Ecological Aspects of Sexual Dimorphism
Bibliographic record
Abstract
Sexual dimorphism represents a striking source of diversity in nature. This thesis focuses on empirically testing and conceptually unifying some of the non-exclusive adaptive causes of sexual dimorphism. I emphasize divergence in traits such as body size, habitat, and feeding morphology, because these â ecologicalâ sexual dimorphisms are: 1) ubiquitous, 2) beg explanations invoking alternative (to sexual selection) models of sexual dimorphism and 3) might play an important role in the structure of ecological communities and the dynamics of evolutionary radiations. In Chapters 2-4, I set out exploring the natural history of sexual divergence in a salamander, the newt. This work, and past studies indicates significant ecological sexual dimorphism in newts and suggests a possible role for at least some direct ecological causal component of dimorphism. I propose a framework for demonstrating an ecological cause of sexual dimorphism, via character displacement between the sexes, and marshal the first direct evidence in support of this hypothesis using microevolutionary field experiments. In Chapter 5, I expand this program to examine how competition-driven disruptive selection, ecological sexual dimorphism and speciation interact during the early stages of adaptive radiation in newts. These analyses suggests clade-wide character displacement between the sexes, and that evolution of ecological sexual dimorphism may play a key role in niche divergence among nascent species In the final two chapters, I examine the evolution of sexual dimorphism across Amphibians. First, using patterns of among-clade variation in allometry of male and female size, I show that much of the variation in sexual dimorphism in amphibians may be due to higher rates of size evolution in females than males. Finally, I show that the evolution of sexual dimorphism is and has been a key driver of amphibian diversification by increasing speciation rates and reducing extinction. These results suggest the novel hypothesis that sexual dimorphism may promote diversification by allowing lineages to exploit sex-specific ecological opportunity. The general conclusions of this thesis are that sexual dimorphism can have significant ecological impact and even direct ecological causes, and contra traditional views, the evolution of sexual dimorphism in ecologically important traits can have important positive impacts on adaptation and diversification.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".