Freshwater fish functional diversity shows diverse responses to human activities, but consistently declines in the tropics
Bibliographic record
Abstract
Freshwater environments are intertwined with human activities and the consequence has been environmental degradation and biodiversity loss. Fish provide key ecological and economic benefits, and fish abundance and diversity can be affected by human activities resulting in functional diversity (FD) changes that might scale up to ecosystem impacts. Changes in FD can be expressed by quantifying its three main FD components: richness, regularity and divergence. There is no consensus about how human activities affect the main components of FD. In addition, human activities might affect the functional diversity of communities differently in temperate and tropical regions because of differences in the regional species pools and the distribution of functional traits. Here, using a meta‐analytical approach, we assess how different human activities (e.g. deforestation, invasion, reservoirs) in freshwater systems affect FD components in fish communities. We compiled information from 2012 to 2023, and we found highly idiosyncratic patterns globally, but consistent loss of functional richness and regularity in face of human activities in the tropics. This idiosyncrasy could be related to high environmental heterogeneity or the multiple ways in which communities can be affected by human activities, including species loss or introduction of non‐native species, or the distribution of functional uniqueness and redundancy. The reduction of functional diversity in tropical regions reveals that high redundancy alone does not prevent declines in functional diversity, and that human activities are removing specific ecological functions from natural environments. Despite the general patterns of reduction observed, local features play a crucial role in shaping how communities respond to human activities. Therefore, it is essential to understand these patterns at a local scale and to investigate the mechanisms by which specific activities impact FD.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".