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Record W4411508505 · doi:10.1016/j.biocon.2025.111313

The spatial distribution of tests of conservation interventions does not align with global conservation needs

2025· article· en· W4411508505 on OpenAlexaffabout
Anindita Anjan, Jonas Geldmann, Mike Harfoot

Bibliographic record

VenueBiological Conservation · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsRegent College
FundersDanmarks Frie ForskningsfondImperial College LondonCambridge Philosophical Society
KeywordsDistribution (mathematics)Nature ConservationGeographyEnvironmental resource managementSpatial distributionEnvironmental planningEnvironmental scienceEcologyBiologyRemote sensingMathematics

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.033
Threshold uncertainty score0.432

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.023
GPT teacher head0.263
Teacher spread0.240 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes2
Has abstractyes

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