Quaternary climate change explains global patterns of tree beta-diversity
Bibliographic record
Abstract
Abstract Both historical and contemporary environmental conditions determine present biodiversity patterns, but their relative importance is not well understood. One way to disentangle their relative effects is to assess how different dimensions of beta-diversity relate to past climatic changes, i.e., taxonomic, phylogenetic and functional compositional dissimilarity, and their components generated by replacement of species, lineages and traits (turnover) and richness changes (nestedness). Here, we quantify global patterns of each of these aspects of beta-diversity among neighboring sites for angiosperm trees using the most extensive global database of tree species-distributions (43,635 species). We found that temperature change since the Last Glacial Maximum (LGM) was the major influence on both turnover and nestedness components of beta-diversity, with a negative correlation to turnover and a positive correlation to nestedness. Moreover, phylogenetic and functional nestedness was higher than expected from taxonomic beta-diversity in regions that experienced large temperature changes since the LGM. This pattern reflects relatively greater losses of phylogenetic and functional diversity in species-poor assemblages, possibly caused by phylogenetically and functionally selective species extinction and recolonization during glacial-interglacial oscillations. Our results send a strong warning that rapid anthropogenic climate change is likely to result in a long-lasting phylogenetic and functional compositional simplification, potentially impairing forest ecosystem functioning.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".