Predicting Pyrodinium bahamense occurrence using weather pattern data in Eastern Visayas, Philippines.

Pyrodinium bahamense, a harmful alga which causes paralytic shellfish poisoning (PSP), has been found in seawater samples collected along bays in Eastern Visayas in the Philippines. Due to its negative impacts and uncertainty in its occurrence, there is a need to develop a real-time monitoring device of the harmful algal bloom (HAB) occurrence. This study aims to determine whether there is significant relationship between the dependent variable, the P. bahamense cell density, and the independent variables, namely, temperature, rainfall and humidity, bays in Eastern Visayas, and sampling month. This was done through data mining technique of P. bahamense cell density and weather patterns and statistically analyzing the datasets, with P. bahamense cell density as the dependent variable and weather patterns, bays and sampling month as independent variables using M5P regression analysis. Through the regression analysis, the usefulness of the predictive model as an initial development can be assessed. Results of the study showed that the correlation coefficient of P. bahamense with the five independent variables is 0.725 which signified a moderate correlation between P. bahamense cell density and bay, sampling month, temperature, rainfall and humidity. Maximum cell density, which was 20 cells/L occurred during months of wet season in the bays in Eastern Visayas, except in Cancabato bay while the minimum cell density, which was 0 cell/L occurred during months of dry season. It can be concluded from the results that there is enough data to say that there is a significant relationship between P. bahamense occurrence and temperature, rainfall and humidity, so the model is meaningful and useful at alpha = 0.05 or 95% and 0.01 or 99% confidence interval. The data can be used to generate a predictive model for P. bahamense occurrence relative to its cell density.

Plant δ15 N reflects the high landscape-scale heterogeneity of soil fertility and vegetation productivity in a Mediterranean semiarid ecosystem.

We investigated the magnitude and drivers of spatial variability in soil and plant δ15 N across the landscape in a topographically complex semiarid ecosystem. We hypothesized that large spatial heterogeneity in water availability, soil fertility and vegetation cover would be positively linked to high local-scale variability in δ15 N. We measured foliar δ15 N in three dominant plant species representing contrasting plant functional types (tree, shrub, grass) and mycorrhizal association types (ectomycorrhizal or arbuscular mycorrhizal). This allowed us to investigate whether δ15 N responds to landscape-scale environmental heterogeneity in a consistent way across species. Leaf δ15 N varied greatly within species across the landscape and was strongly spatially correlated among co-occurring individuals of the three species. Plant δ15 N correlated tightly with soil δ15 N and key measures of soil fertility, water availability and vegetation productivity, including soil nitrogen (N), organic carbon (C), plant-available phosphorus (P), water-holding capacity, topographic moisture indices and normalized difference vegetation index. Multiple regression models accounted for 62-83% of within-species variation in δ15 N across the landscape. The tight spatial coupling and interdependence of the water, N and C cycles in drylands may allow the use of leaf δ15 N as an integrative measure of variations in moisture availability, biogeochemical activity, soil fertility and vegetation productivity (or 'site quality') across the landscape.