Throughout southern Africa, there have been growing concerns about the rates of ecosystem degradation. This issue and natural resource governance concerns in general remain key challenges. We conducted a scoping review of 135 articles to inductively assess how governance challenges have manifested in natural resource management during 2010–2020 in southern Africa. The paper’s findings show that governance challenges in southern Africa are ‘hydra-headed’. The results suggest that governance challenges related to the maintenance of system integrity and functioning – specifically lack of coordination, accountability, capacity, skills and resources to define effective natural resource management – were the most reported in literature. Challenges related to achieving socially equitable governance were the second most mentioned, followed by those related to institutional robustness. Challenges related to the adaptability and flexibility of institutions, specifically learning, monitoring capacity and innovation, were the least identified, showing that these areas are poorly studied or that these attributes are considered less problematic for achieving sustainable outcomes. Furthermore, the review highlights critical gaps – the limited engagement with governance-related frameworks, specifically polycentricity, adaptive governance and social-ecological stewardship. In concluding, we highlight governance reforms and future research needs on the topic of natural resources in southern Africa.

Few studies have highlighted perceptions of urban natural open space systems: land specifically excluded from development to protect ecosystem services. We used a local metropolitan city in South Africa to explore community perceptions of its natural open space system through individual qualitative interviews (n = 40). The objectives were: (1) to identify ecosystem services and disservices associated with the city’s natural open space system, and the reasons thereof, by exploring the relational values of nature held by a diverse socioeconomic spectrum of urban residents; and (2) to identify priorities for protecting the natural open space system by enhancing the benefits and minimizing ecosystem disservices. Reference to ecosystem services and disservices were coded according to the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES) categorization of nature’s contribution to people. Non-material services (relational benefits) were impacted by exploitative material uses, access concerns and (mis)management. Challenges and opportunities identified relate to nature as a resource for supporting livelihoods and lifestyles; community outreach and employment opportunities; personal safety, health and aesthetic concerns; and lack of political accountability and municipal planning in terms of the management of natural open spaces. Innovative collaborative management and stewardship interventions with ecological and socioeconomic benefits should be prioritized to protect the natural open space system.

Globally, there is a trend towards conserving biodiversity by promoting co-management with multiple stakeholders at landscape scales. Environmental policies emphasize stakeholder engagement in decision-making, yet landscape conservation is typically a bureaucratic–scientific endeavour. Building trusting relationships with stakeholders is key to negotiations that minimize trade-offs and maximize synergies. Incorporating shared stakeholder objectives improves co-management, as they act as incentives for participation and trust development. We explored the degree of alignment between the bottom-up stakeholder objectives and top-down management objectives of a landscape-scale conservation initiative on the West Coast of South Africa. We categorized stakeholders into six affiliations representing governmental, private and community organizations, and using a social-ecological inventory we identified ten shared objectives. Of these objectives, three were shared between all affiliations, namely biodiversity conservation, socioeconomic development and coordination of the landscape approach. The first two aligned with the top-down landscape management objectives and the latter did not. The importance of coordinating landscape approaches in multi-stakeholder landscape-scale initiatives is crucial to long-term success, and we recommend that it be formally included as a landscape management objective. Exploring the alignment between bottom-up and top-down objectives can highlight overlooked functions of co-management and can reduce the transaction costs of sustaining conservation efforts in the long term.

Climate change is causing the geographical ranges of some species to track suitable conditions. Habitat specialists, range-restricted species and species with limited dispersal abilities may be unable to track changing conditions, increasing their extinction risk. In response to changing conditions and species movement patterns, there is a need to account for the effects of climate change when designing protected areas and identifying potential climate refugia. We used ecological niche models projected into future climates to identify potential impacts of climate change on the distribution of 11 rupicolous reptile species in the Soutpansberg Mountains, South Africa. Lygodactylus incognitus, Lygodactylus soutpansbergensis, Platysaurus relictus and Vhembelacerta rupicola were identified as being vulnerable to climate change due to substantial reductions in suitable habitat and low spatial overlap between current and future niche envelopes. We identified areas of high conservation importance for the persistence of these species under present-day and projected future conditions. The western Soutpansberg was identified as an area of high conservation priority as it is a potential refuge under future projections. Projecting distributions of vulnerable species into future climate predictions can guide future research and identify potential refugia that will best conserve species with restricted ranges in a world with climate change.

Lockdowns associated with the COVID-19 pandemic temporarily restricted human activity and removed people from many places of work and recreation. The resulting ‘Anthropause’ generated much media and research interest and has become an important storyline in the public history of the pandemic. As an ecological event, the Anthropause is fleeting and unlikely to alter the long-term human impact on the planet. But the Anthropause is also a cultural symbol whose effects may be more enduring. Will the Anthropause inspire people and governments to mobilize for meaningful reform, or does it present a misleading and too-comforting portrayal of resilient nature and wildlife that could ultimately discourage action? While it is too early to gauge the impact of the Anthropause on human behaviour and politics, we use existing research on environmental symbols and metaphors to identify factors that may influence long-term behavioural and political responses to this globally significant period of time.

Globally, water resources are under immense and increasing pressure. This, coupled with the threat of climate change, has increased global interest in water reuse. However, global water reuse remains limited because of public opposition. This paper thus examines public perceptions and attitudes to water reuse across the world. It finds that results from studies of water reuse acceptance have tended to be context specific, although claims can be made about the universal relevance of some predictors, underscoring the need for individual water reuse schemes to carefully consider their local context. Disgust remains a constant in the public psyche, while public trust in delivery agents as well as how water reuse is communicated vis-à-vis perceptions about the quality and safety of recycled water are also critical. The latter particularly highlights public concerns about the indeterminate health risks associated with water reuse.

The semiarid Caatinga is the largest Seasonally Dry Tropical Forest and Woodland (SDTFW) in the Neotropics. Yet the vast majority of the Caatinga is unprotected, with severe chronic anthropogenic use, exotic species and global warming among its most immediate threats. Here, we contrast the current Caatinga protected areas (PAs) scenery with that of other SDTFWs in the Neotropics. We also analyse the growth process of the PAs in the Caatinga over time across Brazilian PA categories and jurisdictions. The percentage of Caatinga that is protected is average among the SDTFWs. Caatinga has more state than federal PAs; however, the size of the PAs is greater under a federal jurisdiction. Nonetheless, in both jurisdictions, Environmental Protected Areas, one of the least restrictive categories, are more representative in terms of total area, corresponding to nearly 80% of the Caatinga PA system. Our results are relevant for international conservation goals because they depict the current PA scenery and clarify the challenges for achieving the actual preservation of the unique Caatinga biome.

Data extraction from unmanned aerial vehicle (UAV) imagery has proved effective in animal surveys and monitoring, but to date has scarcely been used for detailed population analysis and individual animal feature extraction. We assessed the zoometric and feature extraction of the Arabian oryx (Oryx leucoryx) using data acquired from a captive population for comparison with reintroduced populations monitored by UAVs. Highly accurate scaled and geo-rectified imagery derived from UAV surveys allowed precise morphometric measurements of the oryx. The scaled top-view imagery combined with baseline data from known sex, age, weight and pregnancy status of captive individuals were used to develop predictive models. A bracketed index developed from the predictive models showed high accuracy for classifying the age group ≤16 months, animals with a weight >80 kg and pregnancy. The pregnancy classification decision tree model performed with 91.7% accuracy. The polynomial weight predictive model performed well with relatively high accuracy when using the total top-view surface measurement. Photogrammetrically processed UAV-acquired imagery can yield valuable zoometric data, feature extraction and modelling; it is a tool with a practical application for field biologists that can assist in the decision-making process for species conservation management.

Human–wildlife interactions (HWIs) occur in many rural African communities, with potential impacts on livelihood vulnerability. High livelihood vulnerability may force communities to employ strategies that increase the risk of negative HWIs, yet the extent to which HWIs drive or are driven by vulnerability is unclear. We hypothesized that more vulnerable households are more likely to be exposed to wildlife and experience negative interactions. To test this hypothesis, we calculated the Livelihood Vulnerability Index (LVI) of rural households in and around Quirimbas National Park (north-eastern Mozambique) and assessed whether there is a link between livelihood vulnerability and HWIs. We found a two-way association between LVI and HWIs, with more vulnerable households indeed taking greater risks and encountering wildlife when fetching water from rivers, whereas less vulnerable households tended not to employ strategies likely to increase wildlife encounters. We also observed that HWIs exert a strong effect on livelihood vulnerability, suggesting that HWIs should be included as an exposure factor in vulnerability assessments for rural households. We recommend that livelihood strategies and community vulnerability should be considered when designing HWI mitigation schemes and implementing conservation measures.

The prevention of tropical forest deforestation is essential for mitigating climate change. We tested the machine learning algorithm Maxent to predict deforestation across the Peruvian Amazon. We used official annual 2001–2019 deforestation data to develop a predictive model and to test the model’s accuracy using near-real-time forest loss data for 2020. Distance from agricultural land and distance from roads were the predictor variables that contributed most to the final model, indicating that a narrower set of variables contribute nearly 80% of the information necessary for prediction at scale. The permutation importance indicating variable information not present in the other variables was also highest for distance from agricultural land and distance from roads, at 40.5% and 14.3%, respectively. The predictive model registered 73.2% of the 2020 early alerts in a high or very high risk category; less than 1% of forest cover in national protected areas were registered as very high risk, but buffer zones were far more vulnerable, with 15% of forest cover being in this category. To our knowledge, this is the first study to use 19 years of annual data for deforestation risk. The open-source machine learning method could be applied to other forest regions, at scale, to improve strategies for reducing future deforestation.