MIST-NETTING, PASSIVE ULTRASONIC DETECTION, AND STABLE ISOTOPES DETERMINE COMMUNITY STRUCTURE AND TEMPORAL VARIATION IN BATS (CHIROPTERA) AT ACADIA NATIONAL PARK, MAINE By Timothy J. Divoll B.S. Worcester State College, 2005 A THESIS Submitted in Partial Fulfillment of the Requirements for the Degree of Master of Science (in Biology) The Graduate School University of Southern Maine December 18, 2012 Advisory Committee: Dr. David Evers, Adjunct Professor of Biology, Advisor Dr. Christine Maher, Professor of Biology Dr. Jeffrey Walker, Professor of Biology
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MIST-NETTING, PASSIVE ULTRASONIC DETECTION, AND
STABLE ISOTOPES DETERMINE COMMUNITY
STRUCTURE AND TEMPORAL VARIATION IN BATS
(CHIROPTERA) AT ACADIA NATIONAL PARK, MAINE
By
Timothy J. Divoll
B.S. Worcester State College, 2005
A THESIS
Submitted in Partial Fulfillment of the
Requirements for the Degree of
Master of Science
(in Biology)
The Graduate School
University of Southern Maine
December 18, 2012
Advisory Committee:
Dr. David Evers, Adjunct Professor of Biology, Advisor
Dr. Christine Maher, Professor of Biology
Dr. Jeffrey Walker, Professor of Biology
ii
iii
Table of Contents List of Tables ..................................................................................................................... iv
List of Figures ..................................................................................................................... v
Figure 1.5. Number of bat call sequences recorded daily in 2010 at Acadia National Park
before species identification analysis. No data were collected prior to April 14 or after
October 13. ........................................................................................................................ 22
Figure 1.6. Species' calendar heat maps for call sequences recorded daily in 2010 after
automatic identification using SonoBat™ software. Scales for each map are different
despite the same color scheme. No sampling occurred before April 14 or after October 13
at Acadia National Park, Maine. ....................................................................................... 24
Figure 1.7. Abundance of bat captures and bat call sequences recorded nightly in 2010
scaled by the highest one-night abundances. On July 30, 51 bats were captured and on
August 3, 2245 call sequences were recorded. Red lines represent conservative estimates
of the summer bat season, when bats are not migratory, as defined by the USFWS for the
Northeast region (USFWS 2009). Red circles represent likely migration events. Data
equal to zero on the y axis correspond to non-sampling nights or equipment failure.. .... 25
Figure 2.1.a. Comparison of isotopes in adult and juvenile eastern small-footed bats by
tissue type with a small sample size correction. Ellipses encompass 40% of the data
points in each group and represent the isotopic niche width of each group. .................... 49
vi
Figure 2.1.b. Density plots with credible intervals of Bayesian estimates of ellipses
representing the 50th
, 75th
, and 95th
percentiles and the mode shown in black. Groups that
share a letter are not statistically different than one another. ........................................... 50
Figure 2.2.a. Comparison of isotopes in adult and juvenile little brown bats by tissue type
with a small sample size correction. Ellipses encompass 40% of the data points in each
group and represent the isotopic niche width of each group. ........................................... 51
Figure 2.2.b. Density plots with credible intervals of Bayesian estimates of ellipses
representing the 50th
, 75th
, and 95th
percentiles and the mode shown in black. Groups that
share a letter are not statistically different than one another. ........................................... 52 Figure 2.3.a. Comparison of isotopes in adult and juvenile northern long-eared bats by
tissue type with a small sample size correction. Ellipses encompass 40% of the data
points in each group and represent the isotopic niche width of each group. .................... 53
Figure 2.3.b. Density plots with credible intervals of Bayesian estimates of ellipses
representing the 50th
, 75th
, and 95th
percentiles and the mode shown in black. Groups that
share a letter are not statistically different than one another. ........................................... 54
Figure 2.4.a. Blood isotope ellipses per season with a small sample size correction.
Ellipses encompass 40% of the data points in each group and represent the isotopic niche
width of each group. ......................................................................................................... 58
Figure 2.4.b. Density plots with credible intervals of Bayesian estimates of ellipses
representing the 50th
, 75th
, and 95th
percentiles and the mode shown in black. Groups that
share a letter are not statistically different than one another. ........................................... 59
Figure 2.5.b. Hair isotope ellipses per season with a small sample size correction. Ellipses
encompass 40% of the data points in each group and represent the isotopic niche width of
each group. ........................................................................................................................ 60
Figure 2.5.b. Density plots with credible intervals of Bayesian estimates of ellipses
representing the 50th
, 75th
, and 95th
percentiles and the mode shown in black. Groups that
share a letter are not statistically different than one another. ........................................... 61
Figure 2.6.a. Skin isotope ellipses per season with a small sample size correction. Ellipses
encompass 40% of the data points in each group and represent the isotopic niche width of
each group. ........................................................................................................................ 62
Figure 2.6.b. Density plots with credible intervals of Bayesian estimates of ellipses
representing the 50th
, 75th
, and 95th
percentiles and the mode shown in black. Groups that
share a letter are not statistically different than one another. ........................................... 63
1
Acknowledgments
This work would not have been possible if not for logistical support and the
institutional knowledge of Bruce Connery at Acadia National Park. Bik Wheeler (ANP)
provided local knowledge and dedicated field support, especially in my absence and
David Manski, also at ANP, supported this project through permitting and approvals.
John DePue from the Maine Department of Inland Fisheries and Wildlife supported this
work with permitting and a generous donation of nets and poles. No data would have
been collected if not for financial support from L. L. Bean, the University of Southern
Maine (USM), Sheila Colwell at the National Park Service, Kevin Castle from the United
States Geological Survey and in-kind contributions from the Biodiversity Research
Institute. Committed field assistance was provided by Cassandra Alston, Zac Smith,
Chloe Barnett, and Marissa Altmann and many of ANP's natural resource and interpretive
staff. I was greatly pleased to have so many volunteers in the field and have the
opportunity for park staff to transmit current data to the park's visitors, sometimes the
morning after an exciting find. The people of Mount Desert Island add to the charm of
working in such a beautiful place. Particularly, Harry Owens stands out as a local bat
conservationist and proud landlord to a colony of little brown bats in his historic
"Stonebarn". The faculty, staff, and other graduate students at USM were supportive of
the project, providing invaluable ideas and comments. My committee provided a great
blend of freedom, statistical support, and direction to allow the project and this document
to evolve to its current state. Al Hicks helped put my data in ecological context with his
encyclopedic memory and anecdotal observations of bat ecology and behaviors. I have
worked with and learned from "baticompañeros" throughout the New World and Europe,
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too many and too spread out to count, but the fun we've had and the inspiration we share
to research and conserve bats is invaluable and collectively monumental. I am greatly
indebted to David Evers and everyone else at BRI for all the help and support along the
way. Particularly, Dave Yates and David Buck gave great ideas, equipment, and
feedback. Dustin Meattey, Shaylyn Hatch, Kevin Regan, Pedro Ardapple, and Bradley O'
Hanlon helped capture and sample so many bats. Lastly, the continued support of my
family and friends kept me on task, well fed, and loved.
3
ABSTRACT
Recent issues such as white-nose syndrome and wind power development have
undoubtedly affected bat populations in northeastern North America. A lack of baseline
knowledge of bat abundances and spatial and temporal patterns makes it challenging at
best to understand what to consider, how to manage, and how to conserve bat
populations. To better understand the natural history of bats at Acadia National Park
(ANP), I employed several methods to make inferences on the behaviors and patterns of
different species present at this coastal bat refuge. First, I mist-netted bats and
supplemented with acoustic detection and call analysis to map abundance patterns
temporally. Then I collected blood, skin, and hair samples from a subset of captured bats
to explore differences in stable isotopes among tissues, species, sampling season, and
age.
Capture data revealed that Acadia National Park is an important stronghold for a
rare species, Myotis leibii, with a population of 147 individuals. I observed sympatry
between Myotis leibii, M. lucifugus, and M. septentrionalis as evidenced by capture
success and stable isotope analysis. All three of these species exhibit a moderate amount
of site fidelity, returning year after year. Based on temporal acoustic detections, ANP also
appears to be an important site for bat migration.
Stable isotope data revealed that isotopic differences represent distinct isotopic
niches and are useful to assess population dynamics based on differences between adults
and juveniles, different tissue types, and multiple isotopes. Based on isotope results, M.
leibii and M. septentrionalis may maintain residence at ANP, with some new individuals
arriving late in the season. Conversely, M. lucifugus seems to arrive at ANP after the
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other two species are active and then continually move into and out of the local
population. I concluded that Acadia National Park provides optimal habitat and resources
for all bat species encountered through several life stages such as pregnancy, pup rearing,
volancy (first flight), swarming, migration, and likely hibernation.
5
CHAPTER 1: Bat community structure inferred from mist-netting and acoustic
detection
1. 1 Introduction
Bats in northeastern North America face anthropogenic challenges such as heavy
metal contamination (Clark & Shore 2001), habitat loss and degradation (Agosta 2002),
white-nose syndrome (WNS) (Blehert et al. 2009), and wind power development (Kunz
et al. 2007). All of these factors may impact individual bats or species as well as local
populations, particularly if these factors are compounding to increase overall stress on
bats.
Environmental stressors can relate directly to bat population numbers; hence bats
are excellent biotic indicators of environmental health and change (Jones et al. 2009).
Habitat loss and degradation are directly related to environmental contamination in many
cases, as in clearing forests for agriculture and applying pesticides (Jones et al. 2009).
Accumulation of chemicals in bats may increase hormonal stress or tax specific organs or
systems, depending on the toxin (Wada et al. 2010).
It is feasible, therefore, that contamination may hinder bats' immune systems and
potentially allow bats to become more susceptible to diseases like white-nose syndrome
(Kannan et al. 2010). This deadly fungus infects bats during hibernation causing them to
awaken, burn precious energy to groom and then exit caves in winter to search for food
(Reeder et al. 2012). Most bats die when they cannot find prey in winter, precisely the
justification for storing extra fat and hibernating through winter. This disease has killed
over 5 million bats in North America since 2006 (Blehert 2012) and is considered one of
6
the greatest environmental challenges that temperate bats face, alongside wind power
development (Boyles et al. 2011).
Wind power, despite its potential for renewable energy production, poses an
environmental challenge to migrating bats (Kunz et al. 2007). Many bats come into
contact directly with wind turbine blades, and some appear to die from barotrauma, i.e.,
damage to lung capillaries caused by rapid pressure change in the airspace around wind
turbines (Baerwald et al. 2008). Kunz et al. (2007) estimate 33,000 to 111,000 wind
power related bat fatalities in North America by the year 2020. Despite these
environmental challenges, North American bats continue to provide important ecosystem
services such as insect-borne disease management, agricultural crop pest management,
crop pollination, and seed dispersal (Kunz et al. 2011). Globally, estimates range in the
millions to billions of dollars in services provided by bats (Kunz et al. 2011), and in the
United States alone bats provide $22.9 billion in services to industry per year (Boyles et
al. 2010).
Beyond providing economic and ecosystem services, bats warrant conservation to
simply promote biodiversity in the natural world. To conserve bats we must understand
variation in distribution and abundance of bat populations. Rodrigo A Medellín (2003,
page 88), renowned bat conservationist, suggests: "Conservation efforts should
contemplate the preservation of ecological roles and evolutionary processes fulfilled by
bats, and not simply try to stop threats that affect a particular species." To that end, basic
natural history data are necessary to inform decision makers when considering wind
power development, white-nose syndrome management, and other local energy
development that may adversely affect the quantity and quality of bat habitat.
7
Little information is known about the natural history and coastal usage patterns
of bat species in Maine, facilitating the need for baseline population-level data for these
species. There are no satellite transmitters small enough for bats, no large scale banding
efforts, and no ongoing research in the state, creating a significant data gap on spatial and
temporal bat activity along the coast of Maine. All bat species in Maine are insectivores,
all provide services on a local scale, and all may be affected by anthropogenic activities.
Each of the seven species in this state can be classified as either a local migrant,
typically traveling <300km from summer areas to winter hibernacula, or long-distance
migrants, typically traveling greater than 1000 km from summer to winter areas (non-
hibernatory; Table 1.1). These temperate survival strategies put Maine's bats at risk to
disease and mortality based on their necessary seasonal movements. Three of the four
local migrants have recently been petitioned for listing under the Endangered Species Act
due to a lack of natural history data (Mattesson 2010; Kunz and Reichard 2010). Eastern
small-footed bats (Myotis leibii) and northern long-eared bats (Myotis septentrionalis)
have unknown population abundances (Matteson 2010), and little brown bats (Myotis
lucifugus) have experienced severe mortalities from white-nose syndrome (Kunz and
Reichard 2010).
We need to better understand spatial and temporal patterns associated with these
species to build baseline natural history data useful for managing challenges that face bat
species in North America, especially white-nose syndrome and wind power development
because they have caused rapid recent mortalities (Blehert et al. 2009, Kunz et al. 2007)
and are current political and social topics of debate in Maine (Wing Goodale, pers.
comm.). Spatial and temporal patterns directly relate to spread of diseases and behavioral
8
changes associated with poor placement of wind projects as bats forage ‘on the wing’ and
migrate over some distances (Table 1.1).
Table 1.1. Movements documented for Maine bat species using the most conservative observations from the
literature.
Common name Scientific name
Movement
distance Source
local
migrants
little brown bat Myotis lucifugus < 277 km Davis & Hitchcock 1965
northern long-eared bat Myotis septentrionalis < 56 km Nagorsen & Brigham 1993
eastern small-footed bat Myotis leibii < 20 km Hitchcock 1955
big brown bat Eptesicus fuscus < 87 km Neubaum, et al. 2006
long-
distance
migrants
hoary bat Lasiurus cinereus > 1800 km Cryan et. al 2004
red bat Lasiurus borealis continental Cryan 2003
silver-haired bat
Lasionycterus
noctavigans continental Cryan 2003
Since bats fly and are hard to track, classic banding methods (Griffin 1940) need
to be combined with new research technologies to piece together temporal movements
and potential migrations. Acoustic detectors are used to collect bat data temporally via
echolocation calls (Broders et al. 2003, Hayes 1997), and recent technology allows for
unmanned sampling. O'Farrell & Gannon (1999) compared efficacy of mist-netting and
acoustic sampling, and although each method has biases toward detecting certain species,
these authors recommended using both methods for a more complete survey. Mist netting
and banding provide an opportunity to track bats through recaptures while acoustic
sampling measures relative abundance. All of these sampling methods were implemented
on Mount Desert Island (MDI) at Acadia National Park (ANP), Maine, to better
understand bat population dynamics at one location and to investigate the feasibility and
usefulness of employing all three methods simultaneously for bat surveying.
MDI is an island offering natural bat habitat and is situated on the coast of Maine,
isolated from the mainland by water, an ideal site to study bat natural history and
Valley Cove 44.3037 -68.316 34 0.38 forested carriage road
11
Figure 1.1. Locations of mist-netting sites for bats on Mount Desert Island at Acadia National Park, Maine, 2009
- 2011. Sites 7 and 8 highlighted in red were also sites used as acoustic detection locations in 2010.
12
1.3 Methods
1.3.1 Mist-Netting
To determine differences in species diversity at ANP each month, mist-netting
surveys were conducted at all 14 sites using Triple High Forest Filter systems (Bat
Conservation and Management, Carlisle, PA) hung with Avinet (Dryden, NY) bat
specific mist-nets. I conducted a pilot effort in May, June, August, and September 2009
to determine access to and productivity of mist-netting sites. Each night, site selection
was based on weather patterns that can affect capture rates such as wind direction, moon
phase, and temperature (Geluso & Geluso 2012) to predict which sites would be least
windy, darkest, and warmest. I also netted at different sites each night because bats may
become wise to net locations on consecutive nights (Winhold & Kurta 2008). A limiting
factor of mist netting is that some species can detect nets with echolocation and simply
see the nets if there is a fair amount of moonlight or light pollution, allowing them to
actively navigate around and above net (MacCarthy et al. 2006). This problem further
highlights the need for acoustic sampling to be paired with mist netting to accurately
document relative bat activity from all species at ANP.
Based on successful efforts in 2009, I mist-netted 1 – 5 nights per week from mid
April through late September 2010. In 2011, netting only occurred in July and August
and results were compared to capture rates in previous years. Regardless of sampling
year the same sites were used under the same netting protocol. Two triple high mist net
systems (7.8 m tall) were placed over roads or streams with appropriate lengths to fully
cover the travel corridor (6 – 12 m), adding single high nets at the edges where
appropriate. Nets were opened for at least 45 minutes and up to 6.5 hours each night; they
13
were closed if weather inhibited capture or kept open as long as one bat had been
captured within the previous 30 minutes. Following MacCarthy et al. (2006), to
maximize capture rates each night, nets were checked every 10 minutes to reduce both
amount of disturbance at the net and risk of bats chewing their way out of nets.
Due to uncertainties in spread of white-nose syndrome and permitting
requirements, I followed USFWS Bat Decontamination Protocols (USFWS 2012) to
reduce any chance of researcher assisted spread. All bat handling followed Sikes et al.
(2011) and was approved by the University of Southern Maine's Institutional Animal
Care and Use Committee (IACUC), protocol # 051509-03. All captured bats were
identified to species and banded so that each animal was uniquely identifiable upon
subsequent recapture. Individuals were then aged by wing joint ossification (Kunz &
Anthony 1982), sexed by visual inspection, measured (forearm length in mm), and
weighed (g) on a digital balance. I assessed all bats for degree of wing scarring, which
may correlate with prior exposure to white-nose syndrome (Reichard & Kunz 2009). The
final step was to take blood, skin, and hair samples for stable isotope analysis following
established protocols (Voigt & Cruz-Neto 2009; Kunz & Weise 2009; Sullivan et al.
2006) before bats were released on site, unharmed. I submitted all banding data to the
Southern Bat Diversity Network database to inspire collaboration with other researchers
banding bats from the southeastern USA to eastern Canada.
To analyze mist-netting data, I combined all capture locations into one site and
assumed that bats captured at all locations are included in the general population at MDI.
I created heat maps in Microsoft Excel 2007 (Microsoft, Washington, USA) by month of
sampling and species captured to compare capture rates across months and years. Then
14
using 2010 data only I used PAST statistical software (Hammer et al. 2001) to calculate
species richness, equitability, dominance and diversity (Shannon and Simpson indices) by
month of sampling. Rényi diversity profiles are typically used to compare different study
sites (Kindt & Coe 2005), but I used them to visually compare and describe differences
by month. With mist-net capture rates per hour of sampling for each month, I compared
acoustic detection rates per hour of acoustic sampling.
1.3.2 Acoustic Sampling
To detect species that may be difficult to capture in mist-nets, I used two
Pettersson D500x bat detectors (Sweden) placed at Bubble Pond and Jordan Pond Cliff
from April 14 to October 13, 2010 (Fig. 1.1). These two sites were chosen based on high
abundance and diversity of bat captures through mist-netting in 2009 (T. Divoll,
unpublished data). Detectors were passively deployed in a weather-proofed box with a
battery and raised 8 m above the ground to sample an area greater than the mist-netting
area and to detect bats flying higher than mist net sets (7.8 m). Detectors were checked at
least once per week to charge batteries and download data files stored on compact flash
(CF) cards. Pettersson detectors are considered full-spectrum ultrasonic detectors able to
record sounds in the range of 15 to 190 kHz (www.batsound.com). Each bat
echolocation call sequence was captured efficiently by the detectors and then stored as
.wav files with a time and date stamp. I analyzed bat call sequences using SonoBat™v.3
(Arcata, CA) software to determine species by their sonograms. This program, with
guidance, detected and removed non-bat .wav files that were inadvertently recorded at
high frequencies (usually insects). Remaining files were automatically scanned and
matched with a bat call library and identified to species based on full call sequences using
15
quantitative call parameters and hierarchical decision algorithms. Using thresholds of
80% file quality and 90% confidence of species identification. I set the program to
analyze all files recorded in batches by month. No inferences or judgments were made on
files that did not meet 80% quality or 90% confidence of identification. Using only files
identified to the species level, I combined data from both detectors to plot overall and
individual species activity by night of sampling in calendar heat maps created with the
source code "calendarHeat.R" in program R (R Core Team 2012), made available by Dr.
Paul Bleicher (Humedica). These calendar heat maps were visually reviewed to detect
peaks of bat activity on a temporal scale.
1.4 Results
1.4.1 Mist-Netting
I captured 1059 bats in mist-nets during the pilot survey in 2009, the field season
in 2010, and a follow-up survey in 2011 (Fig. 1.2) over the course of 339 hours of mist-
netting effort (Fig. 1.3) for an overall capture rate of 3.1 bats/hr. Though abundance of
each species varied temporally, differing amounts of sampling effort each month strongly
influenced capture rates for each species (Fig. 1.3). Capture rates were highest in June
2009 and little brown bats were captured most frequently. Northern long-eared bats and
little brown bats were captured most frequently in summer, whereas eastern small-footed
bats were captured most frequently in spring to early summer. Red bats and big brown
bats were only captured in late summer, and hoary bats were only captured in August.
Though I did not sample every night, the earliest date that I caught little brown bats was
May 4. I captured northern long-eared bats as early as April 14, whereas I captured
16
eastern small-footed bats as early as April 4. All three species were captured as late as
September 29 (2010). The overall sex ratio of bats throughout the study was 67% male to
33% female (Table 1.3). Males dominated the capture occurrences of most species,
except for the first sampling period in 2009 when females dominated. The warmer and
drier spring in 2009 combined with a cool and wet spring in 2010 may have contributed
to this early season difference. I captured proportionally more eastern small-footed bat
and red bat females than other species (Table 1.3).
Table 1.3. Sex ratios of bats captured by season as percentage of males : percentage of females. Early season
includes sampling in April and May, mid season includes June and July, and late season includes August and
September. Abbreviated species names: "little" = little brown, "northern" = northern long-eared, "eastern" =
eastern small-footed, "big" = big brown.
sampling period little northern eastern red big hoary All species early season-2009 21:79 29:71 0:100 NA NA NA 23:77 mid season-2009 68:32 64:36 59:41 NA NA NA 70:30 late season- 2009 69:31 86:14 61:39 0:100 100:0 50:50 72:28
early season-2010 69:31 77:33 57:43 NA NA NA 65:35 mid season-2010 80:20 73:27 65:35 0:100 NA NA 75:25 late season-2010 77:23 76:24 63:37 80:20 NA NA 74:26
mid season- 2011 80:20 100:0 100:0 0:100 100:0 NA 88:12 late season-2011 68:32 55:45 50:50 50:50 NA NA 62:38
Overall 70:30 72:28 59:41 43:57 100:0 50:50 67:33
To account for inter-year differences, per-month diversity values were only
calculated for the 2010 field season (Table 1.4). Based on a Rényi diversity profile
comparing months sampled in 2010 (Kindt & Coe 2005), each month's diversity was
influenced by species composition at that time (Fig. 1.4). The diversity profile lends itself
well to inherent biases with different indices by incorporating both the Shannon and
Simpson indices. For example, a Shannon index may underestimate diversity when
species richness and evenness are great, whereas a Simpson index underestimates
diversity where there is a dominant species (DeClerck & Salinas 2011). Profiles with a
17
horizontal slope show greater evenness among species, whereas profiles with greater
dominance and less evenness (negative slope) are influenced by one species more than
others (Hammer et al. 2001). In the diversity profile, α = 0 represents species richness, α
= 1 corresponds to a proportional Shannon index, and α = 2 corresponds to the log of the
reciprocal of the Simpson index (Fig 1.4). April had less diversity [1.63 (α = 1), 1.45 (α =
2)] and species richness (n = 2) than all other months. May had slightly higher diversity
[2.96 (α = 1), 2.91 (α = 2)] but equal richness, (n = 3) than both June and September, with
June [2.82 (α = 1), 2.70 (α = 2)] slightly higher than September [2.69 (α = 1), 2.53 (α =
2)]. August had slightly higher diversity [2.54 (α = 1), 2.15 (α = 2)] than July [2.46 (α =
1), 2.00 (α = 2)], and these two months were not comparable to May, June, and
September by this model (Fig. 1.4). Lower diversity in April was dominated by eastern
small-footed bats, whereas other months showed less dominance by a particular species
(Table 1.4). May, June, and September had greater evenness than the other months (Fig.
1.4). May was less dominated by any one species with very high equitability on all 3
species for that month. July and August have a negative slope in the model intersecting
with May, June, and July due to higher richness, lower evenness and higher dominance.
Figure 1.4. Rényi diversity profiles for bats sampled each month on Mount Desert Island, Maine, 2010. Alpha =
0 corresponds to species richness, alpha = 1 corresponds to a proportional Shannon index, alpha = 2
corresponds to the log of the reciprocal of the Simpson index.
20
1.4.2 Recaptures
A total of 22 bats were recaptured alive during the 3 year period (Table 1.5).
These captures included 7 little brown bats, 5 northern long-eared bats and 10 eastern
small-footed bats. Thirteen bats were recaptured at least one year after original capture,
and two bats were originally captured in 2009 and recaptured in 2011, both at the same
locations where they were captured in 2009. At the time of recapture, almost all bats were
adult males (n = 17), with fewer adult females (n = 4), and one juvenile female northern
long-eared bat was recaptured 19 days after her original capture at the same site. One
male little brown bat was originally captured as a juvenile and subsequently recaptured at
the same location one year later. One little brown bat, banded on August 24, 2011 was
recovered dead in a garden in July, 2012 in Brooklin, Maine, 30 km from its banding
location. Anecdotal observations were made about recaptured individuals: DEY0015 (an
eastern small-footed bat) was not pregnant when first captured but was post-lactating
when recaptured one year later. DEY3296 (an eastern small-footed bat) was pregnant
when first captured in May and still pregnant when recaptured 29 days later. DEY3373 (a
northern long-eared bat) was pregnant when first captured and post-lactating when
recaptured 47 days later. DEY4516 (a little brown bat) was captured on the eastern side
of the island in summer and recaptured on the western side in fall (6.7 km away).
DEY4765 (a little brown bat) was originally banded as a juvenile and recaptured a year
later at the same site as an adult. DEY3253 (a small-footed bat) was originally banded in
2009 and recaptured at the same site in 2011. DEY3381 (a small-footed bat) was
pregnant when banded in 2010 and post-lactating when recaptured in 2011, at a different
site. DEY0026 (a small-footed bat) was originally banded in 2009 and recaptured two
years later at the same site.
21
Table 1.5. Individual bats recaptured throughout the 3 years of mist netting at Acadia National Park, Maine.
Distance column highlights straight line map distance between banding location and recapture location. A =
adult, J = juvenile.
1.4.3 Acoustic Sampling
During 2112 hours of sampling, I recorded a total of 90,783 call sequences with
passive detectors between April 14 and October 13, 2010, for a bat detection rate of 43
call sequences/hour. Using full call sequences at 80% quality and 90% confidence of
species identification, SonoBat™ could identify only 9.4% of all calls to species (Table
1.6). In general, the program was most efficient in June, July, and August. During June
the program identified 1,997 calls out of 20,020 (9.9%), in July 2,571 out of 22,122
(11.6%), and in August 2,944 out of 27,379 (10.7%; Table 1.6). I did not analyze data
beyond the automated analysis due to the volume of call sequences recorded, hence data
are only as accurate as the software's capabilities. Call sequences identified to species
Band # Species
Age
Sex
Original
banding
date
Banding
location
Recapture
date
Recapture
location
Distance
(km)
DEY0015 Myotis leibii A ♀ 29-May-09 Marshall 19-Jul-10 Lurvey 3.8
DEY0031 Myotis lucifugus A ♂ 2-Jun-09 Hemlock 11-Aug-10 Hemlock 0
DEY0404 Myotis leibii A ♂ 6-Aug-09 Murphy's 4-Jun-10 Murphy's 0
DEY0416 Myotis lucifugus A ♂ 7-Aug-09 Bubble 29-Jun-10 Bubble 0
DEY0444 Myotis lucifugus A ♂ 7-Aug-09 Bubble 13-Aug-10 Bubble 0
DEY0474 Myotis septentrionalis A ♂ 17-Aug-09 Pooler 2-Jun-10 Pooler 0
DEY3272 Myotis leibii A ♂ 21-Apr-10 Bubble 3-Jun-10 Eagle 1.7
DEY3296 Myotis leibii A ♀ 18-May-10 Hemlock 16-Jun-10 Hemlock 0
DEY3373 Myotis septentrionalis A ♀ 10-Jun-10 Gilmore 27-Jul-10 Gilmore 0
DEY4516 Myotis lucifugus A ♂ 30-Jul-10 Jordan 14-Sep-10 Lurvey 6.7
DEY0067 Myotis leibii A ♂ 4-Jun-09 Bubble 28-Aug-09 Eagle 1.7
DEY0864 Myotis lucifugus A ♂ 11-Jun-09 Lurvey 27-Aug-09 Lurvey 0
DEY3689 Myotis septentrionalis J ♀ 6-Aug-11 Cedar 25-Aug-11 Cedar 0
DEY3400 Myotis septentrionalis A ♂ 16-Jun-10 Hemlock 24-Aug-11 Hemlock 0
DEY4768 Myotis septentrionalis A ♂ 31-Aug-10 Hemlock 21-Jul-11 Hemlock 0
DEY4573 Myotis lucifugus A ♂ 13-Aug-10 Bubble 26-Aug-11 Bubble 0
DEY4765 Myotis lucifugus A ♂ 31-Aug-10 Hemlock 24-Aug-11 Hemlock 0
DEY3253 Myotis leibii A ♂ 24-Sep-09 Jordan 27-Aug-11 Jordan 0
DEY3298 Myotis leibii A ♂ 18-May-10 Hemlock 24-Aug-11 Hemlock 0
DEY3260 Myotis leibii A ♂ 13-Apr-10 Hemlock 24-Aug-11 Hemlock 0
DEY3381 Myotis leibii A ♀ 10-Jun-10 Gilmore 6-Aug-11 Cedar 4.3
DEY0026 Myotis leibii A ♂ 1-Jun-09 Hemlock 21-Jul-11 Hemlock 0
22
level do not correspond to species' abundance in the airspace but rather the software's
ability to identify those species given other acoustic interference, including weather, at
the time of recording. Overall daily totals of call sequences (Fig. 1.5) and monthly call
totals (Table 1.6) provide information on seasonal patterns of bat activity on MDI. More
detailed calendar heat maps for each species are shown in Fig 1.6. SonoBat™ software
identified the same four species that I captured most frequently: red bat, little brown bat,
northern long-eared bat, and eastern small-footed bat (Table 1.6). It also identified big
brown bat and hoary bat, species captured only in 2009 and 2011, along with species that
we did not capture: silver-haired bat (Lasionycteris noctavigans) and tricolored bat
(Perimyotis subflavus), species both previously known to occur in Maine (Fujita & Kunz
1984, Manville 1942), and evening bat (Nycticeius humeralis) and Indiana bat (Myotis
sodalis), species not known to occur in Maine. Overall activity based on acoustic calls
generally increased through the 2010 season, with distinct peaks before May 15 until
reaching the highest points of activity at the end of July and early August. Activity then
decreased through September with sharp peaks of activity after August 15 (Figs. 1.5, 1.7).
Figure 1.5. Number of bat call sequences recorded daily in 2010 at Acadia National Park before species
identification analysis. No data were collected prior to April 14 or after October 13.
23
Table 1.6. Number of bat calls recorded in 2010 at Acadia National Park and subsequently identified (ID) to
species through automated analysis using SonoBat™ software.
Species Apr May Jun Jul Aug Sep Oct 2010
All calls before auto ID 911 9334 20020 22122 27379 10220 797 90783
little brown 1 160 1759 2382 2702 568 0 7572
northern long-eared 60 10 21 30 25 5 2 153
eastern small-footed 1 12 3 1 2 1 1 21
red 3 116 184 128 192 54 2 679
big brown 1 5 21 7 10 0 0 44
hoary 0 0 0 1 0 0 0 1
silver haired 0 0 0 1 1 0 0 2
evening 0 6 9 7 9 1 0 32
tricolored 0 4 0 14 1 1 0 20
Indiana 0 0 0 0 2 0 0 2
Total (ID to species) 66 313 1997 2571 2944 630 5 8526
It was difficult to compare data from different sampling methods due to factors
such as sampling effort and human vs. machine sampling error in ability to identify
species. Nonetheless, I plotted results from mist-netting and acoustic sampling
concurrently to interpret activity peaks (Fig 1.7). However, these results are only useful
for interpreting peaks of activity by date because of differences in sampling effort: bat
detectors were sampling continuously (each night), and capture data are only relevant to
dates sampled. However, peaks in capture data appear to correspond with some peaks in
acoustic data, with the best example occurring in late August and early September.
Where capture rates were greater than call sequence rates (May 10, May 28, September
12), weather strongly influenced bat activity. I captured several bats (n = 4, 5, 4,
respectively) in a relatively short time (< 2 hr) before rain began to fall or temperature
plummeted, despite the overall low activity through the course of those nights. The
overlap between bats captured/hour and calls recorded/hour on July 19 was due to
acoustic equipment failure.
24
Figure 1.6. Species' calendar heat maps for call sequences recorded daily in 2010 after automatic identification
using SonoBat™ software. Scales for each map are different despite the same color scheme. No sampling
occurred before April 14 or after October 13 at Acadia National Park, Maine.
25
Figure 1.7. Abundance of bat captures and bat call sequences recorded nightly in 2010 scaled by the highest one-night abundances. On July 30, 51 bats were
captured and on August 3, 2245 call sequences were recorded. Red lines represent conservative estimates of the summer bat season, when bats are not
migratory, as defined by the USFWS for the Northeast region (USFWS 2009). Red circles represent likely migration events. Data equal to zero on the y axis
correspond to non-sampling nights or equipment failure.
26
1.5 Discussion
1.5.1 Overall Capture Success
My mist-net capture rate (3.1 bats/hour) was greater than previous studies at
Acadia National Park (Fig. 1.3; 0.28 bats/hour in 1996–97; Zimmerman 1998; Table 1.7).
I suspect the majority of this increase in capture success can be attributed to
advancements in trapping equipment and methods in the last 10 years and not to a drastic
increase in abundance. Before 2000, triple high mist-nets were used only for specific
applications, but now they are widely used to capture bats and are so effective that they
are required for Indiana bat surveys related to energy development in the Midwest and
Northeast (USFWS 2009). Furthermore, using mist-nets to sample the subcanopy and
canopy contribute to detection of rare species (Velazco et al. 2011).
It is unlikely that bats are considerably more abundant than they were in the
1990's. Because Acadia National Park has been established since 1919, its resources have
been protected and available to bats since at least that time. Much of the island burned in
a fire in 1947 (NPS 2012), but the park is fully forested today and has remained in that
condition since previous bat studies were conducted in the 1990's. However, composition
of the bat community at ANP seems to have changed. Although Atkins & Glanz (2001)
did not report the number of hours of mist-netting in their 34 night study in 1998, they
reported the species composition. Composition during my study was different when
compared to both previous studies (Table 1.7). Little brown bats have dominated captures
since 1996 with northern long-eared bats as the second most abundant species in all
previous studies.
27
Big brown bats were the third most abundant species before my study, and it is
surprising that I only captured 3 animals in 3 years. They are regarded as an abundant
species in North America (Agosta 2002) and capture rates have increased in the
Northeast even after the advent of white-nose syndrome (Francl et al. 2012).
Conversely, red bats are captured more frequently now that white-nose syndrome
has impacted other species (A. Hicks, pers. comm.), and Ford et al. (2011) found that
abundance of red bats has not declined in NY since the onset of white-nose syndrome.
Though comparing results of mist netting efforts by different researchers operating under
different protocols has limitations, this general observed increase in red bats is consistent
with my findings at ANP.
Table 1.7. Bat community composition from previous and current studies at Acadia National Park, Maine.
species Divoll (2009-11) Zimmerman (1996-98) Atkins (2001)
little brown 58.0% 52.0% 39.0%
northern long-eared 26.0% 39.0% 26.0%
eastern small-footed 14.0% 0.4% 0.0%
red 1.4% 0.0% 0.0%
hoary 0.2% 0.4% 0.0%
big brown 0.3% 1.7% 26.0%
1.5.2 Importance of eastern small-footed bats at ANP
Perhaps the most remarkable difference in mist-netting results is the abundance of
eastern small-footed bats (n = 147) captured since 2009 at ANP. This species has always
been considered one of the rarest North American bats and Maine sits at the northern
extent of this species' range (Best & Jennings, 1997). It has a strong relationship with
talus slopes and rock features and prefers these areas as roosting sites (Erdle & Hobson,
28
2001), of which ANP provides plenty of suitable habitat. The Maine Department of
Inland Fisheries & Wildlife conducted surveys for this species across the state in the early
2000's and found few individuals between 2001 and 2005 (D. Yates, pers. comm.). The
St. John Uplands and Boundary Plateau were surveyed in 2001 and 2002 and the
Aroostook lowlands in 2004 without detection of this species, but eastern small-footed
bats were detected near Augusta and in the Carrabassett Valley, Maine (D.Yates, pers.
comm.). One reproductive female was captured at Farrow Mountain, Washington
County, Maine (Morris & Starr 2005). In 1939, one eastern small-footed bat was taken at
Otter Point, ANP (Manville 1942) and in 1996, one eastern small-footed bat was captured
at Schoodic Peninsula, ANP, confirming prior existence before my study (Zimmerman
1998). Biologists targeting this species in 2008 across 21 sites in the central and western
mountains of Maine did not detect any individuals despite the presence of suitable habitat
(Yates & DePue 2008).
In 2008, I captured 4 individuals at ANP during a pilot study. Since then, I have
captured 147 additional individuals between both the eastern and western sides of Mount
Desert Island. Because prime habitat on this island has remained unchanged over the past
100 years, I searched for historical records of this species from collecting trips and ship
logbooks in the late 19th century and early 20th century. ANP maintains an extensive
collection of documents and logbooks, including the Sawtelle Collections, none of which
mentioned bats of any species being collected on the island. I also searched the Ernst
Mayr Natural History Library at Harvard University without success. I searched the
mammal department collections at Harvard's Museum of Comparative Zoology and
found four study skins collected on Mount Desert Island in 1901 by Charles F.
29
Batchelder, all labeled as Myotis leibii. Upon further review, I realized the specimens are
actually northern long-eared bats, specimens that have been mislabeled for over 100
years. The fact that these specimens were collected on MDI in 1901 and labeled as
eastern small-footed bats suggests some prior knowledge that this species may have
existed there.
The eastern small-footed bat has gained attention recently due to a lack of
knowledge and uncertainties in its susceptibility to white-nose syndrome, including a
petition to list it as a federally endangered species (Matteson 2010). The population at
ANP represents one of the largest concentrations of this species known to date,
comparable to other reported populations: 118 roosting in a dam at Surry Lake, NH (J.
Veilleux, pers. comm.), 47 hibernating in a railroad tunnel in Maryland (Johnson & Gates
2008), 61 roosting in natural talus in West Virginia (Johnson et al. 2011), 33 roosting in
an old wooden cabin at high elevation in North Carolina (O'Keefe & LaVoie 2010), and
29 roosting in natural rock outcrops in southern Illinois (Whitby et al. 2013).
In 2010 at ANP, only 21 eastern small-footed bats were detected by acoustic
methods, with 12 of those bats appearing in May (Table 1.6). These results (0.02% of all
calls identified) do not align with the 15% captured by mist-netting. Of all calls
identified, 89% were little brown bats, and only 1.8% were northern long-eared bats,
which was not surprising since northern long-eared bats forage primarily by gleaning and
are considered "whispering bats", calling at very high frequencies (60–125 kHz) and
potentially avoiding acoustic detection (Faure et al. 1993). On the contrary, eastern small-
footed bats forage mainly by aerial hawking and their calls display similar echolocation
characteristics to those of little brown bats. Mukhida et al. (2004) documented the
30
capacity to shift call frequencies in both eastern small-footed bats and little brown bats
while held in captivity and confined to the same room. Although never previously
documented, eastern small-footed bats may shift call frequencies while free-flying in the
wild, particularly in the presence of little brown bats. This propensity may have hindered
SonoBat™'s ability to distinguish accurately between the two species when little brown
bats dominated the population. The month of May experienced the greatest evenness and
equitability accompanied by the lowest dominance from any one species in the
population (Table 1.4, Fig. 1.4); this equality in community structure may have
contributed to the higher detection rate of eastern small-footed bats in that month versus
other months. In an ecological sense, little brown bats may have congested the airspace
later in the summer, making other species harder to detect. My capture results support
this crowding idea strongly as northern long-eared bats and eastern small-footed bats
were present at ANP before and after detection of little brown bats. Furthermore,
abundance of little brown bats increased dramatically in June, July and August,
supporting the idea that they suppress detection of other species then.
1.5.3 Bat migration at ANP
Red bats were detected acoustically (8% of total) in 2010 more than anticipated
when compared to the number captured in mist-nets (1.5% of total). The presence of red
bats was consistent in the acoustic data throughout the sampling period (May–
September); yet they were only captured in July and August. Menzel et al. (2005)
compared acoustic activity and foraging height in South Carolina across species and
habitats. In that study, red bats foraged above the canopy more often than below the
canopy. My acoustic sampling sites were located on open road corridors 8 m high in the
31
forest. The range of detectors covered an area above the canopy over the road corridor.
Therefore, red bats foraging above the canopy at ANP were easily detected, explaining
the greater percentage (8%) of red bats detected acoustically compared to red bats
captured (1.4%) in mist-nets.
Overall bat activity at Acadia National Park was highest from late July through
early September with several distinct peaks when I considered both sampling methods.
Acoustic detections were most frequent in early August, whereas the highest capture rates
were observed on July 31, 2010 (48 bats) and August 29, 2010 (51 bats). When scaled for
sampling effort, the activity peaks highlighted in Figure 1.7 appear drastic in the early
and late seasons. USFWS considers May 15 to August 15 an accurate representation of
summer habitat use by bats (USFWS 2009), supporting the nature of the sharp activity
peaks experienced at ANP outside that summer season. Peaks in bat activity before May
15 and after August 15 likely correspond to migration events, given these USFWS
acceptable dates for summer bat residence in the Northeast. Total captures from busy
mist-netting nights in late summer under-represent the number of individuals actually
captured. On these nights, several individuals became entangled in the net, and many
chewed their way out of the netting before my assistants or I could extract them. These
events are consistent with a social bat aggregation (Gerth 2010). In many temperate
locales, these aggregations are related to swarming events at winter hibernacula when
bats congregate to mate (Johnson & Gates 2007). Some males during my study displayed
what appeared to be recent penile use. Though these observations are anecdotal and no
hibernacula are known at ANP, these late summer aggregations may suggest
undiscovered hibernacula on MDI. A deep cave may exist, beneath the talus or through a
32
rock fissure on one of ANP's mountains. In Chapter 2 of this thesis, I explore the
importance of these aggregations using stable isotopes.
1..5.4 Potential future research
Future studies to build on this project should include sampling several mist-net
locations in the same night and collecting environmental data such as temperature,
humidity, moon phase, surrounding topography, wind speed and direction at each site to
better correlate bat activity in microhabitats. ANP is well-suited for this study design, and
results would be highly applicable to all mist-netting surveys to aid in site selection and
increase capture rates. Correlations with environmental conditions would also provide
insights on rare species preferences, such as the eastern small-footed bat. Acoustic
surveys at ANP should plan for and include manual analysis of calls recorded after an
initial scan by SonoBat™ software. The determinations by SonoBat™ that Indiana bats
and evening bats were recorded at ANP are nearly impossible based on species' ranges,
but if I had planned several additional months of analysis, manual vetting of suspicious
call identifications may have yielded different results. Furthermore, studies that aim to
quantify correlations between acoustic calls recorded and bats captured in mist-nets could
be useful in many situations where only one method or the other is available.
33
1.6 Literature Cited
Agosta, S. J. 2002. Habitat use, diet and roost selection by the Big Brown Bat (Eptesicus
fuscus) in North America: a case for conserving an abundant species. Mammal
Review, 32, 179–198.
Atkins, L. & Glanz, W. 2001. Roosting habits of bats in Acadia National Park, in
coastal Maine. Department of Biological Sciences, University of Maine. National
Park Service Report, 1–44.
Baerwald, E. F., D'Amours, G. H., Klug, B. J., & Barclay, R. M. 2008. Barotrauma is
a significant cause of bat fatalities at wind turbines. Current Biology, 18, R695–
R696.
Best, T. L., & Jennings, J. B. 1997. Myotis leibii. Mammalian Species 547, 1–6.
Blehert, D. S., Hicks, A. C., Behr, M., Meteyer, C. U., Berlowski-Zier, B. M.,
Buckles, E. L., Coleman, J. T. H., Darling, S. R., Gargas, A., Niver, R.,
Okoniewski, J. C., Rudd, R. J., & Stone, W. B. 2009. Bat white-nose
syndrome: an emerging fungal pathogen? Science 323, 227.
Blehert, D. S. 2012. Fungal disease and the developing story of bat white-nose
CHAPTER 2: Temporal isotopic niche overlap in three sympatric Myotis species
determined using 13
C and 15
N signatures
2.1 Introduction
Stable isotopes have become a powerful tool in ecological studies when research
objectives aim to relate individuals or groups of animals to their environment (Newsome
et al. 2012). Ecological processes such as photosynthetic rates, temperature, rainfall, and
soil moisture can affect the ratio of heavy to light isotopes (signature) in a local
environment (Ben-David & Flaherty 2012). An isotopic signature in an animal is
assimilated from its diet and incorporates a discrimination factor from processes such as
enzymatic reactions as the animal consumes and assimilates food and excretes waste
(Ben-David & Flaherty 2012). Different tissues types may assimilate an isotopic
signature at different rates (Martinez del Rio & Carleton 2012); thus, making inferences
about a consumer's diet is possible by measuring isotopic values in animal tissues
(Phillips 2012).
Given the generally low recapture rates of banded bats (Ellison 2008), many
researchers are turning to intrinsic markers such as stable isotopes to obtain data on
individual bats. Stable isotopes are one new ecological tool that has potential use for
interpreting bat movements and migration (Fleming 1993, Reichard 2010) through
isotopic signatures acquired at geographic locations, thus relating individuals to their
environment. Applications such as bat foraging preferences (Voigt & Kelm 2006),
individual specialization (Cryan et al. 2012), and migration (Cryan et al. 2004; Britzke et
al. 2009; Popa-Lisseaunu et al. 2012) have been investigated in the last 10 years. Stable
deuterium (D) is typically used to infer bat movements and migrations because of its
40
reliability (Britzke et al. 2009, Cryan et al. 2004) and natural precipitation D gradient in
the environment in continental-sized isoscapes (Bowen & Revenaugh 2003). D is useful
for bat species migrating long distances but will not stand alone to provide the resolution
needed to determine local movements and migrations, most of which are less than
continental in distance (Britzke et al. 2009). Furthermore, Cryan et al. (2004) found that
D in bat hair generally correlated with D in local precipitation, whereas Britzke et al.
(2009) found weak relationships between bat hair and precipitation D. Inconsistencies
with D as a stand-alone isotope have inspired multi-isotope analyses to study bat
migration (Popa-Lisseaunu et al. 2012, Fraser 2011) and variation within a population
(Cryan et al. 2012, Fraser 2011). Using several isotopes gives more resolution to
assigning the origin of isotopic accumulation in bat hair (Popa-Lisseaunu et al. 2012).
These aforementioned studies on bat migration and population variation used only bat
hair, which is keratinous, fixing environmental isotopic data at the time of tissue growth
(Voigt et al. 2003). Hair isotopes are informative to isotopic sources at the time of hair
growth. Thus, we can infer foraging strategies based on differences in isotopic input such
as prey type or geographic location.
Stable carbon (13
C) and nitrogen (15
N) isotopes are often used for foraging and
food web studies (Peterson & Fry 1987). In this study, I used 13
C and 15
N to determine
if they can be used at different resolutions, e.g., tissue types, to assess isotopic signature
variation within the population at multiple levels of temporal detection. Hair, skin, and
blood tissue samples each provide isotopic signatures accumulated through bats' diets,
and each tissue type holds these signatures until the tissue “turns over” or regenerates
(Voigt et al. 2003). Unpublished laboratory studies suggest that big brown bats, Eptesicus
41
fuscus, (temperate, insectivorous) turn over hair isotopes once per year, skin every two to
three months, and blood every two to three weeks (Robert Michener, pers. comm.).
Because of unknown differences in local isoscapes in the Northeast and unknown
bat migration pathways, I did not try to assign origins to individual bats. Instead, I used
13
C and 15
N and multiple tissue types to determine how Acadia National Park's (ANP)
bat population changes temporally as a proxy of population mixing. If bats are captured
after travelling from locations outside of Mount Desert Island (MDI), then differences in
isotope signatures representative of distinct foraging locations may be present in samples
collected from bats at ANP. Greater variation in mean isotope values may be correlated
to bats that accumulated those values from a greater variation of locations rather than a
broad prey base at one location. Less variation around mean isotope values may
correspond to an accumulation of those values from bats foraging on a small prey base in
a similar location. All isotope values observed may represent the local signature on MDI,
and bats may not have moved onto or off the island during the sampling period. Without
baseline regional isotopic data, the best we can do is compare significant changes in
isotopic variation with abundance and diversity changes at time points within the
sampling period (April - September, 2010).
To detect significant temporal differences in mean isotope values, a variety of
tests have been used in different situations, as outlined in five studies using bats. Fleming
et al. (1993) compared one isotope, 13
C, across months, and detected significant
differences in nectar-feeding bats. Voigt et al. (2003) and Herrera et al. (1993) found
significant differences in mean values of 13
C across tissues from nectar-feeding bats. In
captivity, bat 13
C values corresponded to diet when bats were fed a diet enriched with
42
either C3 or C4/CAM carbon sources (Voigt et al. 2003). In the wild, stable carbon
isotope signatures in Antrozous pallidus differed depending on temporal agave blooms;
when cacti bloomed, bats accumulated a CAM carbon signature while presumably
preying on insects visiting blooms (Herrera et al. 1993). All of these univariate analyses
used one isotope to detect temporal differences in nectar-feeding bats. Reichard (2010)
analyzed three isotopes (13
C, 15
N, D) and found that discriminant function analysis
(DFA) was not reliable in determining differences in isotope averages between sites in
Mexican free-tailed bats, Tadarida brasiliensis, an insectivorous species. Popa-Lisseaunu
et al. (2012) used DFA to test assignment of isotopic groups to known origination with
three isotopes (13
C, 15
N, D) at 93% correct assignment, showing that DFA can
determine distinct isotopic groups. Pulling out one of the three isotopes, correct
assignment rates fell to less than 90%. The difference in results of DFA are likely related
to the distances bat species were migrating in each study as D differences are most
detectable at larger geographical scales (Reichard 2010, Popa-Lisseaunu et al. 2012).
My study species, Myotis spp., eat insects, so I expect that DFA will not be an
appropriate test to assess variation related to individual geographic variations with only
two isotopes (13
C, 15
N). However, recent advancements in multivariate stable isotope
analyses in wildlife studies have spawned robust Bayesian methods that account for
natural variation and uncertainties in sampling methods (Parnell et al. 2010) and
differences in sample sizes (Jackson et al. 2011). Bayesian inference is not a new method
but has increasingly been used in ecological studies (Ellison 2004). Particularly, Stable
Isotope Bayesian Ellipses in R (SIBER) methods have been used to distinguish invasive
lionfish (Pterois volitans) in the Bahamas (Layman & Allgeier 2012) and invasive
43
crayfish (Procambarus clarkii) and carp (Cyprinus carpio) in Kenya (Jackson et al. 2012)
by comparing isotopic niche widths between groups. These methods allow statistical
comparison of two isotopes in -space (Newsome et al. 2007) as a bivariate unit rather
than plotting two isotopes in isospace and subsequently comparing and making
inferences based on univariate analyses for each isotope. These robust methods have not
been implemented previously in the context of bat population dynamics. I predicted that
using SIBER ellipses with Bayesian estimates would be a useful tool to detect differences
in isotopic niche width and overlap between the three sympatric species at ANP: Myotis
lucifugus, M. septentrionalis, and M. leibii.
2.2 Methods
2.2.1 Sample collection
I attempted to collect blood, hair, and skin samples from at least ten individuals
each of little brown bats (M. lucifugus), eastern small-footed bats (M. leibii), and northern
long-eared bats (M. septentrionalis) monthly in 2010. After capture in mist-nets, I clipped
approximately 0.3 g (2 - 3 small scissor clips) of hair from between the scapulae of each
bat and stored hairs in a 1.5 mL micro-centrifuge tube. Skin samples were collected with
3 mm Acuderm® biopsy punches by placing the bat's wing flat on a paper coin envelope
and stamping through the membrane at a location away from blood vessels. Skin punches
were also stored in 1.5 mL micro-centrifuge tubes. Blood samples were collected with 28
gauge needles by pricking the vein in the uropatagium and collecting approximately 25
µL with a capillary tube before applying styptic gel to clot the blood. Capillary tubes
were then placed in Microtainer® tubes. All samples were labeled with the bat's unique
44
identifier and frozen for 1 to 4 months until they were shipped to Boston University’s
Stable Isotope Laboratory for analysis.
2.2.2 Laboratory analysis
Samples were analyzed using automated continuous-flow isotope ratio mass
spectrometry (Michener & Lajtha, 2007). All specimens were oven dried at 60˚C for 24
hours. They were then powdered using a mortar and pestle. The samples were combusted
in a EuroVector Euro EA elemental analyzer. Combustion gases (N2 and CO2) were
separated on a gas chromatography (GC) column, passed through a reference gas box and
introduced into the GV Instruments IsoPrime isotope ratio mass spectrometer; water was
removed using a magnesium perchlorate water trap. Ratios of 13
C/12
C and 15
N/14
N were
expressed as the relative permil (‰) difference between the samples and international
standards (Vienna Pee Dee Belemnite carbonate and N2 in air) where:
X= (Rsample/ Rstandard-1) x 1000 (‰)
Where X =13
C or 15
N and R=13
C or 15
N/14
N
The sample isotope ratio was compared to a secondary gas standard, whose isotope ratio
was calibrated to international standards. For 13
CV-PDB the gas was calibrated against NBS
20 (Solenhofen Limestone). For 15
Nair the gas was calibrated against atmospheric N2 and
IAEA standards N-1, N-2, and N-3 (all are ammonium sulfate standards). All
international standards were obtained from the National Bureau of Standards in
Gaithersburg, MD. In addition to carbon and nitrogen isotopes from the same sample,
continuous flow also reported % C and % N data.
45
2.2.3 Statistical methods
I tested for differences between adult and juvenile age classes within species as
well as intra- and interspecific comparisons by month and tissue type. Intra- and
interspecific differences were compared using multivariate Stable Isotope Bayesian
Ellipses in R (SIBER) to allow simultaneous comparison of carbon and nitrogen together
in isotopic space as a more robust method over many univariate comparisons with
subsequent inferences (Jackson et al. 2011). Using the Stable Isotope Analysis in R
package (Parnell et al. 2010) in program R (R Core Team 2012) I created ellipses
representing 40% of each group’s data based on the standard ellipse area with a small
sample size correction (SEAc) calculated with frequentist statistics (Jackson et al. 2011).
These ellipses represent each group’s isotopic niche width where a typical individual
from that group normally falls within that ellipse in -space. For comparison, convex
hulls shown around the perimeter of each group correspond to the isotopic niche breadth
of each species (Layman et al. 2007). Ellipses were then estimated using Bayesian
calculations to determine significant differences between groups based on credible
intervals of estimations of ellipses after 104 permutations.
First, adults were compared to juveniles of the same species and the amount of
overlap in isotopic niche calculated. Adults represent individuals captured between April
and September where juveniles are not volant until July and only captured thereafter. To
determine if isotopic differences between adults and juveniles affected grouping results,
Welch two sample t-tests were performed on each isotope for each species and tissue type
(R Core Team 2012), as independent comparisons. I then applied a Benjamini-Hochberg
correction for multiple comparisons to reduce the false discovery rate (Benjamini &
46
Hochberg 1995) using the open source software "Bonferroni Calculator" (Lesack &
Naugler 2011). Results from Welch two tailed t-tests correspond to adult versus juvenile
within isotope, within tissue type. Because most juveniles fell within the isotopic range of
adults for their respective species, juveniles were included in species’ groupings for all
further multivariate isotopic analyses. Skin samples have low variance when compared
within one species (Sullivan et al. 2006). Therefore, isotopic niche overlap comparisons
were conducted with skin isotopes, which represent up to 2 to 3 months of dietary input,
at which point the tissue completely regenerates (Robert Michener, pers. comm.).
Comparisons in overlap would have been biased if based on blood because the resolution
is only two weeks (Robert Michener, pers. comm.). Hair isotopes are acquired at the time
of hair growth and then remain somewhat fixed until the next molt (1 per year), typically
in late summer (Quay 1970; Cryan et al. 2012). Comparisons of niche overlap using hair
would be biased toward June for juveniles when they grow new hair and would be split
between two years for adults.
Next, adults and juveniles were combined in respective groups by species and
sampling season for further Bayesian comparisons of niche widths. For these analyses,
bats captured in April and May were lumped into "early season", May and June were
lumped into "mid season" and August and September were grouped as "late season". To
conserve space in figures in the Results section, eastern small-footed bats were referred to
as “small-foot” and northern long-eared bats were simply called “northern”. A
significance level of α = 0.05 was used in all univariate and bivariate tests.
47
2.3 Results
2.3.1 Adult vs. juvenile isotopic overlap
In most cases, the range of juvenile isotopic values fell within the range of values
for adults of the parent species, with the exception of nitrogen in blood of little brown
bats and nitrogen in hair of eastern small-footed bats (Table 2.1; prior to correction).
When adults were compared to juveniles using multivariate methods, it is possible
to detect significant differences in isotopic niche width (Figs. 2.1.b, 2.2.b, 2.3.b) as well
as to determine where distinct foraging groups overlap in isotopic niche (Figs. 2.1.a,
2.2.a, 2.3.a). Isotopes from skin samples of adult (SEA.B mode = 1.41, 95% CI = 1.07 to
1.88) and juvenile (SEA.B mode = 1.45, 95% CI = 0.706 to 3.62) eastern small-footed
bats did not differ (p = 0.3), with 75% of juveniles falling within the typical niche width
of adults (Figs. 2.1.a, 2.1.b). Adult and juvenile skin isotopes of little brown bats differed
significantly (p = 0.01), and juveniles (SEA.B mode = 9.6, 95% CI = 4.45 to 23.8) far
exceeded the niche width of typical adults (SEA.B mode = 4.89, 95% CI = 3.59 to 6.67;
Figs. 2.2.a, 2.2.b). Northern long-eared bats showed a similar relationship for adult and
juvenile skin isotopes to eastern small-footed bats. Seventy-four percent of juvenile
(SEA.B mode = 1.87, 95% CI = 0.639 to 6.45) northern long-eared bats fell within the
range of typical adults (SEA.B mode = 1.39, 95% CI = 1.06 to 1.89) with no statistical
difference between age classes (p = 0.14; Fig. 2.3.b). Adult hair isotopes covered a
greater isotopic niche and differed significantly from adult blood and skin isotopes in all
species (with the exception of blood in northern long-eared bats, p = 0.84). In contrast,
tissue types in juvenile eastern small-footed bats and northern long-eared bats did not
differ significantly within each species (eastern small footed: p = 0.27, p = 0.38, p = 0.62;
48
northern long-eared: p = 0.51, p = 0.56, p = 0.56; Figs. 2.1.a, 2.3.a). However, little
brown bat juveniles' skin covered a greater isotopic niche than blood (p = 0.006) but not
hair (p = 0.68; Fig. 2.2.a).
Table 2.1. Univariate comparisons of adult and juvenile isotopes by tissue type. P-values marked with *
represent significant differences between adult and juvenile for that category based on Welch two-sample t-tests.
The "fdr" correction is the false discovery rate correction (Benjamini & Hochberg 1995).
Adult n Juvenile n t-test
"fdr"
correction
blood 13C
small-foot –25.41 ± 0.77 49 –25.29 ± 0.93 6 t = –0.30, df = 5.88, p = 0.78 p = 0.88
little brown –26.03 ± 1.80 40 –26.09 ± 1.18 7 t = 0.11, df = 11.55, p = 0.92 p = 0.97
northern –24.82 ± 0.91 47 –24.64 ± 0.47 3 t = –0.58, df = 3.065, p = 0.60 p = 0.87
blood 15N
small-foot 6.11 ± 0.43 49 5.71 ± 0.43 6 t = 2.20, df = 6.34, p = 0.07 p = 0.32
little brown 6.77 ± 1.14 40 7.86 ±0.67 7 t = –3.89, df = 8.77, p = 0.0038* p = 0.07
northern 6.42 ± 0.78 47 6.35 ± 0.32 3 t = 0.34, df = 3.78, p = 0.75 p = 0.88
hair 13C
small-foot –24.18 ± 1.59 49 –24.60 ± 0.45 6 t = 1.41, df = 25.72, p = 0.17 p = 0.61
little brown –26.40 ± 3.53 39 –26.38 ± 3.46 6 t = –0.017, df = 6.70, p = 0.99 p = 0.99
northern –23.93 ± 1.05 47 –24.10 ± 0.37 3 t = 0.63, df = 4.58, p = 0.56 p = 0.87
hair 15N
small-foot 6.63 ± 0.55 49 7.05 ± 0.39 6 t = –2.35, df = 7.66, p = 0.048* p = 0.32
little brown 8.20 ± 1.22 39 8.39 ± 0.92 6 t = –0.44, df = 8.00, p = 0.67 p = 0.87
northern 7.30 ± 0.85 47 8.06 ± 0.40 3 t = –2.88, df = 3.29, p = 0.057 p = 0.32
skin 13C
small-foot –25.03 ± 0.64 49 –25.24 ± 0.71 6 t = 0.69, df = 6.04, p = 0.51 p = 0.87
little brown –26.41 ± 1.67 40 –27.20 ± 3.19 6 t = 0.60, df = 5.42, p = 0.57 p = 0.87
northern –24.31 ± 0.53 47 –24.38 ± 0.24 3 t = 0.45, df = 3.47, p = 0.68 p = 0.87
skin 15N
small-foot 7.36 ±0.71 49 7.58 ±0.37 6 t = –1.18, df = 10.22, p = 0.26 p = 0.78
little brown 9.15 ±1.00 40 8.89 ±1.33 6 t = 0.45, df = 5.88, p = 0.67 p = 0.87
northern 8.04 ±0.79 47 8.33 ±0.71 3 t = –0.68, df = 2.33, p = 0.56 p = 0.87
49
Figure 2.1.a. Comparison of isotopes in adult and juvenile eastern small-footed bats by tissue type with a small sample size correction. Ellipses encompass 40%
of the data points in each group and represent the isotopic niche width of each group.
50
Figure 2.1.b. Density plots with credible intervals of Bayesian estimates of ellipses representing the 50th, 75th, and 95th percentiles and the mode shown in black.
Groups that share a letter are not statistically different than one another.
51
Figure 2.2.a. Comparison of isotopes in adult and juvenile little brown bats by tissue type with a small sample size correction. Ellipses encompass 40% of the
data points in each group and represent the isotopic niche width of each group.
52
Figure 2.2.b. Density plots with credible intervals of Bayesian estimates of ellipses representing the 50th, 75th, and 95th percentiles and the mode shown in black.
Groups that share a letter are not statistically different than one another.
53
Figure 2.3.a. Comparison of isotopes in adult and juvenile northern long-eared bats by tissue type with a small sample size correction. Ellipses encompass 40%
of the data points in each group and represent the isotopic niche width of each group.
54
Figure 2.3.b. Density plots with credible intervals of Bayesian estimates of ellipses representing the 50th, 75th, and 95th percentiles and the mode shown in black.
Groups that share a letter are not statistically different than one another.
55
2.3.2 Comparisons of blood isotopes
Analyzing each species independently by tissue type and sampling season
provides detailed insight on shifts in temporal isotopic niche at an intraspecific level,
whereas analyses of tissue types independently by species and sampling season provides
a broad interspecific view of differences between species within each season (Figs. 2.4.a,
2.4.b, 2.5.a, 2.5.b, 2.6.a, 2.6.b). Blood isotopes offer faster resolution than skin or hair
and are optimal for temporal interspecific isotope analyses. Blood isotopes from eastern
small-footed bat started with a tight grouping in the early season and expanded in the mid
and late seasons (Figs. 2.4.a, 2.4.b). Late season blood isotopes in this species covered a
greater isotopic niche than mid season (p = 0.039) and early season (p = 0.005), but early
and mid season values did not differ (p = 0.809; Fig. 2.4.b). Blood isotopes in little brown
bats did not differ across seasons (Fig. 2.4.b). Northern long-eared bats showed distinct
temporal shifts in blood isotopic niche with a much greater niche in late season when
compared to early season (p = 0.011) and mid season (p = 0.0002), with no difference
between early and mid seasons (p = 0.211; Fig. 2.4.b).
When analyzing blood at an interspecific level within seasons, eastern small-
footed bats and northern long-eared bats did not differ in the early (p = 0.937) and mid (p
= 0.483) seasons. However, in the late season northern long-eared bats covered a larger
isotopic niche (SEA.B mode = 3.77, 95% CI = 2.43 to 5.77) than eastern small-footed
bats (SEA.B mode = 1.89, 95% CI = 1.17 to 3.26; p = 0.03; Figs. 2.4.a, 2.4.b). Little
brown bats exhibited a much broader blood isotope niche (SEA.B mode = 5.95, 95% CI
= 3.08 to 12.80) than both eastern small-footed bats (SEA.B mode = 0.83, 95% CI = 0.54
to 1.27; p < 0.0001) and northern long-eared bats (SEA.B mode = 1.28, 95% CI = 0.72 to
2.70; p = 0.001) in the early season (Figs. 2.4.a, 2.4.b). Mid season blood isotopes in little
56
brown bats (SEA.B mode = 4.12, 95% CI = 2.65 to 6.53) also covered a greater niche
width than both eastern small-footed bats (SEA.B mode = 1.06, 95% CI = 0.68 to 1.71; p
< 0.0001) and northern long-eared bats (SEA.B mode = 1.05, 95% CI = 0.69 to 1.66; p <
0.0001). Little brown bat blood isotopes in the late season (SEA.B mode = 4.65, 95% CI
= 3.03 to 7.62) covered a greater niche width than eastern small-footed bats (p= 0.005)
but not northern long-eared bats (p = 0.217; Figs. 2.4.a, 2.4.b).
2.3.3 Comparisons of hair isotopes
In the early season, all bats' hair represented isotopic values accumulated the
previous summer. Eastern small footed bats covered a significantly reduced niche in hair
isotopes (SEA.B mode = 0.59, 95% CI = 0.41 to 0.95) compared to both little brown bats
(SEA.B mode = 9.22, 95% CI = 5.02 to 20; p < 0.0001) and northern long-eared bats
(SEA.B mode = 1.54, 95% CI 0.83 to 3.2; p = 0.004; Figs. 2.5.a, 2.5.b). Little brown bats'
niche far exceeded northern long-eared bats' in the early season (p < 0.0001; Fig. 2.5.b).
Eastern small-footed bats and northern long-eared bats did not differ in hair isotopes mid-
season (p = 0.865) when juveniles grow new hair and some adults may start molting
(Quay 1970). Little brown bats in mid-season (SEA.B mode = 6.58, 95% CI = 4.25 to
10.30) covered a significantly greater niche based on hair isotopes than other species
(SEA.B mode = 0.93, 95% CI = 0.62 to 1.51; SEA.B mode = 1.36, 95% CI = 0.89 to
2.12; p < 0.0001, each comparison; Figs. 2.5.a, 2.5.b). In the late season, when juveniles
have newly grown hair and adults have already molted (Quay 1970), eastern small-footed
bats and northern long-eared bats did not differ (p = 0.511). However, little brown bats
(SEA.B mode = 13.8, 95% CI = 8.94 to 23) significantly increased in niche width
57
compared to each species (SEA.B mode = 3.55, 95% CI = 2.27 to 3.92; SEA.B mode =
3.58, 95% CI = 2.53 to 5.89; p < 0.0001, each comparison; Figs. 2.5.a, 2.5.b).
2.3.4 Comparisons of skin isotopes
Skin isotopes present a resolution of two to three months of tissue turnover; hence
isotope signatures in this tissue are inter-year representations useful for comparing
different sampling seasons. Early season skin isotopes differed, with eastern small-footed
bats showing a reduced isotopic niche (SEA.B mode = 0.91, 95% CI = 0.62 to 1.47)
compared to little brown bats (SEA.B 3.75, 95% CI = 1.96 to 7.95; p < 0.0001), but no
difference between eastern small-footed bats and northern long-eared bats (SEA.B mode
= 1.47, 95% CI 0.8 to 3.01; p = 0.082; Figs. 2.6.a, 2.6.b). Little brown bats and northern
long-eared bats did not differ significantly in skin isotopes in the early season (p =
0.022), but little brown bats exhibited greater niche width than northern long-eared bats
in both mid and late seasons (p < 0.0001, each season; Figs. 2.6.a, 2.6.b). Little brown
bats had a greater skin isotopic niche than eastern small-footed bats in mid and late
seasons (p < 0.0001, each season), whereas skin isotopes in eastern small-footed bats and
northern long-eared bats did not differ in mid season (p = 0.402) or late season (p =
0.677; Figs. 2.6.a, 2.6.b).
58
Figure 2.4.a. Blood isotope ellipses per season with a small sample size correction. Ellipses encompass 40% of the data points in each group and represent the
isotopic niche width of each group.
59
Figure 2.4.b. Density plots with credible intervals of Bayesian estimates of ellipses representing the 50th, 75th, and 95th percentiles and the mode shown in black.
Groups that share a letter are not statistically different than one another.
60
Figure 2.5.b. Hair isotope ellipses per season with a small sample size correction. Ellipses encompass 40% of the data points in each group and represent the
isotopic niche width of each group.
61
Figure 2.5.b. Density plots with credible intervals of Bayesian estimates of ellipses representing the 50th, 75th, and 95th percentiles and the mode shown in black.
Groups that share a letter are not statistically different than one another.
62
Figure 2.6.a. Skin isotope ellipses per season with a small sample size correction. Ellipses encompass 40% of the data points in each group and represent the
isotopic niche width of each group.
63
Figure 2.6.b. Density plots with credible intervals of Bayesian estimates of ellipses representing the 50th, 75th, and 95th percentiles and the mode shown in black.
Groups that share a letter are not statistically different than one another.
64
2.4 Discussion
2.4.1 Consideration of diet
Stable isotope analyses reveal isotopic information about an individual via
assimilation through dietary uptake (Peterson & Fry 1987). In the case of ANP's bats,
isotopic information relates to the local environment and invertebrates consumed as prey
items. Differences in isotopic variation in a sample group may relate to changes in
foraging strategies. For example, a bat species’ diet may be dominated by one type of
insect and then shift to another type depending on temporal abundances (hatches) and life
cycles of invertebrates (Anthony & Kunz 1977). Variation in invertebrate isotopic
signatures can depend on carbon and nitrogen sources in the local environment, hence
natural variation in invertebrate isotopes can measure population niche width (Bennett &
Hobson 2009). Since prey sources can vary, smaller variation in bat isotopic niches may
correlate with consistent local foraging by most members of that group by way of
isotopic origination in plants consumed by insects at that locale. I postulated that large
variations in bat isotopes may correlate to a broader prey base, or to isotopic inputs from
a non-local source, if bats migrated from elsewhere before being captured and sampled at
ANP.
To compare isotopic niches between species, we must consider what prey each
species may prefer and eat most frequently, as an influence on a group's collective
isotopic variation. Little brown bats are considered generalists. In one Ontario study, they
consumed 66 different prey species, mostly mass emerging aquatic insects (Clare et al.
2011). Similarly, in southern New Hampshire, little brown bats feed mostly on dipterans
(true flies) and lepidopterans (moths) but include a wide variety of other invertebrates as
65
well (Anthony & Kunz 1977). This broad prey base appears to influence isotopic niche
widths in my study because little brown bats tended to have consistently larger SEA.B
values than eastern small-footed bats and northern long-eared bats, irrespective of tissue
type or sampling season.
However, eastern small-footed bats also eat from a broad prey base, and in West
Virginia, they preferred to eat lepidopterans and dipterans most frequently and several
other orders of insects less commonly (Johnson & Gates 2007). A study in New
Hampshire also found that lepidopterans, dipterans, and coleopterans (beetles) dominated
the diet (Moosman et al. 2007). The authors did not detect a significant shift away from
preferred prey items between early, mid, and late season but noticed seasonal
fluctuations. Furthermore, juveniles seemed to eat more beetles than adult males did.
Beetles may be easier (slower, more direct flight than dipterans and lepidopterans) for
juveniles to catch when they are learning to hunt. This observation is supported by other
results showing that little brown juveniles fed randomly when learning to hunt, not
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