Ecology

HFRS distribution in China, 2004–2018From January 1, 2004 to December 31, 2018, 190 203 cases of HFRS were reported nationwide in China, with an average annual incidence rate of 0.950 per 100,000 people, with the highest incidence in 2004 (1.926 per 100,000) and the lowest in 2018 (0.86 per 100,000) (Fig. 1A), and the cases showed obvious seasonal fluctuations (Fig. 1B). HFRS cases existed every month and showed an obvious dual-season mode every year, with a spring peak from May to June and a winter peak from November to December. The highest number of cases were in May and November, with the composition ratios accounting of 9.51% and 17.06%, respectively (Fig. 1B).Figure 1The incidence and number of HFRS cases reported in China, 2004–2018. (A) Number of cases and incidence by year. Trend of the incidence rate of HFRS between 2004 and 2018 shown by the joinpoint regression (upper right corner). The red squares represent the observed crude incidence of HFRS and the lines represent the slope of the annual percentage change (APC). (B) The pink line represents the monthly incidence of HFRS. The bar chart shows the number of cases at peak and trough.Full size imageThe incidence of HFRS in northern regions was higher than that in the south, especially in Heilongjiang, Liaoning, Jining, Shaanxi, Shandong and Hebei provinces. Relatively few cases existed in south China, which were mainly concentrated in Jiangxi, Zhejiang, Hunan and Fujian (Figs. S1 and S2). Spatial autocorrelation analysis indicated that HFRS cases were positively correlated (Moran’s I = 0.09, p  More

Hart, M. B. et al. The search for the origin of the planktic foraminifera. J. Geol. Soc. Lond. 160, 341–343 (2003).Article 

Google Scholar 
Aze, T. et al. A phylogeny of Cenozoic macroperforate planktonic foraminifera from fossil data. Biol. Rev. 86, 900–927 (2011).Article 
PubMed 

Google Scholar 
Gradstein, F., Waskowska, A. & Glinskikh, L. The first 40 million years of planktonic foraminifera. Geosci 11, 1–25 (2021).Article 

Google Scholar 
Ujiié, Y., Kimoto, K. & Pawlowski, J. Molecular evidence for an independent origin of modern triserial planktonic foraminifera from benthic ancestors. Mar. Micropaleontol. 69, 334–340 (2008).Article 
ADS 

Google Scholar 
Darling, K. F. et al. Surviving mass extinction by bridging the benthic/planktic divide. Proc. Natl Acad. Sci. USA 106, 12629–33 (2009).Article 
ADS 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Kucera, M. et al. Caught in the act: anatomy of an ongoing benthic–planktonic transition in a marine protist. J. Plankton Res. 39, 436–449 (2017).
Google Scholar 
Ezard, T. H. G., Aze, T., Pearson, P. N. & Purvis, A. Interplay between changing climate and species’ ecology drives macroevolutionary dynamics. Science 332, 349–352 (2011).Article 
ADS 
PubMed 
CAS 

Google Scholar 
Lowery, C. M., Bown, P. R., Fraass, A. J. & Hull, P. M. Ecological response of plankton to environmental change: thresholds for extinction. Annu. Rev. Earth Planet. Sci. 48, 403–429 (2020).Article 
ADS 
CAS 

Google Scholar 
Pawlowski, J., Holzmann, M. & Tyszka, J. New supraordinal classification of foraminifera: molecules meet morphology. Mar. Micropaleontol. 100, 1–10 (2013).Article 
ADS 

Google Scholar 
Lecroq, B. et al. Ultra-deep sequencing of foraminiferal microbarcodes unveils hidden richness of early monothalamous lineages in deep-sea sediments. Proc. Natl Acad. Sci. USA 108, 13177–13182 (2011).Article 
ADS 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Pawlowski, J. et al. The evolution of early foraminifera. Proc. Natl Acad. Sci. USA 100, 11494–8 (2003).Article 
ADS 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Vachard, D. Macroevolution and biostratigraphy of paleozoic foraminifers. in Stratigraphy and Timescales (Ed. Montenari, M.) Vol. 1, 257–323 (Academic Press, 2016).Ibarbalz, F. M. et al. Global trends in marine plankton diversity across kingdoms of life. Cell 179, 1084–1097.e21 (2019).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Guillou, L. et al. The Protist Ribosomal Reference database (PR2): a catalog of unicellular eukaryote Small Sub-Unit rRNA sequences with curated taxonomy. Nucleic Acids Res. 41, D597–D604 (2013).Article 
PubMed 
CAS 

Google Scholar 
Holzmann, M. & Pawlowski, J. An updated classification of rotaliid foraminifera based on ribosomal DNA phylogeny. Mar. Micropaleontol. 132, 18–34 (2017).Article 
ADS 

Google Scholar 
John, A. W. G. The regular occurrence of Reophax Scottie Chaster, a benthic foraminiferan, in plankton samples from the North Sea. J. Micropalaeontol. 6, 61–63 (1987).Article 

Google Scholar 
Kucera, M. et al. Caught in the act: anatomy of an ongoing benthic-planktonic transition in a marine protist. J. Plankton Res. 39, 436–449 (2017).Darling, K. F., Wade, C. M., Kroon, D. & Brown, A. J. L. Planktic foraminiferal molecular evolution and their polyphyletic origins from benthic taxa. Mar. Micropaleontol. 30, 251–266 (1997).Article 
ADS 

Google Scholar 
Church, S. H., Ryan, J. F. & Dunn, C. W. Automation and evaluation of the SOWH test with SOWHAT. Syst. Biol. 64, 1048–1058 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
Shimodaira, H. An approximately unbiased test of phylogenetic tree selection. Syst. Biol. 51, 492–508 (2002).Article 
PubMed 

Google Scholar 
Pawlowski, J. et al. Extreme differences in rates of molecular evolution of foraminifera revealed by comparison of ribosomal DNA sequences and the fossil record. Mol. Biol. Evol. 14, 498–505 (1997).Article 
PubMed 
CAS 

Google Scholar 
Peijnenburg, K. T. C. A. et al. The origin and diversification of pteropods precede past perturbations in the Earth’s carbon cycle. Proc. Natl Acad. Sci. USA 117, 25609–25617 (2020).Article 
ADS 
PubMed 
PubMed Central 
CAS 

Google Scholar 
O’Brien, C. L. et al. Cretaceous sea-surface temperature evolution: constraints from TEX86 and planktonic foraminiferal oxygen isotopes. Earth-Sci. Rev. 172, 224–247 (2017).Article 
ADS 

Google Scholar 
Olsson, R. K., Berggren, W. A., Hemleben, C. & Huber, B. T. Atlas of Paleocene planktonic foraminifera. Smithson. Contrib. Paleobiol. 1–252 https://doi.org/10.5479/si.00810266.85.1 (1999).Arenillas, I. & Arz, J. A. Benthic origin and earliest evolution of the first planktonic foraminifera after the Cretaceous/Palaeogene boundary mass extinction. Hist. Biol. 29, 25–42 (2017).Article 

Google Scholar 
Huber, B. T., Petrizzo, M. R. & MacLeod, K. G. Planktonic foraminiferal endemism at southern high latitudes following the terminal cretaceous extinction. J. Foraminifer. Res. 50, 382–402 (2020).Article 

Google Scholar 
Arenillas, I., Arz, J. A. & Gilabert, V. An updated suprageneric classification of planktic foraminifera after growing evidence of multiple benthic-planktic transitions. Spanish J. Palaeontol. https://doi.org/10.7203/sjp.22189 (2022).Culver, S. J. Benthic foraminifera across the Cretaceous–Tertiary (K–T) boundary: a review. Mar. Micropaleontol. 47, 177–226 (2003).Article 
ADS 

Google Scholar 
Widmark, J. G. V. & Malmgren, B. A. Benthic foraminiferal changes across the Cretaceous/Tertiary boundary in the deep sea; DSDP sites 525, 527, and 465. J. Foraminifer. Res. 22, 81–113 (1992).Article 

Google Scholar 
Rigaud, S., Martini, R. & Vachard, D. Early evolution and new classification of the order Robertinida (foraminifera). J. Foraminifer. Res. 45, 3–28 (2015).Article 

Google Scholar 
Rigaud, S., Granier, B. & Masse, J. P. Aragonitic foraminifers: an unsuspected wall diversity. J. Syst. Palaeontol. 19, 461–488 (2021).Article 

Google Scholar 
Hull, P. M. et al. On impact and volcanism across the Cretaceous-Paleogene boundary. Science 367, 266–272 (2020).Article 
ADS 
PubMed 
CAS 

Google Scholar 
Morard, R. et al. PFR2: a curated database of planktonic foraminifera 18S ribosomal DNA as a resource for studies of plankton ecology, biogeography and evolution. Mol. Ecol. Resour. 15, 1472–1485 (2015).Article 
PubMed 
CAS 

Google Scholar 
Morard, R. et al. Genetic and morphological divergence in the warm-water planktonic foraminifera genus Globigerinoides. PLoS ONE 14, 1–30 (2019).Article 

Google Scholar 
Morard, R., Vollmar, N. M., Greco, M. & Kucera, M. Unassigned diversity of planktonic foraminifera from environmental sequencing revealed as known but neglected species. PLoS ONE 14, e0213936 (2019).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Camacho, C. et al. BLAST+: Architecture and applications. BMC Bioinforma. 10, 1–9 (2009).Article 

Google Scholar 
R Development Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2014).Liaw, A. & Wiener, M. Classification and Regression by randomForest. R. N. 2, 18–22 (2002).
Google Scholar 
Lang, M. et al. mlr3: a modern object-oriented machine learning framework in R. J. Open Source Softw. 4, 1903 (2019).Article 
ADS 

Google Scholar 
Katoh, K. & Standley, D. M. MAFFT multiple sequence alignment software version 7: improvements in performance and usability. Mol. Biol. Evol. 30, 772–780 (2013).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Darriba, D. et al. ModelTest-NG: a new and scalable tool for the selection of DNA and protein evolutionary models. Mol. Biol. Evol. 37, 291–294 (2020).Article 
MathSciNet 
PubMed 
CAS 

Google Scholar 
Kozlov, A. M. et al. RAxML-NG: a fast, scalable and user-friendly tool for maximum likelihood phylogenetic inference. Bioinformatics 35, 4453–4455 (2019).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Barbera, P. et al. EPA-ng: massively parallel evolutionary placement of genetic sequences. Syst. Biol. 68, 365–369 (2019).Article 
MathSciNet 
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. 49, 293–296 (2021).Article 

Google Scholar 
Löytynoja, A. & Goldman, N. WebPRANK: a phylogeny-aware multiple sequence aligner with interactive alignment browser. BMC Bioinform. 11, 1–7 (2010).Ronquist, F. et al. MrBayes 3. 2: efficient Bayesian phylogenetic inference and model choice across a large model space. Syst. Biol. 61, 539–542 (2012).Article 
PubMed 
PubMed Central 

Google Scholar 
Minh, B. Q. et al. IQ-TREE 2: new models and efficient methods for phylogenetic inference in the genomic era. Mol. Biol. Evol. 37, 1530–1534 (2020).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Dos Reis, M., Donoghue, P. C. J. & Yang, Z. Bayesian molecular clock dating of species divergences in the genomics era. Nat. Rev. Genet. 17, 71–80 (2016).Article 
PubMed 

Google Scholar 
Song, H., Tong, J. & Chen, Z. Q. Evolutionary dynamics of the Permian-Triassic foraminifer size: Evidence for Lilliput effect in the end-Permian mass extinction and its aftermath. Palaeogeogr. Palaeoclimatol. Palaeoecol. 308, 98–110 (2011).Article 

Google Scholar 
Copestake, P. & Johnson, B. Lower Jurassic Foraminifera from the Llanbedr (Mochras Farm) Borehole, North Wales, UK. Monogr. Palaeontogr. Soc. 167, 1–403 (2013).Article 

Google Scholar 
Rigaud, S. & Blau, J. New Robertinid Foraminifers from the Early Jurassic of Adnet, Austria and Their Evolutionary Importance. Acta Palaeontol. Pol. 61, 721–734 (2016).Article 

Google Scholar 
Boudagher-fadel, M. K. Evolution and Geological Significance of Larger Benthic Foraminifera. Evolution and Geological Significance of Larger Benthic Foraminifera (UCL Press, 2018).Piuz, A. & Meister, C. Cenomanian rotaliids (Foraminiferida) from Oman and Morocco. Swiss J. Palaeontol. 132, 81–97 (2013).Article 

Google Scholar 
Kucera, M. & Schönfeld, J. The origin of modern oceanic foraminiferal faunas and Neogene climate change. in Deep-Time Perspectives on Climate Change: Marrying the Signal from Computer Models and Biological Proxies. (ed. The Micropalaeontological Society, S. P.) 409–425 (The Geological Society, 2007).Drummond, A. J. & Suchard, M. A. Bayesian random local clocks, or one rate to rule them all. BMC Biol. 8, 114 (2010).Article 
PubMed 
PubMed Central 

Google Scholar 
Rambaut, A. FigTree version 1.3.1. http://tree.bio.ed.ac.uk (2009).Groussin, M., Pawlowski, J. & Yang, Z. Bayesian relaxed clock estimation of divergence times in foraminifera. Mol. Phylogenet. Evol. 61, 157–166 (2011).Article 
PubMed 

Google Scholar 
Loeblich Jr, A. R. & Tappan, H. Foraminiferal Genera and Their Classification (Springer, 1988). More

Prosser JI, Hink L, Gubry-Rangin C, Nicol GW. Nitrous oxide production by ammonia oxidizers: Physiological diversity, niche differentiation and potential mitigation strategies. Glob Chang Biol. 2020;26:103–18.Article 
PubMed 

Google Scholar 
Huang L, Chakrabarti S, Cooper J, Perez A, John SM, Daroub SH, et al. Ammonia-oxidizing archaea are integral to nitrogen cycling in a highly fertile agricultural soil. ISME Commun. 2021;1:19.Article 

Google Scholar 
Hink L, Gubry-Rangin C, Nicol GW, Prosser JI. The consequences of niche and physiological differentiation of archaeal and bacterial ammonia oxidisers for nitrous oxide emissions. ISME J. 2018;12:1084–93.Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Li Y, Chapman SJ, Nicol GW, Yao H. Nitrification and nitrifiers in acidic soils. Soil Biol Biochem. 2018;116:290–301.Article 
CAS 

Google Scholar 
Ahlgren NA, Fuchsman CA, Rocap G, Fuhrman JA. Discovery of several novel, widespread, and ecologically distinct marine Thaumarchaeota viruses that encode amoC nitrification genes. ISME J 2019;13:618–31.Article 
PubMed 
CAS 

Google Scholar 
Kim J-G, Kim S-J, Cvirkaite-Krupovic V, Yu W-J, Gwak J-H, López-Pérez M, et al. Spindle-shaped viruses infect marine ammonia-oxidizing thaumarchaea. Proc Natl Acad Sci USA. 2019;116:15645–50.Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Emerson JB. Soil Viruses: A New Hope. mSystems 2019;4:e00120–19.Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Santos-Medellín C, Estera-Molina K, Yuan M, Pett-Ridge J, Firestone MK, Emerson JB. Spatial turnover of soil viral populations and genotypes overlain by cohesive responses to moisture in grasslands. Proc Natl Acad Sci USA. 2022;119:e2209132119.Article 
PubMed 
PubMed Central 

Google Scholar 
Wu R, Davison MR, Gao Y, Nicora CD, Mcdermott JE, Burnum-Johnson KE, et al. Moisture modulates soil reservoirs of active DNA and RNA viruses. Commun Biol. 2021;4:992.Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Braga LPP, Spor A, Kot W, Breuil M-C, Hansen LH, Setubal JC, et al. Impact of phages on soil bacterial communities and nitrogen availability under different assembly scenarios. Microbiome 2020;8:52.Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Albright MBN, Gallegos-Graves LV, Feeser KL, Montoya K, Emerson JB, Shakya M, et al. Experimental evidence for the impact of soil viruses on carbon cycling during surface plant litter decomposition. ISME Commun. 2022;2:24.Article 

Google Scholar 
Starr EP, Shi S, Blazewicz SJ, Koch BJ, Probst AJ, Hungate BA, et al. Stable-isotope-informed, genome-resolved metagenomics uncovers potential cross-kingdom interactions in rhizosphere soil. mSphere 2021;6:e0008521.Article 
PubMed 

Google Scholar 
Trubl G, Kimbrel JA, Liquet-Gonzalez J, Nuccio EE, Weber PK, Pett-Ridge J, et al. Active virus-host interactions at sub-freezing temperatures in Arctic peat soil. Microbiome 2021;9:208.Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Lee S, Sieradzki ET, Nicolas AM, Walker RL, Firestone MK, Hazard C, et al. Methane-derived carbon flows into host–virus networks at different trophic levels in soil. Proc Natl Acad Sci USA. 2021;118:e2105124118.Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Nicol GW, Leininger S, Schleper C, Prosser JI. The influence of soil pH on the diversity, abundance and transcriptional activity of ammonia oxidizing archaea and bacteria. Environ Microbiol. 2008;10:2966–78.Article 
PubMed 
CAS 

Google Scholar 
Chaumeil P-A, Mussig AJ, Hugenholtz P, Parks DH. GTDB-Tk: a toolkit to classify genomes with the Genome Taxonomy Database. Bioinformatics 2020;36:1925–7.CAS 

Google Scholar 
Alves RJE, Minh BQ, Urich T, von Haeseler A, Schleper C. Unifying the global phylogeny and environmental distribution of ammonia-oxidising archaea based on amoA genes. Nat Commun. 2018;9:1517.Article 
PubMed 
PubMed Central 

Google Scholar 
Cardinale DJ, Duffy S. Single-stranded genomic architecture constrains optimal codon usage. Bacteriophage 2011;1:219–24.Article 
PubMed 
PubMed Central 

Google Scholar 
Lee S, Sorensen JW, Walker RL, Emerson JB, Nicol GW, Hazard C. Soil pH influences the structure of virus communities at local and global scales. Soil Biol Biochem. 2022;166:108569.Article 
CAS 

Google Scholar 
Jang HB, Bolduc B, Zablocki O, Kuhn JH, Roux S, Adriaenssens EM, et al. Taxonomic assignment of uncultivated prokaryotic virus genomes is enabled by gene-sharing networks. Nat Biotechnol. 2019;37:632–9.Article 

Google Scholar 
Nishimura Y, Yoshida T, Kuronishi M, Uehara H, Ogata H, Goto S. ViPTree: the viral proteomic tree server. Bioinformatics 2017;33:2379–80.Article 
PubMed 
CAS 

Google Scholar 
Kerou M, Offre P, Valledor L, Abby SS, Melcher M, Nagler M, et al. Proteomics and comparative genomics of Nitrososphaera viennensis reveal the core genome and adaptations of archaeal ammonia oxidizers. Proc Natl Acad Sci Usa 2016;113:7937–46.Article 

Google Scholar 
Reyes C, Hodgskiss LH, Kerou M, Pribasnig T, Abby SS, Bayer B, et al. Genome wide transcriptomic analysis of the soil ammonia oxidizing archaeon Nitrososphaera viennensis upon exposure to copper limitation. ISME J 2020;14:2659–74.Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Sieradzki ET, Greenlon A, Nicolas AM, Firestone MK, Pett-Ridge J, Blazewicz SJ, et al. Functional succession of actively growing soil microorganisms during rewetting is shaped by precipitation history. bioRxiv. 2022; 2022.06.28.498032. More

Chironomids have the ability to survive and reproduce in polluted environments, and thus they are included in many ecological studies where approaches may be taxonomic or functional16. The diversity of most macroinvertebrates is controlled by the oxygen level of water, but chironomids may survive in hypoxic conditions where the oxygen concentration may be less than 3 mg l−117. The current study demonstrates that changing seasons, as well as anthropogenic activities, have a significant impact on the levels of DO in aquatic bodies. As observed from the result, DO highly influences the tube length of the chironomid larvae. Since KWC is a wastewater canal, the average oxygen level is lower (5.24 ± 1.14 mg l−1) than KFP (6.63 ± 1.28 mg l−1) which is a normal fish culturing pond. It has also been observed that the average tube length of the chironomid larvae of KWC (8.66 ± 0.88 mm) is higher than KFP (7.68 ± 0.62 mm), which indicates that a low concentration of DO promotes the building of longer tubes in natural conditions. Similar observations were also observed in laboratory conditions. When the oxygen level (7.03 ± 0.41 mg l−1) in the experiment was kept in the normal range, there was negligible variation in tube length (7.61 ± 0.31 mm). But when the concentration of oxygen is gradually reduced by dilution, the tube length starts to increase accordingly, which is explained graphically in Fig. 4. The regression model of both the experimental conditions also supports the hypothesis that the tube length has an inverse relationship with DO. The scatter plot and simple linear regression confirmed the inverse relationship between DO and tube length (Figs. 1 and 2).Chironomid larvae are able to grow in the polluted water of a wastewater pond as dominant macroinvertebrates18. It is observed that those larvae living in the sand tubes are more susceptible to chemical pollutants than the larvae living in silt tubes7. Sand particles are bigger than silt and are not suitable for the survival of larvae19. Chironomus riparius larvae make their tubes from different external particles and their own proteins20. Midge larvae are the inhabitants of sediments, and at the same time, sediment is the depository of different inorganic, organic, and heavy metals. In such cases, the tube of chironomid larvae may act as a defensive structure, which protects them from the adverse effects of undesirable pollutants and may increase their tolerance against such chemicals21,22,23.Larvae can thrive in benthic sediments with high decaying organic content and very low DO concentrations in water bodies24. In poor DO concentration, larvae can survive due to the presence of haemoglobin in their body tissue fluid, which plays an important physiological role in increasing respiratory efficiency, as was observed in Chironomus plumosus. Longer tube length may help larvae generate better respiratory currents so that they can cope with a low DO environment.Tube length is crucial for living in water because primarily tubes protect them from outer environmental factors like predators, and pollution. It was observed during this study that when the DO of water is low, larvae make elongated tubes to reach the upper layer of water, where the DO level is comparatively high. To get their required amount of oxygen, the larvae increase the tube length towards the water surface and increase the DO in tube water by undulating the body and other structures, creating a current inside the tube25,26. On contrary, when the DO level of the surrounding water of chironomid is sufficient, they can manage their normal physiological activities with the available oxygen. They need not to elongate their tube length. That’s why their tube length is inversely related to the DO of their surrounding medium.If tube length does not increase in size in hypoxic water, larvae will not be able to meet their oxygen demand. If the DO of water decreases, tube length will increase and vice versa. Behavioural and physiological adaptations of chironomids larvae make them successful to live in a hypoxic environment. Thus, in hypoxic conditions, larvae with longer tubes are able to gather more oxygen from the upper layer of water and get more space to create a current of water to increase the amount of O2 inside the tube by undulating the preanal papillae, anal gill, ventral gills. This would explain why the tube length of chironomids depends on the DO of water. Hence by measuring the tube length with a standard measuring scale, one may get an idea about the quality of water, especially DO, before doing any chemical analysis. The work seems to be unique and novel for its own kind. More

Wedekind, C. & Küng, C. Shift of spawning season and effects of climate warming on developmental stages of a grayling (Salmonidae). Conserv. Biol. 24, 1418–1423 (2010).PubMed 

Google Scholar 
Capdevila, P., Stott, I., Beger, M. & Salguero-Gómez, R. Towards a comparative framework of demographic resilience. Trends Ecol. Evol. 35, 776–786 (2020).PubMed 

Google Scholar 
Katzner, T. E. et al. Assessing population-level consequences of anthropogenic stressors for terrestrial wildlife. Ecosphere 11, e03046 (2020).
Google Scholar 
Zhou, X. & Hesketh, T. High sex ratios in rural China: declining well-being with age in never-married men. Philos. Trans. R. Soc. B: Biol. Sci. 372, 20160324 (2017). One of the few studies in humans that targets well-being as an outcome, showing concerning mental health implications of sex ratio skew.
Google Scholar 
Schacht, R., Rauch, K. L. & Borgerhoff Mulder, M. Too many men: the violence problem? Trends Ecol. Evol. 29, 214–222 (2014). An influential review of violence and sex ratios across human societies that sets the agenda how reformulated sexual selection theory can inform mating strategies in humans.PubMed 

Google Scholar 
Donald, P. F. Adult sex ratios in wild bird populations. Ibis 149, 671–692 (2007).
Google Scholar 
Székely, T., Weissing, F. J. & Komdeur, J. Adult sex ratio variation: implications for breeding system evolution. J. Evol. Biol. 27, 1500–1512 (2014). A comprehensive overview of mate choice, mating systems and parental care in relation to ASR.PubMed 

Google Scholar 
Du Bois, W. E. B. The Philadelphia Negro (The University of Pennsylvania, 1899).Groves, E. & Ogburn, W. American Marriage and Family Relationships (Henry Holt and Company, 1928).Mayr, E. The sex ratio in wild birds. Am. Naturalist 73, 156–179 (1939).
Google Scholar 
Trivers, R. L. Parental investment and sexual selection. in Sexual Selection & the Descent of Man 136–179 (Aldine de Gruyter, 1972).Kramer, K., Schacht, R. & Bell, A. Adult sex ratios and partner scarcity among hunter–gatherers: Implications for dispersal patterns and the evolution of human sociality. Philos. Trans. R. Soc. B: Biol. Sci. 372, 20160316 (2017).
Google Scholar 
Kappeler, P. M. et al. Sex roles and sex ratios in animals. Biol. Rev. (in press).Kappeler, P. M. Sex roles and adult sex ratios: insights from mammalian biology and consequences for primate behaviour. Philos. Trans. R. Soc. B: Biol. Sci. 372, 20160321 (2017).
Google Scholar 
Clutton-Brock, T. Social evolution in mammals. Science 373, eabc9699 (2021).PubMed 

Google Scholar 
Garamszegi, L. Z., Pavlova, D. Z., Eens, M. & Møller, A. P. The evolution of song in female birds in Europe. Behav. Ecol. 18, 86–96 (2007).
Google Scholar 
Cooney, C. R. et al. Sexual selection predicts the rate and direction of colour divergence in a large avian radiation. Nat. Commun. 10, 1773 (2019).PubMed 
PubMed Central 

Google Scholar 
Ancona, S., Dénes, F. V., Krüger, O., Székely, T. & Beissinger, S. R. Estimating adult sex ratios in nature. Philos. Trans. R. Soc. B: Biol. Sci. 372, 20160313 (2017). A methodology-focused review highlighting the pros and cons of various ASR estimation methods used in wildlife biology.
Google Scholar 
Fitze, P. S. & Le Galliard, J.-F. Operational sex ratio, sexual conflict and the intensity of sexual selection. Ecol. Lett. 11, 432–439 (2008).PubMed 

Google Scholar 
Kokko, H. & Jennions, M. D. Parental investment, sexual selection and sex ratios. J. Evolut. Biol. 21, 919–948 (2008). A landmark theoretical study that explains the complex relationships between parental care, ASR and OSR.
Google Scholar 
Emlen, S. T. & Oring, L. W. Ecology, sexual selection, and the evolution of mating systems. Science 197, 215–223 (1977). A landmark study that introduced the concept of operational sex ratio (OSR).PubMed 

Google Scholar 
Pipoly, I. et al. The genetic sex-determination system predicts adult sex ratios in tetrapods. Nature 527, 91–94 (2015). A pathbreaking phylogenetic study that showed sex determination systems are related to ASR in tetrapods.PubMed 

Google Scholar 
Carmona-Isunza, M. C. et al. Adult sex ratio and operational sex ratio exhibit different temporal dynamics in the wild. Behav. Ecol. 28, 523–532 (2017).
Google Scholar 
Weir, L., Grant, J. & Hutchings, J. The influence of operational sex ratio on the intensity of competition for mates. Am. Naturalist 177, 167–176 (2011).
Google Scholar 
Hays, G. C., Shimada, T. & Schofield, G. A review of how the biology of male sea turtles may help mitigate female-biased hatchling sex ratio skews in a warming climate. Mar. Biol. 169, 89 (2022).
Google Scholar 
Ancona, S., Liker, A., Carmona-Isunza, M. C. & Székely, T. Sex differences in age-to-maturation relate to sexual selection and adult sex ratios in birds. Evolution Lett. 4, 44–53 (2020).
Google Scholar 
Gluckman, P. D. & Hanson, M. A. Evolution, development and timing of puberty. Trends Endocrinol. Metab. 17, 7–12 (2006).PubMed 

Google Scholar 
Veran, S. & Beissinger, S. R. Demographic origins of skewed operational and adult sex ratios: perturbation analyses of two-sex models. Ecol. Lett. 12, 129–143 (2009).PubMed 

Google Scholar 
Wilson, E. O. Sociobiology: The New Synthesis. (Harvard University Press, 1975).Ågren, J. A. & Clark, A. G. Selfish genetic elements. PLoS Genet. 14, e1007700 (2018).PubMed 
PubMed Central 

Google Scholar 
Engelstädter, J. & Hurst, G. D. D. The ecology and evolution of microbes that manipulate host reproduction. Annu. Rev. Ecol., Evolution, Syst. 40, 127–149 (2009).
Google Scholar 
Beukeboom, L. W. & Perrin, N. The Evolution of Sex Determination. (Oxford University Press, 2014). https://doi.org/10.1093/acprof:oso/9780199657148.001.0001.Geffroy, B. & Douhard, M. The adaptive sex in stressful environments. Trends Ecol. Evol. 34, 628–640 (2019).PubMed 

Google Scholar 
Nemesházi, E. et al. Novel genetic sex markers reveal high frequency of sex reversal in wild populations of the agile frog (Rana dalmatina) associated with anthropogenic land use. Mol. Ecol. 29, 3607–3621 (2020).PubMed 

Google Scholar 
Geffroy, B. Energy as the cornerstone of environmentally driven sex allocation. Trends Endocrinol. Metab. 33, 670–679 (2022).PubMed 

Google Scholar 
Janzen, F. J. & Paukstis, G. L. Environmental sex determination in reptiles: ecology, evolution, and experimental design. Q Rev. Biol. 66, 149–179 (1991).PubMed 

Google Scholar 
Cook, J. M. Sex determination in invertebrates. in Sex Ratios: Concepts and Research Methods (ed. Hardy, I. C. W.) 178–194 (Cambridge University Press, 2002). https://doi.org/10.1017/CBO9780511542053.009.Godwin, J., Luckenbach, J. A. & Borski, R. J. Ecology meets endocrinology: environmental sex determination in fishes. Evol. Dev. 5, 40–49 (2003).PubMed 

Google Scholar 
West, S. Sex Allocation. (Princeton University Press, 2009).Geffroy, B. & Wedekind, C. Effects of global warming on sex ratios in fishes. J. Fish. Biol. 97, 596–606 (2020).PubMed 

Google Scholar 
Edmands, S. Sex ratios in a warming world: thermal effects on sex-biased survival, sex determination, and sex reversal. J. Heredity 112, 155–164 (2021).
Google Scholar 
Valenzuela, N. et al. Extreme thermal fluctuations from climate change unexpectedly accelerate demographic collapse of vertebrates with temperature-dependent sex determination. Sci. Rep. 9, 4254 (2019).PubMed 
PubMed Central 

Google Scholar 
Hays, G. C., Mazaris, A. D. & Schofield, G. Different male vs. female breeding periodicity helps mitigate offspring sex ratio skews in sea turtles. Front. Marine Sci. 1, 43 (2014).Maitre, D. et al. Sex differentiation in grayling (Salmonidae) goes through an all-male stage and is delayed in genetic males who instead grow faster. Sci. Rep. 7, 15024 (2017).PubMed 
PubMed Central 

Google Scholar 
Donald, P. F. Lonely males and low lifetime productivity in small populations. Ibis 153, 465–467 (2011).
Google Scholar 
Mabry, K. E., Shelley, E. L., Davis, K. E., Blumstein, D. T. & Vuren, D. H. V. Social mating system and sex-biased dispersal in mammals and birds: a phylogenetic analysis. PLoS ONE 8, e57980 (2013).PubMed 
PubMed Central 

Google Scholar 
Clutton-Brock, T. Mammal Societies. (John Wiley and Sons, 2016).Kalmbach, E. & Benito, M. M. Sexual size dimorphism and offspring vulnerability in birds. in Sex, Size and Gender Roles (Oxford University Press, 2007). https://doi.org/10.1093/acprof:oso/9780199208784.003.0015.Berger, J. & Gompper, M. E. Sex ratios in extant ungulates: products of contemporary predation or past life histories? J. Mammal. 80, 1084–1113 (1999).
Google Scholar 
Christe, P., Keller, L. & Roulin, A. The predation cost of being a male: implications for sex-specific rates of ageing. Oikos 114, 381–384 (2006).
Google Scholar 
Boukal, D. S., Berec, L. & Křivan, V. Does sex-selective predation stabilize or destabilize predator-prey dynamics? PLoS ONE 3, e2687 (2008).PubMed 
PubMed Central 

Google Scholar 
Moore, S. L. & Wilson, K. Parasites as a viability cost of sexual selection in natural populations of mammals. Science 297, 2015–2018 (2002).PubMed 

Google Scholar 
Fairbairn, D., Blanckenhorn, W. & Székely, T. Sex, Size and Gender Roles: Evolutionary Studies of Sexual Size Dimorphism. Sex, Size and Gender Roles: Evolutionary Studies of Sexual Size Dimorphism https://doi.org/10.1093/acprof:oso/9780199208784.001.0001 (2007).Székely, T., Liker, A., Freckleton, R. P., Fichtel, C. & Kappeler, P. M. Sex-biased survival predicts adult sex ratio variation in wild birds. Proc. R. Soc. B: Biol. Sci. 281, 20140342 (2014).
Google Scholar 
Tidière, M. et al. Does sexual selection shape sex differences in longevity and senescence patterns across vertebrates? A review and new insights from captive ruminants. Evolution 69, 3123–3140 (2015).PubMed 

Google Scholar 
Lemaître, J.-F. et al. Sex differences in adult lifespan and aging rates of mortality across wild mammals. Proc. Natl Acad. Sci. USA 117, 8546–8553 (2020).PubMed 
PubMed Central 

Google Scholar 
Wedekind, C. et al. Persistent unequal sex ratio in a population of grayling (Salmonidae) and possible role of temperature increase. Conserv. Biol. 27, 229–234 (2013).PubMed 

Google Scholar 
Eberhart-Phillips, L. J. et al. Demographic causes of adult sex ratio variation and their consequences for parental cooperation. Nat. Commun. 9, 1651 (2018).PubMed 
PubMed Central 

Google Scholar 
Schacht, R., Macfarlan, S. J., Meeks, H., Cervantes, P. L. & Morales, F. Male survival advantage on the Baja California peninsula. Biol. Lett. 16, 20200600 (2020).PubMed 
PubMed Central 

Google Scholar 
Schacht, R., Tharp, D. & Smith, K. R. Sex ratios at birth vary with environmental harshness but not maternal condition. Sci. Rep. 9, 9066 (2019).PubMed 
PubMed Central 

Google Scholar 
Schacht, R. et al. Frail males on the American frontier: the role of environmental harshness on sex ratios at birth across a period of rapid industrialization. Soc. Sci. 10, 319 (2021).
Google Scholar 
Casey, J. A., Gemmill, A., Elser, H., Karasek, D. & Catalano, R. Sun smoke in Sweden: perinatal implications of the Laki volcanic eruptions, 1783–1784. Epidemiology 30, 330–333 (2019).PubMed 
PubMed Central 

Google Scholar 
Catalano, R., Bruckner, T. & Smith, K. R. Ambient temperature predicts sex ratios and male longevity. Proc. Natl Acad. Sci. USA 105, 2244–2247 (2008).PubMed 
PubMed Central 

Google Scholar 
Hollingshaus, M., Utz, R., Schacht, R. & Smith, K. R. Sex ratios and life tables: Historical demography of the age at which women outnumber men in seven countries, 1850–2016. Historical Methods.: A J. Quant. Interdiscip. Hist. 52, 244–253 (2019).
Google Scholar 
Li, X.-Y. & Kokko, H. Sex-biased dispersal: a review of the theory. Biol. Rev. 94, 721–736 (2019).PubMed 

Google Scholar 
Alho, J. S., Matsuba, C. & Merilä, J. Sex reversal and primary sex ratios in the common frog (Rana temporaria). Mol. Ecol. 19, 1763–1773 (2010).PubMed 

Google Scholar 
Sandercock, B. K., Beissinger, S. R., Stoleson, S. H., Melland, R. R. & Hughes, C. R. Survival rates of a neotropical parrot: implications for latitudinal comparisons of avian demography. Ecology 81, 1351–1370 (2000).Budden, A. E. & Beissinger, S. R. Against the odds? Nestling sex ratio variation in green-rumped parrotlets. Behav. Ecol. 15, 607–613 (2004).
Google Scholar 
Thompson, F. J. et al. Reproductive competition triggers mass eviction in cooperative banded mongooses. Proc. Biol. Sci. 283, 20152607 (2016).PubMed 
PubMed Central 

Google Scholar 
Jaccarini, V., AGius, L., Schembri, P. J. & Rizzo, M. Sex determination and larval sexual interaction in Bonellia viridis Rolando (Echiura: Bonelliidae). J. Exp. Mar. Biol. Ecol. 66, 25–40 (1983).
Google Scholar 
Tingley, G. & Anderson, R. Environmental sex determination and density-dependent population regulation in the entomogenous nematode Romanomermis culcivorax. Parasitology 92, 431–449 (1986).
Google Scholar 
Hardisty, M. W. Sex composition of lamprey populations. Nature 191, 1116–1117 (1961).
Google Scholar 
Docker, M. F., William, F. & Beamish, H. Age, growth, and sex ratio among populations of least brook lamprey, Lampetra aepyptera, larvae: an argument for environmental sex determination. Environ. Biol. Fish. 41, 191–205 (1994).
Google Scholar 
Geffroy, B. & Bardonnet, A. Sex differentiation and sex determination in eels: consequences for management. Fish. Fish. 17, 375–398 (2016).
Google Scholar 
Ribas, L., Valdivieso, A., Díaz, N. & Piferrer, F. Appropriate rearing density in domesticated zebrafish to avoid masculinization: links with the stress response. J. Exp. Biol. 220, 1056–1064 (2017).PubMed 

Google Scholar 
García-Cruz, E. L. et al. Crowding stress during the period of sex determination causes masculinization in pejerrey Odontesthes bonariensis, a fish with temperature-dependent sex determination. Comp. Biochem. Physiol. A Mol. Integr. Physiol. 245, 110701 (2020).PubMed 

Google Scholar 
Geffroy, B. et al. Parental selection for growth and early-life low stocking density increase the female-to-male ratio in European sea bass. Sci. Rep. 11, 13620 (2021).PubMed 
PubMed Central 

Google Scholar 
Fricke, H. & Fricke, S. Monogamy and sex change by aggressive dominance in coral reef fish. Nature 266, 830–832 (1977).PubMed 

Google Scholar 
Todd, E. V. et al. Stress, novel sex genes, and epigenetic reprogramming orchestrate socially controlled sex change. Sci. Adv. 5, eaaw7006 (2019).PubMed 
PubMed Central 

Google Scholar 
Kuwamura, T., Nakashimn, Y. & Yogo, Y. Sex change in either direction by growth-rate advantage in the monogamous coral goby, Paragobiodon echinocephalus. Behav. Ecol. 5, 434–438 (1994).
Google Scholar 
Rodgers, E. W., Earley, R. L. & Grober, M. S. Social status determines sexual phenotype in the bi-directional sex changing bluebanded goby Lythrypnus dalli. J. Fish. Biol. 70, 1660–1668 (2007).
Google Scholar 
Munday, P. L., Caley, M. J. & Jones, G. P. Bi-directional sex change in a coral-dwelling goby. Behav. Ecol. Sociobiol. 43, 371–377 (1998).
Google Scholar 
Goikoetxea, A., Todd, E. V. & Gemmell, N. J. Stress and sex: does cortisol mediate sex change in fish? Reproduction 154, R149–R160 (2017).PubMed 

Google Scholar 
Nozu, R. & Nakamura, M. Cortisol administration induces sex change from ovary to testis in the protogynous Wrasse, Halichoeres trimaculatus. Sex. Dev. 9, 118–124 (2015).PubMed 

Google Scholar 
Olivotto, I. & Geffroy, B. Clownfish. in Marine Ornamental Species Aquaculture (eds. Calado, R., Olivotto, I., Oliver, M. P. & Holt, G. J.) 177–199 (John Wiley & Sons, Ltd, 2017). https://doi.org/10.1002/9781119169147.ch12.Bessa, E., Brandão, M. L. & Gonçalves-de-Freitas, E. Integrative approach on the diversity of nesting behaviour in fishes. Fish Fisheries 23, 564–583 (2022).Safari, I. & Goymann, W. The evolution of reversed sex roles and classical polyandry: Insights from coucals and other animals. Ethology 127, 1–13 (2021).
Google Scholar 
Komdeur, J., Székely, T., Long, X. & Kingma, S. A. Adult sex ratios and their implications for cooperative breeding in birds. Philos. Trans. R. Soc. Lond. B Biol. Sci. 372, 20160322 (2017).PubMed 
PubMed Central 

Google Scholar 
Jankowiak, Ł., Tryjanowski, P., Hetmański, T. & Skórka, P. Experimentally evoked same-sex sexual behaviour in pigeons: better to be in a female-female pair than alone. Sci. Rep. 8, 1654 (2018).PubMed 
PubMed Central 

Google Scholar 
Darwin, C. The Descent of Man, and Selection in Relation to Sex (John Murray, 1871).Bleu, J., Bessa-Gomes, C. & Laloi, D. Evolution of female choosiness and mating frequency: effects of mating cost, density and sex ratio. Anim. Behav. 83, 131–136 (2012).
Google Scholar 
Forsgren, E., Amundsen, T., Borg, A. A. & Bjelvenmark, J. Unusually dynamic sex roles in a fish. Nature 429, 551–554 (2004).PubMed 

Google Scholar 
Monier, M., Nöbel, S., Isabel, G. & Danchin, E. Effects of a sex ratio gradient on female mate-copying and choosiness in Drosophila melanogaster. Curr. Zool. 64, 251–258 (2018).PubMed 
PubMed Central 

Google Scholar 
Jirotkul, M. Operational sex ratio influences preference and male–male competition in guppies. Anim. Behav. 58, 287–294 (1999).PubMed 

Google Scholar 
Grant, P. R. & Grant, B. R. Adult sex ratio influences mate choice in Darwin’s finches. Proc. Natl Acad. Sci. USA 116, 12373–12382 (2019).PubMed 
PubMed Central 

Google Scholar 
Queller, D. C. Why do females care more than males? Proc. Biol. Sci. 264, 1555–1557 (1997). A prescient overview that explains why females are more likely than males to provide care, including the explanation that a female-biased ASR means that males have a higher mean mating rate than females, which makes caring more costly for males.PubMed Central 

Google Scholar 
Janicke, T., Häderer, I. K., Lajeunesse, M. J. & Anthes, N. Darwinian sex roles confirmed across the animal kingdom. Sci. Adv. 2, e1500983 (2016).PubMed 
PubMed Central 

Google Scholar 
Liker, A. et al. Evolution of large males is associated with female‐skewed adult sex ratios in amniotes. Evolution 75, 1636–1649 (2021).PubMed 

Google Scholar 
Clutton-Brock, T. H., Harvey, P. H. & Rudder, B. Sexual dimorphism, socionomic sex ratio and body weight in primates. Nature 269, 797–800 (1977).PubMed 

Google Scholar 
Wittenberger, J. F. The evolution of mating systems in grouse. Condor 80, 126–137 (1978).
Google Scholar 
Vahl, W. K., Boiteau, G., Heij, M. E., de, MacKinley, P. D. & Kokko, H. Female fertilization: effects of sex-specific density and sex ratio determined experimentally for colorado potato beetles and drosophila fruit flies. PLoS ONE 8, e60381 (2013).PubMed 
PubMed Central 

Google Scholar 
House, C. M., Rapkin, J., Hunt, J. & Hosken, D. J. Operational sex ratio and density predict the potential for sexual selection in the broad-horned beetle. Anim. Behav. 152, 63–69 (2019).
Google Scholar 
Warner, R. R. & Hoffman, S. G. Population density and the economics of territorial defense in a coral reef fish. Ecology 61, 772–780 (1980).
Google Scholar 
Pröhl, H. Population differences in female resource abundance, adult sex ratio, and male mating success in Dendrobates pumilio. Behav. Ecol. 13, 175–181 (2002).
Google Scholar 
McNamara, J. M., Székely, T., Webb, J. N. & Houston, A. I. A dynamic game-theoretic model of parental care. J. Theor. Biol. 205, 605–623 (2000).PubMed 

Google Scholar 
Davies, N. B. Dunnock Behaviour and Social Evolution. (Oxford University Press, 1992).Pilastro, A., Biddau, L., Marin, G. & Mingozzi, T. Female brood desertion increases with number of available mates in the Rock Sparrow. J. Avian Biol. 32, 68–72 (2001).
Google Scholar 
Rossmanith, E., Grimm, V., Blaum, N. & Jeltsch, F. Behavioural flexibility in the mating system buffers population extinction: lessons from the lesser spotted woodpecker Picoides minor. J. Anim. Ecol. 75, 540–548 (2006).PubMed 

Google Scholar 
Liker, A., Freckleton, R. P. & Székely, T. The evolution of sex roles in birds is related to adult sex ratio. Nat. Commun. 4, 1587 (2013). An important comparative study that shows both social mating system and parenting are associated with ASR in shorebirds.PubMed 

Google Scholar 
Liker, A., Freckleton, R. P. & Székely, T. Divorce and infidelity are associated with skewed adult sex ratios in birds. Curr. Biol. 24, 880–884 (2014).PubMed 

Google Scholar 
Balshine-Earn, S. & Earn, D. J. D. On the evolutionary pathway of parental care in mouth-brooding cichlid fishes. Proc. ofn R. Soc. 265, 2217–2222 (1998).
Google Scholar 
Parra, J. E., Beltrán, M., Zefania, S., Dos Remedios, N. & Székely, T. Experimental assessment of mating opportunities in three shorebird species. Anim. Behav. 90, 83–90 (2014).
Google Scholar 
Székely, T., Cuthill, I. & Kis, J. Brood desertion in Kentish plover: sex differences in remating opportunities. Behav. Ecol. 10, 185–190 (1999). An important early field study showing that intraspecific variation in parental care can be explained by the availability of mates, which in turn depends on the prevailing ASR.
Google Scholar 
Clutton-Brock, T. H. The Evolution of Parental Care. The Evolution of Parental Care (Princeton University Press, 1991). https://doi.org/10.1515/9780691206981.Bessa-Gomes, C., Legendre, S. & Clobert, J. Allee effects, mating systems and the extinction risk in populations with two sexes. Ecol. Lett. 7, 802–812 (2004).
Google Scholar 
Lindström, J. & Kokko, H. Sexual reproduction and population dynamics: the role of polygyny and demographic sex differences. Proc. Biol. Sci. 265, 483–488 (1998).PubMed 
PubMed Central 

Google Scholar 
Lee, A. M., Saether, B.-E. & Engen, S. Demographic stochasticity, allee effects, and extinction: the influence of mating system and sex ratio. Am. Naturalist 177, 301–313 (2011).
Google Scholar 
Leach, D., Shaw, A. K. & Weiss-Lehman, C. Stochasticity in social structure and mating system drive extinction risk. Ecosphere 11, e03038 (2020).
Google Scholar 
Gownaris, N. J. & Boersma, P. D. Sex-biased survival contributes to population decline in a long-lived seabird, the Magellanic Penguin. Ecol. Appl. 29, 1–17 (2019).
Google Scholar 
Le Galliard, J.-F., Fitze, P. S., Ferrière, R. & Clobert, J. Sex ratio bias, male aggression, and population collapse in lizards. Proc. Natl Acad. Sci. USA 102, 18231–18236 (2005).PubMed 
PubMed Central 

Google Scholar 
Lea, J. M. D. et al. Non-invasive physiological markers demonstrate link between habitat quality, adult sex ratio and poor population growth rate in a vulnerable species, the Cape mountain zebra. Funct. Ecol. 32, 300–312 (2018).
Google Scholar 
Dreiss, A. N., Cote, J., Richard, M., Federici, P. & Clobert, J. Age-and sex-specific response to population density and sex ratio. Behav. Ecol. 21, 356–364 (2010).
Google Scholar 
Dale, S. Female-biased dispersal, low female recruitment, unpaired males, and the extinction of small and isolated bird populations. Oikos 92, 344–356 (2001).
Google Scholar 
Morrison, C. A., Robinson, R. A., Clark, J. A. & Gill, J. A. Causes and consequences of spatial variation in sex ratios in a declining bird species. J. Anim. Ecol. 85, 1298–1306 (2016).PubMed 
PubMed Central 

Google Scholar 
Chipman, A. & Morrison, E. The impact of sex ratio and economic status on local birth rates. Biol. Lett. 9, 20130027 (2013).PubMed 
PubMed Central 

Google Scholar 
Krainacker, D. A. & Carey, J. R. Sex ratio in a wild population of twospotted spider mites. Holarct. Ecol. 14, 97–103 (1991).
Google Scholar 
Bunnell, D. B., Madenjian, C. P. & Croley, T. E. Long-term trends of bloater (Coregonus hoyi) recruitment in Lake Michigan: evidence for the effect of sex ratio. Can. J. Fish. Aquat. Sci. 63, 832–844 (2006).
Google Scholar 
Forbes, M. R., McCurdy, D. G., Lui, K., Mautner, S. I. & Boates, J. S. Evidence for seasonal mate limitation in populations of an intertidal amphipod, Corophium volutator (Pallas). Behav. Ecol. Sociobiol. 60, 87–95 (2006).
Google Scholar 
Solberg, E. J., Loison, A., Ringsby, T. H., Sæther, B. E. & Heim, M. Biased adult sex ratio can affect fecundity in primiparous moose Alces alces. Wildl. Biol. 8, 117–128 (2002).
Google Scholar 
Pipoly, I., Székely, T. & Liker, A. Multiple paternity is related to adult sex ratio and sex determination system in reptiles. Journal of Evolutionary Biology (under review).Jones, A. G., Rosenqvist, G., Berglund, A., Arnold, S. J. & Avise, J. C. The Bateman gradient and the cause of sexual selection in a sex–role–reversed pipefish. Proc. R. Soc. Lond. Ser. B: Biol. Sci. 267, 677–680 (2000).
Google Scholar 
Clutton-Brock, T. H., Coulson, T. N., Milner-Gulland, E. J., Thomson, D. & Armstrong, H. M. Sex differences in emigration and mortality affect optimal management of deer populations. Nature 415, 633–637 (2002).PubMed 

Google Scholar 
Lambertucci, S. A., Carrete, M., Speziale, K. L., Hiraldo, F. & Donázar, J. A. Population sex ratios: another consideration in the reintroduction – reinforcement debate? PLoS ONE 8, e75821 (2013).PubMed 
PubMed Central 

Google Scholar 
Snyder, K. T., Freidenfelds, N. A. & Miller, T. E. X. Consequences of sex-selective harvesting and harvest refuges in experimental meta-populations. Oikos 123, 309–314 (2014).
Google Scholar 
Frankham, R. Effective population size/adult population size ratios in wildlife: a review. Genet. Res. 66, 95–107 (1995).
Google Scholar 
Sæther, B.-E. et al. Time to extinction in relation to mating system and type of density regulation in populations with two sexes. J. Anim. Ecol. 73, 925–934 (2004).
Google Scholar 
Milner, J., Nilsen, E. & Andreassen, H. Demographic side effects of selective hunting in ungulates and carnivores. Conserv. Biol.: J. Soc. Conserv. Biol. 21, 36–47 (2007).
Google Scholar 
Heinsohn, R., Olah, G., Webb, M., Peakall, R. & Stojanovic, D. Sex ratio bias and shared paternity reduce individual fitness and population viability in a critically endangered parrot. J. Anim. Ecol. 88, 502–510 (2019).PubMed 

Google Scholar 
Lee, P. L. M., Schofield, G., Haughey, R. I., Mazaris, A. D. & Hays, G. C. A review of patterns of multiple paternity across sea turtle rookeries. Adv. Mar. Biol. 79, 1–31 (2018).PubMed 

Google Scholar 
Wayne, A. F. et al. Sudden and rapid decline of the abundant marsupial Bettongia penicillata in Australia. Oryx 49, 175–185 (2015).
Google Scholar 
Roscoe, P. Dead Birds: The “Theater” of War among the Dugum Dani. Am. Anthropologist 113, 56–70 (2011).
Google Scholar 
Bethmann, D. & Kvasnicka, M. World war ii, missing men and out of wedlock childbearing. Economic J. 123, 162–194 (2013).
Google Scholar 
Schradin, C. et al. Geographic intra-specific variation in social organization is driven by population density. Behav. Ecol. Sociobiol. 74, (2020).Brandner, J. L., Dillon, H. M. & Brase, G. L. Convergent evidence for a theory of rapid, automatic, and accurate sex ratio tracking. Acta Psychologica 210, (2020).Griskevicius, V. et al. The financial consequences of too many men: sex ratio effects on saving, borrowing, and spending. J. Personal. Soc. Psychol. 102, 69–80 (2011).
Google Scholar 
Fritzsche, K., Booksmythe, I. & Arnqvist, G. Sex ratio bias leads to the evolution of sex role reversal in honey locust beetles. Curr. Biol. 26, 2522–2526 (2016).PubMed 

Google Scholar 
Bath, E. et al. Sex ratio and the evolution of aggression in fruit flies. Proc. R. Soc. B: Biol. Sci. 288, 20203053 (2021).
Google Scholar 
Beltran, S., Cézilly, F. & Boissier, J. Adult sex ratio affects divorce rate in the monogamous endoparasite Schistosoma mansoni. Behav. Ecol. Sociobiol. 63, 1363–1368 (2009).
Google Scholar 
Chuard, P., Brown, G. & Grant, J. The effects of adult sex ratio on mating competition in male and female guppies (Poecilia reticulata) in two wild populations. Behavioural Process. 129, 1–10 (2016).
Google Scholar 
Lande, R. Risks of population extinction from demographic and environmental stochasticity and random catastrophes. Am. Naturalist 142, 911–927 (1993).
Google Scholar 
May, R. & Allen, P. Stability and complexity in model ecosystems. Syst., Man Cybern., IEEE Trans. 44, 887–887 (1977).
Google Scholar 
Wobst, H. M. Boundary conditions for paleolithic social systems: a simulation approach. Am. Antiquity 39, 147–178 (1974).
Google Scholar 
Dyson, T. Causes and Consequences of Skewed Sex Ratios. (2012) https://doi.org/10.1146/annurev-soc-071811-145429.Edlund, L. Son preference, sex ratios, and marriage patterns. J. Political Econ. 107, 1275–1304 (1999).
Google Scholar 
Hesketh, T. & Xing, Z. W. Abnormal sex ratios in human populations: causes and consequences. Proc. Natl Acad. Sci. USA 103, 13271–13275 (2006).PubMed 
PubMed Central 

Google Scholar 
Hesketh, T. & Min, J. M. The effects of artificial gender imbalance. EMBO Rep. 13, 487–492 (2012).PubMed 
PubMed Central 

Google Scholar 
Schacht, R. & Kramer, K. L. Patterns of family formation in response to sex ratio variation. PLoS ONE 11, e0160320 (2016).PubMed 
PubMed Central 

Google Scholar 
Schacht, R., Tharp, D. & Smith, K. R. Marriage markets and male mating effort: violence and crime are elevated where men are rare. Hum. Nat. 27, 489–500 (2016).PubMed 

Google Scholar 
Pouget, E. R. Social determinants of adult sex ratios and racial/ethnic disparities in transmission of HIV and other sexually transmitted infections in the USA. Philos. Trans. R. Soc. Lond. B Biol. Sci. 372, 20160323 (2017). An important study on humans that bridges the gap between theory and policy illustrating a societal issue related to sex ratio imbalance and sexually transmitted diseases risk in a vulnerable sub-population in the USA.PubMed 
PubMed Central 

Google Scholar 
Del Giudice, M. Sex ratio dynamics and fluctuating selection on personality. J. Theor. Biol. 297, 48–60 (2012).PubMed 

Google Scholar 
Schacht, R. & Borgerhoff Mulder, M. Sex ratio effects on reproductive strategies in humans. R. Soc. Open Sci. 2, 140402 (2015). A pioneering study of a small-scale population that demonstrates mating strategies vary with the sex ratio at local level.PubMed 
PubMed Central 

Google Scholar 
Jones, J. H. & Ferguson, B. Demographic and Social predictors of intimate partner violence in colombia: a dyadic power perspective. Hum. Nat. 20, 184–203 (2009).PubMed 

Google Scholar 
Uggla, C. & Mace, R. Local ecology influences reproductive timing in Northern Ireland independently of individual wealth. Behav. Ecol. 27, 158–165 (2016).
Google Scholar 
Guttentag, M. & Secord, P. Too Many Women? SAGE Publications Inc (1983). A landmark book that presented historical and quantitative evidence for how sex ratio skew impacts family structure and the societal values applied to men and women.United Nations Population Fund Annual Report. https://www.unfpa.org/annual-report-2020 (2020)Schmitt, D. P. Sociosexuality from Argentina to Zimbabwe: a 48-nation study of sex, culture, and strategies of human mating. Behav. Brain Sci. 28, 247–275 (2005).PubMed 

Google Scholar 
Baumeister, R. F. & Vohs, K. D. Sexual economics: sex as female resource for social exchange in heterosexual interactions. Pers. Soc. Psychol. Rev. 8, 339–363 (2004).PubMed 

Google Scholar 
Reid, P. C. et al. Global impacts of the 1980s regime shift. Glob. Change Biol. 22, 682–703 (2016).
Google Scholar 
Grafe, T. U. & Linsenmair, K. E. Protogynous sex change in the reed frog Hyperolius viridiflavus. Copeia 1989, 1024–1029 (1989).
Google Scholar 
Trochet, A. et al. Population sex ratio and dispersal in experimental, two-patch metapopulations of butterflies. J. Anim. Ecol. 82, 946–955 (2013).PubMed 

Google Scholar 
Thomson, D., Cooch, E. & Conroy, M. Modeling demographic processes in marked populations. https://doi.org/10.1007/978-0-387-78151-8 (2009).Dail, D. & Madsen, L. Models for estimating abundance from repeated counts of an open metapopulation. Biometrics 67, 577–587 (2011).PubMed 

Google Scholar 
Kéry, M. & Royle, J. Andrew. Applied Hierarchical Modeling in Ecology: Analysis of distribution, abundance and species richness in R and BUGS. 783 (2015).US Census Bureau. Accuracy and coverage evaluation of Census 2000: Design and Methodology. (2004).Guillot, M. The dynamics of the population sex ratio in India, 1971-96. Popul. Stud. 56, 51–63 (2002).
Google Scholar 
Dyson, E. A. & Hurst, G. D. D. Persistence of an extreme sex-ratio bias in a natural population. Proc. Natl Acad. Sci. USA 101, 6520–6523 (2004).PubMed 
PubMed Central 

Google Scholar 
Hays, G. C., Mazaris, A. D., Schofield, G. & Laloë, J.-O. Population viability at extreme sex-ratio skews produced by temperature-dependent sex determination. Proc. R. Soc. B. 284, 20162576 (2017).PubMed 
PubMed Central 

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

Google Scholar 
Cockburn, A., Scott, M. P. & Dickman, C. R. Sex ratio and intrasexual kin competition in mammals. Oecologia 66, 427–429 (1985).PubMed 

Google Scholar 
Douglas III, H. & Malenke, J. R. An Extraordinary Host-Specific Sex Ratio in an Avian Louse (Phthiraptera: Insecta)-Chemical Distortion? Environ. Entomol. (2015).Bonnet, X. et al. A prison effect in a wild population: a scarcity of females induces homosexual behaviors in males. Behav. Ecol. 27, 1206–1215 (2016).
Google Scholar 
Beltran, S. & Boissier, J. Male-biased sex ratio: why and what consequences for the genus Schistosoma? Trends Parasitol. 26, 63–69 (2010).PubMed 

Google Scholar 
Beltran, S. & Boissier, J. Schistosome monogamy: who, how, and why? Trends Parasitol. 24, 386–391 (2008).PubMed 

Google Scholar 
Fisher, R. The Genetical Theory of Natural Selection (The Clarendon Press, 1930).Houston, A. & McNamara, J. John Maynard Smith and the importance of consistency in evolutionary game theory. Biol. Philos. 20, 933–950 (2005).
Google Scholar 
Kokko, H. & Jennions, M. D. Sex differences in parental care. in The Evolution of Parental Care (Oxford University Press, 2012). https://doi.org/10.1093/acprof:oso/9780199692576.003.0006.Fromhage, L. & Jennions, M. D. Coevolution of parental investment and sexually selected traits drives sex-role divergence. Nat. Commun. 7, 12517 (2016). A theoretical study showing that under a simple null scenario the sex ratio of male to female care does not evolve in response to ASR, but rather to the sex ratio at maturation.PubMed 
PubMed Central 

Google Scholar 
Long, X. The Evolution of Parental Sex Roles. PhD dissertation, University of Groningen (2020).Seger, J. & Stubblefield, J. W. Models of sex ratio evolution. in Sex Ratios: Concepts and Research Methods (ed. Hardy, I. C. W.) 2–25 (Cambridge University Press, 2002). https://doi.org/10.1017/CBO9780511542053.002.Pen, I. & Weissing, F. J. Optimal sex allocation: steps towards a mechanistic theory. in Sex Ratios: Concepts and Research Methods (ed. Hardy, I. C. W.) 26–46 (Cambridge University Press, 2002). https://doi.org/10.1017/CBO9780511542053.003.Bodmer, W. & Edwards, A. Natural selection and the sex ratio. Ann. Hum. Genet. 239–244, (1960).Sampson, R. J., Laub, J. H. & Wimer, C. Does marriage reduce crime? A counterfactual approach to within-individual causal effects. Criminology 44, 465–508 (2006).
Google Scholar 
Avakame, E. F. Sex ratios, female labor force participation, and lethal violence against women: extending Guttentag and Secord’s Thesis. Violence Women 5, 1321–1341 (1999).
Google Scholar 
Diamond-Smith, N. & Rudolph, K. The association between uneven sex ratios and violence: Evidence from 6 Asian countries. PLoS ONE 13, e0197516 (2018). One of the few studies on crime and sex ratios that uses individual-level data of reported crime as linked to area level sex ratio skew.PubMed 
PubMed Central 

Google Scholar 
Drèze, J. & Khera, R. Crime, gender, and society in India: Insights from homicide data. Popul. Dev. Rev. 26, 335–352 (2000).PubMed 

Google Scholar 
Edlund, L., Li, H., Yi, J. & Zhang, J. Sex ratios and crime: evidence from China. Rev. Econ. Stat. 95, 1520–1534 (2013).
Google Scholar 
Messner, S. F. & Sampson, R. J. The sex ratio, family disruption, and rates of violent crime: the paradox of demographic structure. Soc. Forces 69, 693–713 (1991).
Google Scholar 
Trent, K. & South, S. J. Mate availability and women’s sexual experiences in China. J. Marriage Fam. 74, 201–214 (2012).PubMed 
PubMed Central 

Google Scholar 
Filser, A., Barclay, K., Beckley, A., Uggla, C. & Schnettler, S. Are skewed sex ratios associated with violent crime? A longitudinal analysis using Swedish register data. Evolution Hum. Behav. 42, 212–222 (2021).
Google Scholar 
Barber, N. The sex ratio as a predictor of cross-national variation in violent crime. Cross-Cultural Res. 34, 264–282 (2000).
Google Scholar 
Barber, N. Countries with fewer males have more violent crime: marriage markets and mating aggression. Aggress. Behav. 35, 49–56 (2009).PubMed 

Google Scholar 
Obrien, R. M. Sex ratios and rape rates: a powercontrol theory. Criminology 29, 99–114 (1991).
Google Scholar 
Esmail, A. M., Penny, J. & Eargle, L. A. The impact of culture on crime. Race Gender Class 20, 326–343 (2013).
Google Scholar 
Pollet, T. V., Stoevenbelt, A. H. & Kuppens, T. The potential pitfalls of studying adult sex ratios at aggregate levels in humans. Philos. Trans. R. Soc. B: Biol. Sci. 372, (2017). A critical study that highlights shortcomings inherent in much of the early sex ratio literature, which stems in part from using nation- rather than local-level data.Uggla, C. & Mace, R. Adult sex ratio and social status predict mating and parenting strategies in Northern Ireland. Philos. Trans. R. Soc. B: Biol. Sci. 372, 20160318 (2017). A seminal study on humans demonstrating the impacts of local sex ratio skew depending on individual status on the mating market.
Google Scholar 
Schacht, R. & Uggla, C. Beyond sex: reproductive strategies as a function of local sex ratio variation. in The Oxford Handbook of Human Mating (Oxford University Press, 2022). More

Thrash JC, Boyd A, Huggett MJ, Grote J, Carini P, Yoder RJ, et al. Phylogenomic evidence for a common ancestor of mitochondria and the SAR11 clade. Sci Rep. 2011;1:9.
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.
Google Scholar 
Giovannoni SJ. SAR11 bacteria: the most abundant plankton in the oceans. Annu Rev Mar Sci. 2017;9:231–55.
Google Scholar 
Zhao X, Schwartz CL, Pierson J, Giovannoni SJ, McIntosh RJ, Nicastro D. Three-dimensional structure of the ultraoligotrophic marine bacterium “Candidatus pelagibacter ubique”. Appl Environ Microbiol. 2017;83:807–16.
Google Scholar 
Giovannoni SJ, DeLong EF, Schmidt TM, Pace NR. Tangential flow filtration and preliminary phylogenetic analysis of marine picoplankton. Appl Environ Microbiol. 1990;56:4.
Google Scholar 
Morris RM, Rappé MS, Connon SA, Vergin KL, Siebold WA, Carlson CA, et al. SAR11 clade dominates ocean surface bacterioplankton communities. Nature. 2002;420:806–10.CAS 

Google Scholar 
Rappé MS, Connon SA, Vergin KL, Giovannoni SJ. Cultivation of the ubiquitous SAR11 marine bacterioplankton clade. Nature. 2002;418:630–3.
Google Scholar 
Grote J, Thrash JC, Huggett MJ, Landry ZC, Carini P, Giovannoni SJ, et al. Streamlining and core genome conservation among highly divergent members of the SAR11 clade. mBio. 2012;3:e00252–12.CAS 

Google Scholar 
Field KG, Gordon D, Wright T, Rappé M, Urback E, Vergin K, et al. Diversity and depth-specific distribution of SAR11 cluster rRNA genes from marine planktonic bacteria. Appl Environ Microbiol. 1997;63:63–70.CAS 

Google Scholar 
Suzuki MT, Beja O, Taylor LT, DeLong EF. Phylogenetic analysis of ribosomal RNA operons from uncultivated coastal marine bacterioplankton. Environ Microbiol. 2001;3:323–31.CAS 

Google Scholar 
Carlson CA, Morris R, Parsons R, Treusch AH, Giovannoni SJ, Vergin K. Seasonal dynamics of SAR11 populations in the euphotic and mesopelagic zones of the northwestern Sargasso Sea. ISME J. 2009;3:283–95.CAS 

Google Scholar 
Brown MV, Lauro FM, DeMaere MZ, Muir L, Wilkins D, Thomas T, et al. Global biogeography of SAR11 marine bacteria. Mol Syst Biol. 2012;8:595.
Google Scholar 
Haro‐Moreno JM, Rodriguez‐Valera F, Rosselli R, Martinez‐Hernandez F, Roda‐Garcia JJ, Gomez ML, et al. Ecogenomics of the SAR11 clade. Environ Microbiol. 2020;22:1748–63.
Google Scholar 
Carini P, White AE, Campbell EO, Giovannoni SJ. Methane production by phosphate-starved SAR11 chemoheterotrophic marine bacteria. Nat Commun. 2014;5:4346.CAS 

Google Scholar 
Sun J, Steindler L, Thrash JC, Halsey KH, Smith DP, Carter AE, et al. One carbon metabolism in SAR11 Pelagic marine bacteria. PLoS One. 2011;6:e23973.CAS 

Google Scholar 
Schwalbach MS, Tripp HJ, Steindler L, Smith DP, Giovannoni SJ. The presence of the glycolysis operon in SAR11 genomes is positively correlated with ocean productivity. Environ Microbiol. 2010;12:490–500.CAS 

Google Scholar 
Sun J, Todd JD, Thrash JC, Qian Y, Qian MC, Temperton B, et al. The abundant marine bacterium Pelagibacter simultaneously catabolizes dimethylsulfoniopropionate to the gases dimethyl sulfide and methanethiol. Nat Microbiol. 2016;1:16065.CAS 

Google Scholar 
Halsey KH, Giovannoni SJ, Graus M, Zhao Y, Landry Z, Thrash JC, et al. Biological cycling of volatile organic carbon by phytoplankton and bacterioplankton: VOC cycling by marine plankton. Limnol Oceanogr. 2017;62:2650–61.CAS 

Google Scholar 
Carlson CA, Giovannoni SJ, Hansell DA, Goldberg SJ, Parsons R, Vergin K. Interactions among dissolved organic carbon, microbial processes, and community structure in the mesopelagic zone of the northwestern Sargasso Sea. Limnol Oceanogr. 2004;49:1073–83.CAS 

Google Scholar 
Wagner S, Schubotz F, Kaiser K, Hallmann C, Waska H, Rossel PE, et al. Soothsaying DOM: a current perspective on the future of oceanic dissolved organic carbon. Front Mar Sci. 2020;7:341.
Google Scholar 
Quinn PK, Bates TS. The case against climate regulation via oceanic phytoplankton sulphur emissions. Nature. 2011;480:51–6.CAS 

Google Scholar 
Bolaños LM, Choi CJ, Worden AZ, Baetge N, Carlson CA, Giovannoni S. Seasonality of the microbial community composition in the North Atlantic. Front Mar Sci. 2021;8:624164.
Google Scholar 
Tucker SJ, Freel KC, Monaghan EA, Sullivan CES, Ramfelt O, Rii YM, et al. Spatial and temporal dynamics of SAR11 marine bacteria across a nearshore to offshore transect in the tropical Pacific Ocean. PeerJ. 2021;9:e12274.
Google Scholar 
Giovannoni SJ, Vergin KL. Seasonality in ocean microbial communities. Science. 2012;335:671–6.CAS 

Google Scholar 
Eren AM, Maignien L, Sul WJ, Murphy LG, Grim SL, Morrison HG, et al. Oligotyping: differentiating between closely related microbial taxa using 16S RRNA gene data. Methods Ecol Evol. 2013;4:1111–9.
Google Scholar 
Vergin K, Done B, Carlson C, Giovannoni S. Spatiotemporal distributions of rare bacterioplankton populations indicate adaptive strategies in the oligotrophic ocean. Aquat Microb Ecol. 2013;71:1–13.
Google Scholar 
Salter I, Galand PE, Fagervold SK, Lebaron P, Obernosterer I, Oliver MJ, et al. Seasonal dynamics of active SAR11 ecotypes in the oligotrophic Northwest Mediterranean Sea. ISME J. 2015;9:347–60.CAS 

Google Scholar 
Ortmann AC, Santos TTL. Spatial and temporal patterns in the Pelagibacteraceae across an estuarine gradient. FEMS Microbiol Ecol. 2016;92:fiw133.
Google Scholar 
Vergin KL, Beszteri B, Monier A, Cameron Thrash J, Temperton B, Treusch AH, et al. High-resolution SAR11 ecotype dynamics at the Bermuda Atlantic Time-series Study site by phylogenetic placement of pyrosequences. ISME J. 2013;7:1322–32.CAS 

Google Scholar 
Needham DM, Fichot EB, Wang E, Berdjeb L, Cram JA, Fichot CG, et al. Dynamics and interactions of highly resolved marine plankton via automated high-frequency sampling. ISME J. 2018;12:2417–32.CAS 

Google Scholar 
Benway HM, Lorenzoni L, White AE, Fiedler B, Levine NM, Nicholson DP, et al. Ocean time series observations of changing marine ecosystems: an era of integration, synthesis, and societal applications. Front Mar Sci. 2019;12:6–393.
Google Scholar 
Steinberg DK, Carlson CA, Bates NR, Johnson RJ, Michaels AF, Knap AH. Overview of the US JGOFS Bermuda Atlantic Time-series Study (BATS): a decade-scale look at ocean biology and biogeochemistry. Deep Sea Res Part II Top Stud Oceanogr. 2001;48:1405–47.CAS 

Google Scholar 
Southward AJ, Langmead O, Hardman-Mountford NJ, Aiken J, Boalch GT, Dando PR, et al. Long-term oceanographic and ecological research in the Western English Channel. In: Advances in marine biology. Elsevier. 2005;47:1–105.Gilbert JA, Field D, Swift P, Newbold L, Oliver A, Smyth T, et al. The seasonal structure of microbial communities in the Western English Channel. Environ Microbiol. 2009;11:3132–9.CAS 

Google Scholar 
Gilbert JA, Steele JA, Caporaso JG, Steinbrück L, Reeder J, Temperton B, et al. Defining seasonal marine microbial community dynamics. ISME J. 2012;6:298–308.CAS 

Google Scholar 
Caporaso JG, Paszkiewicz K, Field D, Knight R, Gilbert JA. The Western English Channel contains a persistent microbial seed bank. ISME J. 2012;6:1089–93.CAS 

Google Scholar 
Warwick-Dugdale J, Solonenko N, Moore K, Chittick L, Gregory AC, Allen MJ, et al. Long-read viral metagenomics captures abundant and microdiverse viral populations and their niche-defining genomic islands. PeerJ. 2019;7:e6800.
Google Scholar 
Vergin KL, Done B, Carlson CA, Giovannoni SJ. Spatiotemporal distributions of rare bacterioplankton populations indicate adaptive strategies in the oligotrophic ocean. Aquat Microb Ecol. 2013;71:1–3.
Google Scholar 
Choi CJ, Jimenez V, Needham DM, Poirier C, Bachy C, Alexander H, et al. Seasonal and geographical transitions in eukaryotic phytoplankton community structure in the Atlantic and Pacific Oceans. Front Microbiol. 2020;11:542372.
Google Scholar 
Bolaños LM, Karp-Boss L, Choi CJ, Worden AZ, Graff JR, Haëntjens N, et al. Small phytoplankton dominate western North Atlantic biomass. ISME J. 2020;14:1663–74.
Google Scholar 
Matsen FA, Kodner RB, Armbrust E. pplacer: linear time maximum-likelihood and Bayesian phylogenetic placement of sequences onto a fixed reference tree. BMC Bioinform. 2010;11:1–6.
Google Scholar 
Treusch AH, Vergin KL, Finlay LA, Donatz MG, Burton RM, Carlson CA, et al. Seasonality and vertical structure of microbial communities in an ocean gyre. ISME J. 2009;3:1148–63.
Google Scholar 
Giovannoni SJ, Rappe MS, Vergin KL, Adair NL. 16S rRNA genes reveal stratified open ocean bacterioplankton populations related to the Green Non-Sulfur bacteria. Proc Natl Acad Sci. 1996;93:7979–84.CAS 

Google Scholar 
Morris RM, Vergin KL, Cho J-C, Rappé MS, Carlson CA, Giovannoni SJ. Temporal and spatial response of bacterioplankton lineages to annual convective overturn at the Bermuda Atlantic Time-series Study site. Limnol Oceanogr. 2005;50:1687–96.CAS 

Google Scholar 
Daims H, Brühl A, Amann R, Schleifer K-H, Wagner M. The domain-specific probe EUB338 is insufficient for the detection of all bacteria: development and evaluation of a more comprehensive probe set. Syst Appl Microbiol. 1999;22:434–44.CAS 

Google Scholar 
Lane DJ. Nucleic acid techniques in bacterial systematics. In: Nucleic acid techniques in bacterial systematics. New York: Wiley; p. 115–75.Callahan BJ, McMurdie PJ, Holmes SP. Exact sequence variants should replace operational taxonomic units in marker-gene data analysis. ISME J. 2017;11:2639–43.
Google Scholar 
McMurdie PJ, Holmes S. phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS One. 2013;8:e61217.CAS 

Google Scholar 
Eren AM, Borisy GG, Huse SM, Mark Welch JL. Oligotyping analysis of the human oral microbiome. Proc Natl Acad Sci. 2014;111:E2875–84.CAS 

Google Scholar 
Buchholz HH, Michelsen ML, Bolaños LM, Browne E, Allen MJ, Temperton B. Efficient dilution-to-extinction isolation of novel virus–host model systems for fastidious heterotrophic bacteria. ISME J. 2021;15:1585–98.CAS 

Google Scholar 
R Core Team. R: a language and environment for statistical computing. Vienna, Austria; https://www.R-project.org/Oksanen J, Blanchet FG, Friendly M, Kindt R, Legendre P, McGlinn D, et al. Package “vegan”.Wickham H. ggplot2: ggplot2. Wiley Interdiscip Rev Comput Stat. 2011;3:180–5.
Google Scholar 
Wang W, Yan J. Shape-restricted regression splines with R package splines2. J Data Sci. 2021;19:498–517.
Google Scholar 
Auladell A, Sánchez P, Sánchez O, Gasol JM, Ferrera I. Long-term seasonal and interannual variability of marine aerobic anoxygenic photoheterotrophic bacteria. ISME J. 2019;13:1975–87.CAS 

Google Scholar 
Ahdesmaki M, Fokianos K, Strimmer K, Ahdesmaki MM. Package ‘GeneCycle’ 2015.Roesch A, Schmidbauer H and Roesch MA. Package ‘WaveletComp.’ 2014.Lomas MW, Bates NR, Johnson RJ, Knap AH, Steinberg DK, Carlson CA. Two decades and counting: 24-years of sustained open ocean biogeochemical measurements in the Sargasso Sea. Deep Sea Res Part II Top Stud Oceanogr. 2013;93:16–32.CAS 

Google Scholar 
Lomas MW, Bates NR, Johnson RJ, Steinberg DK, Tanioka T. Adaptive carbon export response to warming in the Sargasso Sea. Nature Commun. 2022;13:1–0.
Google Scholar 
Sargeant SL, Murrell JC, Nightingale PD, Dixon JL. Basin-scale variability of microbial methanol uptake in the Atlantic Ocean. Biogeosciences. 2018;15:5155–67.CAS 

Google Scholar 
Smyth TJ, Allen I, Atkinson A, Bruun JT, Harmer RA, Pingree RD, et al. Ocean net heat flux influences seasonal to interannual patterns of plankton abundance. PLoS One. 2014;9:e98709.
Google Scholar 
Van de Peer Y. A quantitative map of nucleotide substitution rates in bacterial rRNA. Nucleic Acids Res. 1996;24:3381–91.
Google Scholar 
Baker GC, Smith JJ, Cowan DA. Review and re-analysis of domain-specific 16S primers. J Microbiol Methods. 2003;55:541–55.CAS 

Google Scholar 
Vasileiadis S, Puglisi E, Arena M, Cappa F, Cocconcelli PS, Trevisan M. Soil bacterial diversity screening using single 16S rRNA gene V regions coupled with multi-million read generating sequencing technologies. PLoS ONE. 2012;7:e42671.CAS 

Google Scholar 
Stingl U, Tripp HJ, Giovannoni SJ. Improvements of high-throughput culturing yielded novel SAR11 strains and other abundant marine bacteria from the Oregon coast and the Bermuda Atlantic Time-series study site. ISME J. 2007;1:361–71.CAS 

Google Scholar 
Delmont TO, Kiefl E, Kilinc O, Esen OC, Uysal I, Rappé MS, et al. Single-amino acid variants reveal evolutionary processes that shape the biogeography of a global SAR11 subclade. eLife. 2019;8:e46497.
Google Scholar 
Lévy M, Jahn O, Dutkiewicz S, Follows MJ, d’Ovidio F. The dynamical landscape of marine phytoplankton diversity. J R Soc Interface. 2015;12:20150481.
Google Scholar 
Hellweger FL, van Sebille E, Calfee BC, Chandler JW, Zinser ER, Swan BK, et al. The role of ocean currents in the temperature selection of plankton: insights from an individual-based model. PLoS ONE. 2016;11:e0167010.
Google Scholar 
Giovannoni SJ, Tripp HJ, Givan S, Podar M, Vergin KL, Baptista D, et al. Genome streamlining in a cosmopolitan oceanic bacterium. Science. 2005;309:1242–5.CAS 

Google Scholar 
Brown SN, Giovannoni S, Cho JC. Polyphasic taxonomy of marine bacteria from the SAR11 group Ia: Pelagibacter ubiquis (strain HTCC1062) & Pelagibacter bermudensis (strain HTCC7211). Oregon State University; 2012.Auladell A, Barberán A, Logares R, Garcés E, Gasol JM, Ferrera I. Seasonal niche differentiation among closely related marine bacteria. ISME J. 2022;16:178–89.CAS 

Google Scholar 
Tsementzi D, Wu J, Deutsch S, Nath S, Rodriguez-R LM, Burns AS, et al. SAR11 bacteria linked to ocean anoxia and nitrogen loss. Nature. 2016;536:179–83.CAS 

Google Scholar 
Ruiz-Perez CA, Bertagnolli AD, Tsementzi D, Woyke T, Stewart FJ, Konstantinidis KT. Description of Candidatus Mesopelagibacter carboxydoxydans and Candidatus Anoxipelagibacter denitrificans: nitrate-reducing SAR11 genera that dominate mesopelagic and anoxic marine zones. Syst Appl Microbiol. 2021;44:126185.CAS 

Google Scholar 
Yeh YC, Fuhrman JA. Contrasting diversity patterns of prokaryotes and protists over time and depth at the San-Pedro Ocean Time series. ISME Commun. 2022;13:1–12.
Google Scholar 
McCarthy M, Spillane S, Walsh S, Kendon M. The meteorology of the exceptional winter of 2015/2016 across the UK and Ireland. Weather. 2016;71:305–13.
Google Scholar 
Met Office. UK Climate Projections: Headline Findings. 2021. More

Refinetti, R. The diversity of temporal niches in mammals. Biol. Rhythm Res. 39, 173–192 (2008).
Google Scholar 
Hut, R. A., Kronfeld-Schor, N., van der Vinne, V. & De la Iglesia, H. In search of a temporal niche: Environmental factors. Prog. Brain Res. 199, 281–304 (2012).PubMed 

Google Scholar 
Cox, D., Gardner, A. & Gaston, K. Diel niche variation in mammals associated with expanded trait space. Nat. Commun. 12, 1–10 (2021).
Google Scholar 
Grossnickle, D. M., Smith, S. M. & Wilson, G. P. Untangling the multiple ecological radiations of early mammals. Trends Ecol. Evol. 34, 936–949 (2019).PubMed 

Google Scholar 
Baker, J. & Venditti, C. Rapid change in mammalian eye shape is explained by activity pattern. Curr. Biol. 29, 1082–1088. e1083 (2019).PubMed 

Google Scholar 
Crompton, A., Taylor, C. R. & Jagger, J. A. Evolution of homeothermy in mammals. Nature 272, 333–336 (1978).ADS 
PubMed 

Google Scholar 
Maor, R., Dayan, T., Ferguson-Gow, H. & Jones, K. E. Temporal niche expansion in mammals from a nocturnal ancestor after dinosaur extinction. Nat. Ecol. Evol. 1, 1889–1895 (2017).PubMed 

Google Scholar 
Bennie, J. J., Duffy, J. P., Inger, R. & Gaston, K. J. Biogeography of time partitioning in mammals. Proc. Natl Acad. Sci. USA 111, 13727–13732 (2014).ADS 
PubMed 
PubMed Central 

Google Scholar 
Mccain, C. M. & King, S. R. Body size and activity times mediate mammalian responses to climate change. Glob. Change Biol. 20, 1760–1769 (2014).ADS 

Google Scholar 
Veldhuis, M. P. et al. Predation risk constrains herbivores’ adaptive capacity to warming. Nat. Ecol. Evol. 4, 1069–1074 (2020).PubMed 

Google Scholar 
Riede, S. J., van der Vinne, V. & Hut, R. A. The flexible clock: Predictive and reactive homeostasis, energy balance and the circadian regulation of sleep–wake timing. J. Exp. Biol. 220, 738–749 (2017).PubMed 

Google Scholar 
van der Vinne, V. et al. Maximising survival by shifting the daily timing of activity. Ecol. Lett. 22, 2097–2102 (2019).PubMed 
PubMed Central 

Google Scholar 
Harper, G. & Bunbury, N. Invasive rats on tropical islands: Their population biology and impacts on native species. Glob. Ecol. Conserv. 3, 607–627 (2015).
Google Scholar 
Sovie, A. R., Greene, D. U., Frock, C. F., Potash, A. D. & McCleery, R. A. Ephemeral temporal partitioning may facilitate coexistence in competing species. Anim. Behav. 150, 87–96 (2019).
Google Scholar 
Schoener, T. W. Resource partitioning in ecological communities. Science 185, 27–39 (1974).ADS 
PubMed 

Google Scholar 
Richards, S. A. Temporal partitioning and aggression among foragers: Modeling the effects of stochasticity and individual state. Behav. Ecol. 13, 427–438 (2002).
Google Scholar 
Kronfeld-Schor, N. & Dayan, T. Partitioning of time as an ecological resource. Annu. Rev. Ecol., Evol., Syst. 34, 153–181 (2003).
Google Scholar 
Sunarto, S., Kelly, M., Parakkasi, K. & Hutajulu, M. Cat coexistence in central Sumatra: Ecological characteristics, spatial and temporal overlap, and implications for management. J. Zool. 296, 104–115 (2015).
Google Scholar 
Lima, S. L. & Bednekoff, P. A. Temporal variation in danger drives antipredator behavior: The predation risk allocation hypothesis. Am. Naturalist 153, 649–659 (1999).
Google Scholar 
Beschta, R. L. & Ripple, W. J. Large predators and trophic cascades in terrestrial ecosystems of the western United States. Biol. Conserv. 142, 2401–2414 (2009).
Google Scholar 
Duffy, J. E. Biodiversity and ecosystem function: The consumer connection. Oikos 99, 201–219 (2002).
Google Scholar 
Sinclair, A., Mduma, S. & Brashares, J. S. Patterns of predation in a diverse predator–prey system. Nature 425, 288–290 (2003).ADS 
PubMed 

Google Scholar 
Cunningham, C. X., Scoleri, V., Johnson, C. N., Barmuta, L. A. & Jones, M. E. Temporal partitioning of activity: Rising and falling top‐predator abundance triggers community‐wide shifts in diel activity. Ecography 42, 2157–2168 (2019).
Google Scholar 
Hayward, M. W. & Slotow, R. Temporal partitioning of activity in large African carnivores: Tests of multiple hypotheses. South Afr. J. Wildl. Res. 39, 109–125 (2009).
Google Scholar 
Monterroso, P., Alves, P. C. & Ferreras, P. Catch me if you can: Diel activity patterns of mammalian prey and predators. Ethology 119, 1044–1056 (2013).
Google Scholar 
Schemske, D. W., Mittelbach, G. G., Cornell, H. V., Sobel, J. M. & Roy, K. Is there a latitudinal gradient in the importance of biotic interactions? Annu. Rev. Ecol. Evol. Syst. 40, 245–269 (2009).
Google Scholar 
Rovero, F. et al. A standardized assessment of forest mammal communities reveals consistent functional composition and vulnerability across the tropics. Ecography 43, 75–84 (2020).
Google Scholar 
Ahumada, J. A. et al. Community structure and diversity of tropical forest mammals: Data from a global camera trap network. Philos. Trans. R. Soc. B: Biol. Sci. 366, 2703–2711 (2011).
Google Scholar 
Zhang, J. et al. Trophic interactions among vertebrate guilds and plants shape global patterns in species diversity. Proc. R. Soc. B: Biol. Sci. 285, 20180949 (2018).
Google Scholar 
Beaudrot, L. et al. Local temperature and ecological similarity drive distributional dynamics of tropical mammals worldwide. Glob. Ecol. Biogeogr. 28, 976–991 (2019).
Google Scholar 
Janzen, D. H. Why mountain passes are higher in the tropics. Am. Naturalist 101, 233–249 (1967).
Google Scholar 
Khaliq, I., Hof, C., Prinzinger, R., Böhning-Gaese, K. & Pfenninger, M. Global variation in thermal tolerances and vulnerability of endotherms to climate change. Proc. R. Soc. B: Biol. Sci. 281, 20141097 (2014).
Google Scholar 
Willmer, P., Stone, G. & Johnston, I. Environmental Physiology of Animals (John Wiley & Sons, 2009).Cruz, P., Paviolo, A., Bó, R. F., Thompson, J. J. & Di Bitetti, M. S. Daily activity patterns and habitat use of the lowland tapir (Tapirus terrestris) in the Atlantic Forest. Mamm. Biol. 79, 376–383 (2014).
Google Scholar 
Taylor, W. & Skinner, J. Adaptations of the aardvark for survival in the Karoo: A review. Trans. R. Soc. South Afr. 59, 105–108 (2004).
Google Scholar 
Levy, O., Dayan, T., Porter, W. P. & Kronfeld‐Schor, N. Time and ecological resilience: Can diurnal animals compensate for climate change by shifting to nocturnal activity? Ecol. Monogr. 89, e01334 (2019).
Google Scholar 
Simpson, G. G. Splendid Isolation: The Curious History of South American Mammals Vol. 11 (Yale University Press, 1980).Gutiérrez-González, C. E. & López-González, C. A. Jaguar interactions with pumas and prey at the northern edge of jaguars’ range. PeerJ 5, e2886 (2017).PubMed 
PubMed Central 

Google Scholar 
Porfirio, G., Sarmento, P., Foster, V. & Fonseca, C. Activity patterns of jaguars and pumas and their relationship to those of their potential prey in the Brazilian Pantanal. Mammalia 81, 401–404 (2017).
Google Scholar 
Foster, V. C. et al. Jaguar and puma activity patterns and predator‐prey interactions in four Brazilian biomes. Biotropica 45, 373–379 (2013).
Google Scholar 
Ross, J., Hearn, A., Johnson, P. & Macdonald, D. Activity patterns and temporal avoidance by prey in response to S unda clouded leopard predation risk. J. Zool. 290, 96–106 (2013).
Google Scholar 
Lima, S. L. Nonlethal effects in the ecology of predator-prey interactions. Bioscience 48, 25–34 (1998).
Google Scholar 
Santos, F. et al. Prey availability and temporal partitioning modulate felid coexistence in Neotropical forests. PLoS One 14, e0213671 (2019).PubMed 
PubMed Central 

Google Scholar 
Herrera, H. et al. Time partitioning among jaguar Panthera onca, puma Puma concolor and ocelot Leopardus pardalis (Carnivora: Felidae) in Costa Rica’s dry and rainforests. Rev. de. Biol.ía Tropical 66, 1559–1568 (2018).
Google Scholar 
Pratas‐Santiago, L. P., Gonçalves, A. L. S., da Maia Soares, A. & Spironello, W. R. The moon cycle effect on the activity patterns of ocelots and their prey. J. Zool. 299, 275–283 (2016).
Google Scholar 
Gaynor, K. M., Hojnowski, C. E., Carter, N. H. & Brashares, J. S. The influence of human disturbance on wildlife nocturnality. Science 360, 1232–1235 (2018).ADS 
PubMed 

Google Scholar 
Espinosa, S. & Salvador, J. Hunters landscape accessibility and daily activity of ungulates in Yasuní Biosphere Reserve. Ecuad. Therya 8, 45–52 (2017).
Google Scholar 
Butynski, T. M. Ecological survey of the impenetrable (Bwindi) forest, Uganda, and recommendations for its conservation and management. https://doi.org/10.13140/RG.2.1.1719.0487 (1984).Rovero, F. & Ahumada, J. The Tropical Ecology, Assessment and Monitoring (TEAM) Network: An early warning system for tropical rain forests. Sci. Total Environ. 574, 914–923 (2017).ADS 
PubMed 

Google Scholar 
Barnosky, A. D., Koch, P. L., Feranec, R. S., Wing, S. L. & Shabel, A. B. Assessing the causes of late Pleistocene extinctions on the continents. Science 306, 70–75 (2004).ADS 
PubMed 

Google Scholar 
Gorczynski, D. et al. Tropical mammal functional diversity increases with productivity but decreases with anthropogenic disturbance. Proc. R. Soc. B 288, 20202098 (2021).PubMed 
PubMed Central 

Google Scholar 
Frey, S., Fisher, J. T., Burton, A. C. & Volpe, J. P. Investigating animal activity patterns and temporal niche partitioning using camera‐trap data: Challenges and opportunities. Remote Sens. Ecol. Conserv. 3, 123–132 (2017).
Google Scholar 
Bivand, R. et al. Maptools: Tools for Handling Spatial Objects. R package version 1.1-4. http://maptools.r-forge.r-project.org/reference/index.html (2021).Ensing, E. P. et al. GPS based daily activity patterns in European red deer and North American elk (Cervus elaphus): Indication for a weak circadian clock in ungulates. PLoS One 9, e106997 (2014).ADS 
PubMed 
PubMed Central 

Google Scholar 
Vazquez, C., Rowcliffe, J. M., Spoelstra, K. & Jansen, P. A. Comparing diel activity patterns of wildlife across latitudes and seasons: Time transformations using day length. Methods Ecol. Evol. 10, 2057–2066 (2019).
Google Scholar 
Rowcliffe, J. M., Kays, R., Kranstauber, B., Carbone, C. & Jansen, P. A. Quantifying levels of animal activity using camera trap data. Methods Ecol. Evol. 5, 1170–1179 (2014).
Google Scholar 
Rowcliffe, J. M. Activity: Animal Activity Statistics. R package version 1.3.2. https://cran.r-project.org/package=activity (2022).Faurby, S. et al. PHYLACINE 1.2: The phylogenetic atlas of mammal macroecology. Ecology 99, 2626 (2018).PubMed 

Google Scholar 
Wilman, H. et al. EltonTraits 1.0: Species-level foraging attributes of the world’s birds and mammals. Ecology 95, 2027–2027 (2014).
Google Scholar 
Elff, M., Heisig, J. P., Schaeffer, M. & Shikano, S. Multilevel analysis with few clusters: Improving likelihood-based methods to provide unbiased estimates and accurate inference. Br. J. Polit. Sci. 51, 412–426 (2020).Elff, M. Mclogit: mixed conditional logit models. R package version 0.5. 1. https://github.com/melff/mclogit/ (2018).Burnham, K & Anderson, D. Model Selection and Multi-model Inference 2nd edn, Vol. 63, 10 (Springer-Verlag 2004).Carbone, C., Teacher, A. & Rowcliffe, J. M. The costs of carnivory. PLoS Biol. 5, e22 (2007).PubMed 
PubMed Central 

Google Scholar 
Hopcraft, J. G. C., Olff, H. & Sinclair, A. Herbivores, resources, and risks: Alternating regulation along primary environmental gradients in savannas. Trends Ecol. Evol. 25, 119–128 (2010).PubMed 

Google Scholar 
Meredith, M. & Ridout, M. Overlap: Estimates of coefficient of overlapping for animal activity patterns. R package version 0.2. 4, https://cran.r-project.org/package=overlap (2014).Ridout, M. S. & Linkie, M. Estimating overlap of daily activity patterns from camera trap data. J. Agric., Biol., Environ. Stat. 14, 322–337 (2009).MathSciNet 
MATH 

Google Scholar 
RStudio Team. RStudio: Integrated Development for R (PBC, Boston, MA, 2020). More

Among the 325 municipalities in Greece during the period 2010–2021, WNV events, defined as the occurrence of at least one laboratory-confirmed human WNV case during a specific year, were reported in 154 (47%) municipalities, while the remaining 171 did not report any WNV case. WNV events were reported for a period ranging from one to eight years: 54 (35%) municipalities reported laboratory-confirmed WNV cases in only one year, 38 (25%) in two years, 30 (19%) in three years, 12 (8%) in four years, 10 (6%) in five years, 6 (4%) in six years, 1 (1%) in seven years, and 3 (2%) in eight years. This means that in 60% of the positive areas (82 municipalities out of 154), WNV appeared at most for two years, in 27% (42 out of 154) between three and four years, and in the remaining 13% (20 out of 154) for five years or more. Considering the total number of reported laboratory-confirmed human WNV cases across the twelve years (Fig. 1), in approximately 50% of the positive municipalities (78 out of 154), at most 4 cases were reported: 1, 2, 3, and 4 WNV cases were reported in 24, 32, 11, and 11 municipalities, respectively. Overall, 39 municipalities recorded a number of WNV cases ranging from 5 to 10 (third quartile), 34 a number ranging from 11 to 46, while the remaining 3 municipalities recorded a number of WNV cases equal to 56, 71 and 94.Figure 1(a) Map of Greece with total numbers of laboratory-confirmed human WNV cases throughout the 12-year period 2010–2021, with breakdowns by municipality. White denotes municipalities where no human cases were observed. (b) Map of Greece with total numbers of modelled human WNV cases throughout the 12-year period 2010–2021, with breakdowns by municipality. White denotes municipalities where no human cases were modelled.Full size imageModel evaluation and comparison with MIMESISWe investigated the ability of the MIMESIS-2 model to correctly identify the occurrence of WNV events, both in space and time, and its capacity to quantify the annual number of human WNV cases and the timing of the first WNV event in the year. The performance of many quantities of interest, such as the severity and timing of occurrence of human WNV cases, was also compared output from the original MIMESIS model26.Occurrence of WNV eventsStarting with the spatial analysis, we considered the fit of the model to replicate the observed 385 WNV events out of 3,900 (325*12) possible events across municipalities. MIMESIS-2 was able to correctly identify 356 of them, generated only one false alarm, and correctly modelled 3,514 true negatives.The performance of MIMESIS-2 was then evaluated according to four indices: the probability of detection (POD), false alarm rate (FAR), miss rate (MIS), and critical success index (CSI), described in the Methods section. For the POD, MIS and CSI, we considered the 154 municipalities with at least one reported and laboratory-confirmed human WNV case over the 12-year period, while for the FAR, we considered the 153 municipalities where at least one human WNV case was modelled over the same period. We split the (0.0–1.0) index interval into five equally sized bins to derive for each index, the fraction of municipalities falling into each bin. Both the POD and CSI were above 0.8 for 139 municipalities out of 154, while the MIS was below 0.2 for 142 municipalities (out of 154) and the FAR was always below 0.2, with one false alarm produced in a municipality where WNV events were observed in eight out of twelve years (Table 1).Table 1 Capacity of the MIMESIS-2 model to correctly model laboratory-confirmed human WNV cases.Full size tableWe also analysed how the model performed in different years by studying the multiannual evolution of the indices. Both the aggregated POD and CSI were equal to 0.92, with annual variations ranging from 0.72 (2021) to 1 (2011 and 2014). The aggregated MIS was 0.08, ranging from 0.0 (2011 and 2014) to 0.28 (2021). The FAR was virtually 0, being always equal to 0.0, with the only exception being 2017, when it was 0.1 (Table 2).Table 2 Capacity of the MIMESIS-2 model to correctly model laboratory-confirmed human WNV cases by year and in the whole observed time period.Full size tableMagnitude and timing of WNV events: performance and comparison with MIMESISTo evaluate the ability of MIMESIS-2 to capture the magnitude and timing of WNV events, we first considered the discrepancy between the overall number of observed and modelled WNV cases during the 12-year period for each municipality. Out of the 153 municipalities where at least one case was modelled across the 12 years, 76 (50%) had at most 4 modelled cases of WNV: 1, 2, 3, and 4 WNV cases were modelled in 22, 31, 13, and 10 municipalities, respectively. In 42 municipalities, the number of modelled cases ranged from 5 to 10 (which, as for the observed WNV cases, coincided with the third quartile), and in 32 municipalities, the number ranged from 11 to 47, while the remaining 3 municipalities had 55, 70, and 99 modelled cases (Fig. 1).The MIMESIS-2 model closely replicated the total number of laboratory-confirmed WNV cases during the 12-year period. When considering only the 154 municipalities that recorded at least one WNV event during the considered period (excluding the true negatives), for 140 of them, the modelled number of cases fell within a ± 10% error range of the observed value, whereas for 149 the modelled number of cases fell within the ± 25% error margin. Only two municipalities showed a percent error above 50%. These were particular instances where only one WNV case was reported throughout the considered period, while MIMESIS-2 fitted zero human cases. For the original MIMESIS model, 63 and 84 municipalities fell within the ± 10% and ± 25% error margins, respectively, while 31 municipalities—mainly those where few cases were observed— had a relative error ≥ 100% (Fig. 2).Figure 2(a) For MIMESIS-2, modelled (IHMOD) vs. observed (IHOBS) human WNV cases in each municipality in the period 2010–2021. The inner black line represents the main diagonal where ideally the points would lie in case of perfect fit, while the dashed green, black and red lines represent, respectively, the ± 10%, ± 25% and ± 50% error margin. (b) Same quantities for MIMESIS. (c). Breakdown of the week of first WNV incidence by year. Plotted are the modelled quantities (WYMOD) for each of the 325 municipalities on the y-axis and the observed quantities (WYOBS) on the x-axis. The continuous line represents the main diagonal where ideally the points would lie in case of perfect fit, while the dashed lines represent the ± 4-week error margins.Full size imageTo further evaluate the bias of the model across all municipalities and years, we explored the difference between the yearly modelled and observed human WNV cases both with MIMESIS-2 and the original MIMESIS (IHMOD-IHOBS) across municipalities. In MIMESIS-2, we excluded 3,514 true negative cases to avoid distorted conclusions. For the remaining 386 cases, the mean bias was -0.04 indicating a possibly unbiased model, with the standard deviation (SD) of the residuals equal to 0.66 (original MIMESIS: mean bias 0.33, SD 2.07, after removing 3,387 true negatives) (Supplementary Fig. 1).Across the 325 municipalities and the 12 years, 385 WNV events were observed, while on 3,515 occasions, no laboratory-confirmed human WNV cases were reported; on 162 occurrences, 1 case was reported, and on 67 and 39 occasions, 2 and 3 cases were reported, respectively. The maximum yearly number of human WNV cases observed in a single municipality was 38. Considering the modelled human WNV cases with MIMESIS-2, the distribution of the 356 hits ranged between 1 and 37 modelled cases, closely mimicking the distribution of the observed cases, since 1, 2 and 3 human WNV cases were modelled on 129, 72 and 37 occasions, respectively. For the 29 misses, the observed numbers of human cases were 1 (24 times), 2 (3 times), or 3 (2 times). The only false alarm was produced in the Pellas municipality, where WNV events were observed in 8 out of the 12 years.We evaluated the timing of the first occurrence of WNV in humans for any municipality and year. Ignoring the municipalities with zero cases, the observed and MIMESIS-2-modelled first WNV cases occurred between weeks 22 and 44 and weeks 24 and 36, respectively. Modelled values tended to be dispersed around the observed ones: excluding the 3514 true negatives, 290 (75.13%) of the remaining 386 cases fell into the ± 4-week error margins from the observed cases (Fig. 2). This translated into a much lower bias of the week of first appearance (WYMOD-WYOBS) with respect to MIMESIS (Supplementary Fig. 2).Case study: The Pellas municipalityIn addition to presenting the overall performance of the model throughout different years and Greek municipalities, we highlight here the capacity of the model to capture population-specific behaviour and epidemiological features, such as the force of infection, that is, the rate at which susceptible humans, birds, and mosquitoes become infected, by presenting a single municipality case study for the municipality of Pellas. The Pellas municipality had the highest number of observed WNV cases over the 12-year period with a total of 94 human WNV cases, 38 in 2010, 16 in 2018, and 13 in 2021, no cases from 2014 to 2017, and between 4 to 8 cases in the remaining years.We considered the impact arising from the changes in parameters defining the forces of infection. In addition to the introduction of bird (({psi }_{B})) and human (({psi }_{H})) host selections, changes included modifications for the mosquito-to-bird (({p}_{M})) and bird-to-mosquito (({p}_{B})) probabilities of transmission, whose values were made temperature-dependent following Vogels et al.21, and the replacement of the mosquito-to-bird (({varphi }_{B})) and mosquito-to-human (({varphi }_{H})) ratios with their dynamic counterparts, ({N}_{M}/{N}_{B}) and ({N}_{M}/{N}_{H}), respectively (Fig. 3). We used the May–October period for the 12 years that were considered, because this is the part of the year when Culex pipiens mosquitoes are reproductively active and the majority of human WNV cases are reported. In each year of the 12-year period, ({p}_{M}) started from 0.02, reached its peak—ranging from 0.16 to 0.25—in midsummer, and then decreased to the initial values (in the original model, ({p}_{M}=0.9)). Similarly, ({p}_{B}) started from 0.28, peaked in the same time interval—with maximal values ranging from 0.51 to 0.56—and then returned to the initial values (in the original model, ({p}_{B}=0.125)). Additionally, the dynamic specifications of ({varphi }_{B}) and ({varphi }_{H}) were shown to play an important role. Whereas in MIMESIS ({varphi }_{B}=30), in MIMESIS-2 the values started at approximately 8.6 and peaked in late summer when more human WNV cases are reported, reaching values of approximately 57, before decreasing to values ranging from 31.15 to 41.60 in late October. In MIMESIS, ({varphi }_{H}) was calibrated at the municipality level, and for Pellas municipality, it was 0.0001, whereas the dynamic counterpart in MIMESIS-2 showed a temporal evolution with a shape (but different scale) similar to that of ({varphi }_{B}), starting from values of approximately 1, peaking in late summer to values of approximately 7, and then decreasing to values of approximately 4 in late October.Figure 3The temporal evolution during May to October of the (a) mosquito-to-bird probability of transmission, ({p}_{M}), (b) bird-to-mosquito probability of transmission, ({p}_{B},) (c) mosquito-to-bird ratio, ({varphi }_{B},) and (d) mosquito-to-human ratio, ({varphi }_{H},) for both MIMESIS-2 across different years and MIMESIS for each of the 12 years from 2010 to 2021. The plots refer to the simulations for the municipality of Pella.Full size imageChanges in these parameters enter into the expression for the forces of infection. It is of major practical interest to investigate how the values for the forces of infection resulting from MIMESIS-2 may vary for different values of the relative abundance of the vectors with respect to the corresponding carrying capacity and the temperature in different months (Fig. 4). As expected, all forces of infection increased with both the temperature and the relative abundance of the infectious vertebrate hosts. It is worth noting the importance of day length, as this affects the fraction of nondiapausing mosquitoes, ({delta }_{M}), and causes the forces of infection, all other things being equal, to be potentially higher in June and July than in the other months. However, in these two months, the modelled forces of infection tend to be smaller than those in August due to the lower abundance of infectious hosts.Figure 4Contour plots of the forces of infection for May to September for different values of the relative abundance of infected hosts/vectors with respect to the carrying capacity and the temperature. All the other quantities were fixed to the amounts obtained in the simulations for Pellas municipality for 2021 at the end of the corresponding month. (a) Bird-to-mosquito force of infection (({lambda }_{BM})) as a function of the relative abundance of infected birds (({I}_{B})) with respect to the bird carrying capacity (({K}_{B})) and temperature. (b) Mosquito-to-bird force of infection (({lambda }_{MB})) as a function of the relative abundance of infected mosquitoes (({I}_{M})) with respect to the mosquito carrying capacity (({K}_{M})) and temperature. (c) Mosquito-to-human force of infection (({lambda }_{MH})) as a function of the relative abundance of infected mosquitoes (({I}_{M})) with respect to the mosquito carrying capacity (({K}_{M})) and temperature. The ranges for ({I}_{B}/{K}_{B}) and ({I}_{M}/{K}_{M}) were fixed, increasing the maximum modelled value by 20% for the considered period, while the range for the temperature was chosen considering that in the period of interest, the average daily temperature ranged from 16.6 to 27.1 degrees Celsius. Black crosses represent the modelled values for 2021.Full size imageThe bird-to-mosquito force of infection, ({uplambda }_{BM}), took values on the order of 10–4, with possible peaks of approximately 7 × 10–4 in the case of high temperature and high prevalence of birds in June and July, which were nevertheless not reached due to a low abundance of infected birds in that period. Considering the months of July and August 2021 for illustrative purposes, the resulting modelled values were 1.20 × 10–4 and 2.19 × 10–4, respectively, with the increase in August explained by a higher abundance of infected birds in that period. It is worth noting that if the infection across birds had a lead period of two weeks, the resulting ({uplambda }_{BM}) in July would become 3.91 × 10–4 (+ 226%), while an increase in the average temperature in August by 1 °C would result in ({uplambda }_{BM})= 2.36 × 10–4 (+ 8%). The mosquito-to-bird, ({uplambda }_{MB}), and mosquito-to-human, ({uplambda }_{MH}), forces of infection showed similar qualitative behaviours, albeit at different scales, and in this case, they were higher in August due to a higher prevalence of infected Culex mosquitoes in that month. More specifically, ({uplambda }_{MB}) equalled 1.06 × 10–3 and 1.22 × 10–3 at the end of July and August, respectively, and an expected two weeks for the infection of mosquitoes would result in ({uplambda }_{MB})=4.15 × 10–3 (+ 292%) at the end of July, while an increase in the average temperature in August by 1 °C would result in ({uplambda }_{MB})= 1.31 × 10–3 (+ 7%) at the end of August. Finally, ({uplambda }_{MH})=2.86 × 10–6 at the end of July, while ({uplambda }_{MH})=3.26 × 10–6 at the end of August, with the anticipation of the infection among mosquitoes by two weeks resulting in ({uplambda }_{MH})=1.12 × 10–5 (+ 290%) and an increase in the average August temperature by 1 °C leading to ({uplambda }_{MH})=3.51 × 10–6 (+ 8%). It is worth recalling that since we calibrated the model on the number of reported laboratory-confirmed human WNV cases, ({uplambda }_{MH}) represents the rate at which susceptible humans contract infection and become symptomatic leading to a recorded human WNV case.We explored changes in the populations of infectious hosts and the total population number for both mosquitoes and birds over 2010–2021 for the period spanning from May to October (Figs. 5 and 6). The population of infected mosquitoes (({I}_{M})) was initialised by calibration (see the Methods section). Each year, after a short period in which the population of infected mosquitoes slightly decreased due to a very small number of infectious birds (({I}_{B})) that prevented the infection from spreading, it started growing substantially during summer, reaching its peak in late summer, coinciding with the period when most human cases were recorded. The observed increase in ({I}_{M}) was combined with the growth ({I}_{B}) at approximately the same time (with a slightly anticipated peak), which had an amplification effect on the spread of the infection. Both ({I}_{M}) and ({I}_{B}) showed significant yearly variation, with higher modelled numbers in years where more human WNV cases were reported. The modelled total population of mosquitoes (({N}_{M})) did not show significant interannual variability, always peaking in late summer. Finally, the overall population of birds (({N}_{B})) did not show any variability in the first part of the year, when an increase due to immigration and offspring generation was observed, whereas it had a moderate interannual variability in the second half of the year. These differences may be due to heterogeneous numbers of observed infected, dead and immune birds.Figure 5The temporal evolution during May to October of (a) the number of infected mosquitoes modelled by MIMESIS-2 (({I}_{M})), (b) the total number of mosquitoes modelled by MIMESIS-2 (({N}_{M}))(,) (c) the number of infected birds modelled by MIMESIS-2 (({I}_{B}))(,) and (d) the total number of birds modelled by MIMESIS-2 (({N}_{B})) for each of the 12 years from 2010 to 2021. The plots refer to the simulations for the municipality of Pella.Full size imageFigure 6The temporal evolution during May to October of (a) the ratio between the number of WNV-infected mosquitoes modelled by MIMESIS-2 (({I}_{M,MIM-2})) and the ratio modelled by MIMESIS (({I}_{M,MIM})), (b) the ratio between the total number of mosquitoes modelled by MIMESIS-2 (({N}_{M,MIM-2})) and the ratio modelled by MIMESIS (({N}_{M,MIM}))(,) (c) the ratio between the number of infected birds modelled by MIMESIS-2 (({I}_{B,MIM-2})) and the ratio modelled by MIMESIS (({I}_{B,MIM}))(,) and (d) the ratio between the total number of birds modelled by MIMESIS-2 (({N}_{B,MIM-2})) and the ratio modelled by MIMESIS (({N}_{B,MIM})) for each of the years from 2010 to 2021. The plots refer to the simulations for the municipality of Pella.Full size imageComparison of these population dynamics with those of MIMESIS revealed interesting patterns (Fig. 6). Considering the relative number of mosquitoes in MIMESIS-2 with respect to MIMESIS, the populations in MIMESIS tended to grow faster due to a higher mosquito carrying capacity (({K}_{M})) in the original model (({K}_{M}) ≈ 8.3 × 105 in MIMESIS versus ({K}_{M}) ≈ 2.4 × 105 in MIMESIS-2), resulting in a decrease in the ratio between the amounts modelled by MIMESIS-2 and the ones modelled by MIMESIS. Significant interannual variability could be seen in the first part of the year for infectious mosquitoes, where different initial calibration values played an important role. For the populations of birds, until midsummer, the overall number modelled by MIMESIS-2 tended to be approximately 1/4 that of MIMESIS, while as of July, different patterns were observed due to the higher mortality of birds in the original MIMESIS model. In years with higher virus spread, higher mortality was reflected in a sharper decrease in bird populations; therefore, the ratio between the population modelled by MIMESIS-2 and that modelled by MIMESIS increased up to approximately 0.6 (2010). More