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

Abstract

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.

European beewolf (Philanthus triangulum) will expand its geographic range as a result of climate warming

“…The pessimistic scenario (RCP8.5) assumes a CO 2 concentration of 1350 ppm and an average temperature increase of 2.6-4.8 °C by 2100this is a scenario of intensive economic growth with intensive use of fossil fuels (van Vuuren et al 2011;Harris et al 2014;IPCC 2014). To account for uncertainty across different global circulation models (GCMs) used in predicting future values of bioclimatic variables (Thuiller et al 2019;Paź-Dyderska et al 2021) , we used three GCMs and averaged the output to provide a more robust forecasts of range shifts. The three GCMs were -HadGEM2-ES , IPSL-CM5A-LR (Dufresne et al 2013), and MPI-SM-LR (Giorgetta et al 2013), reflecting, respectively, low, moderate, and high levels of change in climate conditions depending on scenarios of CO 2 concentration (Goberville et al 2015) .…”

Diversity in Fruit Morphology and Nutritional Composition of Juglans mandshurica Maxim in Northeast China

“…Besides genetic control, geographical and ecological conditions are important factors in determining the growth and fruit quality of walnut ( Diaz et al, 2005 ; Xu et al, 2016 ; Paź-Dyderska et al, 2021 ). It has been demonstrated that the interaction between genetic effects and environmental factors contribute to the heritability and thus the level of variation of many traits in walnut ( Sarikhani et al, 2014 ).…”

Potential European Geographical Distribution of Gnathotrichus materiarius (Fitch, 1858) (Coleoptera: Scolytinae) under Current and Future Climate Conditions

“…The climate is the primary determinant of species distributions on a large spatial scale, affecting other interactions [122]. Another source of uncertainty in our model is the dependence on GCMs used in the study [110][111][112]. We decreased this uncertainty by averaging model predictions for four different GCMs.…”

“…We applied models to maps of current and future climatic scenarios to obtain predictions of G. materiarius distribution. For each SSP we averaged predicted species occurrence probability across the four GCMs, to reduce uncertainty connected with particular GCMs [110][111][112]. Then, maps with threshold values (true/false) were used to estimate the changes in the potential range, changing the values on maps with the future potential range from 1 to 2 [113].…”