Incorporating historical data into aquaculture planning

Marine historical research has made progress in bridging the gap between science and policy, but examples in which it has been effectively applied remain few. In particular, its application to aquaculture remains unexplored. Using actual examples of natural resource management in the state of South Australia, we illustrate how historical data of varying resolution can be incorporated into aquaculture planning. Historical fisheries records were reviewed to identify data on the now extinct native oyster Ostrea angasi fishery throughout the 1800 and early-1900s. Records of catch, number of boats fishing, and catch per unit effort (cpue) were used to test fishing rates and estimate the total quantity of oysters taken from select locations across periods of time. Catch quantities enabled calculation of the minimum number of oysters per hectare for two locations. These data were presented to government scientists, managers, and industry. As a result, interest in growing O. angasi increased and new areas for oyster aquaculture were included in regulatory zoning (spatial planning). Records of introductions of the non-native oyster Saccostrea glomerata, Sydney rock oysters, from 1866 through 1959, were also identified and used to evaluate the biosecurity risk of aquaculture for this species through semi-quantitative risk assessment. Although applications to culture S. glomerata in South Australia had previously been declined, the inclusion of historical data in risk assessment led to the conclusion that applications to culture this species would be accepted. The examples presented here have been effectively incorporated into management processes and represent an important opportunity for the aquaculture industry in South Australia to diversify. This demonstrates that historical data can be used to inform planning and support industry, government, and societies in addressing challenges associated with aquaculture, as well as natural resource management more broadly.

Weather variability, tides, and Barmah Forest Virus Disease in the Gladstone region, Australia

In this study we examined the impact of weather variability and tides on the transmission of Barmah Forest virus (BFV) disease and developed a weather-based forecasting model for BFV disease in the Gladstone region, Australia. We used seasonal autoregressive integrated moving-average (SARIMA) models to determine the contribution of weather variables to BFV transmission after the time-series data of response and explanatory variables were made stationary through seasonal differencing. We obtained data on the monthly counts of BFV cases, weather variables (e.g., mean minimum and maximum temperature, total rainfall, and mean relative humidity), high and low tides, and the population size in the Gladstone region between January 1992 and December 2001 from the Queensland Department of Health, Australian Bureau of Meteorology, Queensland Department of Transport, and Australian Bureau of Statistics, respectively. The SARIMA model shows that the 5-month moving average of minimum temperature (β = 0.15, p-value < 0.001) was statistically significantly and positively associated with BFV disease, whereas high tide in the current month (β = −1.03, p-value = 0.04) was statistically significantly and inversely associated with it. However, no significant association was found for other variables. These results may be applied to forecast the occurrence of BFV disease and to use public health resources in BFV control and prevention.

Cross-correlations between weather variables in Australia

The information on climate variations is essential for the research of many subjects, such as the performance of buildings and agricultural production. However, recorded meteorological data are often incomplete. There may be a limited number of locations recorded, while the number of recorded climatic variables and the time intervals can also be inadequate. Therefore, the hourly data of key weather parameters as required by many building simulation programmes are typically not readily available. To overcome this gap in measured information, several empirical methods and weather data generators have been developed. They generally employ statistical analysis techniques to model the variations of individual climatic variables, while the possible interactions between different weather parameters are largely ignored. Based on a statistical analysis of 10 years historical hourly climatic data over all capital cities in Australia, this paper reports on the finding of strong correlations between several specific weather variables. It is found that there are strong linear correlations between the hourly variations of global solar irradiation (GSI) and dry bulb temperature (DBT), and between the hourly variations of DBT and relative humidity (RH). With an increase in GSI, DBT would generally increase, while the RH tends to decrease. However, no such a clear correlation can be found between the DBT and atmospheric pressure (P), and between the DBT and wind speed. These findings will be useful for the research and practice in building performance simulation.