Influences of environmental and individual-level covariates on movement behaviour in American lobster Homarus americanus
Bibliographic record
Abstract
Movements reflect important activities and life history events for animals, and therefore understanding what influences movement in organisms is increasingly important as climate change alters environmental conditions at unprecedented rates. This has relevance for predicting the effects of climate change on fitness and interpreting stock status of species such as American lobster Homarus americanus, whose catchability is behaviourally mediated. We analysed movement tracks from tagged lobsters in a natural environment over short (10 d, n = 37) and long timescales (up to 7 mo, n = 16), applying hidden Markov models to investigate the influence of individual-level and environmental covariates on movement patterns. We classified movement tracks to identify behavioural states through time and compared the distribution of states across habitats to understand how movement may relate to bottom composition. In the short-term analysis, we found evidence for 3 behavioural states: Sheltered, Exploratory, and Transit. In the long-term analysis, we found evidence of Sheltered and Exploratory states, but the Transit state was absent. Movement parameters varied across temperature, with higher velocities and more tortuous movements at higher temperatures. Our results demonstrate that lobsters spend most of their time Sheltered, with state probabilities being altered by diel period, time since release, sex, carapace length, temperature, and tide trend. Further, mobile states were typically observed in areas of low algal cover. Our results underscore the importance of environmental and individual-level factors in understanding lobster movement and suggest that such factors could obscure population depletion if not accounted for in a warming environment.
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 | 0.003 |
| 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".