A model of surface fire,climate and forest pattern in the Sierra Nevada,California

A spatially explicit forest gap model was developed for the Sierra Nevada, California, and is the first of its kind because it integrates climate, fire and forest pattern. The model simulates a forest stand as a grid of 15×15 m forest plots and simulates the growth of individual trees within each plot. Fuel inputs are generated from each individual tree according to tree size and species. Fuel moisture varies both temporally and spatially with the local site water balance and forest condition, thus linking climate with the fire regime. Fires occur as a function of the simulated fuel loads and fuel moisture, and the burnable area is simulated as a result of the spatially heterogeneous fuel bed conditions. We demonstrate the model’s ability to couple the fire regime to both climate and forest pattern. In addition, we use the model to investigate the importance of climate and forest pattern as controls on the fire regime. Comparison of model results with independent data indicate that the model performs well in several areas. Patterns of fuel accumulation, climatic control of fire frequency and the influence of fuel loads on the spatial extent of fires in the model are particularly well-supported by data. This model can be used to examine the complex interactions among climate, fire and forest pattern across a wide range of environmental conditions and vegetation types. Our results suggest that, in the Sierra Nevada, fuel moisture can exert an important control on fire frequency and this control is especially pronounced at sites where most of the annual precipitation is in the form of snow. Fuel loads, on the other hand, may limit the spatial extent of fire, especially at elevations below 1500 m. Above this elevation, fuel moisture may play an increasingly important role in limiting the area burned.     

Constraints on global fire activity vary across a resource gradient

We provide an empirical, global test of the varying constraints hypothesis, which predicts systematic heterogeneity in the relative importance of biomass resources to burn and atmospheric conditions suitable to burning (weather/climate) across a spatial gradient of long-term resource availability. Analyses were based on relationships between monthly global wildfire activity, soil moisture, and mid-tropospheric circulation data from 2001 to 2007, synthesized across a gradient of long-term averages in resources (net primary productivity), annual temperature, and terrestrial biome. We demonstrate support for the varying constraints hypothesis, showing that, while key biophysical factors must coincide for wildfires to occur, the relative influence of resources to burn and moisture/weather conditions on fire activity shows predictable spatial patterns. In areas where resources are always available for burning during the fire season, such as subtropical/tropical biomes with mid-high annual long-term net primary productivity, fuel moisture conditions exert their strongest constraint on fire activity. In areas where resources are more limiting or variable, such as deserts, xeric shrublands, or grasslands/savannas, fuel moisture has a diminished constraint on wildfire, and metrics indicating availability of burnable fuels produced during the antecedent wet growing seasons reflect a more pronounced constraint on wildfire. This macro-scaled evidence for spatially varying constraints provides a synthesis with studies performed at local and regional scales, enhances our understanding of fire as a global process, and indicates how sensitivity to future changes in temperature and precipitation may differ across the world.     

Modelling the effect of fire-exclusion and prescribed fire on wildfire size in Mediterranean ecosystems

There is a debate on which factor, fuel accumulation or meteorological variability, is the fundamental control of the occurrence of large fires in Mediterranean-type ecosystems. Its resolution has important management implications, because if the fuel hypothesis proves to be right, then fire-exclusion would enhance the occurrence of large wildfires, and prescribed-fires would be a useful tool to fight them. On the other hand, if large fires were just a direct consequence of some extreme weather situations, neither fire-exclusion nor prescribed fire would have any influence on the size of wildfires. Here we present a simple model of vegetation dynamics and fire spread over homogeneous areas intended to treat quantitatively this issue. In particular, we wanted to address the following questions: (1) What is the effect that different fire fighting capacities have on the total area burnt and, especially, on large fires? (2) What is the effect that different levels of prescribed fire have on the area burnt in wildfires and, especially, in large fires? The model incorporates meteorological variability, different rates of fuel accumulation, number of ignitions per year, fire-fighting capacity, and prescribed burning. The model was calibrated with fire regime data (mean fire size, annual area burnt, and fire size distribution) of Tarragona (NE Spain) and Coimbra (Central Portugal), and it accurately reproduced both data sets, while allowing for multiple behavioural models and prediction uncertainties within the GLUE methodology. Results showed that for a given region, with its particular characteristics of climate, number of ignitions, and vegetation flammability, there was a fairly constant annual area burnt for different fire-fighting capacities. However, higher fire-fighting capacities resulted in a slightly higher proportion of large fires. There was also a quite constant annual area burnt (prescribed and wild fires together) for different prescribed fire intensities in each region. However, the total amount and proportion of large fires decreased as the prescribed burning intensity increased. So, according to the model, it seems that the total area burnt will be more or less the same despite any effort to reduce it by extinguishing fires or by using prescribed burning. Nevertheless, the effect of the fire exclusion policy slightly enhances the dominance of large fires, whereas the use of prescribed fires greatly reduces the importance of large fires.     

Linking 3D spatial models of fuels and fire: Effects of spatial heterogeneity on fire behavior

Crown fire endangers fire fighters and can have severe ecological consequences. Prediction of fire behavior in tree crowns is essential to informed decisions in fire management. Current methods used in fire management do not address variability in crown fuels. New mechanistic physics-based fire models address convective heat transfer with computational fluid dynamics (CFD) and can be used to model fire in heterogeneous crown fuels. However, the potential impacts of variability in crown fuels on fire behavior have not yet been explored. In this study we describe a new model, FUEL3D, which incorporates the pipe model theory (PMT) and a simple 3D recursive branching approach to model the distribution of fuel within individual tree crowns. FUEL3D uses forest inventory data as inputs, and stochastically retains geometric variability observed in field data. We investigate the effects of crown fuel heterogeneity on fire behavior with a CFD fire model by simulating fire under a homogeneous tree crown and a heterogeneous tree crown modeled with FUEL3D, using two different levels of surface fire intensity. Model output is used to estimate the probability of tree mortality, linking fire behavior and fire effects at the scale of an individual tree. We discovered that variability within a tree crown altered the timing, magnitude and dynamics of how fire burned through the crown; effects varied with surface fire intensity. In the lower surface fire intensity case, the heterogeneous tree crown barely ignited and would likely survive, while the homogeneous tree had nearly 80% fuel consumption and an order of magnitude difference in total net radiative heat transfer. In the higher surface fire intensity case, both cases burned readily. Differences for the homogeneous tree between the two surface fire intensity cases were minimal but were dramatic for the heterogeneous tree. These results suggest that heterogeneity within the crown causes more conditional, threshold-like interactions with fire. We conclude with discussion of implications for fire behavior modeling and fire ecology.     

A spatially explicit model to predict future landscape composition of aspen woodlands under various management scenarios

Quaking aspen (Populus tremuloides) is declining across the western United States. Aspen habitats are among the most diverse plant communities in this region and loss of these habitats can result in shifts in biodiversity, productivity, and hydrology across a range of spatial scales. Western aspen occurs on the majority of sites seral to conifer species, and long-term maintenance of these aspen woodlands requires periodic fire. Over the past century, fire intervals, extents, and intensities have been insufficient to regenerate aspen stands at historic rates; however the effects of various fire regimes and management scenarios on aspen vegetation dynamics at broad spatial and temporal scales are unexplored. Here we use field data, remotely sensed data, and fire atlas information to develop a spatially explicit landscape simulation model to assess the effects of current and historic wildfire regimes and prescribed burning programs on landscape vegetation composition across two mountain ranges in the Owyhee Plateau, Idaho. Model outputs depict the future structural makeup and species composition of the landscape at selected time steps under simulated management scenarios. We found that under current fire regimes and in the absence of management activities, loss of seral aspen stands will continue to occur over the next two centuries. However, a return to historic fire regimes (burning 12–14% of the modeled landscape per decade) would maintain the majority of aspen stands in early and mid seral woodland stages and minimizes the loss of aspen. A fire rotation of 70–80 years was estimated for the historic fire regime while the current fire regime resulted in a fire rotation of 340–450 years, underscoring the fact that fire is currently lacking in the system. Implementation of prescribed burning programs, treating aspen and young conifer woodlands according to historic fire occurrence probabilities, are predicted to prevent conifer dominance and loss of aspen stands.     

Multi-season climate synchronized forest fires throughout the 20th century, northern Rockies, U.S.A

We inferred climate drivers of 20th-century years with regionally synchronous forest fires in the U.S. northern Rockies. We derived annual fire extent from an existing fire atlas that includes 5038 fire polygons recorded from 12,070,086 ha, or 71% of the forested land in Idaho and Montana west of the Continental Divide. The 11 regional-fire years, those exceeding the 90th percentile in annual fire extent from 1900 to 2003 (>102,314 ha or approximately 1% of the fire atlas recording area), were concentrated early and late in the century (six from 1900 to 1934 and five from 1988 to 2003). During both periods, regional-fire years were ones when warm springs were followed by warm, dry summers and also when the Pacific Decadal Oscillation (PDO) was positive. Spring snowpack was likely reduced during warm springs and when PDO was positive, resulting in longer fire seasons. Regional-fire years did not vary with El Ni?o-Southern Oscillation (ENSO) or with climate in antecedent years. The long mid-20th century period lacking regional-fire years (1935-1987) had generally cool springs, generally negative PDO, and a lack of extremely dry summers; also, this was a period of active fire suppression. The climate drivers of regionally synchronous fire that we inferred are congruent with those of previous centuries in this region, suggesting a strong influence of spring and summer climate on fire activity throughout the 20th century despite major land-use change and fire suppression efforts. The relatively cool, moist climate during the mid-century gap in regional-fire years likely contributed to the success of fire suppression during that period. In every regional-fire year, fires burned across a range of vegetation types. Given our results and the projections for warmer springs and continued warm, dry summers, forests of the U.S. northern Rockies are likely to experience synchronous, large fires in the future.     

Conditioning uncertainty in ecological models: Assessing the impact of fire management strategies

A simple simulation model has been used to investigate whether large fires in Mediterranean regions are a result of extreme weather conditions or the cumulative effect of a policy of fire suppression over decades. The model reproduced the fire regime characteristics for a wide variety of regions of Mediterranean climate in California, France and Spain. The Generalised Likelihood Uncertainty Estimation (GLUE) methodology was used to assess the possibility of multiple model parameter sets being consistent with the available calibration data. The resulting set of behavioural models was used to assess uncertainty in the predictions. The results suggested that (1) for a given region, the total area burned is much the same whether suppression or prescribed fire policies are used or not; however fire suppression enhances fire intensity and prescribed burning reduces it; (2) the proportion of large fires can be reduced, but not eliminated, using prescribed fires, especially in areas which have the highest proportion of large fires.     

Fire reduces morphospace occupation in plant communities

The two main assembly processes claimed to structure plant communities are habitat filtering and competitive interactions. The set of species growing in fire-prone communities has been filtered in such a way that species without fire-persistence traits have not successfully entered the community. Because plant traits are evolutionarily conserved and fire traits are correlated with other plant traits, communities under high fire frequency should not include all possible trait combinations, and thus the morphospace occupation by species in these communities should be lower than expected by chance (underoccupied). In contrast, communities under low fire frequency would lack the filtering factor, and thus their underoccupation of the morphospace is not expected. We test this prediction by comparing the morphospace occupation by species in communities located in the western Mediterranean Basin, five of them subject to high fire frequency (HiFi) and four to low fire frequency (LowFi). We first compile a set of morphological and functional traits for the species growing on the nine sites, then we compute the morphospace occupation of each site as a convex hull volume, and finally, to assert that our results are not a product of a random branching pattern of evolution, we simulate our traits under a null model of neutral evolution and compare the morphospace occupation of the simulated traits with the results from the empirical data. The results suggest that, as predicted, there is a clear differential morphospace occupation between communities under different fire regimes in such a way that the morphospace is underoccupied in HiFi communities only. The simulation of a neutral evolutionary model does not replicate the observed pattern of differential morphospace occupation, and thus it should be attributed to assembly processes. In conclusion, our results suggest that fire is a strong community assembling process, filtering the species that have fire-persistent traits and thus assembling phenotypically and phylogenetically clustered communities with vacant zones in the morphospace.     

Pyric Herbivory: Rewilding Landscapes through the Recoupling of Fire and Grazing

Abstract:  Our understanding of fire and grazing is largely based on small-scale experimental studies in which treatments are uniformly applied to experimental units that are considered homogenous. Any discussion of an interaction between fire and grazing is usually based on a statistical approach that ignores the spatial and temporal interactions on complex landscapes. We propose a new focus on the ecological interaction of fire and grazing in which each disturbance is spatially and temporally dependent on the other and results in a landscape where disturbance is best described as a shifting mosaic (a landscape with patches that vary with time since disturbance) that is critical to ecological structure and function of many ecosystems. We call this spatiotemporal interaction pyric herbivory (literal interpretation means grazing driven by fire). Pyric herbivory is the spatial and temporal interaction of fire and grazing, where positive and negative feedbacks promote a shifting pattern of disturbance across the landscape. We present data we collected from the Tallgrass Prairie Preserve in the southern Great Plains of North America that demonstrates that the interaction between free-roaming bison ( Bison bison ) and random fires promotes heterogeneity and provides the foundation for biological diversity and ecosystem function of North American and African grasslands. This study is different from other studies of fire and grazing because the fires we examined were random and grazing animals were free to roam and select from burned and unburned patches. For ecosystems across the globe with a long history of fire and grazing, pyric herbivory with any grazing herbivore is likely more effective at restoring evolutionary disturbance patterns than a focus on restoring any large vertebrate while ignoring the interaction with fire and other disturbances .     

Effects of time since fire on birds: how informative are generalized fire response curves for conservation management?

Fire is both a widespread natural disturbance that affects the distribution of species and a tool that can be used to manage habitats for species. Knowledge of temporal changes in the occurrence of species after fire is essential for conservation management in fire-prone environments. Two key issues are: whether postfire responses of species are idiosyncratic or if multiple species show a limited number of similar responses; and whether such responses to time since fire can predict the occurrence of species across broad spatial scales. We examined the response of bird species to time since fire in semiarid shrubland in southeastern Australia using data from surveys at 499 sites representing a 100-year chronosequence. We used nonlinear regression to model the probability of occurrence of 30 species with time since fire in two vegetation types, and compared species' responses with generalized response shapes from the literature. The occurrence of 16 species was significantly influenced by time since fire: they displayed six main responses consistent with generalized response shapes. Of these 16 species, 15 occurred more frequently in mid- or later-successional vegetation (> 20 years since fire), and only one species occurred more often in early succession (< 5 years since fire). The models had reasonable predictive ability for eight species, some predictive ability for seven species, and were little better than random for one species. Bird species displayed a limited range of responses to time since fire; thus a small set of fire ages should allow the provision of habitat for most species. Postfire successional changes extend for decades and management of the age class distribution of vegetation will need to reflect this timescale. Response curves revealed important seral stages for species and highlighted the importance of mid- to late-successional vegetation (> 20 years). Although time since fire clearly influences the distribution of numerous bird species, predictive models of the spatial distribution of species in fire-prone landscapes need to incorporate other factors in addition to time since fire.     

Temporal variability of forest fires in eastern Amazonia

Widespread occurrence of fires in Amazonian forests is known to be associated with extreme droughts, but historical data on the location and extent of forest fires are fundamental to determining the degree to which climate conditions and droughts have affected fire occurrence in the region. We used remote sensing to derive a 23-year time series of annual landscape-level burn scars in a fragmented forest of the eastern Amazon. Our burn scar data set is based on a new routine developed for the Carnegie Landsat Analysis System (CLAS), called CLAS-BURN, to calculate a physically based burn scar index (BSI) with an overall accuracy of 93% (Kappa coefficient 0.84). This index uses sub-pixel cover fractions of photosynthetic vegetation, non-photosynthetic vegetation, and shade/burn scar spectral end members. From 23 consecutive Landsat images processed with the CLAS-BURN algorithm, we quantified fire frequencies, the variation in fire return intervals, and rates of conversion of burned forest to other land uses in a 32 400 km2 area. From 1983 to 2007, 15% of the forest burned; 38% of these burned forests were subsequently deforested, representing 19% of the area cleared during the period of observation. While 72% of the fire-affected forest burned only once during the 23-year study period, 20% burned twice, 6% burned three times, and 2% burned four or more times, with the maximum of seven times. These frequencies suggest that the current fire return interval is 5-11 times more frequent than the estimated natural fire regime. Our results also quantify the substantial influence of climate and extreme droughts caused by a strong El Ni?o Southern Oscillation (ENSO) on the extent and likelihood of returning forest fires mainly in fragmented landscapes. These results are an important indication of the role of future warmer climate and deforestation in enhancing emissions from more frequently burned forests in the Amazon.     

1994—2008年河南省植被净第一性生产力及其时空变化

基于地理信息系统和卫星遥感技术,利用地面气象数据、遥感数据和土地分类等数据,根据河南省实际植被覆盖和自然地理情况,应用改进的CASA模型估算了1994—2008年间河南省植被净第一性生产力及其时空变化。结果表明：河南省植被净第一性生产力与年均温度的相关性好于植被年净第一性生产力与年降水量和年均太阳辐射之间的相关性,温度是限制河南省植被净第一性生产力的主要因子;15年来河南省植被净第一性生产力呈增加趋势,但不明显;河南省主要植被类型的净第一性生产力从大到小依次为林地、灌丛、耕地、草地、其它植被,它们随时间的变化趋势极为一致。     

Effect of sheep grazing and fire on sage grouse populations in southeastern Idaho

This paper describes the development, evaluation, and use of a model that simulates the effect of grazing and fire on temporal and spatial aspects of sagebrush community vegetation and sage grouse population dynamics. The model is represented mathematically as a discrete-time, stochastic compartment model based on difference equations with a time interval of 1 week. In the model, sheep graze through sage grouse breeding habitat during spring and fall, and different portions of the area can burn at different frequencies, creating a habitat mosaic of burned and unburned areas.The model was evaluated by examining predictions of (1) growth of sagebrush canopy cover after fire, (2) seasonal dynamics of grass and forb biomass under historical environmental conditions, and (3) sage grouse population dynamics associated with selected sagebrush canopy covers. Simulated changes in sagebrush canopy cover following fire correspond well with qualitative reports of long-term trends, simulated seasonal dynamics of herbaceous biomass correspond well with field data, and simulated responses of sage grouse population size and age structure to changing sagebrush canopy cover correspond well to qualitative field observations.Simulation results suggest that large fires occurring at high frequencies may lead to the extinction of sage grouse populations, whereas fires occurring at low frequencies may benefit sage grouse if burned areas are small and sheep grazing is absent. Sheep grazing may contribute to sage grouse population decline, but is unlikely to cause extinction under fire regimes that are favorable to sage grouse.     

Grass feedbacks on fire stabilize savannas

Savannas commonly consist of a discontinuous cover of overstory trees and a groundcover of grasses. Savanna models have previously demonstrated that vegetation feedbacks on fire frequency can limit the density of overstory trees, thereby maintaining savannas. Positive feedbacks of either savanna trees alone or trees and grasses together on fire frequency have been shown to result in a stable savanna equilibrium. Grass feedbacks on fire frequency, in contrast, have resulted in stable equilibria in either a grassland or forest state, but not in a savanna. These results, however, were derived from a system of differential equations that assumes that fire occurrence is strictly deterministic and that vegetation losses due to fire are continuous in time. We develop an alternative formulation of the grass-fire feedback model that assumes that fires are discrete and occur stochastically in time to examine the influence of these assumptions on the predicted state of the system. We show that incorporating fire as a discrete event can produce a recurring temporal refuge in which both grass and trees co-occur in a stable, bounded savanna. In our model, tree abundance is limited without invoking demographic bottlenecks in the transition from fire-sensitive to fire-resistant life history stages. An increasing strength of grass feedback on fire results in regular, predictable fires, which suggests that the system can also be modeled using a set of difference equations. We implement this discrete system using modified Leslie/Gower difference equations and demonstrate the existence of a bounded savanna state in this model framework. Our results confirm the potential for grass feedbacks to result in stable savannas, and indicate the importance of modeling fire as a discrete event rather than as a loss rate that is continuous in time.     

Effects of the Size of Prescribed Fire on Insect Predation of Northern Blazing Star, a Rare Grassland Perennial

Abstract: Loss of native grassland habitat in New England has reached>90%. Consequently, remaining grasslands persist as small, geographically isolated fragments, and populations of many plants and animals have declined or disappeared. Given the rarity of the fauna and flora of these habitats, ecological management of many of the remaining native grassland fragments in a manner that attempts to mimic natural processes has been intensive, and the effects of this management on some taxa, such as grassland birds, are now well understood. But the effects of management, especially prescribed fire, on native plants and invertebrates are less well known. I studied the effects of prescribed fire on northern blazing star ( Liatris scariosa var. novae-angliae), a rare grassland perennial endemic to the northeastern United States. Once distributed from southern Maine to northern New Jersey, northern blazing star has disappeared from 69% of the sites where it formerly occurred. Seed predation appears to be a critical proximate factor limiting recruitment of juveniles into local populations. Seven of 8 study sites in Maine and Massachusetts had a 65% average rate of seed predation, and there was no evidence of juvenile recruitment at these sites. None of these sites had been burned in the past 5 years. Experimental research at Kennebunk, Maine, demonstrated that, in the absence of fire, seed viability of northern blazing star was low, the result of larval microlepidopteran ( moth) predators in the flower heads. Prescribed fire temporarily reduced seed predation from approximately 90% to approximately 16% for 1 year following fire, but seed-predation levels once again approached 90% within 2 years. Prescribed fires larger than 13 ha helped reduce predation rates, but fires smaller than 6 ha did not, suggesting that dispersal of adult moths from unburned source areas was spatially limited. Preferably, prescribed burns should be larger than 10 ha, large enough to have core areas larger than 100 m from adjoining unburned units. My results suggest that prescribed fire should be an important component of habitat management for northern blazing star, and they emphasize the need to carefully study the effects of the spatial scale of prescribed fires in other geographic regions and for a broad range of taxa.     

Modelling the productivity of naturalised pasture in the North Island,New Zealand: a decision tree approach

Decision tree, one of the data mining methods, has been widely used as a modelling approach and has shown better predictive ability than traditional approaches (e.g. regression). However, very little is known from the literature about how the decision tree performs in predicting pasture productivity. In this study, decision tree models were developed to investigate and predict the annual and seasonal productivity of naturalised hill-pasture in the North Island, New Zealand, and were compared with regression models with respect to model fit, validation and predictive accuracy. The results indicated that the decision tree models for annual and seasonal pasture productivity all had a smaller average squared error (ASE) and a higher percentage of correctly predicted cases than the corresponding regression models. The decision tree model for annual pasture productivity had an ASE which was only half of that of the regression model, and correctly predicted 90% of the cases in the model validation which was 10.8 percentage points higher than that of the regression model. Furthermore, the decision tree models for annual and seasonal pasture productivity also clearly revealed the relative importance of environmental and management variables in influencing pasture productivity, and the interaction among these variables. Spring rainfall was the most significant factor influencing annual pasture productivity, while hill slope was the most significant factor influencing spring and winter pasture productivity, and annual P fertiliser input and autumn rainfall were the most significant factors influencing summer and autumn pasture productivity. One limitation of using the decision tree to predict pasture productivity was that it did not generate a continuous prediction, and thus could not detect the influence of small changes in environmental and management variables on pasture productivity.     

Simulation of annual productivity and long-term population trends of white-winged doves in the Tamaulipan Biotic Province

We present a model that synthesizes decades of field data on white-winged doves (Zenaida asiatica asiatica; WWDO) in the Tamaulipan Biotic Province. The model is represented as a discrete-time, deterministic compartment model based on difference equations with a one-week time step designed to simulate annual productivity and long-term trends in abundance. We evaluated the model by comparing simulated annual productivity and long-term population trends to field data. Based on simulation results, we identified apparent inconsistencies in the database; we could not generate the observed annual production index with the model parameterized based on field nest success and survivorship data, nor could we generate a stable long-term population trend with the model parameterized based on suggested sustainable harvest rates and empirically-based estimates of migratory return rates. Simulation results suggest that nest success might be closer to 22% (rather than 35%). A similar trend resulted when simulated hunting pressure was increased by 25% (to 31%), or return rates of migrating juveniles and adults were decreased by 5.5 and 5.0%, to 69 and 77%, respectively, with all other values at the baseline level. For these reasons, until better estimates of nest success and migratory return rates are available, model predictions must be viewed with caution.     

Hyperspectral and LiDAR remote sensing of fire fuels in Hawaii Volcanoes National Park.

Alien invasive grasses threaten to transform Hawaiian ecosystems through the alteration of ecosystem dynamics, especially the creation or intensification of a fire cycle. Across sub-montane ecosystems of Hawaii Volcanoes National Park on Hawaii Island, we quantified fine fuels and fire spread potential of invasive grasses using a combination of airborne hyperspectral and light detection and ranging (LiDAR) measurements. Across a gradient from forest to savanna to shrubland, automated mixture analysis of hyperspectral data provided spatially explicit fractional cover estimates of photosynthetic vegetation, non-photosynthetic vegetation, and bare substrate and shade. Small-footprint LiDAR provided measurements of vegetation height along this gradient of ecosystems. Through the fusion of hyperspectral and LiDAR data, a new fire fuel index (FFI) was developed to model the three-dimensional volume of grass fuels. Regionally, savanna ecosystems had the highest volumes of fire fuels, averaging 20% across the ecosystem and frequently filling all of the three-dimensional space represented by each image pixel. The forest and shrubland ecosystems had lower FFI values, averaging 4.4% and 8.4%, respectively. The results indicate that the fusion of hyperspectral and LiDAR remote sensing can provide unique information on the three-dimensional properties of ecosystems, their flammability, and the potential for fire spread.     

Point patterns of forest fire locations

In this paper I demonstrate some of the techniques for the analysis of spatial point patterns that have become available due to recent developments in point process modelling software. These developments permit convenient exploratory data analysis, model fitting, and model assessment. Efficient model fitting, in particular, makes possible the testing of statistical hypotheses of genuine interest, even when interaction between points is present, via Monte Carlo methods. The discussion of these techniques is conducted jointly with and in the context of some preliminary analyses of a collection of data sets which are of considerable interest in their own right. These data sets (which were kindly provided to me by the New Brunswick Department of Natural Resources) consist of the complete records of wildfires which occurred in New Brunswick during the years 1987 through 2003. In treating these data sets I deal with data-cleaning problems, methods of exploratory data analysis, means of detecting interaction, fitting of statistical models, and residual analysis and diagnostics. In addition to demonstrating modelling techniques, I include a discussion on the nature of statistical models for point patterns. This is given with a view to providing an understanding of why, in particular, the Strauss model fails as a model for interpoint attraction and how it has been modified to overcome this difficulty. All actual modelling of the New Brunswick fire data is done only with the intent of illustrating techniques. No substantive conclusions are or can be drawn at this stage. Realistic modelling of these data sets would require incorporation of covariate information which I do not so far have available.

Tree cover in sub-Saharan Africa: rainfall and fire constrain forest and savanna as alternative stable states

Savannas are known as ecosystems with tree cover below climate-defined equilibrium values. However, a predictive framework for understanding constraints on tree cover is lacking. We present (a) a spatially extensive analysis of tree cover and fire distribution in sub-Saharan Africa, and (b) a model, based on empirical results, demonstrating that savanna and forest may be alternative stable states in parts of Africa, with implications for understanding savanna distributions. Tree cover does not increase continuously with rainfall, but rather is constrained to low (<50%, "savanna") or high tree cover (>75%, "forest"). Intermediate tree cover rarely occurs. Fire, which prevents trees from establishing, differentiates high and low tree cover, especially in areas with rainfall between 1000 mm and 2000 mm. Fire is less important at low rainfall (<1000 mm), where rainfall limits tree cover, and at high rainfall (>2000 mm), where fire is rare. This pattern suggests that complex interactions between climate and disturbance produce emergent alternative states in tree cover. The relationship between tree cover and fire was incorporated into a dynamic model including grass, savanna tree saplings, and savanna trees. Only recruitment from sapling to adult tree varied depending on the amount of grass in the system. Based on our empirical analysis and previous work, fires spread only at tree cover of 40% or less, producing a sigmoidal fire probability distribution as a function of grass cover and therefore a sigmoidal sapling to tree recruitment function. This model demonstrates that, given relatively conservative and empirically supported assumptions about the establishment of trees in savannas, alternative stable states for the same set of environmental conditions (i.e., model parameters) are possible via a fire feedback mechanism. Integrating alternative stable state dynamics into models of biome distributions could improve our ability to predict changes in biome distributions and in carbon storage under climate and global change scenarios.