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25 pages, 11053 KiB  
Article
Epiplastic Algal Communities on Different Types of Polymers in Freshwater Bodies: A Short-Term Experiment in Karst Lakes
by Ekaterina Vodeneeva, Yulia Pichugina, Darja Zhurova, Ekaterina Sharagina, Pavel Kulizin, Vyacheslav Zhikharev, Alexander Okhapkin and Stanislav Ermakov
Water 2024, 16(22), 3288; https://doi.org/10.3390/w16223288 (registering DOI) - 15 Nov 2024
Viewed by 261
Abstract
The increasing amount of plastic debris in water ecosystems provides a new substrate (epiplastic microhabitats) for aquatic organisms. The majority of research about epiplastic communities has focused on seawater environments, while research is still quite limited and scattered concerning freshwater systems. In this [...] Read more.
The increasing amount of plastic debris in water ecosystems provides a new substrate (epiplastic microhabitats) for aquatic organisms. The majority of research about epiplastic communities has focused on seawater environments, while research is still quite limited and scattered concerning freshwater systems. In this study, we analyze the first stages of colonization on different types of plastic by a periphytic algae community (its composition and dominant species complex) in freshwater bodies located in a nature reserve (within the Middle Volga Basin). A four-week-long incubation experiment on common plastic polymers (PET, LDPE, PP, and PS), both floating and dipped (~1 m), was conducted in two hydrologically connected karst water bodies in July 2023. The composition of periphytic algae was more diverse (due to the presence of planktonic, benthic, and periphytic species) than the phytoplankton composition found in the water column, being weakly similar to it (less than 30%). Significant taxonomic diversity and the dominant role of periphytic algae were noted for diatoms (up to 60% of the total composition), cyanobacteria (up to 35%), and green (including Charophyta) algae (up to 25%). The composition and structure of periphytic algae communities were distinct between habitats (biotope specificity) but not between the types of plastic, determined primarily by a local combination of factors. Statistically significant higher values of abundance and biomass were demonstrated for some species, particularly for Oedogonium on PP and Nitzschia on LDPE (p-value0.05). As colonization progressed, the number of species, abundance, and dominance of individual taxa increased. In hydrologically connected habitats, different starts of colonization are possible, as well as different types of primary succession (initiated by potentially toxic planktonic cyanobacteria or benthic cyanobacteria and mobile raphid diatoms). Within the transparency zone, colonization was more active on the surface (for example, in relation to green algae on PP (p-value0.05)). These results indicate a tendency for microalgae communities to colonize actively submerged plastic materials in freshwater, and they may be useful in assessing the ecological status of these aquatic ecosystems. Full article
(This article belongs to the Section Biodiversity and Functionality of Aquatic Ecosystems)
24 pages, 18018 KiB  
Article
Analysis of Land Surface Performance Differences and Uncertainty in Multiple Versions of MODIS LST Products
by Ruoyi Zhao, Wenping Yu, Xiangyi Deng, Yajun Huang, Wen Yang and Wei Zhou
Remote Sens. 2024, 16(22), 4255; https://doi.org/10.3390/rs16224255 - 15 Nov 2024
Viewed by 271
Abstract
Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature (LST) products are essential data sources for global and regional climate change research. Currently, several versions of the MODIS LST product have been released, yet the performance differences and uncertainties they introduce in land surface [...] Read more.
Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature (LST) products are essential data sources for global and regional climate change research. Currently, several versions of the MODIS LST product have been released, yet the performance differences and uncertainties they introduce in land surface studies remain insufficiently addressed. To bridge this gap, this study focuses on four distinct versions of the LST product: MxD11A1 Collection 5 (C5), Collection 6 (C6), Collection 6.1 (C6.1), and MxD21A1 Collection 6.1 (MxD21). The spatial resolution of all product generations is 1 km, and the temporal resolution is 0.5 days. This study provides a comprehensive analysis of the errors arising from different generations of these products in various land surface process studies. The error assessment includes cross-comparisons between product versions and evaluations of the absolute errors generated. Absolute errors in evaluation data were collected from 13 surface sites within the Heihe Watershed Allied Telemetry Experimental Research (HiWATER) project during the period 2013–2018. Cross-validation results show that the largest difference between C5 and C6.1 occurs over bare land, with an RMSE of approximately 1.45 K, while there is no significant change between C6 and C6.1. MOD21 shows considerable variation compared to C6.1 at night across different land cover types, with RMSE over cropland exceeding 2 K. The temperature difference between MOD21 and C6.1 is more pronounced at night (2.01 K) than during the day (0.30 K). Validation results based on temperature indicate that C5 has greater uncertainty compared to C6, especially over bare land, where errors are 2.06 K and 1.06 K, respectively. Furthermore, MxD21 demonstrates significant day–night performance discrepancies, with an average bias of 0.10 K at night, while daytime errors over bare land can reach 2 K, potentially influenced by atmospheric conditions. Based on the research in this paper, it is possible to clarify the performance of different versions of MODIS products, reflecting the appropriateness of their past applications; on the other hand, it is recommended to prioritize the use of the MxD11A1 C6 and C6.1 products for monitoring and applications in bare soil areas to ensure higher accuracy. Furthermore, for day and night monitoring, it may be beneficial to alternate between the MxD11A1 and MxD21A1 products to fully leverage their respective advantages and enhance overall monitoring effectiveness. Full article
(This article belongs to the Special Issue Remote Sensing for Land Surface Temperature and Related Applications)
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<p>The study area and the site locations in the Heihe River Basin.</p>
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<p>Scatter plot of the correlation between MOD11A1 C5, MOD11A1 C6, and MOD21A1 C6.1 with MOD11A1 C6.1 LST during the daytime (<b>a</b>–<b>c</b>) and nighttime (<b>d</b>–<b>f</b>).</p>
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<p><b>The</b> BIAS and RMSE of different land surface covers MOD11A1 C5, MOD11A1 C6, and MOD21A1 C6.1 with respect to MOD11A1 C6.1 LST during the daytime (<b>a</b>,<b>b</b>) and nighttime (<b>c</b>,<b>d</b>).</p>
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<p>Boxplot of monthly scale temperature differences between MOD11A1 C5, MOD11A1 C6, and MOD21A1 C6.1 compared to MOD11A1 C6.1 LST during the daytime (<b>a</b>–<b>c</b>) and nighttime (<b>d</b>–<b>f</b>).</p>
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<p>Line graphs of the different land surface covers of MOD11A1 C5, MOD11A1 C6, and MOD21A1 C6.1 temperature differences compared to MOD11A1 C6.1 LST across four seasons during the daytime.</p>
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<p>Line graphs of the different land surface covers of MOD11A1 C5, MOD11A1 C6, and MOD21A1 C6.1 temperature differences compared to MOD11A1 C6.1 LST across four seasons during the nighttime.</p>
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<p>Comparison of emissivity between different land surface covers in C5 and C6.1 (<b>a</b>: Emissivity in MODIS b31, <b>b</b>: Emissivity in MODIS b32, <b>c</b>: Emissivity mean, <b>d</b>: Emissivity difference).</p>
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<p>The annual mean differences for 2013, daytime: (<b>a</b>) C5-C6.1, (<b>b</b>) C6-C6.1, (<b>c</b>) MOD21-C6.1; nighttime: (<b>d</b>) C5-C6.1, (<b>e</b>) C6-C6.1, (<b>f</b>) MOD21-C6.1.</p>
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<p>The temperature difference distribution map for MODIS LST products on the 282nd day, daytime: (<b>a</b>) C5-C6.1, (<b>b</b>) C6-C6.1, (<b>c</b>) MOD21-C6.1; nighttime: (<b>d</b>) C5-C6.1, (<b>e</b>) C6-C6.1, (<b>f</b>) MOD21-C6.1.</p>
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<p>Line graphs of different land surface cover temperature differences from 2013 to 2018 for MOD11A1 C6 and MOD21A1 C6.1 compared to MOD11A1 C6.1 during daytime.</p>
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<p>Line graphs of different land surface cover temperature differences from 2013 to 2018 for MOD11A1 C6 and MOD21A1 C6.1 compared to MOD11A1 C6.1 during nighttime.</p>
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<p>Line plot of monthly average BIASs for MOD11 C6, C6.1, and MOD21 for 2013–2018 during daytime.</p>
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<p>Line plot of monthly average BIASs for MOD11 C6, C6.1, and MOD21 for 2013–2018 during nighttime.</p>
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<p>Line plot of monthly average BIASs for MYD11 C6, C6.1, and MYD21 for 2013–2018 during daytime.</p>
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<p>Line plot of monthly average BIASs for MYD11 C6, C6.1, and MYD21 for 2013–2018 during nighttime.</p>
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22 pages, 7222 KiB  
Article
Karst Ecosystem: Moso Bamboo Intercropping Enhances Soil Fertility and Microbial Diversity in the Rhizosphere of Giant Lily (Cardiocrinum giganteum)
by Jie Zhang, Haoyu Wu, Guibin Gao, Yuwen Peng, Yilin Ning, Zhiyuan Huang, Zedong Chen, Xiangyang Xu and Zhizhuang Wu
Forests 2024, 15(11), 2004; https://doi.org/10.3390/f15112004 - 13 Nov 2024
Viewed by 273
Abstract
Intercropping affects soil microbial community structure significantly; however, the effects on understory medicinal plants in karst areas remain unclear. We investigated the effects of four intercropping systems (Moso bamboo, Chinese fir, bamboo-fir mixed forest, and forest gap) on the rhizosphere microbial communities of [...] Read more.
Intercropping affects soil microbial community structure significantly; however, the effects on understory medicinal plants in karst areas remain unclear. We investigated the effects of four intercropping systems (Moso bamboo, Chinese fir, bamboo-fir mixed forest, and forest gap) on the rhizosphere microbial communities of giant lily (Cardiocrinum giganteum), an economically important medicinal plant in China. We assessed the intercropping impact on rhizosphere microbial diversity, composition, and co-occurrence networks and identified key soil properties driving the changes. Bacterial and fungal diversity were assessed by 16S rRNA and ITS gene sequencing, respectively; soil physicochemical properties and enzyme activities were measured. Moso bamboo system had the highest fungal diversity, with relatively high bacterial diversity. It promoted a distinct microbial community structure with significant Actinobacteria and saprotrophic fungi enrichment. Soil organic carbon, total nitrogen, and available potassium were the most influential drivers of microbial community structure. Co-occurrence network analysis revealed that the microbial network in the Moso bamboo system was the most complex and highly interconnected, with a higher proportion of positive interactions and a greater number of keystone taxa. Thus, integrating Moso bamboo into intercropping systems can enhance soil fertility, microbial diversity, and ecological interactions in the giant lily rhizosphere in karst forests. Full article
(This article belongs to the Special Issue Ecological Research in Bamboo Forests)
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<p>Soil physicochemical properties in the giant lily rhizosphere under different intercropping systems: (<b>a</b>) total organic carbon (TOC), (<b>b</b>) total nitrogen (TN), (<b>c</b>) total phosphorus (TP), (<b>d</b>) available nitrogen (AN), (<b>e</b>) available phosphorus (AP), (<b>f</b>) available potassium (AK), (<b>g</b>) pH, (<b>h</b>) β-D-glucosidase (BDG), (<b>i</b>) acid phosphatase (ACP), (<b>j</b>) N-acetyl-β-D-glucosaminidase (NAG), and (<b>k</b>) leucine aminopeptidase (LAP). Error bars represent standard deviations (n = 5). Different lowercase letters indicate significant differences among systems (LSD post hoc test, <span class="html-italic">p</span> ≤ 0.05).</p>
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<p>Amplicon Sequence Variant (ASV) richness of (<b>a</b>) bacteria and (<b>b</b>) fungi in the giant lily rhizosphere under different intercropping systems; blue circles indicate shared taxa across systems; grey circles indicate non-shared ASVs; black bars indicate the number of shared taxa.</p>
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<p>Alpha diversity indices of (<b>a</b>–<b>c</b>) bacterial and (<b>d</b>–<b>f</b>) fungal communities in the giant lily rhizosphere under different intercropping systems. Lowercase letters indicate significant differences among systems (<span class="html-italic">p</span> = 0.05).</p>
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<p>Principal coordinate analysis (PCoA) and analysis of similarities (ANOSIM) tests of (<b>a</b>,<b>b</b>) bacterial and (<b>c</b>,<b>d</b>) fungal communities in the giant lily rhizosphere under different intercropping systems.</p>
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<p>Composition and linear discriminant analysis effect size (LEfSe) analysis of bacterial and fungal communities in the giant lily rhizosphere under different intercropping systems. (<b>a</b>,<b>b</b>) Relative abundance at the phylum level; (<b>c</b>,<b>d</b>) LEfSe results (phylum to genus level).</p>
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<p>Redundancy analysis (RDA) of (<b>a</b>) bacterial and (<b>b</b>) fungal communities in the giant lily rhizosphere under different intercropping systems.</p>
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<p>Functional predictions and correlations with dominant phyla for (<b>a</b>) bacterial functional annotation of prokaryotic taxa (FAPROTAX) and (<b>b</b>) fungal functional guilds (FUNGuild) in the giant lily rhizosphere under different intercropping systems. (<b>c</b>) Correlation analysis between dominant functional groups and dominant bacterial phyla. Asterisks indicate significance levels: * (0.01&lt; <span class="html-italic">p</span> ≤ 0.05), ** (0.001&lt; <span class="html-italic">p</span> ≤ 0.01).</p>
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<p>Co-occurrence networks of bacterial and fungal communities in the giant lily rhizosphere under different intercropping systems: (<b>a</b>,<b>e</b>) bamboo–giant lily, (<b>b</b>,<b>f</b>) Chinese fir–giant lily, (<b>c</b>,<b>g</b>) Moso bamboo–giant lily, and (<b>d</b>,<b>h</b>) forest gap–giant lily intercropping.</p>
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<p>Co-occurrence networks of bacterial and fungal communities in the giant lily rhizosphere under different intercropping systems: (<b>a</b>,<b>e</b>) bamboo–giant lily, (<b>b</b>,<b>f</b>) Chinese fir–giant lily, (<b>c</b>,<b>g</b>) Moso bamboo–giant lily, and (<b>d</b>,<b>h</b>) forest gap–giant lily intercropping.</p>
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25 pages, 2798 KiB  
Review
A Review of Value Realization and Rural Revitalization of Eco-Products: Insights for Agroforestry Ecosystem in Karst Desertification Control
by Wanmei Hu, Zaike Gu, Kangning Xiong, Yaoru Lu, Zuju Li, Min Zhang, Liheng You and Huan Ruan
Land 2024, 13(11), 1888; https://doi.org/10.3390/land13111888 - 11 Nov 2024
Viewed by 334
Abstract
Amid global rural decline, the main approach to rural revitalization (RR) is to transform rural ecological resources into development advantages by means of ecological product value realization (EPVR). The fragility of the karst ecological environment limits the development of the karst countryside, and [...] Read more.
Amid global rural decline, the main approach to rural revitalization (RR) is to transform rural ecological resources into development advantages by means of ecological product value realization (EPVR). The fragility of the karst ecological environment limits the development of the karst countryside, and agroforestry is an important way to achieve the ecological protection and economic development of the karst countryside. At present, research on EPVR and RR is rapidly developing. Although there is an increasing number of publications on EPVR and RR separately, the literature on their comprehensive analysis is lacking, and how the karst agroforestry ecosystem can be improved is unclear. The objective of this is to provide an overview of the current research status and challenges of EPVR and RR in order to optimize agroforestry ecosystems in karst desertification control (KDC). This paper systematically analyzed 263 relevant articles on EPVR and RR, and the results are as follows: (1) The number of studies increased exponentially after 2017. The research has primarily focused on the relationship between EPVR and RR, as well as the EPVR and the formation mechanisms of the eco-industry and value accounting of eco-products, which account for 95.53% of the total literature. China has published the most research in this area. At the intercontinental scale, this research is mainly concentrated in East Asia, Europe, and North America. (2) The main progress and landmark achievements in the research on EPVR and RR are summarized. Four key scientific questions that need to be addressed in the future are presented. (3) The above information highlights the three key areas for improving the agroforestry ecosystem in karst desertification control (KDC): the value accounting of eco-products, EPVR, and RR. This study found that EPVR and RR can improve the karst agroforestry ecosystem and further promote rural development, providing significant insights for the overall revitalization of rural areas worldwide and the scientific control of karst desertification. Full article
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<p>Publications search progress.</p>
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<p>Literature annual output analysis chart.</p>
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<p>Division research content.</p>
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<p>Distribution of the global literature on EPVR and RR.</p>
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<p>Relationship between agroforestry eco-product value realization and RR.</p>
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18 pages, 8612 KiB  
Article
Climate Sensitivity and Tree Growth Patterns in Subalpine Spruce-Dominated Forests of the North-Western Dinaric Alps
by Marko Orešković, Domagoj Trlin, Igor Anić, Milan Oršanić, Luka Prša and Stjepan Mikac
Forests 2024, 15(11), 1972; https://doi.org/10.3390/f15111972 - 8 Nov 2024
Viewed by 382
Abstract
The mountain forests in Europe, especially the ecosystems dominated by Norway spruce [Picea abies (L.) Karst], are facing major challenges due to climate change. Climatic stress factors such as increased temperatures and drought contribute to reduced growth and increased mortality, especially at [...] Read more.
The mountain forests in Europe, especially the ecosystems dominated by Norway spruce [Picea abies (L.) Karst], are facing major challenges due to climate change. Climatic stress factors such as increased temperatures and drought contribute to reduced growth and increased mortality, especially at lower altitudes. In this study, which was conducted in the northern Velebit region, the growth dynamics and climate sensitivity of Norway spruce were analyzed using standard dendrochronological methods. The focus was on samples collected at altitudes between 1135 and 1545 m. The results show two different growth trends: a positive trend from 1950 to 1977, followed by a negative trend from 1977 to 2013. Precipitation proved to be a key factor for the stability of spruce growth, while the high summer temperatures of the previous year correlated negatively with growth increment. In addition, trees at higher altitudes showed greater resistance to climatic stress. These results underline the crucial role of precipitation and site-specific conditions in maintaining the vitality of spruce forests in mountainous regions, and suggest that climate change could further destabilize spruce ecosystems in the Dinaric Alps. Full article
(This article belongs to the Special Issue Effects of Climate Change on Tree-Ring Growth)
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<p>(<b>a</b>) The location of the experimental plots (P1–P10) in the Northern Velebit National Park. The black areas indicate pure mountain spruce forests. The red line marks the boundary between the Mediterranean and continental biogeographical regions. (<b>b</b>) Z-score of average air temperature values, (<b>c</b>) sum of precipitation in summer (July to September, JAS), for different climate data sources. Pearson correlation coefficients between time series of summer air temperatures (average from July to September, JJA) from local meteorological stations Zavižan and Gospić, and spatially explicit gridded data series, where meteorological stations are ZAV—Zavižan, GOSP—Gospić, EobL—local value of E-OBS v28.0e, and EobR—regional average for [15-20E, 40-45N] of E-OBS v28.0e.</p>
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<p>(<b>a</b>) Results of the cluster analysis of the transformed values (Z-scores) of the basal area increment (BAI) of the chronologies for the period from 1887 to 2018 (<b>a</b>). (<b>b</b>) Time series of average values for each cluster (C1–C4) and (<b>c</b>) moving correlations (over a period of 30 years) between the average values of the time series of basal area increment (BAI) by cluster.</p>
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<p>(<b>a</b>) Regional chronology (average of all studied site chronologies) according to standardization method (<b>a</b>), where RCS—regional curve standardization, RES—residual chronology, SDT—standard chronology, BAI_s—basal area increment combined with a 60-year spline and BAI—basal area increment. (<b>b</b>) Dendrogram of the cluster analysis between regional chronologies and (<b>c</b>) linear trends for the periods from 1950 to 1977 and from 1977 to 2013, together with the corresponding statistical indicators.</p>
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<p>Values of the simple linear (Pearson) correlation coefficient (R) between selected climate factors: air temperature (TEMP) and precipitation totals (PREC) according to the standardization method for each plot separately for the period from 1954 to 2018. * indicates the highest and most important correlation coefficient values.</p>
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<p>Moving correlations between chronologies and the dominant climate factor (total precipitation Psum (JAS) and average air temperature Tavg (JAS) in the summer of the previous year from July to September) using different sources of climate data. Correlations with precipitation (solid line) and air temperature (dashed line). Values of the standardized 35-year average of precipitation and air temperature for the same months.</p>
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<p>(<b>a</b>) Spatial correlation between principal component 1 (PC1) from the PCA of the standard chronologies (SDT) and the average air temperature from July to September of the previous year (jasT) from the EOBS for the period from 1954 to 2018. The black square represents the geographic location of the study area. (<b>b</b>) Correlations (R) of the chronologies with the first (PC1) and second (PC2) principal components according to the standardization method. (<b>c</b>) Simple linear correlation coefficients between PC1 and the total precipitation (Prec.), the average air temperature (Temp.) and the SPEI index for the months from June of the previous year to October of the current year (OCT) and the average seasonal values over a period of 3 months. (<b>d</b>) Moving correlations with dominant climate variables (total precipitation and average air temperature from July to September of the previous year) using different sources of climate data. The gray shaded area indicates the significance threshold for <span class="html-italic">p</span> &gt; 0.05.</p>
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<p>(<b>a</b>) Values of the simple linear (Pearson) moving correlation coefficient (R) between selected climate factors: air temperature (TEMP), (<b>b</b>) total precipitation (PREC) and different chronologies for the period from 1920 to 2018 using the E-OBS data sets. (The gray area represents a period of unexpected lower correlation. (<b>c</b>) shows an example for only one year of the relationship between altitude and air temperature (<b>top</b>) and precipitation (<b>bottom</b>).</p>
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14 pages, 5229 KiB  
Article
Impacts of Bedrocks on Vegetation Carbons in Typical Karst Areas: A Case Study in Puding County, Southwest China
by Xianli Cai, Weijun Luo, Changcheng Liu, Jia Chen, Lin Zhang, Anyun Cheng, Zhongquan He and Shijie Wang
Sustainability 2024, 16(21), 9429; https://doi.org/10.3390/su16219429 - 30 Oct 2024
Viewed by 452
Abstract
An accurate estimation of vegetation carbon pools and their carbon sequestration potential is significant in global carbon cycle research but the existing estimations are still insufficient and largely uncertain. Here, we estimated the vegetation carbon density, carbon stocks, and carbon sequestration potential under [...] Read more.
An accurate estimation of vegetation carbon pools and their carbon sequestration potential is significant in global carbon cycle research but the existing estimations are still insufficient and largely uncertain. Here, we estimated the vegetation carbon density, carbon stocks, and carbon sequestration potential under three main bedrock types (limestone, dolomite, and non-carbonate) in Puding County, Guizhou Province, Southwestern China. The data used here included high-resolution vegetation maps of Puding, data from 274 sample plots, and the carbon contents measured previously in adjacent areas. The land area ratio of natural vegetation at an early stage (namely, grassland and shrub, excluding artificial forests and cultivated land) in carbonate rock areas is significantly larger than that in non-carbonate areas. The average existing carbon densities of vegetation in the non-carbonate, limestone, and dolomite areas were 31.59 ± 7.43, 16.75 ± 4.12, and 8.26 ± 2.45 Mg·ha−1, respectively, while their existing carbon stocks were 752.37 ± 172.85, 855.69 ± 210.65, and 208.49 ± 61.82 Gg, respectively. The maximum vegetation carbon densities of mature forests in the three bedrock types were 156.49 ± 12.92, 130.27 ± 6.05, and 117.41 ± 30.03 Mg·ha−1, respectively. Then, their average vegetation carbon sequestration potentials were 56.07 ± 23.06, 70.13 ± 11.39, and 59.11 ± 33.00 Mg·ha−1, respectively. In other words, vegetation carbon stocks in the non-carbonate, limestone, and dolomite areas increased by 1.34 ± 0.42, 3.58 ± 0.48, and 1.49 ± 0.51 Tg, respectively, after continuous evolution to mature forests. In conclusion, the potential growth of carbon density for karst vegetation is slightly higher than that of non-karst vegetation, despite its lower existing carbon density. Additionally, natural vegetation has a greater potential for carbon sequestration than plantations on all three bedrock types. Full article
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<p>Location of Puding County in Southwest China. The map of China and Guizhou cited by this figure are both derived from <a href="http://bzdt.ch.mnr.gov.cn/" target="_blank">http://bzdt.ch.mnr.gov.cn/</a> with their drawing review numbers of GS(2016)2880 and GS(2019)3333, respectively.</p>
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<p>Distribution map of vegetation types in Puding County. MP: mature plantation, YP: young plantation, MF: mature forest, SF: secondary forest, SW: shrub wood, SL: shrubland, GL: grassland, AC: agricultural crop (including PA (paddy) and DL (dryland)), RI: resident inhabitant area, WA: water area.</p>
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<p>Land area ratios of vegetation types under the three bedrock types. WR: including water area (WA) and resident inhabitant area (RI). The vegetation carbon calculation in this study does not involve the WA and RI regions. Due to the lower vegetation carbon density (VCD) in these two regions, we have merged them into WR in this study.</p>
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<p>Vegetation carbon density (VCD) of each vegetation type under the three bedrock types. PA: paddy field, DL: dryland, AN: average VCD for natural vegetation, AP: average VCD for plantation, AA: average VCD for all vegetation types (including agricultural crops). The a and b for the same vegetation type indicate the carbon densities under different bedrock are significantly different at <span class="html-italic">p</span> &lt; 0.05; multiple comparisons among three bedrocks had not been conducted for AN, AP, and AA.</p>
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<p>Vegetation carbon stocks of different vegetation types in areas of three bedrock types in Puding County.</p>
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<p>Allocation of vegetation carbon among organs under three bedrock types. The carbon amount of the leaves, trunks, and branches in grassland (GL) is considered to be the total value of the aboveground parts.</p>
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<p>Growth potential of vegetation carbon density (<b>A</b>) and stocks (<b>B</b>) under the three bedrock types. NV: natural vegetation, PL: plantation, AV: average value (including agricultural crops).</p>
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<p>Relationships between vegetation carbon density of mature forests and soil thickness or bedrock exposure rates under the three bedrock types. MVCD: maximum vegetation carbon densities of natural vegetation.The a and b for the MVCD indicate the maximum carbon densities under different bedrock are significantly different at <span class="html-italic">p</span> &lt; 0.05.</p>
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16 pages, 6620 KiB  
Article
Both Biotic and Abiotic Factors Shape the Spatial Distribution of Aboveground Biomass in a Tropical Karst Seasonal Rainforest in South China
by Fang Lu, Bin Wang, Jianxing Li, Dongxing Li, Shengyuan Liu, Yili Guo, Fuzhao Huang, Wusheng Xiang and Xiankun Li
Forests 2024, 15(11), 1904; https://doi.org/10.3390/f15111904 - 29 Oct 2024
Viewed by 434
Abstract
Forest biomass accumulation is fundamental to ecosystem stability, material cycling, and energy flow, and pit lays a crucial role in the carbon cycle. Understanding the factors influencing aboveground biomass (AGB) is essential for exploring ecosystem functioning mechanisms, restoring degraded forests, and estimating carbon [...] Read more.
Forest biomass accumulation is fundamental to ecosystem stability, material cycling, and energy flow, and pit lays a crucial role in the carbon cycle. Understanding the factors influencing aboveground biomass (AGB) is essential for exploring ecosystem functioning mechanisms, restoring degraded forests, and estimating carbon balance in forest communities. Tropical karst seasonal rainforests are species-rich and heterogeneous, yet the impact mechanisms of biotic and abiotic factors on AGB remain incompletely understood. Based on the survey data of a 15 ha monitoring plot in a karst seasonal rainforest in Southern China, this study explores the distribution characteristics of AGB and its intrinsic correlation with different influencing factors. The results show that the average AGB of the plot is 125.7 Mg/ha, with notable variations among habitats, peaking in hillside habitats. Trees with medium and large diameters at breast height (DBH ≥ 10 cm) account for 83.94% of the aboveground biomass (AGB) and are its primary contributors; dominant tree species exhibit higher AGB values. Both biotic and abiotic elements substantially influence AGB, with biotic factors exhibiting the largest influence. Among abiotic factors, topographic factors have a strong direct or indirect influence on AGB, while soil physicochemical properties have the smallest indirect impact. This research provides a comprehensive understanding of AGB distribution and its influencing factors in tropical karst forests (KFs), contributing to the management of carbon sinks in these ecosystems. Full article
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<p>Elevation map, geographical location, and habitat outline of Nonggang 15 ha plot.</p>
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<p>Initial SEM analysis of the impacts of biotic factors, soil physicochemical properties, and topographic factors on the amount of biomass above the ground.</p>
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<p>Distribution of aboveground biomass: map of distribution (<b>a</b>) and across different habitats (<b>b</b>). Lowercase letters denote statistically significant variations between distinct habitats (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>The distribution of aboveground biomass analyzed based on different DBH classes (<b>a</b>) and the top 15 species (<b>b</b>).</p>
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<p>Correlation between importance value (<span class="html-italic">IV</span>) and aboveground biomass (AGB). The darkened regions indicate a 95% confidence interval for the models that have been fitted.</p>
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<p>Matrix displaying the precise significance of each ecological element on aboveground biomass. Every row in the dot matrix figure on the right is an environmental component. The single black dot in each column represents the marginal impact of each environmental component. The shared effects between these corresponding environmental elements are indicated by the lines that connect several dots. The variation partitioning process yields the percentage of variance explained by each component, which is shown in the top column graphic. Each environmental element’s individual impact, as determined via hierarchical partitioning, is displayed in the column diagram on the left. Each factor’s value is determined by adding its average shared common effect with other factors to its marginal effect. The notation used for statistical significance is as follows: **, <span class="html-italic">p</span> &lt; 0.01; ***, <span class="html-italic">p</span> &lt; 0.001.</p>
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<p>SEM analysis examining the impact of biotic variables, soil physicochemical parameters, and topographic factors on aboveground biomass (<b>a</b>), as well as the respective contributions of these factors on aboveground biomass (<b>b</b>). The significant effects are represented by black solid arrows (<span class="html-italic">p</span> &lt; 0.05), whereas the non-significant effects are represented by gray solid arrows. The values adjacent to the arrows indicate the standardized coefficients. Abbreviations: StD, structural diversity; InI, individual interactions; Abu, abundance; Ric, richness; Ele, elevation; Slo, slope; Con, convexity. The notation used for statistical significance is as follows: *, <span class="html-italic">p</span> &lt; 0.05; **, <span class="html-italic">p</span> &lt; 0.01; ***, <span class="html-italic">p</span> &lt; 0.001.</p>
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<p>Principal Component Analysis (PCA) used to analyze the indices of structural diversity (<b>a</b>) and soil physicochemical property indices (<b>b</b>).</p>
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17 pages, 8188 KiB  
Article
Identification and Mapping of Eucalyptus Plantations in Remote Sensing Data Using CCDC Algorithm and Random Forest
by Miaohang Zhou, Xujun Han, Jinghan Wang, Xiangyu Ji, Yuefei Zhou and Meng Liu
Forests 2024, 15(11), 1866; https://doi.org/10.3390/f15111866 - 24 Oct 2024
Viewed by 504
Abstract
Eucalyptus plantations are one of the primary artificial forests in southern China, experiencing rapid expansion in recent years due to their significant socio-economic benefits. This expansion has raised concerns about the ecological environment, necessitating accurate mapping of eucalyptus plantations. In this study, the [...] Read more.
Eucalyptus plantations are one of the primary artificial forests in southern China, experiencing rapid expansion in recent years due to their significant socio-economic benefits. This expansion has raised concerns about the ecological environment, necessitating accurate mapping of eucalyptus plantations. In this study, the phenological characteristics of eucalyptus plantations were utilized as the primary classification basis. Long-term time series Landsat and Sentinel-2 data from 2000 to 2022 were rigorously preprocessed pixel by pixel using the Google Earth Engine (GEE) platform to obtain high-quality observation data. The Continuous Change Detection and Classification (CCDC) algorithm was employed to fit the multi-year observation data with harmonic curves, utilizing parameters such as normalized intercept, slope, phase, and amplitude of the fitted curves to characterize the phenological features of vegetation. A total of 127 phenological indices were generated using the Normalized Burn Ratio (NBR), Normalized Difference Fractional Index (NDFI), and six spectral bands, with the top 20 contributing indices selected as input variables for the random forest algorithm to obtain preliminary classification results. Subsequently, eucalyptus plantation rotation features and the Simple Non-Iterative Clustering (SNIC) superpixel segmentation algorithm were employed to filter the results, enhancing the accuracy of the identification results. The producer’s accuracy, user’s accuracy, and overall accuracy of the eucalyptus plantation map for the year 2020 were found to be 96.67%, 89.23%, and 95.83%, respectively, with a total area accuracy of 94.39%. Accurate mapping of eucalyptus plantations provides essential information and evidence for ecological environment protection and the formulation of carbon-neutral strategies. Full article
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<p>Elevation and geographic location of Qinzhou City.</p>
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<p>Workflow for mapping eucalyptus plantations.</p>
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<p>Reflectance (colored) and mean reflectance (black) of candidate endmembers collected for NDFI calculation.</p>
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<p>Visualization results of the NBR fitting for seven typical land cover types in 2020.</p>
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<p>Schematic representation of band correlations with selected normalized intercepts and slopes shown.</p>
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<p>The top 20 parameters ranked by importance and their contribution ratios. The green color represents the top six important parameters that contributed to over 70% of the importance, while the yellow color represents the remaining parameters.</p>
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<p>Differential representation of eucalyptus, other vegetation, and non-vegetation categories under the top 6 ranked features of importance.</p>
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<p>Spatial distribution of eucalyptus plantations in Qinzhou City in 2020.</p>
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<p>Distribution of elevation and slope of eucalyptus plantations in Qinzhou City in 2020.</p>
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<p>Comparative analysis between high-resolution imagery and classification results. (<b>a</b>) Validation Area 1; (<b>b</b>) Validation Area 2.</p>
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16 pages, 8601 KiB  
Article
Ecological Suitability Evaluation of City Construction Based on Landscape Ecological Analysis
by Siyuan Wang, Minmin Zhao, Weicui Ding, Qiang Yang, Hao Li, Changqing Shao, Binghu Wang and Yi Liu
Sustainability 2024, 16(21), 9178; https://doi.org/10.3390/su16219178 - 23 Oct 2024
Viewed by 651
Abstract
Ecological suitability evaluation is a critical component of regional sustainable development and construction, serving as a foundation for optimizing spatial patterns of regional growth. This is particularly pertinent in karst mountainous regions characterized by limited land resources and heightened ecosystem vulnerability, where a [...] Read more.
Ecological suitability evaluation is a critical component of regional sustainable development and construction, serving as a foundation for optimizing spatial patterns of regional growth. This is particularly pertinent in karst mountainous regions characterized by limited land resources and heightened ecosystem vulnerability, where a quantitative assessment of ecological suitability for land development is both crucial and urgent. Based on the fundamental principles of structural and functional dynamics in landscape ecology, this study focuses on Gui’an New Area, a designated urban development zone situated in the karst landscape of Guizhou Province. An index system was established encompassing three dimensions: ecological elements, ecological significance, and ecological resilience, utilizing the integrated ecological resistance (IER) model to evaluate the suitability of regional development and construction. The results reveal that the eastern region exhibits higher suitability compared to the central and western regions, with the northwest region demonstrating the lowest suitability overall. Relatively speaking, the evaluation of geological environment suitability and the comprehensive ecological constraints associated with development and construction indicates that the areas currently planned and ongoing reflect flat terrain and low ecological risk. Furthermore, within the scope of ecosystem dynamic adaptation, developmental activities in these regions exert minimal impact on the natural ecosystem, thereby demonstrating a high suitability for development and construction. In terms of future key development zones, areas with gentle slopes ranging from 8 to 15 degrees are recommended, aligning with the actual requirements for cultivated land protection. The total area designated as prohibited development zones constitutes the smallest proportion, representing only 9.45%, which is significantly lower than that of priority development zones (38.75%) and moderate development zones (22.45%). From the perspective of landscape ecology, this paper provides a comprehensive investigation into the ecological suitability evaluation system for development and construction in the karst regions of Southwest China, offering valuable insights for assessing ecological suitability in similar areas. Full article
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<p>Location of Gui’an New Area.</p>
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<p>Flow chart of integrated ecological resistance model.</p>
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<p>Ecological structure resistance of Gui’an New Area.</p>
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<p>Ecological function resistance of Gui’an New Area.</p>
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<p>Ecological adaption resistance of Gui’an New Area.</p>
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<p>Suitability for development of Gui’an New Area.</p>
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15 pages, 3175 KiB  
Article
Dragonfly Functional Diversity in Dinaric Karst Tufa-Depositing Lotic Habitats in a Biodiversity Hotspot
by Marina Vilenica, Vlatka Mičetić Stanković and Mladen Kučinić
Diversity 2024, 16(10), 645; https://doi.org/10.3390/d16100645 - 17 Oct 2024
Viewed by 418
Abstract
Functional diversity is a key component of biodiversity that reflects various dimensions of ecosystem functioning and the roles organisms play within communities and ecosystems. It is widely used to understand how ecological processes influence biotic assemblages. With an aim to increase our knowledge [...] Read more.
Functional diversity is a key component of biodiversity that reflects various dimensions of ecosystem functioning and the roles organisms play within communities and ecosystems. It is widely used to understand how ecological processes influence biotic assemblages. With an aim to increase our knowledge about dragonfly ecological requirements in tufa-depositing karst habitats, we assessed functional diversity of their assemblages, various life history traits (e.g., stream zonation preference, substrate preference, reproduction type), and relationship between functional diversity and physico-chemical water properties in three types of karst lotic habitats (springs, streams, and tufa barriers) in a biodiversity hotspot in the western Balkan Peninsula. Dragonfly functional diversity was mainly characterized by traits typical for lotic rheophile species with medium dispersal capacity. Among the investigated habitats, tufa barriers, characterized by higher (micro)habitat heterogeneity, higher water velocity, as well as lower conductivity and concentration of nitrates, can be considered as dragonfly functional diversity hotspots. Functional diversity and most of the life history traits were comparable among different substrate types in the studied habitats, indicating higher importance of habitat type in shaping dragonfly functional diversity patterns in karst lotic habitats. Our results should be considered in the management and conservation activities of vulnerable karst freshwater ecosystems and their dragonfly assemblages. Full article
(This article belongs to the Section Freshwater Biodiversity)
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<p>Photo examples of study sites included in the study: springs: (<b>a</b>) Bijela rijeka spring, (<b>b</b>) Crna rijeka spring; streams (and small mountainous rivers): (<b>c</b>) Bijela rijeka middle reaches, (<b>d</b>) Crna rijeka middle reaches, (<b>e</b>) Crna rijeka lower reaches, (<b>f</b>) Plitvica, (<b>g</b>) Korana; tufa barriers: (<b>h</b>) Labudovac, (<b>i</b>) Kozjak–Milanovac, (<b>j</b>) Novakovića Brod.</p>
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<p>Environmental variables at three Dinaric karst lotic habitat types in the Plitvice Lakes NP, Croatia (shown as mean annual values with standard deviation, SD): (<b>a</b>) nitrate concentration, (<b>b</b>) pH, (<b>c</b>) oxygen saturation, (<b>d</b>) water velocity, (<b>e</b>) conductivity, and (<b>f</b>) alkalinity. Significant differences among the habitat types are indicated by different letters (Kruskal–Wallis H test with multiple comparisons <span class="html-italic">post hoc</span> test, <span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Environmental variables at three Dinaric karst lotic habitat types in the Plitvice Lakes NP, Croatia (shown as mean annual values with standard deviation, SD): (<b>a</b>) water temperature, (<b>b</b>) oxygen concentration, and (<b>c</b>) ammonia concentration. Non-significant differences among the habitat types are indicated by the letter a (Kruskal–Wallis H test with multiple comparisons <span class="html-italic">post hoc</span> test, <span class="html-italic">p</span> &gt; 0.05).</p>
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<p>Dragonfly functional diversity (RaoQ index) at three Dinaric karst lotic habitat types in the Plitvice Lakes NP, Croatia (shown as mean with standard deviation, SD). Significant differences among the habitat types are indicated by different letters (Kruskal–Wallis H test with multiple comparisons <span class="html-italic">post hoc</span> test, <span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Dragonfly functional traits at three Dinaric karst lotic habitat types in the Plitvice Lakes NP, Croatia (shown as mean with standard deviation, SD): (<b>a</b>) body shape, (<b>b</b>) dispersal capacity, (<b>c</b>) stream zonation preference, (<b>d</b>) lateral connectivity preference, (<b>e</b>) current preference, (<b>f</b>) substrate type preference, and (<b>g</b>) reproduction type. Significant differences among the habitat types are indicated by different letters (Kruskal–Wallis H test with multiple comparisons <span class="html-italic">post hoc</span> test, <span class="html-italic">p</span> &lt; 0.05). Legend: DC = dispersal capacity; EUC = eucrenal, HYC = hypocrenal, ERH = epirhithral, MRH = metarhithral, HRH = hyporhithral, EPO = epipotamal, MPO = metapotamal, HPO = hypopotamal, LITT = littoral; EUP = eupotamon, PRP = parapotamon, PLP = plesiopotamon, PAP = palaeopotamon, TMP = temporary water bodies; LIP = limnophil, LRP = limno- to rheophil, RLP = rheo- to limnophil, RPH = rheophil; ARG = argyllal, PEL = pelal, PSA = psammal, AKA = akal, LITH = lithal, PHY = phytal, POM = particulate organic matter; ETS = eggs laid attached to substrate, EIS = eggs laid in substrate, SUB = eggs laid not attached to or in substrate, OWA = eggs laid in open water, IPL = eggs laid inside plant tissue, OPL = eggs laid onto plant material, IRS = eggs laid into submerged soil or onto submerged rock.</p>
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<p>Redundancy analysis (RDA) ordination biplot showing the relationship between dragonfly functional traits and six significant environmental variables in Dinaric karst lotic habitats in the Plitvice Lakes NP, Croatia. Abbreviations of the functional (life history) traits are in <a href="#diversity-16-00645-f004" class="html-fig">Figure 4</a>.</p>
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<p>Dragonfly functional diversity (RaoQ index) at four main substrate types in three Dinaric karst lotic habitats in the Plitvice Lakes NP, Croatia (shown as mean with standard deviation, SD) Non-significant differences among the habitat types are indicated by the letter a (Kruskal–Wallis H test with multiple comparisons <span class="html-italic">post hoc</span> test, <span class="html-italic">p</span> &gt; 0.05).</p>
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<p>Dragonfly functional traits at four main substrate types in three Dinaric karst lotic habitats in the Plitvice Lakes NP, Croatia (shown as mean with standard deviation, SD): (<b>a</b>) body shape, (<b>b</b>) dispersal capacity, (<b>c</b>) stream zonation preference, (<b>d</b>) lateral connectivity preference, (<b>e</b>) current preference, (<b>f</b>) substrate type preference, and (<b>g</b>) reproduction type. Significant differences among the habitat types are indicated by different letters (Kruskal–Wallis H test with multiple comparisons post hoc test, <span class="html-italic">p</span> &lt; 0.05). Abbreviations of the functional (life history) traits are in <a href="#diversity-16-00645-f005" class="html-fig">Figure 5</a>.</p>
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18 pages, 9156 KiB  
Article
3D Modelling and Measuring Dam System of a Pellucid Tufa Lake Using UAV Digital Photogrammetry
by Xianwei Zhang, Guiyun Zhou, Jinchen He and Jiayuan Lin
Remote Sens. 2024, 16(20), 3839; https://doi.org/10.3390/rs16203839 - 16 Oct 2024
Viewed by 508
Abstract
The acquisition of the three-dimensional (3D) morphology of the complete tufa dam system is of great significance for analyzing the formation and development of a pellucid tufa lake in a fluvial tufa valley. The dam system is usually composed of the dams partially [...] Read more.
The acquisition of the three-dimensional (3D) morphology of the complete tufa dam system is of great significance for analyzing the formation and development of a pellucid tufa lake in a fluvial tufa valley. The dam system is usually composed of the dams partially exposed above-water and the ones totally submerged underwater. This situation makes it difficult to directly obtain the real 3D scene of the dam system solely using an existing measurement technique. In recent years, unmanned aerial vehicle (UAV) digital photogrammetry has been increasingly used to acquire high-precision 3D models of various earth surface scenes. In this study, taking Wolong Lake and its neighborhood in Jiuzhaigou Valley, China as the study site, we employed a fixed-wing UAV equipped with a consumer-level digital camera to capture the overlapping images, and produced the initial Digital Surface Model (DSM) of the dam system. The refraction correction was applied to retrieving the underwater Digital Elevation Model (DEM) of the submerged dam or dam part, and the ground interpolation was adopted to eliminate vegetation obstruction to obtain the DEM of the dam parts above-water. Based on the complete 3D model of the dam system, the elevation profiles along the centerlines of Wolong Lake were derived, and the dimension data of those tufa dams on the section lines were accurately measured. In combination of local hydrodynamics, the implication of the morphological characteristics for analyzing the formation and development of the tufa dam system was also explored. Full article
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<p>(<b>a</b>) The study site is located in Sichuan Province, China; (<b>b</b>) Jiuzhaigou National Nature Reserve; (<b>c</b>) the tufa dam system of Wolong Lake.</p>
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<p>The workflow for modelling and analyzing dam system of a tufa lake using UAV digital photogrammetry.</p>
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<p>Deviated underwater terrain caused by refraction of light at the interface of water and air.</p>
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<p>The resulting DEM of above-water tufa dam using ground interpolation based on the initial DSM from SfM-MVS processing.</p>
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<p>The centerline of a water channel is obtained using Voronoi-based median axis extraction algorithm. (<b>a</b>) The discrete points sampled on both sides of the water channel; (<b>b</b>) the generated Thiessen polygons; (<b>c</b>) the resulting centerline of the water channel.</p>
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<p>(<b>a</b>) Delineated boundaries of above-water and underwater parts of study site; (<b>b</b>) the initial DSM of study site were divided into above-water and underwater parts; (<b>c</b>) the spatial scopes of UD, SD, and DD delineated out on the complete DEM of study site.</p>
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<p>The complete DEM of the study site by stitching the resulting DEMs after refraction correction and ground interpolation. (<b>a</b>) The initial underwater DSM; (<b>b</b>) the resulting DEM via refraction correction; (<b>c</b>) the initial above-water DSM; (<b>d</b>) the resulting DEM removed of vegetation; (<b>e</b>) the complete DEM of the study site.</p>
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<p>(<b>a</b>) Thiessen polygons of the fluvial channel of Wolong Lake; (<b>b</b>) extracted centerline of the fluvial channel of Wolong Lake; (<b>c</b>) extracted three centerlines for deriving elevation profiles.</p>
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<p>Longitudinal elevation profiles of the tufa dam system belonging to Wolong Lake. (<b>a</b>) Elevation profile along the left centerline; (<b>b</b>) elevation profile along the middle centerline; (<b>c</b>) elevation profile along the right centerline.</p>
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<p>(<b>a</b>) Schematic diagram of downstream-dipping ramp; (<b>b</b>) schematic diagram of downstream-overhanging crest with tufa stalactites; (<b>c</b>) the real scenery of the downstream tufa dam of Wolong Lake; (<b>d</b>) the stepped terrain where the downstream tufa dams formed. (<b>a</b>,<b>b</b>) adapted from Carthew et al. [<a href="#B43-remotesensing-16-03839" class="html-bibr">43</a>].</p>
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18 pages, 18581 KiB  
Article
Spatial–Temporal Variations in Water Use Efficiency and Its Influencing Factors in the Li River Basin, China
by Yanqi Chu, Xiangling Tang and Xuemei Zhong
Water 2024, 16(19), 2864; https://doi.org/10.3390/w16192864 - 9 Oct 2024
Viewed by 508
Abstract
As a vital indicator for measuring the coupled carbon–water cycle of an ecosystem, water use efficiency (WUE) can also reflect the adaptive capacity of plants in different ecosystems. Located in Southwest China, the Li River Basin has a representative karst landform, and the [...] Read more.
As a vital indicator for measuring the coupled carbon–water cycle of an ecosystem, water use efficiency (WUE) can also reflect the adaptive capacity of plants in different ecosystems. Located in Southwest China, the Li River Basin has a representative karst landform, and the uneven rainfall in the region leads to severe water shortage. In this study, we analyzed the spatial–temporal transformation characteristics of the WUE of the basin and its relationship with different influencing factors from 2001 to 2020 based on a correlation analysis and trend analysis. The main conclusions are as follows: (1) The average value of WUE in the Li River Basin was 1.8251 gC· mm−1·m−2, and it kept decreasing at a rate of 0.0072 gC· mm−1·m−2·a−1 in the past 20 years. With respect to the spatial distribution of the multi-year average of WUE, it exhibits a gradual increasing trend from west to east. (2) Between gross primary productivity (GPP) and evapotranspiration (ET), it was found that ET was the primary influencing factor of WUE. Precipitation was positively correlated with WUE in the Li River Basin, accounting for 67.22% of the total area of the basin. The air temperature was negatively correlated with WUE, and the area was negatively correlated with WUE, accounting for 92.67% of the basin area. (3) The normalized difference vegetation index (NDVI) and leaf area index (LAI) were negatively correlated with WUE, and the proportions of negatively correlated areas to the total area of the basin were similar; both were between 60 and 70%. The growth of vegetation inhibited the increase in WUE in the basin to a certain extent. Regarding Vapor Pressure Deficit (VPD), the proportions of positive and negative correlation areas with WUE were similar, accounting for 49.58% and 50.42%, respectively. (4) The occurrence of drought events and the enhancement in its degree led to a continuous increase in WUE in the basin; for different land cover types, the correlation of the standardized precipitation evapotranspiration index (SPEI) was in the following order from strongest to weakest: grassland > cropland > forest > shrubland. Full article
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<p>(<b>a</b>) Location of study area. (<b>b</b>) Land cover types of study area.</p>
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<p>(<b>a</b>) Location of study area. (<b>b</b>) Land cover types of study area.</p>
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<p>Spatial distribution of GPP, ET, and WUE in the Li River Basin, 2001–2020.</p>
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<p>Spatial distribution of trend changes in WUE, GPP, and ET in Li River Basin from 2001 to 2020.</p>
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<p>Temporal variation in mean WUE values for different land cover types in Li River Basin from 2001 to 2020.</p>
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<p>Partial correlation analysis and primary influencing factor analysis of WUE with GPP and ET in Li River Basin from 2001 to 2020.</p>
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<p>Annual average precipitation (<b>a</b>) and temperature (<b>b</b>) data for meteorological stations and raster types in the Li River Basin from 2001 to 2020.</p>
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<p>Spatial distribution of partial correlation analysis of WUE with precipitation and air temperature in Li River Basin from 2001 to 2020. ((<b>a1</b>,<b>b1</b>) Spatial distribution of correlation coefficients in the Analysis Results. (<b>a2</b>,<b>b2</b>) Spatial distribution of various significance Levels).</p>
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<p>Spatial distribution of trend changes in NDVI and LAI in Li River Basin from 2001 to 2020.</p>
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<p>Spatial distribution of correlation coefficients of WUE with NDVI and LAI in Li River Basin from 2001 to 2020.</p>
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<p>Spatial distribution of correlation coefficients of WUE with VPD in Li River Basin from 2001 to 2020. ((<b>a</b>) Spatial distribution of correlation coefficients in the Analysis Results. (<b>b</b>) Spatial distribution of various significance Levels).</p>
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<p>Time series of SPEI in Li River Basin 2001–2020.</p>
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<p>Spatial distribution of correlation analysis between WUE and SPEI in Li River Basin from 2001 to 2020. ((<b>a</b>) Spatial distribution of correlation coefficients in the Analysis Results. (<b>b</b>) Spatial distribution of various significance Levels).</p>
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<p>Relationship between WUE and SPEI in Li River Basin from 2001 to 2020.</p>
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14 pages, 4407 KiB  
Article
Geochemical Characteristics and Genesis of Brine Chemical Composition in Cambrian Carbonate-Dominated Succession in the Northeastern Region of Chongqing, Southwestern China
by Zhi-lin Zheng, Bin Xie, Chun-mei Wu, Lei Zhou, Ke Zhang, Bin-chen Zhang and Ping-heng Yang
Water 2024, 16(19), 2859; https://doi.org/10.3390/w16192859 - 9 Oct 2024
Viewed by 646
Abstract
Deeply situated brine is abundant in rare metal minerals, possessing significant economic worth. To the authors’ knowledge, brine present within the Cambrian carbonate-dominated succession in the northeastern region of Chongqing, Southwestern China, has not been previously reported. In this investigation, brine samples were [...] Read more.
Deeply situated brine is abundant in rare metal minerals, possessing significant economic worth. To the authors’ knowledge, brine present within the Cambrian carbonate-dominated succession in the northeastern region of Chongqing, Southwestern China, has not been previously reported. In this investigation, brine samples were collected from an abandoned brine well, designated as Tianyi Well, for the purpose of analyzing the hydrochemical characteristics and geochemical evolution of the brine. Halide concentrations, associated ions, and their ionic ratios within the sampled brine were analyzed. The brine originating from the deep Cambrian aquifer was characterized by high salinity levels, with an average TDS value of 242 ± 11 g/L, and was dominated by a Na-Cl facies. The studied brine underwent a moderate degree of seawater evaporation, occurring between the saturation levels of gypsum and halite, accompanied by some halite dissolution. Compared to modern seawater evaporation, the depletion of Mg2+, HCO3, and SO42− concentrations, along with the enrichment of Ca2+, Li+, K+, and Sr2+, is likely primarily attributed to water–rock interactions. These interactions include dolomitization, combination of halite dissolution, upwelling of lithium- and potassium-bearing groundwater, calcium sulfate precipitation, biological sulfate reduction (BSR), and the common ion effect within the brine system. This research offers valuable insights into the genesis of the brine within the Cambrian carbonate succession and provides theoretical backing for the development of brine resources in the future. Full article
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<p>(<b>a</b>–<b>c</b>) Location of the studied brine of Tianyi Well, (<b>d</b>) its adjacent outcrops, and (<b>e</b>) cross-section A-A‘ showing an overview of the general lithology of the well and surrounding successions, in which the faults were identified through seismic wave analysis.</p>
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<p>Pumping, recovering water level, and sampling campaign of the studied brine.</p>
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<p>Variation in TDS values during pumping.</p>
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<p>Piper trilinear diagram shows that the studied brine was a Na-Cl hydrochemical facies.</p>
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<p>Gibbs’s diagram depicts the range of brine studied, falling within the evaporation–crystallization process. The plot was adapted from Gibbs (1970) [<a href="#B38-water-16-02859" class="html-bibr">38</a>].</p>
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<p>Cl<sup>−</sup> vs. Br<sup>−</sup> concentrations suggests a moderate level of evaporation between gypsum and halite saturation and some degree of halite dissolution.</p>
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<p>The ionic patterns of the studied brine standardized with respect to the chlorinity of modern seawater (concentrations from Chen, 1983), showing the brine cannot be attributed solely to the evaporation of seawater but also water–rock interactions. The value of dash line is 1 and indicates concentrated seawater. A value of 1 signifies equivalence to seawater, a value greater than 1 denotes an enrichment relative to seawater, whereas a value less than 1 indicates depletion compared to seawater.</p>
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15 pages, 1068 KiB  
Review
Occurrence and Speciation of Pollutants in Guilin Huixian Wetland: Nutrients, Microplastics, Heavy Metals, and Emerging Contaminants
by Hang Gao, Hao Chen, Yue Jin, Ruoting Gao, Chunzhong Wei, Chunfang Zhang and Wenjie Zhang
Water 2024, 16(19), 2816; https://doi.org/10.3390/w16192816 - 3 Oct 2024
Viewed by 1152
Abstract
The Huixian Wetland is a natural ecosystem of immense ecological value, providing crucial ecosystem services such as water purification, water regulation, and a habitat for the region’s flora and fauna. Its karst peak forest landforms and surrounding environment also possess unique ecological and [...] Read more.
The Huixian Wetland is a natural ecosystem of immense ecological value, providing crucial ecosystem services such as water purification, water regulation, and a habitat for the region’s flora and fauna. Its karst peak forest landforms and surrounding environment also possess unique ecological and landscape value. However, with the ongoing socioeconomic development, including the rise of industrial, agricultural, and aquaculture activities in the wetland area, the nutrient composition of the Huixian Wetland has been altered. This paper reviews the current status of nitrogen, phosphorus, heavy metals, emerging pollutants, and biodiversity in various environmental media of the Huixian Wetland. It synthesizes the literature to identify the factors influencing these changes and projects future research directions for the wetland. This work is of significant practical importance, providing scientific foundations for the restoration and protection of the Huixian Wetland. Full article
(This article belongs to the Special Issue Water Treatment Technology for Emerging Contaminants)
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<p>The spatiotemporal distribution of nutrients in the sediment-water-riparian soil system of the Huixian Wetland in 2019: (<b>a</b>) The spatiotemporal distribution of nutrients in the groundwater system; (<b>b</b>) The spatiotemporal distribution of nutrients in riparian soils; (<b>c</b>) The spatiotemporal distribution of nutrients in sediments.</p>
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<p>Summarizes the comparison of heavy metal content in the surface water-sediment system of Huixian Wetland from May to July and August to October in 2018: (<b>a</b>) Comparison of heavy metal concentrations in the surface water; (<b>b</b>) Comparison of heavy metal concentrations in the sediment system.</p>
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18 pages, 10138 KiB  
Review
Knowledge Mapping Analysis of Karst Rocky Desertification Vegetation Restoration in Southwest China: A Study Based on Web of Science Literature
by Xiaxia Lu, Maoyin Sheng and Mengxia Luo
Agronomy 2024, 14(10), 2235; https://doi.org/10.3390/agronomy14102235 - 27 Sep 2024
Viewed by 523
Abstract
Karst rocky desertification (KRD) is a serious ecological and environmental issue, hindering the sustainable socio-economic development of the karst area. To scientifically control this issue, lots of studies on KRD vegetation restoration have been conducted in the past few decades. In the present [...] Read more.
Karst rocky desertification (KRD) is a serious ecological and environmental issue, hindering the sustainable socio-economic development of the karst area. To scientifically control this issue, lots of studies on KRD vegetation restoration have been conducted in the past few decades. In the present study, a systematic review of the research progress and future trends in KRD vegetation restoration was conducted. The results showed the following: (1) Studies on KRD vegetation restoration began in the 1990s and could be divided into the four following stages: germination (1993–2002), initial development (2003–2010), steady growth (2011–2016), and rapid growth (2017–2023); (2) research hot topics included theoretical implications, vegetation restoration strategies and technologies, ecological responses to the KRD vegetation restoration, and the coupling of vegetation restoration with landscape resource enhancement; (3) the research frontiers were as follows: the classification and restoration effectiveness of KRD vegetation types, the impacts of KRD vegetation restoration on soil microorganisms and soil erosion, the influences of ecological engineering and land use on KRD vegetation restoration, and the relationships between KRD vegetation restorations and karst ecosystem structural functions. Finally, research prospects were proposed from the research methods, perspectives, content, and shortcomings. This study provided valuable references for in-depth research in the field of KRD vegetation restoration. Full article
(This article belongs to the Section Agricultural Biosystem and Biological Engineering)
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<p>Analytical framework in the present study.</p>
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<p>The number of study papers and funded projects on KDR vegetation restorations between the years of 1993–2023. Note: The information for the funding amount is obtained from the Chinese National Natural Science Foundation.</p>
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<p>Linkages of journal published (<b>a</b>) and co-cited journals (<b>b</b>) in studies on the karst rocky desertification vegetation restorations.</p>
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<p>Linkages of authors (<b>a</b>) and co-cited authors (<b>b</b>) in studies on the karst rocky desertification vegetation restorations.</p>
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<p>Author countries and their linkages in studies on the karst rocky desertification vegetation restorations.</p>
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<p>Research hotspots in the knowledge map of studies on karst rocky desertification vegetation restorations.</p>
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<p>The time view of literature clustering by study keywords.</p>
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<p>Top 15 keywords with the strongest citation bursts. Note: The blue line segment in the figure represents the time when the keyword started, and the red line segment represents the time period when the keyword was strongly highlighted.</p>
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<p>Knowledge framework of karst rocky desertification vegetation restoration [<a href="#B8-agronomy-14-02235" class="html-bibr">8</a>,<a href="#B17-agronomy-14-02235" class="html-bibr">17</a>,<a href="#B51-agronomy-14-02235" class="html-bibr">51</a>,<a href="#B52-agronomy-14-02235" class="html-bibr">52</a>,<a href="#B54-agronomy-14-02235" class="html-bibr">54</a>,<a href="#B55-agronomy-14-02235" class="html-bibr">55</a>,<a href="#B57-agronomy-14-02235" class="html-bibr">57</a>].</p>
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