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Suburban v Rural Eastern Screech Owls in Texas: Nested earlier (urban heat island) Larger clutches (more food) More and larger fledglings (food and low predation) More recruits into population Higher fitness (McKinney 2002 BioScience 52:883-890) Controlling Sprawl • Sprawl is a strong driver of the urban footprint – results in loss, fragmentation, and degradation of habitat – increases energy use – increases pollution from commuting • Growth Management is needed to control it – Limits most future growth inside Urban Growth Boundary – May just displace the problem if regional planning is not incorporated (leapfrogging) Growth Management Act - King County Comprehensive Plan Subdivision Planning Clustered subdivision has • smaller lots • higher density of homes • majority of the site left as open space Gillham 2002 standard clustered Wildlife Conservation in Urban Areas 1. Preserve large areas of habitat the area, numbers, and connectivity of reserves should be maximized buffers should be maintained around reserves the amount of edge and degree of fragmentation within reserves should be minimized the scale of reserve planning should be expanded beyond the local area to include entire watersheds and bioregions (Marzluff and Ewing 2001) Wildlife Conservation in Urban Areas 2. Enhance habitat locally • Retain as much natural habitat as possible (especially new housing) • Plant native plants, fruitproducing exotics • Retain understory and snags • Minimize lawn cover Wildlife Conservation in Urban Areas 3. Provide essential resources: Places to breed (nest boxes, platforms, trees) Feeding stations (squirrels, birds) Water Cover (vegetation) 4. Provide protection from domestic predators • control dog and cat behavior Wildlife Conservation in Urban Areas 5. Reduce accidental mortality: • Birds crashing into windows • 3.5 million birds/yr • Birds hitting buildings, towers, etc. • 1.5 million birds/yr • Avoid planting fruit-bearing plants/trees next to highways • Clean bird feeders frequently (Salmonella) Wildlife Conservation in Urban Areas 6. Support urban planning initiatives and education • Clustered development • Growth management • Open space preservation Urban Ecology • Traditional view: ‘Natural’ ecosystems impacted by humans HUMANS Abiotic & Biotic Components Urban Ecosystems • Urban Ecology view (one version): Added layers: start with natural ecosystems (biophysical template) built (physical) systems social systems Built systems Abiotic & Biotic Social systems URBAN ECOLOGY • Brings together ecology of nature and ecology of humans in the urban environment. • City as a dynamic organism, composed of multiple systems that interact across different scales with varying intensities. • Requires interdisciplinary approach. Alberti & Marzluff (2005) Impacts of 2nd home development M. Kondo, R. Rivera & S.Rullman • Land conversion in exurban and rural areas has become the nation’s dominant mode of land development • In areas with particular key natural amenities, second homes may be a significant part of this land conversion • Much of the second home development occurs in areas that are ecologically sensitive and high in native biodiversity • Second homes create an increase in the number of households and may lead to more complex ecological consequences than simply the space they occupy Washington State Counties Spatial Analysis Results Common Themes in Both Case Study Counties: • Open space resources drive second home location (shorelines, public land) Unique Characteristics of Case Study Counties: • Okanogan – Small parcels more prone to second home development – Second home location driven by aesthetic rather than recreational preferences • San Juan – Ferry access less important to second home owners than primary – Steep slopes prone to second home development Interview Findings • Maintain strong ties to the metropolitan area • Escape & privacy strong drivers* • Yet “community” in area of second home also a strong driver for many • Design/build or landscape management opportunities • Seeking and maintaining an ideal image or “myth” -looking for unchanging and constant landscape -maintain the character of the area Greenspace, neighborhood walkability, and resident health: J. Tilt, T. Unfried & B. Rocca Walkable Destinations and NDVI NDVI Walkability Low High Low (0-2) Medium (3-5) High (6-12) 1 mile NDVI, BMI and Walkability 25.5 25 24.5 BMI Low NDVI High NDVI 24 23.5 23 Low (0-2) Medium (3-5) High (6-12) Walkability (Number of destination types within 0.4 miles) Low NDVI, High Walkability High NDVI, High Walkability Conclusions Destinations within walking distance from homes Walking Trips Vegetation BMI NDVI Subjective Greenness Using Predicted Land Cover Change to Predict Changes in Biodiversity in the Central Puget Sound, Washington, USA Jeffrey Hepinstall, Marina Alberti, John Marzluff University of Washington Integrated Conceptual Model of Coupled Natural-Human Systems Agents and Mechanisms Demographic, Markets, And Development Behaviors Land Use/ Land Cover Interactions Ecological Processes Computational Models UrbanSim Focal Unit Parcel Land Cover Change Model Pixel Bird Abundance / Species Richness Model Patch Predicting Landscape Change Land Cover Time 1 Land Cover Time 2 Predicted Land Cover Time 3 Explanatory Variables (n = 68) Multinomial Logit equations of Land cover transitions Observed Land Cover Time 3 Monte Carlo Simulations Pixel probabilities of land cover transition Native Forest Land Cover conversions to: -Clearcut forest -Low & Medium Intensity Urban Seattle Cascade Foothills Forest Functionality: A three-dimensional approach using bird richness, home values, and resident satisfaction Dave Oleyar* John Withey Andrew Bjorn Adrienne Greve Forests are Valued in many ways…. • Economic : extraction income, increased property values • Social : recreation and other direct uses, viewshed, psychological and physical health benefits • Ecological : biodiversity protection, wildlife habitat, ecosystem services Different Stakeholders Value Different Forest Functions • How do economic, social, and ecological functions interact with each other in an urbanizing area? Study area is King County, WA Urban Gradient Population Density (-0.817) Distance to nearest forest patch (-0.753) % Forest (0.871) Size of nearest forest patch (0.709) High Low URBAN GRADIENT SCORE • Linking results to common framework (gradient) • Examine relative tradeoffs among different functions Proportion of Maximum Function Value Integrating results 1.0 0.8 Home sales price effects (max = 6.6% premium) 0.6 Satisfaction with neighborhood (max = 1.6 adjusted factor score) Bird species richness (max = 16 species predicted) 0.4 0.2 0.0 -4 -3 -2 -1 0 Gradient Score 1 2 3 4 Integrating results • Identify areas of interest- divergence, convergence Proportion of Maximum Function Value ‘Urban’ ‘Suburban’ ‘Exurban’ 1.0 0.8 Home sales price effects (max = 6.6% premium) 0.6 Satisfaction with neighborhood (max = 1.6 adjusted factor score) Bird species richness (max = 16 species predicted) 0.4 0.2 0.0 -4 -3 -2 -1 0 Gradient Score 1 2 3 4 ‘Urban’ ‘Suburban’ A Locations and examples of on the ground locations of gradient segments. Proportion of Maximum Function Value ‘Exurban’ C B 1.0 0.8 0.6 0.4 0.2 0.0 -4 -3 -2 -1 0 Gradient Score 1 2 3 Wildlife Science Paper: Due next Thursday, 12 November 2009. Questions??? In case you are interested in learning more about current studies of wolf/elk dynamics, Dr. Scott Creel from Montana State Univ. is giving a talk today at 400 in the Biology Dept Seminar: Behavioral, Ecological, Physiological and Demographic Responses of Elk to Wolves Location: Physics-Astronomy A102 See the below link for more details : http://www.biology.washington.edu/index.html?navID=34&q tr=aut