Great progress has been made in addressing global undernutrition over the past several decades, in part because of large increases in food production from agricultural expansion and intensification. Food systems, however, face continued increases in demand and growing environmental pressures. Most prominently, human-caused climate change will influence the quality and quantity of food we produce and our ability to distribute it equitably. Our capacity to ensure food security and nutritional adequacy in the face of rapidly changing biophysical conditions will be a major determinant of the next century's global burden of disease. In this article, we review the main pathways by which climate change may affect our food production systems-agriculture, fisheries, and livestock-as well as the socioeconomic forces that may influence equitable distribution.

One contribution of 20 to a theme issue 'Dynamic and integrative approaches to understanding pathogen spillover'. Subject Areas: health and disease and epidemiology, ecologyHistorically, efforts to assess 'zoonotic risk' have focused mainly on quantifying the potential for cross-species emergence of viruses from animal hosts. However, viruses clearly differ in relative burden, both in terms of morbidity and mortality (virulence) incurred and the capacity for sustained humanto-human transmission. Extending previously published databases, we delineated host and viral traits predictive of human mortality associated with viral spillover, viral capacity to transmit between humans following spillover and the probability of a given virus being zoonotic. We demonstrate that increasing host phylogenetic distance from humans positively correlates with human mortality but negatively correlates with human transmissibility, suggesting that the virulence induced by viruses emerging from hosts at high phylogenetic distance may limit capacity for human transmission. Our key result is that hosts most closely related to humans harbour zoonoses of lower impact in terms of morbidity and mortality, while the most distantly related hosts-in particular, order Chiroptera (bats)-harbour highly virulent zoonoses with a lower capacity for endemic establishment in human hosts. As a whole, our results emphasize the importance of understanding how zoonoses manifest in the human population and also highlight potential risks associated with multi-host transmission chains in spillover.This article is part of the theme issue 'Dynamic and integrative approaches to understanding pathogen spillover'.

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Despite the global investment in One Health disease surveillance, it remains difficult and costly to identify and monitor the wildlife reservoirs of novel zoonotic viruses. Statistical models can guide sampling target prioritisation, but the predictions from any given model might be highly uncertain; moreover, systematic model validation is rare, and the drivers of model performance are consequently under-documented. Here, we use the bat hosts of betacoronaviruses as a case study for the data-driven process of comparing and validating predictive models of probable reservoir hosts. In early 2020, we generated an ensemble of eight statistical models that predicted host–virus associations and developed priority sampling recommendations for potential bat reservoirs of betacoronaviruses and bridge hosts for SARS-CoV-2. During a time frame of more than a year, we tracked the discovery of 47 new bat hosts of betacoronaviruses, validated the initial predictions, and dynamically updated our analytical pipeline. We found that ecological trait-based models performed well at predicting these novel hosts, whereas network methods consistently performed approximately as well or worse than expected at random. These findings illustrate the importance of ensemble modelling as a buffer against mixed-model quality and highlight the value of including host ecology in predictive models. Our revised models showed an improved performance compared with the initial ensemble, and predicted more than 400 bat species globally that could be undetected betacoronavirus hosts. We show, through systematic validation, that machine learning models can help to optimise wildlife sampling for undiscovered viruses and illustrates how such approaches are best implemented through a dynamic process of prediction, data collection, validation, and updating.

It is crucial that a dimensional approach be offered in some form in DSM-V; but it is also vital that any dimensional approach be linked to the categorical definition. The proposal offered herein provides a model for amplifying categorical definitions with a dimensional component in a way that is evolutionary and not disruptive to the existing taxonomy.

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Objective:We collected dietary records over the course of nine months to comprehensively characterize the consumption patterns of Malagasy people living in remote rainforest areas of north-eastern Madagascar.Design:The present study was a prospective longitudinal cohort study to estimate dietary diversity and nutrient intake for a suite of macronutrients, micronutrients and vitamins for 152 randomly selected households in two communities.Setting:Madagascar, with over 25 million people living in an area the size of France, faces a multitude of nutritional challenges. Micronutrient-poor staples, especially rice, roots and tubers, comprise nearly 80 % of the Malagasy diet by weight. The remaining dietary components (including wild foods and animal-source foods) are critical for nutrition. We focus our study in north-eastern Madagascar, characterized by access to rainforest, rice paddies and local agriculture.Participants:We enrolled men, women and children of both sexes and all ages in a randomized sample of households in two communities.Results:Although the Household Dietary Diversity Score and Food Consumption Score reflect high dietary diversity, the Minimum Dietary Diversity–Women indicator suggests poor micronutrient adequacy. The food intake data confirm a mixed nutritional picture. We found that the median individual consumed less than 50 % of his/her age/sex-specific Estimated Average Requirement (EAR) for vitamins A, B12, D and E, and Ca, and less than 100 % of his/her EAR for energy, riboflavin, folate and Na.Conclusions:Malnutrition in remote communities of north-eastern Madagascar is pervasive and multidimensional, indicating an urgent need for comprehensive public health and development interventions focused on providing nutritional security.

31 51 52 Coronaviruses are a diverse family of positive-sense, single-stranded RNA viruses, found widely 53 in mammals and birds 1 . They have a broad host range, a high mutation rate, and the largest 54 genomes of any RNA viruses, but they have also evolved mechanisms for RNA proofreading and 55repair, which help to mitigate the deleterious effects of a high recombination rate acting over a 56 large genome 2 . Consequently, coronaviruses fit the profile of viruses with high zoonotic potential. 57There are seven human coronaviruses (two in the genus Alphacoronavirus and five in 58Betacoronavirus), of which three are highly pathogenic in humans: SARS-CoV, SARS-CoV-2, and 59MERS-CoV. These three are zoonotic and widely agreed to have evolutionary origins in bats 3-6 . 60 61Our collective experience with both SARS-CoV and MERS-CoV illustrate the difficulty of tracing 62 specific animal hosts of emerging coronaviruses. During the 2002-2003 SARS epidemic, SARS-63 CoV was traced to the masked palm civet (Paguma larvata) 7 , but the ultimate origin remained 64 unknown for several years. Horseshoe bats (family Rhinolophidae: Rhinolophus) were implicated 65 as reservoir hosts in 2005, but their SARS-like viruses were not identical to circulating human 66 strains 4 . Stronger evidence from 2017 placed the most likely evolutionary origin of SARS-CoV in 67 Rhinolophus ferrumequinum or potentially R. sinicus 8 . Presently, there is even less certainty in the 68 origins of MERS-CoV, although spillover to humans occurs relatively often through contact with 69 dromedary camels (Camelus dromedarius). A virus with 100% nucleotide identity in a ~200 base 70 pair region of the polymerase gene was detected in Taphozous bats (family Emballonuridae) in 71 Saudi Arabia 9 ; however, based on spike gene similarity, other sources treat HKU4 virus from 72 Tylonycteris bats (family Vespertilionidae) in China as the closest-related bat virus 10,11 . Several 73 bat coronaviruses have shown close relation to MERS-CoV, with a surprisingly broad geographic 74 distribution from Mexico to China 12,13,14,15 . 75 76 Coronavirus disease 2019 (COVID-19) is caused by severe acute respiratory syndrome 77 coronavirus-2 (SARS-CoV-2), a novel virus with presumed evolutionary origins in bats. Although 78 the earliest cases were linked to a wildlife market, contact tracing was limited, and there has been 79 no definitive identification of the wildlife contact that resulted in spillover nor a true "index case." 80 Two bat viruses are closely related to SARS-CoV-2: RaTG13 bat CoV from Rhinolophus affinis 81 (96% identical overall), and RmYN02 bat CoV from Rhinolophus malayanus (97% identical in one 82 gene but only 61% in the receptor-binding domain and with less overall similarity) 6,16 . The 83 divergence time between these bat viruses and human SARS-CoV-2 has been estimated as 40-50 84 years 17 , suggesting that the main host(s) involved in spillover remain unknown. Evidence of viral 85 recombination in pangolins has been proposed but is unresolved 17 . S...