Why biennials are so few: Habitat availability and the species pool
Bibliographic record
Abstract
Plant ecologists have long been intrigued by the relatively small number of plant species that possess the biennial life history. In this paper, we consider a simple explanation for this pattern that is based on the predicted relationship between the size of a species pool and the availability of the habitat type to which the pool of species is adapted. Hence, we predict that there are few species of biennials (relative to annuals and perennials) simply because they are adapted to a relatively uncommon habitat type. We tested this idea using vegetation surveys from a region surrounding Kingston, Ontario, in which the vascular flora has been well documented and recently published as a species checklist of over 1600 flowering species. We found that the relative numbers of annual (18.1%), biennial (3.7%), and perennial flowering species (78.2%) recorded in this region parallelled the contemporary relative abundance of the respective habitat types with which these three life forms are most commonly associated. In vegetation surveys, annuals were more common than biennials in habitats that had been disturbed within the previous 12 months, which comprised 6.2% of the surveyed area. Conversely, biennials were more common than annuals in habitats that had been disturbed one to three years prior to the survey but not subsequently re-disturbed. This ‘biennial’ habitat type comprised only 1% of the surveyed area. Most of the area surveyed (92.8%) consisted of habitats that had been undisturbed for more than three years, where annuals and biennials were both absent. These results suggest that biennials are so few because the type of habitat to which biennials are adapted, and in which they can be expected to speciate, is and always has been relatively rare.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".