Philippa Kaur

1.Forney, K. A. & Wade, P. R. Worldwide distribution and abundance of killer whales. In Whales, Whaling and Ocean Ecosystems (eds Estes, J. A. et al.) 145–162 (University of California Press, 2006).
Google Scholar 
2.Hoelzel, A. R., Dahlheim, M. & Stern, S. J. Low genetic variation among killer whales (Orcinus orca) in the eastern north Pacific and genetic differentiation between foraging specialists. J. Hered. 89, 121–128. https://doi.org/10.1093/jhered/89.2.121 (1998).CAS 
Article 
PubMed 

Google Scholar 
3.Matkin, C. O., Ellis, G. M., Saulitis, E. L., Barrett-Lennard, L. G. & Matkin, D. Killer Whales of Southern Alaska (North Gulf Oceanic Society, 1999).
Google Scholar 
4.Barrett-Lennard, L. G. Population structure and mating patterns of Killer Whales (Orcinus orca) as revealed by DNA analysis. PhD thesis, University of British Columbia (2000). https://open.library.ubc.ca/collections/ubctheses/831/items/1.0099652.5.Ford, J. K. B., Ellis, G. M. & Balcomb, K. C. Killer Whales: The Natural History and Genealogy of Orcinus orca in British Columbia and Washington (UBC Press, 2000).
Google Scholar 
6.Bigg, M. A., Olesiuk, P. F., Ellis, G. M., Ford, J. K. B. & Balcomb, K. C. Social organization and genealogy of resident killer whales (Orcinus orca) in the coastal waters of British Columbia and Washington State. Rep. Int. Whal. Comm. 12, 383–405 (1990).
Google Scholar 
7.Ford, J. K. B. et al. Dietary specialization in two sympatric populations of killer whales (Orcinus orca) in coastal British Columbia and adjacent waters. Can. J. Zool. 76, 1456–1471 (1998).Article 

Google Scholar 
8.Matkin, C. O., Ellis, G. M., Olesiuk, P. & Saulitis, E. L. Association patterns and genealogies of resident killer whales (Orcinus orca) in Prince William Sound, Alaska. Fish. Bull. 97, 900–919 (1999).
Google Scholar 
9.Saulitis, E. L., Matkin, C. O., Barrett-Lennard, L., Heise, K. & Ellis, G. M. Foraging strategies of sympatric killer whale (Orcinus orca) populations in Prince William Sound, Alaska. Mar. Mamm. Sci. 16, 94–109. https://doi.org/10.1111/j.1748-7692.2000.tb00906.x (2000).Article 

Google Scholar 
10.Ford, J. K. B. & Ellis, G. M. Selective foraging by fish-eating killer whales Orcinus orca in British Columbia. Mar. Ecol. Prog. Ser. 316, 185–199. https://doi.org/10.3354/meps316185 (2006).Article 
ADS 

Google Scholar 
11.Ivkovich, T. V., Filatova, O. A., Burdin, A. M., Sato, H. & Hoyt, E. The social organization of resident-type killer whales (Orcinus orca) in Avacha Gulf, Northwest Pacific, as revealed through association patterns and acoustic similarity. Mamm. Biol. 75, 198–210. https://doi.org/10.1016/j.mambio.2009.03.006 (2010).Article 

Google Scholar 
12.Baird, R. W. & Dill, L. M. Occurrence and behaviour of transient killer whales: Seasonal and pod-specific variability, foraging behaviour, and prey handling. Can. J. Zool. 73, 1300–1311 (1995).Article 

Google Scholar 
13.Baird, R. W. & Dill, L. M. Ecological and social determinants of group size in transient killer whales. Behav. Ecol. 7(4), 408–416. https://doi.org/10.1093/beheco/7.4.408 (1996).Article 

Google Scholar 
14.Dahlheim, M. et al. Eastern temperate North Pacific offshore killer whales (Orcinus orca): Occurrence, movements, and insights into feeding ecology. Mar. Mamm. Sci. 24(3), 719–729. https://doi.org/10.1111/j.1748-7692.2008.00206.x (2008).Article 

Google Scholar 
15.Ford, J. K. B. et al. Shark predation and tooth wear in a population of northeastern Pacific killer whales. Aquat. Biol. 11, 213–224. https://doi.org/10.3354/ab00307 (2011).Article 

Google Scholar 
16.Ford, J. K. B., Stredulinsky, E. H., Ellis, G. M., Durban, J. W. & Pilkington, J. F. Offshore Killer Whales in Canadian Pacific Waters: Distribution, Seasonality, Foraging Ecology, Population Status and Potential for Recovery. DFO Canadian Science Advisory Secretariat, Doc. 2014/088 (2014). https://www.dfo-mpo.gc.ca/csas-sccs/publications/resdocs-docrech/2014/2014_088-eng.html.17.Morin, P. et al. Complete mitochondrial genome phylogeographic analysis of killer whales (Orcinus orca) indicates multiple species. Genome Res. 858, 908–915. https://doi.org/10.1101/gr.102954.109 (2010).CAS 
Article 

Google Scholar 
18.Morin, P. A. et al. Geographic and temporal dynamics of a global radiation and diversification in the killer whale. Mol. Ecol. 24(15), 3964–3979. https://doi.org/10.1111/mec.13284 (2015).Article 
PubMed 

Google Scholar 
19.Foote, A. D. et al. Genome-culture coevolution promotes rapid divergence of killer whale ecotypes. Nat. Commun. 7, 11693. https://doi.org/10.1038/ncomms11693 (2016).CAS 
Article 
PubMed 
PubMed Central 
ADS 

Google Scholar 
20.Schevill, W. E. & Watkins, W. A. Sound structure and directionality in Orcinus (killer whale). Zoologica 51, 71–78 (1966).
Google Scholar 
21.Diercks, K. J., Trochta, R. T., Greenlaw, C. F. & Evans, W. E. Recording and analysis of dolphin echolocation signals. J. Acoust. Soc. Am. 49, 1729–1932 (1971).Article 
ADS 

Google Scholar 
22.Diercks, K. J., Trochta, R. T. & Evans, W. E. Delphinid sonar: Measurement and analysis. J. Acoust. Soc. Am. 54, 200–204 (1973).Article 
ADS 

Google Scholar 
23.Steiner, W. W., Hain, J. H., Winn, H. E. & Perkins, P. J. Vocalizations and feeding behavior of the killer whale. J. Mammal. 60, 823–827 (1979).Article 

Google Scholar 
24.Awbrey, F. T., Thomas, J. A., Evans, W. E. & Leatherwood, S. Ross sea killer whale vocalizations: Preliminary description and comparison with those of some Northern Hemisphere killer whales. Rep. Int. Whal. Comm. 32, 667–670 (1982).
Google Scholar 
25.Ford, J. K. B. Acoustic behaviour of resident killer whales (Orcinus orca) off Vancouver Island, British Columbia. Can. J. Zool. 67(3), 727–745 (1989).Article 

Google Scholar 
26.Barrett-Lennard, L. G., Ford, J. K. B. & Heise, K. A. The mixed blessing of echolocation: Differences in sonar used by fish-eating and mammal-eating killer whales. Anim. Behav. 51, 553–565 (1996).Article 

Google Scholar 
27.Ford, J. K. B. Vocal traditions among resident killer whales (Orcinus orca) in coastal waters of British Columbia. Can. J. Zool. 69, 1454–1483 (1991).Article 

Google Scholar 
28.Miller, P. J. O. Mixed-directionality of killer whale stereotyped calls: A direction of movement cue?. Behav. Ecol. Sociobiol. 52, 262–270. https://doi.org/10.1007/s00265-002-0508-9 (2002).Article 

Google Scholar 
29.Miller, P. J. O., Shapiro, A. D., Tyack, P. L. & Solow, A. R. Call-type matching in vocal exchanges of free-ranging resident killer whales, Orcinus orca. Anim. Behav. 67, 1099–1107. https://doi.org/10.1016/j.anbehav.2003.06.017 (2004).Article 

Google Scholar 
30.Filatova, O. A. Independent acoustic variation of the higher- and lower-frequency components of biphonic calls can facilitate call recognition and social affiliation in killer whales. PLoS ONE 15(7), e0236749. https://doi.org/10.1371/journal.pone.0236749 (2020).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
31.Thomsen, F., Franck, D. & Ford, J. K. B. On the communicative significance of whistles in wild killer whales (Orcinus orca). Sci. Nat. 89, 404–407. https://doi.org/10.1007/s00114-002-0351-x (2002).CAS 
Article 

Google Scholar 
32.Riesch, R. & Deecke, V. B. Whistle communication in mammal-eating killer whales (Orcinus orca): Further evidence for acoustic divergence between ecotypes. Behav. Ecol. Sociobiol. 65, 1377–1387. https://doi.org/10.1007/s00265-011-1148-8 (2011).Article 

Google Scholar 
33.Deecke, V. B., Ford, J. K. B. & Slater, P. J. B. The vocal behaviour of mammal-eating killer whales: Communicating with costly calls. Anim. Behav. 69, 395–405. https://doi.org/10.1016/j.anbehav.2004.04.014 (2005).Article 

Google Scholar 
34.Saulitis, E. L., Matkin, C. O. & Fay, F. H. Vocal repertoire and acoustic behavior of the isolated AT1 killer whale subpopulation in southern Alaska. Can. J. Zool. 83, 1015–1029. https://doi.org/10.1139/Z05-089 (2005).Article 

Google Scholar 
35.Deecke, V., Slater, P. & Ford, J. Selective habituation shapes acoustic predator recognition in harbour seals. Nature 420, 171–173. https://doi.org/10.1038/nature01030 (2002).CAS 
Article 
PubMed 
ADS 

Google Scholar 
36.Foote, A. & Nystuen, J. Variation in call pitch among killer whale ecotypes. J. Acoust. Soc. Am. 123(3), 1747. https://doi.org/10.1121/1.2836752 (2008).Article 
PubMed 
ADS 

Google Scholar 
37.Yurk, H., Barrett-Lennard, L. G., Ford, J. K. B. & Matkin, C. O. Cultural transmission within maternal lineages: Vocal clans in resident killer whales in southern Alaska. Anim. Behav. 63(6), 1103–1119. https://doi.org/10.1006/anbe.2002.3012 (2002).Article 

Google Scholar 
38.Filatova, O., Fedutin, I. D., Burdin, A. & Hoyt, E. The structure of the discrete call repertoire of killer whales (Orcinus orca) from southeast Kamchatka. Bioacoustics 16, 261–280. https://doi.org/10.1080/09524622.2007.9753581 (2007).Article 

Google Scholar 
39.Deecke, V. B., Ford, J. K. B. & Spong, P. Quantifying complex patterns of bioacoustic variation: Use of a neural network to compare killer whale (Orcinus orca) dialects. J. Acoust. Soc. Am. 105, 2499. https://doi.org/10.1121/1.426853 (1999).CAS 
Article 
PubMed 
ADS 

Google Scholar 
40.Matkin, C., Barrett-Lennard, L., Yurk, H., Ellifrit, D. & Trites, A. Ecotypic variation and predatory behaviour among killer whales (Orcinus orca) off the eastern Aleutian Islands, Alaska. Fish. Bull. 105, 74–87 (2007).
Google Scholar 
41.Filatova, O. A. et al. Call diversity in the North Pacific killer whale populations: Implications for dialect evolution and population history. Anim. Behav. 83(3), 595–603. https://doi.org/10.1016/j.anbehav.2011.12.013 (2012).Article 

Google Scholar 
42.Danishevskaya, A. Y. et al. Crowd intelligence can discern between repertoires of killer whale ecotypes. Bioacoustics 29(3), 1–13. https://doi.org/10.1080/09524622.2018.1538902 (2018).Article 

Google Scholar 
43.Yurk, H. Vocal culture and social stability in resident killer whales (Orcinus orca) of the northeastern Pacific. PhD Thesis, University of British Columbia (2005).44.Matkin, C. O., Testa, J., Ellis, G. & Saulitis, E. Life history and population dynamics of southern Alaska resident killer whales. Mar. Mamm. Sci. 30(2), 460–469. https://doi.org/10.1111/mms.12049 (2014).Article 

Google Scholar 
45.Matkin, C. O., Olsen, D., Ellis, G., Ylitalo, G. & Andrews, R. Long-Term Killer Whale Monitoring in Prince William Sound/Kenai Fjords. Exxon Valdez Oil Spill Trustee Council Project 16120114-M Final Report (2018). https://www.arlis.org/docs/vol1/EVOS/2018/16120114-M.pdf.46.Matkin, C. O., Matkin, D. R., Ellis, G. M., Saulitis, E. & McSweeney, D. Movements of resident killer whales in southeastern Alaska and Prince William Sound, Alaska. Mar. Mamm. Sci. 13(3), 469–475. https://doi.org/10.1111/j.1748-7692.1997.tb00653.x (1997).Article 

Google Scholar 
47.Parsons, K. M. et al. Geographic patterns of genetic differentiation among killer whales in the northern North Pacific. J. Hered. 104(6), 737–754. https://doi.org/10.1093/jhered/est037 (2013).Article 
PubMed 

Google Scholar 
48.Olsen, D. W., Matkin, C. O., Andrews, R. D. & Atkinson, S. Seasonal and pod-specific differences in core use areas by resident killer whales in the Northern Gulf of Alaska. Deep Sea Res. Part II Top. Stud. Oceanogr. 147, 196–202. https://doi.org/10.1016/j.dsr2.2017.10.009 (2018).Article 
ADS 

Google Scholar 
49.Matkin, C. O. et al. Contrasting abundance and residency patterns of two sympatric populations of transient killer whales (Orcinus orca) in the northern Gulf of Alaska. Fish. Bull. 110(2), 143–155 (2012).
Google Scholar 
50.Matkin, C., Saulitis, E., Ellis, G., Olesiuk, P. & Rice, S. Ongoing population-level impacts on killer whales (Orcinus orca) following the Exxon Valdez oil spill in Prince William Sound, Alaska. Mar. Ecol. Prog. Ser. 356(1983), 269–281. https://doi.org/10.3354/meps07273 (2008).Article 
ADS 

Google Scholar 
51.Scheel, D., Matkin, C. O. & Saulitis, E. Distribution of killer whale pods in Prince William Sound, Alaska 1984–1996. Mar. Mamm. Sci. 17(3), 555–569. https://doi.org/10.1111/j.1748-7692.2001.tb01004.x (2001).Article 

Google Scholar 
52.Matkin, C. O. et al. Monitoring, tagging, feeding habits, and restoration of killer whales in Prince William Sound/Kenai Fjords 2010–2012. Exxon Valdez Oil Spill Trustee Council Project 16120114-M Final Report (2013). https://evostc.state.ak.us/media/2520/2010-10100742-final.pdf.53.Estes, J. A. et al. Trophic downgrading of planet Earth. Science 333(6040), 301–306. https://doi.org/10.1126/science.1205106 (2011).CAS 
Article 
PubMed 
ADS 

Google Scholar 
54.Albouy, C. et al. Global vulnerability of marine mammals to global warming. Sci. Rep. 10, 548. https://doi.org/10.1038/s41598-019-57280-3 (2020).CAS 
Article 
PubMed 
PubMed Central 
ADS 

Google Scholar 
55.Suryan, R. M. et al. Ecosystem response persists after a prolonged marine heatwave. Sci. Rep. 11, 6235. https://doi.org/10.1038/s41598-021-83818-5 (2021).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
56.Williams, R., Lusseau, D. & Hammond, P. S. The role of social aggregations and protected areas in killer whale conservation: The mixed blessing of critical habitat. Biol. Conserv. 142(4), 709–719. https://doi.org/10.1016/j.biocon.2008.12.004 (2009).Article 

Google Scholar 
57.Davis, G. E. et al. Long-term passive acoustic recordings track the changing distribution of North Atlantic right whales (Eubalaena glacialis) from 2004 to 2014. Sci. Rep. 7, 13460. https://doi.org/10.1038/s41598-017-13359-3 (2017).CAS 
Article 
PubMed 
PubMed Central 
ADS 

Google Scholar 
58.Romagosa, M. et al. Baleen whale acoustic presence and behaviour at a Mid-Atlantic migratory habitat, the Azores Archipelago. Sci. Rep. 10, 4766. https://doi.org/10.1038/s41598-020-61849-8 (2020).CAS 
Article 
PubMed 
PubMed Central 
ADS 

Google Scholar 
59.Frasier, K. E. et al. Cetacean distribution models based on visual and passive acoustic data. Sci. Rep. 11, 8240. https://doi.org/10.1038/s41598-021-87577-1 (2021).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
60.Burham, R. E., Palm, R. S., Duffus, D. A., Mouy, X. & Riera, A. The combined use of visual and acoustic data collection techniques for winter killer whale (Orcinus orca) observations. Glob. Ecol. Conserv. 8, 24–30. https://doi.org/10.1016/j.gecco.2016.08.001 (2016).Article 

Google Scholar 
61.Rice, A. et al. Spatial and temporal occurrence of killer whale ecotypes off the outer coast of Washington State, USA. Mar. Ecol. Prog. Ser. 572, 255–268. https://doi.org/10.3354/meps12158 (2017).Article 
ADS 

Google Scholar 
62.Riera, A., Pilkington, J. F., Ford, J. K. B., Stredulinsky, E. H. & Chapman, N. R. Passive acoustic monitoring off Vancouver Island reveals extensive use by at-risk Resident killer whale (Orcinus orca) populations, Endanger. Species Res. 39, 221–234. https://doi.org/10.3354/esr00966 (2019).Article 

Google Scholar 
63.Rice, A. et al. Cetacean occurrence in the Gulf of Alaska from long-term passive acoustic monitoring. Mar. Biol. 168, 72. https://doi.org/10.1007/s00227-021-03884-1 (2021).Article 

Google Scholar 
64.Dahlheim, M. E. & Matkin, C. O. Assessment of injuries to Prince William Sound killer whales. In Marine mammals and the ‘Exxon Valdez’ (ed. Loughlin, T. R.) 163–172 (Academic Press, 1994).Chapter 

Google Scholar 
65.Ford, M. J. et al. Estimation of a killer whale (Orcinus orca) population’s diet using sequencing analysis of DNA from feces. PLoS ONE 11(1), e0144956. https://doi.org/10.1371/journal.pone.0144956 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
66.Hanson, M. B. et al. Endangered predators and endangered prey: Seasonal diet of Southern Resident killer whales. PLoS ONE 16(3), e0247031. https://doi.org/10.1371/journal.pone.0247031 (2021).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
67.Bishop, M. A. & Eiler, J. H. Migration patterns of post-spawning Pacific herring in a subarctic sound. Deep Sea Res. Part II Top. Stud. Oceanogr. 147, 108–115. https://doi.org/10.1016/j.dsr2.2017.04.016 (2018).Article 
ADS 

Google Scholar 
68.Larson, W. A. et al. Single-nucleotide polymorphisms reveal distribution and migration of Chinook salmon (Oncorhynchus tshawytscha) in the Bering Sea and North Pacific Ocean. Can. J. Fish. Aquat. Sci. 70(1), 128–141. https://doi.org/10.1139/cjfas-2012-0233 (2013).CAS 
Article 

Google Scholar 
69.Wright, B. M. et al. Fine-scale foraging movements by fish-eating killer whales (Orcinus orca) relate to the vertical distributions and escape responses of salmonid prey (Oncorhynchus spp.). Mov. Ecol. 5, 3. https://doi.org/10.1186/s40462-017-0094-0 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
70.Olsen, D. W., Matkin, C. O., Mueter, F. J. & Atkinson, S. Social behavior increases in multipod aggregations of southern Alaska resident killer whales (Orcinus orca). Mar. Mamm. Sci. 36, 1150–1159. https://doi.org/10.1111/mms.12715 (2020).CAS 
Article 

Google Scholar 
71.McKinstry, C. A. E. & Campbell, R. W. Seasonal variation of zooplankton abundance and community structure in Prince William Sound, Alaska, 2009–2016. Deep Sea Res. Part II Top. Stud. Oceanogr. 147, 69–78. https://doi.org/10.1016/j.dsr2.2017.08.016 (2018).Article 
ADS 

Google Scholar 
72.Thorne, R. E. Trends in adult and juvenile herring distribution and abundance in Prince William Sound. Exxon Valdez Oil Spill Restoration Project 070830 Final Report (2010).73.Hanson, M. B. et al. Species and stock identification of prey consumed by endangered southern resident killer whales in their summer range. End. Spec. Res. 11(1), 69–82. https://doi.org/10.3354/esr00263 (2010).MathSciNet 
Article 
ADS 

Google Scholar 
74.Filatova, O. A. et al. The function of multi-pod aggregations of fish-eating killer whales (Orcinus orca) in Kamchatka, Far East Russia. J. Ethol. 27, 333. https://doi.org/10.1007/s10164-008-0124-x (2009).Article 

Google Scholar 
75.Yurk, H., Filatova, O., Matkin, C. O., Barrett-Lennard, L. G. & Brittain, M. Sequential habitat use by two resident killer whale (Orcinus orca) clans in Resurrection Bay, Alaska, as determined by remote acoustic monitoring. Aquat. Mamm. 36(1), 67–78. https://doi.org/10.1578/AM.36.1.2010.67 (2010).Article 

Google Scholar 
76.Maniscalco, J. M., Matkin, C. O., Maldini, D., Calkins, D. G. & Atkinson, S. Assessing killer whale predation on Steller sea lions from field observations in Kenai Fjords, Alaska. Mar. Mamm. Sci. 23(2), 306–321. https://doi.org/10.1111/j.1748-7692.2007.00103.x (2007).Article 

Google Scholar 
77.Brown, E. D., Wang, J., Vaughan, S. L., & Norcross, B. L. Identifying seasonal spatial scale for the ecological analysis of herring and other forage fish in Prince William Sound, Alaska. Ecosystem Approaches for Fisheries Management, Alaska Sea Grant College Program AK-SG-99-01, 499–510 (1999). https://seagrant.uaf.edu/lib/aksg/9901/AK-SG-99-01-g.pdf.78.Moran, J. R., O’Dell, M. B., Arimitsu, M. L., Straley, J. M. & Dickson, D. M. S. Seasonal distribution of Dall’s porpoise in Prince William Sound, Alaska. Deep Sea Res. Part II Top. Stud. Oceanogr. 147, 164–172. https://doi.org/10.1016/j.dsr2.2017.11.002 (2018).Article 
ADS 

Google Scholar 
79.Frost, K. J., Lowry, L. F. & Ver Hoef, J. M. Monitoring the trend of harbor seals in Prince William Sound, Alaska, after the Exxon Valdez Oil Spill. Mar. Mamm. Sci. 15(2), 494–506. https://doi.org/10.1111/j.1748-7692.1999.tb00815.x (1999).Article 

Google Scholar 
80.Lowry, L. F., Frost, K. J., Ver Hoef, J. M. & Delong, R. A. Movements of satellite-tagged subadult and adult harbor seals in Prince William Sound, Alaska. Mar. Mamm. Sci. 17(4), 835–861. https://doi.org/10.1111/j.1748-7692.2001.tb01301.x (2001).Article 

Google Scholar 
81.Riera, A., Ford, J. K. & Ross Chapman, N. Effects of different analysis techniques and recording duty cycles on passive acoustic monitoring of killer whales. J. Acoust. Soc. Am. 134(3), 2393–2404. https://doi.org/10.1121/1.4816552 (2013).Article 
PubMed 
ADS 

Google Scholar 
82.Miller, P. J. O. Diversity in sound pressure levels and estimated active space of resident killer whale vocalizations. J. Comp. Physiol. 192, 449. https://doi.org/10.1007/s00359-005-0085-2 (2006).Article 

Google Scholar 
83.Holt, M. M., Noren, D., Veirs, V., Emmons, C. & Veirs, S. R. Speaking up: Killer whales (Orcinus orca) increase their call amplitude in response to vessel noise. J. Acoust. Soc. Am. 125, 27–32. https://doi.org/10.1121/1.3040028 (2009).Article 
ADS 

Google Scholar 
84.Veirs, S., Veirs, V. & Wood, J. D. Ship noise extends to frequencies used for echolocation by endangered killer whales. PeerJ 4, e1657. https://doi.org/10.7717/peerj.1657 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
85.Joy, R. et al. Potential benefits of vessel slowdowns on endangered southern resident killer whales. Front. Mar. Sci. 6, 344. https://doi.org/10.3389/fmars.2019.00344 (2019).Article 

Google Scholar 
86.Williams, R., Veirs, S., Veirs, V., Ashe, E. & Mastick, N. Approaches to reduce noise from ships operating in important killer whale habitats. Mar. Pollut. Bull. 139, 459–469. https://doi.org/10.1016/j.marpolbul.2018.05.015 (2019).CAS 
Article 
PubMed 

Google Scholar 
87.Esler, D. et al. Timelines and mechanisms of wildlife population recovery following the Exxon Valdez oil spill. Deep Sea Res. Part II Top. Stud. Oceanogr. 147, 36–42. https://doi.org/10.1016/j.dsr2.2017.04.007 (2018).Article 
ADS 

Google Scholar 
88.Buckman, A. H. et al. PCB-associated changes in mRNA expression in killer whales (Orcinus orca) from the NE Pacific Ocean. Environ. Sci. Technol. 45(23), 10194–10202. https://doi.org/10.1021/es201541j (2011).CAS 
Article 
PubMed 
ADS 

Google Scholar 
89.Lawson, T. M. et al. Concentrations and profiles of organochlorine contaminants in North Pacific resident and transient killer whale (Orcinus orca) populations. Sci. Total. Environ. 722, 137776. https://doi.org/10.1016/j.scitotenv.2020.137776 (2020).CAS 
Article 
PubMed 
ADS 

Google Scholar 
90.DeMarban, A. Oily water leaks into Port Valdez from pipeline terminal, Alyeska says. Anchorage Daily News, Apr. 14, 2020. https://www.adn.com/business-economy/energy/2020/04/14/oily-water-spills-into-port-valdez-from-pipeline-terminal-alyeska-says/.91.Gillespie, D. et al. PAMGUARD: Semiautomated, open source software for real-time acoustic detection and localisation of cetaceans. Proc. Inst. Acoust. 30, 67–75 (2008).
Google Scholar 
92.Zhong, M. et al. Detecting, classifying, and counting blue whale calls with Siamese neural networks. J. Acoust. Soc. Am. 149, 3086. https://doi.org/10.1121/10.0004828 (2021).Article 
PubMed 
ADS 

Google Scholar 
93.Audacity Team. Audacity®: Free Audio Editor and Recorder [Computer application]. Version 2.3.2 (2019). https://audacityteam.org/.94.R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2013).
Google Scholar 
95.Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67(1), 1–48. https://doi.org/10.18637/jss.v067.i01 (2015).Article 

Google Scholar 
96.Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer, 2016).97.Hijmans, R. J. raster: Geographic Data Analysis and Modeling. R package version 3.4-10. (2021). https://CRAN.R-project.org/package=raster98.GEBCO Compilation Group. GEBCO 2020 Grid. (2020). https://doi.org/10.5285/a29c5465-b138-234d-e053-6c86abc040b9. More

AffiliationsEarth, Atmospheric, and Planetary Sciences Department, Massachusetts Institute of Technology, Cambridge, MA, USAGabriela Serrato MarksSchool of Science, Technology, Accessibility, Mathematics and Public Health, Gallaudet University, Washington DC, USACaroline SolomonScience, Technology & Society Department, Rochester Institute of Technology, Rochester, NY, USAKaitlin Stack WhitneyAuthorsGabriela Serrato MarksCaroline SolomonKaitlin Stack WhitneyCorresponding authorsCorrespondence to
Gabriela Serrato Marks, Caroline Solomon or Kaitlin Stack Whitney. More

1.O’Brien, R. D. & Wolfe, R. S. Nongenetic effects of radiation. In Radiation, Radioactivity, and Insects (eds O’Brien, R. D. & Wolfe, R. S.) 23–54 (Academic Press Inc., Ltd., 1964).Chapter 

Google Scholar 
2.Bakri, A., Mehta, K. & Lance, D. R. Sterilizing insects with ionizing radiation. In Sterile Insect Technique: Principles and Practice in Area-Wide Integrated Pest Management (eds Dyck, V. A. et al.) 233–268 (Springer, 2005).Chapter 

Google Scholar 
3.Koval, T. M. Intrinsic resistance to the lethal effects of X-irradiation in insect and arachnid cells. Proc. Natl. Acad. Sci. USA 80, 4752–4755. https://doi.org/10.1073/pnas.80.15.4752 (1983).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
4.Balock, J. W., Burditt, A. K. & Christenson, L. D. Effects of gamma radiation on various stages of three fruit fly species. J. Econ. Entomol. 56, 42–46 (1963).Article 

Google Scholar 
5.Hooper, G. H. S. The effect of ionizing radiation on reproduction. In Fruit Flies Their Biology, Natural Enemies, and Control (eds Robinson, A. S. & Hooper, G.) 153–164 (World Crop Pests, 1989).
Google Scholar 
6.Robinson, A. S. Genetic basis of the sterile insect technique. In The Sterile Insect Technique: Principles and Practice in Area-Wide Integrated Pest Management (eds Dyck, V. A. et al.) 95–114 (Springer, 2005).Chapter 

Google Scholar 
7.Lauzon, C. R. & Potter, S. E. Description of the irradiated and nonirradiated midgut of Ceratitis capitata Wiedemann (Diptera: Tephritidae) and Anastrepha ludens Loew (Diptera: Tephritidae) used for sterile insect technique. J. Pest Sci. 85, 217–222 (2012).Article 

Google Scholar 
8.Knipling, E. F. Possibilities of insect control or eradication through the use of sexually sterile males. J. Econ. Entomol. 48, 459–469 (1955).Article 

Google Scholar 
9.Riley, P. A. Free radicals in biology: oxidative stress and the effects of ionizing radiation. Int. J. Radiat. Biol. 65, 27–33 (1994).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
10.Foshier, S. Cellular effects of radiation. In Essentials of Radiation, Biology, and Protection (ed. Foshier, S.) 43–62 (Delmar Thomson Learning, 2009).
Google Scholar 
11.Richardson, B. & Harper, M. E. Mitochondrial stress controls the radiosensitivity of the oxygen effect: implications for radiotherapy. Oncotarget 7, 21469–21483 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
12.Monaghan, P., Metcalfe, N. B. & Torres, R. Oxidative stress as a mediator of life history trade-offs: mechanisms, measurements and interpretation. Ecol. Lett. 12, 75–92 (2009).PubMed 
Article 
PubMed Central 

Google Scholar 
13.Brieger, K., Schiavone, S., Miller, F. J. & Krause, K. H. Reactive oxygen species: from health to disease. Swiss Med. Wkly. https://doi.org/10.4414/smw.2012.13659 (2012).Article 
PubMed 
PubMed Central 

Google Scholar 
14.von Schantz, T., Bensch, S., Grahn, M., Hasselquist, D. & Wittzel, H. Good genes, oxidative stress and condition-dependent sexual signals. Proc. R. Soc. Lond. 266, 1–12. https://doi.org/10.1098/rspb.1999.0597 (1999).Article 

Google Scholar 
15.Metcalf, N. B. & Alonso-Alvarez, C. Oxidative stress as a life-history constraint: the role of reactive oxygen species in shaping phenotypes from conception to death. Funct. Ecol. 24, 984–996 (2010).Article 

Google Scholar 
16.Benoit, J. B. & López-Martínez, G. Role of conventional and unconventional stress proteins during the response of insects to traumatic environmental conditions. In Hemolymph Proteins and Functional Peptides: Recent Advances in Insects and Other Arthropods (eds Tufail, M. & Takeda, M.) 128–160 (Bentham Science Publishers, 2012).
Google Scholar 
17.Holbrook, F. R. & Fujimoto, M. S. Mating competitiveness of unirradiated and irradiated Mediterranean fruit flies. J. Econ. Entomol. 63, 1175–1176 (1970).Article 

Google Scholar 
18.Ohinata, K., Chambers, D. L., Fujimoto, M., Kashiwai, S. & Miyabara, R. Sterilization of the Mediterranean fruit fly by irradiation comparative mating effectiveness of treated pupae and adults. J. Econ. Entomol. 64, 781–784 (1971).Article 

Google Scholar 
19.Sharp, J. L. & Webb, J. C. Flight performance and signaling sound of irradiated or unirradiated Anastrepha suspensa. Proc. Hawaii Entomol. Soc. 22, 525–532 (1977).
Google Scholar 
20.Webb, J. C., Sivinski, J. & Smittle, B. J. Acoustical courtship signals and sexual success in irradiated Caribbean fruit flies (Anastrepha suspensa) (Diptera: Tephritidae). Fla. Entomol. 70, 103–109 (1987).Article 

Google Scholar 
21.Moreno, D. S., Sanchez, M., Robacker, D. C. & Worley, J. Mating competitiveness of irradiated Mexican fruit fly (Diptera: Tephritidae). J. Econ. Entomol. 84, 1227–1234 (1991).Article 

Google Scholar 
22.Ponce, W. P., Nation, J. L., Emmel, T. C., Smittle, B. J. & Teal, P. E. A. Quantitative analysis of pheromone production in irradiated Caribbean fruit fly males, Anastrepha suspensa (Loew). J. Chem. Ecol. 19, 3045–3056 (1993).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
23.Heath, R. R., Epsky, N. D., Dueben, B. D., Guzman, A. & Rade, L. E. Gamma radiation effect on production of four pheromonal components of male Mediterranean fruit flies (Diptera: Tephritidae). J. Econ. Entomol. 87, 904–909 (1994).CAS 
Article 

Google Scholar 
24.Lux, S. A. et al. Effects of irradiation on the courtship behavior of medfly (Diptera, Tephritidae) mass reared for the Sterile Insect Technique. Fla. Entomol. 85, 102–112 (2002).Article 

Google Scholar 
25.Barry, J. D., McInnis, D. O., Gates, D. & Morse, J. G. Effects of irradiation on Mediterranean fruit flies (Diptera:Tephritidae): emergence, survivorship, lure attraction and mating competition. J. Econ. Entomol. 96, 615–622 (2003).PubMed 
Article 
PubMed Central 

Google Scholar 
26.Calkins, C. O. & Parker, A. G. Sterile insect quality. In The Sterile Insect Technique: Principles and Practice in Area-Wide Integrated Pest Management (eds Dyck, V. A. et al.) 269–296 (Springer, 2005).Chapter 

Google Scholar 
27.Lance, D. R. & McInnis, D. O. Biological basis of the sterile insect technique. In The Sterile Insect Technique: Principles and Practice in Area-Wide Integrated Pest Management (eds Dyck, V. A. et al.) 69–94 (Springer, 2005).Chapter 

Google Scholar 
28.Thoday, J. M. & Read, J. Effect of oxygen on the frequency of chromosome aberrations produced by X-rays. Nature 160, 608 (1947).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
29.FAO/IAEA/USDA. Product Quality Control for Sterile Mass-Reared and Released Tephritid Fruit Flies V 7.0 (IAEA, 2019).30.FAO/IAEA. Guideline for Packing, Shipping, Holding and Release of Sterile Flies in Area-Wide Fruit Fly Control Programmes (FAO, 2017).31.Langley, P. A. & Maly, H. Control of the Mediterranean fruit fly (Ceratitis capitata) using sterile males: effects of nitrogen and chilling during gamma-irradiation of puparia. Entomol. Exp. Appl. 14, 137–146 (1971).CAS 
Article 

Google Scholar 
32.Hooper, G. H. S. Competitiveness of gamma-sterilized males of the Mediterranean fruit fly: effects of irradiating pupal or adult stage and of irradiating pupae in nitrogen. J. Econ. Entomol. 64, 1364–1368 (1971).Article 

Google Scholar 
33.Hooper, G. H. S. Sterilization of Dacus cucumis French (Diptera: Tephritidae) by gamma radiation. I. Effect of dose on fertility, survival and competitiveness. J. Aust. Entomol. Soc. 14, 81–87 (1975).Article 

Google Scholar 
34.Zumreoglu, A., Ohinata, K., Fujimoto, M., Higa, H. & Harris, E. J. Gamma irradiation of the Mediterranean fruit fly: Effect of treatment of immature pupae in nitrogen on emergence, longevity, sterility, sexual competitiveness, mating ability, and pheromone production of males. J. Econ. Entomol. 72, 173–176 (1979).Article 

Google Scholar 
35.Fisher, K. Irradiation effects in air and in nitrogen on Mediterranean fruit fly (Diptera: Tephritidae) pupae in western Australia. J. Econ. Entomol. 90, 1609–1614 (1997).Article 

Google Scholar 
36.Rull, J., Birke, A., Ortega, R., Montoya, P. & Lopez, L. Quantity and safety vs. quality and performance: conflicting interests during mass rearing and transport affect the efficiency of sterile insect technique programs. Entomol. Exp. Appl. 142, 78–86 (2012).Article 

Google Scholar 
37.Lopez-Martinez, G. & Hahn, D. A. Short-term anoxic conditioning hormesis boosts antioxidant defenses, lowers oxidative damage following irradiation and enhances male sexual performance in the Caribbean fruit fly, Anastrepha suspensa. J. Exp. Biol. 215, 2150–2161 (2012).CAS 
PubMed 
Article 

Google Scholar 
38.Lopez-Martinez, G. & Hahn, D. A. Early life hormetic treatments decrease irradiation-induced oxidative damage, increase longevity, and enhance sexual performance during old age in the Caribbean fruit fly. PLoS ONE 9, e88128 (2014).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
39.Sivinski, J. Lekking and the small-scale distribution of the sexes in the Caribbean fruit fly, Anastrepha suspensa (Loew). J. Insect Behav. 2, 3–13 (1989).Article 

Google Scholar 
40.Teets, N. M. et al. Overexpression of an antioxidant enzyme improves male mating performance after stress in a lek-mating fruit fly. Proc. R. Soc. B. https://doi.org/10.1098/rspb.2019.0531 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
41.Costantini, D. Understanding diversity in oxidative status and oxidative stress: the opportunities and challenges ahead. J. Exp. Biol. https://doi.org/10.1242/jeb.194688 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
42.Beehler, B. M. & Foster, M. S. Hotshots, hotspots, and female preference in the organization of lek mating system. Am. Nat. 131, 203–219 (1988).Article 

Google Scholar 
43.Shelly, T. E. Exposure to alpha-copaene and alpha-copaene-containing oils enhances mating success of male Mediterranean fruit flies (Diptera: Tephritidae). Ann. Entomol. Soc. Am. 94, 497–502 (2001).CAS 
Article 

Google Scholar 
44.Field, S. A., Kaspi, R. & Yuval, B. Why do calling medflies (Diptera: Tephritidae) cluster? Assessing the empirical evidence for models of medfly lek evolution. Fla. Entomol. 85, 63–72 (2002).Article 

Google Scholar 
45.Widemo, F. & Owens, I. P. F. Lek size, male mating skew and the evolution of lekking. Nature 373, 148–151 (1995).ADS 
CAS 
Article 

Google Scholar 
46.Cestari, C., Loiselle, B. A. & Pizo, M. A. Trade-offs in male display activity with lek size. PLoS ONE 11, e0162943 (2016).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
47.Rendon, P., McInnis, D., Lance, D. & Stewart, J. Medfly (Diptera: Tephritidae) genetic sexing: large-scale field comparison of males-only and bisexual sterile fly releases in Guatemala. J. Econ. Entomol. 97, 1547–1553 (2004).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
48.Hendrichs, J., Robinson, A. S., Cayol, J. P. & Enkerlin, W. R. Medfly areawide Sterile Insect Technique programmes for prevention, suppression or eradication: the importance of mating behavior studies. Fla. Entomol. 85, 1–13 (2002).Article 

Google Scholar 
49.Pereira, R. et al. Improving sterile male performance in support of programmes integrating the sterile insect technique against fruit flies. J. Appl. Entomol. 137, S178–S190 (2013).Article 

Google Scholar 
50.Wiley, R. H. Errors, exaggerations and deception in animal communication. In Behavioural Mechanisms in Evolutionary Ecology (ed. Real, L. A.) 157–189 (University of Chicago Press, 1994).
Google Scholar 
51.Cotton, S., Small, J. & Pomiankowski, A. Sexual selection and condition-dependent mate preferences. Curr. Biol. 16, 755–765 (2006).Article 
CAS 

Google Scholar 
52.Sivinski, J. & Burk, T. Reproductive and mating behaviour. In Fruit Flies: Their Biology, Natural Enemies and Control (eds Robinson, A. & Hooper, G.) 343–351 (Elsevier, 1989).
Google Scholar 
53.Hooper, G. H. S. Sterilization of the Mediterranean fruit fly: a review of laboratory data. in Sterile male technique for the control of fruit flies 3–12 (IAEA, 1970).54.Collins, S. R., Weldon, C. W., Banos, C. & Taylor, P. W. Effects of irradiation dose rate on quality and sterility of Queensland fruit flies, Bactrocera tryoni (Froggatt). J. Appl. Entomol. 132, 398–405 (2008).Article 

Google Scholar 
55.Turrens, J. F. Mitochondrial formation of reactive oxygen species. J. Physiol. Lond. 552, 335–344 (2003).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
56.Zhou, Y., Hu, L. F., Wu, H., Jiang, L. W. & Liu, S. Q. Genome-wide identification and transcriptional expression analysis of cucumber superoxide dismutase (SOD) family in response to various abiotic stresses. Int. J. Genomics https://doi.org/10.1155/2017/7243973 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
57.Lesser, M. P. Oxidative stress in marine environments: biochemistry and physiological ecology. Annu. Rev. Physiol. 68, 253–278 (2006).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
58.Martinez-Lendech, N., Golab, M. J., Osorio-Beristain, M. & Contreras-Garduno, J. Sexual signals reveal males’ oxidative stress defences: testing this hypothesis in an invertebrate. Funct. Ecol. 32, 937–947 (2018).Article 

Google Scholar 
59.Romero-Haro, A. A. & Alonso-Alvarez, C. The level of an intracellular antioxidant during development determines the adult phenotype in a bird species: a potential organizer role for glutathione. Am. Nat. 185, 390–405 (2015).PubMed 
Article 
PubMed Central 

Google Scholar 
60.Nestel, D., Nemny-Lavy, E., Islam, S. M., Wornoayporn, V. & Cáceres, C. Effects of pre-irradiation conditioning of medfly pupae (Diptera: Tephritidae): hypoxia and quality of sterile males. Fla. Entomol. 90, 80–87 (2007).Article 

Google Scholar 
61.Bartholomew, N. R., Burdett, J. M., VandenBrooks, J. M., Quinlan, M. C. & Call, G. B. Impaired climbing and flight behaviour in Drosophila melanogaster following carbon dioxide anaesthesia. Sci. Rep. 5, 15298 (2015).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
62.Hermes-Lima, M. et al. Preparation for oxidative stress under hypoxia and metabolic depression: revisiting the proposal two decades later. Free Radic. Biol. Med. 89, 1122–1143 (2015).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
63.Moreira, D. C., Venancio, L. P. R., Sabino, M. A. C. T. & Hermes-Lima, M. How widespread is preparation for oxidative stress in the animal kingdom?. Comp. Biochem. Physiol. A 200, 64–78 (2016).CAS 
Article 

Google Scholar 
64.Giraud-Billoud, M. et al. Twenty years of the ‘preparation for oxidative stress’ (POS) theory: ecophysiological advantages and molecular strategies. Comp. Biochem. Physiol. A 234, 36–49 (2019).CAS 
Article 

Google Scholar 
65.Hermes-Lima, M. & Zenteno-Savin, T. Animal response to drastic changes in oxygen availability and physiological oxidative stress. Comp. Biochem. Phys. C 133, 537–556 (2002).Article 

Google Scholar 
66.Bates, D. et al. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67(1), 1–48. https://doi.org/10.18637/jss.v067.i01 (2015).ADS 
Article 

Google Scholar 
67.Length, R. V. emmeans: Estimated Marginal Means, Aka Least-Squares Means. R package Version 1.6.2-1 (2021). https://CRAN.R-project.org/package=emmeans.68.R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, Vienna, Austria, 2021). https://www.R-project.org/.69.RStudio Team. RStudio: Integrated Development Environment for R. RStudio (PBC, Boston, 2021). http://www.rstudio.com/.70.Fox, J. & Weisberg, S. An R Companion to Applied Regression 3rd edn. (Sage, 2019).
Google Scholar  More

1.Pecl, G. T. et al. Biodiversity redistribution under climate change: Impacts on ecosystems and human well-being. Science 355, 9214 (2017).Article 

Google Scholar 
2.Creamean, J. M. et al. Dust and biological aerosols from the Sahara and Asia influence precipitation in the western US. Science 339, 1572–1578 (2013).ADS 
CAS 
Article 

Google Scholar 
3.Hervàs, A., Camarero, L., Reche, I. & Casamayor, E. O. Viability and potential for immigration of airborne bacteria from Africa that reach high mountain lakes in Europe. Environ. Microbiol. 11, 1612–1623 (2009).Article 

Google Scholar 
4.Barberán, A. et al. Continental-scale distributions of dust-associated bacteria and fungi. Proc. Natl. Acad. Sci. 112, 5756–5761 (2015).ADS 
Article 

Google Scholar 
5.Brown, J. K. & Hovmøller, M. S. Aerial dispersal of pathogens on the global and continental scales and its impact on plant disease. Science 297, 537–541 (2002).ADS 
CAS 
Article 

Google Scholar 
6.Mazar, Y., Cytryn, E., Erel, Y. & Rudich, Y. Effect of dust storms on the atmospheric microbiome in the Eastern Mediterranean. Environ. Sci. Technol. 50, 4194–4202 (2016).ADS 
CAS 
Article 

Google Scholar 
7.Griffin, E. A. & Carson, W. P. The ecology and natural history of foliar bacteria with a focus on tropical forests and agroecosystems. Bot. Rev. 81, 105–149 (2015).Article 

Google Scholar 
8.Guerrieri, R. et al. Partitioning between atmospheric deposition and canopy microbial nitrification into throughfall nitrate fluxes in a Mediterranean forest. J. Ecol. 108, 626–640 (2020).CAS 
Article 

Google Scholar 
9.Fröhlich-Nowoisky, J. et al. Bioaerosols in the earth system: Climate, health, and ecosystem interactions. Atmos. Res. 182, 346–376 (2016).Article 

Google Scholar 
10.Hutchins, D. A. et al. Climate change microbiology—Problems and perspectives. Nat. Rev. Microbiol. 17, 391–396 (2019).CAS 
Article 

Google Scholar 
11.Singh, B. K., Bardgett, R. D., Smith, P. & Reay, D. S. Microorganisms and climate change: Terrestrial feedbacks and mitigation options. Nat. Rev. Microbiol. 8, 779–790 (2010).CAS 
Article 

Google Scholar 
12.Cavicchioli, R. et al. Scientists’ warning to humanity: Microorganisms and climate change. Nat. Rev. Microbiol. 17, 569–586 (2019).CAS 
Article 

Google Scholar 
13.Tipton, L. et al. Fungal aerobiota are not affected by time nor environment over a 13-y time series at the Mauna Loa Observatory. Proc. Natl. Acad. Sci. 116, 25728–25733 (2019).CAS 
Article 

Google Scholar 
14.Cáliz, J., Triadó-Margarit, X., Camarero, L. & Casamayor, E. O. A long-term survey unveils strong seasonal patterns in the airborne microbiome coupled to general and regional atmospheric circulations. Proc. Natl. Acad. Sci. 115, 12229–12234 (2018).ADS 
Article 

Google Scholar 
15.Brągoszewska, E. & Pastuszka, J. S. Influence of meteorological factors on the level and characteristics of culturable bacteria in the air in Gliwice, Upper Silesia (Poland). Aerobiologia 34, 241–255. https://doi.org/10.1007/s10453-018-9510-1 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
16.Ruíz-Gil, T. et al. Airborne bacterial communities of outdoor environments and their associated influencing factors. Environ. Int. 145, 106156 (2020).Article 

Google Scholar 
17.Titos, G. et al. Retrieval of aerosol properties from ceilometer and photometer measurements: Long-term evaluation with in situ data and statistical analysis at Montsec (southern Pyrenees). Atmos. Meas. Tech. 12, 3255–3267 (2019).CAS 
Article 

Google Scholar 
18.Camarero, L., Bacardit, M., de Diego, A. & Arana, G. Decadal trends in atmospheric deposition in a high elevation station: Effects of climate and pollution on the long-range flux of metals and trace elements over SW Europe. Atmos. Environ. 167, 542–552 (2017).ADS 
CAS 
Article 

Google Scholar 
19.Triadó-Margarit, X., Caliz, J., Reche, I. & Casamayor, E. O. High similarity in bacterial bioaerosol compositions between the free troposphere and atmospheric depositions collected at high-elevation mountains. Atmos. Environ. 203, 79–86 (2019).ADS 
Article 

Google Scholar 
20.Els, N. et al. Microbial composition in seasonal time series of free tropospheric air and precipitation reveals community separation. Aerobiologia 35, 671–701 (2019).Article 

Google Scholar 
21.Reche, I., D’Orta, G., Mladenov, N., Winget, D. M. & Suttle, C. A. Deposition rates of viruses and bacteria above the atmospheric boundary layer. ISME J. 12, 1154–1162 (2018).CAS 
Article 

Google Scholar 
22.Triadó-Margarit, X., Cáliz, J. & Casamayor, E. O. A long-term atmospheric baseline for intercontinental exchange of airborne pathogens. Environment International., ENVINT-D-21-01147R1 (2021).23.Barberán, A., Henley, J., Fierer, N. & Casamayor, E. O. Structure, inter-annual recurrence, and global-scale connectivity of airborne microbial communities. Sci. Total Environ. 47, 187–195 (2014).ADS 
Article 

Google Scholar 
24.Ontiveros, V. J., Capitán, J. A., Casamayor, E. O. & Alonso, D. The characteristic time of ecological communities. Ecology 102(2), e03247 (2021).Article 

Google Scholar 
25.Alonso, D., Pinyol-Gallemí, A., Alcoverro, T. & Arthur, R. Fish community reassembly after a coral mass mortality: Higher trophic groups are subject to increased rates of extinction. Ecol. Lett. 18, 451–461 (2015).Article 

Google Scholar 
26.Ontiveros, V. J., Capitán, J. A., Arthur, R., Casamayor, E. O. & Alonso, D. Colonization and extinction rates estimated from temporal dynamics of ecological communities: The island r package. Methods Ecol. Evol. 10, 1108–1117 (2019).Article 

Google Scholar 
27.OPCC-CTP. Le changement climatique dans les Pyrénées: impacts, vulnérabilités et adaptation. Bases de connaissances pour la future stratégie d’adaptation au changement climatique dans les Pyrénées. ISBN: 978-84-09-06268-3. https://www.opcc-ctp.org/sites/default/files/documentacion/opcc-informe-fr-print.pdf (2018).28.Chudobova, D. et al. Effects of stratospheric conditions on the viability, metabolism and proteome of prokaryotic cells. Atmosphere 6, 1290–1306 (2015).ADS 
Article 

Google Scholar 
29.Elbert, W., Taylor, P., Andreae, M. & Pöschl, U. Contribution of fungi to primary biogenic aerosols in the atmosphere: Wet and dry discharged spores, carbohydrates, and inorganic ions. Atmos. Chem. Phys. 7, 4569–4588 (2007).ADS 
CAS 
Article 

Google Scholar 
30.Bowers, R. M., McCubbin, I. B., Hallar, A. G. & Fierer, N. Seasonal variability in airborne bacterial communities at a high-elevation site. Atmos. Environ. 50, 41–49 (2012).ADS 
CAS 
Article 

Google Scholar 
31.Tonkin, J. D., Bogan, M. T., Bonada, N., Rios-Touma, B. & Lytle, D. A. Seasonality and predictability shape temporal species diversity. Ecology 98, 1201–1216 (2017).Article 

Google Scholar 
32.Delort, A. M. et al. Microbial Ecology of Extreme Environments 215–245 (Springer, 2017).Book 

Google Scholar 
33.Catalan, J. et al. High mountain lakes: extreme habitats and witnesses of environmental changes. Limnetica 25, 551–584 (2006).
Google Scholar 
34.Ruiz-González, C., Niño-García, J. P. & del Giorgio, P. A. Terrestrial origin of bacterial communities in complex boreal freshwater networks. Ecol. Lett. 18, 1198–1206 (2015).Article 

Google Scholar 
35.Maignien, L., DeForce, E. A., Chafee, M. E., Eren, A. M. & Simmons, S. L. Ecological succession and stochastic variation in the assembly of Arabidopsis thaliana phyllosphere communities. MBio 5, e00682 (2014).Article 

Google Scholar 
36.Hellberg, R. S. & Chu, E. Effects of climate change on the persistence and dispersal of foodborne bacterial pathogens in the outdoor environment: A review. Crit. Rev. Microbiol. 42, 548–572 (2016).Article 

Google Scholar 
37.Vellend, M. Conceptual synthesis in community ecology. Q. Rev. Biol. 85, 183–206 (2010).Article 

Google Scholar  More

1.Bindoff, N. L. et al. Observations: Oceanic climate change and sea level. In Climate Change 2007: The Physical Science Basis. Contribution of Working Group 1 to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change (eds Solomon, S. et al.) 385–432 (Cambridge University Press, 2007).
Google Scholar 
2.IPCC. Climate Change 2001: Synthesis Report 397 (IPCC, 2001).
Google Scholar 
3.IPCC Climate change 2014: Synthesis report. In Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (eds Pachauri, R. & Meyer, L.) (IPCC, 2014).
Google Scholar 
4.Ye, L. & Grimm, N. B. Modelling potential impacts of climate change on water and nitrate export from a mid-sized, semiarid watershed in the US Southwest. Clim. Change 120(1–2), 419–431 (2013).ADS 
CAS 
Article 

Google Scholar 
5.Zhao, A., Zhu, X., Liu, X., Pan, Y. & Zuo, D. Impacts of land use change and climate variability on green and blue water resources in the Weihe River Basin of northwest China. CATENA 137, 318–327 (2016).Article 

Google Scholar 
6.Langerwisch, F., Václavík, T., von Bloh, W., Vetter, T. & Thonicke, K. Combined effects of climate and land-use change on the provision of ecosystem services in rice agro-ecosystems. Environ. Res. Lett. 13(1), 015003 (2017).ADS 
Article 
CAS 

Google Scholar 
7.Gordon, L. J., Peterson, G. D. & Bennett, E. M. Agricultural modifications of hydrological flows create ecological surprises. Trends Ecol. Evol. 23(4), 211–219 (2008).PubMed 
Article 

Google Scholar 
8.Barry, R. G. The cryosphere—Past, present, and future: A review of the frozen water resources of the world. Polar Geogr. 34(4), 219–227 (2011).Article 

Google Scholar 
9.El-Khoury, A. et al. Combined impacts of future climate and land use changes on discharge, nitrogen and phosphorus loads for a Canadian river basin. J. Environ. Manag. 151, 76–86 (2015).CAS 
Article 

Google Scholar 
10.Van der Pol, T. D., Van Ierland, E. C., Gabbert, S., Weikard, H. P. & Hendrix, E. M. T. Impacts of rainfall variability and expected rainfall changes on cost-effective adaptation of water systems to climate change. J. Environ. Manag. 154, 40–47 (2015).Article 

Google Scholar 
11.Wu, Y., Liu, S., Sohl, T. L. & Young, C. J. Projecting the land cover change and its environmental impacts in the Cedar River Basin in the Midwestern United States. Environ. Res. Lett. 8(2), 024025 (2013).ADS 
Article 

Google Scholar 
12.Zhou, F. et al. Hydrological response to urbanization at different spatio-temporal scales simulated by coupling of CLUE-S and the SWAT model in the Yangtze River Delta region. J. Hydrol. 485, 113–125 (2013).ADS 
Article 

Google Scholar 
13.Malhi, Y. et al. Climate change, deforestation, and the fate of the Amazon. Science 319(5860), 169–172 (2008).ADS 
CAS 
PubMed 
Article 

Google Scholar 
14.Munia, H. A., Guillaume, J. H., Mirumachi, N., Wada, Y. & Kummu, M. How downstream sub-basins depend on upstream inflows to avoid scarcity: Typology and global analysis of transboundary rivers. Hydrol. Earth Syst. Sci. 22(5), 2795–2809 (2018).ADS 
Article 

Google Scholar 
15.Bonan, G. B. Forests and climate change: Forcings, feedbacks, and the climate benefits of forests. Science 320(5882), 1444–1449 (2008).ADS 
CAS 
PubMed 
Article 

Google Scholar 
16.National Research Council. Informing an Effective Response to Climate Change (National Academies Press, 2011).
Google Scholar 
17.Uchida, Y. & Hayashi, T. Effects of hydrogeological and climate change on the subsurface thermal regime in the Sendai Plain. Phys. Earth Planet. Inter. 152(4), 292–304 (2005).ADS 
CAS 
Article 

Google Scholar 
18.Hua, F. et al. Opportunities for biodiversity gains under the world’s largest reforestation program. Nat Commun. 7(1), 1–11 (2016).ADS 

Google Scholar 
19.Penna, D., Borga, M., Aronica, G. T., Brigandì, G. & Tarolli, P. T. infuence of grid resolution on the prediction of natural and road related shallow landslides. Hydrol. Earth Syst. Sci. 18, 2127–2139 (2014).ADS 
Article 

Google Scholar 
20.Fu, Q., Li, B., Hou, Y., Bi, X. & Zhang, X. Effects of land use and climate change on ecosystem services in Central Asia’s arid regions: A case study in Altay Prefecture, China. Sci. Total Environ. 607, 633–646 (2017).ADS 
PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
21.Giuliani, M., Li, Y., Castelletti, A. & Gandolfi, C. A coupled human-natural systems analysis of irrigated agriculture under changing climate. Water Resour. Res. 52(9), 6928–6947 (2016).ADS 
Article 

Google Scholar 
22.Wilson, R. R. et al. Relative influences of climate change and human activity on the onshore distribution of polar bears. Biol. Conserv. 214, 288–294 (2017).Article 

Google Scholar 
23.Mukul, S. A. et al. Combined effects of climate change and sea-level rise project dramatic habitat loss of the globally endangered Bengal tiger in the Bangladesh Sundarbans. Sci. Total Environ. 663, 830–840 (2019).ADS 
CAS 
PubMed 
Article 

Google Scholar 
24.Flynn, D. F. et al. Loss of functional diversity under land use intensification across multiple taxa. Ecol. Lett. 12(1), 22–33 (2009).PubMed 
Article 

Google Scholar 
25.Bregman, T. P. et al. Using avian functional traits to assess the impact of land-cover change on ecosystem processes linked to resilience in tropical forests. Proc. R. Soc. B Biol. Sci. 283, 20161289 (2016).Article 

Google Scholar 
26.Peres, C. A. & Palacios, E. Basin-wide effects of game harvest on vertebrate population densities in Amazonian forests: Implications for animal-mediated seed dispersal. Biotropica 39, 304–315 (2007).Article 

Google Scholar 
27.Bender, I. M. et al. Projected impacts of climate change on functional diversity of frugivorous birds along a tropical elevational gradient. Sci. Rep. 9(1), 1–12 (2019).CAS 
Article 

Google Scholar 
28.Al-Faraj, F. A. & Scholz, M. Impact of upstream anthropogenic river regulation on downstream water availability in transboundary river watersheds. Int. J. Water Resour. Dev. 515(31), 28–49 (2015).Article 

Google Scholar 
29.Drieschova, A., Giordano, M. & Fischhendler, I. Governance mechanisms to address flow variability in water treaties. Glob. Environ. Change 18, 285–295 (2008).Article 

Google Scholar 
30.Beck, L., Bernauer, T., Siegfried, T. & Böhmelt, T. Implications of hydro-political dependency for international water cooperation and conflict: Insights from new data. Polit. Geogr. 42, 23–33 (2014).Article 

Google Scholar 
31.Maleki, S. et al. Human and climate effects on the Hamoun wetlands. Weather Clim. Soc. 11, 609–622 (2019).ADS 
Article 

Google Scholar 
32.Goes, B. J. M., Howarth, S. E., Wardlaw, R. B., Hancock, I. R. & Parajuli, U. N. Integrated water resources management in an insecure river basin: A case study of Helmand River Basin, Afghanistan. Int. J. Water Resour. Dev. 32(1), 3–25 (2016).Article 

Google Scholar 
33.Lua, D. & Weng, Q. Extraction of urban impervious surfaces from an IKONOS image. Int. J. Remote Sens. 30, 1297–1311 (2009).Article 

Google Scholar 
34.Dong, Z. Y. et al. Spatial decision analysis on wetlands restoration in the lower reaches of Songhua River (LRSR), Northeast China, based on remote sensing and GIS. Int. J. Environ. Res. 8(3), 849–860 (2014).
Google Scholar 
35.Ramsar. The List of Wetlands of International Importance 54 (Ramsar, 2016).
Google Scholar 
36.Lumbroso, D. The development of a flood forecasting system for the Sistan plain in Iran. In International Conference on Innovation Advances and Implementation of Flood Forecasting Technology, 17 to 19 October 2005 (2005).37.Scheraga, J. D., Ebi, K. L., Furlow, J. & Moreno, A. R. From science to policy: Developing responses to climate change. In Climate Change and Human Health: Risks and Responses Vol. 237 (eds McMichael, A. J. et al. et al.) 266 (World Health Organization/World Meteorological Organization/United Nations Environment Programme, 2003).
Google Scholar 
38.FAO. Analysis on Water Availability and Uses in Afghanistan River Basins. AFG/3402 FPT (2015).39.Langerwisch, F., Václavík, T., von Bloh, W., Vetter, T. & Thonicke, K. Combined effects of climate and land-use change on the provision of ecosystem services in rice agro-ecosystems. Int. J. Environ. Res. 13(1), 015003 (2017).
Google Scholar 
40.Scullion, J. J., Vogt, K. A., Sienkiewicz, A., Gmur, S. J. & Trujillo, C. Assessing the influence of land-cover change and conflicting land-use authorizations on ecosystem conversion on the forest frontier of Madre de Dios, Peru. Biol. Conserv. 171, 247–258 (2014).Article 

Google Scholar 
41.National Research Council. Informing an Effective Response to Climate Change 346 (National Academies Press, 2010).
Google Scholar 
42.United Nations Development Programme (UNDP). Restoration and Sustainable Use of the Shared Sistan Basin (2005).43.Maleki, S. et al. Habitat mapping as a tool for water birds’ conservation planning in an arid zone wetland: The case study Hamun wetland. Ecol. Eng. 95, 594–603 (2016).Article 

Google Scholar 
44.Alavi Panah, S. K. The Applications of Remote Sensing in the Earth Sciences (University of Tehran, 2003).
Google Scholar 
45.Shamohammadi, Z. & Maleki, S. The Life of Hamun 250 (Jahad Daneshgahi, 2011).
Google Scholar 
46.Bigdeli, B., Samadzadegan, F. & Reinartz, P. A multiple SVM system for classification of hyperspectral remote sensing data. J. Indian Soc. Remote 41, 763–776 (2013).Article 

Google Scholar 
47.Bousbih, S. et al. Potential of sentinel-1 radar data for the assessment of soil and cereal cover parameters. Sensors 17, 2617 (2017).ADS 
PubMed Central 
Article 
PubMed 

Google Scholar 
48.Gregory, R. D., Gibbons, D. W. & Donald, P. F. Bird census and survey techniques. In Bird Ecology and Conservation; A Handbook of Techniques (Sutherland, W. J. & Newton I. Et Green, R. E., eds.) 17–56 (Oxford University Press, 2004).49.Phillips, S. J., Anderson, R. P. & Schapire, R. E. Maximum entropy modeling of species geographic distributions. Ecol. Mod. 190(3–4), 231–259 (2006).Article 

Google Scholar 
50.Young, N., Carter, L. & Evabgelista, P. A Maxent Model v3.3.3e Tutorial (arcgis v10) (Natural Resource Ecology Laboratory at Colorado State University and the National Institute of Invasive Species Science, 2011).
Google Scholar  More

1.Tittensor, D. P. et al. A mid-term analysis of progress toward international biodiversity targets. Science 346, 241–244 (2014).ADS 
CAS 
PubMed 
Article 

Google Scholar 
2.McRae, L., Deinet, S. & Freeman, R. The diversity-weighted living planet index: Controlling for taxonomic bias in a global biodiversity indicator. PloS One 12, e0169156 (2017).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
3.Hortal, J. et al. Seven shortfalls that beset large-scale knowledge of biodiversity. Annu. Rev. Ecol. Evolut. Systemat. 46, 523–549 (2015).Article 

Google Scholar 
4.Meyer, C., Weigelt, P. & Kreft, H. Multidimensional biases, gaps and uncertainties in global plant occurrence information. Ecol. Lett. 19, 992–1006 (2016).PubMed 
Article 

Google Scholar 
5.Dobson, A. Monitoring global rates of biodiversity change: Challenges that arise in meeting the convention on biological diversity (CBD) 2010 goals. Philos. Trans. R. Soc. B Biol. Sci. 360, 229–241 (2005).Article 

Google Scholar 
6.Walpole, M. et al. Tracking progress toward the 2010 biodiversity target and beyond. Science 325, 1503–1504 (2009).PubMed 
Article 

Google Scholar 
7.Butchart, S. H. et al. Global biodiversity: Indicators of recent declines. Science 1164–1168, (2010).8.Jones, J. P. et al. The why, what, and how of global biodiversity indicators beyond the 2010 target. Conserv. Biol. 25, 450–457 (2011).PubMed 
Article 

Google Scholar 
9.Lawton, J. H. et al. Biodiversity inventories, indicator taxa and effects of habitat modification in tropical forest. Nature 391, 72–76 (1998).ADS 
CAS 
Article 

Google Scholar 
10.McKinney, M. L. Extinction vulnerability and selectivity: Combining ecological and paleontological views. Annu. Rev. Ecol. Systemat. 28, 495–516 (1997).Article 

Google Scholar 
11.Cardillo, M. et al. The predictability of extinction: Biological and external correlates of decline in mammals. Proc. R. Soc. Lond. B Biol. Sci. 275, 1441–1448 (2008).
Google Scholar 
12.Newbold, T. et al. A global model of the response of tropical and sub-tropical forest biodiversity to anthropogenic pressures. Proc. R. Soc. Lond. B Biol. Sci. 281, 20141371 (2014).
Google Scholar 
13.on Biodiversity, I. S.-P. P. & Ecosystem Services, I. The methodological assessment report on scenarios and models of biodiversity and ecosystem services. https://doi.org/10.5281/zenodo.3235429 (2016).14.Jetz, W. et al. Essential biodiversity variables for mapping and monitoring species populations. Nat. Ecol. Evolut. 3, 539–551 (2019).Article 

Google Scholar 
15.LPI. Living planet index database. http://www.livingplanetindex.org (2016).16.Dornelas, M. et al. Biotime: A database of biodiversity time series for the anthropocene. Glob. Ecol. Biogeogr. 27, 760–786 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
17.Blowes, S. A. et al. The geography of biodiversity change in marine and terrestrial assemblages. Science. 366, 339–345. https://doi.org/10.1126/science.aaw1620 (2019).ADS 
CAS 
Article 
PubMed 

Google Scholar 
18.Gonzalez, A. et al. Estimating local biodiversity change: A critique of papers claiming no net loss of local diversity. Ecology 97, 1949–1960 (2016).PubMed 
Article 

Google Scholar 
19.Purvis, A. et al. Modelling and projecting the response of local terrestrial biodiversity worldwide to land use and related pressures: The predicts project. Adv. Ecol. Res. 58, 201–241 (2018).Article 

Google Scholar 
20.Leung, B., Greenberg, D. A. & Green, D. M. Trends in mean growth and stability in temperate vertebrate populations. Diversity Distribut. 23, 1372–1380 (2017).Article 

Google Scholar 
21.Scholes, R. & Biggs, R. A biodiversity intactness index. Nature 434, 45–49 (2005).ADS 
CAS 
PubMed 
Article 

Google Scholar 
22.Barnosky, A. D. et al. Has the Earth’s sixth mass extinction already arrived?. Nature. 471, 51–57 (2011).ADS 
CAS 
PubMed 
Article 

Google Scholar 
23.Ceballos, G. & Ehrlich, P. R. Mammal population losses and the extinction crisis. Science 296, 904–907 (2002).ADS 
CAS 
PubMed 
Article 

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

Google Scholar 
25.Hooper, D. U. et al. A global synthesis reveals biodiversity loss as a major driver of ecosystem change. Nature 486, 105–108 (2012).ADS 
CAS 
PubMed 
Article 

Google Scholar 
26.Cardinale, B. J. et al. Biodiversity loss and its impact on humanity. Nature 486, 59–67 (2012).ADS 
CAS 
PubMed 
Article 

Google Scholar 
27.Mace, G. M. et al. Approaches to defining a planetary boundary for biodiversity. Glob. Environ. Change 28, 289–297 (2014).Article 

Google Scholar 
28.Steffen, W. et al. Planetary boundaries: Guiding human development on a changing planet. Science 347, 1259855 (2015).PubMed 
Article 
CAS 

Google Scholar 
29.Mace, G. M. et al. Aiming higher to bend the curve of biodiversity loss. Nat. Sustain. 1, 448–451 (2018).Article 

Google Scholar 
30.Mandl, N., Lehnert, M., Kessler, M. & Gradstein, S. R. A comparison of alpha and beta diversity patterns of ferns, bryophytes and macrolichens in tropical montane forests of southern ecuador. Biodiversity Conservat. 19, 2359–2369 (2010).Article 

Google Scholar 
31.Socolar, J. B., Gilroy, J. J., Kunin, W. E. & Edwards, D. P. How should beta-diversity inform biodiversity conservation? Trends Ecol. Evolut. 31, 67–80 (2016).Article 

Google Scholar 
32.Hillebrand, H. et al. Biodiversity change is uncoupled from species richness trends: Consequences for conservation and monitoring. J. Appl. Ecol. 55, 169–184 (2018).Article 

Google Scholar 
33.Cardinale, B. J., Gonzalez, A., Allington, G. R. & Loreau, M. Is local biodiversity declining or not? A summary of the debate over analysis of species richness time trends. Biol. Conservat. 219, 175–183 (2018).Article 

Google Scholar 
34.Díaz, S. et al. Pervasive human-driven decline of life on earth points to the need for transformative change. Science. https://doi.org/10.1126/science.aax3100 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
35.Newbold, T. et al. Has land use pushed terrestrial biodiversity beyond the planetary boundary? a global assessment. Science 353, 288–291 (2016).ADS 
CAS 
PubMed 
Article 

Google Scholar 
36.Hudson, L. N. et al. The database of the predicts (projecting responses of ecological diversity in changing terrestrial systems) project. Ecol. Evolut. 7, 145–188 (2017).Article 

Google Scholar 
37.Lewis, S. L., Edwards, D. P. & Galbraith, D. Increasing human dominance of tropical forests. Science 349, 827–832 (2015).ADS 
CAS 
PubMed 
Article 

Google Scholar 
38.Jenkins, C. N., Pimm, S. L. & Joppa, L. N. Global patterns of terrestrial vertebrate diversity and conservation. Proc. Natl. Acad. Sci. 110, E2602–E2610. https://doi.org/10.1073/pnas.1302251110 (2013).ADS 
Article 
PubMed 
PubMed Central 

Google Scholar 
39.Bowler, D. E. et al. Mapping human pressures on biodiversity across the planet uncovers anthropogenic threat complexes. People Nat. 2, 380–394. https://doi.org/10.1002/pan3.10071 (2020).Article 

Google Scholar 
40.Sala, O. E. et al. Global biodiversity scenarios for the year 2100. Science 287, 1770–1774 (2000).CAS 
PubMed 
Article 

Google Scholar 
41.Geist, H. J. & Lambin, E. F. Proximate causes and underlying driving forces of tropical deforestation: Tropical forests are disappearing as the result of many pressures, both local and regional, acting in various combinations in different geographical locations. BioScience 52, 143–150 (2002).Article 

Google Scholar 
42.Gibson, L. et al. Primary forests are irreplaceable for sustaining tropical biodiversity. Nature 478, 378–381 (2011).ADS 
CAS 
PubMed 
Article 

Google Scholar 
43.Barlow, J. et al. Anthropogenic disturbance in tropical forests can double biodiversity loss from deforestation. Nature 535, 144–147 (2016).ADS 
CAS 
PubMed 
Article 

Google Scholar 
44.Hansen, M. C. et al. High-resolution global maps of 21st-century forest cover change. Science 342, 850–853 (2013).ADS 
CAS 
Article 

Google Scholar 
45.Pettorelli, N. et al. Framing the concept of satellite remote sensing essential biodiversity variables: Challenges and future directions. Remote Sens. Ecol. Conservat. 2, 122–131 (2016).Article 

Google Scholar 
46.Benítez-López, A., Santini, L. L., Schipper, A. M., Busana, M. & Huijbregts, M. A. J. Intact but empty forests? Patterns of hunting-induced mammal defaunation in the tropics. PLOS Biol. 17, 1–18. https://doi.org/10.1371/journal.pbio.3000247 (2019).CAS 
Article 

Google Scholar 
47.Morales-Hidalgo, D., Oswalt, S. N. & Somanathan, E. Status and trends in global primary forest, protected areas, and areas designated for conservation of biodiversity from the global forest resources assessment 2015. Forest Ecol. Manag. 352, 68–77 (2015).Article 

Google Scholar 
48.Sloan, S. & Sayer, J. A. Forest resources assessment of 2015 shows positive global trends but forest loss and degradation persist in poor tropical countries. Forest Ecol. Manag. 352, 134–145 (2015).Article 

Google Scholar 
49.Smith, R. J., Muir, R. D., Walpole, M. J., Balmford, A. & Leader-Williams, N. Governance and the loss of biodiversity. Nature 426, 67 (2003).ADS 
CAS 
PubMed 
Article 

Google Scholar 
50.Tilman, D. et al. Future threats to biodiversity and pathways to their prevention. Nature 546, 73 (2017).ADS 
CAS 
PubMed 
Article 

Google Scholar 
51.Xu, R. Measuring explained variation in linear mixed effects models. Stat. Med. 22, 3527–3541. https://doi.org/10.1002/sim.1572 (2003).Article 
PubMed 

Google Scholar 
52.Lüdecke, D., Ben-Shachar, M. S., Patil, I., Waggoner, P. & Makowski, D. performance: An R package for assessment, comparison and testing of statistical models. J. Open Source Softw. 6, 3139, 10.21105/joss.03139 (2021).ADS 

Google Scholar 
53.Rockström, J. et al. A safe operating space for humanity. Nature 461, 472–475 (2009).ADS 
PubMed 
Article 
CAS 

Google Scholar 
54.Brook, B. W., Ellis, E. C., Perring, M. P., Mackay, A. W. & Blomqvist, L. Does the terrestrial biosphere have planetary tipping points?. Trends Ecol. Evolut. 28, 396–401 (2013).Article 

Google Scholar 
55.Isbell, F. et al. Linking the influence and dependence of people on biodiversity across scales. Nature 546, 65–72 (2017).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
56.Montoya, J. M., Donohue, I. & Pimm, S. L. Planetary boundaries for biodiversity: Implausible science, pernicious policies. Trends Ecol. Evolut. 33, 71–73. https://doi.org/10.1016/j.tree.2017.10.004 (2018).Article 

Google Scholar 
57.Homer-Dixon, T. Environment, Scarcity, and Violence (Princeton University Press, 2010).Book 

Google Scholar 
58.Murphy, G. E. & Romanuk, T. N. A meta-analysis of declines in local species richness from human disturbances. Ecol. Evolut. 4, 91–103 (2014).Article 

Google Scholar 
59.Betts, M. G. et al. Extinction filters mediate the global effects of habitat fragmentation on animals. Science 366, 1236–1239. https://doi.org/10.1126/science.aax9387 (2019) https://science.sciencemag.org/content/366/6470/1236.full.pdf.ADS 
CAS 
Article 
PubMed 

Google Scholar 
60.Laurance, W. F. et al. Averting biodiversity collapse in tropical forest protected areas. Nature 489, 290–294 (2012).ADS 
CAS 
Article 

Google Scholar 
61.Leung, B. et al. Clustered versus catastrophic global vertebrate declines. Nature 588, 267–271 (2020).ADS 
CAS 
PubMed 
Article 

Google Scholar 
62.Haddad, N. M. et al. Habitat fragmentation and its lasting impact on Earth’s ecosystems. Sci. Adv. 1, e1500052 (2015).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
63.Sloan, S., Jenkins, C. N., Joppa, L. N., Gaveau, D. L. & Laurance, W. F. Remaining natural vegetation in the global biodiversity hotspots. Biol. Conservat. 177, 12–24 (2014).Article 

Google Scholar 
64.Venter, O. et al. Sixteen years of change in the global terrestrial human footprint and implications for biodiversity conservation. Nat. Commun. 7, 12558 (2016).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
65.Potapov, P. et al. The last frontiers of wilderness: Tracking loss of intact forest landscapes from 2000 to 2013. Sci. Adv. 3, e1600821 (2017).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
66.Phillips, H. R., Newbold, T. & Purvis, A. Land-use effects on local biodiversity in tropical forests vary between continents. Biodiversity Conservat. 26, 2251–2270 (2017).Article 

Google Scholar 
67.Newbold, T. et al. Global patterns of terrestrial assemblage turnover within and among land uses. Ecography 39, 1151–1163 (2016).Article 

Google Scholar 
68.Rouget, M., Cowling, R., Vlok, J., Thompson, M. & Balmford, A. Getting the biodiversity intactness index right: The importance of habitat degradation data. Glob. Change Biol. 12, 2032–2036 (2006).ADS 
Article 

Google Scholar 
69.Koh, L. P. & Wilcove, D. S. Is oil palm agriculture really destroying tropical biodiversity?. Conserv. Lett. 1, 60–64 (2008).Article 

Google Scholar 
70.WWF. In Living planet report 2020 (eds. Almond, R. E. A., Grooten, M., & Petersen, T) (WWF, Gland, Switzerland) (2004).71.IPBES. Summary for policymakers of the methodological assessment of scenarios and models of biodiversity and ecosystem services of the intergovernmental science-policy platform on biodiversity and ecosystem services (ed. Ferrier, S), Secretariat of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services, Bonn, Germany, 348 (2016).72.Brauman, K. A. et al. Chapter 2.3. Status and Trends—Nature’s Contributions to People (NCP). https://doi.org/10.5281/zenodo.3832036 (2020).73.Sanderson, E. W. et al. The human footprint and the last of the wild: the human footprint is a global map of human influence on the land surface, which suggests that human beings are stewards of nature, whether we like it or not. BioScience 52, 891–904 (2002).Article 

Google Scholar 
74.Martin, P., Green, R. E. & Balmford, A. Is biodiversity as intact as we think it is?. PeerJ Preprints 7, e27575v1 (2019).
Google Scholar 
75.Newbold, T., Sanchez-Ortiz, K., De Palma, A., Hill, S. L. & Purvis, A. Reply to ‘the biodiversity intactness index may underestimate losses’. Nat. Ecol. Evolut. 3, 864–865 (2019).Article 

Google Scholar 
76.Faith, D. P., Ferrier, S. & Williams, K. J. Getting biodiversity intactness indices right: Ensuring that ‘biodiversity’ reflects ‘diversity’. Glob. Change Biol. 14, 207–217 (2008).ADS 
Article 

Google Scholar 
77.Jetz, W., Wilcove, D. S. & Dobson, A. P. Projected impacts of climate and land-use change on the global diversity of birds. PLoS Biol. 5, e157 (2007).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
78.Newbold, T. Future effects of climate and land-use change on terrestrial vertebrate community diversity under different scenarios. Proc. R. Soc. B 285, 20180792 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
79.Senior, R. A., Hill, J. K., González del Pliego, P., Goode, L. K. & Edwards, D. P. A pantropical analysis of the impacts of forest degradation and conversion on local temperature. Ecol. Evolut. 7, 7897–7908 (2017).Article 

Google Scholar 
80.De Chazal, J. & Rounsevell, M. D. Land-use and climate change within assessments of biodiversity change: a review. Glob. Environ. Change 19, 306–315 (2009).Article 

Google Scholar 
81.Schipper, A. M. et al. Projecting terrestrial biodiversity intactness with globio 4. Glob. Change Biol. 26, 760–771. https://doi.org/10.1111/gcb.14848 (2020).ADS 
Article 

Google Scholar 
82.Wearn, O. R., Reuman, D. C. & Ewers, R. M. Extinction debt and windows of conservation opportunity in the Brazilian amazon. Science 337, 228–232 (2012).ADS 
CAS 
PubMed 
Article 

Google Scholar 
83.De Palma, A. et al. Challenges with inferring how land-use affects terrestrial biodiversity: Study design, time, space and synthesis. In Advances in Ecological Research, vol. 58, 163–199 (Elsevier, 2018).84.De Palma, A. et al. Predicting bee community responses to land-use changes: Effects of geographic and taxonomic biases. Sci. Rep. 6, 31153 (2016).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
85.Bicknell, J. E., Gaveau, D. L., Davies, Z. G. & Struebig, M. J. Saving logged tropical forests: Closing roads will bring immediate benefits. Front. Ecol. Environ. 13, 73–74 (2015).Article 

Google Scholar 
86.Laurance, W. F., Goosem, M. & Laurance, S. G. Impacts of roads and linear clearings on tropical forests. Trends Ecol. Evolut. 24, 659–669 (2009).Article 

Google Scholar 
87.Lloyd, C. T., Sorichetta, A. & Tatem, A. J. High resolution global gridded data for use in population studies. Sci. Data 4, 170001 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
88.Watermeyer, K. E. et al. Using decision science to evaluate global biodiversity indices. Conserv. Biol. 35, 492–501 (2021).89.Olsen, E. et al. Ecosystem model skill assessment. Yes we can!. PLoS One 11, e0146467 (2016).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
90.Hoskins, A. J. et al. Downscaling land-use data to provide global 30 ’ ’estimates of five land-use classes. Ecol. Evolut. 6, 3040–3055 (2016).Article 

Google Scholar 
91.Fulton, E. A., Blanchard, J. L., Melbourne-Thomas, J., Plagányi, É. E. & Tulloch, V. J. Where the ecological gaps remain, a modelers’ perspective. Front. Ecol. Evolut. 7, 424 (2019).Article 

Google Scholar 
92.Watermeyer, K. E. et al. Using decision science to evaluate global biodiversity indices. Conservat. Biol. 35, 492–501 (2021).Article 

Google Scholar 
93.Bradshaw, C. J., Sodhi, N. S. & Brook, B. W. Tropical turmoil: A biodiversity tragedy in progress. Front. Ecol. Environ. 7, 79–87 (2009).Article 

Google Scholar 
94.De Palma, A., Sanchez-Ortiz, K. & Purvis, A. Calculating the Biodiversity Intactness Index: the PREDICTS implementation (2019). This is the first release of a repository from https://github.com/adrianadepalma/BII_tutorial You can also view the document here. https://adrianadepalma.github.io/BII_tutorial/bii_example.html. https://doi.org/10.5281/zenodo.3518067.95.Hudson, L. N. et al. The predicts database: A global database of how local terrestrial biodiversity responds to human impacts. Ecol. Evolut. 4, 4701–4735 (2014).Article 

Google Scholar 
96.Olson, D. M. et al. Terrestrial ecoregions of the world: A new map of life on earth: A new global map of terrestrial ecoregions provides an innovative tool for conserving biodiversity. BioScience 51, 933–938 (2001).Article 

Google Scholar 
97.Newbold, T. et al. Global effects of land use on local terrestrial biodiversity. Nature 520, 45–50 (2015).ADS 
CAS 
PubMed 
Article 

Google Scholar 
98.for International Earth Science Information Network (CIESIN) Columbia University, C. Gridded Population of the World, Version 4 (GPWv4): Population Density Adjusted to Match 2015 Revision of UN WPP Country Totals (NASA Socioeconomic Data and Applications Center (SEDAC), Palisades, NY, 2016). (Accessed 10 November 2017).99.for International Earth Science Information Network (CIESIN) Columbia University, C. & of Georgia, I. T. O. S. I. U. Global Roads Open Access Data Set, Version 1 (gROADSv1) (NASA Socioeconomic Data and Applications Center (SEDAC), Palisades, NY, 2013). (Accessed 19 January 2017).100.Hijmans, R. J., Cameron, S. E., Parra, J. L., Jones, P. G. & Jarvis, A. Very high resolution interpolated climate surfaces for global land areas. Int. J. Climatol. 25, 1965–1978 (2005).Article 

Google Scholar 
101.Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).Article 

Google Scholar 
102.Crawley, M. J. The R Book (Wiley, Chichester, England, 2007).MATH 
Book 

Google Scholar 
103.Chao, A., Chazdon, R. L., Colwell, R. K. & Shen, T.-J. A new statistical approach for assessing similarity of species composition with incidence and abundance data. Ecol. Lett. 8, 148–159 (2005).Article 

Google Scholar 
104.Fox, J. & Weisberg, S. An R companion to applied regression (Sage Publications, 2011).
Google Scholar 
105.Gower, J. C. A general coefficient of similarity and some of its properties. Biometrics. 857–871, (1971).106.van der Loo, M. gower: Gower’s distance (R Foundation for Statistical Computing, 2017). R package version 0.1.2. https://www.R-project.org107.Lichstein, J. W. Multiple regression on distance matrices: A multivariate spatial analysis tool. Plant Ecol. 188, 117–131 (2007).Article 

Google Scholar 
108.Team, R.C. R: A language and environment for statistical computing (R Foundation for Statistical Computing, 2017).
Google Scholar 
109.Zuur, A. F., Ieno, E. N., Walker, N., Saveliev, A. A. & Smith, G. M. Mixed effects models and extensions in ecology with R (Springer New York, 2009).MATH 
Book 

Google Scholar 
110.Hurtt, G. C. et al. Harmonization of land-use scenarios for the period 1500–2100: 600 years of global gridded annual land-use transitions, wood harvest, and resulting secondary lands. Clim. Change 109, 117–161 (2011).ADS 
Article 

Google Scholar 
111.Friedl, M. A. et al. Modis collection 5 global land cover: Algorithm refinements and characterization of new datasets. Remote Sens. Environ. 114, 168–182 (2010).ADS 
Article 

Google Scholar 
112.Goldewijk, K. K. Three centuries of global population growth: A spatial referenced population (density) database for 1700–2000. Populat. Environ. 26, 343–367 (2005).Article 

Google Scholar 
113.Meijer, J. R., Huijbregts, M. A., Schotten, K. C. & Schipper, A. M. Global patterns of current and future road infrastructure. Environ. Res. Lett. 13, 064006 (2018).ADS 
Article 

Google Scholar 
114.van Asselen, S. & Verburg, P. H. Land cover change or land-use intensification: Simulating land system change with a global-scale land change model. Glob. Change Biol. 19, 3648–3667. https://doi.org/10.1111/gcb.12331 (2013).ADS 
Article 

Google Scholar 
115.Brooks, T. M. et al. Analysing biodiversity and conservation knowledge products to support regional environmental assessments. Sci. Data 3, 160007 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
116.World bank national accounts data, and oecd national accounts data files (2017). https://data.worldbank.org/indicator/NY.GDP.PCAP.CD117.Pinheiro, J. et al. Package ‘nlme’. Linear and Nonlinear Mixed Effects Models, version 3–1 (2017). https://CRAN.R-project.org/package=nlme118.Bivand, R., Hauke, J. & Kossowski, T. Computing the jacobian in gaussian spatial autoregressive models: An illustrated comparison of available methods. Geogr. Anal. 45, 150–179 (2013).Article 

Google Scholar 
119.Bivand, R. & Piras, G. Comparing implementations of estimation methods for spatial econometrics. J. Stat. Softw. 63, 1–36 (2015).
Google Scholar 
120.Hartig, F. DHARMa: Residual Diagnostics for Hierarchical (Multi-Level/Mixed) Regression Models (2017). R package version 0.1.5. https://CRAN.R-project.org/package=DHARMa More

Our approach provides data on species richness across independent gradients of land-use intensity and climate. Furthermore, by combining Malaise traps and DNA-metabarcoding, our work is not limited to single factors such as biomass measurements or assessment of single taxa to reveal drivers of insect communities. We found the lowest species richness in arable fields embedded in agricultural landscapes, and the lowest biomass in settlements embedded in urban landscapes. The effects of land-use type were independent of those of local temperatures and climate. Biomass and richness measures differed according to land-use intensity. Our study recorded a difference in insect biomass of 42% from semi-natural to urban environments, but no difference from semi-natural to agricultural environments. This appears to be in contrast with the results documented in a similar analysis6, which showed a temporal decline in insect biomass of >75% in small, protected areas surrounded by an agricultural landscape. Interestingly, in Hallmann et al.6, the few plots in semi-natural landscapes also showed a similar temporal decline as those in agricultural landscapes (Supplementary Fig. 3b). On the other hand, the variation in total BIN richness matched the magnitude of the temporal decline (~35%) determined over a decade in grasslands and forests by Seibold et al.13The hump-shaped seasonal pattern of biomass and associated daily biomass values were in accordance with the time series of Hallmann et al.6, thus demonstrating the comparability of our space-for-time approach with approaches based on time series (Supplementary Fig. 3). However, the contrasting phenological patterns of biomass and total BIN richness after controlling for temperature are evidence that both facets of biodiversity might respond differently, with biomass more strongly driven by pure season, e.g. via plant phenology or day-length, and BIN richness more dependent on local temperature. Divergent responses of biomass variation and species richness have already been described in temporal studies of insects in freshwater systems27 and nocturnal moths in the United Kingdom19,28,29, but not in studies of terrestrial arthropods, including those recorded in comprehensive datasets of hyper-diverse orders such as Diptera and the Hymenoptera.The positive relationships between local temperature and biomass variation and BIN richness were consistent with earlier results6,20 and can be explained (1) by the higher activity of species at higher temperatures, which increases the likelihood of trapping30 and (2) by the fact that insects are ectothermic organisms, i.e., their metabolism is enhanced by increasing temperatures, which in turn can lead to higher reproduction and survival rates and thus to larger populations31. Our additional analyses on the negative effects occurring at the highest temperatures did not provide any such indications for our three measures. Moreover, insects, and in particular many endangered insect species in Central Europe, are thermophilic32, which would explain the observed response of total BIN richness, and especially the very steep response of the richness of red-listed species, to local temperature.Despite the positive or neutral effect of macroclimate and the consistently positive effect of local temperature on insect biomass and BIN richness, global warming can cause shifts in insect communities that threaten biodiversity in specific biomes or elevations7,8,33, by a mismatch between host plant and insect phenology34,35 or by the trait-specific responses of species to climate variations, as shown for butterflies in California33. Nevertheless, the responses of insect populations and insect diversity to climate change are poorly understood, such that clear patterns, with distinct winners and losers, can still not be discerned33. In addition, insect responses to climate change are geographically variable and likely to be disproportionally higher at higher latitudes and elevations or in hot tropical or Mediterranean areas33. However, it is precisely the large topographic variation of mountains that may offer climate pockets that act as refugia, thus allowing insects to survive during periods of extreme climatic conditions or climate variation33,36. Our study supports this possibility, by showing that the responses of total insect richness, the richness of red-listed species, and biomass to higher local temperatures in a cultivated landscape in Central Europe (mean annual temperature of ~5 to 10 °C and annual precipitation between 550 and 2000 mm) are consistently positive. A further rise in temperature, as expected in the near future, poses a high risk of pushing more insect species in our study area to their thermal limits and even to extinction37.The clear biomass patterns which we show indicate a continuous change of biomass from forests to arable fields and further to settlements, of total BIN richness from forests to arable fields, and of red-listed species richness from forests to meadows and arable fields. This underlines the importance of forests as a backbone of insect diversity in cultivated landscapes, and particularly of forest gaps, which are rich in species within forests13,38. Our study is the first to our knowledge to directly compare forests (and forest gaps) with agricultural and urban habitats. Comparable studies using standardized insect sampling across a broad range of land-use types are rare, but data on the biomass of moths obtained by light trapping in different habitats over many decades19 are consistent with our findings and indicate a general pattern that is independent of the sampling method. At the landscape scale, we found biomass was highest in agricultural landscapes and lowest in urban landscapes, whereas red-listed richness was highest in semi-natural landscapes, followed by urban landscapes and lowest in agricultural landscapes. Although we could not confirm the negative effects of agricultural landscapes on biomass, as described by Hallmann et al.6, our results are in line with those of Seibold et al.13, who reported negative effects of surrounding arable fields on the temporal trends in grasslands in terms of species richness but not insect biomass.The contrasting pure seasonal patterns of biomass variation and BIN richness, as well as their different responses to land use, may have methodological or biological causes. A possible methodological reason for the low partial effects of season on BIN richness during summer but high partial effects on total biomass is that high insect biomass occurs particularly during periods of high temperatures, which would have increased evaporation of the ethanol used for preservation, accelerating the degradation of DNA. Similar effects were shown for samples stored over long periods39 of time. However, in our study, the collection bottles contained sufficient amounts of ethanol such that a methodological effect due to ethanol evaporation was unlikely. Moreover, high temperatures and not the pure seasonal effect better explained the higher BIN richness in this study. A second methodological reason for the lower BIN richness is that small species are often “overlooked” in biomass-rich samples40,41,42. To avoid this problem, we divided each sample into two fractions (small and large species) and sequenced them separately. With the exclusion of these methodological reasons, the most likely explanation for our findings is a biological one related to the composition of the samples. An increase in large species in certain habitats or at a certain time of year could influence biomass but not necessarily the total number of species. However, our additional models of total biomass using the BIN richness of the most important taxonomic orders as predictors provided an important clue. Across all habitats, biomass variation was best explained by the increase in BIN richness of three species groups, Orthoptera, Lepidoptera, and Diptera. Of the diverse taxa Coleoptera, Hymenoptera, and Diptera, only the BIN richness of the latter positively affected total biomass, and it was principally the richness of the two groups with many large species (Orthoptera and Lepidoptera) driving the pure seasonal effect. This can be explained by the fact that Lepidoptera abundance peaks in July43, thus coinciding with the higher abundances of most species of hemimetabolous Orthoptera during the summer44, and therefore accounting for the purely seasonal peak of insect biomass in summer.The contrasting responses of biomass variation and BIN richness point to differences in the respective mechanisms. Insect biomass is positively related to productivity and is thus highest in agricultural landscapes and in forests habitats embedded in agricultural landscapes managed to maximize plant productivity and continuous plant biomass45,46. Insect biomass is lowest in urban environments, where productivity is limited due to a high percentage of sealed areas without vegetation. However, insect biomass along gradients of urbanization has been poorly investigated47 such that large differences in the negative effects of urbanization on the abundances of different taxonomic groups cannot be ruled out48. Moreover, urban areas include additional potential stressors, such as light pollution, that might also negatively affect insect biomass49. In contrast to biomass, the richness of all taxa and of threatened species was relatively high in urban habitats. This was especially the case for urban habitats embedded in semi-natural landscapes, although a similar species richness may occur through the interplay of semi-natural habitats with green spaces characterized by a highly variable design and management50 as well as with the natural but also anthropogenically enhanced plant diversity of urban areas47,51,52.The lowest BIN richness generally observed in our study, in arable fields embedded in agricultural landscapes, is consistent with the results of a recent meta-analysis of insect time series9. In that study, the temporal declines in insect populations of terrestrial invertebrates were largest in regions with generally high agricultural land-use intensity, such as Central Europe and the American Midwest. Our direct comparison of different land-use types independent of gradients of macro- and microclimate suggests that the strong declines in insect richness reported for several taxa5 are indeed driven by intensive agriculture and the associated homogenization of the landscape53, not by urban environments. To assess the significance of our two main results on biomass and species richness, however, it is necessary to consider the proportions of the land-use types in question. In our study, agricultural land comprised 48% of the area whereas settlements accounted for ~12%. Since habitat amount is a fundamental parameter for insect populations, it must also be taken into account in a country-wide strategy11.Our finding of a lack of significant interactions between the highly significant local temperature and land use contrasts in part with the previously reported strong effects resulting from the interaction between land use and climate along the elevational gradient of the Kilimanjaro. That finding implied that land-use effects are mediated by climate, especially at high elevations17. Interaction effects between land use and climate may thus occur mainly within more extreme climates54 rather than within the temperate climate exemplified by our study region. By considering macroclimate and the directly measured local temperature and humidity as well as land use, we were able to show that pure land-use effects, when evaluated as habitat effects controlled for local temperature and humidity, strongly influence insect populations. However, despite the increasing awareness among scientists and urban planners that land use at local and landscape scales impacts not only insects but also local climate, the implications have mostly been ignored in international climate negotiations55. Trees, with the reduced local temperatures offered by their canopy layer56 and their hosting of a high species richness of insects, as shown in our study, are thus of particular importance as refuges for insect diversity in temperate zones.By covering the full range of land-use intensities along the climate gradient of a typical cultivated region and measuring both insect biomass and total insect richness, our study’s methodology provided mechanistic insights into the changes of insect populations in areas where a meta-analysis identified the most severe population declines9. Nevertheless, additional studies should focus on biomes other than the cultivated landscapes of the temperate zone, such as cold boreal, dry Mediterranean, or hot tropical areas. Here, the different characteristics of the biome may result in land-use intensification being of less importance than climate change. In addition, the use of metabarcoding to identify all insects within a sample broadens the range for similar space-for-time studies. In contrast to well replicated, standardized time-series data that may require decades to generate the information needed to guide conservation actions, space-for-time approaches covering full gradients of land use and climate are a viable option to identify the drivers of insect decline and thus provide timely information for decision-makers; however, replications from several years should be included to take into account the effects of extreme events.The weak effect of climate variables on insect biomass but the consistently positive effect of local temperature on biomass variation and BIN richness suggests that, at least within the climate range of our temperate study region, the recent warming that has led to higher local temperatures should promote insect biomass and species richness. However, further warming, extreme heat, and drought events may negatively affect biodiversity, although non-linear responses can be expected in other climates or across longer gradients. Moreover, the strong dependency of local temperature on land use indicates that changes in land use impact local climate conditions, such as by accelerating temperature increases in agricultural and urban regions. The contrasting responses of biomass variation and BIN richness to local and landscape-scale land use point to differential effects of shifts in land use on insect populations, with ongoing urbanization leading to a decline in biomass, and conversion to agriculture to a decline in species richness. Based on our results, we recommend that actions aimed at preventing further insect decline should focus on (1) increasing insect biomass, for example by improving “green” habitats in urban environments57 and reducing the extent of vegetation-free sealed surfaces and (2) stopping the ongoing loss of species, by adapting agri-environmental schemes and promoting habitats dominated by trees, even in urban environments. More

Assume n countries ((nge 2)) are located in a transboundary river basin and they are the players of an evolutionary game in which the countries’ strategies concerning water sharing in the basin evolve over time. Each country can choose between a cooperative strategy or a non-cooperative strategy. The game’s interactions and players’ payoffs vary with the number n and the location of the countries within the river basin, specifically, in relation to whether they are upstream-located or downstream-located countries within the river basin. The probability of country i choosing a cooperative strategy is herein denoted by ({x}_{1}^{(i)}), (i=1, 2, dots , n), and there are ({2}^{n}) payoff sets for all the combinations of the countries’ strategies. This paper assesses the interactions between three countries sharing a transboundary river basin.Problem descriptionLet 1, 2, and 3 denote three countries sharing a transboundary river basin. Country 1 is upstream and countries 2 and 3 are located downstream. Country 1 can use maximum amount of the water of the river and choose not to share it with the downstream countries. This strategy, however, may trigger conflict with the two other countries of political, social, economic, security, and environmental natures. Instead, Country 1 can release excess water to be shared by Countries 2 and 3. Countries 2 and 3 are inclined to cooperate with Country 1 unless other benefits emerge by being non-cooperative with Country 1.There are two types of benefits and one type of cost in the payoff matrix of the assumed problem that are economic in nature. The first is a water benefit earned by a country from receiving the water from the transboundary river. The set of benefits related to water use includes economic benefits earned from agricultural, urban, and industrial development benefits. It should be noted that the water benefit for Country 1 means the economic benefit of consuming more water than its water right from the river. So, water benefits of Country 2 and 3 are the economic benefit of consuming excess water of upstream which is released by Country 1.The second is a potential benefit earned from the cooperative strategy of a country. Cooperation benefits stem from sustainability conditions like social interests, environmental benefits and political conjunctures such as international alliances and harmony from amicable interactions with neighboring countries. The parameters F and E (water benefit and potential benefit, respectively) encompass a number of benefit parameters; nevertheless, parameters were simplified to two benefit parameters to simplify the complexity of the water-sharing problem. Costs forced on other countries from non-cooperation by a country involves commercial, security, political, diplomatic, military, and environmental costs. Figure 1 displays the locations of three countries and their shifting interactions in a transboundary river basin.Figure 1Schematic of the transboundary river and riparian countries with their shifting interactions.Full size imageBasic assumptionsThe evolutionary game model of interactions between riparian countries in the transboundary river basin rests on the following assumptions:
Assumption 1

There are three countries (i.e., players) in the game of transboundary water sharing, each seeking to maximize its payoff from the game.

Assumption 2

Country 1 has two possible strategies. One is for Country 1 to release a specified amount of water to the downstream countries (this would be Country 1’s cooperative strategy). The cooperative strategy by Country 1 would produce benefits F2 and F3 to Countries 2 and 3, respectively. By being cooperative Country 1 would attain a benefit E1 called the potential benefit from cooperative responses from the downstream countries. The other strategy is for Country 1 to deny water to the downstream countries (this would be Country 1’s non-cooperative strategy), in which case Country 1 would earn the water benefit F1 from using water that would otherwise be released, but would forego the potential benefit E1. Moreover, by pursuing a non-cooperative strategy Country 1 would inflict a cost C1m to the downstream countries.

Assumption 3

There are two possible strategies for Country 2. One is for Country 2 to accept the behavior of Country 1 (this would be Country 2’s cooperative strategy), which would cause earning a potential benefit E2 to Country 2. Recall that if Country 2 acquiesces to Country 1’s cooperative behavior it would receive a benefit F2. Or, Country 2 may disagree with Country 1 (this would be Country 2’s non-cooperative strategy), in which case, Country 2 would lose benefit E2, and it would inflict a cost C2m to the other countries.

Assumption 4

Similar to Country 2, Country 3 has two possible strategies. One is for Country 3 to agree Country 1’s behavior (this would be Country 3’s cooperative strategy) attaining a potential benefit E3. Recall that if Country 3 agrees with Country 1’s cooperative behavior it would gain a benefit F3. Another strategy for Country 3 is to oppose Country 1 (this would be Country 3’s non-cooperative strategy) missing the benefit E3 and forcing a cost C3m to the other countries.
Table 1 defines the benefits and costs that enter in the transboundary water-sharing game described in this work. The payoff to country (i=mathrm{1,2},3) depends on its own strategy and on the strategies of the other countries, and each country may choose to be cooperative or non-cooperative. The strategies of country (i) are denoted by 1 (cooperation) and 2 (non-cooperation). The probabilities of country (i)’s strategies are denoted by ({x}_{1}^{(i)}) and by ({x}_{2}^{(i)}), in which the former represents cooperation and the latter represents non-cooperation. Clearly, ({x}_{1}^{(i)})+ ({x}_{2}^{(i)}) = 1. The payoff to country (i=1, 2, 3) when the strategies of Countries 1, 2, 3 are (j, k,l), respectively, where (j, k,l) may take the value 1 (cooperation) or 2 (non-cooperation) is denoted by ({U}_{jkl}^{left(iright)}). Thus, for instance, the payoff to country (i=2) is represented by ({U}_{212}^{(2)}) when Countries 1 and 3 are non-cooperative and Country 2’s strategy is cooperative. Evidently, there are 23 payoffs to each country given there are three countries involved and each can be cooperative or non-cooperative. Table 2 shows the symbols for the payoffs that accrue to each country under the probable strategies.Table 1 Benefits and costs.Full size tableTable 2 Payoff matrix under cooperation or non-cooperation.Full size tableFormulation of the transboundary water-sharing strategies as an evolutionary gameThe expected payoff to country (i) is expressed by the following equation:$${U}^{(i)}=sumlimits_{j = 1}^2 {sumlimits_{k = 1}^2 {sumlimits_{l = 1}^2} } {x}_{j}^{(1)}{x}_{k}^{(2)}{x}_{l}^{(3)} {U}_{jkl}^{(i)} quad i=1, 2, 3$$
(1)
The following describe the expected payoffs of Country 1 when it acts cooperatively (({U}_{1}^{(1)})) or non-cooperatively (({U}_{2}^{(1)})):$${U}_{1}^{(1)}={x}_{1}^{(2)}{x}_{1}^{(3)}{U}_{111}^{left(1right)}+{x}_{1}^{(2)}{x}_{2}^{(3)}{U}_{112}^{left(1right)}+{x}_{2}^{(2)}{x}_{1}^{(3)}{U}_{121}^{(1)}+{x}_{2}^{(2)}{x}_{2}^{(3)}{U}_{122}^{(1)}$$
(2)
$${U}_{2}^{(1)}={x}_{1}^{(2)}{x}_{1}^{(3)}{U}_{211}^{left(1right)}+{x}_{1}^{(2)}{x}_{2}^{(3)}{U}_{212}^{left(1right)}+{x}_{2}^{(2)}{x}_{1}^{(3)}{U}_{221}^{left(1right)}+{x}_{2}^{(2)}{x}_{2}^{(3)}{U}_{222}^{left(1right)}$$
(3)
Therefore, the expected payoff of Country 1 is ({U}^{(1)}) which is equal to:$${U}^{(1)}={x}_{1}^{(1)}{U}_{1}^{(1)}+{x}_{2}^{(1)}{U}_{2}^{(1)}= sumlimits_{j = 1}^2 {sumlimits_{k = 1}^2 {sumlimits_{l = 1}^2 } }{x}_{j}^{(1)}{x}_{k}^{(2)}{x}_{l}^{(3)} {U}_{jkl}^{(1)}$$
(4)
The expected payoffs of Countries 2 and 3 can be similarly obtained as done for Country 1. The cooperative and non-cooperative expected payoffs of all countries can be expressed in terms of the payoffs listed in Table 1. The results are found in Appendix A.Replication dynamics equationsThe replication dynamics equations describe the time change of the probabilities of a player’s strategies. The replication dynamics equation of Countries (i) is denoted by ({G}^{(i)}left({x}_{1}^{(i)}right)) which is as follow22:$${G}^{(i)}left({x}_{1}^{(i)}right)=frac{d{x}_{1}^{(i)}}{dt}={x}_{1}^{(i)}left({U}_{1}^{(i)}-{U}^{(i)}right)$$
(5)
The replication dynamics equations of Countries 1, 2 and 3 are presented in Appendix B according to the benefits and costs showed in Table 1.Stability analysis of a country’s strategiesUnder the assumption of bounded rationality each country does not know which strategies may lead to the optimal solution in the game. Therefore, the countries’ strategies change over time until a stable (i.e., time-independent) solution named evolutionary stable strategy (ESS) is attained. The evolutionary stable theorem for replication dynamics equation states that a stable probability of cooperation ({x}_{1}^{(i)}) for country (i) occurs if the following conditions hold25: (1) ({G}^{(i)}left({x}_{1}^{(i)}right)=0), and (2) (d{G}^{(i)}left({x}_{1}^{(i)}right)/d{x}_{1}^{(i)} More