Contribution of biosphere reserves to global biodiversity conservation and climate change
Bibliographic record
Abstract
Biosphere Reserves are areas recognized by UNESCO that represent key terrestrial, coastal, and marine ecosystems. These areas are strictly protected and designed to fulfill three primary functions: conserving biodiversity, promoting sustainable development, and supporting scientific research and education. As of now, 759 biosphere reserves have been established in 136 countries and regions worldwide, supporting the livelihoods of approximately 236 million people. The total area of biosphere reserves covers 7.67 million km2, accounting for 4.10% of global terrestrial area, 5.08% of freshwater surface, and 0.21% of marine areas. The reserves encompass diverse ecosystems, including 2.22 million km2 of forests, 2.24 million km² of grasslands, 0.34 million km2 of wetlands, 0.64 million km2 of deserts, and 1.17 million km2 of aquatic systems. According to species occurrence records from the GBIF database (2010-2024), global biosphere reserves harbor 96 147 plant species (representing 45.10% of all plant species recorded in the database) and 17 603 vertebrate species (covering 56.39% of the global total). Notably, these include 3 351 threatened plant species (29.88%) and 2 551 threatened vertebrate species (36.78%). Global biosphere reserves collectively function as a substantial carbon sink, with an estimated total capacity of 1.35×1015 g C and an average annual sequestration rate of 205.02 g C/m2·yr. These reserves thus play a dual vital role in maintaining global biodiversity and mitigating climate change impacts. However, biosphere reserves face several challenges, including the under-representation of globally threatened plant species, loss of natural ecosystems, and gaps in biodiversity data. Moving forward, it is essential to enhance the role of biosphere reserves in biodiversity protection, promote green development pathways, and strengthen their contributions to local socioeconomic development. Biosphere reserves should serve as pioneering zones and demonstration areas for implementing the Kunming-Montreal Global Biodiversity Framework and achieving the UN Sustainable Development Goals, making meaningful contributions to global biodiversity conservation and sustainable development.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".