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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
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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
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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.
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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).
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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 (
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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
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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
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(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 &
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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.
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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
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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
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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
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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.
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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.
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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.
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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).
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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).
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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
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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
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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.
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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.
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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
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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).
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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
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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
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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
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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.
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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.
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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
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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
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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.
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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).
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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.
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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.
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