Ecology

1.Stocker, T. F. et al. IPCC, 2013: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (Cambridge Univ. Press, 2013).2.Jones, M. W. et al. Climate change increases risk of wildfires. ScienceBrief Review 116, 117 (2020).
Google Scholar 
3.Rogers, B. M., Balch, J. K., Goetz, S. J., Lehmann, C. E. & Turetsky, M. Focus on changing fire regimes: interactions with climate, ecosystems, and society. Environmental Research Letters 15, 030201 (2020).
Google Scholar 
4.Archibald, S., Lehmann, C. E., Gómez-Dans, J. L. & Bradstock, R. A. Defining pyromes and global syndromes of fire regimes. Proceedings of the National Acadam of Science 110, 6442–6447 (2013).CAS 

Google Scholar 
5.Donovan, G. H. & Brown, T. C. Be careful what you wish for: the legacy of Smokey Bear. Frontiers in Ecology and the Environment 5, 73–79 (2007).
Google Scholar 
6.Ghil, M. Natural climate variability. Encyclopedia Global Environmental Change 1, 544–549 (2002).
Google Scholar 
7.Hinnov, L. A. Cyclostratigraphy and its revolutionizing applications in the earth and planetary sciences. Geological Society of America Bulletin 125, 1703–1734 (2013).
Google Scholar 
8.Lantink, M. L., Davies, J. H., Mason, P. R., Schaltegger, U. & Hilgen, F. J. Climate control on banded iron formations linked to orbital eccentricity. Nature Geoscience 12, 369–374 (2019).CAS 

Google Scholar 
9.Berger, A. Milankovitch theory and climate. Reviews of Geophysics 26, 624–657 (1988).
Google Scholar 
10.Laskar, J. Astrochronology in Geological Time Scale 2020 (eds Gradstein, F. M., Ogg, J. G., Schmitz, M. D. & Ogg, G. M.) 139–158 (Elsevier, 2020).11.Shackleton, N. J. & Pisias, S. N. Atmospheric carbon dioxide, orbital forcing, and climate. The Carbon Cycle and Atmospheric ({CO}_{2}): Natural Variations Archean to Present, Geophysics Monograph Series 32, 303–317 (1985).12.Huybers, P. & Wunsch, C. Obliquity pacing of the late Pleistocene glacial terminations. Nature 434, 491–494 (2005).CAS 

Google Scholar 
13.Kutzbach, J. E., Liu, X., Liu, Z. & Chen, G. Simulation of the evolutionary response of global summer monsoons to orbital forcing over the past 280,000 years. Climate Dynamics 30, 567–579 (2008).
Google Scholar 
14.Weedon, G. P. Hemipelagic shelf sedimentation and climatic cycles: the basal Jurassic (Blue Lias) of South Britain. Earth and Planetary Science Letters 76, 321–335 (1986).
Google Scholar 
15.Weedon, G. P., Jenkyns, H. C., Coe, A. L. & Hesselbo, S. P. Astronomical calibration of the Jurassic time-scale from cyclostratigraphy in British mudrock formations. Philosophical Transactions of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences 357, 1787–1813 (1999).
Google Scholar 
16.Van Buchem, F. S. P., McCave, I. N. & Weedon, G. P. Orbitally induced small-scale cyclicity in a siliciclastic epicontinental setting (Lower Lias, Yorkshire, UK) in Orbital forcing and cyclic sequences, Special Publication of the International Association of Sedimentologists (eds de Boer, P. L. & Smith, D. G.) 345–366 (1994).17.Zachos, J. C., McCarren, H., Murphy, B., Röhl, U. & Westerhold, T. Tempo and scale of late Paleocene and early Eocene carbon isotope cycles: Implications for the origin of hyperthermals. Earth and Planetary Science Letters 299, 242–249 (2010).CAS 

Google Scholar 
18.Martinez, M. & Dera, G. Orbital pacing of carbon fluxes by a ∼ 9-My eccentricity cycle during the Mesozoic. Proceedings of the National Academy of Sciences 112, 12604–12609 (2015).CAS 

Google Scholar 
19.Laskar, J. The chaotic motion of the solar system: a numerical estimate of the size of the chaotic zones. Icarus 88, 266–291 (1990).
Google Scholar 
20.Varadi, F., Runnegar, B. & Ghil, M. Successive refinements in long-term integrations of planetary orbits. The Astrophysical Journal 592, 620 (2003).
Google Scholar 
21.Laskar, J. et al. Long term evolution and chaotic diffusion of the insolation quantities of Mars. Icarus 170, 343–364 (2004).
Google Scholar 
22.Imbrie, J. & Imbrie, K. P. Ice ages: solving the mystery (Harvard University Press, 1979).23.Berger, A. & Loutre, M. F. Climate 400,000 years ago, a key to the future? Geophysical Monograph Series 137, 17–26 (2003).
Google Scholar 
24.Laskar, J., Fienga, A., Gastineau, M. & Manche, H. La2010: a new orbital solution for the long-term motion of the Earth. Astronomy & Astrophysics 532, A89 (2011).
Google Scholar 
25.Verardo, D. J. & Ruddiman, W. F. Late Pleistocene charcoal in tropical Atlantic deep-sea sediments: climatic and geochemical significance. Geology 24, 855–857 (1996).CAS 

Google Scholar 
26.Thevenon, F., Bard, E., Williamson, D. & Beaufort, L. A biomass burning record from the West Equatorial Pacific over the last 360 ky: methodological, climatic and anthropic implications. Palaeogeography, Palaeoclimatology, Palaeoecology 213, 83–99 (2004).
Google Scholar 
27.Daniau, A. L. et al. Orbital-scale climate forcing of grassland burning in southern Africa. Proceedings of the National Academy of Sciences 110, 5069–5073 (2013).CAS 

Google Scholar 
28.Inoue, J., Okuyama, C. & Takemura, K. Long-term fire activity under the East Asian monsoon responding to spring insolation, vegetation type, global climate, and human impact inferred from charcoal records in Lake Biwa sediments in central Japan. Quaternary Science Reviews 179, 59–68 (2018).
Google Scholar 
29.Zhang, Z. et al. Precession-scale climate forcing of peatland wildfires during the early middle Jurassic greenhouse period. Global and Planetary Change 184, 103051 (2020).
Google Scholar 
30.Shi, Y. et al. Wildfire evolution and response to climate change in the Yinchuan Basin during the past 1.5 Ma based on the charcoal records of the PL02 core. Quaternary Science Reviews 241, 106393 (2020).
Google Scholar 
31.Martínez-Abarca, L. R. et al. Environmental changes during MIS6-3 in the Basin of Mexico: a record of fire, lake productivity history and vegetation. J. South American Earth Sciences 109, 103231 (2021).
Google Scholar 
32.Whitlock, C. & Larsen, C. Charcoal as a fire proxy in Tracking environmental change using lake sediments (eds Smol, J. P., Birks, H. J. B., Last, W. M., Bradley, R. S. & Alverson, K.) 75–97 (Springer, Dordrecht, 2002).33.Hao, Y., Han, Y., An, Z. & Burr, G. S. Climatic control of orbital time-scale wildfire occurrences since the late MIS 3 at Qinghai Lake, monsoon marginal zone. Quaternary International 550, 20–26 (2020).
Google Scholar 
34.Zhou, B. et al. Elemental carbon record of paleofire history on the Chinese Loess Plateau during the last 420 ka and its response to environmental and climate changes. Palaeogeography, Palaeoclimatology, Palaeoecology 252, 617–625 (2007).
Google Scholar 
35.Kappenberg, A., Lehndorff, E., Pickarski, N., Litt, T. & Amelung, W. Solar controls of fire events during the past 600,000 years. Quaternary Science Reviews 208, 97–104 (2019).
Google Scholar 
36.Han, Y. et al. Asian inland wildfires driven by glacial–interglacial climate change. Proceedings of the National Academy of Sciences 117, 5184–5189 (2020).CAS 

Google Scholar 
37.Scott, A. C. & Glasspool, I. J. Observations and experiments on the origin and formation of inertinite group macerals. International Journal of Coal Geology 70, 53–66 (2007).CAS 

Google Scholar 
38.House, M. R. A new approach to an absolute timescale from measurements of orbital cycles and sedimentary microrhythms. Nature 315, 721–725 (1985).
Google Scholar 
39.Hesselbo, S. P. & Jenkyns, H. C. A comparison of the Hettangian to Bajocian successions of Dorset and Yorkshire. Field Geology of the British Jurassic, Geological Society of London (1995).40.Weedon, G. P. & Jenkyns, H. C. Cyclostratigraphy and the Early Jurassic timescale: data from the Belemnite Marls, Dorset, southern England. Geological Society of America Bulletin 111, 1823–1840 (1999).
Google Scholar 
41.Ruhl, M. et al. Astronomical constraints on the duration of the early Jurassic Hettangian stage and recovery rates following the end-Triassic mass extinction (St Audrie’s Bay/East Quantoxhead, UK). Earth and Planetary Science Letters 295, 262–276 (2010).CAS 

Google Scholar 
42.Hüsing, S. K. et al. Astronomically-calibrated magnetostratigraphy of the lower Jurassic marine successions at St. Audrie’s Bay and East Quantoxhead (Hettangian–Sinemurian; Somerset, UK). Palaeogeography, Palaeoclimatology, Palaeoecology 403, 43–56 (2014).
Google Scholar 
43.Ruhl, M. et al. Astronomical constraints on the duration of the Early Jurassic Pliensbachian Stage and global climatic fluctuations. Earth and Planetary Science Letters 455, 149–165 (2016).CAS 

Google Scholar 
44.Xu, W., Ruhl, M., Hesselbo, S. P., Riding, J. B. & Jenkyns, H. C. Orbital pacing of the Early Jurassic carbon cycle, black‐shale formation and seabed methane seepage. Sedimentology 64, 127–149 (2017).CAS 

Google Scholar 
45.Hinnov, L. A., Ruhl, M. R. & Hesselbo, S. P. Reply to the Comment on “Astronomical constraints on the duration of the Early Jurassic Pliensbachian Stage and global climatic fluctuations” (Ruhl et al. Earth and Planetary Science Letters, 455 149–165). Earth and Planetary Science Letters 481, 415–419 (2018).CAS 

Google Scholar 
46.Storm, M. S. et al. Orbital pacing and secular evolution of the Early Jurassic carbon cycle. Proceedings National Academy Science 117, 3974–3982 (2020).CAS 

Google Scholar 
47.Ogg, J. G., Hinnov, L. A. & Huang, C. Jurassic in The geologic time scale (eds Gradstein, F. M., Ogg, J. G., Schmitz, M. D. & Ogg, G. M.) 731–791 (Elsevier, 2012).48.Hinnov, L. A. & Hilgen, F. J. Cyclostratigraphy and astrochronology in The Geologic Time Scale 2012 (eds Gradstein, F. M., Ogg, J. G., Schmitz, M. D. & Ogg, G. M.) 63–83 (2012).49.Deconinck, J. F., Hesselbo, S. P. & Pellenard, P. Climatic and sea‐level control of Jurassic (Pliensbachian) clay mineral sedimentation in the Cardigan Bay Basin, Llanbedr (Mochras Farm) borehole, Wales. Sedimentology 66, 2769–2783 (2019).
Google Scholar 
50.Chamley, H. Clay sedimentology 623 (Springer, Berlin, Heidelberg, 1989).51.Ruffell, A., McKinley, J. M. & Worden, R. H. Comparison of clay mineral stratigraphy to other proxy palaeoclimate indicators in the Mesozoic of NW Europe. Philosophical Transactions of the Royal Society London A: Mathematical, Physical and Engineering Sciences 360, 675–693 (2002).
Google Scholar 
52.Ghosh, S., Mukhopadhyay, J. & Chakraborty, A. Clay mineral and geochemical proxies for intense climate change in the permian gondwana rock record from eastern india. Research, 8974075 (2019).53.Oboh-Ikuenobe, F. E., Obi, C. G. & Jaramillo, C. A. Lithofacies, palynofacies, and sequence stratigraphy of Palaeogene strata in Southeastern Nigeria. Journal of African Earth Sciences 41, 79–101 (2005).
Google Scholar 
54.Sprovieri, M. et al. Late Cretaceous orbitally-paced carbon isotope stratigraphy from the Bottaccione Gorge (Italy). Palaeogeography, Palaeoclimatology, Palaeoecology 379, 81–94 (2013).
Google Scholar 
55.Raucsik, B. & Varga, A. Climato-environmental controls on clay mineralogy of the Hettangian–Bajocian successions of the Mecsek Mountains, Hungary: an evidence for extreme continental weathering during the early Toarcian oceanic anoxic event. Palaeogeography, Palaeoclimatology, Palaeoecology 265, 1–13 (2008).
Google Scholar 
56.Martinez, M. Mechanisms of preservation of the eccentricity and longer-term Milankovitch cycles in detrital supply and carbonate production in hemipelagic marl-limestone alternations. Stratigraphy & Timescales 3, 189–218 (2018).
Google Scholar 
57.Cochrane, M. A. & Ryan, K. C. Fire and fire ecology: Concepts and principles in Tropical fire ecology, 25–62 (2009).58.Belcher, C. M. & Hudspith, V. A. The formation of charcoal reflectance and its potential use in post-fire assessments. International Journal of Wildland Fire 25, 775–779 (2016).
Google Scholar 
59.Archibald, S. et al. Biological and geophysical feedbacks with fire in the Earth system. Environmental Research Letters 13, 033003 (2018).
Google Scholar 
60.Van de Schootbrugge, B. et al. Early Jurassic climate change and the radiation of organic-walled phytoplankton in the Tethys Ocean. Paleobiology 31, 73–97 (2005).
Google Scholar 
61.Vakhrameyev, V. A. Classopollis pollen as an indicator of Jurassic and Cretaceous climate. International Geology Review 24, 1190–1196 (1982).
Google Scholar 
62.Belcher, C. M., Collinson, M. E. & Scott, A. C. A 450-Million-Year History of Fire in Fire phenomena and the earth system (ed. Belcher, C. M.) 229–249 (Wiley-Blackwell, 2013).63.Rees, P. M., Ziegler, A. M. & Valdes, P. J. Jurassic phytogeography and climates: new data and model comparisons in Warm climates in earth history (eds Huber, B. T., Macleod, K. G. & Wing, S. L.) 297–318 (2000).64.Dera, G. et al. Distribution of clay minerals in Early Jurassic Peritethyan seas: palaeoclimatic significance inferred from multiproxy comparisons. Palaeogeography, Palaeoclimatology, Palaeoecology 271, 39–51 (2009).
Google Scholar 
65.Bonis, N. R., Ruhl, M. & Kürschner, W. M. Milankovitch-scale palynological turnover across the Triassic–Jurassic transition at St. Audrie’s Bay, SW UK. Journal of the Geological Society 167, 877–888 (2010).
Google Scholar 
66.Deconinck, J. F. et al. Diagenetic and environmental control of the clay mineralogy, organic matter and stable isotopes (C, O) of Jurassic (Pliensbachian-lowermost Toarcian) sediments of the Rodiles section (Asturian Basin, Northern Spain). Marine and Petroleum Geology 115, 104286 (2020).CAS 

Google Scholar 
67.Dewhirst, R. A., Smirnoff, N. & Belcher, C. M. Pine Species That Support Crown Fire Regimes Have Lower Leaf-Level Terpene Contents Than Those Native to Surface Fire Regimes. Fire 3, 17 (2020).
Google Scholar 
68.Berger, A., Loutre, M. F. & Dehant, V. Astronomical frequencies for pre‐Quaternary palaeoclimate studies. Terra Nova 1, 474–479 (1989).
Google Scholar 
69.House, M. R. & Gale, A. S. (eds). Orbital forcing timescales and cyclostratigraphy, 85, 1–18 (Geological Society, 1995).70.James, N. P. Facies models 7. Introduction to carbonate facies models. Geoscience Canada 4, 123–125 (1977).
Google Scholar 
71.Nelson, C. S., Keane, S. L. & Head, P. S. Non-tropical carbonate deposits on the modern New Zealand shelf. Sedimentary Geology 60, 71–94 (1988).CAS 

Google Scholar 
72.Chave, K. E. Recent carbonate sediments–an unconventional view. Journal of Geological Education 15, 200–204 (1967).CAS 

Google Scholar 
73.Parrish, J. T. & Curtis, R. L. Atmospheric circulation, upwelling, and organic-rich rocks in the Mesozoic and Cenozoic eras. Palaeogeography, Palaeoclimatology, Palaeoecology 40, 31–66 (1982).
Google Scholar 
74.Crowley, T. J., Baum, S. K. & Hyde, W. T. Milankovitch fluctuations on supercontinents. Geophysical research letters 19, 793–796 (1992).
Google Scholar 
75.Parrish, J. T. Climate of the supercontinent Pangea. The. J. Geology 101, 215–233 (1993).
Google Scholar 
76.Kutzbach, J. E. & Gallimore, R. G. Pangaean climates: megamonsoons of the megacontinent. Journal of Geophysical Research: Atmospheres 94, 3341–3357 (1989).
Google Scholar 
77.Kutzbach, J. E. Idealized Pangean climates: sensitivity to orbital change. Pangea; paleoclimate, tectonics, and sedimentation during accretion, zenith and breakup of a supercontinent. Geological Society of America 15, 41–55 (1994).
Google Scholar 
78.Sellwood, B. W. & Valdes, P. J. Mesozoic climates: General circulation models and the rock record. Sedimentary geology 190, 269–287 (2006).
Google Scholar 
79.Mutti, M. & Hallock, P. Carbonate systems along nutrient and temperature gradients: some sedimentological and geochemical constraints. International Journal of Earth Sciences 92, 465–475 (2003).CAS 

Google Scholar 
80.Clift, P. D., Wan, S. & Blusztajn, J. Reconstructing chemical weathering, physical erosion and monsoon intensity since 25 Ma in the northern South China Sea: a review of competing proxies. Earth-Science Reviews 130, 86–102 (2014).CAS 

Google Scholar 
81.Clift, P. D. et al. Chemical weathering and erosion responses to changing monsoon climate in the Late Miocene of Southwest Asia. Geological Magazine 157, 939–955 (2020).CAS 

Google Scholar 
82.Arocena, J. M. & Opio, C. Prescribed fire-induced changes in properties of sub-boreal forest soils. Geoderma 113, 1–16 (2003).CAS 

Google Scholar 
83.Certini, G. Effects of fire on properties of forest soils: a review. Oecologia 143, 1–10 (2005).
Google Scholar 
84.Reynard-Callanan, J. R., Pope, G. A., Gorring, M. L. & Feng, H. Effects of high-intensity forest fires on soil clay mineralogy. Physical Geography 31, 407–422 (2010).
Google Scholar 
85.Torsvik, T. & Cocks, L. Jurassic in Earth History and Palaeogeography 208–218 (Cambridge University Press, 2016).86.Bjerrum, C. J., Surlyk, F., Callomon, J. H. & Slingerland, R. L. Numerical paleoceanographic study of the Early Jurassic transcontinental Laurasian Seaway. Paleoceanography 16, 390–404 (2001).
Google Scholar 
87.Hesselbo, S. P. & Pieńkowski, G. Stepwise atmospheric carbon-isotope excursion during the Toarcian oceanic anoxic event (Early Jurassic, Polish Basin). Earth and Planetary Science Letters 301, 365–372 (2011).CAS 

Google Scholar 
88.Sellwood, B. W. & Jenkyns, H. G. Basins and swells and the evolution of an epeiric sea: (Pliensbachian–Bajocian of Great Britain). Journal of the Geological Society 131, 373–388 (1975).
Google Scholar 
89.Damborenea, S. E., Echevarría, J. & Ros-Franch, S. Southern hemisphere palaeobiogeography of Triassic-Jurassic marine bivalves. (Springer, 2012).90.Korte, C. et al. Jurassic climate mode governed by ocean gateway. Nature Communications 6, 1–7 (2015).
Google Scholar 
91.Dobson, M. R. & Whittington, R. J. The geology of Cardigan Bay. Proceedings of the Geologists’ Association 98, 331–353 (1987).
Google Scholar 
92.Woodland, A. W. The Llanbedr (Mochras Farm) Borehole Rep. No. 71/18 (Ed. Woodland, A. W.) 115 (Institute of Geological Sciences, 1971).93.Tappin, D. R. et al. The Geology of Cardigan Bay and the Bristol Channel. United Kingdom offshore regional report, British Geological Survey, HMSO, 107 (1994).94.Xu, W. et al. Evolution of the Toarcian (Early Jurassic) carbon-cycle and global climatic controls on local sedimentary processes (Cardigan Bay Basin, UK). Earth and Planetary Science Letters 484, 396–411 (2018).CAS 

Google Scholar 
95.Hesselbo, S. P. et al. Mochras borehole revisited: A new global standard for Early Jurassic earth history. Scientific Drilling 16, 81–91 (2013).
Google Scholar 
96.Moore, D. M. & Reynolds Jr, R. C. X-ray Diffraction and the Identification and Analysis of Clay Minerals (Oxford University Press, 1997).97.Petschick, R. MacDiff 4.1. 2. Powder diffraction software (2000). Available from the author at http://www.geol.uni-erlangen.de/html/software/Macdiff.html.98.Belcher, C. M., Collinson, M. E. & Scott, A. C. Constraints on the thermal energy released from the Chicxulub impactor: new evidence from multi-method charcoal analysis. Journal of the Geological Society 162, 591–602 (2005).
Google Scholar 
99.Scott, A. C. Charcoal recognition, taphonomy and uses in palaeoenvironmental analysis. Palaeogeography, Palaeoclimatology, Palaeoecology 291, 11–39 (2010).
Google Scholar 
100.Li, M., Hinnov, L. & Kump, L. Acycle: Time-series analysis software for paleoclimate research and education. Computers & Geosciences 127, 12–22 (2019).CAS 

Google Scholar 
101.Damaschke, M., Wylde, S., Jiang, M., Hollaar, T. & Ullmann, C. V. LLANBEDR (MOCHRAS FARM) Core Scanning Dataset. NERC EDS National Geoscience Data Centre. (Dataset). https://doi.org/10.5285/c09e9908-6a21-43a8-bc5a-944f9eb8b97e (2021). More

Effects of seasonality on physiochemical properties of waterThe characteristics of the physical and chemical factors of the river are shown in Table 1. Variance analysis showed that the environmental conditions during the four seasons were significantly different (P  More

Well-mixed populationAs shown in the Methods section, in a well-mixed population, the model can be described in terms of the replicator-mutator dynamics. I begin, by a case where the quality of the two resources are similar, r1 = r2 = r, and plot the frequency (solid blue), the average payoffs from the game (dashed red), and the amplitude of fluctuations (dotted blue) for different strategies in Fig. 1a–d. Here, the replicator dynamic is solved starting from a uniform initial condition in which all the strategies’ initial frequency is equal. The results of simulations in finite populations are in good agreement with the replicator dynamics results (see Supplementary Note 2 and Supplementary Figs. 1, 2, 3, and 4 for comparison to simulations). Throughout this manuscript I fix c = 1.Fig. 1: The frequency, game payoffs, and the amplitude of fluctuations for different strategies.The frequency (solid blue), game payoffs (dashed red), and amplitude of fluctuations (dotted blue) of costly cooperators (a), costly defectors (b), non-costly cooperators (c), and non-costly defectors (d), as a function of the enhancement factor, r. As r increases, above a first threshold (r* = 1 + cg), cooperation in the costly institution evolves, and above a second threshold (approximately r = 2) cooperation in both the costly and the free institutions evolves. For medium r, the system shows periodic fluctuations. Parameter values: g = 5, nu = 10−3, π0 = 2, cg = 0.398. The replicator dynamic, derived in the Methods Section, is solved for 9000 time steps, and the time averages are taken over the last 2000 time steps.Full size imageFor small enhancement factors r, the dynamics settle in a fixed point where only defectors in the free institution survive. The advantage of the costly institution becomes apparent as r increases beyond r* = 1 + cg, such that the maximum possible payoff of the costly institution, which is achieved when nobody defects and is equal to r − 1 − cg becomes positive. As shown in the Methods, a focal defector in a group composed of ({n}_{C}^{1}) costly cooperators and ({n}_{D}^{1}) other costly defectors (and ({n}_{C}^{2}+{n}_{D}^{2}) individuals who prefer the free resource), obtains a payoff of ({n}_{C}^{1}r/({n}_{D}^{1}+{n}_{C}^{1}+1)-{c}_{g}). This payoff becomes negative for a small enough value of ({n}_{C}^{1}) (or a large enough value of ({n}_{D}^{1})). Since groups with a small number of costly cooperators are drawn with a high probability when ({rho }_{C}^{1}) is small (these probabilities can be derived in terms of multinational coefficients, see the Methods), the average payoff of a costly defector remains negative in this regime (for instance, given at the transition ({rho }_{C}^{1}approx nu), a mutant costly defector finds itself in a group with no costly cooperator with probability ({(1-{rho }_{C}^{1})}^{g-1}approx {(1-nu )}^{g-1}), which is close to 1 for low mutation rates, and pays a pure cost of −cg). On the other hand, a costly cooperator’s payoff is equal to (({n}_{C}^{1}+1)r/({n}_{D}^{1}+{n}_{C}^{1}+1)-{c}_{g}-1), which for small enough ({n}_{D}^{1}) becomes positive. As in this region, the frequency of costly defectors, ({rho }_{D}^{1}), is small, such group compositions occur with a high probability (at the transition, ({rho }_{D}^{1}approx nu), and thus the probability that a costly cooperator joins a group with no costly defectors is ({(1-{rho }_{D}^{1})}^{g-1}approx {(1-nu )}^{g-1}), which is close to 1 for low mutation rates). Consequently, the average payoff of costly cooperators from the game becomes positive, and thus, larger than the dominant non-costly defectors’ payoff (who receive a payoff of zero). Consequently, the frequency of costly cooperators rapidly increases at r*. However, due to the rapid increase in the frequency of costly cooperators at r*, the probability of formation of such mixed groups increases, and costly defectors start to appear in the system. Further increasing r in this region, the frequency of costly cooperators and costly defectors increases at the expense of non-costly defectors.As r increases above a second threshold, cyclic fluctuations set in, and the dynamics settle in a periodic orbit. An example of this periodic orbit is presented in Fig. 2a, b. Interestingly, the average payoff of costly cooperators, costly defectors, and non-costly defectors in this region remains close to zero despite the evolution of cooperation. Although individuals constantly update their strategy to overcome others, no strategy wins in the evolution. Instead, individuals engage in a winnerless red queen dynamic. The game payoffs of costly cooperators and costly defectors fluctuate around zero (which is equal to the game payoff of non-costly defectors). The dynamics of the system in this regime resembles the frequency-dependent selection in the host-parasite evolution, coined the red queen dynamic based on the fact that no matter how much they run, all end up in the same place53,54. On this basis, I call this periodic orbit the red queen periodic orbit.Fig. 2: Red queen and black queen orbits.The frequency of different strategies (a) and the game payoffs (b) in the red queen, and the black queen (c, d) periodic orbits. In the red queen orbit, cooperators in the costly institution survive. However, the payoff of the surviving strategies fluctuates around zero, and none dominate others. In contrast, cooperators in both institutions evolve in the black queen orbit, and cooperators of each type suppress defection in their opposite institution. Consequently, the payoff of all the strategies starts to deviate from zero. Parameter values: g = 5, nu = 10−3, π0 = 2, and cg = 0.398. In (a, b) r = 1.7, and in (c, d) r = 2.2.Full size imageThe existence of a costly institution can facilitate the evolution of cooperation in its competing free institution too. As the amplitude of fluctuations increases, episodes where most of the individuals prefer the costly institution occur. During these episodes, ({rho }_{D}^{2}) drops to a small value. Consequently, the probability that a mutant non-costly cooperator finds itself in a group devoid of non-costly defectors ({(1-{rho }_{D}^{2})}^{g-1}), increases. In such groups, non-costly cooperators receive a payoff of r−1, which is larger than the payoff of all the other strategies and outcompete other strategies. At this point, a second periodic orbit emerges in which cooperation in both the costly and free institutions evolves. The evolution of cooperation in the free institution can, in turn, have a positive impact on cooperation in the costly institution. This is the case because above the point where cooperation in the free institution evolves, the frequency of individuals who prefer the free institution starts to increase by increasing r. This effect decreases the frequency of those who prefer the costly institution and its effective size. This decreases the mixing probability between costly cooperators and costly defectors and increases the costly cooperators’ payoffs. Consequently, a functional complementation between cooperators with different game preferences emerges, which is reminiscent of a black queen dynamics in which different types crucially depend on each other for performing vital functions36,55. While vulnerable to defectors in their own institution, cooperators complement each other by beating defectors in their opposite institution. Synergistically thus, they can suppress defection in the population and alternately dominate the population (see Fig. 2c, d). At this stage, the game payoff of all the strategies starts to increase beyond zero. I call this periodic orbit the black queen orbit.The picture depicted above is the typical behavior of the model for large enough values of the cost. To see this, in Fig. 3a, I plot the phase diagram of the model in the cg − r plane. Here, the frequency of cooperators in the population, ({rho }_{C}={rho }_{C}^{1}+{rho }_{C}^{2}), is color plotted as well (see Supplementary Fig. 1 for the frequency of different strategies). Red dashed lines show the boundary of the region where the system settles in a periodic orbit. For high costs, as r increases, the system shows a series of successive cross-overs from a defective fixed point to the red queen periodic orbit, black queen periodic orbit, and finally a cooperative fixed point. On the other hand, for small costs, the system possesses a bistable region where both the red queen and black queen periodic orbits (or a partially cooperative fixed point and black queen periodic orbit to the left of the red dashed line in the bistable region) are stable, and the system shows a discontinuous transition between these two orbits. Orange circles show the lower boundary of the bistable region, below which the black queen orbit is unstable. Its upper boundary, above which the red queen orbit becomes unstable, is plotted by red squares, in Fig. 3. The transition between the two periodic orbits becomes a continuous transition at a single critical point (see Supplementary Fig. 4).Fig. 3: Evolution of cooperation.a Time average total frequency of cooperators, ({rho }_{C}={rho }_{C}^{1}+{rho }_{C}^{2}) in the r − cg plane is color plotted. The dynamics can settle in fixed point (FP) (small and large enhancement factors), or two different periodic orbits, red queen periodic orbit (RPO) where cooperation only in the costly institution evolve and black queen periodic orbit (BPO) where cooperation in both institutions evolve. b Time average difference between the probability that an individual in the costly institution is a cooperator from the probability that an individual in the free institution is a cooperator, (gamma ={rho }_{C}^{1}/({rho }_{C}^{1}+{rho }_{D}^{1})-{rho }_{C}^{2}/({rho }_{C}^{2}+{rho }_{D}^{2})). Individuals are more likely to be cooperators in a costly institution. c The time average total frequency of cooperators in the r − cg plane under pure selection dynamic (ν = 0). Red queen and black queen periodic orbit can occur for, respectively, small and large enhancement factors. In other regions, the dynamics settle in a fixed point where either non-costly defectors (small enhancement factors), costly cooperators (inside the region marked with dashed black line), or non-costly cooperators survive. Parameter values: g = 5, and π0 = 2. In (a, b) ν = 10−3, and in (c) ν = 0. In (a, b) the replicator dynamic is solved for 8000 time steps, and the time average is taken over the last 2000 steps. In (c) the replicator dynamic is solved for 200,000 time steps, and the time average is taken over the last 150,000 time steps.Full size imageExamination of the overall cooperation in the population shows that an entrance cost has a contrasting effect on population cooperation for large and small enhancement factors. An entrance cost keeps free-riders away from a costly institution. This fact makes the relative frequency of cooperators to defectors higher in the costly institution than that in the free institution. To see that defectors are less likely to join the costly institution, I plot the difference between the probabilities that an individual in the costly institution is a cooperator and the probability that an individual in the free institution is a cooperator, (gamma ={rho }_{C}^{1}/({rho }_{C}^{1}+{rho }_{D}^{1})-{rho }_{C}^{2}/({rho }_{C}^{2}+{rho }_{D}^{2})) in Fig. 3b, where it can be seen it is always positive. Intuitively, as a costly defector’s payoff in a group with ({n}_{C}^{1}) cooperators and ({n}_{D}^{1}) other defectors is equal to (r{n}_{C}^{1}/({n}_{C}^{1}+{n}_{D}^{1})-{c}_{g}), a costly defector can reach a positive payoff only when ({n}_{C}^{1}) is large. Otherwise, costly defectors are better off hedging the risk of obtaining a negative payoff by joining the free institution, where their payoff is necessarily non-negative. Consequently, the expected number of cooperators in the costly institution, ({rho }_{C}^{1}g), sets a bound for the frequency of costly defectors. This fact increases a costly institution’s profitability, especially for small enhancement factors, and positively impacts cooperation in the population. On the other hand, for high enhancement factors, a large entrance cost is detrimental to cooperation. This is because, although the frequency of defectors in the costly institution remains close to zero, fewer individuals are willing to choose a costly institution with a high cost. This increases the effective size of the free institution and the mixing between cooperators and defectors in the free institution. Since defectors can better exploit cooperators in well-mixed groups, the increased mixing between cooperators and defectors in the free institution hinders cooperation.As shown in the Supplementary Note 3, while the phenomenology of the model remains the same for lower mutation rates, lower mutation rates increase the size of the region where the dynamics settle in a periodic orbit (see Supplementary Fig. 5). Regarding the dependence of the dynamics on the mutation rate, an interesting case is the zero mutation rate, where selection is the sole driver of the dynamics. The time average cooperation for zero mutation rate, starting from an initial condition where all the strategies are equal, is plotted in Fig. 3c (See Supplementary Fig. 6 for the frequency of different strategies). Both the red queen (for small enhancement factors) and the black queen (for large enhancement factors) periodic orbits are observed in this case. However, for zero mutation rate, both the amplitude and period of fluctuations increase: The fluctuating dynamics go through periods where one of the surviving strategies reaches a frequency close to 1 only to be later replaced by another strategy (see Supplementary Fig. 7). The dynamics can also settle in different fixed points. For cg = 0, depending on the enhancement factor, either cooperators or defectors in both institutions survive in equal densities. For nonzero cg, however, only one of the strategies survives. For small enhancement factors, non-costly defectors dominate the population. For larger enhancement factors, either costly cooperators (the region marked with a dashed black line) or non-costly cooperators dominate the population.In the Supplementary Note 2, I consider a case where the two institutions have different productivities, i.e., different enhancement factors, and show that similar phases are at work in this case (see the Supplementary Figs. 2 and 3). For instance, I show that a large entrance cost destabilizes full defection, removes the system’s bistability, and ensures the evolution of cooperation starting from all the initial compositions of the population. In addition, I study the continuous replicator dynamics and show similar phenomenology is at work in this case (see Supplementary Notes 1.4 and 4, and Supplementary Figs. 8 and 9).Finally, I note that a similar phenomenology is at work in a context where instead of a costly and a cost-free institution, two costly institutions interact. To see this, assume institution 1 has a cost cg and institution 2 has a cost ({c}_{g}^{0}). Without loss of generality, assume ({c}_{g} , > , {c}_{g}^{0}). Writing ({c}_{g}=({c}_{g}-{c}_{g}^{0})+{c}_{g}^{0}), it is easy to see that it is possible to absorb ({c}_{g}^{0}) in the base payoff b (as all the individual pay a cost ({c}_{g}^{0}) irrespective of their institution choice). Thus, the model is equivalent to a context where resource 2 has zero cost, resource 1 has a cost of ({c}_{g}-{c}_{g}^{0}), and all the individuals receive a shifted base payoff of (b-{c}_{g}^{0}) (see Supplementary Note 5 and Supplementary Fig. 10).Structured populationIn contrast to the well-mixed population, the model shows no bistability in a structured population, and the fate of the dynamics is independent of the initial condition. To see why this is the case, I note that in a well-mixed population, a situation where all the individuals are defectors, and randomly prefer one of the two institutions, is the worst case for the evolution of cooperation, as in this case, mutant cooperators are in a disadvantage in both institutions. However, in a structured population, starting from such an initial condition, blocks of defectors, most of whom prefer the same institution, form due to spatial fluctuations. A mutant cooperator who prefers the minority institution in these blocks obtains a high payoff and proliferates. This removes the bistability of the dynamics in a structured population.To study the model’s behavior in a structured population, I perform simulations starting from an initial condition in which all the individuals are defectors and prefer one of the two institutions at random. The model shows similar behavior in a structured population to that in a well-mixed population. This can be seen in Fig. 4a–d, where the densities of different strategies are color plotted in the cg − r plane (see Supplementary Note 6 and Supplementary Figs. 11 and 12 for further details). As was the case in a well-mixed population, cooperation does not evolve for too small values of r. As r increases beyond a threshold, cooperation does evolve in the costly institution but not in the free institution. In this region, for a fixed enhancement factor, an optimal cost, approximately equal to cg = r − 1, optimizes the cooperation in the population. On the other hand, cooperation in both the costly and the free institutions evolves for large enhancement factors. In this region, increasing the cost can slightly increases defection in the free institution and have a detrimental effect on the evolution of cooperation, but not as much as it does in a well-mixed population.Fig. 4: The frequency of different strategies in the cg − r plane in a structured population.The time average frequencies of costly cooperators (a), costly defectors (b), non-costly cooperators (c), and non-costly defectors (d) in the cg − r plane are color plotted. The system shows a red queen dynamic in which cooperators only in the costly institution survive in large numbers (for smaller enhancement factors), or a black queen dynamic, where cooperators in both institutions survive and help each other to suppress defection (for larger enhancement factors). Parameter values: g = 5, nu = 10−3, and π0 = 2. The population resides on a 200 × 200 first nearest neighbor square lattice with von Neumann connectivity and periodic boundaries. The simulation is performed for 5000 time steps starting from an initial condition in which all the individuals are defectors and prefer one of the two institutions at random. The time average is taken over the last 2000 steps.Full size imageInstead of periodic orbits observed in the well-mixed population, on a spatial structure the model’s dynamic is governed by the cyclic dominance of different strategies through spatiotemporal fluctuations manifested by traveling waves. In Fig. 5, I present snapshots of the population’s stationary state in different phases. In this figure, I consider a model in which individuals reproduce with a probability proportional to the exponential of their payoff, π, times a selection parameter, β, (exp (beta pi )) (see the Supplementary Note 1.3), with β = 5. The situation in the model where individuals reproduce with a probability proportional to their payoff is similar. In Fig. 5a, I have set r1 = r2 = 1.7, and cg = 0.6. This phase corresponds to the red queen periodic orbit in the well-mixed population case. Here, the majority of the population are non-costly defectors. Costly cooperators experience an advantage over the former and can proliferate in the sea of non-costly defectors. However, costly cooperators are vulnerable to both costly defectors and non-costly cooperators. The former can only survive in small bands around costly cooperators, as they rapidly get replaced by non-costly defectors once they eliminate costly cooperators. This phenomenon shows that spatial competition between defectors with differing institution preferences can positively impact the evolution of cooperation. Non-costly cooperators, in turn, can survive by forming compact domains where they reap the benefit of cooperation among themselves. However, as the effect of network reciprocity is too small to promote cooperation in this region, non-costly cooperators get eliminated by non-costly defectors once costly cooperators are out of the picture. Consequently, the system’s dynamic is governed by traveling waves of costly cooperators followed by small trails of costly defectors and non-costly cooperators in a sea of non-costly defectors (see the Supplementary Video, SV.156, and Supplementary Note 7 for an illustration of the dynamics in this regime).Fig. 5: Snapshots of the population in the stationary state for different parameter values.Different strategies are color codded (legend). In (a,) r1 = 1.7, r2 = 1.7, in (b,) r1 = 3.5 and r2 = 3.5, and in c, r1 = 3, and r2 = 1.8. In all the cases cg = 0.6. For small enhancement factors (a), the red queen dynamics in which cooperators only in the costly institute survive in large numbers occur. For larger enhancement factors (b), the black queen dynamics in which cooperators in both institutions survive and help each other suppress defection occur. By increasing the enhancement factors (c), non-costly cooperators dominate. However, a small frequency of costly cooperators survives and purge the population from defectors by moving along the bands of non-costly defectors. Here, individuals reproduce with a probability proportional to the exponential of their payoff with a selection parameter equal to β = 5. The population resides on a 400 × 400 square lattice with von Neumann connectivity and periodic boundaries. Parameter values: g = 5 and ν = 10−3.Full size imageFigure 5b shows a snapshot of the population for r1 = r2 = 2.2. This phase corresponds to the black queen periodic orbit in a well-mixed population. In this phase, cooperators in both the costly and free institutions evolve. Cooperators are vulnerable to defectors in their institution and lose their territory to defectors of similar type. Defectors are in turn vulnerable to cooperators in their opposite institution and are replaced by them. Consequently, the dynamic of the model is governed by traveling waves of cooperators, chased by defectors of similar type, who are in turn extincted by cooperators of the opposite type. Thus, while cooperators of different types on their own either can not survive (in the case of non-costly cooperators) or are doomed to a winnerless competition with defectors (in the case of costly cooperators), they complement each other to efficiently suppress defection in the population (see the Supplementary Video, SV.256, for an illustration of the dynamics in this regime).Another manifestation of functional complementation between cooperators of different types can be seen in the regime of large enhancement factors. An example of this situation is plotted in Fig. 5c. Here, r1 = r2 = 3.5 and cg = 0.6. In this region, non-costly cooperators dominate the population. However, non-costly defectors can survive in small bands in the sea of non-costly cooperators. While at a disadvantage in the sea of non-costly cooperators, costly cooperators beat non-costly defectors. Consequently, small blocks of costly cooperators are formed within the bands of non-costly defectors. These blocks of costly cooperators move along the bands of non-costly defectors and purge the population from non-costly defectors. In this way, although costly cooperators exist only in small frequency, they play a constructive role in helping non-costly cooperators to dominate the population.In summary, the analysis of the spatial patterns reveals that competition or synergistic relation between individuals with different institution preferences plays an essential role in the evolution of cooperation in the system. Defectors with different institution preferences always appear as competitors who compete over space. By eliminating each other, they play a surprisingly constructive role in the evolution of cooperation. Cooperators, on the other hand, while having direct competition over scarce sites, can also act synergistically and help the evolution of cooperation in their opposite institution since they can eliminate defectors in their opposite institution. In this way, by purging defectors with an opposite game preference, cooperators help fellow cooperators with an opposite game preference. Consequently, cooperators with different game preferences can engage in a mutualistic relation to efficiently suppress defection in the population.Finally, as shown in the Supplementary Note 6, the spatial model shows similar phases in the case where the two public resources have heterogeneous profitability, that is, when r1 ≠ r2 (see the Supplementary Fig. 12). More

Data for all analysed radionuclides are presented in the “Supplementary Material”. Cryoconite samples were collected on Nalli Glacier (Supplementary Fig. S1) on Sept. 25, 2017 (samples 1701–1714) and on Sept. 10, 2018 (samples 1801–1814) at 28 spots (Fig. 2, Supplementary Table S1). Gamma spectrometric analysis of samples showed the presence of anthropogenic radionuclides 137Cs, 241Am, and 207Bi. All quoted radioactivity values were recalculated for the sampling date, except those for 241Am since the concentration of the parent 241Pu isotope is unknown. However, for this isotope, the correction for decay is negligible. The activity of 137Cs reached 8093 (± 69) Bq kg−1 of dry weight, that of 241Am reached 58.3 (± 2.3) Bq kg−1 and that of 207Bi reached 6.3 (± 0.6) Bq kg−1. The natural radionuclides 210Pb and 7Be were also present in all samples. The activity of 210Pb varied in the range of 1280–9750 Bq kg−1. In addition, in the investigated samples, a significant amount of short-lived cosmogenic radionuclide 7Be was found, whose specific activity reached 2418 (± 76) Bq kg−1 (Fig. 3, Supplementary Table S2). To evaluate the contribution of atmospheric components to the total 210Pb activity, 226Ra activity was determined and found to be 17–27 Bq kg−1 (Supplementary Table S2). Based on the 210Pb/226Ra ratio, we conclude that more than 98% of 210Pb was of atmospheric provenance.Figure 2Location of sampling points on Nalli Glacier. A—137Cs activity zone  95%) of corresponding rocks and numerous outcrops likely promoted entrapment of these elements into explosion clouds, and their subsequent precipitation with radionuclides. This feature of the geological structure of the area explains the extremely high enrichment of surface waters in elements such as Zn, Pb, Sr, Ni, As, Cr, Co, Se, Te, Cd, W, Cu, Sb, and Sn; for many of them, the excess reaches 10-fold with respect to the Clrake values51. This hypothesis is supported by obvious correlations between the concentrations of Bi, Ag, Sn, Sb, Pb, Cd, W, and Cu and the activity of anthropogenic radionuclides 137Cs, 241Am and 207Bi. This relationship is obviously related to the simultaneous release of elements and radionuclides from the contaminated ice layer and their entrapment in cryoconite holes. More

Download PDF

For the past five years, I’ve studied oysters — a commercially and environmentally important species in southeast England. My research is very practical: I help to solve problems by working with oyster growers (known locally as oystermen), regulators and other community members. Resulting papers are evidence of work I’ve already done.Most oysters in this area are a non-native species (Crassostrea giga). Locally, it’s well established and has been since the 1960s, but allowing it to spread to nearby estuary systems has been controversial: there are concerns that it could become an invasive species.Working with aquaculture producers, I help to guide efforts to restore the native oyster (Ostrea edulis), populations of which declined owing to overfishing, habitat destruction, pollution and disease. Crassostrea giga oysters have provided enough income for oyster growers to spend time and effort restoring the local species. We’ve done some cool things, including creating one of the largest coastal marine conservation zones in the United Kingdom — more than 284 square kilometres — and all for an unseen brown invertebrate that lacks the charisma of a dolphin.This picture is from a typical day in the field. During high tides, we go out in a boat to take sonar readings to map potential oyster habitats; at low tide, we put on waders and go out on the mud flats to look for juvenile oysters. We focus our conservation efforts on spots where juvenile oysters are already trying to get established.Amazingly, these filter feeders don’t require feeding by humans, and they clean the water as they grow. Bivalve aquaculture such as this has become a cornerstone of the ‘blue economy’ — using marine resources sustainably for economic growth while preserving ocean health. It will take more work to determine how the balance can be reached, but oysters will be part of that conversation.

Nature 600, 182 (2021)
doi: https://doi.org/10.1038/d41586-021-03573-5

Related Articles

How AI is helping the natural sciences

Breeding the sweetest biofuels in the business

Using sound to explore events of the Universe

Subjects

Conservation biology

Agriculture

Latest on:

Agriculture

Even organic pesticides spur change in the wildlife next door
Research Highlight 17 NOV 21

Tracking the origin of Transeurasian languages
News & Views 10 NOV 21

New rules will make UK gene-edited crop research easier
News 30 SEP 21

Jobs

Full Professor in Microbial Biotechnology

University of Groningen (RUG)
Gorningen, Netherlands

Research Faculty Position Cardiovascular Research Center Massachusetts General Hospital

Massachusetts General Hospital
Charlestown, MA, United States

Higher Scientific Officer – Biophysical assay development and protein production

Institute of Cancer Research (ICR)
London, United Kingdom

Group Leaders

John Innes Centre (JIC)
Norwich, United Kingdom More

1.Gese E. M. Monitoring of terrestrial carnivore populations. Carnivore Conservation. (2001).2.Oconnell, A. F. et al. (eds) Camera Traps in Animal Ecology: Methods and Analyses (Springer Science & Business Media, 2010).
Google Scholar 
3.Tobler, M. W., Carrillo-Percastegui, S. E., Pitman, R. L., Mares, R. & Powell, G. An evaluation of camera traps for inventorying large-and medium-sized terrestrial rainforest mammals. Anim. Conserv. 11(3), 169–178 (2008).
Google Scholar 
4.MacKenzie D. I., Nichols J. D., Royle J. A., Pollock K. H., Bailey L. A., Hines J. E. Occupancy Modeling and Estimation (2017).5.Carbone, C. et al. The use of photographic rates to estimate densities of tigers and other cryptic mammals. Anim. Conserv. 4(1), 75–79 (2001).
Google Scholar 
6.Rowcliffe, J. M., Field, J., Turvey, S. T. & Carbone, C. Estimating animal density using camera traps without the need for individual recognition. J. Appl. Ecol. 1, 1228–1236 (2008).
Google Scholar 
7.Karanth, K. U. Estimating tiger Panthera tigris populations from camera-trap data using capture-recapture models. Biol. Conserv. 71(3), 333–338 (1995).
Google Scholar 
8.Silver, S. C. et al. The use of camera traps for estimating jaguar Panthera onca abundance and density using capture/recapture analysis. Oryx 38(2), 148–154 (2004).
Google Scholar 
9.Jhala, Y., Qureshi, Q. & Gopal, R. Can the abundance of tigers be assessed from their signs?. J. Appl. Ecol. 48(1), 14–24 (2011).
Google Scholar 
10.Sollmann, R. et al. Improving density estimates for elusive carnivores: Accounting for sex-specific detection and movements using spatial capture-recapture models for jaguars in central Brazil. Biol. Conserv. 144(3), 1017–1024 (2011).
Google Scholar 
11.Rowcliffe, J. M., Kays, R., Kranstauber, B., Carbone, C. & Jansen, P. A. Quantifying levels of animal activity using camera trap data. Methods Ecol. Evol. 5(11), 1170–1179 (2014).
Google Scholar 
12.Roy, M. et al. Demystifying the Sundarban tiger: Novel application of conventional population estimation methods in a unique ecosystem. Popul. Ecol. 58(1), 81–89 (2016).
Google Scholar 
13.Howe, E. J., Buckland, S. T., Després-Einspenner, M. L. & Kühl, H. S. Distance sampling with camera traps. Methods Ecol. Evol. 8(11), 1558–1565 (2017).
Google Scholar 
14.Bridges, A. S., Vaughan, M. R. & Klenzendorf, S. Seasonal variation in American black bear Ursus americanus activity patterns: Quantification via remote photography. Wildl. Biol. 10(1), 277–284 (2004).
Google Scholar 
15.Beck, H. & Terborgh, J. Groves versus isolates: How spatial aggregation of Astrocaryum murumuru palms affects seed removal. J. Trop. Ecol. 1, 275–288 (2002).
Google Scholar 
16.Kinnaird, M. F., Sanderson, E. W., O’Brien, T. G., Wibisono, H. T. & Woolmer, G. Deforestation trends in a tropical landscape and implications for endangered large mammals. Conserv. Biol. 17(1), 245–257 (2003).
Google Scholar 
17.MacKenzie, D. I. et al. Estimating site occupancy rates when detection probabilities are less than one. Ecology 83(8), 2248–2255 (2002).
Google Scholar 
18.Burton, A. C. et al. Wildlife camera trapping: A review and recommendations for linking surveys to ecological processes. J. Appl. Ecol. 52(3), 675–685 (2015).
Google Scholar 
19.O’Brien, T. G., Kinnaird, M. F. & Wibisono, H. T. Crouching tigers, hidden prey: Sumatran tiger and prey populations in a tropical forest landscape. Anim. Conserv. 6(2), 131–139 (2003).
Google Scholar 
20.Datta, A., Anand, M. O. & Naniwadekar, R. Empty forests: Large carnivore and prey abundance in Namdapha National Park, north-east India. Biol. Cons. 141(5), 1429–1435 (2008).
Google Scholar 
21.Weckel, M., Giuliano, W. & Silver, S. Jaguar (Panthera onca) feeding ecology: Distribution of predator and prey through time and space. J. Zool. 270(1), 25–30 (2006).
Google Scholar 
22.Ramesh, T., Kalle, R., Sankar, K. & Qureshi, Q. Spatio-temporal partitioning among large carnivores in relation to major prey species in Western Ghats. J. Zool. 287(4), 269–275 (2012).
Google Scholar 
23.Ramesh, T., Kalle, R., Sankar, K. & Qureshi, Q. Role of body size in activity budgets of mammals in the Western Ghats of India. J. Trop. Ecol. 32, 315–323 (2015).
Google Scholar 
24.Edwards, S. et al. Making the most of by-catch data: Assessing the feasibility of utilising non-target camera trap data for occupancy modelling of a large felid. Afr. J. Ecol. 56(4), 885–894 (2018).
Google Scholar 
25.Harmsen, B. J., Foster, R. J., Silver, S., Ostro, L. & Doncaster, C. P. Differential use of trails by forest mammals and the implications for camera-trap studies: A case study from Belize. Biotropica 42(1), 126–133 (2010).
Google Scholar 
26.Di Bitetti M. S., Paviolo A. J. & de Angelo C. D. Camera Trap Photographic Rates on Roads vs. Off Roads: Location Does Matter, Vol. 21, 37–46 (2014).27.Blake, J. G. & Mosquera, D. Camera trapping on and off trails in lowland forest of eastern Ecuador: Does location matter?. Mastozool. Neotrop. 21(1), 17–26 (2014).
Google Scholar 
28.Cusack, J. J. et al. Random versus game trail-based camera trap placement strategy for monitoring terrestrial mammal communities. PLoS ONE 10(5), e0126373 (2015).PubMed 
PubMed Central 

Google Scholar 
29.Kolowski, J. M. & Forrester, T. D. Camera trap placement and the potential for bias due to trails and other features. PLoS ONE 12(10), e0186679 (2017).PubMed 
PubMed Central 

Google Scholar 
30.Srbek-Araujo, A. C. & Chiarello, A. G. Influence of camera-trap sampling design on mammal species capture rates and community structures in southeastern Brazil. Biota. Neotrop. 13(2), 51–62 (2013).
Google Scholar 
31.Wearn, O. R., Rowcliffe, J. M., Carbone, C., Bernard, H. & Ewers, R. M. Assessing the status of wild felids in a highly-disturbed commercial forest reserve in Borneo and the implications for camera trap survey design. PLoS ONE 8(11), e77598 (2013).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
32.Sadhu, A. et al. Demography of a small, isolated tiger population in a semi-arid region of western India. BMC Zool. 2(1), 1–13 (2017).
Google Scholar 
33.Sunquist, M. What is a tiger? Ecology and behavior. In Tigers of the World 19–33 (William Andrew Publishing, 2010).
Google Scholar 
34.Gotelli, N. J. & Colwell, R. K. Estimating species richness. Biol. Divers. Front. Meas. Assess. 12, 39–54 (2011).
Google Scholar 
35.Colwell, R. K., Mao, C. X. & Chang, J. Interpolating, extrapolating, and comparing incidence-based species accumulation curves. Ecology 85(10), 2717–2727 (2004).
Google Scholar 
36.Rovero, F. & Marshall, A. R. Camera trapping photographic rate as an index of density in forest ungulates. J. Appl. Ecol. 46(5), 1011–1017 (2009).
Google Scholar 
37.Jhala, Y. V., Qureshi, Q., Nayak, A. K. Status of tigers, copredators and prey in India, 2018. ISBN No. 81-85496-50-1 https://wii.gov.in/tiger_reports (National Tiger Conservation Authority, Government of India and Wildlife Institute of India, 2020).38.Nichols, J. D. et al. Multi-scale occupancy estimation and modelling using multiple detection methods. J. Appl. Ecol. 45(5), 1321–1329 (2008).
Google Scholar 
39.Hines J. E. PRESENCE 3.1 Software to estimate patch occupancy and related parameters. http://www.mbr-pwrc.usgs.gov/software/presence.html. (2006).40.Meredith, M., & Ridout, M. Overview of the overlap package. R. Project. 1–9 (2014).41.Rowcliffe M, Rowcliffe M. M. Package ‘activity’. Animal activity statistics R Package Version. 1 (2016).42.Soberón, M. J. & Llorente, B. J. The use of species accumulation functions for the prediction of species richness. Conserv. Biol. 7(3), 480–488 (1993).
Google Scholar 
43.Broadley, K., Burton, A. C., Avgar, T. & Boutin, S. Density-dependent space use affects interpretation of camera trap detection rates. Ecol. Evol. 9(24), 14031–14041 (2019).PubMed 
PubMed Central 

Google Scholar 
44.Bunnell, F. L. & Gillingham, M. P. Foraging behavior: Dynamics of dining out. Bioenerget. Wild herbiv. 1, 53–79 (1985).
Google Scholar 
45.Mishra H. R. The ecology and behaviour of chital (Axis axis) in the Royal Chitwan National Park, Nepal: with comparative studies of hog deer (Axis porcinus), sambar (Cervus unicolor) and barking deer (Muntiacus muntjak) (Doctoral dissertation, University of Edinburgh). 1982.46.Raman, T. S. Factors influencing seasonal and monthly changes in the group size of chital or axis deer in southern India. J. Biosci. 22(2), 203–218 (1997).
Google Scholar 
47.Karanth, K. U. & Sunquist, M. E. Behavioral correlates of predation by tiger, leopard and dhole in Nagarhole National Park. India. J Zool. 250(2), 255–265 (2000).
Google Scholar 
48.Harmsen, B. J., Foster, R. J., Silver, S. C., Ostro, L. E. & Doncaster, C. P. Spatial and temporal interactions of sympatric jaguars (Panthera onca) and pumas (Puma concolor) in a neotropical forest. J. Mammal. 90(3), 612–620 (2009).
Google Scholar 
49.Nichols, J. D., Karanth, K. U. & O’Connell, A. F. Science, conservation, and camera traps. In Camera Traps in Animal Ecology 45–56 (Springer, 2011).
Google Scholar  More

1.Raffini, F. et al. From nucleotides to satellite imagery: Approaches to identify and manage the invasive pathogen Xylella fastidiosa and its insect vectors in Europe. Sustainability 12, 4508 (2020).CAS 

Google Scholar 
2.Jankowski, T., Collins, A. G. & Campbell, R. Global diversity of inland water cnidarians. In Freshwater Animal Diversity Assessment 35–40 (Springer, 2008).
Google Scholar 
3.Pelosse, J. Étude biologique sur la méduse d’eau douce, Limnocodium Sowerbyi Ray Lankester, du Parc de la Tête-d’Or de Lyon. Publ. Société Linn. Lyon 65, 53–62 (1919).
Google Scholar 
4.Lüskow, F., López-González, P. J. & Pakhomov, E. A. Freshwater jellyfish in northern temperate lakes: Craspedacusta sowerbii in British Columbia, Canada. Aquat. Biol. 30, 69–84 (2021).
Google Scholar 
5.McClary, A. The effect of temperature on growth and reproduction in Craspedacusta sowerbii. Ecology 40, 158–162 (1959).
Google Scholar 
6.McClary, A. Experimental studies of bud development in Craspedacusta sowerbii. Trans. Am. Microsc. Soc. 80, 343–353 (1961).
Google Scholar 
7.McClary, A. Histological changes during regeneration of Craspedacusta sowerbii. Trans. Am. Microsc. Soc. 83, 349–357 (1964).
Google Scholar 
8.Acker, T. S. & Muscat, A. M. The ecology of Craspedacusta sowerbii Lankester, a freshwater hydrozoan. Am. Midl. Nat. 95, 323–336 (1976).
Google Scholar 
9.Boothroyd, I. K., Etheredge, M. K. & Green, J. D. Spatial distribution, size structure, and prey of Craspedacusta sowerbyi Lankester in a shallow New Zealand lake. Hydrobiologia 468, 23–32 (2002).
Google Scholar 
10.Turquin, M. J. Progrès dans la connaissance de la métagenèse chez Craspedacusta sowerbii (= sowerbyi) (Limnoméduse, Olindiidae). Bourgogne-Nat. 9, 162–174 (2010).
Google Scholar 
11.Marchessaux, G. & Bejean, M. From frustules to medusae: A new culture system for the study of the invasive hydrozoan Craspedacusta sowerbii in the laboratory. Invertebr. Biol. 139, e12308 (2020).
Google Scholar 
12.Bouillon, J. & Boero, F. The hydrozoa: A new classification in the ligth of old knowledge. Thalass. Salentina 24, 3–45 (2000).
Google Scholar 
13.Dumont, H. J. The distribution and ecology of the fresh-and brackish-water medusae of the world. In Studies on the Ecology of Tropical Zooplankton 1–12 (Springer, 1994).
Google Scholar 
14.Duggan, I. C. The freshwater aquarium trade as a vector for incidental invertebrate fauna. Biol. Invasions 12, 3757–3770 (2010).
Google Scholar 
15.Marchessaux, G., Gadreaud, J. & Belloni, B. The freshwater jellyfish Craspedacusta sowerbii lankester, 1880: An overview of its distribution in France. Vie Milieu 69, 201–213 (2019).
Google Scholar 
16.Pennak, R. W. The fresh-water jellyfish Craspedacusta in Colorado with some remarks on its ecology and morphological degeneration. Trans. Am. Microsc. Soc. 75, 324–331 (1956).
Google Scholar 
17.Matthews, D. C. A Comparative study of Craspedacusta sowerbyi and Calpasoma dactyloptera life cycles (1966).18.Lundberg, S. & Svensson, J. E. Medusae invasions in Swedish lakes. Fauna Flora 98, 18–28 (2003).
Google Scholar 
19.Jakovčev-Todorović, D., Đikanović, V., Skorić, S. & Cakić, P. Freshwater jellyfish Craspedacusta sowerbyi Lankester, 1880 (Hydrozoa, Olindiidae): 50 years’ observations in Serbia. Arch. Biol. Sci. 62, 123–127 (2010).
Google Scholar 
20.Bosso, L., De Conno, C. & Russo, D. Modelling the risk posed by the zebra mussel Dreissena polymorpha: Italy as a case study. Environ. Manag. 60, 304–313 (2017).ADS 

Google Scholar 
21.Taheri, S., Naimi, B., Rahbek, C. & Araújo, M. B. Improvements in reports of species redistribution under climate change are required. Sci. Adv. 7, eabe1110 (2021).PubMed 
PubMed Central 
ADS 

Google Scholar 
22.Hosmer, D. W., Jovanovic, B. & Lemeshow, S. Best subsets logistic regression. Biometrics 45, 1265–1270 (1989).MATH 

Google Scholar 
23.Allouche, O., Tsoar, A. & Kadmon, R. Assessing the accuracy of species distribution models: Prevalence, kappa and the true skill statistic (TSS). J. Appl. Ecol. 43, 1223–1232 (2006).
Google Scholar 
24.Thuiller, W., Lavorel, S., Araújo, M. B., Sykes, M. T. & Prentice, I. C. Climate change threats to plant diversity in Europe. Proc. Natl. Acad. Sci. 102, 8245–8250 (2005).CAS 
PubMed 
PubMed Central 
ADS 

Google Scholar 
25.Walther, G. Inference and modeling with log-concave distributions. Stat. Sci. 24, 319–327 (2009).MathSciNet 
MATH 

Google Scholar 
26.Mangano, M. C. et al. Moving toward a strategy for addressing climate displacement of marine resources: A proof-of-concept. Front. Mar. Sci. 7, 408 (2020).ADS 

Google Scholar 
27.Perkins-Taylor, I. & Frey, J. Predicting the distribution of a rare chipmunk (Neotamias quadrivittatus oscuraensis): Comparing MaxEnt and occupancy models. J. Mammal. 101, 1035–1048 (2020).PubMed 
PubMed Central 

Google Scholar 
28.Di Pasquale, G. et al. Coastal pine-oak glacial refugia in the Mediterranean basin: A biogeographic approach based on charcoal analysis and spatial modelling. Forests 11, 673 (2020).
Google Scholar 
29.Thapa, A. et al. Predicting the potential distribution of the endangered red panda across its entire range using MaxEnt modeling. Ecol. Evol. 8, 10542–10554 (2018).PubMed 
PubMed Central 

Google Scholar 
30.Fernández, M. & Hamilton, H. Ecological niche transferability using invasive species as a case study. PLoS ONE 10, e0119891 (2015).PubMed 
PubMed Central 

Google Scholar 
31.Sarà, G., Palmeri, V., Rinaldi, A., Montalto, V. & Helmuth, B. Predicting biological invasions in marine habitats through eco-physiological mechanistic models: A case study with the bivalve B rachidontes pharaonis. Divers. Distrib. 19, 1235–1247 (2013).
Google Scholar 
32.Sarà, G., Porporato, E. M., Mangano, M. C. & Mieszkowska, N. Multiple stressors facilitate the spread of a non-indigenous bivalve in the Mediterranean Sea. J. Biogeogr. 45, 1090–1103 (2018).
Google Scholar 
33.Markovic, D., Freyhof, J. & Wolter, C. Where are all the fish: Potential of biogeographical maps to project current and future distribution patterns of freshwater species. PLoS ONE 7, e40530 (2012).CAS 
PubMed 
PubMed Central 
ADS 

Google Scholar 
34.Hamner, W. M., Gilmer, R. W. & Hamner, P. P. The physical, chemical, and biological characteristics of a stratified, saline, sulfide lake in Palau 1. Limnol. Oceanogr. 27, 896–909 (1982).CAS 
ADS 

Google Scholar 
35.Hamner, W. M. & Hauri, I. R. Long-distance horizontal migrations of zooplankton (Scyphomedusae: Mastigias) 1. Limnol. Oceanogr. 26, 414–423 (1981).ADS 

Google Scholar 
36.Duggan, I. C. & Eastwood, K. R. Detection and distribution of Craspedacusta sowerbii: Observations of medusae are not enough. (2012).37.Galarce, L. C., Riquelme, K. V., Osman, D. Y. & Fuentes, R. A. A new record of the non indigenous freshwater jellyfish Craspedacusta sowerbii Lankester, 1880 (Cnidaria) in Northern Patagonia (40 S, Chile). Bioinvasions Rec. 2, 263–270 (2013).
Google Scholar 
38.Stanković, I. & Ternjej, I. New ecological insight on two invasive species: Craspedacusta sowerbii (Coelenterata: Limnomedusae) and Dreissenia polymorpha (Bivalvia: Dreissenidae). J. Nat. Hist. 44, 2707–2713 (2010).
Google Scholar 
39.Stefani, F., Leoni, B., Marieni, A. & Garibaldi, L. A new record of Craspedacusta sowerbii, Lankester 1880 (Cnidaria, Limnomedusae) in northern Italy. J. Limnol. 69, 189 (2010).
Google Scholar 
40.Jankowski, T., Strauss, T. & Ratte, H. T. Trophic interactions of the freshwater jellyfish Craspedacusta sowerbii. J. Plankton Res. 27, 811–823 (2005).CAS 

Google Scholar 
41.Adams, I. B. The effect of light and prey availability on the activity of the freshwater jellyfish, Craspedacusta sowerbii (Hydrozoan) (Mém. B Sc Univ James Madison À Harrisonburg Virginie, 2009).
Google Scholar 
42.Marchessaux, G. & Bejean, M. Growth and ingestion rates of the freshwater jellyfish Craspedacusta sowerbii. J. Plankton Res. 42, 783–786 (2020).CAS 

Google Scholar 
43.Himchik, V., Marenkov, O. & Shmyhol, N. Biology of reproduction of aquatic organisms: The course of oogenesis of freshwater jellyfish Craspedacusta sowerbii Lancester, 1880 in the Dnieper reservoir. World Sci. News 160, 1–15 (2021).
Google Scholar 
44.Caputo, L., Huovinen, P., Sommaruga, R. & Gómez, I. Water transparency affects the survival of the medusa stage of the invasive freshwater jellyfish Craspedacusta sowerbii. Hydrobiologia 817, 179–191 (2018).CAS 

Google Scholar 
45.Bozman, A., Titelman, J., Kaartvedt, S., Eiane, K. & Aksnes, D. L. Jellyfish distribute vertically according to irradiance. J. Plankton Res. 39, 280–289 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
46.Salonen, K. et al. Limnocnida tanganyicae medusae (Cnidaria: Hydrozoa): A semiautonomous microcosm in the food web of Lake Tanganyika. In Jellyfish Blooms IV 97–112 (Springer, 2012).
Google Scholar 
47.Dodson, S. I. & Cooper, S. D. Trophic relationships of the freshwater jellyfish Craspedacusta sowerbyi Lankester 1880. Limnol. Oceanogr. 28, 345–351 (1983).ADS 

Google Scholar 
48.Smith, A. S. & Alexander, J. E. Jr. Potential effects of the freshwater jellyfish Craspedacusta sowerbii on zooplankton community abundance. J. Plankton Res. 30, 1323–1327 (2008).
Google Scholar 
49.Spadinger, R. & Maier, G. Prey selection and diel feeding of the freshwater jellyfish, Craspedacusta sowerbyi. Freshw. Biol. 41, 567–573 (1999).
Google Scholar 
50.Simberloff, D. et al. Impacts of biological invasions: What’s what and the way forward. Trends Ecol. Evol. 28, 58–66 (2013).PubMed 

Google Scholar 
51.Uchida, T. A new sporozoan-like reproduction in the hydromedusa. Gonionemus vertens. Proc. Jpn. Acad. 52, 387–388 (1976).
Google Scholar 
52.Williams, A. B. Shrimps, Lobsters, and Crabs of the Atlantic Coast of the Eastern United States, Maine to Florida (1984).53.Parent, G. H. La découverte lorraine de Craspedacusta sowerbyi Lank. dans son contexte chorologique et écologique européen. Bull. Soc. D’Histoire Nat. Moselle 43, 317–337 (1982).
Google Scholar 
54.Amemiya, I. Freshwater medusa found in the tank of my laboratory. Jpn. J. Zool. Trans. Abstr. 3, Abstract (1930).55.Joshi, M. V. & Tonapi, G. T. A new record of freshwater medusa from India. Curr. Sci. 34, 665–666 (1965).
Google Scholar 
56.El Moussaoui, N. & Beisner, B. L. La méduse d’eau douce Craspedacusta sowerbii: espèce exotique répandue dans les lacs du Québec. Nat. Can. 141, 40–46 (2017).
Google Scholar 
57.Fish, G. R. Craspedacusta sowerbyi Lankester (Coelenterata: Limnomedusae) in New Zealand lakes. N. Z. J. Mar. Freshw. Res. 5, 66–69 (1971).
Google Scholar 
58.Rayner, N. A. First record of Craspedacusta sowerbyi Lankester (Cnidaria: Limnomedusae) from Africa. Hydrobiologia 162, 73–77 (1988).
Google Scholar 
59.Somveille, M., Manica, A., Butchart, S. H. & Rodrigues, A. S. Mapping global diversity patterns for migratory birds. PLoS ONE 8, e70907 (2013).CAS 
PubMed 
PubMed Central 
ADS 

Google Scholar 
60.Newton, I. & Dale, L. C. Bird migration at different latitudes in eastern North America. Auk 113, 626–635 (1996).
Google Scholar 
61.Zhang, J. et al. Determination of original infection source of H7N9 avian influenza by dynamical model. Sci. Rep. 4, 1–16 (2014).
Google Scholar 
62.Fuentes, R., Cárdenas, L., Abarzua, A. & Caputo, L. Southward invasion of Craspedacusta sowerbii across mesotrophic lakes in Chile: Geographical distribution and genetic diversity of the medusa phase. Freshw. Sci. 38, 193–202 (2019).
Google Scholar 
63.Harrell, F. E. Hmisc: Harrell Miscellaneous (Version 4.5-0) (2021).64.Marchessaux, G., Lüskow, F., Sarà, G. & Pakhomov, E. Mapping the global distribution of the freshwater hydrozoan Craspedacusta sowerbii. Pangaea https://doi.org/10.1594/PANGAEA.936074 (2021).65.Fick, S. E. & Hijmans, R. J. WorldClim 2: New 1-km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37, 4302–4315 (2017).
Google Scholar 
66.McGarvey, D. J. et al. On the use of climate covariates in aquatic species distribution models: Are we at risk of throwing out the baby with the bath water?. Ecography 41, 695–712 (2018).
Google Scholar 
67.Zeng, Y. & Yeo, D. C. Assessing the aggregated risk of invasive crayfish and climate change to freshwater crabs: A Southeast Asian case study. Biol. Conserv. 223, 58–67 (2018).
Google Scholar 
68.Wei, T. et al. Package ‘corrplot’. Statistician 56, e24 (2017).
Google Scholar 
69.R. Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2021).70.Eyring, V. et al. Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization. Geosci. Model Dev. 9, 1937–1958 (2016).ADS 

Google Scholar 
71.Phillips, S. J., Anderson, R. P. & Schapire, R. E. Maximum entropy modeling of species geographic distributions. Ecol. Model. 190, 231–259 (2006).
Google Scholar 
72.Elith, J. & Leathwick, J. R. Species distribution models: Ecological explanation and prediction across space and time. Annu. Rev. Ecol. Evol. Syst. 40, 677–697 (2009).
Google Scholar 
73.Phillips, S. J., Anderson, R. P., Dudík, M., Schapire, R. E. & Blair, M. E. Opening the black box: An open-source release of Maxent. Ecography 40, 887–893 (2017).
Google Scholar 
74.Bradie, J. & Leung, B. A quantitative synthesis of the importance of variables used in MaxEnt species distribution models. J. Biogeogr. 44, 1344–1361 (2017).
Google Scholar 
75.Zhang, K., Yao, L., Meng, J. & Tao, J. Maxent modeling for predicting the potential geographical distribution of two peony species under climate change. Sci. Total Environ. 634, 1326–1334 (2018).CAS 
PubMed 
ADS 

Google Scholar 
76.Silva, C., Leiva, F. & Lastra, J. Predicting the current and future suitable habitat distributions of the anchovy (Engraulis ringens) using the Maxent model in the coastal areas off central-northern Chile. Fish. Oceanogr. 28, 171–182 (2019).
Google Scholar 
77.Nenzén, H. K. & Araújo, M. B. Choice of threshold alters projections of species range shifts under climate change. Ecol. Model. 222, 3346–3354 (2011).
Google Scholar 
78.Phillips, S. J. & Dudík, M. Modeling of species distributions with Maxent: New extensions and a comprehensive evaluation. Ecography 31, 161–175 (2008).
Google Scholar 
79.DeLong, E. R., DeLong, D. M. & Clarke-Pearson, D. L. Comparing the areas under two or more correlated receiver operating characteristic curves: A nonparametric approach. Biometrics 837–845 (1988). More

1.Agetsuma, N. Foraging strategies of Yakushima Macaques (Macaca-fuscata Yakui). Int. J. Primatol. 16, 595–609. https://doi.org/10.1007/bf02735283 (1995).Article 

Google Scholar 
2.Hill, D. A. Seasonal variation in the feeding behavior and diet of Japanese macaques (Macaca fuscata yakui) in lowland forest of Yakushima. Am. J. Primatol. 43, 305–322 (1997).CAS 
Article 

Google Scholar 
3.Otani, Y. et al. Factors influencing riverine utilization patterns in two sympatric macaques. Sci. Rep. https://doi.org/10.1038/s41598-020-79259-1 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
4.Maruhashi, T. Feeding behavior and diet of the Japanese monkey Macaca-fuscata-yakui on Yakushima Island, Japan. Primates 21, 141–160. https://doi.org/10.1007/bf02374030 (1980).Article 

Google Scholar 
5.Rothman, J. M., Raubenheimer, D., Bryer, M. A. H., Takahashi, M. & Gilbert, C. C. Nutritional contributions of insects to primate diets: Implications for primate evolution. J. Hum. Evol. 71, 59–69. https://doi.org/10.1016/j.jhevol.2014.02.016 (2014).Article 
PubMed 

Google Scholar 
6.Hanya, G. et al. Not only annual food abundance but also fallback food quality determines the Japanese macaque density: evidence from seasonal variations in home range size. Primates 47, 275–278. https://doi.org/10.1007/s10329-005-1076-2 (2006).ADS 
Article 
PubMed 

Google Scholar 
7.Nakagawa, N. Determinants of the dramatic seasonal changes in the intake of energy and protein by Japanese monkeys in a cool temperate forest. Am. J. Primatol. 41, 267–288. https://doi.org/10.1002/(sici)1098-2345(1997)41:4%3c267::aid-ajp1%3e3.0.co;2-v (1997).CAS 
Article 
PubMed 

Google Scholar 
8.Tsuji, Y., Ito, T. Y., Wada, K. & Watanabe, K. Spatial patterns in the diet of the Japanese macaque Macaca fuscata and their environmental determinants. Mammal Rev. 45, 227–238. https://doi.org/10.1111/mam.12045 (2015).Article 

Google Scholar 
9.Wada, K. & Tokida, E. Habitat utlization by wintering Japanese monkeys Macaca fuscata-fuscata in Shiga Heights Japan. Primates 22, 330–348. https://doi.org/10.1007/bf02381574 (1981).Article 

Google Scholar 
10.Suzuki, A. An ecological study of wild Japanese monkeys in snowy area focused on their food habits. Primates 6, 31–71 (1965).Article 

Google Scholar 
11.Izawa, K. & Nishida, T. Monkeys living in the northern limits of their distribution. Primates 4, 67–88 (1963).Article 

Google Scholar 
12.Enari, H. & Sakamaki-Enari, H. Influence of heavy snow on the feeding behavior of Japanese Macaques (Macaca Fuscata) in Northern Japan. Am. J. Primatol. 75, 534–544. https://doi.org/10.1002/ajp.22128 (2013).Article 
PubMed 

Google Scholar 
13.Agetsuma, N. Dietary selection by Yakushima macaques (Macaca-fustcata Yakui): the influence of food availability and temperature. Int. J. Primatol. 16, 611–627. https://doi.org/10.1007/bf02735284 (1995).Article 

Google Scholar 
14.Agetsuma, N. & Nakagawa, N. Effects of habitat differences on feeding behaviors of Japanese monkeys: comparison between Yakushima and Kinkazan. Primates 39, 275–289. https://doi.org/10.1007/bf02573077 (1998).Article 

Google Scholar 
15.Izumiyama, S. In: High Altitude Primates, Developments in Primatology: Progress and Prospects Vol. 44 (ed N.B. Grow et al.) 153–181 (Springer, New York, 2014).16.Go, M. Seasonal changes in food resource distribution and feeding sites selected by Japanese macaques on Koshima Islet, Japan. Primates 51, 149–158. https://doi.org/10.1007/s10329-009-0179-5 (2010).Article 
PubMed 

Google Scholar 
17.Hanya, G. Diet of a Japanese macaque troop in the coniferous forest of Yakushima. Int. J. Primatol. 25, 55–71. https://doi.org/10.1023/b:ijop.0000014645.78610.32 (2004).Article 

Google Scholar 
18.Sakamaki, H., Enari, H., Aoi, T. & Kunisaki, T. Winter food abundance for Japanese monkeys in differently aged Japanese cedar plantations in snowy regions. Mammal Study 36, 1–10. https://doi.org/10.3106/041.036.0101 (2011).Article 

Google Scholar 
19.Enari, H. In: High Altitude Primates. Developments in Primatology, Progress and Prospects (eds N Grow, S Gursky-Doyen, & Krzton A) (Springer, New York, 2014).20.Tsuji, Y. & Nakagawa, N. Monkeys of Japan: A Mammalogical Studies of Japanese Macaques (University of Tokyo Press, 2017).
Google Scholar 
21.Suzuki, S., Hill, D. A., Maruhashi, T. & Tsukuhara, T. Frog and Lizard-eating behaviour of wild Japanese Macaques in Yakushima, Japan. Primates 31, 421–426 (1990).Article 

Google Scholar 
22.Watanabe, K. Fish: a new addition to the diet of Japanese macaques on Koshima Island. Folia Primatol. 52, 124–131. https://doi.org/10.1159/000156391 (1989).CAS 
Article 

Google Scholar 
23.Leca, J. B., Gunst, N., Watanabe, K. & Huffman, M. A. A new case of fish-eating in Japanese macaques: Implications for social constraints on the diffusion of feeding innovation. Am. J. Primatol. 69, 821–828. https://doi.org/10.1002/ajp.20401 (2007).Article 
PubMed 

Google Scholar 
24.Stewart, A. M. E., Gordon, C. H., Wich, S. A., Schroor, P. & Meijaard, E. Fishing in Macaca fascicularis: a rarely observed innovative behavior. Int. J. Primatol. 29, 543–548. https://doi.org/10.1007/s10764-007-9176-y (2008).Article 

Google Scholar 
25.Hamilton, W. J. & Tilson, R. L. Fishing Baboons at desert waterholes. Am. J. Primatol. 8, 255–257. https://doi.org/10.1002/ajp.1350080308 (1985).Article 
PubMed 

Google Scholar 
26.Tamura, M. Extractive foraging on hard-shelled walnuts and variation of feeding techniques in wild Japanese macaques (Macada fuscata). Am. J. Primatol. 82, e23130 (2020).Article 

Google Scholar 
27.Iwamoto, T. A bioeconomic study on a provisioned troop at a Japanese monkeys Macada fuscata-fuscata at Koshima Islet Miyazaki. Primates 15, 241–262. https://doi.org/10.1007/bf01742286 (1974).Article 

Google Scholar 
28.Tsuji, Y. & Takatsuki, S. Effects of a typhoon on foraging behavior and foraging success of Macaca fuscata on Kinkazan Island, Northern Japan. Int. J. Primatol. 29, 1203–1217. https://doi.org/10.1007/s10764-008-9293-2 (2008).Article 

Google Scholar 
29.Gumert, M. D. & Malaivijitnond, S. Marine prey processed with stone tools by burmese long-tailed macaques (Macaca fascicularis aurea) in intertidal habitats. Am. J. Phys. Anthropol. 149, 447–457. https://doi.org/10.1002/ajpa.22143 (2012).Article 
PubMed 

Google Scholar 
30.Tan, A., Tan, S. H., Vyas, D., Malaivijitnond, S. & Gumert, M. D. There is more than one way to crack an oyster: identifying variation in burmese long-tailed Macaque (Macaca fascicularis aurea) stone-tool use. PLoS ONE https://doi.org/10.1371/journal.pone.0124733 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
31.Urabe, M. The present distribution and issues regarding the control of the exotic snail Potamopyrgus antipodarum in Japan. Jpn. J. Limnol. 68, 491–496 (2007).Article 

Google Scholar 
32.Hamada, K. T. Y. & Urabe, M. Survey of mitochondrial DNA haplotypes of Potamopyrgus antipodarum (Caenogastropoda: Hydrobiidae) introduced into Japan. Limnology 14, 223–228 (2013).Article 

Google Scholar 
33.Izumiyama, S., Mochizuki, T. & Shiraishi, T. Troop size, home range area and seasonal range use of the Japanese macaque in the Northern Japan Alps. Ecol. Res. 18, 465–474. https://doi.org/10.1046/j.1440-1703.2003.00570.x (2003).Article 

Google Scholar 
34.Milner, A. M., Docherty, C., Windsor, F. M. & Tojo, K. Macroinvertebrate communities in streams with contrasting water sources in the Japanese Alps. Ecol. Evol. 10, 7812–7825. https://doi.org/10.1002/ece3.6507 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
35.Vestheim, H. & Jarman, S. N. Blocking primers to enhance PCR amplification of rare sequences in mixed samples: a case study on prey DNA in Antarctic krill stomachs. Front. Zool. https://doi.org/10.1186/1742-9994-5-12 (2008).Article 
PubMed 
PubMed Central 

Google Scholar 
36.Leray, M. et al. A new versatile primer set targeting a short fragment of the mitochondrial COI region for metabarcoding metazoan diversity: application for characterizing coral reef fish gut contents. Front. Zool. https://doi.org/10.1186/1742-9994-10-34 (2013).Article 
PubMed 
PubMed Central 

Google Scholar 
37.Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. 2011 17, 3. doi:https://doi.org/10.14806/ej.17.1.200 (2011).38.Callahan, B. J. et al. DADA2: High-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581. https://doi.org/10.1038/nmeth.3869 (2016).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
39.Ratnasingham, S. & Hebert, P. D. N. BOLD: the barcode of life data system (www.barcodinglife.org). Mol. Ecol. Notes 7, 355–364. doi:https://doi.org/10.1111/j.1471-8286.2007.01678.x (2007).40.Wang, Q., Garrity, G. M., Tiedje, J. M. & Cole, J. R. Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl. Environ. Microbiol. 73, 5261–5267. https://doi.org/10.1128/aem.00062-07 (2007).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
41.McMurdie, P. J. & Holmes, S. Phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE https://doi.org/10.1371/journal.pone.0061217 (2013).Article 
PubMed 
PubMed Central 

Google Scholar 
42.Wickham, H. ggplot2: elegant graphics for data analysis 2nd edn. (Springer, 2016).Book 

Google Scholar  More