Predicting Resource Partitioning and Community Organization of Filter‐Feeding Dabbling Ducks from Functional Morphology
Bibliographic record
Abstract
Resource partitioning due to interspecific differences in phenotype is a key component of ecological and evolutionary theory, but the relationship between morphology and resource use is poorly understood for most species. In addition, ecologists often assume that morphological differences cause distinct resource preferences between species. Using mechanistic models that combine bill morphology and kinetics, I show that filter-feeding dabbling ducks face a morphology-mediated trade-off between particle size selection and water filtration rate. When detritus is absent, mallards (Anas platyrhynchos) and northern shovelers (Anas clypeata) should maximize their intake rates and exhibit high overlap in prey size. When prey and detritus co-occur, species should separate prey from detritus by size, leading to reduced intake rates and size-based prey partitioning. Models for both species correctly predicted variation in water filtration rates, particle retention probabilities, and prey ingestion rates due to variation in prey size, the presence of detritus, and experimental modification of bill morphology. Because species have both shared and distinct resource preferences, duck communities should exhibit strong density-dependent niche shifts (i.e., centrifugal dynamics), a finding that contradicts previous studies that assumed that ducks have distinct resource preferences only. Centrifugal dynamics may be widespread among filter feeders because of the common cost of separating prey from detritus.
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 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 | 0.001 | 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 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".