Modelling Annual Southern Appalachian Acorn Production Using Visual Surveys - Cathryn H. Greenberg (Wildlife Society Bulletin)
ABSTRACT Acorn production varies considerably among species, locations, and years with potential effects on populations of acorn‐consuming wildlife, oak (Quercus spp.) regeneration, and forest ecology. Methods to estimate annual acorn crop size include acorn‐trapping (quantitative) or visual surveys (indices or ranks). Forest managers need a tool for converting visual survey data to quantitative estimates of acorn yield within specific years and areas. I used 7 years (2006–2010, 2012, 2016) of visual acorn survey data with acorn trap data from the same individual trees of 5 common eastern oak species in the Pisgah National Forest, North Carolina, USA, to determine whether a known within‐year relationship between the proportion of oaks bearing acorns (PBA) and acorn density using trap data (PBA‐trap) could be used to predict quantitative (no. of acorns per oak), within‐year acorn crops using visual survey data (PBA‐visual). At the individual tree level, visually determined percent crown with acorns (PCA‐visual) and number of acorns/m2 trap or per crown were correlated for all oak subgroups (species, subgenera, and all oaks combined). At the population level, mean PCA‐visual was correlated with the mean number of acorns/m2 trap or per crown for black oak (Q. velutina). Both PBA‐visual and mean PCA‐visual, and PBA‐visual and PBA‐trap were correlated for all oak subgroups. At the population level, PBA‐visual was a strong predictor of the mean number of acorns/m2 trap or per crown for most oak subgroups. Model results can be used as an index of crop size by comparing number of acorns/m2 trap or per crown among years or applied to oak inventory data to quantitatively estimate annual acorn crop sizes at the stand level or landscape level. This method enables users to estimate and compare the number of acorns produced within specific years and landscapes, rather than rely on qualitative hard‐mast indices. Published 2020. This article is a U.S. Government work and is in the public domain in the USA.