Are ecologists conducting research at the optimal scale?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Aim The spatial extent (scale) at which landscape attributes are measured has a strong impact on inferred species–landscape relationships. Consequently, researchers commonly measure landscape variables at multiple scales to select one scale (the ‘scale of effect’) that yields the strongest species–landscape relationship. Scales of effect observed in multiscale studies may not be true scales of effect if scales are arbitrarily selected and/or are too narrow in range. Miscalculation of the scale of effect may explain why the theoretical relationship between scale of effect and species traits, e.g. dispersal distance, is not empirically well supported. Location World‐wide. Methods Using data from 583 species in 71 studies we conducted a quantitative review of multiscale studies to evaluate whether research has been conducted at the true scale of effect. Results Multiple lines of evidence indicated that multiscale studies are often conducted at suboptimal scales. We did not find convincing evidence of a relationship between observed scale of effect and any of 29 species traits. Instead, observed scales of effect were strongly positively predicted by the smallest and largest scales evaluated by researchers. Only 29% of studies reported biological reasons for the scales evaluated. Scales tended to be narrow in range (the mean range is 0.9 orders of magnitude) and few (the mean number of scales evaluated is four). Many species (44%) had observed scales of effect equal to the smallest or largest scale evaluated, suggesting a better scale was outside that range. Increasing the range of scales evaluated decreased the proportion of species with scales of effect equal to the smallest or largest scale evaluated. Main conclusions To ensure that species–landscape relationships are well estimated, we recommend that the scales at which landscape variables are measured range widely, from the size of a single territory to well above the average dispersal distance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 it