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To supplement the limited publicly available information on rabies risk, the US Centers for Disease Control and Prevention (CDC) performs an annual country-by-country qualitative assessment of rabies risks and protective factors. The results of this assessment are released annually in an open-access database of core metrics consisting of the presence of lyssaviruses (specifically canine or wildlife rabies virus variants, or other bat lyssaviruses), access to rabies immunoglobulins and vaccines, rabies surveillance capacity and canine rabies control capacity18. The analysis presented here builds upon the current CDC evaluation and specifically examines publicly available data to better inform the parameter of rabies surveillance capacity. This study found publicly available data regarding rabies animal testing by species, described testing practices in relation to the country’s human and dog populations, as well as by their stage of DMRVV control (defined by WHO), and used this data to calculate a surveillance testing threshold for DMRVV endemic countries.Data sources were categorized into four tiers, with the order reflecting the preference for selecting the most appropriate data for the purposes of this analysis. Tier 1 data sources were considered to be the preferential data source and included any official government data submitted to a Regional or International data repository. Official data repositories included the WHO GHO, Pan-American Health Organization Regional Information System for Epidemiologic Surveillance of Rabies (PAHO SIRVERA), and the European Rabies Bulletin. Tier 1 data sources also included official country reports found through literature search, so long as they were publicly available. Tier 2 data sources consisted of published reports in peer-reviewed literature or on a ministry of health or agriculture site that includes data from the entire country, as well as unofficial data repositories (e.g., Global Alliance on Rabies Control (GARC) Rabies Epidemiologic Bulletin). Tier 3 data consisted of one-time cross-sectional studies or studies describing sub-national testing activities and which could not be reliably extrapolated to an entire country. Tier 4 data sources include any resource not captured in the previous criteria that were obtained during literature searches. The primary data search was conducted in September 2021, with an update in September 2022. Only Tier 1 and Tier 2 data sources were included in the evaluation of animal testing rates. If multiple data sources contained conflicting testing rates, we prioritized data from surveillance repositories, then reports from ministries of health or agriculture, and, finally, peer-reviewed publications.For Tier 1 data (i.e., surveillance repository), data was included in this study if it described rabies testing conducted between the years 2010 and 2019. As political, economic, and epidemiologic factors directly influence the reliability and transparency of surveillance system data, we decided that a ten-year limit would capture any year-to-year variation in data and better characterize current passive surveillance practices. Additionally, the cutoff of 2019 was chosen so that the effects of the COVID-19 pandemic on rabies surveillance capacity would not affect this comprehensive evaluation and would account for lag time in reporting to Tier 1 data sources19,20. This study assumed data from these surveillance repositories is entered secondary to passive surveillance systems. If data was known to be from active surveillance activities, it was removed from analyses.For Tier 2 data (i.e., peer-reviewed publications), certain publications presented aggregated testing data that included years prior to the Tier 1 cutoff (i.e., 2010). To increase inclusivity of eligible data and keep the findings from this evaluation representative of current practices, eligible data must have had an end year ≥ 2012, regardless of the starting year of data (Table S1). The literature search was conducted on PubMed, Scopus, and Google for “rabies” AND “[country name]” from 2010 to December 2021. “Publicly available” was defined as any result appearing in PubMed or Scopus, or within the first three pages of a Google search. Exceptions to the first three pages were made for similar country names (e.g., Guinea, Congo). The first 10% of Spanish- and French-speaking countries were also searched for “rabia” and “raj,” respectively, to potentially capture any other sources of surveillance data. However, after no additional data was found, this was discontinued. If an article or resource quantifying animal testing capacity within these criteria was not found, the country was deemed to not have readily available data for analysis.For any countries that were part of the surveillance threshold calculation for DMRVV endemic countries, the preferred tiered data was compared to all other data sources. For one country (i.e., Brazil), there was a notable lack of dog testing data and known discrepancies in data reporting between their two reporting systems (i.e., SINAN, SIRVERA)21. In this situation, a median rate was calculated between a Tier 1 and Tier 3 data source. No other such discrepancies were noted. The type of surveillance (active or passive) was noted for each data source; we assumed passive surveillance with Tier 1 data unless compelling evidence existed to display that this was not the case. A strictly active surveillance program was excluded from all analyses. A summary of overall testing practices was performed and standardized according to the number of years each data source contained.As evaluations of rabies testing rates spanned over multiple years, population estimates were obtained to reflect the most recent year in the available data. Three separate testing rates were calculated and standardized based on the human population within the country: [1] All animal, [2] Domestic animal, and [3] Wildlife. There are different social and cultural behaviors that affect the human to dog ratio and interactions between people and animals. These differences can impact the susceptibility of dogs to rabies virus infection and the likelihood of human interactions with rabid animals. Therefore, we additionally calculated country testing rates standardized by the estimated dog population, to provide an additional indicator value of adequate surveillance capacity. Estimated dog populations were obtained from a previous study22. This resulted in up to four calculated rabies testing rates per country, depending upon available data.Equation 1: All-animal per human testing rate (AAHR)$$frac{Average,number,of,all,animals,tested/year}{{Estimated,human,population}} times 100,000$$
(1)
Equation 2: Domestic animal per human testing rate (DAHR)$$frac{Average, number, of, domestic, animals, tested/year}{{Estimated, human, population}} times 100,000$$
(2)
Equation 3: Domestic animal per dog testing rate (DADR)$$frac{Average, number, of, domestic ,animals, tested/year}{{Estimated ,dog, population}} times 100,000$$
(3)
Equation 4: Wildlife per human testing rate (WHR)$$frac{Average, number ,of, wildlife, animals, tested/year}{{Estimated ,human, population}} times 100,000$$
(4)
The WHO rabies epidemiologic Status is divided into five categories in escalating levels of dog rabies control: [1] Endemic dog-transmitted human rabies, [2] Endemic dog rabies, [3] Sporadic dog-transmitted rabies, [4] Controlled dog rabies, and [5] No dog rabies. The WHO Status was established based on existing data and expert knowledge to help better define the level of rabies control for each country23. In addition to these five WHO Statuses, countries in Status [5] were further sub-categorized into [5a] (rabies virus free), and [5b] (wildlife rabies enzootic) based on CDC’s wildlife rabies status; the CDC rabies status was also used for any country without a WHO Status (n = 11)24. Average testing rates for the aforementioned equations were calculated for each WHO Rabies Status category, treating each country as an equally weighted value in the rate calculation. Only descriptive analyses were conducted to describe surveillance and testing data, as data quality was not deemed acceptable for multi-variable statistical analysis and testing rates were heavily left-skewed. Data is presented as median and IQR as the data was noted to not reflect a parametric distribution.Ethics approvalThis activity was reviewed by CDC and was conducted consistent with applicable federal law and CDC policy. (See e.g., 45 C.F.R. part 46, 21 C.F.R. part 56; 42 U.S.C. §241(d); 5 U.S.C. §552a; 44 U.S.C. §3501 et seq.) The views and opinions of the manuscript are of the authors alone and do not represent those of CDC or any other federal agency. More

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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

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

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