Variation in Spatial Predictions Among Species Distribution Modeling Methods

<p>Prediction maps produced by species distribution models (SDMs) influence decision-making in resource management or designation of land in conservation planning. Many studies have compared the prediction accuracy of different SDM modeling methods, but few have quantified the similarity among prediction maps. There has also been little systematic exploration of how the relative importance of different predictor variables varies among model types. Our objective was to expand the evaluation of SDM performance for 45 plant species in southern California to better understand how map predictions vary among model types, and to explain what factors may affect spatial correspondence, including the selection and relative importance of different environmental variables. Four types of models were tested. Correlation among maps was highest between generalized linear models (GLMs) and generalized additive models (GAMs) and lowest between classification trees and GAMs or GLMs. Correlation between Random Forests (RFs) and GAMs was the same as between RFs and classification trees. Spatial correspondence among maps was influenced the most by model prediction accuracy (AUC) and species prevalence; map correspondence was highest when accuracy was high and prevalence was intermediate. Species functional type and the selection of climate variables also influenced map correspondence. For most (but not all) species, climate variables were more important than terrain or soil in predicting their distributions. Environmental variable selection varied according to modeling method, but the largest differences were between RFs and GLMs or GAMs. Although prediction accuracy was equal for GLMs, GAMs, and RFs, the differences in spatial predictions suggest that it may be important to evaluate the results of more than one model to estimate a range of spatial uncertainty before making planning decisions based on map outputs. This may be particularly important if models have low accuracy or if species prevalence is not intermediate.</p>

Characterization and mapping of dwelling types for forest fire prevention

Définition des habitats isolés, diffus et groupés. Méthode de caractérisation et de cartographie de ces types d'habitat dans le contexte de prévention du risque d'incendie. Mise en relation des types d'habitat avec le risque d'incendie. / In a context of forest fire risk , the development of actions concerning wildfire prevention and land management is necessary and essential particularly in wildland urban interfaces (WUI). The term WUI' always includes components such as human presence and wildland vegetation. Both the hazard (probability of fire outbreak, distribution) and the vulnerability of urban areas can be characterized through the spatial organization of houses and vegetation. The first required step is to characterize and map WUI in large areas and at a large scale, which in turn requires qualifying different types of dwellings and mapping them. With this goal in view, the paper presents a brief synthesis of results coming from an exploratory process for the characterization of dwelling types (Lampin et al., 2007), and develops a method based on GIS-geo treatments to characterize different types of dwelling with regard to fire risk. Three types of dwellings were classified: isolated dwellings, scattered dwellings and clustered dwellings, using criteria based on the distance between houses, the size of clusters of houses and housing density, which can be mapped automatically. Within dwelling types, the density value of forest fire ignition changed and was twice as high for isolated dwellings as for clustered dwellings. The spatial organization of dwellings seems to have a real impact on fire occurrence. Thus maps of different dwelling types can be interpreted for use in developing fire fighting strategies or prevention actions concerning end-users such as forest and land planning managers or fire-fighters

A raster-based GIS analysis of the cumulative impacts of humans and beaver on wetland area and types in the Chickahominy River watershed (Virginia, USA) from 1953 to 1994

Despite increased recognition of wetland functions and values, wetland loss and degradation continues in the United States. Digital wetlands and uplands coverages were analyzed to compare the cumulative impacts of humans and beaver (Castor canadensis) on wetland types in the Chickahominy River watershed (Virginia, USA) from 1953 to 1994. A vector-based approach was used for data manipulation, and a raster-based approach was chosen to analyze geographic change over time. Study findings indicated that anthropogenic activities were responsible for both wetland loss and gain in the watershed, and beavers substantially influenced shifting between wetland types. Wetland area increased 4% over 41 years

The importance of geography in forecasting future fire patterns under climate change.

An increasing amount of Californias landscape has burned in wildfires in recent decades, in conjunction with increasing temperatures and vapor pressure deficit due to climate change. As the wildland-urban interface expands, more people are exposed to and harmed by these extensive wildfires, which are also eroding the resilience of terrestrial ecosystems. With future wildfire activity expected to increase, there is an urgent demand for solutions that sustain healthy ecosystems and wildfire-resilient human communities. Those who manage disaster response, landscapes, and biodiversity rely on mapped projections of how fire activity may respond to climate change and other human factors. California wildfire is complex, however, and climate-fire relationships vary across the state. Given known geographical variability in drivers of fire activity, we asked whether the geographical extent of fire models used to create these projections may alter the interpretation of predictions. We compared models of fire occurrence spanning the entire state of California to models developed for individual ecoregions and then projected end-of-century future fire patterns under climate change scenarios. We trained a Maximum Entropy model with fire records and hydroclimatological variables from recent decades (1981 to 2010) as well as topographic and human infrastructure predictors. Results showed substantial variation in predictors of fire probability and mapped future projections of fire depending upon geographical extents of model boundaries. Only the ecoregion models, accounting for the unique patterns of vegetation, climate, and human infrastructure, projected an increase in fire in most forested regions of the state, congruent with predictions from other studies

Housing Arrangement and Location Determine the Likelihood of Housing Loss Due to Wildfire

Surging wildfires across the globe are contributing to escalating residential losses and have major social, economic, and ecological consequences. The highest losses in the U.S. occur in southern California, where nearly 1000 homes per year have been destroyed by wildfires since 2000. Wildfire risk reduction efforts focus primarily on fuel reduction and, to a lesser degree, on house characteristics and homeowner responsibility. However, the extent to which land use planning could alleviate wildfire risk has been largely missing from the debate despite large numbers of homes being placed in the most hazardous parts of the landscape. Our goal was to examine how housing location and arrangement affects the likelihood that a home will be lost when a wildfire occurs. We developed an extensive geographic dataset of structure locations, including more than 5500 structures that were destroyed or damaged by wildfire since 2001, and identified the main contributors to property loss in two extensive, fire-prone regions in southern California. The arrangement and location of structures strongly affected their susceptibility to wildfire, with property loss most likely at low to intermediate structure densities and in areas with a history of frequent fire. Rates of structure loss were higher when structures were surrounded by wildland vegetation, but were generally higher in herbaceous fuel types than in higher fuel-volume woody types. Empirically based maps developed using housing pattern and location performed better in distinguishing hazardous from non-hazardous areas than maps based on fuel distribution. The strong importance of housing arrangement and location indicate that land use planning may be a critical tool for reducing fire risk, but it will require reliable delineations of the most hazardous locations

Microclimate and modeled fire behavior differ between adjacent forest types in northern Portugal

Fire severity varies with forest composition and structure, reflecting micrometeorology and the fuel complex, but their respective influences are difficult to untangle from observation alone. We quantify the differences in fire weather between different forest types and the resulting differences in modeled fire behavior. Collection of in-stand weather data proceeded during two summer periods in three adjacent stands in northern Portugal, respectively Pinus pinaster (PP), Betula alba (BA), and Chamaecyparis lawsoniana (CL). Air temperature, relative humidity and wind speed varied respectively as CL < PP < BA, PP < CL < BA, and CL < BA < PP. Differences between PP and the other types were greatest during the warmest and driest hours of the day in a sequence of 10 days with high fire danger. Estimates of daytime moisture content of fine dead fuels and fire behavior characteristics for this period, respectively, from Behave and BehavePlus, indicate a CL < BA < PP gradient in fire potential. High stand density in CL and BA ensured lower wind speed and higher fuel moisture content than in PP, limiting the likelihood of an extreme fire environment. However, regression tree analysis revealed that the fire behavior distinction between the three forest types was primarily a function of the surface fuel complex, and more so during extreme fire weather conditionsinfo:eu-repo/semantics/publishedVersio

Wildfire ignition-distribution modelling: a comparative study in the Huron-Manistee National Forest, Michigan, USA

Abstract. Wildfire ignition distribution models are powerful tools for predicting the probability of ignitions across broad areas, and identifying their drivers. Several approaches have been used for ignition-distribution modelling, yet the performance of different model types has not been compared. This is unfortunate, given that conceptually similar speciesdistribution models exhibit pronounced differences among model types. Therefore, our goal was to compare the predictive performance, variable importance and the spatial patterns of predicted ignition-probabilities of three ignition-distribution model types: one parametric, statistical model (Generalised Linear Models, GLM) and two machine-learning algorithms (Random Forests and Maximum Entropy, Maxent). We parameterised the models using 16 years of ignitions data and environmental data for the Huron-Manistee National Forest in Michigan, USA. Random Forests and Maxent had slightly better prediction accuracies than did GLM, but model fit was similar for all three. Variables related to human population and development were the best predictors of wildfire ignition locations in all models (although variable rankings differed slightly), along with elevation. However, despite similar model performance and variables, the map of ignition probabilities generated by Maxent was markedly different from those of the two other models. We thus suggest that when accurate predictions are desired, the outcomes of different model types should be compared, or alternatively combined, to produce ensemble predictions