Mechanisms of species coexistence: a field test of theoretical models using intertidal snails
Bibliographic record
Abstract
Competitor coexistence is often facilitated by spatial segregation. Traditionally, spatial segregation is predicted to occur when species differ in the habitat in which they are either superior at competing for resources or less susceptible to predation. However, predictions from a behavioural model demonstrate that spatial segregation and coexistence can also occur in the absence of such interspecific trade‐offs in competitive ability and vulnerability to predation. Unlike other models of competitor coexistence this model predicts that when species rank both habitat productivity and ‘riskinesses’ similarly, but differ slightly in their habitat‐specific vulnerabilities to predators, they will tend to segregate across habitats, with the species experiencing the higher ratio of mortality risk across the habitats occurring primarily in the safer habitat. Here, we investigate the hypothesis that intraspecific trade‐offs between resource availability and mortality risk can lead to spatial segregation of competing species by (1) documenting the spatial (i.e. intertidal) distribution of two marine snails, Littorina sitkana and L. subrotundata and (2) performing field experiments to quantify growth and mortality rates of each species at ‘low’ and ‘high’ intertidal heights. Our results indicate that both species agree on the rankings of habitat riskiness and productivity, experiencing higher predation and higher growth in low‐ than in high‐intertidal habitats. However, L. sitkana and L. subrotundata experienced differences in their habitat‐specific mortality risks and growth rates. Despite both species being similarly at risk of predation in high‐intertidal habitats (where mortality was lower), L. subrotundata was subject to significantly higher mortality than L. sitkana at the low‐intertidal height. In contrast, growth rate differences between habitats were greater for L. sitkana than for L. subrotundata . Whereas both species grew at the same rate at the high‐intertidal level (where growth was lower), L. sitkana individuals grew more rapidly than L. subrotundata snails at the low‐intertidal level. As predicted by the behavioural model, the species that experienced the higher ratio of mortality across habitats (i.e. L. subrotundata ) occurred exclusively in the safer, high‐intertidal habitat. Taken together, these results provide support for the hypothesis that spatial segregation, and potentially competitor coexistence, can occur in the absence of interspecific trade‐offs in resource acquisition ability or vulnerability to predation.
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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) | 0.002 | 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 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".