Philippa Kaur

1.
Muscatine, L. & Porter, J. W. Reef corals: Mutualistic symbioses adapted to nutrient-poor environments. Bioscience 27, 454–460 (1977).
Google Scholar 
2.
Smith, D. C. & Douglas, A. E. The Biology of Symbiosis (Edward Arnold Ltd., London, 1987).
Google Scholar 

3.
Rao, H. Interorganizational Ecology: Haygreeva. In The Blackwell Companion to Organisations (ed. Baum, J. A.) 541–556 (Backwell, Oxford, 2017).
Google Scholar 

4.
Martínez-García, L. B., De Deyn, G. B., Pugnaire, F. I., Kothamasi, D. & van der Heijden, M. G. Symbiotic soil fungi enhance ecosystem resilience to climate change. Glob. Chang. Biol. 23, 5228–5236 (2017).
ADS  PubMed  PubMed Central  Google Scholar 

5.
Compant, S., Van Der Heijden, M. G. & Sessitsch, A. Climate change effects on beneficial plant–microorganism interactions. FEMS Microbiol. Ecol. 73, 197–214 (2010).
CAS  PubMed  Google Scholar 

6.
Chase, T., Pratchett, M., Frank, G. & Hoogenboom, M. Coral-dwelling fish moderate bleaching susceptibility of coral hosts. PLoS ONE 13, e0208545 (2018).
CAS  PubMed  PubMed Central  Google Scholar 

7.
Redman, R. S. et al. Increased fitness of rice plants to abiotic stress via habitat adapted symbiosis: A strategy for mitigating impacts of climate change. PLoS ONE 6, e14823 (2011).
ADS  CAS  PubMed  PubMed Central  Google Scholar 

8.
Stewart, H. L., Holbrook, S. J., Schmitt, R. J. & Brooks, A. J. Symbiotic crabs maintain coral health by clearing sediments. Coral Reefs 25, 609–615 (2006).
ADS  Google Scholar 

9.
Pachauri, R. K. et al. Climate change 2014: Synthesis report. Contribution of Working Groups I, II and III to the fifth assessment report of the Intergovernmental Panel on Climate Change. (IPCC, Switzerland, 2014).

10.
Hoegh-Guldberg, O. Climate change, coral bleaching and the future of the world’s coral reefs. Mar. Freshw. Res. 50, 839–866 (1999).
Google Scholar 

11.
Mieog, J. C. et al. The roles and interactions of symbiont, host and environment in defining coral fitness. PLoS ONE 4(7), e6364 (2009).
ADS  PubMed  PubMed Central  Google Scholar 

12.
Davies, P. S. The role of zooxanthellae in the nutritional energy requirements of Pocillopora eydouxi. Coral Reefs 2, 181–186 (1984).
ADS  Google Scholar 

13.
Cook, C., D’Elia, C. & Muller-Parker, G. Host feeding and nutrient sufficiency for zooxanthellae in the sea anemone Aiptasia pallida. Mar. Biol. 98, 253–262 (1988).
CAS  Google Scholar 

14.
Roopin, M., Henry, R. P. & Chadwick, N. E. Nutrient transfer in a marine mutualism: Patterns of ammonia excretion by anemonefish and uptake by giant sea anemones. Mar. Biol. 154, 547–556 (2008).
CAS  Google Scholar 

15.
Delia, C., Domotor, S. & Webb, K. Nutrient uptake kinetics of freshly isolated zooxanthellae. Mar. Biol. 75, 157–167 (1983).
CAS  Google Scholar 

16.
Steen, R. G. & Muscatine, L. Low temperature evokes rapid exocytosis of symbiotic algae by a sea anemone. Biol. Bull. 172, 246–263 (1987).
Google Scholar 

17.
Roopin, M. & Chadwick, N. E. Benefits to host sea anemones from ammonia contributions of resident anemonefish. J. Exp. Mar. Biol. Ecol. 370, 27–34 (2009).
CAS  Google Scholar 

18.
Brown, B. E. Coral bleaching: Causes and consequences. Coral Reefs 16, 129–138 (1997).
Google Scholar 

19.
Glynn, P. W. Widespread coral mortality and the 1982–83 El Niño warming event. Environ. Conserv. 11, 133–146 (1984).
Google Scholar 

20.
McClanahan, T. R., Ateweberhan, M., Muhando, C. A., Maina, J. & Mohammed, M. S. Effects of climate and seawater temperature variation on coral bleaching and mortality. Ecol. Monogr. 77, 503–525 (2007).
Google Scholar 

21.
Vinoth, R., Gopi, M., Kumar, T. T. A., Thangaradjou, T. & Balasubramanian, T. Coral reef bleaching at Agatti Island of Lakshadweep Atolls India. J. Ocean Univ. China 11, 105–110 (2012).
ADS  Google Scholar 

22.
Hughes, T. P. et al. Global warming transforms coral reef assemblages. Nat. 556, 492–496 (2018).
ADS  CAS  Google Scholar 

23.
Death, G., Fabricius, K. E., Sweatman, H. & Puotinen, M. The 27-year decline of coral cover on the Great Barrier Reef and its causes. Proc. Natl. Acad. Sci. 109, 17995–17999 (2012).
ADS  CAS  Google Scholar 

24.
Hughes, T. P. Catastrophes, phase shifts, and large-scale degradation of a Caribbean coral reef. Science 265, 1547–1551 (1994).
ADS  CAS  PubMed  Google Scholar 

25.
McManus, J. W. & Polsenberg, J. F. Coral–algal phase shifts on coral reefs: Ecological and environmental aspects. Prog. Oceanogr. 60, 263–279 (2004).
ADS  Google Scholar 

26.
Hughes, T. P., Graham, N. A., Jackson, J. B., Mumby, P. J. & Steneck, R. S. Rising to the challenge of sustaining coral reef resilience. Trends Ecol. Evol. 25, 633–642 (2010).
PubMed  Google Scholar 

27.
Garpe, K. C., Yahya, S. A., Lindahl, U. & Öhman, M. C. Long-term effects of the 1998 coral bleaching event on reef fish assemblages. Mar. Ecol. Prog. Ser. 315, 237–247 (2006).
ADS  Google Scholar 

28.
Pratchett, M. S. et al. Effects of climate-induced coral bleaching on coral-reef fishes—ecological and economic consequences. Oceanogr. Mar. Biol. 46, 257–302 (2008).
Google Scholar 

29.
Pratchett, M. S., Hoey, A. S., Wilson, S. K., Messmer, V. & Graham, N. A. Changes in biodiversity and functioning of reef fish assemblages following coral bleaching and coral loss. Divers. 3, 424–452 (2011).
Google Scholar 

30.
Dunn, D. F. The clownfish sea anemones: Stichodactylidae (Coelenterata: Actiniaria) and other sea anemones symbiotic with pomacentrid fishes. Trans. Am. Philos. Soc. 71, 3–115 (1981).
Google Scholar 

31.
Jones, A., Gardner, S. & Sinclair, W. Losing “Nemo”: Bleaching and collection appear to reduce inshore populations of anemonefishes. J. Fish Biol. 73, 753–761 (2008).
Google Scholar 

32.
Scott, A. & Hoey, A. S. Severe consequences for anemonefishes and their host sea anemones during the 2016 bleaching event at Lizard Island, Great Barrier Reef. Coral Reefs 36, 873–873 (2017).
ADS  Google Scholar 

33.
Hobbs, J. P. A. et al. Taxonomic, spatial and temporal patterns of bleaching in anemones inhabited by anemonefishes. PLoS ONE 8, e70966 (2013).
ADS  CAS  PubMed  PubMed Central  Google Scholar 

34.
Hattori, A. Small and large anemonefishes can coexist using the same patchy resources on a coral reef, before habitat destruction. J. Anim. Ecol. 71, 824–831 (2002).
Google Scholar 

35.
Weis, V. M. Cellular mechanisms of Cnidarian bleaching: Stress causes the collapse of symbiosis. J. Exp. Biol. 211, 3059–3066 (2008).
CAS  PubMed  Google Scholar 

36.
Saenz-Agudelo, P., Jones, G., Thorrold, S. & Planes, S. Detrimental effects of host anemone bleaching on anemonefish populations. Coral Reefs 30, 497–506 (2011).
ADS  Google Scholar 

37.
Lönnstedt, O. M. & Frisch, A. J. Habitat bleaching disrupts threat responses and persistence in anemonefish. Mar. Ecol. Prog. Ser. 517, 265–270 (2014).
ADS  Google Scholar 

38.
Beldade, R., Blandin, A., O’Donnell, R. & Mills, S. C. Cascading effects of thermally-induced anemone bleaching on associated anemonefish hormonal stress response and reproduction. Nat. Commun. 8, 716 (2017).
ADS  PubMed  PubMed Central  Google Scholar 

39.
Hoegh-Guldberg, O. & Smith, G. J. Influence of the population density of zooxanthellae and supply of ammonium on the biomass and metabolic characteristics of the reef corals Seriatopora hystrix and Stylophora pistillata. Mar. Ecol. Prog. Ser. 2, 173–186 (1989).
ADS  Google Scholar 

40.
Holbrook, S. J. & Schmitt, R. J. Growth, reproduction and survival of a tropical sea anemone (Actiniaria): Benefits of hosting anemonefish. Coral Reefs 24, 67–73 (2005).
Google Scholar 

41.
Porat, D. & Chadwick-Furman, N. Effects of anemonefish on giant sea anemones: Expansion behavior, growth, and survival. Hydrobiologia 530, 513–520 (2004).
Google Scholar 

42.
Cleveland, A., Verde, E. A. & Lee, R. W. Nutritional exchange in a tropical tripartite symbiosis: Direct evidence for the transfer of nutrients from anemonefish to host anemone and zooxanthellae. Mar. Biol. 158, 589–602 (2011).
Google Scholar 

43.
Fautin, D. G. & Allen, G. R. Field Guide to Anemonefishes and Their Host Sea Anemones (Western Australian Museum, Perth, 1992).
Google Scholar 

44.
Hill, R. & Scott, A. The influence of irradiance on the severity of thermal bleaching in sea anemones that host anemonefish. Coral Reefs 31, 273–284 (2012).
ADS  Google Scholar 

45.
Roughgarden, J. Evolution of marine symbiosis—a simple cost-benefit model. Ecology 56, 1201–1208 (1975).
Google Scholar 

46.
Hobbs, J., Neilson, J. & Gilligan, J. Distribution, abundance, habitat association and extinction risk of marine fishes endemic to the Lord Howe Island region (Report to Lord Howe Island Marine Park (James Cook University, Townsville, 2009).
Google Scholar 

47.
Porat, D. & Chadwick-Furman, N. Effects of anemonefish on giant sea anemones: Ammonium uptake, zooxanthella content and tissue regeneration. Mar. Freshw. Behav. Physiol. 38, 43–51 (2005).
CAS  Google Scholar 

48.
Grottoli, A. G., Rodrigues, L. J. & Palardy, J. E. Heterotrophic plasticity and resilience in bleached corals. Nature 440, 1186–1189 (2006).
ADS  CAS  Google Scholar 

49.
Borell, E. M. & Bischof, K. Feeding sustains photosynthetic quantum yield of a scleractinian coral during thermal stress. Oecologia 157, 593 (2008).
ADS  PubMed  Google Scholar 

50.
Borell, E. M., Yuliantri, A. R., Bischof, K. & Richter, C. The effect of heterotrophy on photosynthesis and tissue composition of two scleractinian corals under elevated temperature. J. Exp. Mar. Biol. Ecol. 364, 116–123 (2008).
Google Scholar 

51.
Nakamura, T. & Van Woesik, R. Water-flow rates and passive diffusion partially explain differential survival of corals during the 1998 bleaching event. Mar. Ecol. Prog. Ser. 212, 301–304 (2001).
ADS  Google Scholar 

52.
Gleason, D. F. & Wellington, G. M. Ultraviolet radiation and coral bleaching. Nature 365, 836 (1993).
ADS  Google Scholar 

53.
Zepp, R. G. et al. Spatial and temporal variability of solar ultraviolet exposure of coral assemblages in the Florida Keys: Importance of colored dissolved organic matter. Limnol. Oceanogr. 53, 1909–1922 (2008).
ADS  CAS  Google Scholar 

54.
Minasian, L. L. Jr. The relationship of size and biomass to fission rate in a clone of the sea anemone, Haliplanella luciae (Verrill). J. Exp. Mar. Biol. Ecol. 58, 151–162 (1982).
Google Scholar 

55.
Miyawaki, M. Temperature as a factor influencing upon the fission of the orange-striped sea-anemone, Diadumene luciae. Zool. 11, 77–80 (1952).
Google Scholar 

56.
Atoda, K. Pedal laceration of the sea anemone, Haliplanella luciae. Pub. Seto Mar. Biol. Lab. 20, 299–313 (1973).
Google Scholar 

57.
Minasian, L. L. Jr. & Mariscal, R. N. Characteristics and regulation of fission activity in clonal cultures of the cosmopolitan sea anemone, Haliplanella luciae (Verrill). Biol. Bull. 157, 478–493 (1979).
PubMed  Google Scholar 

58.
Holbrook, S. J., Brooks, A. J., Schmitt, R. J. & Stewart, H. L. Effects of sheltering fish on growth of their host corals. Mar. Biol. 155, 521–530 (2008).
Google Scholar 

59.
Johnson, L. L. & Shick, J. M. Effects of fluctuating temperature and immersion on asexual reproduction in the intertidal sea anemone Hauplanella luciae (Verrill) in laboratory culture. J. Exp. Mar. Biol. Ecol. 28, 141–149 (1977).
Google Scholar 

60.
Hand, C. & Uhlinger, K. R. Asexual reproduction by transverse fission and some anomalies in the sea anemone Nematostella vectensis. Invertebr. Biol. 2, 9–18 (1995).
Google Scholar 

61.
Tsuchida, C. B. & Potts, D. C. The effects of illumination, food and symbionts on growth of the sea anemone Anthopleura elegantissima (Brandt, 1835). II. Clonal growth. J. Exp. Mar. Biol. Ecol. 183, 243–258 (1994).
Google Scholar 

62.
Hughes, T. P. et al. Global warming and recurrent mass bleaching of corals. Nature 543, 373–377 (2017).
ADS  CAS  PubMed  Google Scholar 

63.
Holbrook, S. J., Forrester, G. E. & Schmitt, R. J. Spatial patterns in abundance of a damselfish reflect availability of suitable habitat. Oecologia 122, 109–120 (2000).
ADS  CAS  PubMed  Google Scholar 

64.
Munday, P. L. Interactions between habitat use and patterns of abundance in coral-dwelling fishes of the genus Gobiodon. Environ. Biol. Fish. 58, 355–369 (2000).
Google Scholar 

65.
Pontasch, S. et al. Photochemical efficiency and antioxidant capacity in relation to Symbiodinium genotype and host phenotype in a symbiotic cnidarian. Mar. Ecol. Prog. Ser. 516, 195–208 (2014).
ADS  CAS  Google Scholar 

66.
IPCC. Climate Change 2014–Impacts, Adaptation and Vulnerability: Regional Aspect (Cambridge University Press, Cambridge, 2014).
Google Scholar 

67.
Siebeck, U., Marshall, N., Klüter, A. & Hoegh-Guldberg, O. Monitoring coral bleaching using a colour reference card. Coral Reefs 25, 453–460 (2006).
ADS  Google Scholar 

68.
Marshall, N. J., Kleine, D. A. & Dean, A. J. CoralWatch: Education, monitoring, and sustainability through citizen science. Front Ecol Environ 10, 332–334 (2012).
Google Scholar 

69.
Association, A. P. H. Standard Methods for the Examination of Water and Wastewater (American Public Health Association, Washington, 2005).
Google Scholar 

70.
AOAC. Official Methods of Analysis of AOAC International (Association of Official Analytical Chemists, Rockville, 2000).
Google Scholar 

71.
Jeffrey, S. T. & Humphrey, G. F. New spectrophotometric equations for determining chlorophylls a, b, c1 and c2 in higher plants, algae and natural phytoplankton. Biochemie und physiologie der pflanzen 167, 191–194 (1974).
Google Scholar  More

1.
Hugelius, G. et al. Estimated stocks of circumpolar permafrost carbon with quantified uncertainty ranges and identified data gaps. Biogeosciences 11, 6573–6593 (2014).
Article  Google Scholar 
2.
Olefeldt, D. et al. Circumpolar distribution and carbon storage of thermokarst landscapes. Nat. Commun. 7, https://doi.org/10.1038/ncomms13043 (2016).

3.
McGuire, A. D. et al. Sensitivity of the carbon cycle in the Arctic to climate change. Ecol. Monogr. 79, 523–555 (2009).
Article  Google Scholar 

4.
Schuur, E. A. G. et al. Climate change and the permafrost carbon feedback. Nature 520, 171–179 (2015).
CAS  Article  Google Scholar 

5.
Chapin, D. M. & Bledsoe, C. S. Nitrogen fixation in Arctic plant communitiesin (eds. Chapin, F. S., Jefferies, R. L., Reynolds, J. F., Shaver, G. R. & Svoboda, J.) Arctic Ecossytems in a changing cliamte: an ecophysiological perspective 1992.

6.
Chapin, F. S. III. et al. The changing global carbon cycle: linking plant-soil carbon dynamics to global consequences. J. Ecol. 97, 840–850 (2009).
CAS  Article  Google Scholar 

7.
Elser, J. J. et al. Global analysis of nitrogen and phosphorus limitation of primary producers in freshwater, marine and terrestrial ecosystems. Ecol. Lett. 10, 1135–1142 (2007).
Article  Google Scholar 

8.
Wookey, P. A. et al. Ecosystem feedbacks and cascade processes: understanding their role in the responses of Arctic and alpine ecosystems to environmental change. Glob. Change Biol. 15, 1153–1172 (2009).
Article  Google Scholar 

9.
Marin-Spiotta, E. et al. Paradigm shifts in soil organic matter research affect interpretations of aquatic carbon cycling: transcending disciplinary and ecosystem boundaries. Biogeochemistry 117, 279–297 (2014).
CAS  Article  Google Scholar 

10.
Tape, K. D., Hallinger, M., Welker, J. M. & Ruess, R. W. Landscape heterogeneity of shrub expansion in arctic Alaska. Ecosystems 15, 711–724 (2012).
CAS  Article  Google Scholar 

11.
Myrstener, M. et al. Persistent nitrogen limitation of stream biofilm communities along climate gradients in the Arctic. Glob Change Biol. 24, 3680–3691 (2018).
Article  Google Scholar 

12.
Salmon, V. G. et al. Nitrogen availability increases in a tundra ecosystem during five years of experimental permafrost thaw. Glob. Change Biol. 22, 1927–1941 (2016).
Article  Google Scholar 

13.
Kling, G. W., Kipphut, G. W. & Miller, M. C. Arctic lakes and streams as gas conduits to the atmosphere-implications for tundra carbon budgets. Science 251, 298–301 (1991).
CAS  Article  Google Scholar 

14.
Kling, G. W., Kipphut, G. W., Miller, M. M. & O’Brien, W. J. Integration of lakes and streams in a landscape perspective: the importance of material processing on spatial patterns and temporal coherence. Freshwater Biol. 43, 477–497 (2000).
Article  Google Scholar 

15.
DelSontro, T., Beaulieu, J. J. & Downing, J. A. Greenhouse gas emissions from lakes and impoundments: upscaling in the face of global change. Limnol. Oceanogr. Lett. 3, 64–75 (2018).
CAS  Article  Google Scholar 

16.
Bring, A. et al. Arctic terrestrial hydrology: A synthesis of processes, regional effects, and research challenges. J. Geophys. Res. Biogeosci. 121, 621–649 (2016).
Article  Google Scholar 

17.
Tank, S. E. et al. Landscape-level controls on dissolved carbon flux from diverse catchments of the circumboreal. Global Biogeochem. Cycles 26, https://doi.org/10.1029/2012gb004299 (2012).

18.
Kling, G. W., Kipphut, G. W. & Miller, M. C. The flux of CO2 and CH4 from lakes and rivers in arctic Alaska. Hydrobiologia 240, 23–36 (1992).
CAS  Article  Google Scholar 

19.
Cory, R. M., Ward, C. P., Crump, B. C. & Kling, G. W. Sunlight controls water column processing of carbon in arctic fresh waters. Science 345, 925–928 (2014).
CAS  Article  Google Scholar 

20.
Kortelainen, P. et al. Carbon evasion/accumulation ratio in boreal lakes is linked to nitrogen. Global Biogeochem. Cycles 27, 363–374 (2013).
CAS  Article  Google Scholar 

21.
Schuur, E. A. G. et al. The effect of permafrost thaw on old carbon release and net carbon exchange from tundra. Nature 459, 556–559 (2009).
CAS  Article  Google Scholar 

22.
Bowden, W. B. et al. Sediment and nutrient delivery from thermokarst features in the foothills of the North Slope, Alaska: Potential impacts on headwater stream ecosystems. J. Geophys. Res. Biogeosci. 113, https://doi.org/10.1029/2007jg000470 (2008).

23.
Curtis, J., Wendler, G., Stone, R. & Dutton, E. Precipitation decrease in the western arctic, with special emphasis on Barrow and Barter Island, Alaska. Int. J. Climatol. 18, 1687–1707 (1998).
Article  Google Scholar 

24.
Yano, Y., Shaver, G. R., Giblin, A. E., Rastetter, E. B. & Nadelhoffer, K. J. Nitrogen dynamics in a small arctic watershed: retention and downhill movement of N-15. Ecol. Monogr. 80, 331–351 (2010).
Article  Google Scholar 

25.
Tape, K., Sturm, M. & Racine, C. The evidence for shrub expansion in Northern Alaska and the Pan-Arctic. Glob. Change Biol. 12, 686–702 (2006).
Article  Google Scholar 

26.
Stackpoole, S. M. et al. Inland waters and their role in the carbon cycle of Alaska. Ecol. Appl. 27, 1403–1420 (2017).
Article  Google Scholar 

27.
Tank, S. E. et al. Landscape matters: Predicting the biogeochemical effects of permafrost thaw on aquatic networks with a state factor approach. Permafr. Periglac. Process. 31, 358–370 (2020).
Article  Google Scholar 

28.
Hobbie, J. E. & Kling, G. W. Alaska’s Changing Arctic: Ecological Consequences for Tundra, Streams, and Lakes. (Oxford University Press, 2014).

29.
Kaufman, D. S. et al. Recent warming reverses long-term Arctic cooling. Science 325, 1236–1239 (2009).
CAS  Article  Google Scholar 

30.
Meyers, P. A. & Ishiwatari, R. Lacustrine organic geochemistry-an overview of indicators of organic matter sources and diagenesis in lake sediments. Organ. Geochem. 20, 867–900 (1993).
CAS  Article  Google Scholar 

31.
Vadeboncoeur, Y. et al. From Greenland to green lakes: cultural eutrophication and the loss of benthic pathways in lakes. Limnol. Oceanogr. 48, 1408–1418 (2003).
Article  Google Scholar 

32.
Gettel, G. M., Giblin, A. E. & Howarth, R. W. Controls of benthic nitrogen fixation and primary production from nutrient enrichment of oligotrophic, Arctic lakes. Ecosystems 16, 1550–1564 (2013).
CAS  Article  Google Scholar 

33.
Cornwell, J. C. & Banahan, S. A silicon budget for an alaskan arctic lake. Hydrobiologia 240, 37–44 (1992).
CAS  Article  Google Scholar 

34.
France, R. L. C-13 enrichment in benthic compared to planktonic algae-foodweb implications. Mar. Ecol. Prog. Ser. 124, 307–312 (1995).
Article  Google Scholar 

35.
Levine, M. A. & Whalen, S. C. Nutrient limitation of phytoplankton production in Alaskan Arctic foothill lakes. Hydrobiologia 455, 189–201 (2001).
Article  Google Scholar 

36.
Hobbie, S. E., Schimel, J. P., Trumbore, S. E. & Randerson, J. R. Controls over carbon storage and turnover in high-latitude soils. Glob. Change Biol. 6, 196–210 (2000).
Article  Google Scholar 

37.
Galman, V., Rydberg, J., de-Luna, S. S., Bindler, R. & Renberg, I. Carbon and nitrogen loss rates during aging of lake sediment: changes over 27 years studied in varved lake sediment. Limnol. Oceanogr. 53, 1076–1082 (2008).
Article  Google Scholar 

38.
Fitzgerald, W. F. et al. Modern and historic atmospheric mercury fluxes in northern Alaska: Global sources and Arctic depletion. Environ. Sci. Technol. 39, 557–568 (2005).
CAS  Article  Google Scholar 

39.
Holtgrieve, G. W. et al. A coherent signature of anthropogenic nitrogen deposition to remote watersheds of the northern hemisphere. Science 334, 1545–1548 (2011).
CAS  Article  Google Scholar 

40.
Hobbs, W. O. et al. Nitrogen deposition to lakes in national parks of the western Great Lakes region: Isotopic signatures, watershed retention, and algal shifts. Global Biogeochem. Cycles 30, 514–533 (2016).
CAS  Article  Google Scholar 

41.
Anderson, N. J. et al. Regional variability in the atmospheric nitrogen deposition signal and its transfer to the sediment record in Greenland lakes. Limnol. Oceanogr. 63, 2250–2265 (2018).
CAS  Article  Google Scholar 

42.
Brock, C. S., Leavitt, P. R., Schindler, D. E., Johnson, S. P. & Moore, J. W. Spatial variability of stable isotopes and fossil pigments in surface sediments of Alaskan coastal lakes: Constraints on quantitative estimates of past salmon abundance. Limnol. Oceanogr. 51, 1637–1647 (2006).
CAS  Article  Google Scholar 

43.
Curtis, C. J. et al. Spatial variations in snowpack chemistry, isotopic composition of NO3- and nitrogen deposition from the ice sheet margin to the coast of western Greenland. Biogeosciences 15, 529–550 (2018).
CAS  Article  Google Scholar 

44.
Post, E. et al. Ecological dynamics across the Arctic associated with recent climate change. Science 325, 1355–1358 (2009).
CAS  Article  Google Scholar 

45.
Jones, V. J. et al. The influence of Holocene tree-line advance and retreat on an arctic lake ecosystem: a multi-proxy study from Kharinei Lake, North Eastern European Russia. J. Paleolimnol. 46, 123–137 (2011).
Article  Google Scholar 

46.
Kittel, T. G. F., Baker, B. B., Higgins, J. V. & Haney, J. C. Climate vulnerability of ecosystems and landscapes on Alaska’s North Slope. Reg. Environ. Change 11, S249–S264 (2011).
Article  Google Scholar 

47.
McKane, R. B. et al. Reconstruction and analysis of historical changes in carbon storage in arctic tundra. Ecology 78, 1188–1198 (1997).
Article  Google Scholar 

48.
Leavitt, P. R. et al. Paleolimnological evidence of the effects on lakes of energy and mass transfer from climate and humans. Limnol. Oceanogr. 54, 2330–2348 (2009).
CAS  Article  Google Scholar 

49.
Tye, A. M. & Heaton, T. H. E. Chemical and isotopic characteristics of weathering and nitrogen release in non-glacial drainage waters on Arctic tundra. Geochim. Cosmochim. Acta 71, 4188–4205 (2007).
CAS  Article  Google Scholar 

50.
Hobbie, J. E. et al. Impact of global change on biogeochemistry and ecosystems of an arctic freshwater system. Polar Res. 18, 207–214 (1999).
Article  Google Scholar 

51.
Whalen, S. C. & Cornwell, J. C. Nitrogen, phosphorus, and organic-carbon cycling in an Arctic lake. Can. J. Fish. Aquat. Sci. 42, 797–808 (1985).
CAS  Article  Google Scholar 

52.
Mantua, N. J., Hare, S. R., Zhang, Y. & Wallace, J. M. Pacific interdecadal climate oscillation with impacts on salmon production. Bull. Am. Meteorol. Soc. 78, 1069–1079 (1997).
Article  Google Scholar 

53.
Galloway, J. N. et al. Transformation of the nitrogen cycle: Recent trends, questions, and potential solutions. Science 320, 889–892 (2008).
CAS  Article  Google Scholar 

54.
Dean, W. E. Determination of carbonate and organic-matter in calcareous sediments and sedimentary-rocks by loss on ignition-comparison with other methods. J. Sediment. Petrol. 44, 242–248 (1974).
CAS  Google Scholar 

55.
Appleby, P. G. In Tracking Environmental Change Using Lake Sediments Volume 1: Basin Analysis, Coring, and Chronological Techniques (eds. Last, W. M. & Smol, J. P.) 171–203 (Kluwer, 2001).

56.
Engstrom, D. R. & Rose, N. L. A whole-basin, mass-balance approach to paleolimnology. J. Paleolimnol. 49, 333–347 (2013).
Article  Google Scholar 

57.
Dietz, R. D., Engstrom, D. R. & Anderson, N. J. Patterns and drivers of change in organic carbon burial across a diverse landscape: insights from 116 Minnesota lakes. Global Biogeochem. Cycles 29, 708–727 (2015).
CAS  Article  Google Scholar 

58.
DeMaster, D. J. The supply and accumulation of silica in the marine environment. Geochim. Cosmochim. Acta 45, 1715–1732 (1981).
CAS  Article  Google Scholar 

59.
Conley, D. J. & Schelske, C. L. In Tracking Environmental Change Using lake Sediments: Biological Methods and Indicators (eds. Smol, J. P., Birks, H. J. B. & Last, W. M.) 281–293 (Kluwer, 2001).

60.
Renberg, I. A procedure for preparing large sets of diatom slides from sediment cores. J. Paleolimnol. 4, 87–90 (1990).
Article  Google Scholar 

61.
Savage, C., Leavitt, P. R. & Elmgren, R. Distribution and retention of effluent nitrogen in surface sediments of a coastal bay. Limnol. Oceanogr. 49, 1503–1511 (2004).
CAS  Article  Google Scholar 

62.
Wood, S. N. Generalized Additive Models: An Introduction with R, 2nd edn. (Chapman and Hall/CRC, 2017).

63.
Wood, S. N. Thin-plate regression splines. J. R. Stat. Soc. (B) 65, 95–114 (2003).
Article  Google Scholar 

64.
Simpson, G. L. Modelling palaeoecological time series using generalized additive models. Front. Ecol. Evol. 6, 149 (2018).
Article  Google Scholar 

65.
Bai, J. & Perron, P. Computation and analysis of multiple structural change models. J Appl. Econ. 181, 1–22 (2003).
Article  Google Scholar 

66.
R Core Team. A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, http://www.R-project.org/ (2013).

67.
Zeileis, A., Leisch, F., Hornik, K. & Kleiber, C. strucchange: An R package for testing for structural change in linear regression models. J. Stat. Softw. 7, 1–38 (2002).
Article  Google Scholar 

68.
Anderson, N. J., Engstrom, D. R., Leavitt, P. R., Flood, S. M. & Heathcote, A. J. Mendeley, https://doi.org/10.17632/xfmpvjmrby.1 (2020).

69.
The Alaska Climate Research Center. Climatological Data-Alaska’s Arctic, http://oldclimate.gi.alaska.edu/Climate/Location/TimeSeries/Data/brwT (2010).

70.
Hastings, M. G., Steig, E. J. & Sigman, D. M. Seasonal variations in N and O isotopes of nitrate in snow at Summit, Greenland: Implications for the study of nitrate in snow and ice cores. J. Geophys. Res. Atmos. 109, https://doi.org/10.1029/2004jd004991 (2004).

71.
Tseng, C.-M., Lamborg, C. H., Fitzgerald, W. F. & Engstrom, D. R. Cycling of dissolved elemental mercury in Arctic Alaskan lakes. Geochim. Cosmochim. Acta 68, 1173–1184 (2004).
CAS  Article  Google Scholar  More

Genotypic heterogeneity in single Rivularia-like colonies
Rivularia-like colonies have a global distribution, occurring in marine or freshwater habitats, where they are usually attached to a rocky substrate; however, many studies have reported that Rivularia spp. are associated with unpolluted environments14. In addition, the relationships between some morphological or physiological features and the environment make these species excellent environmental indicators of changes in running water quality, mainly related to eutrophication processes14,30. Therefore, they have been included in biomonitoring programs21,31,32. On the other hand, because Rivularia colonies sometimes persist for very long periods, avoiding grazing, the toxicity of these colonies is being investigated33. It is undoubted that in all of these approaches, where genera and species must be strictly identified from environmental samples, accurate cyanobacterial characterization is essential.
Traditional identification of cyanobacteria involves assigning a colony to a morphospecies, and conventionally, a bacterial colony is defined as a visible mass of clonal microorganisms, all of which originated from a single cell. However, the results from the present study show that the majority of the analyzed colonies consist of different clones growing together. Among the 28 Rivularia-like colonies, the phylotype corresponding to Rivularia sp. was present in 19 colonies, with abundances ranging from 59.4 to 99.8% depending on the studied colony. Nevertheless, it should also be noted that in most of the colonies, this phylotype dominated, whereby in 14 colonies, it presented an abundance of ≥ 90% (and within 7 of these colonies, the abundance was close to 99%). However, in three colonies, the abundance ranged from 72 to 85%, and in two of them, the abundance decreased to approximately 60%. The other highly abundant phylotypes found in these colonies, which reached abundances up to approximately 21%, corresponded to Calothrix sp. and Oculatella sp., the latter a genus morphologically similar to Leptolyngbya but separated from it because of genetic differences34. These results indicated great variability in the abundance of the phylotype corresponding to Rivularia depending on the analyzed colony, as well as variation in the other phylotypes and their abundances found in these colonies.
One of the surprising findings was that among the twenty-eight analyzed Rivularia-like colonies, seven corresponded to the new, recently described genus Cyanomargarita, which as the authors described, is virtually indistinguishable from Rivularia in field samples15. In these colonies, genotypic heterogeneity was also found, in which the abundance of the phylotype corresponding to Cyanomargarita varied from 57,28% in a colony with clear lamination resembling R. haematites (see Fig. 3b) to 99.2% in a soft colony resembling R. biasolettiana. Interestingly, in these colonies, Phormidium sp. was the dominant nonheterocystous cyanobacterium instead of Oculatella from Rivularia colonies, but the phylotype corresponding to Calothrix was also found.
Furthermore, phylotypes corresponding to Cyanomargarita and Rivularia were never found together in the same colony, although both types of colonies coexisted in the same rivers (e.g., Gordale Beck and Endrinales). Allelopathic effects could explain these results, as previously suggested for other cyanobacteria35. In fact, García-Espín et al.33 showed that extracts obtained from Rivularia colonies affected the photosynthetic activity of several diatoms and a red alga. Further experiments with extracts from both colonies would confirm this possible effect.
Another very surprising finding was that two Rivularia-like colonies did not present any phylotypes corresponding to Rivularia or Cyanomargarita (or contained them at an abundance ≤ 0.7%). In one of these colonies (colony BAT4), five different phylotypes were found at similar abundances (approximately 15–20%), of which three corresponded to different Calothix spp. and the others corresponded to other Nostocaceae and Leptolyngbyaceae. In the other colony (BAT13), the dominant phylotype corresponded to the new genus Macrochaete16. This genus has been described only from cultures, so to the best of our knowledge, this is the first report in which a natural population is morphologically and genetically characterized. Nevertheless, it is noteworthy that the morphological characteristics of filaments and trichomes in this environmental sample were different from those reported in the description of this new genus, in which the phenotypic features resembled those of Calothrix. However, these features corresponded only to isolated strains, which are known to exhibit morphological variability and differences from natural populations7,12,13.
R. biasolettiana vs R. haematites
However, what was very interesting and deserves to be highlighted is that when we tried to differentiate the two typical Rivularia colonies found in calcareous streams, R. biasolettiana and R. haematites, we did not find genetic differences, at least at the studied level, the 16S rRNA gene.
16S rRNA is the most widely used marker gene36,37, which fits the criteria of ubiquity, regions of strong conservation, and regions of hypervariability38,39. This gene is supported by reference databases containing over a million full-length 16S rRNA sequences, therefore spanning a broad phylogenetic spectrum40. The 16S rRNA gene has served as the general framework and as the benchmark for the taxonomy of prokaryotes41. Advances in high-throughput sequencing technologies have enabled almost comprehensive descriptions of bacterial diversity through 16S rRNA gene amplicons, which have been used in surveys of microbial communities to characterize the composition of microorganisms present in environments worldwide42,43,44,45. Although some issues have been raised, such as identification of metabolic or other functional capabilities of microorganisms when studies focus only on this gene, recent studies have shown that the phylogenetic information contained in 16S marker gene sequences is sufficiently well correlated with genomic content to yield accurate predictions when related reference genomes are available46,47,48,49. Therefore, the 16S rRNA gene continues to be the mainstay of sequence-based bacterial analysis, vastly expanding our understanding of the microbial world50.
In particular, in cyanobacteria, as in other prokaryotes, the 16S rDNA gene is currently the most commonly used marker for molecular and phylogenetic studies51,52. The information obtained from 16S rDNA gene phylogenetic reconstructions, together with morphological, ultrastructural, and ecological data, led Komárek et al.53 to propose the current accepted classification of cyanobacteria. There have also been specific studies by this group concerning the problems associated with single-gene phylogenies, in which robust phylogenomic trees of cyanobacteria derived from multiple conserved proteins have also shown congruence between the multilocus and 16S rRNA gene phylogenies, which once again demonstrates the considerable strength of the 16S rRNA gene for phylogenetic inference and evaluation of prokaryote diversity54,55,56,57.
In this study, in contrast to the genetic identity found in R. biasolettiana and R. haematites colonies, showing a dominance of OTU1, the remainder of the studied representatives of Rivulariaceae showed a wide range of variation in the 16S rDNA sequences and with OTU1. Sequence identity between OTU1 and the remaining OTUs belonging to this family was as low as approximately 90%, ranging from 90.73 to 93.41%, and when it was compared with other Rivulariaceae from the databases, in the different clusters of the phylogenetic tree, this value ranged from 87.12 to 93.90%. A large difference between the sequences of this gene was also found in other studies on Rivulariaceae15,16,17,29,58. In fact, several new genera are emerging on the basis of these differences15,16,17. Comparisons of phylogenies using other markers, such as the phycocyanin operon and the intervening intergenic spacer (cpcBA-IGS) with the 16S rRNA gene in previous studies in Rivulariaceae, have shown largely consistent results, with a high level of divergence between the components of this family11.
In addition, the results of the present study showed correlations between morphological characteristics and the analyzed genes in all the cyanobacterial colonies/tufts, except for those of R. biasolettiana and R. haematites. In these two cyanobacteria, only distinct macroscopic phenotypic features were observed due to zonation and different degrees of calcification since no significant differences were found in the size measurements or other microscopic characteristics.
Therefore, although the remainder of the genome has not been studied in these populations, the genetic identity of the studied marker, phenotypic features, together with environmental preferences point out that R. biasolettiana and R. haematites are ecotypes of the same species, as previously suggested59.
R. biasolettiana and R. haematites have very similar morphotypes, and traditional taxonomical classification and studies have distinguished them primarily by their degrees of calcification. R. biasolettiana-type colonies are described as more gelatinous and less calcified, and the crystals are disseminated; however, R. haematites colonies are very hard and exhibit extensive calcification in concentric zones, which leads to clear lamination24,25,60,61. Because of its heavy mineralization, R. haematites is a model for stromatolite-binding organisms25,26.
Microscopic observations from this study showed that some colonies presented typical R. haematites morphology with concentric bands of intense calcification (see Fig. 2a,b), and others were soft and less calcified, such as R. biasolettiana, although all of them presented the same dominant phylotype. Many others with this dominant phylotype have also shown ambiguous morphology with no clear lamination, although some dark/light zones could be observed (see, e.g., Fig. 4b,d, f). Even in Cyanomargarita colonies, whose genotype was clearly separated from that of Rivularia, concentric zones and extensive calcification could be observed (see, e.g., Fig. 3b,d). These results suggested that these phenotypic features are not diagnostic characteristics for further identification.
In a two-year study, Obenlüneschloss and Schneider61 found that not all analyzed R. haematites colonies showed distinct concentric calcification layers. In the stromatolites of both types of Rivularia, the same lamination was observed, and the differences in calcification appeared later60. Pentecost and Franke26 compared populations of R. biasolettiana and R. haematites and argued that although both could be distinguished by their form of calcification and their trichome diameter, some populations of R. biasolettiana were more intensely calcified than others, suggesting that a continuity of forms may exist, even within the same stream, and therefore, a continuum of colony forms probably occurs between these taxa.
Differences in the calcification pattern have been attributed to seasonality and cyanobacterial activity, in particular to photosynthesis24,26,62. The calcification in R. haematites occurred in concentric bands, which varied in thickness and the density of crystals. Since characteristic zonation is formed by filaments of different successive generations, the thickness will vary depending on the growth rate, while crystal density will depend on the rate of calcification. Calcification is the result of photosynthesis (with a maximum of 14%) and evaporation during the warmer seasons, while it is entirely abiogenic during winter as a result of CO2 evasion63. Therefore, dense calcified bands similar to those formed in winter have been described that are caused by a reduction in trichome growth and EPS production, allowing the development of abiotic surface precipitate, and less calcified layers are formed during spring and summer, when calcification is associated with photosynthesis in zones of growth with cell division24,26. Thus, differences in climatic conditions and/or biological activity seem to lead to differences in the degrees of zonation and calcification.
The growth of Rivularia colonies is seasonal and strongly correlated with water temperature24,26. The colony growth rates were 12–14 µm/day in summer and 2 µm/day in winter24. The occurrence of R. biasolettiana was more closely related to high temperatures than that of R. haematites21. Moreover, colonies of R. haematites were generally collected under temperatures below 15 °C in mountain running waters64, and R. haematites stromatolites have been described as preferentially developed in wet periods, particularly in autumn and winter60. Our own field observations during the sampling for this and previous studies were that the gelatinous and weakly calcified R. biasolettiana type was more abundant in warmer locations, and in contrast, R. haematites was dominant in cold locations (data not shown).
One possible explanation for the results found in this study could be related to these differences in the degree of zonation and calcification in relation to climate, which could include microclimatic conditions. In warmer sites or climatic conditions, when growth is rapid, the number of filaments will increase, moving towards the surface in a weakly dense and unaligned arrangement, on which calcite crystals spread, providing a lighter and less calcified structure. Thus, increased growth of Rivularia colonies can lead to the R. biasolettiana type. Under colder conditions, such as in winter, or microclimatic conditions, when growth slows down for other reasons, such as low light, filaments become more densely packed, allowing the development of extensive precipitates and leading to a dark band. When these conditions change, e.g., in the spring and summer, increases in temperatures and/or light will result in increasing and faster growth, leading to a less calcified new layer, and successive seasonal and/or microenvironmental changes will result in the typical lamination of R. haematites. Therefore, warmer places with high temperatures and/or light will allow the occurrence of the R. biasolettiana type, while in colder sites and/or sites with alternating environmental conditions, the R. haematites type will develop. Shaded colonies and colonies that lie in the supratidal spraywater zone often contain small, irregular and more densely packed crystals61.
Cyanobacteria are known to modify EPS production, pigments, and morphology under environmental stimuli6. The production of EPS also varies depending on the cyanobacteria, whereby Rivularia has shown a well-developed exopolymer layer65, which is of great importance for this epilithic cyanobacteria, as it acts as an adhesive that allows cells to stick to the stones in the running waters, and it holds the filaments together, minimizing cell damage during intermittent drying exposure to the air and evaporation in the warmer seasons66. The C/N ratio is an important parameter for the variation in EPS production since high amounts of fixed C compared to N levels drive EPS synthesis to store excess C67,68. Therefore, Rivularia colonies that are exposed, in spring and summer, to high light intensities and temperatures will increase their photosynthetic rates and therefore the amount of EPS, as shown by the R. biasolettiana morphotype. In addition, most of the analyzed populations were dark in color, probably in relation to the accumulation of the yellow–brown scytonemin pigment in the sheaths or EPS, as previously observed in shallow and clear oligotrophic ecosystems, where water clarity allows UV radiation to penetrate well, protecting the cells from the damaging effects of this radiation69,70.
In conclusion, environmental factors can lead to differences in macroscopic phenotypic features, such as those found in the Rivularia colonies studied here. However, further sampling under different climatic conditions and/or microenvironmental conditions or of Rivularia cultures grown under distinct temperature and/or illumination conditions, as well as analysis of other genes, are needed to confirm this hypothesis. More

1.
Zhou, S. et al. Ecosystem-based fisheries management requires a change to the selective fishing philosophy. Proc. Natl. Acad. Sci. U.S.A. 107, 9485–9489 (2010).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 
2.
Fulton, E. A., Smith, A. D. M., Smith, D. C. & Johnson, P. An integrated approach is needed for ecosystem based fisheries management: insights from ecosystem-level management strategy evaluation. PLoS ONE 9, e84242 (2014).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

3.
Murphy, E. J. et al. Understanding the structure and functioning of polar pelagic ecosystems to predict the impacts of change. Proc. R. Soc. B Biol. Sci. 283, 20161646 (2016).
Article  Google Scholar 

4.
Schofield, O. et al. Changes in the upper ocean mixed layer and phytoplankton productivity along the West Antarctic Peninsula. Philos. Trans. R. Soc. A Math. Phys. Eng. Sci. 376, 20170173 (2018).
ADS  Article  CAS  Google Scholar 

5.
Nicol, S., Foster, J. & Kawaguchi, S. The fishery for Antarctic krill-recent developments. Fish Fish. 13, 30–40 (2012).
Article  Google Scholar 

6.
Trathan, P. N. et al. Managing fishery development in sensitive ecosystems: identifying penguin habitat use to direct management in Antarctica. Ecosphere 9, e02392 (2018).
Article  Google Scholar 

7.
Warwick-Evans, V. et al. Using habitat models for chinstrap penguins Pygoscelis antarctica to advise krill fisheries management during the penguin breeding season. Divers. Distrib. 24, 1756–1771 (2018).
Article  Google Scholar 

8.
Santa Cruz, F., Ernst, B., Arata, J. A. & Parada, C. Spatial and temporal dynamics of the Antarctic krill fishery in fishing hotspots in the Bransfield Strait and South Shetland Islands. Fish. Res. 208, 157–166 (2018).
Article  Google Scholar 

9.
Sylvester, Z. T. & Brooks, C. M. Protecting Antarctica through Co-production of actionable science: lessons from the CCAMLR marine protected area process. Mar. Policy 111, 103720 (2020).
Article  Google Scholar 

10.
Agnew, D. J. Review: the CCAMLR ecosystem monitoring programme. Antarct. Sci. 9, 235–242 (1997).
ADS  Article  Google Scholar 

11.
Boyd, I. L., McCafferty, D. J., Reid, K., Taylor, R. & Walker, T. R. Dispersal of male and female antarctic fur seals (Arctocephalus gazella). Can. J. Fish. Aquat. Sci. 55, 845–852 (1998).
Article  Google Scholar 

12.
Vergani, D. F. & Coria, N. R. Increase in numbers of male fur seals Arctocephalus gazella during the summer autumn period at Mossman Peninsula (Laurie Island). Polar Biol. 9, 487–488 (1989).
Article  Google Scholar 

13.
Plagányi, É. E. & Butterworth, D. S. The Scotia Sea krill fishery and its possible impacts on dependent predators: modeling localized depletion of prey. Ecol. Appl. 22, 748–761 (2012).
PubMed  Article  Google Scholar 

14.
Watters, G. M., Hill, S. L., Hinke, J. T., Matthews, J. & Reid, K. Decision-making for ecosystem-based management: evaluating options for a krill fishery with an ecosystem dynamics model. Ecol. Appl. 23, 710–725 (2013).
CAS  PubMed  Article  Google Scholar 

15.
Trivelpiece, W. Z. et al. Variability in krill biomass links harvesting and climate warming to penguin population changes in Antarctica. Proc. Natl. Acad. Sci. U.S.A. 108, 7625–7628 (2011).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

16.
Watters, G. M., Hinke, J. T. & Reiss, C. S. Long-term observations from Antarctica demonstrate that mismatched scales of fisheries management and predator–prey interaction lead to erroneous conclusions about precaution. Sci. Rep. 10, 1–9 (2020).
Article  CAS  Google Scholar 

17.
Hill, S. L., Reid, K., Thorpe, S. E., Hinke, J. & Watters, G. M. A compilation of parameters for ecosystem dynamics models of the Scotia Sea—Antarctic Peninsula region. CCAMLR Sci. 14, 1–25 (2007).
Google Scholar 

18.
Lowther, A. D., Trathan, P., Tarroux, A., Lydersen, C. & Kovacs, K. M. The relationship between coastal weather and foraging behaviour of chinstrap penguins Pygoscelis antarctica. ICES J. Mar. Sci. 75, 1940–1948 (2018).
Article  Google Scholar 

19.
Boehme, L. et al. Technical note: animal-borne CTD-satellite relay data loggers for real-time oceanographic data collection. Ocean Sci. 5, 685–695 (2009).
ADS  Article  Google Scholar 

20.
Lowther, A. D., Lydersen, C., Fedak, M. A., Lovell, P. & Kovacs, K. M. The Argos-CLS Kalman filter: error structures and state-space modelling relative to Fastloc GPS data. PLoS ONE 10, e0124754 (2015).
PubMed  PubMed Central  Article  CAS  Google Scholar 

21.
Johnson, D. S., London, J. M., Lea, M. A. & Durban, J. W. Continuous-time correlated random walk model for animal telemetry data. Ecology 89, 1208–1215 (2008).
PubMed  Article  Google Scholar 

22.
Benhamou, S. Dynamic approach to space and habitat use based on biased random bridges. PLoS ONE 6, e14592 (2011).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

23.
Schlitzer, R. Ocean Data View. https://odv.awi.de (2018).

24.
Hill, S. L., Trathan, P. N. & Agnew, D. J. The risk to fishery performance associated with spatially resolved management of Antarctic krill (Euphausia superba) harvesting. ICES J. Mar. Sci. 66, 2148–2154 (2009).
Article  Google Scholar 

25.
Trivelpiece, W. Z., Trivelpiece, S. G. & Volkman, N. J. Ecological segregation of Adelie, gentoo, and chinstrap penguins at King George Island Antarctica. Ecology 68, 351–361 (1987).
Article  Google Scholar 

26.
Black, C. E. A comprehensive review of the phenology of Pygoscelis penguins. Polar Biol. 39, 405–432 (2016).
Article  Google Scholar 

27.
Payne, M. R. Growth in the Antarctic fur seal Arctocephalus gazella. J. Zool. 187, 1–20 (1979).
Article  Google Scholar 

28.
Reiss, C. S. et al. Overwinter habitat selection by Antarctic krill under varying sea-ice conditions: implications for top predators and fishery management. Mar. Ecol. Prog. Ser. 568, 1–16 (2017).
ADS  CAS  Article  Google Scholar 

29.
Carlini, A. R., Daneri, G. A., Casaux, R. & Márquez, M. E. I. Haul-out pattern of itinerant male Antarctic fur seals (Arctocephalus gazella) at Laurie Island South Orkney Islands. Polar Res. 25, 139–144 (2006).
Article  Google Scholar 

30.
Waluda, C. M., Gregory, S. & Dunn, M. J. Long-term variability in the abundance of Antarctic fur seals Arctocephalus gazella at Signy Island South Orkneys. Polar Biol. 33, 305–312 (2010).
Article  Google Scholar 

31.
Casaux, R., Juares, M., Carlini, A. & Corbalán, A. The diet of the Antarctic fur seal Arctocephalus gazella at the South Orkney Islands in ten consecutive years. Polar Biol. 39, 1197–1206 (2016).
Article  Google Scholar 

32.
Reid, K. & Arnould, J. P. Y. Y. The diet of Antarctic fur seals Arctocephalus gazella during the breeding season at South Georgia. Polar Biol. 16, 105–114 (1996).
Article  Google Scholar 

33.
Boyd, I. L. Estimating food consumption of marine predators: Antarctic fur seals and macaroni penguins. J. Appl. Ecol. 39, 103–119 (2002).
Article  Google Scholar 

34.
CCAMLR. Krill Fishery Report. Commission for the Conservation of Antarctic Marine Living Resources. https://www.ccamlr.org/en/system/files/00%20KRI48%202016%20v1_1.pdf (2016).

35.
Dias, M. P. et al. Identification of marine important bird and biodiversity areas for penguins around the South Shetland Islands and South Orkney Islands. Ecol. Evol. 8, 10520–10529 (2018).
PubMed  PubMed Central  Article  Google Scholar 

36.
Lynch, H. J., Naveen, R. & Casanovas, P. Antarctic site inventory breeding bird survey data, 1994–2013. Ecology 94, 2653–2653 (2013).
Article  Google Scholar 

37.
Humphries, G. R. W. et al. Mapping application for penguin populations and projected dynamics (MAPPPD): data and tools for dynamic management and decision support. Polar Rec. (Gr. Brit) 53, 160–166 (2017).
Article  Google Scholar 

38.
Lea, M. A. et al. Colony-based foraging segregation by Antarctic fur seals at the Kerguelen Archipelago. Mar. Ecol. Prog. Ser. 358, 273–287 (2008).
ADS  Article  Google Scholar 

39.
Staniland, I. J., Reid, K. & Boyd, I. L. Comparing individual and spatial influences on foraging behaviour in Antarctic fur seals Arctocephalus gazella. Mar. Ecol. Prog. Ser. 275, 263–274 (2004).
ADS  Article  Google Scholar 

40.
Bonadonna, F., Lea, M. A. & Guinet, C. Foraging routes of Antarctic fur seals (Arctocephalus gazella) investigated by the concurrent use of satellite tracking and time-depth recorders. Polar Biol. 23, 149–159 (2000).
Article  Google Scholar 

41.
Richerson, K., Santora, J. A. & Mangel, M. Climate variability and multi-scale assessment of the krill preyscape near the north Antarctic Peninsula. Polar Biol. 40, 697–711 (2017).
Article  Google Scholar 

42.
Cleary, A. C., Durbin, E. G., Casas, M. C. & Zhou, M. Winter distribution and size structure of Antarctic krill Euphausia superba populations in-shore along the West Antarctic Peninsula. Mar. Ecol. Prog. Ser. 552, 115–129 (2016).
ADS  Article  Google Scholar 

43.
Lynch, H. J., Naveen, R., Trathan, P. N. & Fagan, W. F. Spatially integrated assessment reveals widespread changes in penguin populations on the Antarctic Peninsula. Ecology 93, 1367–1377 (2012).
PubMed  Article  Google Scholar 

44.
Kokubun, N., Takahashi, A., Mori, Y., Watanabe, S. & Shin, H. C. Comparison of diving behavior and foraging habitat use between chinstrap and gentoo penguins breeding in the South Shetland Islands Antarctica. Mar. Biol. 157, 811–825 (2010).
Article  Google Scholar 

45.
Miller, A. K. & Trivelpiece, W. Z. Chinstrap penguins alter foraging and diving behavior in response to the size of their principle prey Antarctic krill. Mar. Biol. 154, 201–208 (2008).
Article  Google Scholar 

46.
Hinke, J. T., Santos, M. M., Korczak-Abshire, M., Milinevsky, G. & Watters, G. M. Individual variation in migratory movements of chinstrap penguins leads to widespread occupancy of ice-free winter habitats over the continental shelf and deep ocean basins of the Southern Ocean. PLoS ONE 14, e0226207 (2019).
CAS  PubMed  PubMed Central  Article  Google Scholar 

47.
Orgeret, F., Weimerskirch, H. & Bost, C. A. Early diving behaviour in juvenile penguins: improvement or selection processes. Biol. Lett. 12, 20160490 (2016).
PubMed  PubMed Central  Article  Google Scholar 

48.
Marchetti, K. & Price, T. Differences in the foraging of juvenile and adult birds: the importance of developmental constraints. Biol. Rev. Camb. Philos. Soc. 64, 51–70 (1989).
Article  Google Scholar 

49.
Boyd, I. L. Pup production and distribution of breeding antarctic fur seals (Arctocephalus gazella) at South Georgia. Antarct. Sci. 5, 17–24 (1993).
ADS  Article  Google Scholar 

50.
Casaux, R. et al. Geographical variation in the diet of the Antarctic fur seal Arctocephalus gazella. Polar Biol. 26, 753–758 (2003).
Article  Google Scholar 

51.
CCAMLR. Statistical Bulletin. Vol. 31. www.ccamlr.org (2019).

52.
Kawaguchi, S. & Nicol, S. Krill fishery. Fish. Aquac. 9, 137 (2020).
Google Scholar 

53.
Vaughan, D. Fishing effort displacement and the consequences of implementing marine protected area management: an English perspective. Mar. Policy 84, 228–234 (2017).
Article  Google Scholar 

54.
Suuronen, P., Jounela, P. & Tschernij, V. Fishermen responses on marine protected areas in the Baltic cod fishery. Mar. Policy 34, 237–243 (2010).
Article  Google Scholar 

55.
Godø, O. R., Reiss, C., Siegel, V. & Watkins, J. L. Commercial fishing vessel as research vessels in the Antarctic: requirements and solutions exemplified with a new vessel. CCAMLR Sci. 21, 11–18 (2014).
Google Scholar 

56.
Niklitschek, E. J. & Skaret, G. Distribution, density and relative abundance of Antarctic krill estimated by maximum likelihood geostatistics on acoustic data collected during commercial fishing operations. Fish. Res. 178, 114–121 (2016).
Article  Google Scholar 

57.
Watkins, J. L. et al. The use of fishing vessels to provide acoustic data on the distribution and abundance of Antarctic krill and other pelagic species. Fish. Res. 178, 93–100 (2016).
Article  Google Scholar 

58.
Melbourne-Thomas, J., Constable, A., Wotherspoon, S. & Raymond, B. Testing paradigms of ecosystem change under climate warming in Antarctica. PLoS ONE 8, e55093 (2013).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

59.
Matsuoka, K., Skoglund, A. & Roth, G. Quantarctica [Data set]. Norwegian Polar Institute. https://doi.org/10.21334/npolar.2018.8516e961 (2018). More

1.
Daszak, P., Cunningham, A. A. & Hyatt, A. D. Emerging infectious diseases of wildlife: threats to biodiversity and human health. Science 287, 443–449 (2000).
ADS  CAS  PubMed  Article  Google Scholar 
2.
Fey, S. B. et al. Recent shifts in the occurrence, cause, and magnitude of animal mass mortality events. Proc. Natl. Acad. Sci. 112, 1083–1088 (2015).
ADS  CAS  PubMed  Article  Google Scholar 

3.
Fry, W. E. Principles of Plant Disease Management (Academic Press, Cambridge, 2012).
Google Scholar 

4.
Tomley, F. M. & Shirley, M. W. Livestock infectious diseases and zoonoses. Philos. Trans. R. Soc. Lond. B Biol. Sci. (2009).

5.
Frick, W. F. et al. An emerging disease causes regional population collapse of a common North American bat species. Science 329, 679–682 (2010).
ADS  CAS  PubMed  Article  Google Scholar 

6.
Singh, B., Dhand, N. K. & Gill, J. Economic losses occurring due to brucellosis in Indian livestock populations. Prev. Vet. Med. 119, 211–215 (2015).
CAS  PubMed  Article  Google Scholar 

7.
Lafferty, K. D. The ecology of climate change and infectious diseases. Ecology 90, 888–900 (2009).
PubMed  Article  Google Scholar 

8.
Bett, B. et al. Effects of climate change on the occurrence and distribution of livestock diseases. Prev. Vet. Med. 137, 119–129 (2017).
CAS  PubMed  Article  Google Scholar 

9.
Rohr, J. R. et al. Frontiers in climate change-disease research. Trends Ecol. Evol. 26, 270–277 (2011).
PubMed  PubMed Central  Article  Google Scholar 

10.
Harvell, C. D. et al. Climate warming and disease risks for terrestrial and marine biota. Science 296, 2158–2162 (2002).
ADS  CAS  PubMed  Article  Google Scholar 

11.
Boyett, H. V., Bourne, D. G. & Willis, B. L. Elevated temperature and light enhance progression and spread of black band disease on staghorn corals of the Great Barrier Reef. Mar. Biol. 151, 1711–1720 (2007).
Article  Google Scholar 

12.
Paillard, C., Allam, B. & Oubella, R. Effect of temperature on defense parameters in Manila clam Ruditapes philippinarum challenged with Vibrio tapetis. Dis. Aquat. Org. 59, 249–262 (2004).
Article  Google Scholar 

13.
Dalton, S. J., Godwin, S., Smith, S. & Pereg, L. Australian subtropical white syndrome: a transmissible, temperature-dependent coral disease. Mar. Freshwat. Res. 61, 342–350 (2010).
CAS  Article  Google Scholar 

14.
Korkut, G. G., Noonin, C. & Söderhäll, K. The effect of temperature on white spot disease progression in a crustacean Pacifastacus leniusculus. Dev. Comp. Immunol. 89, 7–13 (2018).
PubMed  Article  Google Scholar 

15.
Verant, M. L., Boyles, J. G., Waldrep, W. Jr., Wibbelt, G. & Blehert, D. S. Temperature-dependent growth of Geomyces destructans, the fungus that causes bat white-nose syndrome. PLoS ONE 7, e46280 (2012).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

16.
Ward, J. R., Kim, K. & Harvell, C. D. Temperature affects coral disease resistance and pathogen growth. Mar. Ecol. Prog. Ser. 329, 115–121 (2007).
ADS  Article  Google Scholar 

17.
Albert, V. & Ransangan, J. Effect of water temperature on susceptibility of culture marine fish species to vibriosis. Int. J. Res. Pure Appl. Microbiol. 3, 48–52 (2013).
Google Scholar 

18.
Case, R. J. et al. Temperature induced bacterial virulence and bleaching disease in a chemically defended marine macroalga. Environ. Microbiol. 13, 529–537 (2011).
CAS  PubMed  Article  Google Scholar 

19.
Gattuso, J.-P. et al. Contrasting futures for ocean and society from different anthropogenic CO2 emissions scenarios. Science 349, aac4722 (2015).
PubMed  Article  CAS  Google Scholar 

20.
Laffoley, D. D. A. & Baxter, J. Explaining Ocean Warming: Causes, Scale, Effects and Consequences (IUCN Gland, Switzerland, 2016).
Google Scholar 

21.
Burge, C. A. et al. Climate change influences on marine infectious diseases: implications for management and society. Annu. Rev. Mar. Sci. 6, 249–277 (2014).
ADS  Article  Google Scholar 

22.
Miller, J. et al. Coral disease following massive bleaching in 2005 causes 60% decline in coral cover on reefs in the US Virgin Islands. Coral Reefs 28, 925 (2009).
ADS  Article  Google Scholar 

23.
Eisenlord, M. E. et al. Ochre star mortality during the 2014 wasting disease epizootic: role of population size structure and temperature. Philos. Trans. R. Soc. Lond. B Biol. Sci 371, 20150212 (2016).
PubMed  PubMed Central  Article  CAS  Google Scholar 

24.
Moore, J. D., Robbins, T. T. & Friedman, C. S. Withering syndrome in farmed red abalone Haliotis rufescens: thermal induction and association with a gastrointestinal rickettsiales-like prokaryote. J. Aquat. Anim. Health 12, 26–34 (2000).
PubMed  Article  Google Scholar 

25.
Harvell, D., Altizer, S., Cattadori, I. M., Harrington, L. & Weil, E. Climate change and wildlife diseases: when does the host matter the most?. Ecology 90, 912–920 (2009).
PubMed  Article  Google Scholar 

26.
Malek, J. C. & Byers, J. E. Responses of an oyster host (Crassostrea virginica) and its protozoan parasite (Perkinsus marinus) to increasing air temperature. PeerJ 6, e5046 (2018).
PubMed  PubMed Central  Article  CAS  Google Scholar 

27.
Staehli, A., Schaerer, R., Hoelzle, K. & Ribi, G. Temperature induced disease in the starfish Astropecten jonstoni. Mar. Biodiv. Rec. 2, e78 (2009).
Article  Google Scholar 

28.
Gehman, A.-L.M., Hall, R. J. & Byers, J. E. Host and parasite thermal ecology jointly determine the effect of climate warming on epidemic dynamics. Proc. Natl. Acad. Sci. 115, 744–749 (2018).
CAS  PubMed  Article  Google Scholar 

29.
Studer, A., Thieltges, D. & Poulin, R. Parasites and global warming: net effects of temperature on an intertidal host–parasite system. Mar. Ecol. Prog. Ser. 415, 11–22 (2010).
ADS  Article  Google Scholar 

30.
McCallum, H., Harvell, D. & Dobson, A. Rates of spread of marine pathogens. Ecol. Lett. 6, 1062–1067 (2003).
Article  Google Scholar 

31.
Harvell, C. et al. Emerging marine diseases-climate links and anthropogenic factors. Science 285, 1505–1510 (1999).
CAS  PubMed  Article  Google Scholar 

32.
Morton, J. P., Silliman, B. R. & Lafferty, K. D. In Marine Disease Ecology (eds D.C. Behringer, B.R. Silliman, & K.D. Lafferty) (Oxford University Press, Oxford, 2020).

33.
Lafferty, K. D. et al. Infectious diseases affect marine fisheries and aquaculture economics. Annu. Rev. Mar. Sci. 7, 471–496 (2015).
ADS  Article  Google Scholar 

34.
Food and Agriculture Organization of the United Nations. The state of world fisheries and aquaculture. https://www.fao.org/3/i9540en/i9540en.pdf (2018).

35.
Food and Agriculture Organization of the United Nations. Global aquaculture production statistics, 1950–2017-Fisheries and Aquaculture Information and Statistics Branch. https://www.fao.org/fishery/statistics/global-aquaculture-production/query/en (2017).

36.
Abolofia, J., Asche, F. & Wilen, J. E. The cost of lice: quantifying the impacts of parasitic sea lice on farmed salmon. Mar. Resour. Econ. 32, 329–349 (2017).
Article  Google Scholar 

37.
Jakob, E., Sweeten, T., Bennett, W. & Jones, S. Development of the salmon louse Lepeophtheirus salmonis and its effects on juvenile sockeye salmon Oncorhynchus nerka. Dis. Aquat. Org. 106, 217–227 (2013).
CAS  Article  Google Scholar 

38.
Jones, S. R., Kim, E. & Bennett, W. Early development of resistance to the salmon louse, Lepeophtheirus salmonis (Krøyer), in juvenile pink salmon, Oncorhynchus gorbuscha (Walbaum). J. Fish Dis. 31, 591–600 (2008).
CAS  PubMed  Article  Google Scholar 

39.
Costello, M. J. The global economic cost of sea lice to the salmonid farming industry. J. Fish Dis. 32, 115–118 (2009).
PubMed  Article  Google Scholar 

40.
Skilbrei, O. T. & Wennevik, V. Survival and growth of sea-ranched Atlantic salmon, Salmo salar L., treated against sea lice before release. ICES J. Mar. Sci. 63, 1317–1325 (2006).
Article  Google Scholar 

41.
Grimnes, A. & Jakobsen, P. The physiological effects of salmon lice infection on post-smolt of Atlantic salmon. J. Fish Biol. 48, 1179–1194 (1996).
Article  Google Scholar 

42.
Krkosek, M. et al. Effects of parasites from salmon farms on productivity of wild salmon. Proc. Natl. Acad. Sci. 108, 14700–14704 (2011).
ADS  CAS  PubMed  Article  Google Scholar 

43.
Krkosek, M. et al. Impact of parasites on salmon recruitment in the Northeast Atlantic Ocean. Proc. Biol. Sci. 280, 20122359 (2013).
PubMed  PubMed Central  Google Scholar 

44.
Vollset, K. W. et al. Impacts of parasites on marine survival of Atlantic salmon: a meta-analysis. Fish Fish. 17, 714–730 (2016).
Article  Google Scholar 

45.
Bricknell, I. R., Dalesman, S. J., O’Shea, B., Pert, C. C. & Luntz, A. J. M. Effect of environmental salinity on sea lice Lepeophtheirus salmonis settlement success. Dis. Aquat. Org. 71, 201–212 (2006).
Article  Google Scholar 

46.
Brooks, K. M. The effects of water temperature, salinity, and currents on the survival and distribution of the infective copepodid stage of sea lice (Lepeophtheirus salmonis) originating on Atlantic salmon farms in the Broughton Archipelago of British Columbia Canada. Rev. Fish. Sci. 13, 177–204 (2005).
ADS  Article  Google Scholar 

47.
Hamre, L. A., Bui, S., Oppedal, F., Skern-Mauritzen, R. & Dalvin, S. Development of the salmon louse Lepeophtheirus salmonis parasitic stages in temperatures ranging from 3 to 24 C. Aquacult. Environ. Interact. 11, 429–443 (2019).
Article  Google Scholar 

48.
Johnson, S. & Albright, L. Development, growth, and survival of Lepeophtheirus salmonis (Copepoda: Caligidae) under laboratory conditions. J. Mar. Biol. Assoc. U.K. 71, 425–436 (1991).
Article  Google Scholar 

49.
Bateman, A. W. et al. Recent failure to control sea louse outbreaks on salmon in the Broughton Archipelago, British Columbia. Can. J. Fish. Aquat. Sci. 73, 1164–1172 (2016).
Article  Google Scholar 

50.
Godwin, S. C., Krkosek, M., Reynolds, J. D. & Bateman, A. W. Sea-louse abundance on salmon farms in relation to parasite-control policy and climate change. ICES J. Mar. Sci. (In press).

51.
Jansen, P. A. et al. Sea lice as a density-dependent constraint to salmonid farming. Proc. Biol. Sci. 279, 2330–2338 (2012).
PubMed  PubMed Central  Google Scholar 

52.
Vollset, K. W. Parasite induced mortality is context dependent in Atlantic salmon: insights from an individual-based model. Sci. Rep. 9, 1–15 (2019).
CAS  Article  Google Scholar 

53.
Brewer-Dalton, K., Page, F. H., Chandler, P. & Ratsimandresy, A. Oceanographic conditions of salmon farming areas with attention to those factors that may influence the biology and ecology of sea lice, Lepeophtherius salmonis and Caligus spp., and their control. https://publications.gc.ca/collections/collection_2015/mpo-dfo/Fs70-5-2014-048-eng.pdf (2014).

54.
Greenan, B. J. W. et al. In Canada’s changing climate report (eds E. Bush & D.S. Lemmen) 343–423 (Government of Canada, 2018).

55.
Atlantic Canada Fish Farmers Association. 2017 New Brunswick annual sea lice management report. (2018).

56.
Atlantic Canada Fish Farmers Association. 2018 New Brunswick annual sea lice management report. (2019).

57.
Karlsen, Ø. et al. En vurdering av lakselusinfestasjonen i produksjonsområdene i 2018 og 2019. Report from Marine Research, (2020).

58.
Costello, M. J. Ecology of sea lice parasitic on farmed and wild fish. Trends Parasitol. 22, 475–483 (2006).
PubMed  Article  Google Scholar 

59.
Best, A., White, A. & Boots, M. Maintenance of host variation in tolerance to pathogens and parasites. Proc. Natl. Acad. Sci. 105, 20786–20791 (2008).
ADS  CAS  PubMed  Article  Google Scholar 

60.
Handeland, S. O., Imsland, A. K. & Stefansson, S. O. The effect of temperature and fish size on growth, feed intake, food conversion efficiency and stomach evacuation rate of Atlantic salmon post-smolts. Aquaculture 283, 36–42 (2008).
Article  Google Scholar 

61.
Alzahrani, S. M. & Ebert, P. R. Stress pre-conditioning with temperature, UV and gamma radiation induces tolerance against phosphine toxicity. PLoS ONE 13, e0195349 (2018).
PubMed  PubMed Central  Article  CAS  Google Scholar 

62.
Beitinger, T. L. & Bennett, W. A. Quantification of the role of acclimation temperature in temperature tolerance of fishes. Environ. Biol. Fishes 58, 277–288 (2000).
Article  Google Scholar 

63.
Fischer, K. et al. Environmental effects on temperature stress resistance in the tropical butterfly Bicyclus anynana. PLoS ONE 5, e15284 (2010).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

64.
Huey, R. B. et al. Predicting organismal vulnerability to climate warming: roles of behaviour, physiology and adaptation. Philos. Trans. R. Soc. Lond. B. Biol. Sci. 367, 1665–1679 (2012).
PubMed  PubMed Central  Article  Google Scholar 

65.
Raffel, T. R., Halstead, N. T., McMahon, T. A., Davis, A. K. & Rohr, J. R. Temperature variability and moisture synergistically interact to exacerbate an epizootic disease. Proc. Biol. Sci. 282, 20142039 (2015).
PubMed  PubMed Central  Google Scholar 

66.
Studer, A. & Poulin, R. Differential effects of temperature variability on the transmission of a marine parasite. Mar. Biol. 160, 2763–2773 (2013).
Article  Google Scholar 

67.
Aaen, S. M., Helgesen, K. O., Bakke, M. J., Kaur, K. & Horsberg, T. E. Drug resistance in sea lice: a threat to salmonid aquaculture. Trends Parasitol. 31, 72–81 (2015).
CAS  PubMed  Article  Google Scholar 

68.
Gargan, P. et al. Evidence for sea lice-induced marine mortality of Atlantic salmon (Salmo salar) in western Ireland from experimental releases of ranched smolts treated with emamectin benzoate. Can. J. Fish. Aquat. Sci. 69, 343–353 (2012).
CAS  Article  Google Scholar 

69.
Beamish, R., Mahnken, C. & Neville, C. Evidence that reduced early marine growth is associated with lower marine survival of coho salmon. Trans. Am. Fish. Soc. 133, 26–33 (2004).
Article  Google Scholar 

70.
Peyronnet, A., Friedland, K., Maoileidigh, N., Manning, M. & Poole, W. Links between patterns of marine growth and survival of Atlantic salmon Salmo salar L. J. Fish Biol. 71, 684–700 (2007).
Article  Google Scholar 

71.
Battin, J. et al. Projected impacts of climate change on salmon habitat restoration. Proc. Natl. Acad. Sci. 104, 6720–6725 (2007).
ADS  CAS  PubMed  Article  Google Scholar 

72.
Otero, J. et al. Basin-scale phenology and effects of climate variability on global timing of initial seaward migration of Atlantic salmon (Salmo salar). Global Change Biol. 20, 61–75 (2014).
ADS  Article  Google Scholar 

73.
Lande, R. Risks of population extinction from demographic and environmental stochasticity and random catastrophes. Am. Nat. 142, 911–927 (1993).
PubMed  Article  Google Scholar 

74.
Allendorf, F. W. et al. Prioritizing Pacific salmon stocks for conservation. Conserv. Biol. 11, 140–152 (1997).
Article  Google Scholar 

75.
Chaput, G. Overview of the status of Atlantic salmon (Salmo salar) in the North Atlantic and trends in marine mortality. ICES J. Mar. Sci. 69, 1538–1548 (2012).
Article  Google Scholar 

76.
Nehlsen, W., Williams, J. E. & Lichatowich, J. A. Pacific salmon at the crossroads: stocks at risk from California, Oregon, Idaho, and Washington. Fisheries 16, 4–21 (1991).
Article  Google Scholar 

77.
Parrish, D. L., Behnke, R. J., Gephard, S. R., McCormick, S. D. & Reeves, G. H. Why aren’t there more Atlantic salmon (Salmo salar)?. Can. J. Fish. Aquat. Sci. 55, 281–287 (1998).
Article  Google Scholar 

78.
Groner, M. L. et al. Managing marine disease emergencies in an era of rapid change. Philos. Trans. R. Soc. Lond. B Biol. 371, 20150364 (2016).
Article  CAS  Google Scholar 

79.
Altizer, S., Ostfeld, R. S., Johnson, P. T., Kutz, S. & Harvell, C. D. Climate change and infectious diseases: from evidence to a predictive framework. Science 341, 514–519 (2013).
ADS  CAS  PubMed  Article  Google Scholar 

80.
Columbia Basin Fish and Wildlife Authority PIT Tag Steering Committee. PIT tag marking procedures manual. Columbia Basin Fish and Wildlife Authority, Portland, Oregon, (1999).

81.
Poley, J. D. et al. High level efficacy of lufenuron against sea lice (Lepeophtheirus salmonis) linked to rapid impact on moulting processes. Int. J. Parasitol. Drugs Drug Res. 8, 174–188 (2018).
Article  Google Scholar 

82.
Whyte, S. et al. iAvermectin treatment for Lepeophtheirus salmonis: impacts on host (Salmo salar) and parasite immunophysiology. Aquaculture 501, 488–501 (2019).
CAS  Article  Google Scholar 

83.
Groner, M. L., Gettinby, G., Stormoen, M., Revie, C. W. & Cox, R. Modelling the impact of temperature-induced life history plasticity and mate limitation on the epidemic potential of a marine ectoparasite. PLoS ONE 9, e88465 (2014).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

84.
Chezik, K. A., Lester, N. P. & Venturelli, P. A. Fish growth and degree-days I: selecting a base temperature for a within-population study. Can. J. Fish. Aquat. Sci. 71, 47–55 (2013).
Article  Google Scholar 

85.
Neuheimer, A. B. & Grønkjær, P. Climate effects on size-at-age: growth in warming waters compensates for earlier maturity in an exploited marine fish. Global Change Biol. 18, 1812–1822 (2012).
ADS  Article  Google Scholar 

86.
Jonsson, N., Jonsson, B. & Hansen, L. P. Does climate during embryonic development influence parr growth and age of seaward migration in Atlantic salmon (Salmo salar)?. Can. J. Fish. Aquat. Sci. 62, 2502–2508 (2005).
Article  Google Scholar 

87.
Akaike, H. A new look at the statistical model identification. IEEE Trans. Autom. Control 19, 716–723 (1974).
ADS  MathSciNet  MATH  Article  Google Scholar 

88.
Le Cren, E. The length-weight relationship and seasonal cycle in gonad weight and condition in the perch (Perca fluviatilis). J. Anim. Ecol. 201–219, (1951).

89.
Hurvich, C. M. & Tsai, C.-L. Regression and time series model selection in small samples. Biometrika 76, 297–307 (1989).
MathSciNet  MATH  Article  Google Scholar 

90.
R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing. Vienna, Austria (2019).

91.
Pinheiro J, Bates D, DebRoy S, Sarkar D & R Core Team. nlme: linear and nonlinear mixed effects models. R package version 3.1-139 (2019).

92.
Therneau, T. M. coxme: mixed effects Cox models. R package 2.2-14 (2019). More

1.
IPBES. Intergovernmental science-policy platform on biodiversity and ecosystem services. ttps://ipbes.net (2018).
2.
Kadykalo, A. N. et al. Disentangling ‘ecosystem services’ and ‘nature’s contributions to people’. Ecosyst. People 15(1), 269–287 (2019).
Article  Google Scholar 

3.
Díaz, S. et al. Assessing nature’s contributions to people. Science 359(6373), 270–272 (2018).
ADS  PubMed  PubMed Central  Article  Google Scholar 

4.
Gámez-Virués, S. et al. Landscape simplification filters species traits and drives biotic homogenization. Nat. Commun. 6(1), 8568 (2015).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

5.
Wilson, C. & Tisdell, C. Why farmers continue to use pesticides despite environmental, health and sustainability costs. Ecol. Econ. 39(3), 449–462 (2001).
Article  Google Scholar 

6.
Pimentel, D. et al. Environmental and economic costs of pesticide use. Bioscience 42(10), 750–760 (1992).
Article  Google Scholar 

7.
Horrigan, L., Lawrence, R. & Walker, P. How sustainable agriculture can address the environmental and human health harms of industrial agriculture. Environ Health Perspect 110(5), 445–456 (2002).
PubMed  PubMed Central  Article  Google Scholar 

8.
Popp, J., Pető, K. & Nagy, J. Pesticide productivity and food security. A review. Agron. Sustain. Dev. 33(1), 243–255 (2013).
Article  Google Scholar 

9.
Barbosa, P. Conservation Biological Control (Academic Press, Cambridge, 1998).
Google Scholar 

10.
Greenop, A., Woodcock, B., Wilby, A., Cook, S. & Pywell, R. Functional diversity positively affects prey suppression by invertebrate predators: A meta-analysis. Ecology 99(8), 1771–1782 (2018).
PubMed  PubMed Central  Article  Google Scholar 

11.
Snyder, W. E. Give predators a complement: Conserving natural enemy biodiversity to improve biocontrol. Biol. Control 135, 73–82 (2019).
Article  Google Scholar 

12.
Perez-Alvarez, R., Nault, B. A. & Poveda, K. Effectiveness of augmentative biological control depends on landscape context. Sci. Rep. 9(1), 8664 (2019).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

13.
Hageneder, F. The Heritage of Trees: History, Culture and Symbolism (Floris Books, 2001).

14.
Tews, J. et al. Animal species diversity driven by habitat heterogeneity/diversity: The importance of keystone structures. J. Biogeogr. 31, 79–92 (2004).
Article  Google Scholar 

15.
Müller, J., Jarzabek-Müller, A., Bussler, H. & Gossner, M. M. Hollow beech trees identified as keystone structures for saproxylic beetles by analyses of functional and phylogenetic diversity. Anim. Conserv. 17(2), 154–162 (2013).
Article  Google Scholar 

16.
Jim, C. Urban heritage trees: Natural-cultural significance informing management and conservation. In Greening Cities Advances in 21st Century Human Settlements (eds. Tan, P. & Jim, C.) (Springer, Berlin, 2017).

17.
Hu, L., Li, Z., Liao, W. & Fan, Q. Values of village fengshui forest patches in biodiversity conservation in the Pearl River Delta, China. Biol. Conserv. 144(5), 1553–1559 (2011).
Article  Google Scholar 

18.
Skarpaas, O., Blumentrath, S., Evju, M. & Sverdrup-Thygeson, A. Prediction of biodiversity hotspots in the Anthropocene: The case of veteran oaks. Ecol. Evol. 7(19), 7987–7997 (2017).
PubMed  PubMed Central  Article  Google Scholar 

19.
Siitonen, J., Ranius, T. The Importance of Veteran Trees for Saproxylic Insects. In Europe’s Changing Woods and Forests: From Wildwood to Managed Landscapes (eds. Kirby, K. & Watkins, C.) 140–53 (CAB International, Wallingford, 2015).

20.
Lindenmayer, D. B. et al. New policies for old trees: Averting a global crisis in a keystone ecological structure. Conserv. Lett. 7(1), 61–69 (2014).
Article  Google Scholar 

21.
Tscharntke, T. et al. Multifunctional shade-tree management in tropical agroforestry landscapes—A review. J. Appl. Ecol. 48(3), 619–629 (2011).
Article  Google Scholar 

22.
Wetherbee, R., Birkemoe, T., Skarpaas, O. & Sverdrup-Thygeson, A. Hollow oaks and beetle functional diversity: Significance of surroundings extends beyond taxonomy. Ecol. Evol. 10(2), 819–831 (2020).
PubMed  PubMed Central  Article  Google Scholar 

23.
Pilskog, H., Birkemoe, T., Framstad, E. & Sverdrup-Thygeson, A. Effect of habitat size, quality, and isolation on functional groups of beetles in hollow oaks. Insect Sci. 16, 1–8 (2016).
Article  CAS  Google Scholar 

24.
Lefcheck, J. & Duffy, J. E. Multitrophic functional diversity predicts ecosystem functioning in experimental assemblages of estuarine consumers. Ecology 96(11), 2973–2983 (2015).
PubMed  Article  Google Scholar 

25.
Heemsbergen, D. A. et al. Biodiversity effects on soil processes explained by interspecific functional dissimilarity. Science 306(5698), 1019–1020 (2004).
ADS  CAS  PubMed  Article  Google Scholar 

26.
Ferrante, M., Lo Cacciato, A. & Lovei, G. L. Quantifying predation pressure along an urbanisation gradient in Denmark using artificial caterpillars. Eur. J. Entomol. 111(5), 649–654 (2014).
Article  Google Scholar 

27.
Lovei, G. L. & Ferrante, M. A. Review of the sentinel prey method as a way of quantifying invertebrate predation under field conditions. Insect Sci. 24(4), 528–542 (2017).
PubMed  Article  Google Scholar 

28.
Kidd, N. A., Jervis, M. A. Population dynamics in Insects as Natural Enemies (ed. Jervis, M. A.) 435–523 (Springer, 2005).

29.
Low, P. A., Sam, K., McArthur, C., Posa, M. R. C. & Hochuli, D. F. Determining predator identity from attack marks left in model caterpillars: Guidelines for best practice. Entomol. Exp. Appl. 152(2), 120–126 (2014).
Article  Google Scholar 

30.
Howe, A., Lovei, G. & Nachman, G. Dummy caterpillars as a simple method to assess predation rates on invertebrates in a tropical agroecosystem. Entomol. Exp. Appl. 131, 325–329 (2009).
Article  Google Scholar 

31.
Sam, K., Remmel, T. & Molleman, F. Material affects attack rates on dummy caterpillars in tropical forest where arthropod predators dominate: An experiment using clay and dough dummies with green colourants on various plant species. Entomol. Exp. Appl. 157(3), 317–324 (2015).
Article  Google Scholar 

32.
Moretti, M. et al. Handbook of protocols for standardized measurement of terrestrial invertebrate functional traits. Funct. Ecol. 31(3), 558–567 (2017).
Article  Google Scholar 

33.
Mico, E. et al. Contrasting functional structure of saproxylic beetle assemblages associated to different microhabitats. Sci. Rep. 10(1), 1520 (2020).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

34.
Ranius, T. & Jansson, N. The influence of forest regrowth, original canopy cover and tree size on saproxylic beetles associated with old oaks. Biol. Conserv. 95(1), 85–94 (2000).
Article  Google Scholar 

35.
Parmain, G. & Bouget, C. Large solitary oaks as keystone structures for saproxylic beetles in European agricultural landscapes. Insect Conservation and Diversity 11, 100–115 (2018).
Article  Google Scholar 

36.
Ferrante, M., Barone, G., Kiss, M., Bozóné-Borbáth, E. & Lövei, G. L. Ground-level predation on artificial caterpillars indicates no enemy-free time for lepidopteran larvae. Community Ecol. 18(3), 280–286 (2017).
Article  Google Scholar 

37.
Lövei, G. L. & Sunderland, K. D. Ecology and behavior of ground beetles (Coleoptera: Carabidae). Annu. Rev. Entomol. 41(1), 231–256 (1996).
PubMed  Article  PubMed Central  Google Scholar 

38.
Sverdrup-Thygeson, A., Skarpaas, O. & Ødegaard, F. Hollow oaks and beetle conservation: The significance of the surroundings. Biodivers. Conserv. 19(3), 837–852 (2010).
Article  Google Scholar 

39.
Sverdrup-Thygeson, A., Skarpaas, O., Blumentrath, S., Birkemoe, T. & Evju, M. Habitat connectivity affects specialist species richness more than generalists in veteran trees. For. Ecol. Manag. 403, 96–102 (2017).
Article  Google Scholar 

40.
Gough, L. A., Birkemoe, T. & Sverdrup-Thygeson, A. Reactive forest management can also be proactive for wood-living beetles in hollow oak trees. Biol. Conserv. 180, 75–83 (2014).
Article  Google Scholar 

41.
Hagge, J. et al. Congruent patterns of functional diversity in saproxylic beetles and fungi across European beech forests. J. Biogeogr. 46(5), 1054–1065 (2019).
Article  Google Scholar 

42.
Tschumi, M., Albrecht, M., Entling, M. H. & Jacot, K. High effectiveness of tailored flower strips in reducing pests and crop plant damage. Proc. R. Soc. B. 282(1814), 20151369 (2015).
Article  Google Scholar 

43.
Bowdish, T. I. & Bultman, T. L. Visual cues used by mantids in learning aversion to aposematically colored prey. Am. Midl. Nat. 129(2), 215–222 (1993).
Article  Google Scholar 

44.
Kauppinen, J. & Mappes, J. Why are wasps so intimidating: Field experiments on hunting dragonflies (Odonata: Aeshna grandis). Anim. Behav. 66(3), 505–511 (2003).
Article  Google Scholar 

45.
Prudic, K. L., Stoehr, A. M., Wasik, B. R. & Monteiro, A. Eyespots deflect predator attack increasing fitness and promoting the evolution of phenotypic plasticity. Proc. R. Soc. B. 282(1798), 20141531 (2015).
PubMed  Article  Google Scholar 

46.
Micó, E. Saproxylic insects in tree hollows. In Saproxylic Insects: Diversity, Ecology and Conservation (ed. Ulyshen, M. D.) 693–727 (Springer, Berlin, 2018).

47.
Harmon, J. P., Losey, J. E. & Ives, A. R. The role of vision and color in the close proximity foraging behavior of four coccinellid species. Oecologia 115(1), 287–292 (1998).
ADS  PubMed  Article  Google Scholar 

48.
Hartel, T., Réti, K. O. & Craioveanu, C. Valuing scattered trees from wood-pastures by farmers in a traditional rural region of Eastern Europe. Agric. Ecosyst. Environ. 236, 304–311 (2017).
Article  Google Scholar 

49.
Hougner, C., Colding, J. & Söderqvist, T. Economic valuation of a seed dispersal service in the Stockholm National Urban Park, Sweden. Ecol. Econ. 59(3), 364–374 (2006).
Article  Google Scholar 

50.
Lindenmayer, D. B. & Laurance, W. F. The ecology, distribution, conservation and management of large old trees. Biol. Rev. 92(3), 1434–1458 (2017).
PubMed  Article  Google Scholar 

51.
ARKO. Hule eiker—et hotspot-habitat Sluttrapport under ARKO-prosjektets periode II. https://www.miljodirektoratet.no/globalassets/publikasjoner/dirnat2/attachment/2557/nina-rapport-710_hotspot-hule-eiker_sverdrup-thygeson_2011.pdf. The Norwegian Institute for Nature Research (2011).

52.
NBIC. Norwegian Biodiversity Information Centre. https://www.biodiversity.no (2018).

53.
Majekova, M. et al. Evaluating functional diversity: Missing trait data and the importance of species abundance structure and data transformation. PLoS ONE 11(2), 1–17 (2016).
Article  CAS  Google Scholar 

54.
Laliberte, E. & Legendre, P. A distance-based framework for measuring functional diversity from multiple traits. Ecology 91(1), 299–305 (2010).
PubMed  Article  PubMed Central  Google Scholar 

55.
Zuur, A., Ieno, E., Walker, N., Saveliev, A. & Smith, G. Mixed Effect Models and Extensions in Ecology with R (Springer, New York, 2009).
Google Scholar 

56.
R Development Core Team. R: A language and environment for statistical computing. 3.4.0 ed. (R Foundation for Statistical Computing, Vienna, 2017).

57.
Cailliez, F. The analytical solution of the additive constant problem. Psychometrika 48(2), 305–308 (1983).
MathSciNet  MATH  Article  Google Scholar 

58.
Brooks, M. E. et al. glmmTMB balances speed and flexibility among packages for zero-inflated generalized linear mixed modeling. R J. 9(2), 378–400 (2017).
Article  Google Scholar 

59.
Bolker, B. M. Tools for General Maximum Likelihood Estimation. CRAN.R. R package version 1.0.20 (2017).

60.
Barton, K. MuMIn: Multi-Model Inference. R package version 1421. https://CRAN.R-project.org/package=MuMIn (2018).

61.
Hardin, J. W. & Hilbe, J. M. Generalized Linear Models and Extensions (Stata Press, College Station, 2007).
Google Scholar 

62.
Bolker BM. Linear and generalized linear mixed models. In Ecological Statistics: Contemporary Theory and Application (eds. Fox, G. A., Negrete-Yankelevich, S., Sosa, V. J.) (Oxford University Press, Oxford, 2015).

63.
Sarkar, D. Lattice:Multivariate Data Visualization with R (Springer, New York, 2008).
Google Scholar 

64.
Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer, New York, 2016).
Google Scholar 

65.
Wickham H. François R. Henry L. Müller K. dplyr: A Grammar of Data Manipulation. R package version 078. https://CRAN.R-project.org/package=dplyr (2018). More

1.
Negri, V., Maxted, N., & Veteläinen, M. European landrace conservation: an introduction. European landraces: on farm conservation, management and use. European Cooperative Programme for Plant Genetic Resources. Rome, Italy. Biodivers. Tech. Bull. 15, 1–22 (2009).
2.
Polegri, L., Negri, V. Molecular markers for promoting agro-biodiversity conservation: a case study from Italy. How cowpea landraces were saved from extinction. Genet. Resour. Crop Evol. 57, 867–880 (2010).

3.
Maxim, A. Conservation of genetic diversity in culture plants. ProEnvironment 5, 50–54 (2010).
Google Scholar 

4.
Nyadanu, D., Aboagye, L. M., Akromah, R. & Dansi, A. Agro-biodiversity and challenges of on-farm conservation: the case of plant genetic resources of neglected and underutilized crop species in Ghana. Genet. Resour. Crop Evol. 63, 1397–1409 (2016).
Article  Google Scholar 

5.
Sanchez, E., Sifres, A., Casanas, F., and Nuez, F. The endangered future of organoleptically prestigious European landraces: Ganxet bean (Phaseolus vulgaris L.) as an example of a crop originating in the Americas. Genet. Resour. Crop Evol. 55, 45–52 (2008).

6.
Bertoldo, J. G., Coimbra, J. L. M., Guidolin, A. F., Braatz de Andrade, L. R. & Nodari, R. O. Agronomic potential of genebank landrace elite accessions for common bean genetic breeding. Sci. Agric. 71(2), 120–125 (2014).
Article  Google Scholar 

7.
Derpsch, R., Roth, C.H., Sidiras, N., Köpke, U. Controle de erosão no Paraná, Brasil: Sistema de cobertura do solo, plantio direto e preparo conservacionista do solo. Sonderpublikation der GTZ, No. 245. Rossdorf, Germany, TZ-Verlagsgesellschaft GmbH., 272 (1991).

8.
Maxted, N., Dulloo, M.E., Ford-Lloyd, B.V. Enhancing crop genepool use: capturing wild relative and landrace diversity for crop improvement. CAB eBooks (2016).

9.
Negri, V. Landraces in central Italy: Where and why they are conserved and perspectives for their on farm conservation. Genet. Resour. Crop Evol. 50, 871–885 (2003).
Article  Google Scholar 

10.
German, L., Ramisch, J.J., Verma, R. Beyond the biophysical: knowledge, culture, and power in agriculture and natural resource management. Springer (2010).

11.
Negri, V. Agro-biodiversity conservation in Europe: ethical issues. J. Agric. Environ. Ethics. 18, 3 (2005).
Article  Google Scholar 

12.
Sthapit, B., Lamers, H., Rao, R., & Bailey, A. Tropical fruit tree diversity: good practices for in situ and on-farm conservation. Routledge (2016).

13.
Veteläinen, M., Negri, V., Maxted, N. A European strategic approach to conserving crop landraces. European cooperative programme for plant genetic resources, Rome, Italy. Biodivers. Tech. Bull. 15 (2009).

14.
Spataro, G. & Negri, V. The European seed legislation on conservation varieties: focus, implementation, present and future impact on landrace on farm conservation. Genet. Resour. Crop Evol. 60, 2421–2430 (2013).
Article  Google Scholar 

15.
Lorenzetti, F., Lorenzetti, S., & Negri, V. The Italian Laws on Conservation Varieties and the National Implementation of Commision Directive 2008/62/EC. European landraces: on farm conservation, management and use. European Cooperative Programme for Plant Genetic Resources. Rome, Italy. Biodivers. Tech. Bull. 15, 300–304 (2009).

16.
Thuiller, T., Lavorel, S., Araújo, M. B., Sykes, M. T. & Prentice, I. C. Climate change threats to plant diversity in Europe. Proc. Natl. Acad. Sci. 102(23), 8245–8250 (2005).
ADS  CAS  Article  Google Scholar 

17.
Hammer, K., and Diederichsen, A. Evolution, status and perspectives for landraces in Europe: European cooperative programme for plant genetic resources. Rome, Italy. Biodivers. Tech. Bull. 15, 23–44 (2009).

18.
Maxim, A. et al. Preliminary results concerning the preservation of genetical diversity of different vegetable varieties at USAMV Cluj-Napoca. Bull. UASMV Cluj-Napoca, Agric. 63, 291–296 (2007).
Google Scholar 

19.
Renna, M., Serio, F., Signore, A., Santamaria, P. The yellow–purple Polignano carrot (Daucus carota L.): a multicoloured landrace from the Puglia region (Southern Italy) at risk of genetic erosion. Genet. Resour. Crop Evol. 61, 1611–1619 (2014).

20.
Scholten, M., Maxted, N., Ford-Lloyd, B. V. & Green, N. Hebridean and Shetland oat (Avena strigosa Schreb) and Shetland cabbage (Brassica oleracea L.) landraces: occurrence and conservation issues. Plant Genet. Resour. Newsl. 154, 1–5 (2008).
Google Scholar 

21.
Maxted, N., Ford-Lloyd, B.V., & Hawkes, J.G., Plant genetic conservation: the in situ approach. Chapman and Hall, London (1997).

22.
Malik, S.K., Singh, P.B., Singh, A., Verma, A., Ameta, N., & Bisht, I.S. Community seed banks: Operation and scientific management. New Delhi, India: National Bureau of Plant Genetic Resources, https://www.rainfedindia.org/issues/images/csb.pdf (2013).

23.
Străjeru, S., Ibanescu, M., Constantinovici, D. Landrace Inventories: Nees and Methodologies. European Cooperative Programme for Plant Genetic Resources. Rome, Italy. Biodivers. Tech. Bull. 15, 137–142 (2009).

24.
Maxim, A., Bontea, D., Odagiu, A., Mihalescu, L., & Roman, V. The In Situ and Ex Situ Conservation of Pepper Landraces (Capsicum annuum L.). ProEnvironment 8, 564–570 (2015).

25.
Bailey, K. Methods of social research (2nd edition) (The Free Press, New York, 1982).
Google Scholar 

26.
Lázaro, A., Villar, B., Aceituno-Mata, L., Tardio, J. & De la Rosa, L. The Sierra Norte of Madrid: an agrobiodiversity refuge for common bean landraces. Genet. Resour. Crop Evol. 60, 1641–1654 (2013).
Article  Google Scholar 

27.
Lebot, V., Tuia, V., Ivancic, A., Jackson, G.V.H., Saborio, F., Reyes, G., Rodriguez, S., Robin, G., Traoré, R. Aboagye, L., Onyeka, J., van Rensburg, W., Andrianavalona, V., Mukherjee, A., Prana, M.S., Ferraren, D., Komolong, B., Lawac, F., Winter, S., Pinheiro de Carvalho, M.A.A., & Iosefa, T. Adapting clonally propagated crops to climatic changes: a global approach for taro (Colocasia esculenta (L.) Schott) Genet. Resour. Crop Evol. 1–16 (2017).

28.
Montesano, V., Negro, D., Sarli, G., Logozzo, G. & Zeuli, P. S. Landraces in Inland areas of the Basilicata region, Italy: monitoring and perspectives for on farm conservation. Genet. Resour. Crop Evol. 59, 701–716 (2012).
Article  Google Scholar 

29.
Catalogul oficial al soiurilor de plante din România. Institutul de Stat pentru Testarea și Înregistrarea Soiurilor. Ministerul Agriculturii și Dezvoltării Rurale din România, București, p. 49. Aviable from: https://istis.ro/image/data/download/catalog-oficial/CATALOG%202017.pdf (2017).

30.
Paavilainen, K. National Policies and Support Systems for Landrace Cultivation in Finland. European landraces: on farm conservation, management and use. European Cooperative Programme for Plant Genetic Resources. Rome, Italy. Biodivers. Tech. Bull. 15, 296–299 (2009).

31.
Joshi, B. K., Gauchan, D. Germplasm rescue. Why and how? Proceedings of Sharingshop, 18 Dec 2017, Kathmandu; NAGRC, BI and Crop Trust Nepal. 41–50 (2017).

32.
Joshi, B. K. et al. (eds) Good Practices for Agrobiodiversity Management (NAGRC, LI-BIRD and Alliance of Bioversity International and CIAT, Kathmandu, Nepal, 2020).
Google Scholar 

33.
Kell, S.P., Maxted, N., Allender, C., Astley, D., Ford‐Lloyd, B.V., contributors. Vegetable Landrace Inventory of England and Wales. The University of Birmingham, UK. 117 pp. Available from: https://www.grfa.org.uk/media_files/publications_plant/veg_lr_inventory_england_and_wales.pdf (2009).

34.
Krasteva, L., Stoilova, T., Varbanova, K., & Neykov, S. Bulgarian landrace inventory: significance and use.” In Veteläinen M, Negri V and Maxted N (Eds) European landraces: on-farm conservation, management and use. Bioversity International, Rome. Biodivers. Tech. Bull. 15, 53–68 (2009).

35.
Maxted, N., Veteläinen, M., & Negri, V. Landrace inventories: needs and methodologies. European landraces: on farm conservation, management and use. Biodivers. Tech. Bull. 15, 45–52 (2009).

36.
Mendes Moreira, P.M.R., & Veloso, M.M. Landrace inventory for Portugal. In Veteläinen M., Negri V., and Maxted N., (Eds), European Landraces: On-farm conservation, Management and Use. Bioversity International, Rome. Biovers. Tech. Bull. 15, 124–136 (2009).

37.
Negri, V., Maxted, N., Torricelli, R., Heinonen, M., Veteläinen, M., & Dias, S. Draft descriptors for web-enabled national in situ landrace inventories. PGR Secure. Available from: https://www.pgrsecure.bham.ac.uk/sites/default/files/documents/helpdesk/LR_DESCRIPTORS_PGR_Secure_draft.pdf (2011).

38.
Paprštein, F. & Kloutvor, J. Inventory of fruit landraces. Vědecké práce ovocnářské. 17, 159–162 (2001).
Google Scholar 

39.
Scholten, M., Maxted, N. & Ford-Lloyd, B. V. UK National Inventory of plant genetic resources for food and agriculture (Unpublished Report, Defra, London, 2004).
Google Scholar 

40.
Balyejusa Kizito, E., Chiwona-Karltun, L., Egwang, T., Fregene, M. & Westerbergh, A. Genetic diversity and variety composition of cassava on small scale farms in Uganda: an interdisciplinary study using genetic markers and farmer interviews. Genetica 130, 301–318 (2007).
Article  Google Scholar 

41.
Pistrick, K., Avramiuc, M., Cherecheș, V. & Friesen, N. Collecting plant genetic resources in Romania (Eastern Carpathians, Maramureș, Munții Apuseni). Plant Genet. Resour. Newsl. 1995(104), 10–15 (1994).
Google Scholar 

42.
Maxim, A., Şandor, M., Sima, R., Mihalescu, L., Hapca, A., Papp, R., Opincariu, A., Şandor, V., & Matiş, N. Research concerning biochemical composition and storage capacity of different local varieties of carrot (Daucus carota L.). Bull. UASMV Cluj-Napoca Agric. 66(2), 138–143 (2009).

43.
Maxim, A., Şandor, M., Jidavu, M., Sima, R., Lucian, C., Opincariu, A., Maxim, O., & Bolboacă, V. Research concerning the conservation of genetic diversity of parsley (Petroselinum crispum MILL.). Bull. UASMV Cluj-Napoca Agric. 66(2), 144–149 (2009).

44.
Maxim, A. et al. Romanian landraces of tomates. Bull. UASMV Cluj-Napoca Agric. 69(2), 83–91 (2012).
Google Scholar 

45.
Sudré, C.P., Gonçalves, L.S.A., Rodrigues, R., do Amaral Júnior, A.T., Riva-Souza, E.M., & S. Bento, C. Genetic variability in domesticated Capsicum spp. as assessed by morphological and agronomic data in mixed statistical analysis. Genet. Mol. Res. 9(1), 283–294 (2010).

46.
Boros, L., Wawer, A. & Borucka, K. Morphological, phenological and agronomical characterization of variability among common bean (Phaseolus vulgaris L.): local populations from the national Centre for plant genetic resources: Polish gene bank. J. Hortic. Res. 22(2), 123–130 (2014).
CAS  Article  Google Scholar 

47.
Sabatino, L., Iapichino, G., Vetrano, F., & D’Anna, F. Morphological and agronomical characterisation of Sicilian bottle gourd Lagenaria siceraria (Mol.) Standley. J. Food Agric. Environ. 12(Issue 2), 587–590 (2014).

48.
D’Anna, F. & Sabatino, L. Morphological and agronomical characterization of eggplant genetic resources from the Sicily area. J. Food Agric. Environ. 4–11(1), 401–404 (2013).
Google Scholar 

49.
Dersouni, C. & Chougui, S. Characterisation of two varieties of tomato (Lycopersicon esculentum) with saline resistant. Int. J. Biosci. 12(4), 43–54 (2018).
CAS  Article  Google Scholar 

50.
Casals, J. et al. The risks of succes quality vegetable markets: possible genetic erosion in Marmande tomatoes (Solanum lycopersicum L.) and consumer dissatisfaction. Sci. Hortic. 130, 78–84 (2011).
Article  Google Scholar 

51.
Gibson, R. W., Aritua, V., Byamukama, E., Mpembe, I. & Kayongo, J. Control strategies for sweet potato virus disease in Africa. Virus Res. 100(1), 115–122 (2004).
CAS  Article  Google Scholar 

52.
Onyeka, T. J., Dixon, A. G. O. & Ekpo, E. J. A. Identification of levels of resistance to cassava root rot disease (Botryodiplodia theobromae) in African landraces and improved germplasm using in vitro inoculation method. Euphytica 145, 283–290 (2005).
Article  Google Scholar 

53.
Sarker, A. & Erskine, W. Recent progress in the ancientlentil. J. Agric. Sci. 144, 19–29 (2006).
Article  Google Scholar 

54.
El Tahir, I. M. & Taha, Y. M. Indigenous melons (Cucumis melo L.) in Sudan: a review of their genetic resources and prospects for use as sources of disease and insect resistance. Plant Genet. Res. Newsl. 138, 36–42 (2004).
Google Scholar 

55.
Esquinas-Alcázar, J. Protecting crop genetic diversity for food security: political, ethical and technical challenges. Nature 6, 946–953 (2010).
Google Scholar 

56.
Newton, A.C., Akar, T., Baresel, J.P., Bebeli, P.J., Bettencourt, E., Bladenopoulos, K.V., Czembor, J.H., & Vaz Patto, M.C. Cereal landraces for sustainable agriculture. A review. Agron. Sust. Dev. 30, 237–269 (2010).

57.
Ceccarelli, S. “Landraces: importance and use in breeding and environmentally friendly agronomic systems,” in Agrobiodiversity Conservation Securing the Diversity of Crop Wild Relatives and Landraces, ed. N. Maxted (Wallingford: CAB Interenational), 103 (2011).

58.
Ceccarelli, S. Landraces: importance and use in breeding and environmentally friendly agronomic systems. In Agrobiodiversity Conservation: Securing the Diversity of Crop Wild Relatives and Landraces (Maxted N. et al, eds), CAB International, 103–117 (2012).

59.
QGIS https://qgis.org/en/site/ (2019). More

1.
Clark, J. S. et al. The impacts of increasing drought on forest dynamics, structure, and biodiversity in the United States. Glob. Chang. Biol. 22, 2329–2352 (2016).
ADS  PubMed  Article  PubMed Central  Google Scholar 
2.
Ahmadalipour, A., Moradkhani, H. & Svoboda, M. Centennial drought outlook over the CONUS using NASA-NEX downscaled climate ensemble. Int. J. Climatol. 37, 2477–2491 (2017).
Article  Google Scholar 

3.
Yu, M., Li, Q., Hayes, M. J., Svoboda, M. D. & Heim, R. R. Are droughts becoming more frequent or severe in China based on the standardized precipitation evapotranspiration index: 1951–2010?. Int. J. Climatol. 34, 545–558 (2014).
CAS  Article  Google Scholar 

4.
Allen, C. D., Breshears, D. D. & McDowell, N. G. On underestimation of global vulnerability to tree mortality and forest die-off from hotter drought in the Anthropocene. Ecosphere 6, 1–55 (2015).
Article  Google Scholar 

5.
Huang, C. Y. & Anderegg, W. R. L. Large drought-induced aboveground live biomass losses in southern Rocky Mountain aspen forests. Glob. Chang. Biol. 18, 1016–1027 (2012).
ADS  Article  Google Scholar 

6.
Simeone, C. et al. Coupled ecohydrology and plant hydraulics modeling predicts ponderosa pine seedling mortality and lower treeline in the US Northern Rocky Mountains. New Phytol. 221, 1814–1830 (2019).
PubMed  Article  PubMed Central  Google Scholar 

7.
Anderegg, W. R. L. et al. Pervasive drought legacies in forest ecosystems and their implications for carbon cycle models. Science (80-. ). 349, 528–532 (2015).

8.
Yu, Z. et al. Global gross primary productivity and water use efficiency changes under drought stress. Environ. Res. Lett. 12 (2017).

9.
Smettem, K. R. J., Waring, R. H., Callow, J. N., Wilson, M. & Mu, Q. Satellite-derived estimates of forest leaf area index in southwest Western Australia are not tightly coupled to interannual variations in rainfall: Implications for groundwater decline in a drying climate. Glob. Chang. Biol. 19, 2401–2412 (2013).
ADS  PubMed  Article  PubMed Central  Google Scholar 

10.
Kath, J. et al. Groundwater salinization intensifies drought impacts in forests and reduces refuge capacity. J. Appl. Ecol. 52, 1116–1125 (2015).
CAS  Article  Google Scholar 

11.
Hoylman, Z. H. et al. The topographic signature of ecosystem climate sensitivity in the western United States. Geophys. Res. Lett. 46, 14508–14520 (2019).
ADS  Article  Google Scholar 

12.
Hahm, W. J. et al. Low subsurface water storage capacity relative to annual rainfall decouples Mediterranean plant productivity and water use from rainfall variability. Geophys. Res. Lett. 46, 6544–6553 (2019).
ADS  Article  Google Scholar 

13.
Chaves, M. M., Maroco, J. P. & Pereira, J. S. Understanding plant responses to drought: From genes to the whole plant. Funct. Plant Biol. 30, 239–264 (2003).
CAS  PubMed  Article  PubMed Central  Google Scholar 

14.
Maherali, H., Pockman, W. & Jackson, R. Adaptive variation in the vulnerability of woody plants to xylem cavitation. Ecology 85, 2184–2199 (2004).
Article  Google Scholar 

15.
McDowell, N. et al. Mechanisms of plant survival and mortality during drought: Why do some plants survive while others succumb to drought?. New Phytol. 178, 719–739 (2008).
PubMed  Article  PubMed Central  Google Scholar 

16.
Yang, Y. et al. Contrasting responses of water use efficiency to drought across global terrestrial ecosystems. Sci. Rep. 6, 1–8 (2016).
Article  CAS  Google Scholar 

17.
Malone, S. L. et al. Drought resistance across California ecosystems: Evaluating changes in carbon dynamics using satellite imagery. Ecosphere 7, 1–19 (2016).
ADS  Article  Google Scholar 

18.
Ponce Campos, G. E. et al. Ecosystem resilience despite large-scale altered hydroclimatic conditions. Nature 494, 349–352 (2013).

19.
Choat, B. et al. Global convergence in the vulnerability of forests to drought. Nature 491, 752–755 (2012).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

20.
Vicente-Serrano, S. M. et al. Response of vegetation to drought time-scales across global land biomes. Proc. Natl. Acad. Sci. 110, 52–57 (2013).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

21.
Ahmadi, B., Ahmadalipour, A., Tootle, G. & Moradkhani, H. Remote sensing of water use efficiency and terrestrial drought recovery across the Contiguous United States. Remote Sens. 11 (2019).

22.
Barnes, M. L. et al. Vegetation productivity responds to sub-annual climate conditions across semiarid biomes. Ecosphere 7, e01339 (2016).
Article  Google Scholar 

23.
Susan Moran, M. et al. Functional response of U.S. grasslands to the early 21st-century drought. Ecology 95, 2121–2133 (2014).

24.
Zhang, Y., Voigt, M. & Liu, H. Contrasting responses of terrestrial ecosystem production to hot temperature extreme regimes between grassland and forest. Biogeosciences 12, 549–556 (2015).
ADS  CAS  Article  Google Scholar 

25.
Huete, A. et al. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sens. Environ. 83, 195–213 (2002).
ADS  Article  Google Scholar 

26.
Zhang, Y. et al. Extreme precipitation patterns and reductions of terrestrial ecosystem production across biomes. J. Geophys. Res. Biogeosci. 118, 148–157 (2013).
ADS  Article  Google Scholar 

27.
Norman, S., Koch, F. & Hargrove, W. Detecting and monitoring large-scale drought effects on forests: Toward an integrated approach. in Effects of Drought on Forests and Rangelands in the United States: A Comprehensive Science Synthesis. General Technical Report WO-93b (eds. Vose, J., Clark, J., Luce, C. & Patel-Weynand, T.) 195–230 (U.S. Forest Service, 2015).

28.
Sims, D. A., Brzostek, E. R. & Rahman, A. F. An improved approach for remotely sensing water stress impacts on forest C uptake. Glob. Chang. Biol. 1–11, https://doi.org/10.1111/gcb.12537 (2014).

29.
Brodrick, P. G., Anderegg, L. D. L. & Asner, G. P. Forest drought resistance at large geographic scales. Geophys. Res. Lett. 46, 2752–2760 (2019).
ADS  Article  Google Scholar 

30.
Ma, X., Huete, A., Moran, S., Ponce-Campos, G. & Eamus, D. Abrupt shifts in phenology and vegetation productivity under climate extremes. J. Geophys. Res. Biogeosci. 120, 2036–2052 (2015).
Article  Google Scholar 

31.
McLaughlin, B. et al. Hydrologic refugia, plants and climate change. Glob. Chang. Biol. 23, 1–21 (2017).
Article  Google Scholar 

32.
Tague, C. L. & Moritz, M. A. Plant accessible water storage capacity and tree-scale root interactions determine how forest density reductions alter forest water use and productivity. Front. For. Glob. Chang. 2, 1–18 (2019).
Article  Google Scholar 

33.
Peterman, W., Waring, R., Seager, T. & Pollock, W. Soil properties affect pinyon pine–juniper response to drought. Ecohydrology 6, 455–463 (2013).
Article  Google Scholar 

34.
Law, B. E. & Waring, R. H. Carbon implications of current and future effects of drought, fire and management on Pacific Northwest forests. For. Ecol. Manag. 355, 4–14 (2015).
Article  Google Scholar 

35.
Marlier, M. E. et al. The 2015 drought in Washington State: A harbinger of things to come? Environ. Res. Lett. 12 (2017).

36.
Buttrick, S. et al. Conserving Nature’s Stage: Identifying Resilient Terrestrial Landscapes in the Pacific Northwest. (The Nature Conservancy, 2015).

37.
Potithep, S., Nasahara, N. K., Muraoka, H., Nagai, S. & Suzuki, R. What is the actual relationship between LAI and VI in a deciduous broadleaf forest? Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. ISPRS Arch. 38, 609–614 (2010).

38.
Bigler, C., Gavin, D. G., Gunning, C. & Veblen, T. T. Drought induces lagged tree mortality in a subalpine forest in the Rocky Mountains. Oikos 116, 1983–1994 (2007).
Article  Google Scholar 

39.
Berdanier, A. & Clark, J. Multiyear drought-induced morbidity preceding tree death in southeastern U.S. forests. Ecol. Appl. 26, 17–23 (2016).

40.
Ficklin, D. L. & Novick, K. A. Historic and projected changes in vapor pressure deficit suggest a continental-scale drying of the United States atmosphere. J. Geophys. Res. 122, 2061–2079 (2017).
Article  Google Scholar 

41.
Assal, T. J., Anderson, P. J. & Sibold, J. Spatial and temporal trends of drought effects in a heterogeneous semi-arid forest ecosystem. For. Ecol. Manag. 365, 137–151 (2016).
Article  Google Scholar 

42.
Guarín, A. & Taylor, A. H. Drought triggered tree mortality in mixed conifer forests in Yosemite National Park, California, USA. For. Ecol. Manag. 218, 229–244 (2005).
Article  Google Scholar 

43.
Crausbay, S. D. et al. Defining ecological drought for the twenty-first century. Bull. Am. Meteorol. Soc. 98, 2543–2550 (2017).
ADS  Article  Google Scholar 

44.
Van Loon, A. F. Hydrological drought explained. WIREs. Water 2, 359–392 (2015).
ADS  Google Scholar 

45.
Peterman, W., Waring, R. H., Seager, T. & Pollock, W. L. Soil properties affect pinyon pine-juniper response to drought. Ecohydrology 6, 455–463 (2013).
Article  Google Scholar 

46.
Mildrexler, D., Yang, Z., Cohen, W. B. & Bell, D. M. A forest vulnerability index based on drought and high temperatures. Remote Sens. Environ. 173, 314–325 (2016).
ADS  Article  Google Scholar 

47.
Berner, L. T. & Law, B. E. Water limitations on forest carbon cycling and conifer traits along a steep climatic gradient in the Cascade Mountains, Oregon. Biogeosci. Discuss. 12, 14507–14553 (2015).
ADS  Article  Google Scholar 

48.
Kerns, B. K., Powell, D. C., Mellmann-Brown, S., Carnwath, G. & Kim, J. B. Effects of projected climate change on vegetation in the Blue Mountains ecoregion, USA. Clim. Serv. 10, 33–43 (2018).
Article  Google Scholar 

49.
Michalak, J. L., Withey, J. C., Lawler, J. J. & Case, M. J. Future climate vulnerability—Evaluating multiple lines of evidence. Front. Ecol. Environ. 15, 367–376 (2017).
Article  Google Scholar 

50.
Young, D. J. N. et al. Long-term climate and competition explain forest mortality patterns under extreme drought. Ecol. Lett. 20, 78–86 (2017).
PubMed  Article  PubMed Central  Google Scholar 

51.
Clifford, M. J., Cobb, N. S. & Buenemann, M. Long-term tree cover dynamics in a pinyon-juniper woodland: climate-change-type drought resets successional clock. Ecosystems 14, 949–962 (2011).
Article  Google Scholar 

52.
Baker, W. L. Transitioning western U.S. dry forests to limited committed warming with bet-hedging and natural disturbances. Ecosphere 9 (2018).

53.
Walck, J. L., Hidayati, S. N., Dixon, K. W., Thompson, K. & Poschlod, P. Climate change and plant regeneration from seed. Glob. Chang. Biol. 17, 2145–2161 (2011).
ADS  Article  Google Scholar 

54.
Krawchuk, M. A. et al. Disturbance refugia within mosaics of forest fire, drought, and insect outbreaks. Front. Ecol. Environ. 18, 235–244 (2020).
Article  Google Scholar 

55.
Cartwright, J. Landscape topoedaphic features create refugia from drought and insect disturbance in a lodgepole and whitebark pine forest. Forests 9, 715 (2018).
Article  Google Scholar 

56.
Millar, C. I. et al. Do low-elevation ravines provide climate refugia for subalpine limber pine (Pinus flexilis) in the Great Basin, USA?. Can. J. For. Res. 48, 663–671 (2018).
Article  Google Scholar 

57.
Jaeger, K. L. et al. Probability of streamflow permanence model (PROSPER): A spatially continuous model of annual streamflow permanence throughout the Pacific Northwest. J. Hydrol. X 2, 100005 (2019).
Article  Google Scholar 

58.
AdaptWest Project. Gridded current and future climate data for North America at 1 km resolution. (2015). https://adaptwest.databasin.org/pages/adaptwest-climatena. Accessed 5 Oct 2018.

59.
National Aeronautics and Space Administration (NASA). EarthData Search. (2020). https://search.earthdata.nasa.gov/search. Accessed 8 Jan 2017.

60.
Franklin, S. et al. Building the United States national vegetation classification. Ann. Bot. 2, 1–9 (2012).
Google Scholar 

61.
Vicente-Serrano, S. M., Beguería, S. & López-Moreno, J. I. A multiscalar drought index sensitive to global warming: The standardized precipitation evapotranspiration index. J. Clim. 23, 1696–1718 (2010).
ADS  Article  Google Scholar 

62.
Vogelmann, J. E., Tolk, B. & Zhu, Z. Monitoring forest changes in the southwestern United States using multitemporal Landsat data. Remote Sens. Environ. 113, 1739–1748 (2009).
ADS  Article  Google Scholar 

63.
De’ath, G. & Fabricius, K. E. Classification and regression trees: A powerful yet simple technique for ecological data analysis. Ecology 81, 3178–3192 (2000).

64.
Elith, J., Leathwick, J. R. & Hastie, T. A working guide to boosted regression trees. J. Anim. Ecol. 77, 802–813 (2008).
CAS  PubMed  PubMed Central  Article  Google Scholar 

65.
Hijmans, R., Phillips, S., Leathwick, J. & Elith, J. dismo: Species Distribution Modeling, R package version 1.1–4. (2016). https://cran.r-project.org/web/packages/dismo/dismo.pdf. Accessed: 1 May 2017.

66.
R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria (2017). https://www.r-project.org. Accessed 1 Feb 2017.

67.
Cartwright, J. Analysis of Drought Sensitivity in the Pacific Northwest (Washington, Oregon, and Idaho) from 2000 Through 2016: U.S. Geological Survey Data Release. (2019). https://doi.org/10.5066/P9UNYG2R. Accessed 6 Nov 2019.

68.
Omernik, J. & Griffith, G. Ecoregions of the conterminous United States: Evolution of a hierarchical spatial framework. Environ. Manag. 54, 1249–1266 (2014).
ADS  Article  Google Scholar 

69.
U.S. Geological Survey. GAP/LANDFIRE National Terrestrial Ecosystems Dataset (2010). https://gapanalysis.usgs.gov/gaplandcover/featured-post-1/. Accessed 16 Mar 2017.

70.
U.S. Geological Survey. Global 30 arc-second elevation (GTOPO30). U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center (2015). https://lta.cr.usgs.gov/GTOPO30. Accessed 2 Mar 2018.

71.
Dobrowski, S. Z. et al. Climatic Water Balance and Velocity of Climate Change Data for the Contiguous US During the 20th Century. (2013). https://adaptwest.databasin.org/pages/adaptwest-waterbalance. Accessed 2 Mar 2018.

72.
Hengl, T. et al. SoilGrids1km-global soil information based on automated mapping. PLoS ONE 9, e105992 (2014).
ADS  PubMed  PubMed Central  Article  Google Scholar 

73.
Fan, Y., Li, H. & Miguez-Macho, G. Global patterns of groundwater table depth. Science (80-. ). 339, 940–943 (2013).

74.
Esri. ArcGIS Desktop. (2020). https://www.esri.com/en-us/arcgis/products/arcgis-desktop/overview. Accessed 8 Jan 2017. More