In freshwater, there is more opportunity for silver (Ag) to interact with organic ligands than in seawater. Triclosan is an antibiotic agent which resembles thyroid hormone T3 and is finding its way into aquatic systems. Preliminary toxicology studies for the frogSCOPE program suggest that triclosan and nanosilver (nanoAg), also used as an antibiotic agent, may be chemically interacting, as they seem to synergistically increase the endocrine-disrupting abilities already observed independently in each chemical. Ag speciation methods can be used to determine if triclosan or thyroid hormone T3 are interacting with Ag ion (Ag+), which gets released over time by nanoAg. To fully utilize Ag speciation methods, however, total Ag in the sample must also be independently analyzed. Here we investigated a new total Ag analysis using cadmium sulfide quantum dots (CdS QDs) as fluorescence probes in solution. This method promises results in a fraction of the time of the established competitive ligand equilibration-solvent extraction (CLE-SE) technique utilizing PDC- and DDC- to bind Ag and bring it out of solution. Following this investigation were a series of experiments using CLE-SE for total Ag and Ag speciation in well water used to house bullfrog tadpoles in frogSCOPE Ag exposure studies. CLE-SE for Ag speciation was also applied to well water samples containing the two levels of nanoAg or Ag+ used in frogSCOPE Ag exposures, and used in ligand competition experiments to examine the potential of triclosan or T3 to act as strong Ag-binding ligands, as compared to glutathione and EDTA, two known Ag-binding ligands. The results of the latter experiments could be used to determine if either of these could be forming complexes with Ag which increase or decrease their delivery to amphibian cells. The fluorometric method using CdS QDs showed no ideal analytical response to nanomolar Ag+, even when commercial QDs were modified and used, so it could not be applied to our samples. Using CLE-SE for total Ag, the well water used as a base for toxicity studies in frogSCOPE contained Ag below the method detection limit of 5 pM. Using the speciation variation of the CLE-SE method, no evidence of naturally-occurring ligands which could produce extractable (hydrophobic) or non-extractable (hydrophilic) Ag complexes was found in this well water. EDTA and glutathione responded as model Ag-binding ligands to form non-extractable hydrophilic Ag complexes in fresh water. T3 behaved like these model ligands, while triclosan enhanced the extractability of Ag in the presence of certain concentrations of the added ligand, DDC-. In another set of experiments, coordination of Ag by triclosan or T3 was not detectable within that analytical window. These results suggest that ionic Ag released over time by nanoAg may be binding T3 and preventing it from reaching its receptor, but confirming the interaction of triclosan and Ag+ will require additional experiments using different analytical windows.

The main goal of this thesis was to evaluate the use of high spatial remote imagery to map the location and biophysical parameters of eelgrass in marine areas around Sidney Spit, a part of the Gulf Islands National Park Reserve of Canada (GINPRC). To meet this goal, three objectives were addressed: (1) Define key spectral variables that provide optimum separation between eelgrass and its associated benthic substrates, using in situ hyperspectral measurements, and simulated IKONOS and Landsat 7ETM+ spectral response; (2) evaluate the efficacy of these key variables in classification of the high spatial resolution imagery, AISA and IKONOS, at various levels of processing, to determine the processing methodology that offers the highest eelgrass mapping accuracy; and (3) evaluate the potential of ―value-added‖ classification of two eelgrass biophysical indicators, LAI and epiphyte type. In situ hyperspectral measurements acquired at Sidney Spit in August 2008 provided four different data sets: above water spectra, below water spectral profiles, water-corrected spectra, and pure endmember spectra. In Chapter 3, these data sets were examined with first derivative analysis to determine the unique spectral variables of eelgrass and associated benthic substrates. The most effective variables in discriminating eelgrass from all other substrates were selected using data reduction statistics: M-statistic analysis and multiple discriminant analyses (MDA). These selected spectral variables enabled eelgrass classification accuracy of 98% when separating six classes on above water data: shallow (< 3 m deep) eelgrass, deep (> 3 m) eelgrass, shallow sand, deep sand, shallow green algae, and spectrally deep water. The variables were located mainly in the green wavelengths, where light penetrates to the greatest depth: the slope from 500 – 530 nm, and the first derivatives at 566 nm, 580 nm, and 602 nm. The same data were classified with 96% accuracy after correcting for the water column, using the ratios 566:600 and 566:710. The only source of confusion for all data sets was between green algae and eelgrass, presumably due to their similar pigment composition. IKONOS and Landsat 7ETM+ simulated datasets performed similarly well, with 92% and 94% eelgrass classification accuracy respectively. In Chapter 4, the efficacy of the selected features was tested in the classification of airborne hyperspectral AISA imagery and satellite platform multispectral IKONOS imagery, and compared with various other classifiers, both supervised and unsupervised: K-means, minimum distance (MD), linear spectral unmixing (LSU), and spectral angle mapper (SAM). The selected features achieved the highest eelgrass classification accuracies in the study, when combined with atmospheric correction, glint correction, and optically deep water masking. AISA achieved eelgrass producer and user accuracies of 85% in water shallower than 3 m, and 93% in deeper areas. IKONOS achieved 79% for shallow waters and 82% for deep waters. Endmember classification also showed accuracies over 84% and 71% in AISA and IKONOS imagery respectively. Again, the largest source of confusion was between eelgrass and green algae, as well as between exposed vegetation (sea asparagus and green algae) and exposed eelgrass. Incompatibilities of the automatable processing steps (Tafkaa, LSU and SAM) made automated mapping less accurate than supervised mapping, but suggestions are made toward improvement. The value-added classification of eelgrass LAI and epiphyte type produced poor results in all cases except one; epiphyte presence / absence could be delineated with 87% accuracy. Before applying the findings of this study, one must consider the spatial scale of the intended management goal and select imagery with suitable spatial resolution. Tidal variations, as well as seasonal variability in water conditions and eelgrass phenology must also be considered as they may affect classification accuracies.

Records of abundance of salmon that return to their natal spawning stream (escapements) are important indices that can assist with monitoring, conservation, and management of a salmon population over time. On their own, however these data reveal very little about the habitat, ecosystem and human communities that salmon encounter on their journey from freshwater to sea and back again. This research examines monitoring protocols for Goldstream River salmon stocks (coho, chinook and chum Pacific salmon). It includes and reaches beyond biostatistics from stream surveys to gauge First Nations’ artisanal fishing activities at Goldstream River and Saanich Inlet as well as their commercial chum fishing endeavours in Saanich Inlet on south Vancouver Island, British Columbia. Methods included summations of major themes from interviews on traditional ecological knowledge (TEK) shared by local Saanich First Nation fishers whose families have lived in the communities around Goldstream River and Saanich Inlet for more than 200 years. Analyses of Goldstream salmon escapements for the period 1932 to 2004 and native harvest statistics of chum caught from Saanich Inlet between 1982 and 2004 are integrated with results from analysis of TEK research undertaken for this project. Key recommendations arising from the results of this research are: stream habitat restoration in response to loss and degradation of salmon-bearing streams; modification of stream survey procedures to measure for morphological and physiological attributes including indicators of the health of Goldstream salmon; monitoring and eliminating sources of pollution to Saanich Inlet waters; implementing precautionary measures to ensure that overfishing of Goldstream salmon and shrimp in Saanich Inlet does not recur; and safeguarding naturally abundant Goldstream chum populations at the river. Under current management of the Goldstream chum fishery, the maximum carrying capacity (K) or target escapement of chum that the Goldstream River spawning grounds sustain is 15,000. Based on population assessments as well as physiography and ecosystem dynamics, I infer that Goldstream River’s K for its natural chum population is between ~16,000 and 18,000; ~1,500 for the mixed stocks of natural and hatchery enhanced coho; and ~50 for chinook (based on the river’s naturally occurring populations between 1932 and 1973) or ~385 enhanced chinook (based on the returning population from 1975 to 2002 since hatchery enhancement took place). A co-management relationship exists between Fisheries and Oceans Canada (DFO) resource managers and the Saanich First Nations bands (Saanich Tribal Fisheries councilors). Improvements to communication, collaboration and information sharing between DFO resource managers, Goldstream hatchery operators and Saanich First Nations with regards to decisions made about Goldstream salmon stocks are, however, necessary. In this thesis, I propose a model with recommendations for compatible fisheries management goals and techniques including adaptive management and ecosystem-based management to address this problem.

Records of abundance of salmon that return to their natal spawning stream (escapements) are important indices that can assist with monitoring, conservation, and management of a salmon population over time. On their own, however these data reveal very little about the habitat, ecosystem and human communities that salmon encounter on their journey from freshwater to sea and back again. This research examines monitoring protocols for Goldstream River salmon stocks (coho, chinook and chum Pacific salmon). It includes and reaches beyond biostatistics from stream surveys to gauge First Nations’ artisanal fishing activities at Goldstream River and Saanich Inlet as well as their commercial chum fishing endeavours in Saanich Inlet on south Vancouver Island, British Columbia. Methods included summations of major themes from interviews on traditional ecological knowledge (TEK) shared by local Saanich First Nation fishers whose families have lived in the communities around Goldstream River and Saanich Inlet for more than 200 years. Analyses of Goldstream salmon escapements for the period 1932 to 2004 and native harvest statistics of chum caught from Saanich Inlet between 1982 and 2004 are integrated with results from analysis of TEK research undertaken for this project. Key recommendations arising from the results of this research are: stream habitat restoration in response to loss and degradation of salmon-bearing streams; modification of stream survey procedures to measure for morphological and physiological attributes including indicators of the health of Goldstream salmon; monitoring and eliminating sources of pollution to Saanich Inlet waters; implementing precautionary measures to ensure that overfishing of Goldstream salmon and shrimp in Saanich Inlet does not recur; and safeguarding naturally abundant Goldstream chum populations at the river. Under current management of the Goldstream chum fishery, the maximum carrying capacity (K) or target escapement of chum that the Goldstream River spawning grounds sustain is 15,000. Based on population assessments as well as physiography and ecosystem dynamics, I infer that Goldstream River’s K for its natural chum population is between ~16,000 and 18,000; ~1,500 for the mixed stocks of natural and hatchery enhanced coho; and ~50 for chinook (based on the river’s naturally occurring populations between 1932 and 1973) or ~385 enhanced chinook (based on the returning population from 1975 to 2002 since hatchery enhancement took place). A co-management relationship exists between Fisheries and Oceans Canada (DFO) resource managers and the Saanich First Nations bands (Saanich Tribal Fisheries councilors). Improvements to communication, collaboration and information sharing between DFO resource managers, Goldstream hatchery operators and Saanich First Nations with regards to decisions made about Goldstream salmon stocks are, however, necessary. In this thesis, I propose a model with recommendations for compatible fisheries management goals and techniques including adaptive management and ecosystem-based management to address this problem.

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