Assessing the effects of spatial contingency and environmental filtering on metacommunity phylogenetics
Bibliographic record
Abstract
Patterns in biodiversity and species coexistence are the result of multiple interacting processes including evolutionary history, trait variation, species interactions, dispersal, environmental variation, and landscape heterogeneity. Exploring patterns of biodiversity across space is perhaps the best integrative method (in contrast to the scarcity of temporal data) to interpret the influence of these multiple and interactive effects in determining community assembly, but it is still underdeveloped. Two emerging fields, metacommunity ecology and community phylogenetics, have been making relevant, though rather independent, progress toward understanding how communities are assembled in space. Our main goals were twofold. First, we described a heuristical framework to merge these two fields into “metacommunity phylogenetics.” The main goal of this framework is to provide a way to think about how niche properties of species arranged across the environment and different spatial scales influence the process of community assembly. Second, we developed an analytical framework to link niche properties based on trait and phylogenetics to environmental and spatial variation. In order to assess the performance of the framework, we used extensive computer simulations of community assembly to show that the procedure is robust under a variety of scenarios.
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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 | 0.009 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".