Operationalising social‐ecological systems to meet complex sustainability challenges posed by widespread biological invasions
Bibliographic record
Abstract
Abstract Biological invasions are a major driver of biodiversity loss worldwide. The scale and pace of these invasions often exceed resources available for control, necessitating long‐term management strategies that balance complex sustainability goals. A social‐ecological systems (SES) approach offers a promising framework to guide decision‐making by considering the interconnectedness of human and natural systems. This study focuses on the invasion of Indo‐Pacific lionfish in coastal Belize, demonstrating how operationalising a SES approach can guide the co‐design and selection of coastal management strategies to address the complexities of widespread invasive species. Over 6 years, SES models were developed, refined, and applied to management through a participatory approach involving transdisciplinary knowledge holders and stakeholders. Initially, a broad conceptual model of the coastal system post‐invasion was developed and refined to identify key attributes quantifiable as indicators of the system's response to lionfish suppression efforts on the Belize Barrier Reef. This SES model was used to generate and evaluate potential management scenarios within the Belize National Lionfish Management Strategy. A market‐based approach, which introduced lionfish as a fishery target and incentivised lionfish tourism and a commercial fishery, emerged as the most effective strategy to reduce lionfish populations in Belize. Our approach provides a framework for engaging diverse perspectives and expertise, leading to co‐developed management actions with broad national support. The study underscores the value of SES framing in creating a holistic perspective on invasive species management, integrating ecological and social dimensions. Ultimately, our method of iteratively developing and refining SES models through participatory processes—from a complex conceptual model to a practical, indicator‐driven framework—can be adapted to create and assess durable solutions for managing widespread invasive species in stressed ecosystems worldwide. Read the free Plain Language Summary for this article on the Journal blog.
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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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
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".