Carabid beetles as indicators of tillage disturbance
Bibliographic record
Abstract
Carabid beetles are often considered highly useful indicators of disturbance and environmental stress. They are useful because they are taxonomically well known and are easily captured. However, there is no comprehensive study of carabids using a gradient of similar disturbances and a wide variety of ecological indices and models. I assessed the effect of tillage disturbance on the diversity within and between carabid assemblages by using 14 different measures of diversity from samples collected on individual farms (i.e., treatments) in southern Ontario. I used different diversity indices to test the null hypothesis that for all diversity indices there is no change in species diversity with increasing tillage disturbance. My hypotheses could not be rejected (p > 0.05) because no difference in diversity between farms was found. These tests and results were corroborated by a meta-analysis of 45 published carabid data sets. Thus, my own data, and those of others, indicate that diversity indices are not useful for detecting the effect of disturbance on carabids. Because of the negative findings obtained by using species diversity, I examined the novel hypothesis that diversity in beetle size, a functional measure of a beetle's place in an ecosystem, would be useful. I hypothesized that there would be decreasing diversity of beetle species sizes, increasing mean beetle size and increasing deviation away from log normal distributions of size with increasing disturbance as represented by the tillage practices on the 4 treatments. The log normal distributions of body size on all treatments suggest that carabid beetle assemblages form a functional group. Arguments of niche hierarchy and competitive interactions can be invoked. From the findings of the log normality in sizes, I hypothesized that carabids are a taxonomic functional group by using distribution of species diversity and abundance. My results suggest that they are not because their diversity and abundance is not represented by log normal distributions. I found that carabid populations were geometrically distributed in all treatments. To determine if levels of disturbance can be assessed by using other taxonomic groups I used a meta-analyses of non-carabid species assemblages to test for deviation from log normal distributions of diversity and abundance. Functional groups that should be targeted for disturbance studies are keystone groups which include pollinators, and detritivores. Underlying ecological principles to diversity and abundance would predict that some indicator groups may not show a change in diversity and abundance as the result of a disturbance. (Abstract shortened by UMI.)
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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) | 0.001 | 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 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".