环境科学与生态学最新文献

Coastal aquaculture ponds represented a biogeochemical hotspot in the global carbon cycle. However, there was a limited understanding of their dynamics. In this study, the eddy covariance (EC) technique was applied to quantify the net ecosystem CO₂ exchange (NEE) over coastal aquaculture ponds in the Liaohe River estuary in northern China during 2020, aiming to investigate and quantify the carbon exchange characteristics of this region. The results showed that (a) a predominant "U" shaped diurnal NEE pattern throughout the year. During the sea cucumber monoculture phase, the ponds exhibited a consistent daytime carbon sink and nighttime carbon source pattern. In contrast, during the shrimp and sea cucumber polyculture phase, the ponds mostly remained in a net carbon sink state. (b) NEE was negatively correlated with photosynthetically active radiation (PAR), air temperature (T_air), and wind speed (WS), while showing a positive correlation with atmospheric pressure (AP). (c) Overall, the entire study area (complex underlying surfaces) functioned as a carbon sink in 2020, with a total net carbon sequestration of 281.533 g C·m^-2. This was approximately four times greater than the restored wetlands that naturally formed from decommissioned coastal aquaculture ponds. Adjusting for surface heterogeneity revealed that the complex surfaces led to a 34.28 % underestimation of the aquaculture region's unit area carbon sequestration capacity. This study was crucial for assessing the carbon cycling and sequestration functions of coastal aquaculture pond ecosystems and provided a scientific basis for related ecological restoration projects.

Tea (Camellia sinensis (L.) O. Kuntze) plants have a strong ability to accumulate selenium (Se). However, the question of how tea plants affect Se availability has received little attention. In this study, five tea cultivars, including Soubei (SB), Aolǜ (AL), Longjing43 (LJ), Zhaori (ZR) and Fenglǜ (FL), were chosen for the study. Quantitative Microbial Ecology Chip and high-throughput sequencing were used to explore the effects of five tea cultivars on soil functions, microbial community structures and Se availability. The results showed that the total soil Se content in the FL garden was lower compared to LJ and SB gardens, whereas available Se was highest in the FL garden. Based on the Bray-Curtis distances, tea cultivar was the main factor affecting bacterial and fungal community structures. The abundance of functional genes concerning carbon, nitrogen, phosphorus and sulfur cycling processes varied among tea gardens. The higher soil NH₄⁺ and NO₃^- contents, and higher abundance of functional genes like nifH, amoA1 and narG, whereas lower total nitrogen in the FL garden than in the AL and LJ tea gardens demonstrated that the FL tea plants induced microbes to accelerate soil nitrogen cycling processes. Dominant microbes that positively related with functional genes like nifH, narG, and amoA1 were also positively related with the available Se content. In conclusion, tea cultivars could regulate soil functions through affecting microbial community structures and then affecting the soil Se availability. The soil nitrogen cycle processes are suggested to be closely related with Se transformation in tea gardens.

Background: People are exposed to metals in various ways during their daily lives. However, the association between metal exposure and gallstones remains unclear.

Objectives: To investigate the relationship between serum elemental concentrations and the risk of gallstones.

Methods: Participants (n = 4204) were drawn from the Henan Rural Cohort. Gallstone diagnosis was based on abdominal ultrasound reports during follow-up. Baseline serum elemental concentrations were measured using inductively coupled plasma mass spectrometry. The relationship between serum elemental levels and gallstones was evaluated using robust Poisson regression, restricted cubic spline (RCS), quantile g-computation (Qgcomp), grouped weighted quantile sum (GWQS) and Bayesian kernel machine regression (BKMR).

Results: 121 individuals were diagnosed with gallstone (incidence rate of 2.88 %). In robust Poisson regression, after adjusting for confounding factors, the highest quartile of arsenic concentration compared to the lowest quartile had a 1.90 times higher relative risk (RR) [95 % confidence interval (CI): 1.05, 3.44]. Conversely, the highest quartile of zinc concentration compared to the lowest quartile had a 0.50 times lower RR (95 % CI: 0.28, 0.89). RCS showed an approximately "S"-shaped nonlinear relationship between serum arsenic levels and gallstones, with increasing arsenic concentration leading to a higher risk of gallstones; however, the risk plateaued when arsenic concentration exceeded 0.62 μg/L. Both the Qgcomp and GWQS indicated that arsenic plays a significant role in increasing the risk of gallstones, whereas zinc plays a significant role in reducing the risk of gallstones. BKMR showed that raising arsenic exposure from the 25th to the 75th percentile increased the risk of gallstones, while raising serum zinc concentration reduced it.

Conclusions: Higher serum arsenic concentration increases the risk of gallstones, whereas higher zinc concentration may reduce the risk. Effective prevention of gallstones may require further reduction of arsenic exposure and appropriate increases in zinc intake.

Plastics are common synthetic materials that have been abundantly present as pollutants in natural ecosystems for the past few decades. Thus scientists have investigated the capability of plastic digestion by insects. Here we compare the effectiveness of biodegradation of the specific polymers: expanded polystyrene (EPS), polyvinyl chloride (PVC), low-density polyethylene (LDPE) and polypropylene (PP) altogether with above variants of plastics with microelements and vitamins by the mealworm - the larval form of the beetle Tenebrio molitor - and larvae of the beetle Zophobas morio, known as superworms. Z. morio beetles on all diets were able to complete their life cycle from larvae through pupae and imago, gaining 19 % and 22 % in mass on LDPE and EPS; 8 % and 7 % on PVC and PP. Mealworms (T. molitor) reared on polymers had minimal weight gain, gaining 2 % on LDPE and EPS, and a slight reduction in mass was observed when reared on PP and PVC. Not all specimens of T. molitor were able to pupate and transform to the adult stage. The results suggest that larvae of Z. morio can eat and degrade some types of plastic compounds more effectively than T. molitor. The changes in microbial gut communities were compared between these two species. The highest mass gain for Z. morio is associated with higher diversity in gut microbia and it was more diverse than that of T. molitor. Citrobacter freundii, a bacterium recognized for its ability to degrade long-chain polymers, linear hydrocarbons and cyclic hydrocarbons, was found in the microflora of Z. morio. The results confirm that superworms can survive on polymer feed. Moreover, this diet supplemented with microelements and vitamins increases the number of bacterial species and the diversity in the microbial gut.

The escalating challenges posed by seafood waste generated by the fishing and aquaculture industries underscore the urgent need for innovative solutions that promote both environmental conservation and economic viability within the seafood sector. Seafood waste biorefinery emerges as a promising solution, offering the potential to transform waste materials into valuable products. However, it is essential to recognize that seafood waste biorefinery operations also entail environmental impacts that warrant careful consideration. Environmental assessment tools like Life Cycle Assessment (LCA) provide a valuable framework for assessing these impacts comprehensively. This review critically examines LCA studies in seafood waste biorefinery, focusing on key concepts, emerging technologies, and potential product avenues. Despite the growing body of research in this area, direct comparisons between published studies prove challenging due to discrepancies in feedstocks, processing techniques, value-added products, and LCA methodologies. Nevertheless, the findings consistently demonstrate significant reductions in environmental impacts achieved through seafood waste biorefinery processes. The selection of technologies significantly influences both product quality and sustainability measures. High energy consumption, including diesel fuel consumption in fishing vessels and electricity consumption in processing steps, should be carefully considered and reduced to mitigate associated environmental impacts. In conclusion, while seafood waste biorefinery processes hold significant promise for providing environmental and economic benefits, substantial challenges remain. This review provides invaluable insights for researchers, policymakers, and stakeholders, emphasizing the importance of continuous interdisciplinary collaboration and methodological standardization to advance sustainable waste management practices in the seafood industry.

Wildfires pose significant threats worldwide, requiring accurate prediction for mitigation. This study uses machine learning techniques to forecast wildfire severity in the Upper Colorado River basin. Datasets from 1984 to 2019 and key indicators like weather conditions and land use were employed. Random Forest outperformed Artificial Neural Network, achieving 72 % accuracy. Influential predictors include air temperature, vapor pressure deficit, NDVI, and fuel moisture. Solar radiation, SPEI, precipitation, and evapotranspiration also contribute significantly. Validation against actual severities from 2016 to 2019 showed mean prediction errors of 11.2 %, affirming the model's reliability. These results highlight the efficacy of machine learning in understanding wildfire severity, especially in vulnerable regions.

This comprehensive two-year investigation in the coastal South China Sea has advanced our understanding of marine microbes at both community and genomic levels. By combining metagenomics and metatranscriptomics, we have revealed the intricate temporal dynamics and remarkable adaptability of microbial communities and phytoplankton metagenome-assembled genomes (MAGs) in response to environmental fluctuations. We observed distinct seasonal shifts in microbial community composition and function: cyanobacteria were predominant during warmer months, whereas photosynthetic protists were more abundant during colder seasons. Notably, metabolic marker KOs of photosynthesis were consistently active throughout the year, underscoring the persistent role of these processes irrespective of seasonal changes. Our analysis reveals that environmental parameters such as temperature, salinity, and nitrate concentrations profoundly influence microbial community composition, while temperature and silicate have emerged as crucial factors shaping their functional traits. Through the recovery and analysis of 37 phytoplankton MAGs, encompassing nine prokaryotic cyanobacteria and 28 eukaryotic protists from diverse phyla, we have gained insights into their genetic diversity and metabolic capabilities. Distinct profiles of photosynthesis-related pathways including carbon fixation, carotenoid biosynthesis, photosynthesis-antenna proteins, and photosynthesis among the MAGs indicated their genetic adaptations to changing environmental conditions. This study not only enhances our understanding of microbial dynamics in coastal marine ecosystems but also sheds light on the ecological roles and adaptive responses of different microbial groups to environmental changes.

We assessed the concentrations of metals and other trace elements in two of the most common seabird species breeding on Svalbard, the black-legged kittiwake (Rissa tridactyla) and the Brünnich's guillemot (Uria lomvia). Both of these species feed mostly on fish and crustaceans but have different foraging strategies, kittiwakes being surface feeders while guillemots are divers. We investigated the concentrations of arsenic (As), cadmium (Cd), chromium (Cr), copper (Cu), mercury (Hg), lead (Pb), selenium (Se) and zinc (Zn) in the plasma and body feathers of black-legged kittiwakes (n = 17), as well as in the body feathers of Brünnich's guillemots (n = 13). Samples were collected from adult birds at two time points, one week apart during July 2017 in Kongsfjorden, Svalbard. Of the non-essential trace elements, As was found at the highest median concentration at both the first (56.23 ng/g ww) and second (39.99 ng/g ww) sampling timepoints in the kittiwake plasma. When separating for the sexes, as well as sampling time, males sampled at the first sampling time point had significantly higher concentrations of As (median at 0.087 ng/g versus 0.039 ng/g) and Se (median 0.26 ng/g versus 0.16 ng/g) compared to males sampled at the second time point. There was no significant difference in plasma concentrations between females at first and second sampling time points. Kittiwake feathers contained significantly higher concentrations of As, Cd and Hg than guillemot feathers, while guillemot feathers had significantly higher concentrations of Cu, Pb and Zn. However, of the non-essential elements in both kittiwake and guillemot feathers Hg was found with the highest median concentrations at 5160 and 1080 ng/g, respectively, thus in kittiwakes exceeding the level of 5000 ng/g associated with adverse effect (e.g., impaired reproduction). Levels of Hg and Se found in the kittiwake feathers were higher than previous studies on seabirds in the Arctic.

This study aims to comprehensively evaluate and map the risk of drought in Taiwan by employing a combination of two powerful models, the Analytic Network Process (ANP) and Artificial Neural Network (ANN). This innovative approach utilizes an ensemble learning method, where ANP constructs a logical network and assigns weights to various indicators. Subsequently, ANN leverages these weights to train the model effectively. A total of twenty indicators were incorporated into the analysis to create a holistic drought risk map for Taiwan. These indicators are thoughtfully categorized into three essential components: hazard, exposure, and vulnerability, providing a well-defined representation of drought risk. The trained ANN model showcases remarkable accuracy and performance, boasting values of 0.940 for accuracy, 0.946 for precision, 0.938 for recall, 0.942 for the F1 score, and 0.923 for the Kappa Index. These results unequivocally affirm the model's effectiveness in predicting drought risk. Furthermore, the final drought risk map underwent rigorous validation through fieldwork and statistical data. The validation process yielded high accuracies, ranging from 0.717 to 0.851, for assessing damage to crops, converted damaged areas, and estimated value product loss. This validation, conducted against multiple reference data sources, underscores the map's reliability and its alignment with various goodness-of-fit criteria. In summary, this study underscores the potency of the ANP-ANN ensemble approach, with the trained ANN model proving its robustness in swiftly predicting drought risk across diverse ecological and socioeconomic scenarios.

Exploring the temporal dynamics of biological communities can offer valuable insights into the underlying mechanisms driving changes in biodiversity in the context of short and long-term effects of climate fluctuations. However, an understanding of how temporal shifts in climatic fluctuations influence the spatial patterns of the temporary ecological processes remains unexplored. This study examined the relative importance of temporary deterministic and stochastic processes (i.e., the influence of environmental filtering compared to stochastic variation within the same community) on community dynamics across watersheds in 15 rivers of the European Iberian Peninsula using 21 years of data. This study was divided into two time periods (i.e., 1997-2006 and 2007-2017). The climatic differences between the periods included decreasing levels and heightened variability of precipitation. Additionally, there were declining minimum temperatures and rising maximum temperatures, accompanied by reduced fluctuations in both minimum and maximum temperatures. Water quality and its variations also occur along an elevation pattern and changed over the time period studied. Spatial patterns of the relative importance of the ecological processes shifted between the two decades. The significance of stochastic processes increased with elevation in the earlier period, although no clear elevation pattern emerged in the later period. At the same time, the importance of deterministic processes decreased with elevation in the earlier period, and there was no clear pattern of elevation in the later period. An understanding of the patterns in community dynamics existing at various elevations over time can lay the groundwork for predicting and mitigating the impacts of short-term climate changes on biodiversity and guide appropriate conservation actions.