Can We Compare Effect Size of Spatial Genetic Structure Between Studies and Species Using Moran Eigenvector Maps?
Bibliographic record
Abstract
As the field of landscape genetics is progressing toward comparative empirical studies and meta-analysis, it is important to know how best to compare the strength of spatial genetic structure between studies and species. Moran's Eigenvector Maps are a promising method that does not make an assumption of isolation-by-distance in a homogeneous environment but can discern cryptic structure that may result from multiple processes operating in heterogeneous landscapes. MEMgene uses spatial filters from Moran's Eigenvector Maps as predictor variables to explain variation in a genetic distance matrix, and it returns adjusted R 2 as a measure of the amount of genetic variation that is spatially structured. However, it is unclear whether, and under which conditions, this value can be used to compare the degree of spatial genetic structure (effect size) between studies. This study addresses the fundamental question of comparability at two levels: between independent studies (meta-analysis mode) and between species sampled at the same locations (comparative mode). We used published datasets containing 9,900 haploid, biallelic, neutral loci simulated on a quasi-continuous, square landscape under four demographic scenarios (island model, isolation-by-distance, expansion from one or two refugia). We varied the genetic resolution (number of individuals and loci) and the number of random sampling locations. We considered two measures of effect size, the MEMgene adjusted R 2 and multivariate Moran's I , which is related to Moran's Eigenvector Maps. Both metrics were highly sensitive to the number of locations, even when using standardized effect sizes, SES, and the number of individuals sampled per location, but not to the number of loci. In comparative mode, using the same Moran Eigenvector Maps for all species, even those with missing values at some sampling locations, reduced bias due to the number of locations under isolation-by-distance (stationary process) but increased it under expansion from one or two refugia (non-stationary process). More robust measures of effect size need to be developed before the strength of spatial genetic structure can be accurately compared, either in a meta-analysis of independent empirical studies or within a comparative, multispecies landscape genetic study.
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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 | 0.239 | 0.440 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.023 |
| Bibliometrics | 0.016 | 0.016 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".