Richness–abundance relationships for zooplankton in ballast water: temperate versus Arctic comparisons
Bibliographic record
Abstract
Abstract Species richness and abundance are two commonly measured parameters used to characterize invasion risk associated with transport vectors, especially those capable of transferring large species assemblages. Understanding the relationship between these two variables can further improve our ability to predict future invasions by identifying conditions where high-risk (i.e. species-rich or high abundance or both) and low-risk (i.e. species-poor and low abundance) introduction events are expected. While ballast water is one of the best characterized transport vectors of aquatic non-indigenous species, very few studies have assessed its magnitude at high latitudes. We assessed the arrival potential of zooplankton via ballast water in the Canadian Arctic by examining species richness, total abundance, and the relationship between the two parameters for zooplankton in ships from Europe destined for the Arctic, in comparison with the same parameters for ships bound for Atlantic Canada and the Great Lakes. In addition, we examined whether species richness and/or total abundance were influenced by temperature change and/or ballast water age for each shipping route. We found that species richness and total abundance for Arctic and Great Lakes ships were significantly lower than those for Atlantic ships. Differences in species richness and total abundance for ships utilizing different shipping routes were mostly related to ballast water age. A significant species richness–total abundance relationship for Arctic and Great Lakes ships suggests that these parameters decreased proportionately as ballast water aged. In contrast, the absence of such a relationship for Atlantic ships suggests that decreases in total abundance were accompanied by little to no reduction in species richness. Collectively, our results indicate that the arrival potential of zooplankton in ballast water of Arctic ships may be lower than or similar to that of Atlantic and Great Lakes ships, respectively.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".