Ground vegetation as an indicator of ecological integrity
Bibliographic record
Abstract
Indicators are being sought for monitoring the ecological integrity of forests and other kinds of ecosystems. Biological measures are commonly used as indicators because of their inherent ecological importance and ability to provide insight into environmental change. Such measures are commonly based on data from sets of permanent plots in which the abundances of plant species are monitored. However, the data may be difficult to interpret, especially if corresponding information on natural and anthropogenic stressors is lacking. In this review, we examine general principles of indicator use and discuss the types of plot-based compositional measures obtained from vegetation that may be most relevant for monitoring ecological integrity. Our focus is on the ground vegetation of forested ecosystems, but the principles discussed are relevant to other vegetation types. Individual plant species, guilds, aliens, diversity indices, Ellenberg indicator values, the floristic quality assessment index, multivariate and multimetric indicators are examined, as well as concepts of threshold changes and the need for reference states. The usefulness of any given approach tends to be highly context specific. In particular, the value of using individual species as indicators is highly dependant on factors such as the character of the floristic community of interest and the types and intensities of anthropogenic stressors. Alien species are considered to be especially valuable indicators of changes in ecological integrity due to their established relationships with anthropogenic stressors, known historical state, relevance to all floristic communities, and ability to cause undesirable changes to biodiversity and ecological processes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".