Developing Ecological Criteria for Prescribed Fire in South Florida Pine Rockland Ecosystems

The pine rocklands of South Florida, characterized by a rich herbaceous flora with many narrowly endemic taxa beneath an overstory of south Florida slash pine (Pinus elliottii var. densa), are found in three areas: the Miami Rock Ridge of southeastern peninsular Florida, the Lower Florida Keys, and slightly elevated portions of the southern Big Cypress National Preserve. Fire is an important element in these ecosystems, since in its absence the pine canopy is likely to be replaced by dense hardwoods, resulting in loss of the characteristic pineland herb flora. Prescribed fire has been used in Florida Keys pine forests since the creation of the National Key Deer Refuge (NKDR), with the primary aim of reducing fuels. Because fire can also be an effective tool in shaping ecological communities, we conducted a 4-year research study which explored a range of fire management options in NKDR. The intent of the study was to provide the Fish and Wildlife Service and other land managers with information regarding when and where to burn in order to perpetuate these unique forests.

Improved Criteria for Estimating Calibration Factors for Highway Safety Manual (HSM) Applications

The Highway Safety Manual (HSM) estimates roadway safety performance based on predictive models that were calibrated using national data. Calibration factors are then used to adjust these predictive models to local conditions for local applications. The HSM recommends that local calibration factors be estimated using 30 to 50 randomly selected sites that experienced at least a total of 100 crashes per year. It also recommends that the factors be updated every two to three years, preferably on an annual basis. However, these recommendations are primarily based on expert opinions rather than data-driven research findings. Furthermore, most agencies do not have data for many of the input variables recommended in the HSM. This dissertation is aimed at determining the best way to meet three major data needs affecting the estimation of calibration factors: (1) the required minimum sample sizes for different roadway facilities, (2) the required frequency for calibration factor updates, and (3) the influential variables affecting calibration factors. In this dissertation, statewide segment and intersection data were first collected for most of the HSM recommended calibration variables using a Google Maps application. In addition, eight years (2005-2012) of traffic and crash data were retrieved from existing databases from the Florida Department of Transportation. With these data, the effect of sample size criterion on calibration factor estimates was first studied using a sensitivity analysis. The results showed that the minimum sample sizes not only vary across different roadway facilities, but they are also significantly higher than those recommended in the HSM. In addition, results from paired sample t-tests showed that calibration factors in Florida need to be updated annually. To identify influential variables affecting the calibration factors for roadway segments, the variables were prioritized by combining the results from three different methods: negative binomial regression, random forests, and boosted regression trees. Only a few variables were found to explain most of the variation in the crash data. Traffic volume was consistently found to be the most influential. In addition, roadside object density, major and minor commercial driveway densities, and minor residential driveway density were also identified as influential variables.