Maureen C. Kennedy

Fuel treatments and landform modify landscape patterns of burn severity in an extreme fire event

Under a rapidly warming climate, a critical management issue in semiarid forests of western North America is how to increase forest resilience to wildfire. We evaluated relationships between fuel reduction treatments and burn severity in the 2006 Tripod Complex fires, which burned over 70,000 ha of mixed-conifer forests in the North Cascades range of Washington State and involved 387 past harvest and fuel treatment units. A secondary objective was to investigate other drivers of burn severity including landform, weather, vegetation characteristics, and a recent mountain pine beetle outbreak. We used sequential autoregression (SAR) to evaluate drivers of burn severity, represented by the relative differenced Normalized Burn Ratio index, in two study areas that are centered on early progressions of the wildfire complex. Significant predictor variables include treatment type, landform (elevation), fire weather (minimum relative humidity and maximum temperature), and vegetation characteristics, including canopy closure, cover type, and mountain pine beetle attack. Recent mountain pine beetle damage was a statistically significant predictor variable with red and mixed classes of beetle attack associated with higher burn severity. Treatment age and size were only weakly correlated with burn severity and may be partly explained by the lack of treatments older than 30 years and the low rates of fuel succession in these semiarid forests. Even during extreme weather, fuel conditions and landform strongly influenced patterns of burn severity. Fuel treatments that included recent prescribed burning of surface fuels were particularly effective at mitigating burn severity. Although surface and canopy fuel treatments are unlikely to substantially reduce the area burned in regional fire years, recent research, including this study, suggests that they can be an effective management strategy for increasing forest landscape resilience to wildfires.

Using a stochastic model and cross-scale analysis to evaluate controls on historical low-severity fire regimes

Fire-scarred trees provide a deep temporal record of historical fire activity, but identifying the mechanisms therein that controlled landscape fire patterns is not straightforward. We use a spatially correlated metric for fire co-occurrence between pairs of trees (the Sørensen distance variogram), with output from a neutral model for fire history, to infer the relative strength of top-down vs. bottom-up controls on historical fire regimes. An inverse modeling procedure finds combinations of neutral-model parameters that produce Sørensen distance variograms with statistical properties similar to those observed from two landscapes in eastern Washington, USA, with contrasting topography. We find the most parsimonious model structure that is able to replicate the observed patterns and the parameters of this model provide surrogates for the predominance of top-down vs. bottom-up controls. Simulations with relatively low spread probability produce irregular fire perimeters and variograms similar to those from the topographically complex landscape. With higher spread probabilities fires exhibit regular perimeters and variograms similar to those from the simpler landscape. We demonstrate that cross-scale properties of the fire-scar record, even without historical fuels and weather data, document how complex topography creates strong bottom-up controls on fire spread. This control is weaker in simpler topography, and may be compromised in a future climate with more severe weather events.

Power laws reveal phase transitions in landscape controls of fire regimes

Understanding the environmental controls on historical wildfires, and how they changed across spatial scales, is difficult because there are no surviving explicit records of either weather or vegetation (fuels). Here we show how power laws associated with fire-event time series arise in limited domains of parameters that represent critical transitions in the controls on landscape fire. Comparison to a self-organized criticality model shows that the latter mimics historical fire only in a limited domain of criticality, and is not an adequate mechanism to explain landscape fire dynamics, which are shaped by both endogenous and exogenous controls. Our results identify a continuous phase transition in landscape controls, marked by power laws, and provide an ecological analogue to critical behaviour in physical and chemical systems. This explicitly cross-scale analysis provides a paradigm for identifying critical thresholds in landscape dynamics that may be crossed in a rapidly changing climate.

Functional-structural models optimize the placement of foliage units for multiple whole-canopy functions

I present examples of plant functional–structural models (FSMs) that are used to evaluate how foliage units affect whole-canopy functions, and I show that multi-criteria optimization is an effective tool for these models. FSMs produce plant structures through the repeated application of a set of rules for the placement of foliage units. The models are blind (rules are the same regardless of dynamic simulation conditions), sighted (rules change with interference from other foliage units) or self-regulatory (rules change depending on the conditions of the simulation, i.e., internal conditions). In the examples presented, the models are used to optimize plant morphology for one or more measures of plant performance; these measures include movement of materials and associated hydraulic functions, foliage display, light interception and net carbon, mechanical support and stability, and reproductive success. It is consistently found that no morphology is optimal for any single measure of plant performance, and the rules for plant development are not stationary in space and time. In multi-criteria optimization, alternative morphologies are compared against multiple measures of plant performance; these are optimized simultaneously using Pareto optimality, which yields the set of mutually co-dominant solutions not dominated by any other solution. Two solutions are considered to be mutually co-dominant if improvement with respect to one criterion is at the expense of another criterion. I conclude that multi-criteria optimization is an essential tool for the use of FSMs to relate processes at the foliage level to whole-canopy function and to explain the structural diversity of old-growth forests.

Using Multicriteria Analysis of Simulation Models to Understand Complex Biological Systems

Scientists frequently use computer-simulation models to help solve complex biological problems. Typically, such models are highly integrated, they produce multiple outputs, and standard methods of model analysis are ill suited for evaluating them. We show how multicriteria optimization with Pareto optimality allows for model outputs to be compared to multiple system components simultaneously and improves three areas in which models are used for biological problems. In the study of optimal biological structures, Pareto optimality allows for the identification of multiple solutions possible for organism survival and reproduction, which thereby explains variability in optimal behavior. For model assessment, multicriteria optimization helps to illuminate and describe model deficiencies and uncertainties in model structure. In environmental management and decisionmaking, Pareto optimality enables a description of the trade-offs among multiple conflicting criteria considered in environmental management, which facilitates better-informed decisionmaking.

Pre-fire and post-fire surface fuel and cover measurements collected in the south-eastern United States for model evaluation and development – RxCADRE 2008, 2011 and 2012

A lack of independent, quality-assured data prevents scientists from effectively evaluating predictions and uncertainties in fire models used by land managers. This paper presents a summary of pre-fire and post-fire fuel, fuel moisture and surface cover fraction data that can be used for fire model evaluation and development. The data were collected in the south-eastern United States on 14 forest and 14 non-forest sample units associated with 6 small replicate and 10 large operational prescribed fires conducted during 2008, 2011, and 2012 as part of the Prescribed Fire Combustion and Atmospheric Dynamics Research Experiment (RxCADRE). Fuel loading and fuel consumption averaged 6.8 and 4.1 Mg ha–1 respectively in the forest units and 3.0 and 2.2 Mg ha–1 in the non-forest units. Post-fire white ash cover ranged from 1 to 28%. Data were used to evaluate two fuel consumption models, CONSUME and FOFEM, and to develop regression equations for predicting fuel consumption from ash cover. CONSUME and FOFEM produced similar predictions of total fuel consumption and were comparable with measured values. Simple linear models to predict pre-fire fuel loading and fuel consumption from post-fire white ash cover explained 46 and 59% of variation respectively.

Scaling Laws and Complexity in Fire Regimes

Use of scaling terminology and concepts in ecology evolved rapidly from rare occurrences in the early 1980s to a central idea by the early 1990s (Allen and Hoekstra 1992; Levin 1992; Peterson and Parker 1998). In landscape ecology, use of “scale” frequently connotes explicitly spatial considerations (Dungan et al. 2002), notably grain and extent. More generally though, scaling refers to the systematic change of some biological variable with time, space, mass, or energy. Schneider (2001) further specifies ecological scaling sensu Calder (1983) and Peters (1983) as “the use of power laws that scale a variable (e.g., respiration) to body size, usually according to a nonintegral exponent” while noting that this is one of many equally common technical definitions. He further notes that “the concept of scale is evolving from verbal expression to quantitative expression” (p. 545), and will continue to do so as mathematical theory matures along with quantitative methods for extrapolating across scales.

Applied statistics in ecology: common pitfalls and simple solutions

The most common statistical pitfalls in ecological research are those associated with data exploration, the logic of sampling and design, and the interpretation of statistical results. Although one can find published errors in calculations, the majority of statistical pitfalls result from incorrect logic or interpretation despite correct numerical calculations. There are often simple solutions to avoiding these problems that require only patience, clarity of thinking, probabilistic insight, and a reduced reliance on out-of-the-box approaches. Some of these trouble spots are inherent to all statistical analyses and others are particularly insidious in ecology where true controls or replication are challenging, small sample sizes are common, and correctly linking mathematical constructs and ecological ideas is essential. Here we summarize the most common statistical pitfalls observed over nearly a century of combined consulting and research experience in ecological statistics. We provide short, simple solutions.

Fuel treatment effects on tree mortality following wildfire in dry mixed conifer forests, Washington State, USA

Fuel reduction treatments are increasingly used to mitigate future wildfire severity in dry forests, but few opportunities exist to assess their effectiveness. We evaluated the influence of fuel treatment, tree size and species on tree mortality following a large wildfire event in recent thin-only, thin and prescribed burn (thin-Rx) units. Of the trees that died within the first 3 years, most died in the first year regardless of treatment. First-year mortality was much higher in control and thin-only units (65 and 52%) than in thin-Rx units (37%). Cumulative third-year mortality followed a similar trend (78 and 64% in control and thin-only units) v. 43% in thin-Rx units. Percentage crown scorch is a strong predictor of mortality and is highly dependent on fuel treatment. Across all treatments, Pinus ponderosa had a lower probability of post-fire mortality than did Pseudotsuga menziesii. Finally, the probability of beetle attack on surviving trees was highest in large-diameter trees within thin-only treatments and lowest within thin-Rx treatments. This study contributes further evidence supporting the effectiveness of thinning and prescribed burning on mitigating post-fire tree mortality. We also present evidence that a combination of thinning and prescribed burning is associated with lower incidence of post-fire bark beetle attack.

Assessment of uncertainty in functional–structural plant models

BACKGROUND AND AIMS Constructing functional-structural plant models (FSPMs) is a valuable method for examining how physiology and morphology interact in determining plant processes. However, such models always have uncertainty concerned with whether model components have been selected and represented effectively, with the number of model outputs simulated and with the quality of data used in assessment. We provide a procedure for defining uncertainty of an FSPM and how this uncertainty can be reduced. METHODS An important characteristic of FSPMs is that typically they calculate many variables. These can be variables that the model is designed to predict and also variables that give indications of how the model functions. Together these variables are used as criteria in a method of multi-criteria assessment. Expected ranges are defined and an evolutionary computation algorithm searches for model parameters that achieve criteria within these ranges. Typically, different combinations of model parameter values provide solutions achieving different combinations of variables within their specified ranges. We show how these solutions define a Pareto Frontier that can inform about the functioning of the model. KEY RESULTS The method of multi-criteria assessment is applied to development of BRANCHPRO, an FSPM for foliage reiteration on old-growth branches of Pseudotsuga menziesii. A geometric model utilizing probabilities for bud growth is developed into a causal explanation for the pattern of reiteration found on these branches and how this pattern may contribute to the longevity of this species. CONCLUSIONS FSPMs should be assessed by their ability to simulate multiple criteria simultaneously. When different combinations of parameter values achieve different groups of assessment criteria effectively a Pareto Frontier can be calculated and used to define the sources of model uncertainty.