Facets of functional diversity support niche‐based explanations for Australian biodiversity gradients
Bibliographic record
Abstract
Abstract Aim There is widespread support that species richness increases with the available energy of an ecosystem, but the mechanisms underlying this driver of biodiversity patterns remain elusive. We evaluated gradients of functional diversity to test whether the higher species richness of productive, structurally diverse environments is due to a greater range of niches being supported by the abiotic conditions present (environmental filtering), greater availability of biotic resource and habitat niches (more niches) or increasing functional similarity of species (niche packing). Location Australia. Taxon Birds and mammals. Methods We used structural equation modelling to evaluate the relative contributions of climatic harshness (actual evapotranspiration, AET) and the availability of resource (gross primary productivity, GPP) and habitat (tree height) niches on taxonomic richness and functional richness, dispersion and evenness. We performed parallel analyses within 15 bioclimatic zones and continentally to evaluate the scaling of biodiversity gradients and the shifting balance between niche‐based mechanisms along environmental gradients. Results All continental diversity gradients were primarily associated with energy variables, but while species richness of both taxa and all functional diversity measures of bird assemblages increased with AET, mammal functional diversity was more strongly associated with GPP gradients. Results were more variable at the regional scale, but species richness gradients along tree height (birds and mammals) and GPP (mammals) within bioclimatic zones tended not to be paralleled by increases in functional richness or dispersion. Main Conclusions The niche‐based explanations of biodiversity gradients varied in importance with scale, position on environmental gradients and taxonomic group. At the continental extent, bird biodiversity gradients were structured by environmental filtering by climatic harshness, while mammal biodiversity was related to the increasing availability of resource niches with increasing productivity. Niche packing was more prominent at the regional scale, especially in bioclimatic zones where productivity and vegetation height were less limiting, and in mammal assemblages, suggesting that biodiversity patterns scale differently for birds and mammals.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".