Abstract

Many insect species engage in high-altitude, wind-borne migration, often several hundred meters above the ground. At these heights they can use the wind to travel tens or hundreds of kilometers in a single flight, and hence a knowledge of their movements is essential to understanding their ecology and population dynamics. Direct observation of high-flying insect migrants is very difficult, especially at night, but the remote sensing capabilities of entomological radar provide a solution to this seemingly intractable problem. We describe a novel, nutating-beam, vertical-looking radar with autonomous data analysis software. This system routinely extracts data on size, shape, alignment, and displacement vectors from individual targets, allowing long-term monitoring of migrant insect populations. We discuss the capabilities and limitations of this system and describe some of its applications in the study of insect migration behaviour.

Millions of metric tons of insects are aloft in Earth's atmosphere at any given moment. Much of this multitude comprises insects engaged in high-altitude, wind-borne migration, often at heights several hundred meters (m) above ground level (agl), where they can take advantage of strong winds to fly considerable distances, frequently tens or even hundreds of kilometers (km; Johnson 1969, Drake and Gatehouse 1995). The evolution of this behavior has allowed migratory insects to exploit resources that vary in space and time (Southwood 1977), providing them with adaptive benefits relative to more sedentary species. This enormous aerial “bioflow” has important implications for ecological, physiological, and genetic studies of insects, with applications in pest management, conservation, and environmental change programs (Woiwod and Harrington 1994, Drake and Gatehouse 1995).

The high-altitude flight of migratory insects, together with their relatively small body size and the fact that many species are nocturnal, means that it is very difficult to observe their movements. The study of insect migration has, therefore, relied primarily on the interpretation of indirect evidence of long-distance flights, such as catches in light traps and other ground-based observations. However, this approach has significant limitations: For example, light-trap catches are strongly influenced by the weather and the lunar cycle (Yela and Holyoak 1997), and there may be an interval of several days between an immigration and the resultant peak in light-trap catches (Wada et al. 1987). Moreover, ground-based observations give no indication of the height of the migrants' flight, which is critical in determining the origin of migrant populations because wind speed and direction vary with height (Chapman et al. 2002a). Evidently, what is required is a means of monitoring high-altitude insect migration while it is in progress, but this is a technically challenging enterprise. Maintaining sampling platforms in the air (e.g., aircraft, tethered balloons; Reynolds et al. 1997) is expensive and impracticable over long periods. Also, mechanical sampling devices lack the necessary sampling volume to catch less numerous species and the necessary spatial and temporal sensitivity (i.e., they cannot sample simultaneously from many heights over a large altitude range) to adequately study high-altitude migration.

It seems obvious, at least in hindsight, that there should be a role for a remote sensing technology such as radar, and since G. W. Schaefer's pioneering study in 1968 (Schaefer 1976), groups in several countries have developed entomological scanning radar for observing insect migration at high altitude (see the Radar Entomology Web site, www.ph.adfa.edu.au/a-drake/trews). The great advantages of radar are (a) its unique capacity to detect insects simultaneously at a range of altitudes that can reach more than 1 km agl and (b) the large sampling volume that it provides relative to traditional sampling methodologies (Chapman et al. 2002b). Furthermore, because insects are unaffected by flying through the radar beam, the method provides an unparalleled opportunity to investigate aspects of insect migration behavior, such as orientation in high-flying migrants (Riley and Reynolds 1986, Reynolds and Riley 1997). Early studies used inexpensive X-band scanning radar for case studies of mass migrations of pest insects and revealed many interesting and sometimes spectacular phenomena (Riley and Reynolds 1986, Drake and Farrow 1988, Beerwinkle et al. 1994, Reynolds and Riley 1997). However, these devices are labor-intensive to operate and so are intrinsically unsuitable for long-term monitoring tasks. Vertical-looking radar (VLR) systems allow continuous and autonomous monitoring of pest migrations (Beerwinkle et al. 1995), but early versions of these systems had very limited target identification capabilities. The inclusion of beam nutation in the 1990s (Drake 1993, Smith et al. 1993) has allowed more information to be derived from the returned signals, resulting in improved identification facilities (Smith et al. 1993, Chapman et al. 2002b). This makes nutating VLR amenable to long-term ecological studies in habitats with high insect biodiversity (Smith et al. 2000, Drake et al. 2001), and within the last few years VLR systems have been incorporated in the first long-term research programs monitoring high-altitude insect migration. Separate studies are being carried out by two groups. One group based at the University of New South Wales, Canberra, Australia, is using VLR to characterize the migration pathways of two economically important pests, the Australian plague locust Chortoicetes terminifera and the native budworm Helicoverpa punctigera, from the semiarid inland expanse of eastern Australia to neighbouring cropping regions (Drake et al. 2001). In the United Kingdom, the Rothamsted Radar Entomology Unit (RREU) is employing VLR systems for the first long-term ecological studies of broad-spectrum, high-altitude insect migration in Europe (Smith et al. 2000, Chapman et al. 2002a, 2002b).

We briefly outline this new remote sensing technology and showcase some of the results obtained from the first few seasons of continuous operations by RREU in southern England. Finally, we discuss the future prospects for insect monitoring radar and how the technique might be integrated with other remote sensing and aerial sampling tools.

Vertical-looking radar

Vertical-looking radar has been deployed at two sites in southern England, one at Rothamsted, Harpenden, Hertfordshire (since May 1999; figure 1), and the other at Malvern, Worcestershire, 140 km to the west (since October 1999). The specifications of the system we employ are described below.

Radar equipment.

The VLR emits a circularly symmetric, vertically looking beam in which the plane of linear polarization is continuously rotated by mechanically turning the upward-pointing wave-guide feed about the vertical. In addition, the feed is offset by a very small angle (0.18°) around the vertical axis, making the beam oscillate slightly. This produces nutation, a conical-scan motion similar to that of a spinning top that has started to wobble as it slows down (figure 2). Targets flying through the beam are simultaneously detected within 15 altitude bands (range gates), each 45 m in depth, separated by nonsampling intervals of 26 m; these range gates are located between 150 and 1166 m above the radar (figure 3). Insects and other targets that pass through the radar beam bounce back a signal (figure 4) that is detected by the receiver. Signals captured within the 15 range gates are recorded for 5-minute periods once every 15 minutes, 24 hours a day, giving almost continuous coverage. The returned signal is modulated in a way that is related to the size, shape, position, and speed of the target. During the 10-minute interval following each sampling period, signals are automatically analyzed using a novel iterative procedure based on their complex Fourier transformations (Smith et al. 1993). If the procedure converges to a solution, it yields the target's horizontal speed, displacement direction, body alignment, and distance of closest approach to the beam's central axis; it also yields three terms that describe the radar scattering properties of the target, from which its mass and shape can be estimated (Smith et al. 1993, Chapman et al. 2002b). These seven extracted parameters are used with other products of the procedure to create a simulated signal, and the correlation between this and the radar return provides a quantitative estimate of how well the model has described the overflying target (Smith et al. 1993). Signals with very high correlation coefficients (> 0.9) are well described by our theoretical model; this indicates that the estimated target parameters are highly reliable (Smith et al. 1993, Chapman et al. 2002b). Our analyses of insect migration are generally restricted to targets eliciting correlation coefficients greater than 0.9, and thus we are highly confident that we exclude signals produced by raindrops and other targets of nonbiological origin. On rare occasions, signals from birds and bats may produce correlation coefficients greater than 0.9, but these are excluded because of their large masses and displacement speeds.

The VLR system is not commercially available, and the control and signal analysis software was specially written by one of us (A. D. S.). The component costs of each VLR were approximately $30,000.

Rotating polarization and nutation.

Rotating the radar beam's plane of polarization allows information related to the shape and body alignment of overflying insects to be deduced. For the majority of insects in the United Kingdom, the returned signal is at maximum amplitude when the plane of polarization is parallel to the insect's major body axis (length) and at minimum amplitude when it is parallel to its minor axis (width). Thus, the degree of modulation of the signal (figure 4) indicates the level of disparity between the two principal backscattering terms of the insect, the maximum and minimum radar reflectivity (referred to as σxx and σyy), and hence provides an indication of the body shape of overflying insects (Riley 1985). Large disparities (e.g., σxxyy ratios of 15:1) indicate long, thin-bodied insects, such as lacewings (Neuroptera); smaller disparities (e.g., ratios of 5:1) indicate shorter-bodied insects, such as many beetles; and ratios of 1:1 indicate hemispherical targets, such as ladybirds (Coleoptera: Coccinellidae). Furthermore, the signal modulation allows accurate prediction of the insect's body alignment (i.e., the direction in which the insect is pointing, which may be different from its displacement direction); this alignment can be used to investigate orientation behavior.

To interpret the signal in terms of the target's mass, it is necessary to know the distance of the target from the beam center, as a strong signal may be caused by a large target on the edge of the beam or a small target near its center (Smith et al. 1993). Nutation of the radar beam around the vertical axis allows the position of a target within the horizontal plane of the beam contour to be fixed. The target's mass can therefore be calculated from the signal amplitude. Thus, with a combination of beam nutation and polarization, the size, shape, and orientation of the target can be determined.

Target identification.

Identification procedures currently used by VLR rely on three potential types of information embedded in the radar signals: (1) estimates of body mass, (2) σxxyy ratios, and (3) wingbeat frequency (WBF). Data on WBF are extractable only if the radar nutation cycle is periodically stopped (Drake et al. 2002), which unfortunately means that it is not possible to collect data on mass and WBF simultaneously. Information on body mass is essential for routine monitoring of biomass, so we typically run the VLR in nutation mode. However, after each 5-minute recording period, the VLR collects an additional minute of data in nonnutation mode so that limited WBF data are available. Generally, identity-related information is thus limited to body mass and principal backscattering terms of individual insects. In principle, it should be possible to assign VLR-detected insects to functional groups based on these two parameters alone. Nevertheless, we supplement this information with sample data on the relative abundance and temporal occurrence of different insects in the air column so that provisional identifications can be made. We use data from the Rothamsted Insect Survey's network of light traps and 12-meter-high suction traps throughout the United Kingdom (Woiwod and Harrington 1994) and from intensive periods of sampling airborne insect populations with balloon-supported nets (Reynolds et al. 1997). The high-altitude netting (usually at about 200 m agl) in particular has provided crucial information on the most common species of radar-detectable insects flying within the VLR sampling range. Larger insects sampled at this altitude include lacewings, hoverflies (Syrphidae), ground beetles (Carabidae), diving beetles (Dytiscidae), and the large moth Noctua pronuba (Noctuidae). In a recent study, a combination of data from the radar signals, aerial nets, and ground-based trapping networks resulted in the successful identification of a species—the diamondback moth, Plutella xylostella, a relatively small (1- to 4-milligram [mg]) microlepidopteran—from the mass of insects detected by radar in the summer of 2000 (Chapman et al. 2002a).

Sensed volume and aerial density.

To convert target abundance into biologically meaningful aerial density, it is necessary to calculate the volume sensed by the radar beam for individual targets. This is not trivial, because the volume sensed is dependent on the mass of the target (figure 3). For example, the minimum size resolution of the VLR is approximately 1 mg at a range of 150 m (the height at which the first range gate opens); targets of this size are detectable only in a relatively small volume within the first range gate, while insects weighing more than 15 mg can be detected in much larger volumes throughout all 15 gates. To determine aerial density, we first calculate the maximum range of detection and then the volume enclosed by the isoechoic contour confined by this height (Chapman et al. 2002b). This results in a series of ever-expanding volume contours for insects of increasing mass (figure 3). Once the sensed volume has been calculated for any given target, it is a simple matter to estimate its aerial density. To account for the influence of wind speed on aerial density, the insect's transit time through the beam is included in the calculation. The use of density is advantageous because it allows objective comparison of insect abundance from different size classes and range gates.

Sample results

The two VLR systems have been collecting continuous data on insect flight over southern England since 1999, and we have built up an extensive database of migration flux. This is a unique, replicated data set of aerial insect temporal dynamics that would be impossible to obtain using traditional monitoring techniques. These data are amenable to a whole range of ecological studies; here we briefly discuss examples from the suite of studies we are carrying out.

Insect biomass and long-term monitoring.

Vertical-looking radar provides quantitative estimates of aerial insect density, diversity, and biomass over a range of temporal scales; it therefore has great potential for monitoring insect population dynamics. Data from VLR indicate that the magnitude of high-altitude insect migration over southern England is immense. For example, during July 2001, approximately 450,000 large insects (defined as insects big enough to be detected by the VLR, i.e., > 1 mg) flew through a 15-meter-wide column above the Rothamsted VLR. Extrapolating from these data, we find that a total of 30 million large insects fly through a 1 km stretch of the southern English sky during a typical summer month. However, our experience with aerial netting samples indicates that insects smaller than the minimum VLR detection threshold, such as parasitic Hymenoptera, tiny Diptera, and aphids, generally outnumber the large insects by at least 100 to 1, and often by more. Thus, a conservative estimate of the total bioflow over a 1 km stretch of the southern English countryside is an astounding 3 billion insects per month. This is equivalent to approximately 1 metric ton of insect biomass. Clearly, this enormous airborne biomass has profound implications for population dynamics of aerial insectivores, such as swifts, hirundines (swallows and martins), and bats. Consequently, VLR data provide a unique nutritional resource baseline against which it will be possible to evaluate population studies of vertebrate insectivores. In the longer term, the ever-expanding database will be amenable to studies of the large-scale effects of global warming and land-management changes on insect abundance and biodiversity.

Temporal activity.

Because the temporal sensitivity of VLR sampling (once every 15 minutes) is much greater than is generally practical with traps, the data are particularly suitable for analyzing temporal patterns of flight activity. Thus, it is a simple procedure to calculate mean activity profiles and compare them over long time periods. Figure 5 shows mean monthly patterns of daily flight activity in the first range gate for three months in 1999. There are discrete peaks of activity at dawn and dusk; there is a considerable increase in the late morning, a falloff in the afternoon, and a low level of activity at night. The daily pattern has a complex periodicity, but when aggregated over a month, the pattern becomes highly consistent (figure 5).

Insect layering.

The superior spatial sensitivity of VLR relative to traps also allows detailed study of the vertical distribution of high-flying insects. Density–height profiles of both day-flying and night-flying insects are often highly stratified, with migrants accumulated in dense, well-defined layers, often several hundred meters agl. In the United Kingdom, nighttime layers generally form at lower altitudes (about 200 to 400 m agl) and on occasion areassociated with the development of low-level temperature inversions, with migrants accumulating in the region where the air is warmest and where there is often a local maximum in the wind speed (figure 6). The reasons for the formation of daytime layers, however, are still unclear. Comparison of meteorological data from the United Kingdom Met Office's Unified Model (Dickinson 1999) with the vertical density profiles should clarify the factors underpinning this phenomenon.

Common orientation.

Perhaps the most fascinating and intriguing behavior observed by radar studies of insect migration is the common orientation often exhibited by large nocturnal migrants (Riley and Reynolds 1986, Reynolds and Riley 1997). This collective orientation occurs when the flight headings of a group of migrating insects are not randomly distributed but instead are aligned along a common direction, which may be different from the displacement direction (figure 7). Vertical-looking radar routinely yields the body alignment (heading), displacement direction, and displacement speed of overflying migrants, and thus it is a powerful technique for systematic studies of insect orientation. Preliminary examination of VLR data have indicated that common orientation is a widespread feature of migration over southern England (see figure 7 for an example). Wind speed at high altitude normally exceeds insect-powered flight speed; hence, displacement direction is largely determined by wind direction. However, the flight speed of larger insects is fast enough to influence their displacement direction to a certain extent, and so, if their flight headings are not downwind, flight speed may have important consequences for migration pathways. The ability of a group of migrating insects to select and maintain the same heading while they are several hundred meters up in the night sky indicates that they must have sophisticated orientation mechanisms. If their alignment is related to wind direction, as often seems to be the case, then they must all be able to detect the wind direction independently while flying, as they are too dispersed to observe one another's alignments. However, a flying insect in a steady airstream cannot detect wind direction directly. This is because it moves through space with the wind cell and thus is not capable of any proprioceptive sensation of wind movement, just as a hot-air balloon pilot cannot detect the direction of movement without recourse to other means. A flying insect must therefore achieve orientation to the wind by detecting its direction indirectly, and there seem to be only two possible methods for doing so. It could determine wind direction relative to its heading by making visual reference to the apparent movement of ground features below (Riley and Reynolds 1986). Alternatively, the insect may be able to infer information about wind direction if it can detect a directional component within the turbulence generated by zones of vertical wind shear (Riley and Reynolds 1986). The situation is more complex than this, however, as in some cases high-altitude migrants have been observed orientating in fixed compass directions irrespective of the prevailing wind direction. Thus, some insects may have navigational capabilities analogous to those found in migrant birds, such as the ability to orientate to terrestrial magnetic cues (a magnetic compass) or to celestial cues (a moon compass or stellar compass). Both magnetic and celestial orientation have been postulated for noctuid moths (Baker 1987), but neither of these mechanisms have been demonstrated in free-flying moths. Further work is necessary to elucidate the fascinating phenomenon of common orientation in high-altitude, nocturnal migrants, and VLR is the perfect tool to do this.

Migration case studies.

Because of the increased identification capabilities of VLR, in certain circumstances it is possible to assign targets to a single species, or at least to a group of morphologically similar species (functional group). It then becomes practicable to carry out case studies of economically important species, including agricultural pests and their natural enemies, and document their migration strategies. We have recently used VLR data to investigate the high-altitude, wind-borne migration of the diamondback moth (DBM), the world's most important pest of crucifers (Chapman et al. 2002a). Nocturnal VLR-detected targets presumed to be migrating DBMs were identified by a combination of body mass and radar cross-sectional data from the returned signals, and their identity was verified by comparing the temporal frequency of such targets with actual catches of DBMs in ground-based light-trapping networks (figure 8; Chapman et al. 2002a). Backtracks of the radar-detected DBM verified that the early-season UK population in 2000 resulted from immigration form northwestern continental Europe. We plan to carry out more studies of this nature, particularly on predators of pest aphids, such as ladybirds and lacewings, two groups that should produce characteristic signals because of their distinctive body shapes. A better understanding of the migration strategies of insect pests and their natural enemies should help with the development of more effective control systems.

Future prospects

The most obvious potential improvements to existing entomological VLR systems would be a reduction in the minimum height at which target detection is possible and an increased ability to detect small insect targets at greater heights. Currently, targets cannot be dectected below about 150 m because of the interval (1 microsecond) between the time a radar pulse is transmitted and the time the system can receive the reflected signals. A prototype design for a VLR to detect aphids between ranges of 12 and 250 m envisaged the use of a bistatic (dual-dish) configuration (Bent 1984). The bistatic arrangement would give good receiver isolation, allowing much better radar coverage near the ground. Unfortunately, this more complex system never reached operational status, but it remains an avenue that we would like to explore. Information about lower-altitude movement, and a much greater vertical resolution, can be obtained by using frequency-modulated continuous-wave (FMCW) radar (e.g., McLaughlin 1994). Unlike nutating VLR systems, FMCW radar lacks the target parameterization capabilities that are essential for identification, but it might be an useful adjunct to VLR. Meteorological FMCW radar systems (such as the one used by McLaughlin) are hugely expensive; an entomological version would depend on the availability of suitable equipment at an affordable price.

To detect relatively small insects like aphids over most of their likely heights of migratory flight, one would have to use a shorter wavelength in place of the 3.2-centimeter (cm) wavelength that current VLRs use. A high-frequency (8-millimeter wavelength) entomological scanning radar has been built (Riley 1992) and was successfully used to observe individual brown planthoppers, Nilaparvata lugens (which weigh approximately 2 mg), at heights up to 1000 m agl (Riley et al. 1991). However, in contrast to the 3.2 cm (X-band) technology, there is a comparative scarcity of the inexpensive millimetric radar components that could be used to construct a robust and reliable high-frequency VLR system, and some components would probably have to be custom-made at considerable cost.

Another desirable characteristic would be the measurement of WBF at the same time as the acquisition of the other target characteristics. At present, WBF sampling has to be carried out in a separate period, using a stationary beam rather than nutation (Drake et al. 2002). A possible alternative approach would be to use a monopulse tracker technique, whereby a target's position can be determined from a single transmitted pulse (Skolnik 2000).

Evidently, there is no shortage of technical refinements that could be made to vertical-looking insect monitoring radar systems. With all these developments, however, there will be a severe tradeoff between the desirability of obtaining extra information and the increased costs involved, some of which may be unrealistic within the constraints of entomological budgets.

To place the VLR observations of insect migration more firmly within their biometeorological context, it would be beneficial to operate VLR in conjunction with routinely operating weather surveillance systems, such as the US National Weather Service's Next Generation Radar (NEXRAD) network of weather surveillance Doppler radar (WSR-88D; www.crh.noaa.gov/lmk/soo/88d/doppler.htm) and the National Oceanic and Atmospheric Administration's 404-megahertz NOAA Profiler Network (NPN; www.profiler.noaa.gov/jsp/aboutNpnProfilers.jsp). The WSR-88D can certainly detect insects (e.g., Helicoverpa zea moths; Westbrook and Wolf 1998), but the main benefit of coordinating with these networks would be the integration of the VLR's unique characterization of airborne insect distribution with the detailed information on precipitation and wind obtained by NEXRAD and NPN.

Finally, notwithstanding all the increases in radar-derived identification capability, there will often be a need for a definitive determination of the species involved. This will still require the capture of specimens at high altitude—especially, perhaps, from high-density layers detected by the radar. Apart from netting from small tethered kytoons (Riley et al. 1991, Reynolds and Riley 1997), there may be a role for remotely piloted vehicles. For example, Shields and Testa (1999) have recently used nets attached to model airplanes for sampling the potato leafhopper, Empoasca fabae, at heights up to 150 m. The largest of these planes had a 4 m wingspan and could sample about 12,000 to 15,000 cubic meters in a 30-minute flight.

Conclusions

The insect fauna flying at high altitude can now be monitored continuously and over long periods (many years) by autonomous VLR systems. Combined with aerial sampling technology and sources of biometeorological information, these systems have great potential for areawide monitoring of economically important pests and could clearly be used for pest management and forecasting systems (Drake et al. 2001). Furthermore, the study of phenomena such as layering and common orientation will provide new insights into insect behavior. It is clear that VLR is a powerful new tool that will revolutionize the study of insect migration and make significant contributions to both pure and applied entomology.

Acknowledgements

We thank Ian Woiwod, Joe Riley, and Elspeth Bartlet for comments. Rothamsted Research receives grant-aided support from the Biotechnology and Biological Sciences Research Council (BBSRC). This work was sponsored by BBSRC grant 206/D15558.

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Figure 1. Shown beside author Jason Chapman is the vertical-looking radar (VLR) device on the roof of the Plant and Invertebrate Ecology Division building at Rothamsted. The VLR is an unobtrusive device that, unlike scanning radar, does not require special siting; it can be positioned on a rooftop or on any ground space not overhung by trees. Photograph: Ian Woiwod, Rothamsted Research.

Figure 1. Shown beside author Jason Chapman is the vertical-looking radar (VLR) device on the roof of the Plant and Invertebrate Ecology Division building at Rothamsted. The VLR is an unobtrusive device that, unlike scanning radar, does not require special siting; it can be positioned on a rooftop or on any ground space not overhung by trees. Photograph: Ian Woiwod, Rothamsted Research.

Figure 2. Schematic elevation view of the vertical-looking radar beam. The beam nutates by a fraction of a beam width around its vertical axis (dotted line) so that the top of the beam describes a circle. The vertical components of the diagram are drawn to scale, but the beam width is exaggerated to make the geometry clear.

Figure 2. Schematic elevation view of the vertical-looking radar beam. The beam nutates by a fraction of a beam width around its vertical axis (dotted line) so that the top of the beam describes a circle. The vertical components of the diagram are drawn to scale, but the beam width is exaggerated to make the geometry clear.

Figure 3. Sampling regime of the vertical-looking radar (VLR), showing the range gates at 15 heights (between 150 and 1166 meters) above the radar within which overflying insects are sampled. The maximum altitude of detection and the volume sensed by the VLR are shown diagrammatically for targets with a range of body masses. The vertical components of the diagram are drawn to scale, but the beam width is exaggerated to make the geometry clear.

Figure 3. Sampling regime of the vertical-looking radar (VLR), showing the range gates at 15 heights (between 150 and 1166 meters) above the radar within which overflying insects are sampled. The maximum altitude of detection and the volume sensed by the VLR are shown diagrammatically for targets with a range of body masses. The vertical components of the diagram are drawn to scale, but the beam width is exaggerated to make the geometry clear.

Figure 4. An example of a returned signal from an insect flying through the radar beam. Because of the beam's polarization rotation and nutation, the signal is modulated in a complex way that reflects the alignment, speed, direction, and radar backscattering terms of the target.

Figure 4. An example of a returned signal from an insect flying through the radar beam. Because of the beam's polarization rotation and nutation, the signal is modulated in a complex way that reflects the alignment, speed, direction, and radar backscattering terms of the target.

Figure 5. Diurnal pattern of insect aerial density (per 107 cubic meters) in range gate 1 (between 150 and 195 meters above ground level) in May, June, and July 1999 over Rothamsted. For each 15-minute interval, the value plotted represents the mean daily aerial density for the whole month.

Figure 5. Diurnal pattern of insect aerial density (per 107 cubic meters) in range gate 1 (between 150 and 195 meters above ground level) in May, June, and July 1999 over Rothamsted. For each 15-minute interval, the value plotted represents the mean daily aerial density for the whole month.

Figure 6. Vertical profiles of insect density at 00:00 Greenwich mean time, 23 August 2000, and associated air temperatures and wind speeds. (a) Aerial density (per 107 cubic meters) of insects larger than 15 milligrams each, detected by the Malvern vertical-looking radar and showing a well-defined insect layer centered on gate 3 (approximately 300 meters above ground level). (b) Temperature–height profiles from simulation data for Malvern generated by the United Kingdom Met Office's Unified Model (Metsim) and from radio soundings of two synoptic weather stations in southern England: Herstmonceux, East Sussex, and Larkhill, Wiltshire. (c) Wind speed–height profiles from simulation data for Malvern (Metsim) and from radio soundings from Herstmonceux and Larkhill.

Figure 6. Vertical profiles of insect density at 00:00 Greenwich mean time, 23 August 2000, and associated air temperatures and wind speeds. (a) Aerial density (per 107 cubic meters) of insects larger than 15 milligrams each, detected by the Malvern vertical-looking radar and showing a well-defined insect layer centered on gate 3 (approximately 300 meters above ground level). (b) Temperature–height profiles from simulation data for Malvern generated by the United Kingdom Met Office's Unified Model (Metsim) and from radio soundings of two synoptic weather stations in southern England: Herstmonceux, East Sussex, and Larkhill, Wiltshire. (c) Wind speed–height profiles from simulation data for Malvern (Metsim) and from radio soundings from Herstmonceux and Larkhill.

Figure 7. Circular histogram showing the distribution of body alignments (flight headings) of insects overflying the Rothamsted vertical-looking radar on 29 July 2000. The mean alignment was along the axis of 170° to 350° with a tight angular dispersion (30°), indicating that the insects were demonstrating common orientation (i.e., all were heading in approximately the same direction). The displacement direction was 110°, which would have been largely determined by the wind; thus, the insects were heading at an angle of either approximately 60° or approximately 120° to the wind direction.

Figure 7. Circular histogram showing the distribution of body alignments (flight headings) of insects overflying the Rothamsted vertical-looking radar on 29 July 2000. The mean alignment was along the axis of 170° to 350° with a tight angular dispersion (30°), indicating that the insects were demonstrating common orientation (i.e., all were heading in approximately the same direction). The displacement direction was 110°, which would have been largely determined by the wind; thus, the insects were heading at an angle of either approximately 60° or approximately 120° to the wind direction.

Figure 8. Mean aerial density (per 107 cubic meters) of the diamondback moth, Plutella xylostella, detected in range gate 1 (between 150 and 195 meters above ground level) of the Rothamsted vertical-looking radar in June 2000, and mean catch of P. xylostella in the Rothamsted Insect Survey network of light traps in the same month.

Figure 8. Mean aerial density (per 107 cubic meters) of the diamondback moth, Plutella xylostella, detected in range gate 1 (between 150 and 195 meters above ground level) of the Rothamsted vertical-looking radar in June 2000, and mean catch of P. xylostella in the Rothamsted Insect Survey network of light traps in the same month.

Author notes

1
Jason W. Chapman (e-mail: jason.chapman@bbsrc.ac.uk) and Alan D. Smith are researchers at the Rothamsted Radar Entomology Unit, Rothamsted Research, Harpenden, Hertfordshire, AL5 2JQ,United Kingdom.
2
Don R. Reynolds is a researcher at the Natural Resources Institute,University of Greenwich,Central Avenue, Chatham, Kent, ME4 4TB, United Kingdom.

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