Linking scales and disciplines : an interdisciplinary cross-scale approach to supporting climate-relevant ecosystem management

CITATION: Berger, C. et al. 2019. Linking scales and disciplines : an interdisciplinary cross-scale approach to supporting climate-relevant ecosystem management. Climatic Change, 156:139–150, doi:10.1007/s10584-019-02544-0.The original publication is available at https://www.springer.com/journal/10584Southern Africa is particularly sensitive to climate change, due to both ecological and socioeconomic 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, scalespecific approaches. Decision support systems are used to integrate and communicate the data and models to the local decision-makers.https://link.springer.com/article/10.1007/s10584-019-02544-0Publisher's versio

The application of artificial intelligence models for food security : a review

Emerging technologies associated with Artificial Intelligence (AI) have enabled improvements in global food security situations. However, there is a limited understanding regarding the extent to which stakeholders are involved in AI modelling research for food security purposes. This study systematically reviews the existing literature to bridge the knowledge gap in AI and food security, focusing on software modelling perspectives. The study found the application of AI models to examine various indicators of food security across six continents, with most studies conducted in sub-Saharan Africa. While research organisations conducting AI modelling were predominantly based in Europe or the Americas, their study communities were in the Global South. External funders also supported AI modelling research on food security through international universities and research institutes, although some collaborations with local organisations and external partners were identified. The analysis revealed three patterns in the application of AI models for food security research: (1) the exclusive utilisation of AI models to assess food security situations, (2) stakeholder involvement in some aspects of the AI modelling process, and (3) stakeholder involvement in AI modelling for food security through an iterative process. Overall, studies on AI models for food security were primarily experimental and lacked real-life implementation of the results with stakeholders. Consequently, this study concluded that research on AI, which incorporates feedback and/or the implementation of research outcomes for stakeholders, can contribute to learning and enhance the validity of the models in addressing food security challenges.Hochschule für Angewandte Wissenschaften HamburgPeerReviewe

Exploring survival strategies of African Savanna trees by partial ordering techniques

The resilience of savanna ecosystems to climate and land-use changes is an important ecological and management question. The term ‘resilience' is used to refer to the ability of a tree species to survive in a specific location, even under changing environmental conditions. In this study, vectors of functional traits of selected savanna tree species are studied by applying partial order algorithms to them. Some ecological interpretations are obtained and are compared to published research. One finding is the high rates of nitrogen fixation for the leaves of Acacia nigrescens. In opposition to this well-known fact, we discovered that Sclerocarya birrea has a bigger average leaf area than the other four tree species. Additionally, we found high carbonate values within the leaf from Colophospermum mopane, Combretum apiculatum, and Terminalis sericea. These results correspond to different ecological strategies for the tree species in question. It became obvious that geometric structures gained from partial ordering show a very good correspondence to ecological strategies of these tree species. Concepts of partial order theory may therefore be helpful in ecosystem research

Firewood Collection in South Africa: Adaptive Behavior in Social-Ecological Models

Due to the fact that the South Africa’s savanna landscapes are under changing conditions, the previously sustainable firewood collection system in rural areas has become a social-ecological factor in questions about landscape management. While the resilience of savannas in national parks such as Kruger National Park (KNP) in South Africa has been widely acknowledged in ecosystem management, the resilience of woody vegetation outside protected areas has been underappreciated. Collecting wood is the dominant source of energy for rural households, and there is an urgent need for land management to find sustainable solutions for this complex social-ecological system. However, the firewood collection scenario is only one example, and stands for all “human-ecosystem service” interactions under the topic of over-utilization, e.g., fishery, grazing, harvesting. Agent-based modeling combined with goal-oriented action planning (GOAP) can provide fresh insights into the relationship between individual needs of humans and changes in land use. At the same time, this modeling approach includes adaptive behavior under changing conditions. A firewood collection scenario was selected for a proof-of-concept comprising households, collectors, ecosystem services and firewood sites. Our results have shown that, even when it is predictable what a single human agent will do, massive up-scaling is needed in order to understand the whole complexity of social-ecological systems. Under changing conditions, such as climate and an increasing population, fair distribution of natural goods become an important issue

Evaluating Geospatial Data Adequacy for Integrated Risk Assessments: A Malaria Risk Use Case

International policy and humanitarian guidance emphasize the need for precise, subnational malaria risk assessments with cross-regional comparability. Spatially explicit indicator-based assessments can support humanitarian aid organizations in identifying and localizing vulnerable populations for scaling resources and prioritizing aid delivery. However, the reliability of these assessments is often uncertain due to data quality issues. This article introduces a data evaluation framework to assist risk modelers in evaluating data adequacy. We operationalize the concept of “data adequacy” by considering “quality by design” (suitability) and “quality of conformance” (reliability). Based on a use case we developed in collaboration with Médecins Sans Frontières, we assessed data sources popular in spatial malaria risk assessments and related domains, including data from the Malaria Atlas Project, a healthcare facility database, WorldPop population counts, Climate Hazards group Infrared Precipitation with Stations (CHIRPS) precipitation estimates, European Centre for Medium-Range Weather Forecasts (ECMWF) precipitation forecast, and Armed Conflict Location and Event Data Project (ACLED) conflict events data. Our findings indicate that data availability is generally not a bottleneck, and data producers effectively communicate contextual information pertaining to sources, methodology, limitations and uncertainties. However, determining such data’s adequacy definitively for supporting humanitarian intervention planning remains challenging due to potential inaccuracies, incompleteness or outdatedness that are difficult to quantify. Nevertheless, the data hold value for awareness raising, advocacy and recognizing trends and patterns valuable for humanitarian contexts. We contribute a domain-agnostic, systematic approach to geodata adequacy evaluation, with the aim of enhancing geospatial risk assessments, facilitating evidence-based decisions

Incorporating Multi-Modal Travel Planning into an Agent-Based Model : A Case Study at the Train Station Kellinghusenstraße in Hamburg

This dataset comprises a multi-layer, multi-aspects collection of open data covering the Kellinghusenstraße switch-point in Hamburg, Germany, together with an agent-based simulation model based on the MARS framework (www.mars-group.org). Both parts of this dataset, i.e., the data layers or the model, can be utilized separately or combined under the GNU AGPLv3 license. The MARS Box concept has been invented to support the usage of OpenData and agent-based simulation in education, research, and transfer. Please refer to: Lenfers, U.A., Ahmady-Moghaddam, N., Glake, D., Ocker, F., Ströbele, J., Clemen, T., 2021. Incorporating Multi-Modal Travel Planning into an Agent-Based Model: A Case Study at the Train Station Kellinghusenstraße in Hamburg. Land 10, 1179. https://www.mdpi.com/2073-445X/10/11/1179 for more details.Behörde für Wissenschaft, Forschung, Gleichstellung und Bezirke