The loggerhead sea turtle is an endangered species exposed to many anthropogenic hazards in the Pacific. It is widely held that pelagic longline fisheries pose the major risk for Pacific loggerheads but the effects of other risk factors such as human-induced global climate change have rarely been considered. So we used generalised additive regression modelling and autoregressive-prewhitened cross-correlation analysis to explore whether regional ocean temperatures affect the long-term nesting population dynamics for the 2 Pacific loggerhead genetic stocks (Japan .Australia). We found that both Pacific stocks have been exposed to slowly increasing trends in mean annual sea surface temperature in their respective core regional foraging habitats over the past 50 years. We show that irrespective of whether a population was decreasing or increasing that there was an inverse correlation between nesting abundance and mean annual sea surface temperature in the core foraging region during the year prior to the summer nesting season. Cooler foraging habitat ocean temperatures are presumably associated with increased ocean productivity and prey abundance and consequently increased loggerhead breeding capacity. So warming regional ocean temperatures could lead to long-term decreased food supply and reduced nesting and recruitment unless Pacific loggerheads adapt by shifting their foraging habitat to cooler regions. So the gradual warming of the Pacific Ocean over the past 50 years is a major risk factor that must be considered in any meaningful diagnosis of the long-term declines apparent for some Pacific loggerhead nesting populations.

With increasing evidences of climate change in the prairie region, there is an urgent need to understand the future climate and the responses of small prairie wetlands. This study integrated two regional climate models (RCMs), two weather generators and a distributed hydrological model to examine uncertainties in hydrological responses to climate change in the Assiniboia watershed, Canada. Comparing to baseline conditions (1971–2000), annual water yield and evapotranspiration in the period of 2041–2070 were generally unchanged, while annual reservoir storage was generally reduced. However, projected hydrological regimes were less consistent at monthly level, particularly for March and July. Such uncertainties in simulated hydrological responses were derived from the implementations of different integrated downscaling methods, reflecting our imperfect knowledge of the future climate. We identified a warming temperature trend from climatic projections, but had less confidence in the future pattern of precipitation. Uncertainties in integrated downscaling were primarily derived from the choice of RCM, and were amplified through the incorporation of different weather generators. Results of any climate change study based on only one RCM and/or one weather generator should be interpreted with caution, and the ensemble framework should be advised to generate a comprehensive vision of the future climate. This study demonstrated that the incorporation of precipitation occurrence change contributed to a full translation of RCM outputs, but also introduced additional uncertainty. A balance is thus desired between the information loss and the additional uncertainty in order to effectively utilize RCM outputs.

With increasing evidences of climate change in the prairie region, there is an urgent need to understand the future climate and the responses of small prairie wetlands. This study integrated two regional climate models (RCMs), two weather generators and a distributed hydrological model to examine uncertainties in hydrological responses to climate change in the Assiniboia watershed, Canada. Comparing to baseline conditions (1971–2000), annual water yield and evapotranspiration in the period of 2041–2070 were generally unchanged, while annual reservoir storage was generally reduced. However, projected hydrological regimes were less consistent at monthly level, particularly for March and July. Such uncertainties in simulated hydrological responses were derived from the implementations of different integrated downscaling methods, reflecting our imperfect knowledge of the future climate. We identified a warming temperature trend from climatic projections, but had less confidence in the future pattern of precipitation. Uncertainties in integrated downscaling were primarily derived from the choice of RCM, and were amplified through the incorporation of different weather generators. Results of any climate change study based on only one RCM and/or one weather generator should be interpreted with caution, and the ensemble framework should be advised to generate a comprehensive vision of the future climate. This study demonstrated that the incorporation of precipitation occurrence change contributed to a full translation of RCM outputs, but also introduced additional uncertainty. A balance is thus desired between the information loss and the additional uncertainty in order to effectively utilize RCM outputs