African savannas are increasingly affected by woody encroachment, an increase in the density of woody plants. Woody encroachment often occurs unexpectedly, is difficult to reverse, and has significant economic, cultural and ecological implications. The process of woody encroachment represents a so-called regime shift that results from feedback loops that link vegetation and variables such as fire, grazing and water availability. Much of the work on woody encroachment has focused on the direct drivers of the process, such as the role of fire or grazing in inhibiting or promoting encroachment. However, little work has been done on how ecological changes may provide feedback to affect some of the underlying social processes driving woody encroachment. In this paper, we build on the ecological literature on encroachment to present a qualitative systems analysis of woody encroachment as a social-ecological regime shift. Our analysis highlights the underlying indirect role of human population growth, and we distinguish the key social-ecological processes underlying woody encroachment in arid versus mesic African savannas. The analysis we present helps synthesize the impacts of encroachment, the drivers and feedbacks that play a key role and identify potential social and ecological leverage points to prevent or reverse the woody encroachment process.

Anticipating, avoiding, and managing disruptive environmental change such as regime shifts and the impacts it has on human well-being is a key sustainability challenge. Woody encroachment is a globally important example of a regime shift that occurs in savanna systems, where a large fraction of the world's poor live. Woody encroachment is known to negatively impact a variety of ecosystem services, but few studies have investigated the impact of woody encroachment on local land users and their livelihoods. In this study, we conducted semi-structured interviews to determine how different land users-local subsistence communities and managers of conservation tourism areas-perceive woody encroachment in the Hluhluwe region of South Africa, how it affects the ecosystem services they rely upon, and what costs they incur in undertaking activities to reverse woody encroachment. Most interviewees perceived trees to be increasing in the landscape (83%). However, perceptions about the causes of woody encroachment differed: community members cited the reduced usage of trees as the reason for woody encroachment, whereas conservation managers mostly attributed the change to increased CO 2 . Most community members felt woody encroachment was harmful to their household and general wellbeing, citing loss of grazing for livestock, and fear of attacks by wild animals and criminals as the main impacts. In contrast, conservation managers perceived woody encroachment to have both harmful and beneficial impacts, with the main negative impacts being loss of grazing for wildlife and impacts on tourism through reduced visibility for game viewing. All the conservation areas invested in tree clearing compared to only 20% of respondents in the community areas, where an average of ZAR367 (US$25) was spent per year on clearing, compared to ZAR293,751 (US$20,000) and ZAR163,000 (US$11,000) spent in private game reserves and government reserves, respectively. Our findings highlight the negative impacts of ongoing woody encroachment, the differentiated impacts it has on different land users, and differences in capacity to combat encroachment. These findings highlight the need for state-funded management interventions to support clearing of trees in encroached areas, particularly in communal areas.

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Accurate land use and cover data are essential for effective land-use planning, hydrological modeling, and policy development. Since the Okavango Delta is a transboundary Ramsar site, managing natural resources within the Okavango Basin is undoubtedly a complex issue. It is often difficult to accurately map land use and cover using remote sensing in heterogeneous landscapes. This study investigates the combined value of climate-based regionalization and integration of spectral bands with spectral indices to enhance the accuracy of multi-temporal land use/cover classification using deep learning and machine learning approaches. Two experiments were set up, the first entailing the integration of spectral bands with spectral indices and the second involving the combined integration of spectral indices and climate-based regionalization based on Koppen–Geiger climate zones. Landsat 5 TM and Landsat 8 OLI images, machine learning classifiers (random forest and extreme gradient boosting), and deep learning (neural network and deep neural network) classifiers were used in this study. Supervised classification using a total of 5140 samples was conducted for the years 1996, 2004, 2013, and 2020. Average overall accuracy and Kappa coefficients were used to validate the results. The study found that the integration of spectral bands with indices improves the accuracy of land use/cover classification using machine learning and deep learning. Post-feature selection combinations yield higher accuracies in comparison to combinations of bands and indices. A combined integration of spectral indices with bands and climate-based regionalization did not significantly improve the accuracy of land use/cover classification consistently for all the classifiers (p < 0.05). However, post-feature selection combinations and climate-based regionalization significantly improved the accuracy for all classifiers investigated in this study. Findings of this study will improve the reliability of land use/cover monitoring in complex heterogeneous TDBs.

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How do social-ecological systems change over time? In 2002 Holling and colleagues proposed the concept of Panarchy, which presented social-ecological systems as an interacting set of adaptive cycles, each of which is produced by the dynamic tensions between novelty and efficiency at multiple scales. Initially introduced as a conceptual framework and set of metaphors, panarchy has gained the attention of scholars across many disciplines and its ideas continue to inspire further conceptual developments. Almost twenty years after this concept was introduced we review how it has been used, tested, extended and revised. We do this by combining qualitative methods and machine learning. Document analysis was used to code panarchy features that are commonly used in the scientific literature (N = 42), a qualitative analysis that was complemented with topic modeling of 2177 documents. We find that the adaptive cycle is the feature of panarchy that has attracted the most attention. Challenges remain in empirically grounding the metaphor, but recent theoretical and empirical work offer some avenues for future research.

Understanding the spatial spread pathways and connectivity of Land Use/Cover (LULC) change within basins is critical to natural resources management. However, existing studies approach LULC change as distinct patches but ignore the connectivity between them. It is crucial to investigate approaches that can detect the spread pathways of LULC change to aid natural resource management and decision-making. This study aims to evaluate the utility of the Circuit Theory to detect the spread and connectivity of LULC change within the Okavango basin. Patches of LULC change sites that were derived from change detection of LULC based on the Deep Neural Network (DNN) for the period between 2004 and 2020 were used. The changed sites were categorized based on the nature of the change of the classes, namely Category A (natural classes to artificial classes), Category B (artificial classes to natural classes), and Category C (natural classes to natural classes). In order to generate the resistance layer; an ensemble of machine learning algorithms was first calibrated with social-ecological drivers of LULC change and centroids of LULC change patches to determine the susceptibility of the landscape to LULC change. An inverse function was then applied to the susceptibility layer to derive the resistance layer. In order to analyze the connectivity and potential spread pathways of LULC change, the Circuit Theory (CT) model was built for each LULC change category. The CT model was calibrated using the resistance layer and patches of LULC change in Circuitscape 4.0. The corridor validation index was used to evaluate the performance of CT modeling. The use of the CT model calibrated with a resistance layer (derived from susceptibility modeling) successfully established the spread pathways and connectivity of LULC change for all the categories (validation index > 0.60). Novel maps of LULC change spread pathways in the Okavango basin were generated. The spread pathways were found to be concentrated in the northwestern, central, and southern parts of the basin for Category A transitions. As for category B transitions, the spread pathways were mainly concentrated in the northeastern and southern parts of the basin and along the major rivers. While for Category C transitions were found to be spreading from the central towards the southern parts, mainly in areas associated with semi-arid climatic conditions. A total of 186 pinch points (Category A: 57, Category B: 71, Category C: 58) were detected. The pinch points can guide targeted management LULC change through the setting up of conservation areas, forest restoration projects, drought monitoring, and invasive species control programs. This study provides a new decision-making method for targeted LULC change management in transboundary basins. The findings of this study provide insights into underlying processes driving the spread of LULC change and enhanced indicators for the evaluation of LULC spread in complex environments. Such information is crucial to inform land use planning, monitoring, and sustainable natural resource management, particularly water resources.

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