ECOGRAPHY 27: 157Á/164, 2004

Effects of habitat complexity on assemblages

Scott A. Lassau and Dieter F. Hochuli

Lassau, S. A. and Hochuli, D. F. 2004. Effects of habitat complexity on ant assemblages. Á/ Ecography 27: 157Á/164.

We investigated responses of ant communities to habitat complexity, with the aim of assessing complexity as a useful surrogate for ant diversity. We used pitfall traps to sample at twenty-eight sites, fourteen each of low and high habitat complexity, spread over ca 12 km in Sydney sandstone ridge-top woodland in Australia. Ant species richness was higher in low complexity areas, and negatively associated with ground herb cover, tree canopy cover, soil moisture and leaf litter. Ant community composition was affected by habitat complexity, with morphospecies from the genera Monomorium, Rhytidoponera and Meranoplus being the most significant contributors to compositional differences. Functional group responses to anthropogenic disturbance may be facilitated by local changes in habitat complexity. Habitat complexity, measured as a function of differences in multiple strata in forests, may be of great worth as a surrogate for the diversity of a range of groups including ants.

S. A. Lassau([email protected]) and D. F. Hochuli, Inst. of Wildlife Research, School of Biological Sciences, Heydon-Laurence Bldg A08, The Univ. of Sydney, NSW 2006, Australia.

Patterns in nature which occur over a variety of spatio- patterns, we note that an inherent difficulty in describing temporal scales are sometimes referred to as general laws a general response to complexity is the multitude of of ecology (Lawton 1999). Numerous ecological patterns operational definitions adopted in the literature. Our exist, although many generalisations have been accepted operational definition is consistent with the predominant without substantial evidence (Beck 1997). The general usage at the scale of our work, considering habitat assumption that habitat complexity is positively asso- complexity as the heterogeneity in the arrangement of ciated with the diversity of its constituent fauna (McCoy physical structure in the habitat surveyed. and Bell 1991) may not be strongly supported across Ants respond to a range of disturbances and have scale, taxa and systems being investigated (e.g. Kotze been used as bio-indicators in the wake of forest clearing and Samways 1999). The majority of studies concerning (Majer et al. 1997, King et al. 1998, Gascon et al. 1999), the role of habitat structure and complexity on its fire (Andersen 1991, Vanderwoude et al. 1997), minesites associated biota in undisturbed areas is focussed on (Majer 1984, Majer and Nichols 1998), road construc- aquatic environments (reviewed by Turner et al. 1999). tion (Samways et al. 1997, Lassau and Hochuli 2003), General patterns in terrestrially based work suggests that agricultural practices (Burbidge et al. 1992, Perfecto and species richness of ants (Andersen 1986), a range of Snelling 1995) and general anthropogenic disturbances other (Uetz 1979, Gardner et al. 1995, (Burbidge et al. 1992). Mechanisms by which anthro- Humphrey et al. 1999, Hansen 2000) and mammals pogenic disturbance affect the composition of ants (August 1983, Dueser and Porter 1986) is positively include alterations in shade (King et al. 1998, Hoffman associated with habitat complexity. Habitat complexity et al. 2000), vegetation structure (Greenslade and Green- may also affect the composition of arthropod assem- slade 1977) and plant species richness (Hoffman et al. blages (e.g. Gardner et al. 1995). In outlining these 2000). Ant genera have been assigned to functional

Accepted 30 September 2003 Copyright # ECOGRAPHY 2004 ISSN 0906-7590

ECOGRAPHY 27:2 (2004) 157 groups that are thought to respond predictably to Statistical analyses disturbances (Andersen 1990, Andersen et al. 2002) and help indicate ecosystem health. Functional groups We performed single-factor Analysis of Variance (AN- OVA) to examine differences in the species richness of could thus be used in conservation planning and ants at high versus low complexity habitats and to detect assessment, reducing the expenses associated with sur- the differences for those species that were comparatively veys for arthropod diversity. One of the likely causal most abundant in either low or high complexity areas. mechanisms underpinning functional group composition We tested correlations between ant species richness and is their response to environmental variation, such as individual habitat variable scores using Spearman Rank changes in habitat structure, associated with disturbance Correlations (SRCs). We also tested correlations among (Andersen 1990). habitat variables using SRCs. Using the statistical Our study aims to examine the responses of ants package PRIMER (Anon. 2001), we constructed a to the large physical variability that may occur natural- Bray-Curtis dissimilarity matrix of our ant species data ly in undisturbed habitats. We test whether high com- using a fourth root transformation to allow a more equal plexity habitat supports a greater richness, and/or a contribution of rare species (Clarke 1993). Non-stan- different composition of ant species than low complexity dardised data were used, since throughout the study all habitat. collection sites were treated with equal importance. We then plotted a two-dimensional ordination using non- metric multi-dimensional scaling (MDS), and performed Analysis of Similarities (ANOSIM) with 999 permuta- Methods tions. We used these methods to detect any composi- tional differences in ant assemblages between areas of Survey design low and high habitat complexity. We calculated Similar- ity Percentages (SIMPER) to determine which species We selected fourteen study areas in Sydney sandstone contributed most to differences between treatment types. ridge-top woodland (using the classification of Benson and Howell 1994). Each study area was selected so that it contained a pair of high and low complexity sites situated within 50 m of one another. This resulted in twenty-eight sites in 14 areas and over three National Results Parks in northern Sydney; Marramarra National Park, Low habitat complexity sites supported a much greater Muogamarra Nature Reserve and Ku-Ring-Gai Chase species richness of ants (ANOVA, F(1,26) /9.23, National Park (Fig. 1). Each of the twenty-eight sites pB/0.01) (Fig. 3). The richness of ants was negatively were characterised according to their habitat complexity, associated with the ground herb cover (Spearman using scores between 0 and 3 for six habitat variables, Rho/ /0.52, n/28, pB/0.005), tree canopy cover which is a modified version of the technique (i.e. the (Spearman Rho/ /0.45, n/28, pB/0.05), soil moist- addition of a score for leaf litter) used by Coops and ure (Spearman Rho/ /0.44, n/28, pB/0.05) and leaf Catling (1997) (Table 1). The sites were selected a priori, litter (Spearman Rho/ /0.39, n/28, pB/0.05) fourteen being categorised as low complexity, after (Fig. 4). A surprisingly low number of correlations scoring 8 or less from 18 (Fig. 2a) and fourteen as existed among habitat variables; soil moisture was high complexity, scoring 11 or greater (Fig. 2b). positively associated with tree canopy cover (Spearman

Rho/0.59, n/28, pB/0.001), shrub canopy cover (Spearman Rho/0.40, n/28, pB/0.05), ground herb cover (Spearman Rho/0.47, n/28, pB/0.05) and Sampling methods ground herb cover was positively associated with tree canopy cover (Spearman Rho/0.45, n/28, pB/0.05). We sampled ants using five pitfall traps in each of the The species composition of ants differed between habitat twenty-eight sites during December 2000/January 2001. complexity treatments (ANOSIM, Global R/0.193,

The traps were 9 cm in diameter and were left out for a pB/0.005), and high complexity sites were generally period of 28 d, one-third filled with ethylene glycol for more similar to each other than to low complexity sites preservation of captured arthropods. We identified ants based on ant species composition (Fig. 5), although the to and species or morphospecies, depending on the ordination may not be an accurate representation of the certainty of species-level classification information data considering the stress is /0.2. This indicates the (Shattuck 1999). We refer to morphospecies as ‘‘species’’ species contributing most to the differences between low for the duration of this paper for simplicity. We and high complexity areas thrived in a different range of compared patterns in ant species richness and composi- ecological conditions and/or were dependent of other tion to address the aims of our study. species which have a niche in a particular habitat type.

158 ECOGRAPHY 27:2 (2004) Fig. 1. a) Schematic representation of the sampling design. Within the three National Parks, each square represents paired H and L sites (high and low habitat complexity, respectively). b) Study area location.

Less than 6% of species trapped contributed to /16% ing species and their functional groups revealed that of the dissimilarity in ant composition between low and Monomorium A (ANOVA, F(1,26) /7.89, pB/0.01) high complexity areas. Inspection of the most contribut- (Generalised myrmicine), Rhytidoponera metallica A

Table 1. Visual method for scoring habitat complexity (modified from Coops and Catling 1997).

Structure Score

012 3

Tree canopy (% cover) 0 B/30 30Á/70 /70 Shrub canopy (% cover) 0 B/30 30Á/70 /70 Ground flora (height in m) Sparse* (and B/0.5) Sparse* (and /0.5) Dense** (and B/0.5) Dense** (and /0.5) Logs, rocks, debris etc. (% cover) 0 B/30 30Á/70 /70 Soil moisture Dry Moist Permanent water Water-logged adjacent Leaf litter (% cover) 0 B/30 30Á/70 /70

* Sparse ground flora refers to grasses covering B/50% of a study site. ** Dense ground flora refers to grasses covering /50% of a study site.

ECOGRAPHY 27:2 (2004) 159 Fig. 2. a) Example of low habitat complexity (site 3 in Marramarra National Park). This site scored 6 from 18 (tree canopy cover/1; shrub canopy cover/1; ground herb cover/1; leaf litter/1; moisture/ 0; amount of logs, sticks and debris/2). b) Example of high habitat complexity (site 6 in Marramarra National Park). This site scored 11 from 18 (tree canopy cover/2; shrub canopy cover/2; ground herb cover/2; leaf litter/2; moisture/1; amount of logs, sticks and debris/2).

(ANOVA, F(1,26) /6.23, pB/0.02) (opportunist) and Sydney sandstone ridgetop woodland. The species rich- Meranoplus A (ANOVA, F(1,26) /6.59, pB/0.02) (hot ness of ants was greater in areas of low habitat complex- climate specialist) had a significantly higher abundance ity, contrary to many habitat complexity/fauna diversity in low complexity than high complexity areas. The studies suggesting general relationships. Although there abundance and species richness of total ants belonging are exceptions, it is commonly reported that species to these functional groups was also greater in low richness generally correlates with the complexity of complexity areas (Table 2). physical structure (McCoy and Bell 1991). Our results suggest a combination of habitat characteristics govern the presence and preferences of ant assemblages. Of the six environmental structure variables assessed, ground Discussion herb cover, tree canopy cover, soil moisture and leaf litter We found that habitat complexity was an important were negatively correlated with ant species richness. factor governing ant species richness and composition in Andersen (1986) concluded that higher species diversity

160 ECOGRAPHY 27:2 (2004) more environmental niches and more foraging opportu- nities. Contrary to these comparisons among habitat types, our data suggest higher levels of complexity are not preferable environments to the majority of ant species within a woodland. Furthermore, differences in the complexity of habitat in our study did not appear to reflect a different successional state, but rather the patchy mosaic of eucalypt-dominated sandstone wood- lands on nutrient-poor soils. One possible mechanism for the patterns observed is that ant movement may be more energy efficient in less complex areas, where the ground cover (including leaf litter, ground herb, logs, rocks, sticks and debris) is more negotiable at the scale of a foraging ant (e.g. Kaspari and

Fig. 3. Mean species richness (9/SE) of ants trapped in low and Weiser 1999). The same may apply for following high habitat complexity areas. chemical cues excreted for ‘‘trails’’ by other individuals in a colony. Other advantages of low complexity areas of ants in woodland than heath was probably because may be simpler construction and guarding of nests or areas with a more complex habitat are likely to provide lower soil moisture and shadow levels, leading to greater

Fig. 4. Species richness of ants found in areas of varying comparative a) tree canopy cover b) shrub canopy cover c) ground herb cover d) leaf litter e) soil moisture and f) amount of logs, sticks and debris. A blank column indicates the habitat variable score was not found in the survey.

ECOGRAPHY 27:2 (2004) 161 studies, a range of possible biases may accompany pitfall trapping as a comparative technique, such as digging-in affects (Greenslade 1973) or skewed trap results depend- ing on the habitat being surveyed and the activity of ants (Romero and Jaffe 1989, Melbourne 1999, Bestelmeyer et al. 2000). The immediate surface around all pitfalls in our study were set identically flush with soil to minimise habitat dependent variation, and we consider that twenty-eight days is ample time to allow less active ants to be captured. Melbourne (1999) showed that habitat structure at very fine scales (manipulations within 80 cm) may bias ant catch in pitfall samples. This bias is unlikely to explain the patterns observed in our study, as we observed responses over far greater scales. We suggest that pitfall traps were ideal for our Fig. 5. MDS ordination of sample sites, based on the species study, and for assessing responses to disturbances over composition of ants in low and high complexity habitats. landscape scales, justifying their widespread use (Ander- sen et al. 2002). However, responses detected from our light and heat intensities for energy requirements, since study cannot necessarily be generalised across all ant the vast majority of ant species are strongly thermophilic fauna, and may be only applicable to ground foraging (Holldobler and Wilson 1990). species. Despite being one of the most commonly used Open areas are reported to support a greater percen- methods to survey terrestrial arthropods in ecological tage of opportunistic ants (Andersen 1990, King

Table 2. Ant functional groups and genera abundance totals in low and high complexity areas. Data in brackets is species richness of each genus. Totals of each functional group are given in bold.

Functional Group Genus Low Complexity Areas High Complexity Areas

1. Dominant Froggattella 70(1) 54(1) 671(2) 195(3) Leptomyrmex 13(1) 7(2) 754(4) 256(6) 2. Associated subordinate Camponotus 160(10) 54(7) Camponotinae Polyrhachis 5(1) 0 165(11) 54(7) 3a. Hot climate specialists Cerapachys 0 2(1) Melophorus 179(5) 15(3) Meranoplus 166(1) 18(1) 345(6) 35(5) 3b. Cold climate specialists Dolichoderus 122(4) 25(3) Notoncus 29(1) 14(1) Prolasius 13(1) 21(2) 164(6) 60(6) 4a. Cryptic species Sphinctomyrmex 10 Oligomyrmex 10 Solenopsis 36(1) 18(1) Strumigenys 3(1) 15(1) Discothyrea 11 Hypoponera 10 43(6) 34(3) 4b. Sub-cryptic species Tapinoma 24(1) 9(1) 24(1) 9(1) 5. Opportunists Paratrechina 289(2) 203(2) Tetramorium 22(1) 11(1) Rhytidoponera 205(2) 12(1) 516(5) 226(4) 6. Generalised myrmicines Adlerzia froggatti 4(1) 5(1) Crematogaster 313(4) 201(4) Monomorium 467(7) 69(6) Pheidole 1153(5) 440(5) 1937(17) 715(16) 7. Large, solitary foragers Myrmecia 3(2) 8(3) 3(2) 8(3)

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