The macroecology of spines on woody plants
Bibliographic record
Abstract
Spines are a major ecological innovation supporting plant defence and diversification. Spine anatomy is diverse, having arisen in multiple plant lineages from many different plant organs and parts, which may differ in relative advantages across environmental gradients. Systematic analyses of the correlates of spiny plant diversity are limited, but climate and soil properties may be important. We analysed spatial patterns of the proportional richness of spiny woody plant species (fraction of total woody species richness) and the proportional richness of species with particular spine types (fraction of richness of spiny plants) across three regions with high plant geolocational data density spanning three continents, China (Asia), South Africa (Africa), and Australia. Spiny plants accounted for 12% of woody species, but there are strong phylogenetic biases in the evolution of spiny lineages and lineages bearing different spine types. The proportion of spiny plants increased towards drier environments and higher soil clay contents, and decreased towards soils with greater total N. Species bearing different spine types appear to be distributed differently across climate and soil gradients, suggesting trade-offs across productivity gradients, specialization for climate space, and constraints on environmental adaptability. The spatial richness of spiny plants was positively correlated with estimated historical richness of large herbivorous mammals (body mass >20 kg, diet >90% plant material), and species bearing different spine types also mostly show positive relationships with mammal richness. Plants with spines appear to be advantaged over non-spiny species when exposed to high mammal browsing pressure in arid environments or over certain soil conditions, and species bearing different spine types are differentially advantaged across climate and soil gradients.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".