Significance: This study provides conservation assessments for 600 US native plants that are wild relatives of important agricultural crops. We found that more than one-half of the species may be endangered in their natural habitats, and that the great majority require further conservation action, both ex situ (in gene banks and botanical gardens) and in situ (in protected areas). Diversity hotspots across the nation represent focal regions for further collecting for ex situ conservation as well as for enhanced habitat protection. Wider collaborations, as well as greater awareness, access to, and information about these resources are needed to bolster their conservation and use.

Effective assessments of the current status of biodiversity conservation are needed to support planning, policy and action, from local to global levels. Of particular use would be well documented, reproducible methods based on openly accessible data and tools. Such methods should provide an accurate estimate of the state of conservation of diversity, identifying gaps in current conservation systems, while providing a benchmark against which to measure success, including determining when conservation goals have been met. Here we introduce GapAnalysis, an open source R package developed to support the assessment of the state of conservation of taxa, both ex situ (in genebanks, botanic gardens and other repositories) and in situ (in protected natural areas). The eco‐geographic variation maintained in conservation repositories and that present within protected natural areas is compared with the full extent of eco‐geographic variation predicted within species' native ranges. Eco‐geographic conservation gaps are identified, and taxa are then prioritized for further action. The package enables users to quickly determine conservation status, gaps and priorities for as few as one to as many as thousands of taxa, for any wild flora or fauna for which occurrence data and species distribution models are available. To demonstrate the use of GapAnalysis, we provide case studies for wild plant taxa in the genera Capsicum L. and Cucurbita L, with an accompanying online R tutorial for three of the Cucurbita species.

Deep learning applications on computer vision involve the use of large-volume and representative data to obtain state-of-the-art results due to the massive number of parameters to optimise in deep models. However, data are limited with asymmetric distributions in industrial applications due to rare cases, legal restrictions, and high image-acquisition costs. Data augmentation based on deep learning generative adversarial networks, such as StyleGAN, has arisen as a way to create training data with symmetric distributions that may improve the generalisation capability of built models. StyleGAN generates highly realistic images in a variety of domains as a data augmentation strategy but requires a large amount of data to build image generators. Thus, transfer learning in conjunction with generative models are used to build models with small datasets. However, there are no reports on the impact of pre-trained generative models, using transfer learning. In this paper, we evaluate a StyleGAN generative model with transfer learning on different application domains—training with paintings, portraits, Pokémon, bedrooms, and cats—to generate target images with different levels of content variability: bean seeds (low variability), faces of subjects between 5 and 19 years old (medium variability), and charcoal (high variability). We used the first version of StyleGAN due to the large number of publicly available pre-trained models. The Fréchet Inception Distance was used for evaluating the quality of synthetic images. We found that StyleGAN with transfer learning produced good quality images, being an alternative for generating realistic synthetic images in the evaluated domains.

Despite representing a growing element of the international community’s discourse, the sustainability of food systems and the challenge of its empirical measurement are still highly debated. In this paper, we propose to address this gap by computing a global food system sustainability index which we then use in a cross-country analysis covering 94 countries in low-, middle- and high-income regions. The analysis reveals a strong non-linear but positive correlation between the food system sustainability index and countries’ individual GDP per capita. This relationship suggests some possible degree of endogeneity between food system sustainability and economic development. We then use the Shared Socioeconomic Pathways framework and Individual Conditional Expectations modeling techniques to explore how the sustainability of food systems is projected to evolve in the future as countries move up the economic development ladder. The projections indicate that for lower income countries, the change is usually more significant than for higher income countries. The analysis also reveals that the different dimensions of sustainability will not all contribute equally to future improvements in food system sustainability. In particular, investments targeting social and food security & nutrition dimensions are projected to have a greater effect on the sustainability of food systems than investment/interventions aiming at the environment or economic domains. For countries located at the lower end of the economic development spectrum, this would imply that, even with limited resources, policy-makers could substantially improve the sustainability of countries’ food systems by prioritizing (sub)national policies and interventions focused on social and food security & nutrition domains.

International, regional, and national organizations, alongside policymakers, are increasingly acknowledging the crucial connection between climate, peace, and security. However, there remains a notable gap in research methodologies capable of fully grasping the intricate dynamics of this relationship. This paper introduces the Integrated Climate Security Framework (ICSF), a comprehensive mixed-methods approach designed to unravel the complexities of climate-human security-conflict connections across various scales. By integrating traditional and innovative research methods, the ICSF aims to provide cutting-edge, policy-relevant insights to address five main measurement challenges of the climate security nexus: multiple pathways; context specificity; non-linearity; multiple actors and scales, and multiple geographic and time scales. By drawing on diverse epistemological perspectives and engaging directly with local communities, the framework offers a comprehensive evaluation of the complex social-ecological dynamics at play. Using Kenya as a case study—a country where climate and security risks frequently intersect and amplify each other—we demonstrate the comprehensive insights the framework offers to address the complex challenges at the nexus of climate, human security, and conflict.

Las organizaciones modernas han estimado como inversión prioritaria acceder a las Tecnologías de la Información y las Comunicaciones (TIC), con el principal propósito de obtener información confiable y oportuna que les permita establecer un direccionamiento adecuado con el contexto innovador y competitivo del siglo XXI. Este articulo pretende ilustrar la relación entre la inversión en TIC (Hardware, Software y Telecomunicaciones) y el direccionamiento estratégico en 107 PYMES de Santiago de Cali - Colombia, utilizando herramientas estadísticas como Test de Independencia Chi -cuadrado, imputación múltiple de datos y PLS-PM (Mínimos cuadrados parciales - Modelación de rutas). El principal resultado de la investigación, es que refleja que la inversión en TIC, específicamente en software, es uno de los principales apoyos para el direccionamiento estratégico de las organizaciones.