Association of Recent Incidence of Foliar Disease in Pine Species in the Southeastern United States with Tree and Climate Variables

• Main Authors: Karun Pandit, Jason Smith, Tania Quesada, Caterina Villari, Daniel J. Johnson
• Format: Article
• Language: English
• Published: MDPI AG 2020-10-01
• Series: Forests
• Subjects: loblolly pine; slash pine; longleaf pine; MaxEnt; species distribution model; generalized linear mixed model
• Online Access: https://www.mdpi.com/1999-4907/11/11/1155

Pine forests in the southern United States are a major contributor to the global economy. Through the last three decades, however, there have been concerns about the decline of pine forests attributed mostly to pests and pathogens. A combination of biotic agents and environmental factors and their interaction often influences outbreaks and the resultant damage in the forests. Southern pines experience periodic mortality from bark beetles and root rot fungi and losses from fusiform rust and pitch canker have long been important for management. In recent years, there is also growing evidence of increasing damage from foliar disease in southern pines. Early detection of diseases following changes in foliar characteristics and assessment of potential risks will help us better utilize our resources and manage these forests sustainably. In this study, we used Forest Inventory and Analysis (FIA) data to explore the intensity of foliar disease in three common pines: loblolly (<i>Pinus taeda</i> L.), longleaf (<i>Pinus palustris</i> Mill.), and slash (<i>Pinus elliottii</i> Engelm.) in spatial and temporal terms using tree-level and climatic variables. Results from a tree-level model suggests that crown ratio may be an important factor in pine foliar disease (<i>p</i> < 0.1). We applied the MaxEnt model, a presence-only species distribution model (SDM), to explore any association of foliar disease incidences with the climatic variables at a landscape level. Results indicate that mean dew point temperature, maximum vapor pressure deficit, and precipitation during cold months had more influence over disease incidences than other climatic variables. While the sample size is limited as this is an emerging disease in the region, our study provides a basis for further exploration of disease detection methods, disease etiology studies, and hazard mapping.