Drought sensitivity predicts habitat size sensitivity in an aquatic ecosystem
Bibliographic record
Abstract
Species and trophic richness often increase with habitat size. Although many ecological processes have been evoked to explain both patterns, the environmental stress associated with small habitats has rarely been considered. We propose that larger habitats may be species rich simply because their environmental conditions are within the fundamental niche of more species; larger habitats may also have more trophic levels if traits of predators render them vulnerable to environmental stress. We test this hypothesis using the aquatic insect larvae in water-filled bromeliads. In bromeliads, the probability of desiccation is greatest in small plants. For the 10 most common bromeliad insect taxa, we ask whether differences in drought tolerance and regional abundances between taxa predict community and trophic composition over a gradient of bromeliad size. First, we used bromeliad survey data to calculate the mean habitat size of occurrence of each taxon. Comparing the observed mean habitat size of occurrence to that expected from random species assembly based on differences in their regional abundances allowed us to obtain habitat size sensitivity indices (as Z scores) for the various insect taxa. Second, we obtained drought sensitivity indices by subjecting individual insects to drought and measuring the effects on relative growth rates in a mesocosm experiment. We found that drought sensitivity strongly, predicts habitat size sensitivity in bromeliad insects. However, an increase in trophic richness with habitat size could not be explained by an increased sensitivity of predators to drought, but rather by sampling effects, as predators were rare compared to lower trophic levels. This finding suggests that physiological tolerance to environmental stress can be relevant in explaining the universal increase in species with habitat size.
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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 | 0.001 | 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".