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Introduction: Large-scale investments in restoring California’s forested watersheds are imperative for conserving biodiversity, enhancing water quality, and mitigating the future impacts of climate change. This study explores the underlying incentives, major challenges, and potential strategies associated with such investments. Methods: An online survey was administered to 43 experts in the field to gather their insights on forest watershed restoration investments. The collected responses were then analyzed using a combination of confirmatory factor analysis and regression analysis to elucidate patterns and relationships. Results: The analysis revealed that key environmental outcomes, such as reducing wildfire risks and protecting water supplies, are the principal motivators driving investment. At the same time, significant barriers emerged, including high costs, limited workforce capacity, and insufficient trust among stakeholders. The study also identified a series of effective strategies to overcome these obstacles, such as repositioning forest restoration as an infrastructure investment and clearly demonstrating its ecological, social, and economic benefits. Discussion: Overall, the findings underscore the need for more flexible funding frameworks, enhanced stakeholder engagement, and improved data infrastructures. By addressing these elements, policymakers and practitioners can pave the way for more resilient and sustainable forested-watershed ecosystems in California. 

Recent drought, wildfires and rising temperatures in the western US highlight the urgency of increasing resiliency in overstocked forests. However, limited valuation information hinders the broader participation of beneficiaries in forest management. We assessed how historical disturbances in California's Central Sierra Nevada affected live biomass, forest water use and carbon uptake and estimated marginal values of these changes. On average, low‐severity wildfire caused greater declines in forest evapotranspiration (ET), gross primary productivity (GPP) and live biomass than did commercial thinning. Low‐severity wildfires represent proxies for prescribed burns and both function as biomass removal to alleviate overstocked conditions. Increases in potential runoff over 15 years post‐disturbance were valued at $108,000/km²for commercial thinning versus $234,000/km²for low‐severity wildfire, based on historical water prices. Respective declines in GPP were valued at −$305,000 and −$1,317,000/km², based on an average social cost of carbon. Considering biomass levels created by commercial thinning and low‐severity fire as more‐sustainable management baselines for overstocked forests, carbon uptake over 15 years post‐disturbance can be viewed as a benefit rather than loss. Realizing this benefit upon management re‐entry may require sequestering thinned material. High‐severity wildfire and clearcutting resulted in greater declines in ET and thus greater potential water benefits but also substantial declines in GPP and live carbon. These lessons from historical disturbances indicate what benefit ranges from fuels treatments can be expected from more‐sustainable management of mixed‐conifer forests and the importance of setting an appropriate baseline. 

Abstract The use of recreational ecosystem services is highly dependent on the surrounding environmental and climate conditions. Due to this dependency, future recreational opportunities provided by nature are at risk from climate change. To understand how climate change will impact recreation we need to understand current recreational patterns, but traditional data is limited and low resolution. Fortunately, social media data presents an opportunity to overcome those data limitations and machine learning offers a tool to effectively use that big data. We use data from the social media site Flickr as a proxy for recreational visitation and random forest to model the relationships between social, environmental, and climate factors and recreation for the peak season (summer) in California. We then use the model to project how non-urban recreation will change as the climate changes. Our model shows that current patterns are exacerbated in the future under climate change, with currently popular summer recreation areas becoming more suitable and unpopular summer recreation areas becoming less suitable for recreation. Our model results have land management implications as recreation regions that see high visitation consequently experience impacts to surrounding ecosystems, ecosystem services, and infrastructure. This information can be used to include climate change impacts into land management plans to more effectively provide sustainable nature recreation opportunities for current and future generations. Furthermore, our study demonstrates that crowdsourced data and machine learning offer opportunities to better integrate socio-ecological systems into climate impacts research and more holistically understand climate change impacts to human well-being.