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18 pages, 8958 KiB  
Article
Where is the Eastern Larch Beetle? An Exploration of Different Detection Methods in Northern Wisconsin
by Holly Francart, Amanda M. McGraw, Joseph Knight and Marcella A. Windmuller-Campione
Forests 2025, 16(3), 403; https://doi.org/10.3390/f16030403 - 24 Feb 2025
Viewed by 193
Abstract
Foresters and natural resource managers are increasingly exploring opportunities for the early detection of emerging forest health concerns. One of these emerging concerns is the eastern larch beetle (ELB, Dendroctonus simplex LeConte), a native insect of tamarack (Larix laricina (Du Roi) K., [...] Read more.
Foresters and natural resource managers are increasingly exploring opportunities for the early detection of emerging forest health concerns. One of these emerging concerns is the eastern larch beetle (ELB, Dendroctonus simplex LeConte), a native insect of tamarack (Larix laricina (Du Roi) K., Koch). Historically, the ELB attacked only dead or dying trees, but with climate change, it is now becoming a damaging disturbance agent that affects healthy trees as well. This shift creates a need to evaluate the methods used to detect and quantify the impacted areas. In northern Wisconsin, USA, 50 tamarack stands or aerial detection polygons were surveyed in the field during the 2023 growing season to explore different detection tools for ELBs. We visited 20 polygons identified by aerial sketch map surveys as having ELB mortality, 20 tamarack stands identified by the Astrape satellite imagery algorithm as disturbed, and 10 randomly selected stands from the Wisconsin forest inventory database (WisFIRs) for landscape-level context. For each of the detection methods and the Random stands, information on species composition, mortality, signs of ELB, invasive species, and water presence was quantified. ELBs were common across the landscape, but were not always associated with high levels of mortality. While overstory tree mortality was frequently observed in both aerial sketch map surveys and Astrape, it was not always linked to tamarack mortality. Current methods of detection may need to be re-evaluated in this environment. Tamarack stands in northern Wisconsin were highly heterogeneous in species, which is likely contributing to the difficulties in identifying both tamarack mortality and tamarack mortality specifically caused by ELBs across the two detection methods. Given the evolving impacts of climate change and the shifting dynamics between forests and insects, it is essential to evaluate and innovate detection methods to manage these ecosystems effectively. Full article
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Figure 1
<p>Map of study extent in north central Wisconsin that covers approximately 500,000 hectares. Study sites (black circles) were located on publicly accessible land. The open gray circle represents the extent of the area where sites could be selected.</p>
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<p>The proportion and percentage (4/10 and 40%) of ten random WisFIRS tamarack stands surveyed that can be classified as heavily tamarack-dominant (≥ to 50% of plots with tamarack-dominant overstory) stands with high mortality (≥50% of plots with dead tamarack in overstory) and ELBs present.</p>
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<p>The proportion and percentage (4/20 and 20%) of aerial sketch map polygons surveyed that can be classified as heavily tamarack-dominant (≥ to 50% of plots with tamarack-dominant overstory) stands with high mortality (≥50% of plots with dead tamarack in overstory) and eastern larch beetles (ELBs) present.</p>
Full article ">Figure 4
<p>The proportion and percentage (18/88 and 20%) of disturbed plots detected using the remote sensing algorithm, Astrape, which can be classified as tamarack dominant (≥50% of overstory with tamarack dominant), with tamarack mortality (at least one dead overstory tamarack) and eastern larch beetle (ELB) signs present.</p>
Full article ">Figure 5
<p>The proportion and percentage (6/60 and 10%) of surveyed plots not detected as disturbed that can be classified as tamarack dominant (≥50% of overstory with tamarack dominant) by the remote sensing algorithm, Astrape, with tamarack mortality (at least one dead overstory tamarack), and eastern larch beetle (ELB) signs present.</p>
Full article ">Figure A1
<p>Stand summarization of survey values showing percentage of plots that fall within each category for the twenty aerial sketch map survey polygons (A_#) and the Random stands (R_#) from the Wisconsin forest inventory database (WisFIRs). LALA represents tamarack.</p>
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23 pages, 16858 KiB  
Article
The Extent of Anthropogenic Disturbance on Wetland Area in the Oil Sands Region of Alberta, Canada Between 2000 and 2018
by Joshua Montgomery, Craig Mahoney, Mina Nasr and Danielle Cobbaert
Land 2025, 14(2), 336; https://doi.org/10.3390/land14020336 - 7 Feb 2025
Viewed by 438
Abstract
Wetlands globally have and continue to undergo modification from anthropogenic and natural environmental factors. To bridge this gap, this study utilised a GIS-based approach to quantify the areal extent of human footprint disturbances to wetlands over time. This approach attributed wetland disturbance by [...] Read more.
Wetlands globally have and continue to undergo modification from anthropogenic and natural environmental factors. To bridge this gap, this study utilised a GIS-based approach to quantify the areal extent of human footprint disturbances to wetlands over time. This approach attributed wetland disturbance by wetlands class, disturbance type and sector during two notable disturbance transitions, from 2000 to 2010 and from 2010 to 2018, in the oil sands region (OSR) of northern Alberta, Canada. The wetland disturbance area was calculated using a physical disturbance dataset intersected with the Alberta Merged Wetland Inventory. Results indicate that 3284 km2 (2616 km2 between 2000 and 2010, 668 km2 between 2010 and 2018) of wetlands have undergone disturbance in the OSR. Examination of disturbance by the industrial sector between 2010 and 2018 indicates that the oil and gas and forestry sectors are the greatest sources of disturbance (402 km2 and 179 km2, respectively). Monetary assessment of wetland ecosystem services per year results in a minimum yearly loss of USD 30.05 million for peatlands and USD 197.86 million for marshes and swamps in USD (2007). This analysis is valuable for quantifying the impact of human footprint on wetlands, which is critical for ensuring sustainable development in wetland-rich areas. Full article
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<p>Location of the Oil Sands Region in northeast Alberta, Canada, associated administrative areas (Peace River Oil Sands, Athabasca Oil Sands, Cold Lake Oil Sands), and the Surface Mineable Area. Oil sands project lease boundaries and Indigenous communities are included for geographical context.</p>
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<p>Examples of each wetland class present in the Oil Sands Region: (<b>A</b>) fen; (<b>B</b>) bog; (<b>C</b>) swamp; (<b>D</b>) shallow open water; (<b>E</b>) marsh; and (<b>F</b>) wetland complex featuring multiple wetland classes.</p>
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<p>Workflow for combining three primary products (HFI 2010 and 2018, and AMWI) to create a wetland disturbance product from human footprint change between 2010 and 2018. GIS processes (completed in ESRI ARCGIS) are italicised. Grey boxes indicate primary data products, white indicates geospatial processes in ArcGIS, and blue indicates outputs.</p>
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<p>Wetland disturbed area change by wetland class between 2010 and 2018. Areal disturbance is reported in km<sup>2</sup> and percent area decrease in each wetland class for each region/area based on wetlands present in the Alberta merged wetland inventory (denoted by text adjacent to each bar).</p>
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<p>Wetland disturbed area change by major sector type between 2010 and 2018 from human footprint. Areal disturbance is reported in km<sup>2</sup> with percentage of total disturbance attributable to each sector within each region/area noted adjacent to each bar.</p>
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<p>(<b>A</b>) Summary of proportional disturbance of wetlands in the Surface Mineable Area (SMA) based on wetland class between 2010 to 2018, and (<b>B</b>) wetland disturbance attributed to industrial sector.</p>
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<p>Map of areal disturbance to wetlands and uplands induced by expansion of human footprint in the Oil Sands Area from 2010 to 2018. Size of disturbance areas have been exaggerated for visual interpretation. Insets provide an general view of disturbances and wetland classes in each oil sands area.</p>
Full article ">Figure A2
<p>Map of areal disturbance to wetlands and uplands induced by expansion of human footprint in the Surface Mineable Area from 2010 to 2018. Note, the size of disturbance areas have been exaggerated for visual interpretation. Insets provide a general view of disturbances and wetlands classes within the Surface Mineable Area.</p>
Full article ">Figure A3
<p>Cumulative Oil sands revenue compared to cumulative disturbed wetland ecosystem service value.</p>
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18 pages, 5115 KiB  
Article
Drainage and Afforestation More Strongly Affect Soil Microbial Composition in Fens than Bogs of Subtropical Moss Peatlands
by Putao Zhang, Junheng Yang, Haijun Cui, Weifeng Song, Yingying Liu, Xunxun Shi, Xiaoting Bi and Suyao Yuan
Sustainability 2024, 16(19), 8621; https://doi.org/10.3390/su16198621 - 4 Oct 2024
Cited by 1 | Viewed by 1124
Abstract
Subtropical moss peatlands have important ecological functions, and their protection and restoration are urgent. The lack of understanding of the biogeochemical changes in subtropical moss peatlands after human disturbance, particularly regarding their underground ecological changes, limits the efforts towards their protection and restoration. [...] Read more.
Subtropical moss peatlands have important ecological functions, and their protection and restoration are urgent. The lack of understanding of the biogeochemical changes in subtropical moss peatlands after human disturbance, particularly regarding their underground ecological changes, limits the efforts towards their protection and restoration. In this study, typical subtropical moss peatlands and the Cryptomeria swamp forest (CSF) formed by long-term (more than 20 years) drainage and afforestation in the Yunnan–Guizhou Plateau of China were selected as the research sites. Moreover, 16S rRNA high-throughput sequencing technology was used to study the differences in soil bacterial community diversity and composition among a natural Sphagnum fen (SF), Polytrichum bog (PB), and CSF to explore the effects of drainage and afforestation on different types of moss peatlands and its mechanism combined with soil physicochemical properties. Results showed that (1) drainage and afforestation significantly reduced the α diversity of soil bacterial communities in SF while significantly increasing the α diversity of soil bacterial communities in PB. Soil bacterial communities of SF had the highest α diversity and had many unique species or groups at different taxonomic levels. (2) The impact of drainage and afforestation on the soil bacterial community composition in SF was significantly higher than that in PB. Drainage and afforestation caused significant changes in the composition and relative abundance of dominant groups of soil bacteria in SF at different taxonomic levels, such as significantly reducing the relative abundance of Proteobacteria, significantly increasing the relative abundance of Acidobacteria, and significantly reducing the ratio of Proteobacteria to Acidobacteria, but did not have a significant impact on the corresponding indicators of PB. The changes in the ratio of Proteobacteria to Acidobacteria may reflect changes in the trophic conditions of peatlands. (3) Soil moisture content, available phosphorus content, and pH were key driving factors for changes in soil bacterial community composition and diversity, which should be paid attention to in the restoration of moss peatlands. This study provides insights into the protection and restoration of subtropical moss peatlands. Full article
(This article belongs to the Special Issue Soil Microorganisms, Plant Ecology and Sustainable Restoration)
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<p>Comparison of soil physicochemical properties among <span class="html-italic">Sphagnum</span> fen (SF), <span class="html-italic">Polytrichum</span> bog (PB), and <span class="html-italic">Cryptomeria</span> swamp forest (CSF). TC—soil total carbon content (<b>A</b>); TN—soil total nitrogen content (<b>B</b>); pH—soil pH (<b>C</b>); AP—soil available phosphorus content (<b>D</b>); NO<sub>3</sub><sup>−</sup>-N—soil nitrate nitrogen content (<b>E</b>); NH<sub>4</sub><sup>+</sup>-N—soil ammonium nitrogen content (<b>F</b>); SWW—soil weight water content (<b>G</b>); SBD—soil bulk density (<b>H</b>). Error bars indicate the standard errors (<span class="html-italic">n</span> = 3). Lowercase letters (a, b, c) represent significantly different values of the studied parameter with a 95% confidence interval, confirmed by ANOVA with subsequent LSD comparisons.</p>
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<p>Comparison of soil bacterial α diversity among <span class="html-italic">Sphagnum</span> fen (SF), <span class="html-italic">Polytrichum</span> bog (PB), and <span class="html-italic">Cryptomeria</span> swamp forest (CSF). Sobs—the observed richness (<b>A</b>); ACE—the ACE estimator (<b>B</b>); Chao—the Chao1 estimator (<b>C</b>); Shannon—the Shannon diversity index (<b>D</b>); Simpson—the Simpson diversity index (<b>E</b>); Pd—phylogenetic diversity (<b>F</b>); Shannoneven—a Shannon index-based measure of evenness (<b>G</b>); Simpsoneven—a Simpson index-based measure of evenness (<b>H</b>); Coverage—the Good’s coverage <b>(I</b>). Sobs, ACE, and Chao reflecting on community richness; Shannon, Simpson, and Pd reflecting on community diversity; Shannoneven and Simpsoneven reflecting on community evenness; and Coverage reflecting on community coverage. Error bars indicate the standard errors (<span class="html-italic">n</span> = 3). Lowercase letters (a, b, c) represent significantly different values of the studied parameter with a 95% confidence interval, confirmed by ANOVA with subsequent LSD comparisons.</p>
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<p>Variations of soil bacterial composition among <span class="html-italic">Sphagnum</span> fen (SF), <span class="html-italic">Polytrichum</span> bog (PB), and <span class="html-italic">Cryptomeria</span> swamp forest (CSF) at phylum (<b>A</b>–<b>C</b>), class (<b>D</b>–<b>F</b>), family (<b>G</b>–<b>I</b>), and genus levels (<b>J</b>–<b>L</b>). (1) Comparison of soil bacterial composition among different types of sites by NMDS based on Bray–Curtis distance (<b>A</b>,<b>D</b>,<b>G</b>,<b>J</b>). (2) The number of shared and unique taxa across different types of sites (<b>B</b>,<b>E</b>,<b>H</b>,<b>K</b>). (3) Comparison of the dissimilarities of soil bacterial communities between different types of sites (<b>C</b>,<b>F</b>,<b>I</b>,<b>L</b>). Each box plot represents the maximum, minimum, 75th, and 25th quartiles, respectively; the line of each box plot represents the median, and the red point of each box plot represents the mean (<span class="html-italic">n</span> = 9). Lowercase letters (a, b, c) represent significantly different values of the studied parameter with a 95% confidence interval, confirmed by ANOVA with subsequent LSD comparisons.</p>
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<p>Indicator group analysis of bacterial communities in <span class="html-italic">Sphagnum</span> fen (SF), <span class="html-italic">Polytrichum</span> bog (PB), and <span class="html-italic">Cryptomeria</span> swamp forest (CSF) with LDA SCORE &gt; 3.5.</p>
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<p>Comparison of the relative abundance of major groups of soil bacteria in <span class="html-italic">Sphagnum</span> fen (SF), <span class="html-italic">Polytrichum</span> bog (PB), and <span class="html-italic">Cryptomeria</span> swamp forest (CSF) at phylum (<b>A</b>), class (<b>B</b>), family (<b>C</b>), and genus (<b>D</b>) levels. The main groups of soil bacteria were composed of the top ten groups in the relative abundance of each type of site. Error bars indicate the standard errors (<span class="html-italic">n</span> = 3). Lowercase letters (a, b, c) represent significantly different values of the studied parameter with a 95% confidence interval, confirmed by ANOVA with subsequent LSD comparisons.</p>
Full article ">Figure 5 Cont.
<p>Comparison of the relative abundance of major groups of soil bacteria in <span class="html-italic">Sphagnum</span> fen (SF), <span class="html-italic">Polytrichum</span> bog (PB), and <span class="html-italic">Cryptomeria</span> swamp forest (CSF) at phylum (<b>A</b>), class (<b>B</b>), family (<b>C</b>), and genus (<b>D</b>) levels. The main groups of soil bacteria were composed of the top ten groups in the relative abundance of each type of site. Error bars indicate the standard errors (<span class="html-italic">n</span> = 3). Lowercase letters (a, b, c) represent significantly different values of the studied parameter with a 95% confidence interval, confirmed by ANOVA with subsequent LSD comparisons.</p>
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<p>Redundancy analysis showing the relationship between soil physicochemical properties and major groups of soil bacterial communities in all types of sites at phylum (<b>A</b>), class (<b>B</b>), family (<b>C</b>), and genus (<b>D</b>) levels. SF—<span class="html-italic">Sphagnum</span> fen; PB—<span class="html-italic">Polytrichum</span> bog; CSF—<span class="html-italic">Cryptomeria</span> swamp forest.</p>
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27 pages, 3555 KiB  
Article
Ecological Groups of Coleoptera (Insecta) as Indicators of Habitat Transformation on Drained and Rewetted Peatlands: A Baseline Study from a Carbon Supersite, Kaliningrad, Russia
by Vitalii Alekseev, Maxim Napreenko and Tatiana Napreenko-Dorokhova
Insects 2024, 15(5), 356; https://doi.org/10.3390/insects15050356 - 15 May 2024
Viewed by 1336
Abstract
A total of 281 coleopteran species from 41 families were recorded from different sites of an abandoned cut-over peatland designated as the Carbon Measurement Supersite in Kaliningrad Oblast. This beetle assemblage is considered a baseline (pre-impact) faunal assemblage for further investigations during the [...] Read more.
A total of 281 coleopteran species from 41 families were recorded from different sites of an abandoned cut-over peatland designated as the Carbon Measurement Supersite in Kaliningrad Oblast. This beetle assemblage is considered a baseline (pre-impact) faunal assemblage for further investigations during the ‘before–after’ (BA) or ‘before–after control-impact’ (BACI) study on a peatland that is planned to be rewetted. The spontaneously revegetated peatland has a less specialised beetle assemblage than at an intact raised bog. Tyrphobiontic species are completely absent from the peatland, while some tyrphophiles (5.3% of the total beetle fauna) are still found as remnants of the former raised bog communities. The predominant coenotic coleopteran group is tyrphoneutral generalists from various non-bog habitats (72.9%). The species composition is associated with the vegetation structure of the disturbed peatland (fragmentary Sphagnum cover, lack of open habitats, and widespread birch coppice or tree stand), which does not correspond to that of a typical European raised bog. The sampled coleopteran assemblage is divided into several relative ecological groups, whose composition and peculiarities are discussed separately. Possible responses to the rewetting measurements in different coleopteran groups are predicted and briefly discussed. A complex assemblage of stenotopic peatland-specialised tyrphophiles (15 spp.) and the most abundant tyrphoneutral generalists (31 spp.) were assigned as indicators for the environmental monitoring of peatland development. Full article
(This article belongs to the Section Insect Ecology, Diversity and Conservation)
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<p>Location of the Vittgirrensky peatland (the ‘Rossyanka’ Carbon Measurement Supersite) and the Zehlau raised bog (mentioned in the text for comparison) in Kaliningrad Oblast.</p>
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<p>Vegetation cover in the Vittgirrensky Peatland (after [<a href="#B28-insects-15-00356" class="html-bibr">28</a>,<a href="#B29-insects-15-00356" class="html-bibr">29</a>]) and location of the sampling points for Coleoptera in 2023: 1—Dry shrublands, 2—Wet shrublands, 3—Dry birch stand, 4—Fen-like communities (<span class="html-italic">Juncus</span>), 5—Birch coppice, 6—Reed beds, 7—pitfall traps set in lines, 8—Bare-peat sites, 9—Dense closed-canopy stand, 10—Fen-like communities (<span class="html-italic">Eriophorum</span>/<span class="html-italic">Carex</span>), 11—Wet forest (birch and aspen), 12—Hydrophilic communities in ditches, 13—Dirt road, 14—Places of sweeping with net in water.</p>
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<p>The main typical habitats of the Vittgirrensky peatland: (<b>A</b>) Birch coppice with <span class="html-italic">Calluna</span> (site of the pitfall line 1), habitat for <span class="html-italic">Micrelus ericae</span>, <span class="html-italic">Curimopsis nigrita</span>, <span class="html-italic">Altica longicollis</span>; (<b>B</b>) Open bare peat (site of the pitfall line 3), habitat for <span class="html-italic">Cymindis vaporariorum</span>, <span class="html-italic">Parabolitobius formosus</span>, <span class="html-italic">Pselaphus heisei</span>, <span class="html-italic">Curimopsis nigrita</span>; (<b>C</b>) Dry birch stand (site of pitfall line 2), habitat for <span class="html-italic">Sciaphilus asperatus</span>, <span class="html-italic">Barypeithes pellucidus</span>, <span class="html-italic">Brachysomus echinatus</span>; (<b>D</b>) <span class="html-italic">Phragmites</span>-dominated birch coppice, habitat for <span class="html-italic">Malthodes pumilus</span>, <span class="html-italic">Scymnus suturalis</span>; (<b>E</b>) The drainage ditch drying up in summer, sampling place of <span class="html-italic">Dytiscus dimidiatus</span>, <span class="html-italic">Graphoderus cinereus</span>, <span class="html-italic">Hydaticus seminiger</span>; (<b>F</b>) non-drying drainage ditch, sampling place of <span class="html-italic">Hydrochus elongatus</span>, <span class="html-italic">Enochrus ochropterus</span>.</p>
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<p>Some of the potential indicator species (tyrphophiles and abundant tyrphoneutrals) that can be used, among others, to monitor environmental impacts in the Vittgirrensky peatland: (<b>A</b>) <span class="html-italic">Altica aenescens</span>; (<b>B</b>) <span class="html-italic">Orchestes jota</span>; (<b>C</b>) <span class="html-italic">Platydracus fulvipes</span>; (<b>D</b>) <span class="html-italic">Scymnus suturalis</span>; (<b>E</b>) <span class="html-italic">Lochmaea caprea</span>; and (<b>F</b>) <span class="html-italic">Polydrusus cervinus</span>.</p>
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17 pages, 11618 KiB  
Article
Impact of Large-Scale Fire and Habitat Type on Ant Nest Density and Species Abundance in Biebrza National Park, Poland
by Izabela Sondej and Timo Domisch
Forests 2024, 15(1), 123; https://doi.org/10.3390/f15010123 - 8 Jan 2024
Viewed by 1629
Abstract
Fire can have negative effects on the ant community by reducing species abundance through direct mortality, changes in resource availability, or foraging activity. Fire can also have positive effects, especially for opportunistic species preferring open or disturbed habitats. We assessed the direct effects [...] Read more.
Fire can have negative effects on the ant community by reducing species abundance through direct mortality, changes in resource availability, or foraging activity. Fire can also have positive effects, especially for opportunistic species preferring open or disturbed habitats. We assessed the direct effects of a large-scale fire on ant communities in open habitats (grassland and Carex) and moist forested peatland (birch and alder) sites in Biebrza National Park, testing three hypotheses: (i) the large-scale fire had more significant effects on ant nest density in forests than in open habitats, (ii) the post-fire ant diversity changes within sites are stronger in forests than open habitats, and (iii) ant species preferring disturbed habitats are favoured by the fire event. The fire had negative effects on ant nest density only in the Carex and grassland sites but not in the birch and alder sites, suggesting that fire had a stronger impact in open habitats than in forests. Temporal post-fire ant diversity changes within sites were stronger in forests than in open habitats. We observed higher beta diversity changes between the first and second year of the study in the burned forest sites due to colonisation, indicating a greater fire impact on species community composition followed by a higher recolonisation rate. Ant species preferring disturbed habitats were favoured by the fire. The seed-eating ant species Tetramorium caespitum, a thermophilous and opportunistic species, dominated the burned grassland site. This contrasts with other species, e.g., Lasius alienus, for which nest density decreased after fire, underlining the importance of food resource availability as a major driver of community changes after fire. Our study also underlines the importance of periodic biodiversity monitoring in conservation areas for assessing the recovery of the original status after disturbances and revealing possible habitat changes endangering the survival of local biotic communities. Full article
(This article belongs to the Section Natural Hazards and Risk Management)
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Figure 1
<p>Spatial location of the study area in Biebrza National Park. Red dots indicate the locations of the study sites (GF: burned grassland, GC: unburned control grassland, CF: burned <span class="html-italic">Carex</span>, CC: unburned control <span class="html-italic">Carex</span>, BF: burned birch forest, BC: unburned control birch forest, AF: burned alder forest, AC: unburned control alder forest). Green areas in the subfigure indicate forest areas and yellowish areas are open areas.</p>
Full article ">Figure 2
<p>Example of study plots on grassland communities: the burned grassland site (GF) after 2 weeks (<b>a</b>), after 4 months, coinciding with our first inventory (<b>b</b>), after 16 months, at the time of our second inventory (<b>c</b>), and the unburned control site (GC) (<b>d</b>). The red ribbon delimits the area of one study plot of 10 m<sup>2</sup>.</p>
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<p>Examples of ant nests on some study plots during the first inventory (4 months after the fire): burned <span class="html-italic">Carex</span> plot (<b>a</b>), burned birch forest plot (<b>b</b>), burned alder forest plot (<b>c</b>) and burned grassland plot (<b>d</b>) during ant sampling.</p>
Full article ">Figure 4
<p>(<b>A</b>) Ant nest densities (nests per m<sup>2</sup>), and (<b>B</b>) Shannon diversity index of ant nests for each plot, indicating differences between 2020 and 2021 within the grassland, <span class="html-italic">Carex</span>, birch forest and alder forest sites. Different lowercase letters indicate a statistically significant difference between years and in the same site, and capital letters indicate a statistically significant difference between sites in the same year (<span class="html-italic">p</span> &lt; 0.05). Values are averages of 10 plot-wise values. Note that the value for the Shannon diversity index for the burned alder forest in 2021 is zero (no plots with more than one species).</p>
Full article ">Figure 5
<p>Temporal changes of beta diversities between 2020 and 2021 in the study sites (more details in <a href="#forests-15-00123-t002" class="html-table">Table 2</a>), assessed by presence absence data of ant species (GC: unburned control grassland, GF: burned grassland, CC: unburned control <span class="html-italic">Carex</span>, CF: burned <span class="html-italic">Carex</span>, BC: unburned control birch forest, BF: burned birch forest, AC: unburned control alder forest, AF: burned alder forest). Total changes are additive effects of changes by extinction and colonisation of species.</p>
Full article ">Figure 6
<p>NMDS plots for ant species compositions (<b>A</b>) for 2020 and (<b>B</b>) for 2021. Different sites are depicted with different symbols. Ant species and stress values are indicated. Stress values equal to or below 0.1 are considered as good, while values equal to or below 0.05 indicate a very good fit. A stress value around 0.2 could still be regarded as fair, but values approaching 0.3 indicate that the ordination is arbitrary.</p>
Full article ">
18 pages, 2504 KiB  
Article
Inverted Soil Mounding as a Restoration Approach of Seismic Lines in Boreal Peatlands: Implications on Plant and Arthropod Abundance and Diversity
by Laureen Echiverri, Jaime Pinzon and Anna Dabros
Forests 2023, 14(11), 2123; https://doi.org/10.3390/f14112123 - 25 Oct 2023
Cited by 2 | Viewed by 1678
Abstract
In northern Alberta, Canada, much of treed boreal peatlands are fragmented by seismic lines—linear disturbances where trees and shrubs are cleared for the exploration of fossil fuel reserves. Seismic lines have been shown to have slow tree regeneration, likely due to the loss [...] Read more.
In northern Alberta, Canada, much of treed boreal peatlands are fragmented by seismic lines—linear disturbances where trees and shrubs are cleared for the exploration of fossil fuel reserves. Seismic lines have been shown to have slow tree regeneration, likely due to the loss of microtopography during the creation of seismic lines. Inverted soil mounding is one of the treatments commonly applied in Alberta to restore seismic lines and mitigate the use of these corridors by wildlife and humans. We assessed the effects of mounding on understory plants and arthropod assemblages three years after treatment application. We sampled five mounded and five untreated seismic lines and their adjacent treed fens (reference fens). Compared to reference fens, mounded seismic lines showed on average lower bryophyte (6.5% vs. 98.1%) and total understory cover (47.2% vs. 149.8%), ground-dwelling spider abundance (226.0 vs. 383 individuals), richness (87.2 vs. 106.4 species) and diversity (19.0 vs. 24.6 species), rove beetle abundance (35.2 vs. 84.8 individuals), and ant richness (9.0 vs. 12.9 species). In contrast, rove beetle and ground beetle richness (39.0 and 14.5 species, respectively) and diversity (16.8 and 7.8 species, respectively) were higher on mounded seismic lines compared to reference fens (richness: 18.0 and 7.5 species, respectively; diversity: 7.0 and 3.8 species, respectively). This is one of the first studies to assess arthropod responses to restoration efforts in the context of oil and gas disturbances in North America, and our results highlight the need to incorporate multiple taxa when examining the impact of such treatments. Full article
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<p>Study area showing the location of untreated (green circles) and treated (orange circles) seismic lines within the area of the Canadian Natural Resources Ltd. (CNRL, Calgary, AB, Canada) Kirby South in situ SAGD Plant. At each location, two 50 m long transects were installed parallel to each other, one along the seismic line and one 50 m into the adjacent fen (marked with X on the map). Inset map of Canada highlights the province of Alberta and the location of the study area as a red square). Images of untreated and treated lines, and reference fen are provided for reference (pictures by J.P.).</p>
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<p>Boxplots of percent cover of (<b>a</b>) bryophytes, (<b>b</b>) graminoids, (<b>c</b>) shrubs and (<b>d</b>) total understory, and (<b>e</b>) species richness and (<b>f</b>) diversity (exponential of Shannon’s) by treatment (reference fens, untreated seismic lines, and the top and ground positions of mounded seismic lines). Significant differences between treatments (<span class="html-italic">p</span> &lt; 0.05), based on pairwise comparisons of estimated marginal means, are indicated by different letters. The median is represented by the horizontal line within the boxplot; lower and upper hinges represent the 25th and 75th percentiles, respectively; whiskers are the lowest and highest values within 1.5 times the interquartile range (IQR); dots outside the box and whiskers are outliers (values greater than or less than 1.5 × IQR).</p>
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<p>Assemblage composition (RDA ordination) of (<b>a</b>) understory vegetation, (<b>b</b>) ground−dwelling spiders, (<b>c</b>) rove beetles, (<b>d</b>) ants, and (<b>e</b>) ground beetles for each treatment (reference fens, untreated seismic lines, and mounded seismic lines). Ellipses are 95% confidence intervals around group centroids, and points represent sites (symbolized by treatment: square symbols represent reference fens, circles represent untreated seismic lines, triangles represent mounded seismic lines or top of the mounded seismic lines for vegetation ordination, and crosses represent the ground position of mounded seismic lines).</p>
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<p>Boxplots of catches of (<b>a</b>) ground-dwelling spiders, (<b>b</b>) rove beetles, and (<b>c</b>) ants by treatment (reference fens, untreated seismic lines, and mounded seismic lines). Different letters indicate significant (<span class="html-italic">p</span> &lt; 0.05) differences between treatments based on pairwise comparisons of estimated marginal means. For the statistics used to create the boxplot, see legend of <a href="#forests-14-02123-f002" class="html-fig">Figure 2</a>.</p>
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<p>Estimated species richness and diversity (exponential of Shannon’s) of (<b>a</b>) ground-dwelling spiders, (<b>b</b>) rove beetles, (<b>c</b>) ants, and (<b>d</b>) ground beetles for each treatment (reference fens, untreated seismic lines, and mounded seismic lines), based on coverage-based rarefaction. Error bars represent 95% confidence intervals. Differences between treatments are assessed by visual inspection.</p>
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22 pages, 37016 KiB  
Article
Transformation of Soils and Mire Community Reestablishment Potential in Disturbed Abandoned Peatland: A Case Study from the Kaliningrad Region, Russia
by Olga Antsiferova, Maxim Napreenko and Tatiana Napreenko-Dorokhova
Land 2023, 12(10), 1880; https://doi.org/10.3390/land12101880 - 7 Oct 2023
Cited by 2 | Viewed by 1919
Abstract
Degrading organic soils usually become a source of increased greenhouse gas emissions and fire frequency in disturbed peatlands. As a solution, the rewetting concept should consider not only the detailed hydrological characteristics of the peatland, but should also appraise the properties of the [...] Read more.
Degrading organic soils usually become a source of increased greenhouse gas emissions and fire frequency in disturbed peatlands. As a solution, the rewetting concept should consider not only the detailed hydrological characteristics of the peatland, but should also appraise the properties of the soils. Here, we provide the results of a detailed soil study carried out on an abandoned peatland in the Kaliningrad Region, Russia. The study aims to integrate data on soil properties, hydrology, and the degree of transformation of the current soil cover in terms of how this affects spontaneous revegetation and the potential for further mire community reestablishment. The paper contributes to a greater understanding of rehabilitation patterns of disturbed peatlands depending on the soil’s physical and hydrological properties in the humid climate of the southeastern Baltic region. The present-day soils of the peatland refer to two World Reference Base (WRB) groups: Gleisols and Histosols; the latter change successively from the periphery to the centre of the peatland as follows: Eutric/SapricHemicDystricFibric. Most Histosols are characterised by hydrothermal degradation in the upper layers with patches of pyrogenic degradation. Some local inundated areas show environmental conditions favourable for Sphagnum growth and the formation of mire communities. We have identified six groups of sites with different ecological and time-span potentials for mire community restoration during the implementation of rewetting activities. The rewetting feasibility of the peatland’s sites does not coincide with the degree of transformation of their soil profile, but is rather determined by the hydrological regime. Full article
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<p>Location of the Vittgirrensky Peatland (the Rossyanka Carbon Measurement Supersite site) in the Kaliningrad Oblast.</p>
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<p>The Vittgirrensky Peatland in the archived documents: (<b>a</b>) on the map of the late 19th century [<a href="#B18-land-12-01880" class="html-bibr">18</a>], with the marked sites of peat mining, and (<b>b</b>) on the drainage plan issued in 1962 [<a href="#B21-land-12-01880" class="html-bibr">21</a>].</p>
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<p>Location of the soil profiles and coring wells established in the Vittgirrensky Peatland (basemap source: Google Earth, 2023).</p>
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<p>Sketch map of soils in the Vittgirrensky Peatland.</p>
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<p>Pedo-ecological characteristics of Histosols in profile: HS s (red), HS d (blue), HS f-1 (yellow), HS f-2 (green).</p>
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<p>The degree of anthropogenic transformation of soils in the Vittgirrensky Peatland (map key is given below).</p>
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<p>Relationship between modern-day soils and vegetation cover in the Vittgirrensky Peatland.</p>
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<p>Rewetting potential and feasibility of mire rehabilitation on different soil units in the Vittgirrensky Peatland (see categories listed below).</p>
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<p>Vegetation cover in the central part of the Vittgirrensky Peatland (on fibric Histosols, HS f-1): birch coppice on pyrogenically modified peat substrate (<b>left</b>) and bare peat site (<b>right</b>).</p>
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<p>Fen-like communities on inundated sites in the Vittgirrensky Peatland: <span class="html-italic">Juncus</span>-dominated, HS-d (<b>left</b>) and <span class="html-italic">Carex</span>-dominated, HS f-1 (<b>right</b>).</p>
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<p>Arboreal vegetation on severely drained sites in the Vittgirrensky Peatland: dense closed-canopy stand, HS f-2 (<b>left</b>) and dry birch forest, HS d (<b>right</b>).</p>
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<p>Vegetation on Gleysols (GS u) in the edge zone of the Vittgirrensky Peatland: wet shrubland (<b>left</b>) and wet forest with birch and aspen (<b>right</b>).</p>
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<p>Hydrophilic communities in the Vittgirrensky Peatland: reed beds (<b>left</b>) and <span class="html-italic">Sphagnum</span> lawn with <span class="html-italic">Eriophorum</span> in a drainage ditch (<b>right</b>).</p>
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<p>The soil profile of Histosols (<b>left</b>), occupying the main part of the Vittgirrensky Peatland, and Gleysols (<b>right</b>) from the edge zone of the study area.</p>
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18 pages, 5646 KiB  
Article
Examining Drivers of Post-Fire Seismic Line Ecotone Regeneration in a Boreal Peatland Environment
by Humaira Enayetullah, Laura Chasmer, Chris Hopkinson, Daniel Thompson and Danielle Cobbaert
Forests 2023, 14(10), 1979; https://doi.org/10.3390/f14101979 - 29 Sep 2023
Cited by 2 | Viewed by 2076
Abstract
Seismic lines are the dominant anthropogenic disturbance in the boreal forest of the Canadian province of Alberta, fragmenting over 1900 km2 of peatland areas and accounting for more than 80% of all anthropogenic disturbance in this region. The goal of this study [...] Read more.
Seismic lines are the dominant anthropogenic disturbance in the boreal forest of the Canadian province of Alberta, fragmenting over 1900 km2 of peatland areas and accounting for more than 80% of all anthropogenic disturbance in this region. The goal of this study is to determine whether the wildland fires that burn across seismic lines in peatlands result in the regeneration of woody vegetation within the ecotonal areas adjacent to seismic lines. We use a combination of seismic line and vegetation structural characteristics derived from multi-spectral airborne lidar across a post-fire peatland chronosequence. We found an increasing encroachment of shrubs and trees into seismic lines after many years since a fire, especially in fens, relative to unburned peatlands. Fens typically had shorter woody vegetation regeneration (average = 3.3 m ± 0.9 m, standard deviation) adjacent to seismic lines compared to bogs (average = 3.8 m ± 1.0 m, standard deviation), despite enhanced shrubification closer to seismic lines. The incoming solar radiation and seismic line age since the establishment of seismic line(s) were the factors most strongly correlated with enhanced shrubification, suggesting that the increased light and time since a disturbance are driving these vegetation changes. Shrub encroachment closer to seismic lines tends to occur within fens, indicating that these may be more sensitive to drying conditions and vegetation regeneration after several years post-fire/post-seismic line disturbance. Full article
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<p>Location of lidar survey polygons south of Fort McMurray, Alberta. The peatland–forest complexes within the polygons have been disturbed by industrial land use activities, including petroleum exploration and extraction, infrastructure, and forestry operations (harvesting). Areas that have not been burned recently, according to fire management records (since ~1930) are found in between the fire scars, predominantly between the 1982 and 2002 fires in the western lidar polygon, while the smaller eastern lidar polygon was burned by fires in 1990 and 2015.</p>
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<p>Canopy height model for example peatlands found in the burn chronosequence. Upland and transitional areas: (<b>a</b>) 5 years since fire with seismic lines; (<b>b</b>) 18 years since fire with seismic lines; (<b>c</b>) 30 years since fire with seismic lines; (<b>d</b>) 38 years since fire with seismic lines; (<b>e</b>) unburned reference site with seismic lines; (<b>f</b>) unburned reference site without seismic lines. Uplands and transitional areas have taller vegetation, while peatlands fragmented by seismic lines have vegetation height less than 6 m.</p>
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<p>Average vegetation height and bog/fen forms found across fire scars and in unburned areas.</p>
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<p>Variation in canopy height with distance from seismic lines in bogs determined from randomly located lidar-based height measurements ((<b>a</b>–<b>e</b>) for years since fire). Box plots indicate mean line, with interquartile ranges (25th and 75th percentiles) and outliers (5th and 95th percentiles) indicated by whiskers; (<b>f</b>) illustration of canopy height with distance from seismic line for all years since fire, where ribbons represent interquartile range.</p>
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<p>Variation in canopy height along with distance from seismic lines in fens, determined from randomly located lidar-based height measurements ((<b>a</b>–<b>e</b>) for years since fire). Box plots indicate mean line, with interquartile ranges (25th and 75th percentiles) range and outliers (5th and 95th percentiles) indicated by whiskers; (<b>f</b>) illustration of canopy height against distance from seismic line for all years since fire, where ribbons represent interquartile range.</p>
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<p>Plots of scaled peatland explanatory attribute score and arrows of loadings for the first two principal components (PC1 34% and PC2 20% of variance) of PCA for both bogs and fens. The length of their respective arrows illustrates the component loadings of the indices, while individual bogs and fens found in the study area are also included (n = 155). Circles represent strong clustering of bogs and fens that cluster with years since fire.</p>
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21 pages, 10811 KiB  
Article
Modeling Tool for Estimating Carbon Dioxide Fluxes over a Non-Uniform Boreal Peatland
by Iuliia Mukhartova, Julia Kurbatova, Denis Tarasov, Ravil Gibadullin, Andrey Sogachev and Alexander Olchev
Atmosphere 2023, 14(4), 625; https://doi.org/10.3390/atmos14040625 - 25 Mar 2023
Cited by 5 | Viewed by 2086
Abstract
We present a modeling tool capable of computing carbon dioxide (CO2) fluxes over a non-uniform boreal peatland. The three-dimensional (3D) hydrodynamic model is based on the “one-and-a-half” closure scheme of the system of the Reynolds-Averaged Navier–Stokes and continuity equations. Despite simplifications [...] Read more.
We present a modeling tool capable of computing carbon dioxide (CO2) fluxes over a non-uniform boreal peatland. The three-dimensional (3D) hydrodynamic model is based on the “one-and-a-half” closure scheme of the system of the Reynolds-Averaged Navier–Stokes and continuity equations. Despite simplifications used in the turbulence description, the model allowed obtaining the spatial steady-state distribution of the averaged wind velocities and coefficients of turbulent exchange within the atmospheric surface layer, taking into account the surface heterogeneity. The spatial pattern of CO2 fluxes within and above a plant canopy is derived using the “diffusion–reaction–advection” equation. The model was applied to estimate the spatial heterogeneity of CO2 fluxes over a non-uniform boreal ombrotrophic peatland, Staroselsky Moch, in the Tver region of European Russia. The modeling results showed a significant effect of vegetation heterogeneity on the spatial pattern of vertical and horizontal wind components and on vertical and horizontal CO2 flux distributions. Maximal airflow disturbances were detected in the near-surface layer at the windward and leeward forest edges. The forest edges were also characterized by maximum rates of horizontal CO2 fluxes. Modeled turbulent CO2 fluxes were compared with the mid-day eddy covariance flux measurements in the southern part of the peatland. A very good agreement of modeled and measured fluxes (R2 = 0.86, p < 0.05) was found. Comparisons of the vertical profiles of CO2 fluxes over the entire peatland area and at the flux tower location showed significant differences between these fluxes, depending on the prevailing wind direction and the height above the ground. Full article
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<p>Geographical location, satellite image, and photo of the study area. The white circle indicates the location of the eddy covariance flux station.</p>
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<p>Spatial pattern of the leaf area index (LAI) for the selected modeling domain covering the Staroselsky Moch peatland and surrounding landscapes. The medium-dashed line shows the peatland boundary. The short-dashed line shows the boundaries of abandoned lands with grassy vegetation and open places. The black circle indicates the location of the eddy covariance flux station. The coordinate system is Universal Transverse Mercator (UTM, 36N).</p>
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<p>The wind speed and direction distribution (wind rose) at the Staroselsky Moch peatland in the summer of 2016.</p>
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<p>Modeled vertical (<b>a</b>,<b>d</b>) and horizontal (<b>b</b>,<b>e</b>) flow velocity, and turbulent kinetic energy (<b>c</b>,<b>f</b>) distributions at the heights of 3 (<b>a</b>–<b>c</b>) and 30 (<b>d</b>–<b>f</b>) m above the ground. Calculations were performed using meteorological data at 14:00 on 25 June 2016. The wind direction at the upper boundary of the modeling domain is southwest. The medium-dashed line shows the peatland boundary. The short-dashed line shows the boundaries of abandoned lands with grassy vegetation and open places. The black circle indicates the location of the eddy covariance flux station. The negative vertical flow component corresponds to the downward air flows, and positive, to the upward ones. The coordinate system is Universal Transverse Mercator (UTM, 36N).</p>
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<p>Modeled vertical (<b>a</b>,<b>d</b>) and horizontal (<b>b</b>,<b>e</b>) flow velocity, and turbulent kinetic energy (<b>c</b>,<b>f</b>) distributions at the heights of 3 (<b>a</b>–<b>c</b>) and 30 (<b>d</b>–<b>f</b>) m above the ground. Calculations were performed using meteorological data at 13:30 on 28 June 2016. The wind direction at the upper boundary of the modeling domain is northwest. The medium-dashed line shows the peatland boundary. The short-dashed line shows the boundaries of abandoned lands with grassy vegetation and open places. The black circle indicates the location of the eddy covariance flux station. Negative vertical flow component corresponds to the downward air flows, and positive to the upward ones. Coordinate system is Universal Transverse Mercator (UTM, 36N).</p>
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<p>Modeled vertical (<b>a</b>,<b>c</b>) and horizontal (<b>b</b>,<b>d</b>) CO<sub>2</sub> fluxes (turbulent and advective) at the heights of 3 (<b>a</b>,<b>b</b>) and 30 (<b>c</b>,<b>d</b>) m above the ground. Calculations were performed using meteorological data at 14:00 on 25 June 2016. The medium-dashed line shows the peatland boundary. The short-dashed line shows the boundaries of abandoned lands with grassy vegetation and open places. The black circle indicates the location of the eddy covariance flux station. The coordinate system is Universal Transverse Mercator (UTM, 36N).</p>
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<p>Modeled vertical (<b>a</b>,<b>c</b>) and horizontal (<b>b</b>,<b>d</b>) CO<sub>2</sub> fluxes (turbulent and advective) at the heights of 3 (<b>a</b>,<b>b</b>) and 30 (<b>c</b>,<b>d</b>) m above the ground. Calculations were performed using meteorological data at 13:03 on 28 June 2016. The medium-dashed line shows the peatland boundary. The short-dashed line shows the boundaries of abandoned lands with grassy vegetation and open places. The black circle indicates the location of the eddy covariance flux station. The coordinate system is Universal Transverse Mercator (UTM, 36N).</p>
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<p>Comparisons of vertical CO<sub>2</sub> fluxes simulated by 3D model and measured by the eddy covariance technique for summer 2016. Blue points show the cases of flux measurements under well-developed turbulence and air temperatures below 28 °C. Red points show measured fluxes under friction velocity lower than 0.05 m s<sup>−1</sup> or in the case of air temperatures, above 28 °C. The red line is the linear regression for all cases (R<sup>2</sup> = 0.86, <span class="html-italic">p</span> &lt; 0.05). The green line is the linear regression for the blue points (R<sup>2</sup> = 0.95, <span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Modeled vertical profiles: CO<sub>2</sub> fluxes at the eddy covariance measurement site in the southern part of the peatland and average CO<sub>2</sub> fluxes over the entire peatland area under (<b>a</b>) southwest (25 June 2016 at 14:00) and (<b>b</b>) northwest wind directions (28 June 2016 at 13:30). Green points indicate the eddy covariance flux measurements at the height of 2 m above the ground.</p>
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<p>Top views and modeled cross-sections of vertical CO<sub>2</sub> fluxes along the two directions that cross the peatland and pass through the flux measurement site: (<b>A</b>) 25 June 2016 at 14:00, (<b>B</b>) 28 June 2016 at 13:30. The grey triangle indicates the location of the eddy covariance flux station. White lines in the top views indicate the locations of the cross-sections.</p>
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17 pages, 2798 KiB  
Article
The High-Elevation Peatlands of the Northern Andes, Colombia
by Juan C. Benavides, Dale H. Vitt and David J. Cooper
Plants 2023, 12(4), 955; https://doi.org/10.3390/plants12040955 - 20 Feb 2023
Cited by 7 | Viewed by 3208
Abstract
Andean peatlands are important carbon reservoirs for countries in the northern Andes and have a unique diversity. Peatland plant diversity is generally related to hydrology and water chemistry, and the response of the vegetation in tropical high-elevation peatlands to changes in elevation, climate, [...] Read more.
Andean peatlands are important carbon reservoirs for countries in the northern Andes and have a unique diversity. Peatland plant diversity is generally related to hydrology and water chemistry, and the response of the vegetation in tropical high-elevation peatlands to changes in elevation, climate, and disturbance is poorly understood. Here, we address the questions of what the main vegetation types of peat-forming vegetation in the northern Andes are, and how the different vegetation types are related to water chemistry and pH. We measured plant diversity in 121 peatlands. We identified a total of 264 species, including 124 bryophytes and 140 vascular plants. We differentiated five main vegetation types: cushion plants, Sphagnum, true mosses, sedges, and grasses. Cushion-dominated peatlands are restricted to elevations above 4000 m. Variation in peatland vegetation is mostly driven be elevation and water chemistry. Encroachment of sedges and Sphagnum sancto-josephense in disturbed sites was associated with a reduction in soil carbon. We conclude that peatland variation is driven first by elevation and climate followed by water chemistry and human disturbances. Sites with higher human disturbances had lower carbon content. Peat-forming vegetation in the northern Andes was unique to each site bringing challenges on how to better conserve them and the ecosystem services they offer. Full article
(This article belongs to the Collection Feature Papers in Plant Ecology)
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<p>Variation between the number of species at each site and elevation for vascular plants (<b>top</b>), bryophytes (<b>center</b>) and all plants (<b>bottom</b>) differentiated by four different peat-forming vegetation types from 121 peatlands in the northern Andes.</p>
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<p>(<b>Left</b>) Hierarchical cluster using Ward’s distances on 121 peatlands in the northern Andes indicating the selection of 5 different plant composition groups. (<b>Right</b>) NMDS ordination of 121 peatlands in the northern tropical Andes with matching color codes to the 5 cluster groups. Fitted environmental variables in figure had a significant correlation with NMDS ordination after 999 permutations (<span class="html-italic">p</span> &lt; 0.01).</p>
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<p>The main peat-forming vegetation types in the northern Andes. (<b>Top left</b>): cushion-plant-dominated with <span class="html-italic">Distichia muscoides</span> and <span class="html-italic">Campylopus</span>; (<b>top right</b>): a sedge-dominated site; (<b>bottom left</b>): site dominated by <span class="html-italic">Sphagnum sancto-josephense</span>; (<b>bottom right</b>): a peatland dominated by true mosses and <span class="html-italic">Calamagrostis effussa</span>.</p>
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<p>Box and whisker plots for chemistry of pore water variation between the five vegetation types identified in 121 peatlands in the northern Andes. Cation concentrations are presented in mg L<sup>−1</sup>. Water electrical conductivity was corrected for [H<sup>+</sup>] [<a href="#B37-plants-12-00955" class="html-bibr">37</a>]. Tukey’s homogeneous groups are marked with the same letter. The circles represent outlier values outside the 95% interval.</p>
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<p>Box and whisker plots for variation in peat carbon concentration, peat bulk density and carbon content for 121 peatlands in the northern Andes for the 0–10 cm and 10–20 cm depth intervals. Tukey’s homogeneous groups are marked with the same letter. The circles represent outlier values outside the 95% interval.</p>
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<p>Box and whisker plots for variation in peat water content and peat chemistry for 121 peatlands in the northern Andes. Top row: peat water content as a proportion of the wet peat weight at field capacity subtracting the dry weight. Tukey’s homogeneous groups are marked with the same letter. The circles represent outlier values outside the 95% interval.</p>
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<p>Relationship between carbon content on the 0–10 cm interval and a scaled disturbance observed at each of 121 peatlands in the northern Andes. Higher values of the disturbance index indicate higher evidence of human disturbance in the peatland.</p>
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<p>Map of study area in the northern Andes including a shaded digital elevation model (darker areas are higher). Sampled peatlands (<span class="html-italic">n</span> = 121) are marked with circles. A number of sites are relatively close to each other, and on this, the points overlap.</p>
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18 pages, 9200 KiB  
Article
Microbial Communities of Peaty Permafrost Tundra Soils along the Gradient of Environmental Conditions and Anthropogenic Disturbance in Pechora River Delta in the Eastern European Arctic
by Irina Kravchenko, Denis Grouzdev, Marina Sukhacheva, Tatyana Minayeva and Andrey Sirin
Diversity 2023, 15(2), 251; https://doi.org/10.3390/d15020251 - 10 Feb 2023
Cited by 2 | Viewed by 2038
Abstract
Microbial communities play crucial roles in the global carbon cycle, particularly in peatland and tundra ecosystems experiencing climate change. The latest IPCC assessments highlight the anthropogenic changes in the Arctic peatlands and their consequences due to global climate change. These disturbances could trigger [...] Read more.
Microbial communities play crucial roles in the global carbon cycle, particularly in peatland and tundra ecosystems experiencing climate change. The latest IPCC assessments highlight the anthropogenic changes in the Arctic peatlands and their consequences due to global climate change. These disturbances could trigger permafrost degradation and intensification of the biogeochemical processes resulting in greenhouse gas formation. In this study, we describe the variation in diversity and composition of soil microbial communities from shallow peat tundra sites with different anthropogenic loads and applied restoration interventions in the landscape of remnant fragments of terraces in the Pechora River delta, the Russian Arctic, Nenets Autonomous Okrug. The molecular approaches, including quantitative real-time PCR and high-throughput Illumina sequencing of 16S RNA and ITS, were applied to examine the bacterial and fungal communities in the soil samples. Anthropogenic disturbance leads to a significant decrease in the representation of Acidobacteria and Verrucomicrobia, while the proportion and diversity of Proteobacteria increase. Fungal communities in undisturbed sites may be characterized as monodominant, and anthropogenic impact increases the fungal diversity. Only the verrucomicrobial methanotrophs Methyloacifiphilaceae were found in the undisturbed sites, but proteobacterial methanotrophs Methylobacterium-Methylorubrum, as well as different methylotrophs affiliated with Methylophilaceae, and Beijerinckiaceae (Methylorosula), were detected in disturbed sites. Full article
(This article belongs to the Special Issue Peatland Ecosystems under Climate Change)
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<p>Location of study area in relation to the European mire regions marked with the red frame, the Pechora River delta, and a general view of the site from the quadcopter. The European mire regions are given according to [<a href="#B20-diversity-15-00251" class="html-bibr">20</a>], as simplified from [<a href="#B21-diversity-15-00251" class="html-bibr">21</a>]: I—Arctic seepage and polygon mire region, II—palsa mire region, III—northern fen region, IV—typical raised bog region.</p>
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<p>Characteristic types of micro-landscapes represented in the studied peatlands. The numbers indicate the monitoring plots.</p>
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<p>The number of bacterial sequences (<b>a</b>) and the taxonomic structure at the phylum level of the bacterial community of the studied soils in % of the total number of sequences in the sample (<b>b</b>) according to high-throughput sequencing of 16s rRNA. The median value of the three replicate samples per sampling site is presented. The taxa constituting &gt;1% in at least one library are listed.</p>
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<p>Taxonomic structure of the fungal community at the phylum level in the studied soils, % of the total number of sequences in the sample according to ITS data.</p>
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<p>Non-metric multidimensional scaling (NMDS) plot based on the Bray–Curtis similarity coefficients of experimental soil samples. Points closer to one another in ordination space are more similar than those apart.</p>
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<p>Comparison of the bacterial community structure at the phylum level across all studied sites. Abbreviations: blue boxplots—undisturbed sites, red boxplots—disturbed sites; outliers in a data set are indicated by the blue rhombus. See <a href="#diversity-15-00251-t006" class="html-table">Table 6</a> for details.</p>
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<p>Distribution patterns of methylotrophs retrieved from the peat samples.</p>
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37 pages, 9453 KiB  
Article
After Wildfires and Rewetting: Results of 15+ Years’ Monitoring of Vegetation and Environmental Factors in Cutover Peatland
by Anna Vozbrannaya, Vladimir Antipin and Andrey Sirin
Diversity 2023, 15(1), 3; https://doi.org/10.3390/d15010003 - 20 Dec 2022
Cited by 2 | Viewed by 2487
Abstract
On examples of n × 100 m2 permanent plots laid in 2005 on peatlands disturbed by quarrying and milling peat extraction in Meshchera National Park (central European Russia), changes in vegetation cover and environmental factors during self-revegetation, the impact of wildfire, and [...] Read more.
On examples of n × 100 m2 permanent plots laid in 2005 on peatlands disturbed by quarrying and milling peat extraction in Meshchera National Park (central European Russia), changes in vegetation cover and environmental factors during self-revegetation, the impact of wildfire, and rewetting are considered. Peat extraction pits are overgrown with floating mats, on which mire, predominantly mesotrophic, vegetation is formed. Cofferdams with retained original mire vegetation contribute to the formation of a spatially diverse mire landscape, but they can also be prone to natural fires. The environmental conditions at the abandoned milled peat extraction sites do not favour natural overgrowth. The driest areas can remain with bare peat perennially. Such peatlands are the most frequent targets of wildfires, which have a severely negative impact and interrupt revegetation processes. Alien plant species emerge and disappear over time. To prevent wildfires and create conditions favourable for the restoration of mire vegetation, rewetting is required. With an average ground water level (GWL) during the growing season of −5 to +15 cm, mire vegetation can actively re-establish. Communities with near-aquatic and aquatic plants can form on flooded areas with GWL of +30. This generally contributes to both fire prevention and wetland diversity. Full article
(This article belongs to the Special Issue Peatland Ecosystems under Climate Change)
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Figure 1

Figure 1
<p>The main impacts (milling peat extraction, fires and rewetting), the impact of which is studied on the example of peatlands in the National Park Meshchera, Vladimir region, the center of the European part of Russia. (<b>a</b>) Abandoned milled peat extraction. Ostrovsky peatland. Photo by V. Zheltukhin. (<b>b</b>) Grass wildfire. Tasinsky peatland. Photo by A. Vozbranaya. (<b>c</b>) Rewetting of abandoned milled peat extraction fields. Orlovsky peatland. Photo by V. L’vov.</p>
Full article ">Figure 2
<p>Location of Meshchera National Park in relation to the European mire regions, the administrative boundaries of the center of European Russia: its intact mires (light green), peatlands disturbed by peat extraction (dark green), being rewetted (blue), and the location of permanent monitoring plots, 1–10 - monitoring plot numbers. The European mire regions are given against [<a href="#B19-diversity-15-00003" class="html-bibr">19</a>] as simplified from [<a href="#B46-diversity-15-00003" class="html-bibr">46</a>]: II—palsa mire region, III—northern fen region, IV—typical raised bog region, V—Atlantic bog region, VI—continental fen and bog region, VII—nemoral-sub-meridional fen region.</p>
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<p>Characteristic types of plant communities represented in the studied peatlands. The numbers indicate the monitoring plots.</p>
Full article ">Figure 3 Cont.
<p>Characteristic types of plant communities represented in the studied peatlands. The numbers indicate the monitoring plots.</p>
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<p>Characteristic types of plant communities represented in the studied peatlands. The numbers indicate the monitoring plots.</p>
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<p>Water storage in snow on the sample areas (mm) for the observation period.</p>
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<p>Average GWLs for the growing season (May−August) over the observation period on the monitoring plots not affected by rewetting.</p>
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<p>Average GWLs during the growing season (May−August) for the observation period on the monitoring plots affected by rewetting.</p>
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<p>Pore-water pH values in wells during the growing season (May–August) for the observation period.</p>
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<p>Water electrical conductivity in wells for GWL measurements during the growing season (May−August) for the observation period.</p>
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<p>Changes in vegetation on permanent plot No. 7: 1 <span class="html-italic">Pinus sylvestris</span> – <span class="html-italic">Vaccinium uliginosum</span> + <span class="html-italic">Calamagrostis epigejos</span>; 2 <span class="html-italic">Comarum palustre</span> – <span class="html-italic">Sphagnum riparium</span>; 3 <span class="html-italic">P. sylvestris</span> – <span class="html-italic">V. vitis-idaea</span>; 4 <span class="html-italic">Calla palustris</span> – <span class="html-italic">Sphagnum riparium</span>; 5 <span class="html-italic">P. sylvestris</span> – <span class="html-italic">Ledum palustre</span>; • single trees of <span class="html-italic">Betula pubescens</span>. The colours in this and other drawings highlight the areas: light green — dominated by pine and other conifers; dark green — dominated by deciduous trees; red — bare peat with possible sparse vegetation; blue — open water, with possible aquatic vegetation; blue — hygrophilous vegetation, including sphagnum mosses; yellow — grass vegetation, with possible undergrowth of trees and shrubs.</p>
Full article ">Figure 10
<p>Changes in vegetation on permanent plot No. 6: 1 <span class="html-italic">Pinus sylvestris</span> – <span class="html-italic">Vaccinium uliginosum</span> + <span class="html-italic">Chamaedaphne calyculata</span> – <span class="html-italic">Sphagnum fallax</span>; 2 <span class="html-italic">Phragmites australis</span> – <span class="html-italic">S. fallax</span>; 3 <span class="html-italic">C. calyculata</span> + <span class="html-italic">Oxycoccus palustris</span> – <span class="html-italic">S fallax</span>; 4 <span class="html-italic">Carex rostrata</span> – <span class="html-italic">S. fallax</span>; 5 <span class="html-italic">C. calyculata</span> + <span class="html-italic">P. australis</span> – <span class="html-italic">S. fallax</span>; 6 <span class="html-italic">Calluna vulgaris</span> + <span class="html-italic">Ledum palustre</span> + <span class="html-italic">V. uliginosum</span> – <span class="html-italic">S. fallax</span>; 7 <span class="html-italic">V. uliginosum</span> + <span class="html-italic">L. palustre</span> – <span class="html-italic">S. fallax</span>; 8 <span class="html-italic">C. calyculata</span> + <span class="html-italic">C. rostrata</span> – <span class="html-italic">S fallax</span>.</p>
Full article ">Figure 11
<p>Changes in vegetation on permanent plot No. 5: 1 <span class="html-italic">Pinus sylvestris</span> – <span class="html-italic">Vaccinium uliginosum</span> + <span class="html-italic">Eriophorum vaginatum</span>; 2 <span class="html-italic">P. sylvestris</span> – <span class="html-italic">Andromeda polifolia</span> + <span class="html-italic">E. vaginatum</span> – <span class="html-italic">Sphagnum fallax</span>; 3 water; 4 <span class="html-italic">E. vaginatum</span> – <span class="html-italic">S. fallax</span>; 5 <span class="html-italic">Eriophorum angustifolium</span> + <span class="html-italic">Calla palustris</span> – <span class="html-italic">S. fallax</span>; 6 <span class="html-italic">Betula pubescens</span> – <span class="html-italic">A. polifolia</span> + <span class="html-italic">E. vaginatum</span> – <span class="html-italic">S. fallax</span>; 7 <span class="html-italic">Oxycoccus palustris</span> – <span class="html-italic">E. vaginatum</span> – <span class="html-italic">S. fallax</span>; 8 <span class="html-italic">E. vaginatum</span> + <span class="html-italic">E. angustifolium</span> – <span class="html-italic">S. fallax</span>; 9 <span class="html-italic">A. polifolia</span> + <span class="html-italic">E. vaginatum</span>; 10 <span class="html-italic">B. pubescens</span> – <span class="html-italic">V. uliginosum</span>; 11 <span class="html-italic">A. polifolia</span> + <span class="html-italic">E. vaginatum</span>; 12 <span class="html-italic">C. palustris</span> – <span class="html-italic">S. fallax</span>; 13 <span class="html-italic">Carex rostrata</span> – <span class="html-italic">S. fallax</span>; 14 <span class="html-italic">Sphagnum cuspidatum</span>; 15 <span class="html-italic">P. sylvestris</span> + <span class="html-italic">B. pubescens</span> – <span class="html-italic">V. uliginosum</span> – <span class="html-italic">S. fallax</span>; 16 <span class="html-italic">O. palustris</span> + <span class="html-italic">A. polifolia</span> + <span class="html-italic">E. vaginatum</span> – <span class="html-italic">S. fallax</span>.</p>
Full article ">Figure 12
<p>Changes in vegetation on permanent plot No. 2: 1 bare peat; 2 <span class="html-italic">Betula pubescens</span> – <span class="html-italic">Chamaenerion angustifolium</span> – <span class="html-italic">Ceratodon purpureus</span>; 3 <span class="html-italic">C. angustifolium</span>; 4 <span class="html-italic">Calamagrostis epigejos</span>; 5 <span class="html-italic">C. angustifolium</span> + <span class="html-italic">C. epigejos</span> – <span class="html-italic">C. purpureus</span>; 6 curtains of <span class="html-italic">Polytrichum juniperinum</span>; 7 <span class="html-italic">C. epigejos</span> + <span class="html-italic">C. angustifolium</span>; 8 <span class="html-italic">B. pubescens</span> + <span class="html-italic">Populus tremula</span>.</p>
Full article ">Figure 13
<p>Changes in vegetation on permanent plot No. 10: 1 <span class="html-italic">Carex rostrata</span> + <span class="html-italic">Juncus filiformis</span>; 2 <span class="html-italic">Betula pubescens</span> – <span class="html-italic">Agrostis gigantea</span> + <span class="html-italic">Rumex acetosella</span>; 3 <span class="html-italic">B. pubescens</span> - <span class="html-italic">Salix cinerea</span> – <span class="html-italic">Calamagrostis epigejos</span> – <span class="html-italic">Polytrichum juniperinum</span>; 4 <span class="html-italic">S. cinerea</span> – <span class="html-italic">Juncus conglomeratus</span>; 5 <span class="html-italic">P. juniperinum</span>; 6 <span class="html-italic">Utricularia vulgaris</span>; 7. <span class="html-italic">B. pubescens</span> – <span class="html-italic">C. epigejos</span> + <span class="html-italic">A. gigantea</span> – <span class="html-italic">Polytrichum commune</span>; 8 <span class="html-italic">B. pubescens</span> + <span class="html-italic">Populus tremula</span> – <span class="html-italic">J. conglomeratus</span>; 9 <span class="html-italic">C. rostrata</span> + <span class="html-italic">Comarum palustre</span>; 10 <span class="html-italic">B. pubescens</span> – <span class="html-italic">C. epigejos</span> + <span class="html-italic">Potentilla erecta</span> – <span class="html-italic">P. commune</span>; 11 <span class="html-italic">B. pubescens</span> – <span class="html-italic">C. epigejos</span> + <span class="html-italic">P. erecta</span> – <span class="html-italic">P. commune</span>; 12 <span class="html-italic">B. pubescens</span> + <span class="html-italic">P. tremula</span> – <span class="html-italic">S. cinerea</span> – <span class="html-italic">C. epigejos</span> – <span class="html-italic">P. commune</span>; 13 <span class="html-italic">Nardus stricta</span> + <span class="html-italic">P. erecta</span>; 14 <span class="html-italic">Comarum palustre</span> + <span class="html-italic">Carex rostrata</span>; 15 <span class="html-italic">C. epigejos</span> + <span class="html-italic">C. palustre</span>; 16 <span class="html-italic">B. pubescens</span> + <span class="html-italic">P. tremula</span> + <span class="html-italic">S. cinerea</span>; 17 bushes of <span class="html-italic">S. cinerea</span>.</p>
Full article ">Figure 14
<p>Changes in vegetation on permanent plot No. 8: 1 <span class="html-italic">Betula pubescens</span> – <span class="html-italic">Salix cinerea</span>; 2 <span class="html-italic">Calamagrostis epigejos</span> + <span class="html-italic">Molinia caerulea</span>; 3 <span class="html-italic">Salix aurita</span> – <span class="html-italic">C. epigejos</span> + <span class="html-italic">Epilobium adenocaulon</span> – <span class="html-italic">Marchantia polymorpha</span>; 4 <span class="html-italic">Typha latifolia</span>; 4 quagmire; 5 <span class="html-italic">B. pubescens</span> – <span class="html-italic">S. cinerea</span> – <span class="html-italic">C. epigejos</span>; 6 <span class="html-italic">T. latifolia</span>; 7 <span class="html-italic">S. cinerea</span> – <span class="html-italic">C. epigejos</span>; 8 <span class="html-italic">Carex pseudocyperus</span> + <span class="html-italic">T. latifolia</span>; 9 <span class="html-italic">C. epigejos</span>; 10 <span class="html-italic">T. latifolia</span> + <span class="html-italic">C. pseudocyperus</span>; 11 <span class="html-italic">S. cinerea</span> – <span class="html-italic">Phragmites australis</span> + <span class="html-italic">C. pseudocyperus</span>; 12 <span class="html-italic">B. pubescens</span> – <span class="html-italic">C. epigejos</span>; 13 <span class="html-italic">B. pubescens</span> – <span class="html-italic">S. cinerea</span>; 14 <span class="html-italic">B. pubescens</span> – <span class="html-italic">Calamagrostis canescens</span>; 15 <span class="html-italic">S. cinerea</span> – <span class="html-italic">P. australis</span>; 16 <span class="html-italic">B. pubescens</span> – <span class="html-italic">S. cinerea</span>; 17, 18, 19 <span class="html-italic">B. pubescens</span> – <span class="html-italic">S. cinerea</span> – <span class="html-italic">C. epigejos</span>; 20 lake with single <span class="html-italic">S. cinerea</span>; 21 bushes of <span class="html-italic">S. cinerea</span>.</p>
Full article ">Figure 14 Cont.
<p>Changes in vegetation on permanent plot No. 8: 1 <span class="html-italic">Betula pubescens</span> – <span class="html-italic">Salix cinerea</span>; 2 <span class="html-italic">Calamagrostis epigejos</span> + <span class="html-italic">Molinia caerulea</span>; 3 <span class="html-italic">Salix aurita</span> – <span class="html-italic">C. epigejos</span> + <span class="html-italic">Epilobium adenocaulon</span> – <span class="html-italic">Marchantia polymorpha</span>; 4 <span class="html-italic">Typha latifolia</span>; 4 quagmire; 5 <span class="html-italic">B. pubescens</span> – <span class="html-italic">S. cinerea</span> – <span class="html-italic">C. epigejos</span>; 6 <span class="html-italic">T. latifolia</span>; 7 <span class="html-italic">S. cinerea</span> – <span class="html-italic">C. epigejos</span>; 8 <span class="html-italic">Carex pseudocyperus</span> + <span class="html-italic">T. latifolia</span>; 9 <span class="html-italic">C. epigejos</span>; 10 <span class="html-italic">T. latifolia</span> + <span class="html-italic">C. pseudocyperus</span>; 11 <span class="html-italic">S. cinerea</span> – <span class="html-italic">Phragmites australis</span> + <span class="html-italic">C. pseudocyperus</span>; 12 <span class="html-italic">B. pubescens</span> – <span class="html-italic">C. epigejos</span>; 13 <span class="html-italic">B. pubescens</span> – <span class="html-italic">S. cinerea</span>; 14 <span class="html-italic">B. pubescens</span> – <span class="html-italic">Calamagrostis canescens</span>; 15 <span class="html-italic">S. cinerea</span> – <span class="html-italic">P. australis</span>; 16 <span class="html-italic">B. pubescens</span> – <span class="html-italic">S. cinerea</span>; 17, 18, 19 <span class="html-italic">B. pubescens</span> – <span class="html-italic">S. cinerea</span> – <span class="html-italic">C. epigejos</span>; 20 lake with single <span class="html-italic">S. cinerea</span>; 21 bushes of <span class="html-italic">S. cinerea</span>.</p>
Full article ">Figure 15
<p>Changes in vegetation on permanent plot No. 11: 1 single <span class="html-italic">Vaccinium uliginosum</span>, <span class="html-italic">Andromeda polifolia</span>; 2 <span class="html-italic">Betula pubescens</span> – <span class="html-italic">Eriophorum vaginatum</span> – <span class="html-italic">Polytrichum juniperinum</span>; 3 <span class="html-italic">E. vaginatum</span>; 4 <span class="html-italic">Oxycoccus palustris</span> + <span class="html-italic">E. vaginatum</span>; 5 <span class="html-italic">P. juniperinum</span>; 6 single <span class="html-italic">B. pubescens</span>; 7 water; 8 bare peat; 9 <span class="html-italic">V. uliginosum</span> + <span class="html-italic">Molinia caerulea</span> + <span class="html-italic">E. vaginatum</span> – <span class="html-italic">P. juniperinum</span>; 10 <span class="html-italic">B. pubescens</span> – <span class="html-italic">V. uliginosum</span> – <span class="html-italic">P. juniperinum</span>; 11 Populations of <span class="html-italic">E. vaginatum</span> and <span class="html-italic">Sphagnum cuspidatum</span>; 12 <span class="html-italic">V. uliginosum</span> + <span class="html-italic">E. vaginatum</span>; •—<span class="html-italic">P. juniperinum</span>.</p>
Full article ">Figure 16
<p>Changes in vegetation on permanent plot No. 1: 1 <span class="html-italic">Calla palustris</span> + <span class="html-italic">Typha latifolia</span>; 2 B<span class="html-italic">etula pubescens</span> – <span class="html-italic">Eriophorum vaginatum</span>; 3 <span class="html-italic">E. vaginatum</span>; 4 <span class="html-italic">B. pubescens</span> – <span class="html-italic">Polytrichum juniperinum</span>; 5 bare peat; 6 <span class="html-italic">Phragmites australis</span> – <span class="html-italic">P. juniperinum</span>; 7 <span class="html-italic">B. pubescens</span> – <span class="html-italic">Calamagrostis epigejos</span>; 8 <span class="html-italic">P. australis</span> + <span class="html-italic">Lemna minor</span>; 9 <span class="html-italic">B. pubescens</span> – <span class="html-italic">E. vaginatum</span> – <span class="html-italic">P. juniperinum</span>; 10 <span class="html-italic">B. pubescens</span> – <span class="html-italic">P. australis</span> – <span class="html-italic">P. juniperinum</span>; 11 <span class="html-italic">B. pubescens</span> – <span class="html-italic">E. vaginatum</span>; 12, 13 <span class="html-italic">E. vaginatum</span> – <span class="html-italic">P. juniperinum</span>; 14 <span class="html-italic">B. pubescens</span> – <span class="html-italic">E. vaginatum</span> – <span class="html-italic">P. juniperinum</span>; 15 <span class="html-italic">E. vaginatum</span> – <span class="html-italic">P. juniperinum</span>; 16 <span class="html-italic">C. palustris</span> + <span class="html-italic">P. australis</span>; 17 <span class="html-italic">Salix cinerea</span> – <span class="html-italic">E. vaginatum</span>; 18 <span class="html-italic">B. pubescens</span> – <span class="html-italic">E. vaginatum</span>; 19 <span class="html-italic">E. vaginatum</span> + <span class="html-italic">P. australis</span>; 20 <span class="html-italic">S. cinerea</span> – <span class="html-italic">C. palustris</span> + <span class="html-italic">T. latifolia</span>; 21 <span class="html-italic">Hydrocharis morsus-ranae</span> + <span class="html-italic">Utricularia minor</span>; 22 <span class="html-italic">P. australis</span> + <span class="html-italic">H. morsus-ranae</span>; 23 <span class="html-italic">Agrostis stolonifera</span> + <span class="html-italic">Scirpus radicans</span>; 24 <span class="html-italic">P. australis</span> + <span class="html-italic">Carex canescens</span>; 25 <span class="html-italic">S. cinerea</span> – <span class="html-italic">P. australis</span> + <span class="html-italic">C. palustris</span>; 26 <span class="html-italic">E. vaginatum</span> – <span class="html-italic">Leptodictyum riparium</span>; 27 bushes of <span class="html-italic">S. cinerea</span>; 28 water; 29 <span class="html-italic">P. australis</span> + <span class="html-italic">C. canescens</span> – <span class="html-italic">Sphagnum squarrosum</span>; 30 <span class="html-italic">P. australis</span> – <span class="html-italic">Sphagnum riparium</span>; 31 bushes of <span class="html-italic">P. australis</span>; 32 single <span class="html-italic">Carex rostrata</span>; 33 bushes of <span class="html-italic">P. australis</span>.</p>
Full article ">Figure 17
<p>Changes in vegetation on permanent plot No. 3: 1 <span class="html-italic">Salix cinerea</span> – <span class="html-italic">Carex canescens</span> – <span class="html-italic">Polytrichum juniperinum</span>; 2 <span class="html-italic">Typha latifolia</span>; 3 <span class="html-italic">T. latifolia</span> + <span class="html-italic">Epilobium adenocaulon</span>; 4 <span class="html-italic">C. canescens</span> + <span class="html-italic">Eriophorum angustifolium</span> – <span class="html-italic">Warnstorfia fluitans</span>; 5 bushes of <span class="html-italic">S. cinerea</span>; 6 <span class="html-italic">Scirpus radicans</span>; 7 <span class="html-italic">S. cinerea</span> – <span class="html-italic">T. latifolia</span>; 8 <span class="html-italic">S. cinerea</span> – <span class="html-italic">Bidens cernua</span>; 9 water; 10 bare peat; 11 <span class="html-italic">Carex rostrata</span> + <span class="html-italic">Hydrocharis morsus-ranae</span>; 12 <span class="html-italic">S. cinerea</span> – <span class="html-italic">Phragmites australis</span>; 13 <span class="html-italic">C. rostrata</span> + <span class="html-italic">Eleocharis mammilata</span> – <span class="html-italic">Leptodictyum riparium</span>; 14 <span class="html-italic">S. cinerea</span> – <span class="html-italic">Carex pseudocyperus</span>; 15 <span class="html-italic">P. australis</span> + <span class="html-italic">C. pseudocyperus</span>.</p>
Full article ">Figure 17 Cont.
<p>Changes in vegetation on permanent plot No. 3: 1 <span class="html-italic">Salix cinerea</span> – <span class="html-italic">Carex canescens</span> – <span class="html-italic">Polytrichum juniperinum</span>; 2 <span class="html-italic">Typha latifolia</span>; 3 <span class="html-italic">T. latifolia</span> + <span class="html-italic">Epilobium adenocaulon</span>; 4 <span class="html-italic">C. canescens</span> + <span class="html-italic">Eriophorum angustifolium</span> – <span class="html-italic">Warnstorfia fluitans</span>; 5 bushes of <span class="html-italic">S. cinerea</span>; 6 <span class="html-italic">Scirpus radicans</span>; 7 <span class="html-italic">S. cinerea</span> – <span class="html-italic">T. latifolia</span>; 8 <span class="html-italic">S. cinerea</span> – <span class="html-italic">Bidens cernua</span>; 9 water; 10 bare peat; 11 <span class="html-italic">Carex rostrata</span> + <span class="html-italic">Hydrocharis morsus-ranae</span>; 12 <span class="html-italic">S. cinerea</span> – <span class="html-italic">Phragmites australis</span>; 13 <span class="html-italic">C. rostrata</span> + <span class="html-italic">Eleocharis mammilata</span> – <span class="html-italic">Leptodictyum riparium</span>; 14 <span class="html-italic">S. cinerea</span> – <span class="html-italic">Carex pseudocyperus</span>; 15 <span class="html-italic">P. australis</span> + <span class="html-italic">C. pseudocyperus</span>.</p>
Full article ">Figure 18
<p>Changes in vegetation on permanent plot No. 4: 1 bare peat; 2 <span class="html-italic">Eriophorum vaginatum</span>; 3 <span class="html-italic">Eriophorum angustifolium</span> – <span class="html-italic">Polytrichum juniperinum</span>; 4 <span class="html-italic">Betula pubescens</span> – <span class="html-italic">E. vaginatum</span>; 5 single curtains <span class="html-italic">Sphagnum cuspidatum</span> + <span class="html-italic">Sphagnum fallax</span>; 6 <span class="html-italic">Carex rostrata</span> - <span class="html-italic">Warnstorfia fluitans</span>; 7 <span class="html-italic">E. angustifolium</span> – <span class="html-italic">S. cuspidatum</span>; 8 <span class="html-italic">E. vaginatum</span> – <span class="html-italic">S. cuspidatum</span>; 9 <span class="html-italic">C. rostrata</span> – <span class="html-italic">Sphagnum riparium</span>; 10 open water with <span class="html-italic">C. rostrata</span>; 11 <span class="html-italic">C. rostrata</span> + <span class="html-italic">Calamagrostis canescens</span> – <span class="html-italic">S. riparium</span>; 12 <span class="html-italic">E. vaginatum</span>; 13 <span class="html-italic">E. angustifolium</span> + <span class="html-italic">C. canescens</span> – <span class="html-italic">S. riparium</span>; 14 water; 15 <span class="html-italic">C. rostrata</span>; 16 <span class="html-italic">E. vaginatum</span> + <span class="html-italic">E. angustifolium</span>; 17 <span class="html-italic">C. rostrata</span> – <span class="html-italic">S. cuspidatum</span>; 18 <span class="html-italic">C. rostrata</span> + <span class="html-italic">E. vaginatum</span>; 19 <span class="html-italic">E. angustifolium</span> – <span class="html-italic">S. riparium</span>; 20 <span class="html-italic">C. rostrata</span> + <span class="html-italic">P. australis</span> – <span class="html-italic">S. riparium</span>; 21 <span class="html-italic">C. rostrata</span> – <span class="html-italic">S. fallax</span> + <span class="html-italic">S. riparium</span>; 22 <span class="html-italic">E. angustifolium</span> – <span class="html-italic">S. fallax</span> + <span class="html-italic">S. riparium</span>; 23 <span class="html-italic">C. rostrata</span> – <span class="html-italic">S. fallax</span>; 24 <span class="html-italic">C. rostrata</span> + <span class="html-italic">E. angustifolium</span> – <span class="html-italic">S. fallax</span>.</p>
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<p>Changes in vegetation on permanent plot No. 9: 1 <span class="html-italic">Calamagrostis epigejos</span> + <span class="html-italic">Persicaria maculosa</span>; 2 <span class="html-italic">Scirpus sylvaticus</span> + <span class="html-italic">Juncus conglomeratus</span>; 3 <span class="html-italic">Carex pseudocyperus</span> + <span class="html-italic">S. sylvaticus</span>; 4 <span class="html-italic">Salix cinerea</span> + <span class="html-italic">Salix myrsinifolia</span> – <span class="html-italic">C. epigejos</span> + <span class="html-italic">Lycopus europaeus</span>; 5 bare peat; 6 quagmire; 7 <span class="html-italic">Betula pubescens</span> – <span class="html-italic">J. conglomeratus</span> + <span class="html-italic">C. epigejos</span>; 8 <span class="html-italic">Alisma plantago-aquatica</span> + <span class="html-italic">S. sylvaticus</span>; 9 <span class="html-italic">C. epigejos</span> + <span class="html-italic">J. conglomeratus</span>; 10 <span class="html-italic">S. sylvaticus</span>; 11 <span class="html-italic">B. pubescens</span> – <span class="html-italic">C. epigejos</span> + <span class="html-italic">Agrostis canina</span>; 12 <span class="html-italic">B. pubescens</span> – <span class="html-italic">C. epigejos</span> + <span class="html-italic">J. conglomeratus</span>; 13 <span class="html-italic">A. plantago-aquatica</span> + <span class="html-italic">S. sylvaticus</span> – <span class="html-italic">Sphagnum squarrosum</span>; 14 <span class="html-italic">C. epigejos</span> + <span class="html-italic">A. canina</span>; 15 <span class="html-italic">Typha latifolia</span>; 16 <span class="html-italic">S. sylvaticus</span> – <span class="html-italic">Bidens tripartite</span>; 17 <span class="html-italic">B. pubescens</span> + <span class="html-italic">S. cinerea</span>; 18 water; 19 <span class="html-italic">S. cinerea</span> - <span class="html-italic">Phragmites. australis</span>; 20 <span class="html-italic">Carex rostrata</span> + <span class="html-italic">S. sylvaticus</span>; 21 <span class="html-italic">B. pubescens</span> – <span class="html-italic">Calamagrostis canescens</span> + <span class="html-italic">Carex canescens</span>; 22 <span class="html-italic">P. australis</span> + <span class="html-italic">A. plantago-aquatica</span>; 23 <span class="html-italic">P. australis</span> + <span class="html-italic">Hydrocharis morsus-ranae</span>; 24. <span class="html-italic">Thickets B. pubescens</span>; 25 <span class="html-italic">B. pubescens</span> – <span class="html-italic">Polytrichum juniperinum</span>; 26. <span class="html-italic">A. plantago</span> – <span class="html-italic">aquatica</span>; 27 <span class="html-italic">P. australis</span>; 28 <span class="html-italic">B. pubescens</span> – <span class="html-italic">S. cinerea</span>; 29 single <span class="html-italic">C. pseudocyperus</span>.</p>
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<p>Combined scheme of vegetation change at monitoring plots after peat extraction, fires and rewetting. No. 1: probably flooded in middle 1990’s; by 2003 communities of hygrophilous species with cotton-grass had formed; 2007, runaway grass wildfire; September 2007, flooded; 2012–2020, initial stages of sphagnum communities formation, by 2021 their proportion increased to 13.6%. No. 2: after mining had finished in the middle 1990s, probably, it burned; after the fire—post-pyrogenic communities; 2002—burned out completely; the next year, post-pyrogenic communities, alien species appeared; 2007—burned again; birch–willow–herb and birch–wood–reed communities are forming, appearance of alien species; by 2021 young birch–aspen forest has formed. No. 3: in 2000s probably waterlogged; hygrophilous poplar communities are forming; 2007—runaway grass wildfire; fall 2007—waterlogged, pool with single aquatic plants appeared; lacustrine and riparian vegetation. No. 4: Cutter peat mining until 1990s, then probably flooded; hygrophilous-quagmire communities formed; 2002—grass wildfire; in 2007 rewetting; pool appeared; by 2021 mire communities formed, proportion of sphagnum mosses about 90%. No. 5: 1950–2006—emergence of woody-shrubby vegetation on berms; overgrowth of pools in pits; 2007, berm vegetation burned out; 2008–2010—restoration of vegetation on berms; formation of floating mats on the edges of the pool; 2017—formation of forest and mire vegetation, the pool decreased by 2.6 times at the expense of overgrowth. No. 6: after the end of mining, the banks are overgrown; 1995—presumably wildfire, vegetation of the banks burned out; 2001–2003—formation of reed–sphagnum sedge–sphagnum communities; mire communities formed by 2021. No. 7: formation of woody–shrubby vegetation on berms since 1950s; pits overgrown; pine–shrubby communities formed on ridges by 2003; sphagnum–sphagnum communities formed in pits. No. 8: peat extraction was finished in 1978; from the end of 70s birch overgrowth; fire in 2002; birch–willow and willow–wood–reed communities appeared on the burnt area; by 2008 mixed grass birch forest was formed. No. 9: after completion of peat extraction in 1990s, reclamation with planting of pines was carried out; plantings died because of excessive moistening; by 2003 hygrophilous-quagmire vegetation is formed; 2007—rewetting, increase of pool and flooding of coastal communities; by 2012 lacustrine and coastal-water vegetation dominates. No. 10: production site, used for parking of machinery; 1990–2000 formation of herbaceous plant communities; 2002—runaway grass wildfire; willow–birch and birch–grass communities formed; spring 2009—runaway wildfire, woody vegetation affected; by 2021 forest vegetation. No. 11: peat extraction stopped in 1992; 2002—wildfire; 2003—single mire plants; 2007—wildfire; 2016—birch–birch–moss communities formed.</p>
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27 pages, 7468 KiB  
Article
Quantifying Lidar Elevation Accuracy: Parameterization and Wavelength Selection for Optimal Ground Classifications Based on Time since Fire/Disturbance
by Kailyn Nelson, Laura Chasmer and Chris Hopkinson
Remote Sens. 2022, 14(20), 5080; https://doi.org/10.3390/rs14205080 - 11 Oct 2022
Cited by 5 | Viewed by 2769
Abstract
Pre- and post-fire airborne lidar data provide an opportunity to determine peat combustion/loss across broad spatial extents. However, lidar measurements of ground surface elevation are prone to uncertainties. Errors may be introduced in several ways, particularly associated with the timing of data collection [...] Read more.
Pre- and post-fire airborne lidar data provide an opportunity to determine peat combustion/loss across broad spatial extents. However, lidar measurements of ground surface elevation are prone to uncertainties. Errors may be introduced in several ways, particularly associated with the timing of data collection and the classification of ground points. Ground elevation data must be accurate and precise when estimating relatively small elevation changes due to combustion and subsequent carbon losses. This study identifies the impact of post-fire vegetation regeneration on ground classification parameterizations for optimal accuracy using TerraScan and LAStools with airborne lidar data collected in three wavelengths: 532 nm, 1064 nm, and 1550 nm in low relief boreal peatland environments. While the focus of the study is on elevation accuracy and losses from fire, the research is also highly pertinent to hydrological modelling, forestry, geomorphological change, etc. The study area includes burned and unburned boreal peatlands south of Fort McMurray, Alberta. Lidar and field validation data were collected in July 2018, following the 2016 Horse River Wildfire. An iterative ground classification analysis was conducted whereby validation points were compared with lidar ground-classified data in five environments: road, unburned, burned with shorter vegetative regeneration (SR), burned with taller vegetative regeneration (TR), and cumulative burned (both SR and TR areas) in each of the three laser emission wavelengths individually, as well as combinations of 1550 nm and 1064 nm and 1550 nm, 1064 nm, and 532 nm. We find an optimal average elevational offset of ~0.00 m in SR areas with a range (RMSE) of ~0.09 m using 532 nm data. Average accuracy remains the same in cumulative burned and TR areas, but RMSE increased to ~0.13 m and ~0.16 m, respectively, using 1550 nm and 1064 nm combined data. Finally, data averages ~0.01 m above the field-measured ground surface in unburned boreal peatland and transition areas (RMSE of ~0.19 m) using all wavelengths combined. We conclude that the ‘best’ offset for depth of burn within boreal peatlands is expected to be ~0.01 m, with single point measurement uncertainties upwards of ~0.25 m (RMSE) in areas of tall, dense vegetation regeneration. The importance of classification parameterization identified in this study also highlights the need for more intelligent adaptative classification routines, which can be used in other environments. Full article
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Graphical abstract

Graphical abstract
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<p>Map illustrating the extent of the Horse River Wildfire within the Boreal Plains Ecozone (inset), which extends across Canada from northern British Columbia (BC) and into Alberta (AB), Saskatchewan (SK), and Manitoba (MB) and the study area, including lidar survey polygon and field validation transects/plots.</p>
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<p>Four vegetation categories used to represent time since fire with field photos and lidar point clouds.</p>
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<p>(<b>a</b>) Illustration of lidar laser beam angles, beam divergence, and impact on footprint diameter (Ø) in peatlands with variable microtopography (hollows and hummocks); (<b>b</b>) Samples of validation transects and lidar data demonstrating spatial distribution of validation points throughout the three channels. Note: microtopography in (<b>a</b>) has been exaggerated for demonstration purposes.</p>
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<p>Ground classification results (|<math display="inline"><semantics> <mrow> <mover accent="true"> <mrow> <mi>d</mi> <mi>z</mi> </mrow> <mo stretchy="true">¯</mo> </mover> </mrow> </semantics></math>| and RMSE) along a flat road surface for baseline comparisons using parameterization methods in <a href="#remotesensing-14-05080-t001" class="html-table">Table 1</a>. Classifications were conducted in TerraScan.</p>
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<p>Ground classification results (|<math display="inline"><semantics> <mrow> <mover accent="true"> <mrow> <mi>d</mi> <mi>z</mi> </mrow> <mo stretchy="true">¯</mo> </mover> </mrow> </semantics></math>| and RMSE) along a flat road surface for baseline comparisons using parameterization methods in <a href="#remotesensing-14-05080-t002" class="html-table">Table 2</a>. Classifications were conducted in LAStools.</p>
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<p>Ground classification results (|<math display="inline"><semantics> <mrow> <mover accent="true"> <mrow> <mi>d</mi> <mi>z</mi> </mrow> <mo stretchy="true">¯</mo> </mover> </mrow> </semantics></math>| and RMSE) in burned peatlands with low vegetation regeneration two years post-fire (as a proxy for immediately post-fire). Classifications were conducted in TerraScan.</p>
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<p>Ground classification results (|<math display="inline"><semantics> <mrow> <mover accent="true"> <mrow> <mi>d</mi> <mi>z</mi> </mrow> <mo stretchy="true">¯</mo> </mover> </mrow> </semantics></math>| and RMSE) in burned peatlands with low vegetation regeneration two years post-fire (as a proxy for immediately post-fire). Classifications were conducted in LAStools.</p>
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<p>Ground classification results (|<math display="inline"><semantics> <mrow> <mover accent="true"> <mrow> <mi>d</mi> <mi>z</mi> </mrow> <mo stretchy="true">¯</mo> </mover> </mrow> </semantics></math>| and RMSE) in burned peatlands unsegregated based on vegetation regeneration two years post-fire (true representation of two years post-fire). Classifications were conducted in TerraScan.</p>
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<p>Ground classification results (|<math display="inline"><semantics> <mrow> <mover accent="true"> <mrow> <mi>d</mi> <mi>z</mi> </mrow> <mo stretchy="true">¯</mo> </mover> </mrow> </semantics></math>| and RMSE) in burned peatlands unsegregated based on vegetation regeneration two years post-fire (true representation of two years post-fire). Classifications were conducted in LASTools.</p>
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<p>Ground classification results (|<math display="inline"><semantics> <mrow> <mover accent="true"> <mrow> <mi>d</mi> <mi>z</mi> </mrow> <mo stretchy="true">¯</mo> </mover> </mrow> </semantics></math>| and RMSE) in burned peatlands with tall vegetation regeneration two years post-fire (as a proxy for 3+ years post-fire). Classifications were conducted in TerraScan.</p>
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<p>Ground classification results (|<math display="inline"><semantics> <mrow> <mover accent="true"> <mrow> <mi>d</mi> <mi>z</mi> </mrow> <mo stretchy="true">¯</mo> </mover> </mrow> </semantics></math>| and RMSE) in burned peatlands with tall vegetation regeneration two years post-fire (as a proxy for 3+ years post-fire). Classifications were conducted in LAStools.</p>
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<p>Ground classification results (|<math display="inline"><semantics> <mrow> <mover accent="true"> <mrow> <mi>d</mi> <mi>z</mi> </mrow> <mo stretchy="true">¯</mo> </mover> </mrow> </semantics></math>| and RMSE) in unburned peatlands. Classifications were conducted in TerraScan.</p>
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<p>Ground classification results (|<math display="inline"><semantics> <mrow> <mover accent="true"> <mrow> <mi>d</mi> <mi>z</mi> </mrow> <mo stretchy="true">¯</mo> </mover> </mrow> </semantics></math>| and RMSE) in unburned peatlands. Classifications were conducted in LAStools.</p>
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<p>(<b>a</b>) Expected ground elevation accuracies of lidar data in the years following wildland fire in boreal peatlands; (<b>b</b>) Expected depth of burn (DOB) accuracies of lidar data in the years following wildland fire in boreal peatlands, assuming pre-fire lidar data were collected in “unburned conditions”, where <span class="html-italic">Q</span> = average over- or under-estimation of surface elevation change, and Ea is cumulative error (SD). Note: for all measurements used, SD was equal to RMSE.</p>
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<p>Ground classification results by wavelength along/in: (<b>a</b>) roads, (<b>b</b>) burned peatlands with short regeneration, (<b>c</b>) burned peatlands with tall regeneration, (<b>d</b>) all burned peatlands, and (<b>e</b>) unburned peatlands two years post-fire. Results were identified using TerraScan as determined by lidar channel. Each point represents an iterative parameter set. Note: axis range varies by plot.</p>
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<p>Ground classification results by wavelength along/in: (<b>a</b>) roads, (<b>b</b>) burned peatlands with short regeneration, (<b>c</b>) burned peatlands with tall regeneration, (<b>d</b>) all burned peatlands, and (<b>e</b>) unburned peatlands two years post-fire. Results were identified using LAStools as determined by lidar channel. Each point represents an iterative parameter set. Note: axis range varies by plot.</p>
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19 pages, 2079 KiB  
Article
Variation in the Mercury Concentrations and Greenhouse Gas Emissions of Pristine and Managed Hemiboreal Peatlands
by Arta Bārdule, Linda Gerra-Inohosa, Ivars Kļaviņš, Zane Kļaviņa, Krišs Bitenieks, Aldis Butlers, Andis Lazdiņš and Zane Lībiete
Land 2022, 11(9), 1414; https://doi.org/10.3390/land11091414 - 28 Aug 2022
Cited by 6 | Viewed by 2272
Abstract
We assessed total mercury (THg) concentrations and greenhouse gas (GHG) emissions in pristine and managed hemiboreal peatlands in Latvia, aiming to identify environmental factors that potentially affect their variation. The THg concentrations in soil ranged from <1 µg kg−1 to 194.4 µg [...] Read more.
We assessed total mercury (THg) concentrations and greenhouse gas (GHG) emissions in pristine and managed hemiboreal peatlands in Latvia, aiming to identify environmental factors that potentially affect their variation. The THg concentrations in soil ranged from <1 µg kg−1 to 194.4 µg kg−1. No significant differences between THg concentrations in disturbed and undisturbed peatlands were found, however, the upper soil layer in the disturbed sites had significantly higher THg concentration. During May–August, the mean CO2 emissions (autotrophic and heterotrophic respiration) from the soil ranged from 20.1 ± 5.0 to 104.6 ± 22.7 mg CO2-C m−2 h−1, N2O emissions ranged from −0.97 to 13.4 ± 11.6 µg N2O-N m−2 h−1, but the highest spatial variation was found for mean CH4 emissions—ranging from 30.8 ± 0.7 to 3448.9 ± 1087.8 µg CH4-C m−2 h−1. No significant differences in CO2 and N2O emissions between disturbed and undisturbed peatlands were observed, but CH4 emissions from undisturbed peatlands were significantly higher. Complex impacts of environmental factors on the variation of THg concentrations and GHG emissions were identified, important for peatland management to minimize the adverse effects of changes in the biogeochemical cycle of the biophilic elements of soil organic matter and contaminants, such as Hg. Full article
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Figure 1
<p>Location of the research sites in Latvia.</p>
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<p>THg concentrations and relationships between Hg and the biophilic elements of soil organic matter (Hg/C, Hg/N and Hg/S ratios) in organic soil at 0–10 cm and 50 cm deep, grouped by management-induced disturbance. In the box plots, the median is shown by the bold line, the mean is shown by the dark red square, the box corresponds to the lower and upper quartiles, the whiskers show the minimal and maximal values (within 150% of the interquartile range from the median) and the black dots represent outliers of the datasets. Different uppercase letters show statistically significant differences (<span class="html-italic">p</span> &lt; 0.05) between depths within the same group of management-induced disturbance; different lowercase letters show statistically significant differences (<span class="html-italic">p</span> &lt; 0.05) between disturbed and undisturbed research sites within the same depth.</p>
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<p>Relationships of the Hg/C ratio to the C/N and C/S ratios in the soil at 0–10 cm deep.</p>
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<p>Spearman’s correlations between the THg concentrations in soil at 0–10 cm deep, the mean GHG emissions from the soil during the measurement period (May–August 2019), the soil’s general physico-chemical parameters at 0–10 cm and different environmental factors and vegetation cover. Positive correlations are displayed in blue and negative correlations in red. Colour intensity and the size of the circle are proportional to the correlation coefficients. In the right side of the correlogram, the legend colour shows the correlation coefficients and the corresponding colours. Correlations with <span class="html-italic">p</span> &gt; 0.05 are considered as insignificant (crosses are added).</p>
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<p>GHG emissions from organic soils during the measurement period (May–August 2019) in hemiboreal Latvia. In the boxplots, the median is shown by the bold line, the mean is shown by the black dot, the box corresponds to the lower and upper quartiles, whiskers show the minimal and maximal values (within 150% of the interquartile range from the median) and dots outside the box and whiskers represent outliers of the datasets. Different lowercase letters show statistically significant differences (<span class="html-italic">p</span> &lt; 0.05) between disturbed and undisturbed research sites.</p>
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<p>The proportion of species cover by different species groups and current land use or type of vegetation.</p>
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<p>Canonical correspondence analysis (CCA) ordination of research site groups and environmental factors.</p>
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44 pages, 3089 KiB  
Review
Microbiogeochemical Traits to Identify Nitrogen Hotspots in Permafrost Regions
by Claudia Fiencke, Maija E. Marushchak, Tina Sanders, Rica Wegner and Christian Beer
Nitrogen 2022, 3(3), 458-501; https://doi.org/10.3390/nitrogen3030031 - 12 Aug 2022
Cited by 8 | Viewed by 5303
Abstract
Permafrost-affected tundra soils are large carbon (C) and nitrogen (N) reservoirs. However, N is largely bound in soil organic matter (SOM), and ecosystems generally have low N availability. Therefore, microbial induced N-cycling processes and N losses were considered negligible. Recent studies show that [...] Read more.
Permafrost-affected tundra soils are large carbon (C) and nitrogen (N) reservoirs. However, N is largely bound in soil organic matter (SOM), and ecosystems generally have low N availability. Therefore, microbial induced N-cycling processes and N losses were considered negligible. Recent studies show that microbial N processing rates, inorganic N availability, and lateral N losses from thawing permafrost increase when vegetation cover is disturbed, resulting in reduced N uptake or increased N input from thawing permafrost. In this review, we describe currently known N hotspots, particularly bare patches in permafrost peatland or permafrost soils affected by thermokarst, and their microbiogeochemical characteristics, and present evidence for previously unrecorded N hotspots in the tundra. We summarize the current understanding of microbial N cycling processes that promote the release of the potent greenhouse gas (GHG) nitrous oxide (N2O) and the translocation of inorganic N from terrestrial into aquatic ecosystems. We suggest that certain soil characteristics and microbial traits can be used as indicators of N availability and N losses. Identifying N hotspots in permafrost soils is key to assessing the potential for N release from permafrost-affected soils under global warming, as well as the impact of increased N availability on emissions of carbon-containing GHGs. Full article
(This article belongs to the Special Issue Nitrogen Cycling in Permafrost Soils)
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Figure 1
<p>Map of study sites of N hotspots in the Arctic and the Antarctic in the continuous and discontinuous permafrost zone summarized in this review. Study sites of permafrost peatlands are marked in red, the sites of hillslope thermokarst in yellow, sites of alluvial soils in green and animal-influenced sites in purple. Maps modified after Brown et al. [<xref ref-type="bibr" rid="B31-nitrogen-03-00031">31</xref>].</p>
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<p>Levels of N availability in permafrost-affected soils. (<bold>I</bold>) N limitation level with tight, mainly organic N cycle dominated by depolymerization and zero or negative net N ammonification rates. Plants and soil organisms compete strongly for dissolved organic N (DON) and ammonium in soils with high soil organic matter (SOM) content and high C/N ratio in bulk soil, which is mainly controlled by high water content. (<bold>II</bold>) Intermediate N level with more open N cycle, indicated by positive net N ammonification, therefore higher ammonium content and lower N competition. (<bold>III</bold>) N hotspot level with lower N immobilization (uptake) and therefore more available N for net inorganic N turnover and therefore more open N cycling with aerobic nitrification and anaerobic denitrification, both processes producing the gases nitric oxide (NO) and nitrous oxide (N<sub>2</sub>O) (in addition to N<sub>2</sub> of denitrification) with the highly mobile nitrate for leaching as an intermediate product. This N level is characterized by a low C/N ratio &lt;25 in bulk soil and intermediate moisture. Due to high inorganic N forms ammonium and nitrate, this level is called N hotspot of N availability.</p>
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<p>Photographs of hotspots with high N availability. (<bold>A</bold>) permafrost peatland in Seida, Komi Republic, Russia (N 67°03′, E 62°57′) with bare peat and vegetation on the frozen peat plateau (July 2010, Maija Marushchak), (<bold>B</bold>) palsa mire in Utsjoki, Finland (N 67°45′, E 27°00′) with bare and vegetated palsa underlied by permafrost and wet fen surface without permafrost (August 2009, Maija Marushchak), (<bold>C</bold>) retrogressive thaw slump on Kurungnakh Island, Lena River Delta, Russia (N 72°20, E 126°17′), with bare and revegetated slump floor and thaw mounds (July 2016, Alexander Schütt), (<bold>D</bold>) alluvial soils on Samoylov Island, Lena River Delta, Russia, (N 72°22, E 126°28′) with bare bright organic-poor and dark organic-rich soil surfaces (July 2008, Claudia Fiencke).</p>
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<p>Hotspots of high N availability in a schematic permafrost landscape of a tundra ecosystem with (<bold>A</bold>) permafrost peat plateaus and palsas with bare peat surfaces in the discontinuous permafrost zone; hillslope thermokarst landscapes such as (<bold>B</bold>) retrogressive thaw slump in the continuous and (<bold>C</bold>) thermo-erosion gully in the discontinuous permafrost zone, (<bold>D</bold>) alluvial soils in the transition between terrestrial and aquatic ecosystems, and the occurrence of (<bold>E</bold>) animal-influenced soils and (<bold>F</bold>) wildfire throughout ecosystem. Blue arrows indicate known gaseous (N<sub>2</sub>O, NO, HONOH) and red arrows indicate lateral N (DIN, DON and TN) fluxes, black arrows indicate probability of N<sub>2</sub>O loss. N form without arrow describes enrichment in soil. DIN = dissolved inorganic, DON = dissolved organic nitrogen, TN = total nitrogen, ? = the N<sub>2</sub>O emission is largely unresolved.</p>
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<p>Biochemical traits of (<bold>A</bold>,<bold>C</bold>,<bold>E</bold>,<bold>G</bold>) N hotspots with high N availability and (<bold>B</bold>,<bold>D</bold>,<bold>F</bold>,<bold>H</bold>) adjacent control sites with N limitation using one described site as an example. (<bold>A</bold>,<bold>B</bold>) water-filled pore space (WFPS), C/N ratio (<bold>C</bold>,<bold>D</bold>) dissolved inorganic nitrogen (DIN, ammonium + nitrate), ratio of DIN to total N (DIN/TN) and nitrate. (<bold>E</bold>,<bold>F</bold>) microbial net N turnover: N mineralization, nitrification, denitrification, and (<bold>G</bold>,<bold>H</bold>) abundance of genes of key functional enzymes of nitrification (<italic>amo</italic>, ammonia monooxygenase in % of 16S rRNA and **log10 gdw<sup>−1</sup>), denitrification (<italic>nirS</italic> + <italic>nir K</italic>, nitrite reductases in % of 16S rRNA, *% of N genes and **log10 gdw<sup>−1</sup>) and (<italic>nirS</italic> + <italic>nirK</italic>)/<italic>nosz</italic> ratio (nitrite reductase/N<sub>2</sub>O reductase *** × 10<sup>3</sup>) in subarctic bare (BP) and vegetated peatland (VP, references see <xref ref-type="table" rid="nitrogen-03-00031-t001">Table 1</xref>), Arctic retrogressive thaw slump (RTS) and undisturbed site (URTS, references see <xref ref-type="app" rid="app1-nitrogen-03-00031">Table S1</xref>), thermoerosion-gully (TEG) and undisturbed site (UTEG) on the Tibet Plateau [<xref ref-type="bibr" rid="B218-nitrogen-03-00031">218</xref>], Arctic bare alluvial soils (AS) and vegetated floodplain (VP) [<xref ref-type="bibr" rid="B119-nitrogen-03-00031">119</xref>,<xref ref-type="bibr" rid="B302-nitrogen-03-00031">302</xref>,<xref ref-type="bibr" rid="B304-nitrogen-03-00031">304</xref>] and animal-influenced permafrost affected soils of the Arctic (AIA) [<xref ref-type="bibr" rid="B234-nitrogen-03-00031">234</xref>] and Antarctic (AIAA) [<xref ref-type="bibr" rid="B188-nitrogen-03-00031">188</xref>,<xref ref-type="bibr" rid="B189-nitrogen-03-00031">189</xref>,<xref ref-type="bibr" rid="B236-nitrogen-03-00031">236</xref>] and non-influenced soils of Arctic (NIA) and Antarctic (NIAA). For more detail, see <xref ref-type="app" rid="app1-nitrogen-03-00031">Table S2</xref>.</p>
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