Stream dissolved organic matter bioavailability and composition in watersheds underlain with discontinuous permafrost

We examined the impact of permafrost on dissolved organic matter (DOM) composition in Caribou-Poker Creeks Research Watershed (CPCRW), a watershed underlain with discontinuous permafrost, in interior Alaska. We analyzed long term data from watersheds underlain with varying degrees of permafrost, sampled springs and thermokarsts, used fluorescence spectroscopy, and measured the bioavailabity of dissolved organic carbon (DOC). Permafrost driven patterns in hydrology and vegetation influenced DOM patterns in streams, with the stream draining the high permafrost watershed having higher DOC and dissolved organic nitrogen (DON) concentrations, higher DOC:- DON and greater specific ultraviolet absorbance (SUVA) than the streams draining the low and medium permafrost watersheds. Streams, springs and thermokarsts exhibited a wide range of DOC and DON concentrations (1.5–37.5 mgC/L and 0.14–1.26 mgN/L, respectively), DOC:DON (7.1–42.8) and SUVA (1.5–4.7 L mgC-1 m-1). All sites had a high proportion of humic components, a low proportion of protein components, and a low fluorescence index value (1.3–1.4), generally consistent with terrestrially derivedDOM. Principal component analysis revealed distinct groups in our fluorescence data determined by diagenetic processing and DOM source. The proportion of bioavailable DOC ranged from 2 to 35%, with the proportion of tyrosine- and tryptophan-like fluorophores in the DOM being a major predictor of DOC loss (p\0.05, R2 = 0.99). Our results indicate that the degradation of permafrost in CPCRW will result in a decrease in DOC and DON concentrations, a decline in DOC:DON, and a reduction in SUVA, possibly accompanied by

Ensemble Stream Model for Data-Cleaning in Sensor Networks

Ensemble Stream Modeling and Data-cleaning are sensor information processing systems have different training and testing methods by which their goals are cross-validated. This research examines a mechanism, which seeks to extract novel patterns by generating ensembles from data. The main goal of label-less stream processing is to process the sensed events to eliminate the noises that are uncorrelated, and choose the most likely model without over fitting thus obtaining higher model confidence. Higher quality streams can be realized by combining many short streams into an ensemble which has the desired quality. The framework for the investigation is an existing data mining tool. First, to accommodate feature extraction such as a bush or natural forest-fire event we make an assumption of the burnt area (BA*), sensed ground truth as our target variable obtained from logs. Even though this is an obvious model choice the results are disappointing. The reasons for this are two: One, the histogram of fire activity is highly skewed. Two, the measured sensor parameters are highly correlated. Since using non descriptive features does not yield good results, we resort to temporal features. By doing so we carefully eliminate the averaging effects; the resulting histogram is more satisfactory and conceptual knowledge is learned from sensor streams. Second is the process of feature induction by cross-validating attributes with single or multi-target variables to minimize training error. We use F-measure score, which combines precision and accuracy to determine the false alarm rate of fire events. The multi-target data-cleaning trees use information purity of the target leaf-nodes to learn higher order features. A sensitive variance measure such as ƒ-test is performed during each node's split to select the best attribute. Ensemble stream model approach proved to improve when using complicated features with a simpler tree classifier. The ensemble framework for data-cleaning and the enhancements to quantify quality of fitness (30% spatial, 10% temporal, and 90% mobility reduction) of sensor led to the formation of streams for sensor-enabled applications. Which further motivates the novelty of stream quality labeling and its importance in solving vast amounts of real-time mobile streams generated today.

Stoichiometry, growth, and fecundity responses to nutrient enrichment by invertebrate grazers in sub-tropical turtle grass (Thalassia testudinum) meadows

Although the effectiveness of herbivores in mitigating the effects of nutrient enrichment is well documented, few studies have examined the effects of nutrient enrichment on components of consumer fitness. Enclosures were deployed in shallow turtle grass (Thalassia testudinum) beds in Florida Bay, Florida in fall 2003, spring 2004, and fall 2004 to measure the effects of nitrogen and phosphorous enrichment on the growth, fecundity, and stoichiometry of three invertebrate epiphyte grazers commonly associated with T. testudinum. The gastropod Turbo castanea exhibited significantly greater wet weight gain and lower C:P and N:P in enriched than in ambient treatments. Although nutrient enrichment did not have any significant effects on the growth of caridean shrimp (treatment consisted of several different caridean shrimp species), their C:N was significantly lower in enriched treatments. The final size and stoichiometry of the hermit crab Paguristes tortugae was not significantly affected by nutrient enrichment, nor did nutrient enrichment significantly affect the fecundity of P. tortugae, the only grazer in which gravid individuals or egg masses were present. Our study demonstrated that nutrient enrichment of primary producers can positively affect the growth of marine invertebrate grazers and alter their stoichiometry; however, these effects were species-specific and may be dependent upon the life stage, specific diets, and/or compensatory feeding habits of the grazers.