Hidden trails: Visualizing arthropod silk

Arthropods are known to use silk for a number of different purposes including web construction, shelter building, leaf tying, construction of pupal cocoons, and as a safety line when dislodged from a substrate (Alexander, 1961; Fitzgerald, 1983; Common, 1990). Across the arthropods, silk displays a diversity of material properties and chemical constituents and is produced from glands with different evolutionary origins (Craig, 1997). Among insects, larval Lepidoptera are prolific producers of silk. Because many lepidopteran larvae are pests, an ability to interfere with silk production or, at the very least, an understanding of how silk is used, could provide new options for pest control. After testing many known fluorescent dyes, we found that Fluorescent Brightener 28 (also known as Calcofluor White M2R) (Sigma-Aldrich Pty Ltd, Sydney, NSW, Australia), an optical brightener used in the textile industry, binds to arthropod silk in a simple staining reaction, causing it to fluoresce under ultraviolet (UV) light. Such brighteners have also been used in insect gut content analysis (Schlein & Muller, 1995; Hugo et al., 2003). Here we describe the method of visualizing arthropod silk on plant surfaces, using as a model the thin, barely visible, single strands of silk produced by Helicoverpa armigera (Hübner) (Lepidoptera: Noctuidae) neonates.

A hidden Markov model approach for determining vessel activity from vessel monitoring system data.

Many fisheries worldwide have adopted vessel monitoring systems (VMS) for compliance purposes. An added benefit of these systems is that they collect a large amount of data on vessel locations at very fine spatial and temporal scales. This data can provide a wealth of information for stock assessment, research, and management. However, since most VMS implementations record vessel location at set time intervals with no regard to vessel activity, some methodology is required to determine which data records correspond to fishing activity. This paper describes a probabilistic approach, based on hidden Markov models (HMMs), to determine vessel activity. A HMM provides a natural framework for the problem and, by definition, models the intrinsic temporal correlation of the data. The paper describes the general approach that was developed and presents an example of this approach applied to the Queensland trawl fishery off the coast of eastern Australia. Finally, a simulation experiment is presented that compares the misallocation rates of the HMM approach with other approaches.