Spatial distribution of fibropapillomatosis in green turtles along the Queensland coast and an investigation into the influence of water quality on prevalence

Abstract

AbstractFibropapillomatosis (FP) is a tumor‐forming disease which affects all species of marine turtle, but predominantly the green turtle (Chelonia mydas). Expression of this disease is thought to be precipitated by poor environmental conditions and often linked to anthropogenically induced environmental changes. Although FP is a globally distributed disease, targeted studies on the spatial distribution of the disease in Australia are limited. Here, we present the first comprehensive report of FP prevalence in Queensland, Australia. A retrospective analysis of 25,645 capture records for 15 sites along the Queensland coast were used to determine FP prevalence and trends in foraging green turtles. Within this data set, 791 turtles (3.1%) with FP tumors were recorded. Our analysis showed that prevalence varies between sites and years, with juvenile turtles being the most frequently affected by the disease. We found that survey method has a significant influence on the apparent FP prevalence detected at each site. That is, surveys which were explicitly FP‐targeted detected higher numbers of individual turtles with FP, and therefore generated higher prevalence rates than comprehensive population surveys. We also report the first attempt at developing water quality indices (WQIs) to compare with FP prevalence data in foraging green turtles. The WQIs were built from metrics published in a range of peer‐reviewed papers, reports, and based on expert opinion. Despite utilizing an extensive data set, a relationship between FP prevalence and WQI rankings at each site could not be quantified. The analysis was confounded by a range of limitations, including data gaps, varying temporal scales and data capture methods in the FP prevalence, and water quality data sets. This study has significant implications for management as it highlights the benefits of designing and collecting centralized data that can be integrated and used across multiple projects or programs.