Forest and Biodiversity 2: a tree diversity experiment to understand the consequences of multiple dimensions of diversity and composition for long-term ecosystem function and resilience
Bibliographic record
Abstract
Abstract We introduce a new “low-density” tree diversity experiment at the Cedar Creek Ecosystem Science Reserve in central Minnesota, USA aimed at testing long-term ecosystem consequences of tree diversity and composition. The experiment was designed to provide guidance on forest restoration efforts that will simultaneously advance carbon sequestration goals and contribute to biodiversity conservation and sustainability. The new Forest and Biodiversity (FAB2) experiment uses native tree species in varying levels of species richness, phylogenetic diversity, and functional diversity to test the mechanisms and processes that contribute to forest stability and ecosystem productivity in the face of global change. FAB2 was designed and established in conjunction with a prior experiment (FAB1) in which the same set of twelve species was planted in 16 m 2 plots at high density (0.5 m spacing). In addition to lower density plantings (1 m spacing), FAB2 also has larger plots (100 m 2 and 400 m 2 ) appropriate for testing long-term ecosystem consequences of species composition and diversity. Within the first six years, mortality in the 400 m 2 monoculture plots was significantly higher than in the 100 m 2 plots. The highest mortality among any treatments occurred in Tilia americana and Acer negundo monocultures, but mortality for both species decreased with increasing plot diversity. These results highlight the importance of forest diversity in reducing mortality in some species and point to potential mechanisms, including light and drought stress, that cause tree mortality in vulnerable monocultures. The experiment highlights challenges to maintaining monoculture and low-diversity treatments in tree mixture experiments of large extent. The FAB2 experiment provides a long-term platform for discerning the importance of species and lineage effects and of multiple dimensions of diversity in restoring ecosystem functions and services provided by forests. It also provides a platform for improving remote sensing approaches, including Uncrewed Aerial Vehicles (UAVs) equipped with LiDAR, multispectral, and hyperspectral sensors, to complement ground-based monitoring of forest function and diversity. We aim for the experiment to contribute to international efforts to monitor and manage forests in the face of global change.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".