Climate change shifts environmental space and limits transferability of treeline models

Abstract

Our study aims at gaining insights into the processes determining the current treeline dynamics in Finnish Lapland. Using forest surveys conducted in 1978 and 2003 we modelled the occurrence and abundance of three dominant tree species in Finnish Lapland, i.e. Pinus sylvestris, Picea abies and Betula pubescens, with boosted regression trees. We assessed the importance of climatic, biotic and topographic variables in predicting tree occurrence and abundance based on their relative importance and response curves. We compared temporal and spatial transferability by using an extended transferability index.Site fertility, the abundance of co‐occurring species and growing degree days were generally the most important predictors for both occurrence and abundance across all species and datasets. Climatic predictors were more important for modelling occurrences than for modelling abundances. Occurrence models were able to reproduce the observed treeline pattern within one time period or region. Abundance models underestimated basal area but captured the general pattern of low and high values. Model performance as well as transferability differed considerably between species and datasets. Pinus sylvestris was modelled more successfully than P. abies and B. pubescens. Generally, spatial transferability was greater than temporal transferability. Comparing the environmental space between datasets revealed that transferring models means extrapolating to novel environments, providing a plausible explanation for limited transferability.Our study illustrates how climate change can shift the environmental space and lead to limited model transferability. We identified non‐climatic factors to be important in predicting the distribution of dominant tree species, contesting the widespread assumption of climatically induced range expansion.