Philippa Kaur

Finkelman, S., Navarro, S., Rindner, M. & Dias, R. Effect of low pressure on the survival of Trogoderma granarium Everts, Lasioderma serricorne (F.) and Oryzaephilus surinamensis (L.) at 30°C. J. Stored. Prod. Res. 42, 23–30 (2006).Article 

Google Scholar 
Hosseininaveh, V. A., Bandani, A. P., Azmayeshfard, P. S., Hosseinkhani, S. & Kazzazi, M. Digestive proteolytic and amylolytic activities in Trogoderma granarium Everts (Dermestidae: Coleoptera). J. Stored. Prod. Res. 43, 515–522 (2007).Article 
CAS 

Google Scholar 
Burges, H. D. Development of the khapra beetle, Trogoderma granarium, in the lower part of its temperature range. J. Stored. Prod. Res. 44, 32–35 (2008).Article 

Google Scholar 
Hagstrum D. W & Subramanyam, B. Stored-Product Insect Resource (AACC International, 2009).Beal, R. S. Synopsis of the economic species of Trogoderma occurring in the United States with description of a new species (Coleoptera: Dermestidae). Ann. Entomol. Soc. Am. 49, 559–566 (1956).Article 

Google Scholar 
Kerr, J. A. Khapra beetle returns. Pest Control 49(12), 24–25 (1984).
Google Scholar 
Sinha, R. N. & Utida, S. Climatic areas potentially vulnerable to stored product insects in Japan. Appl. Entomol. Zool. 2, 124–132 (1967).Article 

Google Scholar 
Banks, H. J. Distribution and establishment of Trogoderma granarium Everts (Coleoptera: Dermestidae): Climatic and other influences. J. Stored. Prod. Res. 13, 183–202 (1977).Article 

Google Scholar 
Kavallieratos, N. G., Athanassiou, C. G., Guedes, R. N. C., Drempela, J. D. & Boukouvala, M. C. Invader competition with local competitors: Displacement or coexistence among the invasive khapra beetle, Trogoderma granarium Everts (Coleoptera: Dermestidae), and two other major stored-grain beetles?. Front. Plant. Sci. 8, 1837 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Lampiri, E., Baliota, G. V., Morrison, W. M., Domingue, M. J. & Athanassiou, C. Comparative population growth of the khapra beetle (Coleoptera: Dermestidae) and the warehouse beetle (Coleoptera: Dermestidae) on wheat and rice. J. Econ. Entomol. 115, 344–352 (2021).Article 

Google Scholar 
Athanassiou, C. G., Phillips, T. W. & Wakil, W. Biology and control of the khapra beetle, Trogoderma granarium, a major quarantine threat to global food security. Ann. Rev. Entomol. 64, 131–148 (2019).Article 
CAS 

Google Scholar 
Stibick, J. New pest response guidelines: khapra beetle. APHIS– PPQ–Emergency and Domestic Programs. (U.S Department of Agriculture, 2009).Myers, S. W. & Hagstrum, D. W. Quarantine, In Stored stored product protection, (ed. Hagstrum D.W. Phillips T.W. & Cuperus G.) 297–304 (Kansas State University, 2012).Day, C. & White, B. Khapra beetle, Trogoderma granarium interceptions and eradications in Australia and around the world. In SARE working papers 1609. (Crawley: School of Agricul. Res. Econ. 2016).Burges, H. D. Diapause, pest status and control of the Khapra beetle. Trogoderma Granar. Everts Ann. Appl. Biol. 50, 614–617 (1962).Article 

Google Scholar 
Nair, K. & Desai, A. The termination of diapause in Trogoderma granarium Everts (Coleoptera, Dermestidae). J. Stored. Prod. Res. 8, 275–290 (1973).Article 

Google Scholar 
Burges, H. D. Studies on the Dermestid beetle Trogoderma granarium Everts—IV. Feeding, growth, and respiration with particular reference to diapause larvae. J. Insect. Physiol. 5, 317–334 (1960).Article 
CAS 

Google Scholar 
Wilches, D., Laird, R. A., Floate, K. & Fields, P. G. A review of diapause and tolerance to extreme temperatures in dermestids (Coleoptera). J. Stored Prod. Res. 68, 50–62 (2016).Article 

Google Scholar 
Vick, K. W., Drummond, P. C. & Coffelt, J. A. Trogoderma inclusum and T. glabrum: Effects of time of day on production of female pheromone, male responsiveness and mating. Ann. Entomol. Soc. Am. 66, 1001–1004 (1973).Article 

Google Scholar 
Partida, G. J. & Strong, R. G. Distribution and relative abundance of Trogoderma spp. in relation to climate zones of California. J. Econ. Entomol. 63, 1553–1560 (1970).Article 

Google Scholar 
Hagstrum, D. W. Seasonal variation of stored wheat environment and insect populations. J. Econ. Entomol. 16, 77–83 (1987).
Google Scholar 
Mullen, M. A. & Arbogast, R. T. Insect succession in a stored-corn ecosystem in southeast Georgia. J. Econ. Entomol. 81, 899–912 (1988).
Google Scholar 
Partida, G. J. & Strong, R. G. Comparative studies on the biologies of six species of Trogoderma: T. inclusum. Ann. Entomol. Soc. Am. 68, 91–103 (1975).Article 

Google Scholar 
Beal, R. S. Biology and taxonomy of the nearctic species of Trogoderma. Univ. Calif. Misc. Publ. Entomol. 10, 35–102 (1954).
Google Scholar 
Castañé, C., Agustí, N., del Estal, P. & Riudavets, J. Survey of Trogoderma spp in Spanish mills and warehouses. J. Stored. Prod. Res. 88, 1061 (2020).Article 

Google Scholar 
Levinson, H. Z. & Mori, K. The pheromone activity of chiral isomers of trogodermal for male khapra beetles. Naturwissenschaften 67, 148–149 (1980).Article 
CAS 

Google Scholar 
Silverstein, R. M. et al. Perception by Trogoderma species of chirality and methyl branching at a site far removed from a functional group in a pheromone component. J. Chem. Ecol. 6, 911–917 (1980).Article 
CAS 

Google Scholar 
Vick, K. W. Effects of interspecific matings of Trogoderma glabrum and T. inclusum on oviposition and re-mating. Ann. Entomol. Soc. Am. 66, 237–239 (1973).Article 
MathSciNet 

Google Scholar 
Drijfhout, S. et al. Catalogue of abrupt shifts in intergovernmental panel on climate change climate models. Proc. Natl. Acad. Sci. USA 112, E5777–E5786 (2015).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Phillips, T. W., Pfannenstiel, L. & Hagstrum, D. Survey of Trogoderma species (Coleoptera: Dermestidae) associated with international trade of dried distiller’s grains and solubles in the USA. Julius-Kühn-Archiv 1, 233–238 (2018).
Google Scholar 
Hadaway, A. The biology of the beetles, Trogoderma granarium Everts and Trogoderma versicolor (Creutz). Bull. Entomol. Res. 46, 781–796 (1956).Article 
CAS 

Google Scholar 
Gorham, J. R. Insect and Mite Pests in Food: An Illustrated Key. Vols. 1 and 2, (U.S Department of Agriculture, 1991).Furui, S., Miyanoshita, A., Imamura, T., Minegishi, Y. & Kokutani, R. Qualitative real-time PCR identification of the khapra beetle, Trogoderma granarium (Coleoptera: Dermestidae). Appl. Entomol. Zool. 54, 101–107 (2019).Article 
CAS 

Google Scholar 
Olson, R. L., Farris, R. E., Barr, N. B. & Cognato, A. I. Molecular identification of Trogoderma granarium (Coleoptera: Dermestidae) using the 16s gene. J Pest Sci 87, 701–710 (2014).Article 

Google Scholar 
Wu, Y. et al. Development of an array of molecular tools for the identification of khapra beetle (Trogoderma granarium), a destructive beetle of stored food products. Sci. Rep. 13, 3327 (2023).Article 
PubMed 
PubMed Central 

Google Scholar 
Lampiri, E., Athanassiou, C. & Arthur, F. H. Population growth and development of the khapra beetle (Coleoptera: Dermestidae), on different sorghum fractions. J. Econ. Entomol. 114, 424–429 (2021).Article 
CAS 
PubMed 

Google Scholar 
Athanassiou, C. G., Kavallieratos, N. G. & Boukouvala, M. C. Population growth of the khapra beetle, Trogoderma granarium Everts (Coleoptera: Dermestidae) on different commodities. J. Stored. Prod. Res. 69, 72–77 (2016).Article 

Google Scholar 
Karnavar, G. K. Mating behaviour and fecundity in Trogoderma granarium (Coleoptera: Dermestidae). J. Stored. Prod. Res. 8, 65–69 (1972).Article 

Google Scholar 
Pray, L. A. & Goodnight, C. J. Genetic variation in inbreeding depression in the red flour beetle Tribolium castaneum. Evolution 49, 176–188 (1995).Article 
PubMed 

Google Scholar 
Barzin, S., Naseri, B., Fathi, S. A. A., Razmjou, J. & Aeinehchi, P. Feeding efficiency and digestive physiology of Trogoderma granarium Everts (Coleoptera: Dermestidae) on different rice cultivars. J. Stored. Prod. Res. 84, 101511 (2019).Article 

Google Scholar 
Naseri, B., Aeinehchi, P. & Ashjerdi, A. R. Nutritional responses and digestive enzymatic profile of Trogoderma granarium Everts (Coleoptera: Dermestidae) on 10 commercial rice cultivars. J. Stored. Prod. Res. 87, 101591 (2020).Article 

Google Scholar 
Sarwar, M. & Sattar, M. Varietals assessment of different wheat varieties for their resistance response to Khapra beetle Trogoderma granarium. Pak. J. Seed. Technol. 1(10), 1–7 (2007).
Google Scholar 
Wilches, D., Laird, R., Floate, K. & Fields, P. Effects of acclimation and diapause on the cold tolerance of Trogoderma granarium. Entomol. Exp. Appl. 165, 169–178 (2017).Article 
CAS 

Google Scholar 
Paini, D. R. & Yemshanov, D. Modelling the arrival of invasive organisms via the international marine shipping network: a Khapra beetle study. PLoS ONE 7(9), e44589 (2012).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Morrison, W. R., Grosdidier, R. F., Arthur, F. H., Myers, S. W. & Domingue, M. J. Attraction, arrestment, and preference by immature Trogoderma variabile and Trogoderma granarium to food and pheromonal stimuli. J. Pest Sci. 93, 135–147 (2020).Article 

Google Scholar 
Arthur, F. H. & Morrison, W. M. Methodology for assessing progeny production and grain damage on commodities treated with insecticides. Agronomy 10(6), 804 (2020).Article 
CAS 

Google Scholar  More

Teale, S. A. & Castello, J. D. The past as key to the future: a new perspective on forest health. In Forest Health: An Integrated Perspective (eds Castello, J. D. & Teale, S. A.) 3–16 (Cambridge University Press, 2011). https://doi.org/10.1017/CBO9780511974977.002.Chapter 

Google Scholar 
Jactel, H., Koricheva, J. & Castagneyrol, B. Responses of forest insect pests to climate change: Not so simple. Curr. Opin. Insect Sci. 35, 103–108 (2019).Article 
PubMed 

Google Scholar 
Trumbore, S., Brando, P. & Hartmann, H. Forest health and global change. Science 349, 814–818 (2015).Article 
ADS 
CAS 
PubMed 

Google Scholar 
North, M. P. et al. Operational resilience in western US frequent-fire forests. For. Ecol. Manag. 507, 120004 (2022).Article 

Google Scholar 
Raffa, K. F. et al. A literal use of “forest health” safeguards against misuse and misapplication. J. For. 107, 276–277 (2009).
Google Scholar 
Kolb, T. E., Wagner, M. R. & Covington, W. W. Concepts of forest health: Utilitarian and ecosystem perspectives. J. For. 92, 10–15 (1994).
Google Scholar 
Cale, J. A. et al. A quantitative index of forest structural sustainability. Forests 5, 1618–1634 (2014).Article 

Google Scholar 
Lintz, H. E. et al. Quantifying density-independent mortality of temperate tree species. Ecol. Indic. 66, 1–9 (2016).Article 

Google Scholar 
Stanke, H., Finley, A. O., Domke, G. M., Weed, A. S. & MacFarlane, D. W. Over half of western United States’ most abundant tree species in decline. Nat. Commun. 12, 451 (2021).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Bettinger, P., Boston, K., Siry, J. P. & Grebner, D. L. Chapter 2—Valuing and Characterizing Forest Conditions. In Forest Management and Planning (eds Bettinger, P. et al.) 21–63 (Academic Press, 2017). https://doi.org/10.1016/B978-0-12-809476-1.00002-3.Chapter 

Google Scholar 
Crowther, T. W. et al. Mapping tree density at a global scale. Nature 525, 201–205 (2015).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Fettig, C. J. et al. The effectiveness of vegetation management practices for prevention and control of bark beetle infestations in coniferous forests of the western and southern United States. For. Ecol. Manag. 238, 24–53 (2007).Article 

Google Scholar 
Morin, R. S. & Liebhold, A. M. Invasions by two non-native insects alter regional forest species composition and successional trajectories. For. Ecol. Manag. 341, 67–74 (2015).Article 

Google Scholar 
Nowak, J. T., Meeker, J. R., Coyle, D. R., Steiner, C. A. & Brownie, C. Southern pine beetle infestations in relation to forest stand conditions, previous thinning, and prescribed burning: Evaluation of the southern pine beetle prevention program. J. For. 113, 454–462 (2015).
Google Scholar 
Asaro, C. & Chamberlin, L. A. Outbreak history (1953–2014) of spring defoliators impacting oak-dominated forests in Virginia, with emphasis on gypsy moth (Lymantria dispar L.) and fall cankerworm (Alsophila pometaria Harris). Am. Entomol. 61, 174–185 (2015).Article 

Google Scholar 
Negrón, J. F. Probability of infestation and extent of mortality associated with the Douglas-fir beetle in the Colorado Front Range. For. Ecol. Manag. 107, 71–85 (1998).Article 

Google Scholar 
Negrón, J. F. & Popp, J. B. Probability of ponderosa pine infestation by mountain pine beetle in the Colorado Front Range. For. Ecol. Manag. 191, 17–27 (2004).Article 

Google Scholar 
Schmid, J. M. & Frye, R. H. Spruce Beetle in the Rockies. Gen. Tech. Rep. RM-49 (US Department of Agriculture, Forest Service, Rocky Mountain Forest and Range Experiment Station, 1977).
Google Scholar 
Krivak-Tetley, F. E. et al. Aggressive tree killer or natural thinning agent? Assessing the impacts of a globally important forest insect. For. Ecol. Manag. 483, 118728 (2021).Article 

Google Scholar 
Bradford, J. B. et al. Tree mortality response to drought-density interactions suggests opportunities to enhance drought resistance. J. Appl. Ecol. 59, 549–559 (2022).Article 

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

Google Scholar 
Furniss, T. J., Das, A. J., van Mantgem, P. J., Stephenson, N. L. & Lutz, J. A. Crowding, climate, and the case for social distancing among trees. Ecol. Appl. 32, e2507 (2022).Article 
PubMed 

Google Scholar 
Woodall, C. W. & Weiskittel, A. R. Relative density of United States forests has shifted to higher levels over last two decades with important implications for future dynamics. Sci. Rep. 11, 1–12 (2021).Article 

Google Scholar 
Gandhi, K. J. K., Campbell, F. & Abrams, J. Current status of forest health policy in the United States. Insects 10, 1–14 (2019).Article 

Google Scholar 
Ciesla, W. M. The role of human activities on forest insect outbreaks worldwide. Int. For. Rev. 17, 269–281 (2015).
Google Scholar 
Jactel, H. & Brockerhoff, E. G. Tree diversity reduces herbivory by forest insects. Ecol. Lett. 10, 835–848 (2007).Article 
PubMed 

Google Scholar 
Marini, L., Ayres, M. P. & Jactel, H. Impact of stand and landscape management on forest pest damage. Annu. Rev. Entomol. 67, 181–199 (2022).Article 
PubMed 

Google Scholar 
Guyot, V., Castagneyrol, B., Vialatte, A., Deconchat, M. & Jactel, H. Tree diversity reduces pest damage in mature forests across Europe. Biol. Lett. 12, 20151037 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Kneeshaw, D. D. et al. The vision of managing for pest-resistant landscapes: Realistic or utopic? Curr. For. Rep. 7, 97–113 (2021).PubMed 
PubMed Central 

Google Scholar 
Chisholm, P. J., Stevens-Rumann, C. S. & Davis, T. S. Interactions between climate and stand conditions predict pine mortality during a bark beetle outbreak. Forests 12, 360 (2021).Article 

Google Scholar 
Ferrell, G. T., Otrosina, W. J. & Demars, C. J. Predicting susceptibility of white fir during a drought-associated outbreak of the fir engraver, Scolytus ventralis in California. Can. J. For. Res. 24, 302–305 (1994).Article 

Google Scholar 
Asaro, C., Nowak, J. T. & Elledge, A. Why have southern pine beetle outbreaks declined in the southeastern U.S. with the expansion of intensive pine silviculture? A brief review of hypotheses. For. Ecol. Manag. 391, 338–348 (2017).Article 

Google Scholar 
Nowak, J. T., Klepzig, K. D., Coyle, D. R., Carothers, W. A. & Gandhi, K. J. K. Southern pine beetles in central hardwood forests: Frequency, spatial extent, and changes to forest structure. In Managing Forest Ecosystems Volume 32: Natural Disturbances and Historic Range of Variation (eds Greenberg, C. H. & Collins, B. S.) 73–88 (Springer International Publishing, 2016). https://doi.org/10.1007/978-3-319-21527-3_4.Chapter 

Google Scholar 
Crocker, S. J., Liknes, G. C., McKee, F. R., Albers, J. S. & Aukema, B. H. Stand-level factors associated with resurging mortality from eastern larch beetle (Dendroctonus simplex LeConte). For. Ecol. Manag. 375, 27–34 (2016).Article 

Google Scholar 
Mattson, W. J. & Addy, N. D. Phytophagous insects as regulators of forest primary production. Science 190, 515–522 (1975).Article 
ADS 

Google Scholar 
Thom, D. & Seidl, R. Natural disturbance impacts on ecosystem services and biodiversity in temperate and boreal forests. Biol. Rev. 91, 760–781 (2016).Article 
PubMed 

Google Scholar 
Grégoire, J. C., Raffa, K. F. & Lindgren, B. S. Economics and politics of bark beetles. In Bark Beetles: Biology and Ecology of Native and Invasive Species (eds Vega, F. E. & Hofstetter, R. W.) 585–613 (Academic Press, 2015). https://doi.org/10.1016/B978-0-12-417156-5.00015-0.Chapter 

Google Scholar 
Kolb, T. E. et al. Observed and anticipated impacts of drought on forest insects and diseases in the United States. For. Ecol. Manag. 380, 321–334 (2016).Article 

Google Scholar 
Fettig, C. J. et al. Changing climates, changing forests: A western North American perspective. J. For. 111, 214–228 (2013).
Google Scholar 
Liebhold, A. M. et al. A highly aggregated geographical distribution of forest pest invasions in the USA. Divers. Distrib. 19, 1208–1216 (2013).Article 

Google Scholar 
Siegert, N. W., Mccullough, D. G., Liebhold, A. M. & Telewski, F. W. Dendrochronological reconstruction of the epicentre and early spread of emerald ash borer in North America. Divers. Distrib. 20, 847–858 (2014).Article 

Google Scholar 
Smith, A., Herms, D. A., Long, R. P. & Gandhi, K. J. K. Community composition and structure had no effect on forest susceptibility to invasion by the emerald ash borer (Coleoptera: Buprestidae). Can. Entomol. 147, 318–328 (2015).Article 

Google Scholar 
Aukema, J. E. et al. Historical accumulation of nonindigenous forest pests in the continental United States. Bioscience 60, 886–897 (2010).Article 

Google Scholar 
Hicke, J. A. et al. Effects of biotic disturbances on forest carbon cycling in the United States and Canada. Glob. Chang. Biol. 18, 7–34 (2012).Article 
ADS 

Google Scholar 
Feeny, P. Seasonal changes in oak leaf tannins and nutrients as a cause of spring feeding by winter moth caterpillars. Ecology 51, 565–581 (1970).Article 

Google Scholar 
Schowalter, T. D., Hargrove, W. W. & Crossley, D. A. Herbivory in forested ecosystems. Annu. Rev. Entomol. 31, 177–196 (1986).Article 

Google Scholar 
Seidl, R. et al. Forest disturbances under climate change. Nat. Clim. Chang. 7, 395–402 (2017).Article 
ADS 
PubMed 
PubMed Central 

Google Scholar 
Colautti, R. I., Ricciardi, A., Grigorovich, I. A. & MacIsaac, H. J. Is invasion success explained by the enemy release hypothesis? Ecol. Lett. 7, 721–733 (2004).Article 

Google Scholar 
Catford, J. A., Jansson, R. & Nilsson, C. Reducing redundancy in invasion ecology by integrating hypotheses into a single theoretical framework. Divers. Distrib. 15, 22–40 (2009).Article 

Google Scholar 
Guyot, V. et al. Tree diversity limits the impact of an invasive forest pest. PLoS One 10, 1–16 (2015).Article 

Google Scholar 
Root, R. B. Organization of a plant-arthropod association in simple and diverse habitats: The fauna of collards (Brassica oleracea). Ecol. Monogr. 43, 95–124 (1973).Article 

Google Scholar 
Acker, S. A., Boetsch, J. R., Fallon, B. & Denn, M. Stable background tree mortality in mature and old-growth forests in western Washington (NW USA). For. Ecol. Manag. 532, 120817 (2023).Article 

Google Scholar 
Shive, K. L. et al. Ancient trees and modern wildfires: Declining resilience to wildfire in the highly fire-adapted giant sequoia. For. Ecol. Manag. 511, 120110 (2022).Article 

Google Scholar 
Searle, E. B., Chen, H. Y. H. & Paquette, A. Higher tree diversity is linked to higher tree mortality. Proc. Natl. Acad. Sci. U.S.A. 119, 1–7 (2022).Article 

Google Scholar 
Hart, S. J., Veblen, T. T., Eisenhart, K. S., Jarvis, D. & Kulakowski, D. Drought induces spruce beetle (Dendroctonus rufipennis) outbreaks across northwestern Colorado. Ecology 95, 930–939 (2014).Article 
PubMed 

Google Scholar 
Hart, S. J., Veblen, T. T. & Kulakowski, D. Do tree and stand-level attributes determine susceptibility of spruce-fir forests to spruce beetle outbreaks in the early 21st century? For. Ecol. Manag. 318, 44–53 (2014).Article 

Google Scholar 
Temperli, C. et al. Are density reduction treatments effective at managing for resistance or resilience to spruce beetle disturbance in the southern Rocky Mountains? For. Ecol. Manag. 334, 53–63 (2014).Article 

Google Scholar 
Six, D. L., Biber, E. & Long, E. Management for mountain pine beetle outbreak suppression: Does relevant science support current policy? Forests 5, 103–133 (2014).Article 

Google Scholar 
Black, S. H., Kulakowski, D., Noon, B. R. & Dellasala, D. A. Do bark beetle outbreaks increase wildfire risks in the central U.S. rocky mountains? Implications from recent research. Nat. Areas J. 33, 59–65 (2013).Article 

Google Scholar 
Oswalt, S. N., Smith, W. B., Miles, P. D. & Pugh, S. A. Forest Resources of the United States, 2017: A Technical Document Supporting the Forest Service 2020 RPA Assessment. Gen. Tech. Rep. WO-97 (US Department of Agriculture, Forest Service, 2019). https://doi.org/10.2737/WO-GTR-97.Book 

Google Scholar 
Cleland, D. et al. Terrestrial condition assessment for national forests of the USDA Forest Service in the continental US. Sustainability 9, 1–19 (2017).Article 

Google Scholar 
USDA Forest Service Forest Health Protection. Insect and Disease Detection Survey (IDS) data downloads. https://www.fs.usda.gov/foresthealth/applied-sciences/mapping-reporting/detection-surveys.shtml (2021). Accessed on 9 October 2021.Spruce, J. P. et al. Assessment of MODIS NDVI time series data products for detecting forest defoliation by gypsy moth outbreaks. Remote Sens. Environ. 115, 427–437 (2011).Article 
ADS 

Google Scholar 
Gomez, D. F., Ritger, H. M. W., Pearce, C., Eickwort, J. & Hulcr, J. Ability of remote sensing systems to detect bark beetle spots in the southeastern US. Forests 11, 1–10 (2020).Article 

Google Scholar 
Hanavan, R. P. et al. Supplementing the forest health national aerial survey program with remote sensing during the COVID-19 pandemic: Lessons learned from a collaborative approach. J. For. 120, 125–132 (2021).
Google Scholar 
Johnson, E. W. & Wittwer, D. Aerial detection surveys in the United States. Aust. For. 71, 212–215 (2008).Article 

Google Scholar 
Bright, B. C. et al. Using satellite imagery to evaluate bark beetle-caused tree mortality reported in aerial surveys in a mixed conifer forest in Northern Idaho, USA. Forests 11, 1–19 (2020).Article 

Google Scholar 
Coleman, T. W. et al. Accuracy of aerial detection surveys for mapping insect and disease disturbances in the United States. For. Ecol. Manag. 430, 321–336 (2018).Article 

Google Scholar 
Hicke, J. A., Xu, B., Meddens, A. J. H. & Egan, J. M. Characterizing recent bark beetle-caused tree mortality in the western United States from aerial surveys. For. Ecol. Manag. 475, 118402 (2020).Article 

Google Scholar 
Kosiba, A. M. et al. Spatiotemporal patterns of forest damage and disturbance in the northeastern United States: 2000–2016. For. Ecol. Manag. 430, 94–104 (2018).Article 

Google Scholar 
Meigs, G. W., Kennedy, R. E., Gray, A. N. & Gregory, M. J. Spatiotemporal dynamics of recent mountain pine beetle and western spruce budworm outbreaks across the Pacific Northwest Region USA. For. Ecol. Manag. 339, 71–86 (2015).Article 

Google Scholar 
Bechtold, W. A. & Patterson, P. L. The Enhanced Forest Inventory and Analysis Program—National Sampling Design and Estimation Procedures. Gen. Tech. Rep. SRS-80 (US Department of Agriculture, Forest Service, Southern Research Station, 2005). https://doi.org/10.2737/SRS-GTR-80.Book 

Google Scholar 
Randolph, K. D. C. et al. Past and present individual-tree damage assessments of the US national forest inventory. Environ. Monit. Assess. 193, 116 (2021).Article 
PubMed 

Google Scholar 
Kromroy, K. W., Juzwik, J., Castillo, P. & Hansen, M. H. Using forest service forest inventory and analysis data to estimate regional oak decline and oak mortality. North. J. Appl. For. 25, 17–24 (2008).Article 

Google Scholar 
Coulston, J. W., Edgar, C. B., Westfall, J. A. & Taylor, M. E. Estimation of forest disturbance from retrospective observations in a broad-scale inventory. Forests 11, 1298 (2020).Article 

Google Scholar 
Wilson, B. T., Lister, A. J. & Riemann, R. I. A nearest-neighbor imputation approach to mapping tree species over large areas using forest inventory plots and moderate resolution raster data. For. Ecol. Manag. 271, 182–198 (2012).Article 

Google Scholar 
Blackard, J. A. et al. Mapping U.S. forest biomass using nationwide forest inventory data and moderate resolution information. Remote Sens. Environ. 112, 1658–1677 (2008).Article 
ADS 

Google Scholar 
Brosofske, K. D., Froese, R. E., Falkowski, M. J. & Banskota, A. A review of methods for mapping and prediction of inventory attributes for operational forest management. For. Sci. 60, 733–756 (2014).Article 

Google Scholar 
Lister, A. J. et al. Use of remote sensing data to improve the efficiency of national forest inventories: A case study from the United States national forest inventory. Forests 11, 1–41 (2020).Article 

Google Scholar 
USDA Forest Service Forest Health Protection. Individual Tree Species Parameter (ITSP) maps – GIS data downloads. https://www.fs.usda.gov/foresthealth/applied-sciences/mapping-reporting/indiv-tree-parameter-maps.shtml (2021). Accessed on 9 October 2021.Ellenwood, J. R., Krist, F. J. & Romero, S. A. National Individual Tree Species Atlas. FHTET-15-01 (US Department of Agriculture, Forest Service, Forest Health Technology Enterprise Team, 2015).
Google Scholar 
Krist, F. J. et al. National Insect and Disease Forest Risk Assessment. FHTET-14-01 (US Department of Agriculture, Forest Service, Forest Health Technology Enterprise Team, 2014).
Google Scholar 
Rulequest Inc. Cubist, release 2.07. https://www.rulequest.com/cubist-info.html (2011). Accessed on 15 July 2022.R Core Team. R: A language and environment for statistical computing. https://www.r-project.org (2021). Accessed on 4 March 2022.Esri Inc. ArcGIS Pro 2.8.0. https://www.esri.com/en-us/arcgis/products/arcgis-pro/overview (2021). Accessed on 4 March 2022. More

Post, D. M. Using stable isotopes to estimate trophic position: Models, methods, and assumptions. Ecology 83, 703–718 (2002).Article 

Google Scholar 
Fry, B. Stable Isotope Ecology (Springer, 2007).
Google Scholar 
Boon, P. I. & Bunn, S. E. Variations in the stable isotope composition of aquatic plants and their implications for food web analysis. Aquat. Bot. 48, 99–108 (1994).Article 

Google Scholar 
Kling, G. W., Fry, B. & O’Brien, W. J. Stable isotopes and planktonic trophic structure in arctic lakes. Ecology 73, 561–566 (1992).Article 

Google Scholar 
Nielsen, J. M., Clare, E. L., Hayden, B., Brett, M. T. & Kratina, P. Diet tracing in ecology: Method comparison and selection. Methods Ecol. Evol. 9, 278–291 (2018).Article 

Google Scholar 
Coulter, A. A., Swanson, H. K. & Goforth, R. R. Seasonal variation in resource overlap of invasive and native fishes revealed by stable isotopes. Biol. Invasions 21, 315–321 (2019).Article 

Google Scholar 
Jung, A. S., Van Der Veer, H. W., Van Der Meer, M. T. & Philippart, C. J. Seasonal variation in the diet of estuarine bivalves. PLoS One 14, e0217003 (2019).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Devlin, S. P., Vander Zanden, M. J. & Vadeboncoeur, Y. Depth-specific variation in carbon isotopes demonstrates resource partitioning among the littoral zoobenthos. Freshw. Biol. 58, 2389–2400 (2013).CAS 

Google Scholar 
Possamai, B., Vieira, J. P., Grimm, A. M. & Garcia, A. M. Temporal variability (1997–2015) of trophic fish guilds and its relationships with El Niño events in a subtropical estuary. Estuar. Coast. Shelf Sci. 202, 145–154 (2018).Article 
ADS 

Google Scholar 
Syvaranta, J., Hamalainen, H. & Jones, R. I. Within-lake variability in carbon and nitrogen stable isotope signatures. Freshw. Biol. 51, 1090–1102 (2006).Article 
CAS 

Google Scholar 
Janbu, A. D., Paasche, Ø. & Talbot, M. R. Paleoclimate changes inferred from stable isotopes and magnetic properties of organic-rich lake sediments in Arctic Norway. J. Paleolimnol. 46, 29 (2011).Article 
ADS 

Google Scholar 
Leng, M. et al. Late quaternary palaeoenvironmental reconstruction from Lakes Ohrid and Prespa (Macedonia/Albania border) using stable isotopes. Biogeosciences 7, 3109–3122 (2010).Article 
ADS 
CAS 

Google Scholar 
Jiang, Q., Shen, J., Liu, X., Zhang, E. & Xiao, X. A high-resolution climatic change since holocene inferred from multi-proxy of lake sediment in westerly area of China. Chin. Sci. Bull. 52, 1970–1979 (2007).Article 

Google Scholar 
Finlay, J. C. & Kendall, C. Stable isotope tracing of temporal and spatial variability in organic matter sources to freshwater ecosystems. Stable Isot. Ecol. Environ. Sci. 2, 283–333 (2007).Article 

Google Scholar 
Harvey, C. J. & Kitchell, J. F. A stable isotope evaluation of the structure and spatial heterogeneity of a Lake Superior food web. Can. J. Fish. Aquat. Sci. 57, 1395–1403 (2000).Article 
CAS 

Google Scholar 
Xu, D. et al. Spatial heterogeneity of food web structure in a large shallow eutrophic lake (Lake Taihu, China): Implications for eutrophication process and management. J. Freshw. Ecol. 34, 229–245 (2019).Article 
CAS 

Google Scholar 
Ruokonen, T., Kiljunen, M., Karjalainen, J. & Hämäläinen, H. Invasive crayfish increase habitat connectivity: A case study in a large boreal lake. Knowl. Manag. Aquat. Ecosyst. https://doi.org/10.1051/kmae/2013034 (2012).Article 

Google Scholar 
Veselý, L. et al. The crayfish distribution, feeding plasticity, seasonal isotopic variation and trophic role across ontogeny and habitat in a canyon-shaped reservoir. Aquat. Ecol. 54, 1169–1183 (2020).Article 

Google Scholar 
Kalff, J. Limnology: Inland Water Ecosystems Vol. 592 (Prentice Hall, 2002).
Google Scholar 
Polačik, M., Harrod, C., Blažek, R. & Reichard, M. Trophic niche partitioning in communities of African annual fish: Evidence from stable isotopes. Hydrobiologia 721, 99–106 (2014).Article 

Google Scholar 
Costalago, D., Navarro, J., Álvarez-Calleja, I. & Palomera, I. Ontogenetic and seasonal changes in the feeding habits and trophic levels of two small pelagic fish species. Mar. Ecol. Prog. Ser. 460, 169–181 (2012).Article 
ADS 

Google Scholar 
Matthews, B. & Mazumder, A. Consequences of large temporal variability of zooplankton δ15N for modeling fish trophic position and variation. Limnol. Oceanogr. 50, 1404–1414 (2005).Article 
ADS 
CAS 

Google Scholar 
Taipale, S., Kankaala, P., Tiirola, M. & Jones, R. I. Whole-lake dissolved inorganic 13C additions reveal seasonal shifts in zooplankton diet. Ecology 89, 463–474 (2008).Article 
PubMed 

Google Scholar 
Zohary, T., Erez, J., Gophen, M., Berman-Frank, I. & Stiller, M. Seasonality of stable carbon isotopes within the pelagic food web of Lake Kinneret. Limnol. Oceanogr. 39, 1030–1043 (1994).Article 
ADS 
CAS 

Google Scholar 
Stenroth, P. et al. Stable isotopes as an indicator of diet in omnivorous crayfish (Pacifastacus leniusculus): The influence of tissue, sample treatment, and season. Can. J. Fish. Aquat. Sci. 63, 821–831 (2006).Article 
CAS 

Google Scholar 
R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/ (2021).Moore, J. W. & Semmens, B. X. Incorporating uncertainty and prior information into stable isotope mixing models. Ecol. Lett. 11, 470–480 (2008).Article 
PubMed 

Google Scholar 
Stock, B. C. & Semmens, B. X. Unifying error structures in commonly used biotracer mixing models. Ecology 97, 2562–2569 (2016).Article 
PubMed 

Google Scholar 
Irz, P., Laurent, A., Messad, S., Pronier, O. & Argillier, C. Influence of site characteristics on fish community patterns in French reservoirs. Ecol. Freshw. Fish 11, 123–136 (2002).Article 

Google Scholar 
Sutela, T., Aroviita, J. & Keto, A. Assessing ecological status of regulated lakes with littoral macrophyte, macroinvertebrate and fish assemblages. Ecol. Indic. 24, 185–192 (2013).Article 

Google Scholar 
Hunt, P. & Jones, J. The effect of water level fluctuations on a littoral fauna. J. Fish Biol. 4, 385–394 (1972).Article 

Google Scholar 
Kaster, J. & Jacobi, G. Benthic macroinvertebrates of a fluctuating reservoir. Freshw. Biol. 8, 283–290 (1978).Article 

Google Scholar 
Kraft, K. The effect of unnatural water level fluctuations on benthic invertebrates in Voyageurs National Park. Research⁄Resources Management Report MWR-12. US Department of the Interior, National Park Service. International Falls, Minnesota (1988).Glon, M., Larson, E. R. & Pangle, K. Comparison of 13C and 15N discrimination factors and turnover rates between congeneric crayfish Orconectes rusticus and O. virilis (Decapoda, Cambaridae). Hydrobiologia 768, 51–61 (2016).Article 
CAS 

Google Scholar 
Hesslein, R. H., Hallard, K. & Ramlal, P. Replacement of sulfur, carbon, and nitrogen in tissue of growing broad whitefish (Coregonus nasus) in response to a change in diet traced by δ34S, δ13C, and δ15N. Can. J. Fish. Aquat. Sci. 50, 2071–2076 (1993).Article 
CAS 

Google Scholar  More

Jayathilake PG, Jana S, Rushton S, Swailes D, Bridgens B, Curtis T, et al. Extracellular polymeric substance production and aggregated bacteria colonization influence the competition of microbes in biofilms. Front Microbiol. 2017;8:1865.Article 
PubMed 
PubMed Central 

Google Scholar 
Flemming H-C, Neu TR, Wingender J (eds). The Perfect Slime: Microbial Extracellular Polymeric Substances (EPS). IWA Publishing, London, 2016.Morales-García AL, Bailey RG, Jana S, Burgess JG. The role of polymers in cross-kingdom bioadhesion. Philos Trans R Soc Lond B Biol Sci. 2019;374:20190192.Article 
PubMed 
PubMed Central 

Google Scholar 
Davey ME, O’Toole GA. Microbial biofilms: From ecology to molecular genetics. Microbiol Mol. 2000;64:847–67.Article 
CAS 

Google Scholar 
Peters BM, Jabra-Rizk MA, O’May GA, Costerton JW, Shirtliff ME. Polymicrobial interactions: Impact on pathogenesis and human disease. Clin Microbiol Rev. 2012;25:193–213.Article 
PubMed 
PubMed Central 

Google Scholar 
Karygianni L, Ren Z, Koo H, Thurnheer T. Biofilm matrixome: Extracellular components in structured microbial communities. Trends Microbiol. 2020;28:668–81.Article 
CAS 
PubMed 

Google Scholar 
Morris BEL, Henneberger R, Huber H, Moissl-Eichinger C. Microbial syntrophy: Interaction for the common good. FEMS Microbiol Rev. 2013;37:384–406.Article 
CAS 
PubMed 

Google Scholar 
Decho AW, Gutierrez T. Microbial extracellular polymeric substances (EPSs) in ocean systems. Front Microbiol. 2017;8:922.Article 
PubMed 
PubMed Central 

Google Scholar 
Boleij M, Seviour T, Wong LL, van Loosdrecht MCM, Lin Y. Solubilization and characterization of extracellular proteins from anammox granular sludge. Water Res. 2019;164:114952.Article 
CAS 
PubMed 

Google Scholar 
Kim D, Barraza JP, Arthur RA, Hara A, Lewis K, Liu Y, et al. Spatial mapping of polymicrobial communities reveals a precise biogeography associated with human dental caries. Proc Natl Acad Sci USA. 2020;117:12375–86.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Sadiq FA, Burmølle M, Heyndrickx M, Flint S, Lu W, Chen W, et al. Community-wide changes reflecting bacterial interspecific interactions in multispecies biofilms. Crit Rev Microbiol. 2021;47:338–58.Article 
PubMed 

Google Scholar 
Liu W, Jacquiod S, Brejnrod A, Russel J, Burmølle M, Sørensen SJ. Deciphering links between bacterial interactions and spatial organization in multispecies biofilms. ISME J. 2019;13:3054–66.Article 
PubMed 
PubMed Central 

Google Scholar 
Bridier A, Le Coq D, Dubois-Brissonnet F, Thomas V, Aymerich S, Briandet R. The spatial architecture of Bacillus subtilis biofilms deciphered using a surface-associated model and in situ imaging. PLOS One. 2011;6:e16177.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Lee KWK, Periasamy S, Mukherjee M, Xie C, Kjelleberg S, Rice SA. Biofilm development and enhanced stress resistance of a model, mixed-species community biofilm. ISME J. 2014;8:894–907.Article 
CAS 
PubMed 

Google Scholar 
Myszka K, Czaczyk K. Characterization of adhesive exopolysaccharide (EPS) produced by Pseudomonas aeruginosa under starvation conditions. Curr Microbiol. 2009;58:541–6.Article 
CAS 
PubMed 

Google Scholar 
Harimawan A, Ting YP. Investigation of extracellular polymeric substances (EPS) properties of P. aeruginosa and B. subtilis and their role in bacterial adhesion. Colloids Surf B. 2016;146:459–67.Article 
CAS 

Google Scholar 
Yang X-R, Li H, Nie S-A, Su J-Q, Weng B-S, Zhu G-B, et al. Potential contribution of anammox to nitrogen loss from paddy soils in Southern China. Appl Environ Microbiol. 2015;81:938–47.Article 
PubMed 
PubMed Central 

Google Scholar 
Kartal B, van Niftrik L, Keltjens JT, Op den Camp HJM, Jetten MSM Chapter 3 – Anammox—Growth physiology, cell biology, and metabolism. In: Poole RK, editor. Adv Microb Physiol. 60: Academic Press; 2012. p. 211–62.Lu Y, Natarajan G, Nguyen TQN, Thi SS, Arumugam K, Seviour TW, et al. Species level enrichment of AnAOB and associated growth morphology under the effect of key metabolites. bioRxiv. 2020. 2020.02.04.934877Gonzalez-Gil G, Sougrat R, Behzad AR, Lens PN, Saikaly PE. Microbial community composition and ultrastructure of granules from a full-scale anammox reactor. Micro Ecol. 2015;70:118–31.Article 
CAS 

Google Scholar 
Kindaichi T, Yuri S, Ozaki N, Ohashi A. Ecophysiological role and function of uncultured Chloroflexi in an anammox reactor. Water Sci Technol. 2012;66:2556–61.Article 
CAS 
PubMed 

Google Scholar 
Qin Y, Han B, Cao Y, Wang T. Impact of substrate concentration on anammox-UBF reactors start-up. Bioresour Technol. 2017;239:422–9.Article 
CAS 
PubMed 

Google Scholar 
Chen Z, Meng Y, Sheng B, Zhou Z, Jin C, Meng F. Linking exoproteome function and structure to anammox biofilm development. Environ Sci Technol. 2019;53:1490–500.Article 
CAS 
PubMed 

Google Scholar 
Ali M, Shaw DR, Albertsen M, Saikaly PE. Comparative genome-centric analysis of freshwater and marine ANAMMOX cultures suggests functional redundancy in nitrogen removal processes. Front Microbiol. 2020;11:1637.Article 
PubMed 
PubMed Central 

Google Scholar 
Jia F, Yang Q, Liu X, Li X, Li B, Zhang L, et al. Stratification of extracellular polymeric substances (EPS) for aggregated anammox microorganisms. Environ Sci Technol. 2017;51:3260–8.Article 
CAS 
PubMed 

Google Scholar 
Hou X, Liu S, Zhang Z. Role of extracellular polymeric substance in determining the high aggregation ability of anammox sludge. Water Res. 2015;75:51–62.Article 
CAS 
PubMed 

Google Scholar 
Feng C, Lotti T, Lin Y, Malpei F. Extracellular polymeric substances extraction and recovery from anammox granules: Evaluation of methods and protocol development. Chem Eng J. 2019;374:112–22.Article 
CAS 

Google Scholar 
Lotti T, Carretti E, Berti D, Montis C, Del Buffa S, Lubello C, et al. Hydrogels formed by anammox extracellular polymeric substances: Structural and mechanical insights. Sci Rep. 2019;9:11633.Article 
PubMed 
PubMed Central 

Google Scholar 
Jakubovics NS, Goodman SD, Mashburn-Warren L, Stafford GP, Cieplik F. The dental plaque biofilm matrix. Periodontology 2000. 2021;86:32–56.Article 
PubMed 
PubMed Central 

Google Scholar 
Ðapa T, Leuzzi R, Ng YK, Baban ST, Adamo R, Kuehne SA, et al. Multiple factors modulate biofilm formation by the anaerobic pathogen Clostridium difficile. J Bacteriol. 2013;195:545–55.Article 
PubMed 
PubMed Central 

Google Scholar 
Honma K, Inagaki S, Okuda K, Kuramitsu HK, Sharma A. Role of a Tannerella forsythia exopolysaccharide synthesis operon in biofilm development. Micro Pathog. 2007;42:156–66.Article 
CAS 

Google Scholar 
Li X-R, Du B, Fu H-X, Wang R-F, Shi J-H, Wang Y, et al. The bacterial diversity in an anaerobic ammonium-oxidizing (anammox) reactor community. Syst Appl Microbiol. 2009;32:278–89.Article 
CAS 
PubMed 

Google Scholar 
Cho S, Takahashi Y, Fujii N, Yamada Y, Satoh H, Okabe S. Nitrogen removal performance and microbial community analysis of an anaerobic up-flow granular bed anammox reactor. Chemosphere 2010;78:1129–35.Article 
CAS 
PubMed 

Google Scholar 
Morgenroth E, Sherden T, Van Loosdrecht MCM, Heijnen JJ, Wilderer PA. Aerobic granular sludge in a sequencing batch reactor. Water Res. 1997;31:3191–4.Article 
CAS 

Google Scholar 
Wong LL, Natarajan G, Boleij M, Thi SS, Winnerdy FR, Mugunthan S, et al. Extracellular protein isolation from the matrix of anammox biofilm using ionic liquid extraction. Appl Microbiol Biotechnol. 2020;104:3643–54.Article 
CAS 
PubMed 

Google Scholar 
Law Y, Kirkegaard RH, Cokro AA, Liu X, Arumugam K, Xie C, et al. Integrative microbial community analysis reveals full-scale enhanced biological phosphorus removal under tropical conditions. Sci Rep. 2016;6:25719.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Ondov BD, Bergman NH, Phillippy AM. Interactive metagenomic visualization in a Web browser. BMC Bioinform. 2011;12:385.Article 

Google Scholar 
Liu X, Arumugam K, Natarajan G, Seviour TW, Drautz-Moses DI, Wuertz S, et al. Draft genome sequence of a Candidatus brocadia bacterium enriched from activated sludge collected in a tropical climate. Genome Announc. 2018;6:e00406–18.Article 
PubMed 
PubMed Central 

Google Scholar 
Price MN, Dehal PS, Arkin AP. FastTree 2-approximately maximum-likelihood trees for large alignments. PloS One. 2010;5:e9490–e.Article 
PubMed 
PubMed Central 

Google Scholar 
Arkin AP, Cottingham RW, Henry CS, Harris NL, Stevens RL, Maslov S, et al. KBase: The United States Department of Energy Systems Biology Knowledgebase. Nat Biotechnol. 2018;36:566–9.Article 
CAS 
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.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Buchfink B, Reuter K, Drost H-G. Sensitive protein alignments at tree-of-life scale using DIAMOND. Nat Methods. 2021;18:366–8.Article 
CAS 
PubMed 
PubMed Central 

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

Google Scholar 
Daims H, Nielsen JL, Nielsen PH, Schleifer K-H, Wagner M. In situ characterization of Nitrospira-like nitrite-oxidizing bacteria active in wastewater treatment plants. Appl Environ Microbiol. 2001;67:5273–84.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Seviour T, Wong LL, Lu Y, Mugunthan S, Yang Q, Shankari UDOCS, et al. Phase transitions by an abundant protein in the anammox extracellular matrix mediate cell-to-cell aggregation and biofilm formation. mBio 2020;11:e02052–20.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Protter DSW, Rao BS, Van Treeck B, Lin Y, Mizoue L, Rosen MK, et al. Intrinsically disordered regions can contribute promiscuous interactions to RNP granule assembly. Cell Rep. 2018;22:1401–12.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Fulton KM, Smith JC, Twine SM. Clinical applications of bacterial glycoproteins. Expert Rev Proteom. 2016;13:345–53.Article 
CAS 

Google Scholar 
Upreti RK, Kumar M, Shankar V. Bacterial glycoproteins: Functions, biosynthesis and applications. Proteomics 2003;3:363–79.Article 
CAS 
PubMed 

Google Scholar 
van Teeseling MCF, Maresch D, Rath CB, Figl R, Altmann F, Jetten MSM, et al. The S-layer protein of the anammox bacterium Kuenenia stuttgartiensiss is heavily O-glycosylated. Front Microbiol. 2016;7:1721.PubMed 
PubMed Central 

Google Scholar 
McGonigle JM, Lang SQ, Brazelton WJ, Parales RE. Genomic evidence for formate metabolism by Chloroflexi as the key to unlocking deep carbon in lost city microbial ecosystems. Appl Environ Microbiol. 2020;86:e02583–19.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Vuillemin A, Kerrigan Z, D’Hondt S, Orsi WD. Exploring the abundance, metabolic potential and gene expression of subseafloor Chloroflexi in million-year-old oxic and anoxic abyssal clay. FEMS Microbiol Ecol. 2020;96:fiaa223.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Kartal B, de Almeida NM, Maalcke WJ, Op den Camp HJ, Jetten MS, Keltjens JT. How to make a living from anaerobic ammonium oxidation. FEMS Microbiol Rev. 2013;37:428–61.Article 
CAS 
PubMed 

Google Scholar 
Loera-Muro A, Guerrero-Barrera A, Tremblay DNY, Hathroubi S, Angulo C. Bacterial biofilm-derived antigens: A new strategy for vaccine development against infectious diseases. Expert Rev Vaccines. 2021;20:385–96.Article 
CAS 
PubMed 

Google Scholar 
Hobley L, Harkins C, MacPhee CE, Stanley-Wall NR. Giving structure to the biofilm matrix: An overview of individual strategies and emerging common themes. FEMS Microbiol Rev. 2015;39:649–69.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Elias S, Banin E. Multi-species biofilms: Living with friendly neighbors. FEMS Microbiol Rev. 2012;36:990–1004.Article 
CAS 
PubMed 

Google Scholar 
Teeseling MCFV, Almeida NMD, Klingl A, Speth DR, Camp HJMOD, Rachel R, et al. A new addition to the cell plan of anammox bacteria: Candidatus Kuenenia stuttgartiensis has a protein surface layer as the outermost layer of the cell. J Bacteriol. 2014;196:80–9.Article 
PubMed 
PubMed Central 

Google Scholar 
Paula AJ, Hwang G, Koo H. Dynamics of bacterial population growth in biofilms resemble spatial and structural aspects of urbanization. Nat Commun. 2020;11:1354.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Kragelund C, Caterina L, Borger A, Thelen K, Eikelboom D, Tandoi V, et al. Identity, abundance and ecophysiology of filamentous Chloroflexi species present in activated sludge treatment plants. FEMS Microbiol Ecol. 2007;59:671–82.Article 
CAS 
PubMed 

Google Scholar 
Nierychlo M, Miłobędzka A, Petriglieri F, McIlroy B, Nielsen PH, McIlroy SJ. The morphology and metabolic potential of the Chloroflexi in full-scale activated sludge wastewater treatment plants. FEMS Microbiol Ecol. 2018;95.Kragelund C, Thomsen TR, Mielczarek AT, Nielsen PH. Eikelboom’s morphotype 0803 in activated sludge belongs to the genus Caldilinea in the phylum Chloroflexi. FEMS Microbiol Ecol. 2011;76:451–62.Article 
CAS 
PubMed 

Google Scholar 
Zhang J, Miao Y, Zhang Q, Sun Y, Wu L, Peng Y. Mechanism of stable sewage nitrogen removal in a partial nitrification-anammox biofilm system at low temperatures: Microbial community and EPS analysis. Bioresour Technol. 2020;297:122459.Article 
CAS 
PubMed 

Google Scholar 
Björnsson L, Hugenholtz P, Tyson GW, Blackall LL. Filamentous Chloroflexi (green non-sulfur bacteria) are abundant in wastewater treatment processes with biological nutrient removal. Microbiology 2002;148:2309–18.Article 
PubMed 

Google Scholar 
Boleij M, Pabst M, Neu TR, van Loosdrecht MCM, Lin Y. Identification of glycoproteins isolated from extracellular polymeric substances of full-scale anammox granular sludge. Environ Sci Technol. 2018;52:13127–35.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Pabst M, Grouzdev DS, Lawson CE, Kleikamp HBC, de Ram C, Louwen R, et al. A general approach to explore prokaryotic protein glycosylation reveals the unique surface layer modulation of an anammox bacterium. ISME J. 2022;16:346–57.Article 
CAS 
PubMed 

Google Scholar 
Berlanga M, Guerrero R. Living together in biofilms: The microbial cell factory and its biotechnological implications. Micro Cell Fact. 2016;15:165.Article 

Google Scholar 
Liu T, Tian R, Li Q, Wu N, Quan X. Strengthened attachment of anammox bacteria on iron-based modified carrier and its effects on anammox performance in integrated floating-film activated sludge (IFFAS) process. Sci Total Environ. 2021;787:147679.Article 
CAS 
PubMed 

Google Scholar  More

Beer, C. et al. Terrestrial gross carbon dioxide uptake: global distribution and covariation with climate. Science 329, 834–838 (2010).Article 
CAS 
PubMed 

Google Scholar 
Badgley, G., Field, C. B. & Berry, J. A. Canopy near-infrared reflectance and terrestrial photosynthesis. Sci. Adv.3, e1602244 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Chapin, F. S. III Effects of plant traits on ecosystem and regional processes: a conceptual framework for predicting the consequences of global change. Ann. Bot. 91, 455–463 (2003).Article 
PubMed 
PubMed Central 

Google Scholar 
Chu, C. et al. Does climate directly influence NPP globally? Global Chan. Biol. 22, 12–24 (2016).Article 

Google Scholar 
Yao, Y. et al. Spatiotemporal pattern of gross primary productivity and its covariation with climate in China over the last thirty years. Global Chan. Biol. 24, 184–196 (2018).Article 

Google Scholar 
Fang, J., Lutz, J. A., Wang, L., Shugart, H. H. & Yan, X. Using climate-driven leaf phenology and growth to improve predictions of gross primary productivity in North American forests. Global Chan. Biol. 26, 6974–6988 (2020).Article 

Google Scholar 
Fernández-Martínez, M. et al. The role of climate, foliar stoichiometry and plant diversity on ecosystem carbon balance. Global Chan. Biol. 26, 7067–7078 (2020).Article 

Google Scholar 
Migliavacca, M. et al. The three major axes of terrestrial ecosystem function. Nature 598, 468–472 (2021).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Reichstein, M., Bahn, M., Mahecha, M. D., Kattge, J. & Baldocchi, D. D. Linking plant and ecosystem functional biogeography. Proc. Natl. Acad. Sci. 111, 13697–13702 (2014).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Funk, J. L. et al. Revisiting the Holy Grail: using plant functional traits to understand ecological processes. Biol. Rev. 92, 1156–1173 (2017).Article 
PubMed 

Google Scholar 
Lavorel, S. & Garnier, É. Predicting changes in community composition and ecosystem functioning from plant traits: revisiting the Holy Grail. Fun. Ecol. 16, 545–556 (2002).Article 

Google Scholar 
Enquist, B. J. et al. in Advances in Ecological Research 52 (eds Samraat P, Guy W, & Anthony I. D) 249–318 (Academic Press, 2015).Garnier, E. et al. Plant functional markers capture ecosystem properties during secondary succession. Ecology 85, 2630–2637 (2004).Article 

Google Scholar 
Enquist, B. J. et al. Assessing trait-based scaling theory in tropical forests spanning a broad temperature gradient. Global Ecol. Biogeogr. 26, 1357–1373 (2017).Article 

Google Scholar 
Fyllas, N. M. et al. Solar radiation and functional traits explain the decline of forest primary productivity along a tropical elevation gradient. Ecol. Lett. 20, 730–740 (2017).Article 
PubMed 

Google Scholar 
Van der Plas, F. et al. Plant traits alone are poor predictors of ecosystem properties and long-term ecosystem functioning. Nat. Ecol. Evol. 4, 1602–1611 (2020).Article 
PubMed 

Google Scholar 
Ali, A., Yan, E.-R., Chang, S. X., Cheng, J.-Y. & Liu, X.-Y. Community-weighted mean of leaf traits and divergence of wood traits predict aboveground biomass in secondary subtropical forests. Sci. Total Environ. 574, 654–662 (2017).Article 
CAS 
PubMed 

Google Scholar 
Yang, J., Cao, M. & Swenson, N. G. Why Functional Traits Do Not Predict Tree Demographic Rates. Trend Ecol. Evol. 33, 326–336 (2018).Article 

Google Scholar 
Šímová, I. et al. The relationship of woody plant size and leaf nutrient content to large-scale productivity for forests across the Americas. J. Ecol. 107, 2278–2290 (2019).Article 

Google Scholar 
Li, Y. et al. Leaf size of woody dicots predicts ecosystem primary productivity. Ecol. Lett. 23, 1003–1013 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
He, N. et al. Ecosystem Traits Linking Functional Traits to Macroecology. Trend. Ecol. Evol. 34, 200–210 (2019).Article 

Google Scholar 
Rubio, V. E., Zambrano, J., Iida, Y., Umaña, M. N. & Swenson, N. G. Improving predictions of tropical tree survival and growth by incorporating measurements of whole leaf allocation. J. Ecol. 109, 1331–1343 (2021).Article 

Google Scholar 
Drake, J. E. et al. Increases in the flux of carbon belowground stimulate nitrogen uptake and sustain the long-term enhancement of forest productivity under elevated CO2. Ecol. Lett. 14, 349–357 (2011).Article 
PubMed 

Google Scholar 
Hilty, J., Muller, B., Pantin, F. & Leuzinger, S. Plant growth: the What, the How, and the Why. New Phytol. 232, 25–41 (2021).Article 
PubMed 

Google Scholar 
Xia, J. et al. Joint control of terrestrial gross primary productivity by plant phenology and physiology. Proc. Natl. Acad. Sci. 112, 2788–2793 (2015).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Suding, K. N. et al. Scaling environmental change through the community-level: A trait-based response-and-effect framework for plants. Global Chan. Biol. 14, 1125–1140 (2008).Article 

Google Scholar 
Liu, C., Li, Y., Yan, P. & He, N. How to Improve the Predictions of Plant Functional Traits on Ecosystem Functioning? Front. Plant Sci. 12, 622260 (2021).Reich, P. B., Walters, M. B. & Ellsworth, D. S. From tropics to tundra: global convergence in plant functioning. Proc. Natl. Acad. of Sci. 94, 13730–13734 (1997).Article 
CAS 

Google Scholar 
Reich, P. B. The world-wide ‘fast–slow’ plant economics spectrum: a traits manifesto. J. Ecol. 102, 275–301 (2014).Article 

Google Scholar 
Monteith, J. L. Climate and the efficiency of crop production in Britain. Philosophical Transactions of the Royal Society of London. B. Biol. Sci. 281, 277–294 (1977).
Google Scholar 
Garnier, E. Resource capture, biomass allocation and growth in herbaceous plants. Trend. Ecol. Evol. 6, 126–131 (1991).Article 
CAS 

Google Scholar 
Farnsworth, K. D., Albantakis, L. & Caruso, T. Unifying concepts of biological function from molecules to ecosystems. Oikos 126, 1367–1376 (2017).Article 

Google Scholar 
Zhang, R. et al. Biodiversity alleviates the decrease of grassland multifunctionality under grazing disturbance: A global meta-analysis. Global Ecol. Biogeogr. 31, 155–167 (2022).Article 

Google Scholar 
Jing, X. et al. The links between ecosystem multifunctionality and above-and belowground biodiversity are mediated by climate. Nat. Commun. 6, 1–8 (2015).Article 

Google Scholar 
Peters, M. K. et al. Climate–land-use interactions shape tropical mountain biodiversity and ecosystem functions. Nature 568, 88–92 (2019).Article 
CAS 
PubMed 

Google Scholar 
Hu, W. et al. Aridity-driven shift in biodiversity–soil multifunctionality relationships. Nat. Commun. 12, 1–15 (2021).Article 

Google Scholar 
Jing, X. et al. The influence of aboveground and belowground species composition on spatial turnover in nutrient pools in alpine grasslands. Global Ecol. Biogeogr. 31, 486–500 (2022).Article 

Google Scholar 
Jing, X. et al. Above-and belowground complementarity rather than selection drives tree diversity-productivity relationships in European forests. Funct Ecol. 35, 1756–1767 (2021).He, N. et al. Predicting ecosystem productivity based on plant community traits. Trend. Plant Sci. 28, 43–53 (2023).Maynard, D. S. et al. Global relationships in tree functional traits. Nat. Commun. 13, 1–12 (2022).Article 

Google Scholar 
Michaletz, S. T., Kerkhoff, A. J. & Enquist, B. J. Drivers of terrestrial plant production across broad geographical gradients. Global Ecol. Biogeogr. 27, 166–174 (2018).Article 

Google Scholar 
Shipley, B. Net assimilation rate, specific leaf area and leaf mass ratio: which is most closely correlated with relative growth rate? A meta-analysis. Funct. Ecol. 20, 565–574 (2006).Article 

Google Scholar 
Violle, C. et al. Let the concept of trait be functional! Oikos 116, 882–892 (2007).Article 

Google Scholar 
Jucker, T., Bouriaud, O. & Coomes, D. A. Crown plasticity enables trees to optimize canopy packing in mixed-species forests. Funct. Ecol. 29, 1078–1086 (2015).Article 

Google Scholar 
McGill, B. J. Matters of Scale. Science 328, 575 (2010).Article 
CAS 
PubMed 

Google Scholar 
Penuelas, J. et al. Increasing atmospheric CO2 concentrations correlate with declining nutritional status of European forests. Communi. Biol. 3, 1–11 (2020).
Google Scholar 
Weemstra, M. et al. Towards a multidimensional root trait framework: a tree root review. New Phytol. 211, 1159–1169 (2016).Article 
CAS 
PubMed 

Google Scholar 
Oehri, J., Schmid, B., Schaepman-Strub, G. & Niklaus, P. A. Biodiversity promotes primary productivity and growing season lengthening at the landscape scale. Proc. Natl. Acad. Sci. 114, 10160–10165 (2017).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Wright, I. J. et al. The worldwide leaf economics spectrum. Nature 428, 821–827 (2004).Article 
CAS 
PubMed 

Google Scholar 
Diaz, S. et al. The global spectrum of plant form and function. Nature 529, 167–171 (2015).Article 
PubMed 

Google Scholar 
Liu, Y. et al. The optimum temperature of soil microbial respiration: Patterns and controls. Soil Biol. Biochem. 121, 35–42 (2018).Article 
CAS 

Google Scholar 
Zhao, N. et al. Coordinated pattern of multi-element variability in leaves and roots across Chinese forest biomes. Global Ecol. Biogeogr. 25, 359–367 (2016).Article 

Google Scholar 
Zhang, J. et al. C: N: P stoichiometry in China’s forests: From organs to ecosystems. Funct. Ecol. 32, 50–60 (2018).Article 

Google Scholar 
Karger, D. N. et al. Climatologies at high resolution for the earth’s land surface areas. Sci. Data 4, 1–20 (2017).Article 

Google Scholar 
Dirk Nikolaus, K. et al. Climatologies at high resolution for the earth’s land surface areas. EnviDat. https://doi.org/10.16904/envidat.332 (2021).Kerkhoff, A. J., Enquist, B. J., Elser, J. J. & Fagan, W. F. Plant allometry, stoichiometry and the temperature-dependence of primary productivity. Global Ecol. Biogeogr. 14, 585–598 (2005).Article 

Google Scholar 
Wright, I. J. et al. Global climatic drivers of leaf size. Science 357, 917–921 (2017).Article 
CAS 
PubMed 

Google Scholar 
Zhang, Y. et al. A global moderate resolution dataset of gross primary production of vegetation for 2000-2016. Sci. Data 4, 170165 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Jolliffe, I. T. & Cadima, J. Principal component analysis: a review and recent developments. Philos. Trans. Soc. A Math. Phys. Eng. Sci. 374, 20150202 (2016).Article 

Google Scholar 
Wieczynski, D. J. et al. Climate shapes and shifts functional biodiversity in forests worldwide. Proc. Natl. Acad. Sci. 116, 587–592 (2019).Article 
CAS 
PubMed 

Google Scholar 
Lefcheck, J. S. piecewiseSEM: Piecewise structural equation modelling in r for ecology, evolution, and systematics. Method Ecol. Evol. 7, 573–579 (2016).Article 

Google Scholar 
Bürkner, P.-C. Advanced bayesian multilevel modeling with the R Package brms. R J. 10, 395–411 (2018).Bürkner, P.-C. brms: An R package for Bayesian multilevel models using Stan. J. Stat. Software 80, 1–28 (2017).Article 

Google Scholar 
Vehtari, A., Gelman, A. & Gabry, J. Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC. Stat. Comput. 27, 1413–1432 (2017).Article 

Google Scholar 
Vehtari, A. et al. loo: Efficient leave-one-out cross-validation and WAIC for Bayesian models. R package version 2, 1003 (2019).
Google Scholar 
Gabry, J. & Mahr, T. bayesplot: Plotting for Bayesian models. R package version 1 (2017).Mac Nally, R. & Walsh, C. J. Hierarchical partitioning public-domain software. Biodivers. Conserv. 13, 659 (2004).Article 

Google Scholar 
Murray, K. & Conner, M. M. Methods to quantify variable importance: implications for the analysis of noisy ecological data. Ecology 90, 348–355 (2009).Article 
PubMed 

Google Scholar 
Yan, P., He, N., Yu, K., Xu, L. & Van Meerbeek, K. Integrating multiple functional traits to predict ecosystem productivity. figshare (2023). Dataset. https://doi.org/10.6084/m9.figshare.22081634.v1. More

We performed data acquisition, processing, analysis and visualization using Python23 version 3.8 with the packages Numpy24, Pandas25, Geopandas26, Matplotlib27, Selenium, Beautiful Soup28, SciPy14 and scikit-learn29. The functions used for specific tasks are explicitly mentioned to allow validation and replication studies.Data acquisition and processingHuman PUUV-incidenceHantavirus disease has been notifiable in Germany since 2001. The Robert Koch Institute collects anonymized data from the local and state public health departments and offers via the SurvStat application2 a freely available, limited version of its database for research and informative purposes. We retrieved the reported laboratory-confirmed human PUUV-infections (({text{n}}=text{11,228}) from 2006 to 2021, status: 2022-02-07). From the attributes available for each case, we retrieved the finest temporal and spatial resolution, i.e., the week and the year of notification, together with the district (named “County” in the English version of the SurvStat interface).To avoid bias through underreporting, our dataset was limited to PUUV-infections since 2006. The years 2006–2021 contain 91.9% of the total cases from 2001 to 2021. Human PUUV-incidence was calculated as the number of infections per 100,000 people, by using population data from Eurostat30. For each year, we used the population reported for the January 1 of that year. The population for 2020 was also used for 2021.In the analysis, we only included districts where the total infections were (ge {20}) and the maximum annual incidence was (ge {2}) in the period 2006–2021. The spatial information about the infections provided by the SurvStat application refers to the district where the infection was reported. Therefore, in most of the cases, the reported district corresponds to the residence of the infected person, which may differ from the district of infection. To compensate partially for differences between the reported place of residence and the place of infection, we combined most of the urban districts with their surrounding rural district. The underlying assumption was that most infections reported in urban districts occurred in the neighboring or surrounding rural district. In addition, some urban and rural districts have the same health department. Supplementary Table 1 lists the combined districts.Weather dataFrom the German Meteorological Service31 we retrieved grids of the following monthly weather parameters over Germany from 2004 to 2021: mean daily air temperature—Tmean, minimum daily air temperature—Tmin, and maximum daily air temperature—Tmax (all temperatures are the monthly averages of the corresponding daily values, in 2 m height above ground, in °C); total precipitation in mm—Pr, total sunshine duration in hours—SD, mean monthly soil temperature in 5 cm depth under uncovered typical soil of location in °C—ST, and soil moisture under grass and sandy loam in percent plant useable water—SM. The dataset version for Tmean, Tmin, Tmax, Pr, and SD was v1.0; for ST and SM the dataset version was 0. × . The spatial resolution was 1 × 1 km2.The data acquisition was performed with the Selenium package. The processing was based on the geopandas package26 using a geospatial vector layer for the district boundaries of Germany32. Each grid was processed to obtain the average value of the parameter over each district. We first used the function within to define a mask based on the grid centers contained in the district; we then applied this mask to the grid. In this method, called “central point rasterizing”33, each rectangle of the grid was assigned to a single district, the one that contained its center. The typical processing error was estimated to be about 1%, which agrees with the rasterizing error reported by Bregt et al.33; we consider that most likely this error is significantly less than the uncertainties of the grids themselves, caused by calculation, interpolation, and erroneous or missing observations.Data structureOur analysis was performed at the district level based on the annual infections, acquired by aggregating the weekly cases. From each monthly weather parameter, we created 24 records, for all months of the two previous years. Each observation in our dataset characterized one district in one year. Its target was acquired by transforming the annual incidence, as described in the following section. Each observation comprised all 168 available predictors from the weather parameters (7 parameters × 24 months), thereafter called “variables”. The notation for the naming of the variables follows the format Vx__, where “Vx” can be V1 or V2 that corresponds to one or two years before, respectively;  is the abbreviation of the weather parameter (see previous subsection: “Weather data”); and  is the numerical value of the month, i.e., from 1 to 12.The observations for combined districts retained the label of the rural district. For their infections and populations, we aggregated the individual values, and recalculated the incidence. For their weather variables, we assigned the mean values weighted by the area of each district.Target transformationTo consider the effects that drive the occurrence of high district-relative incidence, we discretized the incidence at the district level. The incidence scaled at its maximum value for each district showed extreme values for minima and maxima. About 49% of all observations were in the range [0, 0.1) and 8% in the range [0.9, 1] (Fig. 5). Therefore, we specifically selected to discretize the scaled incidence with two bins, i.e., to binarize it.Figure 5Histograms of the annual PUUV incidence from 2006 to 2021, scaled to its maximum value for each of the selected districts. Left: Raw incidence. Right: Log-transformed incidence, according to Eq. (6).Full size imageWe first applied a log-transformation to the incidence values34, described in Eq. (6).$${text{Log – incidence}} = log_{10} left( {{text{incidence}} + 1} right)$$
(6)
The addition of a positive constant ensured a noninfinite value for zero incidence, with 1 selected so that the log-incidence is nonnegative, and a zero incidence was transformed into a zero log-incidence. This transformation aimed to increase the influence of nonzero incidence values; values that are not pronounced, but still hint at a nonzero infection risk. Its effect is demonstrated in the right plot of Fig. 5, where the positive skewness of the original data is reduced, i.e., low incidence values are spread to higher values, resulting to more uniform bin heights in the range [0.05, 0.95] after the transformation. Formally, in this case the log-transformation achieves a more uniform distribution for the non-extreme incidence values.For the binarization, we performed unsupervised clustering of the log-transformed incidence, separately for each district, applying the function KBinsDiscretizer of the scikit-learn package29. Our selected strategy was the k-means clustering with two bins, because it does not require a pre-defined threshold, and it can operate with the same fixed number of bins for every district, by automatically adjusting the cluster centroids accordingly.Classification methodWe concentrated only on those variable combinations that led to a linear decision boundary for the classification of our selected target. We selected support vector machines (SVM)35 with a linear kernel, because they combine high performance with low model complexity, in that they return the decision boundary as a linear equation of the variables. In addition, SVM is geometrically motivated36 and expected to be less prone to outliers and overfitting than other machine-learning classification algorithms, such as the logistic regression. For the complete modelling process, the regularization parameter C was set to 1, that is the default value in the applied SVC method of the scikit-learn package29, and the weights for both risk classes were also set to 1.Feature selectionOur aim was to use the smallest possible number of weather parameters as variables for a classification model with sufficient performance. To identify the optimal variable combination, we first applied an SVM with a linear kernel for all 2-variable combinations of the monthly weather variables from V2 and V1, i.e., 168 variables (7 weather parameters × 2 years × 12 months). Only for this step, the variables were scaled to their minimum and maximum values, which significantly reduced the processing time. For all the following steps, the scaler was omitted, because the unscaled support vectors were required for the final model. From the total 14,028 models for each unique pair ((frac{168!}{2!cdot left(168-2right)!})), we kept the 100 models with the best F1-score, i.e., of the harmonic mean of sensitivity and precision, and counted the occurrences of each year-month combination in the variables. The best F1-score was 0.752 for the pair (V1_Tmean_9 and V2_Tmax_4); and the best sensitivity was 83% for the pair (V2_Tmax_9 and V1_ST_9).The year-month combinations with more than 10% occurrences were: V1_9 (September of the previous year, with 49% occurrences), V2_9 (September of two years before, with 12%) and V2_4 (April of two years before, with 10%). To avoid sets with highly correlated variables, we formed 3-variable combinations, with exactly one variable from each year-month combination (threefold Cartesian product). From the total 343 models (73 combinations, i.e., 7 weather parameters for 3 year-month combinations), we selected the model with the best sensitivity and at least 70% precision, i.e., the variable set (V2_ST_4, V2_SD_9, and V1_ST_9). We consider that the criteria for this selection are not particularly crucial; and we expect comparable performance for most variable sets with a high F1-score, because the variables for each dimension of the Cartesian product were highly correlated. The eight variable sets with at least 70% precision and at least 80% sensitivity are shown in Supplementary Table 2.The SVM classifier has two hyperparameters: the regularization parameter C and the class weights. By decreasing C, the decision boundary becomes softer and more misclassifications are allowed. On the other hand, increasing the high-risk class weight, the misclassifications of high-risk observations are penalized higher, which is expected to increase the sensitivity and decrease the precision. The simultaneous adjustment of both hyperparameters ensures that the resulting model has the optimal performance with respect to the preferred metric. However, in order to avoid overfitting, we considered redundant a further model optimization with these two hyperparameters. For completeness, we examined SVM models for different values of the hyperparameters and found that the global maximum for the F1-score is in the region of 0.001 for C and 1.5 for the high-risk class weight. Our selected values C = 1 and high-risk class weight equal to 1 give the second best F1-score, which is a local maximum with comparable performance, mostly insensitive to the selection of C from the range [0.2, 5.5].The addition of a fourth variable from V1_6 (June of the previous year) resulted in a model with higher sensitivity but lower precision and specificity (for V1_Pr_6). The highest F1-score was achieved for the quadruple (V2_ST_4, V2_SD_9, V1_ST_9, V1_Pr_6). Because of the increased complexity without significant improvement in the performance, we considered unnecessary a further expansion of our variable triplet. More

Mounier, A. et al. Deciphering African late middle Pleistocene hominin diversity and the origin of our species. Nat. Commun. https://doi.org/10.1038/s41467-019-11213-w (2019).Schlebusch, C. M. et al. Southern African ancient genomes estimate modern human divergence to 350,000 to 260,000 years ago. Science 358, 652–655 (2017).CAS 
PubMed 

Google Scholar 
Lombard, M. et al. Ancient human DNA: how sequencing the genome of a boy from Ballito Bay changed human history. S Afr. J. Sci. 114, 1–3 (2018).
Google Scholar 
Grün, R. et al. Direct dating of Florisbad hominid. Nature 382, 500–501 (1996).PubMed 

Google Scholar 
Grine, F. et al. The Middle Stone Age human fossil record from Klasies River Main Site. J. Hum. Evol. 103, 53–78 (2017).PubMed 

Google Scholar 
Henshilwood, C. S. et al. A 100,000-year-old ochre-processing workshop at Blombos Cave, South Africa. Science 33, 219–222 (2011).
Google Scholar 
Lombard, M. et al. Four-field co-evolutionary model for human cognition: variation in the Middle Stone Age/Middle Palaeolithic. J. Archeol. Method Theory 28, 142–177 (2021).
Google Scholar 
Wadley, L. What stimulated rapid, cumulative innovation after 100,000 years ago? J. Archeol. Method Theory 28, 120–141 (2021).
Google Scholar 
Tylen, K. et al. The evolution of early symbolic behavior in Homo sapiens. Proc. Natl Acad. Sci. USA 117, 4578–4584 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Rifkin, R. F. et al. Ancient oncogenesis, infection, and human evolution. Evol. Appl. https://doi.org/10.1111/eva.12497 (2017).Pittman, K. J. et al. The legacy of past pandemics: common human mutations that protect against infectious disease. PLoS Pathog. 12, e1005680 (2016).PubMed 
PubMed Central 

Google Scholar 
Andam, C. P. et al. Microbial genomics of ancient plagues and outbreaks. Trends Microbiol. 24, 978–990 (2016).CAS 
PubMed 

Google Scholar 
Houldcroft, C. J. et al. Migrating microbes: what pathogens can tell us about population movements and human evolution. Ann. Hum. Biol. 44, 397–407 (2017).PubMed 

Google Scholar 
Reyes-Centeno, H. et al. Testing modern human out-of-Africa dispersal models using dental nonmetric data. Curr. Anthropol. 58, 406–417 (2017).
Google Scholar 
Pimenoff, V. N. et al. The role of aDNA in understanding the co-evolutionary patterns of human sexually transmitted infections. Genes https://doi.org/10.3390/genes9070317 (2018).Ferwerda, B. et al. Functional consequences of Toll-like Receptor 4 polymorphisms. Mol. Med. 14, 346–352 (2008).CAS 
PubMed 
PubMed Central 

Google Scholar 
Tanabe, K. et al. Plasmodium falciparum accompanied the human expansion out of Africa. Curr. Biol. 20, 1283–1289 (2010).CAS 
PubMed 

Google Scholar 
Linz, B. et al. An African origin for the intimate association between humans and Helicobacter pylori. Nature 445, 915–918 (2007).PubMed 
PubMed Central 

Google Scholar 
Nédélec, Y. et al. Genetic ancestry and natural selection drive population differences in immune responses to pathogens. Cell 167, 657–669 (2016).PubMed 

Google Scholar 
Owers, K. A. et al. Adaptation to infectious disease exposure in indigenous Southern African populations. Proc. Biol. Sci. https://doi.org/10.1098/rspb.2017.0226 (2017).Schlebusch, C. M. et al. Khoe-San genomes reveal unique variation and confirm the deepest population divergence in Homo sapiens. Mol. Biol. Evol. https://doi.org/10.1093/molbev/msaa140 (2020).Kessler, S. E. et al. Selection to outsmart the germs: the evolution of disease recognition and social cognition. J. Hum. Evol. 108, 92–109 (2017).PubMed 

Google Scholar 
Thornhill, R. et al. The parasite-stress theory of sociality, the behavioral immune system, and human social and cognitive uniqueness. Evol. Behav. Sci. 8, 257–264 (2014).
Google Scholar 
Gurven, M. et al. Longevity among hunter‐gatherers: a cross‐cultural examination. Popul Dev. Rev. 33, 321–365 (2007).
Google Scholar 
Pfeiffer, S. et al. The people behind the samples: biographical features of past hunter-gatherers from KwaZulu-Natal who yielded aDNA. Int. J. Paleopathol. 24, 158–164 (2019).PubMed 

Google Scholar 
Schriefer, M. E. et al. Identification of a novel rickettsial infection in a patient diagnosed with murine typhus. J. Clin. Microbiol. 32, 949–954 (1994).CAS 
PubMed 
PubMed Central 

Google Scholar 
Pages, F. et al. The past and present threat of vector-borne diseases in deployed troops. Clin. Microbiol. Infect. 16, 209–224 (2010).CAS 
PubMed 

Google Scholar 
Wood, D. E. et al. Improved metagenomic analysis with Kraken 2. Genome Biol. https://doi.org/10.1186/s13059-019-1891-0 (2019).Jónsson, H. et al. mapDamage 2.0: fast approximate Bayesian estimates of ancient DNA damage parameters. Bioinformatics 29, 1682–1684 (2013).PubMed 
PubMed Central 

Google Scholar 
Gillespie, J. J. et al. Genomic diversification in strains of Rickettsia felis isolated from different arthropods. Genome Biol. Evol. 7, 35–56 (2015).CAS 

Google Scholar 
Cardwell, M. M. et al. The Sca2 autotransporter protein from Rickettsia conorii is sufficient to mediate adherence to and invasion of cultured mammalian cells. Infect. Immun. 77, 5272–5280 (2009).CAS 
PubMed 
PubMed Central 

Google Scholar 
Kay, G. L. et al. Recovery of a Medieval Brucella melitensis genome using shotgun metagenomics. mBio. https://doi.org/10.1128/mBio.01337-14 (2014).Schuenemann, V. J. et al. Genome-wide comparison of medieval and modern Mycobacterium leprae. Science 341, 179–183 (2013).CAS 
PubMed 

Google Scholar 
Müller, R. et al. Genotyping of ancient Mycobacterium tuberculosis strains reveals historic genetic diversity. Proc. Biol. Sci. https://doi.org/10.1098/rspb.2013.3236 (2014).Rasmussen, S. et al. Early divergent strains of Yersinia pestis in Eurasia 5,000 years ago. Cell 163, 571–582 (2015).CAS 
PubMed 
PubMed Central 

Google Scholar 
Vågene, A. J. et al. Salmonella enterica genomes from victims of a major sixteenth-century epidemic in Mexico. Nat. Ecol. Evol. 2, 520–528 (2018).PubMed 

Google Scholar 
Guellil, M. et al. Genomic blueprint of a relapsing fever pathogen in 15th century Scandinavia. Proc. Natl Acad. Sci. USA 115, 10422–10427 (2018).CAS 
PubMed 
PubMed Central 

Google Scholar 
Patterson Ross, Z. et al. The paradox of HBV evolution as revealed from a 16th century mummy. PLoS Pathog. https://doi.org/10.1371/journal.ppat.1006750 (2015).Marciniak, S. et al. Plasmodium falciparum malaria in 1st-2nd century CE southern Italy. Curr. Biol. 26, 1220–1222 (2016).
Google Scholar 
Margaryan, A. et al. Ancient pathogen DNA in human teeth and petrous bones. Ecol. Evol. https://doi.org/10.1002/ece3.3924 (2018).Zhou, Z. et al. Pan-genome analysis of ancient and modern Salmonella enterica demonstrates genomic stability of the invasive Para C lineage for millennia. Curr. Biol. 28, 2420–2428 (2018).CAS 
PubMed 
PubMed Central 

Google Scholar 
Williams, K. M. Update on bone health in paediatric chronic disease. Endocrinol. Metab. Clin. North Am. https://doi.org/10.1016/j.ecl.2016.01.009 (2016).Latham, K.E. et al. DNA recovery and analysis from skeletal material in modern forensic contexts. Forensic Sci. Res. https://doi.org/10.1080/20961790.2018.1515594 (2019).Briggs, H. M. et al. Diagnosis and management of tickborne Rickettsial diseases: rocky mountain spotted fever and other spotted fever group Rickettsioses, Ehrlichioses, and Anaplasmosis – United States. MMWR Recomm. Rep. 65, 1–44 (2016).
Google Scholar 
Jonker, F. A. M. et al. Anaemia, iron deficiency and susceptibility to infection in children in sub‐Saharan Africa, guideline dilemmas. Br. J. Haematol. https://doi.org/10.1111/bjh.14593. (2017).Key, F. M. et al. Emergence of human-adapted Salmonella enterica is linked to the Neolithization process. Nat. Ecol. Evol. 4, 324–333 (2020).
Google Scholar 
Angelakis, E. et al. Rickettsia felis: the complex journey of an emergent human pathogen. Trends Parasitol. https://doi.org/10.1016/j.pt.2016.04.009 (2016).Legendre, K. P. et al. Rickettsia felis: A review of transmission mechanisms of an emerging pathogen. Trop. Med. Infect. Dis. https://doi.org/10.3390/tropicalmed2040064 (2017).Mediannikov, O. et al. Common epidemiology of Rickettsia felis infection and malaria, Africa. Emerg. Infect. Dis. https://doi.org/10.3201/eid1911.130361 (2014).Gonçalves, B. P. et al. Examining the human infectious reservoir for Plasmodium falciparum malaria in areas of differing transmission intensity. Nat. Commun. https://doi.org/10.1038/s41467-017-01270-4 (2017).Snowden, J. et al. Rickettsia rickettsiae (Rocky Mountain Spotted Fever). StatPearls Publishing, available from https://www.ncbi.nlm.nih.gov/books/NBK430881/ (2017).Azad, A. A. Pathogenic Rickettsiae as bioterrorism agents. Ann. N. Y Acad. Sci. 990, 734–738 (2007).
Google Scholar 
Oliveira, R. P. et al. Rickettsia felis in Ctenocephalides spp. fleas, Brazil. Emerg. Infect. Dis. https://doi.org/10.3201/eid0803.010301 (2002).Parola, P. et al. Rickettsia felis: The next mosquito-borne outbreak? Lancet Infect. Dis. https://doi.org/10.1016/S1473-3099(16)30331-0 (2016).Wadley, L. Legacies from the Later Stone Age. S Afr Archaeol Bull. Goodwin Ser. 6, 42–53 (1989).
Google Scholar 
Henn, B. M. et al. The great human expansion. Proc. Natl Acad. Sci. USA 109, 17758–17764 (2012).CAS 
PubMed 
PubMed Central 

Google Scholar 
Yang, D. Y. et al. Technical note: improved DNA extraction from ancient bones using silica-based spin columns. Am. J. Phys. Anthropol. 105, 539–543 (1998).CAS 
PubMed 

Google Scholar 
Malmström, E. M. et al. More on contamination: the use of asymmetric molecular behavior to identify authentic ancient human DNA. Mol. Biol. Evol. 24, 998–1004 (2007).PubMed 

Google Scholar 
Dabney, J. et al. Complete mitochondrial genome sequence of a Middle Pleistocene cave bear reconstructed from ultrashort DNA fragments. Proc. Natl Acad. Sci. USA 110, 15758–15763 (2013).CAS 
PubMed 
PubMed Central 

Google Scholar 
Meyer, M. et al. Illumina sequencing library preparation for highly multiplexed target capture and sequencing. Cold Spring Harbor Protoc. https://doi.org/10.1101/pdb.prot5448 (2010).Li, H. et al. Fast and accurate long-read alignment with Burrows–Wheeler transform. Bioinformatics 26, 589–595 (2010).PubMed 
PubMed Central 

Google Scholar 
Briggs, A. W. et al. Patterns of damage in genomic DNA sequences from a Neandertal. Proc. Natl Acad. Sci. USA 104, 14616–14621 (2007).CAS 
PubMed 
PubMed Central 

Google Scholar 
Borry, M. et al. PyDamage: automated ancient damage identification and estimation for contigs in ancient DNA de novo assembly. PeerJ. https://doi.org/10.7717/peerj.11845 (2021).Schubert, M. et al. AdapterRemoval v2: rapid adapter trimming, identification, and read merging. BMC Res. https://doi.org/10.1186/s13104-016-1900-2 (2016).Langmead, B. et al. Fast gapped-read alignment with Bowtie 2. Nat. Methods. https://doi.org/10.1038/nmeth.1923 (2012).Bankevich, A. et al. SPAdes: A new genome assembly algorithm and its applications to single-cell sequencing. J. Comput. Biol. https://doi.org/10.1089/cmb.2012.0021 (2012).Jain, C. et al. High throughput ANI analysis of 90K prokaryotic genomes reveals clear species boundaries. Nat Commun. https://doi.org/10.1038/s41467-018-07641-9 (2018).Gardner, S. H. et al. kSNP3.0: SNP detection and phylogenetic analysis of genomes without genome alignment or reference genome. Bioinformatics. https://doi.org/10.1093/bioinformatics/btv271 (2015).Contreras-Moreira, B. et al. GET_HOMOLOGUES, a versatile software package for scalable and robust microbial pangenome analysis. Appl. Environ. Microbiol. https://doi.org/10.1128/AEM.02411-13 (2013).Krzywinski, M. et al. Circos: an information aesthetic for comparative genomics. Genome Res. https://doi.org/10.1101/gr.092759.109 (2009).Parks, D. H. et al. CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes Genome Res. https://doi.org/10.1101/gr.186072.114 (2015).Suyama, M. et al. PAL2NAL: robust conversion of protein sequence alignments into the corresponding codon alignments. Nucleic Acids Res. 34, W609–W612 (2006).CAS 
PubMed 
PubMed Central 

Google Scholar 
Dereeper, A. et al. Phylogeny. fr: Robust phylogenetic analysis for the non-specialist. Nucleic Acids Res. https://doi.org/10.1093/nar/gkn180 (2008).Nguyen, L. T. et al. IQ-TREE: a fast and effective stochastic algorithm for estimating maximum-likelihood phylogenies. Mol. Biol. Evol. https://doi.org/10.1093/molbev/msu300 (2015).Hoang, D. T. et al. UFBoot2: improving the ultrafast bootstrap approximation. Mol. Biol. Evol. https://doi.org/10.1093/molbev/msx281 (2018).Kalyaanamoorthy, S. et al. ModelFinder: fast model selection for accurate phylogenetic estimates. Nat. Methods. https://doi.org/10.1038/nmeth.4285 (2017).Price, M. N. et al. FastTree 2–approximately maximum-likelihood trees for large alignments. PLoS One. https://doi.org/10.1371/journal.pone.0009490 (2010).Stamatakis, A. RAxML-VI-HPC: Maximum likelihood-based phylogenetic analyses with thousands of taxa and mixed models. Bioinformatics. https://doi.org/10.1093/bioinformatics/btl446 (2006).Kumar, S. et al. MEGA-CC: Computing core of molecular evolutionary genetics analysis program for automated and iterative data analysis. Bioinformatics. https://doi.org/10.1093/bioinformatics/bts507 (2012).Shimodaira, H. An approximately unbiased test of phylogenetic tree selection. Syst. Biol. https://doi.org/10.1080/10635150290069913 (2002).Olm, M. R. et al. dRep: a tool for fast and accurate genomic comparisons that enables improved genome recovery from metagenomes through de-replication. ISME J. https://doi.org/10.1038/ismej.2017.126 (2017).Posada, D. jModelTest: phylogenetic model averaging. Mol. Biol. Evol. 7, 1253–1256 (2008).
Google Scholar 
Letunic, I. et al. Interactive Tree Of Life (iTOL): an online tool for phylogenetic tree display and annotation. Bioinformatics 23, 127–128 (2007).CAS 
PubMed 

Google Scholar  More

RESEARCH HIGHLIGHT
03 March 2023

Human hunt at least 19% of bat species worldwide — especially flying foxes, which can have wingspans of 1.5 metres. More