Rarity and Sparseness in Plant Communities: Impact of Minor Species Removal on Beta Diversity and Canonical Ordination
Bibliographic record
Abstract
ABSTRACT Question Among the “minor” species present in communities, we distinguish between true “rare” species, with infrequent occurrence (low occupancy) in a given regional data set, and “sparse” species, which may be present over most of the study area, but with low local abundance. Do rare and sparse species play a different role in the evaluation of beta diversity and in the constrained ordination of plant community data sets? Methods Based on their positions in the abundance‐occupancy scatterplots of six contrasted vegetation data sets, we distinguished core, rural, urban, and satellite species. To disentangle the role of rarity and sparseness, we applied to each data set a progressive removal of either the least frequent or the least locally abundant species. We assessed impacts on beta diversity ( q = 0, 1 and 2), and on model performance of RDA, without or after pretransformation of absolute cover values. Results Multiplicative beta diversity decreased with the number of removed rare species, with slightly higher values for q = 2, whereas it increased when removing sparse species, with much higher values for q = 0. With raw data or after binary or by‐site transformation, the fraction of variation explained by RDA increased only slightly when removing rare species, with a more sensible increase of the relative contribution of the first canonical axis. By contrast, progressive elimination of sparse species, which mimics a lower sampling effort within each community, negatively affected model performance. Generally, the removal of rare species clearly improved the performance of RDA after double transformation (chi‐square transformation), contrary to the removal of sparse species. Conclusions The frequently observed positive correlation between occupancy and abundance hides profound differences with critical impacts on vegetation analysis. Providing that meaningful transformations are applied, there is no need to remove rare species prior to RDA. Focusing only on abundant species during sampling is likely to limit the performance of ecological empirical models.
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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 | 0.001 | 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 | 0.000 | 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".