Functional diversity promotes phytoplankton resource use efficiency

Abstract

Understanding the relationship between biodiversity and ecosystem functioning (BEF) is a central topic in ecology. Multi‐trait–based functional diversity has been proposed to improve mechanistic understanding of the BEF relationship; however, how trait‐based functional diversity affects ecosystem functioning and processes has rarely been addressed in aquatic ecosystems. Here, we examined the causal relationships between three phytoplankton functional diversity indices (FAD2, functional diversity based on dendrograms [FDc], FRic) and Shannon diversity index versus resource use efficiency for nitrogen (RUEN), phosphorus (RUEP) and silicate (RUESi), with monthly long‐term datasets from the marine (Western English Channel, 2000–2014) and freshwater (Lake Kasumigaura, 1984–2012) ecosystems. We employed Convergent Cross Mapping (CCM), a novel method developed for identifying causality for nonlinear dynamical systems; this is in contrast to linear approaches that cannot distinguish causality from correlation. CCM found that FDc is the most robust functional diversity index among the selected functional diversity indices (FAD2, FDc, FRic) in predicting phytoplankton resource use efficiency and exhibited a much stronger causal effect than the Shannon index. Furthermore, scenario exploration analysis indicates that most causal effects from phytoplankton diversity indices on resource use efficiency (RUEN, RUEP and RUESi) are on average positive, and FDc exhibited the most consistent positive causal effects on phytoplankton resource efficiency in both marine and freshwater ecosystems. Thus, increasing FDc can enhance phytoplankton resource use efficiency in aquatic ecosystems. Synthesis. Our results show significant causal effects of functional diversity on phytoplankton resource use efficiency in both marine and freshwater ecosystems. Among all selected functional diversity indices, functional diversity based on dendrogram is the most robust functional diversity index in promoting phytoplankton resource efficiency. Our study provides empirical evidences in natural aquatic systems that trait‐based functional diversity represents better species niche partitioning than the Shannon index and thereafter enhances resource use efficiency. This finding can improve our understanding on trophic transfer and nutrient cycling in aquatic ecosystems.

Long‐term warming destabilizes aquatic ecosystems through weakening biodiversity‐mediated causal networks

“…Therefore, nonlinear methods (e.g., CCM) accounting for dynamic interactions and context dependency are more suitable to detect and quantify these causal links in dynamical ecosystems. Based on our findings, we inferred that explicitly resolving the causal pathways connecting species richness and the other key ecosystem components offered a better understanding on the temporal stability of phytoplankton biomass and can be extended to other types of ecosystems (e.g., grassland or microbial ecosystems) with better longterm monitoring (Anneville et al, 2019;Ye et al, 2019) or short-term monitoring with massive spatial replicates (Clark et al, 2015).…”

“…We used chlorophyll-a concentration as a proxy for total phytoplankton biomass. The chlorophyll-a concentration was measured by a standard spectrophotometric approach, so that the approximated biomass is independent of species richness derived from species composition data, following recommendations in the literature (Cardinale, 2011;Ptacnik et al, 2008;Ye et al, 2019).…”

Long-term warming weakens stabilizing effects of biodiversity in aquatic ecosystems

“…are potentially meaningful metrics, but were not consistently observed across systems. We used chlorophyll-a concentration determined by standard spectrophotometric approach (Strickland & Parsons 1977) as a proxy for total phytoplankton biomass instead of estimation from species composition to keep the diversity and biomass data independent, following the recommendation in the literature (Ptacnik et al 2008;Cardinale 2011;Ye et al 2019).…”

Freshwater phytoplankton diversity: models, drivers and implications for ecosystem properties

“…The recently developed 'FD' R package enables one to calculated easily all the aforementioned FD measures (Laliberté & Legendre, 2010;Laliberté et al, 2014). The use of FD components in the context of BEF in phytoplankton has only started very recently (Abonyi et al, 2018a, b;Ye et al, 2019). Trait-based functional diversity measures in BEF have recently been reviewed by Venail (2017).…”

“…The functional approaches-partly due to Colin Reynolds's prominent contribution to this field (Reynolds et al, 2002)-opened new perspectives in phytoplankton diversity research. Functional trait and functional 'group'-based approaches have gained considerable popularity in recent years (Weithoff, 2003; Litchman & Klausmeier, 2008;Borics et al, 2012;Vallina, et al, 2017;Ye et al, 2019). Analysis of large databases enabled to study diversity changes on larger scales in lake area, productivity or temperature (Stomp et al, 2011).…”