Upper Mississippi River Restoration Program Long Term Resource Monitoring

Developing methods for estimating submersed aquatic vegetation biomass in the Upper Mississippi River: Expanding capabilities within the Upper Mississippi River Restoration program and improving the utility of the long-term vegetation data

Drake, D. and E. Lund.  2020. Developing methods for estimating submersed aquatic vegetation biomass in the Upper Mississippi River: Expanding capabilities within the Upper Mississippi River Restoration program and improving the utility of the long-term vegetation data.  A completion report submitted to the U.S. Army Corps of Engineers’ Upper Mississippi River Restoration Program from the U.S. Geological Survey, LTRM-2018BI03. 45 pp. with appendixes.

Abstract

Primary producers’ effects in ecosystems are, in large part, a function of their biomass (mass per unit area). The aquatic vegetation component of the Upper Mississippi River Restoration (UMRR) Long Term Resource Monitoring (LTRM) element has conducted annual surveys in three study reaches of the Upper Mississippi River System (UMRS) since 1998, but currently provides only a rough index of aquatic vegetation biomass (the abundance index). Additionally, most ecological studies and analyses using LTRM aquatic vegetation data have used detection/non-detection (percent frequency occurrence), which does not include a biomass component. Improved biomass estimation would benefit the UMRR program by permitting more accurate and nuanced quantification of critical ecosystem processes such as gas exchange and feedbacks between aquatic vegetation and water clarity.

Although previous studies in the UMRS have found that biomass of submersed aquatic vegetation (SAV) can be reliably estimated from site-level LTRM data, several concerns about the limitations associated with this approach have been debated within the LTRM vegetation component. Estimation of SAV biomass was evaluated here using targeted analyses of several existing data sets and new data collected in 2017 field studies, and the concerns associated with biomass estimation were addressed.