Synthesizing tree biodiversity data to understand global patterns and processes of vegetation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Aims Trees dominate the biomass in many ecosystems and are essential for ecosystem functioning and human well‐being. They are also one of the best‐studied functional groups of plants, with vast amounts of biodiversity data available in scattered sources. We here aim to illustrate that an efficient integration of these data could produce a more holistic understanding of vegetation. Methods To assess the extent of potential data integration, we use key databases of plant biodiversity to: (a) obtain a list of tree species and their distributions; (b) identify coverage of and gaps in different aspects of tree biodiversity data; and (c) discuss large‐scale patterns of tree biodiversity in relation to vegetation. Results Our global list of trees included 58,044 species. Taxonomic coverage varies in three key databases, with data on the distribution, functional traits, and molecular sequences for about 84%, 45% and 44% of all tree species, which is >10% greater than for plants overall. For 28% of all tree species, data are available in all three databases. However, less data are digitally accessible about the demography, ecological interactions, and socio‐economic role of tree species. Integrating and imputing existing tree biodiversity data, mobilization of non‐digitized resources and targeted data collection, especially in tropical countries, could help closing some of the remaining data gaps. Conclusions Due to their key ecosystem roles and having large amounts of accessible data, trees are a good model group for understanding vegetation patterns. Indeed, tree biodiversity data are already beginning to elucidate the community dynamics, functional diversity, evolutionary history and ecological interactions of vegetation, with great potential for future applications. An interoperable and openly accessible framework linking various databases would greatly benefit future macroecological studies and should be linked to a platform that makes information readily accessible to end users in biodiversity conservation and management.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it