The benthic community of the Laurentian Great Lakes: Analysis of spatial gradients and temporal trends from 1998 to 2014
Bibliographic record
Abstract
We used the results of seventeen years of Great Lakes benthic monitoring conducted by the U.S. EPA's Great Lakes National Program Office to describe the spatial and temporal patterns of benthic communities, assess their status, trends, and main drivers, and to infer the potential impact of these community changes on ecosystem functioning. Benthic abundance and diversity were higher at shallow (<70 m in depth) stations with chlorophyll concentrations above 3 μg/L than at deeper sites (<1 μg/L). We infer that lake productivity, measured by chlorophyll was likely the major driver of benthic abundance and diversity across lakes. Consequently, benthic diversity and abundance were the highest in the most productive Lake Erie, followed by lakes Ontario, Michigan, Huron, and Superior. Multivariate analysis distinguished three major communities shared among lakes (littoral, sublittoral, and profundal) that differed in species composition and abundance, functional group diversity, and tolerance to organic pollution. Analysis of temporal trends revealed that the largest changes occurred in profundal communities, apparent in significant shifts in dominant taxa across all lakes except Lake Superior. In lakes Michigan, Huron, and Ontario, the former dominant Diporeia was replaced with Dreissena and Oligochaeta. Profundal species, with the exception of dreissenids, became less abundant, and their depth distribution has shifted. In contrast, density and diversity of native littoral and sublittoral communities increased. The invasion of dreissenids was among the most important drivers of changes in benthic communities. Continued monitoring is critical for tracking unprecedented changes occurring in the Great Lakes ecosystem.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".