Dataset on Potential Distribution of Non-native Tree species in Europe

Dataset on Potential Distribution of Non-native Tree Species in Europe Debojyoti Chakraborty1, João P. Honrado2,3,4, João A. Cabral5, Julian Hafner6, Thomas Wohlgemuth7, Oscar Godoy8, Elisabeth Schatzdorfer (née Pötzelsberger)9&10, Joana Vicente2,3,4 1Austrian Research Centre for Forests BFW, Vienna, Austria 2CIBIO Research Centre in Biodiversity and Genetic Resources, InBIO Associate Laboratory, Campus Agrário de Vairão, Rua Padre Armando Quintas 7, 4485-661 Vairão, Portugal 3BIOPOLIS Program in Genomics, Biodiversity and Land Planning, Campus Agrário de Vairão, 4485-661 Vairão, Portugal 4Department of Biology, Faculty of Sciences, University of Porto, Rua do Campo Alegre s/n, 4169-007 Porto, Portugal 5CITAB - Centre for the Research and Technology of Agro-Environment and Biological Sciences, Institute for Innovation. Capacity Building and Sustainability of Agri-food Production (Inov4Agro), University of Trás-os-Montes e Alto Douro, Vila Real, Portugal 6Stadtgärtnerei, Bau- und Verkehrsdepartement Kt. Basel-Stadt, Basel, Switzerland 7Research Unit Forest Dynamics, Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Switzerland 8Estación Biológica de Doñana (EBD-CSIC), Sevilla, Spain 9University of Natural Resources and Life Sciences (BOKU), Vienna, Austria 10Resilience Programme, European Forest Institute, BONN, Germany Summary The potential distributions (probability of occurrence) of 15 non-native tree species (NNTs) were estimated with a multi-model species distribution model (SDM), which ensembles 10 SDM models implemented through the R package biomod2(Thuiller et al. 2016). These SDMs are based on the correlation between occurrence data and bioclimatic variables. Three sets of SDMs for the NNTs were calibrated, each with the observed occurrence and bioclimatic variables of the NNTs in their i) native range, ii) non-native or introduced range in Europe, and iii) combination of both native and non-native distribution range. Since the True Skill Statistics (TSS) of SDMs calibrated with occurrence data from both native and non-native ranges were significantly higher than those using data from only the i) native range or ii) non-native (introduced) range in Europe, we present only the results of the former in this dataset. The occurrence data for the 15 NNTs at their native and introduced range in Europe was obtained from various sources such as National Forest Inventories, Global biodiversity facilities, etc. collected by the COST Action NNEXT. With this approach, the potential distribution of each species was estimated by each of the 10 SDM models and ensemble model in biomod2 in historical climate (1961-1990) and two future climate scenarios, SSP2-4.5 (comparable to RCP4.5) and SSP5-8.5 (comparable to RCP8.5), for the period 2041-2060, 2061-2080, and 2081-2100 for each of the 13 GCM projections available in the Worldclim database v2.0(Fick and Hijmans 2017). For each species the ensemble model was developed as a consensus model, which combined the median probability over the selected models with the True Skill Statistics threshold (TSS > 0.7). Additional data in this version To account for uncertainty, standard deviation in potential distribution due to 10 SDM models in historical climate (1961-90) and due to 13 GCMs under SSP2-4.5 and SSP5-8.5 scenarios for the period 2041-2060, 2061-2080, and 2081-2100 were mapped. The model ensembles and associated standard deviations are predicted within the geographic boundary of Europe defined by -10.61667, 38.55833, 34.56226, 71.18726 (xmin, xmax, ymin, ymax). The predicted potential distributions for historical and future climates are available as GeoTiff rasters. Description of the dataset Spatial extent (Lon/ Lat): -10.61667, 38.55833, 34.56226, 71.18726 (xmin, xmax, ymin, ymax) Temporal resolution: We modeled for historic climate (1961-90) and three future time frames, which include averages of (2041-2060, 2061-2080, and 2081-2100). The predictions were done for two future climate scenarios, SSP2-4.5 (comparable to RCP4.5) and SSP5-8.5 (comparable to RCP8.5) Spatial resolution: 30 arcsec Geographic projection: WGS 84 (EPSG: 4326) See the Data Description file for details of the SDMs used to generate the dataset.