TY - JOUR T1 - Host density dependence and environmental factors affecting laurel wilt invasion JF - bioRxiv DO - 10.1101/642827 SP - 642827 AU - Robin A. Choudhury AU - Hong L. Er AU - Marc Hughes AU - Jason A. Smith AU - Grechen E. Pruett AU - Joshua Konkol AU - Randy C. Ploetz AU - James J. Marois AU - Karen A. Garrett AU - Ariena H.C. van Bruggen Y1 - 2019/01/01 UR - http://biorxiv.org/content/early/2019/05/21/642827.abstract N2 - Host size, density and distribution, in addition to climate, can affect the likelihood a pathogen will invade and saturate landscapes. Laurel wilt, caused by the vector-borne forest pathogen Raffaelea lauricola, has devastated populations of native Lauraceae in the Southeastern US, and continues to spread. We surveyed 87 plots in six coastal islands in South Carolina, Georgia and North Florida, and one inland site (Archbold Biological Station) in South Florida for laurel wilt-affected and non-affected individual plants belonging to the genus Persea. The coastal island sites were surveyed once in 2008 or 2009, and the inland site was surveyed eight times from 2011 to 2013. Disease incidence per plot ranged from 0% to 96%, with mean disease incidence 45% across all sites. Disease incidence was positively correlated with trunk diameter and density of hosts with trunk diameter > 5 cm, but negatively with the degree of clustering, which was highest for small trees. A recursive partitioning model indicated that higher disease incidence was associated with moderate temperatures, wider trunk diameter, lower relative humidity, and lower wind speeds. Disease progress over time at Archbold followed a Gompertz curve, plateauing at 3% in two years. The dispersal kernel for disease incidence from a focus followed a negative exponential distribution. The number of plots with diseased trees at Archbold was similar for redbay (P. borbonia) and swampbay (P. palustris), but was lower for silkbay (P. humilis). Understanding how host density, size, and diversity interact with environmental effects will help guide future risk prediction efforts. ER -