Data for: Beyond Simple vs Complex: Exploring the Nuanced and Unexpected Effects of Spatial Environmental Complexity on Mating Patterns and Female Fecundity
Bibliographic record
Abstract
The features of the physical environment set the stage upon which sexual selection operates, and consequently can have a significant impact on variation in realized individual fitness, and influence a population’s evolutionary trajectory. This phenomenon has been explored empirically in several studies using fruit flies (Drosophila melanogaster) which found that changing the spatial complexity of the mating environment influenced male-female interaction dynamics, (re)mating rates and realized female fecundities. However, these studies did not explore mating patterns, which can dramatically influence the genetic composition of the next generation, and restricted themselves to a single binary comparison between “simple” and “complex” environments. Our study set out to compare patterns of mating and courtship between large- and small-bodied males and females, and female fecundities in both a simple environment and two distinctly different spatially-complex environments. We found that realized offspring production patterns differed dramatically between all three environments, indicating that that the effects of increasing spatial complexity on mating outcomes are not one-size-fits all, and appear to be sensitive to the specific type of environmental complexity present in the environment. Furthermore, we observed female fecundities were higher for flies in both complex environments compared those in the simple environment, supporting its role as a mediator of sexual conflict. Together, these results show that the union of gametes within a population can be greatly influenced by the specific spatial features of the environment and that simply comparing a “simple” environment with a “complex” environment may underestimate plasticity in mating decisions, and its potentially its consequences for the future. This dataset contains recorded observation data on behaviour and count data on offspring production.
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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 | 0.003 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.106 | 0.022 |
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".