Ecology

Price, P. W. Evolutionary Biology of Parasites (Princeton University Press, 1980).
Google Scholar 
Lima, L. B., Bellay, S., Giacomini, H. C., Isaac, A. & Lima-Junior, D. P. Influence of host diet and phylogeny on parasite sharing by fish in a diverse tropical floodplain. Parasitology 143, 343–349 (2016).CAS 
PubMed 

Google Scholar 
Eizaguirre, C., Lenz, T. L., Kalbe, M. & Milinski, M. Rapid and adaptive evolution of MHC genes under parasite selection in experimental vertebrate populations. Nat. Commun. 3, 1–6 (2012).
Google Scholar 
Bashey, F. Within-host competitive interactions as a mechanism for the maintenance of parasite diversity. Philos. Trans. R. Soc. B Biol. Sci. 370, 20140301 (2015).
Google Scholar 
Jolles, J. W., Mazué, G. P. F., Davidson, J., Behrmann-Godel, J. & Couzin, I. D. Schistocephalus parasite infection alters sticklebacks’ movement ability and thereby shapes social interactions. Sci. Rep. 10, 12282 (2020).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Demandt, N. et al. Parasite-infected sticklebacks increase the risk-taking behaviour of uninfected group members. Proc. R. Soc. B Biol. Sci. 285, 20180956 (2018).
Google Scholar 
Poulin, R. Parasite manipulation of host behavior: An update and frequently asked questions. Adv. Study Behav. 41, 151–186 (2010).
Google Scholar 
Terui, A., Ooue, K., Urabe, H. & Nakamura, F. Parasite infection induces size-dependent host dispersal: Consequences for parasite persistence. Proc. R. Soc. B Biol. Sci. 284, 20171491 (2017).
Google Scholar 
Raeymaekers, J. A. M. et al. Contrasting parasite communities among allopatric colour morphs of the Lake Tanganyika cichlid Tropheus. BMC Evol. Biol. 13, 41 (2013).PubMed 
PubMed Central 

Google Scholar 
Meyer, B. S. et al. An exploration of the links between parasites, trophic ecology, morphology, and immunogenetics in the Lake Tanganyika cichlid radiation. Hydrobiologia 832, 215–233 (2019).PubMed 

Google Scholar 
Gobbin, T. P. et al. Temporally consistent species differences in parasite infection but no evidence for rapid parasite-mediated speciation in Lake Victoria cichlid fish. J. Evol. Biol. 33, 556–575 (2020).PubMed 
PubMed Central 

Google Scholar 
Karvonen, A., Wagner, C. E., Selz, O. M. & Seehausen, O. Divergent parasite infections in sympatric cichlid species in Lake Victoria. J. Evol. Biol. 31, 1313–1329 (2018).PubMed 

Google Scholar 
Bush, S. E. et al. Host defense triggers rapid adaptive radiation in experimentally evolving parasites. Evol. Lett. 3, 120–128 (2019).PubMed 
PubMed Central 

Google Scholar 
Waid, R. M., Raesly, R. L., Mckaye, K. R. & McCrary, J. Zoogeografía íctica de lagunas cratéricas de Nicaragua. Encuentro 51, 65–80 (1999).
Google Scholar 
Barluenga, M., Stölting, K., Salzburger, W., Muschick, M. & Meyer, A. Sympatric speciation in Nicaraguan crater lake cichlid fish. Nature 439, 719–723 (2006).ADS 
CAS 
PubMed 

Google Scholar 
Elmer, K. R., Lehtonen, T. K., Fan, S. & Meyer, A. Crater lake colonization by neotropical cichlid fishes. Evolution 67, 281–288 (2012).PubMed 

Google Scholar 
Kautt, A. F. et al. Contrasting signatures of genomic divergence during sympatric speciation. Nature 588, 106–111 (2020).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Elmer, K. R., Lehtonen, T. K., Kautt, A. F., Harrod, C. & Meyer, A. Rapid sympatric ecological differentiation of crater lake cichlid fishes within historic times. BMC Biol. 8, 1–15 (2010).
Google Scholar 
Kautt, A. F., Machado-Schiaffino, G., Torres-Dowdall, J. & Meyer, A. Incipient sympatric speciation in Midas cichlid fish from the youngest and one of the smallest crater lakes in Nicaragua due to differential use of the benthic and limnetic habitats? Ecol. Evol. 6, 5342–5357 (2016).PubMed 
PubMed Central 

Google Scholar 
Barluenga, M. & Meyer, A. Phylogeography, colonization and population history of the Midas cichlid species complex (Amphilophus spp.) in the Nicaraguan crater lakes. BMC Evol. Biol. 10, 326 (2010).PubMed 
PubMed Central 

Google Scholar 
Elmer, K. R., Lehtonen, T. K. & Meyer, A. Color assortative mating contributes to sympatric divergence of neotropical cichlid fish. Evolution 63, 2750–2757 (2009).PubMed 

Google Scholar 
Kautt, A. F., Machado-Schiaffino, G. & Meyer, A. Lessons from a natural experiment: Allopatric morphological divergence and sympatric diversification in the Midas cichlid species complex are largely influenced by ecology in a deterministic way. Evol. Lett. 2, 323–340 (2018).PubMed 
PubMed Central 

Google Scholar 
Elmer, K. R., Kusche, H., Lehtonen, T. K. & Meyer, A. Local variation and parallel evolution: Morphological and genetic diversity across a species complex of neotropical crater lake cichlid fishes. Philos. Trans. R. Soc. B Biol. Sci. 365, 1763–1782 (2010).
Google Scholar 
Elmer, K. R. et al. Parallel evolution of Nicaraguan crater lake cichlid fishes via non-parallel routes. Nat. Commun. 5, 1–8 (2014).
Google Scholar 
Vanhove, M. P. M. et al. Cichlids: A host of opportunities for evolutionary parasitology. Trends Parasitol. 32, 820–832 (2016).PubMed 

Google Scholar 
Choudhury, A. et al. Trematode diversity in freshwater fishes of the Globe II: ‘New World’. Syst. Parasitol. 93, 271–282 (2016).PubMed 

Google Scholar 
Watson, D. E. Digenea of fishes from Lake Nicaragua. In Investigations of the Ichthyofauna of Nicaraguan Lakes Vol. 15 (ed. Thorson, T. B.) 251–260 (University of Nebraska Press, 1976).
Google Scholar 
Aguirre-Macedo, M. L. et al. Larval helminths parasitizing freshwater fishes from the Atlantic coast of Nicaragua. Comp. Parasitol. 68, 42–51 (2001).
Google Scholar 
Aguirre-Macedo, M. L. et al. Some adult endohelminths parasitizing freshwater fishes from the Atlantic Drainages of Nicaragua. Comp. Parasitol. 68, 190–195 (2001).
Google Scholar 
Mendoza-Franco, E. F., Posel, P. & Dumailo, S. Monogeneans (Dactylogyridae: Ancyrocephalinae) of freshwater fishes from the Caribbean coast of Nicaragua. Comp. Parasitol. 70, 32–41 (2003).
Google Scholar 
Andrade-Gómez, L., Pinacho-Pinacho, C. D. & García-Varela, M. Molecular, morphological, and ecological data of Saccocoelioides Szidat, 1954 (Digenea: Haploporidae) from Middle America supported the reallocation from Culuwiya cichlidorum to Saccocoelioides. J. Parasitol. 103, 257–267 (2017).PubMed 

Google Scholar 
López-Jiménez, A., Pérez-Ponce de León, G. & García-Varela, M. Molecular data reveal high diversity of Uvulifer (Trematoda: Diplostomidae) in Middle America, with the description of a new species. J. Helminthol. 92, 725–739 (2018).PubMed 

Google Scholar 
Vidal-Martínez, V. M., Scholz, T. & Aguirre-Macedo, M. L. Dactylogyridae of cichlid fishes from Nicaragua, Central America, with descriptions of Gussevia herotilapiae sp. n. and three new species of Sciadicleithrum (Monogenea: Ancyrocephalinae). Comp. Parasitol. 68, 76–86 (2001).
Google Scholar 
de Chambrier, A. & Vaucher, C. Proteocephalus gaspari n. sp. (Cestoda: Proteocephalidae), parasite de Lepisosteus tropicus (Gill.) au Lac Managua (Nicaragua). Rev. suisse Zool. 91, 229–233 (1984).
Google Scholar 
González-Solís, A. D. & Jiménez-García, M. I. Parasitic nematodes of freshwater fishes from two nicaraguan crater lakes. Comp. Parasitol. 73, 188–192 (2006).
Google Scholar 
Santacruz, A., Morales-Serna, F. N., Leal-Cardín, M., Barluenga, M. & Pérez-Ponce de León, G. Acusicola margulisae n. sp. (Copepoda: Ergasilidae) from freshwater fishes in a Nicaraguan crater lake based on morphological and molecular evidence. Syst. Parasitol. 97, 165–177 (2020).PubMed 

Google Scholar 
Santacruz, A., Barluenga, M. & Pérez-Ponce de León, G. Taxonomic assessment of the genus Procamallanus (Nematoda) in Middle American cichlids (Osteichthyes) with molecular data, and the description of a new species from Nicaragua and Costa Rica. Parasitol. Res. 120, 1965–1977 (2021).PubMed 

Google Scholar 
Bush, A. O., Lafferty, K. D., Lotz, J. M. & Shostak, A. W. Parasitology meets ecology on its own terms: Margolis et al. revisited. J. Parasitol. 83, 575–583 (1997).CAS 
PubMed 

Google Scholar 
Rózsa, L., Reiczigel, J. & Majoros, G. Quantifying parasites in samples of hosts. J. Parasitol. 86, 228–232 (2000).PubMed 

Google Scholar 
Krebs, C. J. Species diversity measures. In Ecological Methodology (ed. Krebs, C. J.) (Addison-Wesley Educational Publishers, 2014).
Google Scholar 
Dixon, P. VEGAN, a package of R functions for community ecology. J. Veg. Sci. 14, 927–930 (2003).
Google Scholar 
R Core Team. A language and environment for statistical computing. R Found. Stat. Comput. (2018). https://www.R-project.org.Wickham, H. Elegant Graphics for Data Analysis: ggplot2 (Springer, 2008).MATH 

Google Scholar 
Hsieh, T. C., Ma, K. H. & Chao, A. iNEXT-package: Interpolation and extrapolation for species diversity. Methods Ecol. Evol. 7, 1451–1456 (2016).
Google Scholar 
Chao, A. et al. Rarefaction and extrapolation with Hill numbers: A framework for sampling and estimation in species diversity studies. Ecol. Monogr. 84, 45–67 (2014).
Google Scholar 
Poulin, R. Parasite biodiversity revisited: Frontiers and constraints. Int. J. Parasitol. 44, 581–589 (2014).PubMed 

Google Scholar 
Salzburger, W. Understanding explosive diversification through cichlid fish genomics. Nat. Rev. Genet. 19, 705–717 (2018).CAS 
PubMed 

Google Scholar 
Barluenga, M. & Meyer, A. The Midas cichlid species complex: Incipient sympatric speciation in Nicaraguan cichlid fishes? Mol. Ecol. 13, 2061–2076 (2004).CAS 
PubMed 

Google Scholar 
Elmer, K. R. & Meyer, A. Adaptation in the age of ecological genomics: Insights from parallelism and convergence. Trends Ecol. Evol. 26, 298–306 (2011).PubMed 

Google Scholar 
Pérez-Ponce de León, G. & Choudhury, A. Biogeography of helminth parasites of freshwater fishes in Mexico: The search for patterns and processes. J. Biogeogr. 32, 645–659 (2005).
Google Scholar 
Blais, J. et al. MHC adaptive divergence between closely related and sympatric African cichlids. PLoS ONE 2, e734 (2007).ADS 
PubMed 
PubMed Central 

Google Scholar 
Pariselle, A. et al. The monogenean parasite fauna of cichlids: A potential tool for host biogeography. Int. J. Evol. Biol. 2011, 1–15 (2011).
Google Scholar 
Aguilar-Aguilar, R., Salgado-Maldonado, G., Contreras-Medina, R. & Martínez-Aquino, A. Richness and endemism of helminth parasites of freshwater fishes in Mexico. Biol. J. Linn. Soc. 94, 435–444 (2008).
Google Scholar 
Dogiel, V. A. Ecology of parasites of freshwater fish. In Parasitology of Fishes (eds Dogiel, V. A. et al.) 1–47 (Edinburgh Oliver & Boyd, 1961).
Google Scholar 
Poulin, R. & Valtonen, E. T. The predictability of helminth community structure in space: A comparison of fish populations from adjacent lakes. Int. J. Parasitol. 32, 1235–1243 (2002).PubMed 

Google Scholar 
Razo-Mendivil, U., Rosas-Valdez, R. & Pérez-Ponce de León, G. A new Cryptogonimid (Digenea) from the mayan cichlid, Cichlasoma urophthalmus (Osteichthyes: Cichlidae), in several localities of the Yucatán Peninsula, Mexico. J. Parasitol. 94, 1371–1378 (2009).
Google Scholar 
Mendoza-Franco, E. F. et al. Occurrence of Sciadicleithrum mexicanum Kritsky, Vidal-Martinez et Rodríguez-Canul, 1994 (Monogenea: Dactylogyridae) in the Cichlid Cichlasoma urophthalmus from a flooded quarry in Yucatan, Mexico. Mem. Inst. Oswaldo Cruz 90, 319–324 (1995).
Google Scholar 
Blasco-Costa, I. & Poulin, R. Host traits explain the genetic structure of parasites: A meta-analysis. Parasitology 140, 1316–1322 (2013).PubMed 

Google Scholar 
Torchin, M. E., Lafferty, K. D., Dobson, A. P., McKenzie, V. J. & Kuris, A. M. Introduced species and their missing parasites. Nature 421, 628–630 (2003).ADS 
CAS 
PubMed 

Google Scholar 
Salgado-Maldonado, G. et al. Helminth parasites of freshwater fishes of the Balsas River drainage basin of southwestern Mexico. Comp. Parasitol. 68, 196–203 (2001).
Google Scholar 
McCrary, J. K., Murphy, B. R., Stauffer, J. R. & Hendrix, S. S. Tilapia (Teleostei: Cichlidae) status in Nicaraguan natural waters. Environ. Biol. Fishes 78, 107–114 (2007).
Google Scholar 
García-Vásquez, A., Pinacho-Pinacho, C. D., Guzmán-Valdivieso, I., Calixto-Rojas, M. & Rubio-Godoy, M. Morpho-molecular characterization of Gyrodactylus parasites of farmed tilapia and their spillover to native fishes in Mexico. Sci. Rep. 11, 1–17 (2021).
Google Scholar 
Paredes-Trujillo, A., Velázquez-Abunader, I., Torres-Irineo, E., Romero, D. & Vidal-Martínez, V. M. Geographical distribution of protozoan and metazoan parasites of farmed Nile tilapia Oreochromis niloticus (L.) (Perciformes: Cichlidae) in Yucatán, México. Parasit. Vectors 9, 66 (2016).PubMed 
PubMed Central 

Google Scholar 
Zhang, S. et al. Monogenean fauna of alien tilapias (Cichlidae) in south China. Parasite 26, 4 (2019).PubMed 
PubMed Central 

Google Scholar 
Outa, J. O., Dos Santos, Q. M., Avenant-Oldewage, A. & Jirsa, F. Parasite diversity of introduced fish Lates niloticus, Oreochromis niloticus and endemic Haplochromis spp. of Lake Victoria. Kenya. Parasitol. Res. 120, 1583 (2021).PubMed 

Google Scholar 
Smit, N. J., Malherbe, W. & Hadfield, K. A. Alien freshwater fish parasites from South Africa: Diversity, distribution, status and the way forward. Int. J. Parasitol. Parasites Wildl. 6, 386–401 (2017).PubMed 
PubMed Central 

Google Scholar 
Pérez-Ponce de León, G., Lagunas-Calvo, O., García-Prieto, L., Briosio-Aguilar, R. & Aguilar-Aguilar, R. Update on the distribution of the co-invasive Schyzocotyle acheilognathi (= Bothriocephalus acheilognathi), the Asian fish tapeworm, in freshwater fishes of Mexico. J. Helminthol. 92, 279–290 (2018).PubMed 

Google Scholar 
Scholz, T., Šimková, A., Razanabolana, J. R. & Kuchta, R. The first record of the invasive Asian fish tapeworm (Schyzocotyle acheilognathi) from an endemic cichlid fish in Madagascar. Helminthol. 55, 84–87 (2018).CAS 

Google Scholar 
Acosta, A., Carvalho, E. & da Silva, R. First record of Lernaea cyprinacea (copepoda) in a native fish species from a Brazilian river. Neotrop. Helminthol. 7, 7–12 (2013).
Google Scholar 
Choudhury, A. et al. The invasive asian fish tapeworm, Bothriocephalus acheilognathi Yamaguti, 1934, in the chagres river/panama canal drainage, Panama. BioInvas. Rec. 2, 99–104 (2013).
Google Scholar 
Schatz, H. & Behan-Pelletier, V. Global diversity of oribatids (Oribatida: Acari: Arachnida). Hydrobiologia 595, 323–328 (2008).
Google Scholar 
Choudhury, A., Hoffnagle, T. L. & Cole, R. A. Parasites of native and nonnative fishes of the Little Colorado River, Grand Canyon, Arizona. J. Parasitol. 90, 1042–1053 (2004).PubMed 

Google Scholar 
Vanhove, M. P. M. Part 6: Evolutionary parasitology of African freshwater fishes—And its implications for the sustainable management of aquatic resources. In A Guide to the Parasites of African Freshwater Fishes (eds Scholz, T. et al.) 403–412 (Royal Belgian Institute of Natural Sciences, 2018).
Google Scholar 
Catalano, S. R., Whittington, I. D., Donnellan, S. C. & Gillanders, B. M. Parasites as biological tags to assess host population structure: Guidelines, recent genetic advances and comments on a holistic approach. Int. J. Parasitol. Parasites Wildl. 3, 220–226 (2014).PubMed 

Google Scholar 
Baldwin, R. E., Banks, M. A. & Jacobson, K. C. Integrating fish and parasite data as a holistic solution for identifying the elusive stock structure of Pacific sardines (Sardinops sagax). Rev. Fish Biol. Fish. 22, 137–156 (2011).
Google Scholar 
Criscione, C. D. & Blouin, M. S. Parasite phylogeographical congruence with salmon host evolutionarily significant units: Implications for salmon conservation. Mol. Ecol. 16, 993–1005 (2007).CAS 
PubMed 

Google Scholar 
Vanhove, M. P. M. et al. Hidden biodiversity in an ancient lake: Phylogenetic congruence between Lake Tanganyika tropheine cichlids and their monogenean flatworm parasites. Sci. Rep. 5, 1–15 (2015).
Google Scholar 
Matschiner, M., Böhne, A., Ronco, F. & Salzburger, W. The genomic timeline of cichlid fish diversification across continents. Nat. Commun. 11, 1–8 (2020).
Google Scholar 
Choudhury, A., García-Varela, M. & Pérez-Ponce de León, G. Parasites of freshwater fishes and the Great American biotic interchange: A bridge too far? J. Helminthol. 91, 174–196 (2017).CAS 
PubMed 

Google Scholar 
Mendoza-Franco, E. F. & Vidal-Martínez, V. M. Phylogeny of species of Sciadicleithrum (Monogenoidea: Ancyrocephalinae), and their historical biogeography in the Neotropics. J. Parasitol. 91, 253–259 (2005).PubMed 

Google Scholar 
de Chambrier, A., Pinacho-Pinacho, C. D., Hernández-Orts, J. S. & Scholz, T. T. A new genus and two new species of proteocephalidean tapeworms (Cestoda) from cichlid fish (Perciformes: Cichlidae) in the neotropics. J. Parasitol. 103, 83–94 (2017).PubMed 

Google Scholar 
Mendoza-Palmero, C. A., Blasco-Costa, I., Hernández-Mena, D. & Pérez-Ponce de León, G. Parasciadicleithrum octofasciatum n. gen., n. sp. (Monogenoidea: Dactylogyridae), parasite of Rocio octofasciata (Regan) (Cichlidae: Perciformes) from Mexico characterised by morphological and molecular evidence. Parasitol. Int. 66, 152–162 (2017).PubMed 

Google Scholar 
Pinacho-Pinacho, C. D., Hernández-Orts, J. S., Sereno-Uribe, A. L., Pérez-Ponce de León, G. & García-Varela, M. Mayarhynchus karlae n. g., n. sp. (Acanthocephala: Neoechinorhynchidae), a parasite of cichlids (Perciformes: Cichlidae) in southeastern Mexico, with comments on the paraphyly of Neoechynorhynchus Stiles & Hassall, 1905. Syst. Parasitol. 94, 351–365 (2017).PubMed 

Google Scholar 
Razo-Mendivil, U., Vázquez-Domínguez, E., Rosas-Valdez, R., Pérez-Ponce de León, G. & Nadler, S. A. Phylogenetic analysis of nuclear and mitochondrial DNA reveals a complex of cryptic species in Crassicutis cichlasomae (Digenea: Apocreadiidae), a parasite of Middle-American cichlids. Int. J. Parasitol. 40, 471–486 (2010).CAS 
PubMed 

Google Scholar 
Razo-Mendivil, U., Rosas-Valdez, R., Rubio-Godoy, M. & Pérez-Ponce de León, G. The use of mitochondrial and nuclear sequences in prospecting for cryptic species in Tabascotrema verai (Digenea: Cryptogonimidae), a parasite of Petenia splendida (Cichlidae) in Middle America. Parasitol. Int. 64, 173–181 (2015).CAS 
PubMed 

Google Scholar 
Pinacho-Pinacho, C. D., García-Varela, M., Sereno-Uribe, A. L. & Pérez-Ponce de León, G. A hyper-diverse genus of acanthocephalans revealed by tree-based and non-tree-based species delimitation methods: Ten cryptic species of Neoechinorhynchus in Middle American freshwater fishes. Mol. Phylogenet. Evol. 127, 30–45 (2018).PubMed 

Google Scholar 
Martínez-Aquino, A. et al. Detecting a complex of cryptic species within Neoechinorhynchus golvani (Acanthocephala: Neoechinorhynchidae) inferred from ITSs and LSU rDNA gene sequences. J. Parasitol. 95, 1040–1047 (2009).PubMed 

Google Scholar  More

Kumar, N. et al. Peste des petits ruminants virus infection of small ruminants: A comprehensive review. Viruses 6, 2287–2327. https://doi.org/10.3390/v6062287 (2014).Article 
PubMed 
PubMed Central 

Google Scholar 
Zientara, S., Weyer, C. T. & Lecollinet, S. African horse sickness. Rev. Sci. Tech. 34, 315–327. https://doi.org/10.20506/rst.34.2.2359 (2015).CAS 
Article 
PubMed 

Google Scholar 
Rutkowska, D. A., Mokoena, N. B., Tsekoa, T. L., Dibakwane, V. S. & O’Kennedy, M. M. Plant-produced chimeric virus-like particles—A new generation vaccine against African horse sickness. BMC Vet. Res. 15, 1. https://doi.org/10.1016/j.rvsc.2010.05.031 (2019).CAS 
Article 

Google Scholar 
Barnard, B. J. H. Epidemiology of African horse sickness and the role of zebra in South Africa. Arch. Virol. Suppl. 14, 13–19. https://doi.org/10.1007/978-3-7091-6823-3_3 (1998).CAS 
Article 
PubMed 

Google Scholar 
Hamblin, C., Salt, J. S., Mellor, P. S., Graham, S. D. & Wohlsein, P. Donkeys as reservoirs of African horse sickness virus. Arch. Virol. Suppl. 14, 37–47. https://doi.org/10.1007/978-3-7091-6823-3_5 (1998).CAS 
Article 
PubMed 

Google Scholar 
Mellor, P. S., Boorman, J. P. T. & Baylis, M. Culicoides biting midges: their role as arbovirus vectors. Annu. Rev. Entomol. 45, 307–340 (2000).CAS 
Article 

Google Scholar 
Redmond, E. F., Jones, D. & Rushton, J. Economic assessment of african horse sickness vaccine impact. Equine Vet. J. https://doi.org/10.1111/j.2042-3306.1982.tb02404.x (2021).Article 
PubMed 

Google Scholar 
Venter, G. J., Wright, I. M., Linde, T. C. V. D. & Paweska, J. T. The oral susceptibility of South African field populations of Culicoides to African horse sickness virus. Med. Vet. Entomol. 23, 367–378. https://doi.org/10.1111/j.1365-2915.2009.00829.x (2010).Article 

Google Scholar 
Mellor, P. S., Boned, J., Hamblin, C. & Graham, S. D. Isolations of African horse sickness virus from vector insects made during the 1988 epizootic in Spain. Epidemiol. Infect. 105, 447–454. https://doi.org/10.1017/s0950268800048020 (1990).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Meiswinkel, R. & Paweska, J. T. Evidence for a new field Culicoides vector of African horse sickness in South Africa. Prev. Vet. Med. 60, 243–253. https://doi.org/10.1016/s0167-5877(02)00231-3 (2003).CAS 
Article 
PubMed 

Google Scholar 
Howell, P. G. The isolation and identification of further antigenic types of African horsesickness virus. Onderstepoort. J. Vet. Res. 29, 139–149 (1962).
Google Scholar 
Calisher, C. H. & Mertens, P. P. C. Taxonomy of African horse sickness viruses. Arch. Virol. Suppl. 14, 3 (1998).CAS 
PubMed 

Google Scholar 
Rodriguez, M., Hooghuis, H. & Castaño, M. African horse sickness in Spain. Vet. Microbiol. 33, 129–142. https://doi.org/10.1016/0378-1135(92)90041-q (1992).CAS 
Article 
PubMed 

Google Scholar 
Howell, P. G. The 1960 epizootic of African Horsesickness in the Middle East and S.W. Asia (268KB) (268KB). J. South Afr. Vet. Med. Assoc. (1960).King, S., RajkoEnow, P., Ashby, M., Frost, L. & Batten, C. Outbreak of African Horse Sickness in Thailand, 2020. Transbound. Emerg. Dis. (2020).OIE. World Animal Health Information System. https://www.oie.int/wahis_2/public/wahid.php/Reviewreport/Review?page_refer=MapFullEventReport&reportid=33768 (2020).Castillo-Olivares, J. African horse sickness in Thailand: Challenges of Controlling an outbreak by vaccination. Equine Vet. J. (2020).Gibbens, N. Schmallenberg virus: a novel viral disease in northern Europe. Vet. Rec. 170, 58. https://doi.org/10.1136/vr.e292 (2012).Article 
PubMed 

Google Scholar 
Purse, B. V., Brown, H. E., Harrup, L., Mertens, P. & Rogers, D. J. Invasion of bluetongue and other orbivirus infections into Europe: the role of biological and climatic processes. Rev. Sci. Tech. 27, 427–442 (2008).CAS 
Article 

Google Scholar 
Leta, S., Fetene, E., Mulatu, T., Amenu, K. & Revie, C. W. Modeling the global distribution of Culicoides imicola: an Ensemble approach. Sci. Rep. 9, 1 (2019).CAS 
Article 

Google Scholar 
Thepparat, A., Bellis, G., Ketavan, C., Ruangsittichai, J. & Apiwathnasorn, C. T. species of Culicoides Latreille (Diptera: Ceratopogonidae) newly recorded from Thailand. Zootaxa 4033, 48–56. https://doi.org/10.11646/zootaxa.4033.1.2 (2015).Article 
PubMed 

Google Scholar 
Raksakoon, C. & Potiwat, R. Current arboviral threats and their potential vectors in Thailand. Pathogens 10, 80 (2021).CAS 
Article 

Google Scholar 
Gao, S. et al. Transboundary spread of peste des petits ruminants virus in western China: A prediction model. PLoS ONE 16, e0257898–e0257898. https://doi.org/10.1371/journal.pone.0257898 (2021).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Joka, F. R., Van Gils, H., Huang, L. & Wang, X. High probability areas for ASF infection in china along the russian and korean borders. Transbound. Emerg. Dis. https://doi.org/10.1016/j.watres.2015.05.061.Steven et al. Opening the black box: an open-source release of Maxent. Ecography (2017).Gils, H. V., Westinga, E., Carafa, M., Antonucci, A. & Ciaschetti, G. Where the bears roam in Majella National Park, Italy. J. Nat. Conser. 22, 23–34. https://doi.org/10.1016/j.jnc.2013.08.001 (2014).Article 

Google Scholar 
Duque-Lazo, J., Navarro-Cerrillo, R. M., Van Gils, H. & Groen, T. A. Forecasting oak decline caused by Phytophthora cinnamomi in Andalusia : identification of priority areas for intervention. For. Ecol. Manage. 417, 122–136 (2018).Article 

Google Scholar 
Duque-Lazo, J., Gils, H. V., Groen, T. A. & Cerrillo, R. M. N. Transferability of species distribution models: The case of Phytophthora cinnamomi in Southwest Spain and Southwest Australia. Ecol. Model. 320, 62–70 (2016).Article 

Google Scholar 
Zeng, Z., Gao, S., Wang, H.-N., Huang, L.-Y. & Wang, X.-L. A predictive analysis on the risk of peste des petits ruminants in livestock in the Trans-Himalayan region and validation of its transboundary transmission paths. PLoS ONE 16, e0257094–e0257094. https://doi.org/10.1371/journal.pone.0257094 (2021).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Joka, F. R., Wang, H., van Gils, H. & Wang, X. Could wild boar be the Trans-Siberian transmitter of African swine fever?. Transbound. Emerg. Dis. https://doi.org/10.1111/tbed.13814 (2020).Article 
PubMed 

Google Scholar 
Robin, M., Page, P., Archer, D. & Baylis, M. African horse sickness: The potential for an outbreak in disease-free regions and current disease control and elimination techniques. Equine Vet. J. 48, 659–669. https://doi.org/10.1111/evj.12600 (2016).CAS 
Article 
PubMed 

Google Scholar 
Maclachlan, N. J. & Guthrie, A. J. Re-emergence of bluetongue, African horse sickness, and other Orbivirus diseases. Vet. Res. 41, 35. https://doi.org/10.1051/vetres/2010007 (2010).Article 
PubMed 
PubMed Central 

Google Scholar 
M. et al. African horse sickness: The potential for an outbreak in disease-free regions and current disease control and elimination techniques. Equine Vet. J. https://doi.org/10.1111/evj.12600 (2016).Eagles, D., Melville, L., Weir, R. & Davis, S. Long-distance aerial dispersal modelling of Culicoides biting midges: case studies of incursions into Australia. BMC Vet. Res. 10, 1. https://doi.org/10.1186/1746-6148-10-135 (2014).Article 

Google Scholar 
Pedgley, D. E. & Tucker, M. R. Possible spread of African horse sickness on the wind. J. Hygiene 79, 279–298 (1977).CAS 
Article 

Google Scholar 
Riddin, M. A., Venter, G. J., Labuschagne, K. & Villet, M. H. Culicoides species as potential vectors of African horse sickness virus in the southern regions of South Africa. Med. Vet. Entomol. 33, 1 (2019).Article 

Google Scholar 
Carpenter, S., Mellor, P. S., Fall, A. G., Garros, C. & Venter, G. J. African horse sickness Virus: History, transmission, and current status. Annu. Rev. Entomol. 62, 343–358. https://doi.org/10.1146/annurev-ento-031616-035010 (2017).CAS 
Article 
PubMed 

Google Scholar 
https://www.oie.int/wahis_2/public/wahid.php/Countryinformation/Countryreports. (Accessed 12 August 2020).OIE. African horse sickness(updated April 2013). OIE Technical Disease Cards, Paris, France: World Organisation for Animal Health. (2013).Ciss, M. et al. Ecological niche modelling to estimate the distribution of Culicoides, potential vectors of bluetongue virus in Senegal. BMC Ecology 19, doi:https://doi.org/10.1186/s12898-019-0261-9 (2019).Harrup, L. E. et al. Does covering of farm-associated Culicoides larval habitat reduce adult populations in the United Kingdom?. Vet. Parasitol. 201, 137–145. https://doi.org/10.1016/j.vetpar.2013.11.028 (2013).Article 
PubMed 

Google Scholar 
Hoch, A. L., Roberts, D. R. & Pinheiro, F. P. Host-seeking behavior and seasonal abundance of Culicoides paraensis (Diptera: Ceratopogonidae) in Brazil. J. Am. Mosq. Control Assoc. 6, 110–114 (1990).CAS 
PubMed 

Google Scholar 
Carpenter, S., Groschup, M. H., Garros, C., Felippe-Bauer, M. L. & Purse, B. V. Culicoides biting midges, arboviruses and public health in Europe. Antiviral Res. 100, 102–113. https://doi.org/10.1016/j.antiviral.2013.07.020 (2013).CAS 
Article 
PubMed 

Google Scholar 
Carpenter, S., Wilson, A., Barber, J., Veronesi, E. & Gubbins, S. Temperature Dependence of the Extrinsic Incubation Period of Orbiviruses in Culicoides Biting Midges. PLoS ONE 6, e27987. https://doi.org/10.1371/journal.pone.0027987 (2011).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Yanase, T. et al. Molecular Identification of Field-CollectedCulicoidesLarvae in the Southern Part of Japan. J. Med. Entomol. (2013).Meiswinkel, R. Afrotropical Culicoides: C (Avaritia) miombo sp. nov., a widespread species closely allied to C. (A.) imicola Kieffer, 1913 (Diptera: Ceratopogonidae). Onderstepoort. J. Vet. Res. 58, 155–170 (1991).Sloyer, K. E. et al. Ecological niche modeling the potential geographic distribution of four Culicoides species of veterinary significance in Florida, USA. PLoS ONE 14, 1 (2019).Article 

Google Scholar 
Reynolds, D. R., Chapman, J. W. & Harrington, R. The migration of insect vectors of plant and animal viruses. Adv. Virus Res. 67, 453–517 (2006).CAS 
Article 

Google Scholar 
L. et al. Investigating Incursions of Bluetongue Virus Using a Model of Long-Distance Culicoides Biting Midge Dispersal. Transbound. Emerg. Dis. https://doi.org/10.1111/j.1865-1682.2012.01345.x (2013).Notice of the general office of the Ministry of agriculture and rural areas and the general office of the State General Administration of sports on printing and distributing the national horse industry development plan (2020–2025). (Animal Husbandry and Veterinary Bureau, 2020.09.29). More

Mehbub MF, Lei J, Franco C, Zhang W. Marine sponge derived natural products between 2001 and 2010: trends and opportunities for discovery of bioactives. Mar Drugs. 2014;12:4539–77.PubMed 
PubMed Central 

Google Scholar 
Sipkema D, Franssen MCR, Osinga R, Tramper J, Wijffels RH. Marine sponges as pharmacy. Mar Biotechnol. 2005;7:142–62.CAS 

Google Scholar 
Dobson CM. Chemical space and biology. Nature. 2004;432:824–8.CAS 
PubMed 

Google Scholar 
Indraningrat AAG, Micheller S, Runderkamp M, Sauerland I, Becking LE, Smidt H, et al. Cultivation of sponge-associated bacteria from Agelas sventres and Xestospongia muta collected from different depths. Mar Drugs. 2019;17:578.CAS 
PubMed Central 

Google Scholar 
Piel J. Metabolites from symbiotic bacteria. Nat Prod Rep. 2009;26:338–62.CAS 
PubMed 

Google Scholar 
Webster NS, Thomas T. The sponge hologenome. mBio. 2016;7:e00135–16.PubMed 
PubMed Central 

Google Scholar 
de Oliveira MRF, de Maringá UE, da Costa C, Benedito E. Trends and gaps in scientific production on freshwater sponges. Oecologia Austrlis. 2020;24:61–75.
Google Scholar 
Manconi R, Pronzato R. How to survive and persist in temporary freshwater? Adaptive traits of sponges (Porifera: Spongillida): a review. Hydrobiologia. 2016;782:11–22.
Google Scholar 
Manconi R, Pronzato R. Chapter 8 – Phylum Porifera. In: Thorp JH, Rogers DC, editors. Ecology and general biology. Thorp and Covich’s freshwater invertebrates. vol 1 (4th ed.) New York: Academic Press; 2015. p. 133–157.Manconi R, Pronzato R. Chapter 3 – Phylum Porifera. In: Thorp JH, Rogers DC, editors. Keys to Nearctic fauna. Thorp and Covich’s freshwater invertebrates vol 2(4th ed.) San Diego: Academic Press, Elsevier; 2016. p. 39–83.Leidy J. On Spongilla. In: Proceedings of the Academy of Natural Sciences of Philadelphia. Philadelphia: Academy of Natural Sciences of Philadelphia; 1850. p. 278.Smith F. Distribution of the fresh-water sponges of North America. INHS Bull. 1921;14:9–22.
Google Scholar 
Old MC. Environmental selection of the fresh-water sponges (Spongillidae) of Michigan. Trans Am Microsc Soc. 1932;51:129–36.CAS 

Google Scholar 
Ashley JM. Fresh water sponges of Illinois and Michigan. Urbana-Champaign: Master of Arts, University of Illinois; 1913.Jewell ME. An ecological study of the fresh-water sponges of northeastern Wisconsin. Ecol Monogr. 1935;5:461–504.CAS 

Google Scholar 
Kolomyjec SH, Willford RA. The fall 2019 genetics class. Phylogenetic analysis of Michigan’s freshwater sponges (Porifera, Spongillidae) using extended COI mtDNA sequences. bioRxiv. 2020; https://doi.org/10.1101/2020.04.26.062448.Copeland J, Kunigelis S, Tussing J, Jett T, Rich C. Freshwater sponges (Porifera: Spongillida) of Tennessee. Am Midl Nat. 2019;181:310–26.
Google Scholar 
Lauer TE, Spacie A. An association between freshwater sponges and the zebra mussel in a southern Lake Michigan harbor. J Freshw Ecol. 2004;19:631–7.
Google Scholar 
Skelton J, Strand M. Trophic ecology of a freshwater sponge (Spongilla lacustris) revealed by stable isotope analysis. Hydrobiologia. 2013;709:227–35.CAS 

Google Scholar 
Early TA, Glonek T. Zebra mussel destruction by a Lake Michigan sponge: populations, in vivo 31P nuclear magnetic resonance, and phospholipid profiling. Environ Sci Technol. 1999;33:1957–62.CAS 

Google Scholar 
Early TA, Kundrat JT, Schorp T, Glonek T. Lake Michigan sponge phospholipid variations with habitat: A 31P nuclear magnetic resonance study. Comp Biochem Physiol. 1996;114:77–89.
Google Scholar 
Dembitsky VM, Rezanka T, Srebnik M. Lipid compounds of freshwater sponges: family Spongillidae, class Demospongiae. Chem Phys Lipids. 2003;123:117–55.CAS 
PubMed 

Google Scholar 
Řezanka T, Sigler K, Dembitsky VM. Syriacin, a novel unusual sulfated ceramide glycoside from the freshwater sponge Ephydatia syriaca (Porifera, Demospongiae, Spongillidae). Tetrahedron. 2006;62:5937–43.
Google Scholar 
Radnaeva LD, Bazarsadueva SV, Taraskin VV, Tulokhonov AK. First data on lipids and microorganisms of deepwater endemic sponge Baikalospongia intermedia and sediments from hydrothermal discharge area of the Frolikha Bay (North Baikal, Siberia). J Great Lakes Res. 2020;46:67–74.CAS 

Google Scholar 
Manconi R, Piccialli V, Pronzato R, Sica D. Steroids in porifera, sterols from freshwater sponges Ephydatia fluviatilis (L.) and Spongilla lacustris (L.). Comp Biochem Physiol. 1988;91:237–45.
Google Scholar 
Belikov S, Belkova N, Butina T, Chernogor L, Kley AM-V, Nalian A, et al. Diversity and shifts of the bacterial community associated with Baikal sponge mass mortalities. PLoS ONE. 2019;14:e0213926.CAS 
PubMed 
PubMed Central 

Google Scholar 
Costa R, Keller-Costa T, Gomes NCM, da Rocha UN, van Overbeek L, van Elsas JD. Evidence for selective bacterial community structuring in the freshwater sponge Ephydatia fluviatilis. Microb Ecol. 2013;65:232–44.PubMed 

Google Scholar 
Laport MS, Pinheiro U, Rachid CTCC. Freshwater sponge Tubella variabilis presents richer microbiota than marine sponge species. Front Microbiol. 2019;10:2799.PubMed 
PubMed Central 

Google Scholar 
Kenny NJ, Plese B, Riesgo A, Itskovich VB. Symbiosis, selection, and novelty: freshwater adaptation in the unique sponges of Lake Baikal. Mol Biol Evol. 2019;36:2462–80.CAS 
PubMed Central 

Google Scholar 
Gaikwad S, Shouche YS, Gade WN. Microbial community structure of two freshwater sponges using Illumina MiSeq sequencing revealed high microbial diversity. AMB Express. 2016;6:40.PubMed 
PubMed Central 

Google Scholar 
Gernert C, Glöckner FO, Krohne G, Hentschel U. Microbial diversity of the freshwater sponge Spongilla lacustris. Microb Ecol. 2005;50:206–12.CAS 
PubMed 

Google Scholar 
Hernandez A, Nguyen LT, Dhakal R, Murphy BT. The need to innovate sample collection and library generation in microbial drug discovery: a focus on academia. Nat Prod Rep. 2021;38:292–300.CAS 
PubMed 
PubMed Central 

Google Scholar 
Li C-Q, Liu W-C, Zhu P, Yang J-L, Cheng K-D. Phylogenetic diversity of bacteria associated with the marine sponge Gelliodes carnosa collected from the Hainan Island coastal waters of the South China Sea. Microb Ecol. 2011;62:800–12.PubMed 

Google Scholar 
Sipkema D, Schippers K, Maalcke WJ, Yang Y, Salim S, Blanch HW. Multiple approaches to enhance the cultivability of bacteria associated with the marine sponge Haliclona (gellius) sp. Appl Environ Microbiol. 2011;77:2130–40.CAS 
PubMed 
PubMed Central 

Google Scholar 
Montalvo NF, Davis J, Vicente J, Pittiglio R, Ravel J, Hill RT. Integration of culture-based and molecular analysis of a complex sponge-associated bacterial community. PLoS ONE. 2014;9:e90517.PubMed 
PubMed Central 

Google Scholar 
Elfeki M, Alanjary M, Green SJ, Ziemert N, Murphy BT. Assessing the efficiency of cultivation techniques to recover natural product biosynthetic gene populations from sediment. ACS Chem Biol. 2018;13:2074–81.CAS 
PubMed 

Google Scholar 
Dieckmann R, Graeber I, Kaesler I, Szewzyk U, von Döhren H. Rapid screening and dereplication of bacterial isolates from marine sponges of the Sula Ridge by intact-cell-MALDI-TOF mass spectrometry (ICM-MS). Appl Microbiol Biotechnol. 2005;67:539–48.CAS 
PubMed 

Google Scholar 
Costa MS, Clark CM, Ómarsdóttir S, Sanchez LM, Murphy BT. Minimizing taxonomic and natural product redundancy in microbial libraries using MALDI-TOF MS and the bioinformatics pipeline IDBac. J Nat Prod. 2019;82:2167–73.CAS 
PubMed 
PubMed Central 

Google Scholar 
Clark CM, Costa MS, Sanchez LM, Murphy BT. Coupling MALDI-TOF mass spectrometry protein and specialized metabolite analyses to rapidly discriminate bacterial function. Proc Natl Acad Sci USA. 2018;115:4981–6.CAS 
PubMed 
PubMed Central 

Google Scholar 
Clark CM, Costa MS, Conley E, Li E, Sanchez LM, Murphy BT. Using the open-source MALDI TOF-MS IDBac pipeline for analysis of microbial protein and specialized metabolite data. J Vis Exp. 2019;147:e59219.
Google Scholar 
Ryzhov V, Fenselau C. Characterization of the protein subset desorbed by MALDI from whole bacterial cells. Anal Chem. 2001;73:746–50.CAS 
PubMed 

Google Scholar 
Welker M, Moore ERB. Applications of whole-cell matrix-assisted laser-desorption/ionization time-of-flight mass spectrometry in systematic microbiology. Syst Appl Microbiol. 2011;34:2–11.CAS 
PubMed 

Google Scholar 
Sandrin TR, Goldstein JE, Schumaker S. MALDI TOF MS profiling of bacteria at the strain level: a review. Mass Spectrom Rev. 2013;32:188–217.CAS 
PubMed 

Google Scholar 
Seuylemezian A, Aronson HS, Tan J, Lin M, Schubert W, Vaishampayan P. Development of a custom MALDI-TOF MS database for species-level identification of bacterial isolates collected from spacecraft and associated surfaces. Front Microbiol. 2018;9:780.PubMed 
PubMed Central 

Google Scholar 
Strejcek M, Smrhova T, Junkova P, Uhlik O. Whole-cell MALDI-TOF MS versus 16S rRNA gene analysis for identification and dereplication of recurrent bacterial isolates. Front Microbiol. 2018;9:1294.PubMed 
PubMed Central 

Google Scholar 
Giraud-Gatineau A, Texier G, Garnotel E, Raoult D, Chaudet H. Insights into subspecies discrimination potentiality from bacteria MALDI-TOF mass spectra by using data mining and diversity studies. Front Microbiol. 2020;11:1931.PubMed 
PubMed Central 

Google Scholar 
LaMontagne MG, Tran PL, Benavidez A, Morano LD. Development of an inexpensive matrix-assisted laser desorption-time of flight mass spectrometry method for the identification of endophytes and rhizobacteria cultured from the microbiome associated with maize. PeerJ. 2021;9:e11359.PubMed 
PubMed Central 

Google Scholar 
Freiwald A, Sauer S. Phylogenetic classification and identification of bacteria by mass spectrometry. Nat Protoc. 2009;4:732–42.CAS 
PubMed 

Google Scholar 
Croxatto A, Prod’hom G, Greub G. Applications of MALDI-TOF mass spectrometry in clinical diagnostic microbiology. FEMS Microbiol Rev. 2012;36:380–407.CAS 
PubMed 

Google Scholar 
Rodríguez-Sánchez B, Cercenado E, Coste AT, Greub G. Review of the impact of MALDI-TOF MS in public health and hospital hygiene, 2018. Eurosurveillance. 2019;24:1800193. PubMed Central 

Google Scholar 
Rahi P, Vaishampayan P. MALDI-TOF MS application in microbial ecology studies. Front Microbiol. 2019;10:2954.PubMed 

Google Scholar 
Popović NT, Kazazić SP, Strunjak-Perović I, Čož-Rakovac R. Differentiation of environmental aquatic bacterial isolates by MALDI-TOF MS. Environ Res. 2017;152:7–16.PubMed 

Google Scholar 
Rahi P, Prakash O, Shouche YS. Matrix-assisted laser desorption/ionization Time-of-Flight mass-spectrometry (MALDI-TOF MS) based microbial identifications: challenges and scopes for microbial ecologists. Front Microbiol. 2016;7:1359.PubMed 
PubMed Central 

Google Scholar 
Schumann P, Maier T. Chapter 13 – MALDI-TOF mass spectrometry applied to classification and identification of bacteria. In: Methods in microbiology, vol 41, ISSN 0580-9517. Goodfellow M, Sutcliffe I, Chun J, editors. Academic Press; 2014. p. 275–306.Murtagh F, Legendre P. Ward’s hierarchical agglomerative clustering method: which algorithms implement Ward’s criterion? J Classif. 2014;31:274–95.
Google Scholar 
Batagelj V. Generalized Ward and related clustering problems. In: Bock HH, editor. North Holland, Amsterdam: Proceedings of the First Conference of the International Federation of Classification Societies; 1988. p. 67–74.van Santen JA, Jacob G, Singh AL, Aniebok V, Balunas MJ, Bunsko D, et al. The natural products atlas: an open access knowledge base for microbial natural products discovery. ACS Cent Sci. 2019;5:1824–33.PubMed 
PubMed Central 

Google Scholar 
Ghyselinck J, Van Hoorde K, Hoste B, Heylen K, De Vos P. Evaluation of MALDI-TOF MS as a tool for high-throughput dereplication. J Microbiol Meth. 2011;86:327–36.CAS 

Google Scholar 
Pruesse E, Peplies J, Glöckner FO. SINA: accurate high-throughput multiple sequence alignment of ribosomal RNA genes. Bioinformatics. 2012;28:1823–9.CAS 
PubMed 
PubMed Central 

Google Scholar 
Henson MW, Lanclos VC, Pitre DM, Weckhorst JL, Lucchesi AM, Cheng C, et al. Expanding the diversity of bacterioplankton isolates and modeling isolation efficacy with large-scale dilution-to-extinction cultivation. Appl Environ Microbiol. 2020;86:e00943–20.CAS 
PubMed 
PubMed Central 

Google Scholar 
Hoffmann T, Krug D, Bozkurt N, Duddela S, Jansen R, Garcia R, et al. Correlating chemical diversity with taxonomic distance for discovery of natural products in myxobacteria. Nat Commun. 2018;9:1–10.CAS 

Google Scholar 
Jensen PR, Williams PG, Oh D-C, Zeigler L, Fenical W. Species-specific secondary metabolite production in marine actinomycetes of the genus Salinispora. Appl Environ Microbiol. 2007;73:1146–52.CAS 
PubMed 

Google Scholar 
Ziemert N, Lechner A, Wietz M, Millán-Aguiñaga N, Chavarria KL, Jensen PR. Diversity and evolution of secondary metabolism in the marine actinomycete genus Salinispora. Proc Natl Acad Sci USA. 2014;111:E1130–9.CAS 
PubMed 
PubMed Central 

Google Scholar 
Bruns H, Crüsemann M, Letzel A-C, Alanjary M, McInerney JO, Jensen PR, et al. Function-related replacement of bacterial siderophore pathways. ISME J. 2018;12:320–9.CAS 
PubMed 

Google Scholar 
Chase AB, Sweeney D, Muskat MN, Guillén-Matus DG, Jensen PR. Vertical inheritance facilitates interspecies diversification in biosynthetic gene clusters and specialized metabolites. MBio. 2021;12:e0270021.PubMed 

Google Scholar 
Covington BC, Xu F, Seyedsayamdost MR. A natural product chemist’s guide to unlocking silent biosynthetic gene clusters. Annu Rev Biochem. 2021;90:763–88.CAS 
PubMed 

Google Scholar 
Adamek M, Alanjary M, Sales-Ortells H, Goodfellow M, Bull AT, Winkler A, et al. Comparative genomics reveals phylogenetic distribution patterns of secondary metabolites in Amycolatopsis species. BMC Genomics. 2018;19:426.PubMed 
PubMed Central 

Google Scholar 
Chevrette MG, Currie CR. Emerging evolutionary paradigms in antibiotic discovery. J Ind Microbiol Biotechnol. 2019;46:257–71.CAS 
PubMed 

Google Scholar 
Zdouc MM, Iorio M, Maffioli SI, Crüsemann M, Donadio S, Sosio M. Planomonospora: a metabolomics perspective on an underexplored Actinobacteria genus. J Nat Prod. 2021;84:204–19.CAS 
PubMed 
PubMed Central 

Google Scholar 
Kang D, Shoaie S, Jacquiod S, Sørensen SJ, Ledesma-Amaro R. Comparative genomics analysis of keratin-degrading Chryseobacterium species reveals their keratinolytic potential for secondary metabolite production. Microorganisms. 2021;9:1042.CAS 
PubMed 
PubMed Central 

Google Scholar 
Han S, Van Treuren W, Fischer CR, Merrill BD, DeFelice BC, Sanchez JM, et al. A metabolomics pipeline for the mechanistic interrogation of the gut microbiome. Nature. 2021;595:415–20.CAS 
PubMed 

Google Scholar 
Newman DJ, Cragg GM. Natural products as sources of new drugs over the nearly four decades from 01/1981 to 09/2019. J Nat Prod. 2020;83:770–803.CAS 
PubMed 

Google Scholar 
Demain AL, Sanchez S. Microbial drug discovery: 80 years of progress. J Antibiot. 2009;62:5–16.CAS 

Google Scholar 
Chambers MC, Maclean B, Burke R, Amodei D, Ruderman DL, Neumann S, et al. A cross-platform toolkit for mass spectrometry and proteomics. Nat Biotechnol. 2012;30:918–20.CAS 
PubMed 
PubMed Central 

Google Scholar 
Gibb S, Strimmer K. Mass spectrometry analysis using MALDIquant. In: Datta S, Mertens BJA, editors. Statistical analysis of proteomics, metabolomics, and lipidomics data using mass spectrometry. Cham: Springer International Publishing; 2017. p. 101–24.Gibb S, Strimmer K. MALDIquant: a versatile R package for the analysis of mass spectrometry data. Bioinformatics. 2012;28:2270–1.CAS 
PubMed 

Google Scholar 
Weisburg WG, Barns SM, Pelletier DA, Lane DJ. 16S ribosomal DNA amplification for phylogenetic study. J Bacteriol. 1991;173:697–703.CAS 
PubMed 
PubMed Central 

Google Scholar  More

Our general strategy was to compare the performance of four approaches for inferring microbial associations from abundance data with overlying time-series signals. The approaches were (1) pairwise spearman correlation analysis (SCC) [1, 29], (2) Graphical lasso analysis (Glasso) [30, 31], (3) pairwise SCC analysis with a pre-processing step where seasonal and long-term splines were fit to and subtracted from each variable using a GAM (GAM-SCC), and (4) Glasso with the same GAM subtraction approach (GAM-Glasso). Our validation strategy for the GAM transformation consisted of generating mock datasets with underlying associations, masking those associations by adding seasonal and long-term signals to the abundance data, and comparing the predicted associations obtained from each network inference method to the true species-species associations.Data simulation: generating mock abundance data with time-series propertiesWe generated mock abundance datasets that had a predetermined, underlying network structure and contained long-term and seasonal species abundance patterns. First, a covariance matrix was generated to describe the relationships between species in a mock dataset (Fig. S1, Panel 1). The covariance matrices were constructed with underlying network structures that followed either a scale-free Barabási-Albert model, a random Erdős-Rényi model, or a model of network topology based on a real microbial dataset (American Gut dataset; Fig. S1) [32, 33]. The Erdős-Rényi and Barabási-Albert model datasets were generated so that each dataset contained 400 species and 200 samples, and the American Gut datasets were created so that each dataset contained 127 species and 200 samples. A random Bernoulli distribution was used to simulate the covariance matrix for the Erdős-Rényi networks. We set the probability of interactions occurring between species in a given Erdős-Rényi network to 1%. The Barabási-Albert networks were generated using the “sample_pa” function in the igraph package [34]. The “graph2prec” function in the SpiecEasi package was used to predict the covariance matrix of the American Gut dataset [33]. The covariance between species in a dataset was considered “high” or “low” when the true associations in the covariance matrix were set to 100 or 10 respectively (Fig. S1, Panel 1). These covariance matrices describe the “real”, underlying species interactions in our mock datasets.After generating a covariance matrix, the mean abundance for each species was generated from a normal distribution with a mean of 10 and a variance of 1. These mean abundance values and the covariance matrix were used to parameterize a multivariate normal distribution from which species abundance values for all 200 samples in a dataset were drawn (Fig. S1, Panel 2). The values generated from this multivariate normal distribution were the species abundance values without time-series features confounding the relationship between two associated species (Fig. S1, Panel 2).“Gradual” or “abrupt” seasonal trends were added to 0%, 25%, 50% or 100% of the species in each mock dataset. The gradual seasonal trend increased over 5 months, peaked at a specific month, and decreased over 5 months. Conversely, the abrupt seasonal signal increased over 2 months, peaked at a specific month, and decreased over 2 months (Fig. S1, Panel 3). These seasonal signals were generated by plugging a vector of consecutive integers of length 200 (Nt) into the gradual (Eq. (1)) or abrupt (Eq. (2)) seasonal equations (Fig. S1, Panel 3)…$$Gradual:S_t = left( {frac{{cos left( {N_t ast 2 ast frac{pi }{{12}}} right)}}{2}} right) + 0.5$$
(1)
$$Abrupt:,S_t = left( {left( {frac{{cos left( {N_t ast 2 ast frac{pi }{{12}}} right)}}{2}} right) + 0.5} right)^{10}$$
(2)
where N is the random vector of consecutive integers, S is the output seasonal vector, and t is the index of vectors N and S. The starting value of vector Nt was drawn at random for each species to allow the seasonal peaks to be centered at different months. Each element in the seasonal vector (St) was then multiplied by the corresponding element in the abundance vector (Xt) of a specific species to obtain mock species abundance values with a gradual or abrupt seasonal trend (Fig. S1, Panel 3).A long-term time-series trend was added to the abundance values of 0% or 50% of the species in the mock datasets (Fig. S1, Panel 4). When a long-term signal was applied to 50% of the species in a dataset, half of the species were randomly selected to have this long-term trend. Then, a vector of linear values was generated following Eq. (3) such that…$$Long – term,trend:,L_t = pm mleft( {L_{t – 1}} right) + 0.01$$
(3)
where Lt is the point in the line at the next time point and m is the slope of the line. The slope parameter (m) was generated from a random normal distribution with a mean of 0.01 and a variance of 0.01. The slope parameter (m) was also multiplied by −1 half of the time to ensure that half of the long-term trends increased over time and half decreased over time (Fig. S1, Panel 4). After generating the vector of linear values (Lt), each element of this vector was added to each element of the abundance vector (Xt) of a specific species to simulate long-term time-series trends (Fig. S1, Panel 4).Time-series predictor columns were added to each dataset after applying monthly and long-term abundance trends to a portion of the species in the mock datasets. The predictors that were used in the downstream GAM-based data transformation were the month of the year (i.e., 1–12) and the day of the time-series (i.e., 1–200). In total, we generated 100 mock datasets for every combination of conditions (84 combinations total; Table S1), resulting in 8400 mock time-series datasets that were used in the downstream count data transformation, GAM subtraction, and network analysis procedures.Data simulation: Simulating count data from abundance valuesThe 8400 time-series datasets that were generated using the methods described above were transformed to make the abundance values resemble high-throughput sequencing data because microbial time-series sampling efforts are often processed using such molecular methods (e.g., tag-sequencing, meta-omics). Analysis of high-throughput sequencing data is complicated by the compositional (i.e., relative) nature of the data and by the high number of zeros that may be prevalent in a dataset (i.e., zero-inflation; see Supplementary Information) [35, 36]. Relative abundances of different species in natural communities are also highly skewed, so that relatively few species constitute most of the organisms in a sample although many rare species are also present [37, 38]. Therefore, species abundances were first exponentiated to increase the prevalence of abundant species and to decrease the prevalence of rare species (Fig. S1, Panel 5). The exponentiated species abundance values were then converted to relative abundance values by dividing each species count by the sum of all species counts in a sample (Fig. S1, Panel 6). The resulting relative abundance values and time-series predictor variables were used in data normalization and GAM-transformation steps prior to carrying out the network analyses.Network inference: Count data normalization and GAM transformationSeveral steps were taken to back out the species-species relationships in the mock datasets. We advocate these steps to infer network structure from a real time-series dataset. A centered log-ratio (CLR) transformation was first applied to the species relative abundance values to normalize the mock species abundance data across samples using the “clr” function in the compositions package in R (Fig. 1) [39]. This transformation step is important to avoid spurious inferences induced by the inherent compositionality of relative abundance data [31, 33, 36]. In addition to the CLR transformation used in our main network iterations, we carried out additional network iterations using the modified CLR [40], cumulative sum scaling [41], and total sum scaling [42] transformations (see Supplementary Information). In all cases, the normalized dataset was copied, with one copy subjected to a subsequent GAM transformation, and the other one not GAM-transformed.Fig. 1: Steps used to carry out the GAM-based transformation of time-series species abundance data prior to carrying out pairwise spearman correlation (SCC) and graphical lasso (Glasso) ecological network analyses.The raw, species abundance data were first CLR-transformed (1). Generalized additive models (GAMs) were then fit to each species in the dataset (2) and the residuals of each GAM were checked for significant autocorrelation (3). The residuals of each GAM were extracted (4) and were used as input in the SCC and Glasso network analysis methods (5). Finally, the GAM-transformed network outputs were obtained (6; see text for additional details).Full size imageThe GAM transformation was carried out by fitting GAMs to each individual species in the dataset to remove monthly signals, long-term trends, and autocorrelation from the species abundance data. These GAMs were fit using the “gamm” function in the mgcv package in R [43, 44]. The GAMs that were used included the “month of year” parameter as a cyclical spline predictor and the “day of time-series” parameter as a penalized thin-plate spline predictor (“ts” in the mgcv package; Fig. 1), which given our one-dimensional data is analogous to a natural cubic spline. In addition, the first GAM included a continuous AR1 (“corCAR1” in the mgcv package) correlation structure term in the model. This corCAR1 model was revised for specific species when the GAM could not be resolved or when significant autocorrelation was detected in the GAM residuals (Fig. 1). The GAM revision step fit 4 new GAMs with different correlation structure terms (i.e., “AR1”, “CompSymm”, “Exp”, and “Gaus”) to the species that could not be fit using the corCAR1 model or that contained significant autocorrelation in the corCAR1 GAM residuals. Then, the correlation structure term that addressed these issues for the largest number of individuals was used as the GAM model for this group of species. After fitting a GAM to all of the species in the input dataset, the residuals of each GAM were extracted and were used as the new, GAM-transformed abundance values (Fig. 1). These GAM residuals represent species abundance values with a reduced influence of time (Fig. 2) and were used as input in the downstream GAM-SCC and GAM-Glasso network analyses.Fig. 2: A conceptual figure that demonstrates how the GAM transformation can remove seasonal signals while preserving ecologically relevant species co-occurrence patterns.In this example, the co-occurrence pattern between Species A and Species B persists even after the seasonal signals are removed by the GAM transformation.Full size imageNetwork inference: Network runs and statistical analysesThe pre-processed species abundance data with and without the GAM-removal of time-series signals were used in SCC and Glasso networks in order to compare the outputs of the SCC, GAM-SCC, Glasso, and GAM-Glasso network inference approaches (Fig. 1). Additional network iterations were also carried out using the CCLasso [45] and SPRING [40] network inference approaches (see Supplementary Information). For the SCC and Glasso network iterations, a nonparanormal transformation was applied to the species abundance datasets with and without the GAM transformation using the “huge.npn” function in the huge package in R [46]. Spearman correlation networks were then constructed by calculating the correlation between every pair of species in the mock abundance datasets. A Bonferroni-corrected p value of 0.01 was used as a cutoff to identify edges in these SCC networks. The Glasso networks were constructed by testing 30 regularization parameter values (i.e., lambdas) in each network using the “batch.pulsar” (criterion = “stars”; rep.num = 50) function in the pulsar package in R [47]. The lambda that resulted in the most stable network output was selected using the StARS method [48]. Finally, the graph that resulted from the StARS output was used to obtain a species adjacency matrix for the Glasso networks.The species-species associations predicted by the SCC, GAM-SCC, Glasso, and GAM-Glasso networks were compared to the true species-species associations and the F1 scores of the network predictions were calculated. The F1 score is a measure of classification performance (presence or absence of an edge) that accounts for uneven classes, which is essential when dealing with sparse networks. The F1 scores of the GAM-transformed networks were compared to the networks that did not undergo GAM transformation using paired Wilcoxon tests with Bonferroni correction. An adjusted p value of 0.01 was used as a cutoff to identify under what circumstances the GAM significantly improved the F1 score of a Glasso or SCC network.Network inference: Comparison of predicted network structuresAdditional networks were generated using the methods described above to compare the predicted network structures obtained from the GAM-Glasso, Glasso, GAM-SCC, and SCC approaches to the real network structures. These additional networks were constructed using smaller mock datasets to allow for better visualization of the network outputs and contained species with a gradual seasonal signal and high species-species covariance (see Supplementary Information). The average clustering coefficient and the degree distribution of these additional network outputs were calculated and used for the network structure comparisons. The average clustering coefficient of a network describes the likelihood that two species that are both associated with a third species are also associated with each other [49], and in a sense describes the “clumpiness” of a network. The network degree distributions describe the probability distribution of the number of interactions per node in a network [50]. More

IPCC. IPCC Fifth Assessment Report (AR5). 10–12 (IPCC, 2014).Cailleret, M. et al. A synthesis of radial growth patterns preceding tree mortality. Glob. Chang. Biol. 23, 1675–1690 (2017).PubMed 

Google Scholar 
Forzieri, G. et al. Emergent vulnerability to climate-driven disturbances in European forests. Nat. Commun. 12, 1–12 (2021).
Google Scholar 
Bonan, G. B. Forests and climate change: forcings, feedbacks, and the climate benefits of forests. Science https://doi.org/10.1126/science.1155121 (2008).Article 
PubMed 

Google Scholar 
Buras, A. & Menzel, A. Projecting tree species composition changes of European forests for 2061–2090 under RCP 4.5 and RCP 8.5 scenarios. Front. Plant Sci. 9, 1–13 (2019).
Google Scholar 
van der Maaten, E. et al. Species distribution models predict temporal but not spatial variation in forest growth. Ecol. Evol. 7, 2585–2594 (2017).PubMed 
PubMed Central 

Google Scholar 
Lebaube, S., Le Goff, N. L., Ottorini, J. M. & Granier, A. Carbon balance and tree growth in a Fagus sylvatica stand. Ann. Sci. 57, 49–61 (2000).
Google Scholar 
Dobbertin, M. Tree growth as indicator of tree vitality and of tree reaction to environmental stress: a review. Eur. J. For. Res. 124, 319–333 (2005).
Google Scholar 
Büntgen, U. Re-thinking the boundaries of dendrochronology. Dendrochronologia 53, 1–4 (2019).
Google Scholar 
Klesse, S. et al. Continental-scale tree-ring-based projection of Douglas-fir growth: Testing the limits of space-for-time substitution. Glob. Chang. Biol. 26, 5146–5163 (2020).PubMed 

Google Scholar 
Zhao, S. et al. The International Tree-Ring Data Bank (ITRDB) revisited: data availability and global ecological representativity. J. Biogeogr. 46, 355–368 (2019).
Google Scholar 
Babst, F. et al. When tree rings go global: challenges and opportunities for retro- and prospective insight. Quat. Sci. Rev. 197, 1–20 (2018).
Google Scholar 
Klesse, S. et al. Sampling bias overestimates climate change impacts on forest growth in the southwestern United States. Nat. Commun. 9, 1–9 (2018).
Google Scholar 
Yousefpour, R. et al. Realizing mitigation efficiency of European commercial forests by climate smart forestry. Sci. Rep. 8, 1–11 (2018).CAS 

Google Scholar 
Giesecke, T., Hickler, T., Kunkel, T., Sykes, M. T. & Bradshaw, R. H. W. Towards an understanding of the Holocene distribution of Fagus sylvatica L. J. Biogeogr. 34, 118–131 (2007).
Google Scholar 
Fang, J. & Lechowicz, M. J. Climatic limits for the present distribution of beech (Fagus L.) species in the world. J. Biogeogr. 33, 1804–1819 (2006).
Google Scholar 
Luterbacher, J., Dietrich, D., Xoplaki, E., Grosjean, M. & Wanner, H. European seasonal and annual temperature variability, trends, and extremes since 1500. Science 303, 1499–1503 (2004).CAS 
PubMed 

Google Scholar 
Luterbacher, J. et al. European summer temperatures since Roman times. Environ. Res. Lett. 11, 24001 (2016).Nabuurs, G. J. et al. By 2050 the mitigation effects of EU forests could nearly double through climate smart forestry. Forests 8, 1–14 (2017).
Google Scholar 
Walentowski, H. et al. Assessing future suitability of tree species under climate change by multiple methods: a case study in southern Germany. Ann. Res. 60, 101–126 (2017).
Google Scholar 
Mäkelä, A. et al. Process-based models for forest ecosystem management: current state of the art and challenges for practical implementation. Tree Physiol. 20, 289–298 (2000).PubMed 

Google Scholar 
Leech, S. M., Almuedo, P. L. & Neill, G. O. Assisted migration: adapting forest management to a changing climate. BC J. Ecosyst. Manag. 12, 18–34 (2011).
Google Scholar 
Sass-Klaassen, U. G. W. et al. A tree-centered approach to assess impacts of extreme climatic events on forests. Front. Plant Sci. 7, 1069 (2016).PubMed 
PubMed Central 

Google Scholar 
Bowman, D. M. J. S., Brienen, R. J. W., Gloor, E., Phillips, O. L. & Prior, L. D. Detecting trends in tree growth: not so simple. Trends Plant Sci. 18, 11–17 (2013).CAS 
PubMed 

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

Google Scholar 
Dorji, Y., Annighöfer, P., Ammer, C. & Seidel, D. Response of beech (Fagus sylvatica L.) trees to competition-new insights from using fractal analysis. Remote Sens. 11, 2656 (2019).Petit-Cailleux, C. et al. Combining statistical and mechanistic models to unravel the drivers of mortality within a rear-edge beech population. bioRxiv https://doi.org/10.1101/645747 (2019).Weigel, R., Gilles, J., Klisz, M., Manthey, M. & Kreyling, J. Forest understory vegetation is more related to soil than to climate towards the cold distribution margin of European beech. J. Veg. Sci. 30, 746–755 (2019).
Google Scholar 
Etzold, S. et al. Nitrogen deposition is the most important environmental driver of growth of pure, even-aged and managed European forests. Forest Ecol. Manag. 458, 117762 (2020).
Google Scholar 
Martínez-Sancho, E. et al. The GenTree dendroecological collection, tree-ring and wood density data from seven tree species across Europe. Sci. Data 7, 1–7 (2020).PubMed 
PubMed Central 

Google Scholar 
Hartl-Meier, C., Dittmar, C., Zang, C. & Rothe, A. Mountain forest growth response to climate change in the Northern Limestone Alps. Trees 28, 819–829 (2014).
Google Scholar 
Way, D. A. & Montgomery, R. A. Photoperiod constraints on tree phenology, performance and migration in a warming world. Plant Cell Environ. 38, 1725–1736 (2015).PubMed 

Google Scholar 
Martínez del Castillo, E. et al. Spatial patterns of climate – growth relationships across species distribution as a forest management tool in Moncayo Natural Park (Spain). Eur. J. Res. 138, 299 (2019).
Google Scholar 
Hacket-Pain, A. J., Cavin, L., Friend, A. D. & Jump, A. S. Consistent limitation of growth by high temperature and low precipitation from range core to southern edge of European beech indicates widespread vulnerability to changing climate. Eur. J. Res. 135, 897–909 (2016).
Google Scholar 
van der Maaten, E. Climate sensitivity of radial growth in European beech (Fagus sylvatica L.) at different aspects in southwestern Germany. Trees 26, 777–788 (2012).
Google Scholar 
Decuyper, M. et al. Spatio-temporal assessment of beech growth in relation to climate extremes in Slovenia – an integrated approach using remote sensing and tree-ring data. Agric. Meteorol. 287, 107925 (2020).
Google Scholar 
Kraus, C., Zang, C. & Menzel, A. Elevational response in leaf and xylem phenology reveals different prolongation of growing period of common beech and Norway spruce under warming conditions in the Bavarian Alps. Eur. J. Res. 135, 1011–1023 (2016).
Google Scholar 
Martínez del Castillo, E. et al. Living on the edge: contrasted wood-formation dynamics in Fagus sylvatica and Pinus sylvestris under mediterranean conditions. Front. Plant Sci. 7, 370 (2016).PubMed 
PubMed Central 

Google Scholar 
Čufar, K. et al. Temporal shifts in leaf phenology of beech (Fagus sylvatica) depend on elevation. Trees 26, 1091–1100 (2012).
Google Scholar 
Bontemps, J. D., Hervé, J. C. & Dhôte, J. F. Dominant radial and height growth reveal comparable historical variations for common beech in north-eastern France. Forest Ecol. Manag. 259, 1455–1463 (2010).
Google Scholar 
Latte, N., Lebourgeois, F. & Claessens, H. Increased tree-growth synchronization of beech (Fagus sylvatica L.) in response to climate change in northwestern Europe. Dendrochronologia 33, 69–77 (2015).
Google Scholar 
Zimmermann, J., Hauck, M., Dulamsuren, C. & Leuschner, C. Climate warming-related growth decline affects Fagus sylvatica, but not other broad-leaved tree species in central european mixed forests. Ecosystems 18, 560–572 (2015).CAS 

Google Scholar 
Tegel, W. et al. A recent growth increase of European beech (Fagus sylvatica L.) at its Mediterranean distribution limit contradicts drought stress. Eur. J. Res. 133, 61–71 (2014).
Google Scholar 
Hacket-Pain, A. J. & Friend, A. D. Increased growth and reduced summer drought limitation at the southern limit of Fagus sylvatica L., despite regionally warmer and drier conditions. Dendrochronologia 44, 22–30 (2017).
Google Scholar 
Dulamsuren, C., Hauck, M., Kopp, G., Ruff, M. & Leuschner, C. European beech responds to climate change with growth decline at lower, and growth increase at higher elevations in the center of its distribution range (SW Germany). Trees 31, 673–686 (2017).
Google Scholar 
Spiecker, H., Mielikäinen, K., Köhl, M. & Skovsgaard, J. P. Growth trends in European forests: studies from 12 countries. European Forest Institute Research Report (1996).Cavin, L. & Jump, A. S. Highest drought sensitivity and lowest resistance to growth suppression are found in the range core of the tree Fagus sylvatica L. not the equatorial range edge. Glob. Chang. Biol. 23, 1–18 (2016).
Google Scholar 
Mette, T. et al. Climatic turning point for beech and oak under climate change in Central Europe. Ecosphere 4, 1–19 (2013).
Google Scholar 
Michelot, A., Simard, S., Rathgeber, C. B. K., Dufrêne, E. & Damesin, C. Comparing the intra-annual wood formation of three European species (Fagus sylvatica, Quercus petraea and Pinus sylvestris) as related to leaf phenology and non-structural carbohydrate dynamics. Tree Physiol. 32, 1033–1045 (2012).PubMed 

Google Scholar 
Meier, I. C. & Leuschner, C. Belowground drought response of European beech: Fine root biomass and carbon partitioning in 14 mature stands across a precipitation gradient. Glob. Chang. Biol. 14, 2081–2095 (2008).
Google Scholar 
Leuschner, C. & Ellenberg, H. Ecology of Central European Forests. Vegetation Ecology of Central Europe. Vol. I (Springer, 2017).Allen, C. D., Breshears, D. D. & McDowell, N. G. On underestimation of global vulnerability to tree mortality and forest die-off from hotter drought in the Anthropocene. Ecosphere. 6, 1–55 (2015).
Google Scholar 
Pechanec, V., Purkyt, J., Benc, A., Nwaogu, C. & Lenka, Š. Ecological Informatics Modelling of the carbon sequestration and its prediction under climate change. https://doi.org/10.1016/j.ecoinf.2017.08.006 (2017).Speer, J. H. Fundamentals of Tree-Ring Research (University of Arizona Press, 2010).Biondi, F. & Qeadan, F. A theory-driven approach to tree-ring standardization: defining the biological trend from expected basal area increment. Tree-Ring Res. 64, 81–96 (2008).
Google Scholar 
Biondi, F. & Qeadan, F. Removing the tree-ring width biological trend using expected basal area increment. in USDA Forest Service RMRS-P-55 124–131 (2008).Karger, D. N. et al. Climatologies at high resolution for the earth’s land surface areas. Sci. Data 4, 1–20 (2017).
Google Scholar 
De Martonne, E. Une nouvelle fonction climatologique: L’indice d’aridité. La Meteorol. 2, 449–458 (1926).Martínez del Castillo, E., Longares, L. A., Serrano-Notivoli, R. & de Luis, M. Modeling tree-growth: assessing climate suitability of temperate forests growing in Moncayo Natural Park (Spain). Ecol. Manag. 435, 128–137 (2019).
Google Scholar 
Bolker, B. M. et al. Generalized linear mixed models: a practical guide for ecology and evolution. Trends Ecol. Evol. 24, 127–135 (2009).PubMed 

Google Scholar 
Calcagno, V. & Mazancourt, C. De. glmulti: an R package for easy automated model selection with (generalized) linear models. J. Stat. Softw. 34, 1–29 (2010).
Google Scholar 
Detry, M. A. & Ma, Y. Analyzing repeated measurements using mixed models. JAMA J. Am. Med. Assoc. 315, 407 (2016).CAS 

Google Scholar 
Harrison, X. A. et al. A brief introduction to mixed effects modelling and multi-model inference in ecology. PeerJ 2018, 1–32 (2018).
Google Scholar 
Johnson, J. B. & Omland, K. S. Model selection in ecology and evolution. Trends Ecol. Evol. 19, 101–108 (2004).PubMed 

Google Scholar 
Caudullo, G., Welk, E. & San-Miguel-Ayanz, J. Chorological maps for the main European woody species. Data Brief 12, 662–666 (2017).PubMed 
PubMed Central 

Google Scholar 
Meinshausen, M. et al. The shared socio-economic pathway (SSP) greenhouse gas concentrations and their extensions to 2500. Geosci. Model Dev. 13, 3571–3605 (2020).CAS 

Google Scholar 
Karger, D. N. & Zimmermann, N. E. CHELSAcruts – High Resolution Temperature And Precipitation Timeseries For The 20th Century And Beyond. https://doi.org/10.16904/envidat.159 (2018).Norinder, U., Rybacka, A. & Andersson, P. L. Conformal prediction to define applicability domain – a case study on predicting ER and AR binding. SAR QSAR Environ. Res. 27, 303–316 (2016).CAS 
PubMed 

Google Scholar 
Metzger, M. J., Bunce, R. G. H., Jongman, R. H. G., Mücher, C. A. & Watkins, J. W. A climatic stratification of the environment of Europe. Glob. Ecol. Biogeogr. 14, 549–563 (2005).
Google Scholar  More

Beaulieu, J. et al. Genomic selection for resistance to spruce budworm in white spruce and relationships with growth and wood quality traits. Evol. Appl. 13, 2704–2722 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Lenz, P. et al. Multi-trait genomic selection for weevil resistance, growth and wood quality in Norway spruce. Evol. Appl. 13, 76–94 (2020).CAS 
PubMed 

Google Scholar 
Lebedev, V. G., Lebedeva, T. N., Chernodubov, A. I. & Shestibratov, K. A. Genomic selection for forest tree improvement: Methods, achievements and perspectives. Forests 11, 1190 (2020).
Google Scholar 
Mullin, T. J. et al. Economic importance, breeding objectives and achievements. In Genetics, Genomics and Breeding of Conifers (eds Plomion, C. et al.) (Science Publishers & CRC Press, 2011).
Google Scholar 
Zhang, J., Peter, G. F., Powell, G. L., White, T. L. & Gezan, S. A. Comparison of breeding values estimated between single-tree and multiple-tree plots for a slash pine population. Tree Genet. Genomes 11, 48 (2015).CAS 

Google Scholar 
Martínez-García, P. J. et al. Predicting breeding values and genetic components using generalized linear mixed models for categorical and continuous traits in walnut (Juglans regia). Tree Genet. Genomes 13, 109 (2017).
Google Scholar 
Weng, Y., Ford, R., Tong, Z. & Krasowski, M. Genetic parameters for bole straightness and branch angle in Jack pine estimated using linear and generalized linear mixed models. For. Sci. 63, 111–117 (2017).
Google Scholar 
Mrode, R. A. Linear Models for the Prediction of Animal Breeding Values 2nd edn. (CAB International, 2005).
Google Scholar 
Henderson, C. R. Theoretical bias and computational methods for a number of different animal models. J. Dairy Sci. 71, 1–16 (1988).
Google Scholar 
Falconer, D. S. & Mackay, T. F. C. Introduction to Quantitative Genetics 4th edn. (Longman Publishing Group, 1996).
Google Scholar 
Henderson, C. R. A simple method for computing the inverse of a numerator relationship matrix used in prediction of breeding values. Biometrics 32, 69–83 (1976).MATH 

Google Scholar 
Wright, S. Coefficients of inbreeding and relationship. Am. Nat. 56, 330–338 (1922).
Google Scholar 
Hill, W. G. & Weir, B. S. Variation in actual relationship as a consequence of Mendelian sampling and linkage. Genet. Res. 93, 47–64 (2011).CAS 

Google Scholar 
Doerksen, T. K. & Herbinger, C. M. Male reproductive success and pedigree error in red spruce open-pollinated and polycross mating systems. Can. J. For. Res. 38, 1742–1749 (2008).
Google Scholar 
Godbout, J. et al. Development of a traceability system based on SNP array for the large-scale production of high-value white spruce (Picea glauca). Front. Plant Sci. 8, 1264 (2017).PubMed 
PubMed Central 

Google Scholar 
Galeano, E., Bousquet, J. & Thomas, B. R. SNP-based analysis reveals unexpected features of genetic diversity, parental contributions and pollen contamination in a white spruce breeding program. Sci. Rep. 11, 4990 (2021).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Lenz, P. et al. Genomic prediction for hastening and improving efficiency of forward selection in conifer polycross mating designs: An example from white spruce. Heredity 124, 562–578 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Askew, G. R. & El-Kassaby, Y. A. Estimation of relationship coefficients among progeny derived from wind-pollinated orchard seeds. Theor. Appl. Genet. 88, 267–272 (1994).CAS 
PubMed 

Google Scholar 
Doerksen, T. K., Bousquet, J. & Beaulieu, J. Inbreeding depression in intra-provenance crosses driven by founder relatedness in white spruce. Tree Genet. Genomes 10, 203–212 (2014).
Google Scholar 
Meuwissen, T. H. E., Hayes, B. J. & Goddard, M. E. Prediction of total genetic value using genome-wide dense marker maps. Genetics 157, 1819–1829 (2001).CAS 
PubMed 
PubMed Central 

Google Scholar 
Heffner, E. L., Lorenz, A. J., Jannink, J.-L. & Sorrels, M. E. Plant breeding with genomic selection: Gain per unit time and cost. Crop Sci. 50, 1681–1690 (2010).
Google Scholar 
Grattapaglia, D. & Resende, M. D. V. Genomic selection in forest tree breeding. Tree Genet. Genomes 7, 241–255 (2011).
Google Scholar 
Beaulieu, J., Doerksen, T., Clément, S., MacKay, J. & Bousquet, J. Accuracy of genomic selection models in a large population of open-pollinated families in white spruce. Heredity 113, 342–352 (2014).
Google Scholar 
Habier, D., Tetens, J., Seefried, F.-R., Lichtner, P. & Thaller, G. The impact of genetic relationship information on genomic breeding values in German Holstein cattle. Gen. Select. Evol. 42, 5 (2010).
Google Scholar 
Perkel, J. SNP genotyping: six technologies that keyed a revolution. Nat. Methods 5, 447–454 (2008).CAS 

Google Scholar 
Pavy, N. et al. Development of high-density SNP genotyping arrays for white spruce (Picea glauca) and transferability to subtropical and nordic congeners. Mol. Ecol. Res. 13, 324–336 (2013).CAS 

Google Scholar 
Thomson, M. J. High-throughput genotyping to accelerate crop improvement. Plant Breed. Biotechnol. 2, 195–212 (2014).
Google Scholar 
Beaulieu, J., Doerksen, T., MacKay, J., Rainville, A. & Bousquet, J. Genomic selection accuracies within and between environments and small breeding groups in white spruce. BMC Genomics 15, 1048 (2014).PubMed 
PubMed Central 

Google Scholar 
Liu, L., Chen, R., Fugina, C. J., Siegel, B. & Jackson, D. High-throughput and low-cost genotyping method for plant genome editing. Curr. Prot. 1, e100 (2021).CAS 

Google Scholar 
Lenz, P. et al. Factors affecting the accuracy of genomic selection for growth and wood quality traits in an advanced-breeding population of black spruce (Picea mariana). BMC Genomics 18, 335 (2017).PubMed 
PubMed Central 

Google Scholar 
de los Campos, G., Hickey, J. M., Pong-Wong, R., Daetwyler, H. D. & Calus, M. P. L. Whole-genome regression and prediction models applied to plant and animal breeding. Genetics 193, 327–345 (2013).PubMed Central 

Google Scholar 
Hoerl, A. E. & Kennard, R. W. Ridge regression: biased estimation for non-orthogonal problems. Technometrics 12, 55–67 (1970).MATH 

Google Scholar 
Tibshirani, R. Regression shrinkage and selection via the LASSO. J. R. Stat. Soc. Series B. 58, 267–288 (1996).MathSciNet 
MATH 

Google Scholar 
VanRaden, P. M. Efficient methods to compute genomic predictions. J. Dairy Sci. 91, 4414–4423 (2008).CAS 
PubMed 

Google Scholar 
Legarra, A., Aguilar, I. & Misztal, I. A relationship matrix including full pedigree and genomic information. J. Dairy Sci. 92, 4656–4663 (2009).CAS 
PubMed 

Google Scholar 
Zapata-Valenzuela, J., Whetten, R. W., Neale, D., McKeand, S. & Isik, F. Genomic estimated breeding values using genomic relationship matrices in a cloned population of loblolly pine. Genes Genomes Genet. 3, 909–916 (2013).
Google Scholar 
Muñoz, P. R. et al. Unraveling additive from non-additive effects using genomic relationship matrices. Genetics 198, 1759–1768 (2014).PubMed 
PubMed Central 

Google Scholar 
Ratcliffe, B. et al. Single-step BLUP with varying genotyping effort in open-pollinated Picea glauca. Genes Genomes Genet. 7, 935–942 (2017).
Google Scholar 
Gamal El-Dien, O. et al. Multienvironment genomic variance decomposition analysis of open-pollinated Interior spruce (Picea glauca x engelmannii). Mol. Breed. 38, 26 (2018).
Google Scholar 
Zobel, B. J. & Sprague, J. R. Juvenile Wood in Forest Trees (Springer, 1988).
Google Scholar 
Osorio, L. F., White, T. L. & Huber, D. A. Age trends of heritabilities and genotype-by-environment interactions for growth traits and wood density from clonal trials of Eucalyptus grandis Hill ex Maiden. Silv. Genet. 50, 108–117 (2000).
Google Scholar 
Baltunis, B. S., Gapare, W. J. & Wu, H. X. Genetic parameters and genotype by environment interaction in radiata pine for growth and wood quality traits in Australia. Silv. Genet. 59, 113–124 (2010).
Google Scholar 
Gamal El-Dien, O. et al. Prediction accuracies for growth and wood attributes of interior spruce in space using genotyping-by-sequencing. BMC Genomics 16, 370 (2015).PubMed 
PubMed Central 

Google Scholar 
Resende, M. D. V. et al. Genomic selection for growth and wood quality in Eucalyptus: Capturing the missing heritability and accelerating breeding for complex traits in forest trees. New Phytol. 194, 116–128 (2012).PubMed 

Google Scholar 
Chen, Z.-Q. et al. Accuracy of genomic selection for growth and wood quality traits in two control-pollinated progeny trials using exome capture as the genotyping platform in Norway spruce. BMC Genomics 19, 946 (2018).PubMed 
PubMed Central 

Google Scholar 
Beaulieu, J., Perron, M. & Bousquet, J. Multivariate patterns of adaptive genetic variation and seed source transfer in Picea mariana. Can. J. For. Res. 34, 531–545 (2004).
Google Scholar 
Li, P., Beaulieu, J. & Bousquet, J. Genetic structure and patterns of genetic variation among populations in eastern white spruce (Picea glauca). Can. J. For. Res. 27, 189–198 (1997).
Google Scholar 
Namkoong, G. Inbreeding effects on estimation of genetic additive variance. For. Sci. 12, 8–13 (1966).
Google Scholar 
Squillace, A. E. Average genetic correlations among offspring from open-pollinated forest trees. Silv. Genet. 23, 149–156 (1974).
Google Scholar 
Muñoz, P. R. et al. Genomic relationship matrix for correcting pedigree errors in breeding populations: impact on genetic parameters and genomic selection accuracy. Crop Sci. 53, 1115–1123 (2014).
Google Scholar 
Tan, B. et al. Evaluating the accuracy of genomic prediction of growth and wood traits in two Eucalyptus species and their F1 hybrids. BMC Plant Biol. 17, 110 (2017).PubMed 
PubMed Central 

Google Scholar 
Weigel, K. A., VanRaden, P. M., Norman, H. D. & Grosu, H. A 100-year review: Methods and impact of genetic selection in dairy cattle—From daughter-dam comparisons to deep learning algorithms. J. Dairy Sci. 100, 10234–10250 (2017).CAS 
PubMed 

Google Scholar 
Grattapaglia, D. et al. Quantitative genetics and genomics converge to accelerate forest tree breeding. Front. Plant Sci. 9, 1693 (2018).PubMed 
PubMed Central 

Google Scholar 
Park, Y.-S., Beaulieu, J. & Bousquet, J. Multi-varietal forestry integrating genomic selection and somatic embryogenesis. In Vegetative Propagation of Forest Trees (eds Park, Y.-S. et al.) 302–322 (National Institute of Forest Science, 2016).
Google Scholar 
Bousquet, J. et al. Spruce population genomics. In Population Genomics: Forest Trees (ed. Rajora, O. P.) (Springer Nature, 2021).
Google Scholar 
Chamberland, V. et al. Conventional versus genomic selection for white spruce improvement: A comparison of costs and benefits of plantations on Quebec public lands. Tree Genet. Genomes 16, 17 (2020).
Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2019).
Google Scholar 
MacFarland, T. W. & Yates, J. M. Wilcoxon matched-pairs signed-ranks test. In Introduction to Nonparametric Statistics for the Biological Sciences Using R 133–175 (Springer, 2016) https://doi.org/10.1007/978-3-319-30634-6_5.Li, Y. et al. Genomic selection for non-key traits in radiata pine when the documented pedigree is corrected using DNA marker information. BMC Genomics 20, 1026 (2019).PubMed 
PubMed Central 

Google Scholar 
Calleja-Rodriguez, A. et al. Evaluation of the efficiency of genomic versus pedigree predictions for growth and wood quality traits in Scots pine. BMC Genomics 21, 796 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Ukrainetz, N. K. & Mansfield, S. D. Assessing the sensitivities of genomic selection for growth and wood quality traits in lodgepole pine using Bayesian models. Tree Genet. Genomes 16, 14 (2020).
Google Scholar 
Ukrainetz, N. K. & Mansfield, S. D. Prediction accuracy of single-step BLUP for growth and wood quality traits in the lodgepole pine breeding program in British Columbia. Tree Genet. Genomes 16, 64 (2020).
Google Scholar 
Thistlethwaite, F. R. et al. Genomic prediction accuracies in space and time for height and wood density of Douglas-fir using exome capture as the genotyping platform. BMC Genomics 18, 930 (2017).PubMed 
PubMed Central 

Google Scholar 
Suontama, M. et al. Efficiency of genomic prediction across two Eucalyptus nitens seed orchards with different selection histories. Heredity 122, 370–379 (2019).CAS 
PubMed 

Google Scholar 
Müller, B. S. F. et al. Genomic prediction in contrast to a genome-wide association study in explaining heritable variation of complex growth traits in breeding populations of Eucalyptus. BMC Genomics 18, 524 (2017).PubMed 
PubMed Central 

Google Scholar 
Thavamanikumar, S., Arnold, R. J., Luo, J. & Thumma, B. R. Genomic studies reveal substantial dominant effects and improved genomic predictions in an open-pollinated breeding population of Eucalyptus pellita. Genes Genomes Genet. 10, 3751–3763 (2020).CAS 

Google Scholar 
Resende, R. T. et al. Assessing the expected response to genomic selection of individuals and families in Eucalyptus breeding with an additive-dominant model. Heredity 119, 245–255 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
Marco de Lima, B. et al. Quantitative genetic parameters for growth and wood properties in Eucalyptus “urograndis” hybrid using near-infrared phenotyping and genome-wide SNP-based relationships. PLoS ONE 14, e0218747 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
Bouvet, J.-M., Makouanzi, G., Cros, D. & Vigneron, Ph. Modeling additive and non-additive effects in a hybrid population using genome-wide genotyping: Prediction accuracy implications. Heredity 116, 146–157 (2016).CAS 
PubMed 

Google Scholar 
Pégard, M. et al. Favorable conditions for genomic evaluation to outperform classical pedigree evaluation highlighted by a proof-of-concept study in poplar. Front. Plant Sci. 11, 581954 (2020).PubMed 
PubMed Central 

Google Scholar  More

Literature search and study selectionA systematic literature search was conducted in the ISI Web of Science database for observational and experimental studies published from 1975 to 13 January 2020 using the following search terms: TOPIC: (grassland* OR prairie* OR steppe* OR shrubland* OR scrubland* OR bushland*) AND TOPIC: (drought* OR ‘dry period*’ OR ‘dry condition*’ OR ‘dry year*’ OR ‘dry spell*’) AND TOPIC: (product* OR biomass OR cover OR abundance* OR phytomass). The search was refined to include the subject categories Ecology, Environmental Sciences, Plant Sciences, Biodiversity Conservation, Multidisciplinary Sciences and Biology, and the document types Article, Review and Letter. This yielded a total of 2,187 peer-reviewed papers (Supplementary Fig. 1). At first, these papers were screened by title and abstract, which resulted in 197 potentially relevant full-text articles. We then examined the full text of these papers for eligibility and selected 87 studies (43 experimental, 43 observational and 1 that included both types) on the basis of the following criteria:

(1)

The research was conducted in the field, in natural or semi-natural grasslands or shrublands (for example, artificially constructed (seeded or planted) plant communities or studies using monolith transplants were excluded). We used this restriction because most reports on observational droughts are from intact ecosystems, and experiments in disturbed sites or using artificial communities would thus not be comparable to observational drought studies.

(2)

In the case of observational studies, the drought year or a multi-year drought was clearly specified by the authors (that is, we did not arbitrarily extract dry years from a long-term dataset). Please note that some observational data points are from control plots of experiments (of any kind), where the authors reported that a drought had occurred during the study period. We did not involve gradient studies that compare sites of different climates, which are sometimes referred to as ‘observational studies’.

(3)

The paper reported the amount or proportion of change in annual or growing-season precipitation (GSP) compared with control conditions. We consistently use the term ‘control’ for normal precipitation (non-drought) year or years in observational studies and for ambient precipitation (no treatment) in experimental studies hereafter. Similarly, we use the term ‘drought’ for both drought year or years in observational studies and drought treatment in experimental studies. In the case of multi-factor experiments, where precipitation reduction was combined with any other treatment (for example, warming), data from the plots receiving drought only and data from the control plots were used.

(4)

The paper contained raw data on plant production under both control and drought conditions, expressed in any of the following variables: ANPP, aboveground plant biomass (in grassland studies only) or percentage plant cover. In 79% of the studies that used ANPP as a production variable, ANPP was estimated by harvesting peak or end-of-season AGB. We therefore did not distinguish between ANPP and AGB, which are referred to as ‘biomass’ hereafter. We included the papers that reported the production of the whole plant community, or at least that of the dominant species or functional groups approximating the abundance of the whole community.

(5)

When multiple papers were published on the same experiment or natural drought event at the same study site, the most long-term study including the largest number of drought years was chosen.

In addition to the systematic literature search, we included 27 studies (9 experimental, 17 observational and 1 that included both types) meeting the above criteria from the cited references of the Web of Science records selected for our meta-analyses, and from previous meta-analyses and reviews on the topic. In total, this resulted in 114 studies (52 experimental, 60 observational and 2 that included both types; Supplementary Note 9, Supplementary Fig. 2 and ref. 25).Data compilationData were extracted from the text or tables, or were read from the figures using Web Plot Digitizer26. For each study, we collected the study site, latitude, longitude, mean annual temperature (MAT) and precipitation (MAP), study type (experimental or observational), and drought length (the number of consecutive drought years). When MAT or MAP was not documented in the paper, it was extracted from another published study conducted at the same study site (identified by site names and geographic coordinates) or from an online climate database cited in the respective paper. We also collected vegetation type—that is, grassland when it was dominated by grasses, or shrubland when the dominant species included one or more shrub species (involving communities co-dominated by grasses and shrubs). Data from the same study (that is, paper) but from different geographic locations or environmental conditions (for example, soil types, land uses or multiple levels of experimental drought) were collected as distinct data points (but see ‘Statistical analysis’ for how these points were handled). As a result, the 114 published papers provided 239 data points (112 experimental and 127 observational)25.For the observational studies, normal precipitation year or years specified by the authors was used as the control. If it was not specified in the paper, the year immediately preceding the drought year(s) was chosen as the control. When no data from the pre-drought year were available, the year immediately following the drought year(s) (14 data points) or a multi-year period given in the paper (22 data points) was used as the control. For the experimental studies, we also collected treatment size (that is, rainout shelter area or, if it was not reported in the paper, the experimental plot size).For the calculation of drought severity, we used yearly precipitation (YP), which was reported in a much higher number of studies than GSP. We extracted YP for both control (YPcontrol) and drought (YPdrought). For the observational studies, when a multi-year period was used as the control or the natural drought lasted for more than one year, precipitation values were averaged across the control or drought years, respectively. Consistently, in the case of multi-year drought experiments, YPcontrol and YPdrought were averaged across the treatment years. When only GSP was published in the paper (63 of 239 data points), we used this to obtain YP data as follows: we regarded MAP as YPcontrol, and YPdrought was calculated as YPdrought = MAP − (GSPcontrol − GSPdrought). From YPcontrol and YPdrought data, we calculated drought severity as follows: (YPdrought − YPcontrol)/YPcontrol × 100.For production, we compiled the mean, replication (N) and, if the study reported it, a variance estimate (s.d., s.e.m. or 95% CI) for both control and drought. In the case of multi-year droughts, data only from the last drought year were extracted, except in five studies (17 data points) where production data were given as an average for the drought years. When both biomass and cover data were presented in the paper, we chose biomass. For each study, we consistently considered replication as the number of the smallest independent study unit. When only the range of replications was reported in a study, we chose the smallest number.To quantify climatic aridity for each study site, we used an aridity index (AI), calculated as the ratio of MAP and mean annual PET (AI = MAP/PET). This is a frequently used index in recent climate change research27,28. AI values were extracted from the Global Aridity Index and Potential Evapotranspiration (ET0) Climate Database v.2 for the period of 1970–2000 (aggregated on annual basis)29.Because we wanted to prevent our analysis from being distorted by a strongly unequal distribution of studies between the two study types regarding some potentially important explanatory variables, we left out studies from our focal meta-analysis in three steps. First, we left out studies that were conducted at wet sites—that is, where site AI exceeded 1. The value of 1 was chosen for two reasons: above this value, the distribution of studies between the two study types was extremely uneven (22 experimental versus 2 observational data points with AI  > 1)25, and the AI value of 1 is a bioclimatically meaningful threshold, where MAP equals PET. Second, we left out shrublands, because we had only 14 shrubland studies (out of 105 studies with AI  More

Lampert, W. Zooplankton research: The contribution of limnology to general ecological paradigms. Aquat. Ecol. 31, 19–27. https://doi.org/10.1023/A:1009943402621 (1997).Article 

Google Scholar 
Sotton, B. et al. Trophic transfer of microcystins through the lake pelagic food web: Evidence for the role of zooplankton as a vector in fish contamination. Sci. Total Environ. 466–467, 152–163. https://doi.org/10.1016/j.scitotenv.2013.07.020 (2014).ADS 
CAS 
Article 
PubMed 

Google Scholar 
St-Gelais, F. N., Sastri, A. R., del Giorgio, P. A. & Beisner, B. E. Magnitude and regulation of zooplankton community production across boreal lakes. Limnol. Oceanogr. Lett. 2(6), 210–217. https://doi.org/10.1002/lol2.10050 (2017).Article 

Google Scholar 
Dejen, E., Vijverberg, J., Nagelkerke, L. A. J. & Sibbing, F. A. Temporal and spatial distribution of microcrustacean zooplankton in relation to turbidity and other environmental factors in a large tropical lake (L. Tana, Ethiopia). Hydrobiologia 513(1), 39–49. https://doi.org/10.1023/b:hydr.0000018163.60503.b8 (2004).Article 

Google Scholar 
Arendt, K. E. et al. Effects of suspended sediments on copepods feeding in a glacial influenced sub-Arctic fjord. J. Plankton Res. 33, 1526–1537. https://doi.org/10.1093/plankt/fbr054 (2011).CAS 
Article 

Google Scholar 
Carrasco, N. K., Perissinotto, R. & Jones, S. Turbidity effects on feeding and mortality of the copepod Acartiella natalensis (Connell and Grindley, 1974) in the St Lucia Estuary, South Africa. J. Exp. Mar. Biol. Ecol. 446, 45–51. https://doi.org/10.1016/j.jembe.2013.04.016 (2013).Article 

Google Scholar 
Goździejewska, A. et al. Effects of lateral connectivity on zooplankton community structure in floodplain lakes. Hydrobiologia 774, 7–21. https://doi.org/10.1007/s10750-016-2724-8 (2016).CAS 
Article 

Google Scholar 
Zhou, J., Qin, B. & Han, X. The synergetic effects of turbulence and turbidity on the zooplankton community structure in large, shallow Lake Taihu. Environ. Sci. Pollut. Res. 25, 1168–1175. https://doi.org/10.1007/s11356-017-0262-1 (2018).CAS 
Article 

Google Scholar 
Chou, W.-R., Fang, L.-S., Wang, W.-H. & Tew, K. S. Environmental influence on coastal phytoplankton and zooplankton diversity: A multivariate statistical model analysis. Environ. Monit. Assess. 184(9), 5679–5688. https://doi.org/10.1007/s10661-011-2373-3 (2011).CAS 
Article 
PubMed 

Google Scholar 
Du, X. et al. Analyzing the importance of top-down and bottom-up controls in food webs of Chinese lakes through structural equation modeling. Aquat. Ecol. 49(2), 199–210. https://doi.org/10.1007/s10452-015-9518-3 (2015).CAS 
Article 

Google Scholar 
Feitosa, I. B. et al. Plankton community interactions in an Amazonian floodplain lake, from bacteria to zooplankton. Hydrobiologia 831, 55–70. https://doi.org/10.1007/s10750-018-3855-x (2019).CAS 
Article 

Google Scholar 
Kruk, M. & Paturej, E. Indices of trophic and competitive relations in a planktonic network of a shallow, temperate lagoon. A graph and structural equation modeling approach. Ecol. Indic. 112, 106007. https://doi.org/10.1016/j.ecolind.2019.106007 (2020).Article 

Google Scholar 
Kruk, M., Paturej, E. & Artiemjew, P. From explanatory to predictive network modeling of relationships among ecological indicators in the shallow temperate lagoon. Ecol. Indic. 117, 106637. https://doi.org/10.1016/j.ecolind.2020.106637 (2020).Article 

Google Scholar 
Kruk, M., Paturej, E. & Obolewski, K. Zooplankton predator–prey network relationships indicates the saline gradient of coastal lakes. Machine learning and meta-network approach. Ecol. Indic. 125, 107550. https://doi.org/10.1016/j.ecolind.2021.107550 (2021).Article 

Google Scholar 
Oh, H.-J. et al. Comparison of taxon-based and trophi-based response patterns of rotifer community to water quality: Applicability of the rotifer functional group as an indicator of water quality. Anim. Cells Syst. 21, 133–140. https://doi.org/10.1080/19768354.2017.1292952 (2017).Article 

Google Scholar 
Sodré, E. D. O. & Bozelli, R. L. How planktonic microcrustaceans respond to environment and affect ecosystem: A functional trait perspective. Int. Aquat. Res. 11, 207–223. https://doi.org/10.1007/s40071-019-0233-x (2019).Article 

Google Scholar 
Simões, N. R. et al. Changing taxonomic and functional β-diversity of cladoceran communities in Northeastern and South Brazil. Hydrobiologia 847, 3845–3856. https://doi.org/10.1007/s10750-020-04234-w (2020).Article 

Google Scholar 
Goździejewska, A. M., Koszałka, J., Tandyrak, R., Grochowska, J. & Parszuto, K. Functional responses of zooplankton communities to depth, trophic status, and ion content in mine pit lakes. Hydrobiologia 848, 2699–2719. https://doi.org/10.1007/s10750-021-04590-1 (2021).CAS 
Article 

Google Scholar 
Hart, R. C. Zooplankton feeding rates in relation to suspended sediment content: Potential influences on community structure in a turbid reservoir. Fresh. Biol. 19, 123–139. https://doi.org/10.1111/j.1365-2427.1988.tb00334.x (1988).Article 

Google Scholar 
Gliwicz, Z. M. & Pijanowska, J. The role of predation in zooplankton succession. In Plankton Ecology. Succession in Plankton Communities (ed. Sommer, U.) 253–296 (Springer Verlag, 1989).Chapter 

Google Scholar 
Gardner, M. B. Effects of turbidity on feeding rates and selectivity of bluegills. Trans. Am. Fish. Soc. 110(3), 446–450. https://doi.org/10.1577/1548-8659(1981)110%3c446:EOTOFR%3e2.0.CO;2 (1981).Article 

Google Scholar 
Zettler, E. R. & Carter, J. C. H. Zooplankton community and species responses to a natural turbidity gradient in Lake Temiskaming, Ontario-Quebec. Can. J. Fish. Aquat. Sci. 43, 665–673. https://doi.org/10.1139/f86-080 (1986).Article 

Google Scholar 
APHA. Standard Methods for the Examination of Water and Wastewater 20th edn. (American Public Health Association, 1999).
Google Scholar 
Lind, O. T., Chrzanowski, T. H. & D’avalos-Lind, L. Clay turbidity and the relative production of bacterioplankton and phytoplankton. Hydrobiologia 353, 1–18. https://doi.org/10.1023/A:1003039932699 (1997).CAS 
Article 

Google Scholar 
Boenigk, J. & Novarino, G. Effect of suspended clay on the feeding and growth of bacterivorous flagellates and ciliates. Aquat. Microb. Ecol. 34, 181–192. https://doi.org/10.3354/ame034181 (2004).Article 

Google Scholar 
Noe, G. B., Harvey, J. W. & Saiers, J. E. Characterization of suspended particles in Everglades wetlands. Limnol. Oceanogr. 52, 1166–1178. https://doi.org/10.4319/lo.2007.52.3.1166 (2007).ADS 
CAS 
Article 

Google Scholar 
Bilotta, G. S. & Brazier, R. E. Understanding the influence of suspended solids on water quality and aquatic biota. Water Res. 42, 2849–2861. https://doi.org/10.1016/j.watres.2008.03.018 (2008).CAS 
Article 
PubMed 

Google Scholar 
Fernandez-Severini, M. D., Hoffmeyer, M. S. & Marcovecchio, J. E. Heavy metals concentrations in zooplankton and suspended particulate matter in a southwestern Atlantic temperate estuary (Argentina). Environ. Monit. Assess. 185, 1495–1513. https://doi.org/10.1007/s10661-012-3023-0 (2013).CAS 
Article 
PubMed 

Google Scholar 
Paaijmans, K. P., Takken, W., Githeko, A. K. & Jacobs, A. F. G. The effect of water turbidity on the near-surface water temperature of larval habitats of the malaria mosquito Anopheles gambiae. Int. J. Biometeorol. 52(8), 747–753. https://doi.org/10.1007/s00484-008-0167-2 (2008).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Asrafuzzaman, M., Fakhruddin, A. N. M. & Hossain, M. A. Reduction of turbidity of water using locally available natural coagulants. ISRN Microbiol. 1–6, 2011. https://doi.org/10.5402/2011/632189 (2011).Article 

Google Scholar 
Kirk, K. L. & Gilbert, J. J. Suspended clay and the population dynamics of planktonic rotifers and cladocerans. Ecology 71(5), 1741–1755. https://doi.org/10.2307/1937582 (1990).Article 

Google Scholar 
Kirk, K. L. Effects of suspended clay on Daphnia body growth and fitness. Freshwater Biol. 28, 103–109. https://doi.org/10.1111/j.1365-2427.1992.tb00566.x (1992).Article 

Google Scholar 
Levine, S. N., Zehrer, R. F. & Burns, C. W. Impact of resuspended sediment on zooplankton feeding in Lake Waihola, New Zealand. Freshw. Biol. 50, 1515–1536. https://doi.org/10.1111/j.1365-2427.2005.01420 (2005).Article 

Google Scholar 
Moreira, F. W. A. et al. Assessing the impacts of mining activities on zooplankton functional diversity. Acta Limn. Bras. 28, e7. https://doi.org/10.1590/S2179-975X0816 (2016).Article 

Google Scholar 
Kerfoot, W. C. & Sih, A. Predation. Direct and Indirect Impacts on Aquatic Communities Vol. 160 (University Press of New England, 1987).
Google Scholar 
Schou, M. O. et al. Restoring lakes by using artificial plant beds: Habitat selection of zooplankton in a clear and a turbid shallow lake. Freshw. Biol. 54(7), 1520–1531. https://doi.org/10.1111/j.1365-2427.2009.02189.x (2009).Article 

Google Scholar 
Goździejewska, A. M., Gwoździk, M., Kulesza, S., Bramowicz, M. & Koszałka, J. Effects of suspended micro- and nanoscale particles on zooplankton functional diversity of drainage system reservoirs at an open-pit mine. Sci. Rep. 9, 16113. https://doi.org/10.1038/s41598-019-52542-6 (2019).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Ribeiro, F. et al. Silver nanoparticles and silver nitrate induce high toxicity to Pseudokirchneriella subcapitata, Daphnia magna and Danio rerio. Sci. Total Environ. 466–467, 232–241. https://doi.org/10.1016/j.scitotenv.2013.06.101 (2014).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Vallotton, P., Angel, B., Mccall, M., Osmond, M. & Kirby, J. Imaging nanoparticle-algae interactions in three dimensions using Cytoviva microscopy. J. Microsc. 257(2), 166–169. https://doi.org/10.1111/jmi.12199 (2015).CAS 
Article 
PubMed 

Google Scholar 
Shanthi, S. et al. Biosynthesis of silver nanoparticles using a probiotic Bacillus licheniformis Dahb1 and their antibiofilm activity and toxicity effects in Ceriodaphnia cornuta. Microb. Pathogenesis 93, 70e77. https://doi.org/10.1016/j.micpath.2016.01.014 (2016).CAS 
Article 

Google Scholar 
Vijayakumar, S. et al. Ecotoxicity of Musa paradisiaca leaf extract-coated ZnO nanoparticles to the freshwater microcrustacean Ceriodaphnia cornuta. Limnologica 67, 1–6. https://doi.org/10.1016/j.limno.2017.09.004 (2017).CAS 
Article 

Google Scholar 
Hart, R. C. Zooplankton distribution in relation to turbidity and related environmental gradients in a large subtropical reservoir: Patterns and implications. Freshw. Biol. 24(2), 241–263. https://doi.org/10.1111/j.1365-2427.1990.tb00706.x (1990).Article 

Google Scholar 
Pollard, A. I., González, M. J., Vanni, M. J. & Headworth, J. L. Effects of turbidity and biotic factors on the rotifer community in an Ohio reservoir. In Rotifera VIII: A Comparative Approach. Developments in Hydrobiology, Hydrobiologia Vol. 387388 (eds Wurdak, E. et al.) 215–223 (Springer, 1998).
Google Scholar 
Roman, M. R., Holliday, D. V. & Sanford, L. P. Temporal and spatial patterns of zooplankton in the Chesapeake Bay turbidity maximum. Mar. Ecol. Prog. Ser. 213, 215–227. https://doi.org/10.3354/meps213215 (2001).ADS 
Article 

Google Scholar 
Young, I. R. & Ribal, A. Multiplatform evaluation of global trends in wind speed and wave height. Science 364(6440), 548–552. https://doi.org/10.1126/science.aav9527 (2019).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Goździejewska, A. M., Skrzypczak, A. R., Paturej, E. & Koszałka, J. Zooplankton diversity of drainage system reservoirs at an opencast mine. Knowl. Manag. Aquat. Ecosyst. 419, 33. https://doi.org/10.1051/kmae/2018020 (2018).Article 

Google Scholar 
Goździejewska, A. M., Skrzypczak, A. R., Koszałka, J. & Bowszys, M. Effects of recreational fishing on zooplankton communities of drainage system reservoirs at an open-pit mine. Fish. Manag. Ecol. 00, 1–13. https://doi.org/10.1111/fme.12411 (2020).Article 

Google Scholar 
Allesina, S., Bodini, A. & Bondavalli, C. Ecological subsystems via graph theory: The role of strongly connected components. Oikos 110, 164–176. https://doi.org/10.1111/j.0030-1299.2005.13082.x (2005).Article 

Google Scholar 
D’Alelio, D. et al. Ecological-network models link diversity, structure and function in the plankton food-web. Sci. Rep. 6, 21806. https://doi.org/10.1038/srep21806 (2016).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Krebs, C. J. Ecology: The Experimental Analysis of Distribution and Abundance 6th edn. (Benjamin Cummings, 2009).
Google Scholar 
Ejsmont-Karabin, J., Radwan, S. & Bielańska-Grajner, I. Rotifers. Monogononta–Atlas of Species. Polish Freshwater Fauna (Univ of Łódź, 2004).
Google Scholar 
Streble, H. & Krauter, D. Das Leben im Wassertropfen. Mikroflora und Mikrofauna des Süβwassers (Kosmos Gesellschaft der Naturfreunde Franckhsche Verlagshandlung Stuttgart, 1978).
Google Scholar 
Ejsmont-Karabin, J. The usefulness of zooplankton as lake ecosystem indicators: Rotifer trophic state index. Pol. J. Ecol. 60, 339–350 (2012).
Google Scholar 
Gutkowska, A., Paturej, E. & Kowalska, E. Rotifer trophic state indices as ecosystem indicators in brackish coastal waters. Oceanologia 55(4), 887–899. https://doi.org/10.5697/oc.55-4.887 (2013).Article 

Google Scholar 
Dembowska, E. A., Napiórkowski, P., Mieszczankin, T. & Józefowicz, S. Planktonic indices in the evaluation of the ecological status and the trophic state of the longest lake in Poland. Ecol. Indic. 56, 15–22. https://doi.org/10.1016/j.ecolind.2015.03.019 (2015).Article 

Google Scholar 
Sousa, W., Attayde, J. L., Rocha, E. D. S. & Eskinazi-Sant’Anna, E. M. The response of zooplankton assemblages to variations in the water quality of four man-made lakes in semi-arid northeastern Brazil. J. Plankton Res. 30(6), 699–708. https://doi.org/10.1093/plankt/fbn032 (2008).Article 

Google Scholar 
Kak, A. & Rao, R. Does the evasive behavior of H. exarthra influence its competition with cladocerans? In Rotifera VIII: A Comparative Approach. Developments in Hydrobiology, Hydrobiologia Vol. 387/388 (eds Wurdak, E. et al.) 409–419 (Springer, 1998).
Google Scholar 
Hochberg, R., Yang, H. & Moore, J. The ultrastructure of escape organs: Setose arms and crossstriated muscles in Hexarthra mira (Rotifera: Gnesiotrocha: Flosculariaceae). Zoomorphology 136, 159–173. https://doi.org/10.1007/s00435-016-0339-2 (2017).Article 

Google Scholar 
Brooks, J. L. & Dodson, S. I. Predation, body size, and composition of plankton. Science 150, 28–35 (1965).ADS 
CAS 
Article 

Google Scholar 
Connell, J. H. Intermediate-disturbance hypothesis. Science 204(4399), 1345 (1979).CAS 
Article 

Google Scholar 
Martín González, A. M., Dalsgaard, B. & Olesen, J. M. Centrality measures and the importance of generalist species in pollination networks. Ecol. Complex. 7(1), 36–43. https://doi.org/10.1016/j.ecocom.2009.03.008 (2010).Article 

Google Scholar 
Paine, R. T. A note on trophic complexity and community stability. Am. Nat. 104, 91–93 (1969).Article 

Google Scholar 
Schmitz, O. J. & Trussell, G. C. Multiple stressors, state-dependence and predation risk—Foraging trade-offs: Toward a modern concept of trait-mediated indirect effects in communities and ecosystems. Curr. Opin. Behav. Sci. 12, 6–11. https://doi.org/10.1016/j.cobeha.2016.08.003 (2016).Article 

Google Scholar 
Burns, C. W. & Gilbert, J. J. Effects of daphnid size and density on interference between Daphnia and Keratella cochlearis. Limnol. Oceanogr. 31(4), 848–858. https://doi.org/10.4319/lo.1986.31.4.0848 (1986).ADS 
Article 

Google Scholar 
Gilbert, J. J. Suppression of rotifer populations by Daphnia: A review of the evidence, the mechanisms, and the effects on zooplankton community structure. Limnol. Oceanogr. 33(6), 1286–1303. https://doi.org/10.4319/lo.1988.33.6.1286 (1988).ADS 
Article 

Google Scholar 
Conde-Porcuna, J. M., Morales-Baquero, R. & Cruz-Pizarro, L. Effects of Daphnia longispina on rotifer populations in a natural environment: Relative importance of food limitation and interference competition. J. Plankton Res. 16(6), 691–706. https://doi.org/10.1093/plankt/16.6.691 (1994).Article 

Google Scholar 
Ladle, R. J. & Whittaker, R. J. (eds) Conservation Biogeography (Wiley–Blackwell, 2011).
Google Scholar 
Cottee-Jones, H. E. W. & Whittaker, R. J. The keystone species concept: A critical appraisal. Front. Biogeogr. 4(3), 117–127. https://doi.org/10.21425/F5FBG12533 (2012).Article 

Google Scholar 
Remane, A. Die Brackwasserfauna. Verhandlungen Der Deutschen Zoologischen Gesellschaft 36, 34–74 (1934).
Google Scholar 
Skrzypczak, A. R. & Napiórkowska-Krzebietke, A. Identification of hydrochemical and hydrobiological properties of mine waters for use in aquaculture. Aquac. Rep. 18, 100460. https://doi.org/10.1016/j.aqrep.2020.100460 (2020).Article 

Google Scholar 
von Flössner, D. & Krebstiere, C. Kiemen-und Blattfüsser, Branchiopoda, Fischläuse, Branchiura Vol. 382 (VEB Gustav Fischer Verlag, 1972).
Google Scholar 
Koste, W. Rotatoria. Die Rädertiere Mitteleuropas. Überordnung Monogononta. I Textband, II Tafelband 52–570 (Gebrüder Borntraeger, 1978).
Google Scholar 
Rybak, J. I. & Błędzki, L. A. Freshwater Planktonic Crustaceans (Warsaw University Press, 2010).
Google Scholar 
Błędzki, L. A. & Rybak, J. I. Freshwater Crustacean Zooplankton of Europe: Cladocera & Copepoda (Calanoida, Cyclopoida). Key to Species Identification with Notes on Ecology, Distribution, Methods and Introduction to Data Analysis (Springer, 2016).Book 

Google Scholar 
Bottrell, H. H. et al. A review of some problems in zooplankton production studies. Norw. J. Zool. 24, 419–456 (1976).
Google Scholar 
Ejsmont-Karabin, J. Empirical equations for biomass calculation of planktonic rotifers. Pol. Arch. Hydr. 45, 513–522 (1998).
Google Scholar 
Kovach, W. L. MVSP—A Multivariate Statistical Package for Windows, ver. 3.2 (Kovach Computing Services Pentraeth, 2015).
Google Scholar 
Borgatti, S. P. Centrality and network flow. Soc. Netw. 27, 55–71. https://doi.org/10.1016/j.socnet.2004.11.008 (2005).Article 

Google Scholar 
Kamada, T. & Kawai, S. An algorithm for drawing general undirected graphs—Inform. Process Lett. 31, 7–15 (1989).MathSciNet 
Article 

Google Scholar 
Pavlopoulos, G. A. et al. Using graph theory to analyze biological networks. BioData Min 4, 10 (2011).Article 

Google Scholar 
Newman, M. E. J. A measure of betweenness centrality based on random walks. Soc. Netw. 27, 39–54. https://doi.org/10.1016/j.socnet.2004.11.009 (2005).Article 

Google Scholar 
Brandes, U. A. faster algorithm for betweenness centrality. J. Math. Sociol. 25, 163–177. https://doi.org/10.1080/0022250X.2001.9990249 (2001).Article 
MATH 

Google Scholar  More