Bat activity and insect availability in early successional forests: a preliminary analysis
Kathryn M. Womack and Alix D. Fink
Department of Biological and Environmental Sciences
Abstract
Recent research in the United Kingdom found significant differences in
bat use of organic and conventional agricultural land. The elucidated
link between vegetation management, insect communities, and bat
foraging activity also could apply to managed timber lands. We
investigated this potential link in forest stands managed under various
regimes to test the hypothesis that bat activity is higher in sites with
more plant and, therefore, insect diversity. We conducted this research
in the Appomattox-Buckingham State Forest in central Virginia in JuneAugust 2006. We sampled 9 sites representing a continuum from
young (0-1 year-old) released loblolly pine plantations to oak
shelterwood sites.
Introduction
Forests of the southeastern U.S. are changing rapidly, both in terms of
forest composition and extent of forest cover (Wear and Greis 2004).
Compositional changes are being driven by increasing conversion to
pine (Pinus sp.) plantations and increasing management intensity (Siry
2002, Wear and Greis 2004). Concurrent to changes in composition,
forest cover is decreasing as a result of urbanization and sprawl
associated with human population growth (Wear 2002). Moreover, the
nature and extent of impacts of global climate change on Southern
forests are largely speculative (Moore et al. 2002), though further
changes in composition and coverage are expected.
Questions related to the impacts of these changes on wildlife species
are of great conservation interest and management relevance. Highly
mobile species with large home ranges and complex habitat
requirements, such as bats, may be especially vulnerable to increasing
human modification of landscapes. However, the impacts of such
modifications on bat species are poorly understood (e.g., Tibbels and
Kurta 2003).
These relationships were recently explored by researchers in the U.K.
who found significant differences in bat foraging activity in organic and
conventionally managed agricultural land (Wickramasinghe et al. 2004).
The differences in bat foraging were related to differences in insect
abundance in the sites (Wickramasinghe et al. 2003), which in turn was
directly related to intensity of vegetation management and/or control
(Wickramasinghe et al. 2004).
Methods
Study Area
The Virginia Piedmont region provides an interesting context for
addressing research questions related to habitat use in actively managed
forests. Virginia’s Piedmont is still largely forested (>80% forest cover in
some counties; Wear and Greis 2004), and it includes large extents of
intensively managed pine forests. Additionally, in the next few decades
the Piedmont region is expected to lose more forest cover than any other
region in the Southeastern U.S. (Wear and Greis 2004).
Our study area, the Appomattox-Buckingham State Forest (ABSF),
encompasses over 19,000 acres within a largely forested landscape with
industrial and other private landowners. The ABSF is managed by the
Virginia Department of Forestry for multiple uses. In the ABSF, we
selected 9 study sites representing a continuum from young (0-1 yearold) loblolly pine plantations to oak (Quercus sp.) shelterwood sites.
Sampling Arrays
In each site, we established an array with one bat detector and 4 Universal
Black Light Trap (UBLT; BioQuip Products, Inc.) locations (Fig.2).
Bat Sampling
Bat detector systems [with Anabat II detector and ZCAIM (Titley Electronics)
housed in a plastic cooler with 4” PVC elbow joint; after S. Amelon] recorded
data between 1930 and 0630. Each bat detector system was placed on a
stump for elevation from the ground, and the PVC elbow was oriented to
provide the microphone with a clear, unobstructed detection cone (Fig. 3). We
used AnaLook to view the data and count number of passes per hour (Fig. 4).
Fig. 4 Bat calls
Results
3-4 year old pine (not released)
0-1 year old pine
Insect Sampling
Insect samples were collected using UBLTs with photoelectric switches
running on 12-V deep-cycle marine batteries (Fig. 5). Insects were
collected in large glass jars that contained an insect kill strip. We retrieved
the jars each morning, transferred the specimens to freezer bags, and froze
specimens for later analysis. Prior to processing, each sample was dried for
24 hours at 60°C and transferred to plastic storage containers along with a
desiccant.
Dried insects were separated into 3 groups (Lepidoptera, Coleoptera and
other) using magnifying glasses and a dissecting microscope. We
separated Lepidoptera and Coleoptera because these orders made up the
bulk of our samples and are known to be important bat prey. We weighed
each group to determine biomass by group and for the complete sample.
Vegetation
We quantified vegetation density using a density board (3 m x 10 cm;
Fig. 7) at two vegetation sampling points in each study site (Fig. 2). We
photographed the board from a distance of 4 m in each of the four
cardinal directions. In the lab we then quantified percent of vegetation
density using photographs.
8-10 year old pine (released)
4.22 ± 3.85
Regenerating oak shelterwood
3.13 ± 2.24
3.91 ± 2.46
7.06 ± 7.96
Fig. 10 Relative insect abundance by site type. Means are based on two samples per site type; bars represent mean dry mass by insect group and circles represent average percent of total mass.
Mean mass per sample ± SD (g)
3.3 ±1.0
3.6 ±1.3
Discussion
In relating our preliminary results to our proposed
model (Fig. 1), some predictions paralleled
observed results while others did not.
•We predicted a positive relationship between
vegetation density and insect abundance (i.e.,
biomass). However, the sites with the greatest
insect abundance were those with the lowest
vegetation density.
Fig. 1 Community
interactions model
•We predicted a positive relationship between
insect abundance and bat activity. Our
preliminary results support that relationship.
However, the mediating factor of vegetation
clutter on foraging activity is one that we will
explore in more detail.
Fig. 9 Bat passes per hour (pph) by site type. Means are based on 12 nights in the 0-1 year old category and 8 nights in all others.
Overall mean pph ± SD
Results, continued
While these preliminary results should be interpreted with caution, we
look forward to exploring in more detail the following trends:
• In comparing the three pine habitat types, there is a decrease in
observed bat activity as plantations age (Fig. 9).
• Insect biomass was similar in three habitat types, but biomass was
much greater in the youngest of the habitats (Fig. 10).
• Vegetation density and overall height increased with site age (Fig.
11).
3.6 ±1.6
11.6 ±2.6
Future Directions
In continuing the analysis of the data collected this summer, we will
•Conduct a detailed examination of the full data set using the
Information Theoretic approach to linear modeling.
•Analyze fecal pellets collected at our study sites and attempt to
relate their contents to insects collected in the UBLTs.
In future field seasons, we will
•Incorporate multiple sampling arrays per site in different
microhabitats to better understand bat activity.
•Include more mature pine sites to extend the continuum and
assess the trends in insect availability and bat use.
Finally, in the long term we seek to
•Provide information to add to our understanding of how wildlife
species use intensively managed forest systems.
•Relate bat activity in early successional habitats to the better
understood avian use of those habitats to build a more holistic view
for conservation and management decisions, particularly as they
relate to management of sites within broader landscape contexts.
Acknowledgements
Fig. 11 Vegetation density by vertical height interval and site type.
Overall mean % cover (n), max. height (m)
16.7% (16), 1.0
42.6% (15), 1.9
47.7% (6), 2.2
77.3% (6), 2.9
This research was supported by a Longwood University Faculty Research Grant. Additional in-kind
and indirect support were provided by Longwood’s Department of Biological and Environmental
Sciences and the Geographic Information Systems Laboratory, the Virginia Department of Forestry,
and the Kennedy Tree Farm. We also thank the following individuals for their help and support: D.
Horchler, T. Akre, M. Fink, E. Kinman, and S. Amelon.
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