Document Type

Article

Publication Date

2018

Abstract

We analyze a process-based temperature model for the length distribution and population over time of mayfly nymphs. Model parameters are estimated using a Markov Chain Monte Carlo parameter estimation method utilizing length distribution data at five different stream sites. Two different models (a standard exponential model and a modified Weibull model) of mayfly mortality are evaluated, where in both cases mayfly length growth is a function of stream temperature. Based on model-data comparisons to the modeled length distribution and the Bayesian Information Criterion, we found that approaches that length distribution data can reliably estimate 2–3 model parameters. Future model development could include additional factors include such as upstream environmental factors, abiotic conditions, inter- specific competition, predation, or stream salinity. Outputs of this model could be applied to predict mayfly emergence across a geographic domain or to forecast mayfly population responses to climate change.

Comments

The final version of this paper was published in Spora:

Anthony, Jeremy; Baccam, Jennifer; Bier, Imanuel; Gregg, Emily; Halverson, Leif; Mulcahy, Ryan; Okanla, Emmanuel; Osman, Samira A.; Pancoast, Adam R.; Schultz, Kevin C.; Sushko, Alex; Vorarath, Jennifer; Vue, Yia; Wagner, Austin; Schilling, Emily Gaenzle; and Zobitz, John M. (2018) "Modeling Mayfly Nymph Length Distribution and Population Dynamics Across a Gradient of Stream Temperatures and Stream Types," Spora: A Journal of Biomathematics: Vol. 4 : Iss.1, 1.

Available at: https://ir.library.illinoisstate.edu/spora/vol4/iss1/1

Cover Page Footnote:

This manuscript is the product of collaboration with two cohorts of undergraduate students in an upper division mathematical biology course at Augsburg University in Minneapolis, Minnesota. In 2011 and 2016 students completed model analyses and studied elements of scientific writing as part of the course content. Additional acknowledgment is given to Andrew Bohler, Jessica Geisinger, Kayla Johnson, Operolim Marcellino, Baradan Panta, Toua Thao, Alexis Thompson, Andrew Ziolkowski. Funding for this work was provided to JMZ by a Scholarship Grant from Augsburg University. JMZ thanks N. L. Schoenborg for helpful discussions on this manuscript.

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