Data analysis
Generalized Linear Models (GLM) were used for understanding how
environmental variables can be used to predict the current distribution
of pine trees in western Fennoscandia. The spatial extent of the model
includes Norway, Sweden and north Finland, while the temporal extend is
the year 2023 for the current predictions and 2073 for the forecast.
Three independent GLMs with a binomial and logit link function were
specifically used to test the current pine distribution (presence vs.
absence) set as a response to temperature (min) and precipitation as
fixed factors sourced from the CMIP5 data. Each of the GLMs employed
three distinct datasets (PLOT, NFI, ART). An interaction between
temperature and precipitation was also tested, but it was omitted from
this study because it did not improve the fit of the model. The fit of
the model was evaluated by comparing the AIC values. The model diagnosis
was done by assessing the spread of the residual and by plotting a
Receiver Operator Characteristic Curve (ROC), both tools widely used in
SDM . The area under the ROC curve (AUC) was also extracted, which
indicates the efficiency of the model to predict. The value ranges
between 0 and 100, with 100 being a highly accurate model. A p-value
< 0.05 was used as the threshold for a significant
relationship between response and predictors. The forecast of the pine
distribution was made by extrapolating to a map of Fennoscandia the
three GLM responses linked to the future environmental data sourced from
CMIP5. The AUC and the coefficient of determination was used to rank the
models with distinct data collection methods. All statistical analysis
was conducted in “R 4.0.2” . Maps and figures were done in “R” with
the packages “ggplot2” version 3.3.5 and “ggmap” version 3.0.0.903 .