Tip rate estimates can predict future diversification, but are unreliable and context dependent
Bibliographic record
Abstract
Abstract Understanding the variability of processes leading to the emergence of new lineages is one of the major tasks of macroevolution as a scientific field. Recent years have seen the rise of rate-variable diversification models and metrics that estimate the rates of species diversification at the tips of phylogenetic trees and are thus potentially useful for predicting future evolutionary success of individual species. These methods use various assumptions about the variability and heritability of diversification rates. However, the general performance of rate-variable diversification methods have never been consistently tested against real world data. Here we explore the capacity of multiple rate-variable diversification methods to predict near-future diversification using temporal slices of empirical fossil and extant phylogenies. We do this using a newly developed approach similar to generalized linear models, allowing us to quantify the relationship between predictor tip rates and subsequent diversification rates derived from a probability distribution of numbers of daughter species. We find that tip rates estimated from current methods have non-zero but limited capacity to predict diversification in both fossil and extant phylogenies. The quality of the predictions depends not only on the methods used but also on the specific phylogeny, suggesting that diversification dynamics in some taxa may be more predictable in principle. Our results suggest that future cladogenesis can be, to a certain extent, predicted using existing tip rate methods, but the quality of such predictions is highly variable and depends on factors that are difficult to evaluate in practical applications.
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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 | 0.005 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".