Changes in precipitation may alter food preference in an ecosystem engineer, the black land crab, <i>Gecarcinus ruricola</i>
Bibliographic record
Abstract
Gecarcinid land crabs are ecosystem engineers playing an important role in nutrient recycling and seedling propagation in coastal forests. Given a predicted future decline in precipitation for the Caribbean, the effects of dehydration on feeding preferences of the black land crab Gecarcinus ruricola were investigated. G. ruricola were offered novel food items of lettuce, apple, or herring to test for food choice based on water and nutritional (energetic) content in single and multiple choice experimental designs. The effect of dehydration was incorporated by depriving crabs of water for 0, 4, or 8 days, leading to an average body water loss of 0%, 9%, and 17%, respectively, (crabs survived a body water loss of 23% + 2% and 14–16 days without access to water). The results were consistent between the single and multiple choice experiments: crabs consumed relatively more apple and fish and only small amounts of lettuce. Overall, no selective preferences were observed as a function of dehydration, but crabs did consume less dry food when deprived of water and an overall lower food intake with increasing dehydration levels occurred. The decrease in feeding was likely due to loss of water from the gut resulting in the inability to produce ample digestive juices. Future climatic predictions suggest a 25–50% decline in rainfall in the Caribbean, which may lead to a lower food intake by the crabs, resulting in compromised growth. The subsequent reduction in nutrient recycling highlights possible long-term effects on coastal ecosystems and highlights the importance of future work on climate relative behavioral interactions that influence ecosystem function.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".