Linking functional diversity to ecological indicators: a tool to predict anthropogenic effects on ecosystem functioning
Bibliographic record
Abstract
Functional diversity is related to the maintenance of processes and functions in ecosystems. However, there is a lack of a conceptual framework that highlights the application of functional diversity as an ecological indicator. Therefore, we present a new initiative for motivating the development of ecological indicators based on functional diversity. We are interested in showing the challenges and solutions associated with these indicators. We integrated species assemblage theories and literature reviews. We considered plant traits related to ecosystem processes and functions (specific leaf area, leaf dry matter content, wood density, phenology, and seed mass) to show the application of a selection of functional diversity metrics that can be used as ecological indicators (i.e., community weighted-mean, functional divergence, functional richness and functional evenness). We caution that functional diversity as an ecological indicator can be misinterpreted if species composition is unknown. Functional diversity values can be over-represented by weed species (species established in disturbed sites) and do not maintain original processes and functions in ecosystems. Therefore, we searched for evidence to demonstrate that weed species are ecological indicators of functional diversity changes. We found support for two hypotheses that explain the effect of weed species on ecosystem function: functional homogenization, and functional transformation. Likewise, we showed the application of some tools that can help study anthropogenic effects on functional indicators. This perspective shows that the paradigm of addressing the effects of disturbances on ecosystem processes by using functional diversity as an ecological indicator can improve environmental evaluation, particularly in areas affected by human activities.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".