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Canyon Springs DEIR Public Comment #56 (Butler)
1 RAY BUTLER www.rwbutler338@att.net March 6, 2013 Ms. Denyelle Nishimori Town of Truckee Community Development Department 10183 Truckee Airport Rd. Truckee, CA 96161 Via electronic mail this date RE: Comments on Biological Resources Chapter, Canyon Springs Draft Environmental Impact Report (DEIR) Dear Ms. Nishimori: Thank you for the opportunity to comment. The following comments are entirely my own and do not represent the official policy or position of Nevada County, CA. Although I currently hold an appointed position on the County’s Fish and Wildlife Commission, and have for 25 years, my positions is advisory only to the Board of Supervisors. Only the Board of Supervisors can address, decide, and communicate policy matters for the County of Nevada. This review is restricted to Chapter 4.4, Biological Resources and the associated Appendix E, Mule Deer Reports and References. FINDINGS 1. The Biological Resources chapter does not meet new State standards adopted on September 2010 (as required by SB 85, 2007 Session) for describing vegetation types. Details can be found at www.dfg.ca.gov/biogeodata/vegcamp. 2. The basic references used in Chapter 4.4 and Appendix E, including Mayer and Laudenslayer (1988), Sawyer and Todd Keeler-Wolf (1995), Holland (1986), and Barbour and Major (1988), have been significantly revised and published, and more importantly, are integrated into the new State standard. The correct citations for these works are found in the references listed at the end of this letter. 3. Editing and quality control in the document is egregious. Four plant species are incorrectly classified at the genus level (pp. 19 – 26). With the Jepson eFlora website available to all, there is simply no excuse for an error of this magnitude. There is no Martis Creek National Recreation Area (p. 30). Site visit dates for biological surveys are inconsistent between Appendix reports. 4. Other frequently cited references, including those of Jones and Stokes Associates, Albert Beck, PhD, and many of the “personal communications” references (especially 2 Finn), are no longer applicable, outdated and in most cases, are inconsistent with more recent findings in Heal (2011) and CDFG/NDOW Update (2010). 5. Chapter 4.4 and Appendix E do not use Best Available Science (BAS). Standard statistical tests that biologists and ecologists use to determine significance or non- significance are not incorporated. 6. Mitigation proposals offered at Chap. 4.4, p. 48 and elsewhere for re-vegetation and restoration plantings are likely to have poor success in habitats like Canyon Springs (Alpert and Loik 2013). 7. Mitigation measures for wetlands at Bio-3, (Chap. 4.4, p. 55), are not supported by current, peer-reviewed research findings. The less than significant determination is incorrect. Specifically, mitigation ratios offered of 1:1 and 2:1 are insufficient to replace wetland losses and function according to Moilanen et al (2009) and Norton (2008). Mitigation banking and compensatory, payment-in-lieu programs are equally problematic (see Kihslinger 2008). Especially troubling is the poor success record of wetland mitigation measures based on actual, post project assessments (see Minkin and Ladd 2003 and Kihslinger 2008). 8. Methods to control or limit impacts from dogs (Chap. 4.4. p. 49) are completely without merit, as proven by a rigorous scientific study by Lenth et al (2008). Furthermore, the less than significant finding is in error. At this same page the preparer specifies dog control measures, signage, lighting standards, etc. and then makes the statement that “mitigation measures are not required” How is the reader, Town staff, Planning Commissioners, and public supposed to interpret this glaring inconsistency? ISSUES THAT MUST BE ADDRESSED IN THE DEIR 1. The DEIR needs to address the impacts of invasive species on the existing natural communities. Land disturbance is the key cause of exotic invasions and a number of ruderal weeds are already established in the Glenshire area. This is an issue of national priority (U.S. Congress, 1993), as well as being a critical issue for the Placer and Nevada County Agricultural Commissioners, Tahoe National Forest, US Fish and Wildlife Service, Truckee River Watershed Council, California Department of Fish and Wildlife, county Resource and Conservation Districts, US Bureau of Reclamation, US Army Corps of Engineers, Caltrans and for many other agencies and NGOs operating in the Truckee area. A minimal review would include identification and impact assessments of invasive organisms on: a) wildlife habitats and species, b) wildland fire threats and frequency, and c) an inventory of Glenshire area species that could invade new disturbances. 2. The DEIR needs to assess the impacts to native wildlife caused by subsidized predators attracted to the project area by anthropogenic influences. Of special concern are ravens (Fisher, 2013), coyotes and foxes, and black bears (see CDFG/NDOW Update, p 28 (2010). 3 3. Clarification is needed with regard to the exact, defensible space required by Truckee Fire Protection District and CalFire for structures (fences, outbuildings, residences, etc.). The clearance footprints need to be mapped and included in the DEIS so reviewers are able to assess how much land is being disturbed. The Town, as lead agency under CEQA, has the responsibility to insure this information is included. DISCUSSION – WITH A FOCUS ON MULE DEER IMPACTS. Appendix E is titled Mule Deer Report and References and is 168 pages in length. Close examination shows that less than one third of the pages relate to mule deer. Much of that one third consists of verbatim repetition from previous reports. The same statement applies to many of the references. In other words, little new information or new analyses have been provided. 1. Heal (2011) provides new data on deer gathered from remote camera stations at Canyon Springs. Table 1 covers fall 2010 observations collected from October 13th to December 16th. Table 2 covers spring 2011 observations collected from May 4th to July 6th. The data reflects one fall migration and one spring migration. My concerns about the applicability of the data to determine significance or non-significance are: a) The sample size is limited, covering only one fall and one spring movement. This limits the reliability of any conclusions because the data are simply a snapshot and do not include multiple samples in different years. b) The data collected using remote camera techniques cannot be directly compared with data from human observations and sign counts (i.e., tracks and pellets), techniques used by previous surveyors. Specifically, the camera counted 151 deer for fall and spring periods. Tahoe Boca Estates (2004) gives no numbers but refers to a previous study by Eco-Analysts (1990) which states “one or two resident deer use the site during late spring and summer months” (p. 28, Tahoe Boca Estates 2004). Foothill 2008 (p. 14) states, “six to 12 migratory deer do move through the property”. RMT 2009 (p. 3-5) states, “Four mule deer were observed during the 2009 site survey”. No explanation is given to reconcile the differences. c) In Heal (2011), there are apparent important differences deer classified as “unknown” with regard to gender and age class when Table 1(Fall) and Table 2 (Spring) are compared. Unknowns in Table 2 represent 68% of the total deer photographed. Unknowns in Table 1 composed slightly over 3% of the total. Please provide an explanation. 2. In the past, project surveyors have differed in defining migration corridor versus movement corridor and seemed to intimate that a certain number of deer are required to qualify for a corridor label – but no numbers were ever given. In Heal (2011, p. 3-6), deer behaviors like foraging and traveling in groups of < 4 leaves the reader thinking that these can’t be migrating animals. Jones and Stokes Associates (1987) report the direction of deer tracks and conclude they are from resident deer rather than migrants. All of the 4 reports seem to view migration happening within a brief time frame where deer move from point A to point B without delay. In other words, 26 years of deer studies in this area by consultants have produced limited data that can’t be quantified, is internally inconsistent, and does not use best available science. . CANYON SPRINGS FAILED TO USE BEST AVAILABLE SCIENCE Significant advancements in scientific knowledge have been made since Canyon Springs has been proposed. While every consultant claims to have made literature searches they obviously have not. Sawyer and Kauffmann (2011) found that individual mule deer migrate in concert with plant phenology to maximize intake rather than speed. Sawyer et al. (2009) describe and provide model examples and statistical tests for identifying and prioritizing migration corridors. Another significant deficiency is the failure to discuss new discoveries that molecular biologists have made in mule deer genetics. Pease et al. (2009) has especially relevant findings. DNA testing of 587 samples shows that Rocky Mountain Mule Deer – the mule deer that compose the Loyalton-Truckee Herd – is a confirmed subspecies. This was an unexpected result as the original hypothesis formulated that since mule deer are highly mobile, have a broad distribution across the landscape, and share overlapping ranges with other deer species, that high rates of hybridization would occur. The exact opposite was found. In comparison, Bradley et al. (2003) research in Texas showed hybridization rates as high as 24% between mule deer and white-tailed deer. Although interesting comparative findings, the Pease et al explanation of this California/Nevada uniqueness has direct application for the Verdi sub-unit. Clear connections in the genetic clusters of the 5 California subspecies with specific environmental and ecological factors were found. The authors attribute this to timing of reproduction and peak vegetation as well as habitat choice reflecting natal origin as reasons for the persistence of genetic subdivision. Simply put, the transfer of genetic material is restricted to breeding on winter range and the Verdi subunit has very specific winter ranges. CONCLUSIONS The Biological Resources Chapter does not meet CEQA standards. The determinations the document makes on non-significance or potentially significant, are wholly not supported by in the material presented. In my opinion, the Canyon Springs project will have highly significant, negative impacts on local and regional biological resources. The mitigation measures offered are ineffectual. The DEIR is one of the poorest quality and least professional documents I have reviewed in 30 years. FINAL WORDS Attached to my transmittal letter for these comments are two documents that need to be required reading for Town staff, Planning Commissioners and others that will have responsibilities in deliberating outcomes for Canyon Springs: 1) the CDFG/NDOW Update and 2) David Theobald’s work on the landscape pattern of exurban growth. I trust 5 you will make them available as required. They are easy to read and understand. Most of the journal articles in my reference are available in PDF format if requested. Thank you for the opportunity to comment. s/ Ray Butler REFERENCES Alpert, H., Loik, M. E. 2013. Pinus jeffreyi establishment along a forest-shrub ecotone in eastern California, USA. Journal of Arid Environments 90, 12 – 21. Barbour, M. (Editor), Keller-Wolf, T. (Editor), Schoenherr, Allen A. (Editor) 2007. Terrestrial Vegetation of California, 3rd Edition. University of California Press, Berkeley. Bradley, R. D., Bryant, F. C., Bradley, L. C., Haynie, M. L., Baker, R. J. 2003. Implications of hybridization between white-tailed deer and mule deer. The Southwestern Naturalist, 48(4): 654-660. California Department of Fish and Wildlife, Nevada Department of Wildlife. 2010. Interstate Deer Project, Loyalton-Truckee Deer Herd Report and Management Plan Update (Habitat Sections Only). Fisher, F. February 13, 2013. Raven populations continue to grow at Lake Tahoe. Sierra Sun, Tahoe City. Kihslinger, R. L. 2008. Success of wetland mitigation projects. National Wetlands Newsletter 30, 14 – 15. Lenth, B. E., Knight, R. L., Brennan, M. E. 2008. The effects of dogs on wildlife communities. Natural Areas Journal 28: 218-237. Minkin, P., Ladd, R. 2003. Success of Corps-Related Wetland Mitigation in New England. U.S. Army Corps of Engineers, New England District. Moilanen, A., Astrid, J. A., Ben-Haim, Y., Ferrier, S. 2008. How Much Compensation is Enough? A Framework for Incorporating Uncertainty and Time Discounting When Calculating Offset Ratios for Impacted Habitat. Restoration Ecology 17: 470 – 478. Norton, D. A. 2008. Biodiversity Offsets: Two New Zealand Case Studies and Assessment Framework. Environmental Management DOI 10.1007/s00267-008-9192-5. Pease, K. M., Freedman, A. H., Pollinger, J. P., McCormack, J. E., Buermann, W., Rodzen, J., Banks, J., Meredith, E., Bleich, V. C., Schaeffer, R. J., Jones, K., Wayne, R. K. 2009. Landscape genetics of California mule deer (Odocoileus hemionus): the roles of ecological and historical factors in generating differentiation. Molecular Ecology 18: 1848 -1862. 6 Sawyer, J. O., Keeler-Wolf, T., Evens, J. M. 2008. A Manual of California Vegetation, 2nd Edition. The California Native Plant Society Press, Sacramento. Sawyer, H., Kauffmann, M. J., Nielson, R. M., Horn, J. S. 2009. Identifying and prioritizing migration routes for landscape conservation. Ecological Applications 19: 2016 – 2025. Sawyer, H., Kauffmann, M. J. 2011. Stopover ecology of a migratory ungulate. Journal of Animal Ecology 80: 1078 – 1087. Theobald, D. M. 2005. Landscape Patterns of Exurban Growth in the USA from 1980 to 2020. Ecology and Science 10 (1): 32. [online] URL: http://www.ecologyandsociaty.org/vol10/iss1/art32/ U. S. Congress, Office of Technology Assessment. 1993. Harmful Non-indigenous Species in the United States, OTA-F-565. Washington, D. C. 1 INTERSTATE DEER PROJECT Loyalton-Truckee Deer Herd Report and Management Plan Update (Habitat Sections Only) 2010 Table of Contents Introduction …………………………………………………………………………. 2 Land Ownership …………………………………………………………..….……. 3 Vegetation/Land Cover ………………………………………….………….…...… 5 Grazing …………………………………………………………………………..…. 7 Fire History ………………………………………………………………….…….… 8 Seasonal Ranges …………………………………………………………….……. 10 Human Population Change ……………………………………………………..… 14 Exurban Growth ………………………….………………………………………… 15 Land Use Planning …..………………………………………………………..…… 18 Telemetry Studies ……………………………………………….…………..…...... 19 Resident versus Migratory Deer …………………………………………………. 28 Summary …………………………………………………………………….……… 29 Literature Cited …………………………………………………………..……….… 31 2 Introduction In April of 2009 the Interstate Deer Herd Committee of California and Nevada met to establish 2009 tag allocations for the interstate deer herds, and to discuss a project to identify areas of concern within interstate deer herds. The purpose of this project is to produce habitat related information to guide interactions with land management agencies, planning commissions, etc., in regards to mule deer, and possibly other species (sage grouse, antelope). Attending this meeting were California Department of Fish and Game (CDFG) staff from the Deer Management Program in Sacramento: Craig Stowers, Mary Sommer, and David Casady; CDFG Regional biologists: Richard Callas, Terri Weist, Sara Holm, and Tim Taylor: and Nevada Department of Wildlife (NDOW) biologists Mike Cox, Jason Salisbury, Chris Hampson, and Carl Lackey. The discussions about the project revolved around the need to document and quantify what habitat we have now as compared to what we used to have, and identifying the most obvious threats on summer and winter range. This project was initiated in 2007, but little work was accomplished due to work loads and other agency priorities. To get the project (now called the Interstate Deer Project) going, Mary Sommer was designated as the lead. The long term goal of the Interstate Project is to investigate all the interstate deer herds, however the Loyalton-Truckee Deer Herd was chosen as a pilot project due to the belief that development, especially in the Nevada portion of the herd range and in the Truckee, California area, and other issues have led to a critical situation in this herd. These concerns are shared by biologists from CDFG and NDOW. The Loyalton-Truckee deer herd is an interstate herd with winter ranges in both California and Nevada, and summer ranges in California. This herd comprises the bulk of California’s deer zones X7a and X7b, two highly sought after deer hunting areas. A secondary goal of this document is to update habitat related sections in the 1982 Loyalton-Truckee Deer Herd Management Plan. The plan contains information about the herd and its environment that was current in 1982, but much has changed in the years since its writing. While the main elements of vegetation, grazing, fire, seasonal ranges, and land ownership have remained of interest, the details and importance of each has changed over time. Other topics such as human population change, exurban growth, land use, and results of telemetry studies have been added to this report to supplement the original topics. The following sections are intended to accomplish two things: 1. Describe various habitat related issues and their progression over the past 20-30 years. 2. Update sections of the original Loyalton-Truckee Deer Herd Plan relating to habitat and migration patterns of the deer herd. 3 Land Ownership The states of California and Nevada share the land that comprises the Loyalton- Truckee Deer Herd, with approximately 77% within California and 23% Nevada. In California the vast majority is owned by the US Forest Service (50%) and private landowners (44%). Nevada’s portion of the Loyalton-Truckee Deer Herd range consists primarily of Private (38%), BLM (32%), and US Forest Service (29%). Land ownership is shown in Table 1 and the map in Figure 1. CALIFORNIA OWNERSHIP ACRES PERCENTAGE Bureau of Land Management 17,027.76 2.71% CA Dept. of Fish and Game 16,896.64 2.69% CA Dept. of Parks and Rec 3,164.17 0.50% Department of Defense 47.43 0.01% CA State Lands Commission 996.29 0.16% Private 277,164.7244.05% USDA Forest Service 313,902.2749.89% 629,199.27 100.00% NEVADA OWNERSHIP Bureau of Land Management 61,274.2632.23% Department of Defense 1,732.33 0.91% Forest Service 54,773.4628.81% Private 72,298.1538.03% Regional Park 37.75 0.02% 190,115.95 100.00% CA & NV COMBINED Private 349,462.8742.65% USDA Forest Service 368,675.7245.00% Bureau of Land Management 78,302.03 9.56% Department of Defense 1,779.75 0.22% Regional Park (Nevada) 37.75 0.00% CA Dept. of Fish and Game 16,896.64 2.06% CA Dept. of Parks and Rec 3,164.17 0.39% CA State Lands Commission 996.29 0.12% 819,315.21 100.00% 76.80%CA 23.20%NV Table 1. Land Ownership within the Loyalton-Truckee Deer Herd Boundary Data sources California: Public and Conservation Lands, California Resources Agency Legacy Project, data relevant up to 2003. Nevada: Land Status Nevada, U.S. Dept. of the Interior – BLM - Nevada State Office – Mapping Sciences, data relevant for 1998-2007. 4 Figure 1. Land ownership of the Loyalton-Truckee Deer Herd. 5 Vegetation/Land Cover The general land cover types that characterize the habitat of the Loyalton- Truckee Deer Herd are listed in Table 2 below, and illustrated in Figure 2. The shrub/scrub classification is the most extensive, covering approximately 47% of the range mostly in the northeastern section, and dominating the Nevada portion. The next most common land cover type is the Evergreen Forest (36%), occurring in the south and central part of the range, as well as all along the western border. The Herbaceous type occupies approximately 10% of the herd range, mostly in the area of Sierra Valley. Land Cover for the Loyalton-Truckee Deer Herd LANDCOVER TYPEACRESPERCENTAGE Open Water 6,941.97 0.84% Developed, Open Space 10,593.53 1.29% Developed, Low Intensity 12,827.38 1.56% Developed, Medium Intensity 4,183.34 0.51% Developed, High Intensity 980.73 0.12% Barren Land 3,541.36 0.43% Deciduous Forest 106.07 0.01% Evergreen Forest 298,325.42 36.31% Shrub/Scrub 385,249.04 46.88% Herbaceous 85,728.00 10.43% Hay/Pasture 6,070.80 0.74% Cultivated Crops 1,490.63 0.18% Emergent Herbaceous Wetlands 5,679.43 0.69% 821,717.70 100.00% Developed 28,584.99 3.48% Agriculture (Hay/Pasture/Crops)7,561.42 0.92% Data Source: Multi-Resolution Land Characteristics Consortium (MRLC) 2001 National Land Cover Database Source data 2001 remote sensing imagery Publication_Date: 20030901 Table 2. Vegetation/land cover types of the Loyalton-Truckee Deer Herd 6 Figure 2. Land Cover types within the Loyalton-Truckee Deer Herd boundary. 7 Grazing When the 1982 Loyalton-Truckee Deer Herd Plan was written, livestock grazing on USFS and BLM grazing allotments had improved from a historical high level of overuse to a more moderate level of grazing. Since that time the numbers of livestock grazed has continued to decrease within the Loyalton-Truckee Deer Herd boundary. According to Roberta Lim, East Zone Range Management Specialist for the Tahoe National Forest, this is due in large part to the implementation of the National Environmental Policy Act (NEPA) in 1970, and the resulting assessment of the effects of grazing on Federal lands. Standards that were set at NEPA’s inception have been modified over the years and are continually changing, with the trend being more stringent grazing standards. In the 1990’s the health of each individual grazing allotment was evaluated to see if standards were being met, and action was taken to ensure compliance to the latest grazing requirements (personal communication, 4 June 2010). The majority of grazing allotments within the Loyalton-Truckee Deer Herd area lie within the Tahoe National Forest. Reports generated by Roberta Lim, East Zone Range Management Specialist, Tahoe National Forest, 4 June 2010, show that there are currently 14 active allotments for a total of 7,849 AUM’s. The 1982 Loyalton-Truckee Deer Herd Plan lists 24 active allotments for the Tahoe National Forest with 14,257 AUM’s. This represents a substantial reduction in grazing over a large portion of the Loyalton-Truckee Deer Herd range. Of the grazing allotments on the Toiyabe National Forest that were listed in the 1982 plan, it has been confirmed by Courtney Priess, Range Management Specialist, Humboldt-Toiyabe National Forest, that all are currently in vacant status and most have not been grazed since the 1990’s. Two other allotments that used to be BLM allotments (Peavine Watershed S&G and Peavine/Blacksprings S&G) have been permanently closed to grazing (personal communication, 3 June 2010). BLM grazing allotments also show a decline from a total of 16,294 AUM’s (from the 1982 Loyalton-Truckee Deer Herd Plan) to a current number of 13,095 AUM’s (Katrina Leavitt, BLM Carson City District, personal communication, June 2010). While grazing trends on private property are largely unknown, the decrease in competition from livestock grazing on public lands is most likely of benefit to the Loyalton-Truckee Deer Herd. 8 Fire History Despite active fire suppression efforts, fire is a common occurrence on the landscape of the Loyalton-Truckee Deer Herd. The majority of the winter range is composed of sagebrush and bitterbrush communities that are vital to deer populations. The East Side Pine community is an intermediate habitat type that includes conifers with sagebrush and mountain mahogany understory. Deer use this habitat type primarily in the summer. While a cool/light fire can rejuvenate vegetation, particularly in the conifer forest, fire on the shrub dominated winter range tends to burn hot and destroy habitat that recovers slowly at best. Figure 3 shows the locations of fires that burned in the years 1980-2008. Cheatgrass invasion Fire in sagebrush plant communities not only destroys brush forage species that deer depend on, but also opens the way for invasive plants such as cheatgrass (Bromus tectorum) to become established. Cheatgrass is an exceptionally competitive annual grass due to its early germination in the fall and winter, well developed root system for water uptake, abundant seed production, and extended seed dormancy. This grass takes over after fire and outcompetes brush and other grasses. Cheatgrass also provides a fine textured, early maturing fuel that increases the incidence of fire (deVos et al. 2003). Cheatgrass has typically become established following fires in the sagebrush dominated plant communities of the Loyalton-Truckee Deer Herd range. A relatively small portion of the Hallelujah Junction Wildlife Area burned in the Chilcoot fire of 2003, leaving an area of pure cheatgrass (Figure 4). In 2007 the Balls Canyon fire burned a much larger area of HJWLA, destroying 4,400 acres of prime deer habitat. This fire prompted extensive re-vegetation efforts, but it will still be decades before the range has anywhere near the value to deer that it did historically. Cheatgrass appears to be a relatively new addition to the landscape of the Loyalton-Truckee Deer Herd, as there is no mention of it in the 1982 Deer Herd Plan but it is now considered a serious invasive plant problem. 9 Figure 3. Fire perimeters recorded for 1980 – 2008. Data sources: Fire Perimeters (fire08_2), frap.cdf.ca.gov; Nevada Fire History, USDOI BLM Nevada State Office Geographic Sciences (NV_firehistory). 10 Figure 4. Photo of Hallelujah Junction Wildlife Area showing cheatgrass invasion after fire. Fire area is on the left, unburned area on the right. Seasonal Ranges The 1982 Loyalton-Truckee Deer Herd Plan lists 3 well defined, geographically separate winter ranges used (5,000 – 6,000 ft. elevation) All of these winter ranges are dominated by bitterbrush-sagebrush habitat types: 1) Verdi Basin – 5 key ranges: 1)Sunrise basin, 2)Guest Ranch (Donner Trails), 3)Peavine Mtn, 4)Garson, 5)Belli 2) Sierra Valley 3) South Petersen Mtn, including Sand Hills Verdi Basin The Verdi Basin is an important wintering area for deer in the southern portion of this deer herd. The Verdi Basin winter ranges are located mostly in Nevada, and have been impacted extensively by development. 11 Sara Holm, CDFG Associate Wildlife Biologist describes impacts to these key areas: Garson Road is the present day Cabela's exit and runs mostly parallel to Hwy 80. The area is built up but deer are seen there. Beli Ranch is now a series of ranchettes on the south side of Hwy 80 but it does abut the open land as you go up the hill towards the Mt. Rose Wilderness Area. There is lots of use on the hillside but the ability for deer to get water from the Truckee River is very impacted with all the homes, streets and activity along the river. Peavine Mountain is mostly what you see to the north of Hwy 80 and is what we fly for comp counts. We see a lot of deer there but considering it also runs down into Somerset, which is a huge development, it has been highly impacted. This is also the site of the Verdi fire and a lot of habitat was wiped out from that too (personal communication, 14 September 2010). Mike Cox of NDOW estimates the percent loss of winter range in Nevada compared to historic in the following: Development has destroyed 40% (most critical because it was the lowest elevation and key during the killing winters), and severely comprised another 10%, with wildfires destroying 30% and another 10% having severely limited value due to older fires with ever-so-slight vegetation recovery (causing only a handful of deer to survive in it vs. several hundred), leaving only 10% intact winter range left (that could be generous). Some historic deer migrations most likely were all the way into Reno wrapping around to the southeast, but much of that is severed by development (personal communication, 14 September 2010). According to Carl Lackey of NDOW, the herd used to migrate all the way into the Truckee Meadows and east of Reno into the Virginia Range (personal communication, 17 September 2010). The 1982 Loyalton-Truckee Deer Herd Plan mentions that the construction of Hwy 80 in the 1960’s created a barrier to intermingling deer populations on the north and south sides of the Truckee River. There have been no deer collaring projects on the Verdi Basin deer since the 1982 Deer Herd Plan, and no movement studies since the 1970’s. However, a study was initiated in the fall of 2009, which is designed to answer questions regarding the movements of the deer that use the Verdi Basin. Preliminary data show that at least two of the collared does cross Highway 80 to travel between their summer and winter ranges. For more information on this study, see “Telemetry Studies” in this report. The Truckee River Wildlife Area is a complex of CDFG owned units totaling 3,880 acres in Nevada, Placer and Sierra counties, approximately 2 to 7 miles east of Truckee. While these units are located along the Truckee River and were mainly acquired for fisheries values, some provide valuable habitat for deer. The Canyon Unit and Union Ice Unit are the largest and most useful to deer. Recent collaring data show the Union Ice Unit to be a summer concentration and fawning area. 12 Sierra Valley There are two CDFG owned Wildlife Areas that provide important winter habitat to the deer in the Sierra Valley area. These are the Antelope Valley Wildlife Area and the Smithneck Creek Wildlife Area, both located in Sierra County near Loyalton, south of Highway 49. These Wildlife Areas were designated after the 1982 Loyalton-Truckee Deer Herd Plan was written. The Antelope Valley Wildlife Area is 5,616 acres largely covered by sagebrush interspersed with rabbit brush and bitterbrush at the lower elevations. The upper slopes are populated by Jeffrey pine, juniper, mountain mahogany, and chaparral plants. It is considered prime deer winter range, and was acquired to preserve critical deer winter range and migration corridors from development. The wildlife area is considered by sportsmen and the Department as a premier hunting area in California (California Department of Fish and Game, 2008). The Smithneck Creek Wildlife Area consists of 1,395 acres of a variety of habitats typical of the east side of the Sierra. The sagebrush-bitterbrush habitat is a critical deer winter-range area for migratory deer. Limited stands of yellow pine, mountain mahogany and juniper provide additional habitat for resident deer. Wet and dry meadows are found along Bear Valley Creek. Riparian habitat consisting of alders, willows and aspen provide cover along Bear Valley, Smithneck and Badenaugh Creeks. South Petersen Mountain The 1982 Loyalton-Truckee Deer Herd Plan lists South Petersen Mountain in Nevada as a winter range area, although it also stated that during mild winters deer may not cross 395 into Nevada, and instead winter in the Balls Canyon, Evans Canyon, and Coulee Canyon of Sierra County, California. At that time the NDOW fall composition counts in November and December showed large numbers of deer on top of Petersen Mountain. Chris Hampson, the NDOW biologist for the Petersen Mountain area, describes current conditions: “We have a much smaller resident herd now than what we had back in the early 80's. Continued human disturbance and encroachment, plus numerous wildfires have really impacted the Petersen's and surrounding areas. I do see mule deer in the fall but it certainly would not be large numbers by any means, and numbers are generally pretty low as far as the resident herd. Habitat changes on the ground along with the warmer/drier climate have hurt most deer herds in western Nevada.” Chris also points out that fire has been prevalent on the Petersen Mountains, with close to 10,000 acres lost in 2009. Some of the area that burned had been burned previously, but some good unburned habitat was also lost. There is “some important winter range on the SW corner still intact that is keeping some deer alive through the winter and some on 13 the Northern 1/3 of the range as well. Most in between has burned” (personal communication, 26 September 2010). The Hallelujah Junction Wildlife Area is owned and managed by the CDFG, and is an important locale for wintering deer. This Wildlife Area covers 13,394 acres, is located in Lassen and Sierra Counties, and includes part of Balls Canyon and Evans Canyon. The habitat is a mosaic of sagebrush scrub, bitterbrush, Juniper woodlands, wet meadows and wetland habitats. The primary purpose of this land acquisition was the preservation of critical deer winter range and migration corridors from development (California Department of Fish and Game, 2009). This property was acquired after the 1982 Loyalton-Truckee Deer Herd Plan was written. Intermediate and Summer Ranges According to the 1982 Loyalton-Truckee Deer Herd Plan, intermediate and summer ranges cover 67% of the total range, of which 47% is publicly owned. Elevations range between 6,000 and 9,000 feet, and are typically dominated by sagebrush and Jeffrey pine vegetation types. Primary forage species are perennial grasses, green leaf manzanita, sagebrush, bitterbrush, and various species of Ceanothus (California Department of Fish and Game, 1982). While the area of intermediate and summer range is larger than that of the winter range, the quality of much of the habitat is thought to be degraded to a point where all summer range is important and can be considered essential to this deer herd. Patches of relatively rare habitat types such as meadows and aspen are critical as they are often used in summer for fawning. There have been land acquisitions and conservation easements within the summer range negotiated by the Truckee Donner Land Trust, a nonprofit organization that works to preserve and protect important historic, recreational, and scenic open spaces in the greater Truckee Donner region. To date, the Truckee Donner Land Trust has protected 16,296 acres, including 2,000 acres surrounding Independence Lake and 983 acres in Perrazo Meadows. In summary, while many habitats have been severely degraded by development and fire, there have been steps taken to preserve blocks of important deer range. 14 Human Population Change Growth of the human population is an important factor to consider due to the need for resources that an ever-growing population requires. Impacts from various aspects of human population growth, from residential development to recreational use, can influence wildlife populations. Human encroachment on deer habitat can impact habitat suitability in three ways: displacing deer through habitat occupation, reducing habitat suitability by altering the physical characteristics of that habitat, and displacing deer through disturbance, such as noise and activity (Sommer et al. 2007). Deer are displaced when their habitat is occupied by the construction of buildings, roads and other related development, or habitat is converted to another use such as agriculture. With these changes may come additional concerns to deer such as fences, livestock, and dogs. Increased roads can limit access to important habitats and increase mortality by vehicle collisions. Habitat suitability may be decreased when the physical characteristics of that habitat are altered. Unregulated off-highway vehicle (OHV) use can alter habitat characteristics through destruction of vegetation, soil compaction, and increased erosion. Excessive livestock grazing may alter habitat suitability by removing forage and cover species that deer rely on. Other land uses such as mining, energy developments, and landfills can alter habitat suitability by changing vegetation composition and new road installation. Deer are also displaced through disturbance, such as noise and activity. The U.S. Forest Service estimated that OHV use increased 7-fold during a recent 20 year period (Wisdom et al. 2005). Hiking, mountain biking, and ATV use are examples of other disturbances that are common on deer ranges. Recreational use, especially on public lands, occurs primarily in critical summer months, during fawning and lactation periods. Recreational use continues to grow as human populations expand. Table 3 shows the human population change by decade in each county that is part of the Loyalton-Truckee Deer Herd area. While much of the growth in California counties has occurred outside the boundaries of the deer herd (closer to the Sacramento area), local growth such as in the Truckee area and that of Washoe County near Reno has expanded into important habitats of the Loyalton- Truckee Deer Herd. 15 California 20001990198019701960 Lassen County33,82827,59821,66116,79613,597 Nevada County92,03378,51051,64526,34620,911 Placer County248,399172,796117,24777,63256,998 Plumas County20,82419,73917,34011,70711,620 Sierra County3,5553,3183,0732,3652,247 Nevada Washoe County339,486254,667193,623121,06884,743 California 1990 to 20001980 to 19901970 to 1980Total 30 year change Lassen County22.6027.4029.0079.00 Nevada County17.2052.0096.00165.20 Placer County43.8047.4051.00142.20 Plumas County5.5013.8048.1067.40 Sierra County7.108.0029.9045.00 Nevada Washoe County33.3031.5059.90124.70 Source: U.S. Census Bureau, Census 2000 Percent Change of Population HUMAN POPULATION CHANGE FOR COUNTIES OF THE LOYALTON-TRUCKEE DEER HERD Population Table 3. Census data comparison by County. Exurban Growth Residential development beyond the urban fringe, sometimes called exurban sprawl or rural residential development, has resulted in extensive and widespread changes to the landscape across the United States. Theobald, 2005, describes this trend: “the general notion of urban sprawl is that the spatial spread of development proceeds at a greater rate than population growth, resulting in dispersed, low-density development”. As undeveloped rural areas are converted to exurban or possibly urban/suburban land use, natural resource values rapidly diminish. Theobald’s work has shown that nationwide, exurban land use occupies five to ten times more area than urban and suburban densities, and has been growing at a rate of about 10–15% per year, which exceeds the rate of urban development. These exurban areas are often located adjacent to or nearby protected lands, which may expose these lands to growth related impacts. 16 Theobald has produced a nationwide, fine-grained database of historical, current, and forecasted housing density, which can be used to quantify changes in growth patterns to infer possible ecological effects (Theobald, 2005). This database was used to quantify habitat altered by development on the privately owned land within the Loyalton-Truckee Deer Herd from 1960 to 2000 (Table 4). Within the deer herd boundary, undeveloped private land has decreased from 73% in 1960 to 46% in 2000. This represents a loss or conversion of 90,986 acres (26%) of undeveloped private land. This acreage has been redistributed among the other three classes shown in the table below. The greatest increase by percentage however, is in the exurban/urban/built-up classification which includes development of up to 10 acres per housing unit, plus commercial, industrial, and transportation. This is the most intensive type of development of the classes listed, and is the most detrimental to the deer herd. CLASS 2000 Percentage 1980 Percentage 1960 Percentage Undeveloped private 46%55%73% 40 acres and above per unit 32%28%21% 10 - 40 acres per unit 6%9%3% Exurban/urban & Urban/built-up 15%8%3% Exurban/urban/built-up = Up to 10 acres per housing unit, plus commercial, industrial, and transportation. Table 4. Percentages of development classes for selected years on privately owned land of the Loyalton-Truckee Deer Herd. The extent and growth of exurban sprawl within the Loyalton-Truckee Deer Herd area is striking. Figure 5 maps the spatial distribution of the changes in development class that have occurred from 1960 to the year 2000. In addition, the model used by Theobald to forecast future development shows an increasing trend in all development for the Loyalton-Truckee Deer Herd in 2010 (Table 5). Roads and sprawling neighborhoods are replacing and altering deer habitat, putting the survival and reproduction of portions of this deer herd at risk. Habitats have shrunk, fragmented, and in some cases disappeared altogether. Extensive and widespread land-use changes have occurred and are likely to continue. Class Name 2010 Acreage Percentage Undeveloped private Rural 1 111,961 32% 40 acres and above per unit Rural 1 142,691 41% 10 - 40 acres per unit Rural 2 15,177 4% Exurban/urban & Urban/built-up Exurban/urban/built-up 80,030 23% TOTAL 349,859 100% Table 5. Forcasted pattern of development classes for 2010. 17 Figure 5. Levels of development on privately owned land within the boundaries of the Loyalton-Truckee Deer Herd from 1960 to 2010. Blank areas (in white) are public lands. 18 Land Use Planning The California Environmental Quality Act (CEQA) is a statute passed in 1970 that requires California state and local agencies to follow a protocol of analysis and public disclosure of the potential environmental impacts of development projects. Because CEQA makes environmental protection a mandatory part of every California state and local agency's decision making process, it has been somewhat effective in protecting the environment from some development issues. Nevada, unfortunately, does not have the same type of environmental protection. Each county within the Loyalton-Truckee Deer Herd boundary was contacted in an effort to obtain GIS data that could be used to map general plans and/or zoning for the herd area. Due to differences in classification systems between counties, and the absence of current GIS data for some areas, mapping proved to be problematic. However the information collected provides an overview of the trends in land use planning for each county. Following is a summarization of that information. The portion of the Loyalton-Truckee Deer Herd that lies within Washoe County Nevada is heavily impacted by development. Virtually all private property within that area is either under development or there are plans for it in the future. The land within the city limits of Reno is no exception. The City of Reno Master Plan Land Use shows the vast majority of property within the city to be slated for development incompatible with deer use. Plumas County in the northwest corner of the herd area has been zoned for various development levels up to 160 acre lots. Most of the area is zoned for lots of 20 – 160 acres, with some smaller areas zoned for lots less than 20 acres. To the east of Plumas County is a small area of Lassen County within the herd area. The best data available shows this to all be assigned lot size of less than 20 acres, however the reliability of the data are unknown. The Sierra County portion of the deer herd is comprised primarily of public lands and open space, with smaller localized areas zoned for 20 acres or less around Loyalton, Sierraville, Sattley, Calpine, and just west of Verdi. Nevada County has a good amount of public land and open space north of Truckee, however within the city limits of Truckee zoning is all parcels of 20 acres and less, and to the east and west of the city limits are areas of planned development. Placer County in the southern end of this deer herd includes the north and west shore of Lake Tahoe. The Lake Tahoe area, the southern half of Martis Valley, and several ski resorts along highways 89 and 267 comprise the bulk of the development in this county. USFS land is interspersed with private property which is zoned mostly for 20-160 acre parcels, with localized areas of smaller than 20 acre parcels. 19 Telemetry Studies There have been 3 deer telemetry collaring studies conducted on the Loyalton- Truckee Deer Herd since the writing of the 1982 Herd Plan; one in 1992-94, another in 2002-05, and the latest beginning in 2006 and is ongoing. CDFG Associate Wildlife Biologist Syd Kahre conducted the 1992-94 study in an effort to define migration corridors and seasonal use areas for the deer herd. 25 deer were captured at the Hallelujah Junction Wildlife Area and fitted with VHS collars. Aerial surveys were used to track the collared deer, and locations were recorded using GPS. The resulting location information is shown in Figure 6. Jim Lidberg followed Syd Kahre as the next CDFG Associate Wildlife Biologist that worked with the Loyalton-Truckee Deer Herd. The collaring effort Jim conducted during 2002-2005 was a joint effort between the CDFG Sacramento Valley and Central Sierra Region (Region 2) and the Wildlife Programs Branch. The purpose of the study was three fold: 1) provide “markers” for locating individuals in the herd prior to conducting helicopter composition counts in December and March of each year; 2) collect data on distribution of the deer herd at various times of the year; and 3) determine habitat use by the herd. Deer were captured by a net gun fired from a helicopter and by herding deer with the helicopter into linear drive-nets. Deer were captured and fitted with VHS collars made by Telonics, which have a design life of up to 4 years. In 2002 there were 29 deer with collars, in 2003 there were 50, in 2004 there were 42, and in 2005 there were 18 deer with collars. Data were collected on flights conducted regularly over the period of the study. Location information collected for all collars is illustrated in Figure 7. 20 Figure 6. Deer locations from 1992-94 telemetry study. Locations were recorded by GPS during aerial flights. 21 Figure 7. Deer locations from 2002-05 telemetry study. 22 CDFG Associate Wildlife Biologist Sara Holm was the next biologist to study the Loyalton-Truckee Deer Herd with the Hwy 89 Stewardship Team telemetry study, initiated in 2006. This project is an ongoing effort by the Highway 89 Stewardship Team to identify crossing, migration corridor, summer and winter range boundary, and fawning areas. Through a series of grants the Team has completed the first of several anticipated mitigation underpass structures on Hwy 89 between Truckee and Sierraville, and will complete fencing to ensure safe passage across the highway for mule deer in the Loyalton-Truckee Deer Herd as well as other wildlife species. The overall 20-year plan for the Team includes research, mitigation and outreach. The collaring of 15 deer each year has helped to identify priority areas along the highway for crossing structures. The movement seen by these deer will impact land acquisition choices, habitat connectivity and restoration, tag quotas for the draw zones and interstate decisions about the herd. For this project deer were captured on the Hallelujah Junction Wildlife Area, the Antelope Valley Wildlife Area, and a small number on summer ranges, beginning in 2006. Deer were captured by darting from the ground, and immobilized using the drugs telazole and zylazine. All but two of the collared deer were does, and each deer was fitted with a GPS collar. Collars were set to collect data every hour for a month and a half during migration (November and May) and once a day the rest of the year. Deer locations from 2006 through January of 2010 are shown in Figure 8. Using the location data collected in this study, seasonal use areas were delineated using Hawths Tools in ArcGIS. First the fixed kernel density estimator was used to calculate a grid of kernel density, and then 95% volume contours were created for summer and winter use areas. The 95% volume contour contain on average 95% of the points that were used to generate the kernel density estimate. Figures 9 and 10 illustrate summer and winter use areas for the portion of the herd covered by this study. 23 Figure 8. Locations collected by GPS collars 24 Figure 9. Summer contours – 95% of collar locations in July, August, and September occurred within the areas in red. 25 Figure 10. Winter contours – 95% of collar locations in January, February, and March occurred within the areas in blue. 26 Analysis of the telemetry data from 2006 – January 2010 also revealed information regarding the number of deer that did and did not migrate. In 2006, 1 of the 4 collared deer did not migrate (25%), and stayed at Antelope Valley Wildlife Area (AVWLA) near Palen Reservoir. This doe was captured and collared at AVWLA on 5/10/06. All locations recorded for this doe were within approximately 550 meters of each other. In 2007, 4 of the 7 collared deer did not migrate (57%), and all stayed at AVWLA where they were captured and collared. The 3 deer that migrated were captured at locations other than AVWLA. The 4 deer from AVWLA stayed within a 4-5 mile area. In 2008, 2 of the 10 collared deer did not migrate (20%), and stayed at Hallelujah Junction WLA and the private land just south of the WLA, all within 8 miles. Records show both were captured on 3/28/08 at EHJ Guz1 (East Hallelujah Junction, Guzzler 1?). In 2009, all 10 collared deer migrated. These data show that some deer do not migrate in the spring, and remain on what we have considered winter range all year. The data also show that for this sample of collared animals, all that did not migrate stayed in the area where they were captured. Verdi sub-unit The 1982 Loyalton-Truckee Deer Herd Plan describes two sub-units of the herd, the Sierra Valley sub-unit in the north and the Verdi sub-unit in the south. The studies mentioned so far in this report focused on the Sierra Valley sub-unit. A supplemental collaring project was initiated in October of 2009, which intends to track the movements of deer in the Verdi sub-unit. Specific movement related issues will be analyzed such as: 1) how the two sub-units interact, if at all; 2) how much deer movement occurs across hwy 80: and 3) How much movement into Nevada occurs. Initially the captures were conducted by CDFG wildlife staff on the Truckee Wildlife Area, plus a small number captured in the Glenshire area. NDOW has added at least 6 collared deer that were captured by helicopter. Figure 11 illustrates location information collected by 5 satellite collars as of summer 2010. These preliminary data show that the collared deer summer and give birth to their fawns along the Truckee River east of the town of Truckee, and use the Truckee River Wildlife Area extensively. In winter they migrate approximately 10 miles north and east to areas near Verdi. Two of the does cross Hwy 80 to move back and forth between their summer and winter ranges. Further data will be needed to determine where these deer cross the freeway, and where their specific migration routes are. Fall migration occurred in late November through December, and spring movements occurred in May for these collared does. 27 Figure 11. Location information from 5 satellite collared does. 28 Resident versus Migratory Deer There are both migratory and resident deer within the Loyalton-Truckee deer herd, with the resident population generally occupying portions of the winter ranges. Varying proportions of migratory and resident deer within a herd have been observed by researchers studying seasonal movements of deer (Stephenson et al., 2009, Kufeld et al., 1989; Loft et al., 1984). There are a variety of factors involved in determining if migration is advantageous. Migration typically provides access to habitats of higher quality which in turn results in increased nutrition, and often better resting and escape cover. Deer under these conditions potentially would be in better condition, leading to healthier fawns and increased reproductive success (Nicholson et al., 1997). However the possible drawbacks involved with migration may include increased predation, increased energetic costs, and disruption to migration corridors by human disturbance and barriers such as roads, residential development, and recreational use. For migration to be beneficial the costs of making the migration to and from the summer range must be outweighed by the gains associated with using that range. This balance often changes over time, as is illustrated by the following description of the Round Valley Deer study. Deer that winter in Round Valley (Mono and Inyo Counties) have undergone a substantial change in the proportion of deer that migrate over the past 20-25 years. Some of these deer travel over the Sierra Crest to summer on the west slope of the Sierra Nevada range, and the rest remain on the east side all year long. The west side summer ranges are much more mesic and forested compared to the sagebrush dominated habitats on the east side. In 1987 it was determined that >85% of the mule deer wintering in Round Valley migrated to occupy summer ranges west of the Sierra Crest, however by 2009 the proportion of does occupying summer range on either side of the Sierras had shifted to approximately 50:50 (Stephenson et al., 2009). In a long term study of Round Valley deer spanning 1997 – 2009, it was found that does summering on the east side of the Sierras have significantly higher fawn recruitment than the does summering on the west side. The same study revealed that causes of fawn mortality differ between the east and west side, with a large percentage (67%) of fawn mortality due to bear predation on the west side, and only 8% by bears on the east side. The main mortality factor on the east side was coyote predation (25%). In this instance, increased predation on the west side outweighed the benefits of the better habitat conditions, resulting in fewer fawns recruited to the population. The report points out that while bear control on the west side may result in improved fawn recruitment, the increased number of deer on the winter range would only serve to exacerbate the effects of an already forage limited winter range (Stephenson et al., 2009). 29 Each deer population has its own unique set of factors that influence migration. In some areas resident deer appear to be on the increase, and have the potential to severely impact winter range that is already overstocked with deer. During harsh winters the range may not be able to support all deer present, leading to high levels of winter mortality. Increased densities of malnourished deer also provide an environment that invites the spread of disease. Meanwhile summer range may be under-utilized by deer. Lack of deer in the forest in summer and fall is recognized by hunters as well as those that value deer for their intrinsic significance. Summary The Loyalton-Truckee Deer Herd has experienced various changes since the writing of the 1982 deer herd plan, not all of which have been detrimental to deer. Land acquisitions targeting deer habitat have conserved and protected prime deer range. Of particular note are three areas that are used extensively by this deer herd; the Hallelujah Junction and Antelope Valley Wildlife Areas in the north and the Truckee River Wildlife Area in the south. In addition, decreased livestock grazing on US Forest Service and Bureau of Land Management property may be of benefit to the deer herd. Nevertheless, issues remain that have significant negative consequences to this deer herd. The 1982 Loyalton-Truckee Deer Herd Plan described a severe decline in deer numbers during the late 1960’s and early 1970’s, and cited “a combination of factors including, but not limited to, loss of habitat through human encroachment, significant mortality on highways and railroads, reduced habitat productivity resulting from natural vegetational changes, and harassment caused by greatly increased human recreational use.” These issues still exist today, although in some cases in a slightly modified form. Fire continues to be an issue, especially in brush dominated habitats which are vital to the Loyalton-Truckee Deer Herd. A relatively new complication in the fire regime is the introduction of the invasive annual cheatgrass, which often takes over after fire and out-competes native vegetation. Once established, cheatgrass is prone to burning, decreasing the time between fires and preventing establishment of shrub species. Mortality due to highways and railroads may have decreased as the size of the deer herd has declined, however it is still a significant problem in certain areas. The Highway 89 Stewardship Project is addressing this problem in a particularly lethal stretch of Highway 89 that lies across the herd’s migration route in Sierra County. Climate has always been of concern in the context of too little or too much precipitation, and/or excessively cold winter conditions. Current views are also recognizing the issue of climate change and its possible affects on wildlife. For 30 the Loyalton-Truckee Deer Herd, there is speculation that migration into Nevada is decreasing due to warmer, drier winters. It is believed that the more severe the winter, the farther into Nevada these deer travel to reach suitable winter habitat. Habitat changes resulting from residential development and recreational use are currently the biggest concern for the future of this deer herd. Approximately 43% of the land supporting the Loyalton-Truckee Deer Herd is privately owned. A significant issue impacting this herd today involves changes in land use on private land. While changes due to development are most visible around the Reno, Nevada and Truckee, California areas, there are many areas subject to less obvious changes that nonetheless impact deer. The concept of exurban growth (rural residential development) is relatively new, and this type of development has been expanding even more quickly than human population growth. It is fortunate that there exists a large amount of US Forest Service owned land located along the main migration route to and from seasonal ranges in the northern portion of the area. This will help prevent habitat fragmentation, such is occurring in the southern part of the range, and allow migration of a major portion of this deer herd. The Verdi sub-unit of the herd appears to be in trouble, and the future of these migratory deer is not as hopeful. While there are numerous concerns regarding the health of the Loyalton-Truckee Deer Herd, there is significant work being done to ensure the long term viability of the herd. Telemetry studies are essential to expand our knowledge regarding current migration routes and seasonal use areas. Identification of areas used by deer on both public and private property will help to focus conservation efforts efficiently to support this deer herd. Within the Habitat Element of the 1982 Loyalton-Truckee Deer Herd plan the main objective is to “improve fawning success and summer range habitat capacity through habitat alteration and improvement. Protect critical winter ranges from further encroachment due to human activities; improve the capacity of winter habitats wherever possible.” These objectives are still valid, and should continue to guide acquisition and habitat improvement projects. Specific recommendations will need to be coordinated with agency biologists for the project areas. 31 Literature Cited California Department of Fish and Game. 2009. Hallelujah Junction Wildlife Area Land Management Plan. California Department of Fish and Game, Sacramento, USA. California Department of Fish and Game. 2008. Antelope Valley and Smithneck Creek Wildlife Areas Final Land Management Plan, California Department of Fish and Game, Sacramento, USA. California Department of Fish and Game. 1982. Loyalton-Truckee Deer Herd Plan. California Department of Fish and Game, Sacramento, USA. deVos, Jr. J. C, M. R. Conover, and N. E. Headrick. 2003. Mule Deer Conservation: Issues and management Strategies. Berryman Institute Press, Utah State University, Logan, USA. Kufeld, R. C., D. C. Bowden, and D. L. Schrupp. 1989. Distribution and movements of female mule deer in the Rocky Mountain foothills. The Journal of Wildlife Management, 53:871-877. Loft, E. R., J. W. Menke, and T. S. Burton. 1984. Seasonal movements and summer habitats of female black-tailed deer. The Journal of Wildlife Management, 48:1317-1325. Nicholson, M. C., R. T. Bowyer and J. G. Kie. 1997. Habitat Selection and Survival of Mule Deer: Tradeoffs Associated with Migration. Journal of Mammalogy, Vol. 78, No. 2. (May, 1997), pp. 483-504. Sommer, M. L., R. L. Barboza, R. A. Botta, E. B. Kleinfelter, M. E. Schauss and J. R. Thompson. 2007. Habitat Guidelines for Mule Deer: California Woodland Chaparral Ecoregion. Mule Deer Working Group, Western Association of Fish and Wildlife Agencies. Stephenson, T. R. and K. L. Monteith. 2009. Form 872 Post-Project Evaluation Report for Project #608.08 Population Dynamics of an Eastern Sierra Deer Herd, and Assessment of Impacts Associated with Development. Deer Herd Management Plan Implementation Program, California Department of Fish and Game. Theobald, D. 2005. Landscape patterns of exurban growth in the USA from 1980 to 2020. Ecology and Society 10(1): 32. [online] URL: http://www.ecologyandsociety.org/vol10/iss1/art32/ Wisdom, M. J., A. A. Ager, H. K. Preisler, N. J. Cimon, and B. K. Johnson. 2005. Effects of off-road recreation on mule deer and elk. Pages 67-80 in M. J. 32 Wisdom, technical editor. The Starkey Project: a synthesis of long-term studies of elk and mule deer. Reprinted from 2004 Transactions of the North American Wildlife and Natural Resources Conference, Alliance Communications Group, Lawrence, Kansas, USA. Copyright © 2005 by the author(s). Published here under license by the Resilience Alliance. Theobald, D. 2005. Landscape patterns of exurban growth in the USA from 1980 to 2020. Ecology and Society 10(1): 32. [online] URL:http://www.ecologyandsociety.org/vol10/iss1/art32/ Research Landscape Patterns of Exurban Growth in the USA from 1980 to 2020 David M. Theobald1 ABSTRACT. In the United States, citizens, policy makers, and natural resource managers alike have become concerned about urban sprawl, both locally and nationally. Most assessments of sprawl, or undesired growth patterns, have focused on quantifying land-use changes in urban and metropolitan areas. It is critical for ecologists to examine and improve understanding of land-use changes beyond the urban fringe—also called exurban sprawl—because of the extensive and widespread changes that are occurring, and which often are located adjacent to or nearby “protected” lands. The primary goal of this paper is to describe the development of a nationwide, fine-grained database of historical, current, and forecasted housing density, which enables these changes to be quantified as a foundation for inference of possible ecological effects. Forecasted patterns were generated by the Spatially Explicit Regional Growth Model, which relates historical growth patterns with accessibility to urban and protected lands. Secondary goals are to report briefly on the status and trend of exurban land-use changes across the U.S., and to introduce a landscape sprawl metric that captures patterns of land-use change. In 2000, there were 125 729 km2 in urban and suburban (<0.68 ha per unit) residential housing density nationwide (coterminous USA), but there were slightly over seven times that (917 090 km2) in exurban housing density (0.68–16.18 ha per unit). The developed footprint has grown from 10.1% to 13.3% (1980 to 2000), roughly at a rate of 1.60% per year. This rate of land development outpaced the population growth rate (1.18% per year) by 25%. Based on model forecasts, urban and suburban housing densities will expand to 2.2% by 2020, whereas exurban development will expand to 14.3%. Key Words: cross-scale edge; exurban sprawl; forecast model; landscape sprawl metric; land-use change; resilience INTRODUCTION Urban sprawl—or undesirable land-use patterns— and its general effects have been discussed by a variety of researchers, decision makers, and concerned interest groups (Alig and Healy 1987, Ewing 1994, Bank of America 1996, Sierra Club 1998, Benfield et al. 1999, Katz and Liu 2000, Waldie 2000, Ewing et al. 2005). A recent special feature of Ecology and Society directly addressed the interface of urban sprawl and ecology, and its primary outcome was the conclusion that, central to the integration of ecology and society, is better understanding of the consequences of land-use changes on natural resources and ecological processes (Ricketts and Imhoff 2003). Research is needed to improve understanding of the patterns, rates, and ecological effects of urban sprawl, but another type of land-use change is occurring that has important ecological implications as well. Here, I argue that it is critical for ecologists to examine and improve understanding of land-use changes beyond the urban fringe—also called “exurban sprawl” or rural residential development —because of the extensive and widespread changes that are occurring, and because they often are located adjacent to or nearby “protected” lands meant to conserve natural resources and biodiversity. Daniels (1999) defines rural sprawl as low-density residential development scattered outside of suburbs and cities, and as commercial 1Colorado State University Ecology and Society 10(1): 32 http://www.ecologyandsociety.org/vol10/iss1/art32/ strip development along roads outside cites. Nationwide, exurban land use occupies five to ten times more area than urban and suburban densities, and has been growing at a rate of about 10–15% per year (Theobald 2000, Theobald 2001a), which exceeds the rate of urban development (Natural Resources Conservation Service (NRCS) 2001). Many researchers have examined land-use change and sprawl, but they typically focus on urban systems. One frequently used quantitative definition of urban sprawl is a decline in population density over time (El Nasser and Overberg 2001, Firestone 2001). For example, Rusk (1997) found that, for 213 urbanized areas, the population grew by 47% between 1960 and 1990, whereas urbanized land increased by 107%. Thus, the general notion of urban sprawl is that the spatial spread of development proceeds at a greater rate than population growth, resulting in dispersed, low- density development. Most urban sprawl studies have used Census Bureau-defined Urban Areas (UAs) and Metropolitan Statistical Areas (MSAs) (e.g., Pendall 1999, Kolankiewicz and Beck 2001, Lang 2003, Ewing et al. 2005), which can both over- and under-bound locations of urban density (Theobald 2001a). That is, most small cities and towns in the U.S. (census places) were not located within an UA or MSA, and so many towns and cities of generally less than 50 000 residents were excluded from these analyses. Urban density is defined by the U.S. Census Bureau as greater than 1000 people per square mile (about 3.9 people per ha; 1.6 people per acre), whereas rural areas are simply defined as “not urban.” Note that “smart growth” density (i.e., the density that would support mass transit) is much higher at 12 500 people per square mile (about >48 people per ha; >19.5 people per acre). Also, because MSAs are defined by amalgamations of counties, rural areas containing very low population densities within a county are often mixed with highly urbanized areas in these analyses. Some researchers have recognized sprawl as a multidimensional phenomenon rather than simply a decline in the average population density over time (e.g., Torrens and Alberti 2000, Ewing et al. 2002). For example, Ewing (1997) argued that sprawl is characterized by leapfrog,scattered, strip, low- density, or single-use forms of development. Alberti (1999) identified four structural variables: form, density, grain, and connectivity. A useful conceptual definition of sprawl comes from Galster et al. (2000, page 5): “Sprawl (n.) is a pattern of land use in a [urban area] that exhibits low levels of some combination of eight distinct dimensions: density, continuity, concentration, compactness, centrality, nuclearity, diversity, and proximity.” In a comprehensive examination of urban growth, Ewing et al. (2002) characterized sprawl by computing indicators of residential density, mix of services, activity centers, and accessibility of the street network. Most recently, work on urban morphology continues to emphasize multiple characteristics of growth (Song and Knaap 2004). Although progress is being made on characterizing urban sprawl, less work has investigated land-use change in exurban and rural areas. The lack of geographic precision exhibited in most urban sprawl studies is one of the main reasons that a rethinking of the urban–rural framework has been called for (Alonso 1993, page 26): “The existing censal categories are misleading because they present a vision of the United States as a territory tiled with convex, continuous, mutually exclusive types of regions, while the reality is one of a great deal of interpenetration, much of it rather fine- grained.” Revisions to the 2000 Census have partially addressed this issue through “urban clusters,” which contain between 500–1000 people per mile2 in blocks adjacent to UAs. Moreover, it is common to measure and express the pattern and extent of development through population or population density. However, because population data from the Census Bureau are tied to the primary place of residence, measures based on population underestimate landscape change because housing units in the form of vacation and second homes are not represented. Therefore, housing density is a more complete and consistent measure of landscape change than population density. The main difficulties to knowing about exurban land-use changes stem from three related factors. Exurban land-use activities tend to be less intensive than urban land uses, and as a result, are more difficult to define and map (Ward et al. 2000). Typically, spatially explicit efforts to examine spatial patterns of exurban dynamics have been limited to case study assessments (e.g., Theobald et al. 1996, Wilson et al. 2004, Robinson et al. 2005). Although there are a number of federal (and state) efforts to inventory natural resources such as the U. S. Census of Agriculture, Population, and Housing, and the NRCS’s National Resource Inventory Ecology and Society 10(1): 32 http://www.ecologyandsociety.org/vol10/iss1/art32/ (NRI), these tend to provide county-level summaries that are insufficient for ecological inference because important exurban land-use patterns cannot be spatially resolved. The U.S. Geological Survey’s National Land Cover data set (NLCD) (Vogelmann et al. 2001) provides fine resolution (30 m) data, but like the other commonly available inventories, is based on coarse categories that do not differentiate important land-use types beyond the urban fringe (e.g., urban vs. rural). I emphasize here that ecological assessments of development patterns need to be based on data sets that allow fine-grained differentiation of land-use patterns across the urban to exurban to rural spectrum. Moreover, because a primary conservation response to perceived sprawl is to establish protected lands and open space through direct purchase and conservation easements on privately owned lands, then protected lands, in addition to developed lands, need to be resolved in finer detail as well. Many efforts to estimate sprawl based on undifferentiated geographies, such as UAs or MSAs, may, ironically, have overestimated sprawl because protected areas (with no or very low population) within an urban area were not resolved, yet contributed to the density calculations. For example, the population density of Fort Collins, Colorado, USA, computed using the city boundary, declined from 4.05 to 3.72 people per acre from 1980 to 1998, suggesting that it “sprawled.” But, after removing protected lands within city limits (purchased in large part to counteract sprawl), population density increased from 4.05 to 4.27 in 1998 (Theobald 2004). Although the amount of developed area expanded, Fort Collins grew in a more efficient manner (as measured by population density), after adjusting for fine-grained land-use pattern, and incorporating protected lands. My work presented here builds on the premise that to advance understanding of the ecological effects of urbanization and human population growth requires recognition of land-use dynamics beyond the urban fringe, and spatial databases of development and protected lands that contain fine- grained, spatially explicit data. Because ecosystems do not follow political boundaries, a consistent, comprehensive, and seamless data set of regional to nationwide land use is needed to fully support geographical analyses and assessments of ecological effects of land-use change. The overall objective in this paper is to broaden discourse about better understanding of land-use dynamics and ecological effects to include changes beyond the urban fringe. My primary goal is to describe the development of a nationwide, fine-grained database of historical, current, and forecasted housing density. Secondary goals are to briefly report on the status and trend of exurban land-use changes across the U.S., and to introduce a landscape sprawl measure that captures patterns of land-use change —especially the spatial configuration between protected and exurban areas. METHODS This research was conducted in five general steps. First, I estimated historical and current housing densities at a fine grain to examine spatial patterns of development across the coterminous U.S. Decadal sequences of housing density from 1940 to 2000 were constructed. Using historical and current housing density patterns as data inputs, I developed a simulation model to forecast future housing density patterns based on county-level population projections. I evaluated the forecast model by “hind- casting”—i.e., generating a test data set with model runs that started from estimated 1980 patterns and generated forecasted patterns for 1990 and 2000. The results from the hindcasts were then compared with the estimated (“truth”) patterns in 1990 and 2000. I chose to concentrate on recent and near-term patterns from 1980 to 2020 because most conservation planning issues involve roughly 20- year horizons, and these data are the most reliable as well. Next, I developed a metric that measures the spatial pattern and configuration of housing density to better quantify “sprawl” and landscape fragmentation. Based on the Census Bureau’s definition of urban areas, I define “urban” housing densities as less than 0.1 ha per unit, and “suburban” as 0.1–0.68 ha per unit. I define “exurban” density as 0.68–16.18 ha per unit to capture residential land use beyond the urban–suburban fringe comprising parcels or lots that are generally too small to be considered productive agricultural land use (although some high-value crops, such as orchards, are a notable exception). “Rural” is defined as greater than 16.18 ha per unit, where the majority of housing units support agricultural production. In some states, where farming can be productive even for small acreage farms (~8–10 ha), exurban areas could be defined as having between 0.68–8.09 ha per unit. Ecology and Society 10(1): 32 http://www.ecologyandsociety.org/vol10/iss1/art32/ Estimating Historical and Current Housing Density To estimate historical and current housing density patterns, I created maps of housing density using dasymetric mapping techniques (Wright 1936, Robinson et al. 1995). I used the best available, fine- grained and national-extent spatial database on population and housing from the Census Bureau’s block-group and block data for 2000 (U.S. Census Bureau 2001a). Below, I describe census geography basics and a few refinements to common dasymetric techniques that have been detailed in previous work (Theobald 2001a, 2003). Using census geography, the familiar census tract was subdivided into a block-group (containing roughly 250 to 550 housing units). Block-groups were in turn subdivided into blocks that are roughly a block or so in size in urban areas, but may be many square kilometers in rural areas. These block-groups and blocks tessellate or cover the entire U.S. (U.S. Census Bureau 2001a). Nationwide in 2000, there were 207 469 block-groups and 8 185 004 blocks. The boundaries of census blocks typically follow visible physical features, such as streets, roads, streams, railroad tracks, and ridgelines, and occasionally are based on invisible features, such as city or county limits, property lines, or short extensions of streets (U.S. Census 2001a). Blocks vary in shape and size, ranging roughly from 1–2 ha in urban areas to 100–1000 ha in rural areas. Because houses are not allowed on public and protected lands, I removed the portions of blocks that overlapped with protected lands identified in the Protected Areas Database (DellaSala et al. 2001), which is the best available, nationwide data set, and provides data mapped at a scale of roughly 1:100 000 on public lands. Most privately owned protected lands (e.g., through conservation easements) and local government lands (i.e., cities and counties), however, were not included in this database, but are a high priority need for future work. In addition, housing units were precluded from occurring in so-called “water blocks,” which represent hydrological features, such as streams, rivers, ponds, lakes, and reservoirs. As an example, Colorado contains about 141 000 blocks, 75 000 of which contain at least one housing unit (mean = 262.1 ha; SD = 1652.0 ha). Removing the portion of blocks that overlap protected areas refines them considerably (mean = 163.04 ha; SD = 833.9 ha). Using refined blocks results in over 131 600 additional hectares (~18%) being defined as exurban along the wildland interface in western Colorado. Although the refined blocks result in a finer-grained data set, an assumption must be made about the spatial distribution of units within a refined block. Typically, dasymetric mapping techniques assume a homogenous distribution (e.g., Theobald 2000; Eicher and Brewer 2001; Theobald 2001b; Theobald 2003). Another option is to constrain the distribution of units based on land cover types (e.g., Monmonier and Schnell 1984; Holloway et al. 1999; Schumacher et al. 2000; Radeloff et al. 2001), but Eicher and Brewer (2001) found no significant improvement in distribution when land use/cover was incorporated. I modified the spatial distribution of housing units within a block based on the density of major roads, because houses were more likely to be located near roads and less likely to be in portions of blocks that are distant (>~1 km) from roads (Theobald 2003). The allocation of housing units were weighted based on road density (km/km2) computed using a moving neighborhood with an 800-m radius, which is arbitrary but is supported by a moderately strong correlation between housing and road density (Theobald 2003). Density was computed using a commonly available, nationwide data set of major roads generated from the U.S. Census TIGER data set (2004 data and maps from Environmental Systems Research Institute (ESRI), Redlands, California, USA). Road density was classified into four categories to distinguish different levels of development based on an ad hoc comparison of road densities and housing densities around the nation. The classes used were: very low (0.0–0.25 km/km2), low (0.25–1.0 km/km2), medium (1.0–5.0 km/km2), and high (>5.0 km/km2). Weights of 1, 2, 3, and 4 were assigned to very low to high (respectively), and were used to allocate housing density values to cells within a block. The number of housing units per block was obtained from the 100% sample data from the 2000 Census Summary Tape File 1 (U.S. Census Bureau 2001b). Historical patterns of housing density (decadal from 1940 to 1990) were generated from estimates obtained from the “Year Housing Built” question from the sample data Summary File 3 data set (U. S. Census Bureau 2001c). Because the geography of tracts and blocks changes with each census, I estimated historical housing units based on the 2000 Ecology and Society 10(1): 32 http://www.ecologyandsociety.org/vol10/iss1/art32/ Census geography using established methods (Radeloff et al. 2001, Theobald 2001a, Hammer et al. 2004). Housing unit counts for each decade are provided at the block-group level and were adjusted to ensure that the sum of units by block-groups in a county equaled the counts from decadal census. This minimizes systematic underestimation of historical units. Estimates of exurban housing density computed from these data are conservative because units are assumed to be distributed roughly evenly throughout a block. Therefore, estimated housing density will tend to be lower because higher density areas within a block are “averaged out.” Note that the variable-sized analytical units cause possible inaccuracies, which is widely recognized as the modifiable areal unit problem (Openshaw 1984). Analyses based on Census data are subject to these limitations, but to date, there is no easy, practical solution to these difficulties (Longley et al. 2001). It is important to note that the resolution or “grain” of the refined blocks, represented at 100 m resolution, is coarser than land cover information from U.S. Geological Survey’s NLCD (30 m), but because the boundaries of blocks are often based on visible physical boundaries, their shapes often conform to important features on the landscape that control the distribution of houses. Forecasting Future Housing Density Most efforts to forecast land-use change have focused on urban systems (e.g., Landis 1995, Batty 1997, Clarke 1997, Wilson et al. 2003, but see Theobald and Hobbs 1998). In previous work, I created a model to forecast future patterns of housing density across the urban-to-rural gradient, named the Western Futures model (Theobald 2001b, Theobald 2003, Claggett et al. 2004). Here, I describe additional refinements that have resulted in a new model called SERGoM v1 (Spatially Explicit Regional Growth Model). The full urban- to-rural spectrum of housing densities is modeled in SERGoM at broad regional-to-national extents. It uses a supply–demand–allocation approach, and assumes that future growth patterns will be similar to those found in the past decade, although this can be parameterized to reflect alternative scenarios. There are three basic steps in SERGoM to forecast future patterns on a decadal basis (Fig. 1). First, the number of new housing units in the next decade is forced to meet the demands of the projected county- level population. There is significant variability in the population per housing unit ratio (area-weighted mean = 2.509, SD = 2.383), so that in the 2000 Census, 440 counties had <2.0 people/unit and 70 counties <1.5 people/unit. Rather than using a single nationwide conversion factor, population growth was converted to new housing units by the county- specific housing unit per population ratio for 2000. Population estimates were obtained from a demographic–econometric model (NPA Data Services 2003). The second step was to compute a location-specific average growth rate from the previous to current time step (e.g., 1990 to 2000). These growth rates were computed for each 100-m cell using a moving neighborhood (radius = 1.6 km). For each state, I computed the average growth rate for each of 16 development classes. These 16 classes were found by overlaying four density classes (urban, suburban, exurban, and rural) with four accessibility classes measured as travel time (minutes one way) from the nearest urban core (see below): 0–10, 10–30, 30– 60, and >60 minutes. Growth rates averaged over the classes generated from the housing density and accessibility patterns that reflect the previous time step were then joined to a map that depicts the current time step housing density and accessibility pattern. Because these classes and rates are computed locally, both within-county heterogeneity and cross-boundary patterns can be captured. This allows rates of growth to vary across the nation, across a region, and even within a county, and does not assume stationarity. The distribution of new housing units was adjusted according to accessibility to the nearest urban core. That is, growth typically occurs at locations on the urban fringe. Accessibility from all developable land to the nearest urban core was computed—based not simply on straight-line distance, but in terms of minutes of travel time from a location along the main transportation network (major roads and highways) to the nearest urban core. An urban core area is defined here as a contiguous cluster (>100 ha) at urban housing density, but alternative definitions could be developed. Because it is difficult to forecast when roads will be enlarged or where new roads will be constructed, travel time to move across locations that are not on the network of major roads was modeled as an average travel time of 15 miles per hour (24.2 km/hour). Travel speed was assumed Ecology and Society 10(1): 32 http://www.ecologyandsociety.org/vol10/iss1/art32/ Fig. 1. A basic logic diagram of the Spatially Explicit Regional Growth Model (SERGoM v1). to be 70 mph (113 km/hour) on interstates, 55 mph (89 km/hour) on highways, and 45 mph (72.4 km/ hour) on major county roads. An accessibility surface was then created from a cost weight based on travel time from urban areas along major roads. New housing units are allocated as a function of the accessibility surface. Here, the allocation is based on the distribution of new units realized in the previous decade, but other weightings could be applied to develop denser or more dispersed growth scenarios. Accessibility is computed at each decadal time step because new “islands” of urban core may emerge over time. This allows complex growth patterns to be modeled, and incorporates the emergent nature of development patterns. The third step was to add the map layer of new housing density to the current housing density (e.g., adding new housing units to 2000 housing density). SERGoM assumes that housing density cannot decline over time. This is a reasonable assumption when examining patterns of expansion in suburban and exurban areas. However, this current implementation is limited when investigating urban-centric processes, such as urban decay or expansion of commercial land use into urban and suburban residential areas. Future development of SERGoM could include commercial and industrial land cover data (e.g., from the U.S. Geological Survey’s NLCD) to incorporate commercial/ industrial and residential dynamics in urban areas. Evaluation of Forecast Model To evaluate forecasts of urban, exurban, and rural housing density patterns generated by SERGoM, I ran the model with the 1980 estimated housing density pattern as the initial conditions (in place of 2000), and then generated forecasts for 1990 and 2000. I then compared the estimated development patterns to forecasted patterns for 1990 and 2000 data sets. A simple way to examine the accuracy of the forecasted patterns is to generate a “confusion Ecology and Society 10(1): 32 http://www.ecologyandsociety.org/vol10/iss1/art32/ matrix” or cross-tabulation of the area of intersection of the nine possible combinations (of three classes). To examine how accuracy changes with coarser-resolution representations of the data, I followed a multi-resolution approach (Costanza 1989, Pontius 2002) where I averaged the housing density maps from their original resolution of 1 ha to coarser resolutions of 4, 16, 64, and 256 ha. At each resolution, I re-computed the housing density classes (urban/suburban, exurban, and rural) and re- computed a cross-table for each combination of year and resolution. Landscape Measures of Development Patterns Most research effort has measured the pattern of land use, but less work has quantified how land-use change modifies or affects habitat and natural resources. Pattern is defined here as the structural arrangement of different land-use types, which are defined based on housing density. Few researchers have explored the usefulness of common landscape ecology metrics (e.g., Torrens and Alberti 2000). For example, Luck and Wu (2002) used FRAGSTATS (McGarigal and Marks 1995) and found several metrics useful to differentiate the urbanization gradient. Patch-based landscape metrics have also been used to examine patterns of sprawl (Carrion-Flores and Irwin 2004, Hasse and Lathrop 2003, Robinson et al. 2005). However, a primary criterion to judge the usefulness of a metric is establishing a clear link between the landscape metric and the land-use process. Here, I introduce a metric to quantify the effects of exurban and rural development on natural resources, from a landscape perspective. The landscape sprawl metric, LS, quantifies three primary aspects of residential development patterns (Fig. 2): density, continuity, and configuration (the latter roughly incorporates concentration, compactness, centrality, nuclearity, accessibility, and proximity). More work needs to be done to examine the sensitivity of this metric to different regional situations and land-use processes. Commonly the analytical unit or area (the denominator in density calculations) is typically defined by city limits, urbanized areas, or metropolitan statistical areas, but computing the density locally for each block allows fine-grained mapping of development patterns to be examined across the urban-to-rural gradient. Also note that protected open space, parks, and lakes should be excluded from the area developed during calculations. At each location, housing density, D, is computed using the refined blocks. A threshold, t, is used to identify and remove blocks that are presumed to be non-residential land use (e.g., agricultural); here, I assume that lower than exurban densities are primarily non-residential land use. Note that it is difficult to map commercial and industrial land use with these data. Measuring the continuity of residential development patterns can identify leap-frog development and locations adjacent to residential development in the so-called urban “shadow” or “fringe.” I computed a continuity or edge weight (Eq. 1), SE to penalize development locations that contribute to increased edge between developed and undeveloped/ protected land. This is arbitrarily set to the square root of the housing density (maximum of 5.0) so that edges that have higher density adjacent to undeveloped land are weighted higher than edges formed by lower density development. Note that only edges where developed land is adjacent to undeveloped lands, but developable, are considered; development adjacent to large water bodies and commercial/industrial are not considered discontinuous development. Also, density is smoothed by a 600- m radius window to remove small “islands” or narrow strips of housing development to make the identification of edges more robust and less sensitive to possible anomalies. (1) To account for vehicle miles traveled and the effects of broader spatial scales or configurations of development, I computed the accessibility (Eq. 2), ST, of a location of residential development to the nearest urban core areas, computed in terms of minutes of one-way travel time, T, along the major road network. The average person in the U.S. travels roughly 40 miles per day, most of it in a personal vehicle (U.S. Department of Transportation 2003). Development that is more distant from the urban core is assumed to contribute more vehicle miles traveled. Urban core is defined as a contiguous area of at least 25 ha in size, composed of urban density Ecology and Society 10(1): 32 http://www.ecologyandsociety.org/vol10/iss1/art32/ Fig. 2. An illustration of the three components of the Landscape Sprawl (LS) metric. The development pattern (upper left) is based on housing density, classified here to four categories: urban (<0.1 ha per unit) comprises 4% of the area, located at the top, followed by suburban (0.1–0.68 ha per unit; 12%), exurban (0.68 – 16.18 ha per unit; 20%), and rural (>16.18 ha per unit; 44%). Protected land (e.g., public national forest, park, etc.) has no housing density and makes up 20%. LS1–Where rural or protected lands are adjacent to urban, suburban, or exurban densities, an edge contrast weight is computed (upper right). Edges formed by higher density housing lead to a higher edge contrast, and disjoint or “leapfrog” development (>0.5 km) is also penalized by the edge weight. LS2–To get at spatial configuration of development, the distance (square-root transformed) in minutes of travel time along major roads and highways from urban areas is multiplied by housing density to account for the number of trips or vehicle miles traveled (lower right). Within each class, this component increases slightly with increasing distance, but housing density is fairly dominant, so that suburban areas, even though they are closer, account for a greater impact because of the sheer number of housing units. LS3–The inverse of housing density (units per ha) is land consumption (ha per unit). Note that the final LS metric value is the summation of these three components. Ecology and Society 10(1): 32 http://www.ecologyandsociety.org/vol10/iss1/art32/ cells. Both the density threshold and minimum area requirements could be changed to create alternative scenarios. Note that accessibility is rescaled using a square-root transform and constrained to be at least 1.0, so that T increases at a slower rate for longer distances. This transform is performed because, although the majority of vehicle miles traveled occur during long commutes, some portion occurs during errands, which tend to be centralized within urban areas. This transformed time value is then multiplied by the housing density at a location, so that more housing units will result in a higher value, whereas fewer houses will typically generate fewer trips, regardless of the distance from urban areas. (2) The last aspect included in the landscape sprawl metric is land consumption, which represents the efficiency with which land is used in situ (Eq. 3). That is, higher density locations are more efficient from a “per person” or “per housing unit” perspective. The inverse of housing density, 1/D, is the area of land required per unit A. As a result, higher density urban and suburban areas are more efficient in area of land used per unit compared with exurban residential development. Although there is no clear and consistent break point at which agricultural land use no longer becomes viable, there is a rapid threshold that occurs at densities that are often termed hobby ranches or “nonfarm farms.” This threshold varies regionally across the U.S., and depends mostly on ecosystem parameters such as soil productivity and precipitation, but also on the structure of ranch/farm operations. Generally, densities that are around 14 ha or more (40–50 acres) are considered part of the productive agricultural system, particularly in the western U.S. (Hart 1992). The area A was square-root transformed to reduce this aspect from dominating this metric. As A exceeds 10 ha, an increasing portion, u, of a parcel or lot is useable for agricultural production (or alternatively has higher natural habitat values). For urban, suburban, and exurban densities, u = 1.0; for rural densities lower than 0.08 units per ha, u = 0.6; for densities below 0.04, u = 0.1; for densities below 0.03, u = 0.001. These values are estimates, but u could be computed empirically for different regions in the U.S. as the portion of a parcel or lot that does not contribute to agricultural production. For wildlife habitat, u might be an estimate of the proportion of area beyond the “zone of disturbance” associated with housing units (Theobald et al. 1997). (3) The final overall landscape sprawl index, LS (Eq. 4), is computed using an equal weighted combination of the three components, which assumes that all components are equally important in the overall metric LS (Fig. 3). Larger values of LS indicate a more sprawling pattern, whereas smaller values denote a more efficient land-use pattern. Locations that have low housing density, that are nearby agricultural lands, and/or that are further from urban core areas will have higher values of LS. Locations that have higher density, that do not contribute to discontinuous edges, and/ or that are closer to urban core areas will have lower values of LS. Note that the units of LS are expressed roughly in terms of hectares per unit, weighted by accessibility and edge. As a consequence, differences in housing density—between locations, or over time at the same location—have a dominant effect on the landscape sprawl metric. A GIS script (ESRI AML; ESRI, Redlands, California, USA) was used to compute LS (App. 1). (4) Because LS is measured at each location (cell), it is typically averaged over an area of interest, such as a state, county, or watershed. To illustrate how the LS metric captures different development patterns, I generated a simple hypothetical landscape. Figures 4 and 5 illustrate different development patterns and the value of LS for each. To calculate trends, LS can be computed on maps that represent different times (Fig. 5). Ecology and Society 10(1): 32 http://www.ecologyandsociety.org/vol10/iss1/art32/ Fig. 3. An illustration of how the Landscape Sprawl (LS) metric captures different development patterns. Two additional development configurations that total the same number of housing units (center left, bottom left). The LS is computed for each cell within the 100 x 100 landscape, with higher values, represented by a darker hue, indicating more sprawl. With increasing distance from urban core (center), LS increases strongly for suburban density, moderately for exurban density, and weakly for rural areas. Note that the edge contrast weight occurs between the urban/rural and exurban/rural edge, but is clearly visible because of the higher contrast at the urban/rural edge. A general indicator of the overall sprawl score is the mean LS score for a given analytical unit. The mean LS scores increase from top to bottom: 267.8 (top), 338.9 (center), 369.8 (bottom). Ecology and Society 10(1): 32 http://www.ecologyandsociety.org/vol10/iss1/art32/ Fig. 4. The landscape metric also captures “leapfrog” development and development at further distances—note that the scenarios presented here have the same areal proportions as Fig. 3 (and therefore, the same number of housing units). Compared with a fairly compact pattern (top, and also center Fig. 3), the effect of disjunct development is an increase from 338.9 (top) to 373.9 (center). Moving development yet further afield, the score increases to 399.3 (bottom). Ecology and Society 10(1): 32 http://www.ecologyandsociety.org/vol10/iss1/art32/ Fig. 5. The Landscape Sprawl (LS) metric also can be used to capture patterns of development over time. The scenarios here reflect a 10% increase in the original number of housing units (Fig. 3, top). Expanding the area occupied by urban housing density to accommodate the additional housing units increases LS only slightly (274.7, top, compared with 267.8 in the original). The LS metric increases to 286.7 (center) when the suburban class is expanded. LS increases to 403.1 (bottom) when the additional units are developed at exurban densities. Ecology and Society 10(1): 32 http://www.ecologyandsociety.org/vol10/iss1/art32/ Table 1. The extent of development for the coterminous U.S., grouped by housing density class. “Developable” land includes private lands that do not have some protected designation. “Undevelopable” includes public (e.g., Forest Service, parks, etc.) and other protected lands (derived from DellaSala et al. 2001). Density class Extent (km2)Percentage of developable land 198020002020198020002020 Urban/suburban (>0.69 ha/ unit) 95 635125 729174 2261.7%2.2%3.1% Exurban (0.69–16.18 ha/unit) 693 591917 0901 116 04612.2%16.1%19.6% Rural (>16.18 ha/unit) 4 891 9884 638 3954 275 54386.1%81.6%75.2% RESULTS Status and Trends in Developed Lands In 2000, there were 125 729 km2 in urban/suburban residential housing density nationwide, excluding commercial and industrial lands typically associated with urban areas (Table 1). There are slightly over seven times the additional area in exurban housing density (917 090 km2). About 1.6% of land nationwide (coterminous U.S.) was in urban/suburban residential density, whereas 11.8% was in exurban in 2000. The urban/suburban/ exurban development footprint has increased from 10.1% to 13.4% (1980 to 2000), roughly at a rate of 1.60% per year. This rate of land development outstrips by 25% the rate of population growth from the same time period of 1.18% per year—a conservative estimate because rural lands were not included in the computation, but rural population was included. There were 2 107 894 km2 (27%) in non-developable (i.e., public or private-protected lands). The distribution of these development patterns can be seen in Fig. 6 (low resolution), Fig. 7 (high resolution), and App. 2 (Portable Document File). To facilitate easy examination of this database, I provide a spreadsheet containing summaries of these data by county (see App. 3). Also, animations of the development patterns from 1980 to 2020 on a decadal basis allow visualization of spatio- temporal patterns for the nation (Fig. 8) and western (Fig. 9), central (Fig. 10), and eastern U.S. (Fig. 11). Other analytical units, such as hydrologic unit codes (HUC 8-digit codes) or ecoregions, could be used to summarize the housing density data as well. Another way to examine urban and exurban development patterns is to determine what proportion remains rural: that is, either rural housing density (private lands) or public/protected lands. Ruralness is defined here as the proportion of a county (or state) in rural housing density, or the proportion of the developable area in the county (or state) in rural housing density. The amount developed (urban, suburban, and exurban) is roughly the opposite of the rural landscape (Figs. 12, 13, and 14). Notably, some of the “New West” states (Arizona, Colorado, Idaho, Utah) appear less rural than many of the northern Great Plains states (Iowa, Kansas, Montana, Nevada, North Dakota, South Dakota), because much of the open space is provided by public lands that are “undevelopable.” Model Forecasts The SERGoM forecast model performed reasonably well (Table 2), resulting in high accuracy overall for 1990 (urban = 93.0%, exurban = 91.2%, and rural = 99.0%) and reasonably high accuracy for 2000 (urban = 84.2%, exurban = 79.4%, and rural = 99.1%). With coarser resolutions, the accuracy increased minimally for the 1990 pattern and slightly for 2000 (exurban increased from 79.4% to 82.3%. Ecology and Society 10(1): 32 http://www.ecologyandsociety.org/vol10/iss1/art32/ Fig. 6. A low-resolution map showing housing density classes for 2000. Status and Trends of Landscape Sprawl Metric Values of the LS metric ranged from 0.06 to 330.53 throughout the U.S.. LS values increased from 1980 (mean = 232.17, SD = 258.40) to 2000 (mean = 248.06, SD = 258.55) to 2020 (mean = 263.47, SD = 257.11), indicating that exurban development and sprawl have increased throughout the U.S. There is substantial spatial variation of LS between counties, however, and Fig. 15 shows the LS metric averaged by counties for 2000 (and App. 4). Moreover, there is significant local spatial variation within a county (Fig. 16). Surprising spatio-temporal patterns arise in LS because urban core areas may emerge over time, causing a phase change in the LS metric values in a region as municipal goods and services move into new regions (see Fig. 17). DISCUSSION Understanding the patterns and trends of urban sprawl is important, but there are important land- use patterns and dynamics occurring beyond the urban fringe. Not only is the extent of exurban housing density 7–10 times that of urban areas, but per capita land consumption in exurban areas is much greater than in urban locations. Ecology and Society 10(1): 32 http://www.ecologyandsociety.org/vol10/iss1/art32/ Fig. 7. A high-resolution map showing housing density classes for 2000. Fig. 8. An animation of national development patterns from 1980 to 2020. View animated Figure Fig. 9. An animation of western U.S. development patterns from 1980 to 2020. View animated Figure Fig. 10. An animation of central U.S. development patterns from 1980 to 2020. View animated Figure Fig. 11. An animation of eastern U.S. development patterns from 1980 to 2020. View animated Figure Ecology and Society 10(1): 32 http://www.ecologyandsociety.org/vol10/iss1/art32/ Fig. 12. A map showing the proportion of a county in rural housing density for 1980. The housing density database produced in this study is intended to complement other existing land-use/ land-cover databases. Compared with the NRI (NRCS 2001) database, it provides a detailed coverage (based on a census, not a sample) that allows spatially explicit patterns to be examined for potential fragmentation effects. Compared with the U.S. Geological Survey/Environmental Protection Agency NLCD (Vogelmann et al. 2001), it provides insight beyond urban and built-up areas into exurban areas. However, as noted earlier, intense urban land uses, such as commercial and industrial, are not captured well in the census data used here, but are readily identified in the NLCD data. Future work will attempt to integrate these two data sets to a greater degree. The LS metric quantified patterns and locations of urban and exurban sprawl, but requires careful interpretation. Because LS should be summarized by some analytical unit (e.g., a watershed, a county, an MSA), comparisons between different regions must be normalized. LS exhibits a non-linear response to different development patterns that are controlled by two critical parameters. First, at the critical threshold specified by the assumption of the Ecology and Society 10(1): 32 http://www.ecologyandsociety.org/vol10/iss1/art32/ Fig. 13. A map showing the proportion of a county in rural housing density for 2000. size and contiguity of an urban core, LS adjusts to the emergence of a new urban core area. A once- exurban or rural area that results in large LS values because of long distances to the nearest urban core can rapidly have much reduced LS values as a new urban core emerges. Second, as a rural area is developed and converted to exurban or possibly urban/suburban land use, natural resource or ecological values rapidly diminish. This critical threshold exists roughly at the low-density end of the exurban housing density class. In particular, development edge is defined to occur at the interface between exurban and rural and protected lands, but not at the urban/suburban interface with exurban lands. Of course there are a variety of micro- or site- scale conditions (siting of buildings, landscaping, fencing, allowed or prohibited human activities, etc.) that can also have a strong influence on potential ecological effects that are not accounted for in LS. Although these non-linearities and phase changes are more difficult to interpret, this mirrors real-world phenomena that are intrinsic to land-use dynamics (Batty 1997). To progress beyond simple measures of sprawl based on population density changes, these situations need to be explicitly measured. Ecology and Society 10(1): 32 http://www.ecologyandsociety.org/vol10/iss1/art32/ Fig. 14. Proportion of a county in rural housing density for 2020. An important challenge for ecologists is to contribute to better understanding of these critical thresholds, their regional variation, and the potential for micro- or site-level measures to mitigate possible broader-scale effects. In general, an initial analysis of development patterns using LS suggests that development patterns that were more contiguous, higher density, and more compact (not dispersed) had reduced overall effects on natural resources because they resulted in smaller footprints or “disturbance zones,” lower percentage of impervious surface, and reduced pollution because fewer vehicle miles were generated. Moreover, the practical complications of natural resource management are much reduced with more compact patterns of development. LS is a preliminary metric to quantify the general effects (or impacts) of development patterns on ecological systems. Much work remains to develop causal relationships and more conclusive results, but quantifying development patterns in a logical and consistent manner is an important first step. To complement current work that seeks to understand the ecological consequences of urban sprawl (e.g., Blair 2004), the LS metric needs to be tested by empirical, field- Ecology and Society 10(1): 32 http://www.ecologyandsociety.org/vol10/iss1/art32/ Table 2. The results of the test of the SERGoM model comparing estimated areas for urban/suburban, exurban, and rural housing density classes for 1990 and 2000 against forecasted patterns. Resolution 1 ha, Year 1990 Hectares Forecasted % correctForecasted EstimatedUrbanExurbanRuralEstimatedUrbanExurbanRural Urban10,081,521703,87958,201Urban93.0%6.5%0.5% Exurban813,88571,055,4996,050,269Exurban1.0%91.2%7.8% Rural8,2054,723,352473,180,337Rural0.0%1.0%99.0% Resolution 1 ha, Year 2000 Hectares Forecasted % correctForecasted EstimatedUrbanExurbanRuralEstimatedUrbanExurbanRural Urban10,518,4081,717,056260,980Urban84.2%13.7%2.1% Exurban876,04772,573,27917,994,389Exurban1.0%79.4%19.7% Rural21,3924,209,015458,504,582Rural0.0%0.9%99.1% Resolution 4 ha, Year 1990 Hectares Forecasted % correctForecasted EstimatedUrbanExurbanRuralEstimatedUrbanExurbanRural Urban11,068,972785,30849,400Urban93.0%6.6%0.4% Exurban809,68074,892,9886,119,500Exurban1.0%91.5%7.5% Rural3,5044,833,044478,906,016Rural0.0%1.0%99.0% Resolution 4 ha, Year 2000 Hectares Forecasted % correctForecasted EstimatedUrbanExurbanRuralEstimatedUrbanExurbanRural Urban11,550,7361,928,984243,824Urban84.2%14.1%1.8% Exurban857,47676,444,18018,312,696Exurban0.9%80.0%19.2% Rural15,3524,318,952463,796,212Rural0.0%0.9%99.1% (con'd) Ecology and Society 10(1): 32 http://www.ecologyandsociety.org/vol10/iss1/art32/ Resolution 16 ha, Year 1990 Hectares Forecasted % correctForecasted EstimatedUrbanExurbanRuralEstimatedUrbanExurbanRural Urban12,110,992874,30436,560Urban93.0%6.7%0.3% Exurban829,21680,066,7686,181,248Exurban1.0%91.9%7.1% Rural1,5525,034,896488,480,752Rural0.0%1.0%99.0% Resolution 16 ha, Year 2000 Hectares Forecasted % correctForecasted EstimatedUrbanExurbanRuralEstimatedUrbanExurbanRural Urban12,663,8402,178,672203,248Urban84.2%14.5%1.4% Exurban855,93681,695,04018,712,096Exurban0.8%80.7%18.5% Rural12,3044,487,488472,807,664Rural0.0%0.9%99.1% Resolution 64 ha, Year 1990 Hectares Forecasted % correctForecasted EstimatedUrbanExurbanRuralEstimatedUrbanExurbanRural Urban13,213,056944,64022,144Urban93.2%6.7%0.2% Exurban941,44087,619,2006,249,408Exurban1.0%92.4%6.6% Rural1,5365,609,664502,895,232Rural0.0%1.1%98.9% Resolution 64 ha, Year 2000 Hectares Forecasted % correctForecasted EstimatedUrbanExurbanRuralEstimatedUrbanExurbanRural Urban13,864,5762,466,880145,536Urban84.1%15.0%0.9% Exurban943,48889,389,24819,349,632Exurban0.9%81.5%17.6% Rural10,9444,985,664486,340,352Rural0.0%1.0%99.0% Resolution 256 ha, Year 1990 Hectares Forecasted % correctForecasted EstimatedUrbanExurbanRuralEstimatedUrbanExurbanRural Urban14,220,288990,9769,728Urban93.4%6.5%0.1% (con'd) Ecology and Society 10(1): 32 http://www.ecologyandsociety.org/vol10/iss1/art32/ Exurban1,122,56099,425,7926,576,896Exurban1.0%92.8%6.1% Rural1,7926,311,424520,506,624Rural0.0%1.2%98.8% Resolution 256 ha, Year 2000 Hectares Forecasted % correctForecasted EstimatedUrbanExurbanRuralEstimatedUrbanExurbanRural Urban15,015,6802,697,72883,712Urban84.4%15.2%0.5% Exurban1,119,744101,425,40820,720,384Exurban0.9%82.3%16.8% Rural10,7525,472,512502,620,160Rural0.0%1.1%98.9% based research as well. Because quantification of development patterns in general and LS in particular are sensitive to the fine- grained pattern of protected lands, additional effort is needed to create better and more complete databases of protected lands. For example, over 9.4 million acres of land in the U.S. was protected by local and regional land trusts in 2003, a 494% increase over the 1.9 million acres in 1990 (Land Trust Alliance 2004). The vast majority of these lands were not mapped in the Protected Areas Database (PAD) data set used in this study (DellaSala et al. 2001). Moreover, forecast models of land use need to incorporate possible feedbacks of development that locate growth in response to protected lands. Speculation A number of additional uses of the housing density data set are foreseen and underway, such as summarizing the housing densities by watershed to examine the potential effects of development on water quality, and a standard way to map the wildland–urban interface and intermix (WUI). A novel way to assess the dynamics of land-use change at the WUI is through a resilience perspective. Resilience is defined as the amount of change a system can experience before it is forced to reorganize (Peterson 2002). In the context of development patterns, patterns of development can be viewed as the state resulting from processes (largely socio-economic, but some ecological) that structure the landscape at the urban/exurban fringe. Resilience could then measure the amount or degree of control on processes at this interface, providing a way to examine the fundamental tension at the interface zone. Cross-scale edge has been developed to quantify resilience to identify the ecotone of instability, which is commonly identified using percolation theory that identifies thresholds between 0.407 to 0.593, computed at multiple scales (Peterson 2002). As a preliminary query of using the housing density database, I reclassified housing density into urban/suburban/exurban vs. rural/ protected categories and computed cross-scale edge, by calculating the proportion of times a cell is identified as edge (using 0.2, 0.4, 0.6, 0.8, 1.6, and 2.4 km radius; Fig. 18). This method appears to identify the relative instability of the interface, which might provide an alternative way in which to understand how development patterns may constrain or modify ecological processes at a range of scales. CONCLUSION I have argued that it is critical for ecologists to examine and improve understanding of land-use changes beyond the urban fringe to examine the extent, trend, and pattern of “exurban sprawl.” Extensive and widespread land-use changes have occurred and are likely to continue. Based on the nationwide, fine-grained database of historical, current, and forecasted housing density, there were slightly over seven times the additional area (917 090 km2) in exurban housing density (0.68–16.18 ha per unit) compared with 125 729 km2 in urban/ suburban (<0.68 ha per unit). The developed footprint has grown from 10.1% to 13.3% (1980 to 2000), roughly at a rate of 1.60% per year. This rate Ecology and Society 10(1): 32 http://www.ecologyandsociety.org/vol10/iss1/art32/ Fig. 15. Landscape sprawl metric values for the U.S. in 2000, averaged by county. Higher values indicate more sprawl or greater effect on the landscape. of land development outpaced by 25% the population growth rate (1.18% per year). The SERGoM forecast model resulted in overall reasonable accuracies (using hindcasts to 1990 and 2000 from 1980) ranging from 79.4% to 91.2% for exurban densities. Based on these forecasts, urban/ suburban housing densities will expand to 2.2% by 2020, whereas exurban will expand to 14.3.% Numerous possible applications of the housing density database are possible, particularly those that examine the implications of process-level understanding. The landscape sprawl metric is one example, allowing current understanding of the factors that generate undesirable land-use patterns (sprawl) to be explicitly represented and examined in a consistent, national framework. Values of the landscape sprawl metric indicated a general nationwide trend of urban and exurban sprawl. LS values increased from 232 to 248 to 263 (from 1980 to 2000 to 2020). Other investigations of broad-scale ecological issues, such as the wildland–urban interface, nature reserve design, Ecology and Society 10(1): 32 http://www.ecologyandsociety.org/vol10/iss1/art32/ Fig. 16. A detailed map of the Landscape Sprawl (LS) metric values for northern Colorado, USA in 2000. Higher values (red) indicate more sprawl or greater effect on the landscape. Private rural lands with no housing density are shown by gray, and public/protected lands are shown in white. Note that the LS metric is evaluated for all areas, and exurban/rural sprawl can be seen to extend beyond the boundaries of the 2000 Census Urban Areas (black outline). Ecology and Society 10(1): 32 http://www.ecologyandsociety.org/vol10/iss1/art32/ Fig. 17. An animation of the Landscape Sprawl (LS) metric values for northern Colorado, USA for 1980, 2000, and 2020. Higher values (red) indicate more sprawl or greater effect on the landscape. Private rural lands with no housing density are shown in gray, and public/protected lands are shown in white. Note that the LS metric is evaluated for all areas, and rural sprawl can be seen to extend beyond the boundaries of the 2000 Census Urban Areas (black outline). Development can be seen to expand into outlying areas at a more rapid pace than the housing density maps depict. For example, the region to the south and southeast of Denver (near the City of Parker and Douglas County) can be seen to expand rapidly from 1980 to 2000, and is forecast to expand yet again by 2020. Because a key component of the LS metric is distance from urban core, and urban core areas emerge over time, there are some significant phase changes or discontinuities in the LS values. For example, in the region around Evergreen located southwest of Denver, LS values increase rapidly from 1980 to 2000. By 2020, apparently enough land has been converted to urban density that a new urban core area will be established. This complex phase transition is a powerful, but as yet not well understood aspect of exurban land dynamics. Ecology and Society 10(1): 32 http://www.ecologyandsociety.org/vol10/iss1/art32/ Fig. 18. An exploration of the application of the concept of landscape resilience to identifying the dynamic fringe of exurban development. Cross-scale edge was computed on a portion of landscape around Fort Collins, Colorado, USA using developed/undeveloped patterns at six scales (using 0.2, 0.4, 0.6, 0.8, 1.6, and 2.4 km radius). Locations in red depict high probability of instability, orange moderate instability, and yellow low instability (green is stable, lakes are shown in blue, gray lines show major roads). and air quality sources and effects, may find utility in this database. 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Remote Sensing of Environment 86:275–385. Wright, J. K. 1936. A method of mapping densities of population. Geographical Review 26:103–110. Ecology and Society 10(1): 32 http://www.ecologyandsociety.org/vol10/iss1/art32/ APPENDIX 1. The program to compute the Landscape Sprawl metric, written in ArcINFO Arc Macro Language &echo &on &args r y &watch combine_[date -tag].watch &ty [date -full] /************************************* /* Computes Landscape Sprawl metric /* Written by David Theobald June 2004 /* Natural Resource Ecology Lab /* Colorado State University /* ArcINFO AML script, run at GRID prompt /* assumes 100 m resolution grid cells /************************************* /* GRID variables ********************* /* block housing class &sv hdc d:\aft\bhcs_v2\bhc%y% /* block housing densities &sv hd d:\aft\bhds_v2\bhd%y%us /* road cost weights &sv roads d:\aft\rds2_us4 /* county fips &sv cofips d:\aft\pop\cofips /* directory to place sprawl GRIDs &sv psp d:\aft\sprawl\ /* urban threshold for distance calculations &sv turb 8 &sv urban_patch 25 verify off setcell %r% setwindow %r% setmask %cofips% /* convert to devel / undeveloped -- find edge along any protected or undeveloped edge, but /* not if it is skinny unprotected area, because these could also be narrow strips of commercial /* also, do not count on water edges where development can't occur in water cells sdev = con ( %hdc% > 3, 1, 0) s1xxx = con ( isnull( water ), con( isnull( %hdc% ), 0, sdev )) svar = focalvariety ( s1xxx, CIRCLE, 2 ) ssum = focalsum ( sdev, CIRCLE, 6 ) /* find the edge cells sedge = con ( ssum < 70, con( ( svar * s1xxx) == 2, 1.5, 0.0), 0) sedgew = min ( sedge * sqrt( %hd% / 1000.0 ), 5.0 ) /* compute distance adjustment factor /*Adjust by transportation travel time Ecology and Society 10(1): 32 http://www.ecologyandsociety.org/vol10/iss1/art32/ setcell %roads% setmask %cofips% GU4xx1 = con ( %hdc% >= %turb%, 1 ) /* filter urban areas to remove small ones < GU4xxx1 = regiongroup( GU4xX1 ) buildvat GU4XXx1 /* transform distance in minutes traveled so that 60 minutes travel is twice GU4x = expand ( con ( GU4XXX1.COUNT > %urban_patch%, 1 ), 1, LIST, 1) GD4U = COSTDISTANCE( GU4x, %roads% ) / 100000 GD4UT = max ( POW ( GD4U, 0.5 ), 1.0) &ty [date -full] setcell %r% sacc = gd4ut * sqrt( %hd% / 1000.0 ) /* compute efficiency in housing units on landscape seff1 = sqrt( 1.0 / ( %hd% / 1000.0 )) seff = con ( seff1 <= 4.02, seff1, con ( seff1 <= 4.49, seff1 * 0.6, con( seff1 <= 5.68, seff1 * 0.1, seff1 * 0.001 ))) /* compute ls, need to not compute housing density on agricultural areas... %psp%ls%y%%r% = int ( ( sedgew + seff + sacc ) * 100 ) &ty [date -full] &echo &off Ecology and Society 10(1): 32 http://www.ecologyandsociety.org/vol10/iss1/art32/ Appendix 2. A high resolution map of US Housing Density for 2000 (~4.7MB). Please click here to download file ‘appendix2.pdf’. Ecology and Society 10(1): 32 http://www.ecologyandsociety.org/vol10/iss1/art32/ Appendix 3. Summaries of development patterns by county for 1980, 2000, and 2020 in MS Excel format. Please click here to download file ‘appendix3.xls’. Ecology and Society 10(1): 32 http://www.ecologyandsociety.org/vol10/iss1/art32/ Appendix 4. Summaries of Landscape Sprawl metric values by county for 1980, 2000, and 2020 in MS Excel format. Please click here to download file ‘appendix4.xls’.