Protective functions and ecosystem services of global forests in the past quarter-century
Bibliographic record
Abstract
The world’s forests provide fundamental protection of soil and water resources as well as multiple ecosystem services and cultural or spiritual values. We summarized the FRA 2015 data for protective functions and ecosystem services, and analyzed increasing or decreasing trends of protective areas. The global forest area managed for protection of soil and water was 1.002 billion ha as of 2015, which was 25.1% of all global forested areas. Protective forests have increased by 0.181 billion ha over the past 25 years mainly because more countries are now reporting protective forest areas (139 in 2015 vs 114 in 1990). However, average percentage of designated for protective forests did not change significantly from 1990 to 2015. Global forest area managed for ecosystem services is also now at 25.4% of global total forest area and has changed little over the past 25 years. Among the twelve categories of protective forests, flood control, public recreation, and cultural services increased both in terms of percentage of total forest area and the number of reporting countries. Public awareness of the importance of forest resources for functions and services other than production continues to increase as evidenced by the increase of protective forest designations and reporting in many countries. Percentages of total forest area designated for both protective forests and ecosystem services show a dual-peak distribution of numbers of countries concentrated at 0% and 100%. This suggests a socio-economic influence for the designations. We examined five case study countries (Australia, Canada, China, Kenya, and Russia). The most dramatic changes in the past 25 years have been in China where protective forests for soil and water resources increased from about 12% to 28% of forest area. The Russian Federation has also increased percentages of forest area devoted to soil and water resource protection and delivery of ecosystem services. Australia is now reporting in more protective forest categories whereas Kenya and Canada changed little. These five countries have their own classification of forest functions and recalculation methods of reporting for FRA 2015 were different. This demonstrates the difficulty in establishing a universal common designation scheme for multi-functions of forest. Production of more accurate assessments by further improvements in the reporting framework and data quality would help advance the value of FRA as the unique global database for forest functions integrated between forest ecosystems and social sciences.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".