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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".