Exotic plant invasions can notably alter the nitrogen (N) cycle of ecosystems. However, there is large variation in the magnitude and direction of their impact that remains unexplained. We present a structured meta‐analysis of 100 papers, covering 113 invasive plant species with 345 cases of invasion across the globe and reporting impacts on N cycle‐related metrics. We aim to explain heterogeneity of impacts by considering methodological aspects, properties of the invaded site and phylogenetic and functional characteristics of the invaders and the natives. Overall, plant invasions increased N pools and accelerated fluxes, even when excluding N‐fixing invaders. The impact on N pools depended mainly on functional differences and was greater when the invasive plants and the natives differed in N‐fixation ability, plant height and plant/leaf habit. Furthermore, the impact on N fluxes was related mainly to climate, being greater under warm and moist conditions. Our findings show that more functionally distant invaders occurring in mild climates are causing the strongest alterations to the N cycle.

Aim  Many Australian Acacia species have been widely planted around the world. Some taxa are among the most aggressive of invasive alien plants and cause severe ecosystem degradation. We aimed to predict invasiveness of taxa in a large set of Australian Acacia species on the basis of easy‐to‐assess predictors.Location  Global.Methods  We considered three groups of predictors: (1) climatic affinities of species in their native ranges; (2) life history traits; and (3) human usage factors. Logistic multiple regressions were applied to construct predictive models for 85 Australian acacias (species in Acacia subgenus Phyllodineae) that are known to have been transported outside of their native range (17 known to be invasive and 68 non‐invasive). The best model was then applied to predict the probability of an additional 34 Acacia species with unknown invasive status.Results  Water availability in the native range and human uses were significant predictors of invasiveness in all models. Life history index (proportional to plant height, leaf area and seed mass) and climatic amplitude were also positive predictors of invasiveness when human use was not considered. The best model, based on human uses and water availability, correctly classified 92% of the species. Results suggest that Acacia species that evolved under low climatic stress have a greater chance of becoming invasive.Main conclusions  Species that are useful to humans are more likely to be disseminated to and within new regions, thus increasing the risk of invasion. Combining ecological, evolutionary and human‐use criteria is useful for quantifying the risk of Australian acacias becoming invasive. Acacia species can attain invasive status by virtue of intrinsic traits and/or through increased use by humans. Therefore, we predict that the invasion risk of species coming from native areas with high water availability will rise sharply if the interest in exploiting these species increases.

9 páginas.-- 2 figuras.-- 4 tablas.-- 70 referencias.-- Material suplementario.-- Vaz, Ana S. et al...Limitations in the assessment of cultural ecosystem services through quantifiable approaches have constrained our knowledge of how these services can be altered by drivers of global change, such as non-native tree species. Here, we address this caveat by evaluating the effects of non-native tree species, in comparison to native ones, on several categories of cultural services, i.e., recreation and ecotourism, aesthetics, inspiration, and cultural heritage. We propose an indicator-based approach that includes the use of a meta-analysis statistics, the odds ratio, to evaluate photographic, internet and catalogue data, and to infer on the effects of non-native trees on cultural services. We apply our approach to the Iberian Peninsula, exploring potential environmental and socio-economic predictors of non-native tree effects across NUTS-2 administrative regions. Overall, non-native tree effects differed among categories of cultural services and varied with the data type. Non-native trees increased recreation and ecotourism services, when considering data from official tourism entities, but not from nature route users. Data from inventories of urban parks and catalogues of ornamental plant dealers suggested that non-native trees decreased aesthetics services, particularly in Spain, but not in Portugal. Non-native trees also increased cultural heritage services, but no significant effects were observed on inspiration services. Overall, non-native trees showed higher increases in cultural services across regions with lower levels of development (in terms of income, employment and education) and life satisfaction. We suggest that management should emphasise awareness on non-native trees, including the risks involved in promoting the expansion of potentially invasive species. Efforts to raise awareness should prioritise official tourism entities and ornamental plant dealers, with a special focus on less developed regions. Our proposed approach represents a pioneer assessment of the relations between non-native trees and cultural ecosystem services, supporting strategic management in Iberia. The focus on widely available data sources enables reproducibility and application in assessments worldwideFunding: A. S. Vaz was supported by the FSE/MEC and FCT (PhD grant PD/BD/52600/2014); J.R. Vicente by POPH/FSE and FCT (Post-Doc grant SFRH/BPD/84044/2012); O. Godoy by EUH2020 research and innovation program (Marie Sklodowska-Curie grant agreement No 661118-BioFUNC); Á. Bayon by Plan Estatal I+D+i (Spain) and ESF through a pre-doctoral contract. This work was supported by the FPS COST ActionFP1403 NNEXT: Non-native tree species for European forests − experiences, risks and opportunities, and TD1209 AlienChallenge: European Information System for Alien Species; and by the IMPLANTIN project (CGL2015-65346-R) of the Ministerio de Economía y Competitividad of Spain, and the REMEDINAL3-CM MAE-2719 network (Comunidad de Madrid).Peer reviewe