The Date Palm spider mite (DPSM), Oligonychus afrasiaticus (McGregor), is the most critical pest on Date Palm. We studied the standardization of the class-specific sampling method to define the appropriate unit and size and the population dispersion pattern of DPSM in its specific ecological niches and fitting of a Wald,s sequential sampling model on six Date Palm cultivars in five provinces. The sampling was repeated at seven days intervals from May to September. Finally, the dispersion pattern of DPSM in the Date Palm orchards was estimated using indices of mean crowding, Taylor’s Power Law, mass average index, patchiness, and Iwao. As DPSM had a clumped pattern, the first and second-type errors were considered equal to 0.1. The results showed that the means density of DPSM varied from 9.25 (Sayer cultivar) to 41.64 (Berhee cultivar) on 20 fruits. According to Taylor’s power law, the dispersion of this pest was clumped. Average Sample size decreased by increasing the population density of DPSM. By these results, The decreasing of operational curve slopes of sequential sampling models were different in the different Date Palm cultivars so that the slope was reduced to 0.5, 0.3, 0.3, 0.25, 0.1, and 0.1 for Pyaroom, Mazafati Rabi, Sayer Berhee and Kabakab cultivars, respectively. Proposed Wald,s sequential sampling model can be suggested for monitoring DPSM population on different Date Palm cultivars because it could drastically decrease sample size compared with the classical sampling model.
Research Article
Density, spatial dispersion, and sequential sampling of Date Palm spider mite Oligonychus afrasiaticus on six Date Palm cultivars
https://doi.org/10.21203/rs.3.rs-3104367/v1
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Date Palm
mite
spatial
sequential sampling
The Date Palm spider mite (DPSM) Oligonychus afrasiaticus McGregor is an improtatnt Date Palm (Phoenix dactylifera L.) pest in terms of economic. In addition to Date Palm, it has been described on many plants of the family Celastraceae, including Catha edulis Vahl. and family Poaceae including Cynodon dactylon L., Imperata cylindrica L., Lolium sp., Sorghum sp., and Zea mays L. (Ben Chaaban et al. 2011). The pest infestation peak occurs in hot and dry climates in late spring and early summer. The rate of colonization varies depending on the temperature and Date Palm cultivar. Different populations of this pest are rarely found in Date Palm plantations during winter and early spring. However, its population is active on fruit bunches and leaves in late spring and summer (Ba-Angood and Bass’haih, 2000; Ben Chaaban and Chermiti, 2009).
A definition of pest management is using fewer pesticide applications and its replacement by low-risk and safe control methods (Surendra, 2019). The sequential sampling model supports the right decision for an integrated pest management program. (Damos, 2015). Any decision-making program in integrated pest management requires pest sampling information, including appropriate techniques and a proper sampling program (Southwood and Henderson, 2000). First, an accurate and low-cost sampling program should be planned to have a reasonable pest investigation (El-Shafie, 2018) or solve pest population dynamics problems to obtain maximum information from a sampling program (Davis, 1994).
Proper design of the sampling program is most essential for determining the type of spatial dispersion and estimating population density in ecological studies. (Krebs, 1999). Given the importance of integrated pest management, it is endeavored to focus on the information achieved from pest and crop ecology (Radcliffe et al. 2008). It is necessary to reach a scientific sampling program for each insect pest experimentally (Pedigo, 1994). Special tools have been designed that can be used to determine the accuracy of population density estimation (Naranjo and Hutchinson, 1997). Understanding the pest’s spatial is crucial for sampling program design (Lamp and Smith, 1989). The population estimation data are articulated using the number of pests in all sampling methods (Bahlai and Zipkin, 2020). Of course, estimations can also be expressed based on the pest’s injury or production data. These estimations are often called population indices. Measuring the effects of pest injury is the most common pest population density method (Kuno, 1991). The cost of a good sampling program is much less than the cost of running an unsuitable pest management program (Binns et al. 2000). The choice of a sampling method depends on the accuracy of the population estimate (Lamp and Smith, 1989). Different population indices have been widely used for comparing populations, such as relative density, spatial and temporal (Dent, 1991).
A good sampling program must have at least four parameters, including pest sampling stage, size of sampling units, sampling time, and sampling pattern (Binns et al. 2000). The sampling stage varies depending on the pest type and pest management strategies (Binns et al. 2000). The sample sizes depend on the required accuracy and the sampling cost (Dent, 1991). The timing of sampling depends on the various growth stages of the pest's phenology. (Binns et al. 2000). Finally, the sampling pattern depends widely on habitat uniformity and population in the environment (Nyrop and Binns, 1992).
The population is divided into incompatible subgroups or classes in stratified random sampling, and then simple random samples will be selected from each class or sub-population (Bouyer et al. 2010). This method reduces standard deviation, sample sizes, and sampling cost (Young and Young, 1998). Sequential sampling is one of the most crucial decision-making programs in integrated pest management (Carvalho et al. 2007), relying on the pest patterns and economic injury levels. In the widely used sequential sampling designs, up to 50 percent of the costs and time can be expected to be saved (DePalma et al. 2012). Despite the importance of sequential sampling, no specific standard pattern of this model type has been scientifically presented to estimate the population of Date spider mites. The main objectives of this research are determining the dispersion pattern of Date Palm spider mite population in its specific ecological niches and fitting the sequential sampling model for decision-making in the integrated management of this important pest.
This study was conducted on six Date Palm cultivars commercially cultivated in five provinces [Khuzestan (Sayer and Berhee), Hormozgan (Pyarom), Kerman (Mazafati), Bushehr (Kabkab), and Sistan and Baluchestan (Rabi)] of Iran. Sampling was repeated every seven days from late May to early September 2019–2020. To this end, we used a stratified random sampling method to estimate population density because environmental factors affecting population changes may not only be related to environmental conditions of the macro-climate but may also be due to uniformity in the micro-climate (Young and Young, 1998). In these models, the main classes consisted of different trees in the Date Palm gardens, and the sub-classes included bunches in four main geographical directions on each tree. Twenty fruits were randomly selected from each bunch and checked to determine DPSM infestation. The samples were then transferred to the laboratory, and the number of mites were counted and recorded separately. To determine the Date spider mite’s density at the sampling intervals in the field conditions, indicating the relationship of their population densities with Date fruit injury, the parameter Mite-day (Md) was calculated to estimate this index as follows (Machlitt 1998).
$$MD=7\times \left(\frac{M1+M2}{2}\right)$$
1
In this equation M1 and M2 were the mite densities in the current and previous sampling period, respectively.
Stratified Sampling Standardization
The mite environment was classified into K classes. For example, if the mite density of class I was Ni, then:
$$\sum Ni=N$$
2
If ni samples are taken from class i, and their means are ̄\(\stackrel{̄}{y}1......,\stackrel{̄}{yn}\), then the variances are calculated as Eq. 3.
$$\stackrel{̄}{{var}y}i=\left(\raisebox{1ex}{${S}_{i}^{2}$}\!\left/ \!\raisebox{-1ex}{$ni$}\right.\right)(1-\raisebox{1ex}{$ni$}\!\left/ \!\raisebox{-1ex}{$Ni$}\right.)$$
3
In Eq. 2, 
and Ni is the variance of each class and sample size, respectively. The mean population in each region is estimating as Eq. 4.
$$\stackrel{̄}{y}=\sum {\frac{Nix\stackrel{̄}{y}i}{N}}_{i}^{k}$$
4
Because individual ratios from all classes were sampled equally. Then, instead of Ni, ni and instead of N, n were used in Eq. 5.
$$SE\left(\stackrel{̄}{y}\right)=[\sum {w}_{i}^{2}\times \frac{{S}_{i}^{2}}{{n}_{i}}{]}^{\frac{1}{2}}$$
5
When the standard error was determined, interval confidence was calculated as Eq. 6.
$${\stackrel{̄}{\mu }}_{st}\pm {t}_{\alpha /2}\left[SE\right(\stackrel{̄}{y}\left)\right]$$
6
The mean and variance of spider mite population were estimated in four main classes of pest, including north, south, east, and west of tree to fit the population density estimation equation in the stratified sampling method, based on equations 2 to 4. Then the standard error value was calculated by using Eq. 5. Finally, the mean population density models were fitted for studied Date Palm cultivars, based on Eq. 6. The confidence level is often considered at 5 and 1 percent. The n or sample sizes (actual mean population change with 95% probability) was calculated by using Eq. 7. Appropriate and applicable sample sizes were calculated by using population confidence interval (Eq. 7).
$$n=\frac{{S}^{2}}{{L}^{2}\raisebox{1ex}{$N$}\!\left/ \!\raisebox{-1ex}{$4$}\right.+{S}^{2}}$$
7
In Eq. 7, S2, L, N, and n are sample variance, confidence limit, total sample size, and sample size, respectively (Padilla et al. 2017).
Population dispersion
The data included 32 series in each province and on each cultivar. In total, 192 data series were collected. Different indices were used to determine the population dispersion pattern. Sara et al. 2020). The Patchiness index was calculated by using Eq. 8 (Lloyd, 1967).
$$ID=\frac{{S}^{2}}{\stackrel{̄}{x}}(n-1)$$
8
Taylor’s power law is based on an exponential relation between variance and means, as expressed in Eq. 9.
σ2 = a µb (9)
where σ2 is the population variance and µ is the population mean; a and b are constants. This equation is tested on field data by plotting log s2 against log \(\stackrel{-}{\text{X}}\) in Eq. 10
$$Log{S}^{2}=Loga+bLog\stackrel{̄}{X}$$
10
The coefficient of determination (R2) of the regression line indicates how powerful the model is for the mite population. a is a sampling parameter and b can be used as an index of aggregation. If so, b > 1, b = 1 and b < 1 then the population dispersion will be cumulative, random and uniform, respectively (Reiczigel et al. 2005). The coefficient of explanation ICM (crowding mean index based on LIoyd method) indicates competition intensity by other individuals based (Chao et al. 2015). The parameters \(\stackrel{-}{\text{X}}\) or X* is the mean of the effect of another individual in the sampling unit on any other individual in the population. This index was calculated by using Eq. 10 (Reiczigel et al. 2005). The non-patchiness index (IP) was calculated by using Eq. 12.
$${X}^{*}=\stackrel{̄}{X}+\frac{{S}^{2}}{\stackrel{̄}{X}}-1$$
11
$$IP=\frac{{X}^{*}}{\stackrel{̄}{X}}$$
12
The regression curve was adjusted between X* and \(\stackrel{-}{\text{X}}\) to calculate the Iowa index that is defined by Eq. 13 (Iwao and Kuno, 1968).
$${X}^{*}=\alpha +\beta \stackrel{̄}{X}$$
13
The constant α indicates the species’ crowding. If α was positive, the species tended to clumped, and if α was negative, the species tended to disperse. The constant \(\beta\) indicates that how much population tended to clumped at high densities. If so, \(\beta\) >1, \(\beta\) =1 and \(\beta\) <1 then the population will be cumulative, random and uniform, respectively (Iwao and Kuno, 1968).
Waldʹs Sequential sampling model
Two population levels are essential in principles of sequential sampling model (Wald, 1947), including HO: population below a certain level and Hi: population above a certain level (Li et al. 2012). This theory has terms defined as follows. CD, OC, and ASN were defined as critical density, operational curve function, and average sample size curve. Three factors affect OC and ASN functions, including sampling stop lines, population, and population density of mites. Thus, there are two decision lines for the sequential sampling model that is likely to be described by equations 14 and 15(Kafeshani et al. 2018.).
Top stop line \(UBn=n*(cd+Z\times \frac{s}{\sqrt{n}})\) (14)
Bottom stop line \(LBn=n*(cd-Z\times \frac{s}{\sqrt{n}})\) (15)
The ASN curve represents the average of sample sizes needed to decide. The sample sizes were varied by population density fluctuation. According to the clumped of the pest, K, \(\stackrel{-}{x}\), \(\stackrel{-}{{x}_{1}}\) and Sd is accumulation constant, the population means (equivalent to 80% of economic injury level), specified population level (equivalent to 1461.6, 201.9, 222.2, 45.5, 58.1 and 102.0 for Sayer, Barhee, Kabkab, Pyarom, Rabi and Mazafati cultivars respectively) (Latifian et al. 2021) that is important for decision making, and standard deviation, respectively (Fowler and Lynch, 1987). Other model parameters were calculated by using Equations 16 to 20.
، \({h}_{2}=\frac{Ln\frac{1-B}{\propto }}{Ln\frac{{p}_{2}{q}_{1}}{{p}_{1}{q}_{2}}}\)، \(b=k\frac{Ln\frac{{q}_{2}}{{q}_{1}}}{Ln\frac{{p}_{2}{q}_{1}}{{p}_{1}{q}_{2}}}\) (16)
q 2 = 1 + p1 ، q 1=1+p (17)
$$Oc=L\left(A\right)=\frac{{A}^{h\left(M\right)}}{{A}^{h\left(M\right)}-{B}^{h\left(M\right)}}$$
18
$$M=A=K\frac{1-(\frac{{M}_{1}+k}{{M}_{2}+k}{)}^{h\left(M\right)}}{\left(\frac{{M}_{2}\left({M}_{1}+K\right)}{{M}_{1}\left({M}_{2}+K\right)}{)}^{h\left(M\right)}\right)}$$
19
$$ASN=EA\left(N\right)=\frac{b\left(OC\right)+a(1-oc)}{KLn\left(\frac{{M}_{1}+k}{{M}_{2}+k}\right)+M{ln}(\frac{{M}_{2}({M}_{1}+{k}_{1})}{{M}_{1}({M}_{2}+{k}_{2})})}$$
20
In the above equations, b is the slope of the decision lines, and h1 and h2 are the intersections with the y-axis. Suppose the probability of making wrong decisions is above the upper and lower limit lines, respectively. Then, M and P are mite population density and infected to total fruits ratio. In equations 17,19, and 20, K calculated as follows. In Eq. 21, V and m are the variance and mean of the infected fruits.
$$K={m}^{2}/(v-m)$$
21
Standardization of stratified sampling
These calculations were made considering that degrees of freedom and ta/2 were equal to 49 and 1.37, respectively (Table 1). The results are shown for different Date Palm cultivars, in Table 1. Results show that the mean population density of spider mite varied from 9.25 (Sayer cultivar) to 41.64 (Berhee cultivar) in 20 fruits. The absolute population density was also calculated using the equations in column 3 of Table 1.
| Cultivar | N | \({\stackrel{̄}{\mu }}_{st}\pm {t}_{\alpha /2}\left[SE\right(\stackrel{̄}{y}\left)\right]\) | SE(Ῡ) | Ῡ |
|---|---|---|---|---|
| Sayer | 100.87 | \(\stackrel{-}{\mu }\)±15.38 | 11.23 | 9.25 |
| Berhee | 104.26 | \(\stackrel{-}{\mu }\) ±69.21 | 50.53 | 41.64 |
| Kabkab | 103.48 | \(\stackrel{-}{\mu }\) ±30.67 | 22.46 | 18.51 |
| Pyarom | 145.25 | \(\stackrel{-}{\mu }\) ±18.46 | 13.47 | 11.10 |
| Rabi | 130.73 | \(\stackrel{-}{\mu }\) ±55.37 | 40.42 | 33.31 |
| Mazafati | 126.96 | \(\stackrel{-}{\mu }\) ±23.07 | 16.84 | 13.88 |
| Cultivar | Iwao peachiness | IP | ICM | Taylor- Index | ID |
|---|---|---|---|---|---|
| Sayer | 1.63 | 1.73 | 16.04 | 2 | 61.32 |
| Berhee | 1.67 | 1.65 | 68.69 | 2.14 | 275.94 |
| Kabkab | 1.52 | 1.73 | 16.04 | 1.93 | 61.32 |
| Pyarom | 1.92 | 1.72 | 19.05 | 2.54 | 73.58 |
| Rabi | 2.16 | 1.66 | 55.15 | 3.12 | 220.76 |
| Mazafati | 2.01 | 1.69 | 23.56 | 2.46 | 91.98 |
Population density fluctuation of Date Palm spider mite
The activity of Date Palm spider mite populations started in the first week of June and gradually increased with the warming of the weather every year (Fig. 1). The peak of population mite activities (MDs) were around the July and August months of in all studied areas. The highest MDs were observed in August in all Date Palm cultivars. Then gradually the intensity of population activities decreased. In September, which coincides with the beginning of fruit ripening in the most Date Palm plantations areas and the studied Date Palm cultivars, the population density of this mite decreased.
population dispersion pattern in the ecological niches
statistic indices showed that the b value of Taylor’s power law and β-index of the Iowa equation is significantly more than 1. Accordingly, mite dispersion is clumped in Date Palm plantations. Furthermore, Taylor’s power law showed a better fit for the qualification of spider mite pattern by comparing the recognition coefficient of regression models. Therefore, Taylor statistic parameters were used for fitting the mite sequential sampling models.
Fitting the sequential sampling models
Estimating the model indices
The model indices were estimated as described in Table 3 for six different Date Palm cultivars.
| Cultivars | K | \(\stackrel{-}{{x}_{1}}\) | \(\stackrel{-}{x}\) | p1 | p2 | q1 | q2 | Sd | B | A |
|---|---|---|---|---|---|---|---|---|---|---|
| Sayer | 1.17 | 1826.98 | 1.20 | 1558.84 | 1.03 | 1559.84 | 2.03 | 0.95 | 249.26 | 0.11 |
| Berhee | 1.03 | 252.35 | 5.42 | 244.12 | 5.24 | 245.12 | 6.24 | 4.29 | 64.29 | 0.11 |
| Kabkab | 1.07 | 277.78 | 2.41 | 257.39 | 2.23 | 258.39 | 3.23 | 1.90 | 57.99 | 0.11 |
| Pyarom | 1.14 | 56.92 | 1.44 | 49.96 | 1.27 | 50.96 | 2.27 | 1.143 | 15.10 | 0.11 |
| Rabi | 1.043 | 72.61 | 4.33 | 69.65 | 4.158 | 70.65 | 5.158 | 3.429 | 24.22 | 0.11 |
| Mazafati | 1.11 | 127.46 | 1.81 | 114.99 | 1.63 | 115.99 | 2.63 | 1.43 | 29.52 | 0.11 |
Fitting the top stop line (U) and bottom stop line (L)
In this sampling method, the estimated population density of each sample was added to previous samples. The sampling was continued until mite population density crossed one of the stop lines. The mite control was essential if the population density was above the top stop line. Sampling and control operations were stopped if the mite population density was below the bottom stop line. The sampling operation would be continued if its population density was between the top and bottom stop lines. The model of top and bottom stop lines of spider mites on the different Date Palm cultivars are shown in Fig. 2.
Determining the probability of operational curve function and average sample size curve
Plotting the Oc curve helps to understand the accuracy and correctness of the decisions based on the sequential sampling model. The efficiency of the sequential sampling model would be better if the slope of Oc came around Cd. The OC curves of sequential sampling models for decision-making about Date Palm spider mites in studied cultivars are shown in Fig. 3.
Sample size depends on population density in the sequential sampling method (Buntin 1994). In the present study, the sample size was reduced by increasing the population density of spider mites.The decreasing slope was changed in the different Date Palm cultivars so that the slope was reduced to 0.5, 0.3, 0.3, 0.25, 0.1, and 0.1 for Pyaroom, Mazafati Rabi, Sayer Berhee, and Kabakab cultivars, respectively.
The ASN curve represents the average of sample sizes to make a decision. Sample sizes would be changed by variations in mite population density. The sample size also would be small if the population were too low or high because the first sampling stage would determine the pest status. However, more sampling should be done to make the correct decision at the intermediate population densities. The average sample size curve for date spider mites on the different Date Palm cultivars is shown in Figure. 3.
The results showed that the sample size of the different Date Palm cultivars (Table 1 and Figs. 3) varied between 100 and 140 fruits. The model parameters were calculated according to the range of changes in the population density at 1% of probability level .
In this study and similar research, the highest activity of the date mite population was reported on the fruit in June to July and during the fruiting season. At this stage, the date fruit is in the "Kimri" stage, which is very suitable for feeding this mite. Because, it has several nutritional and structural characteristics that allow the date tartan mite population to multiply and develop exponentially. A similar trend has been recorded in all the healthy habitats and all the cultivars studied at the international level (Aldosari and Ali, 2007; Samah and Chermiti, 2010; Palevsky et al. 2005, Ben Chaaban et al. 2017). Here, we provided insights into sequential sampling plans to make a decision system for DPSM management on six Date Palm cultivars in five provinces of Iran. The critical point about this model was its compatibility with the spatial of O. afrasiaticus. Decision-making based on top stop lines (need to control) and bottom stop lines (not need to control) optimized sampling program for mite monitoring. (Akoijam and Telem, 2014). It is recommended that the fruit sampling be performed as described above for the optimal efficiency of these models. After which, the number of infested fruits and mite–day index can be estimated. This process should be checked weekly until their values are equal or more than the upper or lower decision lines. If the total size of infested fruits is equal to or above the upper limit line, it is suggested that DPSM be controlled (Carvalho, 2016). Otherwise, where the estimates fall under the two decision lines, sampling should be continued.When the fruit infestation is below the bottom stop line, sampling can be stopped without pest control. However, it is suggested that at least one additional sampling should be performed for more confidence (Toews et al. 2003).
In this study, the accuracy of sampling was verified by the curve of average sample sizes. (Fig. 3). This curve showed an error in decision-making for the control or uncontrol of DPSM at 1% of probability level. So, there was 1% or less probability that our decisions would be an error. In other words, the probability of Typ1 error that acceptance hypothesis (H0) instead of H1 was 1% or less. This system helps us to estimate proper decisions for IPM of Date Palm spider mites and reducing pesticides. In addition, it may enable us to suppress pest infestation at the beginning of the season because this pest has a clumped behavior, and pest control is more effective in reducing DPSM populations at the beginning of the season (Al-Deeb, 2012). This sampling method helps date growers and experts to monitor date mite populations on an ongoing basis, which helps reduce the cost of monitoring pest populations in small areas under investigation. Due to difficulty in continuous monitoring activity, when the population of date spider mites is randomly distributed, the sample size needed to assess the population density and injury can be increased, up to 50-fold per unit area of Date Palm plantations (Fig. 3).
Applying sequential sampling method for decision-making system of red Date Palm weevil Rhynchophorus ferrugineus Olivier (Coleoptera: Curculionidae) in an IPM program reduced its damage to less than one percent in the Vahah region of Saudi Arabia in 2011 (Faleiro et al. 2010). In another case in a study on the relationship between the number of infested fruits and the total larvae of Ectomyielois ceratoniae (Zeller) (Lepidoptera: Pyralidae),100 fruits were collected from 18 Date Palm bunches, and the above indices were estimated, and the fruit infestation severity was determined using the regression relationship between the top and bottom stop lines (Perring et al. 2005). Consistent with the research mentioned above works regarding the usefulness of sequential sampling in reducing costs and effectiveness of IPM strategies, the results of this study can play an essential role in decreasing the damage of Date Palm spider mites along with other available information on monitoring and forecasting of DPSM in Date Palm plantations (Latifian, 2014; Latifian et al. 2014).
Integrated pest management should balance the insect program cost to increase insect control efficiency and reduce the consequences of contamination. Reducing the sampling program costs and increasing the products quality using economic pest control methods are two ways to reduce production costs. Sequential sampling programs are usually inexpensive because this method requires fewer samples to estimate pest populations when the pest densities are high or close to the economic threshold. To get more accurate and realistic results, the action threshold should be replaced by economic thresholds so that managers can help make decisions about possible errors below 25% (Buntin, 1994). Further research should be done to find the economic thresholds for this pest on other commercial Date Palm cultivars. Also, combining pest management methods with sampling program need to reach an accurate decision to preserve date product from different mite pest attacks.
The date palm is one of the most important horticultural crops from which orchards derive additional income, depending on whether the variety is commercially grown or not.Therefore, it is necessary to use low-cost and straightforward methods in the integrated pest management of spider mites. Overall, this study showed that the sequential sampling model is a thoroughly reliable model for monitoring and estimating DPSM in Date Palm plantations that can adequately be used in IPM. In addition, our results showed that it was more reliable than the stratified sampling method due to its ease of use. Since the spatial pattern of spider mites can be used in the geographical location (Elliott et al. 2003), it can be suggested to monitor the DPSM population on different date cultivars cultivated in geographically distinct regions. In addition, this method drastically reduced the number of samples (7.8 to 50 times) compared to stratified sampling. The sampling was performed, and the obtained data were used to prepare sequential sampling programs to classify the Date Palm spider mite status using SPRT and confidence interval method. The primary purpose of a sequential sampling program is to help the managers to decide whether it needs to perform control measures or not with a minor error. This precision should be made only with several sampling units to be economically sustainable. Our results showed that managers could make the least risk of misjudgment by using SPRT methods. Finally, it is recommended to study the sequential sampling models for monitoring the DPSM population, based on its spatial parameters, for different Date Palm cultivars in other provinces of Iran.
Funding: No funding
Competing interests & Ethics declarations: The authors declare that they have no competing interests. Ethics approval and consent to participate Not applicable
Availability of supporting data: All data and materials mention in the manuscript
Authors’ contributions: All authors read and approved the final manuscript.
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