Mycorrhizal networks and complex systems: Contributions of soil ecology science to managing climate change effects in forested ecosystems
Bibliographic record
Abstract
Soil ecology science has contributed a rich understanding of natural soil patterns and processes, and played a role in revolutionizing the management of ecosystems. This basic understanding is also providing a foundation for predicting and managing the consequences of climate change on ecosystems, including their resilience to disturbance, biotic diversity, and carbon and nutrient dynamics. To help address the challenges of climate change, future soil ecology research and management would benefit from a complex systems approach, where network and dynamics systems theory are used to predict plant community and ecosystem responses to disturbance. A reductionist approach to management that ignores networks and system dynamics, by contrast, is destined to contribute to ecological degradation as climate changes. In this paper, I describe mycorrhizal networks as models of biological networks in the interior Douglas-fir forests of British Columbia, and the role they play in carbon flux and regeneration dynamics following disturbance. I propose a conservationist approach for managing forest mycorrhizal networks and hub trees that can facilitate native plant migrations, limit exotic plant invasions, and bolster ecological resilience. Interdisciplinary research that integrates the dynamics of multiple, overlapping networks will help develop management practices that sustain ecosystems in our changing climate. Key words: Climate change, complex systems, mycorrhizal networks, species migrations
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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 | 0.002 |
| 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 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".