Many conifer forests experience stand-replacing wildfires, and these fires and subsequent recovery can change the amount of carbon released to the atmosphere because conifer forests contain large carbon stores. Stand-replacing fires switch ecosystems to being a net source of carbon as decomposition exceeds photosynthesisa—short-term effect (years to decades) that may be important over the next century if fire frequency increases. Over the long term (many centuries), net carbon storage through a fire cycle is zero if stands replace themselves. Therefore, equilibrium response of landscape carbon storage to changes in fire frequency will depend on how stand age distribution changes, on the carbon storage of different stand ages, and on postfire regeneration. In a case study of Yellowstone National Park, equilibrium values of landscape carbon storage were resistant to large changes in fire frequency because these forests regenerate quickly, the current fire interval is very long, the most rapid changes in carbon storage occur in the first century, and carbon storage is similar for stands of different ages. The conversion of forest to meadow or to sparser forest can have a large impact on landscape carbon storage, and this process is likely to be important for many conifer forests.
Abstract As temperatures continue rising, the direction, magnitude, and tempo of change in disturbance‐prone forests remain unresolved. Even forests long resilient to stand‐replacing fire face uncertain futures, and efforts to project changes in forest structure and composition are sorely needed to anticipate future forest trajectories. We simulated fire (incorporating fuels feedbacks) and forest dynamics on five landscapes spanning the Greater Yellowstone Ecosystem (GYE) to ask the following questions: (1) How and where are forest landscapes likely to change with 21st‐century warming and fire activity? (2) Are future forest changes gradual or abrupt, and do forest attributes change synchronously or sequentially? (3) Can forest declines be averted by mid‐21st‐century stabilization of atmospheric greenhouse gas (GHG) concentrations? We used the spatially explicit individual‐based forest model iLand to track multiple attributes (forest extent, stand age, tree density, basal area, aboveground carbon stocks, dominant forest types, species occupancy) through 2100 for six climate scenarios. Hot‐dry climate scenarios led to more fire, but stand‐replacing fire peaked in mid‐century and then declined even as annual area burned continued to rise. Where forest cover persisted, previously dense forests were converted to sparse young woodlands. Increased aridity and fire drove a ratchet of successive abrupt declines (i.e., multiple annual landscape‐level changes ≥20%) in tree density, basal area, and extent of older (>150 yr) forests, whereas declines in carbon stocks and mean stand age were always gradual. Forest changes were asynchronous across landscapes, but declines in stand structure always preceded reductions in forest extent and carbon stocks. Forest decline was most likely in less topographically complex landscapes dominated by fire‐sensitive tree species ( Picea engelmannii , Abies lasiocarpa , Pinus contorta var. latifolia ) and where fire resisters ( Pseudotsuga menziesii var. glauca ) were not already prevalent. If current GHG emissions continue unabated (RCP 8.5) and aridity increases, a suite of forest changes would transform the GYE, with cascading effects on biodiversity and myriad ecosystem services. However, stabilizing GHG concentrations by mid‐century (RCP 4.5) would slow the ratchet, moderating fire activity and dampening the magnitude and rate of forest change. Monitoring changes in forest structure may serve as an operational early warning indicator of impending forest decline.
Rural landscapes face changing climate, shifting development pressure, and loss of agricultural land. Perennial bioenergy crops grown on existing agricultural land may provide an opportunity to conserve rural landscapes while addressing increased demand for biofuels. However, increased bioenergy production and changing land use raise concerns for tradeoffs within the food–energy–environment trilemma. Heterogeneity of climate, soils, and land use complicate assessment of bioenergy potential in complex landscapes, creating challenges to evaluating future tradeoffs. The hypothesis addressed herein is that perennial bioenergy production can provide an opportunity to avoid agricultural land conversion to development. Using a process-based crop model, we assessed potential bioenergy crop growth through 2100 in a southern Appalachian Mountain region and asked: (1) how mean annual yield differed among three crops (switchgrass Panicum virgatum, giant miscanthus Miscanthus × giganteus, and hybrid poplar Populus × sp.) under current climate and climate change scenarios resulting from moderate and very high greenhouse gas emissions; (2) how maximum landscape yield, spatial allocation of crops, and bioenergy hotspots (areas with highest potential yield) varied among climate scenarios; and (3) how bioenergy hotspots overlapped with current crop production or lands with high development pressure. Under both climate change scenarios, mean annual yield of perennial grasses decreased (−4% to −39%), but yield of hybrid poplar increased (+8% to +20%) which suggests that a switch to woody crops would maximize bioenergy crop production. In total, maximum landscape yield increased by up to 90 000 Mg/yr (6%) in the 21st century due to increased poplar production. Bioenergy hotspots (>18 Mg·ha−1·yr−1) consistently overlapped with high suburban/exurban development likelihood and existing row crop production. If bioenergy production is constrained to marginal (non-crop) lands, landscape yield decreased by 27%. The removal of lands with high development probability from crop production resulted in losses of up to 670 000 Mg/yr (40%). This study demonstrated that tradeoffs among bioenergy production, crop production, and exurban expansion in a mountainous changing rural landscape vary spatially with climate change over time. If markets develop, bioenergy crops could potentially counter losses of agricultural land to development.
Understanding the impacts of changing climate and disturbance regimes on forest ecosystems is greatly aided by the use of process-based models. Such models simulate processes based on first principles of ecology, which requires parameterization. Parameterization is an important step in model development and application, defining the characteristics of trees and their responses to the environment, i.e., their traits. For species-specific models, parameterization is usually done at the level of individual species. Parameterization is indispensable for accurately modeling demographic processes, including growth, mortality, and regeneration of trees, along with their intra- and inter-specific interactions. As it is time-demanding to compile the parameters required to simulate forest ecosystems in complex models, simulations are often restricted to the most common tree species, genera, or plant-functional types. Yet, as tree species composition might change in the future, it is important to account for a broad range of species and their individual responses to drivers of change explicitly in simulations. Thus, species-specific parameterization is a critical task for making accurate projections about future forest trajectories, yet species parameters often remain poorly documented in simulation studies.We compiled and harmonized all existing tree species parameters available for the individual-based forest landscape and disturbance model (iLand). Since its first publication in 2012, iLand has been applied in 50 peer-reviewed publications across three continents throughout the Northern Hemisphere (i.e., Europe, North America, and Asia). The model operates at individual-tree level and simulates ecosystem processes at multiple spatial scales, making it a capable process-based model for studying forest change. However, the extensive number of processes and their interactions as well as the wide range of spatio-temporal scales considered in iLand require intensive parameterization, with tree species characterized by 66 unique parameters in the model. The database presented here includes parameters for 150 temperate and boreal tree species and provenances (i.e., regional variations). Excluding missing values, the database includes a total of 9,249 individual parameter entries. In addition, we provide parameters for the individual susceptibility of tree species to wind disturbance (five parameters) for a subset of 104 tree species and provenances (498 parameter entries). To guide further model parameterization efforts, we provide an estimate of uncertainty for each species based on how thoroughly simulations with the respective parameters were evaluated against independent data.Our dataset aids the future parameterization and application of iLand, and sets a new standard in documenting parameters used in process-based forest simulations. This dataset will support model application in previously unstudied areas and can facilitate the investigation of new tree species being introduced to well-studied systems (e.g., simulating assisted migration in the context of rapid climate change). Given that many process-based models rely on similar underlying processes our harmonized parameter set will be of relevance beyond the iLand community. Our work could catalyze further research into improving the parameterization of process-based forest models, increasing the robustness of projections of climate change impacts and adaptation strategies.
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