Predicting post-fire attack of red turpentine or western pine beetle on ponderosa pine and its impact on mortality probability in Pacific Northwest forests

Forest Ecology and Management 434 (2019) 181–192

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Predicting post-fire attack of red turpentine or western pine beetle on ponderosa pine and its impact on mortality probability in Pacific Northwest forests

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Douglas J. Westlind , Rick G. Kelsey United States Department of Agriculture, Forest Service, Pacific Northwest Research Station, Forestry Sciences Laboratory, 3200 SW Jefferson Way, Corvallis, OR 97331, USA

A R T I C LE I N FO

A B S T R A C T

Keywords: Bole scorch Dendroctonus brevicomis Dendroctonus valens Ethanol Kairomones Mortality models Pinus ponderosa Primary attraction

In ponderosa pine forests of western North America, wildfires are becoming more frequent and affecting larger areas, while prescribed fire is increasingly used to reduce fuels and mitigate potential wildfire severity. Both fire types leave trees that initially survive their burn injuries, but will eventually die. Predicting delayed tree mortality has received considerable research attention to aid in post-fire planning and management. The amount of crown scorched is recognized as the most useful variable for discriminating between trees that live or die, but models gain discrimination with additional variables such as bole scorch, bud or cambium necrosis, and post-fire bark beetle attack (Coleoptera: Curculionidae: Scolytinae). Here, logistic regression was used to determine what fire-injury variables are most associated with red turpentine beetle (RTB; Dendroctonus valens LeConte), or western pine beetle (WPB; D. brevicomis LeConte) attack within three years post-fire. This was tested on 7343 ponderosa pine representing a wide diameter range from 18 wild and prescribed fires in Oregon and Washington, and repeated on a subset of 884 large trees > 53.3 cm diameter. Bole scorch height was most associated with RTB or WPB attack on trees across all diameters, but model predictive ability was poor, whereas for pines > 53.3 cm, the models provided moderate discrimination for predicting attack by each beetle. In addition, mortality models using crown scorch proportion and bole scorch proportion were compared to models with an additional variable for RTB, or WPB attack in year one, or attack by either beetle in year one, or year three. Models including any one of these beetle variables outperformed the models using just bole scorch and crown scorch proportions. For both tree diameter groups, the models including RTB year one performed similar to, or better than models with any other beetle variable, and are preferred for predicting delayed mortality because the RTB attack functions as an additional tree injury indicator similar to cambium kill, not captured by the bole scorch proportion, or crown scorch proportion variables. Furthermore, RTB attack can be assessed within the first year post-fire, and is much faster and easier to evaluate than direct sampling of cambium necrosis.

1. Introduction Fire has been a major disturbance agent in western US ecoregions for thousands of years (Marlon et al., 2012). Beginning in the mid1980’s fire severity and the annual area burned began to increase (Littell et al., 2009) due in part to rising temperatures and drought (Peterson and Marcinkowski, 2014; Littell et al., 2016), in conjunction with changes in forested areas, structure, composition and density resulting from extensive, extended fire exclusion, and other management objectives for human services, including livestock grazing (Belsky and Blumenthal, 1997; Hessburg et al., 2015). Additionally, prescribed fire

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is widely used to decrease fuel loads and mitigate future wildfire severity (Reinhardt et al., 2008). Ponderosa pine (Pinus ponderosa Lawson & C. Lawson) is a fire adapted species widely distributed in western forests of North America that can initially survive all but the highest severity fire (Starker, 1934; Weaver, 1943). However, a portion of fire injured trees will experience delayed mortality, usually due to a combination of reduced physiological function of crown, stem, or root tissues (Kolb et al., 2007; Kavanagh et al., 2010; Michaletz et al., 2012). In addition, these heat stressed trees attract bark beetles, such as red turpentine beetle (RTB, Dendroctonus valens LaConte), western pine beetle (WPB, D. brevicomis

Corresponding author. E-mail addresses: [email protected] (D.J. Westlind), [email protected] (R.G. Kelsey).

https://doi.org/10.1016/j.foreco.2018.12.021 Received 9 October 2018; Received in revised form 11 December 2018; Accepted 13 December 2018 Available online 19 December 2018 0378-1127/ © 2018 Elsevier B.V. All rights reserved.

Forest Ecology and Management 434 (2019) 181–192

D.J. Westlind, R.G. Kelsey

to rapid and extreme population changes, and some have definite tree diameter and bole position attack preferences (Furniss and Carolin, 1977; DeMars and Roettgering, 1982; Kegley et al., 1997; Breece et al., 2008; Owen et al., 2010). The attraction of several bark beetle species to fire damaged ponderosa pine has been well documented (Miller and Keen, 1960; McHugh et al., 2003; Wallin et al., 2003; Breece et al., 2008; Davis et al., 2012), but less is known about how and to what degree specific fire injuries increase the likelihood of attack (Negrón et al., 2016). Understanding these relationships could provide valuable insight into the physiological processes linking fire damaged trees with subsequent beetle attack, the interactions between bark beetle species, and their impact on post-fire tree mortality.

LaConte), and Ips (Ips sp.) leading to additional stress and mortality directly from the beetles, the pathogens they carry, or both (McHugh et al., 2003; Owen et al., 2005, 2010; Parker et al., 2006; Kane et al., 2017). Pioneering RTB are attracted to ethanol released from heat stressed ponderosa pine bole tissues in combination with monoterpene kairomones (Kelsey and Westlind, 2017b, 2017c), with a synergistic response to ethanol+3-carene (Kelsey and Westlind, 2017a). 3-Carene is often the most, or second most abundant monoterpene in ponderosa pine xylem oleoresin (Smith, 1977). Models predicting delayed tree mortality are useful to post-fire management. If salvage logging is planned, quick selection and harvesting of trees most likely to die will help minimize damage to new tree seedlings and other regenerated vegetation (McIver and Starr, 2000), and maximize the commercial value of salvaged timber, as firekilled ponderosa pine wood volume and quality both decline rapidly (Hadfield and Magelssen, 2006). These models can also be used to improve our understanding of how fire impacts the biological and physiological processes leading to mortality. Several key variables have been repeatedly identified and used in a number of logistic regression models to reliably discriminate between ponderosa pine that will live and those that will eventually die (McHugh and Kolb, 2003; McHugh et al., 2003; Sieg et al., 2006; Thies et al., 2006; Breece et al., 2008; Hood et al., 2008a, 2010; Ganio and Progar, 2017). Crown scorch injury is the most valuable predictor of post-fire mortality (Saveland and Neuenschwander, 1990; Fowler and Sieg, 2004; Fowler et al., 2010; Hood et al., 2010), although measurement methods may vary (Woolley et al., 2012). Some measure of stem damage, either as scorch height, char depth, or direct sampling of cambium is included in most models as well (Regelbrugge and Conard, 1993; Thies et al., 2006; Conklin and Geils, 2008; Hood et al., 2010; Ganio and Progar, 2017). The role tree diameter plays in predicting post-fire mortality is unclear. It is a significant variable in some models where mortality decreased with increasing diameter (Harrington and Hawksworth, 1990; Stephens and Finney, 2002; Kobziar et al., 2006; Sieg et al., 2006), but in others the mortality increased with increasing diameter (Ryan and Frandsen, 1991; Hood et al., 2010). However, it is not a significant variable in all models (Thies et al., 2006; Breece et al., 2008; Ganio and Progar, 2017). The role of tree size in mortality prediction may depend on the range of tree diameters included in the data sets used for model building, because the relationship between diameter and mortality may not be linear, with mortality decreasing as diameter increases up to about 50–60 cm, but above which mortality begins increasing with diameter creating a U-shaped mortality curve (McHugh and Kolb, 2003; Kolb et al., 2007). Two competing factors, bark thickness and basal fuels, affect ponderosa pine mortality as diameter increases. Bark thickness increases with diameter, providing an insulation effect protecting the cambium from heat during fire (Ryan and Frandsen, 1991; van Mantgem and Schwartz, 2003; Dickinson and Johnson, 2004; Michaletz and Johnson, 2007). But as trees age, they begin to slough bark, that in the absence of frequent fire creates a mound of litter at the stem base that can produce high heat when burning, or smolder for long periods causing considerable damage to the cambium, depending on fuel moisture conditions (Ryan and Frandsen, 1991). Because of their relative rarity and ecological importance, older ponderosa pine > 53.3 cm have been managed for retention in Pacific Northwest forests (Lowe, 1995), including protection from fire and bark beetle attack (Kolb et al., 2007). Inclusion of additional variables into mortality models can increase their ability to discriminate between outcomes, but also add to model complexity and variable collection time. Post-fire bark beetle attack has provided an additional level of discrimination in many models (McHugh et al., 2003; Sieg et al., 2006; Breece et al., 2008; Hood et al., 2010; Ganio and Progar, 2017). But, the value of including bark beetle variables is still uncertain and may have limitations because beetle species can vary considerably with geography, some species are prone

2. Objectives The mortality models listed above provide good discrimination between fire-damaged ponderosa pine that will live and those that will die. Our goals here are not to promote new mortality models but to: (1) identify the types and levels of ponderosa pine fire injury most associated with post-fire attack by RTB and WPB in Pacific Northwest forests, (2) determine what value RTB and WPB attack presence add in predicting post-fire tree mortality, and (3) determine whether ponderosa pine > 53.3 cm diameter breast height show different outcomes for objectives 1 and 2 compared to trees across all diameters. 3. Methods 3.1. Fires Tree morphology, fire damage, beetle attack, and mortality data was collected on ponderosa pine from seven wildfires and 11 prescribed fires occurring between fall of 2004 and summer 2007 on six National Forests east of the Cascade Range in Oregon and Washington. Crown and bole scorch data from 15 of these fires are a subset of those previously used to validate the Malheur tree mortality model (Thies and Westlind, 2012), but here we include data from three additional fires, and beetle attack data from all fires not reported previously. Fires were selected non-randomly with assistance from local land managers in areas where planned management activities would not interfere with data collection for three years post-fire. 3.2. Sampling Within each fire, trees were selected beginning at a random location along a road or fire boundary. A random azimuth was selected and all trees ≥7.5 cm diameter within 5 m either side of the azimuth were identified with a numbered aluminum tag resulting in a 10 m wide sampling strip. These strips continued until a fire boundary or road was reached, offset 90° a distance of 50–100 m, and then reversed 180° along the back azimuth resulting in parallel sampling strips throughout the burned area. Within wildfires, only areas of light and moderate severity were sampled; high severity areas where tree crowns were completely consumed, or areas having no evidence of fire in the understory, were avoided. At eight sites there were a total of 26 trees < 7.5 cm tagged and left in the data set for analysis, as we felt this small number of trees would not substantially influence the results. A total of 7343 trees were selected (Table 1). 3.3. Data Initial post-fire data were collected the following summer after the spring tree growth and bark beetle flight periods, except for the one spring prescribed fire and two summer wildfires where data were collected later in the same growing season and referred from this point forward as year one. Each tagged tree was categorized as either living, 182

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Table 1 Fire name, location, U.S. National Forest, date, typea, tree number (n), number dead, and % mortality for 18 fires in Oregon and Washington used in this study and sorted by % mortality. Fire Name

Lat/Long

Forest

Date

Fire Type

Tree (n)

Dead (n)

Mortality (%)

Sprinkle Spring Creek Upper Imnaha Burnt Valley 10 Burnt Valley 4 Geophysical Mallory Lick Mitten Stateline Owen Gravel 17 Okanogan 2 Bear Sheep Creek 4712 Road Okanogan 1 Monument

45N, 45N, 45N, 48N, 48N, 48N, 45N, 46N, 42N, 42N, 42N, 43N, 48N, 42N, 46N, 46N, 48N, 44N,

Wallowa-Whitman Wallowa-Whitman Wallowa-Whitman Colville Colville Colville Umatilla Umatilla Fremont-Winema Fremont-Winema Fremont-Winema Malheur Okanogan-Wenatchee Fremont-Winema Umatilla Umatilla Okanogan-Wenatchee Umatilla

Fall 2004 Fall 2004 Fall 2005 Fall 2006 Fall 2006 Fall 2006 Fall 2006 Fall 2006 Fall 2006 Fall 2006 Spring 2007 Summer 2007 Fall 2006 Fall 2006 Fall 2006 Summer 2006 Fall 2006 Summer 2007

Rx Rx Rx Rx Rx Rx Rx Rx Rx Rx Rx WF WF WF WF WF WF WF

105 95 135 200 235 297 494 444 743 749 750 908 521 262 146 135 640 484

0 2 5 12 15 21 44 66 159 211 219 308 200 101 72 67 339 335

0 2.1 3.7 6.0 6.4 7.1 8.9 14.9 21.4 28.2 29.2 33.9 38.4 38.6 49.3 49.6 53.0 69.2

7343

2176

29.6

118W 118W 117W 117W 117W 117W 119W 117W 120W 120W 120W 118W 120W 120W 117W 117W 120W 119W

Totals a

Fire Type: Rx = prescribed fire, WF = wildfire.

having survived the fire with some green needles, or dead (all needles either scorched or consumed). Not included were trees that appeared to be dead prior to the fires based on bark condition, decay, and presence of fine twigs or needles, and a small number of tagged trees that died from other causes, such as firewood harvest and wind throw. For each tree, diameter breast height further referred to as diameter, was measured to the nearest 0.25 cm at 1.4 m above the forest floor. Total tree height, pre-fire live crown base height (based on presence of scorched needles and needle fascicles), crown scorch height, bud kill height, and maximum bole scorch height were measured to the nearest 0.25 m using a laser hypsometer. Bark char depth at the base of each tree was assessed within 30 cm of the forest floor in uphill, left, downhill, and right quadrants, or on flat ground within the four cardinal directions. Char was categorized on a five point scale with 0 = none, having no evidence of char, 1 = superficial, having only light surface charring, 2 = moderate, where bark is uniformly black but bark character remains evident, 3 = deep, where the bark is deeply charred, including the deep fissures and bark character is altered, and 4 = wood, areas where the bark is burned off with wood clearly showing (Thies et al., 2006). Attack by WPB, RTB, mountain pine beetle (MPB, Dendroctonus ponderosae Hopkins), and Ips sp. (not identified to species) were initially assessed at the same time as the initial tree and fire damage measurements. RTB attack was identified by the characteristically large pitch tubes typically occurring below breast height on the lower bole and root collar. WPB and MPB attack was identified by the small to moderate sized reddish pitch tubes typically on the mid bole above breast height with WPB tubes primarily in or at the edges of bark crevices and MPB pitch tubes typically occurring on the flat bark plates. Ips sp. were identified by the presence of reddish boring dust on the bark surface and crevices. Beetle species were confirmed from gallery patterns by removing bark after a tree was considered dead with no green needles or live buds; no bark was removed from live trees. Tree mortality and insect attack were assessed again each late summer or fall for the next two years post-fire, hereafter referred to as year three. Attacks by each beetle species were initially placed into severity categories based on number of attacks, but like Hood et al. (2010), this was reduced to species presence or absence after preliminary analysis showed it worked as well or better for mortality prediction. Pre-fire live crown proportion, an indicator of tree vigor, and fire injury parameters described by Thies et al. (2006) were calculated for each tree as follows: (1) live crown proportion = (tree height – pre-fire

live crown base height)/tree height; (2) bole scorch proportion = maximum bole scorch height/tree height; (3) bud kill proportion = (bud kill height – pre-fire live crown base height)/(tree height – pre-fire live crown base height); (4) crown scorch proportion = (crown scorch height – pre-fire live crown base height)/(tree height – pre-fire live crown base height). Finally, whole tree basal char severity rating from 0 to 4 was calculated as the sum of the number of tree quadrants with a basal bark char damage rating of 3 or higher. 3.3.1. Statistical methods All statistical analysis was completed using SAS 9.4 (SAS, 2014). Data from the wild and prescribed fires were combined during analysis for both beetle attack presence and mortality prediction since previous work has shown mortality is not related to fire type (Thies and Westlind, 2012; Woolley et al., 2012) and our preliminary logistic regression analysis showed no significant relationship between fire type and beetle attack presence. Diameter differences between trees that survived and died for all trees and trees > 53.3 cm were analyzed separately through ANOVA (MIXED procedure, SAS 9.4). All model building for both beetle attack and tree mortality was completed using fire site as a random effect in generalized linear mixed models using the logit link function (GLIMMIX procedure, SAS 9.4) for trees of all diameters and the subset of trees > 53.3 cm. Models were built using the method described in Hosmer et al. (2013) for purposeful selection of covariates. Model fit was assessed through use of the receiver operating characteristic area under the curve, also known as the concordance index (LOGISTIC procedure, SAS 9.4) that describes a model’s ability to discriminate between outcomes (attack/no attack, or live/dead) (Saveland and Neuenschwander, 1990). The concordance index ranges from 0.0 to 1 with values of 0.5 indicating no discrimination, 1.0 indicating perfect discrimination, and values near 0.70 considered acceptable (Hosmer et al., 2013). Bud kill proportion was not used for beetle attack prediction as it was highly correlated with crown scorch proportion, a variable more widely used by others (Pearson correlation statistic R > 0.85 for both all diameter and > 53.3 cm data sets). 3.3.2. Beetle attack prediction Logistic regression was used to determine variables and variable combinations most associated with RTB or WPB attack after year one and year three post-fire. The binary response variable was presence/ absence of attack. Explanatory variables were diameter, live crown 183

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D.J. Westlind, R.G. Kelsey

proportion, bole scorch height, bole scorch proportion, basal char severity, crown scorch height, and crown scorch proportion. We focused on RTB and WPB because their attacks are common, easily identified, and have known association with fire damaged trees. We did not model attack by MPB as the incidence of attack was rare in our data. Likewise, Ips sp. attack, even though more common, was not modeled due to difficulty in consistent identification by field crews resulting from the lack of pitch tubes, ephemeral boring dust, and attack primarily in the upper stem of larger trees (Schwilk et al., 2006).

Table 3 Summary results of model testing and fit for probability of attack by red turpentine beetle (RTB) within one year following fire, including the model intercept, variable coefficients (P value for inclusion), receiver operating curve area under the curve concordance index (CI), and CI 95% confidence limit (CL). Variablea

3.3.3. Tree mortality prediction Logistic regression was used to assess the impact of bark beetle attack variables in predicting post-fire mortality of ponderosa pine, with tree status (live or dead) as the binary response variable. Explanatory variables included crown scorch proportion and bole scorch proportion as used previously in the Malheur model, plus presence of WPB attack in year one, RTB attack in year one, and attack by either beetle in year one and year three. Logistic regression models take the form;

Pa/ m = 1/[1 + exp{− (β0 + β1 X1 + β2 X2. ..+βi Xi + error)}] where Pa/m is the probability of the event, in this case either attack or mortality, β0 is the intercept, β1 is the coefficient for explanatory variable X1, β2 is the coefficient for explanatory variable X2, and βi is the coefficient for the ith explanatory variable Xi.

Intercept

CIb

CI 95% CL

0.689

0.673, 0.706

< 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.024

0.673 0.664 0.646 0.624 0.552 0.544 0.515

0.656, 0.646, 0.629, 0.607, 0.535, 0.530, 0.497,

0.689 0.682 0.662 0.641 0.570 0.559 0.534

< 0.001 < 0.001 < 0.001 < 0.001 0.002 0.007 0.038

0.730 0.727 0.656 0.655 0.563 0.549 0.521

0.683, 0.678, 0.606, 0.605, 0.509, 0.508, 0.474,

0.778 0.775 0.706 0.705 0.616 0.591 0.569

Coefficient

P

All tree diameters Multivariate model −2.7834 BSH CSH

0.1261 0.0739

< 0.001 < 0.001

Univariate models BSH −2.5970 CSH −2.5662 BSP −2.3962 CSP −2.5121 DBH −2.3876 BCS −2.2067 LCP −2.3714

0.2047 0.1036 2.0963 1.2995 0.0117 0.2922 0.6499

Trees > 53.3 cm DBH BSP −2.6865 BSH −2.7436 CSH −2.2563 CSP −2.2289 DBH −3.6626 LCP −3.6545 BCS −2.0606

5.6527 0.2067 0.0621 1.6630 0.0260 2.6670 0.2194

a Variables tested are bole scorch height (BSH), crown scorch height (CSH), bole scorch proportion (BSP), crown scorch proportion (CSP), diameter breast height (DBH), basal char severity (BCS), and pre-fire live crown proportion (LCP). b CI is a measure of the model’s ability to discriminate between attacked and non-attacked trees on a scale of 0–1, with a value of 0.5 indicating no discrimination and 1.0 indicating perfect discrimination.

4. Results 4.1. Beetle attack across all tree diameters In year one, 18.0% of trees across all diameters were attacked by RTB, WPB, or both and by year three 29.7% had been attacked (Table 2). By year three, 23.5% of all trees were attacked by RTB, compared to 12.0% attacked by WPB. A 61.1% majority of RTB attacks occurred in year one, while 57.9% of WPB attacks occurred after year one. Both beetles had attacked 1.4% and 5.8% of the trees at year one and year three, respectively. Of trees attacked by RTB in year one, 9.7% were also attacked by WPB, and by year three this increased to 24.6%. Of all WPB attacked trees, 27.4% were also attacked by RTB in year one post-fire, and 48.0% by year three. Univariate models confirmed bole scorch height, crown scorch height, bole scorch proportion, crown scorch proportion, diameter breast height, basal char severity, and live crown proportion as tree parameters associated with year one and year three attack across all tree diameters by both RTB (Tables 3 and 4) and WPB (Tables 5 and 6) (all P < 0.047, except live crown proportion for WPB in year one). Bole scorch height had the highest or second highest concordance index

values in all instances, but actual discrimination for the univariate models were poor for both year one and year three, and for both beetles (all concordance index ≤0.67, Tables 3–6). Multivariate models using bole scorch height and crown scorch height marginally improved on the best univariate models for both beetles in year one only, but still the overall fit remained poor (all concordance index < 0.69). 4.2. Beetle attack of trees > 53.3 cm diameter In year one, 16.1% of trees > 53.3 cm were attacked by RTB, WPB, or both, and by year three 32.2% had been attacked (Table 2). By year three, 29.0% of large diameter trees were attacked by RTB and 9.4% attacked by WPB. Half (49.2%) of the RTB attacks occurred in post-fire year one, while the majority (67.5%) of WPB attacks occurred after year

Table 2 The number of live and dead ponderosa pine after the third year growing season post-fire (Y3), their DBH mean and range, and number in each Y3 live/dead category that had been attacked by RTB, WPB, or both in year one (Y1) and Y3 post-fire for all diameter trees and subset of trees > 53.3 cm DBH. Number of trees with bark beetle attacks by year Tree status Y3

All tree diameters Live Dead Total

Treesa (n)

5167 2176 7343

Trees > 53.3 cm DBH Live 820 Dead 64 Total 884 a b

DBH cm

RTB only

WPB only

RTB&WPB

RTB total

WPB total

Total

Mean

Range

Y1

Y3

Y1

Y3

Y1

Y3

Y1

Y3

Y1

Y3

Y1

Y3

33.7 20.3 29.7

4.3, 122.7 5.8, 125.5 4.3, 125.5

522 429 951

894 404b 1298

84 186 270

226 234 460

34 68 102

230 194 424

556 497 1053

1124 598 1722

118 254 372

456 428 884

640 683 1323

1350 832 2182

67.5 72.1 67.8

53.3, 122.7 53.8, 125.5 53.3, 125.5

89 26 115

183 19b 202

9 7 16

21 8 29

6 5 11

35 19 54

95 31 126

218 38 256

15 12 27

56 27 83

104 38 142

239 46 285

Y1 live and dead were 6305, 1038, respectively for all tree diameters, and 852, 32, respectively, for trees > 53.3 cm DBH. Y3 tree numbers are lower than Y1 because some were attacked after Y1 by WPB, shifting them to the RTB & WPB Y3 category. 184

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Table 4 Summary results of model testing and fit for probability of attack by red turpentine beetle (RTB) within three years following fire, including the model intercept, variable coefficients (P value for inclusion), receiver operating curve area under the curve concordance index (CI), and CI 95% confidence limit (CL). Variablea

Intercept

Coefficient

P

CIb

CI 95% CL

0.634

0.619, 0.649

All tree diameters Multivariate model −2.2839 BSH CSH DBH

0.0991 0.0379 0.0158

< 0.001 < 0.001 < 0.001

Univariate models BSH −1.6870 DBH −1.9808 BSP −1.4128 CSH −1.5740 BCS −1.3558 CSP −1.3549 LCP −1.9711

0.1610 0.0203 0.9541 0.0659 0.1714 0.3294 1.1585

< 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001

0.627 0.594 0.586 0.586 0.550 0.538 0.531

0.612, 0.580, 0.571, 0.570, 0.538, 0.523, 0.516,

0.641 0.609 0.601 0.601 0.562 0.553 0.546

Trees > 53.3 cm DBH BSP −1.4773 BSH −1.5355 CSH −1.0874 CSP −1.0426 LCP −1.9396 BCS −1.0183 DBH −2.2890

5.3730 0.2015 0.0488 1.1402 1.6871 0.2522 0.0215

< 0.001 < 0.001 < 0.001 < 0.001 0.019 0.002 0.001

0.689 0.688 0.616 0.613 0.549 0.544 0.532

0.651, 0.649, 0.579, 0.577, 0.508, 0.508, 0.490,

0.728 0.727 0.652 0.649 0.591 0.581 0.574

Table 6 Summary results of model testing and fit for probability of attack by western pine beetle (WPB) within three years following fire, including the model intercept, variable coefficients (P value for inclusion), receiver operating curve area under the curve concordance index (CI), and CI 95% confidence limit (CL). Variablea

Variables tested are bole scorch height (BSH), crown scorch height (CSH), bole scorch proportion (BSP), crown scorch proportion (CSP), diameter breast height (DBH), basal char severity (BCS), and pre-fire live crown proportion (LCP). b CI is a measure of the models ability to discriminate between attacked and non-attacked trees on a scale of 0–1, with a value of 0.5 indicating no discrimination and 1.0 indicating perfect discrimination.

CIb

CI 95% CL

0.687

0.657, 0.718

< 0.001 < 0.001 < 0.001 < 0.001 < 0.047 < 0.045 < 0.127

0.668 0.652 0.635 0.631 0.491 0.477 NA

0.636, 0.619, 0.605, 0.599, 0.469, 0.453, NA

0.700 0.685 0.665 0.663 0.513 0.502

< 0.001 < 0.001 0.004 0.003 0.015 0.150 0.767

0.746 0.740 0.684 0.684 0.537 NA NA

0.640, 0.632, 0.574, 0.575, 0.412, NA NA

0.852 0.848 0.793 0.794 0.662

Coefficient

P

All tree diameters Multivariate model −4.9784 BSH CSH

0.1566 0.0546

< 0.001 < 0.001

Univariate models CSH −4.6834 BSH −4.8367 CSP −4.5292 BSP −4.5985 BCS −4.1960 DBH −4.3364 LCP −4.5325

0.0952 0.2133 1.0715 2.3437 0.1269 0.0067 0.6883

Trees > 53.3 cm DBH BSH −5.6471 BSP −5.5373 CSP −5.1690 CSH −5.2562 DBH −7.4503 BCS −5.2925 LCP −5.2174

0.1867 4.9546 1.6854 0.0661 0.0369 0.3704 0.5728

CI 95% CL

0.644

0.620, 0.660

< 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001

0.635 0.596 0.592 0.564 0.544 0.510 0.481

0.615, 0.574, 0.573, 0.547, 0.524, 0.490, 0.466,

0.655 0.617 0.612 0.582 0.564 0.531 0.496

< 0.001 < 0.001 < 0.001 0.003 0.015 0.133 0.531

0.730 0.722 0.646 0.642 0.524 NA NA

0.672, 0.664, 0.585, 0.582, 0.456, NA NA

0.787 0.779 0.706 0.702 0.592

P

All tree diameters Multivariate model −2.9285 BSH CSH

0.1626 0.0260

< 0.001 < 0.001

Univariate models BSH −2.8706 CSH −2.6518 BSP −2.6095 DBH −2.6524 CSP −2.3976 LCP −2.6204 BCS −2.4085

0.1900 0.0670 1.6133 0.0105 0.2973 0.5284 0.1553

Trees > 53.3 cm DBH BSH −3.1614 BSP −3.0979 CSH −2.7379 CSP −2.6895 DBH −4.0793 BCS −2.6388 LCP −2.9066

0.1764 4.7595 0.0469 1.1421 0.0235 0.2020 0.6701

one. Both beetles attacked 1.2% of large pine in year one and 6.1% by year three. Of the large pines attacked by RTB, 8.7% and 21.1% were attacked also by WPB in year one and year three, respectively. Of the large pine attacked by WPB, 40.7% were also attacked by RTB in year one, and 65.1% by year three post-fire. All variables tested in univariate models were significantly associated with RTB attack of large trees > 53.3 cm diameter for both year one and year three (Tables 3 and 4), and all but basal char severity and live crown proportion were significant for WPB attack at both times (Tables 5 and 6). Univariate models using bole scorch proportion or bole scorch height provided the best attack discrimination for both beetles in both years, with concordance indexes of 0.73 and 0.75 for RTB and WPB attack in year one (Table 3 and 5), respectively, and concordance indexes of 0.69 and 0.73 (Tables 4 and 6) at year three, respectively. Fig. 1 compares the large pine attack probability for each beetle species predicted by univariate models using bole scorch height or bole scorch proportion. None of the multivariate models improved on the discriminative ability of the univariate bole scorch proportion or bole scorch height models for either species, at either year one or year three.

Table 5 Summary results of model testing and fit for probability of attack by western pine beetle (WPB) within one year following fire, including the model intercept, variable coefficients (P value for inclusion), receiver operating curve area under the curve concordance index (CI), and CI 95% confidence limit (CL). Intercept

CIb

Coefficient

a Variables tested are bole scorch height (BSH), crown scorch height (CSH), bole scorch proportion (BSP), crown scorch proportion (CSP), diameter breast height (DBH), basal char severity (BCS), and pre-fire live crown proportion (LCP). b CI is a measure of the models ability to discriminate between attacked and non-attacked trees on a scale of 0–1, with a value of 0.5 indicating no discrimination and 1.0 indicating perfect discrimination.

a

Variablea

Intercept

4.3. Mortality across all tree diameters Of the 7343 trees across all diameters that initially survived the fire, 14.1% died from fire or bark beetle related causes in year one post-fire, and by year three 29.6% were dead (Table 2). The mean diameter of live trees at year three was 38.7 cm (31.5, 45.9, 95% CI), whereas the mean diameter of dead trees was statistically smaller, 29.2 cm (22.0, 36.4, 95% CI; F1, 7324 = 573.13, P < 0.001). Of all beetle attacked trees, those colonized only by RTB had a mortality rate of 45.1 and 31.1% in year one and year three, respectively, whereas those attacked only by WPB experienced 68.9 and 50.9% mortality, respectively. Trees attacked by both beetles died at rates similar to those attacked by WPB

a Variables tested are bole scorch height (BSH), crown scorch height (CSH), bole scorch proportion (BSP), crown scorch proportion (CSP), diameter breast height (DBH), basal char severity (BCS), and pre-fire live crown proportion (LCP). b CI is a measure of the models ability to discriminate between attacked and non-attacked trees on a scale of 0–1, with a value of 0.5 indicating no discrimination and 1.0 indicating perfect discrimination.

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year three attack added to models with both crown scorch proportion and bole scorch proportion for trees > 53.3 cm resulted in moderate improvements to mortality discrimination over those with just crown scorch proportion and bole scorch proportion combined (Table 8). The model with either beetle year three had the best data fit, but the concordance index was only 0.001 higher than the model with either beetle year one, and 0.002 above the model with RTB year one. The model with RTB year one improved the concordance index by 0.029 over the model with WPB year one and by 0.034 over the bole and crown scorch proportions model with no attack variable. The probability of mortality for large ponderosa pine at year three as a function of bole and crown scorch proportion in the presence or absence of attack by RTB year one, or WPB year one are graphically presented in Figs. 2 and 3, respectively. 5. Discussion 5.1. Bark beetle attack The attack of more trees by RTB than WPB in year one post-fire is not likely caused by phenology differences in their seasonal flight patterns and abundance overlapping with the fires seasonal timing. Their number of generations per year vary with latitude, but typically WPB will have more than RTB within the same geographic area (Furniss and Carolin, 1977). RTB often has a peak spring or early summer flight period, with fewer individuals during summer, but numbers may increase again in late summer or early fall (Peck et al., 1997; Fettig et al., 2004, 2006; Gaylord et al., 2006; Owen et al., 2010). WPB flight begins in late spring or early summer, then continues through the season sometimes increasing in the fall (DeMars and Roettgering, 1982; Peck et al., 1997; Gaylord et al., 2006; Williams et al., 2014). Studies monitoring both beetles at the same sites show the number of WPB captured at any given time during the season are most often equal to, or multiple times higher than RTB (Peck et al., 1997; Gaylord et al., 2006; Williams et al., 2014). Thus, regardless of the fires timing it appears that phenology would favor a greater abundance of WPB than RTB, yet it is the latter that attacks more trees year one post-fire.

Fig. 1. Probability of attack in year one post-fire by RTB (PRTB1a) and WPB (PWPB1a) in ponderosa pine > 53.3 cm DBH as a function of (A) bole scorch height (BSH) and (B) bole scorch proportion (BSP). Concordance index (CI) is a measure of the model’s ability to discriminate between attacked and non-attacked trees on a scale of 0–1, with a value of 0.5 indicating no discrimination and 1.0 indicating perfect discrimination.

5.1.1. Models predicting attack Bole scorch height is the primary fire injury parameter of ponderosa pine associated with RTB and WPB attack in Pacific Northwest forests. The univariate models using it provided the best, or comparable results to univariate models using other parameters for both tree diameter groups. Multivariate models with crown scorch height, or crown scorch height and diameter (RTB year three only) as additional variables improved model fit minimally. Bole scorch height, slightly but consistently, outperformed bole scorch proportion in predicting attack of both beetles, likely because it more directly measures fire intensity (Alexander and Cruz, 2017) than bole scorch proportion. Bole scorch height is also a more direct measure of injury to portions of the bole most preferred for beetle colonization, whereas bole scorch proportion is influenced by overall tree size, including the upper most uninjured portions of the crown less preferred by these beetles. Bole scorch height predicted RTB attack of ponderosa pine elsewhere as well, with the best models using bole scorch height in Arizona, bole scorch height and diameter breast height in Colorado, diameter in Montana, and crown scorch height in the Black Hills (Negrón et al., 2016). In Nevada Jeffrey pine (Pinus jeffreyi Grev. & Balf.), bole scorch height was also related to RTB attack, though the best model also included bole scorch height squared, crown scorch percent, diameter breast height, and extreme soil burn-index (Bradley and Tueller, 2001).

alone, 66.7 and 45.8% in year one and year three, respectively. Mortality models using both crown scorch proportion and bole scorch proportion showed relatively minor discrimination improvements with the addition of WPB, RTB, either beetle year one, or either beetle year three attack variables (Table 7). Adding WPB year one attack improved the concordance index slightly more (0.004) than attack by RTB year one, but this difference is minor. There was no improvement in concordance index using either beetle year three rather than year one.

4.4. Mortality for trees > 53.3 cm diameter Of the 884 ponderosa pine > 53.3 cm, 3.6% died in year one postfire from fire or bark beetle related causes, and by year three 7.2% were dead (Table 2). The mean diameter of large dead trees at year three was 71.2 cm (67.2, 75.2, 95% CI), and statistically greater than the mean diameter of live trees, 66.8 cm (63.8, 69.7, 95% CI; F1, 865 = 8.39, P = 0.004). For beetle attacked large trees, those colonized only by RTB had 22.6 and 9.4% mortality rates in year one, and year three, respectively, whereas those attacked by WPB alone experienced 43.8 and 27.6% mortality, respectively. Trees attacked by both beetles in year one died at similar rates to those attacked by WPB alone, 45.4%, but by year three the mortality of trees attacked by both beetles, 35.2%, was slightly higher than for those attacked by WPB alone. The variables of WPB, RTB, either beetle year one, or either beetle

5.1.2. RTB attack RTB attack behavior can be attributed largely to their sensitivity for ethanol rapidly produced in heat stressed woody tissues (Kelsey and Westlind, 2017b, 2017c). Eleven days post-wildfire, ethanol 186

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Table 7 Mortality models for ponderosa pine of all diameters, including model intercept, variable coefficients (P value for inclusion), receiver operating curve (ROC) area under the curve (AUC), concordance index (CI), and CI 95% confidence limit (CL). Explanatory variablea

Fit ROC AUC

Model

Intercept

CSP

BSP

WPB1

1

−4.3706 −4.4696

3

−4.5300

4

−4.4097

5

−4.2826

3.5310 (< 0.001) 3.4472 (< 0.001) 3.4988 (< 0.001) 3.5025 (< 0.001) 3.6620 (< 0.001)

1.7682 (< 0.001)

2

6

−3.9934

7

−4.0991

8

−4.0295

9

−3.8723

4.5205 (< 0.001) 4.4378 (< 0.001) 4.5259 (< 0.001) 4.4573 (< 0.001) 4.4619 (< 0.001) 5.1735 (< 0.001) 5.0620 (< 0.001) 5.0873 (< 0.001) 5.1192 (< 0.001)

10

−3.0361

11

−2.9071

12

−2.9694

13

−2.8599

RTB1

EB1

EB3

0.9827 (< 0.001) 0.6374 (< 0.001) 0.7563 (< 0.001)

1.9273 (< 0.001) 1.0770 (< 0.001) 0.8471 (< 0.001)

7.0093 (< 0.001) 7.3002 (< 0.001) 7.0800 (< 0.001) 7.3456 (< 0.001)

1.0687 (< 0.001) 1.4394 (< 0.001) 0.8355 (< 0.001)

CIb

CI 95% CL

0.915

0.907, 0.923

0.914

0.906, 0.921

0.914

0.906, 0.921

0.911

0.903, 0.919

0.909

0.901, 0.917

0.902

0.894, 0.911

0.901

0.892, 0.909

0.896

0.887, 0.905

0.895

0.886, 0.904

0.842

0.832, 0.852

0.837

0.827, 0.848

0.831

0.820 0.841

0.821

0.810, 0.832

a Variables used are crown scorch proportion (CSP), bole scorch proportion (BSP), and attack presence by WPB year one (WPB1), RTB year one (RTB1), either beetle year one (EB1), and either beetle year three (EB3). b CI is a measure of the model’s ability to discriminate between attacked and non-attacked trees on a scale of 0–1, with a value of 0.5 indicating no discrimination and 1.0 indicating perfect discrimination.

Table 8 Mortality models for ponderosa pine > 53.3 cm DBH, including model intercept, variable coefficients (P value for inclusion), receiver operating curve (ROC) area under the curve (AUC) concordance index (CI), and CI 95% confidence limit (CI 95% CL). Explanatory variablea Model

Intercept

CSP

BSP

1

−6.0517

2

−5.56617

3

−5.6496

4.0123 (0.008) 4.1917 (0.006) 4.5102 (0.003)

4

−5.1457

5

−5.0646

6

−5.1214

7

−5.0067

4.4564 (< 0.001) 4.1551 (< 0.001) 4.2879 (< 0.001) 4.7385 (< 0.001) 4.9148 (< 0.001) 4.1682 (< 0.001) 4.0491 (< 0.001)

8

−4.7946

9

−4.5761

10

−4.3747

11

−4.6964

12

−4.5190

13

−4.4482

Fit ROC AUC WPB1

RTB1

EB1

1.9799 (< 0.001) 1.8072 (< 0.001) 2.1173 (< 0.001) 1.9145 (< 0.001)

4.5630 (0.003) 5.1116 (< 0.001) 8.5412 (< 0.001)

4.8686 (< 0.001) 4.8484 (< 0.001)

2.4308 (0.002)

1.816 (< 0.001) 2.6736 (< 0.001)

9.0409 (< 0.001) 9.5489 (< 0.001) 10.0113 (< 0.001)

1.4974 (< 0.001) 2.0630 (0.001)

EB3

CIb

CI 95% CL

1.7390 (< 0.001)

0.911

0.869, 0.952

0.910

0.865, 0.956

0.909

0.863, 0.954

0.895

0.845, 0.946

0.894

0.843, 0.944

0.880

0.825, 0.935

0.875

0.820, 0.930

0.850

0.794, 0.906

0.847

0.785, 0.910

0.836

0.772, 0.899

0.834

0.773, 0.895

0.803

0.736, 0.870

0.786

0.716, 0.857

a Variables used are crown scorch proportion (CSP), bole scorch proportion (BSP), and attack presence by WPB year one (WPB1), RTB year one (RTB1), either beetle year one (EB1), and either beetle year three (EB3). b CI is a measure of the models ability to discriminate between attacked and non-attacked trees on a scale of 0–1, with a value of 0.5 indicating no discrimination and 1.0 indicating perfect discrimination.

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when bole ethanol concentrations decline as they recover from heat injury (Kelsey and Joseph, 2003). Any fire injury near the stem base leading to ethanol release has potential to attract RTB to that area, regardless of the bole scorch height above, or amount of scorch around the circumference. But, increases in bole scorch height may be accompanied by greater heat injury to the stem, and reduced branch or stem xylem conductivity (Kelsey and Joseph, 2003; Kavanagh et al., 2010; Michaletz et al., 2012; Bär et al., 2018) increasing ethanol production and atmospheric release attracting RTB (Kelsey and Westlind, 2017c). Other RTB behavior influenced by ethanol combined with host monoterpenes includes higher attack rates at lower bole scorch heights because they can detect low amounts of ethanol, or possibly ethanol trapped in the stem bark char (Kelsey and Westlind, 2017b) contributing to a higher probability of attack across a wider range of bole scorch height than WPB. Secondary attraction of RTB begins when pioneering females initiate an attack and begin releasing frontalin, a multifunctional aggregation, sex pheromone, especially when mixed with host monoterpenes (Luxova et al., 2007; Owen et al., 2010; Liu et al., 2013), resulting in additional attacks around the stem circumference or up the bole, but typically not a mass attack. 5.1.3. WPB attack WPB attack behavior suggests it lacks the sensitivity to ethanol influencing RTB. WPB prefer pine with higher bole scorch heights, and attack a greater proportion of trees after year one post-fire, than during the first year. Rapid WPB attacks within days like RTB have not been observed by the authors, or reported in the literature to our knowledge, and there is no known primary attractant kairomone for pioneering beetles; they randomly land and sample trees until a suitable host is found (Moeck et al., 1981; Raffa et al., 1993). Blackening of the tree bole may provide them a visual cue, as demonstrated by their response to baited black or white traps (Strom et al., 2001), although blackening by itself is unlikely to result in attack. The role fire plays in WPB outbreaks is not fully known, but there is evidence suggesting fire provides a limited resource “pulse” lasting but a year or two (Davis et al., 2012), while outbreaks are more often associated with stressed trees, especially under periods of extended moisture deficit from competition or drought (Miller and Keen, 1960; DeMars and Roettgering, 1982; Kolb et al., 2016; Fettig et al., 2019). WPB exhibit strong secondary attraction to their pheromones exo-brevicomin or endo-brevicomin, and frontalin when mixed with host monoterpenes, especially myrcene (Bedard et al., 1980; Byers, 1987; Pureswaran et al., 2016), causing a strip or mass attack spreading up and down the bole (Moeck et al., 1981). WPB is an aggressive beetle known to kill healthy trees when their population density is high, but they were at endemic population levels during our studies, so their responses might change if fires occur during an outbreak.

Fig. 2. Influence of RTB year one attack on probability of post-fire mortality at year three for ponderosa pine > 53.3 cm diameter modeled with crown scorch proportion (CSP) and bole scorch proportion (BSP). Concordance index (CI) is a measure of the model’s ability to discriminate between attacked and non-attacked trees on a scale of 0–1, with a value of 0.5 indicating no discrimination and 1.0 indicating perfect discrimination.

5.1.4. RTB and WPB attack interactions Behavior of both beetles suggest they select most host trees independent of one another. Across all diameters, 90.3 and 75.4% of the trees attacked by RTB in year one and three, respectively, were attacked by RTB alone, and 72.6 and 52.0% of the trees attacked by WPB were attacked by WPB alone in year one and three, respectively. The higher percentage of WPB attacked trees also attacked by RTB results from the latter colonizing trees across a wider range of injury, including those with high levels of bole scorch preferred by WPB. Yet, some proportion of trees attacked by both beetles may have involved species cross-attraction as observed between Hylastes parallelus Chapuis and RTB in China (Lu et al., 2007). Here, RTB often attacked first, but some might have responded to trees initially colonized by WPB after releasing a mixture of 3-carene and other host monoterpenes with ethanol from gallery entrances and resin tubes, combined with exo-brevicomin in frass produced by female WPB (Silverstein et al., 1968) and frontalin released by males (Kinzer et al., 1969; Wood et al., 1976). 3-Carene is a strong RTB attractant (Erbilgin et al., 2007), but when

Fig. 3. Influence of WPB year one attack on probability of post-fire mortality at year three for ponderosa pine > 53.3 cm DBH modeled with crown scorch proportion (CSP) and bole scorch proportion (BSP). Concordance index (CI) is a measure of the model’s ability to discriminate between attacked and non-attacked trees on a scale of 0–1, with a value of 0.5 indicating no discrimination and 1.0 indicating perfect discrimination.

concentrations were higher in tissues above RTB galleries on pines with visually uniform scorch and basal stem charring than the non-attacked opposite side of the same bole, and adjacent non-attacked trees (Kelsey and Westlind, 2017b). Ethanol released to the atmosphere in combination with pine monoterpenes, such as 3-carene functions as a strong primary attractant (Kelsey and Westlind, 2017a), resulting in RTB attacks starting within days or weeks after a fire (Ganz et al., 2002; Kelsey and Westlind, 2017b). They attack more trees in year one post-fire when ethanol is most abundant, and fewer trees in subsequent years

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both beetles in year three was a bit higher than for trees attacked by WPB alone (27.6%), or RTB alone (9.4%) in year three, possibly a result of additional stress from colonization by both beetles and their symbionts.

combined with ethanol their response is synergized (Kelsey and Westlind, 2017a), plus frontalin is an RTB aggregation and sex pheromone, especially attractive when mixed with host monoterpenes (Liu et al., 2013). Hall (1983) reported RTB attacks on healthy ponderosa pine baited with three WPB lures together; exo-brevicomin, frontalin, and myrcene. But, exo-brevicomin might also interfere with RTB responses to the mixture of ethanol and monoterpenes from fire injured trees since its addition to traps baited with mixed host monoterpenes and ethanol combined reduced RTB trap capture by nearly 75% (Fettig et al., 2004). Alternatively, some pioneering WPB may be attracted to trees initially attacked by RTB in response to host monoterpenes, especially myrcene, from galleries or resin tubes, and frontalin release by RTB females (Liu et al., 2013). But, trees in the initial phase of WPB attack would have a much stronger attraction with a mixture of exo-brevicomin produced by females, myrcene and other host monoterpenes, plus frontalin from arriving males (Wood et al., 1976; Bedard et al., 1980; Byers, 1987). Other WPB in the vicinity are drawn to re-attack these trees (Moeck et al., 1981), resulting in strip, or mass attacks, thus limiting their colonization of RTB attacked trees. Initial WPB attacks are usually higher up the bole than RTB near the root collar, so there is seldom much overlap (Furniss and Carolin, 1977; DeMars and Roettgering, 1982). To what extent different preferences in bole height between these two species influences any interspecific communication remains to be determined.

5.3. RTB attack, large pine, and delayed mortality The mortality models combining RTB attack year one with bole and crown scorch proportion improved predictions more than WPB for trees > 53.3 cm, and with a larger magnitude of improvement than in the all diameter group with an abundance of smaller trees. This indicates crown and bole scorch proportions when combined, adequately assess fire damage to smaller trees, but not large diameter trees. Crown scorch proportion on large trees has limits because much of the crown will be safely above the scorch heights typical of low and moderate severity fires, and bole scorch proportion on large trees has limits as a reliable indicator of stem tissue damage because of their thicker bark. While consumption of duff near the base of large pines has been related to their mortality (Sackett and Haase, 1998; Kolb et al., 2007), visual assessment of bark char at the stem base lacks accuracy for assessing large ponderosa pine cambium necrosis (Hood et al., 2008b). RTB attack is an additional measure for underlying tissue damage not covered by crown and bole scorch proportion on larger trees with thick bark (Kelsey and Westlind, 2017b), and it appears to share considerable overlap with the basal cambium kill rating variable used by others (Hood et al., 2008a, 2010; Davis et al., 2012; Ganio and Progar, 2017). Their overlap is illustrated with a series of nested mortality models from two wildfires in California (Hood et al., 2010). Full mortality models performing best at both fires included crown scorch variables, cambium kill rating and RTB attack year one. At the fire with smaller trees (mean diameter = 42.4 cm), cambium kill rating was slightly better than RTB year one as a variable for mortality prediction. At the fire with larger trees (mean diameter = 80.5 cm), RTB year one was better than cambium kill rating as a mortality model variable. This is congruent with our Pacific Northwest results. Both cambium kill and RTB attack assess heat damage to the cambium, but RTB attack is a broader indicator of sublethal heat stress experienced by any living stem tissues (Kelsey and Westlind, 2017c), and not just tissue necrosis. This strongly indicates RTB attack can function as a viable alternative to direct assessment of cambium necrosis for mortality prediction of large pines, with the advantages of easy identification and quick and noninvasive assessment.

5.2. Delayed mortality and bark beetles Probability of mortality was higher for trees attacked by RTB or WPB than those not attacked. Hood et al. (2010) and Ganio and Progar (2017) also observed improved mortality predictions for some of their models with inclusion of bark beetle attack. Here mortality was predicted by combining RTB attack year one with bole and crown scorch proportion variables for trees in both diameter groups, with nearly the same or better results as models using WPB attack year one, or either beetle attack at year one or three. Although the model with either beetle year three predicted mortality best, the increase over RTB year one was not enough to warrant an additional two year wait period for collection. Minimal model improvements with three years of attack is likely due to beetles re-attacking trees previously colonized, as shown for RTB in year one (Kelsey and Westlind, 2017b), plus changes in surviving tree mortality rates attacked beyond year one, as mentioned below. Mortality rates for trees attacked by RTB were lower than those attacked by WPB at year one and three in both diameter groups, because RTB colonizes many trees with less severe burn injuries that survive. Our observations support the suggestion by others that RTB post-fire attack does not contribute substantially to tree mortality (Fettig et al., 2008, 2010; Owen et al., 2010; Fettig and McKelvey, 2014; Negrón et al., 2016). Mortality rates for trees attacked in year one were higher than in year three for both beetles. This could result from initially selecting a high proportion of the most stressed host trees as suggested by Fischer (1980), ones likely to die either directly from their fire injuries, or so damaged they are unable to survive any additional stress from beetle colonization. Their loss leaves less injured and recovering trees in subsequent years that may have increased chemical defense resulting from greater resin duct area (Hood et al., 2015), resin flow, and resin pressure (Perrakis and Agee, 2006; Perrakis et al., 2011), making them more resistant to successful beetle attack with lower mortality when attacked (Kane and Kolb, 2010). Davis et al. (2012) noted the decline of WPB attack on fire injured ponderosa pine over time, suggesting it was likely due to decreasing numbers of surviving trees in fire injury categories they can easily colonize. Overall, it is unclear to what extent stress from beetle colonization, including stress from microbial pathogens they vector into the tissues, enhanced mortality. However, the 35.2% mortality for large trees attacked by

5.4. Tree diameter influences Tree diameter had minimal value in predicting the attack of RTB or WPB for either group of trees, or years post-fire. It did however influence mortality model predictions that improved more substantially for large diameter trees when bark beetle attack was included, similar to results of Hood et al. (2010) and Ganio and Progar (2017). Diameter also impacted the value of RTB attack as a visual measure of underlying tissue damage and substitute assessment of cambium necrosis. Dead tree mean diameter was smaller than surviving trees in the all diameter group, but greater than surviving trees in the > 53.3 cm group, further confirming the U-shaped mortality rate curve, with highest mortality for the smallest and largest trees reported previously (Agee, 2003; McHugh and Kolb, 2003; Thies and Westlind, 2012). This mortality change with diameter likely results from stem damage when litter mounds burn at the base of large trees (Ryan and Frandsen, 1991), and may partially explain why diameter has been an inconsistent factor in modelling post-fire mortality. 5.5. Model implications Incorporating bark beetle attack data collected beyond one year may improve mortality model predictions (McHugh et al., 2003; Breece 189

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Appendix A. Supplementary material

et al., 2008; Davis et al., 2012; Ganio and Progar, 2017), but limits the models utility for quick post-fire management decisions. Assessing RTB attack within one year or less post-fire provides a practical option for use in mortality models, since 50% to 85% of post-fire delayed mortality occurs in the first year following a fire, depending on fire season, fire type, and location (Harrington, 1993; McHugh and Kolb, 2003; Thies et al., 2005; Youngblood et al., 2009; Hood et al., 2010). To ensure RTB have ample opportunity to attack, it would be best to assess attacked trees, along with other injury variables, following the first spring peak beetle flight period after the fire. However, assessments of summer fires may be done later the same year as RTB flight and attack continues throughout the summer with some areas experiencing increased activity in early fall (Furniss and Carolin, 1977; Hall, 1983; Peck et al., 1997; Ganz et al., 2002; Gaylord et al., 2006; Owen et al., 2010; Williams et al., 2014). Other factors promoting RTB attack as an indicator of heat injury is their ability to attack within days or weeks of fire damage (Ganz et al., 2002; Kelsey and Westlind, 2017b), a ubiquitous presence, complete range overlap with ponderosa pine, and population densities not subject to rapid fluctuations (Furniss and Carolin, 1977; Owen et al., 2010). The models for predicting post-fire attack of ponderosa pine provide general guidelines for the relationship between bole scorch height and subsequent WPB and RTB attack. Caution should be used when predicting attack on other pine species, or at other geographic areas.

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6. Conclusions Bole scorch height is the primary parameter of fire injury to ponderosa pine associated with RTB and WPB attack in Pacific Northwest forests. Plots of univariate logistic regression models demonstrated higher probability of attack by RTB at all levels of bole scorch height or bole scorch proportion, with better data fit for trees > 53.3 cm than for trees across all diameters. More trees were attacked by RTB than WPB in the months immediately after the fires, and also three years post-fire. These results support the relationships between visible fire damage, and RTB attack linked to ethanol released from heat stressed bole tissues in combination with monoterpenes functioning as a primary attractant for pioneering RTB. Although WPB attacked fewer trees, their host tree mortality rate was higher than for those attacked by RTB, primarily because WPB preferred trees with greater bole scorch injury. Nevertheless, trees attacked in year one by either beetle experienced higher mortality than those attacked over the next two years post-fire. This temporal mortality decline likely resulted from loss of the most injured trees in year one, forcing beetles to attack less stressed hosts, including those with enhanced growth and resin defenses, in years two and three post-fire. Delayed mortality predictions improved with year one WPB or RTB attack presence over models without these attack variables, and there was minimal improvements with attack data at three years. These prediction improvements were modest for trees of all diameters (mean diameter = 29.7 cm) and may not warrant the time and expense of collection or inclusion in mortality modelling. But, mortality prediction accuracy for trees > 53.3 cm diameter was significantly improved by inclusion of either beetle’s presence, especially RTB with the advantage of being more easily detected and occurring more rapidly post-fire than WPB. RTB attack can function as a viable alternative to direct assessment of cambium necrosis in mortality prediction. Acknowledgements Funding for data collection was provided in part by Forest Health Protection of the USDA Forest Service through the Special Technology Development Program, and the Western Wildland Environmental Threat Assessment Center. We thank Dave Shaw for helpful comments during manuscript preparation. 190

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