Impacts of female body size on cannibalism and juvenile abundance in a dominant arctic spider
Bibliographic record
Abstract
Body size influences an individual's physiology and the nature of its intra- and interspecific interactions. Changes in this key functional trait can therefore have important implications for populations as well. For example, among invertebrates, there is typically a positive correlation between female body size and reproductive output. Increasing body size can consequently trigger changes in population density, population structure (e.g. adult to juvenile ratio) and the strength of intraspecific competition. Body size changes have been documented in several species in the Arctic, a region that is warming rapidly. In particular, wolf spiders, one of the most abundant arctic invertebrate predators, are becoming larger and therefore more fecund. Whether these changes are affecting their populations and role within food webs is currently unclear. We investigated the population structure and feeding ecology of the dominant wolf spider species Pardosa lapponica at two tundra sites where adult spiders naturally differ in mean body size. Additionally, we performed a mesocosm experiment to investigate how variation in wolf spider density, which is likely to change as a function of body size, influences feeding ecology and its sensitivity to warming. We found that juvenile abundance is negatively associated with female size and that wolf spiders occupied higher trophic positions where adult females were larger. Because female body size is positively related to fecundity in P. lapponica, the unexpected finding of fewer juveniles with larger females suggests an increase in density-dependent cannibalism as a result of increased intraspecific competition for resources. Higher rates of density-dependent cannibalism are further supported by the results from our mesocosm experiment, in which individuals occupied higher trophic positions in plots with higher wolf spider densities. We observed no changes in wolf spider feeding ecology in association with short-term experimental warming. Our results suggest that body size variation in wolf spiders is associated with variation in intraspecific competition, feeding ecology and population structure. Given the widespread distribution of wolf spiders in arctic ecosystems, body size shifts in these predators as a result of climate change could have implications for lower trophic levels and for ecosystem functioning.
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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".