Landscape-level patterns of mercury contamination of fish in North Texas, USA.

Mercury (Hg) is a toxic metal that is found in aquatic food webs and is hazardous to humans. An emerging conceptual model predicts that the areas of the landscape that have the potential to contain food webs with elevated concentrations of Hg are those that receive high amounts of Hg and sulfate deposition and have high coverage of forests and wetlands and low coverage of agriculture. The objective of the present study was to test this conceptual model using concentrations of Hg in largemouth bass (Micropterus salmoides) from 145 reservoirs in four ecoregions of North Texas. The highest level of Hg contamination in fish was in the South Central Plains, the ecoregion that receives the highest levels of Hg and sulfate deposition and contains extensive forest and wetland habitat and little agriculture. The present study has important implications for other areas of the United States, because the South Central Plains extend into parts of Oklahoma, Louisiana, and Arkansas, covering a total area of 152,132 km(2) of the southern United States.

Uncertainty analysis of predicted disturbance from off-road vehicular traffic in complex landscapes at fort hood.

The US Army Engineering Research Development Center (ERDC) uses a modified form of the Revised Universal Soil Loss Equation (RUSLE) to estimate spatially explicit rates of soil erosion by water across military training facilities. One modification involves the RUSLE support practice factor (P factor), which is used to account for the effect of disturbance by human activities on erosion rates. Since disturbance from off-road military vehicular traffic moving through complex landscapes varies spatially, a spatially explicit nonlinear regression model (disturbance model) is used to predict the distribution of P factor values across a training facility. This research analyzes the uncertainty in this model's disturbance predictions for the Fort Hood training facility in order to determine both the spatial distribution of prediction uncertainty and the contribution of different error sources to that uncertainty. This analysis shows that a three-category vegetation map used by the disturbance model was the greatest source of prediction uncertainty, especially for the map categories shrub and tree. In areas mapped as grass, modeling error (uncertainty associated with the model parameter estimates) was the largest uncertainty source. These results indicate that the use of a high-quality vegetation map that is periodically updated to reflect current vegetation distributions, would produce the greatest reductions in disturbance prediction uncertainty.