Stand Inventories as an Early Detection System for Forest Health Threats

Abstract

Pest-specific inventories require substantial resources and are often infeasible, creating a need for alternative means of early pest detection. We examined the potential for stand inventories to detect forest health threats by using a unique dataset of mapped eastern spruce dwarf mistletoe (Arceuthobium pusillum Peck.) infestations in black spruce (Picea mariana Mill. B.S.P) stands of northern Minnesota, USA. We simulated stand inventories across a range of sampling intensities; that is, current standard (S) methods in Minnesota, adding one plot (S + 1), doubling the intensity (2S), and halving the intensity (S/2), using fixed-radius plots and transect buffers for detection. We categorized stands into low, moderate, and high infestation severity. We simulated detection at multiple viewing distances along S inventory transects in low severity infestation stands. Detection probability increased as sampling intensity increased. Plot-based detection averaged > 50% for moderate and high severity infestations except S/2 in moderate severity infestations. Notably, transect-based detection averaged ≥ 85% at viewing distances of 25 to 100 m. Results suggest stand inventories could provide opportunities to detect forest health threats with unique signatures when transect observations are included. Thus, forest health specialists may consider including pest-specific training for foresters in current inventory methods, requiring modest investment of time and effort.