Shrub Dieback and El Niño Drought in Hawai‘i: Life Stage Demography and Population Rejuvenation
Bibliographic record
Abstract
The powerful El Niño of 1982–1983 precipitated a severe drought in the Hawai‘i Islands and was followed by an unusually dry La Niña year. Our 1983/1984 study of the early successional demography of five shrub and one tree species on volcanic cinder, Big Island of Hawai‘i, inadvertently coincided with the end of the ENSO drought. Life stage structure analyses showed a short-term dieback in the populations but then rapid population recovery. A new demographic tool, population flow diagram analysis, was developed as an aid to interpret the temporal dynamics of life stage structure. Crown size demographic depletion models were also used to describe the species’ vital statistics. The apparent dieback was shown to be a temporary dormancy response to the El Niño/La Niña-induced drought rather than a true case of dieback related to cohort senescence. As precipitation levels returned to normal the populations were rejuvenated by the revival of senescent and dormant individuals. The species showed robust demographic resilience to an unusually powerful drought. The populations of the Devastation Area appear to be members of a non-equilibrium community but there was evidence of a shift towards equilibrium. Climate change may intensify ENSO droughts in Hawai‘i and could cause longer-term diebacks of these populations and possibly their extirpation, affecting the rate and nature of primary succession on volcanic cinder ecosystems. Population viability modelling could determine if the species are likely to face extirpation from climate-change-driven alterations in the historic pattern of El Niño/La Niña events.
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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 | 0.000 | 0.001 |
| 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) | 0.001 | 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".