Philippa Kaur

Cooke, S. J. et al. Knowledge co-production: A pathway to effective fisheries management, conservation, and governance. Fisheries 46, 89–97 (2021).Article 

Google Scholar 
Kobluk, H. M. et al. Indigenous knowledge of key ecological processes confers resilience to a small-scale kelp fishery. People Nat. 3, 723–739 (2021).Article 

Google Scholar 
Lee, L. C. et al. Drawing on indigenous governance and stewardship to build resilient coastal fisheries: People and abalone along Canada’s northwest coast. Mar. Policy 109, 103701 (2019).Article 

Google Scholar 
Reid, A. J. et al. “Two-Eyed Seeing”: An Indigenous framework to transform fisheries research and management. Fish. Fish. 22, 243–261 (2021).Article 

Google Scholar 
Toniello, G., Lepofsky, D., Lertzman-Lepofsky, G., Salomon, A. K. & Rowell, K. 11,500 y of human–clam relationships provide long-term context for intertidal management in the Salish Sea, British Columbia. Proc. Natl Acad. Sci. 116, 22106–22114 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Ahn, J. E. & Ronan, A. D. Development of a model to assess coastal ecosystem health using oysters as the indicator species. Estuar., Coast. Shelf Sci. 233, 106528 (2020).CAS 
Article 

Google Scholar 
Skilbeck, C. G., Heap, A. D. & Woodroffe, C. D. Geology and sedimentary history of modern estuaries. in Applications of Paleoenvironmental Techniques in Estuarine Studies (eds. Weckström, K., Saunders, K. M., Gell, P. A. & Skilbeck, C. G.) 45–74 (Springer Netherlands, 2017). https://doi.org/10.1007/978-94-024-0990-1_3.Durham, S. R., Gillikin, D. P., Goodwin, D. H. & Dietl, G. P. Rapid determination of oyster lifespans and growth rates using LA-ICP-MS line scans of shell Mg/Ca ratios. Palaeogeogr., Palaeoclimatol., Palaeoecol. 485, 201–209 (2017).Article 

Google Scholar 
Lockwood, R. & Mann, R. A conservation palaeobiological perspective on Chesapeake Bay oysters. Philos. Trans. R. Soc. B 374, 20190209 (2019).CAS 
Article 

Google Scholar 
Rick, T. C. et al. Millennial-scale sustainability of the Chesapeake Bay native American oyster fishery. Proc. Natl Acad. Sci. 113, 6568–6573 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Thompson, V. D. et al. Ecosystem stability and Native American oyster harvesting along the Atlantic Coast of the United States. Sci. Adv. 6, eaba9652 (2020).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Zimmt, J. B., Lockwood, R., Andrus, C. F. T. & Herbert, G. S. Sclerochronological basis for growth band counting: A reliable technique for life-span determination of Crassostrea virginica from the mid-Atlantic United States. Palaeogeogr. Palaeoclimatol. Palaeoecol. 516, 54–63 (2019).Article 

Google Scholar 
Alleway, H. K. & Connell, S. D. Loss of an ecological baseline through the eradication of oyster reefs from coastal ecosystems and human memory. Conserv Biol. 29, 795–804 (2015).PubMed 
Article 

Google Scholar 
Beck, M. W. et al. Oyster reefs at risk and recommendations for conservation, restoration, and management. Bioscience 61, 107–116 (2011).Article 

Google Scholar 
Kirby, M. X. Fishing down the coast: Historical expansion and collapse of oyster fisheries along continental margins. Proc. Natl Acad. Sci. 101, 13096 (2004).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Lotze, H. K. et al. Depletion, degradation, and recovery potential of estuaries and coastal seas. Science 312, 1806 (2006).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Zu Ermgassen, P. S. et al. Historical ecology with real numbers: Past and present extent and biomass of an imperilled estuarine habitat. Proc. R. Soc. B: Biol. Sci. 279, 3393–3400 (2012).Article 

Google Scholar 
Carranza, A., Defeo, O. & Beck, M. Diversity, conservation status and threats to native oysters (Ostreidae) around the Atlantic and Caribbean coasts of South America. Aquat. Conserv.: Mar. Freshw. Ecosyst. 19, 344–353 (2009).Article 

Google Scholar 
Pluckhahn, T. J. & Thompson, V. D. Woodland-period mound building as historical tradition: Dating the mounds and monuments at Crystal River (8CI1). J. Archaeological Sci.: Rep. 15, 73–94 (2017).Article 

Google Scholar 
Waselkov, G. A. Shellfish gathering and shell midden archaeology. Adv. Archaeol. Method Theory 10, 93–210 (1987).Article 

Google Scholar 
McNiven, I. J. Ritualized middening practices. J. Archaeol. Method Theory 20, 552–587 (2013).Article 

Google Scholar 
Hawkes, A. D. et al. Relative sea-level change in northeastern Florida (USA) during the last ~8.0 ka. Quat. Sci. Rev. 142, 90–101 (2016).ADS 
Article 

Google Scholar 
Kelley, J. T., Belknap, D. F. & Claesson, S. Drowned coastal deposits with associated archaeological remains from a sea-level “slowstand”: Northwestern Gulf of Maine, USA. Geology 38, 695–698 (2010).ADS 
Article 

Google Scholar 
Khan, N. S. et al. Drivers of Holocene sea-level change in the Caribbean. Quat. Sci. Rev. 155, 13–36 (2017).ADS 
Article 

Google Scholar 
Love, R. et al. The contribution of glacial isostatic adjustment to projections of sea-level change along the Atlantic and Gulf coasts of North America. Earth’s Future 4, 440–464 (2016).ADS 
Article 

Google Scholar 
Shugar, D. H. et al. Post-glacial sea-level change along the Pacific coast of North America. Quat. Sci. Rev. 97, 170–192 (2014).ADS 
Article 

Google Scholar 
Dougherty, A. J. et al. Redating the earliest evidence of the mid-Holocene relative sea-level highstand in Australia and implications for global sea-level rise. PLoS ONE. 14, e0218430 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Bailey, G. N. The role of molluscs in coastal economies: The results of midden analysis in Australia. J. Archaeol. Sci. 2, 45–62 (1975).Article 

Google Scholar 
Habu, J., Matsui, A., Yamamoto, N. & Kanno, T. Shell midden archaeology in Japan: Aquatic food acquisition and long-term change in the Jomon culture. Quat. Int. 239, 19–27 (2011).Article 

Google Scholar 
Hale, J. C. et al. Submerged landscapes, marine transgression and underwater shell middens: Comparative analysis of site formation and taphonomy in Europe and North America. Quat. Sci. Rev. 258, 106867 (2021).Article 

Google Scholar 
Erlandson, J. M. et al. Shellfish, geophytes, and sedentism on Early Holocene Santa Rosa Island, Alta California, USA. J. Isl. Coast. Archaeol. 15, 504–524 (2020).Article 

Google Scholar 
Rick, T. C. Early to Middle Holocene estuarine shellfish collecting on the islands and mainland coast of the Santa Barbara Channel, California, USA. Open Quaternary 6, 9 (2020).Sanger, D. & Sanger, M. J. Boom and bust on the river: The story of the Damariscotta oyster shell heaps. Archaeol. East. North Am. 14, 65–78 (1986).
Google Scholar 
Moss, M. L. Shellfish gender, and status on the Northwest Coast: Reconciling archaeological, ethnographic, and ethnohistoric records of the Tlingit. Am. Anthropologist 95, 631–652 (1993).Article 

Google Scholar 
Cannon, A., Burchell, M. & Bathurst, R. Trends and strategies in shellfish gathering on the Pacific Northwest Coast of North America. in Early Human Impact on Megamolluscs (eds. Antczak, A. & Cipriani, R.) 7–22 (Archaeopress, 2008).Grier, C., Angelbeck, B. & McLay, E. Terraforming and monumentality as long-term social practice in the Salish Sea region of the Northwest Coast of North America. Hunt. Gatherer Res. 3, 107–132 (2017).Article 

Google Scholar 
Pluckhahn, T. J. & Thompson, V. D. New Histories of Village Life at Crystal River. (University Press of Florida, 2018).Thompson, V. D. et al. Ancient engineering of fish capture and storage in southwest Florida. Proc. Natl Acad. Sci. 117, 8374–8381 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Sassaman, K. E. Complex hunter–gatherers in evolution and history: A North American perspective. J. Archaeol. Res. 12, 227–280 (2004).Article 

Google Scholar 
Luby, E. M. & Gruber, M. F. The dead must be fed: Symbolic meanings of the shellmounds of the San Francisco Bay area. Camb. Archaeol. J. 9, 95–108 (1999).Article 

Google Scholar 
Lightfoot, K. G. & Luby, E. M. Mound building by California hunter-gatherers. in The Oxford Handbook of North American Archaeology (ed. Pauketat, T.) 212–223 (Oxford University Press, 2012).Smith, A. D. T. Archaeological expressions of Holocene cultural and environmental change in coastal Southeast Queensland. (The University of Queensland, 2016).Reeder-Myers, L., Rick, T., Lowery, D., Wah, J. & Henkes, G. Human ecology and coastal foraging at Fishing Bay, Maryland, USA. J. Ethnobiol. 36, 595–616 (2016).Article 

Google Scholar 
Petrie, C. C. Tom Petrie’s reminiscences of Early Queensland (dating from 1837). (Watson, Ferguson & Company, 1904).Eipper, C. Statement of the Origin, Condition and Prospects, of the German Mission to the Aborigines at Moreton Bay, etc. (James Reading, 1841).Watkins, G. Notes on the Aboriginals of Stradbroke and Moreton Islands. Proc. R. Soc. Qld. 8, 40–50 (1891).
Google Scholar 
Ross, A. & with members of the Quandamooka Aboriginal Land Council. Aboriginal approaches to cultural heritage management: A Quandamooka case study. in Australian Archaeology ’95: Proceedings of the 1995 Australian Archaeological Association Annual Conference (eds. Ulm, S., Lilley, I. & Ross, A.) vol. Tempus 6 107–112 (Anthropology Museum, University of Queensland, 1996).Jenkins, J. A. & Gallivan, M. D. Shell on earth: Oyster harvesting, consumption, and deposition practices in the Powhatan Chesapeake. J. Isl. Coast. Archaeol. 15, 384–406 (2020).Article 

Google Scholar 
Hatch, M. B. A. & Wyllie-Echeverria, S. Historic distribution of Ostrea lurida (Olympia oyster) in the San Juan Archipelago. Wash. State Tribal Coll. Univ. Res. J. 1, 38–45 (2016).
Google Scholar 
Swanton, J. R. Social Organization and Social Usages of the Indians of the Creek Confederacy. (Bureau of American Ethnology, 1928).Hening, W. W. The Statutes at Large of Virginia. (1809).Wharton, J. The Bounty of the Chesapeake: Fishing in Colonial Virginia. (Virginia 350th Anniversary Celebration Corporation, 1957).Denys, N. Description géographique et historique des Costes de l’Amérique Septentrionale. Avec l’Histoire naturelle du Pais. (Chez Claude Barbin, 1672).Nicolar, J. The Life and Traditions of the Red Man. (Duke University Press, 2007 Print, 1893).Speck, F. G. Penobscot Man: The Life History of a Forest Tribe in Maine. (University of Pennsylvania Press, 1940).Washburn, K. Passamaquoddy tribe conducts oyster project. Bangor Daily News (1979).Kennedy, V. S. Shifting Baselines in the Chesapeake Bay: An Environmental History. (Johns Hopkins University Press, 2018).de Charlevoix, P. F. X. Journal of a Voyage to North America, Vollume II. Translated by Louise Phelps Kellogg. (The Caxton Club, 1923).Ingersoll, E. The Oyster Industry. (United States Bureau of Fisheries, United States Census Office, Government Printing Office, 1881).Brice, J. J. Report on the fish and fisheries of the coastal waters of Florida. in Report of the Commissioner for the Year Ending June 30, 1896 263–242 (U.S. Commission of Fish and Fisheries, U.S. Government Printing Office, 1896).Blake, B. & Zu Ermgassen, P. S. E. The history and decline of Ostrea lurida in Willapa Bay, Washington. J. Shellfish Res. 34, 273–280 (2015).Article 

Google Scholar 
Thurstan, R. H. et al. Charting two centuries of transformation in a coastal social-ecological system: A mixed methods approach. Global Environmental Change 61, 102058 (2020).Schulte, D. M. History of the Virginia oyster fishery, Chesapeake Bay, USA. Front. Mar. Sci. 4, 127 (2017).Fletcher, M.-S., Hamilton, R., Dressler, W. & Palmer, L. Indigenous knowledge and the shackles of wilderness. Proc. Natl Acad. Sci. 118, e2022218118 (2021).Ross, A., Coghill, S. & Coghill, B. Discarding the evidence: The place of natural resources stewardship in the creation of the Peel Island Lazaret Midden, Moreton Bay, southeast Queensland. Quat. Int. 385, 177–190 (2015).Article 

Google Scholar 
Reeder-Myers, L. A. & Rick, T. C. Kayak surveys in estuarine environments: addressing sea level rise and climate change. Antiquity 93, 1040–1051 (2019).Article 

Google Scholar 
Savarese, M., Walker, K. J., Stingu, S., Marquardt, W. H. & Thompson, V. The effects of shellfish harvesting by aboriginal inhabitants of Southwest Florida (USA) on productivity of the eastern oyster: Implications for estuarine management and restoration. Anthropocene 16, 28–41 (2016).Article 

Google Scholar 
Lulewicz, I. H., Thompson, V. D., Cramb, J. & Tucker, B. Oyster paleoecology and native American subsistence practices on Ossabaw Island, Georgia, USA. J. Archaeol. Sci.: Rep. 15, 282–289 (2017).
Google Scholar 
Hesterberg, S. G. et al. Prehistoric baseline reveals substantial decline of oyster reef condition in a Gulf of Mexico conservation priority area. Biol. Lett. 16, 20190865 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
Cannarozzi, N. R. & Kowalewski, M. Seasonal oyster harvesting recorded in a Late Archaic period shell ring. PloS ONE. 14, e0224666 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Cook-Patton, S. C., Weller, D., Rick, T. C. & Parker, J. D. Ancient experiments: Forest biodiversity and soil nutrients enhanced by Native American middens. Landsc. Ecol. 29, 979–987 (2014).Article 

Google Scholar 
Stalter, R. & Kincaid, D. The vascular flora of five Florida shell middens. J. Torre. Botanical Soc. 131, 93–103 (2004).Article 

Google Scholar 
Kirby, M. X. & Miller, H. M. Response of a benthic suspension feeder (Crassostrea virginica Gmelin) to three centuries of anthropogenic eutrophication in Chesapeake Bay. Estuar. Coast. Shelf Sci. 62, 679–689 (2005).ADS 
Article 

Google Scholar 
Suttles, W. Variation in habitat and culture on the Northwest Coast. in Coastal Salish Essays 26–44 (University of Washington Press, 1987).Bliege Bird, R. & Nimmo, D. Restore the lost ecological functions of people. Nat. Ecol. Evolution 2, 1050–1052 (2018).Article 

Google Scholar 
Berkes, F. Indigenous ways of knowing and the study of environmental change. J. R. Soc. N.Z. 39, 151–156 (2009).Article 

Google Scholar 
Tengö, M., Malmer, P., Elmqvist, T. & Brondizio, E. S. A Framework for Connecting Indigenous, Local and Scientific Knowledge Systems. (2012).Ellis, E. C. et al. People have shaped most of terrestrial nature for at least 12,000 years. Proc. Natl Acad. Sci.118, e2023483118 (2021).Roberts, P. et al. Reimagining the relationship between Gondwanan forests and Aboriginal land management in Australia’s “Wet Tropics”. Iscience 24, 102190 (2021).Ogburn, D. M., White, I. & McPhee, D. P. The disappearance of oyster reefs from eastern Australian estuaries—impact of colonial settlement or mudworm invasion? Coast. Manag. 35, 271–287 (2007).Article 

Google Scholar 
Diggles, B. K. Historical epidemiology indicates water quality decline drives loss of oyster (Saccostrea glomerata) reefs in Moreton Bay, Australia. N.Z. J. Mar. Freshw. Res. 47, 561–581 (2013).CAS 
Article 

Google Scholar 
Pritchard, C., Shanks, A., Rimler, R., Oates, M. & Rumrill, S. The Olympia oyster Ostrea lurida: Recent advances in natural history, ecology, and restoration. J. Shellfish Res. 34, 259–271 (2015).Article 

Google Scholar 
Trimble, A. C., Ruesink, J. L. & Dumbauld, B. R. Factors preventing the recovery of a historically overexploited shellfish species, Ostrea lurida Carpenter 1864. J. Shellfish Res. 28, 97–106 (2009).Article 

Google Scholar 
White, J., Ruesink, J. L. & Trimble, A. C. The nearly forgotten oyster: Ostrea lurida Carpenter 1864 (Olympia oyster) history and management in Washington State. J. Shellfish Res. 28, 43–49 (2009).Article 

Google Scholar 
Harding, J. M., Spero, H. J., Mann, R., Herbert, G. S. & Sliko, J. L. Reconstructing early 17th century estuarine drought conditions from Jamestown oysters. Proc. Natl Acad. Sci. 107, 10549–10554 (2010).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Mann, R., Harding, J. M. & Southworth, M. J. Reconstructing pre-colonial oyster demographics in the Chesapeake Bay, USA. Estuar., Coast. Shelf Sci. 85, 217–222 (2009).ADS 
Article 

Google Scholar 
Bayne, B. L. Biology of Oysters. (Elsevier Science & Technology, 2017).Galtsoff, P. S. The American Oyster Crassostrea virginica Gmelin. (United States Government Printing Office, 1964).Kennedy, V. S., Newell, R. I. E. & Eble, A. F. The Eastern Oyster: Crassostrea virginica. (University of Maryland Sea Grant Publications, 1996).Grabowski, J. H., Powers, S. P., Peterson, C. H., Gaskill, D. & Summerson, H. C. Growth and survivorship of non-native (Crassostrea gigas and Crassostrea ariakensis) versus native eastern (Crassostrea virginica) oysters. J. Shellfish Res. 23, 781–793 (2004).
Google Scholar 
Shumway, S. Natural environmental factors. in The eastern oyster Crassostrea virginica (eds. Kennedy, V., Newell, R. & Eble, A.) 467–513 (Maryland Sea Grant, 1996).Lyman, R. L. Paleoenvironmental reconstruction from faunal remains: Ecological basics and analytical assumptions. J. Archaeol. Res. 25, 315–371 (2017).MathSciNet 
Article 

Google Scholar 
Claasen, C. Shells. (Cambridge University Press, 1990).Giovas, C. M. The shell game: Analytic problems in archaeological mollusc quantification. J. Archaeol. Sci. 36, 1557–1564 (2009).Article 

Google Scholar 
Peltier, W. R., Argus, D. F. & Drummond, R. Space geodesy constrains ice age terminal deglaciation: The global ICE-6G_C (VM5a) model: Global Glacial Isostatic Adjustment. J. Geophys. Res.: Solid Earth 120, 450–487 (2015).ADS 
Article 

Google Scholar  More

Fossil dataWe downloaded all fossil occurrences recorded for the order Scleractinia at the species level from the Paleobiology Database (PBDB – paleobiodb.org; accessed on 3 August 2021). This is the most comprehensive repository for palaeontological data in reef corals to date. Due to the nature of the data, no ethics approval was required. To minimize identification issues, we excluded taxa with uncertain generic and species assignments (i.e., classified as aff. and cf.) and only selected species that had accepted names. We also selected the variables classification and palaeoenvironment from the output options to facilitate taxonomic and environmental filters applied in downstream analyses. The full dataset consisted of 24,011 occurrences across 4235 species, spanning over 250 Myr of coral evolution from the Triassic to the present. Although our focus here lies on the Cenozoic, we used the complete fossil dataset (i.e., including all of the occurrences) to have estimates of the diversification dynamics in scleractinian corals throughout the whole timespan of their evolution.Evolutionary ratesWith the full palaeontological dataset, we estimated evolutionary rates through time in scleractinian corals using the Bayesian framework of the program PyRate (v3.0)12,36,37. This program uses fossil occurrence data to calculate the temporal variation in rates of preservation, speciation and extinction, while incorporating multiple sources of uncertainty12. At its core implementation, PyRate jointly estimates the times of origination (Ts) and extinction (Te) for each fossil lineage; the fossilization and sampling parameters that determine preservation rates (q); and the overall rates of speciation (λ) and extinction (μ) through time36. Recently, the program has been upgraded to include a reversible jump Markov Chain Monte Carlo (rjMCMC) algorithm to estimate diversification rate heterogeneity, which provides more accurate and precise estimates than other commonly used methods12. Therefore, despite the inherent bias of the fossil record (i.e., estimates are conditioned on sampled lineages), PyRate is a robust method to quantify speciation and extinction rates, and their respective temporal shifts, from fossil occurrence data.Extant taxa can also be included in the PyRate framework as long as they are also represented in the fossil record. This is done to extend the fossil geologic ranges to the recent times. Hence, the first step in our analysis was to identify which species in our dataset is still alive at the present. To do this, we matched the accepted species names in the PBDB dataset with those from the extant species dataset of Huang et al.38. Subsequently, we split our dataset into eleven independent subsets, with the goal of keeping each subset with an equal number of species. Each data subset included a random selection of species with their respective occurrences, which was enough to calculate Ts and Te (see below). This was done to avoid convergence issues, given the large size of our dataset and the consequent complexity of the model37. For each of our subsets, we generated fifty replicates by resampling the fossil ages from their temporal ranges to account for the uncertainty associated with the age of occurrences. We then used the maximum-likelihood test in PyRate to compare between three models of fossil preservation12: the homogeneous Poisson process (HPP; q is constant through time); the nonhomogeneous Poisson process (NHPP; q varies throughout the lifespan of a species); and the time-variable Poisson process (TPP; q varies across geological epochs). The latter model (TPP) was selected across all of our data subsets (Supplementary Table 2).After selecting the preservation model, we first focused on assessing the estimates of times of origination and extinction in each data subset, rather than using the full dataset to jointly estimate all parameters at once as in the original implementation of PyRate. This further reduced the complexity of the model and allowed for more precise parameter estimates. For each replicate in all of our data subsets, we approximated the posterior distribution of Ts and Te through a 50 million generation run of the rjMCMC algorithm under the TPP, sampling parameters every 40 thousand iterations. At the end of each run, we discarded 20% of the samples as burn-in and assessed chain convergence through the effective sample sizes of posterior parameter estimates, using the software Tracer39 (v1.7.1).From the results of this first set of models, we extracted the median estimates of Ts and Te across replicates, and we merged the estimates from the eleven independent data subsets. This merged data frame contained estimated times of origination and extinction for all coral lineages within our fossil dataset. We then used this merged Ts and Te data frame as input for another rjMCMC chain to finally estimate overall λ and μ through time, by applying the option -d in PyRate. In this option, Ts and Te for all fossil lineages are given as fixed values and, therefore, are not estimated by the model. The chain for this model was run for 100 million generations, sampling parameters at every 40 thousand iterations. Once again, we excluded 20% of the initial samples as burn-in and checked model convergence using Tracer. Finally, we calculated net diversification rates through time by subtracting the post burn-in samples of μ from λ.To explore the taxonomic idiosyncrasies in the evolutionary rates of reef corals, we selected the most abundant families on present-day coral reefs in terms of the number of colonies per area18 (Acroporidae, Agariciidae, Merulinidae, Mussidae, Pocilloporidae, and Poritidae). Altogether, species within these families account for ~40% of the total extant diversity in Scleractinia. These families also account for most of the occurrences in the PBDB fossil dataset (excluding extinct families, which are generally older and had little temporal overlap with extant ones): Acroporidae (1457 occ. in 165 spp.); Agariciidae (722 occ. in 89 spp.); Merulinidae (2464 occ. in 229 spp.); Mussidae (1146 occ. in 100 spp.); Pocilloporidae (615 occ. in 64 spp.); and Poritidae (1149 occ. in 91 spp.). Therefore, from our full dataset, we selected six independent ones encompassing all species in each of the selected families. We also selected only species that are classified as reef-associated within these families, since we were specifically interested in these environments. This selection had a negligible effect on the size of the individual datasets, given that the vast majority of fossil species within these families are reef-associated. In each family, we followed the same modelling steps described above to estimate μ and λ, and diversity trajectories. However, this time it was not necessary to split the datasets into subsets, given that each family has far less occurrences than the full dataset. We started by comparing models of preservation, which showed the TPP as the best supported for all families (Supplementary Table 3). Then we created fifty replicates by resampling fossil ages to accommodate the uncertainty associated with the time of occurrences. For each replicate, we ran the rjMCMC algorithm for 50 million generations under the TPP model, with a sampling frequency of 40 thousand iterations. We discarded initial 20% of the samples as burn-in, and assessed convergence through Tracer. We then combined all replicates, resampling 100 random samples from each replicate to assess the estimates of μ and λ through time for each family. Finally, we extracted diversity trajectories in each family for all of the replicates by applying the -ltt option in PyRate, which generates a table with estimated range-through diversity at every 0.1 Myr. From these trajectories, we calculated the mean difference in diversity (slope in species per 0.1 Myr) between subsequent time samples backwards from the present (i.e., diversity in time t was subtracted from diversity in time t-1) using the diff function in R (v4.0.3).As an alternative to PyRate, we also calculated the diversity dynamics of reef coral fossils using the R package divDyn40, which combines a range of published methods for quantifying fossil diversification rates. Differently from PyRate, the metrics applied in divDyn require that the fossil occurrences are split into discrete time bins. Therefore, these metrics treat the origination and extinction rates as independent parameters in each bin, while PyRate is designed to detect rate heterogeneity through a continuous time setting12. Our goal here, however, was not to compare models but to assess the robustness of our rate patterns and diversity trajectories using alternative methods. We divided our dataset into one-million-year time bins to have enough temporal resolution for rate calculations. To account for the uncertainty in the assignment of fossil ages, we created 50 binned replicates by sampling the age of each occurrence from a random uniform distribution, with bounds defined by the age ranges provided in the PBDB dataset. We then used the divDyn function to calculate the per capita rates of origination and extinction through time (based on the rate equations by Foote41) for all scleractinians (Supplementary Fig. 5a) and for reef-associated acroporids alone (Supplementary Fig. 5b). We also used the same procedure to generate range-through diversity curves for each of the six families selected previously, to compare with the curves generated by PyRate (Fig. 2a). Although the rate results differed between the PyRate (Fig. 1) and the divDyn (Supplementary Fig. 5) approaches, the general patterns remained unchanged. Rates are more volatile through time in divDyn estimates, with larger confidence intervals, which is expected from the metrics applied in the package12,42. Yet, we found the same peaks in extinction for Scleractinia: at the Cretaceous-Paleogene and Eocene-Oligocene boundaries, and at the Pliocene-Pleistocene (Supplementary Fig. 5a). The recent peak in speciation in Acroporidae was also detected, although less strong (Supplementary Fig. 5b). Despite these slight differences in rate estimates, the diversity curves reconstructed through divDyn (Supplementary Fig. 6) mirrored almost exactly the ones found with PyRate (Fig. 2), demonstrating that the overall macroevolutionary trends described herein (Figs. 1 and 2) are robust to methodological choices.Diversity-dependent modelsTo assess the effects of diversity dependency on the evolution of reef coral lineages, we implemented the Multivariate Birth-Death model (MBD)11 within the PyRate framework. This method was first described as the Multiple Clade Diversity Dependence model (MCDD)19, in which rates of speciation and extinction are modelled as having linear correlations with the diversity trajectories of other clades. At its original implementation, the MCDD was developed to assess the effects of negative interactions, where increasing species diversity in one group can suppress speciation rates and/or promote extinction in itself or in other ecologically similar clades19. However, the model also incorporates the possibility of positive interactions, where increasing diversity in one clade can correlate with enhanced rates of speciation or buffered extinction. Through further model developments43, the MCDD was updated to also include a horseshoe prior44 on the diversity-dependence parameters, which helped controlling for overparameterization and enhanced the power of the model to recover true effects43. More recently, this model took its current form as the MBD11, with the additional possibilities of including environmental correlates and setting exponential, rather than just linear, correlations.We first applied the MBD to estimate the diversity-dependent effects of individual extant coral families (i.e., the ones selected in the previous analysis; see Evolutionary rates) in their combined diversity trajectories. From the rjMCMC model results for individual families, we extracted estimates of Ts and Te in each of the fifty replicates and merged them across families. This merged dataset with fifty replicates of Ts and Te was then used as input for the MBD model, where we set the relative diversity trajectories of each individual family as predictors. We also included three key environmental predictors—paleotemperature, sea level and rate of sea-level change—to assess their influence in overall evolutionary rates. The paleotemperature data was obtained from Westerhold et al.45, and consists of global mean temperature estimates for the last 66 million years, averaged across 0.1 Myr time bins. Eustatic sea-level data was downloaded from Miller et al.46, and contains estimates of sea level for the last 100 million years in comparison to present-day levels, also split in ~0.1 Myr time bins. With this dataset, we calculated the average rate of sea-level change per million years, as measured from the absolute difference between subsequent sea-level values backwards in time (i.e., sea level in time t was subtracted from sea level in time t-1). These environmental factors were rescaled between 0 and 1 to maintain all predictors on the same relative scale.Under our MBD model, the speciation and extinction rates of all families combined could change through time and through correlations with the relative diversity of individual families or environmental factors. The strength and directionality (positive or negative) of the correlations are also jointly estimated for each predictor within the model11. We ran both linear and exponential correlation models (see formulas in Lehtonen et al.11) in each of our fifty replicates for 25 million generations, sampling parameters at every 25 thousand iterations. We then compared the linear and exponential models through the posterior harmonic means of their log likelihoods, which supported the exponential one as having a better fit. From the posterior estimates, we summarized the speciation (Fig. 3c) and extinction (Fig. 3d) correlation parameters (i.e., the strength of the effect) by calculating their median and 95% Highest Posterior Density (HPD) interval across replicates. Finally, we also summarized the effect of families on lineage turnover (Fig. 3e), which we conceptualize as the sum of the effects on speciation and extinction.The MBD model also provides posterior samples of the weight of the correlation parameters, which is estimated through the horseshoe prior11. In essence, this prior is able to reliably distinguish correlation parameters that should be considered noise from those that represent a true signal in the data11. The parameterization of the horseshoe prior contains local and global Bayesian shrinkage parameters44 from which shrinkage weights (w) can be calculated (see formulas in Lehtonen et al.11). These shrinkage weights associated with each correlation parameter in the MBD model vary between 0 and 1, with values closer to 0 representing noise and values closer to 1 representing a true signal. Through simulations, it has been shown that values of w  > 0.5 indicate that the correlation parameter in question significantly differs from the background noise, being the correlation positive or negative43. However, as a conservative way to infer the weight of correlation parameters, here we use a value of w  > 0.7 to detect significance. This value was calculated for each diversity-dependence parameter (speciation, extinction and turnover) from the median values drawn from the model posteriors.The spatial distribution of reef-associated taxa varied considerably throughout the Cenozoic, with biodiversity hotspots moving halfway across the globe47. Therefore, the best way to capture this dynamic biogeographic history in reef corals is by analysing global diversity patterns like we did in our main MBD model. However, to assess the robustness of our diversity-dependent results against the influence of geographic scale and site co-occurrences, we repeated all the modelling steps described above with two data subsets. First, we selected only fossil species that have occurrences in the Indo-Pacific Ocean (i.e., 30°W–180°W) within the six families. Second, we excluded sites in which the Acroporidae did not co-occur with the other families. In each of these data subsets, we calculated diversity trajectories and used them as predictors in a separate MBD model. These models had a merged dataset of Ts and Te of all species included in each case (Indo-Pacific and co-occurrences) as a response variable.Finally, we followed the same modelling procedures described above to investigate the diversity-dependent effects in family pairwise analyses. We applied the MBD model to assess the effects of all other families in each individual family at a time, while also estimating correlations with the key environmental predictors. From the rjMCMC model results for individual families, we extracted the fifty replicates of estimated Ts and Te. Each replicate was then used as input for an MBD run using the relative diversity trajectories of each other individual family as predictors, along with the environmental variables. Once again, we ran 25 million generations of the MBD, with a sampling frequency of 25 thousand, using both linear and exponential correlation models in each age replicate. For all families, we found that the exponential model had a better fit. We then summarized the correlation parameters and the shrinkage weights (Supplementary Fig. 7) derived from the exponential models per family by calculating the median and 95% confidence intervals across replicates.Reporting summaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. More

Study siteThe study area covers the campus of Suranaree University of Technology (SUT) and its surrounding landscape in Muang, Nakhon Ratchasima, Thailand (14.879° N, 102.018° E; Fig. 1). The university campus covers about 11.2 km2, and comprises a matrix of human modified lands interspersed with mixed deciduous forest fragments (at the onset of this study we identified there were 37 mixed deciduous forest fragments on campus, mean = 7.36 ± 1.48 ha, range = 0.45–45.6 ha [note, “±” is used for standard error throughout the text]). More than 15,000 students are enrolled at SUT, and there are numerous multi-story classrooms, laboratory and workshop buildings, residential housing, parking areas, eating and sports facilities, an elementary school, and a large hospital on the university campus. During the first term of the 2019 school year, 7622 students, as well as numerous SUT staff, lived in on-campus residential areas. The landscape surrounding the university is primarily dominated by agriculture, though there are also patches of less-disturbed areas as well as several densely populated villages and suburban housing divisions among the monoculture plots of upland crops (e.g., cassava, maize, and eucalyptus).Figure 1Study site map illustrating the land-use types spanning the area where the Malayan kraits (Bungarus candidus) were tracked in Muang Nakhon Ratchasima, Nakhon Ratchasima province, Thailand. Map created using QGIS v.3.8.2 (https://qgis.org/) in combination with Inkscape v.1.1.0 (https://inkscape.org/).Full size imageThe study site is located within the Korat Plateau region with an altitude range of 205–285 m above sea level. Northeast Thailand has a tropical climate, and the average daily temperature from 1 January 2018 to 31 December 2020 in Muang Nakhon Ratchasima was 28.29 °C, with daily averages ranging from 19.3 to 34.1 °C38. The region receives an average annual rainfall ranging from 1270 to 2000 mm39. There are three distinct seasons in northeast Thailand: cold, wet, and hot, each are classified by annual changes in temperature and rainfall. Cold season is typically between mid-October and mid-February, hot season is generally from mid-February to May, while the highly unpredictable rainfall of the wet season is predominantly concentrated between the months May to October39,40.Due to the representation of agriculture, semi-urban, and suburban areas with patches of more natural areas all within a relatively small area, we determined the university campus provided an ideal setting to examine how land-use features and human activity influence the movements of B. candidus. Additionally, past studies have indicated northeast Thailand hosts the most bites by B. candidus in Thailand29,33, making sites like ours ideal.Study animalsWe opportunistically sampled Malayan kraits captured as a result of notifications from locals and ad-hoc encounters during transit due to low detectability in visual encounter surveys, in addition to those discovered through unstandardized visual encounter surveys. Upon capture, we collected morphometric data, including snout-vent length (SVL), tail length (TL), mass, and sex (Table 1, Supp. Table 1). We measured body lengths with a tape measure, measured body mass with a digital scale, and determined sex via cloacal probing, all while the snakes were anesthetized via inhaling vaporized isoflurane. We then housed individuals with an SVL > 645 mm and mass > 50 g in plastic boxes (with refugia and water) prior to surgical transmitter implantation by a veterinarian from the Nakhon Ratchasima Zoo. We attempted to minimize the time snakes were in captivity awaiting implantation; however, delays arose due to the veterinarian’s availability, the snake being mid-ecdysis, or the snake having a bolus that needed to pass through the digestive tract before implantation (n = 21 implantations, mean = 5.02 ± 0.61 days, range = 0.60–13.02 days). The Nakhon Ratchasima Zoo veterinarian implanted radio transmitters (1.8 g BD-2 or 3.6 g SB-2 Holohil Inc, Carp, Canada) into the coelomic cavity using procedures described by Reinert and Cundall41, while the snake was anesthetized. We assigned each individual an ID according to sex and individual detection number (e.g., M02 = a male was the second B. candidus individual documented during the study). We released the implanted individuals as close as possible to their capture locations (mean = 65.31 m ± 13.7 m, range = 0–226.42 m), though on six occasions we moved individuals ≥ 100 m because the individual came from either residential areas or a busy road (all but one were moved  800 mm; thus, nine of the males were adults and four were juveniles (though two of the males had an SVL > 720 mm, and therefore likely sub-adults). The single telemetered female was an adult.Individual tracking durations varied (mean = 106.46 ± 15.36 days, range = 28.5–222.77 days; Supp. Fig. 1), as many individuals were lost due to unexpected premature transmitter failures (n = 5) or unsuccessful recapture efforts due to individuals sheltering under large buildings as the transmitter reached the end of its battery life (n = 4). We only recorded one confirmed mortality in the study, M01, who was killed by a motorized vehicle when crossing a road (n = 1). Another three individuals were lost due to unknown reasons, which may have been due to premature transmitter failure, mortality, or the animal moving beyond radio signal despite extensive search efforts. Thus, we only successfully recaptured and re-implanted five individuals (M01 once, M02 twice, M07 once, M27 once, and M33 twice). Transmitter batteries generally lasted approximately 90–110 days, so we aimed to replace transmitters after ≥ 90 days of use. At the end of the study, only one individual was successfully recaptured to remove the transmitter.Data collectionWe used very high frequency radio-telemetry to locate each telemetered individual on average every 24.20 h (SE ± 0.41, 0.17–410.0 h; see Supp. Fig. 2 for distribution of tracking time lags). We aimed to locate each individual’s shelter locations once each day during the daylight (06:00–18:00 h); however, we were occasionally (n = 34 days) unable to locate a snake for several consecutive days when we were unable to obtain radio signal due to an individual having moved far away or deep underneath a large structure. There were also a few occasions where we were unable to track snakes due to prolonged and heavy rainfall (n = 4 days), as the moisture damages equipment, or other reasons (n = 4 days). We additionally located snakes nocturnally (18:00–06:00 h) ad hoc and in an attempt to observe nocturnal behaviors and movement pathways when animals were active. We defined fixes as any time a telemetered individual was located, and relocations (i.e., moves) as the occasions where we located an individual > 5 m from its previous known location.Each day we manually honed in on signal via a radio receiver to locate individuals (as described by Amelon et al.42, and recorded locations in Universal Transverse Mercator (UTM; 47 N World Geodetic System 84) coordinate reference system with a handheld global positioning system (GPS) unit (Garmin 64S GPS, Garmin International, Inc., Olathe, Kansas) directly above the sheltered snake. We generally approached within one meter of sheltering snakes during daylight to precisely record shelter locations and identify shelter type. Since we could not visually confirm snake locations, we methodically eliminated all possible locations where the snake could possibly be while at close range with the minimum possible gain on the radio receiver.Telemetered kraits tended to be inactive and sheltering underground during the daylight, thus we were confident that our diurnal location checks would not affect their movements. However, in some cases we resorted to determining an individual’s location via triangulation, where multiple lines cast from different vantage points towards the snake intersect on the snake’s location on the GPS, allowing us to determine the animal’s coordinate location from approximately 10–30 m away. This helped ensure that we recorded locations with greater accuracy when snakes sheltered underneath large buildings, as it allowed us to move away from large structures that hindered the GPS accuracy. This technique was also implemented during some nocturnal location checks when a snake was believed to be active among dense vegetation, in an attempt to prevent disturbance of the animals’ natural behavior. While we did hope to gain visual observations of active individuals during the night, we exercised more caution during nocturnal location checks, typically maintaining a minimum distance of approximately 5 m in attempt to lessen the chances of disturbing an active individual’s behavior. If the animal was active we recorded the animal’s observed behavioral state (i.e., moving, feeding, or foraging). When the radio signal was stable and the individual was not visible, we recorded the animal’s behavior as “sheltering”. We strived for an accuracy of  5 m difference), and land-use type (e.g., mixed deciduous forest, human-settlement, semi-natural area, agriculture, plantation; see Supp. Figs. 3 and 4 for photos of land-use types), behavior (e.g., sheltering, moving, foraging, or feeding), and shelter type (e.g., anthropogenic, burrow, or unknown, note we also recorded if we suspected the shelter to be part of a termite tunnel complex due to a close proximity to a visible termite mound; Supp. Fig. 5).During each location check we recorded the straight-line distance between the current and previous locations (distance moved/step length) with the GPS device. We then used step-lengths to summarize their movements by estimating the mean daily displacement (MDD; the total distance moved divided by the number of days the snake was located) and mean movement distance (MMD; the mean relocation distance, excludes distances ≤ 5). In order to limit biases due to some snakes being located multiple times within a given day/night, we limited our sample for estimating MMD and MDD to only include a single location per day. This was accomplished by manually removing “extra” nocturnal location checks that occurred within the same day, making sure to have all shelter relocations present within the dataset. When calculating MDD, we used the total number of daily location checks rather than the number of days between the individual’s tracking start and stop date since there were some days where individuals were not tracked. We also used the same one location check per day dataset to calculate movement/relocation probabilities and to examine each individual’s MMD, MDD, and relocation probability for the overall tracking duration as well as for each season.When feasible, we positioned a Bushnell (Bushnell Corporation, Overland Park, Kansas) time lapse field camera (Trophy Cam HD Essential E3, Model:119837) with infrared night capability on a tripod spaced 2–5 m from occupied shelter sites. We positioned the cameras so that we may gather photos of the focal snake as it exited the shelter site and/or behaviors exhibited near the shelter. We programmed the cameras using a combined setting, including field scan, which continuously captured one photo every minute, along with a motion sensor setting, which took photos upon movement trigger outside of the regular 1-min intervals.Space use and site fidelityAll analyses and most visualizations were done in R v.4.0.5 using RStudio v.1.4.1106 43,44. We attempted to estimate home ranges for the telemetered B. candidus individuals using autocorrelated kernel density estimates (AKDEs) using R package ctmm v.0.6.045,46 in order to better understand the spatial requirements of B. candidus. However, examination of the variograms revealed that the majority of the variograms had not fully stabilized (i.e., limited evidence of range stability in our sample), and many individuals had extremely low effective sample sizes (21.82 ± 9.75, range = 1.49–135.75; Supp. Table 4). Therefore, we do not report home ranges in this text, as the AKDE estimates would violate the assumption of range residency and either underestimate or misrepresent B. candidus spatial requirements. We also examined the speed estimates resulting from fitted movement models. Resulting variograms and tentative home range estimates are included in a supplementary file for viewing only (Supp. Fig. 6, Supp. Table 4). The original code is from Montaño et al.47.Since our data was not sufficient to estimate home range size for the telemetered B. candidus, we instead used Dynamic Brownian Bridge Movement Models (dBBMMs) with the R package move v.4.0.648 to estimate within study occurrence distributions. We caution readers that these are not home range estimates but instead modeling the potential movement pathways animals could have traversed49. Use of dBBMMs not only allows us to estimate occurrence distributions for each individual, thus helping us better understand the animal’s movement pathways and resource use, but it also allows us to examine movement patterns through dBBMM derived motion variance50,51. We selected a window size of 19 and margin size of 5, to catch short resting periods with the margin, while the window size of 19 is long enough to get a valid estimate of motion variance when the animals exhibit activity/movement. Contours however are somewhat arbitrary; therefore, we used three different contours levels (90%, 95%, 99%) to estimate dBBMM occurrence distributions (using R packages adehabitatHR v.0.4.19, and rgeos v.0.5.5), and show the sensitivity to contour choice52,53.All movement data, either including initial capture locations or beginning with the first location check ~ 24 h post release, was used for production of both the AKDEs and dBBMMs for each individual. We also estimated dBBMM occurrence distributions for each telemetered individual with the exception of M29, which only made three small moves within a burrow complex during the short time he was radio-tracked before transmitter failure.We compared space use estimates to two previously published B. candidus tracking datasets34,36, and one unpublished dataset shared on the Zenodo data repository54, all originating from the Sakaerat Biosphere Reserve (approximately 41 km to the south of our study site): two adult males from within the forested area of the reserve [one tracked every 27.8 ± 0.99 h over a period of 103 days, the other tracked every 38.63 ± 11.2 h over a period of 30.58 days]34,54, and a juvenile male from agriculture on a forest boundary [tracked every 50.19 ± h for 66.91 days]36. The previous studies on B. candidus only tracked the movements of a single individual each, had coarser tracking regimes, and used traditional—fundamentally flawed methods55,56—to estimate space use34,36. Therefore, we ran dBBMMs with these previous datasets using the same window (19) and margin size (5).To quantify site reuse and time spent at sites (residency time) we used recursive analysis with the R package recurse v.1.1.257. We defined each site as a circular area with a radius of 5 m around each unique location (matching the targeted GPS accuracy). Then we calculated each individual’s overall number of relocations, each individual’s total number of relocations to each site, and each individual’s site revisit frequency and residency time at each unique site. Then we plotted revisited locations on a land-use map with space use estimates (95% and 99% dBBMM) in an attempt to help identify and highlight activity centers for telemetered individuals (see Supp. Figs. 7–13). All maps were created using Quantum Geographic Information System (QGIS v.3.8.2).Habitat selectionWe used Integrated Step Selection Function models (ISSF) to examine the influence of land-use features on the movements of B. candidus at both the individual and population levels. We included movement data from all male individuals that used more than one habitat feature in our ISSF analysis. Therefore, we excluded F16 and M29 who both only used settlement habitat. Excluding M29 was justified by the individual having been tracked for the shortest duration (19 days) and had the fewest number of moves (n = 3), thus there were not enough relocations for ISSF models to work effectively. Using modified code from Smith et al.51 that used ISSF with Burmese python radio-telemetry data, we used the package amt v.0.1.458 to run ISSF for each individual, with Euclidean distance to particular land-use features (natural areas, agriculture, settlement, buildings, and roads) to determine association or avoidance of features. Cameron Hodges created all land-use shape files in QGIS by digitizing features from satellite imagery and verified all questionable satellite land-use types via on-ground investigation.The semi-natural areas, plantations, mixed deciduous forest and water bodies (such as irrigation canals and ponds which have densely vegetated edges) were all combined into a single layer of less-disturbed habitats which we refer to as “natural areas”. All feature raster layers were then converted into layers with a gradient of continuous values of Euclidean distances to the land-use features, and were inverted in order to avoid zero-inflation of distance to feature values and to make the resulting model directional effects easier to more intuitive. We were able to generate 200 random steps per each observed step (following Smith et al.51), due to the coarse temporal resolution of manually collected radio-telemetry data (i.e., we were not computational limited when deciding the number of random locations). Higher numbers of random steps are preferable as they can aid in detecting smaller effects and rarer landscape features59.To investigate individual selection, we created nine different models testing for association to habitat features, with one being a null model which solely incorporated step-length and turning angle to predict movement60, five examining land-use features individually (agriculture, buildings, settlement, natural areas, roads), and the other three being multi-factor models. Each model considers distance to a land-use variable, step-length, and turn-angle as an aspect of the model. After running each of the nine models for each individual, we then examined the AIC for each model, point estimates (with lower and upper confidence intervals), and p-values in order to identify the best models for each individual and determine the strongest relationships and trends among the samples. We considered models with ∆ AIC  More

Wäckers, F. L. & van Rijn, P. C. J. Food for Protection: An Introduction. In Plant-Provided Food for Carnivorous Insects: A Protective Mutualism and its Applications (eds Wäckers, F. L. et al.) 1–14 (Cambridge University Press, 2005). https://doi.org/10.1017/CBO9780511542220.002.Chapter 

Google Scholar 
Benelli, G. et al. The impact of adult diet on parasitoid reproductive performance. J. Pest Sci. 90, 807–823. https://doi.org/10.1007/s10340-017-0835-2 (2017).Article 

Google Scholar 
Wäckers, F. Assessing the suitability of flowering herbs as parasitoid food sources: Flower attractiveness and nectar accessibility. Biol. Control. 29, 307–314. https://doi.org/10.1016/j.biocontrol.2003.08.005 (2004).Article 

Google Scholar 
Heimpel, G. E. & Jervis, M. A. Does Floral Nectar Improve Biological Control by Parasitoids? In Plant-Provided Food for Carnivorous Insects: A Protective Mutualism and its Applications (eds Wäckers, F. L. et al.) 267–304 (Cambridge University Press, 2009). https://doi.org/10.1017/CBO9780511542220.010.Chapter 

Google Scholar 
Wäckers, F. L. Suitability of (extra-)Floral Nectar, Pollen, and Honeydew as Insect Food Sources. In Plant-Provided Food for Carnivorous Insects: A Protective Mutualism and its Applications (eds Wäckers, F. L. et al.) 17–74 (Cambridge University Press, 2005). https://doi.org/10.1017/CBO9780511542220.003.Chapter 

Google Scholar 
Wäckers, F. L., van Rijn, P. C. & Heimpel, G. E. Honeydew as a food source for natural enemies: Making the best of a bad meal?. Biol. Control. 45, 176–184. https://doi.org/10.1016/j.biocontrol.2008.01.007 (2008).Article 

Google Scholar 
Jervis, M. A., Ellers, J. & Harvey, J. A. Resource acquisition, allocation, and utilization in parasitoid reproductive strategies. Annu. Rev. Entomol. 53, 361–385. https://doi.org/10.1146/annurev.ento.53.103106.093433 (2008).CAS 
Article 

Google Scholar 
Rosenheim, J. A. An evolutionary argument for egg limitation. Evolution 50, 2089–2094 (1996).Article 

Google Scholar 
Rosenheim, J. A. The relative contributions of time and eggs to the cost of reproduction. Evolution 53, 376–385 (1999).Article 

Google Scholar 
Rosenheim, J. A., Jepsen, S. J., Matthews, C. E., Smith, D. S. & Rosenheim, M. R. Time limitation, egg limitation, the cost of oviposition, and lifetime reproduction by an insect in nature. Am. Nat. 172, 486–496 (2008).Article 

Google Scholar 
Rosenheim, J. A., Heimpel, G. E. & Mangel, M. Egg maturation, egg resorption and the costliness of transient egg limitation in insects. Proc. Royal Soc London. Ser. B Biol. Sci. 267, 1565–1573 (2000).CAS 
Article 

Google Scholar 
Takasu, K. & Hirose, Y. Host searching behavior in the parasitoid Ooencyrtus nezarae Ishii (Hymenoptera: Encyrtidae) as influenced by non-host food deprivation. Appl. Entomol. Zool. 26, 415–417. https://doi.org/10.1303/aez.26.415 (1991).Article 

Google Scholar 
Sisterson, M. S. & Averill, A. L. Costs and benefits of food foraging for a braconid parasitoid. J. Insect Behav. 15, 571–588. https://doi.org/10.1023/A:1016389402543 (2002).Article 

Google Scholar 
Jacob, H. S. & Evans, E. W. Influence of food deprivation on foraging decisions of the parasitoid Bathyplectes curculionis (Hymenoptera: Ichneumonidae). Ann. Entomol. Soc. Am. 94, 605–611. https://doi.org/10.1603/0013-8746(2001)094[0605:iofdof]2.0.co;2 (2001).Article 

Google Scholar 
Siekmann, G., Keller, M. A. & Tenhumberg, B. The sweet tooth of adult parasitoid cotesia rubecula: Ignoring hosts for nectar?. J. Insect Behav. 17, 459–476. https://doi.org/10.1023/b:joir.0000042535.76279.c7 (2004).Article 

Google Scholar 
Williams, L., Deschodt, P., Pointurier, O. & Wyckhuys, K. A. Sugar concentration and timing of feeding affect feeding characteristics and survival of a parasitic wasp. J. Insect Physiol. 79, 10–18. https://doi.org/10.1016/j.jinsphys.2015.05.004 (2015).CAS 
Article 

Google Scholar 
Talamas, E. J. et al. A maximalist approach to the systematics of a biological control agent: Gryon aetherium Talamas, sp. nov. (Hymenoptera, Scelionidae). J. Hymenopt. Res. 87, 323–480. https://doi.org/10.3897/jhr.87.72842 (2021).Article 

Google Scholar 
Straser, R. K., Daane, K. M., Talamas, E. & Wilson, H. Evaluation of egg parasitoid Hadronotus pennsylvanicus as a prospective biocontrol agent of the leaffooted bug Leptoglossus zonatus. Biocontrol https://doi.org/10.1007/s10526-022-10131-z (2022).Article 

Google Scholar 
Mitchell, P. L. & Mitchell, F. L. Parasitism and predation of leaffooted bug (Hemiptera: Heteroptera: Coreidae) eggs. Ann. Entomol. Soc. Am. 79, 854–860. https://doi.org/10.1093/aesa/79.6.854 (1986).Article 

Google Scholar 
Yasuda, K. Function of the male pheromone of the leaf-footed plant bug, Leptoglossus australis (Fabricius) (Heteroptera: Coreidae) and its kairomonal effect. Jpn. Agric. Res. Q. 32, 161 (1998).CAS 

Google Scholar 
Bates, S. L. & Borden, J. H. Parasitoids of Leptoglossus occidentalis Heidemann (Heteroptera: Coreidae) in British Columbia. J. Entomol. Soc. Br. Columbia 101, 143–144 (2004).
Google Scholar 
Maltese, M., Caleca, V., Guerrieri, E. & Strong, W. B. Parasitoids of Leptoglossus occidentalis Heidemann (Heteroptera: Coreidae) recovered in western North America and first record of its egg parasitoid Gryon pennsylvanicum (Ashmead) (Hymenoptera: Platygastridae) in California. The Pan-Pacific Entomol. 88, 347–355. https://doi.org/10.3956/2012-23.1 (2012).Article 

Google Scholar 
Roversi, P. F. et al. Pre-release risk assessment of the egg-parasitoid Gryon pennsylvanicum for classical biological control of Leptoglossus occidentalis. J. Appl. Entomol. 138, 27–35. https://doi.org/10.1111/jen.12062 (2013).Article 

Google Scholar 
Nechols, J. R., Tracy, J. L. & Vogt, E. A. Comparative ecological studies of indigenous egg parasitoids (Hymenoptera: Scelionidae: Encyrtidae) of the squash bug, Anasa tristis (Hemiptera: Coreidae). J. Kansas Entomol. Soc. 62, 177–188 (1989).
Google Scholar 
Cornelius, M. L., Buffington, M. L., Talamas, E. J. & Gates, M. W. Impact of the egg parasitoid, Gryon pennsylvanicum (Hymenoptera: Scelionidae), on sentinel and wild egg masses of the squash bug (Hemiptera: Coreidae) in Maryland. Environ. Entomol. 45, 367–375. https://doi.org/10.1093/ee/nvv228 (2016).Article 

Google Scholar 
Cornelius, M. L., Hu, J. S. & Vinyard, B. T. Comparative study of egg parasitism by Gryon pennsylvanicum (Hymenoptera: Scelionidae) on two squash bug species Anasa tristis and Anasa armigera (Hemiptera: Coreidae). Environ. Entomol. https://doi.org/10.1093/ee/nvy145 (2018).Article 

Google Scholar 
Daane, K. M. et al. Stink bugs and leaffooted bugs. Pistachio Prod. Man. Publ. 3545, 225–238 (2016).
Google Scholar 
Joyce, A. L., Higbee, B. S., Haviland, D. R. & Brailovsky, H. Genetic variability of two leaffooted bugs, Leptoglossus clypealis and Leptoglossus zonatus (Hemiptera: Coreidae) in the Central Valley of California. J. Econ. Entomol. 110, 2576–2589. https://doi.org/10.1093/jee/tox222 (2017).CAS 
Article 

Google Scholar 
Zalom, F. G., Haviland, D. R., Symmes, E. T. & Tollerup, K. Almonds: Insects and Mites. University of California, Agriculture and Natural Resources, Oakland, CA, USA, University of California IPM Pest Management Guidelines, Publication 3431 ed. (2018).Michailides, T. J., Rice, R. E. & Ogawa, J. M. Succession and significance of several hemipterans attacking a pistachio orchard. J. Econ. Entomol. 80, 398–406. https://doi.org/10.1093/jee/80.2.398 (1987).Article 

Google Scholar 
Michailides, T. The ‘Achilles heel’of pistachio fruit. Calif. Agric. 43, 10–11 (1989).
Google Scholar 
Michailides, T. J. & Morgan, D. P. Association of botryosphaeria panicle and shoot blight of pistachio with injuries of fruit caused by hemiptera insects and birds. Plant Dis. 100, 1405–1413. https://doi.org/10.1094/pdis-09-15-1077-re (2016).Article 

Google Scholar 
Daane, K. et al. Large bugs damage pistachio nuts most severely during midseason. Calif. Agric. 59, 95–102 (2005).Article 

Google Scholar 
Haviland, D., Bentley, W., Beede, R. & Daane, K. Pistachios: Insects and mites. Univ. California IPM Pest Manag. Guidel. Publ. 3461 (2018).Joyce, A. L., Barman, A. K., Doll, D. & Higbee, B. S. Assessing feeding damage from two leaffooted bugs, Leptoglossus clypealis Heidemann and Leptoglossus zonatus (Dallas) (Hemiptera: Coreidae), on four almond varieties. Insects 10, 333. https://doi.org/10.3390/insects10100333 (2019).Article 

Google Scholar 
Stahl, J. M., Scaccini, D., Pozzebon, A. & Daane, K. M. Comparing the feeding damage of the invasive brown marmorated stink bug to a native stink bug and leaffooted bug on California pistachios. Insects 11, 688. https://doi.org/10.3390/insects11100688 (2020).Article 

Google Scholar 
Olson, D. L. & Nechols, J. R. Effects of squash leaf trichome exudates and honey on adult feeding, survival, and fecundity of the squash bug (Heteroptera: Coreidae) egg parasitoid Gryon pennsylvanicum (Hymenoptera: Scelionidae). Environ. Entomol. 24, 454–458. https://doi.org/10.1093/ee/24.2.454 (1995).Article 

Google Scholar 
Sabbatini Peverieri, G., Furlan, P., Simoni, S., Strong, W. & Roversi, P. Laboratory evaluation of Gryon pennsylvanicum (Ashmead) (Hymenoptera: Platygastridae) as a biological control agent of Leptoglossus occidentalis Heidemann (Heteroptera: Coreidae). Biol. Control. 61, 104–111. https://doi.org/10.1016/j.biocontrol.2012.01.005 (2012).Article 

Google Scholar 
Cornelius, M. L., Vinyard, B. T., Mowery, J. D. & Hu, J. S. Ovipositional behavior of the egg parasitoid Gryon pennsylvanicum (Hymenoptera: Scelionidae) on two squash bug species Anasa tristis (Hemiptera: Coreidae) and Anasa armigera: Effects of parasitoid density, nutrition, and host egg chorion on parasitism rates. Environ. Entomol. 49, 1307–1315. https://doi.org/10.1093/ee/nvaa118 (2020).CAS 
Article 

Google Scholar 
Vogt, E. & Nechols, J. The influence of host deprivation and host source on the reproductive biology and longevity of the squash bug egg parasitoid Gryon pennsylvanicum (Ashmead) (Hymenoptera: Scelionidae). Biol. Control. 3, 148–154. https://doi.org/10.1006/bcon.1993.1022 (1993).Article 

Google Scholar 
Olson, D., Fadamiro, H., Lundgren, J. & Heimpel, G. E. Effects of sugar feeding on carbohydrate and lipid metabolism in a parasitoid wasp. Physiol. Entomol. 25, 17–26 (2000).CAS 
Article 

Google Scholar 
Jervis, M. A., Heimpel, G. E., Ferns, P. N., Harvey, J. A. & Kidd, N. A. C. Life-history strategies in parasitoid wasps: A comparative analysis of “ovigeny”. J. Animal Ecol. 70, 442–458. https://doi.org/10.1046/j.1365-2656.2001.00507.x (2001).Article 

Google Scholar 
Jervis, M. A. & Ferns, P. N. The timing of egg maturation in insects: Ovigeny index and initial egg load as measures of fitness and of resource allocation. Oikos 107, 449–461 (2004).Article 

Google Scholar 
Lee, J. C. & Heimpel, G. E. Effect of floral nectar, water, and feeding frequency on cotesia glomerata longevity. Biocontrol 53, 289–294 (2008).Article 

Google Scholar 
Wu, H., Meng, L. & Li, B. Effects of feeding frequency and sugar concentrations on lifetime reproductive success of Meteorus pulchricornis (Hymenoptera: Braconidae). Biol. Control. 45, 353–359. https://doi.org/10.1016/j.biocontrol.2008.01.017 (2008).CAS 
Article 

Google Scholar 
King, B. H. Offspring sex ratios in parasitoid wasps. Q. Rev. Biol. 62, 367–396. https://doi.org/10.1086/415618 (1987).Article 

Google Scholar 
Berndt, L. A. & Wratten, S. D. Effects of alyssum flowers on the longevity, fecundity, and sex ratio of the leafroller parasitoid Dolichogenidea tasmanica. Biol. Control. 32, 65–69. https://doi.org/10.1016/j.biocontrol.2004.07.014 (2005).Article 

Google Scholar 
Sabbatini Peverieri, G. et al. Host egg age of Leptoglossus occidentalis (Heteroptera: Coreidae) and parasitism by Gryon pennsylvanicum (Hymenoptera: Platygastridae). J. Econ. Entomol. 106, 633–640. https://doi.org/10.1603/ec12344 (2013).Article 

Google Scholar 
Abram, P. K., Brodeur, J., Urbaneja, A. & Tena, A. Nonreproductive effects of insect parasitoids on their hosts. Annu. Rev. Entomol. 64(1), 259–276 (2019).CAS 
Article 

Google Scholar 
Lewis, W. & Takasu, K. Use of learned odours by a parasitic wasp in accordance with host and food needs. Nature 348, 635–636 (1990).ADS 
Article 

Google Scholar 
Takasu, K. & Lewis, W. Importance of adult food sources to host searching of the larval parasitoid Microplitis croceipes. Biol. Control 5, 25–30 (1995).Article 

Google Scholar 
Wäckers, F. The effect of food deprivation on the innate visual and olfactory preferences in the parasitoid Cotesia rubecula. J. Insect Physiol. 40, 641–649 (1994).Article 

Google Scholar 
Lightle, D., Ambrosino, M. & Lee, J. C. Sugar in moderation: Sugar diets affect short-term parasitoid behaviour. Physiol. Entomol. 35, 179–185 (2010).CAS 
Article 

Google Scholar 
Varennes, Y.-D., Gonzalez Chang, M., Boyer, S. & Wratten, S. Nectar feeding increases exploratory behaviour in the aphid parasitoid Diaeretiella rapae (Mcintosh). J. Appl. Entomol. 140, 479–483 (2016).Article 

Google Scholar 
Takano, S. & Takasu, K. Food deprivation increases reproductive effort in a parasitoid wasp. Biol. Control. 133, 75–80. https://doi.org/10.1016/j.biocontrol.2019.03.010 (2019).Article 

Google Scholar 
Landis, D. A., Wratten, S. D. & Gurr, G. M. Habitat management to conserve natural enemies of arthropod pests in agriculture. Annu. Rev. Entomol. 45(1), 175–201 (2000).CAS 
Article 

Google Scholar 
Masner, L. A revision of gryon haliday in North America (Hymenoptera: Proctotrupoidea: Scelionidae). Can. Entomol. 115, 123–174. https://doi.org/10.4039/ent115123-2 (1983).Article 

Google Scholar 
Vogt, E. A. & Nechols, J. R. Diel activity patterns of the squash bug egg parasitoid Gryon pennsylvanicum (Hymenoptera: Scelionidae). Ann. Entomol. Soc. Am. 84, 303–308. https://doi.org/10.1093/aesa/84.3.303 (1991).Article 

Google Scholar 
Wiedemann, L. M., Canto-Silva, C. R., Romanowski, H. P. & Redaelli, L. R. Oviposition behavior of Gryon gallardoi (Hym.: Scelionidae) on eggs of Spartocera dentiventris (Hem.: Coreidae). Braz. J. Biol. 63, 133 (2003).CAS 
Article 

Google Scholar 
Friard, O. & Gamba, M. BORIS: a free, versatile open-source event-logging software for video/audio coding and live observations. Methods Ecol. Evol. 7, 1325–1330. https://doi.org/10.1111/2041-210x.12584 (2016).Article 

Google Scholar 
R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing. Vienna, Austria. https://www.r-project.org/ (2019).Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48. https://doi.org/10.18637/jss.v067.i01 (2015).Article 

Google Scholar 
Hothorn, T., Bretz, F. & Westfall, P. Simultaneous inference in general parametric models. Biom. J. 50, 346–363 (2008).MathSciNet 
Article 

Google Scholar  More

Torsvik, V., Øvreås, L. & Thingstad, T. F. Prokaryotic diversity-magnitude, dynamics, and controlling factors. Science 296, 1064–1066 (2002).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Kuang, J. et al. Predicting taxonomic and functional structure of microbial communities in acid mine drainage. ISME J. 10, 1527–1539 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Mod, H. K. et al. Predicting spatial patterns of soil bacteria under current and future environmental conditions. ISME J. (2021).Pace, N. R. A molecular view of microbial diversity and the biosphere. Science 276, 734–740 (1997).CAS 
PubMed 
Article 

Google Scholar 
Violle, C., Reich, P. B., Pacala, S. W., Enquist, B. J. & Kattge, J. The emergence and promise of functional biogeography. Proc. Natl Acad. Sci. USA 111, 13690–13696 (2004).ADS 
Article 
CAS 

Google Scholar 
Green, J. L., Bohannan, B. J. & Whitaker, R. J. Microbial biogeography: from taxonomy to traits. Science 320, 1039–1043 (2008).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Daly, R. A. et al. Viruses control dominant bacteria colonizing the terrestrial deep biosphere after hydraulic fracturing. Nat. Microbiol. 4, 352–361 (2019).CAS 
PubMed 
Article 

Google Scholar 
Howard-Varona, C. et al. Phage-specific metabolic reprogramming of virocells. ISME J. 14, 881–895 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
Chevallereau, A., Pons, B. J., van Houte, S. & Westra, E. R. Interactions between bacterial and phage communities in natural environments. Nat. Rev. Microbiol. 20, 49–62 (2022).CAS 
PubMed 
Article 

Google Scholar 
Sullivan, M. B., Weitz, J. S. & Wilhelm, S. Viral ecology comes of age. Environ. Microbiol. Rep. 9, 33–35 (2017).PubMed 
Article 

Google Scholar 
Brum, J. R. & Sullivan, M. B. Rising to the challenge: accelerated pace of discovery transforms marine virology. Nat. Rev. Microbiol. 13, 147–159 (2015).CAS 
PubMed 
Article 

Google Scholar 
Roux, S. et al. Minimum information about an uncultivated virus genome (MIUViG). Nat. Biotechnol. 37, 29–37 (2019).CAS 
PubMed 
Article 

Google Scholar 
Brum, J. R. et al. Patterns and ecological drivers of ocean viral communities. Science 348, 1261498 (2015).PubMed 
Article 
CAS 

Google Scholar 
Gregory, A. C. et al. Marine DNA viral macro- and microdiversity from pole to pole. Cell 177, 1109–1123 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Shu, W. S. & Huang, L. N. Microbial diversity in extreme environments. Nat. Rev. Microbiol. (2021).Huang, L. N., Kuang, J. L. & Shu, W. S. Microbial ecology and evolution in the acid mine drainage model system. Trends Microbiol 24, 581–593 (2016).CAS 
PubMed 
Article 

Google Scholar 
Hwang, Y., Rahlff, J., Schulze-Makuch, D., Schloter, M. & Probst, A. J. Diverse viruses carrying genes for microbial extremotolerance in the Atacama desert hyperarid soil. mSystems 6, e00385–21 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
Adriaenssens, E. M. et al. Environmental drivers of viral community composition in Antarctic soils identified by viromics. Microbiome 5, 83 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
Emerson, J. B. et al. Host-linked soil viral ecology along a permafrost thaw gradient. Nat. Microbiol. 3, 870–880 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Andersson, A. F. & Banfield, J. F. Virus population dynamics and acquired virus resistance in natural microbial communities. Science 320, 1047–1050 (2008).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Gao, S. M. et al. Depth-related variability in viral communities in highly stratified sulfidic mine tailings. Microbiome 8, 89 (2020).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Holmfeldt, K. et al. The Fennoscandian Shield deep terrestrial virosphere suggests slow motion ‘boom and burst’ cycles. Commun. Biol. 4, 307 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Rahlff, J. et al. Lytic archaeal viruses infect abundant primary producers in Earth’s crust. Nat. Commun. 12, 4642 (2021).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Hao, Y. Q. et al. Microbial biogeography of acid mine drainage sediments at a regional scale across Southern China. FEMS Microbiol. Ecol. 98, fiac002 (2022).PubMed 
Article 

Google Scholar 
Paez-Espino, D., Pavlopoulos, G. A., Ivanova, N. N. & Kyrpides, N. C. Nontargeted virus sequence discovery pipeline and virus clustering for metagenomic data. Nat. Protoc. 12, 1673–1682 (2017).CAS 
PubMed 
Article 

Google Scholar 
Roux, S., Enault, F., Hurwitz, B. L. & Sullivan, M. B. VirSorter: mining viral signal from microbial genomic data. PeerJ 3, e985 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Nayfach, S. et al. CheckV: assessing the quality of metagenome-assembled viral genomes. Nat. Biotechnol. 39, 578–585 (2021).CAS 
PubMed 
Article 

Google Scholar 
Bin Jang, H. et al. Taxonomic assignment of uncultivated prokaryotic virus genomes is enabled by gene-sharing networks. Nat. Biotechnol. 37, 632–639 (2019).Article 
CAS 

Google Scholar 
Li, Z. et al. Deep sea sediments associated with cold seeps are a subsurface reservoir of viral diversity. ISME J. 15, (2021).Huerta-Cepas, J. et al. eggNOG 5.0: a hierarchical, functionally and phylogenetically annotated orthology resource based on 5090 organisms and 2502 viruses. Nucleic Acids Res 47, D309–D314 (2019).CAS 
PubMed 
Article 

Google Scholar 
Wu, S. et al. DeePhage: distinguishing virulent and temperate phage-derived sequences in metavirome data with a deep learning approach. Gigascience 10, giab056 (2021).PubMed 
PubMed Central 
Article 

Google Scholar 
Chen, L. X. et al. Comparative metagenomic and metatranscriptomic analyses of microbial communities in acid mine drainage. ISME J. 9, 1579–1592 (2015).PubMed 
Article 

Google Scholar 
Liang, J. L. et al. Novel phosphate-solubilizing bacteria enhance soil phosphorus cycling following ecological restoration of land degraded by mining. ISME J. 14, 1600–1613 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Hsieh, Y. J. & Wanner, B. L. Global regulation by the seven-component Pi signaling system. Curr. Opin. Microbiol. 13, 198–203 (2010).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Stasi, R., Neves, H. I. & Spira, B. Phosphate uptake by the phosphonate transport system PhnCDE. BMC Microbiol 19, 79 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Narr, A., Nawaz, A., Wick, L. Y., Harms, H. & Chatzinotas, A. Soil viral communities vary temporally and along a land use transect as revealed by virus-like particle counting and a modified community fingerprinting approach (fRAPD). Front. Microbiol. 8, 1975 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
Santos-Medellin, C. et al. Viromes outperform total metagenomes in revealing the spatiotemporal patterns of agricultural soil viral communities. ISME J. 15, 1956–1970 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Tyson, G. W. & Banfield, J. F. Rapidly evolving CRISPRs implicated in acquired resistance of microorganisms to viruses. Environ. Microbiol. 10, 200–207 (2008).CAS 
PubMed 

Google Scholar 
Sun, C. L. et al. Phage mutations in response to CRISPR diversification in a bacterial population. Environ. Microbiol. 15, 463–470 (2013).CAS 
PubMed 
Article 

Google Scholar 
Hurwitz, B. L., Westveld, A. H., Brum, J. R. & Sullivan, M. B. Modeling ecological drivers in marine viral communities using comparative metagenomics and network analyses. Proc. Natl Acad. Sci. USA 111, 10714–10719 (2014).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Jin, M. et al. Diversities and potential biogeochemical impacts of mangrove soil viruses. Microbiome 7, 58 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Dinsdale, E. A. et al. Functional metagenomic profiling of nine biomes. Nature 452, 629–632 (2008).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Tedersoo, L. et al. Fungal biogeography. Global diversity and geography of soil fungi. Science 346, 1256688 (2014).PubMed 
Article 
CAS 

Google Scholar 
Miraldo, A. et al. An Anthropocene map of genetic diversity. Science 353, 1532–1535 (2016).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Bonnain, C., Breitbart, M. & Buck, K. N. The Ferrojan horse hypothesis: iron-virus interactions in the ocean. Front. Mar. Sci. 3, 82 (2016).Article 

Google Scholar 
Muratore, D. & Weitz, J. S. Infect while the iron is scarce: nutrient-explicit phage-bacteria games. Theor. Ecol. 14, 467–487 (2021).Article 

Google Scholar 
Kyle, J. E., Pedersen, K. & Ferris, F. G. Virus mineralization at low pH in the Rio Tinto. Spain Geomicrobiol. J. 25, 338–345 (2008).CAS 
Article 

Google Scholar 
Kyle, J. E. & Ferris, F. G. Geochemistry of virus–prokaryote interactions in freshwater and acid mine drainage environments, Ontario, Canada. Geomicrobiol. J. 30, 769–778 (2013).CAS 
Article 

Google Scholar 
Hewson, I., O’Neil, J. M., Fuhrman, J. A. & Dennison, W. C. Virus-like particle distribution and abundance in sediments and overlying waters along eutrophication gradients in two subtropical estuaries. Limnol. Oceanogr. 46, 1734–1746 (2001).ADS 
Article 

Google Scholar 
Wu, L. et al. Global diversity and biogeography of bacterial communities in wastewater treatment plants. Nat. Microbiol. 4, 1183–1195 (2019).CAS 
PubMed 
Article 

Google Scholar 
Bates, S. T. et al. Global biogeography of highly diverse protistan communities in soil. ISME J. 7, 652–659 (2013).CAS 
PubMed 
Article 

Google Scholar 
Kuang, J. L. et al. Contemporary environmental variation determines microbial diversity patterns in acid mine drainage. ISME J. 7, 1038–1050 (2013).CAS 
PubMed 
Article 

Google Scholar 
Sant, D. G., Woods, L. C., Barr, J. J. & McDonald, M. J. Host diversity slows bacteriophage adaptation by selecting generalists over specialists. Nat. Ecol. Evol. 5, 350–359 (2021).PubMed 
Article 

Google Scholar 
Betts, A., Gray, C., Zelek, M., MacLean, R. C. & King, K. C. High parasite diversity accelerates host adaptation and diversification. Science 360, 907–911 (2018).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Goldsmith, D. B., Parsons, R. J., Beyene, D., Salamon, P. & Breitbart, M. Deep sequencing of the viral phoH gene reveals temporal variation, depth-specific composition, and persistent dominance of the same viral phoH genes in the Sargasso Sea. Peer. J. 3, e997 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
Goldsmith, D. B. et al. Development of phoH as a novel signature gene for assessing marine phage diversity. Appl. Environ. Microbiol. 77, 7730–7739 (2011).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Martiny, A. C., Coleman, M. L. & Chisholm, S. W. Phosphate acquisition genes in Prochlorococcus ecotypes: evidence for genome-wide adaptation. Proc. Natl Acad. Sci. USA 103, 12552–12557 (2006).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Tetu, S. G. et al. Microarray analysis of phosphate regulation in the marine cyanobacterium Synechococcus sp. WH8102. ISME J. 3, 835–849 (2009).CAS 
PubMed 
Article 

Google Scholar 
Zeng, Q. & Chisholm, S. W. Marine viruses exploit their host’s two-component regulatory system in response to resource limitation. Curr. Biol. 22, 124–128 (2012).CAS 
PubMed 
Article 

Google Scholar 
Kazakov, A. E., Vassieva, O., Gelfand, M. S., Osterman, A. & Overbeek, R. Bioinformatics classification and functional analysis of PhoH homologs. Silico Biol. 3, 3–15 (2003).CAS 

Google Scholar 
Bray, R. H. & Kurtz, L. T. Determination of total, organic, and available forms of phosphorus in soils. Soil Sci. 59, 39–46 (1945).ADS 
CAS 
Article 

Google Scholar 
Hill, A. G. et al. Standardized general method for the determination of iron with 1,10-phenanthroline. Analyst 103, 391–396 (1978).Article 

Google Scholar 
Chesmin, L. & Yien, C. H. Turbidimetric determination of available sulphate. Soil Sci. Soc. Am. Proc. 15, 149–151 (1951).ADS 
Article 

Google Scholar 
Fang, Y. et al. Modified pretreatment method for total microbial DNA extraction from contaminated river sediment. Front. Environ. Sci. Eng. 9, 444–452 (2015).CAS 
Article 

Google Scholar 
Bankevich, A. et al. SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing. J. Comput. Biol. 19, 455–477 (2012).MathSciNet 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Hyatt, D. et al. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinforma. 11, 119 (2010).Article 
CAS 

Google Scholar 
El-Gebali, S. et al. The Pfam protein families database in 2019. Nucleic Acids Res 47, D427–D432 (2019).CAS 
PubMed 
Article 

Google Scholar 
Kanehisa, M., Sato, Y., Kawashima, M., Furumichi, M. & Tanabe, M. KEGG as a reference resource for gene and protein annotation. Nucleic Acids Res 44, D457–D462 (2016).CAS 
PubMed 
Article 

Google Scholar 
Eddy, S. R. Accelerated profile HMM searches. PLOS Comput. Biol. 7, e1002195 (2011).ADS 
MathSciNet 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Roux, S., Hallam, S. J., Woyke, T. & Sullivan, M. B. Viral dark matter and virus-host interactions resolved from publicly available microbial genomes. Elife 4, e08490 (2015).PubMed Central 
Article 

Google Scholar 
Roux, S. et al. Ecogenomics and potential biogeochemical impacts of globally abundant ocean viruses. Nature 537, 689–693 (2016).CAS 
PubMed 
Article 

Google Scholar 
Fu, L., Niu, B., Zhu, Z., Wu, S. & Li, W. CD-HIT: accelerated for clustering the next- generation sequencing data. Bioinformatics 28, 3150–3152 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Kang, D. D. et al. MetaBAT 2: an adaptive binning algorithm for robust and efficient genome reconstruction from metagenome assemblies. PeerJ 7, e7359 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Wu, Y. W., Simmons, B. A. & Singer, S. W. MaxBin 2.0: an automated binning algorithm to recover genomes from multiple metagenomic datasets. Bioinformatics 32, 605–607 (2016).CAS 
PubMed 
Article 

Google Scholar 
Brown, C. T. et al. Unusual biology across a group comprising more than 15% of domain Bacteria. Nature 523, 208–201 (2015).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Alneberg, J. et al. Binning metagenomic contigs by coverage and composition. Nat. Methods 11, 1144–1146 (2014).CAS 
PubMed 
Article 

Google Scholar 
Sieber, C. M. K. et al. Recovery of genomes from metagenomes via a dereplication, aggregation and scoring strategy. Nat. Microbiol. 3, 836–843 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Parks, D. H. et al. Recovery of nearly 8,000 metagenome-assembled genomes substantially expands the tree of life. Nat. Microbiol. 2, 1533–1542 (2017).CAS 
PubMed 
Article 

Google Scholar 
Chaumeil, P. A., Mussig, A. J., Hugenholtz, P. & Parks, D. H. GTDB-Tk: a toolkit to classify genomes with the Genome Taxonomy Database. Bioinformatics 36, 1925–1927 (2019).PubMed Central 

Google Scholar 
Parks, D. H., Imelfort, M., Skennerton, C. T., Hugenholtz, P. & Tyson, G. W. CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res 25, 1043–1055 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Woodcroft, B. J. et al. Genome-centric view of carbon processing in thawing permafrost. Nature 560, 49–54 (2018).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Edwards, R. A., McNair, K., Faust, K., Raes, J. & Dutilh, B. E. Computational approaches to predict bacteriophage-host relationships. FEMS Microbiol. Rev. 40, 258–272 (2016).CAS 
PubMed 
Article 

Google Scholar 
Rho, M., Wu, Y. W., Tang, H., Doak, T. G. & Ye, Y. Diverse CRISPRs evolving in human microbiomes. PLoS Genet. 8, e1002441 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Paez-Espino, D. et al. Uncovering Earth’s virome. Nature 536, 425–430 (2016).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Edgar, R. C. MUSCLE: a multiple sequence alignment method with reduced time and space complexity. BMC Bioinforma. 5, 113 (2004).Article 
CAS 

Google Scholar 
Capella-Gutiérrez, S., Silla-Martínez, J. M. & Gabaldón, T. trimAl: a tool for automated alignment trimming in large-scale phylogenetic analyses. Bioinformatics 25, 1972–1973 (2009).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Minh, B. Q. et al. IQ-TREE 2: new models and efficient methods for phylogenetic inference in the genomic era. Mol. Biol. Evol. 37, 1530–1534 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Letunic, I. & Bork, P. Interactive Tree Of Life (iTOL) v4: recent updates and new developments. Nucleic Acids Res 47, W256–W259 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
R Development Core Team. R: A Language and environment for statistical computing. (2013).Oksanen, J. et al. vegan: Community ecology package. R package version 2.5-5. (2019).Harrell, F. E. Jr. & Dupont, M. C. The hmisc package. R. package version 4, 2–0 (2019).
Google Scholar 
R Development Core Team. The R Stats Package. R package version 4.0.3 (2013).Rosseel, Y. Lavaan: An R package for structural equation modeling and more. Version 0.5-12 (BETA). J. Stat. Soft 48, 1–36 (2012).Article 

Google Scholar 
Flores, C. O., Meyer, J. R., Valverde, S., Farr, L. & Weitz, J. S. Statistical structure of host-phage interactions. Proc. Natl Acad. Sci. USA 108, E288–E297 (2011).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar  More

Loya, Y. et al. Coral bleaching: the winners and the losers. Ecol. Lett. 4, 122–131. https://doi.org/10.1046/j.1461-0248.2001.00203.x (2001).Article 

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

Google Scholar 
Darling, E. S., Alvarez-Filip, L., Oliver, T. A., McClanahan, T. R. & Côté, I. M. Evaluating life-history strategies of reef corals from species traits. Ecol. Lett. 15, 1378–1386 (2012).PubMed 
Article 

Google Scholar 
Toth, L. T. et al. The unprecedented loss of Florida’s reef-building corals and the emergence of a novel coral-reef assemblage. Ecology 100, e02781. https://doi.org/10.1002/ecy.2781 (2019).CAS 
Article 
PubMed 

Google Scholar 
Green, D. H., Edmunds, P. J. & Carpenter, R. C. Increasing relative abundance of Porites astreoides on Caribbean reefs mediated by an overall decline in coral cover. Mar. Ecol. Prog. Ser. 359, 1–10 (2008).ADS 
Article 

Google Scholar 
Alvarez-Filip, L., Carricart-Ganivet, J. P., Horta-Puga, G. & Iglesias-Prieto, R. Shifts in coral-assemblage composition do not ensure persistence of reef functionality. Sci. Rep. 3, 3486. https://doi.org/10.1038/srep03486 (2013).ADS 
Article 
PubMed 
PubMed Central 

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

Google Scholar 
Hughes, T. P. et al. Ecological memory modifies the cumulative impact of recurrent climate extremes. Nat. Clim. Change 9, 40–43 (2019).ADS 
Article 

Google Scholar 
Hoegh-Guldberg, O., Poloczanska, E. S., Skirving, W. & Dove, S. Coral reef ecosystems under climate change and ocean acidification. Front. Mar. Sci. https://doi.org/10.3389/fmars.2017.00158 (2017).Article 

Google Scholar 
Gardner, T. A., Côté, I. M., Gill, J. A., Grant, A. & Watkinson, A. R. Long-term region-wide declines in Caribbean corals. Science 301, 958–960 (2003).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Jackson, J., Donovan, M., Cramer, K. & Lam, V. Status and trends of Caribbean coral reefs. Global Coral Reef Monitoring Network, IUCN, Gland, Switzerland, 1970–2012 (2014).Bruno, J. F., Sweatman, H., Precht, W. F., Selig, E. R. & Schutte, V. G. Ecosystem-based management. Ecology 90, 1478–1484 (2009).PubMed 
Article 

Google Scholar 
Roff, G. & Mumby, P. J. Global disparity in the resilience of coral reefs. Trends Ecol. Evol. 27, 404–413 (2012).PubMed 
Article 

Google Scholar 
Bak, R. P. M., Lambrechts, D. Y. M., Joenje, M., Nieuwland, G. & Van Veghel, M. L. J. Long-term changes on coral reefs in booming populations of a competitive colonial ascidian. Mar. Ecol. Prog. Ser. 133, 303–306 (1996).ADS 
Article 

Google Scholar 
Norström, A. V., Nyström, M., Lokrantz, J. & Folke, C. Alternative states on coral reefs: beyond coral–macroalgal phase shifts. Mar. Ecol. Prog. Ser. 376, 295–306 (2009).ADS 
Article 

Google Scholar 
Lenz, E. A., Bramanti, L., Lasker, H. R. & Edmunds, P. J. Long-term variation of octocoral populations in St. John, US Virgin Islands. Coral Reefs 34, 1099–1109 (2015).ADS 
Article 

Google Scholar 
Pawlik, J. R. & McMurray, S. E. The emerging ecological and biogeochemical importance of sponges on coral reefs. Ann. Rev. Mar Sci. 12, 315–337 (2020).PubMed 
Article 

Google Scholar 
Lasker, H. R., Bramanti, L., Tsounis, G. & Edmunds, P. J. in Advances in Marine Biology Vol. 87 (ed. Riegl, B. M.) 361–410 (Academic Press, 2020).
Google Scholar 
Pearson, R. Recovery and recolonization of coral reefs. Mar. Ecol. Prog. Ser. 4, 105–122 (1981).ADS 
Article 

Google Scholar 
Connell, J. H., Hughes, T. P. & Wallace, C. C. A 30-year study of coral abundance, recruitment, and disturbance at several scales in space and time. Ecol. Monogr. 67, 461–488 (1997).Article 

Google Scholar 
França, F. M. et al. Climatic and local stressor interactions threaten tropical forests and coral reefs. Philos. Trans. R. Soc. B 375, 20190116 (2020).Article 

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

Google Scholar 
Sánchez, J. A. et al. in Mesophotic Coral Ecosystems (eds Loya, Y. et al.) 729–747 (Springer International Publishing, 2019).Chapter 

Google Scholar 
Tsounis, G., Edmunds, P. J., Bramanti, L., Gambrel, B. & Lasker, H. R. Variability of size structure and species composition in Caribbean octocoral communities under contrasting environmental conditions. Mar. Biol. 165, 29. https://doi.org/10.1007/s00227-018-3286-2 (2018).Article 

Google Scholar 
Kinzie, R. A. III. The zonation of West Indian gorgonians. Bull. Mar. Sci. 23, 93–155 (1973).
Google Scholar 
Yoshioka, P. M. & Yoshioka, B. B. A comparison of the survivorship and growth of shallow-water gorgonian species of Puerto Rico. Mar. Ecol. Prog. Ser. 69, 253–260 (1991).ADS 
Article 

Google Scholar 
De’ath, G., Fabricius, K. E., Sweatman, H. & Puotinen, M. The 27–year decline of coral cover on the Great Barrier Reef and its causes. Proc. Natl. Acad. Sci. USA 109, 17995–17999 (2012).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Newman, M. J., Paredes, G. A., Sala, E. & Jackson, J. B. Structure of Caribbean coral reef communities across a large gradient of fish biomass. Ecol. Lett. 9, 1216–1227 (2006).PubMed 
Article 

Google Scholar 
Tilman, D. The ecological consequences of changes in biodiversity: a search for general principles. Ecology 80, 1455–1474 (1999).
Google Scholar 
Lawton, J. H. & Brown, V. K. in Biodiversity and Ecosystem Function (eds Schulze, E. D. & Mooney, H. A.) 255–270 (Springer, 1994).Chapter 

Google Scholar 
Loreau, M. et al. Biodiversity as insurance: from concept to measurement and application. Biol. Rev. 96(5), 2333–2354 (2021).PubMed 
Article 

Google Scholar 
Bellwood, D. R., Stret, R. P., Brandl, S. J. & Tebbett, S. B. The meaning of the term ‘function’ in ecology: a coral reef perspective. Funct. Ecol. 33, 948–961 (2018).Article 

Google Scholar 
Caswell, H. Construction, analysis, and interpretation. Sunderland: Sinauer 585, 258–277 (2001).
Google Scholar 
Bayer, F. M. The shallow-water Octocorallia of the West Indian region. Stud. Fauna Curacao Caribb. Isl. 12, 1–373 (1961).
Google Scholar 
Rossi, S., Bramanti, L., Gori, A. & Orejas, C. An overview of the animal forests of the world. In Marine Animal Forest (ed. Rossi, S.) 1–25 (Springer, 2017).Chapter 

Google Scholar 
Sánchez, J. A. Diversity and evolution of octocoral animal forests at both sides of tropical america. in Marine Animal Forests (eds Rossi, S. et al.) (Springer, 2016).
Google Scholar 
Thibaut, L. M. & Connolly, S. R. Understanding diversity–stability relationships: towards a unified model of portfolio effects. Ecol. Lett. 16, 140–150 (2013).PubMed 
Article 

Google Scholar 
Schindler, D. E., Armstrong, J. B. & Reed, T. E. The portfolio concept in ecology and evolution. Front. Ecol. Environ. 13, 257–263 (2015).Article 

Google Scholar 
Biggs, C. R. et al. Does functional redundancy affect ecological stability and resilience? A review and meta-analysis. Ecosphere 11, e03184 (2020).Article 

Google Scholar 
Anderson, S. C., Moore, J. W., McClure, M. M., Dulvy, N. K. & Cooper, A. B. Portfolio conservation of metapopulations under climate change. Ecol. Appl. 25, 559–572 (2015).PubMed 
Article 

Google Scholar 
Mellin, C., MacNeil, A. M., Cheal, A. J., Emslie, M. J. & Caley, J. M. Marine protected areas increase resilience among coral reef communities. Ecol. Lett. 19, 629–637 (2016).PubMed 
Article 

Google Scholar 
Webster, N. et al. Host-associated coral reef microbes respond to the cumulative pressures of ocean warming and ocean acidification. Sci. rep. 6, 1–9 (2016).Article 
CAS 

Google Scholar 
Tsounis, G. & Edmunds, P. J. Three decades of coral reef community dynamics in St. John, USVI: a contrast of scleractinians and octocorals. Ecosphere 8, e01646 (2017).Article 

Google Scholar 
Hurlbert, S. H. Pseudoreplication and the design of ecological field experiments. Ecol. Monogr. 54, 187–211 (1984).Article 

Google Scholar 
Tsounis, G., Edmunds, P. J., Bramanti, L., Gambrel, B. & Lasker, H. R. Variability of size structure and species composition in Caribbean octocoral communities under contrasting environmental conditions. Mar. Biol. 165, 1–14 (2018).Article 

Google Scholar 
Browning, T. N. et al. Widespread deposition in a coastal bay following three major 2017 hurricanes (Irma, Jose, and Maria). Sci. Rep. 9, 1–13 (2019).CAS 
Article 

Google Scholar 
Edmunds, P. J. Three decades of degradation lead to diminished impacts of severe hurricanes on Caribbean reefs. Ecology 100, e02587 (2019).PubMed 
Article 

Google Scholar 
Clarke, K. & Warwick, R. Quantifying structural redundancy in ecological communities. Oecologia 113, 278–289 (1998).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Menge, B. A., Berlow, E. L., Blanchette, C. A., Navarrete, S. A. & Yamada, S. B. The keystone species concept: variation in interaction strength in a rocky intertidal habitat. Ecol. Monogr. 64, 249–286 (1994).Article 

Google Scholar 
Frost, T. M., Carpenter, S. R., Ives, A. R. & Kratz, T. K. in Linking Species & Ecosystems (eds Jones, C. G. & Lawton, J. H.) 224–239 (Springer, 1995).Chapter 

Google Scholar 
Lasker, H., Martínez-Quintana, Á., Bramanti, L. & Edmunds, P. J. Resilience of octocoral forests to catastrophic storms. Sci. Rep. 10, 1–8 (2020).Article 
CAS 

Google Scholar 
Goffredo, S. & Lasker, H. R. Modular growth of a gorgonian coral can generate predictable patterns of colony growth. J. Exp. Mar. Biol. Ecol. 336, 221–229 (2006).Article 

Google Scholar 
Grigg, R. W. Growth rings: annual periodicity in two gorgonian corals. Ecology 55, 876–881 (1974).Article 

Google Scholar 
Grigg, R. W. Resource management of precious corals a review and application ton shallow water reef building corals. Mar. Ecol. 5, 57–74 (1984).ADS 
Article 

Google Scholar 
Clarke, K. R. & Gorley, R. N. Primer v6: User Manual/Tutorial (PRIMER-E Ltd., 2006).
Google Scholar 
Schutte, V. G., Selig, E. R. & Bruno, J. F. Regional spatio-temporal trends in Caribbean coral reef benthic communities. Mar. Ecol. Prog. Ser. 402, 115–122 (2010).ADS 
Article 

Google Scholar 
Edmunds, P. J. Decadal-scale changes in the community structure of coral reefs of St. John, US Virgin Islands. Mar. Ecol. Prog. Ser. 489, 107–123 (2013).ADS 
Article 

Google Scholar 
Chollett, I., Mumby, P. J., Müller-Karger, F. E. & Hu, C. Physical environments of the Caribbean Sea. Limnol. Oceanogr. 57, 1233–1244 (2012).ADS 
Article 

Google Scholar 
Fowell, S. E. et al. Historical trends in pH and carbonate biogeochemistry on the Belize Mesoamerican Barrier Reef System. Geophys. Res. Lett. 45, 3228–3237. https://doi.org/10.1002/2017GL076496 (2018).ADS 
CAS 
Article 

Google Scholar 
Edmunds, P. J. & Lasker, H. R. Regulation of population size of arborescent octocorals on shallow Caribbean reefs. Mar. Ecol. Prog. Ser. 615, 1–14 (2019).ADS 
Article 

Google Scholar 
Borgstein, N., Beltrán, D. M. & Prada, C. Variable growth across species and life stages in Caribbean reef octocorals. Front. Mar. Sci. 7, 483 (2020).Article 

Google Scholar 
Guizien, K. & Ghisalberti, M. in Marine Animal Forests: The Ecology of Benthic Biodiversity Hotspots (eds Rossi, S. et al.) 1–22 (Springer International Publishing, 2015).
Google Scholar 
Isbell, F. I., Polley, H. W. & Wilsey, B. J. Biodiversity, productivity and the temporal stability of productivity: patterns and processes. Ecol. Lett. 12, 443–451 (2009).PubMed 
Article 

Google Scholar 
Simonson, W. D., Allen, H. D., Coomes, D. A. & Tatem, A. Applications of airborne lidar for the assessment of animal species diversity. Methods Ecol. Evol. 5, 719–729 (2014).Article 

Google Scholar 
Roscher, C. et al. Identifying population- and community-level mechanisms of diversity-stability relationships in experimental grasslands. J. Ecol. 99, 1460–1469 (2011).Article 

Google Scholar 
Yang, Z., Ruijven, V. J. & Du, G. The effects of long-term fertilization on the temporal stability of alpine meadow communities. Plant Soil 345, 315–324 (2011).CAS 
Article 

Google Scholar 
Wilcox, K. R. et al. Asynchrony among local communities stabilises ecosystem function of metacommunities. Ecol. Lett. 20, 1534–1545 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
Rosenfeld, J. S. Logical fallacies in the assessment of functional redundancy. Conserv. Biol. 16, 837–839 (2002).Article 

Google Scholar 
Loreau, M. Does functional redundancy exist?. Oikos 104, 606–611 (2004).Article 

Google Scholar 
Gambrel, B. & Lasker, H. R. Interactions in the canopy among Caribbean reef octocorals. Mar. Ecol. Prog. Ser. 546, 85–95 (2016).ADS 
Article 

Google Scholar 
Zambrano, J. et al. Tree crown overlap improves predictions of the functional neighbourhood effects on tree survival and growth. J. Ecol. 107, 887–900 (2019).Article 

Google Scholar 
Pescador, et al. 2018 The shape is more important than we ever thought: Plant to plant interactions in a high mountain community. Methods Ecol. Evol. 10, 1584–1593 (2019).Article 

Google Scholar 
Cerpovicz, A. F. & Lasker, H. R. Canopy effects of octocoral communities on sedimentation: modern baffles on the shallow-water reefs of St. John, USVI. Coral Reefs 40, 295 (2021).Article 

Google Scholar 
Martinez-Quintana, Á. & Lasker, H. R. Early life-history dynamics of Caribbean octocorals: the critical role of larval supply and partial mortality. Front. Mar. Sci. https://doi.org/10.3389/fmars.2021.705563 (2021).Article 

Google Scholar 
Tsounis, G., Steele, M. A. & Edmunds, P. J. Elevated feeding rates of fishes within octocoral canopies on Caribbean reefs. Coral Reefs 39, 1299–1311 (2020).Article 

Google Scholar 
Girard, J. & Edmunds, P.J. Effects of arborescent octocoral assemblages on the understory benthic communities of shallow Caribbean reefs. J. Exp. Mar. Biol. Ecol. (in review).Privitera-Johnson, K., Lenz, E. A. & Edmunds, P. J. Density-associated recruitment in octocoral communities in St. John, US Virgin Islands. J. Exp. Mar. Biol. Ecol. 473, 103–109. https://doi.org/10.1016/j.jembe.2015.08.006 (2015).Article 

Google Scholar 
Slattery, M. & Lesser, M. P. Gorgonians are foundation species on sponge-dominated Mesophotic Coral Reefs in the Caribbean. Front. Mar. Sci. https://doi.org/10.3389/fmars.2021.654268 (2021).Article 

Google Scholar 
Lasker, H. R. & Porto-Hannes, I. Population structure among octocoral adults and recruits identifies scale dependent patterns of population isolation in The Bahamas. PeerJ 3, e1019. https://doi.org/10.7717/peerj.1019 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
Clark, D. A. & Clark, D. B. Getting to the canopy: tree height growth in a neotropical rain forest. Ecology 82, 1460–1472 (2001).Article 

Google Scholar 
Birkeland, C. Coral Reefs in the Anthropocene 1–15 (Springer, 2015).Book 

Google Scholar 
Petraitis, P. S. & Dudgeon, S. R. Cusps and butterflies: multiple stable states in marine systems as catastrophes. Mar. Freshw. Res. 67, 37–46 (2015).Article 

Google Scholar  More

Curtis, P. G., Slay, C. M., Harris, N. L., Tyukavina, A. & Hansen, M. C. Classifying drivers of global forest loss. Science 361, 1108–1111 (2018).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Duane, A., Castellnou, M. & Brotons, L. Towards a comprehensive look at global drivers of novel extreme wildfire events. Clim. Change 165, 43 (2021).ADS 
Article 

Google Scholar 
Allen, C. D. et al. A global overview of drought and heat-induced tree mortality reveals emerging climate change risks for forests. For. Ecol. Manag. 259, 660–684 (2010). Adaptation of Forests and Forest Management to Changing Climate.Article 

Google Scholar 
Franklin, J. F., Mitchell, R. J. & Palik, B. J. Natural disturbance and stand development principles for ecological forestry. General Technical Report. NRS-19. Newtown Square, PA: US Department of Agriculture, Forest Service, Northern Research Station. 44. p. 19 (2007).Westoby, M., Jurado, E. & Leishman, M. Comparative evolutionary ecology of seed size. Trends Ecol. Evol. 7, 368–372 (1992).CAS 
PubMed 
Article 

Google Scholar 
Smith, C. C. & Fretwell, S. D. The optimal balance between size and number of offspring. Am. Nat. 108, 499–506 (1974).Article 

Google Scholar 
Lord, J., Westoby, M. & Leishman, M. Seed size and phylogeny in six temperate floras: Constraints, niche conservatism, and adaptation. Am. Nat. 146, 349–364 (1995).Article 

Google Scholar 
Moles, A. T. et al. Global patterns in seed size. Glob. Ecol. Biogeogr. 16, 109–116 (2007).Article 

Google Scholar 
Tautenhahn, S. et al. On the biogeography of seed mass in germany – distribution patterns and environmental correlates. Ecography 31, 457–468 (2008).Article 

Google Scholar 
Lidgard, S. & Crane, P. R. Quantitative analyses of the early angiosperm radiation. Nature 331, 344–346 (1988).ADS 
Article 

Google Scholar 
Crisp, M. D. & Cook, L. G. Cenozoic extinctions account for the low diversity of extant gymnosperms compared with angiosperms. New Phytol. 192, 997–1009 (2011).CAS 
PubMed 
Article 

Google Scholar 
Stearns, S. C. Life-history tactics: a review of the ideas. Quart. Rev. Biol. 51, 3–47 (1976).CAS 
PubMed 
Article 

Google Scholar 
Grubb, P. J. The maintenance of species-richness in plant communities: the importance of the regeneration niche. Biol. Rev. 52, 107–145 (1977).Article 

Google Scholar 
Clark, J. S., LaDeau, S. & Ibanez, I. Fecundity of trees and the colonization-competition hypothesis. Ecol. Monogr. 74, 415–442 (2004).Article 

Google Scholar 
Salguero-Gómez, R. et al. Fast-slow continuum and reproductive strategies structure plant life-history variation worldwide. Proc. Natl Acad. Sci. USA 113, 230–235 (2016).ADS 
PubMed 
Article 
CAS 

Google Scholar 
Thomas, S. C. Age-Related Changes in Tree Growth and Functional Biology: The Role of Reproduction, p. 33-64 (Springer Netherlands, 2011).Wenk, E. H. & Falster, D. S. Quantifying and understanding reproductive allocation schedules in plants. Ecol. Evol. 5, 5521–5538 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
Bar-On, Y. M., Phillips, R. & Milo, R. The biomass distribution on earth. Proc. Natl Acad. Sci. USA 115, 6506–6511 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Turnbull, L. A., Rees, M. & Crawley, M. J. Seed mass and the competition/colonization trade-off: a sowing experiment. J. Ecol. 87, 899–912 (1999).Article 

Google Scholar 
Moles, A., Falster, D., Leishman, M. & Westoby, M. Small-seeded species produce more seeds per square metre of canopy per year, but not per individual per lifetime. J. Ecol. 92, 384–396 (2004).Article 

Google Scholar 
Qiu, T. et al. Is there tree senescence? the fecundity evidence. Proc. Natl Acad. Sci. USA 118, e2106130118 (2021).Westoby, M., Falster, D. S., Moles, A. T., Vesk, P. A. & Wright, I. J. Plant ecological strategies: Some leading dimensions of variation between species. Annu. Rev. Ecol. Syst. 33, 125–159 (2002).Article 

Google Scholar 
Henery, M. L. & Westoby, M. Seed mass and seed nutrient content as predictors of seed output variation between species. Oikos 92, 479–490 (2001).Article 

Google Scholar 
Turnbull, L. A., Coomes, D., Hector, A. & Rees, M. Seed mass and the competition/colonization trade-off: competitive interactions and spatial patterns in a guild of annual plants. J. Ecol. 92, 97–109 (2004).Article 

Google Scholar 
Chave, J. et al. Towards a worldwide wood economics spectrum. Ecol. Lett. 12, 351–366 (2009).PubMed 
Article 

Google Scholar 
Poorter, L. et al. The importance of wood traits and hydraulic conductance for the performance and life history strategies of 42 rainforest tree species. New Phytol. 185, 481–492 (2010).PubMed 
Article 

Google Scholar 
Hanley, M. E., Cook, B. I. & Fenner, M. Climate variation, reproductive frequency and acorn yield in english oaks. J. Plant Ecol. 12, 542–549 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Kattge, J. et al. Try plant trait database – enhanced coverage and open access. Glob. Change Biol. 26, 119–188 (2020).ADS 
Article 

Google Scholar 
Ran, E., Arnon, D., Alon, B.-G., Amnon, S. & Uri, Y. Flowering and fruit set of olive trees in response to nitrogen, phosphorus, and potassium. J. Am. Soc. Hortic. Sci. Am. Soc. Hortic. Sci. 133, 639–647 (2008).Article 

Google Scholar 
Fernández-Martínez, M., Vicca, S., Janssens, I. A., Espelta, J. M. & Peñuelas, J. The role of nutrients, productivity and climate in determining tree fruit production in european forests. New Phytol. 213, 669–679 (2017).PubMed 
Article 
CAS 

Google Scholar 
Fortier, R. & Wright, S. J. Nutrient limitation of plant reproduction in a tropical moist forest. Ecology 102, e03469 (2021).Canham, C. D., Ruscoe, W. A., Wright, E. F. & Wilson, D. J. Spatial and temporal variation in tree seed production and dispersal in a new zealand temperate rainforest. Ecosphere 5, art49 (2014).Article 

Google Scholar 
Pérez-Ramos, I. M., Aponte, C., García, L. V., Padilla-Díaz, C. M. & Marañón, T. Why is seed production so variable among individuals? a ten-year study with oaks reveals the importance of soil environment. PLoS ONE 9, e115371 (2014).ADS 
PubMed 
PubMed Central 
Article 
CAS 

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

Google Scholar 
Krinner, G. et al. A dynamic global vegetation model for studies of the coupled atmosphere-biosphere system. Glob. Biogeochem. Cycles 19, 1–33 (2005).Article 
CAS 

Google Scholar 
Fisher, R. A. et al. Vegetation demographics in earth system models: a review of progress and priorities. Glob. Change Biol. 24, 35–54 (2018).ADS 
Article 

Google Scholar 
Hanbury-Brown, A., Ward, R. & Kueppers, L. M. Future forests within earth system models: regeneration processes critical to prediction. New Phytol. in press https://doi.org/10.1111/nph.18131 (2022).Stiles, W. C. & Reid, W. S. Orchard nutrition management. Inf. Bull. (1991). https://ecommons.cornell.edu/bitstream/handle/1813/3305/Orchard%20Nutrition%20Management.pdf?sequence=2&isAllowed=y.Schlesinger, W. H. Some thoughts on the biogeochemical cycling of potassium in terrestrial ecosystems. Biogeochemistry 154, 427–432 (2021).Article 

Google Scholar 
Neilsen, D. & Neilsen, G. Efficient use of nitrogen and water in high-density apple orchards. HortTechnology 12, 19 (2002).Article 

Google Scholar 
Rubio Ames, Z., Brecht, J. K. & Olmstead, M. A. Nitrogen fertilization rates in a subtropical peach orchard: effects on tree vigor and fruit quality. J. Sci. Food Agric. 100, 527–539 (2020).CAS 
PubMed 
Article 

Google Scholar 
Elser, J. J. et al. Growth rate-stoichiometry couplings in diverse biota. Ecol. Lett. 6, 936–943 (2003).Article 

Google Scholar 
Seyednasrollah, B. & Clark, J. S. Where resource-acquisitive species are located: the role of habitat heterogeneity. Geophys. Res. Lett. 47, e2020GL087626 (2020).Rosecrance, R. C., Weinbaum, S. A. & Brown, P. H. Alternate bearing affects nitrogen, phosphorus, potassium and starch storage pools in mature pistachio trees. Ann. Bot. 82, 463–470 (1998).Article 

Google Scholar 
Sala, A., Hopping, K., McIntire, E. J. B., Delzon, S. & Crone, E. E. Masting in whitebark pine (pinus albicaulis) depletes stored nutrients. New Phytol. 196, 189–199 (2012).CAS 
PubMed 
Article 

Google Scholar 
LaDeau, S. L. & Clark, J. S. Rising co2 levels and the fecundity of forest trees. Science 292, 95–8 (2001).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Callahan, H. S., Del Fierro, K., Patterson, A. E. & Zafar, H. Impacts of elevated nitrogen inputs on oak reproductive and seed ecology. Glob. Change Biol. 14, 285–293 (2008).ADS 
Article 

Google Scholar 
Lambers, H. & Poorter, H. Inherent Variation in Growth Rate Between Higher Plants: A Search for Physiological Causes and Ecological Consequences, vol. 23, 187-261 (Academic Press, 1992).Hengl, T. et al. Soilgrids250m: global gridded soil information based on machine learning. PLoS ONE 12, 1–40 (2017).Article 
CAS 

Google Scholar 
Sharma, A., Weindorf, D. C., Wang, D. D. & Chakraborty, S. Characterizing soils via portable x-ray fluorescence spectrometer: 4. cation exchange capacity (cec). Geoderma 239, 130–134 (2015).ADS 
Article 
CAS 

Google Scholar 
Hazelton, P. & Murphy, B. Interpreting Soil Test Results: What Do All The Numbers Mean? (CSIRO publishing, 2016).Chowdhury, S. et al. Chapter Two – Role Of Cultural And Nutrient Management Practices In Carbon Sequestration In Agricultural Soil, vol. 166, 131-196 (Academic Press, 2021).Clark, J. S., Nuñez, C. L. & Tomasek, B. Foodwebs based on unreliable foundations: spatiotemporal masting merged with consumer movement, storage, and diet. Ecol. Monogr. 89, e01381 (2019).Article 

Google Scholar 
Burns, R. M. Silvics Of North America (US Department of Agriculture, Forest Service, 1990).Koenig, W. D. & Knops, J. M. H. Seed-crop size and eruptions of north american boreal seed-eating birds. J. Anim. Ecol. 70, 609–620 (2001).Article 

Google Scholar 
Greene, D. F. & Johnson, E. A. Estimating the mean annual seed production of trees. Ecology 75, 642–647 (1994).Article 

Google Scholar 
Lord, J. M. & Westoby, M. Accessory costs of seed production and the evolution of angiosperms. Evol. Int. J. Org. Evol. 66, 200–210 (2012).Article 

Google Scholar 
Hulme, P. & Benkman, C. Granivory. vol. 23, 132-154 (Oxford: Blackwell, 2002).Bond, W. J. The tortoise and the hare: ecology of angiosperm dominance and gymnosperm persistence. Biol. J. Linn. Soc. 36, 227–249 (1989).Article 

Google Scholar 
Brodribb, T. J. & Feild, T. S. Leaf hydraulic evolution led a surge in leaf photosynthetic capacity during early angiosperm diversification. Ecol. Lett. 13, 175–183 (2010).PubMed 
Article 

Google Scholar 
Davies, T. J. et al. Darwin’s abominable mystery: Insights from a supertree of the angiosperms. Proc. Natl Acad. Sci. USA 101, 1904–1909 (2004).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Berendse, F. & Scheffer, M. The angiosperm radiation revisited, an ecological explanation for darwin’s ‘abominable mystery’. Ecol. Lett. 12, 865–872 (2009).PubMed 
PubMed Central 
Article 

Google Scholar 
Barrett, S. C. H. Influences of clonality on plant sexual reproduction. Proc. Natl Acad. Sci. USA 112, 8859–8866 (2015).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Condamine, F. L., Silvestro, D., Koppelhus, E. B. & Antonelli, A. The rise of angiosperms pushed conifers to decline during global cooling. Proc. Natl Acad. Sci. USA 117, 28867–28875 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Oren, R. et al. Soil fertility limits carbon sequestration by forest ecosystems in a co2-enriched atmosphere. Nature 411, 469–472 (2001).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Reich, P. B. et al. Nitrogen limitation constrains sustainability of ecosystem response to co2. Nature 440, 922–925 (2006).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Firn, J. et al. Leaf nutrients, not specific leaf area, are consistent indicators of elevated nutrient inputs. Nat. Ecol. Evol. 3, 400–406 (2019).PubMed 
Article 

Google Scholar 
Elser, J. et al. Biological stoichiometry from genes to ecosystems. Ecol. Lett. 3, 540–550 (2000).Article 

Google Scholar 
Niklas, K. J., Owens, T., Reich, P. B. & Cobb, E. D. Nitrogen/phosphorus leaf stoichiometry and the scaling of plant growth. Ecol. Lett. 8, 636–642 (2005).Article 

Google Scholar 
Kerkhoff, A. J., Fagan, W. F., Elser, J. J. & Enquist, B. J. Phylogenetic and growth form variation in the scaling of nitrogen and phosphorus in the seed plants. Am. Nat. 168, E103–E122 (2006).PubMed 
Article 

Google Scholar 
Weinbaum, S. A., Johnson, R. S. & DeJong, T. M. Causes and consequences of overfertilization in orchards. HortTechnology 2, 112b (1992).Article 

Google Scholar 
Fernandez-Escobar, R. et al. Olive oil quality decreases with nitrogen over-fertilization. HortScience 41, 215 (2006).CAS 
Article 

Google Scholar 
Han, Q., Kabeya, D., Iio, A. & Kakubari, Y. Masting in fagus crenata and its influence on the nitrogen content and dry mass of winter buds. Tree Physiol. 28, 1269–1276 (2008).PubMed 
Article 

Google Scholar 
Pettigrew, W. T. Potassium influences on yield and quality production for maize, wheat, soybean and cotton. Physiol. Plant. 133, 670–681 (2008).CAS 
PubMed 
Article 

Google Scholar 
Leeper, A. C., Lawrence, B. A. & LaMontagne, J. M. Plant-available soil nutrients have a limited influence on cone production patterns of individual white spruce trees. Oecologia 194, 101–111 (2020).ADS 
PubMed 
Article 

Google Scholar 
Chapin, F. S., Autumn, K. & Pugnaire, F. Evolution of suites of traits in response to environmental stress. Am. Nat. 142, S78–S92 (1993).Article 

Google Scholar 
Westoby, M. & Wright, I. J. Land-plant ecology on the basis of functional traits. Trends Ecol. Evol. 21, 261–268 (2006).PubMed 
Article 

Google Scholar 
Brodribb, T. J., Pittermann, J. & Coomes, D. A. Elegance versus speed: Examining the competition between conifer and angiosperm trees. Int. J. Plant Sci. 173, 673–694 (2012).Article 

Google Scholar 
Clark, J. S., Macklin, E. & Wood, L. Stages and spatial scales of recruitment limitation in southern appalachian forests. Ecol. Monogr. 68, 213–235 (1998).Article 

Google Scholar 
McEuen, A. B. & Curran, L. M. Seed dispersal and recruitment limitation across spatial scales in temperate forest fragments. Ecology 85, 507–518 (2004).Article 

Google Scholar 
Emsweller, L. N., Gorchov, D. L., Zhang, Q., Driscoll, A. G. & Hughes, M. R. Seed rain and disturbance impact recruitment of invasive plants in upland forest. Invasive Plant Sci. Manag. 11, 69–81 (2018).Article 

Google Scholar 
Lindgren, s, Eriksson, O. & Moen, J. The impact of disturbance and seed availability on germination of alpine vegetation in the scandinavian mountains. Arct. Antarct. Alp. Res. 39, 449–454 (2007).Article 

Google Scholar 
Cai, W. H., Liu, Z., Yang, Y. Z. & Yang, J. Does environment filtering or seed limitation determine post-fire forest recovery patterns in boreal larch forests? Front. Plant Sci. 9, 1318 (2018).Darwin, C. On the Origin of Species (John Murray, 1859).Black, M. Darwin and seeds. Seed Sci. Res. 19, 193–199 (2009).Article 

Google Scholar 
FAO. Global forest resources assessment 2020-key findings. un food and agriculture organization. Report (2020).Payn, T. et al. Changes in planted forests and future global implications. For. Ecol. Manag. 352, 57–67 (2015).Article 

Google Scholar 
Clark, J. S. et al. The impacts of increasing drought on forest dynamics, structure, and biodiversity in the united states. Glob. Change Biol. 22, 2329–2352 (2016).ADS 
Article 

Google Scholar 
Gazol, A., Camarero, J. J., Anderegg, W. R. L. & Vicente-Serrano, S. M. Impacts of droughts on the growth resilience of northern hemisphere forests. Glob. Ecol. Biogeogr. 26, 166–176 (2017).Article 

Google Scholar 
Stephens, S. L. et al. Managing forests and fire in changing climates. Science 342, 41–42 (2013).ADS 
CAS 
PubMed 
Article 

Google Scholar 
North, M. P. et al. Tamm review: reforestation for resilience in dry western u.s. forests. For. Ecol. Manag. 432, 209–224 (2019).Article 

Google Scholar 
Seidl, R., Rammer, W. & Spies, T. A. Disturbance legacies increase the resilience of forest ecosystem structure, composition, and functioning. Ecol. Appl. 24, 2063–2077 (2014).PubMed 
PubMed Central 
Article 

Google Scholar 
Serra-Diaz, J. M. et al. Averaged 30 year climate change projections mask opportunities for species establishment. Ecography 39, 844–845 (2016).Article 

Google Scholar 
Davis, F. W. et al. Shrinking windows of opportunity for oak seedling establishment in southern california mountains. Ecosphere 7, e01573 (2016).
Google Scholar 
LeBauer, D. S. & Treseder, K. K. Nitrogen limitation of net primary productivity in terrestrial ecosystems is globally distributed. Ecology 89, 371–379 (2008).PubMed 
Article 

Google Scholar 
Clark, J. S. et al. Continent-wide tree fecundity driven by indirect climate effects. Nat. Commun. 12, 1242 (2021).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Brady, N. C., Weil, R. R. & Weil, R. R. The Nature And Properties Of Soils, vol. 13 (Prentice Hall Upper Saddle River, 2008).Farr, T. G. et al. The shuttle radar topography mission. Rev. Geophys. 45, RG2004 (2007). https://doi.org/10.1029/2005RG000183.Clark, J. S. Landscape interactions among nitrogen mineralization, species composition, and long-term fire frequency. Biogeochemistry 11, 1–22 (1990).Article 

Google Scholar 
Clark, J. S., Bell, D. M., Kwit, M. C. & Zhu, K. Competition-interaction landscapes for the joint response of forests to climate change. Glob. Change Biol. 20, 1979–1991 (2014).ADS 
Article 

Google Scholar 
Begueria, S., Vicente-Serrano, S. M., Reig, F. & Latorre, B. Standardized precipitation evapotranspiration index (spei) revisited: parameter fitting, evapotranspiration models, tools, datasets and drought monitoring. Int. J. Climatol. 34, 3001–3023 (2014).Article 

Google Scholar 
Abatzoglou, J. T., Dobrowski, S. Z., Parks, S. A. & Hegewisch, K. C. Terraclimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958-2015. Sci. Data 5, 170191 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Karger, D. N. et al. Climatologies at high resolution for the earth’s land surface areas. Sci. Data 4, 170122 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
Schneider, R., Calama, R. & Martin-Ducup, O. Understanding tree-to-tree variations in stone pine (pinus pinea l.) cone production using terrestrial laser scanner. Remote Sens. 12, 173 (2020).Article 

Google Scholar 
Gavranović, A., Bogdan, S., Lanšćak, M., Čehulić, I. & Ivanković, M. Seed yield and morphological variations of beechnuts in four european beech (fagus sylvatica l.) populations in croatia. South-East Eur. For. 9, 17–27 (2018).Article 

Google Scholar 
Maitner, B. S. et al. The bien r package: a tool to access the botanical information and ecology network (bien) database. Methods Ecol. Evol. 9, 373–379 (2018).Article 

Google Scholar 
Clark, J. S., Silman, M., Kern, R., Macklin, E. & HilleRisLambers, J. Seed dispersal near and far: patterns across temperate and tropical forests. Ecology 80, 1475–1494 (1999).Article 

Google Scholar 
LePage, P. T., Canham, C. D., Coates, K. D. & Bartemucci, P. Seed abundance versus substrate limitation of seedling recruitment in northern temperate forests of british columbia. Can. J. For. Res. 30, 415–427 (2000).Article 

Google Scholar 
Clark, J. S., LaDeau, S. & Ibanez, I. Fecundity of trees and the colonization-competition hypothesis. Ecol. Monogr. 74, 415–442 (2004).Article 

Google Scholar 
Muller-Landau, H. C., Wright, S. J., Calderon, O., Condit, R. & Hubbell, S. P. Interspecific variation in primary seed dispersal in a tropical forest. J. Ecol. 96, 653–667 (2008).Article 

Google Scholar 
Jones, F. A. & Muller-Landau, H. C. Measuring long-distance seed dispersal in complex natural environments: an evaluation and integration of classical and genetic methods. J. Ecol. 96, 642–652 (2008).Article 

Google Scholar 
Clark, J. S. Individuals and the variation needed for high species diversity in forest trees. Science 327, 1129–1132 (2010).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Clark, J. S. et al. High-dimensional coexistence based on individual variation: a synthesis of evidence. Ecol. Monogr. 80, 569–608 (2010).Article 

Google Scholar 
Clark, J. S., Bell, D. M., Kwit, M. C. & Zhu, K. Competition-interaction landscapes for the joint response of forests to climate change. Glob. Change Biol. 20, 1979–91 (2014).ADS 
Article 

Google Scholar 
Minor, D. M. & Kobe, R. K. Fruit production is influenced by tree size and size-asymmetric crowding in a wet tropical forest. Ecol. Evol. 9, 1458–1472 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Zanne, A. E. et al. Three keys to the radiation of angiosperms into freezing environments. Nature 506, 89–92 (2014).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Pagel, M. Inferring the historical patterns of biological evolution. Nature 401, 877–884 (1999).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Revell, L. J. phytools: an r package for phylogenetic comparative biology (and other things). Methods Ecol. Evol. 3, 217–223 (2012).Article 

Google Scholar 
Felsenstein, J. Phylogenies and the comparative method. Am. Nat. 125, 1–15 (1985).Article 

Google Scholar 
Martins, E. P. & Hansen, T. F. Phylogenies and the comparative method: A general approach to incorporating phylogenetic information into the analysis of interspecific data. Am. Nat. 149, 646–667 (1997).Article 

Google Scholar 
Tung Ho, L. S. & Ané, C. A linear-time algorithm for gaussian and non-gaussian trait evolution models. Syst. Biol. 63, 397–408 (2014).Article 

Google Scholar 
Clark, J. S. Data from: continent-wide tree fecundity driven by indirect climate effects https://doi.org/10.7924/r4348ph5t (2020). More

Boerjan W, Ralph J, Baucher M. Lignin biosynthesis. Annu Rev Plant Biol. 2003;54:519–46. https://doi.org/10.1146/annurev.arplant.54.031902.134938.CAS 
Article 
PubMed 

Google Scholar 
Ragauskas AJ, Beckham GT, Biddy MJ, Chandra R, Chen F, Davis MF, et al. Lignin valorization: improving lignin processing in the biorefinery. Science. 2014;344:1246843. https://doi.org/10.1126/science.1246843.CAS 
Article 
PubMed 

Google Scholar 
Hildén K, Hakala TK, Lundell T. Thermotolerant and thermostable laccases. Biotechnol Lett. 2009;31:1117. https://doi.org/10.1007/s10529-009-9998-0.CAS 
Article 
PubMed 

Google Scholar 
Wilhelm RC, Singh R, Eltis LD, Mohn WW. Bacterial contributions to delignification and lignocellulose degradation in forest soils with metagenomic and quantitative stable isotope probing. ISME J. 2018;1. https://doi.org/10.1038/s41396-018-0279-6.Bugg TDH, Ahmad M, Hardiman EM, Singh R. The emerging role for bacteria in lignin degradation and bio-product formation. Curr Opin Biotechnol. 2011;22:394–400. https://doi.org/10.1016/j.copbio.2010.10.009.CAS 
Article 
PubMed 

Google Scholar 
Kamimura N, Takahashi K, Mori K, Araki T, Fujita M, Higuchi Y, et al. Bacterial catabolism of lignin-derived aromatics: new findings in a recent decade: update on bacterial lignin catabolism. Environ Microbiol Rep. 2017;9:679–705. https://doi.org/10.1111/1758-2229.12597.CAS 
Article 
PubMed 

Google Scholar 
Singh R, Hu J, Regner MR, Round JW, Ralph J, Saddler JN, et al. Enhanced delignification of steam-pretreated poplar by a bacterial laccase. Sci Rep. 2017;7:42121. https://doi.org/10.1038/srep42121.CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Perna V, Meyer AS, Holck J, Eltis LD, Eijsink VGH, Wittrup Agger J. Laccase-catalyzed oxidation of lignin induces production of H2O2. ACS Sustain Chem Eng. 2020;8:831–41. https://doi.org/10.1021/acssuschemeng.9b04912.CAS 
Article 

Google Scholar 
Johnson CW, Salvachúa D, Rorrer NA, Black BA, Vardon DR, St. John PC, et al. Innovative chemicals and materials from bacterial aromatic catabolic pathways. Joule. 2019;3:1523–37. https://doi.org/10.1016/j.joule.2019.05.011.CAS 
Article 

Google Scholar 
Brady AL, Sharp CE, Grasby SE, Dunfield PF. Anaerobic carboxydotrophic bacteria in geothermal springs identified using stable isotope probing. Front Microbiol. 2015;6. https://doi.org/10.3389/fmicb.2015.00897.Grasby SE, Hutcheon I, Krouse HR. The influence of water–rock interaction on the chemistry of thermal springs in western Canada. Appl Geochem. 2000;15:439–54. https://doi.org/10.1016/S0883-2927(99)00066-9.CAS 
Article 

Google Scholar 
Bauchop T, Elsden SR. The growth of micro-organisms in relation to their energy supply. Microbiology. 1960;23:457–69. https://doi.org/10.1099/00221287-23-3-457.CAS 
Article 

Google Scholar 
Neufeld JD, Vohra J, Dumont MG, Lueders T, Manefield M, Friedrich MW, et al. DNA stable-isotope probing. Nat Protoc. 2007;2:860–6. https://doi.org/10.1038/nprot.2007.109.CAS 
Article 
PubMed 

Google Scholar 
Wilhelm RC, Singh R, Eltis LD, Mohn WW. Bacterial contributions to delignification and lignocellulose degradation in forest soils with metagenomic and quantitative stable isotope probing. ISME J. 2019;13:413–29. https://doi.org/10.1038/s41396-018-0279-6.CAS 
Article 
PubMed 

Google Scholar 
Wilhelm R, Szeitz A, Klassen TL, Mohn WW. Sensitive, efficient quantitation of 13C-enriched nucleic acids via ultrahigh-performance liquid chromatography-tandem mass spectrometry for applications in stable isotope probing. Appl Environ Microbiol. 2014;80:7206–11. https://doi.org/10.1128/AEM.02223-14.CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinforma Oxf Engl. 2014;30:2114–20. https://doi.org/10.1093/bioinformatics/btu170.CAS 
Article 

Google Scholar 
Bankevich A, Nurk S, Antipov D, Gurevich AA, Dvorkin M, Kulikov AS, et al. SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing. J Comput Biol. 2012;19:455–77. https://doi.org/10.1089/cmb.2012.0021.CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Lin H-H, Liao Y-C. Accurate binning of metagenomic contigs via automated clustering sequences using information of genomic signatures and marker genes. Sci Rep. 2016;6:24175. https://doi.org/10.1038/srep24175.CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Kang DD, Li F, Kirton E, Thomas A, Egan R, An H, et al. MetaBAT 2: an adaptive binning algorithm for robust and efficient genome reconstruction from metagenome assemblies. PeerJ. 2019;7:e7359. https://doi.org/10.7717/peerj.7359.Article 
PubMed 
PubMed Central 

Google Scholar 
Alneberg J, Bjarnason BS, Bruijn ID, Schirmer M, Quick J, Ijaz UZ, et al. Binning metagenomic contigs by coverage and composition. Nat Methods. 2014;11:1144–6. https://doi.org/10.1038/nmeth.3103.CAS 
Article 
PubMed 

Google Scholar 
Wu Y-W, Simmons BA, Singer SW. MaxBin 2.0: an automated binning algorithm to recover genomes from multiple metagenomic datasets. Bioinforma Oxf Engl. 2016;32:605–7. https://doi.org/10.1093/bioinformatics/btv638.CAS 
Article 

Google Scholar 
Sieber CMK, Probst AJ, Sharrar A, Thomas BC, Hess M, Tringe SG, et al. Recovery of genomes from metagenomes via a dereplication, aggregation and scoring strategy. Nat Microbiol. 2018;3:836–43. https://doi.org/10.1038/s41564-018-0171-1.CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Parks DH, Imelfort M, Skennerton CT, Hugenholtz P, Tyson GW. CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res. 2015;25:1043–55. https://doi.org/10.1101/gr.186072.114.CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Hyatt D, Chen G-L, LoCascio PF, Land ML, Larimer FW, Hauser LJ. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinforma. 2010;11:119. https://doi.org/10.1186/1471-2105-11-119.CAS 
Article 

Google Scholar 
Buchfink B, Xie C, Huson DH. Fast and sensitive protein alignment using DIAMOND. Nat Methods. 2015;12:59–60. https://doi.org/10.1038/nmeth.3176.CAS 
Article 
PubMed 

Google Scholar 
Lombard V, Golaconda Ramulu H, Drula E, Coutinho PM, Henrissat B. The carbohydrate-active enzymes database (CAZy) in 2013. Nucleic Acids Res. 2014;42:D490–5. https://doi.org/10.1093/nar/gkt1178.CAS 
Article 
PubMed 

Google Scholar 
Zhang H, Yohe T, Huang L, Entwistle S, Wu P, Yang Z, et al. dbCAN2: a meta server for automated carbohydrate-active enzyme annotation. Nucleic Acids Res. 2018;46:W95–101. https://doi.org/10.1093/nar/gky418.CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
El-Gebali S, Mistry J, Bateman A, Eddy SR, Luciani A, Potter SC, et al. The Pfam protein families database in 2019. Nucleic Acids Res. 2019;47:D427–32. https://doi.org/10.1093/nar/gky995.CAS 
Article 
PubMed 

Google Scholar 
Haft DH, Loftus BJ, Richardson DL, Yang F, Eisen JA, Paulsen IT, et al. TIGRFAMs: a protein family resource for the functional identification of proteins. Nucleic Acids Res. 2001;29:41–3.CAS 
Article 

Google Scholar 
Aramaki T, Blanc-Mathieu R, Endo H, Ohkubo K, Kanehisa M, Goto S, et al. KofamKOALA: KEGG Ortholog assignment based on profile HMM and adaptive score threshold. Bioinformatics. 2020. https://doi.org/10.1093/bioinformatics/btz859.Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15. https://doi.org/10.1186/s13059-014-0550-8.R Core Team. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing; 2018. https://www.R-project.org.Letunic I, Bork P. Interactive tree of life (iTOL) v3: an online tool for the display and annotation of phylogenetic and other trees. Nucleic Acids Res. 2016;44:W242–5. https://doi.org/10.1093/nar/gkw290.CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Yu G, Smith DK, Zhu H, Guan Y, Lam TT-Y. ggtree: an r package for visualization and annotation of phylogenetic trees with their covariates and other associated data. Methods Ecol Evol. 2017;8:28–36. https://doi.org/10.1111/2041-210X.12628.Article 

Google Scholar 
Brenner AJ, Harris ED. A quantitative test for copper using bicinchoninic acid. Anal Biochem. 1995;226:80–4. https://doi.org/10.1006/abio.1995.1194.CAS 
Article 
PubMed 

Google Scholar 
Brown ME, Barros T, Chang MCY. Identification and characterization of a multifunctional dye peroxidase from a lignin-reactive bacterium. ACS Chem Biol. 2012;7:2074–81. https://doi.org/10.1021/cb300383y.CAS 
Article 
PubMed 

Google Scholar 
Levy-Booth DJ, Hashimi A, Roccor R, Liu L-Y, Renneckar S, Eltis LD, et al. Genomics and metatranscriptomics of biogeochemical cycling and degradation of lignin-derived aromatic compounds in thermal swamp sediment. ISME J. 2021;15:879–93. https://doi.org/10.1038/s41396-020-00820-x.CAS 
Article 
PubMed 

Google Scholar 
Aston JE, Apel WA, Lee BD, Thompson DN, Lacey JA, Newby DT, et al. Degradation of phenolic compounds by the lignocellulose deconstructing thermoacidophilic bacterium Alicyclobacillus Acidocaldarius. J Ind Microbiol Biotechnol. 2016;43:13–23. https://doi.org/10.1007/s10295-015-1700-z.CAS 
Article 
PubMed 

Google Scholar 
Morgan-Lang C, McLaughlin R, Armstrong Z, Zhang G, Chan K, Hallam SJ. TreeSAPP: the tree-based sensitive and accurate phylogenetic profiler. Bioinformatics. 2020. https://doi.org/10.1093/bioinformatics/btaa588.Machczynski MC, Vijgenboom E, Samyn B, Canters GW. Characterization of SLAC: a small laccase from streptomyces coelicolor with unprecedented activity. Protein Sci Publ Protein Soc. 2004;13:2388–97. https://doi.org/10.1110/ps.04759104.CAS 
Article 

Google Scholar 
Berini F, Verce M, Ausec L, Rosini E, Tonin F, Pollegioni L, et al. Isolation and characterization of a heterologously expressed bacterial laccase from the anaerobe Geobacter metallireducens. Appl Microbiol Biotechnol. 2018;102:2425–39. https://doi.org/10.1007/s00253-018-8785-z.CAS 
Article 
PubMed 

Google Scholar 
Yin Q, Zhou G, Peng C, Zhang Y, Kües U, Liu J, et al. The first fungal laccase with an alkaline pH optimum obtained by directed evolution and its application in indigo dye decolorization. AMB Express. 2019;9:151. https://doi.org/10.1186/s13568-019-0878-2.CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Kumar D, Kumar A, Sondhi S, Sharma P, Gupta N. An alkaline bacterial laccase for polymerization of natural precursors for hair dye synthesis. 3 Biotech. 2018;8:182. https://doi.org/10.1007/s13205-018-1181-7.Article 
PubMed 
PubMed Central 

Google Scholar 
Hilgers R, Vincken J-P, Gruppen H, Kabel MA. Laccase/mediator systems: their reactivity toward phenolic lignin structures. ACS Sustain Chem Eng. 2018;6:2037–46. https://doi.org/10.1021/acssuschemeng.7b03451.CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Wu S, Argyropoulos D. An improved method for isolating lignin in high yield and purity. J Pulp Pap Sci. 2003;29:235–40.CAS 

Google Scholar 
Gao R, Li Y, Kim H, Mobley JK, Ralph J. Selective oxidation of lignin model compounds. ChemSusChem. 2018;11:2045–50. https://doi.org/10.1002/cssc.201800598.CAS 
Article 
PubMed 

Google Scholar 
Rahimi A, Azarpira A, Kim H, Ralph J, Stahl SS. Chemoselective metal-free aerobic alcohol oxidation in lignin. J Am Chem Soc. 2013;135:6415–8. https://doi.org/10.1021/ja401793n.CAS 
Article 
PubMed 

Google Scholar 
Schutyser W, Renders T, Bosch SV, den, Koelewijn S-F, Beckham GT, Sels BF. Chemicals from lignin: an interplay of lignocellulose fractionation, depolymerisation, and upgrading. Chem Soc Rev. 2018;47:852–908. https://doi.org/10.1039/C7CS00566K.CAS 
Article 
PubMed 

Google Scholar 
Sun X, Bai R, Zhang Y, Wang Q, Fan X, Yuan J, et al. Laccase-catalyzed oxidative polymerization of phenolic compounds. Appl Biochem Biotechnol. 2013;171:1673–80. https://doi.org/10.1007/s12010-013-0463-0.CAS 
Article 
PubMed 

Google Scholar 
Hu D, Zang Y, Mao Y, Gao B. Identification of molecular markers that are specific to the class Thermoleophilia. Front Microbiol. 2019;10. https://doi.org/10.3389/fmicb.2019.01185.Chen M-Y, Wu S-H, Lin G-H, Lu C-P, Lin Y-T, Chang W-C, et al. Rubrobacter taiwanensis sp. nov., a novel thermophilic, radiation-resistant species isolated from hot springs. Int J Syst Evol Microbiol. 2004;54:1849–55. https://doi.org/10.1099/ijs.0.63109-0.CAS 
Article 
PubMed 

Google Scholar 
Tomariguchi N, Miyazaki K. Complete genome sequence of Rubrobacter xylanophilus strain AA3-22, isolated from Arima Onsen in Japan. Microbiol Resour Announc. 2019;8. https://doi.org/10.1128/MRA.00818-19.Ceballos SJ, Yu C, Claypool JT, Singer SW, Simmons BA, Thelen MP, et al. Development and characterization of a thermophilic, lignin degrading microbiota. Process Biochem. 2017;63:193–203. https://doi.org/10.1016/j.procbio.2017.08.018.CAS 
Article 

Google Scholar 
Clark Mason J, Richards M, Zimmermann W, Broda P. Identification of extracellular proteins from actinomycetes responsible for the solubilisation of lignocellulose. Appl Microbiol Biotechnol. 1988;28:276–80. https://doi.org/10.1007/BF00250455.Article 

Google Scholar 
Yin Y-R, Sang P, Xian W-D, Li X, Jiao J-Y, Liu L, et al. Expression and characteristics of two glucose-tolerant GH1 β-glucosidases from Actinomadura amylolytica YIM 77502T for promoting cellulose degradation. Front Microbiol. 2018;9. https://doi.org/10.3389/fmicb.2018.03149.Zimmermann W, Broda P. Utilization of lignocellulose from barley straw by actinomycetes. Appl Microbiol Biotechnol. 1989;30:103–9. https://doi.org/10.1007/BF00256005.CAS 
Article 

Google Scholar 
Abe T, Masai E, Miyauchi K, Katayama Y, Fukuda M. A tetrahydrofolate-dependent O-demethylase, LigM, is crucial for catabolism of vanillate and syringate in Sphingomonas paucimobilis SYK-6. J Bacteriol. 2005;187:2030–7. https://doi.org/10.1128/JB.187.6.2030-2037.2005.CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Varman AM, He L, Follenfant R, Wu W, Wemmer S, Wrobel SA, et al. Decoding how a soil bacterium extracts building blocks and metabolic energy from ligninolysis provides road map for lignin valorization. Proc Natl Acad Sci USA. 2016;113:E5802–11. https://doi.org/10.1073/pnas.1606043113.CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Studenik S, Vogel M, Diekert G. Characterization of an O-demethylase of Desulfitobacterium hafniense DCB-2. J Bacteriol. 2012;194:3317–26. https://doi.org/10.1128/JB.00146-12.CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Fahrbach M, Kuever J, Remesch M, Huber BE, Kämpfer P, Dott W, et al. Steroidobacter denitrificans gen. nov., sp. nov., a steroidal hormone-degrading gammaproteobacterium. Int J Syst Evol Microbiol. 2008;58:2215–23. https://doi.org/10.1099/ijs.0.65342-0.CAS 
Article 
PubMed 

Google Scholar 
Nogi Y, Yoshizumi M, Hamana K, Miyazaki M, Horikoshi K. Povalibacter uvarum gen. nov., sp. nov., a polyvinyl-alcohol-degrading bacterium isolated from grapes. Int J Syst Evol Microbiol. 2014;64:2712–7. https://doi.org/10.1099/ijs.0.062620-0.CAS 
Article 
PubMed 

Google Scholar 
Sharma V, Siedenburg G, Birke J, Mobeen F, Jendrossek D, Prakash T. Metabolic and taxonomic insights into the Gram-negative natural rubber degrading bacterium Steroidobacter cummioxidans sp. nov., strain 35Y. PLoS ONE. 2018;13:e0197448. https://doi.org/10.1371/journal.pone.0197448.CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Reiss R, Ihssen J, Richter M, Eichhorn E, Schilling B, Thöny-Meyer L. Laccase versus laccase-like multi-copper oxidase: a comparative study of similar enzymes with diverse substrate spectra. PLoS ONE. 2013;8:e65633. https://doi.org/10.1371/journal.pone.0065633.CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Christopher LP, Yao B, Ji Y. Lignin biodegradation with laccase-mediator systems. Front Energy Res. 2014;2. https://doi.org/10.3389/fenrg.2014.00012.Mate DM, Alcalde M. Laccase: a multi‐purpose biocatalyst at the forefront of biotechnology. Micro Biotechnol. 2016;10:1457–67. https://doi.org/10.1111/1751-7915.12422.CAS 
Article 

Google Scholar 
Sirim D, Wagner F, Wang L, Schmid RD, Pleiss J. The Laccase Engineering Database: a classification and analysis system for laccases and related multicopper oxidases. Database J Biol Databases Curation. 2011;2011. https://doi.org/10.1093/database/bar006.Fang Z, Li T, Wang Q, Zhang X, Peng H, Fang W, et al. A bacterial laccase from marine microbial metagenome exhibiting chloride tolerance and dye decolorization ability. Appl Microbiol Biotechnol. 2011;89:1103–10. https://doi.org/10.1007/s00253-010-2934-3.CAS 
Article 
PubMed 

Google Scholar 
Komori H, Miyazaki K, Higuchi Y. X-ray structure of a two-domain type laccase: a missing link in the evolution of multi-copper proteins. FEBS Lett. 2009;583:1189–95. https://doi.org/10.1016/j.febslet.2009.03.008.CAS 
Article 
PubMed 

Google Scholar 
Sherif M, Waung D, Korbeci B, Mavisakalyan V, Flick R, Brown G, et al. Biochemical studies of the multicopper oxidase (small laccase) from Streptomyces coelicolor using bioactive phytochemicals and site-directed mutagenesis. Microb Biotechnol. 2013;6:588–97. https://doi.org/10.1111/1751-7915.12068CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Gunne M, Urlacher VB. Characterization of the alkaline laccase Ssl1 from Streptomyces sviceus with unusual properties discovered by genome mining. PLOS ONE. 2012;7:e52360 https://doi.org/10.1371/journal.pone.0052360CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Dubé E, Shareck F, Hurtubise Y, Beauregard M, Daneault C. Decolourization of recalcitrant dyes with a laccase from Streptomyces coelicolor under alkaline conditions. J Ind Microbiol Biotechnol. 2008;35:1123–9. https://doi.org/10.1007/s10295-008-0391-0CAS 
Article 
PubMed 

Google Scholar 
Koschorreck K, Richter SM, Ene AB, Roduner E, Schmid RD, Urlacher VB. Cloning and characterization of a new laccase from Bacillus licheniformis catalyzing dimerization of phenolic acids. Appl Microbiol Biotechnol. 2008;79:217–24. https://doi.org/10.1007/s00253-008-1417-2CAS 
Article 
PubMed 

Google Scholar 
Mohammadian M, Fathi-Roudsari M, Mollania N, Badoei-Dalfard A, Khajeh K. Enhanced expression of a recombinant bacterial laccase at low temperature and microaerobic conditions: purification and biochemical characterization. J Ind Microbiol Biotechnol. 2010;37:863–9. https://doi.org/10.1007/s10295-010-0734-5CAS 
Article 
PubMed 

Google Scholar 
Ausec L, Berini F, Casciello C, Cretoiu MS, van Elsas JD, Marinelli F, et al. The first acidobacterial laccase-like multicopper oxidase revealed by metagenomics shows high salt and thermo-tolerance. Appl Microbiol Biotechnol. 2017;101:6261–76. https://doi.org/10.1007/s00253-017-8345-yCAS 
Article 
PubMed 

Google Scholar 
Ausec L, Črnigoj M, Šnajder M, Ulrih NP, Mandic-Mulec I. Characterization of a novel high-pH-tolerant laccase-like multicopper oxidase and its sequence diversity in Thioalkalivibrio sp. Appl Microbiol Biotechnol. 2015;99:9987–99. https://doi.org/10.1007/s00253-015-6843-3CAS 
Article 
PubMed 

Google Scholar  More