title: Tracking cyanobacteria blooms: Do different monitoring approaches tell the same story?
abstract: Cyanobacteria blooms are a major environmental issue worldwide. Our understanding of the biophysical processes driving cyanobacterial proliferation and the ability to develop predictive models that inform resource managers and policy makers rely upon the accurate characterization of bloom dynamics. Models quantifying relationships between bloom severity and environmental drivers are often calibrated to an individual set of bloom observations, and few studies have assessed whether differences among observing platforms could lead to contrasting results in terms of relevant bloom predictors and their estimated influence on bloom severity. The aim of this study was to assess the degree of coherence of different monitoring methods in (1) capturing short- and long-term cyanobacteria bloom dynamics and (2) identifying environmental drivers associated with bloom variability. Using western Lake Erie as a case study, we applied boosted regression tree (BRT) models to long-term time series of cyanobacteria bloom estimates from multiple in-situ and remote sensing approaches to quantify the relative influence of physico-chemical and meteorological drivers on bloom variability. Results of BRT models showed remarkable consistency with known ecological requirements of cyanobacteria (e.g., nutrient loading, water temperature, and tributary discharge). However, discrepancies in inter-annual and intra-seasonal bloom dynamics across monitoring approaches led to some inconsistencies in the relative importance, shape, and sign of the modeled relationships between select environmental drivers and bloom severity. This was especially true for variables characterized by high short-term variability, such as wind forcing. These discrepancies might have implications for our understanding of the role of different environmental drivers in regulating bloom dynamics, and subsequently for the development of models capable of informing management and decision making. Our results highlight the need to develop methods to integrate multiple data sources to better characterize bloom spatio-temporal variability and improve our ability to understand and predict cyanobacteria blooms.
authors.affiliation: Water Center, Graham Sustainability Institute, University of Michigan, 625 E. Liberty St., Suite 300, Ann Arbor, MI 48104, USA. Electronic address: ibertani@umich.edu.
authors.forename: Isabella
authors.initials: I
authors.lastname: Bertani
authors.affiliation: Water Center, Graham Sustainability Institute, University of Michigan, 625 E. Liberty St., Suite 300, Ann Arbor, MI 48104, USA.
authors.forename: Cara E
authors.initials: CE
authors.lastname: Steger
authors.affiliation: Department of Civil, Construction, & Environmental Engineering, North Carolina State University, Campus Box 7908, Raleigh, NC 27695-7908, USA.
authors.forename: Daniel R
authors.initials: DR
authors.lastname: Obenour
authors.affiliation: Water Center, Graham Sustainability Institute, University of Michigan, 625 E. Liberty St., Suite 300, Ann Arbor, MI 48104, USA; Great Lakes Research Center, Michigan Technological University, 1400 Townsend Drive, Houghton, MI 49931, USA.
authors.forename: Gary L
authors.initials: GL
authors.lastname: Fahnenstiel
authors.affiliation: Department of Environmental Sciences and Lake Erie Center, University of Toledo, 6200 Bayshore Drive, Oregon, OH 43616, USA.
authors.forename: Thomas B
authors.initials: TB
authors.lastname: Bridgeman
authors.affiliation: Cooperative Institute for Limnology and Ecosystems Research, University of Michigan, 4840 South State St., Ann Arbor, MI 48108, USA.
authors.forename: Thomas H
authors.initials: TH
authors.lastname: Johengen
authors.affiliation: Michigan Tech Research Institute, Michigan Technological University, 3600 Green Ct., Suite 100, Ann Arbor, MI 48105, USA.
authors.forename: Michael J
authors.initials: MJ
authors.lastname: Sayers
authors.affiliation: Michigan Tech Research Institute, Michigan Technological University, 3600 Green Ct., Suite 100, Ann Arbor, MI 48105, USA.
authors.forename: Robert A
authors.initials: RA
authors.lastname: Shuchman
authors.affiliation: Water Center, Graham Sustainability Institute, University of Michigan, 625 E. Liberty St., Suite 300, Ann Arbor, MI 48104, USA.
authors.forename: Donald
authors.initials: D
authors.lastname: Scavia
mesh_terms: D000458:Cyanobacteria / Q000254:growth & development*; D004784:Environmental Monitoring / Q000379:methods*; D005068:Eutrophication; D060106:Lakes; D013696:Temperature; D014919:Wind
keywords: Biophysical drivers; Boosted regression trees; Cyanobacterial harmful algal blooms; Long-term time series; Water quality