Philippa Kaur

1.Lefébure, R. et al. Impacts of elevated terrestrial nutrient loads and temperature on pelagic food-web efficiency and fish production. Glob. Change Biol. 19, 1358–1372 (2013).ADS 

Google Scholar 
2.Roussel, J.-M. et al. Stable isotope analyses on archived fish scales reveal the long-term effect of nitrogen loads on carbon cycling in rivers. Glob. Change Biol. 20, 523–530 (2014).ADS 

Google Scholar 
3.Creed, I. F. et al. Global change-driven effects on dissolved organic matter composition: Implications for food webs of northern lakes. Glob. Change Biol. 24, 3692–3714 (2018).ADS 

Google Scholar 
4.Screen, J. A. & Simmonds, I. The central role of diminishing sea ice in recent Arctic temperature amplification. Nature 464, 1334–1337 (2010).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
5.Kumar, A., Yadav, J. & Mohan, R. Spatio-temporal change and variability of Barents-Kara sea ice, in the Arctic: Ocean and atmospheric implications. Sci. Total Environ. 753, 142046 (2021).ADS 
CAS 
PubMed 

Google Scholar 
6.Vincent, W. F., Laurion, I., Pienitz, R. & Walter Anthony, K. M. Climate Impacts on Arctic Lake Ecosystems. In Climatic Change and Global Warming of Inland Waters (eds Goldman, C. R. et al.) 27–42 (Wiley, 2012). https://doi.org/10.1002/9781118470596.ch2.Chapter 

Google Scholar 
7.Kim, K.-Y. et al. Vertical feedback mechanism of winter Arctic amplification and sea ice loss. Sci. Rep. 9, 1184 (2019).ADS 
PubMed 
PubMed Central 

Google Scholar 
8.Shaver, G. R. & Chapin, F. S. Response to fertilization by various plant growth forms in an Alaskan tundra: Nutrient accumulation and growth. Ecology 61, 662–675 (1980).CAS 

Google Scholar 
9.Meunier, C. L., Gundale, M. J., Sánchez, I. S. & Liess, A. Impact of nitrogen deposition on forest and lake food webs in nitrogen-limited environments. Glob. Change Biol. 22, 164–179 (2016).ADS 

Google Scholar 
10.Arctic Climate Impact Assessment. Arctic climate impact assessment (Cambridge University Press, Cambridge, 2005).
Google Scholar 
11.Hay, W. W. The accelerating rate of global change. Rendiconti Lincei 25, 29–48 (2014).
Google Scholar 
12.Prowse, T. D. et al. Climate change effects on hydroecology of Arctic freshwater ecosystems. AMBIO J. Hum. Environ. 35, 347–358 (2006).CAS 

Google Scholar 
13.Post, E. et al. Ecological dynamics across the Arctic associated with recent climate change. Science 325, 1355–1358 (2009).ADS 
CAS 
PubMed 

Google Scholar 
14.Ward, R. D. Carbon sequestration and storage in Norwegian Arctic coastal wetlands: Impacts of climate change. Sci. Total Environ. 748, 141343 (2020).ADS 
CAS 
PubMed 

Google Scholar 
15.Lin, J., Huang, J., Prell, C. & Bryan, B. A. Changes in supply and demand mediate the effects of land-use change on freshwater ecosystem services flows. Sci. Total Environ. 763, 143012 (2021).ADS 
CAS 
PubMed 

Google Scholar 
16.Bintanja, R. & Andry, O. Towards a rain-dominated Arctic. Nat. Clim. Change 7, 263–267 (2017).ADS 

Google Scholar 
17.Box, J. E. et al. Key indicators of Arctic climate change: 1971–2017. Environ. Res. Lett. 14, 045010 (2019).ADS 
CAS 

Google Scholar 
18.St. Pierre, K. A. et al. Contemporary limnology of the rapidly changing glacierized watershed of the world’s largest High Arctic lake. Sci. Rep. 9, 4447 (2019).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
19.Woelders, L. et al. Recent climate warming drives ecological change in a remote high-Arctic lake. Sci. Rep. 8, 6858 (2018).ADS 
PubMed 
PubMed Central 

Google Scholar 
20.Blaen, P. J., Milner, A. M., Hannah, D. M., Brittain, J. E. & Brown, L. E. Impact of changing hydrology on nutrient uptake in high Arctic rivers: Nutrient uptake in Arctic rivers. River Res. Appl. 30, 1073–1083 (2014).
Google Scholar 
21.Szkokan-Emilson, E. J. et al. Dry conditions disrupt terrestrial-aquatic linkages in northern catchments. Glob. Change Biol. 23, 117–126 (2017).ADS 

Google Scholar 
22.Thackeray, S. J. et al. Food web de-synchronization in England’s largest lake: An assessment based on multiple phenological metrics. Glob. Change Biol. 19, 3568–3580 (2013).ADS 

Google Scholar 
23.Pacheco, J. P. et al. Small-sized omnivorous fish induce stronger effects on food webs than warming and eutrophication in experimental shallow lakes. Sci. Total Environ. 797, 148998 (2021).ADS 
CAS 
PubMed 

Google Scholar 
24.Kuijper, D. P. J., Ubels, R. & Loonen, M. J. J. E. Density-dependent switches in diet: A likely mechanism for negative feedbacks on goose population increase?. Polar Biol. 32, 1789–1803 (2009).
Google Scholar 
25.Sjögersten, S., van der Wal, R., Loonen, M. J. J. E. & Woodin, S. J. Recovery of ecosystem carbon fluxes and storage from herbivory. Biogeochemistry 106, 357–370 (2011).PubMed 
PubMed Central 

Google Scholar 
26.Buij, R., Melman, T. C. P., Loonen, M. J. J. E. & Fox, A. D. Balancing ecosystem function, services and disservices resulting from expanding goose populations. Ambio 46, 301–318 (2017).PubMed 
PubMed Central 

Google Scholar 
27.Nishizawa, K. et al. Long-term consequences of goose exclusion on nutrient cycles and plant communities in the high-Arctic. Polar Sci. 27, 100631 (2021).
Google Scholar 
28.Bjerke, J. W., Tombre, I. M., Hanssen, M. & Olsen, A. K. B. Springtime grazing by Arctic-breeding geese reduces first- and second-harvest yields on sub-Arctic agricultural grasslands. Sci. Total Environ. 793, 148619 (2021).ADS 
CAS 
PubMed 

Google Scholar 
29.Van Geest, G. J. et al. Goose-mediated nutrient enrichment and planktonic grazer control in Arctic freshwater ponds. Oecologia 153, 653–662 (2007).ADS 
PubMed 

Google Scholar 
30.Calizza, E., Rossi, L. & Costantini, M. L. Predators and resources influence phosphorus transfer along an invertebrate food web through changes in prey behaviour. PLoS ONE 8, e65186 (2013).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
31.Rossi, L., di Lascio, A., Carlino, P., Calizza, E. & Costantini, M. L. Predator and detritivore niche width helps to explain biocomplexity of experimental detritus-based food webs in four aquatic and terrestrial ecosystems. Ecol. Complex. 23, 14–24 (2015).
Google Scholar 
32.Caputi, S. S. et al. Seasonal food web dynamics in the Antarctic benthos of Tethys Bay (Ross Sea): Implications for biodiversity persistence under different seasonal sea-ice coverage. Front. Mar. Sci. 7, 594454 (2020).
Google Scholar 
33.Careddu, G., Calizza, E., Costantini, M. L. & Rossi, L. Isotopic determination of the trophic ecology of a ubiquitous key species—The crab Liocarcinus depurator (Brachyura: Portunidae). Estuar. Coast. Shelf Sci. 191, 106–114 (2017).ADS 
CAS 

Google Scholar 
34.Careddu, G. et al. Diet composition of the Italian crested newt (Triturus carnifex) in structurally different artificial ponds based on stomach contents and stable isotope analyses. Aquat. Conserv. Mar. Freshw. Ecosyst. 30, 1505–1520 (2020).
Google Scholar 
35.Zhao, Q., De Laender, F. & Van den Brink, P. J. Community composition modifies direct and indirect effects of pesticides in freshwater food webs. Sci. Total Environ. 739, 139531 (2020).ADS 
CAS 
PubMed 

Google Scholar 
36.Rossi, L., Costantini, M. L., Carlino, P., di Lascio, A. & Rossi, D. Autochthonous and allochthonous plant contributions to coastal benthic detritus deposits: A dual-stable isotope study in a volcanic lake. Aquat. Sci. 72, 227–236 (2010).CAS 

Google Scholar 
37.Rossi, L. et al. Antarctic food web architecture under varying dynamics of sea ice cover. Sci. Rep. 9, 12454 (2019).ADS 
PubMed 
PubMed Central 

Google Scholar 
38.Careddu, G. et al. Effects of terrestrial input on macrobenthic food webs of coastal sea are detected by stable isotope analysis in Gaeta Gulf. Estuar. Coast. Shelf Sci. 154, 158–168 (2015).ADS 
CAS 

Google Scholar 
39.Careddu, G. et al. Gaining insight into the assimilated diet of small bear populations by stable isotope analysis. Sci. Rep. 11, 14118 (2021).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
40.Blais, J. M. Arctic seabirds transport marine-derived contaminants. Science 309, 445–445 (2005).CAS 
PubMed 

Google Scholar 
41.Bentivoglio, F. et al. Site-scale isotopic variations along a river course help localize drainage basin influence on river food webs. Hydrobiologia 770, 257–272 (2016).CAS 

Google Scholar 
42.Rossi, L. et al. Space-time monitoring of coastal pollution in the Gulf of Gaeta, Italy, using δ15N values of Ulva lactuca, landscape hydromorphology, and Bayesian Kriging modelling. Mar. Pollut. Bull. 126, 479–487 (2018).CAS 
PubMed 

Google Scholar 
43.Calizza, E. et al. Isotopic biomonitoring of N pollution in rivers embedded in complex human landscapes. Sci. Total Environ. 706, 136081 (2020).ADS 
CAS 
PubMed 

Google Scholar 
44.Post, D. M. Using stable isotopes to estimate trophic position: Models, methods, and assumptions. Ecology 83, 703–718 (2002).
Google Scholar 
45.Mansouri, F. et al. Evidence of multi-decadal behavior and ecosystem-level changes revealed by reconstructed lifetime stable isotope profiles of baleen whale earplugs. Sci. Total Environ. 757, 143985 (2021).ADS 
CAS 
PubMed 

Google Scholar 
46.Hawley, K. L., Rosten, C. M., Christensen, G. & Lucas, M. C. Fine-scale behavioural differences distinguish resource use by ecomorphs in a closed ecosystem. Sci. Rep. 6, 24369 (2016).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
47.Michener, R. H. & Lajtha, K. Stable Isotopes in Ecology and Environmental Science (Blackwell Publication, 2007).
Google Scholar 
48.Cicala, D. et al. Spatial variation in the feeding strategies of Mediterranean fish: Flatfish and mullet in the Gulf of Gaeta (Italy). Aquat. Ecol. 53, 529–541 (2019).CAS 

Google Scholar 
49.Calizza, E. et al. Stable isotopes and digital elevation models to study nutrient inputs in high-Arctic lakes. Rendiconti Lincei 27, 191–199 (2016).
Google Scholar 
50.Calizza, E., Careddu, G., Sporta Caputi, S., Rossi, L. & Costantini, M. L. Time- and depth-wise trophic niche shifts in Antarctic benthos. PLoS ONE 13, e0194796 (2018).PubMed 
PubMed Central 

Google Scholar 
51.Mehlum, F. Svalbards fugler og pattedyr (Norsk polarinstitutt, 1989).
Google Scholar 
52.Christoffersen, K. Predation on Daphnia pulex by Lepidurus arcticus. Hydrobiologia 442, 223–229 (2001).
Google Scholar 
53.Lakka, H.-K. The ecology of a freshwater crustacean: Lepidurus arcticus (Brachiopoda; Notostraca) in a High Arctic region. Dissertation, University of Helsinky (2013).54.Westergaard-Nielsen, A. et al. Transitions in high-Arctic vegetation growth patterns and ecosystem productivity tracked with automated cameras from 2000 to 2013. Ambio 46, 39–52 (2017).PubMed 
PubMed Central 

Google Scholar 
55.Pyke, G. H., Pulliam, H. R. & Charnov, E. L. Optimal foraging: A selective review of theory and tests. Q. Rev. Biol. 52, 137–154 (1977).
Google Scholar 
56.Kondoh, M. & Ninomiya, K. Food-chain length and adaptive foraging. Proc. R. Soc. B Biol. Sci. 276, 3113–3121 (2009).
Google Scholar 
57.Calizza, E., Costantini, M. L., Rossi, D., Carlino, P. & Rossi, L. Effects of disturbance on an urban river food web: Disturbance of a river food web. Freshw. Biol. 57, 2613–2628 (2012).
Google Scholar 
58.McMeans, B. C., McCann, K. S., Humphries, M., Rooney, N. & Fisk, A. T. Food web structure in temporally-forced ecosystems. Trends Ecol. Evol. 30, 662–672 (2015).PubMed 

Google Scholar 
59.Pimm, S. L. & Lawton, J. H. Number of trophic levels in ecological communities. Nature 268, 329–331 (1977).ADS 

Google Scholar 
60.Elser, J. J. et al. Nutritional constraints in terrestrial and freshwater food webs. Nature 408, 578–580 (2000).ADS 
CAS 
PubMed 

Google Scholar 
61.Hall, S. R. Stoichiometrically explicit food webs: Feedbacks between resource supply, elemental constraints, and species diversity. Annu. Rev. Ecol. Evol. Syst. 40, 503–528 (2009).
Google Scholar 
62.Hessen, D. O., Ågren, G. I., Anderson, T. R., Elser, J. J. & de Ruiter, P. C. Carbon sequestration in ecosystems: The role of stoichiometry. Ecology 85, 1179–1192 (2004).
Google Scholar 
63.Stow, D. A. et al. Remote sensing of vegetation and land-cover change in Arctic Tundra Ecosystems. Remote Sens. Environ. 89, 281–308 (2004).ADS 

Google Scholar 
64.Maher, A. I., Treitz, P. M. & Ferguson, M. A. D. Can Landsat data detect variations in snow cover within habitats of Arctic ungulates?. Wildl. Biol. 18, 75–87 (2012).
Google Scholar 
65.Raynolds, M. K., Walker, D. A., Verbyla, D. & Munger, C. A. Patterns of change within a tundra landscape: 22-year Landsat NDVI trends in an area of the Northern Foothills of the Brooks Range, Alaska. Arct. Antarct. Alp. Res. 45, 249–260 (2013).
Google Scholar 
66.Bokhorst, S. et al. Changing Arctic snow cover: A review of recent developments and assessment of future needs for observations, modelling, and impacts. Ambio 45, 516–537 (2016).PubMed 
PubMed Central 

Google Scholar 
67.Härer, S., Bernhardt, M., Siebers, M. & Schulz, K. On the need for a time- and location-dependent estimation of the NDSI threshold value for reducing existing uncertainties in snow cover maps at different scales. Cryosphere 12, 1629–1642 (2018).ADS 

Google Scholar 
68.Karlsen, S. R., Anderson, H. B., van der Wal, R. & Hansen, B. B. A new NDVI measure that overcomes data sparsity in cloud-covered regions predicts annual variation in ground-based estimates of high Arctic plant productivity. Environ. Res. Lett. 13, 025011 (2018).ADS 

Google Scholar 
69.Karlsen, S. R., et al. Sentinel satellite-based mapping of plant productivity in relation to snow duration and time of green-up. https://zenodo.org/record/4704361. https://doi.org/10.5281/ZENODO.4704361 (2020).70.Beamish, A. et al. Recent trends and remaining challenges for optical remote sensing of Arctic tundra vegetation: A review and outlook. Remote Sens. Environ. 246, 111872 (2020).ADS 

Google Scholar 
71.Layton-Matthews, K., Hansen, B. B., Grøtan, V., Fuglei, E. & Loonen, M. J. J. E. Contrasting consequences of climate change for migratory geese: Predation, density dependence and carryover effects offset benefits of high-Arctic warming. Glob. Change Biol. 26, 642–657 (2020).ADS 

Google Scholar 
72.Owen, M. The selection of feeding site by White-fronted geese in winter. J. Appl. Ecol. 8, 905 (1971).
Google Scholar 
73.Ydenberg, R. C. & Prins, H. HTh. Spring grazing and the manipulation of food quality by Barnacle geese. J. Appl. Ecol. 18, 443 (1981).
Google Scholar 
74.Bos, D. et al. Utilisation of Wadden Sea salt marshes by geese in relation to livestock grazing. J. Nat. Conserv. 13, 1–15 (2005).
Google Scholar 
75.Barrio, I. C. et al. Developing common protocols to measure tundra herbivory across spatial scales. Arct. Sci. https://doi.org/10.1139/as-2020-0020 (2021).Article 

Google Scholar 
76.Jensen, T. C. et al. Changes in trophic state and aquatic communities in high Arctic ponds in response to increasing goose populations. Freshw. Biol. 64, 1241–1254 (2019).CAS 

Google Scholar 
77.Bartoli, M. et al. Denitrification, nitrogen uptake, and organic matter quality undergo different seasonality in sandy and muddy sediments of a turbid estuary. Front. Microbiol. 11, 612700 (2021).PubMed 
PubMed Central 

Google Scholar 
78.van der Wal, R., van Lieshout, S. M. J. & Loonen, M. J. J. E. Herbivore impact on moss depth, soil temperature and Arctic plant growth. Polar Biol. 24, 29–32 (2001).
Google Scholar 
79.Wookey, P. A. et al. Differential growth, allocation and photosynthetic responses of Polygonum viviparum to simulated environmental change at a high Arctic polar semi-desert. Oikos 70, 131 (1994).
Google Scholar 
80.Wookey, P. A. et al. Environmental constraints on the growth, photosynthesis and reproductive development of Dryas octopetala at a high Arctic polar semi-desert, Svalbard. Oecologia 102, 478–489 (1995).ADS 
CAS 
PubMed 

Google Scholar 
81.Jefferies, R. L. Agricultural food subsidies, migratory connectivity and large-scale disturbance in arctic coastal systems: A case study. Integr. Comp. Biol. 44, 130–139 (2004).CAS 
PubMed 

Google Scholar 
82.Hik, D. S. & Jefferies, R. L. Increases in the net above-ground primary production of a salt-marsh forage grass: A test of the predictions of the herbivore-optimization model. J. Ecol. 78, 180 (1990).
Google Scholar 
83.Rautio, M., Mariash, H. & Forsström, L. Seasonal shifts between autochthonous and allochthonous carbon contributions to zooplankton diets in a subarctic lake. Limnol. Oceanogr. 56, 1513–1524 (2011).ADS 
CAS 

Google Scholar 
84.Crump, B. C., Kling, G. W., Bahr, M. & Hobbie, J. E. Bacterioplankton community shifts in an Arctic lake correlate with seasonal changes in organic matter source. Appl. Environ. Microbiol. 69, 2253–2268 (2003).ADS 
PubMed 
PubMed Central 

Google Scholar 
85.Berggren, M., Ziegler, S. E., St-Gelais, N. F., Beisner, B. E. & del Giorgio, P. A. Contrasting patterns of allochthony among three major groups of crustacean zooplankton in boreal and temperate lakes. Ecology 95, 1947–1959 (2014).PubMed 

Google Scholar 
86.Stasko, A. D., Gunn, J. M. & Johnston, T. A. Role of ambient light in structuring north-temperate fish communities: Potential effects of increasing dissolved organic carbon concentration with a changing climate. Environ. Rev. 20, 173–190 (2012).CAS 

Google Scholar 
87.Milardi, M., Käkelä, R., Weckström, J. & Kahilainen, K. K. Terrestrial prey fuels the fish population of a small, high-latitude lake. Aquat. Sci. 78, 695–706 (2016).CAS 

Google Scholar 
88.Vincent, W. F. & Laybourn-Parry, J. Polar Lakes and Rivers (Oxford University Press, 2008). https://doi.org/10.1093/acprof:oso/9780199213887.001.0001.Book 

Google Scholar 
89.Calizza, E., Costantini, M. L., Careddu, G. & Rossi, L. Effect of habitat degradation on competition, carrying capacity, and species assemblage stability. Ecol. Evol. 7, 5784–5796 (2017).PubMed 
PubMed Central 

Google Scholar 
90.Van der Velden, S., Dempson, J. B., Evans, M. S., Muir, D. C. G. & Power, M. Basal mercury concentrations and biomagnification rates in freshwater and marine food webs: Effects on Arctic charr (Salvelinus alpinus) from eastern Canada. Sci. Total Environ. 444, 531–542 (2013).ADS 
PubMed 

Google Scholar 
91.Kozak, N. et al. Environmental and biological factors are joint drivers of mercury biomagnification in subarctic lake food webs along a climate and productivity gradient. Sci. Total Environ. 779, 146261 (2021).ADS 
CAS 
PubMed 

Google Scholar 
92.Longhurst, A. R. A review of the Notostraca. Bull. Br. Mus. Nat. Hist. 3, 1–57 (1955).
Google Scholar 
93.King, J. L. & Hanner, R. Cryptic species in a “living fossil” lineage: Taxonomic and phylogenetic relationships within the genus Lepidurus (Crustacea: Notostraca) in North America. Mol. Phylogenet. Evol. 10, 23–36 (1998).CAS 
PubMed 

Google Scholar 
94.Hessen, D. O., Rueness, E. K. & Stabell, M. Circumpolar analysis of morphological and genetic diversity in the Notostracan Lepidurus arcticus. Hydrobiologia 519, 73–84 (2004).
Google Scholar 
95.Pasquali, V., Calizza, E., Setini, A., Hazlerigg, D. & Christoffersen, K. S. Preliminary observations on the effect of light and temperature on the hatching success and rate of Lepidurus arcticus eggs. Ethol. Ecol. Evol. 31, 348–357 (2019).
Google Scholar 
96.Tanentzap, A. J. et al. Climate warming restructures an aquatic food web over 28 years. Glob. Change Biol. 26, 6852–6866 (2020).ADS 

Google Scholar 
97.Polvani, L. M., Previdi, M., England, M. R., Chiodo, G. & Smith, K. L. Substantial twentieth-century Arctic warming caused by ozone-depleting substances. Nat. Clim. Change 10, 130–133 (2020).ADS 
CAS 

Google Scholar 
98.di Lascio, A. et al. Stable isotope variation in macroinvertebrates indicates anthropogenic disturbance along an urban stretch of the river Tiber (Rome, Italy). Ecol. Indic. 28, 107–114 (2013).
Google Scholar 
99.Moore, I. D., Grayson, R. B. & Ladson, A. R. Digital terrain modelling: A review of hydrological, geomorphological, and biological applications. Hydrol. Process. 5, 3–30 (1991).ADS 

Google Scholar 
100.Vaze, J., Teng, J. & Spencer, G. Impact of DEM accuracy and resolution on topographic indices. Environ. Model. Softw. 25, 1086–1098 (2010).
Google Scholar 
101.Johansen, B. E., Karlsen, S. R. & Tømmervik, H. Vegetation mapping of Svalbard utilising Landsat TM/ETM+ data. Polar Rec. 48, 47–63 (2012).
Google Scholar 
102.Hall, D. K., Riggs, G. A. & Salomonson, V. V. Development of methods for mapping global snow cover using moderate resolution imaging spectroradiometer data. Remote Sens. Environ. 54, 127–140 (1995).ADS 

Google Scholar 
103.Vogel, S. W. Usage of high-resolution Landsat 7 band 8 for single-band snow-cover classification. Ann. Glaciol. 34, 53–57 (2002).ADS 

Google Scholar 
104.Tucker, C. J. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sens. Environ. 8, 127–150 (1979).ADS 

Google Scholar 
105.Dozier, J. Spectral signature of alpine snow cover from the landsat thematic mapper. Remote Sens. Environ. 28, 9–22 (1989).ADS 

Google Scholar 
106.Jensen, J. R. Remote Sensing of the Environment: An Earth Resource Perspective (Pearson Prentice Hall, 2007).
Google Scholar 
107.Gascoin, S., Grizonnet, M., Bouchet, M., Salgues, G. & Hagolle, O. Theia Snow collection: High-resolution operational snow cover maps from Sentinel-2 and Landsat-8 data. Earth Syst. Sci. Data 11, 493–514 (2019).ADS 

Google Scholar 
108.Simon, G., Manuel, G., Tristan, K. & Germain, S. Algorithm Theoretical basis documentation for an operational snow cover product from Sentinel-2 and Landsat-8 data (let-it-snow) (2018). https://doi.org/10.5281/ZENODO.1414452.109.Stahl, J. & Loonen, M. J. Effects of predation risk on site selection of barnacle geese during brood-rearing. In Research on Arctic Geese, 91 (1998).110.McCutchan, J. H., Lewis, W. M., Kendall, C. & McGrath, C. C. Variation in trophic shift for stable isotope ratios of carbon, nitrogen, and sulfur. Oikos 102, 378–390 (2003).CAS 

Google Scholar 
111.Calizza, E., Rossi, L., Careddu, G., Sporta Caputi, S. & Costantini, M. L. Species richness and vulnerability to disturbance propagation in real food webs. Sci. Rep. 9, 19331 (2019).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
112.Mantel, N. & Valand, R. S. A technique of nonparametric multivariate analysis. Biometrics 26, 547 (1970).CAS 
PubMed 

Google Scholar 
113.Signa, G. et al. Horizontal and vertical food web structure drives trace element trophic transfer in Terra Nova Bay, Antarctica. Environ. Pollut. 246, 772–781 (2019).CAS 
PubMed 

Google Scholar  More

1.West-Eberhard, M. J. Developmental plasticity and evolution. (Oxford University Press, 2003).2.de Jong, G. Evolution of phenotypic plasticity: patterns of plasticity and the emergence of ecotypes. N. Phytologist 166, 101–118 (2005).
Google Scholar 
3.Ezard, T. H. G., Prizak, R. & Hoyle, R. B. The fitness costs of adaptation via phenotypic plasticity and maternal effects. Funct. Ecol. 28, 693–701 (2014).
Google Scholar 
4.Williams, C. M. et al. Understanding evolutionary impacts of seasonality: an introduction to the symposium. Integr. Comp. Biol. 57, 921–933 (2017).PubMed 
PubMed Central 

Google Scholar 
5.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 

Google Scholar 
6.Sommer, R. J. Phenotypic plasticity: from theory and genetics to current and future challenges. Genetics 215, 1–13 (2020).PubMed 
PubMed Central 

Google Scholar 
7.Beldade, P., Mateus, A. R. A. & Keller, R. A. Evolution and molecular mechanisms of adaptive developmental plasticity. Mol. Ecol. 20, 1347–1363 (2011).PubMed 

Google Scholar 
8.Lafuente, E. & Beldade, P. Genomics of developmental plasticity in animals. Front. Genet. 10, (2019).9.Marden, J. H. Quantitative and evolutionary biology of alternative splicing: how changing the mix of alternative transcripts affects phenotypic plasticity and reaction norms. Heredity 100, 111–120 (2008).CAS 
PubMed 

Google Scholar 
10.Baralle, F. E. & Giudice, J. Alternative splicing as a regulator of development and tissue identity. Nat. Rev. Mol. Cell Biol. 18, 437–451 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
11.Bush, S. J., Chen, L., Tovar-Corona, J. M. & Urrutia, A. O. Alternative splicing and the evolution of phenotypic novelty. Philos. Trans. R. Soc. B: Biol. Sci. 372, 20150474 (2017).
Google Scholar 
12.Marden, J. H. & Cobb, J. R. Territorial and mating success of dragonflies that vary in muscle power output and presence of gregarine gut parasites. Anim. Behav. 68, 857–865 (2004).
Google Scholar 
13.Kijimoto, T., Moczek, A. P. & Andrews, J. Diversification of doublesex function underlies morph-, sex-, and species-specific development of beetle horns. Proc. Natl Acad. Sci. USA 109, 20526–20531 (2012).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
14.Bear, A., Prudic, K. L. & Monteiro, A. Steroid hormone signaling during development has a latent effect on adult male sexual behavior in the butterfly Bicyclus anynana. PLoS ONE 12, e0174403 (2017).PubMed 
PubMed Central 

Google Scholar 
15.Martin Anduaga, A. et al. Thermosensitive alternative splicing senses and mediates temperature adaptation in Drosophila. eLife 8, e44642 (2019).PubMed 
PubMed Central 

Google Scholar 
16.Deshmukh, R., Lakhe, D. & Kunte, K. Tissue-specific developmental regulation and isoform usage underlie the role of doublesex in sex differentiation and mimicry in Papilio swallowtails. R. Soc. Open Sci. 7, 200792 (2020).ADS 
PubMed 
PubMed Central 

Google Scholar 
17.Grantham, M. E. & Brisson, J. A. Extensive differential splicing underlies phenotypically plastic aphid morphs. Mol. Biol. Evol. 35, 1934–1946 (2018).CAS 
PubMed 
PubMed Central 

Google Scholar 
18.Price, J. et al. Alternative splicing associated with phenotypic plasticity in the bumble bee Bombus terrestris. Mol. Ecol. 27, 1036–1043 (2018).CAS 
PubMed 

Google Scholar 
19.Lees, J. G., Ranea, J. A. & Orengo, C. A. Identifying and characterising key alternative splicing events in Drosophila development. BMC Genomics 16, 608 (2015).PubMed 
PubMed Central 

Google Scholar 
20.Jakšić, A. M. & Schlötterer, C. The interplay of temperature and genotype on patterns of alternative splicing in Drosophila melanogaster. Genetics 204, 315–325 (2016).PubMed 
PubMed Central 

Google Scholar 
21.Healy, T. M. & Schulte, P. M. Patterns of alternative splicing in response to cold acclimation in fish. J. Exp. Biol. 222, jeb193516 (2019).22.Signor, S. & Nuzhdin, S. Dynamic changes in gene expression and alternative splicing mediate the response to acute alcohol exposure in Drosophila melanogaster. Heredity 121, 342–360 (2018).CAS 
PubMed 
PubMed Central 

Google Scholar 
23.Lang, A. S., Austin, S. H., Harris, R. M., Calisi, R. M. & MacManes, M. D. Stress-mediated convergence of splicing landscapes in male and female rock doves. BMC Genomics 21, 251 (2020).PubMed 
PubMed Central 

Google Scholar 
24.Suresh, S., Crease, T. J., Cristescu, M. E. & Chain, F. J. J. Alternative splicing is highly variable among Daphnia pulex lineages in response to acute copper exposure. BMC Genomics 21, 433 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
25.Thorstensen, M. J., Baerwald, M. R. & Jeffries, K. M. RNA sequencing describes both population structure and plasticity-selection dynamics in a non-model fish. BMC Genomics 22, 273 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
26.Singh, A. & Agrawal, A. F. Sexual dimorphism in gene expression: coincidence and population genomics of two forms of differential expression in Drosophila melanogaster. bioRxiv (2021) https://doi.org/10.1101/2021.02.08.429268.27.Rogers, T. F., Palmer, D. H. & Wright, A. E. Sex-specific selection drives the evolution of alternative splicing in birds. Mol. Biol. Evolution 38, 519–530 (2021).CAS 

Google Scholar 
28.Fox, R. J., Donelson, J. M., Schunter, C., Ravasi, T. & Gaitán-Espitia, J. D. Beyond buying time: the role of plasticity in phenotypic adaptation to rapid environmental change. Philos. Trans. R. Soc. B: Biol. Sci. 374, 20180174 (2019).
Google Scholar 
29.Kelly, M. Adaptation to climate change through genetic accommodation and assimilation of plastic phenotypes. Philos. Trans. R. Soc. B: Biol. Sci. 374, 20180176 (2019).
Google Scholar 
30.Oostra, V., Saastamoinen, M., Zwaan, B. J. & Wheat, C. W. Strong phenotypic plasticity limits potential for evolutionary responses to climate change. Nat. Commun. 9, 1–11 (2018).CAS 

Google Scholar 
31.Wang, Y. et al. Mechanism of alternative splicing and its regulation (Review). Biomed. Rep. 3, 152–158 (2015).CAS 
PubMed 

Google Scholar 
32.Ule, J. & Blencowe, B. J. Alternative splicing regulatory networks: functions, mechanisms, and evolution. Mol. Cell 76, 329–345 (2019).CAS 
PubMed 

Google Scholar 
33.McManus, C. J., Coolon, J. D., Eipper-Mains, J., Wittkopp, P. J. & Graveley, B. R. Evolution of splicing regulatory networks in Drosophila. Genome Res. 24, 786–796 (2014).CAS 
PubMed 
PubMed Central 

Google Scholar 
34.Gao, Q., Sun, W., Ballegeer, M., Libert, C. & Chen, W. Predominant contribution of cis-regulatory divergence in the evolution of mouse alternative splicing. Mol. Syst. Biol. 11, 816 (2015).PubMed 
PubMed Central 

Google Scholar 
35.Barbosa-Morais, N. L. et al. The evolutionary landscape of alternative splicing in vertebrate species. Science 338, 1587–1593 (2012).ADS 
CAS 
PubMed 

Google Scholar 
36.Wang, X. et al. Cis-regulated alternative splicing divergence and its potential contribution to environmental responses in Arabidopsis. Plant J. 97, 555–570 (2019).CAS 
PubMed 

Google Scholar 
37.Huang, Y., Lack, J. B., Hoppel, G. T. & Pool, J. E. Parallel and population-specific gene regulatory evolution in cold-adapted fly populations. bioRxiv (2021) https://doi.org/10.1101/795716.38.Lewis, J. J., Van Belleghem, S. M., Papa, R., Danko, C. G. & Reed, R. D. Many functionally connected loci foster adaptive diversification along a neotropical hybrid zone. Sci. Adv. 6, eabb8617 (2020).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
39.Lewis, J. J. & Reed, R. D. Genome-wide regulatory adaptation shapes population-level genomic landscapes in Heliconius. Mol. Biol. Evol. 36, 159–173 (2019).CAS 
PubMed 

Google Scholar 
40.Martin, S. H. et al. Natural selection and genetic diversity in the butterfly Heliconius melpomene. Genetics 203, 525–541 (2016).CAS 
PubMed 
PubMed Central 

Google Scholar 
41.Brakefield, P. M., Beldade, P. & Zwaan, B. J. The African Butterfly Bicyclus anynana: a model for evolutionary genetics and evolutionary developmental biology. Cold Spring Harb. Protoc. 2009, pdb.emo122 (2009).PubMed 

Google Scholar 
42.Mateus, A. R. A. et al. Adaptive developmental plasticity: compartmentalized responses to environmental cues and to corresponding internal signals provide phenotypic flexibility. BMC Biol. 12, 97 (2014).PubMed 
PubMed Central 

Google Scholar 
43.Oostra, V. et al. Ecdysteroid hormones link the juvenile environment to alternative adult life histories in a seasonal insect. Am. Naturalist 184, E79–E92 (2014).
Google Scholar 
44.van Bergen, E. et al. Conserved patterns of integrated developmental plasticity in a group of polyphenic tropical butterflies. BMC Evolut. Biol. 17, 59 (2017).
Google Scholar 
45.Singh, P. et al. Complex multi-trait responses to multivariate environmental cues in a seasonal butterfly. Evol. Ecol. (2020) https://doi.org/10.1007/s10682-020-10062-0.46.Prudic, K. L., Jeon, C., Cao, H. & Monteiro, A. Developmental plasticity in sexual roles of butterfly species drives mutual sexual ornamentation. Science 331, 73–75 (2011).ADS 
CAS 
PubMed 

Google Scholar 
47.Chen, L., Bush, S. J., Tovar-Corona, J. M., Castillo-Morales, A. & Urrutia, A. O. Correcting for differential transcript coverage reveals a strong relationship between alternative splicing and organism complexity. Mol. Biol. Evol. 31, 1402–1413 (2014).PubMed 
PubMed Central 

Google Scholar 
48.Hamid, F. M. & Makeyev, E. V. Emerging functions of alternative splicing coupled with nonsense-mediated decay. Biochem. Soc. Trans. 42, 1168–1173 (2014).CAS 
PubMed 

Google Scholar 
49.Tabrez, S. S., Sharma, R. D., Jain, V., Siddiqui, A. A. & Mukhopadhyay, A. Differential alternative splicing coupled to nonsense-mediated decay of mRNA ensures dietary restriction-induced longevity. Nat. Commun. 8, 306 (2017).ADS 
PubMed 
PubMed Central 

Google Scholar 
50.Uller, T., Moczek, A. P., Watson, R. A., Brakefield, P. M. & Laland, K. N. Developmental bias and evolution: a regulatory network perspective. Genetics 209, 949–966 (2018).PubMed 
PubMed Central 

Google Scholar 
51.Nijhout, H. F. To plasticity and back again. eLife 4, e06995 (2015).PubMed Central 

Google Scholar 
52.Helanterä, H. & Uller, T. Neutral and adaptive explanations for an association between caste-biased gene expression and rate of sequence evolution. Front. Genet. 5, 297 (2014).53.Pespeni, M. H., Ladner, J. T. & Moczek, A. P. Signals of selection in conditionally expressed genes in the diversification of three horned beetle species. J. Evolut. Biol. 30, 1644–1657 (2017).CAS 

Google Scholar 
54.Plass, M. & Eyras, E. Differentiated evolutionary rates in alternative exons and the implications for splicing regulation. BMC Evol. Biol. 6, 50 (2006).PubMed 
PubMed Central 

Google Scholar 
55.Chen, F.-C., Pan, C.-L. & Lin, H.-Y. Independent effects of alternative splicing and structural constraint on the evolution of mammalian coding exons. Mol. Biol. Evolution 29, 187–193 (2012).CAS 

Google Scholar 
56.Peña, C., Nylin, S. & Wahlberg, N. The radiation of Satyrini butterflies (Nymphalidae: Satyrinae): a challenge for phylogenetic methods. Zool. J. Linn. Soc. 161, 64–87 (2011).
Google Scholar 
57.Bhardwaj, S. et al. Origin of the mechanism of phenotypic plasticity in satyrid butterfly eyespots. eLife 9, e49544 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
58.Lewis, B. P., Green, R. E. & Brenner, S. E. Evidence for the widespread coupling of alternative splicing and nonsense-mediated mRNA decay in humans. PNAS 100, 189–192 (2003).ADS 
CAS 
PubMed 

Google Scholar 
59.Akerman, M. & Mandel-Gutfreund, Y. Alternative splicing regulation at tandem 3′ splice sites. Nucleic Acids Res. 34, 23–31 (2006).CAS 
PubMed 
PubMed Central 

Google Scholar 
60.Moran, N. A. The evolutionary maintenance of alternative phenotypes. Am. Naturalist 139, 971–989 (1992).
Google Scholar 
61.Nijhout, H. F. Development and evolution of adaptive polyphenisms. Evolution Dev. 5, 9–18 (2003).
Google Scholar 
62.Mank, J. E. The transcriptional architecture of phenotypic dimorphism. Nat. Ecol. Evolution 1, 1–7 (2017).
Google Scholar 
63.Scheiner, S. M., Barfield, M. & Holt, R. D. The genetics of phenotypic plasticity. XVII. Response to climate change. Evolut. Appl. 13, 388–399 (2020).
Google Scholar 
64.Osada, N., Miyagi, R. & Takahashi, A. Cis- and trans-regulatory effects on gene expression in a natural population of Drosophila melanogaster. Genetics 206, 2139–2148 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
65.Cooper, R. D. & Shaffer, H. B. Allele-specific expression and gene regulation help explain transgressive thermal tolerance in non-native hybrids of the endangered California tiger salamander (Ambystoma californiense). Mol. Ecol. 30, 987–1004 (2021).CAS 
PubMed 

Google Scholar 
66.Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).CAS 
PubMed 
PubMed Central 

Google Scholar 
67.Baruzzo, G. et al. Simulation-based comprehensive benchmarking of RNA-seq aligners. Nat. Methods 14, 135–139 (2017).CAS 
PubMed 

Google Scholar 
68.Schuierer, S. et al. A comprehensive assessment of RNA-seq protocols for degraded and low-quantity samples. BMC Genomics 18, 442 (2017).PubMed 
PubMed Central 

Google Scholar 
69.Broad Institute. Picard toolkit. (2019).70.Liao, Y., Smyth, G. K. & Shi, W. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 30, 923–930 (2014).CAS 
PubMed 
PubMed Central 

Google Scholar 
71.Liao, Y., Smyth, G. K. & Shi, W. The R package Rsubread is easier, faster, cheaper and better for alignment and quantification of RNA sequencing reads. Nucleic Acids Res 47, e47–e47 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
72.Chen, Y., Lun, A. T. L. & Smyth, G. K. From reads to genes to pathways: differential expression analysis of RNA-Seq experiments using Rsubread and the edgeR quasi-likelihood pipeline. F1000Res 5, 1438 (2016).PubMed 
PubMed Central 

Google Scholar 
73.R Core Team. R: A language and environment for statistical computing. (R Foundation for Statistical Computing, 2019).74.Shen, L. GeneOverlap: Test and visualize gene overlaps. (2020).75.Gu, Z., Eils, R. & Schlesner, M. Complex heatmaps reveal patterns and correlations in multidimensional genomic data. Bioinformatics 32, 2847–2849 (2016).CAS 

Google Scholar 
76.Huerta-Cepas, J. et al. eggNOG 5.0: a hierarchical, functionally and phylogenetically annotated orthology resource based on 5090 organisms and 2502 viruses. Nucleic Acids Res 47, D309–D314 (2019).CAS 
PubMed 

Google Scholar 
77.Alexa, A. & Rahnenfuhrer, J. topGO: Enrichment analysis for Gene Ontology. (2016).78.Larsson, J. et al. eulerr: Area-Proportional Euler and Venn Diagrams with Ellipses. (2021).79.Supek, F., Bošnjak, M., Škunca, N. & Šmuc, T. REVIGO summarizes and visualizes long lists of gene ontology terms. PLoS ONE 6, e21800 (2011).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
80.Gu, Z. & Hübschmann, D. simplifyEnrichment: an R/Bioconductor package for Clustering and Visualizing Functional Enrichment Results. 2020.10.27.312116 (2020) https://doi.org/10.1101/2020.10.27.312116.81.Gu, Z. simplifyEnrichment: Simplify Functional Enrichment Results. (Bioconductor version: Release (3.13), 2021). https://doi.org/10.18129/B9.bioc.simplifyEnrichment.82.de Jong, M. A., Wahlberg, N., Eijk, M., van, Brakefield, P. M. & Zwaan, B. J. Mitochondrial DNA signature for range-wide populations of Bicyclus anynana suggests a rapid expansion from recent Refugia. PLoS ONE 6, e21385 (2011).ADS 
PubMed 
PubMed Central 

Google Scholar 
83.de Jong, M. A., Collins, S., Beldade, P., Brakefield, P. M. & Zwaan, B. J. Footprints of selection in wild populations of Bicyclus anynana along a latitudinal cline. Mol. Ecol. 22, 341–353 (2013).PubMed 

Google Scholar 
84.Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet. J. 17, 10–12 (2011).
Google Scholar 
85.Joshi, N. & Fass, J. Sickle: A sliding-window, adaptive, quality-based trimming tool for FastQ files.86.Li, H. Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. arXiv:1303.3997 [q-bio] (2013).87.Korneliussen, T. S., Albrechtsen, A. & Nielsen, R. ANGSD: analysis of next generation sequencing data. BMC Bioinforma. 15, 356 (2014).
Google Scholar 
88.Nowell, R. W. et al. A high-coverage draft genome of the mycalesine butterfly Bicyclus anynana. GigaScience 6, (2017).89.Xu, L. et al. OrthoVenn2: a web server for whole-genome comparison and annotation of orthologous clusters across multiple species. Nucleic Acids Res. 47, W52–W58 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
90.Ranwez, V., Harispe, S., Delsuc, F. & Douzery, E. J. P. MACSE: multiple alignment of coding SEquences accounting for frameshifts and stop codons. PLoS ONE 6, e22594 (2011).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
91.Lucaci, A. G., Wisotsky, S. R., Shank, S. D., Weaver, S. & Kosakovsky Pond, S. L. Extra base hits: widespread empirical support for instantaneous multiple-nucleotide changes. PLoS One 16, e0248337 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
92.Buerkner, P.-C. brms: An R Package for Bayesian Multilevel Models Using Stan. J. Stat. Softw. 80, 1–28 (2017).
Google Scholar 
93.Buerkner, P.-C. Advanced Bayesian multilevel modeling with the R Package brms. R. J. 10, 395–411 (2018).
Google Scholar 
94.Kassambara, A. rstatix: Pipe-Friendly Framework for Basic Statistical Tests. (2021).95.Shen, S. et al. rMATS: Robust and flexible detection of differential alternative splicing from replicate RNA-Seq data. Proc. Natl Acad. Sci. USA 111, E5593–E5601 (2014).CAS 
PubMed 
PubMed Central 

Google Scholar 
96.Alamancos, G. P., Pagès, A., Trincado, J. L., Bellora, N. & Eyras, E. Leveraging transcript quantification for fast computation of alternative splicing profiles. RNA 21, 1521–1531 (2015).CAS 
PubMed 
PubMed Central 

Google Scholar 
97.Wang, Q. & Rio, D. C. JUM is a computational method for comprehensive annotation-free analysis of alternative pre-mRNA splicing patterns. Proc. Natl Acad. Sci. USA115, E8181–E8190 (2018).CAS 
PubMed 
PubMed Central 

Google Scholar 
98.Wickham, H. et al. Welcome to the Tidyverse. J. Open Source Softw. 4, 1686 (2019).ADS 

Google Scholar 
99.Kassambara, A. ggpubr” ‘ggplot2’ based publication-ready plots. (2020).100.Bivand, R. & Rundel, C. rgeos: Interface to Geometry Engine – Open Source (‘GEOS’). (2021).101.South, A. afrilearndata: Small Africa Map Datasets for Learning. (2021).102.Inkscape Project. Inkscape. (2021).103.Steward, R. A., Oostra, V. & Wheat, C. W. B_anynana_differentialSplicing Github. zenodo.org https://zenodo.org/badge/latestdoi/255903232 (2021). More

1.Dynowska, M., Meissner, W. & Pacyńska, J. Mallard duck (Anas platyrhynchos) as a potential link in the epidemiological chain mycoses originating from water reservoirs. Bull. Vet. Inst. Pulawy 57, 323–328 (2013).
Google Scholar 
2.Georgopoulou, I. & Tsiouris, V. The potential role of migratory birds in the transmission of zoonoses. Vet. Ital. 44, 671–677 (2008).PubMed 

Google Scholar 
3.Hubálek, Z. An annotated checklist of pathogenic microorganisms associated with migratory birds. J. Wildl. Dis. 40, 639–659 (2004).PubMed 

Google Scholar 
4.Korniłłowicz, T. K. I. Diversity of fungi in nests and pellets of Montagu’s harrier (Circus pygargus) from eastern Poland—Importance of chemical and ecological factors. Ecol. Chem. Eng. 16, 453–471 (2009).
Google Scholar 
5.Korniłłowicz-Kowalska, T. & Kitowski, I. Aspergillus fumigatus and other thermophilic fungi in nests of wetland birds. Mycopathologia 175, 43–56 (2013).PubMed 

Google Scholar 
6.Kiziewicz B. The occurrence fungi and zoosporic fungi-like organisms on feathers of birds Corvidae. in Corvids of Poland (ed. Jerzak, L.). 147–154. (Bogucki Wydawnictwo Naukowe Poznan, 2005).7.Kasprzykowski, Z. Habitat preferences of foraging Rooks Corvus frugilegus during the breeding period in the agricultural landscape of eastern Poland. Acta Ornithol. 38, 27–31 (2003).
Google Scholar 
8.Czarnecka, J. & Kitowski, I. Seed dispersal by the rook Corvus frugilegus l. In agricultural landscape—Mechanisms and ecological importance. Polish J. Ecol. 58, 511–523 (2010).
Google Scholar 
9.Czarnecka, J. et al. Seed dispersal in urban green space—Does the rook Corvus frugilegus L. contribute to urban flora homogenization?. Urban For. Urban Green. 12, 359–366 (2013).
Google Scholar 
10.Gromadzka, J. Food composition and food consumption of the Rook Corvus frugilegus in agrocoenoses in Poland. Acta Ornithol. 17, 11 (1980).
Google Scholar 
11.Green, A. J., Elmberg, J. & Lovas-Kiss, Á. Beyond scatter-hoarding and frugivory: European corvids as overlooked vectors for a broad range of plants. Front. Ecol. Evolut. 7, 133 (2019).
Google Scholar 
12.Jędrzejewski, S., Majewska, A., Zduniak, P. & Graczyk, T. Parasites of Polish corvids—Knowledge and potential risk for human. in Corvids of Poland (eds. Jerzak, L., Kavanagh, B. P. & Trojanowski, P.). 137–145. (Bogucki Wydawnictwo Naukowe, 2005).13.Kiziewicz, B. The occurrenceof fungy and zoosporic fungus like organisms on feathers of birds Corvids. in Corvids in Poland. (eds. Jerzak, L., Kavanagh, B. P. & Trojanowski, P.). 147–154. (Bogucki Wydawnictwo Naukowe, 2005).14.Camin, A. M., Chabasse, D. & Guiguen, C. Keratinophilic fungi associated with starlings (Sturnus vulgaris) in Brittany, France. Mycopathologia 143, 9–12 (1998).
Google Scholar 
15.Hubálek, Z. Keratinophilic fungi associated with free-living mammals and birds. Biol. Dermatophytes Keratinophilic Fungi 93, 1036 (2000).
Google Scholar 
16.Mandeel, Q., Nardoni, S. & Mancianti, F. Keratinophilic fungi on feathers of common clinically healthy birds in Bahrain. Mycoses 54, 71–77 (2011).PubMed 

Google Scholar 
17.Ciesielska, A., Kawa, A., Kanarek, K., Soboń, A. & Szewczyk, R. Metabolomic analysis of Trichophyton rubrum and Microsporum canis during keratin degradation. Sci. Rep. 11, 1–10 (2021).
Google Scholar 
18.Leibner-Ciszak, J., Dobrowolska, A., Krawczyk, B., Kaszuba, A. & Sta̧czek, P. Evaluation of a PCR melting profile method for intraspecies differentiation of Trichophyton rubrum and Trichophyton interdigitale. J. Med. Microbiol. 59, 185–192 (2010).19.Ciesielska, A., Oleksak, B. & Stączek, P. Reference genes for accurate evaluation of expression levels in Trichophyton interdigitale grown under different carbon sources, pH levels and phosphate levels. Sci. Rep. 9, 1–9 (2019).CAS 

Google Scholar 
20.Calvo, A., Vidal, M. & Guarro, J. Keratinophilic fungi from urban soils of Barcelona, Spain. Mycopathologia 85, 145–147 (1984).
Google Scholar 
21.R.S/, C. Taxonomy of the Onygenales: Arthrodermataceae, Gymnoasceae, Myxotrichaceae and Onygenaceae. Mycotaxon 24, 1–216 (1985).22.Korniłłowicz-Kowalska, T. Studies on the decomposition of keratin wastes by saprotrophic microfungi. P. I. Criteria for evaluating keratinolytic activity. Acta Mycol. 175, 43–56 (1997).
Google Scholar 
23.van Oorschot, C. A. N. A revision of Chrysosporium and allied genera. Stud. Mycol. 20, 1–89 (1980).
Google Scholar 
24.Domsch, K. H. & Gams, W. A. T. H. Compedium of Soil Fungi (Academic, 1980).
Google Scholar 
25.Gan, G. G. et al. Non-sporulating Chrysosporium: An opportunistic fungal infection in a neutropenic patient. Med. J. Malaysia 57, 118–122 (2002).CAS 
PubMed 

Google Scholar 
26.de Hoog, G. S., Guarro, J. & Gene, J. Atlas of clinical fungi. Int. Microbiol 2, 51–52 (2001).
Google Scholar 
27.Manzano-Gayosso, P. et al. Onychomycosis incidence in type 2 diabetes mellitus patients. Mycopathologia 166, 41–45 (2008).PubMed 

Google Scholar 
28.Palma, M. A. G., Espín, L. A. & Pérez, A. F. Invasine sinusal mycosis due to Chrysosporium tropicum. Acta Otorrinolaringol. Esp. 58, 164–166 (2007).
Google Scholar 
29.Stillwell, W. T. & Rubin, B. O. Chrysosporium, a new causative agent in osteomycelitis. Clin. Orthopaed. Relat. Res. 184, 190–192 (1984).
Google Scholar 
30.Gueho, E. V. J. G. R. A new human case of Anixiopsis stercomia mycosis: Discussion of its taxonomy and pathogenicity. Mycoses 28, 430–436 (1985).CAS 

Google Scholar 
31.Nieuwenhuis, B. P. S. & James, T. Y. The frequency of sex in fungi. Philos. Trans. R. Soc. B Biol. Sci. 371, 0540 (2016).
Google Scholar 
32.Neubauer, G. & Sikora, A. C. T. Monitoring populacji ptaków Polski w latach 2008–2009. Biuletyn Monitoringu Przyrody 8, 1–40 (2011).
Google Scholar 
33.Jackson, C. J., Barton, R. C. & Evans, E. G. V. Species identification and strain differentiation of dermatophyte fungi by analysis of ribosomal-DNA intergenic spacer regions. J. Clin. Microbiol. 37, 931–936 (1999).CAS 
PubMed 
PubMed Central 

Google Scholar 
34.Mochizuki, T. et al. Restriction fragment length polymorphism analysis of ribosomal DNA intergenic regions is useful for differentiating strains of Trichophyton mentagrophytes. J. Clin. Microbiol. 41, 4583–4588 (2003).CAS 
PubMed 
PubMed Central 

Google Scholar 
35.Garg, A. P., Gandotra, S., Mukerji, K. G. & Pugh, G. J. F. Ecology of keratinophilic fungi. Proc. Plant Sci. 94, 149–163 (1985).
Google Scholar 
36.Abulreesh, H. H., Goulder, R. & Scott, G. W. Wild birds and human pathogens in the context of ringing and migration. Ringing Migr. 23, 193–200 (2007).
Google Scholar 
37.Prinzinger, R., Preßmar, A. & Schleucher, E. Body temperature in birds. Comp. Biochem. Physiol. Part A Physiol. 99, 499–506 (1991).
Google Scholar 
38.Summerbell, R. C. Form and function in the evolution of dermatophytes. Rev. Iberoam. Micol. 44, 30–43 (2000).
Google Scholar 
39.Warwick, A., Ferrieri, P., Burke, B. & Blazar, B. R. Presumptive invasive Chrysosporium infection in a bone marrow transplant recipient. Bone Marrow Transplant 8, 319–322 (1991).CAS 
PubMed 

Google Scholar 
40.Kitowski, I., Ciesielska, A., Korniłłowicz-Kowalska, T., Bohacz, J., & Świetlicki, M. Estimation of Chrysosporium keratinophilum Dispersal by the Rook Corvus frugilegus in Chełm (East Poland) in Urban Fauna-Animal, Man, and the City—Interactions and Relationships. (Indykiewicz, P. & Böhner, J. eds). 263–269. (Art Studio, 2014)41.Gopal, K. A., Kalaivani, V. & Anandan, H. Pulmonary infection by Chrysosporium species in a preexisting tuberculous cavity. Int. J. Appl. Basic Med. Res. 10, 62 (2020).PubMed 
PubMed Central 

Google Scholar 
42.Krawczyk, B., Samet, A., Leibner, J., Śledzińska, A. & Kur, J. Evaluation of a PCR melting profile technique for bacterial strain differentiation. J. Clin. Microbiol. 44, 2327–2332 (2006).CAS 
PubMed 
PubMed Central 

Google Scholar 
43.Ciesielska, A. et al. Application of microsatellite-primed PCR (MSP-PCR) and PCR melting profile (PCR-MP) method for intraspecies differentiation of dermatophytes. Pol. J. Microbiol. 63, 283–290 (2014).PubMed 

Google Scholar 
44.Orłowski, G. & Czapulak, A. Different extinction risks of the breeding colonies of rooks Corvus frugilegus in rural and urban areas of SW Poland. Acta Ornithologica 42, 145–155 (2007).
Google Scholar 
45.Bohacz, J. & Korniłłowicz-Kowalska, T. Species diversity of keratinophilic fungi in various soil types. Cent. Eur. J. Biol. 7, 259–266 (2012).
Google Scholar 
46.Papini, R., Mancianti, F., Grassotti, G. & Cardini, G. Survey of keratinophilic fungi isolated from city park soils of Pisa, Italy. Mycopathologia 143, 17–23 (1998).CAS 
PubMed 

Google Scholar 
47.Singh, I. K. R. Dermatophytes and related keratinophilic fungi in soil of parks and agricultural fields of Uttar Pradesh, India. Indian J. Dermatol. 55, 306–308 (2010).PubMed 
PubMed Central 

Google Scholar 
48.Gungnani, H. C., Sharma, S. & Gupta, B. Keratinophilic fungi recovered from feathers of different species of birds in St Kitts and Nevis. West Indian Med. J. 61, 912–915 (2012).CAS 
PubMed 

Google Scholar 
49.Jadczyk P, J. Z. Wintering of rooks Corvus frugilegus in Poland. in Corvids of Poland (ed. Jerzak, L.). 541–556. (Bogucki Wydawnictwo Naukowe Poznan, 2005).50.Wilk, T., Chodkiewicz, T., Sikora, A., Chylarecki, P. & Kuczyński, L. Red List of Polish Birds. (OTOP, 2020).51.Oke, T.R. The heat island of the urban boundary layer: Characteristics, causes and effects. in eWind Climate in Cities. NATO ASI Series E (ed. JE, C.). 81–107. (Kluwer Academy, 1995).52.Vidal, P., de Vinuesa, M., Los, A., Sánchez-Puelles, J. M. & Guarro, J. Phylogeny of the anamorphic genus Chrysosporium and related taxa based on rDNA internal transcribed spacer sequences. Rev. Iberoam. Micol. 17, 22–29 (2000).
Google Scholar 
53.Korniłłowicz, T. Studies on mycoflora colonizing raw keratin wastes in arable soil. Mycologica 27, 231–245 (1991).
Google Scholar 
54.Orłowski, G., Kasprzykowski, Z., Zawada, Z. & Kopij, G. Stomach content and grit ingestion by rook Corvus frugilegus nestlings. Ornis Fennica 86, 117–122 (2009).
Google Scholar 
55.Luniak, M. Consumption and digestion of food in the rook, Corvus frugilegus, in the condition of an aviary. Acta Ornithol. 16, 213–234 (1977).
Google Scholar 
56.Liu, D., Coloe, S., Baird, R. & Pedersen, J. Rapid mini-preparation of fungal DNA for PCR [5]. J. Clin. Microbiol. 38, 471 (2000).CAS 
PubMed 
PubMed Central 

Google Scholar 
57.Hunter, P. R. & Gaston, M. A. Numerical index of the discriminatory ability of typing systems: An application of Simpson’s index of diversity. J. Clin. Microbiol. 26, 2465–2466 (1988).CAS 
PubMed 
PubMed Central 

Google Scholar 
58.Greenwell, J. R. Introduction to biostatistics, 2nd edn. By R. R. Sokal and F. J. Rohlf. pp. 363. F. H. Freeman and Co., 1987. £44.99 hardback. ISBN 0 7167 18057. Exp. Physiol. 80, 681 (1995) More

1.Cowen, R. K. & Sponaugle, S. Larval dispersal and marine population connectivity. Ann. Rev. Mar. Sci. 1, 443–466 (2009).PubMed 

Google Scholar 
2.Hellberg, M. E. Gene flow and isolation among populations of marine animals. Annu. Rev. Ecol. Evol. Syst. 40, 291–310 (2009).
Google Scholar 
3.Selkoe, K. A. et al. Taking the chaos out of genetic patchiness: Seascape genetics reveals ecological and oceanographic drivers of genetic patterns in three temperate reef species. Mol. Ecol. 19, 3708–3726 (2010).PubMed 

Google Scholar 
4.Guo, X. et al. Phylogeography of the rock shell Thais clavigera (Mollusca): Evidence for long-distance dispersal in the Northwestern Pacific. PLoS ONE 10, e0129715 (2015).PubMed 
PubMed Central 

Google Scholar 
5.Hoffman, J. I., Clarke, A., Linse, K. & Peck, L. S. Effects of brooding and broadcasting reproductive modes on the population genetic structure of two Antarctic gastropod molluscs. Mar. Biol. 158, 287–296 (2011).
Google Scholar 
6.Modica, M. V., Russini, V., Fassio, G. & Oliverio, M. Do larval types affect genetic connectivity at sea? Testing hypothesis in two sibling marine gastropods with contrasting larval development. Mar. Environ. Res. 127, 92–101 (2017).CAS 
PubMed 

Google Scholar 
7.Je Lee, H. & Boulding, E. G. Spatial and temporal population genetic structure of four northeastern Pacific littorinid gastropods: The effect of mode of larval development on variation at one mitochondrial and two nuclear DNA markers. Mol. Ecol. 18, 2165–2184 (2009).
Google Scholar 
8.Barbosa, S. S., Klanten, S. O., Puritz, J. B., Toonen, R. J. & Byrne, M. Very fine-scale population genetic structure of sympatric asterinid sea stars with benthic and pelagic larvae: Influence of mating system and dispersal potential. Biol. J. Linn. Soc. 108, 821–833 (2013).
Google Scholar 
9.Shanks, A. L. Pelagic larval duration and dispersal distance revisited. Biol. Bull. 216, 373–385 (2009).PubMed 

Google Scholar 
10.Riginos, C., Buckley, Y. M., Blomberg, S. P. & Treml, E. A. Dispersal capacity predicts both population genetic structure and species richness in reef fishes. Am. Nat. 184, 52–64 (2014).PubMed 

Google Scholar 
11.Wort, E. J. G. et al. Contrasting genetic structure of sympatric congeneric gastropods: Do differences in habitat preference, abundance and distribution matter?. J. Biogeogr. 46, 369–380 (2019).
Google Scholar 
12.Ayre, D. J., Minchinton, T. E. & Perrin, C. Does life history predict past and current connectivity for rocky intertidal invertebrates across a marine biogeographic barrier?. Mol. Ecol. 18, 1887–1903 (2009).CAS 
PubMed 

Google Scholar 
13.Meyer, C. P., Geller, J. B. & Paulay, G. Fine scale endemism on coral reefs: Archipelagic differentiation in turbinid gastropods. Evolution (N. Y.) 59, 113–125 (2005).
Google Scholar 
14.White, C. et al. Ocean currents help explain population genetic structure. Proc. R. Soc. B Biol. Sci. 277, 1685–1694 (2010).
Google Scholar 
15.Marko, P. B. ‘What’s larvae got to do with it?’ Disparate patterns of post-glacial population structure in two benthic marine gastropods with identical dispersal potential. Mol. Ecol. 13, 597–611 (2004).CAS 
PubMed 

Google Scholar 
16.Edmands, S. Phylogeography of the intertidal copepod Tigriopus californicus reveals substantially reduced population differentiation at northern latitudes. Mol. Ecol. 10, 1743–1750 (2001).CAS 
PubMed 

Google Scholar 
17.Ni, G., Li, Q., Kong, L. & Yu, H. Comparative phylogeography in marginal seas of the northwestern Pacific. Mol. Ecol. 23, 534–548 (2014).PubMed 

Google Scholar 
18.Vendrami, D. L. J. et al. RAD sequencing sheds new light on the genetic structure and local adaptation of European scallops and resolves their demographic histories. Sci. Rep. 9, 1–13 (2019).CAS 

Google Scholar 
19.Sandoval-Castillo, J., Robinson, N. A., Hart, A. M., Strain, L. W. S. & Beheregaray, L. B. Seascape genomics reveals adaptive divergence in a connected and commercially important mollusc, the greenlip abalone (Haliotis laevigata), along a longitudinal environmental gradient. Mol. Ecol. 27, 1603–1620 (2018).PubMed 

Google Scholar 
20.Hirai, J. Insights into reproductive isolation within the pelagic copepod Pleuromamma abdominalis with high genetic diversity using genome-wide SNP data. Mar. Biol. 167, 1–6 (2020).CAS 

Google Scholar 
21.Hosoya, S. et al. Random PCR-based genotyping by sequencing technology GRAS-Di (genotyping by random amplicon sequencing, direct) reveals genetic structure of mangrove fishes. Mol. Ecol. Resour. 19, 1153–1163 (2019).CAS 
PubMed 

Google Scholar 
22.Losos, J. B. & Ricklefs, R. E. Adaptation and diversification on islands. Nature https://doi.org/10.1038/nature07893 (2009).Article 
PubMed 

Google Scholar 
23.Savolainen, V. et al. Sympatric speciation in palms on an oceanic island. Nature https://doi.org/10.1038/nature04566 (2006).Article 
PubMed 

Google Scholar 
24.Parent, C. E. & Crespi, B. J. Ecological opportunity in adaptive radiation of Galápagos endemic land snails. Am. Nat. https://doi.org/10.1086/646604 (2009).Article 
PubMed 

Google Scholar 
25.Chiba, S. & Cowie, R. H. Evolution and extinction of land snails on oceanic islands. Annu. Rev. Ecol. Evol. Syst. 47, 123–141 (2016).
Google Scholar 
26.Grant, P. R. & Grant, B. R. Unpredictable evolution in a 30-year study of Darwin’s finches. Science (80-.) 296, 707–711 (2002).CAS 
ADS 

Google Scholar 
27.Scheltema, R. The relevance of passive dispersal for the biogeography of Caribbean mollusks. Am. Malacol. Bull. 11, 95–115 (1995).
Google Scholar 
28.Bernardi, G. et al. Darwin’s fishes: Phylogeography of Galápagos Islands reef fishes. Bull. Mar. Sci. 90, 533–549 (2014).
Google Scholar 
29.Eble, J. A., Toonen, R. J. & Bowen, B. W. Endemism and dispersal: Comparative phylogeography of three surgeonfishes across the Hawaiian Archipelago. Mar. Biol. 156, 689–698 (2009).
Google Scholar 
30.Tomokuni, M. M. Aquatic and Semiaquatic Insects of the Bonin Islands (including the Volcano Islands). Mem. Natl. Sci. Museum (1978).31.Sugawara, T., Watanabe, K., Kato, H. & Yasuda, K. Dioecy in Wikstroemia pseudoretusa (Thymelaeaceae) endemic to the Bonin (Ogasawara) islands. APG Acta Phytotaxon. Geobot. https://doi.org/10.18942/apg.KJ00004622804 (2004).Article 

Google Scholar 
32.Chiba, S. Species diversity and conservation of Mandarina, an endemic land snail of the Ogasawara Islands. In Restoring the Oceanic Island Ecosystem: Impact and Management of Invasive Alien Species in the Bonin Islands (eds Kawakami, K. & Okochi, I.) 117–125 (Springer, 2010). https://doi.org/10.1007/978-4-431-53859-2_18.Chapter 

Google Scholar 
33.Mukai, T., Nakamura, S., Suzuki, T. & Nishida, M. Mitochondrial DNA divergence in yoshinobori gobies (Rhinogobius species complex) between the Bonin Islands and the Japan-Ryukyu Archipelago. Ichthyol. Res. 52, 410–413 (2005).
Google Scholar 
34.Shih, H. T., Komai, T. & Liu, M. Y. A new species of fiddler crab from the Ogasawara (Bonin) Islands, Japan, separated from the widely-distributed sister species Uca (Paraleptuca) crassipes (White, 1847) (Crustacea: Decapoda: Brachyura: Ocypodidae). Zootaxa 3746, 175–193 (2013).PubMed 

Google Scholar 
35.Yamazaki, D. et al. Genetic diversification of intertidal gastropoda in an archipelago: The effects of islands, oceanic currents, and ecology. Mar. Biol. https://doi.org/10.1007/s00227-017-3207-9 (2017).Article 

Google Scholar 
36.Nakano, T., Takahashi, K. & Ozawa, T. Description of an endangered new species of Lunella (Gastropoda:Turbinidae) from the Ogasawara Islands, Japan. Venus J. Malacol. Soc. Japan 66, 1–10 (2007).
Google Scholar 
37.Nakano, T., Yazaki, I., Kurokawa, M., Yamaguchi, K. & Kuwasawa, K. The origin of the endemic patellogastropod limpets of the Ogasawara Islands in the northwestern Pacific. J. Molluscan Stud. 75, 87–90 (2009).
Google Scholar 
38.González-Wevar, C. A., Nakano, T., Palma, A. & Poulin, E. Biogeography in cellana (patellogastropoda, nacellidae) with special emphasis on the relationships of southern hemisphere oceanic island species. PLoS ONE 12, 1–16 (2017).
Google Scholar 
39.Tenggardjaja, K. A., Bowen, B. W. & Bernardi, G. Reef fish dispersal in the Hawaiian Archipelago: Comparative phylogeography of three endemic damselfishes. J. Mar. Sci. https://doi.org/10.1155/2016/3251814 (2016).Article 

Google Scholar 
40.Tenggardjaja, K. A., Bowen, B. W. & Bernardi, G. Comparative phylogeography of widespread and endemic damselfishes in the Hawaiian Archipelago. Mar. Biol. 165, 1–21 (2018).
Google Scholar 
41.Kurozumi, T. & Asakura, A. Marine molluscs from the northern Mariana Islands, Micronesia. Nat. Hist. Res. Spec. Issue 1, 121–168 (1994).
Google Scholar 
42.Nakano, D. & Makoto, N. Age structure and growth in a population of Monodonta labio (Linnaeus) at Shima Peninsula, Japan. Venus J. Malacol. Soc. Japan 40, 34–40 (1981).
Google Scholar 
43.Hashino, T. & Tomiyama, K. Life history of Monodonta labio confusa Tapprone-Canefri, 1874 in Kagoshima Bay, Kyushu, Japan and age estimation based on annual ring analysis of shell. Nat. Kagoshima 39, 143–155 (2013).
Google Scholar 
44.Yoh, A. & Sakurai, I. Reproductive cycle and food habits of the herbivorous snail Monodonta confusa off the coast of Suttsu Bay in southwestern Hokkaido, Japan. Proc. Sch. Biol. Sci. Tokai Univ. 6, 17–23 (2017).
Google Scholar 
45.Sasaki, R. Larval identification and occurrence of ezo abalone, Haliotis discus hannai, in the adjacent waters of Kesennuma Bay, Miyagi Prefecture. Suisan Zoushoku 32, 199–206 (1985).
Google Scholar 
46.Yamazaki, D., Miura, O., Uchida, S., Ikeda, M. & Chiba, S. Comparative seascape genetics of co-distributed intertidal snails Monodonta spp. in the Japanese and Ryukyu archipelagoes. Mar. Ecol. Prog. Ser. 657, 135–146 (2020).ADS 

Google Scholar 
47.Ballard, J. W. O. & Whitlock, M. C. The incomplete natural history of mitochondria. Mol. Ecol. 13, 729–744 (2004).PubMed 

Google Scholar 
48.Parham, J. F. et al. Genetic introgression and hybridization in Antillean freshwater turtles (Trachemys) revealed by coalescent analyses of mitochondrial and cloned nuclear markers. Mol. Phylogenet. Evol. 67, 176–187 (2013).CAS 
PubMed 

Google Scholar 
49.Hirano, T. et al. Enigmatic incongruence between mtDNA and nDNA revealed by multi-locus phylogenomic analyses in freshwater snails. Sci. Rep. 9, 6223 (2019).PubMed 
PubMed Central 
ADS 

Google Scholar 
50.Funk, D. J. & Omland, K. E. Species-level paraphyly and polyphyly: Frequency, causes, and consequences, with insights from animal mitochondrial DNA. Annu. Rev. Ecol. Evol. Syst. 34, 397–423 (2003).
Google Scholar 
51.Toews, D. P. L. & Brelsford, A. The biogeography of mitochondrial and nuclear discordance in animals. Mol. Ecol. 21, 3907–3930 (2012).CAS 
PubMed 

Google Scholar 
52.Hirase, S. et al. Integrative genomic phylogeography reveals signs of mitonuclear incompatibility in a natural hybrid goby population. Evolution (N.Y.) 75, 176–194 (2021).
Google Scholar 
53.Zhao, D., Li, Q., Kong, L. & Yu, H. Cryptic diversity of marine gastropod Monodonta labio (Trochidae): Did the early Pleistocene glacial isolation and sea surface temperature gradient jointly drive diversification of sister species and/or subspecies in the Northwestern Pacific?. Mar. Ecol. https://doi.org/10.1111/maec.12443 (2017).Article 

Google Scholar 
54.Mukai, T., Nakamura, S. & Nishida, M. Genetic population structure of a reef goby, Bathygobius cocosensis, in the northwestern Pacific. Ichthyol. Res. 56, 380–387 (2009).
Google Scholar 
55.Keith, S. A., Herbert, R. J. H., Norton, P. A., Hawkins, S. J. & Newton, A. C. Individualistic species limitations of climate-induced range expansions generated by meso-scale dispersal barriers. Divers. Distrib. 17, 275–286 (2011).
Google Scholar 
56.Faurby, S. & Barber, P. H. Theoretical limits to the correlation between pelagic larval duration and population genetic structure. Mol. Ecol. 21, 3419–3432 (2012).PubMed 

Google Scholar 
57.Funk, W. C. et al. Adaptive divergence despite strong genetic drift: Genomic analysis of the evolutionary mechanisms causing genetic differentiation in the island fox (Urocyon littoralis). Mol. Ecol. 25, 2176–2194 (2016).CAS 
PubMed 
PubMed Central 

Google Scholar 
58.Whiteley, A. R. et al. Genetic variation and effective population size in isolated populations of coastal cutthroat trout. Conserv. Genet. 11, 1929–1943 (2010).
Google Scholar 
59.Riginos, C., Douglas, K. E., Jin, Y., Shanahan, D. F. & Treml, E. A. Effects of geography and life history traits on genetic differentiation in benthic marine fishes. Ecography (Cop.) 34, 566–575 (2011).
Google Scholar 
60.Kuriiwa, K., Chiba, S. N., Motomura, H. & Matsuura, K. Phylogeography of Blacktip Grouper, Epinephelus fasciatus (Perciformes: Serranidae), and influence of the Kuroshio Current on cryptic lineages and genetic population structure. Ichthyol. Res. 61, 361–374 (2014).
Google Scholar 
61.Tachikawa, H. Nature profile of the isolated oceanic island, the Bonin Islands. Midoriishi 5, 27–29 (1994).
Google Scholar 
62.Setsuko, S. et al. Genetic diversity, structure, and demography of Pandanus boninensis (Pandanaceae) with sea drifted seeds, endemic to the Ogasawara Islands of Japan: Comparison between young and old islands. Mol. Ecol. 29, 1050–1068 (2020).PubMed 

Google Scholar 
63.Asakura, A. & Nishihama, S. Studies on the biology and ecology of the intertidal animals of Chichijima Island in the Ogasawara (Bonin) Islands III: Description, form and habitat of the trochid snail, Monodonta perplexa boninensis n. subsp. in comparison with those in Monodonta perpl. Venus J. Malacol. Soc. Japan 46, 194–201 (1987).
Google Scholar 
64.Nakano, T. & Minato, R. Marine organisms in the intertidal zone of Nishinoshima Island. Ogasawara Res. 46, 109–121 (2019).
Google Scholar 
65.Sasaki, T. & Horikoshi, K. Marine animals of Minami-Iw-To lsland. Ogasawara Res. 33, 155–171 (2008).
Google Scholar 
66.Williams, S., Apte, D., Ozawa, T., Kaligis, F. & Nakano, T. Speciation and dispersal along continental coastlines and island arcs in the indo-west pacific turbinid gastropod genus lunella. Evolution (N. Y.) 65, 1752–1771 (2011).
Google Scholar 
67.Siddall, M. et al. Sea-level fluctuations during the last glacial cycle. Nature 423, 853–858 (2003).CAS 
PubMed 
ADS 

Google Scholar 
68.Setsuko, S. et al. Genetic variation of pantropical Terminalia catappa plants with sea-drifted seeds in the Bonin Islands: Suggestions for transplantation guidelines. Plant Species Biol. 32, 13–24 (2017).
Google Scholar 
69.Hedgecock, D. Is gene flow from pelagic larval dispersal important in the adaptation and evolution of marine invertebrates?. Bull. Mar. Sci. 39, 550–564 (1986).
Google Scholar 
70.Parsons, K. E. The genetic effects of larval dispersal depend on spatial scale and habitat characteristics. Mar. Biol. 126, 403–414 (1996).CAS 

Google Scholar 
71.Pechenik, J. A. On the advantages and disadvantages of larval stages in benthic marine invertebrate life cycles. Mar. Ecol. Prog. Ser. 177, 269–297 (1999).ADS 

Google Scholar 
72.Scheltema, R. S. Larval dispersal as a means of genetic exchange between geographically separated populations of shallow-water benthic marine gastropods. Biol. Bull. 140, 284–322 (1971).
Google Scholar 
73.Wright, L. I., Tregenza, T. & Hosken, D. J. Inbreeding, inbreeding depression and extinction. Conserv. Genet. 9, 833–843 (2008).
Google Scholar 
74.Caley, M. J. et al. Recruitment and the local dynamics of open marine populations. Annu. Rev. Ecol. Syst. 27, 477–500 (1996).
Google Scholar 
75.Johannesson, K. The paradox of Rockall: Why is a brooding gastropod (Littorina saxatilis) more widespread than one having a planktonic larval dispersal stage (L. littorea)?. Mar. Biol. 99, 507–513 (1988).
Google Scholar 
76.Nakajima, Y., Nishikawa, A., Iguchi, A. & Sakai, K. Regional genetic differentiation among northern high-latitude island populations of a broadcast-spawning coral. Coral Reefs 31, 1125–1133 (2012).ADS 

Google Scholar 
77.Bowen, B. W. et al. Comparative phylogeography of the ocean planet. Proc. Natl. Acad. Sci. U. S. A. 113, 7962–7969 (2016).CAS 
PubMed 
PubMed Central 

Google Scholar 
78.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 

Google Scholar 
79.Palumbi, S. R. Population genetics, demographic connectivity, and the design of marine reserves. Ecol. Appl. 13, 146–158 (2003).
Google Scholar 
80.Jones, G., Srinivasan, M. & Almany, G. Population connectivity and conservation of marine biodiversity. Oceanography 20, 100–111 (2007).
Google Scholar 
81.Colgan, D. J., Ponder, W. F., Beacham, E. & Macaranas, J. M. Gastropod phylogeny based on six segments from four genes representing coding or non-coding and mitochondrial or nuclear DNA. Molluscan Res. https://doi.org/10.1071/MR03002 (2003).Article 

Google Scholar 
82.Griekspoor, A. & Groothuis, T. 4peaks. Ver. 1.7.1. http://nucleobytes.com/4peaks/ (2005).83.Thompson, J. D., Higgins, D. G. & Gibson, T. J. CLUSTAL W: Improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice. Nucleic Acids Res. https://doi.org/10.1093/nar/22.22.4673 (1994).Article 
PubMed 
PubMed Central 

Google Scholar 
84.Edgar, R. C. MUSCLE: Multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. https://doi.org/10.1093/nar/gkh340 (2004).Article 
PubMed 
PubMed Central 

Google Scholar 
85.Excoffier, L. & Lischer, H. E. L. Arlequin suite ver 3.5: A new series of programs to perform population genetics analyses under Linux and Windows. Mol. Ecol. Resour. 10, 564–567 (2010).PubMed 
PubMed Central 

Google Scholar 
86.Excoffier, L., Smouse, P. E. & Quattro, J. M. Analysis of molecular variance inferred from metric distances among DNA haplotypes: Application to human mitochondrial DNA restriction data. Genetics 131, 479–491 (1992).CAS 
PubMed 
PubMed Central 

Google Scholar 
87.Bandelt, H., Forster, P. & Röhl, A. Median-joining networks for inferring intraspecific phylogenies. Mol. Biol. Evol. 16, 37–48 (1999).CAS 
PubMed 
PubMed Central 

Google Scholar 
88.Leigh, J. W. & Bryant, D. POPART: Full-feature software for haplotype network construction. Methods Ecol. Evol. 6, 1110–1116 (2015).
Google Scholar 
89.Peterson, B. K., Weber, J. N., Kay, E. H., Fisher, H. S. & Hoekstra, H. E. Double digest RADseq: An inexpensive method for de novo SNP discovery and genotyping in model and non-model species. PLoS ONE 7, e37135 (2012).CAS 
PubMed 
PubMed Central 
ADS 

Google Scholar 
90.Eaton, D. A. R. & Overcast, I. ipyrad: Interactive assembly and analysis of RADseq datasets. Bioinformatics https://doi.org/10.1093/bioinformatics/btz966 (2020).Article 
PubMed 

Google Scholar 
91.Meirmans, P. G. & Van Tienderen, P. H. genotype and genodive: Two programs for the analysis of genetic diversity of asexual organisms. Mol. Ecol. Notes 4, 792–794 (2004).
Google Scholar 
92.Hammer, Ø., Harper, D. A. T. & Ryan, P. D. PAST: Paleontological statistics software package for education and data analysis. Palaeontol. Electron. 4, 9 (2001).
Google Scholar 
93.Mussmann, S. M., Douglas, M. R., Chafin, T. K. & Douglas, M. E. BA3-SNPs: Contemporary migration reconfigured in BayesAss for next-generation sequence data. Methods Ecol. Evol. 10, 1808–1813 (2019).
Google Scholar 
94.Rambaut, A. & Drummond, A. J. Tracer v1.6. http://tree.bio.ed.ac.uk/software/tracer/ (2013).95.Excoffier, L., Dupanloup, I., Huerta-Sánchez, E., Sousa, V. C. & Foll, M. Robust demographic inference from genomic and SNP data. PLoS Genet. 9, e1003905 (2013).PubMed 
PubMed Central 

Google Scholar  More

1.Falkowski, P. G., Fenchel, T. & Delong, E. F. The microbial engines that drive earth’s biogeochemical cycles. Science 320, 1034–1039 (2008).CAS 
ADS 

Google Scholar 
2.Fuhrman, J. A., Cram, J. A. & Needham, D. M. Marine microbial community dynamics and their ecological interpretation. Nat. Rev. Microbiol. 13, 133–146 (2015).CAS 
PubMed 

Google Scholar 
3.Giovannoni, S. J. & Stingl, U. Molecular diversity and ecology of microbial plankton. Nature 437, 343–348 (2005).CAS 
PubMed 
ADS 

Google Scholar 
4.Kujawinski, E. B. The impact of microbial metabolism on marine dissolved organic matter. Ann. Rev. Mar. Sci. 3, 567–599 (2011).PubMed 

Google Scholar 
5.Moran, M. A. The global ocean microbiome. Science 350, aac8455 (2015).PubMed 

Google Scholar 
6.Pedrós-Alió, C. The rare bacterial biosphere. Ann. Rev. Mar. Sci. 4, 15.1-15.18 (2012).
Google Scholar 
7.Salazar, G. et al. Global diversity and biogeography of deep-sea pelagic prokaryotes. ISME J. 10, 596–608 (2015).PubMed 
PubMed Central 

Google Scholar 
8.Sogin, M. L. et al. Microbial diversity in the deep sea and the underexplored ‘rare biosphere’. Proc. Natl. Acad. Sci. 103, 12115–12120 (2006).CAS 
PubMed 
PubMed Central 
ADS 

Google Scholar 
9.Kirchman, D. L. Growth rates of microbes in the oceans. Ann. Rev. Mar. Sci. 8, 285–309 (2016).PubMed 

Google Scholar 
10.Campbell, B. J., Yu, L., Heidelberg, J. F. & Kirchman, D. L. Activity of abundant and rare bacteria in a coastal ocean. Proc. Natl. Acad. Sci. 108, 12776–12781 (2011).CAS 
PubMed 
PubMed Central 
ADS 

Google Scholar 
11.Salter, I. et al. Seasonal dynamics of active SAR11 ecotypes in the oligotrophic Northwest Mediterranean Sea. ISME J. 9, 347–360 (2015).CAS 
PubMed 

Google Scholar 
12.Giovannoni, S. J., Cameron Thrash, J. & Temperton, B. Implications of streamlining theory for microbial ecology. ISME J. 8, 1553–1565 (2014).PubMed 
PubMed Central 

Google Scholar 
13.Våge, S., Storesund, J. E. & Thingstad, T. F. Adding a cost of resistance description extends the ability of virus-host model to explain observed patterns in structure and function of pelagic microbial communities. Environ. Microbiol. 15, 1842–1852 (2012).
Google Scholar 
14.Våge, S., Storesund, J. E. & Thingstad, T. F. SAR11 viruses and defensive host strains. Nature 499, 9–11 (2013).
Google Scholar 
15.Giovannoni, S., Temperton, B. & Zhao, Y. Giovannoni et al. reply. Nature 499, 9–11 (2013).
Google Scholar 
16.Zhao, Y. et al. Abundant SAR11 viruses in the ocean. Nature 494, 357–360 (2013).CAS 
PubMed 
ADS 

Google Scholar 
17.Herndl, G. J. et al. Contribution of Archaea to total prokaryotic production in the deep Atlantic Ocean. Appl. Environ. Microbiol. 71, 2303–2309 (2005).CAS 
PubMed 
PubMed Central 
ADS 

Google Scholar 
18.Teira, E., Lebaron, P., Van Aken, H. & Herndl, G. J. Distribution and activity of Bacteria and Archaea in the deep water masses of the North Atlantic. Limnol. Oceanogr. 51, 2131–2144 (2006).CAS 
ADS 

Google Scholar 
19.Newton, R. J. & Shade, A. Lifestyles of rarity: Understanding heterotrophic strategies to inform the ecology of the microbial rare biosphere. Aquat. Microb. Ecol. 78, 51–63 (2016).
Google Scholar 
20.Hamasaki, K., Taniguchi, A., Tada, Y., Long, R. A. & Azam, F. Actively growing bacteria in the Inland Sea of Japan, identified by combined bromodeoxyuridine immunocapture and denaturing gradient gel electrophoresis. Appl. Environ. Microbiol. 73, 2787–2798 (2007).CAS 
PubMed 
PubMed Central 
ADS 

Google Scholar 
21.Tada, Y., Makabe, R., Kasamatsu-Takazawa, N., Taniguchi, A. & Hamasaki, K. Growth and distribution patterns of Roseobacter/Rhodobacter, SAR11, and Bacteroidetes lineages in the Southern Ocean. Polar Biol. 36, 691–704 (2013).
Google Scholar 
22.Suttle, C. A. Marine viruses—Major players in the global ecosystem. Nat. Rev. Microbiol. 5, 801–812 (2007).CAS 
PubMed 

Google Scholar 
23.Pernthaler, J. Predation on prokaryotes in the water column and its ecological implications. Nat. Rev. Microbiol. 3, 537–546 (2005).CAS 
PubMed 

Google Scholar 
24.Mena, C. et al. Seasonal niche partitioning of surface temperate open ocean prokaryotic communities. Front. Microbiol. 11, 1749 (2020).PubMed 
PubMed Central 

Google Scholar 
25.Mena, C. et al. Dynamic prokaryotic communities in the dark western Mediterranean Sea. Sci. Rep. 11, 17859 (2021).CAS 
PubMed 
PubMed Central 
ADS 

Google Scholar 
26.Urbach, E., Vergin, K. L. & Giovannoni, S. J. Immunochemical detection and isolation of DNA from metabolically active bacteria. Appl. Environ. Microbiol. 65, 1207–1213 (1999).CAS 
PubMed 
PubMed Central 
ADS 

Google Scholar 
27.Hatzenpichler, R. et al. In situ visualization of newly synthesized proteins in environmental microbes using amino acid tagging and click chemistry. Environ. Microbiol. 16, 2568–2590 (2014).CAS 
PubMed 
PubMed Central 

Google Scholar 
28.Emerson, J. et al. Schrödinger’s microbes: Tools for distinguishing the living from the dead in microbial ecosystems. Micobiome 5, 86 (2017).
Google Scholar 
29.Smriga, S., Samo, T., Malfatti, F., Villareal, J. & Azam, F. Individual cell DNA synthesis within natural marine bacterial assemblages as detected by ‘click’ chemistry. Aquat. Microb. Ecol. 72, 269–280 (2014).
Google Scholar 
30.Reichart, N. et al. Activity-based cell sorting reveals responses of uncultured archaea and bacteria to substrate amendment. ISME J. 14, 2851–2861 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
31.Bakenhus, I. et al. Composition of total and cell-proliferating bacterioplankton community in early summer in the North Sea—Roseobacters are the most active component. Front. Microbiol. 8, 1–14 (2017).
Google Scholar 
32.Morris, R. M. et al. SAR11 clade dominates ocean surface bacterioplankton communities. Nature 420, 806–810 (2002).CAS 
PubMed 
ADS 

Google Scholar 
33.Giovannoni, S. J. SAR11 Bacteria: The most abundant plankton in the oceans. Ann. Rev. Mar. Sci. 9, 231–255 (2017).PubMed 

Google Scholar 
34.Clifford, E. L. et al. Taurine is a major carbon and energy source for marine prokaryotes in the North Atlantic Ocean off the Iberian Peninsula. Microb. Ecol. 78, 299–312 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
35.Tripp, H. J. et al. SAR11 marine bacteria require exogenous reduced sulphur for growth. Nature 452, 741–744 (2008).CAS 
PubMed 
ADS 

Google Scholar 
36.Carlson, C. A. et al. Seasonal dynamics of SAR11 populations in the euphotic and mesopelagic zones of the northwestern Sargasso Sea. ISME J. 3, 283–295 (2009).CAS 
PubMed 

Google Scholar 
37.Winter, C., Bouvier, T., Weinbauer, M. G. & Thingstad, T. F. Trade-offs between competition and defense specialists among unicellular planktonic organisms: The ‘Killing the Winner’ hypothesis revisited. Microbiol. Mol. Biol. Rev. 74, 42–57 (2010).CAS 
PubMed 
PubMed Central 

Google Scholar 
38.Vergin, K. L. et al. High-resolution SAR11 ecotype dynamics at the Bermuda Atlantic Time-series Study site by phylogenetic placement of pyrosequences. ISME J. 7, 1322–1332 (2013).CAS 
PubMed 
PubMed Central 

Google Scholar 
39.Hugoni, M. et al. Structure of the rare archaeal biosphere and seasonal dynamics of active ecotypes in surface coastal waters. Proc. Natl. Acad. Sci. 110, 1–6 (2013).
Google Scholar 
40.Qin, W. et al. Marine ammonia-oxidizing archaeal isolates display obligate mixotrophy and wide ecotypic variation. Proc. Natl. Acad. Sci. 111, 12504–12509 (2014).CAS 
PubMed 
PubMed Central 
ADS 

Google Scholar 
41.Pollard, P. C. & Moriarty, D. J. W. Validity of the tritiated thymidine method for estimating bacterial growth rates: Measurement of isotope dilution during DNA synthesis. Appl. Environ. Microbiol. 48, 1076–1083 (1984).CAS 
PubMed 
PubMed Central 
ADS 

Google Scholar 
42.Wellsbury, P., Herbert, R. A. & John Parkes, R. Incorporation of [methyl-3H]thymidine by obligate and facultative anaerobic bacteria when grown under defined culture conditions. FEMS Microbiol. Ecol. 12, 87–95 (1993).CAS 

Google Scholar 
43.Clausen, A., Matakos, A., Sandrini, M. & Piskur, J. Thymidine kinases in Archaea. Nucleosides Nucleotides Nucleic Acids 25, 1159–1163 (2006).CAS 
PubMed 

Google Scholar 
44.Hamasaki, K., Long, R. A. & Azam, F. Individual cell growth rates of marine bacteria, measured by bromodeoxyuridine incorporation. Aquat. Microb. Ecol. 35, 217–227 (2004).
Google Scholar 
45.Qin, W. et al. Influence of oxygen availability on the activities of ammonia-oxidizing Archaea. Environ. Microbiol. Rep. 9, 250–256 (2017).CAS 
PubMed 

Google Scholar 
46.Reji, L., Tolar, B. B., Smith, J. M., Chavez, F. P. & Francis, C. A. Differential co-occurrence relationships shaping ecotype diversification within Thaumarchaeota populations in the coastal ocean water column. ISME J. 13, 1144–1158 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
47.Sebastián, M. et al. Deep ocean prokaryotic communities are remarkably malleable when facing long-term starvation. Environ. Microbiol. 20, 713–723 (2018).PubMed 

Google Scholar 
48.Vergin, K. L., Done, B., Carlson, C. A. & Giovannoni, S. J. Spatiotemporal distributions of rare bacterioplankton populations indicate adaptive strategies in the oligotrophic ocean. Aquat. Microb. Ecol. 71, 1–13 (2013).
Google Scholar 
49.Tada, Y., Taniguchi, A., Sato-Takabe, Y. & Hamasaki, K. Growth and succession patterns of major phylogenetic groups of marine bacteria during a mesocosm diatom bloom. J. Oceanogr. 68, 509–519 (2012).
Google Scholar 
50.Mestre, M. et al. Sinking particles promote vertical connectivity in the ocean microbiome. Proc. Natl. Acad. Sci. 115, E6799–E6807 (2018).CAS 
PubMed 
PubMed Central 

Google Scholar 
51.Ruiz-González, C. et al. Major imprint of surface plankton on deep ocean prokaryotic structure and activity. Mol. Ecol. 29, 1820–1838 (2020).PubMed 

Google Scholar 
52.Chen, X., Ma, R., Yang, Y., Jiao, N. & Zhang, R. Viral regulation on bacterial community impacted by lysis-lysogeny switch: A microcosm experiment in eutrophic coastal waters. Front. Microbiol. 10, 1763 (2019).PubMed 
PubMed Central 

Google Scholar 
53.McCarren, J. et al. Microbial community transcriptomes reveal microbes and metabolic pathways associated with dissolved organic matter turnover in the sea. Proc. Natl. Acad. Sci. 107, 16420–16427 (2010).CAS 
PubMed 
PubMed Central 
ADS 

Google Scholar 
54.Reintjes, G., Arnosti, C., Fuchs, B. & Amann, R. Selfish, sharing and scavenging bacteria in the Atlantic Ocean: A biogeographical study of bacterial substrate utilisation. ISME J. 13, 1119–1132 (2019).CAS 
PubMed 

Google Scholar 
55.Middelboe, M. Bacterial growth rate and marine virus-host dynamics. Microb. Ecol. 40, 114–124 (2000).CAS 
PubMed 

Google Scholar 
56.Buchan, A., Lecleir, G. R., Gulvik, C. A. & González, J. M. Master recyclers: Features and functions of bacteria associated with phytoplankton blooms. Nat. Rev. Microbiol. 12, 686–698 (2014).CAS 
PubMed 

Google Scholar 
57.Mou, X. et al. Bromodeoxyuridine labelling and fluorescence-activated cell sorting of polyamine-transforming bacterioplankton in coastal seawater. Environ. Microbiol. 17, 876–888 (2014).PubMed 

Google Scholar 
58.Azam, F. & Malfatti, F. Microbial structuring of marine ecosystems. Nat. Rev. Microbiol. 5, 782–791 (2007).CAS 
PubMed 

Google Scholar 
59.Yilmaz, P., Yarza, P., Rapp, J. Z. & Glöckner, F. O. Expanding the world of marine bacterial and archaeal clades. Front. Microbiol. 6, 1524 (2016).PubMed 
PubMed Central 

Google Scholar 
60.Coe, A. et al. Survival of Prochlorococcus in extended darkness. Limnol. Oceanogr. 61, 1375–1388 (2016).ADS 

Google Scholar 
61.Cottrell, M. T. & Kirchman, D. L. Photoheterotrophic microbes in the arctic ocean in summer and winter. Appl. Environ. Microbiol. 75, 4958–4966 (2009).CAS 
PubMed 
PubMed Central 
ADS 

Google Scholar 
62.Zeder, M., Peter, S., Shabarova, T. & Pernthaler, J. A small population of planktonic Flavobacteria with disproportionally high growth during the spring phytoplankton bloom in a prealpine lake. Environ. Microbiol. 11, 2676–2686 (2009).PubMed 

Google Scholar 
63.Cottrell, M. T. & Kirchman, D. L. Natural assemblages of marine proteobacteria and members of the Cytophaga-flavobacter cluster consuming low- and high-molecular-weight dissolved organic matter. Appl. Environ. Microbiol. 66, 1692–1697 (2000).CAS 
PubMed 
PubMed Central 
ADS 

Google Scholar 
64.Banning, E. C., Casciotti, K. L. & Kujawinski, E. B. Novel strains isolated from a coastal aquifer suggest a predatory role for flavobacteria. FEMS Microbiol. Ecol. 73, 254–270 (2010).CAS 
PubMed 

Google Scholar 
65.Uchimiya, M. et al. Coupled response of bacterial production to a wind-induced fall phytoplankton bloom and sediment resuspension in the chukchi sea shelf, Western Arctic Ocean. Front. Mar. Sci. 3, 1–12 (2016).
Google Scholar 
66.Ivancic, I. et al. Seasonal variations in extracellular enzymatic activity in marine snow-associated microbial communities and their impact on the surrounding water. FEMS Microbiol. Ecol. 94, fiy198 (2018).CAS 

Google Scholar 
67.Cram, J. A. et al. Seasonal and interannual variability of the marine bacterioplankton community throughout the water column over ten years. ISME J. 9, 563–580 (2015).PubMed 

Google Scholar 
68.Manca, B. et al. Physical and biochemical averaged vertical profiles in the Mediterranean regions: An important tool to trace the climatology of water masses and to validate incoming data from operational oceanography. J. Mar. Syst. 48, 83–116 (2004).
Google Scholar 
69.Puig, P. & Palanques, A. Temporal variability and composition of settling particle fluxes on the Barcelona continental margin (Northwestern Mediterranean). J. Mar. Res. 56, 639–654 (1998).
Google Scholar 
70.Buesseler, K. O. & Boyd, P. W. Shedding light on processes that control particle export and flux attenuation in the twilight zone of the open ocean. Limnol. Oceanogr. 54, 1210–1232 (2009).CAS 
ADS 

Google Scholar 
71.Alonso-González, I. J., Arístegui, J., Lee, C. & Calafat, A. Regional and temporal variability of sinking organic matter in the subtropical northeast Atlantic Ocean: A biomarker diagnosis. Biogeosciences 7, 2101–2115 (2010).ADS 

Google Scholar 
72.Hunt, D. E. et al. Relationship between abundance and specific activity of bacterioplankton in open ocean surface waters. Appl. Environ. Microbiol. 79, 177–184 (2013).CAS 
PubMed 
PubMed Central 
ADS 

Google Scholar 
73.Campbell, B. J. & Kirchman, D. L. Bacterial diversity, community structure and potential growth rates along an estuarine salinity gradient. ISME J. 7, 210–220 (2013).CAS 
PubMed 

Google Scholar 
74.Alderkamp, A. C., Sintes, E. & Herndl, G. J. Abundance and activity of major groups of prokaryotic plankton in the coastal North Sea during spring and summer. Aquat. Microb. Ecol. 45, 237–246 (2006).
Google Scholar 
75.De Corte, D., Sintes, E., Yokokawa, T. & Herndl, G. J. Comparison between MICRO-CARD-FISH and 16S rRNA gene clone libraries to assess the active versus total bacterial community in the coastal Arctic. Environ. Microbiol. Rep. 5, 272–281 (2013).PubMed 

Google Scholar 
76.Bergauer, K. et al. Organic matter processing by microbial communities throughout the Atlantic water column as revealed by metaproteomics. Proc. Natl. Acad. Sci. 115, E400–E408 (2018).CAS 
PubMed 

Google Scholar 
77.Georges, A. A., El-Swais, H., Craig, S. E., Li, W. K. W. & Walsh, D. A. Metaproteomic analysis of a winter to spring succession in coastal northwest Atlantic Ocean microbial plankton. ISME J. 8, 1301–1313 (2014).CAS 
PubMed 
PubMed Central 

Google Scholar 
78.Couradeau, E. et al. Probing the active fraction of soil microbiomes using BONCAT-FACS. Nat. Commun. 10, 2770 (2019).PubMed 
PubMed Central 
ADS 

Google Scholar 
79.Wu, X. et al. Culturing of ‘unculturable’ subsurface microbes: Natural organic carbon source fuels the growth of diverse and distinct bacteria from groundwater. Front. Microbiol. 11, 610001 (2020).PubMed 
PubMed Central 

Google Scholar 
80.Alonso-Sáez, L., Díaz-Pérez, L. & Morán, X. A. G. The hidden seasonality of the rare biosphere in coastal marine bacterioplankton. Environ. Microbiol. 17, 3766–3780 (2015).PubMed 

Google Scholar 
81.Liu, J., Meng, Z., Liu, X. & Zhang, X.-H. Microbial assembly, interaction, functioning, activity and diversification: A review derived from community compositional data. Mar. Life Sci. Technol. 1, 112–128 (2019).ADS 

Google Scholar 
82.Long, R. A. & Azam, F. Antagonistic interactions among marine pelagic bacteria. Appl. Environ. Microbiol. 67, 4975–4983 (2001).CAS 
PubMed 
PubMed Central 
ADS 

Google Scholar 
83.López-Jurado, J. L. et al. The RADMED monitoring programme as a tool for MSFD implementation: Towards an ecosystem-based approach. Ocean Sci. 11, 897–908 (2015).ADS 

Google Scholar 
84.Strickland, J. D. H. & Parsons, T. R. A Practical Handbook of Seawater Analysis (Fisheries Research Board of Canada, 1968).
Google Scholar 
85.Grasshoff, K., Ehrhardt, M. & Kremling, K. Methods of seawater analysis (Verlag Chemie GmbH, 1983). https://doi.org/10.1002/iroh.19850700232.Book 

Google Scholar 
86.Murphy, J. & Riley, J. P. A modified single solution method for the determination of phosphate in natural waters. Anal. Chim. Acta 27, 31–36 (1962).CAS 

Google Scholar 
87.Brussaard, C. P. D. Optimization of procedures for counting viruses by flow cytometry. Appl. Environ. Microbiol. 70, 1506–1513 (2004).CAS 
PubMed 
PubMed Central 
ADS 

Google Scholar 
88.Gasol, J. M. & del Giorgio, P. A. Using flow cytometry for counting natural planktonic bacteria and understanding the structure of planktonic bacterial communities. Sci. Mar. 64, 197–224 (2000).
Google Scholar 
89.Parada, A. E., Needham, D. M. & Fuhrman, J. A. Every base matters: Assessing small subunit rRNA primers for marine microbiomes with mock communities, time series and global field samples. Environ. Microbiol. 18, 1403–1414 (2016).CAS 
PubMed 

Google Scholar 
90.Bolyen, E. et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME2. Nat. Biotechnol. 37, 852–857 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
91.Callahan, B. J. et al. Dada2: High-resolution sample inference from illumina amplicon data. Nat. Methods 13, 581–583 (2016).CAS 
PubMed 
PubMed Central 

Google Scholar 
92.Katoh, K. & Standley, D. M. MAFFT Multiple sequence aligment software version 7: Improvements in performance and usability. Mol. Biol. Evol. 30, 772–780 (2013).CAS 
PubMed 
PubMed Central 

Google Scholar 
93.Eren, A. M. et al. Oligotyping: Differentiating between closely related microbial taxa using 16S rRNA gene data. Methods Ecol. Evol. 4, 1111–1119 (2013).PubMed Central 

Google Scholar  More

1.Thomas, D. W., Fenton, M. B. & Barclay, R. M. R. Social-behavior of the little brown bat, myotis-lucifugus. 1. mating-behavior. Behav. Ecol. Sociobiol. 6, 129–136. https://doi.org/10.1007/bf00292559 (1979).Article 

Google Scholar 
2.Weiner, J. Physiological limits to sustainable energy budgets in birds and mammals-ecological implications. Trends Ecol. Evol. 7, 384–388. https://doi.org/10.1016/0169-5347(92)90009-z (1992).CAS 
Article 
PubMed 

Google Scholar 
3.Becker, N. I., Encarnação, J. A., Kalko, E. K. V. & Tschapka, M. The effects of reproductive state on digestive efficiency in three sympatric bat species of the same guild. Comp. Biochem. Physiol. A Mol. Integr. Physiol. 162, 386–390. https://doi.org/10.1016/j.cbpa.2012.04.021 (2012).CAS 
Article 
PubMed 

Google Scholar 
4.Becker, N. I., Encarnação, J. A., Tschapka, M. & Kalko, E. K. V. Energetics and life-history of bats in comparison to small mammals. Ecol. Res. 28, 249–258. https://doi.org/10.1007/s11284-012-1010-0 (2012).CAS 
Article 

Google Scholar 
5.Ruf, T. & Bieber, C. Physiological, behavioral, and life-history adaptations to environmental fluctuations in the edible dormouse. Front. Physiol. https://doi.org/10.3389/fphys.2020.00423 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
6.Scholander, P. F., Hock, R., Walters, V. & Irving, L. Adaptation to cold in arctic and tropical mammals and birds in relation to body temperature, insulation, and basal metabolic rate. Biol. Bull. 99, 259–271. https://doi.org/10.2307/1538742 (1950).CAS 
Article 
PubMed 

Google Scholar 
7.Geiser, F. & Ruf, T. Hibernation versus daily torpor in mammals and birds-physiological variables and classification of torpor patterns. Physiol. Zool. 68, 935–966. https://doi.org/10.1086/physzool.68.6.30163788 (1995).Article 

Google Scholar 
8.Aschoff, J. Thermal conductance in mammals and birds-its dependence on body size and circadian phase. Comp. Biochem. Physiol. A Mol. Integr. Physiol. 69, 611–619. https://doi.org/10.1016/0300-9629(81)90145-6 (1981).Article 

Google Scholar 
9.McNab, B. K. The economics of temperature regulation in neotropical bats. Comp. Biochem. Physiol 31, 227–268. https://doi.org/10.1016/0010-406X(69)91651-X (1969).CAS 
Article 
PubMed 

Google Scholar 
10.Speakman, J. R. & Thomas, D. W. in Bat ecology (ed Thomas H. Kunz and M. Brock Fenton) 430–490 (University of Chicago Press, 2003).11.Wang, L. C. H. & Wolowyk, M. W. Torpor in mammals and birds. Can. J. Zool.-Rev. Can. Zool. 66, 133–137. https://doi.org/10.1139/z88-017 (1988).CAS 
Article 

Google Scholar 
12.Geiser, F. Metabolic rate and body temperature reduction during hibernation and daily torpor. Annu. Rev. Physiol. 66, 239–274. https://doi.org/10.1146/annurev.physiol.66.032102.115105 (2004).CAS 
Article 
PubMed 
ADS 

Google Scholar 
13.Geiser, F. & Masters, P. Torpor in relation to reproduction in the mulgara, dasycercus-cristicauda (dasyuridae, marsupialia). J. Therm. Biol. 19, 33–40. https://doi.org/10.1016/0306-4565(94)90007-8 (1994).Article 

Google Scholar 
14.Wojciechowski, M. S., Jefimow, M. & Tęgowska, E. Environmental conditions, rather than season, determine torpor use and temperature selection in large mouse-eared bats (Myotis myotis). Comp. Biochem. Physiol. A Mol. Integr. Physiol. 147, 828–840. https://doi.org/10.1016/j.cbpa.2006.06.039 (2007).CAS 
Article 
PubMed 

Google Scholar 
15.Ruf, T. & Geiser, F. Daily torpor and hibernation in birds and mammals. Biol. Rev. 90, 891–926. https://doi.org/10.1111/brv.12137 (2015).Article 
PubMed 

Google Scholar 
16.Tuttle, M. D. Population ecology of the gray bat (Myotis grisescens): factors Iifluencing growth and survival of newly volant young. Ecology 57, 587–595. https://doi.org/10.2307/1936443 (1976).Article 

Google Scholar 
17.Racey, P. A. & Swift, S. M. Variations in gestation length in a colony of Pipistrelle bats (Pipistrellus pipistrellus) from year to year. J. Reprod. Fertil. 61, 123–129. https://doi.org/10.1530/jrf.0.0610123 (1981).CAS 
Article 
PubMed 

Google Scholar 
18.Audet, D. & Fenton, M. B. Heterothermy and the use of torpor by the bat Eptesicus fuscus (Chiroptera, Vespertilionidae)-a field study. Physiol. Zool. 61, 197–204. https://doi.org/10.1086/physzool.61.3.30161232 (1988).Article 

Google Scholar 
19.Barnes, B. M., Kretzmann, M., Licht, P. & Zucker, I. The influence of hibernation on testis growth and spermatogenesis in the golden mantled ground squirrel, Spermophilus lateralis. Biol. Reprod. 35, 1289–1297. https://doi.org/10.1095/biolreprod35.5.1289 (1986).CAS 
Article 
PubMed 

Google Scholar 
20.Gagnon, M. F., Lafleur, C., Landry-Cuerrier, M., Humphries, M. M. & Kimmins, S. Torpor expression is associated with differential spermatogenesis in hibernating eastern chipmunks. Am. J. Physiol. Regul. Integr. Comp. Physiol. 319, R455–R465. https://doi.org/10.1152/ajpregu.00328.2019 (2020).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
21.McLean, J. A. & Speakman, J. R. Energy budgets of lactating and non-reproductive Brown Long-Eared Bats (Plecotus auritus) suggest females use compensation in lactation. Funct. Ecol. 13, 360–372. https://doi.org/10.1046/j.1365-2435.1999.00321.x (1999).Article 

Google Scholar 
22.Wilde, C. J., Knight, C. R. & Racey, P. A. Influence of torpor on milk protein composition and secretion in lactating bats. J. Exp. Zool. 284, 35–41. https://doi.org/10.1002/(sici)1097-010x(19990615)284:1%3c35::aid-jez6%3e3.0.co;2-z (1999).CAS 
Article 
PubMed 

Google Scholar 
23.Racey, P. A. The prolonged storage and survival of spermatozoa in Chiroptera. J. Reprod. Fertil. 56, 391–402. https://doi.org/10.1530/jrf.0.0560391 (1979).CAS 
Article 
PubMed 

Google Scholar 
24.Racey, P. A. The reproductive cycle in male noctule bats, Nyctalus noctula. J. Reprod. Fertil. 41, 169–182. https://doi.org/10.1530/jrf.0.0410169 (1974).CAS 
Article 
PubMed 

Google Scholar 
25.Gustafson, A. W. Male reproductive patterns in hibernating bats. J. Reprod. Fertil. 56, 317–0 (1979).CAS 
Article 

Google Scholar 
26.Komar, E., Dechmann, D. K. N., Fasel, N. J., Zegarek, M. & Ruczyński, I. Food restriction delays seasonal sexual maturation but does not increase torpor use in male bats. J. Exp. Biol. https://doi.org/10.1242/jeb.214825 (2020).Article 
PubMed 

Google Scholar 
27.Wilkinson, G. S. & McCracken, G. F. in Bat ecology (eds Thomas H. Kunz & M. Brock Fenton) 128–155 (University of Chicago Press, 2003).28.Pescovitz, O. H., Srivastava, C. H., Breyer, P. R. & Monts, B. A. Paracrine control of spermatogenesis. Trends Endocrinol. Metab. 5, 126–131. https://doi.org/10.1016/1043-2760(94)90094-9 (1994).CAS 
Article 
PubMed 

Google Scholar 
29.Sharpe, R. M., Kerr, J. B., McKinnell, C. & Millar, M. Temporal relationship between androgen-dependent changes in the volume of seminiferous tubule fluid, lumen size and seminiferous tubule protein secretion in rats. J. Reprod. Fertil. 101, 193–198 (1994).CAS 
Article 

Google Scholar 
30.Becker, N. I., Tschapka, M., Kalko, E. K. V. & Encarnacao, J. A. Balancing the energy budget in free ranging male Myotis daubentonii bats. Physiol. Biochem. Zool. 86, 361–369. https://doi.org/10.1086/670527 (2013).Article 
PubMed 

Google Scholar 
31.Entwistle, A. C., Racey, P. A. & Speakman, J. R. The reproductive cycle and determination of sexual maturity in male brown long eared bats, Plecotus auritus (Chiroptera: Vespertilionidae). J. Zool. 244, 63–70. https://doi.org/10.1111/j.1469-7998.1998.tb00007.x (1998).Article 

Google Scholar 
32.Fasel, N. J., Kołodziej-Sobocińska, M., Komar, E., Zegarek, M. & Ruczyński, I. Penis size and sperm quality, are all bats grey in the dark?. Curr. Zool. 65, 697–703. https://doi.org/10.1093/cz/zoy094 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
33.Dietz, M. & Kalko, E. K. V. Reproduction affects flight activity in female and male Daubenton’s bats, Myotis daubentoni. Can. J. Zool.-Rev. Can. Zool. 85, 653–664. https://doi.org/10.1139/z07-045 (2007).Article 

Google Scholar 
34.Encarnação, J. A. Spatiotemporal pattern of local sexual segregation in a tree dwelling temperate bat Myotis daubentonii. J. Ethol. 30, 271–278. https://doi.org/10.1007/s10164-011-0323-8 (2012).Article 

Google Scholar 
35.Safi, K. & Kerth, G. Comparative analyses suggest that information transfer promoted sociality in male bats in the temperate zone. Am. Nat. 170, 465–472. https://doi.org/10.1086/520116 (2007).Article 
PubMed 

Google Scholar 
36.Hałat, Z., Dechmann, D. K. N., Zegarek, M. & Ruczyński, I. Male bats respond to adverse conditions with larger colonies and increased torpor use during sperm production. Mamm. Biol. 22, 2109 (2020).
Google Scholar 
37.Dietz, M. & Horig, A. Thermoregulation of tree dwelling temperate bats-a behavioural adaptation to force live history strategy. Folia Zool. 60, 5–16. https://doi.org/10.25225/fozo.v60.i1.a2.2011 (2011).Article 

Google Scholar 
38.Ruczyński, I., Zahorowicz, P., Borowik, T. & Hałat, Z. Activity patterns of two syntopic and closely related aerial-hawking bat species during breeding season in Bialowieza Primaeval Forest. Mammal Res. 62, 65–73. https://doi.org/10.1007/s13364-016-0298-5 (2017).Article 

Google Scholar 
39.Jolly, S. E. & Blackshaw, A. W. Prolonged epididymal sperm storage, and the temporal dissociation of testicular and accessory gland activity in the common sheath-tail bat, Taphozous georgianus, of tropical Australia. J. Reprod. Fertil. 81, 205–211. https://doi.org/10.1530/jrf.0.0810205 (1987).CAS 
Article 
PubMed 

Google Scholar 
40.Boyles, J. G., Dunbar, M. B., Storm, J. J. & Brack, V. Energy availability influences microclimate selection of hibernating bats. J. Exp. Biol. 210, 4345–4350. https://doi.org/10.1242/jeb.007294 (2007).Article 
PubMed 

Google Scholar 
41.Ruczyński, I., Hałat, Z., Zegarek, M., Borowik, T. & Dechmann, D. K. N. Camera transects as a method to monitor high temporal and spatial ephemerality of flying nocturnal insects. Methods Ecol. Evol. https://doi.org/10.1111/2041-210x.13339 (2020).Article 

Google Scholar 
42.Safi, K. Social bats: the males’ perspective. J. Mammal. 89, 1342–1350. https://doi.org/10.1644/08-mamm-s-058.1 (2008).Article 

Google Scholar 
43.Webb, P. I., Speakman, J. R. & Racey, P. A. The implication of small reductions in body temperature for radiant and convective heat loss in resting endothermic brown long eared bats (Pecotus auritus). J. Therm. Biol. 18, 131–135. https://doi.org/10.1016/0306-4565(93)90026-p (1993).Article 

Google Scholar 
44.Boratyński, J. S., Iwińska, K. & Bogdanowicz, W. An intrapopulation heterothermy continuum: notable repeatability of body temperature variation in food deprived yellow necked mice. J. Exp. Biol. 222, 197152. https://doi.org/10.1242/jeb.197152 (2019).Article 

Google Scholar 
45.Christian, N. & Geiser, F. To use or not to use torpor? Activity and body temperature as predictors. Naturwissenschaften 94, 483–487. https://doi.org/10.1007/s00114-007-0215-5 (2007).CAS 
Article 
PubMed 
ADS 

Google Scholar 
46.Smith, L. B. & Walker, W. H. The regulation of spermatogenesis by androgens. Semin. Cell Dev. Biol. 30, 2–13. https://doi.org/10.1016/j.semcdb.2014.02.012 (2014).CAS 
Article 
PubMed 

Google Scholar 
47.Macdonald, J. & Harrison, R. G. Effect of low temperatures on rat spermatogenesis. Fertil. Steril. 5, 205–216 (1954).CAS 
Article 

Google Scholar 
48.Fowler, P. A. & Racey, P. A. Relationship between body and testis temperatures in the European hedgehog, Erinaceus europaeus, during hibernation and sexual reactivation. Reproduction 81, 567. https://doi.org/10.1530/jrf.0.0810567 (1987).CAS 
Article 

Google Scholar 
49.Davis, J. R., Firlit, C. F. & Hollinger, M. A. Effect of temperature on incorporation of l-lysine-U-C14 into testicular proteins. Am. J. Physiol. 204, 696–698. https://doi.org/10.1152/ajplegacy.1963.204.4.696 (1963).CAS 
Article 
PubMed 

Google Scholar 
50.LeVier, R. R. & Spaziani, E. The influence of temperature on steroidogenesis in the rat testis. J. Exp. Zool. 169, 113–120. https://doi.org/10.1002/jez.1401690113 (1968).CAS 
Article 
PubMed 

Google Scholar 
51.Geiser, F. & Brigham, R. M. in Living in a seasonal world (eds Thomas Ruf, Claudia Bieber, Walter Arnold, & Eva Millesi) 109–121 (Springer, 2012).52.Safi, K. Die Zweifarbfledermaus in der Schweiz: Status und Grundlagen zum Schutz. (Haupt Verlag, 2006).53.Hałat, Z., Dechmann, D. K. N., Zegarek, M., Visser, A. F. J. & Ruczyński, I. Sociality and insect abundance affect duration of nocturnal activity of male parti-colored bats. J. Mammal. 99, 1503–1509. https://doi.org/10.1093/jmammal/gyy141 (2018).Article 

Google Scholar 
54.Ruczyński, I. Influence of temperature on maternity roost selection by noctule bats (Nyctalus noctula) and Leisler’s bats (N-leisleri) in Biaowieza Primeval Forest, Poland. Can. J. Zool. 84, 900–907. https://doi.org/10.1139/z06-060 (2006).Article 

Google Scholar 
55.Ruczyński, I. & Bartoń, K. A. Seasonal changes and the influence of tree species and ambient temperature on the fission-fusion dynamics of tree-roosting bats. Behav. Ecol. Sociobiol. 74, 63. https://doi.org/10.1007/s00265-020-02840-1 (2020).Article 

Google Scholar 
56.Linton, D. M. & Macdonald, D. W. Phenology of reproductive condition varies with age and spring weather conditions in male Myotis daubentonii and Myotis nattereri (Chiroptera: Vespertilionidae). Sci. Rep. 10, 6664. https://doi.org/10.1038/s41598-020-63538-y (2020).CAS 
Article 
PubMed 
PubMed Central 
ADS 

Google Scholar 
57.Dammhahn, M., Landry-Cuerrier, M., Reale, D., Garant, D. & Humphries, M. M. Individual variation in energy-saving heterothermy affects survival and reproductive success. Funct. Ecol. 31, 866–875. https://doi.org/10.1111/1365-2435.12797 (2017).Article 

Google Scholar 
58.Boyles, J. G., Johnson, J. S., Blomberg, A. & Lilley, T. M. Optimal hibernation theory. Mammal. Rev. 50, 91–100. https://doi.org/10.1111/mam.12181 (2020).Article 

Google Scholar 
59.Boratyński, J. S., Willis, C. K. R., Jefimow, M. & Wojciechowski, M. S. Huddling reduces evaporative water loss in torpid Natterer’s bats, Myotis nattereri. Comp. Biochem. Physiol. A Mol. Integr. Physiol. 179, 125–132. https://doi.org/10.1016/j.cbpa.2014.09.035 (2015).CAS 
Article 
PubMed 

Google Scholar 
60.Ruczyński, I., Kalko, E. K. V. & Siemers, B. M. The sensory basis of roost finding in a forest bat, Nyctalus noctula. J. Exp. Biol. 210, 3607–3615. https://doi.org/10.1242/jeb.009837 (2007).Article 
PubMed 

Google Scholar 
61.Lovegrove, B. G. Modification and miniaturization of Thermochron iButtons for surgical implantation into small animals. J. Comp. Physiol. B 179, 451–458. https://doi.org/10.1007/s00360-008-0329-x (2009).Article 
PubMed 

Google Scholar 
62.Willis, C. K. R., Lane, J. E., Liknes, E. T., Swanson, D. L. & Brigham, R. M. Thermal energetics of female big brown bats (Eptesicus fuscus). Can. J. Zool. 83, 871–879. https://doi.org/10.1139/z05-074 (2005).Article 

Google Scholar 
63.Willis, C. K. R. An energy-based body temperature threshold between torpor and normothermia for small mammals. Physiol. Biochem. Zool. 80, 643–651. https://doi.org/10.1086/521085 (2007).Article 
PubMed 

Google Scholar 
64.Krutzsch, P. H. in Reproductive Biology of Bats (ed Academic Press) 91–155 (2000).65.Wood, S. N. Generalized Additive Models: An Introduction With R. Vol. 66 (2006).66.Jackman, S. Bayesian Analysis for the Social Sciences. (Wiley, 2009).67.Brooks, S. P. & Gelman, A. General methods for monitoring convergence of iterative simulations. J. Comput. Graph. Stat. 7, 434–455. https://doi.org/10.2307/1390675 (1998).MathSciNet 
Article 

Google Scholar 
68.Kellner, K. jagsUI: A Wrapper Around ‘rjags’ to Streamline ‘JAGS’ Analyses. v.R package version 1.5.1. (2019). More

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

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