Implications of warming temperatures for population outbreaks of a nonindigenous species (<i>Membranipora membranacea</i>, Bryozoa) in rocky subtidal ecosystems
Bibliographic record
Abstract
To quantify and explore the role of temperature on population outbreaks of a nonindigenous bryozoan (Membranipora membranacea) in kelp beds in the western North Atlantic (Nova Scotia, Canada), we constructed an individual‐based model using field‐derived estimates for temperature‐dependent colony settlement and growth. Using temperature as the single input variable, the model successfully simulated the timing of onset of settlement, colony abundance, colony size, and coverage on kelps. We used the model to examine the relative effect on the population of varying temperature by ‐2°C to +2°C each day. The timing of onset of settlement varied by 18°C−1 with changes in temperature from January to August. Variations in temperature had nonlinear effects on the population, with an increase in daily temperature of 1°C and 2°C causing the cover of colonies on kelps to increase by factors of 9 and 62, respectively. Changes in winter and spring temperature had the most pronounced effects on the timing and abundance of colonies, while changes in summer temperature had the most pronounced effect on colony size and coverage on kelp blades. Outbreaks of this species will increase in frequency and intensity if temperatures warm as a result of climate change, causing defoliation of kelp beds and, thus, facilitating the invasion of other nonindigenous benthic 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".