ORIGINAL PAPER
Monitoring arthropods in a tropical landscape: relative effects
of sampling methods and habitat types on trap catches
Olivier Missa ? Yves Basset ? Alfonso Alonso ? Scott E. Miller ?
Gianfranco Curletti ? Marc De Meyer ? Connal Eardley ? Mervyn W. Mansell ?
Thomas Wagner
Received: 7 August 2007 / Accepted: 19 December 2007 / Published online: 11 January 2008

 Springer Science+Business Media B.V. 2008
Abstract To discuss the challenge of monitoring multi-
species responses of tropical arthropods to disturbance, we
considered a large dataset (4 9 105 individuals; 1,682
morphospecies representing 22 focal taxa) based on the
work of parataxonomists to examine the effects of
anthropogenic disturbance on arthropods at Gamba, Gabon.
Replication included three sites in each of four different
stages of forest succession and land use after logging,
surveyed during a whole year with four sampling methods:
pitfall, Malaise, flight-interception and yellow pan traps.
We compared the suitability of each sampling method for
biological monitoring and evaluated statistically their
reliability for 118 arthropod families. Our results suggest
that a range of sampling methods yields more diverse
material than any single method operated with high repli-
cation. Multivariate analyses indicated that morphospecies
composition in trap catches was more strongly influenced
by habitat type than by sampling methods. This implies
that for multi-species monitoring, differences in trap effi-
ciency between habitats may be neglected, as far as habitat
types remain well contrasted. We conclude that for the
purpose of monitoring large arthropod assemblages in the
long-term, a protocol based on operating a set of different
and non-disruptive traps appears superior in design than
summing a series of taxa-specific protocols.
Keywords Africa 
 Biological monitoring 
 Gabon 

Indicator value index 
 Insect trap
Introduction
The Millennium Ecosystem Assessment (2005) report
makes clear that: (1) loss in biodiversity due to human
activities has been more rapid in the past 50 years than at
any time in human history; (2) the most important drivers
of biodiversity loss are habitat change (including loss
and fragmentation of forests) and climate change; (3) in
the tropics, habitat change contributes much more to
O. Missa
Department of Biology, University of York, PO Box 373,
York YO10 5YW, UK
Y. Basset (&)
Smithsonian Tropical Research Institute, Apartado 0843-03092,
Balboa, Ancon, Panama City, Republic of Panama
e-mail: bassety@si.edu
A. Alonso
Smithsonian Institution/Monitoring and Assessment of
Biodiversity Program, 1100 Jefferson Drive, S.W. Suite 3123,
Washington, DC 20560-0705, USA
S. E. Miller
Smithsonian Institution, Washington, DC 20013-7012, USA
G. Curletti
Museo Civico di Storia Naturale, Cas. Post. 89,
10022 Carmagnola (TO), Italy
M. De Meyer
Royal Museum for Central Africa, Leuvensesteenweg 13,
3080 Tervuren, Belgium
C. Eardley
Plant Protection Research Institute, Private Bag X134,
Queenswood, Pretoria 0121, South Africa
M. W. Mansell
Department of Zoology and Entomology, University of Pretoria,
Pretoria 0002, South Africa
T. Wagner
Institut fu?r Integrierte Naturwissenschaften-Biologie, Universita?t
Koblenz-Landau, Universita?tsstr. 1, 56070 Koblenz, Germany
123
J Insect Conserv (2009) 13:103?118
DOI 10.1007/s10841-007-9130-5
biodiversity loss than climate change and this situation will
continue for a significant period (Sala et al. 2000); and (4)
rates of biodiversity loss are projected to accelerate. In
particular, tropical forests are likely to turn into extinction
hotspots (May et al. 1995) and these extinctions will
primarily involve arthropods (Dunn 2005).
Because of their short generation time, invertebrates
respond quickly to modifications of their environment
(Kremen et al. 1993; Basset et al. 2001) and may be more
discriminating in this regard than vertebrates (Moritz et al.
2001). Arthropod populations are thought to be sensitive to
short-term impacts of land management, as well as to
longer-term general ecosystem changes (Kremen et al.
1993; Underwood and Fisher 2006). Relatively high
number of arthropods can be easily collected with a variety
of techniques without harming their populations. For these
various reasons, they represent choice organisms for
biological monitoring (Kremen et al. 1993).
The usual goal of a species inventory is to document as
completely as possible the taxonomy and ecology of taxa
within a certain area (see Longino and Colwell 1997 for a
good example related to ants). In contrast, biological
monitoring seeks to repeat sampling over time to identify
population patterns (Stork et al. 1995; Niemela? 2000;
Yoccoz et al. 2001; Underwood and Fisher 2006; Conrad
et al. 2007). Monitoring goals may include detecting the
presence of invasive species; detecting population trends of
threatened, endangered or keystone species; evaluating
land management decisions; or assessing ecosystem change
(Underwood and Fisher 2006). Our research framework
relates to the latter and seeks to assess the effects of
anthropogenic disturbance, such as land conversion and
clearance, on tropical arthropods. This subject is not well
understood and warrants further investigations since per-
haps 80?90% of tropical taxa have never been the focus of
tropical conservation studies (review in Lewis and Basset
2007).
It is increasingly clear that a multi-species approach,
including functional guilds, appears to be better than using
indicator species to monitor the responses of tropical
invertebrates to disturbance (Kremen et al. 1994; Didham
et al. 1996; Lawton et al. 1998; Kotze and Samways 1999;
Basset et al. 2001). The task of monitoring a sufficient
number of taxa at various locations with adequate time may
appear daunting. In practice, working with parataxonomists
(i.e., local assistants trained by professional biologists)
with adequate taxonomic feedback can help to alleviate
these problems and ensure that statistical replicates are
representative of the system studied (Basset et al. 2004b).
Entomologists have devised quantitative protocols to
survey or monitor specific taxa in the tropics, such as for
example ants (Longino and Colwell 1997; Agosti et al.
2000; Underwood and Fisher 2006), termites (Jones and
Eggelton 2000), or butterflies (Sparrow et al. 1994; DeV-
ries and Walla 2001). While some recommendations are
available to survey whole arthropod assemblages in the
tropics (e.g., Noyes 1989; Gadagkar et al. 1990; Stork
and Brendell 1993; Basset et al. 1997; Adis et al. 1998;
Kitching et al. 2001), few guidelines exist for designing
monitoring protocols targeting multi-assemblages in the
tropics (Finnamore 1997; see Rohr et al. 2007 for one
temperate example). There are multiple reasons for this,
owing notably to complex issues of sampling methodology,
spatio-temporal replication to characterize well assem-
blages and taxonomic impediment (Niemela? 2000; Rohr
et al. 2007). This contribution focuses on sampling meth-
odology and emphasizes the three following questions: (a)
which trap/method may be suitable for monitoring? (b)
Which higher taxa are best collected by particular trapping
method, when several trapping methods are used? (c) Does
trap efficiency vary between different habitat types?
With regard to the first question, entomologists have
devised an impressive range of techniques and traps (e.g.,
Southwood and Henderson 2000). The challenge is less
about collecting insects, but rather how to use available
methods with maximum efficiency, and how best to
interpret the resulting data. Sampling methods can be
broadly classified in three categories. The first category
allows estimating population density by surveying a
defined area/volume of habitat (e.g., visual counts, soil
coring). The second category includes traps that passively
collect arthropods as they move in the habitat (e.g., pitfall,
Malaise and flight interception traps). In this case, differing
levels of activity among species complicate the interpre-
tation of these trap catches. However, large numbers of
individuals can often be collected with relatively little
effort. The last category uses the attraction of arthropods
for a particular scent, food or visual cue to lure and trap
them (e.g., light, pheromone and colored pan traps). These
techniques can be very effective for focal taxa but their
results are the most difficult to interpret as catches are
affected by population density, individual activity and
stimulus attraction, all three of which tend to differ among
species (Southwood and Henderson 2000).
When the emphasis is on comparing species densities,
sampling methods belonging to the first category are often
the best choice. However, since they are often destructive
and labor intensive, they may be unsuitable for biological
monitoring. For baseline surveys and biological monitor-
ing, trapping techniques may be preferable, especially
when comparing different habitats or the same habitat over
time. Trapping methods used in biological monitoring must
fulfill several criteria. Ideally, they should: be simple,
inexpensive, non-destructive and non-disturbing to the
study system; have a negligible impact on arthropod pop-
ulations; be easy to deploy, service and maintain in the
104 J Insect Conserv (2009) 13:103?118
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field; behave more or less consistently across sites
(including both control and impact sites) with respect to the
profile of arthropods collected; be relatively insensitive to
abiotic factors (or the potential effects of abiotic factors on
trap catches should be measurable); quickly provide rep-
resentative baseline data and repeatable results with low
stochastic variance; produce seasonal and annual replicates
of the same sampling units; provide a variety of material
and/or be efficient for specific focal taxa; provide quality
material and taxonomically tractable taxa; and avoid
redundancy of information (Kitching et al. 2001). These
quantitative criteria preclude using specific but mainly
qualitative protocols developed by taxonomists for their
favorite taxa.
While many papers consider the relative merits of
modifying a particular sampling method, relatively few
compared meaningfully different methods of sampling
terrestrial arthropods for biological monitoring (reviews in
Muirhead-Thomson 1991; Basset et al. 1997; Southwood
and Henderson 2000; Kitching et al. 2001). Comparisons
have often been impeded by low spatial and taxonomic
replication. Choice of methods relied more on scientific
traditions than on rigorous statistic analyses. Nevertheless,
four of the most used sampling methods for arthropods are
furthermore recommended for biological monitoring: pit-
fall, Malaise, flight-interception and yellow pan traps
(Finnamore 1997; Niemela? 2000; Southwood and Hen-
derson 2000; Kitching et al. 2001; Rohr et al. 2007). The
first three are passive traps whereas yellow pan traps col-
lect arthropods that are attracted to a small area of water in
a yellow container. In this contribution, we consider these
four methods, which may be complemented by automatic
light traps, if one is more concerned about nocturnal insects
(Wolda et al. 1998; Kitching et al. 2001).
Several authors have emphasized the value of using a
range of sampling methods for inventorying tropical
arthropods (Noyes 1989; Gadagkar et al. 1990; Stork 1994;
Basset et al. 1997; Longino and Colwell 1997; Kitching
et al. 2001). If one of the goals of biological monitoring is
assessing the long-term effects of ecosystem changes on
multiple arthropod assemblages with a range of sampling
methods, then the relative affinities of particular taxa for
particular methods need to be quantified for a sound
interpretation of monitoring data. As for question (b),
above, the entomological literature is surprisingly scarce on
applying rigorous statistics to estimating arthropod affinity
for particular trapping methods. A major exception is
Kitching et al. (2001), who identified subset of both trap-
ping methods and target taxa across a latitudinal transect
spanning from Australia to Borneo. However, these authors
discussed mainly biodiversity inventorying and detailed
information only at the ordinal level. An optimal design for
a monitoring programme would require some information
on the relative affinity of taxa for particular trapping
method at least at the familial level (see Rohr et al. 2007
for a temperate example).
With regard to question (c) above, a dissimilar trap
efficiency in different habitats may result from the effect
of habitat structure on the trappability of different taxa
(Melbourne 1999), from faunistic differences between
habitats, or from both factors. This issue has rarely been well
quantified (see discussions for pitfall, Malaise and light traps
in Bowden 1982; Longino and Colwell 1997; Melbourne
1999; King and Porter 2005) and deserves particular atten-
tion when designing a monitoring programme assessing
long-term ecosystem changes, for example.
Here we consider a study based on the work of trained
parataxonomists in Gabon, which examines the effects of a
wide anthropogenic gradient of disturbance on a range of
focal arthropod taxa that represent diverse taxonomic and
functional guilds. Replication included three sites in each
of four different stages of forest succession and land use
after logging, surveyed during a whole year with four
sampling methods recommended for biological monitoring
(pitfall, Malaise, flight-interception and yellow pan traps).
The major results of this study are reported elsewhere
(Basset et al. 2004a, 2008). Although this specific study
was not designed as a monitoring exercise, we believe that
its scope in terms of diversity of habitats surveyed, sam-
pling methods used, sample size, replication, and
taxonomic coverage allow us to discuss some issues which
may be important for designing monitoring programmes
assessing the effects of ecosystem changes on multiple
assemblages of arthropods in the tropics. In this context,
our key questions are:
? Do the four sampling methods used in this study appear
suitable for biological monitoring? Additionally, what
are their relative efficiency (in terms of abundance and
species richness) and complementarity to collect rap-
idly baseline information?
? Which taxa are most likely to be collected in a baseline
study using these four sampling methods?
? What are the relative effects of sampling methods per
se and habitat types on the composition of trap catches?
Methods
Study area and sites
The study area was in the Shell Gabon oil concession of
Gamba, within the Gamba Complex of Protected Areas in
south-east Gabon (see Alonso et al. 2006 for background and
botanical information). The Gamba oil field includes a mosaic
of old growth secondary rainforests, younger secondary
J Insect Conserv (2009) 13:103?118 105
123
rainforests and savanna areas, resulting mainly from anthro-
pogenic action. Primary rainforests are absent from the Gamba
oil field, following the selective logging of Okoume? (Aucou-
mea klaineana Pierre). The mean annual temperature in the
area is 26C and annual rainfall amounts to 2,093 mm per year,
with the major dry season from June to August (Alonso et al.
2006). The earliest cultivated crop gardens of notable size were
established near the town as recently as 1998.
We considered four distinct habitats of increasing
anthropogenic disturbance (i.e., increasing forest clearing
and introduction of exotic vegetation) and selected three
sites (replicates) within each habitat. The four habitat types
were: (a) the understorey of the interior of old secondary
rainforests, ?old forests?; (b) the understorey of the edge of
young secondary rainforests, ?young forests?; (c) an area of
rainforest cleared to install oil rigs and subsequently
invaded by savanna, ?savanna?; and (d) cultivated crop
gardens, ?gardens?. At the time of the study, there were no
substantial plantations in the area and these four habitat
types were predominant in the Gamba oil field. Salient
characteristics of the study sites (coded A?L) are indicated
in Table 1 (see also Basset et al. 2004a).
Arthropod collecting and processing
Each site was equipped with an identical set of traps rec-
ommended for the biological monitoring of the flying and
epigaeic arthropods of the understorey and litter (Finna-
more 1997; Niemela? 2000; Southwood and Henderson
2000; Kitching et al. 2001; Rohr et al. 2007). At each site,
one ground Malaise trap (hereafter MT), four ground yel-
low pan traps (YPT) and five pitfall traps buried in the
ground (PT) were used. In addition, four flight-intercept
traps were also set up at forest sites (FIT; Table 1).
The collecting surface of one MT was 2.7 m2 (model
similar to Townes 1972; Sante? Traps, 739 Cooper Drive,
Lexington, Kentucky, USA 40502). Collecting fluid was
70% ethanol. YPT were 27 cm in diameter by 8 cm deep
and filled with a mixture of water (ca. 80%), 70% ethanol
(ca. 20%) and a few drops of liquid detergent to break the
surface tension of the water. They were placed in the soil
so that the rim was level with ground surface, thus also
intercepting crawling arthropods (Finnamore 1997). PTs
were small 0.5-l plastic cups (6 cm in diameter) filled with
the same water, ethanol and detergent mixture. At each
site, a MT occupied the center of the set of traps, with four
PTs established to the north, south, east and west, 10 m
distant from the MT. Four YPTs were set up at equal
distances between the PT, again 10 m distant from the MT.
The fifth PT was placed 30 m north of the MT. In addition,
4 FITs were hung 3 m off the ground above the fifth PT, in
four of the six forest sites. The collecting surface of one
FIT was about 4 m2 (Sante? Traps; model similar to
Springate and Basset 1996). All of these traps have specific
advantages and limitations, as discussed for example in
Adis (1979) and Basset et al. (1997).
The 120 traps operated for 3 days each week and were
intended to be surveyed weekly (=one survey) from July
2001 to July 2002. However, the amount of material col-
lected required us to spread surveys and eventually only 38
surveys were obtained during the above period (12 in the dry
season and 26 in the wet season). The longest gap between
two surveys was one month (December 2001). A team of
eight parataxonomists was trained and supervised by a pro-
fessional entomologist throughout the project (see Basset
et al. 2004b for a detailed discussion of this strategy). The
material collected was first sorted into families or higher taxa
by the parataxonomists (see exceptions below). The material
belonging to 22 focal taxa (Table 2) was isolated and pinned,
and each individual was identified by a unique specimen
number. The focal taxa were sorted to morphospecies (i.e.,
unnamed species diagnosed using standard taxonomic
techniques) by the parataxonomists. Formal taxonomic
study of this material is ongoing but sub-samples of the
material belonging to seven taxa have been examined by
taxonomists (Table 2). The rationale for selecting the 22
focal taxa were (a) being well represented in the samples (so
that much information was retained); (b) being workable
taxonomically; (c) taxonomists having expressed interests in
working on the material; and (d) representation of a variety
of functional guilds and orders (Table 2).
There were a few exceptions to the sorting and mounting
pattern. Non-insect material was mostly sorted to order.
Lepidoptera were not sorted to families, since being wet
material they were useless. In the Diptera, the Nematocera,
Brachycera and Aschiza were treated at the family level;
numerous Schizophora were often identified as Calyptera or
Acalyptera because of taxonomic difficulties. A number of
other taxa were identified to superfamily level for the same
reasons: Coccoidea, Aphidoidea, some Cucujoidea, Chal-
cidoidea, Cynipoidea and Proctotrupoidea. Chalcidoidea
smaller than 2 mm were counted, but not morphotyped.
Two focal taxa that were very abundant in samples, Doli-
chopodidae and Formicidae, were partly processed and
morphotyped. Specimens are stored at the Smithsonian
Biodiversity Conservation Center in Gamba, and vouchers
have been deposited at the National Museum of Natural
History (Washington D.C.) and with taxonomists who
helped with species identification.
Statistical methods
Our analyses often considered three datasets of increasing
taxonomic resolution and accuracy: (a) higher taxa (mostly
106 J Insect Conserv (2009) 13:103?118
123
families); (b) morphospecies sorted by parataxonomists
from focal taxa; and (c) species sorted by taxonomists from
focal taxa. Datasets at the ordinal resolution lack discrim-
inating power, as indicated by earlier analyses of part of the
material collected (Basset et al. 2004a). We also often
contrasted data related to forest vs. non-forest habitats. For
most analyses, we pooled the data of a particular sampling
method for a survey and considered this to be a sample
(n = 38). One needs to recall that a sample is equivalent to
three days of collecting of 60 PTs, 48 PYTs, 4 FITs and
12 MTs.
First, we compared the abundance and diversity of the
material collected by the four sampling methods with a
series of standard statistics and curves routinely used in
ecology (Magurran 1988; Colwell 2005): Kruskal?Wallis
tests, coefficient of variation, Coleman rarefaction, non-
parametric Chao1 estimate of species richness, evenness
(=Shannon diversity index/ln[no. morphospecies/species
Table 1 Main characteristics of study sites within the Shell-Gabon Gamba oil field
Code Habitat Coordinates Fragment
size (ha)
Physiognomy Vegetation characteristics
Aa Old forest 02420200 0S 700 Secondary forest, tallest trees = 45 m,
sandy soil
Neochevalierodendron stephanii (A.
Chevalier) Le?onard dominant, Diospyros
zenkeri (Gurke) F. White and D.
vermoeseni De Wild common
09590490 0E
B Old forest 02420540 0S 84 Secondary forest, tallest trees = 45 m,
sandy soil
Neochevalierodendron stephanii dominant,
Diospyros zenkeri, D. vermoeseni and
Palisota ambigua CB. Clarke common
10000000 0E
Ca Old forest 02440270 0S 28 Secondary forest, tallest trees = 40 m,
but many small trees 10?20 m tall,
sandy soil
Diospyros vermoeseni and D. conocarpa
Gurke ex K. Schum common, P.
ambigua and Trichoscypha acuminata
Engler less common
10000110 0E
Da Young
forest
02450380 0S 12 Secondary forest, tallest trees = 20 m,
many small trees and bushes,
sandy soil
Palisota ambigua, Aframomum sp. and
Rauvolfia sp. common; one pioneer
Musanga cecropioides R. Br. ex Tedlie
present
10010370 0E
E Young
forest
02460080 0S 19 Secondary forest, very open canopy,
tallest trees = 30 m, swampy soil
Xylopia hypolampra Mildb. and Xylopia
spp. dominant10020250 0E
Fa Young
forest
02470320 0S 166 Secondary forest, plot at the edge
of a thin tongue of forest
connected to a large forested
area; tallest trees = 30 m,
important re-growth in the
understorey, sandy soil
Pachypodanthium staudtii Engl. and Diels,
Diospyros vermoeseni, Palisota
ambigua, Leptactina mannii Hook.f.,
Ouratea sulcata (Van Tiegh.) Keay,
Sacoglottis gabonensis (Baillon)Urb. and
Bertiera subsessilis Hiern present
10030450 0E
G Savanna 02420510 0S 2.7 Surrounded by forest; isolated bushes
and trees, sandy soil,
bare soil = 50%
Borreria verticillata (L.) GFW Mey and
two unidentified Poaceae dominant,
Cyperus tenax Boeck and Dracaena sp.
present
09590550 0E
H Savanna 02440110 0S 3.0 Surrounded by forest, sandy soil,
bare soil = 25%
Borreria verticillata, Dracaena sp. and one
unidentified Poaceae dominant, Cyperus
halpan J. Kern and Heterotis decumbens
(Pal.Beauv.) H. Jacques-Fe?lix present
10000220 0E
I Savanna 02480230 0S 2.5 Surrounded by forest, sandy soil,
are soil = 25%
Merremia tridentata Hallier f., Cyperus
tenax and one unidentified Poaceae
dominant
10030210 0E
J Garden 02440470 0S 2 Sandy soil fertilized with compost Amaranth, aubergine, cabbage, carrot,
lettuce, pepper, spinach, sweet pepper,
tomato and water melon
10010100 0E
K Garden 02430360 0S 0.5 Clayish sand fertilized with compost Aubergine, banana, maize, manioc, pepper,
pineapple, spinach, sugar cane and taro10020060 0E
L Garden 02440090 0S 0.8 Sandy soil fertilized with compost Amaranth, aubergine, cabbage, cucumber,
gombo, pepper, sorrel, spinach and
tomato
10010060 0E
For gardens, the main crops cultivated during the study period are listed
a Sites equipped with a flight-interception trap
J Insect Conserv (2009) 13:103?118 107
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observed]) of species rank abundance curves, and ran-
domized species accumulation curves. We also compared
the three main sampling methods (PT, YPT and MT) with
regard to the shared number of morphospecies/species
between methods and computed two relevant statistics:
Morisita?Horn index of faunal similarity and complemen-
tarity. The Morisita?Horn index is a special case of the
NESS index where sample size parameter is set to 1 and is
most sensitive to common species (Grassle and Smith
1976). Complementarity between methods was estimated
with the Marczewski?Steinhaus distance (proportion of all
species collected by two methods that were captured by
only one method; varies from 0, when both methods share
all species, to 1, when methods have no species in com-
mon; Colwell and Coddington 1994). Most of these
statistics were calculated with EstimateS version 7.5, with
50 randomizations whenever applicable (Colwell 2005).
Second, to evaluate which higher taxa were best col-
lected by each sampling method, we used the indicator
value index, which ranges from 0 (no indication) to 100
(perfect indication; Dufre?ne and Legendre 1997). Perfect
indication means that presence of a taxon points to a
Table 2 Focal taxa sorted by parataxonomists
Focal taxa Ordera Guildb Ind Indmc Mor Spp. Authority
Mantodea Ma Pr 98 56 22 ? ?
Acrididoidead Or Lc 1,129 360 40 ? ?
Fulgoroideae He Ss 4,022 2,842 242 ? ?
Membracidae He Ss 37 36 15 ? ?
Buprestidae Co Wo 115 95 16 16 GC
Scarabaeidae Co Lc, Sc 2,240 2,031 88 ? ?
Coccinellidae Co Pr 1,409 1,203 34 ? ?
Histeridae Co Pr 682 624 25 ? ?
Cleridae Co Pr 45 38 19 ? ?
Tenebrionidae Co Sc 839 644 60 ? ?
Cerambycidae Co Wo 278 149 53 51 S. Lingafelter
Chrysomelidae Co Lc 2,285 1,961 169 157 TW
Neuropteraf Ne Pr 235 152 25 25 MWM
Asilidae Di Pr 409 351 50 ? ?
Dolichopodidaeg Di Pr 7,339 2,121 38 ? ?
Tephritidae Di Lch 535 429 35 ? ?
Syrphidae Di Pr, Sc 459 375 34 25 C. Thompson
Pipunculidae Di Pa 123 97 16 22 MDM; M. Foldvari
Ichneumonidae Hy Pa 2,302 1,916 429 ? ?
Chalcidoideai Hy Pa 4,577 1,315 179 ? ?
Formicidae Hy An 134,912 na na ? ?
Apoideaj Hy Lck 1,239 1,060 93 51 CE
Ind = no. individuals collected; Indm = no. individuals morphotyped by parataxonomists; Mor = total no. of morphospecies sorted by
parataxonomists from Indm. Spp. = no. of species sorted by taxonomists from a sub-sample of Indm (full data presented and discussed
elsewhere); Authority = taxonomist in charge of the material, abbreviated for co-authors of this article
a Orders: Co = Coleoptera, Di = Diptera, He = Hemiptera, Hy = Hymenoptera, Ma = Mantodea, Ne = Neuroptera, Or = Orthoptera
b Guilds: An = ants, Lc = leaf-chewers, Pa = parasitoids, Pr = predators, Sc = Scavengers , Ss = sap-suckers, Wo = wood-eaters (system of
Moran and Southwood 1982)
c Some damaged or lost material could not be morphotyped
d Including Acrididae, Pyrgomorphidae and many juveniles, not morphotyped
e Including 14 families
f Including eight families
g Only morphotyped from July to December 2001, then kept unassigned in alcohol
h Subguild: fruit-feeders
i Only [2 mm and including 13 families
j Including Apidae, Halictidae and Megachilidae
k Subguild: pollinators
108 J Insect Conserv (2009) 13:103?118
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particular sampling method without error, at least with
the dataset in hand. For this analysis, we considered the
sum of individuals collected within higher taxa for a
particular combination of site and sampling method
(12 9 3 + 4 = 40 samples). We restricted the dataset to
the most abundant higher taxa (C40 individuals; i.e., at
least on average one individual collected in each sample;
151 higher taxa were considered). The significance of the
maximum indicator value was tested for each taxon by a
randomization procedure implemented in PC-ORD (Monte
Carlo permutation tests; 1,000 permutations; McCune and
Medford 1999).
Last, we performed multivariate analyses to estimate the
relative contribution of sampling methods, habitat type and
seasonality on the composition of arthropod catches, for
the higher taxa, morphospecies and species datasets. For
each dataset, we pooled data from a combination of sites,
sampling methods and seasons (12 9 3 9 2 + 4 9 2 = 80
samples). Seasons were defined as being ?dry? (June?
August) or ?wet? (other sampling months). We restricted
datasets to the most abundant higher taxa (C40 individuals,
n = 151, 80 samples), morphospecies (C40 individuals,
n = 86, 80 samples) and species (C20 individuals, n = 43,
67 samples). First, we performed unconstrained ordinations
(detrended correspondence analysis, DCA, ter Braak and
Smilauer 1998) for each dataset to examine the grouping of
samples with regard to sampling methods and habitat types,
especially forest vs. non-forest habitats. Second, we per-
formed constrained ordinations (canonical correspondence
analysis, CCA, ter Braak and Smilauer 1998) for each
dataset to evaluate the effects of independent (factor) vari-
ables. These included sampling methods (four dummy
variables re-coded as advised in Leps and Smilauer 2003),
habitats (four dummy variables) and seasons (two dummy
variables). Partitioning of variance followed Borcard et al.
(1992).
Results
Overall, 430,448 arthropods were collected during the 38
sampling events (4,712 samples), representing 31 orders
and at least 218 families. The 22 focal taxa represented
17,822 individuals and 1,682 morphospecies (Table 2).
Further, 347 species were sorted from the seven focal taxa
which to date have been examined by taxonomists. Most
individuals were collected by PTs and MTs (Table 3).
Catch rates expressed per trap-day were significantly dif-
ferent among methods (Kruskal?Wallis test, W = 1390.9,
P \ 0.001). MTs provided the highest catch rate, but when
considering catch rates per unit surface area, PTs were
most efficient, with the notable high efficiency of YPTs.
Catches with YPTs also had the lowest coefficient of
variation (Table 3). Distribution of arthropod abundance in
the four habitat types was broadly similar for each of the
four sampling methods, being usually high and similar at
forest sites, lowest in the savanna, and intermediate to high
in gardens (Table 3).
Most morphospecies and species were collected by MTs
and YPTs. Rarefaction indicated that this pattern remained
true for morphospecies, but many species were also col-
lected by FITs. Patterns were also broadly similar for
morphospecies and species when estimating the total
number of taxa present in the area or considering the
evenness in rank abundance plots among sampling meth-
ods. The number of singletons collected by MTs and YPTs
was appreciable. The PTs tended to have their collections
dominated by a few morphospecies (Scarabaeidae and
Histeridae; evenness of rank abundance plots, Table 3).
PTs, YPTs and MTs collected higher proportion of sin-
gletons in forest than in non-forest habitats (Table 3). With
larger sampling effort, MTs and YPTs may have collected
many more species in the study area (Chao1, Table 3).
Evenness was highest for FIT catches and lowest for PT
catches (Table 3; morphospecies/species rank abundance
plots not presented here). With the present protocol, one
MT collected as many arthropods as four YPTs or five PTs;
the catches of four YPTs and five PTs were 26 and 48%
less diverse than one MT, respectively (Coleman rarefac-
tion on morphospecies, Table 3).
Our sampling methods only collected a fraction of the
local arthropod fauna, as suggested by morphospecies
accumulation curves (Fig. 1a; patterns were broadly simi-
lar for species and are not presented here). Also, as
indicated by the rarefaction, accumulation of morphospe-
cies was steeper for MTs than for PTs, suggesting that PTs
may have sampled a higher proportion of the epigaeic
fauna when compared to the proportion of flying fauna
sampled by MTs. For PTs, YPTs and MTs, morphospecies
accumulation curves were steeper in forest than in non-
forest habitats, suggesting that a lower fraction of the fauna
was sampled in the former than in the latter (Fig. 1b).
The distribution of unique and shared taxa was broadly
similar for morphospecies and species. Both for morpho-
species and species, MTs produced a high proportion of
unique species (55?60%), whereas this was lower for PTs
(19?26%; Fig. 2). Only 35 morphospecies (2.1% of total
sorted) were collected by the four sampling methods.
Faunal similarity was closest between the catches in MTs
and FITs (Morisita?Horn indices of 0.40 for morphospe-
cies) and furthest between the catches in MTs and PTs
(Fig. 2). For both morphospecies and species, comple-
mentarity was highest between PTs and MTs (Fig. 2).
Sampling methods each collected a different spectrum of
fauna and often substantial variation in trap catches existed
among habitat types (Fig. 3). Furthermore, the distribution
J Insect Conserv (2009) 13:103?118 109
123
of a few abundant taxa equally well collected by the three
main sampling methods was not uniform across habitats (PT,
YPT and MT; Formicidae, Collembola, Phoridae, Acalyp-
tera, Calyptera, Cicadellidae and Scelionidae; G-tests on
3 9 4 matrices, all with P \ 0.001), suggesting that differ-
ent sampling methods collect different subsets of the fauna
that are well-adapted to specific habitat types (and see
multivariate analyses, below).
About half of the higher taxa tested (61 insect families)
could be considered as indicators for particular sampling
methods (Appendix A: indicator values with P \ 0.05, 151
higher taxa tested representing 118 families). These results
can be used by entomologists to compare the relative
reliability of the four sampling methods used in this study
for arthropod taxa. Most of this information has been
previously reported in the literature (sometimes without
Table 3 Statistics related to the abundance and diversity of arthropod material collected by each sampling method
Variable PT YPT FIT MT
No. ind. collected 148,591 106,963 30,044 144,850
Mean ? SE ind. collected per survey 3,910 ? 434 2,814 ? 219 791 ? 96 3,812 ? 306
CV for survey samples (%) 68.4 47.9 75.0 49.4
Catch rate (mean ? SE ind. collected per trap-day) 21.7 ? 1.8 19.5 ? 0.9 65.9 ? 5.6 105.9 ? 6.2
Catch rate (mean ind. 9 day 9 m-2) 7676.3 341.4 16.5 39.2
Mean ? SE ind. collected per survey in old forests 1,359 ? 253.4 656.4 ? 106.6 427.6 ? 64.0 1202.8 ? 175.9
Mean ? SE ind. collected per survey in young forests 1326.7 ? 249.2 830.9 ? 103.4 363.1 ? 38.8 807.9 ? 103.4
Mean ? SE ind. collected per survey in savanna 370.3 ? 44.5 461.8 ? 30.7 ? 562.7 ? 59.5
Mean ? SE ind. collected per survey in gardens 854.2 ? 132.8 865.8 ? 60.0 ? 1238.4 ? 121.3
No. morphospecies collected 274 767 272 1,108
No. spp. collecteda 46 159 100 254
No. of singletons collected (% of total) 139 (50.7) 379 (49.4) 169 (62.1) 504 (45.5)
No. of singletons collected (% of total) in forests 81 (58.3) 244 (55.1 169 (62.1) 306 (57.6)
No. of singletons collected (% of total) in non-forests 76 (48.7) 196 (48.5) ? 296 (42.2)
Evenness of morphospecies rank abundance curve 0.72 0.75 0.80 0.81
Evenness of spp. rank abundance curvea 0.74 0.77 0.87 0.80
Coleman morphospecies; sample size = 1,700 ind. 265.5 ? 0.4 375.1 ? 1.6 277.5 ? 0.3 508.2 ? 1.9
Coleman spp.: sample size = 150 ind.a 45.5 ? 0.2 56.7 ? 0.7 69.2 ? 0.6 68.4 ? 0.8
Chao1 ? SE: morphospecies 504.2 ? 7.8 1447.9 ? 13.8 677.1 ? 12.9 1842.9 ? 12.8
Chao1 ? SE: spp.a 103.0 ? 4.4 299.2 ? 6.0 288.4 ? 10.2 409.9 ? 6.2
CV = coefficient of variation; ind. = individuals; spp. = species
a Seven focal taxa: Table 2
Fig. 1 Morphospecies
accumulation curves (mean of
50 randomizations ? SD)
during 38 arthropod surveys, for
(a) different sampling methods,
all habitats being pooled; and
(b) particular sampling methods
operating either in forest (closed
circles) or non-forest habitats
(open circles)
110 J Insect Conserv (2009) 13:103?118
123
statistical rigor), but other observations may not be widely
known and are detailed below.
PTs are known to sample predominantly arthropods
foraging on the ground, such as Gryllidae, Carabidae,
Formicidae, Acari, Dermaptera and Diplopoda (all with
indicator values with P \ 0.01). YPTs sampled a mixture
of taxa foraging on the ground or flying close to the ground,
such as Dolichopodidae, Encyrtidae, Salticidae, Thysa-
noptera, Ceraphronidae, Sphecidae and Araneae (all with
P \ 0.01). The relative high catches of Stratiomyidae,
Diopsidae and Anthicidae in YPTs are noteworthy. FITs
were especially good to collect flying insects that tend to
drop when hitting surfaces (many Coleoptera), such as
Eucnemidae, Trixagidae, Coniopterygidae, Anthribidae,
Cerambycidae, Cleridae, Scyrtidae, Termitidae, Aderidae
and Psylloidea (all with P \ 0.01). The high incidence of
Coniopterygidae and alate Termitidae in the FITs are
noteworthy. MTs often collected reasonably good fliers,
such as Tabanidae, Buprestidae, Scoliidae, Evaniidae,
Eurytomidae, Braconidae and Myrmeleontidae (all with
P \ 0.01). The good indicator scores for MTs of Bupres-
tidae, Myrmeleontidae and Chrysidae are also noteworthy.
The first and second axes of the DCA explained 23.7 and
11.0% of variance in the composition of arthropod higher
taxa (sum of eigenvalues of DCA = 2.165). They clearly
separated samples on the basis of sampling methods (first
axis) and habitat types, especially forests and non-forests
(second axis; Fig. 4a). The second axis affected more
strongly YPT and MT samples than PT samples (paired
t-tests for differences in sample scores on axis 2 for PT, YPT
and MT samples in forest and non-forest habitats: t =
-2.60, P \ 0.05; t = -13.12, P \ 0.001; t = -10.47,
P \ 0.001, respectively). The CCA confirmed that the effect
of sampling method influenced more strongly the composi-
tion of higher taxa than the effect of habitats. The first
canonical axis explained 49.9% of the variance in the CCA
(21.8% of the total variance). The dummy variable ?PT? was
best correlated with the first axis (r = -0.85, P \ 0.001;
Fig. 4a). The second canonical axis explained 27.2% of the
variance in the CCA (11.9% of the total variance). The
dummy variables ?YPT?, ?Garden?, and ?Old forest? were
best correlated with the second axis (r = 0.61, r = 0.56 and
r = 0.51, respectively, P \ 0.001; Fig. 4a).
Patterns were different when examining morphospecies
composition. In this case, the first and second axes of the
DCA explained a lower fraction of variance (9.6 and 6.1%,
respectively; sum of eigenvalues = 9.305), emphasizing
that many factors may influence morphospecies distribu-
tion. The first axis separated samples on the basis of habitat
type, especially forests and non-forests, whereas the
meaning of the second axis was more difficult to interpret
(Fig. 4b). The first axis affected similarly PTs, YPTs and
MTs (paired t-tests for differences in sample scores on axis
1 for PT, YPT and MT samples in closed and open habitats:
t = -17.00; t = -11.28; t = -14.12; respectively; all
with P \ 0.001). The first canonical axis of the CCA
explained 28.8% of the variance (9.3% of the total vari-
ance) and was best correlated with the dummy variables
?Old forest? and ?Garden? (r = 0.78 and -0.64, respec-
tively, P \ 0.001). The second canonical axis explained
21.6% of the variance 6.9% of the total variance) and was
best correlated with the dummy variables ?PT? and ?MT?
(r = 0.91 and -0.57, respectively, P \ 0.001). Multivari-
ate analyses of the species dataset were broadly similar to
the morphospecies dataset, particularly the first axes of the
DCA and CCA being related to habitat type, and are not
detailed here.
Discussion
Suitability of sampling methods for biological
monitoring
The four sampling methods used in this study were non-
destructive and easily deployed and maintained during a
year at all studied habitats. However, there are at least four
major impediments related to these traps and our study.
First, sedentary arthropods were less likely to be collected
by these rather passive traps. Thus, measurements of faunal
similarity (for example calculated between habitat types)
derived from these data are likely to be rather high.
Our conclusion that many morphospecies/species are
Fig. 2 Shared species statistics between the three main sampling
methods (PT, YPT and MT), for (a) morphospecies and (b) species.
Circles, above: no. of morphospecies/species uniques and shared
between the three methods. Boxes, below: upper matrix of similarity
(Morisita?Horn index) and complementarity between sampling
methods
J Insect Conserv (2009) 13:103?118 111
123
specialized to particular habitats (see below) may thus be
further strengthened. Second, these methods were inade-
quate for many arthropod taxa (e.g., Lepidoptera are better
collected with light traps, Kitching et al. 2001). Third, non-
forest habitats (savanna and gardens) were better surveyed
than forests since traps operated in the understorey. The
rich fauna of the forest canopy (Erwin 1983) was probably
only occasionally collected, as suggested by steep species
accumulation curves and high occurrence of singletons in
forests (Fig. 1b, Table 3). Last, taxonomic impediment
prevented all focal taxa from being examined by taxono-
mists, although the studies of certain taxa are pending. This
limited our analyses related to species richness. In most
cases, our species and morphospecies datasets showed
similar patterns, but species identifications are crucial for
sound biological monitoring.
Fig. 3 The 20 most abundant higher taxa collected by each sampling
method. Mean (?SE) number of individuals collected per survey,
detailed for old forests (closed bars), young forests (stippled bars),
savanna (gray bars) and gardens (open bars). Standard errors relate to
the mean of total individuals collected per survey for each taxon. For
sake of clarity, Formicidae data for PT were all scaled by a factor 0.2
(actual value in brackets)
112 J Insect Conserv (2009) 13:103?118
123
PTs are inexpensive, easy to deploy in the field and allow
high spatial replication for habitat comparisons. However,
particular care must be taken to keep the rim of the pitfall
level with the ground surface, to ensure maximum effi-
ciency. They need to be serviced often to prevent rain from
flooding the trap contents and arthropods to start decom-
posing. A small roof over the trap may stop rain from
diluting the preserving fluid, but this is at the expense
of further trap bias (Adis 1979). YPT share most qualities
and shortcomings of PTs: being inexpensive; easy to use
and possibility of high replication; sensitive to rainfall and
frequent servicing needed. However, no roof can be placed
over the trap to protect it from rainfall, as this would
decrease catches of insects attracted to yellow, which tend
to be phytophagous (Kirk 1984). Unlike most authors, we
emplaced YPTs with the rim level with the ground surface
(Finnamore 1997). This detail explains our high catches of
both crawling and flying insects. Abundant material is
collected in FITs and MTs (and better preserved than in the
case of PTs and YPTs), which takes a lot of time to sort.
However, by restricting operations to a short period with
frequent servicing (3 days at weekly intervals, such as in the
present study), one may greatly decrease catches size and
allow one to consider all taxa within (the smaller) samples.
For long-term studies and monitoring, this approach may
result in more representative samples (Gadagkar et al.
1990). Both types of traps are expensive and, thus, spatial
replication is more difficult to achieve than with the other
two methods. The larger FITs and MTs are also more prone
to disruption from humans and animals (with concomitant
difficulty and costs to replace them) than the smaller PTs
and YPTs, particularly in Africa in areas where large
mammals are abundant.
Complementarity of sampling methods and reliability
for collecting particular taxa
Sampling methods strongly influenced arthropod compo-
sition in trap catches, as they targeted different components
of the fauna, either foraging on the ground (PTs, YPTs) or
flying low in the vegetation (YPTs, FITs, MTs). Trap
complementarity was highest for traps best designed to
exploit these different behaviors, PTs and MTs. Each
sampling method was biased towards specific higher taxa
and morphospecies, resulting in different species rank
abundance distributions. Most of the 35 morphospecies that
were collected by all four collecting methods were more
readily sampled by a particular method. As a result, faunal
similarities calculated between the catches of different
sampling methods were low. The implications are clear: a
range of sampling methods (at the expense of lower rep-
lication) is likely to yield more diverse material for
inventorying and monitoring arthropods than any single
method operated with high replication (Noyes 1989;
Gadagkar et al. 1990; Stork 1994; Basset et al. 1997;
Longino and Colwell 1997; Kitching et al. 2001). Argu-
ably, more systemic sampling methods exist than used in
this study (e.g., pyrethrum knockdown, net sweeping, e.g.,
Noyes 1989; Watt et al. 1997), but they are unsuitable for
long-term monitoring as they dramatically disturb study
sites by fumigation and trampling.
Further, the extensive dataset of this study allowed
statistical testing of the reliability of sampling methods
used in this study for 118 arthropod families (in terms of
relative abundance, Appendix A). This statistical approach
is more accurate than relying on scientific tradition for
choice of taxa and methods, which often prevails in the
entomological literature (Kitching et al. 2001).
Fig. 4 Multivariate analyses of (a) 151 higher taxa and (b) 86
morphospecies, ordered by sites, methods and seasons. Plot of
samples (closed symbols = forests, open symbols = non-forests) in
the first and second axis of the DCA. Inset: plot of centroids of
environmental variables in the first and second canonical axes of the
CCA CCA (Olf = old forest, yof = young forest, sav = savanna,
gar = garden, dry = dry season, wet = wet season)
J Insect Conserv (2009) 13:103?118 113
123
Effects of habitat structure on trap efficiency
The effect of habitat structure on the trappability of dif-
ferent taxa is an important topic (Melbourne 1999).
However true faunistic differences between habitat types,
such as those considered in the present study are likely to
be far more significant, as suggested by the magnitude of
faunal turnover observed (only 39 morphospecies common
to all habitats, 2.4% of all morphospecies sorted; data
presented and discussed elsewhere). As far as higher taxa
are concerned, given that sampling methods had a larger
impact on arthropod composition than habitat type
(Fig. 4a), the most efficient method for a particular taxon is
likely to remain the same across all habitat types. That said,
we observed that the higher taxa composition of MTs
varied more between forests and non-forest habitats than
that of PTs, YPTs being intermediate in this regard. MTs
and YPTs may be more efficient in open habitats because
of increased visibility and attractiveness (Noyes 1989).
Alternatively, MTs and YPTs are more likely to collect
mobile taxa that may forage in different habitats. Inter-
estingly, these patterns were different when considering the
morphospecies/species composition of trap catches: the
effects of habitat type were stronger than the effects of
sampling method. This confirms that many morphospecies/
species are specialized foragers in particular habitats and
that datasets sorted at the level of species/morphospecies
are much more discriminating with regard to arthropod
composition in different habitats than those sorted at the
level of higher taxa (Basset et al. 2004a). This implies that
monitoring should be preferably performed at the level of
morphospecies/species and that for this type of data dif-
ferences in trap efficiency or taxa trappability between
habitat types may be neglected, as far as habitat types
remain well contrasted and contain dissimilar fauna.
Conclusions and recommendations
The two concepts of inventorying and monitoring biodi-
versity are connected, since baseline data are needed for
monitoring (Rohr et al. 2007). For inventorying purposes, a
combination of methods targeting different faunal compo-
nents may be optimal, as they may collect a wider range of
taxa than a single method (Noyes 1989; Gadagkar et al.
1990; Stork 1994; Basset et al. 1997; Longino and Colwell
1997; Kitching et al. 2001). Including a few MTs in the
protocol may be valuable since they accumulate faster
species in trap catches and provide high quality material for
further taxonomic analyses. Note that a possible improve-
ment of the protocol developed in the present study would
be to establish more sites for each habitat type (i.e., nine
sites per habitat instead of three) and to randomly move the
12 traps operating within this set of sites every month (or
other relevant period). The amount of material collected
would be similar but, most likely, more diverse. Further, it
would improve statistical power, with analyses being less-
site and more habitat-dependent. Adequate inventory of the
canopy fauna, often different from that in the understorey
within tall closed rainforests (Basset et al. 2003), remains a
challenge (Basset et al. 1997). With regard specifically to
long-term monitoring, one possibility may include operat-
ing compact FITs, modified YPTs or automatic light traps
lifted on pulleys high in the canopy (Springate and Basset
1996; Wolda et al. 1998; De Dijn 2003).
For general monitoring purposes, the situation is more
complex, as species abundance in traps ideally needs to
reflect actual species abundance. Given that each method
tends to target a different component of the fauna and has
its own biases, it would be preferable to statistically ana-
lyze species data separately for each sampling method, the
results of one complementing the results of the other(s). In
case only one sampling method can be deployed, YPTs
represent an acceptable compromise, as they samples both
crawling and flying arthropods and accumulate specimens
and species reasonably fast. Further, since higher taxa
composition in YPTs differed consistently between forest
and non-forest habitats than in MTs or PTs, YPTs may be
particularly useful for monitoring arthropod recovery in
degraded and open habitats.
As noted in the introduction, several quantitative pro-
tocols exist to survey specific arthropod taxa in the
tropics. For biological monitoring, it may be possible to
implement in parallel taxa-specific protocols, but this
approach is likely to be obtrusive (by trampling required
to performed 5?10 protocols alone), or even destructive
(e.g., ant and termite protocols, Agosti et al. 2000; Jones
and Eggelton 2000), or time-consuming (as different
training skills may be required for operators). The fol-
lowing alternative, based on the present study, may be
considered: implement a common set of passive, non-
obtrusive sampling methods (such as PTs, YPTs and
MTs), extract focal taxa with the help of local paratax-
onomists (Basset et al. 2004b), and refine species
identifications with taxonomists? feedback. In addition to
this multi-taxic monitoring programme, 1?3 taxa-specific
protocols could be implemented in parallel, to validate the
results of the wider monitoring programme. This strategy
has at least four added advantages: (1) development of
baseline datasets that are wider in taxonomic scope; (2)
monitoring of a larger number of focal taxa belonging to
different functional guilds; (3) long-term studies with
non-disruptive methods may allow collection of rare
species, which represent a substantial proportion of trop-
ical arthropod assemblages (Novotny and Basset 2000);
and (4) wider training base for parataxonomists.
114 J Insect Conserv (2009) 13:103?118
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In conclusion, taxa-specific sampling protocols are cer-
tainly the best strategy when evaluating the effects of
anthropogenic disturbance on particular arthropod taxa.
However, for the purpose of monitoring large and diverse
arthropod assemblages in the long-term, a protocol based
on operating a set of different and non-disruptive traps
appears superior in design and rewards than summing a
series of taxa-specific protocols.
Acknowledgements F. Dallmeier, J. Comiskey, M. Lee,
J. Mavoungou and J. B. Mikissa helped to implement the project.
Parataxonomists B. Amvame, N. Koumba, S. Mboumba Ditona,
G. Moussavou, P. Ngoma, J. Syssou, L. Tchignoumba and E. Tobi
collected, processed, sorted and data-based most of the insect material
with great competence. J. Raymakers and S. Mboumba Ditona pro-
vided the vegetational data. M. Foldvari, S. W. Lingafelter and F. C.
Thompson helped with pipunculid, cerambycid and syrphid identifi-
cations, respectively. V. Novotny commented on an early draft of the
manuscript. The project was funded by the Smithsonian Institution,
National Zoological Park, Conservation and Research Center/MAB
Program through grants from the Shell Foundation and Shell Gabon.
This is contribution No. 84 of the Gabon Biodiversity Program.
Appendix A: Results of species indicator analysis with
regard to sampling methods for the 151 most abundant
higher taxa
Taxa Method Indicat. val.
(%)
P-value
Acari PT 54.8 0.001
Araneae YPT 38.1 0.008
Salticidae YPT 65.7 0.001
Archaeognatha
Meinertellidae MT 14.3 0.755
Blattodea FIT 37.7 0.219
Coleoptera
Aderidae FIT 62.8 0.008
Anthicidae YPT 52.2 0.032
Anthribidae FIT 69.8 0.001
Bostrichidae MT 28.7 0.280
Bruchidae MT 39.1 0.452
Buprestidae MT 78.0 0.001
Carabidae PT 68.7 0.001
Cerambycidae FIT 63.0 0.001
Chrysomelidae MT 32.9 0.746
Clambidae YPT 40.5 0.224
Cleridae FIT 78.9 0.002
Coccinellidae YPT 41.7 0.296
Corylophidae FIT 39.4 0.283
Cucujoidea PT 26.2 0.431
Curculionidae YPT 47.4 0.349
Elateridae PT 34.4 0.644
Appendix continued
Taxa Method Indicat. val.
(%)
P-value
Endomychidae MT 74.7 0.013
Eucnemidae FIT 83.9 0.001
Histeridae YPT 43.2 0.109
Hydrophilidae PT 61.6 0.055
Lagriidae FIT 34.4 0.174
Leiodidae YPT 35.4 0.635
Mordellidae MT 44.4 0.076
Mycetophagidae FIT 26.5 0.337
Nitidulidae PT 54.5 0.011
Phalacridae FIT 40.7 0.062
Pselaphidae FIT 47.4 0.018
Ptiliidae PT 51.9 0.091
Scarabaeidae MT 37.8 0.480
Scydmaenidae FIT 35.3 0.461
Scyrtidae FIT 75.3 0.004
Staphylinidae PT 40.3 0.040
Tenebrionidae MT 41.8 0.247
Trixagidae FIT 81.7 0.001
Coleoptera: unknown PT 39.9 0.103
Collembola PT 48.6 0.019
Entomobryidae MT 39.2 0.112
Dermaptera PT 82.2 0.002
Diplopoda PT 67.7 0.009
Diptera
Acalyptera MT 47.2 0.034
Anthomyiidae MT 41.2 0.021
Asilidae MT 61.5 0.013
Calliphoridae YPT 47.4 0.047
Calyptera YPT 51.0 0.031
Cecidomyiidae MT 48.8 0.063
Ceratopogonidae MT 55.0 0.013
Chironomidae FIT 53.2 0.018
Culicidae MT 59.8 0.087
Diopsidae YPT 53.2 0.024
Dolichopodidae YPT 77.2 0.001
Drosophilidae YPT 43.8 0.035
Empididae YPT 52.2 0.180
Limoniidae MT 48.4 0.043
Micropezidae YPT 61.3 0.055
Muscidae YPT 25.6 0.194
Mycetophilidae MT 78.5 0.059
Phoridae MT 65.0 0.016
Pipunculidae MT 47.1 0.031
Platystomatidae YPT 38.4 0.088
Psychodidae MT 38.8 0.145
Sarcophagidae YPT 49.4 0.040
Scatopsidae MT 70.6 0.064
Sciaridae MT 45.8 0.014
J Insect Conserv (2009) 13:103?118 115
123
Appendix continued
Taxa Method Indicat. val.
(%)
P-value
Stratiomyidae YPT 48.6 0.023
Syrphidae MT 63.3 0.046
Tabanidae MT 81.9 0.001
Tephritidae MT 61.1 0.191
Tipulidae MT 54.9 0.021
Diptera: unknown YPT 61.9 0.011
Embioptera YPT 31.2 0.300
Hemiptera (Heteropterans)
Anthocoridae FIT 34.2 0.171
Ceratocombidae FIT 42.5 0.029
Coreidae YPT 29.4 0.313
Cydnidae PT 40.8 0.225
Lygaeidae YPT 36.2 0.453
Miridae MT 36.7 0.232
Reduvidae MT 30.7 0.589
Salpingidae FIT 27.3 0.355
Heteroptera: juveniles FIT 21.4 0.863
Heteroptera: unknown
(Homopterans)
FIT 32.0 0.434
Achilidae FIT 53.4 0.040
Aleyrodidae MT 58.3 0.328
Aphidoidea YPT 54.5 0.098
Aphrophoridae YPT 78.9 0.010
Cercopidae YPT 46.7 0.043
Cicadellidae YPT 38.9 0.035
Cixiidae FIT 51.5 0.035
Coccoidea PT 40.6 0.081
Delphacidae YPT 74.5 0.012
Derbidae MT 57.2 0.043
Fulgoridae MT 25.6 0.395
Meenoplidae MT 57.8 0.079
Psylloidea FIT 49.3 0.008
Fulgoroidea: juveniles MT 39.0 0.419
Hymenoptera
Apidae MT 53.3 0.045
Apoidea MT 34.3 0.380
Aulacidae MT 46.9 0.045
Bethylidae MT 42.4 0.194
Braconidae MT 64.7 0.006
Ceraphronidae YPT 73.4 0.006
Chalcididae MT 65.9 0.014
Chalcidoidea MT 49.4 0.124
Chrysididae MT 42.6 0.035
Crabronidae MT 46.5 0.067
Cynipoidea MT 61.4 0.014
Diapriidae MT 42.1 0.237
Elasmidae MT 31.1 0.113
Encyrtidae YPT 73.3 0.001
Appendix continued
Taxa Method Indicat. val.
(%)
P-value
Eucoilidae YPT 16.4 0.570
Eupelmidae MT 38.4 0.129
Eurytomidae MT 66.9 0.002
Evaniidae MT 72.3 0.002
Formicidae PT 66.6 0.001
Halictidae MT 54.4 0.086
Ichneumonidae YPT 58.5 0.045
Mutilidae YPT 42.7 0.171
Platygastridae MT 30.2 0.577
Pompilidae YPT 47.8 0.018
Proctotrupoidea MT 30.9 0.236
Scelionidae YPT 45.2 0.135
Scoliidae MT 62.2 0.001
Sphecidae YPT 54.2 0.007
Tiphiidae YPT 42.8 0.096
Torymidae MT 58.0 0.016
Vespidae YPT 50.1 0.029
Hymenoptera: juveniles MT 31.8 0.405
Isopoda PT 57.5 0.029
Isoptera FIT 39.0 0.139
Termitidae FIT 55.6 0.005
Lepidoptera MT 54.5 0.017
Geometridae YPT 31.1 0.384
Lepidoptera: juveniles PT 39.7 0.192
Mantodea MT 43.2 0.122
Neuroptera
Coniopterygidae FIT 78.3 0.001
Myrmeleontidae MT 68.1 0.007
Opiliones MT 18.7 0.865
Orthoptera
Acrididae YPT 37.3 0.274
Gryllidae PT 73.0 0.001
Pyrgomorphidae PT 25.3 0.490
Tetrigidae PT 41.9 0.068
Tettigoniidae MT 33.1 0.362
Tridactylidae YPT 36.6 0.270
Pseudoscorpiones PT 21.6 0.521
Psocoptera FIT 47.5 0.043
Thysanoptera YPT 65.7 0.002
Trichoptera FIT 51.7 0.014
Taxa are listed alphabetically by order, detailing the sampling method
for which the maximum indicator value was recorded; the maximum
indicator value; and the P-value of Monte Carlo permutations testing
the statistical significance of the maximum indicator value. Indicator
values for taxa indicated in bold are significant with P \ 0.05
116 J Insect Conserv (2009) 13:103?118
123
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