Constraining the variability of optical properties in the Santa Barbara Channel, CA: A phytoplankton story
Bibliographic record
Abstract
The research presented in this dissertation evaluates the direct relationships of phytoplankton community composition and inherent optical properties (IOP); that is, the absorption and scattering of light in the ocean. Phytoplankton community composition affect IOPs in both direct and indirect ways, thus creating challenges for optical measurements of biological and biogeochemical properties in aquatic systems. Studies were performed in the Santa Barbara Channel (SBC), CA where an array of optical and biogeochemical measurements were made. Phytoplankton community structure was characterized by an empirical orthogonal functional analysis (EOF) using phytoplankton accessory pigments. The results showed that phytoplankton community significantly correlated to all IOPs, e.g. phytoplankton specific absorption, detrital absorption, CDOM absorption and particle backscattering coefficients. Furthermore, the EOF analysis was unique in splitting the microphytoplankton size class into separate diatom and dinoflagellate regimes allowing for assessment optical property differences within the same size class, a technique previously not systematically achievable. The phytoplankton functional group dinoflagellates were particularly influential to IOPs in surprising ways. Dinoflagellates showed higher backscattering efficiencies than would be predicted based on Mie theory, and significantly influenced CDOM absorption via direct association with dissolved mycosproine-like amino acid absorption (MAA) peaks in CDOM spectra. A new index was developed in this work to quantify MAA absorption peaks in CDOM spectra, and was named the MAA Index. Prior to this research dissolved MAA absorption in natural waters was never quantified, and CDOM data containing these peaks were often disregarded and discarded from analysis. CDOM dynamics in the SBC were assessed for a 15-year study period, and this work shows that significantly large MAA Index values, e.g. MAA Index > 1, were present in approximately 16% of surface water data. Variability in CDOM spectral shape was quantified using the EOF technique, and regression analysis with EOF outputs showed that CDOM absorption intensity and spectral shape were well correlated dinoflagellate presence. Furthermore, results showed that phytoplankton biomass played a secondary role in relation to CDOM absorption, and that variability in CDOM absorption coefficients were primarily driven by community composition. CDOM quality in the SBC was also assessed using CDOM fluorescence properties via excitation emission matrix spectroscopy (EEMS). The EEMS data was analyzed using a multivariate statistical procedure, again, an EOF analysis, to identify three dominant CDOM source regimes: the surface pelagic regime, deep-water (up to 300 m) regime and kelp forest pelagic regime. This work also found that while CDOM absorption coefficient was strongly influence by which phytoplankton groups were present, DOM quality was characterized more so by the amount of phytoplankton biomass, hence indicating strong microbial component to DOM production. Lastly, with the use of the EEMS data, and characterization of CDOM absorption properties, e.g. spectral slope, S, slope ratio, SR, specific UV-absorbance, SUVA and MAA Index, we found that terrestrial sources of CDOM were very limited in the SBC. Based on this research, mineral particle concentrations that significantly correlated with IOPs were thought to be associated with suspended sediments from shoaling of the continental shelf rather than from stream/river influence. Thus, the SBC is a unique, optically complex ocean system where IOP dynamics, thus remote sensing reflectance, are strongly influenced by shifts in phytoplankton community structure.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".