Unravelling the evolutionary origins of biogeographic assemblages
Bibliographic record
Abstract
Abstract Aim Floristic and faunal diversity fall within species assemblages that can be grouped into distinct biomes or ecoregions. Understanding the origins of such biogeographic assemblages helps illuminate the processes shaping present‐day diversity patterns and identifies regions with unique or distinct histories. While the fossil record is often sparse, dated phylogenies can provide a window into the evolutionary past of these regions. Here, we present a novel phylogenetic approach to investigate the evolutionary origins of present‐day biogeographic assemblages and highlight their conservation value. Location Southern Africa. Methods We evaluate the evolutionary turnover separating species clusters in space at different time slices to determine the phylogenetic depth at which the signal for their present‐day structure emerges. We suggest present‐day assemblages with distinct evolutionary histories might represent important units for conservation. We apply our method to the vegetation of southern Africa using a dated phylogeny of the woody flora of the region and explore how the evolutionary history of vegetation types compares to common conservation currencies, including species richness, endemism and threat. Results We show the differentiation of most present‐day vegetation types can be traced back to evolutionary splits in the Miocene. The woody flora of the Fynbos is the most evolutionarily distinct, and thus has deeper evolutionary roots, whereas the Savanna and Miombo Woodland show close phylogenetic affinities and likely represent a more recent separation. However, evolutionarily distinct phyloregions do not necessarily capture the most unique phylogenetic diversity, nor are they the most species‐rich or threatened. Main conclusions Our approach complements analyses of the fossil record and serves as a link to the history of diversification, migration and extinction of lineages within biogeographic assemblages that is separate from patterns of species richness and endemism. Our analysis reveals how phyloregions capture conservation value not represented by traditional biodiversity metrics.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".