Simulating Growth and Competition on Wet and Waterlogged Soils in a Forest Landscape Model

Changes in CO2 concentration and climate are likely to alter disturbance regimes and competitive outcomes among tree species, which ultimately can result in shifts of species and biome boundaries. Such changes are already evident in high latitude forests, where waterlogged soils produced by topography, surficial geology, and permafrost are an important driver of forest dynamics. Predicting such effects under the novel conditions of the future requires models with direct and mechanistic links of abiotic drivers to growth and competition. We enhanced such a forest landscape model (PnET-Succession in LANDIS-II) to allow simulation of waterlogged soils and their effects on tree growth and competition. We formally tested how these modifications alter water balance on wetland and permafrost sites, and their effect on tree growth and competition. We applied the model to evaluate its promise for mechanistically simulating species range expansion and contraction under climate change across a latitudinal gradient in Siberian Russia. We found that higher emissions scenarios permitted range expansions that were quicker and allowed a greater diversity of invading species, especially at the highest latitudes, and that disturbance hastened range shifts by overcoming the natural inertia of established ecological communities. The primary driver of range advances to the north was altered hydrology related to thawing permafrost, followed by temperature effects on growth. Range contractions from the south (extirpations) were slower and less tied to emissions or latitude, and were driven by inability to compete with invaders, or disturbance. An important non-intuitive result was that some extant species were killed off by extreme cold events projected under climate change as greater weather extremes occurred over the next 30 years, and this had important effects on subsequent successional trajectories. The mechanistic linkages between climate and soil water dynamics in this forest landscape model produced tight links between climate inputs, physiology of vegetation, and soils at a monthly time step. The updated modeling system can produce high quality projections of climate impacts on forest species range shifts by accounting for the interacting effects of CO2 concentration, climate (including longer growing seasons), seed dispersal, disturbance, and soil hydrologic properties

Forest processes from stands to landscapes: exploring model forecast uncertainties using cross-scale model comparison

Forest management practices conducted primarily at the stand scale result in simplified forests with regeneration problems and low structural and biological diversity. Landscape models have been used to help design management strategies to address these problems. However, there remains a great deal of uncertainty that the actual management practices result in the desired sustainable landscape structure. To investigate our ability to meet sustainable forest management goals across scales, we assessed how two models of forest dynamics, a scaled-up individual-tree model and a landscape model, simulate forest dynamics under three types of harvesting regimes: clearcut, gap, and uniform thinning. Althougth 50– 100 year forecasts predicted average successional patterns that differed by less than 20% between models, understory dynamics of the landscape model were simplified relative to the scaled-up tree model, whereas successional patterns of the scaled-up tree model deviated from empirical studies on the driest and wettest landtypes. The scale dependencies of both models revealed important weaknesses when the models were used alone; however, when used together, they could provide a heuristic method that could improve our ability to design sustainable forest management practices

Simulating dynamic fire regime and vegetation change in a warming Siberia

Background Climate change is expected to increase fire activity across the circumboreal zone, including central Siberia. However, few studies have quantitatively assessed potential changes in fire regime characteristics, or considered possible spatial variation in the magnitude of change. Moreover, while simulations indicate that changes in climate are likely to drive major shifts in Siberian vegetation, knowledge of future forest dynamics under the joint influence of changes in climate and fire regimes remains largely theoretical. We used the forest landscape model, LANDIS-II, with PnET-Succession and the BFOLDS fire extension to simulate changes in vegetation and fire regime characteristics under four alternative climate scenarios in three 10,000-km2 study landscapes distributed across a large latitudinal gradient in lowland central Siberia. We evaluated vegetation change using the fire life history strategies adopted by forest tree species: fire resisters, fire avoiders, and fire endurers. Results Annual burned area, the number of fires per year, fire size, and fire intensity all increased under climate change. The relative increase in fire activity was greatest in the northernmost study landscape, leading to a reduction in the difference in fire rotation period between study landscapes. Although the number of fires per year increased progressively with the magnitude of climate change, mean fire size peaked under mild or moderate climate warming in each of our study landscapes, suggesting that fuel limitations and past fire perimeters will feed back to reduce individual fire extent under extreme warming, relative to less extreme warming scenarios. In the Southern and Mid-taiga landscapes, we observed a major shift from fire resister-dominated forests to forests dominated by broadleaved deciduous fire endurers (Betula and Populus genera) under moderate and extreme climate warming scenarios, likely associated with the substantial increase in fire activity. These changes were accompanied by a major decrease in average cohort age and total vegetation biomass across the simulation landscapes. Conclusions Our results imply that climate change will greatly increase fire activity and reduce spatial heterogeneity in fire regime characteristics across central Siberia. Potential ecological consequences include a widespread shift toward forests dominated by broadleaved deciduous species that employ a fire endurer strategy to persist in an increasingly fire-prone environment

Gap-filling eddy covariance methane fluxes:Comparison of machine learning model predictions and uncertainties at FLUXNET-CH4 wetlands

Time series of wetland methane fluxes measured by eddy covariance require gap-filling to estimate daily, seasonal, and annual emissions. Gap-filling methane fluxes is challenging because of high variability and complex responses to multiple drivers. To date, there is no widely established gap-filling standard for wetland methane fluxes, with regards both to the best model algorithms and predictors. This study synthesizes results of different gap-filling methods systematically applied at 17 wetland sites spanning boreal to tropical regions and including all major wetland classes and two rice paddies. Procedures are proposed for: 1) creating realistic artificial gap scenarios, 2) training and evaluating gap-filling models without overstating performance, and 3) predicting half-hourly methane fluxes and annual emissions with realistic uncertainty estimates. Performance is compared between a conventional method (marginal distribution sampling) and four machine learning algorithms. The conventional method achieved similar median performance as the machine learning models but was worse than the best machine learning models and relatively insensitive to predictor choices. Of the machine learning models, decision tree algorithms performed the best in cross-validation experiments, even with a baseline predictor set, and artificial neural networks showed comparable performance when using all predictors. Soil temperature was frequently the most important predictor whilst water table depth was important at sites with substantial water table fluctuations, highlighting the value of data on wetland soil conditions. Raw gap-filling uncertainties from the machine learning models were underestimated and we propose a method to calibrate uncertainties to observations. The python code for model development, evaluation, and uncertainty estimation is publicly available. This study outlines a modular and robust machine learning workflow and makes recommendations for, and evaluates an improved baseline of, methane gap-filling models that can be implemented in multi-site syntheses or standardized products from regional and global flux networks (e.g., FLUXNET)

Predicting global change effects on forest biomass and composition in south-central Siberia

Multiple global changes such as timber harvesting in areas not previously disturbed by cutting and climate change will undoubtedly affect the composition and spatial distribution of boreal forests, which will, in turn, affect the ability of these forests to retain carbon and maintain biodiversity. To predict future states of the boreal forest reliably, it is necessary to understand the complex interactions among forest regenerative processes (succession), natural disturbances (e.g., fire, wind, and insects), and anthropogenic disturbances (e.g., timber harvest). We used a landscape succession and disturbance model (LANDIS-II) to study the relative effects of climate change, timber harvesting, and insect outbreaks on forest composition, biomass (carbon), and landscape pattern in south-central Siberia. We found that most response variables were more strongly influenced by timber harvest and insect outbreaks than by the direct effects of climate change. Direct climate effects generally increased tree productivity and modified probability of establishment, but indirect effects on the fire regime generally counteracted the direct effects of climate on forest composition. Harvest and insects significantly changed forest composition, reduced living aboveground biomass, and increased forest fragmentation. We concluded that: (1) Global change is likely to significantly change forest composition of south-central Siberian landscapes, with some changes taking ecosystems outside the historic range of variability. (2) The direct effects of climate change in the study area are not as significant as the exploitation of virgin forest by timber harvest and the potential increased outbreaks of the Siberian silk moth. (3) Novel disturbance by timber harvest and insect outbreaks may greatly reduce the aboveground living biomass of Siberian forests and may significantly alter ecosystem dynamics and wildlife populations by increasing forest fragmentation

Using landscape disturbance and succession models to support forest management

Managers of forested landscapes must account for multiple, interacting ecological processes operating at broad spatial and temporal scales. These interactions can be of such complexity that predictions of future forest ecosystem states are beyond the analytical capability of the human mind. Landscape disturbance and succession models (LDSM) are predictive and analytical tools that can integrate these processes and provide critical decision support information. We briefly review the state of the art of LDSMs and provide two case studies to illustrate the application and utility of one LDSM, LANDIS. We conclude that LDSMs are able to provide useful information to support management decisions for a number of reasons: (i) they operate at scale that is relevant to many forest management problems, (ii) they account for interactions among ecological and anthropogenic processes, (iii) they can produce objective and comparable projections of alternative management options or various global change scenarios,(iv) LDSMs are based on current ecological knowledge and theory, (v) LDSMs provide a vehicle for collaboration among decision-makers, resource experts and scientists, (vi) LDSMs are the only feasible research tool that can be used to investigate long-term, large area dynamics

Duplication, Rearrangement and Reconciliation: A Follow-Up 13 Years Later

International audienc