Abstract:
Electronic bioacoustic techniques are providing new and effective ways of monitoring birds and have a number of advantages over other traditional monitoring methods. Given the increasing popularity of bioacoustic methods, and the difficulties associated with automated analyses (e.g. high Type I error rates), it is important that the most effective ways of scoring audio recordings are investigated. In Chapter Two I describe a novel sub-sampling and scoring technique (the ‘10 in 60 sec’ method) which estimates the vocal conspicuousness of bird species through the use of repeated presence-absence counts and compare its performance with a current manual method. The ‘10 in 60 sec’ approach reduced variability in estimates of vocal conspicuousness, significantly increased the number of species detected per count and reduced temporal autocorrelation. I propose that the ‘10 in 60 sec’ method will have greater overall ability to detect changes in underlying birdsong parameters and hence provide more informative data to scientists and conservation managers.
It is often anecdotally suggested that forests ‘fall silent’ and are devoid of birdsong following aerial 1080 operations. However, it is difficult to objectively assess the validity of this claim without quantitative information that addresses the claim specifically. Therefore in Chapter Three I applied the methodological framework outlined in Chapter Two to answer a controversial conservation question: Do New Zealand forests ‘fall silent’ after aerial 1080 operations? At the community level I found no evidence for a reduction in birdsong after the 1080 operation and eight out of the nine bird taxa showed no evidence for a decline in vocal conspicuousness. Only one species, tomtit (Petroica macrocephala), showed evidence for a decline in vocal conspicuousness, though this effect was non-significant after applying a correction for multiple tests.
In Chapter Four I used tomtits as a case study species to compare manual and automated approaches to: (1) estimating vocal conspicuousness and (2) determine the feasibility of using an automated detector on a New Zealand passerine. I found that data from the automated method were significantly positively correlated with the manual method although the relationship was not particularly strong (Pearson’s r = 0.62, P < 0.0001). The automated method suffered from a relatively high false negative rate and the data it produced did not reveal a decline in tomtit call rates following the 1080 drop. Given the relatively poor performance of the automated method, I propose that the automatic detector developed in this thesis requires further refinement before it is suitable for answering management-level questions for tomtit populations. However, as pattern recognition technology continues to improve automated methods are likely to become more viable in the future.
