Overview and Recent Progress on the Biosynthesis and Regulation of Flavonoids in Ginkgo biloba L.

Flavonoids and their derivatives play important roles in plants, such as exerting protective activity against biotic and abiotic stresses, functioning in visual signaling to attract pollinators, and regulating phytohormone activity. They are also important secondary metabolites that are beneficial to humans. Ginkgo biloba L. is a well-known relict plant considered to be a "living fossil". Flavonoids present in ginkgo leaves have antioxidant and anti-aging capacities and show good therapeutic effects on a variety of neurological diseases. To date, studies on flavonoids have mainly focused on their extraction, pharmacological effects, and component analysis and on the expression levels of the key genes involved. However, a systematic review summarizing the biosynthesis and regulatory mechanisms of ginkgo flavonoids is still lacking. Thus, this review was conducted to comprehensively introduce the biological characteristics, value, and utilization status of ginkgo; summarize the effects, biosynthetic pathways, and transcriptional regulation of flavonoids; and finally, discuss the factors (ecological factors, hormones, etc.) that regulate the biosynthesis of flavonoids in ginkgo. This review will provide a reference basis for future research on the biosynthesis and efficient utilization of flavonoids in ginkgo.

Photoperiod decelerates the advance of spring phenology of six deciduous tree species under climate warming.

Vegetation phenology in spring has substantially advanced under climate warming, consequently shifting the seasonality of ecosystem process and altering biosphere-atmosphere feedbacks. However, whether and to what extent photoperiod (i.e., daylength) affects the phenological advancement is unclear, leading to large uncertainties in projecting future phenological changes. Here we examined the photoperiod effect on spring phenology at a regional scale using in situ observation of six deciduous tree species from the Pan European Phenological Network during 1980-2016. We disentangled the photoperiod effect from the temperature effect (i.e., forcing and chilling) by utilizing the unique topography of the northern Alps of Europe (i.e., varying daylength but uniform temperature distribution across latitudes) and examining phenological changes across latitudes. We found prominent photoperiod-induced shifts in spring leaf-out across latitudes (up to 1.7 days per latitudinal degree). Photoperiod regulates spring phenology by delaying early leaf-out and advancing late leaf-out caused by temperature variations. Based on these findings, we proposed two phenological models that consider the photoperiod effect through different mechanisms and compared them with a chilling model. We found that photoperiod regulation would slow down the advance in spring leaf-out under projected climate warming and thus mitigate the increasing frost risk in spring that deciduous forests will face in the future. Our findings identify photoperiod as a critical but understudied factor influencing spring phenology, suggesting that the responses of terrestrial ecosystem processes to climate warming are likely to be overestimated without adequately considering the photoperiod effect.

Robustness of metacommunities with omnivory to habitat destruction: disentangling patch fragmentation from patch loss.

Habitat destruction, characterized by patch loss and fragmentation, is a major driving force of species extinction, and understanding its mechanisms has become a central issue in biodiversity conservation. Numerous studies have explored the effect of patch loss on food web dynamics, but ignored the critical role of patch fragmentation. Here we develop an extended patch-dynamic model for a tri-trophic omnivory system with trophic-dependent dispersal in fragmented landscapes. We found that species display different vulnerabilities to both patch loss and fragmentation, depending on their dispersal range and trophic position. The resulting trophic structure varies depending on the degree of habitat loss and fragmentation, due to a tradeoff between bottom-up control on omnivores (dominated by patch loss) and dispersal limitation on intermediate consumers (dominated by patch fragmentation). Overall, we find that omnivory increases system robustness to habitat destruction relative to a simple food chain.

Spatial pattern characteristics of water footprint for maize production in Northeast China.

BACKGROUND: Water footprint (WF) methodology is essential for quantifying total water consumption of crop production and making efficient water management policies. This study calculated the green, blue, grey and total WFs of maize production in Northeast China from 1998 to 2012 and compared the values of the provinces. This study also analyzed the spatial variation and structure characteristics of the WFs at the prefecture level. RESULTS: The annual average WF of maize production was 1029 m(3) per ton, which was 51% green, 21% blue and 28% grey. The WF of maize production was highest in Liaoning Province, moderate in Heilongjiang Province and lowest in Jilin Province. The spatial differences of the WFs calculated for the 36 major maize production prefectures were significant in Northeast China. There was a moderate positive spatial autocorrelation among prefectures that had similar WFs. Local indicator of spatial autocorrelation index (LISA) analysis identified prefectures with higher WFs in the southeast region of Liaoning Province and the southwest region of Heilongjiang Province and prefectures with lower WFs in the middle of Jilin Province. CONCLUSION: Spatial differences in the WF of maize production were caused mainly by variations in climate conditions, soil quality, irrigation facilities and maize yield. The spatial distribution of WFs can help provide a scientific basis for optimizing maize production distribution and then formulate strategies to reduce the WF of maize production.

[Study on the polarized reflectance hyperspectral characteristics and models of typical saline soil in the west of Jilin Province, China].

In recent years, the area of saline soil in the west of Jilin Province expands increasingly, and soil quality is becoming more and more worsening, which not only caused great damage to the land resources, but also posed a huge threat to agricultural production and ecological environment. We combined with polarized and hyperspectral information to establish the general model and scientifically validated it. The results show that there is a strong relationship between the saline soil hyperspectral polarized information and its physicochemical property parameters, and with regularity. This paper has important theoretical significance for the mechanism of saline soil surface reflection, recognition and classification of saline soil and background, the utilization of soil polarization sensor and the development of quantitative remote sensing.

A novel hierarchical approach to insight to spectral characteristics in surface water of karst wetlands and estimate its non-optically active parameters using field hyperspectral data.

Wetlands cover only around 6 % of the Earth's land surface, and are recognized as one of the three major ecosystems, alongside forests and oceans. The ecological structure and function of karst wetlands are unique due to the influence of geologic structure. At present, the unclear spectral morphology of surface water in karst wetlands poses a significant challenge in remote sensing estimation of non-optically active water quality parameters (NAWQPs). This study proposed a novel multi-scale spectral morphology feature extraction (MSFE) method to insight to spectral characteristics in surface water of karst wetlands, and further screen the sensitive features of NAWQPs. Then we constructed three remote sensing inversion strategies for NAWQPs (TN, TP, NH3_N, DO), including direct estimation, indirect estimation, and auxiliary estimation. Finally, we constructed a novel pH-based hierarchical analysis framework (pH_HA) to thoroughly explore the influence of alkalinity-biased characteristics of karst water on the spectral domain of NAWQPs and its estimation accuracy using in-situ hyperspectral data, respectively. We found that the spectral characteristics of karst waters at the first reflectance peak (580 nm) differed significantly from other water body types. The MSFE successfully captured the sensitive spectral domains for NAWQPs, and focused on between 500 and 600 nm and 900-960 nm. The sensitive features captured by MSFE improved estimation accuracy of NAWQPs (R2 >0.9). Direct estimation presented more stable performance compared to the auxiliary estimation (average RMSE of 0.366 mg/L), and the auxiliary estimation model further improved the retrieval accuracy of TN compared to direct estimation model (R2 increasing from 0.43 to 0.56). The novel hierarchical framework clearly revealed the notable changes in the sensitive spectral domains of NAWQPs under different pH values, and enabled more precise determination of spectral subdomains of NAWQPs, and identified the optimal spectral features. The pH_HA framework effectively improved the estimation accuracy of NAWQPs (R2 increased from 0.514 to over 0.9), and the estimation accuracies (R2) of four NAWQPs were all more than 0.9 when the pH value was over 8.5. Our works provide an effective approach for monitoring water quality in karst wetlands.

[Study on the response to the soil moisture polarized spectrum under visible-near infrared band].

Moisture is one of the important parameters in soil polarized spectrum. It has great significance in soil remote sensing band selection and image interpretation; it also provides the information for soil investigation and analysis on physical and chemical properties. In the present paper we tested and analyzed the soil polarized spectrum with different moisture in 350-2 500 nm wavelength range to study on the relationship between soil polarized spectral data and moisture, to determine the spectral response and changes in soil moisture, to establish models between spectral data and soil moisture and select the best forecast model. The results show that the accuracy of the polarized derivative spectra model is higher than the polarized spectral model and absorbance model. All the models showed a water content threshold, and found that it is a certain regularity that critical value of soil moisture in different polarization is near 30%.

Mapping global distribution of mangrove forests at 10-m resolution.

Mangrove forests deliver incredible ecosystem goods and services and are enormously relevant to sustainable living. An accurate assessment of the global status of mangrove forests warrants the necessity of datasets with sufficient information on spatial distributions and patch patterns. However, existing datasets were mostly derived from âˆ¼30 m resolution satellite imagery and used pixel-based image classification methods, which lacked spatial details and reasonable geo-information. Here, based on Sentinel-2 imagery, we created a global mangrove forest dataset at 10-m resolution, namely, High-resolution Global Mangrove Forests (HGMF_2020), using object-based image analysis and random forest classification. We then analyzed the status of global mangrove forests from the perspectives of conservation, threats, and resistance to ocean disasters. We concluded the following: (1) globally, there were 145,068 km2 mangrove forests in 2020, among which Asia contained the largest coverage (39.2%); at the country level, Indonesia had the largest amount of mangrove forests, followed by Brazil and Australia. (2) Mangrove forests in South Asia were estimated to be in the better status due to the higher proportion of conservation and larger individual patch size; in contrast, mangrove forests in East and Southeast Asia were facing intensive threats. (3) Nearly, 99% of mangrove forest areas had a patch width greater than 100 m, suggesting that nearly all mangrove forests were efficient in reducing coastal wave energy and impacts. This study reports an innovative and up-to-date dataset and comprehensive information on mangrove forests status to contribute to related research and policy implementation, especially for supporting sustainable development.

Salt marsh monitoring along the mid-Atlantic coast by Google Earth Engine enabled time series.

Salt marshes provide a bulwark against sea-level rise (SLR), an interface between aquatic and terrestrial habitats, important nursery grounds for many species, a buffer against extreme storm impacts, and vast blue carbon repositories. However, salt marshes are at risk of loss from a variety of stressors such as SLR, nutrient enrichment, sediment deficits, herbivory, and anthropogenic disturbances. Determining the dynamics of salt marsh change with remote sensing requires high temporal resolution due to the spectral variability caused by disturbance, tides, and seasonality. Time series analysis of salt marshes can broaden our understanding of these changing environments. This study analyzed aboveground green biomass (AGB) in seven mid-Atlantic Hydrological Unit Code 8 (HUC-8) watersheds. The study revealed that the Eastern Lower Delmarva watershed had the highest average loss and the largest net reduction in salt marsh AGB from 1999-2018. The study developed a method that used Google Earth Engine (GEE) enabled time series of the Landsat archive for regional analysis of salt marsh change and identified at-risk watersheds and salt marshes providing insight into the resilience and management of these ecosystems. The time series were filtered by cloud cover and the Tidal Marsh Inundation Index (TMII). The combination of GEE enabled Landsat time series, and TMII filtering demonstrated a promising method for historic assessment and continued monitoring of salt marsh dynamics.

Effects of land use, topography, climate and socio-economic factors on geographical variation pattern of inland surface water quality in China.

The deterioration of water quality has become a primary environmental concern worldwide. Understanding the status of water quality and identifying the influencing factors are important for water resources management. However, reported analyses have mostly been conducted in small and focused areas. It is still unclear if factors driving spatial variation in water quality would be different in extended spatial scales. In this paper, we analyzed spatial pattern of inland surface water quality in China using a dataset with four water quality parameters (i.e., pH, DO, NH4+-N and CODMn) and the water quality level. We tested the effects of anthropogenic (i.e., land use and socio-economic) and natural (i.e., climatic and topographic) factors on spatial variation in water quality. The study concluded that the overall inland surface water quality in China was at level III (fair). Water quality level was strongly correlated with CODMn and NH4+-N concentration. In contrast to reported studies that suggested land use patterns were the determinants of inland surface water quality, this study revealed that both anthropogenic and natural factors played important roles in explaining spatial variation of inland surface water quality in China. Among the tested explanatory variables, mean elevation within watershed appeared as the best predictor for pH, while annual precipitation and mean air temperature were the most important explanatory variables for CODMn and DO, respectively. NH4+-N concentration and water quality level were most strongly correlated with the percent of forest cover in watershed. Compared to studies at smaller spatial scales, this study found different influencing factors of surface water quality, suggesting that factors may play different roles at different spatial scales of consideration. Therefore management policies and measures in water quality control must be established and implemented accordingly. Since currently adopted parameters for monitoring of inland surface water quality in China are largely influenced by natural variables, additional physicochemical and biological indicators are needed for a robust assessment of human impacts on water quality.

Diversity and Spatial-Temporal Distribution of Soil Macrofauna Communities Along Elevation in the Changbai Mountain, China.

The understanding of patterns of vertical variation and diversity of flora and fauna along elevational change has been well established over the past century. However, it is unclear whether there is an elevational distribution pattern for soil fauna. This study revealed the diversity and spatial-temporal distribution of soil macrofauna communities in different vegetation zones from forest to alpine tundra along elevation of the Changbai Mountain, China. The abundance, richness, and Shannon-Wiener diversity index of soil macrofauna communities were compared in four distinguished vegetation zones including the coniferous and broadleaved mixed forest zone, the coniferous forest zone, the subalpine dwarf birch (Betula ermanii) forest zone, and the alpine tundra zone. Soil macrofauna were extracted in May, July, and September of 2009. In each season, the abundance and richness of the soil macrofauna decreased with the ascending elevation. The Shannon-Wiener diversity indices of the soil macrofauna were higher in the vegetation zones of lower elevation than of higher elevation. Significant differences were observed in the abundance, richness, and Shannon-Wiener diversity index for the studied vegetation zones. Soil macrofauna congregated mainly to the litter layer in the low-elevation areas and in the 0-5 cm soil layer of the higher elevation areas. The results emphasized that the diversity of soil macrofauna communities decreased as the elevation increased and possess the distinct characteristics of zonation in the mountain ecosystem. The diversity and distribution of soil macrofauna communities were influenced by mean annual precipitation, altitude, annual radiation quantity, and mean annual temperature.

Evaluation of riparian condition of Songhua River by integration of remote sensing and field measurements.

Riparian zone is crucial to the health of streams and their surrounding environment. Evaluation of riparian condition is essential to achieve and maintain good stream health, as well as to sustain ecological functions that riparian areas provide. This manuscript is aimed to evaluate riparian conditions of Songhua River, the fifth longest river in China, using physical structural integrality (PSI) values derived from remote sensing and validated by field measurements. The variation and clusters of PSI values were discriminated by the spatial statistics to quantify variation of riparian condition in each measurement section. Evaluation results derived from 13 measurement sections indicated that over 60% of the riparian zones have been disturbed by human activities. Analysis of land use patterns of riparian zone in the cold and hot spots found that land-use patterns had an important effect on riparian condition. The build-up and farmland areas had been the main human disturbances to the riparian condition, which were increased from 1976 to 2013. The low-low clusters (low PSI values with low neighbors) of PSI values can be implemented to identify the vulnerability of the riparian zone.

Species extinction thresholds in the face of spatially correlated periodic disturbance.

The spatial correlation of disturbance is gaining attention in landscape ecology, but knowledge is still lacking on how species traits determine extinction thresholds under spatially correlated disturbance regimes. Here we develop a pair approximation model to explore species extinction risk in a lattice-structured landscape subject to aggregated periodic disturbance. Increasing disturbance extent and frequency accelerated population extinction irrespective of whether dispersal was local or global. Spatial correlation of disturbance likewise increased species extinction risk, but only for local dispersers. This indicates that models based on randomly simulated disturbances (e.g., mean-field or non-spatial models) may underestimate real extinction rates. Compared to local dispersal, species with global dispersal tolerated more severe disturbance, suggesting that the spatial correlation of disturbance favors long-range dispersal from an evolutionary perspective. Following disturbance, intraspecific competition greatly enhanced the extinction risk of distance-limited dispersers, while it surprisingly did not influence the extinction thresholds of global dispersers, apart from decreasing population density to some degree. As species respond differently to disturbance regimes with different spatiotemporal properties, different regimes may accommodate different species.

Assessing current and projected suitable habitats for tree-of-heaven along the Appalachian Trail.

The invasion of ecosystems by non-native species is a major driver of biodiversity loss worldwide. A critical component of effective land management to control invasion is the identification and active protection of areas at high risk of future invasion. The Appalachian Trail Decision Support System (A.T.-DSS) was developed to inform regional natural resource management by integrating remote sensing data, ground-based measurements and predictive modelling products. By incorporating NASA's remote sensing data and modelling capacities from the Terrestrial Observation and Prediction System (TOPS), this study examined the current habitat suitability and projected suitable habitat for the invasive species tree-of-heaven (Ailanthus altissima) as a prototype application of the A.T.-DSS. Species observations from forest surveys, geospatial data, climatic projections and maximum entropy modelling were used to identify regions potentially susceptible to tree-of-heaven invasion. The modelling result predicted a 48% increase in suitable area over the study area, with significant expansion along the northern extremes of the Appalachian Trail.