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No description available.

No description available.

No description available.

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Policy-makers increasingly need to use scientific data that are imprecise. This problem is particularly apparent for marine mammal management issues, where practical research constraints leave scientists and managers with the problem of drawing inference from sparse data. Effective use of such data therefore places great demands on our methods of data analysis and statistical inference. In this thesis I introduce novel Bayesian methods for the analysis of data on marine mammal abundance and trends. Bayesian methods are applied to a suite of case studies to inform current management issues of importance both in the UK and overseas. These include estimating the probability of density dependence in the growth of a killer whale (Orcinus orca) population inhabiting the inshore waters of Washington State; estimating the size of a widespread population of bottlenose dolphins (Tursiops truncatus) in the Bahamas; and assessing the population status and abundance trends of bottlenose dolphins within a newly designated Special Area of Conservation in the Moray Firth, NE Scotland. Each of these case studies uses model-based analysis of individual photo- identification data to make inference about unknown population parameters of interest. Specifically, Bayesian inference, based on "posterior" probability distributions and statements, is used to facilitate scientific reporting in the face of uncertainty about these unknowns. Additional issues addressed are the selection of alternative statistical models for inference based on posterior model probabilities; incorporating model selection uncertainty into inference through the estimation of model-averaged parameter estimates; and the use of random effects prior distributions to model the relatedness between unknown parameters and increase estimate precision. The application of these methods is accomplished through the use of Markov chain Monte Carlo sampling methods, which are implemented using the WinBUGS software.

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Aspects of the ecology of the serotine bat (<i>Eptesicus serotinus</i>) were studied at colonies in Southwest Germany and Luxembourg.  Maternity roosts of serotines were situated in slate-covered roof-spaces of buildings, offering a wide variety of microclimates which allow the bats to remain there throughout the period of pregnancy and lactation.  The inferred gestation length was on average 52 days.  Gestation length was not directly related to the temperature inside or outside the roost.  The average data of birth in the study region is 17^th June with a mean birth period of 14 days.  The juveniles emerge from the roost for the first time after a mean of 36 days.  The dispersal of the colony starts with this event and the last bats were observed to emerge in the first week of October.  The number of emerging juveniles never corresponded to the number of emerging females.  This fact and the repeated observation of more serotines re-entering the roost during the night than previously emerging, lead to the conclusion that a colony is part of a metapopulation.  The mean emergencies time of the bats is closely related to sunset.  Emergence time is adjusted to the availability of preferred prey taxa and to light intensity.  Mortality of juveniles is increased by lasting inclement weather.  Although habitat availability differed markedly between the study areas, habitat use by the two colonies was very similar.  The most important habitat type used was woodland and its edges adjacent to grassland, followed by permanent grassland, then settlement areas.  Calculation of habitat selection by means of compositional analysis revealed a preference for settlement areas over woodland and over grassland.  Arable land was always significantly avoided.  Radio-tracked serotine bats of both colonies flew similar maximum distances to foraging sites (4.5 km) and had similar average home range sizes (4.6 km²).