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Data and scripts from: Mechanistic and scale-specific analyses advance the preference-performance hypothesis

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DataCite Commons2026-02-12 更新2026-04-25 收录
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https://hdl.handle.net/1813/119529
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These files contain data and script supporting all results reported in "Mechanistic and scale-specific analyses advance the preference-performance hypothesis". In Slimon et al. we used the specialist seed predator moth Schinia florida and the common evening primrose Oenothera biennis, to investigate the mechanistic drivers of preference and performance and how these dynamics shift across scales, between- versus within-plants. We measured a suite of 126 plant traits, most of which were plant specialized metabolites captured by ultra-high performance liquid chromatography and mass spectrometry, and applied a novel machine learning approach to identify traits that predict both preference and performance. Between-plants, we found no relationship between preference and performance. However, nine overlapping plant traits predicted both preference and performance, with all traits positively correlated with preference showing a negative association with performance. This negative trait-based preference-performance link may result from trade-offs of moths using plant defenses as host finding cues. Overall, weak correlations between the nine predictive traits appear to limit a link between preference and performance between plants. Within plants, moths disproportionally oviposited on flower buds over fruits and concordantly showed higher relative performance on buds. At this scale, only two overlapping traits predicted both preference and performance, and both showed positive associations with preference and performance. Thus, our results show that 1) a specialist herbivore faces trait-based trade-offs between host-selection among plants and larval performance, while making more adaptive decisions at finer scales within plants, and 2) mechanistic and cross-scale approaches offer insight into when adult preference predicts larval performance.
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Cornell University Library eCommons Repository
创建时间:
2025-08-22
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