Approximately 200 million people in Africa derive high-quality and low-cost proteins from fish. However, the consumption of fish is not fully exploited to combat the "triple burden" of malnutrition-obesity, undernutrition, and micronutrient deficiencies which are the leading causes of poor health in the region. There is still limited knowledge on quantitative information to guide policy makers in developing evidence-based actions that can improve the availability of and access to nutritious food for healthy and sustained diets among children and care givers. In this paper, we review the available literature with the aim of assessing and quantifying the extent to which fish contributes towards fighting food and nutrition insecurity in the Eastern Africa subregion. Key results reveal the region is characterized by fish supply deficits, and hence, low levels of fish consumed per person. Nonetheless, the increase in fish imports, and the growing supply of fish from aquaculture are likely to improve the per-capita fish intake. Fish trade is generally bidirectional, with exports exceeding imports in value terms, while significant challenges still hinder domestic and intra-regional fish trade. The Eastern Africa region is projected to realize increased fish consumption from 4.80 kg in 2013 to 5.49 kg by 2022. Rising population growth and income levels imply that the region will need 2.49 million tonnes of fish to fill the demand-supply gaps. We recommend that food security and nutritional programmes should recognize the potential of fish in providing essential micronutrients from the aspects of improved dietary quality, nutritional status, and general wellbeing of the region's fast growing population.

Context Large near-natural rivers have become rare in Europe, a fact reflected in the high conservation status of many riverine ecosystems. While the Balkan still harbors several intact river corridors, most of these are under pressure from planned hydropower constructions. Unfortunately, there is little information available on the hydromorphodynamics and biota of Balkan rivers under threat. Objectives We present a synthesis of research on the Vjosa in Southern Albania. Here, longitudinal continuity in water flow, undisturbed sediment transport and intact fluvial dynamics are still maintained, but threatened by two large dams planned in its downstream section. We intend to provide a first multidisciplinary inventory of this river system as an example of the knowledge base required for sound water management decisions in the Balkans. Methods Based on field work of a multidisciplinary consortium of scientists from Albania and other countries conducted from 2017 onwards, we summarize the most important findings on geomorphology of Electronic supplementary material The online version of this article (

Visual characteristics are among the most important features for characterizing the phenotype of biological organisms. Color and geometric properties define population phenotype and allow assessing diversity and adaptation to environmental conditions. To analyze geometric properties classical morphometrics relies on biologically relevant landmarks which are manually assigned to digital images. Assigning landmarks is tedious and error prone. Predefined landmarks may in addition miss out on information which is not obvious to the human eye. The machine learning (ML) community has recently proposed new data analysis methods which by uncovering subtle features in images obtain excellent predictive accuracy. Scientific credibility demands however that results are interpretable and hence to mitigate the black-box nature of ML methods. To overcome the black-box nature of ML we apply complementary methods and investigate internal representations with saliency maps to reliably identify location specific characteristics in images of Nile tilapia populations. Analyzing fish images which were sampled from six Ethiopian lakes reveals that deep learning improves on a conventional morphometric analysis in predictive performance. A critical assessment of established saliency maps with a novel significance test reveals however that the improvement is aided by artifacts which have no biological interpretation. More interpretable results are obtained by a Bayesian approach which allows us to identify genuine Nile tilapia body features which differ in dependence of the animals habitat. We find that automatically inferred Nile tilapia body features corroborate and expand the results of a landmark based analysis that the anterior dorsum, the fish belly, the posterior dorsal region and the caudal fin show signs of adaptation to the fish habitat. We may thus conclude that Nile tilapia show habitat specific morphotypes and that a ML analysis allows inferring novel biological knowledge in a reproducible manner.

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