Abstract. Under business as usual (BAU) management, stresses posed by climate change may exceed the ability of Great Lake forests to adapt. Temperature and precipitation projections in the Great Lakes region are expected to change forest tree species composition and productivity. It is unknown how a change in productivity and/or tree species diversity due to climate change will affect the relationship between diversity and productivity. We assessed how forests in two landscapes (i.e., northern lower Michigan and northeastern Minnesota, USA) would respond to climate change and explored the diversityproductivity relationship under climate change. In addition, we explored how tree species diversity varied across landscapes by soil type, climate, and management. We used a spatially dynamic forest ecosystem model, LANDIS-II, to simulate BAU forest management under three climate scenarios (current climate, low emissions, and high emissions) in each landscape. We found a positive relationship between diversity and productivity only under a high emissions future as productivity declined. Within landscapes, climate change simulations resulted in the highest diversity in the coolest climate regions and lowest diversity in the warmest climate region in Minnesota and Michigan, respectively. Simulated productivity declined in both landscapes under the high emissions climate scenario as species such as balsam fir (Abies balsamea) declined in abundance. In the Great Lakes region, a high emissions future may decrease forest productivity creating a more positive relationship between diversity and productivity. Maintaining a diversity of tree species may become increasingly important to maintain the adaptive capacity of forests.

Cover PhotoMontane ecosystems, as seen from Mount Jefferson on the White Mountain National Forest. These ecosystems are particularly vulnerable to climate change. Photo by Toni Lyn Morelli, U.S. Geological Survey.

No abstract

The importance of forests for sequestering carbon has created widespread interest among land managers for identifying actions that maintain or enhance carbon storage in forests. Managing for forest carbon under changing climatic conditions underscores a need for resources that help identify adaptation actions that align with carbon management. We developed the Forest Carbon Management Menu to help translate broad carbon management concepts into actionable tactics that help managers reduce risk from expected climate impacts in order to meet desired management goals. We describe examples of real-world forest-management planning projects that integrate climate change information with this resource to identify actions that simultaneously benefit forest carbon along with other project goals. These examples highlight that the inclusion of information on climate vulnerability, considering the implications of management actions over extended timescales, and identifying co-benefits for other management goals can reveal important synergies in managing for carbon and climate adaptation.

No abstract

No abstract

The forests in northern Michigan will be affected directly and indirectly by changing climate during the 21st century. This assessment evaluates the vulnerability of forest ecosystems in the eastern Upper Peninsula and northern Lower Peninsula of Michigan under a range of future climates. We synthesized and summarized information on the contemporary landscape, provided information on past climate trends, and described a range of projected future climates. This information was used to parameterize and run multiple vegetation impact models, which provided a range of potential vegetative responses to climate. Finally, we brought these results before a multidisciplinary panel of scientists and land managers familiar with Michigan forests to assess ecosystem vulnerability through a formal consensus-based expert elicitation process. The summary of the contemporary landscape identifies major forest trends and stressors currently threatening forests in the region. Observed trends in climate over the past century reveal that precipitation increased in the area, particularly in summer and fall, and that daily maximum temperatures increased, particularly in winter. Projected climate trends for the next 100 years using downscaled global climate model data indicate a potential increase in mean annual temperature of 2.2 to 8.1 °F for the assessment area. Projections for precipitation indicate an increase in winter and spring precipitation, and summer and fall precipitation projections vary by scenario. We identified potential impacts on forests by incorporating these climate projections into three forest impact models (Tree Atlas, LANDIS-II, and PnET-CN). Model projections suggest that northern boreal species such as black spruce and paper birch may fare worse under future conditions, but other species such as American elm and white oak may benefit from projected changes in climate. Published literature on climate impacts related to wildfire, invasive species, and diseases also contributed to the overall determination of climate change vulnerability. We assessed vulnerability for nine forest communities in the assessment area, which were a combination of U.S. Department of Agriculture, Forest Service Forest Inventory and Analysis program forest types and Michigan Natural Features Inventory natural communities. The basic assessment was conducted through a formal elicitation process of 27 science and management experts from across the state, who considered vulnerability both in terms of potential impacts on a system and in terms of the system's adaptive capacity. Upland spruce-fir, jack pine, lowland conifers, and red pine-white pine forest communities were determined to be the most vulnerable. Barrens and lowland/riparian hardwood communities were perceived as less vulnerable to projected changes in climate. These projected changes in climate and the associated impacts and vulnerabilities will have important implications for economically valuable timber species, forest-dependent wildlife and plants, recreation, and long-range p...

No abstract