Possible changes in spatial distribution of walnut (Juglans regia L.) in Europe under warming climate

Juglans regia L. is a species of great importance for environmental management due to attractive wood and nutritious fruits, but also high invasive potential. Thus, uncertainties connected with its range shift are essential for environmental management. We aimed to predict the future climatic optimum of J. regia in Europe under changing climate, to assess the most important climatic factors that determine its potential distribution, and to compare the results obtained among three different global circulation models (GCMs). We used distribution data from the Global Biodiversity Information Facility and completed it with data from the literature. Using the MaxEnt algorithm, we prepared a species distribution model for the years 2061-2080 using 19 bioclimatic variables. We applied three emission scenarios, expressed by representative concentration pathways (RCPs): RCP2.6, RCP4.5, and RCP8.5 and three GCMs: HadGEM2-ES, IPSL-CM5A-LR, and MPI-SM-LR. Our study predicted northward shift of the species, with simultaneous distribution loss at the southern edge of the current range, driven by increasing climate seasonality. Temperature seasonality and temperature annual range were the predictors of highest importance. General trends are common for the projections presented, but the variability of our projections among the GCMs or RCPs applied (predicted range will contract from 17.4 to 84.6% of the current distribution area) shows that caution should be maintained while managing J. regia populations. Adaptive measures should focus on maintaining genetic resources and assisted migration at the southern range edge, due to range contraction. Simultaneously, at the northern edge of the range, J. regia turns into an invasive species, which may need risk assessments and control of unintended spread.

Automated summary

The study predicts a northward shift and distribution loss at the southern edge of Juglans regia's current range, driven by increasing climate seasonality. Temperature seasonality and temperature annual range are the most important predictors of its potential distribution.