Community‐wide trait adaptation, but not plasticity, explains ant community structure in extreme environments
Bibliographic record
Abstract
Abstract Quantifying trait–environment associations can help elucidate the processes underpinning the structure of species assemblages. However, most work has focused on trait variation across rather than within species, meaning that processes operating at the intraspecific levels cannot be detected. Incorporating intraspecific trait variation in community‐wide analyses can provide valuable insights about the role of morphological adaptation and plasticity on species persistence and the composition of ecological communities. Here, we assessed geographical variation in the direction (i.e. adaptation) and strength of selection, and the magnitude of plasticity, by examining community‐wide trait variation in ant communities along an environmental gradient spanning 9° latitude in Quebec, Canada. Specifically, we measured 9 morphological traits related to foraging strategies, resource use and thermal regulation at 20 locations across temperate and boreal forests. We then examined how the mean and variance of these traits varied along temperature and precipitation gradients. Moreover, we examined how these trait–environment relationships varied across levels of organization, from individual workers (intraspecific) to colonies (intraspecific) and species (interspecific). We observed changes in mean trait values along environmental gradients, but very little change in variance. Specifically, we observed a decrease in the mean length of antennae and an increase in the mean eye length from mild (warm and wet) to more extreme environments (cold and dry). These shifts in trait means were mostly coordinated across organizational levels (i.e. worker, colony and species). We also observed a general increase in trait variance from mild to extreme environments, but only at the species level. Our findings suggest that stressful environmental conditions exert a strong selection pressure on ant morphology causing shifts in optimal trait values. These adaptations may enable persistence at the northern edge of the boreal forest and therefore influence the composition of these ant communities. Specifically, ants with large eyes and short antennae are overrepresented at the transition zone between the boreal forest and the tundra, possibly representing an adaptation to these more open habitats. Our study demonstrates that combining spatial and community‐wide intraspecific functional trait data provides a promising way forward to gain new insights on trait adaptations and processes underpinning community structure along environmental gradients. Read the free Plain Language Summary for this article on the Journal blog.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".