Graduation Year

2018

Document Type

Thesis

Degree

M.S.

Degree Name

Master of Science (M.S.)

Degree Granting Department

Marine Science

Major Professor

Cameron Ainsworth, Ph.D.

Co-Major Professor

Julien Martin, Ph.D.

Committee Member

Ernst Peebles, Ph.D.

Keywords

citizen science, Florida manatee, Markov Chain Monte Carlo, protection zones, marine mammals, wildlife collisions

Abstract

Wherever wildlife share space with boaters, collisions are a potential source of mortality. Establishing protection and speed zones are the primary actions taken to mitigate collision risk. However, creation of protection zones may be a point of contention with stakeholders as new zones can have significant socioeconomic impacts. The Florida Manatee is a prime example of a species whose abundance and viability are constrained by this balance between the needs of humans and wildlife on a shared landscape. The goal of this work is to help further understand the risk to manatees by quantifying the probability of lethal collisions. I hypothesized that higher boat speeds increase the probability of lethal injury to manatee during a collision and aimed to characterize the relationship between vessel speed and the probability of lethal injury to manatee. I used a logistic regression model implemented with a Bayesian approach and fitted to citizen reported collision data as a feasible method for examining the influence of vessel speed in contributing to lethal injury to a manatee when struck. I also present a method for dealing with uncertainty in data used to report collisions. To conduct this analysis, I used citizen reported collision data. These data are typically collected opportunistically, suffer from low sample sizes, and have uncertainty in reported vessel speeds. Uncertainty associated with speed values in reported collision events was assessed by performing a multiple imputation to replace qualitative vessel speed – in other words, “missing data” – with quantitative values. This procedure involves fitting log-normal distributions to radar data that contained precise vessel speeds along with a physical description like ‘planing’, ‘plowing’, or ‘idle’. For each imputation of the data, a quantitative value was selected randomly from that distribution and used in place of its initial corresponding speed category. I evaluated issues related to quasi-separation and model fit using simulated data sets to explore the importance of sample size and evaluated the effect of key sources of error. The prediction that greater strike speed increases the probability of lethal injury was supported by the data that I analyze

Download

Share

COinS