The biomass-density relationship in seagrasses and its use as an ecological indicator

Abstract

Background: Biomass-density relations have been at the centre of a search for an index which describes the health of seagrass meadows. However, this search has been complicated by the intricacy of seagrass demographics and their complex biomass-density relations, a consequence mainly of their modular growth and clonality. Concomitantly, biomass-density upper boundaries have been determined for terrestrial plants and algae, reflecting their asymptotic maximum efficiencies of space occupation. Each stand's distance to its respective biomass-density upper boundary reflects its effective efficiency in packing biomass, which has proved a reliable ecological indicator in order to discriminate between taxonomic groups, functional groups and clonal vs. non-clonal growth.

Results: We gathered data from 32 studies on 10 seagrass species distributed worldwide and demonstrated that seagrasses are limited by their own boundary line, placed below the boundaries previously determined for algae and terrestrial plants. Then, we applied a new metric-d_grass: each stand's perpendicular distance to the seagrass boundary-and used this parameter to review fundamental aspects such as clonal growth patterns, depth distribution, seasonality, interspecific competition, and the effects of light, temperature and nutrients.

Conclusions: Seagrasses occupy space less efficiently than algae and terrestrial plants. Using only their biomass and density data we established a new and efficient tool to describe space occupation by seagrasses. This was used with success to evaluate their meadows as an ecological indicator for the health of coastal ecosystems.

Keywords: Coastal; Ecosystem; Index; Meadow; Nutrient; Pollution; Seagrass.

Fig. 1

Biomass–density relationships. The trajectories in (a) are a schematic of the generalized observed pattern with the stands’ specific relationships dependent from resource availability. These were not drawn from observed data nor represent any specific taxon. The trajectories in (b) are taxon specific Boundary Lines drawn from data of Weller [9]. The trajectories in (c) are the interspecific boundary lines (IBL) of plants [20], algae [12] and seagrass (estimated in this study)

Fig. 2

Estimation of the perpendicular distances (d_grass). These are estimated from the observed (obs) and estimated (est) biomass (B) and density (D), and the seagrass IBL

Fig. 3

Seagrass biomass-shoot density relations worldwide. Biomass (B) and shoot density (D) of seagrasses, their interspecific boundary line (IBL) given by log₁₀B = 4.569 − 0.438∙log₁₀D, and stands’ distances to the IBL (d_grass). Status of seagrass meadows was labelled as ‘healthy seagrass meadows’ inhabiting favourable environments, and ‘unhealthy seagrass meadows’ inhabiting less favourable environments

Fig. 4

Biomass-shoot density relations specific of each taxon. Green markers. All seagrass observations; black markers—selected seagrass observations

Fig. 5

Seagrass discrimination by efficiency of space occupation. Each stands’ distances to the seagrass IBL (d_grass) is used as a measure of this efficiency. This measure was compared a among taxa worldwide and b for the case study of Dadae Bay [49]. Box and whiskers represent the quartiles of the sample distribution

Fig. 6

Effect of depth on the efficiency of space occupation of seagrasses. Each stands’ distances to the seagrass IBL (d_grass) is used as a measure of this efficiency. Zostera marina was sampled in the Seomjin estuary—South Korea [57] and in the Oresond strait, Baltic Sea [56]. Posidonia sinuosa was sampled in Cockburn Sound and Warnbro Sound, Western Australia [78]. Box and whiskers represent the quartiles of the sample distribution, and asterisks represent outliers

Fig. 7

Seasonality of the seagrass efficiency of space occupation. The seagrass efficiency of space occupation is evaluated from the distance to the seagrass IBL (d_grass)

Fig. 8

Effects of amonium and phosphate on the efficiency of space occupation (d_grass) by Zostera noltii and Thalassia testudinum stands. Stands in Ria Formosa are in a gradient of closeness to a wastewater treatment plant, and measured seasonally. Site 1 was the closest and more polluted and site 4 the furthest away and least polluted [45]. Stands in the Thau Lagoon sampled by Plus et al. [48]. T. testudinum stands sampled by Kaldy and Dunton [66].  Model fits by Ordinary Least Squares (OLS) and Linear-in-the-parameters Oblique Least Squares (LOLS) [81]. The LOLS was fit by the new debuged software provided as Additional file 3

Fig. 9

Abiotic drivers of the efficiency of space occupation (d_grass) by Zostera noltii in the Thao lagoon. The d_grass of stands sampled by Plus et al. [48] is dependent from temperature (T) and photosynthetic active radiation at the bottom (PAR_b). The d_grass fit was performed by Ordinary Least Squares (OLS). All four panels are different perspectives of the same 3D plot showing the surface fit by Eq. (1)

Fig. 10

The d_grass of Zostera noltii stands in southern Iberia. Stands from Ria Formosa, Portugal in natural (R) or highly impacted (I) locations, from Huelva (H) and from Cádiz (C). Correlation coefficients (r) estimated disregarding H1 sampled during 2011