Data from: warming and top-down control of stage-structured prey: linking theory to patterns in natural systems
Data files
Oct 28, 2023 version files 334.51 KB
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abiotic_data.csv
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abundance_data.csv
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CompiledTempData_Master.csv
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metadata.xlsx
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README.md
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sizedep-pred_data.csv
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TDFR_Pantala-Atro_data.csv
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temperature_data.csv
Abstract
Warming has broad and often nonlinear impacts on organismal physiology and traits, allowing it to impact species interactions like predation through a variety of pathways that may be difficult to predict. Predictions are commonly based on short-term experiments and models, and these studies often yield conflicting results depending on the environmental context, spatiotemporal scale, and the predator and prey species considered. Thus, the accuracy of predicted changes in interaction strength, and their importance to the broader ecosystems they take place in, remain unclear. Here, we attempted to link one such set of predictions generated using theory, modeling, and controlled experiments to patterns in the natural abundance of prey across a broad thermal gradient. To do so, we first predicted how warming will impact a stage-structured predator-prey interaction in riverine rock pools between Pantala spp. dragonfly nymph predators and Aedes atropalpus mosquito larval prey. We then described temperature variation across a set of hundreds of riverine rock pools (n = 775) and leveraged this natural gradient to look for evidence for or against our model’s predictions. Our model’s predictions suggested that warming should weaken predator control of mosquito larval prey by accelerating their development and shrinking the window of time that aquatic dragonfly nymphs could consume them in. This was consistent with data collected in rock pool ecosystems, where the negative effects of dragonfly nymph predators on mosquito larval abundance were weaker in warmer pools. Our findings provide additional evidence to substantiate our model-derived predictions, while emphasizing the importance of assessing similar predictions using natural gradients of temperature whenever possible.
README: Warming and top-down control of stage-structured prey: linking theory to patterns in natural systems
https://doi.org/10.5061/dryad.n2z34tn3h
Contains data and models describing rock pool temperature variation and its relationship with the abundance of mosquito larvae and their dragonfly nymph predators at Belle Isle, Richmond VA.
Description of the data and file structure
For variable descriptions, refer to the corresponding tabs in the file "metadata.xlsx".
The files "abundance.rmd" and "abundance_data.csv" contain data and code used to model the distribution and abundance of Ae. atropalpus mosquito larvae as a function of predatory dragonfly nymphs (Pantala spp.) and predicted rock pool temperatures.
The files "pantala_FR.rmd" and "TDFR_Pantala-Atro_data.csv" contain data and code used to fit temperature-dependent functional response models, describing Pantala spp. dragonfly nymphs feeding rates on Ae. atropalpus mosquito larval prey.
The files "sizedep_supplement.rmd" and "sizedep-pred_data.csv" contain supplemental data and code describing the relationship between mosquito larval body size and predation rates by Erythemis simplicicollis and Pachydiplax longipennis dragonfly nymphs.
The files "abiotic_data.csv", "CompiledTempData_Master.csv", "temperature_data.csv", and "temp_analysis.rmd" contain data and code used to model the thermal environment of riverine rock pools as a function of their abiotic characteristics, as well as predicted maximum temperatures for the full rock pool landscape at our field site. Fields with "-" indicate missing data for that pool, and pools with missing data or other peculiarities (e.g., "not a pool", "channel feature", or "filled in" in the Notes column) were excluded from the final analysis.
Code has been annotated for legibility, but please direct any questions to Dr. Andy Davidson at davidsonat@vcu.edu
Sharing/Access information
A more organized and comprehensive version of this data set and codebase, including model outputs and figures, is also available on Github, at https://github.com/davidsonat/2022_tdep-landscape