National Park Service

U.S. Department of the Interior

Natural Resource Program Center

Revised Relative Abundance Estimates and Temporal 

Activity of Bats at Three Great Lakes National Parks 

Based on Acoustic Data

Natural Resource Report NPS/GLKN/NRR—2010/178
ON THE COVER

Passive setup acoustic monitoring station that uses reflector tubes (from Kruger and Peterson 2008). Collected data are used to 

create graphs showing temporal activity between species and dates, such as the example here for Apostle Islands NL. Hoary bat 

and pipistrelle line drawings from Arthur’s Bat Clipart (www.arthursclipart.org/bats/bats.htm). 
Revised Relative Abundance Estimates and Temporal 

Activity of Bats at Three Great Lakes National Parks 

Based on Acoustic Data

Natural Resource Report NPS/GLKN/NRR—2010/178

Bruce W. Miller, Ph.D.

P.O. Box 37, Belize City

Belize, Central America

Phone +501-220-9002

February 2010

U.S. Department of the Interior

National Park Service

Natural Resource Program Center

Fort Collins, Colorado
ii

The National Park Service, Natural Resource Program Center publishes a range of reports that 

address natural resource topics of interest and applicability to a broad audience in the National 

Park Service and others in natural resource management, including scientists, conservation and 

environmental constituencies, and the public. 

The Natural Resource Report Series is used to disseminate high-priority, current natural resource 

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audience, and may contain NPS policy considerations or address sensitive issues of management 

applicability.

All manuscripts in the series receive the appropriate level of peer review to ensure that the 

information is scientifically credible, technically accurate, appropriately written for the intended 

audience, and designed and published in a professional manner. Data in this report were 

collected and analyzed using methods based on established, peer-reviewed protocols and were 

analyzed and interpreted within the guidelines of the protocols.

Views, statements, findings, conclusions, recommendations, and data in this report are those of 

the author(s) and do not necessarily reflect views and policies of the National Park Service, U.S. 

Department of the Interior. Mention of trade names or commercial products does not constitute 

endorsement or recommendation for use by the National Park Service.

This report is available online from the Great Lakes Inventory and Monitoring Network 

(http://science.nature.nps.gov/im/units/GLKN/reportpubs.cfm) and the National Park Service, 

Natural Resource Publications Management (http://www.nature.nps.gov/publications/NRPM).

Please cite this publication as:

Miller, B. W. 2010. Revised relative abundance estimates and temporal activity of bats at three 

Great Lakes national parks based on acoustic data. Natural Resource Report NPS/GLKN/NRR—

2010/178. National Park Service, Fort Collins, Colorado.

NPS 999/101072, February 2010
iii

Contents

Page

Figures............................................................................................................................................. v

Tables............................................................................................................................................ vii

Appendices..................................................................................................................................... ix

Abstract.......................................................................................................................................... xi

Acknowledgements...................................................................................................................... xiii

Introduction..................................................................................................................................... 1

Methods........................................................................................................................................... 3

Acoustic Data File Management................................................................................................. 3

Managing Location Data ............................................................................................................ 4

Management and Addition of Species Data ............................................................................... 4

Assigning Species Identifications............................................................................................... 6

Data Analyses............................................................................................................................. 8

Results........................................................................................................................................... 11

Spatial and Temporal Comparison of Species.......................................................................... 12

Relative Abundance and Temporal Activity of Species at Each Park...................................... 13

Temporal Activity................................................................................................................. 15

Species Temporal Activity Patterns...................................................................................... 15

Apostle Islands National Lakeshore ......................................................................................... 16

Grand Portage National Monument.......................................................................................... 17

Pictured Rocks National Park................................................................................................... 18

Discussion..................................................................................................................................... 21

Recommendations......................................................................................................................... 23

Example of Use of Spatial Data From Acoustic Data Sets....................................................... 25

Literature Cited ............................................................................................................................. 29

v

Figures

Page

Figure 1. An example of the Header portion of an Anabat file as viewed in AnalookW............... 3

Figure 2. Example of unidentifiable call fragments (left) compared to an identifiable call 

sequence (right)............................................................................................................................... 7

Figure 3. Species identification buttons in AnalookW. .................................................................. 8

Figure 4. Passive setups of acoustic monitoring stations showing use of reflector tubes. From 

Nordquist (2006), left, and Kruger and Peterson (2008), right..................................................... 12

Figure 5. Comparison of relative abundance of species by month June-August 2003. The Y 

axis is the AI value standardized per 10 hours of survey time. Species codes: Epfu = 

Eptesicus fuscus, Labo = Lasiurus borealis, Laci= Lasiurus cinereus, Lano= Lasionycteris 

noctivagans, Mylu= Myotis lucifugus, Myse= Myotis septentrionalis......................................... 13

Figure 6. Comparison of relative abundance of species between three parks combining the 

standardized AI values from June-August 2003. The Y axis is the AI value standardized per 

10 hours of survey time. Species codes: Epfu = Eptesicus fuscus, Labo = Lasiurus borealis, 

Laci= Lasiurus cinereus, Lano= Lasionycteris noctivagans, Mylu= Myotis lucifugus, Myse= 

Myotis septentrionalis................................................................................................................... 14

Figure 7. Anabat file structure. Y=year, M=month, D=day, HH=hour, MM=minute, 

SS=seconds, and # the Anabat file extension. .............................................................................. 15

Figure 8. Summary of temporal activity for the three parks during the entire survey period. ..... 16

Figure 9. Comparison of temporal activity patterns for the six species of bats at APIS. ............. 17

Figure 10. Comparison of temporal activity patterns for the six species of bats at GRPO. ......... 18

Figure 11. Comparison of temporal activity patterns for the six species of bats at PIRO............ 19

Figure 12. A target is necessary for monitoring, otherwise it is simply surveillance................... 24

Figure 13. L. cinereus showing a marginal presence in June 2003 based on relative 

abundance. .................................................................................................................................... 25

Figure 14. L. cinereus showing an increase in numbers during July 2003................................... 26

Figure 15. L. cinereus showing the increased numbers and spatial distribution of relative 

abundance in August 2003............................................................................................................ 27

vii

Tables

Page

Table 1. Potential occurrence of bat species in three national parks of the Great Lakes 

Network: Grand Portage National Monument (GRPO); Apostle Islands National Lakeshore 

(APIS); and Pictured Rocks National Lakeshore (PIRO). Species occurrences indicated as X

(verified), L (likely), and U (unknown). Adapted from Kruger and Peterson (2008). ................... 1

Table 2. Percentage of identifiable call sequences versus unidentified call fragments at each 

of the three parks............................................................................................................................. 6

Table 3. Percentage of recorded calls that were unidentifiable fragments at each survey site..... 11

ix

Appendices

Page

Appendix A. A Review of Filters and Filter Parameters Used in AnalookW. ............................. 33

Appendix B. Comparison of Filter Parameters Used for This Analysis....................................... 37

xi

Abstract

In this study I re-analyzed acoustic bat data collected from June-August 2003 that was part of a 

baseline inventory of bat species in three national parks in the Lake Superior region. While the 

original study presented base-line data on the presence/absence of bat species in these parks, this 

reanalysis provides estimates of relative abundance and temporal activity of the identified 

species. Using a suite of recently developed acoustic analysis tools I created species specific 

filters. This allowed me to parse calls from non-fragmented sequences and differentiate between 

two species Myotis septentrionalis and Myotis lucifugus that were combined into a general 

Myotis spp. category. Using the Acoustic Activity Index (AI) it was possible to derive relative 

abundance estimates for each species and park. Temporal activity patterns are also presented as 

summaries for each species and park. An example of how spatially linked acoustic data can be 

useful for park management is also presented. All acoustic files were examined and the textual 

header data was updated with species names, locations and x, y location coordinates. An Access 

relational database with spatially explicit links, and relative abundance values for all species 

identified was also created providing a more robust archive of the data.

xiii

Acknowledgements

I thank Steve Yancho, Chief of Natural Resources, Sleeping Bear Dunes National Lakeshore for 

putting me in contact with the Great Lakes Inventory and Monitoring Network (GLKN). I thank 

Bill Route, Program Coordinator /Ecologist with the GLKN, for the opportunity to undertake this 

project, and Laura Kruger for invaluable help in providing coordinates, location clarification, and 

updates and corrections for all of the acoustic data files. Carolyn Miller proofread an earlier draft 

and provided useful editing comments. Editing and layout provided by Becky Brown and Ted 

Gostomski. This report was peer reviewed by Bill Route and Ron Moen.

1

Introduction

A survey of bat species was conducted in three national parks in the Lake Superior region during 

June, July, and August 2003 (Kruger and Peterson 2008). The three parks surveyed were: 

Pictured Rocks National Lakeshore (PIRO) in the Upper Peninsula of Michigan, Apostle Islands 

National Lakeshore (APIS) in Wisconsin, and Grand Portage National Monument (GRPO) in 

Minnesota. The goals for the project were to determine what bat species occurred in each of the 

three parks, and identify foraging areas, day roosts, maternity sites and hibernacula (Kruger and 

Peterson 2008). The survey sites were not randomly selected therefore the results should be 

viewed as only representative of areas surrounding the survey site and not the entire parks (Route 

pers. comm.).

Survey data on the presence/absence of temperate bat species at the sites within the three 

national parks was collected by: mist netting, roost searches, and acoustic sampling (Table 1). 

Combining multiple survey methods is the most robust means of documenting the bat fauna of a 

given area (Miller 2003, O’Farrell and Miller 2003). Acoustic sampling is a means of 

documenting the occurrence of bat species that may avoid traditional capture methods (e.g., mist 

nets).

Table 1. Potential occurrence of bat species in three national parks of the Great Lakes Network: Grand 

Portage National Monument (GRPO); Apostle Islands National Lakeshore (APIS); and Pictured Rocks 

National Lakeshore (PIRO). Species occurrences indicated as X (verified), L (likely), and U (unknown). 

Adapted from Kruger and Peterson (2008).

Common Name Scientific Name APIS GRPO PIRO

Little brown bat Myotis lucifugus X X X

Northern bat Myotis septentrionalis L L L

Eastern pipistrelle Perimyotis subflavus L U U

Big brown bat Eptesicus fuscus X L L

Silver-haired bat Lasionycteris noctivagans X L L

Eastern red bat Lasiurus borealis X L L

Hoary bat Lasiurus cinereus X X L

Acoustic data files also serve as archived data sets. Each file becomes a voucher record for the 

presence of a species at a given time and place. In addition to being useful in focal studies, these 

data are then available for future analysis as methods and techniques evolve. This is not unlike 

the value of historical museum or herbaria collections with archived voucher specimens 

(Reynolds et al. 1996, Yates et al. 1996). Like traditional research collections serving as 

functional biological libraries (Winker 1996), acoustic data have similar value. As better 

understandings of distinctive vocal signatures evolve and acoustic voucher call libraries are 

expanded, acoustic methods contribute to increasingly important and powerful data sets (Ochoa 

et al. 2000). Having the archived files made it possible to re-evaluate and analyze the acoustical 

data records from the 2003 survey.

In their discussion, Kruger and Peterson (2008) acknowledged that simply using acoustic 

identification as an index did not address potential skewing of the data by a single bat making 
2

several passes at the recording site as opposed to multiple bats flying by. While it is well 

understood that not all acoustic recordings include diagnostic vocal signatures that allow 

unequivocal identification of species (Corben 2004, Miller 2004), it is possible to use an acoustic 

activity index (AI) to estimate relative abundance and to compare both spatial and temporal 

activity of bats. The AI provides a means to standardize results of acoustic surveys after 

adjusting by the survey times (unit effort), and provides a robust means to compare between 

survey sites and dates (Miller 2001).

I have examined and re-analyzed the acoustic data collected by Kruger and Peterson (2008) and 

employed new techniques and used new software tools that were not available during their study. 

The results of this re-analysis provide more detail on the relative abundance and spatial and 

temporal associations for the documented species at the three national parks.

Frequently, re-evaluation of archived acoustic data results in additional species identifications 

because our knowledge of vocal signatures has improved (Miller 2004). In order to reassess the 

acoustic data it was important to ascertain the potential for a given species to be present within 

the study areas prior to scanning the data with a broad range of species specific acoustic filters. 

While it is important to evaluate acoustic data for the presence of all potential species, it is not 

cost effective to scan data for species that are highly unlikely to occur in the areas surveyed 

without adequate justification.
3

Methods

My first task for this project was to determine what species potentially could occur in the area 

surveyed based upon the most recent distribution and range data available. This was necessary to 

make sure I scanned the acoustic data for all potential species and did not a priori limit the 

analysis of the data to only the species the initial study had identified. 

This was accomplished in ArcGIS by overlaying the study site locations in the three parks on the 

North American species distribution ranges. The species spatial data layers used were from the 

IUCN-led Global Mammal Assessment (IUCN 2008). These represented the most recent range 

updates available for bats and were based on recent detailed expert review of taxonomists and 

ecologists from North and Central America and the Caribbean.

Acoustic Data File Management 

I used the most recent versions of the AnalookW software re-analyze the acoustic data, add 

relevant header data such as location names, X-Y coordinates, and species names to the acoustic 

files. While an earlier version of AnalookW was available, Kruger and Peterson (2008) analyzed 

their data using the older DOS platform Analook (Version 4.9, January 2003).

All metadata relating to each Anabat acoustic call file is stored in the header. Header data 

(Figure 1) are the textual data embedded within each acoustic file and includes six descriptive 

fields and four relating to the spatial context of the data. A description of the textual fields 

follows.

Figure 1. An example of the Header portion of an Anabat file as viewed in AnalookW.

The Tape field stores the unique identification of the Compact Flash Zero-Crossing Module (CFZCAIM) used to record the data in the field and is an integral part of each file recorded; it is 

added automatically when data is recorded with the CF-ZCAIM. The Date field is used for the 

date the data was recorded. The Loc field is used for a unique description of the location where 

the recording was made. This field is critical to preserve the value as an acoustic voucher record 

and data must be entered manually as soon as they are downloaded from the CF memory cards.

The Species field is used to link the unique species identification codes to each file and should be 

entered as the identifications are made for each species in each acoustic file. The Spec field is 

used to automatically add information on a given set of extracted acoustic files. The Notes field 

is used for additional clarifications or notes that may be useful to associate with a given data set. 

The right hand section of the header includes the spatial data associated with each file at a unique 
4

location and is comprised of the base-datum used for the coordinates, latitude, longitude and 

altitude, if known.

Managing Location Data

Once the 2003 acoustic survey data (ca. 21,460 acoustic files) was received each acoustic file 

was evaluated for data integrity. Adding the specific location information to acoustic data, 

whether actively recorded or from passive monitoring, was critical for evaluating species 

occurrence at each site both temporally and spatially. While partial descriptive location data had 

been added to some of the data during the original study, this needed to be completed for all files 

and standardized for each location prior to the re-analyses.

The GRPO and PIRO survey locations were based on single points or transects with identical 

location information for each. The APIS survey locations, however, included two or three 

locations, each necessitating unique descriptions and spatial coordinates. The original 

descriptions provided by Kruger (pers. comm.) were retained for each location and a numerical 

suffix was appended to each point or transect so that each location was uniquely identified.

Spatial coordinates (latitude, longitude [X,Y]) and species identifications were then added to 

header files. The acoustic files, which were archived in separate directories for each night’s 

survey, were linked to each unique location using the AnalookW Folder utility Unsplit. This 

moved all of the data files into a single folder containing all of the files recorded at each unique 

location.

Location descriptions were then standardized and all file headers updated with the relevant 

spatial coordinates provided by Kruger (pers. comm.). This was accomplished using the Anahead 

Positions Utility and manually adding the geographical coordinate data to each set of files. This 

was necessary for the files to serve not only as vouchers for the presence of a given species, but 

in order to spatially define each unique location for distribution mapping.

The Anabat file structure is such that the date and time of each recording is encoded into each 

file. This also is used as the file name during the recording and data file saving process. 

Therefore each acoustic record also provides a documented occurrence of a given species not 

only in space but in time. Given that the time and date of every recording is provided, it is 

possible to use the AI to develop species- and location-specific relative activity and abundance 

indices.

Management and Addition of Species Data

The next phase of the data re-analysis was to independently identify the species recorded and add 

the species names into the header data. As noted above, acoustic files are also voucher records; 

files that contained calls of acoustically unknown species can be reviewed at some future time 

and newly recognized species added to the header data. It was necessary to examine every 

acoustic data file to independently determine what species were recorded prior to evaluating the 

relative activity and abundance.

If acoustic data is comprised of calls that are cleanly recorded, it is possible to use speciesspecific filters to rapidly scan data sets and to a large degree automate the addition of 

identifications. For example, during a long term monitoring project in Gallon Jug, Belize it was 
5

possible to scan >800,000 acoustic files recorded at a single site (Miller, unpublished data). The 

data consisted of 3.5 years of nightly recordings collected from dusk until dawn. The data scan 

for this was completed in approximately 3 hours and resulted in a list of species and their relative 

abundance per unit time.

Species-specific filters used in this process are based upon a suite of call metrics that assist in 

identifying species that match the standard acoustic criteria (de Oliveira 1998, Korine and Kalko 

2001). These acoustic criteria include a range of time and frequency values. A brief definition 

taken from Corben (2009a) of each of the basic parameters is provided below.

Duration (Dur) – minimum or maximum time measured in milliseconds (ms) of a pulse.

Maximum frequency (Fmax) – minimum or maximum value range for the highest 

measured frequency of a pulse in kHz.

Minimum frequency (Fmin) – minimum or maximum value range for the lowest 

measured frequency of a pulse in kHz.

Mean frequency (Fmean) – minimum or maximum value range for the mean measured 

frequency of a pulse in kHz.

Characteristic frequency (Fc) – minimum or maximum value range for the characteristic 

frequency of a pulse in kHz.

S1 is the slope at the beginning of the call, calculated over the first five dots in the call. –

minimum or maximum value range for the S1 of a pulse measured in octaves per 

second; Note that SI can be negative.

Characteristic slope (Sc) – minimum or maximum value range for the Sc of a pulse 

measured in octaves per second; as with the S1 this can be negative.

A table of values for each of these acoustic criteria used to identify species in the original study 

was included in Kruger and Peterson (2008). The new AnalookW software uses a different filter 

structure, and includes a larger range of parameters that were not available in the Analook 

(Version 4.9, January 2003) used by Kruger and Peterson (2008). To test the efficacy of the call 

parameters they used to identify species, I constructed a set of the new filters based on their 

values. Each species specific filter was then challenged by scanning datasets comprised only of 

verified voucher calls of the target species to determine how well they preformed.

I created new filters based on Kruger and Peterson’s (2008) parameters. However, these did not 

perform as well as anticipated either when discriminating between, or identifying, focal species. 

Most resulted in a relatively high degree of overlap. Several filters failed to identify verified 

voucher calls of the target species. Their parameters were derived from hand-released bats 

(Kruger, pers. comm.), however, such calls frequently do not provide diagnostic search phase 

calls (Miller 2004). Such overlap occurs when a filter is based on parameters that may be too 

inclusive and include call pulses that have similar components shared between a number of 

species.

With a high degree of parameter overlap, filters will flag all files that contain any fragments of 

call pulses of all species that are within the filters range for the given parameters. For example if 

a filter has a broad range of parameters for a Fc given as 38 kHz but with a range between 28 

kHz to 50 kHz all files that have a Fc within this 22 kHz range would be flagged as a match. 
6

A new set of filters specific to species expected to occur within the study areas were then created 

based on voucher files of search phase calls from reliable sources. Each species specific filter is 

constructed to use a range of the call parameters that reduces potential species specific overlap. 

The definition of a “bat call” used by Kruger and Peterson (2008) appears to more closely match 

the definition of a pulse rather than a call as I have used it for this analysis. They used sequences 

consisting of five or more calls (pulses) when they identified species. Filter parameters also 

include the number of pulses required per unit time in order to be recognized as a call sequence. 

While for some species this may be a minimum of five consecutive pulses within 15 seconds, for 

other species 10 pulses within 10 seconds is often used (e.g., some Myotis spp.). Definitions of 

each filter parameter with comparisons of the values used by Kruger and Peterson (2008) and 

new values for each species are presented in the appendix. Scanning the data using the updated 

criteria and with the new filters resulted in differences in identified species. As a result the 

relative abundance and seasonal composition of species for each location may differ from that 

reported by Kruger and Peterson (2008).

Assigning Species Identifications

It is difficult if not impossible to identify most species of bats from incomplete call sequences 

(O'Farrell 1997, O'Farrell and Miller 1997, Ochoa et al. 2000, Miller 2003). For this re-analysis 

of the acoustic data I used only complete call sequences for most species. In a few cases (e.g., 

species in the genus Lasiurus), a few pulses can be diagnostic for identification as the two 

species occurring in the study area have widely separated characteristic frequencies. 

Given the high percentage of acoustic data from the Kruger and Peterson (2008) study that were 

fragmented or incomplete call sequences (Table 2), direct scanning of the data set to derive 

species composition and relative abundance was not an option. An example of call fragments 

compared to clean identifiable files can be seen in Figure 2.

Table 2. Percentage of identifiable call sequences versus unidentified call fragments at each of the three 

parks.

Park No. of Identified Species Fragments

APIS 36.2 63.8

GRPO 43.3 56.7

PIRO 54.6 45.4

In this re-analysis only search phase calls were used for the species identifications. Even when 

well recorded, not all echolocation calls are useful for species identifications (Fenton and Bell 

1981; O’Farrell et al. 1999; Corben 2004; Corben 2004; Miller, unpublished data). For example 

it is not unusual to record social calls, approach phase, feeding buzzes and commuting calls but 

they have not yet proven to be diagnostic for species identifications.
7

Figure 2. Example of unidentifiable call fragments (left) compared to an identifiable call sequence (right).

Using the new filters the data sets for GRPO, APIS and PIRO were scanned and the results used 

to create an Analist file (ANL). The ANL files enables the user to view all the acoustic files 

which pass a species specific filter, even though they may be dispersed in multiple directories 

and scattered on the hard disk. When the ANL file created using the suite of filters is opened in 

the AnalookW program, the user selects the species they wish to view. The advantage of using 

the ANL file is that one can then review only those files that matched parameters selected by the 

species specific filter. 

In order to determine the relative abundance of species for each location it was important to 

verify that calls were as accurately identified as possible. I used the ANL file utility to review the 

results of each species filter throughout the entire 2003 acoustic data set.

Using the ANL utility facilitated a review of each file identified be the species-specific filter, and 

subjectively matching calls to verified vouchers (Erickson and West 1995, O’Farrell 1997,

O'Farrell et al. 1999). This eliminated errors in identification based on pulses from incomplete 

calls that may have been incorrectly selected by a specific filter.

Questionable calls were visually compared with verified voucher calls and key acoustic 

parameters were measured at the same scales using the side-by-side feature in the AnalookW 

program. When in doubt, or calls could equivocally be assigned to one or more species, they 

were conservatively listed as unidentified fragments. Once confident that the identification was 
8

correct, the species codes were added to the header of each file using the species tool bar in 

AnalookW (Figure 3).

Figure 3. Species identification buttons in AnalookW.

It was not uncommon to have multiple species recorded in a single call file, resulting in several 

species codes being entered in the header of the same file and separated by a comma. Additional 

information was also noted such as feeding buzzes, social calls, incompletely recorded calls or 

unknown fragments. The majority of the latter appeared to be fragmented or incompletely 

recorded calls of either Myotis lucifugus or Myotis septentrionalis. These were identified as 

“Frag” and are included as such in the results as an overall indication of Myotis spp. activity. 

Files that contained hand released bats, insects, and other “noise,” or only 40 kHz calibration 

tones were marked and deleted from the datasets prior to analysis.

Data Analyses

Due to the high percentage of incompletely recorded or fragmented calls it was not possible to 

use recently developed advanced methods to scan the data and add species identifications that 

would yield robust results. Therefore an estimated 70 hours was spent carefully reviewing each 

of the 21,460 files. I used the ANL utility to group files by each unique species filter. During this 

time species identifications or other descriptors (e.g., feeding buzz) were added to the headers of 

each acoustic data file. Once the identification phase was completed, the header data was 

extracted from the files and grouped based on each unique location. Each of these header file 

compilations was subsequently imported into a proprietary acoustic data management and 

analysis system (Miller, in review).

As noted above, it is not uncommon to have multiple species recorded in one Anabat file. To 

accurately analyze such files, all species and additional information recorded were parsed into 

separate species records into three relationally linked database tables when imported into the 

system. These records are stored in a master header data table, a separate table of AI values and a 

location table with relevant spatial data including a description of the location and X, Y 

coordinates. The AI table was automatically updated so that values were adjusted to reflect the 

survey unit effort (hours of survey time) for each location or transect provided by Kruger (pers. 

comm.). The AI values per survey hour were also automatically standardized to 10 survey hours 

to allow comparison between sites and or survey dates. Using the standardized AI allows 
9

comparison of the relative abundance of species between dates and sites and is used in the results 

section below.

Using a suite of queries in the data management system, relevant data was extracted for each 

location and species and is presented in the results. In addition to the Anabat data files, with the 

completed header data, an MS Access database containing the three base tables is available as a 

supplement to this final report.

11

Results

A large percentage of the acoustic files were comprised of call fragments that could not be 

identified to species. These ranged from a low of 30.4% at Miner’s Castle to a high of 85.2% at 

Chaple (Table 3). I attribute the high percent of unidentifiable species to the passive recording 

setup used, and possibly having the gain set too high during several transects, resulting in 

distorted signals. Technology has changed since the early days of passive monitoring and so has 

our understanding of the impact that weather-proofing the Anabat microphone has on the 

recorded data. When Kruger and Peterson (2008) conducted their surveys, the use of curved PVC 

tubes (O'Farrell 1998) was being adapted by many researchers in the region (e.g. Nordquist 

2006) as a means of weather-proofing the Anabat system.

Table 3. Percentage of recorded calls that were unidentifiable fragments at each survey site.

Location Percent fragments

APIS

Devil's Island 80.4

Little Sand Bay 67.7

Outer Island 44.1

Stockton Island 58.2

PIRO

Beaver Lakes 78.4

Chapel 85.2

Grand Sable 51.6

Kingston Lake 31.4

Miner's Castle 30.4

Sand Point 46.3

GRPO

Fort Charlotte 55.4

GP Stockade 48.9

Poplar Creek 78.9

Some setups that used reflector tubes were considerably better than others. For example the 

passive set up illustrated by Nordquist (2006) shows a poorly placed detector microphone cocked 

at what appears to be a 45° angle and not aligned with the tube (Figure 4). With this set up, incoming sound signals may bounce around the tube and secondary echoes may be generated 

within the weather-proof enclosure, further confounding the ability to identify clean signals. 

Kruger and Peterson (2008) show the microphone properly aligned to maximize signal reception 

with the use of the reflector tube (Figure 4).
12

Figure 4. Passive setups of acoustic monitoring stations showing use of reflector tubes. From Nordquist 

(2006), left, and Kruger and Peterson (2008), right.

In a study to evaluate the impacts of weather-proofing and possible loss of bat calls, Livengood 

et al. (2003) demonstrated that curved PVC tubes had a greater impact on frequencies at 25 kHz 

and 80 kHz with a diminished reception distance. The curved tube reduced the length of the 

primary lobe of sound detection. Bats that echolocated within those frequencies could not be 

detected from as far away as they could with the unprotected microphone or with the use of a 

reflector plate (Livengood et al. 2003). This resulted in a signal loss when using reflector tubes 

reducing the received sounds to a narrower sound signal, as opposed to open reflector plates that 

are generally used today that record a wider range of bat calls (Livengood et al. 2003).

There were few problems in identifying the calls attributed to the two species of Lasiurus, given 

the distinctive alternating up and down frequency of the Fc that is characteristic for the genus. 

The remainder of the complete and identifiable sequences were used to summarize the 

standardized AI comparison for species and sites.

Spatial and Temporal Comparison of Species

Using the AI standardized to 10 hours of survey time, it was possible to compare the relative 

abundance of bats between the three parks and look at seasonal differences during the months 

June, July, and August (Figure 5). Lasiurus cinereus was present in all parks from June and 

throughout the survey period. In June, L. borealis was only present in very low numbers with 

more individuals arriving later in July and remaining in August at both at APIS and PIRO and to 

a lesser extent at GRPO. All other species were present at all three parks during all three months 

surveyed, albeit in considerably different numbers. The large number of calls that were 

unidentified fragments were not included in the figures as this would obscure the relative 

abundance and activity of the identifiable calls. It is likely these incomplete call sequences were 

comprised mainly of the two species: Myotis septentrionalis and M. lucifugus. If combined with 

calls that were identifiable it is not unreasonable to assume that these are the two dominant 

species at all sites.
13

Figure 5. Comparison of relative abundance of species by month June-August 2003. The Y axis is the AI 

value standardized per 10 hours of survey time. Species codes: Epfu = Eptesicus fuscus, Labo = 

Lasiurus borealis, Laci= Lasiurus cinereus, Lano= Lasionycteris noctivagans, Mylu= Myotis lucifugus, 

Myse= Myotis septentrionalis.

Relative Abundance and Temporal Activity of Species at Each Park

By combining the standardized AI data, it was possible to examine the relative abundance and 

temporal activity of species at each of the three parks during the three month survey period 

(Figure 6).

Of the six species identified during the survey Lasiurus cinereus and Myotis lucifugus were the 

most abundant at the Apostle Islands National Lakeshore followed by Eptesicus fuscus. The 

remaining three species, Lasiurus borealis, Lasionycteris noctivagans and Myotis septentrionalis

were approximately equally abundant (Figure 6).
14

Figure 6. Comparison of relative abundance of species between three parks combining the standardized 

AI values from June-August 2003. The Y axis is the AI value standardized per 10 hours of survey time. 

Species codes: Epfu = Eptesicus fuscus, Labo = Lasiurus borealis, Laci= Lasiurus cinereus, Lano= 

Lasionycteris noctivagans, Mylu= Myotis lucifugus, Myse= Myotis septentrionalis.

Myotis lucifugus was the most abundant species at GRPO, followed by Eptesicus fuscus (Figure 

6). It is probable that some of the files labeled as “unidentified fragments” included Myotis 

septentrionalis as many of the fragments were in that species frequency range. There were 

considerably fewer unidentified call fragments in this range at APIS and PIRO. Therefore this 

species may have been considerably more abundant than the confirmed identifications would 

suggest. Both species of Lasiurus and Lasionycteris noctivagans were less abundant at GRPO 

during this survey (Figure 6).

Myotis lucifugus was also the most abundant species at PIRO. Lasiurus cinereus and Myotis 

septentrionalis were nearly equal in abundance. Lasionycteris noctivagans was rare and 

Eptesicus fuscus had the next lowest abundance (Figure 6).
15

Temporal Activity

The Anabat data and file structure is ideally suited to deriving temporal activity since the date 

and time is encoded into each file during recording. This also is used as the file name during the 

recording and data file saving process. The file name format stems from the legacy days when 

computer file names were constrained by the 8.3 convention under the DOS operating system. 

Each Anabat file name is comprised of seven parts that encodes both the date and the time 

(Figure 7).

Y M D HH MM SS Ext

D 8 20 21 14 28 #

Figure 7. Anabat file structure. Y=year, M=month, D=day, HH=hour, MM=minute, SS=seconds, and # the 

Anabat file extension.

For the months January through September (months 1-9) a single digit space works well. For the 

remaining three months – October through December, using the months 10-12 – or years beyond 

1999, a single digital character would not suffice. Therefore a modified version of hexadecimal 

numbering is used where numerals 0-9 only used as single digit and from 10 onwards a single 

letter is used to denote the number. For example the Anabat file name D8202114.28# translates 

to August (month 8) 20 (date) 2003 (D) recorded at 21:14:28.

In order to evaluate temporal activity it is assumed that the user has correctly set the time in the 

storage detectors and made certain to adjust for daylight savings shifts and or time zone shifts 

when moving about the landscape. There are utilities included in the AnalookW program to 

correct such time adjustment oversights or errors if discovered at a later date. It is also important 

for storage detectors to have the correct time set to allow the programmable Compact Flash (CF) 

memory cards that both store recorded data and include programming instructions to turn the 

monitoring equipment on and off at the desired times. There were several acoustic data sets for 

several nights and locations that had times incorrect by an hour and these were corrected prior to 

analyzing the data.

Using a suite of acoustic data management and analytical tools I am developing as a new 

acoustic data management and analysis service, it was possible to compare temporal activity 

between the species, parks, and dates. In addition to linking spatial data to the acoustic files, the 

acoustic data analysis system (Miller 2009) includes program code for R, an integrated suite of 

software facilities for data manipulation, calculations and graphical displays (http://www.rproject.org/), and the GGPLOT2 graphics plotting package (http://www.had.co.nz/ggplot/). This 

was used for analyzing and plotting the temporal activity data.

Species Temporal Activity Patterns

A summary of the temporal activity for the six species in the three parks suggests that most 

species were active throughout the night during the surveys (Figure 8). Looking at the temporal 

activity by park and date provides considerable insight into bat activity patterns.
16

Figure 8. Summary of temporal activity for the three parks during the entire survey period.

Apostle Islands National Lakeshore 

There was scant activity throughout the night during the month of June for most species (Figure 

9). There was an increase in activity in July and for most species August. Lasionycteris 

noctivagans was active throughout the night in July only.
17

Figure 9. Comparison of temporal activity patterns for the six species of bats at APIS.

Grand Portage National Monument

There was scant activity for Lasiurus borealis at GRPO throughout sampling period, while 

Lasiurus cinereus was most active in July (Figure 10). Lasionycteris noctivagans activity was 

minimal in June and July but increased considerably in August. With the exception of Lasiurus 

cinereus, nightly activity was greatest in August.
18

Figure 10. Comparison of temporal activity patterns for the six species of bats at GRPO.

Pictured Rocks National Park

Lasiurus borealis and Lasionycteris noctivagans were absent or only marginally active in June, 

respectively (Figure 11). Nightly activity was fairly constant for all other species at PIRO during 

the sampling period. 
19

Figure 11. Comparison of temporal activity patterns for the six species of bats at PIRO.

21

Discussion

Kruger and Peterson’s (2008) study sites were not randomly chosen, thus all conclusions are for 

the sites only. While it is possible to infer relative abundance and activity for the parks the results 

may not be representative (Route pers. comm.). Kruger and Peterson (2008) concluded that six 

of the seven species thought to occur in the three parks were present during a three month survey 

period in 2003. A review of the species distribution maps (IUCN 2008) suggest that possibly the 

seventh species, eastern pipistrelle (Perimyotis subflavus) might occur in the area. It was neither 

captured nor detected acoustically at any of the park survey locations (Kruger and Peterson 

2008). My re-analysis supports the Kruger and Peterson (2008) conclusions that there were six 

verified species. Moreover, during this re-analysis, all acoustic files were subsequently scanned 

with new robust filters and they performed well when challenged with focal species voucher 

files. I found no acoustic files flagged as matches to the P. subflavus vocal signature parameters, 

corroborating the conclusion that the P. subflavus was not present during the survey period. It 

remains possible that the species was present in very small numbers and was not detected 

acoustically or if present it was using areas that were not surveyed.

Kruger and Peterson (2008) assumed that it was not possible to determine relative abundance 

from their acoustic data since calls could have been produced by a few individuals or by many 

individuals. Kruger and Peterson (2008) also assumed that it was not possible to use the acoustic 

data to directly evaluate temporal variation in bat activity between parks or between sites within 

the parks. While they were constrained by limitations of equipment and could not sample all sites 

simultaneously, it was possible to generate relative temporal activity for each species at each

park. Variation in bat species temporal activity at a given site may reflect responses to varied 

environmental conditions. These include seasonal shifts in localized prey availability, transient 

climatic factors (i.e. wind, rain), or lunar phase.

Using the standardized AI during this data re-analysis, I estimated relative abundance for each 

species at each park by month. Given the nature of the Anabat acoustic file structure that 

automatically captures the date and time of each recording it was also possible to evaluate 

temporal activity.

Kruger and Peterson (2008) reported that two species – E. fuscus and Lasiurus cinereus – were 

only verified by acoustic methods. Moreover, this re-analysis shows Lasiurus borealis is 

probably very rare during June. 

Kruger and Peterson (2008) did not distinguish between the two species of M. septentrionalis

and M. lucifugus and combined these into a Myotis spp. category. With a larger library of 

verified reference calls it was possible during the re-analysis to identify these two species from 

the complete call sequences included in the Myotis spp. category. They also had a number of 

sequences they could not identify and placed these into an “unidentifiable” category. The 

majority of these calls were comprised of incomplete call sequences with frequencies were not 

specifically diagnostic search phase calls and with the overlap could match either species of 

Myotis.
22

It is possible they were not aware of the frequency shift that occurs when multiple individual 

conspecifics are present and recorded at the same time. This may have confounded their ability 

to differentiate diagnostic calls of these two species. This occurs when individual bats shift their 

characteristic frequency up or down when another conspecific arrives in the same area (Miller 

2004). These frequency shifts range from 2-3 kHz. In addition to confusing identifications, 

researchers who are unaware of such frequency shifts could over-estimate the variation in the 

basic search phase calls of an individual or species.

This re-analysis of the Kruger and Peterson (2008) data added species identifications and spatial 

attributes to the data set that were not previously included thereby enhancing the archival value. 

Additionally, using the AI (Miller 2001) it was possible to provide reasonable estimates of 

relative abundance for all species occurring during the study that would not have been possible 

using the metric of files per hour (Britzke 1999). The results of this re-analysis also provided 

temporal activity patterns for each species as summarized for each park.
23

Recommendations 

Additional acoustic data files are available for the three parks (L. Kruger, pers. comm.). If 

Kruger’s 2004 survey acoustic data set is also re-analyzed and combined with the re-analyzed 

2003 data set, a better understanding of the temporal and spatial species distributions within each 

park may be possible. Further, if spatial data that includes habitat information is available for 

each sample location, it may also be possible to derive habitat preferences and evaluate niche 

overlap for the species at each of the three parks. This would provide information useful to 

manage the parks for each species in a more proactive and robust manner and further identify 

critical habitat.

Bats are increasingly recognized as excellent taxa for monitoring habitats (Miller 2003; Rainey 

et al. 2006, 2009; Jones et al. 2009). Acoustic sampling is both a powerful and cost effective 

means to monitor bat populations. Future bat surveys in any of the national parks should employ 

reflector plates for the monitoring stations instead of the curved PVC tubes. This will result in a 

higher percentage of identifiable calls, fewer fragmented calls, and more complete vocal 

signatures for species recorded. All future acoustic transects should also employ current 

technology using the Anapocket software and the Anabat SD1 storage detector linked to a PDA 

so that the person(s) conducting the surveys can examine the calls as they are produced during 

transects. This allows immediate adjustment of the microphone sensitivity and gain to eliminate 

overdriving the microphone and resulting distortion of signals. With the addition of a GPS CF 

card receiver linked to a PDA, potential microhabitat associations may be discovered during 

subsequent analysis of the data as the X-Y-Z data is added automatically to each file recorded.

Using acoustic monitoring is a very cost effective means of monitoring bats once the initial 

investment of equipment is made. A number of Anabat acoustic monitoring stations have been 

operating in Yosemite for several years unattended dusk until dawn (W.E. Rainey, pers. comm.). 

Aside from data recovery field time, no costly human resources are necessary for data collection 

during the monitoring periods. Preliminary analysis of this data suggest that even with snow on 

the ground bats may become active during sunny periods, which has management implications 

applicable to winter months. Similar bat monitoring efforts within the GLKN would also be a 

cost effective means of gathering a large amount of data over landscape level and time scale 

monitoring efforts. With the investment of monitoring station equipment and a modest 

investment for training field technicians a program could be implemented that would yield robust 

data for proactive management decisions.

There was no habitat data available for the three parks during this re-analysis. If spatial habitat 

data could be made available for all sites, it would be possible to evaluate the relative abundance 

on a habitat basis to determine habitat preferences and provide better information for on-theground management of these protected areas.

While bats have been a relatively low priority monitoring target they are now very much in the 

forefront of conservation concerns given the spread of white-nose syndrome (Blehert et al., 2009, 

Reichard et al., 2008, Veilleux, 2008). If priorities within the GLKN shift due to the rapid 

spreading and anticipation of white-nose syndrome arrival in Michigan within the next year or so 

(Kurta pers. comm.) it may be desirable to begin a monitoring program. The existing 2004 
24

acoustic data when reanalyzed and combined with the results of this project could serve as a 

starting point for such a monitoring program.

Often, what has been referred to as “monitoring” is actually surveillance, or tracking the change 

over time. Monitoring differs from surveillance as the goal is to track progress over time towards 

a target or objective (Figure 12). If there is no clear idea of what this objective is, then there is 

nothing to monitor. Setting explicit targets lies at the core of effective monitoring. Monitoring 

towards a target provides data verifying whether management efforts are successful, whereas 

surveillance alone will not provide that information (Wilke 2005).

Figure 12. A target is necessary for monitoring, otherwise it is simply surveillance.

I suggest that the monitoring targets initially be equal to or greater than the baseline numbers of 

the conservation targets. The combined results of the 2003 and 2004 acoustic surveys might 

serve as a starting point. If the relative abundance of a bat species of conservation concern drops 

below the established targets, then management intervention may be required. Effective bat 

conservation relies on gathering sufficient information to identify changes in populations that are 

of conservation concern and to measure the population response to management (Walsh et al. 

2006).

The number of acoustic stations required for the simultaneous collection of data will depend on 

the area of the landscape to be monitored. I suggest that a statistical power analysis (Aughney 

and Roche 2006; Barclay et al., 2004; Gerrodette 1987) be used to estimate the optimum number 

of monitoring stations for each site in order to statistically detect trends. This is useful since the 

results will determine whether the number of stations (sample size) is too high or too low. If the 
25

number of stations used is too low, the data may lack the precision to reliably detect trends that 

are being monitored. Conversely if too many are used, time and resources may be wasted, often 

for only minimal gain.

Establishing the locations for each monitoring station will generally be determined by the initial 

baseline sampling period or data on hand. Experience has shown that for baseline data collection, 

a minimum of 3 nights is required for documenting common species, although 5-10 nights is 

considerably better for detecting rare species. Rare species are often those that are of 

conservation concern and may be focal monitoring targets. This is corroborated by using 

rarefaction species accumulation curves (Gotelli and Colwell 2001).

Example of Use of Spatial Data From Acoustic Data Sets

The following examples using data from this re-analysis are illustrative of how acoustic data can 

be linked to spatial analyses and visually provide new and useful information for park 

management. Anecdotal evidence suggests that the north shore of Lake Superior is important for 

migrating bats; however no studies have documented the travel pattern (Nordquist 2006). Three 

species Lasionycteris noctivagans, Lasiurus borealis and Lasiurus cinereus migrate out of the 

state during the winter and return in the spring (Nordquist 2006). The acoustic data linked 

explicitly to spatial survey points suggests that arrival of one of these known migrants (L. 

cinereus) may begin in June with a peak in August in the three parks surveyed (Figures 13-15). 

The increase in relative abundance from the western to the eastern portion of PIRO may be 

coincidental or linked to an important seasonal habitat change.

Figure 13. L. cinereus showing a marginal presence in June 2003 based on relative abundance.
26

Figure 14. L. cinereus showing an increase in numbers during July 2003.
27

Figure 15. L. cinereus showing the increased numbers and spatial distribution of relative abundance in 

August 2003.

29

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33

Appendix A. A Review of Filters and Filter Parameters Used 

in AnalookW.

As the use of filters appears to be a point of uncertainty with users of the AnalookW software, a 

review of the basic filter parameters (O'Farrell and Corben 2001) is presented with additional 

notes on the new filter formats are provided below. This is followed by a species by species 

comparison of the filters constructed using the Kruger and Peterson (2008) call parameters and 

those created specifically for this re-analysis based on a robust set of species specific voucher 

calls. The later set of filters was used for identification of species to scan the entire acoustic data 

set and create the Analist files and verify species identifications.

Filter Parameters

The filter parameters and descriptions (Corben 2009b) are included here as a resource for those 

who may not be familiar with the use of filters and the variables used to use them.

smooth 

This process recognizes groups of dots that are smoothly connected to each other and therefore 

might be bat calls. These groups of dots are treated as bat calls, and are then examined by 

subsequent filter operations. Thus, the selection of an appropriate smoothness parameter is the 

first step in defining a filter. The lower the value, the more stringent the filtering process (i.e., the 

higher the quality a call must have before it can be recognized as a call). Typical values to 

choose would lie between 20 and 100. The default value of 9999 has virtually no effect. As a 

rough guide, when this parameter is set to 100, it allows about a 10% deviation of each point 

from the mean frequency of the points either side of it.

maxposchg

This parameter defines the maximum positive change in frequency that a call can contain. As a 

rough guide, a value of 200 allows one point to be about 10% higher in frequency than the 

previous point.

maxnegchg 

This parameter defines the maximum negative change in frequency that a call can contain. As a 

rough guide, a value of 200 allows one point to be about 10% lower in frequency than the 

previous point.

bodyover 

The bodyover is the number of transitions in the original bat call over which certain other 

parameters are calculated. The number of transitions is divided by the current DivRatio to 

determine the number of dots over which the body is calculated. Using transitions rather than dot 

intervals frees the effect of this value from any dependence on the DivRatio. For example, if the 

bodyover is chosen as 80 when the DivRatio is 16, this gives a value of 5 for the number of dots 

included in the body. Any bat call is then scanned for groups of 5 dots (covering 4 inter-dot 

intervals) until the group of 5 dots is found that has the lowest overall slope. This call fragment is 

treated as the right-hand end of the body of the call and is used to estimate the characteristic 

frequency (Fc) and characteristic slope (Sc). Larger values for the bodyover parameter will make 

the measurement of these parameters more reliable because they will be less affected by small 
34

variations within the call. However, if the body length becomes a large proportion of a non-linear 

call, it will effectively form a chord across the curve and give an inaccurate result. We 

recommend a value of 160 for long duration calls with long, linear segments but a reduced value 

of 80 for short duration calls.

highstart 

When set to 1, this parameter constrains a call to begin at the point of highest frequency. Thus, 

any dots prior to that point will be excluded from the call. The default (0) has no effect.

lowstart 

When set to 1, this parameter constrains a call to begin at the point of lowest frequency. Thus, 

any dots prior to that point will be excluded from the call. The default (0) has no effect.

alldrop 

When set to 1, this parameter constrains a call to consistently decrease in frequency from one dot 

to the next. Thus, positive changes in frequency will be excluded from the call. The default (0) 

has no effect.

minNtrans 

This parameter determines the minimum number of dots a call must have before it can be 

accepted. The number of dots is equal to minNtrans/DivRatio. Normally, this parameter can be 

left at 0, which will have no effect and the call can be constrained using the minDUR parameter.

minDur 

This feature sets the minimum length (ms) a call must have before it is accepted. Shorter calls 

will be rejected.

maxDur 

This feature sets the maximum length (ms) a call must have before it is accepted. Longer calls 

will be rejected.

minFmax 

Any call whose maximum frequency is less than this value is rejected.

maxFmax 

Any call whose maximum frequency is more than this value is rejected.

minFmin

Any call whose minimum frequency is less than this value is rejected.

maxFmin 

Any call whose minimum frequency is more than this value is rejected.

minFmean 

The Mean Frequency (kHz) is the area under a bat call divided by the duration. Any call whose 

mean frequency is less than minFmean is rejected.
35

maxFmean 

Any call whose mean frequency is more than this value is rejected.

minSweep 

Any call in which the difference between the highest and lowest frequencies is less than this 

value will be rejected.

maxSweep 

Any call in which the difference between the highest and lowest frequencies is more than this 

value will be rejected.

minFc 

The characteristic frequency (Fc) is measured as the frequency at the right-hand end of the body 

(see above). Any call in which Fc is less than minFc will be rejected.

maxFc 

Any call in which Fc is greater than maxFc will be rejected.

minS1

S1 is the slope at the beginning of the call, calculated over the first five dots in the call. Any call 

with SI less than minS1 will be rejected. Note that SI can be negative.

maxS1

Any call with SI greater than maxS1 will be rejected.

minSc 

Sc is the slope of the body of the call. Any call with Sc less than minSc will be rejected. Note 

that Sc can be negative.

maxSc 

Any call with Sc greater than maxSc will be rejected.

minNcalls and Tforcalls 

MinNcalls (number of calls) that must be detected within Tforcalls (seconds) in order for any 

calls to be accepted. For example, if these parameters are set to 5 and 0.5, respectively, then 5 

calls must occur within a half second interval or all calls in the file will be rejected.

37

Appendix B. Comparison of Filter Parameters Used for This 

Analysis.

Values used to construct the filters used for this re-evaluation of the data are presented below. 

The filters (KP filters) were based on the Kruger and Peterson (2008), call parameters (see Table 

3). To provide a clear understanding of the parameters used for the identification of species 

during this reanalysis a comparison of the KP filters and those based on a robust set of species 

specific voucher calls is provided below (Tables B1-B7). The new AnalookW filter format 

includes several new parameters that were not included in the older Analook formats. For filters 

based solely on the KP parameters no values were available for these new parameters. Therefore 

the default filter values were used for these parameters and had no effect.

The new filters include 80 parameters that encompass a full range to include distinctive 

separation of species of multiple families and call types. Not all of these are applicable to the 

Vespertilionid species under consideration in this study. Therefore parameters such as harmonic 

content which were not applicable or used in either filter set, are not included here. For other 

parameters that did not prove useful in species discrimination the default filter values were used 

and had no effect.

Table B1. Average acoustic parameters with ranges from Kruger and Peterson (2008) used to construct 

filters. Species codes Eptfus = Eptesicus fuscus, Lascin = Lasiurus cinereus, Lasbor = Lasiurus borealis, 

Myluc = Myotis lucifugus, Myosep = Myotis septentrionalis, Persub = Perimyotis subflavus. Parameter 

values: Dur = msec, Fmax = kHz, Fmin = kHz, Fmean= kHz, Tk = msec, Fk = kHz, Tc = msec, Fc= kHz, 

S1= octaves per second, Sc = octaves per second.

Species Dur Fmax Fmin Fmean Tk Fk Tc Fc S1 Sc

Eptfus 10.12 33.81 24.61 27.17 2.97 27.56 9.67 24.83 151.8 28.66

± 0.16 ± 0.34 ± 0.09 ± 0.12 ± 0.07 ± 0.09 ± 0.15 ± 0.09 ± 6.11 ± 1.26

Lascin 6.75 26.06 20.23 21.64 2.22 21.85 6.34 20.31 153.7 30.96

± 0.09 ± 0.27 ± 0.09 ± 0.13 ± 0.05 ± 0.11 ± 0.09 ± 0.10 ± 4.89 ± 0.80

Lasbor 6.41 58.33 38.3 42.44 2.83 41.07 5.98 38.51 301.2 33.47

± 0.09 ± 0.57 ± 0.15 ± 0.20 ± 0.05 ± 0.16 ± 0.08 ± 0.15 ± 8.33 ± 1.18

Lasnoc 9.98 37.36 26.46 28.81 3.5 28.78 9.33 26.62 221.6 24.64

± 0.14 ± 0.26 ± 0.04 ± 0.06 ± 0.06 ± 0.05 ± 0.13 ± 0.04 ± 5.58 ± 1.00

Myoluc 3.78 62.35 40.28 47.14 1.9 46.04 3.53 41.42 325.9 118.7

± 0.05 ± 0.41 ± 0.10 ± 0.15 ± 0.03 ± 0.13 ± 0.05 ± 0.13 ± 6.20 ± 1.78

Myospe 2.5 70.62 41.54 51.19 1.39 50.38 1.87 46.61 445.8 283.3

± 0.03 ± 0.45 ± 0.10 ± 0.19 ± 0.03 ± 0.19 ± 0.03 ± 0.16 ± 4.07 ± 3.60

Persub 7.01 48.51 41.28 42.86 1.71 43.4 6.63 41.54 166.5 14.77

± 0.07 ± 0.22 ± 0.06 ± 0.07 ± 0.04 ± 0.07 ± 0.07 ± 0.06 ± 5.53 ± 0.46
38

Table B2. Comparison of filter parameters used for this study (Miller) and those used by Kruger and 

Peterson (2008) (KP) for Eptesicus fuscus.

Filter section Parameter Miller KP

Calls smooth 20 20

Times mindur ms 2000 9960

Times maxdur ms 20000 10280

Freq. minFmax kHz 25170000 33470000

Freq. maxFmax kHz 55000000 34150000

Freq. minFmin kHz 23500000 24520000

Freq. maxFmin kHz 39120000 24700000

Freq. minFmean kHz 23990000 27050000

Freq. maxFmean kHz 35000000 27290000

Body bodyover ms 80 80

Body minFc kHz 23700000 24740000

Body maxFc kHz 50000000 24920000

Body minDc ms 0 0

Body maxDc ms 999000 999000

Body minTc ms 1000 9520

Body maxTc ms 20750 9820

Body minFk kHz 24000000 27470000

Body maxFk kHz 50000000 27650000

Body minTk ms 1000 2900

Body maxTk ms 13900 3040

Slopes minS1 OPS -4295000 145.68

Slopes maxS1 OPS 1925000 157.9

Body minSc OPS -1991000 27.4

Body maxSc OPS 2033000 29.92

Sequence minNcalls pulses 5 5

Sequence Tforcalls seconds 10 15
39

Table B3. Comparison of filter parameters used for this study (Miller) and those used by Kruger and 

Peterson (2008) (KP) for Lasiurus borealis.

Filter section Parameter Miller KP

Calls smooth 15 15

Times mindur ms 1000 6320

Times maxdur ms 16200 6500

Freq. minFmax kHz 31000000 57760000

Freq. maxFmax kHz 85000000 58900000

Freq. minFmin kHz 29000000 38150000

Freq. maxFmin kHz 55000000 38450000

Freq. minFmean kHz 30000000 42240000

Freq. maxFmean kHz 55000000 42640000

Body bodyover ms 80 80

Body minFc kHz 29700000 38360000

Body maxFc kHz 62100000 38660000

Body minDc ms 0 0

Body maxDc ms 999000 999000

Body minTc ms 280 5900

Body maxTc ms 14100 6060

Body minFk kHz 30000000 40910000

Body maxFk kHz 66700000 41230000

Body minTk ms 100 2780

Body maxTk ms 3000 2880

Slopes minS1 OPS -14080000 292.85

Slopes maxS1 OPS 2775000 309.51

Body minSc OPS -985000 32.29

Body maxSc OPS 1880000 34.65

Sequence minNcalls pulses 8 5

Sequence Tforcalls seconds 15 15
40

Table B4. Comparison of filter parameters used for this study (Miller) and those used by Kruger and 

Peterson (2008) (KP) for Lasiurus cinereus.

Filter section Parameter Miller KP

Calls smooth 20 20

Times mindur ms 1000 6660

Times maxdur ms 16800 6840

Freq. minFmax kHz 23000000 25790000

Freq. maxFmax kHz 53000000 26330000

Freq. minFmin kHz 18000000 20140000

Freq. maxFmin kHz 29000000 20320000

Freq. minFmean kHz 20000000 21510000

Freq. maxFmean kHz 38000000 21770000

Body bodyover ms 40 40

Body minFc kHz 16000000 20210000

Body maxFc kHz 40000000 20410000

Body minDc ms 0 0

Body maxDc ms 999000 999000

Body minTc ms 3000 6250

Body maxTc ms 30000 6430

Body minFk kHz 20000000 21740000

Body maxFk kHz 30000000 21960000

Body minTk ms 1000 2170

Body maxTk ms 15000 2270

Slopes minS1 OPS -5723000 148.82

Slopes maxS1 OPS 1254000 158.6

Body minSc OPS -490000 30.16

Body maxSc OPS 951000 31.76

Sequence minNcalls pulses 5 5

Sequence Tforcalls seconds 15 15
41

Table B5. Comparison of filter parameters used for this study (Miller) and those used by Kruger and 

Peterson (2008) (KP) for Lasionycteris noctivagans.

Filter section Parameter Miller KP

Calls smooth 20 20

Times mindur ms 400 9840

Times maxdur ms 30000 10120

Freq. minFmax kHz 25100000 37100000

Freq. maxFmax kHz 62700000 37620000

Freq. minFmin kHz 20000000 26420000

Freq. maxFmin kHz 36600000 26500000

Freq. minFmean kHz 23900000 28750000

Freq. maxFmean kHz 51200000 28870000

Body bodyover ms 80 80

Body minFc kHz 24000000 26580000

Body maxFc kHz 35000000 26660000

Body minDc ms 0 0

Body maxDc ms 999000 999000

Body minTc ms 0 9200

Body maxTc ms 999000 9460

Body minFk kHz 24100000 28730000

Body maxFk kHz 57900000 28830000

Body minTk ms 0 3440

Body maxTk ms 999000 3560

Slopes minS1 OPS -4294000 215.98

Slopes maxS1 OPS 1864000 227.14

Body minSc OPS -1990000 23.64

Body maxSc OPS 2032000 25.64

Sequence minNcalls pulses 8 5

Sequence Tforcalls seconds 15 15
42

Table B6. Comparison of filter parameters used for this study (Miller) and those used by Kruger and 

Peterson (2008) (KP) for Myotis lucifugus.

Filter section Parameter Miller KP

Calls smooth 80 80

Times mindur ms 3500 3730

Times maxdur ms 15000 3830

Freq. minFmax kHz 41000000 61940000

Freq. maxFmax kHz 67800000 62760000

Freq. minFmin kHz 28000000 40180000

Freq. maxFmin kHz 60000000 40380000

Freq. minFmean kHz 29000000 46990000

Freq. maxFmean kHz 45000000 47290000

Body bodyover ms 80 80

Body minFc kHz 30640000 41290000

Body maxFc kHz 41750000 41550000

Body minDc ms 0 0

Body maxDc ms 999000 999000

Body minTc ms 2000 3480

Body maxTc ms 10000 3580

Body minFk kHz 34500000 45910000

Body maxFk kHz 49600000 46170000

Body minTk ms 600 1870

Body maxTk ms 5600 1930

Slopes minS1 OPS -9999000 -9999000

Slopes maxS1 OPS 9999000 9999000

Body minSc OPS -13600 116.92

Body maxSc OPS 167000 120.48

Sequence minNcalls pulses 5 5

Sequence Tforcalls seconds 10 15
43

Table B7. Comparison of filter parameters used for this study (Miller) and those used by Kruger and 

Peterson (2008) (KP) for Myotis septentrionalis.

Filter section Parameter Miller KP

Calls smooth 80 80

Times mindur ms 2000 2470

Times maxdur ms 6000 2530

Freq. minFmax kHz 45000000 70170000

Freq. maxFmax kHz 98000000 71070000

Freq. minFmin kHz 40500000 41440000

Freq. maxFmin kHz 50000000 41640000

Freq. minFmean kHz 46000000 51000000

Freq. maxFmean kHz 56000000 51380000

Body bodyover ms 80 80

Body minFc kHz 41000000 46450000

Body maxFc kHz 50000000 46770000

Body minDc ms 0 0

Body maxDc ms 999000 999000

Body minTc ms 0 1840

Body maxTc ms 999000 1900

Body minFk kHz 4000000 50190000

Body maxFk kHz 300000000 50570000

Body minTk ms 0 1360

Body maxTk ms 999000 1420

Slopes minS1 OPS -9999000 441.71

Slopes maxS1 OPS 9999000 449.85

Body minSc OPS -9999000 279.68

Body maxSc OPS 9999000 286.88

Sequence minNcalls pulses 5 5

Sequence Tforcalls seconds 10 15

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