Thesis (MMedVet (Gyn))--University of Pretoria, 2006. / Includes bibliographical references.

The Anthropocene epoch in which we are currently living, also known as the Holocene, has brought about unprecedented losses in planet Earth’s biodiversity. Numerous extirpations of floral and faunal species have been influenced by human encroachment and more specifically, the exploitation of such species and the respective habitats in which they reside. It is this notion that has propelled many scientists to take up intellectual arms in an effort to protect these invaluable resources. The purpose of this research was to develop technologies to measure and evaluate various variables that influence animal physiology, specifically in amphibians who represent the most threatened class of all animal taxa. Species-specific knowledge including life history and an understanding of evolutionary traits are often needed to effectively guide the management decisions surrounding any given animal population. Specific objectives of this project were to develop non-invasive methods, such as hormone monitoring, machine learning-aided ultrasonography, and near-infrared spectroscopy (NIRS), to assess vital physiological traits, such as biological sex, reproductive status, and chytrid fungus pathogen detection in threatened amphibian species. The novel technologies developed and applied in amphibians here may provide insights for addressing conservation related questions in other animal as well as plant species. Additionally, automation of physiological monitoring techniques through the use of machine learning methods reduces barrier to entry and enables these technologies to be operated by a larger practitioner base. This research also serves to advance methods surrounding chemometric analyses as it pertains to the discipline of wildlife spectroscopy, where large multivariate datasets require data manipulation strategies to produce robust prediction models for the physiological trait of interest for qualitative or quantitative assessment. To that end, a multi-model framework is provided for optimizing predictive outcomes to address questions relating to wildlife management and conservation initiatives.

Master of Science in Veterinary Biomedical Science / Department of Clinical Sciences / Robert L. Larson / The selection of young replacement animals can have a significant impact on beef herd reproductive performance. Replacement heifers can be utilized to improve reproductive performance by replacing mature animals that failed to meet the production with young, cycling heifers that can have the potential of improving the reproductive momentum of a herd. The use of yearling bulls in natural breeding herds has the advantage of shortening the generational interval of the herd and has the potential of reducing the cost per cow exposed as additions to the bull battery. This thesis involves two studies that investigated methods used for the selection of peripubertal replacement animals in beef herds. The first study evaluated the ability of the novel Ready-Intermediate-Problem (RIP) replacement heifer evaluation matrix to classify heifers into groups that allow producers to select for replacements that meet production goals. Beef heifers (n=341) were classified according to the RIP matrix guidelines and then exposed to AI breeding, bull breeding, or a combination of both as per the management plans for each participating herd. Following breeding season the heifers were evaluated to determine pregnancy status, pregnancy status to single AI exposure, days bred, and the number of 21 day cycles needed during breeding season to become pregnant. After breeding season, 298 (87%) of the heifers were pregnant, 204 (68%) of which became pregnant in the first 21 days of the breeding season. Probability of overall pregnancy and pregnancy after single AI exposure was not significantly associated with RIP classification. There was a significant interaction in RIP classification by 21 day cycle. The second study was a retrospective study using BSE result data to determine the proportion of yearling beef bulls that are classified as satisfactory potential breeders when reevaluated after failing their initial breeding soundness evaluation (BSE) and to identify any predictive factors at initial BSE for satisfactory performance at revaluation. The study included 2,805 beef bulls between 11 and 14 months of age at first BSE evaluated at KABSU from 2006 to 2014. Generalized linear mixed models were created to assess potential associations among breed, age, and interaction between breed and age and passing the initial evaluation and identify predictive factors for risk of passing BSE after initial failure. The majority (93%) of the study bulls passed one of up to three BSEs. There was a significant interaction between age and breed of bull at initial BSE. Identification of suitable peripubertal replacement animals that will improve herd reproductive performance remains a challenge for producers. There are several factors that can affect replacement animals’ ability to perform according to expectations at the beginning of the breeding season. Classification of heifers into categories that can predict performance during breeding season with reasonable confidence can assist producers in identifying heifers that complement the reproductive performance goals of the herd. Utilizing BSE to identify bulls that have adequate semen quality as well as other traits important for breeding soundness is similarly important in reducing the risks of using young bulls for breeding.