Revealing the ecological content of long-duration audio-recordings of the environment through clustering and visualisation.

Audio recordings of the environment are an increasingly important technique to monitor biodiversity and ecosystem function. While the acquisition of long-duration recordings is becoming easier and cheaper, the analysis and interpretation of that audio remains a significant research area. The issue addressed in this paper is the automated reduction of environmental audio data to facilitate ecological investigations.

Using soundscapes to detect variable degrees of human influence on tropical forests in Papua New Guinea.

There is global concern about tropical forest degradation, in part, because of the associated loss of biodiversity. Communities and indigenous people play a fundamental role in tropical forest management and are often efficient at preventing forest degradation. However, monitoring changes in biodiversity due to degradation, especially at a scale appropriate to local tropical forest management, is plagued by difficulties, including the need for expert training, inconsistencies across observers, and lack of baseline or reference data.

Sampling environmental acoustic recordings to determine bird species richness.

Acoustic sensors can be used to estimate species richness for vocal species such as birds. They can continuously and passively record large volumes of data over extended periods. These data must subsequently be analyzed to detect the presence of vocal species.

The cross-species prediction of bacterial promoters using a support vector machine.

Due to degeneracy of the observed binding sites, the in silico prediction of bacterial sigma(70)-like promoters remains a challenging problem. A large number of sigma(70)-like promoters has been biologically identified in only two species, Escherichia coli and Bacillus subtilis. In this paper we investigate the issues that arise when searching for promoters in other species using an ensemble of SVM classifiers trained on E.

The in silico prediction of promoters in bacterial genomes.

In silico approaches to the identification of bacterial promoters are hampered by poor conservation of their characteristic binding sites. This suggests that the usual position weight matrix models of bacterial promoters are incomplete. A number of methods have been used to overcome this inadequacy, one of which is to incorporate structural properties of DNA.

The prediction of bacterial transcription start sites using SVMs.

Identifying promoters is the key to understanding gene expression in bacteria. Promoters lie in tightly constrained positions relative to the transcription start site (TSS). In this paper, we address the problem of predicting transcription start sites in Escherichia coli.