Ecology

Visser, M. E. & Holleman, L. J. Warmer springs disrupt the synchrony of oak and winter moth phenology. Proc. Biol. Sci. 268, 289–294 (2001).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Duchenne, F. et al. Phenological shifts alter the seasonal structure of pollinator assemblages in Europe. Nat. Ecol. Evol. 4, 115–121 (2020).Article 
CAS 
PubMed 

Google Scholar 
Bartomeus, I. et al. Climate-associated phenological advances in bee pollinators and bee-pollinated plants. Proc. Natl. Acad. Sci. U. S. A. 108, 20645–20649 (2011).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Lehmann, P. et al. Complex responses of global insect pests to climate warming. Front. Ecol. Environ. 18, 141–150 (2020).Article 

Google Scholar 
Organization, W. H. et al. Global vector control response 2017–2030. In Global Vector Control Response 2017–2030 (2017).Reisen, W. K. et al. Effects of warm winter temperature on the abundance and gonotrophic activity of Culex (Diptera: Culicidae) in California. J. Med. Entomol. 47, 230–237 (2010).Article 
PubMed 

Google Scholar 
Denlinger, D. L. & Armbruster, P. A. Mosquito diapause. Annu. Rev. Entomol. 59, 73–93 (2014).Article 
CAS 
PubMed 

Google Scholar 
Mullen, G. R. & Durden, L. A. Medical and Veterinary Entomology. (Academic Press, 2009).Messenger, P. S. Bioclimatic studies with insects. Annu. Rev. Entomol. 4, 183–206 (1959).Article 

Google Scholar 
Hard, J. J., Bradshaw, W. E. & Holzapfel, C. M. The genetic basis of photoperiodism and its evolutionary divergence among populations of the pitcher-plant mosquito, Wyeomyia smithii. Am. Nat. 142, 457–473 (1993).Article 
CAS 
PubMed 

Google Scholar 
Danilevskii, A. S. et al. Photoperiodism and seasonal development of insects. In Photoperiodism and Seasonal Development of Insects (1965).Nietschke, B. S., Magarey, R. D., Borchert, D. M., Calvin, D. D. & Jones, E. A developmental database to support insect phenology models. Crop Prot. 26, 1444–1448 (2007).Article 

Google Scholar 
Vinogradova, E. B. Diapause in aquatic insects, with emphasis on mosquitoes. In Diapause in Aquatic Invertebrates Theory and Human Use (eds. Alekseev, V. R., de Stasio, B. T. & Gilbert, J. J.) 83–113 (Springer Netherlands, 2007).Chown, S. L. & Terblanche, J. S. Physiological diversity in insects: Ecological and evolutionary contexts. Adv. Insect Phys. 33, 50–152 (2006).Article 

Google Scholar 
Belitz, M. W. et al. Climate drivers of adult insect activity are conditioned by life history traits. Ecol. Lett. 24, 2687–2699 (2021).Article 
PubMed 

Google Scholar 
Townroe, S. & Callaghan, A. British container breeding mosquitoes: The impact of urbanisation and climate change on community composition and phenology. PLoS ONE 9, e95325 (2014).Article 
ADS 
PubMed 
PubMed Central 

Google Scholar 
Westby, K. M., Adalsteinsson, S. A., Biro, E. G., Beckermann, A. J. & Medley, K. A. Aedes albopictus populations and larval habitat characteristics across the landscape: Significant differences exist between urban and rural land use types. Insects 12, 196 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Li, D., Stucky, B. J., Deck, J., Baiser, B. & Guralnick, R. P. The effect of urbanization on plant phenology depends on regional temperature. Nat. Ecol. Evol. 3, 1661–1667 (2019).Article 
PubMed 

Google Scholar 
Diniz, D. F. A., de Albuquerque, C. M. R., Oliva, L. O., de Melo-Santos, M. A. V. & Ayres, C. F. J. Diapause and quiescence: Dormancy mechanisms that contribute to the geographical expansion of mosquitoes and their evolutionary success. Parasit. Vectors 10, 310 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Rund, S. S. C., Moise, I. K., Beier, J. C. & Martinez, M. E. Rescuing troves of hidden ecological data to tackle emerging mosquito-borne diseases. J. Am. Mosq. Control Assoc. 35, 75–83 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Dewitz, J., U.S. Geological Survey. National Land Cover Database (NLCD) 2019 Products (ver. 2. 0, June 2021): U.S. Geological Survey data release (ver. 2. 0, June 2021): U.S. Geological Survey data release.Woodring, J. et al. Diapause, transovarial transmission, and filial infection rates in geographic strains of La Crosse virus-infected Aedes triseriatus. Am. J. Trop. Med. Hyg. 58, 587–588 (1998).Article 
CAS 
PubMed 

Google Scholar 
Ellwood, E. R. et al. Disentangling the paradox of insect phenology: Are temporal trends reflecting the response to warming?. Oecologia 168, 1161–1171 (2012).Article 
ADS 
PubMed 

Google Scholar 
Degaetano, A. T. Meteorological effects on adult mosquito (Culex) populations in metropolitan New Jersey. Int. J. Biometeorol. 49, 345–353 (2005).Article 
ADS 
PubMed 

Google Scholar 
Su, T., Webb, J. P., Meyer, R. P. & Mulla, M. S. Spatial and temporal distribution of mosquitoes in underground storm drain systems in Orange County, California. J. Vector Ecol. 28, 79–89 (2003).PubMed 

Google Scholar 
Harbison, J. E., Henry, M., Xamplas, C. & Dugas, L. R. Evaluation of Culex pipiens populations in a residential area with a high density of catch basins in a suburb of Chicago, Illinois. J. Am. Mosq. Control Assoc. 30, 228–230 (2014).Article 
PubMed 

Google Scholar 
Wang, X. et al. Impact of underground storm drain systems on larval ecology of Culex and Aedes species in urban environments of Southern California. Sci. Rep. 11, 12667 (2021).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Geery, P. R. & Holub, R. E. Seasonal abundance and control of Culex spp. in catch basins in Illinois. J. Am. Mosq. Control Assoc. 5, 537–540 (1989).CAS 
PubMed 

Google Scholar 
Nelms, B. M., Macedo, P. A., Kothera, L., Savage, H. M. & Reisen, W. K. Overwintering biology of Culex (Diptera: Culicidae) mosquitoes in the Sacramento Valley of California. J. Med. Entomol. 50, 773–790 (2013).Article 
PubMed 

Google Scholar 
Becker, N. et al. Mosquitoes and Their Control (Springer, 2010).Book 

Google Scholar 
Bartlett-Healy, K., Crans, W. & Gaugler, R. Temporal and spatial synchrony of Culex territans (Diptera: Culicidae) with their amphibian hosts. J. Med. Entomol. 45, 1031–1038 (2008).Article 
PubMed 

Google Scholar 
Reeves, L. E. et al. Identification of Uranotaenia sapphirina as a specialist of annelids broadens known mosquito host use patterns. Commun. Biol. 1, 92 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Burkett-Cadena, N. D. et al. Host reproductive phenology drives seasonal patterns of host use in mosquitoes. PLoS One 6, e17681 (2011).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
van Strien, A. J., Plantenga, W. F., Soldaat, L. L., van Swaay, C. A. M. & Wallisdevries, M. F. Bias in phenology assessments based on first appearance data of butterflies. Oecologia 156, 227–235 (2008).Article 
ADS 
PubMed 

Google Scholar 
Beck, H. E. et al. Present and future Köppen–Geiger climate classification maps at 1-km resolution. Sci. Data 5, 180214 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Giraldo-Calderón, G. I. et al. Vectorbase.org updates: Bioinformatic resources for invertebrate vectors of human pathogens and related organisms. Curr. Opin. Insect Sci. 50, 100860 (2022).Article 
PubMed 

Google Scholar 
Wickham, François, Henry & Müller. dplyr: A grammar of data manipulation. R package version 0.4.Hesselbarth, M. H. K., Sciaini, M., With, K. A., Wiegand, K. & Nowosad, J. landscapemetrics: An open-source R tool to calculate landscape metrics. Ecography 42, 1648–1657 (2019).Article 

Google Scholar 
Abatzoglou, J. T. Development of gridded surface meteorological data for ecological applications and modelling. Int. J. Climatol. 33, 121–131 (2013).Article 

Google Scholar 
Johnson, M. climateR: climateR. R package version 0.1.0. https://github.com/mikejohnson51/climateR.Wickham, H. et al. Welcome to the tidyverse. J. Open Source Softw. 4, 1686 (2019).Article 
ADS 

Google Scholar 
Schmucki, R., Harrower, C. A. & Dennis, E. B.. rbms: Computing generalised abundance indices for butterfly monitoring count data. R package version.Belitz, M. W., Larsen, E. A., Shirey, V., Li, D. & Guralnick, R. P. Phenological research based on natural history collections: Practical guidelines and a lepidopteran case study. Funct. Ecol. https://doi.org/10.1111/1365-2435.14173 (2022).Article 

Google Scholar 
Larsen, E. A., Belitz, M. W., Guralnick, R. P. & Ries, L. Consistent trait-temperature interactions drive butterfly phenology in both incidental and survey data. Sci. Rep. 12, 13370 (2022).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models Usinglme4. J. Stat. Softw. https://doi.org/10.18637/jss.v067.i01 (2015).Article 

Google Scholar 
Fox, J. & Weisberg, S. An R Companion to Applied Regression. (SAGE Publications, 2018).Kuznetsova, A., Brockhoff, P. B. & Christensen, R. H. B. lmerTest Package: Tests in linear mixed effects models. J. Stat. Softw. 82, 1–26 (2017).Article 

Google Scholar 
Lüdecke, D., Ben-Shachar, M., Patil, I., Waggoner, P. & Makowski, D. Performance: An R package for assessment, comparison and testing of statistical models. J. Open Source Softw. 6, 3139 (2021).Article 
ADS 

Google Scholar 
Nakagawa, S., Johnson, P. C. D. & Schielzeth, H. The coefficient of determination R2 and intra-class correlation coefficient from generalized linear mixed-effects models revisited and expanded. J. R. Soc. Interface 14, 20170213 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Lüdecke, D. sjPlot: Data visualization for statistics in social science. R package version. More

Random forest: feature importance and interactivityOur random forests produced highly accurate predictions of local stability when trained on model output from the full dataset (e.g., AUC = 0.998 across all 5 parameters, see Fig. 2A) and all tested subsets. Running random forests on the full results set with all five parameters as predictors indicated both demographic and trophic rates were important to understanding resultant model stability. Moreover, results reveal that whether in multi-stage (red line; Fig. 2A) or single stage herbivory (e.g., ({a}_{2}) = 0, ({a}_{F}) ≥ 0; blue line Fig. 2A), parameters’ contribution to predictive power is related to their interactivity with other parameters (blue line; Fig. 2A). Note, a similar analysis with ({a}_{2})  > 0 and ({a}_{F}) = 0 is not possible because this type of herbivory is always stable.This interactivity was apparent in our attempts to understand how our specific parameters affected the behavior of our model in Eq. (1) via studying their effects as features in driving random forest predictions. Initial investigations into individual feature effects revealed that the effect of any single feature (parameter) on trophic dynamics could change substantially based on the values of our other features (parameters). Specifically, the average marginal effects (e.g., PD plots; Fig. S3) on simulation dynamics belied a high degree of variability in feature effects throughout the simulation data (e.g., ICE plots; Fig. S3).Breaking down results into further subsets of set specific attack rates with varying demographic rates revealed that this variability in feature effects was largely based on the changes in feature importance and effect over different allocations of herbivory on ontogenetic stages. This breakdown affected the relationship between importance and interactivity (Fig. 2A) such that it was inconsistent but still visible in aggregate across our simulation parameters (Fig. 2B,C). Figure 2D–F depict how different allocations and intensity of herbivory across plant ontogeny change the influence of each demographic parameter in driving model stability.Given how the influence of plant demographic rates over model behavior changed across trophic allocation (Fig. 2D–F), we first focused in depth analysis on variable demographic rates across static allocations of herbivore attack rates. By limiting the number of varying features, we use multivariate analysis to develop a fuller understanding of dynamics in subsections of the data which functioned as a scaffolding for further investigation. Specifically, we took a hierarchical approach, first developing an understanding of single-stage herbivory as a basis to study single-stage dominant herbivory (Fig. 3), which then leads us to a better overall understanding of our system’s dynamics across all trophic rates.Figure 3Interactive feature effects on model behavior. Across different herbivory allocations, partial dependence (PD) plots (A,C,E) show interactive effects between maturation rates on categorical simulation stability. Threshold plots (B,D,F) extend this analysis to include gradations of seed production rates. (A,B) Herbivory allocation ({a}_{F}) = 1.0 and ({a}_{2}) = 0.0. (A) Partial dependence plot shows probability of stability across all values of ({r}_{F}). (B) Threshold plot shows the location of the threshold between stable and unstable dynamics in {({g}_{12}),({g}_{2F})} parameter space as a function of seed production levels (({r}_{F})). (C,D) Herbivory allocation ({a}_{F}) = 0.2 and ({a}_{2}) = 1.0. (C) Partial dependence plot shows probability of stability across all values of ({r}_{F}). (D) Threshold plot shows the location of the threshold between stable and unstable dynamics in {({g}_{12}), ({g}_{2F})} parameter space as a function of seed production levels (({r}_{F})). (E,F) Herbivory allocation ({a}_{F}) = 1.0 and ({a}_{2}) = 0.2. (E) Partial dependence plot shows probability of stability across all values of ({r}_{F}). (F) Threshold plot shows the location of the threshold between stable and unstable dynamics in {({g}_{12}), ({g}_{2F})} parameter space as a function of seed production levels (({r}_{F})).Full size imageSingle stage consumptionIn the case of the seedling-only herbivore (({S}_{2}); via ({a}_{2})  > 0 and ({a}_{F}) = 0), all simulations produced stable trophic dynamics. This occurs because density loss in the seedling stage means more juveniles never reach maturity and reproduce themselves19. This essentially reduces the effective reproduction rate, limits the reproductive plant density, and decreases resources available to the herbivore (similar to lowering intrinsic reproduction in the classic Lotka–Volterra model). In fact, seedling herbivory only induced oscillations at higher handling times, a common effect of high handling time (results not shown).On the other hand, concentrating consumption on the fecund stage ((F)) can induce both stable and oscillating trajectories (Fig. S4). Consumption of (F) does not induce the same regulation of reproductive potential that stabilizes under seedling-only consumption, and so is vulnerable to boom/bust populations cycles. We chose the two most consistently important (Fig. 2B) and interactive (Fig. 2C and Fig. S5) parameters, ({g}_{12}) and ({g}_{2F}), in order to search for dominant effects on model behavior and their interactions. These parameters functioned as focal axes for our two-dimensional PD plots36. These PD plots depict the estimates of marginal effect of each parameter on random forest predictions, which in this case is categorical stability (Fig. 3A). We can see that stability estimates are increased by lowering either or both per-capita germination and/or maturation rates (({g}_{12}) and ({g}_{2F})). Demographically, reduced maturation rates shift the ratio of plant population density across its ontogeny, creating a larger juvenile population shielded from consumer pressure. Trophically, this restricts resources for the herbivore, thereby limiting losses in plant density due to herbivory (({theta }_{F})) relative to the overall plant density.This mechanism is so influential in determining trophic dynamics, its effect on stability is statistically detectable pre-simulation via equilibrium values. Losses in plant density due to herbivory are labeled under brackets in Eq. (1) as ({theta }_{F}) and ({theta }_{2}), which we can represent as ({theta }_{F}^{*}) and ({theta }_{2}^{*}) at equilibria. Relative to overall plant density we can define a ratio for plants of consumptive losses to total density (L:D ratio) such that:$$mathrm{L}:mathrm{D ratio}=({theta }_{F}^{*}+ {theta }_{2}^{*})/({S}_{1}^{*} +{S}_{2}^{*}+{F}^{*}).$$
(2)
When applied as a predictor variable on the same adult-herbivory subsection presented in Fig. 3A via a simple linear regression, we can see that L:D ratio alone explains ~ 45% of the variance of the maximum eigenvalue in simple linear models (F-statistic: 4578 on 1 and 5598 DF, p-value:  More

McCutcheon JP, Boyd BM, Dale C. The life of an insect endosymbiont from the cradle to the grave. Curr Biol. 2019;29:R485–95.Article 
CAS 
PubMed 

Google Scholar 
McCutcheon JP, Moran NA. Extreme genome reduction in symbiotic bacteria. Nat Rev Microbiol. 2012;10:13–26.Article 
CAS 

Google Scholar 
Latorre A, Manzano-Marin A. Dissecting genome reduction and trait loss in insect endosymbionts. Ann N Y Acad Sci. 2017;1389:52–75.Article 
PubMed 

Google Scholar 
Salje J. Cells within cells: Rickettsiales and the obligate intracellular bacterial lifestyle. Nat Rev Microbiol. 2021;19:375–90.Article 
CAS 
PubMed 

Google Scholar 
Kaur R, Shropshire JD, Cross KL, Leigh B, Mansueto AJ, Stewart V, et al. Living in the endosymbiotic world of Wolbachia: a centennial review. Cell Host Microbe. 2021;29:879–93.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Pilgrim J, Thongprem P, Davison HR, Siozios S, Baylis M, Zakharov EV, et al. Torix Rickettsia are widespread in arthropods and reflect a neglected symbiosis. Gigascience. 2021;10:giab021.Article 
PubMed 
PubMed Central 

Google Scholar 
Pilgrim J, Ander M, Garros C, Baylis M, Hurst GDD, Siozios S. Torix group Rickettsia are widespread in Culicoides biting midges (Diptera: Ceratopogonidae), reach high frequency and carry unique genomic features. Environ Microbiol. 2017;19:4238–55.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Horn M, Fritsche TR, Gautom RK, Schleifer KH, Wagner M. Novel bacterial endosymbionts of Acanthamoeba spp. related to the Paramecium caudatum symbiont Caedibacter caryophilus. Environ Microbiol. 1999;1:357–67.Article 
CAS 
PubMed 

Google Scholar 
Schulz F, Lagkouvardos I, Wascher F, Aistleitner K, Kostanjsek R, Horn M. Life in an unusual intracellular niche: a bacterial symbiont infecting the nucleus of amoebae. ISME J. 2014;8:1634–44.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Schulz F, Martijn J, Wascher F, Lagkouvardos I, Kostanjsek R, Ettema TJ, et al. A Rickettsiales symbiont of amoebae with ancient features. Environ Microbiol. 2016;18:2326–42.Article 
CAS 
PubMed 

Google Scholar 
Hess S, Suthaus A, Melkonian M. “Candidatus Finniella” (Rickettsiales, Alphaproteobacteria), Novel Endosymbionts of Viridiraptorid Amoeboflagellates (Cercozoa, Rhizaria). Appl Environ Microbiol. 2016;82:659–70.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Castelli M, Sabaneyeva E, Lanzoni O, Lebedeva N, Floriano AM, Gaiarsa S, et al. Deianiraea, an extracellular bacterium associated with the ciliate Paramecium, suggests an alternative scenario for the evolution of Rickettsiales. ISME J. 2019;13:2280–94.Article 
PubMed 
PubMed Central 

Google Scholar 
Floriano AM, Castelli M, Krenek S, Berendonk TU, Bazzocchi C, Petroni G, et al. The genome sequence of “Candidatus Fokinia solitaria”: insights on reductive evolution in Rickettsiales. Genome Biol Evol. 2018;10:1120–6.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
George EE, Husnik F, Tashyreva D, Prokopchuk G, Horak A, Kwong WK, et al. Highly reduced genomes of protist endosymbionts show evolutionary convergence. Curr Biol. 2020;30:925–33.e3.Article 
CAS 
PubMed 

Google Scholar 
Midha S, Rigden DJ, Siozios S, Hurst GDD, Jackson AP. Bodo saltans (Kinetoplastida) is dependent on a novel Paracaedibacter-like endosymbiont that possesses multiple putative toxin-antitoxin systems. ISME J. 2021;15:1680–94.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Castelli M, Lanzoni O, Giovannini M, Lebedeva N, Gammuto L, Sassera D, et al. ‘Candidatus Gromoviella agglomerans’, a novel intracellular Holosporaceae parasite of the ciliate Paramecium showing marked genome reduction. Environ Microbiol Rep. 2022;14:34–49.Article 
CAS 
PubMed 

Google Scholar 
Klinges JG, Rosales SM, McMinds R, Shaver EC, Shantz AA, Peters EC, et al. Phylogenetic, genomic, and biogeographic characterization of a novel and ubiquitous marine invertebrate-associated Rickettsiales parasite, Candidatus Aquarickettsia rohweri, gen. nov., sp. nov. ISME J. 2019;13:2938–53.Article 
PubMed 
PubMed Central 

Google Scholar 
Kroer P, Kjeldsen KU, Nyengaard JR, Schramm A, Funch P. A novel extracellular gut symbiont in the marine worm Priapulus caudatus (Priapulida) reveals an Alphaproteobacterial symbiont clade of the ecdysozoa. Front Microbiol. 2016;7:539.Article 
PubMed 
PubMed Central 

Google Scholar 
Yurchenko T, Sevcikova T, Pribyl P, El Karkouri K, Klimes V, Amaral R, et al. A gene transfer event suggests a long-term partnership between eustigmatophyte algae and a novel lineage of endosymbiotic bacteria. ISME J. 2018;12:2163–75.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Ferla MP, Thrash JC, Giovannoni SJ, Patrick WM. New rRNA gene-based phylogenies of the Alphaproteobacteria provide perspective on major groups, mitochondrial ancestry and phylogenetic instability. PLoS ONE. 2013;8:e83383.Article 
PubMed 
PubMed Central 

Google Scholar 
Szokoli F, Castelli M, Sabaneyeva E, Schrallhammer M, Krenek S, Doak TG, et al. Disentangling the taxonomy of Rickettsiales and description of two novel symbionts (“Candidatus Bealeia paramacronuclearis” and “Candidatus Fokinia cryptica”) sharing the cytoplasm of the ciliate protist Paramecium biaurelia. Appl Environ Microbiol. 2016;82:7236–47.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Munoz-Gomez SA, Hess S, Burger G, Lang BF, Susko E, Slamovits CH, et al. An updated phylogeny of the Alphaproteobacteria reveals that the parasitic Rickettsiales and Holosporales have independent origins. eLife. 2019;8:e42535.Article 
PubMed 
PubMed Central 

Google Scholar 
Sassera D, Beninati T, Bandi C, Bouman EA, Sacchi L, Fabbi M, et al. ‘Candidatus Midichloria mitochondrii’, an endosymbiont of the tick Ixodes ricinus with a unique intramitochondrial lifestyle. Int J Syst Evol Microbiol. 2006;56:2535–40.Article 
CAS 
PubMed 

Google Scholar 
Fokin SI, Görtz HD, Diversity of Holospora bacteria in Paramecium and their characterization. In: Fujishima M, editor. Endosymbionts in Paramecium. Microbiology Monographs, vol 12. Berlin: Springer; 2009. https://doi.org/10.1007/978-3-540-92677-1_7.Min CK, Yang JS, Kim S, Choi MS, Kim IS, Cho NH. Genome-based construction of the metabolic pathways of Orientia tsutsugamushi and comparative analysis within the Rickettsiales order. Comp Funct Genomics. 2008;2008:623145.Article 
PubMed 
PubMed Central 

Google Scholar 
Garushyants SK, Beliavskaia AY, Malko DB, Logacheva MD, Rautian MS, Gelfand MS. Comparative genomic analysis of Holospora spp., intranuclear symbionts of paramecia. Front Microbiol. 2018;9:738.Article 
PubMed 
PubMed Central 

Google Scholar 
Boscaro V, Fokin SI, Schrallhammer M, Schweikert M, Petroni G. Revised systematics of Holospora-like bacteria and characterization of “Candidatus Gortzia infectiva”, a novel macronuclear symbiont of Paramecium jenningsi. Microb Ecol. 2013;65:255–67.Article 
CAS 
PubMed 

Google Scholar 
Serra V, Fokin SI, Castelli M, Basuri CK, Nitla V, Verni F, et al. “Candidatus Gortzia shahrazadis”, a novel endosymbiont of Paramecium multimicronucleatum and a revision of the biogeographical distribution of Holospora-like bacteria. Front Microbiol. 2016;7:1704.Article 
PubMed 
PubMed Central 

Google Scholar 
Takeshita K, Yamada T, Kawahara Y, Narihiro T, Ito M, Kamagata Y, et al. Tripartite symbiosis of an anaerobic scuticociliate with two hydrogenosome-associated endosymbionts, a Holospora-related Alphaproteobacterium and a Methanogenic Archaeon. Appl Environ Microbiol. 2019;85:e00854–19.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Wang Y, Brune A, Zimmer M. Bacterial symbionts in the hepatopancreas of isopods: diversity and environmental transmission. FEMS Microbiol Ecol. 2007;61:141–52.Article 
CAS 
PubMed 

Google Scholar 
Wang Y, Stingl U, Anton-Erxleben F, Zimmer M, Brune A. ‘Candidatus Hepatincola porcellionum’ gen. nov., sp. nov., a new, stalk-forming lineage of Rickettsiales colonizing the midgut glands of a terrestrial isopod. Arch Microbiol. 2004;181:299–304.Article 
CAS 
PubMed 

Google Scholar 
Fraune S, Zimmer M. Host-specificity of environmentally transmitted Mycoplasma-like isopod symbionts. Environ Microbiol. 2008;10:2497–504.Article 
CAS 
PubMed 

Google Scholar 
Dittmer J, Lesobre J, Moumen B, Bouchon D. Host origin and tissue microhabitat shaping the microbiota of the terrestrial isopod Armadillidium vulgare. FEMS Microbiol Ecol. 2016;92:fiw063.Article 
PubMed 

Google Scholar 
Zimmer M, Topp W. Microorganisms and cellulose digestion in the gut of the woodlouse Porcellio scaber. J Chem Ecol. 1998;24:1397–408.Article 
CAS 

Google Scholar 
Zimmer M, Danko JP, Pennings SC, Danford AR, Ziegler A, Uglow RF, et al. Hepatopancreatic endosymbionts in coastal isopods (Crustacea: Isopoda), and their contribution to digestion. Mar Biol. 2001;138:955–63.Article 

Google Scholar 
Bouchon D, Zimmer M, Dittmer J. The terrestrial isopod microbiome: an all-in-one toolbox for animal-microbe interactions of ecological relevance. Front Microbiol. 2016;7:1472.Article 
PubMed 
PubMed Central 

Google Scholar 
Dittmer J, Beltran-Bech S, Lesobre J, Raimond M, Johnson M, Bouchon D. Host tissues as microhabitats for Wolbachia and quantitative insights into the bacterial community in terrestrial isopods. Mol Ecol. 2014;23:2619–35.Article 
CAS 
PubMed 

Google Scholar 
Li H. Minimap2: pairwise alignment for nucleotide sequences. Bioinformatics. 2018;34:3094–100.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Koren S, Walenz BP, Berlin K, Miller JR, Bergman NH, Phillippy AM. Canu: scalable and accurate long-read assembly via adaptive k-mer weighting and repeat separation. Genome Res. 2017;27:722–36.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Loman NJ, Quick J, Simpson JT. A complete bacterial genome assembled de novo using only nanopore sequencing data. Nat Methods. 2015;12:733–5.Article 
CAS 
PubMed 

Google Scholar 
Vaser R, Sovic I, Nagarajan N, Sikic M. Fast and accurate de novo genome assembly from long uncorrected reads. Genome Res. 2017;27:737–46.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Wick RR, Holt KE. Polypolish: short-read polishing of long-read bacterial genome assemblies. PLoS Comput Biol. 2022;18:e1009802.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Bredon M, Dittmer J, Noel C, Moumen B, Bouchon D. Lignocellulose degradation at the holobiont level: teamwork in a keystone soil invertebrate. Microbiome. 2018;6:162.Article 
PubMed 
PubMed Central 

Google Scholar 
Bredon M, Herran B, Bertaux J, Greve P, Moumen B, Bouchon D. Isopod holobionts as promising models for lignocellulose degradation. Biotechnol Biofuels. 2020;13:49.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Li D, Liu CM, Luo R, Sadakane K, Lam TW. MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. Bioinformatics. 2015;31:1674–6.Article 
CAS 
PubMed 

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

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

Google Scholar 
Lagesen K, Hallin P, Rodland EA, Staerfeldt HH, Rognes T, Ussery DW. RNAmmer: consistent and rapid annotation of ribosomal RNA genes. Nucleic Acids Res. 2007;35:3100–8.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Langmead B, Salzberg SL. Fast gapped-read alignment with Bowtie 2. Nat Methods. 2012;9:357–9.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Prjibelski A, Antipov D, Meleshko D, Lapidus A, Korobeynikov A. Using SPAdes de novo assembler. Curr Protoc Bioinformatics. 2020;70:e102.Article 
CAS 
PubMed 

Google Scholar 
Darling AC, Mau B, Blattner FR, Perna NT. Mauve: multiple alignment of conserved genomic sequence with rearrangements. Genome Res. 2004;14:1394–403.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Badawi M, Moumen B, Giraud I, Greve P, Cordaux R. Investigating the molecular genetic basis of cytoplasmic sex determination caused by Wolbachia endosymbionts in terrestrial isopods. Genes. 2018;9:290.Article 
PubMed 
PubMed Central 

Google Scholar 
Wick RR, Judd LM, Gorrie CL, Holt KE. Completing bacterial genome assemblies with multiplex MinION sequencing. Microb Genom. 2017;3:e000132.PubMed 
PubMed Central 

Google Scholar 
Kolmogorov M, Bickhart DM, Behsaz B, Gurevich A, Rayko M, Shin SB, et al. metaFlye: scalable long-read metagenome assembly using repeat graphs. Nat Methods. 2020;17:1103–10.Article 
CAS 
PubMed 

Google Scholar 
Seemann T. Prokka: rapid prokaryotic genome annotation. Bioinformatics. 2014;30:2068–9.Article 
CAS 
PubMed 

Google Scholar 
Mistry J, Chuguransky S, Williams L, Qureshi M, Salazar GA, Sonnhammer ELL, et al. Pfam: the protein families database in 2021. Nucleic Acids Res. 2021;49:D412–9.Article 
CAS 
PubMed 

Google Scholar 
Marchler-Bauer A, Bryant SH. CD-Search: protein domain annotations on the fly. Nucleic Acids Res. 2004;32:W327–31.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ. Basic local alignment search tool. J Mol Biol. 1990;215:403–10.Article 
CAS 
PubMed 

Google Scholar 
Carver T, Harris SR, Berriman M, Parkhill J, McQuillan JA. Artemis: an integrated platform for visualization and analysis of high-throughput sequence-based experimental data. Bioinformatics. 2012;28:464–9.Article 
CAS 
PubMed 

Google Scholar 
Simao FA, Waterhouse RM, Ioannidis P, Kriventseva EV, Zdobnov EM. BUSCO: assessing genome assembly and annotation completeness with single-copy orthologs. Bioinformatics. 2015;31:3210–2.Article 
CAS 
PubMed 

Google Scholar 
Eren AM, Kiefl E, Shaiber A, Veseli I, Miller SE, Schechter MS, et al. Community-led, integrated, reproducible multi-omics with anvi’o. Nat Microbiol. 2021;6:3–6.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Cantalapiedra CP, Hernandez-Plaza A, Letunic I, Bork P, Huerta-Cepas J. eggNOG-mapper v2: functional annotation, orthology assignments, and domain prediction at the metagenomic scale. Mol Biol Evol. 2021;38:5825–9.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Kanehisa M, Sato Y, Morishima K. BlastKOALA and GhostKOALA: KEGG tools for functional characterization of genome and metagenome sequences. J Mol Biol. 2016;428:726–31.Article 
CAS 
PubMed 

Google Scholar 
Abby SS, Cury J, Guglielmini J, Neron B, Touchon M, Rocha EP. Identification of protein secretion systems in bacterial genomes. Sci Rep. 2016;6:23080.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Teufel F, Almagro Armenteros JJ, Johansen AR, Gislason MH, Pihl SI, Tsirigos KD, et al. SignalP 6.0 predicts all five types of signal peptides using protein language models. Nat Biotechnol. 2022;40:1023–5.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Elbourne LD, Tetu SG, Hassan KA, Paulsen IT. TransportDB 2.0: a database for exploring membrane transporters in sequenced genomes from all domains of life. Nucleic Acids Res. 2017;45:D320–4.Article 
CAS 
PubMed 

Google Scholar 
Blin K, Shaw S, Kloosterman AM, Charlop-Powers Z, van Wezel GP, Medema MH, et al. antiSMASH 6.0: improving cluster detection and comparison capabilities. Nucleic Acids Res. 2021;49:W29–35.Article 
CAS 
PubMed 
PubMed Central 

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

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

Google Scholar 
Arndt D, Grant JR, Marcu A, Sajed T, Pon A, Liang Y, et al. PHASTER: a better, faster version of the PHAST phage search tool. Nucleic Acids Res. 2016;44:W16–21.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Emms DM, Kelly S. OrthoFinder: phylogenetic orthology inference for comparative genomics. Genome Biol. 2019;20:238.Article 
PubMed 
PubMed Central 

Google Scholar 
Edgar RC. MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 2004;32:1792–7.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Minh BQ, Schmidt HA, Chernomor O, Schrempf D, Woodhams MD, von Haeseler A, et al. IQ-TREE 2: new models and efficient methods for phylogenetic inference in the genomic era. Mol Biol Evol. 2020;37:1530–4.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Kalyaanamoorthy S, Minh BQ, Wong TKF, von Haeseler A, Jermiin LS. ModelFinder: fast model selection for accurate phylogenetic estimates. Nat Methods. 2017;14:587–9.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Chernomor O, von Haeseler A, Minh BQ. Terrace aware data structure for phylogenomic inference from supermatrices. Syst Biol. 2016;65:997–1008.Article 
PubMed 
PubMed Central 

Google Scholar 
Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 2013;41:D590–6.Article 
CAS 
PubMed 

Google Scholar 
Letunic I, Bork P. Interactive Tree Of Life (iTOL) v5: an online tool for phylogenetic tree display and annotation. Nucleic Acids Res. 2021;49:W293–6.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Schrallhammer M, Castelli M, Petroni G. Phylogenetic relationships among endosymbiotic R-body producer: bacteria providing their host the killer trait. Syst Appl Microbiol. 2018;41:213–20.Article 
PubMed 

Google Scholar 
Gillespie JJ, Kaur SJ, Rahman MS, Rennoll-Bankert K, Sears KT, Beier-Sexton M, et al. Secretome of obligate intracellular Rickettsia. FEMS Microbiol Rev. 2015;39:47–80.CAS 
PubMed 

Google Scholar  More

Lynn, D. H. Ciliated Protozoa: Characterization, Classification, and Guide to the Literature 3rd edn, 1–455 (Springer, 2008).
Google Scholar 
Stoecker, D. K., Michaels, A. E. & Davis, L. H. Grazing by the jellyfish, Aurelia aurita, on microzooplankton. J. Plankton Res. 9, 901–915 (1987).Article 

Google Scholar 
Dolan, J. R., Vidussi, F. & Claustre, H. Planktonic ciliates in the Mediterranean Sea: Longitudinal trends. Deep-Sea Res. I(46), 2025–2039 (1999).Article 

Google Scholar 
Gómez, F. Trends on the distribution of ciliates in the open Pacific Ocean. Acta Oecol. 32, 188–202 (2007).Article 
ADS 

Google Scholar 
Azam, F. et al. The ecological role of water column microbes in the sea. Mar. Ecol. Prog. Ser. 10, 257–263 (1983).Article 
ADS 

Google Scholar 
Pierce, R. W. & Turner, J. T. Ecology of planktonic ciliates in marine food webs. Rev. Aquat. Sci. 6, 139–181 (1992).
Google Scholar 
Calbet, A. & Saiz, E. The ciliate-copepod link in marine ecosystems. Aquat. Microb. Ecol. 38, 157–167 (2005).Article 

Google Scholar 
Kim, Y. O. et al. Tintinnid species as biological indicators for monitoring intrusion of the warm oceanic waters into Korean coastal waters. Ocean Sci. J. 47, 161–172 (2012).Article 
ADS 

Google Scholar 
Wang, C. F. et al. Impact of the warm eddy on planktonic ciliate, with an emphasis on tintinnids as bioindicator species. Ecol. Indic. 133, 108441 (2021).Article 

Google Scholar 
Wang, C. F. et al. Planktonic tintinnid community structure variations in different water masses of the Arctic Basin. Front. Mar. Sci. 8, 775653 (2022).Article 

Google Scholar 
Haney, J. F. Diel patterns of zooplankton behavior. Bull. Mar. Sci. 43, 583–603 (1988).ADS 

Google Scholar 
Vaulot, D. & Marie, D. Diel variability of photosynthetic picoplankton in the equatorial Pacific. J. Geophys. Res-Oceans 104, 3297–3310 (1999).Article 
ADS 
CAS 

Google Scholar 
Hays, G. C., Webb, P. I. & Frears, S. L. Diet changes in the carbon and nitrogen content of the copepod Metridia lucens. J. Plankton Res. 4, 727–737 (1998).Article 

Google Scholar 
Hays, G. C., Harris, R. P. & Head, R. N. Diel changes in the near-surface biomass of zooplankton and the carbon content of vertical migrants. Deep-Sea Res. II(48), 1063–1068 (2001).ADS 

Google Scholar 
Anna, A., Enric, S. & Albert, C. Towards an understanding of diel feeding phythms in marine protists: Consequences of light manipulation. Microb. Ecol. 79, 64–72 (2020).Article 

Google Scholar 
Vaulot, D., Marie, D., Olson, R. J. & Chisholm, S. W. Growth of Prochlorococcus, a photosynthetic prokaryote, in the equatorial Pacific Ocean. Science 268, 1480–1482 (1995).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Binder, B. J. & DuRand, M. D. Diel cycles in surface waters of the equatorial Pacific. Deep-Sea Res. II(49), 2601–2617 (2002).ADS 

Google Scholar 
Li, C. L. et al. Quasi-antiphase diel patterns of abundance and cell size/biomass of picophytoplankton in the oligotrophic ocean. Geophys. Res. Lett. 49, e2022GL097753 (2022).ADS 

Google Scholar 
Ohman, M. D. The demographic benefits of diel vertical migration by zooplankton. Ecol. Monogr. 60, 257–281 (1990).Article 

Google Scholar 
Ringelberg, J. The photo behavior of Daphnia spp. as a model to explain diel vertical migration in zooplankton. Biol. Rev. 74, 397–423 (1999).Article 

Google Scholar 
Tarling, G. A., Jarvis, T., Emsley, S. M. & Matthews, J. B. L. Midnight sinking behaviour in Calanus finmarchicus: A response to satiation or krill predation?. Mar. Ecol. Prog. 240, 183–194 (2002).Article 

Google Scholar 
Cohen, J. H. & Forward, R. B. Diel vertical migration of the marine copepod Calanopia americana. I. Twilight DVM and its relationship to the diel light cycle. Mar. Biol. 147, 387–398 (2005).Article 

Google Scholar 
Cohen, J. H. & Forward, R. B. Diel vertical migration of the marine copepod Calanopia americana. II. Proximate role of exogenous light cues and endogenous rhythms. Mar. Biol. 147, 399–410 (2005).Article 

Google Scholar 
Ringelberg, J. Diel Vertical Migration of Zooplankton in Lakes and Oceans 1–347 (Springer, 2010).
Google Scholar 
Liu, H. J., Zhu, M. L., Guo, S. J., Zhao, X. H. & Sun, X. X. Effects of an anticyclonic eddy on the distribution and community structure of zooplankton in the South China Sea northern slope. J. Mar. Syst. 205, 103311 (2020).Article 

Google Scholar 
Tao, Z. C. et al. The diel vertical distribution and carbon biomass of the zooplankton community in the Caroline Seamount area of the western tropical Pacific Ocean. Sci. Rep. 12, 18908 (2022).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Dale, T. Diel vertical distribution of planktonic ciliates in Lindåspollene, Western Norway. Mar. Microb. Food Webs 2, 15–28 (1987).
Google Scholar 
Jonsson, P. R. Vertical distribution of planktonic ciliates–an experimental analysis of swimming behavior. Mar. Ecol. Prog. Ser. 52, 39–53 (1989).Article 
ADS 

Google Scholar 
Stocker, D. K., Taniguchi, A. & Michaels, A. E. Abundance of autotrophic, mixotrophic and heterotrophic ciliates in shelf and slope waters. Mar. Ecol. Prog. Ser. 50, 241–254 (1989).Article 
ADS 

Google Scholar 
Passow, U. Vertical migration of Gonyaulax catenata and Mesodinium rubrum. Mar. Biol. 110, 455–463 (1991).Article 

Google Scholar 
Suzuki, T. & Taniguchi, A. Temporal change of clustered distribution of planktonic ciliates in Toyama Bay in summers of 1989 and 1990. J. Oceanogr. 53, 35–40 (1997).Article 
CAS 

Google Scholar 
Olli, K. Diel vertical migration of phytoplankton and heterotrophic flagellates in the Gulf of Riga. J. Mar. Syst. 23, 145–163 (1999).Article 

Google Scholar 
Pérez, M. T., Dolan, J. R., Vidussi, F. & Fukai, E. Diel vertical distribution of planktonic ciliates within the surface layer of the NW Mediterrean (May 1995). Deep-Sea Res. I(47), 479–503 (2000).Article 

Google Scholar 
Rossberg, M. & Wickham, S. A. Ciliate vertical distribution and diel vertical migration in a eutrophic lake. Fund. Appl. Limnol. 171, 1–14 (2008).Article 

Google Scholar 
Gu, B. W. et al. High dynamics of ciliate community revealed via short-term, high-frequency sampling in a subtropical estuarine ecosystem. Front. Microbiol. 13, 797638 (2022).Article 
PubMed 
PubMed Central 

Google Scholar 
Su, J. L. Overview of the South China Sea circulation and its influence on the coastal physical oceanography near the Pearl River Estuary. Cont. Shelf Res. 24, 1745–1760 (2004).Article 

Google Scholar 
Cravatte, S., Delcroix, T., Zhang, D., Mcphaden, M. & Leloup, J. Observed freshening and warming of the western pacific warm pool. Clim. Dyn. 33, 565–589 (2009).Article 

Google Scholar 
Feng, M. P., Zhang, W. C., Yu, Y., Xiao, T. & Sun, J. Horizontal distribution of tintinnids in the western South China Sea during summer 2007. J. Trop. Oceanogr. 32, 86–92 (2013).
Google Scholar 
Liu, H. X. et al. Composition and distribution of planktonic ciliates in the southern South China Sea during late summer: Comparison between surface and 75 m deep layer. J. Ocean Univ. China 15, 171–176 (2016).Article 
ADS 

Google Scholar 
Wang, C. F. et al. Vertical distribution of planktonic ciliates in the oceanic and slope areas of the western Pacific Ocean. Deep-Sea Res. II(167), 70–78 (2019).
Google Scholar 
Sun, P., Zhang, S. L., Wang, Y. & Huang, B. Q. Biogeographic role of the Kuroshio Current Intrusion in the microzooplankton community in the boundary zone of the northern South China Sea. Microorganisms 9, 1104 (2021).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Sohrin, R., Imazawa, M., Fukuda, H. & Suzuki, Y. Full-depth profiles of prokaryotes, heterotrophic nanoflagellates, and ciliates along a transect from the equatorial to the subarctic central Pacific Ocean. Deep-Sea Res. II(57), 1537–1550 (2010).ADS 

Google Scholar 
Wang, C. F. et al. Difference of planktonic ciliate communities of the tropical West Pacific, the Bering Sea and the Arctic Ocean. Acta Oceanol. Sin. 39, 9–17 (2020).Article 
CAS 

Google Scholar 
Wang, C. F. et al. Planktonic ciliate trait structure variation over Yap, Mariana and Caroline seamounts in the tropical western Pacific Ocean. J. Oceanol. Limnol. 39, 1705–1717 (2021).Article 
ADS 

Google Scholar 
McLaren, I. A. Demographic strategy of vertical migration by a marine copepod. Amer. Nat. 108, 91–102 (1974).Article 

Google Scholar 
Loose, C. J., Von Elert, E. & Dawidowicz, P. Chemically-induced diel vertical migration in Daphnia: A new bioassay for kairomones exuded by fish. Arch. Hydrobiol. 126, 329–337 (1993).Article 

Google Scholar 
Bandara, K., Varpe, Ø., Wijewardene, L., Tverberg, V. & Eiane, K. Two hundred years of zooplankton vertical migration research. Biol. Rev. 96, 1–43 (2021).Article 

Google Scholar 
Oubelkheir, K. & Sciandra, A. Diel variations in particle stocks in the oligotrophic waters of the Ionian Sea (Mediterranean). J. Mar. Syst. 74, 364–371 (2008).Article 

Google Scholar 
Yang, E. J., Choi, J. K. & Hyun, J. H. Distribution and structure of heterotrophic protist communities in the northeast equatorial Pacific Ocean. Mar. Biol. 146, 1–15 (2004).Article 

Google Scholar 
Wang, C. F. et al. Planktonic ciliate community structure and its distribution in the oxygen minimum zones in the Bay of Bengal (Eastern Indian Ocean). J. Sea Res. 190, 102311 (2022).Article 

Google Scholar 
Daro, M. H. Migratory and grazing behavior of copepods and vertical distribution of phytoplankton. Bull. Mar. Sci. 43, 710–729 (1988).
Google Scholar 
Ursella, L., Cardin, V., Batistić, M., Garić, R. & Gačić, M. Evidence of zooplankton vertical migration from continuous Southern Adriatic buoy current-meter records. Prog. Oceanogr. 167, 78–96 (2018).Article 
ADS 

Google Scholar 
Roman, M. R., Dam, H. G., Le Borgne, R. & Zhang, X. Latitudinal comparisons of equatorial Pacific zooplankton. Deep-Sea Res. II(49), 2695–2711 (2002).ADS 

Google Scholar 
Steinberg, D. K., Cope, J. S., Wilson, S. E. & Kobari, T. A comparison of mesopelagic mesozooplankton community structure in the subtropical and subarctic North Pacific Ocean. Deep-Sea Res. II(55), 1615–1635 (2008).ADS 

Google Scholar 
Isla, A., Scharek, R. & Latasa, M. Zooplankton diel vertical migration and contribution to deep active carbon flux in the NW Mediterranean. J. Mar. Syst. 143, 86–97 (2015).Article 

Google Scholar 
Dolan, J. R. Morphology and ecology in tintinnid ciliates of the marine plankton: Correlates of lorica dimensions. Acta Protozoologica 49, 235–244 (2010).
Google Scholar 
Jacquet, S., Partensky, F., Lennon, J. F. & Vaulot, D. Diel patterns of growth and division in marine picoplankton in culture. J. Phycol. 37, 357–369 (2001).Article 

Google Scholar 
Pitta, P., Giannakourou, A. & Christaki, U. Planktonic ciliates in the oligotrophic Mediterranean Sea: Longitudinal trends of standing stocks, distributions and analysis of food vacuole contents. Aquat. Microb. Ecol. 24, 297–311 (2001).Article 

Google Scholar 
Weisse, T. & Montagnes, D. J. S. Ecology of planktonic ciliates in a changing world: Concepts, methods, and challenges. J. Eukaryot. Microbiol. 69, e12879 (2022).Article 
PubMed 

Google Scholar 
Heinbokel, J. F. Diel periodicities and rates of reproduction in natural populations of tintinnines in the oligotrophic waters off Hawaii, September 1982. Mar. Microb. Food Webs 2, 1–14 (1987).
Google Scholar 
Tsai, A. Y., Chiang, K. P., Chang, J. & Gong, G. C. Seasonal diel variations of picoplankton and nanoplankton in a subtropical western Pacific coastal ecosystem. Limnol. Oceanogr. 50, 1221–1231 (2005).Article 
ADS 
CAS 

Google Scholar 
Ribalet, F. et al. Light-driven synchrony of Prochlorococcus growth and mortality in the subtropical Pacific gyre. Proc. Natl. Acad. Sci. U. S. A. 112, 8008–8012 (2015).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Connell, P. E., Ribalet, F., Armbrust, E. V., White, A. & Caron, D. A. Diel oscillations in the feeding activity of heterotrophic and mixotrophic nanoplankton in the North Pacific Subtropical Gyre. Aquat. Microb. Ecol. 85, 167–181 (2020).Article 

Google Scholar 
Cheung, K. C., Poon, B., Lan, C. Y. & Wong, M. H. Assessment of metal and nutrient concentrations in river water and sediment collected from the cities in the Pearl River Delta, South China. Chemosphere 52, 1431–1440 (2003).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Huang, X. P., Huang, L. M. & Yue, W. Z. The characteristics of nutrients and eutrophication in the Pearl River estuary. South China. Mar. Pollut. Bull. 47, 30–36 (2003).Article 
CAS 
PubMed 

Google Scholar 
Liu, S. M. et al. Nutrient dynamics in the winter thermohaline frontal zone of the northern shelf region of the South China Sea. J. Geophys. Res. 115, C11020 (2010).Article 
ADS 

Google Scholar 
Shu, Y. Q., Wang, Q. & Zu, T. T. Progress on shelf and slope circulation in the northern South China Sea. Sci. China Earth Sci. 61, 560–571 (2018).Article 
ADS 

Google Scholar 
Dai, S. et al. The effects of a warm-core eddy on chlorophyll a distribution and phytoplankton community structure in the northern South China Sea in spring 2017. J. Mar. Syst. 210, 103396 (2020).Article 

Google Scholar 
He, X. Q. et al. Eddy-entrained Pearl River plume into the oligotrophic basin of the South China Sea. Cont. Shelf Res. 124, 117–124 (2016).Article 
ADS 

Google Scholar 
Pan, X. J. et al. Remote sensing of surface [nitrite + nitrate] in river-influenced shelf-seas: The northern South China Sea Shelf-sea. Remote Sens. Environ. 210, 1–11 (2018).Article 
ADS 

Google Scholar 
Xu, J. et al. Phosphorus limitation in the northern South China Sea during late summer: Influence of the Pearl River. Deep-Sea Res. I. 55, 1330–1342 (2008).Article 
CAS 

Google Scholar 
Caron, D. Inorganic nutrients, bacteria, and the microbial loop. Microb. Ecol. 28, 295–298 (1994).Article 
CAS 
PubMed 

Google Scholar 
Kirchman, D. The uptake of inorganic nutrients by heterotrophic bacteria. Microb. Ecol. 28, 255–271 (1994).Article 
CAS 
PubMed 

Google Scholar 
Song, J. M. Biogeochemical Processes of Biogenic Elements in China Marginal Seas 1–657 (Springer, 2011).
Google Scholar 
Zhang, W. C. et al. Review of nutrient (nitrogen and phosphorus) regeneration in the marine pelagic microbial food web. Mar. Sci. Bull. 35, 241–251 (2016).CAS 

Google Scholar 
Ma, J. et al. Effects of Y3 seamount on nutrients influencing the ecological environment in the Western Pacific Ocean. Earth Sci. Front. 27, 322–331 (2020).
Google Scholar 
Li, H. B. et al. Tintinnid diversity in the tropical West Pacific Ocean. Acta Oceanol. Sin. 37, 218–228 (2018).Article 
CAS 

Google Scholar 
Dolan, J. R., Ritchie, M. E. & Ras, J. The, “neutral” community structure of planktonic herbivores, tintinnid ciliates of the microzooplankton, across the SE Tropical Pacific Ocean. Biogeosciences 4, 297–310 (2007).Article 
ADS 
CAS 

Google Scholar 
Dolan, J. R., Ritchie, M. E., Tunin-Ley, A. & Pizay, M. Dynamics of core and occasional species in the marine plankton: Tintinnid ciliates in the north-west Mediterranean Sea. J. Biogeogr. 36, 887–895 (2009).Article 

Google Scholar 
Dolan, J. R. & Marrasé, C. Planktonic ciliate distribution relative to a deep chlorophyll maximum: Catalan Sea, NW Mediterranean, June 1993. Deep-Sea Res. I(42), 1965–1987 (1995).Article 

Google Scholar 
Suzuki, T. & Taniguchi, A. Standing crops and vertical distribution of four groups of marine planktonic ciliates in relation to phytoplankton chlorophyll a. Mar. Biol. 132, 375–382 (1998).Article 

Google Scholar 
Utermöhl, H. Zur vervollkommnung der quantitativen phytoplankton Methodik. Mit. Int. Ver. Theor. Angew. Limnol. 9, 1–38 (1958).
Google Scholar 
Lund, J. W. G., Kipling, C. & Cren, E. D. L. The inverted microscope method of estimating algal numbers and the statistical basis of estimations by counting. Hydrobiologia 11, 143–170 (1958).Article 

Google Scholar 
Kofoid, C. A. & Campbell, A. S. A Conspectus of the Marine and Fresh-Water Ciliata Belonging to the Suborder Tintinnoinea: With Descriptions of New Species Principally from the Agassiz Expedition to the Eastern Tropical Pacific 1904–1905 (University of California Press, 1929).
Google Scholar 
Kofoid, C. A., & Campbell, A. S. Reports on the scientific results of the expedition to the eastern tropical Pacific, in charge to Alexander Agassiz, by US Fish commission steamer “Albatross”, from October 1904 to March 1905, The Ciliata: The Tintinnoinea (Bulletin of the Museum of Comparative Zoology of Harvard College), vol. XXXVII. Cambridge University, Harvard (Lieut.-Commander LM Garrett, USN commanding) (1939).Zhang, W. C., Feng, M. P., Yu, Y., Zhang, C. X. & Xiao, T. An Illustrated Guide to Contemporary Tintinnids in the World 1–499 (Science Press, 2012).
Google Scholar 
Paranjape, M. A. & Gold, K. Cultivation of marine pelagic protozoa. Ann. Inst. Oceanogr. Paris 58, 143–150 (1982).
Google Scholar 
Alder, V. A. Tintinnoinea. In South Atlantic zooplankton (ed. Boltovskoy, D.) 321–384 (Backhuys, 1999).
Google Scholar 
Verity, P. G. & Langdon, C. Relationships between lorica volume, carbon, nitrogen, and ATP content of tintinnids in Narragansett Bay. J. Plankton R. 6, 859–868 (1984).Article 
CAS 

Google Scholar 
Putt, M. & Stoecker, D. K. An experimentally determined carbon: Volume ratio for marine “oligotrichous” ciliates from estuarine and coastal waters. Limnol. Oceanogr. 34, 1097–1103 (1989).Article 
ADS 

Google Scholar 
Yu, Y. et al. Basin-scale variation in planktonic ciliate distribution: A detailed temporal and spatial study of the Yellow Sea. Mar. Biol. Res. 10, 641–654 (2014).Article 

Google Scholar 
Wang, C. F. et al. Hydrographic feature variation caused pronounced differences of planktonic ciliate community in the Pacific Arctic Region in summer 2016 and 2019. Front. Microbiol. 13, 881048 (2022).Article 
PubMed 
PubMed Central 

Google Scholar 
Margalef, R. Information theory in ecology. Gen. Syst. 3, 36–71 (1958).
Google Scholar 
Shannon, C. E. A mathematical theory of communication. Bell Syst. Tech. J. 27, 379–423 (1948).Article 
MathSciNet 
MATH 

Google Scholar 
Dolan, J. R. & Pierce, R. W. Diversity and distributions of tintinnid ciliates. In The Biology and Ecology of Tintinnid Ciliates: Models for Marine Plankton (eds Dolan, J. R. et al.) 214–243 (Wiley-Blackwell, 2013).
Google Scholar 
Xu, Z. L. & Chen, Y. Q. Aggregated intensity of dominant species of zooplankton in autumn in the East China Sea. J. Ecol. 8, 13–15 (1989).
Google Scholar 
Anderson, M. J., Gorley, R. N. & Clarke, K. R. PERMANOVA+ for PRIMER: Guide to Software and Statistical Methods (PRIMER-E, 2008).
Google Scholar 
Jiang, Y., Xu, G. & Xu, H. Use of multivariate dispersion to assess water quality based on species composition data. Environ. Sci. Pollut. Res. 23, 3267–3272 (2016).Article 
CAS 

Google Scholar  More

When studying windfarm developments at the European region scale, the high densities of windfarm developments on blanket bog in Galicia and Greater Manchester (north England) are influenced by the total extent of the recognised blanket bog which is lower in Spain (31.2 km2 total extent) and in Greater Manchester (40.8 km2 total extent) in comparison to other regions (Fig. 1), although no relationship between the total extent of recognised blanket bog and the windfarm developments (wind turbines, tracks and total affected area) was found. Although the rest of the European regions across Spain showed lower densities of windfarm infrastructures (Fig. 2), the total extent of recognised blanket bogs across those regions was under 1 km2 (Fig. 1) meaning that the majority of recognised Spanish blanket bogs could be under threat due to their small size and the potential impact of windfarm infrastructures, if installed. In addition to this, previously unrecognised Spanish blanket bogs that have now been reported17 that could also be under pressure as the lack of formal recognition and protection leaves this habitat exposed to a range of anthropogenic activities, including windfarm developments. In fact, some examples of blanket bogs with extensive damage have been identified and reported in Galicia25, and more recently in Cantabrian blanket bogs17.Spanish unmapped areas of blanket bog at the edge-of-range of this habitat in the south of Europe are, therefore, particularly at risk from windfarm developments, and may disappear before their extent and importance can be defined40. Currently, new renewable energy regulations have been developed as a result of the climate emergency, and several windfarm developments have been proposed in ecologically sensitive areas, where blanket bogs have been reported (e.g. Sierra del Escudo, Spain) increasing the pressure on this habitat. Spanish blanket bogs also have specific characteristics, such as their small size as a consequence of the topographical limitations (e.g. slope) for their development26, meaning that they usually only cover the hill summits, where wind energy potential is at its greatest. Since blanket bogs are small and the windfarm development may cover all of the hill summit for their installation, many blanket bogs will be irrevocably damaged40.Most of the Galician blanket bogs were protected in 1999, under the Natura 2000 network and were declared as Special Area of Conservation (SAC) in 2014. However, between 1999 and 2012, Galician blanket bogs underwent severe and significant alterations in the peatland surface as a consequence of the large number of windfarm developments41 that were established during the period (Table A—Supplementary information), even when the site was incorporated into the Natura 2000 network (Table B—Supplementary information). Despite available scientific evidence that showed the potential environmental risks for these vulnerable ecosystems, windfarms were installed in what this work found to be the most extensive windfarm infrastructures across recognised European blanket bogs (Fig. 2).The incomplete current understanding of the extent of Spanish blanket bogs highlights the need to improve the completeness and representativeness of their current records across the Spanish Atlantic biogeographical region to include, within Natura 2000, a sufficient cover of their occupied area, in proportion to the representation of this natural habitat type in the Member state, for which it could therefore be concluded that the network is complete. Due to the increasing evidence highlighting how important the transitional areas are within the blanket bog complex42, other peatland types and wet heaths should be also considered when recognising and protecting blanket bogs. Mapping unrecorded blanket bogs must be a priority to fully understand the geographical and climatic range of this habitat, and obligatory protection under the Habitats Directive (92/43/EEC) is key to protecting the southern edge-of-range of this habitat.In addition to the lack of protection and updated inventories, the priority status included in the Habitats Directive, key to promoting their protection and restoration, is only for active blanket bogs, excluding other degraded blanket bogs with the potential to be active (carbon sinks), if they are restored. An approach similar to that of Scotland, where degraded blanket bogs are included33,39, could promote blanket bog restoration across Europe and improve the protection of this natural carbon storage.Many countries have also misinterpreted the active status of the blanket bog meaning that it is difficult to define whether the recognised blanket bog habitat is classed as a priority or not. Some countries, such as the Republic of Ireland, have classified as 7130 only active blanket bogs36, meaning that degraded blanket bogs lack appropriate classification and incorrectly applying the Habitat Directive designation as not all blanket bogs are included. The priority status is given when the habitat is particularly vulnerable or unique to the EU and necessitates additional measures for their protection and surveillance; however, whilst some blanket bogs may not act currently as carbon sinks, they still contain large amounts of carbon, and when restored they can recover their carbon sink function1, and then act to mitigate climate change.The issue of windfarm developments across the Republic of Ireland has been previously reported using a peat map43. However, despite researchers highlighting the importance of excluding vulnerable peatland ecosystems in future developments44, new areas of windfarms have been built affecting further recognised blanket bogs. At least 79 wind turbines have been installed in the Republic of Ireland since 2008 on recognised blanket bogs (Table A—Supplementary information) representing the 9.8% of the total onshore turbines installed in the country (Table 3), highlighting the importance of this conflict. The contribution of wind energy production to electricity supply was predicted to be up to 30% by 202044. In 2020, wind energy consumed in the Republic of Ireland represented 36%45. This represented an average annual increase of wind energy consumption of 16.9%45 between 2005 and 2020, which may explain part of the increase of 42% in wind turbines since 2008 (Table A—Supplementary information).Table 3 Total % of turbines on blanket bog (recognised/national inventories) in relation with the total turbines installed by country.Full size tableAcross Europe, several governments have developed climate action plans that over the next decade promote renewable energies to reduce carbon emissions. The government of the Republic of Ireland is aiming to generate up to 80% of electricity from renewable energy by 2030, providing support for onshore windfarm developments with an increase of up to 32% of the renewable energy production by 2030, but with a favourable preference for offshore wind energy production (up to 52% of the renewable energy production)46. This may help to reduce the conflict between blanket bogs and windfarm developments. Currently, windfarm annual energy production on blanket bogs accounts for 263.4 MW, 6.1% of the total production of wind energy in the Republic of Ireland47.The promotion of onshore wind energy production46 and the lack of protection of the full extent of blanket bogs are also threats that need to be considered in the Republic of Ireland. In 2008, a peat map was published showing the distribution of blanket bogs and raised bogs across the Republic of Ireland43. However, the inventory of current recognised blanket bogs under the Habitats Directive does not cover the full extent reported in this research43. While the total extent of recognised blanket bogs under the Habitats Directive 92/43/ECC reported a total of 3621 km2 of blanket bogs36, the real extent of blanket bogs across the country could be up to 2.5 times more (9202 km2)43, highlighting the lack of protection and the potential further increase of the windfarms and peatlands conflict in the Republic of Ireland as it happens in Spain and Scotland.The lack of recognition of blanket bog habitat in combination with the promotion of wind energy production across the island of Ireland could affect further areas of blanket bog, increasing the degradation of blanket bogs. An urgent review of inventories needs to be promoted in both countries, the Republic of Ireland and Northern Ireland, to fully assess the impact of the extensive areas of windfarms across the whole island.In Scotland, the pressure of windfarm developments on blanket bogs is also evident, where the Scottish Planning Policy considers classes 1 and 2 as areas of significant protection; although, windfarm developments may be possible under some circumstances48 as is permitted under the Habitats Directive across the EU29. However, to assess the impacts of windfarms on peatlands in a consistent way and evaluate the environmental impact of potential new developments on carbon-rich soils, a carbon calculator has been developed by the Scottish Government49. The carbon calculator allows users to estimate the carbon savings of windfarms installed on peatlands, although they highlight the importance of long-term management in relation to the final net carbon calculation49. Nonetheless, installing windfarms on non-degraded peatlands has been reported as unlikely to reduce carbon emissions even when the management has been considered carefully and it should be avoided 30. Therefore, peatlands under classes 1 and 2 considered by the Scottish government as a priority should be excluded from any windfarm developments (currently representing over 16% of onshore turbines, Table 3); especially considering the current policy of increasing onshore windfarms in Scotland50. Long-term research is needed to fully assess the impacts before new windfarm developments are installed.The difference between the recognised blanket bogs included in the EU Habitats Directive and the Scottish national inventory highlights the importance of updating and defining the complete extent of blanket bogs to facilitate their protection and restoration.In this novel research, the extent of windfarm developments across all recognised European blanket bogs under the Habitats Directive have been assessed. Large extents of blanket bogs have already been damaged, concentrated in the edge-of-range of this habitat and directly affecting hundreds of hectares of blanket bog across the rest of Europe. The full potential long-term damage to the habitat functionality is still unclear, but scientific evidence supports the negative impacts of windfarm developments on this critical habitat. European blanket bogs need further scientific evidence to demonstrate the real benefit of incentivising the reduction of carbon emissions by installing onshore windfarm infrastructures on peatlands which are causing the degradation of the most important long-term natural carbon sink and storage ecosystems. A strategic restoration plan and appropriate relevant legislation would be beneficial to promote the safeguarding of blanket bogs in the UK after Brexit. An urgent revision and compliance of the legislation regarding the protection of blanket bogs needs to be implemented, especially under the current trend of promotion and increasing legislation on renewable energy to reduce carbon emissions. An improvement of the national inventories across the EU and UK protected area networks is critical to implement the recognition, protection, and restoration of this habitat, in order to guarantee its favourable conservation status and its function as a long-term carbon sink to mitigate climate change. More

Decline in organism size is seen as a major biological response to climate change, and can be particularly pronounced in aquatic ectotherms such as fish, with subsequent implications for fishery yield and food security. However, as well as being modulated by climate factors, the fish population size structure can also be impacted by biotic (competition, predation) and other human factors (harvesting). For migrating species such as salmon, while smaller size may represent reduced size at maturity, it may also indicate faster maturation. More

Shuang Gao from Bjerkens Center for Climate Research in Norway, and colleagues from Germany and the United States explored future changes in marine primary production and carbon uptake under climate scenarios using the Norwegian Earth-system model, with four river transport configurations incorporating established future economic development and nutrient-use efficiency pathways. The researchers find that riverine nutrient inputs lessen nutrient limitation under warmer conditions. In the future, the effect of increased riverine carbon may be larger than the effect of nutrient inputs on the projections of ocean carbon uptake. In the historical period, increased nutrient inputs are considered the most prominent driver of carbon uptake. The results of this study are subject to model limitations, and high-resolution models should be used to assess the future impact. More

Brazil’s new government aims to achieve zero deforestation in the Amazon rainforest. This welcome initiative should be extended to the Cerrado, the world’s most biodiverse woodland savannah.
Competing Interests
The authors declare no competing interests. More