The impact of humanitarian context conditions and individual characteristics on aid worker retention

High employee turnover rates constitute a major challenge to effective aid provision. This study examines how features of humanitarian work and aid workers’ individual characteristics affect retention within one humanitarian organisation, Médecins Sans Frontières (MSF) Holland. The study extends existing research by providing new theoretical explanations of employment opportunities and constraints and by engaging in the first large‐scale quantitative analysis of aid worker retention. Using a database of field staff (N=1,955), a logistic regression is performed of the likelihood of reenlistment after a first mission. The findings demonstrate that only 40 per cent of employees reenlist for a second mission with MSF Holland, and that workplace location and security situation, age, and gender have no significant effect. Individuals are less likely to reenlist if they returned early from the first mission for a personal reason, are in a relationship, are medical doctors, or if they come from highly developed countries. The paper reflects on the findings in the light of policy.     

Uncertainty propagation in vegetation distribution models based on ensemble classifiers

Ensemble learning techniques are increasingly applied for species and vegetation distribution modelling, often resulting in more accurate predictions. At the same time, uncertainty assessment of distribution models is gaining attention. In this study, Random Forests, an ensemble learning technique, is selected for vegetation distribution modelling based on environmental variables. The impact of two important sources of uncertainty, that is the uncertainty on spatial interpolation of environmental variables and the uncertainty on species clustering into vegetation types, is quantified based on sequential Gaussian simulation and pseudo-randomization tests, respectively. An empirical assessment of the uncertainty propagation to the distribution modelling results indicated a gradual decrease in performance with increasing input uncertainty. The test set error ranged from 30.83% to 52.63% and from 30.83% to 83.62%, when the uncertainty ranges on spatial interpolation and on vegetation clustering, respectively, were fully covered. Shannon’s entropy, which is proposed as a measure for uncertainty of ensemble predictions, revealed a similar increasing trend in prediction uncertainty. The implications of these results in an empirical distribution modelling framework are further discussed with respect to monitoring setup, spatial interpolation and species clustering.     

Geostatistical Assessment of the Impact of World War I on the Spatial Occurrence of Soil Heavy Metals

Previous research showed a regional Cu enrichment of 6 mg kg⁻¹ in the top soil of the Ypres war zone (Belgium), caused by corrosion of WWI shell fragments. Further research was required since in addition to Cu, also As, Pb, and Zn were used during the manufacturing of ammunition. Therefore, an additional data collection was conducted in which the initial Cu data set was tripled to 731 data points and extended to eight heavy metals (As, Cd, Cr, Cu, Hg, Ni, Pb, and Zn) which permitted (1) to evaluate the environmental impact of the heavy metals at a regional scale and (2) to assess their regional spatial occurrence by performing an optimized geostatistical modeling. The results showed no pollution at a regional scale, but sometimes locally concentrations exceeded the soil sanitation threshold, especially for Cu, Pb, and Zn. The spatial patterns of Ni and Cr were related to variations in soil texture whereas the occurrences of Cu and Pb were clearly linked to WWI activities. This difference in spatial behavior was confirmed by an analysis of coregionalization.     

How does a nature-based solution for flood control compare to a technical solution? Case study evidence from Belgium

The strategy of reconnecting rivers with their floodplains currently gains popularity because it not only harnesses natural capacities of floodplains but also increases social co-benefits and biodiversity. In this paper, we present an example of a successfully implemented nature-based solution (NBS) in the Dijle valley in the centre of Belgium. The research objective is to retrospectively assess cost and benefit differences between a technical solution (storm basins) and an alternative NBS, here the restoration of the alluvial floodplain. The method is a comparative social cost–benefit analysis. The case study analysis reveals similar flood security, lower costs, more ecosystem services benefits and higher biodiversity values associated with the NBS option in comparison to the technical alternative. However, the business case for working with NBS depends substantially on the spatial and socio-ecological context. Chances for successful NBS implementation increase in conditions of sufficient space to retain flood water, when flood water is of sufficient quality, and when economic activity and housing in the floodplain is limited.Supplementary InformationThe online version of this article contains supplementary material available at (10.1007/s13280-021-01548-4).