Integrative analysis of stressor gradients reveals multiple discrete trait‐defined axes underlie community assembly
Bibliographic record
Abstract
Abstract The generalizable functional attributes of organisms (traits) relate strongly to their environment across multiple levels of biological organization, making trait‐based approaches a powerful mechanistic framework to understand species distributions and community composition in relation to environmental change. To investigate how a wide range of stressor types shape stream macroinvertebrate communities, we conducted an integrative analysis using community and taxon trait information across drying, flooding, eutrophication, fine sediment, and acid mine drainage (AMD) gradients. Each gradient spanned relatively unimpacted to severely impacted sites. To characterize community change in response to stressors, we incorporated abundance‐based trait information from all stressor gradients in a single trait‐based ordination (nonmetric multidimensional scaling), defining the trait space within which each stressor gradient acted. We hypothesized that different stressors would apply different environmental filters, moving communities along distinct axes in trait space and resulting in communities with definable trait combinations. Particularly strong relationships were associated with anthropogenically derived stressors (fine sediment, eutrophication, and AMD) compared to natural stressors (drying and flooding). Anthropogenic stressors instigated significant movement of communities along multiple axes in trait space, likely driven by limited adaptation to these novel stressors. We demonstrate that trait‐based analysis of communities across multiple stressor gradients can support a more comprehensive understanding of how community composition changes than taxonomic methods or investigation of a single stressor type, and could underpin community‐focused management actions.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".