A hybrid reasoning system for supporting the modelling of estuaries

Estuaries are complex natural water systems. Their behaviour depends on many factors, which are possible to analyse only adopting different study approaches. The physical processes within estuaries are generally investigated through computer modelling. However, models are not easily accessible. Their employment is only possible within restricted conditions and assumptions. Furthermore, in depth knowledge is required to interpret the information related to different disciplines and sources for the selection of a correct modelling approach. Therefore, the usability of computational estuarine models appears lower than their actual capability. This thesis describes the application of case-based reasoning methodology to support the design of estuarine models. The system (CBEM—Case-Based reasoning for Estuarine Modelling) aims to provide a general user with the necessary guidance for selecting the model that better matches to his/her goal and the nature of the problem to be solved. The system is based on the co-operative action of three modules: a case-based reasoning scheme and a genetic algorithm and a library of numerical estuarine models. [Continues.

Assessing the Health of Richibucto Estuary with the Latent Health Factor Index

The ability to quantitatively assess the health of an ecosystem is often of great interest to those tasked with monitoring and conserving ecosystems. For decades, research in this area has relied upon multimetric indices of various forms. Although indices may be numbers, many are constructed based on procedures that are highly qualitative in nature, thus limiting the quantitative rigour of the practical interpretations made from these indices. The statistical modelling approach to construct the latent health factor index (LHFI) was recently developed to express ecological data, collected to construct conventional multimetric health indices, in a rigorous quantitative model that integrates qualitative features of ecosystem health and preconceived ecological relationships among such features. This hierarchical modelling approach allows (a) statistical inference of health for observed sites and (b) prediction of health for unobserved sites, all accompanied by formal uncertainty statements. Thus far, the LHFI approach has been demonstrated and validated on freshwater ecosystems. The goal of this paper is to adapt this approach to modelling estuarine ecosystem health, particularly that of the previously unassessed system in Richibucto in New Brunswick, Canada. Field data correspond to biotic health metrics that constitute the AZTI marine biotic index (AMBI) and abiotic predictors preconceived to influence biota. We also briefly discuss related LHFI research involving additional metrics that form the infaunal trophic index (ITI). Our paper is the first to construct a scientifically sensible model to rigorously identify the collective explanatory capacity of salinity, distance downstream, channel depth, and silt-clay content --- all regarded a priori as qualitatively important abiotic drivers --- towards site health in the Richibucto ecosystem.Comment: On 2013-05-01, a revised version of this article was accepted for publication in PLoS One. See Journal reference and DOI belo

A hybrid reasoning system for supporting estuary modelling

In this paper the development of a Case-Based reasoning system for Estuarine Modelling (CBEM) is presented. The aim of the constructed CBEM system is to facilitate the utilisation of complex modelling software by users who lack detailed knowledge about modelling techniques and require training and assistance to implement sophisticated schemes effectively. The system is based on modern computing methods and is constructed as a hybrid of three modules which operate conjunctively to guide the user to obtain the best possible simulation for realistic problems. These modules are: a case-based reasoning scheme a genetic algorithm and a library of numerical estuarine models. Based on the features of a given estuary and the physical phenomenon to be modelled, an appropriate solution algorithm from the system’s library is retrieved by the case-based module after a specifically designed reasoning process. The selected model is then analysed and further treated by the genetic algorithm component to find optimum parameters which can appropriately model the conditions and characteristics of any given estuary. Finally, the user is provided with a procedure that gives the best solution for a problem using the available hydrographic data and under the specified conditions. As an illustrative example and to show the applicability of the present CBEM system under realistic conditions a case study based on the simulation of salinity distribution in the Tay estuary (Scotland, UK) is given in this paper

Joint Report of Peer Review Panel for Numeric Nutrient Criteria for the Great Bay Estuary New Hampshire Department of Environmental Services June, 2009

This peer review was authorized through a collaborative agreement sponsored by the New Hampshire Department of Environmental Services (DES) and the Cities of Dover, Rochester and Portsmouth, New Hampshire. The purpose was to conduct an independent scientific peer review of the document entitled, “Numeric Nutrient Criteria for the Great Bay Estuary,” dated June, 2009 (DES 2009 Report)

Marine Benthic Habitat Mapping of Muir Inlet, Glacier Bay National Park and Preserve, Alaska With an Evaluation of the Coastal and Marine Ecological Classification Standard III

Seafloor geology and potential benthic habitats were mapped in Muir Inlet, Glacier Bay National Park and Preserve, Alaska, using multibeam sonar, ground-truth information, and geological interpretations. Muir Inlet is a recently deglaciated fjord that is under the influence of glacial and paraglacial marine processes. High glacially derived sediment and meltwater fluxes, slope instabilities, and variable bathymetry result in a highly dynamic estuarine environment and benthic ecosystem. We characterize the fjord seafloor and potential benthic habitats using the Coastal and Marine Ecological Classification Standard (CMECS) recently developed by the National Oceanic and Atmospheric Administration (NOAA) and NatureServe. Substrates within Muir Inlet are dominated by mud, derived from the high glacial debris flux. Water-column characteristics are derived from a combination of conductivity temperature depth (CTD) measurements and circulation-model results. We also present modern glaciomarine sediment accumulation data from quantitative differential bathymetry. These data show Muir Inlet is divided into two contrasting environments: a dynamic upper fjord and a relatively static lower fjord. The accompanying maps represent the first publicly available high-resolution bathymetric surveys of Muir Inlet. The results of these analyses serve as a test of the CMECS and as a baseline for continued mapping and correlations among seafloor substrate, benthic habitats, and glaciomarine processes

Automatic identification and enumeration of algae

A good understanding of the population dynamics of algal communities is vital in many ecological and pollution studies of freshwater and oceanic systems. Present methods require manual counting and identification of algae and can take up to 90 min to obtain a statistically reliable count on a complex population. Several alternative techniques to accelerate the process have been tried on marine samples but none have been completely successful because insufficient effort has been put into verifying the technique before field trials. The objective of the present study has been to assess the potential of in vivo fluorescence of algal pigments as a means of automatically identifying algae. For this work total fluorescence spectroscopy was chosen as the observation technique

Administering the Clean Water Act: Do Regulators Have Bigger Fish to Fry When it Comes to Addressing the Practice of Chumming on the Chesapeake Bay?

The Chesapeake Bay is one of the country\u27s most productive estuaries. However, for decades the health of the Bay has been declining due in large part to nutrification. Excessive nutrients encourage algal blooms, which lower dissolved oxygen and increase turbidity in the Bay\u27s waters. More than 40% of the Bay\u27s main stern is now dead largely as a result of this problem. The practice of chumming, the discarding of baitfish, usually menhaden, over the sides of fishing boats to attract game fish like striped bass, is contributing to the Bay\u27s nutrification problem because the decomposing chum raises the waters biological oxygen demand which lowers dissolved oxygen and increases water turbidity causing bay grasses to die and setting in motion destructive positive feedback loops. Chum may also be a source of disease in game fish, and the demand for chum is contributing to the decline of menhaden, an important food and filter fish, on the Atlantic Coast. Despite these problems, the practice of chumming is not regulated by either the federal government or the state of Maryland. This article explores whether citizens can compel regulation by either jurisdiction and concludes that such initiatives would likely fail because of the absence of a duty to regulate. The article examines why regulators decline to regulate and finds that the most likely reasons are an over dependence on economic approaches to environmental regulation, which drives regulators to choose the largest targets of opportunity, and a failure to understand how small disturbances in complex systems like estuaries can set off a cascade of potentially catastrophic and irreversible consequences--here, the loss of the Bay\u27s biodiversity. The article concludes by suggesting that the Precautionary Principle offers a much better approach to identifying regulatory targets in estuarine systems where much is scientifically uncertain; and exhorts citizens to spend time educating regulators of these facts rather than in fruitless and time-consuming litigation