No abstract

Forests play an important role in the life of indigenous communities. However, making non timber forest management a profitable economic activity is a difficult task. Factors contributing to this difficulty include the increasing pressure from the market economy, which leads communities to opt for alternative economic activities such as agroforestry, timber harvest, cacao, and aquaculture. External institutions have implemented rubber projects to reintroduce the rubber extraction activity, but the outcomes of these projects are unknown. To help address this issue, our research was conducted in the Sinchi Roca I native community in Peru. The objectives were (1) to describe the process of wild rubber (Hevea brasiliensis) extraction; ( 2) to analyze the local perception by gender of rubber management; and (3) to evaluate the outcomes of this activity using socioeconomic criteria and indicators. Data collection techniques included indepth interviews, focus group discussions, and intrahousehold surveys. First, we found that locals once extracted rubber with unsuitable techniques, which have improved with technical forest management. Second, wild extraction has a positive socioeconomic perception for the community, mainly because it provides income for basic needs. Surveyed families extract around 28,800 liters of rubber per year, averaging US$ 557.80 per family each year. Finally, we found that men and women participate in wild rubber extraction and decision making. However, women prefer not to actively participate in meetings with external institutions. Despite the benefits found, current use of silviculture techniques and community empowerment should be improved to take better advantage of existing potential.

Tree planting is increasingly being adopted as a strategy to address global change, including mitigation, adaptation, and restoration. Although reforestation has long been central to forest management, the desired outcomes of traditional and emerging tree-planting strategies face barriers linked to a lack of ecological diversity in forest nurseries. In the present article, we outline how insufficient diversity in nursery seedlings among species, genotypes, and stock types has impeded and will continue to hinder the implementation of diverse ecological or climate-suitable planting targets, now and into the future. To support this, we demonstrate disparities in seedling diversity among nursery inventories, focusing on the northern United States. To overcome these challenges, we recommend avenues for improving policy and financing, informational resources and training, and research and monitoring. Absent these advances, current seedling production and practices will fall short of ambitious tree-planting goals proposed for forest restoration and global change mitigation and adaptation.

Background Forests provide the largest terrestrial sink of carbon (C). However, these C stocks are threatened by forest land conversion. Land use change has global impacts and is a critical component when studying C fluxes, but it is not always fully considered in C accounting despite being a major contributor to emissions. An urgent need exists among decision-makers to identify the likelihood of forest conversion to other land uses and factors affecting C loss. To help address this issue, we conducted our research in California, Colorado, Georgia, New York, Texas, and Wisconsin. The objectives were to (1) model the probability of forest conversion and C stocks dynamics using USDA Forest Service Forest Inventory and Analysis (FIA) data and (2) create wall-to-wall maps showing estimates of the risk of areas to convert from forest to non-forest. We used two modeling approaches: a machine learning algorithm (random forest) and generalized mixed-effects models. Explanatory variables for the models included ecological attributes, topography, census data, forest disturbances, and forest conditions. Model predictions and Landsat spectral information were used to produce wall-to-wall probability maps of forest change using Google Earth Engine. Results During the study period (2000–2017), 3.4% of the analyzed FIA plots transitioned from forest to mixed or non-forested conditions. Results indicate that the change in land use from forests is more likely with increasing human population and housing growth rates. Furthermore, non-public forests showed a higher probability of forest change compared to public forests. Areas closer to cities and coastal areas showed a higher risk of transition to non-forests. Out of the six states analyzed, Colorado had the highest risk of conversion and the largest amount of aboveground C lost. Natural forest disturbances were not a major predictor of land use change. Conclusions Land use change is accelerating globally, causing a large increase in C emissions. Our results will help policy-makers prioritize forest management activities and land use planning by providing a quantitative framework that can enhance forest health and productivity. This work will also inform climate change mitigation strategies by understanding the role that land use change plays in C emissions.