[go: up one dir, main page]
More Web Proxy on the site http://driver.im/
You seem to have javascript disabled. Please note that many of the page functionalities won't work as expected without javascript enabled.
 
 
Sign in to use this feature.

Years

Between: -

Subjects

remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline

Journals

Article Types

Countries / Regions

Search Results (45)

Search Parameters:
Keywords = anthropogenic biomes

Order results
Result details
Results per page
Select all
Export citation of selected articles as:
20 pages, 4021 KiB  
Article
The Herpetofauna of the Chihuahuan Desert Biogeographic Province of Mexico: Diversity, Similarity to Other Provinces, and Conservation Status
by Julio A. Lemos-Espinal, Geoffrey R. Smith and Christy M. McCain
Diversity 2024, 16(12), 771; https://doi.org/10.3390/d16120771 - 19 Dec 2024
Viewed by 416
Abstract
The Chihuahuan Desert biogeographic province in Mexico is the largest of the fourteen biogeographic provinces of the country. This biogeographic province hosts a diverse array of amphibian and reptile species, with 262 native species, including 53 amphibians and 209 reptiles, accounting for a [...] Read more.
The Chihuahuan Desert biogeographic province in Mexico is the largest of the fourteen biogeographic provinces of the country. This biogeographic province hosts a diverse array of amphibian and reptile species, with 262 native species, including 53 amphibians and 209 reptiles, accounting for a significant portion of Mexico’s total amphibian (~12%) and reptile diversity (~21%). The Zacatecana subprovince exhibits the highest concentration of species for both groups (89% and 50% of Chihuahuan Desert amphibians and reptiles, respectively), indicating its importance for biodiversity within the Chihuahuan Desert. Comparative analyses with neighboring biogeographic provinces reveal substantial species overlap (48–55%), particularly with the Sierra Madre Oriental, the Transvolcanic Belt, and the Sierra Madre Occidental. These findings suggest strong ecological connections and corridors facilitating species exchange among these regions. Conservation assessments highlight the vulnerability of many species in the Chihuahuan Desert, with a notable percentage listed in the International Union for Conservation of Nature’s (IUCN) Red List (~12%) and higher percentages categorized by the Mexican government as at risk according to their conservation status and the Environmental Vulnerability Score (~40%). Threats primarily stem from habitat loss, pollution, and other anthropogenic factors. In conclusion, the Chihuahua Desert emerges as a biogeographic province of significant biological richness and valuable evolutionary history for amphibians and reptiles. Its conservation is imperative for safeguarding the distinctive species and ecosystems that characterize this desert biome. Full article
(This article belongs to the Special Issue Biology and Evolutionary History of Reptiles)
Show Figures

Figure 1

Figure 1
<p>Topography map of the Chihuahuan Desert biogeographic province of Mexico [<a href="#B5-diversity-16-00771" class="html-bibr">5</a>].</p>
Full article ">Figure 2
<p>Climate map of the Chihuahuan Desert biogeographic province of Mexico [<a href="#B28-diversity-16-00771" class="html-bibr">28</a>].</p>
Full article ">Figure 3
<p>Vegetation map of the Chihuahuan Desert biogeographic province of Mexico [<a href="#B30-diversity-16-00771" class="html-bibr">30</a>].</p>
Full article ">Figure 4
<p>The correlation between the Jaccard distance of amphibians and reptiles among the Chihuahuan Desert and its neighboring biogeographic provinces, with the trend line and 95% confidence intervals.</p>
Full article ">Figure 5
<p>The correlation between the length of the shared border between neighboring biogeographic provinces and the Chihuahuan Desert and the Jaccard distance of (<b>A</b>) amphibians and (<b>B</b>) reptiles and the correlation between the distance between centroids of the Chihuahuan Desert and its neighboring biogeographic provinces and the Jaccard distance of (<b>C</b>) amphibians and (<b>D</b>) reptiles, with the trend line and 95% confidence intervals.</p>
Full article ">Figure 6
<p>Cluster trees for (<b>A</b>) amphibians and (<b>B</b>) reptiles of the Chihuahuan Desert and its neighboring biogeographic provinces.</p>
Full article ">Figure 7
<p>Percentage (±1 S.E.) of amphibian and reptile species with conservation concern status [<a href="#B37-diversity-16-00771" class="html-bibr">37</a>], categorized as threatened (A) or in danger of extinction (P) by the Mexican government [<a href="#B38-diversity-16-00771" class="html-bibr">38</a>], or deemed to have a high Environmental Vulnerability Score (EVS) [<a href="#B39-diversity-16-00771" class="html-bibr">39</a>,<a href="#B40-diversity-16-00771" class="html-bibr">40</a>], for the Chihuahuan Desert biogeographic province of Mexico.</p>
Full article ">
24 pages, 4153 KiB  
Article
Mapping Burned Area in the Caatinga Biome: Employing Deep Learning Techniques
by Washington J. S. Franca Rocha, Rodrigo N. Vasconcelos, Soltan Galano Duverger, Diego P. Costa, Nerivaldo A. Santos, Rafael O. Franca Rocha, Mariana M. M. de Santana, Ane A. C. Alencar, Vera L. S. Arruda, Wallace Vieira da Silva, Jefferson Ferreira-Ferreira, Mariana Oliveira, Leonardo da Silva Barbosa and Carlos Leandro Cordeiro
Fire 2024, 7(12), 437; https://doi.org/10.3390/fire7120437 - 27 Nov 2024
Viewed by 754
Abstract
The semi-arid Caatinga biome is particularly susceptible to fire dynamics. Periodic droughts amplify fire risks, while anthropogenic activities such as agriculture, pasture expansion, and land-clearing significantly contribute to the prevalence of fires. This research aims to evaluate the effectiveness of a fire detection [...] Read more.
The semi-arid Caatinga biome is particularly susceptible to fire dynamics. Periodic droughts amplify fire risks, while anthropogenic activities such as agriculture, pasture expansion, and land-clearing significantly contribute to the prevalence of fires. This research aims to evaluate the effectiveness of a fire detection model and analyze the spatial and temporal patterns of burned areas, providing essential insights for fire management and prevention strategies. Utilizing deep neural network (DNN) models, we mapped burned areas across the Caatinga biome from 1985 to 2023, based on Landsat-derived annual quality mosaics and minimum NBR values. Over the 38-year period, the model classified 10.9 Mha (12.7% of the Caatinga) as burned, with an average annual burned area of approximately 0.5 Mha (0.56%). The peak burned area reached 0.89 Mha in 2021. Fire scars varied significantly, ranging from 0.18 Mha in 1985 to substantial fluctuations in subsequent years. The most affected vegetation type was savanna, with 9.8 Mha burned, while forests experienced only 0.28 Mha of burning. October emerged as the month with the highest fire activity, accounting for 7266 hectares. These findings underscore the complex interplay of climatic and anthropogenic factors, highlighting the urgent need for effective fire management strategies. Full article
Show Figures

Figure 1

Figure 1
<p>Map of the boundaries of the Caatinga biome.</p>
Full article ">Figure 2
<p>Overview of the method for classifying burned areas in Caatinga.</p>
Full article ">Figure 3
<p>The Multi-Layer Perceptron Network‘s structure involves using the spectral bands (RED, NIR, SWIR1, and SWIR2) as input layers and the classes burned and unburned as the output layers.</p>
Full article ">Figure 4
<p>The Multi-Layer Perceptron Network‘s structure involves using the spectral bands (RED, NIR, SWIR1, and SWIR2) as input layers and the classes burned and unburned as the output layers. (<b>A</b>) depicts the cumulative burn area from 1985 to 2023. (<b>B</b>) in contrast, showcases the annual burn area over the same temporal range.</p>
Full article ">Figure 5
<p>The annual distribution of annual burned class areas in the Caatinga biome from 1985 to 2023.</p>
Full article ">Figure 6
<p>The annual distribution of burned areas by land use and land cover types in the Caatinga biome from 1985 to 2023.</p>
Full article ">Figure 7
<p>The paper presents the spatial distribution of fire frequency in Brazil from 1985 to 2023, including the corresponding burned area and proportion by frequency class. (<b>A</b>) shows the map of fire frequency throughout the Caatinga biome, while (<b>B</b>) presents the classes of fire frequency by area and their corresponding percentages.</p>
Full article ">Figure 8
<p>The figures depict the spatial association between accumulated burn scars and various climate parameters. (<b>A</b>) illustrates the correlation between burn scars and accumulated precipitation. (<b>B</b>) showcases the relationship between accumulated burn scars and climate water deficit. Lastly, (<b>C</b>) presents the correlation between burn scars and reference evapotranspiration.</p>
Full article ">
20 pages, 11745 KiB  
Article
Biomass Prediction Using Sentinel-2 Imagery and an Artificial Neural Network in the Amazon/Cerrado Transition Region
by Luana Duarte de Faria, Eraldo Aparecido Trondoli Matricardi, Beatriz Schwantes Marimon, Eder Pereira Miguel, Ben Hur Marimon Junior, Edmar Almeida de Oliveira, Nayane Cristina Candido dos Santos Prestes and Osmar Luiz Ferreira de Carvalho
Forests 2024, 15(9), 1599; https://doi.org/10.3390/f15091599 - 11 Sep 2024
Cited by 1 | Viewed by 965
Abstract
The ecotone zone, located between the Cerrado and Amazon biomes, has been under intensive anthropogenic pressures due to the expansion of commodity agriculture and extensive cattle ranching. This has led to habitat loss, reducing biodiversity, depleting biomass, and increasing CO2 emissions. In [...] Read more.
The ecotone zone, located between the Cerrado and Amazon biomes, has been under intensive anthropogenic pressures due to the expansion of commodity agriculture and extensive cattle ranching. This has led to habitat loss, reducing biodiversity, depleting biomass, and increasing CO2 emissions. In this study, we employed an artificial neural network, field data, and remote sensing techniques to develop a model for estimating biomass in the remaining native vegetation within an 18,864 km2 ecotone region between the Amazon and Cerrado biomes in the state of Mato Grosso, Brazil. We utilized field data from a plant ecology laboratory and vegetation indices from Sentinel-2 satellite imagery and trained artificial neural networks to estimate aboveground biomass (AGB) in the study area. The optimal network was chosen based on graphical analysis, mean estimation errors, and correlation coefficients. We validated our chosen network using both a Student’s t-test and the aggregated difference. Our results using an artificial neural network, in combination with vegetation indices such as AFRI (Aerosol Free Vegetation Index), EVI (Enhanced Vegetation Index), and GNDVI (Green Normalized Difference Vegetation Index), which show an accurate estimation of aboveground forest biomass (Root Mean Square Error (RMSE) of 15.92%), can bolster efforts to assess biomass and carbon stocks. Our study results can support the definition of environmental conservation priorities and help set parameters for payment for ecosystem services in environmentally sensitive tropical regions. Full article
(This article belongs to the Special Issue Modeling Aboveground Forest Biomass: New Developments)
Show Figures

Figure 1

Figure 1
<p>The study area is located within the ecotone zone of the Amazonia and Cerrado biomes in the state of Mato Grosso, Brazil. Field measurements were conducted in 12 sample plots, each measuring 10,000 m² and subdivided into 60 subplots of 2000 m<sup>2</sup> each, in the years 2014, 2018, 2020, and 2021. The year of sampling is indicated in black above each sample plot in the study area.</p>
Full article ">Figure 2
<p>Vegetation indices ((<b>A</b>) = Green Normalized Vegetation Index—GNDV; (<b>B</b>) = Enhanced Vegetation Index—EVI; and (<b>C</b>) = Aerosol Free Vegetation Index—AFRI) retrieved from Sentinel-2 imagery acquired in August 2016, 2018, 2020, and 2021 covering the entire study region.</p>
Full article ">Figure 3
<p>Observed and estimated aboveground biomass in the study area ((<b>A1</b>) = Training; (<b>A2</b>) = Testing; (<b>A3</b>) = Validation) and distribution of residuals ((<b>B1</b>) = Training; (<b>B2</b>) = Testing; (<b>B3</b>) = Validation) for Artificial Neural Network 1 (ANN-1).</p>
Full article ">Figure 4
<p>Architecture of ANN-1 selected for the prediction of aboveground biomass for the study area.</p>
Full article ">Figure 5
<p>Spatial distribution of forest biomass is estimated for the Amazon–Cerrado ecotone zone. Darker areas indicate higher aboveground biomass, while lighter areas indicate lower biomass.</p>
Full article ">
23 pages, 1818 KiB  
Review
Microbial Utilization to Nurture Robust Agroecosystems for Food Security
by Muhammad Qadir, Anwar Hussain, Amjad Iqbal, Farooq Shah, Wei Wu and Huifeng Cai
Agronomy 2024, 14(9), 1891; https://doi.org/10.3390/agronomy14091891 - 24 Aug 2024
Cited by 3 | Viewed by 913
Abstract
In the context of anthropogenic evolution, various sectors have been exploited to satisfy human needs and demands, often pushing them to the brink of deterioration and destruction. One such sector is agrochemicals, which have been increasingly employed to achieve higher yields and bridge [...] Read more.
In the context of anthropogenic evolution, various sectors have been exploited to satisfy human needs and demands, often pushing them to the brink of deterioration and destruction. One such sector is agrochemicals, which have been increasingly employed to achieve higher yields and bridge the gap between food supply and demand. However, extensive and prolonged use of chemical fertilizers most often degrades soil structure over time, resulting in reduced yields and consequently further exacerbating the disparity between supply and demand. To address these challenges and ensure sustainable agricultural production, utilization of microorganisms offers promising solutions. Hence, microorganisms, particularly effective microorganisms (EMs) and plant growth-promoting microbes (PGPMs), are pivotal in agricultural biomes. They enhance crop yields through active contribution to crucial biological processes like nitrogen fixation and phytohormone synthesis, making vital nutrients soluble and acting as natural enemies against pests and pathogens. Microbes directly enhance soil vigor and stimulate plant growth via the exudation of bioactive compounds. The utilization of EMs and PGPMs reduces the need for chemical inputs, leading to lower costs and reduced environmental pollutants. Furthermore, beneficial soil microflora produces growth-related metabolites and phytohormones that augment plant growth and support stress resilience. Microbes also help plants tolerate various abiotic stresses, including metal stress, salt stress, and drought stress, through various mechanisms. Understanding the interactions and activities of microorganisms provides valuable insights into their potential use to manage stress in plants. Thus, by leveraging the full potential of microorganisms, we can develop healthier agroecosystems that contribute sustainably to meet the growing global food demands. Full article
(This article belongs to the Section Farming Sustainability)
Show Figures

Figure 1

Figure 1
<p>Bacterial and fungal metabolites in support of plant growth and development.</p>
Full article ">Figure 2
<p>Microbial species and genera that promote plant growth.</p>
Full article ">Figure 3
<p>Role of plant growth-promoting microbes (PGPMs) in maintaining plant health under biotic stress.</p>
Full article ">Figure 4
<p>Mechanisms of plant growth-promoting microbes (PGPMs) in mitigating abiotic stress.</p>
Full article ">
19 pages, 4778 KiB  
Article
Assessing Historical LULC Changes’ Effect on Ecosystem Services Provisioning and Their Values in a Mediterranean Coastal Lagoon Complex
by Anastasia Mirli, Dionissis Latinopoulos, Georgia Galidaki, Konstantinos Bakeas and Ifigenia Kagalou
Land 2024, 13(8), 1277; https://doi.org/10.3390/land13081277 - 13 Aug 2024
Viewed by 814
Abstract
Urbanization and land claim trends for agriculture have led to land use/land cover (LULC) changes, acting as driving forces for several natural environment alterations. The ecosystem services (ES) concept links ecosystem degradation with direct adverse effects on human welfare, emphasizing the importance of [...] Read more.
Urbanization and land claim trends for agriculture have led to land use/land cover (LULC) changes, acting as driving forces for several natural environment alterations. The ecosystem services (ES) concept links ecosystem degradation with direct adverse effects on human welfare, emphasizing the importance of balancing human activities and ecosystem health. LULC changes and their impacts on ES are crucial for nature conservation and decision-making. To support sustainable management, a historical (75-year) assessment of Nestos Delta lagoons was conducted, using aerial photos and satellite images, providing valuable insights into the drivers and trends of these changes. Until 1960, water-related Biomes were affected the most, in favor of agricultural (Nestos River incubation) and urban ones, but anthropogenic activities development rate reduced after land reclamation. Since their inclusion in the Natura 2000 network and designation as a National Park, they have been protected from rapid development. Over the past two decades, they have increased the economic value of their cultural ES, while deteriorating regulating and having a minimal impact on provisioning services, resulting in a cumulative loss exceeding USD 30 million during the study period. This study strongly indicates the vital importance of legislative protection and the integration of the ES approach in priority habitat management. Full article
Show Figures

Figure 1

Figure 1
<p>Study area: Nestos Delta lagoons.</p>
Full article ">Figure 2
<p>Chronological time-flow representation of Nestos River and Delta historical events.</p>
Full article ">Figure 3
<p>Historical LULC changes in ND lagoons (1945–2015), using CLC codes.</p>
Full article ">Figure 4
<p>Biome transformations in each lagoon at each time step.</p>
Full article ">Figure 5
<p>Graphical representation of the links of Biomes with ES and their reference values.</p>
Full article ">
14 pages, 3146 KiB  
Article
Anthropogenic Impacts on a Temperate Forest Ecosystem, Revealed by a Late Holocene Pollen Record from an Archaeological Site in NE China
by Guangyi Bai, Keliang Zhao, Yaping Zhang, Junchi Liu, Xinying Zhou and Xiaoqiang Li
Forests 2024, 15(8), 1331; https://doi.org/10.3390/f15081331 - 31 Jul 2024
Viewed by 936
Abstract
Pollen records from archaeological sites provide a direct reflection of the vegetation in the immediate vicinity, enabling an accurate depiction of anthropogenic impacts on vegetation. In this study, we applied the biomization technique to fossil pollen data to reconstruct human impact on the [...] Read more.
Pollen records from archaeological sites provide a direct reflection of the vegetation in the immediate vicinity, enabling an accurate depiction of anthropogenic impacts on vegetation. In this study, we applied the biomization technique to fossil pollen data to reconstruct human impact on the biome at the Chengzishan archaeological site in western Liaoning, China, and hence to explore the response of temperate forest vegetation to human activities. The results indicate that the original vegetation at Chengzishan was warm temperate coniferous and broadleaved mixed forest (TEDE). The findings suggest a shift in biome dominance over time, with cool temperate steppe (STEP) replacing TEDE as the dominant biome in response to human activities. Combined with archaeobotanical records, we conclude that the observed vegetation changes in the pollen record were closely linked to deforestation, fire use, and agricultural activities. Full article
(This article belongs to the Special Issue Quaternary Forest Dynamics in Monsoon Asia)
Show Figures

Figure 1

Figure 1
<p>Location (<b>left</b>) and land use (<b>right</b>) of the study area.</p>
Full article ">Figure 2
<p>Main pollen and spore types at the Chengzishan site. (1–3: <span class="html-italic">Pinus</span>; 4–5: <span class="html-italic">Taraxacum</span>; 6: <span class="html-italic">Artemisia</span>; 7: <span class="html-italic">Aster</span>; 8–9: <span class="html-italic">Quercus</span>; 10–11: Polygonaceae (11: <span class="html-italic">Fagopyrum</span>); 12: Rhamnaceae; 13–15: Poaceae; 16–17: <span class="html-italic">Corylus</span>; 18: Polygalaceae; 19: Malvaceae; 20: <span class="html-italic">Glomus</span>; and 21–22: <span class="html-italic">Concentricystis</span>).</p>
Full article ">Figure 3
<p>Lithology, macrofossil records, and percentage pollen diagram for the Chengzishan profile (modified from [<a href="#B23-forests-15-01331" class="html-bibr">23</a>,<a href="#B24-forests-15-01331" class="html-bibr">24</a>]).</p>
Full article ">Figure 4
<p>Biome results and vegetation reconstruction at the Chengzishan site. (TEDE: warm-temperate mixed forest; TEDS: cool-temperate desert steppe; TEFS: cool-temperate forest steppe; STEP: cool-temperate steppe).</p>
Full article ">Figure 5
<p>Distribution of archaeological sites of different cultures in western Liaoning. (Upper: Hongshan Culture and Xiaoheyan Culture, 7000–4000 BP; Below: Lower Xiajiadian Culture, 4000–3500 BP. Red triangles indicate the location of Chengzishan site. The spatiotemporal data for agricultural sites are from [<a href="#B53-forests-15-01331" class="html-bibr">53</a>]).</p>
Full article ">Figure 6
<p>Vegetation composition changes at Chengzishan and other archaeological sites.</p>
Full article ">
11 pages, 1187 KiB  
Article
Mercury Dynamics in the Sea of Azov: Insights from a Mass Balance Model
by Christoph Gade, Rebecca von Hellfeld, Lenka Mbadugha and Graeme Paton
Toxics 2024, 12(6), 417; https://doi.org/10.3390/toxics12060417 - 7 Jun 2024
Viewed by 1169
Abstract
The Sea of Azov, an inland shelf sea bounding Ukraine and Russia, experiences the effects of ongoing and legacy pollution. One of the main contaminants of concern is the heavy metal mercury (Hg), which is emitted from the regional coal industry, former Hg [...] Read more.
The Sea of Azov, an inland shelf sea bounding Ukraine and Russia, experiences the effects of ongoing and legacy pollution. One of the main contaminants of concern is the heavy metal mercury (Hg), which is emitted from the regional coal industry, former Hg refineries, and the historic use of mercury-containing pesticides. The aquatic biome acts both as a major sink and source in this cycle, thus meriting an examination of its environmental fate. This study collated existing Hg data for the SoA and the adjacent region to estimate current Hg influxes and cycling in the ecosystem. The mercury-specific model “Hg Environmental Ratios Multimedia Ecosystem Sources” (HERMES), originally developed for Canadian freshwater lakes, was used to estimate anthropogenic emissions to the sea and regional atmospheric Hg concentrations. The computed water and sediment concentrations (6.8 ng/L and 55.7 ng/g dw, respectively) approximate the reported literature values. The ongoing military conflict will increase environmental pollution in the region, thus further intensifying the existing (legacy) anthropogenic pressures. The results of this study provide a first insight into the environmental Hg cycle of the Sea of Azov ecosystem and underline the need for further emission control and remediation efforts to safeguard environmental quality. Full article
(This article belongs to the Special Issue Monitoring and Assessment of Mercury Pollution)
Show Figures

Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Geographical extent of the Sea of Azov. Red dots indicate large cities, and blue rectangles indicate river mouths.</p>
Full article ">Figure 2
<p>Computed total mercury (THg) concentrations using the Sea of Azov mass balance model.</p>
Full article ">
20 pages, 7942 KiB  
Article
Interannual Variability of Water and Heat Fluxes in a Woodland Savanna (Cerrado) in Southeastern Brazil: Effects of Severe Drought and Soil Moisture
by Lucas F. C. da Conceição, Humberto R. da Rocha, Nelson V. Navarrete, Rafael Rosolem, Osvaldo M. R. Cabral and Helber C. de Freitas
Atmosphere 2024, 15(6), 668; https://doi.org/10.3390/atmos15060668 - 31 May 2024
Viewed by 743
Abstract
The Brazilian Cerrado biome is known for its high biodiversity, and the role of groundwater recharge and climate regulation. Anthropogenic influence has harmed the biome, emphasizing the need for science to understand its response to climate and reconcile economic exploration with preservation. Our [...] Read more.
The Brazilian Cerrado biome is known for its high biodiversity, and the role of groundwater recharge and climate regulation. Anthropogenic influence has harmed the biome, emphasizing the need for science to understand its response to climate and reconcile economic exploration with preservation. Our work aimed to evaluate the seasonal and interannual variability of the surface energy balance in a woodland savanna (Cerrado) ecosystem in southeastern Brazil over a period of 19 years, from 2001 to 2019. Using field micrometeorological measurements, we examined the variation in soil moisture and studied its impact on the temporal pattern of energy fluxes to distinguish the effects during rainy years compared to a severe drought spell. The soil moisture measures used two independent instruments, cosmic ray neutron sensor CRNS, and FDR at different depths. The measures were taken at the Pé de Gigante (PEG) site, in a region of well-defined seasonality with the dry season in winter and a hot/humid season in summer. We gap-filled the energy flux measurements with a calibrated biophysical model (SiB2). The long-term averages for air temperature and precipitation were 22.5 °C and 1309 mm/year, respectively. The net radiation (Rn) was 142 W/m2, the evapotranspiration (ET) and sensible heat flux (H) were 3.4 mm/d and 52 W/m2, respectively. Soil moisture was marked by a pronounced negative anomaly in the 2014 year, which caused an increase in the Bowen ratio and a decrease in Evaporative fraction, that lasted until the following year 2015 during the dry season, despite the severe meteorological drought of 2013/2014 already ending, which was corroborated by the two independent measurements. The results showed the remarkable influence of precipitation and soil moisture on the interannual variability of the energy balance in this Cerrado ecosystem, aiding in understanding how it responds to strong climate disturbances. Full article
(This article belongs to the Special Issue Land-Atmosphere Interactions)
Show Figures

Figure 1

Figure 1
<p>(<b>A</b>) View from the top of the micrometeorological tower at the PEG site, in the southeast direction, with a cup anemometer/wind vane on the right; (<b>B</b>) map of Brazil and São Paulo state with a location box of the site in the city of Santa Rita do Passa Quatro; (<b>C</b>) satellite image (07/2018) of the region with the PEG site (blue polygon); (<b>D</b>) vegetation physiognomy at the PEG site (adapted from [<a href="#B22-atmosphere-15-00668" class="html-bibr">22</a>]).</p>
Full article ">Figure 2
<p>Daily average volumetric water content (θ) measured by CRNS sensor versus FDR sensor at a depth of (<b>a</b>) 10 cm and (<b>b</b>) 20 cm in the Cerrado sensu stricto (woodland savanna) area of the PEG site.</p>
Full article ">Figure 3
<p>Temporal average NDVI between 2013 and 2020, tower location under marked black pin, for the months of (<b>a</b>) January and (<b>b</b>) August.</p>
Full article ">Figure 4
<p>Temporal average LAI between 2013 and 2020, for the months of (<b>a</b>) January and (<b>b</b>) August.</p>
Full article ">Figure 5
<p>Temporal average FPAR between 2013 and 2020, for the months of (<b>a</b>) January and (<b>b</b>) August.</p>
Full article ">Figure 6
<p>Observed and calculated net radiation (Rn) by the SiB2 model: (<b>a</b>) using default initial parameter values; (<b>b</b>) using optimized parameter values. RMSE varied from 34.5 to 30.5, and Nash–Sutcliffe efficiency coefficient (NSE) ranged from 0.97 to 0.9 in the initial and optimized cases, respectively.</p>
Full article ">Figure 7
<p>Observed and calculated net radiation (Rn) by the SiB2 model using (<b>a</b>) default initial parameter values; (<b>b</b>) optimized parameter values. RMSE varied from 72.8 to 66.7, and Nash–Sutcliffe efficiency coefficient (NSE) ranged from 0.72 to 0.76 in the initial and optimized cases, respectively.</p>
Full article ">Figure 8
<p>Monthly average of field measurements at the PEG Cerrado tower for the years 2001–2019: (<b>a</b>) incoming solar radiation (W m<sup>−2</sup>); (<b>b</b>) air temperature (°C); (<b>c</b>) water vapor pressure (hPa); (<b>d</b>) horizontal wind speed (m/s); and (<b>e</b>) precipitation (mm/month).</p>
Full article ">Figure 9
<p>Average annual air temperature (°C) (dashed line), precipitation (mm) (bars) and mean temperature (red line) at the PEG site for the years 2001–2019. The climatological normal of precipitation is shown with the blue line.</p>
Full article ">Figure 10
<p>Diurnal cycle of (<b>a</b>) incoming solar radiation (W/m<sup>2</sup>), (<b>b</b>) water vapor pressure (hPa), (<b>c</b>) air temperature (°C), (<b>d</b>) horizontal wind speed (m/s) and (<b>e</b>) relative air humidity (%) for the PEG site, calculated during the years 2001–2019.</p>
Full article ">Figure 11
<p>Mean monthly precipitation (mm) and soil water index (SWI) for CRNS and FDR for the averaged layer, including levels 10, 20, 50, 80 and 100 cm (black line) (referred to as FDR10_100 cm); levels 150 and 200 cm (black dashed line) (referred to as FDR150_200 cm); and at 250 cm (light blue line), calculated over the common data range at the PEG site.</p>
Full article ">Figure 12
<p>Annual precipitation (bars) in mm/year, and mean SWI measured at the PEG site, estimated for three ranges of the year: January–February–March (JFM—solid and dotted black lines), July–August–September (JAS—solid and dotted blue lines) and October–November (ON—solid and dotted red lines), for (<b>a</b>) FDR measurements, including depths from 10 to 100 cm, from 150 to 200 cm, and (<b>b</b>) CRNS measurements.</p>
Full article ">Figure 13
<p>(<b>a</b>) Monthly and (<b>b</b>) hourly evapotranspiration (ET), Et (transpiration), Es (soil evaporation) and Ei (interception loss of rainfall), calculated by the SIB2 model for PEG, averaged for the years 2011–2019.</p>
Full article ">Figure 14
<p>Boxplot of the daily time series of flows Rn (W/m<sup>2</sup>), LE (W/m<sup>2</sup>) and H (W/m<sup>2</sup>) for PEG, in the range from 2001 to 2019. Dez—December.</p>
Full article ">Figure 15
<p>Monthly mean of soil wetness index (SWI) of FDR100_150 cm (deep) and CRNS (shallow) (in top); net radiation (black line in middle), latent heat flux (LE) (blue line in middle), sensible heat flux (H) (red line in middle), Bowen ratio (β) and evaporative fraction (EF) (red and dashed blue lines in bottom) for the PEG site during the range from 2011 to 2018.</p>
Full article ">
27 pages, 4435 KiB  
Article
Bird Community Traits in Recently Burned and Unburned Parts of the Northeastern Pantanal, Brazil: A Preliminary Approach
by Karl-L. Schuchmann, Kathrin Burs, Filipe de Deus, Carolline Zatta Fieker, Ana Silvia Tissiani and Marinêz I. Marques
Sustainability 2024, 16(6), 2321; https://doi.org/10.3390/su16062321 - 11 Mar 2024
Viewed by 1083
Abstract
Although fire is a natural phenomenon in the dynamics of some biomes around the world, it can threaten the biodiversity of certain ecosystems. Climate change and the expansion of anthropogenic activities have drastically increased the occurrence of large-scale burnings worldwide. The 2020 fire [...] Read more.
Although fire is a natural phenomenon in the dynamics of some biomes around the world, it can threaten the biodiversity of certain ecosystems. Climate change and the expansion of anthropogenic activities have drastically increased the occurrence of large-scale burnings worldwide. The 2020 fire events in the Pantanal marked a historically unprecedented record, burning an area of approximately 40,000 km2. However, how fires affect the local wildlife has yet to be evaluated. The aim of this study was to investigate the recovery of the avifauna in the Pantanal of Mato Grosso by comparing data selected from a previous study conducted between 2014 and 2016 with data collected in burned areas nine to twelve months after the fire. We compared diversity and community composition, investigated the influence of species trait foraging guild, foraging strata, and body mass on their response to fire, and complemented it with species’ individual responses. Bird richness and Shannon diversity were lower in burned areas, and the composition significantly varied between burned and unburned areas. The species’ response toward burned and unburned areas was significantly mediated by their traits, with smaller, piscivorous, omnivorous, ground and water, and midstory to canopy species being the most sensitive toward the environmental changes caused by the fire. Thirty-three species showed a negative response toward burned areas, but 46 species showed the opposite response, and 24 species were similarly abundant in unburned and burned areas. The present study is the first evaluation of the response of birds to the extreme fire events in the Pantanal and provides valuable insight into the recovery and resilience of local avifauna. Full article
Show Figures

Figure 1

Figure 1
<p>Locations of the three sampled unburned areas in 2014–2016 (markings, white arrow) and three sampled burned areas in 2021 (markings, black arrow) in our study area in the Pantanal of Poconé, MT, Brazil.</p>
Full article ">Figure 2
<p>(<b>a</b>) Estimated sample completeness curves as a function of order q between 0 and 2 for bird species data collected in unburned areas (UA, green) (S<sub>obs</sub> = 145, <span class="html-italic">n</span> = 1121) and burned areas (BA, orange) (S<sub>obs</sub> = 129, <span class="html-italic">n</span> = 1088); (<b>b</b>) sample-sized-based rarefaction (solid lines) and extrapolation curves (dashed lines) for diversity of orders <span class="html-italic">q</span> = 0 (species richness), <span class="html-italic">q</span> = 1 (Shannon diversity), and <span class="html-italic">q</span> = 2 (Simpson diversity). Extrapolation up to double the reference sample size (<span class="html-italic">n</span> = 2242 for UA, <span class="html-italic">n</span> = 2176 for BA); (<b>c</b>) asymptotic estimates of diversity profiles (solid lines) and empirical diversity profiles (dashed lines); (<b>d</b>) coverage-based rarefaction (solid lines) and extrapolation (dashed lines) curves up to the corresponding coverage value or a doubling of each reference sample size; (<b>e</b>) evenness profile as a function of order q, for 0 &lt; <span class="html-italic">q</span> ≤ 2, based on the normalized slope of Hill numbers. Solid dots denote observed data points. All shaded areas denote 95% confidence intervals obtained from a bootstrap method with 999 replications. Numerical values corresponding to the gaps are shown in <a href="#sustainability-16-02321-t001" class="html-table">Table 1</a>.</p>
Full article ">Figure 3
<p>Standardized interaction coefficients between (<b>a</b>) bird species abundance and fire impact (burned/unburned) and (<b>b</b>) bird species traits body mass (g), main foraging strata, dominant feeding guild, and fire impact from the fourth-corner models after variable selection using the LASSO penalty. Color shadings represent the strength of interactions and their direction (blue = negative, red = positive). The identified main foraging strata include ground to understory (GU), ground to canopy (GUMC), ground and water (GW), midstory to canopy (MC), understory to midstory (UM), and understory to canopy (UMC); dominant feeding guilds include piscivorous (PIS), omnivorous (OMN), nectarivorous (NEC), insectivorous (INS), granivorous (GRA), frugivorous (FRU), and carnivorous (CAR). A total of 103 (<span class="html-italic">n</span> ≥ 4) of the 183 bird species found during the study were considered for the analysis (see <a href="#sustainability-16-02321-t0A1" class="html-table">Table A1</a> for details).</p>
Full article ">
31 pages, 4651 KiB  
Article
An Integrated Grassland Fire-Danger-Assessment System for a Mountainous National Park Using Geospatial Modelling Techniques
by Olga D. Mofokeng, Samuel A. Adelabu and Colbert M. Jackson
Fire 2024, 7(2), 61; https://doi.org/10.3390/fire7020061 - 19 Feb 2024
Viewed by 1917
Abstract
Grasslands are key to the Earth’s system and provide crucial ecosystem services. The degradation of the grassland ecosystem in South Africa is increasing alarmingly, and fire is regarded as one of the major culprits. Globally, anthropogenic climate changes have altered fire regimes in [...] Read more.
Grasslands are key to the Earth’s system and provide crucial ecosystem services. The degradation of the grassland ecosystem in South Africa is increasing alarmingly, and fire is regarded as one of the major culprits. Globally, anthropogenic climate changes have altered fire regimes in the grassland biome. Integrated fire-risk assessment systems provide an integral approach to fire prevention and mitigate the negative impacts of fire. However, fire risk-assessment is extremely challenging, owing to the myriad of factors that influence fire ignition and behaviour. Most fire danger systems do not consider fire causes; therefore, they are inadequate in validating the estimation of fire danger. Thus, fire danger assessment models should comprise the potential causes of fire. Understanding the key drivers of fire occurrence is key to the sustainable management of South Africa’s grassland ecosystems. Therefore, this study explored six statistical and machine learning models—the frequency ratio (FR), weight of evidence (WoE), logistic regression (LR), decision tree (DT), random forest (RF), and support vector machine (SVM) in Google Earth Engine (GEE) to assess fire danger in an Afromontane grassland protected area (PA). The area under the receiver operating characteristic curve results (ROC/AUC) revealed that DT showed the highest precision on model fit and success rate, while the WoE was used to record the highest prediction rate (AUC = 0.74). The WoE model showed that 53% of the study area is susceptible to fire. The land surface temperature (LST) and vegetation condition index (VCI) were the most influential factors. Corresponding analysis suggested that the fire regime of the study area is fuel-dominated. Thus, fire danger management strategies within the Golden Gate Highlands National Park (GGHNP) should include fuel management aiming at correctly weighing the effects of fuel in fire ignition and spread. Full article
(This article belongs to the Special Issue Remote Sensing of Wildfire: Regime Change and Disaster Response)
Show Figures

Figure 1

Figure 1
<p>Location of the Golden Gate Highlands National Park (GGHNP) in Free State Province, South Africa.</p>
Full article ">Figure 2
<p>Climate of the study area (<b>a</b>) mean monthly precipitation and actual evapotranspiration rate (<b>b</b>); mean monthly minimum and maximum temperature adapted from [<a href="#B84-fire-07-00061" class="html-bibr">84</a>].</p>
Full article ">Figure 3
<p>Percentage of area of fire-danger classes in the Golden Gate Highlands National Park (GGHNP) generated using decision tree (DT), frequency ratio (FR), logistic regression (LR), random forest (RF), support vector machines (SVM), and weight of evidence (WoE) models.</p>
Full article ">Figure 4
<p>Fire-danger mapping in the Golden Gate Highlands National Park (GGHNP) using (<b>a</b>) decision tree (DT); (<b>b</b>) frequency ratio (FR); (<b>c</b>) logistic regression (LR); (<b>d</b>) random forest (RF); (<b>e</b>) support vector machines (SVM); and (<b>f</b>) weight of evidence (WoE) models.</p>
Full article ">Figure 5
<p>ROC/AUC (area under the receiver operating characteristic curve) results of the (<b>a</b>) decision tree (DT), (<b>b</b>) frequency ratio (FR), (<b>c</b>) logistic regression (LR), (<b>d</b>) random forest (RF), (<b>e</b>) support vector machines (SVM), and (<b>f</b>) weight of evidence (WoE) models used in the wildfire-danger assessment in the Golden Gate Highlands National Park (GGHNP).</p>
Full article ">Figure 6
<p>Jack-knife of regularized training gains for modelling wildfire danger in the Golden Gate Highlands National Park (GGHNP); BSI (bare soil index), coarse (coarse fragments), GCI (grass curing index), GVMI (global vegetation moisture index), LST (land surface temperature), prox_structures (proximity from other infrastructure, e.g., built environment and tourist facilities), SMC (soil moisture content), TAWCP (total plant available water-holding capacity), TPI (topographic position index), TRI (topographic ruggedness index), TWI (topographic water index), VCI (vegetation condition index), prox_river (proximity from river), and prox_road (proximity from road).</p>
Full article ">Figure 7
<p>Pearson correlation graph between wildfire-driving factors and fire danger index (SI).</p>
Full article ">
22 pages, 7532 KiB  
Article
Evaluating Spatial Coverage of the Greater Sage-Grouse Umbrella to Conserve Sagebrush-Dependent Species Biodiversity within the Wyoming Basins
by Cameron L. Aldridge, D. Joanne Saher, Julie A. Heinrichs, Adrian P. Monroe, Matthias Leu and Steve E. Hanser
Land 2024, 13(1), 123; https://doi.org/10.3390/land13010123 - 22 Jan 2024
Cited by 2 | Viewed by 2134
Abstract
Biodiversity is threatened due to land-use change, overexploitation, pollution, and anthropogenic climate change, altering ecosystem functioning around the globe. Protecting areas rich in biodiversity is often difficult without fully understanding and mapping species’ ecological niche requirements. As a result, the umbrella species concept [...] Read more.
Biodiversity is threatened due to land-use change, overexploitation, pollution, and anthropogenic climate change, altering ecosystem functioning around the globe. Protecting areas rich in biodiversity is often difficult without fully understanding and mapping species’ ecological niche requirements. As a result, the umbrella species concept is often applied, whereby conservation of a surrogate species is used to indirectly protect species that occupy similar ecological communities. One such species is the greater sage-grouse (Centrocercus urophasianus), which has been used as an umbrella to conserve other species within the sagebrush (Artemisia spp.) ecosystem. Sagebrush-steppe ecosystems within the United States have experienced drastic loss, fragmentation, and degradation of remaining habitat, threatening sagebrush-dependent fauna, resulting in west-wide conservation efforts to protect sage-grouse habitats, and presumably other sagebrush wildlife. We evaluated the effectiveness of the greater sage-grouse umbrella to conserve biodiversity using data-driven spatial occupancy and abundance models for seven sagebrush-dependent (obligate or associated) species across the greater Wyoming Basins Ecoregional Assessment (WBEA) area (345,300 km2) and assessed overlap with predicted sage-grouse occurrence. Predicted sage-grouse habitat from empirical models only partially (39–58%) captured habitats identified by predicted occurrence models for three sagebrush-obligate songbirds and 60% of biodiversity hotspots (richness of 4–6 species). Sage-grouse priority areas for conservation only captured 59% of model-predicted sage-grouse habitat, and only slightly fewer (56%) biodiversity hotspots. We suggest that the greater sage-grouse habitats may be partially effective as an umbrella for the conservation of sagebrush-dependent species within the sagebrush biome, and management actions aiming to conserve biodiversity should directly consider the explicit mapping of resource requirements for other taxonomic groups. Full article
Show Figures

Figure 1

Figure 1
<p>The location of the Wyoming Basins ecoregional area in the western continental United States. Sagebrush habitats include all sagebrush land cover types [<a href="#B61-land-13-00123" class="html-bibr">61</a>] mapped as sagebrush (see [<a href="#B55-land-13-00123" class="html-bibr">55</a>]).</p>
Full article ">Figure 2
<p>Species distribution range maps of individual sagebrush-dependent species (Brewer’s sparrow (<span class="html-italic">Spizella breweri</span>), sagebrush sparrow (<span class="html-italic">Artemisiospiza nevadensis</span>), sage thrasher (<span class="html-italic">Oreoscoptes montanus</span>), pronghorn (<span class="html-italic">Antilocapra americana</span>), green-tailed towhee (<span class="html-italic">Pipilo chlorurus</span>), and greater short-horned lizard (<span class="html-italic">Phrynosoma hernandesi</span>)) within the Wyoming Basins (left panels) with a course spatial estimate of biodiversity (richness, right panel) based on summing the range map overlaps for all six species. Original species range distributions are adapted from Hanser et al. [<a href="#B55-land-13-00123" class="html-bibr">55</a>]. Greater sage-grouse (<span class="html-italic">Centrocercus urophasianus</span>) range shown in hatching.</p>
Full article ">Figure 3
<p>The predicted occurrence of sagebrush-dependent species (Brewer’s sparrow (<span class="html-italic">Spizella breweri</span>), sagebrush sparrow (<span class="html-italic">Artemisiospiza nevadensis</span>), sage thrasher (<span class="html-italic">Oreoscoptes montanus</span>), pronghorn (<span class="html-italic">Antilocapra americana</span>), green-tailed towhee (<span class="html-italic">Pipilo chlorurus</span>) and greater short-horned lizard (<span class="html-italic">Phrynosoma hernandesi</span>)) within the Wyoming Basins based on predicted species abundance or occurrence models (left panels) with combined predictions estimating biodiversity (species richness, right panel) from summing predicted occurrence for all six species. Models are predicted at 90 m pixels and are adapted from Hanser et al. [<a href="#B55-land-13-00123" class="html-bibr">55</a>].</p>
Full article ">Figure 4
<p>Model-predicted habitat occurrence of sagebrush-dependent species (Brewer’s sparrow (<span class="html-italic">Spizella breweri</span>), sagebrush sparrow (<span class="html-italic">Artemisiospiza nevadensis</span>), sage thrasher (<span class="html-italic">Oreoscoptes montanus</span>], pronghorn (<span class="html-italic">Antilocapra americana</span>), green-tailed towhee (<span class="html-italic">Pipilo chlorurus</span>) and greater short-horned lizard (<span class="html-italic">Phrynosoma hernandesi</span>)) in comparison with predicted greater sage-grouse (GRSG; <span class="html-italic">Centrocercus urophasianus</span>; sage-grouse) habitat occurrence restricted to the sage-grouse range within the Wyoming Basin Ecoregional Assessment study area. Each panel shows the concordance (overlap) of each species’ predicted habitat (occurrence) with predicted sage-grouse habitat (occurrence). All models are based on 90 m predictions and are adapted from those developed by Hanser et al. [<a href="#B55-land-13-00123" class="html-bibr">55</a>].</p>
Full article ">Figure 5
<p>Predicted biodiversity (species richness) based on the sum of predicted occurrence for sagebrush-dependent species in the Wyoming Basins Ecoregional Assessment (WBEA) study area (Brewer’s sparrow (<span class="html-italic">Spizella breweri</span>), sagebrush sparrow (<span class="html-italic">Artemisiospiza nevadensis</span>), sage thrasher (<span class="html-italic">Oreoscoptes montanus</span>), green-tailed towhee (<span class="html-italic">Pipilo chlorurus</span>), greater short-horned lizard (<span class="html-italic">Phrynosoma hernandesi</span>), and pronghorn (<span class="html-italic">Antilocapra americana</span>)). (<b>A</b>) Richness across the greater sage-grouse (<span class="html-italic">Centrocercus urophasianus</span>; sage-grouse) range in the WBEA; (<b>B</b>) Richness overlap within sage-grouse model-predicted occurrence of greater sage-grouse (non-gray; predicted absences in gray); and (<b>C</b>) Richness overlap within priority areas for conservation (non-gray) is shown for evaluation of a single species umbrella or identified conservation areas to capture biodiverse habitat for sagebrush vertebrates. All models are based on 90 m predictions and are adapted from those developed by Hanser et al. [<a href="#B55-land-13-00123" class="html-bibr">55</a>].</p>
Full article ">
12 pages, 2794 KiB  
Brief Report
A Survey of the Dung-Dwelling Arthropod Community in the Pastures of the Northern Plains
by Ryan B. Schmid, Kelton D. Welch and Jonathan G. Lundgren
Insects 2024, 15(1), 38; https://doi.org/10.3390/insects15010038 - 6 Jan 2024
Cited by 3 | Viewed by 1733
Abstract
Grassland ecosystems of the Northern Plains have changed substantially since European settlement began in the latter half of the 19th century. This has led to significant changes to the dung-dwelling arthropod community in the region. As humans continue to modify large portions of [...] Read more.
Grassland ecosystems of the Northern Plains have changed substantially since European settlement began in the latter half of the 19th century. This has led to significant changes to the dung-dwelling arthropod community in the region. As humans continue to modify large portions of the landscape, inventories of ecologically significant communities are important to collect in order to monitor the long-term effects of anthropogenic biomes. We conducted a survey of the arthropod community dwelling in cattle dung from 40 pastures extending from northeast South Dakota to central North Dakota during the 2019 and 2020 grazing seasons. In sum, 51,283 specimens were collected from 596 dung pats, comprising a community of 22 orders. Coleoptera, Diptera, and Hymenoptera contributed to the majority (94.5%) of the community abundance. The mean pest abundance was low per pat (0.43 adult pests/pat), with 80% of the pats not containing any adult pest. Ecologically beneficial dung-feeding beetles, predators, and parasitoids were abundant in the region, but it was an inconsistent community, which may hinder ecosystem services. This highlights the need for future work to understand the mechanisms to increase the consistency of dung pat colonization for improved consistency of ecosystem services in the region. Full article
(This article belongs to the Section Insect Ecology, Diversity and Conservation)
Show Figures

Figure 1

Figure 1
<p>Pastures (n = 40) sampled for this study were located in Yellow Medicine County, MN; Burleigh County, ND; Dickey County, ND; Kidder County, ND; La Moure County, ND; Logan County, ND; McIntosh County, ND; Morton County, ND; Ransom County, ND; Codington County, SD; Deuel County, SD; Grant County, SD; Hamlin County, SD; and Roberts County, SD. Counties where sampling occurred are highlighted in black on state county maps.</p>
Full article ">Figure 2
<p>Overall (<b>A</b>) abundance and (<b>B</b>) species richness of arthropod orders collected during the survey.</p>
Full article ">Figure 3
<p>Mean (± SEM) arthropod (<b>A</b>) abundance, (<b>B</b>) species richness, (<b>C</b>) species diversity (exponential Shannon–Wiener H’), and (<b>D</b>) species evenness (Shannon equitability) per dung pat spanning the grazing season (early, mid, and late) during the 2019 and 2020 grazing seasons. Specifically, early sampling took place 12–28 June in 2019 and 2–11 June in 2020, mid-sampling took place 25 July–7 August in 2019 and 14–23 July in 2020, and late sampling took place 27 August–12 September in 2019 and 25 August–3 September in 2020.</p>
Full article ">Figure 4
<p>(<b>A</b>) Abundance and (<b>B</b>) species richness of specimens categorized into functional groups within the arthropod orders Araneae, Hymenoptera, Diptera, and Coleoptera. Quantity of morphospecies categorized in each order were as follows: 14 of 14 Araneae morphospecies, 109 of 122 Hymenoptera morphospecies, 169 of 210 Coleoptera morphospecies, and 132 of 281 Diptera morphospecies. Only the functional groups coprophagous, mycophagous, predator, and parasitoid were applied to these arthropod orders.</p>
Full article ">Figure 5
<p>Abundance of (<b>A</b>) dung-feeding beetles, (<b>B</b>) predators, (<b>C</b>) parasitoids, and (<b>D</b>) pests per dung pat. Each bar represents one sampled dung pat. Pats were sampled during the early, mid, and late periods of the 2019 and 2020 grazing seasons. Mean abundance (±SEM) is represented with red diamonds. Specific sampling dates were 12–28 June 2019 and 2–11 June 2020 (early), 25 July–7 August 2019 and 14–23 July 2020 (mid), and 27 August–12 September 2019 and 25 August–3 September 2020 (late).</p>
Full article ">
21 pages, 52577 KiB  
Article
Use of Remotely Piloted Aircraft System Multispectral Data to Evaluate the Effects of Prescribed Burnings on Three Macrohabitats of Pantanal, Brazil
by Harold E. Pineda Valles, Gustavo Manzon Nunes, Christian Niel Berlinck, Luiz Gustavo Gonçalves and Gabriel Henrique Pires de Mello Ribeiro
Remote Sens. 2023, 15(11), 2934; https://doi.org/10.3390/rs15112934 - 4 Jun 2023
Cited by 1 | Viewed by 2236
Abstract
The controlled use of fires to reduce combustible materials in prescribed burning helps to prevent the occurrence of forest fires. In recent decades, these fires have mainly been caused by anthropogenic activities. The study area is located in the Pantanal biome. In 2020, [...] Read more.
The controlled use of fires to reduce combustible materials in prescribed burning helps to prevent the occurrence of forest fires. In recent decades, these fires have mainly been caused by anthropogenic activities. The study area is located in the Pantanal biome. In 2020, the greatest drought in 60 years happened in the Pantanal. The fire affected almost one third of the biome. The objective of this study is to evaluate the effect of prescribed burnings carried out in 2021 on three macrohabitats (M1: natural grassland flooded with a proliferation of Combretum spp., M2: natural grassland of seasonal swamps, and M3: natural grassland flooded with a proliferation of Vochysia divergens) inside the SESC Pantanal Private Natural Heritage Reserve. Multispectral and thermal data analyses were conducted with remotely piloted aircraft systems in 1 ha plots in three periods of the dry season with early, mid, and late burning. The land use and land cover classification indicate that the predominant vegetation type in these areas is seasonally flooded grassland, with percentages above 73%, except in zone three, which has a more diverse composition and structure, with the presence of arboreal specimens of V. divergem Pohl. The pattern of the thermal range showed differentiation pre- and post-burning. The burned area index indicated that fire was more efficient in the first two macrohabitats because they are natural grasslands, reducing the grass species in the burnings. Early and mid prescribed burnings are a good option to reduce the continuous accumulation of dry forest biomass fuel material and help to promote landscape heterogeneity. The use of multispectral sensor data with high spatial/spectral resolution can show the effects of fires, using highly detailed scales for technical decision making. Full article
Show Figures

Figure 1

Figure 1
<p>Location of the study area.</p>
Full article ">Figure 2
<p>Flowchart of procedures performed in the study.</p>
Full article ">Figure 3
<p>Natural cover within the analysis plots for each period of PB evaluated.</p>
Full article ">Figure 4
<p>Orthomosaics of the three PB periods of each macrohabitat evaluated.</p>
Full article ">Figure 5
<p>Pre-PBs and post-PBs, thermal band behavior.</p>
Full article ">Figure 6
<p>Pre-PB and post-PB thermal band spatial behavior.</p>
Full article ">Figure 7
<p>Effect of burning on study macrohabitats.</p>
Full article ">Figure 8
<p>Fire severity—BAI in each macrohabitat and the evaluated period of PBs.</p>
Full article ">Figure 9
<p>PBs severity—BAI.</p>
Full article ">
33 pages, 5627 KiB  
Article
Wildfire Risk Zone Mapping in Contrasting Climatic Conditions: An Approach Employing AHP and F-AHP Models
by Aishwarya Sinha, Suresh Nikhil, Rajendran Shobha Ajin, Jean Homian Danumah, Sunil Saha, Romulus Costache, Ambujendran Rajaneesh, Kochappi Sathyan Sajinkumar, Kolangad Amrutha, Alfred Johny, Fahad Marzook, Pratheesh Chacko Mammen, Kamal Abdelrahman, Mohammed S. Fnais and Mohamed Abioui
Fire 2023, 6(2), 44; https://doi.org/10.3390/fire6020044 - 24 Jan 2023
Cited by 25 | Viewed by 5324
Abstract
Wildfires are one of the gravest and most momentous hazards affecting rich forest biomes worldwide; India is one of the hotspots due to its diverse forest types and human-induced reasons. This research aims to identify wildfire risk zones in two contrasting climate zones, [...] Read more.
Wildfires are one of the gravest and most momentous hazards affecting rich forest biomes worldwide; India is one of the hotspots due to its diverse forest types and human-induced reasons. This research aims to identify wildfire risk zones in two contrasting climate zones, the Wayanad Wildlife Sanctuary in the Western Ghats and the Kedarnath Wildlife Sanctuary in the Himalayas, using geospatial tools, analytical hierarchy process (AHP), and fuzzy-AHP models to assess the impacts of various conditioning factors and compare the efficacy of the two models. Both of the wildlife sanctuaries were severely battered by fires in the past, with more than 100 fire incidences considered for this modeling. This analysis found that both natural and anthropogenic factors are responsible for the fire occurrences in both of the two sanctuaries. The validation of the risk maps, utilizing the receiver operating characteristic (ROC) method, proved that both models have outstanding prediction accuracy for the training and validation datasets, with the F-AHP model having a slight edge over the other model. The results of other statistical validation matrices such as sensitivity, accuracy, and Kappa index also confirmed that F-AHP is better than the AHP model. According to the F-AHP model, about 22.49% of Kedarnath and 17.12% of Wayanad fall within the very-high risk zones. The created models will serve as a tool for implementing effective policies intended to reduce the impact of fires, even in other protected areas with similar forest types, terrain, and climatic conditions. Full article
Show Figures

Figure 1

Figure 1
<p>(<b>a</b>) Google Earth image showing the location of Kedarnath Wildlife Sanctuary (KWLS) and Wayanad Wildlife Sanctuary (WWLS) in India. (<b>b</b>) Fire incidence locations in KWLS. (<b>c</b>) Fire incidence locations in WWLS.</p>
Full article ">Figure 2
<p>Flowchart of the wildfire risk modeling adopted in this study.</p>
Full article ">Figure 3
<p>FRP distribution. (<b>a</b>) KWLS. (<b>b</b>)WWLS.</p>
Full article ">Figure 4
<p>Natural factors. (<b>a</b>) Land Surface Temperature (LST) of KWLS. (<b>b</b>) LST of WWLS. (<b>c</b>) Land cover types of KWLS. (<b>d</b>) Land cover types of WWLS. (<b>e</b>) Slope of KWLS. (<b>f</b>) Slope of WWLS. (<b>g</b>) Water Ratio Index (WRI) of KWLS. (<b>h</b>) WRI of WWLS. (<b>i</b>) Normalized Difference Water Index (NDWI) of KWLS. (<b>j</b>) NDWI of WWLS.</p>
Full article ">Figure 5
<p>Anthropogenic factors. (<b>a</b>) Distance from the road—KWLS. (<b>b</b>) Distance from the road—WWLS. (<b>c</b>) Distance from the tourist spot and pilgrim/religious center—KWLS. (<b>d</b>) Distance from the tourist spot and pilgrim/religious center—WWLS. (<b>e</b>) Distance from the settlement—KWLS. (<b>f</b>) Distance from the settlement—WWLS. (<b>g</b>) Normalized Difference Built-up Index (NDBI) of KWLS. (<b>h</b>) NDBI of WWLS.</p>
Full article ">Figure 6
<p>(<b>a</b>) Fire risk zone (FRZ) of KWLS (AHP model).(<b>b</b>) FRZ of WWLS (AHP model).(<b>c</b>) FRZ of KWLS (F-AHP model).(<b>d</b>) FRZ of WWLS (F-AHP model).</p>
Full article ">Figure 7
<p>ROC curve of KWLS—training dataset.</p>
Full article ">Figure 8
<p>ROC curve of KWLS—validation dataset.</p>
Full article ">Figure 9
<p>ROC curve of WWLS—training dataset.</p>
Full article ">Figure 10
<p>ROC curve of WWLS—validation dataset.</p>
Full article ">
16 pages, 4577 KiB  
Article
Inventory of China’s Net Biome Productivity since the 21st Century
by Chaochao Du, Xiaoyong Bai, Yangbing Li, Qiu Tan, Cuiwei Zhao, Guangjie Luo, Luhua Wu, Fei Chen, Chaojun Li, Chen Ran, Xuling Luo, Huipeng Xi, Huan Chen, Sirui Zhang, Min Liu, Suhua Gong, Lian Xiong, Fengjiao Song and Biqin Xiao
Land 2022, 11(8), 1244; https://doi.org/10.3390/land11081244 - 4 Aug 2022
Cited by 7 | Viewed by 2029
Abstract
Net biome productivity (NBP), which takes into account abiotic respiration and metabolic processes such as fire, pests, and harvesting of agricultural and forestry products, may be more scientific than net ecosystem productivity (NEP) in measuring ecosystem carbon sink levels. As one of the [...] Read more.
Net biome productivity (NBP), which takes into account abiotic respiration and metabolic processes such as fire, pests, and harvesting of agricultural and forestry products, may be more scientific than net ecosystem productivity (NEP) in measuring ecosystem carbon sink levels. As one of the largest countries in global carbon emissions, in China, however, the spatial pattern and evolution of its NBP are still unclear. To this end, we estimated the magnitude of NBP in 31 Chinese provinces (except Hong Kong, Macau, and Taiwan) from 2000 to 2018, and clarified its temporal and spatial evolution. The results show that: (1) the total amount of NBP in China was about 0.21 Pg C/yr1. Among them, Yunnan Province had the highest NBP (0.09 Pg C/yr1), accounting for about 43% of China’s total. (2) NBP increased from a rate of 0.19 Tg C/yr1 during the study period. (3) At present, NBP in China’s terrestrial ecosystems is mainly distributed in southwest and south China, while northwest and central China are weak carbon sinks or carbon sources. (4) The relative contribution rates of carbon emission fluxes due to emissions from anthropogenic disturbances (harvest of agricultural and forestry products) and natural disturbances (fires, pests, etc.) were 70% and 9.87%, respectively. This study emphasizes the importance of using NBP to re-estimate the net carbon sink of China’s terrestrial ecosystem, which is beneficial to providing data support for the realization of China’s carbon neutrality goal and global carbon cycle research. Full article
(This article belongs to the Special Issue Carbon Cycling in Terrestrial Ecosystems)
Show Figures

Figure 1

Figure 1
<p>The spatial distribution and evolution trend of China’s average NEP from 2000 to 2018 (Lack of data from Hong Kong, Macau, and Taiwan Province): (<bold>a</bold>) spatial distribution of annual average NEP, (<bold>b</bold>) changing trend.</p>
Full article ">Figure 2
<p>Spatial distribution and temporal evolution characteristics of natural and man-made carbon emissions (Lack of data from Hong Kong, Macau, and Taiwan Province): (<bold>a</bold>) Average annual carbon release due to activated carbon and biological ingestion, (<bold>b</bold>) Average annual carbon release from the use of agroforestry and grass products, (<bold>c</bold>) Annual average carbon release caused by forest fire and geological carbon leakage, (<bold>d</bold>) Temporal evolution characteristics of carbon release caused by activated carbon and biological intake, (<bold>e</bold>) Temporal evolution characteristics of carbon release caused by utilization of agricultural, forestry and grass products, (<bold>f</bold>) Temporal evolution characteristics of carbon release caused by forest fire and geological carbon leakage.</p>
Full article ">Figure 3
<p>The spatial distribution and evolution trend of China’s average NBP from 2000 to 2018 (Lack of data from Hong Kong, Macau, and Taiwan Province): (<bold>a</bold>) spatial distribution of annual average NBP, (<bold>b</bold>) changing trend.</p>
Full article ">Figure 4
<p>The evolution of the NBP anomalies in various regions of China from 2000 to 2018.</p>
Full article ">Figure 5
<p>Spatial distribution of contribution rates of FE<sub>RCCI</sub>, FE<sub>AD</sub>, and FE<sub>ND</sub> to NEP decline of the terrestrial ecosystem in China from 2000–2018 (Lack of data from Hong Kong, Macau, and Taiwan Province).</p>
Full article ">Figure 6
<p>Carbon budget components and carbon sink/source formation of terrestrial ecosystems in China during 2000–2018. The NEP in (<bold>a</bold>,<bold>b</bold>) are estimated from this study and data collected in the literature, respectively.</p>
Full article ">
Back to TopTop