A novel single-parameter approach for forecasting algal blooms.

Harmful algal blooms frequently occur globally, and forecasting could constitute an essential proactive strategy for bloom control. To decrease the cost of aquatic environmental monitoring and increase the accuracy of bloom forecasting, a novel single-parameter approach combining wavelet analysis with artificial neural networks (WNN) was developed and verified based on daily online monitoring datasets of algal density in the Siling Reservoir, China and Lake Winnebago, U.S.A. Firstly, a detailed modeling process was illustrated using the forecasting of cyanobacterial cell density in the Chinese reservoir as an example. Three WNN models occupying various prediction time intervals were optimized through model training using an early stopped training approach. All models performed well in fitting historical data and predicting the dynamics of cyanobacterial cell density, with the best model predicting cyanobacteria density one-day ahead (r = 0.986 and mean absolute error = 0.103 × 104 cells mL-1). Secondly, the potential of this novel approach was further confirmed by the precise predictions of algal biomass dynamics measured as chl a in both study sites, demonstrating its high performance in forecasting algal blooms, including cyanobacteria as well as other blooming species. Thirdly, the WNN model was compared to current algal forecasting methods (i.e. artificial neural networks, autoregressive integrated moving average model), and was found to be more accurate. In addition, the application of this novel single-parameter approach is cost effective as it requires only a buoy-mounted fluorescent probe, which is merely a fraction (∼15%) of the cost of a typical auto-monitoring system. As such, the newly developed approach presents a promising and cost-effective tool for the future prediction and management of harmful algal blooms.

An agent-based model to simulate tsetse fly distribution and control techniques: a case study in Nguruman, Kenya.

BACKGROUND: African trypanosomiasis, also known as "sleeping sickness" in humans and "nagana" in livestock is an important vector-borne disease in Sub-Saharan Africa. Control of trypanosomiasis has focused on eliminating the vector, the tsetse fly (Glossina, spp.). Effective tsetse fly control planning requires models to predict tsetse population and distribution changes over time and space. Traditional planning models have used statistical tools to predict tsetse distributions and have been hindered by limited field survey data. METHODOLOGY/RESULTS: We developed an Agent-Based Model (ABM) to provide timing and location information for tsetse fly control without presence/absence training data. The model is driven by daily remotely-sensed environment data. The model provides a flexible tool linking environmental changes with individual biology to analyze tsetse control methods such as aerial insecticide spraying, wild animal control, releasing irradiated sterile tsetse males, and land use and cover modification. SIGNIFICANCE: This is a bottom-up process-based model with freely available data as inputs that can be easily transferred to a new area. The tsetse population simulation more closely approximates real conditions than those using traditional statistical models making it a useful tool in tsetse fly control planning.

Estimating soil organic carbon stocks and spatial patterns with statistical and GIS-based methods.

Accurately quantifying soil organic carbon (SOC) is considered fundamental to studying soil quality, modeling the global carbon cycle, and assessing global climate change. This study evaluated the uncertainties caused by up-scaling of soil properties from the county scale to the provincial scale and from lower-level classification of Soil Species to Soil Group, using four methods: the mean, median, Soil Profile Statistics (SPS), and pedological professional knowledge based (PKB) methods. For the SPS method, SOC stock is calculated at the county scale by multiplying the mean SOC density value of each soil type in a county by its corresponding area. For the mean or median method, SOC density value of each soil type is calculated using provincial arithmetic mean or median. For the PKB method, SOC density value of each soil type is calculated at the county scale considering soil parent materials and spatial locations of all soil profiles. A newly constructed 1∶50,000 soil survey geographic database of Zhejiang Province, China, was used for evaluation. Results indicated that with soil classification levels up-scaling from Soil Species to Soil Group, the variation of estimated SOC stocks among different soil classification levels was obviously lower than that among different methods. The difference in the estimated SOC stocks among the four methods was lowest at the Soil Species level. The differences in SOC stocks among the mean, median, and PKB methods for different Soil Groups resulted from the differences in the procedure of aggregating soil profile properties to represent the attributes of one soil type. Compared with the other three estimation methods (i.e., the SPS, mean and median methods), the PKB method holds significant promise for characterizing spatial differences in SOC distribution because spatial locations of all soil profiles are considered during the aggregation procedure.

Evaluation of MODIS surrogates for meteorological humidity data in east Africa.

Satellite remote sensing technology has shown promising results in characterizing the environment in which plants and animals thrive. Remote sensing scientists, biologists, and epidemiologists are adopting remotely sensed imagery to compensate for the paucity of weather information measured by weather stations. With measured humidity from three stations as baselines, our study reveals that Normalised Difference Vegetation Index (NDVI) and atmosphere saturation deficits at the 780 hPa pressure level (DMODIS), both of which were derived from the MODerate-resolution Imaging Spectroradiometer (MODIS) sensor, were significantly correlated with station saturation deficits (Dstn)(ȣrȣ = 0.42-0.63, p < 0.001). These metrics have the potential to estimate saturation deficits over east Africa. Four to nine days of lags were found in the NDVI responding to Dstn. For the daily estimations of Dstn, DMODIS had a better performance than the NDVI. However, both of them poorly explained the variances in daily Dstn using simple regression models (adj. R2 = 0.17-0.39). When the estimation temporal scale was changed to 16-day, their performances were similar, and both were better than daily estimations. For Dstn estimations at coarser geographic scales, given that many factors such as soil, vegetation, slope, aspect, and wind speed might complicate the NDVI response lags and model construction, DMODIS is more favourable as a proxy of the saturation deficit over ground due to its simple relationship with Dstn.