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40 results for “Kruger National Park”
Woody vegetation composition and structure at long-term monitoring plots on the Stevenson-Hamilton Research Supersite, Kruger National Park, South Africa (2012)
This dataset contains measurements of woody vegetation composition and structural attributes collected in 2012 from long-term ecological monitoring plots located on the Stevenson-Hamilton Research Supersite in the Kruger National Park, South Africa. The study region is characterized by granitic soils, broad-leaved savanna vegetation, and a long history of fire, herbivory, and climate-driven ecological dynamics. Vegetation surveys were conducted in sixteen 0.25-ha sampling plots to quantify woody species composition, stem density, and size structure. Additional measurements of vegetation structure were collected, including grass biomass, canopy cover, canopy height, and canopy diversity, providing a broader assessment of both woody and herbaceous layers. These data establish an important baseline for monitoring ecological change, evaluating woody vegetation dynamics under variable fire and herbivore regimes, and supporting ongoing research on savanna ecosystem functioning within the Kruger National Park.
Riparian Woody Vegetation Composition and Structure in Long-Term Monitoring Plots Along the Sabie River, Kruger National Park (2011-2012)
This dataset contains measurements of woody vegetation composition and structural attributes collected in 2009 and 2010 from 15 long-term riparian monitoring plots located along the Sabie River in the southern region of the Kruger National Park, South Africa. The Sabie River is the park’s most perennial river system and supports diverse riparian plant communities influenced by hydrological variability, flooding dynamics, sediment deposition, herbivory, and climate-driven disturbance. Within each monitoring plot, field teams recorded woody species identity, stem density, plant height, and stem diameter. Additional structural and condition indicators were collected, including canopy breakage, evidence of bark stripping, resprouting status, and whether individuals were toppled or alive at the time of sampling. These structural attributes provide detailed assessments of disturbance impacts and vegetation condition within riparian zones. Data collection followed the same standardized protocols as the Southern Granites long-term vegetation monitoring program, allowing for cross-site comparisons between upland savanna and riparian systems. This dataset provides a baseline for evaluating long-term ecological change in riparian woody plant communities and supports ongoing research on the ecological functioning and resilience of river corridors in Kruger National Park.
Woody Cover Mapping in the Kruger National Park using Sentinel-1 time series and LiDAR data
<p>This data repository presents a workflow to derive woody cover information for the Kruger National Park, South Africa, from freely available Sentinel-1 C-Band time series and LiDAR data (modified from Smit et al. 2016) using machine learning (MLR and Ranger in R). The methodology is described in following publication:</p> <p><em>Urban, M., K. Heckel, C. Berger, P. Schratz, I.P.J. Smit, T. Strydom, J. Baade & C. Schmullius (2020): Woody Cover Mapping in the Savanna Ecosystem of the Kruger National Park Using Sentinel-1 C-Band Time Series Data. Koedoe.</em></p> <p>In order to derive woody cover percentage information, download all files into one folder and run the R-Files consecutively from 01_ to 04_. Follow the instruction within each of the R-Files, which are written as comments in the programming code.</p> <p>The data repository consist of the following files:</p> <p><strong>R-Files:</strong></p> <p>1. Script 1: 01_MLR_tune_spatial_final</p> <p>2. Script 2: 02_MLR_cross_validation_spatial_final</p> <p>3. Script 3: 03_MLR_RANGER_train_final</p> <p>4. Script 4: 04_MLR_prediction_woody_cover_final</p> <p> </p> <p><strong>Training dataset - ENVI FILE (layerstack of Sentinel-1 VH and VV backscatter between 2016 and 2017 and the woody cover reference derived from the LiDAR data) :</strong></p> <p>1. S1_A_VH_VV_16_17_lidar</p> <p> </p> <p><strong>Data for prediction - ENVI FILES (3 example regions in the Kruger National Park):</strong></p> <p>1. S1_A_VH_VV_16_17_subset_example_Letaba_Rest_Camp</p> <p>2. S1_A_VH_VV_16_17_subset_example_Lower_Sabie</p> <p>3. S1_A_VH_VV_16_17_subset_example_Pafuri</p> <p> </p> <p><strong>Final woody cover maps of the Kruger National Park:</strong></p> <p>1. xx_woody_cover_map_final.rar (contains final maps in 10m, 30m, 50m and 100m spatial resolution as .tif and a QGIS project)</p> <p> </p> <p><em>References:</em></p> <p>Smit, I.P.J., Asner, G.P., Govender, N., Vaughn, N.R. & Wilgen, B.W. van, 2016, ‘An examination of the potential efficacy of high-intensity fires for reversing woody encroachment in savannas’, <em>Journal of Applied Ecology</em>, 53(5), 1623–1633.</p>
Figure 4 in Diatom responses to river water quality in the Kruger National Park, South Africa
Figure 4. The relationship between SPI and two physicochemical water quality parameters. Open symbols represent rivers with large catchments outside the KNP. Closed represent rivers with relatively small parts of their catchments outside the KNP. In both cases, the dark- er the symbol, the larger the catchment.
Figure 3 in Diatom responses to river water quality in the Kruger National Park, South Africa
Figure 3. Two-dimension MDS plot of diatom community composition in 1983 and 2015 at different sites along the Olifants (OM and OC), Letaba (LC and LK) and Sabie rivers (SL and SB). Note that all sites' diatom composition shifted significantly from 1983 to 2015 irrespective of whether the index values changed or not (see Table 4). Sample site abbreviations also in Table 4.
Figure 2 in Diatom responses to river water quality in the Kruger National Park, South Africa
Figure 2. Long-term changes in two selected river chemical parameters in the Olifants, Letaba and Sabie rivers between 1983 and 2015. The closed symbols represent data extracted from the Department of Water Affairs and Sanitation. Open symbols represent the values recorded at sites within the three focal rivers during the present study in 2015.
Figure 1. A in Diatom responses to river water quality in the Kruger National Park, South Africa
Figure 1. A map of the Kruger National Park, showing the location of the study area and diatom sampling sites.
Fig. 1 in Epizootic ulcerative syndrome - First report of evidence from South Africa's largest and premier conservation area, the Kruger National Park
Fig. 1. Sampling location from which evidence of genomic material of Aphanomyces invadans was obtained in Clarias gariepinus from Nhlangaluwe Pan on the Limpopo River in the Kruger National Park, South Africa.
Fig. 2 in Epidemiology of Anaplasma marginale and Anaplasma centrale infections in African buffalo (Syncerus caffer) from Kruger National Park, South Africa
Fig. 2. Individual value plots showing the distribution of results for intensity of infection (log-transformed number of copies/reaction) with Anaplasma marginale (a) and Anaplasma centrale (b), using a real-time qPCR from a managed African buffalo (Syncerus caffer) herd from Kruger National Park, South Africa. Error bars represent one standard error, numbers at the top of figure represent sample size.
Fig. 1 in Epidemiology of Anaplasma marginale and Anaplasma centrale infections in African buffalo (Syncerus caffer) from Kruger National Park, South Africa
Fig. 1. The proportion of animals infected with Anaplasma spp. from a managed African buffalo (Syncerus caffer) herd from Kruger National Park, South Africa. a) The mean prevalence of animals with A. marginale single infection, A. centrale single infection, or co-infections with each other over four age groups: calves (0–1 years old), sub-adults (1–5.5 years old), adults (5.5–15 years) and geriatrics (15 years plus); b) the mean prevalence of new infections with A. marginale or A. centrale for each capture event over the two-and-a-half-year study period. Numbers at the top of figure represent sample size.
Fig. 3 in Epidemiology of Anaplasma marginale and Anaplasma centrale infections in African buffalo (Syncerus caffer) from Kruger National Park, South Africa
Fig. 3. Patterns of infection with Anaplasma spp. based on age and sex for a managed African buffalo (Syncerus caffer) herd from Kruger National Park, South Africa. a) The infection intensity (log-transformed number of copies/reaction) of A. marginale and A. centrale based on age (years); b) overall proportion of animals infected with A. marginale or A. centrale based on sex; c) infection intensity (log-transformed number of copies/reaction) results for A. marginale or A. centrale based on sex. * indicates statistical significance (p <0.05). Error bars are standard error.
Buffalo close association matrices: Kruger National Park, South Africa
<div class="ListContainerWrapper SCXW115066823 BCX0"> <ol class="NumberListStyle1 SCXW115066823 BCX0"> <li class="OutlineElement Ltr SCXW115066823 BCX0"> <p class="Paragraph SCXW115066823 BCX0"><span class="TextRun SCXW115066823 BCX0"><span class="NormalTextRun SCXW115066823 BCX0">Many infectious pathogens are shared through social interactions, and examining host connectivity has offered valuable insights for understanding patterns of pathogen transmission across wildlife species. African buffalo are social ungulates and i</span><span class="NormalTextRun SCXW115066823 BCX0">mportant reservoirs of </span><span class="ContextualSpellingAndGrammarError SCXW115066823 BCX0">directly-transmitted</span><span class="NormalTextRun SCXW115066823 BCX0"> pathogens that impact numerous wildlife and livestock species. </span></span><span class="EOP SCXW115066823 BCX0"> </span></p> </li> </ol> </div> <div class="ListContainerWrapper SCXW115066823 BCX0"> <ol class="NumberListStyle1 SCXW115066823 BCX0"> <li class="OutlineElement Ltr SCXW115066823 BCX0"> <p class="Paragraph SCXW115066823 BCX0"><span class="TextRun SCXW115066823 BCX0"><span class="NormalTextRun SCXW115066823 BCX0">Here, we analyzed African buffalo social networks to quantify variation in close contacts, examined drivers of contact heterogeneity, and investigat</span><span class="NormalTextRun SCXW115066823 BCX0">ed how the observed contact patterns affect pathogen invasion likelihoods for a wild social ungulate. </span></span><span class="EOP SCXW115066823 BCX0"> </span></p> </li> </ol> </div> <div class="ListContainerWrapper SCXW115066823 BCX0"> <ol class="NumberListStyle1 SCXW115066823 BCX0"> <li class="OutlineElement Ltr SCXW115066823 BCX0"> <p class="Paragraph SCXW115066823 BCX0"><span class="TextRun SCXW115066823 BCX0"><span class="NormalTextRun SCXW115066823 BCX0">We collected continuous association data using proximity collars and sampled host traits approximately every two months during a 15-month study period in</span><span class="NormalTextRun SCXW115066823 BCX0"> Kruger National Park, South Africa. </span></span><span class="EOP SCXW115066823 BCX0"> </span></p> </li> </ol> </div> <div class="ListContainerWrapper SCXW115066823 BCX0"> <ol class="NumberListStyle1 SCXW115066823 BCX0"> <li class="OutlineElement Ltr SCXW115066823 BCX0"> <p class="Paragraph SCXW115066823 BCX0"><span class="TextRun SCXW115066823 BCX0"><span class="NormalTextRun SCXW115066823 BCX0">Although the observed herd was well connected, with most individuals contacting each other during each bimonthly interval, our analyses revealed striking heterogeneity in close-contact associations among herd members. </span><span class="NormalTextRun SCXW115066823 BCX0">Network analysis showed that individual connectivity was stable over time and that individual age, sex, reproductive status, and pairwise genetic relatedness were important predictors of buffalo connectivity. Calves were the most connected members of the h</span><span class="NormalTextRun SCXW115066823 BCX0">erd, and adult males were the least connected. These findings highlight the role susceptible calves may play in the transmission of pathogens within the herd. We also demonstrate that, at time scales relevant to infectious pathogens found in nature, the ob</span><span class="NormalTextRun SCXW115066823 BCX0">served level of connectivity affects pathogen invasion likelihoods for a wide range of infectious periods and </span><span class="SpellingError SCXW115066823 BCX0">transmissibilities</span><span class="NormalTextRun SCXW115066823 BCX0">. </span></span><span class="EOP SCXW115066823 BCX0"> </span></p> </li> </ol> </div> <div class="ListContainerWrapper SCXW115066823 BCX0"> <ol class="NumberListStyle1 SCXW115066823 BCX0"> <li class="OutlineElement Ltr SCXW115066823 BCX0"> <p class="Paragraph SCXW115066823 BCX0"><span class="TextRun SCXW115066823 BCX0"><span class="NormalTextRun SCXW115066823 BCX0">Ultimately, our study identifies key predictors of social connectivity in a social ungulate and illustrates how contact heter</span><span class="NormalTextRun SCXW115066823 BCX0">ogeneity, even within a highly connected herd, can shape pathogen invasion likelihoods. </span></span><span class="EOP SCXW115066823 BCX0"> </span></p> </li> </ol> </div>
Buffalo close association matrices: Kruger National Park, South Africa
Open the record for dataset details and reuse information.
Distribution. NE South Africa, known only from three small, widely separated locations: Bronberg Ridge area in SE Pretoria, Gauteng Province (18 sites), Nylsvley Nature Reserve and surrounding farms on Nyl floodplain in Modimolle district, Limpopo Province (9 sites), and near Pretoriuskop Camp, Fayi area (Macili and Machulwane), and Numbi Gate in SW Kruger National Park, Mpumalanga Province (4 sites). in Chrysochloridae
Distribution. NE South Africa, known only from three small, widely separated locations: Bronberg Ridge area in SE Pretoria, Gauteng Province (18 sites), Nylsvley Nature Reserve and surrounding farms on Nyl floodplain in Modimolle district, Limpopo Province (9 sites), and near Pretoriuskop Camp, Fayi area (Macili and Machulwane), and Numbi Gate in SW Kruger National Park, Mpumalanga Province (4 sites).
Kruger National Park medium to large mammal species herd sizes & distances to the road - fieldwork data
<p>The dataset consists of systematically recorded presences, from a vehicle, of medium to large mammal species within the Kruger National Park. There is 401 tar and 369 dirt road points within the dataset. Each dataset point has data on species presence, estimated proximity of the animals to the road and herd sizes. Each point also has the following data: estimated cloud cover percentage, surface wetness, rain, co-ordinates, vegetation biome (savanna), road surface, distance to the nearest natural water source and the total number of vehicles passing that point within a 10 minute period.</p>
Fig. 1 in Complexities associated with elephant impact on Sclerocarya birrea subsp. caffra in the Greater Kruger National Park
Fig. 1. Location of the surveyed marula trees across eight transects in Jejane Private Nature Reserve, Greater Kruger National Park.
Fig. 2 in Complexities associated with elephant impact on Sclerocarya birrea subsp. caffra in the Greater Kruger National Park
Fig. 2. Elephant impact levels on marula tree height classes in Jejane Private Nature Reserve. Different letters indicate significant differences in elephant impact levels.
Figure 5 in Landscape-scale surveys reveal patterns of floral visitation by species of Scarabaeidae (Coleoptera) in the Kruger National Park, South Africa
Figure 5. Adult of Pedinorrhina trivittata (Schaum) on flowers of Peltophorum africanum Sonder (Kruger National Park, South Africa).
Figure 4 in Landscape-scale surveys reveal patterns of floral visitation by species of Scarabaeidae (Coleoptera) in the Kruger National Park, South Africa
Figure 4. Adult of Leucocelis amethystina (MacLeay) on flowers of Grewia bicolor Jussieu (Kruger National Park, South Africa).
Figure 2 in Landscape-scale surveys reveal patterns of floral visitation by species of Scarabaeidae (Coleoptera) in the Kruger National Park, South Africa
Figure 2. Adult of Amazula suavis (Burmeister) on flowers of Terminalia sericea Burchell (Kruger National Park, South Africa).
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