Research agenda for integrated landscape modeling

Abstract

Reliable predictions of how changing climate and disturbance regimes will affect forest ecosystems are crucial for effective forest management. Current fire and climate research in forest ecosystem and community ecology offers data and methods that can inform such predictions. However, research in these fields occurs at different scales, with disparate goals, methods, and context. Often results are not readily comparable among studies and defy integration. We discuss the strengths and weaknesses of three modeling paradigms: empirical gradient models, mechanistic ecosystem models, and stochastic landscape disturbance models. We then propose a synthetic approach to multi-scale analysis of the effects of climatic change and disturbance on forest ecosystems. Empirical gradient models provide an anchor and spatial template for stand-level forest ecosystem models by quantifying key parameters for individual species and accounting for broad-scale geographic variation among them. Gradient imputation transfers predictions of fine-scale forest composition and structure across geographic space. Mechanistic ecosystem dynamic models predict the responses of biological variables to specific environmental drivers and facilitate understanding of temporal dynamics and disequilibrium. Stochastic landscape dynamics models predict frequency, extent, and severity of broad-scale disturbance. A robust linkage of these three modeling paradigms will facilitate prediction of the effects of altered fire and other disturbance regimes on forest ecosystems at multiple scales and in the context of climatic variability and change. You may order additional copies of this publication by sending your mailing information in label form through one of the following media. Please specify the publication title and series number. Publishing Services Executive SummaryThe overall goal of the integrated modeling program described in this document is to obtain fine-scale predictions of ecosystem attributes across large geographical areas and project them over time under changing climate and disturbance regimes.Reliable prediction requires focus on mechanisms and responses and attention to spatial heterogeneity and temporal disequilibria.The fundamental unit of ecological analysis is the organism, and fundamental scales are those at which the organism strongly interacts with critical or limiting resources.A multi-scale approach is thus required, incorporating ecological conditions at the scale of treefall gaps, slope facets, catchments, and watersheds.Gradient analysis of environmental tolerances, ecosystem dynamics modeling, and landscape dynamics simulation modeling all contribute components required to predict the ecological responses of forests to changing climate and disturbanceMany of the sources of uncertainty in gradient modeling can be mitigated by addressing biotic interactions, spatial dependence, and scaling relationships with multi-scale modeling and hierarchical variance partitioning within the driver-response paradigm.Predictive vege...

Estimating potential habitat for 134 eastern US tree species under six climate scenarios

“…In this approach, we cannot include changes in land use and land cover likely to occur in the next 100 years, or disturbances such as pests, pathogens, natural disasters, and other human activities. Coupling these outputs with process-based ecosystem dynamics models which include disturbance (e.g., Chaing et al, 2006;Cushman et al, 2007; would be a productive line of research.…”

Wildfire‐mediated vegetation change in boreal forests of Alberta, Canada

“…Given the challenges associated with strictly dynamic models, hybrid approaches may be best suited-or at least most practically implementedfor broad-scale ecological inference in a climatechange context (Cushman et al 2006, Gustafson 2013, Williams and Abatzoglou 2016. We developed such a hybrid approach for northern Alberta, Canada, by simulating scenarios of future fire behavior as a catalyst for climate-driven vegetation change.…”

Managing uncertainty in climate-driven ecological models to inform adaptation to climate change

“…In climateexplicit models, ecosystem responses are directly a function of climate variables, their interactions, and their role in other processes affecting vegetation, such as disturbance. Direct relationships between climate and ecosystem gradients are desirable, and are one way to reduce uncertainty in projections based on false assumptions about causation vs. correlation (Cushman et al 2007).…”

“…Evaluation/validation of empirical models is tractable using numerical methods (e.g., cross validation) and quantifies uncertainty routinely, but it is difficult for them to extrapolate to novel conditions or account for complex interactions. Process models are built from ''first principles'', are generally more difficult to use, Either empirical or mechanistic models can be placed in a spatial (and often contagion) context to develop landscape models, which then extend empirical or mechanistic models (or elements of both) to explicit simulation of ecological processes in space (Cushman et al 2007). Landscape models that address contagion (process interaction across cells) are especially suited to simulations of the effects of disturbance on vegetation.…”