Benefits and trade-offs of optimizing global land use for food, water, and carbon.

Current large-scale patterns of land use reflect history, local traditions, and production costs, much more so than they reflect biophysical potential or global supply and demand for food and freshwater, or-more recently-climate change mitigation. We quantified alternative land-use allocations that consider trade-offs for these demands by combining a dynamic vegetation model and an optimization algorithm to determine Pareto-optimal land-use allocations under changing climate conditions in 2090-2099 and alternatively in 2033-2042. These form the outer bounds of the option space for global land-use transformation. Results show a potential to increase all three indicators (+83% in crop production, +8% in available runoff, and +3% in carbon storage globally) compared to the current land-use configuration, with clear land-use priority areas: Tropical and boreal forests were preserved, crops were produced in temperate regions, and pastures were preferentially allocated in semiarid grasslands and savannas. Transformations toward optimal land-use patterns would imply extensive reconfigurations and changes in land management, but the required annual land-use changes were nevertheless of similar magnitude as those suggested by established land-use change scenarios. The optimization results clearly show that large benefits could be achieved when land use is reconsidered under a "global supply" perspective with a regional focus that differs across the world's regions in order to achieve the supply of key ecosystem services under the emerging global pressures.

Carbon fluxes related to land use and land cover change in Baden-Württemberg.

Spatially explicit information on carbon fluxes related to land use and land cover change (LULCC) is of value for the implementation of local climate change mitigation strategies. However, estimates of these carbon fluxes are often aggregated to larger areas. We estimated committed gross carbon fluxes related to LULCC in Baden-Württemberg, Germany, using different emission factors. In doing so, we compared four different data sources regarding their suitability for estimating the fluxes: (a) a land cover dataset derived from OpenStreetMap (OSMlanduse); (b) OSMlanduse with removal of sliver polygons (OSMlanduse cleaned), (c) OSMlanduse enhanced with a remote sensing time series analysis (OSMlanduse+); (d) the LULCC product of Landschaftsveränderungsdienst (LaVerDi) from the German Federal Agency of Cartography and Geodesy. We produced a high range of carbon flux estimates, mostly caused by differences in the area of the LULCC detected by the different change methods. Except for the OSMlanduse change method, all LULCC methods achieved results that are comparable to other gross emission estimates. The carbon flux estimates of the most plausible change methods, OSMlanduse cleaned and OSMlanduse+, were 291,710 Mg C yr-1 and 93,591 Mg C yr-1, respectively. Uncertainties were mainly caused by incomplete spatial coverage of OSMlanduse, false positive LULCC due to changes and corrections made in OpenStreetMap during the study period, and a high number of sliver polygons in the OSMlanduse changes. Overall, the results showed that OSM can be successfully used to estimate LULCC carbon fluxes if data preprocessing is performed with the suggested methods.

Assessing road criticality and loss of healthcare accessibility during floods: the case of Cyclone Idai, Mozambique 2019.

BACKGROUND: The ability of disaster response, preparedness, and mitigation efforts to assess the loss of physical accessibility to health facilities and to identify impacted populations is key in reducing the humanitarian consequences of disasters. Recent studies use either network- or raster-based approaches to measure accessibility in respect to travel time. Our analysis compares a raster- and a network- based approach that both build on open data with respect to their ability to assess the loss of accessibility due to a severe flood event. As our analysis uses open access data, the approach should be transferable to other flood-prone sites to support decision-makers in the preparation of disaster mitigation and preparedness plans. METHODS: Our study is based on the flood events following Cyclone Idai in Mozambique in 2019 and uses both raster- and network-based approaches to compare accessibility to health sites under normal conditions to the aftermath of the cyclone to assess the loss of accessibility. Part of the assessment is a modified centrality indicator, which identifies the specific use of the road network for the population to reach health facilities. RESULTS: Results for the raster- and the network-based approaches differed by about 300,000 inhabitants (~ 800,000 to ~ 500,000) losing accessibility to healthcare sites. The discrepancy was related to the incomplete mapping of road networks and affected the network-based approach to a higher degree. The modified centrality indicator allowed us to identify road segments that were most likely to suffer from flooding and to highlight potential backup roads in disaster settings. CONCLUSIONS: The different results obtained between the raster- and network-based methods indicate the importance of data quality assessments in addition to accessibility assessments as well as the importance of fostering mapping campaigns in large parts of the Global South. Data quality is therefore a key parameter when deciding which method is best suited for local conditions. Another important aspect is the required spatial resolution of the results. Identification of critical segments of the road network provides essential information to prepare for potential disasters.

Ambulatory Assessment for Physical Activity Research: State of the Science, Best Practices and Future Directions.

Technological and digital progress benefits physical activity (PA) research. Here we compiled expert knowledge on how Ambulatory Assessment (AA) is utilized to advance PA research, i.e., we present results of the 2nd International CAPA Workshop 2019 "Physical Activity Assessment - State of the Science, Best Practices, Future Directions" where invited researchers with experience in PA assessment, evaluation, technology and application participated. First, we provide readers with the state of the AA science, then we give best practice recommendations on how to measure PA via AA and shed light on methodological frontiers, and we furthermore discuss future directions. AA encompasses a class of methods that allows the study of PA and its behavioral, biological and physiological correlates as they unfold in everyday life. AA includes monitoring of movement (e.g., via accelerometry), physiological function (e.g., via mobile electrocardiogram), contextual information (e.g., via geolocation-tracking), and ecological momentary assessment (EMA; e.g., electronic diaries) to capture self-reported information. The strengths of AA are data assessment that near realtime, which minimizes retrospective biases in real-world settings, consequentially enabling ecological valid findings. Importantly, AA enables multiple assessments across time within subjects resulting in intensive longitudinal data (ILD), which allows unraveling within-person determinants of PA in everyday life. In this paper, we show how AA methods such as triggered e-diaries and geolocation-tracking can be used to measure PA and its correlates, and furthermore how these findings may translate into real-life interventions. In sum, AA provides numerous possibilities for PA research, especially the opportunity to tackle within- subject antecedents, concomitants, and consequences of PA as they unfold in everyday life. In-depth insights on determinants of PA could help us design and deliver impactful interventions in real-world contexts, thus enabling us to solve critical health issues in the 21st century such as insufficient PA and high levels of sedentary behavior.

[Assessment of health risks due to heat and fine particles in Germany: an epidemiological study approach]. / Erfassung von hitze- und feinstaubbedingten Gesundheitsrisiken in Deutschland: Ein epidemiologischer Studienansatz.

BACKGROUND: Exposure to heat and particulate matter is a cause of increased mortality. Climate change and increasing climate variability exacerbate these problems. Experts require assessments with which health risks and the success of preventative measures can be estimated. We implemented an ecological study approach to assess these risks at both small and large scales of reference levels (Federal Republic of Germany and territorial authority). METHODS: We utilised a case-crossover design to investigate the relationship between exposure and mortality. This study design uses a logistic regression model. Analogously to a matched case-control study, the odds ratio maps the effect strength. The study period included the years 2002-2006. RESULTS: The analysis demonstrated health risks from exposure to heat for the German population (OR 1.1529, 95% CI 1.1517-1.1541; adjusted OR 1.0658). Significant evidence of a health risk was also documented for exposure to particulate matter (PM10; OR 1.2987, 95% CI 1.2951-1.3024; adjusted OR 1.0128). The risk does not significantly differ for women versus men; the variable age was also not significant at the level of the country-wide analysis, but for a few subordinate units of space. This study approach can be adapted for assessments at varying levels of reference and periods of time as well as for different populations. DISCUSSION: The methodological approach is useful for a reproducible study design. Nevertheless, other influencing factors such as ozone or PM2.5 should be incorporated in subsequent analyses to clarify whether these factors skew the results. Further analysis would also be useful to investigate if and to what extent socio-structural and socio-economic factors affect the associated risk.

A spatio-temporal analysis investigating completeness and inequalities of global urban building data in OpenStreetMap.

OpenStreetMap (OSM) has evolved as a popular dataset for global urban analyses, such as assessing progress towards the Sustainable Development Goals. However, many analyses do not account for the uneven spatial coverage of existing data. We employ a machine-learning model to infer the completeness of OSM building stock data for 13,189 urban agglomerations worldwide. For 1,848 urban centres (16% of the urban population), OSM building footprint data exceeds 80% completeness, but completeness remains lower than 20% for 9,163 cities (48% of the urban population). Although OSM data inequalities have recently receded, partially as a result of humanitarian mapping efforts, a complex unequal pattern of spatial biases remains, which vary across various human development index groups, population sizes and geographic regions. Based on these results, we provide recommendations for data producers and urban analysts to manage the uneven coverage of OSM data, as well as a framework to support the assessment of completeness biases.

Initial response to the COVID-19 pandemic on real-life well-being, social contact and roaming behavior in patients with schizophrenia, major depression and healthy controls: A longitudinal ecological momentary assessment study.

The COVID-19 pandemic strongly impacted people's daily lives. However, it remains unknown how the pandemic situation affects daily-life experiences of individuals with preexisting severe mental illnesses (SMI). In this real-life longitudinal study, the acute onset of the COVID-19 pandemic in Germany did not cause the already low everyday well-being of patients with schizophrenia (SZ) or major depression (MDD) to decrease further. On the contrary, healthy participants' well-being, anxiety, social isolation, and mobility worsened, especially in healthy individuals at risk for mental disorder, but remained above the levels seen in patients. Despite being stressful for healthy individuals at risk for mental disorder, the COVID-19 pandemic had little additional influence on daily-life well-being in psychiatric patients with SMI. This highlights the need for preventive action and targeted support of this vulnerable population.

Mental health in the slums of Dhaka - a geoepidemiological study.

BACKGROUND: Urban health is of global concern because the majority of the world's population lives in urban areas. Although mental health problems (e.g. depression) in developing countries are highly prevalent, such issues are not yet adequately addressed in the rapidly urbanising megacities of these countries, where a growing number of residents live in slums. Little is known about the spectrum of mental well-being in urban slums and only poor knowledge exists on health promotive socio-physical environments in these areas. Using a geo-epidemiological approach, the present study identified factors that contribute to the mental well-being in the slums of Dhaka, which currently accommodates an estimated population of more than 14 million, including 3.4 million slum dwellers. METHODS: The baseline data of a cohort study conducted in early 2009 in nine slums of Dhaka were used. Data were collected from 1,938 adults (≥ 15 years). All respondents were geographically marked based on their households using global positioning systems (GPS). Very high-resolution land cover information was processed in a Geographic Information System (GIS) to obtain additional exposure information. We used a factor analysis to reduce the socio-physical explanatory variables to a fewer set of uncorrelated linear combinations of variables. We then regressed these factors on the WHO-5 Well-being Index that was used as a proxy for self-rated mental well-being. RESULTS: Mental well-being was significantly associated with various factors such as selected features of the natural environment, flood risk, sanitation, housing quality, sufficiency and durability. We further identified associations with population density, job satisfaction, and income generation while controlling for individual factors such as age, gender, and diseases. CONCLUSIONS: Factors determining mental well-being were related to the socio-physical environment and individual level characteristics. Given that mental well-being is associated with physiological well-being, our study may provide crucial information for developing better health care and disease prevention programmes in slums of Dhaka and other comparable settings.

Greenwashing in the US metal industry? A novel approach combining SO2 concentrations from satellite data, a plant-level firm database and web text mining.

This study deals with the issue of greenwashing, i.e. the false portrayal of companies as environmentally friendly. The analysis focuses on the US metal industry, which is a major emission source of sulfur dioxide (SO2), one of the most harmful air pollutants. One way to monitor the distribution of atmospheric SO2 concentrations is through satellite data from the Sentinel-5P programme, which represents a major advance due to its unprecedented spatial resolution. In this paper, Sentinel-5P remote sensing data was combined with a plant-level firm database to investigate the relationship between the US metal industry and SO2 concentrations using a spatial regression analysis. Additionally, this study considered web text data, classifying companies based on their websites in order to depict their self-portrayal on the topic of sustainability. In doing so, we investigated the topic of greenwashing, i.e. whether or not a positive self-portrayal regarding sustainability is related to lower local SO2 concentrations. Our results indicated a general, positive correlation between the number of employees in the metal industry and local SO2 concentrations. The web-based analysis showed that only 8% of companies in the metal industry could be classified as engaged in sustainability based on their websites. The regression analyses indicated that these self-reported "sustainable" companies had a weaker effect on local SO2 concentrations compared to their "non-sustainable" counterparts, which we interpreted as an indication of the absence of general greenwashing in the US metal industry. However, the large share of firms without a website and lack of specificity of the text classification model were limitations to our methodology.

A spatial epidemiological analysis of self-rated mental health in the slums of Dhaka.

BACKGROUND: The deprived physical environments present in slums are well-known to have adverse health effects on their residents. However, little is known about the health effects of the social environments in slums. Moreover, neighbourhood quantitative spatial analyses of the mental health status of slum residents are still rare. The aim of this paper is to study self-rated mental health data in several slums of Dhaka, Bangladesh, by accounting for neighbourhood social and physical associations using spatial statistics. We hypothesised that mental health would show a significant spatial pattern in different population groups, and that the spatial patterns would relate to spatially-correlated health-determining factors (HDF). METHODS: We applied a spatial epidemiological approach, including non-spatial ANOVA/ANCOVA, as well as global and local univariate and bivariate Moran's I statistics. The WHO-5 Well-being Index was used as a measure of self-rated mental health. RESULTS: We found that poor mental health (WHO-5 scores < 13) among the adult population (age ≥15) was prevalent in all slum settlements. We detected spatially autocorrelated WHO-5 scores (i.e., spatial clusters of poor and good mental health among different population groups). Further, we detected spatial associations between mental health and housing quality, sanitation, income generation, environmental health knowledge, education, age, gender, flood non-affectedness, and selected properties of the natural environment. CONCLUSIONS: Spatial patterns of mental health were detected and could be partly explained by spatially correlated HDF. We thereby showed that the socio-physical neighbourhood was significantly associated with health status, i.e., mental health at one location was spatially dependent on the mental health and HDF prevalent at neighbouring locations. Furthermore, the spatial patterns point to severe health disparities both within and between the slums. In addition to examining health outcomes, the methodology used here is also applicable to residuals of regression models, such as helping to avoid violating the assumption of data independence that underlies many statistical approaches. We assume that similar spatial structures can be found in other studies focussing on neighbourhood effects on health, and therefore argue for a more widespread incorporation of spatial statistics in epidemiological studies.

The evolution of humanitarian mapping within the OpenStreetMap community.

In the past 10 years, the collaborative maps of OpenStreetMap (OSM) have been used to support humanitarian efforts around the world as well as to fill important data gaps for implementing major development frameworks such as the Sustainable Development Goals. This paper provides a comprehensive assessment of the evolution of humanitarian mapping within the OSM community, seeking to understand the spatial and temporal footprint of these large-scale mapping efforts. The spatio-temporal statistical analysis of OSM's full history since 2008 showed that humanitarian mapping efforts added 60.5 million buildings and 4.5 million roads to the map. Overall, mapping in OSM was strongly biased towards regions with very high Human Development Index. However, humanitarian mapping efforts had a different footprint, predominantly focused on regions with medium and low human development. Despite these efforts, regions with low and medium human development only accounted for 28% of the buildings and 16% of the roads mapped in OSM although they were home to 46% of the global population. Our results highlight the formidable impact of humanitarian mapping efforts such as post-disaster mapping campaigns to improve the spatial coverage of existing open geographic data and maps, but they also reveal the need to address the remaining stark data inequalities, which vary significantly across countries. We conclude with three recommendations directed at the humanitarian mapping community: (1) Improve methods to monitor mapping activity and identify where mapping is needed. (2) Rethink the design of projects which include humanitarian data generation to avoid non-sustainable outcomes. (3) Remove structural barriers to empower local communities and develop capacity.

How can we make progress with decision support systems in landscape and river basin management? Lessons learned from a comparative analysis of four different decision support systems.

This article analyses the benefits and shortcomings of the recently developed decision support systems (DSS) FLUMAGIS, Elbe-DSS, CatchMODS, and MedAction. The analysis elaborates on the following aspects: (i) application area/decision problem, (ii) stakeholder interaction/users involved, (iii) structure of DSS/model structure, (iv) usage of the DSS, and finally (v) most important shortcomings. On the basis of this analysis, we formulate four criteria that we consider essential for the successful use of DSS in landscape and river basin management. The criteria relate to (i) system quality, (ii) user support and user training, (iii) perceived usefulness and (iv) user satisfaction. We can show that the availability of tools and technologies for DSS in landscape and river basin management is good to excellent. However, our investigations indicate that several problems have to be tackled. First of all, data availability and homogenisation, uncertainty analysis and uncertainty propagation and problems with model integration require further attention. Furthermore, the appropriate and methodological stakeholder interaction and the definition of 'what end-users really need and want' have been documented as general shortcomings of all four examples of DSS. Thus, we propose an iterative development process that enables social learning of the different groups involved in the development process, because it is easier to design a DSS for a group of stakeholders who actively participate in an iterative process. We also identify two important lines of further development in DSS: the use of interactive visualization tools and the methodology of optimization to inform scenario elaboration and evaluate trade-offs among environmental measures and management alternatives.

Mapping physical access to health care for older adults in sub-Saharan Africa and implications for the COVID-19 response: a cross-sectional analysis.

BACKGROUND: Severe acute respiratory syndrome coronavirus 2, the virus causing COVID-19, is rapidly spreading across sub-Saharan Africa. Hospital-based care for COVID-19 is often needed, particularly among older adults. However, a key barrier to accessing hospital care in sub-Saharan Africa is travel time to the nearest health-care facility. To inform the geographical targeting of additional health-care resources, we aimed to estimate travel time at a 1 km × 1 km resolution to the nearest hospital and to the nearest health-care facility of any type for adults aged 60 years and older in sub-Saharan Africa. METHODS: We assembled a dataset on the geolocation of health-care facilities, separately for hospitals and any type of health-care facility and including both private-sector and public-sector facilities, using data from the OpenStreetMap project and the Kenya Medical Research Institute-Wellcome Trust Programme. Population data at a 1 km × 1 km resolution were obtained from WorldPop. We estimated travel time to the nearest health-care facility for each 1 km × 1 km grid using a cost-distance algorithm. FINDINGS: 9·6% (95% CI 5·2-16·9) of adults aged 60 years or older across sub-Saharan Africa had an estimated travel time to the nearest hospital of 6 h or longer, varying from 0·0% (0·0-3·7) in Burundi and The Gambia to 40·9% (31·8-50·7) in Sudan. For the nearest health-care facility of any type (whether primary, secondary, or tertiary care), 15·9% (95% CI 10·1-24·4) of adults aged 60 years or older across sub-Saharan Africa had an estimated travel time of 2 h or longer, ranging from 0·4% (0·0-4·4) in Burundi to 59·4% (50·1-69·0) in Sudan. Most countries in sub-Saharan Africa contained populated areas in which adults aged 60 years and older had a travel time to the nearest hospital of 12 h or longer and to the nearest health-care facility of any type of 6 h or longer. The median travel time to the nearest hospital for the fifth of adults aged 60 years or older with the longest travel times was 348 min (IQR 240-576; equal to 5·8 h) for the entire population of sub-Saharan Africa, ranging from 41 min (34-54) in Burundi to 1655 min (1065-2440; equal to 27·6 h) in Gabon. INTERPRETATION: Our high-resolution maps of estimated travel times to both hospitals and health-care facilities of any type can be used by policy makers and non-governmental organisations to help target additional health-care resources, such as makeshift hospitals or transport programmes to existing health-care facilities, to older adults with the least physical access to care. In addition, this analysis shows the locations of population groups most likely to under-report COVID-19 symptoms because of low physical access to health-care facilities. Beyond the COVID-19 response, this study can inform the efforts of countries to improve physical access to care for conditions that are common among older adults in the region, such as chronic non-communicable diseases. FUNDING: Bill & Melinda Gates Foundation.

Mapping physical access to healthcare for older adults in sub-Saharan Africa: A cross-sectional analysis with implications for the COVID-19 response.

BACKGROUND: SARS-CoV-2, the virus causing coronavirus disease 2019 (COVID-19), is rapidly spreading across sub-Saharan Africa (SSA). Hospital-based care for COVID-19 is particularly often needed among older adults. However, a key barrier to accessing hospital care in SSA is travel time to the healthcare facility. To inform the geographic targeting of additional healthcare resources, this study aimed to determine the estimated travel time at a 1km × 1km resolution to the nearest hospital and to the nearest healthcare facility of any type for adults aged 60 years and older in SSA. METHODS: We assembled a unique dataset on healthcare facilities' geolocation, separately for hospitals and any type of healthcare facility (including primary care facilities) and including both private- and public-sector facilities, using data from the OpenStreetMap project and the KEMRI Wellcome Trust Programme. Population data at a 1km × 1km resolution was obtained from WorldPop. We estimated travel time to the nearest healthcare facility for each 1km × 1km grid using a cost-distance algorithm. FINDINGS: 9.6% (95% CI: 5.2% - 16.9%) of adults aged ≥60 years had an estimated travel time to the nearest hospital of longer than six hours, varying from 0.0% (95% CI: 0.0% - 3.7%) in Burundi and The Gambia, to 40.9% (95% CI: 31.8% - 50.7%) in Sudan. 11.2% (95% CI: 6.4% - 18.9%) of adults aged ≥60 years had an estimated travel time to the nearest healthcare facility of any type (whether primary or secondary/tertiary care) of longer than three hours, with a range of 0.1% (95% CI: 0.0% - 3.8%) in Burundi to 55.5% (95% CI: 52.8% - 64.9%) in Sudan. Most countries in SSA contained populated areas in which adults aged 60 years and older had a travel time to the nearest hospital of more than 12 hours and to the nearest healthcare facility of any type of more than six hours. The median travel time to the nearest hospital for the fifth of adults aged ≥60 years with the longest travel times was 348 minutes (equal to 5.8 hours; IQR: 240 - 576 minutes) for the entire SSA population, ranging from 41 minutes (IQR: 34 - 54 minutes) in Burundi to 1,655 minutes (equal to 27.6 hours; IQR: 1065 - 2440 minutes) in Gabon. INTERPRETATION: Our high-resolution maps of estimated travel times to both hospitals and healthcare facilities of any type can be used by policymakers and non-governmental organizations to help target additional healthcare resources, such as new make-shift hospitals or transport programs to existing healthcare facilities, to older adults with the least physical access to care. In addition, this analysis shows precisely where population groups are located that are particularly likely to under-report COVID-19 symptoms because of low physical access to healthcare facilities. Beyond the COVID-19 response, this study can inform countries' efforts to improve care for conditions that are common among older adults, such as chronic non-communicable diseases.

Studying the impact of built environments on human mental health in everyday life: methodological developments, state-of-the-art and technological frontiers.

Rapid worldwide urbanization benefits humans in many aspects, but the prevalence of common psychiatric disorders is increased in urban populations. While the impact of city living and urban upbringing on mental health is well established, it remains elusive which of the multiple factors of urban living convey risk and resilience for mental disorders. For example, air pollutants, traffic noises and fragmented social networks are some of the highly interdependent and complex influences of city living suggested to be detrimental for mental health. In contrast, urban green spaces, social contacts and physical activity have been associated with increased well-being. Knowledge on underlying mechanisms of these associations is crucial for both city planning and healthcare as it informs on how to build environments and to intervene in a way that fosters mental health yet reduces psychiatric disorders. Thus, real-life studies in urban contexts have been launched making use of recent methodological advancements: Mobile devices (e.g. smartphones) to gather intensive longitudinal mental health data, stationary sensor output providing specific context information (e.g. on weather conditions and air pollution), combinations with traditional and modern neuroimaging techniques (e.g. functional near-infrared spectroscopy and portable magnetic-encephalogram caps) and modern virtual reality setups allowing for increasingly realistic and ecological valid simulation of complex urban environments. Here we review selected methodological developments, state-of-the-art approaches as well as technological frontiers and provide examples for their application, highlighting promising potential of these novel methods for tackling the urgent urbanicity societal issue of the 21st century with a view to improve urban contexts conducive to mental health.

The relevance of using in situ carbon and nitrogen data and satellite images to assess aboveground carbon and nitrogen stocks for supporting national REDD + programmes in Africa.

BACKGROUND: To reduce the uncertainty in estimates of carbon emissions resulting from deforestation and forest degradation, better information on the carbon density per land use/land cover (LULC) class and in situ carbon and nitrogen data is needed. This allows a better representation of the spatial distribution of carbon and nitrogen stocks across LULC. The aim of this study was to emphasize the relevance of using in situ carbon and nitrogen content of the main tree species of the site when quantifying the aboveground carbon and nitrogen stocks in the context of carbon accounting. This paper contributes to that, by combining satellite images with in situ carbon and nitrogen content in dry matter of stem woods together with locally derived and published allometric models to estimate aboveground carbon and nitrogen stocks at the Dassari Basin in the Sudan Savannah zone in the Republic of Benin. RESULTS: The estimated mean carbon content per tree species varied from 44.28 ± 0.21% to 49.43 ± 0.27%. The overall mean carbon content in dry matter for the 277 wood samples of the 18 main tree species of the region was 47.01 ± 0.28%-which is close to the Tier 1 coefficient of 47% default value suggested by the Intergovernmental Panel on Climate Change (IPCC). The overall mean fraction of nitrogen in dry matter was estimated as 0.229 ± 0.016%. The estimated mean carbon density varied from 1.52 ± 0.14 Mg C ha-1 (for Cropland and Fallow) to 97.83 ± 27.55 Mg C ha-1 (for Eucalyptus grandis Plantation). In the same order the estimated mean nitrogen density varied from 0.008 ± 0.007 Mg ha-1 of N (for Cropland and Fallow) to 0.321 ± 0.088 Mg ha-1 of N (for Eucalyptus grandis Plantation). CONCLUSION: The results show the relevance of using the in situ carbon and nitrogen content of the main tree species for estimating aboveground carbon and nitrogen stocks in the Sudan Savannah environment. The results provide crucial information for carbon accounting programmes related to the implementation of the REDD + initiatives in developing countries.

Neural correlates of individual differences in affective benefit of real-life urban green space exposure.

Psychiatric morbidity is high in cities, so identifying potential modifiable urban protective factors is important. We show that exposure to urban green space improves well-being in naturally behaving male and female city dwellers, particularly in districts with higher psychiatric incidence and fewer green resources. Higher green-related affective benefit was related to lower prefrontal activity during negative-emotion processing, which suggests that urban green space exposure may compensate for reduced neural regulatory capacity.

Components of uncertainty in species distribution analysis: a case study of the Great Grey Shrike.

Sophisticated statistical analyses are common in ecological research, particularly in species distribution modeling. The effects of sometimes arbitrary decisions during the modeling procedure on the final outcome are difficult to assess, and to date are largely unexplored. We conducted an analysis quantifying the contribution of uncertainty in each step during the model-building sequence to variation in model validity and climate change projection uncertainty. Our study system was the distribution of the Great Grey Shrike in the German federal state of Saxony. For each of four steps (data quality, collinearity method, model type, and variable selection), we ran three different options in a factorial experiment, leading to 81 different model approaches. Each was subjected to a fivefold cross-validation, measuring area under curve (AUC) to assess model quality. Next, we used three climate change scenarios times three precipitation realizations to project future distributions from each model, yielding 729 projections. Again, we analyzed which step introduced most variability (the four model-building steps plus the two scenario steps) into predicted species prevalences by the year 2050. Predicted prevalences ranged from a factor of 0.2 to a factor of 10 of present prevalence, with the majority of predictions between 1.1 and 4.2 (inter-quartile range). We found that model type and data quality dominated this analysis. In particular, artificial neural networks yielded low cross-validation robustness and gave very conservative climate change predictions. Generalized linear and additive models were very similar in quality and predictions, and superior to neural networks. Variations in scenarios and realizations had very little effect, due to the small spatial extent of the study region and its relatively small range of climatic conditions. We conclude that, for climate projections, model type and data quality were the most influential factors. Since comparison of model types has received good coverage in the ecological literature, effects of data quality should now come under more scrutiny.

Spatial variations and determinants of infant and under-five mortality in Bangladesh.

Reducing child mortality is a Sustainable Development Goal yet to be achieved by many low-income countries. We applied a subnational and spatial approach based on publicly available datasets and identified permanent insolvency, urbanicity, and malaria endemicity as factors associated with child mortality. We further detected spatial clusters in the east of Bangladesh and noted Sylhet and Jamalpur as those districts that need immediate attention to reduce child mortality. Our approach is transferable to other regions in comparable settings worldwide and may guide future studies to identify subnational regions in need for public health attention. Our study adds to our understanding where we may intervene to more effectively improve health, particularly among disadvantaged populations.

Erratum to: Allometric models and aboveground biomass stocks of a West African Sudan Savannah watershed in Benin.

[This corrects the article DOI: 10.1186/s13021-016-0058-5.].