Optimising the sampling window for moth indicator communities

Abstract

In this paper we establish the best period for sampling moth communities within the first half of the night. We stress the
importance of sampling duration in ecological studies that use moths as an indicator taxon, because sample composition changes
throughout the night due to individual species flight behaviour. A total of 20,744 individuals belonging to 562 species were
analysed using diversity and similarity indices. Between-site sub sample comparisons were found to have low discriminant ability
when they included the first hour of the night. Moreover, the moth community sampled at this time showed a low identity with
other sampled portions of the same community, probably because generalist species were present in all the four surveyed sites
at this time. In order to minimise sampling bias, we suggest using three hour-long surveys when the first hour after dusk
is included in the sample, using Fisher’s α for diversity ranking when different sampling durations are used at different sites.

Full text

ORIGINAL PAPER
Optimising the sampling window for moth indicator communities
Stefano Scalercio ÆMarco Infusino Æ
Ian P. Woiwod
Received: 1 October 2008 / Accepted: 4 December 2008
ÓSpringer Science+Business Media B.V. 2008
Abstract In this paper we establish the best period for
sampling moth communities within the first half of the
night. We stress the importance of sampling duration in
ecological studies that use moths as an indicator taxon,
because sample composition changes throughout the night
due to individual species flight behaviour. A total of 20,744
individuals belonging to 562 species were analysed using
diversity and similarity indices. Between-site sub sample
comparisons were found to have low discriminant ability
when they included the first hour of the night. Moreover,
the moth community sampled at this time showed a low
identity with other sampled portions of the same commu-
nity, probably because generalist species were present in all
the four surveyed sites at this time. In order to minimise
sampling bias, we suggest using three hour-long surveys
when the first hour after dusk is included in the sample,
using Fisher’s afor diversity ranking when different
sampling durations are used at different sites.
Keywords Diversity indices Flight behaviour 
Light trapping Moth sampling Similarity indices 
Species assemblage
Introduction
Moth communities are receiving increasing conservation
interest as a species rich, taxonomically tractable group
sensitive to environmental change (Luff and Woiwod
1995), so attaining a high value as an important indicator
group (New 2004). For example, Usher and Keiller (1998)
utilised moth communities in a survey of small woodlands
imbedded within agricultural landscapes, in order to test
their importance as wildlife refuges. In Kitching et al.
(2000) and Summerville et al. (2004) moths proved to be
powerful indicators of forest disturbance. Ricketts et al.
(2001) compared moth communities to assess biodiversity
in native and agricultural habitats in a fragmented land-
scape. Luque et al. (2007) evaluated the impact of forestry
on biodiversity by utilising moth communities. Scalercio
et al. (2007) assessed the role of semi-natural vegetation
patches in sustaining lepidopteran diversity in an agricul-
tural landscape. Also, Conrad et al. (2004,2006) used a
network of permanent light traps to establish long-term
trends of moths over a large area. In all these studies moths
were sampled by light traps. Unfortunately, these were
usually of different design, using different light sources and
were run for different sampling durations making between
study comparisons problematical. The duration of sampling
periods in these studies were 1.5 (Luque et al. 2007), 2
(Ricketts et al. 2001), 3 (Brehm and Fiedler 2005;
Scalercio et al. 2007) and 4 h after sunset (Kitching et al.
2000,2004), or for the entire night (Usher and Keiller
1998; Summerville and Crist 2003; Conrad et al. 2004,
2006). A sampling duration shorter than the entire night
was also applied in a study utilising attractants other than
light (Su
¨ssenbach and Fiedler 1999).
The choice of restricting the sampling to a period during
the first part of the night can be justified by the principle of
S. Scalercio (&)
CRA Centro di Ricerca per l’Olivicoltura e l’Industria Olearia,
Rende, Italy
e-mail: stefano.scalercio@entecra.it
M. Infusino
Dipartimento di Scienze degli Alimenti e dell’Ambiente
‘‘Prof. G. Stagno d’Alcontres’’, Universita
`degli Studi, Messina,
Italy
I. P. Woiwod
Plant and Invertebrate Ecology Department, Rothamsted
Research, Harpenden, UK
123
J Insect Conserv
DOI 10.1007/s10841-008-9206-x

minimising effort while maximising results, and follows
directly from the practical constraints involved in most
monitoring projects. This is facilitated by the usual
behaviour of moths, which tend to come to light in larger
numbers of more species during the first half of the night
(Mikkola 1972; Intachat and Woiwod 1999). While many
papers have been devoted to the effect of different light
sources (e.g. Mikkola 1972; Taylor and French 1974;
Bowden 1982; Intachat and Woiwod 1999) and trap type
(e.g. Taylor and French 1974; Waring 1994; Intachat and
Woiwod 1999; Brehm and Axmacher 2006), there is little
published literature on the effect of trapping period and
duration on the quality of data collected. Nonetheless, it is
known that results are strongly influenced by the flying
behaviour of the studied species, perhaps resulting in the
overestimation of populations of those flying early in the
night and the underestimation of those flying later, partic-
ularly when trapping is restricted to only part of the night.
The few available papers on flight behaviour of moths
mainly concern individual species or a few species
belonging to the same genus or family (but see Williams
1935 ; Lewis and Taylor 1965). Many of the species
studied are pests and have often been observed only under
laboratory conditions (e.g. Edwards 1962; Delisle et al.
1998; Fay and Halfpapp 1999; Saito 2000a,2000b). As
Janzen (1984) argued from field evidence in regard to
Saturniidae and Sphingidae, the pattern of appearance of
species at light may depend on several factors. For exam-
ple, he noticed that males and females or young and old
individuals behave differently, and individual species are
prone to confounding factors such as migratory events,
availability of virgin females, weather conditions and dis-
tance between larval host plant and light sources. Available
data on individual species show a great variability in
diurnal flight patterns, for example: Hepialus fusconebul-
osa (DeGeer 1778) (Hepialidae) flew at dusk, and 80% of
individuals were caught during the first 90 min of the night
(Mikkola 1972); both sexes of Creatonotos transiens
(Walker 1855) (Arctiidae) started their activity rather
abruptly at twilight and flew more or less exclusively
during the first 3 h after dusk (Wunderer and De Kramer
1989); Lambdina fiscellaria (Guene
´e 1858) (Geometridae)
flew mainly in the middle of the night, but differences were
detected between sexes and among nights at different
temperatures (Delisle et al. 1998); Eudocima fullonia
(Clerck 1874) (Noctuidae) occurred at light traps differ-
entially between sexes, males occurring mainly before
midnight while females occurred uniformly throughout the
entire night (Fay and Halfpapp 1999).
The aim of the research reported here is to assess the
effectiveness of moth sampling when restricted to only the
first few hours of the night and to determine an optimal
sampling period for maximising the information on moth
communities when the sampling period needs to be con-
strained. This information should improve the methods
currently used by minimising effort and impact on moth
communities, whilst maximising the information obtained.
We limited this study to the first half of the night because:
(1) this period is easiest to sample from a practical per-
spective, (2) most species are active, (3) most moth
communities studies have been restricted to this period. We
define the ideal times for sampling moths as being those
when communities are most different in between-site
comparisons and yet most similar in within-site compari-
sons. In other words, the larger the differences between
sites, the stronger the discriminant potential of moth
community samples and, at the same time, the smaller the
differences in samples from a given community at different
periods of the night the better the community is
characterised.
Materials and methods
Datasets are from four localities whose moth communities
had been studied previously using the same trapping
method (Scalercio and Infusino 2003,2006; Scalercio
2004; Scalercio et al. 2008). Data were collected in Cala-
bria, southern Italy, from 1999 to 2003. Two samples were
from high altitude communities, a deciduous and a conifer
forest, respectively, and two from low altitude communi-
ties, a dry (xeric) and a wet habitat, respectively (Table 1).
A total of 20,744 individuals belonging to 562 species were
used in our analysis.
Table 1 Study areas
Location Altitude
(m a.s.l.)
UR
mean
(%Ur)
Dominant vegetation cover Sampling nights Sampling period
Lago Angitola 44 95 Salix alba L., Populus sp. pl.,
Phragmites communis Trin.
48 March 2001–February 2003
Fiumara Trionto 90 70 Nerium oleander L., Tamarix sp. pl.,
Helichrysum italicum (Roth) Don
48 March 1999–January 2001
Monte Cocuzzo 1,150 75 Pinus laricio Poiret, Abies alba L. 48 January 2000–January 2002
Monte Curcio 1,690 83 Fagus sylvatica L. and pastureland 32 April 2002–October 2003
J Insect Conserv
123

Moths were collected by using a 160 W mercury-vapour
lamp as light source reflecting onto a white vertical screen.
Manual sampling was chosen instead of automatic col-
lecting because the latter method may be biassed against
smaller moths with this type of light source (Brehm and
Axmacher 2006). Two or three operators were assigned to
collect the moths on the screen surface and on the ground
around the lamp. Samples were collected for a 4-h period
starting from sunset and sampling was repeated at a ten, or
more, day interval. This discontinuous sampling was
deliberately chosen to prevent any carry-over effect on
subsequent samples (White 1991). Two years of sampling
was done in each site to minimise any effect of annual
species turnover within communities (Summerville and
Crist 2005). The sampling sessions were chosen, with very
few exceptions, as those nights with the most favourable
conditions for moth collecting and where the weather did
not deteriorate significantly as the night progressed, i.e.
with no or small new moon, temperatures near the mean of
the study area for a given period, and a light wind. Each
site provided four data subsets; one for each hour of a
night’s sampling session, obtained by partitioning the
moths according to their arrival time at the light. All
specimens were released at the end of each sampling ses-
sion with the exception of the taxonomically uncertain
ones.
In order to establish the most informative period for
moth sampling, similarity and diversity analyses were
carried out on subsets of data obtained from each study site
during (1) one out of the four surveyed hours, (2) two
successive hours, i.e. from the first to the second, from the
second to the third and from the third to the fourth, (3)
three successive hours, i.e. from the first to the third and
from the second to the fourth, and (4) the whole 4-h
sampling session. Although often important in studies
devoted to individual species (Janzen 1984; Delisle et al.
1998; Fay and Halfpapp 1999), the sex ratio was not used
in the present work because of difficulty in assessing this in
the field on live caught individuals before release.
Sampling efficiency attained by each hourly subset of
data was assessed by using the incidence-based coverage
species richness estimator (ICE), a robust non-parametric
estimator (Chazdon et al. 1998), to compute the sampled
fraction of estimated total diversity.
We used four similarity indices: Jaccard Classic (J
c
) and
Sørensen Classic (S
c
), Morisita-Horn (M-H) and Bray-
Curtis (B-C). The former pair is qualitative indices, which
only take into account species occurrence; the latter pair is
quantitative indices which also take into account species
abundance. Although qualitative indices are simple and
may appear to be easy to interpret, quantitative indices are
often more satisfactory because they use more of the
available information by considering the individual
abundances of species, i.e. the relative dominance of a
particular species in a community (Magurran 2004). All the
indices used have values that range between one (maxi-
mum similarity) and zero (minimum similarity).
The sampling period during the night which best char-
acterised a local community was obtained by performing a
within-site analysis that compared the four hourly subsets
of data at a site. The hour with the highest mean similarity
was chosen as the most characteristic. For between-site
comparison, the subset of data showing the lowest mean
similarity was chosen as the most powerful to discriminate
among communities. In order to obtain more homogeneous
data and because altitude is the main parameter influencing
species composition and abundance of insects communities
(Hodkinson 2005), comparisons were carried out sepa-
rately for pairs of sites with similar altitude (Fiumara
Trionto/Lago Angitola and Monte Cocuzzo/Monte Curcio)
and for pairs of sites with different altitude (Fiumara Tri-
onto/Monte Cocuzzo, Fiumara Trionto/Monte Curcio,
Lago Angitola/Monte Cocuzzo and Lago Angitola/Monte
Curcio).
Diversity indices have often been used for ranking
communities according to their naturalness or their con-
servation priority. Here, we assessed the surrogate power of
the various data subsets in providing diversity indicators
for moth communities by finding those subsets which
ranked the sites in the most similar way to the entire
sampled community. Four diversity indices were used:
species richness (S), Fisher’s alpha (a), Simpson inverse (1/
D) and Shannon (H0). These indices are widely used but
have different characteristics and hence provide a good
range of insights into the surrogate power of the tested sub-
samples. Species richness has good discriminant ability
although it is highly sensitive to sample size, Shannon
index has moderate discriminant ability and a moderate
sensitivity to sample size, Simpson inverse index has a
moderate discriminant ability and has low sensitivity to
sample size (Magurran 2004). Fisher’s alpha has a good
discriminant ability, a low sensitivity to sample size and is
particularly suited to the description of moth communities,
at least in temperate regions (Kempton and Taylor 1974;
Taylor et al. 1976; Hayek and Buzas 1997).
The similarity, diversity and species richness estimator
indices were all calculated using the free software Esti-
mateS 7.5 (Colwell 2005).
Results
Within the Lago Angitola site, the sampling efficiency of
total diversity was lower during the fourth hour of sam-
pling (66.0% of the estimated total diversity) than during
the remaining ones, which showed a coverage ranging from
J Insect Conserv
123

74.7 (first hour) to 73.2% (second hour). A similar pattern
was observed within the other study sites.
Along the temporal sequence during the first half of the
night we observed a decrease in abundance (Fig. 1) and
diversity (Fig. 2) of moth communities. Abundance
decreased most steeply. During the first hour a total of
6,590 individuals were collected across the four study sites,
decreasing during the following 3 h by 16.98, 32.34 and
35.90%, respectively in comparison with the first hour
sample. When considering communities separately, at the
fourth sampling hour the largest abundance decrease was
registered at Trionto (42.40%), the smallest at Cocuzzo
(29.83%). Species richness decreased more slowly than
abundance. During the first hour a total of 432 species were
collected across the four study sites, decreasing during the
following 3 h by 6.25, 9.72 and 12.50%, respectively in
comparison with the first hour sample. When communities
were considered separately, the largest diversity decrease
for the fourth sampling hour was registered at Trionto
(25.00%), the smallest at Angitola (12.09%).
All the studied communities showed a similar pattern for
within-site similarity comparisons (Table 2). Sub-samples
of the first and fourth hours had the lowest similarity, while
second and third hour sub-samples attained the highest
similarity, whatever index used. Qualitative indices indi-
cated the first hour of Angitola and Trionto, and the fourth
hour of Cocuzzo and Curcio as the least similar to the
others. Quantitative indices indicated the first hour as the
least similar to the others in all the studied sites except
Angitola where the least similar hour was the fourth. The
most similar hour was often the second and only in few
cases the third one for both qualitative and quantitative
similarity indices (Table 2). The smallest difference
between the largest and the smallest similarity values
within each site was always the qualitative Sorensen
Classic index, whilst the largest difference was given by
the quantitative Morisita-Horn index, with the exception of
the Angitola site.
The between-sites comparison showed a strongly con-
sistent pattern. For those sites located at similar altitude,
sub-samples including the first hour always gave larger
similarity values than sub-samples excluding it (Table 3).
Fig. 1 Number of individuals collected in study sites for each hour of
sampling
Fig. 2 Diversity values obtained for each hour of sampling
J Insect Conserv
123

This pattern occurred whatever index was used. Qualitative
indices increased when the sample duration was increased,
whilst quantitative indices were not linearly related to the
sampling duration. In fact, the mean of the Bray-Curtis
index for the first-hour sample and the mean of the Mori-
sita-Horn index for the sub-samples that excluded the
fourth hour showed higher similarity than those obtained
by the 4-h samples, with the exception of the 3-h sub-
samples. On the other hand, sub-samples including the
fourth hour always had smaller similarity values than those
attained by sub-samples that excluded it (Table 3). Simi-
larity between sub-samples of the same duration varied
from 0.028 (1st/4th, Jaccard Classic) to 0.042 (1st/4th,
Bray-Curtis) for 1-h samples, from 0.026 (1st to 2nd/3rd to
4th, quantitative indices) to 0.042 (1st to 2nd/3rd to 4th,
Sorensen Classic) for 2-h samples, from 0.011 (1st to 3rd/
2nd to 4th, Morisita-Horn) to 0.020 (1st to 3rd/2nd to 4th,
Sorensen Classic) for 3-h samples (Table 3). The longer
the duration of the samples the smaller the variation of
similarity values among samples. In all cases except two
cases, Morisita-Horn index returned a similarity index that
was larger in the sub samples than in the entire sample
(Table 3).
The comparison between sites at different altitudes
showed slightly different patterns (Table 4). The only
difference was displayed by the fourth hour for qualitative
comparison. In fact, the sub-samples including the third
hour always attained smaller similarity values than sub-
samples that excluded it. Similarity between sub-samples
with the same duration varied from 0.034 (1st/3rd, Bray-
Curtis) to 0.053 (1st/4th, Sorensen Classic) for 1-h sam-
ples, from 0.025 (1st to 2nd/2nd to 3rd, Jaccard Classic) to
0.036 (1st to 2nd/2nd to 3rd, Sorensen Classic) for 2-h
samples, from 0.015 (1st to 3rd/2nd to 4th, quantitative
Table 2 Values of similarity (n=3) (mean ±SD), obtained from the within-site comparisons to compare each hour sub-sample
Study site Sampling hour Sorensen Classic Jaccard Classic Bray-Curtis Morisita-Horn
Angitola 1st 0.724 ±0.030 0.568 ±0.038 0.646 ±0.110 0.803 ±0.121
2nd 0.768 ±0.024 0.624 ±0.031 0.696 ±0.083 0.847 ±0.103
3rd 0.751 ±0.040 0.602 ±0.052 0.690 ±0.053 0.854 ±0.040
4th 0.730 ±0.028 0.575 ±0.035 0.623 ±0.089 0.748 ±0.086
Trionto 1st 0.601 ±0.025 0.430 ±0.026 0.590 ±0.080 0.780 ±0.073
2nd 0.653 ±0.025 0.485 ±0.028 0.667 ±0.021 0.884 ±0.027
3rd 0.643 ±0.058 0.476 ±0.061 0.648 ±0.100 0.863 ±0.094
4th 0.645 ±0.039 0.477 ±0.042 0.641 ±0.090 0.839 ±0.103
Cocuzzo 1st 0.698 ±0.032 0.537 ±0.038 0.654 ±0.063 0.789 ±0.030
2nd 0.704 ±0.031 0.544 ±0.037 0.710 ±0.036 0.855 ±0.050
3rd 0.713 ±0.008 0.555 ±0.009 0.704 ±0.058 0.860 ±0.077
4th 0.683 ±0.030 0.519 ±0.035 0.669 ±0.067 0.832 ±0.062
Curcio 1st 0.660 ±0.063 0.495 ±0.071 0.477 ±0.081 0.495 ±0.131
2nd 0.688 ±0.040 0.526 ±0.046 0.629 ±0.067 0.778 ±0.124
3rd 0.662 ±0.021 0.495 ±0.023 0.634 ±0.154 0.761 ±0.270
4th 0.640 ±0.027 0.471 ±0.029 0.590 ±0.169 0.714 ±0.289
Table 3 Values of similarity (n=2) (mean ±SD), obtained by comparing sites at similar altitude
Sub-sample Sorensen Classic Jaccard Classic Bray-Curtis Morisita-Horn
1st 0.380 ±0.053 0.235 ±0.041 0.159 ±0.007 0.136 ±0.008
2nd 0.375 ±0.020 0.231 ±0.015 0.131 ±0.001 0.132 ±0.057
3rd 0.349 ±0.008 0.211 ±0.006 0.119 ±0.016 0.122 ±0.068
4th 0.343 ±0.060 0.207 ±0.044 0.117 ±0.049 0.095 ±0.008
From 1st to 2nd 0.440 ±0.027 0.283 ±0.022 0.152 ±0.002 0.137 ±0.016
From 2nd to 3rd 0.414 ±0.023 0.261 ±0.018 0.132 ±0.007 0.133 ±0.064
From 3rd to 4th 0.398 ±0.023 0.249 ±0.018 0.126 ±0.034 0.111 ±0.032
From 1st to 3rd 0.458 ±0.035 0.297 ±0.030 0.148 ±0.007 0.137 ±0.026
From 2nd to 4th 0.438 ±0.023 0.280 ±0.018 0.134 ±0.023 0.126 ±0.043
From 1st to 4th 0.490 ±0.009 0.324 ±0.008 0.154 ±0.007 0.114 ±0.008
J Insect Conserv
123

indices) to 0.027 (1st to 3rd/2nd to 4th, Sorensen Classic)
for 3-h samples (Table 4). When the 1st hour was included
in the analysis, quantitative indices returned a similarity
that attained larger values in the sub samples than in the
entire sample (Table 4).
The surrogate power of each sub-sample for ranking
sites according to their diversity was evaluated by com-
paring them to results obtained for the entire sample.
When the 4-h sample was taken into account, all chosen
indices ranked all the samples in the same way according
to the sequence Cocuzzo-Angitola-Curcio-Trionto from
the most to the least diverse (Table 5). Sub-samples of
individual hours showed a different behaviour among
diversity indices. In fact, only Fisher’s aranked sites
according to the diversity shown by all the samples,
whatever the examined hour (Fig. 2). Sub-samples longer
than one hour ranked site diversity similarly to the entire
samples only when 3-h samples were used (Table 5). The
most effective index was Fisher’s a, because it ranked
sites wrongly only for the sub-sample from the first to
Table 4 Values of similarity (n=4) (mean ±SD), obtained by comparing sites at different altitudes
Sub-sample Sorensen Classic Jaccard Classic Bray-Curtis Morisita-Horn
1st 0.260 ±0.080 0.151 ±0.056 0.098 ±0.021 0.070 ±0.023
2nd 0.234 ±0.066 0.134 ±0.043 0.086 ±0.022 0.054 ±0.018
3rd 0.207 ±0.067 0.116 ±0.043 0.064 ±0.032 0.038 ±0.028
4th 0.221 ±0.057 0.125 ±0.036 0.066 ±0.024 0.034 ±0.026
From 1st to 2nd 0.291 ±0.075 0.172 ±0.054 0.100 ±0.023 0.066 ±0.020
From 2nd to 3rd 0.255 ±0.067 0.147 ±0.045 0.080 ±0.026 0.048 ±0.022
From 3rd to 4th 0.259 ±0.072 0.150 ±0.048 0.069 ±0.031 0.037 ±0.028
From 1st to 3rd 0.307 ±0.078 0.183 ±0.057 0.094 ±0.025 0.060 ±0.021
From 2nd to 4th 0.280 ±0.074 0.164 ±0.052 0.079 ±0.027 0.045 ±0.025
From 1st to 4th 0.322 ±0.093 0.195 ±0.068 0.088 ±0.028 0.053 ±0.025
Table 5 Diversity values of
sub-samples longer than 1 h for
each site
Sampling period Study site Species richness Shannon index Fisher’s aSimpson inverse
From 1st to 2nd Cocuzzo 249 4.44 63.27 48.42
Angitola 254 4.25 55.22 35.41
Curcio 160 4.10 40.88 37.94
Trionto 149 3.90 41.08 27.35
From 2nd to 3rd Cocuzzo 232 4.40 60.92 47.90
Angitola 224 4.17 49.46 31.89
Curcio 154 3.95 42.10 25.77
Trionto 133 3.95 39.26 29.09
From 3rd to 4th Cocuzzo 223 4.41 59.91 49.16
Angitola 231 4.13 53.69 30.27
Curcio 144 3.79 40.17 20.57
Trionto 119 3.83 35.99 26.83
From 1st to 3rd Cocuzzo 275 4.48 65.21 50.41
Angitola 273 4.27 55.65 36.04
Curcio 185 4.12 44.79 35.38
Trionto 170 3.95 44.66 28.58
From 2nd to 4th Cocuzzo 262 4.45 63.55 49.53
Angitola 257 4.20 53.60 32.17
Curcio 174 3.96 43.92 24.87
Trionto 146 3.94 39.01 28.55
From 1st to 4th Cocuzzo 298 4.51 67.43 51.36
Angitola 294 4.29 57.71 35.98
Curcio 202 4.12 46.98 33.8
Trionto 177 3.97 43.79 28.96
J Insect Conserv
123

second hour. The worst index was the Simpson inverse,
because it ranked sites correctly only for the sub-sample
from the first to third hour.
Discussion
In this paper we have attempted to establish the optimum
sampling period during the first 4 h of the night to maxi-
mise information about moth communities whilst at the
same time minimising sampling effort (i.e. cost). We
stressed the importance of sampling duration in ecological
studies using moths as an indicator taxon by highlighting
the changes that occur in light-trap samples during the
early hours of the night. Our field sampling was over
several trapping nights, in very different habitats and
throughout the season. For this reason biases due to cli-
matic and seasonal changes were reduced, although the
results may still be limited to temperate regions.
We emphasise that this paper does not assess the
effectiveness of sampling to detect the field presence of
species, but is concerned with species periodic respon-
siveness to light traps at different times of night. It is
known that the activity of particular species change
according to several factors. For example, under laboratory
conditions the flight activity pattern can change with the
age and between sexes (Edwards 1962; Saito 2000a).
Otherwise, only their positive phototaxis would be assessed
in the field. Janzen (1984) also emphasised that light
attraction changes during a moth’s lifetime, as does the
flight pattern, even though these changes may not be
related for a given species.
Some researchers have limited moth data collection to
only the first hours of the night (e.g. Ricketts et al. 2001;
Luque et al. 2007), but our results have shown that this
sampling strategy is biassed by the changes in the com-
munity members that come to the light source at different
times. In fact, in between-site comparisons we found a low
discriminant ability of sub-samples which include the first
hour after sunset, and a low similarity of this community
portion compared to later samples of the same community.
The first hour of sampling after dusk seems to be less
informative because the sub-community then is the least
characteristic for within-site comparisons also the least
characteristic for between-sites comparisons, i.e. with low
discriminating potential. As a consequence, a short sam-
pling period could produce an overestimation of similarity
between communities whilst at the same time providing an
underestimation of the most characteristic part of the
community. This implies that species flying during the first
part of the night are frequently shared among sites whilst
those flying later seem to be more rarely shared. Probably,
generalist and more mobile, or even migratory, species are
mainly responsible of such patterns. The 24-species set that
is common to all the four surveyed sites is composed of
species such as the noctuids Agrotis ipsilon (Hufnagel
1766), Autographa gamma (Linnaeus 1758), Hoplodrina
ambigua (Denis and Schiffermu
¨ller 1775), Mythimna uni-
puncta (Haworth 1909), Noctua pronuba (Linnaeus 1758),
Peridroma saucia (Hu
¨bner 1808), Spodoptera exigua
(Hu
¨bner 1808), and the geometrids Gymnoscelis rufifasci-
ata (Haworth, 1809), Idaea filicata (Hu
¨bner 1799),
Menophra abruptaria (Thunberg 1792), Peribatodes
rhomboidaria (Denis and Schiffermu
¨ller 1775). Our results
suggest that these species tend to arrive at light traps at, or
shortly after dusk, but further studies are needed to assess
their detailed temporal patterns of occurrence at light
throughout the night.
Mikkola (1972) reported that the abundance and diver-
sity of moth communities decreased throughout the night.
In contrast, our data showed that diversity is only slightly
affected by a shortened period of moth sampling as only a
small decrease of species richness was detected throughout
the first half of the night. Among the tested indices, Fish-
er’s a(Fisher et al. 1943) was the most suitable for ranking
sub-samples in the same order as the whole samples. The
reliability of this index for measuring moth diversity has
been emphasised also by many other authors in temperate
regions (e.g. Kempton and Taylor 1974; Hayek and Buzas
1997; Usher and Keiller 1998; Brehm and Fiedler 2005),
while in tropical regions the related Qstatistic may be
more appropriate because of the poor fit of such data to the
log-series distribution (Barlow and Woiwod 1989).
Moth abundance showed a stronger decrease than spe-
cies richness, at least until the middle of the night. Several
parameters co-occur to determine such patterns. Tempera-
ture decrease through the night could strongly influence
moth activity pattern of individual species, but evidence is
available only for a few species (Delisle et al. 1998). The
flight activity of moths could also be reduced at the
beginning of the night by bat predation. In fact, when moth
species flying at several metres from the ground were sub-
mitted to bat predation it was found that they could adapt
their activity to minimise the threat (Fullard et al. 2004).
There are several theories about the reason for insect’s
attraction to light, with Janzen (1984) suggesting that it is
probably due to the use of light sources by moths as land-
marks. Clearly, more studies concerning the nocturnal flight
behaviour of individual species are needed in order to refine
and simplify strategies for moth sampling and to understand
exactly why moth communities are more similar during the
first hours of darkness, rather than later on.
For good practical reasons, minimising sampling effort
is a common practise in many field studies, but there is a
lack of related literature about the consequences, particu-
larly for studies on moth communities. Up till now, the
J Insect Conserv
123

sampling duration has varied considerably among authors
and between studies, producing data which is difficult to
compare. Maximising the potential information on a given
taxon should be one of the main goals when a sampling
protocol is designed. Following the results presented here,
we suggest 3-h surveys are optimal for reducing sampling
bias when the first hour after dusk is included in the
sample, and that Fisher’s aindex should be used for
diversity ranking, particularly when different sampling
duration is used at different sites.
Acknowledgments The critical comments of two anonymous
reviewers helped us improve this manuscript. Work was supported
financially by the Museo di Storia Naturale della Calabria e Orto
Botanico, Italy, president prof. P. Brandmayr. Rothamsted Research
receives grant-aided support from the UK Biotechnology and Bio-
logical Sciences Research Council.
References
Barlow HS, Woiwod IP (1989) Moth diversity of a tropical forest in
Peninsular Malaysia. J Trop Ecol 5:37–50
Bowden J (1982) An analysis of factors affecting catches of insects in
light-traps. Bull Entomol Res 72:535–556
Brehm G, Axmacher JC (2006) A comparison of manual and
automatic moth sampling methods (Lepidoptera: Arctiidae,
Geometridae) in a rain forest in Costa Rica. Environ Entomol
35:757–764
Brehm G, Fiedler K (2005) Diversity and community structure of
geometrid moths of disturbed habitat in a montane area in the
Ecuadorian Andes. J Res Lepid 38:1–14
Chazdon RL, Colwell RK, Denslow JS, Guariguata MK (1998)
Statistical methods for estimating species richness of woody
regeneration in primary and secondary rain forests of northeast-
ern Costa Rica. In: Dallmeier F, Comisky JA (eds) Forest
biodiversity research, monitoring and modelling: conceptual
background and world case studies. Parthenon Publishing, Paris,
pp 285–309
Colwell RK (2005) EstimateS: statistical estimation of species
richness and shared species from samples. Version 7.5. Persistent
URL: purl.oclc.org/estimates
Conrad KF, Woiwod IP, Parsons M, Fox R, Warren MS (2004) Long-
term population trends in widespread British moths. J Insect
Conserv 8:119–136
Conrad KF, Warren MS, Fox R, Parsons M, Woiwod IP (2006) Rapid
declines in common moths underscore a biodiversity crisis. Biol
Conserv 132:279–291. doi:10.1016/j.biocon.2006.04.020
Delisle J, West RJ, Bowers WW (1998) The relative performance of
pheromone and light traps in monitoring the seasonal activity of
both sexes of the eastern hemlock looper, Lambdina fiscellaria
fiscellaria. Entomol Exp Appl 89:87–98. doi:10.1023/A:1003
450432755
Edwards DK (1962) Laboratory determinations of the daily flight
times of separate sexes of some moths in naturally changing
light. Can J Zool 40:371–510. doi:10.1139/z62-032
Fay HAC, Halfpapp KH (1999) Activity of fruit-piercing moths,
Eudocima spp. (Lepidoptera: Noctuidae), in north Queensland
crops: some effects of fruit type, locality and season. Aust J
Entomol 38:16–22. doi:10.1046/j.1440-6055.1999.00072.x
Fisher RA, Corbet A, Williams CB (1943) The relation between the
number of species and the number of individuals in a random
sample of an animal population. J Anim Ecol 12:42–58. doi:
10.2307/1411
Fullard JH, Ratcliffe JM, Soutar AR (2004) Extinction of the acoustic
startle response in moths endemic to a bat-free habitat. J Evol
Biol 17:856–861. doi:10.1111/j.1420-9101.2004.00722.x
Hayek L-A, Buzas MA (1997) Surveying natural populations.
Columbia University Press, New York
Hodkinson ID (2005) Terrestrial insects along elevational gradients:
species and community responses to altitude. Biol Rev Camb
Philos Soc 80:489–513. doi:10.1017/S1464793105006767
Intachat J, Woiwod IP (1999) Trap design for monitoring moth
biodiversity in tropical rainforests. Bull Entomol Res 89:153–
163. doi:10.1017/S0007485399000243
Janzen DH (1984) Two ways to be a tropical big moth: Santa Rosa
saturniids and sphingids. Oxf Surv Evol Biol 1:85–140
Kempton RA, Taylor LR (1974) Log-series and log-normal param-
eters as diversity discriminants for the Lepidoptera. J Anim Ecol
43:381–399. doi:10.2307/3371
Kitching RL, Orr AG, Thalib L, Mitchell H, Hopkins MS, Graham
AW (2000) Moth assemblages as indicators of environmental
quality in remnants of upland Australian rain forest. J Appl Ecol
37:284–297. doi:10.1046/j.1365-2664.2000.00490.x
Lewis T, Taylor LR (1965) Diurnal periodicity of flight by insects.
Trans R ent Soc Lond 116:393–469
Luff ML, Woiwod IP (1995) Insect as indicators of land-use change: a
European perspective, focusing on moths and ground beetles. In:
Harrington R, Stork NE (eds) Insects in a changing environment.
Academic Press, London, pp 399–422
Luque C, Gers C, Lauga J, Mariano N, Wink M, Legal L (2007)
Analysis of forestry impacts and biodiversity in two Pyrenean
forests through a comparison of moth communities (Lepidoptera,
Heterocera). Insect Sci 14:323–338. doi:10.1111/j.1744-7917.
2007.00159.x
Magurran AE (2004) Measuring biological diversity. Blackwell
Publishing, Oxford
Mikkola K (1972) Behavioural and electrophysiological responses of
night-flying insects, expecially Lepidoptera, to near-ultraviolet
and visible light. Ann Zool Fenn 9:225–254
New TR (2004) Moths (Insecta: Lepidoptera) and conservation:
background and perspective. J Insect Conserv 8:79–94
Ricketts TH, Daily GC, Ehrlich PR, Fay JP (2001) Countryside
biogeography of moths in a fragmented landscape: biodiversity
in native and agricultural habitats. Conserv Biol 15:378–388.
doi:10.1046/j.1523-1739.2001.015002378.x
Saito O (2000a) Flight activity of three Spodoptera spp., Spodoptera
litura, S. exigua and S. depravata, measured by flight actograph.
Physiol Entomol 25:112–119. doi:10.1046/j.1365-3032.2000.
00172.x
Saito O (2000b) Flight activity changes of the cotton bollworm,
Helicoverpa armigera (Hu
¨bner) (Lepidoptera: Noctuidae), by
ageing and copulation as measured by flight actograph. Appl
Entomol Zool (Jpn) 35:53–61. doi:10.1303/aez.2000.53
Scalercio S (2004) Caratterizzazione della comunita
`dei macrolepi-
dotteri eteroceri fototropici della pino-abetina di Monte Cocuzzo
(Calabria, Italia). Atti del XIX Congresso Nazionale Italiano di
Entomologia, Catania 10–15 giugno 2002, 237–242
Scalercio S, Iannotta N, Brandmayr P (2007) The role of semi-natural
and abandoned vegetation patches in sustaining lepidopteran
diversity in an organic olive orchard. Bull Insectology 60:13–22
Scalercio S, Infusino M (2003) I Macrolepidotteri di fosso Scuotrapiti,
lago dell’Angitola (Calabria, Italia meridionale) (Lepidoptera).
Phytophaga 13:25–52
Scalercio S, Infusino M (2006) I Macrolepidotteri notturni del basso
corso della Fiumara del Trionto (Calabria, Italia meridionale)
(Lepidoptera) Quaderni della Stazione di Ecologia. Civico
Museo Stor Naturale Ferrara 16:179–202
J Insect Conserv
123

Scalercio S, Infusino M, Tuscano J (2008) I Macrolepidotteri notturni
della faggeta di Monte Curcio, Sila Grande (Calabria, Italia
meridionale) (Lepidoptera) Quaderni della Stazione di Ecologia.
Civico Museo Stor Naturale Ferrara 18:5–19
Summerville KS, Crist TO (2003) Determinants of lepidopteran
community composition and species diversity in eastern decid-
uous forests: roles of season, eco-region and patch size. Oikos
100:134–148. doi:10.1034/j.1600-0706.2003.11992.x
Summerville KS, Ritter LM, Crist TO (2004) Forest moth taxa as
indicators of lepidopteran richness and habitat disturbance: a
preliminary assessment. Biol Conserv 116:9–18. doi:10.1016/
S0006-3207(03)00168-X
Summerville KS, Crist TO (2005) Temporal patterns of species
accumulation in a survey of Lepidoptera in a beech-maple forest.
Biodivers Conserv 14:3393–3406. doi:10.1007/s10531-004-05
46-x
Su
¨ssenbach D, Fiedler K (1999) Noctuid Moths attracted to fruit
baits: testing models and methods of estimating species diver-
sity. Nota lepid 22:115–154
Taylor LR, French RA (1974) Effects of light-trap design and
illumination on samples of moths in an English woodland. Bull
Entomol Res 63:583–594
Taylor LR, Kempton RA, Woiwod IP (1976) Diversity statistics and
the log-series model. J Anim Ecol 45:255–271. doi:10.2307/
3778
Usher MB, Keiller SWJ (1998) The Macrolepidoptera of farm
Woodlands: determinants of diversity and community structure.
Biodivers Conserv 7:725–748. doi:10.1023/A:1008836302193
Waring P (1994) Moth traps and their use. Br Wildl 5:136–139
White EG (1991) The changing abundance of moths in a tussock
grassland, 1962–1989, and 50- to 70-year trends. N Z J Ecol
15:5–22
Williams CB (1935) The times of activity of certain nocturnal insects,
chiefly Lepidoptera, as indicated by a light trap. Trans R ent Soc
Lond 83:523–555
Wunderer H, De Kramer JJ (1989) Dorsal ocelli and light-induced
diurnal activity patterns in the arctiid moth Creatonotos transiens.
J Insect Physiol 35:87–95. doi:10.1016/0022-1910(89)90041-3
J Insect Conserv
123