Noninvasive methods for monitoring wildlife species have revolutionized the way population parameters, such as population density and survival and recruitment rates, are estimated while accounting for imperfect detection using capture-recapture models. Reliable estimates of these parameters are vital information required for making sound conservation decisions; however to date, noninvasive sampling methods have been of limited use for a vast number of species which are difficult to identify to the individual level–a general requirement of capture-recapture models. Capture-recapture models that utilize partial identity information have only recently been introduced and have not been extended to most types of noninvasive sampling scenarios in a manner that uses the spatial location where noninvasive samples were collected to further inform complete identity (i.e. spatial partial identity models). Herein, I extend the recently introduced spatial partial identity models to the noninvasive methods of remote cameras for species that are difficult to identify from photographs and DNA from hair or scat samples. The ability of these novel models to improve parameter estimation and extend study design options are investigated and the methods are made accessible to applied ecologists via statistical software. This research has the potential to greatly improve wildlife conservation decisions by improving our knowledge of parameters related to population structure and dynamics that inform those decisions. Unfortunately, many species of conservation concern (e.g., Florida panthers, Andean bears) are managed without having the necessary information on population status or trends, largely a result of the cost and difficulty of studying species in decline and because of the difficulty of applying statistical models to sparse data, which can produce imprecise and biased estimates of population parameters. By leveraging partial identity information in noninvasive samples, the models I developed will improve these parameter estimates and allow noninvasive methods to be used for more species, leading to more informed conservation decisions, and a more efficient allocation of conservation resources across species and populations. / Ph. D. / Noninvasive methods for monitoring wildlife species have revolutionized the way population parameters, such as population density and survival and recruitment rates, are estimated while accounting for imperfect detection using capture-recapture models. Reliable estimates of these parameters are vital information required for making sound conservation decisions; however to date, noninvasive sampling methods have been of limited use for a vast number of species which are difficult to identify to the individual levela general requirement of capture-recapture models. Capture-recapture models that utilize partial identity information have only recently been introduced and have not been extended to most types of noninvasive sampling scenarios in a manner that uses the spatial location where noninvasive samples were collected to further inform complete identity (i.e. spatial partial identity models). Herein, I extend the recently introduced spatial partial identity models to the noninvasive methods of remote cameras for species that are difficult to identify from photographs and DNA from hair or scat samples. The ability of these novel models to improve parameter estimation and extend study design options are investigated and the methods are made accessible to applied ecologists via statistical software. This research has the potential to greatly improve wildlife conservation decisions by improving our knowledge of parameters related to population structure and dynamics that inform those decisions. Unfortunately, many species of conservation concern (e.g., Florida panthers, Andean bears) are managed without having the necessary information on population status or trends, largely a result of the cost and difficulty of studying species in decline and because of the difficulty of applying statistical models to sparse data, which can produce imprecise and biased estimates of population parameters. By leveraging partial identity information in noninvasive samples, the models I developed will improve these parameter estimates and allow noninvasive methods to be used for more species, leading to more informed conservation decisions, and a more efficient allocation of conservation resources across species and populations.

This thesis is presented as a collection of five papers and one report, each of which has been either published after peer review or submitted for publication. It covers a broad range of applied statistical methods, from deterministic modelling to integrated Bayesian modelling using MCMC, via bootstrapping and stochastic simulation. It also covers a broad range of subjects, from analysis of recreational fishing diaries, to genetic mark recapture for wombats. However, it focuses on practical applications of statistics to the management of wild populations. The first chapter (Hoyle and Jellyman 2002, published in Marine and Freshwater Research) applies a simple deterministic yield per recruit model to a fishery management problem: possible overexploitation of the New Zealand longfin eel. The chapter has significant implications for longfin eel fishery management. The second chapter (Hoyle and Cameron 2003, published in Fisheries Management and Ecology) focuses on uncertainty in the classical paradigm, by investigating the best way to estimate bootstrap confidence limits on recreational harvest and catch rate using catch diary data. The third chapter (Hoyle et al., in press with Molecular Ecology Notes) takes a different path by looking at genetic mark-recapture in a fisheries management context. Genetic mark-recapture was developed for wildlife abundance estimation but has not previously been applied to fish harvest rate estimation. The fourth chapter (Hoyle and Banks, submitted) addresses genetic mark-recapture, but in the wildlife context for estimates of abundance rather than harvest rate. Our approach uses individual-based modeling and Bayesian analysis to investigate the effect of shadows on abundance estimates and confidence intervals, and to provide guidelines for developing sets of loci for populations of different sizes and levels of relatedness. The fifth chapter (Hoyle and Maunder 2004, Animal Biodiversity and Conservation) applies integrated analysis techniques developed in fisheries to the modeling of protected species population dynamics - specifically the north-eastern spotted dolphin, Stenella attenuata. It combines data from a number of different sources in a single statistical model, and estimates parameters using both maximum likelihood and Bayesian MCMC. The sixth chapter (Hoyle 2002, peer reviewed and published as Queensland Department of Primary Industries Information Series) results directly from a pressing management issue: developing new management procedures for the Queensland east coast Spanish mackerel fishery. It uses an existing stock assessment as a starting point for an integrated Bayesian management strategy evaluation. Possibilities for further research have been identified within the subject areas of each chapter, both within the chapters and in the final discussion chapter.