Ecology

Bed bugsFour bed bug populations (one laboratory strain and three collected from infested homes) were used in this study (Table 1). All populations were reared in the laboratory as described by DeVries et al.28. Briefly, bed bugs were maintained in 168 cm3 plastic containers on paper substrate at 25 °C, 50% relative humidity, and a photoperiod of 12 h:12 h (Light:Dark). Bed bugs were fed defibrinated rabbit blood (Hemostat Laboratories, Dixon, CA, USA) weekly using an artificial feeding system. This system maintained blood at 35 °C by circulating water through custom-made water-jacketed glass feeders. An artificial membrane (plant budding tape, A.M. Leonared, Piqua, OH, USA) was stretched over the bottom of each glass feeder, containing the blood while simultaneously allowing bed bugs to feed through it. In all experiments, adult males starved for 7–10 days were used. All populations were used for documenting responses to human skin swabs. The WS population was used for bioassays with various human volunteers and hexane extracted swabs, and the JC population was used for testing various lipids.Table 1 Bed bug populations used in this study.Full size tableSkin swab collectionThe North Carolina State University Institutional Review Board approved this study (IRB #14173). Informed consent was obtained from all human participants, and all the methods were performed according to the relevant guidelines and regulations. Six human volunteers (3 males, 3 females) ranging from 25 to 50 years old representing several ethnicities (white/Caucasian, Hispanic, Asian) provided samples for this project. Skin swabs were collected following the exact methods outlined by DeVries et al.16. In our 2019 study, these swabs were reported to attract bed bugs independent of other cues in Y-tube olfactometer assays. Briefly, participants were asked to follow a standard operating procedure, which was reviewed with them prior to sample collection. Before collecting skin swabs, participants were asked to not to eat ‘spicy’ food for at least 24 h, take a morning shower, avoid the use of deodorant and cosmetics after showering, and avoid strenuous physical activity. Skin swabs were collected 4–8 h after showering. Hands were washed with water only before lifting filter paper. Swabs were collected using 4.5 cm diameter filter paper discs (#1; Whatman plc, Madistone, United Kingdom). Both sides of a single filter paper disc were rubbed over the left arm from hand to armpit for 12 s, left leg from lower thigh to ankle for 12 s, and left armpit for 6 s. This procedure was repeated on the right side using a new filter paper disc, so that two samples were collected during each swabbing session. The skin swab samples were then stored in glass vials at − 20 °C, and used within one month of collection. The swabs from all human volunteers were used to compare participants and establish that bed bugs responded similarly to all, and participant A’s skin swabs were used for all subsequent bioassays.Two-choice arrestment bioassaysTwo-choice bioassays were conducted in plastic Petri dishes of 6 cm diameter (Corning Life Sciences, Durham, NC, USA) (Fig. 1). The bottom surface of each Petri dish was roughened so that bed bugs could freely move about the arena. Two tents (3 × 1.5 cm) were created using filter paper (Whatman #1). One tent served as the control tent, and the other served as the treatment tent. Control tents were either untreated (nothing added) or treated with hexane only. Treatment tents were either made directly from human odor swabs, treated with human odor extract (in hexane), or treated with a specific compound (in hexane). Tents were allowed 60 min to acclimate to room conditions and allow for the solvent to evaporate prior to initiating bioassays. The positions of tents (treatment and control) were alternated to account for any side-bias.Figure 1Two-choice behavioral assay (top-view) consisting of two equal size paper shelter tents. A clean filter paper (control) was always paired with a treated filter paper that either represented a human skin swab, hexane extract of swabbed paper, SPE fraction of human skin swab extract, or authentic TAGs. A single male bed bug was introduced into the center of each arena and allowed to select a tent to arrest under.Full size imageAdult male bed bugs were housed in individual vials for 24 h prior to each experiment. A single adult male bed bug was released in the middle of the arena 5 h into the scotophase, by transferring it on its harborage. The harborage material was removed immediately after the bed bug moved off of it (the harborage). Bed bugs were allowed the remaining 7 h of the scotophase to freely move around the arena, with their final position reported 3 h into the photophase. Bed bugs that were in contact with the filter paper with any part of their body were recorded as making a choice (i.e. arrestment state); others not in contact with either filter paper tent were recorded as non-responders, reported in the figures, but not used in data analysis. It should be noted that momentary pauses in movement (feeding or other behaviors) are not referred to as arrestment in this study. In total, 15–39 replicates were performed for each experiment (reported for each bioassay).Bioassays with human skin swabsBioassays with human skin swabs were performed to understand if bed bug arrestment behavior (1) differed among different bed bug populations, and (2) influenced by different host odors. Skin swabs were removed from the freezer, equilibrated to room temperature, divided into three equal parts and trimmed to a rectangular shape corresponding to the size of a shelter tent (Fig. 1). Skin swabs from participant A were used to evaluate the responses of four bed bug populations (Table 1). Skin swabs from all participants A–F were used to evaluate the robustness of our findings across multiple human hosts.Skin swab extraction and fractionationSkin swabs collected from volunteer A were pooled and extracted in hexane. Extraction procedures were carried out sequentially by placing a single skin swab into a 20 ml glass vial containing 5 ml of hexane, vortexing for 30 s, then moving the filter paper to a new 20 ml vial containing 5 ml of hexane and repeating the process. Three sequential extractions were performed for each skin swab, and a minimum of 10 skin swabs (collected over several days) were used for each extraction. After all skin swabs were extracted, all sequential hexane extracts were combined and concentrated to a final concentration of one skin swab equivalent per 300 µl, or one bioassay equivalent (BE) per 100 µl (since each swab was used for 3 bioassays; see “Bioassays with human skin swabs” for more information on the size used for each bioassay). Control swabs were also extracted. These swabs were treated identically to the skin swabs, except they did not contact human skin.To determine what compound classes were responsible for the observed behavior, hexane extracts were fractionated using solid phase extraction (SPE). Extracted samples were concentrated to 1 BE/10 µl hexane, then loaded onto a 1 g silica SPE column (6 ml total volume; J.T. Baker, Phillipsburg, NJ, USA). The column was eluted with the following solvents (4 ml of each, each repeated twice sequentially): hexane, 2% ether (in hexane), 5% ether (in hexane), 10% ether (in hexane), 20% ether (in hexane), 50% ether (in hexane), 100% ether, ethyl acetate, and methanol (all solvents acquired from Sigma Aldrich, St. Louis, MO, USA). Each solvent fraction was then concentrated to a final concentration of 1 BE/100 µl and stored at − 20 °C.Bioassays with extracted and fractionated human skin swabsFor all extraction and fractionation bioassays, filter paper tents were cut to a size of 3 cm × 1.5 cm (Fig. 1) and treated with 100 µl (1 BE) of extracted or fractionated human skin swabs (50 µl on each side). A dose–response bioassay was run first to determine if the compounds responsible for bed bug arrestment responses could be extracted and at what concentration (BE) they were behaviorally active. Dilutions were made in hexane, with control tents receiving extracts of control filter paper. At least 20 replicates were conducted for each concentration. After validating an appropriate BE that could be used in future experiments, SPE fractions were diluted in hexane to 0.1 BE and applied to filter paper tents as previously described (50 µl per side). A minimum of 15 replicates were conducted for each fraction to identify behaviorally active fractions.Compound identificationTo better understand what classes of compounds were present in behaviorally active fractions, we conducted thin layer chromatography (TLC) with known standards. A flexible, silica (250 µm) TLC plate (Whatman) was placed into a glass chamber containing a solvent layer of 1.5 cm. The plate was cleaned twice with acetone, then standards (triglyceride [TAG], wax ester, squalene) and samples (fractions) were each loaded into separate lanes. The plate was developed twice in 10% ether (in hexane), then visualized non-destructively with iodine.In addition, behaviorally active fractions were further evaluated for their composition with GC–MS and LC–MS. GC–MS was employed to analyze free fatty acids, squalene, and cholesterol29, whereas LC–MS was employed to characterize the intact skin lipids as previously described30. Samples were analyzed with a GC 7890A coupled to the MS 5975 VL analyzer (Agilent Technologies, CA, USA) following derivatization. Briefly, 50 µL of the extract dissolved in isopropanol were dried under nitrogen and derivatized with 100 µL BSTFA containing 1% trimethylchlorosilane (TCMS) in pyridine to generate the trimethylsilyl (TMS) derivatives at 60 °C for 60 min. GC separation was performed with a 30 m × 0.250 mm (i.d.) × 0.25 µm film thickness DB-5MS fused silica column (Agilent). Helium was used as the carrier gas. Samples were acquired in scan mode by means of electron impact (EI) MS.Liquid-chromatography coupled to the MS analyzer by means of an electrospray interface (ESI) was used to determine abundance and ESI tandem MS of non-volatile lipids as previously described29,30. LC separation was performed with a reverse phase Zorbax SB-C8 column (2.1 × 100 mm, 1.8 μm particle size, Agilent). Data were acquired in the positive ion mode at unit mass resolving power by scanning ions between m/z 100 and 1000 with G6410A series triple quadrupole (QqQ) (Agilent). LC runs and MS spectra were processed with the Mass Hunter software (B.09.00 version).Bioassays with triglyceridesAfter determining that TAGs were prominent compounds in bioactive skin swab fractions, commercially available TAGs were evaluated for behavioral activity. Filter paper tents were treated with 100 µl of hexane (50 µl to each side) containing TAG standards. First, tripalmitin (16:0/16:0/16:0) (Sigma-Aldrich) was evaluated in a dose–response fashion (60 µg to 0.6 µg) to determine what level of TAG was appropriate for bioassays. The upper level of testing was set at 60 µg as a conservative estimate of the amount of TAGs bed bugs may be exposed to, based on calculations of our arena size and previous reports of TAGs on human skin and sebum. Specifically, previous reports documented that 1.5 mg of sebum could be passively collected using Sebutape from an area of 4.7 cm230,31. Because TAGs typically constitute 60% of human sebum32, it is reasonable to assume that passive collection of sebum can result in  > 190 µg/cm2 of TAGs in a short amount of time (30 min). Our sampling methods involved swabbing rather than passive collection, but our use of 60 µg over a 9 cm2 (two sides of 4.5 cm2) shelter tent (6.67 µg/cm2) is a low-estimate of the amount of TAGs collected (although this was not directly measured in the current study). Other TAGs that we tested at a concentration of 60 µg per 9 cm2 included the saturated TAGs trimyristin (14:0/14:0/14:0) and tristearin (18:0/18:0/18:0) and the unsaturated TAGs triolein (18:1/18:1/18:1), trilinolein (18:2/18:2/18:2), and trilinolenin (18:3/18:3/18:3) (all from Sigma-Aldrich). A minimum of 30 replicates were conducted with each TAG.Statistical analysisA Chi-square goodness of fit test was used to compare the responses of bed bugs to control versus treated tents in all two-choice bioassays, with the null hypothesis that if bed bugs do not respond differentially to treated tents they should display a 1:1 preference ratio for both sides of the assay. All tests were conducted in SPSS Version 26 (IBM Corp., Armonk, NY). More

1.Dirzo, R. et al. Defaunation in the Anthropocene. Science 345, 401–406 (2014).ADS 
CAS 
PubMed 

Google Scholar 
2.Pimm, S. L. et al. The biodiversity of species and their rates of extinction, distribution, and protection. Science 344, 1246752 (2014).CAS 
PubMed 

Google Scholar 
3.Boivin, N. L. et al. Ecological consequences of human niche construction: examining long-term anthropogenic shaping of global species distributions. Proc. Natl Acad. Sci. USA 113, 6388–6396 (2016).CAS 
PubMed 
PubMed Central 

Google Scholar 
4.Asner, G. P., Vaughn, N., Smit, I. P. J. & Levick, S. Ecosystem-scale effects of megafauna in African savannas. Ecography (Cop.). 39, 240–252 (2016).
Google Scholar 
5.Bakker, E. S. et al. Combining paleo-data and modern exclosure experiments to assess the impact of megafauna extinctions on woody vegetation. Proc. Natl Acad. Sci. USA 113, 847–855 (2016).ADS 
CAS 
PubMed 

Google Scholar 
6.Bakker, E. S., Pagès, J. F., Arthur, R. & Alcoverro, T. Assessing the role of large herbivores in the structuring and functioning of freshwater and marine angiosperm ecosystems. Ecography (Cop.). 39, 162–179 (2016).
Google Scholar 
7.Brault, M. O., Mysak, L. A., Matthews, H. D. & Simmons, C. T. Assessing the impact of late Pleistocene megafaunal extinctions on global vegetation and climate. Clim 9, 1761–1771 (2013).ADS 

Google Scholar 
8.Doughty, C. E., Faurby, S. & Svenning, J. C. The impact of the megafauna extinctions on savanna woody cover in South America. Ecography (Cop.). 39, 213–222 (2016).
Google Scholar 
9.Doughty, C. E., Wolf, A. & Malhi, Y. The legacy of the Pleistocene megafauna extinctions on nutrient availability in Amazonia. Nat. Geosci. 6, 761–764 (2013).ADS 
CAS 

Google Scholar 
10.Doughty, C. E. et al. Global nutrient transport in a world of giants. Proc. Natl Acad. Sci. USA 113, 1–6 (2015).
Google Scholar 
11.Malhi, Y. et al. Megafauna and ecosystem function from the Pleistocene to the Anthropocene. Proc. Natl Acad. Sci. USA 113, 838–846 (2016).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
12.Smith, F. A. et al. Exploring the influence of ancient and historic megaherbivore extirpations on the global methane budget. Proc. Natl Acad. Sci. USA 113, 201502547 (2015).
Google Scholar 
13.le Roux, E., Kerley, G. I. H. & Cromsigt, J. P. G. M. Megaherbivores modify trophic cascades triggered by fear of predation in an African Savanna Ecosystem. Curr. Biol. 28, 2493–2499.e3 (2018).PubMed 

Google Scholar 
14.Boulanger, M. T. & Lyman, R. L. Northeastern North American Pleistocene megafauna chronologically overlapped minimally with Paleoindians. Quat. Sci. Rev. 85, 35–46 (2013).ADS 

Google Scholar 
15.Rozas-Dávila, A., Valencia, B. G. & Bush, M. B. The functional extinction of Andean megafauna. Ecology 97, 2533–2539 (2016).PubMed 

Google Scholar 
16.Guthrie, R. D. New Carbon Dates Link Climatic Change with Human Colonization and Pleistocene Extinctions. Nature 441, 207–209 (2006).ADS 
CAS 
PubMed 

Google Scholar 
17.Meltzer, D. J. Overkill, glacial history, and the extinction of North America’s Ice Age megafauna. Proc. Natl. Acad. Sci. USA https://doi.org/10.1073/pnas.2015032117 (2020).18.Sandom, C., Faurby, S., Sandel, B. & Svenning, J.-C. Global late Quaternary megafauna extinctions linked to humans, not climate change. Proc. R. Soc. Lond. B Biol. Sci. 281, 20133254 (2014).
Google Scholar 
19.Martin, P. S. in Quaternary Extinctions: A Prehistoric Revolution (eds. Martin, P. S. & Klein, R. G.) 354–403 (University of Arizona Press, 1984).20.Braje, T. J. & Erlandson, J. M. Human acceleration of animal and plant extinctions: a late Pleistocene, Holocene, and Anthropocene continuum. Anthropocene 4, 14–23 (2013).
Google Scholar 
21.Smith, F. A., Smith, R. E. E. E., Lyons, S. K. & Payne, J. L. Body size downgrading of mammals over the late Quaternary. Science. 360, 310–313 (2018).ADS 
CAS 
PubMed 

Google Scholar 
22.Barnosky, A. D., Koch, P. L., Feranec, R. S., Wing, S. L. & Shabel, A. B. Assessing the causes of late pleistocene extinctions on the continents. Science 306, 70–75 (2004).ADS 
CAS 
PubMed 

Google Scholar 
23.Zimov, S. A. et al. Steppe-Tundra Transition: A Herbivore-Driven Biome Shift at the End of the Pleistocene. Am. Nat. 146, 765–794 (1995).
Google Scholar 
24.Lorenzen, E. D. et al. Species-specific responses of Late Quaternary megafauna to climate and humans. Nature 479, 359–364 (2011).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
25.Mann, D. H., Groves, P., Gaglioti, B. V. & Shapiro, B. A. Climate-driven ecological stability as a globally shared cause of Late Quaternary megafaunal extinctions: the Plaids and Stripes Hypothesis. Biol. Rev. 94, 328–352 (2019).
Google Scholar 
26.Zazula, G. D. et al. American mastodon extirpation in the Arctic and Subarctic predates human colonization and terminal Pleistocene climate change. Proc. Natl Acad. Sci. USA 111, 18460–18465 (2014).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
27.Stuart, A. J. Late Quaternary megafaunal extinctions on the continents: a short review. Geol. J. 50, 414–433 (2015).
Google Scholar 
28.Mann, D. H., Groves, P., Kunz, M. L., Reanier, R. E. & Gaglioti, B. V. Ice-age megafauna in Arctic Alaska: extinction, invasion, survival. Quat. Sci. Rev. 70, 91–108 (2013).ADS 

Google Scholar 
29.Mann, D. H. et al. Life and extinction of megafauna in the ice-age Arctic. Proc. Natl Acad. Sci. USA 112, 14301–14306 (2015).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
30.Rabanus-Wallace, M. T. et al. Megafaunal isotopes reveal role of increased moisture on rangeland during late Pleistocene extinctions. Nat. Ecol. Evol. 1, 1–5 (2017).
Google Scholar 
31.Zimov, S. A., Zimov, N. S., Tikhonov, A. N. & Chapin, I. S. Mammoth steppe: a high-productivity phenomenon. Quat. Sci. Rev. 57, 26–45 (2012).ADS 

Google Scholar 
32.Owen-Smith, N. Pleistocene extinctions: the pivotal role of megaherbivores. Paleobiology 13, 351–362 (1987).
Google Scholar 
33.Willerslev, E. et al. Fifty thousand years of Arctic vegetation and megafaunal diet. Nature 506, 47–51 (2014).ADS 
CAS 
PubMed 

Google Scholar 
34.Jackson, S. T. Representation of flora and vegetation in Quaternary fossil assemblages: known and unknown knowns and unknowns. Quat. Sci. Rev. 49, 1–15 (2012).ADS 

Google Scholar 
35.Froese, D. G. et al. The Klondike goldfields and Pleistocene environments of Beringia. GSA Today 19, 4–10 (2009).
Google Scholar 
36.Murchie, T. J. et al. Optimizing extraction and targeted capture of ancient environmental DNA for reconstructing past environments using the PalaeoChip Arctic-1.0 bait-set. Quat. Res. 99, 305–328 (2021).CAS 

Google Scholar 
37.Haile, J. et al. Ancient DNA reveals late survival of mammoth and horse in interior Alaska. Proc. Natl Acad. Sci. USA 106, 22352–22357 (2009).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
38.Clark, P. U. The last glacial maximum. Science 325, 710–714 (2009).ADS 
CAS 
PubMed 

Google Scholar 
39.Zazula, G. D. et al. A middle Holocene steppe bison and paleoenvironments from the versleuce meadows, Whitehorse, Yukon, Canada. Can. J. Earth Sci. 54, 1138–1152 (2017).ADS 

Google Scholar 
40.Heintzman, P. D. et al. Bison phylogeography constrains dispersal and viability of the Ice Free Corridor in western Canada. Proc. Natl Acad. Sci. USA 113, 8057–8063 (2016).CAS 
PubMed 
PubMed Central 

Google Scholar 
41.Graham, R. W. et al. Timing and causes of mid-Holocene mammoth extinction on St. Paul Island, Alaska. Proc. Natl Acad. Sci. USA 113, 9310–9314 (2016).CAS 
PubMed 
PubMed Central 

Google Scholar 
42.Vartanyan, S. L., Arslanov, K. A., Karhu, J. A., Possnert, G. & Sulerzhitsky, L. D. Collection of radiocarbon dates on the mammoths (Mammuthus primigenius) and other genera of Wrangel Island, northeast Siberia, Russia. Quat. Res. 70, 51–59 (2008).CAS 

Google Scholar 
43.Faith, J. T. & Surovell, T. A. Synchronous extinction of North America’s Pleistocene mammals. Proc. Natl Acad. Sci. USA 106, 20641–20645 (2009).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
44.Signor, P. W. & Lipps, J. H. Sampling bias, gradual extinction patterns and catastrophes in the fossil record. GSA Spec. Pap. 190, 291–296 (1982).
Google Scholar 
45.Fiedel, S. in American Megafaunal Extinctions at the End of the Pleistocene (ed. Haynes, G.) 21–37 (Springer Netherlands, 2009).46.Graf, K. E. Uncharted Territory: Late Pleistocene Hunter-Gatherer Dispersals in the Siberian Mammoth-Steppe (University of Nevada, 2008).47.Kuzmina, S. A. et al. The late Pleistocene environment of the Eastern West Beringia based on the principal section at the Main River, Chukotka. Quat. Sci. Rev. 30, 2091–2106 (2011).ADS 

Google Scholar 
48.Hoffecker, J. F., Elias, S. A. & Rourke, D. H. O. Out of Beringia? Science 343, 979–980 (2014).ADS 
CAS 
PubMed 

Google Scholar 
49.Zazula, G. D. et al. Ice-age steppe vegetation in East Beringia. Nature 423, 603 (2003).ADS 
CAS 
PubMed 

Google Scholar 
50.Guthrie, R. D. Origin and causes of the mammoth steppe: a story of cloud cover, woolly mammal tooth pits, buckles, and inside-out Beringia. Quat. Sci. Rev. 20, 549–574 (2001).ADS 

Google Scholar 
51.Pavelková Řičánková, V., Robovský, J. & Riegert, J. Ecological structure of recent and last glacial mammalian faunas in northern Eurasia: the case of Altai-Sayan refugium. PLoS ONE 9, e85056 (2014).ADS 
PubMed 
PubMed Central 

Google Scholar 
52.Bocherens, H. Isotopic tracking of large carnivore palaeoecology in the mammoth steppe. Quat. Sci. Rev. 117, 42–71 (2015).ADS 

Google Scholar 
53.Ritchie, J. C. & Cwynar, L. C. in Paleoecology of Beringia (eds. Hopkins, D. M. et al.) 113–126 (Academic Press, 1982).54.Zhu, D. et al. The large mean body size of mammalian herbivores explains the productivity paradox during the Last Glacial Maximum. Nat. Ecol. Evol. https://doi.org/10.1038/s41559-018-0481-y (2018).55.Hopkins, D. M., Matthews, J. V., and Schweger, C. E. eds. Paleoecology of Beringia. (Academic Press, 1982).56.Stivrins, N. et al. Biotic turnover rates during the Pleistocene-Holocene transition. Quat. Sci. Rev. 151, 100–110 (2016).ADS 

Google Scholar 
57.Bakker, E. S., Ritchie, M. E., Olff, H., Milchunas, D. G. & Knops, J. M. H. Herbivore impact on grassland plant diversity depends on habitat productivity and herbivore size. Ecol. Lett. 9, 780–788 (2006).PubMed 

Google Scholar 
58.Bradshaw, R. H. W., Hannon, G. E. & Lister, A. M. A long-term perspective on ungulate-vegetation interactions. Ecol. Manag. 181, 267–280 (2003).
Google Scholar 
59.Gill, J. L. Ecological impacts of the late Quaternary megaherbivore extinctions. N. Phytologist 201, 1163–1169 (2014).
Google Scholar 
60.Gill, J. L., Williams, J. W., Jackson, S. T., Donnelly, J. P. & Schellinger, G. C. Climatic and megaherbivory controls on late-glacial vegetation dynamics: a new, high-resolution, multi-proxy record from Silver Lake, Ohio. Quat. Sci. Rev. 34, 66–80 (2012).ADS 

Google Scholar 
61.Gill, J. L., Williams, J. W., Jackson, S. T., Lininger, K. B. & Robinson, G. S. Pleistocene megafaunal collapse, novel plant communities, and enhanced fire regimes in North America. Science 326, 1100–1103 (2009).ADS 
CAS 
PubMed 

Google Scholar 
62.Johnson, C. N. Ecological consequences of Late Quaternary extinctions of megafauna. Proc. Biol. Sci. 276, 2509–2519 (2009).CAS 
PubMed 
PubMed Central 

Google Scholar 
63.Owen-Smith, N. Megaherbivores: The Influence of Very Large Body Size on Ecology (Cambridge University Press, 1992).64.Wright, J. P. & Jones, C. G. The concept of organisms as ecosystem engineers ten years on: progress, limitations, and challenges. Bioscience 56, 203 (2006).
Google Scholar 
65.Gutierrez, J. L. & Jones, C. G. Physical ecosystem engineers as agents of biogeochemical heterogeneity. Bioscience 56, 227 (2006).
Google Scholar 
66.Berke, S. K. Functional groups of ecosystem engineers: a proposed classification with comments on current issues. Integr. Comp. Biol. 50, 147–157 (2010).PubMed 

Google Scholar 
67.Ries, L., Fletcher, R. J. J., Battin, J. & Sisk, T. D. Ecological responses to habitat edges: Mechanisms, models, and variability explained. Annu. Rev. Ecol., Evolution, Syst. 35, 491–522 (2004).
Google Scholar 
68.Rasmussen, S. O. et al. A new Greenland ice core chronology for the last glacial termination. J. Geophys. Res. Atmos. 111, 1–16 (2006).
Google Scholar 
69.Swift, J. A. et al. Micro methods for Megafauna: novel approaches to late quaternary extinctions and their contributions to faunal conservation in the Anthropocene. Bioscience 69, 877–887 (2019).PubMed 
PubMed Central 

Google Scholar 
70.Andersen, K. et al. Meta-barcoding of ‘dirt’ DNA from soil reflects vertebrate biodiversity. Mol. Ecol. 21, 1966–1979 (2012).CAS 
PubMed 

Google Scholar 
71.Comandini, O. & Rinaldi, A. C. Tracing megafaunal extinctions with dung fungal spores. Mycologist 18, 140–142 (2004).
Google Scholar 
72.Säterberg, T., Sellman, S. & Ebenman, B. High frequency of functional extinctions in ecological networks. Nature 499, 468–470 (2013).ADS 
PubMed 

Google Scholar 
73.Courchamp, F., Berec, L. & Gascoigne, J. Allee Effects in Ecology and Conservation. Allee Effects in Ecology and Conservation (Oxford University Press, 2008).74.Allee, W. C. Animal aggregations. Q. Rev. Biol. 2, 367–398 (1927).
Google Scholar 
75.Allee, W. C. & Bowen, E. S. Studies in animal aggregations: mass protection against colloidal silver among goldfishes. J. Exp. Zool. 61, 185–207 (1932).CAS 

Google Scholar 
76.Taberlet, P., Bonin, A., Zinger, L. & Coissac, E. Environmental DNA: For Biodiversity Research and Monitoring. (Oxford University Press, 2018).77.Edwards, M. E. et al. Metabarcoding of modern soil DNA gives a highly local vegetation signal in Svalbard tundra. Holocene 28, 2006–2016 (2018).ADS 

Google Scholar 
78.Slon, V. et al. Neandertal and Denisovan DNA from Pleistocene sediments. Science 356, 605–608 (2017).ADS 
CAS 
PubMed 

Google Scholar 
79.Anderson-Carpenter, L. L. et al. Ancient DNA from lake sediments: bridging the gap between paleoecology and genetics. BMC Evol. Biol. 11, 1–15 (2011).
Google Scholar 
80.Bellemain, E. et al. Fungal palaeodiversity revealed using high-throughput metabarcoding of ancient DNA from arctic permafrost. Environ. Microbiol. 15, 1176–1189 (2013).CAS 
PubMed 

Google Scholar 
81.Ahmed, E. et al. Archaeal community changes in Lateglacial lake sediments: evidence from ancient DNA. Quat. Sci. Rev. 181, 19–29 (2018).ADS 

Google Scholar 
82.Niemeyer, B., Epp, L. S., Stoof-Leichsenring, K. R., Pestryakova, L. A. & Herzschuh, U. A comparison of sedimentary DNA and pollen from lake sediments in recording vegetation composition at the Siberian treeline. Mol. Ecol. Resour. 17, e46–e62 (2017).CAS 
PubMed 

Google Scholar 
83.Rawlence, N. J. et al. Using palaeoenvironmental DNA to reconstruct past environments: progress and prospects. J. Quat. Sci. 29, 610–626 (2014).
Google Scholar 
84.Blum, S. A. E., Lorenz, M. G. & Wackernagel, W. Mechanism of retarded DNA degradation and prokaryotic origin of DNases in nonsterile soils. Syst. Appl. Microbiol. 20, 513–521 (1997).CAS 

Google Scholar 
85.Greaves, M. P. & Wilson, M. J. The degradation of nucleic acids and montmorillonite-nucleic-acid complexes by soil microorganisms. Soil Biol. Biochem. 2, 257–268 (1970).CAS 

Google Scholar 
86.Gardner, C. M. & Gunsch, C. K. Adsorption capacity of multiple DNA sources to clay minerals and environmental soil matrices less than previously estimated. Chemosphere 175, 45–51 (2017).ADS 
CAS 
PubMed 

Google Scholar 
87.Lorenz, M. G. & Wackernagel, W. Adsorption of DNA to sand and variable degradation rates of adsorbed DNA. Appl. Environ. Microbiol. 53, 2948–2952 (1987).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
88.Ogram, A., Sayler, G., Gustin, D. & Lewis, R. DNA adsorption to soils and sediments. Environ. Sci. Technol. 22, 982–984 (1988).ADS 
CAS 
PubMed 

Google Scholar 
89.Lorenz, M. G. & Wackernagel, W. Adsorption of DNA to sand and variable degradation of adsorbed DNA. Appl. Environ. Microbiol. 53, 2948–2952 (1987).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
90.Morrissey, E. M. et al. Dynamics of extracellular DNA decomposition and bacterial community composition in soil. Soil Biol. Biochem. 86, 42–49 (2015).CAS 

Google Scholar 
91.Arnold, L. J. et al. Paper II – Dirt, dates and DNA: OSL and radiocarbon chronologies of perennially frozen sediments in Siberia, and their implications for sedimentary ancient DNA studies. Boreas 40, 417–445 (2011).
Google Scholar 
92.Allentoft, M. E. et al. The half-life of DNA in bone: measuring decay kinetics in 158 dated fossils. Proc. R. Soc. B Biol. Sci. https://doi.org/10.1098/rspb.2012.1745 (2012).93.Kistler, L., Ware, R., Smith, O., Collins, M. & Allaby, R. G. A new model for ancient DNA decay based on paleogenomic meta-analysis. Nucleic Acids Res. 45, 6310–6320 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
94.Cribdon, B., Ware, R., Smith, O., Gaffney, V. & Allaby, R. G. PIA: more accurate taxonomic assignment of metagenomic data demonstrated on sedaDNA from the North Sea. Front. Ecol. Evol. 8, 1–12 (2020).
Google Scholar 
95.Yoccoz, N. G. et al. DNA from soil mirrors plant taxonomic and growth form diversity. Mol. Ecol. 21, 3647–3655 (2012).CAS 
PubMed 

Google Scholar 
96.Doi, H. et al. Environmental DNA analysis for estimating the abundance and biomass of stream fish. Freshw. Biol. 62, 30–39 (2017).CAS 

Google Scholar 
97.Burn, C. R., Michel, F. A. & Smith, M. W. Stratigraphic, isotopic, and mineralogical evidence for an early Holocene thaw unconformity at Mayo, Yukon Territory. Can. J. Earth Sci. 23, 794–803 (1986).ADS 
CAS 

Google Scholar 
98.Kotler, E. & Burn, C. R. Cryostratigraphy of the Klondike ‘muck’ deposits, west-central Yukon Territory. Can. J. Earth Sci. 37, 849–861 (2000).ADS 
CAS 

Google Scholar 
99.Fraser, T. A. & Burn, C. R. On the nature and origin of ‘muck’ deposits in the Klondike area, Yukon Territory. Can. J. Earth Sci. 34, 1333–1344 (1997).ADS 

Google Scholar 
100.Mahony, M. E. 50,000 years of paleoenvironmental change recorded in meteoric waters and coeval paleoecological and cryostratigraphic indicators from the Klondike goldfields, Yukon, Canada. (University of Alberta, 2015). https://doi.org/10.7939/R34T6FF58.101.Lydolph, M. C. et al. Beringian paleoecology inferred from permafrost-preserved fungal DNA. Appl. Environ. Microbiol. 71, 1012–1017 (2005).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
102.Willerslev, E. et al. Diverse plant and animal genetic records from Holocene and Pleistocene sediments. Science 300, 791–795 (2003).ADS 
CAS 
PubMed 

Google Scholar 
103.Haile, J. et al. Ancient DNA chronology within sediment deposits: are paleobiological reconstructions possible and is DNA leaching a factor? Mol. Biol. Evol. 24, 982–989 (2007).CAS 
PubMed 

Google Scholar 
104.Willerslev, E., Hansen, A. J. & Poinar, H. N. Isolation of nucleic acids and cultures from fossil ice and permafrost. Trends Ecol. Evol. 19, 141–147 (2004).PubMed 

Google Scholar 
105.Hansen, A. J. et al. Crosslinks rather than strand breaks determine access to ancient DNA sequences from frozen sediments. Genetics 173, 1175–1179 (2006).CAS 
PubMed 
PubMed Central 

Google Scholar 
106.D’Costa, V. M. et al. Antibiotic resistance is ancient. Nature 477, 457–461 (2011).ADS 
PubMed 

Google Scholar 
107.Johnson, S. S. et al. Ancient bacteria show evidence of DNA repair. Proc. Natl Acad. Sci. USA 104, 14401–14405 (2007).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
108.Hebsgaard, M. B. et al. ‘The Farm Beneath the Sand’- an archaeological case study on ancient ‘dirt’ DNA. Antiquity 83, 430–444 (2009).
Google Scholar 
109.Sadoway, T. R. A Metagenomic Analysis of Ancient Sedimentary DNA Across the Pleistocene-Holocene Transition (McMaster University, 2014).110.Bronk Ramsey, C. Deposition models for chronological records. Quat. Sci. Rev. 27, 42–60 (2008).ADS 

Google Scholar 
111.Reimer, P. J. et al. The IntCal20 Northern Hemisphere Radiocarbon Age Calibration Curve (0-55 cal kBP). Radiocarbon 62, 725–757 (2020).CAS 

Google Scholar 
112.Nichols, R. V. et al. Minimizing polymerase biases in metabarcoding. Mol. Ecol. Resour. 18, 927–939 (2018).CAS 

Google Scholar 
113.Wei, N., Nakajima, F. & Tobino, T. A microcosm study of surface sediment environmental DNA: decay observation, abundance estimation, and fragment length comparison. Environ. Sci. Technol. 52, 12428–12435 (2018).ADS 
CAS 
PubMed 

Google Scholar 
114.Matesanz, S. et al. Estimating belowground plant abundance with DNA metabarcoding. Mol. Ecol. Resour. 19, 1265–1277 (2019).CAS 
PubMed 

Google Scholar 
115.Takahara, T., Minamoto, T., Yamanaka, H., Doi, H. & Kawabata, Z. Estimation of fish biomass using environmental DNA. PLoS ONE 7, 3–10 (2012).
Google Scholar 
116.Doi, H. et al. Use of droplet digital PCR for estimation of fish abundance and biomass in environmental DNA surveys. PLoS ONE 10, 1–11 (2015).
Google Scholar 
117.Debruyne, R. et al. Out of America: ancient DNA evidence for a new world origin of late Quaternary Woolly Mammoths. Curr. Biol. 18, 1320–1326 (2008).CAS 
PubMed 

Google Scholar 
118.Metcalfe, J. Z., Longstaffe, F. J. & Zazula, G. D. Nursing, weaning, and tooth development in woolly mammoths from Old Crow, Yukon, Canada: Implications for Pleistocene extinctions. Palaeogeogr. Palaeoclimatol. Palaeoecol. 298, 257–270 (2010).
Google Scholar 
119.Shapiro, B. et al. Rise and fall of the Beringian steppe bison. Science 306, 1561–1565 (2004).ADS 
CAS 
PubMed 

Google Scholar 
120.Sinclair, P. H., Nixon, W. A., Eckert C. D. & Hughes, N. L.Hughes, eds. Birds of the Yukon Territory. (UBC Press, 2003).121.Keesing, F. & Young, T. P. Cascading consequences of the loss of large mammals in an African Savanna. Bioscience 64, 487–495 (2014).
Google Scholar 
122.Taberlet, P. et al. Power and limitations of the chloroplast trnL (UAA) intron for plant DNA barcoding. Nucleic Acids Res. 35, e14 (2007).PubMed 

Google Scholar 
123.Chevalier, M. et al. Pollen-based climate reconstruction techniques for late Quaternary studies. Earth-Sci. Rev. 210, 103384 (2020).
Google Scholar 
124.Wang, X.-C. & Geurts, M.-A. Post-glacial vegetation history of the Ittlemit Lake basin, southwest Yukon Territory. Arctic 44, 23–30 (1991).
Google Scholar 
125.Wang, X.-C. & Geurts, M.-A. Late Quaternary pollen records and vegetation history of the southwest Yukon Territory: a review. Geogr. Phys. Quat. 45, 175–193 (1991).
Google Scholar 
126.Rainville, R. A. & Gajewski, K. Holocene environmental history of the Aishihik region, Yukon, Canada. Can. J. Earth Sci. 50, 397–405 (2013).ADS 
CAS 

Google Scholar 
127.Lacourse, T. & Gajewski, K. Late Quaternary vegetation history of Sulphur Lake, southwest Yukon Territory, Canada. Arctic 53, 27–35 (2000).
Google Scholar 
128.Bunbury, J. & Gajewski, K. Postglacial climates inferred from a lake at treeline, southwest Yukon Territory, Canada. Quat. Sci. Rev. 28, 354–369 (2009).ADS 

Google Scholar 
129.Gajewski, K., Bunbury, J., Vetter, M., Kroeker, N. & Khan, A. H. Paleoenvironmental studies in Southwestern Yukon. Arctic 67, 58–70 (2014).
Google Scholar 
130.Schofield, E. J., Edwards, K. J. & McMullen, A. J. Modern Pollen-Vegetation Relationships in Subarctic Southern Greenland and the Interpretation of Fossil Pollen Data from the Norse landnám. J. Biogeogr. 34, 473–488 (2007).
Google Scholar 
131.Pennington, W. & Tutin, T. G. Modern pollen samples from west greenland and the interpretation of pollen data from the british late-glacial (late Devesian). N. Phytol. 84, 171–201 (1980).
Google Scholar 
132.Bradshaw, R. H. W. Modern pollen-representation factors for Woods in South-East England. J. Ecol. 69, 45 (1981).
Google Scholar 
133.Roy, I. et al. Over-representation of some taxa in surface pollen analysis misleads the interpretation of fossil pollen spectra in terms of extant vegetation. Trop. Ecol. 59, 339–350 (2018).
Google Scholar 
134.Bryant, J. P. et al. Biogeographic evidence for the evolution of chemical defense by boreal birch and willow against mammalian browsing. Am. Nat. 134, 20–34 (1979).
Google Scholar 
135.Christie, K. S. et al. The role of vertebrate herbivores in regulating shrub expansion in the Arctic: a synthesis. Bioscience 65, 1123 (2015).
Google Scholar 
136.Bryant, J. P. et al. Can antibrowsing defense regulate the spread of woody vegetation in arctic tundra? Ecography (Cop.). 37, 204–211 (2014).137.Bryant, J. P. & Kuropat, P. J. Selection of winter forage by subarctic browsing vertebrates: the role of plant chemistry. Annu. Rev. Ecol. Syst. 11, 261–285 (1980).CAS 

Google Scholar 
138.Fox-Dobbs, K., Leonard, J. A. & Koch, P. L. Pleistocene megafauna from eastern Beringia: Paleoecological and paleoenvironmental interpretations of stable carbon and nitrogen isotope and radiocarbon records. Palaeogeogr. Palaeoclimatol. Palaeoecol. 261, 30–46 (2008).
Google Scholar 
139.Gardner, C., Berger, M. & Taras, M. Habitat assessment of potential wood bison relocation sites in Alaska. Arctic 1–30 (2007).140.Jiménez-Hidalgo, E. et al. Species diversity and paleoecology of late pleistocene horses from Southern Mexico. Front. Ecol. Evol. 7, 1–18 (2019).
Google Scholar 
141.van Geel, B. et al. The ecological implications of a Yakutian mammoth’s last meal. Quat. Res. 69, 361–376 (2008).
Google Scholar 
142.van Geel, B. et al. Palaeo-environmental and dietary analysis of intestinal contents of a mammoth calf (Yamal Peninsula, northwest Siberia). Quat. Sci. Rev. 30, 3935–3946 (2011).ADS 

Google Scholar 
143.Guthrie, R. D. Rapid body size decline in Alaskan Pleistocene horses before extinction. Nature 426, 169–171 (2003).ADS 
PubMed 

Google Scholar 
144.Bourgeon, L. Bluefish Cave II (Yukon Territory, Canada): Taphonomic Study of a Bone Assemblage. PaleoAmerica 1, 105–108 (2015).
Google Scholar 
145.Bourgeon, L., Burke, A. & Higham, T. Earliest human presence in North America dated to the last glacial maximum: new radiocarbon dates from Bluefish Caves, Canada. PLoS ONE 12, e0169486 (2017).PubMed 
PubMed Central 

Google Scholar 
146.Bourgeon, L. Revisiting the mammoth bone modifications from Bluefish Caves (YT, Canada). J. Archaeol. Sci. Rep. 37, 102969 (2021).147.Bourgeon, L. & Burke, A. Horse exploitation by Beringian hunters during the Last Glacial Maximum. Quat. Sci. Rev. 261, (2021).148.Vachula, R. S., Sae-Lim, J. & Russell, J. M. Sedimentary charcoal proxy records of fire in Alaskan tundra ecosystems. Palaeogeogr. Palaeoclimatol. Palaeoecol. 541, 109564 (2020).149.Vachula, R. S. Alaskan lake sediment records and their implications for the Beringian standstill hypothesis. PaleoAmerica 6, 303–307 (2020).
Google Scholar 
150.Vachula, R. S. et al. Evidence of Ice Age humans in eastern Beringia suggests early migration to North America. Quat. Sci. Rev. 205, 35–44 (2019).ADS 

Google Scholar 
151.Vachula, R. S. et al. Sedimentary biomarkers reaffirm human impacts on northern Beringian ecosystems during the Last Glacial period. Boreas 49, 514–525 (2020).
Google Scholar 
152.Abramova, Z. A. in Paleolit Kavkaza i Severnoi Azii (ed. Boriskovskii, P. I.) 145–243 (Nauka, 1989).153.Abramova, Z. A., Astakhov, S. N., Vasil’ev, S. A., Ermolva, N. M. & Lisitsyn, N. F. Paleolit Eniseya. (Nauka, 1991).154.Goebel, T. in Encyclopedia of prehistory. Vol 2: Arctic and Subarctic (eds. Peregrine, P. N. & Ember, M.) 192–196 (Kluwer Academic Publishers, 2002).155.Ermolova, N. M. Teriofauna doliny Angary v pozdem antropogene. (Nauka, 1978).156.Hoffecker, J. F. & Elias, S. A. Human Ecology of Beringia. (Columbia University Press, 2007).157.Johnson, C. N. Determinants of loss of mammal species during the Late Quaternary ‘megafauna’ extinctions: life history and ecology, but not body size. Proc. Biol. Sci. 269, 2221–2227 (2002).CAS 
PubMed 
PubMed Central 

Google Scholar 
158.Laland, K. N. & O’Brien, M. J. Niche Construction Theory and Archaeology. J. Archaeol. Method Theory 17, 303–322 (2010).
Google Scholar 
159.Riede, F. Adaptation and niche construction in human prehistory: a case study from the southern Scandinavian Late Glacial. Philos. Trans. R. Soc. Lond. 366, 793–808 (2011).
Google Scholar 
160.Roos, C. I., Zedeño, M. N., Hollenback, K. L. & Erlick, M. M. H. Indigenous impacts on North American Great Plains fire regimes of the past millennium. Proc. Natl. Acad. Sci. USA https://doi.org/10.1073/pnas.1805259115 (2018).161.Pinter, N., Fiedel, S. & Keeley, J. E. Fire and vegetation shifts in the Americas at the vanguard of Paleoindian migration. Quat. Sci. Rev. 30, 269–272 (2011).ADS 

Google Scholar 
162.Haynes, G. Extinctions in North America’s Late Glacial landscapes. Quat. Int. 285, 89–98 (2013).
Google Scholar 
163.Graf, K. E. in Paleoamerican Odyssey (eds. Graf, K. E., Ketron, C. V. & Waters, M. R.) 65–80 (Texas A&M University Press, 2014).164.Pečnerová, P. et al. Mitogenome evolution in the last surviving woolly mammoth population reveals neutral and functional consequences of small population size. Evol. Lett. 1, 292–303 (2017).165.Conroy, K. J. et al. Tracking late-Quaternary extinctions in interior Alaska using megaherbivore bone remains and dung fungal spores. Quat. Res. https://doi.org/10.1017/qua.2020.19 (2020).166.Bakker, E. S. et al. Combining paleo-data and modern exclosure experiments to assess the impact of megafauna extinctions on woody vegetation. Proc. Natl Acad. Sci. USA 113, 847–855 (2016).ADS 
CAS 
PubMed 

Google Scholar 
167.Dabney, J. et al. Complete mitochondrial genome sequence of a Middle Pleistocene cave bear reconstructed from ultrashort DNA fragments. Proc. Natl Acad. Sci. USA 110, 15758–15763 (2013).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
168.Meyer, M. & Kircher, M. Illumina sequencing library preparation for highly multiplexed target capture and sequencing. Cold Spring Harb. Protoc. 5, pdb.prot5448 (2010).169.Kircher, M., Sawyer, S. & Meyer, M. Double indexing overcomes inaccuracies in multiplex sequencing on the Illumina platform. Nucleic Acids Res. 40, 1–8 (2012).
Google Scholar 
170.Altschul, S. F., Gish, W., Miller, W., Myers, E. W. & Lipman, D. J. Basic local alignment search tool. J. Mol. Biol. 215, 403–410 (1990).CAS 
PubMed 

Google Scholar 
171.Agarwala, R. et al. Database resources of the National Center for Biotechnology Information. Nucleic Acids Res. 44, D7–D19 (2016).CAS 

Google Scholar 
172.Benson, D. A. et al. GenBank. Nucleic Acids Res. 41, D36–D42 (2013).173.Huson, D. H. et al. MEGAN Community Edition – Interactive Exploration and Analysis of Large-Scale Microbiome Sequencing Data. PLoS Comput. Biol. 12, e1004957 (2016).PubMed 
PubMed Central 

Google Scholar 
174.Huson, D. H., Auch, A. F., Qi, J. & Schuster, S. C. MEGAN analysis of metagenomic data. Genome Res. 17, 377–386 (2007).CAS 
PubMed 
PubMed Central 

Google Scholar 
175.Jónsson, H., Ginolhac, A., Schubert, M., Johnson, P. L. F. & Orlando, L. MapDamage2.0: Fast approximate Bayesian estimates of ancient DNA damage parameters. Bioinformatics 29, 1682–1684 (2013).PubMed 
PubMed Central 

Google Scholar 
176.Bronk Ramsey, C. & Lee, S. Recent and planned developments of the program OxCal. Radiocarbon 55, 720–730 (2013).
Google Scholar 
177.Bronk Ramsey, C. Dealing with outliers and offsets in radiocarbon dating. Radiocarbon 51, 1023–1045 (2009).
Google Scholar 
178.Davies, L. J., Jensen, B. J. L., Froese, D. G. & Wallace, K. L. Late Pleistocene and Holocene tephrostratigraphy of interior Alaska and Yukon: key beds and chronologies over the past 30,000 years. Quat. Sci. Rev. 146, 28–53 (2016).ADS 

Google Scholar 
179.Westgate, J. A., Preece, S. J., Kotler, E. & Hall, S. Dawson tephra: a prominent stratigraphic marker of Late Wisconsinan age in west-central Yukon, Canada. Can. J. Earth Sci. 37, 621–627 (2000).ADS 
CAS 

Google Scholar 
180.Froese, D., Westgate, J., Preece, S. & Storer, J. Age and significance of the Late Pleistocene Dawson tephra in eastern Beringia. Quat. Sci. Rev. 21, 2137–2142 (2002).ADS 

Google Scholar 
181.Zazula, G. D. et al. Vegetation buried under Dawson tephra (25,300 14C years BP) and locally diverse late Pleistocene paleoenvironments of Goldbottom Creek, Yukon, Canada. Palaeogeogr. Palaeoclimatol. Palaeoecol. 242, 253–286 (2006).
Google Scholar 
182.Froese, D. G., Zazula, G. D. & Reyes, A. V. Seasonality of the late Pleistocene Dawson tephra and exceptional preservation of a buried riparian surface in central Yukon Territory, Canada. Quat. Sci. Rev. 25, 1542–1551 (2006).ADS 

Google Scholar 
183.Klunk, J. et al. Genetic resiliency and the Black Death: no apparent loss of mitogenomic diversity due to the Black Death in medieval London and Denmark. Am. J. Phys. Anthropol. 169, 240–252 (2019).PubMed 

Google Scholar 
184.Renaud, G., Stenzel, U. & Kelso, J. LeeHom: Adaptor trimming and merging for Illumina sequencing reads. Nucleic Acids Res 42, e141 (2014).PubMed 
PubMed Central 

Google Scholar 
185.Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25, 1754–1760 (2009).CAS 
PubMed 
PubMed Central 

Google Scholar 
186.Adobe Inc. Adobe Illustrator. (2020). https://adobe.com/products/illustrator.187.Lebart, L., Morineau, A. & Tabard, N. Techniques De La Description Statistique Méthodes Et Logiciels Pour L’analyse Des Grands Tableaux. (Dunod, 1977).188.Potter, B. A. et al. Current evidence allows multiple models for the peopling of the Americas. Sci. Adv. 4, 1–9 (2018).
Google Scholar 
189.Grootes, P. M. & Stuiver, M. Oxygen 18/16 variability in Greenland snow and ice with 10-3- to 105-year time resolution. J. Geophys. Res. Ocean. 102, 26455–26470 (1997).ADS 
CAS 

Google Scholar 
190.Wolbach, W. S. et al. Extraordinary Biomass-Burning Episode and Impact Winter Triggered by the Younger Dryas Cosmic Impact ∼12,800 Years Ago. 2. Lake, Marine, and Terrestrial Sediments. J. Geol. 126, 185–205 (2018).ADS 
CAS 

Google Scholar  More

1.Jentsch, A. & White, P. A theory of pulse dynamics and disturbance in ecology. Ecology 100, e02734 (2019).PubMed 

Google Scholar 
2.Stuart-Smith, R. D., Brown, C. J., Ceccarelli, D. M. & Edgar, G. J. Ecosystem restructuring along the Great Barrier Reef following mass coral bleaching. Nature 560, 92–96 (2018).ADS 
CAS 
PubMed 

Google Scholar 
3.Trisos, C. H., Merow, C. & Pigot, A. L. The projected timing of abrupt ecological disruption from climate change. Nature 580, 496–501 (2020).ADS 
CAS 
PubMed 

Google Scholar 
4.Blowes, S. A. et al. The geography of biodiversity change in marine and terrestrial assemblages. Science 366, 339–345 (2019).ADS 
CAS 
PubMed 

Google Scholar 
5.Schwartz, M. W. et al. Increasing elevation of fire in the Sierra Nevada and implications for forest change. Ecosphere 6, art121 (2015).
Google Scholar 
6.Sommerfeld, A. et al. Patterns and drivers of recent disturbances across the temperate forest biome. Nat. Commun. 9, 4355 (2018).ADS 
PubMed 
PubMed Central 

Google Scholar 
7.Giraldo-Ospina, A., Kendrick, G. A. & Hovey, R. K. Depth moderates loss of marine foundation species after an extreme marine heatwave: Could deep temperate reefs act as a refuge?. Proc. R. Soc. B Biol. Sci. 287, 20200709 (2020).
Google Scholar 
8.Fahrig, L. Ecological responses to habitat fragmentation per se. Annu. Rev. Ecol. Evol. Syst. 48, 1–23 (2017).
Google Scholar 
9.Stephens, S. L. et al. Wildfire impacts on California spotted owl nesting habitat in the Sierra Nevada. Ecosphere 7, e01478 (2016).
Google Scholar 
10.Sih, A., Ferrari, M. C. O. & Harris, D. J. Evolution and behavioural responses to human-induced rapid environmental change. Evol. Appl. 4, 367–387 (2011).PubMed 
PubMed Central 

Google Scholar 
11.Duckworth, R. A. The role of behavior in evolution: a search for mechanism. Evol. Ecol. 23, 513–531 (2009).
Google Scholar 
12.Snell-Rood, E. C. An overview of the evolutionary causes and consequences of behavioural plasticity. Anim. Behav. 85, 1004–1011 (2013).
Google Scholar 
13.Schluter, D. Distributions of Galapagos ground finches along an altitudinal gradient: The importance of food supply. Ecology 63, 1504–1517 (1982).
Google Scholar 
14.Fryxell, J. M. & Sinclair, A. R. E. Causes and consequences of migration by large herbivores. Trends Ecol. Evol. 3, 237–234 (1988).CAS 
PubMed 

Google Scholar 
15.Abraham, J. O., Hempson, G. P. & Staver, A. C. Drought-response strategies of savanna herbivores. Ecol. Evol. 9, 7047–7056 (2019).PubMed 
PubMed Central 

Google Scholar 
16.Fryxell, J. M. & Lundberg, P. Diet choice and predator-prey dynamics. Evol. Ecol. 8, 407–421 (1994).
Google Scholar 
17.Heron, S. et al. Impacts of climate change on world heritage coral reefs: Update to the first global scientific assessment. https://apo.org.au/node/193206 (2018).18.Jones, G. P., McCormick, M. I., Srinivasan, M. & Eagle, J. V. Coral decline threatens fish biodiversity in marine reserves. Proc. Natl. Acad. Sci. 101, 8251–8253 (2004).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
19.Bellwood, D. R., Hoey, A. S., Ackerman, J. L. & Depczynski, M. Coral bleaching, reef fish community phase shifts and the resilience of coral reefs. Glob. Change Biol. 12, 1587–1594 (2006).ADS 

Google Scholar 
20.Graham, N. A. J., Jennings, S., MacNeil, M. A., Mouillot, D. & Wilson, S. K. Predicting climate-driven regime shifts versus rebound potential in coral reefs. Nature 518, 94–97 (2015).ADS 
CAS 
PubMed 

Google Scholar 
21.Pratchett, M. S., Thompson, C. A., Hoey, A. S., Cowman, P. F. & Wilson, S. K. Effects of coral bleaching and coral loss on the structure and function of reef fish assemblages. In Coral Bleaching: Patterns, Processes, Causes and Consequences (eds van Oppen, M. J. H. & Lough, J. M.) 265–293 (Springer International Publishing, 2018). https://doi.org/10.1007/978-3-319-75393-5_11.Chapter 

Google Scholar 
22.Baird, A. H. & Marshall, P. A. Mortality, growth and reproduction in scleractinian corals following bleaching on the Great Barrier Reef. Mar. Ecol. Prog. Ser. 237, 133–141 (2002).ADS 

Google Scholar 
23.Gintert, B. E. et al. Marked annual coral bleaching resilience of an inshore patch reef in the Florida Keys: A nugget of hope, aberrance, or last man standing?. Coral Reefs 37, 533–547 (2018).ADS 

Google Scholar 
24.Gold, Z. & Palumbi, S. R. Long-term growth rates and effects of bleaching in Acropora hyacinthus. Coral Reefs 37, 267–277 (2018).ADS 

Google Scholar 
25.Fox, M. D. et al. Limited coral mortality following acute thermal stress and widespread bleaching on Palmyra Atoll, central Pacific. Coral Reefs 38, 701–712 (2019).ADS 

Google Scholar 
26.Thinesh, T., Meenatchi, R., Jose, P. A., Kiran, G. S. & Selvin, J. Differential bleaching and recovery pattern of southeast Indian coral reef to 2016 global mass bleaching event: Occurrence of stress-tolerant symbiont Durusdinium (Clade D) in corals of Palk Bay. Mar. Pollut. Bull. 145, 287–294 (2019).CAS 
PubMed 

Google Scholar 
27.Ritson-Williams, R. & Gates, R. D. Coral community resilience to successive years of bleaching in Kāne‘ohe Bay, Hawai‘i. Coral Reefs 39, 757–769 (2020).
Google Scholar 
28.Sakai, K., Singh, T. & Iguchi, A. Bleaching and post-bleaching mortality of Acropora corals on a heat-susceptible reef in 2016. PeerJ 7, e8138 (2019).PubMed 
PubMed Central 

Google Scholar 
29.Muir, P. R., Marshall, P. A., Abdulla, A. & Aguirre, J. D. Species identity and depth predict bleaching severity in reef-building corals: shall the deep inherit the reef?. Proc. R. Soc. B Biol. Sci. 284, 20171551 (2017).
Google Scholar 
30.Baird, A. H. et al. A decline in bleaching suggests that depth can provide a refuge from global warming in most coral taxa. Mar. Ecol. Prog. Ser. 603, 257–264 (2018).ADS 

Google Scholar 
31.Frade, P. R. et al. Deep reefs of the Great Barrier Reef offer limited thermal refuge during mass coral bleaching. Nat. Commun. 9, 3447 (2018).ADS 
PubMed 
PubMed Central 

Google Scholar 
32.Crosbie, A., Bridge, T., Jones, G. & Baird, A. Response of reef corals and fish at Osprey Reef to a thermal anomaly across a 30 m depth gradient. Mar. Ecol. Prog. Ser. 622, 93–102 (2019).ADS 

Google Scholar 
33.Harrison, H. B. et al. Back-to-back coral bleaching events on isolated atolls in the Coral Sea. Coral Reefs 38, 713–719 (2019).ADS 

Google Scholar 
34.Sheppard, C., Sheppard, A. & Fenner, D. Coral mass mortalities in the Chagos Archipelago over 40 years: Regional species and assemblage extinctions and indications of positive feedbacks. Mar. Pollut. Bull. 154, 111075 (2020).CAS 
PubMed 

Google Scholar 
35.Berumen, M. L., Pratchett, M. S. & McCormick, M. I. Within-reef differences in diet and body condition of coral-feeding butterflyfishes (Chaetodontidae). Mar. Ecol. Prog. Ser. 287, 217–227 (2005).ADS 

Google Scholar 
36.Coker, D. J., Pratchett, M. S. & Munday, P. L. Coral bleaching and habitat degradation increase susceptibility to predation for coral-dwelling fishes. Behav. Ecol. 20, 1204–1210 (2009).
Google Scholar 
37.Glynn, P. W. Corallivore population sizes and feeding effects following El Niño (1982–1983) associated coral mortality in Panama. in Proceedings of the 5th International Coral Reef Congress Symposium vol. 4, 183–188 (1985).38.Gates, R. D. Seawater temperature and sublethal coral bleaching in Jamaica. Coral Reefs 8, 193–197 (1990).ADS 

Google Scholar 
39.Cole, A. J., Pratchett, M. S. & Jones, G. P. Effects of coral bleaching on the feeding response of two species of coral-feeding fish. J. Exp. Mar. Biol. Ecol. 373, 11–15 (2009).
Google Scholar 
40.Pisapia, C., Cole, A. J. & Pratchett, M. S. Changing feeding preferences of butterflyfishes following coral bleaching. in Proceedings of the 12th International Coral Reef Symposium 5 (2012).41.Brooker, R. M., Munday, P. L., Brandl, S. J. & Jones, G. P. Local extinction of a coral reef fish explained by inflexible prey choice. Coral Reefs 33, 891–896 (2014).ADS 

Google Scholar 
42.Rocha, L. A. et al. Mesophotic coral ecosystems are threatened and ecologically distinct from shallow water reefs. Science 361, 281–284 (2018).ADS 
CAS 
PubMed 

Google Scholar 
43.Loya, Y., Puglise, K. A. & Bridge, T. C. L. Mesophotic Coral Ecosystems (Springer, 2019).
Google Scholar 
44.Goldstein, E. D., D’Alessandro, E. K. & Sponaugle, S. Fitness consequences of habitat variability, trophic position, and energy allocation across the depth distribution of a coral-reef fish. Coral Reefs 36, 957–968 (2017).ADS 

Google Scholar 
45.MacDonald, C., Jones, G. P. & Bridge, T. Marginal sinks or potential refuges? Costs and benefits for coral-obligate reef fishes at deep range margins. Proc. R. Soc. B Biol. Sci. 285, 20181545 (2018).
Google Scholar 
46.MacDonald, C., Bridge, T. C. L., McMahon, K. W. & Jones, G. P. Alternative functional strategies and altered carbon pathways facilitate broad depth ranges in coral-obligate reef fishes. Funct. Ecol. 33, 1962–1972 (2019).
Google Scholar 
47.MacDonald, C. Depth as Refuge: Depth Gradients in Ecological Pattern, Process, and Risk Mitigation Among Coral Reef Fishes (James Cook University, 2018).
Google Scholar 
48.MacDonald, C., Tauati, M. I. & Jones, G. P. Depth patterns in microhabitat versatility and selectivity in coral reef damselfishes. Mar. Biol. 165, 138 (2018).
Google Scholar 
49.MacDonald, C., Bridge, T. & Jones, G. Depth, bay position and habitat structure as determinants of coral reef fish distributions: Are deep reefs a potential refuge?. Mar. Ecol. Prog. Ser. 561, 217–231 (2016).ADS 

Google Scholar 
50.Keith, S. A. et al. Synchronous behavioural shifts in reef fishes linked to mass coral bleaching. Nat. Clim. Change 8, 986–991 (2018).ADS 

Google Scholar 
51.Tricas, T. C. Determinants of feeding territory size in the corallivorous butterflyfish, Chaetodon multicinctus. Anim. Behav. 37, 830–841 (1989).
Google Scholar 
52.Coker, D. J., Pratchett, M. S. & Munday, P. L. Influence of coral bleaching, coral mortality and conspecific aggression on movement and distribution of coral-dwelling fish. J. Exp. Mar. Biol. Ecol. 414–415, 62–68 (2012).
Google Scholar 
53.Wismer, S., Tebbett, S. B., Streit, R. P. & Bellwood, D. R. Spatial mismatch in fish and coral loss following 2016 mass coral bleaching. Sci. Total Environ. 650, 1487–1498 (2019).ADS 
CAS 
PubMed 

Google Scholar 
54.Berumen, M. L. & Pratchett, M. S. Trade-offs associated with dietary specialization in corallivorous butterflyfishes (Chaetodontidae: Chaetodon). Behav. Ecol. Sociobiol. 62, 989–994 (2008).
Google Scholar 
55.Brooker, R. M., Jones, G. P. & Munday, P. L. Prey selectivity affects reproductive success of a corallivorous reef fish. Oecologia 172, 409–416 (2013).ADS 
PubMed 

Google Scholar 
56.Burns, C. E. Behavioral ecology of disturbed landscapes: the response of territorial animals to relocation. Behav. Ecol. 16, 898–905 (2005).
Google Scholar 
57.Blowes, S. A., Pratchett, M. S. & Connolly, S. R. Heterospecific aggression and dominance in a guild of coral-feeding fishes: the roles of dietary ecology and phylogeny. Am. Nat. 182, 157–168 (2013).PubMed 

Google Scholar 
58.Pratchett, M. S. Feeding preferences and dietary specialization among obligate coral-feeding butterflyfishes. Biol. Butterflyfishes CRC Press Boca Raton USA 140–179 (2013).59.Penin, L., Vidal-Dupiol, J. & Adjeroud, M. Response of coral assemblages to thermal stress: Are bleaching intensity and spatial patterns consistent between events?. Environ. Monit. Assess. 185, 5031–5042 (2013).
Google Scholar 
60.Wyatt, A. S. J. et al. Heat accumulation on coral reefs mitigated by internal waves. Nat. Geosci. 13, 28–34 (2020).ADS 
CAS 

Google Scholar 
61.Bloomberg, J. & Holstein, D. M. Mesophotic coral refuges following multiple disturbances. Coral Reefs 40, 821–834 (2021).
Google Scholar 
62.Bridge, T. C. L. et al. Variable responses of benthic communities to anomalously warm sea temperatures on a high-latitude coral reef. PLoS One 9, e113079 (2014).ADS 
PubMed 
PubMed Central 

Google Scholar 
63.Grottoli, A. G. et al. The cumulative impact of annual coral bleaching can turn some coral species winners into losers. Glob. Change Biol. 20, 3823–3833 (2014).ADS 

Google Scholar 
64.Hoogenboom, M. O. et al. Environmental drivers of variation in bleaching severity of Acropora species during an extreme thermal anomaly. Front. Mar. Sci. 4, 376 (2017).
Google Scholar 
65.Suggett, D. J. & Smith, D. J. Coral bleaching patterns are the outcome of complex biological and environmental networking. Glob. Change Biol. 26, 68–79 (2020).ADS 

Google Scholar 
66.Starbuck, C. A., Considine, E. S. & Chambers, C. L. Water and elevation are more important than burn severity in predicting bat activity at multiple scales in a post-wildfire landscape. PLoS One 15, e0231170 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
67.Bond, M. L., Bradley, C. & Lee, D. E. Foraging habitat selection by California spotted owls after fire: Spotted Owls and Fire. J. Wildl. Manag. 80, 1290–1300 (2016).
Google Scholar 
68.NOAA. Kaplan SST V2 data provided by the NOAA/OAR/ESRL PSL. https://psl.noaa.gov/ (2020).69.Pinheiro, H. T. et al. Upper and lower mesophotic coral reef fish communities evaluated by underwater visual censuses in two Caribbean locations. Coral Reefs 35, 139–151 (2016).ADS 

Google Scholar 
70.Yabuta, S. & Berumen, M. L. Social structure and spawning behavior of Chaetodon butterflyfishes. in The Biology of Butterflyfishes (CRC Press, 2013).71.Pearl, J., Glymour, M. & Jewell, N. P. Causal Inference in Statistics: A Primer (Wiley, 2016).MATH 

Google Scholar 
72.McElreath, R. Statistical Rethinking: A Bayesian Course with Examples in R and Stan (Chapman and Hall/CRC, 2020). https://doi.org/10.1201/9780429029608.Book 

Google Scholar 
73.Manly, B. F., McDonald, L., Thomas, D. L., McDonald, T. L. & Erickson, W. P. Resource Selection by Animals: Statistical Design and Analysis for Field Studies (Springer Science & Business Media, 2007).
Google Scholar  More

1.Ditchkoff, S. S., Saalfeld, S. T. & Gibson, C. J. Animal behavior in urban ecosystems: Modifications due to human-induced stress. Urban Ecosyst. 9, 5–12 (2006).
Google Scholar 
2.Shochat, E., Warren, P. S., Faeth, S. H., McIntyre, N. E. & Hope, D. From patterns to emerging processes in mechanistic urban ecology. Trends Ecol. Evol. 21, 186–191 (2006).PubMed 

Google Scholar 
3.Witherington, B. E. Behavioral responses of nesting sea turtles to artificial lighting. Herpetologica 48, 31–39 (1992).
Google Scholar 
4.Markovchick-Nicholls, L. et al. Relationships between human disturbance and wildlife land use in urban habitat fragments. Conserv. Biol. 22, 99–109 (2008).PubMed 

Google Scholar 
5.Dunagan, S. P., Karels, T. J., Moriarty, J. G., Brown, J. L. & Riley, S. P. D. Bobcat and rabbit habitat use in an urban landscape. J. Mammal. 100, 401–409 (2019).
Google Scholar 
6.Prange, S., Gehrt, S. D. & Wiggers, E. P. Influences of anthropogenic resources on raccoon (Procyon lotor) movements and spatial distribution. J. Mammal. 85, 483–490 (2004).
Google Scholar 
7.Cooper, D. S., Yeh, P. J. & Blumstein, D. T. Tolerance and avoidance of urban cover in a southern California suburban raptor community over five decades. Urban Ecosyst. https://doi.org/10.1007/s11252-020-01035-w (2020).Article 

Google Scholar 
8.Auman, H. J., Bond, A. L., Meathrel, C. E. & Richardson, A. Urbanization of the silver gull: Evidence of anthropogenic feeding regimes from stable isotope analyses. Waterbirds 34, 70–76 (2011).
Google Scholar 
9.McKinney, M. L. Effects of urbanization on species richness: A review of plants and animals. Urban Ecosyst. 11, 161–176 (2008).
Google Scholar 
10.Faeth, S. H., Warren, P. S., Shochat, E. & Marussich, W. A. Trophic dynamics in urban communities. Bioscience 55, 399–407 (2005).
Google Scholar 
11.Rodewald, A. D., Kearns, L. J. & Shustack, D. P. Anthropogenic resource subsidies decouple predator–prey relationships. Ecol. Appl. 21, 936–943 (2011).PubMed 

Google Scholar 
12.Shochat, E., Lerman, S. B., Katti, M. & Lewis, D. B. Linking optimal foraging behavior to bird community structure in an urban-desert landscape: Field experiments with artificial food patches. Am. Nat. 164, 232–243 (2004).PubMed 

Google Scholar 
13.Baruch-Mordo, S., Breck, S. W., Wilson, K. R. & Theobald, D. M. Spatiotemporal distribution of black bear–human conflicts in Colorado, USA. J. Wildl. Manag. 72, 1853–1862 (2005).
Google Scholar 
14.Bateman, P. W. & Fleming, P. A. Big city life: Carnivores in urban environments. J. Zool. 287, 1–23 (2012).
Google Scholar 
15.Nisbet, I., Veit, R. R., Auer, S. & White, T. Marine Birds of the Eastern United States and the Bay of Fundy: Distribution, Numbers, Trends, Threats, and Management (Nuttall Ornithological Club, 2013).
Google Scholar 
16.Washburn, B. E., Bernhardt, G. E., Kutschbach-Brohl, L., Chipman, R. B. & Francoeur, L. C. Foraging ecology of four gull species at a coastal–urban interface. Condor 115, 67–76 (2013).
Google Scholar 
17.Fuirst, M., Veit, R. R., Hahn, M., Dheilly, N. & Thorne, L. H. Effects of urbanization on the foraging ecology and microbiota of the generalist seabird Larus argentatus. PLoS One 13, 1–22 (2018).
Google Scholar 
18.Shaffer, S. A. et al. Population-level plasticity in foraging behavior of western gulls (Larus occidentalis). Mov. Ecol. 5, 1–13 (2017).
Google Scholar 
19.Rock, P. et al. Results from the first GPS tracking of roof-nesting Herring Gulls Larus argentatus in the UK. Ring. Migr. 31(1), 47–62 (2016).
Google Scholar 
20.Spelt, A. et al. Urban gulls adapt foraging schedule to human-activity patterns. Ibis (Lond. 1859) 163, 274–282 (2021).
Google Scholar 
21.Belant, J. L. Gulls in urban environments: Landscape-level reduce conflict. Landsc. Urban Plan. 38, 245–258 (1997).
Google Scholar 
22.Steenweg, R. J., Ronconi, R. A. & Leonard, M. L. Seasonal and age-dependent dietary partitioning between the great black-backed and herring gulls. Condor 113, 795–805 (2011).
Google Scholar 
23.Maynard, L. D. & Ronconi, R. A. Foraging behaviour of great black-backed gulls Larus marinus near an urban centre in atlantic Canada: Evidence of individual specialization from GPS tracking. Mar. Ornithol. 46, 27–32 (2018).
Google Scholar 
24.Borrmann, R. M., Phillips, R. A., Clay, T. A. & Garthe, S. High foraging site fidelity and spatial segregation among individual great black-backed gulls. J. Avian Biol. 50, 1–10 (2019).
Google Scholar 
25.Smith, J. A., Mazumder, D., Suthers, I. M. & Taylor, M. D. To fit or not to fit: Evaluating stable isotope mixing models using simulated mixing polygons. Methods Ecol. Evol. 4, 612–618 (2013).
Google Scholar 
26.Stock, B. C. et al. Analyzing mixing systems using a new generation of Bayesian tracer mixing models. PeerJ 6, 1–27 (2018).
Google Scholar 
27.Shochat, E. Credit or debit? Resource input changes population dynamics of city-slicker birds. Oikos 106, 622–626 (2004).
Google Scholar 
28.Seress, G. & Liker, A. Habitat urbanization and its effects on birds. Acta Zool. Acad. Sci. Hungar. 61, 373–408 (2015).
Google Scholar 
29.Annett, C. A. & Pierotti, R. Long-term reproductive output in western gulls: Consequences of alternate tactics in diet choice. Ecology 80, 288–297 (1999).
Google Scholar 
30.Anderson, J. G. T., Shlepr, K. R., Bond, A. L. & Ronconi, R. A. Introduction: A historical perspective on trends in some gulls in eastern North America, with reference to other regions. Waterbirds 39, 1–9 (2016).
Google Scholar 
31.Washburn, B. E., Elbin, S. B. & Davis, C. Historical and current population trends of herring gulls (Larus argentatus) and Great Black-Backed Gulls (Larus marinus) in the New York Bight, USA. Waterbirds 39, 74–86 (2016).
Google Scholar 
32.Duhem, C., Roche, P., Vidal, E. & Tatoni, T. Effects of anthropogenic food resources on yellow-legged gull colony size on Mediterranean islands. Popul. Ecol. 50, 91–100 (2008).
Google Scholar 
33.Zorrozua, N. et al. Breeding yellow-legged Gulls increase consumption of terrestrial prey after landfill closure. Ibis (Lond. 1859) 162, 50–62 (2020).
Google Scholar 
34.Pons, J. Effects of changes in the availability of human refuse on breeding parameters in a herring gull. Ardea 1983, 143–150 (1992).
Google Scholar 
35.Ordeñana, M. A. et al. Effects of urbanization on carnivore species distribution and richness. J. Mammal. 91, 1322–1331 (2010).
Google Scholar 
36.Duchamp, J. E., Sparks, D. W. & Whitaker, J. O. Foraging-habitat selection by bats at an urban-rural interface: Comparison between a successful and a less successful species. Can. J. Zool. 82, 1157–1164 (2004).
Google Scholar 
37.USDA. Feedgrains sector at a glance (2021). https://www.ers.usda.gov/topics/crops/corn-and-other-feedgrains/feedgrains-sector-at-a-glance/ (Accessed 10th July 2021).38.Jahren, A. H. & Schubert, B. A. Corn content of French fry oil from national chain vs. small business restaurants. Proc. Natl. Acad. Sci. U.S.A. 107, 2099–2101 (2010).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
39.Hebert, C. E., Shutt, J. L., Hobson, K. A. & Weseloh, D. V. C. Spatial and temporal differences in the diet of Great Lakes herring gulls (Larus argentatus): Evidence from stable isotope analysis. Can. J. Fish. Aquat. Sci. 56, 323–338 (1999).
Google Scholar 
40.Moreno, R., Jover, L., Munilla, I., Velando, A. & Sanpera, C. A three-isotope approach to disentangling the diet of a generalist consumer: The yellow-legged gull in northwest Spain. Mar. Biol. 157, 545–553 (2010).
Google Scholar 
41.Coulson, J. C. Re-evaluation of the role of landfills and culling in the historic changes in the herring gull (Larus argentatus) population in Great Britain. Waterbirds 38, 339–354 (2015).
Google Scholar 
42.Shlepr, K. R., Ronconi, R. A., Hayden, B., Allard, K. A. & Diamond, A. W. Estimating the relative use of anthropogenic resources by herring gull (Larus argentatus) in the Bay of Fundy, Canada. Avian Conserv. Ecol. 16, 1–18 (2021).
Google Scholar 
43.Orians, G. & Pearson, N. On the theory of central place foraging. In Analysis of Ecological Communities (eds Horn, D. et al.) 154–177 (Ohio State University Press, 1979).
Google Scholar 
44.Walter, G. H. What is resource partitioning?. J. Theor. Biol. 150, 137–143 (1991).ADS 
CAS 
PubMed 

Google Scholar 
45.Schoener, T. Resource Partitioning. In Community Ecology: Pattern and Process (eds Kikkawa, J. & Anderson, D.) 91–126 (Blackwell Science Inc, 1986).
Google Scholar 
46.Rome, M. S. & Ellis, J. C. Foraging Ecology and Interactions between Herring Gulls and Great Black-Backed Gulls in New England rocky intertidal. Waterbirds 27, 200–210 (2017). http://www.jstor.org/stable/152243547.Weimerskirch, H., Bartle, J. A., Jouventin, P. & Claude, J. Foraging ranges and partitioning of feeding zones in three species of southern Albatrosses. Condor 90, 214–219 (1998). http://www.jstor.org/stable/136845048.Barger, C. P., Young, R. C., Will, A., Ito, M. & Kitaysky, A. S. Resource partitioning between sympatric seabird species increases during chick-rearing. Ecosphere 7, 1–15 (2016).
Google Scholar 
49.Ronconi, R. A., Steenweg, R. J., Taylor, P. D. & Mallory, M. L. Gull diets reveal dietary partitioning, influences of isotopic signatures on body condition, and ecosystem changes at a remote colony. Mar. Ecol. Prog. Ser. 514, 247–261 (2014).ADS 

Google Scholar 
50.Knoff, A., Macko, S. A., Erwin, R. M. & Brown, K. M. Stable isotope analysis of temporal variation in the diets of pre-fledged laughing gulls. Waterbirds 25, 142–148 (2017).
Google Scholar 
51.Clewley, G. D. et al. Foraging habitat selection by breeding Herring Gulls (Larus argentatus) from a declining coastal colony in the United Kingdom. Estuar. Coast. Shelf Sci. 261, 107564 (2021).
Google Scholar 
52.Evans, B. A. & Gawlik, D. E. Urban food subsidies reduce natural food limitations and reproductive costs for a wetland bird. Sci. Rep. 10, 1–12 (2020).
Google Scholar 
53.Auman, H. J., Meathrel, C. E. & Richardson, A. Supersize me: Does anthropogenic food change the body condition of silver gulls? A comparison between urbanized and remote, non-urbanized areas. Waterbirds 31, 122–126 (2008).
Google Scholar 
54.Pierotti, R. & Annett, C. The ecology of Western Gulls in habitats varying in degree of urban influence. in Avian Ecology and Conservation in an Urbanizing World 307–329 (2001).55.Belant, J. L., Ickes, S. K. & Seamans, T. W. Importance of landfills to urban-nesting herring and ring-billed gulls. Landsc. Urban Plan. 43, 11–19 (1998).
Google Scholar 
56.Murray, M. H., Hill, J., Whyte, P. & St. Clair, C. C. Urban compost attracts coyotes, contains toxins, and may promote disease in urban-adapted wildlife. EcoHealth 13, 285–292 (2016).PubMed 

Google Scholar 
57.Sapolsky, R. & Else, J. Bovine tuberculosis in a wild baboon population: Epidemiological aspects. J. Med. Primatol. 16, 229–235 (1987).CAS 
PubMed 

Google Scholar 
58.Thorne, L. H., Fuirst, M., Veit, R. & Baumann, Z. Mercury concentrations provide an indicator of marine foraging in coastal birds. Ecol. Indic. 121, 106922 (2021).CAS 

Google Scholar 
59.Fauchald, P. & Tveraa, T. Using first-passage time in the analysis of area-restricted reports. Ecology 84, 282–288 (2003).
Google Scholar 
60.Suryan, R. M. et al. Foraging destinations and marine habitat use of short-tailed albatrosses: A multi-scale approach using first-passage time analysis. Deep. Res. Part II Top. Stud. Oceanogr. 53, 370–386 (2006).ADS 

Google Scholar 
61.McCune, B. & Grace, J. B. Nonmetric multidimensional scaling. in Analysis of Ecological Communities 125–142 (2002).62.Hobson, K. A. & Clark, R. G. Assessing avian diets using stable isotopes I: Turnover of 13C in tissues. Condor 94, 181–188 (1992). http://www.jstor.com/stable/136880763.Post, D. M. et al. Getting to the fat of the matter: Models, methods and assumptions for dealing with lipids in stable isotope analyses. Oecologia 152, 179–189 (2007).ADS 
PubMed 

Google Scholar 
64.Sweeting, C. J., Polunin, N. V. C. & Jennings, S. Effects of chemical lipid extraction and arithmetic lipid correction on stable isotope ratios of fish tissues. Rapid Commun. Mass Spectrom. 20, 595–601 (2006).ADS 
CAS 
PubMed 

Google Scholar 
65.Caut, S., Angulo, E. & Courchamp, F. Variation in discrimination factors (Δ15N and Δ13C): The effect of diet isotopic values and applications for diet reconstruction. J. Appl. Ecol. 46, 443–453 (2009).CAS 

Google Scholar 
66.Hobson, K. A. & Clark, R. G. Assessing avian diets using stable isotopes II: Factors influencing diet-tissue fractionation. Condor 94, 189–197 (1992).
Google Scholar 
67.EvansOgden, L. J., Hobson, K. A. & Lank, D. B. Blood isotopic (δ13C and δ15N) turnover and diet-tissue fractionation factors in captive dunlin (Calidris alpina pacifica). Auk 121, 170–177 (2004).
Google Scholar  More

Site characteristics and experimental designIn this study, agricultural soils with five SOM contents were collected in 2015 from the following three different locations with the same climate type (the moderate temperate continental climate) in Northeast China (Table S3 and Fig. 1): Bei’an (BA), Hailun (HL), and Dehui (DH). Their MAT and MAP range from 1.0 to 4.4 and 520 to 550, respectively. After collection, the samples were transported to the Hailun Agricultural Ecological Experimental Station (HL), where the samples were packed into the same PVC tubes. Moving the soil from these three initial sampling points to the HL may have had some influence on the microbes, but compared with longer-distance soil translocation across different climatic zones, the HL site can be regarded as an in situ site that reflects the original climatic conditions. The SOM contents were 2%, 3%, 5%, 7%, and 9% (equivalent to 10, 18, 28, 36, and 56 g C kg−1 soil−1, respectively), and all the soils were classified as Mollisols according to the FAO classification. Here, we designed a unique latitudinal soil translocation experiment to investigate the relationship between the bacterial and fungal community stability and the responses of soil C molecular structure to climate warming. The detailed protocol for the experiment was the following: (1) Forty kilograms of topsoil (0–25 cm) was collected for each SOM. The latitude and longitude of the sampling sites and soil geochemical characteristics are shown in Tables S3 and S4. Detailed data can be found in Supplementary Data 1. (2) The soil was homogenized using a 2 mm sieve and filled with sterilized PVC tubes. The PVC tube was 5 cm in diameter at the bottom and 31 cm in height. Each tube was filled with a 25 cm-high soil column, which corresponded to approximately 1 kg of soil. The bottom of the pipe was filled with 1 cm quartz sand, and a 5 cm space was left at the top. (3) From October to November 2015, 90 PVC pipes containing soil (5 SOM gradients × 3 replicates × 6 climatic conditions) were transported to six ecological research stations with different geoclimatic conditions and SOM contents, and 15 PVC pipes were placed in each station. Once the experiment was set up, the weeds growing in each PVC pipe were manually removed every 2–3 weeks to avoid the impact of plants.The six ecological research stations were the Hailun Agricultural Ecological Experimental Station (HL, N 47°27′, E 126°55′) in Heilongjiang Province, Shenyang Agriculture Ecological Experimental Station (SY, N 41°49′, E 123°33′) in Liaoning Province, Fengqiu Agricultural Ecological Experimental Station (FQ, N 35°03′, E 114°23′) in Henan Province, Changshu Agricultural Ecological Experimental Station (CS, N 31°41′, E 120°41′) in Jiangsu Province, Yingtan Red Soil Ecological Experiment Station (YT, N 28°12′, E 116°55′) in Jiangxi Province and Guangzhou National Agricultural Science and Technology Park (GZ, N 23°23′, E 113°27′) in Guangdong Province. The MAT and MAP at the six ecological research stations ranged from 1.5 to 21.9 °C and from 550 to 1750 mm from north to south, respectively. Details of their climatic conditions (e.g., climatic types) are shown in Table S5. All tubes were removed from each station after 1 year.The soil samples were stored on dry ice and rapidly transported back to the laboratory. The soil pH was measured by the potentiometric method. Nitrate (NO3−-N) and ammonium nitrogen (NH4+-N) were measured by the Kjeldahl method. DOC was measured using a total organic carbon analyzer (Shimadzu Corporation, Kyoto, Japan). SOC was determined by wet digestion using the potassium dichromate method53. Microbial biomass C (MBC) was measured by the chloroform fumigation-incubation method54. All geochemical attributes are shown in Table S4.Solid-state 13C NMR analysis of soil C molecular groupsSolid-state 13C NMR spectroscopy analysis was performed to determine the molecular structure of SOC. A Bruker-Avance-iii-300 spectrometer was used at a frequency of 75 MHz (300 MHz 1H). Before the examination, the soil samples were pretreated with hydrofluoric acid to eliminate the interference of Fe3+ and Mn2+ ions in the soil. Specifically, 5 g of air-dried soil was weighed in a 100 ml centrifuge tube with 50 ml of hydrofluoric acid solution (10% v/v) and shaken for 1 h. The supernatant was then removed by centrifugation at 3000 rpm for 10 min. The residues were washed eight times with a hydrofluoric acid solution (10%) with ultrasonication. The oscillation program consisted of the following: four × 1 h, three × 12 h, and one × 24 h. The soil samples were washed with distilled water four times to remove the residual hydrofluoric acid. The above-mentioned treated soil samples were dried in an oven at 40 °C, ground and passed through a 60-mesh sieve for NMR measurements.The soil samples were then subjected to solid-state magic-angle rotation-NMR measurements (AVANCE II 300 MH) using a 7 mm CPMAS probe with an observed frequency of 100.5 MHz, an MAS rotation frequency of 5000 Hz, a contact time of 2 s, and a cycle delay time of 2.5 s. The external standard material for the chemical shift was hexamethyl benzene (HMB, methyl 17.33 mg kg−1). The spectra were quantified by subdividing them into the following chemical shift regions55: 0–45 ppm (alkyl), 45–60 ppm (N-alkyl and methoxyl), 60–110 ppm (O-alkyl), 110–140 ppm (aryl), 140–160 ppm (O-aryl), 160–185 ppm (carboxy), and 185–230 ppm (carbonyl) (Fig. 3a). We classified O-alkyl, O-aryl, and carboxy C as labile C and alkyl, N-alkyl/methoxyl, and aryl C were classified as recalcitrant C.Soil microbial C metabolic profilesThe soil microbial C metabolic capacities were measured with BIOLOG 96-well Eco-Microplates (Biolog Inc., USA) using 31 different C sources and three replicates in each microplate. These C sources included carbohydrates, carboxylic acids, polymers, amino acids, amines, and phenolic acids (Table S2). Carbohydrates, amino acids, and carboxylic acids are generally considered labile C sources, amines and phenolic acid compounds are relatively resistant C sources, and polymers are recalcitrant C. The diverse nature of these C sources allowed us to identify differences in the capacity of microbes to degrade different C sources56. Soil microbes were extracted as follows: (1) Five grams of soil (dry weight equivalent) was incubated at 25 °C for 24 h, and 45 ml of sterile 0.85% (w/v) sodium chloride solution was added57. (2) At room temperature (25 °C), the mixture was shaken at 200 rpm for 30 min and allowed to stand for 15 min. (3) Subsequently, 0.1 ml of the supernatant was collected and diluted to 100 ml with sterile sodium chloride solution. (4) Soil suspensions were dispensed into each of the 93 wells (150 μl per well), and the plates were then incubated at 25 °C in the dark for 14 days. The optical density (OD, reflecting C utilization) of each well was read at 590 nm (color development) every 12 h. The normalized OD of different C sources was calculated as the OD of the well that contained the C source minus the OD of the well that contained sterile sodium chloride solution (control well). The normalized OD at a single time point (228 h) was used for the posterior analysis when it reached the asymptote.DNA extraction, PCR amplification, and sequencingDNA was extracted from all 90 soil samples. Briefly, well-mixed soil samples (0.6 g) were analyzed using the Power Soil DNA Isolation Kit (MoBio Laboratories, Inc., Carlsbad, CA, USA) following the manufacturer’s instructions. The quality of the DNA extracts was determined by spectrophotometry (OD-1000+, OneDrop Technologies, China). The DNA extracts were considered of sufficient quality if the ratio of OD260 to OD280 (optical density, OD) and the ratio of OD260 to OD230 were approximately 1.8. All eligible DNA samples were stored at −80 °C.Taxonomic profiling of the soil bacterial and fungal communities was performed using an Illumina® HiSeq Benchtop Sequencer. PCR amplification was performed using an ABI GeneAmp® 9700 (ABI, Foster City, CA, USA) with a 20 μl reaction system containing 4 μl of 5× FastPfu Buffer, 0.8 μl of each primer (5 μM), 2 μl of 2.5 mM dNTPs, 2 μl of template DNA, and 0.4 μl of FastPfu Polymerase. For bacterial analysis, the forward the primer 515F (GTGCCAGCMGCCGCGG) and the reverse primer 907R (CCGTCAATTCMTTTRAGTTT) were used to amplify the bacteria-specific V4-V5 hypervariable region of the 16S rRNA gene58. For fungal analysis, the internal transcribed spacer 1 (ITS1) region of the ribosomal RNA gene was amplified with primers ITS1-1737F (GGAAGTAAAAGTCGTAACAAGG) and ITS2-2043R (GCTGCGTTCTTCATCGATGC)59. The PCR protocol for bacteria consisted of an initial predenaturation step of 95 °C for 2 min, 35 cycles of 20 s at 94 °C, 40 s at 55 °C and 1 min at 72 °C, and a final 10 min extension at 72 °C. The PCR protocol for fungi consisted of an initial predenaturation step of 95 °C for 3 min, 35 cycles of 30 s at 95 °C, 30 s at 59.3 °C, and 45 s at 72 °C and a final 10 min extension at 72 °C.Each sample was independently amplified three times. Following amplification, 2 μl of each of the PCR products was checked by agarose gel (2.0%) electrophoresis, and all the PCR products from the same sample were then pooled together. The pooled mixture was purified using the Agencourt AMPure XP Kit (Beckman Coulter, CA, USA). The purified products were indexed in the 16S and ITS libraries. The quality of these libraries was assessed using Qubit@2.0 Fluorometer (Thermo Scientific) and Agilent Bioanalyzer 2100 systems. These pooled libraries (16S and ITS) were subsequently sequenced with an Illumina HiSeq 2500 Sequencer to generate 2 × 250 bp paired-end reads at the Center for Genetic & Genomic Analysis, Genesky Biotechnologies Inc., Shanghai, China.The raw reads were quality filtered and merged as follows: (1) TrimGalore was used for truncation of the raw reads at any site with an average quality score  5%) soils, changes in the C metabolic capacity of microbes under elevated temperatures were characterized using the ratio of the OD of microbes measured in the translocated soils to the OD of microbes in the in situ HL soil. A ratio greater than 1 indicates that translocation warming increases the C metabolism of microbes.Mantel and partial Mantel analysisA previous study showed that partial Mantel analysis is a robust method for evaluating the relationship among three variables65. This approach can control the z-axis and assess only the relationship between the x- and y-axes, avoiding the interaction between the z- and x-axes on the y-axis. In this study, Mantel analysis was employed to assess the relationships between the stability of the bacterial and fungal communities and C metabolic capacity. Stability refers primarily to the ability of the microbial community to resist translocation warming66. A higher similarity between the microbial communities in translocated soil compared with that in the in situ HL area indicates that the community is more resistant to translocation-related warming and that the microbial community is more stable.Calculation of the microbial β-diversityBray-Curtis and Euclidean dissimilarity metrics were calculated to estimate the bacterial and fungal taxonomic dissimilarity (β-diversity) and environmental dissimilarity (e.g., latitude, MAT, and MAP), respectively, using the vegan package (version 2.5–6) in the R statistical program (version 4.0.2, https://www.r-project.org/)67. Corresponding to the 45 C metabolism ratios in soils with the same OM content, the β-diversity values of bacteria and fungi were selected to analyze the relationship between the community similarity (1-β-diversity) of bacteria and fungi and changes in microbial C metabolism.Impact of the SOM content and climate change on changes in microbial communitiesThe distribution patterns of the bacterial and fungal communities under different SOM gradients and climatic regimes were determined through nonmetric multidimensional scaling (NMDS)68. To quantitatively compare the effects of the SOM gradient and climatic regimes on the bacterial and fungal community composition, three nonparametric multivariate statistical analyses were used in this study: nonparametric multivariate analysis of variance (Adonis), analysis of similarity (ANOSIM), and multiple response permutation procedure (MRPP)69. The linear fit between environmental dissimilarity and microbial β-diversity was analyzed using the lm function in R. A significant difference in the bacterial and fungal β-diversity among different SOM contents was evaluated by Student’s paired t-test using the ggpubr (version 0.4.0) package70. RDA was performed to analyze the relationships of bacterial and fungal communities with various environmental factors (soil geochemical attributes and climatic conditions, such as MAP and MAT). In parallel, the Monte Carlo permutation test (999 permutations) was employed to determine whether the explanation of the microbial distribution by individual factors (e.g., pH, SOC, and TN) was significant71.Construction of the structural equation model and random forest modelA SEM was fitted to illustrate the direct or indirect effects of soil properties (e.g., pH, moisture, ammonia, and nitrate nitrogen), climate change (e.g., MAT and MAP), and bacterial and fungal β-diversity on soil C metabolic capacity72. Based on the Euclidean method, the changes in soil properties and climatic conditions of five translocated sites compared with those in the in situ HL site were calculated. A total of 45 ratios were obtained for each OM content. Corresponding to the 45 ratios in soils with the same OM content, the β-diversity values of bacteria and fungi were selected. The model construction process was mainly divided into three steps. In brief, these steps include the establishment of an a priori model, data normality detection, and an overall goodness-of-fit test. The prior model was constructed based on a literature review and our knowledge. For the variables that did not conform to the normal distribution, we performed logarithmic transformation. Here, we used the χ2 test (the model was assumed to exhibit a good fit if p  > 0.05), the goodness-of-fit index (GFI; the model was assumed to show a good fit if GFI  > 0.9), the root mean square error of approximation (RMSEA; the model was assumed to exhibit a good fit if RMSEA  0.05)73 and the Bollen-Stine bootstrap test (the model was assumed to show a good fit if the bootstrap p  > 0.10) to test the overall goodness of fit of the SEM. All SEM analyses were conducted using IBM® SPSS® Amos 21.0 (AMOS, IBM, USA). Additionally, the importance of the metabolic capacity of different types of C on labile and recalcitrant C was assessed by random forest models using the randomForest package (version 4.6-14) in R74, and the model significance and amount of interpretation were evaluated using the rfUtilities package (version 2.1–5)75.Reporting summaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. More

1.Walther, G. et al. Ecological responses to recent climate change. Nature 416, 389–395 (2002).ADS 
CAS 
PubMed 

Google Scholar 
2.Intergovernmental Panel on Climate Change (IPCC). Climate Change 2014: Impacts, Adaptation, and Vulnerability Part A (Cambridge University Press, 2014).
Google Scholar 
3.Smith, V. H. Eutrophication of freshwater and coastal marine ecosystems. A global problem. Environ. Sci. Pollut. Res. 10, 126–139 (2003).CAS 

Google Scholar 
4.Cañedo-Argüelles, M., Kefford, B. & Schäfer, R. Salt in freshwaters: Causes, effects and prospects—introduction to the theme issue. Philos. Trans. R. Soc. B Biol. Sci. 374, 20 (2019).
Google Scholar 
5.Bernhardt, E. S., Rosi, E. J. & Gessner, M. O. Synthetic chemicals as agents of global change. Front. Ecol. Environ. 15, 84–90 (2017).
Google Scholar 
6.Hooper, D. U. et al. A global synthesis reveals biodiversity loss as a major driver of ecosystem change. Nature 486, 105–108 (2012).ADS 
CAS 
PubMed 

Google Scholar 
7.Díaz, S. et al. Summary for policymakers of the global assessment report on biodiversity and ecosystem services of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services. IPBES (2019).8.Parmesan, C. & Yohe, G. A globally coherent fingerprint of climate change impacts across natural systems. Nature 421, 37–42 (2003).ADS 
CAS 
PubMed 

Google Scholar 
9.Hoffmann, A. A. & Sgrò, C. M. Climate change and evolutionary adaptation. Nature 470, 479–485 (2011).ADS 
CAS 
PubMed 

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

Google Scholar 
11.Gienapp, P., Teplitsky, C., Alho, J. S., Mills, J. A. & Merilä, J. Climate change and evolution: Disentangling environmental and genetic responses. Mol. Ecol. 17, 167–178 (2008).CAS 
PubMed 

Google Scholar 
12.Fox, R. J., Donelson, J. M., Schunter, C., Ravasi, T. & Gaitán-Espitia, J. D. Beyond buying time: The role of plasticity in phenotypic adaptation to rapid environmental change. Philos. Trans. R. Soc. B Biol. Sci. 374, 20 (2019).
Google Scholar 
13.Salamin, N., Wüest, R. O., Lavergne, S., Thuiller, W. & Pearman, P. B. Assessing rapid evolution in a changing environment. Trends Ecol. Evol. 25, 692–698 (2010).PubMed 

Google Scholar 
14.Hairston, N. G., Ellner, S. P., Geber, M. A., Yoshida, T. & Fox, J. A. Rapid evolution and the convergence of ecological and evolutionary time. Ecol. Lett. 8, 1114–1127 (2005).
Google Scholar 
15.Govaert, L., Pantel, J. H. & De Meester, L. Eco-evolutionary partitioning metrics: Assessing the importance of ecological and evolutionary contributions to population and community change. Ecol. Lett. 19, 839–853 (2016).PubMed 

Google Scholar 
16.Diamond, S. E. & Martin, R. A. The interplay between plasticity and evolution in response to human-induced environmental change. F1000Research 5, 1–10 (2016).
Google Scholar 
17.Barraclough, T. G. How do species interactions affect evolutionary dynamics across whole communities?. Annu. Rev. Ecol. Evol. Syst. 46, 25–48 (2015).
Google Scholar 
18.De Meester, L. et al. Analysing eco-evolutionary dynamics—The challenging complexity of the real world. Funct. Ecol. 33, 43–59 (2019).
Google Scholar 
19.Kleynhans, E. J., Otto, S. P., Reich, P. B. & Vellend, M. Adaptation to elevated CO2 in different biodiversity contexts. Nat. Commun. 7, 20 (2016).
Google Scholar 
20.Walther, G. R. Community and ecosystem responses to recent climate change. Philos. Trans. R. Soc. B Biol. Sci. 365, 2019–2024 (2010).
Google Scholar 
21.Kooyers, N. J., James, B. & Blackman, B. K. Competition drives trait evolution and character displacement between Mimulus species along an environmental gradient. Evolution (N.Y.) 71, 1205–1221 (2017).CAS 

Google Scholar 
22.Lawrence, D. et al. Species interactions alter evolutionary responses to a novel environment. PLoS Biol. 10, 20 (2012).
Google Scholar 
23.terHorst, C. P., Lennon, J. T. & Lau, J. A. The relative importance of rapid evolution for plant-microbe interactions depends on ecological context. Proc. R. Soc. B Biol. Sci. 281, 20 (2014).
Google Scholar 
24.Lau, J. A., Shaw, R. G., Reich, P. B. & Tiffin, P. Indirect effects drive evolutionary responses to global change. New Phytol. 201, 335–343 (2014).CAS 
PubMed 

Google Scholar 
25.Yoshida, T., Jones, L. E., Ellner, S. P., Fussmann, G. F. & Hairston, N. G. Rapid evolution drives ecological dynamics in a predator–prey system. Nature 424, 303–306 (2003).ADS 
CAS 
PubMed 

Google Scholar 
26.Hart, S. P., Turcotte, M. M. & Levine, J. M. Effects of rapid evolution on species coexistence. Proc. Natl. Acad. Sci. 116, 2112–2117 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
27.Grainger, T. N., Rudman, S. M., Schmidt, P. & Levine, J. M. Competitive history shapes rapid evolution in a seasonal climate. Proc. Natl. Acad. Sci. 118, e22015772118 (2021).
Google Scholar 
28.McGrady-Steed, J., Harris, P. M. & Morin, P. J. Biodiversity regulates ecosystem predictability. Nature 390, 162–165 (1997).ADS 
CAS 

Google Scholar 
29.Altermatt, F. et al. Big answers from small worlds: A user’s guide for protist microcosms as a model system in ecology and evolution. Methods Ecol. Evol. 6, 218–231 (2015).
Google Scholar 
30.Bar-On, Y. M., Phillips, R. & Milo, R. The biomass distribution on Earth. Proc. Natl. Acad. Sci. USA 115, 6506–6511 (2018).CAS 
PubMed 
PubMed Central 

Google Scholar 
31.Stoecker, D. & Pierson, J. Predation on protozoa: Its importance to zooplankton revisited. J. Plankton Res. 41, 367–373 (2019).
Google Scholar 
32.Berninger, U.-G., Finlay, B. J. & Kuuppo-Leinikki, P. Protozoan control of bacterial abundances in freshwater. Limnol. Oceanogr. 36, 139–147 (1991).ADS 

Google Scholar 
33.Williams, W. D. Anthropogenic salinisation of inland waters. Hydrobiologia 466, 329–337 (2001).
Google Scholar 
34.Herbert, E. R. et al. A global perspective on wetland salinization: Ecological consequences of a growing threat to freshwater wetlands. Ecosphere 6, 1–43 (2015).
Google Scholar 
35.Neubauer, S. C. & Craft, C. B. Global change and tidal freshwater wetlands: Scenarios and impacts. Tidal Freshw. Wetl. 20, 20 (2009).
Google Scholar 
36.Osmond, M. M. & de Mazancourt, C. How competition affects evolutionary rescue. Philos. Trans. R. Soc. B Biol. Sci. 368, 20 (2013).
Google Scholar 
37.terHorst, C. P. et al. Evolution in a community context: Trait responses to multiple species interactions. Am. Nat. 191, 368–380 (2018).
Google Scholar 
38.Donelson, J. M. et al. Understanding interactions between plasticity, adaptation and range shifts in response to marine environmental change. Philos. Trans. R. Soc. B Biol. Sci. 374, 20 (2019).
Google Scholar 
39.Vanvelk, H., Govaert, L., van den Berg, E. M., Brans, K. I. & De Meester, L. Interspecific differences, plastic, and evolutionary responses to a heat wave in three co-occurring Daphnia species. Limnol. Oceanogr. 20, 1–20. https://doi.org/10.1002/lno.11675 (2020).Article 

Google Scholar 
40.Svensson, F., Norberg, J. & Snoeijs, P. Diatom cell size, Coloniality and motility: Trade-Offs between temperature, Salinity and nutrient supply with climate change. PLoS One 9, 25 (2014).
Google Scholar 
41.Karp-Boss, L. & Boss, E. The elongated, the squat and the spherical: Selective pressures for phytoplankton shape. In Aquatic Microbial Ecology and Biogeochemistry: A Dual Perspective (eds Glibert, P. & Kana, T.) 25–34 (Springer, 2016).
Google Scholar 
42.Finley, H. E. Toleration of fresh water Protozoa to increased salinity. Ecology 11, 337–347 (1930).
Google Scholar 
43.Chen, H. & Jiang, J. G. Osmotic responses of Dunaliella to the changes of salinity. J. Cell. Physiol. 219, 251–258 (2009).CAS 
PubMed 

Google Scholar 
44.Shetty, P., Gitau, M. M. & Maróti, G. Salinity stress responses and adaptation mechanisms in eukaryotic green microalgae. Cells 8, 1–16 (2019).
Google Scholar 
45.terHorst, C. P. Evolution in response to direct and indirect ecological effects in pitcher plant inquiline communities. Am. Nat. 176, 675–685 (2010).PubMed 

Google Scholar 
46.Stoks, R., Govaert, L., Pauwels, K., Jansen, B. & De Meester, L. Resurrecting complexity: The interplay of plasticity and rapid evolution in the multiple trait response to strong changes in predation pressure in the water flea Daphnia magna. Ecol. Lett. 19, 180–190 (2016).PubMed 

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

Google Scholar 
48.Henn, J. J. et al. Intraspecific trait variation and phenotypic plasticity mediate alpine plant species response to climate change. Front. Plant Sci. 9, 1–11 (2018).ADS 

Google Scholar 
49.Johansson, J. Evolutionary responses to environmental changes:How does competition affect adaptation?. Evolution (N. Y.) 62, 421–435 (2008).
Google Scholar 
50.Li, S. J. et al. Microbial communities evolve faster in extreme environments. Sci. Rep. 4, 1–9 (2014).
Google Scholar 
51.Terhorst, C. P. Experimental evolution of protozoan traits in response to interspecific competition. J. Evol. Biol. 24, 36–46 (2011).CAS 
PubMed 

Google Scholar 
52.Carrara, F., Giometto, A., Seymour, M., Rinaldo, A. & Altermatt, F. Inferring species interactions in ecological communities: A comparison of methods at different levels of complexity. Methods Ecol. Evol. 6, 895–906 (2015).
Google Scholar 
53.Lorts, C. M. & Lasky, J. R. Competition × drought interactions change phenotypic plasticity and the direction of selection on Arabidopsis traits. New Phytol. 227, 1060–1072 (2020).CAS 
PubMed 

Google Scholar 
54.Hoffmann, A. A. & Hercus, M. J. Environmental stress as an evolutionary force. Bioscience 50, 217–226 (2000).
Google Scholar 
55.Klironomos, J. H. et al. Abrupt rise in atmospheric CO2 overestimates community response in a model plant-soil system. Nature 433, 621–624 (2005).ADS 
CAS 
PubMed 

Google Scholar 
56.Parmesan, C. Ecological and evolutionary responses to recent climate change. Annu. Rev. Ecol. Evol. Syst. 37, 637–669 (2006).
Google Scholar 
57.Finlay, B. J., Esteban, G. F., Olmo, J. L. & Tyler, P. A. Global distribution of free-living microbial species. Ecography (Cop.) 22, 138–144 (1999).
Google Scholar 
58.Fox, J. W. & McGrady-Steed, J. Stability and complexity in model ecosystems. J. Anim. Ecol. 71, 749–756 (2002).
Google Scholar 
59.Haddad, N. M. et al. Species’ traits predict the effects of disturbance and productivity on diversity. Ecol. Lett. 11, 348–356 (2008).PubMed 

Google Scholar 
60.Fronhofer, E. A. & Altermatt, F. Eco-evolutionary feedbacks during experimental range expansions. Nat. Commun. 6, 1–9 (2015).
Google Scholar 
61.Sonneborn, T. M. Chapter 12 methods in paramecium research. Methods Cell Biol. 4, 241–339 (1970).
Google Scholar 
62.Berger, H. & Foissner, W. Illustrated guide and ecological notes to ciliate species (Protozoa, Ciliophora) in running waters, lakes, and sewage plants. Handb. Angew. Limnol. Grundlagen-Gewässerbelastung-Restaurierung-Aquatische ökotoxikologie-Bewertung-Gewässerschutz 20, 1–60 (2014).
Google Scholar 
63.Cassidy-Hanley, D. M. Tetrahymena in the laboratory: Strain resources, methods for culture, maintenance, and storage. Methods Cell Biol. 109, 237–276 (2012).PubMed 
PubMed Central 

Google Scholar 
64.Sonzogni, W. C., Richardson, W., Rodgers, P. & Monteith, T. J. Chloride pollution of the Great Lakes. Water Pollut. Control Fed. 55, 513–521 (1983).CAS 

Google Scholar 
65.Lind, L. et al. Salty fertile lakes: How salinization and eutrophication alter the structure of freshwater communities. Ecosphere 9, 25 (2018).ADS 

Google Scholar 
66.Falconer, D. S. Introduction to Quantitative Genetics (Longman Group Ltd, 1981).
Google Scholar 
67.Pennekamp, F., Schtickzelle, N. & Petchey, O. L. BEMOVI, software for extracting behavior and morphology from videos, illustrated with analyses of microbes. Ecol. Evol. 5, 2584–2595 (2015).PubMed 
PubMed Central 

Google Scholar 
68.Pennekamp, F. et al. Dynamic species classification of microorganisms across time, abiotic and biotic environments—a sliding window approach. PLoS One 12, e0176682 (2017).PubMed 
PubMed Central 

Google Scholar 
69.Kuznetsova, A., Brockhoff, P. B., & Christensen, R. H. B. lmerTest: Tests for random and fixed effects for linear mixed effect models (lmer objects of lme4 package). R package version 2.0-6. Retrieved in July 7. (2014).70.Nakagawa, S. & Schielzeth, H. A general and simple method for obtaining R2 from generalized linear mixed-effects models. Methods Ecol. Evol. 4, 133–142 (2013).
Google Scholar 
71.Barton, K. MuMIn: Multi-Model Inference, Version 1.43.6. 1–75 (2019).72.Fronhofer, E. A., Gut, S. & Altermatt, F. Evolution of density-dependent movement during experimental range expansions. J. Evol. Biol. 30, 2165–2176 (2017).CAS 
PubMed 

Google Scholar 
73.Ellner, S. P., Geber, M. A. & Hairston, N. G. Does rapid evolution matter? Measuring the rate of contemporary evolution and its impacts on ecological dynamics. Ecol. Lett. 14, 603–614 (2011).PubMed 

Google Scholar 
74.Govaert, L. Eco-evolutionary partitioning metrics: A practical guide for biologists. Belgian J. Zool. 148, 167–202 (2018).
Google Scholar  More

1.Alvarez-Filip, L., Dulvy, N. K., Gill, J. A., Côté, I. M. & Watkinson, A. R. Flattening of Caribbean coral reefs: Region-wide declines in architectural complexity. Proc. R. Soc. B Biol. Sci. 276, 3019–3025 (2009).
Google Scholar 
2.Hughes, T. P. et al. Coral reefs in the Anthropocene. Nature 546, 82–90 (2017).ADS 
CAS 
PubMed 

Google Scholar 
3.Hughes, T. P. et al. Spatial and temporal patterns of mass bleaching of corals in the Anthropocene. Science 359, 80–83 (2018).ADS 
CAS 
PubMed 

Google Scholar 
4.Halpern, B. S. et al. A global map of human impact on marine ecosystems. Science 319, 948–952 (2008).ADS 
CAS 
PubMed 

Google Scholar 
5.Loya, Y. et al. Coral bleaching: The winners and the losers. Ecol. Lett. 4, 122–131 (2001).
Google Scholar 
6.Anthony, K. R. N., Hoogenboom, M. O., Maynard, J. A., Grottoli, A. G. & Middlebrook, R. Energetics approach to predicting mortality risk from environmental stress: A case study of coral bleaching. Funct. Ecol. 23, 539–550 (2009).
Google Scholar 
7.Depczynski, M. et al. Bleaching, coral mortality and subsequent survivorship on a West Australian fringing reef. Coral Reefs 32, 233–238 (2013).ADS 

Google Scholar 
8.Edmunds, P. J. Implications of high rates of sexual recruitment in driving rapid reef recovery in Mo’orea, French Polynesia. Sci. Rep. 8, 16615. https://doi.org/10.1038/s41598-018-34686-z (2018).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
9.Richmond, R. H., Tisthammer, K. H. & Spies, N. P. The effects of anthropogenic stressors on reproduction and recruitment of corals and reef organisms. Front. Mar. Sci. 5, 266. https://doi.org/10.3389/fmars.2018.00226 (2018).Article 

Google Scholar 
10.Oliver, E. C. J. et al. Marine heatwaves. Ann. Rev. Mar. Sci. 13, 313–342 (2021).PubMed 

Google Scholar 
11.Rinkevich, B. The contribution of photosynthetic products to coral reproduction. Mar. Biol. 101, 259–263 (1989).CAS 

Google Scholar 
12.Lesser, M. P. Using energetic budgets to assess the effects of environmental stress on corals: Are we measuring the right things?. Coral Reefs 32, 25–33 (2013).ADS 

Google Scholar 
13.Muscatine, L., McCloskey, L. & Marian, R. Estimating the daily contribution of carbon from zooxanthellae to coral animal respiration. Limnol. Oceanogr. 26, 601–611 (1981).ADS 
CAS 

Google Scholar 
14.Rodrigues, L. J. & Grottoli, A. G. Energy reserves and metabolism as indicators of coral recovery from bleaching. Limnol. Oceanogr. 52, 1874–1882 (2007).ADS 

Google Scholar 
15.Rädecker, N. et al. Heat stress destabilizes symbiotic nutrient cycling in corals. Proc. Natl. Acad. Sci. USA 118, e2022653118. https://doi.org/10.1073/pnas.2022653118 (2021).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
16.Grottoli, A. G., Rodrigues, L. J. & Palardy, J. E. Heterotrophic plasticity and resilience in bleached corals. Nature 440, 1186–1189 (2006).ADS 
CAS 
PubMed 

Google Scholar 
17.Schoepf, V. et al. Annual coral bleaching and the long-term recovery capacity of coral. Proc. R. Soc. B 282, 20151887. https://doi.org/10.1098/rspb.2015.1997 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
18.Leuzinger, S., Willis, B. L. & Anthony, K. R. N. Energy allocation in a reef coral under varying resource availability. Mar. Biol. 159, 177–186 (2012).
Google Scholar 
19.Oren, U., Benayahu, Y., Lubinevsky, H. & Loya, Y. Colony integration during regeneration in the stony coral Favia favus. Ecology 82, 802–813 (2001).
Google Scholar 
20.Fisch, J., Drury, C., Towle, E. K., Winter, R. N. & Miller, M. W. Physiological and reproductive repercussions of consecutive summer bleaching events of the threatened Caribbean coral Orbicella faveolata. Coral Reefs 38, 863–876 (2019).ADS 

Google Scholar 
21.Ward, S., Harrison, P. & Hoegh-Guldberg, O. Coral bleaching reduces reproduction of scleractinian corals and increases susceptibility to future stress. Proc. 9th Int. Coral Reef Symp. 1123–1128 (2002).22.Levitan, D. R., Boudreau, W., Jara, J. & Knowlton, N. Long-term reduced spawning in Orbicella coral species due to temperature stress. Mar. Ecol. Prog. Ser. 515, 1–10 (2014).ADS 

Google Scholar 
23.Johnston, E. C., Counsell, C. W. W., Sale, T. L., Burgess, S. C. & Toonen, R. J. The legacy of stress: Coral bleaching impacts reproduction years later. Funct. Ecol. 34, 2315–2325 (2020).
Google Scholar 
24.Szmant, A. M. & Gassman, N. J. The effects of prolonged ‘bleaching’ on the tissue biomass and reproduction of the reef coral Montastrea annularis. Coral Reefs 8, 217–224 (1990).ADS 

Google Scholar 
25.Jones, A. M. & Berkelmans, R. Tradeoffs to thermal acclimation: energetics and reproduction of a reef coral with heat tolerant Symbiodinium Type-D. J. Mar. Biol. 2011, 185890. https://doi.org/10.1155/2011/185890 (2011).Article 

Google Scholar 
26.Figueiredo, J. et al. Ontogenetic change in the lipid and fatty acid composition of scleractinian coral larvae. Coral Reefs 31, 613–619 (2012).ADS 

Google Scholar 
27.Hagedorn, M. et al. Potential bleaching effects on coral reproduction. Reprod. Fertil. Dev. 28, 1061–1071 (2016).CAS 

Google Scholar 
28.Michalek-Wagner, K. & Willis, B. L. Impacts of bleaching on the soft coral Lobophytum compactum. I. Fecundity, fertilization and offspring viability. Coral Reefs 19, 231–239 (2001).
Google Scholar 
29.Howells, E. J. et al. Species-specific trends in the reproductive output of corals across environmental gradients and bleaching histories. Mar. Pollut. Bull. 105, 532–539 (2016).CAS 
PubMed 

Google Scholar 
30.Godoy, L. et al. Southwestern Atlantic reef-building corals Mussismilia spp. are able to spawn while fully bleached. Mar. Biol. 168, 15. https://doi.org/10.1007/s00227-021-03824-z (2021).CAS 
Article 

Google Scholar 
31.Veron, J. E. Acropora hyacinthus. in Corals of the World, vol. 1–3. (ed. Veron, J. E.) 404–405 (Australian Institute of Marine Sciences, 2000).32.Pratchett, M. S., McCowan, D., Maynard, J. A. & Heron, S. F. Changes in bleaching susceptibility among corals subject to ocean warming and recurrent bleaching in Moorea, French polynesia. PLoS ONE 8, e70443. https://doi.org/10.1371/journal.pone.0070443 (2013).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
33.Speare, K. E., Adam, T. C., Winslow, E. M., Lenihan, H. S. & Burkepile, D. E. Size-dependent mortality of corals during marine heatwave erodes recovery capacity of a coral reef. Glob. Change Biol. https://doi.org/10.1111/gcb.16000 (2021). Article 

Google Scholar 
34.Holbrook, S. J. et al. Recruitment drives spatial variation in recovery rates of resilient coral reefs. Sci. Rep. 8, 7338. https://doi.org/10.1038/s41598-018-25414-8 (2018).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
35.Carroll, A., Harrison, P. & Adjeroud, M. Sexual reproduction of Acropora reef corals at Moorea, French polynesia. Coral Reefs 25, 93–97 (2006).ADS 

Google Scholar 
36.Tsounis, G. et al. Anthropogenic effects on reproductive effort and allocation of energy reserves in the Mediterranean octocoral Paramuricea clavata. Mar. Ecol. Prog. Ser. 449, 161–172 (2012).ADS 

Google Scholar 
37.Wall, C. B., Ritson-Williams, R., Popp, B. N. & Gates, R. D. Spatial variation in the biochemical and isotopic composition of corals during bleaching and recovery. Limnol. Oceanogr. 64, 2011–2028 (2019).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
38.Jung, E. M. U., Stat, M., Thomas, L., Koziol, A. & Schoepf, V. Coral host physiology and symbiont dynamics associated with differential recovery from mass bleaching in an extreme, macro-tidal reef environment in northwest Australia. Coral Reefs 40, 893–905 (2021).
Google Scholar 
39.Tremblay, P., Gori, A., Maguer, J. F., Hoogenboom, M. & Ferrier-Pagès, C. Heterotrophy promotes the re-establishment of photosynthate translocation in a symbiotic coral after heat stress. Sci. Rep. 6, 38112. https://doi.org/10.1038/srep38112 (2016).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
40.Baumann, J., Grottoli, A. G., Hughes, A. D. & Matsui, Y. Photoautotrophic and heterotrophic carbon in bleached and non-bleached coral lipid acquisition and storage. J. Exp. Mar. Bio. Ecol. 461, 469–478 (2014).CAS 

Google Scholar 
41.Grottoli, A. G. et al. The cumulative impact of annual coral bleaching can turn some coral species winners into losers. Glob. Chang. Biol. 20, 3823–3833 (2014).ADS 
PubMed 

Google Scholar 
42.Graham, E. M., Baird, A. H., Connolly, S. R., Sewell, M. A. & Willis, B. L. Rapid declines in metabolism explain extended coral larval longevity. Coral Reefs 32, 539–549 (2013).ADS 

Google Scholar 
43.Michalek-Wagner, K. & Willis, B. L. Impacts of bleaching on the soft coral Lobophytum compactum. II. Biochemical changes in adults and their eggs. Coral Reefs 19, 240–246 (2001).
Google Scholar 
44.Harii, S., Nadaoka, K., Yamamoto, M. & Iwao, K. Temporal changes in settlement, lipid content and lipid composition of larvae of the spawning hermatypic coral Acropora tenuis. Mar. Ecol. Prog. Ser. 346, 89–96 (2007).ADS 
CAS 

Google Scholar 
45.Wallace, C. C. Reproduction, recruitment and fragmentation in nine sympatric species of the coral genus Acropora. Mar. Biol. 88, 217–233 (1985).
Google Scholar 
46.Ziegler, R. & Ibrahim, M. M. Formation of lipid reserves in fat body and eggs of the yellow fever mosquito, Aedes aegypti. J. Insect Physiol. 47, 623–627 (2001).CAS 
PubMed 

Google Scholar 
47.Baliña, S., Temperoni, B., Greco, L. S. L. & Tropea, C. Losing reproduction: effect of high temperature on female biochemical composition and egg quality in a freshwater crustacean with direct development, the red cherry shrimp, Neocaridina davidi (Decapoda, Atyidae). Biol. Bull. 234, 139–151 (2018).PubMed 

Google Scholar 
48.Levitan, D. R. The relationship between egg size and fertilization success in broadcast-spawning marine invertebrates. Integr. Comp. Biol. 46, 298–311 (2006).PubMed 

Google Scholar 
49.Caballes, C. F., Pratchett, M. S., Kerr, A. M. & Rivera-Posada, J. A. The role of maternal nutrition on oocyte size and quality, with respect to early larval development in the coral-eating starfish, Acanthaster planci. PLoS ONE 11, e0158007. https://doi.org/10.1371/journal.pone.0158007 (2016).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
50.Madin, J. S. et al. The Coral Trait Database, a curated database of trait information for coral species from the global oceans. Sci. Data 4, 160017. https://doi.org/10.1038/sdata.2016.17 (2017).Article 

Google Scholar 
51.Foster, T. & Gilmour, J. Egg size and fecundity of biannually spawning corals at Scott Reef. Sci. Rep. 10, 12313. https://doi.org/10.1038/s41598-020-68289-4 (2020).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
52.Harriott, V. J. Reproductive ecology of four scleratinian species at Lizard Island, Great Barrier Reef. Coral Reefs 2, 9–18 (1983).ADS 

Google Scholar 
53.Vargas-Ángel, B., Colley, S. B., Hoke, S. M. & Thomas, J. D. The reproductive seasonality and gametogenic cycle of Acropora cervicornis off Broward County, Florida, USA. Coral Reefs 25, 110–122 (2006).ADS 

Google Scholar 
54.Hall, V. R. & Hughes, T. P. Reproductive strategies of modular organisms: comparative studies of reef-building corals. Ecology 77, 950–963 (1996).
Google Scholar 
55.Brandt, M. E. The effect of species and colony size on the bleaching response of reef-building corals in the Florida Keys during the 2005 mass bleaching event. Coral Reefs 28, 911–924 (2009).ADS 

Google Scholar 
56.Sakai, K., Singh, T. & Iguchi, A. Bleaching and post-bleaching mortality of Acropora corals on a heat-susceptible reef in 2016. PeerJ 2019, e8138. https://doi.org/10.7717/peerj.8138 (2019).Article 

Google Scholar 
57.Nozawa, Y. & Lin, C. H. Effects of colony size and polyp position on polyp fecundity in the scleractinian coral genus Acropora. Coral Reefs 33, 1057–1066 (2014).ADS 

Google Scholar 
58.Álvarez-Noriega, M. et al. Fecundity and the demographic strategies of coral morphologies. Ecology 97, 3485–3493 (2016).PubMed 

Google Scholar 
59.Bena, C. & Van Woesik, R. The impact of two bleaching events on the survival of small coral colonies (Okinawa, Japan). Bull. Mar. Sci. 75, 115–125 (2004).
Google Scholar 
60.Shenkar, N., Fine, M. & Loya, Y. Size matters: Bleaching dynamics of the coral Oculina patagonica. Mar. Ecol. Prog. Ser. 294, 181–188 (2005).ADS 

Google Scholar 
61.Hughes, T. P. et al. Global warming impairs stock–recruitment dynamics of corals. Nature 568, 387–390 (2019).ADS 
CAS 
PubMed 

Google Scholar 
62.McClanahan, T. R., Maina, J., Moothien-Pillay, R. & Baker, A. C. Effects of geography, taxa, water flow, and temperature variation on coral bleaching intensity in Mauritius. Mar. Ecol. Prog. Ser. 298, 131–142 (2005).ADS 

Google Scholar 
63.Hoogenboom, M. O. et al. Environmental drivers of variation in bleaching severity of Acropora species during an extreme thermal anomaly. Front. Mar. Sci. 4, 376. https://doi.org/10.3389/fmars.2017.00376 (2017).Article 

Google Scholar 
64.Schoepf, V. et al. Thermally variable, macrotidal reef habitats promote rapid recovery from mass coral bleaching. Front. Mar. Sci. 7, 245. https://doi.org/10.3389/fmars.2020.00245 (2020).Article 

Google Scholar 
65.Golbuu, Y. et al. Palau’s coral reefs show differential habitat recovery following the 1998-bleaching event. Coral Reefs 26, 319–332 (2007).
Google Scholar 
66.van Woesik, R. et al. Climate-change refugia in the sheltered bays of Palau: Analogs of future reefs. Ecol. Evol. 2, 2474–2484 (2012).PubMed 
PubMed Central 

Google Scholar 
67.Penin, L., Adjeroud, M., Schrimm, M. & Lenihan, H. S. High spatial variability in coral bleaching around Moorea (French Polynesia): Patterns across locations and water depths. C. R. Biol. 330, 171–181 (2007).PubMed 

Google Scholar 
68.Penin, L., Vidal-Dupiol, J. & Adjeroud, M. Response of coral assemblages to thermal stress: Are bleaching intensity and spatial patterns consistent between events?. Environ. Monit. Assess. 185, 5031–5042 (2013).PubMed 

Google Scholar 
69.Brown, B. E., Downs, C. A., Dunne, R. P. & Gibb, S. W. Exploring the basis of thermotolerance in the reef coral Goniastrea aspera. Mar. Ecol. Prog. Ser. 242, 119–129 (2002).ADS 

Google Scholar 
70.Kenkel, C. D. et al. Evidence for a host role in thermotolerance divergence between populations of the mustard hill coral (Porites astreoides) from different reef environments. Mol. Ecol. 22, 4335–4348 (2013).CAS 
PubMed 

Google Scholar 
71.Burt, J. A. & Bauman, A. G. Suppressed coral settlement following mass bleaching in the southern Persian/Arabian Gulf. Aquat. Ecosyst. Heal. Manag. 23, 166–174 (2020).
Google Scholar 
72.Shlesinger, T. & Loya, Y. Breakdown in spawning synchrony: A silent threat to coral persistence. Science 365, 1002–1007 (2019).ADS 
CAS 
PubMed 

Google Scholar 
73.Edmunds, P., Gates, R. & Gleason, D. The biology of larvae from the reef coral Porites astreoides, and their response to temperature disturbances. Mar. Biol. 139, 981–989 (2001).
Google Scholar 
74.Edmunds, P. J. Spatiotemporal variation in coral recruitment and its association with seawater temperature. Limnol. Oceanogr. 66, 1394–1408 (2021).ADS 

Google Scholar 
75.Bouwmeester, J. et al. Latitudinal variation in monthly-scale reproductive synchrony among Acropora coral assemblages in the Indo-Pacific. Coral Reefs 40, 1411–1418 (2021).
Google Scholar 
76.Edmunds, P. J. MCR LTER: Coral reef: Long-term population and community dynamics: Corals, ongoing since 2005. knb-lter-mcr.4.38. 10.6073/pasta/10ee808a046cb63c0b8e3bc3c9799806 (2020).77.Claar, D. C. & Baum, J. K. Timing matters: Survey timing during extended heat stress can influence perceptions of coral susceptibility to bleaching. Coral Reefs 38, 559–565 (2019).ADS 

Google Scholar 
78.Schneider, C. A., Rasband, W. S. & Eliceiri, K. W. NIH Image to ImageJ: 25 years of image analysis. Nat. Methods 9, 671–675 (2012).CAS 
PubMed 
PubMed Central 

Google Scholar 
79.Leichter, J., Seydel, K. & Gotschalk, C. MCR LTER: Coral reef: Benthic water temperature, ongoing since 2005. knb-lter-mcr.1035.13. 10.6073/pasta/2087a33cdd16986352bed443fecc7fd7 (2020).80.Bradford, M. A rapid and sensitive method for the quantification of microgram quantities of protein utilizing the principle of protein-dye binding. Anal. Biochem. 72, 248–254 (1976).CAS 
PubMed 

Google Scholar 
81.Dubois, M., Gilles, K. A., Hamilton, J. K., Rebers, P. A. & Smith, F. Colorimetric method for determination of sugars and related substances. Anal. Chem. 28, 350–356 (1955).
Google Scholar 
82.Masuko, T. et al. Carbohydrate analysis by a phenol-sulfuric acid method in microplate format. Anal. Biochem. 339, 69–72 (2005).CAS 
PubMed 

Google Scholar 
83.Stimson, J. & Kinzie, R. A. The temporal pattern and rate of release of zooxanthellae from the reef coral Pocillopora damicornis (Linnaeus) under nitrogen-enrichment and control conditions. J. Exp. Mar. Bio. Ecol. 153, 63–74 (1991).
Google Scholar 
84.Szmant-Froelich, A., Rhetter, M. & Riggs, L. Sexual reproduction of Favis fragum (ESPER): lunar patterns of gametogenesis, embryogenesis and planulation in Puerto Rico. Bull. Mar. Sci. 37, 880–892 (1985).
Google Scholar  More

1.Amaja, L. G., Feyssa, D. H. & Gutema, T. M. Assessment of types of damage and causes of Human–wildlife conflict in Gera district, southwestern Ethiopia. J. Ecol. Nat. Environ. 8, 49–54 (2016).Article 

Google Scholar 
2.Decker, D. J., Laube, T. B. & Siemer, W. F. Human–Wildlife Conflict Management: A Practitioner’s Guide (Northeastern Wildlife Damage Management Research and Outreach Cooperative, 2002).
Google Scholar 
3.Habib, A., Nazir, I., Fazili, M. F. & Bhat, B. A. Human–wildlife conflict-causes, consequences and mitigation measures with special reference to Kashmir. J. Zool. Stud. 2, 26–30 (2015).
Google Scholar 
4.Eklund, A., Lopez-Bao, J. V., Tourani, M., Chapron, G. & Frank, J. Author Correction: Limited evidence on the effectiveness of interventions to reduce livestock predation by large carnivores. Sci. Rep. 8, 5770 (2018).ADS 
Article 

Google Scholar 
5.Hussain, S. The status of the snow leopard in Pakistan and its conflict with local farmers. Oryx 37, 26–33 (2003).Article 

Google Scholar 
6.Miller, J. R., Jhala, Y. V. & Schmitz, O. J. Human perceptions mirror realities of carnivore attack risk for livestock: Implications for mitigating human-carnivore conflict. PLoS ONE 11, e0162685 (2016).Article 

Google Scholar 
7.Aryal, P. et al. Human–carnivore conflict: Ecological and economical sustainability of predation on livestock by snow leopard and other carnivores in the Himalaya. Sustain. Sci. 9, 321–329 (2014).Article 

Google Scholar 
8.Khan, B. et al. Pastoralist experience and tolerance of snow leopard, wolf and lynx predation in Karakoram Pamir Mountains. J. Biol. Environ. Sci. 5, 214–229 (2014).
Google Scholar 
9.Jackson, R. M., Ahlborn, G., Gurung, M. & Ale, S. Reducing livestock depredation losses in the Nepalese Himalaya. In Proc. 17th Vertebrate Pest Conference (eds Timm, R. M. & Crabb, A. C.) 241–247 (University of California, 1996).
Google Scholar 
10.Qamar, Q. Z. et al. Human leopard conflict: An emerging issue of common leopard conservation in Machiara National Park, Azad Jammu, and Kashmir, Pakistan. Pak. J. Wildl. 1, 50–56 (2010).
Google Scholar 
11.Atickem, A., Williams, S., Bekele, A. & Thirgood, S. Livestock predation in the Bale Mountains, Ethiopia. Afr. J. Ecol. 48, 1076–1082 (2010).Article 

Google Scholar 
12.Gittleman, J. L., Funk, S. M., Macdonald, D. W. & Wayne, R. K. Carnivore conservation. Cambridge University Press, Cambridge consequences and mitigation measures with special reference to Kashmir. J. Zool. Stud. 2, 26–30 (2001).
Google Scholar 
13.Treves, A. K. & Karanth, K. U. Human–carnivore conflict—Local solutions with global applications (Special section): Introduction. Conserv. Biol. 17, 1489–1490 (2003).Article 

Google Scholar 
14.Li, J., Yin, H., Wang, D., Jiagong, Z. & Lu, Z. Human-snow leopard conflicts in the Sanjiangyuan Region of the Tibetan Plateau. Biol. Conserv. 166, 118–123 (2013).Article 

Google Scholar 
15.McCarthy, T. M. & Chapron, G. Snow Leopard Survival Strategy (IT and SLN, 2003).
Google Scholar 
16.Suryawanshi, K.R. Human carnivore conflicts: Understanding predation ecology and livestock damage by snow leopards. Ph.D. Thesis. Manipal University, India (2013).17.Bocci, A., Lovari, S., Khan, M. Z. & Mori, E. Sympatric snow leopards and Tibetan wolves: coexistence of large carnivores with human-driven potential competition. Eur. J. Wildl. Res. 63, 92 (2017).Article 

Google Scholar 
18.Wang, S. W. & Macdonald, D. Livestock predation by carnivores in Jigme Singye Wangchuck National Park, Bhutan. Biol. Conserv. 129, 558–565 (2006).Article 

Google Scholar 
19.Khan, M. Z., Khan, B., Awan, M. S. & Begum, F. Livestock depredation by large predators and its implications for conservation and livelihoods in the Karakoram Mountains of Pakistan. Oryx 52, 519–525 (2018).Article 

Google Scholar 
20.Ali, H., Younus, M., Din, J. U., Bischof, R. & Nawaz, M. A. Do Marco Polo argali Ovis ammon polii persist in Pakistan?. Oryx 53, 329–333 (2019).Article 

Google Scholar 
21.Dar, N. I., Minhas, R. A., Zaman, Q. & Linkie, M. Predicting the patterns, perceptions, and causes of human-carnivore conflict in and around Machiara National Park, Pakistan. Biol. Conserv. 142, 2076 (2009).Article 

Google Scholar 
22.RC Team. R: A Language and Environment for Statistical Computing (2013).23.Din, J. U. et al. A Tran’s boundary study of spatiotemporal patterns of livestock predation and prey preferences by snow leopard and wolf in the Pamir. Glob. Ecol. Conserv. 20, e00719 (2019).Article 

Google Scholar 
24.Conover, M. R. Resolving Human–Wildlife Conflicts: The Science of Wildlife Damage Management 418 (Lewis Publishers, 2002).
Google Scholar 
25.Graham, K., Beckerman, A. P. & Thirgood, S. Human–predator–prey conflicts: Ecological correlates, prey losses and patterns of management. Biol. Conserv. 122, 159–171 (2005).Article 

Google Scholar 
26.Li, X., Buzzard, P., Chen, Y. & Jiang, X. Patterns of livestock predation by carnivores: Human–wildlife conflict in Northwest Yunnan, China. Environ. Manage. 52, 1334–1340 (2013).ADS 
Article 

Google Scholar 
27.Dar, N. I., Minhas, R. A., Zaman, Q. & Linkie, M. Predicting the patterns, perceptions and causes of human–carnivore conflict in and around Machiara National Park, Pakistan. Biol. Conserv. 142, 2076–2082 (2009).Article 

Google Scholar 
28.Mishra, C., Prins, H. H. T. & van Wieren, S. E. Overstocking in the trans-Himalayan rangelands of India. Environ. Conserv. 28, 279–283 (1997).Article 

Google Scholar 
29.Hayward, M. W. & Kerley, G. I. H. Prey preferences of the lion (Panthera Leo). J. Zool. (Lond.) 267(267), 309–322 (2005).Article 

Google Scholar 
30.Mc Guinness, S. & Taylor, D. Farmers’ perceptions and actions to decrease crop raiding by forest-dwelling primates around a Rwandan Forest fragment. Hum. Dimens. Wildl. 19, 361–372 (2014).Article 

Google Scholar 
31.ICIMOD. Glacial Lakes and Glacial Lake Outburst Floods in Nepal (Gland, 2011).Book 

Google Scholar 
32.Distefano, E. Human–Wildlife Conflict Worldwide: Collection of Case Studies, Analysis of Management Strategies and Good Practices (Food and Agricultural Organization of the United Nations (FAO), 2005).
Google Scholar 
33.Shedayi, A. A., Xu, M., Naseer, I. & Khan, B. Altitudinal gradients of soil and vegetation carbon and nitrogen in a high altitude nature reserve of Karakoram ranges. Springerplus 5, 1–14 (2016).CAS 
Article 

Google Scholar  More