Tangible geospatial modeling for collaborative solutions to invasive species management

Managing landscape-scale environmental problems, such as biological invasions, can be facilitated by integrating realistic geospatial models with user-friendly interfaces that stakeholders can use to make critical management decisions. However, gaps between scientific theory and application have typically limited opportunities for model-based knowledge to reach the stakeholders responsible for problemsolving. To address this challenge, we introduce Tangible Landscape, an open-source participatory modeling tool providing an interactive, shared arena for consensus-building and development of collaborative solutions for landscape-scale problems. Using Tangible Landscape, stakeholders gather around a geographically realistic 3D visualization and explore management scenarios with instant feedback; users direct model simulations with intuitive tangible gestures and compare alternative strategies with an output dashboard. We applied Tangible Landscape to the complex problem of managing the emerging infectious disease, sudden oak death, in California and explored its potential to generate co-learning and collaborative management strategies among actors representing stakeholders with competing management aims. © 2017 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Software and data availability Tangible Landscape is available under GNU General Public License and can be downloaded at http://tangible-landscape.github. io together with installation and setup instructions. Tangible Landscape was developed by Anna Petrasova and Vaclav Petras (Petrasova et al., 2014, 2015). The source code of the epidemiological spread model used in this study is available under GNU General Public License and can be downloaded at https://github. com/f-tonini/SOD-modeling with installation and setup instructions as well as set of GIS layers necessary to run the model. The code was developed by Francesco Tonini and based on the and Sustainability, Michigan University of North Carolina Ltd. This is an open access article u original epidemiological framework presented by Meentemeyer et al. (2011).