Exploring the role of fire, succession, climate, and weather on landscape dynamics using comparative modeling

Understanding and Modeling Forest Disturbance Interactions at the Landscape Level

Disturbances, both natural and anthropogenic, affect the configuration, composition, and function of forested ecosystems. Complex system behaviors emerge from the interactions between disturbance regimes, the vegetation response to those disturbances, and their interplay with multiple drivers (climate, topography, land use, etc.) across spatial and temporal scales. Here, we summarize conceptual advances and empirical approaches to disturbance interaction investigation, and used those insights to evaluate and categorize 146 landscape modeling studies emerging from a systematic review of the literature published since 2010. Recent conceptual advances include formal disaggregation of disturbances into their constituent components, embedding disturbance processes into system dynamics, and clarifying terminology for interaction factors, types, and ecosystem responses. Empirical studies investigating disturbance interactions now span a wide range of approaches, including (most recently) advanced statistical methods applied to an expanding set of spatial and temporal datasets. Concurrent development in spatially-explicit landscape models, informed by these empirical insights, integrate the interactions among natural and anthropogenic disturbances by coupling these processes to account for disturbance stochasticity, disturbance within and across scales, and non-linear landscape responses to climate change. Still, trade-offs between model elegance and complexity remain. We developed an index for the degree of process integration (i.e., balance of static vs. dynamic components) within a given disturbance agent and applied it to the studies from our systematic review. Contemporary model applications in this line of research have applied a wide range process integration, depending on the specific question, but also limited in part by data and knowledge. Non-linear “threshold” behavior and cross-scaled interactions remain a frontier in temperate, boreal, and alpine regions of North America and Europe, while even simplistic studies are lacking from other regions of the globe (e.g., subtropical and tropical biomes). Understanding and planning for uncertainty in system behavior—including disturbance interactions—is paramount at a time of accelerated anthropogenic change. While progress in landscape modeling studies in this area is evident, work remains to increase model transparency and confidence, especially for understudied regions and processes. Moving forward, a multi-dimensional approach is recommended to address the uncertainties of complex human-ecological dynamics.

Developing and testing models of the drivers of anthropogenic and lightning-caused wildfire ignitions in south-eastern Australia

Considerable investments are made in managing fire risk to human assets, including a growing use of fire behaviour simulation tools to allocate expenditure. Understanding fire risk requires estimation of the likelihood of ignition, spread of the fire and impact on assets. The ability to estimate and predict risk requires both the development of ignition likelihood models and the evaluation of these models in novel environments. We developed models for natural and anthropogenic ignitions in the south-eastern Australian state of Victoria incorporating variables relating to fire weather, terrain and the built environment. Fire weather conditions had a consistently positive effect on the likelihood of ignition, although they contributed much more to lightning (57%) and power transmission (55%) ignitions than the 7 other modelled causes (8-32%). The built environment played an important role in driving anthropogenic ignitions. Housing density was the most important variable in most models and proximity to roads had a consistently positive effect. In contrast, the best model for lightning ignitions included a positive relationship with primary productivity, as represented by annual rainfall. These patterns are broadly consistent with previous ignition modelling studies. The models developed for Victoria were tested in the neighbouring fire prone states of South Australia and Tasmania. The anthropogenic ignition model performed well in South Australia (AUC = 0.969) and Tasmania (AUC = 0.848), whereas the natural ignition model only performed well in South Australia (AUC = 0.972; Tasmania AUC = 0.612). Model performance may have been impaired by much lower lightning ignition rates in South Australia and Tasmania than in Victoria. This study shows that the spatial likelihood of ignition can be reliably predicted based on readily available meteorological and biophysical data. Furthermore, the strong performance of anthropogenic and natural ignition models in novel environments suggests there are some universal drivers of ignition likelihood across south-eastern Australia.

Modeling Post-Fire Tree Mortality Using a Logistic Regression Method within a Forest Landscape Model

Fire is a multi-scale process that is an important component in determining ecosystem age structures and successional trajectories across forested landscapes. In order to address questions regarding fire effects over large spatial scales and long temporal scales researchers often employ forest landscape models which can model fire as a spatially explicit disturbance. Within forest landscape models site-level fire effects are often simplified to the species, functional type, or cohort level due to time or computational resource limitations. In this study we used a subset of publicly available U.S. Forest Service forest inventory data (FIA) to estimate short-term fire effects on tree densities across multiple stem diameter classes in two ecological sections in the central and southern United States. We found that FIA plots where low-intensity fires occurred within the preceding five years in the Ozark Highlands ecological section had significantly reduced stem densities in the two smallest diameter classes and in the Gulf Coastal Plains and Flatwoods fire reduced stem densities in the three smallest diameter classes. Using an independent subset of FIA plots we then parameterized and calibrated a forest landscape model to simulate site-level fire effects using a logistic regression based method and compare the results to previous methods of modeling fire effects. When representative landscapes from both study areas were simulated under a low-intensity fire regime using a forest landscape model the logistic regression probability method of modeling fire effects produced a similar reduction in stem densities while the previous age-cohort method overestimated density reductions across diameter classes. A more realistic representation of fire effects, particularly in low intensity fire regimes, increases the utility of forest landscape models as tools for planning and management.

Harvesting interacts with climate change to affect future habitat quality of a focal species in eastern Canada's boreal forest

Many studies project future bird ranges by relying on correlative species distribution models. Such models do not usually represent important processes explicitly related to climate change and harvesting, which limits their potential for predicting and understanding the future of boreal bird assemblages at the landscape scale. In this study, we attempted to assess the cumulative and specific impacts of both harvesting and climate-induced changes on wildfires and stand-level processes (e.g., reproduction, growth) in the boreal forest of eastern Canada. The projected changes in these landscape- and stand-scale processes (referred to as “drivers of change”) were then assessed for their impacts on future habitats and potential productivity of black-backed woodpecker (BBWO; Picoides arcticus), a focal species representative of deadwood and old-growth biodiversity in eastern Canada. Forest attributes were simulated using a forest landscape model, LANDIS-II, and were used to infer future landscape suitability to BBWO under three anthropogenic climate forcing scenarios (RCP 2.6, RCP 4.5 and RCP 8.5), compared to the historical baseline. We found climate change is likely to be detrimental for BBWO, with up to 92% decline in potential productivity under the worst-case climate forcing scenario (RCP 8.5). However, large declines were also projected under baseline climate, underlining the importance of harvest in determining future BBWO productivity. Present-day harvesting practices were the single most important cause of declining areas of old-growth coniferous forest, and hence appeared as the single most important driver of future BBWO productivity, regardless of the climate scenario. Climate-induced increases in fire activity would further promote young, deciduous stands at the expense of old-growth coniferous stands. This suggests that the biodiversity associated with deadwood and old-growth boreal forests may be greatly altered by the cumulative impacts of natural and anthropogenic disturbances under a changing climate. Management adaptations, including reduced harvesting levels and strategies to promote coniferous species content, may help mitigate these cumulative impacts.

Direct and indirect effects of climate change on projected future fire regimes in the western United States

We asked two research questions: (1) What are the relative effects of climate change and climate-driven vegetation shifts on different components of future fire regimes? (2) How does incorporating climate-driven vegetation change into future fire regime projections alter the results compared to projections based only on direct climate effects? We used the western United States (US) as study area to answer these questions. Future (2071-2100) fire regimes were projected using statistical models to predict spatial patterns of occurrence, size and spread for large fires (>400 ha) and a simulation experiment was conducted to compare the direct climatic effects and the indirect effects of climate-driven vegetation change on fire regimes. Results showed that vegetation change amplified climate-driven increases in fire frequency and size and had a larger overall effect on future total burned area in the western US than direct climate effects. Vegetation shifts, which were highly sensitive to precipitation pattern changes, were also a strong determinant of the future spatial pattern of burn rates and had different effects on fire in currently forested and grass/shrub areas. Our results showed that climate-driven vegetation change can exert strong localized effects on fire occurrence and size, which in turn drive regional changes in fire regimes. The effects of vegetation change for projections of the geographic patterns of future fire regimes may be at least as important as the direct effects of climate change, emphasizing that accounting for changing vegetation patterns in models of future climate-fire relationships is necessary to provide accurate projections at continental to global scales.

A new model to simulate climate-change impacts on forest succession for local land management

We developed a new climate-sensitive vegetation state-and-transition simulation model (CV-STSM) to simulate future vegetation at a fine spatial grain commensurate with the scales of human land-use decisions, and under the joint influences of changing climate, site productivity, and disturbance. CV-STSM integrates outputs from four different modeling systems. Successional changes in tree species composition and stand structure were represented as transition probabilities and organized into a state-and-transition simulation model. States were characterized based on assessments of both current vegetation and of projected future vegetation from a dynamic global vegetation model (DGVM). State definitions included sufficient detail to support the integration of CV-STSM with an agent-based model of land-use decisions and a mechanistic model of fire behavior and spread. Transition probabilities were parameterized using output from a stand biometric model run across a wide range of site productivities. Biogeographic and biogeochemical projections from the DGVM were used to adjust the transition probabilities to account for the impacts of climate change on site productivity and potential vegetation type. We conducted experimental simulations in the Willamette Valley, Oregon, USA. Our simulation landscape incorporated detailed new assessments of critically imperiled Oregon white oak (Quercus garryana) savanna and prairie habitats among the suite of existing and future vegetation types. The experimental design fully crossed four future climate scenarios with three disturbance scenarios. CV-STSM showed strong interactions between climate and disturbance scenarios. All disturbance scenarios increased the abundance of oak savanna habitat, but an interaction between the most intense disturbance and climate-change scenarios also increased the abundance of subtropical tree species. Even so, subtropical tree species were far less abundant at the end of simulations in CV-STSM than in the dynamic global vegetation model simulations. Our results indicate that dynamic global vegetation models may overestimate future rates of vegetation change, especially in the absence of stand-replacing disturbances. Modeling tools such as CV-STSM that simulate rates and direction of vegetation change affected by interactions and feedbacks between climate and land-use change can help policy makers, land managers, and society as a whole develop effective plans to adapt to rapidly changing climate.