Data from: The scale of effect of landscape context varies with the species’ response variable measured
Bibliographic record
Abstract
ABSTRACT FROM THE ASSOCIATED PAPER IN LANDSCAPE ECOLOGY: Context: To detect an effect of landscape context on a species’ response, the landscape variables need to be measured within the appropriate distance from the species’ response, i.e. at the scale of effect. However, it is not clear what factors determine the scale of effect. Objective: Our objective was to test the prediction that the scale of effect should be smallest when the species’ response is fecundity, larger for abundance, and larger still for occurrence. Methods: We compared the scale of effect of two landscape variables (road density, forest proportion) on the three responses (fecundity, abundance, occurrence) for the wood frog (Lithobates sylvaticus) in eastern Ontario, Canada. We used egg mass surveys of 34 ponds to estimate fecundity (mean eggs/mass), abundance (number of masses), and occurrence (presence/absence of egg masses). We then empirically estimated the scale of effect of each landscape variable on each response. Results: The scale of effect differed among responses, from 0.2 to 3.0 km radii; however, it did not vary in the predicted order. Furthermore, the order was not consistent between the two landscape variables. Conclusions: Our results show that the scale of effect of a landscape variable on a given species can differ for different response variables. However, they also suggest that these differences in the scale of effect are not predictable. Thus, the most reliable way to ensure a landscape context study is conducted at the correct spatial extent is to estimate the scale of effect empirically, rather than ‘guesstimating’ the extent a priori.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.027 | 0.003 |
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".