Coring has no detrimental impact on tree growth across European forests, but effects on mortality remain uncertain

Tree cores are widely used across a broad range of disciplines in the environmental sciences, most notably as a tool to measure tree growth, estimate tree age, characterise wood anatomy and reconstruct past climate. However, because extracting tree cores is an invasive procedure, concerns about their use are often raised due to perceived risks to trees. Here we comprehensively test the impacts of tree coring on 16 European tree species using a dataset spanning a broad range of forest types across the continent. Over the course of a decade, we tracked the growth and survival of 3201 trees cored in 2012 (including those cored once and twice) and used hierarchical Bayesian models to compare them with those of a cohort of 7262 neighbouring trees that were never cored. We found no evidence that coring had a detrimental impact on tree growth, irrespective of size, species, forest type or the number of times a tree was cored. However, we did observe a small positive stem increment response, which we hypothesise is most likely the result of localised vertical scarring from the coring wound. Impacts of tree coring on mortality were instead more uncertain, with mortality rates of cored trees slightly elevated relative to ones that were not cored, but overlapping widely in their credible intervals. This underscores the challenge of attributing drivers of rare and stochastic processes such as tree mortality, even with relatively large datasets. Our study generally supports the use of tree coring as a low-impact method for characterising the growth, age and function of a wide range of tree species, but highlights that there are circumstances in which it may cause harm. Future work should explore these further by considering the broader impacts of coring on tree health. Additionally, tree cores should always be collected well above or below the point of measurement of tree stem diameters to avoid biasing long-term forest census datasets.