Testing the influence of local forest canopy clearing on phenotypic variation in Trinidadian guppies
Bibliographic record
Abstract
Summary 1. Factors contributing to the maintenance of phenotypic variation in nature are often difficult to determine. Secondary sexual traits might be particularly interesting in this regard due to the interaction they experience between multiple selective agents. One way to examine such effects is to monitor populations following environmental change. Human‐caused changes can be particularly useful here because they often involve an abrupt and extreme alteration of specific habitat features. This alteration can then precipitate phenotypic plasticity, changes in adaptive landscapes, and modified evolutionary trajectories. The consequences of habitat manipulations on local populations can therefore improve our understanding of phenotypic variation in complex ecological systems. 2. We took advantage of a human‐caused environmental disturbance to examine factors influencing phenotypic variation in Trinidadian guppies ( Poecilia reticulata ). Differences in canopy cover along the stream have been hypothesized to explain some of this variation, but this has been hard to test directly. We here attempt a direct test of this hypothesis by monitoring changes in guppy size and colour following a dramatic decrease in canopy cover due to tree removal for agricultural activity. 3. Although male and female body size increased following canopy clearing, little change was observed in the overall amount of melanin‐based colours, carotenoid‐based colours, and structural colours on males. We further compared phenotypes before and after canopy clearing at the disturbed site to those from two nearby reference sites that are at extreme ends of canopy cover. Overall, variation in colour was attributed to differences among sites, irrespective of canopy differences. We also found considerable temporal variation in some colour elements at a given site. 4. Our results suggest that differences in light availability do not cause rapid and dramatic changes in guppy colour. The substantial unexplained variation must therefore be due to factors other than canopy (measured here) and predation regime (all sites were ‘low‐predation’). Because of the multiple and complex interactions involved in the expression and maintenance of sexually selected traits, our study emphasizes the need for a better understanding of both the genetic and environmental sources of co‐variation between sexual ornaments and preferences.
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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".