Nearshore and embayment zooplankton community structure in Lake Michigan, implications for invasion by bigheaded carps
Bibliographic record
Abstract
Zooplankton communities in Lake Michigan’s nearshore and embayment regions are important food sources for planktivorous and larval fish. Geographically extensive surveys of nearshore zooplankton abundance and composition are crucial for understanding spatial patterns in community structure and Lake Michigan’s food web. Over 400 zooplankton samples collected in 2013 and 2014 from 25 locations in Illinois, Michigan, and Wisconsin harbors, open waters, and drowned river mouth lakes of Lake Michigan were used to identify spatial and temporal patterns in zooplankton assemblages. Special focus was placed on the accurate collection of small-bodied taxa such as rotifers, copepod nauplii, and dreissenid veligers to explore the contribution of smaller taxa to total zooplankton biomass. Multivariate analysis identified zooplankton biomass and community structure in Green Bay, Wisconsin and Michigan drowned river mouth lakes as distinct from Illinois and Wisconsin harbor and nearshore areas. Our zooplankton biomass estimates were then applied to an existing bioenergetics model for bigheaded carps (Hypophthalmichthys nobilis and H. molitrix) to quantify their predicted growth, as a proxy for successful establishment in Lake Michigan. Locations where bigheaded carps were predicted to grow were consistent with patterns in zooplankton community structure and were restricted to a handful of sites in Green Bay and Michigan drowned river mouths. These results emphasize the importance of embayment zooplankton communities and small-bodied zooplankton as energy sources for both native and non-native fishes in an increasingly oligotrophic Lake Michigan.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".