Abstract

Research in wildlife management increasingly relies on quantitative population models. However, a remaining challenge is to have end-users, who are often alienated by mathematics, benefiting from this research. I propose a new approach, 'wildlife in the cloud,' to enable active learning by practitioners from cloud-based ecological models whose complexity remains invisible to the user. I argue that this concept carries the potential to overcome limitations of desktop-based software and allows new understandings of human-wildlife systems. This concept is illustrated by presenting an online decision-support tool for moose management in areas with predators in Sweden. The tool takes the form of a user-friendly cloud-app through which users can compare the effects of alternative management decisions, and may feed into adjustment of their hunting strategy. I explain how the dynamic nature of cloud-apps opens the door to different ways of learning, informed by ecological models that can benefit both users and researchers.

Keywords

Population models; Cloud-computing; Wildlife management; Moose; Wolf; Bear

Published in

AMBIO: A Journal of the Human Environment
2015, Volume: 44, number: Supplement 4, pages: S550-S556
Publisher: SPRINGER

SLU Authors

• Chapron, Guillaume

  • Department of Ecology, Swedish University of Agricultural Sciences
    

UKÄ Subject classification

Information Science
Ecology
Zoology


Publication identifier

DOI: https://doi.org/10.1007/s13280-015-0706-0

Permanent link to this page (URI)

https://res.slu.se/id/publ/69899