Exploring the adaptive capacity of a fisheries social-ecological system to global change
Bibliographic record
Abstract
Global change challenges coupled natural-human systems such as fisheries social-ecological systems (SES) because they are confined by spatial and functional ecosystem boundaries. Understanding the capacity of an SES to adapt to changing environmental or socio-economic conditions is complex and entails an analysis of the system's properties such as resilience, resistance, vulnerability, and adaptive capacity. We used a modified Ostrom framework to structure our analyses and to define the SES components, attributes and indicators of the German mixed demersal fishery SES operating in the southern North Sea. Combining analyses of 20-year time series of environmental and socio-economic data with network analysis and semi-structured interviews allowed for a detailed description of past SES adaptations. Hence, our analysis revealed autonomous adaptations of the SES to environmental and socio-economic change, which entailed a shift in target species, fishing strategies as well as a distinct decrease in number of actors. We found that the adaptive capacity of the SES has declined over time, and that the SES is now on the brink of being unable to withstand future environmental and socio-economic change. It is therefore captured in an undesirable state, reflecting a social-ecological trap where social and environmental feedbacks negatively reinforce each other. The main barriers to the adaptive capacity of the SES are related to fishing cultures, economic structures, policy frameworks and increasing conflicts over the use of marine space. An in-depth understanding of the linkages between the identified key SES components and related indicators is a prerequisite for developing future management approaches to enhance the adaptive capacity of SES to global change. Our findings highlight the need for tailored and context-specific co-management approaches for all decision-making processes affecting SES. • Global change trials the resistance and resilience of fisheries socio-ecological systems (SES). • We assessed adaptation strategies of a fisheries SES in the southern North Sea. • Autonomous adaptation strategies comprised changes of target species, fishing strategies, and number of actors. • Barriers to adaptation included prevailing fishing cultures, governance structures, and spatial use conflicts. • Understanding the dynamics of SES components enables tailored management approaches to increase adaptive capacity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".