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Record W4413447588 · doi:10.1002/pan3.70121

Operationalising social‐ecological systems to meet complex sustainability challenges posed by widespread biological invasions

2025· article· en· W4413447588 on OpenAlexaff
Fabian Kyne, Adriel Castañeda, Jennifer K. Chapman, Jennifer Solomon, Stephanie Green

Bibliographic record

VenuePeople and Nature · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine Ecology and Invasive Species
Canadian institutionsUniversity of Alberta
FundersSummit Foundation
KeywordsSustainabilityEnvironmental resource managementEcologyEnvironmental planningBusinessGeographyBiologyEnvironmental science

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.658
Threshold uncertainty score0.751

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.030
GPT teacher head0.274
Teacher spread0.244 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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