ECOGRAPHV 21: 261-26R. Copenhiigeii 1998
Insect diversity of Acacia canopies in Mkomazi game reserve,
north-east Tanzania
Oliver Krugcr and George C. McGavin
Krtigcr. O. and McGavin, G. C. 1998. Insect diversity of Acacia canopies in
Mkoma7i game reserve, north-east Tanzania. - Eeography 21: 261 268,
Here we analyse one of the largest insect samples taken so far from a tropieal
savannah habitat. We used inseetieidal mist blowing to spray the eanopy of 3i trees
of six Aeacia speeies, and obtained 41 099 insects of 492 recognisable taxonomic units
(RTL's). In most cases, there were signifieant differences between individual trees
between and within tree species with regard to their insect community. After we
performed a standardisation procedure because of unequal sample size, cluster
analysis showed that most tree species formed more or less distinct clusters, indieating a moderate level of insect host specificity. Considered by lree localities, clear
elustcrs were visible only if one tree species was sampled. This iinding suggests that
there is no signifieant overlap in the insect communities between tree species at a local
scale. We carried out a multivariate analysis of insect diversity using four different
diversity measurements. Results differed depending on the measurement used and no
significant association of simple tree eharactcristies, such as height, with insect
diversity was detectable. Instead, time of the day and ant biomass seetn to be of
greater importiince. These results emphasise the importance of using different diversity measurements to evaluate habitats with regard to their conservation value.
O. Kyiigey (oliicy.kyuegey(a}biologii'.uni-hic!efeld.de). Dept oj"/.oolag\\ Uniy. aj Oxford,
South Parks Road, Oxfoyd, U.K. OXl 3PS. - G. C. McGiuin. Oxfin-d Univ. Museum
of Natuya! History. Parks Road. O.xford. U.K. OXl 3PW.
The last two decades have seen an enormous increase in
the number of insect studies conducted in tree'canopies
(Erwin 1982, 1983. Moran atid Southwood 1982. Adis et
al. 1984, Morse et al. 1988, Stork 1991, Basset and
Kitching 1991, Basset 1996, Stork et al. 1997). However,
most of these siudies took place in sub-lropical or
tropical forests and there are no major studies for tropical
savannah habitats (see West 1986 for a small study),
although they cover ca Af)% of the land surface of the
tropics (Cole 1986, Solbrig 1996). Consequently, there
are virtually no estimates of inseel diversily for these
habilats (Lewinsohn and Price 1996). This is even more
surprising considering the ideal framework thai trees in
savannah habitats provide for research on community
ecology (Southwood and Kennedy 1983). In addition.
tnultivariate approaches to identify correlates of insect
diversity have only rarely been made (Stork 1987b, 1991,
Basset 1992a, 1996), because in most major studies only
one or a small number of trees have been sampled.
Consequently, there is still a large amount of uncertainty
concerning the factors influencing insect diversity in tree
canopies. We sampled 31 trees of six Aeaeict species in
north-east Tanzania to analyse the insect diversity of
each tree wilh regard to a set ol' variables covering tree
characteristics and guild composition.
Material and methods
Mkomazi Game Reserve is located in north-east Tanzania adjacent to the Kenya border and the Tsavo West
NP. It covers an area of ca 3250 km^ with geographical
Accepted 23 September 1997
Copyright CO LCOGRAPHY 1998
ISSN 0906-7590
Printed in Ireland
nil rights reserved
ECOCiRM'HY 21 ? {]9'-m
261
Table I. Location of (he nine samphng localities, number of trees satnplcd anti tree species composition.
Sample
group
Longitude
Latitude
No. of
trees
Tree species
1
37=5 2,58
3°53.OI
5
2
37''52.60
3'53.02
4
3
37°52.56
3=52.94
5
4
37''52.69
3°52.76
5
4 X iiiloficci
1 X rejiciens
1 X ethaica
3 X rejiciens
3 X nilotiea
2 X Senegal
4 X niiotiea
1 X Senegal
5
38^01.10
4°07.90
4
6
7
37''52.57
37''52,42
3=53.04
3=53.82
1
2
8
9
37=54.06
37=48.20
3°53.35
4°03.04
3
2
borderlines of 37°35' 38°45'E and 3°50'-4°25'S. The
reserve belongs to the East African high pUttcau with
an altitude between 240 and 1609 tn a.s.l. Mkomazi's
climate has becti characterised as semi-arid (Coe 1995),
with a pronounced dry season and high mean temperatures between 23.1 and 37.8°C. Annual precipitation
ranges IVom 300 mm in the eastern part to 900 mm in
the central and western part of the reserve and shows a
bimodal distribution, with the long rainy season between March and tnid-May and the short rainy season
between late October and December.
Insect sampling
The canopies of 31 trees of six Aeacla speeies (1 x ethalca. 4 X nielllfera, \2 x nilotiea, 4 x rejiciens, 8 x
Senegal and 2 x tort ills) were sampled between 30
December 1995 and 18 January 1996 in the northern
part of the reserve. Sampling localities and trees sampled at each locality are given in Table I. The distance
between trees at one locality was between 10 and 304 m
and distances between localities ranged from 50 m to
31 ktn. Groups 5 and 9 were much further away from
all other groups compared to distances between the rest
of the sampling groups. Samples were taken using a
'Hurrican Minor' petrol-driven mist blower, with an
ultra low volume delivery no77]e spraying a mist of
undiluted Pybuthrin 216 into the canopy, a pyrethroid
formulation syiiergised with piperonyl butoxide. Sampling was carried out in as still conditions as possible,
therefore in the morning hours and only if the leaf
surface was dry. Only trees < 10 m high were sampled
and three bursts of the pyreihroid were given from
different directions, lasting 30 s in total. Insects were
collected in 1 m~ funnel-shaped trays, suspended between stakes, over a standard drop-time of 1 h. Ai'ier
262
4 X metlifera
1
1
1
1
3
2
X
X
X
X
X
X
Senegal
Senegal
nitotica
Senegal
Senegal
lortilis
Distance next
gronp [m]
Nearest
group
50
2
50
1
150
1
400
3
24 800
9
50
1450
1
6
2 700
18 250
4
7
the drop-time the material was separated from debris
and stored in !{)'%> alcohol.
During the drop-time period, georeference (using a
geographic position system), tree height, canopy cover
and tree canopy measurements were recorded, the tree
height of trees > 2.5 m being estimated visually against
measured heights. Canopy area was estimated under
the assumption of a near-circular shape.
The material was sorted to taxonomic order and
following that to family level by group specialists.
Individuals were classified into recognisable taxonomic
units (RTUs, in the following referred to as species)
according to the scheme set out by CSIRO (1991).
Larvae were not identified and were excluded from the
species level analysis. Each species was assigned to an
eeological guild, which is defined as a functional grouping of animals which exploit the same resources in
Sanple size
Fig. 1. Scheme showing the standarisation procedure for a
hypothetical data set of nine data points with three different
sample sizes. The thin lines show the chords between the
medium and the small sample size and the large and small
sample size. The arrows indicate the standardised number of
individuals for two arbitrary chosen data points, given by data
weighting functions aecording to the slope of the chord.
F . r O C i R A P H Y 21:3
Table 2. Explanatory
approach.
variable sel used in multivariate
Vanable
Description
Variable
1
2
3
4
5
6
7
8
9
10
Tree species, arranged in order of estimated
Residuals of biomass in mg.
Time of daia collection in h, miti.
Day ol' data collection.
Latitude of tlie tree,
Longitude of the tree.
Heighl of the tree in m.
Estimation of canopy area in m-.
Sample size in m-.
Percentage of canopy area sampled.
Tree species, arranged in order of estimated leafbiomass (Coe atid Beeiitje 1991).
Tree species, arranged in order of geographical
distribution {Coe and Beentje 1991).
Percentage of species in the phytophagous chewer
guild (pc(.
Percentage of species in ihe phytophagous sapsucker guild {ps).
Percentage of species in ihe phytophagous nectarivore guild (pn).
Percentage of species in the epiphyte yrazer tiuild
(e).
Percentage of species in the scavenger guild {s).
Pereeniage ol" species in the predator guild (p).
Percentage of species in the parasotoid guild (pa).
Percentage of species in the ant guild {a).
Percentage of species in the tourist guild (t).
Percentage of biomass in the phytophagous
chewer guild.
Percentage of biomass in the phytophagous sapsucker guild.
Percentage of biomass in the phytophagous nectarivore guild.
Percentage of biomass in the epiphyte guild.
Percentage of biomass in the scavenger guild.
Percentage of biomass in the predator guild.
Percentage of biomass iti the parasitoid guild.
Percentage of biomass in the ant guild.
Percentage of biomass in the tourist guild.
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
similar ways (Root 1973. see Appendix 1 for details).
Larvae were grouped as a whole in the phytophagous
chewers guild, since the great majority collected were
caterpillars. Clearly recognisable larvae such as Neuroptera were separated and placed in a different guild.
In addition to the eight guilds recognised hy Moran
and Southwood (1982), we recognised adult Lepidoptera as phytophagous nectarivores. Allocation of
species into guilds was done by group specialists or
using Scholtz and Holm (1989). The body length of five
randomly selected individuals of each species was measured to the nearest 0.01 mm using a binocular microscope with a calibrated eyepiece graticule. The median
of the measurements was taken to calculate dry weight
biomass per species using the allometric approximation
of Moran and Southwood {1982).
H C O G R A P U Y 21:3
Data analysis
To investigate the similarity of the insect fauna between
trees, we used two approaches. Firstly, the test for
significant of differences in the Shannon index of diversity, provided by Hutcheson (1970) was used. The
Shannon index is calculated by:
(1)
where p is the proportion of the /th species: a /-value
can be obtained by:
(2)
/ ^^
with the variance for a site calculated by:
,
Varir =
,'V
inp,
S- 1
2.'V-
(3)
where S is the number of species and ,V is the number
of individuals. The degrees of freedom obtained by:
{VarH\ + VarH'2)(4)
{VarH\){VarH\)A',
where A', and A', are the total number of individuals in
set 1 and 2 respectively. The second way chosen to
calculate a quantitative measure of dissimilarity is a
similarity coefficient, in this case the Sorenson quantitative (Bray and Curtis 1957, Southwood 1978):
DF =
2/A'
C, , = •
(5)
where C is the similarity, /(V is the lower abundance of
a species present in both samples and A', and N^ are the
total number of individuals in samples 1 and 2 respectively. Since this requires data on species richness and
on abundance and since sample size (total area of
funnel-shaped trays) varied between I and 5 m- in our
study, we standardised the data. Since rarefaction reduces the amount of information available for analysis
greatly (Magurran 1988), we weighted the data of samples with larger sample sizes according to the scheme
shown in Fig. 1. After this standardisation procedure,
hierarchical cluster analysis was used to construct a
dendrogram. No such standardisation was necessary
for the Shannon index since no signifieant correlation
between sample size and the index value was Ibun
(r = 0.091, DF = 29. p = NS). For the multivariate
analysis of insect diversity, we used four difterent measurements of diversity. The most straightibrward tneasure included was species riehness. The disadvantages
of this measure is that it does not take into account
abundance of species, and that it is highly affected by
satnple size (Magurran 1988). Another simple measure
which takes inio account only the most abundant spe263
We used stepwise multiple regression to assess the
association between explanatory variables (Table 2) and
diversity measurements. Prior to analysis the correlation matrix was reviewed to test for multicollinearity
between explanatory variables. In cases where multicollinearity was found, the variable with the lower
partial correlation with the biodiversity measurement
was removed. The entry criterion for the regression
model was set to p < 0,05 and the residuals of the
regression model were tested for their normality using
the Lilliefors-tesi (SPSS),
1
I 10
^ ^ i I.I
8
il i.
ii
i i
Results
^
Diversity of the entire insect fauna
Fig. 2. Divcrsily of the Iburlccn insect orders Lind the anls
(family Fonnicidae) present in this study. Brror bars show the
standard deviation of the alpha index.
cies and the total number of individuals is the BergerParker dominance measure (Berger and Parker 1970). It
takes the genera! form:
A'
N
(6)
where (/ is the dominance and N,^.^^ is the number of
individuals in the most abundant species. The measurement I — d was employed to reflect increasing diversity
with an increasing value. It is also known to be affected
by sample size (Magurran 1988), The a index taken
from the log series is widely used and is relatively
unaffected by sample size (Southwood 1978, Taylor
1978). Alpha is considered to be a good measure even
when the abundance distribution does not follow a log
series model. It takes into account the number of
species and the total number of individuals. It can be
calculated by:
Diversity of the insect orders present in this study,
measured by the alpha index (Fig. 2), shows that
Coleoptcra had the highest diversity, followed by
Hemiptera, Hytiienoptera and Diptera. In terms of
species richness, Hemiptera was highest (121). followed
by Coleoplera (113), Hymenoptera (91) and Diptera
(58). Abundance was also highest in Hemiptera
(11875). followed by Thysanoptera (10 288), Formicidae (7467) and Hymenoptera (3741). The diversity of
the entire insect fauna, measured by the alpha index,
was 78.8.
Similarity between tree species and sampling localities
For the four tree species at least four trees were sampled, the mean number of species fluctuated greatly
imellifera = 62.?i. nilolica = 97.9. refieiens = 2,6.3, seneCoefFicient of dissimilarity
0,6
0,5
0.4
0.7
A, nilotitu •
(7)
with X estimated from the iterative solution of:
S_
(1-,Y)
A' x ( - l n ( l -x))
(8)
and Ihe contidence limits of the index are set by:
- I n (I - . v )
(9)
The last measure chosen was the Shannon index of
diversity (eq. (1)). This is the only index which includes
both species richness and abundance. In cases where
the measurement was correlated with sampie size, the
residuals of the best fitting regression were used as the
dependent variable.
264
Fig. 3. Dendrogram of the 31 trees iinalyscd In this study.
Note that acoefficient of dissimilarity (I -- similarity) is used lis
the scale.
ECOURAPHY 21.? (19')S)
Coefficient of dissimilarity
0.3
0.4
0.5
Group 2
Group 5 -,
,
•^
Group 5
Group 5
Group 5
Group 9
Group 9
Group 7
Group 7
tiroup R
0.6
0.7
Group I-
Group 4
Group 3
Group 4
Group 4
Fig. 4. Dendrogram of tlic trees with regard to their sii
locality. Like in Fig. 2. a coefficient of diisimilarily is used.
gal = 106.6). Sample size did not vary significantly between tree species (ANOVA. F = 1.150, DF = 5,25. p =
0.361), so diffcrcncs between tree speeies eannot be
attributed to differences in sample size.
The test for significant differences in diversity using
the Shannon index revealed that out of 465 comparisons
possible, 367 had a significant t-value and 98 had no
significant t-value. Thus, the overwhelming parts of tree
combinations are different from each other. L'sing a
2 x 2 contingency table, no significant dilTerences in the
number of non-significant comparisons within and between tree species was found (xf = 0.394. p < 0.7) and
also no difference within and between sampling localities
(yj = 0.664. p < 0 . 5 ) .
After the standardisation procedure, no correlation
between sample size and the number of insects was tbun
(both r and r^, p = NS). The percentage similarity between any two trees ranges from 9.3 to 66.7'!^), with most
values around 35'/<L The dendrogram for the tree speeies
(Fig. 3) shows that three Aeaeict species formed distinct
clusters (mellifera, refieien.s, lortills) and two species
(nilotiea and setwgal) were found clustered in some cases
but also distributed over the dendrogram.
Interestingly, the first cluster is formed by A. mellifera,
which is much more different in its leaf structure from
the other species (Coe and Beentje 1991). The fauna of
Ihis species is much more different from the other species
than between these tree speeies, with the exception of an
outlying A. nilotiea. where over 60% of all insects
collected belonged to one psocopteran RTU of the
family Hemipsoeidae. The only sample of A. eihalea
might be similar because of the similar low number of
insects collected from this tree. The distinct cluster of A.
mellifera could also be a result of the big distance to
other sampling localities (Table I). The dendrogram of
the sampling locations (Fig. 4) indicates that local
similarity within and between sampling groups regardless of tree species was low (compare to Table 1). Only
in those occasions when one tree species was sampled at
one location (group 5, mellifera and group 9. toriilis). a
distinct cluster was visible. When only the four tree
species where at least four trees were sampled are
considered, only mellifera and refieiens form a distinct
cluster bt^t not nilotiea and senef;al.
Multivariate analysis of Insect diversity
Significant correlations between sample size and the
biodiversity measurements were found for species richness (r = 0.536, DF = 29, p < 0.01) and the alpha index
(,- = 0.405. DF = 29, p < 0.05). Consequently, the residuals of the best fitting regression were used as the
dependent variable instead.
With regard to species richness residuals (Table 3), the
regression model explained over 87'^ of the variation
with five variables, none of them a guild species share.
Negative associations of ant and predator biomass with
the dependent variable indicate that predation is a likely
factor influencing insect diversity. The picture changes
greatly for the dominance measure (Table 4), where the
model explained 19"A' of the variance. Four guild species
shares are significantly associated with the dominance
index, two of them (pa and pc) had major species shares
in the entire fauna (18.5 and 18.7'^ respectively), while
the others (e and s) had only minor shares (2.2 and 9.6%
respectively). The model for the residuals of the alpha
index (Table 5) also explained 79%i of the varianee with
only three variables (biomass residuals, ant biomass
Table 3. Results of the multiple regression with the residuals of species richness as the dependenl variable. The SE of the estimate
is 10.242.
Variable
Intercept
Biomass res.
Time
a biomass
Latitude
p biomass
Coefficient [i
308.794
3.785
-8.262
-0.622
-54.498
-0.373
l-value
p-value
Ciimulalive R-
3.166
6.653
3.255
4.825
2.218
2.171
0.005
0.0001
0.004
0.0001
0.038
0,046
0.503
0.683
0.787
0.841
0.879
265
Table 4. Results of the multiple reeression with the dominance measure as the dependent variable. The SK of the estimate is
0.061.
Variable
Intercept
e biomass
e RTU
pa RTU
s RTU
LongiliiLle
pe RTU
Coefficient [1
13.708
-0.015
-0.047
0.081
0-017
0.353
-0.009
l-\alue
p-value
Cumulative R-
3.991
4.684
3.283
2.279
3.593
2.920
2.347
0.008
0,0001
0,004
0,034
U.002
0.009
0.030
0.3y9
0.522
0.594
0,656
0.73!
0.791
share and sampling time). Interestingly, these three
variables were also significantly associated with the
residuals of species richness. With regard to the Shannon inde.K H', the regression model expUiined 6H% of
the variance wilh four variables (Table 6) which had
been also significant in the species richness model with
the exception of the ant biomass share. In all four
models, the residuals were distributed in line with a
normal distribution {Lillielbrs-test, DF = 31, p = N S ) .
Discussion
The diversity of the entire insect fauna in this study is
lower than in studies of rain forest canopies. Basset and
Kitching (1991) reported an a value of 162.9 for one
Australian subtropical overstory forest tree, compared
to 78.8 in this study and Stork (1991) found a mean of
617 species per tree in Bornean rain forest trees,
whereas 31 trees in this study lotalled only 492 species.
One small-scale study in East Africa produced y. values
between 2 and 40 for six species of Acacia (West 1986).
a similar range to this study (12-44). In its eomposition. the fauna is similar to rain forest eommunities
with a high beetle diversity (Basset 1991. Stork 1991.
Allison et al. 1993)The eluster analysis indicated a moderate level of
host specifieity with regard to tree species. While some
speeies seem to have a more or less defined inseet
comtiiunity associated with them, some tree species did
not. However, the results must be treated with caution
beeause elear tree speeies cluster coincide with loeality
clusters (meltifera and group 5). so ii is hard to tell
whieh of the two factors (tree species or locality) influences the insect community on a particular tree. Nevertheless the level of clustering is larger with regard to
tree species compared to localities so that it is likely
that there is a moderate level of host specificity related
to tree species. Stork (1987b) reported a low host
specificity from Bornean rain forest irees and suggested
that host specificity is less in the tropics compared to
temperate regions. Basset (1992b) reported a low host
specificity in contrast to Erwin"s findings from the
South Atneriea beetle fauna (Erwin 1982, 1983).
Despite the low host specificity in our study. Stork
(1987b) reported that taxonomic similarity of the trees
was the most important variable affecting species composition in his multivariate approach, stressing that
closely related tree species have a similar inseet fauna.
We could not find such an effeet in our study, probably
because all tree species sampled belong to one gentis. In
addition, we could not detect an iniluenee of any tree
related variables on inseet diversity, which parallels
some results of Stork (1987b), Moran (1980) and Lawton (1986) found a correlation between the architectural
complexity of the host plant and the number of phytophagous speeies. but we could not find such a correlation probably due to the similarity of the tree speeies
sampled. Basset (1992a) found in his multivariate approaeh that most of the variance could be attributed to
young foliage availability. He also stressed the importance of ant abundance as the main predators in insect
communities (Bassei 1996), Our findings support this
view. Ant biomass was a significant predictor of insect
diversity in three out of four approaches. A number of
bolh empirical and theoretical studies have found that
predation is a very important process in local communities (Jeffries and Lawton 1984, Bernays and Graham
1988. Cornell and Lawton 1992). In addition sampling
time seems to be important, indicating a diurnal pattern
in insect activity, and finally some eeological guilds.
Although the guild concept has drawbacks (Simberloff
1976. Stork 1987a) it provides an insight into community processes from a broader perspective.
Table 5. Results of the multiple regression wilh the residuals of alplia log series index as lhe dependent variable. The SE of the
esiimale is 2.629.
Variable
Intercept
Biomass res.
a biomass
Time
266
Coefficieni
t-value
p-value
Cumulative R"
16.547
0,708
-0.160
-1.487
3-393
5-086
5.830
2.371
0.003
0.0001
0.0001
0.027
0.533
0.671
0.793
LCOdllAI'HY
Table 6. Results of the miilliple regression with H' as the dependent variable. The SF of the estimate is 0.208.
Variable
Coefficient
t-value
p-value
Cumulative R
Intercept
e biomass
a biomass
s RTU
e RTU
3.299
-0.048
-0.008
0.031
0.108
17.102
4.655
3.476
2.382
2.226
0.0001
0.0001
0.002
0.027
0.037
0.278
0.511
0.601
0.677
Aeknowledgements - We tlumk J. D. Ismay. B. Levy. J.
Noyes. C. A. OToole and M. Robinson for their help with the
identifieation. S. Nee. M. J- Packer and S. .1. Simpson
provided useful comments on the manuscript. O. K. wiis
funded by the BBSRC and G. C. McG. was funded by the
Initiative.
References
Adis. J., Lubin, Y. D. and Montgomery, G. G. 1984.
Arthropods from the canopy of inundated and terra firme
forests near Manaus, Bnizil, with critical consideration of
the pyrethrum-fogging technique. - Stud. Neotrop. Kauna
Environ. 19: 223-236.
Allison, A., Samuelson, G. A. and Miller. S. E. 1993. Patterns
of beetle species diversity in New Guinea forest as revealed
by canopy foggint;: preliminary ftndinus.
Selbyana 14:
16 20.
Basset, Y. 1991. The taxonomic composition of the arthropod
fauna associated with an Australian rainibrest tree. Aust. J. Zool. 39: 171-190.
- 1992a. Influence ol' leaf traits on the spatial distribution of
arboreal arthropods within an overstory rainforest tree.
Ecol. Hntomol. 17; 8 16.
- 1992b. Host specificity of arboreal and free-living inseet
herbivores in rain forests.
Biol. J. Linn. Soe. 47: 115
133.
- 1996. Local communities of arboreal herbivores in Papua
New Guinea: predictors of insect variables.
Ecology 77'
1906-1919.
- and Kilching. R, L. 1991. Species number, species abundance and body length of arboreal arthropods associated
with an Australian rainforest Iree.
Ecol. Lntomol. 16:
391 402.
Berger, W. II. and Parker, F- L. 1970. Diversily of planktonic
Foraminifera in deep sea sediments. - Science 168: 134.51347.
Bcrnays, E. A. and Graham. M. 1988. On the evolution of
host specifity in phytophagous arthropods.
Hcology 69:
886-892.
Bray, J. R. and Curtis. C. T. 1957. An ordination of the
upland tbrest communities of southern Wisconsin, - Ecoi.
Monogr. 27: 325 349Coe, M. 1995. A preliminary report on the field rescareh of
the Mkomazi Research Programme.
Royal Geographical Soc. London.
- and Beentje. H. 1991. A Iield guide to the acacias of
Kenya. - Oxford Univ. Press.
Cole. M. M. I9K6. The savannahs. Hiogeography and geobotany.
Academic Press.
Cornell, H. V. and Lawton. J. H. 1992. Species interactions,
local and regional processes, and limits to the richness of
ecological communities: a theoretical perspective. - J.
Anim. Ecol. 61: 1-12.
Commonwealth Scientific and Industrial Research Organisation Division of Entomology (CSIRO). 1991. The insects
of Australia: a textbook for students and research workers, 2nd cd. - Melbourne Univ. Press.
KCfXiRAPHY :i,3
Hrwin. T. L. 1982. Tropical forests: their riehness in
Coleoptera and other arthropod speeies.
Coleopt. Bull.
36: 74 75.
- 1983. Tropical forest canopies, the last biotic frontier. Bull. Entomol. Soc. Am. 29: 14-19.
Jeffries. M. J. and Lawton, J. H. 1984. Enemy-free space and
the structure of ecological communities.
Biol. .1. Linn.
Soc. 23: 269 286.
Huieheson. K. 1970. A test for comparing diversities based on
the Shannon formula. - .1. Thcor. Biol. 29: 151-154.
Lawton, J. II. 1986. Surface availability and insect community
structure: the effects of architecture and fraetal dimension
of plants.
In: Juniper. B. E. and Southwood, T. R. E.
(eds). Insects and the plant surface. Edward Arnold, pp.
317 322.
Lewinsohn. T. M. and Price, P. W. 1996. Diversity of herbivorous inseets and ecosystem processes. - In: Solbrig, O.
T., Medina. E. and Silva, P. (eds). Biodiversity and savannah ecosystem processes. Springer, pp. 143 157.
Magurran, A. E. 1988. beologieal diversity and its measurement.
Chapman and Hall.
Moran, V. C. 1980. Interaction between phytophagous insects
and their Opunita hosts. - Ecol. Entomol. 5: 153-164.
- and Southwood. T. R. E. 1982. The guild composition of
arthropod communities in trees.
J. Anim. Ecol. 51:
289 306.
Morse, D. R.. Stork, N. E. and Lawton. J. H. 1988. Species
number, species abundance and body length relationships
of arboreal beetles in Bornean lowland rain forest trees. Ecol. Hntomol. 13: 25 37.
Root, R. B. 1973. Organisation of a plant-arthropod assoeiation in simple and diverse habitats.
Ecol. Monogr. 43:
95-124.
Scholtz, C. H. and Holm, E. 1989. Insects of Southern .Africa.
- Butterworths, Johannesburg.
Simberloff. D. S. 1976. Trophic structure determination and
equilibrium in an arthropod community.
Ecology 57:
395 398.
Solbrig, O. T. 1996. The diversity of the savannah ecosystem.
In: Solbrig, O. T., Medina, E. and Silva. P. (eds).
Biodiversity and savannah ecosystem processes. Springer,
pp. I • 27.
Southwood, T. R. E. 1978. Kcological Methods. - Chapman
and Hall.
and Kennedy. C. E. J, 1983. Trees as islands. Oikos 41:
359-371.
Stork. N. E. 19K7a. Guild strueture of arthropods from
Bornean rain forest trees.
Ecol. Entomol. 12: 69 80.
1987b. Arthropod faunal similarity of Bornean rain forest
trees. - Ecol. Entomol. 12: 219 226.
1991. The eomposition of the arthropod fauna of Bornean
lowland rain forest trees.
J. Trop. Ecol. 7: i61-l80.
, Adis, J. and Didham. K. 1997. Canopy Arthropods. Chapman and Hall.
Taylor. I,. R. 1978. Bates. Williams, Hutchinson
a variety
of diversities. - In: Mound, L. A. and Waloff. D. (eds).
Diversity of insect faunas. Blackwell. pp. 1 IK.
West, C. 1986. Insect eommunities in tree canopies. - In:
Coe, M. (ed.). KORA: an ecological inventory of KORA
National Reserve. Royal Geographical Society. London
pp. 209-222.
267
Appendi\ 1. Allocation of inscci families lo guilds in ihis study.
Laxon
Ciiiilds
Tiixon
(iuilds
Taxon
Giiiids
Isotomidiic
S\mphypleona
Lepismatidae
Blaltidae
BlaUidae
Hymcn.spodidae
Eimpiisidac
s
S
Atlelabidac
Cisiidae
Dermestidae
Myeetophaiiidae
Tenebrionidae
Pliipiphoritlac
Sclei'idac
Monommidiic
Lycidac
Hisicridac
Ascilididac
Chloropidae
Doilichopodidac
Sepsidae
Cecidomyiidae
Ccrtatopogonidae
Pcripsocidae
Cicadcllidae
Alydidiic
Tingldae
Membracidae
Miridac
Psyllidac
AiUhoeoridae
Rcduviidae
Cixiidac
Isomctopidac
Lygaeidac
Carabidae
Cueujidae
Amhribidac
Cocci ncllidac
El ate rid ae
Ccrmabycidac
Brucidae
Mclyridae
Scolytidac
Straphyiinidac
Phalacridao
Nitidnlidac
Belhylidac
[ehneumonidiie
pc
s
pc
s
C"occoidea
Lcslanoidac
Flalidac
Fulgoroidca
.Aphid id ac
Meenoplidac
Pen I a to mid a e
Pyrrhocoridae
Hydromelridac
Dictypliaridac
PhUiCOthripidac
Thripidac
Phoridac
Cliironomidae
Muscidae
Tephrididac
Agromyzidae
Calliphoridae
Scenopinidae
Odiniidae
Scirariidae
liombiliidae
Drosophilidae
Lauxianidae
Conopidac
Pliitystomatidae
Ratopogonidae
Anthomyidae
Ormyridae
Dryinidac
Hliismidac
luirytomidac
Aphelinidat;
T r i e h 0 g ra 111 m a t i d ;i c
Proctopoidea
Sphecidac
Vespidac
Chiysididac
Toiymidac
Sapygidae
Lcucospidae
Megachilidae
lialictidac
Mulillidae
ps
ps
ps
ps
ps
ps
ps
ps
p
ps
p
pc
s
t
s
MaiUidiiC
tJrillidac
Aeridoidcii
humaslacidae
Grilloidca
Gryllidae
Tclogonidae
Pliasmalidac
Ilcmipsocidac
Liposcelidac
Psyllipsocidae
Klipsocidac
Psocidac
l.cpidopsocidac
liclopsoeidae
Moidcllidae
Scrap tiidac
Corylophidac
Bostiyfhidae
i'tiniclae
Anihicidae
Lcpidoplcra
["oimicidac
Braeonidae
liukiphidae
Ijicyrtidac
Cyripoidea
Seclionidae
Pleromalidae
Chrysopidac
Berolhidac
Manlispidac
Curculionidae
Biiprestidac
Clir\somelidac
Cryplophagidae
Anodiidac
Ciithridiidae
268
s
s
s
p
p
p
pc
pc
pc
pc
pc
pc
pf
e
e
c
0
c
c
c
pc
pc
s
pc
s
p
pn
a
pa
pa
pa
pa
pa. p
pa
P
P
P
pc
pc
s
s. pc
s
LUipclmidae
Chalcidae
unknown
pa
P
s
P
P
P
t. s. ps
p
s
ps
p
cs
ps
ps
ps
ps
ps
ps
p
p
ps
ps
ps
p
s
pc
p
pc
pc
pc
p
pc
p, s
pc
pc
pa
pa
pa
pa
ps, t
ps
s
p
s
s
pa
ps
s
pa
s
ps
s
pa
pa
pa
pa
pa
pa
pn
P
P
pa
pa
pa
pa
pa
pa
pa
ECOOR/\I'HY 21:3 (I'WS)