Stochastic approaches for time series forecasting of boron: a case study of Western Turkey

In the present study, a seasonal and non-seasonal prediction of boron concentrations time series data for the period of 1996–2004 from Büyük Menderes river in western Turkey are addressed by means of linear stochastic models. The methodology presented here is to develop adequate linear stochastic models known as autoregressive integrated moving average (ARIMA) and multiplicative seasonal autoregressive integrated moving average (SARIMA) to predict boron content in the Büyük Menderes catchment. Initially, the Box–Whisker plots and Kendall’s tau test are used to identify the trends during the study period. The measurements locations do not show significant overall trend in boron concentrations, though marginal increasing and decreasing trends are observed for certain periods at some locations. ARIMA modeling approach involves the following three steps: model identification, parameter estimation, and diagnostic checking. In the model identification step, considering the autocorrelation function (ACF) and partial autocorrelation function (PACF) results of boron data series, different ARIMA models are identified. The model gives the minimum Akaike information criterion (AIC) is selected as the best-fit model. The parameter estimation step indicates that the estimated model parameters are significantly different from zero. The diagnostic check step is applied to the residuals of the selected ARIMA models and the results indicate that the residuals are independent, normally distributed, and homoscadastic. For the model validation purposes, the predicted results using the best ARIMA models are compared to the observed data. The predicted data show reasonably good agreement with the actual data. The comparison of the mean and variance of 3-year (2002–2004) observed data vs predicted data from the selected best models show that the boron model from ARIMA modeling approaches could be used in a safe manner since the predicted values from these models preserve the basic statistics of observed data in terms of mean. The ARIMA modeling approach is recommended for predicting boron concentration series of a river.     

Interaction between nitrogen and phosphorus cycles in mining-affected aquatic systems—experiences from field and laboratory measurements

The main objectives of this study were to (a) study the interaction between N and P cycles in mining-affected aquatic systems and (b) to quantify release rates of sedimentary soluble reactive phosphorus (SRP) that may be related to this interaction. Sediment cores and water from Lake Bruträsket (Boliden, northern Sweden) were collected and a time series of water sampling and flow measurements was conducted in the Brubäcken stream connected to the lake. Factors affecting SRP release were studied in a sediment incubation experiment and water column experiments. Field and laboratory measurements indicated that pH and dissolved oxygen are two important factors for SRP release. At the end of the low-oxygen incubation, an SRP concentration of 56 μg?L^?1 resulted in a sedimentary flux of 1.1 mg SRP?m^?2?day^?1. This is ～10 times higher than the flux of 0.12 mg SRP?m^?2?day^?1 obtained from depth integration of vertical SRP profiles measured in the lake, and ～100 times higher than the external flux of 0.014 mg SRP?m^?2?d^?1 into the lake (based on catchment area). Field measurements indicated that oxidation of organic matter and mining-related chemicals (ammonium and thiosulphates) may result in increased internal SRP flux from the sediment. Increased P loading in the lake as a result of low-oxygen conditions could change water column total nitrogen/total phosphorus ratios from 27 to 17, consequently changing the lake from being P-limited to be co-limited by N and P. The obtained findings point to possible interaction between the cycles of nitrogen (oxygen consumption) and P (flux from sediment) that may be important for nutrient regulation in mine water recipients.     

Determination of some mineral contents of prickly pear (Opuntia ficus-indica L.) seed flours

The aim of this study was to determine some mineral contents of prickly pear (Opuntia f?cus-indica L.) seeds collected from different locations. The mineral contents of seeds were established by inductively coupled plasma atomic emission spectrometry. All the seeds contained Ca, K, Mg and P at high levels. Calcium content ranged between 268.5 (sample no. 11) and 674.8 ppm (sample no. 4). The level of K changed between 346.7 (sample no. 1) and 676.1 ppm (sample no. 13). Phosphorus content of seeds varied between 1,173.6 (sample no. 14) and 1,871.3 ppm (sample no. 1). It is apparent that seeds are good sources of the macro and micro minerals and can be consumed as a food ingredient to provide nutrition.     

Individual responsiveness to shock and colony-level aggression in honey bees: evidence for a genetic component

The phenotype of the social group is related to phenotypes of individuals that form that society. We examined how honey bee colony aggressiveness relates to individual response of male drones and foraging workers. Although the natural focus in colony aggression has been on the worker caste, the sterile females engaged in colony maintenance and defense, males carry the same genes. We measured aggressiveness scores of colonies and examined components of individual aggressive behavior in workers and haploid sons of workers from the same colony. We describe for the first time, that males, although they have no stinger, do bend their abdomen (abdominal flexion) in a posture similar to stinging behavior of workers in response to electric shock. Individual worker sting response and movement rates in response to shock were significantly correlated with colony scores. In the case of drones, sons of workers from the same colonies, abdominal flexion significantly correlated but their movement rates did not correlate with colony aggressiveness. Furthermore, the number of workers responding at increasing levels of voltage exhibits a threshold-like response, whereas the drones respond in increasing proportion to shock. We conclude that there are common and caste-specific components to aggressive behavior in honey bees. We discuss implications of these results on social and behavioral regulation and genetics of aggressive response.