Competition Drives Clumpy Species Coexistence in Estuarine Phytoplankton

Understanding the mechanisms that maintain biodiversity is a fundamental problem in ecology. Competition is thought to reduce diversity, but hundreds of microbial aquatic primary producers species coexist and compete for a few essential resources (e.g., nutrients and light). Here, we show that resource competition is a plausible mechanism for explaining clumpy distribution on individual species volume (a proxy for the niche) of estuarine phytoplankton communities ranging from North America to South America and Europe, supporting the Emergent Neutrality hypothesis. Furthermore, such a clumpy distribution was also observed throughout the Holocene in diatoms from a sediment core. A Lotka-Volterra competition model predicted position in the niche axis and functional affiliation of dominant species within and among clumps. Results support the coexistence of functionally equivalent species in ecosystems and indicate that resource competition may be a key process to shape the size structure of estuarine phytoplankton, which in turn drives ecosystem functioning.

Chloroplast microsatellites: new tools for studies in plant ecology and systematics

The nonrecombinant, uniparentally inherited nature of organelle genomes
makes them useful tools for evolutionary studies. However, in plants, detecting
useful polymorphism at the population level is often difficult because of the
low level of substitutions in the chloroplast genome, and because of the slow
substitution rates and intramolecular recombination of mtDNA. Chloroplast
microsatellites represent potentially useful markers to circumvent this problem
and, to date, studies have demonstrated high levels of intraspecific variability.
Here,we discuss the use of these markers in ecological and evolutionary
studies of plants, as well as highlighting some of the potential problems
associated with such use.

Modelling and Prediction of NOx Emission in a Coal-Fired Power Generation Plant

In this paper NOx emissions modelling for real-time operation and control of a 200 MWe coal-fired power generation plant is studied. Three model types are compared. For the first model the fundamentals governing the NOx formation mechanisms and a system identification technique are used to develop a grey-box model. Then a linear AutoRegressive model with eXogenous inputs (ARX) model and a non-linear ARX model (NARX) are built. Operation plant data is used for modelling and validation. Model cross-validation tests show that the developed grey-box model is able to consistently produce better overall long-term prediction performance than the other two models.