Community phylogenetics of forest trees along an elevational gradient in the eastern Himalayan region of northeast India
Bibliographic record
Abstract
Large-scale environmental gradients have been invaluable for unravelling the processes shaping the evolution and maintenance of biodiversity. Gradients provide a natural setting to test theories about species diversity and distributions within a landscape with changing biotic and abiotic interactions. Elevational gradients are particularly useful because they often have an extensive climatic range within a constricted geographic region. Arunachal Pradesh is the northeastern-most province in India, located on the southern face of the eastern Himalayas. This region is considered a biodiversity “hotspot”, with an estimated 6000 flowering plant species of which 30-40% are endemic. For this thesis, I analyzed tree communities in plots distributed throughout the province using both species and phylogenetic diversity indices. I explored shifts in community structure across elevation and space as well as the biotic and abiotic forces influencing species assembly throughout the landscape. Species richness and phylogenetic diversity decreased with increasing elevation, as theory predicts. However, species relatedness did not show a clear pattern with elevation. Nonetheless, by exploring beta-diversity (both taxonomic and phylogenetic), I was able to show a strong effect of environmental filtering with elevation. Environmental filtering is generally associated with species clustering on the phylogeny, where co-occurring species in a community are more closely related than expected by chance. Here, however, I suggest that forest community structure is driven by filtering on glacial relicts, resulting in random or over-dispersed community assemblages. These patterns point to possible regions for conservation priority that may provide refugia for species threatened by current warming trends.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".