The impact of non‐reproductive plant species on assessments of community structure and species co‐occurrence patterns
Bibliographic record
Abstract
Abstract Aims Studies of community structure and co‐occurrence patterns rely on the premise that community data reflect where species successfully grow and which species they grow with. However, plant censuses generally do not distinguish between species with reproductive individuals and those only represented by non‐reproductive individuals. We tested whether inclusion of non‐reproductive species, which may not reflect success in that location, significantly impacts evaluations of community structure and co‐occurrence. Location Queen's University Biological Station, Ontario, Canada, old‐field plant communities. Methods We quantified the impact of non‐reproductive species in two plant communities by comparing community structure and co‐occurrence patterns when non‐reproductive species were included or excluded. Results Including non‐reproductive species significantly increased plot‐level species richness in both communities (54% and 13% increases), altered species evenness in both communities, significantly impacted beta‐diversity among plots in one site, and disproportionately impacted assessments of diversity in species‐rich plots. Excluding non‐reproductive species resulted in reduced negative co‐occurrence patterns in both communities, with a substantially larger impact in one community. In that community, the impact of non‐reproductive species was even more pronounced when abundance data were used in analysis, and when pair‐wise co‐occurrence patterns were assessed. Additionally, including non‐reproductive species drastically decreased the number of species pairs with perfect negative co‐occurrence across sites, indicating that these species can add ‘noise’ to co‐occurrence patterns. We examined possible explanations for the presence of non‐reproductive species. In one community, non‐reproductive species were 22 times less abundant (per plot) than reproductive species within plots, although they were not rare overall. Differences in the number of non‐reproductive species per plot across our focal communities were not clearly driven by differences in clonality, stress from extreme weather or low N. While these patterns are consistent with the interpretation that non‐reproductive species are present due to mass effects, this possibility requires further research. Conclusions Including non‐reproductive plant species in censuses can significantly impact assessments of community structure and species co‐occurrence. The divergent impact of their inclusion on our two communities highlights the possibility that excluding non‐reproductive species from surveys may remove noise from community data and clarify theories of plant species co‐existence.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".