Proxy indicators of sand temperature help project impacts of global warming on sea turtles in northern Australia

Abstract

Global warming poses serious threats to sea turtle populations since sex determination and hatching success are dependent on nest temperature. Nest sex ratios may be skewed towards a predominantly female output, and eggs may be consistently exposed to temperatures that exceed thermal mortality thresholds. Consequently, understanding the rates at which sand temperatures are likely to change represents an immediate priority. Here, we use regression analyses to correlate air temperature (AT) and high-resolution sea surface temperature (SST) to sand temperature at 5 rookeries in northern Australia. We show that previous studies using only AT could potentially be improved by including SST as a covariate. At our study sites, combined SST and AT models explained between 79 and 94% of the variance in sand temperature in recent years. Our results suggest that hatchling production will skew towards all females at 3 of our sites by 2070 (Moulter Cay, Milman Island and Bramble Cay) and by as early as 2030 at Ashmore Island and Bare Sand Island. The projections presented here can inform the timely and targeted implementation of local-scale management strategies to reduce the impacts of global warming on sea turtle populations. Identifying and testing new strategies deserves immediate attention, as does further research into the adaptive capacity of sea turtles.

Coastal light pollution and marine turtles: assessing the magnitude of the problem

“…Since successful turtle nesting is strongly hindered by the presence of artificial light (Witherington & Martin 2000) and the effective management of light pollution adjacent to turtle nesting sites may be both expensive and time-intensive (e.g. Fuentes et al 2009), the identification of nesting sites at greatest risk from light pollution is crucial to ensure that limited conservation resources are allocated most effectively (e.g. Fuentes et al 2011).…”

“…Management actions considered necessary to address this issue include the identification of priority areas affected by artificial light. Yet, implementing management strategies can be expensive and time intensive (Fuentes et al 2009). Knowledge of areas at highest risk from light pollution is important to permit management re sources to be allocated most effectively (e.g.…”

Predicting the impacts of climate change on a globally distributed species: the case of the loggerhead turtle

“…More effort is urgently needed to predict how such changes may impact future hatchling sex ratios of populations (Fuentes et al, 2009). If sea turtles do not adapt by shifting their geographical ranges, phenology of breeding or pivotal temperatures, sex ratios, many of which are already highly female biased, will become further skewed.…”

Estimating sex ratios in Caribbean hawksbill turtles: testosterone levels and climate effects

“…Experimental work has yet to be carried out to demonstrate if this is likely to be the case for marine turtles, although with long periods to maturity and slow growth rates, this is a challenge. In addition, early modelling work has suggested that, through climate change, the proportion of female turtles at large in the pop ulation should increase still further (Hawkes et al 2007, Fuentes et al 2009, Mitchell & Janzen 2010, Valverde et al 2010, and thus the pace of change may proceed more quickly than our un derstanding of the system before perturbation. We advocate that all marine turtle research programmes working with foraging aggregations of marine turtles routinely collect blood and tissue samples to enable secondary sex ratio estimates and assignment of source rookeries.…”

“…A problem could arise if future climate warming led to the production of a higher proportion of female hatchlings than at present or if the production of male hatchlings ceased from some beaches (Hawkes et al 2007, Fuentes et al 2009, Mitchell & Janzen 2010, Val verde et al 2010, Witt et al 2010b. Understanding sex ratios is essential for the development of population models (Owens 1997, Hamann et al 2010 for predicting effects of anthropogenic climate change (Hawkes et al 2009).…”