Exploring non-stationary and scale-dependent relationships between walleye (Sander vitreus) distribution and habitat variables in Lake Erie
Bibliographic record
Abstract
Global regression techniques that assume spatial stationarity are usually used to study the interaction between aquatic species distribution and habitat variables. In the present study, a local regression model, named geographically weighted regression (GWR), was used to question the spatial stationarity assumption in exploring the relationships between walleye (Stizostedion vitreum) distribution and habitat variables in Lake Erie. The GWR model resulted in a significant improvement of model performance over the two global linear and non-linear regression methods (a generalised least-squares (GLS) model and a generalised additive mixed model (GAMM)), accounting for residual spatial autocorrelation using the same response and explanatory variables as in the GWR model. The values of local regression coefficients from the GWR model changed among spatial locations significantly, implying spatially varying and scale-dependent relationships between walleye distribution and habitat variables. The k-means cluster analyses based on the t-values of local regression coefficients of GWR model characterised special zones of species–environment relationships of walleye in Lake Erie. In conclusion, spatial stationarity needs to be questioned in studying the relationships between aquatic species distribution and habitat variables and a non-stationary approach, such as GWR, is recommended as a complementary tool.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".