Functional trait complementarity and dominance both determine benthic secondary production in temperate seagrass beds
Abstract Defining relationships between biodiversity and ecosystem functioning (BEF) is key to understanding the consequences of biodiversity loss. Although species functional traits are strongly linked to ecosystem processes, their integration into BEF models has been focused mainly on terrestrial...
Guardado en:
| Autores principales: | , |
|---|---|
| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Wiley
2021
|
| Materias: | |
| Acceso en línea: | https://doaj.org/article/4c140ada89664b9dbad3f0ce9cb206d2 |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| Sumario: | Abstract Defining relationships between biodiversity and ecosystem functioning (BEF) is key to understanding the consequences of biodiversity loss. Although species functional traits are strongly linked to ecosystem processes, their integration into BEF models has been focused mainly on terrestrial ecosystems. Application is limited because functional trait‐based BEF models typically have small‐sample sizes and highly correlated predictors, making it difficult for model selection and identifying underlying drivers. We examine the BEF relationship between secondary production and benthic invertebrate taxonomic and functional diversity for seagrass beds located across a range of environmental conditions. Specifically, we evaluate the role of complementarity (i.e., dissimilarity in species or traits) and dominance (disproportional importance of traits) in determining secondary production at 20 sites using 34 metrics of taxonomic diversity, functional diversity, and functional traits. Here, diversity metrics represent complementarity and functional traits represent dominance. We used elastic‐net regression and commonality analysis to evaluate the BEF model, because its properties (i.e., few observations and many potential, and highly correlated, predictors) precluded more standard approaches and it is well suited to this situation. Functional richness and five functional traits (crawling, surface deposit feeding [SurDF], location on sediment surface, lifespan 1–3 yr, and semi‐continuous breeding) were identified as important determinants of secondary production, explaining 74% of the variance. SurDF was the most important predictor that acted in isolation to influence secondary production, while all other predictors acted together. All six selected variables in three different combinations explained 68% of the total variance in the BEF model. These results indicate that both dominance and complementarity mechanisms were important for the BEF relationship. Our study highlights elastic‐net regression and commonality analysis as a powerful approach to model functional trait‐based BEF relationships. We further show that inclusion of the functional landscape into BEF models is highly valuable, allowing the implications of species loss for ecosystem functioning to be mechanistically understood. |
|---|