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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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Classifying fishing behavioral diversity using high-frequency movement data
Effective management of social-ecological systems (SESs) requires an understanding of human behavior. In many SESs, there are hundreds of agents or more interacting with governance and regulatory institutions, driving management outcomes through collective behavior. Agents in these systems often display consistent behavioral characteristics over time that can help reduce the dimensionality of SES data by enabling the assignment of types. Typologies of resource-user behavior both enrich our knowledge of user cultures and provide critical information for management. Here, we develop a data-driven framework to identify resource-user typologies in SESs with high-dimensional data. To demonstrate policy applications, we apply the framework to a tightly coupled SES, commercial fishing. We leverage large fisheries-dependent datasets that include mandatory vessel logbooks, observer datasets, and high-resolution geospatial vessel tracking technologies. We first quantify vessel and behavioral characteristics using data that encode fishers’ spatial decisions and behaviors. We then use clustering to classify these characteristics into discrete fishing behavioral types (FBTs), determining that 3 types emerge in our case study. Finally, we investigate how a series of disturbances applied selection pressure on these FBTs, causing the disproportionate loss of one group. Our framework not only provides an efficient and unbiased method for identifying FBTs in near real time, but it can also improve management outcomes by enabling ex ante investigation of the consequences of disturbances such as policy actions.
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- Award ID(s):
- 1734999
- PAR ID:
- 10171709
- Date Published:
- Journal Name:
- Proceedings of the National Academy of Sciences
- Volume:
- 116
- Issue:
- 34
- ISSN:
- 0027-8424
- Page Range / eLocation ID:
- 16811 to 16816
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1073/pnas.1906766116
