Philippa Kaur

Plant materialRice (Oryza sativa L.) plants used for the experiment were from the collection of Taiwan Agricultural Research Institute. The rice variety (Tainan No. 11) used in this study has enhanced resistance to rice blast. The use of plant materials complies with international, national, and/or institutional guidelines and legislation.Field areaThis study was carried out in experimental rice fields under low-external-input and conventional farming in central Taiwan (23.5859 N, 120.4083 E; 8.0 ha). The annual average temperature ranged from 23 to 25 °C, the annual average relative humidity ranged from 75 to 92%, and the annual rainfall ranged from 1020 to 2873 mm year−1 (average data between 2006 and 2016 measured at a nearby weather station; Fig. 1). The experimental paddy plots were defined by considering the typical dimensions of the agricultural fields in Taiwan (0.5 to 1.0 ha). A long-term experiment was conducted from 2006 to 2016 to study the effects of different agronomic management on biodiversity, productivity, and environment, including traceability system, soil fertility, nitrogen leaching, production costs, disease incidence and severity, the abundance of pest and beneficial insects, and weed dynamics.The treatments consisted of conventional farming with high chemical fertilizer input (CF) and low-external-input farming with low fertilizer input (LF). In the CF farming, we followed the fertilizer recommendations that are constructed to meet the nutrient requirements of the crop. In the LF farming, the chemical fertilizers were largely reduced compared to the recommendation (see next paragraph for the details). The experiment was conducted as a randomized complete block design (RCBD) with four replicates. In agricultural experiments, the RCBD is a standard procedure by grouping experimental units into blocks. For example, the design can control variation in the experiments by considering spatial influences and adjusting the effects of target factors in fields. Each experimental unit consisted of a 0.58 ± 0.16 ha of the area of the field. Additional nutrient management in the LF system includes (1) nitrogen-fixing and cover crops, (2) manure and compost applications, (3) plant and soil nutrient analyses for adjusting fertilization, and (4) reduced tillage. Soil-available potassium gradually decreased during the 10-year study period in the area of the LF system. Over the study period, the LF system achieved the similar level of crop production as that of the CF system (Fig. S1).In our study area, there were two growing seasons within a year: one in the first half of the year (from February to June) and one in the second half (from August to December). The ground fertilizers were applied before rice seedlings were transplanted, followed by additional fertilizations during the tillering and boosting stages. The total amount of fertilizers used for the CF system included 140–180 and 120–140 kg N ha−1, 70–72 and 60 kg P2O5 ha−1, and 85 and 60 kg K2O ha−1 for the first and second seasons, respectively. For the LF system, 100 and 80 kg N ha−1, 30 and 30 kg P2O5 ha−1, and 30 and 30 kg K2O ha−1 were applied in the first and second seasons, respectively. The larger amount of fertilizers for the first season was due to its longer duration. For each rice growing season, fungicides were applied to both farming systems once during the boosting stage. During the fungicide application, a 10% mixture of Cartap plus Probenazole or 6% probenazole for rice blast (both 30 kg ha−1) and 1.5% Furametpyr for sheath blight (20 kg ha−1) were used.Rice disease monitoringThe major rice disease (rice blast; Fig. S2) was monitored biweekly in the CF and LF systems over the two growing seasons per year, with each growing season including (in chronological order) the tillering, flowering, and maturing stages. There was a total of 123 occasions during our study. The plants were disease free when planted out. When the lesion of the rice blast began to appear in the fields from the tillering stage to the maturing stage, the effects of the two treatments (CF and LF systems) in the paddy fields on the disease incidence of rice blast (caused by Pyricularia oryzae) were investigated. For each plot (or experimental unit), the incidence of rice disease was randomly examined at 5 points and for 25 plexuses (i.e., each derived from one primary tiller) per point. The disease incidence was quantified as the percentage of infected plexuses that were determined based on the presence of infected leaves.The area under the disease progress curve (AUDPC) was used to quantify disease incidences over time, and the relative AUDPC ((RAUDPC)) was used because of unequal sampling duration in the growing seasons during our study period. For each plot (or experimental unit), we used the (RAUDPC) to summarize the incidences of disease during each growing season as follows:$$RAUDPC=frac{sum_{i=1}^{n-1}frac{{y}_{i}+{y}_{i+1}}{2}times left({t}_{i+1}-{t}_{i}right)}{100 times left({t}_{n}-{t}_{1}right)},$$
(1)
where ({y}_{i}) and ({t}_{i}) are the disease incidence (%) and time (day) at the (i)th observation, respectively, and (n) is the total number of observations.Bayesian modelingWe built a mechanistic model that was applied to assess the interplay within a network of relationships among weather fluctuation, farming system, and disease incidence in the paddy fields. The model describes how (1) temperature and relative humidity together influence the development of primary inoculum, (2) rainfall detaches the fungal spores on the host tissues, and (3) rainfall and wind catch the airborne spores onto the leaf area. These environmental processes determine the disease incidence in the model. In addition, this model considers that farming systems can suppress or accelerate disease incidence. By fitting our model to the observed incidence, Bayesian inference was used as the parameter estimation technique for the models. In addition, we tested the alternative mechanistic hypotheses based on a model-selection criterion and cross vaidation (see subsequent paragraphs).With a linearity assumption, the incidences of disease (RAUDPC) were modeled as an inverse-logit function of the progress rate of the development of primary inoculum ((IP) with values between 0 and 1) and the net catchment of the airborne spores by rainfall and wind ((CT) with values between 0 and 1; when subtracting the detachment of spores by rainfall from the host tissue) as follows:$$RAUDPC=invLogitleft({a}_{f}+{b}_{1}bullet logitleft(avg_IPright)+{b}_{2}bullet logitleft(avg_IPbullet avg_CTright)right),$$
(2)
where ({a}_{f}), ({b}_{1}), and ({b}_{2}) describe the constant baseline for different farming systems ((f) = the CF or LF system), the direct effect size of (avg_IP), and the mediating effect size of (avg_CT) through (IP), respectively. The two parameters ((avg_IP) and (avg_CT)) are averaged (IP) and (CT) during the growing season, respectively (see below for details). The effect sizes ({b}_{1}) and ({b}_{2}) have values more than zero. The constant baseline allows the management-specific acting in the model when they can influence the disease incidence.The process rate (IP) was simulated as a function of the temperature response ((fleft(Tright)) with values between 0 and 1) and hourly air relative humidity ((RH,) %) as follows20:$$IP= left{begin{array}{ll}0& mathrm{if}, RH 0) are the steepness and midpoint parameters to control the portion of spores caught by the wind, respectively.The Bayesian framework ‘Stan’49 and its R interface ‘RStan’50 were used to construct and fit the models. There were two competing models: either considering the difference between the CF and LF systems by not fixed to the same values of the constant baseline ({a}_{f}) in Formula (2) or not. For each model, four Markov Chain Monte Carlo (MCMC) chains (for numerical approximations of Bayesian inference) ran, each with 5,000 iterations, and the first half of the iterations of each chain were discarded as burn-in. The R-hat statistic of each parameter approaches a value of 1, indicating model convergence. With a total of 2,000 samples, collected as one sample for every 5 iterations for each chain, the model parameters and their posterior distribution were estimated. To compare the two competing models, we calculated the widely applicable information criterion (WAIC) using the R package ‘loo’51. The best model was determined based on the lowest WAIC. By using the same R package, we also performed the approximate leave-one-out cross-validation (LOO-CV) to estimate the predictive ability of the two Bayesian models. Here, we used the expected log predictive density (ELPD) to be the predictive performance.Compliance with ethical standardsThe authors declare that they have no conflict of interest. This article does not contain any studies involving animals performed by any of the authors. This article does not contain any studies involving human participants performed by any of the authors. More

AffiliationsDepartment of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USAMarjolein Bruijning, Lucas P. Henry, Simon K. G. Forsberg, C. Jessica E. Metcalf & Julien F. AyrolesLewis-Sigler Institute for Integrative Genomics, Princeton, NJ, USALucas P. Henry, Simon K. G. Forsberg & Julien F. AyrolesAuthorsMarjolein BruijningLucas P. HenrySimon K. G. ForsbergC. Jessica E. MetcalfJulien F. AyrolesCorresponding authorCorrespondence to
Marjolein Bruijning. More

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

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

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

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

Hughes, T. P. et al. Coral reefs in the Anthropocene. Nature 546, 82–90 (2017).ADS 
CAS 
PubMed 

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

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

Google Scholar 
Eakin, C. M., Sweatman, H. P. A. & Brainard, R. E. The 2014–2017 global-scale coral bleaching event: Insights and impacts. Coral Reefs 38, 539–545 (2019).ADS 

Google Scholar 
Glynn. Coral reef bleaching: Facts, hypotheses and implications. Glob. Chang. Biol. 2, 495–509 (1996).ADS 

Google Scholar 
Brown, B. E. Coral bleaching: Causes and consequences. Coral Reefs 16, 129–138 (1997).
Google Scholar 
Maynard, J. A. et al. Projections of climate conditions that increase coral disease susceptibility and pathogen abundance and virulence. Nat. Clim. Chang. 5, 688–694 (2015).ADS 

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

Google Scholar 
Anthony, K. R. N., Kline, D. I., Diaz-Pulido, G., Dove, S. & Hoegh-Guldberg, O. Ocean acidification causes bleaching and productivity loss in coral reef builders. Proc. Natl. Acad. Sci. U. S. A. 105, 17442–17446 (2008).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Huang, H. et al. Positive and negative responses of coral calcification to elevated pCO2: Case studies of two coral species and the implications of their responses. Mar. Ecol. Prog. Ser. 502, 145–156 (2014).ADS 
CAS 

Google Scholar 
Hoadley, K. D. et al. Physiological response to elevated temperature and pCO2 varies across four Pacific coral species: Understanding the unique host + symbiont response. Sci. Rep. 5, 1–15 (2015).
Google Scholar 
Schoepf, V. et al. Coral energy reserves and calcification in a high-CO2 world at two temperatures. PLoS One. 8, e75049 (2013).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
IPCC. In IPCC Special Report on the Ocean and Cryosphere in a Changing Climate, (eds. Pörtner, H.-O. et al.) 1–36 (Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 2019).Bahr, K. D., Jokiel, P. L. & Rodgers, K. S. Relative sensitivity of five Hawaiian coral species to high temperature under high-pCO2 conditions. Coral Reefs 35, 729–738 (2016).ADS 

Google Scholar 
Dove, S. G., Brown, K. T., Van Den Heuvel, A., Chai, A. & Hoegh-Guldberg, O. Ocean warming and acidification uncouple calcification from calcifier biomass which accelerates coral reef decline. Commun. Earth Environ. 1, 1–9 (2020).
Google Scholar 
Chow, M. H., Tsang, R. H. L., Lam, E. K. Y. & Ang, P. O. Quantifying the degree of coral bleaching using digital photographic technique. J. Exp. Mar. Bio. Ecol. 479, 60–68 (2016).
Google Scholar 
Amid, C. et al. Additive effects of the herbicide glyphosate and elevated temperature on the branched coral Acropora formosa in Nha Trang, Vietnam. Environ. Sci. Pollut. Res. 25, 13360–13372 (2018).CAS 

Google Scholar 
Anthony, K. R. N., Connolly, S. R. & Willis, B. L. Comparative analysis of energy allocation to tissue and skeletal growth in corals. Limnol. Oceanogr. 47, 1417–1429 (2002).ADS 

Google Scholar 
Edmunds, P. J. & Davies, P. S. An energy budget for Porites porites (Scleractinia). Mar. Biol. 92, 339–347 (1986).
Google Scholar 
Stimson, J. S. Location, quantity and rate of change in quantity of lipids in tissue of Hawaiian hermatypic corals. Bull. Mar. Sci. 41, 889–904 (1987).ADS 

Google Scholar 
Harland, A. D., Navarro, J. C., Spencer Davies, P. & Fixter, L. M. Lipids of some Caribbean and Red Sea corals: Total lipid, wax esters, triglycerides and fatty acids. Mar. Biol. 117, 113–117 (1993).CAS 

Google Scholar 
Grottoli, A. G., Tchernov, D. & Winters, G. Physiological and biogeochemical responses of super-corals to thermal stress from the northern gulf of Aqaba, Red Sea. Front. Mar. Sci. 4, 1–12 (2017).
Google Scholar 
Rodrigues, L. J. & Grottoli, A. G. Energy reserves and metabolism as indicators of coral recovery from bleaching. Limnol. Oceanogr. 52, 1874–1882 (2007).ADS 

Google Scholar 
Anthony, K. R. N., Hoogenboom, M. O., Maynard, J. A., Grottoli, A. G. & Middlebrook, R. Energetics approach to predicting mortality risk from environmental stress: A case study of coral bleaching. Funct. Ecol. 23, 539–550 (2009).
Google Scholar 
Baumann, J. H., Grottoli, A. G., Hughes, A. D. & Matsui, Y. Photoautotrophic and heterotrophic carbon in bleached and non-bleached coral lipid acquisition and storage. J. Exp. Mar. Bio. Ecol. 461, 469–478 (2014).CAS 

Google Scholar 
Hughes, A. D. & Grottoli, A. G. Heterotrophic compensation: A possible mechanism for resilience of coral reefs to global warming or a sign of prolonged stress?. PLoS ONE 8, 1–10 (2013).
Google Scholar 
Grottoli, A. G. et al. The cumulative impact of annual coral bleaching can turn some coral species winners into losers. Glob. Chang. Biol. 20, 3823–3833 (2014).ADS 
PubMed 

Google Scholar 
Grottoli, A. G., Rodrigues, L. J. & Palardy, J. E. Heterotrophic plasticity and resilience in bleached corals. Nature 440, 1186–1189 (2006).ADS 
CAS 
PubMed 

Google Scholar 
Levas, S. J. et al. Can heterotrophic uptake of dissolved organic carbon and zooplankton mitigate carbon budget deficits in annually bleached corals?. Coral Reefs 35, 495–506 (2016).ADS 

Google Scholar 
Jury, C. P., Delano, M. N. & Toonen, R. J. High heritability of coral calcification rates and evolutionary potential under ocean acidification. Sci. Rep. 9, 1–13 (2019).
Google Scholar 
Jury, C. P. & Toonen, R. J. Adaptive responses and local stressor mitigation drive coral resilience in warmer, more acidic oceans. Proc. R. Soc. B Biol. Sci. 286, 20190614 (2019).
Google Scholar 
Concepcion, G. T., Polato, N. R., Baums, I. B. & Toonen, R. J. Development of microsatellite markers from four Hawaiian corals: Acropora cytherea, Fungia scutaria, Montipora capitata and Porites lobata. Conserv. Genet. Resour. 2, 11–15 (2010).

Google Scholar 
Gorospe, K. D. & Karl, S. A. Genetic relatedness does not retain spatial pattern across multiple spatial scales: Dispersal and colonization in the coral, Pocillopora damicornis. Mol. Ecol. 22, 3721–3736 (2013).PubMed 

Google Scholar 
Wall, C. B., Ritson-Williams, R., Popp, B. N. & Gates, R. D. Spatial variation in the biochemical and isotopic composition of corals during bleaching and recovery. Limnol. Oceanogr. 64, 2011–2028 (2019).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Bahr, K. D., Tran, T., Jury, C. P. & Toonen, R. J. Abundance, size, and survival of recruits of the reef coral Pocillopora acuta under ocean warming and acidification. PLoS ONE 15, 1–13 (2020).
Google Scholar 
Rogelj, J. et al. Paris agreement climate proposals need a boost to keep warming well below 2 °C. Nature 534, 631–639 (2016).ADS 
CAS 
PubMed 

Google Scholar 
McLachlan, R. H., Price, J. T., Solomon, S. L. & Grottoli, A. G. Thirty years of coral heat-stress experiments: A review of methods. Coral Reefs 39, 885–902 (2020).
Google Scholar 
Grottoli, A. G. et al. Increasing comparability among coral bleaching experiments. Ecol. Appl. 31, e02262 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
Grottoli, A. G. Variability of stable isotopes and maximum linear extension in reef-coral skeletons at Kaneohe Bay, Hawaii. Mar. Biol. 135, 437–449 (1999).
Google Scholar 
McLachlan, R. H., Dobson, K. L., Grottoli, A. G. Quantification of Total Biomass in Ground Coral Samples. Protocols.io (2020). https://doi.org/10.17504/protocols.io.bdyai7se.McLachlan, R. H., Muñoz-Garcia, A., Grottoli, A. G. Extraction of Total Soluble Lipid from Ground Coral Samples. Protocols.io (2020). https://doi.org/10.17504/protocols.io.bc4qiyvw.McLachlan, R. H., Price, J. T., Dobson, K. L., Weisleder, N. & Grottoli, A. G. Microplate Assay for Quantification of Soluble Protein in Ground Coral Samples. Protocols.io (2020). https://doi.org/10.17504/protocols.io.bdc8i2zw.McLachlan, R. H., Juracka, C. & Grottoli, A. G. Symbiodiniaceae Enumeration in Ground Coral Samples Using Countess™ II FL Automated Cell Counter. Protocols.io (2020). https://doi.org/10.17504/protocols.io.bdc5i2y6.McLachlan, R. H. & Grottoli, A. G. Geometric Method for Estimating Coral Surface Area Using Image Analysis. Protocols.io https://doi.org/10.17504/protocols.io.bdyai7se(2021).Muscatine, L., McCloskey, L. R. & Marian, R. E. Estimating the daily contribution of carbon from zooxanthellae to coral animal respiration. Limnol. Oceanogr. 26, 601–611 (1981).ADS 
CAS 

Google Scholar 
Levas, S. J. et al. Organic carbon fluxes mediated by corals at elevated pCO2 and temperature. Mar. Ecol. Prog. Ser. 519, 153–164 (2015).ADS 
CAS 

Google Scholar 
Perry, C. T. et al. Loss of coral reef growth capacity to track future increases in sea level. Nature 558, 396–400 (2018).ADS 
CAS 
PubMed 

Google Scholar 
Woodley, C. M., Burnett, A. & Downs, C. A. Epidemiological Assessment of Reproductive Condition of ESA Priority Coral (2013).Logan, C. A., Dunne, J. P., Eakin, C. M. & Donner, S. D. Incorporating adaptive responses into future projections of coral bleaching. Glob. Chang. Biol. 20, 125–139 (2014).ADS 
PubMed 

Google Scholar 
Rodrigues, L. J., Grottoli, A. G. & Lesser, M. P. Long-term changes in the chlorophyll fluorescence of bleached and recovering corals from Hawaii. J. Exp. Biol. 211, 2502–2509 (2008).PubMed 

Google Scholar 
Rowan, H. et al. Environmental gradients drive physiological variation in Hawaiian corals. Coral Reefs 40(5), 1505–1523. https://doi.org/10.1007/s00338-021-02140-8 (2021).Article 

Google Scholar 
Houlbrèque, F. & Ferrier-Pagès, C. Heterotrophy in tropical scleractinian corals. Biol. Rev. 84, 1–17 (2009).PubMed 

Google Scholar 
J. T. Price, thesis, The Ohio State University (2020). More