The ability to accurately identify individuals is required for the detailed study of animals. Numerous artificial markers have been developed for this purpose. Negative effects on survival, reproductive success and behavior have been reported for most marking methods, significantly affecting the very parameters being studied. / Birds of prey have suffered the shortcomings of artificial marking methods. In light of the known and potential deleterious effects of marking, attention has been focused on developing new techniques to identify individual raptors without attaching artificial markers. / This study investigated the use of tarsal scale patterns as unique individual identifiers in birds of prey. The American kestrel (Falco sparverius) was chosen as a model. Both legs of seventy-five kestrels were photographed over a two-year period. / Photographic comparisons of 150 scale patterns demonstrated the uniqueness of each and therefore its ability to be used as an individual's natural identifier. Furthermore, patterns were found to remain unchanged from one year to the next. These findings support the hypotheses that tarsal scale patterns are unique to each bird and do not change over time. / A method of coding the tarsal scale patterns was developed. These codes can be used in a computerized data base to significantly enhance the speed of pattern searches.

No description available.

A defining feature of capture-recapture is missing data due to imperfect detection of individuals. The standard approach used to deal with the missing data is to integrate (or sum) over all the possible unknown values. The missing data is completely removed and the resulting likelihood is in terms of the observed data. The problem with this approach is that often biologically unnatural parameters are chosen to make the integration (summation) tractable. A related consequence is that latent variables of interest, such as the population size and the number of births are only available as derived quantities. As they are not explicitly in the model they are not available to be used in the model as covariates to describe population dynamics. Therefore, models including density dependence are unable to be examined using standard methods. Instead of explicitly integrating out missing data, we choose to include it using data augmentation. Instead of being removed, the missing data is now present in the likelihood as if it were actually observed. This means that we are able to specify models in terms of the data we would like to have observed, instead of the data we actually did observe. Having the complete data allows us to separate the processes of demographic interest from the sampling process. The separation means that we can focus on specifying the model for the demographic processes without worrying about the sampling model. Therefore, we no longer need to choose parameters in order to simplify the removal of missing data, but we are free to naturally write the model in terms of parameters that are of demographic interest. A consequence of this is that we are able write complex models in terms of a series of simpler conditional likelihood components. We show an example of this where we fit a CJS model that has an individual-specific time-varying covariate as well as live re-sightings and dead recoveries. Data augmentation is naturally hierarchical, with parameters that are specified as random effects treated as any other latent variable and included into the likelihood. These hierarchical random effects models make it possible to explore stochastic relationships both (i) between parameters in the model, and (ii) between parameters and any covariates that are available. Including all of the missing data means that latent variables of interest, including the population size and the number of births, can now be included and used in the model. We present an example where we use the population size (i) to allow us to parameterize birth in terms of the per-capita birth rates, and (ii) as a covariate for both the per-capita birth rate and the survival probabilities in a density dependent relationship.

Thesis (Ph.D.) - University of St Andrews, April 2009.

Thesis: Ph. D., Joint Program in Applied Ocean Science and Engineering (Massachusetts Institute of Technology, Department of Earth, Atmospheric, and Planetary Sciences; and the Woods Hole Oceanographic Institution), 2018. / Cataloged from PDF version of thesis. / Includes bibliographical references (pages 138-154). / Highly migratory marine fishes support valuable commercial fisheries worldwide. Yet, many target species have proven difficult to study due to long-distance migrations and regular deep diving. Despite the dominance of oceanographic features, such as fronts and eddies, in the open ocean, the biophysical interactions occurring at the oceanic (sub)mesoscale (< 100 km) remain poorly understood. This leads to a paucity of knowledge on oceanographic associations of pelagic fishes and hinders management efforts. With ever-improving oceanographic datasets and modeling outputs, we can leverage these tools both to derive better estimates of animal movements and to quantify fish-environment interactions. In this thesis, I developed analytical tools to characterize the biophysical interactions influencing animal behavior and species' ecology in the open ocean. A novel, observation-based likelihood framework was combined with a Bayesian state-space model to improve geolocation estimates for archival-tagged fishes using oceanographic profile data. Using this approach, I constructed track estimates for a large basking shark tag dataset using a high-resolution oceanographic model and discovered a wide range of movement strategies. I also applied this modeling approach to track archival-tagged swordfish, which revealed affinity for thermal front and eddy habitats throughout the North Atlantic that was further corroborated by synthesizing these results with a fisheries-dependent conventional tag dataset. An additive modeling approach applied to longline catch-per-unit effort data further highlighted the biophysical interactions that characterize variability in swordfish catch. In the final chapter, I designed a synergistic analysis of high-resolution, 3D shark movements and satellite observations to quantify the influence of mesoscale oceanography on blue shark movements and behavior. This work demonstrated the importance of eddies in structuring the pelagic ocean by influencing the movements of an apex predator and governing the connectivity between deep scattering layer communities and deep-diving, epipelagic predators. Together, these studies demonstrate the breadth and depth of information that can be garnered through the integration of traditional animal tagging and oceanographic research with cutting-edge analytical approaches and high-resolution oceanographic model and remote sensing datasets, the product of which provides a transformative view of the biophysical interactions occurring in and governing the structure of the pelagic ocean. / by Camrin Donald Braun. / Ph. D.