The application gap: Genomics for biodiversity and ecosystem service management
Bibliographic record
Abstract
The conservation of biodiversity from the genetic to the community levels is fundamental for the continual provision of ecosystem services (ES), the benefits that ecosystems provide to people. Genetic and genomic diversity enhance the resilience of populations and communities that underpin the provision of ecosystem functions and services. We show that genomics applications are mostly limited to flagship species and that their benefits for biodiversity conservation and ES management are underachieved. We propose a framework on how genomics applications can guide management for biodiversity conservation and sustainable ES to bridge this genomics-ES management ‘application gap’. We review how genomic knowledge in single species (relatedness, potentially adaptive variants) or in interacting species (host-microorganism coevolution, hybridization) can guide effective management actions. These include population supplementation, assisted migration or hybridization to promote climate-adapted variants or adaptive potential, control of invasives, delimitation of conservation or management areas, provenancing strategies for restoration, managing microbial function and solving conservation and ES trade-offs. Genomics-informed management actions for improved conservation and ES outcomes are supported through synergies between scientists and ES managers at local, regional and international levels, through the development of standardized genomic workflows, training to ES managers and incorporation of local information. Such actions facilitate the implementation of biodiversity conservation and ES policies such as the UN 2030 sustainable development goals and the EU Biodiversity strategy for 2030, and support the inclusion of ambitious biodiversity conservation goals in the development of new policies such as the CBD post-2020 Global Biodiversity Framework or conservation policies on hybrids.
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 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 | 0.019 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.008 | 0.018 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.012 | 0.013 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".