Studying citizen science through adaptive management and learning feedbacks as mechanisms for improving conservation

Abstract

Citizen science has generated a growing interest among scientists and community groups, and citizen science programs have been created specifically for conservation. We examined collaborative science, a highly interactive form of citizen science, which we developed within a theoretically informed framework. In this essay, we focused on 2 aspects of our framework: social learning and adaptive management. Social learning, in contrast to individual‐based learning, stresses collaborative and generative insight making and is well‐suited for adaptive management. Adaptive‐management integrates feedback loops that are informed by what is learned and is guided by iterative decision making. Participants engaged in citizen science are able to add to what they are learning through primary data collection, which can result in the real‐time information that is often necessary for conservation. Our work is particularly timely because research publications consistently report a lack of established frameworks and evaluation plans to address the extent of conservation outcomes in citizen science. To illustrate how our framework supports conservation through citizen science, we examined how 2 programs enacted our collaborative science framework. Further, we inspected preliminary conservation outcomes of our case‐study programs. These programs, despite their recent implementation, are demonstrating promise with regard to positive conservation outcomes. To date, they are independently earning funds to support research, earning buy‐in from local partners to engage in experimentation, and, in the absence of leading scientists, are collecting data to test ideas. We argue that this success is due to citizen scientists being organized around local issues and engaging in iterative, collaborative, and adaptive learning.

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