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The dispersal of herbicide-resistance genes via pollen or seed has the potential to connect farms once thought to be independent. The implications of such interconnections have taken on new importance as the appearance of herbicide-resistant biotypes increases together with reliance on herbicide use in genetically modified crops.
The adoption curve for genetically modified glyphosate-resistant soybeans Glycine max L. Merr., cotton Gossypium hirsutum L. and maize Zea mays L. is unprecedented (Carpenter et al. 2002). Glyphosate is a non-selective herbicide now applied to more than 25 million ha and approximately 85% of the total soybean area (USDA-NASS 2005). While its use has reduced overall weed abundance (Mortensen 2005), glyphosate represents a strong selection pressure for glyphosate-resistant survivors. Conyza canadensis L. Cronq. (formerly Erigeron canadensis L.; common names include horseweed, marestail and Canada fleabane) is an increasing problem in no-tillage agriculture (Buhler 1995) and is a cosmopolitan species distributed globally in roadsides, field edges and abandoned fields (Buhler 1992; Holm et al. 1997; Weaver 2001). Glyphosate-resistant C. canadensis was first reported in 1999 (VanGessel 2001) and resistant populations are now found in 44 000 ha in 12 states in the USA (Heap 2006). The rapid expansion in geographical range of glyphosate-resistant C. canadensis is largely the result of long-distance seed dispersal.
Wind dispersal, of seed or pollen, changes the scale at which resistance genes can move (Marvier & Van Acker 2005). Watrud et al. (2004) recorded pollen dispersal distances of 20 km. Regehr & Bazzaz (1979) recorded C. canadensis seeds being transported 100 m in a corn field, and Dauer, Mortensen & Humston (2006) showed that C. canadensis seed probably travels hundreds of metres in controlled environments. The invasion speed of glyphosate-resistant biotypes is further accelerated by propagule pressure. Effective management is essential because crop yield can be reduced by 90% at high densities (100–200 plants m−2) and even one plant can release thousands of wind-dispersed seeds (Bruce & Kells 1990; Weaver 2001). Smisek et al. (1998) reported 96% of the florets were self-pollinated because pollen was released before capitula were fully opened. A single plant produced between 100 000 seeds when densities reached 200 plants m−2 and 200 000 seeds when plant densities were low (10 plants m−2) (Bhowmik & Bekech 1993).
Regehr & Bazzaz (1979) modelled the dispersal distance of C. canadensis seed using a negative exponential function. The negative exponential, and other empirical models, have been used to quantify the spread of forbs (Bullock & Clarke 2000) and trees (Clark et al. 1999; Greene & Calogeropoulos 2001). Such empirical models can provide information about the spread of a species but do so with little knowledge of underlying mechanisms. Mechanistic models have also been used to model the dispersal of forbs (Tackenberg, Poschlod & Kahmen 2003; Skarpaas et al. 2004; Soons et al. 2004) and trees (Greene & Johnson 1989; Nathan, Safriel & Noy-Meir 2001; Katul et al. 2005). These differ from empirical models because they rely on parameters that account for meteorological (horizontal and vertical wind speed) and biological (settlement velocity) dynamics known to be important in seed dispersal. Mechanistic models require precise meteorological measurement to represent accurately seed movement during the dispersal period. Many experiments have averaged wind speed over many months (McEvoy & Cox 1987; Greene & Johnson 1989; Bullock & Clarke 2000), making it impossible to resolve the importance of stochastic wind events that occur over short time intervals. Vertical instability, in the form of infrequent updrafts, may affect dispersal distance even more than wind speed alone, and can be included in mechanistic models by slowing the settlement velocity (Nathan, Safriel & Noy-Meir 2001; Tackenberg, Poschlod & Kahmen 2003; Skarpaas et al. 2004; Boehm & Aylor 2005).
Another important component in dispersal patterns is the variation in horizontal wind direction and speed. Prevailing wind direction is important to measure movement in one dimension, but sampling designs often cannot accurately assess two-dimensional seed dispersal patterns. The two-dimensional spatial distribution of weedy species has been studied frequently, but often for gravity-dispersed seed (R. Humston, unpublished data). Bullock & Clarke (2000) examined seed movement in four directions, analysing each direction independently. While this study revealed a dispersal kernel that was anisotropic, causal mechanisms underlying such distributions are poorly understood.
In this study, seed dispersal was measured in both the prevailing wind direction (PWD) to 500 m and in two-dimensions to 70 m. One-dimensional empirical models were fit to the data to determine which model provided the best fit. A mechanistic model was also evaluated to determine if the meteorological conditions measured could account for seed movement in PWD. The analysis was then expanded to incorporate wind and seed movement in two dimensions. Circular statistics were used to compare the two-dimensional spatial distributions as described by Batschelet (1981) and Jammalamadaka & SenGupta (2001). Wind speed and direction were correlated with seed direction and distance over short time intervals. By understanding the interplay between wind speed and direction of seed dispersal, we can better predict the movement of species that rely on wind as a vector to overcome dispersal constraints. One- and two-dimensional sampling and modelling can elucidate extra-field dispersal and also quantify intrafield spread patterns.
This research attempted to define the dispersal kernel of Conyza seed from a source population. If the spatial extent is sufficiently large, the dispersal kernel would connect individual fields, i.e. one farmer's problem may very well be another farmer's problem. The dispersal of resistance genes via pollen or seed connects farms once thought to be independent. The degree of interconnection was explored by coupling empirical data from several study sites and extending that data through the use of the empirical 2Dt model (Clark et al. 1999). Identification and parameterization of dispersal models will enable us to assess methods of managing the ever-growing problem of glyphosate-resistant weeds in managed landscapes.
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The methodological approach had a number of key features not present in most dispersal sampling experiments, including a defined point source, increased sampling intensity, increased maximum distance of seed collection and inclusion of sampling in two dimensions. The seed source in PA1 contained 492 plants, while the PA2 source contained 1263 plants, at a mean density of 17·2 and 45·1 plants m−2, respectively. The average plant height was 1·34 m (n = 10, SD = 0·221) and 1·44 m (n = 15, SD = 0·125) in PA1 and PA2, respectively, approximately 50 cm taller than the surrounding soybeans at onset of seed release and consistent with naturally occurring C. canadensis populations in soybean fields (M. VanGessel, personal observation). As seeds are approximately normally distributed in the upper third of the plant, the mean seed release height is always less than the plant height. Therefore, the mean seed release height was calculated to be 1·12 m and 1·20 m in PA1 and PA2, respectively.
On average, plants produced 2541 capitula (n = 18, SD = 1541·3), which yielded 54 seeds capitula−1 (n = 10, SD = 3·4). The source strength was calculated to be approximately 70 million seeds in PA1 and approximately 180 million seeds in PA2. In PA1, 32 012 seeds were collected (0·05% of those released), while 71 155 seeds (0·04% of those released) were collected in PA2. The majority (93% in both fields) were collected on traps located in the source, but 2255 and 4689 seeds were trapped outside the source (PA1 and PA2, respectively), providing sufficient data to interpolate seed densities throughout the fields (Fig. 1). An inverse distance weighted interpolation of the seed count data estimated 18 million (PA1) and 43 million (PA2) seeds were deposited in each field, a considerably smaller number than the estimated fecundity of the source populations. The estimated source strength, total seed collected and seed collected outside of the source in PA1 were approximately double those values in PA2. Because of the similar deposition pattern, the model fitting was performed using data from PA1. While seeds collected during experiments were similar in size and filled, seed viability was not evaluated directly and is an area requiring further study.
Seeds were deposited continuously out to the end of the 500-m transects, with a classically distributed dispersal kernel with a very high density centre and very fat tails (Table 3). All three models, 2Dt, GJ with fixed parameters and GJ with estimated parameters, underestimated seed deposition at long distances. The GJ model with estimated parameters had the lowest AIC value and therefore provided the best fit of the three models. The parameters for the GJ model were similar whether these values were fixed or estimated (Table 2). For example, the geometric mean wind velocity in the downwind direction at mean plant height was 1·27 m s−1 (n = 6937 min, SD = 0·46, θ= 38° east-north-east) when measured independently, while MLE resulted in 0·88 m s−1 (Table 2). In addition, the settlement velocity estimated by the model was greater than the empirically derived value of 0·323 m s−1 (Dauer, Mortensen & Humston 2006). The one exception was the value for the variance of the horizontal wind speed, σu. This value may be high as a result of incorporating variation in both horizontal and vertical wind into a single value. While subtracting the vertical wind from the settlement velocity, Fw, and including the vertical wind variance into the horizontal wind variance simplifies the equation, the importance of occasional vertical wind gusts is masked. These stochastic events can affect the timing of seed abscission. Research has shown that abscission of seeds is proportional to wind speed (Greene & Johnson 1996; Skarpaas, Auhl & Shea 2006) but was not examined in this research. Nonetheless, the GJ estimated model had similar parameter estimates to the GJ fixed model, and the fit was improved considerably (lower AIC) by using this technique.
Table 3. Actual and predicted Conyza canadensis seed collections at a subset of distances using the empirical 2Dt, mechanistic GJ with fixed parameters and GJ with estimated parameters. Actual and predicted values represent PA1 but results were proportional in PA2. Because the GJ with fixed parameters has zero estimated parameters, the calculated AIC is −2 times the log-likelihood.
|Seed collected||2Dt||GJ (fixed)||GJ (estimated)|
|20|| 195|| 127·4|| 17·7|| 205·3|
|30|| 80|| 43·9|| 2·0|| 79·2|
|40|| 37|| 20·7|| 0·4|| 40·3|
|50|| 19|| 11·5|| 0·1|| 23·9|
|100|| 4|| 1·5|| 0·0|| 3·8|
|200|| 1|| 0·3|| 0·0|| 0·9|
|300|| 0|| 0·2|| 0·0|| 0·6|
|400|| 3|| 0·1|| 0·0|| 0·5|
|500|| 2|| 0·1|| 0·0|| 0·3|
|AIC|| 313||2409|| 120|| |
The source strength extrapolation found seeds travelling kilometres from the larger source (Fig. 2). As the source strength increased, the number of seeds reaching a field 1 km from the source increased from less than 1 seed m−2 when the infestation was small, to nearly 10 seeds m−2 when the infestation was large.
Figure 2. Seed deposition (predicted, seeds m−2) as source strength (Q, seeds) increases using the empirical 2Dt model. Contours represent the upper bound of seed deposition. Source strength represents a range from a small patch (outbreak) to a field-scale infestation.
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The two-dimensional spatial analysis used a temporal division of the dispersal period into six intervals, where wind and the corresponding pattern of seed deposition were compared. The mean wind direction was variable throughout the season (Table 4). Mean seed direction was also variable during the season, but the angular deviation (κ) around the mean angle was higher than for the wind. The pattern of seed deposition was orientated with wind direction. In 10 of 12 dispersal intervals (two sites and six dispersal intervals) the difference in the mean angles for wind and seeds was less than 90°. While visual assessment found that the angles for the wind and seeds were close, Rao's test for equality of polar vectors found the angles were significantly different in nearly all dispersal intervals (P < 0·05; Table 4). Despite the variability throughout the season, the cumulative mean direction of wind and seed data were not significantly different.
Table 4. Mean angle (degrees) and angular deviation for seeds and wind for individual time periods in PA1. Mean angle and angular deviation were weighted by distance (seeds) and speed (wind) and were relative to PWD. H is the test statistic for Rao's test of equality of polar vectors with the corresponding P-value, where P-values less than 0·05 represent significantly different distributions.
| ||Seed data||Wind data||H||P|
|Mean angle||Angular deviation||Mean angle||Angular deviation|
|Trial 1||344·2|| 62·38||108·7|| 49·33|| 40·93||<0·01|
|Trial 2||352·3||154·11|| 77·5|| 43·80|| 74·08||<0·01|
|Trial 3|| 21·0||100·63|| 63·1|| 34·62||211·45||<0·01|
|Trial 4||163·3|| 52·80||242·5|| 68·75||223·93||<0·01|
|Trial 5||113·8|| 67·41||298·7|| 30·03|| 1·17|| 0·28|
|Trial 6|| 29·6|| 80·07|| 54·8||109·16|| 74·07||<0·01|
|Cumulative|| 42·4|| 39·04|| 38·2|| 23·49|| 1·61|| 0·20|
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- Materials and methods
Long-distance dispersal is inherently difficult to quantify because seed detection decreases with distance and inferring the dispersal distance of seed becomes a study of theoretical approaches. This study examined an empirical method that effectively quantified and accounted for the spatial pattern of seed deposition at distances much greater than previously reported for C. canadensis (Regehr & Bazzaz 1979). While small numbers of seeds were found at the ends of the sample transects, it is almost certain that seeds were dispersing beyond 500 m. The method provided meaningful insights into the distance and direction of C. canadensis seed movement, resulting in a uniquely rich seed-dispersal data set.
As with most long-distance dispersal studies, seeds were collected more frequently near the source but the dispersal tail was considerably longer than related results for this and other wind-dispersed species (Regehr & Bazzaz 1979; McEvoy & Cox 1987; Greene & Johnson 1989; Portnoy & Willson 1993; Bullock & Clarke 2000; Skarpaas et al. 2004). The focus of many of these studies has been to compare dispersal models and determine the model that best fits the data. In addition to those reported on in this study, a number of models were considered for the analysis, including empirical (Bullock & Clarke 2000; Greene & Johnson 2000; Ribbens et al. 1994) and mechanistic models (Soons et al. 2004; Katul et al. 2005). Some were not probability density functions and lacked realistic biological assumptions, while others contained parameters that were not measured in this research. The models selected have been used successfully to model dispersal of lightweight seeds (Clark et al. 1999; Skarpaas et al. 2004), are simple enough that non-mathematicians studying plant dispersal can utilize them, and predict dispersal that is close to observed values. Other empirical models [negative exponential, inverse power, mixed (Bullock & Clarke 2000) and Ribbens (Greene & Calogeropoulos 2002)] fit the data poorly (AIC > 500). Either the 2Dt model (Clark et al. 1999) or log-normal model (Greene & Johnson 2000) could have been used (AIC values were lower for log-normal in PA1 but higher in PA2), but the fewer parameters in the 2Dt model (two compared to four) made it simpler to manipulate. Likewise, the Greene & Johnson (1989) mechanistic model did not rely on variation of the vertical wind speed, σw, which was critical for other models (Nathan et al. 2002; Katul et al. 2005). Both the mechanistic and empirical models have utility in studying long-distance dispersal. A mechanistic approach is useful for evaluating the importance of meteorological and biological factors, while the empirical approach does not depend on specific mechanisms and, with the assumption that the dispersal kernel is representative of a wide range of conditions, can be extended to explore applied questions associated with species spread.
One area of exploration with mechanistic models is the importance of vertical instability affecting dispersal distance. The mechanistic model overestimated seed deposition near the source but vertical wind stochasticity may have led to a conservative estimation of dispersal distances. Longer dispersal distances are possible and, given the low settlement velocity and vertical wind instability expected in a field, updrafts could extend the expected dispersal distances (Nathan et al. 2002; Tackenberg, Poschlod & Kahmen 2003). The empirical data contain some discontinuities, where gaps in seed deposition between 200 and 400 m may reflect a second mode of seed deposition with additional seed deposited at 400 or 500 m (Table 3). Quantifying whether a second mode exists will require either sampling at greater dispersal distances or measuring the vertical seed concentration as distance from the seed source increases.
The mechanistic approach provides the additional benefit that parameters can be collected independently. Where little data are available on dispersal distance of a species, it is possible to measure settlement velocity, monitor wind speed during the dispersal season using a basic meteorology station and estimate seed production to develop a dispersal kernel relevant to the species. The predicted dispersal distances are likely to be underestimates of true distances but will reduce the error in field sampling.
Unfortunately, wind dispersal is never solely in PWD. Sampling in two-dimensions offers insights into seed densities but adds a layer of complexity. Wind speed and direction varied within and among the six dispersal windows and integrating across this variation resulted in a season-long seed distribution that was isotropic (Rayleigh test, P > 0·05; Table 4 and Fig. 1). Comparison of the wind direction and seed deposition during each dispersal interval showed that the two were similar but statistical analysis found most were significantly different. It is likely that a stronger correspondence between wind direction and seed deposition would be found if wind events and resulting seed dispersal were isolated over shorter time periods (Nathan et al. 2002). Tackenberg (2003) found that updrafts are often associated with low wind, cloud-free days, when vertical mixing is greatest, and downdrafts are associated with stormy weather. Wind data could be filtered on other meteorological classifications to test Tackenberg's (2003) findings. For example, high humidity or cloudy days, when updrafts were minimal, could all be removed from the subset to determine if a stronger correspondence occurs. An attempt with this truncated data set revealed a stronger correlation between wind direction and seed deposition in some trials and a weaker correlation in others. Furthermore, removal of wind data to test this correspondence was subjective, based on an estimate of optimal conditions that were not tested during these experiments. Determining optimal flight conditions, with vertical and horizontal wind movement over shorter time intervals, and the corresponding seed distribution will be the subject of ongoing work to increase our ability to predict the spread of this species (Dauer et al. 2006; Shields et al., in press).
Additional benefits of the sampling method include maximizing seed collection and coupling temporal and spatial dynamics. While many studies have elected to use collection plates at ground level (Regehr & Bazzaz 1979; Bullock & Clarke 2000; Skarpaas et al. 2004), elevating the traps provided excellent data and they were easy to locate and replace during the experiments. We assumed that seed collected at the surface of the soybean canopy would have dropped to the soil surface and that secondary dispersal (post-settlement) was limited.
The objective of the sampling design was to quantify accurately the seed dispersal pattern, particularly at long distances (> 200 m). While the 2003 sampling design was large (366 traps, 2196 samples), the percentage of the field sampled was small (less than 0·02% in both fields). Nonetheless, thousands of seeds were trapped, representing an effective methodology for capturing wind-dispersed seed when studying highly fecund species. While a clearly defined dispersal kernel emerged from this work at both source populations, our interpolated kernel indicated that we recovered only one-third to one-quarter of the estimated seed produced by each patch. The disparity in seed recovery could have resulted from overestimating the number of seed produced in the source populations. Overestimation may have been caused by lack of replication of traps within the source or the result of extreme seed deposition coating the source trap and reducing the trapping efficiency. It is also possible that seed movement beyond the sample region may have occurred during extreme storms. Gaining greater resolution on seed movement beyond the currently defined dispersal kernel is the focus of continuing research.
Unfortunately, as with all long-distance dispersal studies, determining the dispersal distance of seeds that travel beyond the sampling design remains elusive. This study was conducted at the field level, but it is likely that seeds travelled well beyond the edges of the field. The utility of the empirical model can now assist in applied questions about source strength. Source populations used in this study were established as small patches, typical of fields that have been recently infested (1–2 years old) by glyphosate-resistant C. canadensis. As the invasion of the glyphosate-resistant population occurs, whole fields quickly become infested. A farmer with a neighbour that fails to control such resistant populations may receive hundreds of thousands of seeds in a single season. We extended this finding by using the fitted model to determine how source strength influences dispersal distance (Fig. 2). The range of source strengths is representative of those commonly observed in the field. As the source strength increases from a patch to a whole field, the maximum dispersal distance increases from less than 0·5 km for a patch to greater than 1·5 km for the whole field. This projection is profound and agrees with results by Rieger et al. (2002), who reported that canola pollen dispersal distances were an order of magnitude greater when originating from whole fields compared with individual plant and small plot sources.
Seeds that can travel beyond 500 m change common agricultural practices that focus on field-level suppression or eradication. Seed dispersal distances may stretch kilometres, effectively decoupling a farmer's management decision from the resulting weed control. A neighbour's field that contains a wind-dispersed weed will continue to act as a source for invasion into adjacent fields irrespective of the intrafield management practices to maximize control in the adjacent fields. In the case of glyphosate-resistant weeds, this work demonstrates that the invasion speed of herbicide-resistant biotypes is far greater than previously thought. Significantly expanding the spatial extent of resistant C. canadensis will increase the cost of managing such populations and compromise weed control, thus limiting the value of glyphosate-resistant crops. One solution is co-operation among farmers in an area-wide approach, to slow the spread of this and other wind-dispersed species.
Co-operation could be used in conjunction with active adaptive management (AAM). This process of making choices about management includes a feedback loop to adjust the management strategy as information about the success or failure is gathered (Shea et al. 2002). One problem noted by the authors is lack of replication in time and space to allow producers to learn from others’ management decisions. With the glyphosate-resistant horseweed biotype now widespread throughout eastern and central USA (Heap 2006), and no definitive management protocol, experiments to determine effective management practices are occurring simultaneously on a large scale. Already farmers who have faced glyphosate-resistant populations have developed useful techniques for slowing the spread out of their fields, often re-incorporating tillage or herbicide mixtures in their management plans (M. VanGessel, personal observation). Unfortunately, lack of co-ordinated or deliberate sharing of results limits the ability of the producers to learn about management decisions that reduce the potential of invasion into their fields. AAM, along with co-operation and information sharing, has the potential to offer farmers applicable solutions that will impact the surrounding network of farms. Providing farmers with options requires understanding the probability that C. canadensis seeds will arrive and establish in their fields. One-dimensional spatial analyses can yield predictions about the distances seeds may disperse, which can be combined with survivorship data to provide a level of risk to surrounding farms. A two-dimensional approach examines more closely the relationship of wind velocity and corresponding spatial seed distribution to yield in-field management decisions at a landscape scale. Collectively, these spatial analyses quantify the dispersal ability for this common agricultural weed and provide a framework for study of other wind-dispersed species.