Effects of habitat complexity on forest beetle diversity: do functional groups respond consistently?


  • Scott A. Lassau,

    Corresponding author
    1. Institute of Wildlife Research, School of Biological Sciences, Heydon-Laurence Building A08, The University of Sydney, NSW 2006, Australia and
    2. Centre for Biodiversity and Conservation Research, The Australian Museum, Sydney, NSW 2010, Australia
      Correspondence: Scott A. Lassau, Centre for Biodiversity and Conservation Research, The Australian Museum, Sydney, NSW 2010, Australia. Tel.: (61–2) 9320–6348; Fax: (61–2) 9361 5479; E-mail: scottl@austmus.gov.au
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  • Dieter F. Hochuli,

    1. Institute of Wildlife Research, School of Biological Sciences, Heydon-Laurence Building A08, The University of Sydney, NSW 2006, Australia and
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  • Gerasimos Cassis,

    1. Centre for Biodiversity and Conservation Research, The Australian Museum, Sydney, NSW 2010, Australia
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  • Chris A. M. Reid

    1. Centre for Biodiversity and Conservation Research, The Australian Museum, Sydney, NSW 2010, Australia
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Correspondence: Scott A. Lassau, Centre for Biodiversity and Conservation Research, The Australian Museum, Sydney, NSW 2010, Australia. Tel.: (61–2) 9320–6348; Fax: (61–2) 9361 5479; E-mail: scottl@austmus.gov.au


We examined the responses of a beetle assemblage to habitat complexity differences within a single habitat type, Sydney sandstone ridgetop woodland, using pitfall and flight-intercept trapping. Six habitat characters (tree canopy cover, shrub canopy cover, ground herb cover, soil moisture, amount of leaf litter, and amount of logs, rocks and debris) were scored between 0 and 3 using ordinal scales to reflect habitat complexity at survey sites. Pitfall trapped beetles were more species rich and of different composition in high complexity sites, compared with low complexity sites. Species from the Staphylinidae (Aleocharinae sp. 1 and sp. 2), Carabidae (Pamborus alternans Latreille), Corticariidae (Cartodere Thomson sp. 1) and Anobiidae (Mysticephala Ford sp. 1) were most clearly responsible for the compositional differences, preferring high complexity habitat. Affinities between general functional groupings of pitfall-trapped beetles and habitat variables were not clear at a low taxonomic resolution (family level). The composition and species richness of flight-intercept-trapped beetles were similar in high and low complexity sites. Our study demonstrates that discrete responses of the various functional groups of beetles are strongly associated with their feeding habits, indicated by differing habitat components from within overall composite habitat complexity measures. Although habitat preferences by beetle species may often reflect their foraging habits, clarification of the causal mechanisms underpinning the relationships between habitat complexity and beetles are critical for the development of general principles linking habitat, functional roles and diversity.


The heterogeneity of natural landscapes contributes to difficulties in ascertaining and quantifying the generality of patterns in nature (Lawton, 1999). Habitat complexity is generally positively associated with the richness of fauna at a range of spatial scales (Uetz, 1979; August, 1983; Huston, 1994; Catling & Burt, 1995; Humphrey et al., 1999; Hansen, 2000). Degrees of habitat complexity in forests may also affect the composition of fauna assemblages (Gardner et al., 1995; Lassau & Hochuli, 2004). Despite the immense amount of literature describing the effects of habitat complexity on fauna (review: Tews et al., 2004), ‘empirical support is almost restricted to studies of vertebrate communities and habitats under anthropogenic influence’ (Tews et al., 2004). Considering their role in natural systems, arthropods are important model taxa for habitat dependent community comparisons. Measures of habitat complexity, integrated with comparative biodiversity levels, may provide land managers with useful insights for making informed decisions regarding biodiversity and conservation.

Beetles are the largest order of insects and occupy a vast array of environments. They affect local communities by various roles in food webs, litter decomposition, and nutrient flow. The functional significance of beetles is reflected in their diversity of foraging behaviour, and they may act as detritivores, herbivores, fungivores or predators (Lawrence & Britton, 1994). Studies of relationships between beetles and habitat characteristics have been focused on either a single family (generally Carabidae) (Kholin, 1993; Tonhasca, 1993; Bortmann, 1996; Niemela et al., 1996; Fournier & Loreau, 1999; Ings & Hartley, 1999; Bonn et al., 2002; Brose, 2003a, 2003b; Jeanneret et al., 2003a, 2003b), disturbed environments (Dangerfield, 1990; Tonhasca, 1993; Bortmann, 1996; Colunga-Garcia et al., 1997; Dennis et al., 1998, 2002; Braendle et al., 2000; Watts & Gibbs, 2002) or differing habitats (Thomas, 1983; Dangerfield, 1990; Ås, 1993; Colunga-Garcia et al., 1997; Braendle et al., 2000; Romero-Alcaraz & Avila, 2000). There remains ‘a significant lack of studies that consider multiple spatial scales and species groups within one ecosystem’ (Tews et al., 2004). To our knowledge, there have been no comparative studies describing overall beetle species richness and composition patterns based on habitat complexity within an undisturbed forest system. Observing patterns in habitat relatively free from anthropogenic impacts will inform conservation management practices, such as habitat restoration.

The microhabitat preferences of the functional groups of beetles are probably most strongly influenced by their feeding habits. Tree canopy cover, shrub canopy cover, and ground herb cover measure the density of vegetation strata, which forms the basis for defining habitat complexity in some studies (August, 1983). Differences in these variables may induce responses from beetle communities through predator-prey relationships stemming from predator foraging success (Bartholomew et al., 2000). The above variables, as well as soil moisture (Dennis et al., 2002), may also affect arthropod distributions because of differing substrate microclimates (e.g. Hölldobler & Wilson, 1990). Variation in the amounts of leaf litter, logs, rocks, and debris is likely to have the most profound effect on ground-dwelling beetles, influencing refuge from predation and facilitating foraging both directly and indirectly. Saproxylic arthropods are those which are, in some stage of their life-cycle, dependent on dead wood, or wood-inhabiting fungi (Schiegg, 2000). Saproxylic species may also directly rely on other species within these systems, and are therefore vulnerable to patches free from dead or dying plant matter (Schiegg, 2000). A combination of the above variables as overall habitat complexity scores is likely to have a major impact on beetle community patterns.

Pitfall traps and flight-intercept traps are designed to collect a majority of ground-dwelling and flying insect species, respectively, and both trapping methods rely on the movement of animals (Southwood, 1978). Flight allows a greater distance to be covered than walking for equal energy cost, therefore we expect species sampled by flight-intercept traps to be more ‘spatially mobile’, and the effects of habitat complexity (if any) to be less pronounced (Chust et al., 2004). In this study, we examined the responses of beetles to naturally contrasting habitat complexity in a single sandstone forest type, with particular reference to functional roles. We tested the following hypotheses: (1) there is greater species richness and abundance of beetles in sites with high habitat complexity than low habitat complexity; (2) high habitat complexity sites have different species composition of beetles from low habitat complexity sites; (3) the above associations are more pronounced for assemblages of ground-dwelling beetles sampled using pitfall traps, than volant assemblages sampled by flight-intercept traps; and (4) responses of beetle functional groups to habitat characters strongly reflect their feeding habits.


Survey design

We selected 14 study areas in open forest approximately 20 km north of Sydney (outlined in greater detail by Lassau & Hochuli, 2004). Each study area contained a pair of high and low complexity sites situated within 50 m of one another, resulting in 28 sites in 14 areas dispersed across approximately 2500 hectares. Each site was defined by a 15 m × 15 m quadrant. The sites were all located in areas classified as belonging to the vegetation unit ‘Sydney Sandstone Ridgetop Woodland’, a structurally diverse component of the Hawkesbury Sandstone vegetation characterized by a dominant canopy of Eucalyptus gummifera and Eucalyptus haemastoma, with a rich sclerophyllous shrubby understorey with many species of Proteaceae, Fabaceae, Epacridaceae and Myrtaceae (Benson & Howell, 1994). Habitat complexity was assessed using six habitat characters (tree canopy cover; shrub canopy cover; ground herb cover; soil moisture; amount of leaf litter; and amount of logs, rocks, and debris), scored between 0 and 3 using ordinal scales (increasing composite scores denotes comparatively greater habitat complexity), a composite scoring system designed by Newsome & Catling (1979). This method is reviewed by Coops & Catling (2000). The habitat variables we scored have often been used to derive measures of habitat structure and complexity in terrestrial systems (Hatchwell et al., 1996; Coops & Catling, 1997; Ecke et al., 2002; Rompola & Anderson, 2002; Williams et al., 2002) and may be useful as a predictive tool for small mammals (Catling & Burt, 1995) and arthropods (Lassau & Hochuli, 2004, 2005).

The sites were allocated to treatments a priori, after visual inspection. Fourteen sites were categorized as low complexity after scoring 8 or less and fourteen as high complexity, scoring 11 or greater (for representative photos see Lassau & Hochuli, 2004). The high complexity sites had a greater abundance of dominant plant species, and more dead vegetation on the forest floor. However, the components causing differences in scores between high and low complexity habitats were not consistent among sites.

Sampling methods

Beetles were sampled using five pitfall traps and a flight-intercept trap at each site during December 2000 and January 2001, with one pitfall trap in the middle of each site, and one at each corner. Pitfall traps were 9 cm in diameter and contained ethylene glycol. These traps were set flush to the ground surface, all opened on the same day and collected after 28 days. The flight-intercept traps were constructed using a rectangular black mesh (1 m × 1.5 m), and a half piece of PVC piping attached across the base to act as a catching tray. The top of each trap was fastened to a tree and, using rope, pegged to the ground to limit movement on windy days. The trough of the flight-intercept traps was suspended approximately 1 m off the ground. Flying beetles connected with the mesh and fell into the catching trough containing ethylene glycol. One flight-intercept trap was suspended for a period of 11 days as close as possible to the middle of each site. Beetles were identified to family (Lawrence & Britton, 1994), then each individual was assigned a species or morphospecies.

Statistical analyses

Differences in the species richness of beetles at high and low habitat complexity sites were examined using single-factor anova. Individual-based rarefaction curves within each treatment were created using EcoSim (Gotelli & Entsminger, 2001). Correlations between beetle species richness and individual habitat variable scores were tested using Spearman rank correlations, which were also used for testing correlations among habitat variables. Pitfall-trapped beetle responses to habitat variables were examined using canonical correspondence analysis (CCA) and biplots, which were generated using canoco (Ter Braak & Smilauer, 1998). A biplot representing the relationships between all beetle families with an abundance of 30 or greater individuals and site habitat variables was initially constructed. Subfamilies of the Staphylinidae were also examined more closely because this was the most abundantly trapped beetle family, and there is a comparatively high variation of trophic functions within the group (Lawrence & Britton, 1994). The statistical significance of the canonical axes were tested together using Monte Carlo permutations (199 permutations), testing the null hypothesis that, given any covariables, there is no relationship between beetle family/subfamily abundances and habitat variables (Ter Braak & Smilauer, 1998).

The differences in composition of beetle assemblages between areas of low and high habitat complexity were assessed as follows. We constructed a Bray-Curtis dissimilarity matrix of our beetle species data using a fourth root transformation to allow a more equal contribution of rare species (Clarke, 1993). Non-standardized data were used to preserve site-specific characteristics and responses. A two-dimensional ordination was plotted using nonmetric multidimensional scaling (nMDS), and an Analysis of Similarities (anosim) was performed with 999 permutations. Similarity Percentages (simper) were calculated, to determine which species contributed most to the significant differences between treatment types (Anon, 2001).


Comparison of trapping techniques

Eight hundred and thirty-five individuals of 126 species of 29 beetle families were collected in pitfall traps and 979 individuals of 271 species of 47 families in flight-intercept traps (Table 1). The Staphylinidae contributed the greatest proportion of species richness (32%, 24%) and abundance (43%, 31%) in both pitfall and flight-intercept traps, respectively (Table 1). The composition of beetle species trapped in pitfalls differed substantially from that trapped in flight-intercept traps (anosim, Global R = 0.81, P < 0.001). Twenty-one families were only collected by flight-intercept traps, most notably seven species of Eucnemidae, and three families were only trapped using pitfalls (Table 1).

Table 1.  Species richness and abundance of beetle families in pitfall and flight-intercept traps
FamilyPitfall trapsIntercept traps
Species richnessAbundanceSpecies richnessAbundanceTotal species (and number of species in common)
Aderidae  2  3 16 33 17 (1)
Anobiidae  2 53 16117 16 (2)
Anthicidae  2  6  1 11  2 (1)
Anthribidae  1  1  1  1  2 (0)
Archaeocrypticidae  1  2  0  0  1 (0)
Belidae  0  0  1  1  1 (0)
Bolboceratidae  0  0  1  1  1 (0)
Bostrichidae  0  0  1  1  1 (0)
Buprestidae  0  0  1  1  1 (0)
Cantharidae  0  0  4  7  4 (0)
Carabidae  6 82  2  9  8 (0)
Cerambycidae  1  1  2  4  3 (0)
Cerylonidae  0  0  1  1  1 (0)
Chrysomelidae  3  3 17 23 18 (2)
Ciidae  1  4  3  4  4 (0)
Clambidae  0  0  1  5  1 (0)
Cleridae  0  0  2  2  2 (0)
Coccinellidae  1  5  6  7  7 (0)
Corticariidae  3134  8 24  9 (2)
Corylophidae  2  2  5 15  6 (1)
Curculionidae  9 30 17 25 25 (1)
Dermestidae  0  0  9 36  9 (0)
Elateridae  5  7 10 29 12 (3)
Eucnemidae  0  0  7 10  7 (0)
Histeridae  0  0  1  2  1 (0)
Hydraenidae  0  0  1  1  1 (0)
Hydrophilidae  1  1  0  0  1 (0)
Laemophloeidae  1  1  1  1  2 (0)
Languridae  1  1  1  1  2 (0)
Leiodidae  4 53  6 21  7 (3)
Lucanidae  0  0  1  2  1 (0)
Lymexylidae  0  0  1  1  1 (0)
Melandryidae  0  0  1  1  1 (0)
Melyridae  1  1  5 10  5 (1)
Mordellidae  3  4  9 67 10 (2)
Mycteridae  0  0  1  3  1 (0)
Nitidulidae  6 11  3  8  8 (1)
Phalacridae  0  0  4  5  4 (0)
Ptiliidae  3 11  4 11  5 (2)
Rhipiphoridae  0  0  2  4  2 (0)
Scarabaeidae 13 38 17 57 25 (5)
Scraptiidae  0  0  1 19  1 (0)
Scydmaenidae  8 14  7 47 12 (3)
Silphidae  1  1  0  0  1 (0)
Silvanidae  0  0  1  1  1 (0)
Sphindidae  1  4  1 28  2 (0)
Staphylinidae 40356 65301 85 (20)
Tenebrionidae  3  5  2 12  5 (0)
Throscidae  0  0  2  7  2 (0)
Zopheridae  1  1  2  2  3 (0)
Totals126835271979347 (50)

Effects of habitat complexity on beetle assemblages

Species richness (anova, F1,26 = 16.1, P < 0.0005) (Fig. 1a) and abundance (anova, F1,26 = 18.5, P < 0.0005) (Fig. 1b) of pitfall-trapped beetles were greatest in high complexity habitat. Sixty-two percent of the beetle species found in low complexity sites were singleton captures, compared with 42% in high complexity sites. Rarefaction curves (Fig. 2a) indicate there may be a higher species diversity of pitfall-trapped beetles in low complexity habitat. However, the species richness of pitfall-trapped beetles was positively associated with the tree canopy cover (rs = 0.63, n = 28, P < 0.0005), leaf litter (rs = 0.45, n = 28, P < 0.02), ground herb cover (rs = 0.47, n = 28, P < 0.02) and soil moisture (rs = 0.43, n = 28, P < 0.05). Among habitat variables, soil moisture was positively associated with; tree canopy cover (rs = 0.59, n = 28, P < 0.001), shrub canopy cover (rs = 0.40, n = 28, P < 0.05) and ground herb cover (rs = 0.47, n = 28, P < 0.05), and ground herb cover was positively associated with tree canopy cover (rs = 0.45, n = 28, P < 0.05). There was a significant relationship (33% variation in the data explained, P = 0.005) between beetle families and site habitat variables (Fig. 3). The CCA biplot shows that the Carabidae were closely associated with the amount of ground herb and logs, rocks and debris, as were the Leiodidae which were also influenced by the amount of leaf litter. Tree canopy cover greatly influenced Scarabaeidae abundance. The abundance of the Anobiidae, although mostly dead wood feeders (Table 2), was negatively associated with the cover of ‘logs, rocks, and debris’. The Corticariidae, Curculionidae and Staphylinidae are plotted close to the centre of the biplot (Fig. 3), suggesting their abundance may be associated with a number of habitat characteristics, or they may be generalists. The subfamilies of the Staphylinidae were significantly related (42% variation in the data explained, P = 0.005) with site habitat variables (Fig. 4). There was little evidence for any habitat preferences for the predatory staphylinids, but the biplot indicates that the detritivores (Oxytelinae) preferred habitat with more leaf litter, and the fungus feeders (Scaphidiinae) preferred habitats with more ground herb and greater tree canopy coverage (Fig. 4).

Figure 1.

Pitfall-trapped (a) species richness and (b) abundance, and flight-intercept-trapped (c) species richness and (d) abundance of beetles in low and high complexity habitat (± SE). ***(anova, P < 0.0005).

Figure 2.

Rarefaction curves of beetles trapped using (a) pitfall and (b) flight-intercept traps.

Figure 3.

CCA ordination, showing the distribution of the more abundant beetle families (> 30 individuals), with respect to six measured habitat variables (▪= predator, ▴= dead wood feeder, ○= herbivore, □= fungus feeder, •= detritivore).

Table 2.  Abundance of pitfall and flight-intercept-trapped beetle families in low and high complexity sites, and the families’ common functional group(s)
FamilyPitfall trapsIntercept trapsFunctional group(s) (Lawrence & Britton, 1994)
Abundance in low complexityAbundance in high complexityAbundance in low complexityAbundance in high complexity
  1. A = aquatic, De = generalist detritivore, DW = dead wood, F = fungus, H = herbivore (leaves, flowers, roots, stems), LW = live wood, P = predator.

Aderidae  3  0 24  9DW
Anobiidae 22 31 52 65DW
Anthicidae  4  2 10  1De
Anthribidae  1  0  0  1DW/F
Archaeocrypticidae  0  2  0  0De
Belidae  0  0  1  0LW
Bolboceratidae  0  0  0  1F
Bostrichidae  0  0  1  0DW
Buprestidae  0  0  1  0H/LW
Cantharidae  0  0  2  5P/H
Carabidae 14 68  3  6P
Cerambycidae  0  1  3  1H/LW
Cerylonidae  0  0  1  0F
Chrysomelidae  2  1 11 12H
Ciidae  0  4  2  2F
Clambidae  0  0  3  2F
Cleridae  0  0  2  0P
Coccinellidae  1  4  2  5P/H/F
Corticariidae 30104 10 14F
Corylophidae  1  1  8  7F
Curculionidae  5 25 14 11H/De
Dermestidae  0  0 27  9De/H
Elateridae  3  3 18 11P/H
Eucnemidae  0  0  6  4DW
Histeridae  0  0  1  1P
Hydraenidae  0  0  0  1A
Hydrophilidae  1  0  0  0H/De
Laemophloeidae  0  1  1  0F/DW
Languridae  1  0  0  1F/De/H
Leiodidae  3 50  6 15De/F
Lucanidae  0  0  0  2DW/H
Lymexylidae  0  0  1  0DW/F
Melandryidae  0  0  0  1DW/F
Melyridae  1  0  7  3H/P/De
Mordellidae  2  2 43 24H/DW/F
Mycteridae  0  0  1  2De
Nitidulidae  4  7  4  4De/F/P
Phalacridae  0  0  3  2F/De
Ptiliidae  6  5  8  3F
Rhipiphoridae  0  0  2  2P
Scarabaeidae  9 29 27 30De/H
Scraptiidae  0  0 11  8De
Scydmaenidae  6  8 32 15P
Silphidae  1  0  0  0De/P
Silvanidae  0  0  0  1De/F
Sphindidae  3  1 19  9F
Tenebrionidae  1  4  8  4De
Throscidae  0  0  4  3DW
Zopheridae  1  0  1  1DW/F
Figure 4.

CCA ordination, showing the distribution of the Staphylinidae subfamilies, with respect to six measured habitat variables (▪= predator, □= fungus feeder, •= detritivore).

High and low complexity habitats supported distinctly different pitfall-trapped beetle assemblages (Fig. 5a) (anosim, Global R = 0.21, P < 0.001). The five most important contributing species were Aleocharinae sp. 1 and sp. 2, Cartodere sp. 1, Mysticephala sp. 1 and Pamborus alternans. Each of these were more abundant in high complexity habitat, and collectively contributed 20% of the compositional dissimilarity between high and low complexity sites (Table 3).

Figure 5.

MDS showing the species composition of beetles trapped using (a) pitfalls and (b) flight-intercepts in low (▴) and high (□) complexity areas.

Table 3.  Abundance of the five beetle species which contributed most to compositional differences between low and high complexity sites (from pitfall traps). ‘Trappability’, the proportion of sites each of the species were trapped in (from each treatment, n = 14), is presented as a percentage in brackets
FamilySpeciesAbundance (and trappability) Habitat complexity
Anobiidae Mysticephala sp. 122 (43%)29 (64%)
Corticariidae Cartodere sp. 110 (50%)75 (100%)
StaphylinidaeAleocharinae sp. 134 (43%)90 (79%)
StaphylinidaeAleocharinae sp. 2 5 (29%)65 (79%)
Carabidae Pamborus alternans  4 (29%)37 (64%)

Neither species richness (Fig. 1c), abundance (Fig. 1d) nor composition (Fig. 5b) of beetles trapped in flight-intercept traps were different in high compared to low complexity habitats. Rarefaction curves of flight-intercept-trapped beetles (Fig. 2b) also indicate a similar diversity in high and low complexity habitats. There were no associations between flight-intercept-trapped beetle species richness and individual habitat complexity scores.


Habitat complexity was a powerful predictor of the species richness and abundance of pitfall-trapped beetles in sandstone woodland. Individual-based rarefaction curves indicate that beetle species richness may actually be higher in low complexity habitat for the same number of sampled individuals. However, for equal sampling effort, we trapped a greater species richness and number of individuals in high complexity habitat, possibly reflecting resource availability (Gotelli & Colwell, 2001). The heterogeneity of structure in high complexity habitats may support more potential niches for a functionally diverse suite of fauna, and is likely to support a greater range of food webs than less complex habitats (Klopfer & MacArthur, 1960). The large number of singletons in low complexity habitat may have been transient individuals passing between high complexity habitats or species with low population levels, rather than species which inhabit low complexity areas in great abundances (Novotny & Basset, 2000).

A suite of habitat characteristics may contribute to the dwelling preferences of the majority of beetle species trapped in pitfalls. We detected positive correlations between beetle species richness and each of: tree canopy cover; ground herb cover; amount of leaf litter; and soil moisture. It is unlikely that single habitat characteristics control beetle diversity, and auto-correlations were detected among habitat scores. However, measures of habitat complexity using a composite score are useful for describing general beetle diversity patterns. Individual habitat variables had no clear effect on general functional groups of pitfall-trapped beetles. This may be a result of more specific responses to habitat differences (i.e. at a finer taxonomic resolution). A closer examination of the staphylinid subfamilies revealed that the Oxytelinae, which are detritivores (Lawrence & Britton, 1994), appropriately preferred habitat with more leaf litter. Similarly, the fungus-feeding Scaphidiinae (Newton, 1984; Hammond & Lawrence, 1989; Lawrence, 1989; Lawrence & Britton, 1994) were more abundantly trapped in sites with greater ground herb cover, most likely reflecting their foraging habits (Hanski, 1989). However, the five predatory subfamilies (Hanski & Hammond, 1986; Wills & Mullins, 1991; Lawrence & Britton, 1994; Schmidt, 1999) showed no compelling evidence for habitat preferences. These subfamilies may be less specialized and utilize a greater range of habitats whilst foraging because they are generally active predators (Lawrence & Britton, 1994).

A different assemblage of beetle species occupied low and high complexity habitats. Species from the Staphylinidae (Aleocharinae sp. 1 and sp. 2), Carabidae (P. alternans), Corticariidae (formerly Lathridiidae, Cartodere sp. 1) and Anobiidae (Mysticephala sp. 1) were most responsible for the compositional differences, preferring high complexity habitat. The Aleocharinae are the largest group of staphylinids, and are generally free-living predators (Lawrence & Britton, 1994). Pamborus alternans is also a predator, predominantly feeding on snails and earthworms (Lawrence & Britton, 1994). Complex habitats provide shelter from predation (if comparisons are made using an equal ratio of predator: prey abundance) (Bartholomew et al., 2000; Hoddle, 2003) and greater moisture levels for these prey items. Greater abundances of prey are therefore most likely supported in complex habitats. The Aleocharinae and P. alternans may therefore prefer complex habitat for rich food sources, from a cascading effect of habitat preferences of their prey items. Cartodere species feed on fungal spores (Lawrence & Newton, 1980; Hammond & Lawrence, 1989; Lawrence, 1989; Lawrence & Britton, 1994; White et al., 1997), and may be more abundant in high complexity habitats because of soil moisture. Similarly, the positive association between habitat complexity and the abundance of Mysticephala sp. 1 is most likely driven by its diet of dead wood (Halperin & Espanol, 1978; Lawrence & Britton, 1994).

Separating the effects of habitat complexity per se from other habitat characteristics such as productivity is a fundamental problem with many studies purporting to examine habitat complexity and its role in driving patterns in diversity (Catling & Burt, 1995; Huston, 1997). There are almost certainly close associations between habitat productivity and our measurements of habitat complexity, three of the six habitat measurements we used being of predominantly living plant tissue. This may partially explain the strength of the patterns in beetle diversity we observed, given the positive relationships often described between habitat productivity and faunal diversity (Huston, 1994, 1997). Floristic composition was relatively similar across our study sites and treatments, indicating that the structural components of the habitat we surveyed most likely had a stronger influence on the beetle community patterns we observed (Catling & Burt, 1995).

Research on associations between biodiversity and habitat complexity is often motivated by a desire to generalize ecological patterns at landscape scales. The applicability of these generalizations may be limited by using single sampling approaches, and the distinct beetle assemblages which were collected by pitfall and flight-intercept traps support the use of multitechnique approaches to arthropod trapping in biodiversity surveys. Although we describe a number of emerging patterns from our data, replication over time could improve the reliability of results to describe general patterns. Temporal variation in the patterns we observed may occur, following differential ‘seasonality’ of food resources for specific beetles. A similar species richness of flight-intercept-trapped beetles in low and high complexity habitat may result from low complexity sites providing easier flight paths. The transit path for flying beetles may be more difficult through more complex vertical habitat structure, causing them to choose to fly through less complex habitat.

Although we trapped a greater abundance of some beetle families in low complexity habitat, there was an overall preference for high complexity habitat by all functional groups. Our results support the hypothesis that discrete responses of the various functional groups of beetles are most likely driven by their feeding habits, indicated by differing habitat components from within overall composite habitat complexity measures. The strong relationship between habitat complexity and several measures of beetle diversity may provide significant new opportunities to incorporate fine-scale assessments of landscape variation into environmental monitoring and management. Comparative studies of responses of invertebrate communities to habitat complexity within undisturbed ecosystems are lacking (Tews et al., 2004). Further examination of the consistency of beetle responses to habitat complexity at multiple scales will offer an indication of the degree of generality of the patterns we describe. Although habitat preferences by beetle species may often reflect their foraging habits, clarification of the causal mechanisms underpinning the relationships between habitat complexity and beetles are critical for the development of general principles linking habitat, functional roles and diversity.


This project was supported by an Australian Research Council grant to DFH and GC. SAL is also grateful for support from the Australian Museum student grant scheme.