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Harnessing the collective intelligence of stakeholders for conservation
Author(s) -
Gray Steven,
Aminpour Payam,
Reza Caitie,
Scyphers Steven,
Grabowski Jonathan,
Murphy Robert,
Singer Alison,
Baltaxe David,
Jordan Rebecca,
Jetter Antonie,
Introne Joshua
Publication year - 2020
Publication title -
frontiers in ecology and the environment
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.918
H-Index - 164
eISSN - 1540-9309
pISSN - 1540-9295
DOI - 10.1002/fee.2232
Subject(s) - crowds , recreation , collective intelligence , natural resource management , computer science , natural resource , the internet , knowledge management , stakeholder , environmental resource management , business , data science , management science , ecology , economics , political science , public relations , computer security , world wide web , biology
Incorporating relevant stakeholder input into conservation decision making is fundamentally challenging yet critical for understanding both the status of, and human pressures on, natural resources. Collective intelligence ( CI ), defined as the ability of a group to accomplish difficult tasks more effectively than individuals, is a growing area of investigation, with implications for improving ecological decision making. However, many questions remain about the ways in which emerging internet technologies can be used to apply CI to natural resource management. We examined how synchronous social‐swarming technologies and asynchronous “wisdom of crowds” techniques can be used as potential conservation tools for estimating the status of natural resources exploited by humans. Using an example from a recreational fishery, we show that the CI of a group of anglers can be harnessed through cyber‐enabled technologies. We demonstrate how such approaches – as compared against empirical data – could provide surprisingly accurate estimates that align with formal scientific estimates. Finally, we offer a practical approach for using resource stakeholders to assist in managing ecosystems, especially in data‐poor situations.