Global trade‐offs of functional redundancy and functional dispersion for birds and mammals
Bibliographic record
Abstract
Abstract Aim The diversity of birds and mammals is typically described in separate analyses, but species may play similar roles. Here, we develop a comparative trait framework for birds and mammals to provide a global quantification of the similarity of species roles (functional redundancy) and the breadth of roles across taxa (functional dispersion). We predict different contributions of birds and mammals to redundancy and dispersion, and unique geographical patterns of redundancy and dispersion by including both taxa. Location Global. Time period Contemporary. Major taxa studied Birds and mammals. Methods We systematically select, compile and impute the same six traits (i.e., a common currency of traits) across 15,485 bird and mammal species from multiple databases. We use these six traits to compute functional redundancy and functional dispersion for birds and mammals across all 825 terrestrial ecoregions. We then calculate the standardized effect size (SES) of these observed values compared with null expectations, based on a randomization of species composition (i.e., independent of differences in species richness). Results We find that species‐rich regions, such as the Neotropics, have high functional redundancy coupled with low functional dispersion, characterizing a global trade‐off. Thus, in general, as species richness increases, the similarity in species functional roles also increases. We therefore suggest that different processes generate species richness/functional redundancy and functional dispersion, leading to a novel, and generally non‐tropical, distribution of hotspots of high functional dispersion across Madagascar, Eastern Asia and Western USA. Main conclusions We recommend consideration of both the similarity and the breadth of functional roles across species pools, including taxa that may play similar roles. We therefore suggest that functional redundancy, as a means of insurance, and functional dispersion, as an indicator of response diversity, should be evaluated further as conservation objectives.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".