Predation scars provide a new method to distinguish native and invasive crab predation on mollusc prey
Bibliographic record
Abstract
Abstract Crab species are increasingly important socioeconomic resources that are threatened by human exploitation, climate change, and invasive species, such as European green crabs ( Carcinus maenas ). However, the continued health of their populations is often uncertain given the limited long‐term population data, necessitating alternate approaches to ensure their continued viability. Furthermore, C. maenas are one of the most highly invasive and destructive marine species globally, posing a threat to local ecosystems and species, including socioeconomically important crabs and their mollusc prey. Improved understanding of C. maenas invasions and their impacts on local crab and mollusc resources is therefore vitally important. Here, we present a new method for identifying species‐level presence and relative abundances of important crab species, including invasive C. maenas , from the scars they leave on their prey. We conducted controlled manipulative feeding experiments in which individuals of Dungeness crabs ( Metacarcinus magister ), red rock crabs ( Cancer productus ), and C. maenas , were allowed to attack snails ( Tegula funebralis ) and produce sublethal shell damage. Resulting shell damage was photographed and landmarked for geometric morphometric analyses to determine any differences in the shape of shell damage between crab species. There were statistically significant differences between the shape of shell damage created by all three crab species ( p < .0001). Shell damage formed a gradient from narrow/deep ( C. productus ) to shallow/wide ( C. maenas ) with M. magister as an intermediate form. Our method provides a novel, cost‐effective tool for long‐term species‐specific reconstructions of crab populations and assessing the broader ecological impacts of C. maenas invasions that can inform management and mitigation for these three important crab species.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".