Research biases create overrepresented “poster children” of marine invasion ecology
Bibliographic record
Abstract
Abstract Nonnative marine species are increasingly recognized as a threat to the world's oceans, yet are poorly understood relative to their terrestrial and freshwater counterparts. Here, we conducted a systematic review of 2,203 research articles on nonnative marine animals to determine whether the current literature reflects the known diversity of marine invaders, how much we know about these species, and how frequently their impacts are measured. We found that only 39% of nonnative animals listed in the World Register of Introduced Marine Species appeared in the peer‐reviewed English literature. Of those, fewer than half were the subject of more than one study. There is currently little focus on the consequences of marine introductions: only 9.9% of studies quantified the impact of nonnative species. Finally, our knowledge of nonnative marine species is heavily limited by strong taxonomic biases consistent across all phyla, resulting in one or two disproportionately well‐studied representatives for each phylum, which we refer to as the “poster children” of invasion. These gaps in the literature make it difficult to effectively triage the most detrimental invasive species for management and illustrate the challenges in achieving the global biodiversity goals of preventing and managing the introduction and establishment of invasive species.
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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 | 0.071 | 0.287 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".