2020 ESA Annual Meeting (August 3 - 6)

COS 81 Abstract - Harnessing local people’s collective intelligence for achieving sustainable ecosystems

Payam Aminpour1, Steven A. Gray1, Rebecca C. Jordan1 and Steven B. Scyphers2, (1)Community Sustainability, Michigan State University, East Lansing, MI, (2)Marine Science Center, Northeastern University, Nahant, MA
Background/Question/Methods

Local ecological knowledge (LEK) of natural resource users, also known as traditional ecological knowledge is constructed while people interact with natural ecosystems through their daily routines. Such LEK constitutes a valuable source of information and can complement scientific knowledge about natural resources abundance, resource dynamics, and human-resource interdependences in data-poor situations. However, information embedded in LEK is predominantly qualitative and thus cannot be easily integrated with scientific assessments which are primarily quantitative. Yet, because of unknown uncertainty of information obtained through LEK, diversity across different groups, and methodological insufficiency, LEK may not lead to accurate assessments and predictions about natural resources abundance and how they respond to various management strategies or external perturbations. To address these challenges, we explored how emerging internet technologies can be used to harness resource users’ collective intelligence (CI). CI refers to a group phenomenon in which the intelligences of individual participants merge into a larger form of intelligence—weather through social interactions or independent aggregation of knowledge— leading to higher problem-solving ability and more accurate decisions. Here, using an example of striped bass fisheries in Massachusetts (MA), we empirically demonstrate how CI of local users, under certain conditions, can be harnessed using cyber-enabled technologies such as online surveys, social swarming platforms, and online mental-modeling tools to inform fisheries management.

Results/Conclusions

Compared against empirical data, our results demonstrate that aggregated responses from a group of diverse stakeholders—which were obtained through an online survey—accurately estimated fish abundance and human pressures on fish resources as measured by the number of licensed fishers. We repeated the experiment by asking stakeholders to participate in an online swarming activity—in which people were virtually connected through an online platform, allowing them to build a synchronous human swarm, socially interact, and provide collective responses. Our results show that swarm yielded accurate results when classifying the sizes of fish that occur most or least frequently. Additionally, using an online mental-modeling practice we show how the system knowledge of local stakeholders about social-ecological relationships, which were elicited through cognitive-mapping techniques, could mathematically aggregate into an accurate model that represents the structure of human and natural resource interdependences and also predicts system responses to natural and anthropogenic perturbations. Therefore, these CI approaches can be used as a concrete basis for developing strategies to better manage ecosystems in participatory and adaptive ways across different natural resources and biodiversity conservation contexts, especially in data-poor situations.