Density data for <scp>L</scp>ake <scp>E</scp>rie benthic invertebrate assemblages from 1930 to 2019
Bibliographic record
Abstract
Benthic invertebrates are important trophic links in food webs and useful bioindicators of environmental conditions, but long-term benthic organism abundance data across broad geographic areas are rare and historic datasets are often not readily accessible. This dataset provides densities of benthic macroinvertebrates collected from 1930 to 2019 during surveys in Lake Erie, a Laurentian Great Lake. The surveys were funded by the governments of the United States and Canada to investigate the status and changes in the benthic community. From the total of 21 lake-wide and basin-wide benthic surveys conducted in Lake Erie from 1929 to 2019, we were able to acquire data for 17 surveys, including species-level data for 10 surveys and data by higher taxonomic groups for seven surveys. Our amassed Lake Erie dataset includes data from 11 surveys (including five with species-level data) conducted in the western basin in 1930-2019, seven surveys (six with species-level data) in the central basin, and eight surveys (seven with species-level data) in the eastern basin (1973-2019). This Lake Erie dataset represents the most extensive temporal dataset of benthic invertebrates available for any of the Laurentian Great Lakes. Benthic samples were collected using Ponar or Shipek bottom dredges and taxa densities were calculated as individuals per square meter using the area of the dredge. Density data are provided for taxa in the Annelida, Arthropoda, Mollusca, Cnidaria, Nemertea, and Platyhelminthes phyla. Current taxonomy was used for most groups but, in a few cases, older taxonomic names were used for consistency with historical data. Analysis of this dataset indicates that eutrophication, water quality improvement, and dreissenid introduction were the major drivers of changes in the benthic community in the western basin, while hypoxia was a major factor in the central basin, and dreissenid introduction was the most important driver in the eastern basin. Considering the rarity of high taxonomic resolution long-term benthic data for lake ecosystems, this dataset could be useful to explore broader aspects of ecological theory, including effects of eutrophication, hypoxia, invasive species, and other factors on community organization, phylogenetic and functional diversity, and spatial and temporal scales of variation in community structure. In addition, the dataset could be useful for studies on individual species, including abundance and distribution, species co-occurrence, and how the patterns of dominance and rarity change over space and time. Use of this dataset for academic or educational purposes is encouraged as long as this data paper is properly cited.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| 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) | 0.017 | 0.008 |
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".