Using multivariate analysis to determine characteristics of sea turtle nest selection along the Florida Panhandle
| dc.contributor.advisor | Halpin, Patrick N | |
| dc.contributor.author | Afford, Heather | |
| dc.date.accessioned | 2016-04-29T10:12:22Z | |
| dc.date.available | 2016-04-29T10:12:22Z | |
| dc.date.issued | 2016-04-29 | |
| dc.department | Nicholas School of the Environment and Earth Sciences | |
| dc.description.abstract | Sea turtle nesting is a highly monitored event in the state of Florida. From this monitoring, important topics in regards to reproductive success are recorded, such as nesting demographics and site selection location. Although these nesting populations are observed and recorded, hatchling studies have still been the main focus in terrestrial based sea turtle studies. This trend is changing. Observations and studies have determined that despite overall physical trends of site selection, such as wide, open beaches with flat slopes among other characteristics (Miller et al. 2003), site selection is quite varied on both large and small scales, therefore, warranting the need for more research on site selection. Biologists and wildlife managers throughout the world have realized the importance of sea turtle nesting in relation to whole species survival, but require more knowledge on how to handle best practices (Hamann et al. 2010). Okaloosa County managers on the Florida Panhandle are working towards more detailed decisions for management practices. For this project, a wide scale study of Okaloosa County and the surrounding region were looked at in regards to three main questions; what variables (e.g. environmental, urban, etc.) contribute to large and small-scale site selection trends and what does this site selection look like, what variables impact site selection individually, and how do urban variables alone affect sea turtle site selection? Two methods were used: site selection prediction analysis through a binomial Bayesian general linear model and tree classification. Site selection analysis looked at three distinct prediction types: a comparison between presence and random nests within the whole study area looking at all variables; a comparison in Okaloosa County and Santa Rosa of present nests in comparison to false crawls looking at all variables; and presence and random nests within the whole study area but only looking at urban variables. Tree classification, similarly, looked at a presence – random nest comparison with all variables, as well as a presence - random nest comparison with urban variables. This complexity in site selection characteristics makes it difficult for management to focus on one particular variable. Yet, this study determines a few main trends: there is a general area between shoreline (high tide) and upper areas with distinct barriers (dunes, roads, urban areas), while within this shoreline area there is a large variability in nest site selection prediction, and finally, urban variables show a complex interaction with and within physical variables. Overall, nest site selection is highly variable, but some trends, which include urban considerations, are involved more than seen in previous studies, although is becoming more prominent in the literature. This study provides a start for what characteristics may contribute to nest site selection, which managers can use to improve best practices for sea turtle management in the Florida Panhandle and around the world. | |
| dc.identifier.uri | ||
| dc.subject | Sea Turtle Florida Panhandle Site Selection | |
| dc.title | Using multivariate analysis to determine characteristics of sea turtle nest selection along the Florida Panhandle | |
| dc.type | Master's project | |
| duke.embargo.months | 0 |