Context Mapping the habitat and distribution of a species is critical for developing effective conservation plans. Koala (Phascolarctos cinereus, Phascolarctidae) distribution is constrained by the nutritional and shelter requirements provided by a relatively small number of key tree species in any given area. Identifying these key species provides a practical foundation for mapping koala habitat and prioritising areas for conservation. Aims To determine key tree species for koalas in Noosa Shire (south-eastern Queensland, Australia) as a basis for mapping koala habitat quality. Methods We applied a faecal-pellet survey methodology in 1996/97 to assess evidence of use by koalas of 4031 trees from 96 randomly stratified survey sites across different eucalypt-forest and woodland communities. Results were compared with those from a later survey undertaken in 2001/02 involving 5535 trees from 195 sites that were distributed across broadly similar areas with the aim to investigate aspects of koala landscape ecology. Key results A total of 66.7% of the 1996/97 survey sites contained koala faecal pellets, recorded under 953 eucalypt trees (14 species) and 1670 non-eucalypt trees (27 species). The proportion of trees at a given survey site that had koala faecal pellets at the base ranged from 2.2% to 94.7% (mean = 31.13 ± 2.59% s.e.). For the 2001/02 dataset, koala pellets were found at 55.4% of sites, from 794 eucalypt and 2240 non-eucalypt trees. The proportion of trees with pellets ranged from 3% to 80% (mean = 21.07 ± 1.77% s.e.). Both the 1996/97 and 2001/02 surveys identified the same three tree species (forest red gum, Eucalyptus tereticornis, swamp mahogany, E. robusta, and tallowwood, E. microcorys) as the highest-ranked for koala use in the study area. Three additional species (red mahogany, E. resinifera, small-fruited grey gum, E. propinqua, and grey ironbark, E. siderophloia) were identified in the 1996/97 surveys as key eucalypt species. Of the non-eucalypts in the 1996/97 dataset, coast cypress pine (Callitris columellaris) and broad-leaved paperbark (Melaleuca quinquenervia) ranked highest for use by koalas, followed by pink bloodwood (Corymbia intermedia) and brush box (Lophostemon confertus). White bottlebrush (Callistemon salignus), hard corkwood (Endiandra sieberi), M. quinquenervia and C. intermedia ranked highest in the 2001/02 dataset. The findings showed significantly greater use of larger eucalypts (i.e. 300-mm to >600-mm diameter at breast height). Conclusions The identified key eucalypt species, being the critical limiting resource for koalas, were used to assign koala habitat-quality classes to mapped regional ecosystem types to create a Koala Habitat Atlas (KHA) for Noosa Shire. The combined two highest quality classes based on abundance of the key eucalypt species comprised only 15.7% of the total land area of the Shire. Implications The KHA approach provides a practical and repeatable method for developing koala habitat-suitability mapping for national-, regional- and local-scale conservation and recovery planning purposes.

Is it possible to develop a meaningful measure for the complexity of a simulation model? Algorithmic information theory provides concepts that have been applied in other areas of research for the practical measurement of object complexity. This article offers an overview of the complexity from a variety of perspectives and provides a body of knowledge with respect to the complexity of simulation models. The key terms model detail, resolution, and scope are defined. An important concept from algorithmic information theory, Kolmogorov complexity, and an application of this concept, normalized compression distance, are used to indicate the possibility of measuring changes in model detail. Additional research in this area can advance the modeling and simulation body of knowledge toward the practical application of measuring simulation model complexity. Examples show that KC and NCD measurements of simulation models can detect changes in scope and detail.

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Is it possible to develop a meaningful measure for the complexity of a simulation model? Algorithmic information theory provides concepts that have been applied in other areas of research for the practical measurement of object complexity. This article offers an overview of complexity from a variety of perspectives and provides a body of knowledge with respect to the complexity of simulation models. Key terms of model detail, resolution, and scope are defined. An important concept from algorithmic information theory, Kolmogorov complexity, and an application of this concept, normalized compression distance, are used to indicate the possibility of measuring changes in model detail. Additional research in this area can advance the modeling and simulation body of knowledge toward the practical application of measuring simulation model complexity. Examples show that KC and NCD measurements of simulation models can detect changes in scope and detail.

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