Post-1935 changes in forest vegetation of Grand Canyon National Park, Arizona, USA: Part 1 – ponderosa pine forest

Forest Ecology and Management 261 (2011) 309–325

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Post-1935 changes in forest vegetation of Grand Canyon National Park, Arizona, USA: Part 1 – ponderosa pine forest John L. Vankat ∗,1 Science Center, Grand Canyon National Park, Grand Canyon, AZ 86023, USA

a r t i c l e

i n f o

Article history: Received 12 March 2010 Received in revised form 17 May 2010 Accepted 18 May 2010 Available online 26 June 2010 Keywords: Ponderosa pine forest Forest change Forest structure Forest composition Southwest Grand Canyon National Park

a b s t r a c t Vegetation plots originally sampled in Grand Canyon National Park (GCNP), Arizona, USA in 1935 are the earliest-known, sample-intensive, quantitative documentation of forest vegetation over a Southwest USA landscape. These historical plots were located as accurately as possible and resampled in 2004 to document multi-decadal changes in never-harvested Southwestern forests. Findings for ponderosa pine forest (PPF) differed among three forest subtypes (dry, mesic, and moist PPF), indicating that understanding the ecology of PPF subtypes is essential for development of ecologically based management practices. Dry PPF, which is transitional with pinyon-juniper vegetation at low elevation, exhibited no changes from 1935 to 2004. Mesic PPF, the core subtype of PPF, had increased densities of total trees, ponderosa pine (Pinus ponderosa), and white fir (Abies concolor) in the 10–29.9 cm diameter class from 1935 to 2004 that may have induced decreased densities of larger ponderosa pines and total tree and ponderosa pine basal areas. Moist PPF, which is transitional with mixed conifer forest at high elevation, was the most dynamic PPF subtype with decreases from 1935 to 2004 in total density and total basal area that are largely attributable to decreases in quaking aspen (Populus tremuloides). Graphical synthesis of datasets with historical and modern values for density and basal area indicates that overall PPF (all subtypes combined) increased in sapling density of all species combined and conifers with canopy potential and decreased in density of quaking aspen trees since the late 19th century. PPF of GCNP has passed through an accretion phase of forest development with increases in density and, depending on PPF subtype and variable being examined, is at or past the point of inflection to recession of density and basal area. Increases in small diameter ponderosa pine and white fir from 1935 to 2004 portend potential additional accretion, but decreases in total basal area, density and basal area of quaking aspen, basal area of ponderosa pine, and density of larger diameter ponderosa pine indicate PPF has passed the inflection point from accretion to recession. Uncertainties about 19th-century PPF structure and composition and about future ecological and societal environments lead to the conclusion that resource managers of GCNP and other natural areas should consider a change in focus from the objective of achieving desired future conditions to an objective of avoiding undesired future conditions. © 2010 Elsevier B.V. All rights reserved.

1. Introduction Information on historical structure and composition of ecological systems and on how historical conditions have changed to the present is essential for understanding the current state of the systems and for developing ecologically oriented management objectives and practices. Such information is particularly important for management of national parks and other areas where ecological systems are to reflect naturalness but past land use or management actions have led to a need for restoration.

∗ Present address: Vankat Consulting, 9505W Hashknife Trail, Flagstaff, AZ 86001, USA. Tel.: +1 928 214 6007. E-mail address: [email protected] 1 Department of Botany, Miami University, Oxford, OH 45056, USA. 0378-1127/$ – see front matter © 2010 Elsevier B.V. All rights reserved. doi:10.1016/j.foreco.2010.05.026

The ponderosa pine forest (PPF) of the Southwest USA has been studied extensively with regard to historical conditions and changes in those conditions. Early verbal accounts consistently described a forest with open structure dominated by large ponderosa pines (Pinus ponderosa; PIPO; see Table 1 for species names and acronyms) and a dense, grass-dominated understory, e.g., We came to a glorious forest of lofty pines. . .every foot being covered with the finest grass, and beautiful broad grassy vales extended in every direction. The forest was perfectly open. . . (Beale, 1858) In addition to historical descriptions of general forest structure and composition, quantitative estimates of historical tree densities and basal areas have been developed by several means, including use of historical data dating to the early 20th century, forest

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reconstructions for the late 19th century, and characterization of reference sites thought to represent historical conditions (e.g., Lang and Stewart, 1910; Fulé et al., 2002). Studies indicated increases in tree densities and basal areas since the late 19th century when the historical fire regime of frequent, low severity surface fires was ended by livestock grazing of the herbaceous surface fuels that had carried fires (e.g., Weaver, 1951; Harrington and Sackett, 1990; Covington and Moore, 1994a,b; Dahms and Geils, 1997; Fulé et al., 2002). Except where harvesting and crown fire have occurred, I am unaware of studies that have reported decreases following the increases in density and basal area in PPF. PPF is the most extensive montane forest in the Southwest, but covers only 6.1% of the combined area of Arizona and New Mexico (Lowry et al., 2005). Extensive commercial cutting in the 19th and 20th centuries resulted in a paucity of old-growth in most areas, but not in Grand Canyon National Park (GCNP), northwestern Arizona. The kinds and distributions of forests in GCNP’s South and North Rim regions have been influenced by the interaction of three major environmental gradients (cf. White and Vankat, 1993). An elevational gradient generally dominates at broad scales, and a topography-driven moisture gradient is important at a finer, more local scale. A gradient in fire frequency and intensity historically ranged from frequent, low-intensity surface fires over essentially all forested areas to infrequent high-intensity crown fires in small patches (Fulé et al., 2000, 2003a,b). The contemporary fire regime includes increased spatial extent of high-intensity crown fire (White and Vankat, 1993; Fulé et al., 2004). From low to high along the elevational gradient, GCNP has three general forest types: PPF, mixed conifer forest (MCF), and spruce-fir forest (SFF). PPF occurs primarily at 2050–2550 m and is dominated by PIPO. At the lower end of its elevational range, PPF intergrades – often patchily – with pinyon-juniper (PJ) vegetation. At its upper elevations on the North Rim, PPF intergrades over a broad transition zone with MCF. A fourth major forest type is quaking aspen forest (QAF), which presently occurs in large patches recently formed by crown fire within the elevational ranges of MCF and SFF. GCNP’s forests are ecologically important in many ways. They make up the largest never-harvested forest area in Arizona (Fulé et al., 2002) and perhaps in the Southwest. They were protected from commercial logging by their remoteness, especially on the North Rim, and by the federal government’s designation of protected areas that began in 1893 and culminated with the expansion of GCNP in 1927 (Vankat, 2010). This absence of commercial logging has enabled fire history studies using fire scars of old trees and long-term monitoring studies without the complexities of postlogging successional changes. Some isolated areas of GCNP’s PPF have additional ecological significance as reference sites of preEuramerican conditions (Fulé et al., 2000, 2002, 2003b; Gildar et al., 2004; Laughlin et al., 2004), because some surface fires continued when they were excluded elsewhere. GCNP reference sites can

serve as a baseline for park management decisions and for restoration of more disturbed areas outside GCNP. GCNP forests also played an essential role in the protection of the Grand Canyon (Vankat, 2010), which began in 1893 with the creation of Grand Canon Forest Reserve (later changed to Grand Canyon Forest Reserve), a 7492 km2 area that included forests of the South and North Rims and the exceedingly sparsely forested canyon between them. There is a long history of forest ecology studies in GCNP. Early studies in PPF did not emphasize historical conditions, but attributed forest changes to livestock and deer. In a 1909 reconnaissance of timber resources of what is now the North Rim of GCNP and the North Rim Ranger District of Kaibab National Forest, Lang and Stewart (1910) described loss of seedlings to trampling by sheep, but did not comment on their PPF diameter-class data, which today appear suggestive of shade-tolerant conifers increasing with reduced fire frequency. PPF changes observed in the 1930s were attributed to intensive browsing by an elevated deer population. For example, Rasmussen (1941) reported that browsing on small PIPO in PPF was so intensive that “. . .trees within reach of the deer had made little growth for the decade preceding 1931.” Others described extensive deer browsing in higher elevation forests (Merkle, 1954, 1962), and recent studies have noted the effects of browsing on population age structure of quaking aspen (Populus tremuloides; POTR) (e.g., Fulé et al., 2003a; Binkley et al., 2006). Field work during 1949–1955 in a white fir-ponderosa pine forest led to the first published comment on the effects of reduced fire frequency on GCNP forests: “. . .nearly impenetrable thickets” of white fir (Abies concolor; ABCO) were present because “[f]ire control for approximately 50 years has favored white fir. . .” (Merkle, 1962). Recognition that the reduced frequency of fire was responsible for changes in GCNP forests shifted the emphasis of research on forest changes from deer to fire, beginning with White and Vankat (1993) in MCF and SFF and continuing into the 21st century. Past fire regimes in PPF have been quantified based on fire dates obtained from fire-scarred trees (Fulé et al., 2000, 2002, 2003b). Comparisons of historical (1935) and more recent (mostly 1992–2002) datasets illustrated changes in PPF related to fire exclusion (Crocker-Bedford et al., 2005a,b). PPF structure and composition prior to fire exclusion have been quantitatively reconstructed using tree rings of contemporary live and dead trees and compared to recent values of density and basal area (Fulé et al., 2002), and these data have been used to model historical changes in forest canopy fuels and fire behavior (Fulé et al., 2004). PPF reference sites with relatively unchanged fire regimes, structure, and composition have been described (Fulé et al., 2000, 2002, 2003b). The direct effects of fire on PPF structure and composition have been examined (Laughlin et al., 2004; Fulé et al., 2006; Fulé and Laughlin, 2007). These PPF studies have concluded that forest structure and composition have changed since the late 19th century; however, the quantitative estimates of change differ.

Table 1 Tree species sampled. Nomenclature follows Integrated Taxonomic Information System (2009). Species acronyms: first two letters of the genus and species, except PISP: Picea species (includes PIEN and PIPU). Scientific name

Common name

Acronym

Abies concolor (Gord. & Glend.) Lindl. ex Hildebr. Abies lasiocarpa (Hook.) Nutt. Juniperus osteosperma (Torr.) Little Picea engelmannii Parry ex Engelm. Picea pungens Engelm. Picea A. Dietr. (P. engelmannii + P. pungens) Pinus edulis Engelm. Pinus ponderosa var. scopulorum Engelm. Populus tremuloides Michx. Pseudotsuga menziesii var. glauca (Beissn.) Franco Quercus gambelii Nutt. Robinia neomexicana Gray

White fir Subalpine fir Utah juniper Engelmann spruce Blue spruce Spruce Pinyon pine Ponderosa pine Quaking aspen Douglas-fir Gambel oak New Mexico locust

ABCO ABLA JUOS PIEN PIPU PISP PIED PIPO POTR PSME QUGA RONE

J.L. Vankat / Forest Ecology and Management 261 (2011) 309–325

My study of changes in Southwestern PPF is based on repeat sampling of vegetation study plots originally sampled in GCNP in 1935. Other studies of Southwestern PPF also have resampled historical plots. The G.A. Pearson Natural Area near Flagstaff, AZ has been resampled periodically since 1920 (Avery et al., 1976), but the site is much smaller and therefore more homogeneous than the area of PPF in GCNP. Some of approximately 55 plots established in National Forests of Arizona and New Mexico in 1909–1915 also have been resampled (Moore et al., 2004), but these plots were widely scattered (and have been harvested). Therefore, the 1935 plots in GCNP are the earliest-known, sample-intensive, quantitative documentation of forest vegetation over a Southwestern landscape. My primary objective is to describe changes in PPF in GCNP since 1935.

2. Methods

311

ommended “. . .implementation of a comprehensive fire control strategy” (Putt, 1995) and the first fire lookout was constructed (Anderson, 1998; National Park Service, 2009). Following the creation of GCNP in 1919, land management transferred from the USFS to the National Park Service (NPS) and fire suppression continued. Other early 20th-century management practices likely affecting GCNP forests included killing of predators (Mead, 1930; Rasmussen, 1941), which likely facilitated a population explosion of deer in the 1910s–1920s (Mitchell and Freeman, 1993; Binkley et al., 2006), and localized cutting of trees for structures, fuel, and insect control (Hinton and Green, 2008). Fire suppression continued with increasing emphasis and effectiveness through most of the 20th century, but a paradigm shift within the NPS (and other land management agencies) regarding the ecological role of fire in forests led to GCNP’s fire management program reintroducing fire into forests beginning in 1980, initially focusing on prescribed burning in PPF and later expanding to include management of lightning-ignited fires.

2.1. Study area 2.2. Sample plots and calculations GCNP is located on the Colorado Plateau in northwestern Arizona and includes 4931 km2 of highly diverse topography dominated by the Grand Canyon of the Colorado River. The South Rim and North Rim of GCNP include part of Coconino Plateau and Kaibab Plateau, respectively, and are the major areas of forests in the park. The South Rim is smaller, relatively flat, and lower in elevation; its only coniferous forest type is PPF. The North Rim is dissected by several drainages that are usually dry; it has PPF, MCF, SFF, and QAF. Most forest soils are Alfisols, but Entisols and Mollisols are also present; limestone is the most common soil parent material. The climate is characterized by a bimodal precipitation regime, with mostly snow from October through March, little precipitation April through June, and a monsoonal season with rainfall in July through August. The “Grand Canyon N P 2” weather station, located on the South Rim near the ecotone between PJ vegetation and PPF at 2070 m, received an annual average of 41.3 cm precipitation, including 112.5 cm snowfall, during 1976–2009 (Western Regional Climate Center, 2010). June was the driest month with an average precipitation of 1.0 cm, August was the wettest month with an average of 5.7 cm, and January had the most snow with an average of 27.2 cm. Average maximum and minimum temperatures for July, the warmest month, were 29.6 and 10.1 ◦ C, respectively, and for January, the coldest month, were 6.7 and −7.8 ◦ C, respectively. The history of forests in GCNP formerly included frequent fires. In many regions, Native Americans historically set fires for hunting, increasing wild food crops, and other reasons (Pyne, 1997), but there is no direct evidence of this being a landscape-scale factor in the fire regimes of Southwestern forests (Allen, 2002), where the high frequency of lightning is sufficient to account for the pre-Euramerican fire regime (Swetnam and Baisan, 1996). Locally, long-time ranchers in the GCNP area have stated that their ancestors learned from Native Americans to set fires to increase forest forage (Michael F. Anderson, former archeologist, GCNP, personal communication). The first major land use by Euramericans in GCNP forests was livestock grazing, beginning on the South Rim in the 1860s and the North Rim in the late 1870s (Hughes, 1978; Anderson, 1998; Anderson, personal communication). The grazing ended frequent surface fires in 1887 on the South Rim and 1879 on the North Rim (Fulé et al., 2002) and began approximately 100 years of fire exclusion on most forested sites. Active fire exclusion, i.e., fire suppression, began around 1905 when management of the Forest Reserve was transferred to the U.S. Forest Service (USFS), an agency that included fire suppression as one of its “fundamental responsibilities” (Anderson, 2000) and whose early Grand Canyon rangers “. . .focused on fire suppression. . .” (Anderson, 1998). In 1909, the first Grand Canyon master plan rec-

In 1935, NPS Branch of Forestry field staff headed by Claude “Bru” Wagner sampled 456 vegetation study plots of 66 ft × 132 ft (20.1 m × 40.2 m) in forest and other vegetation while preparing a vegetation map of GCNP. Field crew members used the sample plot method developed by the USFS for similar projects in California (Wieslander, 1935a,b; Keeley, 2004). Sample plots were to be “. . .representative of the average conditions within a given [vegetation] type. . .” (Coffman, 1934). In each plot, live trees were tallied by species into four diameter classes: 4–12, 12–24, 24–36, and ≥36 in. (10.16–30.48, 30.48–60.96. 60.96–91.44, and ≥91.44 cm; herein reported as 10–29.9, 30–60.9, 61–90.9, and ≥91 cm for brevity; I did not use other vegetation data collected in 1935). Plot locations were recorded on a topographic map but were unmarked in the field. I determined Universal Transverse Mercators (UTMs) for the approximate locations of the 1935 sample plots by (a) matching plot locations shown on the original topographic base map with locations on a digital topographic map (US Topo 24K National Parks, West, Garmin Ltd.) and (b) adjusting for slope aspect, slope inclination, and notes recorded in 1935. I then traveled to each georeferenced plot, searched for the area that matched the slope aspect and inclination recorded in 1935, and within that area selected the site that best matched patterns of trees and ground cover recorded in 1935 and likely to have persisted to 2004. Matching of patterns of vegetation was facilitated in many areas by the lack of fire since 1935. No element of randomization was included because the intent was to (a) locate each 1935 plot as closely as possible to its original location and (b) produce a conservative estimate of forest changes. Of the 191 plots sampled in above-rim forests in 1935, 153 were resampled (Fig. 1), all but one in 2004. Other plots were not on the 1935 base map, were not located in the field, etc. Each of the 153 plots was resampled using a 20 m × 50 m plot oriented in the same direction as the original plots: long axis up and down slope (this plot size was chosen to match that of most forest sampling done in GRCA since 1984). UTMs of the center point and data on several site factors were recorded. Each living and standing dead tree ≥10 cm dbh was recorded by species and dbh, marked with an individually numbered brass tag, and mapped. Numbers of saplings (2.5–9.9 cm dbh) in five 5 m × 10 m nested plots were recorded by species (other vegetation data recorded were not used in this study). To evaluate accuracy of plot relocation, I estimated distance to the original plot location as 0, 1–50, 51–100 m, or unknown, largely based on the size of the area matching the slope aspect, slope inclination, and vegetation recorded in 1935 (the larger the matching area, the greater

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Fig. 1. Distribution of resampled plots. Plots are generally coincident with the distribution of ponderosa pine forest. The east and west portions of the South Rim are dominated by pinyon-juniper vegetation; the north-central and northeast areas of the North Rim are dominated by mixed conifer, spruce-fir, and quaking aspen forests. Multiple plots may be superimposed.

the estimated distance to the 1935 plot). Additional details on plot location and sampling are in Vankat and Crocker-Bedford (2005). I calculated tree densities and basal areas in standard ways, except that diameter-class midpoints had to be used to calculate basal area values for 1935, when trees had been tallied into diameter classes. I assumed a midpoint of 106.68 cm for the largest diameter class. Original data sheets, maps, and photographs from 1935 and 2004 are in GCNP’s Museum Collection. 2.3. Analyses To develop units for statistical analysis of forest changes, I classified the 1935 vegetation of the 153 sample plots using relative basal area of trees (≥10 cm dbh) of species with canopy potential (i.e., ABCO, subalpine fir (Abies lasiocarpa; ABLA), PIPO, spruce (Picea spp.; PISP), Douglas-fir (Pseudotsuga menziesii; PSME), and POTR) so that the classification (a) was based on the 1935 data instead of the 2004 data, (b) ignored differences in absolute basal areas among plots, (c) emphasized larger trees, and (d) ignored subcanopy species Utah juniper (Juniperus osteosperma; JUOS), pinyon pine (Pinus edulis; PIED), Gambel oak (Quercus gambelii; QUGA), and New Mexico locust (Robinia neomexicana; RONE). I first classified plots as QAF if POTR had >80% relative basal area and then classified the remaining plots as PPF if PIPO had >80% relative basal area of conifers. I divided the resultant 99 PPF plots into forest subtypes based on a moisture gradient. The raw data, patterns on ordination diagrams, and personal field observations indicated that the following sites were best characterized by the tree species listed: Dry: Mesic: Moist:

JUOS, PIED PIPO, QUGA ABCO, PISP, PSME, POTR

I assigned weights of 1, 2, and 3 to the dry, mesic, and moist species, respectively. For each plot, I multiplied the 1935 basal area value of each of these species (expressed as a percentage of the total of all eight species) by the species weight and summed the products

to produce a plot score that reflected species composition relative to moisture conditions. Theoretically, plot scores could range from 101 (99% dry species and 1% PIPO) to 284 (80.0% POTR, 16.1% PIPO, and 3.9% moist conifers); actual values ranged from 115 to 274. I divided the theoretical range into thirds and classified 7 plots as dry PPF, 75 as mesic PPF, and 17 as moist PPF. Statistical analyses examined live tree density, live tree basal area, and sapling density. Analysis of variance was used with the following fixed and crossed factors: year (1935 or 2004), historical forest subtype (dry, mesic, and moist), and species (ABCO, ABLA, JUOS, PIED, PIPO, PISP, POTR, PSME, QUGA, and RONE). Plot, which was nested within the (year × historical forest subtype) interaction, was also included and was treated as random, resulting in a repeated measures analysis. Live tree diameter classes (10–29.9, 30–60.9, 61–90.9, and ≥91 cm) and estimated distance to the original plot location (both are fixed factors) were investigated in combination with these factors, but in separate analyses. When significance was determined at the 0.05 level, multiple comparisons were performed using the Bonferroni method and (a) statistically significant differences were determined at 0.05 divided by the number of comparisons of interest and (b) trends of differences were determined at unadjusted 0.10. Non-significant main or interactive effects were dropped from analysis in a hierarchical manner. 3. Results and discussion 3.1. Accuracy of plot location There are two issues regarding accuracy of plot location: (a) the original mapping of plot locations in 1935 and (b) finding these locations in 2004. Accuracy in mapping was a focus of the 1935 field crew because they were mapping vegetation, not just plot locations. The probability of locational error was reduced by crew members working in pairs, with one person sampling plots and the other mapping vegetation (cf. National Park Service n.d.). Accuracy of finding the 1935 plot locations in 2004 was facilitated by remarkable similarity between the topographic base map used in

Table 2 Ponderosa pine forest 1935 and 2004. Trees are ≥10 cm dbh; saplings are 2.5–9.9 cm dbh. M = mean, SD = standard deviation, and species acronyms = first two letters of the genus and species, except PISP = Picea species (includes PIEN and PIPU). P-values provided where there are statistically significant differences (*P ≤ Bonferroni-adjusted 0.05) or trends for differences (P ≤ unadjusted 0.10) between means for 1935 and 2004. Year (# plots)

Total

ABCO SD

Tree density (# ha−1 ) 1935 (99) 405.4 291.9 2004 (99) 368.1 203.0 P-value 2 −1 Tree basal area (m ha ) 1935 (99) 49.7 26.7 2004 (99) 37.4 14.1 * P-value 0.001 Sapling density (# ha−1 ) 2004 (99) 204.8 246.2 Tree density (# ha−1 ) by diameter class 10–29.9 cm 1935 (99) 239.1 269.4 2004 (99) 230.4 187.1 P-value 30–60.9 cm 1935 (99) 123.8 96.5 2004 (99) 103.5 71.9 P-value 61–90.9 cm 1935 (99) 37.3 35.2 2004 (99) 29.9 22.1 P-value ≥91 cm 1935 (99) 5.1 12.7 2004 (99) 4.2 8.1 P-value

M

ABLA SD

JUOS

PIED

PIPO

POTR

PSME

SD

M

SD

M

SD

M

SD

M

SD

M

SD

M

0 0.7

0 7.0

13.0 12.4

46.2 33.5

25.1 21.7

108.2 80.8

263.3 261.1

199.5 171.7

0.6 1.2

6.2 10.1

89.7 15.3 0.0002*

205.0 50.2

0.4 1.1

0 0.02

0 0.2

1.1 0.7

3.5 2.5

1.2 0.7

4.5 2.7

0.03 0.1

6.5 39.1

31.7 98.1

0.6 1.6

2.4 4.5

44.8

121.3

4.8

44.4

7.7

39.5

17.8

53.4

26.1 90.1

0 0.7

0 7.0

8.7 9.1

35.0 25.1

22.0 20.0

3.1 1.7

4.6 33.7 <0.0001*

PISP

M

1.5 5.2

7.1 16.6

4.0 2.9

14.0 11.2

0.4 0.2

2.1 2.0

0.2 0.4

1.7 2.0

42.2 33.0 <0.0001*

27.1 15.1

0.04 0.1

0.4 0.6

74.7

141.4

104.1 73.6

116.8 141.1 <0.0001*

154.0 145.4

0.5 0.7

5.0 6.1

14.7 8.5

105.1 86.6 0.0012*

94.7 70.6

0.1 0.5

1.2 4.1

36.3 29.2

35.0 22.6

5.1 4.2

12.7 8.1

4.4 1.0 0.0049 14.9

9.9 3.8

QUGA SD

RONE

M

SD

3.7 5.1

6.7 15.3

30.6 42.1

0.3 0.4

0.2 0.2

1.0 0.6

M

SD

0 0.2

0 1.4

0 0.004

0 0.0

78.3

1.6

12.7

13.3

46.1

25.1

112.5

186.2 33.8

0.2 0.8

2.5 3.7

6.7 15.3

30.6 42.1

0 0.2

0 1.4

9.9 6.4

29.2 26.2

0.1 0.3

1.2 2.2

0.4 0.1

2.1 1.0

79.5 8.8 <0.0001*

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M

313

314

J.L. Vankat / Forest Ecology and Management 261 (2011) 309–325

1935 and the digital topographic map used for estimating UTMs in 2004 (cf. Section 2.2), at least for the forested areas of GCNP. I was able to match 1935 site characteristics for 94% of the plots sought, and only these plots were resampled. Moreover, statistical analysis indicated that results for tree density, tree basal area, and sapling density are unaffected by the estimated distance to the original plot location, which had been recorded in the field in 2004 as an estimate of accuracy of plot relocation (cf. Section 2.2). 3.2. Forest changes 3.2.1. Overall ponderosa pine forest With all subtypes combined, PPF exhibited no statistically significant change in total density of trees from 1935 to 2004. The means were 405.4 trees ha−1 in 1935 (range: 49.4–1359.1) and 368.1 trees ha−1 in 2004 (range: 10.0–920.0) (Table 2). However, POTR significantly decreased in density by 83% (89.7 to 15.3 trees ha−1 ). (Note: all percentages were calculated from compiled field data, not from rounded data appearing in tables.) In terms of relative density, PIPO remained the most abundant species (65 and 71% in 1935 and 2004, respectively), but the second most abundant species changed from POTR (22%) in 1935 to ABCO (11%) in 2004 (percentages limited to species with ≥10% relative value). In contrast, total basal area significantly decreased 25% from 49.7 m2 ha−1 (range: 2.4–121.3) to 37.4 m2 ha−1 (range: 3.5–70.2), primarily due to a significant decrease of 22% in PIPO (42.2–33.0 m2 ha−1 ) and a trend of a decrease of 76% in POTR (4.4–1.0 m2 ha−1 ). Basal area remained highly concentrated in PIPO with relative values ≥85% in 1935 and 2004. I also examined densities of saplings and each of the four diameter classes listed in Section 2.2. Sapling density was unrecorded in 1935 but averaged 204.8 individuals ha−1 (range: 0–1120.0) in 2004 with PIPO accounting for 36%, followed by ABCO at 22% and RONE at 12%. All tree species except PISP were recorded as saplings. None of the four tree diameter classes exhibited a statistically significant change in total density from 1935 to 2004; however, there were significant changes in individual species, mostly in the smallest diameter class of 10–29.9 cm where POTR decreased 89% (79.5–8.8 trees ha−1 ), PIPO increased 21% (116.8–141.1 trees ha−1 ), and ABCO increased 631% (4.6–33.7 trees ha−1 ). In terms of relative density within this diameter class, PIPO increased (49–61%), while the second most abundant species changed from POTR (33%) to ABCO (15%). In the next larger diameter class of 30–60.9 cm, PIPO decreased by 18% (105.1–86.6 trees ha−1 ); it was the most abundant species in both 1935 and 2004 in this class and the two larger diameter classes (84–100%). The 1935 mean basal area of 49.7 m2 ha−1 is unexpectedly high, and no explanation is readily apparent. A few plots when resampled in 2004 appeared denser than representative of the surroundings, but are unlikely to have markedly inflated the mean value based on 99 plots. The method of sampling trees in 1935 was similar to modern plot-based methods; the plots were not visually approximated, but were outlined by measuring tape (Claude A. Wagner, field crew leader, personal communication). Trees were tallied into diameter classes in 1935, requiring me to use diameter-class midpoints to calculate basal area, but this could lead to both over- and underestimations, depending on diameter distributions within classes in 1935. The inclusion of moist PPF, which is transitional with MCF, increased overall PPF basal area by 4%. Fewer changes occurred in overall PPF from 1935 to 2004 than observed in other GCNP forest types (Vankat, this issue). Even though approximately three-fourths of the resampled plots had been burned in recent surface fire (none had burned in crown fire), Vankat (2010) found no evidence linking surface fire to the 1935–2004 changes in overall PPF. The finding that ABCO and PIPO increased in density in the 10–29.9 cm diameter class and the high

proportions of these two species among saplings may indicate these species are still increasing in density in PPF, although the decrease in PIPO in the next larger diameter class and no change in its overall density suggest that this species may have peaked in density. However, treating GCNP’s PPF as a single forest type can lead to erroneous conclusions, because there are differences among major PPF subtypes. For example, the finding that total basal area decreased in overall PPF differs with no decrease in dry PPF (see Section 3.2.2), none of several findings of decreases in POTR in overall PPF applied to mesic PPF (see Section 3.2.3), and the finding that total density in overall PPF was statistically unchanged from 1935 to 2004 differs with decreased density in moist PPF (see Section 3.2.4). While results for overall PPF may be useful for generalized purposes, division into PPF subtypes is essential for more detailed scientific understanding and therefore for development of ecologically based management practices. 3.2.2. Dry ponderosa pine forest This PPF subtype is codominated by PIPO, JUOS, and PIED (Table 3) and is transitional to PJ vegetation. Dry PPF was the only forest or forest subtype (including MCF, SFF, and QAF; Vankat, this issue) to exhibit no statistically significant changes from 1935 to 2004. Density averaged 478.3 trees ha−1 in 1935 (range: 74.1–1124.3) and 528.6 in 2004 (range: 330.0–870.0). PIED had the highest relative density in both years, 63% in 1935 and 53% in 2004, followed by JUOS (20 and 22%) and PIPO (15 and 17%). Basal area averaged 33.0 m2 ha−1 in 1935 (range: 2.4–54.4) and 25.9 in 2004 (range: 13.0–37.9). PIED had the highest relative basal area in both years (44% in 1935 and 35% in 2004), followed by PIPO (30 and 32%) and JUOS (25 and 31%). Only JUOS, PIED, PIPO, and QUGA were sampled, the lowest number of tree species in any forest or forest subtype. With regard to densities of saplings and each of the four diameter classes listed in Section 2.2, sapling density averaged 302.9 individuals ha−1 in 2004 (range: 120.0–560.0), and PIED had the highest relative value at 62%, followed by PIPO at 19% and JUOS at 11%. Only JUOS, PIED, PIPO, and QUGA were sampled, the lowest number of sapling species in any forest or forest subtype. The smallest tree diameter class was dominated by PIED (72 and 59% relative density in 1935 and 2004, respectively), followed by JUOS (18 and 19%, respectively), PIPO (9 and 12%), and QUGA (1 and 10%). In the 30–60.9 cm class, relative density was nearly equal among JUOS, PIED, and PIPO in both 1935 and 2004. The third largest diameter class (61–90.9 cm) was dominated by PIPO (67% relative density) followed by PIED (33%) in 1935, but was codominated by both species (50%) in 2004. No trees were recorded in the largest diameter class. The absence of statistically significant changes from 1935 to 2004 in dry PPF (and the absence of statistically significant impacts of surface fires; cf. Vankat, 2010) may have been influenced by small sample size (7 plots for dry PPF, compared to 75 and 17 for mesic and moist PPF, respectively). The dominance of PIED in both density and basal area, the second-ranked density of JUOS, and the second-ranked basal area of PIPO support dry PPF being transitional with PJ vegetation. The dominance of PIED in the sapling and 10–29.9 cm diameter class indicates this species has the potential to remain dominant into the foreseeable future. This contrasts with high-elevation PJ woodland vegetation elsewhere in the Southwest where overstory PIED mortality of 40–90% was caused by drought-induced outbreaks of bark beetle (Ips confusus) in 2002–2003 (Breshears et al., 2005, 2009). A similar wave of PIED mortality was not obvious in GCNP’s dry PPF sampled in 2004, when standing dead PIED accounted for 11% (27.1 trees ha−1 ) of the total living + dead PIED in the 10–29.9 cm diameter class and only 6% (1.4 trees ha−1 ) in the 30–60.9 cm diameter class, the largest class with living or dead PIED.

J.L. Vankat / Forest Ecology and Management 261 (2011) 309–325

315

Table 3 Dry ponderosa pine forest 1935 and 2004. Trees are ≥10 cm dbh; saplings are 2.5–9.9 cm dbh. M = mean, SD = standard deviation, and species acronyms = first two letters of the genus and species, except PISP = Picea species (includes PIEN and PIPU). P-values provided where there are statistically significant differences (*P ≤ Bonferroni-adjusted 0.05) or trends for differences (P ≤ unadjusted 0.10) between means for 1935 and 2004. Year (# plots)

Total M

SD

Tree density (# ha−1 ) 1935 (7) 478.3 332.0 2004 (7) 528.6 170.8 P-value Tree basal area (m2 ha−1 ) 1935 (7) 33.0 16.3 2004 (7) 25.9 9.5 P-value Sapling density (# ha−1 ) 2004 (7) 302.9 149.4 Tree density (# ha−1 ) by diameter class 10–29.9 cm 1935 (7) 368.9 309.6 2004 (7) 432.9 154.0 P-value 30–60.9 cm 1935 (7) 98.8 57.5 2004 (7) 84.3 48.6 P-value 61–90.9 cm 1935 (7) 10.6 13.2 2004 (7) 11.4 10.7 P-value ≥91 cm 1935 (7) 2004 (7) P-value

ABCO

ABLA

M

M

SD

JUOS SD

M

PIED SD

PIPO

M

SD

M

PISP SD

M

SD

POTR

PSME

QUGA

M

M

M

SD

SD

RONE SD

97.1 117.1

78.0 25.0

301.8 280.0

295.0 146.4

74.1 90.0

34.2 98.8

5.3 41.4

14.0 92.6

8.4 8.0

7.1 4.8

14.4 9.0

9.7 5.7

10.0 8.3

5.2 7.6

0.2 0.6

0.5 1.2

34.3

42.8

188.6

64.1

57.1

55.9

22.9

45.4

65.3 82.9

60.8 26.3

266.5 255.7

310.6 131.1

31.8 52.9

25.6 84.8

5.3 41.4

14.0 92.6

28.2 28.6

30.0 26.7

35.3 24.3

44.2 23.0

35.3 31.4

21.9 13.5

3.5 5.7

6.0 5.3

7.1 5.7

9.7 11.3

3.2.3. Mesic ponderosa pine forest Mesic PPF, the only PPF subtype on both the South and North Rims of GCNP, had no statistically significant changes in tree density (the apparent increase in ABCO density was obscured by high variability in the data) (Table 4). Total density averaged 327.8 trees ha−1 in 1935 (range: 49.4–1099.6) and 350.0 in 2004 (range: 10.0–920.0). PIPO had the highest relative density (89 and 81% in 1935 and 2004, respectively), with no other species accounting for ≥10%. In contrast, total basal area decreased 23%, from 49.1 m2 ha−1 in 1935 (range: 2.8–121.3) to 37.8 in 2004 (range: 3.5–70.2). This decrease was accounted for by PIPO, which significantly decreased 24% from 47.2 to 35.8 m2 ha−1 , but retained the highest relative value (96 and 95% in 1935 and 2004, respectively). Mesic PPF has the highest number of tree species recorded of any forest or forest subtype, with all species except ABLA and RONE recorded in 1935 and RONE added in 2004. Mean total density of saplings was 186.1 individuals ha−1 in 2004 (range: 0–1120.0). PIPO had the highest relative value at 46%, followed by ABCO at 22% and RONE at 14%. All species except PISP were recorded as saplings, more than any other forest or forest subtype. There were several statistically significant changes in tree diameter classes. In the smallest class, total tree density increased 33% (158.0–209.7 trees ha−1 ) and included a 20% increase in PIPO (125.7–151.1 trees ha−1 ) and a 2351% increase in ABCO (1.2–28.3 trees ha−1 ). PIPO had the highest relative density at 80 and 72% in 1935 and 2004, respectively, and ABCO increased from 1 to 13%, becoming the only other species in either year with a relative density value ≥10%. In the larger diameter classes, total tree density was statistically unchanged but PIPO significantly decreased 17% (118.0–97.6 trees ha−1 ) in the 30–60.9 cm class and exhibited a trend of a decrease of 21% (41.3–32.5 trees ha−1 ) in the 61–90.9 cm class. PIPO had the highest relative density values in the three largest classes at 94–100%. Mesic PPF was intermediate among PPF subtypes in terms of change during 1935–2004. The lack of change in total density is unexpected, giving numerous reports of increased densities of PPF following fire exclusion in GCNP (e.g., Fulé et al., 2002; Crocker-Bedford et al., 2005b) and elsewhere in the Southwest (e.g.,

M

SD

Weaver, 1951; Harrington and Sackett, 1990; Covington and Moore, 1994a,b; Dahms and Geils, 1997). A possible explanation for this discrepancy is that forest densities had already increased by 1935, but also the data suggest another scenario: increases in densities of small (10–29.9 cm dbh) PIPO and ABCO from 1935 to 2004 were largely offset by decreases in densities of larger diameter PIPO. In comparing data from 1935 to data from 1992 to 2002 for PPF in GCNP, Crocker-Bedford et al. (2005b) found statistically significant decreases of PIPO in both the 61–90.9 and ≥91 cm diameter classes (39 and 75%, respectively). A similar study covering a similar time span found PIPO decreased in density in equivalent diameter classes in Yosemite National Park, California (Lutz et al., 2009), and increased tree mortality was reported for old-growth forests in the western United States, including a stand of PPF elsewhere in northern Arizona (van Mantgem et al., 2009). Eighty percent of the resampled mesic PPF plots had evidence of surface fire; however, there is no evidence that surface fire had a statistically significant impact that paralleled the 1935–2004 changes – although the increase in ABCO in the smallest diameter class would have been larger without a trend for reduction by surface fire (Vankat, 2010). The decreased density of large diameter PIPO in GCNP may be related to increased density of smaller trees, because PIPO tree growth rates have been shown to decline with increased competition (Biondi, 1996), as well as with decreased soil moisture resulting from thicker litter (Clary and Ffolliott, 1969; Harrington and Sackett, 1990). In addition, competition from smaller, younger trees reduces the vigor of larger, older trees (Feeney et al., 1998). Measurements before and after thinning of small trees indicated that they reduce canopy growth and uptake of water, carbon, and nitrogen by larger trees (Stone et al., 1999). Older trees are more susceptible to pathogens, drought, and injury because of increased stress through increased competition (Kaufman and Covington, 2001) and, although undocumented for GCNP, possibly to increased air pollutants such as ozone. I did not find evidence that initial management surface fire also decreased the density of large PIPO (Vankat, 2010), but Kaufman and Covington (2001) did find such evidence. Moreover, decreased density in larger diameter classes also may be related to reduced recruitment from smaller trees

316 Table 4 Mesic ponderosa pine forest 1935 and 2004. Trees are ≥10 cm dbh; saplings are 2.5–9.9 cm dbh. M = mean, SD = standard deviation, and species acronyms = first two letters of the genus and species, except PISP = Picea species (includes PIEN and PIPU). P-values provided where there are statistically significant differences (*P ≤ Bonferroni-adjusted 0.05) or trends for differences (P ≤ unadjusted 0.10) between means for 1935 and 2004. Year (# plots)

Total

ABCO SD

Tree density (# ha−1 ) 1935 (75) 327.8 232.3 2004 (75) 350.0 201.7 P-value Tree basal area (m2 ha−1 ) 1935 (75) 49.1 28.1 2004 (75) 37.8 14.3 P-value 0.0012* Sapling density (# ha−1 ) 2004 (75) 186.1 238.5 Tree density (# ha−1 ) by diameter class 10–29.9 cm 1935 (75) 158.0 193.3 2004 (75) 209.7 184.2 P-value 0.0019* 30–60.9 cm 1935 (75) 122.9 103.8 2004 (75) 104.0 73.7 P-value 61–90.9 cm 1935 (75) 41.5 36.8 2004 (75) 32.5 23.5 P-value ≥91 cm 1935 (75) 5.4 13.4 2004 (75) 3.7 8.2 P-value

ABLA

M

SD

1.6 31.2

JUOS

PIPO

PISP

SD

M

SD

8.5 95.2

8.1 5.5

40.1 17.8

4.9 2.5

25.3 11.2

0.2 1.0

1.2 2.8

0.6 0.2

2.7 0.8

0.3 0.1

1.4 0.2

40.5

121.5

6.9

42.8

5.9

21.5

6.5 91.5

5.4 4.3

31.5 14.8

4.1 2.5

20.7 11.2

125.7 151.1 <0.0001*

167.5 145.9

0.7 0.9

0.3 2.9

2.9 8.2

2.6 1.2

11.2 6.4

0.8 0

4.7 0

118.0 97.5 0.0001*

101.4 73.8

0.2 0.5

0.2 0

1.4 0

0.5

SD

PIED

M

1.2 28.3 <0.0001*

M

4.6

POTR

PSME

M

SD

M

SD

M

SD

M

290.4 284.8

211.5 166.0

0.8 1.5

7.1 11.6

14.0 7.2

45.6 42.2

0.5 1.1

0.4 0.7

0.6 0.3

1.8 2.0

0.04 0.1

4.3

18.1

5.7 7.0

13.2 5.6

1.4 4.6

0.8 1.6

47.2 35.8 <0.0001* 84.8

41.3 32.5 0.0945 5.4 3.7

27.9 14.0

0.05 0.1

158.5

36.8 23.5

13.4 8.2

QUGA SD

RONE

M

SD

M

SD

4.3 5.3

7.4 16.0

34.0 39.2

0 0.3

0 1.6

0.3 0.4

0.2 0.2

1.1 0.6

0 0.01

0 0.03

1.6

13.9

15.5

50.9

26.1

115.1

44.3 34.9

0.3 0.8

2.9 3.6

7.4 16.0

34.0 39.2

0 0.3

0 1.6

4.7 8.1

0.2 0.3

1.4 2.3

J.L. Vankat / Forest Ecology and Management 261 (2011) 309–325

M

Table 5 Moist ponderosa pine forest 1935 and 2004. Trees are ≥10 cm dbh; saplings are 2.5–9.9 cm dbh. M = mean, SD = standard deviation, and species acronyms = first two letters of the genus and species, except PISP = Picea species (includes PIEN and PIPU). P-values provided where there are statistically significant differences (*P ≤ Bonferroni-adjusted 0.05) or trends for differences (P ≤ unadjusted 0.10) between means for 1935 and 2004. Year (# plots)

Total

ABCO SD

Tree density (# ha−1 ) 1935 (17) 717.3 311.2 2004 (17) 381.8 200.9 P-value <0.0001* 2 −1 Tree basal area (m ha ) 1935 (17) 59.1 20.2 2004 (17) 40.1 13.2 P-value 0.0095* Sapling density (# ha−1 ) 2004 (17) 247.1 303.4 Tree density (# ha−1 ) by diameter class 10–29.9 cm 1935 (17) 543.6 314.5 2004 (17) 238.2 169.3 P-value <0.0001* 30–60.9 cm 1935 (17) 138.1 74.3 2004 (17) 109.4 73.9 P-value 61–90.9 cm 1935 (17) 29.8 28.0 2004 (17) 25.9 13.3 P-value ≥91 cm 1935 (17) 5.8 12.4 2004 (17) 8.2 8.1 P-value

ABLA

JUOS

M

SD

M

SD

30.5 90.0

71.3 115.2

0 4.1

2.6 4.8

4.9 8.6

82.4

M

PIED SD

M

PIPO SD

PISP

POTR

PSME

QUGA

RONE

M

SD

M

SD

M

SD

M

SD

M

SD

0 17.0

221.7 227.1

124.4 180.8

0 0.6

0 2.4

460.8 57.1 <0.0001*

266.6 70.2

0 1.8

0 5.3

4.4 1.2

18.0 4.9

0 0.1

0 0.5

33.4 30.5

15.5 12.7

0 0.1

0 0.2

23.0 4.5 <0.0001*

11.9 7.3

0 0.1

0 0.3

0.1 0.02

138.9

25.9

106.7

37.6

55.6

179.9

2.4

9.7

21.8 71.8 0.0003*

60.0 94.3

0 4.1

0 17.0

112.7 133.5

108.0 156.2

259.8 30.8

0 1.2

0 4.9

7.3 17.1

15.2 34.4

77.0 61.2

58.7 52.5

51.3 56.3

0 0.6

0 2.4

1.5 1.2

4.1 4.9

26.2 24.1

25.0 13.7

5.8 8.2

12.4 8.1

68.2

404.8 26.5 <0.0001* 0 0.6

0 2.4

53.8 30.0 0.0880 2.2 0.6

4.9 2.4

4.4 1.2

M

SD

30.6

126.1

0.6 0.1

18.0 4.9

J.L. Vankat / Forest Ecology and Management 261 (2011) 309–325

M

317

1.3

f

e

c

d

b

39.1 83.9 303.2 429.2 572.9

368.1

Average of Fire Point, Powell Plateau, Rainbow Plateau, and Grandview in proportion to area sampled in 1997–1998. Average of PIPO-sagebrush, South Rim PIPO-grass, and North Rim PIPO-grass in proportion to area sampled. Average of seven South Rim sites. Average of Fire Point, Powell Plateau, Rainbow Plateau, and Grandview in proportion to area sampled. Average of Fire Point, Powell Plateau, and Rainbow Plateau in proportion to area sampled. Reported as ABCO and/or ABLA, but likely all ABCO. a

1.7 423.7 812.5

435.8

405.4 233.9 219.9

366.2

111.8 270.9 214.2 124.6 111.8 242.8

Crocker-Bedford et al. (2005b) Crocker-Bedford et al. (2005a) Fulé et al. (2002)a Lang and Stewart (1910) This study Merkle (1962)b Rand (1965)c Fulé et al. (2002)d Fulé et al. (2002)e This study ∼1870 ∼1870 1879–1887 1909 1935 1949–1955 1959–1961 1997–1998 1997–1998 2004

149.2

335.8

144.8 261.1 422.0

366.2

111.7 154.7 6.4f 6.5 0.2 60.3f

43.2 227.9

30.2

15.3

89.7

275.0

Trees

≥10 cm dbh ≥10 cm dbh >15.2 cm dbh ≥15.2 cm dbh ≥10 cm dbh ≥3.0 m height >7.6 cm dbh NA >15.2 cm dbh ≥10 cm dbh NA NA ≥2.5 cm dbh ≥91.4 cm height NA ≥0.6 m height NA ≥2.5 cm dbh NA ≥2.5 cm dbh

S+T Trees S+T S+T Trees S+T

Trees

S+T

Trees

S+T

Trees

205.0 145.3 99.6 99.8 263.3 214.2 124.6

Size POTR PIPO ABCO All conifer species with canopy potential All species Source

Table 6 Ponderosa pine forest density data for synthesis (individuals ha−1 ). S + T = saplings + trees, species acronyms = first two letters of genus and species, and NA = not applicable.

3.2.4. Moist ponderosa pine forest Sometimes referred to as high-elevation PPF and considered transitional to MCF, moist PPF is the only PPF subtype to exhibit a statistically significant change in total tree density, having decreased 47%, from 717.3 trees ha−1 in 1935 (range: 234.8–1359.1) to 381.8 in 2004 (range: 40.0–750.0) (Table 5). This decrease is more than accounted for by a statistically significant decrease of 88% in POTR (460.8–57.1 trees ha−1 ). With the large decrease in POTR, the order of relative density shifted from POTR (64%) and PIPO (31%) in 1935 to PIPO (59%), ABCO (24%), and POTR (15%) in 2004. Total basal area decreased by 32% from 59.1 m2 ha−1 (range: 21.8–92.5) to 40.1 (range: 16.5–63.6) and is nearly entirely accounted for by a significant 80% decrease in POTR from 23.0 to 4.5 m2 ha−1 . Basal area was concentrated in PIPO in both 1935 and 2004 (57 and 76%, respectively), followed by POTR in 1935 (39%) and ABCO and POTR in 2004 (12 and 11%, respectively). Four tree species were recorded in 1935 (equaling dry PPF for the lowest value), but three additional species were recorded in 2004 (ABLA, PISP, and PSME), giving moist PPF an intermediate number among PPF subtypes and suggesting important changes. Sapling density in 2004 was 247.1 individuals ha−1 (range: 0–1000.0), with ABCO accounting for 33%, POTR 28%, PIPO 15%, RONE 12%, and ABLA 10%. PSME was the only other species present, giving moist PPF an intermediate number of sapling species among PPF subtypes. In the smallest diameter class, total density significantly decreased 56% (543.6–238.2 trees ha−1 ), which is more than accounted for by a significant 93% decrease in POTR (404.8–26.5 trees ha−1 ) that masks a significant 229% increase in ABCO (21.8–71.8 trees ha−1 ). These changes produced large shifts in the relative density of species, with POTR decreasing from 74 to 11%, PIPO increasing from 21 to 56%, and ABCO increasing from 4 to 30%. In the 30–60.9 cm diameter class, POTR exhibited a trend for a decrease in density of 44% (53.8–30.0 trees ha−1 ). PIPO had a relative density of 56% in both years, and POTR dropped from 39% in 1935 to 27% in 2004, when ABCO (16%) was the only other species with a relative value ≥10% in either year. PIPO had the highest relative density values in the two largest diameter classes at 88–100%. Moist PPF was the most dynamic of the PPF subtypes from 1935 to 2004. Nearly one-third of the plots had evidence of recent surface fire; however, surface fire effects were unrelated to the 1935–2004 changes, except for a trend that surface fire may have favored the increase of ABCO in the smallest diameter class, which Vankat (2010) considered a possible statistical anomaly. The 1935–2004 changes in moist PPF were primarily due to decreases in POTR, a species absent in dry PPF and uncommon in mesic PPF. The decreases in POTR, as well as the increase in density of ABCO in the 10–29.9 cm diameter class (and possibly the additions of ABLA and PISP in 2004), are likely related to fire exclusion. While many studies in PPF and low-elevation MCF have reported that increases in ABCO are due to fire exclusion, including studies in GCNP (e.g., White and Vankat, 1993; Fulé et al., 2002; Mast and Wolf, 2004), decreases in POTR frequently have been attributed to successional changes, as early-successional stands of QAF are replaced by ingrowth and eventual dominance of conifers. However, three lines of evidence indicate that GCNP’s moist PPF was not in an early-successional stage in 1935. First, the 1935 data document that moist PPF had been dominated by PIPO, not POTR. Consid-

Location

made less vigorous by increased competition (Crocker-Bedford et al., 2005b; cf. Biondi, 1996). Possible roles of climate changes are unknown, but 1942–1977 was a period of drought in the region, following a wet period from 1905 to 1941 (Hereford et al., 2002). Increased tree mortality rates in the western United States have been linked to climate warming and consequent increased water deficits (van Mantgem et al., 2009).

Both Rims North Rim Both Rims Kaibab Plateau Both Rims Both Rims South Rim Both Rims North Rim Both Rims

J.L. Vankat / Forest Ecology and Management 261 (2011) 309–325

Date

318

J.L. Vankat / Forest Ecology and Management 261 (2011) 309–325

319

Table 7 Ponderosa pine forest sources of basal area data for synthesis (m2 ha−1 ). Species acronyms = first two letters of genus and species. Date

Source

∼1870 1879–1887 1909 1935 1949–1955 1959–1961 1997–1998 2004

Crocker-Bedford et al. (2005b) Fulé et al. (2002)a Lang and Stewart (1910) This study Merkle (1962)b Rand (1965)c Fulé et al. (2002)d This study

a b c d e

All species

All conifer species with canopy potential

ABCO

13.9

13.3 15.7 42.8 19.6 20.0 21.0 34.8

<0.1 0.9e 0.6

49.7 20.0 24.3 25.4 37.4

<0.1 1.6

PIPO

35.0 13.3 14.1 42.2 19.6 20.0 21.0 33.0

POTR

4.4

<0.1 1.0

Size

Location

≥10 cm dbh ≥2.5 cm dbh ≥91.4 cm height ≥10 cm dbh ≥0.6 m height >7.6 cm dbh ≥2.5 cm dbh ≥10 cm dbh

Both Rims Both Rims Kaibab Plateau Both Rims Both Rims South Rim Both Rims Both Rims

Average of Fire Point, Powell Plateau, Rainbow Plateau, and Grandview in proportion to area sampled in 1997–1998. Average of PIPO-sagebrush, South Rim PIPO-grass, and North Rim PIPO-grass in proportion to area sampled. Average of seven South Rim sites. Average of Fire Point, Powell Plateau, Rainbow Plateau, and Grandview in proportion to area sampled. Reported as ABCO and/or ABLA, but likely all ABCO.

ering trees ≥61 cm dbh, PIPO averaged 32.0 trees ha−1 , compared to 2.2 for POTR and 1.5 for ABCO. Indicative of PIPO dominance, 11 of the moist PPF plots were characterized by the 1935 field crew as PIPO-POTR and 5 as PIPO-ABCO-POTR (one plot was characterized as POTR-PIPO). Second, the historical fire regime of PPF in GCNP and throughout the Southwest was characterized by frequent, lowintensity surface fires (e.g., Swetnam and Baisan, 1996; Fulé et al., 2002), and crowning (often associated with the initiation of QAF stands) was uncommon (Woolsey, 1911; Fulé et al., 2003b) because of the absence of (a) dense understories, which are necessary to produce intense surface fires that cause passive crown fires in GCNP PPF (Chris Marks, Deputy Fire Management Officer, GCNP, personal communication) and (b) extraordinarily high wind speeds well in excess of 100 km h−1 required for active crown fires in GCNP PPF in 1880 (Fulé et al., 2004). Third, there is no evidence of large increases in conifers from 1935 to 2004, as would be expected with successional replacement of POTR. Therefore, successional changes do not account for the decline of POTR in GCNP’s moist PPF. Instead, the decrease in POTR likely involved several factors. First, although there was an 1880s–1890s pulse of survivorship with the end of surface fires (Binkley et al., 2006), there was decreased regeneration by fire-stimulated sprouting during fire exclusion (Moir, 1993; White and Vankat, 1993). Second, recruitment of sprouts and seedlings into sapling and tree diameter classes was reduced by herbivory from an unusually elevated deer population circa 1915–1935 (Fulé et al., 2002, 2003a; Mast and Wolf, 2006; Moore and Huffman, 2004; Binkley et al., 2006) and again in the 1950s (Merkle, 1962), resulting in the loss of cohorts of POTR (Fulé et al., 2003a; Binkley et al., 2006). Third, POTR mortality may have increased with competition from increased density of conifers such as ABCO following the onset of fire exclusion. A fourth factor may have been Sudden Aspen Decline, in which strong drought, high temperatures, late frosts, and repeated defoliation by insects facilitated increased mortality by insects, bark beetles, and canker fungi, as reported elsewhere in the Southwest (Fairweather et al., 2008; Worrall et al., 2008). The drought and high-temperature factors may indicate a link to climate change (cf. Gitlin et al., 2006). A fifth factor may have been elevated ozone levels, which increased in GCNP during 1990–1999 and exceeded levels where effects on tree foliar tissues and seedling growth have been observed in California (National Park Service, 2002). Elsewhere, ozone negatively impacted above-ground growth and physiology of POTR, including accelerated senescence of foliage, decreased photosynthesis, increased respiration, decreased carbon gain, altered carbon allocation, decreased growth (especially in roots), increased foliar pathogens, altered pest occurrence, altered competitive ability, and reduced fitness (Karnosky et al., 1999, 2005, 2007). The possible role of elevated ozone is unstudied in the Southwest where other fac-

tors appear to dominate POTR decline (Mary Lou Fairweather, Plant Pathologist, Southwestern Region, USFS, personal communication). 3.3. Synthesis of historical and recent data Resampling the 1935 plots provided information on changes in GCNP PPF forests over the second half of the period between the beginning of fire exclusion and the 21st century. To integrate this information within the entire period and with other datasets, I collected historical and recent datasets on GCNP PPF and analyzed them graphically. Data from other locations were not used because soil differences can correlate with large differences in PPF (Covington and Moore, 1994a). These graphical analyses used the 1935 and 2004 datasets; therefore, findings complement but are not independent of the findings described in Section 3.2. For consistency, I classified datasets to forest type using the procedure in Section 2.3, rather than use the researcher’s original designation of forest type. I then used all PPF datasets (Tables 6 and 7), except (a) data from Gildar et al. (2004), who sampled a small subset of the plots included in Fulé et al. (2002) and only six 0.1 ha plots in their other study site, and (b) data from Crocker-Bedford et al. (2005a,b), who used the 1935 data also summarized in this study and the PPF data used in Fulé et al. (2002). Studies differed in sites sampled, sampling methods, minimum sizes of saplings and trees sampled, etc. Where a study reported data for two or more sites, I averaged the data (in proportion to the area sampled, when that information was available). This combining of study sites resulted in datasets more directly comparable to the landscape scale of the 1935 and 2004 datasets. I graphed density data for saplings + trees (generally ≥2.5 cm dbh) and for trees (generally ≥10.0 cm dbh) and basal area data by year for (a) all species combined, (b) all conifer species with canopy potential combined (ABCO, ABLA, PIPO, PISP, and PSME), and (c) ABCO, PIPO, and POTR individually. Too few historical datasets are available to analyze PPF subtypes, saplings alone, ABLA, PISP, PSME, and POTR saplings + trees. Graphs were fitted with trendlines selected from exponential, linear, logarithmic, power, and second-order polynomial functions. The fitted trendline with the highest R2 value and a biologically interpretable trend is displayed in each graph. When two lines with different biological interpretations had similar R2 values, both are shown. The following results and discussion focus on findings where R2 ≥ 0.5. The graph of density of saplings + trees for all species combined indicates an increase since the late 19th century with a trendline R2 of 0.9276 (Fig. 2), but the graph for trees indicates highly variable results (R2 = 0.0173) since 1935, the earliest data available. If this variability is interpreted as no change in tree density (which is in accord with my findings for overall PPF in 1935–2004; Section 3.2.1), it is inferred that that saplings account

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Fig. 2. Historical trends in ponderosa pine forest density; saplings + trees in left column, trees in right column. Fitted trendlines have the highest R2 values and biologically interpretable trends. Multiple data points may be superimposed.

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for the increase in density. The two graphs for all conifer species with canopy potential similarly indicate an increase in saplings (R2 = 0.9987 for saplings + seedlings and R2 = 0.3466 for trees), as do the paired graphs for PIPO (R2 = 0.8959 for saplings + seedlings and R2 = 0.1274 for trees). No historical datasets are available for POTR saplings + seedlings, but there has been a decline in POTR tree density (R2 = 1), as well as POTR basal area since the late 19th century (R2 = 0.9075; Figs. 2 and 3); however, these findings add little or no additional insight, because two of the three data points are from this study and the third is an extrapolation for ∼1870 by CrockerBedford et al., 2005a, which was partly based on the 1935 data. All trendlines where R2 ≥ 0.5 indicate unidirectional change or no change (high variability) and all changes are increases, except for decreases in POTR tree density and basal area. The unidirectionality contrasts with many changes in MCF that involve increases followed by decreases (Vankat, this issue). These findings combine with the detailed results for 1935–2004 to document that GCNP PPF has been dynamic since the late 19th century, with changes in density, basal area, and species composition. Previous studies in GCNP and elsewhere in the Southwest indicated that historical dynamics – linked primarily to fire exclusion – have involved increases in forest densities (e.g., Weaver, 1951; Harrington and Sackett, 1990; Covington and Moore, 1994a,b; Dahms and Geils, 1997), except where harvesting or crown fire have occurred. However, this study – the first to exam-

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ine multi-decadal changes across a never-harvested Southwestern PPF landscape using resampled historical plots – documents that changes in PPF in GCNP also have included decreases since 1935. Changes in GCNP PPF can be placed in a general framework of forest accretion, inflection, and recession, in which increases in tree density are followed by an inflection point, which in turn is followed by decreases in density and basal area. Accretion in GCNP PPF appears to have been triggered by the exogenous factor of fire exclusion, but inflection and recession may have been driven by a combination of the endogenous factor of density-dependent mortality and exogenous factors such as climate change and fire. Major differences among GCNP coniferous forests in terms of timing of accretion, inflection, and recession are correlated with elevation (Vankat, this issue). PPF, which increased in density after the beginning of fire exclusion in the late 19th century, has passed through the accretion phase of forest development and, depending on PPF subtype and variable being examined, is at or past the point of inflection. Increases in small diameter PIPO and ABCO from 1935 to 2004 portend potential additional accretion, but decreases in total basal area, POTR density and basal area, PIPO basal area, and density of larger diameter PIPO indicate PPF has passed the inflection point from accretion to recession. Although Vankat (2010) found no evidence of a relationship between these changes and initial management surface fire, fire can effect PPF density in GCNP (Fulé and Laughlin, 2007) and successful reintroduction and establish-

Fig. 3. Historical trends in ponderosa pine forest basal area. Fitted trendlines have the highest R2 values and biologically interpretable trends.

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ment of a surface fire regime with little crown fire will expedite recession from maximum values of density and basal area toward values present before fire exclusion (Vankat, 2010). In more detail, dry PPF may be at the inflection point because it lacks statistically significant changes in tree density and basal area. Mesic and moist PPF appear to have passed the inflection point to recession with decreasing density (moist PPF) and basal area (both subtypes).

4. Conclusions Accurate information on historical PPF structure and composition is not only important for scientific understanding, but also for determining whether GCNP management is leaving forests “. . .unimpaired for the enjoyment of future generations” (NPS Organic Act of August 25, 1916) and for developing management objectives and practices for ensuring this mandated legacy. Because no data were collected on GCNP forest structure and composition in the late 19th century, estimates have had to be developed through (a) extrapolation of data, (b) forest reconstruction, and (c) sampling of reference sites. Each approach has advantages and disadvantages. Crocker-Bedford et al. (2005a,b) extrapolated backwards to circa-1870 structure and composition in GCNP PPF by determining trajectories based on comparing the 1935 dataset with more recent (mostly 1992–2002) datasets. The accuracy of their extrapolations depends on assumptions regarding rates of change. Forest reconstructions for GCNP PPF have used tree rings of living and dead trees to estimate forest structure and composition at the beginning of fire exclusion (Fulé et al., 2002); however, these values may underestimate historical conditions because of the loss of evidence of late 19th-century trees (Fulé et al., 2002). Study of GCNP PPF reference sites, i.e., sites more isolated from human impacts such as fire exclusion, have been used to reflect on pre-Euramerican conditions (Fulé et al., 2000, 2002, 2003b; Gildar et al., 2004; Laughlin et al., 2004), but density and basal area values may over-estimate late 19th-century forest structure and composition because mean fire intervals at the reference sites increased by approximately 4–9 times during the fire-exclusion period (calculated using pre-1879 25% scarring data from Fulé et al., 2003b and post-1879 dates of extensive fires in Fulé et al., 2002). Not only are the accuracies of estimating late 19th-century conditions open to question, but also the methods have produced contrasting results, e.g., forest reconstructions indicated that current densities of reference sites are 155–486% greater and current basal areas are 47–60% greater than in 1879 (Fulé et al., 2002). There are even greater uncertainties regarding the future of GCNP’s PPF. Future climates for the GCNP region will be different (Seager et al., 2007), but specific details are unknown. Fire will increasingly affect forest structure and composition (Westerling et al., 2006), but effects could range from establishment of modern equivalents of late 19th-century PPF to conversion to grassland (Vankat, 2006). Also, PPF likely will have greater abundance of invasive plants (cf. Crawford et al., 2001; Laughlin and Fulé, 2008), but their long-term effects on structure, composition, and ecological processes are uncertain. Impacts of air pollutants on GCNP forests are unknown (Vankat, 2010). Future synergisms among climate, fire, invasive species, air pollution, and other factors such as insect populations and pathogens add dimensions to the ecological uncertainties (e.g., Parker et al., 2006). Moreover, there are uncertainties about societal factors influencing GCNP forests, including which future scenarios will be acceptable to the public, politicians, and GCNP managers. With such ecological and social uncertainties, how should GCNP managers address the future of GCNP forests? For much of the previous two decades, managers of national parks and other natural areas have focused on developing esti-

mates of the historical range of variation (HRV; spatial and temporal variation in historical conditions) or related measures to guide management objectives and practices (Keane et al., 2009). HRV is readily understandable and information on HRV is often highly valuable; however, the use of HRV in defining targets is challenging for various reasons (see Keane et al., 2009 for a review of issues in quantifying and applying HRV in landscape management). Among the challenges is determination of what is HRV. As illustrated above for GCNP PPF, characterizing 19th-century historical conditions is difficult; no approach is flawless, and different approaches give different results. It has been argued that U.S. national parks “. . .will need to drop the well-entrenched tradition of defining desired future conditions in terms of precise targets or ranges of targets (often based on “natural range of variability” or “historical range of variability”) [and instead] define UNdesired future conditions. . .” (Stephenson, 2009). By defining undesirable future conditions, Stephenson’s (2009) proposed approach leaves a range of desirable conditions, but the focus is on what is undesired rather than desired. For example, at least at this point future desirable conditions for GCNP PPF cannot be fully described based on solely on HRV because of uncertainties about both HRV and the future. However, identifying undesirable future conditions for GCNP PPF, such as landscapescale conversion to grasslands, loss of native biodiversity, and loss of key ecosystem functions including resistance and resilience to stresses, while posing its own challenges, is possible. Stephenson’s (2009) suggestion reflects a management approach in which undesirable conditions are defined by management through identification of multiple thresholds of potential concern (TPCs) for different resources (Biggs and Rogers, 2003; Venter et al., 2008). TPCs “. . .are hypotheses of limits of acceptable change in ecosystem structure, function and composition” and collectively define the conditions for which the area is managed, separating undesirable and desirable conditions (South African National Parks, 2009). TPCs are “. . .based on a combination of best available knowledge and plausible ecological hypotheses around understanding complex dynamic ecosystems.” As hypotheses, TPCs are modified through adaptive management as knowledge and experience increase. Therefore research and monitoring are essential to improve the TPCs and identify where rates of approaches to TPCs require timely action by managers to avoid undesired conditions. So rather than attempting to maintain an ecologically viable GCNP PPF through successfully achieving specific desired future conditions, GCNP managers could focus on maintaining a more generalized desired state of PPF through successfully avoiding specific undesired conditions. The contrast between management based solely on HRV and based on TPCs that may be in part based on HRV is illustrated in Fig. 4. It shows a broader range of desirable conditions (including states and transitions shown by squares and arrows within the desirable zone) with management based on TPCs than with management based solely on HRV, because with TPCs the range of desirable conditions is likely to include the HRV as well as additional variation that research and experience have shown is not undesirable (double negative intended). In addition, the boundary of the desirable zone is likely to be more clearly defined (shown in the figure with a solid line) with TPCs at any point in time because it is based on available research and experience. The boundary with HRV is likely more imprecise (shown by a dashed line) because information on HRV is seldom thorough (Keane et al., 2009). However, in both management approaches, additional research, including findings on HRV, can result in changes in the boundaries. These differences between management based solely on HRV and based on TPCs have ramifications for restoration efforts when the system is in the undesirable zone. The greater range of desirable conditions with more desirable states suggests that restoration

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Thomas, Mark White, and two anonymous reviewers provided useful comments on earlier versions of this manuscript. References

Fig. 4. Comparison of management of natural resources using targets defined by historical range of variation vs. thresholds of potential concern. Axes represent two of the multiple dimensions that define desirable zones.

activities typically involve less management activity with TPCs than with HRV (shown in the figure by shorter arrows starting from the same points in the undesirable zone and ending in the desirable zone). Another advantage of using TPCs to define the desirable zone is that this approach can address conditions for which there are no historical analogs, i.e., novel future conditions such as with climate change, invasive species, etc.; HRV cannot do this effectively. A possible disadvantage is that management by TPCs could be more subject to political pressure than management by HRV. Resource managers should evaluate such advantages and disadvantages when considering a shift in focus from achieving desired future conditions to avoiding undesired future conditions. Regardless of approach, information on historical conditions and changes in those conditions, such as provided by this study, valuably informs the management of natural resources. Acknowledgements Cole Crocker-Bedford instituted and thereafter supported this study in numerous ways; Dr. Robert Schaefer conducted statistical analyses; the NPS Washington Office, GCNP, and Southern Colorado Plateau Inventory & Monitoring Network funded the resampling of the 1935 plots; the NPS Colorado Plateau Cooperative Ecosystem Study Unit and the family of Claude “Bru” Wagner (through Grand Canyon Association) funded the statistical analyses and writing; and Dr. Michael F. Anderson provided insight on the history of GCNP. These individuals and Dr. Jim DeCoster, Chris Marks, Lisa

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