University of Rhode Island

About: University of Rhode Island is a education organization based out in Kingston, Rhode Island, United States. It is known for research contribution in the topics: Population & Bay. The organization has 11464 authors who have published 22770 publications receiving 841066 citations. The organization is also known as: URI & Rhode Island College of Agriculture and the Mechanic Arts.

Harmful algal blooms: Their ecophysiology and general relevance to phytoplankton blooms in the sea

Abstract: From 60 to 80 species of phytoplankton have been reported to be harmful; of these, 90% are flagellates, notably dinoflagellates. The effects of turbulence on harmful algal bloom (HAB) taxa, their photoadaptive strategies, growth rate, and nutrient uptake affinity (Ks) are considered. Flagellates, including HAB taxa, collectively have a lower nutrient uptake affinity than diatoms. Four major adaptations are suggested to have been evolved to offset the ecological disadvantages of their low nutrient affinity: nutrient retrieval migrations; mixotrophic tendencies; alle-lelochemically enhanced interspecific competition; and allelopathic, antipredation defense mechanisms. Motility-based behavioral features of flagellates contributing to their blooms include: phototaxis, vertical migration, pattern swimming, and aggregation, which facilitate nutrient retrieval, trace metal detoxification, antipredation, depth-keeping, and turbulence avoidance. Neither a general physiological syndrome nor distinctive physiological profile distinguishes harmful flagellate species from nonharmful taxa. However, HAB flagellates exhibit significant ecophysiological differences when compared to diatoms, including greater biophysical vulnerability to turbulence, greater bloom dependence on water-mass stratification, greater nutritional diversity involving mixotrophic tendencies, greater potential use of allelochemical mechanisms in interspecific competition and antipredation defenses, and unique behaviorial consequences of their motility. Flagellates use a “swim” strategy; diatoms a “sink” strategy.

The mean composition of ocean ridge basalts

Abstract: [1] The mean composition of mid-ocean ridge basalts (MORB) is determined using a global data set of major elements, trace elements, and isotopes compiled from new and previously published data. A global catalog of 771 ridge segments, including their mean depth, length, and spreading rate enables calculation of average compositions for each segment. Segment averages allow weighting by segment length and spreading rate and reduce the bias introduced by uneven sampling. A bootstrapping statistical technique provides rigorous error estimates. Based on the characteristics of the data, we suggest a revised nomenclature for MORB. “ALL MORB” is the total composition of the crust apart from back-arc basins, N-MORB the most likely basalt composition encountered along the ridge >500 km from hot spots, and D-MORB the depleted end-member. ALL MORB and N-MORB are substantially more enriched than early estimates of normal ridge basalts. The mean composition of back-arc spreading centers requires higher extents of melting and greater concentrations of fluid-mobile elements, reflecting the influence of water on back-arc petrogenesis. The average data permit a re-evaluation of several problems of global geochemistry. The K/U ratio reported here (12,340 ± 840) is in accord with previous estimates, much lower than the estimate of Arevalo et al. (2009). The low Sm/Nd and 143Nd/144Nd ratio of all morb and N-MORB provide constraints on the hypothesis that Earth has a non-chondritic primitive mantle. Either Earth is chondritic in Sm/Nd and the hypothesis is incorrect or MORB preferentially sample an enriched reservoir, requiring a large depleted reservoir in the deep mantle.

Estimation of frequencies of multiple sinusoids: Making linear prediction perform like maximum likelihood

Abstract: The frequency-estimation performance of the forward-backward linear prediction (FBLP) method of Nuttall/Uhych and Clayton, is significantly improved for short data records and low signal-to-noise ratio (SNR) by using information about the rank M of the signal correlation matrix. A source for the improvement is an implied replacement of the usual estimated correlation matrix by a least squares approximation matrix having the lower rank M. A second, related cause for the improvement is an increase in the order of the prediction filter beyond conventional limits. Computationally, the recommended signal processing is the same as for the FBLP method, except that the vector of prediction coefficients is formed from a linear combination of the M principal eigenvectors of the estimated correlation matrix. Alternatively, singular value decomposition can be used in the implementation. In one special case, which we call the Kumaresan-Prony (KP) case, the new prediction coefficients can be calculated in a very simple way. Philosophically, the improvement can be considered to result from a preliminary estimation of the explainable, predictable components of the data, rather than attempting to explain all of the observed data by linear prediction.

Ecological Thresholds: The Key to Successful Environmental Management or an Important Concept with No Practical Application?

Abstract: An ecological threshold is the point at which there is an abrupt change in an ecosystem quality, property or phenomenon, or where small changes in an environmental driver produce large responses in the ecosystem. Analysis of thresholds is complicated by nonlinear dynamics and by multiple factor controls that operate at diverse spatial and temporal scales. These complexities have challenged the use and utility of threshold concepts in environmental management despite great concern about preventing dramatic state changes in valued ecosystems, the need for determining critical pollutant loads and the ubiquity of other threshold-based environmental problems. In this paper we define the scope of the thresholds concept in ecological science and discuss methods for identifying and investigating thresholds using a variety of examples from terrestrial and aquatic environments, at ecosystem, landscape and regional scales. We end with a discussion of key research needs in this area.