Hydric Habitats are Important to Foraging Bats in the Bluegrass Region’s Urban Parks
Shelby A. Fulton1, Luke E. Dodd2,*, and Lynne K. Rieske1
1Department of Entomology, University of Kentucky, Lexington, KY 40546. 2Department of Biological Sciences, Eastern Kentucky University, Richmond, KY 40475. *Corresponding author.
Urban Naturalist, No. 3 (2014)
Abstract
Hydric habitats such as riparian areas and wetlands can be valuable resources for bats. These habitats provide corridors for movement, support a rich prey base, and may provide valuable roost locations. In order to better understand the importance of these habitats in urban ecosystems, bat activity was assessed across five sampling locations at two study sites in the Inner Bluegrass region of Fayette County, KY. The hydric habitats assessed included artesian springs and a human-made stormwater wetland at McConnell Springs Natural Area, as well as West Hickman Creek at Veteran’s Park. Activity levels of bats in these habitats were then compared to the surrounding forest habitats by conducting acoustic surveys over 29 nights from May to September of 2012 and 2013. Black-light traps were concurrently deployed on 11 of these nights to assess the abundance and diversity of phototaxic prey. Bats were more active in hydric habitats than in the surrounding forest (P < 0.01). While overall insect abundance was consistent between hydric and forest habitats (P > 0.05), an abundance of Coleoptera and Diptera were observed at the wetland versus other hydric habitats. In addition to structural differences in habitat, availability of these insect prey may have also contributed to the increased activity of bats in the mid-frequency phonic group observed at the wetland. With continued threats of habitat fragmentation, bats may become increasingly dependent upon resources and corridors afforded by hydric areas in urban landscapes. These results underscore the need for management of these habitats as a resource for bats in eastern North America.
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S.A. Fulton, L.E. Dodd, and L.K. Rieske
22001144 URBAN NATURALIST No. 3:N1–o1. 33
Hydric Habitats are Important to Foraging Bats in the
Bluegrass Region’s Urban Parks
Shelby A. Fulton1, Luke E. Dodd2,*, and Lynne K. Rieske1
Abstract - Hydric habitats such as riparian areas and wetlands can be valuable resources
for bats. These habitats provide corridors for movement, support a rich prey base, and may
provide valuable roost locations. In order to better understand the importance of these
habitats in urban ecosystems, bat activity was assessed across five sampling locations at
two study sites in the Inner Bluegrass region of Fayette County, KY. The hydric habitats
assessed included artesian springs and a human-made stormwater wetland at McConnell
Springs Natural Area, as well as West Hickman Creek at Veteran’s Park. Activity levels of
bats in these habitats were then compared to the surrounding forest habitats by conducting
acoustic surveys over 29 nights from May to September of 2012 and 2013. Black-light traps
were concurrently deployed on 11 of these nights to assess the abundance and diversity of
phototaxic prey. Bats were more active in hydric habitats than in the surrounding forest (P <
0.01). While overall insect abundance was consistent between hydric and forest habitats
(P > 0.05), an abundance of Coleoptera and Diptera were observed at the wetland versus
other hydric habitats. In addition to structural differences in habitat, availability of these
insect prey may have also contributed to the increased activity of bats in the mid-frequency
phonic group observed at the wetland. With continued threats of habitat fragmentation, bats
may become increasingly dependent upon resources and corridors afforded by hydric areas
in urban landscapes. These results underscore the need for management of these habitats as
a resource for bats in eastern North America.
Introduction
Riparian areas and other hydric habitats are critical to North American bats.
These habitats can be important foraging locations for many species, such as Myotis
lucifugus (Le Conte) (Little Brown Bat), Perimyotis subflavus (Cuvier) (Tri-Colored
Bat), Eptesicus fuscus (Beauvois) (Big Brown Bat), Lasiurus borealis Gray
(Eastern Red Bat), Myotis septentrionalis (Trouessart) (Northern Long-eared Bat),
Myotis sodalis Miller and Allen (Indiana Bat), and Nycticeius humeralis Rafinesque
(Evening Bat) (Kniowski and Gehrt 2014, Kurta et al. 2005, Schirmacher et
al. 2007). Of these, the Indiana Bat is federally endangered (USFWS 2009), and
the Northern Long-eared Bat is currently being considered for endangered status
(USFWS 2013). Further, hydric habitats have been shown or speculated to be preferred
roosting habitats for maternity colonies of Indiana Bats, Evening Bats, and
Big Brown Bats in both the Midwest and Appalachian regions of North America
(Carter 2006, Kurta et al. 2005, Neubaum et al. 2007). The species known to use
1Department of Entomology, University of Kentucky, Lexington, KY 40546. 2Department
of Biological Sciences, Eastern Kentucky University, Richmond, KY 40475. *Corresponding
author - luke.dodd@eku.edu.
Manuscript Editor: Brooke Maslo
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these habitats for foraging and roosting are found across a wide range of genera,
which highlights the importance of these habitats to a variety of insectivorous bats.
Maintaining high-quality aquatic habitat is a concern across much of the urban
landscape of North America (Paul and Meyer 2001, Walsh et al. 2005) and is of particular
importance to North American bats that may rely upon aquatic habitats for
insect prey (Kalcounis-Rüppell et al. 2007). While urbanization may correlate with
increased bat presence by providing forested habitat within broader arable landscapes
(Gehrt and Chelsvig 2004), urban sprawl is nonetheless a threat to existing
forested foraging and roosting habitats used by bats. Development may have positive
(or little) effects on individual bat species with varied diets (e.g., Coleman and
Barclay 2012, Rollinson et al. 2013), but more generally, richness and diversity of
insectivorous assemblages can be reduced in urban systems, and accumulating data
demonstrate varied species-level impacts in urban ecosystems (Coleman and Barclay
2012, Dixon 2012, Smith and Gehrt 2010, Threlfall et al. 2012a). Considering this
reduction, management initiatives are likely needed both to protect natural areas in
urban systems as resources for bats as well as to mitigate adverse impacts on biodiversity
across the globe (Duchamp and Swihart 2008, Threlfall et al. 2012b).
Our objective is to investigate the relationship between bats and hydric habitats
in an urban landscape. Our study took place in the Bluegrass Region of Kentucky,
where urbanization is known to induce channel instability and lead to watershed
degradation (Hawley 2013). Urban development continues to expand across the
Bluegrass Region (Price 2011). As such, this study lays a foundation for assessing
the impacts of urbanization on bats in the Interior Low Plateau of North America.
These data are valuable not only for bat conservation, but also more generally for
understanding the importance of urban natural areas for maintaining biodiversity.
We expect bats to consistently use hydric habitats more than the surrounding forest
habitat and, following expectations of ecomorphology (Lacki et al. 2007, Smith
and Gehrt 2010), we hypothesize the greatest detection of low- and mid-frequency
echolocators will be in the open-canopy habitat of a wetland and that greater detection
of higher-frequency echolocators (Myotis spp.) will be in closed-canopy habitats
(including an artesian spring and stream corridor). We expect activity patterns
for all bats to positively relate to a presumably higher abundance of insect prey
found in hydric habitats.
Field-site Description
The study was conducted in Fayette County, located in the Inner Bluegrass of
Kentucky (Level IV Ecoregion). The area is known for its limestone bedrock, fertile
soils, and relatively flat landscape with typical topographic relief of 15–46 m. Originally
characterized by savanna woodlands, much of this region has been converted
to agricultural and industrial use, with most remaining deciduous forest occurring
in or near riparian zones (Woods et al. 2002).
Sampling took place at two study sites managed by the Lexington-Fayette
Urban County Government. McConnell Springs Nature Park (38°03'17.9"N,
84°31'40.1"W) is a ~11-ha natural area surrounded by urban development (LFUCG
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Parks and Recreation 2014b) and bordered by railroad tracks and an industrial park.
While several previous owners used the land primarily for agriculture, McConnell
Springs became protected as a city park in 1994 for the purposes of education and
recreation. Since that time, restoration efforts have been undertaken to restore the
integrity of McConnell Springs’ natural and cultural features (O’Malley 2007). The
specific habitats sampled at McConnell Springs included: two locations along a
human-made stormwater wetland (0.40 ha in size with a basal area [BA] of 18.9 ±
6.3 m2/ha, completed in winter 2009–2010), two artesian springs (BA of 20.6 ± 5.7
m2/ha), and two trailside locations within the forested interior of the park (BA of
21.8 ± 1.1 m2/ha at survey points). Veteran’s Park (37°57'23.2"N, 84°30'22.2"W ) is
a 95.3-ha suburban park with 2.25 km of paved walking trails, numerous mountain
biking trails, four picnic shelters, and numerous sports fields (LFUCG Parks and
Recreation 2014a, Pinnacle Homeowners Association 2014). The specific habitats
sampled at Veteran’s Park included two trailside locations within the forested interior
of the park (BA of 21.8 ± 2.3 m2/ha) and two locations along West Hickman
Creek, which runs ~1.25 km through the park (BA of 32.7 ± 4.0 m 2/ha).
Methods
We conducted acoustic assessments of bat activity and black-light insect trapping
concurrently on a monthly schedule from June–August of 2012 and June–
October of 2013. While surveys in 2012 were limited to McConnell Springs, efforts
were expanded in 2013 to include both McConnell Springs and Veteran’s Park,
which were sampled on separate occasions within the same month. In 2012, weekly
temperature lows during survey weeks ranged from 11.7 to 17.8 °C with rainfall
ranging between 0.00 and 91.7 mm. In 2013, temperatures ranged from -1.67 to
17.2 °C with rainfall ranging between 0.00 and 91.4 mm (University of Kentucky
Agricultural Weather Center 2014).
A Song Meter 2 (“Bat+” option, Wildlife Acoustics, Maynard, MA) was used
to record echolocation calls of bats from sunset to sunrise over ≥2 consecutive
nights at each study site within a month. For a survey, we placed detection systems
at single, fixed points at each selected habitat at a study site (n = 3 for McConnell
Springs, n = 2 for Veteran’s Park). Detection systems were placed at the edge of
hydric habitats, and forest sampling locations were ≥ 50 m from the forest edge
(Dodd et al. 2012). Microphones were attached to a 3-m cable and fixed at ~1.5
m in the direction of an open corridor to optimally record an area likely be used
by bats. In the case of hydric habitats, we placed microphones so that the zone of
detection covered as much of the air space above the water as was possible. We
collected full-spectrum data for these surveys using the default settings and a rate
of 16,000 samples/second. Data were downloaded and converted to zero-crossing
format using Kaleidoscope v.1.2 (Wildlife Acoustics, Maynard, MA). These data
were then analyzed using Bat Call Identification v. 2.6a (BCID; Kansas City, MO;
Allen et al. 2011, Romeling et al. 2012). Using the software’s reference library for
species in Kentucky, we analyzed files containing ≥5 echolocation pulses. This
identification procedure yielded total numbers of echolocation pulses and “passes”
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(discrete strings of pulses in a recording) for overall bat activity and phonic groups
(low, mid-, and high, or “Myotis” frequencies as defined by BCID). A 70% confidence
level was used for identification of phonic groups. To maximize autoclassifier
accuracy, we additionally examined acoustic data using Kaleidoscope Pro v.
2.1.0 (hereafter Kaleidoscope; Wildlife Acoustics, Maynard, MA), similarly basing
analyses on files containing ≥5 echolocation pulses and using the software’s reference
library for species in Kentucky. We then assigned the resulting species identifications
(based on a “0”-setting for sensitivity and accuracy) to the same phonic
groups generated by BCID. For both identification programs, the numbers of passes
recorded per night within phonic groups were considered for subsequent statistical
analysis.
Concurrent with acoustic surveys, we used black-light traps (10-watt Universal
Light Trap, Bioquip Products, Gardena, CA) to assess the relative diversity
and abundance of nocturnal insects. Similar to the placement of acoustic detection
systems, black-light traps were simultaneously placed at single, fixed points
for each selected habitat on the same night at a study site. We placed traps at
the edge of hydric habitats, and forest sampling locations were ≥50 m from the
forest edge (Dodd et al. 2012). All traps were located ≥50 m away from acoustic
survey points (Dodd et al. 2012), suspended at 2.5 m, and operated from sunset to
sunrise using digital timers (Flexcharge Programmable Timer, Northern Arizona
Wind and Sun, Inc., Flagstaff, AZ). We captured insects on a single night during
each survey interval for acoustic surveys. A dichlorvos-based insecticide strip
killed trapped insects. While black-light traps preferentially sample phototaxic
insects (Fayle et al. 2007), these traps are the standard technique for surveying assemblages
of nocturnal insects (Southwood 1978) and offer a useful approach for
comparing the relative abundance of bat prey items across habitat types (Lacki et
al. 2007). Following a trap night, specimens were sorted and placed in cold storage
(4 °C) for later identification in the laboratory. We identified insects to order
using reference keys (Triplehorn and Johnson 2005) and enumerated them to estimate
relative abundance of prey across habitats.
Response variables for bat activity included the passes per detector-night for
low-, mid-, and Myotis-frequency phonic groups identified using both BCID and
Kaleidoscope. Response variables for insect occurrence included the abundance per
trap-night for Coleoptera, Diptera, Lepidoptera, and an “other” category for less
numerous insects. We tested response variables for homogeneity of variance using
Variance Ratio F-MAX tests, and conducted analyses on log-transformed values
when variances were heterogeneous (Sokal and Rohlf 1969). We assessed annual
variation and variation between sites using one-way analyses of variance (ANOVAs)
for all response variables. If data varied between years or site, this variation
was partitioned out in subsequent analysis as a covariate. If not, data for both years
were pooled (Dodd et al. 2012). We then performed ANOVAs for our suite of response
variables for acoustic activity and insect occurrence. Main effects in this
model included seasonality (by month) and habitat (hydric versus surrounding forest).
When models were significant, we used the Fisher’s least significant difference
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means-separation procedure to evaluate effects (SAS 2002). The means-separation
procedure was considered significant when the α-level was P ≤ 0.05; summary statistics
are presented as mean ± standard error.
Results
Acoustic surveys spanned 29 nights from June to October, resulting in 48
detector-nights (excluding equipment malfunctions). No differences were observed
between years or sites for any phonic grouping (P > 0.05), save an annual effect
observed for the low-frequency phonic group identified using Kaleidoscope (F1,46 =
6.5, P = 0.01). In this case, fewer low-frequency bat passes per night were recorded
in 2012 (31.4 ± 12.5 passes/night) versus 2013 (77.2 ± 21.3 passes/night). The
model for the mean passes per night of the low-frequency phonic group was significant
using both BCID (F9,38 = 7.11, P < 0.01) and Kaleidoscope (F9,38 = 5.95, P <
0.01). For both models, habitat and seasonality were significant but the interaction
between these main effects was not (Fig. 1). Models for the mean passes per night
of the mid-frequency phonic group were significant using both BCID (F9,38 = 5.41,
Figure 1. Variation
in the activity of
bats in the lowfrequency
phonic
group. Different
letters within a
data series indicates
significance
(P ≤ 0.05).
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P < 0.01) and Kaleidoscope (F9,38 = 6.38, P < 0.01), but only for a habitat effect
(Fig. 2). Finally, models for the mean passes per night of the Myotis phonic group
were significant for both BCID (F9,38 = 15.01, P < 0.01) and Kaleidoscope (F9,38 =
11.35, P < 0.01), with habitat, seasonality, and the interaction between these main
effects being significant (Fig. 3). Across all phonic groups, bat activity was significantly
higher in hydric habitats than in non-hydric habitats, and bat activity peaked
during the middle months of our surveys (Figs. 1–3). Activity patterns for the varied
phonic groups appeared to differ across hydric habitats when comparing BCID and
Kaleidoscope (Fig. 4). Even so, both BCID and Kaleidoscope analyses indicated a
consistent general trend of heightened activity of the mid-frequency phonic group
at the wetland versus other hydric habitats.
Black-light surveys for nocturnal insects spanned a total of 11 nights (n = 30
trap-nights). Over two-thirds of the 19,458 insects captured consisted of Coleoptera,
Diptera, and Lepidoptera. There were no differences between sites, habitats,
and seasons (P > 0.05), but more insects were captured per night in 2012 than in
2013 (1145 ± 294 versus 318 ± 52 insects / trap, F1,25 = 9.45, P < 0.01). Within
Figure 2. Variation
in the activity of
bats in the midfrequency
phonic
group. Different
letters within a
data series indicates
significance
(P ≤ 0.05).
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the hydric habitats surveyed, however, variation was observed for common insect
orders (Fig. 5). While Lepidopteran abundance was similar across these habitats,
Coleoptera and Diptera appeared to be more abundant at the wetl and habitat.
Discussion
Numerous studies have linked forested riparian habitats to bat foraging and
roosting in eastern North America (Carter 2006, Dodd et al. 2008, Kniowski
and Gehrt 2014, Kurta et al. 2005, Neubaum et al. 2007, Schirmacher 2007), and
we found the positive association between hydric habitats and heightened bat
activity extends to an urban landscape. Our results demonstrate greater activity
of bats across all phonic groups in hydric habitats versus surrounding urban
forest. These results underscore data collected elsewhere around the globe
(Threlfell et al. 2012a, b) and stress the importance of management efforts to
protect such habitats for the purposes of bat conservation. While forest patches
are important habitats for bats in urban landscapes (Johnson et al. 2008), our
data further suggest that hydric habitats in these forest patches are particularly
Figure 3. Variation
in the activity of
bats in the Myotis
phonic group. Different
letters within
a data series
indicates significance
(P ≤ 0.05).
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Figure 4. Variation
in activity of bats
recorded across
hydric habitats.
Figure 5. Variation in abundance of insects captured across hydric habitat s.
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important for foraging bats.
Our results also suggest a trend in the activity of mid-frequency echolocators
across hydric habitats, with results from BCID and Kaleidoscope consistently
estimating the activity of this phonic group to be concentrated at the stormwater
wetland at McConnell Springs. This habitat provided a sizeable canopy gap
(0.4 ha) and abundance of forest edge (LFUCG Division of Water Quality 2014).
Mid-frequency echolocators (a phonic group including the Eastern Red Bat, Evening
Bat, and Tri-Colored Bat) are known to utilize such canopy edges and gaps
(Lacki et al. 2007), and dietary preferences of this phonic group also support our
observations of high activity at the wetland habitat. Tri-Colored Bats have a preference
for Dipteran prey, which would likely be abundant by the stagnant waters of an
urban wetland (Carter et al. 2003, Yadav et al. 2012). And, while our spatial replication
was limited to only a single survey location per hydric habitat for placement of
acoustic detectors and black-light traps, our observations for prey abundance corroborate
this. Further, both the Eastern Red Bat and Evening Bat in the region are
known to prey heavily on hard-bodied insects, such as Coleoptera and Hemiptera
(Dodd 2010, Feldhammer et al. 2009, Whitaker and Clem 1992). Trends for Coleoptera
in our study suggest this prey group was also highly abundant at the wetland
survey location, which could further contribute to our observations for activity of
mid-frequency echolocators.
Activity in the low-frequency phonic group (including Lasionycteris noctivagans
Peters [Silver-haired Bat], Lasiurus cinereus [Beauvois] [Hoary Bat],
and the Big Brown Bat) were inconsistently reported across hydric habitats by
BCID and Kaleidoscope. These results are valuable as an illustration of the ongoing
concerns associated with using automated software programs to identify
the echolocation calls of bats (Janos 2013), and likely also reflect limitations in
our study design (i.e., the need for spatial replication across the types of hydric
habitat). While results from BCID suggested relatively similar levels of activity
of low-frequency echolocators across hydric habitats, results from Kaleidoscope
suggested heightened activity of this phonic group at the streamside survey location.
Thus, despite low-frequency echolocators being widely reported to forage
in open, uncluttered habitats (Lacki et al. 2007), we found only limited support
for this suspicion. However, members of the low-frequency phonic group (i.e.,
Big Brown Bat and Silver-haired Bat) are among the most common bats recorded
in urban environments (Gehrt and Chelsvig 2004, Johnson et al. 2008). Further,
Smith and Gehrt (2010) noted that activity of Big Brown Bats was not predictable
in regard to microhabitat parameters measured at parks in the Chicago metropolitan
area. As such, it is possible that the commonality of these bats may have
contributed to our ubiquitous observations across hydric habitats.
Similar to results for low-frequency echolocators, we found varied, albeit reduced,
estimates of activity of the Myotis phonic group between the two acoustic
software programs considered in our study. However, species within this genera
are known to use a variety of forest habitats (Lacki et al. 2007), and while we expected
to find a greater general affiliation of this species group with closed-canopy
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forested and hydric habitats, this suspicion was not confirmed.
Our study is not exhaustive, and our design possesses the biases associated with
acoustic monitoring for bats and with using only a single approach to index prey
availability (Lacki et al. 2007). However, our study provides clear evidence that hydric
habitats are important to insectivorous bats in the Bluegrass Region throughout
their active period. This study provides a benchmark for subsequent research which
should further investigate how different types of hydric features may impact bat
activity, as well as how the size and shape of hydric habitats in urban environments
may influence their use by foraging bats.
Acknowledgments
This project was funded by the Lexington-Fayette Urban County Government and was
made possible by the support of the University of Kentucky’s Forestry Department. The
authors thank T. Culbertson for technical assistance, as well as numerous employees at
McConnell Springs Natural Area. The authors are particular indebted to Laurie Thomas;
without her assistance this project would not have possible. This study is published as Kentucky
Agricultural Experiment Station publication number 14-08-033.
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