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.

The relative contribution of allochthonous and autochthonous production in zooplankton nutrition has been of interest since the net heterotrophy of lakes was recognised to be common. I measured the 13C signature of epilimnetic CO2, particulate organic carbon (POC), and zooplankton in 27 north-temperate lakes in late summer and used the relationships between the POC and zooplankton 13C signatures and the CO2 signature to estimate the autochthonous contribution to these fractions of the plankton. My hypothesis was that POC and zooplankton signature would reflect the 13CO2 signature if they were autochthonous. Conversely, increasing allochthonous C would result in a 13C signature of POC or zooplankton that is increasingly influenced by the allochthonous 13C signature (δ-28‰) and decreasingly dependent on the CO2 signature. The average autochthonous contribution to epilimnetic POC was estimated to be between 62 and 75%. Epilimnetic zooplankton were, on average, between 77 and 91% autochthonous, indicating that zooplankton bias their feeding towards the autochthonous fraction of POC. On average, zooplankton were 1.2‰ enriched in 13C relative to POC, but their biased feeding on phytoplankton means that they can be depleted relative to POC in lakes where POC is highly depleted in 13C. The relationship between 13C POC and 13CO2 allowed us to estimate average photosynthetic fraction as -15.9‰. This estimate is independent of how much allochthonous C contributes to POC. Variation in photosynthetic fractionation was not a major contributor to differences among lakes in POC and zooplankton 13C signature. Allochthonous C is an important, although clearly secondary, source of C to zooplankton of these lakes in late summer. I expanded the above analysis by culling the literature for 13C stable isotope data of lake CO2, POC, and zooplankton. I found that, similar to the lakes that I had sampled, POC signature showed a strong influence of allochthonous C, and inferred that it was close to 50% allochthonous on average. I calculated an autochthonous fractionation of -14.1‰ for the metadata, which was similar to that of the lakes I sampled. While POC had a considerable allochthonous contribution, zooplankton signatures were strongly related to the CO2 signatures, suggesting that their carbon was mostly autochthonous. Therefore, while terrestrial inputs form a major portion of POC, zooplankton C, on average, was largely autochthonous. I also examined the differences in 13C/15N among zooplankton taxa, and differences in 13CO2, 13C/15N of POM, and 13C/15N of zooplankton with depth. There were small differences among the 15N of various taxa, and I did not detect differences in 13C amongst taxa. I found vertical heterogeneity was most marked in 13CO2 signatures, which generally depleted appreciably with increasing lake depth. The signatures of 13C POM and 13C zooplankton also generally depleted with depth, but much less so than did 13CO2. I interpret this as indicating that a large portion of POM and zooplankton C in the metalimnia and hypolimnia of these lakes is derived from C fixed in the epilimnia.

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 (Ph. D.)--Oregon State University, 2001. / Typescript (photocopy). Includes bibliographical references (leaves 155-183). Also available on the World Wide Web.

Thesis (Honors)--College of William and Mary, 2009. / Includes bibliographical references (leaves 159-176). Also available via the World Wide Web.

Thesis (M.M.A.)--University of Washington, 2009. / Title from Web page (viewed on Feb. 3, 2010). Includes bibliographical references (leaves 20-23).

Thesis (M.M.A.)--University of Washington, 2009. / Title from Web page (viewed on Feb. 3, 2010). Includes bibliographical references (leaves 41-45).

Thesis (Ph. D.)--University of Tennessee, Knoxville, 2004. / Title from title page screen (viewed May 14, 2004). Thesis advisors: Robert A. Bohm, Michael J. McKee. Document formatted into pages (xi, 185 p. : ill. (some color)). Vita. Includes bibliographical references (p. 116-122).

Thesis (M.M.A.)--University of Washington, 2009. / Includes bibliographical references (leaves 20-23). Also available on the World Wide Web.

Thesis (M.M.A.)--University of Washington, 2009. / Includes bibliographical references (leaves 41-45). Also available on the World Wide Web.