Land management agencies frequently develop plans to identify future conservation needs and priorities. Creation and implementation of these plans is often required to maintain funding eligibility. Agency conservation plans are typically expert-based and identify large numbers of priority areas based primarily on biological data. As conservation dollars are limited, the challenge is to implement these plans in a manner that is effective, efficient, and considers future threats. Our goal was to improve the utility of existing, expert- and biologically-based plans using a flexible approach for incorporating spatial data on vulnerability to and threat from housing development. We examined two conservation plans for the state of Wisconsin in the United States and related them to current and projected future housing development, a key cause of habitat loss and degradation. Most (54-73%) priority areas were highly vulnerable to future threat, and 18% were already highly threatened by housing development. Existing conservation investments were highly threatened in 8-9% of priority areas, and 25-34% of priority areas were highly vulnerable and highly threatened, meriting immediate conservation attention. Conversely, low threat levels in 20-26% of priority areas may allow time for new, large-scale conservation initiatives to succeed. Our results highlight that vulnerability to and threat from existing and future housing development vary greatly among expert- and biologically-based priority areas. The framework presented here can thus improve the utility of existing plans by helping to target, schedule, and tailor actions to minimize biodiversity loss in highly threatened areas, maximize biodiversity gains, and protect existing conservation investments.
File: Carter_etal_LandscapeUrbanPlanning_2014.pdf
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Numerous measures of human influence on the environment exist, but one that is of particular importance is houses as they can impact the environment from species through the landscape level. Furthermore, because the addition of houses represents an important component of landscape change, housing information could be used to assess ecological responses (e.g., decline in wildlife habitat) to that change. Recently developed housing density data represents a potential source of information to assess landscape and habitat change over long periods of time and at broad spatial extents, which is critically needed for conservation and management. Considering the potential value of housing data, our goal was to demonstrate how changes in the number of houses leads to changes in the amount of habitat across the landscape, and in-turn, how these habitat changes are likely to influence the distribution and abundance for a species of conservation concern, the Ovenbird (Seiurus aurocapillus). Using a relationship between Ovenbird abundance and housing density, we predict suitable habitat in the forests of Massachusetts (USA) from 1970 to 2030. Over this 60-year period, the number of houses was projected to increase from 1.84 to 3.32 million. This magnitude of housing growth translates into a 57 % decline in Ovenbird habitat (6,002 km2 to 2,616 km2), a minimum decline of ~850,000 (48 %) Ovenbirds, and an increase in the number of subpopulations across the landscape. Overall, housing data provide important information to robustly measure landscape and habitat change, and hence predict population change of a species. We suggest that time series of housing data linked to ecological responses (e.g., Ovenbird abundance) offers a novel and underutilized approach to estimating long-term and spatially broad predictions of ecosystem response to landscape change, which in turn can inform conservation and management.
File: Lepczyk-et-al-2013-HousingDynamics.pdf
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Over the past 60 years, housing growth has outpaced population growth in the United States. Conservationists are concerned about the far-reaching environmental impacts of housing development, particularly in rural areas. We use clustering analysis to examine the pattern and distribution of housing development since 1940 in and around the Northern Forest, a heavily forested region with high amenity and recreation use in the Northeastern United States. We find that both proximity to urban areas and an abundance of natural amenities are associated with housing growth at the neighborhood level in this region. In the 1970s, counterurbanization led to higher rates of growth across rural areas. The Northern Forest now has extensive interface between forest vegetation and residential development, which has the potential to profoundly alter the ecological and social benefits of these forests.
File: Mockrin_etal_2013.pdf
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The wildland urban interface (WUI) delineates the areas where wildland fire hazard most directly impacts human communities and threatens lives and property, and where houses exert the strongest influence on the natural environment. Housing data are a major problem for WUI mapping. When housing data are zonal, the concept of a WUI neighborhood can be captured easily in a density measure, but variations in zone (census block) size and shape introduce bias. Other housing data are points, so zonal issues are avoided, but the neighborhood character of the WUI is lost if houses are evaluated individually. Our goal was to develop a consistent method to map the WUI that is able to determine where neighborhoods (or clusters of houses) exist, using just housing location and wildland fuel data. We used structure and vegetation maps and a moving window analysis, with various window sizes representing neighborhood sizes, to calculate the neighborhood density of both houses and wildland vegetation. Mapping four distinct areas (in WI, MI, CA and CO) the method resulted in amounts of WUI comparable to those of zonal mapping, but with greater precision. We conclude that this hybrid method is a useful alternative to zonal mapping from the neighborhood to the landscape scale, and results in maps that are better suited to operational fire management (e.g., fuels reduction) needs, while maintaining consistency with conceptual and U.S. policy-specific WUI definitions.
File: BarMassada_etal_2013_JEM.pdf
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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 species-distribution 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.
File: Bar-Massada-etal-IJWF-2013.pdf
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National-scale analyses of ?re occurrence are needed to prioritize ?re policy and management activities across the United States. However, the drivers of national-scale patterns of ?re occurrence are not well understood, and how the relative importance of human or biophysical factors varies across the country is unclear. Our research goal was to model the drivers of ?re occurrence within ecoregions across the conterminous United States. We used generalized linear models to compare the relative in?uence of human, vegetation, climate, and topographic variables on ?re occurrence in the United States, as measured by MODIS active ?re detections collected between 2000 and 2006. We constructed models for all ?res and for large ?res only and generated predictive maps to quantify ?re occurrence probabilities. Areas with high ?re occurrence probabilities were widespread in the Southeast, and localized in the Mountain West, particularly in southern California, Arizona, and New Mexico. Probabilities for large-?re occurrence were generally lower, but hot spots existed in the western and southcentral United States The probability of ?re occurrence is a critical component of ?re risk assessments, in addition to vegetation type, ?re behavior, and the values at risk. Many of the hot spots we identi?ed have extensive development in the wildland-urban interface and are near large metropolitan areas. Our results demonstrated that human variables were important predictors of both all ?res and large ?res and frequently exhibited nonlinear relationships. However, vegetation, climate, and topography were also signi?cant variables in most ecoregions. If recent housing growth trends and ?re occurrence patterns continue, these areas will continue to challenge policies and management efforts seeking to balance the risks generated by wild?res with the ecological bene?ts of ?re.
File: Hawbaker_etal_2013_EcoApps.pdf
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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.
File: Syphard_etal_2012_PLOS1.pdf
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Forests throughout the US are invaded by non-native invasive plants. Rural housing may contribute to non-native plant invasions by introducing plants via landscaping, and by creating habitat conditions favorable for invaders. The objective of this paper was to test the hypothesis that rural housing is a significant factor explaining the distribution of invasive non-native plants in temperate forests of the Midwestern U.S. In the Baraboo Hills, Wisconsin, we sampled 105 plots in forests interiors. We recorded richness and abundance of the most common invasive non-native plants and measured rural housing, human-caused landscape fragmentation (e.g. roads and forest edges), forest structure and topography. We used regression analysis to identify the variables more related to the distribution of non-native invasive plants (best subset and hierarchical partitioning analyses for richness and abundance and logistic regression for presence/absence of individual species). Housing variables had the strongest association with richness of non-native invasive plants along with distance to edge and elevation, while the number of houses in a 1 km buffer around each plot was the variable most strongly associated with abundance of non-native invasive plants. Rhamnus cathartica and Lonicera spp were most strongly associated with rural housing and fragmentation. Berberis thumbergii and Rosa multiflora were associated with gentle slopes and low elevation, while Alliaria petiolata was associated with higher cover of native vegetation and stands with no recent logging history. Housing development inside or adjacent to forests of high conservation value and the use of non-native invasive plants for landscaping should be discouraged.
File: Gavier_Pizarro_etal_LandEcology2010.pdf
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Understanding the factors related to invasive exotic species distributions at broad spatial scales has important theoretical and management implications, because biological invasions are detrimental to many ecosystem functions and processes. Housing development facilitates invasions by disturbing land cover, introducing nonnative landscaping plants, and facilitating dispersal of propagules along roads. To evaluate relationships between housing and the distribution of invasive exotic plants, we asked (1) how strongly is housing associated with the spatial distribution of invasive exotic plants compared to other anthropogenic and environmental factors; (2) what type of housing pattern is related to the richness of invasive exotic plants; and (3) do invasive plants represent ecological traits associated with speci?c housing patterns? Using two types of regression analysis (best subset analysis and hierarchical partitioning analysis), we found that invasive exotic plant richness was equally or more strongly related to housing variables than to other human (e.g., mean income and roads) and environmental (e.g., topography and forest cover) variables at the county level across New England. Richness of invasive exotic plants was positively related to area of wildland-urban interface (WUI), low-density residential areas, change in number of housing units between 1940 and 2000, mean income, plant productivity (NDVI), and altitudinal range and rainfall; it was negatively related to forest area and connectivity. Plant life history traits were not strongly related to housing patterns. We expect the number of invasive exotic plants to increase as a result of future housing growth and suggest that housing development be considered a primary factor in plans to manage and monitor invasive exotic
File: Gavier_Pizarro_etal_EcoApps2010.pdf
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Maps of the wildland- urban interface (WUI) are both policy tools and powerful visual images. Although the growing number of WUI maps serve similar purposes, this article indicates that WUI maps derived from the same data sets can differ in important ways related to their original intended application. We discuss the use of ancillary data in modifying census data to improve WUI maps and offer a cautionary note about this practice. A comparison of two WUI mapping approaches suggests that no single map is best because users' needs vary. The analysts who create maps are responsible for ensuring that users understand their purpose, data, and methods; map users are responsible for paying attention to these features and using each map accordingly. These considerations should apply to any analysis but are especially important to analyses of the WUI on which policy decisions will be made.
File: Stewart_2009_Forestry.pdf
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