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School of Environment and Natural Resources


TWEL Stephanie Sforza

Spatial Analyses of Northern Bobwhite Occupancy and White-tailed Deer Hunter Distribution and Success in Ohio

Stephanie Sforza, M.S.
Advisor: Robert J. Gates


Geospatial techniques can used to extract fine-scale spatial data and examine temporal or spatial patterns to inform wildlife conservation planning and management.  The overall goal of this thesis was to apply geospatial data and analyses to investigate two systems: conservation of Northern Bobwhite (Colinus virginianus) and harvest management of white-tailed deer (Odocoileus virginianus) in Ohio.  Conclusions based on these broad-scale spatial analyses can be used by managers to devise plans which will be actionable and effective for achieving regional population goals.

Northern bobwhite populations have been declining in Ohio for decades as a result of habitat loss and degradation caused by successional processes and changes in land use.  Landscapes with high juxtaposition and interspersion of early successional, agricultural, and forested vegetation are important to fulfill bobwhite resource requirements throughout all life stages.  I applied land cover composition data to empirically derived distance to cover-type functions with the goal to predict probability of bobwhite occupancy throughout their current range in Ohio.  I then compared final model accuracy to a correlational model of naïve landscape indices that similarly predicted occupancy from landscape metrics.  Eighty five percent of the study area had a probability of occupancy < 0.25 during both breeding and nonbreeding seasons. This is indicative of inadequate habitat at a regional level, which has been suggested as the most appropriate level of management for this species.  I assessed predictive accuracy of both models by predicting occupancy at points where Ohio Division of Wildlife (ODW) whistle count surveys were conducted and comparing predictions to presence or absence of bobwhites.  Though both models were accurate to the commonly accepted threshold of 0.7, the distance to cover type model had higher area under the receiver operating curve (AUC) and kappa statistics.  The empirical distance to cover type model more accurately distinguished cases of bobwhite presence than the landscape metrics model.  This finding could be used to support the value of highly detailed studies done at a fine scale for identifying patterns that can be extrapolated out to scales which are practical and useful for conservation management plans.  However, since user accuracy was higher in the distance to cover type model and producer accuracy was higher in the landscape metrics model, context related to the model purpose may be needed to identify which is appropriate in a given situation.

Management of white-tailed deer is an essential task for many wildlife management agencies due to their economic, recreational, and social importance.  Harvest management is a key tool for capturing the benefits and mitigating some detrimental social and ecological impacts of increasingly abundant white-tailed deer populations in Ohio and other midwestern states.  I used state-wide survey data of deer hunting events during 2011-2014 to evaluate factors that influenced deer hunter distribution and probability of success within potential Ohio deer management units with the goal to provide important information for harvest managers at a regional scale.  While final model results were complex, the strongest relationships captured in all models showed hunters were more likely to hunt but less likely to harvest deer on public compared to private lands.  I found differences in final model covariates and the impact they had on hunter use and success between DMUs, which differ based on aspects of human social, geophysical and landcover composition.  For example, while all DMUs had a clear trend for hunters to select for locations with a higher percentage of forest and public land, strength of selection for these predictors and which cover types were avoided differed by DMU and, therefore, by landscape context.  These results suggest that overall, incentivizing landowners to allow hunting on their property and facilitating access for hunters may be the most effective strategy to increase hunter success.  Additionally, information concerning hunter behavior and outcomes in response to spatial variables can be used to devise region-specific management plans to achieve region-specific deer harvest and population goals.