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    Diversity of life history and population connectivity of threadfin fish Eleutheronema tetradactylum along the coastal waters of Southern China

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    A meta-analysis of the stony coral tissue loss disease microbiome finds key bacteria in unaffected and lesion tissue in diseased colonies

    Summary of SCTLD microbiome studiesInitially, datasets were acquired from 17 SCTLD studies, but one study [24] did not pass quality filtering and was removed from the analysis, resulting in 16 SCTLD studies used in this meta-analysis. In addition, one Acropora spp. rapid tissue loss (RTL) disease study was included for comparison of bacteria which may be associated more generally with coral tissue loss diseases (Supplementary Table 1). The combined dataset included 2425 samples, representing various coral species and environments described below. A total of 63 miscellaneous samples such as lab controls were included in this total (Supplementary Table 1). Samples from the studies were sequenced using five primer pairs: CS1-515F/CS2-806R [31] with additional 5’ linker sequences [32] (n = 79), 515FY [33]/806RB [34] (n = 1219), S-D-Bact-0341-b-S-17/S-D-Bact-0785-a-A-21 [35] (n = 31), 515F/806R [31] (n = 49), and 515F [31]/Arch806R [36] (n = 984; Fig. 1A). Although five primer pairs were used across studies, only the forward reads were evaluated in this analysis (see “Methods”). A description of the differences between 515F primers can be found in detail [34].Fig. 1: The number of aquaria and field samples for each coral species.A small subunit (SSU) rRNA gene primer sets, B sample type, and C disease state. NAs in (A, B) represent sediment and seawater samples. Coral species codes represent the following: Acropora cervicornis (ACER), Acropora palmata (APAL), Colpophyllia natans (CNAT), Diploria labyrinthiformis (DLAB), Dichocoenia stokesii (DSTO), Montastraea cavernosa (MCAV), Meandrina meandrites (MMEA), Orbicella annularis (OANN), Orbicella faveolata (OFAV), Orbicella franksi (OFRA), Porites astreoides (PAST), Pseudodiploria clivosa (PCLI), Pseudodiploria strigosa (PSTR), Stephanocoenia intersepta (SINT), and Siderastrea siderea (SSID).Full size imageSamples were collected throughout Florida and the U.S. Virgin Islands (USVI). Field samples totaled 1274, representing 40 sites, and a further 1088 samples were from aquaria (i.e., laboratory-based experiments; Fig. 1). Thirteen SCTLD-susceptible coral species were included, with Montastraea cavernosa (MCAV; n = 543) and Orbicella faveolata (OFAV; n = 357) most represented and Pseudodiploria clivosa (PCLI; n = 6) and Orbicella franksi (OFRA; n = 7) least represented (Fig. 1). Coral samples (n = 2031) were from three compartments: mucus only (n = 393), mucus and surface tissue (tissue slurry; n = 1585), and skeleton samples with embedded coral tissue (tissue slurry skeleton; n = 53). Seawater (n = 198) and sediment (n = 133) samples from both the field and aquaria experiments also were included to evaluate potential sources of transmission of disease-associated bacteria (Fig. 1B). For seawater from aquaria experiments, 18 L samples were collected [27], while in the field between 60 mL and 1 L samples were collected [11, 25]. In sediment aquaria experiments, 2 mL samples were collected [12], and in the field, approximately 5 mL samples were collected (of the 5 mL, DNA was extracted from 0.25 g sediment [11]). Coral samples represented three SCTLD health states: apparently healthy colonies (AH), which was the most represented (n = 1021), followed by lesions on diseased colonies (DL; n = 661), and unaffected areas on diseased colonies (DU; n = 349; Fig. 1C). AH represents grossly normal tissue, DU grossly normal tissue on diseased colonies, and DL grossly abnormal tissue.Differences in the microbial composition were found in AH corals among zones (vulnerable, endemic, and epidemic)Differences in alpha-diversity were tested among three SCTLD zones: vulnerable (i.e., locations where the disease had not been observed/reported), endemic (i.e., locations where a disease outbreak had moved through the reef and no or few colonies had active lesions), and epidemic (i.e., locations where the outbreak was active and prevalent). For alpha-diversity, for AH field-sourced samples, after filtering, 41,504 amplicon sequence variants (ASVs) remained, which were reduced to 15,021 following rarefaction. Among the filtered AH samples, Shannon (alpha) diversity from the vulnerable zone was slightly higher (estimated marginal means (emmean) = 3.95) compared to the epidemic zone (emmean = 3.70), but this was not significant (Supplementary Fig. 1). For beta-diversity, both within and between-group differences were tested using a filtered counts table. Within-group beta-diversity (variation in microbial composition or dispersion) was not different between zones, but was significant for all comparisons between zones (PERMANOVA, P-adjusted (Padj) More

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    A biome-dependent distribution gradient of tree species range edges is strongly dictated by climate spatial heterogeneity

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    Interpreting random forest analysis of ecological models to move from prediction to explanation

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

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    Climate, landscape, and life history jointly predict multidecadal community mosquito phenology

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    Vole outbreaks may induce a tularemia disease pit that prevents Iberian hare population recovery in NW Spain

    Study siteOur study site is in an intensive agricultural landscape in NW Spain known as “Tierra de Campos”, which occupies part of three out of nine provinces of Castilla-y-León region (Palencia, Valladolid, and Zamora). This area is considered the main “hot-spot” of tularemia in Spain and Southern Europe16 and is characterized by higher-than-average vole abundances during outbreaks17.Iberian hare abundance indexYearly occurrence of vole outbreaks in NW Spain between 1996 and 2020 (i.e., 1997, 1998, 2007, 2008, 2014, 2019) were identified based on reports in the news (historical reconstruction18) and more recently (from 2009 onward) using common vole abundance indices obtained from live-trapping monitoring (i.e.4,19).To study the Iberian hare population trends we used regional hunting statistics available from the regional government (Junta de Castilla-y-León, CAZDATA Project, https://medioambiente.jcyl.es/web/es/caza-pesca/cazdata-banco-datos-actividad.html [Cited 2022 Sep 23]), which included hunting records as well as the number of hunting licences from 1974 to 2020. We used the number of hunted hares divided by the number of hunting licences each year as an abundance index for hares in “Tierra de Campos” (compiling data from the provinces of Palencia, Zamora and Valladolid). CAZDATA Project is an initiative proposed by the Hunting Federation of Castilla y León, which has the support of the regional government and, more importantly, the commitment of almost 60% of the hunting societies in the community to implement a system for monitoring hunting activity. Since this information is gathered by hunters for the benefit of the hunting activity, we are confidence on its reliability to carry out the present study.
    Francisella tularensis prevalence in Iberian haresWe compiled data on F. tularensis prevalence in Iberian hares from 2007 to 2016 using previously published information from a passive surveillance program carried out by the “Regional Network of Epidemiological Surveillance” (Red de Vigilancia Epidemiológica de la Dirección General de Salud Pública) of Castilla-y-León region20. This provided us with information on hare tularemia prevalence (amount of positives/number of screened individual) each year within the three provinces from “Tierra de Campos”.Statistical analysesTo study Iberian hare population trends, we calculated an index of yearly hare population instantaneous growth rate (PGR) using the hunting bag data (hare abundance index) from 1996 to 2020. Hare PGR was calculated as follows:$$PGR= lnleft(frac{{AI}_{t}}{{AI}_{t+1}}right)$$where ln stands for natural logarithm, AIt is Iberian hare abundance index on year t. and AIt+1 is the Iberian hare abundance index on year t + 1. PGRs were estimated yearly from 1996 to 2019. This dependent variable was fitted to a Generalized Linear Mixed Model using the glmmTMB function (GLMMTMB, package glmmTMB21) and a gaussian family distribution and identity link function. The categorical variable vole outbreak year (i.e., with two levels: years with (1) or without vole outbreak (0), hereafter “Vole”) and “Province” (i.e., with three levels: Palencia, Valladolid and Zamora), and their interaction were used as explanatory variables. “Year” of sampling was included as a random factor (i.e., 1996–2019). Significance of the fixed effects in the models was calculated with Type II tests using the function Anova in the car package22. We previously checked the model for overdispersion and distribution fitting using function simulateResiduals (package DHARMa23, simulations = 999). The variable PGR expresses the change between year t and t + 1. We included AI at t as a covariate in the model, in order to take into account density-dependence in hare PGR (the extent to which the abundance changes in between year t and t + 1 depends on the abundance during year t). For this to make biological sense, we rescaled the covariable AI so that it has mean equal to zero. Thus, the effect of the other predictor variables in the model (i.e., “Vole” and “Province”) was interpreted as the effect that these variables have on PGR when the abundance value is at 0. Thus, the effect of “Vole” and “Province” on PGR will be obtained by the mean value of abundance.We assessed the effect of vole outbreak years on the Iberian hare’s population PGR by running a multiple Pearson correlation (function ggscatter) between PGR and AI, considering both, PGR for all the years of the study period (i.e., 1996–2019) and only those years where vole outbreaks were detected (i.e., 1997, 1998, 2007, 2008, 2014, 2019).Finally, we tested for difference in the prevalence of F. tularensis on Iberian hare’s during years with or without vole outbreaks using a GLMMTMB21 with a binomial family distribution and a logit link function, where prevalence of F. tularensis in hares was the dependent variable, and “Vole” outbreak years and “Province” (i.e. Palencia, Valladolid and Zamora) were the responses variables. In this case, the variable “Vole” outbreak years included three levels (i.e. 0 = no vole outbreak, 1 = vole outbreak year, 2 = one year after vole outbreak), to assess if F. tularensis prevalence in hare also persist one year after a vole outbreak. “Year” of sampling was included as a random factor (i.e., 2007–2016). Due to the limited sample size, we did not include the interaction between “Vole” and “Province” to not overfit the model. We also previously checked the model for overdispersion and distribution fitting using function simulateResiduals (package DHARMa23, simulations = 999). All analysis were carried out using the R statistical computing environment24. More