Links between nitrification rates and betaproteobacterial ammonia oxidising bacteria (AOB) community structure in estuarine sediments were determined in relation to changes in salinity and substrate concentrations associated with these environments.  Sediment was collected from the upper, middle and lower reaches of the estuary and incubated with water amended with either a range of salinities from marine to freshwater, or a range of ammonia concentrations.  Ammonia consumption, nitrate and nitrite production were measured at regular intervals as an indicator of nitrification rates and 16S rRNA gene-targeted analysis of betaproteobacterial AOB community structure was carried out by denaturing gradient gel electrophoresis of amplified genes from original sediment, at the beginning of nitrate production, and after a period of incubation.  Salinity and ammonia concentrations were shown to influence both nitrification rates and betaproteobacterial AOB community structure in estuarine sediments, in particular increased ammonia concentrations lead to increased nitrification regardless of the origin of the sediment.  A shift in the dominant betaproteobacterial AOB community structure was observed in microcosms with both salinity and ammonia treatments, but particular treatments did not lead to the selection of a common community structure.  Members of the <i>Nitrosomonas </i>cluster 5 were selected for in most sediments and treatments, regardless of salinity and ammonia treatments, while others, such as members of the <i>Nitrosospira</i>-lineage cluster 1 group, were restricted to low ammonia microcosms.  This study indicates that community members are capable of functioning at a wide range of estuarine salinity and ammonia conditions but that these are eventually replaced by community members better suited to these conditions.

Sediments in the South Slough Estuarine Sanctuary, Coos Bay, Oregon, were sampled and studied in order to determine the sources, dispersal systems and depositional facies of sedimentation. The purpose of the study was to establish baseline measurements and observations on the existing sediment conditions within the relatively undisturbed South Slough Estuarine Sanctuary

Thesis (M.S.)--University of North Carolina at Wilmington, 2006. / Includes bibliographical references (leaves: 65-73)

Assessment of the “health” of an ecosystem is often of great interest to those interested in monitoring and conservation of ecosystems. Traditionally, scientists have quantified the health of an ecosystem using multimetric indices that are semi-qualitative. Recently, a statistical-based index called the Latent Health Factor Index (LHFI) was devised to address many inadequacies of the conventional indices. Relying on standard modelling procedures, unlike the conventional indices, accords the LHFI many advantages: the LHFI is less arbitrary, and it allows for straightforward model inference and for formal statistical prediction of health for a new site (using only supplementary environmental covariates). In contrast, with conventional indices, formal statistical prediction does not exist, meaning that proper estimation of health for a new site requires benthic data which are expensive and time-consuming to gather. As the LHFI modelling methodology is a relatively new concept, it has so far only been demonstrated (and validated) on freshwater ecosystems. The goal of this thesis is to apply the LHFI modelling methodology to estuarine ecosystems, particularly to the previously unassessed system in Richibucto, New Brunswick. Specifically, the aims of this thesis are threefold: firstly, to investigate whether the LHFI is even applicable to estuarine systems since estuarine and freshwater metrics, or indicators of health, are quite different; secondly, to determine the appropriate form that the LHFI model if the technique is applicable; and thirdly, to assess the health of the Richibucto system. Note that the second objective includes determining which covariates may have a significant impact on estuarine health. As scientists have previously used the AZTI Marine Biotic Index (AMBI) and the Infaunal Trophic Index (ITI) as measurements of estuarine ecosystem health, this thesis investigates LHFI models using metrics from these two indices simultaneously. Two sets of models were considered in a Bayesian framework and implemented using Markov chain Monte Carlo techniques, the first using only metrics from AMBI, and the second using metrics from both AMBI and ITI. Both sets of LHFI models were successful in that they were able to make distinctions between health levels at different sites.

Assessment of the “health” of an ecosystem is often of great interest to those interested in monitoring and conservation of ecosystems. Traditionally, scientists have quantified the health of an ecosystem using multimetric indices that are semi-qualitative. Recently, a statistical-based index called the Latent Health Factor Index (LHFI) was devised to address many inadequacies of the conventional indices. Relying on standard modelling procedures, unlike the conventional indices, accords the LHFI many advantages: the LHFI is less arbitrary, and it allows for straightforward model inference and for formal statistical prediction of health for a new site (using only supplementary environmental covariates). In contrast, with conventional indices, formal statistical prediction does not exist, meaning that proper estimation of health for a new site requires benthic data which are expensive and time-consuming to gather. As the LHFI modelling methodology is a relatively new concept, it has so far only been demonstrated (and validated) on freshwater ecosystems. The goal of this thesis is to apply the LHFI modelling methodology to estuarine ecosystems, particularly to the previously unassessed system in Richibucto, New Brunswick. Specifically, the aims of this thesis are threefold: firstly, to investigate whether the LHFI is even applicable to estuarine systems since estuarine and freshwater metrics, or indicators of health, are quite different; secondly, to determine the appropriate form that the LHFI model if the technique is applicable; and thirdly, to assess the health of the Richibucto system. Note that the second objective includes determining which covariates may have a significant impact on estuarine health. As scientists have previously used the AZTI Marine Biotic Index (AMBI) and the Infaunal Trophic Index (ITI) as measurements of estuarine ecosystem health, this thesis investigates LHFI models using metrics from these two indices simultaneously. Two sets of models were considered in a Bayesian framework and implemented using Markov chain Monte Carlo techniques, the first using only metrics from AMBI, and the second using metrics from both AMBI and ITI. Both sets of LHFI models were successful in that they were able to make distinctions between health levels at different sites.

Thesis (M.S.)--University of West Florida, 2007. / Title from title page of source document. Document formatted into pages; contains 117 pages. Includes bibliographical references.

Thesis (Ph. D.)--Rutgers University, 2009. / "Graduate Program in Ecology and Evolution." Includes bibliographical references (165-180).

Thesis (Ph.D.)--Murdoch University, 2009. / Thesis submitted to the Faculty of Sustainability, Environmental and Life Sciences. Includes bibliographical references.

Thesis (M.S.)--University of North Carolina Wilmington, 2009. / Title from PDF title page (February 16, 2010) Includes bibliographical references (p. 16-17)

Thesis (Ph. D.)--University of Washington, 2003. / Vita. Includes bibliographical references (leaves 79-83).