Plant–soil feedbacks: the past, the present and future challenges
Bibliographic record
Abstract
Summary Plant–soil feedbacks is becoming an important concept for explaining vegetation dynamics, the invasiveness of introduced exotic species in new habitats and how terrestrial ecosystems respond to global land use and climate change. Using a new conceptual model, we show how critical alterations in plant–soil feedback interactions can change the assemblage of plant communities. We highlight recent advances, define terms and identify future challenges in this area of research and discuss how variations in strengths and directions of plant–soil feedbacks can explain succession, invasion, response to climate warming and diversity‐productivity relationships. While there has been a rapid increase in understanding the biological, chemical and physical mechanisms and their interdependencies underlying plant–soil feedback interactions, further progress is to be expected from applying new experimental techniques and technologies, linking empirical studies to modelling and field‐based studies that can include plant–soil feedback interactions on longer time scales that also include long‐term processes such as litter decomposition and mineralization. Significant progress has also been made in analysing consequences of plant–soil feedbacks for biodiversity‐functioning relationships, plant fitness and selection. To further integrate plant–soil feedbacks into ecological theory, it will be important to determine where and how observed patterns may be generalized, and how they may influence evolution. Synthesis . Gaining a greater understanding of plant–soil feedbacks and underlying mechanisms is improving our ability to predict consequences of these interactions for plant community composition and productivity under a variety of conditions. Future research will enable better prediction and mitigation of the consequences of human‐induced global changes, improve efforts of restoration and conservation and promote sustainable provision of ecosystem services in a rapidly changing world.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".