Between-year fluctuations in schizophrenia birth rate and associations with the Southern Oscillation Index, cloud cover and sunshine

Our group have recently proposed that low prenatal vitamin D may be a risk-modifying factor for schizophrenia. Climate variability impacts on vitamin D levels in a population via fluctuations in the amount of available UV radiation. In order to explore this hypothesis, we examined fluctuations in the birthrates for people with schizophrenia born between 1920 and 1967 and three sets of variables strongly associated with UV radiation. These included: (a) the Southern Oscillation Index (SOI), a marker of El Nino which is the most prominent meteorological factor that influences Queensland weather: (b) measures of cloud cover and (c) measures of sunshine. Schizophrenia births were extracted from the Queensland Mental Health register and corrected for background population birth rates. Schizophrenia birth rates had several apparently non-random features in common with the SO1. The prominent SO1 fluctuation event that occurred between 1937 and 1943 is congruent with the most prominent fluctuation in schizophrenia birth rates. The relatively flat profile of SOI activity between 1927 and 1936 also corresponds to the flattest period in the schizophrenia time series. Both time series have prominent oscillations in the 3 ~, year range between 1946 and 1960. Significant associations between schizophrenia birth rates and measures of both sunshine and cloud cover were identified,and all three time series shared periodicity in the 3-4 year range. The analyses suggest that the risk of schizophrenia is higher for those born during times of increased cloud cover,reduced sunshine and positive SO1. These ecological analyses provide initial support for the vitamin D hypothesis, however alternative non-genetic candidate exposures also need to be considered. Other sites with year-to-year fluctuations in cloud cover and sunshine should examine patterns of association between these climate variables and schizophrenia birth rates. The Stanley Foundation supported this project.

Long range forecasts of the numbers of Helicoverpa punctigera and H-armigera (Lepidoptera : Noctuidae) in Australia using the Southern Oscillation Index and the Sea Surface Temperature

The use of long-term forecasts of pest pressure is central to better pest management. We relate the Southern Oscillation Index (SOI) and the Sea Surface Temperature (SST) to long-term light-trap catches of the two key moth pests of Australian agriculture, Helicoverpa punctigera (Wallengren) and H. armigera (Hubner), at Narrabri, New South Wales over 11 years, and for H. punctigera only at Turretfield, South Australia over 22 years. At Narrabri, the size of the first spring generation of both species was significantly correlated with the SOI in certain months, sometimes up to 15 months before the date of trapping. Differences in the SOI and SST between significant months were used to build composite variables in multiple regressions which gave fitted values of the trap catches to less than 25% of the observed values. The regressions suggested that useful forecasts of both species could be made 6-15 months ahead. The influence of the two weather variables on trap catches of H. punctigera at Turretfield were not as strong as at Narrabri, probably because the SOI was not as strongly related to rainfall in southern Australia as it is in eastern Australia. The best fits were again given by multiple regressions with SOI plus SST variables, to within 40% of the observed values. The reliability of both variables as predictors of moth numbers may be limited by the lack of stability in the SOI-rainfall correlation over the historical record. As no other data set is available to test the regressions, they can only be tested by future use. The use of long-term forecasts in pest management is discussed, and preliminary analyses of other long sets of insect numbers suggest that the Southern Oscillation Index may be a useful predictor of insect numbers in other parts of the world.

El Niño Southern Oscillation and Ross River Virus Outbreaks in Australia

Various authors have suggested a general predictive value of climatic indices of El Nino/Southem Oscillation events as indicators of outbreaks of arbovirus disease, particularly Ross River virus in Australia. By analyzing over 100 years of historical outbreak data on Ross River virus disease, our data indicate that, although high Southern Oscillation Index and La Nina conditions are potentially important predictors for the Murray Darling River region, this is not the case for the other four ecological zones in Australia. Our study, therefore, cautions against overgeneralization and suggests that, since climate and weather exert different influences and have different biological implications for the multiplicity of vectors involved, it is logical that predictors should be heterogeneous.

El Nino and arboviral disease prediction

Recent El Nino events have stimulated interest in the development of modeling techniques to forecast extremes of climate and related health events. Previous studies have documented associations between specific climate variables (particularly temperature and rainfall) and outbreaks of arboviral disease. In some countries, such diseases are sensitive to Fl Nino. Here we describe a climate-based model for the prediction of Ross River virus epidemics in Australia. From a literature search and data on case notifications, we determined in which years there were epidemics of Ross River virus in southern Australia between 1928 and 1998. Predictor variables were monthly Southern Oscillation index values for the year of an epidemic or lagged by 1 year. We found that in southeastern states, epidemic years were well predicted by monthly Southern Oscillation index values in January and September in the previous year. The model forecasts that there is a high probability of epidemic Ross River virus in the southern states of Australia in 1999. We conclude that epidemics of arboviral disease can, at least in principle, be predicted on the basis of climate relationships.

Deconvolving the delta O-18 seawater component from subseasonal coral delta O-18 and Sr/Ca at Rarotonga in the southwestern subtropical Pacific for the period 1726 to 1997

To reconstruct oceanographic variations in the subtropical South Pacific, 271-year long subseasonal time series of Sr/Ca and delta(18)O were generated from a coral growing at Rarotonga (21.5degreesS, 159.5degreesW). In this case, coral Sr/Ca appears to be an excellent proxy for sea surface temperature (SST) and coral delta(18)O is a function of both SST and seawater delta(18)O composition (delta(18)O(sw)). Here, we focus on extracting the delta(18)O(sw) signal from these proxy records. A method is presented assuming that coral Sr/Ca is solely a function of SST and that coral delta(18)O is a function of both SST and delta(18)O(sw). This method separates the effects of delta(18)O(sw) from SST by breaking the instantaneous changes of coral delta(18)O into separate contributions by instantaneous SST and delta(18)O(sw) changes, respectively. The results show that on average delta(18)O(sw) at Rarotonga explains similar to39% of the variance in delta(18)O and that variations in SST explains the remaining similar to61% of delta(18)O variance. Reconstructed delta(18)O(sw) shows systematic increases in summer months (December-February) consistent with the regional pattern of variations in precipitation and evaporation. The delta(18)O(sw) also shows a positive linear correlation with satellite-derived estimated salinity for the period 1980 to 1997 (r = 0.72). This linear correlation between reconstructed delta(18)O(sw) and salinity makes it possible to use the reconstructed delta(18)O(sw) to estimate the past interannual and decadal salinity changes in this region. Comparisons of coral delta(18)O and delta(18)O(sw) at Rarotonga with the Pacific decadal oscillation index suggest that the decadal and interdecadal salinity and SST variability at Rarotonga appears to be related to basin-scale decadal variability in the Pacific. Copyright (C) 2002 Elsevier Science Ltd.

Forecasting regional crop production using SOI phases: an example for the Australian peanut industry

Using peanuts as an example, a generic methodology is presented to forward-estimate regional crop production and associated climatic risks based on phases of the Southern Oscillation Index (SOI). Yield fluctuations caused by a highly variable rainfall environment are of concern to peanut processing and marketing bodies. The industry could profitably use forecasts of likely production to adjust their operations strategically. Significant, physically based lag-relationships exist between an index of ocean/atmosphere El Nino/Southern Oscillation phenomenon and future rainfall in Australia and elsewhere. Combining knowledge of SOI phases in November and December with output from a dynamic simulation model allows the derivation of yield probability distributions based on historic rainfall data. This information is available shortly after planting a crop and at least 3-5 months prior to harvest. The study shows that in years when the November-December SOI phase is positive there is an 80% chance of exceeding average district yields. Conversely, in years when the November-December SOI phase is either negative or rapidly falling there is only a 5% chance of exceeding average district yields, but a 95% chance of below average yields. This information allows the industry to adjust strategically for the expected volume of production. The study shows that simulation models can enhance SOI signals contained in rainfall distributions by discriminating between useful and damaging rainfall events. The methodology can be applied to other industries and regions.

Spatial and temporal patterns in Australian wheat yield and their relationship with ENSO

Spatial and temporal variability in wheat production in Australia is dominated by rainfall occurrence. The length of historical production records is inadequate, however, to analyse spatial and temporal patterns conclusively. In this study we used modelling and simulation to identify key spatial patterns in Australian wheat yield, identify groups of years in the historical record in which spatial patterns were similar, and examine association of those wheat yield year groups with indicators of the El Nino Southern Oscillation (ENSO). A simple stress index model was trained on 19 years of Australian Bureau of Statistics shire yield data (1975-93). The model was then used to simulate shire yield from 1901 to 1999 for all wheat-producing shires. Principal components analysis was used to determine the dominating spatial relationships in wheat yield among shires. Six major components of spatial variability were found. Five of these represented near spatially independent zones across the Australian wheatbelt that demonstrated coherent temporal (annual) variability in wheat yield. A second orthogonal component was required to explain the temporal variation in New South Wales. The principal component scores were used to identify high- and low-yielding years in each zone. Year type groupings identified in this way were tested for association with indicators of ENSO. Significant associations were found for all zones in the Australian wheatbelt. Associations were as strong or stronger when ENSO indicators preceding the wheat season (April-May phases of the Southern Oscillation Index) were used rather than indicators based on classification during the wheat season. Although this association suggests an obvious role for seasonal climate forecasting in national wheat crop forecasting, the discriminatory power of the ENSO indicators, although significant, was not strong. By examining the historical years forming the wheat yield analog sets within each zone, it may be possible to identify novel climate system or ocean-atmosphere features that may be causal and, hence, most useful in improving seasonal forecasting schemes.

Implications of seasonal climate forecasts on world wheat trade: a stochastic, dynamic analysis

Improvements in seasonal climate forecasts have potential economic implications for international agriculture. A stochastic, dynamic simulation model of the international wheat economy is developed to estimate the potential effects of seasonal climate forecasts for various countries' wheat production, exports and world trade. Previous studies have generally ignored the stochastic and dynamic aspects of the effects associated with the use of climate forecasts. This study shows the importance of these aspects. In particular with free trade, the use of seasonal forecasts results in increased producer surplus across all exporting countries. In fact, producers appear to capture a large share of the economic surplus created by using the forecasts. Further, the stochastic dimensions suggest that while the expected long-run benefits of seasonal forecasts are positive, considerable year-to-year variation in the distribution of benefits between producers and consumers should be expected. The possibility exists for an economic measure to increase or decrease over a 20-year horizon, depending on the particular sequence of years.

Ross River virus disease in Australia, 1886-1998, with analysis of risk factors associated with outbreaks

Ross River virus (RE) is a mosquito-borne arbovirus responsible for outbreaks of polyarthritic disease throughout Australia. To better understand human and environmental factors driving such events, 57 historical reports oil RR Outbreaks between 1896 and 1998 were examined collectively. The magnitude, regularity, seasonality, and locality of outbreaks were found to be wide ranging; however, analysis of climatic and tidal data highlighted that environmental conditions let differently ill tropical, arid, and temperate regions. Overall, rainfall seems to be the single most important risk factor, with over 90% of major outbreak locations receiving higher than average rainfall in preceding mouths. Many temperatures were close to average, particularly in tropical populations; however, in arid regions, below average maximum temperatures predominated, and ill southeast temperate regions, above average minimum temperatures predominated. High spring tides preceded coastal Outbreaks, both in the presence and absence of rainfall, and the relationship between rainfall and the Southern Oscillation Index and Lit Nina episodes suggest they may be useful predictive tools, but only ill southeast temperate regions. Such heterogeneity predisposing outbreaks supports the notion that there are different RE epidemiologies throughout Australia but also Suggests that generic parameters for the prediction and control of outbreaks are of limited use at a local level.

A simple regional-scale model for forecasting sorghum yield across North-Eastern Australia

Sorghum is the main dryland summer crop in NE Australia and a number of agricultural businesses would benefit from an ability to forecast production likelihood at regional scale. In this study we sought to develop a simple agro-climatic modelling approach for predicting shire (statistical local area) sorghum yield. Actual shire yield data, available for the period 1983-1997 from the Australian Bureau of Statistics, were used to train the model. Shire yield was related to a water stress index (SI) that was derived from the agro-climatic model. The model involved a simple fallow and crop water balance that was driven by climate data available at recording stations within each shire. Parameters defining the soil water holding capacity, maximum number of sowings (MXNS) in any year, planting rainfall requirement, and critical period for stress during the crop cycle were optimised as part of the model fitting procedure. Cross-validated correlations (CVR) ranged from 0.5 to 0.9 at shire scale. When aggregated to regional and national scales, 78-84% of the annual variation in sorghum yield was explained. The model was used to examine trends in sorghum productivity and the approach to using it in an operational forecasting system was outlined. (c) 2005 Elsevier B.V. All rights reserved.

Three putative types of El Nino revealed by spatial variability in impact on Australian wheat yield

The El Nino-Southern Oscillation (ENSO) phenomenon significantly impacts rainfall and ensuing crop yields in many parts of the world. In Australia, El Nino events are often associated with severe drought conditions. However, El Nino events differ spatially and temporally in their manifestations and impacts, reducing the relevance of ENSO-based seasonal forecasts. In this analysis, three putative types of El Nino are identified among the 24 occurrences since the beginning of the twentieth century. The three types are based on coherent spatial patterns (footprints) found in the El Nino impact on Australian wheat yield. This bioindicator reveals aligned spatial patterns in rainfall anomalies, indicating linkage to atmospheric drivers. Analysis of the associated ocean-atmosphere dynamics identifies three types of El Nino differing in the timing of onset and location of major ocean temperature and atmospheric pressure anomalies. Potential causal mechanisms associated with these differences in anomaly patterns need to be investigated further using the increasing capabilities of general circulation models. Any improved predictability would be extremely valuable in forecasting effects of individual El Nino events on agricultural systems.

Forecasting Helicoverpa populations in Australia: A comparison of regression based models and a bio-climatic based modeling approach

Long-term forecasts of pest pressure are central to the effective management of many agricultural insect pests. In the eastern cropping regions of Australia, serious infestations of Helicoverpa punctigera (Wallengren) and H. armigera (Hübner)(Lepidoptera: Noctuidae) are experienced annually. Regression analyses of a long series of light-trap catches of adult moths were used to describe the seasonal dynamics of both species. The size of the spring generation in eastern cropping zones could be related to rainfall in putative source areas in inland Australia. Subsequent generations could be related to the abundance of various crops in agricultural areas, rainfall and the magnitude of the spring population peak. As rainfall figured prominently as a predictor variable, and can itself be predicted using the Southern Oscillation Index (SOI), trap catches were also related to this variable. The geographic distribution of each species was modelled in relation to climate and CLIMEX was used to predict temporal variation in abundance at given putative source sites in inland Australia using historical meteorological data. These predictions were then correlated with subsequent pest abundance data in a major cropping region. The regression-based and bioclimatic-based approaches to predicting pest abundance are compared and their utility in predicting and interpreting pest dynamics are discussed.

Effect of rainfall as a component of climate change on estuarine fish production in Queensland, Australia

The speculation that climate change may impact on sustainable fish production suggests a need to understand how these effects influence fish catch on a broad scale. With a gross annual value of A$ 2.2 billion, the fishing industry is a significant primary industry in Australia. Many commercially important fish species use estuarine habitats such as mangroves, tidal flats and seagrass beds as nurseries or breeding grounds and have lifecycles correlated to rainfall and temperature patterns. Correlation of catches of mullet (e.g. Mugil cephalus) and barramundi (Lates calcarifer) with rainfall suggests that fisheries may be sensitive to effects of climate change. This work reviews key commercial fish and crustacean species and their link to estuaries and climate parameters. A conceptual model demonstrates ecological and biophysical links of estuarine habitats that influences capture fisheries production. The difficulty involved in explaining the effect of climate change on fisheries arising from the lack of ecological knowledge may be overcome by relating climate parameters with long-term fish catch data. Catch per unit effort (CPUE), rainfall, the Southern Oscillation Index (SOI) and catch time series for specific combinations of climate seasons and regions have been explored and surplus production models applied to Queensland's commercial fish catch data with the program CLIMPROD. Results indicate that up to 30% of Queensland's total fish catch and up to 80% of the barramundi catch variation for specific regions can be explained by rainfall often with a lagged response to rainfall events. Our approach allows an evaluation of the economic consequences of climate parameters on estuarine fisheries. thus highlighting the need to develop forecast models and manage estuaries for future climate chan e impact by adjusting the quota for climate change sensitive species. Different modelling approaches are discussed with respect to their forecast ability. (c) 2006 Elsevier Ltd. All rights reserved.

Refractive index tomography of turbid media by bifocal optical coherence refractometry

We demonstrate tomographic imaging of the refractive index of turbid media using bifocal optical coherence refractometry (BOCR). The technique, which is a variant of optical coherence tomography, is based on the measurement of the optical pathlength difference between two foci simultaneously present in a medium of interest. We describe a new method to axially shift the bifocal optical pathlength that avoids the need to physically relocate the objective lens or the sample during an axial scan, and present an experimental realization based on an adaptive liquid-crystal lens. We present experimental results, including video clips, which demonstrate refractive index tomography of a range of turbid liquid phantoms, as well as of human skin in vivo.

The Coefficient of Variance as an index of L2 lexical processing skill

The Coefficient of Variance (mean standard deviation/mean Response time) is a measure of response time variability that corrects for differences in mean Response time (RT) (Segalowitz & Segalowitz, 1993). A positive correlation between decreasing mean RTs and CVs (rCV-RT) has been proposed as an indicator of L2 automaticity and more generally as an index of processing efficiency. The current study evaluates this claim by examining lexical decision performance by individuals from three levels of English proficiency (Intermediate ESL, Advanced ESL and L1 controls) on stimuli from four levels of item familiarity, as defined by frequency of occurrence. A three-phase model of skill development defined by changing rCV-RT.values was tested. Results showed that RTs and CVs systematically decreased as a function of increasing proficiency and frequency levels, with the rCV-RT serving as a stable indicator of individual differences in lexical decision performance. The rCV-RT and automaticity/restructuring account is discussed in light of the findings. The CV is also evaluated as a more general quantitative index of processing efficiency in the L2.