[Truncated abstract] The identification of individual animals based on acoustic parameters is a non-invasive method of identifying individuals with considerable advantages over physical marking procedures. One requirement for an effective and practical method of acoustic individual identification is that it is call-independent, i.e. determining identity does not require a comparison of the same call or song type. This means that an individuals identity over time can be determined regardless of any changes to its vocal repertoire, and different individuals can be compared regardless of whether they share calls. Although several methods of acoustic identification currently exist, for example discriminant function analysis or spectrographic cross-correlation, none are call-independent. Call-independent identification has been developed for human speaker recognition, and this thesis aimed to: 1) determine if call-independent identification was possible in birds, using similar methods to those used for human speaker recognition, 2) examine the impact of noise in a recording on the identification accuracy and determine methods of removing the noise and increasing accuracy, 3) provide a comparison of features and classifiers to determine the best method of call-independent identification in birds, and 4) determine the practical limitations of call-independent identification in birds, with respect to increasing population size, changing vocal characteristics over time, using different call categories, and using the method in an open population. ... For classification, Gaussian mixture models and probabilistic neural networks resulted in higher accuracy, and were simpler to use, than multilayer perceptrons. Using the best methods of feature extraction and classification resulted in 86-95.5% identification accuracy for two passerine species, with all individuals correctly identified. A study of the limitations of the technique, in terms of population size, the category of call used, accuracy over time, and the effects of having an open population, found that acoustic identification using perceptual linear prediction and probabilistic neural networks can be used to successfully identify individuals in a population of at least 40 individuals, can be used successfully on call categories other than song, and can be used in open populations in which a new recording may belong to a previously unknown individual. However, identity was only able to be determined with accuracy for less than three months, limiting the current technique to short-term field studies. This thesis demonstrates the application of speaker recognition technology to enable call-independent identification in birds. Call-independence is a pre-requisite for the successful application of acoustic individual identification in many species, especially passerines, but has so far received little attention in the scientific literature. This thesis demonstrates that call-independent identification is possible in birds, as well as testing and finding methods to overcome the practical limitations of the methods, enabling their future use in biological studies, particularly for the conservation of threatened species.

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With declines in abundance and changing distribution of White-nose Syndrome (WNS)-affected bat species, increased reliance on acoustic monitoring is now the new "normal". As such, the ability to accurately identify individual bat species with acoustic identification programs has become increasingly important. Additionally, how bat distribution and habitat associations have changed at the local to sub-landscape scale in the post WNS environment is important to understand. The significance of these changes, relative to bat activity, may be based on the species-specific susceptibility to WNS. We used data collected from Fort Drum Military Installation, New York from the summers of 2003-2017 to analyze the accuracy of acoustic software programs, and assess the changes in relative bat activity, occupancy, and distribution induced by WNS. Our results indicate that continued acoustic monitoring of bat species, such as the little brown bat (Myotis lucifugus) in the Northeast, to assess ongoing temporal and spatial changes, habitat associations, and as a guide to direct future mist-netting should rely more on relative activity as the metric of choice. Furthermore, the continuous spread of WNS across North America will have strong negative effects on bat populations and communities, this study points to how individual species (both impacted and non-impacted) will respond to WNS. We believe that our results can help users choose automated software and MLE thresholds more appropriate for their needs to accurately address potential changes in communities of bat species due to impacts of WNS or other factors. / MS