The relative importance of COVID‐19 pandemic impacts on biodiversity conservation globally
Bibliographic record
Abstract
The COVID-19 pandemic has had an enormous impact on almost all aspects of human society and endeavor; the natural world and its conservation have not been spared. Through a process of expert consultation, we identified and categorized, into 19 themes and 70 subthemes, the ways in which biodiversity and its conservation have been or could be affected by the pandemic globally. Nearly 60% of the effects have been broadly negative. Subsequently, we created a compendium of all themes and subthemes, each with explanatory text, and in August 2020 a diverse group of experienced conservationists with expertise from across sectors and geographies assessed each subtheme for its likely impact on biodiversity conservation globally. The 9 subthemes ranked highest all have a negative impact. These were, in rank order, governments sidelining the environment during their economic recovery, reduced wildlife-based tourism income, increased habitat destruction, reduced government funding, increased plastic and other solid waste pollution, weakening of nature-friendly regulations and their enforcement, increased illegal harvest of wild animals, reduced philanthropy, and threats to survival of conservation organizations. In combination, these impacts present a worrying future of increased threats to biodiversity conservation but reduced capacity to counter them. The highest ranking positive impact, at 10, was the beneficial impact of wildlife-trade restrictions. More optimistically, among impacts ranked 11-20, 6 were positive and 4 were negative. We hope our assessment will draw attention to the impacts of the pandemic and, thus, improve the conservation community's ability to respond to such threats in the future.
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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 | 0.014 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".