Functional diversity promotes phytoplankton resource use efficiency
Bibliographic record
Abstract
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, RUEPand 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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".