James D. A. Millington

GIS analysis of spatial patterns of human-caused wildfire ignition risk in the SW of Madrid (Central Spain)

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.

Regression Techniques for Examining Land Use/Cover Change: A Case Study of a Mediterranean Landscape

In many areas of the northern Mediterranean Basin the abundance of forest and scrubland vegetation is increasing, commensurate with decreases in agricultural land use(s). Much of the land use/cover change (LUCC) in this region is associated with the marginalization of traditional agricultural practices due to ongoing socioeconomic shifts and subsequent ecological change. Regression-based models of LUCC have two purposes: (i) to aid explanation of the processes driving change and/or (ii) spatial projection of the changes themselves. The independent variables contained in the single ‘best’ regression model (that is, that which minimizes variation in the dependent variable) cannot be inferred as providing the strongest causal relationship with the dependent variable. Here, we examine the utility of hierarchical partitioning and multinomial regression models for, respectively, explanation and prediction of LUCC in EU Special Protection Area 56, ‘Encinares del río Alberche y Cofio’ (SPA 56) near Madrid, Spain. Hierarchical partitioning estimates the contribution of regression model variables, both independently and in conjunction with other variables in a model, to the total variance explained by that model and is a tool to isolate important causal variables. By using hierarchical partitioning we find that the combined effects of factors driving land cover transitions varies with land cover classification, with a coarser classification reducing explained variance in LUCC. We use multinomial logistic regression models solely for projecting change, finding that accuracies of maps produced vary by land cover classification and are influenced by differing spatial resolutions of socioeconomic and biophysical data. When examining LUCC in human-dominated landscapes such as those of the Mediterranean Basin, the availability and analysis of spatial data at scales that match causal processes is vital to the performance of the statistical modelling techniques used here.

Research on Coupled Human and Natural Systems (CHANS): Approach, Challenges, and Strategies

William J. McConnell, James D. A. Millington, Nicholas J. Reo, Marina Alberti, Heidi Asbjornsen, Lawrence A. Baker, Nicholas Brozov, Laurie E. Drinkwater, Scott A. Drzyzga, Jose, Fragoso, Daniel S. Holland, Claire A. Jantz, Timothy Kohler, Herbert D. G. Maschner, Michael Monticino, Guillermo Podesta, Robert Gilmore Pontius, Jr., Charles L. Redman, David Sailor, Gerald Urquhart, and Jianguo Liu. (2011). Research on Coupled Human and Natural Systems (CHANS): Approach, Challenges, and Strategies. Bulletin of the Ecological Society of America April: 218-228.

Modelling Mediterranean landscape succession-disturbance dynamics: A landscape fire-succession model

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. This 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 @b 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.

Agent-Based Models – Because They’re Worth It?

We address the question of when the relative complicatedness of spatial agent-based models (ABMs) compared to alternative modelling approaches can be justified. The spectrum of ABM types from simple, abstract models to complicated models aspiring to realism makes a single answer impossible. Therefore we focus on identifying circumstances where the advantages of ABMs outweigh the additional effort involved. We first recall the reasons for building any model: to simplify the phenomena at hand to improve understanding. Thus, the representational detail of ABMs may not always be desirable. We suggest that critical aspects of the phenomena of interest that help us to assess the likely usefulness of ABMs are the nature of the decisions which actors make, and how their decisions relate to the spatio-temporal grain and extent of the system. More specifically, the heterogeneity of the decision-making context of actors, the importance of interaction effects, and the overall size and organization of the system must be considered. We conclude by suggesting that there are good grounds based on our discussion for ABMs to become a widely used approach in understanding many spatial systems.

Participatory evaluation of agent-based land use models

A key issue facing contemporary agent-based land-use models (ABLUMs) is model evaluation. In this article, we outline some of the epistemological problems facing the evaluation of ABLUMs, including the definition of boundaries for modelling open systems. In light of these issues and given the characteristics of ABLUMs, participatory model evaluation by local stakeholders may be a preferable avenue to pursue. We present a case study of participatory model evaluation for an agent-based model designed to examine the impacts of land-use/cover change on wildfire regimes for a region of Spain. Although model output was endorsed by interviewees as credible, several alterations to model structure were suggested. Of broader interest, we found that some interviewees conflated model structure with scenario boundary conditions. If an interactive participatory modelling approach is not possible, an emphasis on ensuring that stakeholders understand the distinction between model structure and scenario boundary conditions will be particularly important.

Connecting Earth observation to high-throughput biodiversity data

Understandably, given the fast pace of biodiversity loss, there is much interest in using Earth observation technology to track biodiversity, ecosystem functions and ecosystem services. However, because most biodiversity is invisible to Earth observation, indicators based on Earth observation could be misleading and reduce the effectiveness of nature conservation and even unintentionally decrease conservation effort. We describe an approach that combines automated recording devices, high-throughput DNA sequencing and modern ecological modelling to extract much more of the information available in Earth observation data. This approach is achievable now, offering efficient and near-real-time monitoring of management impacts on biodiversity and its functions and services.

Models, data and mechanisms: quantifying wildfire regimes

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.

Simple or complicated agent-based models? A complicated issue

Agent-based models (ABMs) are increasingly recognized as valuable tools in modelling human-environmental systems, but challenges and critics remain. One pressing challenge in the era of Big Data and given the flexibility of representation afforded by ABMs, is identifying the appropriate level of complicatedness in model structure for representing and investigating complex real-world systems. In this paper, we differentiate the concepts of complexity (model behaviour) and complicatedness (model structure), and illustrate the non-linear relationship between them. We then systematically evaluate the trade-offs between simple (often theoretical) models and complicated (often empirically-grounded) models. We propose using pattern-oriented modelling, stepwise approaches, and modular design to guide modellers in reaching an appropriate level of model complicatedness. While ABMs should be constructed as simple as possible but as complicated as necessary to address the predefined research questions, we also warn modellers of the pitfalls and risks of building mid-level models mixing stylized and empirical components. We clarify the terms complexity and complicated in the context of ABM.We comprehensively discuss pros and cons of simple and complicated ABMs.We identify challenges and pitfalls for simple and complicated ABMs.We provide recommendations and good practices for dealing with complicatedness.

Mixed qualitative-simulation methods Understanding geography through thick and thin

Across geography there has been variable engagement with the use of simulation and agent-based modelling. We argue that agent-based simulation provides a complementary method to investigate geographical issues which need not be used in ways that are epistemologically different in kind from some other approaches in contemporary geography. We propose mixed qualitative-simulation methods that iterate back-and-forth between ‘thick’ (qualitative) and ‘thin’ (simulation) approaches and between the theory and data they produce. These mixed methods accept simulation modelling as process and practice; a way of using computers with concepts and data to ensure social theory remains embedded in day-to-day geographical thinking.