Effects of land use, cover, and protection on stream and riparian ecosystem services and biodiversity
Bibliographic record
Abstract
Protected areas are an important part of broader landscapes that are often used to preserve biodiversity or natural features. Some argue that protected areas may also help ensure provision of ecosystem services. However, the effect of protection on ecosystem services and whether protection affects the provision of ecosystem services is known only for a few services in a few types of landscapes. We sought to fill this gap by investigating the effect of watershed protection status and land use and land cover on biodiversity and the provision of ecosystem services. We compared the ecosystem services provided in and around streams in 4 watershed types: International Union for Conservation of Nature category II protected forests, unprotected forests, unprotected forests with recent timber harvesting, and unprotected areas with agriculture. We surveyed 28 streams distributed across these watershed types in Quebec, Canada, to quantify provisioning of clean water, carbon storage, recreation, wild foods, habitat quality, and terrestrial and aquatic biodiversity richness and abundance. The quantity and quality of ecosystem services and biodiversity were generally higher in sites with intact forest-whether protected or not-relative to those embedded in production landscapes with forestry or agriculture. Clean-water provision, carbon storage, habitat quality, and tree diversity were significantly higher in and around streams surrounded by forest. Recreation, wild foods, and aquatic biodiversity did not vary among watershed types. Although some services can be provided by both protected and unprotected areas, protection status may help secure the continued supply of services sensitive to changes in land use or land cover. Our findings provide needed information about the ecosystem service and biodiversity trade-offs and synergies that result from developing a watershed or from protecting it.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".