A proposed microcosm for landscape ecology – beyond the binary to the patch-mosaic model
Bibliographic record
Abstract
Abstract Background Microcosms such as pitcher plants, or patches of mosses on a rock surface, have been used worldwide to allow for manipulative experiments that test hypotheses for patterns observed at larger extents, such as dispersal or community assemblage. Such microcosms can also be applied to questions in landscape ecology, but are limited by their binary (patch/non-patch structure). Here we examine a more realistic model landscape system that shares the patch-mosaic structure common to kilometres-extent landscapes. This system of lichen thalli on tree trunks has been shown to have consistent spatial patterns across replicate microcosms, but only when sampling within a limited area. To be relevant for experimentation across scales, it is necessary to determine whether previously observed patterns are consistent when sampling across a broader region and when using different tree species. Here, we test for consistent landscape patch pattern in both maco- and micro-lichens across 21 balsam fir ( Abies balsamea ) and yellow birch ( Betula alleghaniensis ) trees. Methods We measured spatial pattern of lichen thalli along the trunks of two species of trees, at two spatial resolutions; trees within a single stand (∼100 × 50 m) and trees dispersed across a larger region (500 km 2 ). We used a “lichen ladder” comprised of 5 10 × 10 cm sampling blocks to quantify number of species and individuals in a 50 cm section of the tree trunk. We tested for similar patterns along the trunk and between the north and south sides at both sampling intensities using perMANOVA. Results We find that lichen patches on tree trunks can function as replicate microscoms for landscape ecology. Patterns of thalli along the trunks of trees and between the north and south aspects of the trunk are statistically significantly consistent, although there is variation between tree species, and groups of lichens included. Our microcosm could be used as a model system for landscape ecology research; but researchers should test for consistent patterns first.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".