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18 pages, 5529 KiB  
Article
Integrated Evaluation of the Ecological Security Pattern in Central Beijing Using InVEST, MSPA, and Multifactor Indices
by Xiaodan Li, Haoyu Tao, Jing Wang, Bo Zhang, Zhen Liu, Zhiping Liu and Jing Li
Land 2025, 14(1), 205; https://doi.org/10.3390/land14010205 - 20 Jan 2025
Viewed by 218
Abstract
Scientific identification of ecological sources and corridors is crucial in constructing an ecological security pattern (ESP). To develop an ESP tailored to the scale of central urban areas in megacities, this study takes Central Beijing as the research object. It innovatively integrates the [...] Read more.
Scientific identification of ecological sources and corridors is crucial in constructing an ecological security pattern (ESP). To develop an ESP tailored to the scale of central urban areas in megacities, this study takes Central Beijing as the research object. It innovatively integrates the integrated valuation of ecosystem services and tradeoffs (InVEST), the morphological spatial pattern analysis (MSPA), and the Conefor software to identify ecological sources. Seven indicators related to topographic, natural conditions, and human disturbance factors are selected to build the ecological resistance surface, which is then combined with circuit theory to construct the ESP. The results show the following: (1) Central Beijing contains 157 ecological sources, primarily distributed in the western, northern, and eastern regions, with woodland as the dominant land type. (2) A total of 439 ecological corridors were extracted, including 317 key ecological corridors and 122 inactive ecological corridors. (3) The identified ecological pinch points are mainly the Jingmi Diversion Canal and the West Moat. (4) The identified ecological barriers are spread throughout the entire study area. The results of this study are highly significant for improving the quality of ecological security and protecting biodiversity in the study area and other urban centers. Full article
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<p>Geographical location and land cover types of Central Beijing.</p>
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<p>Research flowchart design.</p>
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<p>Spatial distribution of ESs. (<b>a</b>) Habitat Quality; (<b>b</b>) Carbon Storage and Sequestration; (<b>c</b>) Sediment Delivery Ratio; (<b>d</b>) Spatial distribution of ESs.</p>
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<p>Spatial distribution of MSPA.</p>
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<p>Spatial distribution of ecological sources.</p>
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<p>Resistance surface of Central Beijing.</p>
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<p>Spatial distribution of CWD.</p>
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<p>Spatial distribution of ecological corridors.</p>
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<p>Spatial patterns of ecological pinch points and barriers.</p>
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26 pages, 19399 KiB  
Article
The Status of Wild Grapevine (Vitis vinifera L. subsp. sylvestris (C.C. Gmel.) Hegi) Populations in Georgia (South Caucasus)
by Gabriele Cola, Gabriella De Lorenzis, Osvaldo Failla, Nikoloz Kvaliashvili, Shengeli Kikilashvili, Maia Kikvadze, Londa Mamasakhlisashvili, Irma Mdinaradze, Ramaz Chipashvili and David Maghradze
Plants 2025, 14(2), 232; https://doi.org/10.3390/plants14020232 - 15 Jan 2025
Viewed by 483
Abstract
Repeated expeditions across various regions of Georgia in the early 2000s led to the identification of 434 wild grapevine individuals (Vitis vinifera L. subsp. sylvestris (C.C. Gmel.) Hegi) across 127 different sites, with 45% of these sites containing only a single vine [...] Read more.
Repeated expeditions across various regions of Georgia in the early 2000s led to the identification of 434 wild grapevine individuals (Vitis vinifera L. subsp. sylvestris (C.C. Gmel.) Hegi) across 127 different sites, with 45% of these sites containing only a single vine and only 7% more than 9 vines. A total of 70 accessions were propagated in a germplasm collection, 41 of them were descripted from the ampelographic point of view and 32 from the phenological one. The geographical and ecological analysis confirmed that wild grapevines primarily grow in humid environments with warm and fully humid climates, often near rivers. They favor deep, fertile, and evolved soils, mainly alluvial and cinnamonic types (80%), with a marginal presence on strongly eroded soils. Their main natural vegetations are forests and open woodlands, with some individuals in the Southeast found in steppes. The altitudinal range spans from 0 to 1200 m, with 80% of vines distributed between 400 and 900 m. The phenological analysis revealed significant differences among the accessions but no difference among populations, with only a slight variation in bud-break timing, indicating a high level of synchronicity overall. Flowering timing proved to be the most uniform stage, suggesting minimal environmental pressure on genetic adaptation. The mature leaf morphology exhibited significant polymorphism, though leaves were generally three- or five-lobed, weak-wrinkling, and -blistering, with a low density of hairs. Bunch and berry morphology were more uniform. Bunches were consistently very small, cylindrical, and never dense or winged. Berries were also very small, mostly globular, always blue-black in color, and non-aromatic. A striking feature was the frequency of red flesh coloration, which ranged from weak to strong, with uncolored flesh being rare. The Georgian population of wild grapevines was found to be fragmented, often consisting of scattered single individuals or small groups. Therefore, we believe it is urgent for Georgia to implement specific protection measures to preserve this vital genetic resource. Full article
(This article belongs to the Section Plant Ecology)
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<p>Distribution map of wild <span class="html-italic">Vitis vinifera</span> L. populations in Georgia for the second half of the 20th century [<a href="#B57-plants-14-00232" class="html-bibr">57</a>]. (1) Saingilo, (2) Kakheti—the banks of Alazani and Iori rivers, (3) Lower Kartli, (4) Inner Kartli, (5) Upper Imereti, (6), Racha-Lechkhumi, (7) the Black See Regions of Adjara and Abkhazeti, (8) Samtskhe–Javakheti (i.e., Meskheti).</p>
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<p>Box and whiskers plot, showing the population phenological course of 2019 (<b>a</b>), 2020 (<b>b</b>), and 2021 (<b>c</b>). For each value, X represents the average, the horizontal line is the median, and the box extends from upper to lower quartile. The whiskers (vertical lines outside the box) represent data variability outside the upper and lower quartiles. Points outside the whisker line represent the outlier data. Legend: 1 = beginning of bud swelling; 9 = bud break; 61 = beginning of flowering; 65 = full flowering; 71 = fruit set; 75 = berries pea-sized; 79 = majority of berries touching; 81 = beginning of ripening; 85 = softening of berries; 89 = berries ripe.</p>
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<p>3D scatter plot of the deviation from the population average of the date of occurrence of BBCH 9 (bud break) as a function of elevation and longitude.</p>
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<p>(<b>a</b>) Site distribution in relation to the number of wild vines per site and (<b>b</b>) frequency distribution of the detected sites in relation to the number of individuals growing in the site.</p>
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<p>(<b>a</b>) Map of wild grapevine sampling sites and Köppen climate types [<a href="#B65-plants-14-00232" class="html-bibr">65</a>,<a href="#B66-plants-14-00232" class="html-bibr">66</a>], (<b>b</b>) distribution of the individuals along the climate types. The frequency of distribution (%) is shown above the bars.</p>
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<p>(<b>a</b>) Map of wild grapevine sampling sites and administrative regions, (<b>b</b>) distribution of the individuals along the regions. The frequency of distribution (%) is shown above the bars.</p>
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<p>(<b>a</b>) Map of wild grapevine sampling sites and elevation, (<b>b</b>) distribution of the individuals along the elevation ranges. The frequency of distribution (%) is shown above the bars.</p>
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<p>(<b>a</b>) Map of wild grapevine sampling sites and soil types, (<b>b</b>) distribution of the individuals along the soil types. The frequency of distribution (%) is shown above the bars. Soil classifications [<a href="#B67-plants-14-00232" class="html-bibr">67</a>] provided in <a href="#plants-14-00232-t0A4" class="html-table">Table A4</a>.</p>
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<p>(<b>a</b>) Map of wild grapevine sampling sites and main water catchment basins, (<b>b</b>) distribution of the individuals along the basins. The frequency of distribution (%) is shown above the bars.</p>
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<p>(<b>a</b>) Map of wild grapevine sampling sites and vegetation formations, (<b>b</b>) distribution of the individuals along the vegetation formations. The frequency of distribution (%) is shown above the bars. Botanical classification [<a href="#B68-plants-14-00232" class="html-bibr">68</a>] is provided in <a href="#plants-14-00232-t0A5" class="html-table">Table A5</a>.</p>
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<p>Frequency distribution of the ampelographic polymorphic traits.</p>
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<p>Location of the populations selected for ampelography and phenology analysis conducted in the Jighaura collection.</p>
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37 pages, 27014 KiB  
Article
Five New Species of Pezizales from Northeastern China
by Zhengqing Chen and Tolgor Bau
J. Fungi 2025, 11(1), 60; https://doi.org/10.3390/jof11010060 - 14 Jan 2025
Viewed by 479
Abstract
Species belonging to the Pezizales are mainly saprobes in nature. They are most commonly observed in woodlands and humid environments. As a result of recent research conducted on the distribution of species in sandy areas and some National Forests Parks, five new species [...] Read more.
Species belonging to the Pezizales are mainly saprobes in nature. They are most commonly observed in woodlands and humid environments. As a result of recent research conducted on the distribution of species in sandy areas and some National Forests Parks, five new species belonging to three genera were identified. A total of five species of disk fungi from Northeast China were identified and described based on morphological classification and molecular phylogenetics. These included Pulvinula (Pulvinula elsenensis, Pulvinula sublaeterubra), Microstoma (Microstoma jilinense, Microstoma changchunense), and Sarcoscypha (Sarcoscypha hongshiensis). Maximum likelihood and Bayesian analyses were performed using a combined nuc rDNA internal transcribed spacer region (ITS) and nuc 28S rDNA (nrLSU) dataset for the construction of phylogenetic trees. Morphological descriptions, line illustrations, and photographs of the ascocarps of these new species are provided, along with lists of the salient attributes exhibited by the species in the three genera under consideration. Full article
(This article belongs to the Special Issue Advanced Research of Ascomycota)
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<p>The phylogeny of <span class="html-italic">Pulvinula</span> by Bayesian inference based on the ITS and LSU dataset.</p>
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<p>The phylogeny of Sarcoscyphaceae as assessed by Bayesian inference based on the ITS dataset.</p>
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<p>The phylogeny of Sarcoscyphaceae as assessed by Bayesian inference based on the ITS and LSU dataset.</p>
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<p>Ascocarps of <span class="html-italic">Pulvinula elsenensis</span> (<b>A</b>,<b>B</b>); <span class="html-italic">Pulvinula sublaeterubra</span>; (<b>C</b>,<b>D</b>) <span class="html-italic">Microstoma jilinense</span> (<b>E</b>,<b>F</b>); <span class="html-italic">Microstoma changchunense</span> (<b>G</b>,<b>H</b>); <span class="html-italic">Sarcoscypha hongshiensis</span> (<b>I</b>,<b>J</b>). Scale bars: (<b>A</b>–<b>D</b>) = 0.3 cm; (<b>E</b>–<b>J</b>) = 1 cm. Collection site and collection time: (<b>A</b>,<b>B</b>): Jilin Province, 2023; (<b>C</b>): Inner Mongolia Autonomous Region, 2022, (<b>D</b>): Inner Mongolia Autonomous Region, 2023; (<b>E</b>): Liaoning Province, 2024, (<b>F</b>): Jilin Province, 2024; (<b>G</b>, <b>H</b>): Jilin Province, 2024; (<b>I</b>, <b>J</b>): Jilin Province, 2023.</p>
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<p>Ascocarps of <span class="html-italic">Pulvinula elsenensis</span> (<b>A</b>,<b>B</b>); <span class="html-italic">Pulvinula sublaeterubra</span>; (<b>C</b>,<b>D</b>) <span class="html-italic">Microstoma jilinense</span> (<b>E</b>,<b>F</b>); <span class="html-italic">Microstoma changchunense</span> (<b>G</b>,<b>H</b>); <span class="html-italic">Sarcoscypha hongshiensis</span> (<b>I</b>,<b>J</b>). Scale bars: (<b>A</b>–<b>D</b>) = 0.3 cm; (<b>E</b>–<b>J</b>) = 1 cm. Collection site and collection time: (<b>A</b>,<b>B</b>): Jilin Province, 2023; (<b>C</b>): Inner Mongolia Autonomous Region, 2022, (<b>D</b>): Inner Mongolia Autonomous Region, 2023; (<b>E</b>): Liaoning Province, 2024, (<b>F</b>): Jilin Province, 2024; (<b>G</b>, <b>H</b>): Jilin Province, 2024; (<b>I</b>, <b>J</b>): Jilin Province, 2023.</p>
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<p><span class="html-italic">Pulvinula elsenensis</span>: (<b>A</b>) ascocarps; (<b>B</b>) ascospores; (<b>C</b>) asci; (<b>D</b>) paraphyses. Scale bars: (<b>A</b>) = 0.3 cm; (<b>B</b>) = 8 μm; (<b>C</b>,<b>D</b>) = 55 μm.</p>
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<p><span class="html-italic">Pulvinula sublaeterubra</span>: (<b>A</b>) ascocarps; (<b>B</b>) ascospores; (<b>C</b>) asci; (<b>D</b>) paraphyses. Scale bars: (<b>A</b>) = 0.3 cm; (<b>B</b>) = 15 μm; (<b>C</b>,<b>D</b>) = 50 μm.</p>
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<p><span class="html-italic">Microstoma jilinense</span>: (<b>A</b>) ascocarps; (<b>B</b>) ascospores; (<b>C</b>) asci; (<b>D</b>) paraphyses. Scale bars: (<b>A</b>) = 0.5 cm; (<b>B</b>) = 12 μm; (<b>C</b>–<b>E</b>) = 55 μm.</p>
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<p><span class="html-italic">Microstoma changchunense</span>: (<b>A</b>) ascocarps; (<b>B</b>) ascospores; (<b>C</b>) asci; (<b>D</b>) hairs; (<b>E</b>) paraphyses. Scale bars: (<b>A</b>) = 0.8 cm; (<b>B</b>) = 15 μm; (<b>C</b>–<b>E</b>) = 55 μm)</p>
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<p><span class="html-italic">Sarcoscypha hongshiensis</span>: (<b>A</b>) ascocarps; (<b>B</b>) ascospores; (<b>C</b>) asci; (<b>D</b>) paraphyses. Scale bars: (<b>A</b>) = 0.5 cm; (<b>B</b>) = 15 μm; (<b>C</b>,<b>D</b>) = 60 μm.</p>
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26 pages, 6157 KiB  
Article
Assessing the Impact of Climate Change on the Landscape Stability in the Mediterranean World Heritage Site Based on Multi-Sourced Remote Sensing Data: A Case Study of the Causses and Cévennes, France
by Mingzhuo Zhu, Daoye Zhu, Min Huang, Daohong Gong, Shun Li, Yu Xia, Hui Lin and Orhan Altan
Remote Sens. 2025, 17(2), 203; https://doi.org/10.3390/rs17020203 - 8 Jan 2025
Viewed by 437
Abstract
Global climate fluctuations pose challenges not only to natural environments but also to the conservation and transmission of human cultural and historical heritage. World Heritage Sites are pivotal regions for studying climate change impacts and devising adaptation strategies, with remote sensing technology showcasing [...] Read more.
Global climate fluctuations pose challenges not only to natural environments but also to the conservation and transmission of human cultural and historical heritage. World Heritage Sites are pivotal regions for studying climate change impacts and devising adaptation strategies, with remote sensing technology showcasing significant utility in monitoring these impacts, especially in the Mediterranean region’s diverse and sensitive climate context. Although existing work has begun to explore the role of remote sensing in monitoring the effects of climate change, detailed analysis of the spatial distribution and temporal trends of landscape stability remains limited. Leveraging remote sensing data and its derived products, this study assessed climate change impacts on the Causses and Cévennes Heritage Site, a typical Mediterranean heritage landscape. Specifically, this study utilized remote sensing data to analyze the trends in various climatic factors from 1985 to 2020. The landscape stability model was developed utilizing land cover information and landscape indicators to explore the landscape stability and its distribution features within the study area. Finally, we adopted the Geographical Detector to quantify the extent to which climatic factors influence the landscape stability’s spatial distribution across different periods. The results demonstrated that (1) the climate showed a warming and drying pattern during the study period, with distinct climate characteristics in different zones. (2) The dominance of woodland decreased (area proportion dropped from 76% to 66.5%); transitions primarily occurred among woodland, cropland, shrubland, and grasslands; landscape fragmentation intensified; and development towards diversification and uniformity was observed. (3) Significant spatiotemporal differences in landscape stability within the heritage site were noted, with an overall downward trend. (4) Precipitation had a high contribution rate in factor detection, with the interactive enhancement effects between temperature and precipitation being the most prominent. The present study delivers a thorough examination of how climate change affects the Causses and Cévennes Heritage Landscape, reveals its vulnerabilities, and offers crucial information for sustainable conservation efforts. Moreover, the results offer guidance for the preservation of similar Mediterranean heritage sites and contribute to the advancement and deepening of global heritage conservation initiatives. Full article
(This article belongs to the Section Environmental Remote Sensing)
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Graphical abstract
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<p>Location (<b>a</b>), topography (<b>b</b>), and climatic zones (<b>c</b>) of the Causses and Cévennes World Heritage Site (Cf: temperate oceanic; Cs: Mediterranean; Df: temperate continental).</p>
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<p>Research framework.</p>
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<p>The annual cycle of temperature and precipitation in the heritage site (1985–2020).</p>
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<p>Temporal dynamics of climate factors in the heritage site from 1985 to 2020; (<b>a</b>) temperature, (<b>b</b>) precipitation, (<b>c</b>) potential evaporation, and (<b>d</b>) relative humidity.</p>
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<p>Spatial distribution of landscape types across different time periods in the Causses and Cévennes World Heritage Site; (<b>a</b>) 1985, (<b>b</b>) 2010, and (<b>c</b>) 2020.</p>
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<p>Landscape-type transition trajectory map of the heritage site, 1985–2020 (in km<sup>2</sup>).</p>
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<p>Spatial distribution of landscape-type transitions in the heritage site; (<b>a</b>) 1985–2010 and (<b>b</b>) 2010–2020.</p>
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<p>Changes in landscape indices.</p>
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<p>Spatial distribution of landscape stability in the heritage site from 1985 to 2020; (<b>a</b>) 1985, (<b>b</b>) 2010, and (<b>c</b>) 2020.</p>
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<p>Spatial dynamics of landscape stability from 1985 to 2020; (<b>a</b>) 1985–2010, (<b>b</b>) 2010–2020, and (<b>c</b>) 1985–2020.</p>
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<p>Contribution of climatic factors to the spatial divergence of landscape stability in the heritage site. (TMP, temperature; PRE, precipitation; RH, relative humidity; PET, potential evaporation).</p>
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<p>Climate trends and sub-regional variations in the heritage site (1985–2020); (<b>a</b>) temperature, (<b>b</b>) precipitation, (<b>c</b>) potential evaporation, (<b>d</b>) relative humidity.</p>
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15 pages, 774 KiB  
Article
The Key Factors That Influence Farmers’ Participating Behavior in Forest Management Plan Formulation Based on 1752 Households in China
by Zongfei Liu, Qianqian Yan, Yinxue Zhang and Mei Qu
Forests 2025, 16(1), 73; https://doi.org/10.3390/f16010073 - 5 Jan 2025
Viewed by 369
Abstract
Forest management plans are the bibles of forest management. On the basis of these plans, farmers play essential roles in forest cultivation, protection, and utilization. After the forest tenure reform in the 2000s in China, the status of farmers has changed. For example, [...] Read more.
Forest management plans are the bibles of forest management. On the basis of these plans, farmers play essential roles in forest cultivation, protection, and utilization. After the forest tenure reform in the 2000s in China, the status of farmers has changed. For example, collective management has decreased and household management has become a leading structure of operation and management. Farmer’s dependence on income from forests has increased, which is reflected in their increased participation in management. However, insights into farmers’ perceptions of and willingness to participate in the formulation of forest management plans are insufficient. This study analyzes the factors influencing farmers’ participation by using an econometric model based on 1752 samples of farmer households from 10 counties. The empirical results reveal that according to farmers, forest type and property rights influence their willingness to participate in the formulation of forest management plans. In addition, whether there is a village leader, the village distance from town, the circulation of forest land, the area of woodland, timber price, and forestry income have a significant positive impact on farmers’ willingness to participate in forest management plan formulation; the level of education and non-agricultural income have a significant negative impact on farmers’ willingness to participate in forest management program development. Finally, this study proposes to improve and deepen the reform of the forest ownership system, encourage land circulation, and give play to the role of village leaders in promoting the participation of farmers in forest management plans, so as to improve the efficiency of forest management. Full article
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<p>Distribution of study counties.</p>
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22 pages, 6301 KiB  
Article
Phytophthora Species and Their Associations with Chaparral and Oak Woodland Vegetation in Southern California
by Sebastian N. Fajardo, Tyler B. Bourret, Susan J. Frankel and David M. Rizzo
J. Fungi 2025, 11(1), 33; https://doi.org/10.3390/jof11010033 - 4 Jan 2025
Viewed by 690
Abstract
Evidence of unintended introductions of Phytophthora species into native habitats has become increasingly prevalent in California. If not managed adequately, Phytophthora species can become devastating agricultural and forest plant pathogens. Additionally, California’s natural areas, characterized by a Mediterranean climate and dominated by chaparral [...] Read more.
Evidence of unintended introductions of Phytophthora species into native habitats has become increasingly prevalent in California. If not managed adequately, Phytophthora species can become devastating agricultural and forest plant pathogens. Additionally, California’s natural areas, characterized by a Mediterranean climate and dominated by chaparral (evergreen, drought-tolerant shrubs) and oak woodlands, lack sufficient baseline knowledge on Phytophthora biology and ecology, hindering effective management efforts. From 2018 to 2021, soil samples were collected from Angeles National Forest lands (Los Angeles County) with the objective of better understanding the diversity and distribution of Phytophthora species in Southern California. Forty sites were surveyed, and soil samples were taken from plant rhizospheres, riverbeds, and off-road vehicle tracks in chaparral and oak woodland areas. From these surveys, fourteen species of Phytophthora were detected, including P. cactorum (subclade 1a), P. multivora (subclade 2c), P. sp. cadmea (subclade 7a), P. taxon ‘oakpath’ (subclade 8e, first reported in this study), and several clade-6 species, including P. crassamura. Phytophthora species detected in rhizosphere soil were found underneath both symptomatic and asymptomatic plants and were most frequently associated with Salvia mellifera, Quercus agrifolia, and Salix sp. Phytophthora species were present in both chaparral and oak woodland areas and primarily in riparian areas, including detections in off-road tracks, trails, and riverbeds. Although these Mediterranean ecosystems are among the driest and most fire-prone areas in the United States, they harbor a large diversity of Phytophthora species, indicating a potential risk for disease for native Californian vegetation. Full article
(This article belongs to the Special Issue Fungal Communities in Various Environments)
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<p>Examples of areas of the Angeles National Forest from which soil samples were taken to determine the presence of <span class="html-italic">Phytophthora</span> species. These include transition areas between chaparral and oak woodland areas (<b>A</b>), montane chaparral areas (<b>B</b>), rip riparian areas with dry and wet riverbeds (<b>C</b>), and oak woodlands (<b>D</b>). <span class="html-italic">Adenostoma</span> fasciculatum, <span class="html-italic">Eriodictyon crassifolium</span>, <span class="html-italic">Salvia mellifera</span>, and <span class="html-italic">Quercus agrifolia</span> were the most sampled native plant species in these areas.</p>
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<p>Location of the 40 sites sampled in the Angeles National Forest (ANF). Three main areas were sampled: the southwestern (SWA, <b>B</b>) area, with six sites; the northwestern (NWA, <b>C</b>) area, with two sites; and the northeastern (NEA, <b>E</b>) area, with 32 sites. Red dots indicate <span class="html-italic">Phytophthora</span>-positive sites, and yellow indicate <span class="html-italic">Phytophthora</span>-negative sites (<b>B</b>,<b>C</b>,<b>E</b>). Map (<b>A</b>) displays the location of the ANF in Southern California, with black box indicating Los Angeles County, and map (<b>D</b>) shows the general location of the sampling areas.</p>
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<p>Diversity and frequency of <span class="html-italic">Phytophthora</span> taxa isolated from surveys of burnt areas of the Angeles National Forest from 2018 through 2021. Upland areas and streams beds were sampled in chaparral and oak woodlands, including restoration areas. Multiple isolates of a <span class="html-italic">Phytophthora</span> taxon from the same sample were considered as one record.</p>
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<p>Maximum likelihood tree of <span class="html-italic">Phytophthora</span> subclade 7a. The tree is inferred with IQ-TREE 2 from single-locus ITS rDNA alignment. Support values above the branches are ultrafast bootstrap approximations ≥ 50, and those below are posterior probabilities ≥ 0.90, according to an analysis with MrBayes v2.3.7a. The isolates in bold are from this study.</p>
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<p>Maximum likelihood tree of <span class="html-italic">Phytophthora</span> subclade 7a. The tree is inferred with IQ-TREE 2 from a mitochondrial cox1 alignment. Support values above the branches are ultrafast bootstrap approximations ≥ 50, and those below are posterior probabilities ≥ 0.90, according to an analysis with MrBayes v2.3.7a. Isolates in bold are from this study. Isolates PDA2194 and PDA1795 were uploaded as <span class="html-italic">P. abietivora</span> and are included in the original description of the species.</p>
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<p>Maximum likelihood tree of <span class="html-italic">Phytophthora</span> clade 8. The tree is inferred with IQ-TREE 2 from single-locus ITS rDNA alignment. Support values above the branches are ultrafast bootstrap approximations ≥ 50, and those below are posterior probabilities ≥ 0.90, according to an analysis with MrBayes. The isolate in bold is from this study.</p>
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<p>Maximum likelihood tree of <span class="html-italic">Phytophthora</span> clade 8. The tree is inferred with IQ-TREE 2 from a mitochondrial cox1 alignment. Support values above branches are ultrafast bootstrap approximations ≥ 50, and those below are posterior probabilities ≥ 0.90, according to an analysis with MrBayes v2.3.7a. The isolate in bold is from this study.</p>
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<p><span class="html-italic">Phytophthora</span>-positive plants and streams in fire areas of the Angeles National Forest: (<b>A</b>) <span class="html-italic">Eriodictyon crassifolium</span> positive for <span class="html-italic">P. cactorum</span>; (<b>B</b>) white arrow indicates dead <span class="html-italic">Quercus agrifolia</span> restoration plant positive for <span class="html-italic">P. gonapodyides</span>; (<b>C</b>) dead <span class="html-italic">Salvia mellifera</span> positive for <span class="html-italic">P. multivora</span>; (<b>D</b>) <span class="html-italic">E. crassifolium</span> with mild symptoms of crown thinning, close to a <span class="html-italic">P. crassamura</span>-positive stream; (<b>E</b>) thinning <span class="html-italic">Adenostoma fasciculatum</span> from which <span class="html-italic">P. gonapodyides</span> was isolated; (<b>F</b>–<b>I</b>) sites positive for <span class="html-italic">P. crassamura</span> and other clade-6 <span class="html-italic">Phytophthora</span> species.</p>
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21 pages, 5477 KiB  
Article
Bioinformatics and Expression Profiling of the DHHC-CRD S-Acyltransferases Reveal Their Roles in Growth and Stress Response in Woodland Strawberry (Fragaria vesca)
by Si Gu, Xinghua Nie, Amal George, Kyle Tyler, Yu Xing, Ling Qin and Baoxiu Qi
Plants 2025, 14(1), 127; https://doi.org/10.3390/plants14010127 - 4 Jan 2025
Viewed by 549
Abstract
Protein S-acyl transferases (PATs) are a family of enzymes that catalyze protein S-acylation, a post-translational lipid modification involved in protein membrane targeting, trafficking, stability, and protein–protein interaction. S-acylation plays important roles in plant growth, development, and stress responses. Here, we report the genome-wide [...] Read more.
Protein S-acyl transferases (PATs) are a family of enzymes that catalyze protein S-acylation, a post-translational lipid modification involved in protein membrane targeting, trafficking, stability, and protein–protein interaction. S-acylation plays important roles in plant growth, development, and stress responses. Here, we report the genome-wide analysis of the PAT family genes in the woodland strawberry (Fragaria vesca), a model plant for studying the economically important Rosaceae family. In total, 21 ‘Asp-His-His-Cys’ Cys Rich Domain (DHHC-CRD)-containing sequences were identified, named here as FvPAT1-21. Expression profiling by reverse transcription quantitative PCR (RT-qPCR) showed that all the 21 FvPATs were expressed ubiquitously in seedlings and different tissues from adult plants, with notably high levels present in vegetative tissues and young fruits. Treating seedlings with hormones indole-3-acetic acid (IAA), abscisic acid (ABA), and salicylic acid (SA) rapidly increased the transcription of most FvPATs. A complementation assay in yeast PAT mutant akr1 and auto-S-acylation assay of one FvPAT (FvPAT19) confirmed its enzyme activity where the Cys in the DHHC motif was required. An AlphaFold prediction of the DHHC and the mutated DHHC155S of FvPAT19 provided further proof of the importance of C155 in fatty acid binding. Together, our data clearly demonstrated that S-acylation catalyzed by FvPATs plays important roles in growth, development, and stress signaling in strawberries. These preliminary results could contribute to further research to understand S-acylation in strawberries and plants in general. Full article
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<p>Phylogenetic tree and DHHC domain analysis of the 21 FvPATs from woodland strawberry (<span class="html-italic">F. vesca</span>). (<b>a</b>) Phylogenetic analysis of FvPATs. The tree is constructed based on the maximum-likelihood method using the protein sequences of FvPATs. AtPATs were used as references. FvPATs and AtPATs were divided into three groups. Group 1 (G1) is shaded darker blue, group 2 (G2) lighter blue, group 3 (G3) orange. (<b>b</b>) Analysis of DHHC-CRDs of FvPATs. Top panel, sequence alignment. Same color indicates similarity. Bottom panel, conserved domain display. The larger the font size, the more conserved the amino acid(s) among the FvPATs. Numbers at the bottom indicate amino acid positions.</p>
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<p>The protein/gene structure and chromosome location of the 21 FvPATs from the woodland strawberry (<span class="html-italic">Fragaria vesca</span>). (<b>a</b>) Protein and gene structure analysis. G1, G2, and G3 correspond to group 1, 2, and 3 in <a href="#plants-14-00127-f001" class="html-fig">Figure 1</a>a. Left panel, protein structure (shaded light grey) showing the five conserved motifs. Numbers at the bottom indicate the amino acid number; right panel, gene structure (shaded blue) indicating the UTR and exons. Numbers at the bottom indicate the nuclide acid number. Note that a similar gene structure was found for FvPATs within the same group. (<b>b</b>) Chromosome location of the <span class="html-italic">FvPATs</span>. The seven chromosomes are represented by different colored bars and their location and size are indicated by the mb on the circle.</p>
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<p>Gene duplication and collinearity analysis of <span class="html-italic">FvPATs</span> from woodland strawberry (<span class="html-italic">F. vesca</span>). (<b>a</b>) Gene duplication events. The seven chromosomes are color-coded and their sizes were indicated by the number of 5 Mbs. The grey lines represent the duplicated fragments in the genome. The duplicated pair of <span class="html-italic">FvPAT11</span> and <span class="html-italic">FvPAT19</span> are indicated by the purple line. (<b>b</b>) Collinearity analysis. The 10 FvPATs that are confirmed to be collinear with AtPATs are joined by purple lines. The woodland strawberry chromosomes (Fv-chr 1–7) are shown on top as thick blue lines and Arabidopsis at the bottom as orange lines (At-chr 1–5). Grey lines in the background indicate the collinear region.</p>
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<p>Prediction of the regulatory elements of <span class="html-italic">FvPATs</span> of the woodland strawberry (<span class="html-italic">F. vesca</span>). The promoter region of 1500 bp upstream of the start codon of each <span class="html-italic">FvPAT</span> was analyzed by PlantCARE. CREs, cis-regulatory elements. The positions and different types of the CREs are indicated as different colored boxes and their details are given on the right panel. All CREs predicted were classified into three categories: hormone response elements including ABA, JA, SA, GA, and auxin, the internal regulation element, and basic elements for initiating transcription. The bottom line indicates the 1500 bp of the promotor region.</p>
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<p>Prediction of transcription factors (TFs) of <span class="html-italic">FvPATs</span> of woodland strawberry (<span class="html-italic">F. vesca</span>). (<b>a</b>) TFs. The font size is positively correlated to the number of TFs. The larger the font size, the higher the number of that TF. Therefore, the order is ERF &gt; Dof &gt; MYB &gt; NAC &gt; C2H2 &gt; GATA &gt; MIKC_MADS &gt; BBR-BPC &gt; Trihelix &gt; TALE. (<b>b</b>) Correlation between <span class="html-italic">FvPATs</span> and the enriched TFs. Individual <span class="html-italic">FvPATs</span> are shown in blue and TFs in red circles. Different sizes of the blue circles indicate different numbers of TFs that each <span class="html-italic">FvPAT</span> has, where the larger the circle, the higher the number. Similarly, the size of the red circle positively correlates to the number of each TF. For a particular <span class="html-italic">FvPAT</span>, the larger the red circle for a particular TF, the higher this TF is enriched for this <span class="html-italic">FvPAT</span>. The number of TFs is indicated next to the circle. Only the top 17 TFs were analyzed.</p>
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<p>Expression profiles of FvPATs in vegetative of woodland strawberry (<span class="html-italic">F. vesca</span>). (<b>a</b>) Comparison of expression of individual FvPATs between different tissues. (<b>b</b>) Comparison of expression of the 21 <span class="html-italic">FvPATs</span> in the same tissue. RT-qPCR was carried out on total RNAs isolated from mature roots, stems, newly expanded leaves, and 14-day-old seedlings. At least three replicates were included in each run. The transcript level was calculated by the 2<sup>−∆∆Ct</sup> method using <span class="html-italic">FvActin</span> as the internal control and the average of the transcript levels of all 21 <span class="html-italic">FvPATs</span> in seedlings as 1. The data are visualized using the heatmap module in Tbtools software after being normalized by the Z-score.</p>
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<p>Expression profiles of <span class="html-italic">FvPATs</span> in reproductive tissues of woodland strawberry (<span class="html-italic">F. vesca</span>). (<b>a</b>) Comparison of the expression of individual <span class="html-italic">FvPATs</span> in fruits of 0, 4, 14–16, 22–24, 28–30, and 34–36 days after flowering (DAF). (<b>b</b>) Comparison of expression of the 21 <span class="html-italic">FvPATs</span> in the same aged fruits of 0, 4, 14–16, 22–24, 28–30, and 34–36 DAF. RT-qPCR was carried out on total RNAs isolated from reproductive tissues including the fully opened flower (0 d) and receptacle (fruit) tissues of 4, 14–16, 22–24, 28–30, and 34–36 DAF. At least three replicates were included in each run. The transcript level was calculated by the 2<sup>−∆∆Ct</sup> method using <span class="html-italic">FvActin</span> as the internal control and the average of the transcript levels of all 21 <span class="html-italic">FvPATs</span> in fully opened flowers as 1. The data are visualized using the heatmap module in Tbtools software after being normalized by the Z-score.</p>
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<p>Effect of IAA, ABA, and SA on the Expression profiles of <span class="html-italic">FvPATs</span> in the woodland strawberry (<span class="html-italic">F. vesca</span>). (<b>a</b>) Expression profiles of <span class="html-italic">FvPATs</span> treated with IAA, ABA, and SA for 6 h. (<b>b</b>) Expression profiles of <span class="html-italic">FvPATs</span> treated with IAA, ABA, and SA for 12 h. Seedlings were grown on ½ MS for 14 days and transferred to medium containing 20 µM IAA, 50 µM ABA, and 100 µM SA for 6 and 24 h. At least three replicates were included in each run. The transcript level of each gene was calculated by the 2<sup>−∆∆Ct</sup> method using <span class="html-italic">FvActin</span> as the internal control and the average transcript level of all 21 <span class="html-italic">FvPATs</span> in non-treated seedlings at 0 h as 1. The data are visualized using the heatmap module in Tbtools software after being normalized by the Z-score.</p>
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<p>Protein S-acyltransferase 19 of woodland strawberry (<span class="html-italic">F. vesca</span>), FvPAT19 has S-acyltransferase activity. (<b>a</b>) Yeast growth assay. The wild-type (WT), <span class="html-italic">akr1</span>, FvPAT19-, and FvPAT19CS-expressing <span class="html-italic">akr1</span> cells were grown at 28 °C and 37 °C for 3 days. The WT yeast grew well but <span class="html-italic">akr1</span> did not at 37 °C (<b>left panel</b>), although this growth defect was less obvious at 28 °C (<b>right</b>). The growth defect of <span class="html-italic">akr1</span> at 37 °C was largely restored by expressing FvPAT19 (FvPAT19/<span class="html-italic">akr1</span>) but not by FvPAT19CS (FvPAT19CS/<span class="html-italic">akr1</span>) in <span class="html-italic">akr1.</span> Five microliters of serial dilutions of 1:5, 1:10, 1:20, and 1:40 from 1 OD600 cells were spotted on solid selective medium supplemented with 2% galactose and grown at 28 °C or 37 °C. (<b>b</b>) Cell morphology. Cells grown at 37 °C were observed by phase contrast microscopy. The WT cells are individual, small, and round, while the <span class="html-italic">akr1</span> cells are large, irregular, and elongated. The <span class="html-italic">akr1</span> cells transformed with FvPAT19 largely restored the phenotype with rounder although still larger cells, whilst those transformed with FvPAT19CS resemble the <span class="html-italic">akr1</span> cells. The cells were inoculated in liquid selective medium supplemented with 2% galactose and grown at 37 °C for 4 days. Bars = 5 µm. (<b>c</b>) Acyl-PEG exchange assays were performed on transgenic <span class="html-italic">akr1</span> cells expressing FvPAT19 and FvPAT19CS using 10 kD mPEG-maleimide and analyzed on immunoblots probed with anti-V5 antibody and detected by ECL. The position of PEGylated FvPAT19 is indicated with an asterisk. The molecular weight of FvPAT19 is ~35 kDa. Both FvPAT19 and FvPAT19CS were detected in loading control (LC) with (+) or without (-) NH<sub>2</sub>OH treatment. In the experimental samples (EX), when 10kD mPEG was present, a molecular weight shift by 10 kD for FvPAT19 was detected in the NH<sub>2</sub>OH-treated sample (+), while no such band in the non NH<sub>2</sub>OH-treated sample was present, indicating that FvPAT19 is S-acylated. Therefore, cysteine in DHHC is required for the auto-acylation of FvPAT19.</p>
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<p>3D structure prediction of FvPAT19, its DHHC and DHHC155S. (<b>a</b>) The 3D protein structure of FvPAT19 predicted by AlphaFold 2. The 4 TMs as α-helices (blue, purple, pink, and raspberry-red) were embedded between the lipid bilayers (brown dotted lines) of the membrane, while the N- and C-termini are in cytosol. The DHHC-CRD domain (green dotted box) is also in cytosolic, where DHHC is in the proximity of the membrane. (<b>b</b>) 3D structure of DHHC motif. The length between D<sup>152</sup>- and C<sup>155</sup> is 4.4 (yellow dotted line) supporting C<sup>155</sup> binding to the fatty acyl chain. D<sup>152</sup>, H<sup>153</sup>, and H<sup>154</sup> (colored cyan), and C<sup>155</sup> (colored yellow) are indicated. (<b>c</b>) 3D structure of DHHS. The length between D<sup>152</sup> and S<sup>155</sup> was reduced to approx. 2.5 (yellow dotted line) due to the formation of a hydrogen bond with H<sup>153</sup> after the mutation from cysteine (-SH) to serine (-OH). The oxygen atom is colored red.</p>
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15 pages, 1809 KiB  
Article
Defecation Site Preferences and Spatial Ecological Segregation of Forest Musk Deer and Siberian Roe Deer in North China
by Yixin Li, Luyao Hai, Pengfei Luo, Wangshan Zheng, Xuelin Jin, Jiangcheng Liu, Haiyan Wang and Defu Hu
Animals 2025, 15(1), 61; https://doi.org/10.3390/ani15010061 - 30 Dec 2024
Viewed by 457
Abstract
The forest musk deer (Moschus berezovskii) and Siberian roe deer (Capreolus pygargus) are browsers with a broad sympatric distribution in North and Southwest China. However, little is known about their spatial utilization of microhabitats and habitats. This study, conducted [...] Read more.
The forest musk deer (Moschus berezovskii) and Siberian roe deer (Capreolus pygargus) are browsers with a broad sympatric distribution in North and Southwest China. However, little is known about their spatial utilization of microhabitats and habitats. This study, conducted on Huanglong Mountain in China, analyzed the defecation site distribution, indicating preferences of forest musk deer and Siberian roe deer for their habitat demands. Using generalized linear mixed models (GLMMs), we compared the defecation site preferences of both species and further examined their spatial utilization patterns. The results indicated that the primary factors influencing defecation site preferences for forest musk deer were slope (15.79%), elevation (4.26%), herbaceous cover (19.93%), herb height (33.73%), and tree diversity (15.64%). Conversely, for Siberian roe deer, elevation (54.63%) and herbaceous cover (29.31%) were the key factors. Significant differences were found in elevation (p < 0.001) and herbaceous diversity (p < 0.01) between the defecation sites of the two species, with additional notable differences in slope position, tree diversity, and average tree height (p < 0.05). Furthermore, forest musk deer primarily utilized broadleaf forests, coniferous forests, mixed conifer-broadleaf forests, and sparse woodlands. In contrast, Siberian roe deer utilized broadleaf forests, sparse woodlands, and coniferous forests, showing a significant difference (p = 0.01). These findings suggest distinct spatial ecological segregation between forest musk deer and Siberian roe deer regarding their microhabitat preferences and vegetation type utilization at the habitat scale. Full article
(This article belongs to the Section Ecology and Conservation)
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<p>Geographic location of the study area and placement of sampling plots and transects.</p>
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<p>Defecation site vegetation type proportions for forest musk deer and Siberian roe deer.</p>
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<p>Comparison of environmental variables showing significant differences in defecation sites between forest musk deer and Siberian roe deer.</p>
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28 pages, 52897 KiB  
Article
How to Coordinate Urban Ecological Networks and Street Green Space Construction? Insights from a Multi-Scale Perspective
by Shujun Hou, Ying Yu, Taeyeol Jung and Xin Han
Land 2025, 14(1), 26; https://doi.org/10.3390/land14010026 - 26 Dec 2024
Viewed by 519
Abstract
Rapid socio-economic development and imbalanced ecosystem conservation have heightened the risk of species extinction, reduced urban climate adaptability, and threatened human health and well-being. Constructing ecological green space networks is an effective strategy for maintaining urban ecological security. However, most studies have primarily [...] Read more.
Rapid socio-economic development and imbalanced ecosystem conservation have heightened the risk of species extinction, reduced urban climate adaptability, and threatened human health and well-being. Constructing ecological green space networks is an effective strategy for maintaining urban ecological security. However, most studies have primarily addressed biodiversity needs, with limited focus on coordinating street spaces in human settlement planning. This study examines the area within Chengdu’s Third Ring Road, employing the following methodologies: (1) constructing the regional ecological network using Morphological Spatial Pattern Analysis (MSPA), the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model, and circuit theory; (2) analyzing the street green view index (GVI) through machine learning semantic segmentation techniques; and (3) identifying key areas for the coordinated development of urban ecological networks and street green spaces using bivariate spatial correlation analysis. The results showed that (1) Chengdu’s Third Ring Road exhibits high ecological landscape fragmentation, with 41 key ecological sources and 94 corridors identified. Ecological pinch points were located near urban rivers and surrounding woodlands, while ecological barrier points were concentrated in areas with dense buildings and complex transportation networks. (2) Higher street GVI values were observed around university campuses, urban parks, and river-adjacent streets, while lower GVI values were found near commercial areas and transportation hubs. (3) To coordinate the construction of ecological networks and street green spaces, the central area of the First Ring Road and the northwestern region of the Second and Third Ring Roads were identified as priority restoration areas, while the northern, western, and southeastern areas of the Second and Third Ring Roads were designated as priority protection areas. This study adopts a multi-scale spatial perspective to identify priority areas for protection and restoration, aiming to coordinate the construction of urban ecological networks and street green spaces and provide new insights for advancing ecological civilization in high-density urban areas. Full article
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<p>Location and land use of the study area.</p>
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<p>Research framework and technical route.</p>
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<p>Spatial characteristics of each resistance surface. (<b>a</b>) habitat quality; (<b>b</b>) NDVI; (<b>c</b>) distance from rivers; (<b>d</b>) land use type; (<b>e</b>) distance from railroads; (<b>f</b>) distance from roads.</p>
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<p>The process of collecting and calculating the GVI of streets. (<b>a</b>) The process of collecting street view images; (<b>b</b>) Image segmentation process of street view images.</p>
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<p>Landscape classification of MSPA. (<b>a</b>–<b>c</b>) Examples from the northern, western, and southeastern regions.</p>
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<p>Selection of ecological source areas. Numbers indicate extracted ecological source areas.</p>
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<p>Integrated ecological resistance surfaces.</p>
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<p>Ecological corridors and important levels. Numbers indicate extracted ecological source areas.</p>
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<p>Pinch point identification. Numbers indicate extracted ecological source areas.</p>
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<p>Barrier point identification. Numbers indicate extracted ecological source areas.</p>
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<p>Spatial distribution of pinch points and Barrier points. Numbers indicate extracted ecological source areas. (<b>a</b>–<b>d</b>) Typical examples of ecological pinch points, mainly located in forested areas along urban rivers; (<b>e</b>–<b>h</b>) Typical examples of ecological barrier points, predominantly found in densely built-up areas and road intersections.</p>
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<p>GVI distribution of Street Sites.</p>
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<p>GVI distribution at the subdistrict level.</p>
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<p>GVI hot and cold spots analysis. (<b>a</b>,<b>b</b>) Examples of hot spot areas near parks and universities; (<b>c</b>,<b>d</b>) Examples of cold spot areas near commercial streets and transport hubs.</p>
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<p>Spatial correlation analysis of GVI and ecological resistance values: (<b>a</b>) Global Moran’s I for GVI; (<b>b</b>) Global Moran’s I for ecological resistance; (<b>c</b>) Global Moran’s I for ecological resistance and GVI; (<b>d</b>) Local autocorrelation analysis of GVI; (<b>e</b>) Local autocorrelation analysis of ecological resistance; (<b>f</b>) Local autocorrelation analysis of GVI and ecological resistance; (<b>g</b>) Identification of priority restoration and conservation areas.</p>
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<p>Comparison of edge width analysis results at 30 m, 60 m, and 90 m in MSPA analysis.</p>
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24 pages, 9084 KiB  
Article
Resilience of the Miombo Woodland to Different Fire Frequencies in the LevasFlor Forest Concession, Central Mozambique
by Osvaldo M. Meneses, Natasha S. Ribeiro, Zeinab Shirvani and Samora M. Andrew
Forests 2025, 16(1), 10; https://doi.org/10.3390/f16010010 - 24 Dec 2024
Viewed by 486
Abstract
Fires play a significant role in shaping the Miombo woodlands. Understanding how fire affects the Miombo region’s resilience is crucial for ensuring its sustainability. This study evaluated plant composition and structure across different fire frequencies in the Miombo woodlands of the LevasFlor Forest [...] Read more.
Fires play a significant role in shaping the Miombo woodlands. Understanding how fire affects the Miombo region’s resilience is crucial for ensuring its sustainability. This study evaluated plant composition and structure across different fire frequencies in the Miombo woodlands of the LevasFlor Forest Concession (LFC), central Mozambique. Fire frequency clusters-high (HFF), moderate (MFF), and low (LFF)-were identified using a 21-year remote-sensing dataset. In each cluster, 90 random sampling plots were established (30 per cluster). In each plot, the diameter at breast height (DBH) and total height of the saplings and trees were measured. Subplots were used to count and identify seedlings, herbs, climbers, and grasses. Plant species richness, evenness,—diversity, the importance value index (IVI), and similarity were computed to assess plant composition. For the structure, stem density, biomass, basal area, diameter, and height were assessed. A total of 124 plant species-including trees, saplings, seedlings, herbs, climbers, and grasses-were identified across the three clusters. The Bray-Curtis Dissimilarity Index, tested with an ANOSIM similarity test, revealed significant differences in plant species composition among clusters (p < 0.0003), with an overall average dissimilarity of 71.98%. In the HFF cluster, fire-tolerant species were among the five species with the highest IVI, while fire-sensitive species predominated in the LFF. Additionally, the Kruskal-Wallis test indicated significant differences in seedling stem density (p < 0.005) between the LFF and other clusters. However, overall, the composition and structure attributes suggested that current fire regime does not significantly compromise the plant species resilience of the Miombo woodlands in the LFC. Still, it is essential to concentrate management and conservation efforts on seedlings of some key Miombo species, such as Brachystegia spiciformis, whose ecology is particularly affected by fire. Full article
(This article belongs to the Section Natural Hazards and Risk Management)
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<p>The study area map, based on 21 years of MODIS Burned Area data [<a href="#B33-forests-16-00010" class="html-bibr">33</a>], illustrates the spatial distribution of fire frequency within the LevasFlor Forest Concession (LFC), central Mozambique. The map displays the fire-frequency categories, high (red), moderate (orange), and low (yellow). Sampling points, marked by dots, represent data collection locations. The inset map shows the geographical location of the LFC within Mozambique, a Southern African country.</p>
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<p>Fire frequency clusters: (<b>a</b>) cluster 1 (1 to 2 years)-high fire frequency (HFF), open woodland with low tree density, presence of tall trees, and occasional dead or fallen trees. The under storey is dominated by grasses, sometimes mixed with debris; (<b>b</b>) cluster 2 (2 to 4 years)-moderate fire frequency (MFF), characterized by tall to medium trees, moderate tree density, and low grass density, (<b>c</b>) cluster 3 (up to 4 years)-low fire frequency (LFF), dense woodland, including riverine forest, with minimal grass cover and density of seedlings and saplings.</p>
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<p>Plot design used to assess the effects of fire frequencies on the community structure and growth of vegetation in the LFC, central Mozambique. The main plot is 10 m × 10 m, oriented northward, containing five 1 m × 1 m subplots: one at each corner and one at the center. The main plot is used for measuring DBH and total height of adult trees and saplings, while the subplots are designed for counting seedlings, herbs, climbers, and evaluating grass cover.</p>
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<p>Rarefication curve showing species accumulation with increasing sample size in the ecological study conducted at the LevasFlor Forest Concession, central Mozambique. The curve helps assess sampling effort and species richness, indicating when additional sampling yields a minimal number of new species. This analysis ensures adequate representation of the collected sample in this study.</p>
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<p>Simplified flowchart illustrating the study’s methodological framework to assess Miombo woodland resilience to varying fire frequencies. The flowchart delineates the process of evaluating community structure under low-, moderate-, and high-, fire-, frequency conditions. Key metrics for community structure include richness, diversity, evenness, abundance, and importance value index, while community growth is assessed through density, basal area, height variation, diameter at breast height (DBH) variation, and biomass variation.</p>
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<p>Plant species richness across fire clusters in the LevasFlor Forest Concession, central Mozambique. The box plot illustrates the interquartile range, where 50% of plant species richness values are located. The line within the box marks the median, and the “×” symbol represents the mean.</p>
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<p>Plant species diversity across fire clusters in the LevasFlor Forest Concession, central Mozambique. The box plot illustrates the interquartile range, where 50% of plant species Shannon and Simpson diversity index values are located. The line within the box marks the median, and the ‘×’ symbol represents the mean.</p>
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<p>The dendrogram illustrate the dissimilarities among clusters based on the Bray-Curtis test. It also highlights the relationship between dominant woody plant species observed in the clusters. Below the dendrogram of the clusters are presented. The The yellow and red colors with higher values indicate species with higher contribution to dissimilarity among clusters based on the Bray-Curtis dissimilarity index in the Miombo woodland, LevasFlor Forest Concession, central Mozambique.</p>
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<p>Visualization of the distribution of dissimilarities across fire clusters in the LevasFlor Forest Concession, central Mozambique. The box plots represent the degree of ranked dissimilarity, where LFF present the highest ranked distance.</p>
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<p>DBH Class variation among fire clusters. The bar colors represent the number of individuals recorded per DBH class in LevasFlor Forest Concession, central Mozambique.</p>
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<p>This image evidences the high resilience of some Miombo plant species observed in the Miombo woodlands within the LevasFlor Forest Concession. The image shows rapid resprout (one to two years) for <span class="html-italic">Diplorhynchus condylocarpon</span> (<b>a</b>) and <span class="html-italic">Millettia stuhlmannii</span> (<b>b</b>). Image (<b>c</b>) illustrates a bark of <span class="html-italic">Pterocarpus angolensis</span> one-year post-fire in a high fire frequency cluster.</p>
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14 pages, 5885 KiB  
Review
Rewilding Landscape Creation in Country Parks Based on Wilderness Thinking—Taking Shanghai Heqing Country Park as an Example
by Jing Li, Yi Zhu, Haoran Yu and Lang Zhang
Land 2024, 13(12), 2254; https://doi.org/10.3390/land13122254 - 23 Dec 2024
Viewed by 503
Abstract
In recent years, rapid urbanization in China has driven cities to expand uncontrollably into surrounding rural areas. Within the global context of ecological restoration, protecting and rehabilitating natural spaces have become key issues in landscape design. The concept of rewilding, rooted in wilderness [...] Read more.
In recent years, rapid urbanization in China has driven cities to expand uncontrollably into surrounding rural areas. Within the global context of ecological restoration, protecting and rehabilitating natural spaces have become key issues in landscape design. The concept of rewilding, rooted in wilderness philosophy, has gained significant attention. This article explores the background, significance, and theoretical foundations of rewilding urban ecological spaces using the woodland area of Heqing Country Park in Shanghai as a case study. It examines the feasibility, methods, and strategies for implementing rewilding in urban settings, considering both natural and human-influenced activities, with a focus on minimizing human intervention. This approach enhances biodiversity, promotes the sustainable development of ecosystems, and helps the park maintain its rural character despite its tourist appeal. Four years after the project’s implementation, research data show that the richness of native plant species, the diversity of bird species, and insect populations have exceeded those of the previously maintained forest. Moreover, the rewilding landscapes have supported the natural succession of habitat communities, leading to a stable and revitalized ecosystem. The landscape improvements and visitor experiences have been highly positive. The reconstruction strategy developed in this project is expected to inform future ecological initiatives, such as country parks, promoting the harmonious development of human and natural environments and serving as a model for creating urban ecosystems where both can coexist sustainably. Full article
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<p>Scientific methods of rewilding implemented in country parks.</p>
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<p>Forest chart.</p>
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<p>Overlook of the forest.</p>
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<p>Forest road.</p>
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<p>Forest road 2.</p>
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17 pages, 4303 KiB  
Article
Evaluating Domestic Herbivores for Vegetation Structure Management in Transitional Woodland–Shrubland Systems
by Inês Ribeiro, Tiago Domingos, Davy McCracken and Vânia Proença
Forests 2024, 15(12), 2258; https://doi.org/10.3390/f15122258 - 23 Dec 2024
Viewed by 998
Abstract
Mediterranean landscapes are shaped by natural disturbances such as herbivory and fire that regulate vegetation structure and fuel loads. As a result of the cessation of traditional agricultural practices, land abandonment is a widespread phenomenon in these landscapes, leading to shrub encroachment and [...] Read more.
Mediterranean landscapes are shaped by natural disturbances such as herbivory and fire that regulate vegetation structure and fuel loads. As a result of the cessation of traditional agricultural practices, land abandonment is a widespread phenomenon in these landscapes, leading to shrub encroachment and heightened fire hazard. This study reports the effects of grazing by domestic herbivores on vegetation structure in transitional woodland–shrubland systems across three case study areas in Portugal. The effects of low and moderate grazing intensity by cattle and horses on vegetation structure were assessed on three vegetation strata—canopy, shrubs, and grasses—using indicators to evaluate the influence of grazing on both horizontal and vertical vegetation structure. Moderate grazing shaped vertical vegetation structure by reducing shrub and grass height and by browsing and thinning the lower branches, creating a discontinuity between understorey and canopy layers. These effects on vertical fuel continuity are anticipated to limit the upward spread of flames and reduce the potential for crown fires. In contrast, low-intensity grazing showed limited effects on both vertical and horizontal vegetation structure. This work highlights the potential of using domestic herbivores as a tool to manage vegetation structure and its contribution to mitigating local wildfire hazards. Full article
(This article belongs to the Section Forest Ecology and Management)
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<p>Location of case study sites in Portugal. Grazing regimes and the spatial arrangement of survey quadrats, nested at survey plots (see “Survey scheme” for more details), are shown for each site: Site 1 (top-right map), Site 2 (bottom-right map), Site 3 (bottom-left map).</p>
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<p>Overall aspect of the landscape at Site 1, Site 2, and Site 3 (<b>left</b> column). Example of the high-resolution land cover maps (20 cm pixel) of the landscape surrounding survey plots (40 m × 40 m), with the nested 10 m × 10 m quadrats (black squares). One example is shown for each study site (<b>middle</b> column). Land cover in mapped landscape mosaics in Site 1, Site 2, and Site 3. The bars show the percentage cover of the main land cover classes for all survey plots (<b>right</b> column).</p>
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<p>Metrics of vegetation structure at (<b>a</b>,<b>d</b>,<b>g</b>,<b>j</b>,<b>m</b>) Site 1, (<b>b</b>,<b>e</b>,<b>h</b>,<b>k</b>,<b>n</b>) Site 2, and (<b>c</b>,<b>f</b>,<b>i</b>,<b>l</b>,<b>o</b>) Site 3, for the grazing intervention and the control areas. Boxplots show the distribution of values in 10 m × 10 m quadrats (mean: asterisk, median: line). Treatments differences (Wilcoxon Rank Sum; <span class="html-italic">p</span> &lt; 0.05) are marked in bold.</p>
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<p>Metrics of landscape structure at (<b>a</b>,<b>d</b>,<b>g</b>,<b>j</b>) Site 1, (<b>b</b>,<b>e</b>,<b>h</b>,<b>k</b>) Site 2, and (<b>c</b>,<b>f</b>,<b>i</b>,<b>l</b>) Site 3, measured in the landscape mosaics (100 m × 100 m) surrounding survey plots. Largest patch index, mean patch size, and clumpiness were estimated for shrub cover, and edge density was estimated for all land cover classes.</p>
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14 pages, 1907 KiB  
Article
A Study on the Characteristics of Nitrification and Denitrification of Three Small Watersheds During the Wet and Dry Seasons with Various Sources of Pollution: A Case Study of the Jinjing Basin
by Lingling Tong, Murni Karim, Fatimah M. Yusoff, Ahmad Zaharin Aris, Ahmad Fikri Abdullah, Feng Liu, Dejun Li and Puvaneswari Puvanasundram
Agriculture 2024, 14(12), 2330; https://doi.org/10.3390/agriculture14122330 - 20 Dec 2024
Viewed by 777
Abstract
Nitrogen cycling in freshwater ecosystems is critical for maintaining water quality, and understanding the processes of nitrification and denitrification is essential for effective nitrogen management, particularly in areas with diverse pollution sources. This study investigated the nitrification and denitrification processes in three tributaries [...] Read more.
Nitrogen cycling in freshwater ecosystems is critical for maintaining water quality, and understanding the processes of nitrification and denitrification is essential for effective nitrogen management, particularly in areas with diverse pollution sources. This study investigated the nitrification and denitrification processes in three tributaries of the Jinjing River—Tuojia (agricultural), Jinjing (residential), and Guanjia (woodland)—during both the wet and dry seasons. The potential nitrification rates (PNRs) and potential denitrification rates (PDNRs) were measured across these sites. The highest rates were observed in Tuojia during the wet season, with the PNR reaching 39.7 μg·kg−1 h−1 and the PDNR reaching 3.25 mg·kg−1·h−1, while the rates were considerably lower in Jinjing and Guanjia. The ammonia-oxidizing archaea (AOA) abundance was higher than the ammonia-oxidizing bacteria (AOB) abundance at all sites, with Tuojia exhibiting the highest AOA abundance (5.9 × 10⁷ copies·g−1) during the wet season. The nitrate-nitrogen (NO₃-N) content was a key factor influencing denitrification, and the AOA abundance was significantly correlated with nitrification rates (r = 0.69; p < 0.05). These findings highlight the spatial and seasonal variability in nitrogen cycling and emphasize the importance of developing targeted nitrogen management strategies in regions with mixed land uses and pollution sources. Full article
(This article belongs to the Section Agricultural Water Management)
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<p>PNRs and PDNRs in sub-basin sediment of Jinjing watershed. Data are represented as means ± SEM (<span class="html-italic">n</span> = 3). Same letters indicate no significant difference (<span class="html-italic">p</span> &gt; 0.05). Small letters represent rivers, and capital letters represent seasons. JR = Jinjing River, GR = Guanjia River, GRS = Guanjia sub-stream, TR = Tuojia River, and TRS = Tuojia River sub-stream.</p>
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<p>Total bacterial abundances in sediment of Jinjing watershed. Data are represented as means ± SEM (n = 3). Same letters indicate no significant differences (<span class="html-italic">p</span> &gt; 0.05). Small letters represent rivers, and capital letters represent seasons. JR = Jinjing River, GR = Guanjia River, GRS = Guanjia sub-stream, TR = Tuojia River, and TRS = Tuojia River sub-stream.</p>
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<p>Abundances of AOA (left) and AOB (right) in sediment of Jinjing watershed. Data are represented as means ± SEM (n = 3). Same letters indicate no significant difference (<span class="html-italic">p</span> &gt; 0.05). Small letters represent rivers, and capital letters represent seasons. JR = Jinjing River, GR = Guanjia River, GRS = Guanjia sub-stream, TR = Tuojia River, and TRS = Tuojia River sub-stream.</p>
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<p>Gene abundances of nirS nirK and narG in sediment of Jinjing watershed. Data are represented as means ± SEM (n = 3). Same letters indicate no significant difference (<span class="html-italic">p</span> &gt; 0.05). Small letters represent rivers, and capital letters represent seasons. JR = Jinjing River, GR = Guanjia River, GRS = Guanjia sub-stream, TR = Tuojia River, and TRS = Tuojia River sub-stream.</p>
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<p>Structural equation model of effects of different environmental factors on nitrification potential of sediment. Dotted lines mean insignificant; solid lines mean significant at 0.05 level; numbers represent path coefficients between variables; and WT, WV, WEh, and WAN and WNN represent water temperature, flow rate, REDOX potential, and NH<sub>4</sub><sup>+</sup>-N and NO<sub>3</sub><sup>−</sup>-N concentrations, respectively.</p>
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<p>Structural equation model of effects of different environmental factors on denitrification potential of sediment. Dotted lines mean insignificant; solid lines mean significant at level of 0.05; numbers represent path coefficients between variables; and WV, SOM, and SAN and SNN represent water velocity, organic matter content in sediment, and NH<sub>4</sub><sup>+</sup>-N and NO<sub>3</sub><sup>−</sup>-N concentrations in sediment, respectively.</p>
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18 pages, 3550 KiB  
Article
Wildfire Severity to Valued Resources Mitigated by Prescribed Fire in the Okefenokee National Wildlife Refuge
by C. Wade Ross, E. Louise Loudermilk, Joseph J. O’Brien, Steven A. Flanagan, Grant Snitker and J. Kevin Hiers
Remote Sens. 2024, 16(24), 4708; https://doi.org/10.3390/rs16244708 - 17 Dec 2024
Viewed by 585
Abstract
Prescribed fire is increasingly utilized for conservation and restoration goals, yet there is limited empirical evidence supporting its effectiveness in reducing wildfire-induced damages to highly valued resources and assets (HVRAs)—whether natural, cultural, or economic. This study evaluates the efficacy of prescribed fire in [...] Read more.
Prescribed fire is increasingly utilized for conservation and restoration goals, yet there is limited empirical evidence supporting its effectiveness in reducing wildfire-induced damages to highly valued resources and assets (HVRAs)—whether natural, cultural, or economic. This study evaluates the efficacy of prescribed fire in reducing wildfire severity to LANDFIRE-defined vegetation classes and HVRAs impacted by the 2017 West Mims event, which burned across both prescribed-fire treated and untreated areas within the Okefenokee National Wildlife Refuge. Wildfire severity was quantified using the differenced normalized burn ratio (dNBR) index, while treatment records were used to calculate the prescribed frequency and post-treatment duration, which is defined as the time elapsed between the last treatment and the West Mims event. A generalized additive model (GAM) was fit to model dNBR as a function of post-treatment duration, fire frequency, and vegetation type. Although dNBR exhibited considerable heterogeneity both within and between HVRAs and vegetation classes, areas treated with prescribed fire demonstrated substantial reductions in burn severity. The beneficial effects of prescribed fire were most pronounced within approximately two years post-treatment with up to an 88% reduction in mean wildfire severity. However, reductions remained evident for approximately five years post-treatment according to our model. The mitigating effect of prescribed fire was most pronounced in Introduced Upland Vegetation-Shrub, Eastern Floodplain Forests, and Longleaf Pine Woodland when the post-treatment duration was within 12 months. Similar trends were observed in areas surrounding red-cockaded woodpecker nesting sites, which is an HVRA of significant ecological importance. Our findings support the frequent application of prescribed fire (e.g., one- to two-year intervals) as an effective strategy for mitigating wildfire severity to HVRAs. Full article
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<p>The West Mims wildfire. (<b>a</b>) Location of the Okefenokee National Wildlife Refuge (tan) and b, the West Mims wildfire perimeter (gray, 675 km<sup>2</sup>). The map also depicts the prescribed fire burn units (<b>b</b>, green) and red-cockaded woodpecker (<span class="html-italic">Picoides borealis</span>) clusters (black dots).</p>
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<p>The 2017 West Mims wildfire. (<b>a</b>), The West Mims fire perimeter illustrates the variation in wildfire severity (dNBR) with regions of low and high severity depicted in blues and yellows, respectively. (<b>b</b>), Unmanaged areas within the wildfire perimeter that have not been treated with prescribed fire, and (<b>c</b>) actively managed areas that have been treated with prescribed fire. (<b>d</b>) Violin plots illustrate the variability of wildfire severity within managed areas. The x-axis represents the duration of time (in months) between the prescribed fires and the wildfire event. The red curve was fit to the 97th percentile of data using the NLS exponential growth model to characterize the upper limits of wildfire severity in actively managed areas.</p>
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<p>Wildfire severity (dNBR) grouped by LANDFIRE-defined vegetation classes. The top panel corresponds to vegetation classes found within actively managed areas, while the bottom panel corresponds to unmanaged areas. The boxplots are color coded by LANDFIRE-defined physiognomy classes.</p>
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<p>Percent change between unmanaged and managed areas, grouped by post-treatment duration, for LANDFIRE-defined vegetation classes. Negative values indicate that dNBR was lower in areas treated with prescribed fire relative to untreated areas.</p>
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<p>Percent change between unmanaged and managed areas, grouped by post-treatment duration, for highly valued resources and assets (HVRAs). Negative values indicate that dNBR was lower in areas treated with prescribed fire relative to untreated areas.</p>
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<p>(<b>a</b>), The relationship between post-treatment duration in months and the NLS model fit for dNBR. (<b>b</b>) Residual plot showing the difference between observed and fitted response values. (<b>c</b>) Histogram indicates that the residuals are normally distributed. (<b>d</b>) Probability plot of the residuals is approximately linear supporting the condition that the error terms are normally distributed.</p>
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<p>(<b>a</b>) Probability plot of the residuals is approximately linear supporting the condition that the error terms are normally distributed. (<b>b</b>) Residual plot showing the difference between observed and fitted response values. (<b>c</b>) Histogram indicates that the residuals are normally distributed. (<b>d</b>) Actual vs. fitted dNBR values.</p>
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<p>Partial effects of LANDFIRE vegetation classes from the best-performing model (GAM). The zero line represents the overall mean of the response; negative values on the y-axis indicate where the effect of the covariate reduces the response below the average value, and positive values indicate those covariate values where the response is increased above the average (all conditional upon the other estimated model terms). The gray bands represent the 95% confidence intervals for the fitted values, indicating the range of uncertainty around the estimated trend.</p>
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22 pages, 32327 KiB  
Article
Dynamic Simulation of Land Use Change and Assessment of Carbon Storage Based on the PLUS Model: A Case Study of the Most Livable City, Weihai, China
by Xudong Li, Chuanrong Li, Shouchao Yu, Lijuan Cheng, Dan Li, Jiehui Wang and Hongxia Zhao
Sustainability 2024, 16(24), 10826; https://doi.org/10.3390/su162410826 - 11 Dec 2024
Viewed by 849
Abstract
Analyzing and monitoring land use/cover (LULC) changes is critical for improving regional ecosystem service functions and developing strategies for long-term socio-economic development. Exploring future changes in land use and carbon storage under different scenarios is important for optimizing regional ecosystem service functions and [...] Read more.
Analyzing and monitoring land use/cover (LULC) changes is critical for improving regional ecosystem service functions and developing strategies for long-term socio-economic development. Exploring future changes in land use and carbon storage under different scenarios is important for optimizing regional ecosystem service functions and formulating sustainable socio-economic development policies. In the present work, we evaluate LULC changes and carbon storage changes in the Rapid Urbanization Area (RUA) of Weihai City from 2000 to 2020 using satellite images. Using five Landsat images, the spatio-temporal dynamics of the LULC changes were measured, using a supervised classification algorithm of the neural net and the intensity analysis techniques in GIS. The Landsat images from 2000, 2005, 2010, 2015, and 2020 were categorized into five main land use categories in the researched region: urban areas, woodlands, cultivated areas, bare soil, and water bodies. Our results reveal that urban areas, woodlands, and bare soil increased by about 129.63 km2 (13.29%), 53.07 km2 (5.44%), and 40.99 km2 (4.2%) from 2000 to 2020, respectively. On the contrary, the cultivated areas decreased by 218.35 km2 (22.36%) and the water bodies decreased by 5.44 km2 (0.56%). To summarize, the conversion of cultivated areas into urban areas has been the most significant transformation in the RUA during the period 2000–2020. Regarding carbon storage, in the study area, it decreased by 14.92 × 104 t from 2000 to 2020. Moreover, according to the prediction of the LULC changes for 2030 by the patch-generating land use simulation (PLUS) model, the cultivated areas and carbon storage will continue to decline. The slow increase in woodland brings good ecological benefits. But the sharp reduction in the per capita cultivated areas will bring environmental and socio-economic problems to the RUA. Therefore, it is time to strengthen the implementation of cultivated area protection policy. Monitoring and managing LULC changes are critical for establishing relationships between policy choices, regulatory measures, and future LULC operations, especially because many potential concerns remain in the RUA territories. Full article
(This article belongs to the Section Soil Conservation and Sustainability)
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<p>Study area map for the RUA, Weihai City, Shandong Province, China (using Landsat8 OLI 2020).</p>
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<p>Research framework. Note: CA—cellular automata. (1): Land use classification; (2): Land use simulation. (3): Land use analysis.</p>
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<p>Sixteen driving factors affecting LULC.</p>
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<p>LULC distribution dynamics of RUA dated 2000, 2005, 2010, 2015, 2020, and 2030.</p>
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<p>Pie chart and cumulative pie chart of LULC classes to the total area.</p>
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<p>Gain/loss (%) of LULC classes for temporal dataset (2000–2020).</p>
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<p>LULC change detection map for RUA.</p>
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<p>LULC conversion detection.</p>
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<p>Spatial distribution of carbon reserves in Huancui District in 2000, 2005, 2010, 2015, and 2020.</p>
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