Variation in the behaviour of an obligate corallivore is influenced by resource availability
Bibliographic record
Abstract
Abstract Marine environments are subject to increasing disturbance events, and coral reef ecosystems are particularly vulnerable. During periods of environmental change, organisms respond initially through rapid behavioural modifications. Whilst mean population level modifications to behaviour are well documented, how these shifts vary between individuals, and the relative trade-offs that are induced, are unknown. We test whether the frequency and time invested in different behaviours varies both between and within individuals with varying resource availability. To do this, we quantify differences in four key behavioural categories (aggression, exploration, feeding and sociability) at two sites of different resource availability, using an obligate corallivore butterflyfish species (Chaetodon lunulatus). Individuals on a low resource site held larger territories, investing more time in exploration, which was traded off with less time invested on aggression, feeding and sociability. Repeatability measures indicated that behavioural differences between sites could plausibly be driven by both plasticity of behaviour within individuals and habitat patchiness within feeding territories. By combining population-level means, co-correlation of different behaviours and individual-level analyses, we reveal potential mechanisms behind behavioural variation in C. lunulatus due to differences in resource availability. Significance statement Using observational methods, we identify differences in the behaviour of an obligate corallivorous butterflyfish (Chaetodon lunulatus) between a high and a low resource site. We use a combination of density surveys, territory mapping and behavioural observation methods in a comparative analysis to relate behaviour directly to the environment in which it occurs. Bringing together insights from game theory and optimal foraging, we also use our results to highlight how understanding the correlations of different behaviours can inform our understanding of the extent to which behaviours are plastic or fixed. Furthermore, by considering how multiple behaviours are correlated, we move away from exploring individual behaviours in isolation and provide an in-depth insight into how differences in behaviour both between individuals and at the population level can affect responses to declining resource availability.
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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 | 0.001 | 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".