Modeling Mayfly Nymph Length Distribution and Population Dynamics Across a Gradient of Stream Temperatures and Stream Types

• DOI: 10.30707/spora4.1anthony
• Publication Date: 2018-06-06T17:59:27Z
• Authors: Jeremy Anthony; Jennifer Baccam; Imanuel Bier; Emily Gregg; Leif Halverson; Ryan Mulcahy; Emmanuel Okanla; Samira Osman; Adam Pancoast; Kevin Schultz; Alex Sushko; Jennifer Vorarath; Yia Vue; Austin Wagner; Emily Schilling; John Zobitz
• Keywords: 0106 biological sciences; mayfly; Applied Statistics; Ordinary Differential Equations and Applied Dynamics; population dynamics; Markov Chain Monte Carlo; data assimilation; Other Environmental Sciences; 01 natural sciences

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, interspecific 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.