Philippa Kaur

DNA sample collectionTo analyze the population structure of wild medaka populations, we selected samples from the DNA collection of Takehana et al.29, deposited in University of Shizuoka. The original DNA collection had been made throughout 1980s and 2000s. The selected samples covered the major mitotypes and contained more than three individuals of each population (Table S11, Fig. 3), which were collected from three collection sites for O. sakaizumii and 12 collection sites for O. latipes. We also examined several artificial strains: HNI and Hd-rR, which are inbred strains derived from O. sakaizumii and O. latipes, respectively, and four Himedaka individuals from commercial stock (Uruma city, Okinawa Prefecture, Japan).In addition, samples were newly collected at Kunigami Village, Okinawa Prefecture. Live fish were anesthetized with MS-222 (aminobenzene methanesulfonate, FUJIFILM Wako Pure Chemical Corporation, Osaka, Japan) and then fixed in 99% ethanol. Genomic DNA was extracted using a DNeasy kit (Qiagen Inc., Hilden, Germany) from ethanol-fixed pectoral fin samples according to the manufacturer’s protocol. The DNA concentration was measured using a spectrophotometer (Nanodrop 1000, Thermo Fisher Scientific, Waltham, Massachusetts, USA), and the DNA was diluted with PCR-grade water to a concentration of c.a. 10 ng/µl (UltraPure™ DNase/RNase-Free Distilled Water, Thermo Fisher Scientific).Ethic statementAll methods were carried out in accordance with the Regulation for Animal Experiments at University of the Ryukyus for handling live fish. All experiments were approved by the Animal Care Ethics Committee of University of the Ryukyus (R2019035). All experimental methods are reported in accordance with ARRIVE guidelines.PCR primer designThe following steps were used to select primers for MAAS (Fig. 1). (1) All possible 10-mer sequence combinations (i.e., 410 = 1,048,576 sequences) were generated in silico. (2) The sequences containing simple sequence repeats, some of which had been used in the MIG-seq method17, were excluded. (3) Sequences containing a functional motif, such as a transcription factor-binding site, were also excluded because they may not be suitable for examining neutral genetic markers. We obtained a catalog of motifs from the JASPAR CORE40 (http://jaspar.genereg.net). (4) To avoid taxon-dependency in primer performance, we used information about the k-mer (k = 10) frequency of reference genomes from multiple phyla. Sequences that showed marked differences in frequency among taxa were excluded. The frequencies of each 10-mer sequence in the reference genomes of 17 species belonging to 12 phyla of metazoa were counted (Table S12) using the “oligonucleotideFrequency” function in the “Biostrings” package ver. 2.441. In each of these taxa, the frequencies of sequences were stratified into three grades ( 103). We then selected the sequences that showed the same grade in more than 80% (14/17) of the species. (5) To avoid synthesizing primer dimers, self-complementary sequences were excluded, taking Illumina adapter sequences (5′-CGCTCTTCCGATCT-3′ and 5′-TGCTCTTCCGATCT-3′) into account. Self-complementation of two bases at the 3′-end or every three continuous bases in primer sequences was then evaluated using a custom script in R ver. 3.5.0 (R Development Core Team, http://cran.r-project.org). Based on the selected 10-mer sequences (i.e., 129 sequences, Fig. 1), 7-mer primer sequences were designed by removing the 3 bases at the 3′ end. Finally, we selected 24 candidate sequences for both 10-mer and 7-mer primers for the subsequent step (Table S1).The primer sequence consisted of three parts17: partial sequence of the Illimina adapter, 7 N bases, and a short priming sequence, e.g., 5′-CGCTCTTCCGATCTNNNNNNNGTCGCCC-3′. PCR amplification was performed using the candidate primers using the first PCR protocol described below (Table S1). Banding patterns were observed by electrophoresis on 1% agarose gels (agarose S; TaKaRa, Japan). Of the candidate primers, we selected four 7-mer primers and four 10-mer primers that each gave a smeared banding pattern with amplification products ranging from 500 to 2000 bp, indicating uniform amplification of multiple target sequences (Table S1).Library construction and sequencingThe library was constructed by a two-step PCR approach using a modification of a MIG-seq protocol14. In the first PCR step, multiple regions of genomic DNA were amplified using a cocktail of primers with a Multiplex PCR Assay Kit Ver.2 (TaKaRa) (Table 1). The volume of the PCR reaction mixture was 7 μl, containing 1 μl of template DNA, 2 μM of each PCR primer, 3.5 μl of 2 × Multiplex PCR Buffer, and 0.035 μl of Multiplex PCR Enzyme Mix. PCR was performed under the following conditions: denaturation at 94 °C for 1 min; 25 cycles of 94 °C for 30 s, 38 °C for 1 min, and 72 °C for 1 min, followed by a final extension step at 72 °C for 10 min.The primers in the second PCR step contained the Illumina sequencing adapter and an index sequence to identify each sample. Following the Truseq indexes, we used the combinations of eight forward indexes (i5) and 12 reverse indexes (i7), which resulted in a total of 96 combinations. To be used as a template for the second PCR, the first PCR product from each sample was diluted 50 times with PCR-grade water. The second PCR was performed in a 15-μl reaction mixture containing, 3 μl of diluted first PCR product, 3 μl of 5 × PrimeSTAR GXL Buffer, 200 μM of each dNTP, 0.2 μM of forward index primer and reverse index primer, 0.375 U of PrimeSTAR GXL DNA Polymerase (TaKaRa). The PCR conditions were as follows: 12 cycles at 98 °C for 10 s, 54 °C for 15 s, and 68 °C for 30 s.The second PCR product of each sample was pooled by equal volume and size-selected from 600 to 1000 bp using solid phase reversible immobilization (SPRI) select beads (Beckman Coulter Inc, Brea, California, USA) according to the manufacturer’s protocol. The DNA concentration of the pooled library was measured using a Qubit fluorometer (Thermo Fisher Scientific). We sequenced the libraries using two NGS platforms, MiSeq (Illumina, MiSeq Reagent Kit v2 Micro, Paired-End (PE), 150 bp) and HiSeq X (Illumina, PE, 150 bp). Sequencing using the HiSeq X platform was performed by Macrogen Japan (Tokyo, Japan).To compare primer performance, the DNA libraries constructed using the 7-mer and 10-mer primers for one individual were sequenced using MiSeq. Then, a 7-mer primer cocktail containing four sets of mixed primers was used for the subsequent analyses (Table 1). We also constructed DNA libraries using 7-mer and MIG-seq primer cocktails for three individuals and sequenced them using the HiSeq X platform. Finally, we constructed DNA libraries using 7-mer primer cocktails for 67 wild individuals and six artificial strain individuals for population genetics analyses (Table S11, Fig. 3).Mapping and SNV callingGenotyping was conducted using the following BWA-GATK best-practices pipeline for each sample42. Primer sequences were removed using cutadapt with the –b option selected43. The Illumina adapter sequences were also removed and quality filtering was performed using fastp ver. 0.20.0 with the “–detect_adapter_for_pe, –cut_front” option selected44. The remaining reads were mapped on the reference genome of medaka, Hd-rR strain, GCA_002234675.1; ASM223467v127 using Burrows-Wheeler Alignment tool, BWA mem ver. 0.7.1745. After mapping, output files were converted to Binary Alignment/Map (BAM) format using SAMtools ver. 1.746. SNVs and InDels in the sample were determined following the best practice guidelines set out in the Genome Analysis Tool Kit (GATK ver. 3.8.0)42. We then filtered out SNVs and InDels based on the following criteria: “QD  60.0 || MQ  More

Study settingUganda covers 241,037 km2 (4° N, 2° S, 29° E, 35° E) and averages 1100 m above sea level25. Uganda has a total population of 34.6 million people, with 7.4 and 27.2 million living in urban and rural areas, respectively26. It borders Lake Victoria and has an equatorial climate. The study area is mainly plateau, with a few mountains. About 20 percent of Uganda’s area is covered by swamps and water bodies, including the four Great Lakes of East Africa (Lake Edward, Lake Victoria, Lake Albert, and Lake Kyoga). The country has ten national parks housing a high diversity of wildlife and endangered species26. By 2009, agriculture ranked as the second leading contributor to the country’s Gross Domestic Product27.Surveillance data of Bacillus anthracis infectionSurveillance data of livestock and human cases from 2018 were provided by the Ministry of Health (MOH) in Uganda through the Field Epidemiology and Laboratory Training Program and the Uganda Ministry of Agriculture, Animal Industry and Fisheries (MAAIF). Geographical coordinates of anthrax cases among wildlife (from 2004 to 2010) in Queen Elizabeth National Park were obtained from a recently published study4. These cases were mapped to show the distribution of anthrax across Uganda (Fig. 1). It is only recently that Uganda has mandated systematic anthrax surveillance across the country following the outbreak that started in 2018. GPS coordinates gathered by field personnel during outbreak responses were used to map outbreak locations. The human anthrax cases were defined based on the CDC’s clinical criteria (signs and symptoms), presumptive laboratory diagnosis (Gram staining), and confirmatory laboratory diagnosis (bacterial culture, immunohistochemistry, ELISA, and PCR)28. The animal anthrax cases were also defined based on the clinical presentation, presumptive laboratory diagnosis, and confirmatory laboratory diagnosis. All cases were classified as either ‘probable’ or ‘confirmed,’ with probable defined as cases that met both the clinical and presumptive laboratory diagnostic criteria and confirmed defined as cases that met the clinical and confirmatory laboratory diagnostic criteria. A total of 497 livestock (n = 171), humans (n = 32), and wildlife cases (n = 294), both confirmed (n = 32) and probable (n = 465), occurring from 2004 to 2018 were compiled. All methods were performed in accordance with the relevant guidelines and regulations.Figure 1Distribution of anthrax presence and pseudo-absence locations across Uganda. The navy-blue circles show wildlife cases (n = 294) used for model training, the blue triangles show livestock cases (n = 171), and the red diamonds represent human cases (n = 32). The blue polygons show the locations of the 50 km, 75 km, and 100 km buffers which were constructed around the wildlife cases with a distance of 10 km between the buffers and the presence locations. The pink dots show the pseudo-absence points selected within the 50 km buffer, the orange dots show the pseudo-absence points selected within the 75 km buffer, and the white dots show the pseudo-absence points selected within the 100 km buffer. Prediction maps were developed using the Quantum Geographic Information System software (QGIS). URL: https://qgis.osgeo.org (2020).Full size imageEnvironmental variable processingCorrelative studies of environmental risk factors for anthrax outbreaks suggest that temperature6,29,30,31,32,33,34,35,36,37, precipitation6,29,30,31,32,33,34,35,36,37,38,39, elevation6,29,31,32,34,35,37,38,39, soil (type, calcium concentration, pH, carbon content, and moisture)6,30,31,33,34,36,37,39,40,41,42,43, vegetation6,29,31,34,36,37,38,39,40,42,43, and hydrology37,44 are some of the major drivers of B. anthracis suitability. A total of 26 environmental predictors (Fig. 2) were selected for this study based on these known variables. These comprised 19 bioclimatic variables (the mean for the years 1970–2000) collected from the WorldClim database version 2 (https://www.worldclim.org/data/worldclim21.html) at a resolution of 30 s (~ 1 km2)45. Four soil variables, including exchangeable calcium at a depth of 0–20 cm, soil water availability, soil pH (10×) in H2O at a depth of 0 cm, and soil organic carbon at a depth of 0–5 cm, were retrieved from the International Soil Reference and Information Centre (ISRIC) data hub at a resolution of 250 m (https://data.isric.org/geonetwork/srv/eng/catalog.search#/home). Distance to permanent water bodies was derived from a global hydrology map provided by ArcGIS online version 10.6.146. Elevation data of 1 km2 in resolution was obtained from the Global Multi-resolution Terrain Elevation Data (GMTED2010) dataset available from the United States Geological Service. Finally, the monthly Enhanced Vegetation Index (EVI) data for the years 2004, 2005, and 2010 (36 tiles in total) were obtained from The Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Vegetation Indices (MYD13A3 v.6) at a spatial resolution of 1 km (https://lpdaac.usgs.gov/products/myd13a3v006/). The single variable, mean EVI, was calculated in QGIS by averaging all 36 tiles. The EVI minimizes variations in the canopy background and maintains precision over conditions with dense vegetation.Figure 2Results of correlation between covariates using Pearson’s correlation test. Correlation between covariates was shown by red numbers (negative correlation) and blue numbers (positive correlation). BIO1 = Annual Mean Temperature, BIO2 = Mean Diurnal Range, BIO3 = Isothermality, BIO4 = Temperature Seasonality, BIO5 = Max Temperature of Warmest Month, BIO6 = Min Temperature of Coldest Month, BIO7 = Temperature Annual Range, BIO8 = Mean Temperature of Wettest Quarter, BIO9 = Mean Temperature of Driest Quarter, BIO10 = Mean Temperature of Warmest Quarter, BIO11 = Mean Temperature of Coldest Quarter, BIO12 = Annual Precipitation, BIO13 = Precipitation of Wettest Month, BIO14 = Precipitation of Driest Month, BIO15 = Precipitation Seasonality, BIO16 = Precipitation of Wettest Quarter, BIO17 = Precipitation of Driest Quarter, BIO18 = Precipitation of Warmest Quarter, BIO19 = Precipitation of Coldest Quarter.Full size imageAll environmental variables were resampled using the R package ‘Resample’47 to a resolution of 1 km and clipped to the extent of Uganda. Since data sampling for wildlife data (used in model training) was not done systematically across the study area, target backgrounds buffers (polygon buffers created at certain radii from the training points and used for the random selection of pseudo-absences) were created for model calibration to reduce sampling bias. As there was no information on the sampling radius, a sensitivity analysis was conducted by creating target backgrounds using circular buffers of radius 50 km, 75 km, and 100 km around the presence points used for model training, leaving 10 km between the presence points and the various buffers (Fig. 1). Quantum Global Information System (QGIS) version 3.16 (https://qgis.org) software was then used to add 294 random pseudo-absence points within each buffer polygon giving a ratio of 1:1 for the training presences to pseudo-absences. A recent study explored how four approaches of pseudo-absence creation affect the performance of models across different species and three model types by building both terrestrial and marine models using boosted regression trees, generalised additive mixed models, and generalised linear mixed models48. They then tested four methods for generating pseudoabsences across all the different model types: (1) correlated random walks (RW); (2) reverse correlated RW; (3) sampling pseudoabsences within a buffer area surrounding the presence points; (4) background sampling48. The findings of the study suggested that the separation or distance in the environmental space between the presence locations and the pseudoabsences was the most significant driver of the model predictive ability and explanatory power, and thus finding was consistent across the three model types (boosted regression trees, generalised additive mixed models, and generalised linear mixed models) and both the terrestrial and marine habitats48.The values of the environmental variables were then extracted for each presence and pseudo-absence location using the raster package in R. With these, we did an initial data exploration to check for outliers within the covariates, collinearity, and to explore the relationships between the covariates and the response variables (presence or absence of anthrax). Cleveland dot plots were used to check for possible outliers. Following the outlier checks, variance inflation factors (VIF), pairwise plots, and Pearson correlation coefficients with correction for multiple comparisons were used to measure the statistically significant correlation between the covariates (Fig. 2). For variables that were highly correlated (correlation greater than 0.6) or those with high variance inflation (VIF  > 3), only one was used in the modelling process. Five variables were selected following this analysis: Temperature seasonality (BIO4), elevation, distance to water, soil calcium, and soil water (Table 1).Table 1 Summary of the environmental variables used.Full size tableModelling anthrax suitability across UgandaQGIS v.3.16 (https://qgis.org) and the R statistical package version 4.1.049 were used to conduct data visualization, cleaning, and model analysis (R code used available in a Github repository: https://github.com/valentinandolo/Uganda-Spatial_Model/tree/master). The wildlife cases (n = 294) were used for model training and testing. The remaining human (n = 32) and livestock (n = 171) cases were used for model evaluation. Since the wildlife case locations were recorded from 2004 to 2010, while the livestock and human cases were recorded in 2018, the latter locations were both spatially and temporally distinct from the wildlife cases, making an excellent basis for block cross-validation of the final model performance50. Random partitioning of the data into training and testing sets can inflate the performance of a model and underestimate the error in the spatial prediction evaluation50. Block cross-validation uses spatial blocks that separate the testing and training datasets; thus, the method has been suggested to be a good technique for error estimation and a robust approach for measuring a model’s predictive performance50.The INLA package in R was applied to model the suitability of B. anthracis across Uganda. INLA calculates the spatial interaction effects using a Stochastic Partial Differential Equation (SPDE) method, which estimates a continuous Gaussian Markov Random Field (GMRF) where the correlation between two locations in space is specified by the Matérn correlation which is explained in more detail elsewhere51. The initial step in fitting an SPDE model is the creation of a Constrained Refined Delaunay Triangulation or a mesh to illustrate the spatial process51. R-INLA uses a function called ‘inla.mesh.2d()’ that applies a variety of arguments to build the mesh, these include: loc, loc.domain, boundary, max.edge, and cutoff51. The loc argument contains the point locations which provide information about the area of study and are used to create the triangulation nodes. Alternatively, a polygon of the study area can be used to identify the extent of the domain via loc.domain. We applied the point locations using the loc argument. We then specified the boundary of the mesh as a convex hull. We used the max.edge argument to specify the maximum edge length for the inner mesh domain/triangles and the outer triangles. We did this by first studying the distribution of distances between the point locations for the training presences and pseudo-absences. Most points were within a distance of about 90–100 km away from each other, thus, a possible guess for the range at which spatial autocorrelation persists was 100 km. We used a distance of 20 km as a range guess to create a finer mesh which has been shown to produce more precise models. We specified the maximum edge length for the inner triangles as 20 km divided by 5 (4 km) and the maximum edge length for the outer triangles as 20 km. Finally, the cutoff argument sets the minimum distance allowed between point locations. We divided the maximum edge length for the inner triangles by 5 (4 km divided by 5 = 0.8 km), meaning that points that were closer in distance than 0.8 km were replaced by one vertex to avoid the occurrence of small triangles.The spatial effect, which is a numeric vector, then links each observation within the data to a spatial location, thus, accounting for region-specific variation that cannot be accounted for by the covariates. Following the recommendation by Lindgren and Rue52, multivariate Gaussian distributions with means of zero and a spatially defined covariance matrix were used to model the spatial effect. Several versions of Bayesian hierarchical additive models were created by estimating a Bernoulli generalized additive model (GAM) with and without spatially correlated random effects. The Bernoulli GAM is defined as shown in Eqs. (1) and (2)$${C}_{i} sim Bernoulli left({p}_{i}right),$$
(1)
$$logit left({p}_{i}right)= alpha +sum_{j=1}^{m}{beta }_{j}left({X}_{j,i}right)+ sum_{k=1}^{l}{f}_{k}left({X}_{k,i}right)+ {mu }_{i},$$
(2)
where ({C}_{i}) denotes the observed value, such that: B. anthracis presence or absence at a given location i (i = 1, …, n; n = 588) is given as ({C}_{i}), where ({C}_{i}) =1 if B. anthracis was present, and ({C}_{i})=0 if absent. Logit is the link function for binomial family, ({p}_{i}) is the expected value of the response variable (the probability of B. anthracis suitability) at location i, α is the intercept, ({X}_{j,i}) and ({X}_{k,i}) are the j th and the k th covariates at a location i, ({beta }_{j}) are the beta coefficients, ({f}_{k}) are the smooth functions (cubic regression splines) for k th covariates, and ({mu }_{i}) is the spatial random effect at the location i53. The number of variables in linear term (m) and the non-liner term (l) are different because the variables employed in each term are different. We estimated both linear and non-linear effects for the covariates. Our overall database had 294 B. anthracis pseudo-absences generated randomly across the target background buffers to match the number of species presences recorded. Because we had no prior information, a Gaussian prior distribution with a mean of zero (default no effect prior unless data is informative) was applied for all the model parameters. The posterior mean, standard deviation, and 95% credible intervals were estimated for all the parameters.Model selectionSeveral different candidate models were examined. First, a baseline model was built using only the intercept. A second baseline model was then built, which included the intercept and spatial random effects only. Covariates were added to the second baseline model (intercept plus spatial effects model) without any smoothing function (i.e., only linear effects). The contribution of the spatial random effect was then re-examined by taking it out from the model. Smoothing functions were then added to all covariates, and the same procedure was repeated. Model selection was done using this forward stepwise approach. The final model was run using the three different target background buffers to identify the buffer distance with the lowest Deviance Information Criterion (DIC). The various options were assessed using the DIC54, Watanabe-Akaike information criterion (WAIC), and the Conditional Predictive Ordinate (CPO). The DIC and WAIC were chosen because they are commonly used to assess model performance by measuring the compromise between the goodness of fit and complexity. For CPO, the logarithmic score (− mean(log (CPO)) (LCPO) was calculated and used55. CPO can also be used to conduct internal cross-validation of models using a leave-one-out approach to evaluate the predictive performance of the model. Lower LCPO, DIC, and WAIC estimates suggest superior model performance. Thus, the favoured model had the lowest values across the 3 metrics.Model validation and evaluationModel validation for the favoured model was conducted using an independent evaluation dataset comprising of livestock and human outbreaks occurring at different spatial locations and 8 years after the training data. The omission rate, which indicates the proportion of positive test locations that end up in pixels predicted to be unsuitable for B. anthracis56, was used to validate the model. A low omission rate indicates good model performance. The threshold for suitability was the probability threshold that maximized the sensitivity and specificity of the model. The sensitivity was then derived by calculating the proportion of positive test locations that end up in pixels predicted to be suitable for B. anthracis56. A high sensitivity indicates good model performance.Model predictionThe favoured model selected using the criteria described above was used to generate countrywide prediction maps showing the posterior mean values, standard deviation, and the 95% credible intervals of the probability of B. anthracis suitability. The raster package in R was used to make the prediction maps. Bayesian kriging was done by treating all model parameters as random variables to include uncertainty in the prediction57. This kriging is built into the INLA framework via the SPDE, which allows a Delaunay triangulation to be constructed around the presence and absence locations within the sampling frame52. INLA then conducts model inference and prediction at the same time by considering the prediction points as locations missing the response variable (set to NA)51. Following successful model prediction, additional linear interpolation functions then generate the output for the whole study area scaled from 0 to 1.Ethical approvalEthical approval for this study was provided by the Human Biology Research Ethics Committee, University of Cambridge, UK (Ref: HBREC.2019.02) the School of Veterinary Medicine and Animal Resources Institutional Animal Care and Use Committee, Makerere University, Uganda (Ref: SVAREC/21/2019); and the Uganda National Council for Science and Technology. Informed consent was obtained from all subjects and/or their legal guardian(s). All methods were performed in accordance with the relevant guidelines and regulations. 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

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

Colpaert, J. V. In Stress in Yeasts and Filamentous Fungi Vol. 27 (eds Avery, S. V. et al.) 157–173 (Elsevier, Academic Press, 2008).Chapter 

Google Scholar 
Dalvi, A. A. & Bhalerao, S. A. Response of plants towards heavy metal toxicity: An overview of avoidance, tolerance and uptake mechanism. Ann. Plant Sci. 2, 362–368 (2013).
Google Scholar 
Langer, I. et al. Ectomycorrhizal impact on Zn accumulation of Populus tremula L. grown in metalliferous soil with increasing levels of Zn concentration. Plant Soil 355, 283–297 (2012).Article 

Google Scholar 
Agrahar-Murugkar, D. & Subbulakshmi, G. Nutritional value of edible wild mushrooms collected from the Khasi hills of Meghalaya. Food Chem. 89, 599–603 (2005).Article 

Google Scholar 
Das, N., Charumathi, D. & Vimala, R. Effect of pretreatment on Cd2+ biosorption by mycelial biomass of Pleurotus florida. Afr. J. Biotechnol. 6, 2555–2558 (2007).Article 

Google Scholar 
Dulay, R. M. R. et al. Effects and myco-accumulation of lead (Pb) in five Pleurotus mushrooms. Int. J. Biol. Pharm. Allies Sci. 4, 1664–1677 (2015).
Google Scholar 
Prasad, A. A., Varatharaju, G., Anushri, C. & Dhivyasree, S. Biosorption of lead by Pleurotus florida and Trichoderma viride. Br. Biotechnol. J. 3, 66–78 (2013).Article 

Google Scholar 
Tay, C. C. et al. Biosorption of cadmium ions using Pleurotus ostreatus: Growth kinetics, isotherm study and biosorption mechanism. Korean J. Chem. Eng. 28, 825–830 (2011).Article 

Google Scholar 
Kulshreshtha, S., Mathur, N. & Bhatnagar, P. Mushroom as a product and their role in mycoremediation. AMB Express 4, 1–7 (2014).Article 

Google Scholar 
Khan, I. et al. Mycoremediation of heavy metal (Cd and Cr)-polluted soil through indigenous metallotolerant fungal isolates. Environ. Monit. Assess. 191, 1–11 (2019).
Google Scholar 
Mosa, K. A., Saadoun, I., Kumar, K., Helmy, M. & Dhankher, O. P. Potential biotechnological strategies for the cleanup of heavy metals and metalloids. Front. Plant Sci. 7, 303 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Velásquez, L. & Dussan, J. Biosorption and bioaccumulation of heavy metals on dead and living biomass of Bacillus sphaericus. J. Hazard. Mater. 167, 713–716 (2009).Article 
PubMed 

Google Scholar 
Fawzy, E. M., Abdel-Motaal, F. F. & El-zayat, S. A. Biosorption of heavy metals onto different eco-friendly substrates. J. Toxicol. Environ. Health Sci. 9, 35–44 (2017).
Google Scholar 
Kumar, V. & Dwivedi, S. K. Mycoremediation of heavy metals: Processes, mechanisms, and affecting factors. Environ. Sci. Pollut. Res. 28, 10375–10412 (2021).Article 

Google Scholar 
Barros, L., Baptista, P., Estevinho, L. M. & Ferreira, I. C. Bioactive properties of the medicinal mushroom Leucopaxillus giganteus mycelium obtained in the presence of different nitrogen sources. Food Chem. 105, 179–186 (2007).Article 

Google Scholar 
Kim, H. G. et al. Phellinus linteus inhibits inflammatory mediators by suppressing redox-based NF-κB and MAPKs activation in lipopolysaccharide-induced RAW 264.7 macrophage. J. Ethnopharmacol. 114, 307–315 (2007).Article 
PubMed 

Google Scholar 
Sarikurkcu, C., Tepe, B. & Yamac, M. Evaluation of the antioxidant activity of four edible mushrooms from the Central Anatolia, Eskisehir–Turkey: Lactarius deterrimus, Suillus collitinus, Boletus edulis, Xerocomus chrysenteron. Bioresour. Technol. 99, 6651–6655 (2008).Article 
PubMed 

Google Scholar 
Giannopolitis, C.N. & Ries, S.K. Superoxide Dismutases II. Purification and quantitative relationship with water-soluble protein in seedlings.Plant Physiol. 59, 315–318 (1977).Article 
PubMed 
PubMed Central 

Google Scholar 
Dhindsa, R.S., Plumb-Dhindsa, P. & Thorpe, T.A. Leaf senescent: correlated with increased levels of membrane permeability and lipid peroxidation, and decreased levels of superoxide dismutase and catalase. J. Exp. Bot. 32, 93–101 (1981).Article 

Google Scholar 
Marx, D. H. Ectomycorrhizae as biological deterrents to pathogenic root infections. Annu. Rev. Phytopathol. 10, 429–454 (1972).Article 
PubMed 

Google Scholar 
Calvillo-Medina, R. P. Determination of fungal tolerance index to heavy metals and heavy metal resistance tests. Bio-Protoc. 11, e4218 (2021).PubMed 
PubMed Central 

Google Scholar 
Ouzounidou, G., Eleftheriou, E. & Karataglis, S. Ecophysical and ultrastructural effects of copper in Thlaspi ochroleucum (Cruciferae). Can. J. Bot. 70, 947–957 (1992).Article 

Google Scholar 
Carrillo-González, R., González-Chávez, M. & del Carmen, A. Tolerance to and accumulation of cadmium by the mycelium of the fungi Scleroderma citrinum and Pisolithus tinctorius. Biol. Trace Elem. Res. 146, 388–395 (2012).Article 
PubMed 

Google Scholar 
Mkhize, S. S., Simelane, M. B. C., Gasa, N. L. & Pooe, O. J. Evaluating the antioxidant and heavy metal content of Pleurotus ostreatus mushrooms cultivated using sugar cane agro-waste. Pharmacogn. J. 13, 1–9 (2021).Article 

Google Scholar 
Kumar, V. In Microbial Cell Factories (eds Sharma, D. & Saharan, B. S.) 149–174 (CRC Press, 2018).Chapter 

Google Scholar 
El-Sayed, M. T., Ezzat, S. M., Taha, A. S. & Ismaiel, A. A. Iron stress response and bioaccumulation potential of three fungal strains isolated from sewage-irrigated soil. J. Appl. Microbiol. 132, 1936–1953 (2022).Article 
PubMed 

Google Scholar 
Li, X. et al. Mechanisms of Cd and Cr removal and tolerance by macrofungus Pleurotus ostreatus HAU-2. J. Hazard. Mater. 330, 1–8 (2017).Article 
ADS 
PubMed 

Google Scholar 
Chuang, H.-W., Wang, I.-W., Lin, S.-Y. & Chang, Y.-L. Transcriptome analysis of cadmium response in Ganoderma lucidum. FEMS Microbiol. Lett. 293, 205–213 (2009).Article 
PubMed 

Google Scholar 
Jones, D. & Muehlchen, A. Effects of the potentially toxic metals, aluminium, zinc and copper on ectomycorrhizal fungi. J. Environ. Sci. Health A Environ. Sci. Eng. Technol. 29, 949–966 (1994).
Google Scholar 
Tam, P. C. Heavy metal tolerance by ectomycorrhizal fungi and metal amelioration by Pisolithus tinctorius. Mycorrhiza 5, 181–187 (1995).Article 

Google Scholar 
Purkayastha, R., Mitra, A. K. & Bhattacharyya, B. Uptake and toxicological effects of some heavy metals on Pleurotus sajor-caju (Fr.) Singer. Ecotoxicol. Environ. Saf. 27, 7–13 (1994).Article 
PubMed 

Google Scholar 
Akkin, N. Mycoremediation by oyster mushroom. Acta Sci. Agric. 5, 47–48 (2021).Article 

Google Scholar 
Ogbo, E. & Okhuoya, J. Bio-absorption of some heavy metals by Pleurotus tuber-regium Fr. Singer (an edible mushroom) from crude oil polluted soils amended with fertilizers and cellulosic wastes. Int. J. Soil Sci. 6, 34–48 (2011).Article 

Google Scholar 
Brunnert, H. & Zadražil, F. The translocation of mercury and cadmium into the fruiting bodies of six higher fungi. Eur. J. Appl. Microbiol. Biotechnol. 17, 358–364 (1983).Article 

Google Scholar 
Singh, V., Singh, M. P. & Mishra, V. Bioremediation of toxic metal ions from coal washery effluent. Desalin. Water Treat. 197, 300–318 (2020).Article 

Google Scholar 
Lazarova, N., Krumova, E., Stefanova, T., Georgieva, N. & Angelova, M. The oxidative stress response of the filamentous yeast Trichosporon cutaneum R57 to copper, cadmium and chromium exposure. Biotechnol. Biotechnol. Equip. 28, 855–862 (2014).Article 
PubMed 
PubMed Central 

Google Scholar  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

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