LiDAR data as a proxy for light availability improve distribution modelling of woody species

Modern multifunctional forest management can profit from high-quality information on the potential distribution of woody species generated by species distribution models (SDMs). Forest structure is an important factor in determining the distribution of woody species in forests, for example because it affects light conditions within forest stands. Remotely sensed data from light detection and ranging (LiDAR) can capture this three-dimensional structure of forests, leading to the expectation that LiDAR-derived data should enhance the predictive performance of SDMs. We test if and how LiDAR-derived data increases the predictive performance of SDMs for light-demanding and shade-tolerant shrub and tree species in Swiss forests. Our analyses suggest that LiDAR-derived data generally increases predictive performance of models. However, the response to including LiDAR-derived data varies depending on plant functional type: the increase in predictive performance is largest for light-demanding shrubs, reduced for light-demanding trees, and is lost for shade-tolerant species. We further find that shade-tolerant and light-demanding species show opposing responses along the LiDAR-derived predictors. Our results suggest that LiDAR-derived data indeed capture some aspects of light availability in forests, and that including LiDAR-derived predictors in SDMs should be considered for light-demanding shrubs, but may be of less use for trees (especially if shade-tolerant). We conclude that improving SDMs and resulting maps by including LiDAR-derived predictors potentially helps to ameliorate multifunctional, biodiversity-friendly forest stand management.