To promote the resilience and sustainability of coastal social-ecological systems (SES), it is necessary to implement adaptive and participatory management schemes. Successful examples of adaptation to the rapid change in coastal SES exist, but the question of whether these cases may be scalable to other regions and contexts remains. To this end, the present study aimed to identify how successful management strategies implemented in a fishing cooperative in Baja California, Mexico, can be adapted to other coastal SES. In particular, this study aimed to understand whether adaptive co-management of Isla Natividad (IN) could be replicated in Isla Todos Santos (ITS), a biophysically similar coastal SES to IN but with different results with regard to fisheries management. We found that the resource systems and resources in both SESs were similar. However, there were substantial differences with regard to governance and resource users. In Isla Natividad, the level of organization orchestrated by the resource users has contributed to establishing rules and sanctions that have supported the sustainable use of fishery resources. On the contrary, in ITS, the number of resource users and their socioeconomic attributes have impeded the establishment of effective rules or sanctions. The results of this study suggest that the ITS governance system needs to be improved in order to adapt some of the IN management strategies to increase its adaptive capacity. To promote successful adaptive management, it is necessary to develop context-specific adaptive pathways that contribute to greater resilience in the SESs of this region and in other regions that face similar conditions.
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This content will become publicly available on December 1, 2026
Predicting hardness in refractory high-entropy alloys using machine learning
We developed an ML framework to predict Vickers hardness in RHEAs. Feature selection reduced descriptors, enabling Kernel Ridge Regression to achieve the highest accuracy. SHAP identified valence electron concentration as a key predictor, highlighting ML’s potential in alloy design.
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- PAR ID:
- 10630038
- Publisher / Repository:
- Elsevier
- Date Published:
- Journal Name:
- Materials Letters
- Volume:
- 400
- Issue:
- C
- ISSN:
- 0167-577X
- Page Range / eLocation ID:
- 138940
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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