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Debate about what proportion of the Earth to protect often overshadows the question of how nature should be conserved and by whom. We present a systematic review and narrative synthesis of 169 publications investigating how different forms of governance influence conservation outcomes, paying particular attention to the role played by Indigenous peoples and local communities. We find a stark contrast between the outcomes produced by externally controlled conservation, and those produced by locally controlled efforts. Crucially, most studies presenting positive outcomes for both well-being and conservation come from cases where Indigenous peoples and local communities play a central role, such as when they have substantial influence over decision making or when local institutions regulating tenure form a recognized part of governance. In contrast, when interventions are controlled by external organizations and involve strategies to change local practices and supersede customary institutions, they tend to result in relatively ineffective conservation at the same time as producing negative social outcomes. Our findings suggest that equitable conservation, which empowers and supports the environmental stewardship of Indigenous peoples and local communities represents the primary pathway to effective long-term conservation of biodiversity, particularly when upheld in wider law and policy. Whether for protected areas in biodiversity hotspots or restoration of highly modified ecosystems, whether involving highly traditional or diverse and dynamic local communities, conservation can become more effective through an increased focus on governance type and quality, and fostering solutions that reinforce the role, capacity, and rights of Indigenous peoples and local communities. We detail how to enact progressive governance transitions through recommendations for conservation policy, with immediate relevance for how to achieve the next decadeâ s conservation targets under the U

Skill shortages are a drain on society. They hamper economic opportunities for individuals, slow growth for firms, and impede labor productivity in aggregate. Therefore, the ability to understand and predict skill shortages in advance is critical for policy-makers and educators to help alleviate their adverse effects. This research implements a high-performing Machine Learning approach to predict occupational skill shortages. In addition, we demonstrate methods to analyze the underlying skill demands of occupations in shortage and the most important features for predicting skill shortages. For this work, we compile a unique dataset of both Labor Demand and Labor Supply occupational data in Australia from 2012 to 2018. This includes data from 7.7 million job advertisements (ads) and 20 official labor force measures. We use these data as explanatory variables and leverage the XGBoost classifier to predict yearly skills shortage classifications for 132 standardized occupations. The models we construct achieve macro-F1 average performance scores of up to 83 per cent. Our results show that job ads data and employment statistics were the highest performing feature sets for predicting year-to-year skills shortage changes for occupations. We also find that features such as 'Hours Worked', years of 'Education', years of 'Experience', and median 'Salary' are highly important features for predicting occupational skill shortages. This research provides a robust data-driven approach for predicting and analyzing skill shortages, which can assist policy-makers, educators, and businesses to prepare for the future of work.

Targeting seedable clouds with silver iodide in complex terrain adds considerable uncertainty in weather modification studies. This study explores the geographic and temporal distribution of silver iodide associated with an active cloud seeding program in central Idaho snowpack using trace chemistry. Over 4,000 snow samples were analyzed for the presence of a cloud seeding silver iodide (AgI) signature over two winter seasons. The results indicate the following. (1) At sites within 70 km of AgI sources, silver enrichments were detected at 88% of cases involving seeding efforts from ground generators, but none from aircraft seeded cases. (2) Real-time snow collection methods were replicable within 0.41 ppt and confirmed seeding signatures for the entire duration of a seeded storm (n=3). (3) Sites sampled beyond 70 km of AgI sources (n=13) lacked detectable seeding signatures in snow. The results of this study demonstrate some of the strengths and limitations of chemical tracers to evaluate cloud seeding operations and provide observational data that can inform numerical simulations of these processes. The results also indicate that this chemical approach can be used to help constrain the spatiotemporal distribution of silver from cloud seeding efforts.

Assessing and managing children who are underweight is an integral part of paediatric practice. Young people with anorexia nervosa (AN) are mainly cared for in the community by specialist eating disorder services. However, increasing numbers require admission to paediatric wards with medical instability due to the complications of starvation. Despite recommendations published in the junior MARSIPAN report in 2012, many paediatricians still feel poorly equipped to care for these highrisk patients. This article aims to provide a safe and structured approach to the assessment and management of children and adolescents with medically unstable AN.Copyright © 2017. Produced by BMJ Publishing Group Ltd under licence.