Behavioral signature of intraspecific competition and density dependence in colony‐breeding marine predators
Bibliographic record
Abstract
In populations of colony-breeding marine animals, foraging around colonies can lead to intraspecific competition. This competition affects individual foraging behavior and can cause density-dependent population growth. Where behavioral data are available, it may be possible to infer the mechanism of intraspecific competition. If these mechanics are understood, they can be used to predict the population-level functional response resulting from the competition. Using satellite relocation and dive data, we studied the use of space and foraging behavior of juvenile and adult gray seals (Halichoerus grypus) from a large (over 200,000) and growing population breeding at Sable Island, Nova Scotia (44.0 (o)N 60.0 (o)W). These data were first analyzed using a behaviorally switching state-space model to infer foraging areas followed by randomization analysis of foraging region overlap of competing age classes. Patterns of habitat use and behavioral time budgets indicate that young-of-year juveniles (YOY) were likely displaced from foraging areas near (<10 km) the breeding colony by adult females. This displacement was most pronounced in the summer. Additionally, our data suggest that YOY are less capable divers than adults and this limits the habitat available to them. However, other segregating mechanisms cannot be ruled out, and we discuss several alternate hypotheses. Mark-resight data indicate juveniles born between 1998 and 2002 have much reduced survivorship compared with cohorts born in the late 1980s, while adult survivorship has remained steady. Combined with behavioral observations, our data suggest YOY are losing an intraspecific competition between adults and juveniles, resulting in the currently observed decelerating logistic population growth. Competition theory predicts that intraspecific competition resulting in a clear losing competitor should cause compensatory population regulation. This functional response produces a smooth logistic growth curve as carrying capacity is approached, and is consistent with census data collected from this population over the past 50 years. The competitive mechanism causing compensatory regulation likely stems from the capital-breeding life-history strategy employed by gray seals. This strategy decouples reproductive success from resources available around breeding colonies and prevents females from competing with each other while young are dependent.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".