Aspects of the life history and breeding biology of the Mountain Pygmy-possum (Burramys parvus), (Marsupialia: Burramyidae) in alpine Victoria.

From February 1982 to November 1989, a trapping and captive breeding program was undertaken to examine the life history of the vulnerable Mountain Pygmy-possum (Burramys parvus), the only Australian mammal restricted to the alpine and subalpine region. During the active season (Oct.-Apr.) over 38 000 trapnights in the habitat throughout Victoria yielded over 900 individual B. parvus captured on over 3 800 occasions. Burramys parvus is a polyoestrous species (mean cycle period of 20.3 days) and produces supernumerary young, up to twice the number of available teats (4). In the wild, breeding is highly synchronised to spring and a single litter is carried p.a. (x = 3.6; mode = 4). Whilst B. parvus retains the capacity to produce a second litter, this is a rare event in the wild and would be selected against as there would be insufficient time to obtain fat reserves for hibernation. At birth and independence the sex ratio of the cohort is at parity, yet at any time the sex ratio of the B. parvus population is always biased toward females due to differential survival of the sexes (at breeding I M: 4-6 F). In autumn, females that would survive winter were on average &amp;gt; 12 % heavier than those not retrapped. One female was at least 11 years old whilst the oldest male recorded was 4 years. Burramys parvus is the longest lived small terrestrial mammal known. Aspects of the life histo ry are discussed in the context of adaptions to the alpine environment and are compared to other small mammals in the habitat and in other cold climates, and to other small marsupial diprotodonts.</jats:p

Diet of the Mountain Pygmy-possum, Burramys parvus (Marsupialia: Burramyidae) and other small mamma ls in the alpine environment at Mt Higginbotham, Victoria.

The diets of Burramys parvus, Rattus fuscipes and Antechinus swainsonii in Podocarpus lawrencei heathland in alpine Victoria during the non-winter period were determined from analysis of 264 faecal pellets. Both frequency of occurrence and mean percentage composition of dietary items in the samples were determined; the Iatter being used to assess the diet. We found B. parvus to be an omnivore concentrating on insec ts. It eats a variety of foods but the Bogong Moth (Agrolis infusa) is heavily exploited (31 % of the composition of faecal samples) especially by females during the breeding season (46 %). Other components of the diet are other invertebrates, predominantly insects (32 %) and vegetative material (16 %). Seasonal differences in the composition of the diet of B. parvus were due to the exploitation of fruit in the non-breeding season. No differences in diet were observed between age-classes and the sexes but females are sedentary in food resource-rich habitats, whilst when not breeding, males occur in areas of poorer food resources. Circumstantial evidence suggests that during facultative hibernation a major dietary component of B. parvus may be seeds, cached from the previous summer-autumn. The diet of R. fuscipes consists mainly of insects (12 %) and the largest vegetative component was seeds (10.1 %). Fungi were found in 53 % of faecal samples of R. fuscipes but could not be quantified as a percentage composition of diet. The species is classified as a selective omnivore. The diet of A. swainsonii consisted of 68 % in sec ts, 10.4 % of insect setae and worms with the major vegetative component (5.2 %) being soft fruits. Burramys parvus, relative to the other small mammals with which it cohabits, has become a specialist in exploiting the abundant and rich food resource of Bogong moths.</jats:p

Spatial modelling of biodiversity at the community level

1. Statistical modelling is often used to relate sparse biological survey data to remotely derived environmental predictors, thereby providing a basis for predictively mapping biodiversity across an entire region of interest. The most popular strategy for such modelling has been to model distributions of individual species one at a time. Spatial modelling of biodiversity at the community level may, however, confer significant benefits for applications involving very large numbers of species, particularly if many of these species are recorded infrequently. 2. Community-level modelling combines data from multiple species and produces information on spatial pattern in the distribution of biodiversity at a collective community level instead of, or in addition to, the level of individual species. Spatial outputs from community-level modelling include predictive mapping of community types (groups of locations with similar species composition), species groups (groups of species with similar distributions), axes or gradients of compositional variation, levels of compositional dissimilarity between pairs of locations, and various macro-ecological properties (e.g. species richness). 3. Three broad modelling strategies can be used to generate these outputs: (i) 'assemble first, predict later', in which biological survey data are first classified, ordinated or aggregated to produce community-level entities or attributes that are then modelled in relation to environmental predictors; (ii) 'predict first, assemble later', in which individual species are modelled one at a time as a function of environmental variables, to produce a stack of species distribution maps that is then subjected to classification, ordination or aggregation; and (iii) 'assemble and predict together', in which all species are modelled simultaneously, within a single integrated modelling process. These strategies each have particular strengths and weaknesses, depending on the intended purpose of modelling and the type, quality and quantity of data involved. 4. Synthesis and applications. The potential benefits of modelling large multispecies data sets using community-level, as opposed to species-level, approaches include faster processing, increased power to detect shared patterns of environmental response across rarely recorded species, and enhanced capacity to synthesize complex data into a form more readily interpretable by scientists and decision-makers. Community-level modelling therefore deserves to be considered more often, and more widely, as a potential alternative or supplement to modelling individual species