Pollen deposition and distance relationship

Pollen deposition was measured at 216 sites with distances to the nearest pollen source ranging from 0.2 m (within the field) to 4.45 km away from the nearest edge of the maize field. The values of maize pollen deposition varied from 23.3 million pollen grains/m2 (2,330 pollen/cm2) within the field to 2,857 pollen grains/m2 at further distances from the field.

The amount of maize pollen deposition over the main flowering period decreased markedly with increasing distance. Nevertheless, the spatial distribution exhibited a long-tailed shape, with pollen detected as far as 4.45 km away from the nearest field. Comparing the fits of the power model and the exponential model, the relationship between maize pollen deposition and distance to the next maize field was fitted best by the following power function:

Y = 1.271 · 1 0 6 · X - 0.585 in n m 2 (3)

where:

Y is the deposition of maize pollen (n/m2), X is the distance to the nearest maize pollen source/field edge [m], and n is the number of pollen grains.

The above power function was highly significant (p <0.001) with distance accounting for 70.9% of the total variation in deposition (R2 = 0.709).

Figure 3 shows the data and the fitted curve with confidence intervals on log-log transformed axes. This presentation turns the power relationship between the X and Y variable into a linear relationship. The scale on the left vertical axis denotes pollen deposition in the standard unit (n/m2). The horizontal axis shows the distance to the next maize field, ranging from within the same field (0.2 m) to 4,450 m. Table 2 shows the estimated regression parameters.

Table 2 Results of linear regression analysis for the linearized power model Full size table

Figure 3 shows the good fit over the data range. It also shows that data points are scattered around the regression line with nearly constant variation over the distance range, as required by the assumptions for standard regression analysis. This finding is confirmed by the standardized residuals plot (Figure 4).

Figure 4 Standardized residuals for regression line shown in Figure3. X, distance from nearest maize field in m. Full size image

Fitting an exponential model to the data resulted in a much weaker determination coefficient of R2 = 0.222 (p <0.001) compared with the R2 = 0.709 for the power model. The corresponding equation is as follows:

Y = e 5.418 - 0.0005049 · X in n m 2 (4)

A fitted purely linear regression line has the following equation:

Y = 964 , 347 – 403.8 · X in n m 2 (5)

with a coefficient of determination R2 = 0.021 (p =0.035). The least-squares fitted cubic polynomial has the following equation:

Y = 1 , 305,760 - 4,380 · X + 2.695 · X 2 – 0.0004024 · X 3 in n m 2 (6)

with a coefficient of determination R2 = 0.078 (p <0.001). The cubic polynomial shows a slightly better, but nevertheless poor, quality of fit with 7.8% of the variation in Y explained by X. For the linear function, all predictions for distances greater than approximately 2,400 m are negative. For the cubic polynomial, distances between 380 and 1,900 m and greater than 4,400 m generate negative predictions for pollen deposition, interrupted by positive predictions for distances between 1,900 and 4,400 m, with a local maximum at about 3,200 m. The polynomial model for the relation between deposition and distance previously published by Lang et al. [36] (equation: Y = 74.75 − 9.71 · X + 1.32 · X2 − 0.08 · X3) was similarly inacceptable, predicting negative deposition for all distances greater than 13 m. These model fits are numerical examples of the fact that (non-constant) polynomials tend to plus or minus infinity for large distances instead of converging to zero, as is required for a reasonable model. Even worse, as demonstrated by the third-order polynomial, high-order polynomials can exhibit erratic behavior outside the X data range and even in the observed X range.

Consistency of the power regression over the distance range and sampling periods

A segmented linear regression showed no improvement of fit compared with that of a single linear regression line on a log-log scale over the whole distance range, from within the same field to 4 km (p >0.050). The improvement in the goodness of fit after adding a nonlinear smooth component to the power model was practically zero (p =1), and the R2 value was increased by only 0.00014. For this reason, and because neither the graphical check of the regression line (Figure 3) nor the residual plot (Figure 4) suggested deviations from linearity, the power model was accepted and no further nonlinear models for the distance effect were investigated.

The regression equations were not significantly different between the two sampling periods of 2001 to 2007 and 2008 to 2010 (F test; p =0.107).

Summary of model selection

The results from the goodness of fit calculations and the consistency checks confirmed the superiority of the power model (Equation 3) over the exponential model (Equation 4) and confirmed the validity of the power model for pollen deposition over the whole distance range examined.

Several previous studies [29],[39],[42],[44] have arrived at the same conclusion, namely, that the decrease in pollen deposition with increasing distance follows a power function. The exponential model used in some studies [37],[76],[77] and the polynomial model used in others [36] are not appropriate in the light of the present dataset because of their poor fit and their implausible predictions of pollen behavior over large distances.

The difference between the power function and the exponential function fitted to our data lies markedly in the curve tail, where the exponential curve underestimates pollen deposition, especially at long distances. This raises questions about the accuracy of some risk assessments of genetically modified plants in the EU based on an exponential relationship [71]-[75].

The reason why different authors proposed different curve shapes for the relationship between deposition and distance may be explained by the different distance ranges of the data used in the analyses. Looking at the data from a narrow distance range, a power function, an exponential function, a polynomial, or even a straight line may fit the data quite well. However, a model derived from short distance range data of a few meters cannot reasonably be extrapolated and expected to fit long-distance data. The exponential curve for pollen deposition used in the EU risk assessment is given in Perry et al. [71], page 4, equation 2.3, 2.4, and related text], based on the data of Wraight et al. [76]. Amending the stated factors, the equation for the exponential curve of maize pollen deposition on slides (n/cm2) versus distance (m) is log 10 Y = 2.368 − 0.145 · X. The dataset used was limited to a distance range of 7 m from the field edge. In this range, both the exponential model of Perry et al. and the power function derived here produce acceptable predictions. However, as illustrated in Figure 5, extrapolating the exponential curve to distances greater than 10 m leads to a rapidly descending curve with increasing discrepancies from field measurement data and from the respective power regression line.

Figure 5 Exponential model of EU risk assessment compared with power model described in this study. For maize pollen deposition versus distance to nearest maize field. Circles, data points from field measurements 2001 to 2010 (N =216). Blue solid line, expected mean deposition calculated using power regression equation Y =1.271 · 106 · X−0.585; Y, deposition in n/m2; X, distance from nearest maize field in m; dashed lines, confidence interval for expected deposition; dotted lines, 95% confidence intervals for single predictions; red line, exponential model used in EU risk assessment (Perry et al. [71], deposition values on slides used in equation 2.3 corrected for stated factor 3); solid, predictions within database up to 7 m from field edge; dashed line, extrapolation of exponential model over distance >7 m. Full size image

Mean regression and confidence intervals

The power regression line (Equation 3) provides estimates for the mean expected pollen deposition at given distances from the pollen source. The mean values predicted from the regression equation range from 3.26 million pollen grains/m2 (326 pollen/cm2) close to the pollen source to 9,340 pollen grains/m2 (0.934 pollen/cm2) at 4,450 m away from the next field margin (see also Table 3). The uncertainty of this prediction is expressed by the 95% confidence interval (CI) of the mean regression line (Figure 3, fine dotted lines).

Table 3 Predicted maize pollen deposition with confidence intervals Full size table

Also shown in Figure 3 is the 95% CI for single observations (solid red lines). This interval describes the range in which a single observation is expected to lie with a probability of 95%. The upper boundary of a single value CI at a given distance indicates the deposition, which is exceeded by a single observation with a probability of no more than 2.5%. Vice versa, to find the distance at which the exposure can be expected to lie below a defined level, while accounting for variations in the data, the intersection between the upper confidence limit and a horizontal line at the defined level must be determined. Table 3 shows predicted values for maize pollen deposition and their confidence intervals for selected distances on the usual linear scale. The standard deviations of the confidence intervals were originally calculated on the logarithmic scale (sd_log), but are shown in Table 3 as 10sd_log so that they are on the same scale as the pollen counts. It must be emphasized, however, that 10sd_log is not the standard deviation of a Y prediction, neither for mean nor for single values. In both cases, for a given X, the relationship between 10sd_log and the confidence interval (CI low , CI high ) on the linear scale is expressed as follows:

C I low , C I high = 1.271 · 10 6 · X · 0.585 · 1 0 s d _ log ± c in n m 2 (7)

where the constant c depends on the number of observations and the coverage probability of the confidence interval. In contrast to the usual form of a confidence interval (‘mean value ± constant c’), here, the multiplicative forms ‘mean value/factor c’ and ‘mean value · factor c’ apply. For the present data, with n = 216 and a coverage probability of 95%, c = 1.97 holds for mean and single value predictions. The way to calculate sd_log differs for mean and single value predictions. Details are given in standard texts on linear regression, e.g. Neter et al. [78] chapter 2].

As an example, Table 3 shows that at the closest distance (within the maize field), the mean value for maize pollen deposition is expected to be 3,258,000 pollen grains/m2 with a confidence interval of (2,462,000; 4,311,000) pollen grains/m2. This confidence interval can be described as (mean prediction/1.32; mean prediction × 1.32). While this consideration is for mean values, the numbers for single observations are quite different. The factor for the confidence interval of a predicted single observation is 7.33, indicating large variations among single values. This means that overall, 95% of maize pollen deposition values from single measurement sites within the field vary between the lower boundary (444,000 n/m2) and the upper boundary (23,876,000 n/m2). Half of the remaining 5% (2.5%) will be above the upper boundary, so the probability of values not exceeding the upper boundary will be 97.5%. At 1,000-m distance, single observations of maize pollen deposition are expected to be between 3,100 and 164,000 n/m2 with a mean of 22,500 n/m2 (2.25 n/cm2). Consequently, a distance of 1,000 m would be necessary to exclude a maize pollen deposition higher than 164,000 n/m2 (16 n/cm2) with a probability of 97.5% for distinct sites.

Dispersal range

In Germany, maize flowers in summer (July to August) and pollen release is favored by warm and windy (i.e., drying and turbulent) weather conditions during the daytime [29],[45],[59],[79],[80]. In turbulent wind conditions, pollen grains are transported higher above the ground and are dispersed over further distances than they are under non-turbulent conditions [29],[32],[39],[40],[42],[44],[59],[80]. Pollen dispersal itself varies according to wind speed and direction, other climatic conditions, topography, and factors that affect the settling velocity of maize pollen, for example, dehydration of the pollen grain [33].

A considerable portion of our dataset represents the longer distance range (>100 m), which has been underrepresented in the literature so far. Compared with that stated in our earlier report [47], the maximum distance for which pollen data is reported is extended from 3.3 to 4.45 km with a total of 81 data points representing distances greater than 100 m.

The results shown in Figure 3 illustrate that maize pollen dispersal is not restricted to close distances (<100 m), but extends well beyond to longer distances up to the kilometer range. This is consistent with the findings of other studies [29],[39]-[42],[44].

Maize is a wind-pollinated plant and produces enormous amounts of pollen (1011 to 1013 pollen grains/ha). Maize pollen is relatively large (80 to 125 μm diameter; 1.25 g/cm3; approx. 500 μg) and its settling velocity in air is approximately 0.2 m/s. Assuming that the average height of the maize tassel is 2 to 3.5 m above the ground, we can estimate that maize pollen settles in the range of 20 to 40 m from the field margin on average. However, this assumption would be only true under still air conditions. In the field, still air conditions are practically non-existent. As our data from real environmental conditions (Figure 3) show, maize pollen was deposited even at the farthest distance measured (4.45 km). In fact, considerable amounts of maize pollen drift on the wind over longer distances (>100 m) and even further than 1,000 m, with deposition values of 3,000 to 164,000 maize pollen grains per square meter (Figure 3). This is consistent with observations of long-distance maize pollen dispersal in other studies [29],[39],[42]. Brunet et al. [40] observed that maize pollen was dispersed at even higher altitudes of several km and was transported over distances as far as 70 km.

The database includes distances up to 4.45 km, which covers a distance range relevant to questions related to Bt maize dispersal. The respective power function for the distance relationship expressed in Equation 3 is highly significant within the distance range of the database. We recommend not to extrapolate to distances far beyond without specific validation. One possible estimation method would be to combine the use of the deposition database with an appropriate dispersal model. The standardized PMF deposition data serve to calibrate the model within the data range, while model predictions may be used to extrapolate over larger distances [32],[80]. These predictions are open to validation based on further standardized measurements.

Pollen deposition under common cultivation

Under common agricultural practice, maize is rarely grown in isolated fields. The pollen deposition of several fields is expected to overlap, and more than one pollen source contributes to exposure at any single site. The potential overlap with other surrounding fields, even if they are distant, should be considered as a factor affecting the distance relationship to the nearest maize field, especially for distances greater than 100 m. This assumption has been supported in various scenarios using a dispersal model [32],[59],[80]. The relative proportion of overlap will depend on several factors, such as distance, the relative position of other maize fields, wind direction, other meteorological conditions, and the flowering behavior in each field. Therefore, it is difficult or not feasible to extrapolate from single field experiments to the complex situations that exist in common cultivation, that is, those situations relevant for the risk assessment and management of GMOs.

The large number of locations and years represented in our dataset covers a broad range of environmental and agricultural conditions. In fact, the data used in our analyses reflect the variable conditions of common maize cultivation in central European countries such as Germany. The data cover small, single-field settings as well as complex field arrangements with no preferences for field size or shape, variety, topography, nor for the relative location of the pollen trap to the main wind direction nor to other maize fields. The distance relationship expressed in Equation 3, therefore, represents the expected values of maize pollen deposition under common agricultural practice in the studied countries.

The reference to common agricultural practice is important when interpreting the variability in deposition under real field conditions. As described before, the results of the regression analysis show a relatively wide confidence interval for individual values (see Figure 3, Table 3). This can be expected, as our data include variability in pollen production, release, and dispersal resulting from different relative positions of the traps to the main wind direction, and other factors such as field size, plant density, maize variety, growing conditions, agricultural management, and weather conditions [29],[45].

Consequences for risk assessment and management of Bt maize

The power curves and exponential curves fitted to our data showed different shapes over the distance range. Our results indicate that the exponential model previously used by some authors and currently used for risk assessment and management of Bt maize [37],[71]-[77] is inferior to the power function in terms of goodness of fit. It underestimates deposition, and thus underestimates the exposure of non-target organisms to GMO maize pollen for distance ranges greater than 10 m, with increasing inaccuracy over longer distances (see Figure 5).

The toxin concentration varies among different Bt maize varieties [81],[82], and the various target insect species and their different larval instars show variations in their sensitivity to the toxin [83]. Therefore, in risk assessment, event and species-specific variations should be considered when establishing dose-effect relationships between Bt maize pollen and sensitive species. Nevertheless, it is not practical to implement specific legal regulations for any single event. Therefore, more general measures based on the precautionary principle are required for risk management of Bt maize cultivation.

Bt maize pollen, even at low pollen densities, adversely affects sensitive Lepidoptera species. For example, Felke et al. reported that the LD 50 value of Bt maize pollen (Bt-176) for the fourth larval instar of Plutella xystostella could be as low as nine Bt maize pollen grains/cm2 (92,000 n/m2) [24], and that a single uptake of four or more Bt pollen grains was sufficient to kill larvae, with earlier instars being even more sensitive [83],[84].

The current recommendation of the event-specific EU risk assessment for Bt maize states, for example, buffer distances of 20 to 30 m between Bt maize and protected habitats of extremely sensitive butterfly species [73]-[75]. These recommendations have been based on an exponential curve for exposure assessment (see Figure 5 and related discussion above). The results of the present study indicate that these buffer zones may be inappropriately small. Instead, conclusions on risk assessment and risk management should be updated based on the model described here for the distance relationship of maize pollen deposition under common cultivation.

With respect to a general risk management of Bt maize cultivation, and based on the precautionary principle, the upper boundary of the confidence interval of the regression for single value predictions indicates that buffer distances in the kilometer range are required to prevent exposure of protected and/or sensitive species to Bt pollen, rather than ranges of tens of meters as proposed in the actual EU risk management.