Phenotype‐dependent selection underlies patterns of sorting across habitats: the case of stream‐fishes
Bibliographic record
Abstract
Spatial and temporal heterogeneity within landscapes influences the distribution and phenotypic diversity of individuals both within and across populations. Phenotype–habitat correlations arise either through phenotypes within an environment altering through the process of natural selection or plasticity, or phenotypes remaining constant but individuals altering their distribution across environments. The mechanisms of non‐random movement and phenotype‐dependent habitat choice may account for associations within highly heterogeneous systems, such as streams, where local adaptation may be negated, plasticity too costly and movement is particularly important. Despite growing attention, however, few empirical tests have yet to be conducted. Here we provide a test of phenotype‐dependent habitat choice and ask: 1) if individuals collected from a single habitat type continue to select original habitat; 2) if decisions are phenotype‐dependent and functionally related to habitat requirements; and 3) if phenotypic‐sorting continues despite increasing population density. To do so we both conducted experimental trials manipulating the density of four stream‐fish species collected from either a single riffle or pool and developed a game‐theoretical model exploring the influence of individuals’ growth rate, sampling and competitive abilities as well as interference on distribution across two habitats as a function of density. Our experimental trials show individuals selecting original versus alternative habitats differed in their morphologies, that morphologies were functionally related to habitat‐type swimming demands, and that phenotypic‐sorting remained significant (although decreased) as density increased. According to our model this only occurs when phenotypes have contrasting habitat preferences and only one phenotype disperses (i.e. selects alternatives) in response to density pressures. This supports our explanation that empirical habitat selection was due to a combination of collecting a fraction of mobile individuals with different habitat preferences and the exclusion of individuals via scramble competition at increased densities. Phenotype‐dependent habitat choice can thereby account for observed patterns of natural stream‐fish distribution.
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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 | 0.001 | 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".