Identifying the loci of speciation: the challenge beyond genome scans
Bibliographic record
Abstract
In their review on the genomic landscape of speciation, Ravinet et al. (in press) highlight difficulties when attempting to draw inferences about speciation based on heterogeneous patterns of genome differentiation. These problems arise because various factors that are either causally unlinked or only spuriously associated with speciation can induce genomic heterogeneity and thus complicate the interpretation of genome scans. In the light of these difficulties, it is important to not restrict speciation research to genome scans but to view them as an additional tool that can be applied complementary to more traditional methodologies, for example field observations, experiments and crosses. In our opinion, interpreting the genomic landscape of speciation faces additional difficulties that were not addressed by Ravinet et al. (in press), mainly due to limitations in our understanding of whether genomic patterns caused by processes involved in speciation can be differentiated from those associated with adaptation within lineages. While the latter likely play an important role in many cases of speciation (Gavrilets, 2003; Schluter & Conte, 2009; Nosil, 2012), in other cases, they might not (Presgraves, 2010; Maheshwari & Barbash, 2011). We thus argue that reduced gene flow attributable to divergent adaptation, and potentially noticeable in genome scans, might contain limited information about speciation, as it is difficult to predict the future role of adaptive alleles in facilitating the evolution of reproductive isolation. This parallels the notion that current isolating barriers may be uninformative about their past importance during speciation (Coyne & Orr, 2004). The unpredictable future holds for any isolation barrier, but specifically so for those solely relying on environmental factors (e.g. Seehausen et al., 2008). We thus focus here on whether genome scans can identify barriers to gene flow that are potentially able to keep species separated in the long run and that may be less susceptible to or independent of environmental changes, for example intrinsic hybrid incompatibilities or mating preferences (Seehausen et al., 2014). While environmentally independent processes that potentially advance speciation (e.g. genomic conflict or assortative mating) can differ from processes involved in divergent adaptation between lineages, their genomic signatures may be difficult to distinguish in an empirical context. However, it is critical to understand the relative importance and interaction of different classes of barriers for the evolution of reproductive isolation and speciation.
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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 | 0.036 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.005 | 0.029 |
| Open science | 0.007 | 0.008 |
| Research integrity | 0.008 | 0.014 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".