Directional biases and resource‐dependence in dispersal generate spatial patterning in a consumer–producer model
Bibliographic record
Abstract
Directional dispersal plays a large role in shaping ecological processes in diverse systems such as rivers, coastlines and vegetation communities. We describe an instability driven by directional dispersal in a spatially explicit consumer-producer model where spatial patterns emerge in the absence of external environmental variation. Dispersal of the consumer has both undirected and directed components that are functions of producer biomass. We demonstrate that directional dispersal is required for the instability, while undirected diffusive dispersal sets a lower bound to the spatial scale of emerging patterns. Furthermore, instability requires indirect feedbacks affecting consumer per capita dispersal rates, and not activator-inhibitor dynamics affecting production and mortality as is described in previous theory. This novel and less-restrictive mechanism for generating spatial patterns can arise over realistic parameter values, which we explore using an empirically inspired model and data on stream macroinvertebrates.
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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".