Using hierarchies of cause to inform conservation of a naturally rare but critically endangered shrub Lasiopetalum pterocarpum (Malvaceae s.l.)
Bibliographic record
Abstract
Understanding the causes of rarity and ways of managing populations of rare species is essential for their successful conservation. The present study applies the conceptual model of a hierarchy of causes to Lasiopetalum pterocarpum E.M.Benn. & K.Shep. (a critically endangered species) to understand better its reproductive and ecological attributes, possible reasons for its rarity and to determine whether this model assists in developing management strategies. L. pterocarpum subpopulations from Serpentine National Park were censused to record abundance, plant health, phenology, flower and fruit production and the presence of any seed bank. These characteristics were matched to criteria in the hierarchies of cause model. There was no evidence of recent seedling recruitment at any subpopulation. Hand-pollination produced a flower-to-fruit conversion proportion similar to that found in the field and self- and cross-pollinations produced virtually the same fruit set. Seed store in soil from beneath the native subpopulations and at a translocation site showed seed was patchily distributed and infrequent. L. pterocarpum is an obligate seeder, killed by fire and dependent on disturbance to break seed dormancy. However, smoke has no effect on germination. Seed production does not constrain population growth, because seedling regeneration after fire in 1999 was prolific at sites where plants had been growing. In the hierarchies of cause framework, the main causes of rarity for this species are taxon ecology, life-history strategy and stochasticity. Thus, concentrating active management on factors related to life history such as mosaic patch burning, fencing after fire to exclude vertebrate grazers, weed control and establishment of translocated populations will aid the preservation of this species in the wild.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".