Trophic interactions and abiotic factors drive functional and phylogenetic structure of vertebrate herbivore communities across the Arctic tundra biome
Bibliographic record
Abstract
Communities are assembled from species that evolve or colonise a given geographic region, and persist in the face of abiotic conditions and interactions with other species. The evolutionary and colonisation histories of communities are characterised by phylogenetic diversity, while functional diversity is indicative of abiotic and biotic conditions. The relationship between functional and phylogenetic diversity infers whether species functional traits are divergent (differing between related species) or convergent (similar among distantly related species). Biotic interactions and abiotic conditions are known to influence macroecological patterns in species richness, but how functional and phylogenetic diversity of guilds vary with biotic factors, and the relative importance of biotic drivers in relation to geographic and abiotic drivers is unknown. In this study, we test whether geographic, abiotic or biotic factors drive biome‐scale spatial patterns of functional and phylogenetic diversity and functional convergence in vertebrate herbivores across the Arctic tundra biome. We found that functional and phylogenetic diversity both peaked in the western North American Arctic, and that spatial patterns in both were best predicted by trophic interactions, namely vegetation productivity and predator diversity, as well as climatic severity. Our results show that both bottom–up and top–down trophic interactions, as well as winter temperatures, drive the functional and phylogenetic structure of Arctic vertebrate herbivore assemblages. This has implications for changing Arctic ecosystems; under future warming and northward movement of predators potential increases in phylogenetic and functional diversity in vertebrate herbivores may occur. Our study thus demonstrates that trophic interactions can determine large‐scale functional and phylogenetic diversity just as strongly as abiotic conditions.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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 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".