How much space is needed for biodiversity conservation?
Bibliographic record
Abstract
The EU's 2030 Biodiversity Strategy and the Kunming-Montreal Global Biodiversity Framework call for 30% coverage of land and sea protected areas and strict protection for 10% of land area to prevent and reverse biodiversity loss. Ukraine has declared its aspiration to integrate into the European Union and must back up its statements with action and do everything to achieve such ambitious conservation goals. Like many European countries, Ukraine faces great challenges on this route. The significant level of anthropogenic transformation of the territories makes it very difficult to find new areas to expand the boundaries of the nature reserve fund. The practical steps to create nature reserves are significantly limited by legal mechanisms that guarantee land users' ownership of the relevant land plots. An important task in nature conservation is to develop indicators that can clearly and easily demonstrate the importance of areas for conservation. Such tools are necessary to convince policy makers and land users of the need to protect the relevant areas. The indicators of importance for biodiversity conservation should be scale-independent, as both large areas and small areas are important for conservation. In this article, we consider the case of a project to expand the boundaries of the Dnipro-Orylskiy Nature Reserve by adding five areas directly adjacent to it. The number of species included in the various Red Data Lists was chosen as an indicator of the conservation value of the territory. The species-area relationship was used to assess the role of scale. The number of species on the Red Lists was considered instead of the classical relationship that considers the total number of species in a community. The normalised deviation from the regression relationship was considered as an indicator of the conservation value of the respective area, which is statistically independent of the area of the site. The different Red Lists are compiled according to different criteria, so the indicator of conservation value for each Red List focuses on the relevant conservation aspect. The results of the conservation value assessment can be presented graphically, which clearly demonstrates the role of the respective areas in the maintenance of biological diversity. The proposed algorithm for assessing conservation value can be applied to a wide range of environmental protection tasks. In terms of further research, it is important to assess the role of ecosystem function assessment in the design of protected areas.
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 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".