Greener on the other side of the fence: density-dependent habitat selection by a unicellular alga
Bibliographic record
Abstract
Background: When populations have the opportunity to occupy multiple habitats, individuals that move to habitats with higher mean fitness will expect to produce more descendants than will individuals that are incapable of such adaptive movements. The ability to make an optimal decision depends on habitat quality, the quality and reliability of the cue for habitat choice, and the ability of the organism to respond to the cue appropriately. Questions: Do simple motile organisms select habitats that maximize fitness? Do they move such that densities in different habitats equalize fitness? Does the ability to select between habitats depend on the difference in habitat quality? Organism: Chlamydomonas reinhardtii, a motile, chemo- and phototactic single-celled alga. Experiments: We introduced clonal populations, at 15 different densities, into either shaded or fully lighted aqueous habitats in one side of Petri dishes. The other side of the dishes contained either unused (high quality) or used (lower quality) growth media. We estimated fitness in control dishes containing a single habitat. We used the estimates to predict the density expected when algae could choose between light and shaded habitats in the same dish. Results: Fitness declined linearly with increasing density. Mean fitness was higher in control light than in control shade. Patterns of density and fitness in treatment dishes depended on which habitat the cells were introduced into, and on the quality of growth media. Cells were more abundant in light than in shade when introduced into the light side of habitat-selection dishes containing unused media. There was no difference in fitness between habitats in this treatment. Cells introduced into the shaded side with unused media attained similar densities in both habitats. Fitness was higher in light. Cell densities in the two habitats were also similar in both treatments containing used media. Fitness was higher in the light habitat in each of these treatments. Conclusion: Motile non-sentient species with simple sensory abilities can move among habitats to increase fitness. They can also attain distributions that equalize fitness differences between habitats. The threshold for adaptive habitat choice depends on the cues for habitat quality and the costs of habitat selection. Whether fitness and density differ between habitats depends on the quality of the environment in which habitat selection occurs.
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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 | 0.001 | 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) | 0.002 | 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".