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The vulnerability of global forest ecoregions to future climate change represents a major threat to biodiversity and ecosystems worldwide. Therefore, it is important to investigate this vulnerability to improve the global conservation management network for biodiversity and ecosystems. We used species–area relationship coupled with correlative distribution modelling to conduct a global vulnerability assessment on 387 forest ecoregions under future climate change across different (1) biomes, (2) biogeographical realms and (3) conservation statuses. We found that 8.8% of global forest ecoregions were highly vulnerable in a low-greenhouse-gas-concentration scenario, and 32.6% of the global forest ecoregions were highly vulnerable in the high-greenhouse-gas-concentration scenario. Furthermore, the overall vulnerability of forest ecoregions was significantly greater for the high-rather than the low-greenhouse-gas-concentration scenario. In particular, critical or endangered forest ecoregions of Temperate Broadleaf and Mixed Forests, Temperate Conifer Forests, Tropical and Subtropical Dry Broadleaf Forests and Tropical and Subtropical Moist Broadleaf Forests were highly vulnerable in Nearctic, Neotropic and Palearctic realms. Furthermore, relatively stable and intact Tropical and Subtropical Moist Broadleaf Forests may be threatened in Neotropic and Afrotropic realms due to their climate change vulnerability. Hence, due to increasing greenhouse gas concentrations, future climate change must be incorporated into forest ecoregion conservation management to improve the effectiveness of global conservation network systems for biodiversity and ecosystems. Keywords: Climatic change, Conservation status, Correlative distribution modelling, Species–area relationship, Vulnerability assessment

Background: Historical and contemporary climates may shape the distributional patterns of plant species richness across different scales. However, few studies have focused on the effects of historical and contemporary climate changes on the distributional patterns of plant richness in Chinese protected areas across different taxonomic levels. Hypotheses: Historical and contemporary climates can have an important legacy effect on the large-scale distributional patterns of plant richness across different taxonomic levels. Studied species: Vascular plants. Study site: China. Method: We used data on plant richness at the family, genus, and species levels from Chinese protected areas and applied regression modelling to explore the relationships between climate change and plant richness among vascular, fern, seed, gymnosperm, and angiosperm plants based on paleoclimate (Last Glacial Maximum; LGM, ca. 22,000 years ago) and contemporary climate data. Results: The large-scale distributional patterns of plant richness could be predicted across different taxonomic levels on the basis of paleoclimate and contemporary climate data. Specifically, historical and contemporary climate variables were found to better correlate with fern plant richness than seed plant richness. For seed plants, the explanatory power of historical and contemporary climate variables was found to be stronger for the richness of gymnosperms than for the richness of angiosperms. Conclusions: The distributional pattern of plant richness could be predicted across different taxonomic levels after including paleoclimate (LGM, ca. 22,000 years ago) and contemporary climate data from China. Our study could support the effectiveness of the management of protected areas in China.

The investigation of factors affecting the spatial distribution of forest vegetation on a large scale is a hot topic in forestry and ecology. Numerous studies have reported that climate and human activities have a considerable effect on the spatial distribution of forest vegetation. However, few studies have focused on the effects of soil properties on the spatial distribution of forest vegetation across China. In the present study two indicators were used to explore such soil effects, namely the percentage contribution of soil properties to the spatial distribution of forest vegetation and the similarity of the potential distributions based on climate variables and both climate and soil variables under distribution modelling. We found that 1) soil parameters (e.g., coarse fragment volume and organic carbon stock) contribute to the spatial distribution of forest vegetation in China, although this contribution may vary among different biomes and vegetation classes and 2) the spatial distribution of forest vegetation differs among the different vegetation classes and biomes. Furthermore, soil variables (e.g., coarse fragment volume and organic carbon stock) could play an important role in the spatial distribution of conifer-broadleaf forest vegetation and mountain broad-leaved and conifer-leaved forest vegetation. However, climate variables were more important than soil properties across most vegetation types. It is therefore suggested that 1) coarse fragment volume and organic carbon stock should be used as indicators to monitor forest vegetation and 2) soil properties should be conserved to facilitate reforestation programs in China. The present study provides evidence that soil parameters affect the spatial distribution of forest vegetation in China, facilitating the development of effective management strategies. Keywords: China, Distribution modelling, Forest vegetation, Organic carbon stock, Soil factors

Climate change is increasing the risk of invasive plant expansion worldwide. However, few studies have specified the relationship between invasive plant expansion and ecoregions at the global scale under climate change. To address this gap, we provide risk maps highlighting the response of invasive plant species (IPS), with a focus on terrestrial and freshwater ecoregions to climate change, and further explore the climatic features of ecosystems with a high potential for invasive plant expansion under climate change. We use species distribution modelling to predict the suitable habitats of IPS with records at the global scale. Hotspots with a potential risk of IPS (such as aquatic plants, trees, and herbs) expanding in global ecoregions were distributed in Northern Europe, the UK, South America, North America, southwest China, and New Zealand. Temperature changes were related to the potential of IPS expansion in global ecoregions under climate change. Coastal and high latitude ecoregions, such as temperate forests, alpine vegetation, and coastal rivers, were severely infiltrated by IPS under climate change. Monitoring strategies should be defined for climate change for IPS, particularly for aquatic plants, trees, and herbs in the biomes of regions with coastal or high latitudes. The role of climate change on the potential for IPS expansion should be taken into consideration for biological conservation and risk evaluation of IPS at ecoregional scales.

Hair loss disorders such as androgenetic alopecia have caused serious disturbances to normal human life. Animal models play an important role in exploring pathogenesis of disease and evaluating new therapies. NIH hairless mice are a spontaneous hairless mouse discovered and bred in our laboratory. In this study, we resequenced the genomes of NIH normal mice and NIH hairless mice and obtained 3,575,560 high-quality, plausible SNP loci and 995,475 InDels. The Euclidean distance algorithm was used to assess the association of SNP loci with the hairless phenotype, at a threshold of 0.62. Two regions of chromosome 18 having the highest association with the phenotype contained 345 genes with a total length of 13.98 Mb. The same algorithm was used to assess the association of InDels with the hairless phenotype at a threshold of 0.54 and revealed a region of 25.45 Mb in length, containing 518 genes. The mutation candidate gene Lama3 (NM_010680.2: c.652C>T; NP_034810.1: p. Arg217Cys) was selected based on the results of functional gene analysis and mutation prediction screening. Lama3 (R217C) mutant mice were further constructed using CRISPR/Cas9 technology, and the relationship between Lama3 point mutations and the hairless phenotype were clarified by phenotypic observation. The results showed that male Lama3 point mutation mice started to lose hair on the 80th day after birth, and the hair loss area gradually expanded over time. H&E staining of skin sections showed that the point mutation mice had increased sebaceous glands in the dermis and missing hair follicle structure (i.e., typical symptoms of androgenetic alopecia). This study is a good extension of the current body of knowledge about the function of Lama3, and the constructed Lama3 (R217C) mutant mice may be a good animal model for studying androgenetic alopecia. [ABSTRACT FROM AUTHOR]

The functional composition of plant communities (FCPC) makes a significant contribution to ecosystem properties, functions, and services. Here, we used 18 plant functional traits from the sPlot database v2.1 and the global forest management type dataset to explore the links between forest management and the FCPC. We used the post hoc Tukey test to explore the differences in the community-weighted mean (CWM) and community-weighted variance (CWV) among different forest management types [i.e., intact forests, managed forests with natural regeneration, planted forests, plantation forests (with up to a 15-year rotation), and agroforestry]. We found that different forest management types can result in significant variability in plant communities' functional composition. Plantation forests could result in significantly higher CWM and CWV compared to intact forests, and significant differences could occur between natural and managed forests with natural regeneration. Furthermore, the relationship between forest management practices and the FCPC depends on ecozone type changes. There were significant differences between natural and plantation forests for CWM and CWV in temperate forests. Our study provides an effective reference for applying plant functional traits to regulate and optimize the functions and services of forest ecosystems. [ABSTRACT FROM AUTHOR]

Cross-modal image-text retrieval aims at retrieving the images according to the given query texts and vice versa, which is a challenging task due to the inherent heterogeneous gap between computer vision and natural language processing. Most previous methods mine the intra-modal interactions and inter-modal interactions independently, which may lead to a fragmented understanding of the visual-linguistic modalities. Different from them, in this paper, we address this challenge by proposing a unified multi-modal Co-Occurrence transformer Reasoning Network, dubbed as COREN, to comprehensively discover the semantic correlations of the two modalities. Specifically, we resort to a unified multi-modal transformer encoder to decompose the intra-modal and inter-modal co-occurrence relationships reasoning into a two-stage learning architecture. In the first learning stage, we utilize the multi-modal transformer as a shared siamese encoder for both visual and textual branch to reason the intra-modal co-occurrence relationships. In this way, we obtain modality-specific contextualized representations for each input image and text instance, and the model is equipped with the representation and reasoning ability of both visual and textual entities. In the second learning stage, we stack the visual and textual features together and jointly feed them into the same multi-modal transformer encoder to reason the inter-modal co-occurrence relationships between the two modalities. Additionally, we propose a novel Adaptive Similarity Aggregation (ASA) module to achieve a more accurate cross-modal similarity measurement based on the generated contextualized representations. The experimental results on benchmark datasets demonstrate the effectiveness and superiority of our proposed method. [ABSTRACT FROM AUTHOR]