Philippa Kaur

1.Paerl, H. W. & Otten, T. G. Harmful cyanobacterial blooms: causes, consequences, and controls. Microb. Ecol. 65, 995–1010 (2013).CAS 
PubMed 
Article 

Google Scholar 
2.Visser, P. M. et al. How rising CO2 and global warming may stimulate harmful cyanobacterial blooms. Harmful Algae 54, 145–159 (2016).CAS 
PubMed 
Article 

Google Scholar 
3.Lürling, M., Mendes e Mello, M., van Oosterhout, F., de Senerpont Domis, L. & Marinho, M. M. Response of natural cyanobacteria and algae assemblages to a nutrient pulse and elevated temperature. Front. Microbiol. 9, 1851 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
4.Low-Décarie, E., Fussmann, G. F. & Bell, G. Aquatic primary production in a high-CO2 world. Trends Ecol. Evol. 29, 223–232 (2014).PubMed 
Article 

Google Scholar 
5.Stanley, S. M. Estimates of the magnitudes of major marine mass extinctions in earth history. PNAS 113, E6325–E6334 (2016).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
6.Sun, Y. D. et al. Lethally hot temperatures during the Early Triassic Greenhouse. Science 338, 366–370 (2012).ADS 
CAS 
PubMed 
Article 

Google Scholar 
7.Frank, T. D. et al. Pace, magnitude, and nature of terrestrial climate change through the end Permian extinction in southeastern Gondwana. Geology 49, https://doi.org/10.1130/G48795.1 (2021).8.Wu, Y. et al. Six-fold increase of atmospheric pCO2 during the Permian–Triassic mass extinction. Nat. Commun. 12, 2137 (2021).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
9.Burgess, S. D., Muirhead, J. D. & Bowring, S. A. Initial pulse of Siberian Traps sills as the trigger of the end-Permian mass extinction. Nat. Commun. 8, 164 (2017).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
10.Mays, C. et al. Refined Permian–Triassic floristic timeline reveals early collapse and delayed recovery of south polar terrestrial ecosystems. GSA Bull. 132, 1489–1513 (2020).CAS 
Article 

Google Scholar 
11.Chu, D. et al. Ecological disturbance in tropical peatlands prior to marine Permian-Triassic mass extinction. Geology 48, 288–292 (2020).ADS 
Article 

Google Scholar 
12.Retallack, G. J., Veevers, J. J. & Morante, R. Global coal gap between Permian–Triassic extinction and Middle Triassic recovery of peat-forming plants. GSA Bull. 108, 195–207 (1996).CAS 
Article 

Google Scholar 
13.Fielding, C. R. et al. Age and pattern of the southern high-latitude continental end-Permian extinction constrained by multiproxy analysis. Nat. Commun. 10, 385 (2019).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
14.Fielding, C. R. et al. Sedimentology of the continental end-Permian extinction event in the Sydney Basin, eastern Australia. Sedimentology 68, 30–62 (2021).CAS 
Article 

Google Scholar 
15.Metcalfe, I., Crowley, J. L., Nicoll, R. S. & Schmitz, M. High-precision U-Pb CA-TIMS calibration of Middle Permian to Lower Triassic sequences, mass extinction and extreme climate-change in eastern Australian Gondwana. Gondwana Res. 28, 61–81 (2015).ADS 
CAS 
Article 

Google Scholar 
16.Vajda, V. et al. End-Permian (252 Mya) deforestation, wildfires and flooding—an ancient biotic crisis with lessons for the present. Earth Planet. Sci. Lett. 529, 115875 (2020).CAS 
Article 

Google Scholar 
17.McLoughlin, S. et al. Dwelling in the dead zone—vertebrate burrows immediately succeeding the end-Permian extinction event in Australia. Palaios 35, 342–357 (2020).ADS 
Article 

Google Scholar 
18.Lamb, A. L., Wilson, G. P. & Leng, M. J. A review of coastal palaeoclimate and relative sea-level reconstructions using δ13C and C/N ratios in organic material. Earth-Sci. Rev. 75, 29–57 (2006).ADS 
CAS 
Article 

Google Scholar 
19.Mays, C., Vajda, V. & McLoughlin, S. Permian–Triassic non-marine algae of Gondwana—distributions, natural affinities and ecological implications. Earth-Sci. Rev. 212, 103382 (2021).CAS 
Article 

Google Scholar 
20.McLoughlin, S. et al. Age and paleoenvironmental significance of the Frazer Beach Member—a new lithostratigraphic unit overlying the end-Permian extinction horizon in the Sydney Basin, Australia. Front. Earth Sci. 8, 600976 (2021).Article 

Google Scholar 
21.Huber, J. K. A postglacial pollen and nonsiliceous algae record from Gegoka Lake, Lake County, Minnesota. J. Paleolimnol. 16, 23–35 (1996).ADS 
Article 

Google Scholar 
22.Woodward, C. A. & Shulmeister, J. A Holocene record of human induced and natural environmental change from Lake Forsyth (Te Wairewa), New Zealand. J. Paleolimnol. 34, 481–501 (2005).ADS 
Article 

Google Scholar 
23.Pacton, M., Gorin, G. & Fiet, N. Occurrence of photosynthetic microbial mats in a Lower Cretaceous black shale (central Italy): a shallow-water deposit. Facies 55, 401–419 (2009).Article 

Google Scholar 
24.Pacton, M., Gorin, G. E. & Vasconcelos, C. Amorphous organic matter—Experimental data on formation and the role of microbes. Rev. Palaeobot. Palynol. 166, 253–267 (2011).Article 

Google Scholar 
25.Tyson, R. V. Sedimentary Organic Matter: Organic Facies and Palynofacies (Chapman & Hall, 1995).26.Retallack, G. J. Earliest Triassic claystone breccias and soil-erosion crisis. J. Sediment. Res. 75, 679–695 (2005).ADS 
Article 

Google Scholar 
27.Augland, L. E. et al. The main pulse of the Siberian Traps expanded in size and composition. Sci. Rep. 9, 18723 (2019).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
28.Retallack, G. J. Post-apocalyptic greenhouse paleoclimate revealed by earliest Triassic paleosols in the Sydney Basin. Aust. GSA Bull. 111, 52–70 (1999).CAS 
Article 

Google Scholar 
29.Woodward, C., Shulmeister, J., Larsen, J., Jacobsen, G. E. & Zawadzki, A. The hydrological legacy of deforestation on global wetlands. Science 346, 844–847 (2014).ADS 
CAS 
PubMed 
Article 

Google Scholar 
30.Stevenson, R. J. & Smol, J. P. In Freshwater Algae of North America: Ecology and Classification (eds Wehr, J. D., Sheath, R. G. & Kociolek, P.) Ch. 21, 921–962 (Academic Press, 2015).31.Lindström, S., Bjerager, M., Alsen, P., Sanei, H. & Bojesen-Koefoed, J. The Smithian–Spathian boundary in North Greenland: implications for extreme global climate changes. Geol. Mag. 157, 1547–1567 (2020).ADS 
Article 
CAS 

Google Scholar 
32.de Leeuw, J. W., Versteegh, G. J. M. & van Bergen, P. F. in Plants and Climate Change, Plant Ecology (eds Rozema, J., Aerts, R. & Cornelissen, H.) Vol. 182, 209–233 (Springer, 2006).33.Baudelet, P.-H., Ricochon, G., Linder, M. & Muniglia, L. A new insight into cell walls of Chlorophyta. Algal Res 25, 333–371 (2017).Article 

Google Scholar 
34.Graham, L. E. & Gray, J. In Plants Invade the Land: Evolutionary and Environmental Perspectives (eds Gensel, P. G. & Edwards, D.) 140–158 (Columbia University Press, 2001).35.Demura, M., Ioki, M., Kawachi, M., Nakajima, N. & Watanabe, M. M. Desiccation tolerance of Botryococcus braunii (Trebouxiophyceae, Chlorophyta) and extreme temperature tolerance of dehydrated cells. J. Appl. Phycol. 26, 49–53 (2014).CAS 
PubMed 
Article 

Google Scholar 
36.Del Cortona, A. et al. Neoproterozoic origin and multiple transitions to macroscopic growth in green seaweeds. PNAS 117, 2551–2559 (2020).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
37.Wheeler, A., Van de Wetering, N., Esterle, J. S. & Götz, A. E. Palaeoenvironmental changes recorded in the palynology and palynofacies of a Late Permian Marker Mudstone (Galilee Basin, Australia). Palaeoworld 29, 439–452 (2020).Article 

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

Google Scholar 
39.Low-Décarie, E., Fussmann, G. F. & Bell, G. The effect of elevated CO2 on growth and competition in experimental phytoplankton communities. Glob. Change Biol. 17, 2525–2535 (2011).ADS 
Article 

Google Scholar 
40.von Alvensleben, N., Magnusson, M. & Heimann, K. Salinity tolerance of four freshwater microalgal species and the effects of salinity and nutrient limitation on biochemical profiles. J. Appl. Phycol. 28, 861–876 (2016).Article 
CAS 

Google Scholar 
41.Chu, D. et al. Microbial mats in the terrestrial Lower Triassic of North China and implications for the Permian–Triassic mass extinction. Palaeogeog. Palaeoclimatol. Palaeoecol. 474, 214–231 (2017).ADS 
Article 

Google Scholar 
42.Guo, W. et al. Secular variations of ichnofossils from the terrestrial Late Permian–Middle Triassic succession at the Shichuanhe section in Shaanxi Province, North China. Glob. Planet. Change 181, 102978 (2019).Article 

Google Scholar 
43.Lee, J. Y. et al. Future global climate: Scenario-based projections and near-term information. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (eds Masson-Delmotte, V., et al.) 195 pp. (Cambridge University Press, 2021).44.de Jersey, N. J. Palynology of the Permian-Triassic transition in the western Bowen Basin. Geol. Surv. Qld. Publ. 374, 1–39 (1979).
Google Scholar 
45.Lindström, S. & McLoughlin, S. Synchronous palynofloristic extinction and recovery after the end-Permian event in the Prince Charles Mountains, Antarctica: Implications for palynofloristic turnover across Gondwana. Rev. Palaeobot. Palynol. 145, 89–122 (2007).Article 

Google Scholar 
46.Grebe, H. Permian plant microfossils from the Newcastle Coal Measures/Narrabeen Group Boundary, Lake Munmorah, New South Wales. Rec. Geol. Surv. NSW 12, 125–136 (1970).
Google Scholar 
47.Mishra, S. et al. A new acritarch spike of Leiosphaeridia dessicata comb. nov. emend. from the Upper Permian and Lower Triassic sequence of India (Pranhita-Godavari Basin): its origin and palaeoecological significance. Palaeogeog. Palaeoclimatol. Palaeoecol. 567, 110274 (2021).ADS 
Article 

Google Scholar 
48.Grice, K. et al. Photic zone euxinia during the Permian-Triassic superanoxic event. Science 307, 706–709 (2005).ADS 
CAS 
PubMed 
Article 

Google Scholar 
49.Kershaw, S. et al. Microbialites and global environmental change across the Permian–Triassic boundary: a synthesis. Geobiology 10, 25–47 (2012).CAS 
PubMed 
Article 

Google Scholar 
50.Schneebeli-Hermann, E. et al. Palynofacies analysis of the Permian–Triassic transition in the Amb section (Salt Range, Pakistan): implications for the anoxia on the South Tethyan Margin. J. Asian Earth Sci. 60, 225–234 (2012).ADS 
Article 

Google Scholar 
51.van Soelen, E. E. & Kürschner, W. M. Late Permian to Early Triassic changes in acritarch assemblages and morphology in the Boreal Arctic: new data from the Finnmark Platform. Palaeogeog. Palaeoclimatol. Palaeoecol. 505, 120–127 (2018).ADS 
Article 

Google Scholar 
52.Spina, A., Cirilli, S., Utting, J. & Jansonius, J. Palynology of the Permian and Triassic of the Tesero and Bulla sections (Western Dolomites, Italy) and consideration about the enigmatic species Reduviasporonites chalastus. Rev. Palaeobot. Palynol. 218, 3–14 (2015).Article 

Google Scholar 
53.Thomas, B. M. et al. Unique marine Permian‐Triassic boundary section from Western Australia. Aust. J. Earth Sci. 51, 423–430 (2004).ADS 
Article 

Google Scholar 
54.Schneebeli-Hermann, E. & Bucher, H. Palynostratigraphy at the Permian-Triassic boundary of the Amb section, Salt Range, Pakistan. Palynology 39, 1–18 (2015).Article 

Google Scholar 
55.Lei, Y. et al. Phytoplankton (acritarch) community changes during the Permian-Triassic transition in South China. Palaeogeog. Palaeoclimatol. Palaeoecol. 519, 84–94 (2019).ADS 
Article 

Google Scholar 
56.Algeo, T. J. et al. Plankton and productivity during the Permian–Triassic boundary crisis: An analysis of organic carbon fluxes. Glob. Planet. Change 105, 52–67 (2013).ADS 
Article 

Google Scholar 
57.van Soelen, E. E., Twitchett, R. J. & Kürschner, W. M. Salinity changes and anoxia resulting from enhanced run-off during the late Permian global warming and mass extinction event. Climate 14, 441–453 (2018).
Google Scholar 
58.Kaiho, K. et al. Effects of soil erosion and anoxic–euxinic ocean in the Permian–Triassic marine crisis. Heliyon 2, e00137 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
59.Bond, D. P. G. & Grasby, S. E. On the causes of mass extinctions. Palaeogeog. Palaeoclimatol. Palaeoecol. 478, 3–29 (2017).ADS 
Article 

Google Scholar 
60.Lindström, S., Erlström, M., Piasecki, S., Nielsen, L. H. & Mathiesen, A. Palynology and terrestrial ecosystem change of the Middle Triassic to lowermost Jurassic succession of the eastern Danish Basin. Rev. Palaeobot. Palynol. 244, 65–95 (2017).Article 

Google Scholar 
61.Garel, S. et al. Paleohydrological and paleoenvironmental changes recorded in terrestrial sediments of the Paleocene–Eocene boundary (Normandy, France). Palaeogeog. Palaeoclimatol. Palaeoecol. 376, 184–199 (2013).ADS 
Article 

Google Scholar 
62.van de Schootbrugge, B. & Gollner, S. In Ecosystem Paleobiology and Geobiology, The Paleontological Society Papers (eds Bush, A. M., Pruss, S. B. & Payne, J. L.) 19, 87–114 (The Paleontological Society, 2013).63.Mata, S. A. & Bottjer, D. J. Microbes and mass extinctions: paleoenvironmental distribution of microbialites during times of biotic crisis. Geobiology 10, 3–24 (2012).CAS 
PubMed 
Article 

Google Scholar 
64.Peterffy, O., Calner, M. & Vajda, V. Early Jurassic microbial mats—a potential response to reduced biotic activity in the aftermath of the end-Triassic mass extinction event. Palaeogeog. Palaeoclimatol. Palaeoecol. 464, 76–85 (2016).ADS 
Article 

Google Scholar 
65.Schoene, B. et al. U-Pb constraints on pulsed eruption of the Deccan Traps across the end-Cretaceous mass extinction. Science 3636, 862–866 (2019).ADS 
Article 
CAS 

Google Scholar 
66.Hull, P. M. et al. On impact and volcanism across the Cretaceous-Paleogene boundary. Science 367, 266–272 (2020).ADS 
CAS 
PubMed 
Article 

Google Scholar 
67.Vajda, V., Ocampo, A., Ferrow, E. & Bender Koch, C. Nano particles as the primary cause for long-term sunlight suppression at high southern latitudes following the Chicxulub impact—evidence from ejecta deposits in Belize and Mexico. Gondwana Res. 27, 1079–1088 (2015).ADS 
CAS 
Article 

Google Scholar 
68.Sepúlveda, J., Wendler, J. E., Summons, R. E. & Hinrichs, K.-U. Rapid resurgence of marine productivity after the Cretaceous-Paleogene mass extinction. Science 326, 129–132 (2009).ADS 
PubMed 
Article 
CAS 

Google Scholar 
69.Bralower, T. J. et al. Origin of a global carbonate layer deposited in the aftermath of the Cretaceous-Paleogene boundary impact. Earth Planet. Sci. Lett. 548, 116476 (2020).CAS 
Article 

Google Scholar 
70.Schaefer, B. et al. Microbial life in the nascent Chicxulub crater. Geology 48, 328–332 (2020).ADS 
CAS 
Article 

Google Scholar 
71.Milligan, J. N., Royer, D. L., Franks, P. J., Upchurch, G. R. & McKee, M. L. No evidence for a large atmospheric CO2 spike across the Cretaceous‐Paleogene boundary. Geophys. Res. Lett. 46, 3462–3472 (2019).ADS 
CAS 
Article 

Google Scholar 
72.Strother, P. K. & Wellman, C. H. The Nonesuch Formation Lagerstätte: a rare window into freshwater life one billion years ago. J. Geol. Soc. 178, jgs2020–jgs2133 (2021).Article 

Google Scholar 
73.Sepkoski, J. J., Bambach, R. K. & Droser, M. L. In Cycles and Events in Stratigraphy (eds Einsele, G., Ricken, W. & Seilacher, A.) 298–312 (Springer-Verlag, 1991).74.Tyson, R. V. Calibration of hydrogen indices with microscopy: a review, reanalysis and new results using the fluorescence scale. Org. Geochem. 37, 45–63 (2006).CAS 
Article 

Google Scholar 
75.Benninghoff, W. S. Calculation of pollen and spore density in sediments by addition of exotic pollen in known quantities. Pollen et. Spores 4, 332–333 (1962).
Google Scholar 
76.Maher, L. J. Statistics for microfossil concentration measurements employing samples spiked with marker grains. Rev. Palaeobot. Palynol. 32, 153–191 (1981).Article 

Google Scholar 
77.Simpson, M. G. Plant Systematics (Academic Press, 2019).78.Evitt, W. R. A discussion and proposals concerning fossil dinoflagellates, hystrichospheres, and acritarchs, II. PNAS 49, 298–302 (1963).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
79.Rampino, M. R. & Eshet, Y. The fungal and acritarch events as time markers for the latest Permian mass extinction: an update. Geosci. Front. 9, 147–154 (2018).Article 

Google Scholar 
80.Combaz, A. Les palynofaciès. Rev. Micropaléontol. 7, 205–218 (1964).
Google Scholar 
81.Hammer, Ø., Harper, D. A. T. & Ryan, P. D. PAST: paleontological statistics software package for education and data analysis. Palaeontol. Electron. 4, 4 (2001).
Google Scholar 
82.Wei, W. & Algeo, T. J. Elemental proxies for paleosalinity analysis of ancient shales and mudrocks. Geochim. Cosmochim. Acta 287, 341–366 (2020).ADS 
CAS 
Article 

Google Scholar 
83.Rowe, H., Hughes, N. & Robinson, K. The quantification and application of handheld energy-dispersive x-ray fluorescence (ED-XRF) in mudrock chemostratigraphy and geochemistry. Chem. Geol. 324–325, 122–131 (2012).ADS 
Article 
CAS 

Google Scholar 
84.Blakey, R. C. Global paleogeography and tectonics in deep time. https://deeptimemaps.com/global-series-details/. Accessed 16 June 2020 (2016).85.Zhuravlev, A. Y. & Wood, R. A. Anoxia as the cause of the mid-Early Cambrian (Botomian) extinction event. Geology 24, 311–314 (1996).ADS 
CAS 
Article 

Google Scholar 
86.Zhang, W., Shi, X., Jiang, G., Tang, D. & Wang, X. Mass-occurrence of oncoids at the Cambrian Series 2–Series 3 transition: Implications for microbial resurgence following an Early Cambrian extinction. Gondwana Res. 28, 432–450 (2015).ADS 
Article 
CAS 

Google Scholar 
87.Vecoli, M. Fossil microphytoplankton dynamics across the Ordovician–Silurian boundary. Rev. Palaeobot. Palynol. 148, 91–107 (2008).Article 

Google Scholar 
88.Xie, S. et al. Contrasting microbial community changes during mass extinctions at the Middle/Late Permian and Permian/Triassic boundaries. Earth Planet. Sci. Lett. 460, 180–191 (2017).ADS 
CAS 
Article 

Google Scholar 
89.Eshet, Y., Rampino, M. R. & Visscher, H. Fungal event and palynological record of ecological crisis and recovery across the Permian-Triassic boundary. Geology 23, 967–970 (1995).ADS 
Article 

Google Scholar 
90.Richoz, S. et al. Hydrogen sulphide poisoning of shallow seas following the end-Triassic extinction. Nat. Geosci. 5, 662–667 (2012).ADS 
CAS 
Article 

Google Scholar 
91.Lindström, S. et al. No causal link between terrestrial ecosystem change and methane release during the end-Triassic mass extinction. Geology 40, 531–534 (2012).ADS 
Article 
CAS 

Google Scholar 
92.van de Schootbrugge, B. et al. End-Triassic calcification crisis and blooms of organic-walled “disaster species”. Palaeogeog. Palaeoclimatol. Palaeoecol. 244, 126–141 (2007).ADS 
Article 

Google Scholar 
93.Slater, S. M., Twitchett, R. J., Danise, S. & Vajda, V. Substantial vegetation response to Early Jurassic global warming with impacts on oceanic anoxia. Nat. Geosci. 12, 462–467 (2019).ADS 
CAS 
Article 

Google Scholar 
94.Polgári, M. et al. Mineral and chemostratigraphy of a Toarcian black shale hosting Mn-carbonate microbialites (Úrkút, Hungary). Palaeogeog. Palaeoclimatol. Palaeoecol. 459, 99–120 (2016).ADS 
Article 

Google Scholar 
95.Xu, W. et al. Carbon sequestration in an expanded lake system during the Toarcian oceanic anoxic event. Nat. Geosci. 10, 129–134 (2017).ADS 
CAS 
Article 

Google Scholar 
96.Kashiyama, Y. et al. Diazotrophic cyanobacteria as the major photoautotrophs during mid-Cretaceous oceanic anoxic events: nitrogen and carbon isotopic evidence from sedimentary porphyrin. Org. Geochem. 39, 532–549 (2008).CAS 
Article 

Google Scholar 
97.Jarvis, I. et al. Microfossil assemblages and the Cenomanian-Turonian (late Cretaceous) oceanic anoxic event. Cretac. Res 9, 3–103 (1988).Article 

Google Scholar 
98.Layeb, M., Ben Fadhel, M., Layeb-Tounsi, Y. & Ben Youssef, M. First microbialites associated to organic-rich facies of the Oceanic Anoxic Event 2 (Northern Tunisia, Cenomanian–Turonian transition). Arab. J. Geosci. 7, 3349–3363 (2014).CAS 
Article 

Google Scholar 
99.Pearce, M. A., Jarvis, I. & Tocher, B. A. The Cenomanian–Turonian boundary event, OAE2 and palaeoenvironmental change in epicontinental seas: new insights from the dinocyst and geochemical records. Palaeogeog. Palaeoclimatol. Palaeoecol. 280, 207–234 (2009).ADS 
Article 

Google Scholar 
100.Kuypers, M. M. M., Pancost, R. D., Nijenhuis, I. A. & Sinninghe Damsté, J. S. Enhanced productivity led to increased organic carbon burial in the euxinic North Atlantic basin during the late Cenomanian oceanic anoxic event. Paleoceanography 17, 1051 (2002).ADS 
Article 

Google Scholar 
101.Dodsworth, P., Eldrett, J. S. & Hart, M. B. Cretaceous Oceanic Anoxic Event 2 in eastern England: further palynological and geochemical data from Melton Ross. figshare https://doi.org/10.6084/m9.figshare.c.4987205.v3 (2020).102.Schwab, K. W., Bayliss, G. S., Smith, M. A. & Yoder, N. B. Mushroom and broccoli-head shaped algal fragments from the Eagle Ford Shale of south Texas and Coahuila, Mexico. Search and Discovery 70134 (2013).103.Lyson, T. R. et al. Exceptional continental record of biotic recovery after the Cretaceous–Paleogene mass extinction. Science 366, 977–983 (2019).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
104.Scasso, R. A. et al. A high-resolution record of environmental changes from a Cretaceous-Paleogene section of Seymour Island. Antarctica. Palaeogeog. Palaeoclimatol. Palaeoecol. 555, 109844 (2020).ADS 
Article 

Google Scholar 
105.Sosa-Montes de Oca, C. et al. Minor changes in biomarker assemblages in the aftermath of the Cretaceous-Paleogene mass extinction event at the Agost distal section (Spain). Palaeogeog. Palaeoclimatol. Palaeoecol. 569, 110310 (2021).ADS 
Article 

Google Scholar 
106.Sluijs, A. et al. Environmental precursors to rapid light carbon injection at the Palaeocene/Eocene boundary. Nature 450, 1218–1222 (2007).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
107.Junium, C. K., Dickson, A. J. & Uveges, B. T. Perturbation to the nitrogen cycle during rapid Early Eocene global warming. Nat. Commun. 9, 3186 (2018).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
108.Pagani, M. et al. Arctic hydrology during global warming at the Palaeocene/Eocene thermal maximum. Nature 442, 671–675 (2006).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
109.Kender, S. et al. Marine and terrestrial environmental changes in NW Europe preceding carbon release at the Paleocene–Eocene transition. Earth Planet. Sci. Lett. 353–354, 108–120 (2012).ADS 
Article 
CAS 

Google Scholar 
110.Huisman, J. et al. Cyanobacterial blooms. Nat. Rev. Microbiol. 16, 471–483 (2018).CAS 
PubMed 
Article 

Google Scholar 
111.Paerl, H. W., Hall, N. S. & Calandrino, E. S. Controlling harmful cyanobacterial blooms in a world experiencing anthropogenic and climatic-induced change. Sci. Tot. Environ. 409, 1739–1745 (2011).CAS 
Article 

Google Scholar  More

1.Darwin, C. On the origin of species. (D. Appleton and Co., 1871). https://doi.org/10.5962/bhl.title.28875.2.Thompson, J. N. Mutualistic webs of species. Science (80–) 312, 372–373 (2006).CAS 
Article 

Google Scholar 
3.Bronstein, J. L. The exploitation of mutualisms. Ecol. Lett. 4, 277–287 (2001).Article 

Google Scholar 
4.Bshary, R. & Grutter, A. S. Experimental evidence that partner choice is a driving force in the payoff distribution among cooperators or mutualists: The cleaner fish case. Ecol. Lett. 5, 130–136 (2002).Article 

Google Scholar 
5.Kiers, E. T., Rousseau, R. A., West, S. A. & Denison, R. F. Host sanctions and the legume–rhizobium mutualism. Nature 425, 78–81 (2003).ADS 
CAS 
Article 

Google Scholar 
6.Heil, M., Barajas-Barron, A., Orona-Tamayo, D., Wielsch, N. & Svatos, A. Partner manipulation stabilises a horizontally transmitted mutualism. Ecol. Lett. 17, 185–192 (2014).Article 

Google Scholar 
7.Hindsbo, O. Effects of Polymorphus (Acanthocephala) on colour and behaviour of Gammarus lacustris. Nature 238, 333 (1972).ADS 
Article 

Google Scholar 
8.Thomas, F., Renaud, F., de Meeus, T. & Poulin, R. Manipulation of host behaviour by parasites: Ecosystem engineering in the intertidal zone?. Proc. R. Soc. B Biol. Sci. 265, 1091–1096 (1998).Article 

Google Scholar 
9.Thomas, F. et al. Do hairworms (Nematomorpha) manipulate the water seeking behaviour of their terrestrial hosts?. J. Evol. Biol. 15, 356–361 (2002).Article 

Google Scholar 
10.Kadoya, E. Z., Ishii, H. S. & Williams, N. M. Host manipulation of bumble bee queens by Sphaerularia nematodes indirectly affects foraging of non-host workers. Ecology 96, 1361–1370 (2015).Article 

Google Scholar 
11.Hojo, M. K., Pierce, N. E. & Tsuji, K. Lycaenid caterpillar secretions manipulate attendant ant behavior. Curr. Biol. 25, 2260–2264 (2015).CAS 
Article 

Google Scholar 
12.Poulin, R., Brodeur, J. & Moore, J. Parasite manipulation of host behaviour: Should hosts always lose?. Oikos 70, 479 (1994).Article 

Google Scholar 
13.Heil, M. et al. Divergent investment strategies of Acacia myrmecophytes and the coexistence of mutualists and exploiters. Proc. Natl. Acad. Sci. USA. 106, 18091–18096 (2009).ADS 
CAS 
Article 

Google Scholar 
14.Watanabe, S., Murakami, T., Yoshimura, J. & Hasegawa, E. Color polymorphism in an aphid is maintained by attending ants. Sci. Adv. 2, (2016).15.Watanabe, S., Yoshimura, J. & Hasegawa, E. Ants improve the reproduction of inferior morphs to maintain a polymorphism in symbiont aphids. Sci. Rep. 8, (2018).16.Sakata, H. Density-dependent predation of the ant Lasius niger (Hymenoptera: Formicidae) on two attended aphids Lachnus tropicalis and Myzocallis kuricola (Homoptera: Aphididae). Res. Popul. Ecol. (Kyoto) 37, 159–164 (1995).Article 

Google Scholar 
17.Evans, P. D. Biogenic Amines in the insect nervous system. Adv. In Insect Phys. 15, 317–473 (1980).CAS 
Article 

Google Scholar 
18.Aonuma, H. & Watanabe, T. Octopaminergic system in the brain controls aggressive motivation in the ant Formica japonica. Acta Biol. Hung. 63, 63–68 (2012).Article 

Google Scholar 
19.Stevenson, P. A., Dyakonova, V., Rillich, J. & Schildberger, K. Octopamine and experience-dependent modulation of aggression in crickets. J. Neurosci. 25, 1431–1441 (2005).CAS 
Article 

Google Scholar 
20.Kostowski, W. & Tarchalska, B. The effects of some drugs affecting brain 5-HT on the aggressive behaviour and spontaneous electrical activity of the central nervous system of the ant Formica rufa. Brain Res. 38, 143–149 (1972).CAS 
Article 

Google Scholar 
21.Szczuka, A. et al. The effects of serotonin, dopamine, octopamine and tyramine on behavior of workers of the ant Formica polyctena during dyadic aggression tests. Acta Neurobiol. Exp. (Wars) 73, 495–520 (2013).
Google Scholar 
22.Way, M. J. Mutualism between ants and honeydew-producing homoptera. Annu. Rev. Entomol. 8, 307–344 (1963).Article 

Google Scholar 
23.Hafer-Hahmann, N. Behavior out of control: Experimental evolution of resistance to host manipulation. Ecol. Evol. 9, 7237–7245 (2019).Article 

Google Scholar 
24.Martinez, J., Fleury, F. & Varaldi, J. Heritable variation in an extended phenotype: The case of a parasitoid manipulated by a virus. J. Evol. Biol. 25, 54–65 (2012).Article 

Google Scholar 
25.Engelstädter, J. & Hurst, G. D. D. The ecology and evolution of microbes that manipulate host reproduction. Annu. Rev. Ecol. Evol. Syst. 40, 127–149 (2009).Article 

Google Scholar 
26.Rosenthal, G. G. & Servedio, M. R. Chase-away sexual selection: Resistance to ‘resistance’. Evolution (N.Y.) 53, 296 (1999).
Google Scholar 
27.Woodring, J., Wiedemann, R., Fischer, M. K., Hoffmann, K. H. & Völkl, W. Honeydew amino acids in relation to sugars and their role in the establishment of ant-attendance hierarchy in eight species of aphids feeding on tansy (Tanacetum vulgare). Physiol. Entomol. 29, 311–319 (2004).CAS 
Article 

Google Scholar 
28.Stadler, B. & Dixon, A. F. G. Ecology and evolution of aphid-ant interactions. Annu. Rev. Ecol. Evol. Syst. 36, 345–372 (2005).Article 

Google Scholar 
29.Tsuji, K. & Dobata, S. Social cancer and the biology of the clonal ant Pristomyrmex punctatus (Hymenoptera: Formicidae). Myrmecological News 15, 91–99 (2011).
Google Scholar 
30.Vellend, M. Conceptual synthesis in community ecology. Q. Rev. Biol. 85, 183–206 (2010).Article 

Google Scholar 
31.Agawa, H. & Kawata, M. The effect of color polymorphism on mortality in the aphid Macrosiphoniella yomogicola. Ecol. Res. 10, 301–306 (1995).Article 

Google Scholar 
32.Watanabe, S., Murakami, Y. & Hasegawa, E. Effects of attending ant species on the fate of colonies of an aphid, Macrosiphoniella yomogicola (Matsumura) (Homoptera: Aphididae), in an ant-aphid symbiosis. Entomol. News 128, 325 (2019).Article 

Google Scholar 
33.Wada-Katsumata, A., Yamaoka, R. & Aonuma, H. Social interactions influence dopamine and octopamine homeostasis in the brain of the ant Formica japonica. J. Exp. Biol. 214, 1707–1713 (2011).CAS 
Article 

Google Scholar 
34.Aonuma, H. & Watanabe, T. Changes in the content of brain biogenic amine associated with early colony establishment in the queen of the ant, formica japonica. PLoS One 7, (2012).35.Aonuma, H. Serotonergic control in initiating defensive responses to unexpected tactile stimuli in the trap-jaw ant Odontomachus kuroiwae. J. Exp. Biol. 223, jeb228874 (2020).Article 

Google Scholar 
36.R Core Team. R: A Language and Environment for Statistical Computing. (2020).37.Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using {lme4}. J. Stat. Softw. 67, 1–48 (2015).Article 

Google Scholar 
38.Venables, W. N. & Ripley, B. D. Modern Applied Statistics with S. (Springer, 2002).39.Hothorn, T. & Hornik, K. exactRankTests: Exact Distributions for Rank and Permutation Tests. (2019). More

Macro- and micro-evolutionary integration between jaw complexesWe examined phenotypic associations between the lower oral and pharyngeal jaws (LOJ and LPJ, respectively) of 88 cichlid species from across Africa, primarily sampling from lakes in the East African Rift Valley: lakes Malawi, Tanganyika, and Victoria (Supplementary Data 1). We characterized jaw shapes based on 107 individuals using 3D geometric morphometrics by placing landmarks in positions that capture functionally (e.g., bony processes, sutures, etc.) and developmentally (e.g., distinct cellular origins) important aspects of morphology, including placing mirrored landmarks across midlines to gain symmetric configurations (Fig. 1e, Supplementary Fig. 1). We conducted a Procrustes superimposition, removed the effects of allometry to account for size differences, and then removed the effects of asymmetry to account for developmental noise. We performed a two-block partial least squares (PLS) analysis on the species mean landmark configurations and corrected for phylogenetic non-independence using a Bayesian time-calibrated tree31. We found the LOJ and LPJ were evolutionarily correlated (r-PLS = 0.482, P = 0.002, effect size (Z) = 2.585), but some taxa, particularly those with unique diets and/or modes of feeding, appeared to deviate from the best-fit line, indicating lower levels (or different patterns) of integration between jaws (Fig. 2a). Indeed, we found numerous taxa, typically from Lake Malawi, whereby covariation between the LOJ and LPJ appeared much different relative to other African cichlids. Taxa placed far from the best-fit line either (1) exhibited a specialized feeding morphology to better exploit an foraging niche shared with many competitors (i.e., Labeotropheus, algae; Copadichromis, zooplankton; Taeniolethrinops, insects), or (2) exhibited a specialized feeding morphology to take advantage of a more challenging food source (i.e., Trematocranus, snails). However, not all taxa that consume specialized prey were far from the best-fit line; Pungu, (primarily a sponge-feeder) and Perissodus (a scale-feeder), while exhibiting specialized feeding apparatuses to consume such prey, exhibited a relationship between their LOJ and LPJ that was in-line with other African cichlids (Supplementary Fig. 2). We also noted, that while Malawi cichlids exhibit a range of LOJ-LPJ relationships (from weak to strong), most Tanganyikan cichlids reside close to the best-fit line. However, when we examine the strength of integration in the Tanganyika group (n = 29, r-PLS = 0.698, P = 0.001, Z = 2.954) and Malawi group (n = 40, r-PLS = 0.541, P = 0.020, Z = 2.155), despite Tangyanika cichlids exhibiting higher Z-scores, consistent with stronger integration, a statistical comparison between groups finds no significant difference (Z pairwise = 1.188, P = 0.235). Comparisons between Tanganyikan and Malawi cichlids should not be influenced by sampling bias, as principal components analyses (PCA) on the LOJ and LPJ landmark data (Supplementary Data 2 and 3) showed that our sampling of Tanganyikan cichlids includes many species with extreme morphologies that reside at the outer edges of LOJ and LPJ morphospace (Supplementary Fig. 3). Indeed, cichlids from Lake Tanganyika exhibited similar LOJ morphological disparity (Malawi Procrustes variance (PV) = 0.074; Tanganyika PV = 0.057, P = 0.253) and greater LPJ morphological disparity (Malawi PV = 0.015; Tanganyika PV = 0.023, P = 0.012), relative to cichlids from Lake Malawi. Taken together, this indicates that while Tanganyikan cichlids exhibit comparable (i.e., LOJ), or greater (i.e., LPJ) morphological variation compared to Malawi cichlids, covariation between LOJ and LPJ shapes was generally similar between groups.Fig. 2: Phylogenetic two-block partial least squares analysis to assess macroevolutionary associations between lower oral and pharyngeal jaws.a Jaw shape associations across a broad sample of African cichlids (n = 88). Taxa from Lake Malawi are placed into two groups based on phylogenetic position: an mbuna ‘rock-dwellers’ group, and a non-mbuna group consisting of the utaka ‘sand-dwellers’ alongside other benthic species88. b Jaw shape associations across the Tropheops sp. species complex from across a depth gradient (n = 22). Oral and pharyngeal jaw wireframes denote morphologies at either end of the correlational axis. Source data are provided as a Source Data file.Full size imageWe next investigated the degree of integration at lower taxonomic levels. First, we analyzed the jaws of cichlids within the Tropheops species complex from Lake Malawi that is diverse and known to partition habitat by depth32,33. While Tropheops exhibited strong integration between jaws in on our macroevolutionary assessment, species within this genus occupy a broader niche space. Investigating integration within such a species complex provided an opportunity to understand whether habitat differences could lead to differences in integration between jaw complexes. Using the same landmarking procedure as described above, we characterized shape variation in the LOJs and LPJs of 22 wild-caught Tropheops taxa from 60 individuals, concentrating on members from localities across the southern portion of Lake Malawi (Supplementary Data 4). We again performed a two-block PLS analysis on the mean landmark configurations and accounted for phylogenetic non-independence using an amplified fragment length polymorphism tree33. Again, we found the LOJ and LPJ were evolutionarily correlated (r-PLS = 0.795, P = 0.006, Z = 2.521), indicating jaw integration does not appear to vary by habitat (Fig. 2b).Finally, we measured and compared integration between a species pair that exhibited relatively strong versus weak covariation between LOJ and LPJ shapes in our macroevolutionary assessments, Tropheops sp. ‘red cheek’ (TRC, relatively stronger integration) and Labeotropheus fuelleborni (LF, relatively weaker integration). Using the same landmarking protocol we performed separate two-block PLS analyses between LOJs and LPJs of LF and TRC (Supplementary Data 5). Notably, we found strong and significant integration between jaw complexes in TRC (n = 11, r-PLS = 0.957, P = 0.001, Z = 3.038; Fig. 3a) relative to LF (n = 17, r-PLS = 0.669, P = 0.22, Z = 0.794; Fig. 3b). Further, we found the effect sizes of jaw integration within TRC and LF to be statistically distinct (Z pairwise = 1.678, P = 0.047). Altogether, our data support the assertion that the LOJ and LPJ are evolutionarily integrated at multiple taxonomic levels, but they also appear to indicate that certain taxa, such as Labeotropheus, can more readily generate adaptive morphological variation in each jaw complex independently.Fig. 3: Two-block partial least squares analysis to assess microevolutionary associations between lower oral and pharyngeal jaws.a Shape associations among Tropheops sp. “red cheek” (TRC) individuals (n = 11). b Shape associations among Labeotropheus fuelleborni (LF) individuals (n = 17). c Shape associations among members of a hybrid cross between TRC and LF (n = 409). Oral and pharyngeal jaw wireframes denote morphologies at either end of the correlational axis. Source data are provided as a Source Data file.Full size imageGenetic basis for oral and pharyngeal jaw shape covariationTo understand whether phenotypic covariation between the LOJ and LPJ is genetically determined we performed a quantitative trait loci (QTL) analysis to identify prospective genomic regions involved in jaw shape variation for both the LOJ and LPJ. Specifically, we extended an existing genetic cross between the more strongly integrated TRC and the more weakly integrated LF to the F5 generation. Details of the pedigree may be found in34 and in the supplement. For this experiment, we genotyped 636 F5 hybrids and produced a genetic map containing 812 single-nucleotide polymorphisms (SNPs) spread across 24 linkage groups (Supplementary Data 6). With a total length of 1431 cM, our high-resolution linkage map contained a marker every 1.83 cM, on average, allowing us to leverage the increased number of recombination events that occurred to reach an F5 population. We then characterized LOJ and LPJ shape in 409 F5 hybrids using the same landmarking scheme described above, and performed a two-block PLS analysis. In concordance with our findings from natural populations, we documented an association between jaw complexes in this laboratory pedigree (r-PLS = 0.491, P = 0.001, effect size = 6.189; Fig. 3c).We next performed a PCA on the hybrid landmark configurations to distill the data down to a series of orthogonal axes that best explain shape variation among individuals. We extracted the first two PCs from the LOJ and LPJ as each axis represented more than 10% of the shape variation (Supplementary Data 7; Supplementary Figs. 4 and 5). The first axis of the LOJ reflected more general variation in depth, width, and length of the element (41.8% of variation), while the second axis reflected more specific variation in the length of the ascending arm of the articular––the process for which jaw closing muscles attach (12.7% of variation). The first axis of the LPJ reflected width, length, and wing process size (33.7% of variation), while the second axis reflected depth and the size of the anterior keel – the process for which the pharyngeal jaw pharyngohyoideus muscle attaches and controls jaw adduction (14.2% of variation). We then utilized these PC scores as traits to run in our QTL analyses to investigate the genetic basis for variation in these structures.QTL mapping implicates pleiotropic control of LOJ and LPJ shape variationIntegration between LOJ and LJP shapes in the F5 predicts that this pattern of covariation will be reflected in the genotype-phenotype map. Specifically, we predict that we will find overlapping QTL for both jaws. We used a multiple QTL mapping (MQM) approach to test this prediction. Specifically, we performed QTL scans for all four traits and quantitatively assessed the evidence for significant QTL marker(s) using a permutation procedure that reshuffles the phenotypic data relative to genotypic data 1000 times to generate a null distribution, disassociating any possible relationship between genotype and phenotype, to then compare the empirical distribution against35. Once candidate QTL markers were identified, we calculated an approximate Bayesian credible interval to determine the region in which a potential candidate locus would reside. We uncovered a total of five QTL for LOJ traits, and four QTL for LPJ traits (Fig. 4a; Supplementary Data 8). While most QTL localize to different linkage groups, we also identified some QTL that colocalized. Two traits (LOJ PC1, LPJ PC1) share a marker on LG4, while three traits (LOJ PC1, LOJ PC2, LPJ PC1) colocalized to the same markers on LG7. These data are consistent with pleiotropy on LG7 and possibly LG4.Fig. 4: Genetic analyses to identify regions of the genome responsible for major changes in jaw shape.All plots are based on 409 LFxTRC F5 hybrids. a QTL analysis to identify positions in the genome most associated with each trait. b Pleiotropy analysis on linkage group seven to determine whether the oral jaw PC1 trait colocalizes to the same region as the pharyngeal jaw PC1 trait. Significance was determined using a likelihood ratio test (LLRT). c Pleiotropy analysis on linkage group seven to determine whether the oral jaw PC2 trait colocalizes to the same region as the pharyngeal jaw PC1 trait. Significance was determined using a LLRT. d Fine mapping all traits across the entirety of LG7. Values furthest from 0 reflect the largest differences between hybrids with LF and TRC genotypes at a given marker. We find peak genotype-phenotype association at ~50 mb that coincides with our Bayes credible interval (grey bar). Intervals that surround the average phenotypic effect line denote standard error of the mean. e Fine mapping all traits across the Bayes credible interval. Population level genetic diversity (FST) data are applied to the map (black dots) with the opacity of each SNP dependent on the degree of segregation between LF and TRC, with those falling above an empirical Z-score threshold of 0.6 determined to be significant, and those above 0.9 deemed highly significant (green lines). Within the credible interval there are four SNPs with FST values of 1.0, but a single SNP that falls within a genotype-phenotype peak residing within an intron of dym (black circle). Source data are provided as a Source Data file.Full size imageWe then quantitatively assessed the evidence for pleiotropy using a likelihood ratio test (LLRT) to compare the null hypothesis of a common pleiotropic QTL to the alternative hypothesis that they are affected by separate QTL36,37. The overlap on LG4 at a single marker (43.57 cM) was deemed significant (LLRT = 1.85, P = 0.03), indicating that we can reject the null hypothesis and that these peaks likely represent separate QTL for each trait (Supplementary Fig. 6). The three traits that overlap on LG7 spanned two markers (19.12 cM–28.04 cM) and were all deemed non-significant (LOJpc1-LPJpc1: LLRT = 0.02, P = 0.66, Fig. 4b; LOJpc2-LPJpc1: LLRT = 0.20, P = 0.41, Fig. 4c), leading us to accept the null hypothesis and conclude that this interval likely contains a single pleiotropic QTL. Whether a single gene, or multiple closely linked genes drive this pleiotropic signal requires a fine-mapping approach.Notably, this locus on LG7 has been implicated previously in underlying LOJ and LPJ shape in another Lake Malawi cichlid cross between LF and Maylandia zebra38,39. Maylandia species, like Tropheops, were generally more integrated in our macroevolutionary analysis (Fig. 2a), and thus another cross between LF and a species with higher integration values point to the same locus. This suggests that the genetic mechanism of integration may be conserved.Fine mapping identifies two candidate genes critical for bone formationTo gain insights into which gene(s) may be pleiotropically regulating LOJ and LPJ jaw shape variation on LG7 we constructed a fine map with greater marker density to investigate genotype-phenotype associations with greater resolution. To that end, we anchored QTL intervals to particular stretches of physical sequence of the Maylandia zebra genome40. We then identified additional RAD-seq SNPs across the linkage group of interest and genotyped them in the F5. Based on this we created two fine maps: the first spanned the entirety of LG7 group with an average spacing of around one marker every 490 kb (Supplementary Data 9), the second matched the QTL marker range revealed by the Bayesian credible interval analysis with an average spacing of around one marker every 180 kb (Supplementary Data 10). We also calculated FST from a panel of wild-caught LF (n = 20) and TRC (n = 20), and primarily focused on FST values of 1.0 that would indicate complete segregation of a SNP between LF and TRC. At every marker on our LG7 fine maps, we calculated the difference in the values of our three colocalized traits between those hybrids homozygous for the LF allele and those homozygous for the TRC allele.We identified a small region on LG7 that exhibited large differences in the average phenotypic effect of those hybrids with either LF or TRC alleles. In our full LG7 map we identified a ~2 mb region (46.7 mb–48.7 mb) that peaked in all three traits (Fig. 4d; Supplementary Data 11). Notably, the traces of all three traits across our LG7 fine maps track together in an almost identical fashion. In our fine map that centered on the Bayes credible interval, we found evidence for both large phenotypic effects among all traits, and the presence of several FST markers approaching or equal to 1.0 (Fig. 4e; Supplementary Data 12). One marker combined an FST score of 1.0, indicating complete segregation of that allele between LF and TRC, with high average phenotypic effects across all traits (Supplementary Fig. 7). This SNP fell within an intron of dymeclin (dym), a gene that is necessary for correct organization of Golgi apparatus and controls endochondral bone formation during early development. Dym is critical for chondrocyte development and previous research using the zebrafish model found an expression pattern that spanned the presumptive mandibular and ceratobranchial regions at larval stages41. Mutations in this gene lead to profound effects on the size and shape of bones due to misregulated chondrocyte development42. Just downstream (8 kb, Supplementary Fig. 7) of dym is mothers against decapentaplegic homolog 7 (smad7), an antagonist of both TGF-β and BMP signaling and a suppressor of bone formation. As an inhibitory Smad, smad7 negatively regulates genes from the BMP and TGF-β signaling pathways (i.e., bmp-2, -4, -7, nodal, etc.) that are known to shape phenotypic differences in the craniofacial skeleton across a wide range of taxa including cichlids25,38,43, Geospiza finches44,45, and Anolis lizards46, primarily because these genes have the capacity to influence size in structures of trophic importance such as the mandible47. Both of these genes represent good candidates for controlling shape variation in the LOJ and the LPJ simultaneously. While two of the three traits peak at the dym SNP, when considering markers just outside the credible interval another peak is visible (especially for LOJ PC1) that sits close to notch1a, a gene involved in skeletal development and homeostasis. Notch1a is flanked by two fully segregated FST markers. The upstream marker is around ~60 kb from the promoter region, while the downstream marker resides around ~52 kb away from the gene within an intron of kcnt1, a gene involved in potassium channel development that appears to regulate brain function48. While kcnt1 reflects a poor candidate gene for our analysis, the intronic SNP could act as a distant enhancer of notch1a. Thus, given the combined results from QTL and fine-mapping, dym and smad7 represent strong candidates, but we cannot rule out notch1a.Correlated expression of key genes between LOJ and LPJWe used quantitative real-time PCR (qPCR) to assess and compare the expression levels of dym, smad7, and notch1a in the LOJ and LPJ of three mbuna genera from lake Malawi (Tropheops n = 6, Labeotropheus n = 8, Maylandia n = 8). We used Labeotropheus and Tropheops to complement our quantitative genetic analysis, and all three taxa were represented in our phenotypic assessments of integration, permitting a comparison between macroevolutionary associations of the LOJ and LPJ with the underlying genetic architecture and expression for jaw complex correlation. We collected tissue samples from young juveniles of these four taxa, taking the LOJ and LPJ, alongside the caudal fin to act as an internal control, and performed a phenol/chloroform RNA extraction. We designed primers with high amplification efficiency ( >90%) for our three genes (Supplementary Data 13), and used β-actin as our control gene. We calculated relative expression of the LOJ and LPJ using the 2-ΔΔCT method49, and compared expression across taxa and between tissues (Supplementary Data 14 and 15).We initially compared tissue level expression levels between Labeotropheus and Tropheops and found small differences in dym expression, with LF typically exhibiting slightly higher levels (t-test LOJ t = 2.863, P = 0.014; LPJ t = 1.212, P = 0.249; Fig. 5a). These results are consistent with previous expression studies that demonstrated how Labeotropheus typically has up-regulated bone and collagen markers and as a consequence has greater bone deposition and a more robust craniofacial skeleton50,51. Expression level differences were also noted for notch1a and smad7 (Fig. 5b-c); both showed reduced expression in LF, which is expected based on each genes role as negative regulators of bone formation52,53. While the differences between species were fairly small in smad7 between taxa (t-test LOJ t = −1.869, P = 0.086; LPJ t = −0.359, P = 0.726), they were more notable in notch1a (t-test LOJ t = −1.947, P = 0.080; LPJ t = −3.221, P = 0.009). Notch1a is involved in skeletal remodeling, previous research has shown LF exhibits a minimal plastic response to environmental stimuli51. Thus, the relatively low expression of notch1a in Labeotropheus compared to Tropheops is consistent with this observation. While only representing a single life-history stage, the expression differences between species suggest that all three genes may underlie the development of species-specific shapes for the LOJ and/or LPJ. However, visualizing the data this way cannot speak to whether one or more of these loci underlie the covariation of the jaws.Fig. 5: Comparing expression levels of dym, smad7, and notch1a via qPCR in the oral and pharyngeal jaws.a dym bar plot; (b) notch1a bar plot; (c), smad7 bar plot; (d), dym scatter plot; (e), notch1a scatter plot; (f), smad7 scatter plot. a–c bar plots depict mean relative expression levels, error bars denote standard error. d–f Scatterplots depict relative expression levels of the LOJ and LPJ, error bounds surrounding the linear regression line denote standard error. e inset, linear regression for each genus. Three cichlid taxa were included: Labeotropheus n = 8, Tropheops n = 6, Maylandia n = 8. Bar plot significance determined via t-tests: ●P  More

Understanding the ‘push’ and ‘pull’ influence of environment on the migration and sustainability of peoples in northern North America over the last millennia is arguably one of the most important elements of understanding how recent climate change may affect society and lead to genetic adaptations1,2. The timing of migration has often been associated with paleo- temperature reconstructions that link evidence of distinctive material culture3 as well as the impact of subsistence practices in areas where hunting camps were established4 with shifting conditions. For the Dorset people, who were reliant on ice-dependent species such as walrus5, climate may have served as a “push” factor that served as a mechanism for northern migration during periods of time such as the Medieval Climate Anomaly (MCA). Conversely, the Thule were able to take advantage of increased activity of belugas and narwhals during longer open-water seasons, and migrations associated with the Thule expansion (circa 1250 CE) may have followed this transition until cooling associated with the Little Ice Age in the fifteenth century3,6. The Sadlermiut of Southampton Island (Nunavut, Arctic Canada) have often been referred to as descendants of the Dorset culture7,8 even though recent genetic evidence suggests they were a long isolated Thule population9,10. Archaeological evidence of stone-carved tools for walrus hunting, which is much more related to Dorset cultural practices than Thule4,5, is a prominent feature of winter hunting camps concentrated on the eastern side of Southampton Island in proximity to polynyas and ample walrus hunting grounds. Small, shallow ponds that are widespread in this area were used as staging grounds for the cleaning and preparation of subsistence harvest, and serve as sedimentary archives of the past presence and influence of the Sadlermiut, and their cultural practices, on the landscape.High latitude freshwater ecosystems are often referred to as sentinels of environmental changes caused by climate variability and human activity11. Small and shallow lakes and ponds that characterize Arctic landscapes have a low resilience to buffer environmental change12,13,14, as well as catchment disturbances induced by prehistoric Inuit whalers15. Likewise, diffuse and point source disturbances can have disproportional effects due to the suboptimal environmental thresholds characteristic of biological communities of northern aquatic ecosystems16. Here, we show that a small subarctic pond in proximity of the archaeological site “Native Point” on Southampton Island evolved atypically after human activities initiated almost 800 years ago when Sadlermiut settled in the area. Our multi-proxy paleolimnological investigation uses geochemical and biological indicators to infer direct and indirect anthropogenic impacts. The lacustrine sediments collected from this site are highly sensitive environmental recorders that also allow us to pinpoint the first arrival of Sadlermiut culture, define their dietary shifts, and summarize the legacy of anthropogenic activities at “Native Point” since their first arrival.The legacy of the Sadlermiut on the environmentOne of the richest archaeological sites found in the Canadian Arctic, the “Native Point” site was occupied by the Sadlermiut ca. 1250–1325 CE until decimated by disease introduced by European whalers in 19033,4,5. The Sadlermiut village, referred to as the Tunermiut site4, consisted of numerous sod and winter houses that bordered a small shallow freshwater body (c. 20,000 m2), “Bung Stick Pond”. This site (Fig. 1A–C), and others in the well-known archaeological area of Native Point, offer a fascinating glimpse of an isolated society that evolved independently of modern-day Inuit and incorporated cultural elements of the Dorset peoples that vacated the area prior to the Thule migration10.Figure 1Bung Stick Pond and its catchment at Native Point, Southampton Island, Nunavut; (A) Aerial photo of Native Point (Orthoimage GéoBase, Natural Resources Canada), yellow circle—Bung Stick Pond; contains information licensed under the Open Government Licence—Canada; (B) Simplified geological map of Southampton Island17 and location of nine reference lakes and ponds; (Source: Geological Survey of Canada, “A” Series Map 1404A, 1977, 1 sheet, https://doi.org/10.4095/108900; contains information licensed under the Open Government Licence—Canada; georeferenced with Grass GIS 7.8.3; https://grass.osgeo.org/) (C) Photo of Bung Stick Pond facing northward, note scattered bones and antler fragments and partly paleozoic limestone gravel, informed consent for the publication of image has been obtained from Gabriel Bruce.Full size imageThe heavy influence of Sadlermiut families processing food and leaving the remains of butchered carcasses to degrade in the pond is both visible and likely the main contributing factor for the difference in water chemistry that persists until today (Fig. 2). Southampton Island is characterized by a short vegetation period, ultra-oligotrophic freshwater ecosystems, and low sedimentation rates18,19. As such, the lakes and ponds of the area have low nutrient concentrations (i.e. total N and P; see Fig. 2), and the concentration of ions is dependent on soluble bedrock geology in their catchment, basin evolution since the last glaciation, distance to shore, and inputs from wildlife14,18,20,21,22. Here, the water chemistry of our study site, Bung Stick Pond, is an order or magnitude higher in concentrations of nutrients and organic carbon than in other lakes and ponds investigated on Southampton Island during the sampling period (Fig. 2). The only other eutrophic systems known in the region are those affected by waterfowl colonies18. Furthermore, the pond is characterized by an unusual high alkalinity caused by the catchment’s surface geology, which consists of Paleozoic limestone.Figure 2Box and whiskers diagram of water chemistry of nine lakes and ponds sampled on Southampton Island compared to Bung Stick Pond (red circle) (see Fig. 1). Nutrient indicators (top row) and major ion concentrations (bottom row) in mg L−1.Full size imageThe arrival and harvesting practices of the SadlermiutThe sediment history collected from Bung Stick Pond offers the possibility to track the aquatic system’s evolution since the arrival of the Sadlermiut when the site was used by the community for butchering of the collected harvest (Fig. 3). There is little archaeological evidence to suggest that the diet of Sadlermiut contained fish or any plants4,5, and the pond’s littoral zone is littered with skulls/skeletons at the bottom (see Fig. 1C). The predominant role of marine resources in Sadlermiut culture is also mirrored by the stable isotope signal in their adult bone collagen measured from burials23,24,25 (Fig. 4). Similarly, the surplus of organic material from the decaying process of carcasses in or around Bung Stick Pond carried the species specific isotope signal in the sediment. In general, heavier isotopes of nitrogen are enriched in predators relative to its food, which leads to high values in top predators of a food web26,27,28,29,30. Carbon isotope ratios usually show much less trophic enrichment, however a secondary fractionation process causes a positive offset in bone collagen in relation to soft tissue26,27,28,29,30 and apparently sediment samples.Figure 3Nitrogen isotope analysis from paleo-Inuit harvesting sites and distinguishable phases at Bung Stick Pond cores. Inferred August air temperature based on chironomid remains from Southampton Island19. Earlier pronounced stable δ15N isotope record from sediment core tracingprehistoric Inuit whalers on Somerset Island15. Stable δ15N isotope record and TOC:TN-ratio from bulk sediment samples of core NP-3; iron (Fe) record from bulk sediment samples of core NP-2; selected relative abundance of chironomids of core NP-2, with Tanytarsus gracilentus (pale blue) and sum percentage of Paratanytarsus (dark blue); enumerated Daphnia ephippia (resting eggs) and Fabaeformiscandona harmsworthi (Ostracoda) valves of core NP-2 in individuals per cm3 with; adults (dark green), juveniles (pale green); interpreted activity phases I–IV at Native Point; sediment colors of age-corrected core NP-1.Full size imageFigure 4Relationship of δ13C and δ15N in organic material of sediment core NP-3 and bone collagen of the Sadlermiut and their potential diet. Circles indicate isotope excursion in organic material (sediment) in different time intervals; green (Phase 1):  1767 CE; triangles show isotope data from human skeletal remains (bone collagen) in Sadlermiut burials from Coltrain (up)23, (down)24,25; whisker plots indicate modern range of isotope composition in muscle and blubber tissue of mammals supposedly included in the Sadlermiut diet from Hudson Bay or the Canadian Arctic/reports26,27,28,29,30.Full size imageThe stratigraphic analysis of biological and geochemical indicators revealed four distinguishable phases that are attributable to the arrival and cultural practices of the Sadlermiut (Fig. 3). The reference condition of the pristine environment prior to Sadlermiut settlement (Phase 1; Fig. 3) is inferred by the low abundance of aquatic organisms (e.g., chironomids, cladocerans ephippia, ostracods) and δ15N values of around 8‰ at the base of the sediment core. During this time, the carbon:nitrogen ratio (TOC:TN) indicated mostly allochthonous inputs from the terrestrial environment31. An abrupt shift in geochemical indicators (Phase 2) suggests that the arrival of the Sadlermiut occurred between 1250 and 1300 CE. This period leads the earliest radiocarbon dated materials (1325 CE) found at the Sadlermiut heritage site4. Isotope analyses show a substantial increase in δ15N from about + 8 to + 19‰ (Fig. 3) and depletion of δ13C from about − 18 to − 21‰ (Fig.S2). Likewise, a decline in TOC:TN from 13 to 9 in bulk sediments indicates a large difference in the source of materials entering the lake and a sharp increase in aquatic production during this period32. Abnormally high iron concentrations were also observed starting from 1250 CE, potentially from blood washed into the system from butchered marine harvest.The onset of Phase 3 (~ 1400 CE) suggests that settlement of the Sadlermiut camp supplied less external materials to the lake basin and a shift in the harvest of the Sadlermiut from a diet primarily comprised of marine mammals (e.g., seals, whales), which are characterized by the heavier δ15N and depleted δ13C (see Figs. 3 & S2), to one dominated by a more terrestrial origin (i.e., caribou). The shift in isotopic indicators, including the decrease of TOC:TN, during Phase 3 is concurrent with loss of macrophyte habitat as inferred from the chironomid data, notably the reduction of Paratanytarsus from 35 to  2 (Table S5). The sediment concentrations of each of the metals showed major increases from pre-industrial (~ 1850) to modern times consistent with industrial air-borne pollution (Fig. 5). Ag and Zn increased beginning ~ 1750–1800, while Bi, Pb, Sb and Sn showed increases occurring after 1900. The most striking EF was for tin (Sn), which had a rapid rise in concentrations from about 1900 (Fig. 5) and an EF of 72. Other trace elements including As, Cd, Cu, and Se showed modest enrichment (EFs 1.6–1.9) in post-1900 horizons (Table S5). So far, there is only one reference in subarctic Hudson Bay region that significant anthropogenic enrichment of Pb in post-1900 horizons (EFs 2–5×) has occurred38. Enrichment of metals is better known from ice cores from the Devon Ice cap (Devon Island Nunavut, Arctic Canada), which are in good agreement or show higher EFs than observations in the NP2 core. Noteworthy are anthropogenic enrichment of As and Bi39, Sb40, Pb41, Ag and Thallium (Tl)42, which originate from urban and industrial areas and linked to coal combustion and metal smelting. The overall comparison of ice cap ice cores and NP-2 EFs suggests that the inputs of Ag, Bi, Pb, Sb, and Ag are influenced by long-range transport from Eurasian sources40,42. Historical profiles are not available for Sn in Arctic sediment, peat, or ice core archives. Elsewhere, peat cores in the UK record deposition of Sn from regional tin mining and smelting43.Figure 5Metal concentrations of industrial air-borne pollution in sediment core NP-2; concentrations in ppm; interpreted activity phases I–IV at Native Point; sediment colors of age-corrected core NP-1.Full size imageIn concert with recent anthropogenic deposition of contaminants, an eutrophication trend can be inferred from more abundant remains of aquatic microfauna (i.e., chironomids, cladocerans, and ostracods) in the uppermost lake sediments (Fig. 3). Likewise, the sediments are composed of highly organic material (mean 15 wt%), which accumulates toward the core top exceeding 30 wt% (Fig. 3).All these data indicate the extreme vulnerability and low resilience of small Arctic ponds as the effects of human activities at this site are still prevalent after more than 750 years. The sediment archive ipso facto records the influence of the Sadlermiut on the environment since their arrival and until the last of their population succumbed to disease in 1903. Furthermore, the continued contamination by airborne metal pollutants of remote Arctic landscapes since industrialisation is evident. More

The MTS proboscidean tracks and trackmakersRounded-to-elliptical tracks, with an axial length range from 9.6 to 54.5 cm (pes), were found mostly isolated and as manus-pes couples, or associated forming at least eight short trackways (see Table 1). They reveal good preservation in one 6-footprint trackway (see below), two converging trackways and some couples, showing anteriorly directed, wide, short and blunt toe impressions (Figs. 2, 3 and 4). Toe impressions are not commonly visible in elephant footprints9,13, (but see27), which attests to cases of exceptional preservation in Matalascañas tracks. Preservation as true tracks is identified through expulsion marginal rims (e.g., Fig. 4a, g) and possible ejecta (Fig. 3b,e). Large and flat sole surfaces sometimes show evidence of pockmarks23 (Fig. 4f).Table 1 Measurements of Proboscipeda tracks, ordered from smallest to largest in length.Full size tableFigure 2Proboscidean tracks (Proboscipeda panfamilia) attributed in the MTS to straight-tusked elephants. (a–h) Morphological features of small-sized tracks produced by calves and juveniles. Examples of manus impressions in (a) PAT/MTS/011a, (b) PAT/MTS/016 and (f) PAT/MTS/015x, and for further interpretation of (a) see Fig. 3; the latter two with drag marks made during the foot-off event. (c) and (g) PAT/MTS/002a,b: Manus-pes couple found isolated showing heteropody and different number of toe impressions (interpretation as left-side tracks by peak pressure deformation in the left side of the track according to27); interpretation in (c). (d) PAT/MTS/014 and (e) PAT/MTS/007a: Calf-sized pes with three toe impressions. (h) PAT/MTS/011 h: Badly preserved manus of a calf. Scale bar = 5 cm.Full size imageFigure 3Photograph, outline, high-resolution 3D and false-coloured 3D images of the PAT/MTS/0011a track representing the best preserved manus of a juvenile-sized Proboscipeda track. (a) and (c) From the photograph and high-resolution images, five toe impressions in the anterior part of the rounded track are clear (especially toes I–IV). (b) and (f) The false coloured images in orthogonal (b) and oblique angle views (f) highlight the deepening of the track fore- and outwards, thus revealing a peak pressure pattern typical of left forefoot (toes III–IV), as well as a possible ejecta mound in front of the track. The poorly evident and narrow expulsion rim developed around the track is the result of the high cohesiveness and plasticity of the clayey fine-sand substrate. (d) Contour map supporting previous interpretation. (e) The cross-section of the track details the anterior migration of the foot pressure during its rotation, creating a peak pressure in the foot-off event that is represented in the deepest part of the track. Scale bars are 10 cm.Full size imageFigure 4Large-sized Proboscipeda tracks attributed to P. antiquus adults. (a) to (d) PAT/MTS/001: Right manus showing clearly 5 toe impressions and the frontal and lateral displacement rims (morphological interpretation based on the orthogonal (b) and oblique (d) depth and contour (c) maps). (e) and (f) PAT/MTS/010e: Deeper manus with pockmarks; toe pad impressions indicated (I–III). (g) PAT/MTS/004a,b: large manus-pes couple where the hind foot deformed the fore foot during overstepping, and revealing a typical elephantine gait; the toe impressions in both tracks indicate the direction of movement. Scale bar = 10 cm.Full size imageIrrespective of the track size, pes are elliptical to sub-rounded, with the length axis larger than the width and manus are circular or elliptical, with the width axis larger than the length (Figs. 2c and 4d, g for small and large size tracks, respectively). The safest way to differentiate between pes and manus is through the orientation of the track provided by the toe impressions, or by the orientation of the longer axis in trackways. When arranged in trackways, manus-pes couples show the typical elephantine gait, showing a short pace resulting from the fore- and hind feet on the same side swinging forward simultaneously below the body, as it is known from modern elephant gait28. In some cases, the partial impression of a pes overstepping the proximal part of a manus can be seen (Fig. 2c, g). Based on similar preservational style and opposing directions of movement without overlapping at the meeting point, a converging pair of trackways was apparently produced contemporaneously by an adult and a rather small juvenile. Sharp edges of the toe impressions indicate the presence of nails. These are found mostly in well preserved, smaller-sized tracks (Fig. 2a, d, e) because nails are commonly worn down in adult elephants and not always shown in their tracks13. These morphological features allow us to attribute the MTS trackways to the ichnospecies Proboscipeda panfamilia used previously for describing, among other tracksites, those tracks attributed confidently to the straight-tusked elephant Palaeoloxodon antiquus in the paleogeographical context of southern Europe11,14 (see supplementary Table S1).Manus-pes couples, when showing overstepping, were not considered in Table 1 (Fig. 2c, g). Overstepping depends on the speed of walking; at faster speeds the overstepping is only partial or there is no overstepping; elephants maintain the footfall pattern at all speeds, shifting toward a calculated 25% phase offset between limbs as they increase speed28 (Fig. 2g). The smallest tracks usually do not show overstepping possibly because of the greater activity, with longer pace and stride lengths, demonstrated by calves and juveniles when compared to adults. Manus or pes showing a large width-length ratio (below 0.80–0.96 sensu25) were not considered for the estimates since they represent slippage.Younger elephants have more pliable skin and musculature than adults. Also, the greater expansion and distribution of the weight in heavier adult animals is enough to reduce or negate toe impressions in some types of sediments, such as compacted substrates24,29. Interpreting the sedimentological data for the paleosol where MTS was developed15,17,30, suggests a drying clayey-sandy substrate14 that was still plastic enough to absorb the impact of the limbs during the locomotion of the elephants (presence of expulsion rims and absence of radial pressure cracks), and preserving, in many cases, the morphological details of the feet in good condition (Figs. 2a, 3, 4a; see Fig. 2h for a badly preserved example).Ichnological inference about the height, body mass and age of Palaeoloxodon antiquus in the MTSSeveral methods have been proposed for estimating the height at the shoulders for proboscideans, and the relationship between body mass and age with shoulder height 1,31,32. A linear relationship between foot length and shoulder height was confirmed by Lee and Moss33 from extant elephants and compared with fossil examples by Pasenko24. Pes length has been especially used in studies as an indicator of shoulder height21,34,35,36. Among Asian elephants, manus circumference has been shown to have a similar predictive relationship with shoulder height33. These parameters were determined for each isolated track (or representative track in a trackway), including manus and pes (Table 1), using equations previously proposed31,33 (see Methods). A similar approach has been applied to mammoth track studies in North America21,27, where modern ontogenetic and body-mass data has been used to provide age and size estimates from fossil tracks.From the skeletal record, sexual dimorphism of P. antiquus was observed to be more accentuated than in extant elephants, especially in terms of size differences1. During the first 10 years of life, both male and female African bush elephant foot lengths increase rapidly, with the fastest growth shown in the first two years for calves33,37. In P. antiquus, males would have continued to grow until their fifties according to bone data1, while females would have been much smaller as result of energy expenditure with reproduction, flattening the growth curve just after puberty. That is why the equations of Lee and Moss33 that discriminates the shoulder height from tracks for males and females have been applied. However, by comparison with the study of Marano and Palombo32 (based on the progress of eruption and degree of wear of teeth compared to extant elephants), and the body mass correlation of Larramendi et al.1 for calculating the age of P. antiquus, our MTS ages obtained from the application of the regression curve of Lee and Moss33 are underestimated and must be analysed as minimum age approximations for track lengths corresponding to adolescent and adult animals, especially for males. The obtained estimations from tracks are subject to a level of uncertainty related to biotic and abiotic factors that can distort the data (i.e., taphonomy) as it happens also with the calculations taken from skeletal proportions. Therefore, McNeil et al.21 even included data from frozen mammoth carcasses on the growth curve of Lee and Moss33 for correcting size discrepancies along ontogeny. For P. antiquus, our best data for comparison comes, however, from the flesh reconstructions1.Ontogenetic implicationsBased on the best fossil site found for this species in Europe, corresponding to 70 individual Palaeoloxodon antiquus specimens recovered in Geiseltal, Germany, Larramendi et al.1 developed the best reconstruction, so far, of the life appearance of this species and discussed size, body mass, ontogeny and sexual dimorphism. The Neumark-Nord bone site may be contemporary or slightly older than MTS, corresponding to late Middle Pleistocene-to-Eemian interglacial period1. The authors found that the body mass of P. antiquus males was up to three times more that of male Asian elephants and twice that of extant male African bush elephants. The large size determined for straight-tusked elephants (with an estimated  > 400 cm shoulder height in the flesh and body mass of 13 tonnes) and a later complete epiphyseal-diaphyseal fusion of limb bones (not yet totally fused at an estimated age of 47 years), in comparison with extant elephants, suggests that this species had a longer lifespan of 80 years or more1. Sexual dimorphism of P. antiquus was observed to be more accentuated than in extant elephants, with females generally not exceeding 300 cm at the shoulders with an estimated weight of not more than 5.5 tonnes, while males continued to grow until their fifties1. Males in extant elephant species grow more rapidly than females after puberty (i.e., around 7 years in age), which are affected by a trade-off between growth and reproduction. Under normal nutritional conditions, the growth rate is generally higher in males than females leading to a marked difference in size between sexes at already around 10 years in age33,37,38,39.The ontogenetic variation in growth projected for the MTS, when compared to what we known from extant proboscideans, is expressed in the track size distribution plot, with the definition of five age classes (Fig. 5; see also Table 1): calves under 2 years in age (when extant elephants experience fastest growth rates in both sexes), juveniles between 2 and 7 years in age (up to when elephant females reach their sexual maturity and therefore experience a strong reduction of growth rate in comparison to males), 7–15 years in age which include pre-puberty males and young female adults, over 15 years in age and  More

1.Estes, J. A. et al. Trophic downgrading of planet earth. Science 333, 301–306 (2011).ADS 
CAS 
PubMed 
Article 

Google Scholar 
2.DeLong, J. P. et al. The body size dependence of trophic cascades. Am. Nat. 185, https://doi.org/10.1086/679735 (2015).3.Ellner, S. P. et al. Habitat structure and population persistence in an experimental community. Nature 412, 538–543 (2001).ADS 
CAS 
PubMed 
Article 

Google Scholar 
4.Lin Jiang & Peter J., Morin Predator diet Breadth Influences the relative importance of bottom-up and top-down control of prey biomass and diversity. Am. Nat. 165, 350–363 (2005).PubMed 
Article 

Google Scholar 
5.Johnke, J. et al. Multiple micro-predators controlling bacterial communities in the environment. Curr. Opin. Biotechnol. 27, 185–190 (2014).CAS 
PubMed 
Article 

Google Scholar 
6.Suttle, C. A. Marine viruses: major players in the global ecosystem. Nat. Rev. Micro 5, 801–812 (2007).CAS 
Article 

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

Google Scholar 
8.Rotem, O. et al. in The Prokaryotes: Deltaproteobacteria and 740 Epsilonproteobacteria (eds Rosenberg, R. et al.) 3–17 (Springer, 2014).9.Chen, H., Athar, R., Zheng, G. & Williams, H. N. Prey bacteria shape the community structure of their predators. ISME J. https://doi.org/10.1038/ismej.2011.4 (2011).10.Koval, S. F. et al. Bdellovibrio exovorus sp. nov., a novel predator of Caulobacter crescentus. Int. J. Syst. Evol. Microbiol 63, 146–151 (2012).PubMed 
Article 
PubMed Central 

Google Scholar 
11.Jurkevitch, E., Minz, D., Ramati, B. & Barel, G. Prey range characterization, ribotyping, and diversity of soil and rhizosphere Bdellovibrio spp. isolated on phytopathogenic bacteria. Appl. Environ. Microbiol. 66, 2365–2371 (2000).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
12.Kadouri, D. E., To, K., Shanks, R. M. Q. & Doi, Y. Predatory bacteria: a potential ally against multidrug-resistant gram-negative pathogens. PLoS ONE 8, e63397 (2013).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
13.Williams, H. N. et al. Halobacteriovorax, an underestimated predator on bacteria: potential impact relative to viruses on bacterial mortality. ISME J. 10, 491–499 (2016).CAS 
PubMed 
Article 

Google Scholar 
14.Feng, S. et al. Predation by Bdellovibrio bacteriovorus significantly reduces viability and alters the microbial community composition of activated sludge flocs and granules. FEMS Microbiol. Ecol. 93, fix020–fix020 (2017).Article 
CAS 

Google Scholar 
15.Chauhan, A., Cherrier, J. & Williams, H. N. Impact of sideways and bottom-up control factors on bacterial community succession over a tidal cycle. Proc. Natl Acad. Sci. USA 106, 4301–4306 (2009).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
16.Kandel, P. P., Pasternak, Z., van Rijn, J., Nahum, O. & Jurkevitch, E. Abundance, diversity and seasonal dynamics of predatory bacteria in aquaculture zero discharge systems. FEMS Microbiol. Ecol. 89, 149–161 (2014).CAS 
PubMed 
Article 

Google Scholar 
17.Li, N. & Williams, H. 454 Pyrosequencing reveals diversity of Bdellovibrio and like organisms in fresh and salt water. Antonie van Leeuwenhoek 107, 305–311 (2015).PubMed 
Article 

Google Scholar 
18.Daims, H., Taylor, M. W. & Wagner, M. Wastewater treatment: a model system for microbial ecology. Trends Biotechnol. 24, 483–489 (2006).CAS 
PubMed 
Article 

Google Scholar 
19.Dolinšek, J., Lagkouvardos, I., Wanek, W., Wagner, M. & Daims, H. Interactions of nitrifying bacteria and heterotrophs: identification of a Micavibrio-like putative predator of Nitrospira spp. Appl. Environ. Microbiol. 79, 2027–2037 (2013).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
20.Yu, R., Zhang, S., Chen, Z. & Li, C. Isolation and application of predatory Bdellovibrio-and-like organisms for municipal waste sludge biolysis and dewaterability enhancement. Front. Env. Sci. Eng. 11, 10 (2017).Article 
CAS 

Google Scholar 
21.Pineiro, S. et al. Niche partition of Bacteriovorax operational taxonomic units along salinity and temporal gradients in the chesapeake bay reveals distinct estuarine strains. Microb. Ecol. 65, 652–660 (2013).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
22.Cohen, Y. et al. Bacteria and microeukaryotes are differentially segregated in sympatric wastewater microhabitats. Environ. Microbiol. 21, 1757–1770 (2019).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
23.Mahmoud, K. K., McNeely, D., Elwood, C. & Koval, S. F. Design and performance of a 16S rRNA-targeted oligonucleotide probe for detection of members of the genus Bdellovibrio by fluorescence in situ hybridization. Appl. Environ. Microbiol. 73, 7488–7493 (2007).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
24.Albertsen, M., Karst, S. M., Ziegler, A. S., Kirkegaard, R. H. & Nielsen, P. H. Back to basics – the influence of DNA extraction and primer choice on phylogenetic analysis of activated sludge communities. PLOS ONE 10, e0132783 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
25.Welsh, R. M. et al. Bacterial predation in a marine host-associated microbiome. ISME J. 10, 1540–1544 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
26.Chow, C.-E. T., Kim, D. Y., Sachdeva, R., Caron, D. A. & Fuhrman, J. A. Top-down controls on bacterial community structure: microbial network analysis of bacteria, T4-like viruses and protists. ISME J. 8, 816–829 (2014).CAS 
PubMed 
Article 
PubMed Central 

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

Google Scholar 
28.Ju, F. & Zhang, T. Bacterial assembly and temporal dynamics in activated sludge of a full-scale municipal wastewater treatment plant. ISME J. 9, 683–695 (2015).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
29.Tudor, J. J. & Conti, S. F. Characterization of bdellocysts of Bdellovibrio sp. J. Bacteriol. 131, 314–322 (1977).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
30.Eloe-Fadrosh, E. A., Ivanova, N. N., Woyke, T. & Kyrpides, N. C. Metagenomics uncovers gaps in amplicon-based detection of microbial diversity. Nat. Microbiol. 1, 15032 (2016).CAS 
PubMed 
Article 

Google Scholar 
31.Parks, D. H. et al. Recovery of nearly 8,000 metagenome-assembled genomes substantially expands the tree of life. Nat. Microbiol. 2, 1533–1542 (2017).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
32.Williams, H. N. The recovery of high numbers of bdellovibrios from the surface water microlayer. Can. J. Microbiol. 33, 572–575 (1987).Article 

Google Scholar 
33.Liu, L., Yang, J., Yu, Z. & Wilkinson, D. M. The biogeography of abundant and rare bacterioplankton in the lakes and reservoirs of China. ISME J https://doi.org/10.1038/ismej.2015.29 (2015).34.Wilén, B.-M., Jin, B. & Lant, P. The influence of key chemical constituents in activated sludge on surface and flocculating properties. Water Res. 37, 2127–2139 (2003).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
35.Phuong, K., Kakii, K. & Nikata, T. Intergeneric coaggregation of non-flocculating Acinetobacter spp. isolates with other sludge-constituting bacteria. J. Biosci. Bioeng. 107, 394–400 (2009).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
36.Kadouri, D. & O’Toole, G. A. Susceptibility of biofilms to Bdellovibrio bacteriovorus attack. Appl. Environ. Microbiol. 71, 4044–4051 (2005).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
37.Im, H., Dwidar, M. & Mitchell, R. J. Bdellovibrio bacteriovorus HD100, a predator of Gram-negative bacteria, benefits energetically from Staphylococcus aureus biofilms without predation. ISME J. https://doi.org/10.1038/s41396-018-0154-5 (2018).38.Feng, S., Tan, C. H., Cohen, Y. & Rice, S. A. Isolation of Bdellovibrio bacteriovorus from a tropical wastewater treatment plant and predation of mixed species biofilms assembled by the native community members. Environ. Microbiol. 18, 3923–3931 (2016).CAS 
PubMed 
Article 

Google Scholar 
39.Rice, T. D., Williams, H. N. & Turng, B. F. Susceptibility of bacteria in estuarine environments to autochthonous bdellovibrios. Microb. Ecol. 35, 256–264 (1998).CAS 
PubMed 
Article 

Google Scholar 
40.Szabó, E. et al. Comparison of the bacterial community composition in the granular and the suspended phase of sequencing batch reactors. AMB Express 7, 168 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
41.Wilén, B.-M., Jin, B. & Lant, P. Impacts of structural characteristics on activated sludge floc stability. Water Res. 37, 3632–3645 (2003).PubMed 
Article 
CAS 

Google Scholar 
42.Hahn, M. W. & Hofle, M. G. Flagellate predation on a bacterial model community: interplay of size-selective grazing, specific bacterial cell size, and bacterial community composition. Appl. Environ. Microbiol. 65, 4863–4872 (1999).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
43.Kadouri, D., Venzon, N. C. & O’Toole, G. A. Vulnerability of pathogenic biofilms to Micavibrio aeruginosavorus. Appl. Environ. Microbiol. 73, 605–614 (2007).ADS 
CAS 
PubMed 
Article 

Google Scholar 
44.Dashiff, A., Junka, R., Libera, M. & Kadouri, D. Predation of human pathogens by the predatory bacteria Micavibrio aeruginosavorus and Bdellovibrio bacteriovorus. J. Appl. Microbiol. https://doi.org/10.1111/j.1365-2672.2010.04900.x (2011).45.Winder, M. Photosynthetic picoplankton dynamics in Lake Tahoe: temporal and spatial niche partitioning among prokaryotic and eukaryotic cells. J. Plankton Res. 31, 1307–1320 (2009).Article 

Google Scholar 
46.Dini-Andreote, F. et al. Dynamics of bacterial community succession in a salt marsh chronosequence: evidences for temporal niche partitioning. ISME J 8, 1989 (2014).47.Kelley, J., Turng, B., Williams, H. & Baer, M. Effects of temperature, salinity, and substrate on the colonization of surfaces in situ by aquatic bdellovibrios. Appl. Environ. Microbiol. 63, 84–90 (1997).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
48.Thingstad, T. A theoretical approach to structuring mechanisms in the pelagic food web. Hydrobiologia 363, 59–72 (1998).Article 

Google Scholar 
49.Shapiro, O. H., Kushmaro, A. & Brenner, A. Bacteriophage predation regulates microbial abundance and diversity in a full-scale bioreactor treating industrial wastewater. ISME J. 4, 327–336 (2009).PubMed 
Article 

Google Scholar 
50.Dwidar, M., Nam, D. & Mitchell, R. J. Indole negatively impacts predation by Bdellovibrio bacteriovorus and its release from the bdelloplast. Environ. Microbiol. 17, 1009–1022 (2015).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
51.Mun, W. et al. Cyanide production by chromobacterium piscinae shields it from Bdellovibrio bacteriovorus HD100 predation. mBio https://doi.org/10.1128/mBio.01370-17 (2017).52.Sathyamoorthy, R. et al. To hunt or to rest: prey depletion induces a novel starvation survival strategy in bacterial predators. ISME J. 15, 109–123 (2021).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
53.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. Molec. Biol. Rev. 74, 42–57 (2010).CAS 
Article 

Google Scholar 
54.Chanyi, R. M., Ward, C., Pechey, A. & Koval, S. F. To invade or not to invade: two approaches to a prokaryotic predatory life cycle. Can. J. Microbiol. 59, 273–279 (2013).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
55.Lu, F. & Cai, J. The protective effect of Bdellovibrio-and-like organisms (BALO) on tilapia fish fillets against Salmonella enterica ssp. enterica serovar Typhimurium. Lett. Appl. Microbiol. 51, 625–631 (2010).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
56.Peura, S., Bertilsson, S., Jones, R. I. & Eiler, A. Resistant microbial cooccurrence patterns inferred by network topology. Appl. Environ. Microbiol. 81, 2090–2097 (2015).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
57.Meerburg, F. A. et al. High-rate activated sludge communities have a distinctly different structure compared to low-rate sludge communities, and are less sensitive towards environmental and operational variables. Water Res. 100, 137–145 (2016).CAS 
PubMed 
Article 

Google Scholar 
58.de Celis, M. et al. Tuning up microbiome analysis to monitor WWTPs’ biological reactors functioning. Sci. Rep. 10, 1–8 (2020).ADS 
Article 
CAS 

Google Scholar 
59.Hashimoto, T., Diedrich, D. L. & Conti, S. F. Isolation of a bacteriophage for Bdellovibrio bacteriovorus. J. Virol. 5, 87–98 (1970).Article 

Google Scholar 
60.Varon, M. & Levisohn, R. Three-membered parasitic systems: a bacteriophage, Bdellovibrio bacteriovorus, and Escherichia coli. J. Virol. 9, 519–525 (1972).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
61.Johnke, J., Boen–igk, J., Harms, H. & Chatzinotas, A. Killing the killer: predation between protists and predatory bacteria. FEMS Microbiol. Lett. 364, fnx089–fnx089 (2017).Article 
CAS 

Google Scholar 
62.Johnke, J. et al. A generalist protist predator enables coexistence in multitrophic predator–prey systems containing a phage and the bacterial predator Bdellovibrio. Front. Ecol. Evol. https://doi.org/10.3389/fevo.2017.00124 (2017).63.Berleman, J. E., Chumley, T., Cheung, P. & Kirby, J. R. Rippling is a predatory behavior in Myxococcus xanthus. J. Bacteriol. 188, 5888–5895 (2006).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
64.Shimkets, L. J. Social and developmental biology of myxobacteria. Microbiol. Rev. 54, 473–501 (1990).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
65.Friman, V.-P. & Buckling, A. Phages can constrain protist predation-driven attenuation of Pseudomonas aeruginosa virulence in multienemy communities. ISME J. 8, 1820 (2014).66.Matassa, S., Verstraete, W., Pikaar, I. & Boon, N. Autotrophic nitrogen assimilation and carbon capture for microbial protein production by a novel enrichment of hydrogen-oxidizing bacteria. Water Res. 101, 137–146 (2016).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
67.Semblante, G. U. et al. The role of microbial diversity and composition in minimizing sludge production in the oxic-settling-anoxic process. Sci. Tot. Environ. 607–608, 558–567 (2017).Article 
CAS 

Google Scholar 
68.Xia, Y., Kong, Y., Thomsen, T. R. & Halkjær Nielsen, P. Identification and ecophysiological characterization of epiphytic protein-hydrolyzing saprospiraceae (“Candidatus Epiflobacter” spp.) in activated sludge. Appl. Environ. Microbiol. 74, 2229–2238 (2008).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
69.Niu, T. et al. Effects of dissolved oxygen on performance and microbial community structure in a micro-aerobic hydrolysis sludge in situ reduction process. Water Res. 90, 369–377 (2016).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
70.Ju, F., Xia, Y., Guo, F., Wang, Z. & Zhang, T. Taxonomic relatedness shapes bacterial assembly in activated sludge of globally distributed wastewater treatment plants. Environ. Microbiol. 16, 2421–2432 (2014).CAS 
PubMed 
Article 

Google Scholar 
71.Günther, S. et al. Correlation of community dynamics and process parameters as a tool for the prediction of the stability of wastewater treatment. Environ. Sci. Technol. 46, 84–92 (2012).ADS 
PubMed 
Article 
CAS 

Google Scholar 
72.Nettmann, E. et al. Development of a flow-fluorescence in situ hybridization protocol for the analysis of microbial communities in anaerobic fermentation liquor. BMC Microbiol. 13, 278–278 (2013).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
73.Kim, J. M. et al. Analysis of the fine-scale population structure of “Candidatus accumulibacter phosphatis” in enhanced biological phosphorus removal sludge, using fluorescence In Situ hybridization and flow cytometric sorting. Appl. Environl. Microbiol. 76, 3825–3835 (2010).ADS 
CAS 
Article 

Google Scholar 
74.Wallner, G., Erhart, R. & Amann, R. Flow cytometric analysis of activated sludge with rRNA-targeted probes. Appl. Environ. Microbiol. 61, 1859–1866 (1995).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
75.Pasternak, Z. et al. In and out: an analysis of epibiotic vs periplasmic bacterial predators. ISME J. 8, 625–635 (2014).CAS 
PubMed 
Article 

Google Scholar 
76.Spencer, S. J. et al. Massively parallel sequencing of single cells by epicPCR links functional genes with phylogenetic markers. ISME J. 10, 427–436 (2016).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
77.Pernthaler, J. & Amann, R. Fate of heterotrophic microbes in pelagic habitats: focus on populations. Microbiol. Mol. Biol. Rev. 69, 440–461 (2005).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
78.Jurkevitch, E. In The Ecology of Predation at the Microscale (eds Mitchell, R. J.) 37–64 (Springer, 2020).79.Delmont, T. O. et al. Accessing the soil metagenome for studies of microbial diversity. Appl. Environ. Microbiol. 77, 1315–1324 (2011).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
80.Green, S. J., Venkatramanan, R. & Naqib, A. Deconstructing the polymerase chain reaction: understanding and correcting bias associated with primer degeneracies and primer-template mismatches. PloS ONE 10, e0128122 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
81.Schloss, P. D. et al. Introducing Mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl. Environ. Microbiol. 75, 7537–7541 (2009).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
82.Quast, C. et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res 41, D590–D596 (2012).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
83.Huse, S. M., Welch, D. M., Morrison, H. G. & Sogin, M. L. Ironing out the wrinkles in the rare biosphere through improved OTU clustering. Environ. Microbiol. 12, 1889–1898 (2010).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
84.Edgar, R. C., Haas, B. J., Clemente, J. C., Quince, C. & Knight, R. UCHIME improves sensitivity and speed of chimera detection. Bioinformatics 27, 2194–2200 (2011).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
85.Yarza, P. et al. Uniting the classification of cultured and uncultured bacteria and archaea using 16S rRNA gene sequences. Nat. Rev. Microbiol. 12, 635 (2014).CAS 
PubMed 
Article 

Google Scholar 
86.McMurdie, P. J. & Holmes, S. Waste not, want not: why rarefying microbiome data is inadmissible. PLOS Comput. Biol. 10, e1003531 (2014).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
87.Mather, P. Computational Methods of Multivariate Analysis in Physical Geography (J Wiley and Sons, 1976).88.Berry, K. J. & Mielke, P. W. Computation of exact probability values for multi-response permutation procedures (MRPP). Commun. Stat. – Simul. Comput. 13, 417–432 (1984).MathSciNet 
Article 

Google Scholar 
89.Edgar, R. C. MUSCLE: a multiple sequence alignment method with reduced time and space complexity. BMC Bioinf. 5, 113 (2004).Article 
CAS 

Google Scholar 
90.Tamura, K., Stecher, G., Peterson, D., Filipski, A. & Kumar, S. MEGA6: molecular evolutionary genetics analysis version 6.0. Mol Biol Evol https://doi.org/10.1093/molbev/mst197 (2013).91.Kendall, M. G. Rank Correlation Methods 2nd edn, (Hafner, 1955).92.Bastian, M., Heymann, S. & Jacomy, M. Gephi: an open source software for exploring and manipulating networks. Icwsm 8, 361–362 (2009).
Google Scholar 
93.Sathyamoorthy, R. et al. Bacterial predation under changing viscosities. Environ. Microbiol. 21, 2997–3010 (2019).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
94.Jurkevitch, E. In Current Protocols in Microbiology (ed Coico, R. et al.) (John Wiley and Sons, 2012).95.Whelan, J. A., Russell, N. B. & Whelan, M. A. A method for the absolute quantification of cDNA using real-time PCR. J. Immunol. Methods 278, 261–269 (2003).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
96.Nakatsuji, T. et al. The microbiome extends to subepidermal compartments of normal skin. Nat. Commun. https://www.nature.com/articles/ncomms2441 (2013).97.Van Essche, M. et al. Development and performance of a quantitative PCR for the enumeration of Bdellovibrionaceae. Environ. Microbiol. Rep. 1, 228–233 (2009).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
98.Zheng, G., Wang, C., Williams, H. N. & Pineiro, S. A. Development and evaluation of a quantitative real-time PCR assay for the detection of saltwater. Bacteriovorax. Environ. Microbiol. 10, 2515–2526 (2008).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
99.Liu, Z. et al. Neutral mechanisms and niche differentiation in steady-state insular microbial communities revealed by single cell analysis. Environ. Microbiol. 21, 164–181 (2019).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
100.Amann, R. I. et al. Combination of 16S rRNA-targeted oligonucleotide probes with flow cytometry for analyzing mixed microbial populations. Appl. Environ. Microbiol. 56, 1919–1925 (1990).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
101.Cichocki, N. et al. Bacterial mock communities as standards for reproducible cytometric microbiome analysis. Nat. Protoc. 15, 2788–2812 (2020).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar  More

1.Perry, A. L., Low, P. J., Ellis, J. R. & Reynolds, J. D. Climate change and distribution shifts in marine fishes. Science 308, 1912–1915 (2005).ADS 
CAS 
PubMed 
Article 

Google Scholar 
2.Nye, J. A., Link, J. S., Hare, J. A. & Overholtz, W. J. Changing spatial distribution of fish stocks in relation to climate and population size on the Northeast United States continental shelf. Mar. Ecol. Prog. Ser. 393, 111–129 (2009).ADS 
Article 

Google Scholar 
3.Fodrie, F. J., Heck, K. L. Jr., Powers, S. P., Graham, W. M. & Robinson, K. L. Climate-related, decadal-scale assemblage changes of seagrass-associated fishes in the northern Gulf of Mexico. Glob. Change Biol. 16, 48–59 (2010).ADS 
Article 

Google Scholar 
4.Pinsky, M. L., Worm, B., Fogarty, M. J., Sarmiento, J. L. & Levin, S. A. Marine taxa track local climate velocities. Science 341, 1239–1242 (2013).ADS 
CAS 
PubMed 
Article 

Google Scholar 
5.Free, C. M. et al. Impacts of historical warming on marine fisheries production. Science 363, 979–983 (2019).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
6.Simpson, S. D. et al. Continental shelf-wide response of a fish assemblage to rapid warming of the sea. Curr. Biol. 21, 1565–1570 (2011).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
7.Pecl, G. T. et al. Biodiversity redistribution under climate change: Impacts on ecosystems and human well-being. Science 355 (2017).8.Poloczanska, E. S. et al. Responses of marine organisms to climate change across oceans. Front. Mar. Sci. 3, 62 (2016).Article 

Google Scholar 
9.Oswald, S. A. & Arnold, J. M. Direct impacts of climatic warming on heat stress in endothermic species: seabirds as bioindicators of changing thermoregulatory constraints. Integr. Zool. 7, 121–136 (2012).PubMed 
Article 
PubMed Central 

Google Scholar 
10.Boyles, J. G., Seebacher, F., Smit, B. & McKechnie, A. E. Adaptive thermoregulation in endotherms may alter responses to climate change. Integr. Comp. Biol. 51, 676–690 (2011).PubMed 
Article 
PubMed Central 

Google Scholar 
11.Khaliq, I., Hof, C., Prinzinger, R., Böhning-Gaese, K. & Pfenninger, M. Global variation in thermal tolerances and vulnerability of endotherms to climate change. Proc. R. Soc. B Biol. Sci. 281, 20141097 (2014).Article 

Google Scholar 
12.Gibson-Reinemer, D. K., Sheldon, K. S. & Rahel, F. J. Climate change creates rapid species turnover in montane communities. Ecol. Evol. 5, 2340–2347 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
13.Pörtner, H.-O. et al. Climate induced temperature effects on growth performance, fecundity and recruitment in marine fish: developing a hypothesis for cause and effect relationships in Atlantic cod (Gadus morhua) and common eelpout (Zoarces viviparus). Cont. Shelf Res. 21, 1975–1997 (2001).ADS 
Article 

Google Scholar 
14.Neuheimer, A., Thresher, R., Lyle, J. & Semmens, J. Tolerance limit for fish growth exceeded by warming waters. Nat. Clim. Chang. 1, 110–113 (2011).ADS 
Article 

Google Scholar 
15.Pörtner, H.-O. Climate variations and the physiological basis of temperature dependent biogeography: systemic to molecular hierarchy of thermal tolerance in animals. Comp. Biochem. Physiol. A Mol. Integr. Physiol. 132, 739–761 (2002).PubMed 
Article 
PubMed Central 

Google Scholar 
16.Buckley, L. B., Hurlbert, A. H. & Jetz, W. Broad-scale ecological implications of ectothermy and endothermy in changing environments. Glob. Ecol. Biogeogr. 21, 873–885 (2012).Article 

Google Scholar 
17.Loarie, S. R. et al. The velocity of climate change. Nature 462, 1052–1055 (2009).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
18.Burrows, M. T. et al. The pace of shifting climate in marine and terrestrial ecosystems. Science 334, 652–655 (2011).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
19.Przeslawski, R., Falkner, I., Ashcroft, M. B. & Hutchings, P. Using rigorous selection criteria to investigate marine range shifts. Estuar. Coast. Shelf Sci. 113, 205–212 (2012).ADS 
Article 

Google Scholar 
20.Sunday, J. M. et al. Species traits and climate velocity explain geographic range shifts in an ocean-warming hotspot. Ecol. Lett. 18, 944–953 (2015).PubMed 
Article 
PubMed Central 

Google Scholar 
21.MacLeod, C. D. Global climate change, range changes and potential implications for the conservation of marine cetaceans: a review and synthesis. Endanger. Species Res. 7, 125–136 (2009).Article 

Google Scholar 
22.Sydeman, W., Poloczanska, E., Reed, T. & Thompson, S. Climate change and marine vertebrates. Science 350, 772–777 (2015).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
23.Bowen, W. Role of marine mammals in aquatic ecosystems. Mar. Ecol. Prog. Ser. 158, 74 (1997).Article 

Google Scholar 
24.Williams, T. M., Estes, J. A., Doak, D. F. & Springer, A. M. Killer appetites: assessing the role of predators in ecological communities. Ecology 85, 3373–3384 (2004).Article 

Google Scholar 
25.Roman, J. et al. Whales as marine ecosystem engineers. Front. Ecol. Environ. 12, 377–385 (2014).Article 

Google Scholar 
26.Neutel, A.-M., Heesterbeek, J. A. & de Ruiter, P. C. Stability in real food webs: weak links in long loops. Science 296, 1120–1123 (2002).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
27.Bascompte, J., Melián, C. J. & Sala, E. Interaction strength combinations and the overfishing of a marine food web. Proc. Natl. Acad. Sci. U.S.A. 102, 5443–5447 (2005).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
28.Macnab, B. K. The Physiological Ecology of Vertebrates: A View from Energetics (Cornell University Press, 2002).
Google Scholar 
29.Robinson, R. A. et al. Climate change and migratory species (2005).30.Worthy, G. A. & Edwards, E. F. Morphometric and biochemical factors affecting heat loss in a small temperate cetacean (Phocoena phocoena) and a small tropical cetacean (Stenella attenuata). Physiol. Zool., 432–442 (1990).31.Koopman, H. N. Phylogenetic, ecological, and ontogenetic factors influencing the biochemical structure of the blubber of odontocetes. Mar. Biol. 151, 277–291 (2007).Article 

Google Scholar 
32.Adamczak, S. K., Pabst, D. A., McLellan, W. A. & Thorne, L. H. Do bigger bodies require bigger radiators? Insights into thermal ecology from closely related marine mammal species and implications for ecogeographic rules. J. Biogeogr. 47, 1193–1206 (2020).Article 

Google Scholar 
33.Silber, G. K. et al. Projecting marine mammal distribution in a changing climate. Front. Mar. Sci. 4, 413 (2017).Article 

Google Scholar 
34.Kaschner, K., Tittensor, D. P., Ready, J., Gerrodette, T. & Worm, B. Current and future patterns of global marine mammal biodiversity. PLoS ONE 6, e19653 (2011).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
35.Salvadeo, C. J., Lluch-Belda, D., Gómez-Gallardo, A., Urbán-Ramírez, J. & MacLeod, C. D. Climate change and a poleward shift in the distribution of the Pacific white-sided dolphin in the northeastern Pacific. Endanger. Species Res. 11, 13–19 (2010).Article 

Google Scholar 
36.Kovacs, K. M., Lydersen, C., Overland, J. E. & Moore, S. E. Impacts of changing sea-ice conditions on Arctic marine mammals. Mar. Biodivers. 41, 181–194 (2011).Article 

Google Scholar 
37.MacLeod, C. D. et al. Climate change and the cetacean community of north-west Scotland. Biol. Cons. 124, 477–483 (2005).Article 

Google Scholar 
38.Higdon, J. W. & Ferguson, S. H. Loss of Arctic sea ice causing punctuated change in sightings of killer whales (Orcinus orca) over the past century. Ecol. Appl. 19, 1365–1375 (2009).PubMed 
Article 

Google Scholar 
39.Evans, P. G. & Hammond, P. S. Monitoring cetaceans in European waters. Mammal Rev. 34, 131–156 (2004).Article 

Google Scholar 
40.Kaschner, K., Quick, N. J., Jewell, R., Williams, R. & Harris, C. M. Global coverage of cetacean line-transect surveys: status quo, data gaps and future challenges. PLoS ONE 7, e44075 (2012).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
41.Taylor, B. L., Martinez, M., Gerrodette, T., Barlow, J. & Hrovat, Y. N. Lessons from monitoring trends in abundance of marine mammals. Mar. Mamm. Sci. 23, 157–175 (2007).Article 

Google Scholar 
42.Pyenson, N. D. The high fidelity of the cetacean stranding record: insights into measuring diversity by integrating taphonomy and macroecology. Proc. R. Soc. B Biol. Sci. 278, 3608–3616 (2011).Article 

Google Scholar 
43.Leeney, R. H. et al. Spatio-temporal analysis of cetacean strandings and bycatch in a UK Wsheries hotspot. Biodivers. Conserv. 17, 2323–2338 (2008).Article 

Google Scholar 
44.Lambert, E. et al. Quantifying likely cetacean range shifts in response to global climatic change: implications for conservation strategies in a changing world. Endanger. Species Res. 15, 205–222 (2011).Article 

Google Scholar 
45.Poloczanska, E. S. et al. Global imprint of climate change on marine life. Nat. Clim. Chang. 3, 919–925 (2013).ADS 
Article 

Google Scholar 
46.Pershing, A. J. et al. Slow adaptation in the face of rapid warming leads to collapse of the Gulf of Maine cod fishery. Science 350, 809–812 (2015).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
47.Nawojchik, R., St. Aubin, D. J. & Johnson, A. Movements and dive behavior of two stranded, rehabilitated long-finned pilot whales (Globicephala melas) in the northwest Atlantic. Mar. Mammal Sci. 19, 232–239 (2003).Article 

Google Scholar 
48.Bloch, D. et al. Short-term movements of long-finned pilot whales Globicephala melas around the Faroe Islands. Wildl. Biol. 9, 47–8 (2003).Article 

Google Scholar 
49.Hayes, S., Josephson, E., Maze‐Foley, K. & Rosel, P. US Atlantic and Gulf of Mexico marine mammal stock assessments–2019. NOAA Tech Memo NMFS‐NE 264 (2020).50.Gannon, D., Read, A., Craddock, J., Fristrup, K. & Nicolas, J. Feeding ecology of long-finned pilot whales Globicephala melas in the western North Atlantic. Mar. Ecol. Prog. Ser. Oldendorf 148, 1–10 (1997).ADS 
Article 

Google Scholar 
51.Harden Jones, F. R. In Animal migration. Soc. Exp. Biol. Sem. Ser. 13 (ed. Aidley, D. J.) 139–165 (Cambridge Univ. Press, 1981).
Google Scholar 
52.Alerstam, T., Hedenström, A. & Åkesson, S. Long-distance migration: evolution and determinants. Oikos 103, 247–260 (2003).Article 

Google Scholar 
53.Heide-Jørgensen, M. P. et al. Diving behaviour of long-finned pilot whales Globicephala melas around the Faroe Islands. Wildl. Biol. 8, 307–313 (2002).Article 

Google Scholar 
54.Baird, R. W., Borsani, J. F., Hanson, M. B. & Tyack, P. L. Diving and night-time behavior of long-finned pilot whales in the Ligurian Sea. Mar. Ecol. Prog. Ser. 237, 301–305 (2002).ADS 
Article 

Google Scholar 
55.Adamczak, S. K., McLellan, W. A., Read, A. J., Wolfe, C. L. & Thorne, L. H. The impact of temperature at depth on estimates of thermal habitat for short‐finned pilot whales. Mar. Mammal Sci. (2020).56.Jorda, G. et al. Ocean warming compresses the three-dimensional habitat of marine life. Nat. Ecol. Evolut. 4, 109–114 (2020).Article 

Google Scholar 
57.Burrows, M. T. et al. Ocean community warming responses explained by thermal affinities and temperature gradients. Nat. Clim. Chang. 9, 959–963 (2019).ADS 
Article 

Google Scholar 
58.Kleisner, K. M. et al. The effects of sub-regional climate velocity on the distribution and spatial extent of marine species assemblages. PLoS ONE 11, e0149220 (2016).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
59.Kleisner, K. M. et al. Marine species distribution shifts on the US Northeast Continental Shelf under continued ocean warming. Prog. Oceanogr. 153, 24–36 (2017).ADS 
Article 

Google Scholar 
60.Kavanaugh, M. T., Rheuban, J. E., Luis, K. M. & Doney, S. C. Thirty-three years of ocean benthic warming along the US northeast continental shelf and slope: Patterns, drivers, and ecological consequences. J. Geophys. Res. Oceans 122, 9399–9414 (2017).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
61.Grady, J. M. et al. Metabolic asymmetry and the global diversity of marine predators. Science 363 (2019).62.Williams, T. M. et al. The diving physiology of bottlenose dolphins (Tursiops truncatus). III. Thermoregulation at depth. J. Exp. Biol. 202, 2763–2769 (1999).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
63.Pabst, D. A., Rommel, S. A. & McLELLAN, W. A. The emergence of whales 379–397 (Springer, 1998).Book 

Google Scholar 
64.McNab, B. K. Short-term energy conservation in endotherms in relation to body mass, habits, and environment. J. Therm. Biol 27, 459–466 (2002).Article 

Google Scholar 
65.Yeates, L. C. & Houser, D. S. Thermal tolerance in bottlenose dolphins (Tursiops truncatus). J. Exp. Biol. 211, 3249–3257 (2008).PubMed 
Article 
PubMed Central 

Google Scholar 
66.Saba, V. S. et al. Enhanced warming of the Northwest Atlantic Ocean under climate change. J. Geophys. Res. Oceans (2016).67.Kenney, R. D., Scott, G. P., Thompson, T. J. & Winn, H. E. Estimates of prey consumption and trophic impacts of cetaceans in the USA northeast continental shelf ecosystem. J. Northwest Atl. Fish. Sci. 22, 155–171 (1997).Article 

Google Scholar 
68.Read, A. J. & Brownstein, C. R. Considering other consumers: fisheries, predators, and Atlantic herring in the Gulf of Maine. Conserv. Ecol. 7, 2 (2003).
Google Scholar 
69.Overholtz, W. & Link, J. Consumption impacts by marine mammals, fish, and seabirds on the Gulf of Maine-Georges Bank Atlantic herring (Clupea harengus) complex during the years 1977–2002. ICES J. Mar. Sci. J. Conseil 64, 83–96 (2007).Article 

Google Scholar 
70.Smith, L. A., Link, J. S., Cadrin, S. X. & Palka, D. L. Consumption by marine mammals on the Northeast US continental shelf. Ecol. Appl. 25, 373–389 (2015).PubMed 
Article 

Google Scholar 
71.Estes, J. A., Tinker, M. T., Williams, T. M. & Doak, D. F. Killer whale predation on sea otters linking oceanic and nearshore ecosystems. Science 282, 473–476 (1998).ADS 
CAS 
PubMed 
Article 

Google Scholar 
72.Pace, M. L., Cole, J. J., Carpenter, S. R. & Kitchell, J. F. Trophic cascades revealed in diverse ecosystems. Trends Ecol. Evol. 14, 483–488 (1999).CAS 
PubMed 
Article 

Google Scholar 
73.Myers, R. A., Baum, J. K., Shepherd, T. D., Powers, S. P. & Peterson, C. H. Cascading effects of the loss of apex predatory sharks from a coastal ocean. Science 315, 1846–1850 (2007).ADS 
CAS 
PubMed 
Article 

Google Scholar 
74.Durant, J. M., Hjermann, D. Ø., Ottersen, G. & Stenseth, N. C. Climate and the match or mismatch between predator requirements and resource availability. Clim. Res. (CR) 33, 271–283 (2007).ADS 
Article 

Google Scholar 
75.Schweiger, O., Settele, J., Kudrna, O., Klotz, S. & Kühn, I. Climate change can cause spatial mismatch of trophically interacting species. Ecology 89, 3472–3479 (2008).PubMed 
Article 

Google Scholar 
76.Both, C., Van Asch, M., Bijlsma, R. G., Van Den Burg, A. B. & Visser, M. E. Climate change and unequal phenological changes across four trophic levels: constraints or adaptations?. J. Anim. Ecol. 78, 73–83 (2009).PubMed 
Article 

Google Scholar 
77.Evans, K. et al. Periodic variability in cetacean strandings: links to large-scale climate events. Biol. Let. 1, 147–150 (2005).CAS 
Article 

Google Scholar 
78.Overholtz, W., Hare, J. & Keith, C. Impacts of interannual environmental forcing and climate change on the distribution of Atlantic mackerel on the US Northeast continental shelf. Mar. Coastal Fish. 3, 219–232 (2011).Article 

Google Scholar 
79.Roper, C., Lu, C. & Vecchione, M. A revision of the systematics and distribution of Illex species (Cephalopoda: Ommastrephidae). Smithsonian Contrib. Zool., 405–424 (1998).80.Brodziak, J. & Hendrickson, L. An analysis of environmental effects on survey catches of squids Loligo pealei and Illex illecebrosus in the northwest Atlantic. Fish. Bull. 97, 9–24 (1999).
Google Scholar 
81.Henderson, M. E., Mills, K. E., Thomas, A. C., Pershing, A. J. & Nye, J. A. Effects of spring onset and summer duration on fish species distribution and biomass along the Northeast United States continental shelf. Rev. Fish Biol. Fisheries 27, 411–424 (2017).Article 

Google Scholar 
82.Sosebee, K. A. & Cadrin, S. X. A historical perspective on the abundance and biomass of northeast demersal complex stocks from NMFS and Massachusetts inshore bottom trawl surveys, 1963–2002. (2006).83.Brito-Morales, I. et al. Climate velocity can inform conservation in a warming world. Trends Ecol. Evol. 33, 441–457 (2018).PubMed 
Article 
PubMed Central 

Google Scholar 
84.Burrows, M. T. et al. Geographical limits to species-range shifts are suggested by climate velocity. Nature 507, 492–495 (2014).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
85.García Molinos, J., Schoeman, D. S., Brown, C. J. & Burrows, M. T. VoCC: an r package for calculating the velocity of climate change and related climatic metrics. Methods Ecol. Evolut. 10, 2195–2202 (2019).Article 

Google Scholar  More

1.Huai, J. J. Dynamics of resilience of wheat to drought in Australia from 1991–2010. Sci. Rep. 7, 9532 (2017).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
2.Li, M., Peterson, C. A., Tautges, N. E., Scow, K. M. & Gaudin, A. C. M. Yields and resilience outcomes of organic cover crop, and conventional practices in a Mediterranean climate. Sci. Rep. 9, 12283 (2019).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
3.Keersmaecker, W. D. et al. A model quantifying global vegetation resistance and resilience to short-term climate anomalies and their relationship with vegetation cover. Glob. Ecol. Biogeogr. 24, 539–548 (2015).Article 

Google Scholar 
4.Griffith, G. P. et al. Ecological resilience of Arctic marine food webs to climate change. Nat. Clim. Change 9, 868–872 (2019).ADS 
Article 

Google Scholar 
5.You, N. S., Meng, J. J. & Zhu, L. K. Sensitivity and resilience of ecosystems to climate variability in the semi-arid to hyper-arid areas of Northern China: a case study in the Heihe River Basin. Ecol. Res. 33, 161–174 (2018).Article 

Google Scholar 
6.Reijers, V. C. et al. Resilience of beach grasses along a biogeomorphic successive gradient: resource availability vs. clonal integration. Oceologia https://doi.org/10.1007/s00442-019-04568-w (2019).Article 

Google Scholar 
7.Chambers, J. C. et al. Resilience to stress and disturbance, and resistance to Bromus tectorum L. invasion in clod desert shrublands of western North America. Ecosystems 17, 360–375 (2014).CAS 
Article 

Google Scholar 
8.Driessen, M. M. Fire resilience of a rare, freshwater crustacean in a fire-prone ecosystem and the implications for fire management. Austral Ecol. 44, 1030–1039 (2019).Article 

Google Scholar 
9.Ren, H., Lu, H. F., Li, Y. D. & Wen, Y. G. Vegetation restoration and its research advancement in Southern China. J. Trop. Subtrop. Bot. 27(5), 469–480 (2019).
Google Scholar 
10.Yan, H. M., Zhan, J. Y. & Zhang, T. Review of ecosystem resilience research progress. Prog. Geogr. 31(3), 303–314 (2012).
Google Scholar 
11.Zhan, J. Y., Yan, H. M., Deng, X. Z. & Zhang, T. Assessment of forest ecosystem resilience in Lianhua County of Jiangxi Province. J. Nat. Resour. 27(8), 1304–1315 (2012).
Google Scholar 
12.Pérez-Girón, J. C., Álvarez-Álvarez, P., Díaz-Valera, E. R. & Lopes, D. M. M. Influence of climate variations on primary production indicators and on the resilience of forest ecosystems in a future scenario of climate change: application to sweet chestnut agroforestry systems in the Iberian Peninsula. Ecol. Indic. 113, 106199 (2020).Article 

Google Scholar 
13.Meng, Y. Y. et al. Analysis of ecological resilience to evaluate the inherent maintenance capacity of a forest ecosystem using a dense Landsat time series. Ecol. Inform. 57, 101064 (2020).Article 

Google Scholar 
14.Han, L. et al. Species composition, community structure, and floristic characteristics of desert riparian forest community along the mainstream of Tarim River. Plant Sci. J. 37(3), 324–336 (2019).
Google Scholar 
15.Zhou, H. H. et al. Climate change may accelerate the decline of desert riparian forest in the lower Tarim River, Northwestern China: evidence from tree-rings of Populus euphratica. Ecol. Indic. 111, 105997 (2020).Article 

Google Scholar 
16.Aini, A. et al. Analysis of stakeholders’ cognition on desert riparian forest ecosystem services in the lower reaches of Tarim River, China. Res. Soil Water Conserv. 23(1), 205–209 (2016).
Google Scholar 
17.Li, Y. Q., Chen, Y. N., Zhang, Y. Q. & Xia, Y. Rehabilitating China’s largest inland river. Conserv. Biol. 23(3), 531–536 (2009).PubMed 
Article 

Google Scholar 
18.Dai, J. S. Evaluation of eco-environment and socio-economic benefits on comprehensive reclamation projects on the Tarim River Basin. Doctoral Dissertation of Xinjiang Agricultural University (2015).19.Han, L., Wang, H. Z., Niu, J. L., Wang, J. Q. & Liu, W. Y. Response of Populus euphratica communities in a desert riparian forest to the groundwater level gradient in the Tarim River Basin. Acta Ecol. Sin. 37, 6836–6846 (2017).
Google Scholar 
20.Yang, G. & Guo, Y. P. The change and prospect of vegetation in the end of the lower reaches of Tarim River after ecological water delivery. J. Desert Res. 24(2), 167–172 (2004).
Google Scholar 
21.Yan, H. M., Zhan, J. Y. & Zhang, T. Resilience of forest ecosystems and its influencing factors. Procedia Environ. Sci. 10, 2201–2206 (2011).Article 

Google Scholar 
22.Abenayake, C. C., Mikami, Y., Matsuda, Y. & Jayasinghe, A. Ecosystem service-based composite indicator for assessing community resilience to floods. Environ. Dev. 27, 34–46 (2018).Article 

Google Scholar 
23.Maestas, J. D., Campbell, S. B., Chambers, J. C., Pellant, M. & Miller, R. F. Tapping soil survey information for rapid assessment of sagebrush ecosystem resilience and resistance. Rangelands 38(3), 120–128 (2016).Article 

Google Scholar 
24.Ponce-Campos, G. E. et al. Ecosystem resilience despite large-scale altered hydroclimatic conditions. Nature 494, 349–352 (2013).ADS 
CAS 
PubMed 
Article 

Google Scholar 
25.Frazier, A. E., Renschler, C. S. & Miles, S. B. Evaluating post-disaster ecosystem resilience using MODIS GPP data. Int. J. Appl. Earth Obs. Geoinform. 21, 43–52 (2013).ADS 
Article 

Google Scholar 
26.Kahiluoto, H. et al. Decline in climate resilience of European wheat. PNAS 116(1), 123–128 (2019).CAS 
PubMed 
Article 

Google Scholar 
27.Li, X. Y. et al. Temporal trade-off between gymnosperm resistance and resilience increases forest sensitivity to extreme drought. Nat. Ecol. Evolut. 4, 1075–1083 (2020).Article 

Google Scholar 
28.Li, C. H., Zhou, M., Wang, Y. T., Zhu, T. B., Sun, H., Yin, H. H., Cao, H. J., Han, H. Y. Inter-annual variations of vegetation net primary productivity and their spatial-temporal contribution and climate driving in arid Northwest China: a case study of Hexi Corridor. Chin. J. Ecol. (2020).29.Song, J. et al. A global database of plant production and carbon exchange from global change manipulative experiments. Sci. Data 7, 1–7 (2020).Article 
CAS 

Google Scholar 
30.Yang, G. et al. Research progress of ecosystem resilience assessment. Zhejiang Agric. Sci. 60(3), 508–513 (2019).
Google Scholar 
31.Liu, J. Z. & Chen, Y. N. Analysis on converse succession of plant communities at the lower reaches of Tarim River. Arid Land Geogr. 25(3), 231–236 (2002).
Google Scholar 
32.Chen, X., Bao, A. M., Wang, X. P., Guli, J. P. E. & Huang, Y. Recent ecological effectiveness assessment of integrated management projects in the Tarim River. Bull. Chin. Acad. Sci. 32(1), 20–28 (2017).
Google Scholar 
33.Zhao, H., Yan, L. & Ji, F. The dynamics of land utilization in the upper reaches of Tarim River. J. Arid Land Resour. Environ. 15(4), 40–43 (2001).
Google Scholar 
34.Sun, F., Wang, Y. & Chen, Y. N. Dynamics of desert-oasis ecotone and its influencing factors in the Tarim Basin. Chin. J. Ecol. 39(10), 1–11 (2020).
Google Scholar 
35.Xu, G. H. A genetic explanation of the recent changes of ecological environment in the Tarim River Basin, southern Xinjiang. Xinjiang Meteorol. 28–31 (2005).36.Kamkin, A. & Lozinsky, I. Mechanically Gated Channels and Their Regulation (Springer, 2012).Book 

Google Scholar 
37.Feyisa, K. et al. Effects of enclosure management on carbon sequestration, soil properties and vegetation attributes in East African rangelands. CATENA 159, 9–19 (2017).Article 

Google Scholar 
38.Wang, G. H., Ren, Y. J. & Gou, Q. Q. The changes of soil physical and chemical property during the enclosure process in a typical desert oasis ecotone of the Hexi Corridor in northwestern China. J. Desert Res. 40(2), 222–231 (2020).
Google Scholar 
39.Xu, H. L., Ye, M. & Li, J. M. Changes in groundwater levels and the response of natural vegetation to the transfer of water to the lower reaches of the Tarim River. J. Environ. Sci. 19(10), 1199–1207 (2007).Article 

Google Scholar 
40.Zhang, P. F., Guli, J., Bao, A. M., Meng, F. H. & Guo, H. Ecological effects evaluation for short term planning of the Tarim River. Arid Land Geogr. 40(1), 156–164 (2017).
Google Scholar 
41.Gulimire, H., Wang, G. Y., Zhang, Y., Liu, Q. Q. & Su, L. T. Influence mechanisms of intermittent ecological water conveyance on groundwater level and vegetation in arid land. Arid Land Geogr. 41(4), 726–733 (2018).
Google Scholar 
42.Guo, H. W., Xu, H. L. & Ling, H. B. Study of ecological water transfer mode and ecological compensation scheme of the Tarim River Basin in dry years. J. Nat. Resour. 32(10), 1705–1717 (2017).
Google Scholar 
43.Wu, T. Z., Ding, J., Guan, W. K., Ruan, C. J. & Guan, Y. Populus euphratica forest replacement and photosynthetic characteristics in Tarim Populus euphratica national nature reserve. Prot. For. Sci. Technol. 8, 1–4 (2020).
Google Scholar 
44.Zhu, C. G., Aikeremu, A., Li, W. H. & Zhou, H. H. Ecosystem restoration of Populus euphratica forest under the ecological water conveyance in the lower reaches of Tarim River. Arid Land Geography, 44(3), 629–636 (2021).
Google Scholar 
45.Chen, Y. N. Study on Eco-hydrological Problems of the Tarim River Basin in Xinjiang (Science Press, 2010).
Google Scholar 
46.Halik, U., Aishan, T., Betz, F., Kurban, A. & Rouzi, A. Effectiveness and challenges of ecological engineering for desert riparian forest restoration along China’s largest inland river. Ecol. Eng. 127, 11–22 (2019).Article 

Google Scholar 
47.Xinjiang Morning News. In the past three years, the area of the Populus euphratica forest reserve in the Tarim River Basin has increased by 569.95 km2. https://www.sohu.com/a/308626663_100034331?sec=wd (2019).48.China News Service. Ecological water transfer for desert vegetation in lower reaches of Konqi River in Xinjiang. https://news.sina.com.cn/o/2020-02-22/doc-iimxyqvz4945915.shtml (2020). More