Data for: Female foraging strategy co-evolve with sexual harassment intensity in the Trinidadian guppy
Data files
Mar 27, 2023 version files 9.81 KB
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data_behavior_archive.Rda
2.64 KB
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data_growth_archive.Rda
4.59 KB
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README.md
2.58 KB
Abstract
Sexual harassment is a widespread evolutionary outcome of sexual conflict over mating rates. Male harassment can impose costs on females, and females often change their behaviors to avoid unwanted attention. In Trinidadian guppies (Poecilia reticulata), females experience two potential sources of male harassment: coercive sneak mating behavior (more harmful, and common in high-predation populations) and courtship displays (less harmful, and common in low-predation populations). Here, we tested whether female foraging strategy co-evolves with decreasing levels of male harassment as guppies colonize low-predation environments. We set up outdoor stream mesocosms with ecologically naïve males and females from either a high- or a low-predation population in a 2x2 design and tested whether populations diverge in female response to male harassment. We found that low-predation males used more courtship and fewer sneak tactics than their natural high-predation ancestors. Male sneak behavior reduced female foraging efficiency, and this effect was stronger for high-predation females. We then tested whether a similar pattern evolved in a population where high-predation guppies were artificially introduced to a low-predation habitat 12 years ago. Unexpectedly, the introduced males had evolved decreased courtship and increased sneak tactics. Here, increased male courtship, but not increased sneak behavior, reduced female foraging efficiency, and this effect existed only in the introduced, low-predation population. Altogether, our results suggest that both male mating behaviors harass females to differing degrees; females evolve foraging behavior in response to divergence in male mating strategies, but populations may differ in strategies of enduring or ignoring unwanted attention.
Methods
Data was collected on a physical datasheet, entered into an Excel file, and processed in R. Saved as an Rda file.
Usage notes
Data is saved as Rda file, and code is organized in an R markdown file with annotation. R and R Studio is needed to open data file and code.