Habitat specialization and climate affect arthropod fitness: a comparison of generalist vs. specialist spider species in Arctic and temperate biomes
Bibliographic record
Abstract
Life history trade-offs are a key notion in evolutionary biology, notably for understanding how selection shapes the diversity of traits among species. Despite the frequent study of such trade-offs, few studies synchronously investigate the effects of multiple factors, such as niche specialization and adaptation to harsh environments. We compared reproduction (fecundity and egg quality) in two sympatric couples (one habitat generalist and one specialist) of congeneric wolf spider species, in both Arctic and temperate habitats. We found that specialist species at both latitudes invested more in clutch size than did generalist species. We interpret this result as an optimization of clutch production. In the Arctic, the specialist was able to invest in fecundity with increasing body size at a much higher rate than the generalist species. In the temperate habitat, both species showed similar strategies: they increased quantity and quality of offspring relative to body size at the same rate. These results are consistent with the hypothesis that Arctic species must develop distinct strategies in order not to overlap each other’s ecological niches as a consequence of limited food resources or niche space. We emphasize the need to test the role of plasticity and environmentally mediated effects of competition on arthropod fitness.
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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) | 0.001 | 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".