Predicting Present and Future Suitable Climate Spaces (Potential Distributions) for an Armillaria Root Disease Pathogen (Armillaria solidipes) and Its Host, Douglas-fir (Pseudotsuga menziesii), Under Changing Climates

Abstract

Climate change and associated disturbances are expected to exacerbate forest root diseases because of altered distributions of existing and emerging forest pathogens and predisposition of trees due to climatic maladaptation and other disturbances. Predictions of suitable climate space (potential geographic distribution) for forest pathogens and host trees under contemporary and future climate scenarios will guide the selection of appropriate management practices by forest managers to minimize adverse impacts of forest disease within forest ecosystems. A native pathogen ( Armillaria solidipes ) that causes Armillaria root disease of conifers in North America is used to demonstrate bioclimatic models (maps) that predict suitable climate space for both pathogen and a primary host ( Pseudotsuga menziesii , Douglas-fir) under contemporary and future climate scenarios. Armillaria root disease caused by A. solidipes is a primary cause of lost productivity and reduced carbon sequestration in coniferous forests of North America, and its impact is expected to increase under climate change due to tree maladaptation. Contemporary prediction models of suitable climate space were produced using Maximum Entropy algorithms that integrate climatic data with 382 georeferenced occurrence locations for DNA sequence-confirmed A. solidipes . A similar approach was used for visually identified P. menziesii from 11,826 georeferenced locations to predict its climatic requirements. From the contemporary models, data were extrapolated through future climate scenarios to forecast changes in geographic areas where native A. solidipes and P. menziesii will be climatically adapted. Armillaria root disease is expected to increase in geographic areas where predictions suggest A . solidipes is well adapted and P. menziesii is maladapted within its current range. By predicting areas at risk for Armillaria root disease, forest managers can deploy suitable strategies to reduce damage from the disease.

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