Widespread woody encroachment is a prominent concern for savanna systems as it is often accompanied by losses in productivity and biodiversity. Extensive ecosystem‐level work has advanced our understanding of its causes and consequences. However, there is still debate over whether local management can override regional and global drivers of woody encroachment, and it remains largely unknown how encroachment influences woody community assemblages. Here, we examined species‐level changes in woody plant distributions and size structure from the late 1980s to the late 2000s based on spatially intensive ground‐based surveys across Kruger National Park, South Africa. This study region spans broad gradients in rainfall, soil texture, fire frequency, elephant density, and other topographic variables. Species‐level changes in frequency of occurrence and size class proportion reflected widespread woody encroachment primarily byDichrostachys cinereaandCombretum apiculatum, and a loss of large trees mostly ofSclerocarya birreaandAcacia nigrescens. Environmental variables determining woody species distributions across Kruger varied among species but did not change substantially between two sampling times, indicating that woody encroachers were thickening within their existing ranges. Overall, more areas across Kruger were found to have an increased number of common woody species through time, which indicated an increase in stem density. These areas were generally associated with decreasing fire frequency and rainfall but increasing elephant density. Our results suggest that woody encroachment is a widespread but highly variable trend across landscapes in Kruger National Park and potentially reflects an erosion of local heterogeneity in woody community assemblages. Many savanna managers, including in Kruger, aim to manage for heterogeneity in order to promote biodiversity, where homogenization of vegetation structure counters this specific goal. Increasing fire frequency has some potential as a local intervention. However, many common species increased in commonness even under near‐constant disturbance conditions, which likely limits the potential for managing woody encroachment in the face of drivers beyond the scope of local control. Regular field sampling coupled with targeted fire management will enable more accurate monitoring of the rate of encroachment intensification.

Smallholder farmers in semi-arid areas depend on both cropping and livestock as the main sources of livelihoods. Rangeland productivity varies on both spatial and temporal scales and provides the major source of feed for livestock. Rangeland productivity is expected to decline with climate change thereby reducing livestock feed availability and consequently livelihoods that depend on livestock. This study was carried out to assess the impacts of climate change on rangeland productivity and consequently livestock population dynamics using a 30-year simulation modeling approach. The climate scenarios used in the simulations are built from the localized predictions by General Circulation Models (GCMs). The primary climate variables under consideration are rainfall (+/−7% change), carbon dioxide (CO₂ up to 650 ppm) and temperature (+4 °C change). This was done by applying the SAVANNA ecosystem model which simulates rangeland processes and demographic responses of herbivores on a temporal and spatial scale using a weekly internal time step and monthly spatial and temporal outputs. The results show that rainfall levels of less than 600 mm/year have the largest negative effect on herbaceous biomass production. The amount of biomass from the woody layer does not change much during the year. The carbon dioxide (CO₂) effects are more influential on the tree and shrub layers (C₃ plants) than the herbaceous layer (C₄ grasses). The CO₂ effect was more dominant than the effects of rainfall and temperature. In the baseline simulations, the shrub plant layer increased significantly over 30 years while there is a three-fold increase in the woody plant layer (trees and shrubs) where biomass increased from a 1980 production to that of 2010. The biomass of the herbaceous layer was stable over the historical period (1980 to 2010) with values fluctuating between 200 and 400 g/m². Grass green biomass has a variable distribution where most production occurred in the fields and cleared areas while lower levels of production were found in the forested areas. The spatial distribution of shrub green biomass was less directly linked to yearly rainfall. Shrub biomass was mostly found in forested areas, and it showed a steady increase in production. Cattle, donkey, and goat populations rose slowly from 1980 but the rise was disrupted by a dry period during the late 1980s to the early 1990s causing a decline in all populations primarily due to grass unavailability. The populations of cattle goats and donkeys started to rise again from 1995 onwards due to improvements in rainfall. Cattle and donkey populations were rising faster than that of goats while sheep population was not changing much for most of the simulation period, otherwise they declined significantly during the drought of 2002. Similar changes in simulated grass biomass (g/m²) were observed in almost all climate scenarios, except for the peak and low years. The livestock population simulation showed few variations in livestock population under all scenarios. The main conclusion from the study is that CO₂ effects on rangeland productivity are much more dominant than the localized effects of rainfall and temperature. This has implications of favoring the growth of the tree and shrub layers over herbaceous layer, which meant that in the long run, the species that are able to use tree and shrub layers may be kept as a livelihood source as they will have a feed source.

The goal of this study was to evaluate the age-differentiated health risks associated with exposure to mercury in drinking water from artisanal small-scale gold mining (ASGM) sites on nearby communities in Yolombo, Colombia. In 2017, nine samples were collected from a local regulatory agency to report mercury concentrations in locations near mining sites. We performed a risk assessment to find 100% of the water samples collected downstream of mining sites exceed Hazard Quotient (HQ) risk thresholds (set by the US-Environmental Protection Agency and Health Canada). HQ model, coupled with global sensitivity and uncertainty analysis (GSUA), was used to conclude infants as the most vulnerable, with 50% of the population exceeding HQ thresholds. Length of exposure was the most significant input that contributed to risk variance, explaining 30-55% of risk across all age groups. Monte-Carlo filtering was used to identify effective strategies to reduce the number of individuals exceeding allowable HQ thresholds. After Monte-Carlo filtering intervention strategies, all individuals are below HQ thresholds. This work shows the importance of combining risk assessment tools with sensor data to inform the need for filters, stakeholder education, and alternative mining approaches to gain a multi-perspective risk approach. This work provides a valuable risk and decision modeling methodology and baseline information to gain a deeper understanding of the probability of experiencing detrimental health effects from water contamination in ASGM communities.

In this review, we discuss the role of sensor analytics point solutions (SNAPS), a reduced complexity machine-assisted decision support tool. We summarize the approaches used for mobile phone-based chemical/biological sensors, including general hardware and software requirements for signal transduction and acquisition. We introduce SNAPS, part of a platform approach to converge sensor data and analytics. The platform is designed to consist of a portfolio of modular tools which may lend itself to dynamic composability by enabling context-specific selection of relevant units, resulting in case-based working modules. SNAPS is an element of this platform where data analytics, statistical characterization and algorithms may be delivered to the data either via embedded systems in devices, or sourced, in near real-time, from mist, fog or cloud computing resources. Convergence of the physical systems with the cyber components paves the path for SNAPS to progress to higher levels of artificial reasoning tools (ART) and emerge as data-informed decision support, as a service for general societal needs. Proof of concept examples of SNAPS are demonstrated both for quantitative data and qualitative data, each operated using a mobile device (smartphone or tablet) for data acquisition and analytics. We discuss the challenges and opportunities for SNAPS, centered around the value to users/stakeholders and the key performance indicators users may find helpful, for these types of machine-assisted tools.