The impacts of multiple anthropogenic environmental drivers on plant–soil feedbacks: A systematic review protocol
Bibliographic record
Abstract
Abstract Plant–soil feedbacks (PSFs) regulate plant growth, plant community dynamics and ecosystem functioning and are important for global biogeochemical cycles. However, human activities and their associated impacts on the environment can alter the strength and direction of PSFs, but these effects, and especially the interactions among human impacts, are poorly understood. In urbanised and other human‐modified landscapes, anthropogenic sources of change are more varied and pronounced, resulting in a myriad of biotic and abiotic human‐caused drivers simultaneously affecting ecological processes across multiple scales. These anthropogenic environmental drivers can have severe consequences for the delivery of ecosystem services in urbanised areas and beyond. Here, we systematically review the literature on the impacts of environmental drivers on PSFs to address the question: how do multiple anthropogenic drivers impact PSFs? Further, we will determine the dominant and interactive drivers of changes to PSFs across 21 potential anthropogenically influenced environmental drivers and assess the relative importance of biotic and abiotic drivers. We will assess how these drivers shape the plant and soil microbial communities involved in PSFs to determine their scale and directionality. We will also outline research gaps to guide future studies on PSFs in anthropogenically impacted ecosystems and especially urban environments. Besides extracting key variables, such as the range of values of the driver and impacts on plant growth or microbial diversity from reviewed articles, we will also determine how attributes of the studies themselves, such as location or duration of studies, influence the strength of findings. Practical implication : This work will be crucial to understand not only human impacts on ecosystems, but also developing mitigation and management solutions to reduce the negative consequences of altered PSF, and so can be instrumental for managing ecosystem services in human‐dominated landscapes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".