A trait-based framework for seagrass ecology: Trends and prospects

Abstract

In the last three decades, quantitative approaches that rely on organism traits instead of taxonomy have advanced different fields of ecological research through establishing the mechanistic links between environmental drivers, functional traits, and ecosystem functions. A research subfield where trait-based approaches have been frequently used but poorly synthesized is the ecology of seagrasses; marine angiosperms that colonized the ocean 100M YA and today make up productive yet threatened coastal ecosystems globally. Here, we compiled a comprehensive trait-based response-effect framework (TBF) which builds on previous concepts and ideas, including the use of traits for the study of community assembly processes, from dispersal and response to abiotic and biotic factors, to ecosystem function and service provision. We then apply this framework to the global seagrass literature, using a systematic review to identify the strengths, gaps, and opportunities of the field. Seagrass trait research has mostly focused on the effect of environmental drivers on traits, i.e., "environmental filtering" (72%), whereas links between traits and functions are less common (26.9%). Despite the richness of trait-based data available, concepts related to TBFs are rare in the seagrass literature (15% of studies), including the relative importance of neutral and niche assembly processes, or the influence of trait dominance or complementarity in ecosystem function provision. These knowledge gaps indicate ample potential for further research, highlighting the need to understand the links between the unique traits of seagrasses and the ecosystem services they provide.

Keywords: ecosystem service vulnerability; functional ecology; response-effect framework; seagrass traits database; trait-based approach.

Figure 1

Conceptual TBF (trait-based response-effect framework) synthetized combining the concepts introduced by Lavorel and Garnier (2002); Suding et al. (2008); Violle et al. (2012); Díaz et al. (2013) and Mouillot et al. (2013). A given array of species at the regional level (Sp1-Sp6) are influenced by several filters until the final local community assemblage. The circles indicate the species abundance at the regional level (top) and at the local level after the filters (bottom). Traits can be used to study if community assemblage is a neutral or niche assembly process, meaning that it is mainly stochastic or affected by environmental drivers (Filters 1 and 2, Hypothesis #1). Once settled, the relative importance of the abiotic filter (i.e. environmental drivers) and the biotic filter (Filter 3) (i.e. competition for resources and biotic interactions) can be studied through trait convergence and divergence (Hypothesis #2). Functional trait abundance (CWM) and diversity (FD) can be calculated in a community to study the influence of traits in ecosystem function delivery (Hypothesis #5). Functional traits can, however, be phylogenetically controlled (Hypothesis #3) and their inter- and intraspecific variability may change among species and communities (Hypothesis #4). Ecosystem function delivery not only depends on traits, but also the environmental constraints may play a central role in it (Hypothesis #6). The correlation of the ecosystem function delivery and response will determine the function vulnerability (Hypothesis #7). Finally, ecosystem functions and their perception by humankind determine the ecosystem services provided and, therefore, their vulnerability.

Figure 2

Number of studies included in the systematic review classified by (A) Year, (B) Bioregion and (C) Seagrass species. The light gray color indicates the number of studies including the word “trait” and the dark gray color the number of studies classified as using a trait-based frameworks (TBF). Studies from the year 2022 (n=5 until March 31st) are not included in this figure.

Figure 3

Results of the systematic literature review adapted to the conceptual TBF proposed by this manuscript. The studies were classified in “Study types” (top), “Study subcategories” (middle, a posteriori classification with the environmental drivers and ecosystem functions found in the review process) and “Trait categories” (bottom, trait classification decided before the review process). The size of the circle indicates the number of trait studies. The darker circles within indicate the number of studies classified as using TBFs. The horizontal lines indicate the number of connections among the “Study types”. The darker lines indicate the number of connections among study types using a TBF.