Importance of long-term intensive monitoring programs for understanding multiple drivers influencing Lake Ontario zooplankton communities
Bibliographic record
Abstract
Drivers of lower food web composition and productivity in Lake Ontario have undergone extensive changes in the last 40 years, including nutrient abatement, fluctuations in planktivores (Alewife), and invasion by dreissenid mussels and predatory cladocerans. Temporally intensive long-term index stations are critical for understanding these drivers and interpreting the results of periodic lake-wide spatially intensive surveys such as Cooperative Science and Monitoring Initiative (CSMI). We compare epilimnetic physical–chemical parameters and zooplankton metrics at a Kingston Basin biomonitoring site (Station 81) over three time stanzas (1981–1986, 1987–1995 and 2007–2017). In the most recent stanza, mean May-October temperature increased by 2.5 °C, and despite static total phosphorus levels, chlorophyll has significantly decreased and Secchi depth has increased. Between Stanzas 2 and 3, epilimnetic density, biomass and production of crustacean zooplankton have declined by 88%, 79% and 67%, respectively. Bosminids, Daphnia retrocurva, Diacyclops and juvenile cyclopoids are most impacted, whereas larger taxa (calanoids, Daphnia galeata, Holopedium and predatory cladocerans) have remained stable or increased. While some taxa have increased in size over time, zooplankton egg ratios have remained stable. Dreissenid veligers are now numerically dominant and have replaced some of the lost crustacean production. Redundancy Analysis showed environmental drivers (Secchi and temperature) significantly influenced zooplankton during the 1981–1995 period but not in the recent stanza. Alewife were not a significant driver despite substantial declines since the 1990s. Resource competition by Dreissena for the strongly reduced phytoplankton productivity, combined with predation by invasive cladocerans Cercopagis and Bythotrephes have also likely influenced Kingston Basin zooplankton.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".