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Data from: A stochastic model for predicting age and mass at maturity of insects Legault, Geoffrey; Kingsolver, Joel
Description
<b>Abstract</b><br/>
Variation in age and mass at maturity is commonly observed in populations, even among individuals with the same genetic and environmental backgrounds. Accounting for such individual variation with a stochastic model is important for estimating optimal evolutionary strategies and for understanding potential trade-offs among life history traits. However, most studies employ stochastic models that are either phenomenological or account for variation in only one life history trait. We propose a model based on the developmental biology of the moth <em>Manduca sexta</em> that accounts for stochasticity in two key life history traits, age and mass at maturity. The model is mechanistic, describing feeding behavior and common insect developmental processes including the degradation of juvenile hormone prior to molting. We derive a joint probability density function for the model and explore how the distribution of age and mass at maturity is affected by different parameter values. We find that the joint distribution is generally non-normal and highly sensitive to parameter values. In addition, our model predicts previously observed effects of temperature change and nutritional quality on the expected values of insect age and mass. Our results highlight the importance of integrating multiple sources of stochasticity into life history models.</p>; <b>Usage notes</b><br />
This ZIP archive contains R files for simulating the stochastic maturation and growth model, and for evaluating the joint probability density function.</p>
Item Metadata
Title |
Data from: A stochastic model for predicting age and mass at maturity of insects
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Creator | |
Date Issued |
2021-05-19
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Description |
<b>Abstract</b><br/>
Variation in age and mass at maturity is commonly observed in populations, even among individuals with the same genetic and environmental backgrounds. Accounting for such individual variation with a stochastic model is important for estimating optimal evolutionary strategies and for understanding potential trade-offs among life history traits. However, most studies employ stochastic models that are either phenomenological or account for variation in only one life history trait. We propose a model based on the developmental biology of the moth <em>Manduca sexta</em> that accounts for stochasticity in two key life history traits, age and mass at maturity. The model is mechanistic, describing feeding behavior and common insect developmental processes including the degradation of juvenile hormone prior to molting. We derive a joint probability density function for the model and explore how the distribution of age and mass at maturity is affected by different parameter values. We find that the joint distribution is generally non-normal and highly sensitive to parameter values. In addition, our model predicts previously observed effects of temperature change and nutritional quality on the expected values of insect age and mass. Our results highlight the importance of integrating multiple sources of stochasticity into life history models.</p>; <b>Usage notes</b><br /> This ZIP archive contains R files for simulating the stochastic maturation and growth model, and for evaluating the joint probability density function.</p> |
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Type | |
Notes |
Dryad version number: 3</p> Version status: submitted</p> Dryad curation status: Published</p> Sharing link: https://datadryad.org/stash/share/N2G5Y2ClsOyv4oe4QcIy01Ku4YaeRK52TGhwZiPkUeI</p> Storage size: 55583</p> Visibility: public</p> |
Date Available |
2020-04-22
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Provider |
University of British Columbia Library
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License |
This dataset is made available under a Creative Commons CC0 license with the following additional/modified terms and conditions: CC0 Waiver
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DOI |
10.14288/1.0397795
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URI | |
Publisher DOI | |
Grant Funding Agency |
National Science Foundation
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Rights URI | |
Aggregated Source Repository |
Dataverse
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Item Media
Item Citations and Data
Licence
This dataset is made available under a Creative Commons CC0 license with the following additional/modified terms and conditions: CC0 Waiver