Does structural connectivity influence tree species distributions and abundance in a naturally discontinuous tropical forest formation?
Bibliographic record
Abstract
Abstract Aims The study of naturally discontinuous forest systems could help further our understanding of the relative roles of abiotic factors and spatial connectivity in influencing species turnover and plant metacommunity structure, compared to continuous forest formations, where local communities are often arbitrarily defined and where ‘mass effects’ and source–sink dynamics tend to confound the roles of dispersal and environment. Here we studied a tropical montane landscape where old‐growth evergreen forest occurs as patchy formations in a matrix of natural grasslands, to test the influence of environment and connectivity on species turnover and woody plant metacommunity structure. Location Western and southern regions of the Upper Nilgiri Plateau, Western Ghats, southern India. Methods We sampled 85 vegetation plots located across a 600‐km2 landscape, assembled environmental data, constructed contrasting spatial connectivity models, including models for the effects of topography on structural connectivity, and used RDA‐based variation partitioning to assess the relative influence of environment and space on woody plant metacommunity structure. Results Considering several environmental and multi‐scale spatial predictors, we could explain half of the variation in plant community structure. Environmental and habitat factors such as precipitation, temperature seasonality, elevation, fragment size and landscape context play a dominant role, explaining approximately 40% of variation. Spatial predictors based on Euclidean distance performed better than those that accounted for topographic resistance. Spatial predictors accounted for only 9% of the variation in plant metacommunity structure. Conclusion Our results support the species sorting paradigm of metacommunity structure, as abiotic effects and biotic interactions play dominant roles in influencing community structure and species turnover in these old‐growth forests, with a comparatively small influence of spatial connectivity. Effective management of woody species diversity would therefore require conservation of these forests across the range of environmental conditions under which they occur.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".