THE RELATIONSHIP IN LAKE COMMUNITIES BETWEEN PRIMARY PRODUCTIVITY AND SPECIES RICHNESS
Bibliographic record
Abstract
An understanding of the relationship between species richness and productivity is crucial to understanding biodiversity in lakes. We investigated the relationship between the primary productivity of lake ecosystems and the number of species for lacustrine phytoplankton, rotifers, cladocerans, copepods, macrophytes, and fish. Our study includes two parts: (1) a survey of 33 well-studied lakes for which data on six major taxonomic groups were available; and (2) a comparison of the effects of short- and long-term whole-lake nutrient addition on primary productivity and planktonic species richness. In the survey, species richness of all six taxa showed a significant quadratic response to increased annual primary productivity (14C estimate, g C·m−2·yr−1) when lake area is taken into account. However, the richness–productivity relationship for phytoplankton and fish was strongly dependent on lake area. The relationship for phytoplankton, rotifers, cladocerans, copepods, and macrophytes was significantly unimodal. Species richness generally peaked at levels of primary productivity in the range of 30–300 g C·m−2·yr−1. For the average lake size, the highest biodiversity tended to occur in lakes with relatively low primary productivity, such as those found in the Northern Temperate Lakes Long-Term Ecological Research (LTER) site in the upper Midwest (United States) and in the Experimental Lakes Area of Ontario (Canada). Based on short-term (3 yr) and long-term (21–24 yr) experiments, we tested whether individual lakes respond to whole-lake enrichment experiments in the manner suggested by analyses of survey data. Experimental addition of nutrients produced varied and unpredictable responses in species richness, probably due to transient dynamics and time lags. Responses to nutrient addition were taxon and lake specific. Phytoplankton showed a variety of relationships between species richness and pelagic primary productivity (PPR), depending on the history of enrichment and recovery. No significant effect of primary productivity on rotifer richness occurred in any of the experimental lakes, whereas richness of crustacean zooplankton was negatively correlated with primary productivity in both the short- and long-term experiments.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".