Effects of spatial scale and choice of statistical model (linear versus tree-based) on determining species–habitat relationships
Bibliographic record
Abstract
The choice of spatial scale and modelling technique used to capture species–habitat relationships needs to be considered when ascertaining environmental determinants of habitat quality for species and communities. Fish densities and environmental data were collected at three Laurentian lakes using underwater surveys by four snorkelers collecting fine spatial data acquired through geographic positioning systems. At both fine (20 m) and broad (100 m) spatial scales, tree-based approaches, which incorporated both linear and nonlinear relationships, explained more variation than their linear counterparts. At the finest spatial scale considered (20 m), local environmental conditions, such as habitat structure and heterogeneity, were important determinants of fish habitat selection. At the broadest spatial scale considered (100 m), fish tended to select habitat based on both local environmental features and riparian development. Moran’s eigenvector maps further revealed that fish–habitat associations were operating at broader spatial scales than the predefined analytical units, which can be partially attributed to the spatial structure of environmental conditions acting at spatial scales greater than 100 m. This study highlights the importance of evaluating statistical approaches at different spatial scales to identify key determinants of habitat quality for species, ultimately to assess the effects of perturbations on ecosystems.
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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 | 0.059 | 0.119 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| 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 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".