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    Viruses infecting a warm water picoeukaryote shed light on spatial co-occurrence dynamics of marine viruses and their hosts

    Isolation and characterisation of viruses infecting the picoeukaryote Bathycoccus Clade BIIBathycoccus BII isolates RCC716 and RCC715 used in our experiments were originally cultured from a nutrient-limited region in the Indian Ocean. Clade BII as a whole has been reported extensively in warm oligotrophic ocean gyres based on metagenome analyses [22,23,24]. Peak abundances occurr when well-developed deep chlorophyll maxima are present, or throughout the photic zone during mixing periods at Station ALOHA of the Hawaii Ocean Time-series [12]. We targeted BATS for viral isolation in springtime because Bathycoccus has been observed at relatively high abundance during this period using qPCR [74]. Here, three viruses were isolated against RCC716 [12] using seawater flown from BATS/Bermuda to the laboratory, obviating bringing this finicky strain into the field for use as a viral host. We then purified the viruses by serial dilutions and sequenced the partial PolB gene to determine whether they were evolutionarily different from other cultured viruses. BLASTn and preliminary phylogenetic analysis using GenBank nr sequences indicated they were distinct from described viruses with deposited sequences, with best BLASTn hits to Bathycoccus prasinos viruses (62–74% nucleotide identity). Transmission electron microscopy (TEM) revealed that all three viruses have similar morphology to other characterised prasinoviruses [75], with icosahedral capsids diameter ranging between 120 and 140 nm (Fig. 1A).Fig. 1: Morphology and evolutionary relationships of newly discovered Bathycoccus viruses.A Transmission electron micrographs of BII-V1, BII-V2 and BII-V3 (scale bar, 50 nm). The capsid diameters (n = 6 virions) measured 138 ± 2 nm (BII-V1), 150 ± 5 nm (BII-V2) and 152 ± 11 nm (BII-V3). B Maximum Likelihood (ML) phylogenetic reconstruction of green algal viruses inferred from a concatenated alignment of 22 core proteins shared among the viruses (7,001 positions) under the LG + G + F model. Node support was calculated from 1000 bootstrap (BS) replicates, with all branches acquired support values of 100% (white dots). Viruses infecting Chlorella were used as an outgroup and the branch connecting the prasinoviruses to the outgroup was truncated for display purpose. The new Bathycoccus viruses isolated against Bathycoccus Clade II (sensu [12]) isolate RCC716 (named as species Bathycoccus calidus herein, see below) are in bold. Colours reflect different host species within each genus. Letters alongside vertical lines (a and b) correspond to Bathycoccus viral clades. C Venn diagram of the shared and unique protein-encoding genes in the genome sequences of the new Bathycoccus viruses.Full size imageGenomic sequencing and multi-gene evolutionary analysesAssembly of DNA sequences from the viral isolates after deep sequencing by Illumina rendered one complete dsDNA genome sequence (BII-V3), and two others may still be partial (Table 1). The BII-V2 genome, which was in one contig, was similar in size (~208 kb) to that of BII-V3 (~212 kb). The BII-V1 genome assembly was ~174 kb and comprised of four linear dsDNA scaffolds. The viral concentrate was deeply sequenced ( >50x coverage) and minor fragmentation of the genome was partially related to repeats that were not resolved during assembly. The total number of putative open reading frames (ORFs) varied from 220 in BII-V1 to 235 in BII-V2 (Table 1). Gene synteny was globally well-conserved across the BII-Vs and the BpV1 and BpV2 viruses of B. prasinos (Fig. S1), with limited genomic rearrangements. Other genome characteristics such as the coding proportion (~90%) and G + C % (~36%) were similar to other described prasinoviruses infecting Mamiellophyceae [64, 75], for which the reported number of proteins range from 203 to 268 and G + C % from 37 to 45%. However, the full-length PolB gene from the genome assemblies differed for BII-V3 from the other two, in having a 329 amino acid intein (Supplementary information table S3). Likewise, inteins have been reported at the same PolB position in uncultivated prasinoviruses from the subtropical Pacific Ocean [76], where Bathycoccus BII is abundant [12].Table 1 Genomic characteristics of the three Bathycoccus viruses (BII-Vs) isolated against Clade BII isolate RCC716.Full size tableTo reconstruct a robust phylogeny for the new viruses, we employed 22 proteins previously identified as being shared across all available green algal virus genomes, including both prasinoviruses and chloroviruses [65]. We found all 22 in the predicted coding sequences of BII-V1; however, DNA helicase (SNF2) was not found in BII-V2 or -V3, FAD-dependent thymidylate synthase (thy1) and the topoisomerase IV were not found in BII-V2, nor was the prolyl 4-hydroxylase in the BII-V3 genome. Additional searches with tBLASTn did not recover these genes or fragments of them, suggesting they have been lost. Phylogenomic reconstruction grouped the three BII-Vs with the two BpVs [32], in a fully supported clade that branched adjacent to a large group of viruses that infect various species of Ostreococcus and Micromonas (Fig. 1B). The clade of Bathycoccus viruses was segregated in two subclades with BII-V2 and BII-V3 clustering together adjacent to BII-V1 and BpVs (Fig. 1B). While better resolution of the position of BII-V1 awaits greater taxonomic sampling, our results demonstrated that the three new viruses branch adjacent or basally to BpVs.Variation in prasinovirus gene content and functions encodedThe three Bathycoccus Clade BII viruses had 72–77% of their proteins held in common, and ~30 unique proteins as well as a few proteins shared by just two of the three viruses (Fig. 1C). The 170 shared proteins had higher amino acid identities between BII-V2 and BII-V3 (73% aa identity) than to BII-V1 (69% and 68%, respectively). Generally, only 19–21% of Bathycoccus viral genes could be assigned a functional category, based on EggNOG classification. Similar functional category distributions were observed across both prasinoviruses and chloroviruses, including lipid metabolism, RNA processing and modification, and nucleotide metabolism and transport (Fig. 2A). Other functional categories were more variable, such as cell wall/membrane/envelope biogenesis genes prevalent in chloroviruses (potentially related to their enveloped nature), as well as genes involved in modification of the capsid with compounds such as with chitin and hyaluronan [77, 78] that are absent from prasinoviruses sequenced to date (Fig. 2A). Within prasinoviruses, most of the unique proteins in the Bathycoccus viruses lack defined functional categories. Among those with functional assignments, all five Bathycoccus viruses encoded a P2X receptor in the intracellular trafficking and secretion category, and both BII-V2 and -V3 encode two proteins putatively involved in degrading the aromatic compound 4-hydroxy-2-oxopentanoate to acetyl-CoA (secondary metabolite category), that otherwise are only encoded by one other prasinovirus, MpV1 [32]. Similar to the phylogenetic relationships, the functional category distributions of BII-V1 were closer to those of BpVs than to BII-Vs. The primary difference was in carbohydrate metabolism, where BII-V2 and -V3 each encodes ribulose-phosphate 3-epimerase (involved in the pentose phosphate pathway and carbon fixation; not found in any other available virus genomes, but encoded by B. prasinos) and TDP-glucose 4,6-dehydratase (involved in biosynthesis of rhamnose and encoded by most other chloroviruses and prasinoviruses [79]). Notably, the putative high-affinity phosphate transporter (PHO4, also termed HAPT) was only present in BII-V1 and BpV1, as well as OtV2 (isolated against the Ostreococcus Clade OII ecotype), and most sequenced viruses of O. lucimarinus (Supplementary information table S3). This gene is hypothesised to enhance phosphate uptake during infection under phosphorus‐limited host growth [25], as observed for the PstS phosphate transport system expressed by cyanophages [80], mitigating limitation of this key component of viral genomes. However, most isolated prasinovirus genomes come from waters that are not considered phosphate-limited, hence presence of this gene may connect to poising the host for responding to sudden availability of other nutrients, such as nitrogen, which is often limiting in the ecosystems from which these viruses were isolated. Studies of virus-cell responses under various limiting nutrients are required to understand the retention of this host-derived HGT.Fig. 2: Distribution of functions and orthologous protein families across genome-sequenced prasinoviruses.A Functional category distributions across 21 genome-sequenced prasinoviruses and chloroviruses based on EggNOG categorisation. Viruses are clustered by similarity in their distribution of the functional categories on the y-axis and the frequency of each category across the viral genomes determines clustering along the x-axis ordering. Genes with homology to proteins in the EggNOG database but could not be assigned a function are in the “function unknown” category. B Orthogroups presence/absence patterns ordered along the x-axis by ranking according to the total number of genes in the orthogroup. For inclusion, the orthogroup was required to include protein sequences from at least two different viral genomes. Viruses are ordered along the vertical by their presence/absence pattern reconstructed by hierarchical clustering (topology on the left). Top histogram: frequency of each orthogroup in sequenced prasinoviruses. C Genes in each virus (number) not assigned to any orthogroup, with viruses in the same vertical order as B.Full size imageHierarchical clustering of orthologous proteins revealed patterns across prasinoviruses that generally corresponded with phylogenetic relationships. The BII- and Bp-viruses shared 130 orthologous proteins and hierarchical clustering (Fig. 2B) followed the clade structure of the phylogenomic reconstruction (Fig. 1B) with the exception of BII-V1 that grouped with BII-Vs, as well as OtV6, which grouped with Micromonas viruses. These orthologous proteins had on average 72% amino acid identity between BII-V2 and BII-V3, and 88% between the two B. prasinos viruses, but between 65 to 67% when comparing members of these two groups (Table 2). BII-V1 orthologs also had 67% and 66% amino acid identity to BII-V2 and BII-V3, respectively, while they had 83% and 80% identity to BpV1 and BpV2, respectively. Collectively, these results indicate that BII-V2 and -V3 diverged from BpVs prior to the divergence of BII-V1.Table 2 Average percent amino acid identity of the orthologous proteins between the five Bathycoccus viruses.Full size tableOf the 130 orthologous Bathycoccus virus proteins, 37% were assigned putative functions revealing core components of this viral group (Supplementary information table S3). These included genes involved in DNA replication and transcription, including PolB (type II), a DNA topoisomerase, a transcription factor S-II, mRNA capping enzymes, ribonucleases, a ribonucleotide reductase, and a dUTPase. Several others are necessary for viral particle synthesis, such as genes encoding structural elements for assembling the virion, including capsid proteins (5–6 copies per genome), as well as transcriptional regulators connected to the replication cycle. The BII viruses showed a number of differences among orthologous protein families. In addition to each having “unique” protein sets, there was a set of BII-V specific orthogroups, as well as some shared with BpVs, and/or other prasinoviruses (Fig. 1C and Supplementary information table S3). First, six predicted proteins showed orthologs across the three BII-Vs, but were not present in other prasinoviruses sequenced to date. Only one of these six was assigned putative function, belonging to the XRE family of transcriptional regulators. Additionally, all BII viruses harboured a tandem duplication of the FstH gene, while other sequenced prasinoviruses (including the two Clade BI viruses) have one copy (Supplementary information table S3). This ATP-dependent metalloprotease has been shown to be involved in photosystem II repair in cyanobacteria [81], and is present in genomes of photosynthetic eukaryotes, including all Mamiellophyceae [15, 16]. In Arabidopsis and Chlamydomonas it has been shown to be involved in protein quality control in the thylakoid membranes [82]. A gene of unknown function was also duplicated in the BII-virus genomes, that is a single copy in BpVs and absent from other sequenced prasinoviruses. Genes putatively encoding a glucose-1-phosphate adenylyltransferase, a glycosyltransferase and a thiamine pyrophosphate-requiring enzyme involved in amino acid biosynthesis were sporadically found in BII-viruses.Considering the two Bathycoccus virus subclades (Fig. 1B), there is one predicted protein of unknown function exclusive to BpV1, BpV2 and BII-V1 and six predicted proteins shared only by BII-V2 and BII-V3. Among the latter, one belonged to the Ribulose-5-Phosphate-3-Epimerase (RPE) family, which catalyses the interconversion of D-ribulose 5-phosphate (Ru5P) into d-xylulose 5-phosphate, as part of the Calvin cycle (although no transit peptide was detected using TargetP) and in the oxidative pentose phosphate pathway. The ortholog analyses further showed that among prasinoviruses, 9, 17 and 18 genes were unique to BII-V1, BII-V2 and BII-V3, respectively (Fig. 2B). Apart from one nucleotidyltransferase and one glycosyltransferase (group 1) in BII-V1, none of these unique genes had known functions.To study the evolutionary aspects of the shared prasinovirus proteins, we constructed and examined 130 phylogenies of orthogroups shared between Bathycoccus viruses. Nine showed a topology where all three BII-Vs grouped together with full support (100% bootstrap support), separate from the BpV orthologs, and in contrast to the multi-gene phylogeny where BII-V1 grouped with BpVs (Fig. 1B). The average amino acid similarities within each of these nine protein ortholog groups ranged from 85 to 88% between BII-Vs proteins, while they were 77 to 81% between BII-Vs and BpVs, different from overall amino acid similarity averages (Table 2). Interestingly, proteins from three of these nine ortholog groups, all lacking known functions, were adjacent to each other in the genome, or separated by only one gene. This synteny and co-location likely reflects the acquisition of these genes before co-infecting viruses diverged via viral recombination [83].Infection dynamics of Bathycoccus virusesGeneral host specificity of BII-viruses was assessed using two B. prasinos isolates (CCMP1898 and RCC4222, Clade BI), the two available Clade BII isolates (RCC715 and RCC716), four Ostreococcus species and one Micromonas species (Table 3). None were able to infect the B. prasinos, Ostreococcus or Micromonas isolates tested, suggesting BII-V specificity for Bathycoccus Clade BII. Similar host specificity has been observed in O. lucimarinus viruses, none of which infect O. tauri [64], and other viruses of eukaryotic and prokaryotic algae [84, 85]. Some other prasinoviruses appear to have broader host ranges [85,86,87], or their host species are less divergent than the two known Bathycoccus clades. For example, generalist viruses isolated against Micromonas commoda can infect M. bravo [85]. Further investigations are necessary to determine the extent to which the six shared proteins in BII-Vs (absent from BpVs), are responsible for the differences in host and virus specificity of interactions, versus variations in the shared Bathycoccus virus proteins (65–83% similarity). Importantly, host specificity tests for the new viruses described herein were limited by weak sampling of Bathycoccus diversity (in culture; all that we could acquire were tested).Table 3 Results of cross infectivity tests of BII-V1, BII-V2 and BII-V3 against isolates representing various picoprasinophyte species within the Class Mamiellophyceae.Full size tableAlthough specific for the BII clade, the three BII-Vs exhibited variations in infectivity of the two cultured BII strains available, despite their isolation from the same sample and having identical ITS1 and ITS2 sequences. BII-V1 lysed and cleared RCC715 and RCC716 cultures after four days (Table 3). The same was true for BII-V2 and BII-V3, when incubated with RCC716. Different from results for BII-V1, we found that while BII-V2 and -V3 initially lysed RCC715 cultures, resistant populations became evident at day 7 of infectivity tests, and measureable lysis of RCC715 could not be achieved thereafter. These results underscored the need to further examine host-virus interactions for the three new viruses.Infection dynamics over time course experiments further illuminated differences in BII-V impacts on hosts. In these experiments, growth rates of the uninfected (control) RCC715 and RCC716 cultures were 0.45 ± 0.04 day−1 and 0.49 ± 0.06 per day, respectively, similar to rates during the pre-experiment acclimation period (T-test, p  > 0.05). Host and virus dynamics were similar for RCC715 and RCC716 infected with BII-V1 (Fig. S2 and Fig. 3), with cell numbers starting to diverge from control abundances 10 h after inoculation (T-test, p  More

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    Semiparametric model selection for identification of environmental covariates related to adult groundfish catches and weights

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    Quantitative modeling of radioactive cesium concentrations in large omnivorous mammals after the Fukushima nuclear power plant accident

    Data setsRadioactivity measurement data for several species of wild game mammals and birds in Fukushima Prefecture from May 2011 to March 2018 were released to the public by the Fukushima Prefecture Government (https://emdb.jaea.go.jp/emdb/en/portals/1040501000/). We extracted the data for wild boar (Sus scrofa), 1404 samples, and Asian black bear (Ursus thibetanus), 422 samples. The resulting boar and bear data sets contained total radioactive cesium activity (134Cs + 137Cs isotopes) values (in Bq/kg) from animals captured at different times and locations within Fukushima Prefecture. The data were imported for analysis into R 4.0.3 software21.We ln-transformed the cesium activity values to bring their distribution closer to normal, creating the variable LnCsTot. To facilitate regression analyses (described below), we removed instances of missing data and cesium levels below detection: 20 samples (1.4%) for boar and 15 samples (3.3%) for bears. The time when each sample was taken (labeled “Day of collection” in the Fukushima Prefecture Government data set) was converted to years since the Fukushima accident (since March 11, 2011), assuming that 1 year = 365.25 days. This time of sample collection in years was called variable T.Since for each sample some time passed between sample collection and radioactivity measurement (labeled “Result found Date”, called Tr in our notation), we needed to correct the reported LnCsTot values for physical decay over this time, which was different for different samples. The procedure used to perform this correction is described in Supplementary methods. The data with corrected total cesium values (LnCsc) are provided in Supplementary data (Supplementary_Dataset_File_Full).Mathematical modelTo describe the data on ln-transformed total radioactive cesium levels (LnScc) in each species as function of time after the accident (T), we developed the following simple mathematical model (Eqs. 1A, 1B):$${LnCs}_{c}=X+Q-mu times {T}^{nu }+Atimes mathrm{sin}left[2times pi times left(T+Pright)right], $$
    (1A)
    $$X=mathrm{ln}left[mathrm{exp}left(LnCs{134}_{t{0}_{r}}right)times {2}^{-frac{T}{{Th}_{Cs134}}}+mathrm{exp}left(LnCs{137}_{t{0}_{r}}right)times {2}^{-frac{T}{{Th}_{Cs137}}}right]$$
    (1B)
    Here the term X represents the estimated average radioactive cesium level in the studied area, based on the intercepts (LnCs134t0r for 134Cs and LnCs137t0r for 137Cs, respectively) from robust regression discussed in Supplementary methods, and taking into account only physical decay for each isotope (with half-lives of ThCs134 for 134Cs and ThCs134 for 137Cs, respectively). The terms Q, µ, ν, A and P represent adjustable model parameters. Parameter Q represents the fitted relationship between radioactive cesium levels in the animal (Bq/kg), relative to the external environment (Bq/m2). Parameter µ represents the net rate of radioactive cesium reduction in animal tissues over time due to all processes except physical decay (e.g. decrease in bioavailability due to migration of cesium into deeper soil layers, human-mediated cleanup efforts, etc.). Parameter ν is a potential power dependence for these processes. By default, ν was set to ν = 1, but exploratory calculations using ν = 2 or treating ν as a freely adjustable parameter (≥ 0.1) were performed as well. Parameters A and P in the sine function represent a sinusoidal approximation for seasonal changes in radioactive cesium levels in animal tissues (e.g. due to seasonal variations in diet and life style), where A is the amplitude of the oscillations, P is the phase shift, and the period is set to 1 year. For simplicity, these parameters were assumed to be the same for both studied cesium isotopes. The descriptions of each parameter are also presented in Table 1.Table 1 The meanings of all parameters used in our mathematical model (Eq. 1A, 1B) for radioactive cesium levels in wild boar (Sus scrofa) and Asian black bear (Ursus thibetanus).Full size tableModel fitting approachesInitially, we used nonlinear ordinary least squares (OLS) regression (nls R function) to fit the model (Eq. 1A, 1B) to the data. To find the global optimum fit, we repeated the fitting procedure 2000 times with slightly different random initial parameter values and recorded the solution with the smallest root mean squared error (RMSE). Diagnostics on this regression included checking of convergence criteria and analyses of residuals (by scatter plot and histogram, regressing residuals as function of T, visualizing the QQ plot, autocorrelation and partial autocorrelation functions with 95% confidence intervals, performing the Shapiro–Wilk normality test, and calculating skewness and kurtosis). For boar data, diagnostics revealed problems with convergence (both X-convergence and relative convergence) and non-normality of residuals: e.g. Shapiro–Wilk p-value = 1.476 × 10–7, skewness = − 0.37, kurtosis = 3.50. For black bear data similar problems occurred with convergence, but residuals were closer to the normal distribution (perhaps due to smaller sample size): e.g. Shapiro–Wilk p-value = 0.0526, skewness = − 0.058, kurtosis = 2.45.Due to these issues, we used robust nonlinear regression (nlrob R package) to reduce the effects of “outlier” data points. To find the global optimum, we repeated the fitting procedure 2000 times with slightly different random initial parameter values and selected the solution with the smallest absolute value of median residuals. The best-fit parameters for OLS and robust regressions were somewhat different for both boar and bear data. For boar data, the minimum robustness weight was 0.339 and the median was 0.762, and the corresponding values for black bear data were 0.557 and 0.821, respectively.For each species, we compared the performances of model variants with different assumptions about parameter ν: (1) The default case with ν = 1, which represents an exponential rate of radioactive cesium decrease due to processes other than physical decay. (2) The case with ν = 2, which represents quadratic decay. (3) The case with ν being freely adjustable (≥ 0.1). The comparisons were based on Akaike information criterion (AIC)22,23. The purpose of these calculations was to better assess the shape of the time course for non-physical factors involved in radioactive cesium level decline in animal tissues over time after the accident.In addition to analyzing the full data set for each species, we also performed separate analyses on subsets of data from specific locations: from those districts of Fukushima Prefecture where the mean radioactive cesium levels in animal samples were the highest, and where a sufficiently large number of samples was present. For wild boar, the two selected districts for this subset analysis were Soso and Kenpoku (819 samples), and for black bear they were Kenpoku and Kenchu (163 samples).To further assess the sensitivity of model results to geographical and temporal factors, we also constructed a separate subset of data for each species. This subset excluded data from the Aizu and Minamiaizu districts, which are separated by mountains from the Fukushima Daiichi Nuclear Power Plant, and excluded data collected ≤ 6 months after the accident. These restrictions were intended to determine model performance on data collected in a more geographically contiguous area after the initial abrupt changes in contamination levels were completed and the system entered the phase of more stable kinetics. The purpose of all these analyses was to assess whether the time course of radioactive cesium levels in the bodies of each species differed between locations with high contamination vs. those with lower contamination, and as function of time after the accident.We were interested in quantifying not only the center of the distribution of radioactive cesium values in each species over time, but also in assessing the lower and upper tails of this distribution. For this purpose, we fitted the model (Eq. 1A, 1B) for each species using quantile regression (nlrq function in quantreg R package) for the median (50th percentile), and also for the 25th and 75th percentiles. Initial parameter estimates for the quantile regressions were taken from best-fit parameters from robust regression described above. The 25th and 75th percentiles were selected instead of more extreme values (e.g. 5th and 95th) because the latter resulted in poor quality fits due to limited amounts of data at the fringes of the distribution.To assess the variability of model parameters by location in more detail, we used mixed effects modeling (nlme R package) on the data from each species. Since original OLS fits suggested substantial deviations of residuals from the normality assumption, we performed mixed effects modeling on data with some outlier data points removed. The OutlierDetection package in R removed 43 boar samples and 5 bear samples. These outliers are listed in the Supplementary_outlier_data_points file. The remaining samples were used for mixed effects model fitting, but model performance metrics like coefficient of determination (R2) and RMSE were assessed on the full data set (with outliers included) for each species.Since the Fligner-Killeen test of homogeneity of variances by district generated low p-values for both species (4.6 × 10–14 for boar and 0.018 for black bear), we allowed modelled variances to differ by district (using the weights option in nlme). We investigated several random effects structures for some or all model parameters, with randomness by district only, or by district and municipality within district. Model diagnostics were the same as for fixed effects OLS modeling described above, and also included boxplots of model residuals by district. The mixed effects model variants with different random effects structures were compared using the anova function in R, and also by assessing convergence criteria, normality of residuals, skewness, and kurtosis. Consequently, preferred mixed effects model variants were selected for the full data as well as for the subset of two districts with high radioactive cesium levels, separately for each species.Model extrapolation from training to testing dataTo investigate how the robust and quantile regression fits of our model could extrapolate beyond the time range that was used for model fitting, we split the data for each species into “training” (early times) and “testing” (later times) parts. The split was done based on time since the accident (T variable), so that approximately ½ of the samples were assigned to the training and testing sets, respectively. For wild boar data, the training set included times between 0.20 and 3.45 years after the accident, and the testing set included times between 3.45 and 7.03 years. For black bear data, the training set included times between 0.42 and 3.46 years after the accident, and the testing set included times between 3.46 and 6.87 years.We also evaluated an alternative approach to splitting the data, where the split was done randomly instead of by time. In other words, any data point regardless of time had an equal probability of being assigned to either the training or the testing data set. Both the training and testing data subsets generated by this random split included the complete time range. This approach was implemented in context of the sensitivity analysis described above.For each species, robust and quantile regressions were fitted to training data, and their predictions were calculated for testing data. For robust regression, RMSE was calculated on testing data for two scenarios: (1) for the model fitted to training data only, and (2) for the model fitted over the entire data range (training + testing). These RMSE values for conditions 1 and 2 were compared to assess the quality of model extrapolation. Extrapolation performance for robust and quantile regressions was also assessed visually by plotting the model predictions and data.Application of the model to wild boar data from the Chernobyl accident areaTo compare the results of our analysis of wild boar contamination with radioactive cesium in the area affected by the Fukushima accident with data from another location, we also analyzed wild boar data from the Chernobyl accident area. These data were published by Gulakov14 and contain summaries of 137Cs contamination levels in the muscles of 188 boar collected between 1991 and 2008 (i.e. from 5 to 22 years after the 1986 accident). Sampling was carried out in three zones with different land contamination levels with 137Cs. This data set provides important information on radioactive cesium contamination in wild boar in the Chernobyl area. Unfortunately, 137Cs measurements in each sampled boar were not provided by Gulakov14, and only summary statistics are available for each zone and year after the accident (Tables 1–3 in reference14): number of animals, mean, minimum and maximum 137Cs levels.We could not apply the full model (Eq. 1A, 1B) to these summary data which lacked seasonality information and 134Cs data. However, we were able to perform a weighted linear regression to quantify the ecological half-life of 137Cs in Chernobyl boar and the relationship between 137Cs levels in the animals (Bq/kg), relative to the external environment (Bq/m2). The data used for this analysis, derived from Gulakov14, are provided in Supplementary data (Supplementary_Dataset_File_Full). They contain the following variables. Zone = location of sample collection (Alienation, Permanent control or Periodic control). Time = time in years after the Chernobyl accident. LnMeanCs = ln-transformed mean 137Cs level in boar muscle (Bq/kg). LnMeanCs_c = LnMeanCs − X, where X is ln-transformed 137Cs land contamination (Bq/m2) in the given zone, corrected for physical decay of 137Cs. Weight = weighting of each data point used for regression. Weight = number of animals/(ln[maximum 137Cs level] − ln[minimum 137Cs level])2. These approximately inverse-variance weights were normalized by the overall mean across all data points, so that the mean weight across all data points was set to 1.These data were analyzed by weighted linear regression in R, where LnMeanCs_c was allowed to depend on Time and Zone variables. Model variants containing all possible combinations and pairwise interactions between these predictor variables were fitted and their performances were compared using the Akaike information criterion with correction for small sample size (AICc). These calculations were performed using the glmulti R package. Multimodel inference (MMI) was performed on this collection of fitted model variants. It resulted in the calculation of model-averaged parameter estimates, 95% CIs and importance scores, corrected for model selection uncertainty. Of main interest here were the intercept parameter, which is analogous to parameter Q in the full model (Eq. 1A, 1B), and the Time parameter, which is analogous to parameter µ in the full model. The ecological half-life for 137Cs was calculated based on the Time parameter. More

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    The severity and extent of the Australia 2019–20 Eucalyptus forest fires are not the legacy of forest management

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    Relationship of contact angle of spray solution on leaf surfaces with weed control

    The results were divided into two factors, both were an independent process that was occurring on the surfaces, together, the two factors explained 60.2% of the total variation of the original data. Factor 1, which consists of the contact angle and the weed control, represent 60.1% of the data, and Factor 2, which consists of the contact angle of the abaxial surface of I. grandifolia and the adaxial surface of A. curassavica, represent 0.9% of the total variation (Table 1).Table 1 Result of the analysis of factors containing the first two factors (processes) with their respective factorial loads that represent the correlation coefficients between the foliar surfaces and control and each Factor.Full size tableThe process contained in the contact angle and weed control is the most important for this study since it is derived from the higher eigenvalue and has a higher percentage of explanation (60.1%), and the variables that contribute the most are represented by artificial surface (0.98), lantana adaxial and abaxial face (0.75 and 0.68, respectively), shakeshake adaxial and abaxial face (0.95 and 0.96, respectively), morning glory adaxial face (0.81), sicklepod adaxial and abaxial face (0.88 and 0.94), castor bean adaxial and abaxial face (0.52 and 0.85), bloodflower milkweed abaxial face (0.73), control in shakeshake 5, 11 and 16 DAA (−0.50, −0.77 and −0.92, respectively), control in lantana 5, 11 and 16 DAA (−0.51, −0.84 and −0.95, respectively). Furthermore, according to the signs of the factorial loads, the contact angle and weed control factor are directly and strongly correlated with the surfaces, because the contact angle showed the same positive sign for both surfaces, as well as to the control shakeshake and lantana showed the same negative sign.Considering that the factors are orthogonal (uncorrelated), the processes retained in the contact angle, weed control (Factor 1), and the factor morning glory abaxial surface, bloodflower milkweed adaxial surface (Factor 2) are considered independent. Thus, the analysis was performed with the scores of the contact angle and control factor (Fig. 1) and the morning glory abaxial surface factor, and bloodflower milk weed adaxial surface (Fig. 2). A significant difference (F = 109.58; p = 0.0001) was found between the treatments when the surfaces and weed control were evaluated (Factor 1). There were differences between the spray solutions and water treatment.Figure 1Graphical representation with the scores of Factor 1 (surface and control) as a function of the evaluated samples. Vertical bars represent confidence intervals of 0.95 (F = 109.58; p = 0.0001). Spray solution: 1-No adjuvant; 2-herbicide associated with vegetable oil; 3- herbicide associated with mineral oil; 4-herbicide associated with lecithin, and Control.Full size imageFigure 2Graphical representation with Factor 2 (Ipomoea grandifolia (abaxial) and Asclepias curassavica (adaxial) scores) as a function of the evaluated samples. Vertical bars represent confidence intervals of 0.95 (F = 4.036, p = 0.009). Spray solution: 1-No adjuvant; 2-herbicide associated with vegetable oil; 3-herbicide associated with mineral oil; 4-herbicide associated with lecithin, and Control.Full size imageFor factor 2 there was also a difference (F = 4.036; p = 0.009) when the plants were evaluated together. The spray solution did not differ from the control. When the spray solutions were compared, there was a difference only between the herbicide spray solution with no lecithin and the herbicide spray solution with lecithin (Fig. 2).Correlating with the results of the univariate analysis, it is possible to observe that the separation of factors is related to the result obtained from the contact angle of the bloodweed milkweed and morningglory surfaces, because, for bloodflower milkweed, the treatments and control were not significant (p  > 0.05), with no significance for the dosage and adjuvant interaction (p  > 0.05). The two experiments to control the weeds were complementary, did not differ from each other (p  > 0.05).For lantana on the adaxial surface, the control treatment differed from the treatments (p = 0.001), however, the spray solution, doses, and interaction were not significant (p  > 0.05, 0.0844, and 0.0616, respectively) (Table 2). For the abaxial surface, the spray solutions and the interaction of the factors were not significant (p = 0.0535 and 0.1353, respectively). However, the herbicide doses were statistically different (p  > 0.0001) (Figs. 3, 4).Table 2 Average and standard deviation of the contact angle (°) after drop deposition in surfaces of the leaves of the weeds.Full size tableFigure 3Percentage of control of Crotalaria incana L. plants after herbicide solution spraying. (A) Aminopyralid + fluroxypyr at 155.3 L of active ingredient ha−1. (B) Aminopyralid + fluroxypyr at 360.6 L of active ingredient ha−1. Equal letters within the evaluation days, the adjuvants do not differ from each other by the Tukey test (p  > 0.05).Full size imageFigure 4Percentage of control of Lantana camara L. plants after herbicide solution spraying. (A) Aminopyralid + fluroxypyr at 155.3 L of active ingredient ha−1. (B) Aminopyralid + fluroxypyr at 360.6 L of active ingredient ha−1. Equal letters within the evaluation days, the adjuvants do not differ from each other by the Tukey test (p  > 0.05).Full size imageThe contact angle of the shakeshake adaxial surface did not differ for spray solutions (p = 0.666), dose and control factors versus the treatments were significant (p  > 0.0001 and 0.0001), but for interaction, there was no significance (p = 0.5327) (Table 2). The abaxial surface, the spray solution, doses, and the control treatment versus the treatments were significant (p = 0.0056, 0.0428, and 0.0001), but the interaction was not significant (0.4453) (Table 2). For sicklepods adaxial and abaxial surfaces, doses, spray solution, control versus treatments and interaction were significant (p  > 0.0001). The surfaces of the species shakeshake and sicklepod showed higher values to the control, the abaxial surface sicklepod presented the contact angle value of approximately 177° (Table 2; Figs. 5 and 6), for shakeshake the addition of vegetable oil decreased the contact angle on the adaxial surface at any dose, and for sicklepod the addition of lecithin resulted in the lowest contact angle (Table 2).Figure 5Surface adaxial in plants of the Crotalaria incana L. (A), Lantana camara L. (B), Asclepias curassavica L. (C), Senna obtusifolia (L.) H.S.Irwin & Barneby (D), Ricinus communis L. (E) and Ipomoea grandifolia (Dammer) O’Donell (F) taken on the Stereoscopic Microscope with × 1.0.Full size imageFigure 6Surface abaxial in plants of the Crotalaria incana L. (A), Lantana camara L. (B), Asclepias curassavica L. (C), Senna obtusifolia (L.) H.S.Irwin & Barneby (D), Ricinus communis L. (E) and Ipomoea grandifolia (Dammer) O’Donell (F) taken on the Stereoscopic Microscope with × 1.0.Full size imageFor the castor bean, on the adaxial surface, the spray solution and doses were not significant (p = 0.06126 and 0.1761, respectively), the control treatment and the interaction were significant (p  > 0.0001), but for the abaxial surface the spray solution, dose, interaction, and control treatment were significant (p  > 0.0001). The morningglory on the adaxial surface showed a significant difference between the spray solution, doses, control treatment (p = 0.0001, 0.0072, and 0.0001) but it was not significant for the interaction (p = 0.2283), on the abaxial surface, the spray solution and the doses were not significant (p = 0.0755 and 0.3025), but the interaction and the control treatment were significant (p = 0.0007 and 0.0018). For morningglory the addition of lecithin resulted in the lowest contact angle on the adaxial surface, for the abaxial addition of mineral oil and lecithin presented the lowest values, for the castor beans the lecithin resulted in the lowest contact angle on the adaxial surface for the dose of 155.3 L ha−1, however, at a dose of 310.6 L ha−1, the spray solution without adjuvant or mineral or vegetable oil showed lower values of contact angle.For the milkweed bloodflower, on the adaxial surface, the sprays solution, doses, interaction, and control treatment were not significant (p = 0.1320, 0.6804, 0.0848, and 0.800, respectively), the abaxial surface, the sprays solution, doses, and interaction were not significant (p = 0.2016, 0.7371 and 0.8916, respectively), so the contact angle values were statistically similar to each other. However, its species presented a lower average value of the contact angle (43.93°) compared to other plants (Fig. 5 and 6).The standard surface (Parafilm) sprays solutions, control treatment and doses were significant (p  > 0.0001) but there was no significance for interaction (p = 0.2077), thus, regardless of the dose, the addition of lecithin resulted in the lowest contact angle of the drops.In the weed control experiment, shakeshake and lantana plants showed rapid damage caused by the herbicide aminopyralid + fluroxypyr (Fig. 3 and 4). For shakeshake, at 5 DAP the spray solution and dose were not significant (p  > 0.05), the control treatment versus the treatments and the interaction was significant (p = 0.0019 and 0.0149, respectively), the interaction was significant only in mineral oil in that the level of control was lower at the dose of 155.3 L ha−1. At 11 DAP the spray solution was not significant (p  > 0.05), but the doses, the interaction, and the control treatment versus the treatments were significant (p = 0.0009, 0.0413, and 0.0001, respectively), the interaction was significant because there were differences in lecithin at the control level, in which the dose of 155.3 L ha−1 resulted in the lowest level of control. In the last evaluation at 16 DAP, the spray solution was not significant (p  > 0.05), dose, interaction, and control versus treatments were significant (p = 0.0001, 0.0353 and 0.001), the interaction was significant for the spray solutions that had the addition of an adjuvant, in which the highest level of control was observed at the dose of 310.6 L ha−1 (Fig. 3 and 4).The level of lantana control at 5 DAP showed significance for spray solution (p = 0.0257) and control versus treatments (0.007), the dose and interaction were not significant (p  > 0.05), at 11DAP the spray solution, dose, and interaction were not significant (p = 0.1377, 0.0706 and 0.6540, respectively) there was only significance for control versus treatments (p = 0.0001). At 16 DAP the spray solution, dose, and interaction were not significant (p = 0.4703, 0.1734, and 0.4210, respectively), there was significance for the control versus treatments (p = 0.0004) (Fig. 3 and 4).In general, at 5 DAP, both species showed symptoms of twisting of the leaves next of the apical region was observed differences between treatments with no adjuvant and the doses used. The dose of 310.6 L ha−1 showed a better percentage of control in both species, and independent of the treatments used (Fig. 3 and 4).At 16 DAP, lantana plants showed 100% control independent of the dose used, but the shakeshake plants had control of close to 80% independent of the dose (Fig. 4). Shakeshake plants have a lower percentage of control due to the surface being more hydrophobic to the surface of the lantana, hydrophobicity can cause the drops to ricochet, with no drops being deposited, therefore, the product does not absorb (Fig. 3).The addition of adjuvants to the spray solution did not result in differences between the treatment without addition, despite the decrease in the contact angle with the addition of the herbicide. In the shakeshake, a higher percentage of control was observed in the treatment without the addition of an adjuvant, in the dose of 155.3 L ha−1, in the dose of 310.6 L ha−1 with the addition of lecithin resulted in a higher percentage of control (Fig. 3). For lantana, the addition or not of the adjuvant did not result in different control percentage values, independent of the dose, that is, only the herbicide was necessary to obtain 100% control of the plants, this is due to the lower angle value of contact compared to shakeshake (Fig. 4). 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