quantifying the roles of ecology and geography in spatial genetic divergence. Ecol Lett 16:175–182
PubMed  Article  Google Scholar 

Weir BS, Cockerham CC (1984) Estimating F-statistics for the analysis of population structure. Evolution 38:1358–1370
CAS  Google Scholar 

Winiarski KJ, Peterman WE, Whiteley AR, McGarigal K (2020) Multiscale resistant kernel surfaces derived from inferred gene flow: an application with vernal pool breeding salamanders. Mol Ecol Resour 20:97–113
CAS  PubMed  Article  Google Scholar 

Wogan GOU, Yuan ML, Mahler DL, Wang IJ (2020) Genome-wide epigenetic isolation by environment in a widespread Anolis lizard. Mol Ecol 29:40–55
CAS  PubMed  Article  Google Scholar 

Wright S (1943) Isolation by distance. Genetics 28:114–138
CAS  PubMed  PubMed Central  Google Scholar  More

Adams DC, Church JO (2008) Amphibians do not follow Bergmann’s rule. Evol: Int J Org Evol 62(2):413–420
Article  Google Scholar 

Alho J, Herczeg G, Laugen A, Räsänen K, Laurila A, Merilä J (2011) Allen’s rule revisited: quantitative genetics of extremity length in the common frog along a latitudinal gradient. J Evol Biol 24(1):59–70
CAS  PubMed  Article  Google Scholar 

Allen JA (1877) The influence of physical conditions in the genesis of species. Radic Rev 1:108–140
Google Scholar 

Amado TF, Bidau CJ, Olalla-Tárraga MÁ (2019) Geographic variation of body size in New World anurans: energy and water in a balance. Ecography 42(3):456–466
Article  Google Scholar 

Ashton KG (2002) Do amphibians follow Bergmann’s rule? Can J Zool 80(4):708–716
Article  Google Scholar 

Bates D, Maechler M, Bolker B, Walker S (2015) Fitting linear mixed-effects models using lme4. J Stat Softw 67(1):1–48. https://doi.org/10.18637/jss.v067.i01
Article  Google Scholar 

Belden LK, Rubbo MJ, Wingfield JC, Kiesecker JM (2007) Searching for the physiological mechanism of density dependence: does corticosterone regulate tadpole responses to density? Physiol Biochem Zool 80(4):444–451
CAS  PubMed  Article  Google Scholar 

Berven KA (1982a) The genetic basis of altitudinal variation in the wood frog Rana sylvatica II. An experimental analysis of larval development. Oecologia 52(3):360–369
PubMed  Article  Google Scholar 

Berven KA (1982b) The genetic basis of altitudinal variation in the wood frog Rana sylvatica. I. An experimental analysis of life history traits. Evolution 36(5):962–983
PubMed  Google Scholar 

Berven KA (1990) Factors affecting population fluctuations in larval and adult stages of the wood frog (Rana sylvatica). Ecology 71(4):1599–1608
Article  Google Scholar 

Berven KA (2009) Density dependence in the terrestrial stage of wood frogs: evidence from a 21-year population study. Copeia 2009(2):328–338
Article  Google Scholar 

Berven KA, Gill DE (1983) Interpreting geographic-variation in life-history traits. Am Zool 23(1):85–97
Article  Google Scholar 

Bijlsma R, Loeschcke V (2012) Genetic erosion impedes adaptive responses to stressful environments. Evol Appl 5(2):117–129
CAS  PubMed  Article  PubMed Central  Google Scholar 

Bjornstad ON (2020) ncf: spatial covariance functions. R package version 1.2-9. https://cran.r-project.org/package=ncf

Castellano S, Balletto E (2002) Is the partial Mantel test inadequate? Evolution 56(9):1871–1873
PubMed  Article  PubMed Central  Google Scholar 

Chaparro-Pedraza PC, de Roos AM (2020) Density-dependent effects of mortality on the optimal body size to shift habitat: Why smaller is better despite increased mortality risk. Evolution 74(5):831–841
PubMed  PubMed Central  Article  Google Scholar 

Conover DO, Schultz ET (1995) Phenotypic similarity and the evolutionary significance of countergradient variation. Trends Ecol Evol 10(6):248–252
CAS  PubMed  Article  PubMed Central  Google Scholar 

Cordero GA, Epps CW (2012) From desert to rainforest: phenotypic variation in functionally important traits of bushy-tailed woodrats (Neotoma cinerea) across two climatic extremes. J Mamm Evol 19(2):135–153
Article  Google Scholar 

Costanzo JP, do Amaral MCF, Rosendale AJ, Lee RE (2013) Hibernation physiology, freezing adaptation and extreme freeze tolerance in a northern population of the wood frog. J Exp Biol 216(18):3461–3473
CAS  PubMed  Article  PubMed Central  Google Scholar 

Crespi EJ, Warne RW (2013) Environmental conditions experienced during the tadpole stage alter post-metamorphic glucocorticoid response to stress in an amphibian. Integr Comp Biol 53(6):989–1001
CAS  PubMed  Article  PubMed Central  Google Scholar 

Dahl E, Orizaola G, Nicieza AG, Laurila A (2012) Time constraints and flexibility of growth strategies: geographic variation in catch‐up growth responses in amphibian larvae. J Anim Ecol 81(6):1233–1243
PubMed  Article  PubMed Central  Google Scholar 

Davenport JM, Hossack BR (2016) Reevaluating geographic variation in life‐history traits of a widespread Nearctic amphibian. J Zool 299(4):304–310
Article  Google Scholar 

Denver RJ (1997) Environmental stress as a developmental cue: corticotropin-releasing hormone is a proximate mediator of adaptive phenotypic plasticity in amphibian metamorphosis. Horm Behav 31(2):169–179
CAS  PubMed  Article  PubMed Central  Google Scholar 

DeWitt TJ, Scheiner SM (2004) Phenotypic plasticity: functional and conceptual approaches. Oxford University Press, New York, NY USA

Dorcas ME, Gibbons JW (2008) Frogs and Toads of the Southeast. University of Georgia Press, Athens, GA, USA

Dormann CF, Elith J, Bacher S, Buchmann C, Carl G, Carré G et al. (2013) Collinearity: a review of methods to deal with it and a simulation study evaluating their performance. Ecography 36(1):27–46
Article  Google Scholar 

Duncan SI, Crespi EJ, Mattheus NM, Rissler LJ (2015) History matters more when explaining genetic diversity within the context of the core–periphery hypothesis. Mol Ecol 24(16):4323–4336
PubMed  Article  PubMed Central  Google Scholar 

Elith J, Phillips SJ, Hastie T, Dudík M, Chee YE, Yates CJ (2011) A statistical explanation of MaxEnt for ecologists. Diversity Distrib 17(1):43–57
Article  Google Scholar 

Fitzpatrick MJ, Zuckerberg B, Pauli JN, Kearney MR, Thompson KL, Werner LC et al. (2019) Modeling the distribution of niche space and risk for a freeze‐tolerant ectotherm, Lithobates sylvaticus. Ecosphere 10(7):e02788
Article  Google Scholar 

Fox J, Weisberg S (2019) An R Companion to Applied Regression, 3rd Edition. Sage, Thousand Oaks, CA

GBIF.org (2014) GBIF Occurrence Download. https://doi.org/10.15468/dl.e3k4ag

Gouveia SF, Correia I (2016) Geographical clines of body size in terrestrial amphibians: water conservation hypothesis revisited. J Biogeogr 43(10):2075–2084
Article  Google Scholar 

Hahn DA, Martin AR, Porter SD (2008) Body size, but not cooling rate, affects supercooling points in the red imported fire ant, Solenopsis invicta. Environ Entomol 37(5):1074–1080
PubMed  Article  Google Scholar 

Hangartner S, Laurila A, Rasanen K (2012) Adaptive divergence in moor frog (Rana arvalis) populations along an acidification gradient: inferences from Q(st) -F(st) correlations. Evolution 66(3):867–881
PubMed  PubMed Central  Article  Google Scholar 

Hijmans R, Cameron S, Parra J, Jones P, Jarvis A, Richardson K (2005) WorldClim version 1.3. University of California, Berkeley
Google Scholar 

Holderegger R, Kamm U, Gugerli F (2006) Adaptive vs. neutral genetic diversity: implications for landscape genetics. Landsc Ecol 21(6):797–807
Article  Google Scholar 

Kawakami T, Morgan TJ, Nippert JB, Ocheltree TW, Keith R, Dhakal P et al. (2011) Natural selection drives clinal life history patterns in the perennial sunflower species, Helianthus maximiliani. Mol Ecol 20(11):2318–2328
PubMed  Article  Google Scholar 

Kierepka E, Latch E (2015) Performance of partial statistics in individual‐based landscape genetics. Mol Ecol Resour 15(3):512–525
CAS  PubMed  Article  Google Scholar 

Kingsolver JG, Diamond SE (2011) Phenotypic selection in natural populations: what limits directional selection? Am Naturalist 177(3):346–357
Article  Google Scholar 

Kingsolver JG, Pfennig DW (2004) Individual-level selection as a cause of cope’s rule of phyletic size increase. Evolution 58(7):1608–1612
PubMed  Article  Google Scholar 

Laugen AT, Laurila A, Jönsson KI, Söderman F, Merilä J (2005) Do common frogs (Rana temporaria) follow Bergmann’s rule? Evol Ecol Res 7(5):717–731
Google Scholar 

Laugen AT, Laurila A, Räsänen K, Merilä J (2003) Latitudinal countergradient variation in the common frog (Rana temporaria) development rates–evidence for local adaptation. J Evol Biol 16(5):996–1005
CAS  PubMed  Article  Google Scholar 

Laurila A, Karttunen S, Merila J (2002) Adaptive phenotypic plasticity and genetics of larval life histories in two Rana temporaria populations. Evolution 56(3):617–627
PubMed  Article  Google Scholar 

Lee-Yaw JA, Irwin JT, Green DM (2008) Postglacial range expansion from northern refugia by the wood frog, Rana sylvatica. Mol Ecol 17(3):867–884
CAS  PubMed  Article  Google Scholar 

Leinonen T, Cano JM, Mäkinen H, Merilä J (2006) Contrasting patterns of body shape and neutral genetic divergence in marine and lake populations of threespine sticklebacks. J Evol Biol 19(6):1803–1812
CAS  PubMed  Article  Google Scholar 

Leinonen T, McCairns RJ, O’Hara RB, Merila J (2013a) Q(ST)-F(ST) comparisons: evolutionary and ecological insights from genomic heterogeneity. Nat Rev Genet 14(3):179–190
CAS  PubMed  PubMed Central  Article  Google Scholar 

Leinonen T, McCairns RS, O’hara RB, Merilä J (2013b) Q ST–F ST comparisons: evolutionary and ecological insights from genomic heterogeneity. Nat Rev Genet 14(3):179
CAS  PubMed  PubMed Central  Article  Google Scholar 

Lenker MA, Savage AE, Becker CG, Rodriguez D, Zamudio KR (2014) Batrachochytrium dendrobatidis infection dynamics vary seasonally in upstate New York, USA. Dis Aquat Organ 111(1):51–60
PubMed  Article  Google Scholar 

Lind M, Johansson F (2011) Testing the role of phenotypic plasticity for local adaptation: growth and development in time‐constrained Rana temporaria populations. J Evol Biol 24(12):2696–2704
CAS  PubMed  Article  Google Scholar 

Lind MI, Ingvarsson PK, Johansson H, Hall D, Johansson F (2011) Gene flow and selection on phenotypic plasticity in an island system of Rana temporaria. Evolution 65(3):684–697
PubMed  Article  Google Scholar 

Lindgren B, Laurila A (2009) Physiological variation along a geographical gradient: is growth rate correlated with routine metabolic rate in Rana temporaria tadpoles? Biol J Linn Soc 98(1):217–224
Article  Google Scholar 

Lomolino MV, Heaney LR (2004) Frontiers of biogeography: new directions in the geography of nature. Sinauer Associates, Sunderland, MA, USA

Manis ML, Claussen DL (1986) Environmental and genetic influences on the thermal physiology of Rana sylvatica. J Therm Biol 11(1):31–36
Article  Google Scholar 

Mantel N (1967) The detection of disease clustering and a generalized regression approach. Cancer Res 27(2 Part 1):209–220
CAS  Google Scholar 

Martof BS, Humphries RL (1959) Geographic variation in the wood frog Rana sylvatica. Am Midl Naturalist 61(2):350–389
Article  Google Scholar 

Merilä J, Crnokrak P (2001) Comparison of genetic differentiation at marker loci and quantitative traits. J Evol Biol 14(6):892–903
Article  Google Scholar 

Merilä J, Laurila A, Laugen AT, Räsänen K, Pahkala M (2000) Plasticity in age and size at metamorphosis in Rana temporaria‐comparison of high and low latitude populations. Ecography 23(4):457–465
Article  Google Scholar 

Mitchell-Olds T, Willis JH, Goldstein DB (2007) Which evolutionary processes influence natural genetic variation for phenotypic traits? Nat Rev Genet 8(11):845–856
CAS  Article  Google Scholar 

Morrison C, Hero JM (2003) Geographic variation in life‐history characteristics of amphibians: a review. J Anim Ecol 72(2):270–279
Article  Google Scholar 

Mueller LD (1997) Theoretical and empirical examination of density-dependent selection. Annu Rev Ecol Syst 28(1):269–288
Article  Google Scholar 

Muir AP, Biek R, Thomas R, Mable BK (2014) Local adaptation with high gene flow: temperature parameters drive adaptation to altitude in the common frog (Rana temporaria). Mol Ecol 23(3):561–574
CAS  PubMed  PubMed Central  Article  Google Scholar 

Nosil P, Vines TH, Funk DJ (2005) Reproductive isolation caused by natural selection against immigrants from divergent habitats. Evolution 59(4):705–719
PubMed  PubMed Central  Google Scholar 

Oksanen J, Blanchet FG, Kindt R, Legendre P, Minchin PR, O’hara R et al. (2019) vegan: Community Ecology Package. R package version 2.5-6. https://CRAN.R-project.org/package=vegan

Olalla-Tárraga MÁ, Rodríguez MÁ (2007) Energy and interspecific body size patterns of amphibian faunas in Europe and North America: anurans follow Bergmann’s rule, urodeles its converse. Glob Ecol Biogeogr 16(5):606–617
Article  Google Scholar 

Orizaola G, Quintela M, Laurila A (2010) Climatic adaptation in an isolated and genetically impoverished amphibian population. Ecography 33(4):730–737
Article  Google Scholar 

Padgham M, Sumner MD (2020) geodist: fast, dependency-free geodesic distance calculations. R package version 0.0.6. https://CRAN.R-project.org/package=geodist

Palo JU, O’Hara RB, Laugen AT, Laurila A, Primmer CR, Merila J (2003) Latitudinal divergence of common frog (Rana temporaria) life history traits by natural selection: evidence from a comparison of molecular and quantitative genetic data. Mol Ecol 12(7):1963–1978
CAS  PubMed  Article  PubMed Central  Google Scholar 

Peters RH, Peters RH (1986) The ecological implications of body size, vol 2. Cambridge University Press, New York, NY, USA

Phillips SJ, Anderson RP, Schapire RE (2006) Maximum entropy modeling of species geographic distributions. Ecol Model 190(3-4):231–259
Article  Google Scholar 

Pigliucci M (2001) Phenotypic plasticity: beyond nature and nurture. Johns Hopkins University Press, Baltimore, MA, USA

Powell R, Conant R, Collins JT (2016) Peterson field guide to reptiles and amphibians of eastern and central North America. Houghton Mifflin Harcourt, New York, NY, USA

R Core Team (2020) R: a language and environment for statistical computing. R Foundation for Statistical Computing. Vienna, Austria. https://www.R-project.org/

Raufaste N, Rousset F (2001) Are partial Mantel tests adequate? Evolution 55(8):1703–1705
CAS  PubMed  Article  Google Scholar 

Rice KC, Jung RE (2004) Water-quality and amphibian population data for Maryland, Washington, DC, and Virginia, 2001–2004. US Geological Survey

Richter-Boix A, Quintela M, Kierczak M, Franch M, Laurila A (2013) Fine-grained adaptive divergence in an amphibian: genetic basis of phenotypic divergence and the role of nonrandom gene flow in restricting effective migration among wetlands. Mol Ecol 22(5):1322–1340
CAS  PubMed  PubMed Central  Article  Google Scholar 

Richter-Boix A, Teplitsky C, Rogell B, Laurila A (2010) Local selection modifies phenotypic divergence among Rana temporaria populations in the presence of gene flow. Mol Ecol 19(4):716–731
PubMed  Article  Google Scholar 

Richter‐Boix A, Katzenberger M, Duarte H, Quintela M, Tejedo M, Laurila A (2015) Local divergence of thermal reaction norms among amphibian populations is affected by pond temperature variation. Evolution 69(8):2210–2226
PubMed  Article  Google Scholar 

Rissler LJ (2016) Union of phylogeography and landscape genetics. Proc Natl Acad Sci USA 113(29):8079–8086
CAS  PubMed  Article  Google Scholar 

Roff D (1980) Optimizing development time in a seasonal environment: the ‘ups and downs’ of clinal variation. Oecologia 45(2):202–208
PubMed  Article  Google Scholar 

Santos M, Borash DJ, Joshi A, Bounlutay N, Mueller LD (1997) Density‐dependent natural selection in Drosophila: evolution of growth rate and body size. Evolution 51(2):420–432
PubMed  Google Scholar 

Schemske DW, Bierzychudek P (2007) Spatial differentiation for flower color in the desert annual Linanthus parryae: was Wright right? Evol: Int J Org Evol 61(11):2528–2543
Article  Google Scholar 

Schueler FW (1975) Geographic variation in the size of Rana septentrionalis in Quebec, Ontario, and Manitoba. J Herpetol 9(2):177–185
Article  Google Scholar 

Semlitsch RD, Scott DE, Pechmann JHK (1988) Time and size at metamorphosis related to adult fitness in Ambystoma talpoideum. Ecology 69(1):184–192
Article  Google Scholar 

Shafer AB, Wolf JB (2013) Widespread evidence for incipient ecological speciation: a meta‐analysis of isolation‐by‐ecology. Ecol Lett 16(7):940–950
PubMed  PubMed Central  Article  Google Scholar 

Sheridan JA, Caruso NM, Apodaca JJ, Rissler LJ (2018) Shifts in frog size and phenology: testing predictions of climate change on a widespread anuran using data from prior to rapid climate warming. Ecol Evol 8(2):1316–1327
PubMed  Article  PubMed Central  Google Scholar 

Smith-Gill SJ, Berven KA (1979) Predicting amphibian metamorphosis. Am Naturalist 113(4):563–585
Article  Google Scholar 

Spitze K (1993) Population structure in Daphnia obtusa: quantitative genetic and allozymic variation. Genetics 135(2):367–374
CAS  PubMed  PubMed Central  Article  Google Scholar 

Stevens CE, Paszkowski CA (2004) Using chorus-size ranks from call surveys to estimate reproductive activity of the wood frog (Rana sylvatica). J Herpetol 38(3):404–410
Article  Google Scholar 

Therneau TM (2020) coxme: Mixed effects cox models. R package version 2.2-16. https://CRAN.R-project.org/package=coxme

Thomassen HA, Cheviron ZA, Freedman AH, Harrigan RJ, Wayne RK, Smith TB (2010) Spatial modelling and landscape‐level approaches for visualizing intra‐specific variation. Mol Ecol 19(17):3532–3548
PubMed  Article  Google Scholar 

Van Buskirk J (2017) Spatially heterogeneous selection in nature favors phenotypic plasticity in anuran larvae. Evolution 71(6):1670–1685
PubMed  Article  PubMed Central  Google Scholar 

Venables W, Ripley B (2002) Modern Applied Statistics with S. 4th Edition. Springer, New York, NY, USA

Wang IJ, Bradburd GS (2014) Isolation by environment. Mol Ecol 23(23):5649–5662
Article  Google Scholar 

Wang IJ, Summers K (2010) Genetic structure is correlated with phenotypic divergence rather than geographic isolation in the highly polymorphic strawberry poison‐dart frog. Mol Ecol 19(3):447–458
PubMed  Article  PubMed Central  Google Scholar 

Warne RW, Crespi EJ (2015) Larval growth rate and sex determine resource allocation and stress responsiveness across life stages in juvenile frogs. J Exp Zool A Ecol Genet Physiol 323(3):191–201
CAS  PubMed  Article  PubMed Central  Google Scholar 

Weber MM, Stevens RD, Diniz‐Filho JAF, Grelle CEV (2017) Is there a correlation between abundance and environmental suitability derived from ecological niche modelling? A meta‐analysis. Ecography 40(7):817–828
Article  Google Scholar 

Weir L (2001) NAAMP unified protocol: call surveys. North American Amphibian Monitoring Program. Patuxtent Wildlife Research Center, Patuxtent, MA, USA

Whitlock MC (2008) Evolutionary inference from QST. Mol Ecol 17(8):1885–1896
PubMed  Article  PubMed Central  Google Scholar 

Whitlock MC, Guillaume F (2009) Testing for spatially divergent selection: comparing QST to FST. Genetics 183(3):1055–1063
PubMed  PubMed Central  Article  Google Scholar 

Whitlock MC, Phillips PC (2000) The exquisite corpse: a shifting view of the shifting balance. Trends Ecol Evol 15(9):347–348
CAS  PubMed  Article  PubMed Central  Google Scholar 

Wilbur HM (1976) Density‐dependent aspects of metamorphosis in Ambystoma and Rana sylvatica. Ecology 57(6):1289–1296
Article  Google Scholar 

Wilbur HM, Collins JP (1973) Ecological aspects of amphibian metamorphosis. Science 182(4119):1305–1314
CAS  PubMed  Article  PubMed Central  Google Scholar 

Wright S (1943) Isolation by distance. Genetics 28(2):114
CAS  PubMed  PubMed Central  Google Scholar  More

1.
Alerstam, T., Hedenström, A. & Åkesson, S. Long-distance migration: evolution and determinants. Oikos 103, 247–260 (2003).
Article  Google Scholar 
2.
Gilroy, J. J., Gill, J. A., Butchart, S. H. M., Jones, V. R. & Franco, A. M. A. Migratory diversity predicts population declines in birds. Ecol. Lett. 19, 308–317 (2016).
Article  Google Scholar 

3.
Gill, J. A., Alves, J. A. & Gunnarsson, T. G. Mechanisms driving phenological and range change in migratory species. Philos. Trans. R. Soc. B 374, 20180047 (2019).
Article  Google Scholar 

4.
Méndez, V., Gill, J. A., Alves, J. A., Burton, N. H. K. & Davies, R. G. Consequences of population change for local abundance and site occupancy of wintering waterbirds. Divers. Distrib. 24, 24–35 (2018).
Article  Google Scholar 

5.
Finch, T., Butler, S. J., Franco, A. M. A. & Cresswell, W. Low migratory connectivity is common in long-distance migrant birds. J. Anim. Ecol. 86, 662–673 (2017).
Article  Google Scholar 

6.
Phillips, R. A., Silk, J. R. D., Croxall, J. P., Afanasyev, V. & Bennett, V. J. Summer distribution and migration of nonbreeding albatrosses: Individual consistencies and implications for conservation. Ecology 86, 2386–2396 (2005).
Article  Google Scholar 

7.
Newton, I. The Migration Ecology of Birds (Academic Press, New York, 2008).
Google Scholar 

8.
Grist, H. et al. Site fidelity and individual variation in winter location in partially migratory European shags. PLoS ONE 9, e98562 (2014).
ADS  Article  Google Scholar 

9.
Alves, J. A. et al. Costs, benefits, and fitness consequences of different migratory strategies. Ecology 94, 11–17 (2013).
ADS  Article  Google Scholar 

10.
Evans, D. R. et al. Individual condition, but not fledging phenology, carries over to affect post-fledging survival in a neotropical migratory songbird. Ibis (Lond. 1859) 162, 331–344 (2020).
Article  Google Scholar 

11.
Gill, J. A. et al. Why is timing of bird migration advancing when individuals are not?. Proc. Biol. Sci. 281, 20132161 (2014).
PubMed  PubMed Central  Google Scholar 

12.
Meyburg, B.-U. et al. Orientation of native versus translocated juvenile lesser spotted eagles (Clanga pomarina) on the first autumn migration. J. Exp. Biol. 220, 2765–2776 (2017).
Article  Google Scholar 

13.
Gill, J. A. Does competition really drive population distributions?. Wader Study 126, 166–168 (2019).
Article  Google Scholar 

14.
Berthold, P. Control of Bird Migration (Chapman & Hall, Boca Raton, 1996).
Google Scholar 

15.
Sutherland, W. J. Evidence for flexibility and constraint in migration systems. J. Avian Biol. 29, 441 (1998).
Article  Google Scholar 

16.
Harrison, X. A. et al. Cultural inheritance drives site fidelity and migratory connectivity in a long-distance migrant. Mol. Ecol. 19, 5484–5496 (2010).
Article  Google Scholar 

17.
Piersma, T., Loonstra, A. H. J., Verhoeven, M. A. & Oudman, T. Rethinking classic starling displacement experiments: evidence for innate or for learned migratory directions?. J. Avian Biol. 51, jav.02337 (2020).
Article  Google Scholar 

18.
Þórisson, B. et al. Population size of oystercatchers Haematopus ostralegus wintering in Iceland. Bird Study 65, 274–278 (2018).
Article  Google Scholar 

19.
Méndez, V. et al. Individual variation in migratory behaviour in a sub-Arctic partial migrant shorebird. Behav. Ecol. https://doi.org/10.1093/beheco/araa010 (2020).
Article  Google Scholar 

20.
van de Pol, M. et al. A global assessment of the conservation status of the nominate subspecies of Eurasian oystercatcher Haematopus ostralegus ostralegus. Int. Wader Stud. 20, 47–61 (2014).
Google Scholar 

21.
Méndez, V. et al. Effects of migratory behaviour on breeding phenology and success in a sub-arctic partially migratory shorebird. J. Anim. Ecol. (under review).

22.
Ens, B. J., Safriel, U. N. & Harris, M. P. Divorce in the long-lived and monogamous oystercatcher, Haematopus ostralegus: incompatibility or choosing the better option?. Anim. Behav. 45, 1199–1217 (1993).
Article  Google Scholar 

23.
Ens, B. J., Choudhury, S. & Black, J. M. Mate fidelity and divorce in monogamous birds. In Partnerships in Birds: The Study of Monogamy (ed. Black, J. M.) 344–401 (Oxford University Press, Oxford, 1996).
Google Scholar 

24.
Winger, B. M., Auteri, G. G., Pegan, T. M. & Weeks, B. C. A long winter for the Red Queen: rethinking the evolution of seasonal migration. Biol. Rev. 94, 737–752 (2019).
Article  Google Scholar 

25.
Bulla, M. et al. Unexpected diversity in socially synchronized rhythms of shorebirds. Nature 540, 109–113 (2016).
ADS  CAS  Article  Google Scholar 

26.
Nol, E. Sex roles in the American oystercatcher. Behaviour 95, 232–260 (1985).
Article  Google Scholar 

27.
Reynolds, J. D. & Székely, T. The evolution of parental care in shorebirds: life histories, ecology, and sexual selection. Behav. Ecol. 8, 126–134 (1997).
Article  Google Scholar 

28.
Lazarus, J. The logic of mate desertion. Anim. Behav. 39, 672–684 (1990).
Article  Google Scholar 

29.
Safriel, U. N., Ens, B. J., Kaiser, A. & Goss-Custard, J. D. Rearing to independence. In The Oystercatcher: From Individuals to Populations (ed. Goss-Custard, J. D.) 219–250 (Oxford University Press, Oxford, 1996).
Google Scholar 

30.
Gunnarsson, T. G., Gill, J. A., Sigurbjörnsson, T. & Sutherland, W. J. Pair bonds: arrival synchrony in migratory birds. Nature 431, 646 (2004).
ADS  CAS  Article  Google Scholar 

31.
Gunnarsson, T. G., Gill, J. A., Newton, J., Potts, P. M. & Sutherland, W. J. Seasonal matching of habitat quality and fitness in a migratory bird. Proc. R. Soc. B 272, 2319–2323 (2005).
Article  Google Scholar 

32.
Gunnarsson, T. G. Monitoring wader productivity during autumn passage in Iceland. Wader Study Gr. Bull. 110, 21–29 (2006).
Google Scholar 

33.
Cramp, S. & Simmons, K. E. L. Birds of the Western Palearctic (Oxford University Press, Oxford, 1983).
Google Scholar 

34.
Alves, J. A., Gunnarsson, T. G., Sutherland, W. J., Potts, P. M. & Gill, J. A. Linking warming effects on phenology, demography, and range expansion in a migratory bird population. Ecol. Evol. 9, 2365–2375 (2019).
Article  Google Scholar 

35.
Liebezeit, J. R. et al. Assessing the development of shorebird eggs using the flotation method: species-specific and generalized regression models. Condor 109, 32–47 (2007).
Article  Google Scholar 

36.
Burnham, K. & Anderson, D. Model Selection and Multi-model Inference: A Practical Information-Theoretic Approach (Springer, Berlin, 2002).
Google Scholar 

37.
R Core Team. A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, Vienna, 2020).
Google Scholar  More

1.
Litchman, E. et al. Global biogeochemical impacts of phytoplankton: a trait-based perspective. J. Ecol. 103, 1384–1396 (2015).
CAS  Article  Google Scholar 
2.
De Senerpont Domis, L. N. et al. Plankton dynamics under different climatic conditions in space and time. Freshw. Biol. 58, 463–482 (2013).
Article  Google Scholar 

3.
Reynolds, C. Ecology of Phytoplankton. (Cambridge University Press, 2006).

4.
Phillips, G. et al. Water Framework Directive Intercalibration: Central Baltic Lake Phytoplankton Ecological Assessment Methods. 189 (Join Research Center, 2014).

5.
Ptacnik, R., Solimini, A. & Brettum, P. Performance of a new phytoplankton composition metric along a eutrophication gradient in Nordic lakes. Hydrobiologia 633, 75–82 (2009).
CAS  Article  Google Scholar 

6.
Pollard, A. I., Hampton, S. E. & Leech, D. M. The promise and potential of continental-scale limnology using the U.S. Environmental Protection Agency’s National Lakes. Assessment. Limnol. Oceanogr. Bull. 27, 36–41 (2018).
Article  Google Scholar 

7.
de Hoyos, C. et al. Water Framework Directive Intercalibration: Mediterranean Lake Phytoplankton Ecological Assessment Methods. 189 (Join Research Center, 2014).

8.
Mischke, U., Riedmüller, U., Hoehn, E., Schönfelder, I. & Nixdorf, B. Description of the German System for Phytoplankton-Based Assessment of Lakes for Implementation of the EU Water Framework Directive (WFD). 31 (Univ. Cottbus, 2008).

9.
Laplace-Treyture, C. & Feret, T. Performance of the Phytoplankton Index for Lakes (IPLAC): A multimetric phytoplankton index to assess the ecological status of water bodies in France. Ecol. Indic. 69, 686–698 (2016).
CAS  Article  Google Scholar 

10.
Xue, Y. et al. Distinct patterns and processes of abundant and rare eukaryotic plankton communities following a reservoir cyanobacterial bloom. ISME J. 12, 2263–2277 (2018).
CAS  Article  Google Scholar 

11.
Barbe, J. et al. Actualisation de la Méthode de Diagnose Rapide des Plans d’Eau: Analyse Critique des Indices de Qualité des Lacs et Propositions d’Indices de Fonctionnement de l’Écosystème Lacustre. 107 (Cemagref, 2003).

12.
Marchetto, A., Padedda, B., Mariani, M., Luglie, A. & Sechi, N. A numerical index for evaluating phytoplankton response to changes in nutrient levels in deep mediterranean reservoirs. J. Limnol. 68, 106–121 (2009).
Article  Google Scholar 

13.
Kruk, C., Mazzeo, N., Lacerot, G. & Reynolds, C. S. Classification schemes for phytoplankton: A local validation of a functional approach to the analysis of species temporal replacement. J. Plankton Res. 24, 901–912 (2002).
Article  Google Scholar 

14.
Reynolds, C. S. Phytoplankton designer – or how to predict compositional responses to trophic-state change. Hydrobiologia 424, 123–132 (2000).
Article  Google Scholar 

15.
Reynolds, C. S., Huszar, V., Kruk, C., Naselli-Flores, L. & Melo, S. Towards a functional classification of the freshwater phytoplankton. J. Plankton Res. 24, 417–428 (2002).
Article  Google Scholar 

16.
Mieleitner, J. & Reichert, P. Modelling functional groups of phytoplankton in three lakes of different trophic state. Ecol. Model. 211, 279–291 (2008).
Article  Google Scholar 

17.
Rangel, L. M., Soares, M. C. S., Paiva, R. & Silva, L. H. S. Morphology-based functional groups as effective indicators of phytoplankton dynamics in a tropical cyanobacteria-dominated transitional river–reservoir system. Ecol. Indic. 64, 217–227 (2016).
Article  Google Scholar 

18.
Salmaso, N., Naselli-Flores, L. & Padisák, J. Functional classifications and their application in phytoplankton ecology. Freshw. Biol. 60, 603–619 (2015).
Article  Google Scholar 

19.
Padisák, J., Borics, G., Grigorszky, I. & Soróczki-Pintér, É. Use of phytoplankton assemblages for monitoring ecological status of lakes within the water framework directive: The assemblage index. Hydrobiologia 553, 1–14 (2006).
Article  Google Scholar 

20.
Borics, G. et al. A new evaluation technique of potamo-plankton for the assessment of the ecological status of rivers. Large Rivers 17, 465–486 (2007).
Google Scholar 

21.
European Parliament. Directive 2000/60/CE du Parlement Européen et du Conseil du 23 Octobre 2000 Établissant un Cadre pour une Politique Communautaire dans le Domaine de l’Eau. 72 (Communauté Européenne, 2000).

22.
Padisák, J., Crossetti, L. O. & Naselli-Flores, L. Use and misuse in the application of the phytoplankton functional classification: A critical review with updates. Hydrobiologia 621, 1–19 (2009).
Article  Google Scholar 

23.
Kruk, C. et al. Classification of Reynolds phytoplankton functional groups using individual traits and machine learning techniques. Freshw. Biol. 62, 1681–1692 (2017).
CAS  Article  Google Scholar 

24.
Wentzky, V. C., Tittel, J., Jäger, C. G., Bruggeman, J. & Rinke, K. Seasonal succession of functional traits in phytoplankton communities and their interaction with trophic state. J. Ecol. 108, 1649–1663 (2020).
CAS  Article  Google Scholar 

25.
Olenina, I. et al. Biovolumes and Size-Classes of Phytoplankton in the Baltic Sea. 144 (Baltic Marine Environnment Protection Commission, 2006).

26.
Kremer, C. T., Gillette, J. P., Rudstam, L. G., Brettum, P. & Ptacnik, R. A compendium of cell and natural unit biovolumes for >1200 freshwater phytoplankton species. Ecology 95, 2984–2984 (2014).
Article  Google Scholar 

27.
Druart, J. C. & Rimet, F. Protocole d’Analyse du Phytoplancton de l’INRA: Prélèvement, Dénombrement et Biovolume. 96 (INRA, 2008).

28.
Rimet, F. & Druart, J.-C. A trait database for phytoplankton of temperate lakes. Ann. Limnol. – Int. J. Limnol. 54, 18 (2018).
Article  Google Scholar 

29.
John, D. M., Whitton, B. A. & Brook, A. J. The Freshwater Algal Flora of the British Isles: an Identification Guide to Freshwater and Terrestrial Algae. Second Edition. (Cambridge University Press, 2011).

30.
Wehr, J. D., Sheath, R. G. & Kociolek, P. Freshwater Algae of North America: Ecology and Classification. (Academic press, 2015).

31.
Laplace-Treyture, C., Hadoux, E., Plaire, M., Dubertrand, A. & Esmieu, P. PHYTOBS v3.0: Outil de Comptage du Phytoplancton en Laboratoire et de Calcul de l’IPLAC. Version 3.0. Application JAVA. https://hydrobio-dce.inrae.fr/phytobs-software/ (2017).

32.
Hillebrand, H., Dürselen, C. D., Kirschtel, D., Pollinger, U. & Zohary, T. Biovolume calculation for pelagic and benthic microalgae. J. Phycol. 35, 403–424 (1999).
Article  Google Scholar 

33.
Hutorowicz, A. Opracowanie Standardowych Objętości Komórek do Szacowania Biomasy w Wybranych Taksonów Glonów Planktonowych Wraz z Określeniem Sposobu Pomiarów i Szacowania. 42 (Instytutu Rybactwa Śródlądowego, 2005).

34.
Padisak, J. & Adrian, R. In Methoden der Biologischen Wasseruntersuchung 2. Biologische Gewässeruntersuchung (ed. Friedrich, W. und G.) Biovolumen und Biomasse (Gustav Fischer Verlag, 1999).

35.
NF EN 16695. Qualité de l’eau – Lignes Directrices pour l’Estimation du Biovolume des Microalgues. 106 (2015).

36.
Menden-Deuer, S. & Lessard, E. J. Carbon to volume relationships for dinoflagellates, diatoms, and other protist plankton. Limnol. Oceanogr. 45, 569–579 (2000).
ADS  CAS  Article  Google Scholar 

37.
Sieburth, J. M., Smetacek, V. & Lenz, J. Pelagic ecosystem structure: Heterotrophic compartments of the plankton and their relationship to plankton size fractions. Limnol. Oceanogr. 23, 1256–1263 (1978).
ADS  Article  Google Scholar 

38.
Ignatiades, L. Redefinition of cell size classification of phytoplankton – a potential tool for improving the quality and assurance of data interpretation. Mediterr. Mar. Sci. 17, 56 (2015).
Article  Google Scholar 

39.
Whitton, B. A. Ecology of Cyanobacteria II. Their Diversity in Space and Time. (Springer Verlag, 2012).

40.
Dittmann, E., Gugger, M., Sivonen, K. & Fewer, D. P. Natural product biosynthetic diversity and comparative genomics of the Cyanobacteria. Trends Microbiol. 23, 642–652 (2015).
CAS  Article  Google Scholar 

41.
Sanseverino, I., Conduto, D., Pozzoli, L., Dobricic, S. & Lettieri, T. Algal Bloom and its Economic Impact. 48 (Join Research Center, 2016).

42.
Sanseverino, I., Conduto Antonio, D., Loos, R. & Lettieri, T. Cyanotoxins: Methods and Approaches for their Analysis and Detection. 64 (Join Research Center, 2017).

43.
Meriluoto, J., Spoof, L. & Codd, G. A. Handbook of Cyanobacterial Monitoring and Cyanotoxin Analysis. (John Wiley & Sons, 2017).

44.
Lwoff, A., Van Niel, C. B., Ryan, P. J. & Tatum, E. L. Nomenclature of Nutritional Types of Microorganisms. In Cold Spring Harbor Symposia on Quantitative Biology. XI (5th ed.) 302–303 (1946).

45.
Morris, J. Biology: How Life Works. (W. H. Freeman/Macmillan Learning, 2018).

46.
Laplace-Treyture, C. et al. Phytoplankton morpho-functional trait dataset from French water-bodies. Portail Data INRAE https://doi.org/10.15454/GJGIAH (2020).

47.
Morabito, G., Oggioni, A., Caravati, E. & Panzani, P. Seasonal morphological plasticity of phytoplankton in Lago Maggiore (N. Italy). Hydrobiologia 578, 47–57 (2007).
Article  Google Scholar 

48.
Naselli-Flores, L., Padisák, J. & Albay, M. Shape and size in phytoplankton ecology: Do they matter? Hydrobiologia 578, 157–161 (2007).
Article  Google Scholar 

49.
Strathmann, R. R. Estimating the organic carbon content of phytoplankton from cell volume or plasma volume. Limnol. Oceanogr. 12, 411–418 (1967).
ADS  CAS  Article  Google Scholar 

50.
Chaffin, J. D., Stanislawczyk, K., Kane, D. D. & Lambrix, M. M. Nutrient addition effects on chlorophyll a, phytoplankton biomass, and heterocyte formation in Lake Erie’s central basin during 2014–2017: Insights into diazotrophic blooms in high nitrogen water. Freshw. Biol. 00, 1–15 (2020).
Google Scholar 

51.
Hadoux, E. & Laplace-Treyture, C. PHYTOBS: Phytoplankton Counting Tool in Laboratory. Version 1.0. JAVA Application. https://hydrobio-dce.inrae.fr/phytobs-software/ (2009).

52.
Huber Pestalozzi, G. & Thienemann, A. Das Phytoplankton des Susswassers Systematik und Biologie: 5 Teil Chlorophyceae (Grünalgen) Ordnung: Volvocales. (E. Schweizerbart’sche verlagsbuchhandlung, 1974).

53.
Komarek, J., Fott, B. & Huber Pestalozzi, G. Das Phytoplankton des Susswassers Systematik und Biologie: 7 Teil 1 Halfte Chlorophyceae (Grunalgen) Ordnung: Chlorococcales. (E. Schweizerbart’sche verlagsbuchhandlung, 1983).

54.
Coesel, P. F. M. & Meesters, K. J. Desmids of the Lowlands: Mesotaeniaceae and Desmidiaceae of the European Lowlands. (KNNV Publishing, 2007).

55.
Coesel, P. F. M. & Meesters, K. European Flora of the Desmid Genera Staurastrum and Staurodesmus. (KNNV Publishing, 2013).

56.
Starmach, K. Chrysophyceae und Haptophyceae. (VEB Gustav Fischer Verlag, 1985).

57.
Komarek, J. & Anagnostidis, K. Cyanoprokaryota 1.Teil: Chroococcales. (Gustav Fischer, 1999).

58.
Komarek, J. & Anagnostidis, K. Cyanoprokaryota 2.Teil: Oscillatoriales. (Elsevier, 2005).

59.
Komarek, J. Cyanoprokaryota: 3. Teil/Part 3: Heterocytous Genera. (Springer Spektrum Verlag, 2013).

60.
Anses. Evaluation des Risques Liés aux Cyanobactéries et leurs Toxines dans les Eaux Douces. Avis de l’Anses. 438 (Anses, 2020).

61.
Bey, M.-Y. & Ector, L. Atlas des Diatomées des Cours d’Eau de la Région Rhône-Alpes. (DREAL Rhône-Alpes, 2013).

62.
Cox, E. J. Identification of Freshwater Diatoms from Live Material. (Chapman & Hall, 1996).

63.
Druart, J. C. & Straub, F. Description de deux nouvelles Cyclotelles (Bacillariophyceae) de milieux alcalins et eutrophes: Cyclotella costei nov. sp. et Cyclotella wuethrichiana nov. sp. Swiss J. Hydrol. 50, 182–188 (1988).
Article  Google Scholar 

64.
Houk, V. Atlas of Freshwater Centric Diatoms with a Brief Key and Descriptions Part I Melosiraceae, Orthoseiraceae, Paraliaceae and Aulacoseiraceae. vol. 1 (Czech Phycological Society, Prague & Palacký University Olomouc, 2003).

65.
Houk, V. & Klee, R. Atlas of freshwater centric diatoms with a brief key and descriptions Part II Melosiraceae and Aulacoseiraceae (Supplement to Part I). Fottea J. Czech Phycol. Soc. 7, 85–255 (2007).
Google Scholar 

66.
Houk, V., Klee, R. & Tanaka, H. Atlas of Freshwater Centric Diatoms with a Brief Key and Descriptions Part IV Stephanodiscaceae B. vol. 14 (Czech Phycological Society, Prague & Palacký University Olomouc, 2014).

67.
Houk, V., Klee, R. & Tanaka, H. Atlas of Freshwater Centric Diatoms with a Brief Key and Descriptions Part III Steogabiduscaceae A Cyclotella, Tertiarius, Discostella. vol. 10 (Czech Phycological Society, Prague & Palacký University Olomouc, 2010).

68.
Houk, V., Klee, R. & Tanaka, H. Atlas of freshwater centric diatoms with a brief key and descriptions: second emended edition of Part I and II Melosiraceae, Orthoseiraceae, Paraliaceae and Aulacoseiraceae. Fottea J. Czech Phycol. Soc. 17, 1–615 (2017).
Google Scholar 

69.
Krammer, K. & Lange Bertalot, H. Bacillariophyceae. 1. Teil: Naviculaceae. (Specktrum Akademischer Verlag GmbH Heidelberg, 1999).

70.
Krammer, K. & Lange Bertalot, H. Bacillariophyceae. 4. Teil: Achnanthaceae Kritische Ergänzungen zu Achnanthes s.l., Bavicula s. str., Gomphonema. (Spektrum, 2004).

71.
Krammer, K. & Lange Bertalot, H. Bacillariophyceae. 2. Teil: Bacillariaceae, Epithemiaceae, Surirellaceae. (Elsevier, 2007).

72.
Krammer, K. & Lange-Bertalot, H. Bacillariophyceae. 3. Teil: Centrales, Fragilariaceae, Eunotiaceae. (Gustav Fischer Verlag, 2004).

73.
Lange-Bertalot, H., Hofmann, G., Werum, M. & Cantonati, M. Freshwater Benthic Diatoms of Central Europe: Over 800 Common Species Used in Ecological Assessment. (Koeltz Botanical Books, 2017).

74.
Siver, P. A. et al. Observations on Fragilaria longifusiformis comb. nov. et nom. nov. (Bacillariophyceae), a widespread planktic diatom documented from North America and Europe. Phycol. Res. 54, 183–192 (2006).
Article  Google Scholar 

75.
Popovsky, J. & Pfiester, L. A. Dinophyceae (Dinoflagellida). (Gustav Fischer Verlag, 1990).

76.
Moestrup, Ø. & Calado, A. J. Dinophyceae. vol. 6 (Spektrum Akademischer Verlag, 2018).

77.
Huber Pestalozzi, G. Das Phytoplankton des Susswassers Systematik und Biologie: 4 Teil Euglenophyceen. (E. Schweizerbart’sche verlagsbuchhandlung, 1969).

78.
Ettl, H. Xanthophyceae: 1. Teil. (Gustav Fischer Verlag, 1978).

79.
Rieth, A. Xanthophyceae: 2. Teil. (Gustav Fischer Verlag, 1980). More

1.
Sender, R., Fuchs, S. & Milo, R. Revised estimates for the number of human and bacteria cells in the body. PLOS Biol. 14, e1002533 (2016).
PubMed  PubMed Central  Article  CAS  Google Scholar 
2.
Kho, Z. Y. & Lal, S. K. The human gut microbiome—A potential controller of wellness and disease. Front. Microbiol. 9, 1835 (2018).
PubMed  PubMed Central  Article  Google Scholar 

3.
Kostic, A. D., Xavier, R. J. & Gevers, D. The microbiome in inflammatory bowel disease: Current status and the future ahead. Gastroenterology 146, 1489–1499 (2014).
CAS  PubMed  PubMed Central  Article  Google Scholar 

4.
Thaiss, C. A., Zmora, N., Levy, M. & Elinav, E. The microbiome and innate immunity. Nature 535, 65–74 (2016).
CAS  PubMed  Article  ADS  PubMed Central  Google Scholar 

5.
Das, B. & Nair, G. B. Homeostasis and dysbiosis of the gut microbiome in health and disease. J. Biosci. 44, 117 (2019).
PubMed  Article  PubMed Central  Google Scholar 

6.
Shreiner, A. B., Kao, J. Y. & Young, V. B. The gut microbiome in health and in disease. Curr. Opin. Gastroenterol. 31, 69–75 (2015).
CAS  PubMed  PubMed Central  Article  Google Scholar 

7.
Petersen, C. & Round, J. L. Defining dysbiosis and its influence on host immunity and disease: How changes in microbiota structure influence health. Cell. Microbiol. 16, 1024–1033 (2014).
CAS  PubMed  PubMed Central  Article  Google Scholar 

8.
Karlsson, F. H. et al. Gut metagenome in European women with normal, impaired and diabetic glucose control. Nature 498, 99–103 (2013).
CAS  PubMed  PubMed Central  Article  ADS  Google Scholar 

9.
Koren, O. et al. Human oral, gut, and plaque microbiota in patients with atherosclerosis. Proc. Natl. Acad. Sci. 108, 4592–4598 (2011).
CAS  PubMed  Article  ADS  PubMed Central  Google Scholar 

10.
Karlsson, F. H. et al. Symptomatic atherosclerosis is associated with an altered gut metagenome. Nat. Commun. 3, 1245 (2012).
PubMed  PubMed Central  Article  ADS  CAS  Google Scholar 

11.
Chatelier, L. M. et al. Richness of human gut microbiome correlates with metabolic markers. Nature 500, 541–546 (2013).
PubMed  Article  CAS  PubMed Central  Google Scholar 

12.
Franzosa, E. A. et al. Gut microbiome structure and metabolic activity in inflammatory bowel disease. Nat. Microbiol. 4, 293–305 (2019).
CAS  PubMed  Article  PubMed Central  Google Scholar 

13.
Becker, C., Neurath, M. F. & Wirtz, S. The intestinal microbiota in inflammatory bowel disease. ILAR J. 56, 192–204 (2015).
CAS  PubMed  Article  PubMed Central  Google Scholar 

14.
Kostic, A. D. et al. Fusobacterium nucleatum potentiates intestinal tumorigenesis and modulates the tumor-immune microenvironment. Cell Host Microbe 14, 207–215 (2013).
CAS  PubMed  PubMed Central  Article  Google Scholar 

15.
The Human Microbiome Project Consortium. Structure, function and diversity of the healthy human microbiome. Nature 486, 207–214 (2012).
PubMed Central  Article  ADS  CAS  Google Scholar 

16.
Johnson, A. J. et al. Daily sampling reveals personalized diet–microbiome associations in humans. Cell Host Microbe 25, 789-802.e5 (2019).
CAS  PubMed  Article  PubMed Central  Google Scholar 

17.
Villmones, H. C. et al. Species level description of the human ileal bacterial microbiota. Sci. Rep. 8, 1–9 (2018).
CAS  Article  Google Scholar 

18.
Gevers, D. et al. The treatment-naive microbiome in new-onset Crohn’s disease. Cell Host Microbe 15, 382–392 (2014).
CAS  PubMed  PubMed Central  Article  Google Scholar 

19.
David, L. A. et al. Diet rapidly and reproducibly alters the human gut microbiome. Nature 505, 559–563 (2014).
CAS  PubMed  Article  ADS  PubMed Central  Google Scholar 

20.
Zhou, J., Deng, Y., Luo, F., He, Z. & Yang, Y. Phylogenetic molecular ecological network of soil microbial communities in response to elevated CO2. MBio 2, e00122-e211 (2011).
PubMed  PubMed Central  Article  Google Scholar 

21.
Lupatini, M. et al. Network topology reveals high connectance levels and few key microbial genera within soils. Front. Environ. Sci. 2, 10 (2014).
Article  Google Scholar 

22.
Eiler, A., Heinrich, F. & Bertilsson, S. Coherent dynamics and association networks among lake bacterioplankton taxa. ISME J. 6, 330–342 (2012).
CAS  PubMed  Article  Google Scholar 

23.
Kara, E. L., Hanson, P. C., Hu, Y. H., Winslow, L. & McMahon, K. D. A decade of seasonal dynamics and co-occurrences within freshwater bacterioplankton communities from eutrophic Lake Mendota, WI, USA. ISME J. 7, 680–684 (2013).
PubMed  Article  Google Scholar 

24.
Shetty, S. A., Hugenholtz, F., Lahti, L., Smidt, H. & de Vos, W. M. Intestinal microbiome landscaping: Insight in community assemblage and implications for microbial modulation strategies. FEMS Microbiol. Rev. 41, 182–199 (2017).
CAS  PubMed  PubMed Central  Article  Google Scholar 

25.
Gould, A. L. et al. Microbiome interactions shape host fitness. Proc. Natl. Acad. Sci. 115, E11951–E11960 (2018).
CAS  PubMed  Article  Google Scholar 

26.
Hibbing, M. E., Fuqua, C., Parsek, M. R. & Peterson, S. B. Bacterial competition: Surviving and thriving in the microbial jungle. Nat. Rev. Microbiol. 8, 15–25 (2010).
CAS  PubMed  PubMed Central  Article  Google Scholar 

27.
Fox, G. E., Magrum, L. J., Balcht, W. E., Wolfef, R. S. & Woese, C. R. Classification of methanogenic bacteria by 16S ribosomal RNA characterization (comparative oligonucleotide cataloging/phylogeny/molecular evolution). Evolution (N.Y.) 74, 4537–4541 (1977).
CAS  Google Scholar 

28.
Venter, J. C. et al. Environmental genome shotgun sequencing of the Sargasso Sea. Science 304, 66 (2004).
CAS  PubMed  Article  ADS  Google Scholar 

29.
Větrovský, T. & Baldrian, P. The variability of the 16S rRNA gene in bacterial genomes and its consequences for bacterial community analyses. PLoS ONE 8, e57923 (2013).
PubMed  PubMed Central  Article  ADS  CAS  Google Scholar 

30.
Edgar, R. C. Accuracy of taxonomy prediction for 16S rRNA and fungal ITS sequences. PeerJ 6, 1–29 (2018).
Google Scholar 

31.
Ranjan, R., Rani, A., Metwally, A., McGee, H. S. & Perkins, D. L. Analysis of the microbiome: Advantages of whole genome shotgun versus 16S amplicon sequencing. Biochem. Biophys. Res. Commun. 469, 967–977 (2016).
CAS  PubMed  Article  Google Scholar 

32.
Laudadio, I. et al. Quantitative assessment of shotgun metagenomics and 16S rDNA amplicon sequencing in the study of human gut microbiome. Omi. A J. Integr. Biol. 22, 248–254 (2018).
CAS  Article  Google Scholar 

33.
Gloor, G. B., Macklaim, J. M., Pawlowsky-Glahn, V. & Egozcue, J. J. Microbiome datasets are compositional: And this is not optional. Front. Microbiol. 8, 1–6 (2017).
Article  Google Scholar 

34.
Friedman, J. & Alm, E. J. Inferring correlation networks from genomic survey data. PLoS Comput. Biol. 8, e1002687 (2012).
CAS  PubMed  PubMed Central  Article  ADS  Google Scholar 

35.
Layeghifard, M., Hwang, D. M. & Guttman, D. S. Disentangling interactions in the microbiome: A network perspective. Trends Microbiol. 25, 217–228 (2017).
CAS  PubMed  Article  Google Scholar 

36.
Aitchison, J. The statistical analysis of compositional data. J. R. Stat. Soc. Ser. B 44, 40 (1982).
MathSciNet  MATH  Google Scholar 

37.
Kurtz, Z. D. et al. Sparse and compositionally robust inference of microbial ecological networks. PLoS Comput. Biol. 11, 1–25 (2015).
Article  CAS  Google Scholar 

38.
Friedman, J., Hastie, T. & Tibshirani, R. Sparse inverse covariance estimation with the graphical lasso. Biostatistics 9, 432–441 (2008).
PubMed  MATH  Article  Google Scholar 

39.
Falony, G. et al. Population-level analysis of gut microbiome variation. Science 352, 560–564 (2016).
CAS  PubMed  Article  ADS  Google Scholar 

40.
Efron, B. & Tibshirani, R. Bootstrap methods for standard errors, confidence intervals, and other measures of statistical accuracy. Stat. Sci. 1, 54–75 (1986).
MathSciNet  MATH  Article  Google Scholar 

41.
Su, W., Bogdan, M., Candès, E. & Candes, E. False discoveries occur early on the lasso path. Ann. Stat. 45, 2133–2150 (2017).
MathSciNet  MATH  Article  Google Scholar 

42.
Saunders, A. M., Albertsen, M., Vollertsen, J. & Nielsen, P. H. The activated sludge ecosystem contains a core community of abundant organisms. ISME J. 10, 11–20 (2016).
CAS  PubMed  Article  PubMed Central  Google Scholar 

43.
Tsvetovat, M. & Kouznetsov, A. Social network analysis for startups. Zhurnal Eksperimental’noi i Teoreticheskoi Fiziki (2011).

44.
Stadtfeld, C., Takács, K. & Vörös, A. The emergence and stability of groups in social networks. Soc. Netw. 60, 129–145 (2020).
Article  Google Scholar 

45.
Cordasco, G. & Gargano, L. Community detection via semi-synchronous label propagation algorithms. 2010 IEEE Int. Work. Bus. Appl. Soc. Netw. Anal. BASNA 2010 (2010). https://doi.org/10.1109/BASNA.2010.5730298.

46.
Prettejohn, B. J., Berryman, M. J. & McDonnell, M. D. Methods for generating complex networks with selected structural properties for simulations: A review and tutorial for neuroscientists. Front. Comput. Neurosci. 5, 11 (2011).
PubMed  PubMed Central  Article  Google Scholar 

47.
Guimerà, R., Sales-Pardo, M. & Amaral, L. A. N. Modularity from fluctuations in random graphs and complex networks. Phys. Rev. E 70, 25101 (2004).
Article  ADS  CAS  Google Scholar 

48.
Trosvik, P. & de Muinck, E. J. Ecology of bacteria in the human gastrointestinal tract—Identification of keystone and foundation taxa. Microbiome 3, 44 (2015).
PubMed  PubMed Central  Article  Google Scholar 

49.
Verster, A. J. & Borenstein, E. Competitive lottery-based assembly of selected clades in the human gut microbiome. Microbiome 6, 186 (2018).
PubMed  PubMed Central  Article  Google Scholar 

50.
Berry, D. & Widder, S. Deciphering microbial interactions and detecting keystone species with co-occurrence networks. Front. Microbiol. 5, 219 (2014).
PubMed  PubMed Central  Article  Google Scholar 

51.
Foster, K. R. & Bell, T. Competition, not cooperation, dominates interactions among culturable microbial species. Curr. Biol. 22, 1845–1850 (2012).
CAS  PubMed  Article  PubMed Central  Google Scholar 

52.
Nemergut, D. R. et al. Patterns and processes of microbial community assembly. Microbiol. Mol. Biol. Rev. 77, 342–356 (2013).
PubMed  PubMed Central  Article  Google Scholar 

53.
Hamilton, W. D. The genetical evolution of social behaviour. I. J. Theor. Biol. 7, 1–16 (1964).
CAS  PubMed  Article  PubMed Central  Google Scholar 

54.
Hardin, G. The competitive exclusion principle. Science 131, 1292–1297 (1960).
CAS  PubMed  Article  ADS  Google Scholar 

55.
Jackson, M. A. et al. Detection of stable community structures within gut microbiota co-occurrence networks from different human populations. PeerJ 6, e4303 (2018).
PubMed  PubMed Central  Article  CAS  Google Scholar 

56.
Darcy, J. L. et al. A phylogenetic model for the recruitment of species into microbial communities and application to studies of the human microbiome. ISME J. 14, 1359–1368 (2020).
PubMed  Article  Google Scholar 

57.
Pacheco, A. R., Moel, M. & Segrè, D. Costless metabolic secretions as drivers of interspecies interactions in microbial ecosystems. Nat. Commun. 10, 103 (2019).
PubMed  PubMed Central  Article  ADS  CAS  Google Scholar 

58.
Chase, J. M. & Leibold, M. A. Spatial scale dictates the productivity–biodiversity relationship. Nature 416, 427–430 (2002).
CAS  PubMed  Article  ADS  PubMed Central  Google Scholar 

59.
Zarrinpar, A., Chaix, A., Yooseph, S. & Panda, S. Diet and feeding pattern affect the diurnal dynamics of the gut microbiome. Cell Metab. 20, 1006–1017 (2014).
CAS  PubMed  PubMed Central  Article  Google Scholar 

60.
Mark Welch, J. L., Hasegawa, Y., McNulty, N. P., Gordon, J. I. & Borisy, G. G. Spatial organization of a model 15-member human gut microbiota established in gnotobiotic mice. Proc. Natl. Acad. Sci. 114, E9105–E9114 (2017).
CAS  PubMed  Article  Google Scholar 

61.
Fung, T. C., Artis, D. & Sonnenberg, G. F. Anatomical localization of commensal bacteria in immune cell homeostasis and disease. Immunol. Rev. 260, 35–49 (2014).
CAS  PubMed  PubMed Central  Article  Google Scholar 

62.
Donaldson, G. P., Lee, S. M. & Mazmanian, S. K. Gut biogeography of the bacterial microbiota. Nat. Rev. Microbiol. 14, 20–32 (2016).
CAS  PubMed  Article  Google Scholar 

63.
Stachowicz, J. J. Mutualism, facilitation, and the structure of ecological communities. Bioscience 51, 235 (2001).
Article  Google Scholar 

64.
Lozupone, C. A., Stombaugh, J. I., Gordon, J. I., Jansson, J. K. & Knight, R. Diversity, stability and resilience of the human gut microbiota. Nature 489, 220–230 (2012).
CAS  PubMed  PubMed Central  Article  ADS  Google Scholar 

65.
Lynd, L. R., Weimer, P. J., van Zyl, W. H. & Pretorius, I. S. Microbial cellulose utilization: Fundamentals and biotechnology. Microbiol. Mol. Biol. Rev. 66, 72 (2002).
Article  Google Scholar 

66.
Turroni, F. et al. Glycan cross-feeding activities between bifidobacteria under in vitro conditions. Front. Microbiol. 6, 1030 (2015).
PubMed  PubMed Central  Article  Google Scholar 

67.
Hall, C. V. et al. Co-existence of network architectures supporting the human gut microbiome. iScience 22, 380–391 (2019).
PubMed  PubMed Central  Article  ADS  Google Scholar 

68.
Fisher, C. K. & Mehta, P. Identifying keystone species in the human gut microbiome from metagenomic timeseries using sparse linear regression. PLoS ONE 9, e102451 (2014).
PubMed  PubMed Central  Article  ADS  CAS  Google Scholar 

69.
Jones, M. B. et al. Library preparation methodology can influence genomic and functional predictions in human microbiome research. Proc. Natl. Acad. Sci. 112, 14024–14029 (2015).
CAS  PubMed  Article  ADS  Google Scholar 

70.
Lahti, L., Salojärvi, J., Salonen, A., Scheffer, M. & de Vos, W. M. Tipping elements in the human intestinal ecosystem. Nat. Commun. 5, 4344 (2014).
CAS  PubMed  PubMed Central  Article  ADS  Google Scholar 

71.
Dhakan, D. B. et al. The unique composition of Indian gut microbiome, gene catalogue, and associated fecal metabolome deciphered using multi-omics approaches. Gigascience 8, giz004 (2019).
PubMed  PubMed Central  Article  CAS  Google Scholar 

72.
Qin, J. et al. A human gut microbial gene catalogue established by metagenomic sequencing. Nature 464, 59–65 (2010).
CAS  PubMed  PubMed Central  Article  Google Scholar 

73.
Yachida, S. et al. Metagenomic and metabolomic analyses reveal distinct stage-specific phenotypes of the gut microbiota in colorectal cancer. Nat. Med. 25, 968–976 (2019).
CAS  PubMed  Article  Google Scholar 

74.
Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: A flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).
CAS  PubMed  PubMed Central  Article  Google Scholar 

75.
Langmead, B. & Salzberg, S. Bowtie2. Nat. Methods 9, 357–359 (2013).
Article  CAS  Google Scholar 

76.
O’Leary, N. A. et al. Reference sequence (RefSeq) database at NCBI: Current status, taxonomic expansion, and functional annotation. Nucleic Acids Res. 44, D733–D745 (2016).
PubMed  Article  CAS  Google Scholar 

77.
Xia, L. C., Cram, J. A., Chen, T., Fuhrman, J. A. & Sun, F. Accurate genome relative abundance estimation based on shotgun metagenomic reads. PLoS ONE 6, e27992 (2011).
CAS  PubMed  PubMed Central  Article  ADS  Google Scholar 

78.
Parks, D. H., Imelfort, M., Skennerton, C. T., Hugenholtz, P. & Tyson, G. W. CheckM: Assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res. 25, 1043–1055 (2015).
CAS  PubMed  PubMed Central  Article  Google Scholar 

79.
Hyatt, D. et al. Prodigal: Prokaryotic gene recognition and translation initiation site identification. BMC Bioinform. 11, 119 (2010).
Article  CAS  Google Scholar 

80.
Jones, P. et al. InterProScan 5: Genome-scale protein function classification. Bioinformatics 30, 1236–1240 (2014).
CAS  PubMed  PubMed Central  Article  Google Scholar 

81.
Haft, D. H. TIGRFAMs: A protein family resource for the functional identification of proteins. Nucleic Acids Res. 29, 41–43 (2001).
CAS  PubMed  PubMed Central  Article  Google Scholar 

82.
Liao, Y., Smyth, G. K. & Shi, W. featureCounts: An efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 30, 923–930 (2014).
CAS  PubMed  PubMed Central  Article  Google Scholar 

83.
R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, Vienna, 2017).
Google Scholar 

84.
Zhao, T., Liu, H., Roeder, K., Lafferty, J. & Wasserman, L. The huge package for high-dimensional undirected graph estimation in R. J. Mach. Learn. Res. 13, 6 (2016).
MathSciNet  MATH  Google Scholar 

85.
Liu, H., Roeder, K. & Wasserman, L. Stability approach to regularization selection (stars) for high dimensional graphical models. Advances in Neural Information Processing Systems (2010).

86.
Hagberg, A., Swart, P. & Chult, D. S. Exploring network structure, dynamics, and function using NetworkX. No. LA-UR-08-05495; LA-UR-08-5495 (Los Alamos National Lab. (LANL), Los Alamos, 2008).

87.
Newman, M. E. J. Networks: An Introduction 168–234 (Oxford University Press, Oxford, 2010).
Google Scholar 

88.
Newman, M. E. J. Modularity and community structure in networks. Proc. Natl. Acad. Sci. 103, 8577–8582 (2006).
CAS  PubMed  Article  ADS  PubMed Central  Google Scholar 

89.
Newman, M. E. J. Mixing patterns in networks. Phys. Rev. E 67, 26126 (2003).
MathSciNet  CAS  Article  ADS  Google Scholar 

90.
Fortunato, S. Community detection in graphs. Phys. Rep. 486, 75–174 (2010).
MathSciNet  Article  ADS  Google Scholar 

91.
Brandes, U. A faster algorithm for betweenness centrality*. J. Math. Sociol. 25, 163–177 (2001).
MATH  Article  Google Scholar  More

1.
Keller, R. P., Geist, J., Jeschke, J. M. & Kühn, I. Invasive species in Europe: ecology, status, and policy. Environ. Sci. Eur. 23, 1–17 (2011).
Article  Google Scholar 
2.
Parker, M., Thompson, J. N. & Weller, S. G. The population biology of invasive species. Annu. Rev. Ecol. Syst. 32, 305–332 (2001).
Article  Google Scholar 

3.
Allendorf, F. W. & Lundquist, L. L. Introduction: population biology, evolution, and control of invasive species. Conserv. Biol. 17, 24–30 (2003).
Article  Google Scholar 

4.
Crowl, T. A., Crist, T. O., Parmenter, R. R., Belovsky, G. & Lugo, A. E. The spread of invasive species and infectious disease as drivers of ecosystem change. Front. Ecol. Environ. 6, 238–246 (2008).
Article  Google Scholar 

5.
van der Veer, G. & Nentwig, W. Environmental and economic impact assessment of alien and invasive fish species in Europe using the generic impact scoring system. Ecol. Freshw. Fish 24, 646–656 (2015).
Article  Google Scholar 

6.
Clavero, M. & García-Berthou, E. Invasive species are a leading cause of animal extinctions. Trends Ecol. Evol. 20, 110 (2005).
PubMed  Article  PubMed Central  Google Scholar 

7.
Scalera, R. How much is Europe spending on invasive alien species?. Biol. Invasions 12, 173–177 (2010).
Article  Google Scholar 

8.
Sala, O. E. et al. Global biodiversity scenarios for the year 2100. Science 287, 1770–1774 (2000).
CAS  PubMed  Article  PubMed Central  Google Scholar 

9.
McLellan, R., Iyengar, L., Jeffries, B. & Oerlemans, N. Living Planet Report 2014: Species and Spaces, People and Places (WWF International, Gland, 2014).
Google Scholar 

10.
García-Berthou, E. et al. Introduction pathways and establishment rates of invasive aquatic species in Europe. Can. J. Fish. Aquat. Sci. 62, 453–463 (2005).
Article  Google Scholar 

11.
Karatayev, A. Y., Burlakova, L. E., Padilla, D. K., Mastitsky, S. E., & Olenin, S. Invaders are not a random selection of species. Biol. Invasions, 11, 2009. https://doi.org/10.1007/s10530-009-9498-0 (2009).
Article  Google Scholar 

12.
Verdonschot, R. C. M., Vos, J. H., & Verdonschot, P. F. M. Exotische macrofauna en macrofyten in de Nederlandse zoete wateren: voorkomen en beleid in 2012. (WOt-werkdocument 334) (Wettelijke Onderzoekstaken Natuur & Milieu, 2013).

13.
Holdich, D. M., Reynolds, J. D., Souty-Grosset, C. & Sibley, P. J. A review of the ever increasing threat to European crayfish from non-indigenous crayfish species. Knowl. Manag. Aquat. Ecosyst. 394–395, 11 (2009).
Article  Google Scholar 

14.
Chucholl, C. Invaders for sale: trade and determinants of introduction of ornamental freshwater crayfish. Biol. Invasions 15, 125–141 (2013).
Article  Google Scholar 

15.
Barbaresi, S. & Gherardi, F. The invasion of the alien crayfish Procambarus clarkii in Europe, with particular reference to Italy. Biol. Invasions 2, 259–264 (2000).
Article  Google Scholar 

16.
Gherardi, F. Crayfish invading Europe: the case study of Procambarus clarkii. Mar. Freshw. Behav. Physiol. 39, 175–191 (2006).
Article  Google Scholar 

17.
Kouba, A., Petrusek, A. & Kozák, P. Continental-wide distribution of crayfish species in Europe: update and maps. Knowl. Manag. Aquat. Ecosyst. 413, 5 (2014).
Article  Google Scholar 

18.
Lowe, S., Browne, M., Boudjelas, S., & De Poorter, M. 100 of the world’s worst invasive alien species: a selection from the global invasive species database in Aliens vol. 12 (Invasive Species Specialist Group, 2000).

19.
Padilla, D. K. & Williams, S. L. Beyond ballast water: aquarium and ornamental trades as sources of invasive species in aquatic ecosystems. Front. Ecol. Environ. 2, 131–138 (2004).
Article  Google Scholar 

20.
Faulkes, Z. The global trade in crayfish as pets. Crustacean Res. 44, 75–92 (2015).
Article  Google Scholar 

21.
Soes, D. M., & Koese, B. Invasive Crayfish in the Netherlands: A Preliminary Risk Analysis. (Bureau Waardenburg bv, Stichting EIS-Nederland, Invasive Alien Species Team, 2010).

22.
Chucholl, C. & Wendler, F. Positive selection of beautiful invaders: long-term persistence and bio-invasion risk of freshwater crayfish in the pet trade. Biol. Invasions 19, 197–208 (2017).
Article  Google Scholar 

23.
Zeng, Y., Chong, K. Y., Grey, E. K., Lodge, D. M. & Yeo, D. C. Disregarding human pre-introduction selection can confound invasive crayfish risk assessments. Biol. Invasions 17, 2373–2385 (2015).
Article  Google Scholar 

24.
Blackburn, T. M. et al. A proposed unified framework for biological invasions. Trends Ecol. Evol. 26, 333–339 (2011).
PubMed  Article  Google Scholar 

25.
Statzner, B., Bonada, N. & Dolédec, S. Biological attributes discriminating invasive from native European stream macroinvertebrates. Biol. Invasions 10, 517–530 (2008).
Article  Google Scholar 

26.
Whitney, K. D. & Gabler, C. A. Rapid evolution in introduced species, ‘invasive traits’ and recipient communities: challenges for predicting invasive potential. Divers. Distrib. 14, 569–580 (2008).
Article  Google Scholar 

27.
Kolar, C. S. & Lodge, D. M. Progress in invasion biology: predicting invaders. Trends Ecol. Evol. 16, 199–204 (2001).
PubMed  Article  Google Scholar 

28.
Marchetti, M. P., Moyle, P. B. & Levine, R. Invasive species profiling? Exploring the characteristics of non-native fishes across invasion stages in California. Freshw. Biol. 49, 646–661 (2004).
Article  Google Scholar 

29.
Grabowski, M., Bacela, K. & Konopacka, A. How to be an invasive gammarid (Amphipoda: Gammaroidea)-comparison of life history traits. Hydrobiologia 590, 75–84 (2007).
Article  Google Scholar 

30.
Thiébaut, G. Invasion success of non-indigenous aquatic and semi-aquatic plants in their native and introduced ranges. A comparison between their invasiveness in North America and in France. Biol. Invasions 9, 1–12 (2007).
Article  Google Scholar 

31.
Swart, C., Visser, V. & Robinson, T. B. Patterns and traits associated with invasions by predatory marine crabs. NeoBiota 39, 79 (2018).
Article  Google Scholar 

32.
Larson, E. R. & Olden, J. D. Latent extinction and invasion risk of crayfishes in the southeastern United States. Conserv. Biol. 24, 1099–1110 (2010).
PubMed  Article  PubMed Central  Google Scholar 

33.
Tricarico, E., Vilizzi, L., Gherardi, F. & Copp, G. H. Calibration of FI-ISK, an invasiveness screening tool for nonnative freshwater invertebrates. Risk Anal. Int. J. 30, 285–292 (2010).
Article  Google Scholar 

34.
Larson, E. R. & Olden, J. D. Using avatar species to model the potential distribution of emerging invaders. Glob Ecol. Biogeogr. 21, 1114–1125 (2012).
Article  Google Scholar 

35.
Veselý, L., Buřič, M. & Kouba, A. Hardy exotics species in temperate zone: can “warm water” crayfish invaders establish regardless of low temperatures?. Sci. Rep. 5, 16340 (2015).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

36.
Jaklič, M. & Vrezec, A. The first tropical alien crayfish species in European waters: the redclaw Cherax quadricarinatus (Von Martens, 1868) (Decapoda, Parastacidae). Crustaceana 84, 651–665 (2011).
Article  Google Scholar 

37.
Colautti, R. I., Grigorovich, I. A. & MacIsaac, H. J. Propagule pressure: a null model for biological invasions. Biol. Invasions 8, 1023–1037 (2006).
Article  Google Scholar 

38.
Marchetti, M. P., Moyle, P. B. & Levine, R. Alien fishes in California watersheds: characteristics of successful and failed invaders. Ecol. Appl. 14, 587–596 (2004).
Article  Google Scholar 

39.
Bennett, S. N., Olson, J. R., Kershner, J. L. & Corbett, P. Propagule pressure and stream characteristics influence introgression: cutthroat and rainbow trout in British Columbia. Ecol. Appl. 20, 263–277 (2010).
PubMed  Article  PubMed Central  Google Scholar 

40.
Cruz, M. J. & Rebelo, R. Colonization of freshwater habitats by an introduced crayfish, Procambarus clarkii Southwest Iberian Peninsula. Hydrobiologia 575, 191–201 (2007).
Article  Google Scholar 

41.
Lynas, J., Storey, A. W. & Knott, B. Aggressive interactions between three species of freshwater crayfish of the genus Cherax (Decapoda: Parastacidae). Mar. Freshw. Behav. Physiol. 40, 105–116 (2007).
Article  Google Scholar 

42.
Corey, S. Comparative fecundity of four species of crayfish in southwestern Ontario, Canada (Decapoda, Astacidea). Crustaceana 52(3), 276–286 (1987).
Article  Google Scholar 

43.
Somers, K. M. Characterizing size-specific fecundity in crustaceans. Crustacean Egg Prod. 7, 357–378 (1991).
Google Scholar 

44.
Maguire, I., Klobučar, G. I. V. & Erben, R. The relationship between female size and egg size in the freshwater crayfish Austropotamobius torrentium. Bulletin Français de la Pêche et de la Pisciculture 376–377, 777–785 (2005).
Article  Google Scholar 

45.
Pilotto, F. et al. The invasive crayfish Faxonius limosus in Lake Varese: estimating abundance and population size structure in the context of habitat and methodological constraints. J. Crustacean Biol. 28, 633–640 (2008).
Article  Google Scholar 

46.
Hobbs Jr, H. H. A checklist of the North and Middle American crayfishes (Decapoda: Astacidae and Cambaridae). Smithsonian Contrib. Zool. 166, 1–161 (1974).
Google Scholar 

47.
Mrugała, A. et al. Trade of ornamental crayfish in Europe as a possible introduction pathway for important crustacean diseases: crayfish plague and white spot syndrome. Biol. Invasions 17, 1313–1326 (2015).
Article  Google Scholar 

48.
Svoboda, J., Mrugała, A., Kozubíková-Balcarová, E. & Petrusek, A. Hosts and transmission of the crayfish plague pathogen Aphanomyces astaci: a review. J. Fish Dis. 40, 127–140 (2017).
CAS  PubMed  Article  PubMed Central  Google Scholar 

49.
Grandjean, F. et al. Status of Pacifastacus leniusculus and its role in recent crayfish plague outbreaks in France: improving distribution and crayfish plague infection patterns. Aquat. Invasions, 12, 541–549 (2017).
Article  Google Scholar 

50.
Crandall, K. A. & De Grave, S. An updated classification of the freshwater crayfishes (Decapoda: Astacidea) of the world, with a complete species list. J. Crustacean Biol. 37, 615–653 (2017).
Article  Google Scholar 

51.
Freshwater Crayfish: A Global Overview. (ed. Kawai, T., Faulkes, Z., & Scholtz, G.) (CRC Press, Boca Raton, 2015).

52.
Buřič, M., Kouba, A. & Kozak, P. Reproductive plasticity in freshwater invader: from long-term sperm storage to parthenogenesis. PLoS ONE 8, e77597. https://doi.org/10.1371/journal.pone.0077597 (2013).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

53.
Kaldre, K., Meženin, A., Paaver, T., & Kawai, T. A preliminary study on the tolerance of marble crayfish Procambarus fallax f. virginalis to low temperature in Nordic climate in Freshwater crayfish: global overview, 54–62 (2016).

54.
Vogt, G. Marmorkrebs: natural crayfish clone as emerging model for various biological disciplines. J. Biosci. 36, 377–382 (2011).
PubMed  Article  Google Scholar 

55.
Chucholl, C. Predicting the risk of introduction and establishment of an exotic aquarium animal in Europe: insights from one decade of Marmorkrebs (Crustacea, Astacida, Cambaridae) releases. Biol. Invasions 5, 309–318 (2014).
Article  Google Scholar 

56.
Chucholl, C., Morawetz, K. & Groß, H. The clones are coming–strong increase in Marmorkrebs [Procambarus fallax (Hagen, 1870) f. virginalis] records from Europe. Aquat. Invasions 7, 511–519 (2012).
Article  Google Scholar 

57.
Soes, D. M. & van Eekelen, R. Rivierkreeften, een oprukkend probleem?. De Levende Natuur 107, 56–59 (2006).
Google Scholar 

58.
Mauvisseau, Q., Tönges, S., Andriantsoa, R., Lyko, F. & Sweet, M. Early detection of an emerging invasive species: eDNA monitoring of a parthenogenetic crayfish in freshwater systems. Manag. Biol. Invasions 10, 461 (2019).
Article  Google Scholar 

59.
Strand, D. A. et al. Monitoring a Norwegian freshwater crayfish tragedy: eDNA snapshots of invasion, infection and extinction. J. Appl. Ecol. 56, 1661–1673 (2019).
CAS  Article  Google Scholar 

60.
Beentjes, K. K., Speksnijder, A. G., Schilthuizen, M., Schaub, B. E. & van der Hoorn, B. B. The influence of macroinvertebrate abundance on the assessment of freshwater quality in The Netherlands. Metabarcoding Metagenom. 2, e26744 (2018).
Article  Google Scholar 

61.
Melo-Merino, S. M., Reyes-Bonilla, H. & Lira-Noriega, A. Ecological niche models and species distribution models in marine environments: a literature review and spatial analysis of evidence. Ecol. Model. 415, 108837 (2020).
Article  Google Scholar 

62.
Zhang, Z. et al. Impacts of climate change on the global potential distribution of two notorious invasive crayfishes. Freshw. Biol. 65, 353–365 (2020).
Article  Google Scholar 

63.
Capinha, C., Leung, B. & Anastácio, P. Predicting worldwide invasiveness for four major problematic decapods: an evaluation of using different calibration sets. Ecography 34, 448–459 (2011).
Article  Google Scholar 

64.
Havel, J. E., Kovalenko, K. E., Thomaz, S. M., Amalfitano, S. & Kats, L. B. Aquatic invasive species: challenges for the future. Hydrobiologia 750, 147–170 (2015).
PubMed  PubMed Central  Article  Google Scholar 

65.
Früh, D., Stoll, S. & Haase, P. Physicochemical and morphological degradation of stream and river habitats increases invasion risk. Biol. Invasions 14, 2243–2253 (2012).
Article  Google Scholar 

66.
Ghalambor, C. K., McKay, J. K., Carroll, S. P. & Reznick, D. N. Adaptive versus non-adaptive phenotypic plasticity and the potential for contemporary adaptation in new environments. Funct. Ecol. 21, 394–407 (2007).
Article  Google Scholar 

67.
Scalici, M. et al. The new threat to Italian inland waters from the alien crayfish “gang”: the Australian Cherax destructor Clark, 1936. Hydrobiologia 632, 341–345 (2009).
Article  Google Scholar 

68.
Koese, B. & Evers, C. H. M. A National Inventory of Invasive Freshwater Crayfish in the Netherlands in 2010 (EIS, Stichting European Invertebrate Survey Nederland, 2011).
Google Scholar 

69.
Clement, J., & van Puijenbroek, P. Basiskaart Aquatisch: de Watertypenkaart Het oppervlaktewater in de TOP10NL geclassificeerd naar watertype (No. 500067004). (Planbureau voor de Leefomgeving 2010).

70.
Peel, M. C., Finlayson, B. L. & McMahon, T. A. Updated world map of the Köppen-Geiger climate classification. Hydrol. Earth Syst. Sci. Discuss. 4, 439–473 (2007).
ADS  Google Scholar 

71.
Lyko, F. The marbled crayfish (Decapoda: Cambaridae) represents an independent new species. Zootaxa 4363(4), 544–552 (2017).
PubMed  Article  PubMed Central  Google Scholar 

72.
Usseglio-Polatera, P. & Tachet, H. Theoretical habitat templets, species traits, and species richness: Plecoptera and Ephemeroptera in the Upper Rhône River and its floodplain. Freshw. Biol. 31, 357–375 (1994).
Article  Google Scholar 

73.
Poff, N. L. et al. Functional trait niches of North American lotic insects: traits-based ecological applications in light of phylogenetic relationships. J. North Am. Benthological. Soc. 25, 730–755 (2006).
Article  Google Scholar 

74.
Wyse, S. V. et al. A quantitative assessment of shoot flammability for 60 tree and shrub species supports rankings based on expert opinion. Int. J. Wildland Fire 25, 466–477 (2016).
Article  Google Scholar 

75.
Hill, M. O. TWINSPAN. A FORTRAN program for arranging multivariate data in an ordered two-way table by classification of the individuals and attributes. (Ecology and Systematics, Cornell University, 1979).

76.
Hu, G. et al. Regeneration of different plant functional types in a Masson pine forest following pine wilt disease. PLoS ONE 7, e36432. https://doi.org/10.1371/journal.pone.0036432 (2012).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

77.
Agir, S. U., Kutbay, H. G. & Surmen, B. Plant diversity along coastal dunes of the Black Sea (North of Turkey). Rendiconti Lincei 27, 443–453 (2016).
Article  Google Scholar 

78.
Andrej, P. & Andraž, Č. Functional response traits and plant community strategy indicate the stage of secondary succession. Hacquetia 11, 209–225 (2012).
Article  Google Scholar 

79.
Hill, M.O. & Šmilauer, P. TWINSPAN for Windows version 2.3. (Centre for Ecology and Hydrology & University of South Bohemia, Huntingdon & Ceske Budejovice, 2005).

80.
Roleček, J., Tichý, L., Zelený, D. & Chytrý, M. Modified TWINSPAN classification in which the hierarchy respects cluster heterogeneity. J. Veg. Sci. 20, 596–602 (2009).
Article  Google Scholar  More

1.
Angilletta, M. J. Thermal Adaptation: A Theoretical And Empirical Analysis (Oxford University Press, 2009).
2.
Chown, S. L., Sinclair, B. J., Leinaas, H. P. & Gaston, K. J. Hemispheric asymmetries in biodiversity—a serious matter for ecology. PLoS Biol. 2, e406 (2004).
PubMed  PubMed Central  Article  CAS  Google Scholar 

3.
Sunday, J. M., Bates, A. E. & Dulvy, N. K. Thermal tolerance and the global redistribution of animals. Nat. Clim. Change 2, 686–690 (2012).
ADS  Article  Google Scholar 

4.
Kellermann, V., van Heerwaarden, B., Sgrò, C. M. & Hoffmann, A. A. Fundamental evolutionary limits in ecological traits drive Drosophila species distributions. Science 325, 1244–1246 (2009).
ADS  CAS  PubMed  Article  Google Scholar 

5.
Araújo, M. B. et al. Heat freezes niche evolution. Ecol. Lett. 16, 1206–1219 (2013).
PubMed  Article  Google Scholar 

6.
García-Robledo, C., Kuprewicz, E. K., Staines, C. L., Erwin, T. L. & Kress, W. J. Limited tolerance by insects to high temperatures across tropical elevational gradients and the implications of global warming for extinction. Proc. Natl Acad. Sci. USA 113, 680–685 (2016).
ADS  PubMed  Article  CAS  Google Scholar 

7.
Geerts, A. N. et al. Rapid evolution of thermal tolerance in the water flea, Daphnia. Nat. Clim. Change 5, 665–668 (2015).
ADS  Article  Google Scholar 

8.
Iossa, G. Sex-specific differences in thermal fertility limits. Trends Ecol. Evol. 34, 490–492 (2019).
PubMed  Article  Google Scholar 

9.
Walsh, B. S. et al. The impact of climate change on fertility. Trends Ecol. Evol. 34, 249–259 (2019).
PubMed  Article  Google Scholar 

10.
Vasudeva, R. et al. Adaptive thermal plasticity enhances sperm and egg performance in a model insect. eLife 8, e49452 (2019).
PubMed  PubMed Central  Article  Google Scholar 

11.
Hurley, L. L., McDiarmid, C. S., Friesen, C. R., Griffith, S. C. & Rowe, M. Experimental heatwaves negatively impact sperm quality in the zebra finch. Proc. R. Soc. B 285, 20172547 (2018).
PubMed  Article  Google Scholar 

12.
Dahlke, F., Wohlrab, S., Butzin, M. & Pörtner, H. Thermal bottlenecks in the lifecycle define climate vulnerability of fish. Science 369, 65–70 (2020).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

13.
Bathiany, S., Dakos, V., Scheffer, M. & Lenton, T. M. Climate models predict increasing temperature variability in poor countries. Sci. Adv. 4, 1–11 (2018).
Article  Google Scholar 

14.
Vázquez, D. P., Gianoli, E., Morris, W. F. & Bozinovic, F. Ecological and evolutionary impacts of changing climatic variability. Biol. Rev. 92, 22–42 (2017).
PubMed  Article  Google Scholar 

15.
Chevin, L.-M., Lande, R. & Mace, G. M. Adaptation, plasticity, and extinction in a changing environment: towards a predictive theory. PLoS Biol. 8, e1000357 (2010).
PubMed  PubMed Central  Article  CAS  Google Scholar 

16.
Sgrò, C. M. & Hoffmann, A. A. Genetic correlations, tradeoffs and environmental variation. Heredity 93, 241–248 (2004).
PubMed  Article  Google Scholar 

17.
Wood, C. W. & Brodie, E. D. Environmental effects on the structure of the G-matrix. Evolution 69, 2927–2940 (2015).
PubMed  Article  Google Scholar 

18.
Brommer, J. E., Merila, J., Sheldon, B. C. & Gustavsson, L. Natural selection and genetic variation for reproductive reaction norms in a wild bird population. Evolution 59, 1362–1371 (2005).
PubMed  Article  Google Scholar 

19.
Brommer, J. E., Rattiste, K. & Wilson, A. J. Exploring plasticity in the wild: laying date–temperature reaction norms in the common gull Larus canus. Proc. R. Soc. B 275, 687–693 (2008).
PubMed  Article  Google Scholar 

20.
Nussey, D. H., Postma, E., Gienapp, P., Visser, M. E. & Gienapp, P. Selection on heritable phenotypic plasticity in a wild bird population. Science 310, 304–306 (2005).
ADS  CAS  PubMed  Article  Google Scholar 

21.
Charmantier, A. et al. Adaptive phenotypic plasticity in response to climate change in a wild bird population. Science 320, 800–803 (2008).
ADS  CAS  PubMed  Article  Google Scholar 

22.
Matthysen, E., Adriaensen, F. & Dhondt, A. A. Multiple responses to increasing spring temperatures in the breeding cycle of blue and great tits (Cyanistes caeruleus, Parus major). Glob. Change Biol. 17, 1–16 (2011).
ADS  Article  Google Scholar 

23.
Both, C. & Visser, M. E. Adjustment to climate change is constrained by arrival date in a long-distance migrant bird. Nature 411, 296–298 (2001).
ADS  CAS  PubMed  Article  Google Scholar 

24.
Schiegg, K., Pasinelli, G., Walters, J. R. & Daniels, S. J. Inbreeding and experience affect response to climate change by endangered woodpeckers. Proc. R. Soc. B 269, 1153–1159 (2002).
PubMed  Article  Google Scholar 

25.
Wilson, S., Norris, D. R., Wilson, A. G. & Arcese, P. Breeding experience and population density affect the ability of a songbird to respond to future climate variation. Proc. R. Soc. B 274, 2539–2545 (2007).
PubMed  Article  Google Scholar 

26.
Dunn, P. O. & Winkler, D. W. Climate change has affected the breeding date of tree swallows throughout North America. Proc. R. Soc. B 266, 2487–2490 (1999).
CAS  Article  Google Scholar 

27.
Hällfors, M. H. et al. Shifts in timing and duration of breeding for 73 boreal bird species over four decades. Proc. Natl Acad. Sci. USA 117, 18557–18565 (2020).
PubMed  Article  CAS  Google Scholar 

28.
Gienapp, P., Postma, E. & Visser, M. E. Why breeding time has not responded to selection for earlier breeding in a songbird population. Evolution 60, 2381 (2006).
PubMed  Article  Google Scholar 

29.
Jàrvinen, A. Global warming and egg size of birds. Ecography 17, 108–110 (1994).
Article  Google Scholar 

30.
Kitaysky, A. S. & Golubova, E. G. Climate change causes contrasting trends in reproductive performance of planktivorous and piscivorous alcids. J. Anim. Ecol. 69, 248–262 (2000).
Article  Google Scholar 

31.
Julliard, R., Clavel, J., Devictor, V., Jiguet, F. & Couvet, D. Spatial segregation of specialists and generalists in bird communities. Ecol. Lett. 9, 1237–1244 (2006).
PubMed  Article  Google Scholar 

32.
Weatherhead, P. J. Effects of climate variation on timing of nesting, reproductive success, and offspring sex ratios of red-winged blackbirds. Oecologia 144, 168–175 (2005).
ADS  PubMed  Article  Google Scholar 

33.
Auer, S. K. & Martin, T. E. Climate change has indirect effects on resource use and overlap among coexisting bird species with negative consequences for their reproductive success. Glob. Change Biol. 19, 411–419 (2013).
ADS  Article  Google Scholar 

34.
Riddell, E. A., Iknayan, K. J., Wolf, B. O., Sinervo, B. & Beissinger, S. R. Cooling requirements fueled the collapse of a desert bird community from climate change. Proc. Natl Acad. Sci. USA116, 21609–21615 (2019).
CAS  PubMed  Article  Google Scholar 

35.
Visser, M. E., Van Noordwijk, A. J., Tinbergen, J. M. & Lessells, C. M. Warmer springs lead to mistimed reproduction in great tits (Parus major). Proc. R. Soc. B 265, 1867–1870 (1998).
Article  Google Scholar 

36.
Both, C., Bouwhuis, S., Lessells, C. M. & Visser, M. E. Climate change and population declines in a long-distance migratory bird. Nature 441, 81–83 (2006).
ADS  CAS  PubMed  Article  Google Scholar 

37.
Magige, F. J., Stokke, B. G., Sortland, R. & Røskaft, E. Breeding biology of ostriches (Struthio camelus) in the Serengeti ecosystem, Tanzania. Afr. J. Ecol. 47, 400–408 (2009).
Article  Google Scholar 

38.
Bertram, B. C. R. The Ostrich Communal Nesting System (Princeton University Press, New Jersey, 1992).

39.
Kimwele, C. N. & Graves, J. A. A molecular genetic analysis of the communal nesting of the ostrich (Struthio camelus). Mol. Ecol. 12, 229–236 (2003).
CAS  PubMed  Article  Google Scholar 

40.
Maloney, S. K. Thermoregulation in ratites: a review. Aust. J. Exp. Agric. 48, 1293–1301 (2008).
Article  Google Scholar 

41.
Hassan, S. M., Siam, A. A., Mady, M. E. & Cartwright, A. L. Egg storage period and weight effects on hatchability of ostrich (Struthio camelus) eggs. Poult. Sci. 84, 1908–1912 (2005).
CAS  PubMed  Article  Google Scholar 

42.
Gonzalez, A., Satterlee, D. G., Moharer, F. & Cadd, G. G. Factors affecting ostrich egg hatchability. Poult. Sci. 78, 1257–1262 (1999).
CAS  PubMed  Article  Google Scholar 

43.
Roff, D. A. & Wilson, A. J. Quantifying genotype-by-environment interactions in laboratory systems. In Genotype‐by‐Environment Interactions and Sexual Selection (eds. Hunt, J. & Hosken, D.) 100–136 (John Wiley & Sons, Ltd, 2014).

44.
Christians, J. K. Avian egg size: variation within species and inflexibility within individuals. Biol. Rev. Camb. Philos. Soc. 77, 1–26 (2002).
PubMed  Article  Google Scholar 

45.
Lack, D. The Natural Regulation of Animal Numbers (Clarendon Press, 1954).

46.
Perrins, C. M. The timing of birds‘ breeding seasons. Ibis 112, 242–255 (1970).
Article  Google Scholar 

47.
Sales, K. et al. Experimental heatwaves compromise sperm function and cause transgenerational damage in a model insect. Nat. Commun. 9, 1–11 (2018).
ADS  CAS  Article  Google Scholar 

48.
McAfee, A. et al. Vulnerability of honey bee queens to heat-induced loss of fertility. Nat. Sustain 3, 367–376 (2020).
Article  Google Scholar 

49.
Pérez-Crespo, M., Pintado, B. & Gutiérrez-Adán, A. Scrotal heat stress effects on sperm viability, sperm DNA integrity, and the offspring sex ratio in mice. Mol. Reprod. Dev. 75, 40–47 (2008).
PubMed  Article  CAS  Google Scholar 

50.
Hansen, P. J. Effects of heat stress on mammalian reproduction. Philos. Trans. R. Soc. B 364, 3341–3350 (2009).
Article  Google Scholar 

51.
Moreno, R. D., Lagos-Cabre, R., Bunay, J., Urzua, N. & Bustamante-Marin, X. Molecular basis of heat stress damage in mammalian testis. In Testis: Anatomy, Physiology and Pathology (eds. Nemoto, Y. & Inaba, N.) 127–155 (Nova Science, 2012).

52.
Karaca, A. G., Parker, H. M., Yeatman, J. B. & McDaniel, C. D. The effects of heat stress and sperm quality classification on broiler breeder male fertility and semen ion concentrations. Br. Poult. Sci. 43, 621–628 (2002).
CAS  PubMed  Article  Google Scholar 

53.
Mita, P., Hinton, B. T. & Dufour, J. M. The blood–testis and blood–epididymis barriers are more than just their tight junctions. Biol. Reprod. 84, 851–858 (2011).
Article  CAS  Google Scholar 

54.
Smith, C. C. & Fretwell, S. D. The optimal balance between size and number of offspring. Am. Nat. 108, 499–506 (1974).
Article  Google Scholar 

55.
Ojanen, M. Composition of the eggs of the great tit (Parus major) and pied flycatcher (Ficedula hypoleuca). Ann. Zool. Fenn. 20, 57–63 (1983).
Google Scholar 

56.
Krist, M. Egg size and offspring quality: a meta-analysis in birds. Biol. Rev. 86, 692–716 (2011).
PubMed  Article  Google Scholar 

57.
Falconer, D. S. & Mackay, T. F. C. Introduction to Quantitative Genetics (Pearson, 1996).

58.
Lynch, M. & Gabriel, W. Environmental tolerance. Am. Nat. 129, 283–303 (1987).
Article  Google Scholar 

59.
Gilchrist, G. W. Specialists and generalists in changing environments. I. Fitness landscapes of thermal sensitivity. Am. Nat. 146, 252–270 (1995).
Article  Google Scholar 

60.
Whitlock, M. C. The red queen beats the jack-of-all-trades: the limitations on the evolution of phenotypic plasticity and niche breadth. Am. Nat. 148, S65 (1996).
Article  Google Scholar 

61.
Pen, I. & Weissing, F. J. Towards a unified theory of cooperative breeding: the role of ecology and life history re-examined. Proc. R. Soc. B 267, 2411–2418 (2000).
Article  Google Scholar 

62.
Emlen, S. T. The evolution of helping. I. An ecological constraints model. Am. Nat. 119, 29–39 (1982).
Article  Google Scholar 

63.
Rubenstein, D. R. Spatiotemporal environmental variation, risk aversion, and the evolution of cooperative breeding as a bet-hedging strategy. Proc. Natl Acad. Sci. USA 108, 10816–10822 (2011).
ADS  CAS  PubMed  Article  Google Scholar 

64.
Cornwallis, C. K. et al. Cooperation facilitates the colonization of harsh environments. Nat. Ecol. Evol. 1, 0057 (2017).
Article  Google Scholar 

65.
Rubenstein, D. R. & Lovette, I. J. Temporal environmental variability drives the evolution of cooperative breeding in birds. Curr. Biol. 17, 1414–1419 (2007).
CAS  PubMed  Article  Google Scholar 

66.
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, 201613625 (2017).
Google Scholar 

67.
Vincze, O. et al. Parental cooperation in a changing climate: fluctuating environments predict shifts in care division. Glob. Ecol. Biogeogr. 26, 347–358 (2017).
Article  Google Scholar 

68.
Nord, A. & Nilsson, J. Å. Heat dissipation rate constrains reproductive investment in a wild bird. Funct. Ecol. 33, 250–259 (2019).
Article  Google Scholar 

69.
Cloete, S. W. P. et al. Variance components for live weight, body measurements and reproductive traits of pair-mated ostrich females. Br. Poult. Sci. 47, 147–158 (2006).
CAS  PubMed  Article  Google Scholar 

70.
Rybnik, P. K., Horbanczuk, J. O., Naranowicz, H., Lukaszewicz, E. & Malecki, I. A. Semen collection in the ostrich (Struthio camelus) using a dummy or a teaser female. Br. Poult. Sci. 48, 635–643 (2007).
CAS  PubMed  Article  Google Scholar 

71.
Brand, T. S., Olivier, T. R. & Gous, R. M. The response in food intake and reproductive parameters of breeding ostriches to increasing dietary energy. South Afr. J. Anim. Sci. 40, 434–437 (2010).
Google Scholar 

72.
Brand, T. S., Olivier, T. R. & Gous, R. M. The reproductive response of female ostriches to dietary protein. Br. Poult. Sci. 56, 232–238 (2015).
CAS  PubMed  Article  Google Scholar 

73.
Martin, P. A., Reimers, T. J., Lodge, J. R. & Dziuk, P. J. The effect of ratios and numbers of spermatozoa mixed from two males on proportions of offspring. J. Reprod. Fertil. 39, 251–258 (1974).
CAS  PubMed  Article  Google Scholar 

74.
Birkhead, T. R. & Møller, A. P. Sperm Competition and Sexual Selection (Academic Press, 1998).

75.
Birkhead, T. R. & Biggins, J. D. Sperm competition mechanisms in birds: models and data. Behav. Ecol. 9, 253–260 (1998).
Article  Google Scholar 

76.
Soley, J. T. & Roberts, J. C. Ultrastructure of ostrich (Struthio camelus) spermatozoa. II. Scanning electron microscopy. Onderstepoort J. Vet. Res. 61, 239–246 (1994).
CAS  PubMed  Google Scholar 

77.
Lake, P. E. & Stewart, J. M. Artificial Insemination in Poultry. Ministry of Agriculture Fisheries and Food, Bulletin 213 (Her Majesty’s Stationery Office, 1978).

78.
Bonato, M., Malecki, I. A., Rybnik-Trzaskowska, P. K., Cornwallis, C. K. & Cloete, S. W. P. Predicting ejaculate quality and libido in male ostriches: effect of season and age. Anim. Reprod. Sci. 151, 49–55 (2014).
PubMed  Article  Google Scholar 

79.
Bonato, M., Rybnik, P. K., Malecki, I. A., Cornwallis, C. K. & Cloete, S. W. P. Twice daily collection yields greater semen output and does not affect male libido in the ostrich. Anim. Reprod. Sci. 123, 258–264 (2011).
PubMed  Article  Google Scholar 

80.
Muvhali, P. T. et al. Ostrich ejaculate characteristics and male libido around equinox and solstice dates. Trop. Anim. Health and Prod. 52, 2609–2619 (2020).
CAS  Article  Google Scholar 

81.
Brand, Z., Cloete, S. W. P., Brown, C. R. & Malecki, I. A. Systematic factors that affect ostrich egg incubation traits. South Afr. J. Anim. Sci. 38, 315–325 (2008).
Google Scholar 

82.
Bronneberg, R. G. G. et al. The relation between ultrasonographic observations in the oviduct and plasma progesterone, luteinizing hormone and estradiol during the egg laying cycle in ostriches. Domest. Anim. Endocrinol. 32, 15–28 (2007).
CAS  PubMed  Article  Google Scholar 

83.
Van Schalkwyk, S. J., Cloete, S. W. P. & De Kock, J. A. Repeatability and phenotypic correlations for body weight and reproduction in commercial ostrich breeding pairs. Br. Poult. Sci. 37, 953–962 (1996).
PubMed  Article  Google Scholar 

84.
Jones, R. C. & Lin, M. Spermatogenesis in birds. In Oxford Reviews of Reproductive Biology, Vol. 15 (ed. Milligan, S. R.) (Oxford University Press, 1993).

85.
R Core Team. R: A Language and Environment for Statistical Computing (R Core Team, 2020).

86.
Hadfield, J. D. MCMC methods for multi-response generalized linear mixed models: the MCMCglmm R package. J. Stat. Softw. 33, 1–22 (2010).
Article  Google Scholar 

87.
Araya-Ajoy, Y. G. & Dingemanse, N. J. Repeatability, heritability, and age-dependence of seasonal plasticity in aggressiveness in a wild passerine bird. J. Anim. Ecol. 86, 227–238 (2017).
PubMed  Article  Google Scholar 

88.
Araya-Ajoy, Y. G., Mathot, K. J. & Dingemanse, N. J. An approach to estimate short-term, long-term and reaction norm repeatability. Methods Ecol. Evol. 6, 1462–1473 (2015).
Article  Google Scholar 

89.
Scheiner, S. M. Genetics and evolution of phenotypic plasticity. Annu. Rev. Ecol. Syst. 24, 35–68 (1993).
Article  Google Scholar 

90.
Wilson, A. J. Why h2 does not always equal VA/VP. J. Evol. Biol. 21, 647–650 (2008).
CAS  PubMed  Article  Google Scholar 

91.
de Villemereuil, P., Morrissey, M. B., Nakagawa, S. & Schielzeth, H. Fixed-effect variance and the estimation of repeatabilities and heritabilities: Issues and solutions. J. Evol. Biol. 31, 621–632 (2018).
PubMed  Article  Google Scholar 

92.
de Villemereuil, P., Schielzeth, H., Nakagawa, S. & Morrissey, M. General methods for evolutionary quantitative genetic inference from generalized mixed models. Genetics 204, 1281–1294 (2016).
PubMed  PubMed Central  Article  Google Scholar 

93.
BirdLife International. BirdLife International and Handbook of the Birds of the World. Bird Species Distribution Maps of the World (BirdLife International, 2019).

94.
Fick, S. E. & Hijmans, R. J. WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37, 4302–4315 (2017).
Article  Google Scholar  More