Basics of Spatial Data Analysis: Linking Landscape and Genetic Data for Landscape Genetic Studies
Bibliographic record
Abstract
Most ecological and genetic data show inherent structure in space, time, or phylogeny. This chapter focuses solely on spatial structure, acknowledging that the other types of dependency occur. The power of landscape genetics analyses to detect significant spatial relationships between genetic and landscape data is directly linked to the sampling design, the spatial analysis methods, and the genetic markers used. The chapter discusses the main issues of relating genetic variation to landscape predictors in the familiar context of regression analysis or, more generally, in the framework of the linear model. It then presents different approaches through which space can be incorporated into the linear model. Finally, the chapter explains two common goals of spatial analysis in landscape genetics: first, how to test for the presence of significant isolation-by-distance (IBD) and then how to account for IBD by incorporating it into the null model when testing for other landscape effects.
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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 | 0.010 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.013 | 0.008 |
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".