Resource pulses increase the diversity of successful competitors in a multi‐species stream fish assemblage
Bibliographic record
Abstract
Abstract Food resources are often patchily distributed through space and time and are classified as resource pulses when hyperabundant. Resource pulses can benefit growth, reproduction, and abundance of various consumers. Yet, it is relatively unknown how such resources are partitioned among competing consumers and how this is influenced by the magnitude of the pulse. Here, we examined how the magnitude of a pulsed resource influences resource partitioning among diverse sizes and species of consumers in a natural setting over small spatial and temporal scales. We focused on salmon egg subsidies to stream fish consumers. We experimentally added different quantities of pink salmon eggs to five meter long experimental stream sections. Egg additions spanned three orders of magnitude from 6 to 3575 eggs. Stream fish (egg consumers) were captured and gastric lavaged at each experimental section to determine how many eggs each individual fish consumed. We modeled taxon‐specific individual egg consumption as a function of egg availability, individual mass, community composition, number of competitors, and stream velocity using hurdle models in a Bayesian framework. We found that there were diminishing returns for increasing egg abundance increasing egg consumption (i.e., type II functional response) for individual size classes of fish, but that higher egg numbers were needed to benefit diverse consumers. Top models indicated that egg availability and individual fish characteristics (size and taxon) drove egg consumption, while community characteristics (species composition and number of competitors) were not supported. Our results suggest that resource pulses can provide rare opportunities for less dominant sizes and species of fish to consume abundant resources. The current paradigm in the stream fish literature suggests that stream fish communities are structured by dominance hierarchies; however, dominance hierarchies may be less influential where pulsed resources comprise a large portion of the resource base.
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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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".