Premium
Formal analysis and evaluation of allometric methods for estimating above‐ground biomass of eelgrass
Author(s) -
EchavarriaHeras H.,
Lee K.S.,
SolanaArellano E.,
FrancoVizcaíno E.
Publication year - 2011
Publication title -
annals of applied biology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.677
H-Index - 80
eISSN - 1744-7348
pISSN - 0003-4746
DOI - 10.1111/j.1744-7348.2011.00511.x
Subject(s) - zostera marina , seagrass , biomass (ecology) , allometry , estuary , biology , bay , quadrat , sampling (signal processing) , ecology , tree allometry , environmental science , habitat , biomass partitioning , oceanography , transect , computer science , filter (signal processing) , computer vision , geology
Eelgrass ( Zostera marina ) populations supply substantial amounts of organic materials to food webs in shallow coastal environments, provide habitat for many fishes and their larvae and abate erosion. The characterisation of eelgrass biomass dynamics is an important input for the assessment of the function and values for this important seagrass species. We here present original allometric methods for the non‐destructive estimation of above‐ground biomass of eelgrass. These assessments are based on measurements of lengths and areas of leaves and sheaths and mathematical models that can be identified by means of standard regression procedures. The models were validated by using data obtained from Z. marina meadows in the Punta Banda estuary B.C., Mexico, and in Jindong Bay, Korea. Using available data and concordance correlation index criteria we show that the values projected thorough the presented allometric paradigm reproduces observed values in a consistent way. The annual average value for observed above‐ground biomass was 1.46 ± 0.15 g shoot −1 , while the corresponding calculated value was 1.40 ± 0.13 g shoot −1 . We suggest that our method can be applied to other studies in which the architecture and growth form of leaves and sheaths are similar to those of eelgrass. This would provide reliable and simplified estimations of biomass while eliminating tedious laboratory processing and avoiding destructive sampling.