Response of ant functional composition to fire
Bibliographic record
Abstract
Little is known about the impact of disturbances on functional diversity and the long‐term provisioning of ecosystem services, especially in animals. In this work we analyze the effect of wildfire on the functional composition of Mediterranean ant communities. In particular, we asked whether a) fire changes functional composition (mean and dissimilarity of trait values) at the community level; and b) such fire‐induced functional modification is driven by changes in the relative abundance‐dominance of species or by a replacement of species with different traits. We sampled ant communities in burned and unburned plots along 22 sites in a western Mediterranean region, and we computed two complementary functional trait composition indices (‘trait average’ and ‘trait dissimilarity’) for 12 functional traits (related to resource exploitation, social structure and reproduction) and with two different datasets varying in the way species abundance is considered (i.e. abundance and occurrence data). Our results suggest a set of functional responses that seem to be related to direct mortality by fire as well as to indirect fire‐induced modifications in environmental conditions relevant for ants. Trait average of colony size, worker size, worker polymorphism and the ratio between queen and worker size, as well as the trait dissimilarity of the proportion of behaviorally dominant species and of liquid food consumption, and overall functional diversity, were higher in burned than in unburned areas. Interestingly, different patterns arise when comparing results from abundance and occurrence data. While the response to fire in trait averages is quite similar, in the case of trait dissimilarity, the higher values in response to fire are much more marked when considering occurrence rather than abundance data. Our results suggest that changes in trait average are driven at the same time by replacement of species with different traits and by changes in the relative abundance‐dominance of species, while fire promotes a higher diversity of functions that is primarily driven by rare species that are functionally unique. Overall, we observed major fire‐induced changes in functional composition in Mediterranean ant communities that might have relevant consequences for ecosystem processes and services.
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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 | 0.001 |
| 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".