A Neutral Theory for Interpreting Correlations between Species and Genetic Diversity in Communities
Bibliographic record
Abstract
Spatial patterns of biological diversity have been extensively studied in ecology and population genetics, because they reflect the forces acting on biodiversity. A growing number of studies have found that genetic (within-species) and species diversity can be correlated in space (the so-called species-gene diversity correlation [SGDC]), which suggests that they are controlled by nonindependent processes. Positive SGDCs are generally assumed to arise from parallel responses of genetic and species diversity to variation in site size and connectivity. However, this argument implicitly assumes a neutral model that has yet to be developed. Here, we build such a model to predict SGDC in a metacommunity. We describe how SGDC emerges from competition within sites and variation in connectivity and carrying capacity among sites. We then introduce the formerly ignored mutation process, which affects genetic but not species diversity. When mutation rate is low, our model confirms that variation in the number of migrants among sites creates positive SGDCs. However, when considering high mutation rates, interactions between mutation, migration, and competition can produce negative SGDCs. Neutral processes thus do not always contribute positively to SGDCs. Our approach provides empirical guidelines for interpreting these novel patterns in natura with respect to evolutionary and ecological forces shaping metacommunities.
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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".