Ecology

Fish distributionWe rasterized a detailed reef distribution vector map35 at 5 × 5 latitude/longitude degrees (by considering as reef area each cell in the raster intersecting a polygon in the original shapefile). We collected all the occurrences of fish species intersecting the rasterized reef area from both the Ocean Biogeographic Information System36 and the Global Biodiversity Information Facility37. We used taxonomic and biogeographical (i.e., latitudinal/longitudinal extremes for a given species) information from FishBase38 to exclude potential incorrect occurrences (i.e., all the records falling outside the known species ranges). We also restricted the list to all the species for which FishBase provided relevant ecological information (as these were needed to evaluate prey-predator species interactions and identify indirect links between fish species and coral, see below). The filtered list comprises 9143 fish species.For these species, we used occurrence data to generate species ranges. For this, we used the α-hull procedure39, but instead of pre-selecting an α parameter and using it for all species, we developed a procedure to obtain conservative species ranges while including most of the known occurrences. First, we selected a very small α (0.001), to obtain a hull including most of the occurrences. Then, we progressively incremented α in small amounts (0.005) by computing, for each increment, the ratio between the relative reduction in the resulting hull area (in respect to the previous hull), and the relative reduction of occurrences included in the hull (in respect to the total number of available occurrences for the target species). We stopped increasing α when the ratio became 0.97.The random forest predictor was used to assess the probability of trophic interaction between a large list of potential interactions generated by combining all fish species from our reef fish occurrence dataset known to rely mainly or exclusively on fish for their survival (i.e. “true piscivores”, FishBase trophic level  > 3.5), with all the fish in the dataset. The full list included 31,768,450 potential interactions, that we reduced to 6,721,450 interactions by keeping only the interacting pairs identified by the random forest classifier with a probability ≥0.9.(3) If the ecological dependency between two species is actually manifested then the two species must obviously co-occur at some locations, and vice-versa, co-occurrence is a necessary pre-requisite for an ecological dependency. Following this logic, we took a final, additional step to further filter and improve the fish → fish interaction list. In particular, we quantified the tendency for species to co-occur in the same locality as one potential proxy layer for species interactions, complementary to our other approaches. There are various factors that can affect the co-occurrence of two species. In a simplification, this can emerge from stochasticity, shared environmental requirements, shared evolutionary history, and ecological dependencies. We attempted to disentangle the effect of the last factor from the first three.For each target species pair, we computed overlap in distribution as the raw number of reef localities where both target species were found. Then, we compared this number with the null expectation obtained by randomizing the distribution of species occurrences across reef localities. We designed a null model accounting for randomness, species niche and biogeographical history, and hence randomizing the occurrence of species only within areas where they could have possibly occurred according to environmental conditions and biogeographical factors (e.g., in the absence of hard or soft barriers). To implement the null model, we first excluded from the list of potential localities all the areas outside the biogeographical regions where the target species had been recorded, with regions identified according to Spalding et al.49. Then, within the remaining areas, we identified all the reef localities with climate envelopes favourable to target species survival. For this, we identified the min and max of major environmental drivers (mean annual surface temperature, salinity, pH) where the target species occurred, and then we identified all the localities with conditions not exceeding these limits. We generated, for each pairwise species comparison, one thousand randomized sets of species occurrences by rearranging randomly species occurrence within all suitable localities. We quantified co-occurrence between the species pair in each random scenario. Finally, we compared the observed co-occurrence with the random co-occurrences, computing a p-value as the fraction of null models with co-occurrence identical or higher than the observed one. We kept only the pairs with a p-value  More

1.Silvy, Y., Guilyardi, E., Sallee, J.-B. & Durack, P. J. Human-induced changes to the global ocean water masses and their time of emergence. Nat. Clim. Change 10, 1030–1036 (2020).ADS 
CAS 

Google Scholar 
2.Laufkötter, C., Zscheischler, J. & Frölicher, T. L. High-impact marine heatwaves attributable to human-induced global warming. Science 369, 1621–1625 (2020).ADS 

Google Scholar 
3.Henson, S. A. et al. Rapid emergence of climate change in environmental drivers of marine ecosystems. Nat. Commun. 8, 14682 (2017).ADS 
PubMed Central 
PubMed 

Google Scholar 
4.Grothmann, T. & Patt, A. Adaptive capacity and human cognition: The process of individual adaptation to climate change. Glob. Environ. Change 15, 199–213 (2005).
Google Scholar 
5.Adger, W. N. Vulnerability. Glob. Environ. Change 16, 268–281 (2006).
Google Scholar 
6.Cinner, J. E. et al. Building adaptive capacity to climate change in tropical coastal communities. Nat. Clim. Change 8, 117–123 (2018).ADS 

Google Scholar 
7.van Putten, I. E. et al. Empirical evidence for different cognitive effects in explaining the attribution of marine range shifts to climate change. ICES J. Mar. Sci. 73, 1306–1318 (2016).
Google Scholar 
8.Salinger, J. et al. Decadal-scale forecasting of climate drivers for marine applications. in Advances in Marine Biology (ed. Curry, BE) vol. 74, 1–68 (2016).9.Williams, J. W. & Jackson, S. T. Novel climates, no-analog communities, and ecological surprises. Front. Ecol. Environ. 5, 475–482 (2007).
Google Scholar 
10.Pershing, A. J. et al. Challenges to natural and human communities from surprising ocean temperatures. Proc. Natl. Acad. Sci. U. S. A. 116, 18378–18383 (2019).CAS 
PubMed Central 
PubMed 

Google Scholar 
11.Overland, J. E. et al. Climate controls on marine ecosystems and fish populations. J. Mar. Syst. 79, 305–315 (2010).
Google Scholar 
12.Merryfield, W. J. et al. Current and emerging developments in subseasonal to decadal prediction. Bull. Am. Meteorol. Soc. 101, E869–E896 (2020).
Google Scholar 
13.Deser, C. et al. Insights from Earth system model initial-condition large ensembles and future prospects. Nat. Clim. Change 10, 277–286 (2020).ADS 

Google Scholar 
14.Palmer, T. N. & Stevens, B. The scientific challenge of understanding and estimating climate change. Proc. Natl. Acad. Sci. U. S. A. 116, 24390–24395 (2019).ADS 
CAS 
PubMed Central 
PubMed 

Google Scholar 
15.Parmesan, C. et al. Beyond climate change attribution in conservation and ecological research. Ecol. Lett. 16, 58–71 (2013).
Google Scholar 
16.Myers, R. A. When do environment-recruitment correlations work?. Rev. Fish Biol. Fish. 8, 285–305 (1998).
Google Scholar 
17.Litzow, M. A. et al. Non-stationary climate–salmon relationships in the Gulf of Alaska. Proc. R. Soc. B Biol. Sci. 285, 20181855 (2018).
Google Scholar 
18.Deyle, E. R. et al. Predicting climate effects on Pacific sardine. Proc. Natl. Acad. Sci. U. S. A. 110, 6430–6435 (2013).ADS 
CAS 
PubMed Central 
PubMed 

Google Scholar 
19.Planque, B. Projecting the future state of marine ecosystems, ‘la grande illusion’?. ICES J. Mar. Sci. 73, 204–208 (2016).MathSciNet 

Google Scholar 
20.Schindler, D. E. & Hilborn, R. Prediction, precaution, and policy under global change. Science 347, 953–954 (2015).ADS 
CAS 

Google Scholar 
21.Maguire, K. C., Nieto-Lugilde, D., Fitzpatrick, M. C., Williams, J. W. & Blois, J. L. Modeling species and community responses to past, present, and future episodes of climatic and ecological change. Annu. Rev. Ecol. Evol. Syst. 46, 343–368 (2015).
Google Scholar 
22.Glaser, S. M. et al. Complex dynamics may limit prediction in marine fisheries. Fish Fish. 15, 616–633 (2014).
Google Scholar 
23.Pershing, A. J. et al. Slow adaptation in the face of rapid warming leads to collapse of the Gulf of Maine cod fishery. Science 350, 809–812 (2015).ADS 
CAS 

Google Scholar 
24.Palmer, M. C., Deroba, J. J., Legault, C. M. & Brooks, E. N. Comment on “Slow adaptation in the face of rapid warming leads to collapse of the Gulf of Maine cod fishery”. Science 352, 423 (2016).ADS 
CAS 

Google Scholar 
25.Swain, D. P., Benoit, H. P., Cox, S. P. & Cadigan, N. G. Comment on “Slow adaptation in the face of rapid warming leads to collapse of the Gulf of Maine cod fishery”. Science 352, 423 (2016).ADS 
CAS 

Google Scholar 
26.Pershing, A. J. et al. Response to comments on “Slow adaptation in the face of rapid warming leads to collapse of the Gulf of Maine cod fishery”. Science 352, 423 (2016).CAS 

Google Scholar 
27.Stott, P. A., Stone, D. A. & Allen, M. R. Human contribution to the European heatwave of 2003. Nature 432, 610–614 (2004).ADS 
CAS 

Google Scholar 
28.Stott, P. A. et al. Attribution of extreme weather and climate-related events. Wiley Interdiscip. Rev. Clim. Change 7, 23–41 (2016).
Google Scholar 
29.Walsh, J. E. et al. The high latitude heat wave of 2016 and its impacts on Alaska. Bull. Am. Meteorol. Soc. 99, S39–S43 (2018).
Google Scholar 
30.Schwalm, C. R., Glendon, S. & Duffy, P. B. RCP85 tracks cumulative CO2 emissions. Proc. Natl. Acad. Sci. U. S. A. 117, 19656–19657 (2020).ADS 
CAS 
PubMed Central 
PubMed 

Google Scholar 
31.Dorn, M. W. et al. Assessment of the walleye pollock stock in the Gulf of Alaska. https://www.fisheries.noaa.gov/resource/data/2020-assessment-walleye-pollock-stock-gulf-alaska (2020).32.Barbeaux, S. J. et al. Assessment of the Pacific cod stock in the Gulf of Alaska. https://www.fisheries.noaa.gov/resource/data/2020-assessment-pacific-cod-stock-gulf-alaska (2020).33.Litzow, M. A. et al. Evaluating ecosystem change as Gulf of Alaska temperature exceeds the limits of preindustrial variability. Prog. Oceanogr. 186, 102393 (2020).
Google Scholar 
34.Caley, M. J. et al. Recruitment and the local dynamics of open marine populations. Annu. Rev. Ecol. Syst. 27, 477–500 (1996).
Google Scholar 
35.Barbeaux, S. J., Holsman, K. & Zador, S. Marine heatwave stress test of ecosystem-based fisheries management in the Gulf of Alaska Pacific cod fishery. Front. Mar. Sci. 7, 703 (2020).
Google Scholar 
36.Piatt, J. F. et al. Extreme mortality and reproductive failure of common murres resulting from the northeast Pacific marine heatwave of 2014–2016. PLoS ONE 15, e0226087 (2020).CAS 
PubMed Central 
PubMed 

Google Scholar 
37.Harley, C. D. G. et al. The impacts of climate change in coastal marine systems. Ecol. Lett. 9, 228–241 (2006).ADS 

Google Scholar 
38.Hsieh, C.-H. et al. Fishing elevates variability in the abundance of exploited species. Nature 443, 859–862 (2006).ADS 
CAS 

Google Scholar 
39.Laurel, B. J. & Rogers, L. A. Loss of spawning habitat and prerecruits of Pacific cod during a Gulf of Alaska heatwave. Can. J. Fish. Aquat. Sci. 77, 644–650 (2020).
Google Scholar 
40.Koenker, B. L., Laurel, B. J., Copeman, L. A. & Ciannelli, L. Effects of temperature and food availability on the survival and growth of larval Arctic cod (Boreogadus saida) and walleye pollock (Gadus chalcogrammus). ICES J. Mar. Sci. 75, 2386–2402 (2018).
Google Scholar 
41.Rogers, L. A., Wilson, M. T., Duffy-Anderson, J. T., Kimmel, D. G. & Lamb, J. F. Pollock and “the Blob”: Impacts of a marine heatwave on walleye pollock early life stages. Fish. Oceanogr. 30, 142–158 (2021).
Google Scholar 
42.Filbee-Dexter, K. et al. Quantifying ecological and social drivers of ecological surprise. J. Appl. Ecol. 55, 2135–2146 (2018).
Google Scholar 
43.Allen, M. Liability for climate change. Nature 421, 891–892 (2003).ADS 
CAS 

Google Scholar 
44.Lloyd, E. A. & Oreskes, N. Climate change attribution: When is it appropriate to accept new methods?. Earths Future 6, 311–325 (2018).ADS 

Google Scholar 
45.Kirchmeier-Young, M. C., Gillett, N. P., Zwiers, F. W., Cannon, A. J. & Anslow, F. S. Attribution of the influence of human-induced climate change on an extreme fire season. Earths Future 7, 2–10 (2019).ADS 

Google Scholar 
46.Frame, D. J. et al. Climate change attribution and the economic costs of extreme weather events: A study on damages from extreme rainfall and drought. Clim. Change 162, 781–797 (2020).ADS 

Google Scholar 
47.Frame, D. J., Wehner, M. F., Noy, I. & Rosier, S. M. The economic costs of Hurricane Harvey attributable to climate change. Clim. Change 160, 271–281 (2020).ADS 

Google Scholar 
48.Winkler, A. J. et al. Slowdown of the greening trend in natural vegetation with further rise in atmospheric CO2. Biogeosciences 18, 4985–5010 (2021).ADS 

Google Scholar 
49.Shepherd, T. G. Storyline approach to the construction of regional climate change information. Proc. R. Soc. A Math. Phys. Eng. Sci. 475, 20190013 (2019).ADS 

Google Scholar 
50.Litzow, M. A. et al. Quantifying a novel climate through changes in PDO-climate and PDO-salmon relationships. Geophys. Res. Lett. 47, 2020GL087972 (2020).ADS 

Google Scholar 
51.Laurel, B. J. et al. Regional warming exacerbates match/mismatch vulnerability for cod larvae in Alaska. Prog. Oceanogr. 193, 102555 (2021).
Google Scholar 
52.Bailey, K. M. Shifting control of recruitment of walleye pollock Theragra chalcogramma after a major climatic and ecosystem change. Mar. Ecol. Prog. Ser. 198, 215–224 (2000).ADS 

Google Scholar 
53.Jutfelt, F. Metabolic adaptation to warm water in fish. Funct. Ecol. 34, 1138–1141 (2020).
Google Scholar 
54.Walsh, J. E. et al. Downscaling of climate model output for Alaskan stakeholders. Environ. Model. Softw. 110, 38–51 (2018).
Google Scholar 
55.Lott, F. C. & Stott, P. A. Evaluating simulated fraction of attributable risk using climate observations. J. Clim. 29, 4565–4575 (2016).ADS 

Google Scholar 
56.Freeland, H. & Ross, T. `The Blob’—or, how unusual were ocean temperatures in the Northeast Pacific during 2014–2018?. Deep-Sea Res. I: Oceanogr. Res. Pap. 150, 103061 (2019).
Google Scholar 
57.Knutti, R. & Sedlacek, J. Robustness and uncertainties in the new CMIP5 climate model projections. Nat. Clim. Change 3, 369–373 (2013).ADS 

Google Scholar 
58.Adamson, M. W. & Hilker, F. M. Resource-harvester cycles caused by delayed knowledge of the harvested population state can be dampened by harvester forecasting. Theor. Ecol. 13, 425–434 (2020).
Google Scholar 
59.Dorn, M. W. & Zador, S. G. A risk table to address concerns external to stock assessments when developing fisheries harvest recommendations. Ecosyst. Heal. Sustain. 6, 2 (2020).
Google Scholar 
60.Rudnick, D. L. & Davis, R. E. Red noise and regime shifts. Deep-Sea Res. I: Oceanogr Res. Pap. 50, 691–699 (2003).ADS 

Google Scholar 
61.Lauffenburger, N., Williams, K. & Jones, D. Results of the acoustic-trawl surveys of walleye pollock (Gadus chalcogrammus) in the Gulf of Alaska, March 2019. https://repository.library.noaa.gov/view/noaa/23711/ (2019).62.Stone, D. A., Rosier, S. M. & Frame, D. J. The question of life, the universe and event attribution. Nat. Clim. Change 11, 276–278 (2021).ADS 

Google Scholar 
63.Zuur, A. F., Tuck, I. D. & Bailey, N. Dynamic factor analysis to estimate common trends in fisheries time series. Can. J. Fish. Aquat. Sci. 60, 542–552 (2003).
Google Scholar 
64.Holmes, E. E., Ward, E. J. & Wills, K. MARSS: Multivariate autoregressive state-space models for analyzing time-series data. R J. 4, 11–19 (2012).
Google Scholar 
65.Yau, K. K. W., Wang, K. & Lee, A. H. Zero-inflated negative binomial mixed regression modeling of over-dispersed count data with extra zeros. Biom. J. 45, 437–452 (2003).MathSciNet 
MATH 

Google Scholar 
66.Zuur, A. F., Ieno, N., Walker, N., Saveliev, A. A. & Smith, G. M. Mixed Effects Models and Extensions in Ecology with R (Springer, 2009).MATH 

Google Scholar 
67.Wood, S. N. Thin plate regression splines. J. R. Stat. Soc. Series B Stat. Methodol. 65, 95–114 (2003).MathSciNet 
MATH 

Google Scholar 
68.Carpenter, B. et al. Stan: A probabilistic programming language. J. Stat. Softw. 76, 1–29 (2017).
Google Scholar 
69.R Core Team. R: A language and environment for statistical computing. v4.0.2. http://www.r-project.org/ (2020).70.Buerkner, P.-C. brms: An R package for Bayesian multilevel models using Stan. J. Stat. Softw. 80, 1–28 (2017).
Google Scholar 
71.Gabry, J., Simpson, D., Vehtari, A., Betancourt, M. & Gelman, A. Visualization in Bayesian workflow. J. R. Stat. Soc. Series Stat. Soc. 182, 389–402 (2019).MathSciNet 

Google Scholar  More

1.Kelly, D. The evolutionary ecology of mast seeding. Trends Ecol. Evol. 9, 465–470 (1994).CAS 
PubMed 

Google Scholar 
2.Janzen, D. H. Seed predation by animals. Annu. Rev. Ecol. Syst. 2, 465–492 (1971).
Google Scholar 
3.Silvertown, J. W. The evolutionary ecology of mast seeding in trees. Biol. J. Linn. Soc. 14, 235–250 (1980).
Google Scholar 
4.Koenig, W. D. Global patterns of environmental synchrony and the Moran effect. Ecography 25, 283–288 (2002).
Google Scholar 
5.Moran, P. A. P. The statistical analysis of the Canadian Lynx cycle. Aust. J. Zool. 1, 291–298 (1953).
Google Scholar 
6.Ranta, E., Veijo, K. & Lindströom, J. Spatially autocorrelated disturbances and patterns in population synchrony. Proc. R. Soc. Lond. B 266, 1851–1856 (1999).
Google Scholar 
7.Liebhold, A., Koenig, W. D. & Bjørnstad, O. N. Spatial synchrony in population dynamics. Annu. Rev. Ecol. Evol. Syst. 35, 467–490 (2004).
Google Scholar 
8.Sanguinetti, J. Producción y Predación de Semillas, Efectos de Corto y Largo Plazo Sobre el Reclutamiento de Plántulas. Caso de Estudio: Araucaria araucana (Universidad Nacional del Comahue, 2008).9.Schauber, E. M. et al. Masting by eighteen New Zealand plant species: the role of temperature as a synchronizing cue. Ecology 83, 1214–1225 (2002).
Google Scholar 
10.Fletcher, M.-S. Mast seeding and the El Niño-Southern Oscillation: a long-term relationship? Plant Ecol. 216, 527–533 (2015).
Google Scholar 
11.Koenig, W. D. & Knops, J. M. H. Scale of mast-seeding and tree-ring growth. Nature 396, 225 (1998).CAS 

Google Scholar 
12.Hacket-Pain, A. J. et al. Climatically controlled reproduction drives interannual growth variability in a temperate tree species. Ecol. Lett. 21, 1833–1844 (2018).PubMed 
PubMed Central 

Google Scholar 
13.Hadad, M. A., Roig, F. A., Arco Molina, J. G. & Hacket-Pain, A. Growth of male and female Araucaria araucana trees respond differently to regional mast events, creating sex-specific patterns in their tree-ring chronologies. Ecol. Indic. 122, 107245 (2021).
Google Scholar 
14.Thompson, D. W. J., Wallace, J. M. & Hegerl, G. C. Annular modes in the extratropical circulation. Part II: trends. J. Clim. 13, 1018–1036 (2000).
Google Scholar 
15.Holz, A., Kitzberger, T., Paritsis, J. & Veblen, T. T. Ecological and climatic controls of modern wildfire activity patterns across southwestern South America. Ecosphere 3, 1–25 (2012).
Google Scholar 
16.Mundo, I. A., Kitzberger, T., Roig Juñent, F. A., Villalba, R. & Barrera, M. D. Fire history in the Araucaria araucana forests of Argentina: human and climate influences. Int. J. Wildland Fire 22, 194–206 (2013).
Google Scholar 
17.Mundo, I. A., Roig Juñent, F. A., Villalba, R., Kitzberger, T. & Barrera, M. D. Araucaria araucana tree-ring chronologies in Argentina: spatial growth variations and climate influences. Trees-Struct. Funct. 26, 443–458 (2012).
Google Scholar 
18.Veblen, T. T., Kitzberger, T., Villalba, R. & Donnegan, J. Fire history in Northern Patagonia: the roles of humans and climatic variation. Ecol. Monogr. 69, 47–67 (1999).
Google Scholar 
19.Sanguinetti, J. & Kitzberger, T. Patterns and mechanisms of masting in the large-seeded southern hemisphere conifer Araucaria araucana. Austral Ecol. 33, 78–87 (2008).
Google Scholar 
20.Wigley, T. M. L., Briffa, K. & Jones, P. D. On the average value of correlated time series, with applications in dendroclimatology and hydrometeorology. J. Clim. Appl. Meteorol. 23, 201–213 (1984).
Google Scholar 
21.Swetnam, T. W. & Lynch, A. M. Multicentury, regional-scale patterns of Western Spruce budworm outbreaks. Ecol. Monogr. 63, 399–424 (1993).
Google Scholar 
22.Kitzberger, T., Veblen, T. T. & Villalba, R. Tectonic influences on tree growth in northern Patagonia, Argentina: the roles of substrate stability and climatic variation. Can. J. Res. 25, 1684–1696 (1995).
Google Scholar 
23.Mundo, I. A. et al. Austrocedrus chilensis growth decline in relation to drought events in northern Patagonia, Argentina. Trees Struct. Funct. 24, 561–570 (2010).
Google Scholar 
24.Rozas, V. et al. Climatic cues for secondary growth and cone production are sex-dependent in the long-lived dioecious conifer Araucaria araucana. Agric. Meteorol. 274, 132–143 (2019).
Google Scholar 
25.Pearse, I. S., Koenig, W. D. & Kelly, D. Mechanisms of mast seeding: resources, weather, cues, and selection. New Phytol. 212, 546–562 (2016).CAS 
PubMed 

Google Scholar 
26.Sanguinetti, J. & Kitzberger, T. Factors controlling seed predation by rodents and non-native Sus scrofa in Araucaria araucana forests: potential effects on seedling establishment. Biol. Invasions 12, 689–706 (2010).
Google Scholar 
27.Sanguinetti, J. & Kitzberger, T. Efectos de la producción de semillas y de la heterogeneidad vegetal sobre la supervivencia de semillas y el patrón espacio-temporal de establecimiento de plántulas en Araucaria araucana. Rev. Chil. Hist. Nat. 82, 319–335 (2009).
Google Scholar 
28.Kelly, D. et al. Of mast and mean: differential-temperature cue makes mast seeding insensitive to climate change. Ecol. Lett. 16, 90–98 (2013).PubMed 

Google Scholar 
29.Ostfeld, R. S. & Keesing, F. Pulsed resources and community dynamics of consumers in terrestrial ecosystems. Trends Ecol. Evol. 15, 232–237 (2000).CAS 
PubMed 

Google Scholar 
30.Holmgren, M., Scheffer, M., Ezcurra, E., Gutiérrez, J. R. & Mohren, G. M. J. El Niño effects on the dynamics of terrestrial ecosystems. Trends Ecol. Evol. 16, 89–94 (2001).CAS 
PubMed 

Google Scholar 
31.Swetnam, T. W. & Betancourt, J. L. Mesoscale disturbance and ecological response to decadal climatic variability in the American southwest. J. Clim. 11, 3128–3147 (1998).
Google Scholar 
32.Kitzberger, T., Swetnam, T. W. & Veblen, T. T. Inter-hemispheric synchrony of forest fires and the El Niño-Southern Oscillation. Glob. Ecol. Biogeogr. 10, 315–326 (2001).
Google Scholar 
33.Marshall, G. J. Trends in the Southern Annular Mode from observations and reanalyses. J. Clim. 16, 4134–4143 (2003).
Google Scholar 
34.Silvestri, G. E. & Vera, C. S. Antarctic Oscillation signal on precipitation anomalies over southeastern South America. Geophys. Res. Lett. 30, 2115 (2003).
Google Scholar 
35.Cai, W. et al. Climate impacts of the El Niño–Southern Oscillation on South America. Nat. Rev. Earth Environ. 1, 215–231 (2020).
Google Scholar 
36.Piovesan, G. & Adams, J. M. Masting behaviour in beech: linking reproduction and climatic variation. Can. J. Bot. 79, 1039–1047 (2001).
Google Scholar 
37.Drobyshev, I., Niklasson, M., Mazerolle, M. J. & Bergeron, Y. Reconstruction of a 253-year long mast record of European beech reveals its association with large scale temperature variability and no long-term trend in mast frequencies. Agric. Meteorol. 192–193, 9–17 (2014).
Google Scholar 
38.Fernández-Martínez, M., Vicca, S., Janssens, I. A., Espelta, J. M. & Peñuelas, J. The North Atlantic Oscillation synchronises fruit production in western European forests. Ecography 40, 864–874 (2017).
Google Scholar 
39.Ascoli, D. et al. Two centuries of masting data for European beech and Norway spruce across the European continent. Ecology 98, 1473 (2017).PubMed 

Google Scholar 
40.Thompson, D. W. J. et al. Signatures of the Antarctic ozone hole in Southern Hemisphere surface climate change. Nat. Geosci. 4, 741–749 (2011).CAS 

Google Scholar 
41.Jacques-Coper, M., Brönnimann, S., Martius, O., Vera, C. & Cerne, B. Summer heat waves in southeastern Patagonia: an analysis of the intraseasonal timescale. Int. J. Climatol. 36, 1359–1374 (2016).
Google Scholar 
42.Estudio de la Variabilidad Climáticas en Chile para el Siglo XXI (CONAMA, 2006).43.Garreaud, R. D. et al. The 2010–2015 megadrought in central Chile: impacts on regional hydroclimate and vegetation. Hydrol. Earth Syst. Sci. 21, 6307–6327 (2017).
Google Scholar 
44.Tortorelli, L. A. La explotación racional de los bosques de Araucaria de Neuquén. Su importancia económica. Servir (separata) VI, 1–74 (1942).45.Veblen, T. T., Burns, B. R., Kitzberger, T., Lara, A. & Villalba, R. in Ecology of the Southern Conifers (eds Enright, N. J. & Hill, R. S) 120–155 (Melbourne Univ. Press, 1995).46.Lara, A. et al. Mapeo de la Ecoregión de los Bosques Valdivianos de Argentina y Chile, en escala 1:500.000 (Fundación Vida Silvestre Aregentina, 1999).47.R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2020).48.Cook, E. R., Briffa, K., Shiyatov, S. & Mazepa, V. in Methods of Dendrochronology—Applications in the Environmental Sciences (eds Cook, E. & Kairiukstis, L. A.) 104–132 (Kluwer Academic Publishers, 1990).49.Yamaguchi, D. K. A simple method for cross-dating increment cores from living trees. Can. J. Res. 21, 414–416 (1991).
Google Scholar 
50.Holmes, R. L. Computer-assisted quality control in tree-ring dating and measurement. Tree-Ring Bull. 43, 69–78 (1983).
Google Scholar 
51.Visser, H. Note on the relation between ring widths and basal area increments. Forest Sci. 41, 297–304 (1995).52.Pedersen, B. S. The role of stress in the mortality of midwestern oaks as indicated by growth prior to death. Ecology 79, 79–93 (1998).
Google Scholar 
53.Cook, E. R. A Time Series Analysis Approach to Tree Ring Standardization (University of Arizona, School of Renewable Natural Resources, 1985).54.Melvin, T. M., Briffa, K. R., Nicolussi, K. & Grabner, M. Time-varying-response smoothing. Dendrochronologia 25, 65–69 (2007).
Google Scholar 
55.Biondi, F. Comparing tree-ring chronologies and repeated timber inventories as forest monitoring tools. Ecol. Appl. 9, 216–227 (1999).
Google Scholar 
56.Battipaglia, G. et al. Long tree-ring chronologies provide evidence of recent tree growth decrease in a Central African tropical forest. PLoS ONE 10, e0120962 (2015).PubMed 
PubMed Central 

Google Scholar 
57.Blasing, T. J., Solomon, A. M. & Duvick, D. N. Response function revisited. Tree-Ring Bull. 44, 1–15 (1984).
Google Scholar 
58.Sanguinetti, J. Producción de semillas de Araucaria araucana (Molina) K. Koch durante 15 años en diferentes poblaciones del Parque Nacional Lanín (Neuquén-Argentina). Ecol. Austral 24, 265–275 (2014).
Google Scholar 
59.Ficha de Valorización de Resultados. Proyecto Producción, Técnicas de Poscosecha y Desarrollo de Productos a partir del Piñón (FIA, 2011).60.Delignette-Muller, M. L. & Dutang, C. fitdistrplus: an R package for fitting distributions. J. Stat. Softw. 64, 1–34 (2015).
Google Scholar 
61.Villalba, R. et al. Unusual Southern Hemisphere tree growth patterns induced by changes in the Southern Annular Mode. Nat. Geosci. 5, 793–798 (2012).CAS 

Google Scholar 
62.Grissino-Mayer, H. D. Tree-ring Reconstructions of Climate and Fire at El Malpais National Monument, New Mexico (Univ. of Arizona, 1995).63.Torrence, C. & Compo, G. P. A practical guide to wavelet analysis. Bull. Am. Meteorol. Soc. 79, 61–78 (1998).
Google Scholar 
64.Grinsted, A., Moore, J. C. & Jevrejeva, S. Application of the cross wavelet transform and wavelet coherence to geophysical time series. Nonlinear Process. Geophys. 11, 561–566 (2004).
Google Scholar 
65.Gouhier, T., Grinsted, A. & Simko, V. Biwavelet: Conduct Univariate and Bivariate Wavelet Analyses. R package version 0.20.19 https://github.com/tgouhier/biwavelet (2019).66.Mundo, I. A. Historia de incendios en bosques de Araucaria araucana (Molina) K. Koch de Argentina a través de un análisis dendroecológico (Universidad Nacional de La Plata, 2011).67.Emile-Geay, J., Cobb, K. M., Mann, M. E. & Wittenberg, A. T. Estimating Central Equatorial Pacific SST variability over the past millennium. Part I: methodology and validation. J. Clim. 26, 2302–2328 (2013).
Google Scholar 
68.Mundo, I. A. et al. Multi-century tree-ring based reconstruction of the Neuquén River streamflow, northern Patagonia, Argentina. Clim. Past 8, 815–829 (2012).
Google Scholar  More

1.Ripple, W. J. et al. World Scientists’ Warning of a Climate Emergency 2021. BioScience. https://doi.org/10.1093/biosci/biab079 (2021).2.Liu, P. R. & Raftery, A. E. Country-based rate of emissions reductions should increase by 80% beyond nationally determined contributions to meet the 2 C target. Commun. Earth Environ. 2, 1–10 (2021).
Google Scholar 
3.IPBES. (eds Brondizio, E. S., Settele, J., Díaz, S. & Ngo, H. T.) 56 (IPBES, 2019).4.CBD Secretariat. The Strategic Plan for Biodiversity 2011-2020 and the Aichi Biodiversity Targets Vol. Document UNEP/CBD/COP/DEC/X/2 (Secretariat of the Convention on Biological Diversity, 2010).5.Trisos, C. H., Merow, C. & Pigot, A. L. The projected timing of abrupt ecological disruption from climate change. Nature 580, 496–501 (2020).CAS 

Google Scholar 
6.United State of America. The United States of America Nationally Determined Contribution- Reducing Greenhouse Gases in the United States: A 2030 Emissions Target. 24 (Submitted to the UNFCCC Secretariat under the Paris Agreement; https://www4.unfccc.int/sites/ndcstaging/PublishedDocuments/United%20States%20of%20America%20First/United%20States%20NDC%20April%2021%202021%20Final.pdf, 2021).7.Nelson, M. D. et al. Defining the United States land base: a technical document supporting the USDA Forest Service 2020 RPA assessment. Gen. Tech. Rep. NRS-191. 191, 1–70 (2020).
Google Scholar 
8.Pörtner, H. O. & et al. IPBES-IPCC co-sponsored workshop report on biodiversity and climate change. (IPBES and IPCC, https://doi.org/10.5281/zenodo.4782538, 2021).9.Elsen, P. R., Monahan, W. B., Dougherty, E. R. & Merenlender, A. M. Keeping pace with climate change in global terrestrial protected areas. Sci. Adv. 6, eaay0814 (2020).
Google Scholar 
10.Dinerstein, E. et al. A “Global Safety Net” to reverse biodiversity loss and stabilize Earth’s climate. Sci. Adv. 6, eabb2824 (2020).
Google Scholar 
11.Dinerstein, E. et al. An ecoregion-based approach to protecting half the terrestrial realm. BioScience 67, 534–545 (2017).
Google Scholar 
12.Griscom, B. W. et al. Natural climate solutions. Proc. Natl Acad. Sci. 114, 11645–11650 (2017).CAS 

Google Scholar 
13.Friedlingstein, P. et al. Global carbon budget 2020. Earth Syst. Sci. Data 12, 3269–3340 (2020).
Google Scholar 
14.Sexton, J. O. et al. Conservation policy and the measurement of forests. Nat. Clim. Chang. 6, 192–196 (2016).
Google Scholar 
15.Kreft, H. & Jetz, W. Global patterns and determinants of vascular plant diversity. Proc. Natl Acad. Sci. 104, 5925–5930 (2007).CAS 

Google Scholar 
16.Houghton, R. A., Hall, F. & Goetz, S. J. Importance of biomass in the global carbon cycle. J. Geophys. Res. 114, G00E03 (2009).
Google Scholar 
17.Mackey, B. et al. Understanding the importance of primary tropical forest protection as a mitigation strategy. Mitig. Adapt. Strateg. Glob. Chang. 25, 763–787 (2020).
Google Scholar 
18.Buotte, P. C., Law, B. E., Ripple, W. J. & Berner, L. T. Carbon sequestration and biodiversity co‐benefits of preserving forests in the western United States. Ecol. Appl.30, e02039 (2020).
Google Scholar 
19.Ruefenacht, B. et al. Conterminous US and Alaska forest type mapping using forest inventory and analysis data. Photogramm. Eng. Remote Sensing 74, 1379–1388 (2008).
Google Scholar 
20.USGS GAP. Protected Areas Database of the United States (PAD-US) 2.1: U.S. Geological Survey data release, https://doi.org/10.5066/P92QM3NT (2020).21.USGS. Gap Analysis Project Species Habitat Maps CONUS_2001. U.S. Geological Survey, https://doi.org/10.5066/F7V122T2 (2018).22.Wilson, B. T., Lister, A. J., Riemann, R. I. & Griffith, D. M. Live tree species basal area of the contiguous United States (2000-2009). (USDA Forest Service, Rocky Mountain Research Station, 2013).23.Wilson, B. T., Woodall, C. & Griffith, D. Imputing forest carbon stock estimates from inventory plots to a nationally continuous coverage. Carbon Balance Management 8, 1–15 (2013).
Google Scholar 
24.Oleson, K. et al. Technical Descriptioin of Version 4.5 of the Community Land Model (CLM) (National Center for Atmospheric Research, 2013).25.Buotte, P. C. et al. Near‐future forest vulnerability to drought and fire varies across the western United States. Glob. Chang. Biol. 25, 290–303 (2019).
Google Scholar 
26.Noss, R. F. et al. Bolder thinking for conservation. Conserv. Biol. 26, 1–4 (2012).
Google Scholar 
27.Allen, C. D. & Breshears, D. D. Drought-induced shift of a forest–woodland ecotone: rapid landscape response to climate variation. Proc. Natl Acad. Sci. 95, 14839–14842 (1998).CAS 

Google Scholar 
28.Watson, J. E. et al. The exceptional value of intact forest ecosystems. Nat. Ecol. Evol. 2, 599–610 (2018).
Google Scholar 
29.Lecina‐Diaz, J. et al. The positive carbon stocks–biodiversity relationship in forests: co‐occurrence and drivers across five subclimates. Ecol. Appl. 28, 1481–1493 (2018).
Google Scholar 
30.Di Marco, M., Ferrier, S., Harwood, T. D., Hoskins, A. J. & Watson, J. E. Wilderness areas halve the extinction risk of terrestrial biodiversity. Nature 573, 582–585 (2019).
Google Scholar 
31.Glaser, C., Romaniello, C. & Moskowitz, K. Costs and consequences: the real price of livestock grazing on America’s public lands. Tucson, AZ: Center for Biological Diversity (2015).32.Flather, C. H. Species endangerment patterns in the United States. Vol. 241 (US Department of Agriculture, Forest Service, Rocky Mountain Forest and …, 1994).33.Beschta, R. L. et al. Adapting to climate change on western public lands: addressing the ecological effects of domestic, wild, and feral ungulates. Environ. Manag. 51, 474–491 (2013).
Google Scholar 
34.Betts, M. G., Gutiérrez Illán, J., Yang, Z., Shirley, S. M. & Thomas, C. D. Synergistic effects of climate and land-cover change on long-term bird population trends of the western USA: a test of modeled predictions. Front. Ecol. Evol. 7, https://doi.org/10.3389/fevo.2019.00186 (2019).35.Berner, L. T., Law, B. E., Meddens, A. J. & Hicke, J. A. Tree mortality from fires, bark beetles, and timber harvest during a hot and dry decade in the western United States (2003–2012). Environ. Res. Lett. 12, 065005 (2017).
Google Scholar 
36.Law, B. E. et al. Land use strategies to mitigate climate change in carbon dense temperate forests. Proc. Natl Acad. Sci. 115, 3663 (2018).CAS 

Google Scholar 
37.Ouren, D. S. et al. Environmental effects of off-highway vehicles on Bureau of land management lands: a literature synthesis, annotated bibliographies, extensive bibliographies, and internet resources. US Geol. Survey Open-File Rep. 1353, 225 (2007).
Google Scholar 
38.Talty, M. J., Mott Lacroix, K., Aplet, G. H. & Belote, R. T. Conservation value of national forest roadless areas. Conserv. Sci. Pract. 2, e288 (2020).
Google Scholar 
39.Belote, R. T. & Wilson, M. B. Delineating greater ecosystems around protected areas to guide conservation. Conserv. Sci. Pract. 2, e196 (2020).
Google Scholar 
40.DellaSala, D. A., Karr, J. R. & Olson, D. M. Roadless areas and clean water. J. Soil Water Conserv. 66, 78–84 (2011).
Google Scholar 
41.McLaren, D. P., Tyfield, D. P., Willis, R., Szerszynski, B. & Markusson, N. O. Beyond “net-zero”: a case for separate targets for emissions reduction and negative emissions. Front. Clim. 1, 4 (2019).
Google Scholar 
42.Mildrexler, D. J., Berner, L. T., Law, B. E., Birdsey, R. A. & Moomaw, W. R. Large Trees Dominate Carbon Storage in Forests East of the Cascade Crest in the United States Pacific Northwest. Front. For. Glob. Chang. 3, https://doi.org/10.3389/ffgc.2020.594274 (2020).43.Hudiburg, T. W., Luyssaert, S., Thornton, P. E. & Law, B. E. Interactive effects of environmental change and management strategies on regional forest carbon emissions. Environ. Sci. Tech. 47, 13132–13140 (2013).CAS 

Google Scholar 
44.Noss, R. F. & Daly, K. M. In Connectivity Conservation (eds K. Crooks & M. Sanjayan) 587–619 (Cambridge Univ. Press, 2010).45.Geldmann, J. et al. Effectiveness of terrestrial protected areas in reducing habitat loss and population declines. Biol. Conserv. 161, 230–238 (2013).
Google Scholar 
46.Omernik, J. M. Perspectives on the nature and definition of ecological regions. Environ. Manag. 34, S27–S38 (2004).
Google Scholar 
47.Hudiburg, T. et al. Carbon dynamics of Oregon and Northern California forests and potential land-based carbon storage. Ecol. Appl. 19, 163–180 (2009).
Google Scholar 
48.Leu, M., Hanser, S. E. & Knick, S. T. The human footprint in the west: a large‐scale analysis of anthropogenic impacts. Ecol. Appl. 18, 1119–1139 (2008).
Google Scholar 
49.Haight, J. & Hammill, E. Protected areas as potential refugia for biodiversity under climatic change. Biol. Conserv. 241, 108258 (2020).
Google Scholar 
50.Dobrowski, S. Z. A climatic basis for microrefugia: the influence of terrain on climate. Glob. Chang. Biol. 17, 1022–1035 (2011).
Google Scholar 
51.Jantz, P., Goetz, S. & Laporte, N. Carbon stock corridors to mitigate climate change and promote biodiversity in the tropics. Nat. Clim. Chang. 4, 138–142 (2014).CAS 

Google Scholar 
52.McMenamin, S. K., Hadly, E. A. & Wright, C. K. Climatic change and wetland desiccation cause amphibian decline in Yellowstone National Park. Proc. Natl Acad. Sci. 105, 16988–16993 (2008).CAS 

Google Scholar 
53.Scott, J. M. et al. Recovery of imperiled species under the Endangered Species Act: the need for a new approach. Front. Ecol. Environ. 3, 383–389 (2005).
Google Scholar 
54.Miller, S. L. et al. Recent population decline of the Marbled Murrelet in the Pacific Northwest. Condor 114, 771–781 (2012).
Google Scholar 
55.Noon, B. R. & McKelvey, K. S. Management of the spotted owl: a case history in conservation biology. Annu. Rev. Ecol. System. 27, 135–162 (1996).
Google Scholar 
56.Ripple, W. J. et al. Ruminants, climate change and climate policy. Nat. Clim. Chang. 4, 2–5 (2014).CAS 

Google Scholar 
57.King, T. W. et al. Will Lynx lose their edge? Canada Lynx occupancy in Washington. J. Wildl. Manag. 84, 705–725 (2020).
Google Scholar 
58.Cayan, D. R. et al. Future dryness in the southwest US and the hydrology of the early 21st century drought. Proc. Natl Acad. Sci. 107, 21271–21276 (2010).CAS 

Google Scholar 
59.Rhoades, A. M., Ullrich, P. A. & Zarzycki, C. M. Projecting 21st century snowpack trends in western USA mountains using variable-resolution CESM. Clim. Dyn. 50, 261–288 (2018).
Google Scholar 
60.Williams, A. P. et al. Large contribution from anthropogenic warming to an emerging North American megadrought. Science 368, 314 (2020).CAS 

Google Scholar 
61.Mote, P. W., Hamlet, A. F., Clark, M. P. & Lettenmaier, D. P. Declining mountain snowpack in western north America. Bull. Am. Meteorol. Soc. 86, 39–49 (2005).
Google Scholar 
62.Cook, B. et al. Twenty‐first century drought projections in the CMIP6 forcing scenarios. Earth’s Futur. 8, e2019EF001461 (2020).
Google Scholar 
63.Vörösmarty, C. J. et al. Global threats to human water security and river biodiversity. Nature 467, 555–561 (2010).
Google Scholar 
64.Johnson, Z. C., Leibowitz, S. G. & Hill, R. A. Revising the index of watershed integrity national maps. Sci. Total Environ. 651, 2615–2630 (2019).CAS 

Google Scholar 
65.Anderegg, W. R. et al. Climate-driven risks to the climate mitigation potential of forests. Science 368, eaaz7005 (2020).CAS 

Google Scholar 
66.Buotte, P., Levis, S. & Law, B. E. NACP forest carbon stocks, fluxes, and productivity estimates, Western USA, 1979-2099. ORNL Distributed Active Archive Center, https://doi.org/10.3334/ORNLDAAC/1662 (2019).67.Williams, A. P. et al. Temperature as a potent driver of regional forest drought stress and tree mortality. Nat. Clim. Chang. 3, 292–297 (2012).
Google Scholar 
68.McDowell, N. G. et al. Multi-scale predictions of massive conifer mortality due to chronic temperature rise. Nat. Clim. Chang. 6, 295–300 (2015).
Google Scholar 
69.Williams, A. P. et al. Correlations between components of the water balance and burned area reveal new insights for predicting forest fire area in the southwest United States. Int. J. Wildland Fire 24, 14–26 (2014).
Google Scholar 
70.Abatzoglou, J. T. & Williams, A. P. Impact of anthropogenic climate change on wildfire across western US forests. Proc. Natl Acad. Sci. 113, 11770–11775 (2016).CAS 

Google Scholar 
71.Dennison, P. E., Brewer, S. C., Arnold, J. D. & Moritz, M. A. Large wildfire trends in the western United States, 1984–2011. Geophys. Res. Lett. 41, 2928–2933 (2014).
Google Scholar 
72.Balch, J. K. et al. Human-started wildfires expand the fire niche across the United States. Proc. Natl Acad. Sci. 114, 2946–2951 (2017).CAS 

Google Scholar 
73.Schoennagel, T. et al. Adapt to more wildfire in western North American forests as climate changes. Proc. Natl Acad. Sci. 114, 4582–4590 (2017).CAS 

Google Scholar 
74.Law, B. E. & Waring, R. H. Carbon implications of current and future effects of drought, fire and management on Pacific Northwest forests. For. Ecol. Management 355, 4–14 (2015).
Google Scholar 
75.Donato, D. C., Campbell, J. L. & Franklin, J. F. Multiple successional pathways and precocity in forest development: can some forests be born complex? J. Veg. Sci. 23, 576–584 (2012).
Google Scholar 
76.Campbell, J. L., Harmon, M. E. & Mitchell, S. R. Can fuel‐reduction treatments really increase forest carbon storage in the western US by reducing future fire emissions? Front. Ecol. Environ. 10, 83–90 (2012).
Google Scholar 
77.Harris, N. et al. Attribution of net carbon change by disturbance type across forest lands of the conterminous United States. Carbon Balanc. Management 11, 24 (2016).CAS 

Google Scholar 
78.Ghimire, B. et al. Large carbon release legacy from bark beetle outbreaks across Western United States. Glob. Chang. Biol. 21, 3087–3101 (2015).
Google Scholar 
79.Mitchell, S. R., Harmon, M. E. & O’connell, K. E. Forest fuel reduction alters fire severity and long‐term carbon storage in three Pacific Northwest ecosystems. Ecol. Appl. 19, 643–655 (2009).
Google Scholar 
80.Rhodes, J. J. & Baker, W. L. Fire probability, fuel treatment effectiveness and ecological tradeoffs in western US public forests. Open For. Sci. J. 1, 1–7 (2008).
Google Scholar 
81.Law, B. E. & Harmon, M. E. Forest sector carbon management, measurement and verification, and discussion of policy related to climate change. Carbon Management 2, 73–84 (2011).
Google Scholar 
82.Hudiburg, T. W., Law, B. E., Wirth, C. & Luyssaert, S. Regional carbon dioxide implications of forest bioenergy production. Nat. Clim. Chang. 1, 419–423 (2011).CAS 

Google Scholar 
83.Bonan, G. B. & Doney, S. C. Climate, ecosystems, and planetary futures: the challenge to predict life in Earth system models. Science 359, eaam8328 (2018).
Google Scholar 
84.Law, B. E. Regional analysis of drought and heat impacts on forests: current and future science directions. Glob. Chang. Biol. 20, 3595–3599 (2014).
Google Scholar 
85.Spawn, S. A., Sullivan, C. C., Lark, T. J. & Gibbs, H. K. Harmonized global maps of above and belowground biomass carbon density in the year 2010. Sci. Data 7, 1–22 (2020).
Google Scholar 
86.Kullberg, P. & Moilanen, A. How do recent spatial biodiversity analyses support the convention on biological diversity in the expansion of the global conservation area network? Natureza Conservacao 12, 3–10 (2014).
Google Scholar 
87.R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2020).88.Hijmans, R. J. raster: Geographic Analysis and Modeling. R package version 3.0-12. http://CRAN.R-project.org/package=raster (2019).89.Bivand, R., Keitt, T. & Rowlingson, B. rgdal: Bindings for the ‘Geospatial’ Data Abstraction Library. R package version 1.4-8. https://CRAN.R-project.org/package=rgdal (2019).90.O’Brien, J. gdalUtilities: Wrappers for ‘GDAL’ Utilities Executables. R package version 1. https://CRAN.R-project.org/package=gdalUtilities (2019).91.Dawle, M. & Srinivasan, A. data.table: Extension of ‘data.frame’. R package version 1.12.8. https://CRAN.R-project.org/package=data.table. (2019).92.Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer-Verlang New York, 2016).93.Hurrell, J. W. et al. The community earth system model: a framework for collaborative research. Bull. Am. Meteorol. Soc. 94, 1339–1360 (2013).
Google Scholar 
94.Conservation Biology Institute. Protected Areas Database of the United States, CBI Edition Version 2. http://consbio.org/products/projects/pad-us-cbi-edition (2012).95.USDA Forest Service. Forests to Faucets 2.0 [spatial data set]. Retrieved from https://usfs-public.app.box.com/v/Forests2Faucets[Sept 21, 2021] (2019). More

1.Remaudiere, G., & Remaudiere, M. Catalogue of the World’s Aphididae: Homoptera Aphidoidea. 473–1275. (Institut National de la Recherche Agronomique (INRA), 1997).2.Fereres, A., Irwin, M.E., & Kamppeier, G.E. Aphid movement: Process and consequences. in (van Emden H.F.R.H. ed.) Aphids as Crop Pests. 2nd edn. 196–200. (CABI, 2017).3.Ng, J. C. K. & Perry, K. L. Transmission of plant viruses by aphid vectors. Mol. Plant Pathol. 5(5), 505–511. https://doi.org/10.1111/j.1364-3703.2004.00240.x (2004).Article 
PubMed 

Google Scholar 
4.Whitfield, A. E., Falk, B. W. & Rotenberg, D. Insect vector-mediated transmission of plant viruses. Virology 479–480, 278–289. https://doi.org/10.1016/j.virol.2015.03.026 (2015).CAS 
Article 
PubMed 

Google Scholar 
5.Elena, S. F., Bernet, G. P. & Carrasco, J. L. The games plant viruses play. Curr. Opin. Virol. 8, 62–67. https://doi.org/10.1016/j.coviro.2014.07.003 (2014).CAS 
Article 
PubMed 

Google Scholar 
6.Casteel, C.L., & Falk, B.W. Plant virus-vector interactions: More than just for virus transmission. in (Wang, A., & Zhou, X. eds.) Current Research Topics in Plant Virology. 2016. 217–240. https://doi.org/10.1007/978-3-319-32919-2_9 (2016).7.Eigenbrode, S. D., Bosque-Pérez, N. A. & Davis, T. S. Insect-borne plant pathogens and their vectors: Ecology, evolution, and complex interactions. Annu. Rev. Entomol. 63, 169–191. https://doi.org/10.1146/annurev-ento-020117-043119 (2018).CAS 
Article 
PubMed 

Google Scholar 
8.Blanc, S. & Michalakis, Y. Manipulation of hosts and vectors by plant viruses and impact of the environment. Curr. Opin. Insect Sci. 16, 36–43. https://doi.org/10.1016/j.cois.2016.05.007 (2016).Article 
PubMed 

Google Scholar 
9.Ingwell, L. L., Eigenbrode, S. D. & Bosque-Pérez, N. A. Plant viruses alter insect behavior to enhance their spread. Sci. Rep. 2(1), 578. https://doi.org/10.1038/srep00578 (2012).ADS 
Article 
PubMed 
PubMed Central 

Google Scholar 
10.Stafford, C. A., Yang, L. H., Mcmunn, M. S. & Ullman, D. E. Virus infection alters the predatory behavior of an omnivorous vector. Oikos 123, 1384–1390. https://doi.org/10.1111/oik.01148 (2014).Article 

Google Scholar 
11.Wang, Q. et al. Rice dwarf virus infection alters green rice leafhopper host preference and feeding behavior. PLoS ONE 13(9), 1–16. https://doi.org/10.1371/journal.pone.0203364 (2018).CAS 
Article 

Google Scholar 
12.Stafford, C. A., Walker, G. P. & Ullman, D. E. Infection with a plant virus modifies vector feeding behavior. Proc. Natl. Acad. Sci. 108(23), 9350–9355. https://doi.org/10.1073/pnas.1100773108 (2011).ADS 
Article 
PubMed 
PubMed Central 

Google Scholar 
13.Zhang, Y. C., Cao, W. J., Zhong, L. R., Godfray, H. C. J. & Liu, X. D. Host plant determines the population size of an obligate symbiont (Buchnera aphidicola) in aphids. Appl. Environ. Microbiol. 82(8), 2336–2346. https://doi.org/10.1128/AEM.04131-15 (2016).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
14.Hansen, A. K. & Moran, N. A. Aphid genome expression reveals host-symbiont cooperation in the production of amino acids. Proc. Natl. Acad. Sci. 108(7), 2849–2854. https://doi.org/10.1073/pnas.1013465108 (2011).ADS 
Article 
PubMed 
PubMed Central 

Google Scholar 
15.Nakabachi, A. et al. Transcriptome analysis of the aphid bacteriocyte, the symbiotic host cell that harbors an endocellular mutualistic bacterium, Buchnera. Proc. Natl. Acad. Sci. 102(15), 5477–5482. https://doi.org/10.1073/pnas.1013465108 (2005).ADS 
Article 
PubMed 
PubMed Central 

Google Scholar 
16.Wernegreen, J. J. Strategies of genomic integration within insect-bacterial mutualisms. Biol Bull. 223(1), 112–122. https://doi.org/10.1086/BBLv223n1p112 (2012).Article 
PubMed 
PubMed Central 

Google Scholar 
17.Zhang, Y. et al. Genetic structure of the bacterial endosymbiont, Buchnera aphidicola, from its host aphid, Schlechtendalia chinensis, and evolutionary implications. Curr. Microbiol. 75(3), 309–315. https://doi.org/10.1007/s00284-017-1381-0 (2018).CAS 
Article 
PubMed 

Google Scholar 
18.Zhang, F. et al. Bacterial symbionts, Buchnera, and starvation on wing dimorphism in English grain aphid, Sitobion avenae (F) (Homoptera: Aphididae). Front. Physiol. 6, 155. https://doi.org/10.3389/fphys.2015.00155 (2015).ADS 
Article 
PubMed 
PubMed Central 

Google Scholar 
19.Machado-Assefh, C. R., Lopez-Isasmendi, G., Tjallingii, W. F., Jander, G. & Alvarez, A. E. Disrupting Buchnera aphidicola, the endosymbiotic bacteria of Myzus persicae, delays host plant acceptance. Arthropod. Plant Interact. 9(5), 529–541. https://doi.org/10.1007/s11829-015-9394-8 (2015).Article 

Google Scholar 
20.Douglas, A. E. Nutritional interactions in insect-microbial symbioses: Aphids and their symbiotic bacteria Buchnera. Annu. Rev. Entomol. 43(1), 17–37. https://doi.org/10.1146/annurev.ento.43.1.17 (1998).CAS 
Article 
PubMed 

Google Scholar 
21.Tamas, I. et al. 50 million years of genomic stasis in endosymbiotic bacteria. Science 296(5577), 2376–2379. https://doi.org/10.1126/science.1071278 (2002).ADS 
CAS 
Article 
PubMed 

Google Scholar 
22.Van Ham, R. C. H. J. et al. Reductive genome evolution in Buchnera aphidicola. Proc. Natl. Acad. Sci. 100(2), 581–586. https://doi.org/10.1073/pnas.0235981100 (2003).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
23.Bouvaine, S., Boonham, N. & Douglas, A. E. Interactions between a Luteovirus and the GroEL chaperonin protein of the symbiotic bacterium Buchnera aphidicola of aphids. J. Gen. Virol. 92(6), 1467–1474. https://doi.org/10.1099/vir.0.029355-0 (2011).CAS 
Article 
PubMed 

Google Scholar 
24.Rana, V. S., Singh, S. T., Priya, N. G., Kumar, J. & Rajagopal, R. Arsenophonus GroEL interacts with CLCuV and is localized in midgut and salivary gland of whitefly B. tabaci. PLoS ONE 7(8), e42168. https://doi.org/10.1371/journal.pone.0042168 (2012).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
25.Kliot, A. & Ghanim, M. The role of bacterial chaperones in the circulative transmission of plant viruses by insect vectors. Viruses 5(6), 1516–1535. https://doi.org/10.3390/v5061516 (2013).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
26.Filichkin, S. A., Brumfield, S., Filichkin, T. P. & Young, M. J. In vitro interactions of the aphid endosymbiotic SymL chaperonin with Barley yellow dwarf virus. J. Virol. 71(1), 569–577. https://doi.org/10.1128/JVI.71.1.569-577.1997 (1997).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
27.van den Heuvel, J. F., Verbeek, M. & van der Wilk, F. Endosymbiotic bacteria associated with circulative transmission of Potato leafroll virus by Myzus persicae. J. Gen. Virol. 75(Pt 10), 2559–2565. https://doi.org/10.1099/0022-1317-75-10-2559 (1994).Article 
PubMed 

Google Scholar 
28.Gray, S. M. & Gildow, F. E. Luteovirus-aphid interactions. Annu. Rev. Phytopathol. 41(1), 539–566. https://doi.org/10.1146/annurev.phyto.41.012203.105815 (2003).CAS 
Article 
PubMed 

Google Scholar 
29.Li, C., Cox-Foster, D., Gray, S. M. & Gildow, F. Vector specificity of Barley yellow dwarf virus (BYDV) transmission: Identification of potential cellular receptors binding BYDV-MAV in the aphid, Sitobion avenae. Virology 286(1), 125–133. https://doi.org/10.1006/viro.2001.0929 (2001).CAS 
Article 
PubMed 

Google Scholar 
30.Dombrovsky, A., Gollop, N., Chen, S., Chejanovsky, N. & Raccah, B. In vitro association between the helper component-proteinase of Zucchini yellow mosaic virus and cuticle proteins of Myzus persicae. J. Gen. Virol. 88(5), 1602–1610. https://doi.org/10.1099/vir.0.82769-0 (2007).CAS 
Article 
PubMed 

Google Scholar 
31.van den Heuvel, J. F. et al. The N-terminal region of the luteovirus readthrough domain determines virus binding to Buchnera GroEL and is essential for virus persistence in the aphid. J. Virol. 71(10), 7258–7265. https://doi.org/10.1128/JVI.71.10.7258-7265.1997 (1997).Article 
PubMed 
PubMed Central 

Google Scholar 
32.Morin, S. et al. A GroEL homologue from endosymbiotic bacteria of the whitefly Bemisia tabaci is implicated in the circulative transmission of Tomato yellow leaf curl virus. Virology 256(1), 75–84. https://doi.org/10.1006/viro.1999.9631 (1999).CAS 
Article 
PubMed 

Google Scholar 
33.Chaudhary, R., Atamian, H. S., Shen, Z., Briggs, S. P. & Kaloshian, I. GroEL from the endosymbiont Buchnera aphidicola betrays the aphid by triggering plant defense. Proc. Natl. Acad. Sci. 111(24), 8919–8924. https://doi.org/10.1073/pnas.1407687111 (2014).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
34.Vandermoten, S. et al. Comparative analyses of salivary proteins from three aphid species. Insect Mol. Biol. 23(1), 67–77. https://doi.org/10.1111/imb.12061 (2014).CAS 
Article 
PubMed 

Google Scholar 
35.Gray, S. M., Cilia, M. & Ghanim, M. Circulative, “nonpropagative” virus transmission: An orchestra of virus-, insect-, and plant-derived instruments. Adv. Virus Res. 2014, 89. https://doi.org/10.1016/B978-0-12-800172-1.00004-5 (2014).Article 

Google Scholar 
36.Eigenbrode, S. D., Ding, H., Shiel, P. & Berger, P. H. Volatiles from potato plants infected with Potato leafroll virus attract and arrest the virus vector, Myzus persicae (Homoptera: Aphididae). Proc. Biol. Sci. 269(1490), 455–460. https://doi.org/10.1098/rspb.2001.1909 (2002).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
37.Rajabaskar, D., Wu, Y., Bosque-Pérez, N. A. & Eigenbrode, S. D. Dynamics of Myzus persicae arrestment by volatiles from Potato leafroll virus-infected potato plants during disease progression. Entomol. Exp. Appl. 148(2), 2. https://doi.org/10.1111/eea.12087 (2013).Article 

Google Scholar 
38.Patton, M. F., Bak, A., Sayre, J. M., Heck, M. L. & Casteel, C. L. A polerovirus, Potato leafroll virus, alters plant–vector interactions using three viral proteins. Plant Cell Environ. 43(2), 387–399. https://doi.org/10.1111/pce.13684 (2020).CAS 
Article 
PubMed 

Google Scholar 
39.Sadowy, E., Juszczuk, M., David, C., Gronenborn, B. & Danuta Hulanicka, M. D. Mutational analysis of the proteinase function of Potato leafroll virus. J. Gen. Virol. 82(Pt 6), 1517–1527. https://doi.org/10.1099/0022-1317-82-6-1517 (2001).CAS 
Article 
PubMed 

Google Scholar 
40.DeBlasio, S. L. et al. Insights into the polerovirus– plant interactome revealed by coimmunoprecipitation and mass spectrometry. Mol. Plant-Microbe Interact. 28(4), 467–481. https://doi.org/10.1094/MPMI-11-14-0363-R (2015).CAS 
Article 
PubMed 

Google Scholar 
41.Zhong, S. et al. High-throughput illumina strand-specific RNA sequencing library preparation. Cold Spring Harb. Protoc. 2011(8), 940–949. https://doi.org/10.1101/pdb.prot5652 (2011).Article 

Google Scholar 
42.Anders, S. et al. Count-based differential expression analysis of RNA sequencing data using R and bioconductor. Nat. Protoc. 8(9), 1765–1786. https://doi.org/10.1038/nprot.2013.099 (2013).CAS 
Article 
PubMed 

Google Scholar 
43.Morgan, M. et al. ShortRead: A bioconductor package for input, quality assessment and exploration of high-throughput sequence data. Bioinformatics 25(19), 2607–2608. https://doi.org/10.1093/bioinformatics/btp450 (2009).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
44.Andrews, S. FastQC: A Quality Control Tool for High Throughput Sequence Data. http://www.bioinformatics.babraham.ac.uk/projects/fastqc/. (2010).45.Gauthier, J. P., Legeai, F., Zasadzinski, A., Rispe, C. & Tagu, D. AphidBase: A database for aphid genomic resources. Bioinformatics 23(6), 783–784. https://doi.org/10.1093/bioinformatics/btl682 (2007).CAS 
Article 
PubMed 

Google Scholar 
46.Kim, D. et al. TopHat2: Accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions. Genome Biol. 14, R36. https://doi.org/10.1186/gb-2013-14-4-r36 (2013).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
47.Anders, S., Pyl, P. T. & Huber, W. HTSeq-A Python framework to work with high-throughput sequencing data. Bioinformatics 31(2), 166–169. https://doi.org/10.1093/bioinformatics/btu638 (2015).CAS 
Article 

Google Scholar 
48.Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15(12), 550. https://doi.org/10.1186/s13059-014-0550-8 (2014).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
49.Conesa, A. et al. Blast2GO: A universal tool for annotation, visualization and analysis in functional genomics research. Bioinformatics 21(18), 3674–3676. https://doi.org/10.1093/bioinformatics/bti610 (2005).CAS 
Article 
PubMed 

Google Scholar 
50.Patton, M. F., Arena, G. D., Salminen, J. P., Steinbauer, M. J. & Casteel, C. L. Transcriptome and defence response in Eucalyptus camaldulensis leaves to feeding by Glycaspis brimblecombei Moore (Hemiptera: Aphalaridae): A stealthy psyllid does not go unnoticed. Austral. Entomol. 57(2), 247–254. https://doi.org/10.1111/aen.12319 (2017).Article 

Google Scholar 
51.Casteel, C. L. et al. Disruption of ethylene responses by Turnip mosaic virus mediates suppression of plant defense against the green peach aphid vector. Plant Physiol. 169(1), 209–218. https://doi.org/10.1104/pp.15.00332 (2015).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
52.Nikoh, N. et al. Bacterial genes in the aphid genome: Absence of functional gene transfer from Buchnera to its host. PLoS Genet. 6(2), e1000827. https://doi.org/10.1371/journal.pgen.1000827 (2010).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
53.Hansen, A. K. & Degnan, P. H. Widespread expression of conserved small RNAs in small symbiont genomes. ISME J. 8(12), 2490–2502. https://doi.org/10.1038/ismej.2014.121 (2014).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
54.Hogenhout, S. A., van der Wilk, F., Verbeek, M., Goldbach, R. W. & van den Heuvel, J. F. Potato leafroll virus binds to the equatorial domain of the aphid endosymbiotic GroEL homolog. J. Virol. 72(1), 358–365. https://doi.org/10.1128/JVI.72.1.358-365.1998 (1998).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
55.Camberg, J.L., Doyle, S.M., Johnston, D.M., & Wickner, S. Molecular Chaperones. in Brenner’s Encyclopedia of Genetics. 2nd Edn. 456–60. (Elsevier, 2013). https://doi.org/10.1016/B978-0-12-809633-8.06723-6.56.Segal, G. & Ron, E. Z. Regulation and organization of the groE and dnaK operons in Eubacteria. FEMS Microbiol. Lett. 138(1), 1–10. https://doi.org/10.1111/j.1574-6968.1996.tb08126.x (1996).CAS 
Article 
PubMed 

Google Scholar 
57.Zhang, L., Pelech, S. & Uitto, V. J. Bacterial GroEL-like heat shock protein 60 protects epithelial cells from stress-induced death through activation of ERK and inhibition of caspase 3. Exp. Cell Res. 292(1), 231–240. https://doi.org/10.1016/j.yexcr.2003.08.012 (2004).CAS 
Article 
PubMed 

Google Scholar 
58.Shigenobu, S., Watanabe, H., Hattori, M., Sakaki, Y. & Ishikawa, H. Genome sequence of the endocellular bacterial symbiont of aphids Buchnera sp. APS Nat. 407(6800), 81–86. https://doi.org/10.1038/35024074 (2000).CAS 
Article 

Google Scholar 
59.Dombrovsky, A., Sobolev, I., Chejanovsky, N. & Raccah, B. Characterization of RR-1 and RR-2 cuticular proteins from Myzus persicae. Comp. Biochem. Physiol. B Biochem. Mol. Biol. 146(2), 256–264. https://doi.org/10.1016/j.cbpb.2006.11.013 (2007).CAS 
Article 
PubMed 

Google Scholar 
60.Dombrovsky, A., Huet, H., Zhang, H., Chejanovsky, N. & Raccah, B. Comparison of newly isolated cuticular protein genes from six aphid species. Insect Biochem. Mol. Biol. 33(7), 709–715. https://doi.org/10.1016/s0965-1748(03)00065-1 (2003).CAS 
Article 
PubMed 

Google Scholar 
61.Liang, Y. & Gao, X. W. The cuticle protein gene MPCP4 of Myzus persicae (Homoptera: Aphididae) plays a critical role in cucumber mosaic virus acquisition. J. Econ. Entomol. 110(3), 848–853. https://doi.org/10.1093/jee/tox025 (2017).CAS 
Article 
PubMed 

Google Scholar 
62.Silva, A. X., Jander, G., Samaniego, H., Ramsey, J. S. & Figueroa, C. C. Insecticide resistance mechanisms in the green peach aphid Myzus persicae (Hemiptera: Aphididae) I: A transcriptomic survey. PLoS ONE 7(6), e36366. https://doi.org/10.1371/journal.pone.0036366 (2012).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
63.Deshoux, M., Monsion, B. & Uzest, M. Insect cuticular proteins and their role in transmission of phytoviruses. Curr. Opin. Virol. 33, 137–143. https://doi.org/10.1016/j.coviro.2018.07.015 (2018).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
64.Gallot, A. et al. Cuticular proteins and seasonal photoperiodism in aphids. Insect Biochem. Mol. Biol. 40(3), 235–240. https://doi.org/10.1016/j.ibmb.2009.12.001 (2010).CAS 
Article 
PubMed 

Google Scholar 
65.Cilia, M. et al. Genetics coupled to quantitative intact proteomics links heritable aphid and endosymbiont protein expression to circulative polerovirus transmission. J. Virol. 85(5), 2148–2166. https://doi.org/10.1128/JVI.01504-10 (2011).CAS 
Article 
PubMed 

Google Scholar 
66.Wang, H., Wu, K., Liu, Y., Wu, Y. & Wang, X. Integrative proteomics to understand the transmission mechanism of Barley yellow dwarf virus-GPV by its insect vector Rhopalosiphum padi. Sci. Rep. 5, 10971. https://doi.org/10.1038/srep10971 (2015).ADS 
Article 
PubMed 
PubMed Central 

Google Scholar 
67.Seddas, P. et al. Rack-1, GAPDH3, and actin: proteins of Myzus persicae potentially involved in the transcytosis of Beet western yellows virus particles in the aphid. Virology 325(2), 399–412. https://doi.org/10.1016/j.virol.2004.05.014 (2004).CAS 
Article 
PubMed 

Google Scholar 
68.Yang, Z., Zhang, F., Zhu, L. & He, G. Identification of differentially expressed genes in brown planthopper Nilaparvata lugens (Hemiptera: Delphacidae) responding to host plant resistance. Bull. Entomol. Res. 96(1), 53–59. https://doi.org/10.1079/ber2005400 (2006).CAS 
Article 
PubMed 

Google Scholar 
69.Bass, C. et al. Gene amplification and microsatellite polymorphism underlie a recent insect host shift. Proc. Natl. Acad. Sci. 110(48), 19460–19465. https://doi.org/10.1073/pnas.1314122110 (2013).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
70.Ramsey, J. S. et al. Adaptation to nicotine feeding in Myzus persicae. J. Chem. Ecol. 40(8), 869–877. https://doi.org/10.1007/s10886-014-0482-5 (2014).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
71.Casteel, C. L. & Jander, G. New synthesis: Investigating mutualisms in virus-vector interactions. J. Chem. Ecol. 39(7), 809. https://doi.org/10.1007/s10886-013-0305-0 (2013).CAS 
Article 
PubMed 

Google Scholar 
72.Götz, M. et al. Implication of Bemisia tabaci HEAT SHOCK PROTEIN 70 in Begomovirus-whitefly interactions. J. Virol. 86(24), 13241–13252. https://doi.org/10.1128/JVI.00880-12 (2012).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
73.Porras, M. F. et al. Enhanced heat tolerance of viral-infected aphids leads to niche expansion and reduced interspecific competition. Nat. Commun. 11(1), 1184. https://doi.org/10.1038/s41467-020-14953-2 (2020).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
74.Syller, J. The influence of temperature on transmission of potato leaf roll virus by Myzus persicae Sulz. Potato Res. 30(1), 47–58. https://doi.org/10.1007/BF02357683 (1987).Article 

Google Scholar 
75.Syller, J. The effects of temperature on the susceptibility of potato plants to infection and accumulation of Potato Leafroll Virus. J. Phytopathol. 133(3), 216–224. https://doi.org/10.1111/j.1439-0434.1991.tb00156.x (1991).Article 

Google Scholar 
76.Chung, B. N. et al. The effects of high temperature on infection by Potato virus Y, Potato virus A, and Potato leafroll virus. Plant Pathol. J. 32(4), 321–328. https://doi.org/10.5423/PPJ.OA.12.2015.0259 (2016).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
77.Hansen, A. K. & Moran, N. A. The impact of microbial symbionts on host plant utilization by herbivorous insects. Mol. Ecol. 23(6), 1473–96 (2014).Article 

Google Scholar 
78.Jiang, Z. et al. Comparative analysis of genome sequences from four strains of the Buchnera aphidicola Mp endosymbion of the green peach aphid, Myzus persicae. BMC Genom. 14(1), 917. https://doi.org/10.1186/1471-2164-14-917 (2013).CAS 
Article 

Google Scholar 
79.Enders, L. S. et al. Abiotic and biotic stressors causing equivalent mortality induce highly variable transcriptional responses in the soybean aphid. G3 (Bethesda) 5(2), 261–270. https://doi.org/10.1534/g3.114.015149 (2014).Article 

Google Scholar 
80.Wilcox, J. L., Dunbar, H. E., Wolfinger, R. D. & Moran, N. A. Consequences of reductive evolution for gene expression in an obligate endosymbiont. Mol. Microbiol. 48(6), 1491–1500. https://doi.org/10.1046/j.1365-2958.2003.03522.x (2003).CAS 
Article 
PubMed 

Google Scholar 
81.Karp, P. D. et al. The BioCyc collection of microbial genomes and metabolic pathways. Brief Bioinform. 20(4), 1085–1093. https://doi.org/10.1093/bib/bbx085 (2019).CAS 
Article 
PubMed 

Google Scholar 
82.Zhang, B., Leonard, S. P., Li, Y. & Moran, N. A. Obligate bacterial endosymbionts limit thermal tolerance of insect host species. Proc. Natl. Acad. Sci. 116(49), 24712–24718. https://doi.org/10.1073/pnas.1915307116 (2019).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
83.Chong, R. A. & Moran, N. A. Intraspecific genetic variation in hosts affects regulation of obligate heritable symbionts. PNAS 113(46),13114–13119. https://doi.org/10.1073/pnas.1610749113 (2016).ADS 
CAS 
Article 

Google Scholar 
84.Pers, D. & Hansen, A. K. The boom and bust of the aphid’s essential amino acid metabolism across nymphal development. G3 (Bethesda). 11(9), jkab115. https://doi.org/10.1093/g3journal/jkab115 (2021).Article 

Google Scholar 
85.Dunbar, H. E., Wilson, A. C. C., Ferguson, N. R. & Moran, N. A. Aphid thermal tolerance is governed by a point mutation in bacterial symbionts. PLoS Biol. 5(5), e96. https://doi.org/10.1371/journal.pbio.0050096 (2007).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
86.Moran, N. A. & Yun, Y. Experimental replacement of an obligate insect symbiont. Proc. Natl. Acad. Sci. 112(7), 2093–2096. https://doi.org/10.1073/pnas.1420037112 (2015).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
87.Fares, M. A., Barrio, E., Sabater-Muñoz, B. & Moya, A. The evolution of the heat-shock protein GroEL from Buchnera, the primary endosymbiont of aphids, is governed by positive selection. Mol. Biol. Evol. 19(7), 1162–1170. https://doi.org/10.1093/oxfordjournals.molbev.a004174 (2002).CAS 
Article 
PubMed 

Google Scholar 
88.Kliot, A., Cilia, M., Czosnek, H., & Ghanim, M. Implication of the bacterial endosymbiont Rickettsia spp. in interactions of the whitefly Bemisia tabaci with Tomato yellow leaf curl virus. J. Virol. 88(10), 5652–5660. https://doi.org/10.1128/JVI.00071-14 (2014).89.Dheilly, N. M. et al. Who is the puppet master? Replication of a parasitic wasp-associated virus correlates with host behaviour manipulation. Proc. R. Soc. B Biol. Sci. 2015(282), 20142773 (1803).
Google Scholar 
90.Mohan, P. & Sinu, P. A. Does the solitary parasitoid Microplitis pennatulae use a combinatorial approach to manipulate its host?. Entomol. Exp. Appl. 168(4), 295–303 (2020).CAS 
Article 

Google Scholar 
91.Smith, T. E. & Moran, N. A. Coordination of host and symbiont gene expression reveals a metabolic tug-of-war between aphids and Buchnera. Proc. Natl. Acad. Sci. 117(4), 2113–2121. https://doi.org/10.1073/pnas.1916748117 (2020).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar  More

1.Klein, A. M. et al. Importance of pollinators in changing landscapes for world crops. Proc. R. Soc. B 274(1608), 303–313 (2007).Article 

Google Scholar 
2.Aizen, M. A. & Harder, L. D. The global stock of domesticated honey bees is growing slower than agricultural demand for pollination. Curr. Biol. 19, 915–918 (2009).CAS 
Article 

Google Scholar 
3.Aizen, M. A. et al. Global agricultural productivity is threatened by increasing pollinator dependence without a parallel increase in crop diversification. Glob. Change Biol. 25, 3516–3527 (2019).ADS 
Article 

Google Scholar 
4.Ribbands, C. R. The scent perception of the honey bee. Proc. R. Soc. Lond. B 143(912), 367–379 (1955).ADS 
Article 

Google Scholar 
5.von Frisch, K. The Dance Language and Orientation of Bees (Harvard University Press, 1967).
Google Scholar 
6.Reinhard, J., Srinivasan, M. V., Guez, D. & Zhang, S. W. Floral scents induce recall of navigational and visual memories in honeybees. J. Exp. Biol. 207(25), 4371–4381 (2004).Article 

Google Scholar 
7.Arenas, A., Fernández, V. M. & Farina, W. M. Floral odor learning within the hive affects honeybees’ foraging decisions. Naturwissenschaften 94, 218–222 (2007).ADS 
CAS 
Article 

Google Scholar 
8.Farina, W. M., Grüter, C. & Díaz, P. C. Social learning of floral odours inside the honeybee hive. Proc. R. Soc. Lond. B. 272(1575), 1923–1928 (2005).
Google Scholar 
9.Farina, W. M., Grüter, C., Acosta, L. & Mc Cabe, S. Honeybees learn floral odors while receiving nectar from foragers within the hive. Naturwissenschaften 94(1), 55–60 (2007).ADS 
CAS 
Article 

Google Scholar 
10.Grüter, C., Acosta, L. E. & Farina, W. M. Propagation of olfactory information within the honeybee hive. Behav. Ecol. Sociobiol. 60(5), 707–715 (2006).Article 

Google Scholar 
11.Arenas, A., Fernández, V. M. & Farina, W. M. Floral scents experienced within the colony affect long-term foraging preferences in honeybees. Apidologie 39, 714–722 (2008).Article 

Google Scholar 
12.Balbuena, M. S., Arenas, A. & Farina, W. M. Floral scents learned inside the honeybee hive have a long-lasting effect on recruitment. Anim. Behav. 84, 77–83 (2012).Article 

Google Scholar 
13.Farina, W. M., Arenas, A., Díaz, P. C., Susic Martin, C. & Estravis Barcala, M. C. Learning of a mimic odor within beehives improves pollination service efficiency in a commercial crop. Curr. Biol. 30, 4284–4290. https://doi.org/10.1016/j.cub.2020.08.018 (2020).CAS 
Article 
PubMed 

Google Scholar 
14.Stevenson, P. C., Nicolson, S. W. & Wright, G. A. Plant secondary metabolites in nectar: Impacts on pollinators and ecological functions. Funct. Ecol. 31(1), 65–75 (2017).Article 

Google Scholar 
15.Baker, H. G. Non-sugar chemical constituents of nectar. Apidologie 8(4), 349–356 (1977).Article 

Google Scholar 
16.Chalisova, N. I. et al. Effect of encoding amino acids on associative learning of honeybee Apis mellifera. J. Evol. Biochem. Fisiol. 47(6), 607 (2011).Article 

Google Scholar 
17.Strauss, S. Y. & Whittall, J. B. Non-pollinator agents of selection on floral traits. In Ecology and Evolution of flowers (eds. Harder, L. D. & Barret, S. C. H.) 120–138 (Oxford University Press, 2006).18.McArt, S. H., Koch, H., Irwin, R. E. & Adler, L. S. Arranging the bouquet of disease: Floral traits and the transmission of plant and animal pathogens. Ecol. Lett. 17, 624–636 (2014).Article 

Google Scholar 
19.Gatica Hernández, I., Palottini, F., Macri, I., Galmarini, C. R. & Farina, W. M. Appetitive behavior of the honey bee Apis mellifera in response to phenolic compounds naturally found in nectars. J. Exp. Biol. https://doi.org/10.1242/jeb.189910 (2019).Article 

Google Scholar 
20.Stevenson, P. C. For antagonists and mutualists: The paradox of insect toxic secondary metabolites in nectar and pollen. Phytochem. Rev. 19(3), 603–614 (2020).CAS 
Article 

Google Scholar 
21.Carlesso, D., Smargiassi, S., Pasquini, E., Bertelli, G. & Baracchi, D. Nectar non-protein amino acids (NPAAs) do not change nectar palatability but enhance learning and memory in honey bees. Sci. Rep. 11(1), 1–14 (2021).Article 

Google Scholar 
22.Kretschmar, J. A. & Baumann, T. W. Caffeine in Citrus flowers. Phytochemistry 52(1), 19–23 (1999).CAS 
Article 

Google Scholar 
23.Wright, G. A. et al. Caffeine in floral nectar enhances a pollinator’s memory of reward. Science 339(6124), 1202–1204 (2013).ADS 
CAS 
Article 

Google Scholar 
24.Couvillon, M. J. et al. Caffeinated forage tricks honeybees into increasing foraging and recruitment behaviors. Curr. Biol. 25, 2815–2818 (2015).CAS 
Article 

Google Scholar 
25.Arnold, S. E. et al. Bumble bees show an induced preference for flowers when primed with caffeinated nectar and a target floral odor. Curr. Biol. 31, 1–5 (2021).Article 

Google Scholar 
26.Gardener, M. C. & Gillman, M. P. Analyzing variability in nectar amino acids: Composition is less variable than concentration. J. Chem. Ecol. 27(12), 2545–2558 (2001).CAS 
Article 

Google Scholar 
27.Power, E. F., Stabler, D., Borland, A. M., Barnes, J. & Wright, G. A. Analysis of nectar from low-volume flowers: A comparison of collection methods for free amino acids. Methods Ecol. Evol. 9, 734–743 (2018).Article 

Google Scholar 
28.Terrab, A. et al. Analysis of amino acids in nectar from Silene colorata Poiret (Caryophyllaceae). Bot. J. Linn. Soc. 155, 49–56 (2007).Article 

Google Scholar 
29.Taha, E. K. A., Al-Kahtani, S. & Taha, R. Protein content and amino acids composition of bee-pollens from major floral sources in Al-Ahsa, eastern Saudi Arabia. Saudi J. Biol. Sci. 26, 232–237 (2019).CAS 
Article 

Google Scholar 
30.Müller, U. Inhibition of nitric oxide synthase impairs a distinct form of long-term memory in the honeybee, Apis mellifera. Neuron 16, 541–549 (1996).Article 

Google Scholar 
31.Müller, U. The nitric oxide system in insects. Prog. Neurobiol. 51, 363–381 (1997).Article 

Google Scholar 
32.Lopatina, N. G., Zachepilo, T. H., Kamyshev, N. G. & Chalisova, N. I. The influence of combinations of encoded amino acids on associative learning in the honeybee Apis mellifera L. J. Evol. Biochem. Physiol. 53(2), 123–128 (2017).CAS 
Article 

Google Scholar 
33.Marchi, I. L., Palottini, F. & Farina, W. M. Combined secondary compounds naturally found in nectars enhance honeybee cognition and survival. J. Exp. Biol. 224(6), jeb239616 (2021).Article 

Google Scholar 
34.Müller, U. Prolonged activation of cAMP-dependent protein kinase during conditioning induces long-term memory in honeybees. Neuron 27(1), 159–168 (2000).Article 

Google Scholar 
35.Negri, P. et al. Nitric oxide participates at the first steps of Apis mellifera cellular immune activation in response to non-self recognition. Apidologie 44(5), 575–585 (2013).CAS 
Article 

Google Scholar 
36.Lu, Y. H. et al. Identification of immune regulatory genes in Apis mellifera through caffeine treatment. Insects 11(8), 516 (2020).Article 

Google Scholar 
37.Delaplane, K. S. & Mayer, D. F. Crop Pollination by Bees (CAB International, 2000).
Google Scholar 
38.Cabrera, A. L. & Willink, A. Biogeografía de América Latina (Organización de Estados Americanos, 1973).
Google Scholar 
39.Gabai, A. et al. Protocol for Using Pollinators in Hybrid Seed Production: An Outline for Improving Pollinator Effectiveness. (International Seed Federation, 2018).40.Torretta, J. P., Medan, D., Roig Alsina, A. H. & Montaldo, N. H. Visitantes florales diurnos del girasol (Helianthus annuus L., Asterales: Asteraceae) en la Argentina. Rev. Soc. Entomol. Argent. 69(1–2), 17–32 (2010).
Google Scholar 
41.Sáez, A., Sabatino, M. & Aizen, M. A. Interactive effects of large-and small-scale sources of feral honeybees for sunflower in the Argentine Pampas. PLoS One 7(1), e30968 (2012).ADS 
Article 

Google Scholar 
42.Farina, W. M., Díaz, P. C. & Arenas, A. Patent AR082846B1: Una Formulación que Promueve la Polinización Dirigida de Abejas Melíferas Hacia Cultivos de Girasol (Instituto Nacional de Propiedad Intelectual, 2017).43.Noetzel, D. M. Insect Pollination Results on Sunflower 108–112 (Department of Entomology North Dakota State University Fargo USA, 1968).44.Johannsmeier, M. F. & Mostert, J. N. Crop pollination. In Beekeeping in South Africa, 3rd ed. 235–250 (ed. Johannsmeier, M. F.) (Plant Protection Research Institute handbook 14. Agricultural Research Council of South Africa, 2001).45.R Development Core Team. R: A language and environment for statistical computing (R Foundation for Statistical Computing). https://www.R-project.org/ (2021).46.Brooks, M. E. et al. glmmTMB balances speed and flexibility among packages for zero-inflated generalized linear mixed modeling. R J. 9(2), 378–400 (2017).Article 

Google Scholar 
47.Crawley, M. J. The R Book (Wiley, 2013).MATH 

Google Scholar 
48.Hartig, F. DHARMa: Residual Diagnostics for Hierarchical (Multi-Level/Mixed) Regression Models. R Package Version 0.2.7. https://CRAN.R-project.org/package=DHARMa (2021).49.Chambers, J. M. & Hastie, T. J. Statistical Models in S (Chapman and Hall, 1992).MATH 

Google Scholar 
50.Lenth, R. emmeans: Estimated Marginal Means, aka Least-Squares Means. R Package Version 1.4.4., https://CRAN.R-project.org/package=emmeans (2021). More

1.Hebert, P. D. N., Muncaster, B. W. & Mackie, G. L. Ecological and genetic studies on Dreissena polymorpha (Pallas): A new mollusc in the Great Lakes. Can. J. Fish. Aquat. Sci. 46, 1587–1591 (1989).
Google Scholar 
2.May, B. & Marsden, J. E. Genetic identification and implications of another invasive species of dreissenid mussel in the Great Lakes. Can. J. Fish. Aquat. Sci. 49, 1501–1506 (1992).
Google Scholar 
3.Ackerman, J. D., Cottrell, C. M., Ethier, C. R., Allen, D. G. & Spelt, J. K. Attachment strength of zebra mussels on natural, polymeric, and metallic materials. J. Environ. Eng. ASCE 122, 141–148 (1996).CAS 

Google Scholar 
4.Kobak, J. Attachment strength of Dreissena polymorph on artificial substrates. In The Zebra Mussel in Europe (eds van der Velde, G. et al.) 349–354 (Margraf Publishers, 2010).
Google Scholar 
5.Karatayev, A. Y., Burlakova, L. E. & Padilla, D. K. Zebra versus quagga mussels: A review of their spread, population dynamics, and ecosystem impacts. Hydrobiologia 746, 97–112 (2015).CAS 

Google Scholar 
6.Karatayev, V. A., Karatayev, A. Y., Burlakova, L. E. & Padilla, D. K. Lakewide dominance does not predict the potential for spread of dreissenids. J. Great Lakes Res. https://doi.org/10.1016/j.jglr.2013.09.007 (2013).Article 

Google Scholar 
7.Peyer, S. M., McCarthy, A. J. & Lee, C. E. Zebra mussels anchor byssal threads faster and tighter than quagga mussels in flow. J. Exp. Biol. https://doi.org/10.1242/jeb.028688 (2009).Article 
PubMed 
PubMed Central 

Google Scholar 
8.Amini, S. et al. Preventing mussel adhesion using lubricant-infused materials. Science 357, 668–673 (2017).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
9.Matsui, Y. et al. Attachment strength of Limnoperna fortunei on substrates, and their surface properties. Biofouling 17, 29–39 (2001).
Google Scholar 
10.Marsden, J. E. & Lansky, D. M. Substrate selection by settling zebra mussels, Dreissena polymorpha, relative to material, texture, orientation, and sunlight. Can. J. Zool. 78, 787–793 (2000).
Google Scholar 
11.Kobak, J. Factors influencing the attachment strength of Dreissena polymorpha (Bivalvia). Biofouling 22, 141–150 (2006).
Google Scholar 
12.Ackerman, J. D., Ethier, C. R., Allen, D. G. & Spelt, J. K. Investigation of zebra mussel adhesion strength using rotating disks. J. Environ. Eng. 118, 708–724 (1992).
Google Scholar 
13.Ackerman, J. D., Ethier, C. R., Spelt, J. K., Allen, D. G. & Cottrell, C. M. A wall jet to measure the attachment strength of zebra mussels. Can. J. Fish. Aquat. Sci. 52, 126–135 (1995).
Google Scholar 
14.Balogh, C., Serfőző, Z., bij de Vaate, A., Noordhuis, R. & Kobak, J. Biometry, shell resistance and attachment of zebra and quagga mussels at the beginning of their co-existence in large European lakes. J. Great Lakes Res. 45, 777–787 (2019).
Google Scholar 
15.Grutters, B. M. C., Verhofstad, M. J. J. M., van der Velde, G., Rajagopal, S. & Leuven, R. S. E. W. A comparative study of byssogenesis on zebra and quagga mussels: The effects of water temperature, salinity and light–dark cycle. Biofouling 28, 121–129 (2012).PubMed 

Google Scholar 
16.Naddafi, R. & Rudstam, L. G. Predator-induced behavioural defences in two competitive invasive species: The zebra mussel and the quagga mussel. Anim. Behav. 86, 1275–1284 (2013).
Google Scholar 
17.Bell, E. C. & Gosline, J. M. Mechanical design of mussel byssus: Material yield enhances attachment strength. J. Exp. Biol. 199, 1005–1017 (1996).CAS 
PubMed 

Google Scholar 
18.Brazee, S. L. & Carrington, E. Interspecific comparison of the mechanical properties of mussel byssus. Biol. Bull. 211, 263–274 (2006).PubMed 
PubMed Central 

Google Scholar 
19.Burkett, J. R., Wojtas, J. L., Cloud, J. L. & Wilker, J. J. A method for measuring the adhesion strength of marine mussels. J. Adhes. 85, 601–615 (2009).CAS 

Google Scholar 
20.Desmond, K. W., Zacchia, N. A., Waite, J. H. & Valentine, M. T. Dynamics of mussel plaque detachment. Soft Matter 11, 6832–6839 (2015).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
21.Hamada, N., Roman, V., Howell, S. & Wilker, J. Examining potential active tempering of adhesive curing by marine mussels. Biomimetics 2, 16 (2017).
Google Scholar 
22.Farsad, N. & Sone, E. D. Zebra mussel adhesion: Structure of the byssal adhesive apparatus in the freshwater mussel, Dreissena polymorpha. J. Struct. Biol. 177, 613–620 (2012).PubMed 
PubMed Central 

Google Scholar 
23.Stalder, A. F. et al. Low-bond axisymmetric drop shape analysis for surface tension and contact angle measurements of sessile drops. Colloids Surf. A Physicochem. Eng. Asp. 364, 72–81 (2010).CAS 

Google Scholar 
24.Claxton, W. T., Wilson, A. B., Mackie, G. L. & Boulding, E. G. A genetic and morphological comparison of shallow- and deep-water populations of the introduced dreissenid bivalve Dreissena bugensis. Can. J. Zool. 76, 1269–1276 (1998).
Google Scholar 
25.Peyer, S. M., Hermanson, J. C. & Lee, C. E. Developmental plasticity of shell morphology of quagga mussels from shallow and deep-water habitats of the Great Lakes. J. Exp. Biol. 213, 2602–2609 (2010).PubMed 
PubMed Central 

Google Scholar 
26.Sprung, M. Field and laboratory observations of Dreissena polymorpha larvae: Abundance, growth, mortality and food demands. Arch. Hydrobiol. 115, 537–561 (1989).
Google Scholar 
27.Nichols, S. J. Maintenance of the zebra mussel (Dreissena polymorpha) under laboratory conditions. In Zebra Mussels: Biology, Impacts, and Control (eds Nalepa, T. F. & Schloesser, D. W.) 733–747 (Lewis Publishers, 1992).
Google Scholar 
28.Porter, A. E. & Marsden, J. E. Adult zebra mussels (Dreissena polymorpha) avoid attachment to mesh materials. Northeast. Nat. 15, 589–594 (2008).
Google Scholar 
29.Kimmins, K. M., James, B. D., Nguyen, M. T., Hatton, B. D. & Sone, E. D. Oil-infused silicone prevents zebra mussel adhesion. ACS Appl. Bio Mater. https://doi.org/10.1021/acsabm.9b00832 (2019).Article 

Google Scholar 
30.Peyer, S. M., Hermanson, J. C. & Lee, C. E. Effects of shell morphology on mechanics of zebra and quagga mussel locomotion. J. Exp. Biol. 214, 2226–2236 (2011).PubMed 
PubMed Central 

Google Scholar 
31.Berkman, P. A., Garton, D. W., Haltuch, M. A., Kennedy, G. W. & Febo, L. R. Habitat shift in invading species: Zebra and quagga mussel population characteristics on shallow soft substrates. Biol. Invasions https://doi.org/10.1023/A:1010088925713 (2000).Article 

Google Scholar 
32.Skaja, A., Tordonato, D. & Merten, B. Coatings for invasive mussel control: Colorado river field study. In Biol. Manag. Invasive Quagga Zebra Mussels West. United States 451–466 (2015) https://doi.org/10.1201/b18447-37https://doi.org/10.1201/b18447-37.33.Zhao, H., Robertson, N. B., Jewhurst, S. A. & Waite, J. H. Probing the adhesive footprints of Mytilus californianus byssus. J. Biol. Chem. https://doi.org/10.1074/jbc.M510792200 (2006).Article 
PubMed 
PubMed Central 

Google Scholar 
34.Kimmins, K. Freshwater Mussel Adhesion: Interfacial Structures & Antifouling Surfaces (Univesity of Toronto, 2020).
Google Scholar 
35.Kobak, J. Behavior of juvenile and adult zebra mussels (Dreissena polymorpha). In Quagga Zebra Mussel Biol. Impacts, Control 331–344 (2013) https://doi.org/10.1201/b15437-28.36.Waite, J. H. Adhesion in byssally attached bivalves. Biol. Rev. 58, 209–231 (1983).CAS 

Google Scholar 
37.Lachance, A. A., Myrand, B., Tremblay, R., Koutitonsky, V. & Carrington, E. Biotic and abiotic factors influencing attachment strength of blue mussels Mytilus edulis in suspended culture. Aquat. Biol. 2, 119–129 (2008).
Google Scholar 
38.Lee, H., Scherer, N. F. & Messersmith, P. B. Single-molecule mechanics of mussel adhesion. Proc. Natl. Acad. Sci. 103, 12999–13003 (2006).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
39.Rzepecki, L. M. & Waite, J. H. The byssus of the zebra mussel, Dreissena polymorpha. I: Morphology and in situ protein processing during maturation. Mol. Mar. Biol. Biotechnol. 2, 255–266 (1993).CAS 
PubMed 
PubMed Central 

Google Scholar 
40.Waite, J. H. & Qin, X. Polyphosphoprotein from the adhesive pads of Mytilus edulis. Biochemistry 40, 2887–2893 (2001).CAS 
PubMed 
PubMed Central 

Google Scholar 
41.Zhao, H. & Waite, J. H. Linking adhesive and structural proteins in the attachment plaque of Mytilus californianus. J. Biol. Chem. 281, 26150–26158 (2006).CAS 
PubMed 
PubMed Central 

Google Scholar 
42.Petrone, L. et al. Mussel adhesion is dictated by time-regulated secretion and molecular conformation of mussel adhesive proteins. Nat. Commun. 6, 8737 (2015).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
43.Waite, J. H. Mussel adhesion—Essential footwork. J. Exp. Biol. 220, 517–530 (2017).PubMed 
PubMed Central 

Google Scholar 
44.Lee, B. P., Messersmith, P. B., Israelachvili, J. N. & Waite, J. H. Mussel-inspired adhesives and coatings. Annu. Rev. Mater. Res. 41, 99–132 (2011).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
45.Ou, X. et al. Structure and sequence features of mussel adhesive protein lead to its salt-tolerant adhesion ability. Sci. Adv. 6, eabb7620 (2020).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
46.Maier, G. P., Rapp, M. V., Waite, J. H., Israelachvili, J. N. & Butler, A. Adaptive synergy between catechol and lysine promotes wet adhesion by surface salt displacement. Science 349, 628–632 (2015).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
47.Bilotto, P. et al. Adhesive properties of adsorbed layers of two recombinant mussel foot proteins with different levels of DOPA and tyrosine. Langmuir 35, 15481–15490 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
48.Kim, S. et al. Cation–π interaction in DOPA-deficient mussel adhesive protein mfp-1. J. Mater. Chem. B 3, 738–743 (2015).CAS 
PubMed 
PubMed Central 

Google Scholar  More

1.Hall EK, Bernhardt ES, Bier RL, Bradford MA, Boot CM, Cotner JB, et al. Understanding how microbiomes influence the systems they inhabit. Nature Microbiol. 2018;3:977–82.CAS 

Google Scholar 
2.Gilbert JA, Blaser MJ, Caporaso JG, Jansson JK, Lynch SV, Knight R. Current understanding of the human microbiome. Nat. Med. 2018;24:392–400.3.Suttle CA. Marine viruses-major players in the global ecosystem. Nat Rev Microbiol. 2007;5:801–12.CAS 
PubMed 

Google Scholar 
4.Zimmerman AE, Howard-Varona C, Needham DM, John SG, Worden AZ, Sullivan MB, et al. Metabolic and biogeochemical consequences of viral infection in aquatic ecosystems. Nat Rev Microbiol. 2020;18:21–34.5.Howard-Varona C, Lindback MM, Bastien GE, Solonenko N, Zayed AA, Jang HB, et al. Phage-specific metabolic reprogramming of virocells. ISME J. 2020;14:881–95.PubMed 
PubMed Central 

Google Scholar 
6.Sullivan MB, Lindell D, Lee JA, Thompson LR, Bielawski JP, Chisholm SW. Prevalence and evolution of core photosystem II genes in marine cyanobacterial viruses and their hosts. PLoS Biol. 2006;4:1344–57.CAS 

Google Scholar 
7.Lindell D, Jaffe JD, Johnson ZI, Church GM, Chisholm SW. Photosynthesis genes in marine viruses yield proteins during host infection. Nature. 2005;438:86–9.CAS 
PubMed 

Google Scholar 
8.Hurwitz BL, Hallam SJ, Sullivan MB. Metabolic reprogramming by viruses in the sunlit and dark ocean. Genome Biol. 2013;14:R123.PubMed 
PubMed Central 

Google Scholar 
9.Thompson LR, Zeng Q, Kelly L, Huang KH, Singer AU, Stubbe J, et al. Phage auxiliary metabolic genes and the redirection of cyanobacterial host carbon metabolism. Proc Natl Acad Sci. 2011;108:E757–E764.CAS 
PubMed 
PubMed Central 

Google Scholar 
10.Gazitúa MC, Vik DR, Roux S, Gregory AC, Bolduc B, Widner B, et al. Potential virus-mediated nitrogen cycling in oxygen-depleted oceanic waters. ISME J. 2021;15:981–98.PubMed 

Google Scholar 
11.Vik D, Gazitúa MC, Sun CL, Zayed AA, Aldunate M, Mulholland MR, et al. Genome-resolved viral ecology in a marine oxygen minimum zone. Environ Microbiol. 2021;23:2858–74.CAS 
PubMed 

Google Scholar 
12.Rosenwasser S, Ziv C, Creveld SG, van, Vardi A. Virocell metabolism: metabolic innovations during host–virus interactions in the ocean. Trends Microbiol. 2016;24:821–32.CAS 
PubMed 

Google Scholar 
13.Emerson JB, Roux S, Brum JR, Bolduc B, Woodcroft BJ, Jang HB, et al. Host-linked soil viral ecology along a permafrost thaw gradient. Nat Microbiol. 2018;3:870–80.CAS 
PubMed 
PubMed Central 

Google Scholar 
14.Trubl G, Jang HB, Roux S, Emerson JB, Solonenko N, Vik DR, et al. Soil viruses are underexplored players in ecosystem carbon processing. mSystems. 2018;3:1–21.
Google Scholar 
15.Zhong Z-P, Tian F, Roux S, Gazitúa MC, Solonenko NE, Li Y-F, et al. Glacier ice archives nearly 15,000-year-old microbes and phages. Microbiome. 2021;9:160.PubMed 
PubMed Central 

Google Scholar 
16.Zhong Z-P, Rapp JZ, Wainaina JM, Solonenko NE, Maughan H, Carpenter SD, et al. Viral ecogenomics of arctic cryopeg brine and sea ice. mSystems. 2020;5:e00246–20.17.Anantharaman K, Duhaime MB, Breier JA, Wendt KA, Toner BM, Dick GJ. Sulfur oxidation genes in diverse deep-sea viruses. Science. 2014;344:757–60.CAS 
PubMed 

Google Scholar 
18.Gao S-M, Schippers A, Chen N, Yuan Y, Zhang M-M, Li Q, et al. Depth-related variability in viral communities in highly stratified sulfidic mine tailings. Microbiome. 2020;8:89.CAS 
PubMed 
PubMed Central 

Google Scholar 
19.Correa AMS, Howard-Varona C, Coy SR, Buchan A, Sullivan MB, Weitz JS. Revisiting the rules of life for viruses of microorganisms. Nat Rev Microbiol. 2021;19:501–13.CAS 
PubMed 

Google Scholar 
20.Blazanin M, Turner PE. Community context matters for bacteria-phage ecology and evolution. ISME J. 2021;1–10.21.Gregory AC, Zayed AA, Conceição-Neto N, Temperton B, Bolduc B, Alberti A, et al. Marine DNA viral macro- and microdiversity from pole to pole. Cell. 2019;177:1109–23. e14CAS 
PubMed 
PubMed Central 

Google Scholar 
22.Gregory AC, Zablocki O, Zayed AA, Howell A, Bolduc B, Sullivan MB. The gut virome database reveals age-dependent patterns of virome diversity in the human gut. Cell Host and Microbe. 2020;28:724–40. e8CAS 
PubMed 

Google Scholar 
23.Roux S, Páez-Espino D, Chen IA, Palaniappan K, Ratner A, Chu K, et al. IMG/VR v3: an integrated ecological and evolutionary framework for interrogating genomes of uncultivated viruses. Nucleic Acids Res. 2021;49:1–12.24.Nayfach S, Páez-Espino D, Call L, Low SJ, Sberro H, Ivanova NN, et al. Metagenomic compendium of 189,680 DNA viruses from the human gut microbiome. Nat Microbiol. 2021;6:960–70.CAS 
PubMed 
PubMed Central 

Google Scholar 
25.Roux S, Matthijnssens J, Dutilh BE. Metagenomics in virology. Encycloped Virol. 2021;133–40. Published online 2021 Mar 1. https://doi.org/10.1016/B978-0-12-809633-8.20957-6.26.Warwick-Dugdale J, Solonenko N, Moore K, Chittick L, Gregory AC, Allen MJ, et al. Long-read viral metagenomics captures abundant and microdiverse viral populations and their niche-defining genomic islands. PeerJ. 2019;7:e6800.PubMed 
PubMed Central 

Google Scholar 
27.Roux S, Solonenko NE, Dang VT, Poulos BT, Schwenck SM, Goldsmith DB, et al. Towards quantitative viromics for both double-stranded and single-stranded DNA viruses. PeerJ. 2016;4:e2777.PubMed 
PubMed Central 

Google Scholar 
28.Simmonds P, Adams MJ, Benkő M, Breitbart M, Brister JR, Carstens EB, et al. Consensus statement: Virus taxonomy in the age of metagenomics. Nat Rev Microbiol. 2017;15:161–8.CAS 
PubMed 

Google Scholar 
29.Roux S, Adriaenssens EM, Dutilh BE, Koonin EV, Kropinski AM, Krupovic M, et al. Minimum Information about an Uncultivated Virus Genome (MIUViG): a community consensus on standards and best practices for describing genome sequences from uncultivated viruses. Nat Biotechnol. 2018;37:29–37.PubMed 
PubMed Central 

Google Scholar 
30.Jang HB, Bolduc B, Zablocki O, Kuhn JH, Roux S, Adriaenssens EM, et al. Taxonomic assignment of uncultivated prokaryotic virus genomes is enabled by gene-sharing networks. Nat Biotechnol 2019;37:632–9.
Google Scholar 
31.Nishimura Y, Yoshida T, Kuronishi M, Uehara H, Ogata H, Goto S. ViPTree: the viral proteomic tree server. Bioinformatics. 2017;33:2379–80.32.Moraru C, Varsani A, Kropinski AM. VIRIDIC-a novel tool to calculate the intergenomic similarities of prokaryote-infecting. Viruses. 2020;12:1268.CAS 
PubMed Central 

Google Scholar 
33.Pons JC, Paez-Espino D, Riera G, Ivanova N, Kyrpides NC, Llabrés M. VPF-Class: taxonomic assignment and host prediction of uncultivated viruses based on viral protein families. Bioinformatics. 2021;37:1805–13.34.Bolduc B, Youens-Clark K, Roux S, Hurwitz BL, Sullivan MB. iVirus: facilitating new insights in viral ecology with software and community data sets imbedded in a cyberinfrastructure. ISME J. 2017;11:7–14.PubMed 

Google Scholar 
35.Merchant N, Lyons E, Goff S, Vaughn M, Ware D, Micklos D, et al. The iPlant Collaborative: cyberinfrastructure for enabling data to discovery for the life sciences. PLOS Biol. 2016;14:e1002342.PubMed 
PubMed Central 

Google Scholar 
36.Teytelman L, Stoliartchouk A, Kindler L, Hurwitz BL. Protocols.io: virtual communities for protocol development and discussion. PLOS Biol. 2016;14:e1002538.PubMed 
PubMed Central 

Google Scholar 
37.Kindler L, Stoliartchouk A, Gomez C, Thornton J, Teytelman L, Hurwitz BL. VERVENet: the viral ecology research and virtual exchange network. PeerJ. 2021; in press.38.Arndt D, Grant JR, Marcu A, Sajed T, Pon A, Liang Y, et al. PHASTER: a better, faster version of the PHAST phage search tool. Nucleic Acids Res. 2016;44:W16–21.CAS 
PubMed 
PubMed Central 

Google Scholar 
39.Sousa AL de, Maués D, Lobato A, Franco EF, Pinheiro K, Araújo F, et al. PhageWeb—web interface for rapid identification and characterization of prophages in bacterial genomes. Front Genet. 2018; 9.40.Tynecki P, Guziński A, Kazimierczak J, Jadczuk M, Dastych J, Onisko A. PhageAI—bacteriophage life cycle recognition with machine learning and natural language processing. bioRxiv 2020; 2020.07.11.198606.41.Wommack KE, Bhavsar J, Polson SW, Chen J, Dumas M, Srinivasiah S, et al. VIROME: a standard operating procedure for analysis of viral metagenome sequences. Standards Genom Sci. 2012;6:427–39.
Google Scholar 
42.Roux S, Faubladier M, Mahul A, Paulhe N, Bernard A, Debroas D, et al. Metavir: a web server dedicated to virome analysis. Bioinformatics. 2011;27:3074–5.CAS 
PubMed 

Google Scholar 
43.Arkin AP, Cottingham RW, Henry CS, Harris NL, Stevens RL, Maslov S, et al. KBase: The United States department of energy systems biology knowledgebase. Nat Biotechnol. 2018;36:566–9.CAS 
PubMed 
PubMed Central 

Google Scholar 
44.Roux S, Enault F, Hurwitz BL, Sullivan MB. VirSorter: mining viral signal from microbial genomic data. PeerJ. 2015;3:e985.PubMed 
PubMed Central 

Google Scholar 
45.Bolduc B, Jang HB, Doulcier G, You Z-QZ, Roux S, Sullivan MB. vConTACT: an iVirus tool to classify double-stranded DNA viruses that infect Archaea and Bacteria. PeerJ. 2017;5:e3243.PubMed 
PubMed Central 

Google Scholar 
46.Hurwitz BL, Westveld AH, Brum JR, Sullivan MB. Modeling ecological drivers in marine viral communities using comparative metagenomics and network analyses. Proc Natl Acad Sci. 2014;111:10714–9.CAS 
PubMed 
PubMed Central 

Google Scholar 
47.Guo J, Bolduc B, Zayed AA, Varsani A, Dominguez-Huerta G, Delmont TO, et al. VirSorter2: a multi-classifier, expert-guided approach to detect diverse DNA and RNA viruses. Microbiome. 2021;9:37.PubMed 
PubMed Central 

Google Scholar 
48.Ren J, Kai S, Chao D, Nathan A, Ahlgren, JA, Fuhrman, YL, et al. Identifying viruses from metagenomic data using deep learning. Quant Biol. 2020;8:64–77. https://doi.org/10.1007/s40484-019-0187-4.49.Amgarten D, Braga LPP, da Silva AM, Setubal JC. MARVEL, a tool for prediction of bacteriophage sequences in metagenomic bins. Front Genet. 2018;9:1–8.
Google Scholar 
50.Pratama A, Bolduc B, Zayed AA, Zhong Z-P, Guo J, Vik DR, et al. Expanding standards in viromics: in silico evaluation of dsDNA viral genome identification, classification, and auxiliary metabolic gene curation. PeerJ. 2021; In Press.51.Kieft, K., Zhou, Z. & Anantharaman, K. VIBRANT: automated recovery, annotation and curation of microbial viruses, and evaluation of viral community function from genomic sequences. Microbiome. 2020;8:90. https://doi.org/10.1186/s40168-020-00867-0.52.Karner MB, DeLong EF, Karl DM. Archaeal dominance in the mesopelagic zone of the Pacific Ocean. Nature. 2001;409:507–10.CAS 
PubMed 

Google Scholar 
53.Vik DR, Roux S, Brum JR, Bolduc B, Emerson JB, Padilla CCC, et al. Putative archaeal viruses from the mesopelagic ocean. PeerJ. 2017;5:e3428.PubMed 
PubMed Central 

Google Scholar 
54.Vik D, Bolduc B, Roux S, Krupovic M, Sullivan MB. MArVDv2: a machine learning approach to metagenomic archaeal virus detection. bioRxiv 2021; In Press..55.Tisza MJ, Pastrana DV, Welch NL, Stewart B, Peretti A, Starrett GJ, et al. Discovery of several thousand highly diverse circular DNA viruses. eLife. 2020;9:1–26.
Google Scholar 
56.Tisza MJ, Belford AK, Domínguez-Huerta G, Bolduc B, Buck CB. Cenote-Taker 2 democratizes virus discovery and sequence annotation. Virus Evolut. 2021;7:1–12.
Google Scholar 
57.Shaffer M, Borton MA, McGivern BB, Zayed AA, La Rosa SL, Solden LM, et al. DRAM for distilling microbial metabolism to automate the curation of microbiome function. Nucleic Acids Res. 2020;48:8883–8900.CAS 
PubMed 
PubMed Central 

Google Scholar 
58.Edwards RA, McNair K, Faust K, Raes J, Dutilh BE. Computational approaches to predict bacteriophage–host relationships. FEMS Microbiol Rev. 2016;40:258–72.CAS 
PubMed 

Google Scholar 
59.Galiez C, Siebert M, Enault F, Vincent J, Söding J. WIsH: who is the host? Predicting prokaryotic hosts from metagenomic phage contigs. Bioinformatics. 2017;33:3113–14.60.Nayfach S, Camargo AP, Schulz F, Eloe-Fadrosh E, Roux S & Kyrpides NC. CheckV assesses the quality and completeness of metagenome-assembled viral genomes. Nat Biotechnol 2021;39:578–85. https://doi.org/10.1038/s41587-020-00774-7.61.Gregory AC, Solonenko SA, Ignacio-Espinoza JC, LaButti K, Copeland A, Sudek S, et al. Genomic differentiation among wild cyanophages despite widespread horizontal gene transfer. BMC Genom. 2016;17:930.
Google Scholar 
62.Brum JR, Sullivan MB. Rising to the challenge: accelerated pace of discovery transforms marine virology. Nat Rev Microbiol. 2015;13:147–59.CAS 
PubMed 

Google Scholar 
63.Gregory AC, Gerhardt K, Zhong Z-P, Bolduc B, Temperton B, Konstantinidis KT, et al. MetaPop: a pipeline for macro- and micro-diversity analyses and visualization of microbial and viral metagenome-derived populations. bioRxiv 2020; 2020.11.01.363960.64.Eren AM, Esen ÖC, Quince C, Vineis JH, Morrison HG, Sogin ML, et al. Anvi’o: an advanced analysis and visualization platform for ‘omics data. PeerJ. 2015;3:e1319.PubMed 
PubMed Central 

Google Scholar 
65.Mitchell AL, Almeida A, Beracochea M, Boland M, Burgin J, Cochrane G, et al. MGnify: the microbiome analysis resource in 2020. Nucleic Acids Res. 2019;48:D570–D578.PubMed Central 

Google Scholar 
66.Solonenko SA, Ignacio-Espinoza JC, Alberti A, Cruaud C, Hallam S, Konstantinidis K, et al. Sequencing platform and library preparation choices impact viral metagenomes. BMC Genom. 2013;14:320.CAS 

Google Scholar 
67.Wood-Charlson EM, Anubhav, Auberry D, Blanco H, Borkum MI, Corilo YE, et al. The National Microbiome Data Collaborative: enabling microbiome science. Nat Rev Microbiol. 2020;18:313–4.CAS 
PubMed 

Google Scholar  More