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Ecosystem models show divergent responses of the terrestrial carbon cycle to global change over the next century. Individual model evaluation and multimodel comparisons with data have largely focused on individual processes at subannual to decadal scales. Thus far, data-based evaluations of emergent ecosystem responses to climate and CO at multidecadal and centennial timescales have been rare. We compared the sensitivity of net primary productivity (NPP) to temperature, precipitation, and CO in ten ecosystem models with the sensitivities found in tree-ring reconstructions of NPP and raw ring-width series at six temperate forest sites. These model-data comparisons were evaluated at three temporal extents to determine whether the rapid, directional changes in temperature and CO in the recent past skew our observed responses to multiple drivers of change. All models tested here were more sensitive to low growing season precipitation than tree-ring NPP and ring widths in the past 30 years, although some model precipitation responses were more consistent with tree rings when evaluated over a full century. Similarly, all models had negative or no response to warm-growing season temperatures, while tree-ring data showed consistently positive effects of temperature. Although precipitation responses were least consistent among models, differences among models to CO drive divergence and ensemble uncertainty in relative change in NPP over the past century. Changes in forest composition within models had no effect on climate or CO sensitivity. Fire in model simulations reduced model sensitivity to climate and CO , but only over the course of multiple centuries. Formal evaluation of emergent model behavior at multidecadal and multicentennial timescales is essential to reconciling model projections with observed ecosystem responses to past climate change. Future evaluation should focus on improved representation of disturbance and biomass change as well as the feedbacks with moisture balance and CO in individual models.

Overabundant populations of ungulates have caused environmental degradation and loss of biological diversity in ecosystems throughout the world. Culling or regulated harvest is often used to control overabundant species. These methods are difficult to implement in national parks, other types of conservation reserves, or in residential areas where public hunting may be forbidden by policy. As a result, fertility control has been recommended as a non-lethal alternative for regulating ungulate populations. We evaluate this alternative using white-tailed deer in national parks in the vicinity of Washington, D.C., USA as a model system. Managers seek to reduce densities of white-tailed deer from the current average (50 deer per km2) to decrease harm to native plant communities caused by deer. We present a Bayesian hierarchical model using 13 years of population estimates from 8 national parks in the National Capital Region Network. We offer a novel way to evaluate management actions relative to goals using short term forecasts. Our approach confirms past analyses that fertility control is incapable of rapidly reducing deer abundance. Fertility control can be combined with culling to maintain a population below carrying capacity with a high probability of success. This gives managers confronted with problematic overabundance a framework for implementing management actions with a realistic assessment of uncertainty.

 Reanalyses provide decades-long model-data-driven harmonized and continuous data sets for new scientific discoveries  Novel global scale reanalyses quantify the biogeochemical ocean cycle, terrestrial carbon cycle, land surface and hydrologic processes  New observation technology and modeling capabilities allow in the near future production of advanced terrestrial ecosystem reanalysis

Aim Climate change is occurring at accelerated rates in high latitude regions such as Alaska, causing alterations in woody plant growth and associated ecosystem patterns and processes. Our aim is to assess the magnitude and speed that climate‐induced changes in woody plant distribution and volume may be reduced and/or slowed by relatively static landscape features like physical characteristics (e.g. depth to gravel, mineral cover percent and slope degree) and/or edaphic properties (e.g. soil organic matter, soil pH and site wetness rating) that resist climate‐vegetation responses. Location We leveraged a large field data set collected across a network of Alaskan national parks, which allows for comprehensive spatial data analysis over a uniquely large spatial extent. Methods To learn about the conditions that may either impede or accelerate vegetation changes in northern Alaska, we used a Bayesian hierarchical model to identify which landscape features may decelerate change or offer refuge for plant species. Our model quantifies the contribution of fast (‘dynamic’) versus slow (‘static’) changing variables to predict plant volume and categorize landscape types into either robust or nonrobust to climate changes. Results We found that two landscape features, low soil wetness and low soil organic matter comprising 63.1% of sites in the data set, were the most likely landscape features to inhibit vegetation expansion. We also found that fewer numbers of sites have the potential to offer refuge to existing plant species (5.43% on average) because few sites had high soil wetness as a landscape feature. Main conclusions Our analyses highlight the importance of incorporating static covariates representing landscape resistance to vegetation change for improving realism in forecasts of vegetation change in Alaska.