Monitor indicators of genetic diversity from space using Earth Observation data
Bibliographic record
Abstract
Use Earth Observation (EO) for monitoring and re-porting on the Kunming-Montreal Global BiodiversityFramework (GBF) indicators of genetic diversity. EOhas high potential and practical importance for advancingbiodiversity monitoring within the GBF. We proposethat these advances are artificially limited by the consensusthat genetic variation cannot be observed from space.Here, we explain how EO can also advance genetic diversitymonitoring within the GBF, especially by helping to developthe headline and complimentary indicators of genetic diver-sity recently adopted at the 15th Conference of the Partiesto the CBD (COP15). Ahead of the 2024 COP and the pre-ceding meeting of its Subsidiary Body on Scientific, Tech-nical and Technological Advice (SBSTTA), we propose thatEO should be rapidly integrated into genetic diversity moni-toring workflows, to accelerate the ongoing development ofthese indicators while helping Parties to fulfill their reportingcommitments.The genetic variation distributed across the individuals andpopulations of Earth’s species is essential for their adapta-tion and persistence in changing environments, and for themaintenance of biodiversity. Its importance is recog-nized within the monitoring framework of the GBF adoptedat COP15, which includes a headline indicator on themaintenance of genetic diversity in species populations. Yetdespite rapid advances in sequencing technology, it remainslaborious and expensive to monitor changes in genetic diver-sity by repeatedly sampling populations and sequencing theirDNA. Fortunately, the COP15 headline indicator and otherkey indicators of genetic diversity can be assessed based oninformation about species populations inferred from localknowledge, field surveys, and other sources, and do not nec-essarily require genetic sequence data. This rep-resents a useful but indirect means of genetic diversity assess-ment, and additional biodiversity observation data is neededto improve indicator quality. Here, we present a frame-work and show examples for how existing, public data fromEO satellites can provide complementary biodiversity obser-vations that could immediately be used to improve monitor-ing and reporting on indicators of genetic diversity.EO is generally not considered for genetic diversity assess-ment because genetic information cannot be retrieved eas-ily or directly from satellite observations. However, EOproducts can directly help countries to locate and delineatespecies populations, and monitor their change over time. Wenot only show how EO can facilitate genetic diversity mon-itoring as implemented within the GBF, but also look aheadto potential EO contributions in the assessment of genetic Es-sential Biodiversity Variables (EBVs). We call for the advis-ing of Parties on how to use existing EO products for geneticdiversity monitoring and for the co-development and dissem-ination of accessible tools.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".