Prenatal diagnosis of friedreich ataxia: Improved accuracy by using new genetic flanking markers

Friedreich ataxia is a neurodegerative disorder with autosomal recessive inheritance. Since the gene causing mutation has not yet been identified, prenatal, predictive, and carrier diagnoses are based on indirect haplotype analysis with closely linked markers. Until recently, only distal markers were available and their physical distance to the Friedreich ataxia (FRDA) gene remained elusive. The identification of close flanking markers that mark out the boundaries of the FRDA locus and reduce the critical genomic region which contains the gene allows for the first time misdiagnosis due to undetectable recombination to be avoided and diagnosis accuracy to be greatly improved. In this sense, we have verified a prenatal diagnosis in which the fetus was diagnosed as an unaffected carrier last year with a confidence of 95 per cent. By using the new flanking markers, the diagnosis improved and confidence reached almost 100 per cent.     

Integrated assessment of agricultural policies with dynamic land use change modelling

With about half of its territory being farmed, agriculture is the main land use in the European Union (EU). As over 10% of the total EU manufacturing output comes from the agri-food sector, it also is an economic factor of great importance. Moreover, EU policy in this sector has far-reaching consequences ranging from the EU's status as a global trade partner to landscape preservation and development. The LUMOCAP Policy Support System is targeted towards policy makers in the European Commission (EC) and its Member States (MS) and aims to provide support in the field of sustainable agricultural and rural development. To this end it incorporates an integrated model with socio-economic and bio-physical processes, operating at different spatial scales. For supporting integrated assessment, a large number of policy levers is included as inputs for these models and outputs are transformed into policy-relevant social, economic and environmental indicators. The whole system is framed in a flexible, modular and easy to use software package that is useable for process experts and policy-analysts alike.This paper describes the integrated model, the individual models and a first calibration of the system. It demonstrates the system's behaviour for typical scenario runs and concludes with a reflection on the current status of the system and some recommendations for further development.     

Predicting landscape-scale biodiversity recovery by natural tropical forest regrowth

Natural forest regrowth is a cost-effective, nature-based solution for biodiversity recovery, yet different socioenvironmental factors can lead to variable outcomes. A critical knowledge gap in forest restoration planning is how to predict where natural forest regrowth is likely to lead to high levels of biodiversity recovery, which is an indicator of conservation value and the potential provisioning of diverse ecosystem services. We sought to predict and map landscape-scale recovery of species richness and total abundance of vertebrates, invertebrates, and plants in tropical and subtropical second-growth forests to inform spatial restoration planning. First, we conducted a global meta-analysis to quantify the extent to which recovery of species richness and total abundance in second-growth forests deviated from biodiversity values in reference old-growth forests in the same landscape. Second, we employed a machine-learning algorithm and a comprehensive set of socioenvironmental factors to spatially predict landscape-scale deviation and map it. Models explained on average 34% of observed variance in recovery (range 9–51%). Landscape-scale biodiversity recovery in second-growth forests was spatially predicted based on socioenvironmental landscape factors (human demography, land use and cover, anthropogenic and natural disturbance, ecosystem productivity, and topography and soil chemistry); was significantly higher for species richness than for total abundance for vertebrates (median range-adjusted predicted deviation 0.09 vs. 0.34) and invertebrates (0.2 vs. 0.35) but not for plants (which showed a similar recovery for both metrics [0.24 vs. 0.25]); and was positively correlated for total abundance of plant and vertebrate species (Pearson r = 0.45, p = 0.001). Our approach can help identify tropical and subtropical forest landscapes with high potential for biodiversity recovery through natural forest regrowth.