Determinants of fire regime variability in lower elevation forests of the northern Greater Yellowstone Ecosystem by Jeremy Scott Littell A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science in Land Resources and Environmental Science Montana State University © Copyright by Jeremy Scott Littell (2002) Abstract: Understanding how multivariate influences on fire regime cause changes in the spatio-temporal distribution of fire regimes and fire events is critical to the management of forest ecosystems. Climatic change, land-use change, and changes in vegetation patterns during the 20th century in the Greater Yellowstone Ecosystem have been documented, and understanding the past fire history of the ecosystem is necessary to gauge uncertainty and opportunity surrounding management decisions. Previous research in Greater Yellowstone has primarily focused on slow-growing forests with fire return intervals of approximately 200-400 years. Relatively little research has focused on lower elevation forests with shorter fire return intervals. I produced three 400-year dendrochronologically precise fire histories in biophysically similar but geographically separate watersheds in northern Greater Yellowstone. The watersheds are primarily composed of lower elevation Douglas-fir (.Pseudotsuga menziesii) forests with smaller components of lodgepole pine {Pirns contortd), subalpine fir {Abies lasiocarpa), Engelmann spruce {Picea engelmannii), and limber pine {Pirns flexilis). I correlated site-specific and regional fire regime with climatic variability using a variety of multi-taper and bootstrapping time series analysis techniques. At one of the three watersheds, I also related spatial fire regime variability to spatial and topographic variables that influence soil moisture using partial Mantel analysis. Results suggest fire regimes in northern Greater Yellowstone lower elevation forests are defined as much by their variability as the mean fire interval (20-35 years) and that this variability is related to multi-decadal and sub-decadal synoptic Pacific climate phenomena that affect local climate. In addition, these forests exhibit mixed-severity fire regimes throughout the fire history record. Spatial variability in fire regimes is primarily related to elevation. Given the variability in fire return interval and the mixed-severity classification of these fire regimes, it is doubtful that any significant differentiation between current forest structure and the natural range of variability- in forest structure can be made. Instead, the current and predicted future ecological and climatological environment must be considered a proximate driver of fire regime in these systems and management scenarios must be crafted with this uncertainty in mind.  DETERMINANTS OF FIRE REGIME VARIABILITY IN LOWER ELEVATION FORESTS OF THE NORTHERN GREATER YELLOWSTONE ECOSYSTEM by Jeremy Scott Littell A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science in Land Resources and Environmental Science MONTANA STATE UNIVERSITY Bozeman, Montana April 2002 APPROVAL of a thesis submitted by Jeremy S. Littell This thesis has been read by each member of the thesis committee and has been found to be satisfactory regarding content, English usage, format, citations, bibliographic style, and consistency, and is ready for submission to the College of Graduate Studies. Lisa J. Graumlich / / A j A ( ^ T ( I^ J Io 2— (Signature) Date Approved for the Department of Land Resources and Environmental Sciences Jeffrey S. Jacobsen (Signature) A iZfzt/O-L, Date Approved for the College of Graduate Studies Date Bruce R. McIxod (Signature) iii STATEMENT OF PERMISSION TO USE In presenting this thesis in partial fulfillment of the requirements for a master’s degree at Montana State University, I agree that the Library shall make it available to borrowers under the rules of the Library. If I have indicated my intention to copyright this thesis by including a copyright notice page, copying is allowable only for scholarly purposes, consistent with “fair use” as prescribed in the U.S. Copyright Law. Requests for permission for extended quotation from or reproduction of this thesis in whole or in parts may be granted only by the copyright holder. Signature ———✓ * Date Za Af&si- 0.67 of the samples in the plot were within 15 years of the center of the distribution described by the median and the mode, the initiation dates of the trees were within a maximum of 40 years standard deviation, and a fire detected elsewhere in the site suggested a possible disturbance event, I counted the plot as strong evidence of stand replacing fire in that plot for that fire year. If no fire detected elsewhere in the site corresponded with these stand initiation dates, I counted the plot as strong evidence of a fire event prior to the stand initiation date but did not attempt to reconstruct minimum fire perimeters. Plots that were temporally coherent with known fires were combined with fire scar dates to produce heuristic maps of minimum estimated fire perimeters for known fire years. Results A total of 127 fire-scarred trees were sampled: 43 at Soda Butte Creek, 41 at Crevice Creek, and 43 at Cinnamon Creek. Of the 127 fire scarred samples, 108 cross dated and contained interpretable evidence of fire events. At Soda Butte Creek, I also cored 541 trees in 36 plots. I will first address the site-specific results from the combined partial cross sections and plot study at Soda Butte Creek, then focus on the Crevice Creek and Cinnamon Creek sites, and finally address the summary results from all fire history information in a regional context. 24 Soda Butte Creek. The summary fire history chart from fire-scarred trees at SBC (Figure 3) shows the results from 35 cross-dated samples. Eight samples either did not cross date well or contained scars that could not be reliably distinguished from other types of injuries. Notable fire events (scarring > 3 recorder trees) occurred in 1584, 1634, 1681, 1693, 1695, 1744, 1756, 1800, 1855, and 1870 (Table I). Other events occurred in 1774, 1790, 1822, 1855, and 1905. The mean fire interval for the entire 1400 ha SBC site for all events recorded on three or more trees is 35.75 years. The standard deviation is 22.33 years; the minimum fire return interval is 2 years, and the maximum fire return interval is 63 years. Table I. Fire history of Soda Butte Creek, Yellowstone National Park Year Total Scars Interval (years) 1584 4 1634 7 50 1681 3 47 1693 8 12 1695 4 2 1744 4 49 1756 15 12 1800 19 44 1855 5 55 1870 12 15 A Kolmogorov-Smirnov goodness of fit test was used to evaluate the null hypothesis that the observed distribution of return intervals for fires that scarred three or more trees could be modeled by an empirical distribution, and the test was not significant (di = 0.371, n = 8,p = 0.22). Similarly, a Weibull distribution models the fire return interval adequately (rf,- = 0.311, n = 8, p = 0.42). The Weibull median fire return interval ' " " T 1 h+^™ '.......1.......1 -I-+- -^--- 1- z " " 4 " - t 1 + ----- 4+ ' X:i, 'I - T " + ' 4 - i ............................. SBC003 -----------------------x SBC004 ...............- ......... SBC005 SBC006X -----------------------x SBC007 ----------------------- x. SBC008 ............................ x SBC009X .............................. -x SBCOIO ----------------------- x SBC01I — .............-x SBC012 ..................... x SBC013 — ..................—..............-x SBC014 -----------------------.x SBC015X --------------------- x. SBC016 — .............-x SBC018 ---------------------- -x SBC019 ....... ............. x S8C022 — - -x SBC023 --------------------- -x SBC024 .......................... -x SBC025 -x SBC026X ------------------ -x SBC028X S8C029X ------------x SBC030X SBC031X --- 4-.................... -x SBC032 . — -----------------x SBC034 ------ ----------------- x S8C036 ................................x SBC037 — ......................x SBC038X --------------- x SBC039X -----------------------x SBC040 ---------------------- „x SBC041 ........................ -x SBC043X 11 Il 11 111 1111111111111111111111111 11111111111 11111111111 111111111 III 1111 MF ttP 1111 111111111111111111 11111111111111111111 1450 1500 1550 1600 1650 1700 1750 1800 1850 1900 1950 2000 Figure 3. Soda Butte Creek, Yellowstone National Park Master Fire Chart. Each dark vertical bar represents a fire scar detected on the tree sampled on the Y axis on the right hand side of the chart. The dotted lines along the X axis represent the time span of the sample. Prominent fire years recorded on many samples are 1696,1756,1800, and 1870. 26 is 30.33. The significance of the goodness-of fit tests does not change if fires that scarred two trees only are included in the analysis. Fire evidence in cores from the SBC plots primarily reconfirmed fire dates already established in the fire scar record, but plot evidence suggested the 1822 and 1844 fires were more widespread than indicated by the fire scar record alone. Nine plots met the criteria outlined in the methods section for stand replacing fires. An additional six plots contained trees that yielded fire scars when cored. Fire history information derived from the plot cores is presented in Table 2. Adding these events to the fire chronology for Soda Butte Creek decreases the mean fire return interval to 30 years with a standard deviation of 18.23. Table 2. Age-class Cohort Plots With Detected Fire Evidence. Plot Method of detection Fire year Estimated Plot Median Estimated Plot Mode Estimated Plot SD I age class 1822? 1855 1855 18.01 2 age class 1855 1878 1887 24.76 3 age class unknown 1938 1946 15.20 4 age class 1870 1899 1883 13.26 6 dated scar 1870 - - - 8 dated scar 1870 - - - 9 dated scar 1870 - - - 10 dated scar 1870 - - - 11 documented burn 1988 - - - 16 age class 1844 1849 1845 38.89 17 dated scar 1800, 1844 - - - 21 age class unknown 1922 1900 11.13 27 age class 1870 1880 1863 16.76 30 age class 1855 1870 1885 17.54 33 age class 1870 1895 1895 31.90 35 dated scar 1870 - - - 27 Reconstructed fire perimeters (Appendix I) suggest the relative size of fires varied through time, but there are insufficient fire events to determine the significance of any hypothesized watershed level response through time. There is no evidence to suggest a relationship at the watershed scale between time since fire and estimated area of the fire event. Crevice Creek. The summary fire chart for Crevice Creek shows the master fire chronology derived from 39 cross dated samples (Figure 4). Two samples did not cross­ date. Notable fire events (scarring > 3 recorder trees) occurred in 1696, 1729, 1732, 1740, 1771, 1775, 1784, 1800, and 1867 (Table 3). Other events (2 recorder trees scarred) occurred in 1691, 1705, 1714, 1748, 1751, 1863, and 1875. The mean fire interval for the entire 1400 ha CCR site for all events recorded on three or more trees is 21.38 years. The standard deviation is 21.74 years; the minimum fire return interval is 3 years, and the maximum fire return interval is 67 years. Table 3. Fire history of Crevice Creek, Yellowstone National Park Year Total Scars Interval (years 1696 12 1729 3 33 1732 3 3 1740 3 8 1771 11 31 1775 8 4 1784 4 9 1800 6 16 1867 7 67 CCROOI CCR002 CCR003 CCR004X CCR005 CCR006 CCR007X CCR008X CCR009 CCRO10 CCR01I CCRO12 CCROI 3 CCROUX CCROI 5X CCRO16 CCRO17 CCROI 9 CCR020X CCR021X CCR022 CCR023 CCR024 CCR025 CCR026 CCR027X CCR029 CCR030X CCR031 CCR032B CCR033 CCR034 CCR035X CCR036 CCR037 CCR038X CCR039X CCR040X CCR041 1450 1500 1550 1600 1650 1700 1750 1800 1850 1900 1950 2000 Figure 4 . Crevice Creek, Yellowstone National Park Master Fire Chart. Each dark vertical bar represents a fire scar detected on the tree sampled on the Y axis on the right hand side of the chart. The dotted lines along the X axis represent the time span of the sample. Prominent fire years recorded on many samples are 1695,1771,1775,1800, and 1867. 29 Kolmogorov-Smirnov goodness of fit tests suggested the CCR fire return interval can be adequately modeled by an empirical distribution (di = 0.386, n = 8, p = 0.18). A Weibull distribution models the fire return interval adequately (di = 0.182, n = 8,p = 0.95). The Weibull median fire return interval is 15.65 years. If fire events scarring only two trees are included in the test, Kolmogorov-Smirnov goodness of fit test suggests the CCR fire return interval are not adequately modeled by an empirical distribution (dt = 0.616, n = 15, p = 0.0001). Reconstructed fire perimeters suggest the relative size of fires varied through v „ - time, but there are insufficient fire events to determine the significance of any hypothesized watershed level response through time. There is no evidence to suggest a relationship at the watershed scale between time since fire and estimated area of the fire event. Cinnamon Creek. The summary fire chart for Cinnamon Creek shows the master fire chronology derived from 35 cross dated samples (Figure 5), Eight samples either did not cross-date or contained scars not identifiable as fire scars. Notable fire events (scarring > 3 recorder trees) occurred in 1702, 1734, 1743,1750, and 1800 (Table 4). Other events (2 recorder trees scarred) occurred in 1715,1759, 1769, 1805, 1840, an 1870. The mean fire interval for the entire 1400 ha CIN site for all events recorded on three or more trees is 24.50 years. The standard deviation is 20.44 years; the minimum fire return interval is 7 years, and the maximum fire return interval is 50 years. CINO01X CIN002X CIN003X CIN004X CIN006 CIN007X CIN008X CIN009X CIN010 CIN013 CIN014 CIN015 CIN016 CIN017 CIN018X CIN020 CIN021 CIN022 CIN023X CIN024 CIN025 CIN027 CIN028 CIN030X CIN031 CIN032 CIN033 CIN034 CIN035X CIN036 CIN037X CIN038 CIN039 CIN040X CIN043 1450 1500 1550 1600 1650 1700 1750 1800 1850 1900 1950 2000 Figure 5. Cinnamon Creek, Gallatin National Forest Master Fire Chart. Each dark vertical bar represents a fire scar detected on the tree sampled on the Y axis on the right hand side of the chart. The dotted lines along the X axis represent the time span of the sample. Prominent fire years recorded on many samples are 1702 and 1800 . 31 Table 4. Fire history of Cinnamon Creek, Gallatin National Forest Year Total Scars Fire Interval 1702 12 1734 5 32 1743 4 9 1750 4 7 1800 27 50 Kolmogorov-Smirnov goodness of fit tests suggested the CCR fire return interval can be adequately modeled by an empirical distribution (J, = 0.320, n = 4, /? = 0.81). A Weibull distribution models the fire return interval adequately (45 yr) intervals between fires contributing to high standard deviations about the mean fire return intervals. This evidence fits the criteria to reject the first alternative hypothesis and the criteria to fail to reject the second null hypothesis. Past fire regime research in northern Greater Yellowstone implied that Douglas- fir forests in Gallatin National Forests had relatively short fire return intervals of approximately 11 years (Losensky.1993). Similar forests in the northern range of Yellowstone National Park exhibited fire return intervals of 25 to 50 years (Houston 1973). The results presented here appear to confirm previous research when fire scarred samples alone are included, but when stand age class data is included, fire regimes appear more mixed, even at the local scale (hectares to kilometers) of a single site. The relatively fine scale variation suggested by the combined stand age class and fire scarred tree data implies a blanket fire regime can not be applied over a given forest type at a regional scale. Instead, the heterogeneity at a given site in determinants of forest structure and fuel moisture might have an important influence on the variability of “typical” fire intensities across space. Thus the hypothesis that fire regimes are not equally consistent in space and time best fits the data: detectable temporal and spatial patterns of fire events exist within and between watersheds, but vary through time and across space. 36 Caveats associated with failure to reject the hypothesis that fire regimes vary in space and time are primarily associated with the evidence I used to reconstruct patterns of fire regime. First and foremost, fire evidence declines in quality as time progresses after a given fire event. This is a function of everything from insect damage of fire-scarred trees to the replacement of stands or trees containing fire regime evidence. In addition, not all fires leave evidence that can be detected 400 years post fire. Thus, reconstructing the perimeter of fire events becomes less certain as time passes. However, the conclusions presented here are derived from evidence that supports the maximum fire return interval for lower elevation forests of the Greater Yellowstone Region is around 25 to 30 years on average, but, more importantly, there is substantial variability about that average. The next chapters of this thesis document the search for mechanisms leading to that variability. Conclusion If fire regimes vary substantially between and within biophysically similar sites in a region through time, the utility of the mean fire return interval derived from fire-history research as a management tool must be called into serious question. It is possible that the statistical mean of fire return intervals bisects a bimodal or multimodal distribution of return intervals driven by temporal variation in the most important drivers of fire regime. For example, the existence of multi-decadal periods of anomalously high precipitation as well as the existence of multi-decadal droughts is substantiated by millennial length tree­ ring chronologies sampled in the vicinity of these fire history sites (Graumlich et al. in prep, Douglas and Stockton 1975). During multi-decadal drought episodes, regional 37 climate is probably the most important driver of fire regime and thus fire regimes are more likely to be synchronous over large spatial scales. In contrast, during multi-decadal periods with anomalously high precipitation, local factors such as aspect, elevation, topographic convergence and slope probably have a much higher influence on fire regime and regional fire regimes as a result are probably relatively heterogeneous. If this is the case, managing for the mean fire return interval might be the most effective way to impose temporary, artificial homogeneity on forested ecosystems instead of managing for the variation that is suggested by past and present climate trends and disturbance history. Homogeneous systems tending toward equilibrium are almost never found under natural disturbance regimes if long enough temporal scales are examined; apparently stationary systems eventually satisfy the second law of thermodynamics with a large, relatively sudden increase in entropy which results in reorganization of the system’s structure and function. For agencies charged with managing for natural resource extraction, preservation of native biodiversity, recreation opportunities and landscape connectivity, or more commonly, some or all of the above, the inherent variability in disturbance drivers and responses demands close scrutiny of the benchmarks used to interpret fire regime and how to mange it. 38 CHAPTER 3 CLIMATIC DRIVERS OF FIRE REGIME IN THE NORTHERN GREATER YELLOWSTONE ECOSYSTEM Introduction Forest fire events are well correlated with dry “fire weather” when fuel moisture drops below critical thresholds and the ignition and spread of fires becomes more likely. The influence of long-term regional climate on the frequency of fire weather has been inferred from contemporary reconstructions of long-term precipitation and fire frequency (Grissino-Mayer 1995, Heyerdahl 1997, Swetnam 1993). Understanding the influence of regional climate on fire regime is critical both in reconstructing the mechanisms leading to past fires but also in forecasting how future climate change will likely affect future fire regime. In the Greater Yellowstone Ecosystem, global and regional climate models have hypothesized not only different magnitudes of precipitation change under global warming scenarios, but also different directions of change (Bartlein et al. 1997, Romme and Turner 1991, Whitlock and Bartlein 1993). Over the last several hundred years, lower elevation forests of the GYR exhibit mixed fire regimes that, depending on the temporal window and spatial location, have varied between classic surface fire regimes characterized by relatively high-frequency, low intensity fires and mixed fire regimes characterized by lower frequency, higher intensity fires (Chapter 2, Barrett 1994, Houston 1973, Romme 1982). Future changes in precipitation may have a marked influence on the trajectory of 39 GYR forests and the likely fire frequency, intensity, and scale. Thus it is important to document the strength of the relationship between precipitation variability of known fire events in the GYR and whether that relationship has changed over time. Variation in long-term climate in the Greater Yellowstone Ecosystem since the last glacial maximum has been documented from analysis of pollen and sediment deposition in lake sediments (Whitlock and Bartlein 1993). However, the annual resolution necessary for reconstructing the past relationship between precipitation anomalies and fire events is not usually present in sediment. Instead, tree-ring based reconstructions of annual precipitation for the last thousand years can be used to model annually resolved estimates of precipitation variability as well as to determine the temporal distribution of fire. Therefore tree-rings provide a good basis for determining the relationship between precipitation variability and fire regime. The dominant synoptic climate phenomena in a region often have a profound influence on fire regime. Research in the southwestern United States, for example, has documented a significant influence of the El Nino Southern Oscillation (ENSO) on fire events in the southwestern United States (Grissino-Mayer 1995, Swetnam and Betancourt 1993). ENSO is a sub-decadal (2 to 7 years) periodic phenomenon with two dominant phases. In El Nino years, when the Southern Oscillation Index (SOI) is low, the southwestern United States is subject to incursions of moist air from the Pacific Ocean during the spring and fall, which significantly increases precipitation in the region prior to and during the potential fire season. This effect increases fuel moisture, thus decreasing the probability of fire. In La Nina years when SOI is high, the southwestern U.S. usually experiences drier conditions and fires are more probable. In contrast, the 40 Pacific Northwest experiences a decrease in winter precipitation when the SOI is low (El Nino conditions) and an increase when the SOI is high. The influence of these patterns on the GYR is less dramatic than in the Southwest during the 20th century, but the Yellowstone River basin experiences similar ENSO-driven shifts to the rest of the Pacific Northwest (Dettinger et al. 1993). ENSO is not the only climate pattern documented to exert an influence on climate in the Greater Yellowstone Ecosystem. The Pacific Decadal Oscillation (Hare and Francis 1995), a multi-decadal climate phenomenon with currently unspecified patterns of sea surface temperature and atmospheric circulation causes, exhibits two multi-decadal phases. Cool-phase PDO (negative values of PDO) typically results in cooler springs and increased winter/early spring precipitation, whereas warm-phase PDO (positive values of PDO) often results in below average October to March precipitation, spring snow pack, and water year stream flow in the northwestern United States (Dettinger et al. 1998). Documented PDO cycles during the 20th century (e.g. Mantua et al. 1997, Minobe 1997) suggest cool PDO (low values) regime from 1890-1924 as well as 1947-1976, while warm PDO (high values) regimes prevailed from 1925-1946 and from 1977 through the mid-1990s. Thus both ENSO and PDO have been demonstrated to affect precipitation in the Greater Yellowstone Ecosystem during the 20th century. The.relative strength of ENSO and PDO have been modeled back in time for several centuries by evaluating the statistical relationship between tree-rings and 20th century climate parameters and then “backcasting” the relationship through time (Stable et al. 1998, Cook 2000, Biondi et al. 2001). In contrast, the relationship between fire occurrence and long-term climatic 41 variability is difficult to “backcast” empirically because the period of relatively precise climate observation (the 20th century in the GYR) is also the period of active fire exclusion and fire suppression in forests of Greater Yellowstone. Therefore reliable relationships between past fire and climate must be derived from tree-ring based reconstructions of climate and fire regime. I am unaware of any previous attempt to relate long-term precipitation records to an established fire history reconstruction using tree- rings in this region. The goal of this chapter is to explore the relationships between the regional fire history developed in chapter one and local tree-ring reconstructions of precipitation proxies for the Greater Yellowstone Region. A second goal is to evaluate the relationship between PDO, ENSO, and fire regime in the GYR. To that end, I will evaluate four hypotheses. Note that each alternative hypothesis has an implied null associated with it and that the labeled null hypothesis is an overall null accounting for a “no relationship” scenario. HO: There are no coherent relationships between regional climate patterns and fire events in the Greater Yellowstone Region; fire events occur during “fire weather”. HA l: Regional fire events are fully explained by the effects of the Pacific Decadal Oscillation (PDO) on climate in the GYR. HA2: Regional fire events are fully explained by the effects of the Southern Oscillation (ENSO) on climate in the GYR. HA3: The relationship between regional fire events and climate is a function of both ENSO and PDO. 42 The null hypothesis is intended to address the possibility that seasonal precipitation anomalies of days to weeks in length that result in fires are the primary manifestation of a fire-climate relationship in the GYR. Evidence leading to the rejection of this hypothesis would include statistically significant relationships between established long-term periodic climate patterns and regional fire events. Evidence leading to a failure to reject the null hypothesis would entail a lack of significant relationships between long­ term climate patterns and fire events in the GYR. The first alternative hypothesis is intended to address the possibility that the Pacific Decadal Oscillation, through its influence on GYR seasonal precipitation, is the primary mechanism behind dry periods that lead to fire. Evidence leading to the rejection of this hypothesis would include lack of a significant relationship between years when PDO values are above normal and the occurrence of fire events. Evidence leading to the failure to reject this hypothesis would be a significant relationship between above average PDO values and fire events. The second alternative hypothesis is intended to address the possibility that the El Nino Southern Oscillation, through its influence on GYR precipitation, is the primary mechanism behind dry periods that lead to fire. Evidence leading to the rejection of this hypothesis would be lack of a significant relationship between low values of ENSO and fire events. Evidence leading to the failure to reject this hypothesis would be a significant relationship between ENSO and fire events. The third alternative hypothesis is intended to address the possibility that both PDO and ENSO have a significant influence on GYR climate/fire relationships. Evidence leading to the rejection of this hypothesis would be lack of any significant 43 combination of PDO and ENSO that explained the occurrence of regional fire years. Evidence leading to the failure to reject this hypothesis would be a significant combination of PDO and ENSO that explained the occurrence of regional fire years. Methods To test these hypotheses, I first evaluated the relationship between ENS0, PDO, and 20th century precipitation patterns at three meteorological stations near the fire history sites identified in chapter one. Seasonal precipitation variables were assembled from consecutive 4-month precipitation totals at each of the three sites and correlated with 20th century instrumental values of PDO (Mantua et al. 1997, Mantua 2001) and reconstructed values of ENSO (Urban et al. 2000). I also evaluated the relationship between the full timer period of the regional fire chronology developed in chapter two and tree-ring based reconstructions of climate related phenomena: a GYR specific reconstruction of Palmer Drought Severity Index (PDSI) (Pisaric and Graumlich 2002), a PDO reconstruction derived from Southern California and Baja, Mexico (Biondi et al. 2001), and an ENSO reconstruction derived from tree rings in the Southwestern United States, Mexico, and Indonesia (Stable et al. 1998). Fire History A composite regional fire chronology for the northern GYR was reconstructed from fire-scarred trees at three lower elevation forest sites (Chapter 2). The regional fire chronology spans 1550 to 1900, beginning with the period for which there are at least 10% of the trees at any fire history site available to record fires and truncating with the 44 rise of active fire suppression in the early 20th century. Site specific chronologies have different spans, with Soda Butte Creek spanning 1550 to 1900, Crevice Creek and Cinnamon Creek both spanning 1650 to 1900. Climate Data PDSI is a composite estimate of the departure of soil water balance from the average. Methods for calculating PDSI vary, but all incorporate monthly temperature, precipitation, and soil recharge rates to evaluate conditions against the long-term mean. PDSI can often be reconstructed from drought-sensitive tree-ring chronologies by comparing instrumentally derived PDSI values for the 20th century with tree-ring variability for the same time period. The relationship between PDSI and tree-rings is then modeled back in time as far as the tree-ring chronology permits. PDSI used in this chapter is derived from two millennial length tree-ring chronologies in the GYR, one at Yellow Mountain Ridge (YMR) near Big Sky, MT, and the other at Mount Everts (MEV) between Gardiner, MT and Mammoth, Yellowstone National Park, WY. The reconstruction was calibrated against 20th century PDSI at gridpoint 36 derived from 20th century climate data in the region centered around 45° N latitude and 110.5° W longitude (Pisaric and Graumlich 2002). The PDO reconstruction used here (Biondi et al 2001b) was derived from Big-cone Douglas-fir and Jeffrey Pine tree-ring chronologies from southern California, U.S., and Baja California, Mexico. 20th century values of instrumentally derived PDO were used to calibrate the relationship and extend it back in time (Biondi et al. 2001). The ENSO reconstruction was derived from factor analysis of a network of tree-ring chronologies in the southwestern United States, northern Mexico, 45 and Java, Indonesia and calibrated against 20th century values of instrumentally derived ENSO (Stable et al. 1998). Analysis To evaluate the relationship between ENSO, PDO, and seasonal 20th century precipitation patterns, I used Pearson correlations. I used three methods to explore the relationship between regional fire history, drought, and global climate phenomena. To explore the relationship between drought and fire, PDO and fire, and ENSO and fire, I used Superposed Epoch Analysis (SEA) (Kelly and Sear 1984, Lough and Fritts 1987, Grissino-Mayer 1995). SEA employs a bootstrapping / re-sampling procedure to estimate statistical confidence intervals for the relationship between an event-based (discrete) response time series and a continuous predictor time series. To evaluate the relative importance of PDO and ENSO through time, I used multi-taper spectral analysis (MTM) (Thomson 1982) and evolutive multi-taper spectral analysis (EMTM) (e.g. Mann and Lees 1996). Multi-taper methods utilize multiple numerical filters to evaluate power spectra for time series that violate stationarity assumptions required by traditional spectral analysis techniques. Finally, to provide confirmation of the SEA results given low sample sizes of fires and the potential spuriousness of SEA for climate phenomena with periodicities as long as PDO, I used a bootstrapping and resampling procedure. Relationships between fire years and unusually dry years were evaluated with Superposed Epoch Analysis (SEA) to determine if there was a statistically significant relationship between Palmer Drought Severity Index and regional fire events. 1000 SEA iterations were run for the 30-year period surrounding each fire event for the fire 46 chronology period between 1550 and 1900. This analysis was repeated for the common period of PDO, ENSO, and fire history reconstructions between 1661 and 1900 with the same SEA parameters. Next, to determine the relative importance of PDO and ENSO to the distribution of dry years and to evaluate how likely a combined PDO-ENSO signal was, I used multi­ taper time series methods to examine the power spectrum distribution of the reconstructed PDSI chronology. I also used evolutive multi-taper analysis to determine how stationary the PDSI power spectrum is over time. For the multi-taper power spectrum density analysis, I used a value of p, = 3 resulting in k=2(p;)-l= 5 adaptive tapers, which is an appropriate value for a chronology the length of the PDSI reconstruction. I used a spectrum-derived red noise modeling procedure to filter the power spectrum density. For the evolutive multi-taper procedure, I retained the above parameters and used 250-year windows stepped forward 5 years for each iteration of the evolutive procedure. 250-year windows are approximately five to ten times the wavelength expected for PDO-type climate signals, so this window should be sufficient. Significance for the power spectra were determined by 95% confidence intervals calculated by an f-test of chi-square values determined by the multi-taper software used to produce the power spectrum density. SEA was then run on subsets of the regional fire chronology against PDO and ENSO to determine the relative importance of each phenomenon to distinct classes of fire events. Specifically, the regional fire data were split into bins of events that occurred during warm phase PDO or cool phase PDO and El Nino or La Nina ENSO events. These 47 groups were then evaluated separately to partial out the potentially interacting effects of PDO and ENSO in the GYR. Because of the relatively long fire return intervals described by the fire chronology, this procedure resulted in groups of fire events that were fairly small. Therefore, in order to substantiate the SEA results, I used a bootstrapping and resampling procedure to evaluate the probability of selecting at random groups of years equal to the sizes of the groups of fire years with means lower than the means of the climate phenomena for the fire years. This expands on the SEA analysis by testing more than the local context for a given fire event year; groups of years are sampled iteratively from the distribution of all years to determine whether the fire years sampled are significantly different from random groups of years. Specifically, I evaluated the mean PDO value for 15 warm-phase PDO fire events against 1000 random means of samples of 15 PDO values taken from the entire PDO distribution and the mean of the 6 El Nino-phase SOI events against 1000 means of random samples of 6 SOI values taken from the entire SOI distribution. Results Table 5 shows the results of correlation analysis of 20th century precipitation at 3 meteorological stations near fire history sites with independent 20th century PDO and ENSO values. Figure 7 illustrates the relationship between the summary regional fire chronology and PDSL Fire year relationships at the regional scale are plotted on the SOI reconstruction (Figure 8) and the PDO reconstruction (Figure 9). 48 Table 5. Best Fire History Site Seasonal Meteorological Relationships With PDO and ENSO for the 20th Century. Abbreviated months (DJFM = December January _______February March) indicate time spans of best correlation Meteorological Site (Associated Fire History Site) West Yellowstone Gardiner Lamar Ranger Station (Cinnamon Creek) (Crevice Creek) (Soda Butte Creek) 1926-1998 1956-1998 1940-1998 Dec/Jan/Feb DJFM DJFMA DJFM PDO r = -0.447; r = -0.388; r = -0.536; (Mantua 2001) p = 0.002 p = 0.018 p < 0.001 Winter JFMA DJFM JFMA ENSO r = 0.441; r = 0.512; r = 0.500; (Urban et al. 2000) p = 0.001 p = 0.002 p = 0.001 Superposed Epoch Analysis Site-specific Superposed Epoch Analysis suggested a relationship between PDSI and fire events at two of the three sites. At Soda Butte Creek, the year of fire has a significantly lower PDSI value than simulated years (p< 0.05) (Figure 10). SEA did not detect any significant relationship between fire events and PDSI at Crevice Creek (Figure 11). At Cinnamon Creek, the year prior to fire had a significantly higher PDSI value than simulated years (p< 0.05) (Figure 12). When all the site-specific fire chronologies were combined into a single regional chronology, none of the implied site-specific relationships were significant (Figure 13). However, both relationships are close to the 10 a> -2 - Figure 7. Fire Event Years plotted on a Time Series of Reconstructed PDSI (Pisaric and Graumlich 2002). The time series is composed of annual values. Fire events occur during low PDSI years, but also occur in years when PDSI is high. 2- ■ I I I I I I I i i i i — r — i 1650 1700 1750 1800 1850 1900 1950 Figure 8. Fire Event Years Plotted on a Time Series of Reconstructed PDO (Biondi et al. 2001). The dark time series is composed of reconstructed annual PDO values. Fire events occur more often during warm phase (positive) PDO, but some events occur during cool phase PDO. 12 1700 1750 1800 1850 1900 1950 Figure 9. Fire Event Years Plotted on a Time Series of Reconstructed SOI (Stable et al. 2000). The dark time series is composed of annual values of ENSO. Fire events occur during both El Nino (low SOI values) and La Nina (high SOI values). 2-3 4 - -20 —I---------1-------------1-------------1------------1------------1------------r -15 -10 -5 0 5 10 15 Lag Event Year -------- PDSI .......... 95% Cl .......... 99% Cl ------- 99.9% Cl Figure 10. Superposed Epoch Analysis Simulation Results for SBC Fire Years Versus PDSI1550- 1900. The year of fire is significantly drier than expected at random. 0.5 - 0.0 - -0.5 - - 1.0 - -1.5 - - 2.0 - Lag Event Year ------- PDSI • •• 95% Cl • • 99% Cl 99.9% Cl Uiu> Figure 11. Superposed Epoch Analysis Simulation Results for CCR Fire Years Versus PDSI 1650-1900. The year of fire is not significantly drier than expected at random. 2------- PDSI 95% Cl ......... 99% Cl -------99.9% Cl Lag Event Year Ui Figure 12. Superposed Epoch Analysis Simulation Results for CIN Fire Years Versus PDSI 1650- 1900. The year of fire is not significantly drier than expected at random, but the year prior is significantly wetter than expected at random. 1.5 a SI £0) P(r=0) Cl 97.5% Cl fire ~ xy 0.08 0.01* 0.99 0.02 0.04 0.13 fire ~ dem 0.15 0.01* I 0.01 0.1 0.21 fire ~ slope 0.02 0.33 0.67 0.72 -0.02 0.05 fire ~ tasp 0.03 0.23 0.77 0.53 0 0.06 fire ~ tci 0.01 0.39 0.61 0.84 -0.02 0.03 fire ~ xy + dem 0.04 0.13 0.88 0.25 0 0.08 fire ~ xy + slope 0.08 0.01* 0.99 0.01 0.04 0.13 fire ~ xy + tasp 0.08 0.01* 0.99 0.01 0.04 0.13 fire ~ xy + tci 0.08 0.01* 0.99 0.01 0.04 0.13 fire ~ xy + dem + slope 0.04 0.15 0.85 0.27 0 0.08 fire ~ xy + dem + tasp 0.03 0.13 0.87 0.32 0 0.07 fire ~ xy + dem + tci 0.04 0.14 0.86 0.25 0 0.08 fire ~ xy + slope + tasp 0.08 0.02* 0.98 0.02 0.04 0.12 fire ~ xy + slope + tci 0.08 0.02* 0.98 0.02 0.04 0.13 fire ~ xy + tasp + tci 0.08 0.01* 0.99 0.02 0.04 0.12 fire ~ xy + dem + slope + tasp 0.03 0.14 0.86 0.29 0 0.08 fire ~ xy + dem + slope + tci 0.04 0.14 0.87 0.24 0 0.07 fire ~ xy + slope + tasp + tci 0.03 0.14 0.86 0.29 0 0.08 fire ~ xy + dem + slope + tasp + tci 0.03 0.17 0.83 0.31 0 0.08 fire ~ dem + slope 0.02 0.34 0.66 0.72 -0.02 0.05 fire ~ dem + tasp 0.03 0.22 0.78 0.5 0 0.07 fire ~ dem + tci 0.01 0.35 0.65 0.79 -0.02 0.04 fire ~ slope + tasp 0.01 0.34 0.66 0.8 -0.02 0.05 fire ~ slope + tci 0 0.44 0.57 0.97 -0.02 0.04 fire ~ tasp + tci 0 0.43 0.57 0.97 -0.03 0.03 fire ~ dem + slope + tasp 0.03 0.25 0.75 0.54 0 0.06 fire ~ dem + tasp + tci 0.15 0* I 0 0.1 0.22 fire ~ dem + slope + tci 0.15 0* I 0 0.1 0.21 fire ~ dem + slope + tasp + tci 0.03 0.23 0.77 0.53 0 0.07 fire ~ slope + tasp + tci 0.01 0.37 0.63 0.82 -0.03 0.05 xy ~ dem 0.31 0* I 0 0.28 0.35 xy ~ tasp 0.12 0* I 0 0.07 0.17 xy ~ tci 0.03 0.2 0.8 0.41 0.01 0.06 xy ~ slope 0 0.46 0.55 0.92 -0.02 0.03 tasp ~ dem 0 0.46 0.54 0.98 -0.04 0.03 tasp ~ tci 0.25 0* I 0 0.16 0.3 tasp ~ slope 0.22 0* I 0 0.15 0.26 slope ~ tci 0.43 0* I 0 0.35 0.53 F igure 20. Spatia l in ten sity (X) o f F ire S carred T rees and A ge C lass P lo ts a t Soda Butte Creek . Spatia l c lu ster ing o f sam p le p o in ts is strong and d riven by the prox im ity o f fire scarred trees to each other. F igure 21 . K W ith S im u la tion E nvelop es fo r S am p lin g Po in ts at S oda Bu tte C reek . S econd ord er spatia l p a ttern ing is s ign ifican tly c lu stered a t a ll d istances betw een 0 and 2300 m . D ark po in ts are va lu es o f K ; d ashed lines ind icate upper and low er sim u la ted 95% con fid en ce in terva ls; grey line ind ica tes the expected va lue. 1.0 - I I I I I 0 500 1000 1500 2000 Distance (m) 2500 F igure 22. F fo r S am p lin g Po in ts at S oda B u tte C reek . N earest n eighbors are m ost c lu stered a t shorter d istances. D ark po in ts are va lu es o f F . Aspect Elevation Fire Regime Topographic Convergence F igure 23. C oncep tua l D iagram o f M an te l’s T est R esu lts 79 Discussion th e clustering demonstrated by Ripley’s K (Figure 21) combined with X plots (Figure 20) suggests fire scarred trees are strongly clustered. It should be noted that the site is not square; bounding box changes didn’t seem to have much effect on Ripley’s K , but intensity might change if the site better approximated a rectangular grid. Spatial clustering is a pattern observed in fire-history studies globally, and is probably an artifact of the continuous nature of fire and the similar response of similar trees to disturbance. The clustering does suggest that fire regime is not the same everywhere in the sample site. Simple Mantel’s test results suggest that the spatial matrix is significantly correlated with elevation (Table 6). This result is not surprising because even though SBC is topographically dissected, there is an elevation trend from one edge of the site (west) to another edge (east) and this is reflected in the sampling points also. Simple Mantel’s tests for fire regime matrix / environmental matrix correlations were dominated by elevation even when space was controlled for. None of the other environmental variables were significant even when space and elevation were controlled for (Table 6). Thus the null hypothesis that point estimates of fire regime are distributed randomly across the SBC site must be rejected. Mantel’s test results, however, suggests that the null hypotheses associated with the first three alternative hypotheses, that topographic convergence, aspect, and slope affect the distribution of fire regime across the site, must not be rejected (Figure 23). Only the implied null for the fourth alternative 80 hypothesis can be rejected; point elevation is correlated with point fire regime once spatial effects have been removed. Combinations of the variables would therefore not be significant. Thus, environmental controls on fire regime at local scales, if they exist, are masked by elevation. Elevation is commonly spatially autocorrelated with precipitation, temperature, evapotranspiration, and light intensity, all of which could affect fire regime. Without specific biophysical modeling to determine the effects of elevation on the ecophysiological responses at SBC, it is doubtful if I would be able to partial out the effects of elevation on fire regime. This might in turn merely demonstrate that the controls on fire regime operate on scales of kilometers along elevation gradients rather than on scales of meters in response to local biophysical variables. However, if the ultimate application of this work was to inform prescribed fire treatments, the results bolster previous results (Heyerdahl 1997, Swetnam 1990) that suggest elevation is a strong determinant of fire regime in much of the western United Sates. hi summary, although there are hypothetical reasons why soil moisture proxies should be correlated with fire regime type once spatial variability is accounted for, such relationships were not detected here except for the distinct significant relationship between fire regime and elevation. This relationship may be cause for a more detailed mechanism to explain the variation of fire regime at scales of hectares instead of kilometers. However, it is likely that larger scale phenomena that influence climate have a larger impact on local fire regime than local biophysical controls, at least for the period of record for this study. 81 CHAPTER 5 SUMMARY: FIRE REGIME VARIABILrrY IN SPACE AND TIME IN LOWER ELEVATION FORESTS OF THE GREATER YELLOWSTONE REGION Key Findings The goal of this thesis was to determine the past fire history of three lower elevation watersheds in the northern Greater Yellowstone Region and relate the variability, if any, in fire regime to the biophysical template of the region. Fire regime differed in its manifestation in three watersheds of the Greater Yellowstone Ecosystem. Each watershed had a unique fire history, and only a few fire years in the past five hundred years were detected at more than one watershed. Eire episodes, however, in which all three sites burned within a few years of each other were not uncommon. In addition, sites cannot necessarily be characterized at the scale of several hundred hectares as having a single fire regime. More intensive, plot based research at Soda Butte Creek in Yellowstone National Park demonstrated that at least some watersheds in low elevation forests of the Greater Yellowstone Region are characterized by a mixed fire regime wherein some fire events burn stand replacing, some bum as understory burns, still other fires burn as both stand replacing and understory fires, and fire frequency varies substantially through time. However, the statistical properties of fire regimes in the three watersheds are quite similar: all three watersheds have fire return intervals between 20 and 30 years, but the standard deviation, minimum and maximum fire return intervals varied substantially between watersheds and through time. 82 Regional fire years are related to periodic climate phenomena arising in the Pacific Ocean. The Pacific Decadal Oscillation, or PDO, has a significant influence on drought in Greater Yellowstone, but this effect too is non-stationary. During the mid 1700s, the influence of PDO-Iength periodicities on local precipitation became insignificant, and during that period the El Nino Southern Oscillation became more important to precipitation variability and significantly influenced fire events in the GYR. Finally, aside from the effect of elevation, the spatial variability in biophysical variables that influence soil moisture was unrelated to the spatial variability in fire regime. This suggests that, although synoptic climate phenomena that influence precipitation have a deterministic effect on when fires occur, the behavior and thus the effects of fire on the ground can vary markedly. Two fires detected at Soda Butte Creek burned both as understory fires in open Douglas-fir forests and as stand replacing fires in lodgepole pine forests with closer fuel continuity. This may be related to the distribution of fuel moisture during fire events; in years that are dry enough, fuel moisture could be homogenously below the threshold needed to burn and fire propagates easily through different forest types. Forecasting when thresholds of fuel moisture are exceeded homogeneously across the landscape may be the single most important predictive effort in determining fire spread and the effects of fire on a variety of ecological processes. These results suggest that the variability in climate and its relationship to variability in fire regime are more important to the characterization of fire in a spatiotemporal context than mean fire return interval. Fire, like any ecological process with a suite of non-linear interacting drivers that produce feedbacks, does not conform to normal distributions and varies significantly in both space an time. 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USDA Forest Service General Technical Report WO-26. 92 APPENDICES 93 APPENDIX A FIRE PERIMETER MAPS — Contour Interval 60 m A . Fire Scarred Tree A Tree Not Scarred in This Fire © Hot w/o Fire Record 1584 Fire Soda Butte Creek Yellowstone National Park 2 Kilometers I: 50 000 1634 Fire 1681 Fire Key — Contour Interval 60 m A Fire Scarred Tree A Tree Not Scarred in This Fire © Hot w/o Fire Record 1693 Fire Soda Butte Creek Yellowstone National Park 1695 Fire — Contour Interval 60 m A Fire Scarred Tree A Tree Not Scarred in This Eire © Plot w/o Fire Record 1744 Fire Soda Butte Creek Yellowstone National Park § 2 Kilometers I: 50 000 1756 Fire Soda Butte Creek Yellowstone National Park — Contour Interval 60 m A Fire Scarred Tree A Tree Not Scarred in This Fire • Hot w/ Fire Record ® Hot w/o Fire Record 1800 Fire Soda Butte Creek Yellowstone National Park 2 Kilometers I: 50 000 1800 Fire 1855 Fire 104 1696 Fire Crevice Creek Yellowstone National Park 1729 Fire Crevice Creek Yellowstone National Park Key Contour Interval 60 m A Fire Scarred Tree A Tree Not Scarred in This Fire 1784 Fire Crevice Creek Yellowstone National Park Contour Interval 60 maim Fire Scarred Tree Tree Not Scarred in This Firemm Yellowstone Rn 1:50,000 2 Kilometers — Contour Interval 30 m A Fire Scarred Tree / \ Tree Not Scarred in This Fire 1800 Fire Cinnamon Creek Gallatin National Forest A 1:40 000 120 APPENDIX B Data for Chapter 4 121 Estimated Sample Latitude Northing (m) Longitude Easting (m) Slope (%) Topographic Convergence Transformed Aspect Elevation (m) Fire Return Interval SBC_002 566305.74 4967799.73 37.05975 47 -0.842696200 2075.383 70 SBCL003 566283.41 4967793.94 30.07349 40 -0.518978200 2075.688 63 SBC_004 566443.92 4967299.37 35.61506 26 -0.271460200 2168.042 67 SBC_005x 566553.34 4967543.07 28.57163 27 -0.811534300 2123.542 54 SBC_006x 566534.98 4967663.23 26.47888 82 -0.887216800 2097.329 15 SBC_007 566644.82 4967738.43 12.05719 101 -0.910366500 2095.5 27 SBC_008 566704.23 4967724.24 59.07614 31 0.095308670 2124.456 65 SBC_009x 566736.95 4967996.76 49.93813 16 0.668964700 2083.918 81 SBC_010 566739.06 4967920.87 57.77386 44 -0.245156800 2081.784 36 SBC_011 566960.41 4968162.03 39.46441 82 -0.257662800 2071.726 56 SBC_012 567360.65 4968419.89 35.44068 62 -0.248375600 2065.325 44 SBC_013x 567583.69 4968372.24 45.83452 50 -0.274721300 2112.264 28 SBC_014 567832.19 4968532.25 23.91028 41 0.398726100 2121.408 50 SBC_015x 567780.67 4968548.37 21.27147 44 0.771930100 2114.093 44 SBC_016x 567977.39 4968498.61 49.29902 66 -0.152057100 2129.638 28 SBC_018 568439.06 4968918.28 31.49278 70 0.721387200 2158.898 118 SBC_019 568466.72 4968916.72 40.02451 57 0.757768700 2164.08 59 SBC_022 568134.50 4969452.00 15.61613 90 0.316227600 2082.089 44 SBC_023 568006.58 4969098.84 36.62355 62 0.047565280 2098.243 118 SBC_024x 568941.22 4969729.11 36.71637 61 0.521450000 2190.902 38 SBC_025x 568872.85 4969843.17 38.28695 66 0.559857300 2164.385 43 SBC_026x 569023.81 4970005.89 44.82436 58 0.088932070 2183.587 38 SBC_028x 569000.76 4970068.59 41.52402 58 0.059892190 2168.042 57 SBC_029x 568908.63 4970065.75 28.40597 68 0.196116200 2153.717 70 SBC_030x 568853.65 4970037.39 30.97472 63 0.235599800 2147.621 70 SBC_031x 569175.71 4969959.38 51.00027 53 0.400818600 2236.622 44 SBC_032 569216.61 4969950.57 43.96554 53 0.439867500 2250.338 83 SBC_034 569328.30 4969970.29 62.18096 35 0.307820300 2307.641 70 SBC_036 567187.74 4967075.67 56.57306 44 0.198359400 2412.187 72 SBC_037 567111.93 4967147.09 52.39295 52 0.108936800 2370.43 87 SBC_038x 566984.79 4967340.18 53.64144 59 0.312320900 2284.781 72 SBC_039x 566880.93 4967322.43 70.43312 79 0.772836400 2239.975 51 SBC_040 568188.94 4967054.75 50.11161 46 0.221621000 2421.026 32 SBC_041 568217.87 4967182.82 47.1009 51 0.246399000 2404.872 86 SBC_043x 568113.56 4967207.63 41.86582 55 0.256434600 2378.964 63 Pl 567593 4968452 43.21072 55 -0.338719700 2092.452 178 P2 567889 4968222 66.89948 33 -0.392098600 2257.958 145 P3 568744 4968629 32.72094 68 0.990492300 2218.03 115 P4 568756 4968964 53.99063 35 0.346896000 2232.66 130 122 P5 569008 4969287 23.84264 P6 568288 4969511 45.50138 P7 567153 ' 4967902 13.51969 P8 567115 4967935 17.36707 P9 566741 4967610 49.9755 PlO 566173 4966999 68.79409 PU 565903 4967341 47.03909 P12 569895 4968617 45.22208 P B 569911 4968278 39.25562 PM 569547 4967905 41.13368 P15 569210 4967617 48.36646 P16 569425 4967434 40.9229 P17 568875 4967486 45.43207 P18 568528 4967763 34.46869 P19 568640 4967587 34.61868 P20 568390 4967195 45.07589 P21 567146 4967274 50.79768 P22 570547 4969444 60.6621 P23 570492 4969686 44.9327 P24 570165 4969171 54.99641 P25 569675 4969362 61.69086 P26 568257 4969189 41.81031 P27 569616 4969878 50.28279 P28 569363 4970071 62.44363 P29 569300 4969006 64.80956 P30 569434 4970278 62.33603 P31 568964 4970562 45.71584 P32 568738 4970324 45.23529 P33 568632 4970198 46.94195 P34 568301 4969763 25.99665 P35 569865 4968132 43.97996 P36 568524 4967655 51.19401 0.960000000 2223.821 155 0.295306300 2103.73 75 -0.294085700 2143.658 200 -0.734803400 2140.001 130 0.757156700 2138.477 65 -0.438964200 2175.967 65 0.543251300 2090.014 130 -0.017541340 2546.299 145 0.627329400 2496.007 200 0.586967500 2474.062 200 0.141421300 2464.613 200 -0.040782460 2534.412 151 -0.121891600 2482.901 100 -0.206010700 2356.714 200 0.051214670 2332.634 160 0.158678300 2454.25 70 0.475818500 2357.628 78 0.904142000 2571.598 100 0.124035000 2540.813 200 0.965615800 2524.354 200 -0.238052200 2392.07 250 0.316227600 2129.942 150 0.499298700 2396.033 130 -0.498124700 2278.075 250 -0.066519220 2369.21 70 0.129735100 2268.626 130 0.184000000 2108.302 70 -0.035066110 2123.846 168 0.379918700 2111.045 200 0.688749300 2084.222 100 0.146548700 2506.066 87 0.255892500 2374.392 225 65 49 74 75 39 31 42 41 78 54 60 56 36 46 52 51 51 34 63 32 43 52 55 31 48 39 47 41 49 71 52 43 MONTANA STATE UNIVERSITY BOZEMAN