National Park Visitation and Species Diversity

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National Park Visitation and Species Diversity
Haberkorn, Flora Mae
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Analyzing the relationship between National Park visitation in 2015 and species diversity in endangered species, native species, and non-native species groups. ( en )
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Awarded Bachelor of Arts, summa cum laude, on May 8, 2018. Major: Economics
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College or School: College of Liberal Arts and Sciences
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Advisor: Michelle Phillips. Advisor Department or School: Economics

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University of Florida
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Copyright Flora Mae Haberkorn. Permission granted to the University of Florida to digitize, archive and distribute this item for non-profit research and educational purposes. Any reuse of this item in excess of fair use or other copyright exemptions requires permission of the copyright holder.

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1 Flora Haberkorn Honors Thesis NATIONAL PARK VISITATION AND SPECIES DIVERSITY I. INTRODUCTION The Department of Interior National Park Service marked its 100 th year of operation in 2016. The Parks were created to protect natural, historical, and cultur al resources a nd secure the preservation of unique scenic landscapes found within the boundaries of the U.S. and its territories. Every year millions of people from across the U.S. and around the world visit Parks to experience nature and view animals in their natural habitats. Parks offer many activities such as special ecosystems. The Parks also harbor thousands of species with its boundaries from birds, mammal s fish, and reptiles to insects and plants In 1973 Congress passed the Threatened and Endangered Species Act ( ESA ) to begin to identify and begin conservation of species whose populations are threatened due to factors such as habitat loss, pollution, an d excess hunting. The habitats of these species are protected by law and often are within the boundaries of the Parks and have further added to the purpose of the Parks. T here is also a growing number of non native animals found within the Parks that add t o the total number of species This influx make s planning how to manage park resources more complex as how these animals interact with the local environment can disrupt ecosystems as well as tourist locations with their presence This investigat ion wi ll de termine whether there is a correlation between the total number of native species, the total number of non native species or the total number of ESA species and the number of visitors that enter the Parks. For instance, does the presence of lower numbers of species on the ESA list, increase the volume of visitors at the Park s or does the presence of high


2 populations of non native species affect the number of visitors. This study intends to focus on high profile taxonomic groups includi ng birds, mammals fi sh plants and reptiles II. SAMPLE The sample for this study in cludes National Parks (Parks) within the U.S. for which data can be found on the number visitors and species This information is available on the internet free of charge on National Park Service websites. National Parks known to focus on historical or cultural resources are excluded from the study as well as Parks with insufficient data across all the variables. Therefore leaving 82 Parks as the final number of observations. Sufficient data are av ailable for the year 2016 but it marked the 100 th Anniversary of the National Park Service and would have data heavily influenced by the events surrounding the celebration. Thus, the year of focus will be 2015. III. DEPENDENT VARIABLE Visitors ( Park V isitors) The d ependent variable in this study is the number of visitors in 2015 in each o f the Parks. The number of visitors that come to each Park is counted by each of the Parks themselves through their entrance ticketing systems The visitors that are being counted a re people entering the Parks for recreation purposes exclusively through the official Park entrances not including people conducting business in the Parks, employees, and citizens using the Parks for civic or local government business. Discerning if certa in groups of species affect the number of visitors that attend the Parks is important to consider for Park Service management and expansion. Visitors coming to the Parks generate a large amount of revenue for Park services such as research funding, Park to urs,


3 as well as conservation projects within the Parks. The factors that influence Park visitation is also imperative to analyze to understand why the most popular Parks, such as Yellowstone, are so popular. The visitor data from 2015 for each Park will be used in this study. IV. INDEPENDENT VARIABLES ESA Species ( T otal ESA ) The number of ESA species differ from Park to Park due to differing ecosystems across the U.S., level of diversity within the ecosystems, as well as many different factors attributing to th e species being added to the ESA list. Species are added to the ESA list due to a multitude of factors such as habitat loss or hunting. P arks around the globe have a standard to protect ESA species while protecting their visitors. The balance as mentioned previously, is difficult to maintain and brings up concerns about the best way for federal land managers in the United States to protect, preserve, and restore their natural resources (Bednar Fried, 2012). The protection of biodiversity as a fundamental b rick in the architecture of federal park management calls for necessary actions to please stakeholders while remaining consistent with the original goal of the Parks Stakeholders that ultimately matter the most are P ark visitors who pay entrance fees, whe n required, or spread the word of their appreciation for what opportunities the P ark is providing. There seems to be visitors in every N ational P ark who are there for passive recreation such as picnicking or to take photographs. Though many P arks offer fis hing, canoeing, biking and hiking, a high number of national park websites emphasize their P ESA species that may be a draw for visitors by offering the opportunity to have a potentially rare experience of seeing a rare species in the wild. For the pa st two decades, economists have attempted to translate the importance of ESA species protection into measurable terms that potentially make project planning easier. In other


4 words, the priority of protecting listed species and giv ing them an inherit value to the ecological benefits of biodiversity and then p ut ting them into economic terms so that projects protecting ESA species can be planned and prioritized in quantifiable terms directly with other park management activities has been difficult (Richardson, 2009). Overall, it should be mentioned it is difficult to find value for any goods that are not transacted in typical markets that normally have a clear determined value of the goods. These species will be taken from Parks total species lists as a separat e variable and c ollected from the largest taxonomic categories: mammals, birds, fish, plants, and reptiles. The ESA species data set will be cultivated by referencing the total species lists and separating the ESA species into a separate data set for each Park. To control any influence regarding the size of the park has on the number of species, the number of species will be divided by the number of acres in the park to create a measure of species density This will be applied to all the variables regardin g species. To prevent double counting species, two regressions will be conducted. Total ESA in the Main Regression Table 3 contain s the ESA species (excluding Non Native and Native species variables) along with the other control variables. I predict that this variable will be pos itively correlated and significant. Since ESA species are rare, I expect more visitors would go to Parks the more ESA species per acre there is because it increases the chance of visitors to view them. Non Native Specie s ( Total Non Native ) Non native speci es arrive at Parks in various ways. They could have been once pets and then released into the Parks as an act of mercy by pet owners, or escaped from households and zoos, and even integrate into the local ecosystem after getting lost during annual migrator y travels. Many non native species just add to the local biodiversity of the Parks ecosystems


5 without disrupting the current order, while others cause serious ecological problems that can devastate native species populations. For instance, in the Everglade s the python is considered a non native species and integrated into the local ecosystem after continuous release of these snakes because often python pet owners underestimate their size. The python population within the Everglades subsequently exploded bec ause they were able to thrive in the warm climate of Florida and has since become one of the top predator for more fragile local species causing many conservation issues These species will be taken from the total species lists as well as created into a s eparate variable taken from the largest taxonomic categories in the species lists : mammals, birds, fish, plants, and reptiles. The number of non native species varies from Park to Park because of similar factors as to the reason the number of ESA species v ary from Park to Park. It should be noted that within Parks there could be more non native species than on the provided lists due to the constant flow of human traffic (visitor and otherwise) within the Parks. Even though the effects of the presence of non native species have a large range of varying results from no effect to significantly detrimental consequences, overall non native species are viewed in a negative light as intruders in the local ecosystems. I predict the variable to be significant and neg atively related to Park visitation. The more non native species per acre in Parks could be associated with It is assumed people would like to visit Parks because of their original natural wonders and too many and expectation Native Specie s ( Total Native ) Native species in Parks make up for most of the species within Parks since they are close to or within the species ori ginal habitat. Native species populations can be affected by non native


6 species intrusion, as well as habitat loss and other factors. Many Parks were created in their current location to preserve local ecosystems and harbor local species. Thus, the managem ent of local species is very important to Park conservation efforts as well as research. The native species variable will be separated from the total species list s of the Parks and be constructed from the same largest taxonomic categories that were mentio ned previously The focus will be compiled from these categories of species beca use they are found in each of the National Parks included in the study therefore this creat es a uniform analysis of each Park regarding the species variables. The complete spe cies lists for most of the Parks was last completed in 2015 The total number of species found in the Parks will provide as a control for the determination of the relationship between ESA and non native species and Park visitation. The variable will be inc luded in the Total Non/Native regression I predict the variable to be significant and positively related to Park visitation. Since the protection of native species is one of the primary functions of Parks, I expect that the more native species there are in Parks, the mo re visitors would come to see a great representation of the original ecosystems and environment of the U.S. Price of Admiss ion ( Park Fee ) The price of admission for each Park varies from no fee to $25 for each Park in 20 15. The price of admission is determ ined by a multitude of factors including Park popularity and budget. The effect of entrance fees per vehicle on the number of visitors has not been shown to be clearly positive or negative by some researchers. One study looked at entrance fees for 29 natio nal parks and the number of visitors over an 11 year period. Though no clear correlation was found, the study recommended that there may be correlation that would be revealed if more factors that may influence park visitation were included in the analysis (Factor, 2007) However,


7 another study in 2014 revealed park entrance fees have a significant effect on visitation between 1993 2010, but had a relatively small impact. Factors that influence visitation more were found to be travel costs and consumer incom es (Stevens, 2014). Price of admission will act as a control because of its potential influential impact on the choice of a visitor to visi t a Park or not. As mentioned n ot all Parks have admission fees and some Parks are more expensive to visit than othe rs which can skew the data if not accounted for. I predict the value to be significant, but negatively related to Park visitation. The higher the entrance fee, the less number of visitors to the Park. It would have been preferable to account for travel cos ts and consumer incomes, however, the consumer level data is unavailable to me at this time. The data on Park fees will be the most current price as of 2017. Historical price fees could not be found, however, the entrance fees for Parks were last increased in 2016 at a consistent level from 2015. Distance of Nearest Large Cit y ( Distance ) Many Parks are located next to large cities or in very close proximity to them, while other Parks are more isolated. The location for most businesses is significant because it influences the cost of doing business for customers. The further away a business is, the more effort it takes customers to interact with the business. The same is assumed applies to National Parks. The cities that are considered the nearest large city to the individual Parks was chosen by picking the closest city to the Park that held 100,000 or more people and calculating the distance between that city and the Park boundary as of 2015. This variable serves as a proxy for determining the ease of transpo rtation to the Parks. I predict the variable will be significant and negatively related to Park visitation. The further the large cities are to the Parks, the less number of visitors to the Park. Table 5


8 Number of Visitors to Each Stat e ( T ourists) The number of visitors f o r each state will be a proxy for the level of tourism of the state where the Park is located Visitors who enter the Parks could be visiting the Parks as part of their trip, and not limiting their travels to the Parks. The variable will serve as one of the controls for the species variables. The visitor data will be focused on the year 2015. I expect the variable to be significant and positive. The higher amount of tourism a stat e has, the more likely people will visit the nearby Parks. 5 V. RESULTS In a ddition to the T otal Species variables, the bird and mammal taxonomic group s were analyzed individually the res ults are avai lable in Table 4, b ut my discussion will remain focus ed on the results for the Total Species variables because they did not provide different results separately. E SA Species ( Total ESA ) The variable for ESA species was not statistically significant as viewed in Main Regression Table 3 The t value was 0.377 which has significan ce a t low levels This is contrary to the original prediction and could be heavily affected by the fact that there are not enough ESA species per Park to analyze a relationship between their populations and Park v isit ation. There are various reasons why people may visit Parks which include to see famous landmarks that are preserved within the Parks as well as to review famous/attractive species such as exclusively bears or eagles. However, because it is difficult t o control for these factors, instead I controll ed what can be uniformly measured which is species diversity.


Table1:CorrelationMatrix TouristsParkVisitorsParkFeeDistanceTotalESATotalNativeTotalNon-Native Tourists1.000.050.01-0.32-0.07-0.07-0.07 ParkVisitors0.051.000.36-0.23-0.09-0.09-0.09 ParkFee0.010.361.00-0.12-0.11-0.11-0.11 Distance-0.32-0.23-0.121.00-0.06-0.06-0.06 TotalESA-0.07-0.09-0.11- TotalNative-0.07-0.09-0.11- TotalNon-Native-0.07-0.09-0.11- Table2:SummaryStatistics TouristsParkVisitorsParkFeeDistanceTotalESATotalNativeTotalNon-Native nbr.val78.0078.0078.0078.0078.0078.0078.00 nbr.null0.000.0034. nbr.na0. min2.07e61133.000.0013. max2.51e81.07e730.001205.430.053.970.44 range2.49e91.07e730.001192.330.053.970.44 sum5.61e91.02e8664.0014700.080.054.310.46 median4.10e76.29e57.50139.730.000.000.00 mean7.20e71.31e68.51188.460.000.060.01 SE.mean8.89e61.88e51.0423. CI.mean.0.951.77e73.75e52.0845.890.000.100.01 var6.17e152.76e1284.9041430. std.dev7.85e71.66e69.21203.540.010.450.05 coef.var1. Table3:MainRegressionResults Dependentvariable: ParkVisitors ESATotalNon/NativeTotal Tourists-0.001-0.001 .0020.002 ParkFee5.77e4 5.75e4 .92e4.94e4 Distance-1.50e3 -1.73e3 .62e2.33e2 TotalESA-2.03e7 .41e7 TotalNative-1.97e7 .47e7 TotalNon-Native1.77e8 .15e8 Constant1.17e6 1.24e6 .81e5.94e5 Observations8079 R 2 0.1660.172 AdjustedR 2 0.1220.115 Note: p < 0.1; p < 0.05; p < 0.01 9 9 9


Table4:AddedRegressionResults Dependentvariable: ParkVisitors ESABirdsNon/NativeBirdsESAMammalsNon/NativeMammals Tourists-0.001-0.001-0.0004-0.0003 . ParkFee5.76e4 5.65e4 6.04e4 5.22e4 .94e4.94e4.92e4.99e4 Distance-1.54e3 -1.60e3 -1.4e3-1.13e3 .8e2.8e2.6e2.75e2 ESABirds-196e9 .65e9 NativeBirds-2.21e10 .20e10 Non-NativeBirds1.74e8 .36e8 ESAMammals1.94e9 .68e9 DensityofNativeMammals 6.35e8 .84e8 Non-NativeMammals 9.27e9 .60e9 Constant1.20e9 1.25e9 1.08e9 7.79e8 .18e8.08e8.81e8.20e8 Observations80808080 R 2 0.1640.1700.1660.194 AdjustedR 2 0.1200.1140.1210.140 ResidualStd.Error1.54e6df=751.55e6df=741.54e6df=751.52e6df=74 FStatistic3.690 df=4;753.033 df=5;743.721 df=4;753.568 df=5;74 Note: p < 0.1; p < 0.05; p < 0.01 10


11 Non Native Specie s ( T otal N on N a ti v e ) The variable for non native species was not statistically significant contrary to the prediction as viewed in Main Regression Table 3 The t value was 1.186 is significant at low levels regarding Park visitation. This could be related to the low number of available observations. Further research on this topic would include more years of data. Native Specie s ( T otal N a ti v e ) The variable for native species was not statistically significant as viewed in Table 3 which conflicts with the original prediction The t value was 1.224 just below significance at the 90% level. Price of Admissio n ( P ark F ee) The variable for price of admission was statistically significant in both regressions at the 99% level as viewed in T able 3 This is almost consistent with the hypothesis that Park entrance fees affect visitation, however, it reveals a positive relationship with a posi tive coefficient Increasing Park entrance fees will increase the number of visitors that attend the Parks. The reason for this relationship could be influenced by the fact that there are Parks with no entrance fee or an entrance fee of $0 and that these P arks also have some of the lowest number of visitors. While the Parks with the largest number of visitors have the highest amount of entrance fees. The relationship between price of admission and Park visitation could be that because the Parks are so popul ar, Parks take advantage of the popularity and charge visitors. While the unpopular barrier to entry to the Parks at the risk of losing visitors. Further, another element could be


12 impacting both price and visitation such as a Park lacking a popular attraction such as a famous landmark or species. Thus, the relationship could be endogenous between price and visitation. Distance of Nearest Large Cit y ( Dist ance ) T he variable was statistically significant in only Non/Native r egression in Table 3 at the 95% level. The coefficient is also negative which is exactly consistent with my hypothesis. The further away the Parks are from big cities, the more difficult it is to travel to the Parks which is associated with lower Park visitation levels. Number of Visitors to Each Stat e ( T ourists) The variable was not statistically significant in both regressions as seen in Table 3 It had t value o f 0.022 and 0.002 respectively which holds a low level of significance This conclusion is interesting since it was high ly expected to be a factor in Park visitation in terms of Park state tourism potential. Parks are generally seen as a tourist attractio n and I predicted it to behave like one (e.g. visitors would actively plan to visit them as part of their vacations and the more popular the state, the overall higher volume of visitors) Thus, it was expected that more visitors would visit popular states or states with a high level of tourism. Conclusion The primary independent variables that were being tested were all insignificant which could be influenced by many factors. Limited observations and unaccounted for influences could all have affected the re choice to visit Parks may not be influenced by the species diversity within the Park, but more conventional economic reasons such ease of travel to these Parks and cost of entry. Addi tionally, other elements that could be in important that influence a n individual


13 include whether the terrain of the Park is unique (canyons, waterfalls, mountains) or if they have famous species, but were not able to be accounted f or in this study. The R squared value of this regression is also low thus there is potential for creating better models of Park visitation In Table 4 more specific regressions that focused on bird and mammals, but they did not hold significant differences separately from the main regressions so even further changes to the taxonomic variables must occur. Further study will be done separating the species into different distinct categories and analyzing separate regressions to see i f visitation is influenced by specific descriptive group s or if it is not affected at all by any descriptive group, but a different variable. Nonetheless, this study is the beginning of the analysis of determining what Parks have that attract visitors and discourage visitors in addition to what Parks can do to improve their services and conservation efforts.


14 I. WORKS CITED Bednar Friedl, B., Behrens, D.A. & Getzner, M. Optimal Dynamic Control of Visitors and Endangered Species in a National Park. Environm ental Resource Economy (2012) 52: 1. 011 9515 5 Factor, Seth. 2007. Effects of Per vehicle Entrance Fees on U.S. National Park Visitation Rates. A thesis submitted in partial fulfillment of a Masters Degree. Duke University. Durham, North Carolina. Richardson, Leslie, and John Loomis. 2009. The total economic value of threatened, endangered, and rare species: An updated meta analysis. 68(5):1535 1548. Stevens, Thomas. H., and Mar la Markowski Lindsay. 2014. Declining Park Visitation: An Economic Analysis. Journal of Lecture Research. 46(2):153 164. II. DATA REFERENCES National Park Service park names, size (area in acres [federal land only]), year authorized: National Park Service, 20 16. The National Parks: Index 2012 216. Office of Communications and the Office of Legislative and Congressional Affairs. National Park Service. U.S. Department of the Interior. Washington, D.C. National Park Service total number of visitors by park unit and year: NPS Stats, October, 2017, National Park Service total number of species by taxonomic group, subdivided into native, non native, and ESA listed species: ior, Oct. 2017, Census of people per state in 2015, size of state, cities with population of at least 100,000 in 2016: Distance between park and closest city of at least 100,000 in 2016: .com/ Number of visitors, domestic overnight stays taking into account international arrivals, to state in 2015 (all retrieved October 2017:


15 Table 5 : Self Collected Data National Park State of park Number of tourists in 2015 Nearest major city ( 100,000 pe ople ) State of nearest major city Distance to nearest major city ( miles ) Population of nearest city in 2016 Acadia Maine 33,000,000 Manchester NH 258.41 110506 Arches Utah 52,729,516 Provo UT 197.98 116868 Badlands South Dakota 13,700,000 Fort Collins C O 357.13 164207 Big Bend Texas 17,500,000 Odessa TX 213.53 117871 Biscayne Florida 105,000,000 Miami FL 21.81 453579 Black Canyon of the Gunnison Colorado 77,000,000 Colorado Springs CO 203.33 465101 Bryce Canyon Utah 52,729,516 Henderson NV 236.19 292 969 Canyonlands Utah 52,729,516 Provo UT 217.01 116868 Capitol Reef Utah 52,729,516 Provo UT 167.77 116868 Carlsbad Caverns New Mexico 34,000,000 El Paso TX 138.11 683080 Channel Islands California 251,000,000 San Buenaventura CA 39.73 109592 Congaree South Carolina 30,000,000 Columbia SC 26.46 134309 Crater Lake Oregon 82,900,000 Eugene OR 125.22 166575 Cuyahoga Valley Ohio 207,000,000 Akron OH 16.92 197633 Death Valley California 251,000,000 Las Vegas NV 141.35 632912 Dry Tortugas Florida 105,000 ,000 Cape Coral FL 160.28 179804 Everglades Florida 105,000,000 Hialeah FL 56.14 236387 Glacier Montana 11,700,000 Spokane WA 272.83 215973 Grand Canyon Arizona 43,000,000 Henderson NV 194.99 292969 Grand Teton Wyoming 10,500,000 Billings MT 245.8 1103 23 Great Basin Nevada 36,084,507 Provo UT 214.75 116868 Great Smoky Mountains North Carolina 50,000,000 Knoxville TN 40.66 186239 Guadalupe Mountains Texas 17,500,000 El Paso TX 112.3 683080 Haleakala Hawaii 8,600,000 Honolulu HI 117 351792 Hawai'i Vo lcanoes Hawaii 8,600,000 Honolulu HI 211 351792 Hot Springs Arkansas 28,000,000 Little Rock AR 59.09 198541 Isle Royale Michigan 113,400,000 Saint Paul MN 428.37 302398 Joshua Tree California 251,000,000 Moreno CA 90.6 205499 Kenai Fjords Alaska 2,070,000 Anchorage AK 178.92 298192 Kings Canyon California 251,000,000 Visalia CA 69.36 131074 Kobuk Valley Alaska 2,070,000 Anchorage AK 1180.24 298192 Lassen Volcanic California 251,000,000 Sacramento CA 41.37 495234 Mammoth Cave Kentucky 20,580, 000 Louisville KY 96.1 616261


16 Mesa Verde Colorado 77,000,000 Albuquerque NM 224.15 559277 Mount Rainier Washington 39,400,000 Kent WA 64.62 127514 America Samoa American Samoa 128,000 Honolulu HI 2620.63 351792 North Cascades Washington 39,400,000 Ever ett WA 107.28 109043 Olympic Washington 39,400,000 Seattle WA 105.5 704352 Petrified Forest Arizona 43,000,000 Scottsdale AZ 198.28 246645 Pinnacles California 251,000,000 Salinas CA 36.77 157218 Redwood California 251,000,000 Sacramento CA 378.2 49523 4 Rocky Mountain Colorado 77,000,000 Boulder CO 42.88 108090 Saguaro Arizona 28,880,000 Tuscan AZ 13.1 530706 Sequoia California 251,000,000 Visalia CA 52.16 131074 Shenandoah Virginia 41,000,000 Richmond VA 106 223170 Theodore Roosevelt North Dakota 25,000,000 Billings MT 369.96 110323 Virgin Islands Virgin Islands 2,643,021 Miami FL 1209.94 453579 Voyageurs Minnesota 41,000,000 Fargo ND 320.95 120762 Wind Cave South Dakota 13,700,000 Fort Collins CO 302.3 164207 Yellowstone Wyoming 10,500,000 Bil lings MT 184.15 110323 Yosemite California 251,000,000 Clovis CA 89.72 106583 Zion Utah 52,729,516 North Las Vegas NV 173.78 238702 Assateague Island Maryland 39,800,000 Virginia Beach VA 115.25 452602 Canaveral Florida 105,000,000 Orlando FL 47.69 277 173 Cape Cod Massachusetts 26,800,000 Boston MA 82.27 673184 Cape Hatteras North Carolina 50,000,000 Virginia Beach VA 141.45 452602 Cape Lookout North Carolina 50,000,000 Wilmington NC 136.75 117525 Cumberland Island Georgia 103,358,000 Jacksonville F L 44.63 880619 Fire Island New York 8,517,000 Bridgeport CT 45.99 145936 Gulf Islands Florida 105,000,000 Clear Springs FL 62.69 114361 Padre Island Texas 17,500,000 Brownsville TX 62.86 183823 Point Reyes California 251,000,000 Santa Rosa CA 35.90 175 155 Apostle Islands Wisconsin 105,200,000 Saint Paul MN 264.71 302398 Indiana Dunes Indiana 27,400,000 Chicago IL 46.40 2704958 Pictured Rocks Michigan 113,400,000 Grand Rapids MI 351.08 196445 Sleeping Bear Dunes Michigan 113,400,000 Grand Rapids MI 2 08.63 196445 Buffalo NR Arkansas 28,000,000 Springfield MO 110.77 167319 New River Gorge NR Virginia 41,000,000 Winston Salem NC 159.59 242203 Obed Wild and Scenic River Tennessee 110,000,000 Knoxville TN 46.50 186239 Ozark National Scenic Riverway Mis souri 39,000,000 Springfield MO 146.38 167319


17 Saint Croix National Scenic Riverway Wisconsin 105,200,000 Saint Paul MN 146.95 302398 Bering Land Bridge NPRES Alaska 2,070,000 Anchorage AK 1205.43 298192 Big Cypress NPRES Florida 105,000,000 Hialeah FL 5 6.54 236387 Big Thicket NPRES Texas 17,500,000 Beaumont TX 22.32 118299 Denali NP and NPRES Alaska 2,070,000 Anchorage AK 381.76 298192 Great Sand Dunes NP and NPRES Colorado 77,000,000 Pueblo CO 73.53 110291 Katmai NP and NPRES Alaska 2,070,000 Anchor age AK 510.11 298192 Little River Canyon NPRES Alabama 6,100,000 Chattanooga TN 148.20 177571 Mojave NPRES California 251,000,000 Henderson NV 87.09 292969 Noatak NPRES Alaska 2,070,000 Anchorage AK 1316.92 298192 Tallgrass Prairie NPRES Kansas 24,000, 000 Topeka KS 81.84 126808