Background Herbs are an important part of the forest ecosystem, and their diversity and biomass can reflect the restoration of vegetation after forest thinning disturbances. Based on the near-mature secondary coniferous and broad-leaved mixed forest in Jilin Province Forestry Experimental Zone, this study analyzed seasonal changes of species diversity and biomass of the understory herb layer after different intensities of thinning. Results The results showed that although the composition of herbaceous species and the ranking of importance values were affected by thinning intensity, they were mainly determined by seasonal changes. Across the entire growing season, the species with the highest importance values in thinning treatments included Carex pilosa, Aegopodium alpestre, Meehania urticifolia, and Filipendula palmata, which dominated the herb layer of the coniferous and broad-leaved mixed forest. The number of species, Margalef index, Shannon-Wiener index and Simpson index all had their highest values in May, and gradually decreased with months. Pielou index was roughly inverted “N” throughout the growing season. Thinning did not increase the species diversity. Thinning can promote the total biomass, above- and below-ground biomass. The number of plants per unit area and coverage were related to the total biomass, above- and below-ground biomass. The average height had a significantly positive correlation with herb biomass in May but not in July. However, it exerted a significantly negative correlation with herb biomass in September. The biomass in the same month increased with increasing thinning intensity. Total herb biomass, above- and below-ground biomass showed positive correlations with Shannon-Winner index, Simpson index and Pielou evenness index in May. Conclusions Thinning mainly changed the light environment in the forest, which would improve the plant diversity and biomass of herb layer in a short time. And different thinning intensity had different effects on the diversity of understory herb layer. The findings provide theoretical basis and reference for reasonable thinning and tending in coniferous and broad-leaved mixed forests.

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Onfocus detection aims at identifying whether the focus of the individual captured by a camera is on the camera or not. Based on the behavioral research, the focus of an individual during face-to-camera communication leads to a special type of eye contact, i.e., the individual-camera eye contact, which is a powerful signal in social communication and plays a crucial role in recognizing irregular individual status (e.g., lying or suffering mental disease) and special purposes (e.g., seeking help or attracting fans). Thus, developing effective onfocus detection algorithms is of significance for assisting the criminal investigation, disease discovery, and social behavior analysis. However, the review of the literature shows that very few efforts have been made toward the development of onfocus detector owing to the lack of large-scale public available datasets as well as the challenging nature of this task. To this end, this paper engages in the onfocus detection research by addressing the above two issues. Firstly, we build a large-scale onfocus detection dataset, named as the onfocus detection in the wild (OFDIW). It consists of 20623 images in unconstrained capture conditions (thus called “in the wild”) and contains individuals with diverse emotions, ages, facial characteristics, and rich interactions with surrounding objects and background scenes. On top of that, we propose a novel end-to-end deep model, i.e., the eye-context interaction inferring network (ECIIN), for onfocus detection, which explores eye-context interaction via dynamic capsule routing. Finally, comprehensive experiments are conducted on the proposed OFDIW dataset to benchmark the existing learning models and demonstrate the effectiveness of the proposed ECIIN.

Predicting photosynthetic acclimation to elevated CO2 and warming is difficult because they have opposite effects. We investigated physiological and morphological responses in white birch (Betula papyrifera Msrsh.) to a combination of CO2 and temperature (ACT: 400 µmol mol−1 CO2, current temperature; ECT: 750 µmol mol−1 CO2, current + 4 ºC temperature). ECT reduced photosynthesis, maximum Rubisco carboxylation (Vcmax), maximum electron transport rate (Jmax), photorespiration, daytime respiration, leaf N, stomatal and mesophyll conductance, but increased biomass, height, total leaf area, electron partitioning to carboxylation and oxygenation ratio and CO2 compensation point. The photosynthetic acclimation is consistent with the optimal carbon gain theory (carbon gain drives the coordination of carboxylation, electron transport and respiration). While the photosynthetic acclimation was similar to acclimation to elevated CO2, ECT reduced Jmax/Vcmax, which is consistent with the response to warming but opposite to the response to elevated CO2, suggesting that thermal acclimation may be the primary mechanism of photosynthetic acclimation to ECT and ECT probably altered N allocation between machinery for carboxylation and that for RuBP regeneration. The increase in total leaf area by ECT more than offset the negative effect of photosynthetic downregulation on carbon sequestration, resulting in faster growth and greater biomass under ECT.