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

Acknowledgments............................................................................................................... 1

ABSTRACT ........................................................................................................................ 3

CHAPTER 1: Bat community structure inferred from mist-netting and acoustic detection

............................................................................................................................................. 5

1. 1 Introduction .............................................................................................................. 5

1.2 Study Area ............................................................................................................... 9

1.3 Methods.................................................................................................................. 12

1.3.1 Mist-Netting ................................................................................................ 12

1.3.2 Acoustic Sampling ....................................................................................... 14

1.4 Results .................................................................................................................... 15

1.4.1 Mist-Netting ................................................................................................ 15

1.4.2 Recaptures................................................................................................... 20

1.4.3 Acoustic Sampling ....................................................................................... 21

1.5 Discussion .......................................................................................................... 26

1.5.1 Overall Capture Success ............................................................................. 26

1.5.2 Importance of eastern small-footed bats at ANP ........................................ 27

1.5.3 Bat migration at ANP.................................................................................. 30

1.5.4 Potential future research ............................................................................ 32

1.6 Literature Cited ...................................................................................................... 33

CHAPTER 2: Temporal isotopic niche overlap in three sympatric Myotis species,

determined using 13

C and 15

N signatures ...................................................................... 39

2.1 Introduction ............................................................................................................ 39

2.2 Methods.................................................................................................................. 43

2.2.1 Sample collection ........................................................................................ 43

2.2.2 Laboratory analysis .................................................................................... 44

2.2.3 Statistical methods ...................................................................................... 45

2.3 Results .................................................................................................................... 47

2.3.1 Adult vs. juvenile isotopic overlap .............................................................. 47

2.3.2 Comparisons of blood isotopes .................................................................. 55

2.3.3 Comparisons of hair isotopes ..................................................................... 56

2.3.4 Comparisons of skin isotopes...................................................................... 57

iv

2.4 Discussion .............................................................................................................. 64

2.4.1 Consideration of diet................................................................................... 64

2.4.2 Niche width comparisons ............................................................................ 66

2.4.3 Potential hibernacula.................................................................................. 69

2.5 Literature Cited ...................................................................................................... 70

CONCLUSIONS............................................................................................................... 74

List of Tables

Table 1.1. Movements documented for Maine bat species using the most conservative

observations from the literature. ......................................................................................... 8

Table 1.2. Mist-net locations for capturing bats at Acadia National Park, Maine, 2009 -

2011................................................................................................................................... 10

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. .......................................................................... Error! Bookmark not defined.

Table 1.4. Diversity calculations based on bats captured on Mount Desert Island for each

month sampled in 2010. ..................................................... Error! Bookmark not defined.

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. ................................... 21

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. ............................................................................................................................ 23

Table 1.7. Bat community composition from previous and current studies at Acadia

National Park, Maine. ....................................................................................................... 27

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). ................................................................ 48

v

List of Figures

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. ............................................................................ 11

Figure 1.2. Monthly capture abundances of bat on Mount Desert Islands without

consideration of sampling time per month. Green to red relational color scale displays

highest abundances in red across all species to lowest in green. Blue to grey relational

color scale displays highest totals in blue to lowest totals in grey. Abbreviated species

names: "little" = little brown, "northern" = northern long-eared, "eastern" = eastern small-

footed, "big" = big brown. ................................................................................................ 18

Figure 1.3. Capture rates for each month of sampling bats as individuals/hour on Mount

Desert Island, Maine. Green to red scheme reflects rates compared across species with

highest rates in red, lowest rates in green. Blue to grey schemes correspond to respective

totals across species and across sampling month as well as hours of sampling per month,

respectively: highest totals rates are in blue with lowest in grey. Abbreviated species

names: "little" = little brown, "northern" = northern long-eared, "eastern" = eastern small-

footed, "big" = big brown. ................................................................................................ 18

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. .................................................................................................................. 19

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,

2

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

4

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

9

movements. Previous studies at ANP (Manville 1942, Zimmerman 1998, Zimmerman &

Glanz 2000) identified all 7 species listed in Table 1.1 and considered the habitat at ANP

important for bat populations. Hence, MDI is a suitable location for a temporal bat study

to monitor changes in abundance, diversity, and isotopic variation in the overall bat

population. My research at ANP focused on seasonal movements of local and long-

distance migrant bats and variation in the population over time in an effort to identify

critical temporal data points where there is high dynamicity in the population (i.e., bats

are on the move and under increased environmental stress). Data from my research can

be used to compare a healthy bat population from a protected park to populations that

have been impacted by WNS or wind power development to make informed decisions

about species management and conservation.

1.2 Study Area

Historic parklands and national parks provide important foraging habitat for bats

(Glendell & Vaughn 2002, Loeb et al. 2009). I surveyed bats in 2009, 2010 and 2011 at

Acadia National Park, Maine, which comprises 15,233 ha of protected land in eastern

coastal Maine. Three tracts of land make up the majority of the park: Mount Desert Island

(MDI), Isle au Haut, and Schoodic Peninsula. These areas contain marshes, old growth

forest, mixed forest, ridges, lakes and ponds, and carriage roads with approximately 20%

of this area classified as wetland habitat (Calhoun et al. 1994). I used MDI as one study

site to determine seasonal variation in the population. I established 14 trapping locations

on the island to stay mobile and maximize nightly captures based on wind, moon, and fog

patterns, which can affect capture rates (Morrison 1978; Fig. 1.1). These 14 locations

10

were situated on park lands and conservation easements in mixed forest dominated by

mature eastern hemlock (Tsuga canadensis), northern white cedar (Thuja occidentalis),

spruce (Picea spp.), poplar (Populus spp.), American beech (Fagus grandifolia), birch

(Betula spp.), and maple (Acer spp.). Locations occurred at low elevation (2 - 122 m),

and the average distance to the closest water source was 0.53 km (Table 1.2). Different

sites, particularly on the eastern side of the island, offered topographic protection from

moonlight or wind depending on moon phase and wind direction.

Table 1.2. Mist-net locations for capturing bats at Acadia National Park, Maine, 2009 - 2011.

Trapping location Latitude Longitude Elevation (m) Distance to water (km) Habitat type

Amphitheater bend 44.323 -68.2716 112 1.07 forested carriage road

Bubble Pond 44.3375 -68.2357 122 0.34 forested carriage road

Cedar Swamp Mtn. 44.3246 -68.2842 78 0.27 forested carriage road

Eagle Lake South 44.3493 -68.2484 106 0.19 forested carriage road

Gilmore Meadow 44.3617 -68.2696 75 0.22 forested carriage road

Hio Rd. 44.2547 -68.3367 12 0.31 forested carriage road

Jordan Pond Cliff 44.3306 -68.26 111 0.24 forested carriage road

Lurvey Spring Rd. 44.3044 -68.3355 41 1.01 forested carriage road

Marshall Brook Rd. 44.2725 -68.3521 7 0 forested grass road, tidal creek

Murphy's Lane 44.3486 -68.1843 23 0.17 forested road bed

Pooler property 44.4207 -68.3215 2 0 forested grass road, tidal creek

Stonebarn property 44.4179 -68.3069 5 3 forested grass road, tidal creek

Hemlock Trail 44.3632 -68.2074 15 0.15 forested carriage road

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.

18

little

brown northern

long-eared

eastern small-footed

red big brown

hoary Total May-09 14 7 1 0 0 0 22 June-09 97 36 22 0 0 0 155

August-09 143 36 16 2 1 2 200 September-09 10 5 7 0 0 0 22

April-10 0 4 17 0 0 0 21 May-10 16 14 21 0 0 0 51 June-10 46 41 19 0 0 0 106 July-10 86 33 9 3 0 0 131

August-10 116 58 12 5 0 0 191 September-10 10 10 3 0 0 0 23

July-11 5 5 4 1 2 0 17 August-11 71 29 16 4 0 0 120

Total 614 278 147 15 3 2 1059 Figure 1.2. Monthly capture abundances of bat on Mount Desert Islands without consideration of sampling time

per month. Green to red relational color scale displays highest abundances in red across all species to lowest in

green. Blue to grey relational color scale displays highest totals in blue to lowest totals in grey. Abbreviated

species names: "little" = little brown, "northern" = northern long-eared, "eastern" = eastern small-footed,

"big" = big brown.

Hours little brown

bro brown

northern long-eared

eastern small-footed

red big brown

hoary Total May-09 5.42 2.58 1.29 0.18 0 0 0 4.1 June-09 20.83 4.66 1.73 1.06 0 0 0 7.4

August-09 50.82 2.81 0.71 0.31 0.04 0.02 0.04 3.9 September-09 10.75 0.93 0.47 0.65 0 0 0 2.1

April-10 11.67 0 0.34 1.46 0 0 0 1.8 May-10 26.63 0.6 0.53 0.79 0 0 0 1.9 June-10 50.32 0.91 0.81 0.38 0 0 0 2.1 July-10 39.18 2.19 0.84 0.23 0.08 0 0 3.3

August-10 55.75 2.08 1.04 0.22 0.09 0 0 3.4 September-10 23.42 0.43 0.43 0.13 0 0 0 1

July-11 4.5 1.11 1.11 0.89 0.22 0.44 0 3.8 August-11 40.08 1.77 0.72 0.4 0.1 0 0 3

Total 339.37 1.8 0.82 0.43 0.04 0.01 0.01 3.1

Figure 1.3. Capture rates for each month of sampling bats as individuals/hour on Mount Desert Island, Maine.

Green to red scheme reflects rates compared across species with highest rates in red, lowest rates in green. Blue

to grey schemes correspond to respective totals across species and across sampling month as well as hours of

sampling per month, respectively: highest totals rates are in blue with lowest in grey. Abbreviated species

names: "little" = little brown, "northern" = northern long-eared, "eastern" = eastern small-footed, "big" = big

brown.

19

Table 1.4. Diversity calculations based on bats captured on Mount Desert Island for each month sampled in

2010.

April May June July August September

Species richness 2 3 3 4 4 3 Individuals captured 21 51 106 131 191 23 Dominance 0.69 0.34 0.37 0.50 0.47 0.40 Simpson index 0.31 0.66 0.63 0.50 0.53 0.61 Shannon index 0.49 1.08 1.04 0.89 0.93 0.99 Evenness 0.70 0.99 0.95 0.65 0.67 0.90

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

syndrome. PLoS Pathogens, 8, e1002779. doi:10.1371/journal.ppat.1002779.

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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

selecting prey items but catching prey opportunistically (Anthony & Kunz 1977).

Northern long-eared bats forage similarly to little brown bats and eastern small-

footed bats, preferring dipterans, lepidopterans, and coleopterans in one Indiana study

(Whitaker 2004). A separate study in Indiana found similar results, with northern long-

eared bats preferring lepidopterans, coleopterans, and dipterans; similar prey preferences

to little brown bats, but little brown bats also consumed trichopterans (caddis flies; Lee &

McCracken 2004). Behaviorally, northern long-eared bats and little brown bats can both

aerially hawk and glean prey items, though northern long-eared bats may be better built

to glean than little brown bats (Ratcliff & Dawson 2003, Faure et al. 1993). Though less

66

is known about foraging in eastern small-footed bat, they are built as aerial hawkers but

may glean prey from the ground, as evidenced by soil detected in fecal samples (Johnson

& Gates 2007). Further evidence that eastern small-footed bats glean insects came from

spiders and crickets found in fecal samples from New Hampshire (Moosman et al. 2007).

In short, all three of these myotine species prefer moths, true flies and beetles, all eat a

variety of prey items, and all are capable of aerial hawking and gleaning. In New

England, little brown bats, northern long-eared bats, and eastern small-footed bats share a

community diet similarity value of 71% (Thomas et al. 2012). In that same study, little

brown bat and eastern small-footed bat diets were statistically 78% similar (Thomas et al.

2012). Despite these similarities, I cannot ignore the possibility of niche partitioning

when these bat species compete for food. However, the fact that none of these species are

prey specialists suggests that observed differences in isotopic niche variation may have

been influenced at certain times of year by migrating bats carrying non-local isotopic

signatures to Acadia National Park from previous foraging bouts elsewhere. If this is true,

my data support this idea with larger niche widths observed in little brown bats in all

Bayesian isotope analyses (see density plots (b) in all isotope figures).

2.4.2 Niche width comparisons

Within species using skin samples, I found most juveniles isotopically situated

within the niche width of adults. Indeed, 75% of eastern small-footed (Fig. 2.1.a) and

74% of northern long-eared juveniles (Fig. 2.3.a) fell within the range of adults, but little

brown bat juveniles (Fig. 2.2.a) had a greater niche width than adults. Anthony & Kunz

(1977) obtained similar results when they used fecal analysis to determine that juveniles

of this species were not selectively foraging while learning to hunt. Based on univariate

67

analysis, little brown juveniles had significantly higher nitrogen levels than adults. Since

nitrogen isotopes relate to trophic position (Ben-David & Flaherty 2012), ANP’s little

brown juveniles are feeding at higher trophic levels than adults. It is possible that juvenile

bats had a higher nitrogen signature because of high levels of nitrogen gained through

milk. However, juveniles in other species at ANP had lower average nitrogen than adults

(Table 2.1). Myotis species are income breeders, generating milk from daily intake of

insects (Henry et al. 2002). In other income breeders such as deer mice, Peromyscus

maniculatus, there is little enrichment in nitrogen in juvenile tissues when compared to

adult female's milk (Miller et al. 2011). Nitrogen is a key component of protein and the

predominant elemental component of bat milk (Studier & Kunz 1995). The difference in

nitrogen signatures may be explained by juveniles accreting nitrogen during parturition

and by suckling milk, decreasing adult levels as nitrogen is transferred from parent to

offspring. Juveniles only start to consume solid foods once they can fly independently

(Voigt et al. 2008). Since the turnover in skin tissue is 2 – 3 months, nitrogen from milk

likely inflated signatures in juvenile little brown bats in my study. Why does this pattern

not hold true for eastern small-footed bats and northern long-eared bats?

Juvenile bats captured closer to the date that they learn to fly and forage on their

own (i.e., not receiving milk) will have more nitrogen from milk remaining in their

tissues compared to juvenile bats captured later in the season. Because I sampled weekly

throughout the season, juveniles of each species were captured within 7 days of first

juvenile flight. Little brown juveniles were captured an average of 37 ± 7 days (n = 6

bats) after first juvenile flight; northern long-eared bats averaged 34 ± 7 days (n = 3)

days, and eastern small-footed bats averaged 20 ± 7 days (n = 7). Based on these

68

differences, I would expect little brown juveniles, captured later in the season on average,

to have less nitrogen compared to adults than other species. My capture data discussed in

Chapter 1 support that ANP may be a fall swarming aggregation site. The purpose is

unknown, but hibernacula may exist on the island, or ANP may be a social gathering

location when bats begin to migrate. In any case, differences in little brown bats' nitrogen

levels may have been influenced by juveniles that were not born at ANP but subsequently

migrated there.

This pattern may hold true for eastern small-footed bats as well. A significant

difference in hair nitrogen (Table 2.1) may be related to non-native (to ANP) juvenile

bats that dispersed. Hair isotopes should represent the environment in which juveniles

were born and in which adults were present during molt. Despite low variation in hair

isotopes from eastern small-footed bats, two adult outliers (Fig. 2.5.a) had a strong effect

on the SEA.B of late season hair samples in this species. I propose the following

scenario: if a hibernaculum is present at ANP, early season small-footed bats should all

have similar isotope values from local foraging upon spring emergence. In mid-season,

juvenile input increases the isotopic niche of the species and then in the late season,

outliers may be explained by individual bats arriving at ANP to mate or hibernate from a

different location.

Considering blood isotopes, with the fastest turnover, eastern small-footed bats

and northern long-eared bats had smaller niche widths in the early season, with

significant increases in niche width in the late season. These late season inputs may be

from non-residents. Interestingly, little brown bats showed no significant differences in

blood isotopes across seasons but had the largest blood isotope niche in the early season.

69

Either little brown bats are less selective in prey choice in the early season, or they

arrived from different locations. Little brown bats were not captured until May while both

other species were captured in April (Chapter 1). Could this suggest that eastern small-

footed bats and northern long-eared bats hibernate at ANP, but little brown bats only stop

over during migration?

2.4.3 Potential hibernacula

In winter 2011, 95 little brown bats were either found dead and tested positive for

white-nose syndrome (WNS) or observed flying during the day, a behavior that may

indicate WNS (B. Connery, pers. comm). However, no eastern small-footed or northern

long-eared bats were found. Because researchers only relied on the presence of dead bats,

absence of other species does not mean that the latter two species do not also have white-

nose syndrome at ANP. However, if all three species hibernated in the same cave, I

expect that all 3 species would be found dead. Susceptibility to the disease may account

for species differences at ANP, or they truly are not hibernating together. Future studies

at ANP should assess stable isotopes alongside fecal diet analysis to quantify isotopic

differences between species related strictly to diet. Insects should also be sampled and

source partitioning performed using the SIAR package in R to determine isotopic

differences in diet. These additional methods may help to explain whether observed

differences arise mostly from differences in temporal prey selection or from mixing of

populations that aggregate in the late season at ANP. Tracking of Myotis spp. with radio

transmitters in late September or into October may also lead researchers to hibernacula at

Acadia National Park.

70

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CONCLUSIONS

The three years spent researching bats at Acadia National Park (ANP) have

answered some basic questions. We can reliably infer that ANP is an excellent location to

support robust bat populations and have a good sense of which species are currently

present at ANP, when they are generally present at ANP, and that they appear to partition

resources, at least in some capacity. The combination of survey methods provided

complimentary data gathered with each technique to better understand bat behavior and

activity at ANP. However, the information gathered has highlighted the general lack of

knowledge pertaining to bat natural history, particularly in such a large and wild state like

Maine. We are left with more complex questions such as:

Do other places in Maine also support large populations of bats, particularly

eastern small-footed bats?

Are Myotis species truly competing for resources or are observed isotopic

differences influenced by foreign foraging?

Where do the bats at ANP hibernate?

How should we manage for bat species given data gaps and current threats such as

white-nose syndrome (WNS)?

Fortunately, ANP is already protected as a national heritage site so by default bats there

benefit from this first level of management. But is that enough to maintain the population

of bats at ANP? In a location with unique species, possibly endangered species, it is

necessary to learn more about the specific behaviors of ANP's bats. Location of maternity

sites and hibernacula, along with critical foraging areas will greatly apply to targeted park

management for bat species. Intrinsic markers such as stable isotopes give clues toward

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species dynamics and differences in life histories and should continue to be used in

conjunction with other survey methods. Specifically, a follow-up study with stable

isotopes in Myotis species and their full range of prey items will allow for comparative

source partitioning to determine a) if species compete for or share the same resources and

b) if individual species are always prey generalists or if they are temporal specialists,

shifting preferred prey items depending on temporal availability. Insights on these

interactions will allow for more accurate interpretation of my isotope study and the

potential for observed differences related to foreign input rather than differences in local

prey items.