An evaluation of nest predator impacts and the efficacy of plastic meshing on marine turtle nests on the western Cape York Peninsula, Australia

Last modified: 
December 13, 2019 - 12:31pm
Type: Journal Article
Year of publication: 2019
Date published: 10/2019
Authors: Eric Nordberg, Stewart Macdonald, Gina Zimny, Andrew Hoskins, Anders Zimny, Ruchira Somaweera, Janine Ferguson, Justin Perry
Journal title: Biological Conservation
Volume: 238
Pages: 108201
ISSN: 00063207

Nest predation is considered to be one of the most significant biotic threats to marine turtle populations globally. The introduction of feral predators to nesting beaches has dramatically increased nest predation, reaching near total egg loss in some regions. We monitored a 48 km stretch of beach along western Cape York Peninsula, Australia, from June – November 2018. We recorded a total of 360 nests comprising 117 flatback and 243 olive ridley nests. We installed plastic meshing (90 cm × 100 cm) on 110 olive ridley nests (45.2% of total olive ridley clutches laid) within the study area. We classified all nest predation attempts into three categories: complete, partial, or failed predation events. In total, 109 (30.2%) of all marine turtle nests were depredated by a variety of predators, including feral pigs, dingoes, goannas, and humans. The addition of plastic meshing reduced the likelihood of dingoes gaining access to eggs, but not goannas or feral pigs. Further, we found no difference in the proportion of hatchling emergence between meshed and un-meshed nests. Additionally, while hatchling emergence was reduced in nests that had been partially depredated, these nests still produced live hatchlings and contributed to recruitment. The success of particular predator control methods is often predator, and/or regionally, specific. Our findings highlight a thorough understanding of predator guilds and their relative impacts is required to deploy targeted and predator-specific strategies to maximize conservation results. We present a strong case for data-driven adaptive management that has implications for designing optimal predator management plans.

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