Integrating Biodiversity into the State Factor Framework of Ecosystem Ecology: An Artist's Rendition
Bibliographic record
Abstract
Research in the field of Biodiversity and Ecosystem Functioning has shown species diversity to be a major driver of ecosystem properties, including carbon sequestration in plants and soils. However, most of this research has occurred in small, carefully controlled experimental plots that have not assessed effects of abiotic ecosystem controls such as climate and topography. To date, fewer studies have examined the effects of biodiversity on ecosystem processes at the whole-landscape scale. Because of this, some ecosystem ecologists question biodiversity as a major contributor to ecosystem processes, instead identifying species functional traits and abiotic factors as the primary significant ecosystem drivers. In the article, "Ecosystem context illuminates conflicting roles of plant diversity in carbon storage" (Adair et al., in review), the authors examine how plant diversity, plant functional traits, and abiotic factors together affect carbon sequestration across temperate and boreal forests in Québec. They combine plot-scale measurements across broad spatial scales with structural equation modeling to identify direct and indirect effects of these different controls. This poster seeks to provide a concise visual depiction of the processes involved in carbon sequestration in the landscape, and illustrate the main findings of the paper in a format accessible to a general audience. Specifically, it shows biodiversity included under the umbrella of "biotic factors" in the State Factor Framework commonly used in ecosystem ecology, and discusses the ways in which plant diversity influenced carbon pools in these forests. I aim to make this poster simple enough to be accessible to a layperson, but with enough detailed information drawn from the Adair et al. article to serve as a useful tool in understanding the results of the paper for scientists who already have a working knowledge of ecosystem processes and carbon cycling.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".