The spatial distribution of tests of conservation interventions does not align with global conservation needs
Bibliographic record
Abstract
Aligning research effort to address major threats to biodiversity is key to bending the curve of biodiversity loss with limited resources. Recent global threat mapping for terrestrial amphibians, birds, and mammals enabled us to assess whether there was alignment between the spatial distributions of key threats (logging, hunting, agriculture, pollution, Invasive Non-Native Species, and climate change) and published tests of conservation interventions addressing these threats (1025 English and 147 non-English language studies from 15 languages collated in the Conservation Evidence database). We ran Generalised Linear Models (GLMs) to determine the key predictors (including socio-economic factors) of the number of studies that test interventions on amphibians, birds, and mammals per country. We found poor spatial alignment between studies and the impact probabilities of threats for each taxon. Studies were distributed across 92 countries with 64 % of all studies conducted in the United States, United Kingdom, Canada, and Germany. Most studies focused on interventions against agricultural threats (60 %, 79 %, and 58 % for amphibians, birds, and mammals, respectively) – next most common were interventions tackling climate change (18 % of bird studies), hunting/trapping (29 % of mammal studies), and logging (20 % of amphibian studies). Countries with the highest threat levels typically had few or no studies, although several countries had both high threat levels and numbers of studies, including: Germany for amphibian interventions against agriculture, New Zealand for bird interventions against invasive species, and Brazil for mammal interventions against hunting. However, our modelling suggested that certain socio-economic factors, not levels of conservation risk posed by threats, were associated with more studies testing interventions. These factors included countries with higher levels of Gross Domestic Product (GDP) (amphibians and birds) and greater levels of government effectiveness (mammals). We call for rapid action to remove barriers to (and incentivise) reporting tests of interventions, such as through specific funding to test conservation actions and a global reporting database in both English and non-English languages to fill evidence gaps in underrepresented regions. Building ‘testing partnerships’ to link Global North and Global South institutions through networks of funders, local community, governmental, non-governmental and research organisations would also help to better coordinate testing of interventions to address conservation needs. This will require international coordination to build capacity for testing interventions and reporting their effects, whilst avoiding parachute science.
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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 | 0.203 | 0.544 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.015 | 0.024 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".