MECHANISTIC ANALYSIS OF INTERSPECIFIC COMPETITION USING FORAGING TRADE-OFFS: IMPLICATIONS FOR DUCK ASSEMBLAGES
Bibliographic record
Abstract
The role of interspecific competition and resource partitioning in determining the composition of species assemblages is often controversial. In many cases data on species co-occurrence or resource use (prey or habitat) have been interpreted without a clear understanding of how, or even whether, phenotypic differences constrain performance to allow resource partitioning or how these constraints and the density of resources and competitors should shape resource selection by each species. Instead, predictions have been based on assumed constraints, possibly leading to conflicting results. One such controversy involves the role of bill morphology in mediating resource partitioning among dabbling ducks (Anas spp.). To determine whether incorrect assumptions may have contributed to this controversy, I constructed mechanistic models that predict filter-feeding performance for seven species of ducks directly from bill morphology and kinetics and compared these predictions to those of earlier studies that tested the bill morphology hypothesis. The models predicted that species should share a preference for their most profitable (primary) prey while partitioning their less profitable (secondary) prey by size. Consequently, ducks should forage in the same habitats and exhibit high overlap in prey size when competitor/resource ratios are either high or low. In contrast, earlier studies expected that resource partitioning should always be evident, which implicitly assumes that species partition their primary resources. The models also predicted that the ecological similarity of species in assemblages should increase as prey abundance and size variability declines, contrary to the expectations of an earlier study. A more consistent understanding of the mechanisms regulating assemblages of dabbling ducks, and other species, might emerge if patterns of resource use and species co-occurrence were predicted directly from a mechanistic understanding of how performance trade-offs affect resource selection in the context of varying resource and competitor densities.
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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) | 0.000 | 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".