Synthesizing the scientific evidence to inform the development of the post-2020 Global Framework on Biodiversity:Earth Commission Meeting Report to the Convention on Biological Diversity. Subsidiary Body on Scientific, Technical and Technological Advice, Convention on Biological Diversity, 24th meeting Montreal, Canada 17-22 August 2020
Bibliographic record
Abstract
This report is the result of a meeting which aimed to offer scientific guidance to the development under the Convention on Biological Diversity (CBD) of the post-2020 Global Biodiversity Framework focussing on its contribution to the 2030 Mission and 2050 Vision.We provide a synthesis of the scientific and technical justification, evidence base and feasibility for outcome-oriented goals on nature and its contributions to people, including biodiversity at different levels from genes to biomes.The report is structured to respond to the Zero Draft of the post-2020 Global Biodiversity Framework. We commend: The focus of the 5 high-level goals on the conservation of nature (Goals a-c), its sustained provision of benefits to people (Goal d) and fair and equitable sharing of benefits (Goal e); The focus, at this high level, on outcomes (results to be achieved) for nature and people; The focus on different facets of nature (or levels of organization within biodiversity): ecosystems, species and genetic diversity within species, each of them receiving the same level of importance.We stress: That these goals cannot be fully achieved in isolation.Rather, each of them contributes synergistically to the achievement of the others. That condensing the goals into fewer more compound goals would risk obscuring the multidimensionality of living nature and the complementarity of the outcome goals in achieving the long-term vision of the CBD. The need to consider all ecosystems under the double perspective of conserving nature and ensuring the long-term provision of benefits to people."Natural" ecosystems provide essential benefits to people.At the same time, "managed" ecosystems should not be considered as "lost for nature"; they are places where certain functions of nature are managed to provide specific benefits, Ecosystems (Goal a): No additional loss of critical ecosystems.No net loss by 2030 in both the area and integrity of all "natural" ecosystems compared to 2020, and increases of at least 20% in the area and integrity of "natural" ecosystems by 2050.No net loss of integrity of "managed" ecosystems by 2030, and net gain by 2050.Critical elements: Take 2020 as reference year for evaluating no net loss, achieving no net loss between 2020 and 2030. Ensure achieving no loss of critical ecosystems, i.e., ecosystems that are rare, vulnerable or essential for planetary function. Ensure like-for-like compensation by having a clear ecosystem definition and no substitution between different ecosystems. Aim for no net loss of both area and integrity in "natural" ecosystems and no net loss of integrity of "managed" ecosystems by 2030.Integrity of "managed" areas should be increased by 2050 to ensure recovery of nature's contributions to people. Maintain a restoration ambition as part of the goals ("net gain in area and integrity") with implementation through integrated planning to optimize benefits for nature and people. Species (Goal b):Species extinction rate and extinction risk are reduced progressively by 2030 and 2050, across the whole Tree of Life, and the local abundance and distributional extent of key functional species and threatened species is stabilized by 2030 and recovered by 2050.Critical elements: Reduce the rate of extinction progressively. Minimize the loss of evolutionary history, recognizing that species are not equal in this respect. Focus on threatened species to 2030 to prioritize species needing urgent attention, but for 2050, reduce extinction risk across all species, not just the most threatened. Re-establish population abundance within local ecological communities, rather than increasing total population abundance overall, prioritizing species with key functional roles. Include a qualitative statement about retention and eventual recovery of a natural distributional extent of species. Genes (Goal c):By 2030, genetic erosion of all wild and domesticated species is halted and, by 2050, the genetic diversity of populations is restored [to XX%] and their adaptive capacity is safeguarded.Only the highest level of ambition and the consideration of all goals in a synergistic manner are sufficient to achieve the CBD's 2050 Vision.
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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 | 0.054 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.014 | 0.014 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".