Grasping at water: a gap‐oriented approach to bridging shortfalls in freshwater biodiversity conservation
Bibliographic record
Abstract
Freshwater biodiversity is the fastest declining part of the global biota, threatened by multiple stressors including habitat loss and fragmentation, climate change, invasive species, water pollution, and abstraction by humans. A multitude of recent agenda-setting publications have pointed out key objectives and goals for addressing this freshwater biodiversity crisis, but important gaps must be overcome to reach ambitious conservation targets. In this perspective, we complement these high-level papers in freshwater conservation by highlighting important gaps in knowledge, governance, and implementation. This gap-oriented approach is designed to facilitate meaningful action by highlighting missing 'pieces' in the conservation process, and their connection to existing and emerging solutions in the literature. We derive 13 overarching gaps from a conference session and informal synthesis of recent literature in freshwater biodiversity conservation to catalyse research, advocacy, and action to meet freshwater goals for the post-2020 Kunming-Montreal Global Biodiversity Framework (GBF). Key gaps include inventory data on global freshwater biodiversity, collating and mobilizing conservation evidence in practice, improving coordination of ecological governance at scale -including within and across catchments-and navigating trade-offs between economic development, resource consumption, and priorities for freshwater biodiversity. Finally, we apply this gap-oriented approach to key language describing GBF goals for freshwater biodiversity conservation, and point out existing and emerging solutions which may help address important gaps. Major themes that address multiple gaps include the use of Nature-based Solutions and Other Effective Area-based Conservation Measures (OECMs), navigation of water management trade-offs between human and environmental needs, co-production of knowledge with Indigenous and local people and other stakeholders, integration of conservation research and action between aquatic and terrestrial ecosystems, and funding and policy mechanisms to facilitate conservation action and support meaningful monitoring of conservation evidence across hydrological scales.
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 | 0.030 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.015 | 0.048 |
| Scholarly communication | 0.022 | 0.038 |
| Open science | 0.008 | 0.029 |
| Research integrity | 0.012 | 0.013 |
| Insufficient payload (model declined to judge) | 0.010 | 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".