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One of the most interesting observations from the time series presented in the section above is the following: when the magnitude of the Allee parameter (theta) is low, vegetation and prey densities are confined to low values. However, the predator densities deviate very significantly away from their mean. Now for very small (theta) the system is attracted to a periodic orbit, and so the large deviations are completely correlated with time and occur periodically. So they cannot be considered to be extreme events, as they are neither aperiodic, nor rare. But for larger (theta), both predator and prey densities can sometime shoot up over 7 standard deviations away from the mean value. This is evident clearly in Fig. 2c,e where one can see that both predator and prey populations exceed the (7sigma) threshold from time to time. The instants at which prey and predator populations exceed the (7sigma) threshold are now completely uncorrelated with time. This is consistent with the underlying chaotic dynamics that emerges under increasing Allee parameter (theta).In order to illustrate this, we mark the time instances at which a population exceeds the (7sigma) threshold, for different values of Allee parameter (theta). Figure 4 shows this for the vegetation, prey and predator populations. The density of points signifying the occurrence of extreme events is clearly the highest for the predator population. This indicates that the predator population has the greatest propensity for large deviations. It is also clear that vegetation has the least number of extreme events in the same time window. The uncorrelated nature of the extreme events is also evident in the scatter of these points, except in the small periodic windows that occur for certain special ranges of (theta). The increasing density of these points also illustrate the increasing probability of extreme events in the populations with increasing Allee parameter (theta).Figure 4Figure marking the time instances at which a population exceeds the (7sigma) threshold, for different values of Allee parameter (theta), for the case of (top to bottom) vegetation, prey and predator populations.Full size imageIn order to understand the phenomena quantitatively, we first estimate the maximum densities of vegetation, prey and predator populations (denoted by (u_{max}), (v_{max}) and (w_{max}) respectively) for varying the Allee parameter (theta). To estimate this, we find the global maximum of the populations sampled over a time interval (T=1000), averaged over a large set of random initial conditions.Figure 5 shows (u_{max}), (v_{max}) and (w_{max}), for Allee parameter (theta in [0,theta _{c})), scaled by their values at (theta = 0). These scaled maxima help us gauge the relative change in the maximum population densities arising due to the Allee effect. It is evident from our simulation results that the magnitude of the global maximum of vegetation does not change very significantly for increasing Allee parameter (theta), with its magnitude around (theta _c) being approximately 4 fold the value at (theta =0). However, the magnitude of maximum prey and predator populations change very significantly with respect to Allee parameter (theta) and exceeds over 10 fold the value obtained for (theta =0).Figure 5Global maximum of vegetation (u_{max}) (blue), prey (v_{max}) (red) and predator (black) populations, with respect to the Allee parameter (theta), scaled by their values obtained for (theta =0). Clearly, when Allee parameter (theta) is sufficiently large, the maximum prey and predator populations are an order of magnitude larger than that obtained in systems with no Allee effect.Full size imageWe then go on to numerically calculate the probability density of the vegetation, prey, and predator population densities, for increasing Allee effect parameter (theta). The tail of this probability density function reflects the influence of the Allee effect on the probability of obtaining extreme events. To illustrate this, we show the probability density function for the prey population in Fig. 6, for three different values of (theta). Extreme events are confined to the tail of the distribution that lie beyond the vertical red line, marking the (mu + 7 sigma) value in the figure. So it is clear from these probability distributions that the Allee effect in prey population promotes the occurrence of extreme events as the tail of the distribution is flatter and extends further with increasing Allee parameter (theta).Figure 6Probability Density Function (PDF) of the prey population v, for the system given by Eq. (1), with increasing magnitude of (theta) with (a) (theta =0), (b) (theta =0.015) and (c) (theta =0.02). The threshold for extreme event (mu + 7sigma) is denoted by vertical red dashed line.Full size imageIn order to ascertain that the extreme values are uncorrelated and aperiodic we examine the time intervals between successive extreme events in the population. Figure 7 (left panel) shows representative results for the return map of the intervals between extreme events in the prey population and it is clearly shows no regularity. The probability distribution of the intervals is also Poisson distributed and so the extreme population buildups are uncorrelated aperiodic events, as clearly evident from the right panel of the figure.Figure 7(Left) Return Map of (Delta t_{i+1}) versus (Delta t_i), and (right) Probability distribution of (Delta t_i) fitted with exponentially decaying function, where (Delta t_i) is the ith interval between successive extreme events, where an extreme event is defined at the instant when the prey population crosses the (mu +7sigma) line (cf. Fig. 2). Here (theta =0.024).Full size imageIn order to further quantify how Allee effect influences extreme events, we estimate the probability of obtaining large deviations, in a large sample of initial states tracked over a long period of time. We denote this probability by (P_{ext}), and we calculate it by following a large set of random initial conditions and recording the number of occurrences of the population crossing the threshold value in a prescribed period of time, with this time window being several orders of magnitude larger than the mean oscillation period. This time-averaged and ensemble-averaged quantity yields a good estimate of (P_{ext}). With no loss of generality, we choose the threshold for determining extreme events to be (mu + 7 sigma), i.e. when the variable crosses the (7 sigma) level, it is labelled as extreme.This probability, estimated for all three populations is shown in Fig. 8. First, it is clear from Fig. 8, that the probability of the occurrence of extreme events is the lowest for vegetation, and the highest for predator populations, for any value of the Allee parameter (theta in [0,theta _{c})). We also observe that, for values of the Allee parameter (theta) lower than a critical value denoted by (theta ^{u}_{c}) the probability of obtaining extreme events in the vegetation population tends to zero. Beyond the critical value (theta ^u_c), the vegetation population starts to exhibit extreme events. A similar trend emerges for the prey population. However, the critical value of the Allee parameter (theta) necessary for the emergence of a finite probability of extreme events, denoted by (theta ^{v}_{c}), is much smaller than (theta ^u_c). So for the prey population, a weaker Allee effect can induce extreme events.Figure 8Probability of obtaining extreme event in unit time ((P_{ext})), with respect to Allee parameter (theta), estimated by sampling a time series of length (T=5000), and averaging over 500 random initial states. Here we consider that an extreme event occurs when a population level crosses the threshold (mu + 7sigma). (P_{ext}) for vegetation, prey and predator are displayed in blue, red and black colors respectively. Note that there exists a narrow periodic window around (theta sim 0.02) (cf. Fig. 9), and so the large deviations in this window of Allee parameter are not associated with true extreme events, as they occur periodically.Full size imageNote that some mechanisms have been proposed for the generation of extreme events in deterministic dynamical systems, which typically have been excitable systems. These include interior crisis, Pomeau-Manneville intermittency, and the breakdown of quasiperiodic motion. However the extreme events generated by these mechanisms occur typically at very specific critical points in parameter space, or narrow windows around it. The first important difference in our system here is that the extreme events do not emerge only at some special values alone. Rather, there is a broad range in Allee parameter space where extreme events have a very significant presence. This makes our extreme event phenomenon more robust, and thus increases its potential observability. This also rules out the intermittency-induced mechanisms that have been proposed, as is evident through the lack of sudden expansion in attractor size in our bifurcation diagram (Fig. 3) in general.However, interestingly, the system does have one parameter window where there is attractor widening and this gives rise to a markedly enhanced extreme event count. The peak observed in Fig. 8 can be directly correlated with a sudden attractor widening leading to a marked increase of extreme event in a narrow window of parameter space located near the crisis (see Fig. 9). Additionally, for a narrow window around (theta sim 0.02), the emergent dynamics is periodic. So the large deviations are no longer uncorrelated, and so they are not extreme events in the true sense.Figure 9Bifurcation diagram of prey populations with respect to Allee parameter, in the range (theta in [0.0189 : 0.0191]). Here we display the local maxima of the prey population. The parameter values in Eq. (1) are as mentioned in the text.Full size imageLastly we notice that the predator population shows extreme events for all values of (theta in [0,theta _{c})). So the predator population is most prone to experiencing unusually large deviations from the mean. We also observe that the probability of occurrence of extreme events in the predator population is not affected significantly by the Allee effect. This is in marked contrast to the case of vegetation and prey, where the Allee effect crucially influences the advent of extreme events. Also, for the predator population there is no marked transition from zero to finite (P_{ext}) under increasing Allee parameter (theta), as evident for vegetation and prey populations. More

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Global statistical analysisOur first attempt to identify plausible effects of meteorological covariates on COVID-19 spread applied a comparative regression analysis. To this end, we focused on the exponential onset of the disease, as it is the epidemic phase that allows for a better comparison between countries or regions, without the confounding effect of intervention policies. We first determined, for each of the spatial units (either countries or NUTS (nomenclature of territorial units for statistics) 2 regions), the day in which 20 or more cumulative cases were officially reported. We then fitted the first-order polynomial function f(t) = x0 + rt for the next 20 days of log-transformed data, where t represents time (in days) and ({{x}_0}) is the value at initial condition t = 0. The r parameter can be understood as the exponential growth rate, and is then used to estimate the basic reproduction number (R0) using the estimated serial interval T for COVID-19 of 4.7 days53, such that R0 = 1 + rT (ref. 54). (We note that we are interested here in the relationship between the reproductive number and not in the actual inference of R0.) Once R0 was obtained for all our spatial units, we filtered our meteorological data to match the same fitting period (with a 10-day negative delay to account for an incubation and reporting lapse) for every spatial unit. To compute a single average of the meteorological variables per regional unit, we computed a weighted average on the basis of the population contribution of each grid cell to the total population of the region. We did so to have an aggregated value that would better represent the impact of these factors on the population transmission of COVID-19, as the same variation in weather in a high-density urban area is more likely to contribute to a change in population-level transmission than that of an unpopulated rural area. We then averaged the daily values of temperature and AH for each country and computed univariate linear models for each of these variables as predictors of R0. Given the somewhat arbitrary criteria to select the dates to estimate the R0 in each country, a sensitivity analysis was run to test the robustness of the regressions to changes in the related parameters. We tested 70 different combinations of two parameters: the total number of days used for the fit (18–27) and the threshold of cumulative COVID-19 cases used to select the initial day of the fit (15–45). We also calculated the weather averages by shifting the selected dates accordingly. Then, a linear model for each of the estimates was fitted for both T and AH. A summary of the distribution of parameter estimates (the regression slope coefficients and the R2 of the models) is shown in Extended Data Fig. 3.Bivariate time-series analysis with scale-dependent correlationsTo examine associations between cases and climate factors in more detail, SDC was performed on the daily time series of both COVID-19 incidence and a given meteorological variable. SDC is an optimal method for identifying dynamical couplings in short and noisy time series20,21. In general, Spearman correlations between incidence and a meteorological time series assess whether there is a monotonic relation between the variables. SDC analysis was specifically developed to study transitory associations that are local in time at a specified temporal scale corresponding to the size of the time intervals considered (s). The two-way implementation (TW-SDC) is a bivariate method that computes non-parametric Spearman rank correlations between two time series, for different pairs of time intervals along these series. Different window sizes (s) can be used to examine increasingly finer temporal resolution. The results are sensitive to the value of this window size, s, with expected significant and highest correlation values at the scale of the transient coupling between variables. Correlation values decrease in magnitude as window size increases, and averages are computed over too long a time interval. Values can also decrease and become non-significant for small windows when correlations are spurious. Here, the method was applied for windows of different length (from s = 75 to 14 days) and, despite a weekly cycle showing up in some cases for small s, results removing this cycle were robust. We therefore did not remove this cycle.The results are typically displayed in a figure with the following subplots: (1) the two time series, to the left and top of the matrix of correlation values, respectively; (2) the matrix or grid of correlation values itself in the center, with significant correlations colored in blue when positive and in red when negative, with rows and columns corresponding to the temporal localization of the moving window along the time series on the left and top, respectively; (3) a time series at the bottom, below this grid, with the highest significant correlations for a given time (vertically, and therefore for the variable that acts as the driver, here the meteorological time series). To read the results, one starts at the diagonal and moves vertically down from it to identify a given lag for which significant correlations are found (the closest to the main diagonal). In some of the SDC figures, the time intervals with high local correlations are highlighted with boxes. These intervals alternate with other ones (left blank) for which no significant correlation is found. All colored areas correspond to significance levels of at least P  fs/fr, where fs is the sampling rate and fr the minimum frequency. Another strategy is that M be large enough that the M-lagged vector incorporates the temporal scale of the time series that is of interest. The larger the M, the more detailed the resulting decomposition of the signal. In particular, the most detailed decomposition is achieved when the embedding dimension is approximately equal to half of the total signal length. A compromise must be reached, however, as a large M implies increased computation, and too large a value may produce mixing of components. SSA is especially well suited for separating components corresponding to different frequencies in nonlinear systems. Here, we applied it to remove the weekly cycle.MSDC analysisMSDC provides a scan of the SDC analyses over a range of different scales (here, S from 5 to 100 days at 5-day intervals), by selecting the maximum correlation values (positive or negative) closer to the diagonal. The goal is to consider the evolution of transient correlations at all scales pooled together in a single analysis. The MSDC plot displays time on the x axis and scale (S) on the y axis, and positive and negative correlations either jointly or separately. The rationale behind MSDC is that correlations at very small scales can occur by chance because of coincident similar patterns, but that as one moves up to larger scales (by increasing S), the correlation patterns that are spurious tend to vanish, whereas those reflecting mechanistic links increase in strength. This increase in correlation values should occur up to the real scale of interaction, decreasing afterwards. By ‘real’, we mean here the temporal scale covering the extent of the interaction between the driver and the response process (in this case, the response of disease transmission to a given climate factor). Thus, continuity of the same sign correlations together with transitions to larger values are indicative of causal effects, whereas the rapid vanishing of small-scale significant correlations signals spurious ones.Process-based modelDescriptionThe dynamical model is a discrete stochastic model that incorporates seven different compartments: S, E, I, C, Q, R and D. The model structure is illustrated in Fig. 4. The transition probabilities of the stochastic model are based on the corresponding rates of the transitions between classes in the deterministic (mean-field) model (specified in Fig. 4b). These probabilities are defined as follows. P(e) = (1.0 − exp(−β dt)) is the probability of infection exposure of the susceptible class, where β = (1/N)(βII + βQQ) is the infection rate (of the deterministic model). P(i) = (1.0 − exp(−γ dt)) is the probability that an new exposed individual becomes infectious, where γ denotes the incubation rate. P(r) = (1.0 − exp(−Λ dt)) is the recovery probability, where λ0(1 − exp(λ1t)) is the (deterministic) recovery rate. P(p) = (1.0 − exp(−α dt)) is the protection probability, where α = α0exp(α1t). P(d) = (1.0 − exp(−K dt)) is the mortality probability, with K = k0exp(k1t). P(re) = (1.0 − exp(−τ dt)) is the release probability from confinement, where τ = τ0exp(τ1t). Finally, P(q) = (1.0 − exp(−δ  dt)) is the detection probability, where δ is the quarantine rate (for example, at which infected individuals are isolated from the rest of the population).In the model, both infected non-detected and infected detected individuals can infect susceptible ones. In the model incorporating temperature in the transmission rate, the respective values of βI and βQ are calculated as follows:$${beta }_{I}(t)={beta }_{I},T_{mathrm{inv}}(t);quad {beta }_{Q}(t)={beta }_{Q},T_{mathrm{inv}}(t)$$where (T_{mathrm{inv}}=fleft(frac{1-T(t)}{bar{T}}right)), with (bar{T}) corresponding to the overall mean of the temperature time series and f(·) to a Savitzky–Golay filter, used to smooth the temperature series with a window size of 50 data points and a polynomial order of 3. When the infection rate is constant, we simply omit the temperature term. For further comparison, in a third model, β is specified with a sinusoidal function of period equal to 12 months and an estimated phase.The number of individuals transitioning from compartment i to j at time t are determined by means of binomial distributions P(Xi,P(y)), where Xi corresponds to one of the compartments S, E, I, Q, R, D, C, and P(y) to the respective transition probability defined above. Thus,

e(t) = P(S(t), P(e)), new exposed individuals at time t

p(t) = P(S(t), P(p)), protected individuals at time t

i(t) = P(E(t), P(i)), new infected not detected individuals at time t

q(t) = P(I(t), P(q)), new infected and detected individuals at time t

r(t) = P(Q(t), P(r)), total recovered individuals at time t

d(t) = P(Q(t), P(d)), total dead individuals at time t

re(t) = P(C(t), P(re)), individuals released from confinement at time t

Then, the final dynamics are given by the following equations:$$S(t)=S(t-{rm{d}}t)-e(t)-p(t)+re(t)$$$$E(t)=E(t-{rm{d}}t)+e(t)-i(t)$$$$I(t)=I(t-{rm{d}}t)+i(t)-q(t)$$$$Q(t)=Q(t-{rm{d}}t)+q(t)-r(t)-d(t)$$$$R(t)=R(t-{rm{d}}t)+r(t)$$$$D(t)=D(t-{rm{d}}t)+d(t)$$$$C(t)=C(t-{rm{d}}t)+p(t)-re(t)$$CalibrationThe model was implemented using Python and calibrated by means of the least squares algorithm of the scipy library. The error function minimized with this algorithm was obtained from the normalized residuals on the basis of total cases (Q + R + D) and deaths (D).To search parameter space, we ran 100 calibrations starting from different initial choices of parameter combinations. The tolerance for termination in the change of the cost function was set to 1 × 10−10. Tolerance for termination by the norm of the gradient was also set to 1 × 10−10, and the tolerance for termination by the change of the independent variables was set to 1 × 10−10. The solver was the lsmr method (which is suitable for problems with sparse and large Jacobian matrices) with a differential step of 1 × 10−5. With this configuration, each fitting run usually converged after ~500 iterations.ValidationTo compare the model including an effect of T in the transmission rate to those without it, we calculated the chi-square, Akaike information criterion (AIC) and Bayesian information criterion (BIC) indices for the residuals obtained from the optimization process. The resulting values are shown in Supplementary Table 1.Our choice of T to modulate the infection rate (β) instead of AH underlies the fact that the temporal dynamics of both factors roughly follow the same shape, with the advantage that T shows less oscillatory behavior than AH. This fact adds stability to the model when the inverse relationship is used in the calculation of β (Supplementary Information). This selection is further reinforced by the results from the SDC analyses, which yielded larger correlations for temperature, even when penalizing for the larger autocorrelation structure.Our choice to modulate β using T instead of AH follows from the fact that the temporal dynamics of both climate variables present roughly the same shape, with the advantage that T exhibits weaker oscillations. This less fluctuating pattern provides stability to the model fitting when the inverse relationship is used in the calculation of β (Supplementary Information). Additionally, the transient correlations obtained with SDC yielded higher values for T than for AH (even when accounting for concurrent levels of autoregression in the two variables). More

Study sites and samplingThe study was conducted in Garhwal region (Western Himalaya) from 2016 to 2017 at eight Rhododendron arboreum rich areas in four hill districts (Chamoli, Tehri, Pauri and Rudraprayag). Voucher specimen of Rhododendron arboreum collected and have been deposited in the Herbarium, Botany department, HNB Garhwal University (specimen no. GUH 8510)6. Identification of R. arboreum has been done through A Field Guide book authored by Rai et al.7. Since it is a wild species and flowers have been collected for our research and field study under the permission from competent authority of State Forest Department, Govt. of Uttarakhand. According to IUCN’s Red List Categories and Criteria, globally Rhododendron arboreum comes under Least Concern (LC) category8. These sites are situated between 30°08′47″ to 30°24′06″ N latitude and 78°25′05″ to 79°12′39″ E longitude with altitudes from 1820 m asl in Nandasain and 2270 m asl in Jadipani (Table 1; Fig. 1). All sites were well stocked (mean stand density ≥ 500 tree/ha) with Rhododendron arboreum trees mixed with Quercus leucotrichophora. We referred these resource rich sites as R. arboreum habitats (Table 1). Stratified random sampling method (i.e. stand density and CBH class’s strata) were carried out these eight sites. Total sampled area 0.2 ha in each site; two sample plots (size of each plot is 0.1 ha or 31.62 × 31.62 m) nested within 0.2 ha in each site were laid out for trees enumeration. Sample size (number of R. arboreum tree) for a total population in each site were 166 in Phadkhal; 110 in Khirsu; 104 in Khadpatiya; 166 in Ghimtoli; 80 in Jadipani; 74 in Ranichauri; 74 in Nandasain and 96 in Nauti. Out of the standing trees in sample plots, flower bearing trees were 96 in Phadkhal; 90 in Khirsu; 102 in Khadpatiya; 126 in Ghimtoli; 64 in Jadipani; 58 in Ranichauri; 68 in Nandasain and 82 in Nauti, and without flower or smaller trees were 70 in Phadkhal; 20 in Khirsu; 02 in Khadpatiya; 40 in Ghimtoli; 16 in Jadipani; 16 in Ranichauri; 06 in Nandasain and 14 in Nauti. The individuals of all tree species in each plot were recorded along with their CBH (circumference at breast height, 1.3 m above from the ground). Individuals were categorized as mature trees (≥ 31 cm CBH), saplings (11–30 cm CBH) and seedlings (≤ 10 cm CBH)9. Further all the tree individuals have been grouped into 8 CBH classes: (A) 5–15 cm, (B) 16–25 cm, (C) 26–35 cm, (D) 36–45 cm, (E) 46–55 cm, (F) 56–65 cm, (G) 66–75 cm, (H) 76–85 cm. Recorded data were used for the analysis of density10.Table 1 Physical characteristics of study sites in four districts of Garhwal region.Full size tableFigure 1Locations of Rhododendron arboreum study sites in Garhwal region (ARC GIS software 10.5 version was used for map preparation. The map was created by Mr. Raman Patel, Research scholar, Dept. of Geology, HNB Garhwal University, Srinagar, Uttarakhand, India).Full size imageFlower yield estimationFlower yield (kg/tree) was estimated during full bloom (flowering season/harvest season February–April 2017). In each sample plot, numbers of flower bearing trees varied from 29–63 trees/0.1 ha. At each site of 0.2 ha sample plot, total 40 trees, 05 flower bearing trees in each of the 08 CBH classes were marked for estimation of flower yield. The number of main branches, the number of sub- branches/offshoots per main branches (i.e. average per five randomly selected main branches per tree), and the amount of flower per sub-branches/offshoot (i.e. the average per five offshoots from the low, middle and upper canopy of each tree) were counted form marked individuals. This way flower yield/tree was calculated9,11,12.The flowers from all CBH classes in each site were mixed and weighted in 5 lots of 1 kg each. The number of flowers in each lot was then counted and the mean value (400.0 ± 9.56) was considered as a standard for conversion into kilograms. Based on this conversion flower yield kg/tree was obtained. Flower yield data were pooled and mean yield (kg/tree) for each CBH class (A–H) calculated. For each site, flower yield in kg/0.2 ha was obtained by multiplying flower yield/tree by the density of flower bearing trees/0.2 ha. The total yield kg/ha for each site was calculated as total yield = (yield/ha) × density of flower bear trees/ha9,11,12.Extraction/harvesting and marketing trendsFlower extraction and collection were totally dependent on market availability and accessibility of site; one of the selected sites (Ranichauri) was easily accessible, while Phadkhal, Khirsu and Jadipani were moderately accessible. Khadpatiya, Ghimtoli, Nandasain and Nauti sites were far-flung from market (Table 1). The highest extraction was recorded between second week of February and first week of April. During this period, data was obtained for three consecutive days at each site.Questionnaire based survey was carried out in selective forest fringe villages. Across the sites, total sixteen villages were selected for questionnaire survey, three villages each in Jadipani, Ranichauri, Nandasain and Nauti sites, while one village each in Phadkhal, Khirsu, Khadpatiya and Ghimtoli. In each village 15 families were randomly chosen for semi-structured questionnaire survey.Considering the market availability for trading of the R. arboreum flower products, Nandasain and Nauti sites are located nearest to local market whereas Khadpatiya and Ghimtoli sites are farthest from local market. As far as the access to resources is concerned, four sites represent open and easy access to resource and four sites represent open and moderate access of resource (Table 1). During questionnaire survey, villagers were asked about the number of persons involved in resource collection (hereafter referred as collectors), age of collectors, timing of collection (early morning and late evening) etc. Ten individuals in each group (adults and children) were randomly interviewed on their harvest load to generate data on the average collection per individual, the number of days spent in flower collection, and the total income generated through this activity.Squash/juice making factories are generally located nearby urban centers; local NGOs and small entrepreneurs are engaged in this work. These peoples purchase flower from the collectors or middleman for preparation of value product (squash). Collectors of each families (varied from n = 15 in Nanadasain to n = 31 in Jadipani) and buyers (n = 5 each site) were contacted to obtain information on the benefits accrued. The income values are given in Indian rupees (USD 1 = Rs. 68.00, 2017 exchange rates). Projections of potential (probable/-could generate) income (with flower processed into juice or squash) were made. The involvement of rural inhabitants as flowers collectors and the income that subsequently accrued (within a 10 km radius of fringe area) was also analyzed for sixteen villages across the sites. One adult member from each household was contacted in a village to collect information on involvement of flower collection/extraction.Juice/squash preparation methods and value-added productsThe collected flowers are graded for their size and healthiness and the stamens are separated from petals by laborers in the juice processing unit. Petals are cleaned washed with tap water and grinded into small pieces. The petal mass is retained in the water and then boiled for one hour. The slurry (aqueous solution) obtained in this process is left at room temperature for cooling and when it get cold, filtered through linen cloth. The filtrate solution is the pure juice of the flower. For the preparation of squash from the pure juice, about 2 kg of sugar is boiled in one liter of water. Further one liter of pure juice and a small quantity of citric acid (10 g/2 kg sugar) are added to this solution. The mixture is boiled again for 30 min and then left to cool at room temperature13. The obtained solution known as squash is then filtered through linen cloth and stored into containers and bottles for marketing. For long term storage and good test and aroma small amount of sodium benzoate and vanilla or kawra is also mixed in the squash.Cost–benefit analysis of value- added productsThe cost–benefit analysis of value added products prepared from the R. arboreum flowers was calculated in Rs./day which includes labour charges of workers involved in flower collection and materials/items required for preparation of different value added products viz: sugar, preservatives, essence, plastic containers/bottles, packaging materials etc. Labour charge was calculated on the basis of existing daily wages as per market rates. The monetary output was calculated as per the current market rates of the products (Table 2). The cost- benefit analysis of the squash product prepared from the flowers was calculated as Rs./day which includes: (i) Man days incumbent for the flowers extraction from the forest and for the preparation of squash product, (ii) Essential items such as sugar, preservatives etc. and their monetary equivalents, (iii) The total quantity of squash product and their monetary equivalents.Table 2 Market cost in rupees (Rs.) of essential commodity in the preparation of R. arboreum juice/squash in Garhwal region.Full size tableStatistical analysisData failed to meet the assumption of normality (Shapiro–Wilk test) as well as homogeneity (Levene statistic); therefore, a non-parametric test (i.e. Independent–Samples Kruskal–Wallis test) was applied for one-way ANOVA. However, to find the interaction of site and cbh on flower production (yield), the same data set was subjected to two-way analysis using univariate analysis. 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Toxicity of spirotetramat to F. occidentalis eggsThe indoor toxicity of spirotetramat to 0-h-, 12-h-, and 24-h-old eggs of F. occidentalis using egg dipping method and leaf dipping method was shown in Table 1 after egg hatching was observed for 144 h. The results suggested that the LC50 value gradually decreased as the egg age increased. The two methods have the same trend. And in the leaf dipping method, according to the confidence limits analysis, 0-h-old eggs are significantly more sensitive to spirotretramat than 24-h-old eggs.Table 1 Toxicity of spirotetramat to F. occidentalis eggs.Full size tableEgg external shape observationsExternal morphology of normally developed isolated 0-h-old eggs of F. occidentalis in the control treatment (Fig. 1a) were compared with those treated with spirotetramat (Fig. 1b, c). After spirotetramat treatment, some eggs appeared darker, yellowish-brown, and with embryonic development abnormalities (Fig. 1b); some of the egg embryo cells treated with spirotetramat appeared atrophied, and there were obvious gaps between the outer and the inner egg embryo cells, compared with the control treatment (Fig. 1c). Some eggs ruptured at the top after the egg shell was treated with spirotetramat, and the internal egg embryo cells flowed out, thus failing to form a complete embryo (Fig. 1d); the full egg embryo cells formed a control treatment.Figure 1The effect of spirotetramat on external morphology of isolated 0-h-old eggs of F. occidentalis. (a) 0-h-old isolated eggs of normal developing thrips; (b) yellowish-brown, developmentally deformed eggs after spirotetramat treatment; (c) eggs with shrunken oocytes after spirotetramat treatment; (d) eggs with apical rupture of the eggshell after spirotetramat treatment.Full size imageThe effect of spirotetramat on external morphology of live F. occidentalis 0-h-old eggs (Fig. 2b, c) was compared with normal development in the control treatment (Fig. 2a). Similar to the effect on external morphology of isolated 0-h-old eggs of F. occidentalis, the eggs treated with spirotetramat also showed abnormal embryonic development (Fig. 2b), egg embryo cell atrophy (Fig. 2c) and the phenomenon of rupture of the egg shell and outflow of embryo cells.Figure 2The effect of spirotetramat on external morphology of living 0-h-old eggs of F. occidentalis. (a) External morphology of live 0-h-old eggs of normal developing thrips; (b) developmentally deformed eggs after spirotetramat treatment; (c) eggs with shrunken oocytes after spirotetramat treatment; (d) eggs with apical rupture of the eggshell after spirotetramat treatment.Full size imageCompared with the control (Fig. 3a), the isolated 24-h-old eggs treated with spirotetramat (Fig. 3b) did not show obvious external morphological differences. After spirotetramat treatment, the eggs were still white and plump and with no embryonic deformities, egg cell atrophy or egg shell rupture, and could still develop normally. There were clear red eye spots on the head end, and embryo movement was clearly seen under the super-depth microscope (Fig. 3b). Similarly, the live 24-h-old eggs in the control treatment (Fig. 4a) and those treated with spirotetramat (Fig. 4b) showed no obvious external morphological differences, and the eggs developed normally.Figure 3The effect of spirotetramat on external morphology of isolated 24-h-old eggs of F. occidentalis. (a) Normally developing 24-h-isolated eggs in the control group; (b) 24-h-old eggs after spirotetramat treatment.Full size imageFigure 4The effect of spirotetramat on external morphology living of 24-h-old eggs of F. occidentalis. (a) Normally developing 24-h-live eggs in the control group; (b) live 24-h-old eggs after spirotetramat treatment.Full size imageEffect of egg hatchingThe 0-h-old eggs of F. occidentalis treated with spirotetramat did not hatch normally, and the mortality rate was 100% (Fig. 5). Among them, 77 eggs eventually showed rupture of the egg shell, the internal egg embryo cells flowed out and they did not hatch; 23 eggs showed no changes in external morphology, but did not hatch after continuous observation for 144 h, and showed no developmental phenomena such as embryo movement under a super-depth microscope, which was regarded as egg death. In the control treatment, 96 eggs hatched normally, and only six eggs did not rupture but did not hatch normally and were considered dead.Figure 5Effect of spirotetramat on hatching rate of F. occidentalis 0-h-old eggs.Full size imageThere was no significant difference between the 24-h-old eggs of F. occidentalis treated with spirotetramat and the control treatment. After spirotetramat treatment, 93 eggs hatched normally, and the shells of seven eggs were not ruptured (Fig. 6). Any eggs not hatched after 144 h of continuous observation were considered dead. In the control treatment, 95 eggs hatched normally and five eggs did not rupture but did not hatch normally, and so were considered dead.Figure 6The effect of spirotetramat on hatching rate of F. occidentalis 24-h-old eggs.Full size imageSEM observationsThe F. occidentalis eggs in the control treatment were kidney-shaped, with regular egg morphology, smooth surfaces and no folds or protrusions (Fig. 7a). At 24 h after spirotetramat treatment, part of the egg shells treated with spirotetramat had fallen off the chorion, and the embryonic material was exposed (Fig. 7b). The surface of the egg shell was uneven and severely wrinkled (Fig. 7c). The pores of some eggs treated with spirotetramat were sunken down and shrunken (Fig. 7d). Spirotetramat treatment of 0-h-old eggs affect clearly egg shells, resulting in shrinkage of egg shells, ovarian depression and egg malformations, and destroyed the egg shell structure. Thus, normal embryonic development was affected, and disrupted normal hatching.Figure 7The effect of spirotetramat on the surface of egg shells of F. occidentalis 0-h-old eggs. (a) 0-h-old eggs in the control treatment; (b) eggs shells were shed 24 h after treatment with spirotetramat; (c) the surface of the egg shell was uneven and severely wrinkled; (d) the pores of some eggs were sunken down and shrunken after treatment with spirotetramat.Full size imageThe shells of eggs treated with spirotetramat (Fig. 8b) showed no significant difference compared with controls (Fig. 8a). The eggs of the two groups of F. occidentalis were regular in shape, with smooth surfaces and without folds or protrusions. Thus, development of 24-h-old eggs showed some resistance to spirotetramat. Spirotetramat did not destroy the egg shell surface structure of 24-h-old eggs, indicating a high resistance to spirotetramat.Figure 8The effect of spirotetramat on the egg shell surface of F. occidentalis 24-h-old eggs. (a) 24-h-old eggs in the control treatment; (b) 24-h-old eggs in the spirotetramat treatment.Full size imageTEM observationsThe TEM observations showed that the egg structure of the control treatment was complete, the protoplasm and yolk were clearly observed inside the egg and the yolk was packed in the void of the protoplasm network (Fig. 9a). The egg shell structure was clear, and the outer and inner egg shell were clearly observed, as was the yolk membrane and the dense layer structure (Fig. 9c). Eggs treated with spirotetramat were flocculent, and no clear internal material was observed. The protoplasm and yolk structure were blurred, and flocculation in the protoplasm appeared to agglomerate and form blocks (Fig. 9b). The egg shell structure was unclear, and no clear outer egg shell, inner egg shell, yolk membrane and lamellar structures were observed. The egg shell was also filled with many flocs (Fig. 9d).Figure 9The effect of spirotetramat on the structure of F. occidentalis 0-h-old eggs. (a) and (b) 0-h-old eggs in the control treatment; (c) and (d) 0-h-old eggs in the spirotetramat treatment.Full size imageEffect on embryonic developmentThe initial eggs of the control group were kidney-shaped, white and full of vitellin (Fig. 10a). After 12 h of development, the eggs were larger and of oval shape (Fig. 10b). After 24 h of development, the egg had increased in volume, a partially transparent region appeared in the embryo and the embryo had transparent top follicles (Fig. 10c). After 36 h of development, some yolk granules disappeared and eggs became smooth and translucent (Fig. 10d). After 48 h of development, the insect outline was visible within the egg, a pair of antennae were visible on the head and a red eye point was clearly observed on the head during the blastokinesis phenomenon (Fig. 10e). After 60 h of development, embryo color deepened, the eye point was clearer and the head, femur, tibia and tarsus were clear (Fig. 10f). After 72 h of development, the egg shell began to break at the head, the tail constantly jittered, internal fluid flowed and the larva hatched from the top of the egg (Fig. 10g).Figure 10The embryonic development process of control 0-h-old eggs of F. occidentalis. (a) Control initial eggs; (b) eggs after 12 h of development; (c) eggs after 24 h of development; (d) eggs after 36 h of development; (e) eggs after 48 h of development; (f) eggs after 60 h of development; (g) eggs hatching as larvae after 72 h of development.Full size imageEggs of F. occidentalis were initially white, kidney-shaped and full of vitellin (Fig. 11a). Following treatment with spirotetramat, after 12 h of development, the eggs became large and oval, and the embryo was a pale brown color (Fig. 11b). After 24 h of development, color of the egg deepened to dark brown. There was a gap between the egg and the egg shell, and a small amount of spillage appeared at the end of the egg (Fig. 11c). After 36 h of development, the egg shell ruptured, material flowed out of the egg and embryo development did not proceed (Fig. 11d).Figure 11Effects of spirotetramat on development of 0-h-old eggs of F. occidentalis. (a) Frankliniella occidentalis initial eggs; (b) eggs developing 12 h after spirotetramat treatment; (c) eggs developing 24 h after spirotetramat treatment; (d) eggs developing 36 h after spirotetramat treatment.Full size imageIn the control treatment, the egg volume increased at 24 h, the embryo had a partially transparent area and there was a transparent follicle on the top of the embryo (Fig. 12a). After 12 h of development, some of the yolk particles disappeared and the egg body was smooth and translucent (Fig. 12b). After 24 h of development, the body outline, a pair of antennae and red eye spots were visible, and there was obvious embryo movement (Fig. 12c). At 36 h of development, the head, leg segments, tibia and tarsus were apparent (Fig. 12d). After 48 h of development, the embryo moved violently, internal body fluid flowed and the larva was ready to hatch (Fig. 12e). After 60 h of development, the larva emerged from its shell (Fig. 12f).Figure 1224-h-old eggs embryo development of F. occidentalis in control treatment. (a) Control 24-h-old eggs; (b) eggs after 12 h of development; (c) eggs after 24 h of development; (d) eggs after 36 h of development; (e) eggs after 48 h of development; (f) eggs hatching as larvae after 60 h of development.Full size imageThe 24 h old eggs of F. occidentalis showed enlarged volume, and there were transparent follicles on the top of the embryo (Fig. 13a). After 24 h eggs were treated with spirotetramat, they developed for 12–36 h, and the developmental status was the same as that of the control. The embryos developed normally, and there was no egg body discoloration or egg shell rupture (Fig. 13b–d). After 48 h of development, hairy scales appeared on the surface of the egg shell, and the egg body turned yellowish-brown in color, but the egg shell was not broken and no internal material overflow was seen (Fig. 13e). After 60 h of development, larvae hatched normally (Fig. 13f).Figure 13The effect of spirotetramat on embryonic development of F. occidentalis 24-h-old eggs. (a) Frankliniella occidentalis 24-h-old eggs; (b) eggs developing 12 h after spirotetramat treatment; (c) eggs developing 24 h after spirotetramat treatment; (d) eggs developing 36 h after spirotetramat treatment; (e) eggs developing 48 h after spirotetramat treatment; (f) eggs hatching as larvae after 60 h of development.Full size image More