From logs to landscapes: determining the scale of ecological processes affecting the incidence of a saproxylic beetle
Bibliographic record
Abstract
1. Species incidence is influenced by environmental and intrinsic factors operating at multiple scales. The incidence of a dispersal‐limited beetle, Odontotaenius disjunctus (Coleoptera: Passalidae), was surveyed within hierarchically nested organisational levels of its environment (log sections < logs < 10‐m radius subplots < 0.66‐ha plots) in Louisiana, U.S.A. The finest level was the size of a single territory. Passalid beetles are an ecologically prominent group, but little is known of the factors affecting their incidence. 2. Three scale‐sensitive aspects of O. disjunctus incidence were evaluated: (i) the extent (52–3600 ha) within which forest cover was most associated with incidence; (ii) the hierarchical level at which environmental variables best predicted incidence; and (iii) the hierarchical level at which incidence exhibited the greatest spatial autocorrelation as a result of intrinsic factors (e.g. dispersal limitation). 3. Forest cover best predicted incidence at 225 ha, but accounted for only 1.2% of variation in incidence. Incidence was most sensitive to environmental factors measured at the finest scale (i.e. territories). Incidence was positively associated with moderately decayed wood and increased surface area of logs (9.9% and 3.1% of variance, respectively). When environmental factors were accounted for, spatial autocorrelation in incidence was greatest within subplots and logs, consistent with the hypothesis that intrinsic autocorrelation is associated with O. disjunctus average dispersal distance (<5 m). 4. This study indicates the influences of factors acting at multiple scales, but suggests that environmental conditions at the scale of territories may be most important for species incidence.
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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 | 0.001 | 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 | 0.001 | 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 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".