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Effects of Habitat Complexity on Ant Assemblages

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Effects of Habitat Complexity on Ant Assemblages ECOGRAPHY 27: 157Á/164, 2004 Effects of habitat complexity on ant 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 species diversity. We used pitfall traps to sample ants 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 arthropod 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 arthropods (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 genus 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.
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