Democratising forest management: Applying multiwinner approval voting to tree selection

Climate change, biodiversity losses, global health threats, changing recreation patterns are but a few of the many challenges that currently, and for some time to come, the world has to cope with. To address these challenges and to mitigate some of them, ecosystem and particularly conservation management increasingly have to adopt strategies never considered before. One such new possibility is crowdsourcing, a variant of public consultation, where a number of experts are invited and, for example, asked to mark trees that - in their opinion - should be removed in order to improve or restore a forest ecosystem. This type of crowdsourcing has recently been carried out in many European countries and overseas as part of what commonly is referred to as marteloscope. In this paper, we addressed the question of how the rating or voting of such a crowd of experts is best aggregated to obtain one final, consolidated list of trees to be evicted. Standard approval voting often leads to a domination by the majority of voters and important contributions by minority experts are largely ignored. To avoid this and to better represent the pluralism of expertise and opinions in matters, where currently no best-practice guidelines exist, we analysed the effects of three proportional multiwinner rules used in political science by applying them to 50 marteloscope experiments in Great Britain. Our results indicated that proportional rules - particularly in situations where the invited expert markers disagree - achieved a better representation of different opinions than standard approval voting. Proportional rules also act as a safety mechanism reducing risks when the majority decisions prove inappropriate and as a consequence forest development could completely go astray.