Disentangling vegetation and climate as drivers of Australian vertebrate richness
Bibliographic record
Abstract
Determining drivers of species richness is recognised as highly complex, involving many synergies and interactions. We examine the utility of newly available remote sensing representations of vegetation productivity and vegetation structure to examine drivers of species richness at continental and regional scales. We related richness estimates derived from stacked species distribution models for birds, mammals, amphibians, and reptiles to estimates of actual and potential evapotranspiration (AET and PET), forest structure, and forest productivity across Australia as a whole as well as by bioclimatic zones. We used structural equation modeling to partition correlations between climate energy and vegetation attributes and their subsequent associations with species richness. Continentally, vertebrate richness patterns were strongly related to patterns of energy availability. Richness of amphibians, mammals, and birds were positively associated with AET. However, reptile richness was most strongly associated with PET. Regionally, forest structure and productivity associations with bird, mammal, and amphibian richness were strongest. Again, reptile richness associated most strongly with PET. Our results suggest that a hierarchy of drivers of broad‐scale vertebrate richness patterns exist (reptiles excluded): 1) climate energy is most important at the continental scale; next, 2) vegetation productivity and vegetation structure are most important at the regional scale; except 3) at low extremes of climate energy when energy becomes limiting.
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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 | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".