Measuring spatial variation in secondary production and food quality using a common consumer approach in Lake Erie
Bibliographic record
Abstract
Lake Erie is a large lake straddling the border of the USA and Canada that has become increasingly eutrophic in recent years. Eutrophication is particularly focused in the shallow western basin. The western basin of Lake Erie is hydrodynamically similar to a large estuary, with riverine inputs from the Detroit and Maumee Rivers mixing together and creating gradients in chemical and physical conditions. This study was driven by two questions: (1) How does secondary production and food quality for consumers vary across this large mixing zone? and (2) Are there correlations between cyanobacterial abundance and secondary production or food quality for consumers? Measuring spatial and temporal variation in secondary production and food quality is difficult for a variety of logistical reasons, so here a common consumer approach was used. In a common consumer approach, individuals of a single species are raised under similar conditions until placed in the field across environmental gradients of interest. After some period of exposure, the response of that common consumer is measured to provide an index of spatial variation in conditions. Here, a freshwater mussel (Lampsilis siliquoidea) was deployed at 32 locations that spanned habitat types and a gradient in cyanobacterial abundance in the western basin of Lake Erie to measure spatial variation in growth (an index of secondary production) and fatty acid (FA) content (an index of food quality). We found secondary production was highest within the Maumee river mouth and lowest in the open waters of the lake. Mussel tissues in the Maumee river mouth also included more eicosapentaenoic and docosapentaenoic fatty acids (EPA and DPA, respectively), but fewer bacterial FAs, suggesting more algae at the base of the food web in the Maumee river mouth compared to open lake sites. The satellite-derived estimate of cyanobacterial abundance was not correlated to secondary production, but was positively related to EPA and DPA content in the mussels, suggesting more of these important FAs in locations with more cyanobacteria. These results suggest that growth of secondary consumers and the availability of important fatty acids in the western basin are centered on the Maumee river mouth.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".