Intraspecific competition in size-structured populations: ontogenetic shift in the importance of interference competition in a key marine herbivore
Bibliographic record
Abstract
Individuals rarely have equal competitive abilities, with body size being one of the most important attributes affecting the mechanism (i.e. exploitative and interference) and consequences of competition. Competitive interactions within size-structured populations are complex and can have major implications for population dynamics, community structure and evolutionary processes. Destructive grazing of kelp beds by the green urchinStrongylocentrotus droebachiensiscreates barrens where high-quality food is scarce and intraspecific competition may have an important role in structuring populations. In this study, we experimentally identified the mechanisms underlying size-asymmetric competition between small, medium, and large size classes of the green urchin. A field-based mesocosm experiment showed that small and medium sea urchins grew less and produced smaller gonads when competing for food with large conspecifics. Surprisingly, when food was provided ad libitum but large urchins were present, small individuals’ growth and foraging behavior were reduced, providing strong evidence for interference competition between small and large sea urchins. Interactions between medium and large sea urchins were, however, more influenced by exploitative competition, suggesting that sea urchins shift ontogenetically from a situation of intense interference competition to one dominated by exploitative competition. The size structure of the population can thus determine the relative importance of interference and exploitative competition. In turn, the importance of interference competition may influence size structure by inhibiting the growth of smaller urchins, a pattern consistent with the prediction of theoretical models. The consideration of size-asymmetric competitive interactions can lead to a better understanding of population size structure and dynamics.
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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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".