A hierarchical Bayesian Beta regression approach to study the effects of geographical genetic structure and spatial autocorrelation on species distribution range shifts
Bibliographic record
Abstract
Global climate change (GCC) may be causing distribution range shifts in many organisms worldwide. Multiple efforts are currently focused on the development of models to better predict distribution range shifts due to GCC. We addressed this issue by including intraspecific genetic structure and spatial autocorrelation (SAC) of data in distribution range models. Both factors reflect the joint effect of ecoevolutionary processes on the geographical heterogeneity of populations. We used a collection of 301 georeferenced accessions of the annual plant Arabidopsis thaliana in its Iberian Peninsula range, where the species shows strong geographical genetic structure. We developed spatial and nonspatial hierarchical Bayesian models (HBMs) to depict current and future distribution ranges for the four genetic clusters detected. We also compared the performance of HBMs with Maxent (a presence-only model). Maxent and nonspatial HBMs presented some shortcomings, such as the loss of accessions with high genetic admixture in the case of Maxent and the presence of residual SAC for both. As spatial HBMs removed residual SAC, these models showed higher accuracy than nonspatial HBMs and handled the spatial effect on model outcomes. The ease of modelling and the consistency among model outputs for each genetic cluster was conditioned by the sparseness of the populations across the distribution range. Our HBMs enrich the toolbox of software available to evaluate GCC-induced distribution range shifts by considering both genetic heterogeneity and SAC, two inherent properties of any organism that should not be overlooked.
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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 | 0.000 | 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 | 0.000 |
| 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".