Traits of litter‐dwelling forest arthropod predators and detritivores covary spatially with traits of their resources
Bibliographic record
Abstract
The functional trait approach proposes that relating traits of organisms within a community to variation in abiotic and biotic characteristics of their environment will provide insight on the mechanisms of community assembly. As traits at a given trophic level might act as filters for the selection of traits at another trophic level, we hypothesized that traits of consumers and of their resources covary in space. We evaluated complementary predictions about top-down (negative) and bottom-up (positive) trait covariation in a detrital food web. Additionally, we tested whether positive trait covariation was better explained by the Resource Concentration Hypothesis (i.e., most commonly represented trait values attract abundant consumers) or the Resource Specialization Hypothesis (i.e., resource diversity increases niche availability for the consumers). Macroarthopods were collected with pitfall traps over two summers in three forested sites of southern Quebec in 110 plots that varied in tree species composition. Six feeding traits of consumers (detritivores and predators) and six palatability traits of their resources (leaf litter and prey) were matched to assess spatial covariation. Trait matches included consumer biting force/resource toughness, detritivore mandibular gape/leaf thickness, predator/prey body size ratio, etc. Our results demonstrate for the first time a covariation between feeding traits of detritivores and palatability traits of leaf litter (31-34%), and between feeding traits of litter-dwelling predators and palatability traits of potential prey (38-44%). The observed positive covariation supports both the Resource Concentration Hypothesis and Resource Specialization Hypothesis. Spatial covariation of consumer and resource traits provides a new tool to partially predict the structure of the detrital food web. Nonetheless, top-down regulation remains difficult to confirm. Further research on top-down processes will be undoubtedly necessary to refine our capacity to interpret the effect of biotic interactions on co-distribution.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".