Using old fields for new purposes: ecosystem service outcomes of restoring marginal agricultural land to forests
Bibliographic record
Abstract
Context: Human activities, particularly intensive agriculture, have caused significant environmental degradation, reduced ecosystem diversity, and increased vulnerability to global change. Recent international policies, such as the Global Biodiversity Framework's 30 × 30 target, advocate for nature-based solutions (NbS) such as ecological restoration to address these impacts. In agricultural landscapes, however, there are concerns that restoration may impact food production. Objectives: We investigated how forest restoration, as an NbS, changes the supply of ecosystem services (ES), including potential trade offs with agricultural output. Using the Montérégie region of Québec (southeastern Canada) as a case study, we assessed the influence of restoration extent, spatial configuration, and the original agricultural site conditions on the ES outcomes. Methods: We modeled ES outcomes for seven ES (crop production, maple syrup production, deer hunting, water quality, carbon storage, pollination, and outdoor recreation) under nine scenarios, which varied by total amount of the landscape restored (3.3%, 10.8%, 30%) and initial conditions of the agricultural fields restored (randomly selected, degraded agricultural field, or abandoned agricultural field). Results: Our findings indicate that increasing the amount of land restored enhances provision of most ES, though improvement varied by service. The initial condition of restored sites minimally influences ES outcomes. However, the spatial pattern of restoration plays a significant role in determining ES delivery, as restored sites enhance most ES through spillover effects up to 500 m. Conclusion: This study underscores the potential for combining landscape ecology approaches and ES tools to forecast NbS outcomes and inform landscape planning. Supplementary Information: The online version contains supplementary material available at 10.1007/s10980-025-02121-0.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".