Understanding the dynamics of tree species and their demography is necessary for predicting future developments in savanna ecosystems. In this contribution, elephant-tree and firewood collector-tree interactions are compared using a multiagent model. To investigate these dynamics, we compared three different tree species in two plots. The first plot is located in the protected space of Kruger National Park (KNP), South Africa, and the second plot in the rural areas of the Bushbuckridge Municipality, South Africa. The agent-based modeling approach enabled the modeling of individual trees with characteristics such as species, age class, size, damage class, and life history. A similar level of detail was applied to agents that represent elephants and firewood collectors. Particular attention was paid to modeling purposeful behavior of humans in contrast to more instinct-driven actions of elephants. The authors were able to predict future developments by simulating the time period between 2010 and 2050 with more than 500,000 individual trees. Modeling individual trees for a time span of 40 years might yield more detailed information than a simple woody mass aggregation. The results indicate a significant trend toward more and thinner trees together with a notable reduction in mature trees, while the total aboveground biomass appears to stay more or less constant. Furthermore, the KNP scenarios show an increase in young Combretum apiculatum, which may correspond to bush encroachment.
Conservation areas, like national parks, are hotspots of social-ecological and social-economic activities. The resulting interactions contribute to an inherent complexity of these systems, making simulation models a vital form of support for their management activities. These models are often unimodal, i.e., limited by design to only one particular question or a specific temporal and spatial scale. We implemented the cross-scale and multi-modal base model MARS KNP for the Kruger National Park, South Africa that combines the agent-based paradigm with a dynamic vegetation model. As a proof-of-concept, we developed an elephant movement model within MARS KNP to evaluate the base model's decision-support capabilities. The study was mainly focused on the underlying software mechanisms that allow easy integration of multi-scale spatio-temporal data objects. MARS agents can probe, interact with, and modify these objects. We found that this feature is essential for a cross-scale integration of different modeling approaches. Additionally, we propose a definition of the term ‘base model’ to shorten the provisioning time of decision-support tools. A spatio-temporal mechanism couples the large-scale multi-agent modeling and simulation framework MARS and the dynamic vegetation model aDGVM. We propose a definition of the term ‘base model’ to shorten the provisioning time of decision-support tools. A base model including prototypical elephant agents was created for the Kruger National Park, South Africa.
We studied the impacts of livestock grazing on carbon budgets in the semi-arid South African Karoo by comparing two sites under different grazing intensities. The previously overgrazed site, characterised by unpalatable grasses and thus poorly suited as pasture, sequestered more carbon over the four-year measurement period, compared to the lenient-grazed site. The studied ecosystems act as either carbon sinks or sources depending on precipitation.
Savannas are heterogeneous ecosystems, composed of varied spatial combinations and proportions of woody and herbaceous vegetation. Most field-based inventory and remote sensing methods fail to account for the lower stratum vegetation (i.e., shrubs and grasses), and are thus underrepresenting the carbon storage potential of savanna ecosystems. For detailed analyses at the local scale, Terrestrial Laser Scanning (TLS) has proven to be a promising remote sensing technology over the past decade. Accordingly, several review articles already exist on the use of TLS for characterizing 3D vegetation structure. However, a gap exists on the spatial concentrations of TLS studies according to biome for accurate vegetation structure estimation. A comprehensive review was conducted through a meta-analysis of 113 relevant research articles using 18 attributes. The review covered a range of aspects, including the global distribution of TLS studies, parameters retrieved from TLS point clouds and retrieval methods. The review also examined the relationship between the TLS retrieval method and the overall accuracy in parameter extraction. To date, TLS has mainly been used to characterize vegetation in temperate, boreal/taiga and tropical forests, with only little emphasis on savannas. TLS studies in the savanna focused on the extraction of very few vegetation parameters (e.g., DBH and height) and did not consider the shrub contribution to the overall Above Ground Biomass (AGB). Future work should therefore focus on developing new and adjusting existing algorithms for vegetation parameter extraction in the savanna biome, improving predictive AGB models through 3D reconstructions of savanna trees and shrubs as well as quantifying AGB change through the application of multi-temporal TLS. The integration of data from various sources and platforms e.g., TLS with airborne LiDAR is recommended for improved vegetation parameter extraction (including AGB) at larger spatial scales. The review highlights the huge potential of TLS for accurate savanna vegetation extraction by discussing TLS opportunities, challenges and potential future research in the savanna biome.
Climatic and land management factors, such as water availability and grazing intensity, play an important role in seasonal and annual variability of the ecosystem–atmosphere exchange of CO2 in semi-arid ecosystems. However, the semi-arid South African ecosystems have been poorly studied. Four years of measurements (November 2015–October 2019) were collected and analysed from two eddy covariance towers near Middelburg in the Karoo, Eastern Cape, South Africa. We studied the impact of grazing intensity on the CO2 exchange by comparing seasonal and interannual CO2 fluxes for two sites with almost identical climatic conditions but different intensity of current and historical livestock grazing. The first site represents lenient grazing (LG) and the vegetation comprises a diverse balance of dwarf shrubs and grasses, while the second site has been degraded through heavy grazing (HG) in the past but then rested for the past 10 years and mainly consists of unpalatable grasses and ephemeral species. Over the observation period, we found that the LG site was a considerable carbon source (82.11 g C m−2), while the HG site was a slight carbon sink (−36.43 g C m−2). The annual carbon budgets ranged from −90 ± 51 g C m−2 yr−1 to 84 ± 43 g C m−2 yr−1 for the LG site and from −92 ± 66 g C m−2 yr−1 to 59 ± 46 g C m−2 yr−1 for the heavily grazed site over the four years of eddy covariance measurements. The significant variation in carbon sequestration rates between the last two years of measurement was explained by water availability (25 % of the precipitation deficit in 2019 compared to the long-term mean precipitation). This indicates that studied ecosystems can quickly switch from a considerable carbon sink to a considerable carbon source ecosystem. Our study shows that the CO2 dynamics in the Karoo are largely driven by water availability and the current and historical effects of livestock grazing intensity on aboveground biomass (AGB). The higher carbon uptake at the HG site indicates that resting period after overgrazing, together with the transition to unpalatable drought-tolerant grass species, creates conditions that are favourable for carbon sequestration in the Karoo ecosystems, but unproductive as Dorper sheep pasture. Furthermore, we observed a slight decrease in carbon uptake peaks at the HG site in response to resuming continuous grazing (July 2017).
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Anthropogenic climate change is expected to impact ecosystem structure, biodiversity and ecosystem services in Africa profoundly. We used the adaptive Dynamic Global Vegetation Model (aDGVM), which was originally developed and tested for Africa, to quantify sources of uncertainties in simulated African potential natural vegetation towards the end of the 21st century. We forced the aDGVM with regionally downscaled high‐resolution climate scenarios based on an ensemble of six general circulation models (GCMs) under two representative concentration pathways (RCPs 4.5 and 8.5). Our study assessed the direct effects of climate change and elevated CO2 on vegetation change and its plant‐physiological drivers. Total increase in carbon in aboveground biomass in Africa until the end of the century was between 18% to 43% (RCP4.5) and 37% to 61% (RCP8.5) and was associated with woody encroachment into grasslands and increased woody cover in savannas. When direct effects of CO2 on plants were omitted, woody encroachment was muted and carbon in aboveground vegetation changed between –8 to 11% (RCP 4.5) and –22 to –6% (RCP8.5). Simulated biome changes lacked consistent large‐scale geographical patterns of change across scenarios. In Ethiopia and the Sahara/Sahel transition zone, the biome changes forecast by the aDGVM were consistent across GCMs and RCPs. Direct effects from elevated CO2 were associated with substantial increases in water use efficiency, primarily driven by photosynthesis enhancement, which may relieve soil moisture limitations to plant productivity. At the ecosystem level, interactions between fire and woody plant demography further promoted woody encroachment. We conclude that substantial future biome changes due to climate and CO2 changes are likely across Africa. Because of the large uncertainties in future projections, adaptation strategies must be highly flexible. Focused research on CO2 effects, and improved model representations of these effects will be necessary to reduce these uncertainties. Climate change and elevated CO2 are expected to drive vegetation changes in Africa. We used an ensemble of dynamic vegetation model simulations to assess the impacts of these drivers on carbon stocks and biomes until 2099. Climate change and elevated CO2 led to an 18% to 61% increase in carbon stocks, which was primarily driven by CO2 fertilization. Associated biome changes are likely across Africa, especially changes from savanna to forest. Disabling CO2 fertilization resulted in a −22% to +11% change in carbons stocks. These large uncertainties in future projections imply that adaptation strategies need to be flexible.
berbewirtschaftung, Verbuschung und Klimawandel-Was geschieht mit den Bäumen in der Savanne Südafrikas? Ergebnisse einer Agenten-basierten Modellierung des Savannen-Ökosystems in zwei verschiedenen Nutzungsformen Lenfers, U. A.; Glake, D.; Ocker, F.; Clemen, T. Zusammenfassung: Bäume in der Savanne werden in hohem Maße von Tieren und Menschen genutzt. In dieser Studie werden unterschiedliche Baumarten in einem Simulationszeitraum von 2010 bis 2050 unter zwei verschiedenen Klimaszenarien mittel Agenten-basierter Modellierung verglichen. Besondere Betrachtung gilt dem Vergleich zwischen natur-naher Nutzung im Krüger Nationalpark (Elefanten) und einer Nutzung im ländlichen Raum (Feuerholzsammler). Agenten-basierte Modellierung ermöglicht es sowohl aggregierte Biomassewerte aller Bäume über den Zeitverlauf zu beobachten, als auch die verschiedenen Baumarten und Altersgruppen miteinander zu vergleichen. Es zeigt sich, dass bereits innerhalb der Arten und der Altersklassen Veränderungen zu beobachten sind, die in den aggregierten Werten so nicht zu erkennen sind. Auch wenn Bäume sehr langsam auf Veränderungen reagieren, lassen sich aus Altersgruppenverschiebungen und den Veränderungen in der Artenzusammensetzung Tendenzen ableiten, die es erlauben einen Blick in zukünftige Verteilungen unterschiedlicher Baumarten unter verschiedenen Nutzungen zu wagen. Damit können die Ergebnisse dieser Arbeit auch als ein Frühwarnsystem für die Entscheider vor Ort dienen.
The savanna ecosystems in South Africa, which are predominantly characterised by woody vegetation (e.g. shrubs and trees) and grasslands with annual phenological cycles, are shaped by ecosystem processes such as droughts, fires and herbivory interacting with management actions. Therefore, monitoring of the intra- and inter-annual vegetation structure dynamics is one of the essential components for the management of complex savanna ecosystems such as the Kruger National Park (KNP). To map the woody cover in the KNP, data from European Space Agency’s (ESA) Copernicus Sentinel-1 radar satellite (C-Band vertical–vertical [VV]/vertical–horizontal [VH]) for the years 2016 and 2017, at 10 m spatial resolution and repeated acquisitions every 12 days, were utilised. A high-resolution light detection and ranging (LiDAR) data set was reclassified to produce woody cover percentages and consequently used for calibration and validation. Woody cover estimation for different spatial resolutions was carried out by fitting a random forest (RF) model. Model accuracy was assessed via spatial cross-validation and revealed an overall root mean squared error (RMSE) of 22.8% for the product with a spatial resolution of 10 m and improved with spatial averaging to 15.8% for 30 m, 14.8% for 50 m and 13.4% for 100 m. In addition, the product was validated against a second LiDAR data set, confirming the results of the spatial cross-validation of the model. The methodology of this study is designed for savanna vegetation structure mapping based on height estimates by using open-source software and open-access data, to allow for a continuation of woody cover classification and change monitoring in these types of ecosystems. Conservation implications: Information about the state and changes in woody cover are important for park management and conservation efforts. Both increasing (e.g. because of atmospheric carbon fertilisation) and decreasing (e.g. because of elephant impact) woody cover patterns will have cascading effects on other ecosystem processes such as fire and herbivory.
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 using machine learning (MLR and Ranger in R). The methodology is described in following publication: 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 (in press).
A network of remotely-monitored common garden experiments to explore the response of plant functional types from different biomes to changing climate by collecting physiological information of plant species that are charactaristic of their biomes with NDVI cameras and soil and air sensors (temperature and moisture).
Southern Africa is particularly sensitive to climate change, due to both ecological and socio-economic factors, with rural land users among the most vulnerable groups. The provision of information to support climate-relevant decision-making requires an understanding of the projected impacts of change and complex feedbacks within the local ecosystems, as well as local demands on ecosystem services. In this paper, we address the limitation of current approaches for developing management relevant socio-ecological information on the projected impacts of climate change and human activities. We emphasise the need for linking disciplines and approaches by expounding the methodology followed in our two consecutive projects. These projects combine disciplines and levels of measurements from the leaf level (ecophysiology) to the local landscape level (flux measurements) and from the local household level (socio-economic surveys) to the regional level (remote sensing), feeding into a variety of models at multiple scales. Interdisciplinary, multi-scaled, and integrated socio-ecological approaches, as proposed here, are needed to compliment reductionist and linear, scale-specific approaches. Decision support systems are used to integrate and communicate the data and models to the local decision-makers.
Trees in savanna ecosystems are highly used by animals and humans. In this study, we compare the different effects of elephant utilization and firewood collector preferences for Sclerocarya birrea (Marula), Senegalia nigrescens (Acacia nigrescens), and Combretum apiculatum. The potential distribution and, therefore, the structure of the savanna ecosystem under different scenarios is determined by the establishment, growth, and mortality of individual trees. A critical step to understand the future distribution of tree species is the understanding of the number of mature trees and especially their dispersal capabilities. Different strategies of the tree species have different effects on usability for elephants and firewood collectors. For example, the high content of carbon in leaves such as in Combretum apiculatum is accompanied by less palatability for the elephants but induces higher firewood quality at the same time. On the other hand, marula trees which are highly impacted by elephants are prized for their edible fruit in rural communities adjacent to the Kruger National Park, and are protected by customary law. It is a criminal offence to cut down living adult marula trees without consent of the local chief. Thus, we find a higher number of large marula trees here. Two different scenarios with elephants and trees on the one side and firewood collectors and trees on the other side where modelled to simulated tree distribution until 2100. We will present our conceptual model and first results of the comparison between the future distribution of mature trees inside and outside the KNP under different elephant management, land use, and IPCC scenarios.
How do different human groups act and interact in the same social-ecological system? Multi-agent modelling and simulation (MAMS) can help to find out. A key element of human modelling is how agents plan their behaviour. Goal-oriented action planning (GOAP) allows agents to adapt their behaviour in relation to their own setup, personal traits and the different goals they try to fulfil. GOAP was originally developed by game developers years ago. Additionally, simulating large numbers of agents with complex behaviour demands specialized algorithms and frameworks. The massive multi-agent modelling and simulation framework MARS is under development at Hamburg University of Applied Sciences, Germany. It provides a mechanism to bridge spatial scales from the individual level up to landscapes or even entire countries. To overcome the problem of the static and sometimes non-authentic behaviour of agents we integrated GOAP into MARS. As a proof-of-concept, a firewood collection scenario was developed. In here, competing demands for land use around villages were analysed by simulation runs. Beside human agents, e.g. firewood collectors, we modelled ecosystem services in the landscape by agents also. By that, temporal changes of service provisioning could be easily described. First results show that GOAP is a suitable paradigm for modelling complex and adaptive behaviour in MAMS scenarios. Describing ecosystem services by agents obviously raises the need for large-scale multi-agent modelling and simulation frameworks. MARS can be seen as a proper step in this direction. Our results will form the foundation for a web-based decision-support information system for environmental management in regions with high social-ecological influences like, for example, the Kruger to Canyon (K2C) Biosphere in South Africa.
Land use and climate changes induce shifts in plant functional diversity and community structure. Comparing the past, current, and potential future distribution of indicator species is important for detecting these biodiversity shifts. Therefore, a thorough understanding of the main factors affecting the distribution of those species is necessary. Because of the complexity of the underlying systems, a multivariate method should be applied. The mathematical concept of partial orders was recently used to analyse existing data in some domains, e.g. chemistry. For this study, it was applied to plant ecology in two different plant communities in South Africa. The resulting partial order ranking is visualized by Hasse diagrams. One example shows different tree species of the savanna ecosystems within the Kruger National Park, South Africa. The second example is concerning plant species in the shrub plant community within the Karoo. Different leaf traits were mathematically contrasted against different species in the same ecosystem. We found that partial orders in conjunction with Hasse diagrams are interesting tools to analyse multivariate systems. It can be utilized to sharpen new research hypotheses and is also very valuable for conceptual modelling.
Our poster presented at the 4th South African National Conference on Global Change 2018 presents the main themes of the EMSAfrica project. Southern Africa is particularly sensitive to climate change due to both ecological and socioeconomic factors. Rapidly growing population and threats to the sustainability of ecosystem service delivery at local, regional and national levels pose increasing challenges to policy and land-use decision makers. The German-South African collaborative research project “Ecosystem Management Support for Climate Change in Southern Africa” (EMSAfrica) provides an interdisciplinary, multi-scale approach to inferring management-relevant information on the impacts of climate change and human activities in rural Southern Africa. It is a follow-up of our previous project called “Adaptive Resilience in the South African Ecosystems” (ARS AfricaE, 2014-2018). During ARS AfricaE, we established six observation sites along an aridity gradient in South Africa representing different land use intensities. Our approach combines several disciplines and measurements made at a range of spatial scales, including: measurements of ecosystem-scale carbon fluxes through Eddy Covariance flux tower infrastructures; experiments and observations of plant ecophysiological traits; characterisation of vegetation structure using remote sensing; and socioeconomic surveys of human use of fuelwood. These data are used for creating, calibrating and testing dynamic vegetation and species distribution models, and further, for scaling up the information to the level of biomes. Our final objective is to produce information adapted to the needs of land-use decision makers. To this end, we have set up a cloud-based information system to integrate and operationalise the spatio-temporal data and models of ecosystems and human agents. We will use this system to provide combined and upscalable methods able to generate information that are relevant to land-use management in the local ecosystems of rural Southern Africa.