The missing link in biogeographic reconstruction: Accounting for lineage extinction rewrites history
Bibliographic record
Abstract
Abstract Aim In the most widely used family of methods for ancestral range estimation (ARE), dispersal, speciation and extirpation events are estimated from information on extant lineages. However, this approach fails to consider the geographic distribution of extinct species and their position on the phylogenetic tree, an omission that could compromise reconstruction. Here, we present a method that models the geographic distribution of extinct species and we quantify the potential inaccuracy in ancestral range estimation when extinction rates are above zero. Location Global applications, with an example from the Americas. Taxon All taxa, with an example from hummingbirds ( Amazilia ). Methods Methods capable of explicitly modelling extinct branches along with their reconstructed geographic information (GeoSSE) have been overlooked in ARE analysis, perhaps due to the inherent complexity of implementation. We develop a user‐friendly platform, which we term LEMAD (Lineage Extinction Model of Ancestral Distribution) that generalizes the likelihood described in GeoSSE for any number of areas and under several sets of geographic assumptions. We compare LEMAD and extinction‐free approaches using extensive simulations under different macroevolutionary scenarios. We apply our method to revisit the historical biogeography of Amazilia hummingbirds. Results We find that accounting for the lineages removed from a tree by extinction improves reconstructions of ancestral distributions, especially when rates of vicariant speciation are higher than rates of in situ speciation, and when rates of extinction and range evolution are high. Rates of in situ and vicariant speciation are accurately estimated by LEMAD in all scenarios. North America as the most likely region for the common ancestor of hummingbirds. Main conclusions Methods that neglect lineage extinction are less likely to accurately reconstruct true biogeographic histories of extant clades. Our findings on an empirical dataset reconcile the Eurasian origin of Amazilia with biogeographic reconstructions when lineage extinction is considered.
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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 | 0.007 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".