High performance liquid chromatography phytoplankton pigment timeseries for the northern Salish Sea and central coast, British Columbia
Bibliographic record
Abstract
High performance liquid chromatography (HPLC) is a method that quantifies concentrations of phytoplankton pigments from bulk water samples. Phytoplankton species groupings (i.e. diatoms, dinoflagellates, etc.) typically contain different pigments, and concentrations of pigments, used for photosynthesis. These differences make it possible to use statistical methods (e.g. chemotaxonomic analysis, CHEMTAX) to estimate phytoplankton group-level biomass contributions within a bulk water sample. These methods provide unique and valuable insight into phytoplankton community dynamics as they quantify the full phytoplankton size-spectrum, are relatively fast and cost-effective and, are directly relatable to remote sensing (i.e. pigments drive differences in light measured by satellites). Furthermore, HPLC is considered the gold-standard for quantifying phytoplankton total chlorophyll a concentrations (TChla, proxy for bulk phytoplankton biomass) required for satellite remote sensing validation. The Hakai institute has been collecting HPLC samples in the northern Salish Sea (NSS) at the QU39 long-term ecological research (LTER) station and at various stations on the central coast of British Columbia since 2015. These data are used to monitor phytoplankton group level dynamics, build knowledge of different environmental conditions driving their variability (Del Bel Belluz et al., 2021), investigate linkages to the food web and carbonate system, evaluate long-term change and, to build regional remote sensing models (Vishnu et al., 2022). Data from station QU39 within the NSS are collected weekly and from 2015-2019 at 5m depth and afterwards at 0, 5, 10 and 20m depth. On the central coast of British Columbia, data are collected monthly at 5m depth. Samples are analyzed at the University of South Carolina Baruch Institute for Marine and Coastal Sciences using the USC method. This method was evaluated and is detailed in the NASA Fourth SeaWiFS HPLC Analysis Round-Robin Experiment (SeaHARRE-4, Hooker et al., 2010). Phytoplankton form the base of the marine food web and play key roles in biogeochemical cycling and carbon sequestration. The high turnover rates of phytoplankton species make them ideal sentinels of environmental change as they quickly respond to perturbations; however, a paucity of data, notably in terms of community composition, exists across coastal systems hindering the derivation of baseline conditions to assess change. This knowledge gap is especially pertinent when considering that coastal regions are experiencing rapid climate-driven change including increased temperature and acidification, reduced oxygen and altered freshwater dynamics. The synergy of these influences has the potential to alter phytoplankton community and size structure having large downstream implications on ecosystem resiliency, food production and climate regulation.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".