Insights on community dynamics from large-scale, long-term data: time series for zooplankton and environmental variates from 35 North American lakes
Bibliographic record
Abstract
The NCEAS Community Dynamics Working Group brought together theoretical and empirical ecologists to evaluate the relative importance of intrinsic and extrinsic sources of variability in natural communities. We focused on zooplankton community dynamics, in part because we had access to extensive time series data on zooplankton community structure from 35 North American lakes. Thirty-four of the lakes were concentrated at 5 sites: the North Temperate Lakes LTER site in Wisconsin; the University of Notre Dame Environmental Research Center in Michigan; the Dorset Environmental Research Centre, the Experimental Lakes Area, and the Sudbury region, all in Ontario. We also have data for saline Mono Lake in California. Our database has many strengths, including the length of the time series (5-21 years); consistent sampling methodologies, particularly for zooplankton; and the availability of information on environmental factors, including phytoplankton, physical parameters, and water chemistry. Another unique aspect of our dataset is that it includes data from both undisturbed reference lakes and experimentally manipulated lakes. The whole-lake experiments include food web manipulations, nutrient enrichments, acidifications and limings, and an unplanned invasion of an exotic species. Some of the experimental manipulations are even replicated within and among sites, providing a unique opportunity to explore the extent to which responses to a particular perturbation are consistent across lakes. In addition to determining how manipulations change the dynamics of zooplankton communities, we are using the data from the reference lakes to establish bounds on normal ranges of variability and to explore how this variability differs among lakes at a site vs. between sites. Thus, our joint database is facilitating extensive comparisons of zooplankton community dynamics at the scale of whole lakes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
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; both teacher heads agree on what is shown here.
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".