Thesis (M.A.)--York University, 2002. Graduate Programme in Social Anthropology. / Typescript. Includes bibliographical references (leaves 102-107). Also available on the Internet. MODE OF ACCESS via web browser by entering the following URL: http://wwwlib.umi.com/cr/yorku/fullcit?pMQ71574.

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Msinsi Holdings (Pty) Ltd are considering the introduction of nyala to Shongweni Dam and Game Reserve in KZN. This reserve has a naturally resident population of bushbuck and is located beyond the natural distribution of nyala. Concerns for competition between these two species causing declines in bushbuck numbers elsewhere prompted the present study. The main aim of the present study was to determine some aspects of the ecology of bushbuck within the reserve to assist with decision-making regarding the introduction of nyala and species specific-management of bushbuck at the study site. Bushbuck home range and habitat utilisation was investigated with the aid of radio telemetry and Geographical Information Systems. Estimates of total home range size for males using minimum convex polygons (MCPs) and fixed kernels (FKs) were 33.9 ha and 32.1 ha respectively. Estimates of total home range size for females using MCPs and FKs were 12.0 ha and 13.5 ha respectively. A significant difference between total home range size for gender (male and female) was found but there was no significant difference for age (adult and subadult). Bushbuck typically utilised one core area within their home ranges in which 50 % of their time was spent in approximately 17 % and 11.7 % of their total home range for males and females respectively. A substantial overlap in total home range and core areas between animals was found. Bushbuck showed preference for short thickets and avoidance of low closed grasslands. High reedbeds were utilised in proportion to their availability and tallwoodlands were not utilised by the study animals, but were observed to be utilised by other non radio-collared bushbuck. Habitat preference was a consequence of favourable cover being provided by the structure of the vegetation and the occurrence of favourable foraging species. Bushbuck utilisation of topographical aspect was largely determined by the vegetation type that occurred on the respective slopes. Estimations of bushbuck density and abundance were made using sighting efforts, drive counts, and mark-resightings. Sighting efforts using distance sampling during spring were found to be the most effective in terms of accuracy and man-hour costs, however, these were still not considered to be precise estimations of the total bushbuck population at SDGR, but would be useful for monitoring population trends as a result of the high repeatability and simplicity of the method. Sex, age ratios and nocturnal activity were determined using field classification. The field classification method of age and sex ratio determination used during the present study was found to be very subjective and was therefore suggested to have produced ratios which may be largely biased towards the female component of the population. This in turn also effected the determination of social organization and was evident when compared to previous studies. Bushbuck activity determined from radio telemetry and sighting efforts produced results that corresponded with all previous studies, showing bushbuck to be largely nocturnal, moving much larger distances at night than during the day, and spending most of their time walking and feeding at night. The status and management of synoptic bushbuck and nyala in KwaZulu-Natal was also investigated by means of a questionnaire survey. From the opinions of landowners and reserve managers, the status of bushbuck sharing a sympatric relationship with nyala in KwaZulu-Natal (KZN) appeared to be stable to declining, whereas nyala status was increasing. This trend was suggested to be a result of competition for resources between the two species. Northern KZN recorded a higher frequency of this trend (57.7%, n = 26) compared to the Midlands (35.7%, n = 14), as did Ezemvelo KZN Wildlife Reserves (85.7%, n = 7) compared to privately owned properties (42.4%, n = 33). Very little species-specific management for nyala and bushbuck occurred in reserves that participated in the present survey. / Thesis (M.Sc.)-University of KwaZulu- Natal, Pietermaritzburg, 2003.

Understanding population structure and resource selection are essential for wildlife management and conservation. I assessed the population structure and resource selection of elk (Cervus elaphus) in central Ontario. I used fuzzy and hierarchical cluster analyses to elucidate elk population structure based on spatial data collected from 41 radio-collared elk. I assessed impacts of habitat quality on space use using the minimum convex polygon (MCP) and fixed kernel methods. I evaluated resource selection by elk in winter, spring, summer, and fall, from December 2011 to August 2013, using resource selection functions (RSF’s). I used a generalized linear mixed model (GLMM) to evaluate resource selection functions and used Akaike information criterion (AICc ) to select the best model of 20 candidate models constructed a priori. Models included parameters representing resources known to be important to elk: elevation, aspect, slope, distance to roads and water, and habitat. Both fuzzy and hard clustering indicated that elk in Central Ontario occur in a metapopulation that includes 5 subpopulations. The largest cluster consisted of a core group of 22 radio-collared elk located in Burwash with several satellite subpopulations spread along a 50km long north-south axis and a small subpopulation to the west located in Worthington. Survival rates among subpopulations were similar ranging from 0.71 to 0.83; however, anthropogenic causes of mortality were predominant only in the Burwash subpopulation. Space use and density of elk differed between core and satellite subpopulations. Resource selection by elk differed by time of day and season. In all seasons, elk selected open habitats at night and more forested areas during the day. Elk avoided areas close to roads in spring, but selected them in winter at night. Elk selected higher elevations in winter and for south facing slopes in spring and fall. Elk displayed strong crepuscular activity patterns in all seasons; however, movements were limited in winter. Understanding population structure is important in order to develop appropriate management plans. My results support the conclusion that population structure can be reliably assessed using spatial data. Resource selection is a dynamic process that changes with seasons, as well as animal activity across the diel period. Resource selection should include time of day in order to obtain a complete picture of resources important to a particular species and to support the conservation of habitats used for various animal activities.

<p> After nearly being extirpated from the state, black bears in Maryland have rebounded to a point where recreational harvest has now become an important management tool. Having a better understanding of bear population parameters, movements, and harvest vulnerability allows managers to implement hunting more effectively and responsibly. To estimate demographics of the Maryland bear population, we implemented noninvasive genetic sampling of bear hair during summer 2011. We used a model-based sampling design that allowed us to collect samples more efficiently. We used presence-only maximum entropy (Maxent) modeling to classify the study area based on predicted probability of bear occurrence, and allocated the majority of our hair snares to areas with high or medium probabilities. Using microsatellite analysis and mark-recapture methods, we estimated the bear population at 701 individuals. This represents a nearly doubling of the population since the previous estimate in 2005. Our density estimate (0.25 bears/km²) is comparable to other estimates from southeastern and mid-Atlantic states. Our sampling approach did lead to more efficient sample collection, with more hair samples collected at snares located in areas with predicted high or medium probability of bear occurrence than those in low probability areas. However, in the eastern portion of our study area, where bear occurrence is presumed to be much lower, our sampling effort seemed insufficient to collect enough samples for reliable abundance estimation. As a first step toward quantifying harvest vulnerability, we used Global Positioning System (GPS) units to record movements and spatial behaviors of 108 bear hunters during the 2005&ndash;2007 Maryland bear hunting seasons. Median values showed that hunters traveled 2.9 km per hunting event, but only 0.6 km from their starting point. Hunters did not seem to show any preferential use of areas based on the landscape metrics we examined (e.g., elevation, distance from nearest road) except cover type, where 81% of locations were in deciduous forests. We found few differences between spatial behaviors of groups of hunters based on harvest success, residency, and previous bear hunting experience, as classified using post-hunt mail surveys. One notable difference is that successful hunters used steeper slopes than unsuccessful hunters. We also found that hunter perceptions of total distance traveled and distance from nearest roads were often highly inaccurate, showing that hunter surveys are not a useful tool for collecting those data. For Garrett County, Maryland, we used the hunter locations to create a Maxent model of the spatial distribution of harvest pressure. We also created a model using fall telemetry locations of female bears and compared the models to identify areas of high (i.e., high hunter and high bear occurrence) and low (i.e., low hunter and high bear occurrence) harvest vulnerability. Both models showed higher probability of occurrence on public lands. Both high and low vulnerability areas comprised small portions of the county. The low vulnerability areas included 9 larger blocks (>1 km²), which were 2.3 times steeper, 2.0 times farther from roads, and 1.5 times farther from streams than the medians for the study area. Those characteristics may limit hunter access to and use of the areas. Our predicted high vulnerability areas did not correspond to most previous bear harvest locations, indicating that our definition of harvest vulnerability often does not translate to actual harvest. Finally, we used GPS collars to track female bear locations in Garrett County and examine home range dynamics. Fixed kernel estimates for annual, spring, summer, and fall home ranges were 10.40 km², 8.93 km², 16.08 km², and 19.35 km², respectively. Fall and summer home ranges were larger than spring home ranges, but summer and fall ranges were similar. Solitary females had mean spring home ranges 6.9 times larger than females with cubs-of-the-year, but ranges did not differ during other seasons. Bears exhibited high levels of home range fidelity, with home range centroids shifting little among seasons or years. Intraspecific overlap of home ranges occurred during all 3 seasons, but was most common in summer. The results of this study provide Maryland bear biologists and managers with essential information about the state&rsquo;s bear population. Home range estimates represent important baseline information to determine appropriate spatial scales of management. The abundance estimates will be used to set proper harvest quotas with the goal of slowing the bear population growth. The hunter movement analysis and harvest vulnerability modeling may be used by managers to adjust harvest regulations to increase the efficacy of the hunting seasons.</p>

<p> The Golden-winged Warbler (<i>Vermivora chrysoptera</i>) is an imperiled migratory songbird that nests in young forest habitats of eastern North America. As such, this species has recently been the focus of an intensive multi-year, range-wide, breeding ecology study. A major focus of this research involved spot-mapping color banded males to examine relationships between nesting success and territory-scale habitat variables. I compared differences in space and habitat use of individual male Golden-winged Warblers that were monitored using both spot mapping and radio telemetry. An individual's telemetry delineated use area was on average 3.6 times larger than its spot-mapped territory. Almost half (46%) of all telemetry locations were located outside their respective male's spot-mapped territory. Number of saplings was higher in telemetry use areas (22.49 &plusmn; 2.14) than spot-mapped territories (11.80 &plusmn; 1.86). Although the exact motive for extra-territorial movements is unknown, foraging and/or suggestive observations of extra-pair copulation are likely motivating factors. The results of my study suggest Golden-winged Warblers are seeking resources outside their spot-mapped delineated territories. Furthermore, Golden-winged Warblers were found to have more telemetry locations in mature forest than found through spot-mapping. Ultimately, spot mapping alone does not accurately reflect Golden-winged Warbler space use and habitat needs.</p>

<p> My research was focused on greater sage-grouse (<i> Centrocercus urophasianus</i>; hereafter "sage-grouse") nest-site selection, nest success, and hen survival in relation to avian predators. The trade-off between using habitat and avoiding predators is a common decision for prey species including sage-grouse. In Chapter 2, I compared avian predator densities at sage-grouse nest and brood locations to random locations. Sage-grouse were located where densities of small, medium, and large avian predators were 65-68% less than random locations. </p><p> The effects of anthropogenic and landscape features on habitat use of sage-grouse hens have not been evaluated relative to avian predator densities. In Chapter 3, I compared anthropogenic and landscape features and avian predator densities among sage-grouse locations (nest, early-brood, late-brood) and random locations. I found sage-grouse hens chose locations with lower avian predator densities compared to random locations, and selected locations farther away from anthropogenic and landscape features. </p><p> Depredation of sage-grouse nests can be an influential factor limiting their productivity. Predator removal has been simultaneously proposed and criticized as a potential mitigation measure for low reproductive rates of sage-grouse. In Chapter 4, I hypothesized that sage-grouse nest success would be greater in areas where Wildlife Services lowered common raven (<i> Corvus corax</i>: hereafter "raven") density. I found that Wildlife Services decreased raven density by 61% during 2008&ndash;2011 but I did not detect a direct improvement to sage-grouse nest success. However, sage-grouse nest success was 22% when ravens were detected within 550 m of a sage-grouse nest and 41% when no raven was detected within 550 m. In Chapter 5, I assessed interactive effects of corvid densities relative to anthropogenic and landscape features on sage-grouse nest success. I found that sage-grouse nest success was positively correlated with rugged habitat. </p><p> Survival of breeding-age birds is the most important demographic parameter driving sage-grouse abundance. In Chapter 6, I evaluated the effect of raptor densities, proximity to anthropogenic and landscape features, and hen behavior on survival of sage-grouse hens. I found that sage-grouse hen survival was negatively correlated with golden eagle (<i>Aquila chrysaeto</i>s) density, proximity to anthropogenic and landscape features, and hen parental investment (nesting and brood-rearing).</p>

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<p> Early detection of an invasive species facilitates control and eradication. Slender false brome <i>(Brachypodium sylvaticum)</i> was first discovered in the Santa Cruz Mountains of Central California in 2003 as a non-native grass in redwood forests, competing with native vegetation. The current infestation in the Santa Cruz Mountains, estimated to be 300 acres, is concentrated in San Mateo County and could be eradicated. This study sought to determine most likely locations of slender false brome in the Santa Cruz Mountains by assessing environmental attributes of known presence locations using species distribution modeling and Maxent software. The study used 1,320 species presence points collected in field surveys conducted from 2009 to 2012, GIS environmental layers covering a 940 km² study area, and the machine-learning program Maxent to identify slender false brome habitat at a 30 m resolution in the Santa Cruz Mountains. Maxent models successfully identified locations of potential distribution of slender false brome (training AUC = 0.961, test AUC = 0.960). Annual precipitation, average annual maximum or minimum temperature, and soils were the most important predictors. An independent dataset corroborated the performance of the Maxent model. Maxent could be used by land managers for targeting field surveys by predicting most likely <i> B. sylvaticum</i> habitat in the Santa Cruz Mountains.</p>

<p>Rice not collected by harvesters and natural seeds are important foods for waterfowl. Estimation of abundance of these seeds is necessary for calculating waterfowl habitat conservation needs in the Louisiana Chenier Plain (LCP) and Texas Mid-Coast (TMC). My objectives were to quantify dry mass of rice and other seeds from August-November 2010, and estimate waterbird abundances on farmed and idle ricelands in these regions from December 2010-March 2011. Rice abundance in farmed ricelands ranged from 159.7 kg/ha (CV = 66.6%) to 1,014.0 kg/ha (CV = 8.3%). Natural seed abundance in idle ricelands ranged from 99.7 kg/ha (CV = 32.9%) to 957.4 kg/ha (CV = 17.2%). Greatest waterbird densities occurred in shallowly flooded disked ricelands (mean = 7.35 waterbirds/ha, 90%; CI = 2.37-19.70). Ratoon, disked, and shallowly flooded ricelands are important habitat for non-breeding waterbirds but variable estimates of seed and waterbird abundances warrant continuation of this study.