Faculty Opinions recommendation of The roles of spatial heterogeneity and adaptive movement in stabilizing (or destabilizing) simple metacommunities.
Bibliographic record
Abstract
Adaptive consumer movement and between-patch heterogeneity have both been suggested to reduce population fluctuations in spatially subdivided systems. These conjectures are explored using models of two-patch consumer-resource systems with fitness dependent consumer movement and cyclic dynamics in at least one of the patches; neither conjecture applies generally to such systems. Under relatively low heterogeneity, highly accurate and rapid adaptive movement most often increases both the between-patch correlation of density and the variation in the total density of both species compared to a similar system having a low rate of random movement. However, such adaptive movement can decrease between-patch correlation and global population variability when (1) the consumer's movement is moderately sensitive to fitness differences and heterogeneity is relatively low, or (2) one of the patches would be stable in isolation, and the stable patch supports a sufficiently large consumer population. In both cases, the dynamics are typically either a stable equilibrium or a simple anti-phase cycle with low variation in total population size. Under adaptive movement, population variability is often lowest for intermediate levels of heterogeneity, but monotonic increases or decreases with increasing spatial heterogeneity are possible, depending on the fitness sensitivity of movement and how the characteristic that differs between patches affects within-patch stability and population size. High rates of random movement can lead to greater stability than adaptive movement when consumers are very efficient.Copyright © 2011 Elsevier Ltd. All rights reserved. PMID: 21945147
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.215 | 0.080 |
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".