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Conyza canadensis seed ascent in the lower atmosphere
agricultural and forest meteorology 149 (2009) 526–534
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Conyza canadensis seed ascent in the lower atmosphere Joseph T. Dauer a,*, David A. Mortensen a, Edward C. Luschei b, Scott A. Isard c, Elson Shields d, Mark J. Van-Gessel e a
Intercollege Graduate Degree Program in Ecology, The Pennsylvania State University, University Park, PA 16802, United States Department of Agronomy, University of Wisconsin – Madison, 1575 Linden Dr., Madison, WI 53706, United States c Departments of Plant Pathology and Meteorology, The Pennsylvania State University, University Park, PA 16802, United States d Department of Entomology, Cornell University, Ithaca, NY 14853, United States e Plant and Soil Sciences Department, University of Delaware, Georgetown, DE 19947, United States b
article info
abstract
Article history:
Long-distance dispersal of wind dispersed plant seed is dependent on seeds ascending in the
Received 29 February 2008
atmosphere’s surface boundary layer (SL). However, seed dispersal research often focuses
Received in revised form
on the near-surface dynamics of abscission and deposition, with little attention paid to seed
29 September 2008
ascent and transport beyond the surface boundary layer. Our research sought to estimate
Accepted 3 October 2008
the vertical seed density flux of Conyza canadensis, a common agricultural weed, in the surface boundary layer. Using a 2.7 ha field infested with Conyza canadensis, the vertical seed concentration profile was measured on 5 days, three times per day while simultaneously
Keywords:
monitoring micrometeorological conditions to connect the mechanisms of release with the
Wind dispersal
process of ascent. Vertical seed sampling was conducted from two to six meters above
Long-distance dispersal
ground level using removable screen frames. Remotely piloted airplanes were used to
Glyphosate-resistance
quantify seed concentration at 68 and 120 m above ground level and provide a connection
Erigeron canadensis
between near surface dynamics and dynamics higher in the SL. Seed flux density (area under seed concentration curve) decreased with height and decreased from morning to afternoon. The seed flux density was significantly correlated with mean mechanical turbulence and the interaction of mean mechanical and mean thermal turbulence, supporting earlier research of meteorological effects on seed dispersal. While fewer total seeds were collected in the afternoon, more seeds were collected at 68 and 120 m during this period and may suggest more seeds were present in the upper SL where dispersal distance may be greater. Seeds collected above the SL will potentially be carried for hours before descending, depositing seeds in the range of 2–122 m, and influencing a much greater area than seeds unable to ascend through the lower SL. While this range is very wide, the dispersal distances reported herein are several orders of magnitude greater than previously reported for plants. These findings are all the more striking since the study species has evolved resistance to glyphosate herbicide, an herbicide now widely used in soybean, cotton, and corn crops in the US. Therefore, aerial transport of C. canadensis seeds carrying genes coding for glyphosate resistance enables seed to move tens or hundreds of kilometers in a single dispersal event, a spread rate corroborated by number of cases of reported glyphosate resistance occurrences in North America. # 2008 Elsevier B.V. All rights reserved.
* Corresponding author. Present address: Crop and Soil Science, 335 Crops Building, Oregon State University, Corvallis, OR 97331, United States. E-mail address: [email protected] (J.T. Dauer). 0168-1923/$ – see front matter # 2008 Elsevier B.V. All rights reserved. doi:10.1016/j.agrformet.2008.10.005
agricultural and forest meteorology 149 (2009) 526–534
1.
Introduction
The importance of long-distance seed dispersal in determining plant population spread has been well documented in the last 10 years (Nathan, 2006; Clark et al., 2003). Central to this literature is the challenge of collecting data to validate predictive models of seed dispersal. Should one collect seeds at increasing distances to show how far seeds can travel (Green and Figuerola, 2005) or sample at short time intervals to quantify the mechanisms of dispersal (Cain et al., 2000)? To aid in our understanding of seed dispersal, two types of dispersal models have emerged: phenomenological and mechanistic. The phenomenological approach quantifies the dispersal pattern and infers process (Clark et al., 1999; Bullock and Clarke, 2000) and can be used when extrapolating seed dispersal in space and time (Clark, 1998). The mechanistic approach, used in this paper, matches the sampling interval to the time scale of the system responsible for dispersal (Nathan, 2006). This approach measures the trajectory of the dispersal vector (water, air, and animals), and assuming one can predict how that vector will change in the environment, projects the path of the seeds over longer distances. Mechanistic models of transport by insects include behavioral characteristics (Zanen and Carde, 1999), whereas mechanistic models of abiotic vectors (water and atmosphere) typically focus on plant characteristics and hydrological and meteorological conditions that impact the distance of transport (see Kuparinen (2006) for review). In this study, we focus on collecting samples with sufficient numbers of seeds over relatively short time intervals that correspond to important meteorological controls over which the transport process vary (Katul et al., 2005; Yao et al., 1997; Nathan et al., 2002; Tackenberg, 2003). Dauer et al. (2007) were unable to link seed deposition with wind direction and speed with a 7 day seed sampling interval. The lack of correspondence was probably because meteorological monitoring should be on the order of hours or shorter to capture turbulence effects without encountering synoptic weather patterns (Stull, 1988). Researchers have successfully coupled empirical samples on a small time interval (30 min) with Lagrangian models for heavy particles (which may include seed) that ‘‘track’’ individual or cohorts of particles as they move through the atmosphere, constantly adjusting particle trajectory with wind speed and direction (Nathan et al., 2002; Boehm and Aylor, 2005; Aylor et al., 2006; Boehm et al., 2008). In this study, sampling was conducted over 30 min periods at multiple heights above and downwind of a large seed source to trap seeds when atmospheric conditions may have resulted in seed uplift. Aerial seed transport depends on both buoyancy to suspend seeds in the air and wind to push the seeds. To understand how seed utilize the atmosphere, we must better understand the components of the atmospheric boundary layers. The planetary boundary layer (PBL) is the region of the atmosphere in which air flow is influenced by the physical and biological features of the earth below, with the majority of the influence taking place in the lowest 10% and termed the surface boundary layer (SL, Stull, 1988). The PBL extends, during the daytime, to 1–3 km above ground level (AGL) and is characterized by increasing wind speed with height. When
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seed sampling was conducted during the daytime, the layer above the SL (and comprising the remaining portion of the PBL) is convectively mixed and termed the convective boundary layer or mixed layer (CBL). All sampling for this paper was conducted in the SL. Turbulence is ‘‘eddies superimposed on an underlying smooth flow having constant speed and direction (Lowry and Lowry, 1989).’’ Within the PBL, turbulence decrease with height (Stull, 1988). Turbulence is composed of both a thermal component caused by unequal heating of the earth’s surface and a mechanical component caused by friction between the airflow and surface obstructions or slower moving air streams (Lowry and Lowry, 1989). While both types of turbulence can transport seeds vertically, a feature critical of long-distance dispersal (Nathan et al., 2002; Nathan and Katul, 2005), mechanical turbulence can also result in local deposition. In the daytime SL, short-period mechanical turbulence is generally pronounced (Lowry and Lowry, 1989) leading to seed deposition close to their source (from settlement velocity plus downdrafts). Consequently, many wind-dispersed organisms are adapted to ascend and stay aloft in order to facilitate long-ranged transport. Insects, pollen, and seeds respond to fluctuations in humidity, pressure, and vertical wind to determine the optimal release time to facilitate this ascent (Shields and Testa, 1999; Rodriguez et al., 2000; MaldonadoRamirez et al., 2005), and we expect a similar Conyza canadensis response. While researchers have not examined seed adaptation to ascent, seeds are adapted to release in response to certain meteorological conditions (Nathan et al., 1999; Greene, 2005). Seed collection in the SL often characterizes seed density at canopy level (e.g. Bullock and Clarke, 2000; Dauer et al., 2007; Skarpaas and Shea, 2008) and only rarely at multiple levels within the canopy (e.g. Nathan et al., 2002). Isaacs and Byrne (1998) used a tower sampler to assess whitefly (Bemisia tabaci) abundance at multiple heights in the lower SL which allowed them to characterize the change in insect density with height. Detecting seeds in the upper SL can be difficult due to their very low concentrations, but researchers have successfully collected aphids (Johnson, 1957; Isard et al., 1990), potato leafhoppers (Shields and Testa, 1999), and corn pollen (Aylor et al., 2006). Seed collection in the upper SL has been limited to two studies including a general organism sampling in Australia (White, 1970) and more recently, sampling for C. canadensis (Shields et al., 2006). The focus of this study was to quantify the vertical seed concentration profile of a wind-dispersed plant. Conyza canadensis is a winter annual broadleaf weed that invades agricultural fields, roadsides, abandoned fields and waste areas (Holm et al., 1997; Weaver, 2001). Conyza canadensis seed have an unbeaked pappus which reduces settlement velocity to 0.3 m s1 (Dauer et al., 2006; Andersen, 1993) and allows seeds to remain aloft longer than most wind-dispersed plant seeds. Quantifying the dispersal distance for this species is critical because populations have developed resistance to the common agricultural herbicide, glyphosate (VanGessel, 2001), and have spread to at least 40 thousand hectares of agricultural land in the US (Heap, 2007). Glyphosate-resistant C. canadensis, along with other weeds that already have or will likely develop glyphosate-resistance, will dramatically alter
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current agricultural practices that rely on glyphosate for weed management. Predicting the potential dispersal distance will allow producers to proactively adjust their weed management to consider herbicide resistance. Successful predictions about C. canadensis dispersal distance, as with other wind-dispersed organisms, will require knowledge of conditions leading to ascent within the SL. Our objectives for this study were to (1) quantify the atmospheric seed concentration profile of C. canadensis accounting for changes in daytime conditions, and (2) correlate the observed seed density flux with meteorological conditions during the sampling interval.
2.
Materials and methods
Experiments were conducted near Georgetown, Delaware, USA in a 2.73 ha fallow field infested with Conyza canadensis. C. canadensis occurs naturally in Delaware and plant densities within the field ranged from 0.1 to 70–130 plants m2 (Fig. 1). C. canadensis was in near monoculture and constituted the ‘crop’, with heights approximately homogeneous at 2.5 m. Assuming 130,000 seeds per plant (Dauer et al., 2006), the resulting seed source strength, was estimated to be 125 billion seeds. Seed and meteorological sampling took place over 5 days in midSeptember 2006 during the maximum seed release period (M. VanGessel, personal observation). Extensive daily sampling determined that maximum seed release occurred during early afternoon (Middle, 12:30–14:00, J.T. Dauer, unpublished data). Two additional sampling periods were chosen: Early (11:00– 12:30) and Late (14:00–15:30) to provide a thorough quantification of the change in seed concentration within and among days. Sampling in the Middle and Late periods was conducted approximately 1 h after completion of the previous sampling due to equipment constraints. For example, on day 4, sampling was initiated at 11:45, continued at 13:30 and 15:00.
Fig. 1 – Schematic view of field and general relationship of tower samplers (A, photo inset) to heaviest concentration of C. canadensis (slashed areas, 10–130 plants mS2). Screens on the tower could be swiveled so screens were perpendicular to wind direction. Airplane sampling was conducted downwind at a 458 angle vertical to the source at approximately 68 and 120 m AGL. The 3-dimensional sonic anemometer was located near the tower samplers (B).
Seed collection was conducted at 2–6 m, 60 m, and 120 m AGL. For the purpose of this study, we refer to lower and upper SL, with the former represented by measurements near the ground surface (2–6 m) and the later by the samples collected at 60 and 120 m. Seed density in the lower SL was quantified using a set of towers located in the center of the field (Fig. 1). Individual towers consisted of 7.6 m long 10 cm diameter PVC, centrally reinforced with wood (3.8 cm 8.8 cm 150 cm) and supported by guide wires. One stationary, pivot tower and four positional towers located at 3408, 2958, 2508, and 2058 (08 is north), made it possible to connect a positional tower closest to perpendicular to wind direction with the pivot tower. Five 15 cm collars separated by 1 m were connected to the towers and raised and lowered via pulley guided ropes. Two 1 m 0.5 m PVC frames were attached to these collars and 1.5 mm mesh fiberglass screen was attached to the frames. During each sample period, sticky adhesive (Tangle-Trap, exterior formulation; TangleFoot Company, Michigan, USA) was applied to the screens, and screens were raised to 2–6 m AGL. The heights were selected to sample at multiple levels within the SL. Sampling in the upper SL was conducted by simultaneously flying two large radio controlled monoplanes (Remote Piloted Vehicle, RPV) at approximately 60 and 120 m AGL (Shields and Testa, 1999; Shields et al., 2006). A third plane was flown during the first 2 days which sampled the atmosphere upwind of the source population ranging from 60 to 120 m AGL. Zero seeds were collected upwind of the field during these days and for the subsequent sampling periods we assumed no seed advection throughout the study area. The lower sampling altitude of 60 m was set for reasons that included increased likelihood of plane crashes below this altitude and greater interference from a local cell phone towers. The RPVs were controlled by ground-based pilots using hand-held transmitters. Each RPV held two seed sampling devices, attached near each wing tip to minimize influence of turbulent air streaming off the propeller. RPVs were flown at full throttle setting (96 km h1) because full throttle was easily repeatable and maximized the air volume sampled. Fuel consumption restricted flights to 30 min. The trapping surface within each airplane collector was a removable unit built from a standard 90 mm plastic Petri plate. Each individual trapping surface was constructed from the smaller portion of the Petri plate by removing the bottom of the plate while leaving a 3 mm lip around the outside diameter. A 30 micron cloth mesh was glued to the lip. When the collector was closed, the trapping surface was protected from the airflow and when the collector was opened during the flight, the trapping surface was perpendicular to the airflow. Just prior to a sampling flight, the trapping surfaces were sprayed with a sticky adhesive (Dupont Super X-77TM) to retain the seeds striking the trapping surface during flight. Each trapping surface was exposed to approximately 10,000 L of air per min (10 m3/min), or a total of 300,000 L of air (300 m3) during the typical 30 min sampling flight. Each RPV used in this research carried a data logger, GPS antenna, and a telemetry downlink. Data logger information was utilized to calculate the volume of air sampled per flight and additional information about on-board electronics can be found in Shields et al. (2006). The telemetry downlink from the
agricultural and forest meteorology 149 (2009) 526–534
RPV was used to transmit real time data to an ‘‘air traffic control trailer’’ where altitude information was relayed by a technician to the pilot to assist them in maintaining the desired altitude during the duration of the sampling flight. Flight pattern was an approximately 300 m long by 200 m wide oval, at a predetermined height, oriented perpendicular to the wind direction, which essentially provided crosswind lineintegrated concentrations. Meteorological conditions were measured concurrently at two locations. Meteorological conditions (wind direction, wind speed, relative humidity, and temperature) were monitored using the University of Delaware weather station located 1 km from the source field (Fig. 1). Turbulence was measured with a 3-dimensional sonic anemometer (CSAT-3, Campbell Scientific, Logan, UT) located 12 m from the seed sampling towers and positioned at 1.5 m above ground level (approximately 0.5 m above the soybean canopy, Fig. 1). The sonic anemometer measured horizontal wind speed in the direction of the sensor orientation (Ux) and the direction perpendicular to the sensor orientation (Uy), vertical wind speed (Uz), and temperature (T). Data were recorded at 10 Hz and averaged over 1 min intervals during each trial and recorded on a CR23X datalogger (Campbell Scientific, Logan, UT). Maximum wind speed was defined as the largest recorded value (1 min averaged value) during a 30 min sampling period. Vector analysis of Ux and Uy resulted in the wind speed and wind direction for each minute interval. The datalogger calculated the product of instantaneous variation of the vertical wind velocity (w), air temperature (T0 ), and horizontal wind velocity (u0 ) from their respective means and the covariances wT and wu. Covariances were averaged each minute and recorded on the datalogger. The covariance wT represents thermal turbulence and a negative value signifies an upward directed force since temperature generally decreases with height in the lower atmosphere during the daylight hours. The covariance wu represents mechanical turbulence and a positive value is a lifting force since wind speed generally increases with height above the earth’s surface. The turbulence statistics can be combined to provide a measure of atmospheric stability (Soons et al., 2004; Aylor et al., 2006; Finnigan, 2007). Mechanical effects are the result of friction velocity (u*) calculated by u ¼
pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi covw0 u0
3.
3
u ½k ðg=To Þ covw0 T0
(Gillies and Berkofsky, 2004). An error in the sampling device at 10 m on the first 2 days necessitated using the three remaining days to calculate the average wind gradient during the 3 test periods. This average gradient was then applied to the 2 missing days to calculate the wind speed at each height. The volume of air was calculated as the product of screen area (1 m2) and wind run (wind speed time) for the period. Sampling frames were realigned before each sampling period and wind direction never varied more than 908 during the sampling period. However, wind direction did vary slightly, which affected the sampling area available for seed collection. The sine of the wind direction relative to the sampling surface represented the proportion of the wind contacting the sampling screen, assuming 100% screen trapping efficiency. Seed concentration at each height was finally calculated by dividing the observed seed collection by the predicted wind volume. Seed concentrations in the upper SL were quantified by the seeds collected divided by the volume of air sampled by the RPVs. Following other aerobiological researchers, we assume that seed concentration declines as a negative exponential function of height above the ground within the SL (Isaacs and Byrne, 1998). Seed concentration data were divided into periods (Early, Middle, and Late) and a linear model was used to quantify the average decline in log seed concentration with increasing height in the SL (R Development Core Team, 2007). The rate at which seeds entered the air per unit area of field (seed flux density in units of seeds m2 per 30 min sample) was calculated as the area under the seed concentration curve for each period and also averaged across 5 days. The seed flux was calculated from 1 to 120 m, above which additional seed flux density contributed minimally to the overall seed flux density. Germination was assessed for a subsample of seeds collected on tower screens and all seeds collected on airplane samplers during one trial each day. Seeds were placed in 9 cm Petri dishes on moistened filter paper in a germination chamber set at 21 8C and photoperiod of 16:8 light:dark. Germinated seeds were recorded and removed daily for 2 weeks. Logistic regression was used to determine whether germination varied with height of collection.
Analysis
(1)
The Monin–Obukhov length (LMO) gives the relative contribution of mechanical and thermal turbulence to the atmospheric stability and is calculated by
LMO ¼
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(2)
The Monin–Obukhov lengths were calculated for each 1 min-averaged covariance, before being averaged for the 30 min sampling period. A greater magnitude of the LMO represent a more unstable atmosphere, although this scaling does not speak directly to the independent contribution of either the thermal or mechanical turbulence. Wind speed at each height (2–6 m) was calculated by fitting a logarithmic model between 3, 6, and 10 m cup anemometers
Generalized linear models using least squares parameter estimation were used to test the effects of day and period on both biological and meteorological factors. We assumed that sampling periods were continuous, but recognize that inference from this study is limited to late morning to afternoon. In many cases, errors were correlated within days and were included in the analysis (Pinheiro and Bates, 2000). Seed fluxes were separated into 5 days and three periods (15 seed fluxes) for analysis with turbulence. A generalized linear model was fit to the flux data for the interaction of mean convective turbulence and mean mechanical turbulence. An identical analysis was conducted with the maximum convective and mechanical turbulence recorded during the sampling period. We made no assumptions about correlation of mean or maximum turbulence among or within days. Lastly, a model was fit to the Monin–Obukhov length to test
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1158 on day 2, 3408 on day 3, 1808 on day 4, and 2058 on day 5 and wind speed decreased during the day at both 1.5 and 10 m AGL. The logarithmic model fit to wind speed resulted in a slope of 0.223 s1 (S.E. = 0.068, r2 = 0.07) and the slope was not significantly different among (P > 0.60, F = 0.30, df = 1,5) or within days (P > 0.70, F = 0.17, df = 1,5) (Table 1). A total of 52,214 seeds were collected in the lower SL and 15 seeds were collected in the upper SL (Table 2). More seeds were collected in the early period and 1 day (September 20th) seed collection was three times greater than any other single day (Table 2). Within days and within periods, seed collection in the lower SL always decreased with height AGL and seed collection was greater during periods of higher mean wind
whether this measure of stability was correlative with the seed flux in the SL.
4.
Results
Temperature and relative humidity fluctuated within and among days. Mean temperature significantly increased from 20.49 8C (S.E. = 1.6 103, Early) to 21.18 8C (S.E. = 4.8 103, Late) while relative humidity dropped significantly from 40.38% (S.E. = 1.0 102) to 36.16% (S.E. = 3.5 105) during the same period. Mean wind direction shifted significantly (P < 0.05) among days with a mean direction on day 1 of 708,
Table 1 – Mean (WS.E.) horizontal wind speed (m sS1), mean and maximum thermal turbulence (J mS2 sS1), and magnitude (absolute value) of mean and maximum mechanical turbulence (kg mS1 sS2) for each sampling period, as measured by sonic anemometer centrally located in the source field (Fig. 1) and placed at 1.5 m AGL. The magnitudes of mechanical turbulence are reported for comparison with thermal turbulence. Wind speed Mean 1
ms
Thermal turbulence Mean
Max 2
Jm
s
1
Mechanical turbulence
Monin–Obukhov length
Mean Max kg m1 s2
m
11 September
Early Middle Late
3.8 (0.10) 3.5 (0.08) 3.7 (0.09)
0.11 (0.007) 0.08 (0.003) 0.12 (0.006)
0.28 0.12 0.22
0.23 (0.014) 0.22 (0.012) 0.22 (0.011)
0.39 0.46 0.45
87.93 108.10 68.26
12 September
Early Middle Late
2.5 (0.10) 2.4 (0.09) 2.0 (0.09)
0.14 (0.006) 0.15 (0.009) 0.16 (0.008)
0.25 0.26 0.26
0.11 (0.011) 0.11 (0.008) 0.08(0.007)
0.37 0.24 0.22
23.85 27.84 13.64
20 September
Early Middle Late
2.1 (0.06) 2.5 (0.08) 2.5 (0.08)
0.11 (0.006) 0.16 (0.006) 0.10 (0.007)
0.22 0.24 0.18
0.14 (0.009) 0.23 (0.014) 0.18(0.014)
0.33 0.40 0.46
48.97 69.01 83.08
21 September
Early Middle Late
1.7 (0.09) 1.5 (0.09) 1.3 (0.08)
0.15 (0.006) 0.14 (0.010) 0.10 (0.008)
0.30 0.25 0.17
0.06 (0.008) 0.06 (0.008) 0.03(0.006)
0.26 0.25 0.14
10.33 11.20 8.40
22 September
Early Middle Late
2.2 (0.07) 1.8 (0.05) 1.7 (0.06)
0.13 (0.007) 0.16 (0.008) 0.11 (0.009)
0.25 0.31 0.31
0.17 (0.014) 0.12 (0.008) 0.12 (0.010)
0.38 0.27 0.37
49.61 22.17 33.37
Table 2 – Total C. canadensis seeds collected at a subset of heights within the surface boundary layer during 5 sampling days across three sampling periods (Early, Middle, and Late). Sampling efficiency of 100% was assumed for RPV and tower sampling devices. Period
Height (m)
11 September
12 September
20 September
21 September
22 September
Seeds Early
2 6 68 120
3106 650 0 0
605 53 0 0
4082 1813 2 1
152 60 0 0
926 159 0 0
Middle
2 6 68 120
636 200 0 0
980 169 0 3
3132 2531 0 0
152 56 1 0
342 140 0 2
Late
2 6 68 120
521 10 1 0
82 26 2 1
850 617 0 0
36 39 0 0
241 57 0 2
10453
3857
32750
1127
4042
Total
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Table 3 – Conyza canadensis mean (WS.E.) seed collection, air volume, seed concentration (assuming 100% sampling efficiency) and within the surface boundary layer. Ground seed collections were only conducted during the last three sampling days. Height,
Seeds/day
Volume/day
Concentration
Germination
(m3)
seeds (m3)
(percent)
m 0 2 3 4 5 6 68.2 (0.42) 120.6 (0.83)
225 3169 2447 1968 1543 1316 1 2
(156.3) (1381.5) (1388.7) (1183.5) (955.9) (919.4) (0.4) (0.9)
– 3921 6031 7528 8689 9638 3396 2860
– 3.080 1.567 1.017 0.712 0.549 0.001 0.002
(305.0) (467.3) (582.7) (672.2) (745.4) (95.9) (292.0)
(1.193) (0.824) (0.581) (0.431) (0.369) (3.4 104) (8.1 104)
– 60% 53% 54% 43% 51% 40% 20%
(N (N (N (N (N (N (N
= = = = = = =
40) 40) 39) 40) 39) 5) 5)
Table 4 – Seed flux density (seeds mS2 30 minS1) in the surface boundary layer. Density was calculated as the area under the negative exponential curve fit to tower and RPV data for each period. Period
11 September
12 September
20 September
21 September
22 September
Seeds (m2) Early Middle Late
9.09 1.89 0.53
1.79 3.24 0.14
speed (Tables 1 and 2). Seed collection in the lower SL decreased during the day so that the fewest seeds were always collected during the Late period. In contrast, periods of large seed collection in the lower SL did not translate to more seeds collected in the upper SL where the frequency and quantity of seeds collected were greater in the Middle and Late periods (Table 2). Because of the wind variation in wind speed, seed
Fig. 2 – Modeled lower SL seed concentration gradients during three within day sampling periods (Early, Middle, and Late) and averaged over 5 sampling days. Gradients were calculated using a negative exponential model and represent the mean parameter estimates of gradients fit to each sampling period within days. Points represent seed concentration during a single day and corresponding period. Seed concentrations in the upper SL are not shown for increase clarity in the lower SL.
9.11 18.25 1.62
0.74 0.54 0.15
3.16 1.17 0.25
concentration (seeds m3) is used in the subsequent analysis instead of seed quantity. Mean seed concentration decreased from 3.080 seeds m3 at 2 m AGL to 0.0044 seeds m3 at 120 m (Table 3, Fig. 2). Seed flux density is the area under the concentration curve and generally decreased from Early to Middle to Late, although there was considerable variation in the density among days (Table 4). Atmospheric seed flux density was hypothesized to correspond to changes in atmospheric conditions, principally turbulent eddies that can uplift seeds through the SL. Mean mechanical turbulence was highest in the Middle period and varied significantly among periods and days (P < 0.03, F = 2.83, df = 4758, Table 1). No within day pattern was evident in mean thermal turbulence, but as expected, it varied considerably from day to day (Table 1). Both the mean and maximum mechanical and thermal turbulence per time period were tested against the SL seed flux density for the same period. The maximum thermal and maximum mechanical turbulence did not significantly affect the SL seed flux density (P > 0.25). The mean thermal turbulence also had no effect (P 0.47), but the mean mechanical turbulence did significantly relate to SL seed flux density (P < 0.01, F = 11.82, df = 1,11). There was a significant interaction between mean mechanical and mean thermal turbulence in their effect on SL seed flux (P 0.052, F = 4.75, df = 1,11). The atmosphere was unstable (negative Monin–Obukhov length) during every sampling period, but varied within and among days (Table 1). However, the variation in the Monin–Obuhkov length, was not significantly related to variation in the seed flux (P 0.13, F = 2.63, df = 1,13). Interestingly, height of seed collection did not significantly affect seed viability (P > 0.14, z = 1.47, Table 3) though sample size may have affected our ability to detect differences. For example, in a germination test, 50% of seeds collected from 2– 6 m germinated while 40% of seeds collected at 68 m and 20%
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at 120 m germinated with only 5 seeds tested at each height (Table 3).
5.
Discussion
This study sought to describe the ascent of C. canadensis seeds by simultaneously sampling the lower and upper surface layer. This unique data set captured seeds in the process of dispersal instead of sampling the resulting deposition. The small sampling interval provided an opportunity to connect this dispersal process with micro-meteorological conditions. While this data set was possible because of the use of RPVs, their use also introduced some limitations. Because of challenges associated with flying the RPVs in high winds, sampling was limited to days when wind speeds at ground level were less than 24 km h1 (6.67 m s1). In spite of the challenges, 30 flights were conducted and supported earlier findings that seeds are present in the upper SL (Shields et al., 2006). However, there remains an atmospheric zone (6–68 m, >120 m AGL) within the SL that was unexplored. Further sampling within the SL would considerably strengthen our ability to link ascent dynamics with transport dynamics and facilitate the use of a Lagrangian model to better assess seed trajectories. With the current data set, only 15 seeds were collected in the upper SL, a small number compared to the thousands of pollen grains collected by Boehm et al. (2008) used to verify their Lagrangian model. The day to day variation in quantity of seeds collected may be tied to both biological and environmental factors (Tables 1 and 2). Greene (2005) and Jongejans et al. (2007) determined seed abscission was governed by horizontal wind speed. However, seeds released at high horizontal wind speeds, or gusts, may not necessarily be uplifted into the upper SL. Uplift is governed by turbulence in the atmosphere which overcome the settlement velocity of the organism. Strong horizontal winds are often associated with mechanical turbulence which can increase seed uplift (Soons et al., 2004). Tackenberg (2003) argued that convective forces (thermal turbulence) must be driving seed uplift but convective currents are generally present when horizontal wind speeds are low. Soons et al. (2004) state that strong convective forces may increase dispersal distance, but only marginally. We believe convective forces interact with the mechanical turbulence to create sufficient uplift for seed ascent in the SL. Our findings do support the Soons et al. claim that mean rather than maximum (gust) mechanical turbulence may play a greater role in seed uplift. However, the maximum turbulence, or vertical wind gusts, may increase seed abscission (Greene, 2005; Skarpaas et al., 2006; Jongejans et al., 2007). Connecting abscission with transport will ultimately require extensive sampling at very small time scales to quantify the interaction of convective and mechanical turbulence. The range of 1.1–4.4 seeds/1000 m3 at 68–120 m AGL observed in this study are similar to those reported by Shields et al. (2006) in a related study system and also similar to those reported for potato leafhopper (0.4/1000 m3) and for Fusarium spores (30/1000 m3; Shields and Testa, 1999). Daily variations in C. canadensis seed concentration in the upper SL were consistent with reports for fungal spores, insects and pollen
(Yao et al., 1997; Shields and Testa, 1999; Jarosz et al., 2003); suggesting that plants are similarly adapted to wind dispersal under optimal conditions. Fewer seeds were released in the Middle and Late periods, and the total seed flux during these periods was lower, but their impact may be more important than seeds released in the Early period. The ramifications of finding seed in the upper SL are significant because even moderate wind speeds at high altitudes can transport C. canadensis seeds many kilometers downwind (Shields et al., 2006). To calculate a maximum potential distance, we assumed seeds collected at 120 m AGL remained in the SL and encountered a wind speed of 4.9 m s1 (17.5 km h1; assuming a logarithmic wind speed gradient of 0.52 s1). Over 5 h (minimum time between seed collection in upper SL and sunset), the seeds would have traveled approximately 87 km. The few seed collected at 120 m during the Early period would remain aloft for 7 or more hours and travel up to 122 km before falling with gravity and being deposited. After sunset, the mixed layer shrinks rapidly (Finnigan, 2007). Relaxing the first assumption (PBL remains mixed) yields a scenario where seeds ascended through the SL late in the day or daily conditions changed to prevent convective mixing and thus the seeds fell from 120 m without any additional upward movement. Conyza canadensis seed have an unbeaked pappus which reduces settlement velocity to 0.3 m s1 (Dauer et al., 2006; Andersen, 1993), requiring 6 min to reach the ground from 120 m, all the while encountering the slowing wind speed (logarithmic decrease of 0.52 s1) with loss of altitude. Even with the decreasing wind speed, seeds would be carried horizontally at least 1800 m. Therefore, the long-distance C. canadensis seed dispersal events likely lie in the range of 2–122 km. While this range is very wide, the dispersal distances reported herein are several orders of magnitude greater than previously reported (Dauer et al., 2007). Furthermore, seed collection from near ground level to 120 m AGL sheds light on stratified dispersal of C. canadensis seed (Shigesada and Kawasaki, 1997). An average plant produces 130,000 seeds that are released over 6 weeks, and the majority of these seeds will land near the source (Dauer et al., 2007), but the few long-distance dispersers will have the greatest impact on population spread (Clark et al., 2003). It takes only a fraction of a percent of seeds (or a single seed!!) to ascend into the upper SL where their potential long distances dispersal can infest fields many kilometers from the source field. Based on the information presented in this paper, we present a compelling case that long-range transport of C. canadensis seed carrying genes coding for glyphosate resistance occurs via the ascent and possible transport in the SL. Further, we argue jumps of tens or hundreds of kilometers can occur from an individual dispersal event. Such phenomenal dispersal rates are corroborated by the rate of reported glyphosate resistance occurrences in North America (Heap, 2007). The implications are profound for management of this species, and other wind-dispersed plant species, if they are able to ascend in the SL. Maldonado-Ramirez et al. (2005) noted that movement of Fusarium in the atmosphere impacts the required extent of management to control the species. Controlling the pathogens in individual fields, as is commonly
agricultural and forest meteorology 149 (2009) 526–534
practiced, will have little regional impact on reducing Fusarium populations. The concept can be extended to this system. Even if a few glyphosate-resistant C. canadensis produce seeds that travel a conservative distance of 2 km, potentially hundreds of surrounding fields could be impacted by a single dispersal event. When infestation of the resistant biotype equates to increased expenditures for management, every neighboring farmer feels the effects of a local glyphosate resistant population. The impact of wind dispersal in conservation and invasion biology has been recognized (Trakhtenbrot et al., 2005; Nathan, 2006; Isard and Gage, 2001) and focuses on the connectivity of suitable habitat patches (Soons et al., 2005). In current agro-ecosystems, suitable habitat for glyphosateresistant C. canadensis in the US may be defined by fields where glyphosate is the sole method of weed control. Already glyphosate and glyphosate-resistant crops are increasing in abundance and may increase the number of suitable patches for survival of the resistant biotype (USDA-NASS, 2007). While atmospheric transport increases the connectivity of hundreds of farmsteads, when coupled with increases in suitable habitat, spread across the landscape can be very rapid. Long distance transport is difficult to quantify, but the effects are substantial and should not be ignored.
Acknowledgements This project required extensive coordination and the authors would like to thank the USDA National Research Initiative Weedy and Invasive Plants competitive grants program for financial support of this collaboration (grant #2004-02158). The authors thank G. Neumann, A. Testa, J. Scortino, D. Heggenstaller, S. Kinneer, B. Scott, and Q. Johnson for their assistance with field and laboratory work. The authors also thank O. Bjornstad, W. Curran, J.M. Dauer and two anonymous reviewers for critical discussion of ideas explored in this paper.
references
Andersen, M.C., 1993. Diaspore morphology and seed dispersal in several wind-dispersed Asteraceae. Am. J. Bot. 80, 487– 492. Aylor, D.E., Boehm, M.T., Shields, E.J., 2006. Quantifying aerial concentrations of maize pollen in the atmospheric surface layer using remote-piloted airplanes and Lagrangian stochastic modeling. J. Appl. Meteorol. Climatol. 45, 1003– 1015. Boehm, M.T., Aylor, D.E., 2005. Lagrangian stochastic modeling of heavy particle transport in the convective boundary layer. Atm. Environ. 39, 4841–4850. Boehm, M.T., Aylor, D.E., Shields, E.J., 2008. Maize pollen dispersal under convective conditions. J. Appl. Meteorol. Climatol. 47, 291–307. Bullock, J.M., Clarke, R.T., 2000. Long distance seed dispersal by wind: measuring and modelling the tail of the curve. Oecologia 124, 506–521. Cain, M.L., Milligan, B.G., Strand, A.E., 2000. Long-distance seed dispersal in plant populations. Am. J. Bot. 87 (9), 1217–1227.
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Clark, J.S., 1998. Why trees migrate so fast: Confronting theory with dispersal biology and the paleorecord. Am. Natural. 152 (2), 204–224. Clark, J.S., Silman, M., Kern, R., Macklin, E., HilleRisLambers, J., 1999. Seed dispersal near and far: Pattern across temperate and tropical forests. Ecology 80 (5), 1475–1494. Clark, J.S., Lewis, M., McLachlan, J.S., HilleRisLambers, J., 2003. Estimating population spread: what can we forecast and how well? Ecology 84 (8), 1979–1988. Dauer, J.T., Mortensen, D.A., Humston, R., 2006. Controlled experiments to predict horseweed (Conyza canadensis) dispersal distances. Weed Sci. 54, 484–489. Dauer, J.T., Mortensen, D.A., VanGessel, M.J., 2007. Temporal and spatial dynamics of long-distance Conyza canadensis seed dispersal. J. Appl. Ecol. 44, 105–114. Finnigan, J.J., 2007. The turbulent wind in plant and forest canopies. In: Johnson, E.A., Miyanishi, K. (Eds.), Plant Disturbance Ecology: The Process and the Response. Academic Press, Amsterdam, pp. 15–58. Gillies, J.A., Berkofsky, L., 2004. Eolian suspension above the saltation layer, the concentration profile. J. Sedimentary Res. 74, 176–183. Green, A.J., Figuerola, J., 2005. Recent advances in the study of long-distance dispersal of aquatic invertebrates via birds. Diversity Distributions 11, 149–156. Greene, D.F., 2005. The role of abscission in long-distance seed dispersal by the wind. Ecology 86, 3105–3110. Heap, I. 2007. International weed resistance monitoring site.
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Nathan, R., 2006. Long-distance dispersal of plants. Science 313, 786–788. Nathan, R., Katul, G.G., 2005. Foliage shedding in deciduous forests lifts up long-distance seed dispersal by wind. In: Proceedings of the National Academy of Sciences of the United States of America, Vol. 102.23, pp. 8251–8256. Nathan, R., Safriel, U.N., Noy-Meir, I., Schiller, G., 1999. Seed release without fire in Pinus halepensis, a Mediterranean serotinous wind dispersed tree. J. Ecol. 87 (4), 659–669. Nathan, R., Katul, G.G., Horn, H.S., Thomas, S.M., Oren, R., Avissar, R., Pacala, S.W., Levin, S.A., 2002. Mechanism of long distance dispersal of seeds by wind. Nature 418, 409–413. Pinheiro, J.C., Bates, D.M., 2000. Mixed-Effects Models in S and SPLUS. Springer, New York, NY. R Development Core Team (2007). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, URL
Skarpaas, O., Auhl, R., Shea, K., 2006. Environmental variability and the initiation of dispersal: turbulence strongly increases seed release. In: Proceedings of the Royal Society B: Biological Sciences, Vol. 273. pp. 751–756. Soons, M.B., Heil, G.W., Nathan, R., Katul, G.G., 2004. Determinants of long-distance seed dispersal by wind in grasslands. Ecology 85, 3056–3068. Soons, M.B., Messelink, J.H., Jongejans, E., Heil, G.W., 2005. Habitat fragmentation reduces grassland connectivity for both short-distance and long-distance wind-dispersed forbs. J. Ecol. 93, 1214–1225. Stull, R.B., 1988. An Introduction to Boundary Layer Meteorology. Kluwer Academic Publishers, Dordrecht, The Netherlands. Tackenberg, O., 2003. Modeling long-distance dispersal of plant diaspores by wind. Ecol. Monogr. 73, 173–189. Trakhtenbrot, A., Nathan, R., Perry, G., Richardson, D.M, 2005. The importance of long-distance dispersal in biodiversity conservation. Diversity Distributions 11, 173–181. USDA-NASS publications. (2007) Acreage U.S. Department of Agriculture.
available at www.sciencedirect.com
journal homepage: www.elsevier.com/locate/agrformet
Conyza canadensis seed ascent in the lower atmosphere Joseph T. Dauer a,*, David A. Mortensen a, Edward C. Luschei b, Scott A. Isard c, Elson Shields d, Mark J. Van-Gessel e a
Intercollege Graduate Degree Program in Ecology, The Pennsylvania State University, University Park, PA 16802, United States Department of Agronomy, University of Wisconsin – Madison, 1575 Linden Dr., Madison, WI 53706, United States c Departments of Plant Pathology and Meteorology, The Pennsylvania State University, University Park, PA 16802, United States d Department of Entomology, Cornell University, Ithaca, NY 14853, United States e Plant and Soil Sciences Department, University of Delaware, Georgetown, DE 19947, United States b
article info
abstract
Article history:
Long-distance dispersal of wind dispersed plant seed is dependent on seeds ascending in the
Received 29 February 2008
atmosphere’s surface boundary layer (SL). However, seed dispersal research often focuses
Received in revised form
on the near-surface dynamics of abscission and deposition, with little attention paid to seed
29 September 2008
ascent and transport beyond the surface boundary layer. Our research sought to estimate
Accepted 3 October 2008
the vertical seed density flux of Conyza canadensis, a common agricultural weed, in the surface boundary layer. Using a 2.7 ha field infested with Conyza canadensis, the vertical seed concentration profile was measured on 5 days, three times per day while simultaneously
Keywords:
monitoring micrometeorological conditions to connect the mechanisms of release with the
Wind dispersal
process of ascent. Vertical seed sampling was conducted from two to six meters above
Long-distance dispersal
ground level using removable screen frames. Remotely piloted airplanes were used to
Glyphosate-resistance
quantify seed concentration at 68 and 120 m above ground level and provide a connection
Erigeron canadensis
between near surface dynamics and dynamics higher in the SL. Seed flux density (area under seed concentration curve) decreased with height and decreased from morning to afternoon. The seed flux density was significantly correlated with mean mechanical turbulence and the interaction of mean mechanical and mean thermal turbulence, supporting earlier research of meteorological effects on seed dispersal. While fewer total seeds were collected in the afternoon, more seeds were collected at 68 and 120 m during this period and may suggest more seeds were present in the upper SL where dispersal distance may be greater. Seeds collected above the SL will potentially be carried for hours before descending, depositing seeds in the range of 2–122 m, and influencing a much greater area than seeds unable to ascend through the lower SL. While this range is very wide, the dispersal distances reported herein are several orders of magnitude greater than previously reported for plants. These findings are all the more striking since the study species has evolved resistance to glyphosate herbicide, an herbicide now widely used in soybean, cotton, and corn crops in the US. Therefore, aerial transport of C. canadensis seeds carrying genes coding for glyphosate resistance enables seed to move tens or hundreds of kilometers in a single dispersal event, a spread rate corroborated by number of cases of reported glyphosate resistance occurrences in North America. # 2008 Elsevier B.V. All rights reserved.
* Corresponding author. Present address: Crop and Soil Science, 335 Crops Building, Oregon State University, Corvallis, OR 97331, United States. E-mail address: [email protected] (J.T. Dauer). 0168-1923/$ – see front matter # 2008 Elsevier B.V. All rights reserved. doi:10.1016/j.agrformet.2008.10.005
agricultural and forest meteorology 149 (2009) 526–534
1.
Introduction
The importance of long-distance seed dispersal in determining plant population spread has been well documented in the last 10 years (Nathan, 2006; Clark et al., 2003). Central to this literature is the challenge of collecting data to validate predictive models of seed dispersal. Should one collect seeds at increasing distances to show how far seeds can travel (Green and Figuerola, 2005) or sample at short time intervals to quantify the mechanisms of dispersal (Cain et al., 2000)? To aid in our understanding of seed dispersal, two types of dispersal models have emerged: phenomenological and mechanistic. The phenomenological approach quantifies the dispersal pattern and infers process (Clark et al., 1999; Bullock and Clarke, 2000) and can be used when extrapolating seed dispersal in space and time (Clark, 1998). The mechanistic approach, used in this paper, matches the sampling interval to the time scale of the system responsible for dispersal (Nathan, 2006). This approach measures the trajectory of the dispersal vector (water, air, and animals), and assuming one can predict how that vector will change in the environment, projects the path of the seeds over longer distances. Mechanistic models of transport by insects include behavioral characteristics (Zanen and Carde, 1999), whereas mechanistic models of abiotic vectors (water and atmosphere) typically focus on plant characteristics and hydrological and meteorological conditions that impact the distance of transport (see Kuparinen (2006) for review). In this study, we focus on collecting samples with sufficient numbers of seeds over relatively short time intervals that correspond to important meteorological controls over which the transport process vary (Katul et al., 2005; Yao et al., 1997; Nathan et al., 2002; Tackenberg, 2003). Dauer et al. (2007) were unable to link seed deposition with wind direction and speed with a 7 day seed sampling interval. The lack of correspondence was probably because meteorological monitoring should be on the order of hours or shorter to capture turbulence effects without encountering synoptic weather patterns (Stull, 1988). Researchers have successfully coupled empirical samples on a small time interval (30 min) with Lagrangian models for heavy particles (which may include seed) that ‘‘track’’ individual or cohorts of particles as they move through the atmosphere, constantly adjusting particle trajectory with wind speed and direction (Nathan et al., 2002; Boehm and Aylor, 2005; Aylor et al., 2006; Boehm et al., 2008). In this study, sampling was conducted over 30 min periods at multiple heights above and downwind of a large seed source to trap seeds when atmospheric conditions may have resulted in seed uplift. Aerial seed transport depends on both buoyancy to suspend seeds in the air and wind to push the seeds. To understand how seed utilize the atmosphere, we must better understand the components of the atmospheric boundary layers. The planetary boundary layer (PBL) is the region of the atmosphere in which air flow is influenced by the physical and biological features of the earth below, with the majority of the influence taking place in the lowest 10% and termed the surface boundary layer (SL, Stull, 1988). The PBL extends, during the daytime, to 1–3 km above ground level (AGL) and is characterized by increasing wind speed with height. When
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seed sampling was conducted during the daytime, the layer above the SL (and comprising the remaining portion of the PBL) is convectively mixed and termed the convective boundary layer or mixed layer (CBL). All sampling for this paper was conducted in the SL. Turbulence is ‘‘eddies superimposed on an underlying smooth flow having constant speed and direction (Lowry and Lowry, 1989).’’ Within the PBL, turbulence decrease with height (Stull, 1988). Turbulence is composed of both a thermal component caused by unequal heating of the earth’s surface and a mechanical component caused by friction between the airflow and surface obstructions or slower moving air streams (Lowry and Lowry, 1989). While both types of turbulence can transport seeds vertically, a feature critical of long-distance dispersal (Nathan et al., 2002; Nathan and Katul, 2005), mechanical turbulence can also result in local deposition. In the daytime SL, short-period mechanical turbulence is generally pronounced (Lowry and Lowry, 1989) leading to seed deposition close to their source (from settlement velocity plus downdrafts). Consequently, many wind-dispersed organisms are adapted to ascend and stay aloft in order to facilitate long-ranged transport. Insects, pollen, and seeds respond to fluctuations in humidity, pressure, and vertical wind to determine the optimal release time to facilitate this ascent (Shields and Testa, 1999; Rodriguez et al., 2000; MaldonadoRamirez et al., 2005), and we expect a similar Conyza canadensis response. While researchers have not examined seed adaptation to ascent, seeds are adapted to release in response to certain meteorological conditions (Nathan et al., 1999; Greene, 2005). Seed collection in the SL often characterizes seed density at canopy level (e.g. Bullock and Clarke, 2000; Dauer et al., 2007; Skarpaas and Shea, 2008) and only rarely at multiple levels within the canopy (e.g. Nathan et al., 2002). Isaacs and Byrne (1998) used a tower sampler to assess whitefly (Bemisia tabaci) abundance at multiple heights in the lower SL which allowed them to characterize the change in insect density with height. Detecting seeds in the upper SL can be difficult due to their very low concentrations, but researchers have successfully collected aphids (Johnson, 1957; Isard et al., 1990), potato leafhoppers (Shields and Testa, 1999), and corn pollen (Aylor et al., 2006). Seed collection in the upper SL has been limited to two studies including a general organism sampling in Australia (White, 1970) and more recently, sampling for C. canadensis (Shields et al., 2006). The focus of this study was to quantify the vertical seed concentration profile of a wind-dispersed plant. Conyza canadensis is a winter annual broadleaf weed that invades agricultural fields, roadsides, abandoned fields and waste areas (Holm et al., 1997; Weaver, 2001). Conyza canadensis seed have an unbeaked pappus which reduces settlement velocity to 0.3 m s1 (Dauer et al., 2006; Andersen, 1993) and allows seeds to remain aloft longer than most wind-dispersed plant seeds. Quantifying the dispersal distance for this species is critical because populations have developed resistance to the common agricultural herbicide, glyphosate (VanGessel, 2001), and have spread to at least 40 thousand hectares of agricultural land in the US (Heap, 2007). Glyphosate-resistant C. canadensis, along with other weeds that already have or will likely develop glyphosate-resistance, will dramatically alter
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current agricultural practices that rely on glyphosate for weed management. Predicting the potential dispersal distance will allow producers to proactively adjust their weed management to consider herbicide resistance. Successful predictions about C. canadensis dispersal distance, as with other wind-dispersed organisms, will require knowledge of conditions leading to ascent within the SL. Our objectives for this study were to (1) quantify the atmospheric seed concentration profile of C. canadensis accounting for changes in daytime conditions, and (2) correlate the observed seed density flux with meteorological conditions during the sampling interval.
2.
Materials and methods
Experiments were conducted near Georgetown, Delaware, USA in a 2.73 ha fallow field infested with Conyza canadensis. C. canadensis occurs naturally in Delaware and plant densities within the field ranged from 0.1 to 70–130 plants m2 (Fig. 1). C. canadensis was in near monoculture and constituted the ‘crop’, with heights approximately homogeneous at 2.5 m. Assuming 130,000 seeds per plant (Dauer et al., 2006), the resulting seed source strength, was estimated to be 125 billion seeds. Seed and meteorological sampling took place over 5 days in midSeptember 2006 during the maximum seed release period (M. VanGessel, personal observation). Extensive daily sampling determined that maximum seed release occurred during early afternoon (Middle, 12:30–14:00, J.T. Dauer, unpublished data). Two additional sampling periods were chosen: Early (11:00– 12:30) and Late (14:00–15:30) to provide a thorough quantification of the change in seed concentration within and among days. Sampling in the Middle and Late periods was conducted approximately 1 h after completion of the previous sampling due to equipment constraints. For example, on day 4, sampling was initiated at 11:45, continued at 13:30 and 15:00.
Fig. 1 – Schematic view of field and general relationship of tower samplers (A, photo inset) to heaviest concentration of C. canadensis (slashed areas, 10–130 plants mS2). Screens on the tower could be swiveled so screens were perpendicular to wind direction. Airplane sampling was conducted downwind at a 458 angle vertical to the source at approximately 68 and 120 m AGL. The 3-dimensional sonic anemometer was located near the tower samplers (B).
Seed collection was conducted at 2–6 m, 60 m, and 120 m AGL. For the purpose of this study, we refer to lower and upper SL, with the former represented by measurements near the ground surface (2–6 m) and the later by the samples collected at 60 and 120 m. Seed density in the lower SL was quantified using a set of towers located in the center of the field (Fig. 1). Individual towers consisted of 7.6 m long 10 cm diameter PVC, centrally reinforced with wood (3.8 cm 8.8 cm 150 cm) and supported by guide wires. One stationary, pivot tower and four positional towers located at 3408, 2958, 2508, and 2058 (08 is north), made it possible to connect a positional tower closest to perpendicular to wind direction with the pivot tower. Five 15 cm collars separated by 1 m were connected to the towers and raised and lowered via pulley guided ropes. Two 1 m 0.5 m PVC frames were attached to these collars and 1.5 mm mesh fiberglass screen was attached to the frames. During each sample period, sticky adhesive (Tangle-Trap, exterior formulation; TangleFoot Company, Michigan, USA) was applied to the screens, and screens were raised to 2–6 m AGL. The heights were selected to sample at multiple levels within the SL. Sampling in the upper SL was conducted by simultaneously flying two large radio controlled monoplanes (Remote Piloted Vehicle, RPV) at approximately 60 and 120 m AGL (Shields and Testa, 1999; Shields et al., 2006). A third plane was flown during the first 2 days which sampled the atmosphere upwind of the source population ranging from 60 to 120 m AGL. Zero seeds were collected upwind of the field during these days and for the subsequent sampling periods we assumed no seed advection throughout the study area. The lower sampling altitude of 60 m was set for reasons that included increased likelihood of plane crashes below this altitude and greater interference from a local cell phone towers. The RPVs were controlled by ground-based pilots using hand-held transmitters. Each RPV held two seed sampling devices, attached near each wing tip to minimize influence of turbulent air streaming off the propeller. RPVs were flown at full throttle setting (96 km h1) because full throttle was easily repeatable and maximized the air volume sampled. Fuel consumption restricted flights to 30 min. The trapping surface within each airplane collector was a removable unit built from a standard 90 mm plastic Petri plate. Each individual trapping surface was constructed from the smaller portion of the Petri plate by removing the bottom of the plate while leaving a 3 mm lip around the outside diameter. A 30 micron cloth mesh was glued to the lip. When the collector was closed, the trapping surface was protected from the airflow and when the collector was opened during the flight, the trapping surface was perpendicular to the airflow. Just prior to a sampling flight, the trapping surfaces were sprayed with a sticky adhesive (Dupont Super X-77TM) to retain the seeds striking the trapping surface during flight. Each trapping surface was exposed to approximately 10,000 L of air per min (10 m3/min), or a total of 300,000 L of air (300 m3) during the typical 30 min sampling flight. Each RPV used in this research carried a data logger, GPS antenna, and a telemetry downlink. Data logger information was utilized to calculate the volume of air sampled per flight and additional information about on-board electronics can be found in Shields et al. (2006). The telemetry downlink from the
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RPV was used to transmit real time data to an ‘‘air traffic control trailer’’ where altitude information was relayed by a technician to the pilot to assist them in maintaining the desired altitude during the duration of the sampling flight. Flight pattern was an approximately 300 m long by 200 m wide oval, at a predetermined height, oriented perpendicular to the wind direction, which essentially provided crosswind lineintegrated concentrations. Meteorological conditions were measured concurrently at two locations. Meteorological conditions (wind direction, wind speed, relative humidity, and temperature) were monitored using the University of Delaware weather station located 1 km from the source field (Fig. 1). Turbulence was measured with a 3-dimensional sonic anemometer (CSAT-3, Campbell Scientific, Logan, UT) located 12 m from the seed sampling towers and positioned at 1.5 m above ground level (approximately 0.5 m above the soybean canopy, Fig. 1). The sonic anemometer measured horizontal wind speed in the direction of the sensor orientation (Ux) and the direction perpendicular to the sensor orientation (Uy), vertical wind speed (Uz), and temperature (T). Data were recorded at 10 Hz and averaged over 1 min intervals during each trial and recorded on a CR23X datalogger (Campbell Scientific, Logan, UT). Maximum wind speed was defined as the largest recorded value (1 min averaged value) during a 30 min sampling period. Vector analysis of Ux and Uy resulted in the wind speed and wind direction for each minute interval. The datalogger calculated the product of instantaneous variation of the vertical wind velocity (w), air temperature (T0 ), and horizontal wind velocity (u0 ) from their respective means and the covariances wT and wu. Covariances were averaged each minute and recorded on the datalogger. The covariance wT represents thermal turbulence and a negative value signifies an upward directed force since temperature generally decreases with height in the lower atmosphere during the daylight hours. The covariance wu represents mechanical turbulence and a positive value is a lifting force since wind speed generally increases with height above the earth’s surface. The turbulence statistics can be combined to provide a measure of atmospheric stability (Soons et al., 2004; Aylor et al., 2006; Finnigan, 2007). Mechanical effects are the result of friction velocity (u*) calculated by u ¼
pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi covw0 u0
3.
3
u ½k ðg=To Þ covw0 T0
(Gillies and Berkofsky, 2004). An error in the sampling device at 10 m on the first 2 days necessitated using the three remaining days to calculate the average wind gradient during the 3 test periods. This average gradient was then applied to the 2 missing days to calculate the wind speed at each height. The volume of air was calculated as the product of screen area (1 m2) and wind run (wind speed time) for the period. Sampling frames were realigned before each sampling period and wind direction never varied more than 908 during the sampling period. However, wind direction did vary slightly, which affected the sampling area available for seed collection. The sine of the wind direction relative to the sampling surface represented the proportion of the wind contacting the sampling screen, assuming 100% screen trapping efficiency. Seed concentration at each height was finally calculated by dividing the observed seed collection by the predicted wind volume. Seed concentrations in the upper SL were quantified by the seeds collected divided by the volume of air sampled by the RPVs. Following other aerobiological researchers, we assume that seed concentration declines as a negative exponential function of height above the ground within the SL (Isaacs and Byrne, 1998). Seed concentration data were divided into periods (Early, Middle, and Late) and a linear model was used to quantify the average decline in log seed concentration with increasing height in the SL (R Development Core Team, 2007). The rate at which seeds entered the air per unit area of field (seed flux density in units of seeds m2 per 30 min sample) was calculated as the area under the seed concentration curve for each period and also averaged across 5 days. The seed flux was calculated from 1 to 120 m, above which additional seed flux density contributed minimally to the overall seed flux density. Germination was assessed for a subsample of seeds collected on tower screens and all seeds collected on airplane samplers during one trial each day. Seeds were placed in 9 cm Petri dishes on moistened filter paper in a germination chamber set at 21 8C and photoperiod of 16:8 light:dark. Germinated seeds were recorded and removed daily for 2 weeks. Logistic regression was used to determine whether germination varied with height of collection.
Analysis
(1)
The Monin–Obukhov length (LMO) gives the relative contribution of mechanical and thermal turbulence to the atmospheric stability and is calculated by
LMO ¼
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(2)
The Monin–Obukhov lengths were calculated for each 1 min-averaged covariance, before being averaged for the 30 min sampling period. A greater magnitude of the LMO represent a more unstable atmosphere, although this scaling does not speak directly to the independent contribution of either the thermal or mechanical turbulence. Wind speed at each height (2–6 m) was calculated by fitting a logarithmic model between 3, 6, and 10 m cup anemometers
Generalized linear models using least squares parameter estimation were used to test the effects of day and period on both biological and meteorological factors. We assumed that sampling periods were continuous, but recognize that inference from this study is limited to late morning to afternoon. In many cases, errors were correlated within days and were included in the analysis (Pinheiro and Bates, 2000). Seed fluxes were separated into 5 days and three periods (15 seed fluxes) for analysis with turbulence. A generalized linear model was fit to the flux data for the interaction of mean convective turbulence and mean mechanical turbulence. An identical analysis was conducted with the maximum convective and mechanical turbulence recorded during the sampling period. We made no assumptions about correlation of mean or maximum turbulence among or within days. Lastly, a model was fit to the Monin–Obukhov length to test
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1158 on day 2, 3408 on day 3, 1808 on day 4, and 2058 on day 5 and wind speed decreased during the day at both 1.5 and 10 m AGL. The logarithmic model fit to wind speed resulted in a slope of 0.223 s1 (S.E. = 0.068, r2 = 0.07) and the slope was not significantly different among (P > 0.60, F = 0.30, df = 1,5) or within days (P > 0.70, F = 0.17, df = 1,5) (Table 1). A total of 52,214 seeds were collected in the lower SL and 15 seeds were collected in the upper SL (Table 2). More seeds were collected in the early period and 1 day (September 20th) seed collection was three times greater than any other single day (Table 2). Within days and within periods, seed collection in the lower SL always decreased with height AGL and seed collection was greater during periods of higher mean wind
whether this measure of stability was correlative with the seed flux in the SL.
4.
Results
Temperature and relative humidity fluctuated within and among days. Mean temperature significantly increased from 20.49 8C (S.E. = 1.6 103, Early) to 21.18 8C (S.E. = 4.8 103, Late) while relative humidity dropped significantly from 40.38% (S.E. = 1.0 102) to 36.16% (S.E. = 3.5 105) during the same period. Mean wind direction shifted significantly (P < 0.05) among days with a mean direction on day 1 of 708,
Table 1 – Mean (WS.E.) horizontal wind speed (m sS1), mean and maximum thermal turbulence (J mS2 sS1), and magnitude (absolute value) of mean and maximum mechanical turbulence (kg mS1 sS2) for each sampling period, as measured by sonic anemometer centrally located in the source field (Fig. 1) and placed at 1.5 m AGL. The magnitudes of mechanical turbulence are reported for comparison with thermal turbulence. Wind speed Mean 1
ms
Thermal turbulence Mean
Max 2
Jm
s
1
Mechanical turbulence
Monin–Obukhov length
Mean Max kg m1 s2
m
11 September
Early Middle Late
3.8 (0.10) 3.5 (0.08) 3.7 (0.09)
0.11 (0.007) 0.08 (0.003) 0.12 (0.006)
0.28 0.12 0.22
0.23 (0.014) 0.22 (0.012) 0.22 (0.011)
0.39 0.46 0.45
87.93 108.10 68.26
12 September
Early Middle Late
2.5 (0.10) 2.4 (0.09) 2.0 (0.09)
0.14 (0.006) 0.15 (0.009) 0.16 (0.008)
0.25 0.26 0.26
0.11 (0.011) 0.11 (0.008) 0.08(0.007)
0.37 0.24 0.22
23.85 27.84 13.64
20 September
Early Middle Late
2.1 (0.06) 2.5 (0.08) 2.5 (0.08)
0.11 (0.006) 0.16 (0.006) 0.10 (0.007)
0.22 0.24 0.18
0.14 (0.009) 0.23 (0.014) 0.18(0.014)
0.33 0.40 0.46
48.97 69.01 83.08
21 September
Early Middle Late
1.7 (0.09) 1.5 (0.09) 1.3 (0.08)
0.15 (0.006) 0.14 (0.010) 0.10 (0.008)
0.30 0.25 0.17
0.06 (0.008) 0.06 (0.008) 0.03(0.006)
0.26 0.25 0.14
10.33 11.20 8.40
22 September
Early Middle Late
2.2 (0.07) 1.8 (0.05) 1.7 (0.06)
0.13 (0.007) 0.16 (0.008) 0.11 (0.009)
0.25 0.31 0.31
0.17 (0.014) 0.12 (0.008) 0.12 (0.010)
0.38 0.27 0.37
49.61 22.17 33.37
Table 2 – Total C. canadensis seeds collected at a subset of heights within the surface boundary layer during 5 sampling days across three sampling periods (Early, Middle, and Late). Sampling efficiency of 100% was assumed for RPV and tower sampling devices. Period
Height (m)
11 September
12 September
20 September
21 September
22 September
Seeds Early
2 6 68 120
3106 650 0 0
605 53 0 0
4082 1813 2 1
152 60 0 0
926 159 0 0
Middle
2 6 68 120
636 200 0 0
980 169 0 3
3132 2531 0 0
152 56 1 0
342 140 0 2
Late
2 6 68 120
521 10 1 0
82 26 2 1
850 617 0 0
36 39 0 0
241 57 0 2
10453
3857
32750
1127
4042
Total
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Table 3 – Conyza canadensis mean (WS.E.) seed collection, air volume, seed concentration (assuming 100% sampling efficiency) and within the surface boundary layer. Ground seed collections were only conducted during the last three sampling days. Height,
Seeds/day
Volume/day
Concentration
Germination
(m3)
seeds (m3)
(percent)
m 0 2 3 4 5 6 68.2 (0.42) 120.6 (0.83)
225 3169 2447 1968 1543 1316 1 2
(156.3) (1381.5) (1388.7) (1183.5) (955.9) (919.4) (0.4) (0.9)
– 3921 6031 7528 8689 9638 3396 2860
– 3.080 1.567 1.017 0.712 0.549 0.001 0.002
(305.0) (467.3) (582.7) (672.2) (745.4) (95.9) (292.0)
(1.193) (0.824) (0.581) (0.431) (0.369) (3.4 104) (8.1 104)
– 60% 53% 54% 43% 51% 40% 20%
(N (N (N (N (N (N (N
= = = = = = =
40) 40) 39) 40) 39) 5) 5)
Table 4 – Seed flux density (seeds mS2 30 minS1) in the surface boundary layer. Density was calculated as the area under the negative exponential curve fit to tower and RPV data for each period. Period
11 September
12 September
20 September
21 September
22 September
Seeds (m2) Early Middle Late
9.09 1.89 0.53
1.79 3.24 0.14
speed (Tables 1 and 2). Seed collection in the lower SL decreased during the day so that the fewest seeds were always collected during the Late period. In contrast, periods of large seed collection in the lower SL did not translate to more seeds collected in the upper SL where the frequency and quantity of seeds collected were greater in the Middle and Late periods (Table 2). Because of the wind variation in wind speed, seed
Fig. 2 – Modeled lower SL seed concentration gradients during three within day sampling periods (Early, Middle, and Late) and averaged over 5 sampling days. Gradients were calculated using a negative exponential model and represent the mean parameter estimates of gradients fit to each sampling period within days. Points represent seed concentration during a single day and corresponding period. Seed concentrations in the upper SL are not shown for increase clarity in the lower SL.
9.11 18.25 1.62
0.74 0.54 0.15
3.16 1.17 0.25
concentration (seeds m3) is used in the subsequent analysis instead of seed quantity. Mean seed concentration decreased from 3.080 seeds m3 at 2 m AGL to 0.0044 seeds m3 at 120 m (Table 3, Fig. 2). Seed flux density is the area under the concentration curve and generally decreased from Early to Middle to Late, although there was considerable variation in the density among days (Table 4). Atmospheric seed flux density was hypothesized to correspond to changes in atmospheric conditions, principally turbulent eddies that can uplift seeds through the SL. Mean mechanical turbulence was highest in the Middle period and varied significantly among periods and days (P < 0.03, F = 2.83, df = 4758, Table 1). No within day pattern was evident in mean thermal turbulence, but as expected, it varied considerably from day to day (Table 1). Both the mean and maximum mechanical and thermal turbulence per time period were tested against the SL seed flux density for the same period. The maximum thermal and maximum mechanical turbulence did not significantly affect the SL seed flux density (P > 0.25). The mean thermal turbulence also had no effect (P 0.47), but the mean mechanical turbulence did significantly relate to SL seed flux density (P < 0.01, F = 11.82, df = 1,11). There was a significant interaction between mean mechanical and mean thermal turbulence in their effect on SL seed flux (P 0.052, F = 4.75, df = 1,11). The atmosphere was unstable (negative Monin–Obukhov length) during every sampling period, but varied within and among days (Table 1). However, the variation in the Monin–Obuhkov length, was not significantly related to variation in the seed flux (P 0.13, F = 2.63, df = 1,13). Interestingly, height of seed collection did not significantly affect seed viability (P > 0.14, z = 1.47, Table 3) though sample size may have affected our ability to detect differences. For example, in a germination test, 50% of seeds collected from 2– 6 m germinated while 40% of seeds collected at 68 m and 20%
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at 120 m germinated with only 5 seeds tested at each height (Table 3).
5.
Discussion
This study sought to describe the ascent of C. canadensis seeds by simultaneously sampling the lower and upper surface layer. This unique data set captured seeds in the process of dispersal instead of sampling the resulting deposition. The small sampling interval provided an opportunity to connect this dispersal process with micro-meteorological conditions. While this data set was possible because of the use of RPVs, their use also introduced some limitations. Because of challenges associated with flying the RPVs in high winds, sampling was limited to days when wind speeds at ground level were less than 24 km h1 (6.67 m s1). In spite of the challenges, 30 flights were conducted and supported earlier findings that seeds are present in the upper SL (Shields et al., 2006). However, there remains an atmospheric zone (6–68 m, >120 m AGL) within the SL that was unexplored. Further sampling within the SL would considerably strengthen our ability to link ascent dynamics with transport dynamics and facilitate the use of a Lagrangian model to better assess seed trajectories. With the current data set, only 15 seeds were collected in the upper SL, a small number compared to the thousands of pollen grains collected by Boehm et al. (2008) used to verify their Lagrangian model. The day to day variation in quantity of seeds collected may be tied to both biological and environmental factors (Tables 1 and 2). Greene (2005) and Jongejans et al. (2007) determined seed abscission was governed by horizontal wind speed. However, seeds released at high horizontal wind speeds, or gusts, may not necessarily be uplifted into the upper SL. Uplift is governed by turbulence in the atmosphere which overcome the settlement velocity of the organism. Strong horizontal winds are often associated with mechanical turbulence which can increase seed uplift (Soons et al., 2004). Tackenberg (2003) argued that convective forces (thermal turbulence) must be driving seed uplift but convective currents are generally present when horizontal wind speeds are low. Soons et al. (2004) state that strong convective forces may increase dispersal distance, but only marginally. We believe convective forces interact with the mechanical turbulence to create sufficient uplift for seed ascent in the SL. Our findings do support the Soons et al. claim that mean rather than maximum (gust) mechanical turbulence may play a greater role in seed uplift. However, the maximum turbulence, or vertical wind gusts, may increase seed abscission (Greene, 2005; Skarpaas et al., 2006; Jongejans et al., 2007). Connecting abscission with transport will ultimately require extensive sampling at very small time scales to quantify the interaction of convective and mechanical turbulence. The range of 1.1–4.4 seeds/1000 m3 at 68–120 m AGL observed in this study are similar to those reported by Shields et al. (2006) in a related study system and also similar to those reported for potato leafhopper (0.4/1000 m3) and for Fusarium spores (30/1000 m3; Shields and Testa, 1999). Daily variations in C. canadensis seed concentration in the upper SL were consistent with reports for fungal spores, insects and pollen
(Yao et al., 1997; Shields and Testa, 1999; Jarosz et al., 2003); suggesting that plants are similarly adapted to wind dispersal under optimal conditions. Fewer seeds were released in the Middle and Late periods, and the total seed flux during these periods was lower, but their impact may be more important than seeds released in the Early period. The ramifications of finding seed in the upper SL are significant because even moderate wind speeds at high altitudes can transport C. canadensis seeds many kilometers downwind (Shields et al., 2006). To calculate a maximum potential distance, we assumed seeds collected at 120 m AGL remained in the SL and encountered a wind speed of 4.9 m s1 (17.5 km h1; assuming a logarithmic wind speed gradient of 0.52 s1). Over 5 h (minimum time between seed collection in upper SL and sunset), the seeds would have traveled approximately 87 km. The few seed collected at 120 m during the Early period would remain aloft for 7 or more hours and travel up to 122 km before falling with gravity and being deposited. After sunset, the mixed layer shrinks rapidly (Finnigan, 2007). Relaxing the first assumption (PBL remains mixed) yields a scenario where seeds ascended through the SL late in the day or daily conditions changed to prevent convective mixing and thus the seeds fell from 120 m without any additional upward movement. Conyza canadensis seed have an unbeaked pappus which reduces settlement velocity to 0.3 m s1 (Dauer et al., 2006; Andersen, 1993), requiring 6 min to reach the ground from 120 m, all the while encountering the slowing wind speed (logarithmic decrease of 0.52 s1) with loss of altitude. Even with the decreasing wind speed, seeds would be carried horizontally at least 1800 m. Therefore, the long-distance C. canadensis seed dispersal events likely lie in the range of 2–122 km. While this range is very wide, the dispersal distances reported herein are several orders of magnitude greater than previously reported (Dauer et al., 2007). Furthermore, seed collection from near ground level to 120 m AGL sheds light on stratified dispersal of C. canadensis seed (Shigesada and Kawasaki, 1997). An average plant produces 130,000 seeds that are released over 6 weeks, and the majority of these seeds will land near the source (Dauer et al., 2007), but the few long-distance dispersers will have the greatest impact on population spread (Clark et al., 2003). It takes only a fraction of a percent of seeds (or a single seed!!) to ascend into the upper SL where their potential long distances dispersal can infest fields many kilometers from the source field. Based on the information presented in this paper, we present a compelling case that long-range transport of C. canadensis seed carrying genes coding for glyphosate resistance occurs via the ascent and possible transport in the SL. Further, we argue jumps of tens or hundreds of kilometers can occur from an individual dispersal event. Such phenomenal dispersal rates are corroborated by the rate of reported glyphosate resistance occurrences in North America (Heap, 2007). The implications are profound for management of this species, and other wind-dispersed plant species, if they are able to ascend in the SL. Maldonado-Ramirez et al. (2005) noted that movement of Fusarium in the atmosphere impacts the required extent of management to control the species. Controlling the pathogens in individual fields, as is commonly
agricultural and forest meteorology 149 (2009) 526–534
practiced, will have little regional impact on reducing Fusarium populations. The concept can be extended to this system. Even if a few glyphosate-resistant C. canadensis produce seeds that travel a conservative distance of 2 km, potentially hundreds of surrounding fields could be impacted by a single dispersal event. When infestation of the resistant biotype equates to increased expenditures for management, every neighboring farmer feels the effects of a local glyphosate resistant population. The impact of wind dispersal in conservation and invasion biology has been recognized (Trakhtenbrot et al., 2005; Nathan, 2006; Isard and Gage, 2001) and focuses on the connectivity of suitable habitat patches (Soons et al., 2005). In current agro-ecosystems, suitable habitat for glyphosateresistant C. canadensis in the US may be defined by fields where glyphosate is the sole method of weed control. Already glyphosate and glyphosate-resistant crops are increasing in abundance and may increase the number of suitable patches for survival of the resistant biotype (USDA-NASS, 2007). While atmospheric transport increases the connectivity of hundreds of farmsteads, when coupled with increases in suitable habitat, spread across the landscape can be very rapid. Long distance transport is difficult to quantify, but the effects are substantial and should not be ignored.
Acknowledgements This project required extensive coordination and the authors would like to thank the USDA National Research Initiative Weedy and Invasive Plants competitive grants program for financial support of this collaboration (grant #2004-02158). The authors thank G. Neumann, A. Testa, J. Scortino, D. Heggenstaller, S. Kinneer, B. Scott, and Q. Johnson for their assistance with field and laboratory work. The authors also thank O. Bjornstad, W. Curran, J.M. Dauer and two anonymous reviewers for critical discussion of ideas explored in this paper.
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