The use of regional phylogenies in exploring the structure of plant assemblages
Bibliographic record
Abstract
Unravelling the complexities of biological communities has long interested community ecologists. Molecular, computational and conceptual advances in the last fifteen years have led to the increasing incorporation of phylogenetic information into ecological analyses, creating a new field of 'community phylogenetics'. Although the field of community phylogenetics has improved our understanding of the processes determining community composition, more recently the field has been challenged because of its reliance on several critical assumptions. In this thesis, I address some of these critiques by exploring novel empirical and analytical approaches. In Chapter 2, I describe the major challenges to sampling a comprehensive species pool, essential for generating regional phylogenies and testing community assembly mechanisms. I use a case study based on my work at Mont St. Hilaire, Québec to illustrate these challenges, and I make several recommendations for sampling that can be used by other researchers pursuing similar efforts in the future. I switch focus from regional species pools to plant communities in Chapters 3 and 4, where I combine co-occurrence and phylogenetic data on sedges (i.e. plants of the Cyperaceae family) near Schefferville, Québec to address assumptions related to species coexistence. I use an individual-based focal sampling approach in Chapter 3 to show that the phylogenetic neighbourhood of a plant varies with the clade membership of the species, addressing the critique that it is individuals of a species and not communities that are 'filtered' by environment. I explore the assumption that competitive exclusion is most probable between closely related species in Chapter 4 by examining whether specialists are better competitors than generalists. My results suggest that niche width differences translate into differences in competitive abilities and that co-occurrence is more likely between distantly related species with differing niche widths, adding further insights into the relationship between phylogenetic relatedness and competitive abilities. Finally, in Chapter 5 I evaluate the effectiveness of phylogenetic beta diversity methods for delineating ecological boundaries. Previous work has focused on regional and global scales, whereas similar methods might not be as effective at differentiating local scale patches. As I demonstrate in this thesis, the field of community phylogenetics will remain relevant for unravelling the complexities of biological communities by using novel approaches.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".