Climate change reduces elevational and latitudinal differences in spring phenology of pine caterpillar (Dendrolimus spectabilis Bulter).

The pine caterpillar (Dendrolimus spectabilis Bulter, Lepidoptera: Lasiocampidae), as an ectotherm, temperature plays a crucial role in its development. With climate change, earlier development of insect pests is expected to pose a more frequent threat to forest communities. Yet the quantitative research about the extent to which global warming affects pine caterpillar populations is rarely understood, particularly across various elevations and latitudes. Spring phenology of pine caterpillars showed an advancing trend with 0.8 d/10a, 2.2 d/10a, 2.2 d/10a, and 3.3 d/10a under the SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5 scenario, respectively. There was a maximum advance of 20 d in spring phenology of pine caterpillars during the 2090s, from mid-March to early March, and even late February. This study highlighted the significant advance in spring phenology at elevations >1000 m and lower latitudes. Consequently, the differences in elevational and latitudinal gradients were relatively small as the increasing temperatures at the end of the 21st century. And the average temperature in February-March was effective in explaining theses variability. These findings are crucial for adapting and mitigating to climate change.

Rapid Determination of Low Heavy Metal Concentrations in Grassland Soils around Mining Using Vis-NIR Spectroscopy: A Case Study of Inner Mongolia, China.

Proximal sensing offers a novel means for determination of the heavy metal concentration in soil, facilitating low cost and rapid analysis over large areas. In this respect, spectral data and model variables play an important role. Thus far, no attempts have been made to estimate soil heavy metal content using continuum-removal (CR), different preprocessing and statistical methods, and different modeling variables. Considering the adsorption and retention of heavy metals in spectrally active constituents in soil, this study proposes a method for determining low heavy metal concentrations in soil using spectral bands associated with soil organic matter (SOM) and visible-near-infrared (Vis-NIR). To rapidly determine the concentration of heavy metals using hyperspectral data, partial least squares regression (PLSR), principal component regression (PCR), and support vector machine regression (SVMR) statistical methods and 16 preprocessing combinations were developed and explored to determine an optimal combination. The results showed that the multiplicative scatter correction and standard normal variate preprocessing methods evaluated with the second derivative spectral transformation method could accurately determine soil Cr and Ni concentrations. The root-mean-square error (RMSE) values of Vis-NIR model combinations with PLSR, PCR, and SVMR were 0.34, 3.42, and 2.15 for Cr, and 0.07, 1.78, and 1.14 for Ni, respectively. Soil Cr and Ni showed strong spectral responses to the Vis-NIR spectral band. The R2 value of the Vis-NIR-based PLSR model was higher than 0.99, and the RMSE value was 0.07-0.34, suggesting higher stability and accuracy. The results were more accurate for Ni than Cr, and PLSR showed the best performance, followed by SVMR and PCR. This perspective has critical implications for guiding quantitative biogeochemical analysis using proximal sensing data.

Environmental Risk Assessment of Metals in the Volcanic Soil of Changbai Mountain.

Tianchi volcano is a dormant active volcano with a risk of re-eruption. Volcanic soil and volcanic ash samples were collected around the volcano and the concentrations of 21 metals (major and trace elements) were determined. The spatial distribution of the metals was obtained by inverse distance weight (IDW) interpolation. The metals' sources were identified and their pollution levels were assessed to determine their potential ecological and human health risks. The metal concentrations were higher around Tianchi and at the north to the west of the study area. According to the geo-accumulation index (Igeo), enrichment factor (EF) and contamination factor (CF) calculations, Zn pollution was high in the study area. Pearson's correlation analysis and principal component analysis showed that with the exception of Fe, Mn and As, the metals that were investigated (Al, K, Ca, Na, Mg, Ti, Cu, Pb, Zn, Cr, Ni, Ba, Ga, Li, Co, Cd, Sn, Sr) were mostly naturally derived. A small proportion of Li, Pb and Zn may have come from vehicle traffic. There is no potential ecological risk and non-carcinogenic risk because of the low concentrations of the metals; however, it is necessary to pay attention to the carcinogenic risk of Cr and As in children.