Extending community ecology to landscapes
Bibliographic record
Abstract
A goal of landscape ecology is to infer processes or constraints that generate spatial pattern in communities and ecosystems. The rich tradition of plant community ecology is now being extended to address spatial pattern in vegetation over large spatial extents. The challenge in this is that vegetation pattern on landscapes is fine-grained, which presents sampling problems for large study areas. Further, spatial autocorrelation in ecological data, coupled with strong patterns of correlation among environmental factors (such as the gradient complexes governed by elevation) make it difficult to make clear inferences about the agents patterning landscape-scale vegetation. Here we review the methods of plant community ecology as extended to landscapes and illustrate the challenges with a case study from Sequoia-Kings Canyon National Park in California’s southern Sierra Nevada. We outline an iterative approach to such studies, with three stages. The first stage is a pilot study to characterize the spatial scaling of environmental factors presumed to be important to vegetation; this stage can often be conducted virtually, using digital terrain data. The second stage is iterative and consists of building a preliminary explanatory model using a combination of ordination, classification, and Mantel tests: all analyses based on the same ecological distance or dissimilarity matrices. This preliminary model is then attacked to find its uncertain or sensitive parts, and these parametric conditions are mapped into geographic space to identify candidate sites for follow-up field studies in the third stage. This approach ensures that the most uncertain aspects of the preliminary model are refined in an efficient manner. As the approach proceeds toward a richer understanding of species-environment relationships and vegetation pattern, a need emerges for new kinds of field studies and novel extensions to existing statistical analyses. We discuss possible extensions of these as a natural consequence of this iterative process of model construction and revision.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".