Combining quadrat, rake and echosounding to estimate submerged aquatic vegetation biomass at the ecosystem scale

Abstract

Measuring freshwater submerged aquatic (SAV) biomass at large spatial scales is challenging and no single technique can cost effectively accomplish this while maintaining accuracy. We propose to combine and intercalibrate accurate quadrat-scuba diver technique, fast rake sampling and large scale echosounding. We found that the relationship between quadrat and rake biomass is moderately strong (R2 = 0.62, RMSECV = 2.19 g/m2) and varies with substrate type and SAV growth form. Rake biomass was also successfully estimated from biovolume10 and its error (R2 = 0.53, RMSECV = 5.95 g/m2), a biomass proxy derived from echosounding, at a resolution of 10 m radius from rake sampling point. However, the relationship was affected by SAV growth form, depth, acoustic data quality and wind conditions. Sequential application of calibrations yielded predictions in agreement with quadrat observations, but echosounding predictions underestimated biomass in shallow areas (< 1.5 m) while outperforming point estimation in deep areas (> 3 m). Whole-system biomass was more accurately estimated by calibrated echosounding than rake point surveys, owing to the large sample size and better representation of spatial heterogeneity of echosounding. We recommend developing as a one-time event a series of quadrat and rake calibration equations for each growth form and substrate type. Because the relationship between biovolume and biomass depends on SAV growth form, rake and echosounding calibration needs to be conducted frequently. With the two calibrations, rake can thus be used as a rapid ground truthing or in shallow areas where echosounding is inadequate.

Periphyton and benthivorous fish affect charophyte abundance and indicate hidden nutrient loading in oligo‐ and mesotrophic temperate hardwater lakes

“…Their current state of charophytes, occurrence of major species and maximum colonisation depths were assessed by mappings performed with underwater cameras or scuba diving along transects in 2017–2018 (Table 1). There are different methods to determine macrophyte coverage in lakes (e.g., Botrel et al, 2022; Lauridsen et al, 2015), but we opted for a rather simple approach. Similar to the five ecological classes that lakes have to be assigned to in the EU Water Framework Directive (e.g.…”

Global historical trends and drivers of submerged aquatic vegetation quantities in lakes

“…Optical remote techniques will likely become more common in assessing SAV areas with the increase in hyperspectral capacity of new satellites, and greater accessibility of drones with improved sensors that could potentially be used in smaller lakes (Rowan & Kalacska, 2021). Hydroacoustics, however, have the advantage of being a tridimensional assessment of SAV quantities by reporting both SAV height and coverage across depths, thus simultaneously providing an estimation of SAV extent and biomass (Botrel et al, 2022; Duarte, 1987). Additionally, these techniques are best suited in turbid waters as in these conditions, only reasonable SAV detection is achieved from object‐based image analysis and optic sensors are typically blind at only 1‐ or 2‐m depth (de Grandpré et al, 2022; Krause et al, 2021; Rowan & Kalacska, 2021).…”

Climate‐Driven Variations in Nitrogen Retention From a Riverine Submerged Aquatic Vegetation Meadow

“…Daily velocity was estimated from Delft3D simulations, a tridimensional hydrodynamic model established for our study area (Bulat et al., 2019). Model simulations were run for each year using median, minimum, and maximum input discharges at Saint‐François over the SUNA mooring period and yearly spatial polygons of SAV height established from SAV echosounding surveys (Botrel et al., 2022). Mean depth‐average velocity was computed on the resulting spatial polygon grid of 70 m resolution at the outflow transect (7 polygons).…”

“…SAV species composition was dominated by Vallisneria americana and Potamogeton richardsonii , but also included Heteranthera dubia , Stuckenia pectinata , Elodea spp., Myriophyllum spp., and Chara spp. Because rake biomass collections are underestimated due to loss of plant material during sampling, rake estimates were converted to quadrat equivalency using a previously determined general relationship (Botrel et al., 2022).…”

“…Accurate estimates of N loss in large rivers are particularly challenging because methods for measuring SAV quantities and N retention throughout or over multiple growing seasons are restricted and time consuming. Capturing changes in SAV density, or biomass, over a growing season requires the processing of many plant samples, or in typically turbid waters, conducting numerous remote sensing surveys using a sonar (Botrel et al., 2022). One potential solution to track temporal SAV biomass is to use the difference in slope in the surface water level between gauging stations around large riverine meadows.…”