Integrative and Comparative Biology Advance Access originally published online on June 18, 2008
Integrative and Comparative Biology 2008 48(1):24-39; doi:10.1093/icb/icn051
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This article appears in the following Integrative and Comparitive Biology issue: Aeroecology: Probing and Modeling the Aerosphere–The Next Frontier [View the issue table of contents]
Analyzing NEXRAD doppler radar images to assess nightly dispersal patterns and population trends in Brazilian free-tailed bats (Tadarida brasiliensis)
*Center for Ecology and Conservation Biology, Department of Biology, Boston University, Boston, MA 02215 USA
Correspondence: 1E-mail: jason{at}jwhorn.com
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Operators of early weather-surveillance radars often observed echoes on their displays that did not behave like weather pattern, including expanding ring-like shapes they called angels. These echoes were caused by high-flying insects, migrating birds, and large colonies of bats emerging from roosts to feed. Modern weather-surveillance radar stations in the United States (NEXt-generation RADar or NEXRAD) provide detailed images that clearly show evening bat emergences from large colonies. These images can be used to investigate the flight behavior of groups of bats and population trends in large colonies of Brazilian free-tailed bats (Tadarida brasiliensis) in south-central Texas which are clearly imaged by local NEXRAD radar stations. In this study, we used radar reflectivity data from the New Braunfels, Texas NEXRAD station to examine relative colony size, direction of movement, speed of dispersion, and altitude gradients of bats from these colonies following evening emergence. Base reflectivity clear-air-mode Level-II images were geo-referenced and compiled in a GIS along with locations of colonies and features on the landscape. Temporal sequences of images were filtered for the activity of bats, and from this, the relative size of bat colonies, and the speed and heading of bat emergences were calculated. Our results indicate cyclical changes in colony size from year to year and that initial headings taken by bats during emergence flights are highly directional. We found that NEXRAD data can be an effective tool for monitoring the nightly behavior and seasonal changes in these large colonies. Understanding the distribution of a large regional bat population on a landscape scale has important implications for agricultural pest management and conservation efforts.
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The ability to observe free-ranging animals interacting within their environment is essential for understanding their behavior and ecology. Increasingly, studies have broadened the scope of ecological research from local processes and interactions to changes at the level of the landscape by employing remote sensing methods. One such approach is the use of radar (Radio Detection and Ranging) systems to study behaviors and patterns of movement that occur at altitudes and distances not visible to human observers.
Operators of early radars were often challenged with interpreting echoes in images that they believed were of empty airspace (Lack and Varley 1945
; McKay 1945; Buss 1946; Plank 1956). Researchers have since successfully employed radar to describe the distribution and movement of smoke, dust, birds, bats, and insects. The timing, altitude, speed, and density of nightly and seasonal flights of birds has been studied extensively by examining echoes created by groups of animals aloft in "clear" (no precipitation) air (Schnell 1965; Cooper et al. 1991; Cooper and Ritchie 1995; Bruderer 1997; Gauthreaux 1998; Alerstam and Gudmundsson 1999; Klaassen and Biebach 2000; Burger 2001; Williams et al. 2001; Dinevich et al. 2003). Entomologists have found that radar echoes can also be attributable to insects flying at altitudes higher than previously thought (Crawford 1949; Glover et al. 1966; Richter et al. 1973; Wolf et al. 1995). Williams et al. (1973) confirmed by helicopter that the reflectivity on airport radars in Texas was caused by groups of Brazilian free-tailed bats (Tadarida brasiliensis) dispersing from caves to forage.
Early long-range weather-surveillance radars (WSR-57D) also detected biological phenomena. Operators noted shapes and movements on their displays during clear-sky conditions, such as expanding ring-like shapes and long finger-like formations that did not correspond to weather events. The current United States National Weather Service weather-surveillance radars known as WSR-88D or NEXRAD (NEXt-Generation RADar) have been used extensively for studying bird migration on a larger geographic scale (Gauthreaux and Belser 1992; Gauthreaux and Belser 1998; Russell and Gauthreaux 1998; Diehl and Larkin 2005). Nocturnal and diurnal exodus flights (large-scale departure of groups of migrating birds from a roost), overwater migrations, arrival times, and selection of roost sites can be examined using images produced from the first 0.5° elevation scan (base reflectivity). Flights of large groups that occur along migration routes can be monitored, and thus long-term studies to examine changes in the intensity and scope of migration are possible. Insect migrations are also detectable in NEXRAD images (Westbrook and Isard 1999). Westbrook et al. (1998) were able to confirm that NEXRAD echoes were due to migrating corn earworm moths (Helicoverpa zea) by using additional 3 cm radar that revealed the orientation and size of the targets.
Biologists and meteorologists have also noted that the timing, location, and seasonal changes in some clear-air NEXRAD images are consistent with bat activity. The first report of bats appearing in NEXRAD images described a unique dispersal pattern of reflectivity from the location of a known large colony located in a cave in Oklahoma (Ruthi 1994). NEXRAD radial-velocity images indicated that bats were dispersing at 25–32 km/h away from the cave and that large numbers of bats were flying well above 2000 m above ground level (AGL). Similar emergences have been observed to originate and then disperse from most large maternity colonies of Brazilian free-tailed bats (T. brasiliensis) in Texas and Oklahoma (McCracken and Westbrook 2002; Cleveland et al. 2006).
An important and frequently posed question about NEXRAD images and bats is whether it is possible to distinguish echoes that are due to bats from those produced by weather, insects, birds, and other airborne objects. Reflectivity that is caused by Brazilian free-tailed bats during evening emergence from roosts is visually identifiable in sequences of NEXRAD images because its appearance and movement are correlated with known flight behavior and life history patterns. Areas of intense reflectivity that expand and disperse are viewable in NEXRAD images 30–40 min after the onset of evening emergence from roosts. These expanding shapes are located directly over the known geographic position of several large, well-known colonies. These emergence patterns do not appear in winter when bats are not present, and appear strongest during June and July when these colonies are at their peak numbers. Bird migrations and morning departures from roosts occur at times of night and the early morning that generally do not overlap with the timing of evening bat emergence (Diehl and Larkin 2005). Insect concentrations appear morphologically distinct on NEXRAD (Westbrook et al. 1998) and do not originate from bat colony locations. However, NEXRAD reflectivity may be a mix of birds, bats, insects, dust, and anomalous reflections from objects on the ground. Few attempts have been made to quantify the underlying flight behavior and population fluctuations of bats by attempting to isolate reflectivity from bats from other sources based on the timing, geographic location, and known emergence behavior of bats.
The present study has two primary goals: (1) to establish methodology for systematically quantifying magnitude, movement, and dispersal patterns of groups of Brazilian free-tailed bats from large numbers of NEXRAD reflectivity images and (2) to demonstrate that these data can be used to investigate population changes and feeding ecology of Brazilian free-tailed bats in Texas.
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Species studied and study area
The Brazilian free-tailed bat is a semi-tropical species that typically migrates annually between Mexico and the south-central United States. It roosts in buildings, mines, and bridges, but is best known for the enormous colonies that it forms annually during the summer in Texas, Oklahoma, and New Mexico. These colonies are comprised mostly of females, 96% of which are pregnant or lactating (Davis et al. 1962
; McCracken and Gustin 1991). These females each give birth to a single pup, on average, around 10 June (McCracken and Gustin 1991). Young Brazilian free-tailed bats begin to forage when they are 
6 weeks of age (Kunz and Robson 1995), during which time, mothers return to the roost cave nightly to nurse their pups. Historical accounts describe the seasonal maximum population size of these colonies in the tens of millions (Davis et al. 1962). McCracken's (2003) review of available reports estimated the total population of Brazilian free-tailed bats in the southwestern United States during this peak to be 150 million individuals. Brazilian free-tailed bats are also noteworthy for their high-altitude and long-distance nightly foraging flights (Davis et al. 1962; McCracken et al. this symposium). As Brazilian free-tailed bats emerge from caves to forage, they typically form a closely-packed stream which rises several hundred meters above ground before breaking up into smaller groups that disappear from view. Williams et al. (1973) found that these bats dispersed from caves at an average maximum speed of 53 km/h and reached an average maximum altitude of 2500 m AGL. Brazilian free-tailed bats radio-tracked from Carlsbad Caverns in New Mexico have flown as far as 56 km to reach foraging areas (Best and Geluso 2003) and echolocation and terminal feeding buzzes have been recorded at 300 m AGL (Griffin and Thompson 1982; McCracken et al. 2008).
For our analysis, we selected four colonies inhabiting caves and two colonies inhabiting bridges from among the many colonies that are located in the Balcones Escarpment area between Austin and San Antonio, Texas, where the Edwards plateau lies to the northwest and agricultural areas to the south and east (Fig. 8). The two colonies inhabiting Congress Avenue Bridge and McNeil Bridge are located near Austin Texas. Congress Avenue Bridge is located just south of the downtown area and McNeil Bridge is located 25 km to the northeast along Interstate 35. Two colonies inhabiting Frio Cave and Ney Cave are located 65 and 115 km west of San Antonio, respectively. Two other colonies that inhabit Davis Cave and Eckert James River Cave are located in the foothills 83 and 156 km northwest of Austin, respectively. Agricultural landscapes comprised largely of cotton, corn, and beets lie northeast of Austin and south of San Antonio in the "Winter Garden" area of Texas, where bats are known to feed on the adult forms of crop pests such as the corn earworm moth (H. zea) (Lee and McCracken 2005; Cleveland et al. 2006). The study area contains a total of 54 known colonies of Brazilian free-tailed bats; 14 in caves, 38 in bridges, and 2 in sinkholes. These colonies also contain small numbers of the cave bat, Myotis velifer, and perhaps other species (Ritzi 1999). Bracken Cave, widely regarded as housing the largest colony in the study area, reportedly contains upwards of 20 million individuals at its peak population (Davis et al. 1962; McCracken 2003; but see Betke et al. 2008). The geographic area where these colonies are found, and the range over which they are thought to forage (50 km from the roost), is 100,238 km2. The NEXRAD station (KEWX) in New Braunfels, Texas, is centrally located in this area.
Sampling NEXRAD data
To quantify the nightly emergence behavior of Brazilian free-tailed bats, we examined archived NEXRAD data available from the NOAA National Climatic Data Center (NCDC, http://hurricane.ncdc.noaa.gov/pls/plhas/has.dsselect). For our analysis, we only used "clear-air"-mode images which are produced by NEXRAD stations when there is no local precipitation, and which are more sensitive to the presence of bats. We selected days for which clear-air mode data were available while attempting to achieve as close to an even distribution as possible for each period of time we examined.
To examine intra-annual seasonal changes in relative colony size at each of the six sites, we assembled a data set that included 27 days beginning January 12, 2005 and ending December 20, 2005. We collected 904 separate images of bat emergence, with each day containing between 33 and 34 images taken at 10 min intervals. We also tested for directional trends in initial headings taken by bats following evening emergence using a sub-set of 19 days of this data set. To examine long-term inter-annual changes in relative colony size, we obtained images of emergence from the six colonies spanning an 11-year period (1995–2005). To improve our confidence in estimating colony size for any given year, we used images from the 5–6 week period following parturition, when mothers are nursing their developing pups and are unlikely to relocate to new roosts. For each year, we selected two days between June 20 and July 8, from which we used the mean value as an estimate of the most stable number of adults in each colony.
Spatially explicit image processing
We estimated relative colony size and initial heading from radar images of emerging bats by creating a software-based NEXRAD data analysis system. The system consists primarily of a custom-written software framework and applications using the scripting language php 5 (http://www.php.net) that extends the open-source GRASS GIS (Geographic Resources Analysis and Support System, http://www.geog. uni-hannover.de/grass/index.php). Together, the applications make possible geo-referencing, geographic processing and spatial analysis, data storage and statistical analysis, and visualization and animation of NEXRAD data. Native NEXRAD data "bins", each representing the amount of reflectivity in a wedge-shaped volume of air, are first converted into a grid pattern using the NCDC Java NEXRAD Exporter version 1.0.12 (http://www.ncdc.noaa.gov/oa/radar/jnx/batch.html). The NEXRAD exporter geo-references the cells as a grid of latitudinal and longitudinal coordinates based on a World Geodetic System spheroid (WGS84) model of the earth. Latitude and longitude coordinate grids must be projected onto a flat surface (a map projection) before measurement of area, distance, and scale can be calculated without distortion errors. We used a custom-written application to project large numbers of NEXRAD images onto the Texas State Plane Mapping System (TSMS), a Lambert conformal conic projection based on a GRS80 spheroid model of the earth and the 1983 North American Map Datum (NAD83) that minimizes distortion of distance and area in maps of Texas. The resulting geo-referenced NEXRAD layers are raster grids of 1 km2 cells that are 1072 km wide (east-west) and 994 km high (north-south) and are centered at the New Braunfels, Texas NEXRAD station. NEXRAD reflectivity is measured in units of decibels Z on a logarithmic scale. Z is a ratio of the amount of RADAR energy that is emitted by the station to the strength of the reflected echo received for a given volume of airspace. The range (including zero) of values found in typical Level-II NEXRAD images that include ground clutter, insects, birds, and bats is from –25 to 25 dBZ. Object density cannot be inferred by comparing or summing dBZ values from volumes of airspace, as dBZ values are on a log10 scale. To allow comparison of reflectivities in our analysis, we transformed the dBZ values for each pixel into Z-values; the ratio of the emitted signal to the strength of the return echo. In a typical clear-air-mode image containing bats, these Z-values range from 1 to 10,000, with 0 indicating no reflectivity. Because NEXRAD Z-values are linearly proportional to the number of birds aloft in a volume of space (Gauthreaux and Belser 1998, 1999; Black and Donaldson 1999), we used Z-values as estimates of the relative number of bats aloft in a volume of airspace.
Modeling the emergence of bats
To identify activity of bats in NEXRAD images, it was necessary to build a model of bat behavior during emergence. We developed a set of qualitative and quantitative criteria, based on the foraging ecology of Brazilian free-tailed bats and the physics of the NEXRAD beam propagation, for determining which areas (or patches) within an image contain reflectivity due to bats. (1) Emergences of free-tailed bats contain large dispersing groups of individuals. We chose a threshold-sized area that was large enough to confidently identify bats, but small enough to filter unwanted NEXRAD reflectivity. Therefore, we define only patches of reflectivity covering at least 35 km2 (35 pixels) to be a group of bats. (2) Emergences originate from specific roosting locations and bats disperse outward from these locations. The first patch of reflectivity from an emergence must occur no further that 15 km distant from the location of the colony. (3) Emergences of bats on NEXRAD manifest as developing, dispersing patterns. Therefore, reflectivities should at first appear weak, then become centralized and stronger, then become weaker and ultimately disappear. (4) The patch must conform to one of three common shapes: a characteristic expanding ring centered over the colony, a partial expanding ring or curved wave front, or a funnel shape, expanding outward as distance from the colony increases.
Identifying bat activity
Quantifying the reflectivity in NEXRAD images attributable to emergence of Brazilian free-tailed bats requires two steps. First, areas or patches of reflectivity in the images that are due to bats aloft must be identified and retained while all other reflectivity is removed. Second, patches of reflectivity in successive images must be tracked through time to attribute them to a single emergence event. In the first step, a series of nine image-analysis procedures are performed in sequence (Fig. 1). In Procedure 1, an area is selected near the colony in which bats are expected to be found (see the next section on "Tracking movements of bats" for a detailed explanation of how these areas were calculated). In Procedure 2, a contrast filter is applied that generates a sigmoidal response (from 0 to 1) for all the pixel values in the masked image using the following formula
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where rx,y is the reflectivity value of any pixel at position x,y in the image, rmin is the minimum reflectivity value, rmax is the maximum value, and r is the mean reflectivity value in the image.
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This has the effect of lowering lower-than-mean values and raising higher-than-mean values, condensing patches of reflectivity and separating them from one another. Procedure 3 masks this image using the mask from Procedure 1. Procedure 4 removes the values below the mean value in the Procedure-3 image. Procedure 5 uses the Procedure-4 image as a mask for the original data, selecting only the pixels from the original image that match the contrast-filtered image. Procedure 6 reintroduces original pixels into the image that are above a threshold value by using a dilation procedure. This approach retains below-mean reflectivities that are spatially associated with likely bat patches, while removing the same values elsewhere in the image where they may represent clutter or noise. The threshold value is based on the mean value of the pixels in Procedure 1, but is modulated slightly by the pixel density (the proportion of non-zero pixels). This is an important aspect of this procedure because it is common in NEXRAD sequences for pixel density to increase as the night sky becomes saturated with dispersed bats, leading to images where there are a few high concentrations of bats surrounded by a sea of lower reflectivities. Thus, the threshold value is raised slightly as pixel density increases. The threshold is calculated as follows, where r is reflectivity.
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Procedure 7 separates patches of bats that are connected by only a few pixels. This is a critical step because emergence is a dispersal phenomenon and as emergence progresses, patches of bats from different source colonies may begin to overlap, leading to over counting. To separate patches, a series of 25-pixel neighborhood kernels are passed over each value in the image, and the center value is either retained or removed, based on the value of its neighbors in the kernel. These functions have the effect of breaking small bridges 1 or 2 pixels wide that connect two larger patches. An additional kernel function uses two alternate 9-pixel patterns and effectively removes any diagonally oriented small bridges connecting two larger patches. Procedure 8 fills any small holes with original pixels. The final Procedure (9) uses a recursive connected-components algorithm that assigns an ID number to each of the remaining patches.
Tracking movements of bats
To attribute multiple patches of reflectivity in successive images to a single bat emergence event, we applied a tracking algorithm (the Tracker) to the filtered images (Fig. 2). To accomplish this, the Tracker first determines the position of each patch by calculating its value-weighted spatial mean (Dacey 1962). Thus, the center of the patch is determined both by the geometric shape of the patch, and by the distribution of values within the patch. The spatial mean coordinates of a raster image are given by the following where ri is the reflectivity at location xi or yi.
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The Tracker's algorithm is based on the criteria given previously and proceeds as follows: (1) The Tracker searches for the first patch of reflectivity within a circular zone of 15 km radius (707 km2) around the colony's geographic location. Once a patch is found in this zone, its center is recorded as the first point of a new branch that will become a line of successive points that describes the movement of the center of the patch through time. In subsequent images, the Tracker searches for patches whose centers fall within an elliptical model of the probability of the position of that patch. One focus of the ellipse is placed at the endpoint of a branch. The position of the second focus is determined by the average distance moved by the patch in the previous two images (T–2 and T–1) and the angle of the vector from the center of the T–2 patch to the T–1 patch. The ellipse is then drawn using the formula given below where e is the eccentricity of the ellipse, a is 1/2 the major axis length, and r is a radius from the focus to a point on the ellipse at angle 
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If the center of the present patch falls within this ellipse, and the endpoint was recorded in the T–1 image, the new patch is added to the endpoint's branch. If not, the Tracker again determines if this new patch is within the circular zone around the colony. If the patch is in this area, again, it is recorded as the first point of a new branch. If the center of the patch does not fall into any of these areas, the patch is assumed to be unassociated with the emergence from the colony being examined and is discarded. Subsequently, all recorded patches are measured and the following values are recorded to a database: (1) number of pixels, (2) sum of all the reflectivity, (3) mean reflectivity, (4) standard deviation, (5) coordinates of the spatial mean, (6) weighted standard distance (spatial standard deviation), and (7) distance to, and heading from, the previous point in the branch. The output of the combined filtering and tracking steps is a group of patches which, when considered together, represent the total of all of the reflectivity generated by a single colony during an emergence period.
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Our filtering and tracking system identified and recorded 642 patches of bats in 548 images. Each emergence event contained between 1 and 21 patches (x = 7.3 ± SD = 4.6). Both weak (low Z-value) and strong (high Z-value) areas of bat reflectivity were identified and tracked. The Tracker successfully recorded one or more branches of bat patches when emergence proceeded in a series of several waves. This observation correlates with observations of emergence at roost sites, which, when at peak colony size, typically proceeds in 2–4 waves separated by pauses of 15–30 min (Frank et al. 2003
; Betke et al. 2007). The number of branches recorded per emergence does not necessarily indicate the number of emergence waves, however, as the Tracker occasionally broke a single wave into one or more branches. Tracking performance was, by design, conservative, and some weak patches that were visually identifiable as part of an emergence branch were not counted. Only in very rare cases did the filtering and tracking system select areas of reflectivity that did not appear related to bats. The seasonal variation in the total reflectivity conformed to the pattern of activity typically observed on the ground at these colonies (Fig. 3). No reflectivity was recorded in January. Reflectivity from bats was first observed on February 21 at Congress Avenue Bridge. Total reflectivity increased at the Frio Cave, Ney Cave, Congress Avenue Bridge, and McNeil Bridge colonies throughout March and April. Strength varied in May, June, and July, but decreased during the first week of June. Reflectivity decreased markedly in August. Sporadic reflectivity was recorded in September and October, with some colonies showing little or no reflectivity (Davis Cave and Eckert James River Cave), and others showing a marked increase (McNeil Bridge). No reflectivity was recorded in December.
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During the period from April 1, to August 1, 2005, Frio Cave produced the highest total reflectivity per night (1.2 MZ). Ney Cave, Congress Avenue Bridge, and McNeil Bridge produced similar amounts of reflectivity (712, 947, 628 kZ, respectively). Davis and Eckert James River caves produced much smaller amounts of reflectivity (14 and 127 kZ), respectively.
The patterns of nightly reflectivity measured at Frio Cave and Ney Cave are highly correlated with the expected seasonal migration of Brazilian free-tailed bats. In early May, a large increase in numbers of bats typically occurs at these caves, and there is a corresponding increase in nightly reflectivity. Toward the end of the summer maternity period, there is a drop in reflectivity as pups are weaned and adult females begin to move among different roosts at the onset of migration. We also observed a corresponding decrease in reflectivity in the last week of July. The pattern at Frio Cave, Ney Cave, and Congress Avenue Bridge may also correspond to the timing of the onset of parturition at these colonies, which typically occurs between the first and second week of June. Both Frio Cave and Ney Cave experienced a significant drop in reflectivity during this time.
The pattern of reflectivity differed significantly between bridge colonies and cave colonies. Bridge colonies both showed their first significant increases in reflectivity in early March, two months before increases occurred at cave colonies. We observed a peak of activity at the Congress Avenue and McNeil bridge colonies on April 15 and April 3, respectively, while during the same period, almost no reflectivity was recorded for both Frio Cave and Ney Cave. Also, the bridge colonies showed higher reflectivity through the end of October, whereas cave colonies showed little to no reflectivity after August 1 (Fig. 4). These data suggest that bats first occupy bridge colonies when they arrive in Texas following spring migration, and then later expand into caves in late spring and early summer. A reverse pattern of reflectivity is evident in the autumn prior to migration, when bats appear to move from caves to bridges.
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Mean and total reflectivity both varied annually (Fig. 5). The highest mean nightly reflectivity (mean of the total cumulative Z for the two sampled nights for that year) occurred in 2000, and the lowest in 2002. An increasing trend in reflectivity was observed from 1995 through 2000, followed by a precipitous decrease to the lowest recorded measurements in 2001, 2002, and 2003. From 2002 through 2005, an increase in annual reflectivity was observed, reaching the pre-2001 levels. We found no evidence of a general downward trend in total reflectivity from the six colonies sampled over an 11-year period.
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Nightly emergences were observed on 17 out of the 19 nights we evaluated in 2005. Using Rayleigh tests (a statistical measure of directionality) we found the distributions of headings of patches to be significantly directional (P < 0.05) for each of the six colonies. Mean heading varied from 15° (NNW) to 128° (SW). Typically, individual colonies moved in a predominant direction during nightly emergence. Bat colonies at Frio Cave and Ney Cave produced emergence reflectivity that typically moved in a southeasterly direction (x = 111.9°, ± SD = 25.7° and x = 99.2°, ± SD = 58.2°). Bats from the McNeil Bridge colony typically moved toward the northeast (x = 54.6°, ± SD = 49.7°). Bats from the Congress Avenue colony typically dispersed in a northeasterly direction, but also exhibited several emergences distinctly oriented toward the southwest as well (x = 109.3°, ± SD = 70.7°). Bats from Eckert James River Cave and Davis Cave both exhibited mean directionality toward the northeast, but this observation may not be significant owing to the small number of times they were observed (n = 5, n = 3, respectively).
Significant differences existed between the mean vectors for all cave colonies versus all bridge colonies (Fig. 6). The mean vector for emergence from bridge colonies was directional toward the southeast (x = 116.9°, ± SD = 43.3°, Rayleigh test = 0.75, P < 0.001) and that for cave colonies was oriented to the northeast, (x = 76.3°, ± SD = 66.0°, Rayleigh test = 0.51, P < 0.001). Moreover, the mean vectors for colony emergences from caves and bridges were significantly different from each other (Watson t-test = 0.44, P < 0.05).
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We also tested for the differences in headings between cave colonies and bridge colonies during the spring mating/migration period (before May 15), the summer maternity and lactation period (May 15 to August 15), and the fall migration period (after August 15). Emergence headings from bridges in spring, summer, and autumn and from caves in spring and summer were significantly directional. Emergences from caves in autumn were not significantly directional, most likely owing to the small number of emergences observed during this period (n = 3). During spring and summer, there were significant differences in the mean vector of emergence between cave- and bridge-colonies. Thus, we observed no effect of season on the directionality of emergences from caves or bridges. We continued this analysis to the level of the individual colony, but again found no effect of season on the distribution of headings.
We analyzed the mean heading of emergences for all six colonies combined over the same inter-annual period (1995–2004) for which we found large variation in total reflectivity. Eight of the ten years showed significant directionality in emergence heading. Mean heading varied between years, but was typically in an easterly direction. Emergences were significantly directional in years 1995 through 2001, and in 2004, but were not significantly directional for years 2002 and 2003. Significance in the Rayleigh test was correlated with the inter-annual pattern of total reflectivity. As total reflectivity increases, the variance in direction of emergence decreases. During the 2001–2003 period, when reflectivity was at its lowest point, direction of emergence was most variable.
To test the effect of wind heading on observed headings of emerging bats, we compared the mean surface wind heading measured during the emergence period (17:00–23:00 CDT) to the mean emergence heading of all colonies. Mean wind heading and mean heading of emerging bats were significantly different in 2005 for all colonies (Watson = 0.449, P < 0.05, Fig. 7). A circular–circular regression (Sarma and Jammalamadaka 1993) showed that wind heading was not a significant predictor of emergence headings of bats (
= 1.70, px = 0.65, py = 0.10). When considering only Frio Cave and Ney Cave and wind measurements taken from the KSAT station (San Antonio), mean heading of emerging bats was close to mean wind heading (mean wind heading = 136.7° ± SD 25.5°, mean heading emerging bats = 116.9° ± SD = 43.3°), but the two were still significantly different (Watson = 0.221, P < 0.05, Fig. 7). Again, a circular–circular regression showed no effect of wind on emergence direction (px = 0.89, py = 0.66). The difference between the mean wind heading at the Austin station (KAUS) and the heading of the emergences at the two bridges, Congress Avenue and McNeil, was larger than that for the two caves (mean wind heading = 75.8° ± SD 47.7°, mean heading of emergences = 158.7° ± SD = 62.1°, Fig. 7). Again, the mean headings were significantly different (Watson = 0.531, P < 0.05), and there was no effect of surface wind on direction of emergence ( = 2.69, px = 0.42, py = 0.07). Speed of dispersal was measured as the mean distance traveled by patches of reflectivity during an emergence, divided by the time between images (10 min). Mean speed of dispersal of colonies for 2005 was 27.4 km/h ± SD 6.14. We observed no discernible pattern in annual variation in speed of dispersal from all colonies, from bridges alone, or from caves alone. Wind speed at the time of emergence (14.12 km/h ± SD 5.07) was also not a predictor of emergence speed, nor did wind speed predict the total distance traveled by patches of bat reflectivity. We estimated total distance traveled during dispersal by calculating the distance of the furthest point in each tracked path of an emerging wave of bats. Total mean distance of dispersal of the center of patches of reflectivity was 15.1 km. Patches of reflectivity from bats emerging from Frio Cave traveled the furthest (18.6 km), whereas patches from Congress Avenue Bridge traveled the shortest distance (10 km).
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A primary question raised by this study is whether patterns in NEXRAD reflectivity accurately reflect changes in sizes of colonies and nightly dispersal of Brazilian free-tailed bats. Taken together, the absence of organized reflectivity near known colonies in November, December, and January, the seasonal peak of total Z occurring when these colonies are known to be largest, and the synchronous drop in Z in August when migration begins, are strong indicators that NEXRAD images contain valuable information about nightly behavior and colony dynamics.
An important finding is that Brazilian free-tailed bats appear to populate bridge roosts earlier in the season, and again later in the season, than cave roosts. Cavernous limestone caves and modern precast concrete bridges are clearly very different types of roosting habitat for bats. The environment of highway or urban bridges includes noise, vibration, constant air pollution from vehicle emissions, and the increased potential for disturbance by humans. There may be a number of benefits from roosting in bridges that would explain the attractiveness of these sites to Brazilian free-tailed bats in spite of these potential drawbacks. One possibility is that bats gain an energetic advantage by using crevices in bridges during cooler weather in spring and fall (L.C. Allen-Hristova, personal communication). Brazilian free-tailed bats depend on group living for energetic benefits. Maternity roosts in caves are warm, which benefits individual females by reducing gestation times and reducing energetically costly re-warming after bouts of torpor induced by lower temperatures (Racey and Swift 1981; Kunz and Robson 1995). Crevices and expansion joints in precast concrete bridges in which Brazilian free-tailed bats typically roost have lower thermal inertia than does the rock mass of cave walls and ceilings, and thus may have shorter temperature cycles. For a small group of bats, or even large groups of bats on cooler days, these spaces may warm more quickly, and thus bats may benefit energetically from the warmth of group huddling (Herreid 1963, 1967; Vickery and Millar 1984; Kurta 1985). Temperatures recorded inside bridge crevices in the study area (L.C. Allen-Hristova, personal communication) indicate that temperatures in bridge roosts are indeed warmer and more stable than those in caves. Before precast concrete bridges were available, Brazilian free-tailed bats may have used small crevices in buildings and other structures as stopover roosts during migration or during the mating season. Concrete bridges with expansion joints may represent a significant thermal advantage to bats over such spaces.
Another possibility is that bridges represent a reduction in competition for roosting space. Davis (1962) reported that many limestone caves in Texas were unoccupied by bats, but those that were occupied were concentrated in the Balcones escarpment (our study area), and shared characteristics, such as large, domed ceilings which trap heat generated by roosting bats. Suitable roosts may be limited in caves, which are experiencing increasing intrusion and recreational use by humans, while new bridges are increasing in number. Occupancy of bridges may relieve overcrowding in the limited number of suitable roosting space in caves. This factor may be combined with the thermal advantages of bridges, such that when all available thermally "good" roosting spaces in caves are occupied during the maternity season, bats may roost in bridges as an alternative.
Finally, the extended activity period in early spring suggests that bridge colonies may also provide ideal sites for courtship and mating. Keeley and Keeley (2004) reported that a bridge within the study area (Williamson County, Texas) is used for courtship and mating during late March and early April. There may be some intrinsic characteristics of bridges that make them favorable sites for mating, such as the ability of males to move easily among groups of females roosting in crevices (Keeley and Keeley 2004). Combining radar monitoring with detailed ground observations might shed light on any plasticity in this gregarious mating behavior.
It has been suggested or reported many times that there may be a decline in the size of many large, well-known Brazilian free-tailed bat colonies (Cockrum 1969; Mohr 1973; Altenbach et al. 1979; McCracken 1986, 1989, 2003; Clark 2001; Betke et al. 2008). Use of pesticides, increased intrusion by humans into caves, and shifting use of roost and feeding areas by bats have been variously suggested as the cause. We found no evidence of a net decline in the reflectivity from colonies that we monitored. We did, however, find a nonrandom pattern of annual fluctuation in total reflectivity that suggests a cyclical fluctuation in colony sizes over an 11-year period. The 5-year steady increase in reflectivity from 1995 to 2000, as well as the marked decrease in 2001–2003 may be caused by several factors. One possibility is that climatological events, such as droughts, floods, or storms may affect the timing of seasonal movements, foraging success, reproductive rates, and survival of bats, leading to differential population of colonies. Events that occur in over-wintering areas in Mexico also may result in population changes that are measurable downstream over the range of northward migration. For example, a warmer spring may allow individuals to conserve energy and continue moving further north to large caves in Oklahoma, rather than stop in Texas, resulting in smaller sizes of colonies in Texas than in cooler years. Moreover, severe meteorological events, such as tropical storms may further influence patterns of migration and movement. The decrease in reflectivity that we observed in 2002 is coincident with a damaging flood that occurred that year in our study area. Although our measurements were taken before the rains began, the floods were caused by a large circulating tropical storm system fed by moisture from the Gulf of Mexico, known to occur in cyclic patterns. Storm-prone years could have effects in overwintering areas in Mexico, or along migration routes that result in smaller sizes of colonies. An El Niño-Southern Oscillation (ENSO) occurred in 2002–2003, and could have contributed to the pattern we observed. If this hypothesis were correct, reductions in reflectivity in one region might be offset by increases in other regions. This question could be addressed by examining NEXRAD images from several stations along the migration path of Brazilian free-tailed bats.
Another important finding of this study is that nightly dispersal of Brazilian free-tailed bats is highly directional. Surface wind headings at the time of emergence do not appear to affect the initial direction in which bats set out to forage, nor does surface wind speed predict either the total distance traveled or the speed of emergence. The absence of an influence from wind suggests that some other factor(s) are important in determining the highly directional behavior exhibited by Brazilian free-tailed bats. This is reasonable from an energetic perspective. If bats used winds to their advantage to conserve energy en route to foraging areas, they would likely pay the opposite penalty on the reverse trip, expending additional energy to fly against the wind when returning to their roosts. However, Westbrook et al. (1995) showed that there is an altitudinal profile of wind speeds in the south-central United States, with slower speeds near ground, peak velocities of 9–15 m/s between 400 and 600 m above ground, and slower speeds again with increasing altitude. Thus, surface wind speed may have a weak relationship to dispersal phenomena because it does not correlate with winds at higher altitudes where bats are dispersing. Moreover, Brazilian free-tailed bats may minimize or overcome the energetic disadvantage of having to fly into prevailing winds by flying at different altitudes on outgoing and return trips.
A standing hypothesis is that Brazilian free-tailed bats increase foraging success by pursuing masses of emerging, dispersing, or migrating insects at high altitudes (Cleveland et al. 2006). Our observation that nightly emergence is highly directional generally supports this hypothesis. That nearby colonies share common directionality, as in the case with the bridge colonies near Austin and Frio Cave and Ney Cave in the San Antonio/Winter Garden area, suggests that there may be a common local food resource in those areas. It is not surprising that emergences from Ney Cave and Frio Cave tend to move toward the southeast, as this is the general direction of dense areas of corn and cotton agriculture where pest insects are highly abundant (Fig. 8). For the two bridge colonies, an initial northeasterly heading may be explained by similarly dense plantings of field crops in the regions immediately to the north and east. Lee and McCracken (2005) reported that the daily composition of the diet that consisted of adult corn earworms and fall armyworms in these colonies was correlated with the emergence of noctuid moths from crop fields. Bats may intersect high densities of insects within altitudinal bands where insects are taking advantage of winds for dispersal. If so, bats may improve their foraging success by flying over, but not descending entirely to, large areas of field crops from which insects often emerge in large number (Westbrook 2008). We also noted in our images, several instances of "radar fine lines" which have been attributed to insect densities in the atmosphere (Russell and Wilson 1997). Such fine lines moved across the study area from southeast to northwest, directionally against the prevailing winds. We did not observe direct convergence of emerging patches of bats with fine lines, but if such interactions occur, they may occur when bats have dispersed to the point that they are indistinguishable from background reflectivity and thus are unlikely to be detected in NEXRAD images. Future studies could use images from a combination of vertical profiling radar (T.A. Kelly, personal communication) and NEXRAD to test for evidence of spatial and temporal convergence of bats and insects.
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Although our results show there is strong evidence that NEXRAD reflectivity measured during emergence of bats reflects real events observed on the ground, there are several factors that can introduce error. NEXRAD images themselves contain inherent variability. Sensitivity of the radar decreases with distance from the station. Although this is compensated for to some extent in the Level-II data that we used, patches of bats (particularly low-intensity patches) are less likely to be detected when further from the station. This is partly the reason for the weaker returns from colonies at Eckert James River Cave and Davis Cave. Another reason is that the elevation of the NEXRAD beam means that the altitude band both increases in width and height as distance from the station increases. Therefore, patches of bats appear weaker or stronger at various distances from the NEXRAD station. We used a tracking algorithm, rather than counting reflectivity based on an arbitrary distance from the station or arbitrary strength. This means that we do not count reflectivity unless it originates from a colony location, which gives us a high degree of confidence that the reflectivity counted was due to bats.
Timing and duration of precipitation were other important variables we considered. We used only clear-air-mode data that is generated by the NEXRAD station during times when there is no reflectivity caused by precipitation. Whenever possible, we selected dates that did not follow long periods of precipitation. Bat emergences may be larger, denser, or have altered timing after many days of precipitation because females may have consequently experienced low foraging success, and may change their emergence behavior to compensate. Another source of error is the filtering and tracking algorithms themselves. Although our analytical approach successfully isolates and measures bat reflectivity, there are some scenarios that are problematic, such as when two patches of emerging bats merge. Applying additional ecologically-based rules to the tracking algorithm will help limit this source of variation.
The behavior of bats in the aerosphere is unknown once they climb to higher altitudes following emergence. Bats likely disperse in a variety of patterns and change their rate of climb as they pass through the volume of aerosphere being sampled by the radar beam. This causes them to appear in a variable number of images, leading to over- or under-estimation by our sampling method. Unfortunately, we cannot hold constant the behavior of bats while adjusting the performance of our detection or tracking algorithms. Thus, the accuracy of the filtering and tracking system we used cannot be easily evaluated. Vertically integrating all of the reflectivity in all elevation scans into a three-dimensional data set for analysis (rather than just base reflectivity 0.5° scan as we did) should provide a more complete understanding of the spatial and temporal patterns of nightly emergence behavior of Brazilian free-tailed bats.
Finally, our measurements of colony size are based on variation in relative strength and geographic distribution of reflectivity. To compare actual numbers of bats present during evening emergences, a relationship must be developed between radar reflectivity and the actual density of bats in the aerosphere. One possible approach is to correlate censuses of colonies taken on the ground with radar observations to create a calibration curve as has been done for migrating birds with NEXRAD data (Gauthreaux and Belser 1998, 1999). The number of bats present in each volume of the aerosphere can then be estimated from the reflectivity value for that volume, and total counts can be estimated by integrating this over all the reflectivity associated with the event in question. Ongoing research that is generating accurate ground-based censuses of emerging bats using high-speed thermal infrared imaging (Betke et al. 2008; Hristov et al. 2008) should make this possible in the near future.
Analysis of NEXRAD reflectivity can be a powerful and flexible remote sensing tool for studying nightly dispersal and population changes in Brazilian free-tailed bats. Our approach has potential for uncovering population trends, for detecting changes in the timing of migration, and for characterizing differential use of roosts by bats. The large NEXRAD repository provides opportunities to use both current and historical data to understand long-term population trends and to examine how anthropogenic factors may be affecting this species. Thus, this approach also holds promise for assessing conservation efforts and for improving understanding of the foraging ecology of this wide-ranging bat species.
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We wish to thank Ray Dezzani of the University of Idaho Department of Geography for guidance with spatial analysis and statistical techniques and Margrit Betke of the Boston University Department of Computer Science for her expertise in computer vision analysis. We would also like to thank the following individuals for invaluable field assistance and logistical support: Steve and Bill Rafferty, Tommy Reardon, and Clinton Schulze for assistance at the Eckert James River Cave; Lynn and Tex Barnett and Thomas (Bub), and Marge Keese for assistance at Ney Cave, Pat Morton at Texas Parks and Wildlife; and Jim Kennedy from Bat Conservation International. For contributing additional coordinates of bat colonies, we would like to thank Brian Keeley of Bat Conservation International and Jerry Fant of the Texas Speleological Survey. We also thank Theresa Labriola for assistance with analysis. Finally, we wish to thank the Society for Integrative and Comparative Biology for waiving our registration fees and for their support in hosting the symposium. This research was funded in part by grants from NSF: DBI-9808396 (to T.H.K. and C.J. Cleveland) and EIA-ITR 0326483(to T.H.K., M. Betke, G.F. McCracken, J.K. Westbrook, and P.W. Morton). We also wish to thank the Air Force Office of Scientific Research, through a grant to Boston University (FA9550-7-1-0449 to T.H.K.) for providing partial travel support to participate in this symposium.
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From the symposium "Aeroecology: Probing and Modeling the Aerosphere—The Next Frontier" presented at the annual meeting of the Society for Integrative and Comparative Biology, January 2–6, 2008, San Antonio, Texas.
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