Ecology

Data acquisitionOur analysis was based on open access crop production data from the United Nation’s Food and Agricultural Organization (FAO) spanning from 1961 to 201718. We extracted data on area harvested (in ha) for 339 FAO-defined crop groups being grown in all UN-recognized countries. Since our research centred on understanding, quantifying, and mapping changes in crop diversity in current agricultural lands, countries that cease to exist (e.g., Yugoslavia) were not included in our analysis, resulting in data for 201 countries (Table S1). Prior to analyses, we adjusted certain crop group listings following our previous analyses of global changes in crop diversity8. Specifically, “Cottonlint” and “Cottonseed” were duplicated in our dataset and were therefore compiled as “Seedcotton”, while “Palmkernels” were renamed as “Oilpalmfruit.” Additionally, “Fruitpomenes”, “Fruitstonenes”, and “Grainmixed” were removed from analysis since these crop groupings are not associated with any specific crop species in the FAO database18. Finally, “Mushroomsandtruffles” were removed since it relates to non-plant species, and “Coir” was removed because it is a plant by-product.Changes in crop richness over timeAll statistical analyses were performed using R version 3.3.3 statistical software (R Foundation for Statistical Computing, Vienna, Austria). The initial step in our analysis was to calculate both crop richness and evenness for each country, at each individual year, using the vegan R package38. Based on these datasets, we then used the analytical framework developed by8 to evaluate how crop species richness and evenness have changed in each individual country across its entire data range.Specifically, in their analysis Martin et al.8 found that piecewise linear regression models provided the strongest descriptions of crop species richness change over time, across 21 of 22 FAO-defined regions globally. We therefore followed this approach by fitting a piecewise linear regression model for each country individually, that predicts changes in species richness over time. Piecewise model fitting was a two-step process, whereby for each country we first fit a linear regression model of the form:$$S = a + left( {b times {text{year}}} right)$$
(1)
where a is the intercept and b represents the rate of change in crop group richness (S) through time. This linear model (Eq. 1) was then used as the basis of a piecewise linear regression model, which was fitted in order to estimate breakpoints in the relationship between S and year. Specifically, piecewise models were fit using the segmented function in the segmented R package39, and were of the form:$$S = a + bleft( {{text{year}}} right) + left( {left( {c({text{year}} -uppsi _{1} } right) times Ileft( {{text{year}} >uppsi _{1} } right)} right) + left( {dleft( {{text{year}} -uppsi _{2} } right) times Ileft( {{text{year}} >uppsi _{2} } right)} right)$$
(2)
where a is as in Eq. (1), and b represents the slope of the S-year relationship prior to the first breakpoint (ψ1). Here, c represents the difference in the slope of the S-year relationship between the first and second piecewise model segments; the c parameter therefore applies only when the first conditional indicator function (denoted by “I”) is true. Similarly, d represents the difference in slopes for the S-year relationship between the first, second, and third segments, which only applies when the second conditional indicator function is true. In sum, the slope of the relationship between S and year is equal to b prior to the ψ1, is equal to b + c between ψ1and ψ2, and is equal to b + c + d after ψ2. Piecewise models were fit with initial starting parameters of 1975 and 2000 for ψ1 and ψ2, respectively. The ψ1 and ψ2 parameters were tuned manually for 29 countries with a shortened data range, following visual inspection of data (see Tables S1 and S2).Based on this piecewise regression model procedure, we then used parameters from Eq. (2) to determine three key indicator points of crop diversity change through time for each country (displayed visually in Fig. 1). Indicator 1 reflects the onset of diversification in each country, and was calculated as Breakpoint 1 (ψ1) in Eq. (2); this indicator therefore corresponds to the year in which notable changes in species richness began. Indicator 2 reflects the duration of the crop diversification period in each country, and was calculated as the difference between breakpoints 2 and 1 (i.e., ψ2-ψ1 from Eq. 2); this indicator therefore represents the duration of the period when crop prominent changes in crop diversity occurred. Finally, Indicator 3 reflects the rate at which crop diversity changed throughout the diversification period in each country; this indicator was calculated as the rate of crop diversity change (between ψ1 and ψ2), which in our models corresponded to the sum of the slopes (1) prior to the first breakpoint, and (2) between the first and second breakpoints (i.e., corresponding to b + c in Eq. 2). For each indicator we then calculated summary statistics as either mean ± standard deviations or median ± median absolute deviations (m.a.d.), where data was normally or log-normally distributed, respectively. Country values for each indicator were mapped using the mapCountryData function in the rworldmap R package40.Changes in crop evenness over timeEvaluations of temporal changes in crop evenness at national scales followed this same analytical approach as above. First, for each country-by-year combination we calculated Pielou’s evenness index (J′)—which ranges from 0 to 1, with values closer to 0 indicating less evenness or greater abundance of a few dominant crop groups, and values closer to 1 representing more equitable abundances of crop groups—as:$$J^{prime} = frac{H^prime }{{ln left( S right)}}$$
(3)
where S is again crop richness, and H′ is the Shannon–Weiner diversity index calculated as:$$H^prime = – mathop sum limits_{i = 1}^{S} p_{i} ln p_{i}$$
(4)
where pi represents the relative proportion of the ith crop group for a given country-by-year combination. In these evenness calculations, all values of pi were estimated as the relative proportion of agricultural area (measured in ha) occupied by a given crop commodity group, within a country at a given year; this analytical approach was employed by Martin et al.8 when assessing crop group composition at supra-national scales. We then evaluated how J′ values changed in each country through time by replicating our stepwise modelling analyses above, substituting J′ for S in Eqs. (1) and (2), and extracting the same model indicators (Fig. 1). Finally, we calculated summary statistics and mapped each of these indicators, as described above.Changes in crop composition across countries and over timeWe used multivariate analyses to evaluate how temporal changes in S and J′ influenced crop composition across countries and over time. To do so, we created a community composition matrix whereby national-level crop assemblages were estimated for each of the country-by-year combinations. In this matrix, area harvested was taken as an approximation of the abundance of each crop group within each country-by-year combination (again following Martin et al.8). Since these abundances (or area harvested) across country-by-year combinations varied over orders of magnitude, we used non-metric multidimensional scaling (NMDS) to analyze and visualize spatial (country) and temporal (year) differences in crop diversity. Specifically, we used the vegan R package38 to calculate all 58,899,231 Bray–Curtis dissimilarities among all 10,854 data points (i.e., crop group composition in every country-by-year data point), as:$$BC_{jk} = frac{{sum i left| {x_{ij} – x_{ik} } right|}}{{sum i left( {x_{ij} + x_{ik} } right)}}$$
(5)
where BCjk represents the dissimilarity between the jth and kth community, xij represents the abundance (i.e., area harvested) of crop group i in sample j, and xik represents the abundance of crop group i in sample k. We then used a multivariate analysis of variance (i.e., an Adonis test), to test for significant differences in Bray–Curtis distances as a function of country, year, and a country-by-year interaction. Significance was assessed using a permutation test, with 99 permutations used.Latitudinal gradients in crop richnessTo test our hypotheses surrounding the presence of, and temporal changes in, latitudinal gradients in crop group diversity, we focused on 164 countries for which crop group diversity was available in both 1961 and 2017. For each of these two datasets, we fit a separate linear regression model that predicts crop group richness as a function of latitude (expressed as an absolute value) and a 2nd-order polynomial term for the ‘latitude2’ variable. From both of these models, we extracted and compared latitude value at which crop group richness was estimated/ modelled to peak.Predictors of change in crop diversity and compositionWe tested if Human Development Index (HDI) was correlated with patterns of change in crop diversity and composition. Briefly, the HDI is a composite index of four metrics related to socio-economic status, including life expectancy at birth, expected years of schooling for children at a school-centring age, mean years of schooling for adults ≥ 25 years of age, and log-transformed gross national income per capita. These values are then aggregated on a per country basis, into an HDI index that ranges from 0–1 with higher scores denoting higher performance in these indicators. We employed 2017 HDI values in our analysis here, in order to include the most countries possible in each analysis (since earlier HDI scores are less readily available)41.We then used linear mixed effects models to test if patterns of change in crop diversity and evenness varied systematically with HDI values. This entailed fitting six linear mixed models, where each of our six indicators (i.e., Indicators 1–3 for both S and J′) were predicted as a function of HDI; these models also accounted for potential spatial autocorrelation in Indicator values by including the FAO-defined continent identity and FAO-defined region identity of each country, as a nested random variable. Models were fit using the lme function in the nlme R package41. We then estimated the proportion of variation in each indicator that is explained by HDI, continent identity, and region identity, using the varcomp function in the ape R package42—which partitioned explained variation across continents and regions—as well as the sem.model.fits function in the piecewiseSEM R package43—which partitioned explained variation across the fixed (i.e., model intercept and HDI) vs. random (i.e., continent and region) effects. Due to differences in HDI data availability and in the number of piecewise models that converged, n = 152 countries for all models of S indicators and n = 139 countries for all models of J′ indicators. Log-transformed values of Indicators were used in these analyses where they better approximated a log-normal distribution, as determined using the fitdistrplus function in the fitdistrplus R package44. More

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

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

1.Fisher, R. et al. Species richness on coral reefs and the pursuit of convergent global estimates. Curr. Biol. 25, 500–505 (2015).Article 

Google Scholar 
2.Costanza, R. et al. Changes in the global value of ecosystem services. Glob. Environ. Change 26, 152–158 (2014).Article 

Google Scholar 
3.Moberg, F. & Folke, C. Ecological goods and services of coral reef ecosystems. Ecol. Econ. 29, 215–233 (1999).Article 

Google Scholar 
4.Wilkinson, C. Status of Coral Reefs of the World: 2008 (Global Coral Reef Monitoring Network, 2008).5.Spalding, M. et al. Mapping the global value and distribution of coral reef tourism. Mar. Policy 82, 104–113 (2017).Article 

Google Scholar 
6.Hoegh-Guldberg, O. Climate change, coral bleaching and the future of the world’s coral reefs. Mar. Freshw. Res. 50, 839–866 (1999). This paper projects loss and degradation of coral reefs on a global scale before it became common knowledge.
Google Scholar 
7.Hughes, T. P. et al. Global warming transforms coral reef assemblages. Nature 556, 492–496 (2018).Article 

Google Scholar 
8.Porter, J. W. & Meier, O. W. Quantification of loss and change in Floridian reef coral populations. Am. Zool. 32, 625–640 (1992).Article 

Google Scholar 
9.Ruzicka, R. R. et al. Temporal changes in benthic assemblages on Florida Keys reefs 11 years after the 1997/1998 El Niño. Mar. Ecol. Prog. Ser. 489, 125–141 (2013).Article 

Google Scholar 
10.Somerfield, P. J. et al. Changes in coral reef communities among the Florida Keys, 1996–2003. Coral Reefs 27, 951–965 (2008).Article 

Google Scholar 
11.Lapointe, B. E., Brewton, R. A., Herren, L. W., Porter, J. W. & Hu, C. Nitrogen enrichment, altered stoichiometry, and coral reef decline at Looe Key, Florida Keys, USA: a 3-decade study. Mar. Biol. 166, 108 (2019).Article 

Google Scholar 
12.Suggett, D. J. & Smith, D. J. Coral bleaching patterns are the outcome of complex biological and environmental networking. Global Change Biol. https://doi.org/10.1111/gcb.14871 (2019).Article 

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

Google Scholar 
14.Lesser, M. P. in Coral Reefs: An Ecosystem in Transition (eds Dubinsky, Z. & Stambler, N.) 405–419 (Springer, 2011).15.Rädecker, N. et al. Heat stress destabilizes symbiotic nutrient cycling in corals. Proc. Natl Acad. Sci. USA 118, e2022653118 (2021). This paper demonstrates that algal symbionts cease photosynthate transfer to coral hosts under heat stress long before visual signs of bleaching (symbiont loss) become evident.Article 

Google Scholar 
16.Allen, M. R. et al. in Sustainable Development, and Efforts to Eradicate Poverty (eds Masson-Delmotte, V. et al.) 41–91 (IPCC, 2018).17.Gattuso, J.-P. et al. Contrasting futures for ocean and society from different anthropogenic CO2 emissions scenarios. Science 349, aac4722 (2015).Article 

Google Scholar 
18.Hughes, D. J. et al. Coral reef survival under accelerating ocean deoxygenation. Nat. Clim. Chang. 10, 296–307 (2020).Article 

Google Scholar 
19.Durack, P.J., Wijffels, S.E. & Matear, R.J. Ocean salinities reveal strong global water cycle intensification during 1950 to 2000. Science 336, 455-458 (2012).Article 

Google Scholar 
20.Morris, L. A., Voolstra, C. R., Quigley, K. M., Bourne, D. G. & Bay, L. K. Nutrient availability and metabolism affect the stability of coral–Symbiodiniaceae symbioses. Trends Microbiol. 27, 678–689 (2019).Article 

Google Scholar 
21.Muller, E. M., Sartor, C., Alcaraz, N. I. & van Woesik, R. Spatial Epidemiology of the Stony-Coral-Tissue-Loss Disease in Florida. Front. Mar. Sci. 7, 163 (2020).Article 

Google Scholar 
22.Hughes, T. P. et al. Climate change, human impacts, and the resilience of coral reefs. Science 301, 929–933 (2003).Article 

Google Scholar 
23.Nyström, M., Folke, C. & Moberg, F. Coral reef disturbance and resilience in a human-dominated environment. Trends Ecol. Evol. 15, 413–417 (2000).Article 

Google Scholar 
24.Wiedenmann, J. et al. Nutrient enrichment can increase the susceptibility of reef corals to bleaching. Nat. Clim. Chang. 3, 160–164 (2012).Article 

Google Scholar 
25.D’Angelo, C. & Wiedenmann, J. Impacts of nutrient enrichment on coral reefs: new perspectives and implications for coastal management and reef survival. Curr. Opin. Environ. Sust. 7, 82–93 (2014).Article 

Google Scholar 
26.Thurber, R. L. V. et al. Chronic nutrient enrichment increases prevalence and severity of coral disease and bleaching. Glob. Change Biol. 20, 544–554 (2014).Article 

Google Scholar 
27.Donovan, M. K. et al. Local conditions magnify coral loss after marine heatwaves. Science 372, 977–980 (2021).Article 

Google Scholar 
28.Climate change widespread, rapid, and intensifying. IPCC (9 August 2021); https://www.ipcc.ch/2021/08/09/ar6-wg1-20210809-pr.29.Radchuk, V. et al. Adaptive responses of animals to climate change are most likely insufficient. Nat. Commun. 10, 3109 (2019).Article 

Google Scholar 
30.Kleypas, J. et al. Designing a blueprint for coral reef survival. Biol. Conserv. 257, 109107 (2021).Article 

Google Scholar 
31.Gattuso, J.-P. et al. Ocean solutions to address climate change and Its effects on marine ecosystems. Front. Mar. Sci. 5, 337 (2018).Article 

Google Scholar 
32.Knowlton, N. et al. Rebuilding Coral Reefs: A Decadal Grand Challenge (International Coral Reef Society and Future Earth Coasts, 2021) https://doi.org/10.53642/NRKY9386.33.Hoegh-Guldberg, O., Kennedy, E. V., Beyer, H. L., McClennen, C. & Possingham, H. P. Securing a long-term future for coral reefs. Trends Ecol. Evol. 33, 936–944 (2018).Article 

Google Scholar 
34.Zoccola, D. et al. The World Coral Conservatory (WCC): a Noah’s ark for corals to support survival of reef ecosystems. PLoS Biol. 18, e3000823 (2020).Article 

Google Scholar 
35.Kleinhaus, K. et al. Science, diplomacy, and the Red Sea’s unique coral reef: it’s time for action. Front. Mar. Sci. 7, 90 (2020).Article 

Google Scholar 
36.van Oppen, M. J. H., Oliver, J. K., Putnam, H. M. & Gates, R. D. Building coral reef resilience through assisted evolution. Proc. Natl Acad. Sci. USA 112, 2307–2313 (2015).Article 

Google Scholar 
37.Baums, I. B. et al. Considerations for maximizing the adaptive potential of restored coral populations in the western Atlantic. Ecol. Appl. 29, e01978 (2019).Article 

Google Scholar 
38.Peixoto, R. S., Sweet, M. & Bourne, D. G. Customized medicine for corals. Front. Mar. Sci. 6, 686 (2019).Article 

Google Scholar 
39.Rinkevich, B. The active reef restoration toolbox is a vehicle for coral resilience and adaptation in a changing world. J. Mar. Sci. Eng. 7, 201 (2019).Article 

Google Scholar 
40.Boström-Einarsson, L. et al. Coral restoration — a systematic review of current methods, successes, failures and future directions. PLoS ONE 15, e0226631 (2020).Article 

Google Scholar 
41.Voolstra, C. R. et al. Standardized short-term acute heat stress assays resolve historical differences in coral thermotolerance across microhabitat reef sites. Glob. Chang. Biol. 26, 4328–4343 (2020). This paper highlights the potential of mobile acute heat stress assays to resolve fine-scale differences in coral thermotolerance, suitable for large-scale identification of resilient genotypes/reefs for conservation and restoration approaches.Article 

Google Scholar 
42.Parkinson, J. E. et al. Molecular tools for coral reef restoration: beyond biomarker discovery. Conserv. Lett. 13, e12687 (2020).Article 

Google Scholar 
43.Voolstra, C. R. et al. Contrasting heat stress response patterns of coral holobionts across the Red Sea suggest distinct mechanisms of thermal tolerance. Mol. Ecol. https://doi.org/10.1111/mec.16064 (2021).Article 

Google Scholar 
44.Morikawa, M. K. & Palumbi, S. R. Using naturally occurring climate resilient corals to construct bleaching-resistant nurseries. Proc. Natl Acad. Sci. USA 116, 10586–10591 (2019).Article 

Google Scholar 
45.Sweet, M. & Brown, B. in Oceanography and Marine Biology — An Annual Review (eds Hughes R.N. et al.) 271–314 (CRC, 2016).46.Voolstra, C. R. & Ziegler, M. Adapting with microbial help: microbiome flexibility facilitates rapid responses to environmental change. Bioessays 42, e2000004 (2020). This paper proposes microbiome flexibility as a mechanism to aid adaptation to environmental change and posits that capacity for dynamic restructuring of the microbiome is host specific.Article 

Google Scholar 
47.Jaspers, C. et al. Resolving structure and function of metaorganisms through a holistic framework combining reductionist and integrative approaches. Zoology 133, 81–87 (2019).Article 

Google Scholar 
48.Torda, G. et al. Rapid adaptive responses to climate change in corals. Nat. Clim. Chang. 7, 627–636 (2017).Article 

Google Scholar 
49.Ziegler, M., Seneca, F. O., Yum, L. K., Palumbi, S. R. & Voolstra, C. R. Bacterial community dynamics are linked to patterns of coral heat tolerance. Nat. Commun. 8, 14213 (2017). This paper provides the first putative link between bacterial community composition and coral heat tolerance.Article 

Google Scholar 
50.Morgans, C. A., Hung, J. Y., Bourne, D. G. & Quigley, K. M. Symbiodiniaceae probiotics for use in bleaching recovery. Restor. Ecol. 28, 282–288 (2020).Article 

Google Scholar 
51.Liew, Y. J. et al. Intergenerational epigenetic inheritance in reef-building corals. Nat. Clim. Chang. 10, 254–259 (2020).Article 

Google Scholar 
52.Craggs, J. et al. Inducing broadcast coral spawning ex situ: closed system mesocosm design and husbandry protocol. Ecol. Evol. 7, 11066–11078 (2017).Article 

Google Scholar 
53.Camp, E. F., Schoepf, V. & Suggett, D. J. How can “super corals” facilitate global coral reef survival under rapid environmental and climatic change? Glob. Chang. Biol. 24, 2755–2757 (2018).Article 

Google Scholar 
54.Peixoto, R. S. et al. Coral probiotics: premise, promise, prospects. Annu. Rev. Anim. Biosci. 9, 265–288 (2021). This paper reviews coral probiotics and critical assessment of applicability.Article 

Google Scholar 
55.Doering, T. et al. Towards enhancing coral heat tolerance: a “microbiome transplantation” treatment using inoculations of homogenized coral tissues. Microbiome 9, 102 (2021).Article 

Google Scholar 
56.Howells, E. J. et al. Enhancing the heat tolerance of reef-building corals to future warming. Sci. Adv. 7 (2021).57.Devlin-Durante, M. K., Miller, M. W., Caribbean Acropora Research Group, Precht, W. F. & Baums, I. B. How old are you? Genet age estimates in a clonal animal. Mol. Ecol. 25, 5628–5646 (2016).Article 

Google Scholar 
58.Irwin, A. et al. Age and intraspecific diversity of resilient Acropora communities in Belize. Coral Reefs 36, 1111–1120 (2017).Article 

Google Scholar 
59.Palumbi, S. R., Barshis, D. J., Traylor-Knowles, N. & Bay, R. A. Mechanisms of reef coral resistance to future climate change. Science 344, 895–898 (2014). This paper demonstrates that acclimation and adaptation contribute to coral thermal tolerance and climate resistance at about equal contribution.Article 

Google Scholar 
60.Barott, K. L. et al. Coral bleaching response is unaltered following acclimatization to reefs with distinct environmental conditions. Proc. Natl Acad. Sci. USA 118, e2025435118 (2021).Article 

Google Scholar 
61.Thomas, L., López, E. H., Morikawa, M. K. & Palumbi, S. R. Transcriptomic resilience, symbiont shuffling, and vulnerability to recurrent bleaching in reef-building corals. Mol. Ecol. 28, 3371–3382 (2019).Article 

Google Scholar 
62.Bellantuono, A. J., Granados-Cifuentes, C., Miller, D. J., Hoegh-Guldberg, O. & Rodriguez-Lanetty, M. Coral thermal tolerance: tuning gene expression to resist thermal stress. PLoS ONE 7, e50685 (2012).Article 

Google Scholar 
63.Barshis, D. J. et al. Genomic basis for coral resilience to climate change. Proc. Natl Acad. Sci. USA 110, 1387–1392 (2013).Article 

Google Scholar 
64.Savary, R. et al. Fast and pervasive transcriptomic resilience and acclimation of extremely heat-tolerant coral holobionts from the northern Red Sea. Proc. Natl Acad. Sci. USA 118, e2023298118 (2021).Article 

Google Scholar 
65.Liew, Y. J. et al. Epigenome-associated phenotypic acclimatization to ocean acidification in a reef-building coral. Sci. Adv. 4, eaar8028 (2018).Article 

Google Scholar 
66.Durante, M. K., Baums, I. B., Williams, D. E., Vohsen, S. & Kemp, D. W. What drives phenotypic divergence among coral clonemates of Acropora palmata? Mol. Ecol. 28, 3208–3224 (2019).Article 

Google Scholar 
67.Rodríguez-Casariego, J. A. et al. Genome-Wide DNA Methylation Analysis Reveals a Conserved Epigenetic Response to Seasonal Environmental Variation in the Staghorn Coral Acropora cervicornis. Front. Mar. Sci. 7, 822 https://doi.org/10.3389/fmars.2020.560424 (2020).68.Putnam, H. M. & Gates, R. D. Preconditioning in the reef-building coral Pocillopora damicornis and the potential for trans-generational acclimatization in coral larvae under future climate change conditions. J. Exp. Biol. 218, 2365–2372 (2015).Article 

Google Scholar 
69.Putnam, H. M., Davidson, J. M. & Gates, R. D. Ocean acidification influences host DNA methylation and phenotypic plasticity in environmentally susceptible corals. Evol. Appl. 9, 1165–1178 (2016).Article 

Google Scholar 
70.Putnam, H. M., Ritson-Williams, R., Cruz, J. A., Davidson, J. M. & Gates, R. D. Environmentally-induced parental or developmental conditioning influences coral offspring ecological performance. Sci. Rep. 10, 13664 (2020).Article 

Google Scholar 
71.Drury, C. et al. Genomic variation among populations of threatened coral: Acropora cervicornis. BMC Genomics 17, 286 (2016).Article 

Google Scholar 
72.Bay, R. A., Rose, N. H., Logan, C. A. & Palumbi, S. R. Genomic models predict successful coral adaptation if future ocean warming rates are reduced. Sci. Adv. 3, e1701413 (2017).Article 

Google Scholar 
73.Prada, C. et al. Empty niches after extinctions increase population sizes of modern corals. Curr. Biol. 26, 3190–3194 (2016).Article 

Google Scholar 
74.Robitzch, V., Banguera-Hinestroza, E., Sawall, Y., Al-Sofyani, A. and Voolstra, C.R., 2015. Absence of genetic differentiation in the coral Pocillopora verrucosa along environmental gradients of the Saudi Arabian Red Sea. Front. Mar. Sci. 2, 5 (2015).Article 

Google Scholar 
75.Van Oppen, M. J. H., Souter, P., Howells, E. J., Heyward, A. & Berkelmans, R. Novel genetic diversity through somatic mutations: fuel for adaptation of reef corals? Diversity 3, 405–423 (2011).Article 

Google Scholar 
76.Vasquez Kuntz, K. L. et al. Juvenile corals inherit mutations acquired during the parent’s lifespan. Preprint at bioRxiv https://doi.org/10.1101/2020.10.19.345538 (2020).Article 

Google Scholar 
77.Matz, M. V., Treml, E. A., Aglyamova, G. V. & Bay, L. K. Potential and limits for rapid genetic adaptation to warming in a Great Barrier Reef coral. PLoS Genet. 14, e1007220 (2018).Article 

Google Scholar 
78.Guest, J. R. et al. Contrasting patterns of coral bleaching susceptibility in 2010 suggest an adaptive response to thermal stress. PLoS ONE 7, e33353 (2012).Article 

Google Scholar 
79.Coles, S. L. et al. Evidence of acclimatization or adaptation in Hawaiian corals to higher ocean temperatures. PeerJ 6, e5347 (2018).Article 

Google Scholar 
80.Sully, S., Burkepile, D. E., Donovan, M. K., Hodgson, G. & van Woesik, R. A global analysis of coral bleaching over the past two decades. Nat. Commun. 10, 1264 (2019).Article 

Google Scholar 
81.Camp, E. F. et al. The future of coral reefs subject to rapid climate change: lessons from natural extreme environments. Front. Mar. Sci. 5, 4 (2018).Article 

Google Scholar 
82.Oliver, T. A. & Palumbi, S. R. Do fluctuating temperature environments elevate coral thermal tolerance? Coral Reefs 30, 429–440 (2011).Article 

Google Scholar 
83.Morgan, K. M., Perry, C. T., Smithers, S. G., Johnson, J. A. & Daniell, J. J. Evidence of extensive reef development and high coral cover in nearshore environments: implications for understanding coral adaptation in turbid settings. Sci. Rep. 6, 29616 (2016).Article 

Google Scholar 
84.Middlebrook, R., Hoegh-Guldberg, O. & Leggat, W. The effect of thermal history on the susceptibility of reef-building corals to thermal stress. J. Exp. Biol. 211, 1050–1056 (2008).Article 

Google Scholar 
85.Brown, B. E., Dunne, R. P., Edwards, A. J., Sweet, M. J. & Phongsuwan, N. Decadal environmental ‘memory’ in a reef coral? Mar. Biol. 162, 479–483 (2015).Article 

Google Scholar 
86.Dixon, G., Liao, Y., Bay, L. K. & Matz, M. V. Role of gene body methylation in acclimatization and adaptation in a basal metazoan. Proc. Natl Acad. Sci. USA 115, 13342–13346 (2018).Article 

Google Scholar 
87.Humanes, A. et al. An experimental framework for selectively breeding corals for assisted evolution. Front. Mar. Sci. 8, 626 (2021).Article 

Google Scholar 
88.Dixon, G. B. et al. Genomic determinants of coral heat tolerance across latitudes. Science 348, 1460–1462 (2015). This paper demonstrates applicability of assisted evolution via selective breeding.Article 

Google Scholar 
89.van Oppen, M. J. H. et al. Shifting paradigms in restoration of the world’s coral reefs. Glob. Chang. Biol. 23, 3437–3448 (2017).Article 

Google Scholar 
90.Fukami, H. et al. Conventional taxonomy obscures deep divergence between Pacific and Atlantic corals. Nature 427, 832–835 (2004).Article 

Google Scholar 
91.Voolstra, C. R. et al. Consensus guidelines for advancing coral holobiont genome and specimen voucher deposition. Front. Mar. Sci. 8, 1029 (2021).Article 

Google Scholar 
92.Seneca, F. O. & Palumbi, S. R. The role of transcriptome resilience in resistance of corals to bleaching. Mol. Ecol. 24, 1467–1484 (2015).Article 

Google Scholar 
93.Evensen, N. R., Fine, M., Perna, G., Voolstra, C. R. & Barshis, D. J. Remarkably high and consistent tolerance of a Red Sea coral to acute and chronic thermal stress exposures. Limnol. Oceanogr. https://doi.org/10.1002/lno.11715 (2021).Article 

Google Scholar 
94.Cleves, P. A., Strader, M. E., Bay, L. K., Pringle, J. R. & Matz, M. V. CRISPR/Cas9-mediated genome editing in a reef-building coral. Proc. Natl Acad. Sci. USA 115, 5235–5240 (2018).Article 

Google Scholar 
95.Cleves, P. A. et al. Reduced thermal tolerance in a coral carrying CRISPR-induced mutations in the gene for a heat-shock transcription factor. Proc. Natl Acad. Sci. USA 117, 28899–28905 (2020).Article 

Google Scholar 
96.Fuller, Z. L. et al. Population genetics of the coral Acropora millepora: toward genomic prediction of bleaching. Science 369, eaba4674 (2020).Article 

Google Scholar 
97.Yetsko, K. et al. Genetic differences in thermal tolerance among colonies of threatened coral Acropora cervicornis: potential for adaptation to increasing temperature. Mar. Ecol. Prog. Ser. 646, 45–68 (2020).Article 

Google Scholar 
98.Kenkel, C. D., Almanza, A. T. & Matz, M. V. Fine-scale environmental specialization of reef-building corals might be limiting reef recovery in the Florida Keys. Ecology 96, 3197–3212 (2015).Article 

Google Scholar 
99.D’Angelo, C. et al. Local adaptation constrains the distribution potential of heat-tolerant Symbiodinium from the Persian/Arabian Gulf. ISME J. 9, 2551–2560 (2015).Article 

Google Scholar 
100.Safaie, A. et al. High frequency temperature variability reduces the risk of coral bleaching. Nat. Commun. 9, 1671 (2018).Article 

Google Scholar 
101.Quigley, K. M., Bay, L. K. & van Oppen, M. J. H. Genome-wide SNP analysis reveals an increase in adaptive genetic variation through selective breeding of coral. Mol. Ecol. 29, 2176–2188 (2020).Article 

Google Scholar 
102.Craggs, J., Guest, J., Bulling, M. & Sweet, M. Ex situ co culturing of the sea urchin, Mespilia globulus and the coral Acropora millepora enhances early post-settlement survivorship. Sci. Rep. 9, 12984 (2019).Article 

Google Scholar 
103.Quigley, K. M. et al. Variability in fitness trade-offs amongst coral juveniles with mixed genetic backgrounds held in the wild. Front. Mar. Sci. 8, 161 (2021).Article 

Google Scholar 
104.LaJeunesse, T. C. et al. Systematic revision of Symbiodiniaceae highlights the antiquity and diversity of coral endosymbionts. Curr. Biol. 28, 2570–2580.e6 (2018). This paper provides a revised coral symbiont taxonomy and shows that Symbiodiniaceae diversification coincides with the radiation of reef-building corals.Article 

Google Scholar 
105.Muscatine, L. The role of symbiotic algae in carbon and energy flux in reef corals. Coral Reefs 25, 75–87 (1990).
Google Scholar 
106.Trench, R. K. Microalgal–invertebrate symbiosis, a review. Endocytobiosis Cell Res. 9, 135–175 (1993).
Google Scholar 
107.Pogoreutz, C. et al. in Cellular Dialogues in the Holobiont (eds Bosch, T. C. G. & Hadfield, M. G.) 91–118 (CRC, 2020). https://doi.org/10.1201/9780429277375-7.108.Hume, B. C. C. et al. SymPortal: a novel analytical framework and platform for coral algal symbiont next-generation sequencing ITS2 profiling. Mol. Ecol. Resour. 19, 1063–1080 (2019).Article 

Google Scholar 
109.Decelle, J. et al. Worldwide occurrence and activity of the reef-building coral symbiont Symbiodinium in the open ocean. Curr. Biol. 28, 3625–3633.e3 (2018).Article 

Google Scholar 
110.Aranda, M. et al. Genomes of coral dinoflagellate symbionts highlight evolutionary adaptations conducive to a symbiotic lifestyle. Sci. Rep. 6, 39734 (2016).Article 

Google Scholar 
111.González-Pech, R. A., Bhattacharya, D., Ragan, M. A. & Chan, C. X. Genome evolution of coral reef symbionts as intracellular residents. Trends Ecol. Evol. 34, 799–806 (2019).Article 

Google Scholar 
112.Hume, B. C. C., Mejia-Restrepo, A., Voolstra, C. R. & Berumen, M. L. Fine-scale delineation of Symbiodiniaceae genotypes on a previously bleached central Red Sea reef system demonstrates a prevalence of coral host-specific associations. Coral Reefs 39, 583–601 (2020).Article 

Google Scholar 
113.Howells, E. J. et al. Corals in the hottest reefs in the world exhibit symbiont fidelity not flexibility. Mol. Ecol. 29, 899–911 (2020).Article 

Google Scholar 
114.Turnham, K. E., Wham, D. C., Sampayo, E. & LaJeunesse, T. C. Mutualistic microalgae co-diversify with reef corals that acquire symbionts during egg development. ISME J. https://doi.org/10.1038/s41396-021-01007-8 (2021).Article 

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

Google Scholar 
116.LaJeunesse, T. C., Smith, R. T., Finney, J. & Oxenford, H. Outbreak and persistence of opportunistic symbiotic dinoflagellates during the 2005 Caribbean mass coral ‘bleaching’ event. Proc. R. Soc. B Biol. Sci. 276, 4139–4148 (2009).Article 

Google Scholar 
117.Grégoire, V., Schmacka, F., Coffroth, M. A. & Karsten, U. Photophysiological and thermal tolerance of various genotypes of the coral endosymbiont Symbiodinium sp. (Dinophyceae). J. Appl. Phycol. 29, 1893–1905 (2017).Article 

Google Scholar 
118.Quigley, K. M., Baker, A. C., Coffroth, M. A., Willis, B. L. & van Oppen, M. J. H. in Coral Bleaching: Patterns, Processes, Causes and Consequences (eds van Oppen, M. J. H. & Lough, J. M.) 111–151 (Springer International, 2018).119.Ziegler, M., Arif, C. & Voolstra, C. R. in Coral Reefs of the Red Sea (eds Voolstra, C. R. & Berumen, M. L.) 69–89 (Springer International, 2019).120.Suggett, D. J., Warner, M. E. & Leggat, W. Symbiotic dinoflagellate functional diversity mediates coral survival under ecological crisis. Trends Ecol. Evol. 32, 735–745 (2017).Article 

Google Scholar 
121.Hume, B. C. C. et al. Ancestral genetic diversity associated with the rapid spread of stress-tolerant coral symbionts in response to Holocene climate change. Proc. Natl Acad. Sci. USA 113, 4416–4421 (2016).Article 

Google Scholar 
122.Ochsenkühn, M. A., Röthig, T., D’Angelo, C., Wiedenmann, J. & Voolstra, C. R. The role of floridoside in osmoadaptation of coral-associated algal endosymbionts to high-salinity conditions. Sci. Adv. 3, e1602047 (2017).Article 

Google Scholar 
123.Baumgarten, S. et al. Integrating microRNA and mRNA expression profiling in Symbiodinium microadriaticum, a dinoflagellate symbiont of reef-building corals. BMC Genomics 14, 704 (2013).Article 

Google Scholar 
124.Klein, S. G. et al. Symbiodinium mitigate the combined effects of hypoxia and acidification on a noncalcifying cnidarian. Glob. Chang. Biol. 23, 3690–3703 (2017).Article 

Google Scholar 
125.Liew, Y. J., Li, Y., Baumgarten, S., Voolstra, C. R. & Aranda, M. Condition-specific RNA editing in the coral symbiont Symbiodinium microadriaticum. PLoS Genet. 13, e1006619 (2017).Article 

Google Scholar 
126.Warner, M. E. & Suggett, D. J. in The Cnidaria, Past, Present and Future: The World of Medusa and Her Sisters (eds Goffredo, S. & Dubinsky, Z.) 489–509 (Springer International, 2016).127.Levin, R. A. et al. Sex, scavengers, and chaperones: transcriptome secrets of divergent symbiodinium thermal tolerances. Mol. Biol. Evol. 33, 3032 (2016).Article 

Google Scholar 
128.Nand, A. et al. Genetic and spatial organization of the unusual chromosomes of the dinoflagellate Symbiodinium microadriaticum. Nat. Genet. 53, 618–629 (2021).Article 

Google Scholar 
129.Buerger, P. et al. Heat-evolved microalgal symbionts increase coral bleaching tolerance. Sci. Adv. 6, eaba2498 (2020).Article 

Google Scholar 
130.Thornhill, D. J., Howells, E. J., Wham, D. C., Steury, T. D. & Santos, S. R. Population genetics of reef coral endosymbionts (Symbiodinium, Dinophyceae). Mol. Ecol. 26, 2640–2659 (2017).Article 

Google Scholar 
131.LaJeunesse, T. C. et al. Long-standing environmental conditions, geographic isolation and host–symbiont specificity influence the relative ecological dominance and genetic diversification of coral endosymbionts in the genus Symbiodinium. J. Biogeogr. 37, 785–800 (2010).Article 

Google Scholar 
132.Parkinson, J. E. et al. Gene expression variation resolves species and individual strains among coral-associated dinoflagellates within the genus Symbiodinium. Genome Biol. Evol. 8, 665–680 (2016).Article 

Google Scholar 
133.Baker, A. C. Flexibility and specificity in coral–algal symbiosis: diversity, ecology, and biogeography of Symbiodinium. Annu. Rev. Ecol. Evol. Syst. 34, 661–689 (2003).Article 

Google Scholar 
134.Boulotte, N. M. et al. Exploring the Symbiodinium rare biosphere provides evidence for symbiont switching in reef-building corals. ISME J. 10, 2693–2701 (2016).Article 

Google Scholar 
135.Ziegler, M., Eguíluz, V. M., Duarte, C. M. & Voolstra, C. R. Rare symbionts may contribute to the resilience of coral–algal assemblages. ISME J. 12, 161–172 (2018).Article 

Google Scholar 
136.Mies, M., Sumida, P. Y. G., Rädecker, N. & Voolstra, C. R. Marine Invertebrate Larvae Associated with Symbiodinium: A Mutualism from the Start? Front. Ecol. Evol. 5, 56 https://www.frontiersin.org/article/10.3389/fevo.2017.00056 (2017).137.Cumbo, V. R., Baird, A. H. & van Oppen, M. J. H. The promiscuous larvae: flexibility in the establishment of symbiosis in corals. Coral Reefs 32, 111–120 (2013).Article 

Google Scholar 
138.Quigley, K. M., Willis, B. L. & Bay, L. K. Heritability of the Symbiodinium community in vertically- and horizontally-transmitting broadcast spawning corals. Sci. Rep. 7, 8219 (2017).Article 

Google Scholar 
139.National Academies of Sciences, Engineering, and Medicine. A Research Review of Interventions to Increase the Persistence and Resilience of Coral Reefs (National Academies Press, 2019). This book reviews restoration interventions, detailing latest emerging technologies and approaches.140.Quigley, K. M., Randall, C. J., van Oppen, M. J. H. & Bay, L. K. Assessing the role of historical temperature regime and algal symbionts on the heat tolerance of coral juveniles. Biol. Open 9, bio047316 (2020).Article 

Google Scholar 
141.McIlroy, S. E. et al. The effects of Symbiodinium (Pyrrhophyta) identity on growth, survivorship, and thermal tolerance of newly settled coral recruits. J. Phycol. 52, 1114–1124 (2016).Article 

Google Scholar 
142.Thornhill, D. J., Daniel, M. W., LaJeunesse, T. C., Schmidt, G. W. & Fitt, W. K. Natural infections of aposymbiotic Cassiopea xamachana scyphistomae from environmental pools of Symbiodinium. J. Exp. Mar. Bio. Ecol. 338, 50–56 (2006).Article 

Google Scholar 
143.Coffroth, M. A., Lewis, C. F., Santos, S. R. & Weaver, J. L. Environmental populations of symbiotic dinoflagellates in the genus Symbiodinium can initiate symbioses with reef cnidarians. Curr. Biol. 16, R985–R987 (2006).Article 

Google Scholar 
144.Fujise, L. et al. Unlocking the phylogenetic diversity, primary habitats, and abundances of free-living Symbiodiniaceae on a coral reef. Mol. Ecol. 30, 343–360 (2021).Article 

Google Scholar 
145.Levin, R. A. et al. Engineering strategies to decode and enhance the genomes of coral symbionts. Front. Microbiol. 8, 1220 (2017).Article 

Google Scholar 
146.Chen, J. E., Barbrook, A. C., Cui, G., Howe, C. J. & Aranda, M. The genetic intractability of Symbiodinium microadriaticum to standard algal transformation methods. PLoS ONE 14, e0211936 (2019).Article 

Google Scholar 
147.Sheykhali, S. et al. Robustness to extinction and plasticity derived from mutualistic bipartite ecological networks. Sci. Rep. 10, 9783 (2020).Article 

Google Scholar 
148.Quigley, K. M., Bay, L. K. & Willis, B. L. Leveraging new knowledge of Symbiodinium community regulation in corals for conservation and reef restoration. Mar. Ecol. Prog. Ser. 600, 245–253 (2018).Article 

Google Scholar 
149.LaJeunesse, T. C. et al. Host–symbiont recombination versus natural selection in the response of coral–dinoflagellate symbioses to environmental disturbance. Proc. R. Soc. B: Biol. Sci. 277, 2925–2934 (2010).Article 

Google Scholar 
150.Poland, D. M. & Coffroth, M. A. Trans-generational specificity within a cnidarian–algal symbiosis. Coral Reefs 36, 119–129 (2017).Article 

Google Scholar 
151.Sampayo, E. M. et al. Coral symbioses under prolonged environmental change: living near tolerance range limits. Sci. Rep. 6, 36271 (2016).Article 

Google Scholar 
152.Abrego, D., van Oppen, M. J. H. & Willis, B. L. Onset of algal endosymbiont specificity varies among closely related species of Acropora corals during early ontogeny. Mol. Ecol. 18, 3532–3543 (2009).Article 

Google Scholar 
153.Pettay, D. T., Wham, D. C., Smith, R. T., Iglesias-Prieto, R. & LaJeunesse, T. C. Microbial invasion of the Caribbean by an Indo-Pacific coral zooxanthella. Proc. Natl Acad. Sci. USA 112, 7513–7518 (2015).Article 

Google Scholar 
154.Qin, Z. et al. Diversity of Symbiodiniaceae in 15 coral species from the Southern South China Sea: potential relationship with coral thermal adaptability. Front. Microbiol. 10, 2343 (2019).Article 

Google Scholar 
155.Claar, D. C. et al. Dynamic symbioses reveal pathways to coral survival through prolonged heatwaves. Nat. Commun. 11, 6097 (2020).Article 

Google Scholar 
156.Lim, E.-P. et al. Continuation of tropical Pacific Ocean temperature trend may weaken extreme El Niño and its linkage to the Southern Annular Mode. Sci. Rep. 9, 17044 (2019).Article 

Google Scholar 
157.Pollock, F. J. et al. Coral larvae for restoration and research: a large-scale method for rearing Acropora millepora larvae, inducing settlement, and establishing symbiosis. PeerJ 5, e3732 (2017).Article 

Google Scholar 
158.McFall-Ngai, M. et al. Animals in a bacterial world, a new imperative for the life sciences. Proc. Natl Acad. Sci. USA 110, 3229–3236 (2013).Article 

Google Scholar 
159.Bosch, T. C. G. & McFall-Ngai, M. J. Metaorganisms as the new frontier. Zoology 114, 185–190 (2011).Article 

Google Scholar 
160.Robbins, S. J. et al. A genomic view of the reef-building coral Porites lutea and its microbial symbionts. Nat. Microbiol. 4, 2090–2100 (2019).Article 

Google Scholar 
161.Bang, C. et al. Metaorganisms in extreme environments: do microbes play a role in organismal adaptation? Zoology 127, 1–19 (2018).Article 

Google Scholar 
162.Williams, A. D., Brown, B. E., Putchim, L. & Sweet, M. J. Age-related shifts in bacterial diversity in a reef coral. PLoS ONE 10, e0144902 (2015).Article 

Google Scholar 
163.Roder, C., Bayer, T., Aranda, M., Kruse, M. & Voolstra, C. R. Microbiome structure of the fungid coral Ctenactis echinata aligns with environmental differences. Mol. Ecol. 24, 3501–3511 (2015).Article 

Google Scholar 
164.Sweet, M. J., Brown, B. E., Dunne, R. P., Singleton, I. & Bulling, M. Evidence for rapid, tide-related shifts in the microbiome of the coral Coelastrea aspera. Coral Reefs 36, 815–828 (2017).Article 

Google Scholar 
165.Ziegler, M. et al. Coral bacterial community structure responds to environmental change in a host-specific manner. Nat. Commun. 10, 3092 (2019).Article 

Google Scholar 
166.Reshef, L., Koren, O., Loya, Y., Zilber-Rosenberg, I. & Rosenberg, E. The coral probiotic hypothesis. Environ. Microbiol. 8, 2068–2073 (2006).Article 

Google Scholar 
167.Pogoreutz, C. et al. Dominance of Endozoicomonas bacteria throughout coral bleaching and mortality suggests structural inflexibility of the Pocillopora verrucosa microbiome. Ecol. Evol. 8, 2240–2252 (2018).Article 

Google Scholar 
168.Neave, M. J. et al. Differential specificity between closely related corals and abundant Endozoicomonas endosymbionts across global scales. ISME J. 11, 186–200 (2017).Article 

Google Scholar 
169.Neave, M. J., Apprill, A., Ferrier-Pagès, C. & Voolstra, C. R. Diversity and function of prevalent symbiotic marine bacteria in the genus. Endozoicomonas. Appl. Microbiol. Biotechnol. 100, 8315–8324 (2016).Article 

Google Scholar 
170.Nissimov, J., Rosenberg, E. & Munn, C. B. Antimicrobial properties of resident coral mucus bacteria of Oculina patagonica. FEMS Microbiol. Lett. 292, 210–215 (2009).Article 

Google Scholar 
171.Sharp, K. H., Sneed, J. M., Ritchie, K. B., Mcdaniel, L. & Paul, V. J. Induction of larval settlement in the reef coral Porites astreoides by a cultivated marine roseobacter strain. Biol. Bull. 228, 98–107 (2015).Article 

Google Scholar 
172.Rosado, P. M. et al. Marine probiotics: increasing coral resistance to bleaching through microbiome manipulation. ISME J. 13, 921–936 (2019).Article 

Google Scholar 
173.Sunagawa, S. et al. Bacterial diversity and white plague disease-associated community changes in the Caribbean coral Montastraea faveolata. ISME J. 3, 512–521 (2009).Article 

Google Scholar 
174.Ushijima, B., Smith, A., Aeby, G. S. & Callahan, S. M. Vibrio owensii induces the tissue loss disease Montipora white syndrome in the Hawaiian reef coral Montipora capitata. PLoS ONE 7, e46717 (2012).Article 

Google Scholar 
175.Mouchka, M. E., Hewson, I. & Harvell, C. D. Coral-associated bacterial assemblages: current knowledge and the potential for climate-driven impacts. Integr. Comp. Biol. 50, 662–674 (2010).Article 

Google Scholar 
176.Glasl, B., Herndl, G. J. & Frade, P. R. The microbiome of coral surface mucus has a key role in mediating holobiont health and survival upon disturbance. ISME J. 10, 2280–2292 (2016).Article 

Google Scholar 
177.Peixoto, R. S. et al. Beneficial Microorganisms for Corals (BMC): proposed mechanisms for coral health and resilience. Front. Microbiol. 8, 341 (2017).Article 

Google Scholar 
178.Mueller, E. A., Wisnoski, N. I., Peralta, A. L. & Lennon, J. T. Microbial rescue effects: how microbiomes can save hosts from extinction. Funct. Ecol. 34, 2055–2064 (2020).Article 

Google Scholar 
179.Leite, D. C. A. et al. Coral bacterial-core abundance and network complexity as proxies for anthropogenic pollution. Front. Microbiol. 9, 833 (2018).Article 

Google Scholar 
180.Fragoso Ados Santos, H. et al. Impact of oil spills on coral reefs can be reduced by bioremediation using probiotic microbiota. Sci. Rep. 5, 18268 (2015).Article 

Google Scholar 
181.Silva, D. P. et al. Multi-domain probiotic consortium as an alternative to chemical remediation of oil spills at coral reefs and adjacent sites. Microbiome 9, 118 (2021).Article 

Google Scholar 
182.Welsh, R. M. et al. Alien vs. predator: bacterial challenge alters coral microbiomes unless controlled by Halobacteriovorax predators. PeerJ 5, e3315 (2017).Article 

Google Scholar 
183.Santoro, E. P. et al. Coral microbiome manipulation elicits metabolic and genetic restructuring to mitigate heat stress and evade mortality. Sci. Adv. 7, eabg3088 (2021).Article 

Google Scholar 
184.Assis, J. M. et al. Delivering Beneficial Microorganisms for Corals: rotifers as carriers of probiotic bacteria. Front. Microbiol. 11, 608506 (2020).Article 

Google Scholar 
185.Damjanovic, K., Blackall, L. L., Webster, N. S. & van Oppen, M. J. H. The contribution of microbial biotechnology to mitigating coral reef degradation. Microb. Biotechnol. 10, 1236–1243 (2017).Article 

Google Scholar 
186.van Oppen, M. J. H. & Blackall, L. L. Coral microbiome dynamics, functions and design in a changing world. Nat. Rev. Microbiol. 17, 557–567 (2019).Article 

Google Scholar 
187.Sweet, M. et al. Insights into the cultured bacterial fraction of corals. mSystems 6, e0124920 (2021).Article 

Google Scholar 
188.Brussaard, C. P. D., Baudoux, A.-C. & Rodríguez-Valera, F. in The Marine Microbiome: An Untapped Source of Biodiversity and Biotechnological Potential (eds Stal, L. J. & Cretoiu, M. S.) 155–183 (Springer International, 2016).189.Levin, R. A., Voolstra, C. R., Weynberg, K. D. & van Oppen, M. J. H. Evidence for a role of viruses in the thermal sensitivity of coral photosymbionts. ISME J. 11, 808–812 (2017).Article 

Google Scholar 
190.Messyasz, A. et al. Coral bleaching phenotypes associated with differential abundances of nucleocytoplasmic large DNA viruses. Front. Mar. Sci. https://doi.org/10.3389/fmars.2020.555474 (2020).Article 

Google Scholar 
191.Thurber, R. L. V. et al. Metagenomic analysis indicates that stressors induce production of herpes-like viruses in the coral Porites compressa. Proc. Natl Acad. Sci. USA 105, 18413–18418 (2008).Article 

Google Scholar 
192.Sweet, M. & Bythell, J. The role of viruses in coral health and disease. J. Invertebr. Pathol. 147, 136–144 (2017).Article 

Google Scholar 
193.Thurber, R. V., Payet, J. P., Thurber, A. R. & Correa, A. M. S. Virus–host interactions and their roles in coral reef health and disease. Nat. Rev. Microbiol. 15, 205–216 (2017). This paper reviews the role of viruses in coral holobiont biology.Article 

Google Scholar 
194.Frazão, N., Sousa, A., Lässig, M. & Gordo, I. Horizontal gene transfer overrides mutation in Escherichia coli colonizing the mammalian gut. Proc. Natl Acad. Sci. USA 116, 17906–17915 (2019).Article 

Google Scholar 
195.Lepage, P. et al. Dysbiosis in inflammatory bowel disease: a role for bacteriophages? Gut 57, 424–425 (2008).Article 

Google Scholar 
196.Barr, J. J. et al. Bacteriophage adhering to mucus provide a non-host-derived immunity. Proc. Natl Acad. Sci. USA 110, 10771–10776 (2013).Article 

Google Scholar 
197.Silveira, C. B. & Rohwer, F. L. Piggyback-the-winner in host-associated microbial communities. NPJ Biofilms Microbiomes 2, 16010 (2016).Article 

Google Scholar 
198.Roach, T. N. F. et al. A multiomic analysis of in situ coral–turf algal interactions. Proc. Natl Acad. Sci. USA 117, 13588–13595 (2020).Article 

Google Scholar 
199.Cárdenas, A. et al. Coral-associated viral assemblages from the central Red Sea align with host species and contribute to holobiont genetic diversity. Front. Microbiol. 11, 572534 (2020).Article 

Google Scholar 
200.Bondy-Denomy, J. & Davidson, A. R. When a virus is not a parasite: the beneficial effects of prophages on bacterial fitness. J. Microbiol. 52, 235–242 (2014).Article 

Google Scholar 
201.Weynberg, K. D., Voolstra, C. R., Neave, M. J., Buerger, P. & van Oppen, M. J. H. From cholera to corals: viruses as drivers of virulence in a major coral bacterial pathogen. Sci. Rep. 5, 17889 (2015).Article 

Google Scholar 
202.Silveira, C. B. et al. Genomic and ecological attributes of marine bacteriophages encoding bacterial virulence genes. BMC Genomics 21, 126 (2020).Article 

Google Scholar 
203.Soffer, N., Brandt, M. E., Correa, A. M. S., Smith, T. B. & Thurber, R. V. Potential role of viruses in white plague coral disease. ISME J. 8, 271–283 (2014).Article 

Google Scholar 
204.Weynberg, K. D. et al. Prevalent and persistent viral infection in cultures of the coral algal endosymbiont Symbiodinium. Coral Reefs 36, 773–784 (2017).Article 

Google Scholar 
205.Brüwer, J. D., Agrawal, S., Liew, Y. J., Aranda, M. & Voolstra, C. R. Association of coral algal symbionts with a diverse viral community responsive to heat shock. BMC Microbiol. 17, 174 (2017).Article 

Google Scholar 
206.Jacquemot, L. et al. Therapeutic potential of a new jumbo phage that infects Vibrio coralliilyticus, a widespread coral pathogen. Front. Microbiol. 9, 2501 (2018).Article 

Google Scholar 
207.Efrony, R., Loya, Y., Bacharach, E. & Rosenberg, E. Phage therapy of coral disease. Coral Reefs 26, 7–13 (2007).Article 

Google Scholar 
208.Cohen, Y., Joseph Pollock, F., Rosenberg, E. & Bourne, D. G. Phage therapy treatment of the coral pathogen Vibrio coralliilyticus. Microbiologyopen 2, 64–74 (2013).Article 

Google Scholar 
209.Efrony, R., Atad, I. & Rosenberg, E. Phage therapy of coral white plague disease: properties of phage BA3. Curr. Microbiol. 58, 139–145 (2009).Article 

Google Scholar 
210.Atad, I., Zvuloni, A., Loya, Y. & Rosenberg, E. Phage therapy of the white plague-like disease of Favia favus in the Red Sea. Coral Reefs 31, 665–670 (2012).Article 

Google Scholar 
211.Sweet, M. J. & Bulling, M. T. On the importance of the microbiome and pathobiome in coral health and disease. Front. Mar. Sci. 4, 9 (2017).Article 

Google Scholar 
212.Pollock, F. J., Morris, P. J., Willis, B. L. & Bourne, D. G. The urgent need for robust coral disease diagnostics. PLoS Pathog. 7, e1002183 (2011).Article 

Google Scholar 
213.Lesser, M. P., Bythell, J. C., Gates, R. D., Johnstone, R. W. & Hoegh-Guldberg, O. Are infectious diseases really killing corals? Alternative interpretations of the experimental and ecological data. J. Exp. Mar. Bio. Ecol. 346, 36–44 (2007).Article 

Google Scholar 
214.Roder, C., Arif, C., Daniels, C., Weil, E. & Voolstra, C. R. Bacterial profiling of white plague disease across corals and oceans indicates a conserved and distinct disease microbiome. Mol. Ecol. 23, 965–974 (2014).Article 

Google Scholar 
215.Soffer, N., Zaneveld, J. & Vega Thurber, R. Phage–bacteria network analysis and its implication for the understanding of coral disease. Environ. Microbiol. 17, 1203–1218 (2015).Article 

Google Scholar 
216.Ubeda, C. et al. Antibiotic-induced SOS response promotes horizontal dissemination of pathogenicity island-encoded virulence factors in staphylococci. Mol. Microbiol. 56, 836–844 (2005).Article 

Google Scholar 
217.Cárdenas, A. et al. Excess labile carbon promotes the expression of virulence factors in coral reef bacterioplankton. ISME J. 12, 59–76 (2018).Article 

Google Scholar 
218.Anthony, K. et al. New interventions are needed to save coral reefs. Nat. Ecol. Evol. 1, 1420–1422 (2017).Article 

Google Scholar 
219.Allard, S. M. et al. Introducing the mangrove microbiome initiative: identifying microbial research priorities and approaches to better understand, protect, and rehabilitate mangrove ecosystems. mSystems https://doi.org/10.1128/mSystems.00658-20 (2020).Article 

Google Scholar 
220.Zickfeld, K. et al. Long-term climate change commitment and reversibility: An EMIC intercomparison. J. Clim. 26, 5782–5809 (2013).Article 

Google Scholar 
221.Humanes, A. et al. A framework for selectively breeding corals for assisted evolution. Preprint at bioRxiv https://doi.org/10.1101/2021.02.23.432469 (2021).Article 

Google Scholar 
222.National Academies of Sciences, Engineering, and Medicine. A Decision Framework for Interventions to Increase the Persistence and Resilience of Coral Reefs (National Academies Press, 2019).223.Page, C. A., Muller, E. M. & Vaughan, D. E. Microfragmenting for the successful restoration of slow growing massive corals. Ecol. Eng. 123, 86–94 (2018).Article 

Google Scholar 
224.Schopmeyer, S. A. et al. Regional restoration benchmarks for Acropora cervicornis. Coral Reefs 36, 1047–1057 (2017).Article 

Google Scholar 
225.Suggett, D. J., Edmondson, J., Howlett, L. & Camp, E. F. Coralclip®: a low-cost solution for rapid and targeted out-planting of coral at scale. Restor. Ecol. 28, 289–296 (2020).Article 

Google Scholar 
226.Woesik, R. et al. Differential survival of nursery-reared Acropora cervicornis outplants along the Florida reef tract. Restor. Ecol. 29, e13302 (2021).Article 

Google Scholar 
227.Ware, M. et al. Survivorship and growth in staghorn coral (Acropora cervicornis) outplanting projects in the Florida Keys National Marine Sanctuary. PLoS ONE 15, e0231817 (2020).Article 

Google Scholar 
228.Ladd, M. C., Shantz, A. A., Bartels, E. & Burkepile, D. E. Thermal stress reveals a genotype-specific tradeoff between growth and tissue loss in restored Acropora cervicornis. Mar. Ecol. Prog. Ser. 572, 129–139 (2017).Article 

Google Scholar 
229.Goergen, E. A. & Gilliam, D. S. Outplanting technique, host genotype, and site affect the initial success of outplanted Acropora cervicornis. PeerJ 6, e4433 (2018).Article 

Google Scholar 
230.Chamberland, V. F. et al. New seeding approach reduces costs and time to outplant sexually propagated corals for reef restoration. Sci. Rep. 7, 18076 (2017).Article 

Google Scholar 
231.Craggs, J., Guest, J., Davis, M. & Sweet, M. Completing the life cycle of a broadcast spawning coral in a closed mesocosm. Invertebr. Reprod. Dev. 64, 244–247 (2020).Article 

Google Scholar 
232.Hock, K. et al. Connectivity and systemic resilience of the Great Barrier Reef. PLoS Biol. 15, e2003355 (2017).Article 

Google Scholar 
233.Quigley, K. M., Bay, L. K. & van Oppen, M. J. H. The active spread of adaptive variation for reef resilience. Ecol. Evol. 9, 11122–11135 (2019).Article 

Google Scholar 
234.Sangsawang, L. et al. 13C and 15N assimilation and organic matter translocation by the endolithic community in the massive coral Porites lutea. R. Soc. Open Sci. 4, 171201 (2017).Article 

Google Scholar 
235.Pernice, M. et al. Down to the bone: the role of overlooked endolithic microbiomes in reef coral health. ISME J. 14, 325–334 (2020).Article 

Google Scholar 
236.Kwong, W. K., Del Campo, J., Mathur, V., Vermeij, M. J. A. & Keeling, P. J. A widespread coral-infecting apicomplexan with chlorophyll biosynthesis genes. Nature 568, 103–107 (2019).Article 

Google Scholar 
237.Fine, M., Gildor, H. & Genin, A. A coral reef refuge in the Red Sea. Glob. Chang. Biol. 19, 3640–3647 (2013).Article 

Google Scholar 
238.Osman, E. O. et al. Thermal refugia against coral bleaching throughout the northern Red Sea. Glob. Chang. Biol. 24, e474–e484 (2018).Article 

Google Scholar 
239.Camp, E. F. et al. Corals exhibit distinct patterns of microbial reorganisation to thrive in an extreme inshore environment. Coral Reefs 39, 701–716 (2020).Article 

Google Scholar 
240.Grottoli, A. G. et al. Increasing comparability among coral bleaching experiments. Ecol. Appl. 31, e02262 (2021).Article 

Google Scholar 
241.Putnam, H. M., Barott, K. L., Ainsworth, T. D. & Gates, R. D. The vulnerability and resilience of reef-building corals. Curr. Biol. 27, R528–R540 (2017).Article 

Google Scholar 
242.Hagedorn, M. & Spindler, R. The reality, use and potential for cryopreservation of coral reefs. Adv. Exp. Med. Biol. 753, 317–329 (2014).Article 

Google Scholar 
243.Hagedorn, M. et al. Successful demonstration of assisted gene flow in the threatened coral Acropora palmata across genetically-isolated caribbean populations using cryopreserved sperm. Cold Spring Harb. Lab. https://doi.org/10.1101/492447 (2018).Article 

Google Scholar 
244.Hagedorn, M., Spindler, R. & Daly, J. Cryopreservation as a tool for reef restoration: 2019. Adv. Exp. Med. Biol. 1200, 489–505 (2019).Article 

Google Scholar 
245.Daly, J. et al. Successful cryopreservation of coral larvae using vitrification and laser warming. Sci. Rep. 8, 15714 (2018).Article 

Google Scholar 
246.Chakravarti, L. J., Beltran, V. H. & van Oppen, M. J. H. Rapid thermal adaptation in photosymbionts of reef-building corals. Glob. Chang. Biol. 23, 4675–4688 (2017).Article 

Google Scholar 
247.Quigley, K. M., Alvarez Roa, C., Torda, G., Bourne, D. G. & Willis, B. L. Co-dynamics of Symbiodiniaceae and bacterial populations during the first year of symbiosis with Acropora tenuis juveniles. Microbiologyopen 9, e959 (2020).Article 

Google Scholar 
248.Teplitski, M. & Ritchie, K. How feasible is the biological control of coral diseases? Trends Ecol. Evol. 24, 378–385 (2009).Article 

Google Scholar  More

1.Wickham, D. E. A new species of Carcinonemertes (Nemertea: Carcinonemertidae) with notes on the genus from the Pacific Coast. Proc. Biol. Soc. 91, 197–202 (1978).
Google Scholar 
2.Kuris, A. M. & Wickham, D. E. Effect of nemertean egg predators on crustaceans. Bull. Mar. Sci. 41, 151–164 (1987).
Google Scholar 
3.Shields, J.D. & Kuris, A.M. Temporal variation in abundance of the egg predator Carcinonemertes epialti (Nemertea) and its effect on egg mortality of its host, the shore crab, Hemigrapsus oregonensis in Recent Advances in Nemertean Biology, Developments in Hydrobiology 31–38 (eds. Sundberg, P., Gibson, R. & Berg, G). (Springer, 1988).4.Shields, J.D., Wickham, D.E., Blau, S.F. & Kuris, A.M. Some implications of egg mortality caused by symbiotic nemerteans for data acquisition and management strategies of red king crabs Paralithodes camtschaticus. In Proceedings of the International King Tanner King Crab Symposium. Alaska Sea Grant Report, 383–395 (1990).5.Wickham, D. E. Aspects of the Life History of Carcinonemertes errans (Nemertea: Carcinonemertidae), an Egg Predator of the Crab Cancer magister. Biol. Bull. 159, 247–257 (1980).Article 

Google Scholar 
6.Wickham, D. E. & Kuris, A. M. The comparative ecology of nemertean egg predators. Am. Zool. 25, 127–134 (1985).Article 

Google Scholar 
7.Campbell, A. & Brattey, J. Egg Loss from the American Lobster, Homarus americanus, in Relation to Nemertean, Pseudocarcinonemertes homari, Infestation. Can. J. Fish. Aquat. Sci. 43, 772–780 (1986).Article 

Google Scholar 
8.Kuris, A. M., Blau, S. F., Paul, A. J., Shields, J. D. & Wickham, D. E. Infestation by brood symbionts and their impact on Egg Mortality of the Red King Crab, Paralithodes camtschatica, in Alaska: Geographic and temporal variation. Can. J. Fish. Aquat. Sci. 48, 559–568 (1991).Article 

Google Scholar 
9.Gonzalez-Cueto, J. A. & Quiroga, S. First record of Carcinonemertes conanobrieni Simpson, Ambrosio & Baeza, 2017 (Nemertea, Carcinonemertidae), an egg predator of the Caribbean spiny lobster Panulirus argus (Latreille, 1804), on the Caribbean Coast of Colombia. CheckList 14, 425–429 (2018).Article 

Google Scholar 
10.Simpson, L.A., Ambrosio, L.J. & Baeza, J.A. A new species of Carcinonemertes, Carcinonemertes conanobrieni sp. nov. (Nemertea: Carcinonemertidae), an egg predator of the Caribbean spiny lobster, Panulirus argus. PLoS ONE 12, e0177021 (2017).11.Shields, J. D. & Wood, F. E. Impact of parasites on the reproduction and fecundity of the blue sand crab Portunus pelagicus from Moreton Bay, Australia. Mar. Ecol. Prog. Ser. 92, 159–159 (1993).ADS 
Article 

Google Scholar 
12.Botsford, L. W. & Wickham, D. E. Behavior of age-specific, density-dependent models and the Northern California Dungeness Crab (Cancer magister) Fishery. J. Fish. Res. Bd. Can. 35, 833–843 (1978).Article 

Google Scholar 
13.Santos, C., Bueno, S. L. S. & Norenburg, J. L. Infestation by Carcinonemertes divae (Nemertea: Carcinonemertidae) in Libinia spinosa (Decapoda: Pisidae) from São Sebastião Island, SP, Brazil. J. Nat. Hist. 40, 999–1005 (2006).Article 

Google Scholar 
14.Wickham, D.E. & Kuris, A.M. Diversity among nemertean egg predators of decapod crustaceans in Recent Advances in Nemertean Biology, Developments in Hydrobiology 23–30 (eds. Sundberg, P., Gibson, & R., Berg, G) (Springer, 1988).15.Kuris, A.M. Life cycles of nemerteans that are symbiotic egg predators of decapod Crustacea: adaptations to host life histories in Advances in Nemertean Biology, Developments in Hydrobiology (eds. Gibson, R., Moore, J., & Sundberg). Springer 1–14 (1993).16.Wickham, D. E. A new species of Carcinonemertes (Nemertea: Carcinonemertidae) with notes on the genus from the Pacific coast. Proc. Biol. Soc. Wash. 91, 197–202 (1996).
Google Scholar 
17.Segonzac, M. & Shields, J. D. New Nemertean Worms (Carcinonemertidae) on Bythograeid Crabs (Decapoda: Brachyura) from Pacific Hydrothermal Vent Sites. J. Crustac. Biol. 27, 681–692 (2007).Article 

Google Scholar 
18.Humes, A. G. The morphology, taxonomy, and bionomics of the nemertean genus Carcinonemertes. Ill. Biol. Monogr. 18, 1–105 (1942).
Google Scholar 
19.Shields, J. D. Parasites and symbionts of the Crab Portunus Pelagicus from Moreton Bay Eastern Australia. J. Crustac. Biol. 12, 94–100 (1992).Article 

Google Scholar 
20.McDermott, J. J. & Gibson, R. Carcinonemertes pinnotheridophila sp. Nov. (Nemertea, Enopla, Carcinonemertidae) from the branchial chambers of Pinnixa chaetopterana (Crustacea, Decapoda, Pinnotheridae): Description, incidence and biological relationships with the host. Hydrobiologia 266, 57–80 (1993).Article 

Google Scholar 
21.Sadeghian, P. S. & Santos, C. Two new species of Carcinonemertes (Hoplonemertea: Carcinonemertidae) living in association with leucosiid crabs from California and Tasmania. J. Nat. Hist. 44, 2395–2409 (2010).Article 

Google Scholar 
22.Shields, J. D., Wickham, D. E. & Kuris, A. M. Carcinonemertes regicides n. sp. (Nemertea), a symbiotic egg predator from the red king crab, Paralithodes camtschatica (Decapoda: Anomura), in Alaska. Can. J. Zool. 67, 923–930 (1989).Article 

Google Scholar 
23.Shields, J. D. & Kuris, A. M. Carcinonemertes wickhami n. sp. (Nemertea), a symbiotic egg predator from the spiny lobster Panulirus interruptus in southern California, with remarks on symbiont-host adaptations. Fish. Bull. 88, 279–287 (1990).
Google Scholar 
24.Campbell, A., Gibson, R. & Evans, L. H. A new species of Carcinonemertes (Nemertea: Carcinonemertidae) ectohabitant on Panulirus cygnus (Crustacea: Palinuridae) from Western Australia. Zool. J. Linn. Soc. 95, 257–268 (1989).Article 

Google Scholar 
25.Fischer, S. & Thatje, S. Temperature effects on life-history traits cause challenges to the management of brachyuran crab fisheries in the Humboldt Current: A review. Fish. Res. 183, 461–468 (2016).Article 

Google Scholar 
26.Retamal, M.A. et al. Estado actual del conocimiento de las principales especies de jaibas a nivel nacional. Fondo de Investigación Pesquera, Informe Técnico FIP-IT/2007-39 1-237 (2009).27.Retamal, M.A. Catálogo ilustrado de los crustáceos decápodos de Chile. Universidad de Concepción (1981).28.Aedo, G.A. & Arancibia, H. Pesca comercial de jaiba limón, Cancer porteri, con trampas Fathom Plus. Proyecto FONDEF D97I-1058 “Nuevas pesquerías en Chile central”. Manual Técnico N°2, UNITEP. Universidad de Concepción 1–13 (2000).29.Muñoz, C. A., Pardo, L. M., Henríquez, L. A. & Palma, Á. T. Seasonal variations in the composition and abundance of four Cancer species (Decapoda: Brachyura: Cancridae) trapped in San Vicente Bay, Concepción (central Chile). Investig. Mar. 34, 9–21 (2006).Article 

Google Scholar 
30.Bustamante, R. H. & Castilla, J. C. The shellfishery in Chile: An analysis of 26 years of landings (1960–1985). Biología Pesquera 16, 79–97 (1987).
Google Scholar 
31.SUBPESCA. Decreto 9: Establece normas de regulación para el recurso jaiba en todo el territorio nacional. http://bcn.cl/2nfdj (1990).32.SERNAPESCA. Anuario estadístico de pesca y acuicultura 2019. http://www.sernapesca.cl/informacion-utilidad/anuarios-estadisticos-de-pesca-y-acuicultura (2019).33.Wickham, D. E. Predation by the nemertean Carcinonemertes errans on eggs of the Dungeness crab Cancer magister. Mar. Biol. 55, 45–53 (1979).Article 

Google Scholar 
34.Kuris, A. M. & Lafferty, K. B. Modelling crustacean Fisheries: Effects of parasites on management strategies. Can. J. Fish. Aquat. Sci. 49, 327–336 (1992).Article 

Google Scholar 
35.Baeza, J. A. et al. Active parental care, reproductive performance, and a novel egg predator affecting reproductive investment in the Caribbean spiny lobster Panulirus argus. BMC Zool. 1, 6 (2016).Article 

Google Scholar 
36.Abramoff, M. D., Magelhaes, P. J. & Ram, S. J. Image Processing with ImageJ. Biophotonics Intern. 11, 6–42 (2004).
Google Scholar 
37.Legendre, P. & Legendre, L. Numerical Ecology (Elsevier, 1998).38.Vivanco, M., Vivanco, & J. M., Dapena, M. Análisis estadístico multivariable: Teoría y práctica (Editorial Universitaria, 1999).39.Miller, S. A., Dykes, D. D. & Polesky, H. F. A simple salting out procedure for extracting DNA from human nucleated cells. Nucl. Acids. Res. 16, 1215–1215 (1988).CAS 
Article 

Google Scholar 
40.Folmer, O., Hoeh, W. R., Black, M. B. & Vrijenhoek, R. C. Conserved primers for PCR amplification of mitochondrial DNA from different invertebrate phyla. Mol. Mar. Biol. Biotechnol. 3, 294–299 (1994).CAS 
PubMed 

Google Scholar 
41.Filatov, D. A. Proseq: A software for preparation and evolutionary analysis of DNA sequence data sets. Mol. Ecol. Notes. 2, 621–624 (2002).CAS 
Article 

Google Scholar 
42.Larkin, M. A. et al. Clustal W and Clustal X version 2.0. Bioinformatics 23, 2947–2948 (2007).CAS 
Article 

Google Scholar 
43.Santorum, J. M., Darriba, D., Taboada, G. L. & Posada, D. jmodeltest.org: Selection of nucleotide substitution models on the cloud. Bioinformatics 30, 1310–1311 (2014).CAS 
Article 

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

Google Scholar 
45.Ronquist, F. et al. MrBayes 3.2: Efficient Bayesian phylogenetic inference and model choice across a large model space. Syst. Biol. 61, 539–542 (2012).Article 

Google Scholar 
46.Nguyen, L. T., Schmidt, H. A., von Haeseler, A. & Minh, B. Q. IQ-TREE: A fast and effective stochastic algorithm for estimating maximum-likelihood phylogenies. Mol. Biol. Evol. 32, 268–274 (2015).CAS 
Article 

Google Scholar 
47.Miller, M.A., Pfeiffer, W. & Schwartz, T. Creating the CIPRES Science Gateway for inference of large phylogenetic trees. In Gateway Computing Environments Workshop (GCE) 1–8. https://doi.org/10.1109/GCE.2010.567612948.Tamura, K., Stecher, G., Peterson, D., Filipski, A. & Kumar, S. MEGA6: Molecular evolutionary genetics analysis Version 6.0. Mol. Biol. Evol. 30, 2725–2729 (2013).CAS 
Article 

Google Scholar 
49.Kimura, M. A simple method for estimating evolutionary rates of base substitutions through comparative studies of nucleotide sequences. J. Mol. Evol. 16, 111–120 (1980).ADS 
CAS 
Article 

Google Scholar  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