Characterizing Trophic State in Tropical/Subtropical Reservoirs: Deviations among Indexes in the Lower Latitudes.

Trophic state indexes (TSI) guide management strategies regarding eutrophication control worldwide. Such indexes usually consider chlorophyll-a (Chl-a), total phosphorus (TP), and Secchi disk depth (SDD) as independent variables for estimating aquatic productivity and the degree of impairment. TSIs for each of these components are frequently averaged to produce a single TSI value associated with a trophic state classification (e.g., oligotrophic, mesotrophic, or eutrophic). The potential divergence among equations and classification systems originally developed for temperate lakes or tropical/subtropical reservoirs might be particularly relevant in the tropics, where there is a lack of data and the use of equations originally developed for temperate systems may be inappropriate. We calculated two widely used TSIs for temperate lakes (TSItemp) or tropical reservoirs (TSItrop) and explored the deviations among TSI components in Brazilian reservoirs. When applied to our tropical/subtropical reservoirs, the TSItemp provided a conservative approach, with lower limits anticipating increasing trophic state classification. TSI components for Chl-a and SDD significantly deviated for both sets of equations, and these discrepancies were related to turbidity, water temperature, and cyanobacterial biomass. For TSItemp, but not for TSItrop, TSI values in relation to Chl-a and TP were also significantly different. All such deviations have important management implications especially when Chl-a, TP, and SDD are averaged in a single TSI, representing loss of information and less useful trophic state classifications. Our results demonstrate that tropical water bodies may respond to drivers of eutrophication differently than temperate systems, highlighting the need for more data to better inform management of these understudied ecosystems. As managers collect data from more tropical water bodies, regional models may offer even better understanding of factors influencing trophic state.

Small molecule-based binding environments: combinatorial construction of microarrays for multiplexed affinity screening.

This paper describes the construction of a combinatorial artificial receptor array (CARA) and the application of the array to differentiation of proteins based on their binding patterns. Microarrays displaying 5035 unique binding environments were prepared using a library of 19 small molecule building blocks. Each building block was equipped with a carboxylic acid handle, allowing mixtures of the building blocks to be spotted onto the surface of an amine functionalized glass slide for covalent immobilization as subunits of the binding environments. This strategy employs the microarray surface as the receptor synthesis platform, which allows for flexibility in array preparation and agility in application. An advantage of the CARA strategy is the enormous flexibility it enables in the construction of alternate microarray configurations, which facilitates rapid access to the breadth and depth of binding space. Four fluorescently labeled proteins, ubiquitin, myoglobin, alpha-1-acid glycoprotein and lysozyme, were incubated with the arrays to demonstrate the reproducibility of binding and the level of differentiation that can be achieved. The binding environments are stable, scalable, and adaptable to other formats.

Multivariate analysis of the ecoregion delineation for aquatic systems.

The ecoregion concept is a popular method of understanding the spatial distribution of the environment', however, it has yet to be adequately demonstrated that the environment is distributed in accordance with these bounded units. In this paper, we generated a testable hypothesis based on the current usage of ecoregions: the ecoregion classification will allow for discrimination between lakes of different water quality. The ecoregion classification should also be more effective better than a comparably scaled classification based on political boundaries, land-use class, or random grouping. To test this hypothesis we used the Environmental Monitoring and Assessment Program (EMAP) lake water chemistry data from the northeast United States. The water chemistry data were reduced to four components using principal component analysis. For comparison to an optimal grouping of these data we used K-means cluster analysis to define the extent at which these lakes could be segregated into distinct classes. Jackknifed discriminant analysis was used to determine the classification rate of ecoregions, the three alternative spatial classification methods, and the clustering algorithm. The classification based on ecoregions was successful for 35% of the lakes included in this study, in comparison to the clustered groups accuracy of 98%. These results suggest that the large scale spatial distribution of ecosystem types is more complicated than that suggested by the present ecoregion boundaries. Further tests of ecoregion delineations are needed and alternative large-scale management strategies should be investigated.