Last week I was at 2009 US-IALE in Snowbird, Utah. It was a great meeting; my presentations went down well, I participated in two stimulating symposia and a statistics workshop, heard interesting presentations that spanned a range of subjects, made new friends, talked about potential collaborations and even found time at the end of the week for a spot of Spring snowboarding. There was so much going on that I'm going to devote two other blog posts to the 'Complexity in Human-Nature Interactions across Landscapes' symposium and the 'Global Land Project Symposium on Agent-Based Modeling of Land Use Effects on Ecosystem Processes and Services'.
The conference plenary, entitled 'Facilitating the Conduct of Naturally Humane and Humanely Natural Research', was given by Thomas Baerwald, Senior Science Advisor at the National Science Foundation. In-keeping with his position, Baerwald dealt with several issues related to the execution of coupled human-natural type research, from the scientific or policy questions that need to be addressed to the mechanics of putting together a research team or proposal. Broadly, his comments could be interpreted (respectively) as i) CHANS research needs to provide a better understanding of the processes underlying observed dynamics, and ii) that effective teamwork (including developing a common language between researchers from diverse backgrounds) are required in the interdisciplinary research projects his department funds. Many questions and issues raised in the plenary were later addressed in the Complexity in Human-Nature Interactions symposium.
Two areas of research caught my attention in the Fire and Landscapes session. First was the ongoing work of Don McKenzie and his PostDoc Maureen Kennedy at USFS. Don has been examining the mechanisms behind scaling laws in wildfire regimes such as those I worked on during my Masters with Bruce Malamud. In particular, Don and Maureen are trying to determine whether scaling relationships like the power-law frequency-area wildfire distribution arise from physical mechanisms or are numerical artifacts of the way data are quantified.
In his presentation Don proposed that topographic controls on fire spread are the underlying driver for more proximate mechanisms that govern the observed scaling relationships. Maureen then demonstrated how they used a raster-based neutral model for fire history to generate fire history patterns to examine this. Using the neutral model, Maureen has found the expected value of Sorensen distance (a metric for fire co-occurrence between pairs of trees) depends both on the probability two trees are both in a given fire, and on the probability a tree that is in a fire records that fire with a scar [this is important given much wildfire regime data come from paleorecords of wildfire scars]. In turn, this is related to the topographic complexity of the simulated landscape.
In conclusion, Don suggested that "the search for mechanisms behind scaling laws in landscape ecology may be fruitful only when the scope of observed phenomena is sufficiently local to be in the domain of a contagious process... Power laws and other scaling relationships at broader scales, even if not simply numerical artifacts, are likely to be phenomenological in nature rather than governed by identifiable mechanisms." Thus, Don is arguing against trying to find mechanisms driving broad-scale patterns in wildfire regimes like those Bruce Malamud, George Perry, and I found for the ecoregions of the conterminous USA. The neutral model approach is certainly appealing and provides a definitive way to test the importance of a variety of variables. We've stalled lately on following-up on our PNAS paper, but the work Don and Maureen are tackling definitely provides some food for thought.
The second area of fire research that interested came from a distinctly different background. Francisco Seijo Maceiras discussed the governance of wildfire regimes. Following-up on previous work, Francisco developed the idea that the disruption of 'Pre-Industrial Anthropogenic Fire Regimes' (PIAFRs) - and the livelihoods and lifestyles of the social groups that generated them - is an important factor in changes in wildfire regimes in recent decades. Using Spain as an example, Francisco argues that changes in understanding regarding the ecological role of wildfire in landscapes (e.g. see Perry 2002) "provides an excellent opportunity for both re-enfranchising local communities regarding fire use and improving fire management." I am no expert in the history of Spanish wildfire policy but I can certainly see potential uses of my Landscape Fire Succession Model I to examine potential consequences of a change in wildfire management strategies from top-down, state-organised management towards those favoured by local community fire practitioners.
In another session I happened to drop in on, Virginia Dale gave an interesting presentation on climate change, land-use change, and energy use. What specifically caught my attention was her discussion of the use of the net environmental benefit framework for landscape ecologists to explore the land and water resource effects climate change and different energy options might bring. Papers will be appearing with more on that soon I believe.
On the final day of the meeting I attended the bayesian statistics workshop led by Mevin Hooten from Utah State University. The introduction looked at hierarchical models and the difference between forward models (e.g. forest simulation modelling: set the parameters, run the model, look at the data produced) and inverse model (e.g. linear regression: collect the data, think about how the process works, fit the parameters). Bayesian modelling is inverse modelling that uses conditional probability: first we specify a stochastic model that explains where the data come from (i.e. a likelihood) and a stochastic model for the parameters (i.e. a prior), then we fit the model by finding the posterior distribution of the parameters give the data. That's a very simplified explanation of the approach and the workshop proceeded to get technical. What re-affirmed my determination to experiment with this approach in the future were the examples Mevin's graduate students provided: Ephraim Hanks presented his work and a tutorial on the prediction of dwarf mistletoe incidence in Black Spruce stands of Northern Minnesota using Bayesian methods, and Ryan Wilson presented his work and a tutorial that used Bayesian methods to examine uncertainty, and multi-scale clustering in core area (habitat) characterisation of a variety of mammals (hopefully forthcoming in Ecology).
Even without my notes on the comments on the 'Complexity in Human-Nature Interactions across Landscapes' symposium and the 'Global Land Project Symposium on Agent-Based Modeling of Land Use Effects on Ecosystem Processes and Services' this has turned into a long blog post. There really was a lot on at the US-IALE this year. I hope to post on those symposia very soon.
Environmental Modelling and Software paper In Press
It took a while (first submitted late February 2008) but the manuscript I submitted with colleagues to Environmental Modelling and Software has now been accepted for publication. The paper describes the bio-physical component of the integrated socio-ecological simulation model I developed during my PhD. I don't envision it changing it much so the abstract is copied below. When it's in print I'll holler again...
Modelling Mediterranean Landscape Succession-Disturbance Dynamics: A Landscape Fire-Succession Model James D.A. Millington, John Wainwright, George L.W. Perry, Raul Romero-Calcerrada and Bruce D. Malamud
Abstract We present a spatially explicit Landscape Fire Succession Model (LFSM) developed to represent Mediterranean Basin landscapes and capable of integrating modules and functions that explicitly represent human activity. Plant functional types are used to represent spatial and temporal competition for resources (water and light) in a rule-based modelling framework. Vegetation dynamics are represented using a rule-based community-level modelling approach that considers multiple succession pathways and vegetation ‘climax’ states. Wildfire behaviour is represented using a cellular automata model of fire spread that accounts for land-cover flammability, slope, wind and vegetation moisture. Results show that wildfire spread parameters have the greatest influence on two aspects of the model: land cover change and the wildfire regime. Such sensitivity highlights the importance of accurately parameterising this type of grid-based model for representing landscape-level processes. We use a ‘pattern-oriented modelling’ approach in conjunction with wildfire power-law frequency-area scaling exponent beta to calibrate the model. Parameters describing the role of soil moisture on vegetation dynamics are also found to significantly influence land-cover change. Recent improvements in understanding the role of soil moisture and wildfire fuel loads at the landscape-level will drive advances in Mediterranean LFSMs.
The main reason I haven't blogged much recently is because all my spare time has been taken up working on revisions to a paper submitted to Environmental Modelling and Software. Provisionally entitled 'Modelling Mediterranean Landscape Succession-Disturbance Dynamics: A Landscape Fire-Succession Model', the paper describes the biophysical component of the coupled human-natural systems model I started developing during my PhD studies. This biophysical component is a vegetation state-and-transition model combined with a cellular-automata to represent wildfire ignition and spread.
The reviewers of the paper wanted to see some changes to the seed dispersal mechanism in the model. Greene et al. compared three commonly used empirical seed dispersal functions and concluded that the log-normal distribution is generally the most suitable approximation to observed seed dispersal curves. However, dispersal functions using an exponential function have also been used. A good example is the LANDIS forest landscape simulation model that calculates the probability of seed fall (P) in a region between the effective (ED) and maximum (MD) seed distance from the seed source. For distances from the seed source (x) < ED, P = 0.95. For x > MD, P = 0.001. For all other distances P is calculated using the negative exponential distribution function is used as follows: where b is a shape parameter.
Recently Syphard et al. modified LANDIS for use in the Mediterranean Type Environment of California. The two predominant pine species in our study area in the Mediterran Basin have different seed types: one (Pinus pinaster) has has wings and can fly large distances (~1km), but the other (Pinus pinea) does not. In this case a negative exponential distribution is most appropriate. However, research on the dispersal of acorns (from Quercus ilex) found that the distance distribution of acorns was best modeled by a log-normal distribution. I am currently experimenting with these two alternative seed dispersal distributions and comparing them with spatially random seed dispersal (dependent upon quantity but not locations of seed sources).
The main thing that has kept me occupied the last couple of weeks has been the implementation of these approaches in a manner that is computationally feasible. I need to run and test my model over several hundred (annual) timesteps for a landscape grid of data ~1,000,000 pixels. Keeping computation time down so that model execution does not take hundreds of hours is clearly important if sufficient model executions are to be run to ensure some form of statistical testing is possible. A brute-force iteration method was clearly not the best approach.
One of my co-authors suggested I look into the use of Quadtrees. Quadtrees are a tree data structure that are often used to partition a two dimensional space by recursively subdividing regions into quadrants (nodes). A region Quadtree partitions a region of interest into four equal quadrants. Each of these quadrants is subdivided into four subquadrants, each of which is subdivided and so on to the finest level of spatial resolution required. The University of Maryland have a nice Java applet example that helps illustrate the concept.
For our seed dispersal purposes, a region quadtree with n levels of may be used to represent an landscape of 2n × 2n pixels, where each pixel is assigned a value of 0 or 1 depending upon whether it contains a seed source of the given type or not. The distance of all landscape pixels to a seed source can then be quickly calculated using this data structure - staring at the top level we work our way down the tree querying whether each quadrant contains a pixel(s) that is a seed source. In this way, large areas of the grid can be discounted as not containing a seed source, thereby speeding the distance calculation.
Now that I have my QuadTree structure in place model execution time is much reduced and a reasonable number of model executions should be possible over the next month or so of model testing, calibration and use. My next steps are concerned with pinning down the appropriate values for ED and MD in the seed dispersal functions. This process of parameterization will take into account values previously used by similar models in similar situations (e.g. Syphard et al.) and empirical research and data on species found within our study area (e.g. Pons and Pausas). The key thing to keep in mind with these latter studies is their focus on the distribution of individual seeds from individual trees - the spatial resolution of my model is 30m (i.e. each pixel is 30m square). Some translation of values for individuals versus aggregated representation of individuals (in pixels) will likely be required. Hopefully, you'll see the results in print early next year.
One of the examples I used in class this week when talking about 'Complex Systems' and associated modelling approaches was the Forest Fire Cellular Automata model. I've produced an implementation of the model in NetLogo, complete with plots to illustrate the frequency-area scaling relationship of the resulting wildfire regime. I've updated the wildfire behaviour page on my website to include an applet of the NetLogo model (if that page gets changed in the future, you can view and experiment with the model here).
Regional partitioning for wildfire regime characterization
Fighting wildfires is a strategic operation. In fire-prone areas of the world, such as California and the Mediterranean Basin, it is important that managers allocate and position fire trucks, water bombers and human fire-fighters in locations that minimize the response time to reach new fires. Not only is this important to reduce risk to human lives and livelihoods, the financial demands of fighting a prolonged campaign against multiple fires demands that resources be used as economically as possible.
Characterizing the wildfire regime of an area (the frequency, timing and magnitude of all fires) can be very useful for this sort of planning. If an area burns more frequently, or with greater intensity, on average, fire-fighting resources might be better placed in or near these areas. The relationship between the frequency of fires and the area they burn is one the characteristics that is particularly interesting from this perspective.
As I've written about previously with colleagues, although it is well accepted that the frequency-area distribution of wildfires is 'heavy-tailed' (i.e. there are many, many more small fires than large fires), the exact nature of this distribution is still debated. One of the distributions that is frequently used is the power-law distribution. Along with my former advisors Bruce Malamud and George Perry, I examined how this characteristic of the wildfire regime, the power-law frequency-area distribution, varied for different regions across the continental USA (see Malamud et al. 2005). Starting with previously defined 'ecoregions' (area with characterized by similar vegetation, climate and topography) we mapped how the frequency-area relationship varied in space, finding a systematic change from east to west across the country.
More recently, Paolo Fiorucci and colleagues (Fiorucci et al. 2008) have taken a slightly different approach. Rather than starting with pre-defined spatial regions and calculating the frequency-area distribution of all the fires in each region, they have devised a method that splits a large area into smaller regions based on the wildfire data for the entire area. Thus, they use the data to define the spatial differentiation of regions with similar wildfire regime characteristics a posteriori rather than imposing the spatial differentiation a priori.
Fiorucci and his colleagues apply their method to a dataset of 6,201 fires (each with an area greater than 0.01 sq km) that burned between 1987 and 2004 in the Liguria region of Italy (5400 sq km). They show that estimates of a measure of the wildfire frequency-area relationship (in this case the power-law distribution) of a given area varies significantly depending on how regions within that area are partitioned spatially. Furthermore, they found differences in spatial patterns of the frequency-area relationship between climatic seasons.
Using both a priori (the approach of Malamud et al. 2005) and a posteriori (the approach of Fiorucci et al. 2008) spatial delineation of wildfire regime areas, whilst simultaneously considering patterns in the processes believed to be driving wildfire regimes (such as climate, vegetation and topography), will lead to better understanding of wildfire regimes. That is, future research in this area will be well advised to look at the problem of wildfire regime characterization from both perspectives - data-driven and process-driven. The approach developed by Fiorucci et al. also provide much promise for a more rigorous, data-driven, approach to make decisions about the allocation and positioning of wildfire fire-fighting resources.
Citation and Abstract Fiorucci, P., F. Gaetani, and R. Minciardi (2008) Regional partitioning for wildfire regime characterization, Journal of Geophysical Research, 113, F02013 doi:10.1029/2007JF000771
Wildfire regime characterization is an important issue for wildfire managers especially in densely populated areas where fires threaten communities and property. The ability to partition a region by articulating differences in timing, frequency, and intensity of the phenomena among different zones allows wildfire managers to allocate and position resources in order to minimize wildfire risk. Here we investigate “wildfire regimes” in areas where the ecoregions are difficult to identify because of their variability and human impact. Several studies have asserted that wildfire frequency-area relationships follow a power law distribution. However, this power law distribution, or any heavy-tailed distribution, may represent a set of wildfires over a certain region only because of the data aggregation process. We present an aggregation procedure for the selection of homogeneous zones for wildfire characterization and test the procedure using a case study in Liguria on the northwest coast of Italy. The results show that the estimation of the power law parameters provides significantly different results depending on the way the area is partitioned into its various components. These finds also show that it is possible to discriminate between different wildfire regimes characterizing different zones. The proposed procedure has significant implications for the identification of ecoregion variability, putting it in a more mathematical basis.
This week one of the papers I have been working on as a result of my PhD research has been accepted for publication in the Journal of Artificial Societies and Social Simulation (JASSS). The paper, written with Raúl Romero-Calcerrada, John Wainwright and George Perry, describes the agent-based model of agricultural land-use decision-making we constructed to represent SPA 56 in Madrid, Spain. We then present results from our use of the model to examine the importance of land tenure and land use on future land cover and the potential consequences for wildfire risk. The abstract is below, and I'll post again here when the paper is published and online.
An Agent-Based Model of Mediterranean Agricultural Land-Use/Cover Change for examining Wildfire Risk
Abstract Humans have a long history of activity in Mediterranean Basin landscapes. Spatial heterogeneity in these landscapes hinders our understanding about the impacts of changes in human activity on ecological processes, such as wildfire. Use of spatially-explicit models that simulate processes at fine scales should aid the investigation of spatial patterns at the broader, landscape scale. Here, we present an agent-based model of agricultural land-use decision-making to examine the importance of land tenure and land use on future land cover. The model considers two ‘types’ of land-use decision-making agent with differing perspectives; ‘commercial’ agents that are perfectly economically rational, and ‘traditional’ agents that represent part-time or ‘traditional’ farmers that manage their land because of its cultural, rather than economic, value. The structure of the model is described and results are presented for various scenarios of initial landscape configuration. Land use/cover maps produced by the model are used to examine how wildfire risk changes for each scenario. Results indicate land tenure configuration influences trajectories of land use change. However, simulations for various initial land-use configurations and compositions converge to similar states when land-tenure structure is held constant. For the scenarios considered, mean wildfire risk increases relative to the observed landscape. Increases in wildfire risk are not spatially uniform however, varying according to the composition and configuration of land use types. These unexpected spatial variations in wildfire risk highlight the advantages of using a spatially-explicit ABM/LUCC.
Final preparations are underway for the US-IALE Symposium in Madison, WI, next week. I've finished the poster that we'll be presenting there on the progress we're making withour ecological-economic forest landscape model. We've also been putting the finishing touches on our posters for the wildfire session at EGU in Vienna (which Raul will be attending and presenting our posters at). Links to .pdf versions of the posters are below. Thoughts and photos from Madison and Chicago (where I'll be stopping off for a couple of days on the way home) on my return.
This week's edition of Nature devotes an editorial, a special report and an interview to the subject of tropical rainforests and their deforestation. The articles highlight both the proximate causes and underlying driving forces of tropical deforestation, and the importance of human activity as an agent of change (via fire for example), in these socio-ecological systems.
The editorial considers the economics of rainforest destruction, with regards to global carbon emissions. It suggests that deforestation must be integrated into international carbon markets, to reward those countries that have been able to control the removal of forest land (such as India and Costa Rica). Appropriate accounting of tropical rainforest carbon budgets is required however, and the authors point to the importance of carbon budget modelling and the monitoring of (via satellite imagery for example) change in rainforest areas over large spatial extents. Putting an economic price on 'ecosystem services' is key to this issue, and the editorial concludes:
One of the oddly positive effects of global warming is that it has given the world the opportunity to build a more comprehensive and inclusive economic model by forcing all of us to grapple with our impact on the natural environment. We are entering a phase in which new ideas can be developed, tested, refined and rejected as necessary. If we find just one that can beat the conventional economic measure of gross domestic product, and can quantify some of the basic services provided by rainforests and other natural ecosystems, it will more than pay for itself.
The special report focuses on the efforts of the Brazilian government to curb the rate of deforestation in the their Amazonian forests. The Brazilian police force is blockading roads, conducting aerial surveys and inspecting agricultural and logging operations, to monitor human activities on the ground. Brazilian scientists meanwhile are monitoring the situation from space, and have developed methodologies and techniques that are leading the way globally in the remote monitoring of forests. The Brazilian government is a keen advocate of the sort of economic approaches to the issues of rainforest destruction highlighted in the editorial outlined above, and sees this rigorous monitoring as key to be able to show how much carbon they can save by preventing deforestation.
Halting the removal of forest cannot simply be left to carbon trading alone, however, and local initiatives need to be pursued. To ensure the forest's existence is sustainable, local communities need to be able make money for themselves without chopping down the trees - if they can do this it will be their in their interests NOT to remove forest. But developing this incentive has not been straightforward. For example, some researchers have have suggested that as commodity prices for crops such as soya beans have increased (possibly due to increased demand for corn-based ethanol in the US) deforestation has increased as a result. Although the price of soya beans may be a contributing factor to rainforest removal, Ruth DeFries (who will be visiting CSIS and MSU next week as part of the Rachel Carson Distinguished Lecture Series) suggests that it is not the main driver. Morton et al. found that during for the period 2001-04, conversion of forest to agriculture peaked in 2003. This situation makes it clear that there are both proximate causes and underlying driving forces of tropical deforestation. The Nature special report suggests:
If the international community is serious about tackling deforestation, it will probably need to use a hybrid approach: helping national governments such as Brazil to fund traditional policies for enforcement and monitoring and enabling communities to experiment with a market-based approach.
But how long do policy-makers have to discuss this and get these measures in place? One set of research suggests 55% of the Amazon rainforest could be removed over the next two decades, and the complexity of the rainforest system means that a 'tipping point' (i.e., an abrupt transition) beyond which the system might not recover (i.e., reforestation would not be possible). The Nature interview with Carlos Nobre highlights this issue - the interactions of climate change with soil moisture and the potential for fire indicate that the there is risk of rapid 'savannization' in the eastern to southeastern Amazon as the regional climate changes. When asked what the next big question scientists need to address in the Amazon is, Nobre replies that the role of human-caused fire will be key:
Fire is such a radical transformation in a tropical forest ecosystem that biodiversity loss is accelerated tremendously — by orders of magnitude. If you just do selective logging and let the area recover naturally, perhaps in 20–30 years only a botanist will be able to tell that a forest has been logged. If you have a sequence of vegetation fires going through that area, forget it. It won’t recover any more.
As I've previously discussed, considering the feedbacks and interactions between systems is important when examining landscape vulnerabilities to fire. Along with colleagues I have examined the potential effects of changing human activity on wildfire regimes in Spain (recently we had this paper published in Ecosystems and you can see more wildfire work here). However, the integrated study of socio-economic and ecological systems is still very much in its infancy. And the processes of landscape change in the northern Mediterranean Basin and the Amazonian rainforest are very different; practically inverse (increases in forest in the former and decreases in the latter). As always, plenty more work needs to be done on these subjects, and with the potential presence of 'tipping points', now is an important time to be doing it.
We were informed this week that the paper I have been working on with Raul Romero Calcerrada and other colleagues at Universidad Rey Juan Carlos has been accepted by Landscape Ecology. I've copied the abstract below. It should be out later in 2008, but email me if you want a pre-print.
Currently I'm working on two paper with colleagues describing the construction and initial results of the model I constructed during my PhD research. We're also submitting abstracts to the European Geophysics Union General Assembly 2008 on this and work related to the Landscape Ecology paper.
Tomorrow I'm heading back to Europe for a couple of weeks. I have my PhD graduation ceremony next week (maybe I'll post some photos of me looking scholarly/awkward in my academic dress/get-up), a couple days snowboarding in the Swiss Alps, and a couple of days working with Bruce Malamud at King's following up on the work we published on US wildfire regimes in PNAS. Should be a fun couple of weeks!
GIS analysis of spatial patterns of wildfire human-caused ignition risk in the SW of Madrid (Central Spain) (In Press) Landscape Ecology
Raul Romero Calcerrada; Carlos J. Novillo Camacho; James DA Millington; Inmaculada Gomez-Jimenez
Abstract: The majority of wildfires in Spain are caused by human activities. However, much wildfire research has focused on the biological and physical aspects of wildfire, with comparatively less attention given to the importance of socio-economic factors. With recent changes in human activity and settlement patterns in many parts of Spain, potentially contributing to the increases in wildfire occurrence recently observed, the need to consider human activity in models of wildfire risk for this region are apparent. Here we use a method from Bayesian statistics, the Weights of Evidence (WofE) model, to examine the causal factors of wildfires in the south west of the Madrid region for two differently defined wildfire seasons. We also produce predictive maps of wildfire risk. Our results show that spatial patterns of wildfire ignition are strongly associated with human access to the natural landscape, with proximity to urban areas and roads found to be the most important causal factors. We suggest these characteristics and recent socio-economic trends in Spain may be producing landscapes and wildfire ignition risk characteristics that are increasingly similar to Mediterranean regions with historically stronger economies, such as California, where the urban-wildland interface is large and recreation in forested areas is high. We also find that the WofE model is useful for estimating future wildfire risk. We suggest the methods presented here will be useful to optimize time, human resources and fire management funds in areas where urbanization is increasing the urban-forest interface and where human activity is an important cause of wildfire ignition.
Update 06/02/08: This paper is now online here and here.
Seeing the Wood for the Trees: Pattern-Oriented Modelling
A while back I wrote about the potentially misplaced preoccupation with statistical power in species distribution models. Our attempts at drawing out some relationships between our deer distribution data and descriptors of land cover is proving taxing - the relationships evident at a more coarse spatial resolution (e.g. county level) than we are considering aren't found in our stand-level data. As a result we moving toward taking a modelling approach that is driven less by our empirical data and more by inferences based on multiple information sources. Particularly I'm drawn toward emphasising an approach I first encountered in my undergraduate landscape ecology class taught by George Perry - 'Pattern-Oriented Modelling'.
A prime example of the POM approach is its use to model the spread of rabies through central Europe. The rabies virus has been observed to spread in a wave-like manner, carried by foxes. Grimm et al. (1996) describe how they developed a cellular automate-type model that considers cells (of fox territory) to be in either a healthy, infected or empty state. Through an iterative model development process, their model was gradually refined (i.e. its assumptions and parameters modified) by comparing model results with empirical patterns.
The idea underpinning this iterative POM approach is
"... if we decide to use a pattern for model construction because we believe this pattern contains information about essential structures and processes, we have to provide a model structure which in principle allows the pattern observed to emerge Whether it does emerge depends on the hypotheses we have built into the model."
This approach has been found particularly useful for the development of 'bottom-up' agent-based models. Often understanding of the fine-scale processes driving broad-scale system dynamics and patterns is poor, making it difficult to both structure and parameterise mechanistic models. However, whilst the logical fallacy of affirming the consequent remains, if a model of low-level interactions is able to reproduce higher-level patterns, we can be confident that our model is a better representation of the system mechanics than one that doesn't. Furthermore, the more patterns at different scales that the model reproduces, the mode confident we can be in it. Thus, in POM
"multiple patterns observed in real systems at different hierarchical levels and scales are used systematically to optimize model complexity and to reduce uncertainty."Grimm et al. (2005)
Grimm and Berger outline the general protocol of a pattern-oriented modelling approach (whilst reminding us that there is no standard recipe for model development):
Formulate the question or problem
Assemble hypotheses about essential processes and structures
Assemble (observed) patterns
Choose state variables, parameters and structures
Construct the model
Analyse, test and revise the model
Use patterns for parameterisation
Search for independent predictions
Several iterations of this process will be required to refine the model. In initial iterations, steps 2 and 4 may need to be largely inferential if the state of knowledge about the system is poor. However, by moving iteratively back through these steps, and in particular exploiting steps 6 and 7 to inform us about model performance relative to system behaviour, we can improve our knowledge about the system whilst simultaneously ensuring our model recreates observed patterns. For example, during the development of the landscape fire-succession model in my PhD, I compared the landscape-level model results of different sets of (unknown) flammability probabilities (parameters) of each vegetation type required by the model with empirically observed wildfire regime behaviour. By modifying parameters for individual vegetation types I was able to reproduce the appropriate wildfire frequency-area distribution for Mediterranean-type environments that had previously been found (I'm currently writing this up for publications - more soon).
But what does this all have to do with our model of the relationship between deer browse and timber harvest in Michigan's Upper Pensinsula? Well, right now I think we're at steps 2,3 and 4 (all at the same time). As our deer and land cover relationships are weak at the stand-level (which is the level we are considering so that we can integrate the model with an economic module), I am currently developing hypotheses (i.e. assumptions) about the structure of the system from previous research on different specific aspects of similar systems. Furthermore, we're continuing to look for spatial patterns in both vegetation and deer distribution so that we can compare the results of our hypothetical model.
For example, one thing I'm struggling with right now is is how to establish the probability of which individual trees (or saplings) will be removed from a stand due to a given level of deer browse (which in turn is dependent upon a deer density). This is not something that has been explicitly studied (and would be very difficult to study at the landscape level). Therefore we need to parameterise this process in order for the model to function. We should be able to do this by comparing several different parameterisations to empirically observed patterns such as spatial configuration of forest types classified by age class or age/species distributions at the stand-level. That's the idea anyway - we'll see how it goes over the next months...
In the meantime, next week I head back to the study area for the first stage of our seedling experiment. We're planting seedlings now across a gradient of browse and site conditions with the intention of returning in the spring to see what has been browsed and count deer pellets. This should improve our understanding of the link between pellet counts and browse pressure and provide us with some more empirical patterns which we can use in our ongoing model development.
As in previous years, I'm a co-convener of the Wildfires session at the 2008 European Geophysics Union General Assembly (along with Rosa Lasaponara, Luciano Telesca and Don McKenzie). We hope this year's session will be as successful as ever, and are expecting the best papers presented to compose a special issue of Ecological Modelling. The call for abstracts is now open (copied below). Abstracts should be submitted at the conference website. Important deadlines are:
Abstract Submission: 14 January 2008 Financial Applications 07 December 2007 Pre-registration: 31 March 2008
Subject: Call for Abstracts: Wildfires session at EGU 2008
5 November 2007
Dear Colleagues [Apologies for cross-posting],
The European Geosciences Union (EGU) General Assembly 2008 is to be help from 13-18 April 2008 in Vienna, Austria. We invite you to participate in the session 'Spatial and temporal patterns of wildfires: models, theory, and reality' (NH8.4/BG2.16 - co-organized by the Natural Hazards & Biogeosciences divisions).
Session description: Wildfires are the result of a large variety and number of interacting components, producing patterns that vary significantly both spatially and temporally. This session will examine models, theory, and empirical studies in wildfire research. We encourage submissions in any one or combination of these three main areas, and envision bringing together wildfire hazard managers, applied researchers, and theoreticians. Posters are also very much encouraged, as we plan to have both lively oral and poster sessions.
The best papers will be considered for publication in a Special Issue of Ecological Modelling
Please note that the deadline for financial applications is 07 December 2007, and for pre-registration is 31 March 2008. We look forward to seeing you in Vienna. Please forward this message also to your colleagues.
With best regards,
Lasaponara, R. (Convener) Telesca, L.; McKenzie, D.; Millington, J. (Co-conveners)
Lasaponara Rosa, PhD Research on Remote Sensing and Signal Processing CNR-IMAA Italy lasaponara at imaa.cnr.it
Luciano Telesca Research on geoscience and Signal Processing CNR-IMAA Italy luciano.telesca at imaa.cnr.it
Don McKenzie Research Ecologist Pacific WIldland Fire Sciences Lab US Forest Service
Affiliate Professor College of Forest Resources CSES Climate Impacts Group University of Washington
dmck at u.washington.edu donaldmckenzie at fs.fed.us
James D.A. Millington, PhD Research Associate Center for Systems Integration and Sustainability Michigan State University jmil at msu.edu
Integrated, multi- and inter-disciplinary studies are becoming increasingly demanded and required to understand the consequences of human activity on the natural environment. In a recent paper, Sandra Lavorel and colleagues highlight the importance of considering the feedbacks and interactions between several systems when examining landscape vulnerabilities to fire. They present a framework for integrated fire research that considers the fire regime as the central subsystem (FR in the figure below) and two feedback loops, the first with consequences for atmospheric and biochemical systems (F1) and the second that represents ecosystems services and human activity (F2). It is this second feedback loop in their framework that my research focuses.
To adequately quantify the fire-related vulnerability of different regions of the world the authors suggest that a better understanding of the relative contributions of climate, vegetation and human activity to the fire regime is required. For example, they suggest that an examination of the statistical relationships between spatio-temporal patterns evident in wildfire regimes and data on ecosystem structure, land use and other socio-economic factors. We made a very similar point in our PNAS paper and hope to continue to use the exponent (Beta) of the power-law frequency-area relationship that is evident in many model and empirical wildfire regimes to examine these interactions. One statistical relationship that might be investigated is between Beta and the level of forest fragmentations, thought to be a factor confounding research on the effects of fire suppression of wildfire regimes.
But the effects of landscape fragmentation can also be examined in a more mechanistic fashion using dynamic simulation models. Lavorel et al. mention the impacts of agricultural abandonment on the connectivity of fuels in Mediterranean landscapes which are attributed, in conjunction with a drier than average climate, to the exceptionally large fires that burned there during the 1990s. My PhD research examined the impacts of agricultural land abandonment on wildfire regimes in central Spain. I'm currently working on writing this work up for publication, but I plan on continuing to develop the model to more explicitly represent the F2 feedbacks loop shown in the figure above.
The potential socio-economic consequences of changing fire regimes are an area with a lot of room to improve our understanding. For example, some regions of the world, such as the Canadian boreal forest, are transitioning from a net sink for carbon to a net source (due to emission during burning). If carbon sinks are considered in future emission trading systems, regions such as are losing a potential future economic commodity. Lavorel et al. also discuss the interesting subject of potential land conflict due to mismatches in the time scales between land planning and fire occurrence. In Indonesia for example, years which burn large areas force re-allocation of land development plans by local government. Often however the processes of developing these plans is not fast enough to forestall the exploitation by local residents of the new land available for occupation and use.
The need for increased research in this area is highlighted by the case studies for Alaskan and African savannah ecosystems presented by Lavorel et al. Whilst discussion of the wildfire regime and atmospheric/biochemical feedbacks can be discussed in detail, poor understanding of the ecosystem services/human activity feedbacks prevents such detailed discussion.
The framework Lavorel et al. present is a very useful way to conceptualise and plan for future research in this field. They suggest (p.47-48) that "Assessments of vulnerability of land systems to fire demand regional studies that use a systemic approach that focuses on the feedback loops described here" and "... will require engaging a collection of multiscale and interdisciplinary regional studies". In many respects, I expect my future work to contribute to this framework, particularly with regards the human activity (F2) feedback loop.
"Increasing drought conditions across Michigan have increased the fire danger to very high. Department of Natural Resources wildfire officials are asking outdoor enthusiasts to use caution with outdoor fires."
Over the weekend erratic winds have fanned a fire to greater than 12,000 acres in the UP, just north of Tahquamenon Falls State Park. More here.
Update - 4th January 2008 On 29th August 2007 Michigan DNR reported the Sleeper Lake fire was 95% contained and at ~18,000 acres was the third largest fire in Michigan history.
We plan to use the Forest Vegetation Simulator (FVS), developed by the USFS over the previous couple of decades, in our ecological-economic model of a managed forest landscape. This week I've been thinking a lot about how best to link a representation of white-tailed deer browse with the FVS.
The Fire and Fuels Extension to the Forest Vegetation Simulator (FFE-FVS) links the existing FVS, models that represent fire and fire-effects, and fuel dynamics and crowning submodels. The overall model is currently calibrated for northern Idaho, western Montana, and northeastern Washington. More details on the FFE-FVS can be found here, where you can also download this video about the extension:
The Westwide Pine Beetle Model simulates impacts of mountain beetle (Dendroctonus ponderosae Hokpins), western pine beetle (D. brevicomis Leconte), and Ips species for which western pines are a host. The model simulates the movement of beetles between the forest stands in the landscape using the Parallel Processor Extension (PPE) to represent multiple forest stands in FVS.
We simulated management scenarios with and without thinning over 60 years, coupled with a mountain pine beetle outbreak (at 30 years) to examine how thinning might affect bark beetle impacts, potential fire behavior, and their interactions on a 16,000-ha landscape in northeastern Oregon. We employed the Forest Vegetation Simulator, along with sub-models including the Parallel Processing Extension, Fire and Fuels Extension, and Westwide Pine Beetle Model (WPBM). We also compared responses to treatment scenarios of two bark beetle-caused tree mortality susceptibility rating systems. As hypothesized, thinning treatments led to substantial reduction in potential wildfire severity over time. However, contrary to expectations, the WPBM predicted higher bark beetle-caused mortality from an outbreak in thinned versus unthinned scenarios. Likewise, susceptibility ratings were also higher for thinned stands. Thinning treatments favored retention of early seral species such as ponderosa pine, leading to increases in proportion and average diameter of host trees. Increased surface fuel loadings and incidence of potential crown fire behavior were predicted post-outbreak; however, these effects on potential wildfire behavior were minor relative to effects of thinning. We discuss apparent inconsistencies between simulation outputs and literature, and identify improvements needed in the modeling framework to better address bark beetle-wildfire interactions.
Whilst I'm still in the early stages of working out how our model will all fit together, it seems like an approach that takes a similar approach will be suitable for our purposes. We'll need to develop a model that is able to represent the spatial distribution of the deer population across the landscape and that can specify the impact of those deer densities on the vegetation for given age-height classes (for each veg species). This model would likely then be linked with FVS via the the PPE. So concurrently over the next few months I'm going to be working on developing a model of deer density and browse impacts, coding this model into a structure that will link with FVS-PPE, and acquiring and developing data for model initialization.
Reference Ager, A.A., McMahan, A., Hayes, J.L. and Smith, E.L. (2007) Modeling the effects of thinning on bark beetle impacts and wildfire potential in the Blue Mountains of eastern Oregon Landscape and Urban Planning80:3 p.301-311
The days of Smokey Bear, an enduring American icon of wildland management and its efforts to communicate with the public, are apparently numbered. Whilst his message about taking precautions against starting wildfires remains necessary, the underlying ethos of forest (and environmental) management has changed. Once, ecologists' theoretical foundation was the 'balance of nature' and the presence of equilibrium and stability within ecosystems. But over the past three decades this perception has dramatically shifted and now 'change is natural' would be a more apt motto. Ecosystems are dynamic. Disturbance, such a wildfire, is now seen as an inherent and necessary component of many landscapes to ensure ecosystem health. This shift in thinking is evident on the Smokey website, with sections discussing the use of prescribed fire, fire's role in ecosystem function, and the potential pitfalls of excluding fire entirely. George Perry has written an excellent review of these shifts in ecological understanding.
So what about Smokey Bear? His message about taking precautions in wilderness areas still remain of course. But with this new ecological ethos in mind, Botkin was asked for suggestions for a new management mascot. He came up with Morph the Moose. I haven't seen anything about Morph previously, and a quick Google search currently only throws up 7 hits, so we'll have to watch out for Morph wandering around with his new message soon.
The second post that got my eye is related to the evaluation of the forest growth model JABOWA that Botkin developed. JABOWA is an individual-based model that considers the establishment, growth and senescence of individual trees. In 1991 JABOWA was used to forecast how potential global warming would influence the Kirtland’s warbler, an endangered species that nests only in Michigan. Botkin and his colleagues forecast that by 2015 the Jack pine habitat of the warbler would decline significantly with detrimental consequences for the warbler. On his blog he suggests that matching this prediction with contemporary observations will be an ideal test to validate the predictions of the JABOWA model. Given my previous discussion about 'affirming the consequent' (i.e. deeming a model a true representation of reality if its predictions match observed reality, and false if it does not) it's good to see Botkin does not suggest a valid prediction indicates the validity of the model itself. We're advised us to stay tuned for the results. Given the subject matter and quality of the articles on the new renegade blog I certainly will.
A simulation model is an internally logically-consistent theory of how a system functions. Simulation models are currently recognised by environmental scientists as powerful tools, but the ways in which these tools should be used, the questions they should be used to examine, and the ways in which they can be ‘validated’ are still much debated. Whether a model aims to represent an 'open' or 'closed' systems has implications for the process of validation.
Issues of validation and model assessment are largely absent in discussions of abstract models that purport to represent the fundamental underlying processes of 'real world' phenomena such as wildfire, social preferences and human intelligence. These ‘metaphor models’ do not require empirical validation in the sense that environmental and earth systems modellers use it, as the very formulation of the system of study ensures it is 'closed'. That is, the system the model examines is logically self-contained and uninfluenced by, nor interactive with, outside statements or phenomena. The modellers do not claim to know much about the real world system which their model is purported to represent, and do not claim their model is the best representation of it. Rather, the modelled system is related to the empirical phenomena via ‘rich analogy’ and investigators aim to elucidate the essential system properties that emerge from the simplest model structure and starting conditions.
In contrast to these virtual, logically closed systems, empirically observed systems in the real world are ‘open’. That is, they are in a state of disequilibrium with flows of mass and energy both into and out of them. Examples in environmental systems are flows of water and sediment into and out of watersheds and flows of energy into (via photosynthesis) and out of (via respiration and movement) ecological systems. Real world systems containing humans and human activity are open not only in terms of conservation of energy and mass, but also in terms of information, meaning and value. Political, economic, social, cultural and scientific flows of information across the boundaries of the system cause changes in the meanings, values and states of the processes, patterns and entities of each of the above social structures and knowledge systems. Thus, system behaviour is open to modification by events and phenomena outside the system of study.
Alongside being ‘open’, these systems are also ‘middle-numbered’. Middle-numbered systems differ from small-numbered systems (controlled situations with few interacting components, e.g. two billiard balls colliding) that can be described and studied well using Cartesian methods, and large-numbered systems (many, many interacting components, e.g. air molecules in a room) that can be described and studied using techniques from statistical physics. Rather, middle-numbered systems have many components, the nature of interactions between which is not homogenous and is often dictated or influenced by the condition of other variables, themselves changing (and potentially distant) in time and space. Such a situation might be termed complex (though many perspectives on complexity exist). Systems at the landscape scale in the real world are complex and middle-numbered. They exist in a unique time and place. In these systems history and location are important and their study is necessarily a ‘historical science’ that recognises the difficulty of analysing unique events scientifically through formal, laboratory-type testing and the hypothetico-deductive method. Most real-world systems possess these properties, and coupled human-environment systems are a prime example.
Traditionally laboratory science has attempted to isolate real world systems such that they become closed and amenable to the hypothetico-eductive method. The hypothetico-deductive method is based upon logical prediction of phenomena independent of time and place and is therefore useful for generating knowledge about logically, energetically and materially ‘closed’ systems. However, the ‘open’ nature of many real-world, environmental systems (which cannot be taken into the laboratory and instead must be studies in situ) is such that the hypothetico-deductive method is often problematic to implement in order to generate knowledge about environmental systems from simulation models. Any conclusions draw using the hypothetico-deductive method for open systems using a simulation model will implicitly be about the model rather than the open system it represents. Validation has also frequently been used, incorrectly, as synonymous with demonstrating that the model is a truly accurate representation of the real world. By contrast, validation in the discussion presented in this series of blog posts refers to the process by which a model constructed to represent a real-world system has been shown to represent that system well enough to serve that model’s intended purpose. That is, validation is taken to mean the establishment of model legitimacy – usually of arguments and methods.
In the next few posts I'll examine the rise of (critical) realist philosophies in the environmental sciences and environmental modelling and will explore the philosophy underlying these problems of model validation in more detail.
I'm not attending the European Geophysics Union General Assembly this year as I have done the past couple. However, I do have a poster there (today, thanks to Bruce Malamud for posting it) on some work I have been doing with Raul Romero Calcerrada at Universidad Rey Juan Carlos in Madrid, Spain. We have been using various spatial statistical modelling techniques to examine the spatial patterns and causes (including both socioeconomic and biophysical) of wildfire ignition probabilities in central Spain. The poster abstract is presented below and we're working on writing a couple of papers related to this right now.
Spatial analysis of patterns and causes of fire ignition probabilities using Logistic Regression and Weights-of-Evidence based GIS modelling R. Romero-Calcerrada, J.D.A. Millington
In countries where more than 95% of wildfires are caused by direct or indirect human activity, such as those in the Iberian Peninsula, ignition risk estimation must consider anthropic influences. However, the importance of human factors has been given scant regard when compared to biophysical factors (topography, vegetation and meteorology) in quantitative analyses of risk. This disregard for the primary cause of wildfires in the Iberian Peninsula is owed to the difficulties in evaluating, modelling and representing spatially the human component of both fire ignition and spread. We use logistic regression and weights-of-evidence based GIS modelling to examine the relative influence of biophysical and socio-economic variables on the spatial distribution of wildfire ignition risk for a six year time series of 508 fires in the south west of the Autonomous Community of Madrid, Spain. We find that socioeconomic variables are more important than biophysical to understand spatial wildfire ignition risk, and that models using socioeconomic data have a greater accuracy than those using biophysical data alone. Our findings suggest the importance of socioeconomic variables for the explanation and prediction of the spatial distribution of wildfire ignition risk in the study area. Socioeconomic variables need to be included in models of wildfire ignition risk in the Mediterranean and will likely be very important in wildfire prevention and planning in this region.
So, finally, it is done. As I write, three copies of my PhD Thesis are being bound ready for submission tomorrow! I've posted a short abstract below. If you want a more complete picture of what I've done you can look at the Table of Contents and read the online versions of the Introduction and Discussion and Conclusions. Email me if you want a copy of the whole thesis (all 81,000 words, 277 pages of it).
So just the small matter of defending the thesis at my viva voce in May. But before that I think it's time for a celebratory beer on the South Bank of the Thames in the evening sunshine...
Modelling Land-Use/Cover Change and Wildfire Regimes in a Mediterranean Landscape
James D.A. Millington March 2007
Department of Geography King’s College, London
Abstract
This interdisciplinary thesis examines the potential impacts of human land-use/cover change upon wildfire regimes in a Mediterranean landscape using empirical and simulation models that consider both social and ecological processes and phenomena. Such an examination is pertinent given contemporary agricultural land-use decline in some areas of the northern Mediterranean Basin due to social and economic trends, and the ecological uncertainties in the consequent feedbacks between landscape-level patterns and processes of vegetation- and wildfire-dynamics.
The shortcomings of empirical modelling of these processes are highlighted, leading to the development of an integrated socio-ecological simulation model (SESM). A grid-based landscape fire succession model is integrated with an agent-based model of agricultural land-use decision-making. The agent-based component considers non-economic alongside economic influences on actors’ land-use decision-making. The explicit representation of human influence on wildfire frequency and ignition in the model is a novel approach and highlights biases in the areas of land-covers burned according to ignition cause. Model results suggest if agricultural change (i.e. abandonment) continues as it has recently, the risk of large wildfires will increase and greater total area will be burned.
The epistemological problems of representation encountered when attempting to simulate ‘open’, middle numbered systems – as is the case for many ‘real world’ geographical and ecological systems – are discussed. Consequently, and in light of recent calls for increased engagement between science and the public, a shift in emphasis is suggested for SESMs away from establishing the truth of a model’s structure via the mimetic accuracy of its results and toward ensuring trust in a model’s results via practical adequacy. A ‘stakeholder model evaluation’ exercise is undertaken to examine this contention and to evaluate, with the intent of improving, the SESM developed in this thesis. A narrative approach is then adopted to reflect on what has been learnt.
This is my fifth contribution to JustScience week.
The last couple of days I've discussed some techniques and case studies of statistical model of landscape processes. Monday and Tuesday I looked at the power-law frequency-area characteristics of wildfire regimes in the US, Wednesday and Thursday I looked at regression modelling for predicting and explaining land use/cover change (LUCC). The main alternative to these empirical modelling methods are simulation modelling techniques.
When a problem is not analytically tractable (i.e. equations cannot be written down to represent the processes) simulation models may be used to represent a system by making certain approximations and idealisations. When attempting to mimic a real world system (for example a forest ecosystem), simulation modelling has become the method of choice for many researchers. This may have become the case since simulation modelling can be used when data is sparse. Also, simulation modelling overcomes many of the problems associated with the large time and space scales involved in landscapes studies. Frequently, study areas are so large (upwards of 10 square kilometres - see photo below of my PhD study area) that empirical experimentation in the field is virtually impossible because of logistic, political and financial constraints. Experimenting with simulation models allows experiments and scenarios to be run and tested that would not be possible in real environments and landscapes.
Spatially-explicit simulation models of LUCC have been used since the 1970s and have dramatically increased in use recently with the growth in computing power available. These advances mean that simulation modelling is now one of the most powerful tools for environmental scientists investigating the interaction(s) between the environment, ecosystems and human activity. A spatially explicit model is one in which the behaviour of a single model unit of spatial representation (often a pixel or grid cell) cannot be predicted without reference to its relative location in the landscape and to neighbouring units. Current spatially-explicit simulation modelling techniques allow the spatial and temporal examination of the interaction of numerous variables, sensitivity analyses of specific variables, and projection of multiple different potential future landscapes. In turn, this allows managers and researchers to evaluate proposed alternative monitoring and management schemes, identify key drivers of change, and potentially improve understanding of the interaction(s) between variables and processes (both spatially and temporally).
Early spatially-explicit simulation models of LUCC typically considered only ecological factors. Because of the recognition that landscapes are the historical outcome of multiple complex interactions between social and natural processes, more recent spatially-explicit LUCC modelling exercises have begun to integrate both ecological and socio-economic process to examine these interactions.
A prime example of a landscape simulation model is LANDIS. LANDIS is a spatially explicit model of forest landscape dynamics and processes, representing vegetation at the species-cohort level. The model requires life-history attributes for each vegetation species modelled (e.g. age of sexual maturity, shade tolerance and effective seed-dispersal distance), along with various other environmental data (e.g. climatic, topographical and lithographic data) to classify ‘land types’ within the landscape. Previous uses of LANDIS examined the interactions between vegetation-dynamics and disturbance regimes , the effects of climate change on landscape disturbance regimes , and simulated the impacts of forest management practices such as timber harvesting.
Recently, LANDIS-II was released with a new website and a paper published in Ecological Modelling;
LANDIS-II advances forest landscape simulation modeling in many respects. Most significantly, LANDIS-II, 1) preserves the functionality of all previous LANDIS versions, 2) has flexible time steps for every process, 3) uses an advanced architecture that significantly increases collaborative potential, and 4) optionally allows for the incorporation of ecosystem processes and states (eg live biomass accumulation) at broad spatial scales.
During my PhD I've been developing a spatially-explicit, socio-ecological landscape simulation model. Taking a combined agent-based/cellular automata approach, it directly considers:
human land management decision-making in a low-intensity Mediterranean agricultural landscape [agent-based model]
landscape vegetation dynamics, including seed dispersal and disturbance (human or wildfire) [cellular automata model]
the interaction between 1 and 2
Read more about it here. I'm nearly finished now, so I'll be posting results from the model in the near future. Finally, some other useful spatial simulation modelling links:
We examined wildfires statistics for the conterminous United States (U.S.) in a spatially and temporally explicit manner. Using a high-resolution data set of 88,916 U.S. Department of Agriculture Forest Service wildfires over the time period 1970-2000 to consider wildfire occurrence as a function of biophysical landscape characteristics. We used Bailey's ecoregions as shown by Figure 1A below.
Figure 1.
In Bailey's classification, the conterminous U.S. is divided into ecoregion divisions according to common characteristics of climate, vegetation, and soils. Mountainous areas within specific divisions are also classified. In the paper, we used ecoregion divisions to geographically subdivide the wildfire database for statistical analyses as a function of ecoregion division. Figure 1B above shows the location of USFS lands in the conterminous U.S.
We found that wildfires exhibit robust frequency-area power-law behaviour in the 18 different ecoregions and used power-law exponents (normalized by ecoregion area and the temporal extent of the wildfire database) to compare the scaling of wildfire-burned areas between ecoregions. Normalizing the relationships allowed us to map the frequency-area relationships, as shown in Figure 2A below.
Figure 2.
This mapping exercise shows a systematic change east-to-west gradient in power-law exponent beta values. This gradient suggests that the ratio of the number of large to small wildfires decreases from east to west across the conterminous U.S. Controls on the wildfire regime (for example, climate and fuels) vary temporally, spatially, and at different scales, so it is difficult to attribute specific causes to this east-to-west gradient. We suggested that the reduced contribution of large wildfires to total burned area in eastern ecoregion divisions might be due to greater human population densities that have increased forest fragmentation compared with western ecoregions. Alternatively, the gradient may have natural drivers, with climate and vegetation producing conditions more conducive to large wildfires in some ecoregions compared with others.
Finally, this method allowed us to calculate recurrence intervals for wildfires of a given burned area or larger for each ecoregion (Figure 2B above). In turn this allowed for the classification of wildfire regimes for probabilistic hazard estimation in the same vein as is now used for earthquakes.
This post is the first of my contribution to JustScience week.
Wildfire is considered an integral component of ecosystem functioning, but often comes into conflict with human interests. Thus, understanding and managing relationship between wildfire, ecology and human activity is of particular interest to both ecologists and wildfire managers. Quantifying the wildfire regime is useful in this regard. The wildfire regime is the name given to the combination of the timing, frequency and magnitude of all fires in a region. The relationship between the frequency and magnitude of fires, the frequency-area distribution, is one particular aspect of the wildfire regime that has become of interest recently.
where is the frequency of fires with size , and is a constant. is a measure of the ratio of small to medium to large size fires and how frequently they occur. The smaller the value of , the greater the contribution of large fires (compared to smaller fires) to the total burned area of a region. The greater the value, the smaller the contribution. Such a power-law relation is represented on a log-log plot as straight line, as the example from Malamud et al. 2005 shows:
Shown circles are number of wildfires per "unit bin" of 1 km^2 (in this case normalized by database length in years and area in km^2) plotted as a function of wildfire area. Also shown is a solid line (best least-squares fit) with coefficient of determination r^2. Dashed lines represent lower/upper 95% confidence intervals, calculated from the standard error. Horizontal error bars on burned area are due to measurement and size binning of individual wildfires. Vertical error bars represent two standard deviations of the normalized frequency densities and are approximately the same as the lower and upper 95% confidence interval.
As a result of their work on the forest fire cellular automata Malamud et al. 1998 wondered whether the same relation would hold for empirical wildfire data. They found the power-law relationship did indeed hold for observed wildfire data for parts of the US and Australia. As Millington et al. 2006 discuss, since this seminal publication several other studies have suggested a power-law relationship is the best descriptor of the frequency-size distribution of wildfires around the world.
During my Master's Thesis I worked with Dr. Bruce Malamud to examine wildfire frequency-area statistics and their ecological and anthropogenic drivers. Work resulting from this thesis led to the publication of Malamud et al. 2005 which I'll discuss in more detail tomorrow.
The preliminary program and schedule of sessions for the 2007 AAG (Association of American Geographers) National Meeting in San Francisco, April 17-21, is now available online.
It looks like I should have some time during April, and several colleagues from King's Geography Dept. are going to San Francisco, so it might be good to go. Unfortunately, I wasn't banking on having the opportunity so I haven't submitted anything to present.
Spatial analysis of patterns and causes of fire ignition probabilities using Logistic Regression and Weights-of-Evidence based GIS modelling Romero-Calcerrada, R. and Millington, J.D.A Session NH8.04/BG1.04: Spatial and temporal patterns of wildfires: models, theory, and reality (co-organized by BG & NH)
It'a been out for a while (so there are several reviews available ) but I only just got and started reading Jared Diamond's Collapse (How societies choose to fail or survive). I've only read the first part (Modern Montana) so far, but already I've come across several parallels between the socio-economic changes, and their potential ecological impacts, occuring in the landscapes under Montana's Big Sky and Madrid's Sun-Blessed Skies.
The broad similarity between the change Diamond describes in Montana and that occurring in my PhD study area (SPA 56, an EU protection area for endangered bird species to the west of Madrid, Spain) is the shift from an economy and landscape driven by agricultural activity to one driven by recreational activities. Such a shift reflects both the differing visions of multiple stakeholders within these landscapes, but also generational changes in attitude between older inhabitants and their children and grandchildren. In Montana's Bitteroot Valley larger macroeconomic changes nationally and internationally have made previously profitable extractive industries (forestry, mining and agriculture) largely unsustainable economically. This has come about as land is now valued not according to resource and agricultural production but according to real-estate potential for incoming retirees, second-homers and tourists. Incoming (usually older) 'out-of-staters' arrive to enjoy the outdoor recreation (fishing, hiking, etc.), beauty and lifestyle opportunities, replacing the younger generation of Montanans going the other way to seek modern urban lifestyle opportunities and lifestyles;
"It's a wonderful lifestyle to get up before dawn and see the sunrise, to watch fly hawks overhead, and to see deer jump through your hay field to avoid your haying equipment. ... Occasionally I get up at 3 AM and work until 10 AM. This isn't a 9 to 5 job. But none of our children will sign up for being a farmer if it is 3 AM to 10 PM every day." Dairy Farmer, Montana
Locals in SPA 56 have expressed similar feelings and ideas when I have visited over the last few years. Younger generations that would have previously continued the family farm that has passed through generation upon generation of farmers, are now seeking out employment in construction and service sectors to secure what is understood as a more 'modern' lifestyle. A lifestyle that affords leisure time at specified times of the week and at regular intervals (i.e. the weekends and paid holidays);
"Most farmers are part-time, maintaining the tradition agriculture. The children or grandchildren of those [farmers] do not have interest [in agriculture] because is it not profitable and requires a lot of dedication. The youths go or they seek other work." Local Development Official, Madrid (2006)
In Montana, Diamond describes the conflicts that have arisen between existing inhabitants and the new-comers, each with differing world-views, priorities and values. For example, contrast the attitudes of the third generation dairy farmer fighting to ensure the survival of his farm in the global economy vs. the lady who complained to him when she got manure on her white running shoes. Of course, these multiple perspectives within the landscape are inevitable in a changing world and tools and strategies must be found and employed to ensure appropriate decisions and compromises are made. In my simulation model of agricultural decision-making I have attempted to represent the influence of two differing world-views on landscape change (as have other modellers). I have termed the representative agents 'commercial' and 'traditional'; the former behaving as a perfectly rational actor (in economic terms), the latter designed to reflect the importance of traditional cultural values in land-use decision-making;
"Whoever has a vineyard nowadays is like a gardener... they like to keep it, even if they lose money. They maintain vineyards because they have done it all their life and they like it, even having to pay for it. If owners were looking for profitability there would be not a singe vineyard... People here grow wine because of a matter of feeling, love for the land..." Vinter, Madrid (2005)
As the primary thesis of his book Diamond highlights, for both contemporary and historical societies, the impacts of social, economic and technological change on the physical environment, and the sustainability of those changes. Of the several issues of concern in Montana, those related to forestry and water availability are likely to be of most concern in SPA 56. One particular interest of my PhD thesis is the importance of changes in the landscape for wildfire regimes, which Diamond discusses with reference to previous management strategies of the Unites States Forest Service (USFS). Commercial forestry has not been a widespread activity in SPA 56, the nature and human history of Mediterranean ecosystems restricting contemporary timber productivity. However, the problems of increased fuel loads due to the fire suppression policies of the USFS during the 20th century may be beginning to present themselves in SPA 56. If the agricultural sector continues to decline due to the social and economic trends just outlined, farmland will (continue to) be abanoned or converted to recreational uses (for example, hunting reservations). In turn this will leading to increased biomass and fuel loads in the landscape. As yet the consequences of such change on the frequency and magnitude of fires in the region is unclear due to spatial relationships and feedbacks between vegetation growth and burning. In the very near future the results of my simulation model will be able shed some light on this aspect of the region's changing landscape and ecology.
In all of this the book is quite careful as to matters of fact – in detail all its statements are cautiously worded and filled with subtle caveats. However its broad message is very different, implying that abstract physics-style models have been successful at identifying some general laws and tendencies in social phenomena. It does this in two ways: firstly, by slipping between statements about the behaviour of the models and statements about the target social phenomena, so that it is able to make definite pronouncements and establish the success and relevance of its approach; and secondly, by implying that it is as well-validated as any established physics model but, in fact, only establishing that the models can be used as sophisticated analogies – ways of thinking about social phenomena. The book particularly makes play of analogies with the phase transitions observed in fluids since this was the author's area of expertise.
This book is by no means unique in making these kinds of conflation – they are rife within the world of social simulation.
And not only within social simulation. In a previous paper, I highlighted with some colleagues that the name given to the 'Forest Fire Cellular Automata' made famous by Per Bak and colleagues, is better treated as a metaphor than an accurate representation of the dynamics of a real world forest fire (Millington et al 2006). This may be a seemingly an obvious point to make, but simulation models can provide an unjustified sense of verisimilitude and the appearance of the reproduction of complex empirical systems' behaviour by simple models can lead to the false conclusion that those simple mechanisms are the cause of the observed complexity.
In a forthcoming paper with Dr. George Perry in a special issue of Perspectives in Plant Ecology, Evolution and Systematics, we discuss the lure of these 'metaphor models' and other issues regarding the approaches to spatial modelling of succession-disturbance dynamics in terrestrial ecological systems. I'll keep you posted on the paper's progress...
Some guys at the University of Granada, Spain, have developed software for managing wildfire-fighting efforts. SIADEX is designed to speed decision-making for resource allocation, as an article in New Scientist describes:
"Computerised maps are already used by people in charge of managing the fire-fighting effort. These maps are used to plan which areas to focus on and which resources to deploy, such as fire engines, planes and helicopters.
But working out the details of such a plan involves coordinating thousands of people, hundreds of vehicles and many other resources. SIADEX is able to help by rapidly weighing up different variables. Shift patterns
For example, it calculates which fire engines could reach an area first, where aircraft could be used, and even how to organise the shift patterns of individual fire fighters. It then very quickly produces several different detailed plans. ... One plan might be the cheapest, another the fastest, and a third the least complicated."
I wonder how Normal Maclean would have felt about this approach to fire-fighting. I imagine like me he'd be interested in how this new tool can be used to aid and protect wildland fire-fighters, but the given the unpredictability of fire behaviour (in the light of current understanding) would still maintain that human experience, gained over many years dealing with unique situations, will be invaluable in managing fire-fighters and their resources. As with much computer software, this should remain as a tool to aid human decision-making, not replace it.
MILLINGTON, J.D.A, Perry, G.L.W. and Malamud, B.D. (2006) Models, data and mechanisms: quantifying wildfire regimes In: Cello G. & Malamud B. D. (Eds.) Fractal Analysis for Natural Hazards. Geological Society, London, Special Publications
Abstract The quantification of wildfire regimes, especially the relationship between the frequency with which events occur and their size, is of particular interest to both ecologists and wildfire managers. Recent studies in cellular automata (CA) and the fractal nature of the frequency–area relationship they produce has led some authors to ask whether the power-law frequency–area statistics seen in the CA might also be present in empirical wildfire data. Here, we outline the history of the debate regarding the statistical wildfire frequency–area models suggested by the CA and their confrontation with empirical data. In particular, the extent to which the utility of these approaches is dependent on being placed in the context of self-organized criticality (SOC) is examined. We also consider some of the other heavy-tailed statistical distributions used to describe these data. Taking a broadly ecological perspective we suggest that this debate needs to take more interest in the mechanisms underlying the observed power-law (or other) statistics. From this perspective, future studies utilizing the techniques associated with CA and statistical physics will be better able to contribute to the understanding of ecological processes and systems.
I expect I was wheeling my bike through the tourists, guzzling on my choco-milk after a session in the gym. Those book stalls under Waterloo Bridge on the South Bank get me everytime. This one must have jumped at me, out of the titles and authors streaming by, me the fly.
I'm sure it was because I'd read Norman Maclean before - A River Runs Through It, the story of Montana fly-fishing. But the title also intrigued me; Young Men and Fire. It wasn't until I'd parted with my £3.50 and began reading just recently that I really discovered how intrigued I would become.
Other reviews will offer you a better description of the story and more evocative excerpts, but I'll concentrate less on the story and more on the storytelling. The story is the events of 5th August 1949 when 12 USFS smokejumpers died on a fire in Mann Gulch, Montana, and Maclean's exploits to understand the tragic events years later. The telling is part story, part history,part science.
"Historical questions the storyteller must face, although in a place of his own choosing, but his most immediate question as he faces new material is always, Will anything strange or wonderful happen here? The rights and wrongs come later and likewise the scientific know how."
The first third of the book is the story of the tragedy. It's only later that the detective story begins, where we start "... examining how all the little cockeyed things all fit together to explain one big cockeyed thing". This is where Maclean begins to suggest that not only did the events of the day happen because 'everything was just right' but that the route to discovering what happened also depends on everything being 'just right'. The process of discovering is often as historically contingent as the history.
Maclean describes his 'ah-ha!' moment, his 'eureka!', when he thought "that's funny". On a boat trying to piece the bits of puzzle together in his mind of how and why those men got caught by the fire, he sees a wave on the water 'going the wrong way'. Or rather, going in a direction he wasn't expecting because of the winds that come and go.
Wind is the whip of the fire, spurring it on, pointing the way. The winds on the day of the fire all came together at the right time across the unique topography of the gulch to cause a 'blowup', an explosion of fire throwing flames tens of metres high and accelerating fire spread to speeds faster than a man can run. Faster even than a man running for his life.
Everything that had to fit that day did fit that day - but the evidence of those conditions may still be observed in the broader patterns of the landscape that are shaped by the prevailing winds. Processes acting at different rates and extents leave their evidence at different rates and extents. Maclean saw those patterns one day by chance and thought "that's funny".
So just as the tragedy was dependent upon "everything fitting together", so too is the path or route to discovery? The patterns are there but they must coincide with our observation for us to understand? Or is this just the way we tell the story of discovery, linearizing the complex web of our thought processes? How can we know what subconscious links are being made when we think "that's funny"? Or is the most important skill knowing when "that's funny" really is funny?
The scientist in the storytelling is Richard C. Rothermel, he of mathematical fire modelling fame. Maclean asks for Rothermel's help to use his mathematical models to plot the race between the young men and fire on the axes of distance and time. Maclean seems reasonably confident with results of the model - the numbers seem to fit with his qualitative understanding of the events. But he's not totally convinced by the numbers alone. Just as fire requires the triangle of heat, fuel and oxygen, the events and his understanding of them require story, history and science;
"We are beyond where arithmetic can explain what was happening in the piece of nature that had been the head of Mann Gulch ... Near the end of many tragedies it seems right that there should be moments when the story stops and looks back for something it left behind and finds it and finds it because of the things it learned, as it were, by having lived through the story."
Young Men and Fire is quintessentially 'Direction not Destination'. The route to discovery is important. The modelling is as important as the model. Hindsight is a wonderful thing because of contingency and history. But hindsight is also painful; it allows us to understand the tragedies that befell the young, who could not see it until it was upon them.
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Maclean, N (1992) Young Men and Fire Chicago: University of Chicago Press. ISBN: 0226500624
Two articles caught my eye today, (one in Science and the other in PNAS) both suggesting future climates are going to send the world up in flames (or drown it in seawater, or starve it of rainwater).
Westerling et al. find that changes in the timing of snowpack melt in the mountains of the western US, due to changes in climate, has led to an increased number of wildfires and a higher large-fire frequency across the period 1970 - 2003. They suggest this is due to a longer fire season (i.e. spring snow-melt is occuring earler in the year and the onset of winter freezing is occurring later) and that, generally, wildfire regimes at broad scales across this region are more senstive to climate change than human land-use histories.
I find this interesting for two reasons:
In work I've done with others we found that for a similar time period (1970 - 2000) and across similar broad space scales there was no significant change in the frequency-area distribution of wildfire through time (i.e. decadal scale). I'd like to extend on this empirical work and examine causal factors as Westerling et al. have
I plan to examine the influence of both land-use and climate change on wildfire regimes using the simulation model I'm currently working; it will be interesting to see which I find is more important...
Today I was also thinking about the importance of vegetation flammability on the frequency-area scaling of wildfires in a region. Which is most important;
total flammability of all vegetation in a region (related to broad scale climate)
distributed of risk between different vegetation species (composition of the landscape)
spatial distribution of risk across a landscape (configuration of the landscape)?
Something to examine with a CA model in the future maybe...
It seems many of the fires that have been burning in Spain recently have been caused by arson. Unfortunately this isn't uncommon in Spain (or the Med as a whole) where over 95% of fires are human-caused (indirectly or directly).
So, how do we account for this in any model of wildfire regimes? Regarding the location of ignition, is arson more likely near or far from other human activity? What is the frequency of arson ignitions in a region linked to? Economic conditions? High levels of land tenure fragmentation (and therefore more borders across which farmers might conflict)?
As always there's much work to do on these questions. In large part, accurate assessment of arson ignitions is likely to be dependent upon psychological understanding as much as anything else. For me, I'll concentrate on the potential influence of increased tourism in rural areas and the consequent 'accidental' ignitions.
In the midst of writing a PhD thesis crises of confidence are like those Routemaster buses I was talking about the other day (but on a different temporal scale); days and weeks without a worry and then a couple come along on the same day. Sometimes the end feels infintely far away. Today has been such a day.
However, I've found by writing down my specific aims and objectives and then reviewing my progress toward them I can calm myself down before any lasting damage is done. So here's what I wrote today:
Aim
Examine the impacts of human land use/cover change upon wildfire regimes in a Mediterranean landscape
Explore and evaluate novel methods to 'validate' simulation models (and processes of modelling) of environmental change considering human activity
impacts of change in land use/cover configuration (specifically fragmentation) on future wildfire regime (spread component)
impacts of change in vegetation (land cover) composition on future wildfire regime (spread and ignition risk components)
impacts of change in human population (size and 'type' of inhabitant) on future wildfire regime (ignition risk component)
To achieve aim ii):
Explore ways of using local stakeholder input to 'validate' (or assess the 'warrantability' of) the construction of the model (emphasis on the 'realism' of the model rather than dynamics
Discuss potential uses of narrative approaches to present processes of model construction and interpretation of results
Examine use of 'table of inductions' as proposed by W. Whewell
Think about discussion of potential of online tools for collaborative/participatory approaches to environmental modelling
is the frequency of fires with size 
, and 
is a constant. 
