We found a match
Your institution may have rights to this item. Sign in to continue.
- Title
Using artificial intelligence for CyanoHAB niche modeling: discovery and visualization of Microcystis-environmental associations within western Lake Erie.
- Authors
Millie, David F.; Weckman, Gary R.; Fahnenstiel, Gary L.; Carrick, Hunter J.; Ardjmand, Ehsan; Young, William A.; Sayers, Michael J.; Shuchman, Robert A.
- Abstract
Cyanobacterial harmful algal blooms (CyanoHABs), mainly composed of the genus Microcystis, occur frequently throughout the Laurentian Great Lakes. We used artificial neural networks (ANNs) involving 31 hydrological and meteorological predictors to model total phytoplankton (as chlorophyll a) and Microcystis biomass from 2009 to 2011 in western Lake Erie. Continuous ANNs provided modeled-measured correspondences (and modeling efficiencies) ranging from 0.87 to 0.97 (0.75 to 0.94) and 0.71 to 0.90 (0.45 to 0.88) for training-cross-validation and test data subsets of chlorophyll a concentrations and Microcystis biovolumes, respectively. Classification ANNs correctly assigned up to 94% of instances for Microcystis presence-absence. The influences of select predictors on phytoplankton and CyanoHAB niches were visualized using biplots and three-dimensional response surfaces. These then were used to generate mathematical expressions for the relationships between modeled CyanoHAB outcomes and the direct and interactive influences of environmental factors. Based on identified conditions (∼40 to 50 μg total phosphorus (TP)·L−1, 22 to 26 °C, and prolonged wind speeds less than ∼19 km·h−1) underlying the likelihood of occurrence and accumulation of phytoplankton and Microcystis, a 'target' concentration of 30 μg TP·L−1 appears appropriate for alleviating blooms. ANNs generated robust ecological niche models for Microcystis, providing a predictive framework for quantitative visualization of nonlinear CyanoHAB-environmental interactions.
- Subjects
LAKE Erie; ARTIFICIAL intelligence research; ALGAE; MICROCYSTIS; HYDROLOGY; METEOROLOGY
- Publication
Canadian Journal of Fisheries & Aquatic Sciences, 2014, Vol 71, Issue 11, p1642
- ISSN
0706-652X
- Publication type
Article
- DOI
10.1139/cjfas-2013-0654