Macroecology of local insect communities

Acta Oecologica 21 (1) (2000) 21−28 / © 2000 Éditions scientifiques et médicales Elsevier SAS. All rights reserved
Macroecology of local insect communities
Oliver Krüger a*§, George C. McGavin b
Department of Zoology, University of Oxford, South Parks Road, Oxford OX1 3PS, UK.
Oxford University Museum of Natural History, Parks Road, Oxford OX1 3PW, UK.
* Corresponding author (e-mail: [email protected])
Received May 20, 1999; revised November 30, 1999; accepted December 8, 1999
Abstract — The inter-relationships between animal body weight, range size, species richness and abundance are currently the basis of
macroecology. Using 41 099 insects sampled from 31 Acacia tree canopies in north-east Tanzania, we first documented the basic
macroecological patterns. The relationship between body weight and both species richness and abundance was polygonal with the highest insect
species richness and abundance occurring at intermediate body weights. Across individual tree communities, the most statistically robust
relationships were found between range size, abundance and species richness and they were all linear. In a second part, we focused on the
positive abundance-range size relationship and we could test predictions of six of the eight proposed hypotheses to explain this widely
documented pattern of community structure. The relationship is most likely explained by the metapopulation hypothesis stating that with more
patches being occupied, local abundance in a given patch increases due to a higher rate of immigration from nearby patches. In addition, we
found high slopes for the species-area relationship, typical of island systems and thus it seems reasonable to characterise Acacia trees in the
savannah as habitat islands for insects. © 2000 Éditions scientifiques et médicales Elsevier SAS
Abundance / Acacia / body weight / insects / range size / species richness
Macroecology can be described as the study of
emergent patterns of assemblages of species distributed over geographical spatial scales and evolutionary
time scales [11, 21, 36]. These patterns are frequently
characterised by four variables: body weight, range
size, species richness and abundance [2-6, 12-16,
18-20, 22-25, 28, 34, 37, 44, 45, 52, 53, 58, 59].
Among the possible combinations of these variables, a
central role is played by the three interspecific relationships between abundance, range size and body size
(see [21] and references therein) and enormous
progress has been made concerning bivariate relationships. Over 300 interspecific species-body size,
500 interspecific abundance-body size and well over
100 abundance-range size relationships have been
published (however, only around 25 studies reported
Present address: Department of Animal Behaviour, University of
Bielefeld, Postfach 100131, 33501 Bielefeld, Germany.
on the range size-body size relationship, see [21]).
These relationships are not only of interest to macroecologists but a deeper understanding of the responsible mechanisms would greatly enhance our understanding of the causes of rarity and commonness and
thus are a helpful tool for all conservationists
alike [21]. However, there is an enormous bias in the
publication record. Over 75 % of abundance-range
size reports come from North America and Europe and
over 75 % of abundance-body size reports focused on
vertebrates only so that the regions and taxa of high
species richness are currently very much understudied.
Thus, although documenting these patterns can be
regarded as ‘stamp-collecting’ [21], we cannot be sure
that the reported patterns also apply in the tropics and
to more speciose taxa such as insects.
The widespread use of knockdown methods and
other techniques to collect arthropods has resulted in a
number of large data sets which have been analysed
with regard to the relationships between body weight,
species richness and abundance [1, 48, 50, 54, 56].
These data are very suitable for analysis, since they
cover a wide range of taxonomic orders, body types
and ecological niches [47, 56] and can provide insights
into community ecology. Range size, however, has
rarely been included since most studies were conducted at one or a few sites only so that no data on
local range sizes could be collected (but see [54] for an
example concerning insects).
Here we analyse the local insect communities associated with Acacia tree canopies in Mkomazi Game
Reserve, north-east Tanzania, with regard to the relationships between body weight, range size, species
richness (total number of species) and abundance.
First, we look at the entire community where all
samples were combined. Second, we divide the community into body weight classes and explore the
inter-relationships between the four variables across
body weight classes, because body weight is commonly regarded as the variable with the biggest
predictive power in macroecology [2]. Third, we compare communities across trees and focus on one
variable combination, the abundance-range size relationship. This, very often positive, relationship is of
greatest importance in times of great biodiversity loss
since it ultimately asks why certain species have a low
extinction risk (widespread and abundant) while others
exhibit a high risk (localised and rare). Eight different
hypotheses have been proposed to account for the
relationship [24]. They are not mutually exclusive [21]
and a test of their predictions using a single assemblage has rarely been made. Two regard the relationship as artefactual (sampling artefact [10] and artefact
of phylogeny [31]) and six put forward a biological
explanation (range position [8], breadth of resource
usage [10], resource availability [29], habitat selection [51], metapopulation [29] and vital rates [33]).
We do not test two hypotheses (range position and
vital rates) since they require either large scale geographical range size data or birth and death rates.
Sadly, there is no sufficient data for Afrotropical
insects. Instead, we focus on the two artefactual as
well as four biological hypotheses (breath of resource
usage, resource availability, habitat selection and
metapopulation) because they can be reasonably tested
using regional scale data. We explicitly test their
predictions in order to see which is most likely to
explain the relationship in our data.
Thirty-one Acacia trees of six species (A. etbaica,
mellifera, nilotica, reficiens, senegal, tortilis) were
sampled in Mkomazi Game Reserve, north-east Tan-
O. Krüger, G.C. McGavin
zania, between 30 December 1995 and 18 January
1996 at nine localities between 50 m and 31 km apart
(for a detailed basic data breakdown, see [40]). The
area is a semi-arid savannah with a pronounced dry
season and mean rainfall between 300 and 900 mm.
Insects were sampled using the mist-blowing
technique [39-41] using a fast-acting insecticide
(Pybuthrin 216). Insects were collected in 1-m2
funnel-shaped trays, suspended between stakes, over a
standard drop-time of 1 h. The material was identified
to family level by specialists and then morphotyped
(RTU, in the following species). Body length was used
to calculate dry biomass, using an allometric formula
given by Moran and Southwood [47]. To avoid overestimation of species richness, larvae were excluded
from the analysis, so that underestimation is more
likely. The lightest species was taken as the starting
point and other species were grouped into body weight
octaves which represent a body weight doubling. For
each tree, geometric mean body weight of all species
present, mean range size (proportion of trees where a
species was found averaged for all species on a tree),
species richness (total number of species found) and
abundance (ind⋅m–_) were calculated. Species abundances include only those trees where a species
occurred. We used partial correlation analysis with
P < 0.05 as a significance threshold to see which
variables were most likely significantly correlated with
each other. Because of sampling errors, relationships
cannot be analysed using ordinary least square regression which is considered to be unreliable [27, 46].
Instead, the reduced major axis method is strongly
recommended [27, 46] and was applied. We normalised the binomial distributed range size variable by
arcsine-transformation and statistical analysis was
done in SPSS and STATISTICA.
3.1. Macroecological patterns in the entire
We obtained 41 099 insects of 133 families and
492 species, which is estimated to be around 77 % of
the true richness of the 31 trees (for a detailed taxonomic breakdown of the current data set, see [39]).
The inter-relationships between body weight, abundance and range size (figure 1) indicate that body
weight has weak negative relationships with abundance (figure 1A) and range size (figure 1B). The
Acta Oecologica
Insect macroecology
Figure 1. Relationship between body weight and mean density for all species (A); relationship between body weight and range size (B);
relationship between range size and mean density (C); relationship between range size and density for those species for which at least twenty
individuals were sampled (D).
abundance-range size relationship, in contrast, was
strong and positive (figure 1C). The relationship
between body weight and abundance was weakly
significant when range size was controlled for (partial
r = –0.092, df = 489, P = 0.041) while both the body
weight-range size and abundance-range size relationships were highly significant (partial r = –0.171,
df = 489, P < 0.0001, controlling for abundance and
partial r = 0.682, df = 489, P < 0.0001, controlling for
body weight, respectively). Sampling inevitably introduces errors, so we performed a control analysis where
only those species were included of which at least
twenty individuals were sampled (n = 133 species or
27 % of the total). Body weight was not significantly
partially correlated with abundance (r = –0.045,
df = 129, P = 0.611) but negatively with range size
(r = –0.275, df = 129, P < 0.001). The abundanceVol. 21 (1) 2000
range size relationship was still highly significant and
positive (figure 1D, r = 0.482, df = 129, P < 0.0001).
After grouping species into body weight classes,
body weight and both species richness and abundance
had a non-linear relationship with peaks at intermediate body weights (figure 2A, B). The same was found
for the four main insect orders (Hemiptera,
Coleoptera, Diptera and Hymenoptera) for species
richness and abundance with the exception of Diptera,
where the relationship between abundance and body
weight was linear (see [38]). To see which other
inter-relationships were strongest, partial correlation
analysis was performed and the results are given in
table I. Body weight was not significantly correlated
with any of the other three variables while these were
strongly inter-correlated.
O. Krüger, G.C. McGavin
expected between different tree species and sampling
localities due to host-specificity or spatial heterogeneity. However, two-way ANOVA showed that this was
not the case (table II). None of the four variables
exhibited significant differences between tree species,
sampling localities and there was also no significant
interaction of the two factors. In addition, host specificity does not play an important role, indicated by
cluster analysis (see [40]). It is therefore reasonable to
look for correlations among the variables across the
whole number of trees sampled. Results of partial
correlation analysis are shown in table III and were
similar to the results of the body weight classes
analysis. Geometric mean body weight was not correlated with species richness and abundance but negatively with range size. The inter-relationships between
range size, species richness and abundance were all
highly significant. Lack of significant interactions
between body weight and both species richness and
abundance was not caused by a non-linear relationship. Thus, mean body weight was not a significant
predictor across trees except for range size which
decreased with body weight.
3.3. Abundance-range size relationship
Figure 2. Relationship between RTU richness and log2 body weight
classes (A), fitted by a polynomial regression (R2 = 0.970, df = 14,
P < 0.0001); relationship between abundance and log2 body weight
classes (B), fitted by a polynomial regression (R2 = 0.763, df = 14,
P < 0.001).
3.2. Macroecological patterns across trees
In this section, the 31 tree communities will be
compared. Differences in the four variables might be
Table I. Partial correlations between body weight classes (log2),
mean range size, species richness and abundance (df = 15). The two
left-out variables are controlled for, P-values are in parenthesis.
Mean range
Body weight
–0.129 (0.621)
Mean range size
Species richness
0.282 (0.272)
–0.745 (0.001)
–0.303 (0.237)
0.827 (0.001)
0.939 (0.001)
In this section, we test predictions of the six selected
hypotheses in order to see which is most likely for our
1) Sampling artefact. Rare species, although potentially widespread, are rarely sampled and their ranges
are consequently underestimated. This is unlikely to be
the causal factor for our data for two reasons. First, it
can be estimated that a good proportion (77 %) of the
true species richness has been sampled. Second and
more important, restricting the analysis to those species for which at least twenty individuals were
sampled also provides a positive relationship
(figure 1D); even if only species for which at least fifty
individuals were sampled (n = 68 species or 13.8 % of
the total) are included, the positive relationship still
holds (r = 0.395, df = 66, P < 0.01). Thus underestimation of range size due to sampling bias alone cannot
account for the relationship in our data. In the following, we use the restricted data set (> 20 ind. sampled)
for hypotheses testing to minimise the effect of sampling bias on the outcome of the tests.
2) Artefact of phylogeny. Because of their phylogenetic relatedness, species are not independent data
points. If taxonomic units differ significantly in range
size or abundance, a cross-species correlation might be
significant because of phylogeny and not ecology.
There was no significant difference between the fourteen insect orders in mean range size (F13,119 = 1.234,
P = 0.264) but there was one in abundance
(F13,119 = 5.377, P < 0.0001). As an explicit, although
Acta Oecologica
Insect macroecology
Table II. Two-way ANOVA for the four macroecological variables between tree species sampled, between sampling localities and for the
interaction of the two factors.
Tree species
Mean body weight
Mean range size
Species richness
admittedly crude test, we used independent contrast
analysis at the order level based on a phylogenetic
tree [9]. Contrasts in range size were positively correlated with contrasts in abundance (figure 3A, r = 0.751,
df = 11, P < 0.01). The relationship is thus unlikely to
be a phylogenetic artefact which has been found in
other studies [20, 23, 25, 49, 54].
3) Breadth of resource usage. Wide ranging species
have higher densities because they use a wider range
of resources compared to narrow ranging species
which should be more specialised. This hypothesis
makes three predictions of which one can be tested
here, although on a rather crude level. Species occupying fewer and fewer sites should have narrower and
narrower niche breadths [24]. To obtain an estimate of
niche breadth, we calculated on how many different
tree species an insect species was present, assuming
that different tree species reflect in some way niche
breadth. We only used the restricted data set (> 20 ind.
sampled) because a species sampled only once is
constrained to be found on one tree. For the restricted
data set, this constraint does not play a major role. A
positive correlation between this niche breadth measure and range size and, more important, abundance,
would support the prediction. Indeed there were highly
significant positive correlations between the niche
breadth measure and both range size (r = 0.726,
df = 131, P < 0.0001) and abundance (r = 0.209,
df = 131, P = 0.016). At a crude level, there is support
for this hypothesis in our data.
Table III. Partial correlations between geometric mean body weight,
mean range size, species richness and abundance across the 31 trees
studied (df = 26). The two left-out variables and sample size were
controlled for, P-values are in parenthesis.
Range size
Body weight
–0.439 (0.019)
Range size
Species richness
Vol. 21 (1) 2000
Species × Locality
Sampling locality
–0.104 (0.599)
–0.772 (0.001)
0.239 (0.221)
0.394 (0.039)
0.665 (0.001)
Figure 3. Relationship between independent contrasts in range size
and abundance for the fourteen insect orders present in this study (A);
regression was forced through the origin [17] but is still significant
(R2 = 0.433, df = 11, P < 0.05). Relationship between range size and
abundance for those species for which at least twenty individuals
were sampled in comparison with species of Diptera and
Hymenoptera for which at least twenty individuals were sampled (B).
Slopes are 41.4 for the entire community, 58.8 for Diptera and 54.6
for Hymenoptera.
4) Resource availability. Wide ranging species are
abundant because they use abundant and widespread
resources. This hypothesis makes two predictions
amenable for testing. First, species that share a common resource base should not show a positive
abundance-range size relationship. We used the phytophagous sapsuckers in the order Hemiptera with
sampling > 20 individuals to test this because their
food niche can be considered to be very common on
Acacia trees. They should have, at least, a much
weaker abundance-range size relationship compared to
the entire community since they comprise a clearly
defined ecological guild within a clearly defined taxonomic unit. However, the relationship is stronger
compared to the entire community (b = 0.024 and
r = 0.515 for the community and b = 0.022 and
r = 0.780 for hemipteran sapsuckers). Second,
resource specialists can be abundant and widespread.
Support would be no positive relation between niche
breadth and range size and abundance. Since we found
positive correlations, this predictions is not supported
5) Habitat selection. Density-dependent habitat
selection produces the abundance-range size relationship. A further development [43] leads to a testable
prediction which is that the slope of the abundancerange size relationship should be proportional to the
number of resources. We tested this by using tree
species as surrogates for resources; slopes should be
lower for the species set found on one tree species
compared to those found on more or all tree species.
This is not the case for the restricted data set. Species
found on one tree species exhibit an abundance-range
size relationship with a slope of –0.076, species found
on two tree species have a slope of 0.047, for three tree
species the slope is –0.094, for four tree species it is
–0.060, for five it is 0.030 and species found on all six
tree species have a slope of 0.029. The proposed
proportionality thus does not hold for our data.
6) Metapopulation hypothesis. The rescue effect
proposes that immigration decreases the probability of
local population extinction and that the immigration
rate for a given habitat increases with the proportion of
habitats occupied. Among five predictions of this
hypothesis, two can be tested. First, there should be a
positive abundance-range size relationship even where
there is no difference in niche breadth. In line with this
prediction, there was a highly significant correlation
between abundance and range size (restricting the
analysis to species with sampling > 20 individuals
which were found on all trees r = 0.797, df = 25,
P < 0.0001). Second, species with high dispersal rates
should have a wider range size for a given abundance
compared to less dispersive species. Figure 3B shows
the abundance-range size relationship for the commu-
O. Krüger, G.C. McGavin
nity and two more dispersive orders such as Diptera
and Hymenoptera using the restricted data set. In order
to quantify differences, we used the reduced major axis
slope for the entire community and compared it with
the ones of Diptera and Hymenoptera (figure 3B). For
a given abundance of 1.0 ind⋅m–_, range size is 53.4 %
for the entire community, 65.3 % for Diptera and
79.3 % for Hymenoptera. For Hymenoptera, the value
is significantly larger compared to the community
(Fischer’s exact test: t = 2.560, df = ∞, P < 0.02) while
there is a trend for Diptera (t = 1.805, df = ∞,
P < 0.10). The metapopulation hypothesis is thus well
supported by our data.
Our data set is one of the first examples where
Afrotropical insect data was usable for testing and
distinguishing between macroecological hypotheses
(see [47] for another such example, and [54] for a
temperate region). It is also one of the largest samples
ever collected from a tropical savannah habitat. Therefore the taxonomic broadness as well as the large
sample size makes it suitable for the kind of analysis
we have performed here. However, it has been clearly
emphasised that broad generalisations from the analysis of a single assemblage should be avoided [21].
Here we just want to point out the differences and
similarities to other studies.
The results are in line with the reported patterns in
macroecology in some aspects but not in others.
Non-linear relationships between body weight and
both species richness and abundance are common
within taxa [3, 4, 7, 12, 14, 16, 20, 35, 48, 56].
Although RTUs are not synonymous with species, it is
unlikely that errors during the morphotyping process
are responsible for this relationship. The 23 % of
RTUs estimated to be absent in the samples might
constitute a biased subset [2], possibly towards smaller
organisms, but this would not change the bell-shaped
patterns reported here qualitatively.
The polygonal negative relationship between body
weight and abundance is frequently reported from
local studies [5] while regional or continental studies
mainly reported linear negative relationships. The
slope of –0.5 is in good agreement with Blackburn and
Gaston’s findings [5] who reported a mean slope of
–0.51 from 291 studies and the results of Griffiths [27]
who proposed a broad general negative relationship
with a slope around –1.
In contrast to most other studies, the relationship
between body weight and range size was linear and
negative instead of positive. It has been pointed
Acta Oecologica
Insect macroecology
out [19] that local studies more frequently report
negative relationships and that these might involve
artefacts, so the result should be treated with caution.
The strongest relationships, however, in the entire
community and across trees, are those between range
size, species richness and abundance which has been
found elsewhere [20, 49].
With regard to the abundance-range size relationship, among the six hypotheses for which predictions
could be tested, the metapopulation hypothesis is best
supported and there is also some support for the
breadth of resource usage hypothesis. The other four
(sampling artefact, phylogeny, resource availability,
habitat selection) are unlikely to explain the relationship for our data. It has been shown [30] that a positive
relationship can be expected a priori on the basis of
random distribution of species. Nevertheless Gaston et
al. [26] point out that it is difficult to predict a priori
what form the relationship should take and that random draw alone cannot account for it.
The metapopulation hypothesis makes intuitive
sense for this habitat where scattered trees in the
savannah might be habitat islands. In addition,
species-area relationships (area measured as sampling
area in m_) in the community and for the four most
speciose taxa show exponents around 0.35 [39], typical of island systems [28, 42, 55]. But slopes are also
inflated by sampling errors [32], so their high exponents are questionable. However, the support for the
metapopulation hypothesis explaining the abundancerange size relationship and the slopes of the speciesarea curves are two pieces of evidence that trees in the
savannah can be characterized as habitat islands for
insects (but see [57]).
We thank J.D. Ismay, B. Levy, J. Noyes, C.A. O’Toole and M.
Robinson for insect identification. K.J. Gaston, I. Hanski, J.H.
Lawton, S. Nee, M.J. Packer, S.J. Simpson and W. Traunsburger
provided comments on earlier drafts of the manuscript. O. Krüger
was funded by the BBSRC and the German National Scholarship
Foundation and G.C. McGavin by the Darwin Initiative.
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