Mapping lodgepole pine stand structure susceptibility to mountain pine beetle attack across the western United States

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Forest Ecology and Management 255 (2008) 1536–1547 www.elsevier.com/locate/foreco

Mapping lodgepole pine stand structure susceptibility to mountain pine beetle attack across the western United States Jeffrey A. Hicke a,*, Jennifer C. Jenkins b a

Department of Geography, University of Idaho, McClure Hall Room 203, PO Box 443021, Moscow, ID 83844-3021, United States b Gund Institute for Ecological Economics and The Rubenstein School of Environment and Natural Resources, University of Vermont, Burlington, VT 05405, United States Received 30 June 2007; received in revised form 14 November 2007; accepted 16 November 2007

Abstract Mountain pine beetle (MPB) outbreaks are important disturbances of forests in the western United States. Stand conditions influence the success of MPB attack through food availability, shelter, and effects on tree vigor and thus the ability of a tree to withstand MPB infestation. We estimated the contribution of stand structure to susceptibility to MPB-caused mortality in lodgepole pine forests by applying a model that incorporates stand age, stem density, and basal area by species. We utilized tree-level measurements across the western United States from the USDA Forest Service Resources Planning Act inventory database that permitted plot-level and county-level analysis. In the western United States, we found that stands were typically in age classes (60–120 years) and stem densities (>400 stem ha1) that are highly susceptible. A third structural factor, the percentage of susceptible pine basal area within a stand (compared to all trees within that stand), typically reduced stand susceptibility across the region. However, when we considered the susceptibility of only the pine component instead of the entire stand, we found substantially increased susceptibility index values. Regional susceptibility was high (pine component susceptibility index >50, which has been related in past studies to 34% loss of stand basal area) for 2.8 Mha, or 46%, of lodgepole pine forest. Maps revealed variability among regions, with the highest susceptibility in the southern Rocky Mountains and the lowest in the coastal states. Our analysis provides useful information to land managers concerned with future forest ecosystem dynamics and forest susceptibility to MPB outbreak. # 2007 Elsevier B.V. All rights reserved. Keywords: Mountain pine beetle; Lodgepole pine; Stand susceptibility; Forest inventory; Western United States

1. Introduction Insect infestations are widespread and important disturbances in western North America (Taylor and Carroll, 2004; Breshears et al., 2005; USDA Forest Service, 2005a) with the potential to kill millions of trees, thereby impacting forest ecosystems for decades. Mountain pine beetle (MPB; Dendroctonus ponderosae Hopkins) epidemics occur in many regions in western North America (Amman et al., 1990). Recent major outbreaks include 2 million ha affected in the early 1980s and 1.2 million ha today in the western United States (USDA Forest Service, 2005a), and 9.2 million ha affected as part of an ongoing infestation in British Columbia (British Columbia Ministry of Forests and Range, 2007).

* Corresponding author. Tel.: +1 208 885 6240; fax: +1 208 885 2855. E-mail address: [email protected] (J.A. Hicke). 0378-1127/$ – see front matter # 2007 Elsevier B.V. All rights reserved. doi:10.1016/j.foreco.2007.11.027

Mountain pine beetles are aggressive bark beetles, killing healthy trees during an outbreak. Host tree species have developed defenses that repel beetle attacks through enhanced resin production when attack numbers are low and/or trees are healthy enough to respond (Amman et al., 1990). Beetles can overcome these defenses through synchronized mass attacks, however. Mountain pine beetles spend much of their life under the bark of host trees, feeding on phloem. Adults emerge in late summer, fly to new hosts, and burrow under the bark of host trees, where eggs are laid. Larvae develop into adults in the trees, completing the life cycle. Life cycles take 1–2 years, with 1-year life cycles (‘‘univoltinism’’) conducive to building epidemics. MPB attacks several tree species, primarily lodgepole pine (Pinus contorta Douglas) and ponderosa pine (Pinus ponderosa Lawson) (Amman et al., 1990). Here we focus on lodgepole pine as it is valued for timber, wildlife habitat, and recreational use (Burns and Honkala, 1990), is a well-studied and widespread host species, and has been the focus of susceptibility studies,

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including a model developed and tested for application (Shore et al., 2000). Both weather and host stand conditions influence MPB outbreaks (e.g., Safranyik, 1978; Shore and Safranyik, 1992; Safranyik and Linton, 1998; Logan and Powell, 2001; Carroll et al., 2004). Because stand conditions vary slowly in time with stand development whereas weather has much higher temporal variability, stand conditions set the stage for an outbreak, and suitable weather conditions may actually trigger the epidemic. Influences of weather include drought and temperatures, which influence epidemics both directly via effects on beetles and indirectly via effects on trees (Logan and Powell, 2001; Carroll et al., 2004). Stand conditions that facilitate successful attacks are those that attract beetles as well as reduce tree vigor and thus compromise the ability of a tree to mount a defense (Berryman, 1982; Shore and Safranyik, 1992). Larger trees attract MPB for several reasons: they are good food sources with thicker phloem, they have thicker bark that insulates against cold, and they tend to be older and therefore less vigorous. Competition with other trees in high stem density stands and older stands leads to enhanced susceptibility to attack (Shore and Safranyik, 1992). Several models of susceptibility have been developed to aid land managers in determining whether pine stands are susceptible to attack by MPB. Different models include different controls on MPB susceptibility: some models include stand characteristics only (e.g., Schenk et al., 1980; Stuart, 1984; Anhold and Jenkins, 1987; Anhold et al., 1996), some are designed to assess weather suitability only (Logan and Powell, 2001; Carroll et al., 2004), and others are combinations of stand and weather (or climate) characteristics (Amman et al., 1977; Berryman, 1978; Shore and Safranyik, 1992). The terms ‘‘susceptibility’’, ‘‘hazard’’, and ‘‘risk’’ relate to whether a tree or stand will be attacked by MPB, and are often used interchangeably in the literature. In this paper, we follow the terms defined by Shore and Safranyik (1992). ‘‘Susceptibility’’ (synonymous with ‘‘hazard’’) is the term that describes whether a tree, stand, or landscape is suitable for supporting mountain pine beetle populations, and is calculated from inherent stand and site characteristics. ‘‘Risk’’ assesses whether a location is susceptible as well as whether sufficient beetle populations are in the region. Several hazard rating systems/susceptibility models have been evaluated in the past. Bentz et al. (1993) applied four different hazard/risk systems to lodgepole pine stands attacked by MPB measured in Montana. The models used different stand measures, including age, diameter at breast height (DBH), periodic growth ratio, vigor, and phloem thickness, for estimating susceptibility. None of the models provided adequate estimates of lodgepole pine mortality. The authors suggested a variety of possible reasons for this, including the lack of inclusion of appropriate relationships, incorrect representation of relationships, and variability in the phase of beetle populations at the time of measurement. Similarly, Shore et al. (1989) and Shore et al. (2006) reviewed a number of different systems, and also found that most had significant problems in accurately predicting mortality.

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The Shore and Safranyik (1992) model determines stand susceptibility to mortality in the event of an infestation of MPB. In this model, stand characteristics that facilitate epidemics are used to compute an index of susceptibility. Different stand characteristics might be used to determine whether a stand can support endemic populations of beetles. For example, Bentz et al. (1993) suggested that individual tree vigor is important for determining stand resistance to endemic populations, while stand and site characteristics are related to susceptibility to epidemics. We do not address the characteristics such as tree vigor needed to assess suitability for endemic (background) populations of beetles. The Shore and Safranyik (1992) model uses percentage of lodgepole pine basal area within a stand, stand age, stem density, and location to compute a stand susceptibility index. Location is a surrogate for climate suitability for beetles, and is computed from elevation, latitude, and longitude. In a second step, beetle pressure is calculated from the size of and proximity to the nearest MPB infestation. The stand susceptibility index and beetle pressure index are combined to estimate a stand risk index. The stand susceptibility index from the model was tested following subsidence of MPB attack in 38 stands in southern British Columbia (Shore et al., 2000). A linear relationship between susceptibility index and percent basal area killed within a stand was developed. To test this relationship, 41 additional stands were assessed, and very good agreement was found: 40 stands fell within the 95% confidence intervals of the linear relationship. Shore et al. (2000) attributed to their success to the design of the susceptibility model. The inclusion of stem density as well as its appropriate functional form improve the ability to capture important forest processes related to vigor, microclimate, and phloem thickness, making the model more widely applicable. The USDA Forest Service produced a map of future risk of mortality resulting from insects and disease, with locations defined as ‘‘at risk’’ if 25% mortality is expected over the next 15 years (Lewis, 2002). For mountain pine beetle, a variety of stand susceptibility models, including those mentioned above, were used with inventory data and combined with forest cover type maps and expert opinion to determine risk. The stand susceptibility technique differed by region, and reported methods ranged from specific formulas to professional judgment. In this paper, we focus on susceptibility to MPB based on stand conditions only, for several reasons. First, because stand structure determines whether conditions are suitable for MPB outbreaks on longer time scales, stand structure patterns across the western United States are useful decision tools. In contrast to climate or weather, managers may have the potential to manipulate stand structure to reduce susceptibility to MPB outbreak. Second, temperatures in recent decades have increased by 1–2 8C in western states (www.nrel.colostate.edu/jhicke/climate_data; CIRMOUNT Committee, 2006), and studies have related this warming as well as temperature increases in British Columbia to recent MPB infestations (Logan and Powell, 2001; Carroll et al., 2004). Model projections predict continued warming and enhanced climate suitability for MPB outbreaks in higher locations (Williams and Liebhold, 2002; Hicke et al., 2006). With continued warming in these locations, the

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severity and location of MPB epidemics may be more influenced by the susceptibility of stands to infestation than climate because of continually suitable temperatures. In this study, we quantify and map susceptibility of lodgepole pine stands across the western United States to MPB. We took advantage of a large-scale database of forest inventories produced by the USDA Forest Service. These inventories collected tree- and stand-level information that can be used to assess susceptibility to insect and disease attacks. The USDA Forest Service has collected inventory information nominally every 10 years for the past several decades across the United States. These data are available at the plot level, and can be scaled up to the county level for mapping purposes. We used the structural aspects of the Shore and Safranyik (1992) model to estimate susceptibility across the western United States at the plot-level. We then mapped susceptibility at the county-level. We also report patterns of the structural characteristics that were used to estimate susceptibility.

To compute the stand structure susceptibility index (SSSI), we used the following equations from Shore et al. (2006): SSSI ¼ P  A  D

where P is the percentage of susceptible lodgepole pine basal area within a stand, A is the age factor, and D is the density factor P¼

basal area of pine  15 cm DBH  100 basal area of all species  7:5 cm DBH

8 0:1; > >   > > > ðage  40Þ 1:585 > > 0:1 þ 0:1 ; > < 10 A ¼ 1:0;   > > ðage  120Þ > > > 1:0  0:05 ; > > 20 > : 0:1;

8   sph 2:0 > > ; 0:0824 > > > 250 > >   > < 3  sph 0:5 ; D ¼ 1:0  0:7 250 > > > 1:0; > > > > 1:0 > : ; ½0:9 þ ½0:1 exp ð0:4796ðsph=250  6ÞÞ

2. Methods 2.1. Stand structure susceptibility model The susceptibility model we used calculates an index of potential loss of basal area within a stand in the event of an outbreak of MPB. Shore and Safranyik (1992) published an original version of the stand susceptibility model based on four factors: the percentage of susceptible pine within a stand; stand age; stem density; and location. Because (i) location was included to capture the effects of climate, (ii) advances in modeling will lead to improvements in climate influences, and (iii) we focus here on structure alone, we did not include the location variable. Shore and Safranyik also described a beetle pressure index indicating presence of local beetle populations that, when combined with the stand structure and location information, resulted in an overall stand risk index. Again, because we are interested in isolating forest structural conditions, we did not compute a beetle pressure index. Shore et al. (2006) updated the original susceptibility model by replacing categorical variables with piecewise continuous functions. These updates lead to smoother transitions between states. In addition, the update included a conversion of the original stand susceptibility index to a pine susceptibility index. This new index quantifies susceptibility in the pine-only component of a stand in contrast to the original index, which is a stand-level susceptibility measure and includes non-pine (non-host) species within the stand.

(1)

(2)

age < 40 40  age  80 80 < age  120

(3)

120 < age  510 age > 510

sph < 650 650  sph  750

(4)

750 < sph  1500 sph > 1500

Basal area has units of m2 ha1; age is the stand age; sph is stems per ha. P indicates the susceptibility of an entire stand regardless of species (e.g., percentage of all trees that are susceptible to attack). That is, if all the lodgepole pine within a stand were killed by mountain pine beetle, but the lodgepole pine was only a small fraction of the stand, the stand would be rated with low susceptibility. To address this, Shore et al. (2006) added a new calculation that represents the susceptibility of the pine component only of a stand. Here we designate P* as the rescaled percentage of pine basal area within a stand, and use P* to compute the Pine Structure Susceptibility Index (PSSI). P* is calculated from P ¼

100:0 ð1 þ exp ððP  22:7Þ=5:3ÞÞ

(5)

PSSI is computed the same as SSSI (Eq. (1)) except replacing P with P*. 2.2. Forest inventory database We used information from the Resources Planning Act (RPA) inventory database collected and reported by the USDA Forest Service. The RPA database is a combination of plots on public and private lands across the United States (Smith et al., 2004). Prior to the late 1990s, the Forest Inventory and Analysis (FIA) surveyed private lands, while the National Forest System (NFS) was

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responsible for surveying national forest lands. These ‘‘periodic’’ inventories occurred nominally every 10 years (Smith, 2002). In the late 1990s, responsibility for the nationwide forest inventory of all lands was assumed by the FIA, and methods shifted to an ‘‘annual’’ inventory in which some plots are measured every year. Under this annualized system, few states have made substantial progress towards a complete inventory of all established plots. For example, as of 2004, Colorado had completed only 3 of 10 subcycles. For this reason, we chose to analyze the RPA database, which is older but more complete. The inventories consist of Phase 1 and Phase 2 plots. ‘‘Phase 1’’ plots are established using remotely sensed imagery to classify forest cover based on type, volume, and other stand characteristics (see Smith (2002) for details). The classification is also used to develop expansion factors that allow plot-level information to be scaled to counties. ‘‘Phase 2’’ fixed-radius or variable-radius ground plots are established roughly every 2500 ha. At these plots, field crews record multiple tree and stand characteristics. DBH is measured and species is noted for each tree within a plot. Stand age is determined by coring 2–3 dominant or co-dominant trees identified in the field (USDA Forest Service, 2005b). In the absence of severe disturbance, the stand age from a previous inventory may simply be updated

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to the current year and recorded. Age is either identified to the nearest year or binned into 10-year classes for ages <100 years, 20-year classes for 100–200-year-old stands, and 100-year classes for stands >200 years old. No information is recorded in the database about which trees were used to identify stand age. We downloaded the most recent (2002) RPA database from the Forest Inventory and Analysis (FIA) web site (http:// fia.fs.fed.us). We included states west of the Great Plains that have lodgepole pine. We limited our susceptibility analysis to lodgepole pine forest type plots containing live trees 7.5 cm DBH that have recorded stand ages. We computed area of lodgepole pine forest type within a county by summing plot area ‘‘expansion factors’’ (EXPCURR, units of area) included in the RPA database (Alerich et al., 2004). The expansion factors indicate how much area within a county (A) is represented by a subsample of plots (for this study, lodgepole pine forest type plots with live lodgepole pine) X A¼ ðEXPCURRi  CONDPROPi Þ: (6) i

‘‘Conditions’’ are areas within an inventory with distinct forest characteristics, such as forest type. Plots typically have one condition, but may have more. CONDPROPi is the

Fig. 1. (a) Mean of plot measurement year by county. Plots in lodgepole pine regions were typically measured in the 1990s except in Colorado and southern Wyoming, where inventories occurred 10–15 years earlier. (b) Area of lodgepole pine forest (ha) and percent of total county area (c) from the RPA database. (d) For comparison, lodgepole pine forest type from the USDA Forest Service satellite classification (blue pixels) (Zhu and Evans, 1994) and from the Little (1971) distribution (red polygons). Lodgepole pine is distributed in the United States Rockies from Colorado north; on the west slope of the Cascades; in the Blue Mountains of northeast Oregon; and in the Sierra Nevada.

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Fig. 2. Plot-level values of (a) stand age (years), (b) age factor, (c) stem density (number ha1), and (d) density factor. Gray lines on (a) and (c) are the age and density factors (Eqs. (3) and (4), respectively) from the Shore et al. (2006) updated model. Most plots had stand ages and stem densities that were in high susceptibility ranges.

Fig. 3. Plot-level values of (a) basal area (m2 ha1) of lodgepole pine trees with diameter at breast height (DBH) >15 cm, (b) basal area (m2 ha1) of all trees with DBH >7.5 cm, (c) percentage of susceptible lodgepole pine basal area (P), and (d) rescaled percentage of susceptible lodgepole pine basal area (P*). Most plots had higher percentages of susceptible basal areas; when susceptible pine was accounted for through the rescaled percentage, most plots were at the maximum susceptibility.

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proportion of the plot with the desired condition (unitless), here, lodgepole pine forest type with live lodgepole pine trees. Inputs to the Shore and Safranyik (1992) stand structure susceptibility model include stand age, stem density, and basal area. Stand age is a condition-level variable in the RPA database (in contrast to, for example, DBH, which is a tree-level variable). We calculated stem density and basal area using treelevel information for each plot. Age, density, and basal area were then aggregated to county values by area weighting using EXPVOL (units of area), the expansion factor for volume. Stem density (sph; units of number ha1) was computed by using TPACURR (‘‘trees per acre’’, in this study converted to ‘‘tree per ha’’ by multiplying by 2.47), which is the inverse of the area represented by a tree within a plot, and has units of per area X  TPACURRi  sph ¼ (7) CONDPROPi i

amount of lodgepole pine forest within a county. Thus, we also distributed the county-level susceptibility values to lodgepole pine forest type pixels in the Zhu and Evans (1994) satellite classification, assigning the same county-level value to each lodgepole pine forest type pixel in the county. In 65 counties, satellite-derived lodgepole pine area was less than inventoryderived area. For these counties, we added pixels from other forest types, beginning with conifers, until the inventoryderived area was reached. These additional pixels contributed little to the maps, but were included for completeness. These resulting maps combine susceptibility with lodgepole pine locations.

Basal area (BA; m2 ha1) was computed using DBH and TPACURR of each tree together with CONDPROP   2 X DBHi TPACURRi BA ¼  p (8) 2 CONDPROPi i

In the western United States, 129,997 plots were inventoried for the 2002 RPA report. Of these, 4850 were identified as lodgepole pine forest type. The number of plots with sufficient information to compute SSSI (e.g., recorded stand age or large enough trees) was 4454. There were 150,297 trees on these plots with DBH >7.5 cm; of these, 120,320 were lodgepole pines. We used observations from USDA Forest Service inventories in the region that generally occurred in the 1990s (Fig. 1a). A few counties were surveyed in the late 1980s, particularly in Idaho and Montana. In southern Wyoming and Colorado, plots were inventoried in the late 1970s or early 1980s.

For comparison with RPA lodgepole pine distributions, we plotted the distribution from Little (1971). We also mapped lodgepole pine forest type using a satellite classification of United States forest types (Zhu and Evans, 1994). The spatial resolution of the RPA database is at the countylevel, and therefore we mapped susceptibility at this scale. However, county-level mapped values do not indicate the

3. Results 3.1. Lodgepole pine in the western United States

Fig. 4. Distribution of plot-level values of Stand Structure Susceptibility Index (SSSI) (a) and Pine Structure Susceptibility Index (PSSI) (b).

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Plots identified as lodgepole pine forest type were used to calculate lodgepole pine area (Fig. 1b) (note that this is a superset of the plots used to determine susceptibility as indicated above). A total of 6.03 Mha of lodgepole pine forest type occurs in the United States. Lodgepole pine is widely distributed from a few stands in southern California north to the Canadian border, and throughout the Rockies north of New Mexico. Locations where large areas of lodgepole pine are found include southern Oregon, northern Idaho/western Montana, and the southern Rocky Mountains. Comparisons with the Little (1971) distribution and the satellite classification reveal general similarities (Fig. 1d). The RPA estimate of total area was less than from the satellite map, which included 11.7 Mha of lodgepole pine forest type. A notable difference occurred in Yellowstone National Park (northwestern Wyoming), where the RPA database reported less area than the satellite estimate. The patterns change somewhat when plotting lodgepole pine area as a percentage of county area (Fig. 1c). This map is useful for determining local (i.e., county-level) amounts of lodgepole

pine, where smaller counties are emphasized. Large percentages occurred in the northern and southern Rockies and in southern Oregon. 3.2. Stand characteristics The histogram of plot-level stand ages for lodgepole pine peaks at 80–100 years (Fig. 2a), and the distribution falls off rapidly for younger and older stand ages. A secondary peak occurs in the youngest stands. The resulting A is highly skewed towards 1, with 49% having A = 1 and 15% having values between 0 and 0.1 (Fig. 2b). The stem density distribution peaks at low values, 400–800 stems ha1, with a long tail in the higher densities (Fig. 2c). A large number of plots, >40%, had D equal to 1; the remaining plots were distributed evenly between 0 and 1 (Fig. 2d). Basal area of lodgepole pine trees with DBH > 15 cm was distributed between 0 and 35 m2 ha1, with a peak at 5– 10 m2 ha1, and a long tail out to 60 m2 ha1 (Fig. 3a). The typical basal area of all trees with DBH > 7.5 cm on each plot

Fig. 5. County-level maps of (a) stand age (years), (b) age factor, (c) stem density (number ha1), and (d) density factor.

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was higher, peaking at 25–30 m2 ha1, with most plots having between 0 and 45 m2 ha1 (Fig. 3b). The resulting P ranges from 0 to 100, with the minimum number of plots at 0–10 and the maximum number at 90–100 (Fig. 3c). Fifteen percent of the plots had percentage values 90, and 72% had values 50. P* is highly skewed toward values of 100 (Fig. 3d), the highest susceptibility class, with 84% of the plots having P* > 90. The resulting distribution of SSSI decreased dramatically from a maximum at the lowest susceptibility class (Fig. 4a), 0– 10, which had 40% of the plots. The number of PSSI plots was also high in the lowest susceptibility class (Fig. 4b). Higher PSSI values, however, were associated with increasing numbers of plots, such that 27% of the plots were in the highest susceptibility class (90–100), and 47% of the plots had PSSI 50. Maps of structural characteristics revealed spatial patterns of plot-level information. Lodgepole pine stand ages varied across the western United States, with the youngest stands in the Cascades (<40 years) and northern Rockies (<80 years),

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intermediate age stands in the southern Rockies (80–120 years), and oldest stands in California (Fig. 5a). As a result, high values of A generally occurred throughout the region. Exceptions occurred in northeastern Washington, northern Idaho, northwestern Montana, and the Blue Mountains of northeastern Oregon, although A was still high in these locations (>50) (Fig. 5b). Stem densities were variable across the study region (Fig. 5c). Lower densities (<600 ha1) were found in the coastal states and parts of Idaho and Wyoming. Higher stem densities (>1200 ha1) occurred in western Montana, northern Washington, and in the southern Rockies. Higher density factor (D) classes related to stem density were found in the Rocky Mountains and northern Cascades (Fig. 5d). Lower D values occurred in California, Oregon, and northwestern Wyoming, though these regions often had values >0.4. Basal area of susceptible lodgepole pine was relatively high in the Sierra Nevada and southern California and in other

Fig. 6. County-level maps of (a) basal area (m2 ha1) of lodgepole pine trees with diameter at breast height (DBH) >15 cm, (b) basal area (m2 ha1) of all trees with DBH >7.5 cm, (c) percentage of susceptible lodgepole pine basal area (P), and (d) rescaled percentage of susceptible lodgepole pine basal area to account for pineonly within a stand (P*).

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counties scattered across the western United States (Fig. 6a). Basal area of all trees with DBH >7.5 cm had a similar pattern, though with higher values (Fig. 6b). Resulting P was typically high across the western United States (Fig. 6c), with values >50, though exceptions occurred in the northern Cascade Mountains and the northern Rockies, where P was around 50, and in the Blue Mountains of northeastern Oregon, where P was <40. Rescaling these percentages to estimate the susceptibility of only the pine component of a stand (P*) dramatically increased the values compared to P (Fig. 6d). Most of the western United States had extremely high values with the exception of eastern Oregon, where P* was around 50–60. These high values resulted from our selection within the RPA database of only stands of lodgepole pine forest type. 3.3. Susceptibility We plotted the distribution of county-level lodgepole pine area throughout the western United States by SSSI and PSSI (Fig. 7a). Distributions were similar to those calculated from plot-level data (Fig. 4): SSSI followed a negative exponential shape, whereas PSSI decreased with increasing index from large areas at low values until PSSI = 80, after which area increased. The ‘‘inverse cumulative distribution’’ (Fig. 7b) shows the area of stands with indices greater than or equal to a given index value. For instance, at an index value of 50, 28% of the area (1.67 Mha) and 46% of the area (2.8 Mha) had SSSI and PSSI >50, respectively. About 25–30% (about 1.5 Mha) of the total area had low susceptibility values (SSSI and PSSI <10). Little area was associated with the highest SSSI values, but almost 20% of the area, 1.14 Mha, had PSSI values >95. Maps indicated that SSSI was highest in Rocky Mountains, particularly in Colorado/southern Wyoming and southwestern Montana/central Idaho with values of 50 or greater (Fig. 8a and c). Lower SSSI occurred in the northernmost part of the Rockies, in the Cascades, and in the Sierra Nevada. PSSI (Fig. 8b and d) followed similar spatial patterns, though with higher values; for example, stands in the southern Rocky Mountain and southwestern Montana and southern Idaho as well as stands in other counties scattered around the western United States had PSSI >70. In addition, several counties in Oregon and northern California exhibited relatively larger changes from SSSI to PSSI, indicating that the stands identified as lodgepole pine forest type in those specific counties had higher tree species diversity (and therefore lower SSSI) than other counties in the region. 4. Discussion Many lodgepole pine stands across the western United States have structural characteristics that are susceptible to attack by MPB. Susceptibility of stands was highest in the southern part of the Rocky Mountains and lowest in the Sierra Nevada, with the Cascades and northernmost Rockies having intermediate values. Considering only the pine component of stands (PSSI) increased susceptibility index values.

Fig. 7. (a) Distribution of area (million ha) associated with stand (black) and pine (gray) structure susceptibility index values. (b) ‘‘Inverse cumulative distribution’’ showing area greater than or equal to a given index. For example, 27% of the total lodgepole pine area, or about 1.8 Mha, has a stand structure susceptibility index of 50 or greater; 46% of the area (2.6 Mha) has a Pine Structure Susceptibility Index of 50 or greater. Top x-axis shows conversion of stand susceptibility index to basal area mortality following Shore et al. (2000).

To assess general differences in stand characteristics and susceptibility, we qualitatively ranked these variables by region (Table 1). Across the Rocky Mountains, regions of lower susceptibility occurred in the northernmost locations and in northwestern Wyoming, with higher susceptibility in central Idaho/southwestern Montana and in the southernmost locations. Reduced stem density played a role in lowering susceptibility in Wyoming and the Sierra Nevada, whereas multiple factors contributed in the northernmost Rockies. Low values of multiple factors also resulted in lowered susceptibility in the Blue Mountains of northeastern Oregon. The Cascade Mountains had reduced susceptibility as a result of younger stands with lower percentages of susceptibility pine basal area. The southern Cascades typically had reduced susceptibility compared with the northern Cascades, though variability occurred in both regions. In a separate study, an assessment of the interior Columbia River Basin used subsampling at the subwatershed level, aerial photography, and topographical and biophysical information to

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Fig. 8. County-level maps of (a) Stand Structure Susceptibility Index (SSSI) and (b) Pine Structure Susceptibility Index (PSSI). (c) and (d) map county-level values for only lodgepole pine forest type locations within a county as derived from a satellite classification.

estimate vegetation type and structural attributes in a spatially explicit manner (Hessburg et al., 1999b). Models of vulnerability to various insects and pathogens were applied to these vegetation maps (Hessburg et al., 1999b). Despite the different methods, the

general patterns were similar to those reported here: lower vulnerability in the Blue Mountains and in southern Oregon; intermediate vulnerability in the Cascades, and highest vulnerability in the northern Rocky Mountains (Hessburg et al., 1999a).

Table 1 General behavior of stand susceptibility factors and indices within regions with significant lodgepole pine forest area Region

Age factor (A)

Density factor (D) % Susceptible pine factor (P)

Rescaled % susceptible Stand susceptibility Pine susceptibility pine factor (P*) (SSSI) (PSSI)

Southern Wyoming and Colorado Northwestern Wyoming Central Idaho/Southwestern Montana Northeastern Washington/ northern Idaho/ northwestern Montana Northeastern Oregon Northern Cascades Southern Cascades Sierra Nevada

High High High Low to high

High Medium High Medium to high

Medium to high High High Medium to high

High High High High

Medium Low Medium Low

High Medium Medium to high Low to medium

Medium to high Medium to high Low to high High

Low to medium High Medium to high Low

Low Medium Medium High

Medium to high High High High

Low Low to medium Low to medium Low

Low Low to high Low to high Low

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How should SSSI and PSSI be interpreted for assessing mortality within a stand? Shore et al. (2000) applied the stand susceptibility index to locations that experienced epidemics in southern British Columbia. The study found good agreement between the predicted susceptibility and occurrence of attack by MPB. A linear relationship was developed to relate stand susceptibility index (including the location factor; SSI) to percent basal area killed within a stand (M) (M = 0.68  SSI). For example, an SSI value of 50 corresponds to 34% of the basal area of a stand killed by MPB. Here we applied this relationship to the SSSI to estimate the percent basal area killed across the western United States, assuming that climate will be suitable at some point for establishing epidemic populations at each location (Fig. 5b). We estimated that 28% of the lodgepole pine area, 1.67 Mha, would sustain mortality of 34% of the basal area within a stand. The relationship between the pine basal area within a stand, as represented by PSSI, and basal area killed has not been explored. However, if we apply the M/SSI relationship to PSSI, we found that 46% of the area of lodgepole pine (2.8 Mha) is susceptible to losses of 34% basal area or greater. Estimates of stand structure susceptibility indicate where the highest mortality will occur in the event of an outbreak. Given current and future climate influences on MPB epidemics (Hicke et al., 2006), and the widespread distribution of mountain pine beetle throughout the western United States (e.g., USDA Forest Service, 2005a), the likelihood of an infestation occurring in susceptible stands in the coming decades is high. To predict when they are likely to happen, however, assessments of weather influences (both short-term and long-term variations) and spatial patterns of existing beetle populations need to be considered as well (Shore and Safranyik, 1992; Shore et al., 2006). An uncertainty of using the Shore and Safranyik susceptibility model is the lack of validation across the range of lodgepole pine in the United States. Although Shore et al. (2000) showed that the model performed well in a given location as well as for stands located across southern British Columbia, there has not been a similar evaluation in the United States. Additional studies are needed to establish model performance across the broad range of climates in the western United States. Uncertainties exist when using the RPA inventory database. These inventories are not designed to capture stand variability at watershed scales. Instead, the RPA database is useful for identifying larger spatial patterns. Estimates for individual stands or watersheds should be developed from local stand exams. In some states (CA, OR, WA), national forest lands were not included in the inventory. In addition, some ‘‘reserved’’ lands such as national parks were not represented in this RPA database. For example, although plots within Yellowstone and Grand Teton National Parks exist in the database, they were used only for determining forest area and do not have diameter or stand age measurements. Large discrepancies between the RPA area estimates and the FIA satellite-derived estimates in the Yellowstone region, as well as comparisons with other data sources (National Park Service, 1989), suggest undersampling

by the RPA inventory in this region. Other reserved lands, such as wilderness, were measured as part of the inventory and were included in our analysis. Differences between the RPA and satellite-derived lodgepole pine forest type areas outside of the Yellowstone region may result from sampling issues within the RPA database that result in underrepresentation and/or uncertainties in the 1-km satellite results. Finally, because of the way in which stand age was determined, species other than lodgepole pine may have been selected for determining stand age; no information is available to assess this. 5. Conclusions With measurements of hundreds of thousands of trees on thousands of plots in the western United States, the USDA Forest Service RPA inventory database is a valuable tool for evaluating large-scale forest susceptibility to insect epidemics. The Shore and Safranyik (1992) model of lodgepole pine susceptibility to mountain pine beetle outbreak includes components related to stand structure, climate, and beetle populations. In this analysis, we assessed susceptibility related to stand structure. Forest characteristics required by the Shore and Safranyik (1992) susceptibility model – stand age, stem density, and basal area by species – were measured in the field and recorded in the RPA database, and allowed us to estimate stand susceptibility of lodgepole pine forests to mountain pine beetle attack. The sampling design of the RPA database then allowed us to scale the plot-level information up to the countylevel to assess regional patterns of susceptibility. Patterns of SSSI and PSSI resulted from combinations of patterns of stand age, stem densities, and percent pine basal area. A large fraction of lodgepole pine forest in the western United States is highly susceptible to MPB attack. We estimated that 46% of the lodgepole pine in the region, 2.8 Mha, could sustain mortality of 34% of basal area during MPB epidemics. Higher susceptibility occurred in southern Wyoming and Colorado, in southwestern Montana and central Idaho, and in some counties of the Cascades. We estimated low susceptibility in the Blue Mountains of northeast Oregon, the northernmost Rocky Mountains, and the Sierra Nevada. In addition to stand structure, climate is another major influence on bark beetle outbreaks (Logan and Powell, 2001; Carroll et al., 2004; Hicke et al., 2006). Future work will combine stand structural characteristics with models of climate suitability and locations of beetle populations. However, the large area of susceptible pine we report here coupled with recent and projected climate variability and trends (e.g., CIRMOUNT Committee, 2006; Hicke et al., 2006) suggest the possibility of extensive outbreaks in many locations in the western United States. Acknowledgments We thank T. Frieswyk, E. LaPoint, and S. Woudenberg for their assistance in understanding the RPA inventory database. We thank two anonymous reviewers for their suggestions that improved the manuscript. Funding was provided by the USDA

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