Law, B. E. & Waring, R. H. Carbon implications of current and future effects of drought, fire and management on Pacific Northwest forests. For. School. Management 355, 4â14 (2015). Allen, C. D. & Breshears, D. D.

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Chang. 6, 192-196 (2016). We assessed the current extent and conservation status of forest lands in the western United States, as well as additional forest areas that would need to be protected to meet the 30% and 50% conservation goals. We characterized the current forest area using a 250 m resolution forest-type dataset generated by the United States Forest Service (USFS) Forest Inventory and Analysis Program (FIA) using forest inventories, MODIS satellites, and additional spatial datasets.19 We characterized the conservation status of the land using the United States Protected Areas Database (PAD-US Version 2.1). The PAD-US is the official national inventory of protected areas in the United States and is compiled by the USGS GAP20. The PAD-US contains spatial information on known protected areas for public and private lands in the 50 states, as well as the status of each protected area in accordance with guidelines developed by the International Union for Conservation of Nature (IUCN). Conservation status is indicated by GAP status codes, which describe management`s intention to conserve biodiversity. CAMP 1 and 2 designate areas with permanent protection from anthropogenic land cover transformation and management plans to maintain an entirely natural or predominant state. CAP 1 generally corresponds to IUCN categories Ia, Ib and II and GAP 2 corresponds to IUCN categories III to VI (supplementary table).

We adapted the forest area and PAD-US datasets to the region, aggregated the total forest extent primarily at a resolution of 1 km, and rasterized the PAD-US GAP status code with a resolution of 1 km using the lowest GAP state in case of overlap. We then assessed the total area of land and forests in each state currently permanently protected (CAP 1 or 2). In addition, we calculated the additional area needed if the goal is to protect 30 and 50 percent of the total land and forest area in each state. In summary, we show not only that additional forest protection is needed to achieve conservation goals (i.e., 30-30, 50-50) in the western United States, but also to determine where it would be most effective to preserve additional forests for mitigation and adaptation objectives that minimize species loss and ecosystem disruption. Our prioritization framework ensures that conservation opportunities are spread across the region, protecting many facets of regional biodiversity, promoting connectivity and creating local opportunities for participation in decision-making. Drought and forest fires are becoming more frequent in this region and could destabilize the carbon content and biodiversity of forests in some regions. Therefore, our framework includes simulations of ecosystem models to identify highly vulnerable forests in the future and reduce their priority for protection. Achieving conservation objectives would increase the protection of forest carbon stocks and accumulation, animal and tree habitat, and surface drinking water in the western United States. We focus on forest areas, but find that these areas often contain a mosaic of non-forest ecosystems (e.g. grasslands, wetlands, scrubland) that are also important for biodiversity conservation and carbon storage and accumulation. The development of a broader landscape PPR system that includes non-forest ecosystems would require standardized spatial datasets related to current ecosystem carbon stocks (e.g., Ref. 85), habitat distribution for non-woody plant species and, ideally, invertebrate species, and multi-taxa simulations of potential future ecological dynamics.

To support conservation efforts, our new forest prioritization datasets can be combined with local knowledge and finer local analysis using higher-resolution spatial datasets. The next steps are to apply this framework to all countries, include non-forest ecosystems, simulate future ecological conditions with higher spatial resolution, and examine how uncertainty in the underlying spatial datasets affects conservation prioritization.