Scientific and technical innovations to revolutionize knowledge production for the conservation and management of migratory and straddling biodiversity
Bibliographic record
Abstract
Migratory species and straddling populations, including highly mobile fish, elasmobranchs, sea turtles, seabirds, and marine mammals, are increasingly impacted by overexploitation, fisheries bycatch, habitat degradation, and climate change. Conserving these species and sustainably managing fisheries and other human activities present one of the most complex challenges in contemporaneous multilateral governance. Effective conservation and management efforts require harmonizing data collection, sustainable harvesting practices, monitoring, control, and enforcement across numerous jurisdictions, including the high seas. However, inconsistent and fragmented data collection and knowledge production remains a significant barrier. To address this, scaling and mainstreaming innovative approaches and technologies in management, monitoring, and data collection is essential for building the foundational knowledge required for sustainable and equitable operations, particularly under the pressures of a changing climate.This study conducts an expert-driven global solution scan of scientific and technical innovations across fisheries and regions, highlighting best practices to inform estimates about the status, abundance, and distribution of marine migratory species. It also assesses measures to understand the scope and scale of fisheries impacting them, from local artisanal to industrial operations. Scientific and technical knowledge is categorized into four key themes: species abundance and distribution, fleet activity, fisheries catch composition and traceability, and climate change adaptation. Technological advancements such as vessel tracking and monitoring hardware, innovative stock assessment methodologies, and novel genomics applications are instrumental in tracking population abundances and the scale of our impacts on them. Breakthroughs in computer modeling, artificial intelligence, and satellite remote sensing offer invaluable tools to inform, monitor, and enforce management decisions on a large scale. Species distribution and density modeling are also a fundamental tool for tracking current and predicting future range shifts of species under environmental changes. To ensure scalability, cost efficiency, institutional arrangements, and capacity development are considered, especially given resource and training limitations in many regions. A critical element in advancing the scientific foundation for the conservation and management of migratory biodiversity is the emphasis on independent, well-funded science. Independent research ensures the transparency and integrity of data collection and analysis, which are crucial for informed and unbiased decision-making. Independence in research also provides a foundation of trust and reliability, which supports the credibility and adoption of conservation strategies across stakeholders. To this end, we also identify examples of independent regional scientific bodies informing managers and outline examples of principles to guide the transparent, accessible, and interoperable production of actionable data and knowledge.Consistent and sufficient funding is essential to support long-term monitoring and stimulate the development of advanced technologies, particularly in regions that may lack the resources to independently invest in these areas. Together, these innovations provide a roadmap for strengthening and scaling the science available to local, national, and regional fisheries frameworks to inform adaptive, science-based methods that address the complex dynamics of migratory biodiversity. While improved scientific knowledge cannot guarantee successful conservation outcomes on its own, it lays the groundwork for evidence-based, scalable approaches to managing migratory and straddling species effectively across geopolitical boundaries.
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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 | 0.037 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".