Population dynamics of Caladenia: Bayesian estimates of transition and extinction probabilities
Bibliographic record
Abstract
A disproportionate number of threatened plant species in Australia are found in the genus Caladenia, although little has been published on their life history. Here we examine data from nine species to evaluate some of the basic life-history strategies in Caladenia, specifically the transitions between life-history stages. We constructed life-history transition models of the orchids by using a Bayesian approach, we evaluated the growth rate of populations, compared transition values among species and determined which stage influenced the population growth most. We assessed extinction likelihood and considered the effect of variation in transitions among states on the probability of extinction. Bayesian model selection showed differences between species regarding their life cycle. The probability of individuals flowering in two consecutive years is extremely rare and was found to be common in only one species, C. amoena. All other species had a high likelihood of returning to a vegetative state, and some were likely to enter dormancy after flowering. High elasticities in the transition from the dormant to dormant stage suggest that dormancy has a large impact on population persistence. The quasi-extinction rate suggests that C. rosella, C. clavigera, C. graniticola and C. macroclavia are most at risk when all species have an equal initial population size. Conservation management should focus on studies to identify cues that influence flowering in consecutive years, emergence from dormancy and increasing recruitment.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".