Multidecadal Time Series of Measured Chlorophyll-a in Lakes and Estuarine-Coastal Ecosystems, 1966-2024
Bibliographic record
Abstract
The photosynthetic pigment chlorophyll-a is a commonly measured index of phytoplankton biomass and water quality across all aquatic ecosystem types. Some monitoring and research programs have sustained chlorophyll-a measurements for decades at monthly or higher frequency. Each of these time series is an invaluable record of phytoplankton variability at a particular location. The patterns of that variability have been essential for identifying the underlying processes of phytoplankton change at time scales of days, months, seasons, years and decades. Multidecadal series are rare and valuable because they provide empirical records of phytoplankton changes over the recent decades of unprecedented global change. These records also provide an empirical basis for comparing patterns and rates of change across geographic regions and ecosystem types. This data package contains multidecadal time series of measured chlorophyll-a concentration in three ecosystem types: 134 freshwater lakes (including a small number of reservoirs) that do not freeze; 78 high latitude lakes that do freeze; and 176 coastal ecosystems defined as water bodies where freshwater and seawater mix, including estuaries, coastal bays and lagoons, tidal rivers, and the Baltic Sea. Although there are other published compilations of chlorophyll-a time series, this package was compiled specifically to report observations made at monthly or higher frequency and sustained over multiple decades. The mean time series duration in this package is 33 years, and the mean number of sampling dates per site was 503 (range 186 to 2381). Thus, this data package provides an empirical basis for analyses to measure and compare decadal-scale patterns and rates of phytoplankton biomass variability between inland lakes and water bodies at the land-ocean interface. All chl-a measurements reported here were accessed from published repositories, except these four sites. We acknowledge and thank the following data providers for permission to include data from their study sites in this package: - Bahía Blanca, Argentina: Valeria Ana Guinder, Instituto Argentino de Oceanografía (IADO) Consejo, Nacional de Investigaciones Científicas y Técnicas (CONICET) - Neuse River Estuary US: Hans W. Paerl, Departments of Earth, Marine and Environmental Sciences and Environmental Sciences and Engineering, University of North Carolina Institute of Marine Sciences - Lake Tahoe US: S. Geoffrey Schladow and Shohei Watabe, Tahoe Environmental Research Center, University of California, Davis - Experimental Lakes, Canada: Sonya Havens and Chris Hay, IISD Experimental Lakes Area, Org ID: https://ror.org/05revcs89, Email: eladata@iisd-ela.org, Online URL: https://www.iisd.org/ela/ Each time series in this data package resulted from heroic investments of time and resources and an unwavering commitment to repeated observations to reveal patterns and understand processes of changes in our aquatic ecosystems. This data package is an homage to those heroes. Each observational program is listed and acknowledged in the file MetadataTable Sampling Locations.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".