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Birch pollen grains (BPGs) are major aeroallergens in Europe, causing allergies in millions of people. Although background concentrations are provided by stationary pollen monitoring stations, they do not accurately reflect personal exposure. Knowing personal exposure makes it possible to establish a link between inhaled pollen grains and symptoms. Characterizing personal exposure to pollen using portable devices is challenging and requires time-consuming visual counting of pollen grains. We have developed a method for counting sedimented BPGs based on easy sampling using a handheld hoover and automated analysis by gas chromatography-mass spectrometry (GC-MS). This work is a feasibility study on the use of a lipid tracer (heptacosane) for the mass quantification of sedimented BPGs in outdoor and indoor environments. Before a lipid tracer could be used for the determination of BPGs, we ensured that the variability of total lipid mass was low (around 17%) for pollen samples from various geographical origins and for several pollen seasons. The limit of quantification of sedimented BPGs by GC-MS was estimated to be 100 µg (equivalent to 16,000 BPGs), i.e. about 1.6 BPG cm −2 for a sampled surface of 1 m 2 . This method of assessing individual exposure was implemented during the birch pollen season indoors (between 70 and 225 sedimented BPGs per cm 2 ) and outdoors, directly on the ground under a birch tree (over 6600 BPGs per cm 2 ). This method of counting sedimented pollen grains is suitable for large sample series, and the data obtained could be used as an indicator of individual exposure to indoor air pollen in a large number of patients as part of epidemiological surveys.

Zinc is an essential trace element and a vital microelement for human health. Zinc can be toxic when exposures exceed physiological needs. Toxic effects in humans are most evident from inhalation exposure to high concentrations of Zn compounds. Urban air pollution can be especially dangerous due to the Zn content in airborne dust. Tree leaves can absorb significant levels of zinc. In this study, leaf deposition of Zn was investigated in Chelyabinsk, Russia. Russian zinc production plant and metallurgical plant are located in Chelyabinsk. Extremely high concentrations of Zn (316–4000 mg kg−1) were found in the leaves of birch trees. It is well known that traffic also is Zn source in an urban environment. Trees, growing at the different distances from zinc production and metallurgical plants and road to identify the contribution of each source (road or industry), were studied. Through SEM analysis, the prevalence of small particulates (PM10 and less), containing Zn, illustrated leaf Zn deposition from the air by passing root accumulation. It was shown that emission of zinc production plant and the metallurgical plant is the main source of leaf Zn deposition in Chelyabinsk.

The aim of this study was to investigate degraded birch trees (Betula pendula Roth) that suffered from a harmful fungus called Piptoporus betulinus. The main chemical analysis of B. pendula degraded by the fungus, included the holocellulose, alpha-cellulose, and lignin contents and was determined in cold and hot water and alcohol-benzene solubility in 1% NaOH mixtures. This fungus caused B. pendula to lose mass and chemical properties. The declining amount of holocellulose mass loss was 6.7% according to the holocellulose test. This decrement caused the quality of the birch holocellulose to decline. The total loss difference was 9.8% according to the alkaline solubility analysis of the 1% NaOH test and 14.3% according to the density analysis of the test. The loss difference was 4.2% according to the alcohol-benzene analysis of the test.

In the Czech Republic, the silver birch (Betula pendula Roth.) is considered as a pioneer and a soil preparing tree species. It occurs mainly on clearcutting areas after disturbances. The aim of this study was to fit breast height diameter increment model for birch with respect to tree age, share of birch trees and forest site type (ecological series – ES and forest vegetation zones – FVZ). We used data of both cycles of National Forest Inventory of the Czech Republic. We evaluated production potential of this species. We tested Korf and Michailoff increment models in variant of nonlinear least squares model (NLS) and nonlinear mixed effects model (NLME). Michailoff models performed better. We found seven statistically significant and practically applicable models. The greatest influence on increment of diameter at breast height have forest vegetation zone and ecological series whereas the influence of the share of birch in forest stand is smaller. The highest absolute values of diameter increment were on gleyed or enriched with water sites in the fourth forest vegetation zone.

Accurate information regarding forest tree species composition is useful for a wide range of applications, both for forest management and scientific research. Remote sensing is an efficient tool for collecting spatially explicit information on forest attributes. With the launch of the Sentinel-2 mission, new opportunities have arisen for mapping tree species owing to its spatial, spectral, and temporal resolution. The short revisit cycle (five days) is crucial in vegetation mapping because of the reflectance changes caused by phenological phases. In our study, we evaluated the utility of the Sentinel-2 time series for mapping tree species in the complex, mixed forests of the Polish Carpathian Mountains. We mapped the following nine tree species: common beech, silver birch, common hornbeam, silver fir, sycamore maple, European larch, grey alder, Scots pine, and Norway spruce. We used the Sentinel-2 time series from 2018, with 18 images included in the study. Different combinations of Sentinel-2 imagery were selected based on mean decrease accuracy (MDA) and mean decrease Gini (MDG) measures, in addition to temporal phonological pattern analysis. Tree species discrimination was performed using the Random Forest classification algorithm. Our results showed that the use of the Sentinel-2 time series instead of single date imagery significantly improved forest tree species mapping, by approximately 5–10% of overall accuracy. In particular, combining images from spring and autumn resulted in better species discrimination.

Birch ( Betula ) trees and forests are found across much of the temperate and boreal zones of the Northern Hemisphere. Yet, despite being an ecologically significant genus, it is not well studied compared to other genera like Pinus , Picea , Larix , Juniperus , Quercus , or Fagus . In the Himalayas, Himalayan birch ( Betula utilis ) is a widespread broadleaf timberline species that survives in mountain rain shadows via access to water from snowmelt. Because precipitation in the Nepalese Himalayas decreases with increasing elevation, we hypothesized that the growth of birch at the upper timberlines between 3900 and 4150 m above sea level is primarily limited by moisture availability rather than by low temperature. To examine this assumption, a total of 292 increment cores from 211 birch trees at nine timberline sites were taken for dendroecological analysis. The synchronous occurrence of narrow rings and the high interseries correlations within and among sites evidenced a reliable cross-dating and a common climatic signal in the tree-ring width variations. From March to May, all nine tree-ring-width site chronologies showed a strong positive response to total precipitation and a less-strong negative response to temperature. During the instrumental meteorological record (from 1960 to the present), years with a high percentage of locally missing rings coincided with dry and warm pre-monsoon seasons. Moreover, periods of below-average growth are in phase with well-known drought events all over monsoon Asia, showing additional evidence that Himalayan birch growth at the upper timberlines is persistently limited by moisture availability. Our study describes the rare case of a drought-induced alpine timberline that is comprised of a broadleaf tree species.

Environmental stress conditions such as drought, heat, salinity, cold, or pathogen infection can have a devastating impact on plant growth and yield under field conditions. Nevertheless, the effects of these stresses on plants are typically being studied under controlled growth conditions in the laboratory. The field environment is very different from the controlled conditions used in laboratory studies, and often involves the simultaneous exposure of plants to more than one abiotic and/or biotic stress condition, such as a combination of drought and heat, drought and cold, salinity and heat, or any of the major abiotic stresses combined with pathogen infection. Recent studies have revealed that the response of plants to combinations of two or more stress conditions is unique and cannot be directly extrapolated from the response of plants to each of the different stresses applied individually. Moreover, the simultaneous occurrence of different stresses results in a high degree of complexity in plant responses, as the responses to the combined stresses are largely controlled by different, and sometimes opposing, signaling pathways that may interact and inhibit each other. In this review, we will provide an update on recent studies focusing on the response of plants to a combination of different stresses. In particular, we will address how different stress responses are integrated and how they impact plant growth and physiological traits.

As northern latitudes experience rapid winter warming, there is an urgent need to assess the effect of varying winter conditions on tree growth and forest carbon sequestration potential. We examined tree growth responses to variability in cold-season (November–April) frequency of freeze days (FFD) over 1951 to 2018 using tree-ring data from 35,217 trees and 57 species at 4,375 sites distributed across Canada. We found that annual radial growth responses to FFD varied by species, with some commonalities across genera and clades. The growth of gymnosperms with late spring leaf-out strategies was negatively related to FFD; years with high FFD were most detrimental to the annual growth of Pinus banksiana, Pinus contorta, Larix lyalli, Abies amabilis, and Abies lasiocarpa. In contrast, the growth of angiosperms with early leaf-out strategies, namely, Populus tremuloides and Betula papyrifera, was better in the coldest years, and gymnosperms with intermediate leaf-out timing, such as widespread Picea mariana and Picea glauca, had no consistent relationship to FFD. Tree growth responses to FFD were further modulated by tree size, tree age, regional climate (i.e., mean cold-season temperature), and local site conditions. Overall, our results suggest that moderately warming winters may temporarily improve the growth of widespread pines and some high-elevation conifers in western Canada, whereas warming winters may be detrimental to the growth of widespread boreal angiosperms. Our findings also highlight the value of using species-specific climate-growth relationships to refine predictions of forest carbon dynamics.

Interest in drone solutions in forestry applications is growing. Using drones, datasets can be captured flexibly and at high spatial and temporal resolutions when needed. In forestry applications, fundamental tasks include the detection of individual trees, tree species classification, biomass estimation, etc. Deep neural networks (DNN) have shown superior results when comparing with conventional machine learning methods such as multi-layer perceptron (MLP) in cases of huge input data. The objective of this research is to investigate 3D convolutional neural networks (3D-CNN) to classify three major tree species in a boreal forest: pine, spruce, and birch. The proposed 3D-CNN models were employed to classify tree species in a test site in Finland. The classifiers were trained with a dataset of 3039 manually labelled trees. Then the accuracies were assessed by employing independent datasets of 803 records. To find the most efficient set of feature combination, we compare the performances of 3D-CNN models trained with hyperspectral (HS) channels, Red-Green-Blue (RGB) channels, and canopy height model (CHM), separately and combined. It is demonstrated that the proposed 3D-CNN model with RGB and HS layers produces the highest classification accuracy. The producer accuracy of the best 3D-CNN classifier on the test dataset were 99.6%, 94.8%, and 97.4% for pines, spruces, and birches, respectively. The best 3D-CNN classifier produced ~5% better classification accuracy than the MLP with all layers. Our results suggest that the proposed method provides excellent classification results with acceptable performance metrics for HS datasets. Our results show that pine class was detectable in most layers. Spruce was most detectable in RGB data, while birch was most detectable in the HS layers. Furthermore, the RGB datasets provide acceptable results for many low-accuracy applications.

Silver birch (Betula pendula) is a pioneer boreal tree that can be induced to flower within 1 year. Its rapid life cycle, small (440-Mb) genome, and advanced germplasm resources make birch an attractive model for forest biotechnology. We assembled and chromosomally anchored the nuclear genome of an inbred B. pendula individual. Gene duplicates from the paleohexaploid event were enriched for transcriptional regulation, whereas tandem duplicates were overrepresented by environmental responses. Population resequencing of 80 individuals showed effective population size crashes at major points of climatic upheaval. Selective sweeps were enriched among polyploid duplicates encoding key developmental and physiological triggering functions, suggesting that local adaptation has tuned the timing of and cross-talk between fundamental plant processes. Variation around the tightly-linked light response genes PHYC and FRS10 correlated with latitude and longitude and temperature, and with precipitation for PHYC. Similar associations characterized the growth-promoting cytokinin response regulator ARR1, and the wood development genes KAK and MED5A.