analyzingnexraddopplerradarimagestoassessnightly ...bat emergences from large colonies. these images...

Analyzing NEXRAD doppler radar images to assess nightlydispersal patterns and population trends in Brazilian free-tailedbats (Tadarida brasiliensis)Jason W. Horn1,* and Thomas H. Kunz*�Center for Ecology and Conservation Biology, Department of Biology, Boston University, Boston, MA 02215 USA

Synopsis 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.

Introduction

The ability to observe free-ranging animals interact-

ing within their environment is essential for under-

standing 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) con-

firmed 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 expand-

ing ring-like shapes and long finger-like forma-

tions that did not correspond to weather events.

From the symposium ‘‘Aeroecology: Probing and Modeling the Aerosphere—The Next Frontier’’ presented at the annual meeting of the Societyfor Integrative and Comparative Biology, January 2–6, 2008, San Antonio, Texas.1E-mail: [email protected]

24

Integrative and Comparative Biology, volume 48, number 1, pp. 24–39

doi:10.1093/icb/icn051

Advanced Access publication June 18, 2008

� The Author 2008. Published by Oxford University Press on behalf of the Society for Integrative and Comparative Biology. All rights reserved.

For permissions please email: [email protected].

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 0.58elevation 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 move-

ment 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 emer-

gence from roosts. These expanding shapes are

located directly over the known geographic position

of several large, well-known colonies. These emer-

gence 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 con-

centrations appear morphologically distinct on

NEXRAD (Westbrook et al. 1998) and do not origi-

nate 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 fluc-

tuations 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 inves-

tigate population changes and feeding ecology of

Brazilian free-tailed bats in Texas.

Methods

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 build-

ings, 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 sympo-

sium). As Brazilian free-tailed bats emerge from

caves to forage, they typically form a closely-packed

Analyzing NEXRADDoppler radar images 25

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 echoloca-

tion 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 inhab-

iting 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 com-

prised 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, report-

edly 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 confi-

dence in estimating colony size for any given year,

we used images from the 5–6 week period following

parturition, when mothers are nursing their devel-

oping 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 soft-

ware 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 appli-

cations 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.

26 J.W. Horn and T. H. Kunz

http://hurricane.ncdc

http://www.php.net

http://www.geog

http://www.ncdc.noaa.gov/oa/radar/

Latitude and longitude coordinate grids must be pro-

jected 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 attrib-

utable to emergence of Brazilian free-tailed bats

requires two steps. First, areas or patches of reflec-

tivity 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

response ¼1

1 þ e�12rx,yþ6where rx,y ¼

rx,y � rmin

�r þ ð�r � rminÞ

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.

This has the effect of lowering lower-than-mean

values and raising higher-than-mean values, condens-

ing 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.

thresholda ¼ �r þ ð2 density� 1Þ � 0:5rstddev

Procedure 7 separates patches of bats that are con-

nected by only a few pixels. This is a critical step

A B

C D

E F

Fig. 1 The main steps in the processing of NEXRAD images to isolate reflectivity generated by dispersing Brazilian free-tailed bats.

Values from a mask (B) of the original image (A) are used to apply a contrast function that removes low Z-values (C). The image is

then threshold (D) to remove lower values. Neighborhood kernel operators are applied to remove small bridges interconnecting

larger patches (E). Finally, a recursive algorithm identifies distinct patches in the image (F).

28 J. W. Horn and T. H. Kunz

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.

�x ¼rixi

riand �y ¼

riyi

ri

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 move-

ment 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 �.

r ¼a 1 � e2� �

1 þ e cos �

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

Fig. 2 A sequence of filtered NEXRAD image patches that

indicate dispersing Brazilian free-tailed bats are tracked from

Frio Cave on May 19, 2005. Each is a snapshot of every other

step in the tracking progression. The left panel of each image

shows the unprocessed data, while the right shows processed

data, and the progress of the Tracker. Crosses mark the location

of the spatial mean of the patch. Circles and ellipses mark the

areas that were used as criteria to accept the patches tracked

in that image. The colony location is marked in magenta. The first

wave of the emergence is tracked in yellow, the second in red.

As the emergence progresses, the Tracker effectively records

only the reflectivity that originates from Frio Cave.


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.

Results

Our filtering and tracking system identified and

recorded 642 patches of bats in 548 images. Each

emergence event contained between 1 and 21 patches

(x¼ 7.3� SD¼ 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 occa-

sionally 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 reflectiv-

ity that did not appear related to bats.

The seasonal variation in the total reflectivity con-

formed to the pattern of activity typically observed

on the ground at these colonies (Fig. 3). No reflec-

tivity 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.

During the period from April 1, to August 1,

2005, Frio Cave produced the highest total reflectiv-

ity per night (1.2 MZ). Ney Cave, Congress Avenue

Bridge, and McNeil Bridge produced similar

amounts of reflectivity (712, 947, 628 kZ, respec-

tively). 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 correspond-

ing 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.

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.

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 (P50.05) for each of the

six colonies. Mean heading varied from 158 (NNW)

to 1288 (SW). Typically, individual colonies moved

in a predominant direction during nightly emer-

gence. Bat colonies at Frio Cave and Ney Cave

produced emergence reflectivity that typically moved

in a southeasterly direction (x¼ 111.98, � SD¼ 25.78and x¼ 99.28, � SD¼ 58.28). Bats from the McNeil

Bridge colony typically moved toward the northeast

(x¼ 54.68, � SD¼ 49.78). Bats from the Congress

Avenue colony typically dispersed in a northeasterly

direction, but also exhibited several emergences

distinctly oriented toward the southwest as well

(x¼ 109.38, � SD¼ 70.78). Bats from Eckert James

River Cave and Davis Cave both exhibited mean

directionality toward the northeast, but this obser-

vation may not be significant owing to the small

number of times they were observed (n¼ 5, n¼ 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

(x¼ 116.98, � SD¼ 43.38, Rayleigh test¼ 0.75,

P50.001) and that for cave colonies was oriented

to the northeast, (x¼ 76.38, � SD¼ 66.08, Rayleigh

test¼ 0.51, P50.001). Moreover, the mean vectors

for colony emergences from caves and bridges were

significantly different from each other (Watson

t-test¼ 0.44, P50.05).

We also tested for the differences in headings

between cave colonies and bridge colonies during the

date

Mea

n R

efle

ctiv

ity (

Z)

Feb Apr Jun Aug Octdate

Feb Apr Jun Aug Oct

dateFeb Apr Jun Aug Oct




020

000

5000

0

Mea

n R

efle

ctiv

ity (

Z)

020

000

5000

0

Mea

n R

efle

ctiv

ity (

Z)

020

000

5000

0

Mea

n R

efle

ctiv

ity (

Z)

020

000

5000

0

Mea

n R

efle

ctiv

ity (

Z)

020

000

5000

0

Mea

n R

efle

ctiv

ity (

Z)

020

000

5000

0

Frio

A B

C D

E F

Ney

(c) Davis James River

Congress McNeil

Fig. 3 Seasonal patterns in the level of NEXRAD radar reflectivity recorded for each of six colonies of Brazilian free-tailed bat

colonies in south-central Texas. Cave colonies and bridge colonies reflect a similar population increase beginning in early May (A, B,

E, and F), but bridge colonies (E and F) show radar reflectivity in March and April that is absent from caves (A–D) and may

represent formation of colonies for courtship and mating.


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 signif-

icantly directional, most likely owing to the small

number of emergences observed during this period

(n¼ 3). During spring and summer, there were

significant differences in the mean vector of emer-

gence 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 varia-

tion in total reflectivity. Eight of the ten years

showed significant directionality in emergence head-

ing. 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

Mean Total Annual Reflectivity 1995–2005

Date

Mea

n R

efle

ctiv

ity (

Z)

1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005

020

000

050

000

080

000

01

100

000

140

000

0 all coloniescave coloniesbridge colonies

Fig. 5 Total NEXRAD reflectivity for six colonies of Brazilian free-tailed bat colonies over 11 years in south-central Texas.

Each measurement is the average of two days of reflectivity measure during the June 20–July 8 period, a time when females

are lactating and populations are expected to be stable with the maximum number of adults.

Frio Ney Davis Jms Rvr Congress McNeil

May–August 2005A

B

Colony

Frio Ney Davis Jms Rvr Congress McNeil

Colony

Ref

lect

ivity

(Z

)

0e+

004e

+05

8e+

05

September–October 2005

Ref

lect

ivity

(Z

)

0e+

004e

+05

8e+

05

Fig. 4 Differences in the amount of NEXRAD reflectivity

from dispersing Brazilian free-tailed bats between cave colonies

(in grey) and bridge colonies (in black) during (A) May–August

2005 versus (B) August–November 2005. Bridge colonies show

activity in the fall when there is little to none at cave colonies.


emergence period (17:00–23:00 CDT) to the mean

emergence heading of all colonies. Mean wind head-

ing and mean heading of emerging bats were signi-

ficantly different in 2005 for all colonies (Watson¼

0.449, P50.05, Fig. 7). A circular–circular regression

(Sarma and Jammalamadaka 1993) showed that wind

heading was not a significant predictor of emergence

headings of bats (k¼ 1.70, px¼ 0.65, py¼ 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

heading¼ 136.78� SD 25.58, mean heading emerging

bats¼ 116.98� SD¼ 43.38), but the two were still

significantly different (Watson¼ 0.221, P50.05,

Fig. 7). Again, a circular–circular regression showed

no effect of wind on emergence direction (px¼ 0.89,

py¼ 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 heading¼ 75.88� SD

47.78, mean heading of emergences¼ 158.78�SD¼ 62.18, Fig. 7). Again, the mean headings were

significantly different (Watson¼ 0.531, P50.05), and

there was no effect of surface wind on direction of

emergence (k¼ 2.69, px¼ 0.42, py¼ 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).

Discussion

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.

90

180

270

0.05

0.10

0.15

0.20

0.25 n=21

p < 0.001

Caves

90

180

270

0.05

0.10

0.15 n=34

p < 0.001

Bridges

Fig. 6 Circular histograms showing that mean heading of dispersing Brazilian free-tailed bats during nightly emergence is both

highly directional, and significantly different between cave colonies and bridge colonies. The single black indicator on the circle

indicates the mean emergence heading for the number of emergence events (n) shown. The standard deviation is marked in grey.

Reflectivity from Frio Cave and Ney Cave typically moves in an east-southeasterly direction. Reflectivity from bridges more often

moves toward the east or northeast and may be a bimodal pattern.


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) indi-

cate that temperatures in bridge roosts are indeed

90

180

270

0.05

0.10

0.15 n=34

p < 0.001

Emergence

90

180

270 0.05

0.100.150.200.250.30 n=34

p < 0.001

Wind

All colonies

90

180

270

270

0.05

0.10

0.15

0.20 n=29

p < 0.001

Emergence

90

0.050.100.150.200.250.300.35 n=29

p < 0.001

Wind

Austin Wind and Bridges

90

180

270

0.050.100.150.200.25 n=21

p < 0.001

Emergence

90

180

270

0.10.20.30.40.5 n=21

p < 0.001

Wind

San Antonio Wind and Caves

180

Fig. 7 Headings of surface winds measured at nearby weather stations do not predict headings of nightly emergences of Brazilian

free-tailed bats from colonies of Brazilian free-tailed bats in south-central Texas.


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 repre-

sent 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 escarp-

ment (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 plastic-

ity 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, reproduc-

tive rates, and survival of bats, leading to differential

population of colonies. Events that occur in over-

wintering areas in Mexico also may result in popu-

lation 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 Nino-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 direc-

tion 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 perspec-

tive. If bats used winds to their advantage to con-

serve 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 relation-

ship 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 disadvan-

tage 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

Fig. 8 Mean heading of emergences in March, April, and May 2005, overlaid on the geographic study area in the foothills and

agricultural plains near San Antonio and Austin, Texas. Cave colonies are marked as circles, bridge colonies as triangles, and agricultural

areas as yellow patches. Headings of bat emergences may be related to the location of agricultural areas that may be dense with

potential insects prey for Brazilian free-tailed bats.


radar (T.A. Kelly, personal communication) and

NEXRAD to test for evidence of spatial and temporal

convergence of bats and insects.

Limitations and future work

Although our results show there is strong evidence

that NEXRAD reflectivity measured during emer-

gence 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 compen-

sated 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 possi-

ble, 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 conse-

quently 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 reflec-

tivity, 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 emer-

gence. 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 track-

ing 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 0.58scan 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 emer-

ging 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 uncover-

ing population trends, for detecting changes in the

timing of migration, and for characterizing differ-

ential use of roosts by bats. The large NEXRAD

repository provides opportunities to use both current

and historical data to understand long-term popu-

lation trends and to examine how anthropogenic

factors may be affecting this species. Thus, this

approach also holds promise for assessing con-

servation efforts and for improving understanding

of the foraging ecology of this wide-ranging bat

species.

Acknowledgments

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 Conserva-

tion International. For contributing additional coor-

dinates 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.

References

Alerstam T, Gudmundsson GA. 1999. Migration patterns

of tundra birds: tracking radar observations along the

northeast passage. Arctic 52:346–71.

Altenbach JS. Geluso KN, Wilson DE. 1979. Population size

of Tadarida brasiliensis at Carlsbad Caverns in 1973. In:

Genoways HH, Baker RJ, editors. Biological investigations

in the Guadalupe Mountains National Park, Texas.

National Park Service Proceedings and Transactions,

Series No. 4. Washington (DC): National Park Service.

p. 341–8.

Best TL, Geluso KN. 2003. Summer foraging range of Mexican

free-tailed bats (Tadarida brasiliensis mexicana) from

Carlsbad Cavern, New Mexico. Southwest Nat 48:590–6.

Betke M, Hirsh D, Bagchi A, Hristov NI, Makris NC, Kunz

TH. 2007. Tracking large variable numbers of objects in

clutter. Proceedings of IEEE Computer Society Conference

on Computer Vision and Pattern Recognition.

Minneapolis, MN. p. 8.

Betke M, et al. 2008. Thermal imaging reveals significantly

smaller Brazilian free-tailed bat colonies than previously

estimated. J Mammal 89:18–24.

Black JE, Donaldson NR. 1999. Comments on ‘‘display of

bird movements on the WSR-88D: patterns and quantifica-

tion’’. Weather Forecast 14:1039–40.

Bruderer B. 1997. The study of bird migration by radar 2.

Major achievements. Naturwissenschaften 84:45–54.

Burger AE. 2001. Using radar to estimate populations and

assess habitat associations of marbled murrelets. J Wildl

Manage 65:696–715.

Buss I. 1946. Bird detection by radar. Auk 63:315–8.

Clark DR. 2001. DDT and the decline of free-tailed bats

(Tadarida brasiliensis) at Carlsbad Cavern, New Mexico.

Arch Environ Con Tox 40:537–43.

Cleveland CJ, et al. 2006. Economic value of the pest control

service provided by Brazilian free-tailed bats in south-

central Texas. Front Ecol Environ 5:238–43.

Cockrum EL. 1969. Migration in the guano bat. Miscellaneous

Publications, Museum of Natural History, University of

Kansas. Lawrence, KS. 51:303–36.

Cooper BA, Day RH, Ritchie RJ, Cranor CL. 1991. An

improved marine radar system for studies of bird

migration. J Field Ornithol 62:367–77.

Cooper BA, Ritchie RJ. 1995. The altitude of bird migration

in east-central Alaska: a radar and visual study. J Field

Ornithol 66:590–608.

Crawford AB. 1949. Radar reflections in the lower atmo-

sphere. P IRE 37:404–5.

Davis RB, Herreid CF II, Short HL. 1962. Mexican free-tailed

bats in Texas. Ecol Monogr 32:311–46.

Dacey MF. 1962. Analysis of central place and point pattern

analysis by a nearest neighbor method. Lund Studies in

Geography, B. Human Geog 24:55–75.

Diehl RH, Larkin RP. 2005. Introduction to the WSR-88D

(NEXRAD) for Ornithological Research. In: Ralph CJ,

Rich TD, editors. Bird Conservation Implementation and

Integration in the Americas: Proceedings of the Third

International Partners in Flight Conf, March 20–24, 2002,

Asilomar, CA. Gen Tech Rep PSW-GTR-191. U.S. Dept of

Agr, Forest Service, Pacific Southwest Research Station,

Albany, CA. p. 876–88.

Dinevich L, Matsyura A, Leshem Y. 2003. Temporal charac-

teristics of night bird migration above Central Israel – a

radar study. Acta Ornithol 38:103–10.

Frank JD, Kunz TH, Horn JW, Cleveland CJ, Petronio C.

2003. Advanced infrared detection and image

processing for automated bat censusing. Infrared

Technology and Applications XXIX. Proceedings of SPIE

5074:261–71.

Gauthreaux SA. 1992. The use of weather radar to monitor

long-term patterns of trans-Gulf migration in spring.

In: Hagan JM, Johnston DW, editors. Ecology and

conservation of neotropical migrant landbirds.

Washington (DC): Smithsonian Institution Press. p. 96–100.

Gauthreaux SA, Belser CG. 1998. Displays of bird movements

on the WSR-88D: patterns and quantification. Weather

Forecast 13:453–64.

Gauthreaux SA, Belser CG. 1999. Reply to ‘‘Comments on

display of bird migration on the WSR-88D: patterns and

quantification’’. Weather and Forecast 14:1041–2.

Glover KM, Hardy KR, Konrad TG, Sullivan WN,

Michaels AS. 1966. Radar observations of insects in free

flight. Science 154:967–72.


Griffin DR, Thompson D. 1982. High altitude echolocation of

insects by bats. Behav Ecol Sociobiol 10:303–6.

Herreid CF II. 1963. Temperature regulation of Mexican

free-tailed bats in cave habitats. J Mammal 44:560–73.

Herreid CF, Kessel B. 1967. Thermal conductance in birds

and mammals. Comp Biochem Physiol 21:405–14.

Hristov NI, Betke M, Kunz TH. 2008. Applications of

thermal infrared imaging for research in aeroecology.

Integr Comp Biol. doi:10.1093/icb/icn053.

Keeley ATH, Keeley BW. 2004. The mating system of

Tadarida brasiliensis (Chiroptera: Molossidae) in a large

highway bridge colony. J Mammal 85:113–9.

Klaassen M, Biebach H. 2000. Flight altitude of trans-Sahara

migrants in autumn: a comparison of radar observations

with predictions from meteorological conditions and water

and energy balance models. J Avian Biol 31:47–55.

Kunz TH, Arnett EB, Cooper BA, Erickson WIP, Larkin RP,

Mabee T, Morrison ML, Strickland JD, Szewczak JM. 2007.

Assessing impacts of wind energy development on noctur-

nally active birds and bats: a guidance document. J Wildl

Manage 71:2449–86.

Kunz TH, Robson SK. 1995. Postnatal-growth and develop-

ment in the Mexican free-tailed bat (Tadarida brasiliensis):

birth size, growth-rates, and age estimation. J Mammal

76:769–83.

Kurta A. 1985. External insulation available to a non-nesting

mammal, the little brown bat (Myotis lucifugus). Comp

Biochem Phy A 82:413–20.

Lack D, Varley GC. 1945. Detection of birds by radar. Nature

156:446.

Lee YF, McCracken GF. 2005. Dietary variation of Brazilian

free-tailed bats links to migratory populations of pest

insects. J Mammal 86:67–76.

McCracken GF. 1986. Why are we losing our Mexican

free-tailed bats? Bats 3:1–2.

McCracken GF. 1989. Cave conservation: special problems

with bats. Bull Natl Speleol Soc 51:47–51.

McCracken GF. 2003. Estimates of population sizes in

summer colonies of Brazilian free-tailed bats (Tadarida

brasiliensis). In: O’Shea TJ, Bogan MA, editors. Monitoring

trends in bat populations of the United States and

Territories: problems and prospects. Washington (DC):

U.S. Geological Survey, Biological Resources Discipline,

Information and Technology Report, USGS/BRD/ITR-

2003-003. p. 21–30.

McCracken GF, Gustin MK. 1991. Nursing behavior in Mexican

free-tailed bat maternity colonies. Ethology 89:305–21.

McCracken GF, Westbrook JK. 2002. Scientists discover that

high-flying mammals are bad news for bugs. Natl Geogr

201:114–23.

McCracken GF, Gillam EH, Westbrook JK, Lee Y, Jensen ML,

Balsley BB. 2008. Brazilian free-tailed bats (Tadarida

brasiliensis: Molossidae, Chiroptera) at high altitude: links

to migratory insect populations. doi:10.1093/icb/icn033.

McKay HAC. 1945. Detection of birds by radar. Nature

156:446.

Mohr CE. 1973. The status of threatened species of cave-

dwelling bats. Bulletin of the National Speleological

SocietyBull Natl Speleol Soc 34:33–47.

Plank VG. 1956. Geophysical research paper No. 52. Air Force

Cambridge Research Center. Bedford, MA.

Racey PA, Swift SM. 1981. Variations in gestation length in a

colony of pipistrelle bats (Pipistrellus pipistrellus) from year

to year. J Reprod Fertil 61:123–9.

Richter JH, Jensen DR, Noonkester VR, Kreasky JB,

Stimmann MW, Wolf WW. 1973. Remote radar sensing:

atmospheric structure and insects. Science 180:1176–8.

Ritzi CM. 1999. Utilization of cliff swallow (Petrochelidon

pyrrhonata) nests in west Texas by cave myotis (Myotis

velifer). Southwest Nat 44:414–5.

Russell KR, Gauthreaux SA. 1998. Use of weather radar to

characterize movements of roosting purple martins.

Wildlife Society Bulletin 26:16.

Russell RW, Wilson JW. 1997. Radar-observed ‘‘fine lines’’ in

the optically clear boundary layer: reflectivity contributions

from aerial plankton and its predators. Bound-Lay

Meteorol 82:235–62.

Ruthi L. 1994. Observation of bat emergence from Reed

Bat Cave (HR-004) with the WSR-88D RADAR. Okla

Underground 17:54–6.

Sarma Y, Jammalamadaka S. 1993. Circular regression.

Statistical science and data analysis. Proc 3rd Pacific Area

Stat Conf. VSP: Utrecht, Netherlands. p. 109–28.

Schnell G. 1965. Recording the flight speed of birds by

Doppler radar. Living Bird 4:79–87.

Vickery WL, Millar JS. 1984. The energetics of huddling by

endotherms. Oikos 43:88–93.

Westbrook JK. 2008. Noctuid migration in Texas within the

nocturnal aeroecological boundary layer. Integr Comp Biol.

doi:10.1093/icb/icn040.

Westbrook JK, Esquivel JF, Lopez JD, Jones GD, Wolf WW,

Raulston JR. 1998. Validation of bollworm (Lepidoptera:

Noctuidae) migration across south-central Texas in 1994-

1996. Southwest Entomol 23:209–19.

Westbrook JK, Isard SA. 1999. Atmospheric scales of biotic

dispersal. Agr Forest Meteorol 97:263–74.

Westbrook JK, Raulston JR, Wolf WW, Pair SD, Eyster RS,

Lingren PD. 1995. Field observations and simulations

of atmospheric transport of noctuids from northeastern

Mexico and the south-central US. Southwest Entomol

Suppl 18:25–44.

Williams T, Ireland L, Williams J. 1973. High altitude flights

of the free-tailed bat, Tadarida brasiliensis, observed with

radar. J Mammal 54:807–21.

Williams TC, Williams JM, Williams PG, Stokstad P. 2001.

Bird migration through a mountain pass studied with

high resolution radar, ceilometers, and census. Auk

118:389–403.

Wolf WW, Westbrook JK, Raulston JR, Pair SD, Lingren PD.

1995. Radar observations of orientation of noctuids

migrating from corn fields in the lower Rio-Grande

Valley. Southwest Entomol 18:45–61.


analyzingnexraddopplerradarimagestoassessnightly ...bat emergences from large colonies. these images...

Documents