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Changes in temperature and extreme environmental conditions can affect the dynamics of vector-borne pathogens. These include leishmaniasis, transmitted by phlebotomine sandflies, as well as mosquitoes that spread arboviruses like dengue, encephalitis, yellow fever, West Nile fever, and lymphatic filariasis19,20,21.The CCA analysis showed that maximum temperature significantly influenced the abundance of mosquito populations in the study area. In addition, the NMDS showed two different groupings that consisted of samples collected during the rainy and dry seasons. Accordingly, Refs.22,23 report that changes in temperature and relative humidity determine the abundance of mosquitoes, which can disappear entirely during the dry season. Moreover, Refs.22,24,25 note that certain species of mosquitoes increase proportionally with the regional rainfall regime. This is consistent with Ref.10, who find alternating patterns in tropical and temperate climates in some Brazilian regions.As shown by the geometric regression, there is a positive correlation between cumulative rainfall in the days before collection and the number of species found in the study period. Likewise, Ref.26 reported that under the conditions observed in the Serra do Mar State Park, climate variables directly influenced the abundance of Cq. chrysonotum and Cq. venezuelensis, favoring the occurrence of culicids during the more warm, wet, and rainy months.The current climate scenario and future projections about climate, environmental, demographic, and meteorological factors directly influence the distribution and abundance of mosquito vectors and/or diseases27,28,29,30. Environmental temperature alters mosquito population dynamics, thereby affecting the development of immature stages as well as reproduction31. While temperature has an important effect on population dynamics, rainfall and drought also affect the density and dispersal of mosquitoes in temperate and tropical regions32.To be sure, environmental changes other than climate can modify the behavior of vector insects and, subsequently, the mechanism of transmission of parasites20. Specifically, human impacts on the environment can result in drastically different disease transmission cycles in and around inhabited areas33.A previous study34 reported that changes in land use influence the mosquito communities with potential implications for the emergence of arboviruses. Another study35 noted that environmental changes negatively affect natural ecosystems with accelerated biodiversity loss. This is due to the modification and loss of natural habitat and unsustainable land use, which leads to the spread of pathogens and disease vectors.Hence, understanding the relationship between humans and the environment becomes increasingly critical, given the way in which climate changes can lead to alterations in the epidemiology of diseases such as dengue in areas considered free of the disease, as well as in endemic areas36.We found that the abundance and diversity of Mansoniini were directly influenced by the effect of the rainy season and other climatic factors. The rainfall regime has been shown to affect the development of immature forms12,37; explaining the greater frequency of these specimens in the warmer and wetter months38,39,40. According to Ref.41, stable ecosystems such as forests contain great species diversity. On the other hand, diversity tends to be reduced in biotic communities suffering from stress.Studies of insect populations in natural areas are important because they allow a direct analysis of how environmental factors influence phenomena such as the choice of breeding sites by females for oviposition, hematophagous behavior, and the distribution of species along a vegetation gradient12,26,42,43.Throughout the experimental period of the present study, we observed that Shannon light traps are an effective method for catching mosquitoes from the Mansoniini tribe. Interestingly, Ref.44 reported a species richness pattern strongly influenced by Coquillettidia fasciolata (Lynch Arribálzaga, 1891) on mosquito samples from different capture points by using CDC and Shannon light traps as sampling methods. In contrast to the results of Ref.44, where the highest population density of mosquitoes was captured with CDC traps, we observed that these traps were not effective at capturing specimens of Mansoniini in spite of being used in large numbers in the present study. Moreover, Ref.45 conducted another study on faunal diversity in an Atlantic Forest remnant of the state of Rio de Janeiro and observed the highest abundance of Cq. chrysonotum (Peryassú, 1922) and Cq. venezuelensis by using Shannon light traps, while the numbers of captures of Ma. titillans were very similar using CDC and Shannon traps.The results of this study indicate that the makeup of culicid fauna remains quite similar throughout the year, despite seasonal variations in abundance, though there was a lower variability of fauna in the dry season. Therefore, although the seasonality did not affect the temporal variation of the faunal composition in a generalized way, it was possible to detect a partial effect of the seasonality on fauna abundance.
Reference46 report that the incidence peaks of mosquitoes in the warmer and wetter months, as well as mosquito populations remaining between tolerance limits for most of the year, indicate the sensitivity of some species to the local climate.The elevated abundance and diversity of species of Mansoniini in the study area were influenced by the favorable maintenance of breeding sites, including specific water accumulations with emerging vegetation that remain present throughout the year and the well-defined rainy season in the region. In addition, the representatives of Mansoniini, which prefer breeding sites containing macrophytes, made up nearly all of the species collected7.Besides providing a greater awareness of mosquito populations’ ecological and biological aspects, research carried out in wild areas also provides information on the relationship between species diversity and the area in which they are found. Considering that wild insects may become potential vectors of diseases, research in wild areas also provides helpful information for understanding relevant epidemiological aspects. These studies facilitate the identification, monitoring, and control of mosquito populations following environmental changes caused by direct human action, which can lead to major epidemics26.We observed considerable heterogeneity among Mansoniini fauna, and the months with the highest rainfall directly influence the structure of the communities and contribute to the increase in mosquito diversity and abundance, possibly due to variations in the availability of habitat for their immature forms. More

Case studyThe experimental setup for the field studies that provided the bacterial population and weather data used here was previously described by Belias et al. [9]. Briefly, baby spinach and lettuce plants were spray-inoculated with E. coli and S. enterica (Salmonella) onto field plots established in Davis, CA (University of California, Plant Sciences Field Research Facility); Freeville, NY (Homer C. Thompson Research Farm, Cornell University); and Murcia, Spain (La Matanza Research Farm). The spinach and lettuce varieties were selected based on their suitability for baby leaf production: lettuce var. Tamarindo, and spinach var. Acadia F1 and Seaside F1. Four replicate trials at different times of the regional growing season were carried out per location. The plants were spray-inoculated with a 104 CFU/mL cocktail of rifampin-resistant strains of commensal E. coli and attenuated S. enterica serovar Typhimurium (Salmonella), and samples were collected for bacterial cell quantification by plate counts on selective and differential media at 0, 4, 8, 24, 48, 72 and 96 h post-inoculation. Concurrent with leaf sample collection, weather variables (temperature, relative humidity (RH), solar radiation intensity, and wind velocity) were recorded hourly for the respective field locations. The hourly dew point (DP) was calculated as a function of both the hourly temperature and RH.Model for persister formation on plantsMathematical modeling to characterize the switch rate from a non-persister bacterial cell (hereafter termed “normal cell”) to a persister cell in the phyllosphere under laboratory conditions was performed as described in our previously published study [24]. Briefly, persister cell fractions were quantified in culturable EcO157 populations after inoculation onto young lettuce plants cultivated in plant growth chambers. Persister cells recovered from the lettuce phyllosphere were identified using the antibiotic lysing method [23]. The greatest persister fraction in the EcO157 population on lettuce in our laboratory investigation above was observed during population decline on leaf surfaces of plants left to dry after inoculation. Using mathematical modeling, we calculated the switch rate from an EcO157 normal to persister cell on dry lettuce plants based on these data [24]. Importantly, our laboratory conditions mimicked inoculation conditions in which E. coli arrived via water on leaves, the surfaces of which progressively dried like under prevailing weather conditions in the field.Based on the main dynamic observed in the field study data [9] and building on our previous study [24], we assumed that the total enteric pathogen population is composed of (i) non-persister (normal) cells consisting of two sub-populations, characterized by fast (n1) (CFU/100g) and slow (n2) (CFU/100g) decay, and (ii) the persister population, leading to the following model from Munther et al. [24]:$$frac{{dn_1}}{{dt}} = – theta _{n_1}n_1 – alpha _dn_1 + beta _dleft( {1 – sigma } right)hat p,$$
(1a)
$$frac{{dn_2}}{{dt}} = – theta _{n_2}n_2 – alpha _dn_2 + beta _dsigma hat p,$$
(1b)
$$frac{{dhat p}}{{dt}} = – mu _{hat p}hat p – beta _dhat p + alpha _dleft( {n_1 + n_2} right),$$
(1c)
$$n_1left( 0 right) = n_{10},n_2left( 0 right) = n_{20},, hat pleft( 0 right) = widehat {p_0},$$
(1d)
where (theta _{n_i})(1/h) is the death rate of the normal cells (subscript i = 1 for fast and i = 2 for slow), (hat p) (CFU/100 g) represents the persister cell population at time t (h), (mu _{hat p}) (1/h) reflects the persister population inactivation rate, αd (1/h) is the switch rate from normal to persister state, βd (1/h) is the switch rate from persister to the normal state, and σ ∈ (0,1) is a constant, describing the fraction of persister cells switching back to the normal, slowly decaying state. Equation (1a) and (1b) reflect the assumption that times between switching states are exponentially distributed, using the expected values (frac{1}{{alpha _d}}) (h) and (frac{1}{{beta _d}}) (h) of the respective distributions.Lacking data for potential persister populations from the field trials, we assumed the persister population is a fraction 1  > k  > 0 of the tail population, as observed in Munther et al. [24]. Regarding the model above, this implies that (hat p approx kn_2) for (t ge t^ ast), where (t^ ast approx frac{1}{{theta _{n_1}}}) (the time scale of survival for the fast-decaying population (n1)). In accord with bi-phasic decay, for (t ge t^ ast), the main dynamics for slow decaying population (n2) is dictated by (- theta _{n_2}n_2) in Eq. (1b). This suggests that the effective switch rates from n2 to (hat p) and from (hat p) back to n2 balance, so that (beta _dsigma hat p approx alpha _dn_2) in Eq. (1b). Following these ideas, we simplified the model in Eq. (1a)–(1d) to:$$frac{{dn_1}}{{dt}} = – theta _{n_1}n_1 – alpha _dn_1,$$
(2a)
$$frac{{dn_2}}{{dt}} = – theta _{n_2}n_2,$$
(2b)
$$frac{{dhat p}}{{dt}} = – theta _{hat p}hat p + alpha _dn_1,$$
(2c)
$$n_1left( 0 right) = n_{10},n_2left( 0 right) = n_{20},, hat pleft( 0 right) = widehat {p_0},$$
(2d)
where we ignored (beta _dleft( {1 – sigma } right)hat p) in (1a) since the decay rate ((theta _{n_1})) dominates. Also, by setting (theta _{hat p} = mu _{hat p} + beta _d(1 – sigma )), and using (beta _dsigma hat p approx alpha _dn_2), we obtained Eq. (2c). Furthermore, because (hat p approx kn_2) for (t ge t^ ast), (theta _{hat p} approx) (theta _{n_2}).In particular, the assumption that (hat p approx kn_2) for (t ge t^ ast) characterizes the switch rate from normal to persister cells, αd, as (alpha _d approx kalpha), where α is a hypothetical switch rate assuming that the population is composed only of fast decaying normal cells (n1) and a hypothetical persister cell population (p). In this case, the hypothetical population p starts small at (widehat {p_0}), initially increases due to switching from population n1 and then slowly decays as the n1 population is effectively inactivated (i.e., the tail of the total population is comprised entirely of p). From this perspective we utilized the following equations:$$frac{{dn_1}}{{dt}} = – theta _{n_1}n_1 – alpha n_1,$$
(3a)
$$frac{{dp}}{{dt}} = alpha n_1 – theta _pp.$$
(3b)
$$n_1left( 0 right) = n_0,, pleft( 0 right) = widehat {p_0},$$
(3c)
For mathematical justification regarding the relationship (alpha _d approx kalpha), please see the appendix (Supplementary Information).The utility of the relationship (alpha _d approx kalpha), is twofold. First, we used model fitting (Eqs. (3a)–(3c)) to determine α from the respective field study data [9]. Note that using Eqs. (3a)–(3c), we actually fit for (theta _{n_1}), θp, and α using the field study data [9]. Please reference the “model fitting procedure” section as well as the appendix for details concerning the unique determination of the aforementioned parameters, i.e., the practical identifiability of these parameters, and justification regarding the legitimacy of measured tail populations relative to the respective field trial data [9]. Second, because we wanted to examine Spearman’s correlations (corr) between αd and various weather factors, given a particular weather factor (vec w) across trials (i = 1, ldots ,n), let k be the maximum persister fraction (of the tail) across these n trials, that is, for each i, we have (alpha _{d_i} approx k_ialpha _i), so (alpha _{d_i} lesssim kalpha _i). Thus kαi represents the maximum persister switch rate for each trial i, and since corr((kvec alpha ,vec w)) =corr((vec alpha ,vec w)), we conducted the correlation analysis with the fitted α values in lieu of the actual persister switch rate αd.The assumptions behind our approach are summarized below:

A.

The tails of pathogen populations surviving on plants in the field study [9] are comprised of some fraction k ∈(0,1) of persister cells since their decay rate is quite small and they remain culturable.

B.

Because (alpha _d approx kalpha), we hereafter utilize α from model (3a)–(3c) as the representative persister switch rate.

C.

Given that the experimental context [24] for modeling persister switching occurred during population decline, we only employed trials from Belias et al. [9] that exhibited bi-phasic decay. Namely, we did not include trials in which significant bacterial growth was observed at the time scale of successive data points (the time scale in the field study is on the order of 4–16 h for the 1st day and then 24 h thereafter.)

D.

The switch rate from normal to persister cell is on average a monotonic function of some measure of environmental stress.

Based on assumptions A–D above, we applied the model (3a)–(3c) to published pathogen population size and weather data from four replicate trials in Spain, two in California, and one in NY [9]. More specifically, we fit model (3a)–(3c) to the respective population data in order to:

1.

determine values for the maximum switch rate α relative to the produce/bacteria type at the field scale,

2.

describe the correlative relationship between α and weather factors in the respective field trials.

Model fitting procedureIn model (3a)–(3c) above, we supposed dp/dtt = 0  > 0, i.e., we assumed that bacteria experience stress from the change in conditions from culture growth and inoculum suspension preparation to those on the plant surface and therefore, that persister formation increases in the phyllosphere immediately following inoculation. The report that EcO157 persister formation increases as early as 1 h after inoculation into leaf wash water [23], which could be considered as a proxy for the average oligotrophic environment that bacterial cells experience after spray inoculation onto leaves or through irrigation in the field, supports this assumption. To avoid identifiability issues between the initial persister population (widehat {p_0}) and α regarding the model fits above, we assumed that (widehat {p_0})= 1 ((widehat {p_0}) = 0 gives the same results). Thus, the initial persister population at inoculation is at its lowest, an assumption supported by Munther et al. [24], who observed an average fraction of EcO157 persisters of 0.0043% in the inoculum population. This imparts the largest possible switch rate, α, onto the population, corresponding to the largest and hence most conservative food safety risk.Let yk (CFU/100 g of produce) be the average bacteria population measurement at time tk (h) and let Pk,X (CFU/100 g of produce) represent the model prediction (total population) at time tk relative to the parameter vector (X = [ {theta _{n_1} , theta_p , alpha } ]^T). Following Eqs. (3a) and (3b), this means that ({{{{{{{mathrm{P}}}}}}}}_{k,X} = n_1left( {t_k,X} right) + p(t_k,X)). Since the population data spans multiple orders of magnitude, we calculated the residuals as (e_{k,X} = log _{10}y_k – log _{10}P_{k,X}). To determine the optimal model fit (see the appendix for details regarding a priori bounds on parameter ranges), we utilized the fminsearch function in MATLAB (MATLAB 2020b, The MathWorks, Inc., Natick, Massachusetts, United States) to determine the parameter vector X that minimizes the 2-norm of the following function F:$$| | Fleft( X right) | |_2 = left( {mathop {sum }limits_k e_{k,X}^2} right)^{frac{1}{2}}$$Correlation analysisTo provide a statistical foundation from which to relate the switch rate α and measured weather factors, we utilized Spearman and partial Spearman correlation. First, we calculated the Spearman correlation coefficients between α and each of the respective factors: 8-h average of temperature, RH, solar radiation, wind speed post-inoculation, and then we calculated the partial Spearman correlation coefficients for each respective weather factor, while controlling for the other three factors and simultaneously controlling for produce type (using lettuce =1 and spinach =0) (For details regarding why 8-h weather variables were used, see the “model fitting” subsection of the results.) The correlation coefficients were determined using the corr and partialcorr functions in MATLAB 2020b (The MathWorks, Inc., Natick, MA, USA). Considering the significant association of Salmonella α with RH and temperature, we also examined the correlation between α and dew point. Figure 1 presents a logical flow of the statistical analysis. Partial correlations with a P value of less than 0.05 were deemed significant. If the 8-h average of a weather factor exhibited a significant correlation with the switch rate, the 8-h minimum and range of the weather factor were also tested.Fig. 1: Logical flow diagram for statistical analysis.Factors in Step 1: UV (average ultraviolet radiation intensity), RH (average air relative humidity), Wind (average wind speed), and Temp (average air temperature). All weather data used in the statistical analysis were obtained over 8 h post-inoculation of E. coli and Salmonella onto lettuce and spinach leaves in the field.Full size image More

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Zhang BH, Salam N, Cheng J, Li HQ, Yang JY, Zha DM, Zhang YQ, Ai MJ, Hozzein WN, Li WJ. Modestobacter lacusdianchii sp. nov., a phosphate-solubilizing actinobacterium with ability to promote Microcystis growth. PLoS ONE. 2016;11:e0161069.PubMed 
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