A Comparison of Acoustic and Capture Methods as Means of Assessing Bat Diversity and Activity in Honduras_Hopkins 2004

7/31/2019 A Comparison of Acoustic and Capture Methods as Means of Assessing Bat Diversity and Activity in Honduras_Hop

1/39

A Comparison of Acoustic and CaptureMethods as Means of Assessing BatDiversity and Activity in Honduras

Claire Hopkins

Supervisor: Prof. John Altringham

M.Sc. ThesisUniversity of Leeds

August 2004


2/39

2

Contents

Abstract 3

Introduction 4

Echolocation in microchiropteran bats 4Call frequency 5Intensity and effective range 5Echolocation and foraging behaviour 6Call flexibility 7Sampling and detection methods 8Aims and objectives 9

Materials and Methods 10Study area 10Site selection 10Capture survey 11Acoustic survey 12Sound analysis 13Bat activity 13

Results 15Capture data 15Species accumulation curves 17Capture times 19Sonar reference library analysis 19

Indices of bat activity 23

Discussion 25

Interspecific echolocation call properties of Neotropical bats 25Intraspecific sonar characteristics 26Timing of capture 28Measures of bat diversity in the Neotropics 29Indices of relative bat activity 30Future studies 32

Conclusion 33

Acknowledgements 34

References 35


3/39

3

Abstract

The use of bat detectors in conjunction with traditional capture methods for

creating inventories of microchiropteran communities is becoming increasingly

widespread. However the extent to which bats can be distinguished by theproperties of their echolocation calls is still in dispute. The current study

compares two techniques for detecting bats and evaluates their effectiveness

in contributing toward species lists and elucidating activity patterns in a

previously unstudied area.

Mist nets were deployed at a number of sites in three locations in the

Merendon Mountains of northern Honduras (Base Camp, Buenos Aires and El

Paraiso). A total of 266 bats of 28 species were captured over the 6-week

study period and successfully recorded calls formed the basis of a reference

library. An acoustic survey based on the number of bat passes detected with

a Tranquility detector along a 250m transect was also carried out.

Interspecific variability in sonar properties was found to be low in relation to

intraspecific variation and no statistical differences were found between call

parameters of bats representing similar guilds. While this negates the

reliability of acoustic methods to carry out accurate biodiversity assessments

it highlights potential for recognition of bats according to their foraging guild.

Mist netting remains the most reliable way of identifying bats in the field but

tends to be biased toward Phyllostomid bats foraging in the understorey.

Acoustic monitoring is found to be a convenient method of assessing bat

activity in an area but is sensitive to small scale variations in bat abundance,

foraging patterns and habitat configurations. Future research should aim to

supplement the call reference library with bats from different guilds and further

elucidate ways of recognizing bats acoustically. Patterns of bat activity should

also be established in order to maximize the effectiveness of surveys carried

out in limited time periods.


4/39

4

Introduction

Bats are one of the major taxonomic groups in Honduras comprising

the most species-rich and ecologically diverse mammalian taxon at the local

community level in the Neotropics (Patterson et al. 2001, Kalko 1995). Theecological importance of bats in tropical forest ecosystems as seed dispersal

and pollination agents and their contribution to the diversity of vertebrate

communities is becoming increasingly recognized. Around 98 species of

microchiropteran bats are currently recognised in Honduras (IUCN, 1994).

This is dominated by the family Phyllostomidae (New World leaf-nosed bats)

which represents a diverse radiation that is endemic to the Neotropics (Kalko

& Handley 2001). Patterns of diversity and abundance in local bat

communities reflect differences in ecological conditions such as levels of

disturbance (Medellin et al. 2000) and the availability of roost sites and

foraging habitats (Wunder & Carey 1996). Attempting to understand the

factors which underlie such patterns has presented important practical

problems for conservation. As such Honduras has been identified as an area

of priority for the investigation of rainforest mammalian diversity in the

Neotropical region (Voss and Emmons 1996). This is a reflection of the

current paucity of data collected in the area and the increasingly fragmentary

nature of prime habitats.

Echolocation in microchiropteran bats

Flight and echolocation are two attributes of microchiropteran bats that

help to explain the diversity of form and functional niches, having allowed

adaptation to pursue previously inaccessible resources (Fenton 1995).

Research has therefore centred on investigating the aspects of flight and

echolocation which correlate best with patterns of diversity.

All microchiropterans use echolocation - vocalisations produced in the

larynx and emitted through the mouth or nose - to orientate, and some use it

to detect insect prey. Time comparisons between pulse and echo are vital for

acquiring information about the presence, location and structure of prey, and

on changes in position in relation to the surroundings (Dear et al. 1993).


5/39

5

There are a number of essential acoustic features of bat sonar signals.

Signals are typically brief to save energy and avoid pulse-echo overlap, and

vary in duration from 0.2 to 50ms and in frequency from 12 to 200kHz (Fenton

1995).

Call frequency

Ultrasonic orientation sounds may be either broad- or narrowband.

Broadband signals cover a range of frequencies while narrowband signals

focus most energy into a smaller range of frequencies (Fenton et al. 1995).

Sounds emitted should have a similar wavelength to the dimensions of the

target object in order to give information about target range, direction, size,

texture and velocity (Altringham 1996). Calls therefore vary widely according

to the species and the interests of the bat in frequency composition and

amplitude, and may contain frequency modulated (FM) components or

constant frequency (CF) components. Patterns of frequency-time structure

based on CF and FM components have been described for some

microchiropteran species. For example, Phyllostomus spp. use multiple-

harmonic sounds with relatively broad FM sweeps and a large overall

bandwidth which gives high resolution information about targets in complex

habitats (Simmons & Stein 1980). The Mormoopid Pteronotus davyiproduces

high-intensity sounds with short CF component at around 68kHz, a downward

FM sweep and short terminal CF component at around 58kHz (OFarrell &

Miller 1997).

Intensity and effective range

In addition to frequency, calls also vary in their intensity. Aerial

insectivores including bats in the Emballonuridae, Mormoopidae and

Vespertilionidae families use intense (> 110 dB SPL) echolocation calls to

detect, track and assess moving targets (Bogdanowicz et al 1999) and

separate pulse and echo in time with low duty cycles. Whispering bats

including many Phyllostomids, use directional calls of lower intensity (


6/39

6

intensity is lost in an inverse square relationship with spreading distance from

the source and sounds show additional atmospheric attenuation that is

proportional to the distance travelled dependent upon factors affecting the

speed of sound in air (Griffin 1971, Laurence & Simmons 1982). This limits

the effectiveness of echolocation used by bats. Broadband calls at higher

frequency are more resistant to clutter (acoustic interference generated by

complex backgrounds such as vegetation) but suffer from attenuation.

Conversely, narrowband low frequency sounds are good for long-range prey

detection but can lead to confusion by background clutter (Aldridge &

Rautenbach 1987). Bats must therefore reach a compromise between range

and resolution of detail, which is itself dependent on foraging strategy (Fenton

et al1998).

Echolocation and foraging behaviour

Significant correlations have been found between wing morphology,

foraging behaviour and echolocation call design as measured by

characteristic sonagram shapes including frequency, bandwidth and duration

(Aldridge & Rautenbach 1987). Insectivorous bats can be categorised into

guilds (groups of species assigned according to functional similarities in their

foraging behaviours and echolocation calls), defined by the degree of clutter

or vegetation structure to which they are specialised. Open space bats such

as Emballonurids and Molossids tend to use narrow bandwidth calls of simple

designs to pick out airborne prey against soft backgrounds. Phyllostomids, the

dominant species foraging in cluttered environments in the Neotropics (Kalko

& Handley 2001) tend with a few exceptions to use a gleaning approach to

foraging. Unlike insectivores which are dependent on echolocation for

foraging, leaf-nosed bats have a propensity to use short, multiharmonic, FM,

low intensity ultrasonic calls (Belwood 1988), and to supplement sonar with

other sensory modalities for orientation, foraging and communication. These

include vision, passive hearing and olfaction (Fenton et al. 1995, Kalko &

Schnitzler 1998, Altringham & Fenton 2003, Laska 1990). This has been

suggested as a contributory factor in the radiation of Phyllostomid bats into

other trophic areas including blood (eg Desmodus rotundus), fruit, nectar andpollen (Gardner 1977). However the role of echolocation in the foraging


7/39

7

behaviour of microchiropterans feeding on pollen, nectar, fruit and blood

remain poorly understood at present and the extent to which sound, light,

odour and other senses interact to allow the bat to interpret its immediate

surroundings are unknown (Altringham & Fenton 2003).

The ability to separate target (food) echoes from interfering signals

including clutter echoes and other bats is fundamental to the effectiveness of

echolocation as a means of foraging. As the acoustic characteristics of a bats

call are important in determining its comparative success at foraging in a

particular habitat, it is expected that bats sampled from habitats of a particular

structural complexity will be dominated by bats whose calls reflect the nature

of available foraging space. This is especially true as clutter manipulation

experiments have demonstrated the negative effects of clutter on the foraging

activity of bats according to constraints of wing morphology (Neuweiler 1983,

Brigham et al. 1997).

These interpretations have implications for the design of acoustic surveys of

bats in the field. Call intensity is difficult to measure as it is influenced by the

position of the bat in relation to the recording source (a function of frequency,

intensity and distance) as well as by the sensitivity of acoustic software.

Call Flexibility

A degree of intra- and inter-individual variety has been demonstrated

by studies of individual bats over extended time periods (Obrist 1995). At least

some of this variation could be explained by genetic (morphometric) variety,

differences between populations and learning (Obrist 1995). Differences

between sex and age also exist (Jones et al. 1992). These call differences

serve several functions in the transmission of information. Unique calls have

the benefit of ensuring self-recognition in the presence of conspecifics and

reduced ambiguity when communicating information about the surroundings

to oneself. Plasticity in the physical properties of calls enabling adjustment to

the environment allows access to a greater variety of habitats. This has been

demonstrated in Pipistrelle bats which adjust their calls to avoid overlap

between echoes from potential prey and obstacles in different vegetation

densities (Kalko & Schnitzler 1993). Studies of the nature of call changeabilityhave been extended to the Neotropical insectivorous bat Myotis nigricans


8/39

8

(Kalko & Schnitzler 2001, OFarrell & Miller 1999) but the full extent of

plasticity in non-insectivorous bats including many members of the

Phyllostomidae has not been investigated in detail. This identifies a lack of

knowledge about the extent to which this group can be used as candidates for

acoustic surveys, given that Phyllostomid bats dominate Neotropical

communities (Read 2002, Kalko & Handley 2001).

Inter-specific variation in call characteristics holds the information

necessary for understanding differences between species (Simmons & Stein

1980). While variation in the frequency-time structure (e.g. frequency

bandwidth, call duration) of insectivorous bats has been used as a means of

species identification in the field (Fenton & Bell 1981, Vaughan et al. 1997,

Rydell et al. 2002), assessments of intraspecific variation with respect to

habitat and behaviour have also been made (Obrist 1995). As Phyllostomid

bats have been observed to have calls which are less variable inter-

specifically (Kalko 1995) and more difficult to detect and record in the field

(OFarrell & Miller 1997) research is needed to find out the extent to which

different species can be recognised and distinguished by their calls. This is a

fundamental aspect governing the reliability of acoustic surveys (Barclay

1999).

Sampling and detection methods

The sampling method employed is an important determinant of species

representation in inventory studies. To date studies of bats in the Neotropics

have focused largely on traditional capture methods along foraging flyways

(Kunz & Kurta 1988) such as mist netting and harp trapping (eg. Medellin et

al. 2000, Kalko & Handley 2001). These methods are found to be inherently

biased towards species that forage in the understorey and certain gap types

and have resulted in a high representation by Phyllostomid bats (Kalko &

Handley 2001). In recent years, however, attention has been turned to the

potential of using acoustic techniques to carry out inventories of bats (e.g.

Crome & Richards 1988, Duffy et al. 2000). Acoustic methods are less labour-

intensive than trapping and potentially sample a greater area; especially of

bats calling at lower frequencies which attenuate less quickly (Laurence &


9/39

9

Simmons 1982, Griffin 1971). They are frequently used to complement

capture data (e.g. OFarrell & Miller 1997).

Establishment of species-specific vocal signatures has been identified

as an important priority in order to perform rapid inventories and to establish

activity patterns, habitat uses and other aspects of behavioural ecology of bat

communities (Kalko 1995). However acoustic methods have not previously

been found to be a reliable method of making assessments of bat diversity as

it can be difficult to distinguish between species even with a good call library

(Barclay 1999).

Only a small number of previous studies have set out to directly

compare capture and call recordings (Mills et al 1996, OFarrell & Gannon

1999, Duffy et al 2000). While ultrasonic surveying was found to be more

successful than harp trapping in terms of mean numbers of species detected

within an area (Mills et al. 1996) not all species are found to be equally

susceptible to each detection method, and not all calls were identifiable.

Concomitant use of capture methods is expected to provide a more complete

inventory (OFarrell & Gannon 1999). The study is a response to Moreno &

Halffters suggestion that a comparison of the efficacy of different sampling

techniques is required to optimise bat species detection and improve

inventory completeness (Moreno & Halffter 2000).

Aims and objectives

The current investigation aims to compare acoustic sampling and mist

net capture techniques as methods of assessing patterns of bat diversity in

northern Honduras. This will be achieved by building up a call library from

bats captured in mist nets and investigating the extent to which species can

be distinguished on the basis of their calls. The effectiveness of mist netting

and acoustic sampling with bat detectors as means of assessing relative bat

activity in the Neotropics will also be evaluated.


10/39

10

Materials and Methods

Study area

An acoustic bat monitoring study was undertaken in three locations in

northern Honduras between July 26th and August 7th 2004. The area of

interest (15 29.8 - 15 32.1 N, 88 13.0 - 18 16.3 W) comprises part of the

Merendon Mountain range and supports a range of habitats and distinct

vegetation types. These include lowland wet deciduous rainforests (up to

1500m), coniferous forest (800 1500m) and cloudforest at the highest

altitudes (up to 2242m). This reflects the moist, aseasonal climate and

moderately high temperatures. The highest parts are protected within Cusuco

National Park (7,690ha), which was established as a National Park in 1987

and is now under the management of COHDEFOR, the Honduran Forestry

Department. Cusuco National Park is part of the Central American Montane

Forest and consists of patches of forest types situated on isolated tops and

slopes of mountains. These support high levels of endemism and biodiversity.

There were three bases in the area around which research was carried out.

The camp at the entrance to the core zone (Base Camp) is within

predominantly closed deciduous and non-deciduous broadleaf forest with a

canopy up to 30m high and with an understorey dominated by ferns and

saplings (Lennkh 2003). The second base was the adjacent village of Buenos

Aires (1200m) which lies within the 15,750ha buffer zone of the National Park.

Heavily influenced by anthropological activities the area is characterised by

plantation crops and secondary forest including semi-arid pine. The third

location was in the El Paraiso Valley (0 800m) which consists of lowland

and secondary regenerating forest protected within a privately owned reserve.

Site selection

Four sites were chosen in the vicinity of each location. Sites selected

represented a range of different habitats, elevations and levels of disturbance

representative of the region, including forest trails, habitat edges and other

areas likely to have concentrated bat activity (Crome & Richards 1988).

Riparian habitats were also sampled as bats frequently use rivers as travelcorridors (Brigham et al1992). Sites were not selected on the basis of known


11/39

11

presence or abundance of bats as no thorough pilot studies have been

previously carried out in the area. Two capture nights were obtained at each

site and a site was never sampled on two consecutive nights to avoid the

problem of reduced capture success as a result of net shyness (Kunz & Kurta

1988) and to avoid pseudoreplication. The resulting number of capture nights

exceeds the number suggested to obtain reliable estimates of the number of

bat species in forest areas and takes into account variation in weather

conditions (Mills et al. 1996).

Capture survey

Following standard protocol, four 5-shelf mist nets of two different lengths (2 x

9m, 2 x 6m) were deployed at each site (e.g. Kunz & Kurta, 1988). The

orientation of nets was influenced directly by the physical characteristics of

the sampling location although where possible nets were places perpendicular

to flyways in such a way as to maximize the chances of capturing bats.

Additional disturbance to the local vegetation was kept to a minimum.

Sampling effort was standardized in terms of number of capture hours

(between 19.30 and 00.00 - coinciding with sunset at this time of year),

frequency of checking (once every 5 10 minutes) and total net length.

Captured bats were identified to species level using a key (M. B. Fenton, pers.

comm.) and field guide (Read 2002). Sex and reproductive condition were

recorded as Jones et al. (1992) have demonstrated call differences between

age and sex groups. Bats were subjected to wing biopsy as a means of

marking captured bats and also biometric analyses including forearm and

mass measurements were performed, as is standard for confirmation of

species identity. Attempts were made to minimize stress to the bats and

heavily pregnant females or highly stressed individuals were released without

analysis.

Species accumulation curves were obtained by taking the number of survey

nights as sampling effort and calculating the cumulative number of species

captured with capture effort. The order in which samples were included in a

species accumulation curve influence its overall shape so sample nights were

randomised to smooth the curve (Magurran 2004). Estimates of overallspecies richness at each of the three sites were made using Chaos


12/39

12

presence/absence estimator as this method is a nonparametric method that

has been observed to perform effectively with variable data (Magurran 2004).

Species Diversity and Richness software (http://www.pisces-

conservation.com) was used to carry out the calculations.

To complement capture data an ever-expanding library of calls was created

as bats were hand-released close to the net of capture (OFarrell et al. 1999,

Aldridge & Rautenbach 1987). Call recordings were made using a Tranquility

Bat detector connected to a Sony Minidisk recorder held 2 3m from the bat.

This was the first such library to be carried out in the area and was developed

to illustrate the range of calls and to establish the extent to which species can

be identified on the basis of their calls. Ultimately this could provide a

reference source of call parameters of known species against which unknown

species can be compared.

Acoustic survey

A 250m linear transect method was employed to sample free-flying bats of

unknown identity around each site. Calls were recorded using a time

expansion bat detector set to record for 40 ms intervals when triggered by a

bat call. This allows retention of the spectral content of sound information

(Parsons et al. 2000). The detector was placed at a 45 angle to the ground

pointing along the trail or across the open area so as to maximize the

likelihood of detecting high-quality calls (OFarrell et al. 1999). This was done

for at least five minutes at 25m points along the transect - a distance selected

in order to avoid sampling overlapping foraging grounds. Acoustic transect

surveys commenced around 90 minutes after sunset (approx. 19.30) to

ensure constant light intensities across sampling nights and to coincide with

maximum foraging activity (Aldridge & Rautenbach 1987). Using this method

allows bat activity to be sampled regardless of the purpose of the flight (i.e.

foraging or commuting between foraging grounds or roost sites) and sampling

for at least 1 hour each night gives sufficient data to make comparisons

between sites. Recording ceased during periods of rain to minimize risk of

microphone damage and was not carried out within 20m of nets to avoid

detection of any trapped bats. Start points for the transect were rotated on thesecond capture night at a site to avoid bias and to take into account


13/39

13

differential habitat use by bats. Transects typically followed the forest trails or

natural vegetation breaks as these have been shown to be used by forest-

dwelling bats (reviewed in Wunder & Carey 1996, Brigham et al1997, Grindal

1995, Crome & Richards 1988). Transects incorporated parts of the trail

before, around and beyond the netted area in order to sample a comparable

area and acoustic sampling was carried out simultaneously with capture.

Sound analysis

Recorded calls (defined as individual, discrete pulses of sound; OFarrell et al.

1999) were processed using Batsound Pro software on a desktop PC at a

sampling rate of 44100Hz and a Hanning window. Bandwidth, maximum and

minimum frequency (kHz), frequency of maximum energy (frequency at which

the intensity was greatest in kHz) and duration (time in milliseconds from the

beginning to the end of the pulse) were identified in the fundamental call

where possible (Figure 1). Qualitative observations of sonagram shape and

the presence of harmonics were also made. Inter-pulse interval and duty cycle

were not calculated due to the fragmentary nature of calls. Call intensity

measurements were also omitted as this is very sensitive to the direction

traveled and the distance of the bat from the microphone.

Bat activity

I used an index of activity (IA) taken from acoustic monitoring data as a

measure of activity levels in bats (Hayes 1997). On nights when bats were

successfully recorded using the transect method in BA and EP sites the total

number of passes was calculated for each night sampled, where a pass is

defined as a single change of track on the minidisk recorder. Because

recording was limited to 1 disk per night (11 x 5-minute intervals) over

standardized recording hours this method was used to compare activity levels

on a night to night basis. The acoustic IA was compared with a similar IA

obtained from capture data, where activity was equal to the number of bats

caught in the time period covered by the acoustic technique. The acoustic IA

is expected to vary in direct proportion with the IA from captures if the two

methods are sampling similar assemblages of bats such that xnumber of batpasses would correspond with ynumber of captures.


14/39

14

Figure 1: Hanning windows showing call spectrograms recorded from bats captured during thestudy period. Power spectra are included showing how energy is distributed across frequenciesand harmonics. (A) Sturnira lilium;(B) Artibeus jamaicensis;(C) Molossus sinoloae; (D)Rhogeessa turmida. (E) and (F) represent two call sequences emitted by Glossophaga soricina.


15/39

15

Results

Capture Data

A combined total of 266 bats were captured on 29 survey nights over the 6-

week study period (Table 1). Representatives from 28 species (plus 4 which

we could not identify), 20 genera and four families were captured in mist nets

at 17 sites over three study locations (Table 2).

LocationFamily Species BC BA EP

Phyllostomidae Anoura geoffroyi 1

Artibeus intermedius 7 2

Artibeus jamaicensis 18 14

Artibeus lituratus 34

Artibeus phaeotis 3

Artibeus toltecus 9 11 1

Carollia brevicauda 3 2 11

Carollia perspicillata 13

Chiroderma salvini 1

Centurio senex 1 2

Desmodus rotundus 5 9

Glossophaga soricina 2 5 17

Hylonycteris underwoodi 1

Micronycteris microtis 1

Phyllostomus discolor 7Phyllostomus hastatus 1

Platyrrhina helleri 2

Sturnira lilium 1 16 21

Sturnira ludovici 8 8 5

Tonatia saurophila 1

Tonatia sylvicolor 1

Uroderma bilobatum 3

Vampyrodes caraccioli 1

Vampyressa pusilla 3

Vespertilionidae Eptesicus brasiliensis 5

Myotis keaysi 2 2

Noctilionidae Noctilio leperinus 1

Mormoopidae Pteronotus davyi 1

Unknown 1 1 2

Total 34 79 153

Table 1 Numbers of each species captured in mist nets in the three survey locations (BC =Base Camp, BA = Buenos Aires, EP = El Paraiso). Captured bats were predominantly fromthe family Phyllostomidae although the most abundant species varied between locations.


16/39

16

Total survey nightsLocation Vegetation type Survey

night

Number ofsampling

sitesCapture

onlyCombinedacoustic/capture

Riparian habitat 1 2 0Secondary cloudforest(broadleaved trees)

4 6 0

Secondary forest (pine) 1 1 0

BaseCamp

Plantation (banana) 1 1 0Plantation (banana, coffee) 1, 2, 5, 8 2 4 4Buenos

Aires Landscape mosaic(plantation/regeneratingforest)

3, 4, 6, 7 2 4 4

Secondary lowlandrainforest

2, 3, 5, 8 2 5 4

Plantation (banana) 2 2 0Ornamental garden near

beach

1, 6 1 2 2

El Paraiso

Man-made clearing 4, 7 1 2 2

Table 2: Capture effort for the Neotropical bat inventory. Mist nets were deployed at all 17sites representing capture and call library data; combined acoustic monitoring andcapture/call library data were obtained from 2 non-consecutive survey nights (numbered) ateach of 4 sites in EP and BA.

Some trap sites were more successful than others and there was

considerable heterogeneity in relative species abundance between sites and

locations (Figure 3). There were distinct between-night effects evident from

variability in both species composition and total number trapped on the 1st and

2nd nights at a site. The limited number of climate recordings taken limits

further analysis of distribution patterns. (Mills et al 1996).

-1

0

1

23

4

5

6

BC BA EP

Location

Meancaptu

rerate

(bats/hour)

Figure 2: Mean capture rates (Bats per hour standard deviation) were generallyhigh at the lower altitude sites (BA 2.25 1.179; EP 3.40 2.047) and relatively low at

Base Camp sites (0.76 0.932).


17/39

17

El Paraiso was the most successful location in terms of both numbers of bats

and numbers of species captured. Mean capture rates were higher at lower

elevations (Figure 2) although high deviation in capture rates at Base Camp

reflect disproportionately high numbers on a night when sampling a riparian

habitat. In addition mean capture rates were significantly larger on the 8

combined acoustic/capture study nights in EP compared with BA (Mann

Whitney U test: z= -2.107, p= 0.035, n = 16). Due to low capture rates and

unfavourable weather conditions during survey nights at all Base Camp sites

and three El Paraiso sites (10 survey nights in total), combined

acoustic/capture data from these sites have been omitted from analyses

although echolocation calls collected from bats captured on these nights were

represented in the call library to demonstrate more of the variation that

occurs.

Species accumulation curves

Patterns of bat species accumulation were analysed against sampling effort

for bats identified from mist net captures at the three locations (Figure 3). Bat

surveys were carried out over a relatively small area, but rare species

(including those that could not be identified) continued to appear with

increased sampling time. The rate of new species captures decreased

markedly with effort despite sampling different habitat configurations and

communities within each location and sampling different sites on consecutive

nights. Curves reflected the relatively small numbers of common species and

large numbers of relatively rare species but did not approach the asymptote at

Base Camp and Buenos Aires suggesting insufficient sampling effort at this

location. Chao presence/absence estimators of species richness were

calculated based on night-by-night analysis of species abundance (Base

Camp estimate = 17 4.257, Buenos Aires estimate = 20.25 5.889, El

Paraiso estimate = 22.56 1.17; Colwell & Coddington 1994).


18/39

18

Figure 3 Species accumulation curves for Base Camp (BC), Buenos Aires (BA) and ElParaiso (EP). Blue diamonds represent average cumulative species number against sampleeffort where cumulative species numbers represent average numbers taken from survey datarandomly rearranged according to the number of survey nights in that location (BA = 10, BA =8, EP = 11). Total species numbers differed nonsignificantly between sites (BC = 11, BA = 14,

EP = 21: 2

=3.435, df = 2, p = 0.1795).

BC

0

2

4

6

8

10

12

0 2 4 6 8 10 12

Randomised sample

Cumulativenumberofspecie

BA

0

2

4

6

8

10

12

14

16

0 2 4 6 8 10

Randomised sample

Cumulativenumberofspe

cie

EP

0

5

10

15

20

25

0 2 4 6 8 10 12

Randomised sample

Cumulativespeciesnumber


19/39

19

Capture time

Capture frequencies of all species for each combined acoustic/capture study

night at BA and EP were grouped into 30-minute time intervals and tested for

differences between sites. At Buenos Aires sites there were no significant

differences between capture nights in timing of captures so data from different

nights were pooled together (2 = 66.827, p = 0.153, df = 56, n = 8). A second

2 analysis was carried out to test the hypothesis that there is no temporal

variation in capture numbers, where expected values are represented by the

total caught over all sites divided by the number of 30-minute time intervals

(9). This test yielded a non significant result suggesting that over the 4 hour

sample period at this time of year and at these sites in BA bat activity appears

to be relatively evenly spread over the night (2 = 9.778, p = 0.281, df = 8).

At El Paraiso there was significant variation in capture times between nights

(2 = 91.589, p = 0.002, df = 56). Capture rates at each site were therefore

analysed on a night by night basis to elucidate patterns of activity. Five out of

8 nights had significantly more captures early on in the evening (between

19:30 and 21:00) than would be expected if activity was uniformly distributed

over the night (df = 8: EP2: 2 = 17.0, p =0.030; EP3: 2 = 16.8, p = 0.032;

EP4: 2 = 38.6, p


20/39

20

bats captured at the same sites on different nights by another bat team. This

represented 16 individuals from 6 species making a combined total of 221

calls for analysis.

In order to make comparisons between species call characteristics only those

calls made by individuals of species with more than one individual

representing it were used.

Five parameters and aspects of call structure were measured in the

fundamental component per call. Examination of the echolocation call library

identified considerable variation in call design (Figures 1 & 4).

Call shapeSpecies a b c d e f

A.intermedius XA.lituratus XA.jamaicensis X X XA.toltecus X XC.brevicauda X

C.perspicillata XE.brasiliensis X XG.soricina X X XM.keaysi XM.sinoloae XP.davyi XP.discolor X XR.turmida X XS.lilium X XS.ludovici X

Table 3 Comparison of call designs used by 15 species of echolocating bat represented inthe call library. Many species were observed to use more than one call type, and some types

are typical across species. Only two bats (Molossus sinoloae and Rhogeessa turmida)appeared to have distinctive call patterns.

Figure 4 Typical call structures ofbats captured during the study

period that were represented inthe call library. (a) Steepbroadband FM sweep, often withharmonics overlapping infrequency. (b) A combination ofsteep and shallow FM sweepswith initial CF component. (c)Short FM sweep over smallfrequency range, often withdistinct harmonics. (d) Sharpbroadband downward FM sweep,most energy at the base of thesteepest section. (e) Initial drop in

frequency followed by brief CFcomponent and steep FM sweep.(f) Initially shallow followed bysteep FM sweep.


21/39

21

Calls were significantly dominated by sounds with the frequency of maximum

energy in the 20 60 kHz range, and less so by the 60kHz ranges

(2 = 13.687, df = 2, p < 0.05). Most signals were tonal broadband signals

ranging in time from 1.6 to 11.7 ms. Strong positive intercorrelations were

observed between frequency maximum, frequency minimum and frequency of

maximum energy (Figure 5).

Given that there appears to be an association between frequency

characteristics multivariate statistical analyses were carried out in order to test

the hypothesis that there is no variation between individuals of the same

species. The error variance across species groups was tested using all five

call variables to increase discrimination power, and found to be non-

homoscedastic which violated the assumption of a parametric multivariate

analysis (Levenes test: Fmax: F = 2.968, Fmin: F = 5.443, F range: F =

2.432, Duration: F = 3.644, F max E: F = 3.865; df = 92, 128; n = 221).

Assuming that combinations of sound variables potentially hold information for

species recognition (Fenton & Bell 1981) a Principal Components Analyses

(PCA) would be expected to identify clusters of similar call parameter

groupings and derive classification rules by which to discriminate between

species according to their calls. A PCA was carried out to investigate which

linear combination of variables from the library as a whole best explains the

variation in the multivariate data set. The ordination of the sample points on

the first and second principal component axes is shown in Figure 6.


22/39

22

Figure 6 Principal Components Analysis scores plotted with species markers. Cumulatively62.706% and 86.466% of the variation observed between species can be explained by PCA1and PCA2 respectively. Clumping appears to exist in R. turmidaand M. sinoloaebut most

other species have overlapping values.

-2-1.5

-1

-0.5

0

0.5

1

1.5

2

2.5

3

-4 -2 0 2 4

PCA1

PCA2

A. intermedius

A. lituratus

A. jamaicensis

A. toltecus

C. brevicauda

C. perspicillata

E. brasiliensis

G. soricina

M. keaysi

M. sinoloae

P. davyi

P. discolor

R. turmidaS. lilium

S. ludovici

(A)

0

1020

30

40

50

60

70

80

90

0 20 40 60 80 100 120

Frequency maximum kHz

Fr

equencyminkH

(B)

0

1020

30

40

50

60

70

80

90

100

0 20 40 60 80 100 120

Frequency maximum kHz

Fre

quencyofmaximu

EnergykHz

(C)

0

20

40

60

80

100

0 20 40 60 80 100

Frequency minimun kHz

Frequencyofmaximum

EnergykHz

Figure 5 Correlation results between soundvariables of 221 bat echolocation calls. (A)There is a strong positive correlation betweenfrequency maximum (F max) and frequencyminimum (F min) (Spearmans correlation r =0.747, p < 0.05). (B) There is a strong positivecorrelation between frequency of maximum

energy (F max E) and Fmax (Spearmanscorrelation: r = 0.770). (C) There is a strongpositive correlation between Fmin and FmaxE(Spearmans correlation: r= 0.795, p


23/39

23

Principal Component Analysis scores show that although a high proportion of

individual variance can be explained by the principal components Neotropical

bats there is considerable overlap between species, making discrimination on

the basis of these call factors alone unreliable under these circumstances.

Indices of Bat Activity

Indices of bat activity were derived from acoustic monitoring and mist net

capture data (Table 4). It is expected that if variation in bat activity levels is

equally detectable by the two indices a linear relationship will exist between

them.

The acoustic activity index results were inconsistent both between nights and

between sites (figure 7). Although acoustic counts were high on nights where

there were high total captures at a landscape mosaic site in Buenos Aires this

was not reflected in the capture index for this time period. Conversely a night

with a high capture index does not necessarily yield large acoustic index

values.

Transect IA

Location Date Site Start time Finish time Duration Acoustic Capture

Total

capturesBuenos Aires 11/7/04 1 20:10 21:19 1:09 8 3 5

12/7/04 2 20:03 21:14 1:11 6 1 4

13/07/04 3 20:18 21:29 1:11 54 3 17

15/07/04 4 20:55 22:13 1:18 26 2 7

18/07/04 1 20:04 21:14 1:10 13 3 9

19/07/04 4 20:19 21:27 1:08 3 4 8

20/07/04 3 20:16 21:46 1:30 88 2 18

22/07/04 2 20:15 21:31 1:16 6 3 13

El Paraiso 27/07/04 1 19:58 21:12 1:14 63 3 10

29/07/04 2 20:11 21:38 1:26 9 2 9

30/07/04 3 19:48 20:58 1:10 30 6 15

1/8/04 4 20:11 21:34 1:22 54 7 10

2/8/04 2 20:07 21:19 1:12 24 9 18

3/8/04 1 20:07 21:10 1:03 51 3 19

4/8/04 4 19:58 21:31 1:33 22 8 19

5/8/04 3 20:10 21:25 1:15 8 13 36

Table 4 Index of Activity (IA) scores for bats detected by acoustic and mist nettingtechniques. There were 16 independent sample nights (2 repetitions at each of 4 sites in BA

and EP Table 2). Mean number of bat passes (passes S.E.) were 25.5 10.731 (BA) and

32.625 7.412 (EP).


24/39

24

Figure 7 Graphs showing the correlation between Acoustic Index of Activity (IA) and CaptureIA (A) and between Acoustic IA and total captures by location (B). In each case acousticindices were not significantly correlated with captures. When data from the two locations weregrouped together acoustic indices were not significantly correlated with either capture IA or

total captures respectively on a night by night basis (Spearmans rank correlation r= -0.034,p = 0.900; r = 0.385 p = 0.141; n = 16).

B

Total Capture

403020100

AcousticAI

100

80

60

40

20

0

Location

EP

BA

A

Capture AI

14121086420

AcousticAI

100

80

60

40

20

0

Location

EP

BA


25/39

25

DiscussionInterspecific echolocation call properties of Neotropical bats

In the current study bats were not found to be readily distinguishable on the

basis of their echolocation call structure or design. Interspecific differences in

sonar properties were seen to be small given the amount of variation

observed in conspecifics and in addition bats from different species were

observed to use similar call designs. This contrasts with the limited number of

previous studies investigating echolocation call characteristics of foraging

Neotropical bats, which have found calls to be distinguishable at least to

genus level (OFarrell & Miller 1997, Rydell et al. 2002). While these findings

confounded attempts to differentiate between species on the basis of their

sonar pulse properties alone, it provided useful information pertaining to the

ecology of the bats that use them.

Comparative field studies have demonstrated that inter- and intraspecific

trends in echolocation behaviour are closely associated with ecological

conditions including habitat type, foraging mode and diet, and in turn with

aspects of maneuverability and morphology (Aldridge & Rautenbach 1987,

Crome & Richards 1988, Schnitzler & Kalko 1998). In particular members of

the same feeding guild tend to have evolved similar calls as signal structure

and pattern correspond closely with the environment in which they hunt

(Denzinger et al. 2004). Captures revealed a prevalence of gleaning

Phyllostomid bats and especially frugivores which have been shown to have a

preference for feeding in the understorey (Fleming 1982). Adopting the guild

classification concept developed by Kalko and Handley (2001), the species

represented in the call library upon which analyses were based were

dominated by those suited to highly cluttered space which reflects the bias inour sampling areas towards forest trails and cluttered habitats.

Sonar signals were dominated by short duration signals typically between 2

and 5 ms long. As emitted pulses greater than 5.9ms long will cause overlap

between pulse and echo from objects 1m away, short pulses avoid this

overlap and are well suited to bats foraging in close proximity to obstacles

(Altringham 1996). Steep broadband sweeps of mid-frequency range, often

supplemented with harmonics that overlap (Figure 1), give large overallbandwidth and sharp target ranging acuity. Such calls have been


26/39

26

demonstrated to be resistant to clutter interference and atmospheric

attenuation allowing echoes from prey to be distinguished from the substrate,

and are typically used for spatial orientation in narrow space (Simmons &

Stein 1980, Laurence & Simmons 1982). The two species which did appear to

stand out on the basis of PCA and call shape analyses (R. turmidaand M.

sinoloae) represented a different guild sampled from an open space habitat.

In addition, many bats have been observed to use similar calls under similar

situations such as obstacle avoidance, dominated by steep sweeps (Rydell et

al. 2002). Given the novelty of the situation and the presence of the recorder

in front of the bat as it was being recorded it is perhaps not surprising that

particular call types dominated the assemblage.

Previous studies investigating the characteristics of foraging bats have

concentrated primarily on aerial-feeding insectivores and open space feeders

and on describing their call properties. By focusing on echolocation strategies

this study helps shed some light on foraging ecology in closed space non-

insectivorous New World bats and shows potential for echolocation calls to be

used to assign bats according to their guild.

Intraspecific call properties

Despite distinct correlations between the frequency characteristics of bats

representing the call library consistent with the close relationships that exist

between call form and function; conspecific bats were observed to emit a

variety of different call types and intraspecifically the distinction between call

parameters was not so obvious. For example, Glossophaga soricina was

observed to emit narrowband shallow-modulated elements associated with

gap habitats as well as those typical of more cluttered environments as

described above (Figure 1). These observations are consistent with field and

flight cage experiments investigating flexibility in call behaviour (Kalko &

Schnitzler 1993, Fenton et al1995, Obrist 1995). Given that changes in signal

structure depend largely on the horizontal distance of a bat to obstacles

(Kalko & Schnitzler 1993) it is conceivable that call structure can be altered in

order to orientate within the novel surroundings presented to a bat on its

release from the hand. While this illustrates the plasticity in echolocation callstructure bats are capable of, it presents problems in basing identification of


27/39

27

different species of free flying bats on call libraries obtained from bats

adjusting to atypical situations. Variation within and among populations and

variation according to recording differences can augment the problems of

using echolocation calls to identify bats (Barclay 1999). Reliable identification

of bats could at this stage only be increased by supplementing call data with

other morphometric data including size and shape of the bat which are difficult

to obtain without capture. Rydell et al. (2002) found that open situations give

more typical calls as it is least difficult for bats to obtain supplementary

information visually, and it may be possible to standardize call recording

methods by always releasing bats in a similarly open habitat.

It appears that variation within species is larger than can be explained by

variation in survey site alone given that different individuals belonging to the

same species were observed to emit calls of different designs even if

captured at the same site (pers. obs). Small sample size, context, behaviour

and genetic differences should not be discounted as proximal causes of the

observed variation. Low success rates for call recording possibly reflected the

fact that Phyllostomids are whispering bats that emit low intensity sounds

that are rarely registered with detectors (Arita & Fenton 1997, Kalko 1997),

and that are less reliant upon echolocation for navigation and foraging

(Altringham & Fenton 2003). This may also account for poor call quality and

the fragmentary nature of calls recorded.

Duffy et al. (2000) suggested that a library of between 15 and 40 reference

calls would be necessary to represent the variation between individuals in

search-phase calls in bats at each site. On the basis of this study it is

suggested that even more calls would be required to accurately represent the

full range of call variations achievable by each species over the range of

habitats sampled and in order to identify trends according to geographical

distance (OFarrell et al. 2000), sex and reproductive condition (Jones et al.

1992).

The current study has provided a baseline acoustic reference which should be

supplemented by thorough future sampling efforts to provide a representative

sample of the full repertoire of bats.


28/39

28

Timing of capture

The non significant difference between capture rates at different times in the

night highlights that caution should be applied when basing acoustic

monitoring studies on specific time periods during a survey night. The

assumption had been made that maximum periods of activity in different bat

species would coincide around 2 hours of sunset when opportunistic feeding

takes place (Aldridge & Rautenbach 1987). Although El Paraiso data show a

general trend toward early activity patterns of bat activity consistent with

Eckerts and Hayes observations (Eckert 1982, Hayes 1997), the pattern was

less obvious in Buenos Aires. There appeared to be persistent overall activity

levels at moderate levels with multiple peaks during the 4.5 hour sampling

period each night and additionally, the distribution of 30-minute periods with

no captures did not differ significantly on a night to night basis. In

concordance with the findings of Crome and Richards (1988) no consistent

differences were observed between species in timing of foraging activity and

Phyllostomid bats showed a tendency to be active over long time periods

(Eckert 1982). Fluctuation in activity patterns has been demonstrated to be

influenced strongly by prevailing external and physiological conditions (Eckert

1982). Furthermore, a number of other factors were seen to affect timing of

capture. There appeared to be less activity on moonlit nights (pers. obs.)

consistent with Morrisons theory of lunarphobia (Morrison 1978). The

influence of foraging strategy and conspecifics on flight times and capture

numbers was also observed as some frugivores forage in groups (Fleming

1982) and Artibeus species tended to be trapped in quick succession,

possibly as a result of attraction to the distress calls of conspecifics

(Altringham & Fenton 2003).

On the basis of the implications of this study two trap nights do not appear to

be adequate to obtain reliable estimates of bat species in forest areas (cf.

Mills et al. 1996). A more in-depth understanding of activity patterns in

conjunction with more detailed knowledge of bat composition in the area will

have important implications for the design of acoustic surveys. For the

purposes of carrying out biodiversity assessments whole night sampling is

recommended to detect bats with more elusive foraging patterns.


29/39

29

Measures of bat diversity in the Neotropics

Levels of bat biodiversity appear to be highest in El Paraiso and diminish

progressively with altitude to sites in the National Park. Consistent with

Patterson et al.s1996 findings, these data showed highland bat faunas to be

attenuated versions of lowland sites with no apparent replacement with

higher-elevation specialists. The variation in diversity of bat assemblages,

occurrence of rare species among different sites and observed clime in

species richness may plausibly have been a function of elevation although the

effects of large-scale biogeographic factors such as altitude on bat diversity

were beyond the scope of this investigation.

Species diversity estimates for each location were lower than for the

combined total of species captured over all locations despite considerable

overlap in community composition. This, in combination with the observation

that species abundance curves did not attenuate towards an asymptote,

suggest that estimates of species richness in northern Honduras are likely to

be conservative and there is low level of completeness at the level of effort

invested (Magurran 2004). Results also inferred that spatial variation appears

to be a greater contributor to overall species numbers than temporal variation

under the weather conditions experienced. This implies that in order to obtain

a more representative species inventory including hard to document species it

may be more effective to replicate with more traps, trap configurations and

detection methods over a larger area than to extend to more nights.

Estimates of species richness were limited by the biases implicit with mist

netting which tend to under represent adept fliers foraging more than 2-3m

above the ground such as Vespertilionids (Aldridge & Rautenbach 1987),

above-canopy foragers such as Mormoopids (Kalko 1997), and riparian

foragers such as Noctilionids and do not take into account differential use of

habitats by opportunistic species. Species distribution and abundance may

differ significantly from the canopy to the understorey in a vertical stratification

and the composition of the understorey community may not be a good

representation of the community as a whole (Bernard 2001).

Species richness is only one indicator of species diversity and despite low

overall richness the diversity shown in trophic mode was high, includingvampires, nectarivorous, frugivorous, carnivorous and fish eating bats. This


30/39

30

implies that given the number of trophic niches available the number of

species actually represented in the inventory were relatively low. Biodiversity

estimates calculated in this study were based on relative abundance although

as has been discussed, absence from a site at a particular time and using a

particular detection method does not categorically define it as being absent.

Differences in flight frequency, variation in habitat structure, differences in

vertical movements and the proportion of time spent in the sampling zone will

all have an effect on captures. It should not be assumed that such influences

have no significant effect on the number and relative proportions of bat

species bats captured at a site (Remsen & Good 1996).

The failure of the acoustic reference library to demonstrate means of

differentiating between species based on echolocation calls meant that

ultrasound surveys could not be used to complement mist netting data in

compilation of species lists (cf. OFarrell & Miller 1997, OFarrell & Gannon

1999). Establishing a bigger call library and overcoming the biases inherent

with mist netting should be priorities for the design of future biodiversity

assessments. In future it may be prudent to limit biodiversity measures to

families of bats belonging to common and ubiquitous taxonomic and

biogeographic units, and especially to polytypic genera such as Glossophaga,

Sturniraand Artibeusspp. which can be sampled using standardised protocol

(Moreno & Halffter 2000). This will allow data between inventories to be

directly compared and, importantly, for analysis of biodiversity in relation to

community structure and ecosystem modification.

Analysis of species composition at the landscape level can be useful for

detecting and evaluating the effects of habitat change for example as a result

of anthropogenic activities, and for comparing the biodiversity in different

geographical areas, communities or guilds (Moreno & Halffter 2000, Medellin

et al. 2000). The level of completeness of species lists therefore has the

potential to influence studies of diversity, macroecology and conservation by

providing a predictive tool.

Indices of relative bat activity

Capture and acoustic activity indices were nonsignificantly correlated implyingthat of the communities sampled bats were unequally susceptible to detection


31/39

31

by each method. This may be a reflection of the limitations associated with

comparing bat passes, a relative index, with captures. Having anticipated that

acoustic methods would be less successful at detecting bat activity than

capture given the tendency of bats dominating neotropical assemblages to

call at low intensity (Arita & Fenton 1997) and to supplement echolocation

with olfaction and vision (Altringham & Fenton 2003) these results suggest

that the relationship between calls and capture is not simple. This implies that

if a method of discriminating between bats on the basis of their calls is

established then call and capture techniques could yet provide

complementary contributions to inventories as has been done in other studies

(e.g. OFarrell & Miller 1997).

That acoustic monitoring surveys revealed inconsistent numbers of bat

passes is possibly the result of subtle differences in habitat structure and

vegetation composition at points on the transect. Such differences are likely to

affect activity and flight patterns of bats according to the constraints of

morphology (Brigham et al. 1997, Aldridge & Rautenbach 1987) or

echolocation call structure (Denzinger et al. 2004). Habitat differences may

also influence the ability to detect ultrasonic calls leading to false conclusions

being drawn about the ecology of bats (Patriquin et al. 2003). This helps to

explain why some 5-minute intervals had disproportionately large or small

index values. Adoption of a mobile, manual ultrasonic transect survey similar

to that used by Mills et al. (1996) is suggested to offer a means of monitoring

a comparable area without the bias toward certain habitat configurations and

guilds of bats better than using fixed detector positions (Mills et al. 1996). In

combination with careful observations about forest structure preferably

including taxonomic information on vegetation composition this method has

the potential to allow the effects of environmental heterogeneity on bat activity

to be characterised.

Duffy et al. (2000) observed that harp traps performed better than bat

detectors in dense vegetation with discrete flyways. This may explain why

relative activity from detectors was higher in more open and edge habitats in

Buenos Aires where bats are less constrained by the structure of vegetation in

habitats that were often significantly influenced by anthropogenic activity.


32/39

32

Accurate and precise estimates of activity levels derived using bat detectors

will be obtainable only with intensive sampling effort (Hayes 1997) and

ultrasonic monitoring studies should be designed to minimise the effects of

high variability in bat activity at a site among nights.

Future studies

This study was a pilot which may form the basis of forthcoming regional

surveys of microchiropteran bats. The study has revealed that not all species

and not all individuals of a species are equally susceptible to different forms of

detection. The reasons for this are not known although they are thought to be

due to differential use of space and vocalization (OFarrell & Gannon 1999).

Bat guilds can comprise many different species that have each evolved

differently in order to catch specific prey, but forest interior guilds of bats in

Honduras appear not to be partitioned according to echolocation behaviour or

timing of foraging using the parameters measured. This contrasts with Heller

& von Helversons 1987 study, which demonstrated resource partitioning

according to sonar frequency bands in Rhinolophid bats. Further investigation

into the correlates of species distribution and resource partitioning should aim

to investigate the alternative parameters of echolocation call design including

pulse interval, duty cycle and the properties of harmonics (Fenton et al1998)

as well as prey specificity and morphology and the means by which bats in

the Neotropics partition resources.

Future studies monitoring bat activity and habitat use should concentrate on

identifying ways of boosting capture rates for example by using harp traps

which are better suited to forest trails and flyways, as variation in capture

numbers may have been due to the structural characteristics of flyways

influencing the trappability of bats (Kunz & Kurta 1988). Use of canopy nets

will also increase the completeness of biodiversity surveys by sampling

different guilds (Bernard 2001, Wunder & Carey 1996, Kalko & Handley

2001).


33/39

33

Conclusion

Neotropical bats captured in mist nets in the understorey were found to have

indistinguishable sonar characteristics which may be attributable to the

similarities in their foraging habitat. Not all bat species are equally susceptibleto different forms of detection and this was reflected by the absence of

correlation between activity levels monitored using mist nets and bat

detectors. This study has demonstrated that mist netting is currently the most

reliable and accurate way of creating inventories of bats, especially given the

difficulties with standardization of acoustic methods in the field. However mist

netting is seen to under represent particular guilds of bats. Supplementation

of call reference libraries with calls from more individuals and species is

expected to increase the resolution at which correct species identification can

be made and will allow quicker and more representative biodiversity

assessments to be produced.


34/39

34

Acknowledgements

Id like to thank all of the members of team bat who worked with me in

Honduras: John Altringham, Paula Senior, Sally Griffiths, Tory Bennett, Zoe

Davies and Oscar Arostegui. I would also like to thank Operation Wallacea forproviding the logistical arrangements necessary for carrying out the project,

COHDEFOR for the permits and to Roberto Downing for his help and advice.


35/39

35

References

Aldridge, H. D. & Rautenbach, I. L. (1987). Morphology, echolocation andresource partitioning in insectivorous bats. Journal of Animal Ecology56, 763 778.

Altringham, J. D. (1996). Bats Biology and Behaviour. OUP, Oxford.

Altringham, J. D. & Fenton, M. B. (2003). Sensory Ecology andcommunication in Chiroptera. In Bat Ecology (Kunz, T. H. & Fenton, M. B.eds). University of Chicago Press, Chicago.

Barclay, R. M. (1999). Bats are not birds a cautionary note on usingecholocation calls to identify bats a comment. Journal of Mammalogy80,290 296.

Belwood, J. J. (1988). Foraging behaviour, prey selection and echolocation inphyllostomine bats (Phyllostomidae). In Animal Sonar 601 605. (edsNachtigall, P. E. & Moore, P. W. B.). Plenum Press, NY.

Bogdanowicz, W.,, Fenton, M. B. & Daleszczyk, K. (1999). The relationshipsbetween echolocation calls, morphology and diet in insectivorous bats.Journal of Zoology (London) 247, 381 393.

Brigham, R. M., Grindal, S. D., Firman, M. C. & Morissette, J. L. (1997). Theinfluence of structural clutter on activity patterns of insectivorous bats.Canadian Journal of Zoology75, 131 136.

Brigham, R. M., Aldridge, H. D. & Mackey, R. L. (1992). Variation in habitatuse and prey selection by Yuma bats, Myotis yumanensis. Journal ofMammalogy73, 640 645.Bernard, E. (2001). Vertical stratification of bat communities in primary forestsof Central Amazon, Brazil. Journal of Tropical Ecology17, 115 126.

Colwell, R. K. & Coddington, J. A. (1995). Estimating terrestrial biodiversitythrough extrapolation. Philosophical Transactions of the Royal Society ofLondonser. B. (Biological Sciences)345, 101 118.

Dear, S.P., Simmons, J. A., & Fritz, J. (1993). A possible neuronal basis forrepresentation of acoustic scenes in the auditory cortex of the big brown bat.Nature364, 620 623.

Denzinger, A., Kalko, E. K. V. & Jones, G. (2004). Ecological and evolutionaryaspects of echolocation in bats. Pp 311 327 in Echolocation in Bats andDolphins, ed. J. A. Thomas, C. F. Moss & M. Vater. University of ChicagoPress, Chicago.


36/39

36

Duffy, A. M., Lumsden, L., Caddle, C. R. & Chick, R. (2000). The efficacy ofAnabat ultrasonic detectors and harp traps for surveying microchiropterans insouth-eastern Australia. Acta Chiropterologica2, 145 153.

Eckert, H. G. (1982). Ecological aspects of bat activity rhythms. Pp. 201 242

in Ecology of Bats, ed. T. H. Kunz. Plenum Press, New York.

Fenton, M. B., Portfors, C. V., Rautenbach, I. L. & Waterman, J. M. (1998).Compromises: sound frequencies used in echolocation by aerial-feeding bats.Canadian Journal of Zoology76, 1174 1182.

Fenton, M. B., Audet, D., Obrist, M. K. & Rydell, J. (1995). Signal strength,timing and self-deafening: the evolution of echolocation in bats. Paleobiology21, 229 242.

Fenton, M. B. & Bell, G. P. (1981). Recognition of species of insectivorous

bats by their echolocation calls. Journal of Mammalogy62, 233 243.

Fenton, M. B. (1982). Echolocation, Insect hearing, and feeding ecology ofinsectivorous bats. Pp261 285 in Ecology of Batsed. T. H. Kunz. PlenumPress, New York.

Fleming, T. H. (1982). Foraging strategies of plant visiting bats. Pp. 287 325in Ecology of Bats, ed. T. H. Kunz. Plenum Press, New York.

Griffin, D. R. (1971). The importance of atmospheric attenuation for theecholocation of bats (Chiroptera). Animal Behaviour19, 55 61.

Grindal, S. D. (1995). Impacts of forest harvesting on habitat use by foragingbats in southern British Colombia. M.Sc Thesis, University of Regina, Regina,Sask.

Heller, K-G. & v. Helverson, O. (1989). Resource partitioning of sonarfrequency bands in Rhinolophid bats. Oecologica80, 178 186.

IUCN Red List of Threatened Species (2003) IUCN World ConservationUnion, Gland, Switzerland. www.redlist.org.

Jones, G., Gordon, T. & Nightingale, J. (1992). Sex and age differences in theecholocation calls of the lesser horseshoe bat Rhinolophus hipposideros.Mammalia56 189 193.

Kalko, E. K. V. (1998). Organisation and diversity of tropical bat communitiesthrough space and time. Zoology101, 281 297.

Kalko, E. K. V. & Schnitzler, H-U. (1998). The roles of echolocation andolfaction in two Neotropical fruit-eating bats, Carollia perspicillata and C.castanea. Behavioural Ecology and Sociobiology42, 397 409.


37/39

37

Kalko, E. K. V. & Schnitzler, H-U. (1993). Plasticity in echolocation signals ofEuropean Pipistrelle bats in search flight: implications for habitat use and preydetection. Behavioural Ecology and Sociobiology33, 415 428.

Kalko, E. K. V. & Schnitzler, H-U. (2001). Echolocation behaviour and signal

plasticity in the Neotropical bat Myotis nigricans (Vespertilionidae): aconvergent case with European species of Pipistrellus? Behavioural Ecologyand Sociobiology50, 317 328.

Kalko, E. K. V. & Handley, C. O. (2001). Neotropical bats in the canopy:diversity, community structure and implications for conservation. PlantEcology153, 319 333.

Kalko, E. K. V. (1995). Echolocation signal design, foraging habitats and guildstructure in six neotropical sheath-tailed bats (Emballonuridae). Symposium ofthe Zoological Society of London67, 259 273.

Kalko, E. K. V. (1997). Diversity in tropical bats. Tropical biodiversity andsystematics. Proceedings of the International Symposium on Biodiversity andSystematics on Tropical Ecosystems (ed. H. Ulrich), pp. 13 43.Zoologisches Forschungsinstitut und Museum Alexander Koenig, Bonn,Germany.

Kunz, T. H. & Kurta, A. (1988). Capture methods and holding devices. InEcological and behavioural methods for the study of bats (Kunz, Ed.).Smithsonian Institute Press, Washington.

Laska, M. (1990). Olfactory sensitivity to food odour components in the short-tailed fruit bat Carollia perspicillata (Chiroptera: Phyllostomidae). Journal ofComparative Physiology166, 395 400.

Laurence, B. D. & Simmons, J. (1982). Measurements of atmosphericattenuation at ultrasonic frequencies and the significance for echolocatingbats. Journal of the Acoustic Society of America71, 585 590.

Lennkh, C. A. M. (2003). Background Information on Cusuco National Park.MSc Thesis, University of Glamorgan, UK.

Magurran, A. E. (2004). Measuring Biological Diversity. Blackwell Science,London.

Medellin, R. A., Equihua, M. & Amin, M. A. (2000). Bat diversity andabundance as indicators of disturbance in Neotropical rainforests.Conservation Biology14, 1666 1675.

Mills, D. J., Norton, T., Parnaby, H., Cunningham, R. & Nix, H. (1996)Designing surveys for Microchiropteran bats in complex forest landscapes apilot study from south east Australia. Forest Ecology and Management 85,

149 161.


38/39

38

Moreno, C. E. & Halffter, G. (2000). Assessing the completeness of batbiodiversity inventories using species accumulation curves. Journal of AppliedEcology37, 149 158.

Morrison, D. W. (1978). Lunar phobia in a neotropical fruit bat, Artibeus

jamaicensis(Chiroptera: Phyllostomidae). Animal Behaviour26, 852 855.

Neuweiler, G. (1983). Echolocation and adaptivity to ecological constraints. InNeuroethology and Behavioural Physiology: Roots and Growing Points. (ed.Huber & Markl), 280 302. Springer-Verlag, Berlin.

Obrist, M. K. (1995). Flexible bat echolocation: the influence of individual,habitat and conspecifics on sonar signal design. Behavioural Ecology andSociobiology36, 207 219.

OFarrell, M. J. & Gannon, W. L. (1999). A comparison of acoustic versus

capture techniques for the inventory of bats. Journal of Mammalogy80, 24 30.

OFarrell, M. J., Miller, B. W. & Gannon, W. L. (1999). Qualitative identificationof free-flying bats using the Anabat detector. Journal of Mammalogy80, 11 23.

OFarrell, M. J. & Miller, B. W. (1997). A new examination of echolocationcalls of some Neotropical bats (Emballonuridae and Mormoopidae). Journal ofmammalogy78, 954 963.

OFarrell, M. J. & Miller, B. W. (1999). Use of vocal signatures for theinventory of free-flying Neotropical bats. Biotropica31, 507 516.

OFarrell, M. J., Corben, C. & Gannon, W. J. (2000). Geographic variation inthe echolocation calls of the hoary bat Lasiurus cinereus. ActaChiropterologica2, 185 196.

Parsons, S., Booman, A. & Obrist, M. (2000). Advantages and disadvantagesof techniques for transforming and analyzing Chiropteran echolocation calls.Journal of Mammalogy81, 927 938.

Patriquin, K.J., Hogberg, L.K., Chruszcz, B.J. & Barclay, R. M. (2003). Theinfluence of habitat structure on the ability to detect ultrasound using batdetectors. Wildlife Society Bulletin31, 475-481.

Patterson, B. D., Pacheco, V. & Solari, S. (1996). Distributions of bats alongan elevational gradient in the Andes of Peru

Racey, P. A. & Entwistle, A. C. (2003). Conservation Ecology of Bats. InEcology of Bats (Kunz, T. H. & Fenton, M. B. eds.). University of ChicagoPress, Chicago.


39/39

Read, F. (2002). A Field Guide to the Mammals of Central America. OxfordUniversity Press, UK.

Remsen, J. V. & Good, D. A. (1996). Misuse of data from mist net captures toassess relative abundance in bird populations. The Auk113, 381 398.

Rydell, J., Arita, H., Santos, M. & Granados, J. (2002). Acoustic information ofinsectivorous bats (Order Chiroptera) of Yucatan, Mexico. Journal ofZoology(London)257, 27 36.

Schnitzler, H-U & Kalko, E. K. V. (1998). How echolocating bats search andfind food. Pp 183 196 in Bat Biology and Conservation, ed. T. H. Kunz andP. A. Racey. Washington D. C. Smithsonian Institute Press.

Vaughan, N., Jones, G. & Harris, S. (1997). Identification of British Batspecies by multivariate analysis of echolocation call parameters.

Bioaccoustics7. 189 207.

Voss, R. S. & Emmons, L. H. (1996). Mammalian diversity in Neotropicallowland forests: a preliminary assessment. Bulletin of the American Museumof Natural History230, 1 115.

Wunder, L. & Carey, A. (1996). Use of the forest canopy by bats. NorthwestScience70, 79 85.

Documents

A Comparison of Acoustic and Capture Methods as Means of Assessing Bat Diversity and Activity in Honduras_Hopkins 2004