Searching for predictors of the variability of impacts caused by non-native trees on regulating ecosystem services worldwide.

Humans have introduced non-native trees (NNT) all over the world to take advantage of the plethora of benefits they provide. However, depending on the context, NNT may present a diverse range of effects on ecosystem services (ES), from benefits to drawbacks, which may hinder the development of policies for these species. Unfortunately, the attempts so far to understand the impacts of NNT on ES only explained a low proportion of their variation. Here we analyze the variation in impacts of NNT on regulating ecosystem services (RES) by using a global database, which covers the effect size of multiple NNT species on six RES (climate regulation, soil erosion regulation, soil fertility, soil formation, hydrological cycle regulation, and fire protection). We used a wide range of predictors to account for the context-dependency of impacts distributed in five groups: the RES type, functional traits of both the NNT and the dominant NT of the recipient ecosystem, phylogenetic and functional distances between NNT and NT, climatic context, and human population characteristics. Using boosted regression trees and regression trees, we found that the most influential predictors of NNT impacts on RES were annual mean temperatures and precipitation seasonality, followed by the type of RES, human population density, and NNT height. In regions with warm temperatures and low seasonality, NNT tended to increase RES. NNT impacts were greater in densely populated regions. Smaller NNT exerted greater positive impacts on climate regulation and soil erosion regulation in tropical regions than in other climates. We highlight that benign climates and high population density exacerbate the effects of NNT on RES, and that soil fertility is the most consistently affected RES. Knowledge of the factors that modulate NNT impacts can help to predict their potential effects on RES in different parts of the world and at various environmental settings.