Impact of forest fire on radial growth of tree rings and their element concentrations of Pinus sylvestris and Larix gmelinii in northern China

AimsThrough analyzing the responses of the radial growth and element concentrations (B, Mg, Al, K, Ca, Mn, Fe, Zn, Na, P, Ni, and Cu) of tree rings of two dominant tree species to forest fires, we aimed to investigate the relationship between tree rings and the fires. MethodsWe sampled wood cores of Pinus sylvestris and Larix gmelinii in the northern forest region of China, where forest fires happened in 1990 and 2008. The ring-width growth of P. sylvestris and L. gmelinii from 1986 to 1995 and 2004 to 2013 in two sites of Tahe County were measured. Element concentrations in tree rings were determined using inductively coupled plasma mass spectrometry (ICP-MS). ResultsOur results showed that tree-ring radial growth was largely reduced after the fire, together with the increase in concentrations of B, Al, Mn, and Fe but the decrease in some samples in K. Strong correlations were observed between tree-ring growth and concentrations of Mg and Mn of P. sylvestris and Znof L. gmelinii. DiscussionThe results provide evidence that variations in tree-ring growth and element concentrations, particularly concentrations of B, Al, Mn, and Fe, are potentially useful to monitor forest fires, which add new insights into the study of forest fire history

Evaluation of geographically weighted logistic model and mixed effect model in forest fire prediction in northeast China

IntroductionForest fires seriously threaten the safety of forest resources and human beings. Establishing an accurate forest fire forecasting model is crucial for forest fire management.MethodsWe used different meteorological and vegetation factors as predictors to construct forest fire prediction models for different fire prevention periods in Heilongjiang Province in northeast China. The logistic regression (LR) model, mixed-effect logistic (mixed LR) model, and geographically weighted logistic regression (GWLR) model were developed and evaluated respectively.ResultsThe results showed that (1) the validation accuracies of the LR model were 77.25 and 81.76% in spring and autumn fire prevention periods, respectively. Compared with the LR model, both the mixed LR and GWLR models had significantly improved the fit and validated results, and the GWLR model performed best with an increase of 6.27 and 10.98%, respectively. (2) The three models were ranked as LR model < mixed LR model < GWLR model in predicting forest fire occurrence of Heilongjiang Province. The medium-and high-risk areas of forest fire predicted by the GWLR model were distributed in western and eastern parts of Heilongjiang Province in spring, and western part in autumn, which was consistent with the observed data. (3) Driving factors had strong temporal and spatial heterogeneities; different factors had different effects on forest fire occurrence in different time periods. The relationship between driving factors and forest fire occurrence varied from positive to negative correlations, whether it’s spring or autumn fire prevention period.DiscussionThe GWLR model has advantages in explaining the spatial variation of different factors and can provide more reliable forest fire predictions

A Renewable and Ultrasensitive Electrochemiluminescence Immunosenor Based on Magnetic RuL@SiO2-Au∼RuL-Ab2 Sandwich-Type Nano-Immunocomplexes

An ultrasensitive and renewable electrochemiluminescence (ECL) immunosensor was developed for the detection of tumor markers by combining a newly designed trace tag and streptavidin-coated magnetic particles (SCMPs). The trace tag (RuL@SiO2-Au∼RuL-Ab2) was prepared by loading Ru(bpy)32+(RuL)-conjuged secondary antibodies (RuL-Ab2) on RuL@SiO2 (RuL-doped SiO2) doped Au (RuL@SiO2-Au). To fabricate the immunosensor, SCMPs were mixed with biotinylated AFP primary antibody (Biotin-Ab1), AFP, and RuL@SiO2-Au∼RuL-Ab2 complexes, then the resulting SCMP/Biotin-Ab1/AFP/RuL@SiO2-Au∼RuL-Ab2 (SBAR) sandwich-type immunocomplexes were absorbed on screen printed carbon electrode (SPCE) for detection. The immunocomplexes can be easily washed away from the surface of the SPCE when the magnetic field was removed, which made the immunosensor reusable. The present immunosensor showed a wide linear range of 0.05–100 ng mL−1 for detecting AFP, with a low detection limit of 0.02 ng mL−1 (defined as S/N = 3). The method takes advantage of three properties of the immunosensor: firstly, the RuL@SiO2-Au∼RuL-Ab2 composite exhibited dual amplification since SiO2 could load large amount of reporter molecules (RuL) for signal amplification. Gold particles could provide a large active surface to load more reporter molecules (RuL-Ab2). Accordingly, through the ECL response of RuL and tripropylamine (TPA), a strong ECL signal was obtained and an amplification analysis of protein interaction was achieved. Secondly, the sensor is renewable because the sandwich-type immunocomplexes can be readily absorbed or removed on the SPCE’s surface in a magnetic field. Thirdly, the SCMP modified probes can perform the rapid separation and purification of signal antibodies in a magnetic field. Thus, the present immunosensor can simultaneously realize separation, enrichment and determination. It showed potential application for the detection of AFP in human sera

Dissecting the Shared Genetic Architecture of Suicide Attempt, Psychiatric Disorders, and Known Risk Factors

Background Suicide is a leading cause of death worldwide, and nonfatal suicide attempts, which occur far more frequently, are a major source of disability and social and economic burden. Both have substantial genetic etiology, which is partially shared and partially distinct from that of related psychiatric disorders. Methods We conducted a genome-wide association study (GWAS) of 29,782 suicide attempt (SA) cases and 519,961 controls in the International Suicide Genetics Consortium (ISGC). The GWAS of SA was conditioned on psychiatric disorders using GWAS summary statistics via multitrait-based conditional and joint analysis, to remove genetic effects on SA mediated by psychiatric disorders. We investigated the shared and divergent genetic architectures of SA, psychiatric disorders, and other known risk factors. Results Two loci reached genome-wide significance for SA: the major histocompatibility complex and an intergenic locus on chromosome 7, the latter of which remained associated with SA after conditioning on psychiatric disorders and replicated in an independent cohort from the Million Veteran Program. This locus has been implicated in risk-taking behavior, smoking, and insomnia. SA showed strong genetic correlation with psychiatric disorders, particularly major depression, and also with smoking, pain, risk-taking behavior, sleep disturbances, lower educational attainment, reproductive traits, lower socioeconomic status, and poorer general health. After conditioning on psychiatric disorders, the genetic correlations between SA and psychiatric disorders decreased, whereas those with nonpsychiatric traits remained largely unchanged. Conclusions Our results identify a risk locus that contributes more strongly to SA than other phenotypes and suggest a shared underlying biology between SA and known risk factors that is not mediated by psychiatric disorders.Peer reviewe

Modeling Anthropogenic Fire Occurrence in the Boreal Forest of China Using Logistic Regression and Random Forests

Frequent and intense anthropogenic fires present meaningful challenges to forest management in the boreal forest of China. Understanding the underlying drivers of human-caused fire occurrence is crucial for making effective and scientifically-based forest fire management plans. In this study, we applied logistic regression (LR) and Random Forests (RF) to identify important biophysical and anthropogenic factors that help to explain the likelihood of anthropogenic fires in the Chinese boreal forest. Results showed that the anthropogenic fires were more likely to occur at areas close to railways and were significantly influenced by forest types. In addition, distance to settlement and distance to road were identified as important predictors for anthropogenic fire occurrence. The model comparison indicated that RF had greater ability than LR to predict forest fires caused by human activity in the Chinese boreal forest. High fire risk zones in the study area were identified based on RF, where we recommend increasing allocation of fire management resources

Investigating Drought Events and Their Consequences in Wildfires: An Application in China

Understanding the impact of drought on fire dynamics is crucial for assessing the potential effects of climate change on wildfire activity in China. In this study, we present a series of multiple linear regression (MLR) models linking burned area (BA) during mainland China's fire season from 2001 to 2019, across seven regions, to concurrent drought, antecedent drought, and time trend. We estimated burned area using Collection 6 Moderate Resolution Imaging Spectradiometer (MODIS) and drought indicators using either the Standardized Precipitation Evapotranspiration Index (SPEI) or the self-calibrated Palmer Drought Severity Index (sc-PDSI). Our findings indicate that the wildfire season displays a spatial variation pattern that increases with latitude, with the Northeast China (NEC), North China (NC), and Central China (CC) regions identified as the primary areas of wildfire occurrence. Concurrent and antecedent drought conditions were found to have varying effects across regions, with concurrent drought as the dominant predictor for NEC and Southeast China (SEC) regions and antecedent drought as the key predictor for most regions. We also found that the Northwest China (NWC) and CC regions exhibit a gradual decrease in burned area over time, while the NEC region showed a slight increase. Our multiple linear regression models exhibited a notable level of predictive power, as evidenced by the average correlation coefficient of 0.63 between the leave-one-out cross-validation predictions and observed values. In particular, the NEC, NWC, and CC regions demonstrated strong correlations of 0.88, 0.80, and 0.76, respectively. This indicates the potential of our models to contribute to the prediction of future wildfire occurrences and the development of effective wildfire management and prevention strategies. Nevertheless, the intricate relationship among fire, climate change, human activities, and vegetation distribution may limit the generalizability of these findings to other conditions. Consequently, future research should consider a broad range of factors to develop more comprehensive models

Anthropogenic and Biophysical Factors Associated with Vegetation Restoration in Changting, China

Changting has been promoted by the Chinese government as a demonstration for the soil and water conservation works in recent years. As the experience in Changting is now being explored and summarized and will be further promoted to the nation, it is important to understand the factors affecting the vegetation restoration process. Random forest and multiple linear regression approaches were applied to investigate the influential factors and their relative importance on the dynamic change of vegetation coverage of Changting during the period of 2000–2010. The Normalized Difference Vegetation Index (NDVI) was used to calculate Fractional Vegetation Cover (FVC) dynamics in response to topographic, climatic, infrastructure, and economic factors. The results show that overall, there was a dramatic increase in the FVC of Changting from 2000–2010. The percentage of the FVC-increased area reached 87.86% with an increase rate of 0.142. Factors such as precipitation, temperature, elevation, slope and financial investment for soil conservation were important drivers of local FVC change. Our findings reveal that climatic factors along with the strict implementation of government policies played a role in driving vegetation cover dynamics, and the continuation of implementation of soil erosion management in Changting is required

Effects of fire disturbance on soil respiration in the non-growing season in a <i>Larix gmelinii</i> forest in the Daxing'an Mountains, China

<div><p>In boreal forests, fire is an important part of the ecosystem that greatly influences soil respiration, which in turn affects the carbon balance. Wildfire can have a significant effect on soil respiration and it depends on the fire severity and environmental factors (soil temperature and snow water equivalent) after fire disturbance. In this study, we quantified post-fire soil respiration during the non-growing season (from November to April) in a <i>Larix gmelinii</i> forest in Daxing'an Mountains of China. Soil respiration was measured in the snow-covered and snow-free conditions with varying degrees of natural burn severity forests. We found that soil respiration decreases as burn severity increases. The estimated annual C efflux also decreased with increased burn severity. Soil respiration during the non-growing season approximately accounted for 4%–5% of the annual C efflux in all site types. Soil temperature (at 5 cm depth) was the predominant determinant of non-growing season soil respiration change in this area. Soil temperature and snow water equivalent could explain 73%–79% of the soil respiration variability in winter snow-covering period (November to March). Mean spring freeze–thaw cycle (FTC) period (April) soil respiration contributed 63% of the non-growing season C efflux. Our finding is key for understanding and predicting the potential change in the response of boreal forest ecosystems to fire disturbance under future climate change.</p></div

Are Climate Factors Driving the Contemporary Wildfire Occurrence in China?

Understanding the drivers of wildfire occurrence is of great value for fire prevention and management, but due to the variation in research methods, data sources, and data resolution of those studies, it is challenging to conduct a large-scale comprehensive comparative qualitative analysis on the topic. China has diverse vegetation types and topography, and has undergone rapid economic and social development, but experiences a high frequency of wildfires, making it one of the ideal locations for wildfire research. We applied the Random Forests modelling approach to explore the main types of wildfire drivers (climate factors, landscape factors and human factors) in three high wildfire density regions (Northeast (NE), Southwest (SW), and Southeast (SE)) of China. The results indicate that climate factors were the main driver of wildfire occurrence in the three regions. Precipitation and temperature significantly impacted the fire occurrence in the three regions due to the direct influence on the moisture content of forest fuel. However, wind speed had important influence on fire occurrence in the SE and SW. The explanation power of the landscape and human factors varied significantly between regions. Human factors explained 40% of the fire occurrence in the SE but only explained less than 10% of the fire occurrence in the NE and SW. The density of roads was identified as the most important human factor driving fires in all three regions, but railway density had more explanation power on fire occurrence in the SE than in the other regions. The landscape factors showed nearly no influence on fire occurrence in the NE but explained 46.4% and 20.6% in the SE and SW regions, respectively. Amongst landscape factors, elevation had the highest average explanation power on fire occurrence in the three regions, particularly in the SW. In conclusion, this study provides useful insights into targeted fire prediction and prevention, which should be more precise and effective under climate change and socio-economic development.Forestry, Faculty ofNon UBCReviewedFacultyResearche

Vegetative composition of the experimental plots.

<p>Vegetative composition of the experimental plots.</p