Predicting ectotherm disease vector spread—benefits from multidisciplinary approaches and directions forward

The occurrence of ectotherm disease vectors outside of their previous distribution area and the emergence of vector-borne diseases can be increasingly observed at a global scale and are accompanied by a growing number of studies which investigate the vast range of determining factors and their causal links. Consequently, a broad span of scientific disciplines is involved in tackling these complex phenomena. First, we evaluate the citation behaviour of relevant scientific literature in order to clarify the question “do scientists consider results of other disciplines to extend their expertise?” We then highlight emerging tools and concepts useful for risk assessment. Correlative models (regression-based, machine-learning and profile techniques), mechanistic models (basic reproduction number R ₀) and methods of spatial regression, interaction and interpolation are described. We discuss further steps towards multidisciplinary approaches regarding new tools and emerging concepts to combine existing approaches such as Bayesian geostatistical modelling, mechanistic models which avoid the need for parameter fitting, joined correlative and mechanistic models, multi-criteria decision analysis and geographic profiling. We take the quality of both occurrence data for vector, host and disease cases, and data of the predictor variables into consideration as both determine the accuracy of risk area identification. Finally, we underline the importance of multidisciplinary research approaches. Even if the establishment of communication networks between scientific disciplines and the share of specific methods is time consuming, it promises new insights for the surveillance and control of vector-borne diseases worldwide.     

Does including physiology improve species distribution model predictions of responses to recent climate change?

Thermal constraints on development are often invoked to predict insect distributions. These constraints tend to be characterized in species distribution models (SDMs) by calculating development time based on a constant lower development temperature (LDT). Here, we assessed whether species-specific estimates of LDT based on laboratory experiments can improve the ability of SDMs to predict the distribution shifts of six U.K. butterflies in response to recent climate warming. We find that species-specific and constant (5 degrees C) LDT degree-day models perform similarly at predicting distributions during the period of 1970-1982. However, when the models for the 1970-1982 period are projected to predict distributions in 1995-1999 and 2000-2004, species-specific LDT degree-day models modestly outperform constant LDT degree-day models. Our results suggest that, while including species-specific physiology in correlative models may enhance predictions of species' distribution responses to climate change, more detailed models may be needed to adequately account for interspecific physiological differences.     

Participation and empowerment-based development of socio-cultural indicators supporting regional decision-making for eco-efficiency

Abstract

This article describes a process of developing socio-cultural indicators to support local and regional decision-making for eco-efficiency. Although eco-efficiency is calculated using environmental and economic variables, decisions on local and regional levels cannot ignore the socio-cultural impacts. The indicators were developed by a participatory process that was based on involvement and empowerment of local actors. The process ensured policy relevance and meaningful indicators and advanced their future use in the decision-making.     

Trace element contents in fine particulate matter (PM<Subscript>2.5</Subscript>) in urban school microenvironments near a contaminated beach with mine tailings,Chañaral,Chile

Air quality in schools is an important public health issue because children spend a considerable part of their daily life in classrooms. Particulate size and chemical composition has been associated with negative health effects. We studied levels of trace element concentrations in fine particulate matter (PM_2.5) in indoor versus outdoor school settings from six schools in Chañaral, a coastal city with a beach severely polluted with mine tailings. Concentrations of trace elements were measured on two consecutive days during the summer and winter of 2012 and 2013 and determined using X-ray fluorescence. Source apportionment and element enrichment were measured using principal components analysis and enrichment factors. Trace elements were higher in indoor school spaces, especially in classrooms compared with outdoor environments. The most abundant elements were Na, Cl, S, Ca, Fe, K, Mn, Ti, and Si, associated with earth’s crust. Conversely, an extremely high enrichment factor was determined for Cu, Zn, Ni and Cr; heavy metals associated with systemic and carcinogenic risk effects, whose probably origin sources are industrial and mining activities. These results suggest that the main source of trace elements in PM_2.5 from these school microenvironments is a mixture of dust contaminated with mine tailings and marine aerosols. Policymakers should prioritize environmental management changes to minimize further environmental damage and its direct impact on the health of children exposed.     

Identification of sources of environmental lead in South Africa from surface soil geochemical maps

The bioavailability of lead in soil is of considerable importance to human and animal health. Although selective extraction has been explored as a more appropriate technique than total heavy metal analysis in environmental pollution assessments, such studies remain scarce globally and are almost non-existent in developing countries. Results for a large-scale study of extractable lead levels in undisturbed soil samples in South Africa identify several geographic areas of concern. Lead levels are considerably elevated relative to background levels in the Johannesburg urban and industrial area. Areas of active lead mining also exhibit higher surface soil values. Interestingly, areas of active and intensive coal mining activity display relatively low soil Pb values, possibly attributable to the relatively low heavy metal content of South African coal. In all instances, distribution of cadmium, a carcinogenic element, correlates with that of lead. The results demonstrate the usefulness of the quick and easy Mehlich-3 single extractant technique, an established technique in micronutrient studies, to simultaneously provide valuable environmental data for toxic metals such as Pb and Cd.     

Understanding the effects of rainfall on elephant-vegetation interactions around waterholes

The distribution of surface water affects herbivore-vegetation interactions in arid and semi-arid regions. Limited access to surface water typically results in the emergence of vegetation gradients around natural and artificial water sources. In particular, African elephants can create large-scale gradients of woody vegetation. Understanding the dynamics of these gradients is of particular importance for the conservation of other, less mobile herbivores that depend on woody vegetation in areas close to water. While rainfall is known to be a key determinant of herbivore-vegetation interactions in dry areas, we only have limited understanding on how it impacts woody vegetation gradients around waterholes. To address this problem, we developed a deterministic simulation model that describes the interplay of rainfall, elephants and woody vegetation in the vicinity of waterholes. The model is based on elephant telemetry data and the ecological conditions in Etosha National Park (ENP), Namibia. We found that decreasing amounts of rainfall led to an increased degradation of woody vegetation, which was particularly severe in areas close to water. Based on this result we conclude that low rainfall was an important driver of recently observed patterns of vegetation degradation in ENP. More generally, rainfall appears to be a key factor that determines elephant-vegetation interactions and thus dynamics of woody vegetation gradients around waterholes. Using long-term rainfall data from ENP, we also demonstrate that an increase in the number of water sources during periods of low rainfall can mitigate the destructive impact of elephants in areas close to water. However, more research is required to assess the sustainability and effectiveness of rainfall-adapted strategies of artificial water provisioning in more detail. In particular it is important to investigate potential effects on elephant population dynamics.     

Changes in extreme events and the potential impacts on human health

Extreme weather and climate-related events affect human health by causing death, injury, and illness, as well as having large socioeconomic impacts. Climate change has caused changes in extreme event frequency, intensity, and geographic distribution, and will continue to be a driver for change in the future. Some of these events include heat waves, droughts, wildfires, dust storms, flooding rains, coastal flooding, storm surges, and hurricanes. The pathways connecting extreme events to health outcomes and economic losses can be diverse and complex. The difficulty in predicting these relationships comes from the local societal and environmental factors that affect disease burden. More information is needed about the impacts of climate change on public health and economies to effectively plan for and adapt to climate change. This paper describes some of the ways extreme events are changing and provides examples of the potential impacts on human health and infrastructure. It also identifies key research gaps to be addressed to improve the resilience of public health to extreme events in the future.

Implications: Extreme weather and climate events affect human health by causing death, injury, and illness, as well as having large socioeconomic impacts. Climate change has caused changes in extreme event frequency, intensity, and geographic distribution, and will continue to be a driver for change in the future. Some of these events include heat waves, droughts, wildfires, flooding rains, coastal flooding, surges, and hurricanes. The pathways connecting extreme events to health outcomes and economic losses can be diverse and complex. The difficulty in predicting these relationships comes from the local societal and environmental factors that affect disease burden.     

Modeling Watershed‐Scale Impacts of Stormwater Management with Traditional versus Low Impact Development Design

Stormwater runoff and associated pollutants from urban areas in the greater Chesapeake Bay Watershed (CBW) impair local streams and downstream ecosystems, despite urbanized land comprising only 7% of the CBW area. More recently, stormwater best management practices (BMPs) have been implemented in a low impact development (LID) manner to treat stormwater runoff closer to its source. This approach included the development of a novel BMP model to compare traditional and LID design, pioneering the use of comprehensively digitized storm sewer infrastructure and BMP design connectivity with spatial patterns in a geographic information system at the watershed scale. The goal was to compare total watershed pollutant removal efficiency in two study watersheds with differing spatial patterns of BMP design (traditional and LID), by quantifying the improved water quality benefit of LID BMP design. An estimate of uncertainty was included in the modeling framework by using ranges for BMP pollutant removal efficiencies that were based on the literature. Our model, using Monte Carlo analysis, predicted that the LID watershed removed approximately 78 kg more nitrogen, 3 kg more phosphorus, and 1,592 kg more sediment per square kilometer as compared with the traditional watershed on an annual basis. Our research provides planners a valuable model to prioritize watersheds for BMP design based on model results or in optimizing BMP selection.