Rapid monitoring of seagrass biomass using a simple linear modelling approach, in the field and from space

Abstract

Seagrass meadows are globally significant carbon sinks and increasingly threatened; and seagrass habitat provides critical ecosystem services, for which above-ground biomass is a key indicator. The capacity to quantify biomass in seagrass ecosystems is both critical and urgent, yet no methods exist to perform this at the large spatial scale required for management (e.g. regional/continental). We built linear model relationships between in situ above-ground biomass and seagrass percentage cover per seagrass species to estimate biomass from both point-based and landscape scale (>100 km2) seagrass data. First we used a set of linear models to estimate the biomass component of each seagrass species in over 20 000 benthic photos. We then adapted this approach to estimate biomass from a time-series of remote sensing derived seagrass percentage cover and dominant species maps. We demonstrate accurate estimation of above-ground biomass using a set of methods that is not only more time and resource efficient than existing methods, but is sufficiently robust and generalisable for application at large spatial or temporal scales. Our method allows for quantification of above-ground biomass in seagrass ecosystems over spatial scales larger than can be tractably assessed using current site- and point-based measurement approaches, and at scales that are required to understand and manage seagrass systems to tackle anthropogenic climate change and other impacts.