Australian Glow-worms in Caves

Glow-worms are the larvae of a fly from the family Keroplatidae. Their closest relatives are the “fungus flies” that seek out mushrooms for their larvae to consume. Glow-worms have gone out on an evolutionary limb, albeit a successful one. They have lost their association with fungi and have instead become carnivorous. The unique feature of glow-worms is their ability to bioluminesce—to produce light.

Glow worms as a tourist attraction in Springbrook National Park: Visitor attitudes and economic issues

nsect-based tourism mainly caters to a niche market, but its popularity has been growing in recent years. Despite its popularity this form of tourism has remained under-researched and in a sense its contribution to the tourism industry has gone mostly unnoticed. This paper reports the results of a study undertaken on one form of popular insect-based tourism, namely glow worms. The study was undertaken in Springbrook National Park (Natural Bridge section) southeast Queensland, which has one of the largest glow worm colonies in Australia that attracts thousands of visitors each year. A study of this form of tourism is important and useful for several reasons. It is important to understand this hitherto under-studied tourism activity to determine the type of visitors, their socio-economic attributes, economic benefits to the local economy, visitors’ knowledge of glow worms, education imparted, visitor satisfaction of glow worm viewing and visitor attitudes for the introduction of a user fee system to view glow worms. An understanding of these issues could not only help to better manage this valuable biological resource, but can be used to develop the industry to cater to a growing number of visitors. Tourism in glow worms can potentially be used not only to educate the public on the threats affecting glow worms and their colonies, but could also be used to conserve them. Lessons learnt from glow worms as an attraction to Springbrook National Park can be used to better manage and further develop other existing and new glow worm sites in Australia and elsewhere for tourism. Furthermore, it could provide some guidance for the management and development of other forms of current insect-based tourism activities (eg. butterflies) and develop new tourism ventures based on species such as stick insects and jewel beetles for which Australia is well known (Reader’s Digest, 1997)

The number of rational points of a class of Artin-Schreier curves

We determine the number of F-q-rational points of a class of Artin-Schreier curves by using recent results concerning evaluations of some exponential sums. In particular, we determine infinitely many new examples of maximal and minimal plane curves in the context of the Hasse-Weil bound. (C) 2002 Elsevier Science (USA).

Species accumulation curves and their applications in parasite ecology

Species accumulation curves (SACs) chart the increase in recovery of new species as a function of some measure of sampling effort. Studies of parasite diversity can benefit from the application of SACs, both as empirical tools to guide sampling efforts and predict richness, and because their properties are informative about community patterns and the structure of parasite diversity. SACs can be used to infer interactivity in parasite infra-communities, to partition species richness into contributions from different spatial scales and different levels of the host hierarchy (individuals, populations and communities) or to identify modes of community assembly (niche versus dispersal). A historical tendency to treat individual hosts as statistically equivalent replicates (quadrats) seemingly satisfies the sample-based subgroup of SACs but care is required in this because of the inequality of hosts as sampling units. Knowledge of the true distribution of parasite richness over multiple host-derived and spatial scales is far from complete but SACs can improve the understanding of diversity patterns in parasite assemblages.

Precision-recall operating characteristic (P-ROC) curves in imprecise environments

Traditionally, machine learning algorithms have been evaluated in applications where assumptions can be reliably made about class priors and/or misclassification costs. In this paper, we consider the case of imprecise environments, where little may be known about these factors and they may well vary significantly when the system is applied. Specifically, the use of precision-recall analysis is investigated and compared to the more well known performance measures such as error-rate and the receiver operating characteristic (ROC). We argue that while ROC analysis is invariant to variations in class priors, this invariance in fact hides an important factor of the evaluation in imprecise environments. Therefore, we develop a generalised precision-recall analysis methodology in which variation due to prior class probabilities is incorporated into a multi-way analysis of variance (ANOVA). The increased sensitivity and reliability of this approach is demonstrated in a remote sensing application.