Philippa Kaur

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

EDITORIAL
08 December 2021

The UN must get on with appointing its new science board

The decision to appoint a board of advisors is welcome — and urgent, given the twin challenges of COVID and climate change.

Twitter

Facebook

Email

Download PDF

UN secretary-general António Guterres announced plans for a new science board in September, but is yet to release further details.Credit: Juancho Torres/Anadolu Agency/Getty

Scientists helped to create the United Nations system. Today, people look to UN agencies — such as the UN Environment Programme or the World Health Organization — for reliable data and evidence on, say, climate change or the pandemic. And yet, shockingly, the UN leader’s office has not had a department for science advice for most of its 76-year history. That is about to change.UN secretary-general António Guterres is planning to appoint a board of scientific advisers, reporting to his office. The decision was announced in September in Our Common Agenda (see go.nature.com/3y1g3hp), which lays out the organization’s vision for the next 25 years, but few other details have been released.Representatives of the scientific community are excited about the potential for science to have a position at the centre of the UN, but are rightly anxious for rapid action, given the twin challenges of COVID-19 and climate change, which should be urgent priorities for the board. The International Science Council (ISC), the Paris-based non-governmental body representing many of the world’s scientists, recommended such a board in its own report on science and the intergovernmental system, published last week (see go.nature.com/3rjdjos). Council president Peter Gluckman, former chief science adviser to New Zealand’s prime minister, has written to Guterres to say the ISC is ready to help.
COP26 didn’t solve everything — but researchers must stay engaged
But it’s been more than two months since the announcement, and the UN has not yet revealed the names of the board members. Nature spoke to a number of serving and former UN science advisers who said they know little about the UN chief’s plans. So far, there are no terms of reference and there is no timeline.Nature understands that the idea is still being developed, and that Guterres is leaning towards creating a board that would draw on UN agencies’ existing science networks. Guterres is also aware of the need to take into account that both the UN and the world have changed since the last such board was put in place. All the same, the UN chief needs to end the suspense and set out his plans. Time is of the essence.Guterres’s predecessor, Ban Ki-moon, had a science advisory board between 2014 and 2016. Its members were tasked with providing advice to the secretary-general on science, technology and innovation for sustainable development. But COVID-19 and climate change have pushed science much higher up the international agenda. Moreover, global challenges are worsening — the pandemic has put back progress towards the UN’s flagship Sustainable Development Goals (SDGs), a plan to end poverty and achieve sustainability by 2030. There is now widespread recognition that science has an important part to play in addressing these and other challenges.
How science can put the Sustainable Development Goals back on track
Research underpins almost everything we know about the nature of the virus SARS-CoV-2 and the disease it causes. All countries have access to similar sets of findings, but many are coming to different decisions on how to act on those data — for example, when to mandate mask-wearing or introduce travel restrictions. The UN’s central office needs advice that takes this socio-cultural-political dimension of science into account. It needs advice from experts who study how science is applied and perceived by different constituencies and in different regions.Science advice from the heart of the UN system could also help with another problem highlighted by the pandemic — how to reinvigorate the idea that it is essential for countries to cooperate on solving global problems.Climate change is one example. Advice given by the Intergovernmental Panel on Climate Change (IPCC) is being read and applied in most countries, albeit to varying degrees. But climate is also an area in which states are at odds. Despite Guterres’s calls for solidarity, there were times during last month’s climate conference in Glasgow when the atmosphere was combative. Science advisers could help the secretary-general’s office to find innovative ways to encourage cooperation between countries in efforts to meet the targets of the 2015 Paris climate agreement.
Reset Sustainable Development Goals for a pandemic world
The SDGs are also, to some extent, impeded by competition within the UN system. To tackle climate change, manage land and forests, and protect biodiversity, researchers and policymakers need to work collegially. But the UN’s scientific bodies, such as the IPCC, are set up along disciplinary lines with their own objectives, work programmes and rules, all guided by their own institutional histories. The IPCC and the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES), for example, have only begun to collaborate in the past few years .Independence will be key for an advisory role to be credible. Guterres needs to consider an organizational architecture through which UN agencies are represented, and funding could come from outside the UN. But all of those involved would have to accept that their contributions were for common goals — not to promote their own organization’s interests.Leadership matters, as do communication and support. Guterres should ensure that his scientific advisers are chosen carefully to represent individuals from diverse disciplines and across career stages, and to ensure good representation from low-income countries. The board needs to be well staffed and have a direct line to his office. And it will need a decent budget. Guterres should quickly publish the terms of reference so that the research community has time to provide input and critique.At its most ambitious, a scientific advisory board to the secretary-general could help to break the culture of individualism that beleaguers efforts to reach collective, global goals, and bring some coherence to the current marketplace of disciplines, ideas and outcomes. This will be a monumental task, requiring significant resources and the will to change. But if the advisers succeed, there will also be valuable lessons for the practice of science, which, as we know all too well, still largely rewards individual effort.

Nature 600, 189-190 (2021)
doi: https://doi.org/10.1038/d41586-021-03615-y

Related Articles

COP26 didn’t solve everything — but researchers must stay engaged

Ending Hunger: Science must stop neglecting smallholder farmers

Reset Sustainable Development Goals for a pandemic world

How science can put the Sustainable Development Goals back on track

Subjects

Sustainability

Biodiversity

Climate change

Government

Latest on:

Sustainability

Battery-powered trains offer a cost-effective ride to a cleaner world
Research Highlight 22 NOV 21

All aboard the climate train! Scientists join activists for COP26 trip
News 02 NOV 21

Machine learning enables global solar-panel detection
News & Views 27 OCT 21

Biodiversity

Link knowledge and action networks to tackle disasters
Correspondence 16 NOV 21

COP26 climate pledges: What scientists think so far
News 05 NOV 21

The answer to the biodiversity crisis is not more debt
Editorial 26 OCT 21

Climate change

An IPCC reviewer shares his thoughts on the climate debate
Career Q&A 08 DEC 21

Brazil is in water crisis — it needs a drought plan
Comment 08 DEC 21

Build solar-energy systems to last — save billions
Comment 07 DEC 21

Jobs

Postdoc in Formulation Development for Gene Delivery Therapies

Technical University of Denmark (DTU)
2800 Kgs. Lyngby, Denmark

​​​​​​​Postdoc in Molecular Biology for Gene Delivery Project

Technical University of Denmark (DTU)
2800 Kgs. Lyngby, Denmark

Post-doctoral Research Fellows

Brigham and Women’s Hospital (BWH)
Boston, MA, United States

HPC/Research Computing Engineer

Francis Crick Institute
London, United Kingdom More

Large-scale, long-term field data from the GBR Marine ParkThe field data for CoTS, hard coral cover (here referred to as coral cover) and coral reef fish were obtained from the Australian Institute of Marine Science’s (AIMS) Long-Term Monitoring Programme (LTMP), while fisheries retained catch data were supplied by the Queensland Department of Agriculture and Fisheries (QDAF). The LTMP has been surveying CoTS populations and coral cover at reefs across the length and breadth of the GBR Marine Park since 198350 and has quantified the status and trend of benthic and reef fish assemblages since 1995. Specific examination of the effectiveness of zoning within the GBR Marine Park has also been undertaken24. The surveyed reefs are located within zones open to fishing (i.e. General Use, Habitat Protection and Conservation Park) and zones closed to fishing (i.e. Marine National Park Zones, Preservation and Scientific Research Zones) (Supplementary Table 1). The QDAF fisheries data comprise annual retained catch data from the Coral Reef Fin Fish Fishery including commercial, recreational (including charters) and Indigenous fisheries, as well as the Marine Aquarium Fish Fishery (Supplementary Data 1–3). Monthly catch return logbooks became compulsory for all trawlers and line fisheries on 1 January 198830. Retained catch data from each of these fisheries is collected separately and differently by QDAF (please see details below). Use of these data is by courtesy of the State of Queensland, Australia, through the Department of Agriculture and Fisheries.For both the LTMP and QDAF data, the data sets are chronologically divided into report (LTMP) or financial (QDAF) years, respectively, from 01 July to 30 June. This means that, for instance, the second semester of 2017 belongs to the 2018 report or financial year. Hereafter we will refer to report or financial year as simply year. Below we explain each of these data sets in more detail.LTMP CoTS and coral cover dataLTMP CoTS and coral cover data are available from 1983 to 2020. Both observed CoTS and coral cover data are based on field observations that employ manta tow surveys around the perimeter of each reef following AIMS’ Standard Operational Procedure51. Within this period, manta tows were conducted once per year but not all reefs were sampled every year. Briefly, manta tow surveys are a broad-scale technique that covers large areas of reef quickly and provides an assessment of broad changes in the distribution and abundance of corals and CoTS. During surveys, two boats each tow an observer clockwise and anti-clockwise around reef perimeters in a series of 2-min tows until they meet at the other end of the reef. Each observer records categorical coral cover (Supplementary Table 8) and the number and size of any CoTS observed (Supplementary Table 9) at the end of each 2-min tow51. Manta tow surveys are a non-targeting, rapid assessment method, and therefore it under-samples CoTS individuals that are More

1.Friedlingstein, P. et al. Global carbon budget 2019. Earth Syst. Sci. Data 11, 1783–1838 (2019).ADS 

Google Scholar 
2.Ballantyne, A. P., Alden, C. B., Miller, J. B., Tans, P. P. & White, J. W. C. Increase in observed net carbon dioxide uptake by land and oceans during the past 50 years. Nature 488, 70–72 (2012).CAS 
PubMed 
ADS 

Google Scholar 
3.Sitch, S. et al. Recent trends and drivers of regional sources and sinks of carbon dioxide. Biogeosciences 12, 653–679 (2015).ADS 

Google Scholar 
4.Keenan, T. F. et al. Recent pause in the growth rate of atmospheric CO2 due to enhanced terrestrial carbon uptake. Nat. Commun. 7, 13428 (2016).CAS 
PubMed 
PubMed Central 
ADS 

Google Scholar 
5.Schimel, D., Stephens, B. B. & Fisher, J. B. Effect of increasing CO2 on the terrestrial carbon cycle. Proc. Natl Acad. Sci. USA 112, 436–441 (2015).CAS 
PubMed 
ADS 

Google Scholar 
6.Huntzinger, D. N. et al. Uncertainty in the response of terrestrial carbon sink to environmental drivers undermines carbon-climate feedback predictions. Sci. Rep. 7, 4765 (2017).CAS 
PubMed 
PubMed Central 
ADS 

Google Scholar 
7.Walker, A. P. et al. Integrating the evidence for a terrestrial carbon sink caused by increasing atmospheric CO2. New Phytol. 229, 2383–2385 (2020).
Google Scholar 
8.Sun, Z. et al. Evaluating and comparing remote sensing terrestrial GPP models for their response to climate variability and CO2 trends. Sci. Total Environ. 668, 696–713 (2019).CAS 
PubMed 
ADS 

Google Scholar 
9.Smith, W. K. et al. Large divergence of satellite and Earth system model estimates of global terrestrial CO2 fertilization. Nat. Clim. Change 6, 306–310 (2016).ADS 

Google Scholar 
10.Li, W. et al. Recent changes in global photosynthesis and terrestrial ecosystem respiration constrained from multiple observations. Geophys. Res. Lett. 45, 1058–1068 (2018).ADS 

Google Scholar 
11.Wenzel, S., Cox, P. M., Eyring, V. & Friedlingstein, P. Projected land photosynthesis constrained by changes in the seasonal cycle of atmospheric CO2. Nature 538, 499–501 (2016).PubMed 
ADS 

Google Scholar 
12.Ehlers, I. et al Detecting long-term metabolic shifts using isotopomers: CO2-driven suppression of photorespiration in C3 plants over the 20th century. Proc. Natl Acad. Sci. USA 112, 15585–15590 (2015).CAS 
PubMed 
PubMed Central 
ADS 

Google Scholar 
13.Campbell, J. E. et al. Large historical growth in global terrestrial gross primary production. Nature 544, 84–87 (2017).CAS 
PubMed 
ADS 

Google Scholar 
14.Eyring, V. et al. Taking climate model evaluation to the next level. Nat. Clim. Change 9, 102–110 (2019).ADS 

Google Scholar 
15.Winkler, A. J., Myneni, R. B. & Brovkin, V. Investigating the applicability of emergent constraints. Earth Syst. Dyn. 10, 501–523 (2019).ADS 

Google Scholar 
16.Hall, A., Cox, P., Huntingford, C. & Klein, S. Progressing emergent constraints on future climate change. Nat. Clim. Change 9, 269–278 (2019).ADS 

Google Scholar 
17.Keenan, T. F. & Williams, C. A. The terrestrial carbon sink. Annu. Rev. Environ. Resour. 43, 219–243 (2018).
Google Scholar 
18.Ryu, Y., Berry, J. A. & Baldocchi, D. D. What is global photosynthesis? History, uncertainties and opportunities. Remote Sens. Environ. 223, 95–114 (2019).ADS 

Google Scholar 
19.Winkler, A. J., Myneni, R. B., Alexandrov, G. A. & Brovkin, V. Earth system models underestimate carbon fixation by plants in the high latitudes. Nat. Commun. 10, 95 (2019).ADS 

Google Scholar 
20.Ainsworth, E. A. & Long, S. P. What have we learned from 15 years of free-air CO2 enrichment (FACE)? A meta-analytic review of the responses of photosynthesis, canopy properties and plant production to rising CO2. New Phytol. 165, 351–372 (2005).PubMed 

Google Scholar 
21.De Kauwe, M. G., Keenan, T. F., Medlyn, B. E., Prentice, I. C. & Terrer, C. Satellite based estimates underestimate the effect of CO2 fertilization on net primary productivity. Nat Clim. Change 6, 892–893 (2016).ADS 

Google Scholar 
22.Cernusak, L. A. et al Robust response of terrestrial plants to rising CO2. Trends Plant Sci. 24, 578–586 (2019).CAS 
PubMed 

Google Scholar 
23.Piao, S. et al. Evaluation of terrestrial carbon cycle models for their response to climate variability and to CO2 trends. Glob. Change Biol. 19, 2117–2132 (2013).ADS 

Google Scholar 
24.Haverd, V. et al. Higher than expected CO2 fertilization inferred from leaf to global observations. Glob. Change Biol. 26, 2390–2402 (2020).ADS 

Google Scholar 
25.Friedlingstein, P. et al. Uncertainties in CMIP5 climate projections due to carbon cycle feedbacks. J. Clim. 27, 511–526 (2014).ADS 

Google Scholar 
26.Zhao, F. et al. Role of CO2, climate and land use in regulating the seasonal amplitude increase of carbon fluxes in terrestrial ecosystems: a multimodel analysis. Biogeosciences 13, 5121–5137 (2016).CAS 
ADS 

Google Scholar 
27.Le Quéré, C. et al. Global carbon budget 2017. Earth Syst. Sci. Data 10, 405–448 (2018).ADS 

Google Scholar 
28.Running, S. W. & Zhao, M. Daily GPP and Annual NPP (MOD17A2/A3) Products NASA Earth Observing System MODIS Land Algorithm User’s Guide v. 3 (MODIS Land Team, 2015).29.Jung, M. et al. Global patterns of land-atmosphere fluxes of carbon dioxide, latent heat, and sensible heat derived from eddy covariance, satellite, and meteorological observations. J. Geophys. Res. 116, https://doi.org/10.1029/2010JG001566 (2011).30.Zeng, N. et al. Agricultural Green Revolution as a driver of increasing atmospheric CO2 seasonal amplitude. Nature 515, 394–397 (2014).CAS 
PubMed 
ADS 

Google Scholar 
31.Long, S. P. Modification of the response of photosynthetic productivity to rising temperature by atmospheric CO2 concentrations: has its importance been underestimated? Plant Cell Environ. 14, 729–739 (1991).CAS 

Google Scholar 
32.Stevens, N., Lehmann, C. E. R., Murphy, B. P. & Durigan, G. Savanna woody encroachment is widespread across three continents. Glob. Change Biol. 23, 235–244 (2017).ADS 

Google Scholar 
33.Fleischer, K. et al. Amazon forest response to CO2 fertilization dependent on plant phosphorus acquisition. Nat. Geosci. 12, 736–741 (2019).CAS 
ADS 

Google Scholar 
34.Myneni, R. B. et al. Global products of vegetation leaf area and fraction absorbed PAR from year one of MODIS data. Remote Sens. Environ. 83, 214–231 (2002).ADS 

Google Scholar 
35.Cernusak, L. A. et al. Tropical forest responses to increasing atmospheric CO2: current knowledge and opportunities for future research. Funct. Plant Biol. 40, 531–551 (2013).CAS 
PubMed 

Google Scholar 
36.Ainsworth, E. A. & Rogers, A. The response of photosynthesis and stomatal conductance to rising [CO2]: mechanisms and environmental interactions. Plant Cell Environ. 30, 258–270 (2007).CAS 
PubMed 

Google Scholar 
37.Baig, S., Medlyn, B. E., Mercado, L. M. & Zaehle, S. Does the growth response of woody plants to elevated CO2 increase with temperature? A model-oriented meta-analysis. Glob. Change Biol. 21, 4303–4319 (2015).ADS 

Google Scholar 
38.Yang, J. et al. Low sensitivity of gross primary production to elevated CO2 in a mature eucalypt woodland. Biogeosciences 17, 265–279 (2020).CAS 
ADS 

Google Scholar 
39.McMurtrie, R. E., Comins, H. N., Kirschbaum, M. U. F. & Wang, Y. P. Modifying existing forest growth models to take account of effects of elevated CO2. Aust. J. Bot. 40, 657–677 (1992).CAS 

Google Scholar 
40.Luo, Y., Sims, D. A., Thomas, R. B., Tissue, D. T. & Ball, J. T. Sensitivity of leaf photosynthesis to CO2 concentration is an invariant function for C3 plants: a test with experimental data and global applications. Global Biogeochem. Cycles 10, 209–222 (1996).CAS 
ADS 

Google Scholar 
41.Li, Q. et al. Leaf area index identified as a major source of variability in modeled CO2 fertilization. Biogeosciences 15, 6909–6925 (2018).CAS 
ADS 

Google Scholar 
42.Graven, H. D. et al. Enhanced seasonal exchange of CO2 by northern ecosystems since 1960. Science 341, 1085–1089 (2013).CAS 
PubMed 
ADS 

Google Scholar 
43.Zaehle, S. et al. Evaluation of 11 terrestrial carbon-nitrogen cycle models against observations from two temperate free-air CO2 enrichment studies. New Phytol. 202, 803–822 (2014).CAS 
PubMed 
PubMed Central 

Google Scholar 
44.De Kauwe, M. G. et al. Where does the carbon go? A model-data intercomparison of vegetation carbon allocation and turnover processes at two temperate forest free-air CO2 enrichment sites. New Phytol. 203, 883–899 (2014).PubMed 
PubMed Central 

Google Scholar 
45.Stocker, B. D. et al Drought impacts on terrestrial primary production underestimated by satellite monitoring. Nat. Geosci. 12, 264–270 (2019).CAS 
ADS 

Google Scholar 
46.Williamson, M. S. et al Emergent constraints on climate sensitivities. Rev. Mod. Phys. 93, 025004 (2021).MathSciNet 
CAS 
ADS 

Google Scholar 
47.Sanderson, B. et al. On structural errors in emergent constraints. Earth Syst. Dyn. Discuss. https://doi.org/10.5194/esd-2020-85 (2021).48.Fisher, J. B., Huntzinger, D. N., Schwalm, C. R. & Sitch, S. Modeling the terrestrial biosphere. Annu. Rev. Environ. Resour. 39, 91–123 (2014).
Google Scholar 
49.Arora, V. K. et al. Carbon-concentration and carbon-climate feedbacks in CMIP5 earth system models. J. Clim. 26, 5289–5314 (2013).ADS 

Google Scholar 
50.Ballantyne, A. et al. Accelerating net terrestrial carbon uptake during the warming hiatus due to reduced respiration. Nat. Clim. Change 7, 148–152 (2017).CAS 
ADS 

Google Scholar 
51.Forkel, M. et al. Enhanced seasonal CO2 exchange caused by amplified plant productivity in northern ecosystems. Science 351, 696–699 (2016).CAS 
PubMed 
ADS 

Google Scholar 
52.Friedlingstein, P. et al. On the contribution of CO2 fertilization to the missing biospheric sink. Global Biogeochem. Cycles 9, 541–556 (1995).CAS 
ADS 

Google Scholar 
53.Farquhar, G. D., von Caemmerer, S. & Berry, J. A. A biochemical model of photosynthetic CO2 assimilation in leaves of C3 species. Planta 149, 78–90 (1980).CAS 
PubMed 

Google Scholar 
54.Myneni, R. B., Keeling, C. D., Tucker, C. J., Asrar, G. & Nemani, R. R. Increased plant growth in the northern high latitudes from 1981 to 1991. Nature 386, 698–702 (1997).CAS 
ADS 

Google Scholar 
55.Zhu, Z. et al. Greening of the Earth and its drivers. Nat. Clim. Change 6, 791–795 (2016).CAS 
ADS 

Google Scholar 
56.Keenan, T. F. et al. Increase in forest water-use efficiency as atmospheric carbon dioxide concentrations rise. Nature 499, 324–327 (2013).CAS 
PubMed 
ADS 

Google Scholar 
57.Ukkola, A. M., Keenan, T. F., Kelley, D. I. & Prentice, I. C. Vegetation plays an important role in mediating future water resources. Environ. Res. Lett. 11, 094022 (2016).ADS 

Google Scholar 
58.Donohue, R. J., Roderick, M. L., McVicar, T. R. & Farquhar, G. D. Impact of CO2 fertilization on maximum foliage cover across the globe’s warm, arid environments. Geophys. Res. Lett. 40, 3031–3035 (2013).CAS 
ADS 

Google Scholar 
59.Smith, N. G. & Dukes, J. S. Plant respiration and photosynthesis in global-scale models: incorporating acclimation to temperature and CO2. Glob. Change Biol. 19, 45–63 (2013).ADS 

Google Scholar 
60.De Kauwe, M. G. et al. A test of the ‘one-point method’ for estimating maximum carboxylation capacity from field-measured, light-saturated photosynthesis. New Phytol. 210, 1130–1144 (2016).PubMed 

Google Scholar 
61.Maire, V. et al. The coordination of leaf photosynthesis links C and N fluxes in C3 plant species. PLoS ONE 7, e0038345 (2012).ADS 

Google Scholar 
62.Smith, N. G. & Keenan, T. F. Mechanisms underlying leaf photosynthetic acclimation to warming and elevated CO2 as inferred from least-cost optimality theory. Glob. Change Biol. 26, 806–834 (2020).
Google Scholar 
63.Lloyd, J. & Farquhar, G. The CO2 dependence of photosynthesis, plant growth responses to elevated atmospheric CO2 concentrations and their interaction with soil nutrient status. I. General principles and forest ecosystems. Funct. Ecol. 10, 4–32 (1996).
Google Scholar 
64.Ehleringer, J. & Björkman, O. Quantum yields for CO2 uptake in C3 and C4 plants: dependence on temperature, CO2, and O2 concentration. Plant Physiol. 59, 86–90 (1997).
Google Scholar 
65.Bernacchi, C. J., Singsaas, E. L., Pimentel, C., Portis, A. R. Jr & Long, SP. Improved temperature response functions for models of Rubisco-limited photosynthesis. Plant, Cell Environ. 24, 253–259 (2001).CAS 

Google Scholar 
66.Prentice, I. C., Dong, N., Gleason, S. M., Maire, V. & Wright, I. J. Balancing the costs of carbon gain and water transport: testing a new theoretical framework for plant functional ecology. Ecol. Lett. 17, 82–91 (2014).PubMed 

Google Scholar 
67.Wang, H. et al. Towards a universal model for carbon dioxide uptake by plants. Nat. Plants 3, 734–741 (2017).CAS 
PubMed 

Google Scholar 
68.Huber, M. L. et al. New international formulation for the viscosity of H2O. J. Phys. Chem. Ref. Data 38, 101–125 (2009).CAS 
ADS 

Google Scholar 
69.Still, C. J., Berry, J. A., Collatz, G. J. & DeFries, R. S. Global distribution of C3 and C4 vegetation: carbon cycle implications. Global Biogeochem. Cycles 17, 6-1–6-14 (2003).ADS 

Google Scholar 
70.Zhu, Z. et al. Global data sets of vegetation leaf area index (LAI)3g and fraction of photosynthetically active radiation (FPAR)3g derived from global inventory modeling and mapping studies (GIMMS) normalized difference vegetation index (NDVI3g) for the period 1981 to 2. Remote Sens. 5, 927–948 (2013).ADS 

Google Scholar 
71.Zhao, M. & Running, S. W. Drought-induced reduction in global terrestrial net primary production from 2000 through 2009. Science 329, 940–943 (2010).CAS 
PubMed 
ADS 

Google Scholar 
72.Gallego-Sala, A. et al. Bioclimatic envelope model of climate change impacts on blanket peatland distribution in Great Britain. Clim. Res. 45, 151–162 (2010).
Google Scholar 
73.Veroustraete, F. On the use of a simple deciduous forest model for the interpretation of climate change effects at the level of carbon dynamics. Ecol. Modell. 75–76, 221–237 (1994).
Google Scholar 
74.Jiang, C. & Ryu, Y. Multi-scale evaluation of global gross primary productivity and evapotranspiration products derived from Breathing Earth System Simulator (BESS). Remote Sens. Environ. 186, 528–547 (2016).ADS 

Google Scholar 
75.Zhang, S. et al. Evaluation and improvement of the daily boreal ecosystem productivity simulator in simulating gross primary productivity at 41 flux sites across Europe. Ecol. Modell. 368, 205–232 (2018).CAS 

Google Scholar 
76.Liu, Y., Hejazi, M., Li, H., Zhang, X. & Leng, G. A hydrological emulator for global applications-HE v1.0.0. Geosci. Model Dev. 11, 1077–1092 (2018).ADS 

Google Scholar 
77.Yuan, W. et al. Increased atmospheric vapor pressure deficit reduces global vegetation growth. Sci. Adv. 5, aax1396 (2019).ADS 

Google Scholar 
78.Haverd, V. et al. A new version of the CABLE land surface model (Subversion revision r4601) incorporating land use and land cover change, woody vegetation demography, and a novel optimisation-based approach to plant coordination of photosynthesis. Geosci. Model Dev. 11, 2995–3026 (2018).CAS 
ADS 

Google Scholar 
79.Melton, J. R. & Arora, V. K. Competition between plant functional types in the Canadian Terrestrial Ecosystem Model (CTEM) v. 2.0. Geosci. Model Dev. 9, 323–361 (2016).CAS 
ADS 

Google Scholar 
80.Oleson, K. W. et al. Technical Description of Version 4.0 of the Community Land Model (CLM) (National Center for Atmospheric Research, 2013).81.Tian, H. et al. North American terrestrial CO2 uptake largely offset by CH4 and N2O emissions: toward a full accounting of the greenhouse gas budget. Clim. Change 129, 413–426 (2015).CAS 
PubMed 
ADS 

Google Scholar 
82.Jain, A. K., Meiyappan, P., Song, Y. & House, J. I. CO2 emissions from land-use change affected more by nitrogen cycle, than by the choice of land-cover data. Glob. Change Biol. 19, 2893–2906 (2013).ADS 

Google Scholar 
83.Reick, C. H., Raddatz, T., Brovkin, V. & Gayler, V. Representation of natural and anthropogenic land cover change in MPI-ESM. J. Adv. Model Earth Syst. 5, 459–482 (2013).ADS 

Google Scholar 
84.Clark, D. B. et al. The Joint UK Land Environment Simulator (JULES), model description—Part 2: Carbon fluxes and vegetation dynamics. Geosci. Model Dev. 4, 701–722 (2011).ADS 

Google Scholar 
85.Smith, B. et al. Implications of incorporating N cycling and N limitations on primary production in an individual-based dynamic vegetation model. Biogeosciences 11, 2027–2054 (2014).ADS 

Google Scholar 
86.Sitch, S. et al. Evaluation of ecosystem dynamics, plant geography and terrestrial carbon cycling in the LPJ dynamic global vegetation model. Glob. Chang. Biol. 9, 161–185 (2003).ADS 

Google Scholar 
87.Keller, K. M. et al. 20th century changes in carbon isotopes and water-use efficiency: tree-ring-based evaluation of the CLM4.5 and LPX-Bern models. Biogeosciences 14, 2641–2673 (2017).CAS 
ADS 

Google Scholar 
88.Krinner, G. et al. A dynamic global vegetation model for studies of the coupled atmosphere-biosphere system. Global Biogeochem. Cycles 19, GB1015 (2005).ADS 

Google Scholar 
89.Guimberteau, M. et al. ORCHIDEE-MICT (v8.4.1), a land surface model for the high latitudes: model description and validation. Geosci. Model Dev. 11, 121–163 (2018).CAS 
ADS 

Google Scholar 
90.Zeng, N., Mariotti, A. & Wetzel, P. Terrestrial mechanisms of interannual CO2 variability. Global Biogeochem. Cycles 19, https://doi.org/10.1029/2004GB002273 (2005).91.Kato, E., Kinoshita, T., Ito, A., Kawamiya, M. & Yamagata, Y. Evaluation of spatially explicit emission scenario of land-use change and biomass burning using a process-based biogeochemical model. J. Land Use Sci. 8, 104–122 (2013).
Google Scholar 
92.Fernández-Martínez, M. et al. Atmospheric deposition, CO2, and change in the land carbon sink. Sci. Rep. 7, 9632 (2017).PubMed 
PubMed Central 
ADS 

Google Scholar 
93.Ciais, P. et al. Large inert carbon pool in the terrestrial biosphere during the Last Glacial Maximum. Nat. Geosci. 5, 74–79 (2012).CAS 
ADS 

Google Scholar 
94.Cheng, L. et al. Recent increases in terrestrial carbon uptake at little cost to the water cycle. Nat. Commun. 8, 110 (2017).PubMed 
PubMed Central 
ADS 

Google Scholar 
95.Ueyama, M. et al. Inferring CO2 fertilization effect based on global monitoring land-atmosphere exchange with a theoretical model. Environ. Res. Lett. 15, 084009 (2020).CAS 
ADS 

Google Scholar 
96.Pastorello, G. et al. The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data. Sci. Data 7, 225 (2020).PubMed 
PubMed Central 

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

PatientsStool samples from a total of 52 patients with varying stages of CKD were collected in this study: CKD3A (n = 12), CKD3B (n = 11), CKD4 (n = 15), CKD5 (n = 4) and ESRD (n = 10) (Table 1). Patients’ characteristics are summarized in Table 1. Among 52 patients, 31 were reported to have Type 2 diabetes mellitus and 7 patients were reported to have human immunodeficiency virus (HIV) infection. As expected, urine protein creatinine ratio, serum creatinine and blood urea nitrogen level increased with progressing stages of CKD (CKD 3A to ESRD). There was no significant difference in fat, protein, carbohydrates, dietary fiber and calorie intake between CKD patients with different stages (Supplementary Table S1).Table 1 Patients’ characteristics.Full size tableAlpha and beta-diversityRichness and Shannon index were not significantly different between different patient groups, meanwhile the CKD5 group showed a significant decrease in Simpson diversity compared with CKD 3A (FDR  More