| 1. |
- Zhang, Guojie, et al.
(författare)
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Comparative genomics reveals insights into avian genome evolution and adaptation
- 2014
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Ingår i: Science. - : American Association for the Advancement of Science (AAAS). - 0036-8075 .- 1095-9203. ; 346:6215, s. 1311-1320
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Tidskriftsartikel (refereegranskat)abstract
- Birds are the most species-rich class of tetrapod vertebrates and have wide relevance across many research fields. We explored bird macroevolution using full genomes from 48 avian species representing all major extant clades. The avian genome is principally characterized by its constrained size, which predominantly arose because of lineage-specific erosion of repetitive elements, large segmental deletions, and gene loss. Avian genomes furthermore show a remarkably high degree of evolutionary stasis at the levels of nucleotide sequence, gene synteny, and chromosomal structure. Despite this pattern of conservation, we detected many non-neutral evolutionary changes in protein-coding genes and noncoding regions. These analyses reveal that pan-avian genomic diversity covaries with adaptations to different lifestyles and convergent evolution of traits.
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| 2. |
- Chen, Zixuan, et al.
(författare)
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Basin-Scale Daily Drought Prediction Using Convolutional Neural Networks in Fenhe River Basin, China
- 2024
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Ingår i: Atmosphere. - 2073-4433. ; 15:2
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Tidskriftsartikel (refereegranskat)abstract
- Drought is a natural disaster that occurs globally and can damage the environment, disrupt agricultural production and cause large economic losses. The accurate prediction of drought can effectively reduce the impacts of droughts. Deep learning methods have shown promise in drought prediction, with convolutional neural networks (CNNs) being particularly effective in handling spatial information. In this study, we employed a deep learning approach to predict drought in the Fenhe River (FHR) basin, taking into account the meteorological conditions of surrounding regions. We used the daily SAPEI (Standardized Antecedent Precipitation Evapotranspiration Index) as the drought evaluation index. Our results demonstrate the effectiveness of the CNN model in predicting drought events 1~10 days in advance. We evaluated the predictions made by the model; the average Nash–Sutcliffe efficiency (NSE) between the predicted and true values for the next 10 days was 0.71. While the prediction accuracy slightly decreased with longer prediction lengths, the model remained stable and effective in predicting heavy drought events that are typically difficult to predict. Additionally, key meteorological variables for drought predictions were identified, and we found that training the CNN model with these key variables led to higher prediction accuracy than training it with all variables. This study approves an effective deep learning approach for daily drought prediction, particularly when considering the meteorological conditions of surrounding regions.
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| 3. |
- Hu, Guojie, et al.
(författare)
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Water and heat coupling processes and its simulation in frozen soils : Current status and future research directions
- 2023
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Ingår i: Catena. - : Elsevier BV. - 0341-8162. ; 222
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Forskningsöversikt (refereegranskat)abstract
- To date, most studies on coupled-water-and-heat processes in frozen soils haves focused on the mechanism of changes in frozen soil and the contribution of climate change, hydrological processes, and ecosystems in cold regions. Several studies have demonstrated considerable improvements in the accuracy of simulating water and heat transfer processes in cold regions. However, substantial differences remain among the different models and parameterizations because of the lack of observations and in-depth understanding of the water and heat transfer processes. Hence, it is necessary to summarize recent advances in the simulation of water-and-heat-coupling processes and challenges for further research. Therefore, we present a theory-focused summary of progress in this field considering the aspects of water flow and coupled-water-and-heat transfer. The simulation progress is discussed in terms of physical process models; one type of model only considers the heat conduction transfer processes without water flow, and the other considers coupled-water-and-heat transfer processes. Aspects of model deficiencies related to non-conductive heat transfer and soil water transfer processes in the frozen soil are also summarized. Moreover, the major parameterizations are reviewed, including phase changes, freeze–thaw fronts, thermal conductivity, hydraulic conductivity, snow processes, surface parameterization schemes, ground ice, and lower boundary conditions. While models and parameterizations can suitably capture local-scale water and heat transfer processes in frozen soil, their applications are spatiotemporally constrained, requiring further improvement. We provide a theoretical basis for further studying water and heat transfer processes in frozen soil and suggest that future research should enhance the accuracy of frozen soil parameterization and improve the understanding of the coupling of water and heat transfer processes based on improved observation techniques and high-resolution data.
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| 4. |
- Hu, Yifan, et al.
(författare)
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Reconstructing long-term global satellite-based soil moisture data using deep learning method
- 2023
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Ingår i: Frontiers in Earth Science. - : Frontiers Media SA. - 2296-6463. ; 11
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Tidskriftsartikel (refereegranskat)abstract
- Soil moisture is an essential component for the planetary balance between land surface water and energy. Obtaining long-term global soil moisture data is important for understanding the water cycle changes in the warming climate. To date several satellite soil moisture products are being developed with varying retrieval algorithms, however with considerable missing values. To resolve the data gaps, here we have constructed two global satellite soil moisture products, i.e., the CCI (Climate Change Initiative soil moisture, 1989–2021; CCIori hereafter) and the CM (Correlation Merging soil moisture, 2006–2019; CMori hereafter) products separately using a Convolutional Neural Network (CNN) with autoencoding approach, which considers soil moisture variability in both time and space. The reconstructed datasets, namely CCIrec and CMrec, are cross-evaluated with artificial missing values, and further againt in-situ observations from 12 networks including 485 stations globally, with multiple error metrics of correlation coefficients (R), bias, root mean square errors (RMSE) and unbiased root mean square error (ubRMSE) respectively. The cross-validation results show that the reconstructed missing values have high R (0.987 and 0.974, respectively) and low RMSE (0.015 and 0.032 m3/m3, respectively) with the original ones. The in-situ validation shows that the global mean R between CCIrec (CCIori) and in-situ observations is 0.590 (0.581), RMSE is 0.093 (0.093) m3/m3, ubRMSE is 0.059 (0.058) m3/m3, bias is 0.032 (0.037) m3/m3 respectively; CMrec (CMori) shows quite similar results. The added value of this study is to provide long-term gap-free satellite soil moisture products globally, which helps studies in the fields of hydrology, meteorology, ecology and climate sciences.
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| 5. |
- Li, Cai, et al.
(författare)
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Two Antarctic penguin genomes reveal insights into their evolutionary history and molecular changes related to the Antarctic environment
- 2014
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Ingår i: GigaScience. - 2047-217X. ; 3
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Tidskriftsartikel (refereegranskat)abstract
- Background: Penguins are flightless aquatic birds widely distributed in the Southern Hemisphere. The distinctive morphological and physiological features of penguins allow them to live an aquatic life, and some of them have successfully adapted to the hostile environments in Antarctica. To study the phylogenetic and population history of penguins and the molecular basis of their adaptations to Antarctica, we sequenced the genomes of the two Antarctic dwelling penguin species, the Adelie penguin [Pygoscelis adeliae] and emperor penguin [Aptenodytes forsteri]. Results: Phylogenetic dating suggests that early penguins arose similar to 60 million years ago, coinciding with a period of global warming. Analysis of effective population sizes reveals that the two penguin species experienced population expansions from similar to 1 million years ago to similar to 100 thousand years ago, but responded differently to the climatic cooling of the last glacial period. Comparative genomic analyses with other available avian genomes identified molecular changes in genes related to epidermal structure, phototransduction, lipid metabolism, and forelimb morphology. Conclusions: Our sequencing and initial analyses of the first two penguin genomes provide insights into the timing of penguin origin, fluctuations in effective population sizes of the two penguin species over the past 10 million years, and the potential associations between these biological patterns and global climate change. The molecular changes compared with other avian genomes reflect both shared and diverse adaptations of the two penguin species to the Antarctic environment.
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| 6. |
- Wu, Tonghua, et al.
(författare)
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Storage, patterns, and environmental controls of soil organic carbon stocks in the permafrost regions of the Northern Hemisphere
- 2022
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Ingår i: Science of the Total Environment. - : Elsevier BV. - 0048-9697. ; 828
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Tidskriftsartikel (refereegranskat)abstract
- Large stocks of soil organic carbon (SOC) accumulated in the Northern Hemisphere permafrost regions may be vulnerable to climatic warming, but global estimates of SOC distribution and magnitude in permafrost regions still have large uncertainties. Based on multiple high-resolution environmental variables and a compiled soil sample dataset (>3000 soil profiles), we used machine-learning methods to estimate the size and spatial distribution of SOC for the top 3 m soils in the Northern Hemisphere permafrost regions. We also identified key environmental predictors of SOC. The results showed that the SOC storage for the top 3 m soil was 1079 ± 174 Pg C across the Northern Hemisphere permafrost regions (20.8 × 106 km2), including 1057 ± 167 Pg C in the northern permafrost regions and 22 ± 7 Pg C in the Third Pole permafrost regions. The mean annual air temperature and NDVI are the main controlling factors for the spatial distribution of SOC stocks in the northern and the Third Pole permafrost regions. Our estimations were more accurate than the existing global SOC stock maps. The results improve our understanding of the regional and global permafrost carbon cycle and their feedback to the climate system.
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| 7. |
- Zhang, Wenxin, et al.
(författare)
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Convergence and divergence emerging in climatic controls of polynomial trends for northern ecosystem productivity over 2000–2018
- 2023
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Ingår i: Science of the Total Environment. - : Elsevier BV. - 1879-1026 .- 0048-9697.
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Tidskriftsartikel (refereegranskat)abstract
- Southwest China has been the largest terrestrial carbon sink in China over the past 30 years, but has recently experienced a succession of droughts caused by high precipitation variability, potentially threatening vegetation productivity in the region. Yet, the impact of precipitation anomalies on the vegetation primary productivity is poorly known. We used an asymmetry index (AI) to explore possible asymmetric productivity responses to precipitation anomalies in Southwest China from 2003 to 2018, using a precipitation dataset, combined with gross primary productivity (GPP), net primary productivity (NPP), and vegetation optical depth (VOD) products. Our results indicate that the vegetation primary productivity of Southwest China shows a negative asymmetry, suggesting that the increase of vegetation primary productivity during wet years exceeds the decrease during dry years. However, this negative asymmetry of vegetation primary productivity was shifted towards a positive asymmetry during the period of analysis, suggesting that the resistance of vegetation to drought, has increased with the rise in the occurrence of drought events. Among the different biomes, grassland vegetation primary productivity had the highest sensitivity to precipitation anomalies, indicating that grasslands are more flexible than other biomes and able to adjust primary productivity in response to precipitation anomalies. Furthermore, our results showed that the asymmetry of vegetation primary productivity was influenced by the effects of temperature, precipitation, solar radiation, and anthropogenic and topographic factors. These findings improve our understanding of the response of vegetation primary productivity to climate change over Southwest China.
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