Ajith H. Perera

Sensitivity of landscape pattern indices to input data characteristics on real landscapes: implications for their use in natural disturbance emulation

Resource management strategies have begun to adopt natural landscape disturbance emulation as a means of minimizing risk to ecosystem integrity. Detailed understanding of the disturbance regime and the associated spatial landscape patterns are required to provide a “natural” baseline for comparison with the results of emulation strategies. Landscape pattern indices provide a useful tool to quantify spatial pattern for developing these strategies and evaluating their success. Despite an abundance of indices and tools to calculate these, practical knowledge of interpretation is rare. Quantifying changes in landscape pattern indices and the meaning of these changes is confounded by index sensitivity to input data characteristics such as spatial extent, spatial resolution, and thematic resolution. Sensitivity has been examined for simulated landscapes but rarely using real data for large areas as real landscapes are more difficult to manipulate systematically than simulated data. While simulated data offer a control, they do not provide an accurate portrayal of reality for practical applications. Our goal was to test the sensitivity of a suite of landscape pattern indices useful for disturbance emulation strategy development and evaluation to spatial extent, spatial resolution, and thematic resolution using current land cover data for a case study of the managed forest of Ontario, Canada. We also examined how sensitivity varies spatially across the study area. We used Landsat TM-based land cover data (> 45.5 million ha), controlling spatial extent (2,500 to 2,560,000 ha), spatial resolution (1 to 16 ha), and thematic resolution (2 to 26 classes). For each index we tested a hypothesis of insensitivity to changes in each input data characteristic using a combination of ANOVA and regression and compared our results with previous studies. Of the 18 indices studied, significant (p< 0.01) effects were found for 17 indices with changes in spatial extent, 13 indices with changes in spatial resolution and 18 indices with changes in thematic resolution. A significant (p < 0.01) linear trend accounted for the majority of the variance for all of the significant relationships identified. Most of the mean index responses were consistent with those interpreted from previous studies of simulated and real landscapes; however, sensitivity varied greatly among indices and over space. We suggest that variation in sensitivity to input data characteristics among indices and over space must be explicitly incorporated in the design of future natural disturbance emulation efforts.

Temporal fire disturbance patterns on a forest landscape

Abstract Potential temporal fire disturbance patterns on a forest landscape were investigated using a fire regime model with four different fire probability functions: (1) forest age-independent; (2) hyperbolic increase with forest age; (3) sigmoid increase with age; and (4) linear increase with age. Different combinations of parameter values for a logistic equation were used to approximate different fire probability functions. An extensive model behavior study suggested that fire regimes similar to the observations in Ontario could result from any of the fire probability functions, but with different parameter values. Simulation results on the case study area indicated that when the fire rotation period was fixed to 200 years (corresponding to fire regimes in the southern part of northwestern Ontario), the predicted temporal disturbance patterns (average interval between two successive fires) were similar for small and intermediate fires, but different for large and severe fires. The results from the fire probability function with a sigmoid shape appeard the most appropriate among the four tested fire probability functions. The average interval between two successive fires for each size group is: small fires every 5.8 years, intermediate fires every 34.4 years, and large fires every 151.6 years. Better prediction of a temporal disturbance pattern, especially for large and severe fires, will require an explicit understanding of the quantitative relationship between fire probability and forest age.

Toward rigorous use of expert knowledge in ecological research

Practicing ecologists who excel at their work (“experts”) hold a wealth of knowledge. This knowledge offers a wide range of opportunities for application in ecological research and natural resource decision-making. While experts are often consulted ad-hoc, their contributions are not widely acknowledged. These informal applications of expert knowledge lead to concerns about a lack of transparency and repeatability, causing distrust of this knowledge source in the scientific community. Here, we address these concerns with an exploration of the diversity of expert knowledge and of rigorous methods in its use. The effective use of expert knowledge hinges on an awareness of the spectrum of experts and their expertise, which varies by breadth of perspective and critical assessment. Also, experts express their knowledge in different forms depending on the degree of contextualization with other information. Careful matching of experts to application is therefore essential and has to go beyond a simple fitting of the expert to the knowledge domain. The standards for the collection and use of expert knowledge should be as rigorous as for empirical data. This involves knowing when it is appropriate to use expert knowledge and how to identify and select suitable experts. Further, it requires a careful plan for the collection, analysis and validation of the knowledge. The knowledge held by expert practitioners is too valuable to be ignored. But only when thorough methods are applied, can the application of expert knowledge be as valid as the use of empirical data. The responsibility for the effective and rigorous use of expert knowledge lies with the researchers.

What do we know about forest fire size distribution, and why is this knowledge useful for forest management?

Forest fire size distribution (FSD) is one of the suite of indicators of forest fire regimes. It is applied in forest fire management, particularly for planning and evaluating suppression efforts. It is also used in forest management in the context of emulating natural fire disturbances. Given the recent growth in research and applied interest in this topic, we review and synthesise the state of knowledge on FSD, and identify sources of knowledge uncertainties and future research directions. Based on literature, it is common for forest fires to follow the power law probability distribution, particularly the truncated subtype, under a variety of forest types and forest and fire management practices. Other types of FSD are also observed, but under specific circumstances. Although there is evidence that observed FSDs vary both over space and time, the knowledge is too fragmented to generalise the cause–effect relationships for such variation. As well, it is not clear how the various methods of studying FSD and their spatio-temporal scales influence derivations of FSDs. We suggest that a hypothetico-deductive research approach, combining empirical studies with process-based simulations is an effective means to advance the knowledge of FSD. We suggest caution in the use of FSD in forest management because applying different distributions or even different parameters for the same distribution may result in great fire size class differences and thus different implications for forest management.

Modelling the effect of spatial scale and correlated fire disturbances on forest age distribution

Abstract With the exponential model, Van Wagner (1978) gave us valuable insight in understanding stand age and forest age distribution in fire-disturbed landscapes. He showed that, under certain conditions, the probability distribution of the age of a stand subject to periodic renewal by fire is exponential. The extension of this model to the landscape-level results, also under certain conditions, in an exponential shape for the forest age distribution. Empirical studies have supported this hypothesis in some landscapes and not in others. The results are believed to depend on the size of the landscape in question, the patterns of fire disturbance, and changes in the disturbance regime over time and space. In this paper, we present additional insight into some of the fundamental factors that determine the forest age distribution. We analyzed some alternative spatial models of fire disturbance, and used a spatial simulation model (FLAP-X) to explore whether the forest age distribution has an exponential shape, and whether it would be stable or variable over time under different conditions. We use different spatial and temporal disturbance patterns, some of which represent correlation due to fire growth and episodes of high fire disturbance. We describe FLAP-X and give the results of computational tests based on hypothetical data. We found that, under characteristic boreal fire disturbance regimes, we should not expect to find forest age distribution stability even at very large spatial scales due to the spatial and temporal correlation of disturbances.

A spatially explicit stochastic model to simulate boreal forest cover transitions: general structure and properties

We describe a spatially explicit simulation model of large-scale forest cover transition in the North American boreal biome. This model is a time-dependent Markov chain with discrete states corresponding to dominant tree species in the forest canopy. To parameterize the model, we used three temporal variables, extracted from published data from field studies: period of persistence of a given cover type, the time interval of forest cover replacement by another, and the time of complete replacement. Environmental domains based on climate, soil moisture regime, and soil nutrient status stratified all forest cover transitions. Probability matrices of forest cover transition were derived for 15 discrete states at 20-year intervals for each of the environmental domains. Five spatial databases from boreal North America were used as input to the model: forest cover composition, time since last forest cover change, climate zone, soil moisture regime, and soil nutrient status. The model output consists of spatially explicit prediction of (a) time since last disturbance, (b) transition age, (c) forest cover composition, and (d) canopy age in 20-year time steps at 1 ha resolution. As a case study, we simulated forest cover transition in a 3.7 million ha area in boreal Canada. These 200-year simulations show that spatio-temporal transition of forest cover type is significant even in the absence of catastrophic disturbances.

Fire mapping in a northern boreal forest: assessing AVHRR/NDVI methods of change detection

Understanding natural fire regimes is crucial to developing harvesting scenarios and conducting sustainable resource management in the boreal forest. To gain this understanding, resource professionals need efficient and cost-effective data collection methods that can operate over vast and isolated landscapes. We compared three Advanced Very High Resolution Radiometer (AVHRR)/Normalized Difference Vegetation Index (NDVI) methods of fire detection and mapping for a case study in northern Ontario, Canada, of the 1992, 1993, and 1995 fire seasons. Fire mapping accuracy was assessed by the spatial coincidence between mapped fires and ground-truthed information using a decision-tree approach and by testing the hypothesis that various calculated accuracy components were equal within an ANOVA design. Ground-truthed fire sizes and shapes were correlated with the AVHRR/NDVI-mapped areas; however, light cloud contamination increased false detection of fires due to NDVI suppression. The multiple threshold technique of change detection provided better estimates of fire areas than did single threshold methods.

A comparison of large-scale spatial vegetation patterns following clearcuts and fires in Ontario’s boreal forests

Abstract The role of wildfires as the most significant source of disturbance in boreal forests has been equaled by clearcuts during the past five decades. Post-disturbance revegetation patterns are important because they have a direct influence on many ecological processes. However, the knowledge of post-disturbance changes in spatial patterns of forest cover is scarce, especially at large scales. We examined spatial patterns of forest cover in a four decade series of post-fire and post-clearcut landscapes in boreal Canada. A suite of indices was used to quantify spatial patterns of post-disturbance vegetation, based on Landsat TM imagery, and edaphic conditions. Indices were grouped in terms of patch geometry, contagion and composition. We used a general linear model to compare the effects of disturbance type, time since disturbance, edaphic conditions, and their interactions on these indices. Clearcuts produced more heterogeneous landscapes after disturbances in comparison to fires. Time since disturbance also had a significant effect on spatial patterns of vegetation: the older disturbances had more landcover types with higher interspersion. Edaphic conditions also significantly affected spatial patterns of vegetation. Landscapes with complex spatial patterns of edaphic conditions also had complex spatial patterns of vegetation.

Spatial variability of stand-scale residuals in Ontario’s boreal forest fires

Knowledge of postfire residuals in boreal forest landscapes is increasingly important for ecological applications and forest management. While many studies provide useful insight, knowledge of stand-scale postfire residual occurrence and variability remains fragmented and untested as formal hypotheses. We examined the spatial variability of stand-scale postfire residuals in boreal forests and tested hypotheses of their spatial associations. Based on the literature, we hypothesized that preburn forest cover characteristics, site conditions, proximity to water and fire edge, and local fire intensity influence the spatial variability of postfire residuals. To test these hypotheses, we studied live-tree and snag residuals in 11 boreal Ontario forest fires, using 660 sample points based on high resolution photography (1:408) captured immediately after the fires. The abundance of residuals varied considerably within and among these fires, precluding attempts to generalize estimates. Based on a linear mixed-effe...

Modelling land cover transitions: A solution to the problem of spatial dependence in data

Raster-based spatial land cover transition models (LCTMs) are widely used in landscape ecology. However, many LCTMs do not account for spatial dependence of the input data, which may artificially fragment the output spatial configuration. We demonstrate the consequences of ignoring spatial dependence, thus assigning probabilities randomly in space, using a simple LCTM. We ran the model from four different initial conditions with distinct spatial configurations and results indicated that, after 20 simulation steps, all of them converged towards the spatial configuration of the random data set. From an ecological perspective this is a serious problem because ecological data often exhibit distinct spatial configuration related to ecological processes. As a solution, we propose an approach (region approach) that accounts for spatial dependence of LCTM input data. Underlying spatial dependence was used to apply spatial bias to probability assignment within the model. As a case study we applied a region approach to a Vegetation Transition Model (VTM); a semi-Markovian model that simulates forest succession. The VTM was applied to approximately 500,000 ha of boreal forest in Ontario, at 1 ha pixel resolution. When the stochastic transition algorithms were applied without accounting for spatial dependence, spatial configuration of the output data became progressively more fragmented. When the VTM was applied using the region approach to account for spatial dependence output fragmentation was reduced. Accounting for spatial dependence in transition models will create more reliable output for analyzing spatial patterns and relating those patterns to ecological processes.