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We present here a copper speciation model that accounts for the long-term (‘coastal-acidification’) and short-term (daily and seasonal variation) variability in water pH and water temperature. The developed model is applied to a sub-tropical estuary (Moreton Bay, Australia) at a one hundred year time scale so that outputs are consistent with climate change projections. The model predicts that the mean cupric ion concentration (Cu2+) in the estuary will increase by 115% over the next 100 years as a result of the projected decrease in pH and increase in water temperature. Through calibration, the estimated concentration of copper-complexing dissolved organic matter (DOM) in the estuary is found to be 22.5nM. An increase in the concentration of Cu2+, which is the most toxic and bioavailable form of copper, has implications for ecosystem health and may have a negative effect on the detoxifying capacity of DOM. Models that provide a framework for coupling biological, chemical and physical processes are important for providing a holistic perspective of coastal systems, especially for better understanding a system within the context of climatic and non-climatic drivers. [Copyright &y& Elsevier]

The Hawaiian green sea turtle genetic stock is endemic to the Hawaiian Archipelago. This stock was depleted over the past century mainly due to over-exploitation that ceased during the 1970s following protection under the US Endangered Species Act. Nesting trends suggest the stock has been recovering but no formal stock assessment has been undertaken. So, we used a Bayesian state-space surplus-production model to describe Hawaiian green turtle population dynamics given limited data and uncertainty about sea turtle demography. Data series comprised commercial landings of green turtles reported from the Archipelago (1944–1973) and nester abundance recorded at the primary rookery on East Island, French Frigate Shoals (1973–2004). The model incorporated process and observation error and was fitted using Markov chain Monte Carlo simulation with a mix of informative and non-informative priors. We estimated that the Hawaiian green turtle stock was ca. 20% of pre-exploitation biomass when monitoring and protection began in the 1970s. The stock is estimated to be now ca. 83% of pre-exploitation biomass with an intrinsic growth rate ca. 5.4% pa (95% Bayesian credible interval: 3.1–8.9%). Rebound or recovery potential (also exploitation rate at MSP) of this stock was estimated to be 3.4% (1.6–6.2%), which is consistent with estimates for other long-lived late-maturing marine species. So, this once-seriously-depleted green turtle stock is well on the way to recovery and a limited harvest might now be demographically feasible. These findings are relevant for supporting informed public policy debate on the restoration of indigenous hunting rights in the Archipelago. Parameter estimates and model structure from the Bayesian surplus-production model were incorporated in an interactive easy-to-use stochastic simulation model to help support policy analysts in stock recovery planning and to explore sustainable harvest potential. [Copyright &y& Elsevier]

A stochastic simulation model was developed for the southern Great Barrier Reef (sGBR) green sea turtle stock to foster better insight into local population dynamics and to assess the risk of harvesting on long-term stock viability. The model was ageclass-structured with time-varying, nonlinear and stochastic demographic processes that were parameterised using recent findings on sex- and ageclass-specific growth and survivorship and female breeding behaviour subject to environmental stochasticity. Monte Carlo based uncertainty analysis was used to estimate population growth given demographic parameters subject to sampling error and environmental stochasticity. Model validity was based on replication of stock reference behaviours with sensitivity evaluated using parameter perturbation and Monte Carlo simulation within a fractional factorial sampling design. Fertility and adult survival were the most important high level parameters affecting population growth, where fertility is a function of fecundity and temporal variability in breeding likelihood. A dynamic stochastic form of the model was then used to evaluate stock viability given various turtle harvesting scenarios. It was found, given model assumptions, that even limited turtle harvesting would result in the sGBR stock being categorised, as vulnerable under IUCN criteria for listing of threatened species. It is apparent that extensive harvesting of either eggs or turtles is probably not a prudent management policy, if the long-term viability of the sGBR green sea turtle stock is the primary conservation objective. [Copyright &y& Elsevier]