Stand-scale tree mortality factors differ by site and species following drought in southwestern mixed conifer forests

Forest Ecology and Management 330 (2014) 171–182

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Stand-scale tree mortality factors differ by site and species following drought in southwestern mixed conifer forests Jeffrey M. Kane a,⇑, Thomas E. Kolb b, Joel D. McMillin c a

Department of Forestry and Wildland Resources, Humboldt State University, Arcata, CA 95521, USA School of Forestry, Northern Arizona University, Flagstaff, AZ 86011, USA c Forest Health Protection, USDA Forest Service, Boise, ID 83709, USA b

a r t i c l e

i n f o

Article history: Received 20 January 2014 Received in revised form 24 June 2014 Accepted 26 June 2014

Keywords: Abies concolor Drought Pinus flexilis Populus tremuloides Pseudotsuga menziesii Tree death

a b s t r a c t Impacts of drought on tree mortality in high-elevation mixed-conifer forests of southwestern U.S. are poorly understood. A recent extended and severe drought in the region provided an opportunity to investigate the patterns and factors associated with tree mortality in this forest type. Specifically, we quantified mortality that occurred between 1995 and 2008 of four tree species, white fir (Abies concolor), limber pine (Pinus flexilis), trembling aspen (Populus tremuloides), and Douglas-fir (Pseudotsuga menziesii), in mixed-conifer forests over three sites in northern Arizona within 84, 0.02 ha plots. We found: (1) varied but substantial tree mortality (4–56% by basal area) in most species between 1996 and 2006 in association with recent severe and prolonged drought; (2) tree mortality differed among sites and species with aspen and white fir having the most mortality (>30% by basal area); (3) relationships between tree mortality and most climatic factors (e.g. temperature, precipitation, Palmer Drought Severity Index) were lagged 1–4 yr; (4) bark beetle attack and intraspecific tree basal area were consistently and positively related to tree mortality for most species and sites, whereas topographic and other stand characteristics were less consistently related to mortality. Results show that aspen, Douglas-fir, and white fir were more vulnerable to recent drought-associated mortality than limber pine. Associations between tree mortality and intraspecific basal area support further evaluation of treatments that reduce intraspecific competition within stands to lower risks of tree mortality in southwestern mixed conifer forests. Ó 2014 Elsevier B.V. All rights reserved.

1. Introduction The southwestern United States recently experienced an unusual pulse of tree mortality in multiple ecosystems (Gitlin et al., 2006) associated with a prolonged and severe drought from 1996 to 2003 (Weiss et al., 2009). Several studies in this region have investigated this mortality event in lower-elevation woodlands and forests (Breshears et al., 2005; Mueller et al., 2005; Floyd et al., 2009; Negrón et al., 2009; Clifford et al., 2011; Koepke et al., 2010; McDowell et al., 2010), and most attribute mortality to drought and associated bark beetle attacks. Increased drought stress and insect attacks are often associated with increased tree density, altered tree spatial arrangement, and shifted forest composition that have resulted from fire exclusion, grazing, and past logging (Cooper, 1960; Covington et al., 1994). These changes in forest structure may exacerbate tree mortality due to increased

⇑ Corresponding author. Tel.: +1 707 826 5622. E-mail address: [email protected] (J.M. Kane). http://dx.doi.org/10.1016/j.foreco.2014.06.042 0378-1127/Ó 2014 Elsevier B.V. All rights reserved.

competition among trees (Peat and Christensen, 1987; Biging and Dobbertin, 1992; Das et al., 2011). Higher elevation forests in the southwestern U.S., such as mixed-conifer and spruce-fir forests, are less well studied and may be more climatically buffered from drought than lower elevation forests due to cooler temperatures and greater precipitation (Adams and Kolb, 2005). Additionally, higher elevation forests have not missed as many fire cycles and have not undergone as many changes due to fire suppression and other management activities compared to lower elevation forest types (Fulé et al., 2003; Cocke et al., 2005). Alternatively, higher elevation forests may be more susceptible to drought-related mortality than lower elevation forests due to poorer genetic adaptation to water stress (Ogle et al., 2000; Bigler et al., 2006). Aerial surveys conducted between 2002 and 2006 within mixed-conifers forests of Arizona and New Mexico documented a recent pulse of tree mortality (USDA, 2007). This pulse has been corroborated by a recent field study, conducted between 1997 and 2007, where mortality of mixed-conifer tree species averaged between 5% and 80% depending on the species (Ganey and Vojta,

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2011). Additionally, recent increases in mortality have been reported for trembling aspen, Populus tremuloides (Fairweather et al., 2008; Hogg et al., 2008; Worrall et al., 2008, 2013; Zegler et al., 2012), which occurs in southwestern mixed-conifer forests. Despite these reports, tree mortality processes have not been thoroughly investigated in this forest type. Tree mortality processes are an essential component of ecosystems through their influence on forest population and community structure, nutrient cycling, habitat creation, and food sources (Franklin et al., 1987). However, novel climatic conditions or rapid climate change can intensify or alter these processes and decrease ecological, ecosystem, and climatic services (Anderegg et al., 2013a). Recent evidence suggests that increases in temperature and reduced water availability are associated with increased tree mortality in the western United States (van Mantgem et al., 2009) and in many forests globally (Allen et al., 2010). Understanding factors that contribute to observed patterns and processes of tree mortality resulting from climate change-related events is needed to better anticipate future changes and to consider potential management solutions. Tree mortality is a complex process that often varies among species and across temporal and spatial scales (Franklin et al., 1987; Manion, 1991). The complex process of tree mortality is exemplified by recent advances in understanding and debate about the physiological mechanisms of tree mortality (McDowell et al., 2008; Adams et al., 2009; Sala et al., 2010; Anderegg et al., 2013b; Sevanto et al., 2014). Factors contributing to tree mortality at the stand scale can be generally characterized into topographic, climatic, stand, and disturbance factors. Topographic factors (e.g. elevation, aspect, and slope) contribute to spatial variability in the microclimate, which can enhance or buffer broader climatic influences. Climatic factors often include temperature, precipitation, and measures of drought, which have been strongly related to tree mortality in many forest types (Bigler et al., 2006; van Mantgem and Stephenson, 2007). Stand factors that contribute to tree mortality include structure, composition, and productivity. These stand factors are indirect measures of competition and resource availability, which have been associated with tree mortality (Biging and Dobbertin, 1992; Das et al., 2008, 2011; Stephenson et al., 2011). Furthermore, biotic agents (e.g., insects and fungal diseases) also can contribute to tree mortality (Ferrell et al., 1994; Negrón et al., 2009; Egan et al., 2011). These tree mortality factors likely are not mutually exclusive, have non-additive effects, and vary among species in importance (Bigler et al., 2005; Kenaley et al., 2008). The main objective of our study was to examine patterns of mortality that occurred between 1995 and 2008 of the four most common tree species in montane southwestern mixed-conifer forests of northern Arizona. More specifically, the aims of this research were to: (1) quantify tree mortality in southwestern mixed-conifer forests by species across three mountain sites; (2) investigate relationships between temporal trends in tree mortality and climatic factors; (3) determine the relative importance of topography, stand characteristics, and biotic factors on tree mortality for each species.

2. Methods 2.1. Study sites and design Sampling occurred during 2009 and 2010 within southwestern mixed-conifer montane forests of northern Arizona, USA at three study sites: San Francisco Peaks (SFP), Sitgreaves Mountain (SIT), and Bill Williams Mountain (BWM), located on distinct major mountains in the region (Fig. 1). Climate data from 1909 to 2009

for the Fort Valley weather station (www.wrcc.dri.edu) near Flagstaff (5 km south of SFP) show that the region is characterized by cold winters with January daily temperatures ranging from 5.4 °C (maximum) to 12.2 °C (minimum) and warm summers with July temperatures ranging from 26.9 °C (maximum) and 7.2 °C (minimum). Mean annual precipitation is 55.9 cm and is bimodal in distribution with approximately half falling as snow in the winter and half falling as rain in the late summer from monsoonal thunderstorms. Climate for the three study sites we sampled was slightly cooler and wetter than the weather station data due to higher altitudes. These sites were selected because they are geographically distinct mountains located at least 20 km from one another, and contained mixed-conifer forests on most aspects and over a wide range of elevations (Table 1). Additionally, none of these sites had been harvested or experienced recent intense fire at the time of our sampling. Soil parent material for all three sites was primarily volcanically derived basalt. BWM has the oldest substrate (3.9 my), followed by SIT (2.4 my), and SFP (1.0 my; Tanaka et al., 1986). Southwestern mixed-conifer forests vary in composition throughout Arizona and New Mexico, but they are usually divided into two types: a warm–dry type and a cool–moist type (Jones, 1974). The warm–dry type frequently occurs at lower elevation, on south-facing slopes and often contains ponderosa pine (Pinus ponderosa), whereas the cool–moist type frequently occurs at higher elevation, on north-facing slopes, and often contains Engelmann spruce (Picea englemannii), cork-bark fir (Abies lasiocarpa var. arizonica) or sub-alpine fir (Abies lasiocarpa var. lasiocarpa). Both forest types were present in our study, but we defined mixed-conifer forests as those containing a majority (>50% by basal area) of Douglas-fir (Pseudotsuga menziesii), limber pine (Pinus flexilis), trembling aspen, and white fir (Abies concolor) with other species (ponderosa pine, Engelmann spruce, and corkbark fir) as a smaller component. While mixed-conifer forests in the southwestern U.S. can occur along lower elevation canyons, we did not include these areas in our sampling because they were not common or in close proximity to our study sites. Additionally, areas with greater than 50% aspen cover by basal area were avoided because stands dominated by aspen were considered a separate forest type. For simplicity, we refer to the white pine species occurring on our plots as P. flexilis, however, the taxonomic identification of the species in our study area is uncertain and may be Pinus strobiformis or a combination of the two species. At each site, we randomly established three 0.02 ha (12.6 m radius) circular plots in mixed-conifer forests that were stratified by three elevation categories (low, middle, high) on each aspect (N, E, S, W). From a point along a road or trail, we randomly selected an azimuth by spinning the dial of a compass until the azimuth was generally parallel (within 10–15°) to the slope. Along this transect we stratified plot locations along three elevation categories for each aspect. The lower elevational bounds of mixedconifer forests were defined by an overstory dominance (>50% basal area) change from Douglas-fir and other associated conifers to ponderosa pine, while the upper bound was defined by a transition in dominance to Engelmann spruce and corkbark fir. Once the elevation and composition criteria were met, we randomly selected an azimuth and distance (0–360 m) to determine plot center. All plots were >500 m from a major road and least 500 m from one another. If the random plot locations were not in the mixedconifer type, had experienced a recent fire (<50 y) based on available fire perimeter data, or the slopes were steeper than 35°, we took a random compass bearing and walked until the desired conditions were met. At sites where Engelmann spruce and corkbark fir did not occur, the highest elevation plots were limited to the mountaintop. At the BWM site we only surveyed two elevation categories (low and moderate) and two aspects (north and west) due

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Fig. 1. Map of study sites and plots in southwestern mixed-conifer forests of northern Arizona.

Table 1 Characteristics of sampled mixed conifer forest sites in northern Arizona (BWM = Bill Williams Mountain; SFP = San Francisco Peaks; SIT = Sitgreaves Mountain). Characteristic

Site BWM

Location (lat., long.) Elevation range (m) Slope range (°) Aspects Tree density (# ha1) Soil parent material

SFP 0

112°12 , 35°12 2360–2785 12–28 N, W 600–3420 Basalt

0

SIT 0

0

112°00 , 35°20 2585–3013 9–29 N, E, S, W 480–2520 Basalt

111°410 , 35°200 2466–2843 11–33 N, E, S, W 500–3260 Basalt

to the presence of a lookout tower and road at the top of the mountain, the occurrence of steep eastern slopes (>40°), and the absence of mixed-conifer forest types on the southern aspect. We established a total of 84 plots (36 SFP, 36 SIT, and 12 BWM). 2.2. Data collection Each established plot center point was marked with a low-set rebar and the global positioning system (GPS) generated location was recorded. For each plot we recorded the slope (°) with a clinometer and aspect (°) with a standard compass. We used the GPS locations of each plot to generate elevation values (m) in ArcGIS (ESRI, Redlands, CA) using a digital elevation model layer that covered all study sites. Within each plot, we measured the diameter at breast height (dbh) of all live and dead trees that were equal to or taller than breast height (1.37 m). Both standing and down dead trees were included and we considered trees dead when no green foliage was visible. We categorized the decay class of snag and down woody debris using a separate five class system for each (Vanderwel et al., 2006). Classes for snags ranged from recently dead trees that had intact tops, bark, and fine branches (class 1), to short (<6 m) snags that had broken tops, no coarse branches,

and/or missing bark (class 5). Classes for down woody debris ranged from recently dead trees that had intact bark, fine branches, and hard wood (class 1) to soft, well-decayed and resembled organic soil layers (class 5). For all recently dead trees (decay classes 1 and 2), we used a hatchet to remove bark from the bole and the base of each tree to examine signs of bark beetle galleries and fungal pathogens. If present, we recorded the species of the damaging agent using a regional field guide to forest insects and pathogens (Fairweather et al., 2006). We also looked for and recorded other potential mortality agents, such as lightning, mechanical damage (tree fall and wind damage), and animal rubbing damage (e.g. elk, deer). For Douglas-fir, which can be infested by Douglas-fir dwarf mistletoe (Arceuthobium douglasii), we assigned a broom volume rating that ranged between 0 and 6 for all live and recently dead trees based on Tinnin (1998). All recently dead trees were cored once at breast height using a large-gauge (12 mm) increment borer. Approximately 20% of all qualifying trees were too decayed to core in the field, had eroded rings (missing exterior rings due to decay), or were unreadable due to decay once sanded in the lab and were subsequently discarded. For 1/3 of all plots, we cored all live trees near the base (<30 cm above ground) that were greater than 10 cm dbh using a small-gauged (5 mm) increment borer to determine approximate tree age. These age plots were randomly stratified to cover one plot per elevation (low, moderate, high) and aspect (N, E, S, W) category for each site. Tree cores were prepared and analyzed using standard dendrochronology techniques (Stokes and Smiley, 1968). All cores were mounted and progressively sanded to 400 or 600 grit and scanned to create a high-resolution image (1600 dpi) using a flatbed scanner. The scanned images were entered into WinDendro (Regent Instruments, Quebec, Canada) to delineate ring boundaries and calculate ring widths for each designated annuli. All cores from living trees were cross-dated visually and checked for errors using

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COFECHA (Grissino-Mayer, 2001). Once a reliable chronology was developed with the live tree cores, all cores from dead trees were visually cross-dated and assessed again using COFECHA. We did not remove any processed cores regardless of their correlation with the overall chronology to avoid any unintended biases. Our best efforts were made to reach the pith of each live tree age core, but 36% of the cores were missed and we estimated the pith date for off-centered samples using the concentric circle method (Applequist, 1958). We then generated equations for diameterage relationships for all species at each site and used the equations to assign ages to trees in plots not sampled for ages. We then calculated mean and variance for age values for all plots. 2.3. Calculations and data analysis For each plot we calculated absolute and percent mortality by both tree density (# of trees ha1) and basal area (m2 ha1) for recently dead trees by each species and for all species combined. We tested for differences among sites using both percent mortality metrics for each species separately and for all species combined. We used a two-way analysis of variance (ANOVA) to test for significant site, species, and site by species interaction effects on ranktransformed percent tree mortality. Because data did not always meet the equal variance assumptions for one-way ANOVA, we used the non-parametric Kruskal–Wallis test on ranks with a Dunn’s multiple comparison test to determine differences among species and sites. Statistical analyses were performed using NCSS (Heintz, 2007) and significance for these and all subsequent analyses was set at a = 0.05. We examined the relationship between climate variables and temporal patterns of mortality with consideration for potential lagged relationships. Poisson regression was conducted with the statistical package R (R Core Team, 2013) using the glm function to determine the relationship between nine climatic factors (i.e., precipitation, temperature, and drought) and the number of dated dead trees pooled across all plots and sites by year (1995–2008) for each species. Climate factors included four precipitation (mm) measures: annual (total precipitation of the same calendar year), water year (total precipitation from the previous October through current September), monsoon (total precipitation from current July, August, September), and winter (total precipitation from previous November through February). Temperature (°C) measures included minimum, average, and maximum annual temperature. We also compared tree mortality frequency to annual and water year Palmer Drought Severity Index (PDSI) for the same period. All climate data from 1995 to 2008 were measured at the Fort Valley weather station and were obtained from the United States Historical Climatology Network (http://cdiac.ornl.gov/epubs/ndp/ ushcn/ushcn.html). In addition to comparing annual mortality frequency of each species with climate variables for a given year, we also compared relationships with climate variables for the preceding four years and the proceeding year. The proceeding year was included to account for the possibility of missing rings (i.e. no radial growth) during the year of death. The preceding years were included to assess the importance of temporal lags between tree mortality and climatic stresses. We report the test statistic (Z) and significance level (P) for each of the 54 separate analyses for each species. Spearman rank-order correlation analyses were used to test relationships between plot-level percent tree mortality (by basal area) and potential factors (topographic, stand, and biotic). Topographic factors included elevation, slope, folded aspect, and estimated direct solar radiation (McCune and Keon, 2002). Stand factors included plot-level tree density (# of trees ha1), tree basal area (m2 ha1), intraspecific basal area (m2 ha1), mean dbh (cm), variance of dbh, mean age (y), variance of age, and slope of the

diameter distribution (Zhang et al., 2011). All stand factors were calculated using both live trees and recently dead trees (decay < 3) to approximate conditions prior to the recent drought. For aspen only, we calculated percent of stand basal area and tree density in conifers to compare with aspen mortality since previous studies have shown this to be an important factor (Bartos and Campbell, 1998; Kulakowski et al., 2004). Additionally, we determined the slope of the diameter distribution using linear regression by comparing the relationship between the log of tree frequency and tree dbh in 4 cm classes for each plot, for all trees below 88 cm dbh. Larger trees were present but infrequent and occurred on less than 10% of all plots for each site. We calculated the percent occurrence of potential mortality agents for the four focal species in all plots and tested for significant differences in mortality agents among sites for each species using a one-way Kruskal–Wallis ANOVA. We report results for mortality agents that were found in >1% of all dead trees on at least one site. Where differences in percent incidences by mortality type were detected among sites, we used post hoc Dunn’s multiple comparison tests to determine which sites statistically differed from one another. 3. Results 3.1. Tree mortality by species and sites Tree composition and stand characteristics varied among sites (Appendix A), as did recent tree mortality as a percentage of basal area among sites (F = 3.9, P = 0.02) and among species (F = 7.8, P < 0.0001). The site  species interaction was also significant for tree mortality (F = 5.9, P < 0.0001). Similar results were detected for percent mortality based on tree density (data not shown). In general, the BWM site had the most tree mortality for all species combined (23.8%) and for white fir (51.3%). The SFP site had the next highest level of overall tree mortality (19.8%), followed by SIT with the least overall tree mortality (8.2%). Site-level differences in percent tree mortality by basal area varied among species (Table 2). Percent trembling aspen mortality ranged between 22% and 56% and differed among sites (H = 6.3, P = 0.04). Mortality of white fir ranged between 5% and 51% and differed among sites (H = 13.3, P = 0.0001). Douglas-fir mortality did not differ among sites (H = 1.6, P = 0.38) and ranged between 13% and 16%. Lastly, limber pine mortality differed between the SIT (4%) and SFP (13%) sites (H = 15.0, P = 0.0001). These results for tree mortality based on basal area were consistent with results based on tree density (Table 2). 3.2. Temporal and climatic trends in tree mortality A pulse of tree mortality occurred for all species between 1995 and 2008 (Fig. 2). Temporal variation in tree mortality was associated with climatic data for the region. Most commonly, we detected strong relationships between climate and mortality that were lagged between 1 and 4 years (Table 3). Aspen mortality was related to climatic data that was primarily lagged the year before mortality. Limber pine and Douglas-fir mortality were consistently lagged by three years and white fir was lagged by four years. The strongest climatic relationships with aspen mortality were a positive relationship with annual mean minimum daily temperature of the same year (Z = 5.5, P < 0.00001), a negative relationship with monsoonal precipitation lagged by three years (Z = 5.1, P =< 0.00001), and mean annual precipitation lagged by one year (Z = 4.9, P =< 0.00001). Limber pine mortality was negatively related to water year precipitation (Z = 2.5, P = 0.011) and PDSI

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Table 2 Mean (S.E.) absolute and percent recent tree mortality by (a) basal area and (b) tree density for four species: white fir (Abies concolor), limber pine (Pinus flexilis), trembling aspen (Populus tremuloides), Douglas-fir (Pseudotsuga menziesii), at three southwestern mixed-conifer forest sites (BWM = Bill Williams Mountain; SFP = San Francisco Peaks; SIT = Sitgreaves Mountain) in northern Arizona, USA. Species with significant (a < 0.05) differences in mortality among sites are represented with bolded P-values determined using the chi-squared statistic (H) from a Kruskal–Wallis one-way ANOVA. Individual site differences are denoted with superscripted letters as determined using post hoc Dunn’s multiple comparison tests. Species

(a) White fir Limber pine Trembling aspen Douglas-fir All species

Tree mortality basal area (m2 ha1) BWM

SFP

2.9 (0.8)a Not present 0.8 (0.3) 1.0 (0.7) 4.3 (0.8)a

0.4 0.8 1.3 1.9 4.8

Tree mortality (% basal area) SIT

(0.2)b (0.2)a (0.3) (0.4) (0.5)a

0.2 0.2 0.6 0.9 1.5

(0.1)b (0.1)b (0.2) (0.4) (0.5)b

H

P

BWM

SFP

20.5 15.0 1.9 0.1 27.5

>0.0001 0.0002 0.38 0.97 >0.0001

51.3 (8.5)a Not present 44.2 (12.1)ab 15.9 (6.4) 23.8 (3.2)a

34.9 12.5 22.0 14.7 19.8

Tree mortality density (# tree ha1)

(b) White fir Limber pine Trembling aspen Douglas-fir All species

(9.3)ab (2.7)a (5.4)b (4.9) (1.8)a

SIT

H

P

4.5 (7.6)b 3.7 (2.7)b 56.0 (8.6)a 13.2 (3.7) 8.2 (1.8)b

13.3 15.0 6.3 1.6 21.3

0.0001 0.0001 0.04 0.43 <0.0001

Tree mortality (% tree density)

BWM

SFP

SIT

H

P

BWM

SFP

SIT

H

P

353.3 (70.3)a Not present 113.3 (85.9) 76.7 (43.6) 493.3 (88.2)a

20.0 (77.0)b 60.0 (8.1)a 162.7 (38.4) 133.3 (25.2) 412.4 (88.2)b

50.7 (62.9)b 14.4 (8.1)b 141.7 (60.7) 150.0 (25.2) 270.6 (60.4)b

18.2 16.8 0.5 1.9 9.9

0.0001 >0.0001 0.78 0.39 0.007

34.5 (7.5)a Not present 42.6 (11.1)ab 7.2 (2.4) 26.4 (3.2)a

24.4 (8.3)ab 12.0 (1.5)a 35.0 (5.0)b 8.3 (1.4) 21.7 (1.9)a

4.4 (6.7)b 3.2 (1.5)b 67.2 (7.9)a 8.8 (1.4) 13.3 (1.9)

15.1 15.9 8.3 0.6 14.4

0.0005 <0.0001 0.02 0.73 0.0007

Fig. 2. Number of dead trees by estimated death year (last year of radial growth) pooled across all study sites for four tree species: white fir (Abies concolor), limber pine (Pinus flexilis), trembling aspen (Populus tremuloides), and Douglas-fir (Pseudotsuga menziesii).

(Z = 2.4, P = 0.014) lagged by three years. Douglas-fir mortality had the strongest positive relationships with annual mean minimum daily temperature of the same year (Z = 3.4, P = 0.0006) and a negative relationship with monsoonal precipitation lagged by three years (Z = 3.7, P < 0.0001). White fir mortality had a strong positive relationship with mean annual minimum temperature lagged by one year (Z = 3.2, P = 0.001) and a strong negative relationship with annual precipitation lagged four years (Z = 3.3, P = 0.001). 3.3. Topographic and stand characteristics related to tree mortality Tree mortality (by basal area) was variably related to topographic and stand characteristics (Table 4). Pooled across sites,

mortality for each species had a strong and consistent positive relationship (P < 0.0001) to intraspecific basal area (i.e. the basal area of the same species). White fir mortality was negatively correlated with elevation (r = 0.40, P = 0.01). Limber pine mortality was positively related to stand basal area (r = 0.38, P = 0.0009) and elevation (r = 0.31, P = 0.007), and negatively related to slope (r = 0.31, P = 0.006). Aspen mortality was negatively correlated with% conifer basal area (r = 0.71, P < 0.00001), and variance in dbh (r = 0.21, P = 0.0052). A positive correlation with aspen mortality was detected with total stand basal area (r = 0.30, P = 0.038). Douglas-fir mortality was negatively related to stand mean dbh (r = 0.19, P = 0.0074), variance in dbh (r = 0.21, P = 0.0052), and slope of the diameter distribution (r = 0.23, P = 0.033).

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Table 3 Direction (DIR) and Poisson regression results (Z-score) for the relationships between the number of dated dead trees pooled over all three sites by year (1995–2008) and nine climate metrics from Flagstaff, AZ for each of four species: white fir (Abies concolor); limber pine (Pinus flexilis); trembling aspen (Populus tremuloides); and Douglas-fir (Pseudotsuga menziesii) during the proceeding year (1), same year (0), and four preceding years (1 through 4). Only significant (a < 0.05) results are presented (no subscript: P = 0.05–0.01; a: P =< 0.01–0.001; b: P < 0.001). DIR

White fir 1

AnTmin AnTavg AnTmax PDSI WYPDSI AnPrecip WYPrecip MonPrecip WinPrecip

Limber pine 0

1

2

3

4

2.1

2.0

2.0a 2.4

2.9a 2.9a

2.7a 2.5a 2.6a 2.5a 3.2b 3.0a

1

2

3

4

2.0 2.3 2.4a 2.3 2.2 2.5a 2.0 2.2 Douglas-fir

1 + + +      

0

2.5a Aspen

AnTmin AnTavg AnTmax PDSI WYPDSI AnPrecip WYPrecip MonPrecip WinPrecip

1

3.2b

+ + +      

0 5.5b 3.6b

3.4 3.6b 2.7a

2.7 2.2

1

2

3

4

1

0

1

2

3

4

2.0 2.0 3.3b 3.3b 2.8a 3.1a 3.7b 2.6a

2.1 2.6a

3.4b 2.5 2.6a 4.4b 4.3 4.9 4.4b 2.1

2.9a

2.3

2.3 2.7 4.0 2.4 2.1

3.3b 5.1b 2.7a

2.0 2.1 3.8b

2.8a

3.0a

AnTmin = annual mean minimum daily temperature (°); AnTavg = annual mean daily temperature (°); AnTmax = annual mean maximum daily temperature (°); PDSI = Palmer Drought Severity Index; WYPDSI = water year PDSI; AnPrecip = annual precipitation (mm); WYPrecip = water year precipitation (mm); MonPrecip = monsoon precipition (mm); WinPrecip = winter precipitation (mm).

Table 4 Matrix of Spearman rank-order correlation results comparing topographic and stand characteristic factors to tree mortality by basal area (m2 ha1) of four species, white fir (Abies concolor), limber pine (Pinus flexilis), trembling aspen (Populus tremuloides), and Douglas-fir (Pseudotsuga menziesii) pooled across all sites. Symbol type (/+) represent the direction of a significant (a < 0.05) relationship and number of symbols represent the significance level (/+, P = 0.05–0.01;  /++, P = <0.01–0.001;   /+++, P = <0.001– 0.0001;    /++++, P < 0.0001).

a b

Factors

Units

Topographic Folded aspect Elevation Slope Incident radiationa

° m ° MJ cm2 y1

Stand characteristics Basal area (BA) Tree density (TD) Species BA Mean DBH Variance DBH DiamDistSlope Mean age Variance age Conifer BA Conifer TD

m2 ha1 # ha1 m2 ha1 cm No units No units y No units % %

Bioticb Dwarf mistletoe Fir engraver Douglas-fir beetle Mountain pine beetle

% % % %

White fir

Limber pine



++ 

++++

Trembling aspen

+++

+

++++

++++ 

 



Douglas-fir

++   



++++ ++++ ++++

+ +

Potential annual direct incident radiation calculated from McCune and Keon (2002). All biotic factors were calculated as the percentage of dead trees that demonstrated signs of biotic agents.

3.4. Biotic and other potential agents of mortality In addition to climatic, topographic, and stand factors, many biotic and other agents were associated with tree mortality. For all conifer species studied, phytophagous insects (e.g. bark beetles) were positively associated with tree mortality (Table 4). Specifically, fir engraver (Scolytus ventralis) signs were prevalent in 22– 77% of recently dead white fir trees, differed among sites

(H = 8.3, P = 0.0159; Table 5), and were positively correlated with plot-level white fir mortality (r = 0.60, P = 0.002). BWM contained the most white fir (Appendix A) and had the most fir engraverrelated mortality. Mountain pine beetle (Dendroctonus ponderosae) presence did not vary among sites but occurred in approximately 27% of dead limber pine trees, and was significantly related to plot-level limber pine mortality pooled across sites (r = 0.54, P = 0.0002). Bark beetle species were present in Douglas-fir,

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Table 5 Mean (SE) percent occurrence of potential mortality agents for recently dead trees for four species, white fir (Abies concolor), limber pine (Pinus flexilis), trembling aspen (Populus tremuloides), and Douglas-fir (Pseudotsuga menziesii), at three southwestern mixed-conifer forest sites (BWM = Bill Williams Mountain; SFP = San Francisco Peaks; SIT = Sitgreaves Mountain) in northern Arizona, USA. Species with significant (a < 0.05) differences in mortality among sites are represented with bolded P-values determined using the chisquared statistic (H) from a Kruskal–Wallis one-way ANOVA. Individual site differences are denoted with superscripted letters as determined using post hoc Dunn’s multiple comparison tests. Species

Mortality agent

BWM

SFP

SIT

H

P

White fir

Fir engraver Mechanical

76.7 (8.0)a 2.6 (1.4)

53.3 (13.0)ab 0.0 (2.3)

21.7 (11.0)b 1.2 (1.9)

8.28 0.85

0.0159 0.6545

Limber pine

Mountain pine beetle Mechanical Animal damage Lightning

26.4 (7.7) 17.5 (5.4) 10.2 (4.9) 9.9 (3.9)

27.1 (12.0) 2.8 (8.4) 10.4 (7.6) 0.0 (6.1)

2.61 2.31 0.00 2.29

0.9592 0.1281 0.9478 0.1305

Not present

Aspen

Black canker Cytospora Sooty bark False tinder conk Animal damage Mechanical

0.0 (1.3) 19.4 (10.0) 14.5 (8.9) 25.2 (6.6)a 0.0 (2.8) 13.7 (5.5)

1.0 (0.7) 29.6 (5.4) 15.1 (4.8) 5.8 (3.5)b 2.8 (1.5) 2.7 (2.9)

0.0 (1.1) 11.0 (8.6) 0.0 (7.6) 11.3 (5.6)ab 0.4 (2.4) 1.5 (4.7)

1.39 3.62 5.94 7.25 1.43 3.46

0.4999 0.1636 0.0513 0.0266 0.4874 0.1771

Douglas-fir

Douglas-fir Beetles Douglas-fir engraver Dwarf mistletoe Mechanical Animal damage Lightning

28.3 (9.2) 22.1 (4.5) 3.0 (10.5)b 0.0 (4.6) 2.2 (3.1) 1.0 (0.7)

29.8 (5.0) 11.0 (2.5) 29.9 (6.0)a 2.4 (2.5) 4.6 (1.7) 0.6 (0.4)

28.3 (4.9) 6.9 (2.4) 36.4 (6.0)a 5.4 (2.5) 2.8 (1.7) 0.0 (0.4)

0.09 5.82 6.93 1.61 2.09 3.09

0.9566 0.0545 0.0313 0.4467 0.3525 0.2128

including the Douglas-fir beetle (Dendroctonus pseudotsugae), Douglas-fir pole beetle (Pseudohylesinus nebulosus), and Douglas fir engraver (Scolytus monticolae). Douglas-fir beetle presence in dead Douglas-fir was similar across sites (28%) whereas Douglas-fir engraver did not quite differ among sites (7–22%; H = 5.8, P = 0.0545), and both were strongly and positively related to Douglas-fir mortality (r > 0.45, P < 0.0001). Additionally, presence of Douglas-fir dwarf mistletoe differed among sites and ranged between 3% and 36% (H = 6.9, P = 0.031) and was positively related to Douglas-fir mortality. Other potential agents such as root diseases, elk damage, mechanical agents (e.g. tree fall, wind throw) and lightning were rarely to occasionally present in dead Douglas-fir but none of these agents differed in occurrence among sites (Table 5). Aspen also had signs of biotic agents that are often associated with tree mortality though none of them differed significantly among sites (Table 5). The most common fungal agent present on aspen was cytospora canker (Cytospora chrysosperma), which was visible on 11–30% of recently dead trees. Other fungal agents detected on dead aspens included black canker (Ceratocystis fimbriata), sooty bark canker (Encoelia pruinosa), and false tinder conk (Phellinus tremulae). Animal damage was low on recently dead aspen across all sites (<3%).

4. Discussion 4.1. Tree mortality by species and sites The incidence and magnitude of tree mortality (9–24% of total basal area) in mixed conifer forests of northern Arizona quantified in our study are consistent with other recent studies in the region (Gitlin et al., 2006; Fairweather et al., 2008; Ganey and Vojta, 2011; Zegler et al., 2012) and elsewhere (Ferrell et al., 1994; Guarín and Taylor, 2005; Hogg et al., 2008; Worrall et al., 2008, 2013; Egan et al., 2011) for the same species. These combined reports show that recent high levels of drought-related tree mortality in the southwestern U.S. were not limited to lower elevation ecosystems and that higher-elevation forests in the region are not fully buf-

fered from severe drought by relatively cooler, wetter climates. These results are consistent with reports that drought limits growth of many high-elevation mixed-conifer tree species in the southwestern U.S. (Adams and Kolb, 2005). Overall, we conclude that recent tree mortality in southwestern mixed-conifer forests is a complex, multifaceted process that involved numerous abiotic and biotic factors. Stand-level tree mortality differed among sites for all species examined in our study except for Douglas-fir. We hypothesize that site-specific variation in tree species composition, soil factors (e.g. soil age), prior disturbance histories (e.g. fire, bark beetle attacks), and pathogen abundance (e.g. dwarf mistletoe) may have caused some of the differences in tree mortality among sites. Generally, most species had greatest mortality at BWM and lowest mortality at SIT. Possible explanations for this difference may be due to BWM’s lower overall elevation of sample plots or because BWM had a greater proportion of white fir than the other sites (Appendix A). Overall tree mortality was positively related to site substrate age, with older soils generally having greater tree mortality. Substrate age in our study ranged among sites from approximately 1 my old to 3.9 my old with the oldest site (BWM) having the greatest mortality. However, the intermediate-aged site (SIT) had the least overall mortality. Substrate age has been positively associated with mortality of piñon pine (Pinus edulis) (Looney et al., 2012). Volcanic soils over 0.75 my old often have low availability of nitrogen and phosphorus (Selmants and Hart, 2008, 2010), which may have influenced mortality patterns observed in our study. Prior fire regimes at our study sites also may have affected subsequent tree mortality. While fire history has not been determined for all sites in our study, it is known that fires were mostly excluded since the late 1800s at the SFP site (Cocke et al., 2005) and there was no evidence of recent intense burning at the other sites at the time of our sampling. Subtle differences in fire history among sites may partially explain current differences in stand conditions and subsequent tree mortality. Biotic agents (e.g. insects, fungi, and dwarf mistletoe) also varied among most sites in our study (Table 5). Greater infestation or background population levels of these agents may have caused higher tree mortality at

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specific sites. For example, sites with historically high levels of dwarf mistletoe would likely have greater levels of bark beetle activity during periods of drought (e.g., Kenaley et al., 2008). 4.2. Temporal and climatic trends in tree mortality Most tree mortality in our study occurred since 2002, which is estimated to have been one of the driest years in the last 1400 years for the southwestern U.S. (Weiss et al., 2009). This result may have been influenced by our sampling methods. For instance, the number of detectable dead trees decreases with time since present, which could be the result of reduced sampling depth over time due to tree decay. However, we reduced this possibility by limiting our analysis to recently dead trees (1995 and 2008) in decay classes 1 and 2. Additionally, we accounted for this possibility by visually inspecting the temporal pattern of dead trees by decay class. Assuming that decay is related to time of death, where more-decayed trees (decay class 4 and 5) died longer ago, recently dead trees were on average 200–300% more common than decay classes 4 or 5 over all of our plots. In other words, substantially more trees died in our plots recently than in the past, which supports our interpretation that most tree mortality in our study was recent and not simply an artifact of our sampling methods. Our finding of a tree mortality pulse between 1995 and 2008 is consistent with previous reports in the southwestern U.S. for mixed-conifer forests (Ganey and Vojta, 2011) and lower elevation forests and woodlands (Allen and Breshears, 1998; Breshears et al., 2005; Mueller et al., 2005; Floyd et al., 2009; Negrón et al., 2009; Clifford et al., 2011). These mortality events coincided with prolonged and severe drought throughout much of the southwestern U.S. (Weiss et al., 2009; Williams et al., 2013). Additionally, our results corroborate these findings based on significant relationships between tree mortality and temperature, precipitation, and PDSI for all species; however, many of these relationships were lagged by one to four years. Lags in tree mortality following severe drought have been previously reported (e.g. Bigler et al., 2007). All species in our study had a lagged mortality response to at least some climatic variables. There is a possibility that we underestimated tree death dates in our study due to partial or absent cambial growth prior to the actual tree death date (Stan et al., 2011; Jones and Daniels, 2012). However, underestimated death dates, if consistent, would suggest that the estimated lag effect with climate factors we detected was conservative and a greater lag time is possible. Additionally, the lagged relationships we detected were consistent with the ecological characteristics of the species. For instance, aspen mortality was predominately related to climatic events that happened the preceding year, whereas conifer mortality was more typically related to climate factors lagged by three or four years. These lagged relationships provide insight about mortality agents for each species. For example, aspen mortality was lagged with temperature, precipitation, and PDSI the preceding year, which we suggest is because aspen was directly impacted by severe drought years (1996, 2000, 2002) and thus succumbed quickly after drought years, possibly due to cavitation fatigue (Anderegg et al., 2013b). This conclusion is also supported by observational and experimental data showing that recent aspen death in Colorado was the result of root mortality or hydraulic failure during drought (Worrall et al., 2010; Anderegg et al., 2012). However, other agents of aspen mortality that we could not measure directly, such as tent caterpillars and frost damage, may have contributed to aspen mortality in the region of our study (Fairweather et al., 2008). Conifers had delayed tree mortality following drought in our study. We speculate that the lag in conifer mortality is related to

poor tree defenses (Kane and Kolb, 2010; Ferrenberg et al., 2014) and a subsequent bark beetle population increase over several years during and after drought (Raffa et al., 2008). The magnitude of bark beetle attacks in our study varied among tree species. For instance, limber pine had low tree mortality and relatively low to moderate incidences of mountain pine beetle attacks, whereas white fir had greater tree mortality and high incidences of fir engraver attacks (Table 4). A possible explanation for the lagged relationship between climate and conifer mortality is that many of the bark beetle species that were associated with tree mortality in our study (Douglas-fir beetle and mountain pine beetle) are univoltine, which produce only one generation per year (Bentz et al., 1991). Thus, bark beetle populations sufficient to kill drought stressed trees may take multiple years to accumulate. In contrast, multivoltine species of bark beetles commonly occur in lower elevation conifer forests and woodlands and respond rapidly to drought. For instance, engraver beetles (Ips spp.) populations increased dramatically during the severe drought year of 2002, which impacted ponderosa pine and piñon pine that same year (Negrón et al., 2009; Santos and Whitham, 2010). 4.3. Topographic and stand characteristics related to tree mortality Topographic factors were moderately related to stand-level tree mortality for some species in our study. Elevation was negatively related to mortality of white fir over all sites (Table 4). The negative relationship between elevation and mortality in this case is likely due to a climatic buffering of higher elevation trees during drought. A negative relationship between aspen mortality and elevation is widely reported (Fairweather et al., 2008; Worrall et al., 2008; Zegler et al., 2012), but was not detected in our study. The absence of strong and consistent relationships between tree mortality and elevation over all species in our study, as well as others (Ganey and Vojta, 2011), was somewhat unexpected but was likely due to lack of sampling of sites below 2300 m elevation. Stand characteristics were more consistently related to plotlevel tree mortality across sites and species than topographic characteristics (Table 4). The stand characteristic that was most strongly and positively related to tree mortality was intraspecific basal area. This result likely reflects one of two possibilities acting alone or in combination. One likely possibility is that greater basal area of the same species increases intraspecific competition for resources. Another possibility is that greater basal area of the same species promotes species-specific pathogens and insects. Stands that contain more of a single species may have greater endemic levels of pathogens or insects. Other stand characteristics (total basal area, tree density, mean dbh, variance of dbh, and slope of the diameter distribution) were not consistently related to tree mortality over all species. Explanations for these inconsistent or weak associations may be related to the presence of confounding factors (topography, elevation, productivity) with stand characteristics, which cannot be clearly tested with an observational study. We were surprised to find that aspen mortality was negatively related to conifer basal area and density given previous reports of positive relationships (Bartos and Campbell, 1998; Kulakowski et al., 2004; Zegler et al., 2012). One possible reason for the absence of this relationship is that we did not include stands dominated by aspen (>50% by basal area). 4.4. Biotic and other potential agents of mortality Aspen mortality was not strongly associated with biotic agents, although numerous fungal agents were present in aspen across our study sites. Our interpretation of this result is that these agents are

J.M. Kane et al. / Forest Ecology and Management 330 (2014) 171–182

contributing factors to aspen death, but other inciting or predisposing factors such as drought and intraspecific competition were more important. While some studies support our interpretation (Worrall et al., 2010; Anderegg et al., 2012), others have highlighted an important role of woodborers in aspen mortality (Worrall et al., 2008; Zegler et al., 2012). Other potential aspen mortality agents that we did not investigate include tent caterpillars and late frosts (Fairweather et al., 2008). Biotic agents were more strongly related to mortality for conifers than aspen. Bark beetles were strongly associated with tree death of limber pine, Douglas-fir, and white fir. However, our estimates of bark beetle evidence are likely conservative because we only looked for galleries along the lower tree bole. Larger trees may have had bark beetle attacks higher up on the bole that would have gone undetected in our survey. For Douglas-fir, many studies have demonstrated that the Douglas-fir beetle can be a primary biotic agent of tree death (Negrón, 1998; Negrón et al., 2001; McMillin and Allen, 2003). In most of these studies, the amount of Douglas-fir beetle-killed trees was positively related to Douglas-fir basal area (Negrón, 1998; Negrón et al., 2001). Droughtrelated mortality of white fir was positively related to fir engraver attack in our study and supports observations from other regions (Ferrell et al., 1994; Egan et al., 2011). The importance of bark beetles in killing large numbers of conifers in the western U.S., especially during periods of drought, has long been known, but evidence of recent increases in bark beetle activity is growing (Raffa et al., 2008). For Douglas-fir, another biotic agent, dwarf mistletoe (A. douglasii), was detected at high amounts at two of our study sites (Table 5) and was positively related to Douglas-fir mortality (Table 4). Our results support previous findings that high levels of dwarf mistletoe infestation contribute to tree mortality of Douglas-fir (Mathiasen et al., 1990). 4.5. Climate change implications Warmer temperatures and more frequent and severe drought are increasing tree mortality and turn-over rates globally (Phillips et al., 2004; van Mantgem et al., 2009; Allen et al., 2010). In the southwestern U.S., an exceptional pulse of tree mortality occurred during a recent severe drought in many forest types (Allen and Breshears, 1998; Breshears et al., 2005; Mueller et al., 2005; Floyd et al., 2009; Negrón et al., 2009; Koepke et al., 2010; Clifford et al., 2011). Though pulses of tree mortality have occurred regionally in the past (Allen and Breshears, 1998), the frequency and severity of these events are projected to increase in the near future (Williams et al., 2013). Climatic models for the southwestern U.S. predict continued warming, greater variability in precipitation, and increased drought (Westerling et al., 2006; Seager et al., 2007; Seager and Vecchi, 2010). These climatic changes likely will contribute to continued and perhaps increasing tree mortality, which may lead to large shifts and contractions in the range of dominant trees throughout much of the region (Rehfeldt et al., 2006, 2009). 4.6. Management implications Since fire exclusion, mixed-conifer forests have increased in tree density and basal area by as much as ten-fold (Cocke et al., 2005; Fulé et al., 2009; but see Vankat, 2011). Much of this increase in density has been from shade tolerant and fire susceptible species, such as white fir and Douglas-fir, but in most instances all tree species have increased in density. Threats of widespread intense burning and intensification of drought have prompted an interest in treating southwestern mixed-conifer forests to mitigate impacts

179

(Evans et al., 2011; Korb et al., 2012). Many of the treatment approaches involve thinning, prescribed burning or both to reduce stand density, which has been shown to be effective to varying degrees in reducing drought- and insect-induced mortality in many western U.S. forests (Fettig et al., 2007; Wallin et al., 2008). The effectiveness of these treatments in southwestern mixed-conifer forests has not been fully evaluated. Korb et al. (2012) found that a combination of thinning and burning of mixed-conifer forests in southwestern Colorado resulted in the greatest reductions in stand density and produced a stand composition and structure more similar to presettlement (i.e. pre-fire exclusion) conditions. Our results suggest that targeted thinning may be effective in reducing drought- and insect-induced tree mortality in southwestern mixed conifer forests. Specifically, we found a consistent positive relationship between tree mortality and intraspecific basal area for all species that strongly suggests a role of intraspecific competition in mortality. As a result, we suggest future evaluation of treatments that reduce intraspecific density and basal area with the goal of minimizing future mortality of all species we studied (aspen, Douglas-fir, white fir, limber pine). Our finding of a negative relationship between stand-level variability in diameter and tree mortality for aspen and Douglas-fir may inform treatment decisions. We suggest evaluation of treatments that reduce intraspecific tree density and basal area but also increase diversity in tree size and age to promote greater resistance and survival during drought and insect attacks (Fettig et al., 2007). While the effectiveness of these treatments is understudied, managers will be increasingly reliant on adaptive approaches to treat mixed-conifer forests as climatic models predict that increased temperatures and greater incidences of drought will possibly promote unprecedented events. These novel climatic conditions will likely require flexible approaches that build in redundancies and heterogeneous conditions that may better allow for management success in an uncertain future. Acknowledgements We thank the many people who provided field and lab assistance over the course of the study: B. Dechant, D. Carlson, D. Kennedy, M. McKinney, A. Coble, M. Gaylord, C. Erikson, and C. West. We received helpful editorial and analysis comments from J. Berrill, M. DeSilva, S. Eyes, G. Koch, A. Livingston, P. van Mantgem, K. Waring and three anonymous reviewers on earlier drafts. A Science Foundation of Arizona Fellowship and a McIntire-Stennis grant to the School of Forestry, Northern Arizona University funded this research, with supplemental support provided by scholarships from Achievement Rewards for College Scientists Program and by Kay and Irene Haffen. Appendix A Mean (SE) tree species composition and stand characteristics for southwestern mixed-conifer forests at three sites (BWM = Bill Williams Mountain; SFP = San Francisco Peaks; SIT = Sitgreaves Mountain) in northern Arizona, USA. Species include (white fir = Abies concolor, corkbark fir = Abies lasiocarpa var. arizonica, Engelmann spruce = Picea englemannii, limber pine = Pinus flexilis, ponderosa pine = Pinus ponderosa, trembling aspen = Populus tremuloides, Douglas-fir = Pseudotsuga menziesii, All species = all species combined). BA = basal area, TPH = trees per hectare, DBH = mean tree diameter at breast height). Values are based on mean plot values of live and recently dead (decay class <3) trees.

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Species White fir

Limber pine

Trembling aspen

Douglas-fir

Units

BWI

SFP

SIT

Species

BA TPH

m2 ha1 # ha1 %

0.4 (0.2) 68.9 (30) 3.5 (1.7)

TPH

%

DBH

cm

BA TPH

m2 ha1 # ha1

BA

%

–

TPH

%

–

DBH

cm

–

BA TPH

m2 ha1 # ha1

2.1 (1.2) 167 (75)

1.2 (0.4) 267 (90) 10.3 (3.3) 10.7 (3.2) 10.3 (1.2) 2.8 (0.4) 320 (49) 21.4 (3.0) 19.2 (2.5) 15.7 (1.4) 0.4 (0.2) 87 (48)

Corkbark fir

BA

5.5 (0.9) 915 (145) 37.2 (4.9) 44.2 (4.7) 11.9 (1.0) – –

BA

%

8.7 (4.7)

TPH

%

9.8 (4.0)

DBH

cm

BA TPH

m2 ha1 # ha1

14.2 (2.5) 6.2 (4.3) 410 (70)

BA

%

TPH

%

DBH

cm

38.3 (6.5) 22.6 (4.5) 22.6 (3.8)

5.5 (2.5) 11.3 (2.6) 5.8 (0.7) 501 (58) 30.6 (3.4) 30.4 (2.9) 19.2 (1.4) 3.7 (0.6) 361 (66) 18.1 (2.8) 19.0 (2.8) 22.5 (1.1) 7.6 (0.9) 554 (63) 36.7 (2.8) 32.3 (2.1) 20.1 (1.6)

Units

BWI

SFP

SIT

BA TPH

m2 ha1 # ha1

– –

1.3 (0.4) 177 (63)

– –

BA

%

–

6.1 (2.0)

–

TPH

%

–

8.8 (2.7)

–

DBH

cm

–

18.5 (2.5)

–

BA TPH

m2 ha1 # ha1

– –

0.1 (0.03) 14 (7)

– –

BA

%

–

0.3 (0.2)

–

TPH

%

–

0.7 (0.3)

–

DBH

cm

–

13.4 (1.7)

–

BA TPH

m2 ha1 # ha1

0.1 (0.1) 8 (4)

0.6 (0.2) 31 (11)

1.5 (0.4) 149 (31)

3.1 (1.3)

BA

%

1.1 (0.6)

4.1 (1.4)

13.5 (2.9)

3.9 (1.7)

TPH

%

0.4 (0.2)

2.4 (1.0)

9.9 (2.2)

15.8 (1.7) 6.6 (0.7) 908 (92) 51.6 (4.2) 55.4 (3.7) 14.6 (1.1)

DBH

cm

25.6 (3.2)

32.4 (7.1)

18.7 (2.0)

BA TPH

m2 ha1 # ha1

BA

%

16.1 (1.7) 2047 (201) –

19.5 (1.0) 1712 (131) –

12.5 (0.7) 1753 (153) –

TPH

%

–

–

–

DBH

cm

14.7 (1.2)

19.4 (1.1)

15.0 (0.9)

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