Philippa Kaur

1.
Tisdale, H. The process of urbanization. Soc. Forces 20, 311–316 (1942).
Article  Google Scholar 
2.
McKinney, M. L. Urbanization, biodiversity, and conservation. Bioscience 52, 883–890 (2002).
Article  Google Scholar 

3.
Grimm, N. B. et al. Global change and the ecology of cities. Science 319, 756–760 (2008).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

4.
Johnson, M. T. J. & Munshi-South, J. Evolution of life in urban environments. Science 358, eaam8327 (2017).
PubMed  Article  CAS  PubMed Central  Google Scholar 

5.
Turrini, T., Sanders, D. & Knop, E. Effects of urbanization on direct and indirect interactions in a tri-trophic system. Ecol. Appl. 26, 664–675 (2016).
Article  Google Scholar 

6.
Theodorou, P. et al. Genome-wide single nucleotide polymorphism scan suggests adaptation to urbanization in an important pollinator, the red-tailed bumblebee (Bombus lapidarius L.). Proc. R. Soc. B Biol. Sci. 285, 20172806 (2018).
Article  Google Scholar 

7.
Thompson, K. A., Renaudin, M. & Johnson, M. T. J. Urbanization drives the evolution of parallel clines in plant populations. Proc. R. Soc. B Biol. Sci. 283, 20162180 (2016).
Article  Google Scholar 

8.
Theodorou, P., Baltz, L. M., Paxton, R. J. & Soro, A. Urbanisation is associated with shifts in bumblebee body size, with cascading effects on pollination. Evol. Appl. 10, 1–16 (2020).
Google Scholar 

9.
Ollerton, J., Winfree, R. & Tarrant, S. How many flowering plants are pollinated by animals?. Oikos 120, 321–326 (2011).
Article  Google Scholar 

10.
Potts, S. G., Vulliamy, B., Dafni, A., Nee’man, G. & Willmer, P. Linking bees and flowers: how do floral communities structure pollinator communities?. Ecology 84, 2628–2642 (2003).
Article  Google Scholar 

11.
Steffan-Dewenter, I. & Tscharntke, T. Succession of bee communities on fallows. Ecography 24, 83–93 (2001).
Article  Google Scholar 

12.
Fründ, J., Linsenmair, K. E. & Blüthgen, N. Pollinator diversity and specialization in relation to flower diversity. Oikos 119, 1581–1590 (2010).
Article  Google Scholar 

13.
Ebeling, A., Klein, A. M., Schumacher, J., Weisser, W. W. & Tscharntke, T. How does plant richness affect pollinator richness and temporal stability of flower visits?. Oikos 117, 1808–1815 (2008).
Article  Google Scholar 

14.
Theodorou, P. et al. The structure of flower visitor networks in relation to pollination across an agricultural to urban gradient. Funct. Ecol. 31, 838–847 (2017).
Article  Google Scholar 

15.
Biesmeijer, J. C. et al. Parallel declines in pollinators and insect-pollinated plants in Britain and the Netherlands. Science 313, 351–354 (2006).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

16.
Ghazoul, J. Floral diversity and the facilitation of pollination. J. Ecol. 94, 295–304 (2006).
Article  Google Scholar 

17.
Clough, Y. et al. Density of insect-pollinated grassland plants decreases with increasing surrounding land-use intensity. Ecol. Lett. 17, 1168–1177 (2014).
PubMed  Article  PubMed Central  Google Scholar 

18.
Lundgren, R., Totland, Ø. & Lázaro, A. Experimental simulation of pollinator decline causes community-wide reductions in seedling diversity and abundance. Ecology 97, 1420–1430 (2016).
PubMed  Article  PubMed Central  Google Scholar 

19.
Papanikolaou, A. D. et al. Wild bee and floral diversity co-vary in response to the direct and indirect impacts of land use. Ecosphere 8, e02008 (2017).
Article  Google Scholar 

20.
Brosi, B. J. & Briggs, H. M. Single pollinator species losses reduce floral fidelity and plant reproductive function. Proc. Natl. Acad. Sci. U. S. A. 110, 13044–13048 (2013).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

21.
Vázquez, D. P., Blüthgen, N., Cagnolo, L. & Chacoff, N. P. Uniting pattern and process in plant–animal mutualistic networks: a review. Ann. Bot. 103, 1445–1457 (2009).
PubMed  PubMed Central  Article  Google Scholar 

22.
Albrecht, J. et al. Plant and animal functional diversity drive mutualistic network assembly across an elevational gradient. Nat. Commun. 9, 3177 (2018).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

23.
Kremen, C. et al. Pollination and other ecosystem services produced by mobile organisms: a conceptual framework for the effects of land-use change. Ecol. Lett. 10, 299–314 (2007).
PubMed  Article  Google Scholar 

24.
Harrison, T. & Winfree, R. Urban drivers of plant-pollinator interactions. Funct. Ecol. 29, 879–888 (2015).
Article  Google Scholar 

25.
Baldock, K. C. R. et al. Where is the UK’s pollinator biodiversity? The importance of urban areas for flower-visiting insects. Proc. R. Soc. B Biol. Sci. 282, 20142849 (2015).
Article  Google Scholar 

26.
Bates, A. J. et al. Changing bee and hoverfly pollinator assemblages along an urban–rural gradient. PLoS ONE 6, e23459 (2011).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

27.
Fortel, L. et al. Decreasing abundance, increasing diversity and changing structure of the wild bee community (Hymenoptera: Anthophila) along an urbanization gradient. PLoS ONE 9, e104679 (2014).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

28.
Theodorou, P. et al. Urban areas as hotspots for bees and pollination but not a panacea for all insects. Nat. Commun. 11, 576 (2020).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

29.
Buchholz, S., Gathof, A. K., Grossmann, A. J., Kowarik, I. & Fischer, L. K. Wild bees in urban grasslands: urbanisation, functional diversity and species traits. Landsc. Urban Plan. 196, 103731 (2020).
Article  Google Scholar 

30.
Hung, K. J., Ascher, J. S., Davids, J. A. & Holway, D. A. Ecological filtering in scrub fragments restructures the taxonomic and functional composition of native bee assemblages. Ecology 100, e02654 (2019).
PubMed  Article  PubMed Central  Google Scholar 

31.
Buchholz, S. & Egerer, M. H. Functional ecology of wild bees in cities: towards a better understanding of trait-urbanization relationships. Biodivers. Conserv. 29, 2779–2801 (2020).
Article  Google Scholar 

32.
Cane, J. H., Minckley, R. L., Kervin, L. J., Roulston, T. H. & Williams, N. M. Complex responses within a desert bee guild (Hymenoptera: Apiformes) to urban habitat fragmentation. Ecol. Appl. 16, 632–644 (2006).
PubMed  Article  PubMed Central  Google Scholar 

33.
Banaszak-Cibicka, W. & Żmihorski, M. Wild bees along an urban gradient: winners and losers. J. Insect Conserv. 16, 331–343 (2011).
Article  Google Scholar 

34.
Neame, L. A., Griswold, T. & Elle, E. Pollinator nesting guilds respond differently to urban habitat fragmentation in an oak-savannah ecosystem. Insect Conserv. Divers. 6, 57–66 (2013).
Article  Google Scholar 

35.
Fitch, G. et al. Does urbanization favour exotic bee species? Implications for the conservation of native bees in cities. Biol. Lett. 15, 20190574 (2019).
PubMed  PubMed Central  Article  Google Scholar 

36.
Knapp, S., Kühn, I., Schweiger, O. & Klotz, S. Challenging urban species diversity: contrasting phylogenetic patterns across plant functional groups in Germany. Ecol. Lett. 11, 1054–1064 (2008).
PubMed  Article  PubMed Central  Google Scholar 

37.
Kühn, I., Brandl, R. & Klotz, S. The flora of German cities is naturally species rich. Evol. Ecol. Res. 6, 749–764 (2004).
Google Scholar 

38.
Knapp, S., Winter, M. & Klotz, S. Increasing species richness but decreasing phylogenetic richness and divergence over a 320-year period of urbanization. J. Appl. Ecol. 54, 1152–1160 (2016).
Article  Google Scholar 

39.
Lososová, Z. et al. Patterns of plant traits in annual vegetation of man-made habitats in central Europe. Perspect. Plant Ecol. Evol. Syst. 8, 69–81 (2006).
Article  Google Scholar 

40.
Pysek, P. Alien and native species in Central European urban floras: a quantitative comparison. J. Biogeogr. 25, 155–163 (1998).
Article  Google Scholar 

41.
Schleuning, M. et al. Ecological networks are more sensitive to plant than to animal extinction under climate change. Nat. Commun. 7, 13965 (2016).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

42.
Ollerton, J. Pollinator diversity: distribution, ecological function, and conservation. Annu. Rev. Ecol. Evol. Syst. 48, 353–376 (2017).
Article  Google Scholar 

43.
Schleuning, M., Fründ, J. & García, D. Predicting ecosystem functions from biodiversity and mutualistic networks: an extension of trait-based concepts to plant–animal interactions. Ecography 38, 380–392 (2014).
Article  Google Scholar 

44.
Mallinger, R. E., Gaines-Day, H. R. & Gratton, C. Do managed bees have negative effects on wild bees?: A systematic review of the literature. PLoS ONE 12, e0189268 (2017).
PubMed  PubMed Central  Article  CAS  Google Scholar 

45.
Potts, S. G. et al. Role of nesting resources in organising diverse bee communities in a Mediterranean landscape. Ecol. Entomol. 30, 78–85 (2005).
Article  Google Scholar 

46.
Pardee, G. L. & Philpott, S. M. Native plants are the bee’s knees: local and landscape predictors of bee richness and abundance in backyard gardens. Urban Ecosyst. 17, 641–659 (2014).
Article  Google Scholar 

47.
Ballare, K. M., Neff, J. L., Ruppel, R. & Jha, S. Multi-scalar drivers of biodiversity: local management mediates wild bee community response to regional urbanization. Ecol. Appl. 29, e01869 (2019).
PubMed  Article  PubMed Central  Google Scholar 

48.
Torné-Noguera, A. et al. Determinants of spatial distribution in a bee community: nesting resources, flower resources, and body size. PLoS ONE 9, e97255 (2014).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

49.
Baldock, K. C. R. et al. A systems approach reveals urban pollinator hotspots and conservation opportunities. Nat. Ecol. Evol. 3, 363–373 (2019).
PubMed  PubMed Central  Article  Google Scholar 

50.
Fetridge, E. D., Ascher, J. S. & Langellotto, G. A. The bee fauna of residential gardens in a suburb of New York City (Hymenoptera: Apoidea). Ann. Entomol. Soc. Am. 101, 1067–1077 (2008).
Article  Google Scholar 

51.
Stang, M., Klinkhamer, P. G. L. & van der Meijden, E. Size constraints and flower abundance determine the number of interactions in a plant–flower visitor web. Oikos 112, 111–121 (2006).
Article  Google Scholar 

52.
Scolozzi, R. & Geneletti, D. A multi-scale qualitative approach to assess the impact of urbanization on natural habitats and their connectivity. Environ. Impact Assess. Rev. 36, 9–22 (2012).
Article  Google Scholar 

53.
Cheptou, P.-O., Hargreaves, A. L., Bonte, D. & Jacquemyn, H. Adaptation to fragmentation: evolutionary dynamics driven by human influences. Philos. Trans. R. Soc. B Biol. Sci. 372, 2 (2017).
Google Scholar 

54.
Hennig, E. I. & Ghazoul, J. Plant–pollinator interactions within the urban environment. Perspect. Plant Ecol. Evol. Syst. 13, 137–150 (2011).
Article  Google Scholar 

55.
Winfree, R., Aguilar, R., Vázquez, D. P., LeBuhn, G. & Aizen, M. A. A meta-analysis of bees’ responses to anthropogenic disturbance. Ecology 90, 2068–2076 (2009).
PubMed  Article  PubMed Central  Google Scholar 

56.
Quantum GIS Development Team. Quantum GIS Geographic Information System. Open Source Geospatial Foundation Project. Available at: http://qgis.osgeo.org. (2014).

57.
Greenleaf, S. S., Williams, N. M., Winfree, R. & Kremen, C. Bee foraging ranges and their relationship to body size. Oecologia 153, 589–596 (2007).
ADS  PubMed  Article  PubMed Central  Google Scholar 

58.
Westphal, C. et al. Measuring bee diversity in different European habitats and biogeographical regions. Ecol. Monogr. 78, 653–671 (2008).
Article  Google Scholar 

59.
Amiet, F. & Gesellschaft, S. E. Insecta Helvetica. A, Fauna: 12. Hymenoptera. Apidae.-T. 1. Allgemeiner Teil, Gattungsschlüssel, Gattungen Apis, Bombus und Psithyrus. (Musée d’Histoire naturelle, 1996).

60.
Amiet, F., Herrmann, M., Müller, A. & Neumeyer, R. Fauna Helvetica 6. Apidae 3: Halictus, Lasioglossum. Fauna Helv. 6. Apidae 3 Halictus, Lasioglossum (2001).

61.
Amiet, F., Müller, A. & Neumeyer, R. Apidae 2: Colletes, Dufourea, Hylaeus, Nomia, Nomioides, Rhophitoides, Rophites, Sphecodes, Systropha. 4 (Schweizerische Entomologische Gesellschaft, 1999).

62.
Hebert, P. D. N., Cywinska, A., Ball, S. L. & de Waard, J. R. Biological identifications through DNA barcodes. Proc. R. Soc. Lond. B Biol. Sci. 270, 313–321 (2003).
CAS  Article  Google Scholar 

63.
Bäßler, M., Jäger, J. E. & Werner, K. Rothmaler, W. (Begr.): Exkursionsflora von Deutschland. Bd.2: Gefäßpflanzen. 17.Aufl (Berlin: Spektrum, 1999).

64.
Jäger, J. E., Wesche, K., Ritz, C., Müller, F. & Welk, E. Rothmaler – Exkursionsflora von Deutschland, Gefäßpflanzen: Atlasband (Springer-Verlag, 2013).

65.
Westrich, P. Die Wildbienen Deutschlands (Verlag Eugen Ulmer, 2018).

66.
Kattge, J. et al. TRY plant trait database—enhanced coverage and open access. Glob. Change Biol. 26, 119–188 (2020).
ADS  Article  Google Scholar 

67.
Botta-Dukát, Z. Rao’s quadratic entropy as a measure of functional diversity based on multiple traits. J. Veg. Sci. 16, 533–540 (2005).
Article  Google Scholar 

68.
Laliberté, E. & Legendre, P. A distance-based framework for measuring functional diversity from multiple traits. Ecology 91, 299–305 (2010).
PubMed  Article  Google Scholar 

69.
Rader, R., Bartomeus, I., Tylianakis, J. M. & Lalibert, E. The winners and losers of land use intensification: pollinator community disassembly is non-random and alters functional diversity. Divers. Distrib. 20, 908–917 (2014).
Article  Google Scholar 

70.
Faith, D. P. Conservation evaluation and phylogenetic diversity. Biol. Conserv. 61, 1–10 (1992).
Article  Google Scholar 

71.
Bartoń, K. MuMIn: Multi-Model Inference. R package version 1.15.1 (2013).

72.
Burnham, K. P. & Anderson, D. R. Multimodel inference. Sociol. Methods Res. 33, 261–304 (2004).
MathSciNet  Article  Google Scholar 

73.
Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).
Article  Google Scholar 

74.
Legendre, P., Galzin, R. & Harmelin-Vivien, M. L. Relating behavior to habitat: solutions to the fourth-corner problem. Ecology 78, 547–562 (1997).
Google Scholar 

75.
Wang, Y., Naumann, U., Eddelbuettel, D., Wilshire, J. & Warton, D. mvabund: Statistical Methods for Analysing Multivariate Abundance Data. R package version 4.1.3 (2020).

76.
Lefcheck, J. S. piecewiseSEM: piecewise structural equation modelling in r for ecology, evolution, and systematics. Methods Ecol. Evol. 7, 573–579 (2016).
Article  Google Scholar 

77.
Shipley, B. Confirmatory path analysis in a generalized multilevel context. Ecology 90, 363–368 (2009).
PubMed  Article  PubMed Central  Google Scholar 

78.
Sobel, M. E. Sociological methodology. In: Sociological Methodology (ed. Leinhart, S.) 290–312 (1982).

79.
Zuur, A., Ieno, E. N., Walker, N., Saveliev, A. A. & Smith, G. M. Mixed effects models and extensions in ecology with R (Springer, New York, 2009).
Google Scholar 

80.
Paradis, E., Claude, J. & Strimmer, K. APE: analyses of phylogenetics and evolution in R language. Bioinformatics 20, 289–290 (2004).
CAS  PubMed  Article  PubMed Central  Google Scholar 

81.
R Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing. http://www.r-project.org (2016). More

Deformation measurement accuracy
To evaluate the accuracy of ICT, synthetic deformation fields were added to the R-specimen datasets (Fig. 3a). For this purpose, constant strain was simultaneously introduced in both R and L directions ((varepsilon_{RR}),({ }varepsilon_{LL})). Absolute accuracy (hat{varepsilon }) was measured by adding synthetic deformation to the reference state #1 (#1 + synthetic), and then measuring with ICT strain of #1 + synthetic with respect to #1. Differential accuracy ({Delta }hat{varepsilon }) was measured by adding synthetic deformation to the deformed state #2 (#2 + synthetic), measuring with ICT strain of #2 + synthetic with respect to the reference state #1, and finally subtracting the ICT measured strain between #2 and #1.
Figure 3 shows ICT strain estimates for tracheids and wood rays. The accuracy is highest along the cell-cross section (RT for tracheids, LT for wood rays) and lowest in the cell longitudinal direction (L for tracheids, R for wood rays). Due to the tubular cell geometry (Fig. 2b,c) of both cell types, tracking is more challenging in the longitudinal cell axis, for which symmetry reduces the available landmarks (for instance, wood pits in Fig. 2) and deformation tracking accuracy. Absolute accuracy is limited by both cell segmentation and deformation parametrization. Differential accuracy is further influenced by experimental uncertainties between #1 and #2 acquisitions, such as vibration artifacts and sample relaxation strains. While absolute and differential accuracy are similar in cell-cross sections, differential accuracy is reduced with respect to absolute accuracy along the longitudinal cell axis. The sensitivity limit for strain measurements in tracheids is (varepsilon_{RR})  More

1.
Convention on Biological Diversity. https://www.cbd.int/doc/meetings/cop-bureau/cop-bur-2007/cop-bur-2007-10-14-en.pdf (2007).
2.
Coates, D. J., Byrne, M. & Moritz, C. Genetic diversity and conservation units: dealing with species-population continuum in the age of genomics. Front. Ecol. Evol. 6, 165. https://doi.org/10.3389/fevo.2018.00165 (2018).
Article  Google Scholar 

3.
Ralls, K., Ballou, J. D., Dudash, M. R., Eldridge, M. D. B. & Fenster, C. B. Call for a paradigm shift in the genetic management of fragmented populations. Conserv. Lett. 11, 1–6 (2018).
Article  Google Scholar 

4.
Ryder, O. A. Species conservation and systematics: the dilemma of subspecies. Trends Ecol. Evol. 1, 9–10 (1986).
Article  Google Scholar 

5.
Moritz, C. Defining evolutionary significant units. Trends Ecol. Evol. 9, 373–375 (1994).
CAS  PubMed  Article  Google Scholar 

6.
Waples, R. S. Pacific salmon, Oncorhynchus spp., and the definition of “species” under the Endangered Species Act. Marine Fish. Rev. 53, 11–22 (1991).
Google Scholar 

7.
Funk, W. C., McKay, J. K., Hohenlohe, P. A. & Allendorf, F. W. Harnessing genomics for delineating conservation units. Trends Ecol. Evol. 27, 489–496 (2012).
PubMed  PubMed Central  Article  Google Scholar 

8.
Green, D. M. Designatable units for status assessment of endangered species. Conserv. Biol. 19, 1813–1820 (2005).
Article  Google Scholar 

9.
Brodie, J. F., Redford, K. H. & Doak, D. F. Ecological function analysis: incorporating species roles into conservation. Trends Ecol. Evol. 33, 840–850 (2018).
PubMed  Article  Google Scholar 

10.
Paine, R. T. Food web complexity and species diversity. Am. Nat. 100, 65–75 (1966).
Article  Google Scholar 

11.
Decker, E., Linke, S., Hermoso, V. & Geist, J. Incorporating ecological functions in conservation decision making. Ecol. Evol. 7, 8273–8281 (2017).
PubMed  PubMed Central  Article  Google Scholar 

12.
Leclerc, C., Villéger, S., Marino, C. & Bellard, C. Global changes threaten functional and taxonomic diversity of insular species worldwide. Divers. Distrib. 26, 402–414 (2020).
Article  Google Scholar 

13.
Zipkin, E. F., DiRenzo, G. V., Ray, J. M., Rossman, S. & Lips, K. P. Tropical snake diversity collapses after widespread amphibian loss. Science 367, 814–816 (2020).
ADS  CAS  PubMed  Article  Google Scholar 

14.
Hedges, S. B. & Woods, C. A. Caribbean hot spot. Nature 364, 375. https://doi.org/10.1038/364375a0 (1993).
ADS  Article  Google Scholar 

15.
Heinicke, M. P., Duellman, W. E. & Hedges, S. B. Major Caribbean and Central American frog faunas originated by ancient oceanic dispersal. Proc. Natl. Acad. Sci. 104, 10092–10097 (2007).
ADS  CAS  PubMed  Article  Google Scholar 

16.
ITWG (Iguana Taxonomy Working Group). A checklist of the iguanas of the world (Iguanidae; Iguaninae). Herpetol. Conserv. Biol. 11, 4–46 (2016).

17.
Henderson, R. W. Consequences of predator introductions and habitat destruction on amphibians and reptiles in the post-Columbus West Indies. Caribb. J. Sci. 28, 1–10 (1992).
Google Scholar 

18.
Alberts, A. C. Developing recovery strategies for West Indian Rock Iguanas. Endangered Species UPDATA 16, 107–110 (1999).
Google Scholar 

19.
Hartley, L. M., Glor, R. E., Sproston, A. L., Powell, R. & Parmer-Lee, J. S. Jr. Germination rates of seeds consumed by two species of Rock Iguanas (Cyclura spp.) in the Dominican Republic. Caribb. J. Sci. 36, 149–151 (2000).
Google Scholar 

20.
Malone, C. L., Wheeler, T., Taylor, J. F. & Davis, S. K. Phylogeography of the Caribbean rock Iguana (Cyclura): implications for conservation and insights on the biogeographic history of the West Indies. Mol. Phylog. Evol. 17, 269–279 (2000).
CAS  Article  Google Scholar 

21.
Alberts, A. C. et al. (eds) Iguanas-Biology and Conservation (University of California Press, California, 2004).
Google Scholar 

22.
US Fish and Wildlife Report. Caribbean Iguana Conservation Workshop. Exploring a region-wide approach to recovery. San Juan, Puerto Rico. https://www.fws.gov/international/pdf/Caribbean-Iguana-Workshop-Proceedings.pdf (2013).

23.
González-Rossell, A. Ecologia y conservación de la iguana (Cyclura nubila nubila) en Cuba. Dissertation, Universitat d’Alacant (Alcalá de Henares, España, 2018).

24.
Rodríguez-Schettino, L. (ed.) The Iguanid lizards of Cuba (Florida University Press, Gainesville, 1999).
Google Scholar 

25.
Day, M. Cyclura nubila. The IUCN Red List of Threatened Species; e.T6030A12338655. https://dx.doi.org/https://doi.org/10.2305/IUCN.UK.1996.RLTS.T6030A12338655.en (1996)

26.
Knapp, C. R. & Malone, C. L. Patterns of reproductive success and genetic variability in a translocated iguana population. Herpetologica 59, 195–202 (2003).
Article  Google Scholar 

27.
Malone, C. L., Knapp, C. R., Taylor, J. F. & Davis, S. K. Genetic consequences of Pleistocene fragmentation: isolation, drift, and loss of diversity in rock iguanas (Cyclura). Conserv. Genet. 4, 1–15 (2003).
CAS  Article  Google Scholar 

28.
An, J., Sommer, J., Shore, G. D. & Williamson, J. E. Characterization of 20 microsatellite marker loci in the West Indian Rock Iguana (Cyclura nubila). Conserv. Genet. 5, 121–125 (2004).
CAS  Article  Google Scholar 

29.
Wildlife Conservation Society. Global Conservation Strategy. Mesoamerica and Western Caribbean. https://www.wcs.org/about-us/2020-strategy (2020).

30.
Critical Ecosystem Partnership Fund (CEPF). The Caribben Islands Biodiversity Hotspot. https://www.cepf.net/sites/default/files/final_caribbean_ep.pdf (2010).

31.
Waples, R. S. & Do, C. Linkage disequilibrium estimates of contemporary Ne using highly variable genetic markers: a largely untapped resource for applied conservation and evolution. Evol. Appl. 3, 244–262 (2010).
PubMed  Article  Google Scholar 

32.
Soltis, P. S. & Soltis, D. E. Applying the bootstrap in phylogeny reconstruction. Stat. Sci. 18, 256–267 (2003).
MathSciNet  MATH  Article  Google Scholar 

33.
Sites, J. W., Davis, S. K., Guerra, T., Iverson, J. B. & Snell, H. L. Character congruence and phylogenetic signal in molecular and morphological data sets: a case study in the living iguanas (Squamata, Iguanidae). Mol. Biol. Evol. 13, 1087–1105 (1996).
CAS  PubMed  Article  Google Scholar 

34.
Starostova, Z., Rehak, I. & Frynta, D. New haplotypes of Cyclura nubila nubila from Cuba changed the phylogenetic tree of rock iguanas: a challenge for conservation strategies?. Amphib-reptil 31, 134–143 (2010).
Article  Google Scholar 

35.
Allendorf, F. W. & Luikart, G. Conservation and the Genetics of Populations (Wiley-Blackwell, New York, 2006).
Google Scholar 

36.
England, P. R., Cornuet, J. M., Berthier, P., Tallmon, D. A. & Luikart, G. Estimating effective population size from linkage disequilibrium: severe bias in small samples. Conserv. Genet. 7, 303–308 (2007).
Article  Google Scholar 

37.
Sunny, A., Monroy-Vilchis, O., Fajardo, V. & Aguilera-Reyes, U. Genetic diversity and structure of an endemic and critically endangered stream river salamander (Caudata: Ambystoma leorae) in Mexico. Conserv. Genet. 15, 49–59 (2014).
CAS  Article  Google Scholar 

38.
Franklin, I. R. & Frankham, R. How large must populations be to retain evolutionary potential?. Anim. Conserv. 1, 79–70 (1998).
Article  Google Scholar 

39.
Vázquez-Domínguez, E., Suárez-Atilano, M., Booth, W., González-Baca, C. & Cuarón, A. D. Genetic evidence of a recent successful colonization of introduced species on islands: Boa constrictor imperator on Cozumel Island. Biol. Invasions 14, 2101–2116 (2012).
Article  Google Scholar 

40.
Frankham, R., Ballou, J. & Briscoe, D. Introduction to Conservation Genetics (Cambridge University Press, Cambridge, 2005).
Google Scholar 

41.
Iturralde-Vinent, M. A. Meso-Cenozoic Caribbean paleogeography: Implications for the historical biogeography of the region. Int. Geol. Rev. 48, 791–827 (2006).
Article  Google Scholar 

42.
Iturralde-Vinent, M. A. & MacPhee, R. D. E. Paleogeography of the Caribbean region: Implications for Cenozoic Biogeography. Bull. Am. Mus. Nat. Hist. 238, 1–95 (1999).
Google Scholar 

43.
Rodríguez, A. Biogeographic origin and radiation of Cuban Eleutherodactylus frogs of the auriculatus species group, inferred from mitochondrial and nuclear gene sequences. Mol. Phylog. Evol. 54, 179–186 (2010).
Article  Google Scholar 

44.
Cobos, M. E. & Bosch, R. A. Recent and future threats to the endangered Cuban toad Peltophryne longinasus: potential additive impacts of climate change and habitat loss. Oryx 52, 116–125 (2018).
Article  Google Scholar 

45.
Robertson, J. M. et al. Identifying evolutionarily significant units and prioritizing populations for management of islands. West. N. Am. Nat. 7, 397–411 (2014).
Google Scholar 

46.
Burton, F. J. Revision to species of Cyclura nubila lewisi, the Grand Cayman Blue Iguana. Caribb. J. Sci. 40, 198–203 (2004).
Google Scholar 

47.
Dinerstein, E. & Olson, D. M. A Conservation Assessment of the Terrestrial Ecoregions of Latin America and the Caribbean (The World Bank in Association with WWF, Washington, 1995).
Google Scholar 

48.
Wasser, S. K. et al. Assigning African elephant DNA to geographic region of origin: applications to the ivory trade. Proc. Natl. Acad. Sci. 101, 14847–14852 (2004).
ADS  CAS  PubMed  Article  Google Scholar 

49.
Zhang, H. et al. Molecular tracing of confiscated pangolin scales for conservation and illegal trade monitoring in Southeast Asia. Global Ecol. Conserv. 4, 412–422 (2015).
Google Scholar 

50.
Shaney, K. J. et al. A suite of potentially amplifiable microsatellite loci for reptiles of conservation concern from Africa and Asia. Conserv. Genet. Res. 8, 307–311 (2016).
Article  Google Scholar 

51.
de Miranda, E. B. P. The plight of reptiles as ecological actors in the tropics. Front. Ecol. Evol. 5, 159. https://doi.org/10.3389/fevo.2017.00159 (2017).
Article  Google Scholar 

52.
Beovides-Casas, K. & Mancina, C. A. Natural history and morphometry of the Cuban iguana (Cyclura nubila Gray, 1831) in Cayo Sijú Cuba. Anim. Biodiv. Conserv. 29, 1–8 (2006).
Google Scholar 

53.
HACC. Guidelines for use of live amphibians and reptiles in field and laboratory research. Revised by the Herpetological Animal Care and Use Committee of the American Society of Ichthyologists and Herpetologists (Committee Chair: Steven J. Beaupre, Members: Elliott R. Jacobson, Harvey B. Lillywhite, and Kelly Zamudio) (2014).

54.
Chatterji, S. & Pachter, L. Reference based annotation with GeneMapper. Genome Biol. 7, 29. https://doi.org/10.1186/gb-2006-7-4-r29 (2006).
CAS  Article  Google Scholar 

55.
Arévalo, E., Davis, S. K. & Sites, J. W. Mitochondrial DNA sequence divergence and phylogenetic relationships among eight chromosome races of the Sceloporus grammicus complex (Phrynosomatidae) in central Mexico. Syst. Biol. 43, 387–418 (1994).
Article  Google Scholar 

56.
Kearse, M., Moir, R., Wilson, M. & Stones-Havas, S. Geneious Basic: an integrated and extendable desktop software platform for the organization and analysis of sequence data. Bioinformatics 28, 1647–1649 (2012).
PubMed  PubMed Central  Article  Google Scholar 

57.
Raymond, M. & Rousset, F. GENEPOP (version 1.2): Population genetics software for exact tests and ecumenicism. J. Heredity 86, 248–249 (1995).
Article  Google Scholar 

58.
Rice, W. R. Analysing tables of statistical test. Evolution 43, 223–225 (1989).
PubMed  Article  Google Scholar 

59.
Van Oosterhout, C., Hutchinson, W. F., Willis, D. P. & Shipley, P. MICRO-CHECKER: software for identifying and correcting genotyping error in microsatellites data. Mol. Ecol. Notes 4, 535–538 (2004).
Article  CAS  Google Scholar 

60.
Nei, M. Estimation of average heterozygosity and genetic distance from a small number of individuals. Genetics 89, 583–590 (1978).
CAS  PubMed  PubMed Central  Google Scholar 

61.
Peakall, R. & Smouse, P. E. GENALEX 6: genetic analysis in Excel. Population genetic software for teaching and research. Mol. Ecol. Notes 6, 288–295 (2006).
Article  Google Scholar 

62.
Jombart, T. adegenet: a R package for the multivariate analysis of genetic markers”. Bioinformatics 24, 1403–1405 (2008).
CAS  Article  Google Scholar 

63.
Do, C. et al. NeEstimator V2: Sre-implementation of software for estimation of contemporary effective population size (Ne) from genetic data. Mol. Ecol. 14, 209–214 (2014).
CAS  Article  Google Scholar 

64.
Chybicki, I. J. & Burczyk, J. Simultaneous estimation of null alleles and inbreeding coefficients. J. Heredity 100, 106–113 (2009).
CAS  Article  Google Scholar 

65.
Cornuet, J. M. & Luikart, G. Description and power analysis of two tests for detecting recent population bottlenecks from allele frequency data. Genetics 144, 2001–2014 (1996).
CAS  PubMed  PubMed Central  Google Scholar 

66.
Garza, J. C. & Williamson, E. G. Detection of reduction in population size using data from microsatellite loci. Mol. Ecol. 10, 305–318 (2001).
CAS  PubMed  Article  Google Scholar 

67.
Foll, M. & Gaggiotti, O. E. Identifying the environmental factors that determine the genetic structure of populations. Genetics 174, 875–891 (2006).
CAS  PubMed  PubMed Central  Article  Google Scholar 

68.
Goudet, J. FSTAT (Version 1.2): a computer program to calculate F-Statisitics. J. Heredity 86, 485–486 (1995).
Article  Google Scholar 

69.
Nei, M. Genetic distance between populations. Am. Nat. 106, 283–292 (1972).
Article  Google Scholar 

70.
Pritchard, J. K., Stephens, M. & Donnelly, P. Inference of population structure using multilocus genotype data. Genetics 155, 945–959 (2000).
CAS  PubMed  PubMed Central  Google Scholar 

71.
Evanno, G., Regnaut, S. & Goudet, J. Detecting the number of clusters of individuals using the software Structure: a simulation study. Mol. Ecol. 14, 2611–2620 (2005).
CAS  PubMed  Article  Google Scholar 

72.
Earl, D. A. & von Holdt, B. M. Structure-harvester: a website and program for visualizing Structure output and implementing the Evanno method. Conserv. Genet. Res. 4, 359–361 (2012).
Article  Google Scholar 

73.
Excoffier, L., Laval, G. & Schneider, S. Arlequin ver. 3.0: an integrated software package for population genetics data analysis. Evol. Bioinform. Online 1, 47–50 (2005).
CAS  Article  Google Scholar 

74.
Kalinowski, S. T., Wagner, A. P. & Taper, M. L. ML-Relate: a computer program for maximum likelihood estimation of relatedness and relationship. Mol. Ecol. Notes 6, 576–579 (2006).
CAS  Article  Google Scholar 

75.
Guindon, S. et al. New algorithms and methods to estimate maximum-likelihood phylogenies: assessing the performance of PhyML 3.0. Syst. Biol. 59, 307–321 (2010).
CAS  PubMed  Article  Google Scholar 

76.
Leigh, J. W. & Bryant, D. PopART: full-feature software for haplotype network construction. Methods Ecol. Evol. 6, 1110–1116 (2015).
Article  Google Scholar 

77.
Bandelt, H. J., Forster, P. & Röhl, A. Median-joining networks for inferring intraspecific phylogenies. Mol. Ecol. Biol. 16, 37–48 (1999).
CAS  Article  Google Scholar 

78.
Suchard, M. A. et al. Bayesian phylogenetic and phylodynamic data integration using BEAST 110. Virus Evol. https://doi.org/10.1093/ve/vey016 (2018).
Article  PubMed  PubMed Central  Google Scholar 

79.
Blankers, T. et al. Contrasting global-scale evolutionary radiations: Phylogeny, diversification, and morphological evolution in the major clades of iguanian lizards. Biol. J. Linn. Soc. 108, 127–143 (2012).
Article  Google Scholar 

80.
Pyron, R. A., Burbrink, F. T. & Wiens, J. J. A phylogeny and revised classification of Squamata, including 4161 species of lizards and snakes. BMC Evol. Biol. 13, 93. https://doi.org/10.1186/1471-2148-13-93 (2013).
CAS  Article  PubMed  PubMed Central  Google Scholar 

81.
Carroll, R. L. Vertebrate Paleontology and Evolution (WH Freeman, New York, 1988).
Google Scholar 

82.
Townsend, T. M. et al. Phylogeny of iguanian lizards inferred from 29 nuclear loci, and a comparison of concatenated and species-tree approaches for an ancient, rapid radiation. Mol. Phylogen. Evol. 61, 363–380 (2011).
Article  Google Scholar 

83.
MacLeod, A. Hybridization masks speciation in the evolutionary history of the Galápagos marine iguana. Proc. R. Soc. B 282, 20150425. https://doi.org/10.1098/rspb.2015.0425 (2015).
Article  PubMed  Google Scholar  More

1.
Tilman, D. Competition and biodiversity in spatially structured habitats. Ecology 75, 2–16 (1994).
Article  Google Scholar 
2.
Matesanz, S., Gimeno, T. E., de la Cruz, M., Escudero, A. & Valladares, F. Competition may explain the fine-scale spatial patterns and genetic structure of two co-occurring plant congeners: Spatial genetic structure of congeneric plants. J. Ecol. 99, 838–848 (2011).
CAS  Article  Google Scholar 

3.
Fridley, J. D., Grime, J. P. & Bilton, M. Genetic identity of interspecific neighbours mediates plant responses to competition and environmental variation in a species-rich grassland. J. Ecol. 95, 908–915 (2007).
Article  Google Scholar 

4.
Baron, E., Richirt, J., Villoutreix, R., Amsellem, L. & Roux, F. The genetics of intra- and interspecific competitive response and effect in a local population of an annual plant species. Funct. Ecol. 29, 1361–1370 (2015).
Article  Google Scholar 

5.
McGoey, B. V. & Stinchcombe, J. R. Interspecific competition alters natural selection on shade avoidance phenotypes in Impatiens capensis. New Phytol. 183, 880–891 (2009).
PubMed  Article  PubMed Central  Google Scholar 

6.
Vellend, M. The Consequences of genetic diversity in competitive communities. Ecology 87, 304–311 (2006).
PubMed  Article  PubMed Central  Google Scholar 

7.
Turkington, R. The growth, distribution and neighbours relationships of Trifolium repens in a permanent pasture. VI. Conditioning effects by neighbours. J. Ecol. 77, 734 (1989).
Article  Google Scholar 

8.
Sultan, E. Phenotypic plasticity and plant adaptation. Acta Bot. Neerl. 44, 363–383 (1995).
Article  Google Scholar 

9.
Via, S. et al. Adaptive phenotypic plasticity: Consensus and controversy. Trends Ecol. Evol. 10, 212–217 (1995).
CAS  PubMed  Article  PubMed Central  Google Scholar 

10.
Vermeulen, P. J. On selection for flowering time plasticity in response to density. New Phytol. 205, 429–439 (2015).
PubMed  Article  PubMed Central  Google Scholar 

11.
Geber, M. A. & Griffen, L. R. Inheritance and natural selection on functional traits. Int. J. Plant Sci. 164, S21–S42 (2003).
Article  Google Scholar 

12.
Dudley, S. A. & Schmitt, J. Testing the adaptive plasticity hypothesis: Density-dependent selection on manipulated stem length in Impatiens capensis. Am. Nat. 147, 445–465 (1996).
Article  Google Scholar 

13.
Boege, K. Induced responses to competition and herbivory: Natural selection on multi-trait phenotypic plasticity. Ecology 91, 2628–2637 (2010).
PubMed  Article  PubMed Central  Google Scholar 

14.
Munguía-Rosas, M. A., Ollerton, J., Parra-Tabla, V. & De-Nova, J. A. Meta-analysis of phenotypic selection on flowering phenology suggests that early flowering plants are favoured. Ecol. Lett. 14, 511–521 (2011).
PubMed  Article  PubMed Central  Google Scholar 

15.
Weis, A., Wadgymar, S., Sekor, M. & Franks, S. The shape of selection: Using alternative fitness functions to test predictions for selection on flowering time. Evol. Ecol. 28, 885–904 (2014).
Article  Google Scholar 

16.
Juenger, T., Lennartsson, T. & Tuomi, J. The evolution of tolerance to damage in Gentianella campestris: Natural selection and the quantitative genetics of tolerance. Evol. Ecol. 14, 393 (2000).
Article  Google Scholar 

17.
Kenney, A. M., McKay, J. K., Richards, J. H. & Juenger, T. E. Direct and indirect selection on flowering time, water-use efficiency (WUE, δ13 C), and WUE plasticity to drought in Arabidopsis thaliana. Ecol. Evol. 4, 4505–4521 (2014).
PubMed  PubMed Central  Article  Google Scholar 

18.
Leverett, L. D., Iv, G. F. S. & Donohue, K. The fitness benefits of germinating later than neighbors. Am. J. Bot. 105, 20–30 (2018).
PubMed  Article  PubMed Central  Google Scholar 

19.
Weinig, C., Johnston, J., German, Z. M. & Demink, L. M. Local and global costs of adaptive plasticity to density in Arabidopsis thaliana. Am. Nat. 167, 826–836 (2006).
CAS  PubMed  Article  PubMed Central  Google Scholar 

20.
Callahan, H. S. & Pigliucci, M. Shade-Induced plasticity and its ecological significance in wild populations of Arabidopsis thaliana. Ecology 83, 1965–1980 (2002).
Article  Google Scholar 

21.
Manzano-Piedras, E., Marcer, A., Alonso-Blanco, C. & Picó, F. X. Deciphering the adjustment between environment and life history in annuals: Lessons from a geographically-explicit approach in Arabidopsis thaliana. PLoS ONE 9, e87836 (2014).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

22.
Sandring, S., Riihimäki, M.-A., Savolainen, O. & Ågren, J. Selection on flowering time and floral display in an alpine and a lowland population of Arabidopsis lyrata. J. Evol. Biol. 20, 558–567 (2007).
CAS  PubMed  Article  PubMed Central  Google Scholar 

23.
Weinig, C. Differing selection in alternative competitive environments: Shade-avoidance responses and germination timing. Evolution 54, 124–136 (2000).
CAS  PubMed  Article  PubMed Central  Google Scholar 

24.
Lande, R. & Arnold, S. J. The measurement of selection on correlated characters. Evolution 37, 1210–1226 (1983).
PubMed  Article  PubMed Central  Google Scholar 

25.
Pigliucci, M. & Kolodynska, A. Phenotypic plasticity to light intensity in Arabidopsis thaliana: Invariance of reaction norms and phenotypic integration. Evol. Ecol. 16, 27–47 (2002).
Article  Google Scholar 

26.
Pigliucci, M. & Preston, K. A. Phenotypic Integration. Studying the Ecology and Evolution of Complex Phenotypes (Oxford University Press, Oxford, 2004).
Google Scholar 

27.
Schlichting, C. D. Phenotypic integration and environmental change. Bioscience 39, 460–464 (1989).
Article  Google Scholar 

28.
Brock, M. T. & Weinig, C. Plasticity and environment-specific covariances: An investigation of floral–vegetative and within flower correlations. Evolution 61, 2913–2924 (2007).
PubMed  Article  PubMed Central  Google Scholar 

29.
Lind, M. I., Yarlett, K., Reger, J., Carter, M. J. & Beckerman, A. P. The alignment between phenotypic plasticity, the major axis of genetic variation and the response to selection. Proc. R. Soc. B Biol. Sci. 282, 20151651 (2015).
Article  Google Scholar 

30.
Crespi, B. J. The evolution of maladaptation. Heredity 84, 623 (2000).
PubMed  Article  Google Scholar 

31.
DeWitt, T. J. & Scheiner, S. M. Phenotypic Plasticity: Functional and Conceptual Approaches (Oxford University Press, Oxford, 2004).
Google Scholar 

32.
Nicotra, A. B. et al. Plant phenotypic plasticity in a changing climate. Trends Plant Sci. 15, 684–692 (2010).
CAS  PubMed  Article  Google Scholar 

33.
Scheiner, S. M. Genetics and evolution of phenotypic plasticity. Annu. Rev. Ecol. Syst. 24, 35–68 (1993).
Article  Google Scholar 

34.
Palacio-López, K., Beckage, B., Scheiner, S. & Molofsky, J. The ubiquity of phenotypic plasticity in plants: A synthesis. Ecol. Evol. 5, 3389–3400 (2015).
PubMed  PubMed Central  Article  Google Scholar 

35.
Turcotte, M. M. & Levine, J. M. Phenotypic plasticity and species coexistence. Trends Ecol. Evol. 31, 803–813 (2016).
PubMed  Article  Google Scholar 

36.
Goldberg, D. E. & Barton, A. M. Patterns and consequences of interspecific competition in natural communities: A review of field experiments with plants. Am. Nat. 139, 771–801 (1992).
Article  Google Scholar 

37.
Van Kleunen, M. & Fischer, M. Constraints on the evolution of adaptive phenotypic plasticity in plants: Research review. New Phytol. 166, 49–60 (2005).
PubMed  Article  Google Scholar 

38.
Stinchcombe, J. R., Dorn, L. A. & Schmitt, J. Flowering time plasticity in Arabidopsis thaliana: A reanalysis of Westerman & Lawrence (1970): Flowering time plasticity in Arabidopsis. J. Evol. Biol. 17, 197–207 (2003).
Article  Google Scholar 

39.
Scheiner, S. M. & Holt, R. D. The genetics of phenotypic plasticity. X. Variation versus uncertainty: Plasticity, variation, and uncertainty. Ecol. Evol. 2, 751–767 (2012).
PubMed  PubMed Central  Article  Google Scholar 

40.
Scheiner, S. M. Bet-hedging as a complex interaction among developmental instability, environmental heterogeneity, dispersal, and life-history strategy. Ecol. Evol. 4, 505–515 (2014).
PubMed  PubMed Central  Article  Google Scholar 

41.
DeWitt, T. J., Sih, A. & Wilson, D. S. Costs and limits of phenotypic plasticity. Trends Ecol. Evol. 13, 77–81 (1998).
CAS  PubMed  Article  PubMed Central  Google Scholar 

42.
Dechaine, J. M., Johnston, J. A., Brock, M. T. & Weinig, C. Constraints on the evolution of adaptive plasticity: Costs of plasticity to density are expressed in segregating progenies. New Phytol. 176, 874–882 (2007).
PubMed  Article  PubMed Central  Google Scholar 

43.
Murren, C. J. et al. Constraints on the evolution of phenotypic plasticity: Limits and costs of phenotype and plasticity. Heredity 115, 293–301 (2015).
CAS  PubMed  PubMed Central  Article  Google Scholar 

44.
Auld, J. R., Agrawal, A. A. & Relyea, R. A. Re-evaluating the costs and limits of adaptive phenotypic plasticity. Proc. R. Soc. B Biol. Sci. 277, 503–511 (2010).
Article  Google Scholar 

45.
Callahan, H. S., Maughan, H. & Steiner, U. K. Phenotypic plasticity, costs of phenotypes, and costs of plasticity. Ann. N. Y. Acad. Sci. 1133, 44–66 (2008).
ADS  PubMed  Article  Google Scholar 

46.
Rausher, M. D. The measurement of selection on quantitative traits: Biases due to environmental covariances between traits and fitness. Evolution 46, 616–626 (1992).
PubMed  Article  Google Scholar 

47.
Calsbeek, B., Lavergne, S., Patel, M. & Molofsky, J. Comparing the genetic architecture and potential response to selection of invasive and native populations of reed canary grass. Evol. Appl. 4, 726–735 (2011).
PubMed  PubMed Central  Article  Google Scholar 

48.
Siepielski, A. M. et al. Precipitation drives global variation in natural selection. Science 355, 959–962 (2017).
ADS  CAS  PubMed  Article  Google Scholar 

49.
Agrawal, A. F. & Whitlock, M. C. Environmental duress and epistasis: How does stress affect the strength of selection on new mutations?. Trends Ecol. Evol. 25, 450–458 (2010).
PubMed  Article  Google Scholar 

50.
Arbuthnott, D. & Whitlock, M. C. Environmental stress does not increase the mean strength of selection. J. Evol. Biol. 31, 1599–1606 (2018).
PubMed  Article  PubMed Central  Google Scholar 

51.
Osmond, M. M. & de Mazancourt, C. How competition affects evolutionary rescue. Philos. Trans. R. Soc. B Biol. Sci. 368, 20120085 (2013).
Article  Google Scholar 

52.
Wood, C. W. & Brodie, E. D. Evolutionary response when selection and genetic variation covary across environments. Ecol. Lett. 19, 1189–1200 (2016).
PubMed  Article  PubMed Central  Google Scholar 

53.
Rowiński, P. K. & Rogell, B. Environmental stress correlates with increases in both genetic and residual variances: A meta-analysis of animal studies. Evolution 71, 1339–1351 (2017).
PubMed  Article  CAS  PubMed Central  Google Scholar 

54.
Stanton, M. L., Roy, B. A. & Thiede, D. A. Evolution in stressful environments. I. Phenotypic variability, phenotypic selection, and response to selection in five distinct environmental stresses. Evolution 54, 93–111 (2000).
CAS  PubMed  Article  PubMed Central  Google Scholar 

55.
Weigelt, A., Steinlein, T. & Beyschlag, W. Does plant competition intensity rather depend on biomass or on species identity?. Basic Appl. Ecol. 3, 85–94 (2002).
Article  Google Scholar 

56.
Dostál, P. Plant competitive interactions and invasiveness: Searching for the effects of phylogenetic relatedness and origin on competition intensity. Am. Nat. 177, 655–667 (2011).
PubMed  Article  PubMed Central  Google Scholar 

57.
Gaudet, C. L. & Keddy, P. A. A comparative approach to predicting competitive ability from plant traits. Nature 334, 242–243 (1988).
ADS  Article  Google Scholar 

58.
Goldberg, D. E. & Werner, P. A. Equivalence of competitors in plant communities: A null hypothesis and a field experimental approach. Am. J. Bot. 70, 1098–1104 (1983).
Article  Google Scholar 

59.
Débarre, F. & Gandon, S. Evolution in heterogeneous environments: Between soft and hard selection. Am. Nat. 177, E84–E97 (2011).
PubMed  Article  PubMed Central  Google Scholar 

60.
Kelley, J. L., Stinchcombe, J. R., Weinig, C. & Schmitt, J. Soft and hard selection on plant defence traits in Arabidopsis thaliana. Evol. Ecol. Res. 7, 287–302 (2005).
Google Scholar 

61.
Austen, E. J., Rowe, L., Stinchcombe, J. R. & Forrest, J. R. K. Explaining the apparent paradox of persistent selection for early flowering. New Phytol. 215, 929–934 (2017).
PubMed  Article  PubMed Central  Google Scholar 

62.
Lorts, C. M. & Lasky, J. R. Competition × drought interactions change phenotypic plasticity and the direction of selection on Arabidopsis traits. New Phytol. https://doi.org/10.1111/nph.16593 (2020).
Article  PubMed  PubMed Central  Google Scholar 

63.
Franks, S. J., Sim, S. & Weis, A. E. Rapid evolution of flowering time by an annual plant in response to a climate fluctuation. Proc. Natl. Acad. Sci. 104, 1278–1282 (2007).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

64.
Forrest, J. R. K. Plant size, sexual selection, and the evolution of protandry in dioecious plants. Am. Nat. 184, 338–351 (2014).
PubMed  Article  PubMed Central  Google Scholar 

65.
Wilczek, A. M. et al. Effects of genetic perturbation on seasonal life history plasticity. Science 323, 930–934 (2009).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

66.
Elzinga, J. A. et al. Time after time: Flowering phenology and biotic interactions. Trends Ecol. Evol. 22, 432–439 (2007).
PubMed  Article  PubMed Central  Google Scholar 

67.
Mitchell-Olds, T. Genetic constraints on life-history evolution: Quantitative-trait loci influencing growth and flowering in Arabidopsis thaliana. Evolution 50, 140 (1996).
PubMed  Article  PubMed Central  Google Scholar 

68.
Fournier-Level, A. et al. Paths to selection on life history loci in different natural environments across the native range of Arabidopsis thaliana. Mol. Ecol. 22, 3552–3566 (2013).
CAS  PubMed  Article  PubMed Central  Google Scholar 

69.
Hall, M. C., Dworkin, I., Ungerer, M. C. & Purugganan, M. Genetics of microenvironmental canalization in Arabidopsis thaliana. Proc. Natl. Acad. Sci. 104, 13717–13722 (2007).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

70.
Cho, L.-H., Yoon, J. & An, G. The control of flowering time by environmental factors. Plant J. 90, 708–719 (2017).
CAS  PubMed  Article  PubMed Central  Google Scholar 

71.
Pérez-Pérez, J. M., Serrano-Cartagena, J. & Micol, J. L. Genetic analysis of natural variations in the architecture of Arabidopsis thaliana vegetative leaves. Genetics 162, 24 (2002).
Google Scholar 

72.
Samis, K. E., Stinchcombe, J. R. & Murren, C. J. Population climatic history predicts phenotypic responses in novel environments for Arabidopsis thaliana in North America. Am. J. Bot. 106, 1068–1080 (2019).
PubMed  PubMed Central  Google Scholar 

73.
Taylor, M. A. et al. Large-effect flowering time mutations reveal conditionally adaptive paths through fitness landscapes in Arabidopsis thaliana. Proc. Natl. Acad. Sci. 116, 17890–17899 (2019).
CAS  PubMed  Article  PubMed Central  Google Scholar 

74.
Donohue, K., Messiqua, D., Pyle, E. H., Heschel, M. S. & Schmitt, J. Evidence of adaptive divergence in plasticity: Density- and site-dependent selection on shade-avoidance responses in Impatiens capensis. Evolution 6, 13 (2000).
Google Scholar 

75.
Huber, H. et al. Frequency and microenvironmental pattern of selection on plastic shade-avoidance traits in a natural population of Impatiens capensis. Am. Nat. 163, 548–563 (2004).
PubMed  Article  PubMed Central  Google Scholar 

76.
Stinchcombe, J. R., Agrawal, A. F., Hohenlohe, P. A., Arnold, S. J. & Blows, M. W. Estimating nonlinear selection gradients using quadratic regression coefficients: Double or nothing?. Evolution 62, 2435–2440 (2008).
PubMed  Article  PubMed Central  Google Scholar 

77.
Callahan, H. S., Dhanoolal, N. & Ungerer, M. C. Plasticity genes and plasticity costs: A new approach using an Arabidopsis recombinant inbred population. New Phytol. 166, 129–140 (2005).
CAS  PubMed  Article  PubMed Central  Google Scholar 

78.
Arnold, P. A., Nicotra, A. B. & Kruuk, L. E. B. Sparse evidence for selection on phenotypic plasticity in response to temperature. Philos. Trans. R. Soc. B Biol. Sci. 374, 20180185 (2019).
Article  Google Scholar 

79.
Acasuso-Rivero, C., Murren, C. J., Schlichting, C. D. & Steiner, U. K. Adaptive phenotypic plasticity for life-history and less fitness-related traits. Proc. R. Soc. B Biol. Sci. 286, 20190653 (2019).
Article  Google Scholar 

80.
Crispo, E. Modifying effects of phenotypic plasticity on interactions among natural selection, adaptation and gene flow. J. Evol. Biol. 21, 1460–1469 (2008).
CAS  PubMed  Article  PubMed Central  Google Scholar 

81.
Scheiner, S. M. The genetics of phenotypic plasticity. XII. Temporal and spatial heterogeneity. Ecol. Evol. 3, 4596–4609 (2013).
PubMed  PubMed Central  Article  Google Scholar 

82.
Hendry, A. P. Key questions on the role of phenotypic plasticity in eco-evolutionary dynamics. J. Hered. 107, 25–41 (2016).
PubMed  Article  PubMed Central  Google Scholar 

83.
Fordyce, J. A. The evolutionary consequences of ecological interactions mediated through phenotypic plasticity. J. Exp. Biol. 209, 2377–2383 (2006).
PubMed  Article  PubMed Central  Google Scholar 

84.
Agrawal, A. A. Phenotypic plasticity in the interactions and evolution of species. Science 294, 321–326 (2001).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

85.
Matesanz, S., Gianoli, E. & Valladares, F. Global change and the evolution of phenotypic plasticity in plants: Global change and plasticity. Ann. N. Y. Acad. Sci. 1206, 35–55 (2010).
ADS  PubMed  Article  PubMed Central  Google Scholar 

86.
Valladares, F., Gianoli, E. & Gómez, J. M. Ecological limits to plant phenotypic plasticity. New Phytol. 176, 749–763 (2007).
PubMed  Article  PubMed Central  Google Scholar 

87.
Callaway, R. M., Pennings, S. C. & Richards, C. L. Phenotypic plasticity and interactions among plants. Ecology 84, 1115–1128 (2003).
Article  Google Scholar 

88.
Chevin, L.-M. & Hoffmann, A. A. Evolution of phenotypic plasticity in extreme environments. Philos. Trans. R. Soc. B Biol. Sci. 372, 20160138 (2017).
Article  Google Scholar 

89.
Pigliucci, M. Ecology and evolutionary biology of Arabidopsis. Arab. Book 1, e0003 (2002).
Article  Google Scholar 

90.
Volis, S., Verhoeven, K. J. F., Mendlinger, S. & Ward, D. Phenotypic selection and regulation of reproduction in different environments in wild barley. J. Evol. Biol. 17, 1121–1131 (2004).
CAS  PubMed  Article  PubMed Central  Google Scholar 

91.
Sgrò, C. M. & Hoffmann, A. A. Genetic correlations, tradeoffs and environmental variation. Heredity 93, 241–248 (2004).
PubMed  Article  PubMed Central  Google Scholar 

92.
Reger, J., Lind, M. I., Robinson, M. R. & Beckerman, A. P. Predation drives local adaptation of phenotypic plasticity. Nat. Ecol. Evol. 2, 100–107 (2018).
PubMed  Article  PubMed Central  Google Scholar 

93.
Gianoli, E. & Palacio-López, K. Phenotypic integration may constrain phenotypic plasticity in plants. Oikos 118, 1924–1928 (2009).
Article  Google Scholar 

94.
Godoy, O., Valladares, F. & Castro-Díez, P. The relative importance for plant invasiveness of trait means, and their plasticity and integration in a multivariate framework. New Phytol. 195, 912–922 (2012).
PubMed  Article  PubMed Central  Google Scholar 

95.
Crawford, K. M. & Whitney, K. D. Population genetic diversity influences colonization success. Mol. Ecol. 19, 1253–1263 (2010).
CAS  PubMed  Article  PubMed Central  Google Scholar 

96.
Vasseur, F. et al. Climate as a driver of adaptive variations in ecological strategies in Arabidopsis thaliana. Ann. Bot. https://doi.org/10.1101/404210 (2018).
Article  PubMed  PubMed Central  Google Scholar 

97.
Hovick, S. M. & Whitney, K. D. Propagule pressure and genetic diversity enhance colonization by a ruderal species: A multi-generation field experiment. Ecol. Monogr. 89, e01368sa (2019).
Article  Google Scholar 

98.
Platt, A. et al. The scale of population structure in Arabidopsis thaliana. PLoS Genet. 6, e1000843 (2010).
PubMed  PubMed Central  Article  CAS  Google Scholar 

99.
Roach, D. A. & Wulff, R. D. Maternal effects in plants. Annu. Rev. Ecol. Syst. 18, 209–235 (1987).
Article  Google Scholar 

100.
McGlothlin, J. W. & Galloway, L. F. The contribution of maternal effects to selection response: An empirical test of competing models. Evolution 68, 549–558 (2014).
PubMed  Article  PubMed Central  Google Scholar 

101.
Dechaine, J., Brock, M. & Weinig, C. Maternal environmental effects of competition influence evolutionary potential in rapeseed (Brassica rapa). Evol. Ecol. 29, 77–91 (2015).
Article  Google Scholar 

102.
Beddows, A. R. Lolium Multiflorum Lam. J. Ecol. 61, 587–600 (1973).
Article  Google Scholar 

103.
Vilà, M., Gómez, A. & Maron, J. L. Are alien plants more competitive than their native conspecifics? A test using Hypericum perforatum L. Oecologia 137, 211–215 (2003).
ADS  PubMed  Article  PubMed Central  Google Scholar 

104.
Veiga, R. S. L. et al. Arbuscular mycorrhizal fungi reduce growth and infect roots of the non-host plant Arabidopsis thaliana. Plant Cell Environ. 36, 1926–1937 (2013).
PubMed  PubMed Central  Google Scholar 

105.
Scheiner, S. M. & Callahan, H. S. Measuring natural selection on phenotypic plasticity. Evolution 53, 1704–1713 (1999).
PubMed  Article  PubMed Central  Google Scholar 

106.
Wender, N. J., Polisetty, C. R. & Donohue, K. Density-dependent processes influencing the evolutionary dynamics of dispersal: A functional analysis of seed dispersal in Arabidopsis thaliana (Brassicaceae). Am. J. Bot. 92, 960–971 (2005).
PubMed  Article  PubMed Central  Google Scholar 

107.
Brachi, B., Aimé, C., Glorieux, C., Cuguen, J. & Roux, F. Adaptive value of phenological traits in stressful environments: Predictions based on seed production and laboratory natural election. PLoS ONE 7, e32069 (2012).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

108.
Li, B., Suzuki, J.-I. & Hara, T. Latitudinal variation in plant size and relative growth rate in Arabidopsis thaliana. Oecologia 115, 293–301 (1998).
ADS  PubMed  Article  PubMed Central  Google Scholar 

109.
Ågren, J., Oakley, C. G., McKay, J. K., Lovell, J. T. & Schemske, D. W. Genetic mapping of adaptation reveals fitness tradeoffs in Arabidopsis thaliana. Proc. Natl. Acad. Sci. 110, 21077–21082 (2013).
ADS  Article  CAS  Google Scholar 

110.
Sokal, R. R. & James, R. F. Biometry the Principles and Practice of Statistics in Biological Research (W.H. Freeman, New York, 1995).
Google Scholar 

111.
Stinchcombe, J. R. et al. Testing for environmentally induced bias in phenotypic estimates of natural selection: Theory and practice. Am. Nat. 160, 13 (2002).
Article  Google Scholar 

112.
Fischer, E. K., Ghalambor, C. K. & Hoke, K. L. Plasticity and evolution in correlated suites of traits. J. Evol. Biol. 29, 991–1002 (2016).
CAS  PubMed  Article  PubMed Central  Google Scholar 

113.
Handelsman, C. A., Ruell, E. W., Torres-Dowdall, J. & Ghalambor, C. K. Phenotypic plasticity changes correlations of traits following experimental introductions of Trinidadian guppies (Poecilia reticulata). Integr. Comp. Biol. 54, 794–804 (2014).
PubMed  Article  PubMed Central  Google Scholar  More

1.
Cossins, A. R. & Bowler, K. Temperature Biology of Animals (Chapman and Hall, London, 1987).
Google Scholar 
2.
Hochachka, P. W. & Somero, G. N. Biochemical Adaptation (Oxford University Press, Oxford, 2002).
Google Scholar 

3.
Wilmer, P., Stone, G. & Johnston, I. Environmental Physiology of Animals (Blackwell Publishing, Oxford, 2005).
Google Scholar 

4.
Huey, R. B., Berrigan, D., Gilchrist, G. W. & Herron, J. C. Testing the adaptive significance of acclimation: a strong inference approach. Am. Zool. 39, 323–336 (1999).
Article  Google Scholar 

5.
IUPS Thermal Commission. Glossary of terms for thermal physiology. Third edition. J. Therm. Biol. 28, 75–106 (2003).
Article  Google Scholar 

6.
Hazel, J. R. Influence of thermal acclimation on membrane lipid composition of rainbow trout liver. Am. J. Physiol. 236, R91-101 (1979).
CAS  PubMed  Google Scholar 

7.
Overgaard, J. et al. Effects of acclimation temperature on thermal tolerance and membrane phospholipid composition in the fruit fly Drosophila melanogaster. J. Insect Physiol. 54, 619 (2008).
CAS  PubMed  Article  Google Scholar 

8.
Moon, T. W. & Hochachka, P. W. Temperature and enzyme activity in poikilotherms. Biochem. J. 123, 695–705 (1971).
CAS  PubMed  PubMed Central  Article  Google Scholar 

9.
Storey, K. B. & Storey, J. M. Biochemical strategies of overwintering in the gall gly larva, Eurosta solidaginis: effect of low temperature acclimation on the activities of enzymes of intermediary metabolism. J. Comp. Physiol. 144, 191–199 (1981).
CAS  Article  Google Scholar 

10.
Tomanek, L. & Somero, G. N. Evolutionary and acclimation-induced variation in the heat-shock responses of congeneric marine snails (genus Tegula) from different thermal habitats: implications for limits of thermotolerance and biogeography. J. Exp. Biol. 202, 2925–2936 (1999).
CAS  PubMed  Google Scholar 

11.
Colinet, H., Overgaard, J., Com, E. & Sørensen, J. G. Proteomic profiling of thermal acclimation in Drosophila melanogaster. Insect Biochem. Mol. Biol. 43, 352–365 (2013).
CAS  PubMed  Article  Google Scholar 

12.
Lagerspetz, K. Y. H. & Vainio, L. A. Thermal behaviour of crustaceans. Biol. Rev. 81, 237–258 (2006).
PubMed  Article  Google Scholar 

13.
Bowler, K. Acclimation, heat shock and hardening. J. Therm. Biol. 30, 125–130 (2005).
Article  Google Scholar 

14.
Loeschcke, V. & Sørensen, J. G. Acclimation, heat shock and hardening—a response from evolutionary biology. J. Therm. Biol. 30, 255–257 (2005).
Article  Google Scholar 

15.
Collier, R. J., Baumgard, L. H., Zimbelman, R. B. & Xiao, Y. Heat stress: physiology of acclimation and adaptation. Anim. Front. 9, 12–19 (2019).
PubMed  Article  Google Scholar 

16.
Collier, R. J. et al. Use of gene expression microarrays for evaluating environmental stress tolerance at the cellular level in cattle. J. Anim. Sci. 84, E1-13 (2006).
PubMed  Article  Google Scholar 

17.
Kristensen, T. N., Kjeldal, H., Schou, M. F. & Nielsen, J. L. Proteomic data reveal a physiological basis for costs and benefits associated with thermal acclimation. J. Exp. Biol. 219, 969–976 (2016).
PubMed  Article  Google Scholar 

18.
MacMillan, H. A. et al. Cold acclimation wholly reorganizes the Drosophila melanogaster transcriptome and metabolome. Sci. Rep. 6, 28999 (2016).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

19.
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 

20.
Yao, C. L. & Somero, G. N. The impact of acute temperature stress on hemocytes of invasive and native mussels (Mytilus galloprovincialis and Mytilus californianus): DNA damage, membrane integrity, apoptosis and signaling pathways. J. Exp. Biol. 215, 4267–4277 (2012).
CAS  PubMed  Article  Google Scholar 

21.
Huey, R. B. & Stevenson, R. D. Integrating thermal physiology and ecology of ectotherms: a discussion of approaches. Am. Zool. 19, 357–366 (1979).
Article  Google Scholar 

22.
Huey, R. B. & Kingsolver, J. G. Evolution of thermal sensitivity of ectotherm performance. Trends Ecol. Evol. 4, 131–135 (1989).
CAS  PubMed  Article  Google Scholar 

23.
Angilletta, M. J., Niewiarowski, P. H. & Navas, C. A. The evolution of thermal physiology in ectotherms. J. Therm. Biol. 27, 249–268 (2002).
Article  Google Scholar 

24.
Schulte, P. M., Healy, T. M. & Fangue, N. A. Thermal performance curves, phenotypic plasticity, and the time scales of temperature exposure. Integr. Comp. Biol. 51, 691–702 (2011).
PubMed  Article  Google Scholar 

25.
Deere, J. A. & Chown, S. L. Testing the beneficial acclimation hypothesis and its alternatives for locomotor performance. Am. Nat. 168, 630–644 (2006).
PubMed  Article  Google Scholar 

26.
Gibert, P. & Huey, R. B. Chill-coma temperature in Drosophila: effects of developmental temperature, latitude, and phylogeny. Physiol. Biochem. Zool. 74, 429–434 (2001).
CAS  PubMed  Article  Google Scholar 

27.
Ayrinhac, A. et al. Cold adaptation in geographical populations of Drosophila melanogaster: phenotypic plasticity is more important than genetic variability. Funct. Ecol. 18, 700–706 (2004).
Article  Google Scholar 

28.
Lachenicht, M. W., Clusella-Trullas, S., Boardman, L., Le Roux, C. & Terblanche, J. S. Effects of acclimation temperature on thermal tolerance, locomotion performance and respiratory metabolism in Acheta domesticus L. (Orthoptera: Gryllidae). J. Insect Physiol. 56, 822–830 (2010).
CAS  PubMed  Article  Google Scholar 

29.
Colinet, H. & Hoffmann, A. A. Comparing phenotypic effects and molecular correlates of developmental, gradual and rapid cold acclimation responses in Drosophila melanogaster. Funct. Ecol. 26, 84–93 (2012).
Article  Google Scholar 

30.
Kellermann, V., van Heerwaarden, B. & Sgrò, C. M. How important is thermal history? Evidence for lasting effects of developmental temperature on upper thermal limits in Drosophila melanogaster. Proc. Biol. Sci. 31, 20170447 (2017).
Google Scholar 

31.
Schou, M. F. et al. Metabolic and functional characterization of effects of developmental temperature in Drosophila melanogaster. Am. J. Physiol. Regul. Integr. Comp. Physiol. 312, R211–R222 (2017).
PubMed  Article  Google Scholar 

32.
Klepsatel, P., Girish, T. N., Dircksen, H. & Gáliková, M. Reproductive fitness of Drosophila is maximised by optimal developmental temperature. J. Exp. Biol. 222, jeb202184 (2019).
PubMed  Article  Google Scholar 

33.
Frazier, M. R., Harrison, J. F., Kirkton, S. D. & Roberts, S. P. Cold rearing improves cold-flight performance in Drosophila via changes in wing morphology. J. Exp. Biol. 211, 2116–2122 (2008).
PubMed  Article  Google Scholar 

34.
Kristensen, T. N. et al. Costs and benefits of cold acclimation in field-released Drosophila. Proc. Natl. Acad. Sci. USA 105, 216–221 (2008).
ADS  CAS  PubMed  Article  Google Scholar 

35.
Zamudio, K. R., Huey, R. B. & Crill, W. D. Bigger isn’t always better: body size, developmental and parental temperature and male territorial success in Drosophila melanogaster. Anim. Behav. 49, 671–677 (1995).
Article  Google Scholar 

36.
Zwaan, B. J., Bijlsma, R. & Hoekstra, R. F. On the developmental theory of ageing. II. The effect of developmental temperature on longevity in relation to adult body size in D. melanogaster. Heredity 68, 123–130 (1992).
CAS  PubMed  Article  Google Scholar 

37.
Gibert, P., Huey, R. B. & Gilchrist, G. W. Locomotor performance of Drosophila melanogaster: interactions among developmental and adult temperatures, age, and geography. Evolution 55, 205–209 (2001).
CAS  PubMed  Article  Google Scholar 

38.
Rion, S. & Kawecki, T. J. Evolutionary biology of starvation resistance: what we have learned from Drosophila. J. Evol. Biol. 20, 1655–1664 (2007).
CAS  PubMed  Article  Google Scholar 

39.
Sokolova, I. M. Energy-limited tolerance to stress as a conceptual framework to integrate the effects of multiple stressors. Integr. Comp. Biol. 53, 597–608 (2013).
PubMed  Article  Google Scholar 

40.
Klepsatel, P., Gáliková, M., Xu, Y. & Kühnlein, R. P. Thermal stress depletes energy reserves in Drosophila. Sci. Rep. 6, 33667 (2016).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

41.
Klepsatel, P., Wildridge, D. & Gáliková, M. Temperature induces changes in Drosophila energy stores. Sci. Rep. 9, 5239 (2019).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

42.
Leroi, A. M., Bennett, A. F. & Lenski, R. E. Temperature acclimation and competitive fitness: an experimental test of the beneficial acclimation assumption. Proc. Natl. Acad. Sci. USA 91, 1917–1921 (1994).
ADS  CAS  PubMed  Article  Google Scholar 

43.
Beller, M., Thiel, K., Thul, P. J. & Jäckle, H. Lipid droplets: a dynamic organelle moves into focus. FEBS Lett. 584, 2176–2182 (2010).
CAS  PubMed  Article  Google Scholar 

44.
Olzmann, J. A. & Carvalho, P. Dynamics and functions of lipid droplets. Nat. Rev. Mol. Cell Biol. 20, 137–155 (2019).
CAS  PubMed  PubMed Central  Article  Google Scholar 

45.
Arrese, E. L. & Soulages, J. L. Insect fat body: energy, metabolism, and regulation. Annu. Rev. Entomol. 55, 207–255 (2010).
CAS  PubMed  PubMed Central  Article  Google Scholar 

46.
Giesy, J. P. & Graney, R. L. Recent developments in and intercomparisons of acute and chronic bioassays and bioindicators. Hydrobiologia 188(189), 21–60 (1989).
Article  Google Scholar 

47.
Smolders, R., Bervoets, L., De Coen, W. & Blust, R. Cellular energy allocation in zebra mussels exposed along a pollution gradient: linking cellular effects to higher levels of biological organization. Environ. Pollut. 129, 99–112 (2004).
CAS  PubMed  Article  Google Scholar 

48.
Angilletta, M. J. Thermal Adaptation: A Theoretical and Empirical Synthesis (Oxford University Press, Oxford, 2009).
Google Scholar 

49.
Schuler, M. S., Cooper, B. S., Storm, J. J., Sears, M. W. & Angilletta, M. J. Isopods failed to acclimate their thermal sensitivity of locomotor performance during predictable or stochastic cooling. PLoS ONE 6, e20905 (2011).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

50.
Ferguson, L. V., Heinrichs, D. E. & Sinclair, B. J. Paradoxical acclimation responses in the thermal performance of insect immunity. Oecologia 181, 77–85 (2016).
ADS  PubMed  Article  Google Scholar 

51.
MacLean, H. J. et al. Evolution and plasticity of thermal performance: an analysis of variation in thermal tolerance and fitness in 22 Drosophila species. Philos. Trans. R. Soc. Lond. B Biol. Sci. 374, 20180548 (2019).
PubMed  PubMed Central  Article  Google Scholar 

52.
Johnson, T. & Bennett, A. The thermal acclimation of burst escape performance in fish: an integrated study of molecular and cellular physiology and organismal performance. J. Exp. Biol. 198, 2165–2175 (1995).
CAS  PubMed  Google Scholar 

53.
Seebacher, F., Ducret, V., Little, A. G. & Adriaenssens, B. Generalist-specialist trade-off during thermal acclimation. R. Soc. Open. Sci. 2, 140251 (2015).
ADS  PubMed  PubMed Central  Article  Google Scholar 

54.
da Silva, C. R. B., Riginos, C. & Wilson, R. S. An intertidal fish shows thermal acclimation despite living in a rapidly fluctuating environment. J. Comp. Physiol. B 189, 385–398 (2019).
PubMed  Article  Google Scholar 

55.
Kingsolver, J. G. & Huey, R. B. Selection and evolution of morphological and physiological plasticity in thermally varying environments. Am. Zool. 38, 545–560 (1998).
Article  Google Scholar 

56.
Woods, H. A. & Harrison, J. F. The beneficial acclimation hypothesis versus acclimation of specific traits: physiological change in water-stressed Manduca sexta caterpillars. Physiol. Biochem. Zool. 74, 32–44 (2001).
CAS  PubMed  Article  Google Scholar 

57.
Gabriel, W. & Lynch, M. The selective advantage of reaction norms for environmental tolerance. J. Evol. Biol. 5, 41–59 (1992).
Article  Google Scholar 

58.
Cooper, B. S., Czarnoleski, M. & Angilletta, M. J. Acclimation of thermal physiology in natural populations of Drosophila melanogaster: a test of an optimality model. J. Evol. Biol. 23, 2346–2355 (2010).
CAS  PubMed  Article  Google Scholar 

59.
Nilsson-Örtman, V. & Johansson, F. The rate of seasonal changes in temperature alters acclimation of performance under climate change. Am. Nat. 190, 743–761 (2017).
PubMed  Article  Google Scholar 

60.
Shah, A. A., Funk, W. C. & Ghalambor, C. K. Thermal acclimation ability varies in temperate and tropical aquatic insects from different elevations. Integr. Comp. Biol. 57, 977–987 (2017).
PubMed  Article  Google Scholar 

61.
Angilletta, M. J., Condon, C. & Youngblood, J. P. Thermal acclimation of flies from three populations of Drosophila melanogaster fails to support the seasonality hypothesis. J. Therm. Biol. 81, 25–32 (2019).
PubMed  Article  Google Scholar 

62.
Hoffmann, A. A. & Watson, M. Geographical variation in the acclimation responses of Drosophila to temperature extremes. Am. Nat. 142, S93–S113 (1993).
PubMed  Article  Google Scholar 

63.
Bubliy, O. A., Riihimaa, A., Norry, F. M. & Loeschcke, V. Variation in resistance and acclimation to low-temperature stress among three geographical strains of Drosophila melanogaster. J. Therm. Biol. 27, 337–344 (2002).
Article  Google Scholar 

64.
Chown, S. L. Physiological variation in insects: hierarchical levels and implications. J. Insect Physiol. 47, 649–660 (2001).
CAS  PubMed  Article  Google Scholar 

65.
Terblanche, J. S., Sinclair, B. J., Klok, C. J., McFarlane, M. L. & Chown, S. L. The effects of acclimation on thermal tolerance, desiccation resistance and metabolic rate in Chirodica chalcoptera (Coleoptera: Chrysomelidae). J. Insect Physiol. 51, 1013–1023 (2005).
CAS  PubMed  Article  Google Scholar 

66.
Woods, H. A. & Harrison, J. F. Interpreting rejections of the beneficial acclimation hypothesis: when is physiological plasticity adaptive?. Evolution 56, 1863–1866 (2002).
PubMed  Article  Google Scholar 

67.
Somero, G. N. Comparative physiology: a ‘crystal ball’ for predicting consequences of global change. Am. J. Physiol. Regul. I(301), R1–R14 (2011).
ADS  Google Scholar 

68.
Abele, D., Heise, K., Pörtner, H. O. & Puntarulo, S. Temperature-dependence of mitochondrial function and production of reactive oxygen species in the intertidal mud clam Mya arenaria. J. Exp. Biol. 205, 1831–1841 (2002).
CAS  PubMed  Google Scholar 

69.
Martinez, E., Menze, M. A. & Agosta, S. J. Reduced mitochondrial efficiency explains mismatched growth and metabolic rate at supraoptimal temperatures. Physiol. Biochem. Zool. 90, 294–298 (2017).
PubMed  Article  Google Scholar 

70.
Kukal, O. & Dawson, T. E. Temperature and food quality influences feeding behavior, assimilation efficiency and growth rate of arctic woolly-bear caterpillars. Oecologia 79, 526–532 (1989).
ADS  PubMed  Article  Google Scholar 

71.
Butterworth, F. M. Adipose tissue of Drosophila melanogaster. V. Genetic and experimental studies of an extrinsic influence on the rate of cell death in the larval fat body. Dev. Biol. 28, 311–325 (1972).
CAS  PubMed  Article  Google Scholar 

72.
Aguila, J. R., Suszko, J., Gibbs, A. G. & Hoshizaki, D. K. The role of larval fat cells in adult Drosophila melanogaster. J. Exp. Biol. 210, 956–963 (2007).
PubMed  Article  Google Scholar 

73.
Gáliková, M., Klepsatel, P., Xu, Y. & Kuhnlein, R. P. The obesity-related Adipokinetic hormone controls feeding and expression of neuropeptide regulators of Drosophila metabolism. Eur. J. Lipid Sci. Technol. 119, 1600138 (2017).
Article  CAS  Google Scholar 

74.
Gáliková, M., Klepsatel, P., Münch, J. & Kühnlein, R. P. Spastic paraplegia-linked phospholipase PAPLA1 is necessary for development, reproduction, and energy metabolism in Drosophila. Sci Rep. 7, 46516 (2017).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

75.
Tennessen, J. M., Barry, W. E., Cox, J. & Thummel, C. S. Methods for studying metabolism in Drosophila. Methods 68, 105–115 (2014).
CAS  PubMed  PubMed Central  Article  Google Scholar 

76.
Gáliková, M. et al. Energy homeostasis control in Drosophila Adipokinetic hormone mutants. Genetics 201, 665–683 (2015).
PubMed  PubMed Central  Article  CAS  Google Scholar 

77.
Waner, S. & Constenoble, S. Finite Mathematics and Applied Calculus 7th edn. (Cengage Learning, Boston, MA, 2017).
Google Scholar 

78.
Bruce, P. S. Introductory Statistics and Analytics: A Resampling Perspective (Wiley, Hoboken, 2014).
Google Scholar  More

The meso-scale habitat simulation model MesoHABSIM21 was used to assess the impact of low-head HPPs on fish populations. MesoHABSIM is a physical habitat modelling system developed for e-flow assessment and river channel restoration planning. It describes the utility of instream habitat conditions for aquatic fauna, allowing to simulate change in habitat quality and quantity in response to alterations of flow and river hydromorphology. Meso-scale habitats are defined as geomorphic units (GUs, such as pools, riflles, rapids, glides22) that can be used by species and life stages for a significant part of their diurnal routine23. A meso-habitat can be considered suitable or optimal when the configuration of hydraulic patterns, together with the attributes that provide shelter, create favourable conditions for survival and development of animals. MesoHABSIM approach is based on the aggregation of three models24:
1.
A hydromorphological model that describes the spatial mosaic of fish-relevant hydro-morphological features.

2.
A biological model describing the relationship between the presence and abundance of fish and the physical environment of the river.

3.
A habitat model quantifying the amounts, frequency and duration of the available habitat depending on the flow regime and local river morphology.

For the modelling, the time series of daily water discharge data in natural and altered (downstream HPPs) conditions were created for wet, normal and dry years in order to describe the habitat suitability in all possible hydrological conditions. Conditional habitat suitability criteria (CHSC) were developed to define the relationship between fish distribution and physical environment. Physical spatial measurements of river hydraulic and fish shelter attributes (current velocity, depth, discharge, sediments, woody debris, boulders, etc.) were conducted on a scale of mesohabitat during field surveys. SimStream plugin of QGIS25 was used to organize collected data for mesohabitat modelling.
Hydrological data and hydromorphological surveys
The daily time series of discharge data of three water gauging stations (WGSs; Bartuva-Skuodas, Venta-Leckava and Mūša-Ustukiai) were taken from the hydrological yearbook of the Lithuanian Hydrometeorological Service for the periods of 1970–2000 (period before construction of HPPs) and of 2001–2015 (period after construction). The WGSs are located downstream the selected HPPs, and their data were used for the assessment of the altered discharge conditions and the impact of HPPs on fish communities. Two additional WGSs of Minija River-Kartena (for the Bartuva and Venta rivers) and Nemunėlis River-Tabokinė (for the Mūša River) were chosen for the restoration of natural conditions of river discharge at case study sites according to the analogy method26. The selection of a river analogue was based on the same hydrological region, similar catchment area, similarity in physico-geographical and hydrometeorological characteristics, and absence of anthropogenic structures which interrupt the continuity of the river, e.g. dams. The regression equation between case study river and river-analogue was prepared using daily water discharge data of 1970–2000 (period before construction of HPPs). The natural regime of investigated rivers after construction of HPPs (2001–2015) was restored using regression equations. In this way, we obtain the annual hydrographs of the investigated rivers in natural and altered conditions. In order to evaluate the habitat suitability in all possible hydrological conditions, hydrographs were prepared for wet, normal and dry hydrological years (probability of 5, 50 and 95%, respectively), according to average discharge data in the period of 2001–2015.
Four different discharge values (from minimal to average) were defined for hydromorphological measurements in each site of the selected river. These discharges represented the minimum, average and maximum low flow discharges of 30 consecutive days (Q30_min, Q30_ave, Q30_max) in the warm period (May–September), and multi-annual mean water discharge (Qannual_mean) in 1970–2000 (before HPPs construction). According to the Lithuanian law, environmental flow (Qenv) is defined at each HPP as 80% or 95% probability of the mean minimum discharge of 30 consecutive days of the warm period11. A Laser Rangefinder (distance, inclination, azimuthal measurements) connected via Bluetooth with the field tablet was used for the mapping of hydromorphological units (HMUs, also called mesohabitats). The maps of HMUs polygons were digitized in the .shp format using MapStream plugin of QGIS25,27. The length of an analysed river reach was defined as 20 times the mean river width28. The depth and flow velocity measurements in each defined HMU were done using a propeller-type flow meter mounted on a wading rod. Depending on the polygon area, from 5 to 30 measurements were carried out in each HMU, while the measurement density (point/m2) was kept as constant as possible in each case study considering its size (on average one point per 6 m2 in the Bartuva, 20 m2 in the Mūša and 25 m2 in the Venta rivers).
The presence/absence of fish shelters and vegetation were assessed visually (see21 for details). All measurements were carried out as close as it is possible to four defined discharges (minimum low flow (Q30_min), average low flow (Q30_ave), maximum low flow (Q30_max) and annual mean (Qannual_mean)) of each selected case study (Table 1).
Fish data and conditional habitat suitability criteria
Four Cyprinidae fish species, which are common in cyprinid-dominated lowland rivers of Lithuania20, but differ in rheophily and reproduction habitat were selected for the assessment of HPPs impact: lithophilic rheophilic schneider Alburnoides bipunctatus and dace Leuciscus leuciscus, phyto-lithophilic eurytopic roach Rutilus rutilus, and diadromous lithophilic eurytopic vimba Vimba vimba (fish guilds according to29). Based on the classification of fish species in European rivers according to their overall resistance to habitat degradation30, the selected species also represent different guilds of tolerance capacity: schneider is intolerant species, dace and vimba are intermediate, and roach is tolerant31. These four species are all benthopelagic, and in this respect they are similar, but due to their different preferences for rheophilic conditions, spawning habitat and overall habitat quality, it was expected that their response to changes in flow conditions should also be different. Currently access for diadromous vimba to most rivers is limited by dams; therefore, habitat availability for vimba was modelled only in the Venta River, which is still accessible for this species and contains its spawning grounds.
To define conditional habitat suitability criteria (CHSC)21, the river monitoring database for 2008–2015 was used. Data on the physical, chemical and hydrological characteristics of river sites was collected by the Lithuanian Environmental Protection Agency (EPA). Fish monitoring and assessment of hydromorphological characteristics of the site at the time of sampling was carried out by the Nature Research Centre under agreement with EPA. Standardized single-pass electric fishing took place in mid-July–September on river sections with a minimum length of at least 10 times the wetted width (but not less than 50 m) using backpack pulse current electrofisher (type IG200-2; HANS GRASSL GmbH) with a maximum output of 800 V and a maximum power of 10.0 kW per pulse.
For CHSC construction, only river sites in natural conditions (from good to high ecological status according to the European Water Framework Directive) with a catchment area of 100–5000 km2 and sampled by wading were selected from the database. In total, 245 river sites were selected. 160 sites in 75 rivers (2/3 of the selected sites) were randomly selected and used to build CHSC. The remaining 85 locations in 53 rivers (1/3 of all locations) were used for calibration. Once the locations were selected, their depth and current velocity were classified into intervals of 0.15 m and 0.15 m s-1 following the MesoHABSIM protocol (up to 0.15, 0.15–0.3, 0.3–0.45, etc.). The preference of schneider, dace and roach for depth and current velocity was determined by their frequency of occurrence in each of the intervals. In order to minimize the impact of random catches, species were considered present only when the number of individuals exceeded 25th percentile of the number of individuals in all places where they were found. Species were considered abundant when the number of individuals was greater than the median abundance in all places where they were found. A species was considered present in a particular interval of depth or current velocity only when its frequency of occurrence was  > 40%. Accordingly, a species was considered abundant only in those groups of depth and velocity where the number of individuals was greater than the median in more than 50% of the sites. The preference for the type of substrate and shelters was determined according to the analysis of these environmental variables in the river sites where the species should be present based on the criteria of depth and current velocity. According to the geomorphological and ecological definition of mesohabitat21,22, 10 m2 was considered the minimum surface that an HMU must have to be considered a suitable (species present) or optimal (species abundant) habitat for fish. When tested on an independent dataset (85 sites), CHSC were considered satisfactory for the presence of species when the species were present in  > 60% of the sites meeting the criteria (total accuracy  > 0.6). CHSC were considered satisfactory for the abundance of species when the species were present in  > 60% of the sites meeting the abundance criteria and the abundance of individuals was higher than the median in at least 50% of these sites.
CHSC for vimba were selected by an expert judgement, analysing common features of the river sites where this species was observed. Migration of vimba to the majority of former spawning grounds is currently restricted by dams. Therefore, this species is constantly found in a limited number of rivers, in which vimba is present not only during spawning in spring, but is also common in specific habitats in summer and autumn.
For the validation of CHSC for schneider, dace and roach, a single-pass electric fishing was performed in 42 HMUs of 4 natural rivers (Minija, Dubysa, Šventoji and Merkys), in river stretches with a length of 150–400 m, a maximum depth up to 1.5 m, and a catchment size of 315–3040 km2, during the low flow season, with high transparency of water. Fish were sampled by wading by a team of 3 persons using a backpack pulse current unit of a similar type as for fish monitoring (IG200-2D; HANS GRASSL GmbH). CHSC verification for vimba was carried out only in 14 out of 42 HMUs, since this species is constantly found in only one of the natural rivers selected for verification. A single-pass electric fishing was also conducted in all HMUs which were identified in the studied river stretches below HPPs at the low flow. Fish sampling was accomplished by wading and using pulse current backpack electric fishing gear. A single-pass electric fishing strategy was used, as the CHSC criteria were also developed based on single-pass sampling data. Studies show that in most cases species composition and rank abundance of common species do not change significantly after the first pass32,33,34.
To assess the predictive performance of CHSC, correctly classified instances, sensitivity, specificity, and true skill statistic were calculated based on confusion matrix analysis35.
Assessment of HPPs impact
The habitat area available for the species was modelled at different discharges of rivers. The impact of HPPs on habitat availability was assessed based on the comparison of the modelled available habitat area (i) at reference conditions during a dry year, (ii) under HPPs functioning in dry, normal and wet years, and (iii) at environmental Qenv. The flow value that exceeded 97% of the time at reference conditions (Q97)36 during a dry year and the corresponding area of species habitat (expressed in m2, hereafter, the minimum threshold area) were used as common denominators. Deviation of temporal availability of suitable habitats for modelled fish species due to HPPs functioning at different flows was assessed based on relative increase in the cumulative continuous duration of days when the area of the habitat falls below the minimum threshold values (hereafter, the stress days alteration; SDA). SDA analysis is based on the assumption that minimum habitat availability is a limiting factor for fish species, and events occurring rarely in nature create stress to aquatic fauna and shape the community. Therefore, for the selected minimum habitat threshold (expressed in m2), the number of habitat stress days that occur under those conditions was calculated and used as a benchmark for comparative analysis using the SDA metric, (see e.g.28,36,37 for details). Finally, we normalize SDA values between 0 and 1 by using the index of temporal habitat availability (ITH) as it is described by Rinaldi et al.28.
The relative abundance of fish species that are common in the cyprinid-dominated rivers of Lithuania (the frequency of occurrence in the natural river sites is  > 50%) was also compared in river reaches with natural (42 sites, 85 fishing occasions) and regulated (below HPPs; 20 sites, 39 fishing occasions) flows, which met at least good water quality criteria and fell within the same range of catchment size and slope as the rivers selected for modelling did. The sites were selected from the same river monitoring database for 2008–2015, which was used for selection of sites for CHSC development. The significance of identified differences was assessed using the Mann–Whitney U test. More

Ethical considerations
This study was carried out in accordance with the guidelines for ethical treatment of animals of the International Society of Applied Ethology. It was approved by the Institutional Animal Care and Use Committee of the Institute of Animal Science and the Czech Central Committee for Protection of Animals, Ministry of Agriculture (Permit Number 27356/2016-MZE-17214). Calves on the low-milk schedule received a milk allowance of approx. 12% of their body weight, equalling the traditional calf feeding practices26.
Animals and housing
The study was conducted at the Netluky research station of the Institute of Animal Science in Prague, Czech Republic. Data were collected from August 2016 until April 2017.
Seventy-two Holstein Friesian dairy calves (31 heifers and 41 bulls) were included in the study. Calves were separated from their dams at approximately 12 h after birth and housed individually either in outdoor hutches or in individual pens in a naturally ventilated open barn equipped with curtains. In both cases, the area available for each calf was 1.4 m × 1.4 m straw-bedded lying area and 1.2 m × 1.2 m solid walking area. While in individual housing, calves were fed 3 l of milk twice per day through teat-buckets at 06.00 and 18.00 and received concentrates and water ad libitum. Calves entered the experiment at an average age of 13.3 ± 3.1 days (mean ± S.D.) and were then housed in groups of three. Calves were allocated to groups balanced by sex, age and weight. Groups entered the experiment consecutively with 1–2 groups per week. Groups were housed in a naturally ventilated open barn with curtains. Group pens were 10.1 m2 consisting of a straw-bedded lying area (4.2 m × 1.4 m; approx. 2.0 m2 per calf) and a concrete walking and feeding area (3.5 m × 1.2 m). The group pens were covered with visual barriers in order to avoid direct visual contact of other calves. The visual barriers in front of the respective pens were removed in order to allow video recording; however, groups that were recorded simultaneously were allocated in the barn in a way that precluded visual contact without the front visual barriers of the pens. The calves received water, hay and concentrates ad libitum, offered in buckets and were provided with fresh straw bedding three times per week. All routine farm work was done before 10.00. Calves were hot-iron disbudded at 24.4 ± 3.1 days of age (mean ± S.D.). Disbudding wounds are painful for more than three weeks27, however no difference in play behaviour after disbudding was found after 27 hours23, thus we do not expect an effect of disbudding on play in our study. On the recording days, the air temperature in the barn ranged between -4.5 °C and 29 °C with the average (± S.D.) being 7.9 (± 8.6) °C.
Experimental design and procedures
Experimental design
Groups were allocated to treatments balanced by sex composition, age, weight and point of time entering the experiment. Milk allowance, group composition and number of groups assigned to each of the treatments are displayed in Fig. 1. Calves in all treatments received three milk meals per day at approximately 06.00, 12.00 and 18.00. All calves were offered 6 l of milk per day at the beginning of week 3. For UHigh and MHigh calves, the offered milk was gradually increased to 9 l of milk per day in week four and 12 l of milk per day in week six (Fig. 1). Therefore, the total milk amount offered from the start of the experiment until the end of week eight (42 days) was 240 l for ULow and MLow calves and 420 l for UHigh and MHigh calves. Milk was offered in teat buckets. Calves were tethered for the duration of the milk meal using neck collars and were released when all calves of the group had finished their meals (i.e. calves had either emptied the buckets or stopped drinking milk; approx. 5 min). If calves did not finish the offered milk meal, the volume of the remaining milk amount was measured. The volume of unconsumed milk was then summed from the point of entering the experiment until the respective day of behaviour recording. For ULow and MLow the total volume of unconsumed milk amounted to 0.3 ± 0.9 l (mean ± S.D.; median/interquartile range: 0/0 – 0). For UHigh and MHigh the total volume of unconsumed milk amounted to 16.7 ± 18.0 l (median/interquartile range: 10/2.5–25). The average daily amount of milk refusal was 0.1 ± 0.3 l and 0.3 ± 0.6 l, when calves were four weeks and eight weeks old, respectively.
Figure 1

Experimental design of treatments, group composition and milk allowance. Sample size is the number of calves included in statistical analysis.

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Data from two groups were excluded from statistical analysis: in one ULow group, a calf died from health issues unrelated to the experiment and one UHigh group was treated for severe diarrhoea for a prolonged time and therefore was not offered 12 l of milk in order to avoid further digestive problems.
Health and weight assessment
Calves’ health state was assessed once per week by two assessors. The following indicators of compromised health were recorded: diarrhoea, coughing/sneezing and increased respiratory rate (adapted from Gratzer, et al.28; Supplementary Table 1). The overall health score was set to 0 when calves showed no or one symptom of diarrhoea or coughing/sneezing and 1 when calves showed either combined diarrhoea and coughing/sneezing or increased respiratory rate. The ratio of calves with a health score of 1 is shown in Table 1.
Table 1 Ratio of calves with a health score of 1 by treatment and age group.
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Calves were weighed once per week between Monday and Thursday. To allow for comparison, daily weight gain for every respective week was calculated and weights were subsequently corrected for Monday as reference weighing day. Body weights from the start until the end of the experiment (three to eight weeks of age) are presented in Supplementary Figure S1. These data show that higher milk provision in MHigh and UHigh calves resulted in faster growth.
Quantification of play behaviour
Data recording
Locomotor play behaviour of calves was quantified through leg-attached accelerometers, using a previously validated method29. In this study, accelerometers were used to record running, turning and bucking/buck-kicking, as defined in Größbacher, et al.30. The data used to validate accelerometer recordings for these behaviours30 were a subset of the data used in this study. Accelerometers (HOBO Pendant G Acceleration Data Logger, Onset Computer Corporation, Pocasset, MA, USA; product specifications described in detail in Luu, et al.29) were attached to calves’ hind legs with elastic cohesive bandages. The accelerometers were oriented with the x-axis perpendicular to the ground. Acceleration was measured on the vertical axis at 1 Hz, i.e. with one measurement per second, from 05.00 until 23.04 on two consecutive days (Tuesday and Wednesday) when calves were four and eight weeks of age and recordings were stored on the device. Accelerometers were fitted to calves from the evening before until the morning after recording days, after being programmed with an optical infrared base station with USB interface and the HOBOware Pro Software (Version 3.7.8; Onset Computer Corporation, Pocasset, MA, USA).
Behaviour classification
Data processing was performed in SAS 9.4. Always 10 acceleration measurements, representing a period of 10 s each, were evaluated. These 10 s periods were categorized into lying, standing or play behaviour using quadratic discriminant analysis. This categorization was based on six predictor variables, which were calculated for each period with the respective 10 values: mean of two highest acceleration measurements, mean of two lowest acceleration measurements, variance, maximum of absolute value of change in acceleration measurement, mean change in acceleration measurements, and total sum of absolute values of change in acceleration measurements30.
In order to develop the discriminant function, a reference data set was created with randomly selected short sections of accelerometer data obtained from recordings of calves in this study. This reference data set consisted of 52 recordings with a mean (± S.D.) duration of 37.8 ± 16.8 min. Lying, standing and play behaviour were visually identified from video of these recordings applying one-zero-sampling of the respective 10 s periods, for which predictor variables were calculated. This was used as the gold standard.
The discriminant function was then applied to the entire data set in two steps to identify periods that contained locomotor play, i.e. included events of running, turning and/or bucking30, based on the six predictor variables: The first discriminant function classified the acceleration data into lying and standing, based on equal prior probabilities (50:50 chance of both behaviours occurring). The second discriminant function classified all standing-periods according to their presence or absence of locomotor play, based on prior probabilities of the reference data set (3:97 chance of play occurring across all treatments). The transitions from lying to standing and vice versa were almost always falsely classified as playing, as identified from video, and reclassified into standing.
The validation of processing the raw acceleration data was accomplished through checking the agreement between the acceleration-based method and visually identified play of the reference data set30. It proved that although the absolute play-levels were overestimated with the acceleration method, the method was able to truthfully quantify the inter-individual differences in locomotor play in dairy calves30.
Data analysis
Data processing
The last four minutes of each recording were omitted to obtain observation durations of exactly 18 h. The number of 10 s periods of locomotor play was converted into minutes of locomotor play per recording day (18 h). Recordings were excluded for the duration of disturbance when any calf in the barn escaped their pen or a person entered the pen. If more than 1 h was missing or compromised, the entire recording day was excluded for the calves affected. If less than 1 h of the recording was missing, locomotor play was calculated on a per hour basis and extrapolated to the ‘standard’ duration of 18 h. Out of 264 recordings (4 recordings per calf overall with 2 recordings at the age of four and eight weeks, respectively), 17 recordings were excluded or missing and in 13 recordings a mean (± S.D.) of 23.9 ± 13.0 min were missing and locomotor play duration and bout frequency were extrapolated. This resulted in 245 recordings, i.e. data points, included in the model. Play bouts were assessed by counting standalone periods of play, i.e. single 10 s periods not proceeded and not followed by periods classified as play were counted as one play bout, or by counting consecutive periods of play, i.e. two or more play periods occurred in a row as one play bout. Mean bout durations were assessed by recording the duration of each bout, e.g. a play bout consisting of one play period was recorded as 10 s and a play bout consisting of 3 play periods was recorded as 30 s.
Individual play was defined as one calf performing play in a 10-s-period when no other calf in the group was performing play. Dyadic play was defined as one calf performing play in the same 10-s-period as any one other calf of the group. Individual and dyadic play were calculated as minutes per recording day (18 h). Data were only included when observations of all three calves of the group were available. If data of one of the calves in the group were partially missing, observations of the other calves for the same period of time were excluded. Then the duration of individual and dyadic play was extrapolated on a per hour basis as described above. 237 recordings, i.e. data points, were included in the analysis, whereof 15 recordings were extrapolated.
The observed and randomly expected proportion of dyadic synchronized play were calculated on the basis of dyads, i.e. the combination of always two calves of a group, according to Šilerová, et al.31. Both were calculated for each pair combination:

$$ Sync_{obs} = frac{{left( {2*C_{sync} } right)}}{{left( {C_{A} + C_{B} } right)}} $$

$$ Sync_{exp} = frac{{(2* C_{A} *C_{B} *1/P_{dyad} )}}{{left( {C_{A} + C_{B} } right)}} $$

where Csync is the number of synchronous play periods of the pair, CA is the total number of play periods of calf A, CB is the total number of play periods of calf B and Pdyad is the total number of recorded periods for each dyad. The randomly expected proportion of play is the proportion of synchronized play occurring by chance if the calves played independently of each other31.
Triadic play was defined as all three calves of a group performing play in the same 10-s-period and calculated as minutes per recording day. Only periods in which data for all calves of the group were available were included. Extrapolation of play duration (due to partially missing data) was done in 10 out of 79 recordings.
Statistical analysis
All data was analysed in SAS Version 9.4. Five separate linear mixed effects models were run with total duration of play, frequency of play bouts, mean bout duration, duration of dyadic play or duration of individual play as dependent variables. Treatment (ULow, MLow, MHigh, UHigh), age (week four, week eight) and overall health score (0,1) were included as fixed class effects, while volume of unconsumed milk and maximum daily temperature were included as fixed quantitative effects, i.e. as covariates. Age (week), nested in calf and group were included as random effects. Furthermore, the date of recording was included as a crossed random effect. The same model was used for all dependent variables. Initially, the full model contained the interaction effect of treatment and age, however this was never significant and therefore removed from the model. An auto-regressive covariance structure was selected based on the Akaike Information Criterion (AIC). All models were visually inspected for normal distribution of residuals devoid of skewness. More

The growing interest in understanding the complex bovine udder microbiome has resulted in several published studies in recent years. These studies aimed to uncover how this microbiome influences the udder health and possibly has an important role during mastitis. In this study, we contribute to this knowledge by exploring the milk microbiota with a cross-sectional study of the milk microbiota as elucidated from over 400 quarter milk samples obtained from 60 lactating cows.
Sampling of udder milk for microbiome analysis is difficult and challenging15,16. In order to accomplish a more representative overview of the milk microbiota that colonize the upper interior part of the udder, the sampling method used in this study was different to those previously used to study milk microbiota2,6. The sampling was performed after the cow was regularly milked with the intent to remove microbial contaminations from the teat apex and to avoid the sampling of milk present in the cistern which might contain bacteria able to enter the udder between milking. The complete removal of contaminations from the teat apex or the environment cannot be ensured by the results obtained in this study, and additional experiments are needed to compare microbiota from milk samples obtained pre- and post-milking. However, although some taxa associated with the environment, such as Bacillaceae and Pseudomonadaceae, were detected in the dataset their abundance was much lower ( More