Date
Summer 2015
Document Type
Master's Thesis (Open Access)
Degree Name
Master of Science (M.S.)
Department
Science & Environmental Policy
Abstract
Much importance has been placed on understanding and quantifying how diversity, community structure and mechanistic processes change over different scales. There is a need in ecology for multiscale approaches quantitatively linking scales to characterize, quantify and better understand how ecological patterns and processes functioning at different scales interact. Many of the structural mechanisms established in rocky intertidal systems are driven or influenced by larger scale oceanographic processes that scale down to sub-meter scale effects. Here I develop and integrate two models that link these two scales. Velocity was measured in a swath along shore using an array of dynamometers. Intertidal and subtidal landscapes were mapped using field survey techniques. Digital elevation models were created using GIS, from which landscape characteristics such as slope, curvature and aspect were quantified at a fine scale. A large scale model (p-value < 0.001, R2 = 0.803) uses buoy data, local bathymetry and landscape characteristics to predict the mean maximum velocity experienced on rocky intertidal shorelines. A fine scale model (p-value < 0.001, R2 = 0.633) uses the mean maximum velocity and fine scale landscape characteristics to predict the fine scale distribution of flow speeds across rocky intertidal landscapes. Their integration (p-value < 0.001, R2 = 0.6849) links large scale oceanographic conditions to the fine scale distribution of wave velocity across rocky intertidal landscapes. The multiscale models presented here address the need in ecology for multiscale modeling approaches to quantify and link multiscale patterns to better understand how processes and mechanisms interact at different scales.
Recommended Citation
Orr, Daniel, "Multiscale Predictive Modeling of Wave Velocity and Its Distribution Across a Rocky Intertidal Landscape" (2015). SNS Master's Theses. 4.
https://digitalcommons.csumb.edu/sns_theses/4