@article{gaydos_jones_jones_millar_petras_petrasova_mitasova_meentemeyer_2021, title={Evaluating online and tangible interfaces for engaging stakeholders in forecasting and control of biological invasions}, volume={9}, ISSN={["1939-5582"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85115251448&partnerID=MN8TOARS}, DOI={10.1002/eap.2446}, abstractNote={Abstract}, number={8}, journal={ECOLOGICAL APPLICATIONS}, publisher={Wiley}, author={Gaydos, Devon A. and Jones, Chris M. and Jones, Shannon K. and Millar, Garrett C. and Petras, Vaclav and Petrasova, Anna and Mitasova, Helena and Meentemeyer, Ross K.}, year={2021}, month={Sep} }
 @article{jones_jones_petrasova_petras_gaydos_skrip_takeuchi_bigsby_meentemeyer_2021, title={Iteratively forecasting biological invasions with PoPS and a little help from our friends}, volume={6}, ISSN={["1540-9309"]}, url={http://dx.doi.org/10.1002/fee.2357}, DOI={10.1002/fee.2357}, abstractNote={Ecological forecasting has vast potential to support environmental decision making with repeated, testable predictions across management‐relevant timescales and locations. Yet resource managers rarely use co‐designed forecasting systems or embed them in decision making. Although prediction of planned management outcomes is particularly important for biological invasions to optimize when and where resources should be allocated, spatial–temporal models of spread typically have not been openly shared, iteratively updated, or interactive to facilitate exploration of management actions. We describe a species‐agnostic, open‐source framework – called the Pest or Pathogen Spread (PoPS) Forecasting Platform – for co‐designing near‐term iterative forecasts of biological invasions. Two case studies are presented to demonstrate that iterative calibration yields higher forecast skill than using only the earliest‐available data to predict future spread. The PoPS framework is a primary example of an ecological forecasting system that has been both scientifically improved and optimized for real‐world decision making through sustained participation and use by management stakeholders.}, number={7}, journal={FRONTIERS IN ECOLOGY AND THE ENVIRONMENT}, publisher={Wiley}, author={Jones, Chris M. and Jones, Shannon and Petrasova, Anna and Petras, Vaclav and Gaydos, Devon and Skrip, Megan M. and Takeuchi, Yu and Bigsby, Kevin and Meentemeyer, Ross K.}, year={2021}, month={Jun} }
 @article{gaydos_petrasova_cobb_meentemeyer_2019, title={Forecasting and control of emerging infectious forest disease through participatory modelling}, volume={374}, ISSN={["1471-2970"]}, url={https://doi.org/10.1098/rstb.2018.0283}, DOI={10.1098/rstb.2018.0283}, abstractNote={
            Epidemiological models are powerful tools for evaluating scenarios and visualizing patterns of disease spread, especially when comparing intervention strategies. However, the technical skill required to synthesize and operate computational models frequently renders them beyond the command of the stakeholders who are most impacted by the results. Participatory modelling (PM) strives to restructure the power relationship between modellers and the stakeholders who rely on model insights by involving these stakeholders directly in model development and application; yet, a systematic literature review indicates little adoption of these techniques in epidemiology, especially plant epidemiology. We investigate the potential for PM to integrate stakeholder and researcher knowledge, using
            Phytophthora ramorum
            and the resulting sudden oak death disease as a case study. Recent introduction of a novel strain (European 1 or EU1) in southwestern Oregon has prompted significant concern and presents an opportunity for coordinated management to minimize regional pathogen impacts. Using a PM framework, we worked with local stakeholders to develop an interactive forecasting tool for evaluating landscape-scale control strategies. We find that model co-development has great potential to empower stakeholders in the design, development and application of epidemiological models for disease control.
          }, number={1776}, journal={PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY B-BIOLOGICAL SCIENCES}, publisher={The Royal Society}, author={Gaydos, Devon A. and Petrasova, Anna and Cobb, Richard C. and Meentemeyer, Ross K.}, year={2019}, month={Jul} }