The interaction between the spatial distribution of resource patches and population density: consequences for intraspecific growth and morphology
Bibliographic record
Abstract
How individuals within a population distribute themselves across resource patches of varying quality has been an important focus of ecological theory. The ideal free distribution predicts equal fitness amongst individuals in a 1 : 1 ratio with resources, whereas resource defence theory predicts different degrees of monopolization (fitness variance) as a function of temporal and spatial resource clumping and population density. One overlooked landscape characteristic is the spatial distribution of resource patches, altering the equitability of resource accessibility and thereby the effective number of competitors. While much work has investigated the influence of morphology on competitive ability for different resource types, less is known regarding the phenotypic characteristics conferring relative ability for a single resource type, particularly when exploitative competition predominates. Here we used young-of-the-year rainbow trout (Oncorhynchus mykiss) to test whether and how the spatial distribution of resource patches and population density interact to influence the level and variance of individual growth, as well as if functional morphology relates to competitive ability. Feeding trials were conducted within stream channels under three spatial distributions of nine resource patches (distributed, semi-clumped and clumped) at two density levels (9 and 27 individuals). Average trial growth was greater in high-density treatments with no effect of resource distribution. Within-trial growth variance had opposite patterns across resource distributions. Here, variance decreased at low-population, but increased at high-population densities as patches became increasingly clumped as the result of changes in the levels of interference vs. exploitative competition. Within-trial growth was related to both pre- and post-trial morphology where competitive individuals were those with traits associated with swimming capacity and efficiency: larger heads/bodies/caudal fins and less angled pectoral fins. The different degrees of within-population growth variance at the same density level found here, as a function of spatial resource distribution, provide an explanation for the inconsistencies in within-site growth variance and population regulation often noted with regard to density dependence in natural landscapes.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".