The Scale-Dependent Role of Biological Traits in Landscape Ecology: A Review
Bibliographic record
Abstract
We describe current approaches that evaluate how the influence of species traits on the relationship between environmental variables and ecological responses varies among scales (i.e. the scale-dependent role of traits). We quantify which traits and ecological responses have been assessed, and discuss the main challenges associated with quantifying the scale-dependent effect of traits. We identify three main approaches used to evaluate the scale-dependent role of traits, based on whether 1) traits are used as predictors or responses, 2) intraspecific variation in single traits is considered, or 3) trait diversity indices are used. Our review identifies several gaps that include the following: 1) evidence of the scale-dependent role of traits is biased towards studies of plants; 2) we lack evidence of whether the traits of interacting species groups are consistently related across spatial scales; and 3) interactions between species traits and landscape structure are usually ignored. The explicit inclusion of landscape structure effects in trait-based approaches at multiple scales will benefit the integration of approaches from community ecology and landscape ecology. This is important for describing the mechanisms that operate simultaneously across scales and for predicting the impact of landscape change on a broad range of ecological responses, including species diversity and interspecific interactions.
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.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".