Spatio-temporal divergence in the responses of Finland’s boreal forests to climate variables

The effect of urban morphological characteristics on the spatial variation of PM2.5 air quality in downtown Nanjing

“…While PLS regression was rst applied in the social sciences, its usefulness in geosciences has been recently proved in many studies. [40][41][42][43] The statistical signicancy of the PLS regression analysis is shown using the cross-validated R 2 , which is indicating the square of the correlation between the actual and predicted values and it is called Q 2 in the PLS analysis. A PLS analysis is assumed to be statistically signicant if the Q 2 value is greater than or equal to 0.0975.…”

“…While PLS regression was first applied in the social sciences, its usefulness in geosciences has been recently proved in many studies. 40–43…”

Spatiotemporal changes in the boreal forest in Siberia over the period 1985–2015 against the background of climate change

“…Considering the delay response of the forest to the climate change, the climate data of the time intervals 5-year, 10-year and 15-year before the forest change were chosen for the influence analysis respectively, to determine which the driving interval was. The climate variables used in this study included the average annual temperature (TEM_Year), the growing season temperature (TEM_Grow: the average temperature during the growing season (June to September)), the average maximum temperature during the growing season (TEM_Growmax), the average minimum temperature during the growing season (TEM_Growmin), the total annual precipitation (PRE_Year) and the total precipitation during the growing season (PRE_Grow), which have also been used in some previous researches (Hou, et al,2020, He, et al, 2020. The statistics of climate variables in the study area from 1971 to 2015 are shown in Figure 10.…”

“…The VIP scores for the six climate variables were ranked in descending order. The independent variable with the highest VIP score was considered to be the most important variable (Hou et al, 2020). To clearly show the effect of each climate variable on the forest cover and species type for each time interval (5 years, 10 years or 15 years), we also calculated the correlation coefficients (Table 5) and Q 2 value for each PLS regression model.…”

“…It has been observed that the growth of boreal forest has been influenced by global warming in the past decade or more. However, there are clear spatiotemporal differences in these effects (Hou et al, 2020;Alibakhshi et al, 2020). Models and investigations have suggested that warming will induce northern migration of the treeline and an alteration in the mosaic structure of boreal forests; it has also been shown that, as temperatures increase, white spruce tree growth is declining (Soja et al, 2007).…”