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22 results for “protection zones”
Burnt forest area and CO2 emissions from fires in Russian forests by fire protection zones in 2010-2020
<p>The dataset is Supplementary Materials for the article ''<em>Reassessment of carbon emissions from fires and a new estimate of net carbon uptake in Russian forests in 2010-2020</em>'' in the Carbon Balance and Management journal. It contains files with burnt forest area and carbon dioxide emissions from fires data in Russia 2010-2020.<br> Article Supplementary materials are stored in the file Supplementary Tables and contains:</p> <p>- Table 1. Burnt forest area from NIR and MODIS (MCD64A1) in 2010-2020;</p> <p>- Table 2. Burnt forest area in the ground, aviation and the control (no fire protection) zones in 2010-2020 using MCD64A1;</p> <p>- Table 3. Carbon emissions from forest fires from National Inventory Report (NIR) and Copernicus Atmosphere Monitoring Service (CAMS) in 2010-2020</p> <p>Also, there is Supplementary Figure 1 with the Federal Districts of Russia schematic map.</p> <p>There are 22 files in GeoTIFF format for every year: </p> <p>1. Burnt forest area obtained using MODIS product MCD64A1 (250 m pixel, ESRI:102025). Coverage: -4064059.5401764437556267,1967242.6686790268868208 : 3658440.4598235562443733, 6012242.6686790268868208</p> <p>2. CO2 emissions using Copernicus Atmosphere Monitoring System (CAMS) (0.1 degrees, VGS 84). Coverage: 27.9493818283081055,42.9493612670349520 : 190.0498617200859712,78.0494651794433594<br> <br> In addition, we share Shapefiles:</p> <p>1. Russian borders (necessary to cut Russia from CO2 GeoTIFFs), EPSG:4326. Coverage: -180.0000000000000000,41.1888656599999976 : 180.00000000000000000,81.8562469499999992;</p> <p>2. Forest Fire Protection zoning in 2019: ground zone, aviation zone, the so-called control zone (no fire protection), EPSG:4326. Coverage: 27.4019779002987676,41.3483353426717599 : 173.8255532772949721,72.6575707670955353.</p>
Test Result for IIA Zone protection method.
<p>Test results related to the paper:</p> <p>"Application of IIA Method and Virtual Bus Theory for Backup Protection of a Zone Using PMU Data in a WAMPAC System", Energies Magazine, 2022. </p> <p>Link Energies: https://www.mdpi.com/1996-1073/15/9/3470</p> <p>DOI: <a href="https://doi.org/10.3390/en15093470">https://doi.org/10.3390/en15093470</a></p> <p>Authors of paper: Aníbal Antonio Prada Hurtado, Eduardo Martinez Carrasco, Maria Teresa Villén Martínez and Jose Saldaña.</p> <p> </p>
Fig. 5 in Assessment of coastal fish assemblages before the establishment of a new marine protected area in the central Mediterranean: its role in formulating a zoning proposal Abstract
Fig. 5: Size class frequency distributions of species of recreational value (i.e. attractive for SCUBA diving) recorded along the transects at each sector and depth range.
Fig. 4 in Assessment of coastal fish assemblages before the establishment of a new marine protected area in the central Mediterranean: its role in formulating a zoning proposal Abstract
Fig. 4: Total density (mean number of individuals/125 m2 ± S.E.) of fishes recorded along the transects at each (a) sector and (b) depth range.
Fig. 2 in Assessment of coastal fish assemblages before the establishment of a new marine protected area in the central Mediterranean: its role in formulating a zoning proposal Abstract
Fig. 2: Scatter plot of the canonical analysis of principal coordinates (CAP) based on the Bray–Curtis dissimilarities on the effects of (a) habitat type (RAR=rocky-algal reef; POM=Posidonia oceanica meadow; SOB=soft bottom) and (b) depth range.
Figure 2 in Rare and protected species of Caraboidea (Coleoptera) of the Steppe zone of Ukraine
Figure 2. Some habitats of rare Caraboidea species recorded within Steppe zone of Ukraine: A, B – steppe areas along Sivash Gulf (Kherson Region, Chaplynka district, vicinity of Novovolodymyrivka village; A – July 2019, B – September 2019); C – forest belt overgrown with Ulmus sp. mainly in the vicinity of Mala Ternivka village (Zaporizhzhia Region, Yakymivka district, May 2019); D, E – steppe areas in the vicinity of Shelyuhy village (Ukraine, Zaporizhzhia Region, Yakymivka district, June 2019); F – sparse forest belt used as pasture, vicinity of Myrne village (Zaporizhzhia Region, Yakymivka district, August 2019).
Figure 1 in Rare and protected species of Caraboidea (Coleoptera) of the Steppe zone of Ukraine
Figure 1. Cephalota besseri (Dejean, 1826) and its habitat: A – total view of imago of the species; B. – solonchaks near Bulakhivskyy Lyman Lake (June 2012).
Supplementary material 1 from: Virkkala R, Rajasärkkä A (2012) Preserving species populations in the boreal zone in a changing climate: contrasting trends of bird species groups in a protected area network. Nature Conservation 3: 1-20. https://doi.org/10.3897/natureconservation.3.3635
Mean densities and number of observations of species in 1981–1999 and in 2000–2009
Fig. 1 in Assessment of coastal fish assemblages before the establishment of a new marine protected area in the central Mediterranean: its role in formulating a zoning proposal Abstract
Fig. 1: Map of the study area with indication of sampling sectors around the promontory of Milazzo.
Supporting information from: Chorotypes, zones for the conservation of Scarabaeoidea, and representativity in protected areas of El Salvador
Open the record for dataset details and reuse information.
Data from: Quantification and characterization of vegetation and functional trait diversity of riparian zones of the protected forest of Kashmir Himalaya, India
Open the record for dataset details and reuse information.
Figure 6 from: Moulin N (2020) A cryptic new species of Chlidonoptera Karsch, 1892 from the south west protected zone of the Central African Republic (Insecta, Mantodea, Hymenopodidae). ZooKeys 917: 63-83. https://doi.org/10.3897/zookeys.917.39270
Figure 6 Differences between genitalia: left to right, C. lestoni, C. vexillum, and C. roxanae sp. nov. Scale bar: 1.00 mm.
Figure 2 from: Moulin N (2020) A cryptic new species of Chlidonoptera Karsch, 1892 from the south west protected zone of the Central African Republic (Insecta, Mantodea, Hymenopodidae). ZooKeys 917: 63-83. https://doi.org/10.3897/zookeys.917.39270
Figure 2 A Male Chlidonoptera roxanae sp. nov. photographed in the Dzanga-Ndoki National Park (CAR), by Philippe Annoyer B female Chlidonoptera vexillum group photographed in the forest surrounding Sanaga Yong Chimpanzee Rescue Centre, Belabo, East Province (Cameroon), by Sean Brogan.
Figure 7 from: Moulin N (2020) A cryptic new species of Chlidonoptera Karsch, 1892 from the south west protected zone of the Central African Republic (Insecta, Mantodea, Hymenopodidae). ZooKeys 917: 63-83. https://doi.org/10.3897/zookeys.917.39270
Figure 7 Barcode tree of Chlidonoptera from Central Africa created in BOLD using a Neighbour-Joining analysis.
Figure 1 from: Moulin N (2020) A cryptic new species of Chlidonoptera Karsch, 1892 from the south west protected zone of the Central African Republic (Insecta, Mantodea, Hymenopodidae). ZooKeys 917: 63-83. https://doi.org/10.3897/zookeys.917.39270
Figure 1 Distribution map of Chlidonoptera species. Source: http://www.gadm.org Global Administrative areas Data and Maps (GADM).
Figure 4 from: Moulin N (2020) A cryptic new species of Chlidonoptera Karsch, 1892 from the south west protected zone of the Central African Republic (Insecta, Mantodea, Hymenopodidae). ZooKeys 917: 63-83. https://doi.org/10.3897/zookeys.917.39270
Figure 4 Chlidonoptera, dorsal habitus: AC. vexillum, male, Mbalmayo, Cameroon, BOLD NMMAN11-0541 BC. vexillum, male, Arboretum Raponda Walker, Gabon, BOLD MANGAB15-094 CC. vexillum, male, Biosphere Reserve of Dimonika, Republic of the Congo, BOLD NMMAN11-0487 DC. vexillum, male, Minziro Forest, Tanzania, BOLD NMMAN11-0533 EC. vexillum, male, Kalinzu Forest, Uganda FC. roxanae sp. nov., holotype male, base camp, lake #1, Dzanga-Ndoki NP, CAR, BOLD NMMAN11-0404 GC. lestoni, paratype male, Tafo, Ghana (S. Poulain) HC. chopardi, male, San Pedro, Ivory Coast.
Figure 5 from: Moulin N (2020) A cryptic new species of Chlidonoptera Karsch, 1892 from the south west protected zone of the Central African Republic (Insecta, Mantodea, Hymenopodidae). ZooKeys 917: 63-83. https://doi.org/10.3897/zookeys.917.39270
Figure 5 Chlidonoptera roxanae sp. nov., holotype male, Genitalia. afa = phalloid apophysis; paa = apical processof left phallomere, titillator; bl = basal lobe of ventral phallomere; loa = membranous lobe; pda = primary distal process; sdpl = lateral secondary distal process; sdpm = median secondary distal process; vla = ventral lobe of ventral phallomere. Scale bars: 1.00 mm.
Figure 3 from: Moulin N (2020) A cryptic new species of Chlidonoptera Karsch, 1892 from the south west protected zone of the Central African Republic (Insecta, Mantodea, Hymenopodidae). ZooKeys 917: 63-83. https://doi.org/10.3897/zookeys.917.39270
Figure 3 Chlidonoptera roxanae sp. nov., holotype male, dorsal and ventral habitus. Scale bar: 10.00 mm.
Data from: Integrating encounter theory with decision analysis to evaluate collision risk and determine optimal protection zones for wildlife
1. Better understanding human-wildlife interactions and their links with management can help improve the design of wildlife protection zones. One important example is the problem of wildlife collisions with vehicles or human-built structures (e.g. power lines, wind farms). In fact, collisions between marine wildlife and watercraft are among the major threats faced by several endangered species of marine mammals. Natural resource managers are therefore interested in finding cost-effective solutions to mitigate these threats. 2. We combined abundance estimators with encounter rate theory to estimate relative lethal collision risk of the Florida manatee (Trichechus manatus latirostris) from watercraft. We first modeled seasonal abundance of watercraft and manatees using a Bayesian analysis of aerial survey count data. We then modeled relative lethal collision risk in space and across seasons. Finally, we applied decision analysis and Linear Integer Programming to determine the optimal design of speed zones in terms of relative risk to manatees and costs to waterway users. We used a Pareto efficient frontier approach to evaluate the performance of alternative zones, which included additional practical considerations (e.g. spatial aggregation of speed zones) in relation to the optimal zone configurations. 3. Under the various relationships for probability of death given strike speed that we considered, the current speed zones reduced the relative lethal collision risk by an average of 51.5% to 70% compared to the scenario in which all speed regulations were removed (i.e. the no-protection scenario). We identified optimal zones and near-optimal zones with additional management considerations that improved upon the current zones in terms of cost or relative risk. 4. Policy Implications: Our analytical framework combines encounter rate theory and decision analysis to quantify the effectiveness of speed zones protecting manatees while accounting for uncertainty. Our approach can be used to optimize the design of protection zones intended to reduce conflicts between human waterborne activity and marine mammals. This framework could be extended to address many other problems of human-wildlife interactions, such as the optimal placement of wind farms to minimize collisions with wildlife or the optimal allocation of ranger effort to mitigate poaching threats.
Fig. 3 in Assessment of coastal fish assemblages before the establishment of a new marine protected area in the central Mediterranean: its role in formulating a zoning proposal Abstract
Fig. 3: Species richness (mean number of species ± S.E.) of fishes recorded along the random courses at each (a) sector, (b) habitat type (POM=Posidonia oceanica meadow; RAR=rockyalgal reef; SOB=soft bottom) and (c) depth range.
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