Philippa Kaur

Logan, C. A., Dunne, J. P., Ryan, J. S., Baskett, M. L. & Donner, S. D. Nat. Clim. Chang. 11, 537–542 (2021).Article 

Google Scholar 
Cook, C. N. & Sgrò, C. M. Conserv. Biol. 31, 501–512 (2017).Article 

Google Scholar 
Gonzalez, A., Ronce, O., Ferriere, R. & Hochberg, M. E. Phil. Trans. R. Soc. Lond. B 368, 1–8 (2013).
Google Scholar 
Kovach, R. P., Gharrett, A. J. & Tallmon, D. A. Proc. R. Soc. Lond. B 279, 3870–3878 (2012).
Google Scholar 
Bonnet, T. et al. Science 376, 1012–1016 (2022).CAS 
Article 

Google Scholar 
Norberg, J. et al. Nat. Clim. Chang. 2, 747–751 (2012).Article 

Google Scholar 
Torda, G. et al. Nat. Clim. Chang. 7, 627–636 (2017).Article 

Google Scholar 
Catullo, R. A., Llewelyn, J., Phillips, B. L. & Moritz, C. C. Curr. Biol. 29, R996–R1007 (2019).CAS 
Article 

Google Scholar 
Keppel, G. et al. Glob. Ecol. Biogeogr. 21, 393–404 (2012).Article 

Google Scholar 
Vos, C. C. et al. J. Appl. Ecol. 45, 1722–1731 (2008).Article 

Google Scholar 
Isaak, D. J. et al. Glob. Change Biol. 21, 2540–2553 (2015).Article 

Google Scholar 
Beyer, H. L. et al. Conserv. Lett. 11, e12587 (2018).Article 

Google Scholar 
Tingley, M. W., Estes, L. D. & Wilcove, D. S. Nature 500, 271–272 (2013).CAS 
Article 

Google Scholar 
Schindler, D. E., Armstrong, J. B. & Reed, T. E. Front. Ecol. Environ. 13, 257–263 (2015).Article 

Google Scholar 
Cornwell, B. et al. eLife 10, e64790 (2021).CAS 
Article 

Google Scholar 
National Academies. of Sciences Engineering & Medicine. A Decision Framework for Interventions to Increase the Persistence and Resilience of Coral Reefs. (The National Academies Press, 2019).Palumbi, S. R., Barshis, D. J., Traylor-Knowles, N. & Bay, R. A. Science 344, 895–898 (2014).CAS 
Article 

Google Scholar 
Matz, M. V., Treml, E. A. & Haller, B. C. Glob. Change Biol. 26, 3473–3481 (2020).Article 

Google Scholar 
Bay, R. A. & Palumbi, S. R. Curr. Biol. 24, 2952–2956 (2014).CAS 
Article 

Google Scholar 
Donovan, M. K. et al. Science 372, 977–980 (2021).CAS 
Article 

Google Scholar 
Anthony, K. et al. Nat. Ecol. Evol. 1, 1420–1422 (2017).Article 

Google Scholar 
Morrison, T. H. et al. Nature 573, 333–336 (2019).CAS 
Article 

Google Scholar 
van Oppen, M. J. H., Oliver, J. K., Putnam, H. M. & Gates, R. D. Proc. Natl Acad. Sci. USA 112, 2307–2313 (2015).Article 

Google Scholar 
DeFilippo, L. B. et al. Ecol. Appl. https://doi.org/10.1002/eap.2650 (2022).Steneck, R. S. et al. Front. Mar. Sci. 6, 265 (2019).Article 

Google Scholar 
Dixon, G. B. et al. Science 348, 1460–1462 (2015).CAS 
Article 

Google Scholar 
McManus, L. C. et al. Glob. Change Biol. 27, 4307–4321 (2021).CAS 
Article 

Google Scholar 
Kleypas, J. A. et al. Glob. Change Biol. 22, 3539–3549 (2016).Article 

Google Scholar 
McManus, L. C. et al. Ecology 102, e03381 (2021).Article 

Google Scholar 
Walsworth, T. E. et al. Nat. Clim. Chang. 9, 632–636 (2019).Article 

Google Scholar  More

Futuyma, D. J. & Agrawal, A. A. Macroevolution and the biological diversity of plants and herbivores. Proc. Natl. Acad. Sci. 106, 18054–18061 (2009).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Frago, E., Dicke, M. & Godfray, H. C. J. Insect symbionts as hidden players in insect–plant interactions. Trends Ecol. Evol. 27, 705–711 (2012).PubMed 
Article 

Google Scholar 
Gurung, K., Wertheim, B. & Salles, J. F. The microbiome of pest insects: It is not just bacteria. Entomol. Exp. Appl. 167, 156–170 (2019).Article 

Google Scholar 
Douglas, A. E. Multiorganismal insects: Diversity and function of resident microorganisms. Annu. Rev. Entomol. 60, 17–34 (2015).CAS 
PubMed 
Article 

Google Scholar 
Engel, P. & Moran, N. A. The gut microbiota of insects—diversity in structure and function. FEMS Microbiol. Rev. 37, 699–735 (2013).CAS 
PubMed 
Article 

Google Scholar 
Giron, D. et al. Chapter seven—influence of microbial symbionts on plant-insect interactions. In Advances in Botanical Research Vol. 81 (eds Sauvion, N. et al.) 225–257 (Academic Press, 2017).
Google Scholar 
Chen, B. et al. Biodiversity and activity of the gut microbiota across the life history of the insect herbivore Spodoptera littoralis. Sci. Rep. 6, 29505 (2016).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Vacher, C. et al. The phyllosphere: Microbial jungle at the plant–climate interface. Annu. Rev. Ecol. Evol. Syst. 47, 1–24 (2016).Article 

Google Scholar 
Griffin, E. A. & Carson, W. P. Tree endophytes: cryptic drivers of tropical forest diversity. In Endophytes of Forest Trees: Biology and Applications (eds Pirttilä, A. M. & Frank, A. C.) 63–103 (Springer International Publishing, 2018). https://doi.org/10.1007/978-3-319-89833-9_4.Chapter 

Google Scholar 
Peñuelas, J., Rico, L., Ogaya, R., Jump, A. S. & Terradas, J. Summer season and long-term drought increase the richness of bacteria and fungi in the foliar phyllosphere of Quercus ilex in a mixed Mediterranean forest. Plant Biol. 14, 565–575 (2012).PubMed 
Article 

Google Scholar 
Laforest-Lapointe, I., Paquette, A., Messier, C. & Kembel, S. W. Leaf bacterial diversity mediates plant diversity and ecosystem function relationships. Nature 546, 145–147 (2017).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Kembel, S. W. et al. Relationships between phyllosphere bacterial communities and plant functional traits in a neotropical forest. Proc. Natl. Acad. Sci. USA. 111, 13715–13720 (2014).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Kembel, S. W. & Mueller, R. C. Plant traits and taxonomy drive host associations in tropical phyllosphere fungal communities. Botany 92, 303–311 (2014).Article 

Google Scholar 
Faeth, S. H. & Hammon, K. E. Fungal endophytes in oak trees: Long-term patterns of abundance and associations with leafminers. Ecology 78, 810–819 (1997).Article 

Google Scholar 
Broderick, N. A., Raffa, K. F., Goodman, R. M. & Handelsman, J. Census of the bacterial community of the gypsy moth larval midgut by using culturing and culture-independent methods. Appl. Environ. Microbiol. 70, 293–300 (2004).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Pinto-Tomás, A. A. et al. Comparison of midgut bacterial diversity in tropical caterpillars (Lepidoptera: Saturniidae) fed on different diets. Environ. Entomol. 40, 1111–1122 (2011).PubMed 
Article 

Google Scholar 
Ravenscraft, A., Berry, M., Hammer, T., Peay, K. & Boggs, C. Structure and function of the bacterial and fungal gut microbiota of Neotropical butterflies. Ecol. Monogr. 89, e01346 (2019).Article 

Google Scholar 
Hammer, T. J., Sanders, J. G. & Fierer, N. Not all animals need a microbiome. FEMS Microbiol. Lett. 366, 117 (2019).Article 
CAS 

Google Scholar 
Mason, C. J. et al. Diet influences proliferation and stability of gut bacterial populations in herbivorous lepidopteran larvae. PLoS ONE 15, e0229848 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Montagna, M. et al. Evidence of a bacterial core in the stored products pest Plodia interpunctella: The influence of different diets. Environ. Microbiol. 18, 4961–4973 (2016).CAS 
PubMed 
Article 

Google Scholar 
Phalnikar, K., Kunte, K. & Agashe, D. Disrupting butterfly caterpillar microbiomes does not impact their survival and development. Proc. R. Soc. B Biol. Sci. 286, 20192438 (2019).CAS 
Article 

Google Scholar 
Somerville, J., Zhou, L. & Raymond, B. Aseptic rearing and infection with gut bacteria improve the fitness of transgenic diamondback moth, Plutella xylostella. Insects 10, 89 (2019).PubMed Central 
Article 

Google Scholar 
González-Serrano, F. et al. The gut microbiota composition of the moth brithys crini reflects insect metamorphosis. Microb. Ecol. 79, 960–970 (2020).PubMed 
Article 
CAS 

Google Scholar 
Goharrostami, M. & JalaliSendi, J. Investigation on endosymbionts of Mediterranean flour moth gut and studying their role in physiology and biology. J. Stored Prod. Res. 75, 10–17 (2018).Article 

Google Scholar 
Vilanova, C., Baixeras, J., Latorre, A. & Porcar, M. The generalist inside the specialist: Gut bacterial communities of two insect species feeding on toxic plants are dominated by Enterococcus sp. Front. Microbiol. 7, 1005 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
Minard, G., Tikhonov, G., Ovaskainen, O. & Saastamoinen, M. The microbiome of the Melitaea cinxia butterfly shows marked variation but is only little explained by the traits of the butterfly or its host plant. Environ. Microbiol. 21, 4253–4269 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Shapira, M. Gut microbiotas and host evolution: Scaling up symbiosis. Trends Ecol. Evol. 31, 539–549 (2016).PubMed 
Article 

Google Scholar 
Chen, B. et al. Gut bacterial and fungal communities of the domesticated silkworm (Bombyx mori) and wild mulberry-feeding relatives. ISME J. 12, 2252–2262 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Mason, C. J. & Raffa, K. F. Acquisition and structuring of midgut bacterial communities in gypsy moth (Lepidoptera: Erebidae) larvae. Environ. Entomol. 43, 595–604 (2014).PubMed 
Article 

Google Scholar 
Paniagua Voirol, L. R., Frago, E., Kaltenpoth, M., Hilker, M. & Fatouros, N. E. Bacterial symbionts in Lepidoptera: Their diversity, transmission, and impact on the host. Front. Microbiol. 9, 556 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Laforest-Lapointe, I., Messier, C. & Kembel, S. W. Host species identity, site and time drive temperate tree phyllosphere bacterial community structure. Microbiome 4, 27 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
Meyer, K. M. & Leveau, J. H. J. Microbiology of the phyllosphere: A playground for testing ecological concepts. Oecologia 168, 621–629 (2012).ADS 
PubMed 
Article 

Google Scholar 
Gomes, T., Pereira, J. A., Benhadi, J., Lino-Neto, T. & Baptista, P. Endophytic and epiphytic phyllosphere fungal communities are shaped by different environmental factors in a Mediterranean ecosystem. Microb. Ecol. 76, 668–679 (2018).PubMed 
Article 

Google Scholar 
Rastogi, G. et al. Leaf microbiota in an agroecosystem: Spatiotemporal variation in bacterial community composition on field-grown lettuce. ISME J. 6, 1812–1822 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Whitaker, M. R. L., Salzman, S., Sanders, J., Kaltenpoth, M. & Pierce, N. E. Microbial communities of lycaenid butterflies do not correlate with larval diet. Front. Microbiol. 7, 1920 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
Zheng, Y. et al. Midgut microbiota diversity of potato tuber moth associated with potato tissue consumed. BMC Microbiol. 20, 58 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Griffin, E. A., Harrison, J. G., McCormick, M. K., Burghardt, K. T. & Parker, J. D. Tree diversity reduces fungal endophyte richness and diversity in a large-scale temperate forest experiment. Diversity 11, 234 (2019).Article 

Google Scholar 
Kim, M. et al. Distinctive phyllosphere bacterial communities in tropical trees. Microb. Ecol. 63, 674–681 (2012).PubMed 
Article 

Google Scholar 
Hammer, T. J., Janzen, D. H., Hallwachs, W., Jaffe, S. P. & Fierer, N. Caterpillars lack a resident gut microbiome. Proc. Natl. Acad. Sci. 114, 9641–9646 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Višňovská, D. et al. Caterpillar gut and host plant phylloplane mycobiomes differ: A new perspective on fungal involvement in insect guts. FEMS Microbiol. Ecol. 96, fiaa116 (2020).PubMed 
Article 
CAS 

Google Scholar 
Voříšková, J. & Baldrian, P. Fungal community on decomposing leaf litter undergoes rapid successional changes. ISME J. 7, 477–486 (2013).PubMed 
Article 
CAS 

Google Scholar 
Pochon, X., Zaiko, A., Fletcher, L. M., Laroche, O. & Wood, S. A. Wanted dead or alive? Using metabarcoding of environmental DNA and RNA to distinguish living assemblages for biosecurity applications. PLoS ONE 12, e0187636 (2017).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Schlechter, R. O., Miebach, M. & Remus-Emsermann, M. N. P. Driving factors of epiphytic bacterial communities: A review. J. Adv. Res. 19, 57–65 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Seabloom, E. W. et al. Effects of nutrient supply, herbivory, and host community on fungal endophyte diversity. Ecology 100, e02758 (2019).PubMed 
Article 

Google Scholar 
Berlec, A. Novel techniques and findings in the study of plant microbiota: Search for plant probiotics. Plant Sci. 193–194, 96–102 (2012).PubMed 
Article 
CAS 

Google Scholar 
Unterseher, M., Reiher, A., Finstermeier, K., Otto, P. & Morawetz, W. Species richness and distribution patterns of leaf-inhabiting endophytic fungi in a temperate forest canopy. Mycol. Prog. 6, 201–212 (2007).Article 

Google Scholar 
Gilbert, G. S., Reynolds, D. R. & Bethancourt, A. The patchiness of epifoliar fungi in tropical forests: Host range, host abundance, and environment. Ecology 88, 575–581 (2007).PubMed 
Article 

Google Scholar 
Stone, B. W. G. & Jackson, C. R. Canopy position is a stronger determinant of bacterial community composition and diversity than environmental disturbance in the phyllosphere. FEMS Microbiol. Ecol. 95, fiz032 (2019).CAS 
PubMed 
Article 

Google Scholar 
Copeland, J. K., Yuan, L., Layeghifard, M., Wang, P. W. & Guttman, D. S. Seasonal community succession of the phyllosphere microbiome. Mol. Plant. Microbe Interact. 28, 274–285 (2015).CAS 
PubMed 
Article 

Google Scholar 
Stone, B. W. G. & Jackson, C. R. Seasonal patterns contribute more towards phyllosphere bacterial community structure than short-term perturbations. Microb. Ecol. https://doi.org/10.1007/s00248-020-01564-z (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Truchado, P., Gil, M. I., Reboleiro, P., Rodelas, B. & Allende, A. Impact of solar radiation exposure on phyllosphere bacterial community of red-pigmented baby leaf lettuce. Food Microbiol. 66, 77–85 (2017).PubMed 
Article 

Google Scholar 
Wang, X. et al. Variability of gut microbiota across the life cycle of Grapholita molesta (Lepidoptera: Tortricidae). Front. Microbiol. 11, 1366 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
Toju, H. & Fukatsu, T. Diversity and infection prevalence of endosymbionts in natural populations of the chestnut weevil: Relevance of local climate and host plants. Mol. Ecol. 20, 853–868 (2011).PubMed 
Article 

Google Scholar 
Yun, J.-H. et al. Insect gut bacterial diversity determined by environmental habitat, diet, developmental stage, and phylogeny of host. Appl. Environ. Microbiol. 80, 5254–5264 (2014).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Sánchez, N. E., Pereyra, P. C. & Luna, M. G. Spatial patterns of parasitism of the solitary parasitoid Pseudapanteles dignus (Hymenoptera: Braconidae) on Tuta absoluta (Lepidoptera: Gelechiidae). Environ. Entomol. 38, 365–374 (2009).PubMed 
Article 

Google Scholar 
Santos, A. M. C. & Quicke, D. L. J. Large-scale diversity patterns of parasitoid insects. Entomol. Sci. 14, 371–382 (2011).Article 

Google Scholar 
Mereghetti, V., Chouaia, B. & Montagna, M. New insights into the microbiota of moth pests. Int. J. Mol. Sci. 18, 2450 (2017).PubMed Central 
Article 
CAS 

Google Scholar 
Floater, G. J. Estimating movement of the processionary caterpillar Ochrogaster zunifer Herrich-Schäffer (Lepidoptera: Thaumetopoeidae) between discrete resource patches. Aust. J. Entomol. 35, 279–283 (1996).Article 

Google Scholar 
Turčáni, M. & Patočka, J. Does intraguild predation of Cosmia trapezina L. (Lep.: Noctuidae) influence the abundance of other Lepidoptera forest pests?. J. For. Sci. 57, 472–482 (2011).Article 

Google Scholar 
Hikisz, J. & Soszynska-Maj, A. What moths fly in winter? The assemblage of moths active in a temperate deciduous forest during the cold season in Central Poland. J. Entomol. Res. Soc. 17, 59–71 (2015).
Google Scholar 
Bell, J. R., Bohan, D. A., Shaw, E. M. & Weyman, G. S. Ballooning dispersal using silk: World fauna, phylogenies, genetics and models. Bull. Entomol. Res. 95, 69–114 (2005).CAS 
PubMed 
Article 

Google Scholar 
Griffin, E. A. & Carson, W. P. The ecology and natural history of foliar bacteria with a focus on tropical forests and agroecosystems. Bot. Rev. 81, 105–149 (2015).Article 

Google Scholar 
Qian, X. et al. Mainland and island populations of Mussaenda kwangtungensis differ in their phyllosphere fungal community composition and network structure. Sci. Rep. 10, 952 (2020).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Herren, C. M. & McMahon, K. D. Keystone taxa predict compositional change in microbial communities. Environ. Microbiol. 20, 2207–2217 (2018).PubMed 
Article 

Google Scholar 
Humphrey, P. T. & Whiteman, N. K. Insect herbivory reshapes a native leaf microbiome. Nat. Ecol. Evol. 4, 221–229 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
Müller, T., Müller, M., Behrendt, U. & Stadler, B. Diversity of culturable phyllosphere bacteria on beech and oak: The effects of lepidopterous larvae. Microbiol. Res. 158, 291–297 (2003).PubMed 
Article 

Google Scholar 
Hrcek, J., Miller, S. E., Quicke, D. L. J. & Smith, M. A. Molecular detection of trophic links in a complex insect host-parasitoid food web. Mol. Ecol. Resour. 11, 786–794 (2011).PubMed 
Article 

Google Scholar 
Bateman, C., Šigut, M., Skelton, J., Smith, K. E. & Hulcr, J. Fungal associates of the Xylosandrus compactus (Coleoptera: Curculionidae, Scolytinae) are spatially segregated on the insect body. Environ. Entomol. 45, 883–890 (2016).CAS 
PubMed 
Article 

Google Scholar 
Toju, H., Tanabe, A. S., Yamamoto, S. & Sato, H. High-coverage ITS primers for the DNA-based identification of ascomycetes and basidiomycetes in environmental samples. PLoS ONE 7, e40863 (2012).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Chelius, M. K. & Triplett, E. W. The diversity of archaea and bacteria in association with the roots of Zea mays L. Microb. Ecol. 41, 252–263 (2001).CAS 
PubMed 
Article 

Google Scholar 
Redford, A. J., Bowers, R. M., Knight, R., Linhart, Y. & Fierer, N. The ecology of the phyllosphere: Geographic and phylogenetic variability in the distribution of bacteria on tree leaves. Environ. Microbiol. 12, 2885–2893 (2010).PubMed 
PubMed Central 
Article 

Google Scholar 
Bolyen, E. et al. QIIME 2: Reproducible, Interactive, Scalable, and Extensible Microbiome Data Science https://peerj.com/preprints/27295 (2018) https://doi.org/10.7287/peerj.preprints.27295v2.Rivers, A. R., Weber, K. C., Gardner, T. G., Liu, S. & Armstrong, S. D. ITSxpress: Software to rapidly trim internally transcribed spacer sequences with quality scores for marker gene analysis. F1000Research 7, 1418 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Callahan, B. J. et al. DADA2: High-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581–583 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Bokulich, N. A. et al. Optimizing taxonomic classification of marker-gene amplicon sequences with QIIME 2’s q2-feature-classifier plugin. Microbiome 6, 90 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Quast, C. et al. The SILVA ribosomal RNA gene database project: Improved data processing and web-based tools. Nucleic Acids Res. 41, D590–D596 (2013).CAS 
PubMed 
Article 

Google Scholar 
Nilsson, R. H. et al. The UNITE database for molecular identification of fungi: Handling dark taxa and parallel taxonomic classifications. Nucleic Acids Res. 47, D259–D264 (2019).CAS 
PubMed 
Article 

Google Scholar 
UNITE Community. UNITE QIIME Release for Fungi 2. (2019).Davis, N. M., Proctor, D. M., Holmes, S. P., Relman, D. A. & Callahan, B. J. Simple statistical identification and removal of contaminant sequences in marker-gene and metagenomics data. Microbiome 6, 226 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2020).Ter Braak, C. J. F. ter & Smilauer, P. Canoco reference manual and user’s guide: software for ordination, version 5.0. (2012).Ondov, B. D., Bergman, N. H. & Phillippy, A. M. Interactive metagenomic visualization in a Web browser. BMC Bioinform. 12, 385 (2011).Article 

Google Scholar 
Chrostek, E., Pelz-Stelinski, K., Hurst, G. D. D. & Hughes, G. L. Horizontal transmission of intracellular insect symbionts via plants. Front. Microbiol. 8, 2237 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
Fox, J. & Weisberg, S. An R Companion to Applied Regression (SAGE Publications, 2018).
Google Scholar 
Oksanen, J. et al. vegan: Community Ecology Package. (2020).Anderson, M. J. Distance-based tests for homogeneity of multivariate dispersions. Biometrics 62, 245–253 (2006).MathSciNet 
PubMed 
MATH 
Article 

Google Scholar 
Renkonen, O. Statistisch-ökologische Untersuchungen über die terrestrische Käferwelt der finnischen Bruchmoore. Ann. Zool. Soc. Zool.-Bot. Fenn. Vanamo 6, 1–231 (1938).
Google Scholar 
Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).Article 

Google Scholar 
Roberts, D. W. labdsv: Ordination and Multivariate Analysis for Ecology (2019).Cáceres, M. D. & Legendre, P. Associations between species and groups of sites: Indices and statistical inference. Ecology 90, 3566–3574 (2009).PubMed 
Article 

Google Scholar 
Dufrêne, M. & Legendre, P. Species assemblages and indicator species: The need for a flexible asymmetrical approach. Ecol. Monogr. 67, 345–366 (1997).
Google Scholar 
Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: A practical and powerful approach to multiple testing. J. R. Stat. Soc. Ser. B Methodol. 57, 289–300 (1995).MathSciNet 
MATH 

Google Scholar  More

Overall strategyThe aim of this project was to synthesize publicly available data on land use, pesticide use, and toxicity to generate a ‘toolkit’ of data resources enabling improved landscape-scale research on pesticide-pollinator interactions. The main outcomes are several novel datasets covering ten major crops or crop groups in each of the 48 contiguous U.S. states:

I)

Average application rate (kg/ha/yr) of >500 common pesticide active ingredients (1997–2017),

II)

Aggregate bee toxic load (honey bee lethal doses/ha/yr) of all insecticides combined (1997–2014), (Note that this dataset ends in 2014 because after that year, data on seed-applied pesticides were excluded29, and these contribute significantly to bee toxic load21)

III)

Reclass tables relating these pesticide-use indicators to land use/land cover classes to enable the creation of maps predicting annual pesticide loading at 30–56 m resolution.

An overview of the steps, inputs, and outcomes are provided in Fig. 1.Fig. 1Overview of the data synthesis workflow described in this paper.Full size imageData inputsA summary of input datasets is provided in Table 1.Table 1 Data inputs used in this study.Full size tablePesticide dataPesticide use data were last downloaded from the USGS National Pesticide Synthesis Project30,31 in June 2020. This dataset reports total kg applied of 508 common pesticide active ingredients by combinations of state, crop group, and year for the contiguous U.S. from 1992–2017 (crop groups explained in Table 2). The data are derived primarily from farmer surveys conducted by a private firm (Kynetec). For California, USGS obtains data from the state’s pesticide use reporting program32. USGS then aggregates and standardizes both data sources into a common national dataset that is released to the public and was used in this effort. The USGS dataset includes both a ‘high’ and a ‘low’ estimate of pesticide use, varying based on the treatment of missing values in the source data31. Because previous work on this dataset suggested that the ‘low’ estimate more closely matches independent pesticide estimates33, we used the ‘low’ estimate throughout, but assess the influence of this choice on the resulting estimates (see Technical Validation). While we focus on the ‘low’ estimate for the data and outputs presented in this manuscript, the workflow we developed can accommodate both the low and high estimates.Table 2 USGS crop categories in pesticide source data, based on metadata from USGS30,31 and personal communication with USGS staff scientists.Full size tableCrop area dataTo translate pesticide use estimates into average application rates, it was necessary to divide total kg of pesticide applied by the land area to which it was potentially applied. Crop area data were last downloaded from the Quick Stats Database of the USDA34 in May 2020, using data files downloaded from the ‘developer’ page. This USDA dataset contains crop acreage estimates generated from two sources: the Census of Agriculture (Census), which is comprehensive but conducted only once every five years35 and the crop survey conducted by the National Agricultural Statistics Service (NASS), which is an annual survey based on a representative sample of farmers in major production regions for a more limited subset of crops36.Honey bee toxicity dataTranslating insecticide application rates into estimates of bee toxic load (honey bee lethal doses/ha/yr) required toxicity values for each insecticide active ingredient in the USGS dataset. We used LD50 values for the honey bee (Apis mellifera) because this is the standard terrestrial insect species used in regulatory procedures, and so has the most comprehensive data available. This species is also of particular concern as an important provider of pollination services to agriculture. As previously reported21, the LD50 values were derived from two sources, the ECOTOX database37 of the U.S. Environmental Protection Agency (US-EPA), and the Pesticide Properties Database (PPDB)038. ECOTOX was queried in July 2017, by searching for all LD50 values for the honey bee (Apis mellifera) that were generated under laboratory conditions. Acute contact and oral LD50 values for the honey bee were recorded manually from the PPDB in June 2018.Land cover dataMapping pesticides to the landscape requires land use/land cover data indicating where crops are grown. We used the USDA Cropland Data Layer (CDL)39, a land cover dataset at 30–56 m resolution produced through remote sensing. This dataset is available starting in 2008 for states in the contiguous U.S., with some states (primarily in the Midwest and Mid-South) available back to the early 2000s.Data preparationRelating datasetsA major challenge in this data synthesis effort was relating the various data sources to each other, given that each dataset has unique nomenclature and organization. We created the following keys (summarized in Table 3) to facilitate joining datasets:

I)

USGS-USDA crop keys – Using documentation and metadata associated with the USGS pesticide dataset31,33,40, we created keys relating the USGS surveyed crop names (‘ePest’ crops) and the ten USGS crop categories to the large number of corresponding crop acreage data items in the Census and NASS datasets. For annual crops and hay crops we used ‘harvested acres,’ and for tree crops we used ‘acres bearing & non-bearing.’ These choices were made to maximize data availability and to correspond as closely as possible to the crop acreage from which the pesticide data were derived31. A separate key was developed for California because California pesticide data derives from different source data and covers a larger range of crops.

II)

USGS-CASRN compound key – Using USGS documentation as well as background information on pesticide active ingredients38,41, we generated keys relating USGS active ingredient names to chemical abstracts service (CAS) registry numbers to facilitate matching compounds to the ECOTOX and PPDB databases.

III)

USGS compound-category key – In this key we classified active ingredients into major groups (insecticides, fungicides, nematicides, etc.) and into mode-of-action classes on the basis of information from pesticide databases and resistance action committees38,41,42,43,44.

IV)

USGS-USDA compound key – To facilitate our data validation effort, we generated a key relating USGS compound names to USDA compound names, on the basis of information from several pesticide databases38,41.

V)

USGS-CDL land use-land cover keys – Using documentation from the USGS pesticide dataset describing the crop composition of each of the ten crop categories31, we created a key that matches these categories to land cover classes in the CDL. A separate key was developed for California given the differences in surveyed crops in this state, noted above.

Table 3 Keys generated to relate datasets.Full size tableProcessing crop area dataBecause of differences in the crops included in pesticide use estimates, crop acreage data were processed separately for California and for all other states, and then re-joined, as follows: Acreage data were first filtered to include only data at the state level, reporting total annual acreage for states in the contiguous U.S. after 1996. Acreage data were joined to the appropriate USGS-USDA crop key and only those crops represented in the pesticide dataset were retained. We then generated an acreage dataset with single rows for each combination of crop, state, and year using data from the Census when available (1997, 2002, 2007, 2012, 2017), data from NASS in non-Census years, and temporal interpolation to fill in remaining missing values (i.e. linear interpolation between values in the same state and crop in the nearest surrounding years). This process was repeated for California, using acreage data for only that state in combination with the CA crop key. Finally, acreage data in the two datasets were recombined, converted to hectares, and summed by USGS crop group.Processing honey bee toxicity dataProcessing for the honey bee toxicity data has been described in detail elsewhere21. Briefly, toxicity values were categorized as contact, oral, or other and standardized where possible into µg/bee. Records were retained if they represented acute exposure (4 days or less) for adult bees representing contact or oral LD50 values in µg/bee. To generate a consensus list of contact and oral LD50 values for all insecticides reported in the USGS dataset, we gave preference to point estimates and estimates generated through U.S. or E.U. regulatory procedures, taking a geometric mean if multiple such estimates were available. Unbounded estimates (“greater than” or “less than” some value) were only used when point estimates were unavailable, using the minimum (for “less than”) or the maximum (for “greater than”). If values for a compound were unavailable in both datasets, we used the median toxicity value for the insecticide mode-of-action group. And finally, in rare cases (n = 1/148 compounds for contact toxicity and 8/148 compounds for oral toxicity) we were still left without a toxicity estimate for a particular insecticide. In those cases, we used the median value for all insecticides.Data synthesisCompound-specific application rates for state-crop-year combinationsUSGS data on pesticide application were joined to data on crop area. Average pesticide application rates were calculated by dividing kg applied by crop area (ha) for each combination of compound, crop group, state, and year.Aggregate insecticide application rates for state-crop-year combinationsThe dataset from the previous step was filtered to include only insecticides, and then joined to LD50 data by compound name. Bee toxic load associated with each insecticide active ingredient was calculated by dividing the application rate by the contact or oral LD50 value (µg/bee) to generate a number of lethal doses applied per unit area. These values were then summed across compounds to generate estimates of kg and bee toxic load per ha for combinations of crop group, state, and year.Missing values were estimated using temporal interpolation, where possible (i.e. linear interpolation between values in the same state and crop group in the nearest surrounding years). This dataset ends in 2014 because after that year seed-applied pesticides were excluded from the source data29, and they constitute a major contribution to bee toxic load21.We focused bee toxic load on insecticides for three reasons. First, quality of LD50 data is highest for insecticides and uneven for fungicides and herbicides. Point estimates make up the majority of LD50 values for insecticides, whereas  100 µg/bee”, increasing the uncertainty of downstream estimates). Second, insecticides tend to have greater acute toxicity toward insects than fungicides and herbicides (median [IQR] LD50 = 100 [44–129] µg/bee for fungicides, 100 [75–112] µg/bee for herbicides, and 1.36 [0.16–12] µg/bee for insecticides). As a result, insecticides account for > 95% of bee toxic load nationally, even when herbicides and fungicides are included (and even though insecticides make up only 6.5% of pesticides applied on a weight basis). Third, focusing these values on insecticides increases their interpretability, reflecting efforts directed toward insect pest management, rather than a mix of insect, weed, and fungal pest management (which often have distinct dynamics and constraints for farmers).While we chose to include only insecticides in this aggregate value, users are welcome to adjust the workflow to include fungicides and herbicides if desired. To this end, we provide our best estimates for LD50 values for fungicides and herbicides in the USGS dataset (Table 4).Table 4 Data outputs generated by this study.Full size tableReclassification tablesTo generate reclassification tables for the CDL, the pesticide datasets described above were joined by crop group to CDL land use categories. The output of these processes was a set of reclassification tables for combinations of compound, state, and year. Also generated was a set of reclassification tables for aggregate insecticide use for combinations of state and year.Of the 131 land use categories in the CDL, 16 represent two crops grown sequentially in the same year (double crops, found on ~2% of U.S. cropland in 201245), which required a modified accounting in our workflow. Pesticide use practices on double crops are not well described, but one study suggested that pesticide expenditures on soybean grown after wheat were similar to pesticide expenditures in soybean grown alone46. Therefore, we assumed that pesticide use on double crops would be additive (e.g. for a wheat-soybean double crop, the annual pesticide use estimate was generated by summing pesticide use associated with wheat and soybean).Missing values in the reclassification tables resulted from several distinct issues. Some values were missing because a particular crop was not included in the underlying pesticide use survey (e.g. oats was not included in the Kynetec survey), or because the land use category was not a crop at all (e.g. deciduous forest). These two issues were indicated with values of ‘1’ in columns called ‘unsurveyed’ and ‘noncrop,’ respectively. For double crops, a value of 0.5 in the ‘unsurveyed’ column indicates that one of the crops was surveyed and the other was not. For compound-specific datasets, missing values may reflect that a given compound was not used in a state-crop group-year combination. For the aggregate insecticide dataset, even after interpolation there were some missing values, usually when a state had very little area of a particular crop or crop group.Finally, missing data for double crops were treated slightly differently in the aggregate vs. compound-specific reclassification tables. For the aggregate insecticide dataset, estimates for double crops were only included if estimates were available for both crops; otherwise the value was reported as missing. For the compound-specific datasets, estimates for double crops were included if there was an estimate for at least one of the crops, since specific compounds may be used in one crop but not another. More

Davis, M. B. & Shaw, R. G. Range shifts and adaptive responses to Quaternary climate change. Science 292, 673–679 (2001).CAS 
PubMed 
Article 

Google Scholar 
Parmesan, C. Ecological and evolutionary responses to recent climate change. Annu. Rev. Ecol. Evol. Syst. 37, 637–669 (2006).Article 

Google Scholar 
Hewitt, G. The genetic legacy of the Quaternary ice ages. Nature 405, 907–913 (2000).CAS 
PubMed 
Article 

Google Scholar 
Williams, J. E. & Blois, J. L. Range shifts in response to past and future climate change: can climate velocities and species’ dispersal capabilities explain variation in mammalian range shifts? J. Biogeogr. 45, 2175–2189 (2018).Article 

Google Scholar 
Parmesan, C. & Yohe, G. A globally coherent fingerprint of climate change impacts across natural systems. Nature 421, 37–42 (2003).CAS 
PubMed 
Article 

Google Scholar 
Thomas, C. D. Climate, climate change and range boundaries. Divers. Distrib. 16, 488–495 (2010).Article 

Google Scholar 
Bradshaw, A. D. & McNeilly, T. Evolutionary response to global climatic change. Ann. Bot. 67, 5–14 (1991).Article 

Google Scholar 
Harter, D. E. V. et al. Impacts of global climate change on the floras of oceanic islands—projections, implications and current knowledge. Perspect. Plant Ecol. Evol. Syst. 17, 160–183 (2015).Article 

Google Scholar 
Veron, S., Haevermans, T., Govaerts, R., Mouchet, M. & Pellens, R. Distribution and relative age of endemism across islands worldwide. Sci. Rep. 9, 1–12 (2019).Article 
CAS 

Google Scholar 
Román-Palacios, C. & Wiens, J. J. Recent responses to climate change reveal the drivers of species extinction and survival. Proc. Natl Acad. Sci. USA 117, 4211–4217 (2020).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Jump, A. S. & Peñuelas, J. Running to stand still: adaptation and the response of plants to rapid climate change. Ecol. Lett. 8, 1010–1020 (2005).PubMed 
Article 

Google Scholar 
Freeman, B. G., Scholer, M. N., Ruiz-Gutierrez, V. & Fitzpatrick, J. W. Climate change causes upslope shifts and mountaintop extirpations in a tropical bird community. Proc. Natl Acad. Sci. USA 115, 11982–11987 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Gilbert, K. J. & Whitlock, M. C. The genetics of adaptation to discrete heterogeneous environments: frequent mutation or large-effect alleles can allow range expansion. J. Evol. Biol. 30, 591–602 (2017).CAS 
PubMed 
Article 

Google Scholar 
Christmas, M. J., Breed, M. F. & Lowe, A. J. Constraints to and conservation implications for climate change adaptation in plants. Conserv. Genet. 17, 305–320 (2015).Article 
CAS 

Google Scholar 
Barrett, R. D. H. & Schluter, D. Adaptation from standing genetic variation. Trends Ecol. Evol. 23, 38–44 (2008).PubMed 
Article 

Google Scholar 
Lai, Y. T. et al. Standing genetic variation as the predominant source for adaptation of a songbird. Proc. Natl Acad. Sci. USA 116, 2152–2157 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Hoban, S. et al. Finding the genomic basis of local adaptation: Pitfalls, practical solutions, and future directions. Am. Nat. 188, 379–397 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
Hoffmann, A. A. & Sgrò, C. M. Climate change and evolutionary adaptation. Nature 470, 479–485 (2011).CAS 
PubMed 
Article 

Google Scholar 
Catullo, R. A., Llewelyn, J., Phillips, B. L. & Moritz, C. C. The potential for rapid evolution under anthropogenic climate change. Curr. Biol. 29, R996–R1007 (2019).CAS 
PubMed 
Article 

Google Scholar 
Botkin, D. B. et al. Forecasting the effects of global warming on biodiversity. BioScience 57, 227–236 (2007).Article 

Google Scholar 
Wiens, J. A., Stralberg, D., Jongsomjit, D., Howell, C. A. & Snyder, M. A. Niches, models, and climate change: assessing the assumptions and uncertainties. Proc. Natl Acad. Sci. USA 106, 19729–19736 (2009).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Smith, A. B., Godsoe, W., Rodríguez-Sánchez, F., Wang, H. H. & Warren, D. Niche estimation above and below the species level. Trends Ecol. Evol. 34, 260–273 (2019).PubMed 
Article 

Google Scholar 
Waldvogel, A.-M. et al. Evolutionary genomics can improve prediction of species’ responses to climate change. Evol. Lett. 4, 4–18 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
Razgour, O. et al. An integrated framework to identify wildlife populations under threat from climate change. Mol. Ecol. Resour. 18, 18–31 (2018).PubMed 
Article 

Google Scholar 
Razgour, O. et al. Considering adaptive genetic variation in climate change vulnerability assessment reduces species range loss projections. Proc. Natl Acad. Sci. USA 116, 10418–10423 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Aguirre-Liguori, J. A., Ramírez-Barahona, S., Tiffin, P. & Eguiarte, L. E. Climate change is predicted to disrupt patterns of local adaptation in wild and cultivated maize. Proc. R. Soc. B 286, 20190486 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Evans, T. G., Diamond, S. E. & Kelly, M. W. Mechanistic species distribution modelling as a link between physiology and conservation. Conserv. Physiol. 3, cov056 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
Hall, S. J. G. Haemoglobin polymorphism in the bank vole, Clethrionomys glareolus, in Britain. J. Zool. 187, 153–160 (1979).Article 

Google Scholar 
Kotlík, P. et al. Adaptive phylogeography: functional divergence between haemoglobins derived from different glacial refugia in the bank vole. Proc. R. Soc. B 281, 20140021 (2014).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Searle, J. B. et al. The Celtic fringe of Britain: Insights from small mammal phylogeography. Proc. R. Soc. B 276, 4287–4294 (2009).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Escalante, M. A., Horníková, M., Marková, S. & Kotlík, P. Niche differentiation in a postglacial colonizer, the bank vole Clethrionomys glareolus. Ecol. Evol. 11, 8054–8070 (2021).PubMed 
PubMed Central 
Article 

Google Scholar 
Reischl, E., Dafre, A. L., Franco, J. L. & Wilhelm Filho, D. Distribution, adaptation and physiological meaning of thiols from vertebrate hemoglobins. Comp. Biochem. Physiol. Part C. Toxicol. Pharmacol. 146, 22–53 (2007).Article 
CAS 

Google Scholar 
Storz, J. F. & Wheat, C. W. Integrating evolutionary and functional approaches to infer adaptation at specific loci. Evolution 64, 2489–2509 (2010).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Rossi, R. et al. Different metabolizing ability of thiol reactants in human and rat blood. Biochemical and pharmacological implications. J. Biol. Chem. 276, 7004–7010 (2001).CAS 
PubMed 
Article 

Google Scholar 
Vitturi, D. A. et al. Antioxidant functions for the hemoglobin β93 cysteine residue in erythrocytes and in the vascular compartment in vivo. Free Radic. Biol. Med. 55, 119–129 (2013).CAS 
PubMed 
Article 

Google Scholar 
Petersen, A. G. et al. Hemoglobin polymerization via disulfide bond formation in the hypoxia-tolerant turtle Trachemys scripta: Implications for antioxidant defense and O2 transport. Am. J. Physiol. Regul. Integr. Comp. Physiol. 314, R84–R93 (2018).PubMed 
Article 
CAS 

Google Scholar 
Paital, B. et al. Longevity of animals under reactive oxygen species stress and disease susceptibility due to global warming. World J. Biol. Chem. 7, 110–127 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
Jacobs, P. J., Oosthuizen, M. K., Mitchell, C., Blount, J. D. & Bennett, N. C. Heat and dehydration induced oxidative damage and antioxidant defenses following incubator heat stress and a simulated heat wave in wild caught four-striped field mice Rhabdomys dilectus. PLoS One 15, e0242279 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Kotlík, P., Marková, S., Horníková, M., Escalante, M. A. & Searle, J. B. The bank vole (Clethrionomys glareolus) as a model system for adaptive phylogeography in the European theater. Front. Ecol. Evol. 10, 866605 (2022).Article 

Google Scholar 
Strážnická, M., Marková, S., Searle, J. B. & Kotlík, P. Playing hide-and-seek in beta-globin genes: Gene conversion transferring a beneficial mutation between differentially expressed gene guplicates. Genes 9, 492 (2018).PubMed Central 
Article 
CAS 

Google Scholar 
Stocker, T. Climate Change 2013: the Physical Science Basis: Working Group I Contribution to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (Cambridge University Press, 2013).Araújo, M. B., Pearson, R. G., Thuiller, W. & Erhard, M. Validation of species-climate impact models under climate change. Glob. Chang. Biol. 11, 1504–1513 (2005).Article 

Google Scholar 
Peterson, A. T., Papeş, M. & Soberón, J. Rethinking receiver operating characteristic analysis applications in ecological niche modeling. Ecol. Modell. 213, 63–72 (2008).Article 

Google Scholar 
Warren, D. L., Glor, R. E. & Turelli, M. Environmental niche equivalency versus conservatism: quantitative approaches to niche evolution. Evolution 62, 2868–2883 (2008).PubMed 
Article 

Google Scholar 
Warren, D. L. et al. ENMTools 1.0: an R package for comparative ecological biogeography. Ecography 44, 504–511 (2021).Article 

Google Scholar 
Mayes, J. & Wheeler, D. Regional weather and climates of the British Isles—part 1: introduction. Weather 68, 3–8 (2013).Article 

Google Scholar 
Kotlík, P., Marková, S., Konczal, M., Babik, W. & Searle, J. B. Genomics of end-Pleistocene population replacement in a small mammal. Proc. R. Soc. B 285, 20172624 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Capblancq, T., Fitzpatrick, M. C., Bay, R. A., Exposito-Alonso, M. & Keller, S. R. Genomic prediction of (mal)adaptation across current and future climatic landscapes. Annu. Rev. Ecol. Evol. Syst. 51, 245–269 (2020).Article 

Google Scholar 
Benito Garzón, M., Robson, T. M. & Hampe, A. ΔTraitSDMs: species distribution models that account for local adaptation and phenotypic plasticity. N. Phytol. 222, 1757–1765 (2019).Article 

Google Scholar 
Wisz, M. S. et al. Effects of sample size on the performance of species distribution models. Divers. Distrib. 14, 763–773 (2008).Article 

Google Scholar 
Phillips, S. J., Dudík, M. & Schapire, R. E. A maximum entropy approach to species distribution modeling. in Twenty-first International Conference on Machine Learning – ICML ’04 9, 83 (ACM Press, 2004).Hijmans, R. J., Cameron, S. E., Parra, J. L., Jones, P. G. & Jarvis, A. Very high resolution interpolated climate surfaces for global land areas. Int. J. Climatol. 25, 1965–1978 (2005).Article 

Google Scholar 
Zeng, Y., Low, B. W. & Yeo, D. C. J. Novel methods to select environmental variables in MaxEnt: A case study using invasive crayfish. Ecol. Modell. 341, 5–13 (2016).Article 

Google Scholar 
Warren, D. L. & Seifert, S. N. Ecological niche modeling in Maxent: the importance of model complexity and the performance of model selection criteria. Ecol. Appl. 21, 335–342 (2011).PubMed 
Article 

Google Scholar 
Warren, D. L., Glor, R. E. & Turelli, M. ENMTools: a toolbox for comparative studies of environmental niche models. Ecography 33, 607–611 (2010).Article 

Google Scholar 
Gent, P. R. et al. The community climate system model version 4. J. Clim. 24, 4973–4991 (2011).Article 

Google Scholar 
Dufresne, J. L. et al. Climate change projections using the IPSL-CM5 Earth System Model: from CMIP3 to CMIP5. Clim. Dyn. 40, 2123–2165 (2013).Article 

Google Scholar 
Watanabe, S. et al. MIROC-ESM 2010: model description and basic results of CMIP5-20c3m experiments. Geosci. Model Dev. 4, 845–872 (2011).Article 

Google Scholar 
Giorgetta, M. A. et al. Climate and carbon cycle changes from 1850 to 2100 in MPI-ESM simulations for the Coupled Model Intercomparison Project phase 5. J. Adv. Model. Earth Syst. 5, 572–597 (2013).Article 

Google Scholar 
Schoener, T. W. The anolis lizards of Bimini: resource partitioning in a complex fauna. Ecology 49, 704–726 (1968).Article 

Google Scholar  More

Costanza, R. et al. Changes in the global value of ecosystem services. Glob. Environ. Change 26, 152–158 (2014).Article 

Google Scholar 
Mittermeier, R. A. et al. Wilderness and biodiversity conservation. Proc. Natl. Acad. Sci. USA 100, 10309–10313 (2003).CAS 
Article 

Google Scholar 
Dirzo, R. & Raven, P. H. Global state of biodiversity and loss. Annu. Rev. Env. Resour. 28, 137–167 (2003).Article 

Google Scholar 
Marengo, J. A. et al. Changes in climate and land use over the amazon region: current and future variability and trends. Front. Earth Sci. 6, 1–21 (2018).Article 

Google Scholar 
Anderson-Teixeira, K. J. et al. Climate-regulation services of natural and agricultural ecoregions of the Americas. Nat. Clim. Change 2, 177–181 (2012).Article 

Google Scholar 
Marengo, J. A. et al. The drought of Amazonia in 2005. J. Clim. 21, 495–516 (2008).Article 

Google Scholar 
Lewis, S. L., Brando, P. M., Phillips, O. L., Van Der Heijden, G. M. F. & Nepstad, D. The 2010 Amazon drought. Science 331, 554 (2011).CAS 
Article 

Google Scholar 
Jiménez-Muñoz, J. C. et al. Record-breaking warming and extreme drought in the Amazon rainforest during the course of El Niño 2015–2016. Sci. Rep. 6, 33130 (2016).Article 
CAS 

Google Scholar 
Phillips, O. L. et al. Drought sensitivity of the amazon rainforest. Science 323, 1344–1347 (2009).Koren, G. et al. Widespread reduction in sun-induced fluorescence from the Amazon during the 2015/2016 El Niño. Philos. Trans. R. Soc. Lond. B Biol. Sci 373, 20170408 (2018).Article 
CAS 

Google Scholar 
Feldpausch, T. R. et al. Amazon forest response to repeated droughts. Glob. Biogeochem. Cycles 30, 964–982 (2016).CAS 
Article 

Google Scholar 
Sousa, T. R. et al. Palms and trees resist extreme drought in Amazon forests with shallow water tables. J. Ecol. 108, 2070–2082 (2020).CAS 
Article 

Google Scholar 
Barros, F. et al. Hydraulic traits explain differential responses of Amazonian forests to the 2015 El Niño-induced drought. New Phytol. 223, 1253–1266 (2019).CAS 
Article 

Google Scholar 
Magney, T. S. et al. Mechanistic evidence for tracking the seasonality of photosynthesis with solar-induced fluorescence. Proc. Natl Acad. Sci. USA https://doi.org/10.1073/pnas.1900278116 (2019).Ciemer, C. et al. Higher resilience to climatic disturbances in tropical vegetation exposed to more variable rainfall. Nat. Geosci. 12, 174–179 (2019).CAS 
Article 

Google Scholar 
Gloor, E. et al. Tropical land carbon cycle responses to 2015/16 El Niño as recorded by atmospheric greenhouse gas and remote sensing data. Philos. Trans. R. Soc. B 373, 20170302 (2018).Article 
CAS 

Google Scholar 
Jiménez-Muñoz, J. C., Sobrino, J. A., Mattar, C. & Malhi, Y. Spatial and temporal patterns of the recent warming of the Amazon forest. J. Geophys. Res. Atmos. 118, 5204–5215 (2013).Article 

Google Scholar 
Choat, B. et al. Global convergence in the vulnerability of forests to drought. Nature 491, 752–755 (2012).CAS 
Article 

Google Scholar 
Esquivel-Muelbert, A. et al. Seasonal drought limits tree species across the Neotropics. Ecography 60, 12 (2016).
Google Scholar 
Fisher, R. A., Williams, M., de Lourdes Ruivo, M., de Costa, A. L. & Meir, P. Evaluating climatic and soil water controls on evapotranspiration at two Amazonian rainforest sites. Agric. For. Meteorol. 148, 850–861 (2008).Article 

Google Scholar 
Marthews, T. R. et al. High-resolution hydraulic parameter maps for surface soils in tropical South America. Geosci. Model Dev. 7, 711–723 (2014).Article 

Google Scholar 
Esteban, E. J. L., Castilho, C. V., Melgaço, K. L. & Costa, F. R. C. The other side of droughts: wet extremes and topography as buffers of negative drought effects in an Amazonian forest. New. Phytol. 229, 1995–2006 (2021).CAS 
Article 

Google Scholar 
Castro, A. O. et al. OCO-2 solar-induced chlorophyll fluorescence variability across ecoregions of the amazon basin and the extreme drought effects of El Niño (2015–2016). Remote Sens. 12, 1202 (2020).Article 

Google Scholar 
Sullivan, M. J. P. et al. Long-term thermal sensitivity of Earth’s tropical forests. Science 368, 869–874 (2020).CAS 
Article 

Google Scholar 
Sombroek, W. Spatial and temporal patterns of amazon rainfall. Ambio 30, 388–396 (2001).CAS 
Article 

Google Scholar 
Quesada, C. A. et al. Basin-wide variations in Amazon forest structure and function are mediated by both soils and climate. Biogeosciences 9, 2203–2246 (2012).Fan, Y., Li, H. & Miguez-Macho, G. Global patterns of groundwater table depth. Science 339, 940–943 (2013).CAS 
Article 

Google Scholar 
Joetzjer, E., Douville, H., Delire, C. & Ciais, P. Present-day and future Amazonian precipitation in global climate models: CMIP5 versus CMIP3. Clim. Dyn. 41, 2921–2936 (2013).Article 

Google Scholar 
Schietti, J. et al. Vertical distance from drainage drives floristic composition changes in an Amazonian rainforest. Plant. Ecol. Divers. 7, 241–253 (2014).Oliveira, R. S. et al. Embolism resistance drives the distribution of Amazonian rainforest tree species along hydro‐topographic gradients. New Phytol. 221, 1457–1465 (2018).Fyllas, N. M. et al. Basin-wide variations in foliar properties of Amazonian forest: phylogeny, soils and climate. Biogeosciences 6, 2677–2708 (2009).Sterck, F., Markesteijn, L., Schieving, F. & Poorter, L. Functional traits determine trade-offs and niches in a tropical forest community. PNAS 108, 20627–20632 (2011).CAS 
Article 

Google Scholar 
Oliveira, R. S. et al. Linking plant hydraulics and the fast–slow continuum to understand resilience to drought in tropical ecosystems. New Phytol. 230, 904–923 (2021).Article 

Google Scholar 
Guillemot, J. et al. Small and slow is safe: On the drought tolerance of tropical tree species. Glob. Chang. Biol. 28, 2622–2638 (2022).CAS 
Article 

Google Scholar 
DeSoto, L. et al. Low growth resilience to drought is related to future mortality risk in trees. Nat. Commun. 11, 545 (2020).CAS 
Article 

Google Scholar 
Rowland, L. et al. Death from drought in tropical forests is triggered by hydraulics not carbon starvation. Nature 528, 119–122 (2015).CAS 
Article 

Google Scholar 
de Almeida Castanho, A. D. et al. Changing Amazon biomass and the role of atmospheric CO2 concentration, climate, and land use. Glob. Biogeochem. Cycles 30, 18–39 (2016).Article 
CAS 

Google Scholar 
Friedlingstein, P. et al. Global carbon budget 2020. Earth Syst. Sci. Data 12, 3269–3340 (2020).Article 

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

Google Scholar 
Lathière, J. et al. Impact of climate variability and land use changes on global biogenic volatile organic compound emissions. Atmos. Chem. Phys. 6, 2129–2146 (2006).Article 

Google Scholar 
Galbraith, D. et al. Multiple mechanisms of Amazonian forest biomass losses in three dynamic global vegetation models under climate change. New Phytol. 187, 647–65 (2010).Article 

Google Scholar 
Johnson, M. O. et al. Variation in stem mortality rates determines patterns of above-ground biomass in Amazonian forests: implications for dynamic global vegetation models. Glob. Chang. Biol. 22, 3996–4013 (2016).Article 

Google Scholar 
Thonicke, K. et al. Simulating functional diversity of European natural forests along climatic gradients. J. Biogeogr. 47, 1069–1085 (2020).Article 

Google Scholar 
Feldpausch, T. R. et al. Height-diameter allometry of tropical forest trees. Biogeosciences 8, 1081–1106 (2011).Article 

Google Scholar 
Feldpausch, T. R. et al. Tree height integrated into pantropical forest biomass estimates. Biogeosciences 9, 3381–3403 (2012).Article 

Google Scholar 
Potapov, P. et al. The last frontiers of wilderness: tracking loss of intact forest landscapes from 2000 to 2013. Sci. Adv. 3, 1–14 (2017).Article 

Google Scholar 
Olson, D. M. et al. Terrestrial ecoregions of the world: a new map of life on Earth. BioScience 51, 933–938 (2001).Article 

Google Scholar 
Running, Steve, Mu, Qiaozhen & Zhao, Maosheng. MOD16A2 MODIS/Terra net evapotranspiration 8-day L4 global 500m. https://doi.org/10.5067/MODIS/MOD16A2.006 (2017).van Schaik, E. et al. Improved SIFTER v2 algorithm for long-term GOME-2A satellite retrievals of fluorescence with a correction for instrument degradation. https://doi.org/10.5194/amt-2019-384 (2020).Kooreman, M. L. et al. GOME-2A SIFTER v2 (2007-2018) [Data set]. SIFTER sun-induced vegetation fluorescence data from GOME-2A (Version 2.0) [Data set]. Royal Netherlands Meteorological Institute (KNMI). https://doi.org/10.21944/gome2a-sifter-v2-sun-induced-fluorescence.Hoese, D. et al. pytroll/pyresample: Version 1.23.0. Zenodo, https://doi.org/10.5281/zenodo.6375741 (2022).Kooreman, M., Tuinder, O., Boersma, K. F. & van Schaik, E. Algorithm Theoretical Basis Document for the GOME-2 NRT, Offline and Data Record Sun-Induced Fluorescence Products. (2019).Wigneron, J.-P. et al. Tropical forests did not recover from the strong 2015–2016 El Niño event. Sci. Adv. 6, eaay4603 (2020).CAS 
Article 

Google Scholar 
Gatti, L. V. et al. Drought sensitivity of Amazonian carbon balance revealed by atmospheric measurements. Nature 506, 76–80 (2014).CAS 
Article 

Google Scholar 
Doughty, R. et al. TROPOMI reveals dry-season increase of solar-induced chlorophyll fluorescence in the Amazon forest. Proc. Natl. Acad. Sci. USA 116, 22393–22398 (2019).CAS 
Article 

Google Scholar 
Porcar-Castell, A. et al. Chlorophyll a fluorescence illuminates a path connecting plant molecular biology to Earth-system science. Nat. Plants 7, 998–1009 (2021).CAS 
Article 

Google Scholar 
Sun, Y. et al. OCO-2 advances photosynthesis observation from space via solar-induced chlorophyll fluorescence. Science 358, eaam5747 (2017).Article 
CAS 

Google Scholar 
Wood, J. D. et al. Multiscale analyses of solar-induced florescence and gross primary production. Geophys. Res. Lett. 44, 533–541 (2017).Article 

Google Scholar 
Verma, M. et al. Effect of environmental conditions on the relationship between solar-induced fluorescence and gross primary productivity at an OzFlux grassland site. J. Geophys. Res. Biogeosci. 122, 716–733 (2017).Article 

Google Scholar 
Parazoo, N. C. et al. Terrestrial gross primary production inferred from satellite fluorescence and vegetation models. Glob. Chang Biol. 20, 3103–3121 (2014).Article 

Google Scholar 
Copernicus Climate Change Service (C3S). ERA5: Fifth generation of ECMWF atmospheric reanalyses of the global climate. https://cds.climate.copernicus.eu/cdsapp#!/home (2017).Goddard Earth Sciences Data and Information Services Center (GES DISC). Tropical Rainfall Measuring Mission (TRMM) – TRMM (TMPA/3B43) Rainfall Estimate L3 1 month 0.25 degree x 0.25 degree V7. https://doi.org/10.5067/TRMM/TMPA/MONTH/7 (2011).Aragão, L. E. O. C. et al. Spatial patterns and fire response of recent Amazonian droughts. Geophys. Res. Lett. 34 (2007).Paca, V. H. et al. The spatial variability of actual evapotranspiration across the Amazon River Basin based on remote sensing products validated with flux towers. Ecol. Process. 8, 6 (2019).Phillips, O. L. et al. Drought–mortality relationships for tropical forests. New Phytol. 187, 631–646 (2010).Article 

Google Scholar 
Maeda, E. E. et al. Evapotranspiration seasonality across the Amazon Basin. Earth Syst. Dyn. 8, 439–454 (2017).Article 

Google Scholar 
Hengl, T. et al. SoilGrids250m: Global gridded soil information based on machine learning. PLoS One 12, e0169748 (2017).Article 
CAS 

Google Scholar 
Costa, F. R. C., Schietti, J., Stark, S. C. & Smith, M. N. The other side of tropical forest drought: do shallow water table regions of Amazonia act as large-scale hydrological refugia from drought? New Phytol. https://nph.onlinelibrary.wiley.com/doi/10.1111/nph.17914 .Walsh, R. P. D. & Lawler, D. M. Rainfall seasonality: description, spatial patterns and change through time. Weather 36, 201–208 (1981).Article 

Google Scholar 
Gorelick, N. et al. Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sens. Environ. https://doi.org/10.1016/j.rse.2017.06.031 (2017).Heinze, G., Wallisch, C. & Dunkler, D. Variable selection – a review and recommendations for the practicing statistician. Biom. J. 60, 431–449 (2018).Article 

Google Scholar 
Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer-Verlag New York, 2016).QGIS.org. QGIS Geographic Information System (QGIS Association, 2022).Fancourt, M. Repository for Code, Data and Figures. https://zenodo.org/badge/latestdoi/514231211 (2022). More

Galindo, I. & Alonso, C. African swine fever virus: A review. Viruses 9, 103 (2017).PubMed Central 
Article 
CAS 

Google Scholar 
Blome, S., Franzke, K. & Beer, M. African swine fever: A review of current knowledge. Virus Res. 2020, 198099 (2020).Article 
CAS 

Google Scholar 
Li, X. & Tian, K. African swine fever in China. Vet. Rec. 183, 300 (2018).PubMed 
Article 

Google Scholar 
Wang, T., Sun, Y. & Qiu, H. J. African swine fever: An unprecedented disaster and challenge to China. Infect. Dis. Poverty 7, 66–70 (2018).Article 

Google Scholar 
Gaudreault, N. N., Madden, D. W., Wilson, W. C., Trujillo, J. D. & Richt, J. A. African swine fever virus: An emerging DNA arbovirus. Front. Vet. Sci. 7, 215 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
Ge, S. et al. Molecular characterization of African swine fever virus, China, 2018. Emerg. Infect. Dis. 24, 2131–2133 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Mason-D’Croz, D. et al. Modelling the global economic consequences of a major African swine fever outbreak in China. Nat. Food 1, 221–228 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
Woonwong, Y., Do, T. D. & Thanawongnuwech, R. The future of the pig industry after the introduction of African swine fever into Asia. Anim. Front. 10, 30–37 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
Mulieri, P. R. & Patitucci, L. D. Using ecological niche models to describe the geographical distribution of the myiasis-causing Cochliomyia hominivorax (Diptera: Calliphoridae) in southern South America. Parasitol. Res. 118, 1077–1086 (2019).PubMed 
Article 

Google Scholar 
Escobar, L. E. Ecological niche modeling: An introduction for veterinarians and epidemiologists. Front. Vet. Sci. 7, 519059 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
Bosso, L. et al. The rise and fall of an alien: why the successful colonizer Littorina saxatilis failed to invade the Mediterranean Sea. Biol. Invasions https://doi.org/10.1007/s10530-022-02838-y (2022).Article 

Google Scholar 
Wen, X. et al. Prediction of the potential distribution pattern of the great gerbil (Rhombomys opimus) under climate change based on ensemble modelling. Pest Manag. Sci. 78, 3128–3134 (2022).CAS 
PubMed 
Article 

Google Scholar 
Cheng, Y. et al. Evaluating the risk for Usutu virus circulation in Europe: Comparison of environmental niche models and epidemiological models. Int. J. Health Geogr. 17, 1–14 (2018).Article 

Google Scholar 
Naimi, B. & Araújo, M. B. Sdm: A reproducible and extensible R platform for species distribution modelling. Ecography 39, 368–375 (2016).Article 

Google Scholar 
Georges, D. & Thuiller, W. An example of species distribution modeling with biomod2. https://r-forge.r-project.org/…/inst/doc/Simple_species_modelling.pdf?root=biomod (2013).Thuiller, W. BIOMOD: Optimizing predictions of species distributions and projecting potential future shifts under global change. Glob. Change Biol. 9, 1353–1362 (2003).ADS 
Article 

Google Scholar 
Thuiller, W., Lafourcade, B., Engler, R. & Araújo, M. B. BIOMOD: A platform for ensemble forecasting of species distributions. Ecography 32, 369–373 (2009).Article 

Google Scholar 
Thuiller, W. Editorial commentary on “BIOMOD: Optimizing predictions of species distributions and projecting potential future shifts under global change”. Glob. Change Biol. 20, 3591–3592 (2014).ADS 
Article 

Google Scholar 
Navarro-Cerrillo, R. M., Duque-Lazo, J., Manzanedo, R. D., Sánchez-Salguero, R. & Palacios-Rodriguez, G. Climate change may threaten the southernmost Pinus nigra subsp. salzmannii (Dunal) Franco populations: An ensemble niche-based approach. iForest Biogeosci. For. 11, 396–405 (2018).Article 

Google Scholar 
Assefa, A., Tibebu, A., Bihon, A., Dagnachew, A. & Muktar, Y. Ecological niche modeling predicting the potential distribution of African horse sickness virus from 2020 to 2060. Sci. Rep. 12, 1748 (2022).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Raffini, F. et al. From nucleotides to satellite imagery: Approaches to identify and manage the invasive pathogen Xylella fastidiosa and its insect vectors in Europe. Sustainability 12, 4508 (2020).CAS 
Article 

Google Scholar 
Wani, I. A. et al. Predicting habitat suitability and niche dynamics of Dactylorhiza hatagirea and Rheum webbianum in the Himalaya under projected climate change. Sci. Rep. 12, 13205 (2022).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Boulanger-Lapointe, N. et al. Herbivore species coexistence in changing rangeland ecosystems: First high resolution national open-source and open-access ensemble models for Iceland. Sci. Total Environ. 845, 157140 (2022).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Sillero, N. & Barbosa, A. M. Common mistakes in ecological niche models. Int. J. Geogr. Inf. Sci. 35, 213–226 (2020).Article 

Google Scholar 
Varela, S., Anderson, R. P., García-Valdés, R. & Fernández-González, F. Environmental filters reduce the effects of sampling bias and improve predictions of ecological niche models. Ecography 37, 1084–1091 (2014).
Google Scholar 
Barbet-Massin, M., Jiguet, F., Albert, C. H. & Thuiller, W. Selecting pseudo-absences for species distribution models: How, where and how many?. Methods Ecol. Evol. 3, 327–338 (2010).Article 

Google Scholar 
Allouche, O., Tsoar, A. & Kadmon, R. Assessing the accuracy of species distribution models: Prevalence, kappa and the true skill statistic (TSS). J. Appl. Ecol. 43, 1223–1232 (2006).Article 

Google Scholar 
Xiao-Ge, X. et al. Introduction of BCC models and its participation in CMIP6. Clim. Change Res. 5, 533–539 (2019).
Google Scholar 
Wu, T. et al. The Beijing Climate Center Climate System Model (BCC-CSM): The main progress from CMIP5 to CMIP6. Geosci. Model Dev. 12, 1573–1600 (2019).ADS 
Article 

Google Scholar 
Thomson, A. M. et al. RCP4.5: A pathway for stabilization of radiative forcing by 2100. Clim. Change 109, 77–94 (2011).ADS 
CAS 
Article 

Google Scholar 
Assefa, A., Tibebu, A., Bihon, A. & Yimana, M. Global ecological niche modelling of current and future distribution of peste des petits ruminants virus (PPRv) with an ensemble modelling algorithm. Transbound Emerg. Dis. 68, 3601–3610 (2021).PubMed 
Article 

Google Scholar 
Jori, F. & Bastos, A. D. Role of wild suids in the epidemiology of African swine fever. EcoHealth 6, 296–310 (2009).PubMed 
Article 

Google Scholar 
Teklue, T., Sun, Y., Abid, M., Luo, Y. & Qiu, H. J. Current status and evolving approaches to African swine fever vaccine development. Transbound Emerg. Dis. 67, 529–542 (2020).PubMed 
Article 

Google Scholar 
Arias, M. et al. Approaches and perspectives for development of African swine fever virus vaccines. Vaccines 5, 35 (2017).PubMed Central 
Article 
CAS 

Google Scholar 
Chenais, E. et al. Epidemiological considerations on African swine fever in Europe 2014–2018. Porcine Health Manag. 5, 1–10 (2019).Article 

Google Scholar 
Quembo, C. J., Jori, F., Vosloo, W. & Heath, L. Genetic characterization of African swine fever virus isolates from soft ticks at the wildlife/domestic interface in Mozambique and identification of a novel genotype. Transbound Emerg. Dis. 65, 420–431 (2018).CAS 
PubMed 
Article 

Google Scholar 
Torres, J. R. et al. Chikungunya fever: Atypical and lethal cases in the Western hemisphere: A Venezuelan experience. IDCases 2, 6–10 (2015).PubMed 
Article 

Google Scholar 
Nuanualsuwan, S. et al. Persistence of African swine fever virus on porous and non-porous fomites at environmental temperatures. Porc. Health Manag. 8, 34 (2022).Article 

Google Scholar 
Davies, K. et al. Survival of African swine fever virus in excretions from pigs experimentally infected with the Georgia 2007/1 isolate. Transbound Emerg. Dis. 64, 425–431 (2017).CAS 
PubMed 
Article 

Google Scholar 
Carlson, J. et al. Stability of African swine fever virus in soil and options to mitigate the potential transmission risk. Pathogens 9, 977 (2020).CAS 
PubMed Central 
Article 

Google Scholar 
Salari, L. S., Vatandoost, H., Telmadarraiy, Z., Entezar, M. R. & Kia, E. Seasonal activity of ticks and their importance in tick-borne infectious diseases in West Azerbaijan, Iran. J. Arthropod. Borne Dis. 2, 28–34 (2008).
Google Scholar 
Vial, L. Biological and ecological characteristics of soft ticks (Ixodida: Argasidae) and their impact for predicting tick and associated disease distribution. Parasite 16, 191–202 (2009).CAS 
PubMed 
Article 

Google Scholar 
Jian, L. et al. WANG potential adaptability of soft tick vectors of African swine fever to China. Chin. J. Vect. Biol. Control 21, 317–320 (2010).
Google Scholar 
Cwynar, P., Stojkov, J. & Wlazlak, K. African swine fever status in Europe. Viruses 11, 310 (2019).PubMed Central 
Article 

Google Scholar 
Marmion, M., Parviainen, M., Luoto, M., Heikkinen, R. K. & Thuiller, W. Evaluation of consensus methods in predictive species distribution modelling. Divers. Distrib. 15, 59–69 (2009).Article 

Google Scholar  More

We confirm that Cuba is home to the world’s largest European honeybee population that has naturally become Varroa-resistant, with an estimated 220,000 colonies being maintained without any form of chemical treatment for over two decades19 although some drone-trapping occurred during the early years of the transition period This is despite the presence of the K-haplotype of the mite20 and the widespread occurrence of DWV19 throughout Cuba. Hence, the Cuban honeybee population is the first major case of Varroa-resistant European bees occupying an entire country of a large size (109,884 km2). In Europe the proportion of varroa-resistant honeybee populations in each country is highly variable21,22, but they still consist of small, isolated populations within any country. For example, the second largest known area of European Varroa-resistant honeybees is in North Wales, UK where 104 beekeepers have managed around 500 honey bee colonies over an area of 2500 km2 without treatment for over a decade23.It has long been established that sub-Sharan African and Africanised honeybees are Varroa-resistant and both populations cover much larger areas than Cuba, but these honeybee races are not capable of thriving in temperate regions or are rejected by beekeepers in Northern hemispheres. However, previous studies on African/Africanised and European honeybees4,5,6,9 all appear to have evolved with the same resistance mechanism7 and Cuban honeybees follow this pattern showing high recapping behaviour, high mite removal behaviour and low mite reproduction (Figs. 1, 4, Table 1).The strongest evidence that increased recapping behaviour is a direct response to the presence of Varroa, is the very low recapping rates in Varroa-naïve colonies. This is evidenced by the recapping baseline data that has now been collected from four different Varroa-naïve (Varroa free) honeybee populations (Australia, UK [two populations] and Hawaii [this study]) all producing similar results (Fig. 1). Across the four populations, a total of 9542 worker cells from 15 colonies have been studied with an average recapping rate of 2.0% (+ SD 3.2). Interestingly, only two of the colonies had atypical recapping rates of 8.5% and 10.7%, from Australia and Kauai respectively. This may suggest increased sensitivity in these colonies as no obvious causes e.g., wax moth or dead pupa, were detected in either colony. The data summary in Fig. 1 indicates that even in Varroa-treated populations the workers are still able to detect mite infested cells, but the average consistently falls significantly below that found in resistant populations. That is, in non-infested worker cells recapping rates are significantly higher in resistant populations in comparison to susceptible populations (Fig. 1) t4, 5 = − 4.185, p = 0.0023 as well as for infested cells t4, 5 = − 6.905, p = 0.00007.The ability of Cuban honeybees to detect infested cells causes not only high recapping levels but also high removal rates of artificially mite-infested cells. A mean removal rate of 81% is among one of the highest recorded in Apis mellifera7. The average control rate of 45% is driven by three colonies that all removed more than 75% of the controls, while the average of the remaining seven colonies was 28%. During the mite-removal studies in March 2022 natural Varroa infestation was 23%, whereas in December 2021 it was only 13%. This is due to decreasing worker brood rearing, caused by a shortage of nectar during the annual dry season. During this time there is an increase in hygienic behaviour in the colonies24, which could help explain the higher-than-expected removal of control cells.The reproductive ability of Varroa to produce viable i.e., mated, female offspring (r) in infested worker cells in resistant colonies in South Africa4 (r = 0.9), Brazil4 (r = 0.8), Mexico18 (r = 0.73), Europe3 (r = 0.84) is similar to the 0.87 found in Cuba (this study). In Cuba ‘r’ reduces to 0.77 when both single and multiple infested cells are considered. This reduction in mite reproduction, relative to susceptible colonies that have values of r greater than one, is directly linked to the increased ability of resistant workers to both detect and remove, by cannibalisation, the infested pupa. Hence, this ensures the invading mite fails to reproduce7 or reduces mite fertility due to the recapping process4. Although, in this study no significant difference was found in the reproduction of Varroa in recapped or non-recapped cells, supporting the findings of two previous studies5,9. Therefore, recapping may be playing a minor role in resistance. However, recapping remains the best indicator or ‘proxy’ of resistance within the vast majority of honeybee populations since it’s easier, quicker, and it requires less skill to measure recapping rates than mite removal rates. However, recapping is a highly variable trait7, hence both many cells (200–300) per colony and many colonies ( > 10) per population ideally need to be studied to help reduce the variablity, also in temperate countries measuring recapping when mite-infestation rates peak in autumn maximises detecting infested cells since the recapping of cells is spatially associated with infested cells11.Despite the current focus on what is happening in worker cells, studies focusing on the role of recapping in drone brood are still in their infancy with. Currently, data is only available from South Africa9 (Fig. 1) and now Cuba (this study). Interestingly, both studies indicate no significant difference in recapping rates between infested and non-infested brood. This is caused by some colonies performing no recapping of drone brood, while some colonies do recap cells but in a non-targeted manner. Whereas there is a significant increase in the size of the recapped area between infested (3.1 mm) and non-infested (2.3 mm) worker cells (Fig. 3), this does not occur in drone brood, as it appears that the holes are entirely exploratory. However, the lack of removal of infested drone brood may be playing an important role in mite-resistance (see below).The mite infestation of worker cells currently varies between 23 and 13% in Cuba (this study), roughly 25 years after it was first detected (1996). Whereas, in Mexico and Brazil, infestation rates of worker brood have fallen from around 20% in 1996/1999 down to 4% in 2018/197. Although, Varroa was first detected in Brazil much earlier, in 197225 and the Africanised honeybees adapted to the mite and spread northward replacing the susceptible European colonies. Therefore, we predict that the worker infestation rate in Cuba will continue to fall over the next 20 years, especially if high mite-removal rates persist. Correspondingly, we would expect to see the infestation rates of the drone brood (currently at 40%) to remain high as mites potentially avoid reproduction in worker cells. This potentially is a key, but currently overlooked part, of the resistance mechanism. Since an empirical model26 indicated that negative mite population growth occurs in (resistant) Africanised honeybee colonies only when the initial drone cells are present. This is thought to arise because mites also show a tenfold preference to reproduce in drone cells (which comprises only 1–5% of all the honeybee brood) and they soon become overcrowded as the mite population increases. This leads to inter-mite competition for the limited food and space, causing an increase in mite mortality27, resulting in negative reproductive success for mites entering these overcrowded drone cells. Thus, mite population growth in drone brood cells is limited by a density-dependent mechanism. In Cuba it has been observed that strong colonies typically with drone brood do not weaken during the drought season, whereas colonies without drone brood are weak and often die during the drought (APP personal comm).Although Cuban beekeepers have been aware of their mite-resistant honeybees for 15 to 20 years’, Cuba’s situation has only recently come to light16,18. The main reason for Varroa-resistance in Cuba is due to the centralised decision to allow natural resistance to evolve, as also was done successfully in South Africa3, rather than becoming locked into using miticides, as has happened throughout the Northern hemisphere. The CIAPI and Veterinarian Services central decision to ‘not treat’ was greatly assisted by all Cuban beekeepers being professional, registered and embedded within a strong locally based beekeeping community where colony movement and exchange of queens is within each province.There is also a large feral population and due to Cuba’s sub-tropical climate, queens are replaced annually in managed colonies because of almost continuous egg-laying, similar to honeybees in Hawaii. This rapid queen turnover speeds up natural selection relative to honeybee populations in more temperate climates. Finally, Cuba’s 60-year ban on honeybee importation has helped isolate the country from been invaded by Africanised bees which has occurred in many nearby regions (eg. Mexico, Southern USA, Puerto Rico, neighbouring Dominican Republic13 and Haiti (D. Macdonald, Apiary Inspector, Min. of Agi BC, Canada, pers. Comm.). Cuba has many managed European colonies coupled with many queen rearing stations. These colonies are productive and mild mannered. Thus, Cuba is an excellent example of the power of natural selection in honeybees when they are allowed to adapt naturally to Varroa with minimal human interference. More

Field, C. B., Behrenfeld, M. J., Randerson, J. T. & Falkowski, P. Primary production of the biosphere: integrating terrestrial and oceanic components. Science 281, 237–240 (1998).CAS 
PubMed 
Article 

Google Scholar 
Boyce, D. G., Lewis, M. R. & Worm, B. Global phytoplankton decline over the past century. Nature 466, 591–596 (2010).CAS 
PubMed 
Article 

Google Scholar 
Henson, S. A., Cael, B. B., Allen, S. R. & Dutkiewicz, S. Future phytoplankton diversity in a changing climate. Nat. Commun. 12, 5372 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Vaulot, D., Eikrem, W., Viprey, M. & Moreau, H. The diversity of small eukaryotic phytoplankton (≤3 μm) in marine ecosystems. FEMS Microbiol. Rev. 32, 795–820 (2008).CAS 
PubMed 
Article 

Google Scholar 
Agawin, N. S. R., Duarte, C. M. & Agustí, S. Nutrient and temperature control of the contribution of picoplankton to phytoplankton biomass and production. Limnol. Oceanogr. 45, 591–600 (2000).CAS 
Article 

Google Scholar 
Morán, X. A. G., López-Urrutia, Á., Calvo-Díaz, A. & Li, W. K. W. Increasing importance of small phytoplankton in a warmer ocean. Glob. Change Biol. 16, 1137–1144 (2010).Article 

Google Scholar 
Li, W. K. W., McLaughlin, F. A., Lovejoy, C. & Carmack, E. C. Smallest algae thrive as the arctic ocean freshens. Science 326 https://doi.org/10.1126/science.1179798 (2009).Benner, I., Irwin, A. J. & Finkel, Z. V. Capacity of the common Arctic picoeukaryote Micromonas to adapt to a warming ocean. Limnol. Oceanography Lett. 5, 221–227 (2020).Sunda, W. G. & Huntsman, S. A. Iron uptake and growth limitation in oceanic and coastal phytoplankton. Mar. Chem. 50, 189–206 (1995).CAS 
Article 

Google Scholar 
Raven, J. A. The twelfth Tansley Lecture. Small is beautiful: the picophytoplankton. Funct. Ecol. 12, 503–513 (1998).Article 

Google Scholar 
Morel, F. M. M. & Price, N. M. The biogeochemical cycles of trace metals in the oceans. Science 300, 944–947 (2003).CAS 
PubMed 
Article 

Google Scholar 
Gao, X., Bowler, C. & Kazamia, E. Iron metabolism strategies in diatoms. J. Exp. Bot. 72, 2165–2180 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Caputi, L. et al. Community-level responses to iron availability in open ocean plankton ecosystems. Glob. Biogeochemical Cycles 33, 391–419 (2019).CAS 
Article 

Google Scholar 
Carradec, Q. et al. A global ocean atlas of eukaryotic genes. Nat. Commun. 9, 373 (2018).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Morrissey, J. et al. A novel protein, ubiquitous in marine phytoplankton, concentrates iron at the cell surface and facilitates uptake. Curr. Biol. 25, 364–371 (2015).CAS 
PubMed 
Article 

Google Scholar 
Moore, C. M. et al. Processes and patterns of oceanic nutrient limitation. Nat. Geosci. 6, 701–710 (2013).CAS 
Article 

Google Scholar 
Kumar, A. & Bera, S. Revisiting nitrogen utilization in algae: a review on the process of regulation and assimilation. Bioresour. Technol. Rep. 12, 100584 (2020).Article 

Google Scholar 
Smith, S. R. et al. Evolution and regulation of nitrogen flux through compartmentalized metabolic networks in a marine diatom. Nat. Commun. 10, 4552 (2019).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Berg, G. M., Glibert, P. M., Lomas, M. W. & Burford, M. A. Organic nitrogen uptake and growth by the chrysophyte Aureococcus anophagefferens during a brown tide event. Mar. Biol. 129, 377–387 (1997).CAS 
Article 

Google Scholar 
Andersen, R. A., Saunders, G. W., Paskind, M. P. & Sexton, J. P. Ultrastructure and 18s rRNA gene sequence for Pelagomonas calceolata gen. et sp. nov. and the description of a new algal class, the pelagophyceae classis nov. J. Phycol. 29, 701–715 (1993).CAS 
Article 

Google Scholar 
Choi, C. J. et al. Seasonal and geographical transitions in eukaryotic phytoplankton community structure in the Atlantic and Pacific Oceans. Front. Microbiol. 11, 542372 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
Duerschlag, J. et al. Niche partitioning by photosynthetic plankton as a driver of CO2-fixation across the oligotrophic South Pacific Subtropical Ocean. ISME J 1–12 https://doi.org/10.1038/s41396-021-01072-z (2021).Worden, A. Z. et al. Global distribution of a wild alga revealed by targeted metagenomics. Curr. Biol. 22, R675–R677 (2012).CAS 
PubMed 
Article 

Google Scholar 
Dimier, C. é, Brunet, C., Geider, R. & Raven, J. Growth and photoregulation dynamics of the picoeukaryote Pelagomonas calceolata in fluctuating light. Limnol. Oceanogr. 54, 823–836 (2009).CAS 
Article 

Google Scholar 
Dupont, C. L. et al. Genomes and gene expression across light and productivity gradients in eastern subtropical Pacific microbial communities. ISME J. 9, 1076–1092 (2015).CAS 
PubMed 
Article 

Google Scholar 
Kang, Y. et al. Transcriptomic responses of four pelagophytes to nutrient (N, P) and light stress. Front. Mar. Sci. 8, 636699 (2021).Huff, J. T., Zilberman, D. & Roy, S. W. Mechanism for DNA transposons to generate introns on genomic scales. Nature 538, 533–536 (2016).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Waterhouse, R. M. et al. BUSCO applications from quality assessments to gene prediction and phylogenomics. Mol. Biol. Evol. 35, 543–548 (2018).CAS 
PubMed 
Article 

Google Scholar 
Nambiar, M. & Smith, G. R. Repression of harmful meiotic recombination in centromeric regions. Semin Cell Dev. Biol. 54, 188–197 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Pessia, E. et al. Evidence for widespread GC-biased gene conversion in eukaryotes. Genome Biol. Evol. 4, 675–682 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Chi, J., Mahé, F., Loidl, J., Logsdon, J. & Dunthorn, M. Meiosis gene inventory of four ciliates reveals the prevalence of a synaptonemal complex-independent crossover pathway. Mol. Biol. Evol. 31, 660–672 (2014).CAS 
PubMed 
Article 

Google Scholar 
Ramesh, M. A., Malik, S.-B. & Logsdon, J. M. A phylogenomic inventory of meiotic genes; evidence for sex in Giardia and an early eukaryotic origin of meiosis. Curr. Biol. 15, 185–191 (2005).CAS 
PubMed 

Google Scholar 
Schurko, A. M. & Logsdon, J. M. Using a meiosis detection toolkit to investigate ancient asexual ‘scandals’ and the evolution of sex. Bioessays 30, 579–589 (2008).CAS 
PubMed 
Article 

Google Scholar 
Ibarbalz, F. M. et al. Global trends in marine plankton diversity across kingdoms of life. Cell 179, 1084–1097.e21 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Frémont, P. et al. Restructuring of plankton genomic biogeography in the surface ocean under climate change. Nat. Clim. Chang. 12, 393–401 (2022).Article 

Google Scholar 
Ward, D. M. & Kaplan, J. Ferroportin-mediated iron transport: expression and regulation. Biochim Biophys. Acta 1823, 1426–1433 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Gobler, C. J., Lonsdale, D. J. & Boyer, G. L. A review of the causes, effects, and potential management of harmful brown tide blooms caused by Aureococcus anophagefferens (Hargraves et sieburth). Estuaries 28, 726–749 (2005).Article 

Google Scholar 
Agusti, S., Lubián, L. M., Moreno-Ostos, E., Estrada, M. & Duarte, C. M. Projected changes in photosynthetic picoplankton in a warmer subtropical ocean. Front. Mar. Sci. 5, 506 (2019).Article 

Google Scholar 
Anderson, S. I., Barton, A. D., Clayton, S., Dutkiewicz, S. & Rynearson, T. A. Marine phytoplankton functional types exhibit diverse responses to thermal change. Nat. Commun. 12, 6413 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Martin, J. H. et al. Testing the iron hypothesis in ecosystems of the equatorial Pacific Ocean. Nature 371, 123–129 (1994).CAS 
Article 

Google Scholar 
Shi, D., Xu, Y., Hopkinson, B. M. & Morel, F. M. M. Effect of ocean acidification on iron availability to marine phytoplankton. Science 327, 676–679 (2010).CAS 
PubMed 
Article 

Google Scholar 
McQuaid, J. B. et al. Carbonate-sensitive phytotransferrin controls high-affinity iron uptake in diatoms. Nature 555, 534–537 (2018).CAS 
PubMed 
Article 

Google Scholar 
Turnšek, J. et al. Proximity proteomics in a marine diatom reveals a putative cell surface-to-chloroplast iron trafficking pathway. eLife 10, e52770 (2021).PubMed 
PubMed Central 
Article 

Google Scholar 
Urzica, E. I. et al. Systems and trans-system level analysis identifies conserved iron deficiency responses in the plant lineage[W][OA]. Plant Cell 24, 3921–3948 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Mao, X. et al. Diversity, prevalence, and expression of cyanase genes (cynS) in planktonic marine microorganisms. ISME J. 16, 602–605 (2022).CAS 
PubMed 
Article 

Google Scholar 
Ou, L., Cai, Y., Jin, W., Wang, Z. & Lu, S. Understanding the nitrogen uptake and assimilation of the Chinese strain of Aureococcus anophagefferens (Pelagophyceae). Algal Res. 34, 182–190 (2018).Article 

Google Scholar 
Shu, C. J., Ulrich, L. E. & Zhulin, I. B. The NIT domain: a predicted nitrate-responsive module in bacterial sensory receptors. Trends Biochem Sci. 28, 121–124 (2003).CAS 
PubMed 
Article 

Google Scholar 
Wu, S. Q., Chai, W., Lin, J. T. & Stewart, V. General nitrogen regulation of nitrate assimilation regulatory gene nasR expression in Klebsiella oxytoca M5al. J. Bacteriol. 181, 7274–7284 (1999).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Durand, N. C. et al. Juicer provides a one-click system for analyzing loop-resolution Hi-C experiments. Cell Syst. 3, 95–98 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Li, R., Li, Y., Kristiansen, K. & Wang, J. SOAP: short oligonucleotide alignment program. Bioinformatics 24, 713–714 (2008).CAS 
PubMed 
Article 

Google Scholar 
Alberti, A. et al. Viral to metazoan marine plankton nucleotide sequences from the Tara Oceans expedition. Sci. Data 4, 170093 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Kopylova, E., Noé, L. & Touzet, H. SortMeRNA: fast and accurate filtering of ribosomal RNAs in metatranscriptomic data. Bioinformatics 28, 3211–3217 (2012).CAS 
PubMed 
Article 

Google Scholar 
Kim, D., Song, L., Breitwieser, F. P. & Salzberg, S. L. Centrifuge: rapid and sensitive classification of metagenomic sequences. Genome Res. https://doi.org/10.1101/gr.210641.116 (2016).Vurture, G. W. et al. GenomeScope: fast reference-free genome profiling from short reads. Bioinformatics 33, 2202–2204 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Vaser, R. & Šikić, M. Yet another de novo genome assembler. BioRxiv. https://doi.org/10.1101/656306 (2019).Liu, H. et al. SMARTdenovo: a de novo assembler using long noisy reads. Gigabyte 2021, 1–9 (2021).Article 

Google Scholar 
Kolmogorov, M., Yuan, J., Lin, Y. & Pevzner, P. A. Assembly of long, error-prone reads using repeat graphs. Nat. Biotechnol. 37, 540–546 (2019).CAS 
PubMed 
Article 

Google Scholar 
Ruan, J. & Li, H. Fast and accurate long-read assembly with wtdbg2. Nat. Methods 17, 155–158 (2020).CAS 
PubMed 
Article 

Google Scholar 
Wick, R. R., Schultz, M. B., Zobel, J. & Holt, K. E. Bandage: interactive visualization of de novo genome assemblies. Bioinformatics 31, 3350–3352 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Vaser, R., Sović, I., Nagarajan, N. & Šikić, M. Fast and accurate de novo genome assembly from long uncorrected reads. Genome Res 27, 737–746 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Aury, J.-M. & Istace, B. Hapo-G, haplotype-aware polishing of genome assemblies with accurate reads. NAR Genomics Bioinform. 3, lqab034 (2021).Benson, G. Tandem repeats finder: a program to analyze DNA sequences. Nucleic Acids Res. 27, 573–580 (1999).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Morgulis, A., Gertz, E. M., Schäffer, A. A. & Agarwala, R. A fast and symmetric DUST implementation to mask low-complexity DNA sequences. J. Comput Biol. 13, 1028–1040 (2006).CAS 
PubMed 
Article 

Google Scholar 
Smit, A. F. A., Hubley, R. & Green, P. RepeatMasker. http://repeatmasker.org/ (2013).Price, A. L., Jones, N. C. & Pevzner, P. A. De novo identification of repeat families in large genomes. Bioinformatics 21, i351–i358 (2005).CAS 
PubMed 
Article 

Google Scholar 
Quinlan, A. R. & Hall, I. M. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841–842 (2010).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Pedersen, B. S. & Quinlan, A. R. Mosdepth: quick coverage calculation for genomes and exomes. Bioinformatics 34, 867–868 (2018).CAS 
PubMed 
Article 

Google Scholar 
Schulz, M. H., Zerbino, D. R., Vingron, M. & Birney, E. Oases: robust de novo RNA-seq assembly across the dynamic range of expression levels. Bioinformatics 28, 1086–1092 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Zerbino, D. R. & Birney, E. Velvet: algorithms for de novo short read assembly using de Bruijn graphs. Genome Res. 18, 821–829 (2008).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Li, H. et al. The sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Marchler-Bauer, A. et al. CDD: NCBI’s conserved domain database. Nucleic Acids Res. 43, D222–D226 (2015).CAS 
PubMed 
Article 

Google Scholar 
Niang, G. et al. METdb: A genomic reference database for marine species. F1000Research, https://doi.org/10.7490/f1000research.1118000.1 (2020).Kent, W. J. BLAT–the BLAST-like alignment tool. Genome Res. 12, 656–664 (2002).CAS 
PubMed 
PubMed Central 

Google Scholar 
Altschul, S. F., Gish, W., Miller, W., Myers, E. W. & Lipman, D. J. Basic local alignment search tool. J. Mol. Biol. 215, 403–410 (1990).CAS 
PubMed 
Article 

Google Scholar 
Birney, E., Clamp, M. & Durbin, R. GeneWise and genomewise. Genome Res. 14, 988–995 (2004).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Stanke, M. et al. AUGUSTUS: ab initio prediction of alternative transcripts. Nucleic Acids Res. 34, W435–W439 (2006).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Dubarry, M. et al. Gmove a tool for eukaryotic gene predictions using various evidences. F1000Research, https://doi.org/10.7490/f1000research.1111735.1 (2016).Sibbald, S. J., Lawton, M. & Archibald, J. M. Mitochondrial genome evolution in pelagophyte algae. Genome Biol. Evol. 13, evab018 (2021).Quevillon, E. et al. InterProScan: protein domains identifier. Nucleic Acids Res. 33, W116–W120 (2005).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Buchfink, B., Reuter, K. & Drost, H.-G. Sensitive protein alignments at tree-of-life scale using DIAMOND. Nat. Methods 18, 366–368 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Aramaki, T. et al. KofamKOALA: KEGG Ortholog assignment based on profile HMM and adaptive score threshold. Bioinformatics 36, 2251–2252 (2020).CAS 
PubMed 
Article 

Google Scholar 
Delmont, T. O. et al. Functional repertoire convergence of distantly related eukaryotic plankton lineages abundant in the sunlit ocean. Cell Genomics 2, 100123 (2022).CAS 
Article 

Google Scholar 
Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics 25, 1754–1760 (2009).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Pesant, S. et al. Open science resources for the discovery and analysis of Tara Oceans data. Sci. Data 2, 150023 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Aumont, O., Ethé, C., Tagliabue, A., Bopp, L. & Gehlen, M. PISCES-v2: an ocean biogeochemical model for carbon and ecosystem studies. Geoscientific Model Dev. 8, 2465–2513 (2015).CAS 
Article 

Google Scholar 
Clayton, S. et al. Biogeochemical versus ecological consequences of modeled ocean physics. Biogeosciences 14, 2877–2889 (2017).CAS 
Article 

Google Scholar 
Ravindra, K., Rattan, P., Mor, S. & Aggarwal, A. N. Generalized additive models: building evidence of air pollution, climate change and human health. Environ. Int. 132, 104987 (2019).CAS 
PubMed 
Article 

Google Scholar 
Günther, F. & Fritsch, S. neuralnet: training of neural networks. R. J. 2, 30–38 (2010).Article 

Google Scholar 
Gobler, C. J. et al. Niche of harmful alga Aureococcus anophagefferens revealed through ecogenomics. Proc. Natl Acad. Sci. USA 108, 4352–4357 (2011).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Guo, L. et al. Genome assembly of Nannochloropsis oceanica provides evidence of host nucleus overthrow by the symbiont nucleus during speciation. Commun. Biol. 2, 1–12 (2019).CAS 
Article 

Google Scholar 
Bowler, C. et al. The Phaeodactylum genome reveals the evolutionary history of diatom genomes. Nature 456, 239–244 (2008).CAS 
PubMed 
Article 

Google Scholar 
Armbrust, E. V. et al. The genome of the diatom thalassiosira pseudonana: ecology, evolution, and metabolism. Science 306, 79–86 (2004).CAS 
PubMed 
Article 

Google Scholar 
Worden, A. Z. et al. Green evolution and dynamic adaptations revealed by genomes of the marine picoeukaryotes micromonas. Science 324, 268–272 (2009).CAS 
PubMed 
Article 

Google Scholar 
Palenik, B. et al. The tiny eukaryote Ostreococcus provides genomic insights into the paradox of plankton speciation. PNAS 104, 7705–7710 (2007).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Moreau, H. et al. Gene functionalities and genome structure in Bathycoccus prasinos reflect cellular specializations at the base of the green lineage. Genome Biol. 13, R74 (2012).PubMed 
PubMed Central 
Article 

Google Scholar 
Read, B. A. et al. Pan genome of the phytoplankton Emiliania underpins its global distribution. Nature 499, 209–213 (2013).CAS 
PubMed 
Article 

Google Scholar  More