Water loss in tree weta (<i>Hemideina</i>): adaptation to the montane environment and a test of the melanisation–desiccation resistance hypothesis
Bibliographic record
Abstract
Montane insects are at a higher risk of desiccation than their lowland counterparts and are expected to have evolved reduced water loss. Hemideina spp. (tree weta; Orthoptera: Anostostomatidae) have both lowland (Hemideina femorata, Hemideina crassidens and Hemideina thoracica) and montane (Hemideina maori and Hemideina ricta) species. H. maori has both melanic and yellow morphs. We use these weta to test two hypotheses: that montane insects lose water more slowly than lowland species, and that cuticular water loss rates are lower in darker insects than lighter morphs, because of incorporation of melanin in the cuticle. We used flow-through respirometry to compare water loss rates among Hemideina species and found that montane weta have reduced cuticular water loss by 45%, reduced respiratory water loss by 55% and reduced the molar ratio of V̇H2 O:V̇CO2 by 64% compared with lowland species. Within H. maori, cuticular water loss was reduced by 46% when compared with yellow morphs. Removal of cuticular hydrocarbons significantly increased total water loss in both melanic and yellow morphs, highlighting the role that cuticular hydrocarbons play in limiting water loss; however, the dark morph still lost water more slowly after removal of cuticular hydrocarbons (57% less), supporting the melanisation-desiccation resistance hypothesis.
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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 | 0.000 |
| 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 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".