Unifying measures of biodiversity: understanding when richness and phylogenetic diversity should be congruent
Bibliographic record
Abstract
Abstract Aim Biogeographical theory and conservation valuation schemes necessarily involve assessing how biodiversity is distributed through space and ‘biodiversity’ encapsulates many different aspects of biological organization and information. While biogeography may try to explain biodiversity patterns, successful conservation strategies should attempt to maximize different aspects of diversity. Ultimately, diversity patterns are the product of evolutionary history, and research and conservation efforts seek to understand the unequal distribution of evolutionary history. For conservation efforts, results have been inconsistent as to whether species richness ( SR ) provides sufficient surrogacy for evolutionary history. Here, we provide a conceptual framework allowing for the direct comparison of taxonomic richness and phylogenetic diversity ( PD ), both in terms of their mechanistic relationship and the relationship between their spatial distributions. Location Global. Methods We present a framework that relates regional SR , PD , biogeographically weighted evolutionary distinctiveness and biogeographically weighted SR . Further, we use simulations to illustrate how the size of the species pool, topological patterns within the phylogeny and autocorrelation in spatial distributions affect the correlation among metrics. Results In regions that include both recently diversified groups and ancient species poor lineages, large species pools and low spatial autocorrelation, the correlation between biodiversity measures is lower than regions with low richness, balanced phylogenetic trees and high spatial autocorrelation. Main conclusions We can now understand and predict when regional richness and PD should be strongly correlated. This congruency is the product of evolutionary and ecological processes that determine species pool membership and community assembly. Further, in regions where SR is not expected to be congruent with phylogenetic distinctiveness, re‐examining how existing reserve networks protect the multiple aspects of biodiversity is critically important.
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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 | 0.011 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".