Is it common to be rare on the landscape? A test using a novel model system
Bibliographic record
Abstract
Understanding how rare species are distributed can be difficult due to heterogeneity between landscape units. Lack of statistical replication of landscapes can make it difficult to carry out testing. Model systems may be a solution. We test whether lichen thalli along the trunk of a tree are analogous to habitat patches in a kilometers-extent landscape and hence can function as a model system. This model system allows for increased statistical power. We use this system to test whether landscapes with rare species are different from those without. We sampled macrolichen diversity along the trunk of 24 balsam fir trees in a stand on the Avalon Peninsula, Newfoundland, Canada, along with microclimate variables. We analysed difference in pattern by aspect and along the gradient of 1 m up the trunk as well as between trees containing the rare Erioderma pedicellatum and those without. We found no difference in total patch richness or abundance between the micro-landscapes. We found significantly consistent patterns in lichen patches along the trunk. These patterns were similar on the trees with the rare species. Lichen species richness did not differ between trees containing the rare species versus those that did not. Lichen patch pattern is statistically similar between trees and as such, these can be considered as replicate landscape units. Thus, landscape ecologists can use micro-landscapes as model systems to conduct observational and manipulative experiments to test questions about spatial pattern and process, such as those concerning distribution of rare species.
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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 | 0.013 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".