This is a repository copy of Ten people-centered rules for socially sustainable ecosystem restoration.

The importance of non-timber forest products (NTFPs) to rural livelihoods is widely acknowledged globally, as is the income generated from casual or fulltime trade on village and urban markets. However, there is less understanding of how the condition or status of the neighboring landscapes influence the use of and trade in NTFPs. Here we report on the use and trade in NTFPs in four villages situated along a gradient of decreasing forest cover in southwest Malawi using a mixed-methods approach. Data were sourced via a survey of 286 households, value chain analysis of the four most commonly traded NTFPs (thatch grass, edible orchids, mushrooms, and wild fruits), key informant interviews with NTFP traders and direct observations. All households used at least one NTFP, with the most widely used being firewood (100% of households), bamboo (96%), thatch grass (94%), and timber for construction (92%). Overall, 15% of households sold at least one NTFP and the prevalence of selling within a village was correlated with forest cover, whereas buying of NTFPs was inversely correlated with forest cover. There was a wide range in mean annual income (US$20-456) from selling NTFPs based on the product, whether the trader sold on a casual or full-time basis and the market. Of those households selling NTFPs, approximately two-thirds sold more than one NTFP product, which is rarely recognized in income studies of individual market chains. The returns to labor were variable between villages and products, but were generally double or more than the national minimum hourly wage. The NTFP value chains were short, dominated by traders and some intermediaries. Most of the products were sold in local markets with little value addition. Overall, NTFPs were an integral part of the household economy, with multiple participants and users, partly shaped by the forest cover of the surrounding landscapes.

Global interest and investment in food system transformation should be accompanied by critical analysis of its justice implications. Multiple forms of injustice, and the potential role that research might play in exacerbating these, are key considerations for those engaging with food system transformation and justice.

Although it has long been recognised that human activities affect fire regimes, the interactions between humans and fire are complex, imperfectly understood, constantly evolving, and lacking any kind of integrative global framework. Many different approaches are used to study human-fire interactions, but in general they have arisen in different disciplinary contexts to address highly specific questions. Models of human-fire interactions range from conceptual local models to numerical global models. However, given that each type of model is highly selective about which aspects of human-fire interactions to include, the insights gained from these models are often limited and contradictory, which can make them a poor basis for developing fire-related policy and management practices. Here, we first review different approaches to modelling human-fire interactions and then discuss ways in which these different approaches could be synthesised to provide a more holistic approach to understanding human-fire interactions. We argue that the theory underpinning many types of models was developed using only limited amounts of data and that, in an increasingly data-rich world, it is important to re-examine model assumptions in a more systematic way. All of the models are designed to have practical outcomes but are necessarily simplifications of reality and as a result of differences in focus, scale and complexity, frequently yield radically different assessments of what might happen. We argue that it should be possible to combine the strengths and benefits of different types of model through enchaining the different models, for example from global down to local scales or vice versa. There are also opportunities for explicit coupling of different kinds of model, for example including agent-based representation of human actions in a global fire model. Finally, we stress the need for co-production of models to ensure that the resulting products serve the widest possible community.