The effect of prey identity and substrate type on the functional response of a globally invasive crayfish
Bibliographic record
Abstract
Biological invasions threaten biodiversity on a global scale, therefore, developing predictive methods to understand variation in ecological change conferred is essential. Trophic interaction strength underpins community dynamics, however, these interactions can be profoundly affected by abiotic context, such as substrate type. The red swamp crayfish (Procambarus clarkii) has successfully invaded a number of freshwater ecosystems. We experimentally derive the Functional Response (FR) (density dependent predation) of the red swamp crayfish preying upon both a benthic prey; chironomid larvae, and a pelagic prey; Daphnia magna, on a no substrate control, sand, and gravel substrates to determine whether (1) there is a higher impact on prey that are benthic, and (2) whether the presence of different substrate types can dampen the interaction strength. We apply and demonstrate the utility of the Functional Response Ratio (FRR) metric in unravelling differences in ecological impact not obvious from traditional FR curves. Procambarus clarkii is capable of constantly utilising high numbers of both benthic and pelagic prey items, showing a Type II functional response under all scenarios. The presence of gravel and sand substrate each independently decreased the magnitude FR upon D. magna. Though, with regards to chironomid larvae the FR curves showed no difference in magnitude FR, the FRR reveals that the highest impact is conferred when foraging on sand substrate. This reinforces the need for impact assessments to be contextually relevant.
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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 | 0.000 | 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".