2012 NORTHEASTERN NATURALIST 19(1):87–98
Bat Activity at Woodland/Farmland Interfaces in Central
Delaware
Kelly A. Wolcott1,2,* and Kevina Vulinec1
Abstract - Bats vary their activity with different features of habitat, resource availability,
predation risk, and other factors. Agricultural fields may provide an abundance of insect
prey, but are also risky habitats due to their exposure. How bats use mixed landscapes is important
information for biologists, as increasing development affects the amount of suitable
habitat and impacts bat populations in the region. Using acoustic recording, we monitored
relative bat activity in open areas and edges of the woodland/farmland interface of agricultural
fields in Kent County, DE. We examined bat activity among different sites, in openings
versus edges, among crop types, relative to nearby forest fragment size and shape, and under
different weather conditions. Bat activity was significantly higher along edges than in
the open in the agricultural fields for passes/night, but we found no differences among crop
types or sites and no interaction effects. We also found no effect of size or amount of edge
of a fragment on bat activity. We found significant negative correlations between passes
and temperature and wind speed, and significant positive correlations between passes and
relative humidity and barometric pressure. Bats use agricultural areas for foraging, and the
woodland interfaces along these fields are important for bat activity. This study provides
data that may help engender conservation practices, such as retention of forested edges and
maintenance of tree lines, and perhaps crop selection and pest control management, in the
region’s farming community.
Introduction
Bats use small woodlands and riparian-farmland interfaces as foraging or commuting
corridors (Geggie and Fenton 1985, Hein et al. 2009, Sparks et al. 2005).
However, these landscapes are rapidly vanishing as development accelerates—a
particular problem on the Delmarva Peninsula, where both forests and farmland
are rapidly being converted to residential developments (DNREC Wildlife 2006,
Weber et al. 2006). As the amount of suitable habitat decreases, bat populations
may decline. Agricultural pests, particularly moths and beetles, are common in the
region (Kee 2007, Tipping et al. 2005), and bats contribute to their control (Jones et
al. 2009). Bats’ efficiency as predators has increasingly been studied, and with the
population declines due to White-nose Syndrome, details of their roles in ecosystem
integrity may emerge (Federico et al. 2008, Lacki et al. 2007).
Vertical landscape features, such as trees and hedgerows, often increase insect
populations (Lewis and Dibley 1970) and are important as flyways and foraging sites
for bats (Verboom and Spoelstra 1999). Trees on farms contribute to the diversity of
wildlife, and a decrease in this habitat feature is expected to lead to a significant decline
in bats and other wildlife (Fischer et al. 2010). Vaughan et al. (1997) advocated
1Department of Agriculture and Natural Resources, Delaware State University, 1200
North DuPont Highway, Dover, DE 19901. 2Current address - Federal Energy Regulatory
Commission, Washington, DC 20426. *Corresponding author - kelly.wolcott@ferc.gov.
88 Northeastern Naturalist Vol. 19, No. 1
that land management plans for agricultural areas, woodlands, and freshwater areas
should include attention to variation in vegetation structure to allow for diversity
in insects and other fauna, including bats. Most studies on agricultural fields have
focused on how habitat type (i.e., water sources, arable land, hedgerows, tree spacing,
etc.) influences bat activity (Downs and Racey 2006; Federico et al. 2008; Law
and Chidel 2006; Wickramasinghe et al. 2003, 2004), yet little research has explored
crop type and bat activity. Current studies of bats in croplands have generally looked
only at one crop type (Davy et al. 2007) or at the difference between conventional
and organic farms (Federico et al. 2008, Gibson et al. 2007).
Development has recently intensified in Delaware (Liu and Lynch 2011); thus,
we wanted to understand the current activity levels of the local bat populations in
relation to increasing development. We were particularly interested in monitoring
farmlands, which are often the site of the only remaining forested lands in the region
(Allen 2009). Delaware lost at least 50,000 acres of farmland and connected
woodland between 1997 and 2002, with consequent negative impacts on wildlife
(Mix and Hurley 2008). Several factors may affect bat activity on farmlands,
including habitat type, vegetation and forested area present, pesticide usage, and
prey abundance (Wickramasinghe et al. 2004). To examine if bats are using both
farmland and forest-agriculture edges (riparian and woodlands), we investigated
bat activity using ultrasonic acoustic detectors at woodland/farmland interfaces
in four sites in central Delaware. Our objectives were to determine if: 1) bat
activity varied among selected agricultural sites within the central Delaware
region, 2) bat activity differed between open agricultural field versus field edge,
3) bat activity differed among crop types, 4) there was a difference in bat activity
among surrounding forest fragments by size and amount of edge, and 5) bat activity
in our area was affected by weather variables (temperature, relative humidity,
barometric pressure, and wind speed).
Field-site Description
Our study areas were located at (1) the Bombay Hook National Wildlife Refuge
(BHNWR), (2) Smyrna Outreach and Research Center (SORC), (3) Woodland
Beach Wildlife Area (WBWA), and (4) the Little Creek Wildlife Area (LCWA)
(Fig. 1). All sites were near Dover, DE. We chose agricultural areas within each
study site that were geographically accessible, were bordered on at least one side
by forest, and were similar in surrounding vegetation associations. We limited
our sampling to forest edges and open areas, as Delaware has limited large tracts
of forest (and interior forest) due to agricultural and suburban development
(Jones et al. 2009). The forested strips we used were embedded in a matrix of
agricultural land and suburban and urban developments (Fig. 1). All sites except
SORC had protected marshes nearby.
BHNWR is 6475 hectares, of which 728 hectares are devoted to agriculture
(USFWS 2004). The refuge participates in cooperative farming of corn and
soybean and some winter wheat and clover. Winter wheat is planted as a cover
crop, and clover fields serve as feeding areas for wildlife. SORC is owned by
Delaware State University (DSU) and is 78 hectares in size (R. Barczewski,
Delaware State University, Dover, DE, pers. comm.). Over the course of this
2012 K.A. Wolcott and K. Vulinec 89
research, three crops were grown on a rotational basis: soybean, corn, and hay.
LCWA and WBWA are owned by the state of Delaware and are managed by
Delaware Natural Resources and Environmental Control (DNREC). We examined
only soybean fields at LCWA and corn at WBWA, as the two fields with
Figure 1. A map of all study sites in Kent County, DE. White areas are forest fragment
polygons at each site: Little Creek Wildlife Area (LCWA), Bombay Hook National Wildlife
Refuge (BHNWR), Smyrna Agricultural Outreach and Research Center (SORC), and
Woodland Beach Wildlife Area (WBWA).
90 Northeastern Naturalist Vol. 19, No. 1
these crops were the only ones that had at least one side bordered by a forest
fragment. We considered BHNWR, SORC, LCWA, and WBWA to be separate
sites. Fields containing hay, winter wheat, and clover were pooled as small
grain/forage fields because their height and density were similar; corn and
soybean were the other crop types. Fields of the same crop type located within
200 m to each other were not considered to be independent and were therefore
pooled and considered as one plot.
Methods
Effects of habitat, crop type, site, and weather variables on bat activity
We recorded bat activity using a Pettersson Ultrasound Detector D 240x™
(Pettersson Electronik, AB, Uppsala, Sweden). Because the detector does not have
recording capabilities beyond the previous 1.7 seconds, data were stored in one of
two ways. First, the detector was connected to a 512 megabyte (MB) iriver T30 MP3
player (Reigncom, Ltd.), which saved the passes until they could be later downloaded
onto a laptop computer for analysis. The second method was to connect the
detector directly to the laptop via a patch cable to record the files to the laptop. We
set the Pettersson unit to record on time expansion (TE) with an automatic trigger of
1.7 seconds, the trigger level set to high frequency (HF), and the trigger source set
to high. We set the expansion factor for the TE recordings at 10. The HF setting allows
the Pettersson to be triggered by any high frequency signal, and the high setting
allows for better recording in areas of high insect noise. Bat passes were quantified
according to Fenton (1970), who “defined each sequence of one or more echolocation
pulses with <1 s between sequential pulses as a pass by a bat.” All passes were
then downloaded into SonoBat™ 2.5.8 (SonoBat, Arcata, CA) for analysis.
We used a modified point-count protocol for sampling bat activity level. We
sampled random observation points along the perimeter of the fields where the field
edge meets the wooded edge and three random points from the middle of the fields
to sample activity in open habitat. Sites were pre-selected each night by a random
draw from a gridded map. Each habitat type was sampled three times each night.
Since we were using active recording to examine bat activity at its peak—the first
few hours after sunset—only one field in one site could be sampled per night. If the
selected site contained more than one field (e.g., BHNWR), then the fields were
given numbers, and a second random draw was performed to determine which field
within the site was to be sampled. We defined open habitat as an open area in fields
(of 2 ha or more) that was at least 30 m from the wooded edge. Since we had recorded
the maximum detection distance for the Pettersson D240x in TE mode in an
open habitat at 31.92 m (Wolcott 2008), point counts in areas >30 m from the forest
edge would detect activity only in the open field habitat. We sampled passes from
edges and center of the fields for alternating periods of ten minutes each (Verboom
and Huitema 1997). Crop types were matched with fields of the same crop type at
other sites as independent replicates.
Bat activity was measured as the number of passes/night and included commuting,
searching, and feeding calls (Kuenzi and Morrison 2003, Murray and
Kurta 2004). We measured relative activity because only the number of passes
and not the absolute number of bats can be measured using acoustic detectors
2012 K.A. Wolcott and K. Vulinec 91
(Kunz et al. 2007). We used passes/night as opposed to passes/minute to satisfy
the assumption of independence among the replicates and to account for the issue
of some loss of data during the time the data were recording to files from the Pettersson’s
D240x (Kunz et al. 2007). We recorded passes/night for the same total
time period between open and edge habitats each night. We collected data over a
period of 21 days between June and October 2007, and all data were collected in
the first two hours after sunset, as this time period is the peak of bat activity in
the area near our study sites (Fox 2007). Collection dates were heavily dependent
on weather conditions.
We monitored weather variables during each sampling period using a Kestrel
3000 (Nielsen-Kellerman, Boothwyn, PA) handheld weather monitor. The weather
variables recorded included: temperature (°C), average wind speed (kph), and
relative humidity. For barometric pressure we used measurements from local
forecasts at the Dover Air Force Base provided by the National Ocean and Atmospheric
Association’s National Weather Service website (www.nws.noaa.gov).
We used passes/minute for this analysis so that differences among the 10-minute
recording intervals could be examined.
Area and edge effects on bat activity
We took global positioning system (GPS) location points from all sites
in February 2008 to generate maps of the study sites and determine the area,
perimeter, and edge-to-interior ratios of the forested fragments adjacent to the
agricultural fields. We overlaid all points onto aerial photos of Kent County
taken in 2006. These images were accurate to within a meter. All maps were
produced using ArcGIS 9.2 and digitized to a resolution of 1:7090. We calculated
the interior-to-edge ratio by dividing the area of each fragment by its
perimeter (Riley et al. 1998). We pooled the fields that shared fragments at each
site and used a regression analysis to determine the effect of the fragment area
and interior-to-edge ratio on bat activity (both commuting and foraging passes).
One field at Bombay Hook was excluded from analysis because it was shared
by two separate fragments. We constructed six polygons in ArcGIS 9.2 depicting
the continuous fragments at all four study sites. Most fields were bordered
by the same fragments or were divided by a hedgerow in the fragment. Hedgerows
were included as part of the fragment in the polygons. One polygon each
was made for SORC, LCWA, and WBWA. Three polygons were produced for
the fields at BHNWR. We calculated the perimeter, area, and edge-to-interior
ratio for each polygon in all sites (Table 1).
Statistical analyses
Due to the non-normality of passes/night count data, we used a generalized
linear model (GLM) procedure with a negative binomial distribution using a log
link function for acoustic analyses (Morris et al. 2010). Negative binomial fit the
data better than Poisson (AIC = 392 vs. 878, respectively). Significances were
calculated with Wald’s chi-square statistic. We compared bat activity (passes/
night) to site, habitat type, and crop type, and examined interactions between
site x habitat and crop x habitat. There were not enough replicates of each crop
type per site to compare crop x site or the three-way interaction. In addition, we
92 Northeastern Naturalist Vol. 19, No. 1
compared bat activity to weather variables (temperature, wind speed, relative humidity,
and barometric pressure) using a Kendall rank correlation. We examined
fragment size and interior-to-edge ratio of each site and localized bat activity using
the GLM procedure described above. We used SPSS Statistics Version 17.0
for Windows (SPSS, Inc., Chicago, IL) for all statistical tests.
Results
Effects of site, habitat type, crop type, and weather variables on bat activity
We sampled 11 fields (3 corn, 5 soybean, and 3 small grain/forage). We collected
a total of 1672 passes over 21 days during the main activity season (early
June to late September). We sampled each site the following number of nights:
BHNWR = 16, LCWA = 10, SORC = 6, and WBWA = 8. We sampled habitat
and crop types for the following number of collecting-nights: edge habitat = 21 and
open habitat = 21, and corn crop= 12, grain crop= 12, and soybean crop = 18.
Bat activity was not significantly different among sites (χ2 = 5.14, df = 3, P =
0.16; Fig. 2), but was significantly different between the center of the fields and
edges (χ2 = 16.44 df = 1, P ≤ 0.001; Fig. 3). Bat activity was not significantly different
among crop types, although grain/forage has a lower average bat activity
than the other two crop types (χ2 = 3.16, df = 2, P = 0.21; Fig. 4). The interactions
between site x habitat or between crop x habitat were not significant (χ2 = 0.84,
df = 2, P = 0.84; χ2 = 2.19, df = 3, P = 0.33; respectively).
Passes/minute were negatively correlated with temperature (τ = -0.142, P =
0.023) and wind speed (τ = -0.139, P = 0.047), but were positively correlated
with relative humidity (τ = 0.213, P = 0.001) and barometric pressure (τ =
0.196, P = 0.002).
Area and edge effects on bat activity
Fragment area did not significantly affect passes/minute (F=1.47, df =5, P=
0.29). There was also no significant difference between interior-to-edge ratio and
passes/minute (F =2.25, df = 5, P = 0.21).
Table 1. Perimeter, area, and interior-to-edge ratio for the polygons from the 4 sites sampled with
Pettersson bat detectors during the field season.
Area-to-perimeter
Site1 Fields2 Perimeter (km) Area (km2) ratio
LCWA 6.03 0.27 0.04
BHNWR Parson’s Point 1.32 0.02 0.02
BHNWR Finnis Pool/Parson’s Point 7.74 0.64 0.08
BHNWR Allee House 7.32 0.42 0.06
SORC 4.77 0.10 0.02
WBWA 9.42 0.36 0.04
1Bombay Hook National Wildlife Refuge (BHNWR), Little Creek Wildlife Area (LCWA),
Smyrna Agricultural Outreach and Research Center (SORC), and Woodland Beach Wildlife Area
(WBWA).
2Refers to description of the geographic location of each field in the site and does not relate to crop
type. This is only relevant to BHNWR, which is the only site with multiple fragments.
2012 K.A. Wolcott and K. Vulinec 93
Discussion
Effects of habitat, crop type, site, and weather variables on bat activity
Our results show that bats were more active near the interface of the agricultural
fields and the forest (i.e., edge habitats; Fig. 3) than open fields, a result
generally consistent with previous research on bat activity (Furlonger et al. 1987,
Hayes and Loeb 2007, Morris et al. 2010, Rogers et al. 2006). There are several
hypotheses to explain this behavior, including protection from wind during the
bats’ foraging near vertical strata or the use of edges for navigation guides (Verboom
and Huitema 1997, Verboom and Spoelstra 1999). Other studies indicate
that insect abundance is also higher in edge habitat, which may increase foraging
opportunities (Verboom and Spoelstra 1999). In our study, crop type was
not found to be a significant factor influencing bat activity, but edges were. This
result suggests that the edges themselves are important to bats.
The level of bat activity at each site may be influenced by land management
practices (e.g., use of pesticides; Wickramasinghe et al. 2004). An estimated 445
hectares at BHNWR are designated for agriculture. The agricultural practices at the
refuge include fertilizing, green manure cropping, and liming (O. Reed, US Fish
and Wildlife Service, Bombay Hook National Wildlife Refuge, Smyrna, DE, pers.
comm.). SORC treats the commercial fields (corn and soybeans) with herbicides
Figure 2. Comparison of average bat activity (passes per night) at each study site. The
central line in the shaded box represents the median, the box encompasses the 25th and
75th quartiles, and the whiskers include the range of data. Sample size is included above
each box. (χ2 = 5.14, df = 3, P = 0.16).
94 Northeastern Naturalist Vol. 19, No. 1
to prevent weed growth but does not treat the fields with insecticides or fungicides
(R. Peiffer, Delaware State University, Dover, DE, pers. comm.). These practices
are compatible with biological pest management, and may allow more insect abundance
that encourages bat foraging. However, managers at this site plant Bt corn, a
pest-resistant strain, which may negatively affect insect abundance.
DNREC leases out the agricultural fields in the State Wildlife Areas to local
farmers who manage the fields within certain guidelines issued by the agency.
The state sprays the edges with herbicides to control woody brush. LCWA also
contains mosquito impoundments that are managed by the state. These impoundments
are treated with pesticides to control the mosquito population (W. Lehman,
Delaware Division of Fish and Wildlife, Viola, DE, pers. comm.) and thus, may
also contain chemicals that alter prey availability.
Our study showed that bat activity might be influenced by barometric pressure,
which is consistent with other results in the same area (Fox 2007). For
example, Perimyotis subflavus Cuvier (Tri-colored Bat) uses cues from changes
in barometric pressure to evaluate foraging opportunities (Paige 1995). We found
an increase in activity with increasing barometric pressure. Past studies have also
found that ambient temperature affects bat activity (Vaughan et al. 1997). In our
Figure 3. A comparison of bat activity (passes per night) in each habitat type (edge versus
open). The central line in the shaded box represents the median, the box encompasses the
25th and 75th quartiles, and the whiskers include the range of data. Values more than 1.5
interquartile ranges (IQR’s) but less than 3 IQR’s from the end of the box are labeled as
outliers (•). Sample size is included above each box. (χ2 = 16.44, df = 1, P ≤ 0.001).
2012 K.A. Wolcott and K. Vulinec 95
study, bat activity declined with temperatures between 15 and 30 °C; however,
Fox (2007) found a positive relationship between activity and temperature at
BHNWR. Another study determined that activity increased with increasing temperature
to a maximum between 17 and 21 °C, but declined with temperatures
over 21 °C (Brooks 2009). Weather effects are also highly species specific (Burles
et al. 2009); however, we did not examine individual species in this study.
Adjoining forest fragment size did not have a significant impact on bat activity
at any of the sites. Nevertheless, BHNWR had the largest amount of continuous
forested tracts compared to the other sites (Table 1) and had higher levels of bat
activity than the other sites. The refuge has 445 hectares of impounded freshwater
pools and swamps (USFWS 2004), providing ample water sources for wildlife
populations, including bats. SORC has a small stream within the forest fragment
that may be utilized as a water source, and the farm also has a substantial pond
that may also be large enough for bats to drink from while on foraging bouts.
LCWA and WBWA are adjacent or in close proximity to Delaware Bay; however,
the Bay is not a suitable water source for bats due to high salinity (Gay and
O’Donnell 2008).
This study provides data which suggests that edge habitat is important to
sustain high levels of bat activity in Kent County, DE. Land-use practices should
Figure 4. Comparison of average bat activity (passes per night) in different crop types.
The central line in the shaded box represents the median, the box encompasses the 25th
and 75th quartiles, and the whiskers include the range of data. Sample size is included
above each box. (χ2 = 3.16, df = 2, P = 0.21).
96 Northeastern Naturalist Vol. 19, No. 1
endeavor to conserve forested habitats within the state; nevertheless, this forest is
likely to remain as thin strips of edge habitat for the near future. Thus, the availability
of forest interior for bats in Delaware is expected to be low, making the
conservation of thin strips of edge habitat even more important. Local wildlife
refuges are managed for wildlife species such as deer, waterfowl, and migratory
birds (DNREC 2006, USFWS 2004), so it is possible that management programs
for bats may be developed and implemented as well. The total amount of edge or
area was not significant in our study, thus we suggest that even small edges along
crop fields provide habitat for foraging bats. With increasing development on the
Delmarva Peninsula, bats must contend with decreasing habitat availability. We
recommend that farms retain or replant forest edges around fields and maintain
tree lines. Management strategies should also emphasize the need to conserve
corridors and riparian strips to serve as flyways. Pesticide use and the planting
of pest-resistant crop strains like Bt corn decrease prey availability in these areas;
these factors could affect bat populations not only in terms of fewer food
resources, but also have the potential of harming bats through the accumulation
of pesticides in the body (Clawson and Clark 1989, Wickramasinghe et al. 2004).
Further research in this area is required to determine if the use of pest-resistant
crops and pesticides affect prey availability for bats.
Acknowledgments
Funding for this project was provided by the United States Department of Agriculture
(USDA) Natural Resource Conservation Services; the USDA Cooperative State Research,
Education, and Extension Service; the First State Resource Conservation and Development
Council, Inc; and Delaware State University. We are also grateful to Richard Barczewski,
Robert Naczi, Dana Limpert, and 2 anonymous reviewers for their comments on the study.
Special thanks are also owed to Darren A. Miller for his valuable comments on an earlier
version of the manuscript. Lori Brown assisted with the development of the GIS maps of
our agricultural sites. Gary Page also provided technical assistance and comments on the
manuscript and for that we are grateful. Thanks to Liang Liu for clarification of some statistical
issues. Many thanks are also due to Ashleigh Green, Ayasha Jones, David Mellow,
Johnna Fay, Jonathan McKenzie, and Kevin Neves for their help in the field. We would also
like to thank Wayne Lehman at DNREC and the staff at Bombay Hook National Wildlife
Refuge for their expertise and assistance with permits, support, and background information
on our study sites. The opinions and views expressed in this article do not necessarily
represent the views of the United States, the Federal Energy Regulatory Commission
(FERC), individual FERC Commissioners, or FERC staff.
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