This study was a comprehensive evaluation of population reconstruction techniques. Population reconstruction techniques are population estimation methods that calculate a minimum population size based on age-specific harvest data (Downing 1980, Roseberry and Woolf 1991). Population reconstruction techniques share the following characteristics: 1) utilization of catch-at-age data and 2) backward addition of cohorts to estimate a minimum population size. I developed a questionnaire to survey the biologists participating in this survey to determine the most common reconstruction technique used to estimate population sizes of exploited white-tailed deer (Odocoileus virginianus) and black bear (Ursus americanus). Downing reconstruction (Downing 1980) was the most commonly used reconstruction technique among biologists participating in this study. Based on a comprehensive literature review and discussions with state biologists, I decided to evaluate virtual reconstruction (Roseberry and Woolf 1991) and develop a new reconstruction technique: Reverse Order reconstruction. I developed a quantitative population model in Microsoft Visual Basic 6.0 to evaluate the ability of the 3 reconstruction techniques to estimate population sizes given a variety of conditions. I evaluated the effects of stochasticity on reconstruction population estimates by incorporating different levels of environmental stochasticity (i.e. process error) and measurement error in the generated or "known" population. I also evaluated the effects of collapsing age classes and aging biases on population estimates. In all conditions, Downing and virtual reconstruction were underestimates of the actual population size. Reverse Order reconstruction more closely estimated the actual population size, but is also more data-intensive than the other 2 methods. Measurement error introduces more uncertainty in the reconstructed population estimates than does process error. The population simulation model proved that Downing and virtual reconstruction are consistently underestimates and the percent underestimation is due to lack of inclusion of a natural mortality rates in population estimation. I used the results of the questionnaire to characterize the harvest datasets of the states participating in this study. From these results, I chose two harvest datasets to further analyze: a white-tailed deer harvest dataset from North Carolina and a black bear harvest dataset from Pennsylvania. I analyzed these datasets with Downing and virtual reconstruction. I also applied the quantitative population model to these datasets to evaluate the effect of increasing levels of measurement error on the variance of the population estimates. I found that Downing and virtual reconstruction estimated the population sizes very closely to one another, within 5%, for both datasets, and the reconstructed estimates closely tracked the actual harvest numbers. I also found that increasing levels of measurement error increased the variance associated with reconstructed population estimates and may decrease the ability of these techniques to accurately capture population trends. / Master of Science

The long-term success of wildlife conservation depends on maximizing the benefits of limited funds and data in pursuit of population and habitat objectives. The ultimate currency for wildlife management is progress toward long-term preservation of ample, wild, free wildlife populations and to this end, funds must be wisely spent and maximal use made from limited data. Through simulation-based analyses, I evaluated the efficacy of various models for estimating population abundance from harvest data. Because managers have different estimators to choose from and can also elect to collect additional data, I compared the statistical performance of different estimation strategies (estimator + dataset) relative to the financial cost of data collection. I also performed a value of information analysis to measure the impact that different strategies have on a representative harvest management decision. The latter analysis is not based on the cost of data, but rather on the management benefit derived from basing decisions on different datasets. Finally, I developed a hybrid modeling framework for mapping habitat quality or suitability. This framework makes efficient use of expert opinion and empirical validation data in a single, updatable statistical structure. I illustrate this method by applying it across an entire state.

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Thesis (M. Sc. (Agriculture, Horticulture)) --University of Limpopo, 2016 / ‘Reed’ avocado is a late season cultivar introduced to South Africa from California. The cultivar has shown good adaptation and produces quality fruit with export potential. Its pre-harvest adaptation and production aspects have been researched and documented. However, the effect of harvest time, post-harvest storage and ripening temperature has not been comprehensively studied on this newly introduced ‘Reed’ avocado cultivar. Therefore, the aim of this work was to investigate the effect of different harvest time, post-harvest storage and ripening temperature on the quality of late season ‘Reed’ avocado fruit. Matured ‘Reed’ avocado fruit were harvested based on moisture content indexing in December (2015) and January (2016). The experiment was carried out in a factorial, arranged in a completely randomised design (CRD) with three replicates. Treatment factors were: 2 x harvest time (mid-and late), 2 x post-harvest storage (2.0 and 5.5°C), 3 x ripening temperature (16, 21 and 25°C) and 5 x ripening day (0, 2, 4, 6 and 8). Fruit were stored at 2.0 and 5.5°C for 28 days, thereafter, ripened at 16, 21 and 25°C until fully ripe. During ripening, fruit were evaluated for weight loss, skin colour, firmness, respiration rate, physiological and pathological disorders. Mid-harvest fruit had higher moisture content when compared with late harvest fruit. However, harvest time, post-harvest storage, ripening temperature and ripening time (days) significantly influenced fruit weight loss, firmness, respiration rate, ripening percentage of ‘Reed’ avocado fruit during ripening. Moreover, fruit firmness decreased faster at higher temperatures (25 and 21°C) with fruit ripening within 4 and 6 days, respectively. In addition, ripening at a lower temperature (16°C) was slower with fruit fully ripened within 8 days after withdrawal from cold storage at both harvest times. ‘Reed’ avocado fruit respiration rate followed a climacteric pattern, however, significantly higher rate at higher temperature (25°C) when compared with lower temperature (16°C) after withdrawal from cold storage during both harvest times. Interestingly, mid-harvest fruit showed high electrolyte damage after withdrawal from 2.0°C when compared with late harvest fruit at the same temperature. Furthermore, mid-harvest fruit stored at 2.0ºC and ripened at 21°C showed higher chilling injury when compared with fruit ripened at 16 and 25°C. High electrolyte leakage positively correlated (R2 = 0.242) with high chilling damage for xiv mid-harvest fruit stored 5.5°C. Treatment factors had a significant effect (P < 0.05) on lightness (L *) and hue angle (h ) but no significant effect (P > 0.05) on chroma (C *) and eye colour of ‘Reed’ avocado fruit during ripening, irrespective of harvest time. Overall results showed a visual change in ‘Reed’ avocado skin colour, with eye colour changing from green to bright yellow. Furthermore, late harvest fruit showed high post-harvest pathological diseases after removal from high temperature (5.5°C) when compared with mid-harvest fruit stored at low storage temperature (2.0°C). Ripening at a higher temperature (21 and 25°C) resulted in higher incidence of stem-end rot and body rot when compared with lower temperature (16°C) for both harvest times. Late harvest fruit showed a higher incidence of vascular browning at higher ripening temperatures (21 and 25°C) when compared with lower temperature (16°C) after withdrawal from cold storage. Moreover, overall results showed that harvest time, post-harvest storage and the ripening temperature had a profound influence on the quality of ‘Reed’ avocado fruit. In conclusion, ‘Reed’ avocado fruit can be harvested during mid- or late season and stored at recommended low temperature (2.0ºC); and thereafter, ripened at either 16 or 21ºC. In addition, future studies should focus on identifying pre-harvest practices that promote higher post-harvest fruit quality for ‘Reed’ avocado fruit under South African production environment. Keywords: ‘Reed’ avocado fruit; firmness; electrolyte leakage; respiration rate; stem-end rot; body rot; vascular browning