Spatiotemporal patterns of mortality in declining balsam fir and spruce stands

Forest Ecology and Management 253 (2007) 188–201 www.elsevier.com/locate/foreco

Spatiotemporal patterns of mortality in declining balsam fir and spruce stands Sarah L. Taylor *, David A. MacLean Faculty of Forestry and Environmental Management, University of New Brunswick, P.O. Box 44555, Fredericton, NB E3B 5A3, Canada Received 17 March 2007; received in revised form 17 July 2007; accepted 18 July 2007

Abstract We used spatiotemporal inventory data from declining balsam fir (Abies balsamea (L.) Mill.) and spruce (Picea spp.) stands in New Brunswick, Canada, to determine the extent of tree-to-tree interactions and their effect on individual-tree mortality and stand break-up. Fifty circular 11.3 m radius permanent sample plots, established from 1987 to 1989 and remeasured every 3–5 years, were stem-mapped for tree and dead wood data in 2003–2005. Data included 1332 live trees and 1160 dead trees. Tree-to-tree interactions were assessed for trees within a 5 m circle of all subject trees in 12 m  12 m subplots. Logistic regression models of individual-tree mortality were generated for two analysis periods, corresponding to about the end of a spruce budworm (Choristoneura fumiferana (Clem.)) outbreak (1990–1992) and about 10 years after the cessation of budworm defoliation (1999–2003). From 1990–1992, significant predictor variables included annual diameter increment, moderate–severe spruce budworm defoliation, and stem defects. The mortality model also includes a distance-dependent competition index that outperformed all other measures of competition. By 1999–2003, annual diameter growth had increased by 30% following cessation of defoliation. Predictors of individual-tree mortality of trees alive in 1999–2003 included stem defects and annual wind-related mortality of neighbouring trees. Wind damage was the primary cause of death, and increased as canopies became more open. Treefall events occurred mostly in the winter, as indicated by alignment of easterly treefall direction with winter prevailing winds. Dead treefall collisions accounted for 1.1% of tree deaths and damaged 1.3% of live trees. Mortality model results suggest that when stands reach a critical stage of decline, probability of mortality for residual trees is dramatically increased due to absence of protection from neighbouring trees and increased wind exposure, effecting rapid stand break-up. # 2007 Elsevier B.V. All rights reserved. Keywords: Balsam fir; Competition indices; Individual-tree mortality; Logistic regression; Spruce budworm outbreaks; Stem maps; Treefall damage

1. Introduction Mortality is an important component of most stand growth and yield models, and yet it is one of the most difficult and least reliable aspects of evaluating forest growth (Glover and Hool, 1979; Woollons, 1998; Yang et al., 2003). Difficulty in understanding tree mortality, particularly at the individual level (Bigler and Bugmann, 2003), is attributed to uncertainty in timing of relatively rare events like tree death and complexity of mortality processes (Yang et al., 2003). This results from multiple interactions of biotic and abiotic agents that serve to weaken a tree’s resilience or to cause death directly over the short or long-term (Manion, 1981; Waring, 1987). Although mortality is complicated, and will probably never be perfectly predictable (Glover and Hool, 1979), variation in tree mortality

* Corresponding author. Tel.: +1 506 458 7552; fax: +1 506 453 3538. E-mail address: [email protected] (S.L. Taylor). 0378-1127/$ – see front matter # 2007 Elsevier B.V. All rights reserved. doi:10.1016/j.foreco.2007.07.016

is related to four contributing factors identified by Hamilton (1986): individual tree size; individual tree vigor (growth rate); individual tree competition; and stand density. External influences such as insect defoliation or wind events also determine mortality. High probability of mortality typically occurs in small individuals (<10 cm DBH), as a result of self-thinning due to competition for resources such as light (Oliver and Larson, 1996). As tree size increases, probability of mortality decreases to a plateau around 10–20 cm DBH (e.g., Lang, 1985), above which trees are apparently large enough to escape the most deleterious effects of shading and other overstory influences (Harcombe, 1987). A common approach in stand-based studies is to divide groups of individuals into size classes and then assume a similar probability of death within a size class (e.g., Glover and Hool, 1979; Hamilton, 1986). Fate of an individual tree depends to some extent on growth in the preceding period and on fate of its neighbours (Yao et al., 2001). Suppressed slow-growing individuals generally have

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higher mortality rates than fast-growing individuals (Kobe and Coates, 1997; Umeki, 2002; but see Bigler and Bugmann, 2003). Factors that do not reduce growth, such as heavy winds or the fall of a neighbouring tree, also can kill trees (Robert, 2003). Approximately 15% of mortality in mature and old-growth Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco) stands in the Pacific Northwest consists of trees knocked over, broken, or crushed by falling trees (Franklin et al., 1987). Furthermore, probability of mortality is increased by proximity to trees affected by diseases or pests. For example, spruce budworm (Choristoneura fumiferana (Clem.)) and Armillaria root disease (Armillaria ostoyae (Romagn.) Herink) result in aggregated tree death (Baskerville and MacLean, 1979; Bruhn et al., 1996; Candau et al., 1998). Abiotic processes can also result in aggregated tree death, such as landscape-scale wave regeneration patterns caused by sequential tree death at the leading edge of forests facing the prevailing wind (Sprugel, 1975). Stand-level approaches have been successful in predicting growth reduction and mortality due to spruce budworm using large-scale historical defoliation records and stand-level dendrometrical variables in Quebec (Pothier et al., 2005; Pothier and Mailly, 2006). However, it is likely that data on tree-to-tree interactions could improve predictability of tree death at tree and stand levels (Moeur, 1993), since they more accurately represent spatial structural complexity of trees in a stand. And yet most forest growth and yield forecasts only incorporate spatial effects indirectly through inclusion of competition variables such as stand density, which assume competitive forces are applied equally throughout a stand (Moeur, 1993). Recently, this was addressed by incorporating distance-dependent competition indices, such as the Hegyi competition index (Hegyi, 1974), into logistic models of individual-tree mortality (e.g., Bigler and Bugmann, 2003) and growth (Mailly et al., 2003). Such models are hampered by a lack of studies on spatial pattern of dead trees (Kenkel, 1988; Moeur, 1993; Rouvinen and Kuuluvainen, 2001), unavailability of spatial data in forest inventories, and small size of most forest inventory plots, which severely limits point pattern analysis (Woodall and Graham, 2004). For this study, we added a spatial component to a 15–20 year repeat-measurement inventory of 50 permanent sample plots in declining balsam fir (Abies balsamea (L.) Mill.) and spruce (Picea spp.) stands, to determine the extent of tree-to-tree interactions and their effect on individual-tree mortality and stand break-up. Our objectives were to: (i) identify characteristics indicative of predisposition to death; (ii) develop logistic regression models of individual-tree mortality; (iii) quantify occurrence and spatial orientation of tree contact events resulting from treefall; (iv) relate probability of mortality to time since the last spruce budworm outbreak. 2. Methods 2.1. New Brunswick permanent sample plot data and plot selection We used New Brunswick Department of Natural Resources (NBDNR) permanent sample plot data (Porter et al., 2001) in

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the boreal and Acadian forest of central and northwest New Brunswick, Canada (Rowe, 1972). Stands are dominated by balsam fir, black spruce (Picea mariana (Mill.) BSP), red spruce (P. rubens Sarg.), and white spruce (P. glauca (Moench) Voss), and have a continental climate with hot summers and cold winters (NBDNRE, 2003). Plots were established between 1987 and 1989 and remeasured three to five times over different time intervals to 2004. At plot establishment, all live trees 5.1 cm diameter at breast height (DBH) were numbered, except for alder (Alnus spp.) and mountain maple (Acer spicatum (Lam.)), and upon remeasurement, trees that had grown to meet this size criterion were added. Trees that died since the last measurement were assigned a cause of death (broken top, insects, other, overmaturity, stem breakage, stem wounds, suppression, unknown, or wind throw), and excluded from future remeasurements (Porter et al., 2001). All live trees were assessed for tree health, including estimates of live crown ratio (crown length/tree height  100), type and extent of stem wounds or leader damage, degree of stem lean, cumulative spruce budworm defoliation, and age class (Porter et al., 2001). Age class was not determined directly, but was based on maturity classes that broadly correlate to DBH (e.g., 8.1– 11.0 cm young; 11.1–16.0, immature; 16.1–30.0, mature; and >30.0, overmature; NBGYU, 2005). The 50 sampled plots were selected from 585 balsam fir-spruce (BFSP) and sprucebalsam fir (SPBF) plots analyzed by Taylor and MacLean (2005), extending the database up to year 2005. Field plot selection criteria included >60% balsam fir or spruce live volume, stand age >50 years, absence of harvesting 100 m from the plot, and fluctuating or decreasing live merchantable volume between 1987 and 1998. Further details on the study area and plot selection criteria can be found in Taylor and MacLean (2005, 2007). 2.2. Field measurements (2003–2005) Location of plot centre was recorded with a GPS and slope and aspect at plot centre measured with a Suunto compass clinometer. An inventory was made of all tagged trees live at plot establishment, non-tagged trees of unknown history 9.1 cm DBH within the plot, and all trees next to the plot boundary. Trees near the edge of measured plots might be influenced by their neighbours outside the plot (Radtke and Burkhart, 1998), and ignoring them can introduce edge bias when computing spatial analyses (Monserud and Ek, 1974). Tree location was mapped relative to plot centre, using a compass clinometer to record azimuth and a hypsometer (Vertex III and Transponder T3, Haglof, Sweden AB) to record horizontal distance at breast height. For each tree, DBH, height, species, status (live/dead), position (understory, overstory), crown class (dominant, codominant, intermediate, suppressed), cause of death (as per Porter et al., 2001), bole lean and direction, stem deformities indicative of past leader damage (e.g., crook, fork, etc.), defoliation, decay class, direction of treefall for downed dead trees, type of tree-to-tree interactions (e.g., hit by fallen tree),

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Fig. 1. An example of a stem map of live and dead trees (a) and crown projections of live trees (b) for the full plot, a 144 m2 subplot (square), and a 5 m search area around a given subject tree.

and outcome of contact (killed, damaged or leaning, supporting tree, suspended dead wood) were recorded. Distance from centre of bole to crown drip line in four cardinal directions was measured to determine canopy projection (Fraver and White, 2005). Tagged trees previously assigned causes of death by NBDNR were verified. A detailed description of dead wood field measurements is given in Taylor and MacLean (2007). 2.3. Spatiotemporal data Analyses were based on six measurement periods from 1987 to 2005. Plots were established from 1987 to 1989, and remeasured every 3 years: 1990–1992 (49 plots), 1993–1995 (39 plots), 1996–1998 (40 plots); and thereafter every 5 years: 1999–2003 (47 plots), and 2004–2008 (three plots by NBDNR, plus our field measurements of all 50 plots). Fluctuations in number of plots between periods resulted from the shift from 3to 5-year remeasurements, or from a few unmeasured plots in a given period (Taylor and MacLean, 2005). To avoid temporal correlation between data (Yao et al., 2001; Yang et al., 2003), logistic regression analyses were confined to only two of the six periods (1990–1992, 1999–2003), selected on the basis of their relation to the last spruce budworm outbreak. Spruce budworm outbreaks, which historically cycled approximately every 35 years (Royama, 1984), are the primary driving force of forest succession in balsam fir-spruce stands (Baskerville, 1975), and result in elevated mortality and reduced growth rates followed by intervening periods of quiescence in which growth rates recover and mortality resumes to background levels (Erdle and MacLean, 1999). Spruce budworm-caused mortality usually starts after 4–5 years of defoliation, but can take upwards of 6–7 years, and is generally complete after about 10 years (MacLean, 1980). Data from 1990–1992 and 1999–2003 were therefore selected for analysis, as they broadly correspond to the end of the last spruce

budworm outbreak in 1993 (Porter et al., 2004), and the end of residual spruce budworm-caused mortality. One plot in 1990– 1992 and three plots in 1999–2003 were excluded from logistic regression analysis due to the lack of measurement data in the corresponding analysis period. All spatial mapping was performed in ArcView GIS 3.3 (Environmental Systems Research Institute Inc.) using Jenness Enterprises (2005)1 freeware extension tools. Plot and tree location data were imported as text files, and stem maps plotted by projecting tree position from plot centre. To avoid using complicated edge-bias compensation indices (Radtke and Burkhart, 1998; Mailly et al., 2003), only tagged balsam fir and black/red spruce trees located within a square 12 m  12 m (144 m2) subplot centred inside the circular 11.28 m radius (400 m2) plot were selected as subject trees for spatial analysis (Fig. 1a). Black and red spruce were grouped together, as they occur as hybrid swarms in central New Brunswick (Manley, 1972). Reduction in effective plot size allowed the use of measured trees as off-plot trees in addition to the boundary trees around the plot, producing a variable width buffer ranging between 2.7 and 5.7 m. Stem plot distance and azimuth matrices were linked to the field database in MS Access 2003. The search radius for inclusion of competitor trees was based on Lorimer’s (1983) recommendation of 3.5 times the mean crown radius of overstory trees with co-dominant or intermediate crown classes. Mean crown radius averaged 1.45 m (range 1–2.1 m), and we used a fixed-radius search area of 5 m (Fig. 1a). Spatial maps were used to ensure that all subject tree search areas were wholly included within the mapped tree area. The 37 subject trees that included boundary trees in their search area were excluded from analysis, because

1 Distance and Azimuth Tools v. 1.6; Distance Matrix v. 2.1; Convex Hull around Points v. 1.21. Available at http://www.jennessent.com.

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of the absence of historical data for boundary trees, such as past DBH measurements and year of death. Mapping of live canopy projections and estimates of canopy crown closure were based on 2003–2005 field measurements, as crown dimensions were not included in previous measurements. Live canopy projection was assumed to take the form of four quarter ellipses (Lorimer and Frelich, 1989): six interpolated points were calculated, using the formula for an ellipse perimeter on each quarter-ellipse arc between the four cardinal points (Fraver and White, 2005), and joined to produce polygons (Fig. 1b). Species-site specific (Porter et al., 2001) regression models of crown radius based on DBH, crown length and/or modelled tree height of live trees (r2 > 0.5, data not shown) were used to estimate crown radius of trees that died in 1999–2005; projections of reconstructed canopy were assumed to take the form of a circle. Changes in canopy projection due to lateral growth were assumed to be negligible. Canopy was not reconstructed for 1990–1992. A stand-level index of crown closure for trees 9.1 cm DBH in 1999–2003 was calculated by dividing subplots into cells of 0.01 m  0.01 m and calculating proportion of horizontal ground area covered by all encroaching live crowns (Radtke and Burkhart, 1998), shown by black shading in Fig. 1b. Trees <9.1 cm DBH, which ranged in height from 1 to 12 m, did not reach the upper canopy, and in the absence of overtopping crowns, were considered to represent canopy gaps. Swiftly moving gusts of wind usually flow over the canopy of well-stocked, even-aged coniferous forest, and rarely penetrate into stands except where openings occur (Gratkowski, 1956). Hence, crown closure was used as a gauge of tree-level wind exposure. TOPEX, which is a measure of site exposure determined from the sum of the angles in eight compass directions to the horizon (Ruel et al., 1997), was used to assess stand-level wind exposure. TOPEX-to-distance values (Hannah et al., 1995) were derived from 10 m resolution digital elevation models with horizon limiting distances of 0.5, 1, and 2 km. Negative TOPEX values indicate an exposed site, while positive values indicate a sheltered site. Rose diagrams depicting orientation of treefall and collision events were generated using GEOrient 9.2 (Holcombe Coughlin and Associates, Australia, 2006) and compared to prevailing wind direction and slope aspect. All datasets were reclassified to eight cardinal directions to match the resolution of the climate data. Prevailing wind direction of >20 km/h winds was based on Canadian Climate Normals (1971–2000) from nine weather stations located across New Brunswick (Environment Canada, 2006a). For consistency, axial data were converted to circular data as per standard method of Mardia and Jupp (1999): azimuth between contact trees was bearing from tree initiating contact to tree receiving contact, and wind direction was direction wind was blowing to (e.g., winds blowing from NW to SE shown as SE). 2.4. Tree mortality model parameters Model parameters, corresponding to the four categories of Hamilton (1986), were calculated for all live subject trees (Fig. 1a) 9.1 cm DBH during the analysis period. Parameters

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were based on direct measurements of individual-tree DBH, basal area, and number of trees in the subplot, and empirical estimates of volume using site-specific relationships (Porter et al., 2001). Tree size, based on DBH (Eid and Tuhus, 2001; Yang et al., 2003) is a better variable than tree age, as discrepancies between age of recruitment and age of release are common in species like balsam fir and spruce that retain advanced regeneration (e.g., Peters et al., 2002). Individual tree vigour was calculated as annual diameter increment (DIN; mm/year) in the previous 3–5 years: DIN ¼

dbh2  dbh1 L

(1)

where dbh2 and dbh1 were the consecutive DBH measurements for the analysis period and preceding measurement period, and L is the length of the interval in years, calculated as follows: L ¼ year2  year1 þ ðadj2  adj1 Þ

(2)

where year1 and year2 were the consecutive measurement years, and adj1 and adj2 are the consecutive month adjustments. The month adjustment was defined according to the biological growth period in boreal forests (Huang, 1992), equal to 0.0, 0.2, 0.5, 0.9, and 1, when month was January–April, May, June, July, and August–December, respectively (Yao et al., 2001). Only our field inventory was subjected to month adjustment, as NBDNR plot measurements were carried out in the fall after growth was complete. The previous growth period was based on a 3-year interval for 1990–1992 analyses, and a 5-year interval for 1999–2003 analyses. Live crown ratio, stem lean, and presence/absence of stem defects were included as measures of tree health. Stem lean was recoded into two classes, <108 lean and 108 lean. The six cumulative spruce budworm defoliation classes (0–5, 6–20, 21– 40, 41–60, 61–80, and 81–100%) were recoded to broadly correspond to nil-light defoliation (0–30%) and moderate– severe defoliation (31–100%), but because of the defoliation classes, a 20% cut off was used rather than 30%. Nil-light cumulative spruce budworm defoliation was used as a reference condition on the basis that nil-light defoliation of current-year foliage does not cause tree mortality (Vanguard Forest Management Services Ltd., 1993; Erdle and MacLean, 1999), and repeated current annual defoliation has to exceed the removal of one full-age class of foliage in order to cause >10% growth reduction (Ostaff and MacLean, 1995). In the absence of crown position data, one-sided competition for light resources, which assumes that larger trees are at a competitive advantage over smaller neighbours (Cannell et al., 1984), was described indirectly by means of competition indices. Mailly et al. (2003) compared a variety of indices of competition, and found that Hegyi’s (1974) diameter-distance competition index performed 12% better than the best distanceindependent indices when predicting basal area increment of black spruce. The Hegyi competition index (Hegyi CI) was calculated as:  X  dbhn 1 Hegyi CI ¼  (3) dbhs distns

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where dbhn is the DBH of a neighbouring tree within 5 m of the subject, dbhs the DBH of the subject tree, and distns is the distance between them (Hegyi, 1974). Not all studies found distance-dependent indices to be superior (e.g., Lorimer, 1983), therefore a distance-independent competitive status (CS) index was also calculated as: CS ¼

BAns  100 BAn

(4)

where BAns is the basal area of neighbouring trees with DBH equivalent to or less than that of the subject tree, and BAn is the basal area of all live neighbouring trees (Vanguard Forest Management Services Ltd., 1993), for the 5 m search area and the plot. The CS index was selected as it is used in the calculation of growth and survival functions in the STAMAN stand dynamics model, used by industry to determine stand volume yields for forest management planning (NBGYU, 2005). Two-sided competition was represented by live basal area, which, as it combines both tree size and density, is a good measure of stand crowding (Yang et al., 2003). The proportion of balsam fir and black/red spruce live basal area was used to distinguish stand types, and annual mortality in the past 3–5 years was used to represent stand condition. Annual mortality from wind-related causes of death (wind throw, stem breakage, and broken top) was also included to incorporate effects of treefall damage and canopy opening. 2.5. Logistic regression model Logistic regression, using the logit function, was used to assess association of a suite of variables with fate (0, live; 1, dead) of a tree in the next measurement period. Most mortality models using logistic functions are based on data with equal measurement intervals (Yao et al., 2001; Yang et al., 2003). However, in our study, interval length (as per Eq. (2)) between analysis period and fate (live or dead) assessment period ranged from 3 to 9 years for 1990–1992 and 0.2 to 4.2 years for 1999–2003. As increasing length of interval increases likelihood of a tree death event (Yao et al., 2001), interval length was included as an explanatory variable in the logistic model (Monserud, 1976; Hamilton, 1986). All parameters were used as potential indicators, and the maximum likelihood method used to estimate parameters. Crown ratio, moderate–severe cumulative spruce budworm defoliation, >108 stem lean and presence of stem defects were added as categorical variables. A backward stepwise logistic regression using the log–likelihood ratio and a removal level of 0.1 was used to identify key parameters associated with tree death. Final model selection was based on the log–likelihood ratio, significance of parameter coefficients, and model performance. In addition, a non-spatial traditional model was calculated for 1999–2003 for comparison with the spatially explicit model, using plot-level estimates of CS index and live basal area.

2.6. Statistical analysis ANOVAs were used to assess significance of differences in key characteristics between live and dead trees, and differences in survivorship by species groups and analysis period. Percent mortality data were arcsine transformed to meet assumptions of normality (Kolmogrov–Smirnov test) and homogeneity of variance (Bartlett’s test, Levene test). Post hoc analysis was performed when the significance level was p < 0.05 using Tukey’s test. Pearson’s correlation was used to assess relationship between DBH and annual diameter increment. The Hosmer–Lemeshow goodness-of-fit test (Hosmer and Lemeshow, 1989) was used to evaluate the logistic regression model fit, and a Pearson Chi-squared statistic used to test for deviations between predicted and observed values. Parameter coefficients were compared between the 1990–1992 and 1999–2003 analyses, to evaluate effect of the last spruce budworm outbreak on individual-tree mortality factors. Stepwise logistic regression was executed in SPSS, release 14.0 for Windows (SPSS Inc., Chicago, IL, 2005); and all other statistical tests performed in Minitab1 statistical software, release 14 for Windows (Minitab Inc., State College, PA, 2003). 3. Results 3.1. Completeness of spatiotemporal dataset A total of 1332 (99%) of live tagged trees and 1160 (93%) of dead tagged trees 9.1 cm that died in the 15–18 year period since plot establishment were relocated. Numbers of dead tagged trees not relocated were 46, 22, 9, 7, and 1, for trees that died in 1990–1992, 1993–1995, 1996–1998, 1999–2003, and 2004–2008, respectively; these likely contributed to the inventory of non-tagged trees of unknown history. In addition, 10 trees alive in 1999–2003 were not relocated and are presumed dead. From 1990 to 1992 onwards, number of trees live in a given period that were not relocated did not exceed four per plot, and plot average decreased from 1 tree per plot in 1990–1992 to 0.2 trees per plot in 1999–2003. Therefore, missing trees were unlikely to impact spatial analyses of live trees, but resulted in a small underestimate of dead trees in 1990–1992. 3.2. Changes in mortality rate over time by species group and stand type Fig. 2 shows the proportion of 711 subject trees with 9.1 cm DBH at plot establishment in 1987–1989 that died over time. Trends differed by species groups and stand types (Fig. 2), but overall show a rapid loss of trees prior to 1997 followed by slower losses thereafter. Balsam fir in declining BFSP plots had significantly higher mortality from 1997 onwards than in other stand types (asterisks in Fig. 2a). By the time of our field inventory 15–18 years after plot establishment, 73% of balsam fir in declining BFSP plots had died, compared to 37% in SPBF plots, and 28% in

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3.3. Characteristics indicative of predisposition to death

Fig. 2. Mortality of balsam fir (a) and black/red spruce (b) 9.1 cm DBH at plot establishment in non-declining (ND) and declining balsam fir-spruce (BFSP) and spruce-balsam fir (SPBF) stands. Plot means  one standard error are shown; asterisks indicate statistically significant ANOVA at p < 0.01 significance level, and shading denotes periods used in logistic regression analyses.

non-declining BFSP plots. Equivalent annual mortality rates are 4.6, 2.3, and 1.7%, respectively. Mortality trends did not significantly differ by stand type for black/red spruce (1.3–2% annual mortality), and were similar to balsam fir trends for SPBF plots. The shading in Fig. 2 indicates the two analysis periods used. For species group and stand type pooled, 15% and 54% of trees live at plot establishment were dead by 1990–1992 and 1999–2003, respectively. Ingrowth of undersized trees to meet the 9.1 cm DBH size limit contributed just four trees (0.6% of live trees) in 1990–1992 and 26 trees (6.7% of live trees) in 1999–2003. From here on in this paper, analyses are confined to balsam fir subject trees, as the lower mortality rates of spruce provided inadequate dead tree sample sizes for modeling purposes.

Table 1 quantifies individual tree and neighbouring tree characteristics, by fate of subject tree and analysis period. Tree death accounted for 19% of 416 subject trees alive in 1990– 1992 and 20% of 235 subject trees alive in 1999–2003. Interval length was 0.5–1.5 years longer for trees that died than for those that remained alive (Table 1). Within 5 m of subject trees, a total of 4571 trees in 1990–1992 and 2676 trees in 1999–2003 were used to determine local stand conditions. On average, a subject tree had 10 live neighbours in 1990–1992 (1325 trees/ ha) and seven live neighbours in 1999–2003 (843 trees/ha) for live and dead fate classes combined. Distributions of live and dead balsam fir for the two analysis periods are shown in Fig. 3 for five key characteristics summarized in Table 1. Trees that died had larger DBH than their live counterparts for both analysis periods (S = 5.578, p < 0.05 in 1999–2003; Table 1). In 1990–1992, dead trees were evenly distributed across the DBH size classes, while in 1999–2003 there was a peak in the 20–24.9 cm DBH size class (Fig. 3a and b). Annual diameter increment of live balsam fir in 1990–1992 was 70% of that in 1999–2003 (Table 1), as a consequence of sustained spruce budworm defoliation (Fig. 3e and f). Diameter increment was negatively correlated with cumulative spruce budworm defoliation (r = 0.286, p < 0.001), and subject trees with moderate–severe defoliation had significantly lower growth rates than those with nil-light defoliation (1.3 mm/year versus 1.9 mm/year, respectively; S = 1.159, p < 0.001). In 1990–1992, 59% of trees had moderate–severe spruce budworm defoliation, but by 1999– 2003 this had diminished to 10% of subject trees. Annual diameter increments of dead balsam fir were approximately 80% of surviving tree growth rates for both periods (Table 1), but the difference was non-significant in 1990–1992, and marginally non-significant in 1999–2003 (S = 1.544, p = 0.068). Subject trees that died had lower crown ratios for both analysis periods (Table 1); 41% of live trees and 19% of dead trees in 1990–1992 had crown ratios >40%. Stem deformities were twice as common in trees that died compared to those that survived for both analysis periods (e.g., 33% versus 15% for 1990–1992). Subject trees in 1990–1992 that subsequently died had lower live density, lower live basal area, and less competition (Table 1). By 1999–2003, live basal area and Hegyi CI were equivalent for live and dead trees (e.g., S = 2.029, p = 0.854 for Hegyi CI). However, fewer subject trees experienced high levels of competition from neighbours in 1999–2003 (6.5% had Hegyi CI > 6) than in 1990–1992 (16% had Hegyi CI > 6; Fig. 3g and h). Mortality rates of neighbouring trees decreased by one-third from 1990–1992 to 1999–2003, and trees that subsequently died experienced higher mortality rates of neighbouring trees (S = 0.6823, p < 0.001 in 1999–2003; Table 1). Species composition of surrounding live trees did not differ by fate of tree in 1990–1992, but in 1999–2003, dead balsam fir were surrounded by a higher proportion of live balsam fir and fewer black/red spruce relative to trees that survived (Table 1).

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Table 1 Summary statistics for balsam fir (bF) subject trees by fate of tree (live or dead) and analysis period Fate of bF live in 1990–1992 a

Fate of bF live in 1999–2003b

Live

Dead

Live

Dead

Number of plots Number of subject trees c Number of neighbours e Interval length (years)f

39 336 3858 3.4  0.05g

30 80 (72) d 713 4.9  0.25

37 188 2129 2.1  0.08

18 47 (89)d 547 2.6  0.11

Characteristics of subject treec DBH (cm) Diameter increment (mm/year) h Crown ratio (%) i Cumulative SBW defoliation (%) j Presence of stem defects (%n)k Stem lean >108 (%n)l Contact events (%n)m

18.1  0.25 1.6  0.06 27.1  0.6 26.5  0.9 15 11 –

19.5  0.65 1.4  0.15 22.4  1.1 27.1  2.6 33 10 3.7

19.2  0.41 2.3  0.11 31.6  1.2 6.9  0.8 16 17 3.7

21.4  0.8 1.8  0.20 22.7  1.5 3.1  0.6 38 28 8.5

Characteristics of neighbours e Live density (n/ha) Live basal area (m2/ha) Live volume (m3/ha) Competitive status indexn Hegyi competition indexo Balsam fir (%live BA) Black/red spruce (%live BA) Hardwood (%live BA) Mortality (m2/ha/year) p Mortality (vol/ha/year) p Wind-related mortality (m2/ha/year) p Plot mortality (m2/ha/year)q Crown closure (%) r Evergreen crown closure (%)r,s

1345  31 34.4  0.6 209  4 31.4  1.4 4.0  0.1 80  1.4 7  1.0 7  0.7 0.84  0.07 4.89  0.44 0.37  0.06 – – –

1000  54 31.3  1.4 197  9 27.5  3.1 3.0  0.2 79  2.8 7  2.2 8  1.3 1.40  0.17 8.84  1.14 0.98  0.16 – – –

926  32 27.5  0.9 174  6 39.5  2.5 2.79  0.1 72  2.3 9  1.6 11  1.5 0.58  0.06 3.43  0.37 0.30  0.04 0.94  0.04 59  1.2 50  1.3

843  56 27.4  1.9 176  12 46.8  5.3 2.7  0.4 84  3.7 3  1.9 9  2.6 1.03  0.14 6.40  0.96 0.90  0.14 1.14  0.09 46  2.9 36  2.6

Parameters

a b c d e f g h i j k l m n o p q r s

Near the end of the last spruce budworm outbreak. About 10 years following cessation of spruce budworm defoliation. Live 9.1 cm DBH balsam fir located in a 12 m  12 m subplot. Proportion of trees that died from wind-related causes. 9.1 cm DBH trees located in a 5 m radius search area centred on a subject tree. Assessment interval for fate of subject. Mean  one standard error. Annual diameter increment for last 3–5 years. Crown length as proportion of tree height. Proportion of foliage defoliated by spruce budworm (SBW) over 5–8 years. Dead/broken main stem/top, stem wounds, and/or leader damage. Deviate from vertical by >108. Treefall collisions observed in 2003–2005. Proportion of total live basal area of neighbouring trees with DBH  subject tree DBH (Vanguard Forest Management Services Ltd., 1993). Difference between neighbours and subject DBH, weighted by inverse of distance between them (Hegyi, 1974). Annual mortality for last 3–5 years. Annual mortality for last 11–16 years since plot establishment. Crown closure for portion of search area that falls inside the subplot. Only tree species that retain foliage in winter included (balsam fir, spruce, pine).

3.4. Mortality model Interval length, DBH, and annual diameter increment were common variables included in the logistic regression models of tree death for the two analysis periods (Table 2). The CS index was not associated with tree death in either period, and although live basal area and proportion of live balsam fir were marginally positively associated in 1999–2003 (coefficient of 0.05, data not shown), the stepwise logistic regression consistently removed them from the final spatially explicit models. Moderate–severe cumulative spruce budworm defoliation

was only included in the 1990–1992 model, since there were few trees in the moderate–severe category in 1999–2003. Although evergreen crown closure was weakly associated with tree death (coefficient close to zero), it appeared consistently in the final stepwise model for 1999–2003 (Table 2). All models were statistically significant; the calculated Hosmer–Lemeshow’s goodness-of-fit statistic indicated no significant difference between observed data and model predictions ( p > 0.05; Table 3). The spatially explicit models correctly predicted fate of 83% of subject trees in 1990–1992, and 86% of subject trees in 1999–2003 (Table 3). The traditional

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Fig. 3. Distributions of live and dead balsam fir trees in 1990–1992 and 1999–2003, as a function of DBH, diameter increment, spruce budworm (SBW) defoliation, Hegyi competition index, and wind-related mortality (i.e., model parameters selected).

model performed poorly, correctly classifying only 15% of dead trees, compared to 47% of dead trees for the spatially explicit multifactor model in 1999–2003 (Table 3). Table 4 presents observed and predicted mortality rates for the spatially explicit model in 1999–2003, by maturity class, live basal area category, and CS index. Modeled mortality rates were the same as observed rates for 44% of the 18 classes, and ranged from 27% below to 12% above observed rates. Generally, mortality increased with maturity, basal area, and CS index classes. DBH was only marginally associated with tree death (coefficient close to zero), but was still included in the models, as it was positively correlated with diameter increment (e.g., r = 0.4, p < 0.001 for balsam fir in 1990–1992), and therefore distinguished small slow growing suppressed trees from larger successfully competing trees. Fig. 4 compares probability of mortality for the spatially explicit logistic model parameters. DBH had a sigmoidal trend with mortality in 1999–2003, and a bell-shaped trend in 1990–1992 (Fig. 4a), hence the insignificance of DBH in 1990–1992 (Table 2). The >30 cm DBH size class (1.2% of trees in 1990–1992 and 4.4% in 1999–2003; Fig. 3a and b), experienced increased probability of death in 1999–2003, and decreased probability of death in 1990–1992 (Fig. 4a). Diameter increment was negatively associated with tree death (Table 2), and did not change for the three smaller growth rates, but declined for diameter increment >3 mm/year for both periods (Fig. 4b). Probability of tree death was negatively associated with Hegyi CI in 1990–1992, and positively associated in 1999–2003 (Table 2). The positive trend in 1999–2003 is likely the result of an outlier in the Hegyi CI > 8 class (Fig. 4d), in which 75% of the four trees died and were correctly predicted by the model.

In both models, the largest coefficient was generated for the stem defects categorical parameter. Presence of stem defects, such as stem wounds or broken top, was positively associated with tree death, increasing odds of a tree dying by 2.5 in 1990– 1992 and 3.9 in 1999–2003 (Table 2, Fig. 4c). Presence of moderate–severe cumulative spruce budworm defoliation generated the second largest coefficient in 1990–1992, approximately doubling the odds of a tree dying (Table 2). This was closely followed by diameter increment and mortality in 1990–1992. In 1999–2003, after stem defects, wind-related mortality had the strongest positive association with balsam fir death, and far exceeded that occurring in 1990–1992 (Table 2, Fig. 4f). Wind-related mortality comprised 70% of all mortality in 1990–1992, and 87% of all mortality in 1999–2003 for subject trees that died in the following measurement (Table 1). A unit increase of 1 m2/ha/year mortality increased the odds of a tree dying by 2.4 in 1999–2003 and 1.2 in 1990–1992 (Table 2). The lower odds ratio for 1990–1992 could reflect our low success rate in finding tagged trees that died >10 years old. Evergreen crown closure was negatively correlated with windrelated mortality (r = 0.327, p < 0.001) and negatively associated with tree death. Probability of tree death dropped dramatically at a threshold of 40% evergreen crown closure, above which probability of tree death remained consistently low (Fig. 4e). 3.5. Occurrence and spatial orientation of treefall and contact events Random death events, such as a wind thrown tree killing a neighbour as it falls, cannot be predicted by our models, as they

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Table 2 Estimated parameters for logistic regression model of individual-tree mortality, by analysis period Parameters

Coefficient a

Zb

pc

Odds ratio (CI)d

bF in 1990–1992 (spatially explicit) Constant DBH (cm) Diameter increment (mm/year) Cumulative defol (MS)e Stem defect (presence)f Hegyi CI Wind-related mortality (m2/ha/year) Interval length (years)

3.50  1.12 0.02  0.04 0.34  0.15 0.63  0.38 0.90  0.33 0.17  0.10 0.22  0.11 0.72  0.12

3.11 0.47 2.22 1.66 2.76 1.66 1.91 6.05

0.002 0.636 0.026 0.097 0.006 0.097 0.056 0.000

0.98 0.71 1.88 2.46 0.84 1.24 2.05

(0.91, (0.53, (0.89, (1.29, (0.68, (0.99, (1.62,

1.06) 0.96) 3.97) 4.68) 1.03) 1.55) 2.58)

bF in 1999–2003 (spatially explicit) Constant DBH (cm) Diameter increment (mm/year) Stem defect (presence)f Hegyi CI Evergreen crown closure (%) Wind-related mortality (m2/ha/year) Interval length (years)

4.58  1.35 0.13  0.05 0.44  0.19 1.36  0.44 0.28  0.15 0.05  0.01 0.86  0.28 0.74  0.21

3.38 2.72 2.34 3.07 1.87 3.64 3.06 3.63

0.001 0.006 0.019 0.002 0.061 0.000 0.002 0.000

1.14 0.64 3.92 1.33 0.96 2.36 2.11

(1.04, (0.44, (1.64, (0.99, (0.93, (1.36, (1.41,

1.26) 0.93) 9.35) 1.78) 0.98) 4.08) 3.16)

bF in 1999–2003 (traditional) Constant DBH (cm) Diameter increment (mm/year) Competitive status indexg Live basal area (m2/ha)h Interval length (years)

2.35  1.12 0.05  0.06 0.56  0.16 0.01  0.01 0.03  0.02 0.61  0.19

2.11 0.81 3.44 1.03 1.38 3.22

0.035 0.420 0.001 0.303 0.167 0.001

1.05 0.57 1.01 0.97 1.84

(0.93, (0.42, (0.99, (0.93, (1.27,

1.18) 0.79) 1.04) 1.01) 2.67)

See Table 1 for definitions of parameters. a Mean of coefficient  one standard error; sign indicates direction of relationship. b Standardised Z-score of coefficient, values close to zero indicate association not important. c Significance level of 0.1; null hypothesis: no association. d Odds of tree dying with unit increase of parameter, if confidence interval (CI) includes 1 not significant. e Binary categorical variable, compares nil-light and moderate–severe (MS) cumulative defoliation. f Binary categorical variable, compares absence/presence of stem defects. g All plot trees included. h Includes all plots 9.1 cm DBH for immature subject trees, and 14.0 cm DBH for mature or overmature subject trees.

Table 3 Assessment of logistic regression model coefficient significance, goodness-offit, and classification accuracy, by analysis period 1990–1992 Significance of coefficients b G (d.f.) 93.05 (7) pc 0.001 Goodness-of-fit testsd Hosmer–Lemeshow p (d.f. = 8)e

8.834 0.356

Fate classification accuracyf All trees 83.4 (416)g Live trees 96.7 (336) Dead trees 27.5 (80) a

1999–2003

1999–2003 traditionala

71.08 (7) 0.001

28.036 (5) 0.001

8.480 0.388 86.4 (235) 96.3 (188) 46.8 (47)

3.631 0.889 81.3 (235) 97.9 (188) 14.9 (47)

Traditional model parameters based on aspatial data from the entire plot. Test that all slopes (coefficients) are zero. c Significance level of 0.05; null hypothesis: slopes are equal. d Measure of how well occurrence of death is predicted by the model. e Significance level of 0.1; null hypothesis: model fits data. f Proportion of trees with predicted fate classification same as observed fate class. Cut value of 0.50. g Number of observed trees given in parentheses. b

are based on current conditions and past mortality and growth (Table 2). Therefore, we attempted to quantify prevalence and outcome of treefall collisions using 2003–2005 field observations. A total of 68 (5.1% of 1332) live plot trees and 73 (6.3% of 1160) dead plot trees were involved in 164 contact events with neighbouring trees. Fig. 5 illustrates the different types of contact events and relative proportions of plot trees affected. Most contact events were caused by dead treefall (86% of 164 contact events); however, 14% of contact events were a consequence of live trees leaning onto neighbours. Approximately half of all contact events resulted in suspended or leaning dead wood getting caught in canopies of neighbouring trees, sometimes linking several trees together (Fig. 5), which may decrease health and/or stability of the affected trees. It was not possible to distinguish trees that were previously leaning on a tree prior to death from trees supported by a neighbour on or after death. Treefall collision events killed four plot trees, equivalent to 0.3% of the 1160 plot trees that died over the 15– 18 year assessment period, and knocked down 14 snags (standing dead wood). An additional nine trees (0.8% of 1160 trees) were uprooted with a bigger DBH neighbour. Non-fatal injuries resulting from collisions, such as stem wounds and

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Table 4 Predicted and observed balsam fir mortality rates, by basal area category, maturity class, and competitive status (CS) class, for 1999–2003 spatially explicit model Maturity classa

CS index (%) b

Basal area category (m2/ha)c 11–20 Model

21–30 Obs.

I

0–25

M

0–25 26–50 51–75

16.7e

16.7

O

0–25 26–50 51–75 76–100

0.0 37.5

0.0 25.0

a b c d e

d

31–40

Model

Obs.

0.0

0.0

7

6

0.0 0.0 0.0

0.0 0.0 0.0

5 8

15.4 13.3 22.2 40.0

30.8 13.3 33.3 60.0

n

n

Model

Obs.

n

3 5 5

4.9 10.0 0.0

9.7 10.0 25.0

41 10 4

13 15 9 10

20.0 4.2 15.0 16.7

46.7 20.8 30.0 16.7

15 24 20 12

Relative development stage used as a surrogate for tree age broadly based on DBH: I, immature; M, mature; and O, overmature. Competitive status (CS) used as a surrogate for crown class, proportion of plot basal area contained in trees of equal or smaller DBH than subject. Basal area is calculated for all plot trees 9.1 cm DBH for immature trees, and 14.0 cm DBH for mature and overmature trees. Only classes with 3 trees shown. Proportion of trees that died.

stem lean, affected 1.3% of live trees, potentially affecting their future survival. The apparent overall rarity of dead treefall collisions is an artefact reflecting the difficulty of identifying >10 year old collision events. Of the 47 subject trees that died after 1999–2003, 8.5% were observed to have been involved in non-fatal contact events as they fell (Table 1). In 1990–1992, just 3.7% of 80 subject trees that died were observed to be in contact with neighbours, one of which resulted from post-death snag fall. Values in Fig. 5 are probably underestimates of actual values.

Distance between trees initiating and receiving contact averaged 3.6 m, and ranged between 0.1 and 15.6 m. Threequarters (77%) of contact events occurred within the 5 m search area around a subject tree. Inter-tree distance differed significantly by type of contact (S = 2.968, p < 0.001), and averaged 4.9 m for treefall collisions, 4.4 m for suspended dead wood, 2.9 m for tree support, and 0.9 m for trees uprooted with a neighbour. Collision direction (from initiating to receiving tree) was aligned with treefall direction, with an overall trend to the east and southeast (Fig. 6a and b). Suspended dead wood,

Fig. 4. Probability of tree death events as a function of logistic regression model variables in Table 2, by analysis period. Means  one standard error.

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Fig. 5. Illustration of contact events observed between neighbouring trees: (a) live tree leaning on neighbour; (b) live tree supporting neighbour; (c) live tree with no contacts; (d) live tree leaning as result of treefall; (e) tree uprooted with neighbour; (f) dead tree leaning on neighbour; (g) live tree with suspended dead wood; (h) suspended dead wood recruited from dead tree; (i) tree killed by falling neighbour. Percentages indicate proportions observed among 1332 live and 1160 dead trees.

however, was generally northwest, in the opposite direction to treefall, suggesting that as trees fall, dead wood gets caught in crowns of trees behind the direction of fall (data not shown). To eliminate confounding effects of slope steepness and aspect (Harmon et al., 1986), treefall data analysis was limited to the 33 plots that occupied flat ground, with slopes of less than five degrees. Direction of treefall can be influenced by prevailing storms winds (Harmon et al., 1986). Sites had positive TOPEX values indicating that they were all relatively sheltered (data not shown). Fig. 6c illustrates prevailing direction of >20 km/h storm winds for comparison to tree collision and fall direction data (Fig. 6a and b). Treefall aligns with prevailing winter wind direction, which blow to the east and south east (Fig. 6c). Summer prevailing winds blowing to the south and southwest (Environment Canada, 2006b) may account for the less abundant westward treefall (Fig. 6b). 4. Discussion 4.1. Mortality model and relation to the last spruce budworm outbreak Although individual-tree mortality is a stochastic, rare, and irregular event (Eid and Tuhus, 2001), spatially explicit logistic

models presented in our study provided a good fit to observed mortality, correctly classifying as live or dead 84% of 651 subject trees (Table 3), equivalent to classification of nearly 80% of 119 trees by Bigler and Bugmann (2003). The 1999– 2003 spatially explicit model more accurately predicted tree death events than the traditional non-spatial model (Table 3). The models developed in our study are unique in that they not only evaluate natural mortality in stands undergoing decline and break-up, but also incorporate both regular mortality due to suppression or competition and irregular mortality due to wind and insect outbreaks. Most models address either regular mortality (e.g., Yang et al., 2003) or irregular mortality (e.g., Reams et al., 1988); however, both should be incorporated in order to meet requirements of large-scale forest scenarios (e.g., Eid and Tuhus, 2001). The models were developed for two separate time periods, the first for about the end of the last spruce budworm outbreak (1990–1992) and the second about 10 years after cessation of defoliation (1999–2003). Because plots were established in 1987–1989, towards the end of the outbreak, tree-level data on outbreak severity, total outbreak spruce budworm-caused mortality, and efficacy of insecticide spraying were not available. Tree-level data on cumulative spruce budworm defoliation over the last 5–8 years, collected by NBDNR as part of plot remeasurements, provided a means of quantifying impact of recent defoliation. Model parameters were related to tree size, tree growth rate, competition and stand density, and reflected a shift from insect to wind-related causes of death following the end of the spruce budworm outbreak in 1993 (Taylor and MacLean, 2005, 2007). For example, in 1990– 1992, moderate–severe defoliation doubled the odds of a tree dying relative to the nil-light reference condition, while in 1999–2003 an increase of 1 m2/ha/year of neighbouring windrelated mortality more than doubled the odds of a tree dying (Table 2). Reams et al. (1988) suggested that risk of death from spruce budworm defoliation and blow down, although related, are somewhat mutually exclusive. Intense spruce budworm defoliation may kill a tree before it can blow down, and wind-related mortality during a budworm outbreak decreased with time since the start of the outbreak and increased with decreasing cumulative plot defoliation (Reams et al., 1988). This was because trees susceptible to wind-related mortality in

Fig. 6. Rose diagrams of (a) collision direction of 98 contact events, (b) fall direction of 1009 pieces of downed dead wood >1.5 m length, and (c) prevailing wind direction of 20 km/h winds from nine climate stations across New Brunswick (source data: Environment Canada, 2006a). Grey shaded triangles indicate mean direction.

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heavily defoliated stands were blown over or broken within the first 4 years of the outbreak, as spruce budworm feeding opened up the stands, and the remaining residual trees were less susceptible to blow down, due to spruce budworm feeding reducing crown resistance (Reams et al., 1988). Reams et al. (1988) only included the first 9 years of the outbreak, prior to significant fragmentation and fall of insect-killed snags. Taylor and MacLean (2007) estimated that by 10–15 years after tree death, 50% of insect-killed snags have fallen, resulting in further opening of the canopy, and exposure of residual trees to wind. In 1990–1992, at the end of the outbreak, our study suggests that subject trees with moderate–severe cumulative defoliation were surrounded by significantly higher levels of wind-related mortality than their nil-light defoliated counterparts (0.57 m2/ha/year versus 0.35 m2/ha/year; S = 1.145, p < 0.05). This supports the hypothesis of Taylor and MacLean (2005, 2007) of an interaction between spruce budworm-caused mortality and subsequent wind throw. 4.2. Effect of interactions with neighbours The distance-dependent Hegyi CI, which takes in to account tree location within the search area, outperformed all other measures of competition. Other studies have also found the Hegyi CI to be equivalent or superior to other competition indices (e.g., Holmes and Reed, 1991; Mailly et al., 2003). CS index was not associated with tree death at the level of the 5 m search area (data not shown), or the plot (Table 2). Cole and Lorimer (1994) suggest that significant competitive stress on individual trees may be induced only by competitor trees immediately surrounding a subject tree, hence success of the Hegyi CI, which gives a greater weight to closer competitors. The non-significance of plot-level live basal area (Table 2) indicates that density-dependent mortality resulting from competition identified in other studies (e.g., Kenkel, 1988) was not an important driving force of tree death in our study. In addition to competitive effects, neighbouring trees can also have a protective effect against harsh conditions, such as strong winds (Umeki, 2002). Damping of wind energy by collisions with neighbouring crowns may prevent trees from toppling during windstorms, especially in dense spruce-fir stands (Reams et al., 1988; Gardiner, 1995). This likely explains the negative correlation between tree death and crown closure in 1999–2003 (Fig. 4e). Interplay between competitive forces and protective forces of neighbouring trees is demonstrated by the U-shaped trend of probability of mortality with increasing Hegyi CI in 1999–2003 (Fig. 4d). Umeki (2002) also observed a combination of protective and competitive effects, which were species-specific and complicated. The mortality model results suggest that when stands reach a critical stage of decline, probability of mortality for residual trees is dramatically increased due to absence of protection from neighbouring trees and increased wind exposure (Fig. 4). Interactions between neighbouring trees as a result of treefall collisions affected only a minority of trees. The minimum estimate of 1.1% mortality resulting from fatal treefall collisions in our study, was much lower than the 15%

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reported by Franklin et al. (1987) in the Pacific Northwest. The strong association between presence of stem defects and impending death (Table 2) demonstrates the possible future fate of the 1.3% of live trees that incurred non-fatal damage as a result of treefall collisions. Removing the defect parameter from the model resulted in a reduction of dead tree prediction accuracy from 47 to 36% in 1999–2003. Ruel (2000) observed that balsam fir with cracks had a higher proportion of decay and were more susceptible to wind-related mechanical damage. The variability of treefall directions indicated that treefall events occurred throughout the year, but were most correlated with winter storm winds (Fig. 6), as also shown by Falinksi (1978) and Gratkowski (1956). Although storm winds in the fall and winter predominantly blow to the south and southeast (Fig. 6c), storms in the fall associated with hurricanes, such as the >100 km/h winds that resulted in the Christmas Mountains blow down on November 7th 1994 (Repap New Brunswick, 1995), generally blow in the opposite direction. None of the plots in the vicinity of the Christmas Mountains blow down had extensive wind damage for the coinciding measurement period (Taylor and MacLean, 2007). 4.3. Importance of tree death events and implications for forest management Mortality was the driving force causing stand decline and break-up in softwood stands in New Brunswick (Taylor and MacLean, 2005), and was strongly associated with tree death in the individual-tree mortality model. Removing previous windrelated mortality from the 1999–2003 model resulted in a reduction of dead tree prediction accuracy from 47 to 43%. Evergreen crown closure, which was negatively correlated with wind-related mortality and reflects the death of canopy trees, was the most important factor in terms of dead tree prediction, and removing it from the model reduced dead prediction accuracy from 47 to 25%. The association of canopy gaps and previous mortality in a 5 m search area with the fate of a subject tree indicates a contagion effect of death. Tree death at the stand-level can occur in two non-mutually exclusive patterns for biotic agents: tree-to-tree dieback, i.e., where many adjacent trees are affected (contagious distribution); or salt-and-pepper dieback, i.e., where dying trees occur repeatedly in a matrix of healthy trees due to certain species being ‘‘targeted’’ (MullerDombois, 1987). Spruce budworm causes both patterns. Following a 1950s spruce budworm outbreak in stands of the Green River watershed in northern New Brunswick sequential mortality tended towards a contagious distribution causing holes to develop in the stand as mortality progressed, leaving an open appearance with distinct clumps of surviving trees (Baskerville and MacLean, 1979). Candau et al. (1998) similarly observed ‘‘hot spots’’ of cumulative defoliation surrounded by approximately radial gradients of decreasing defoliation. Canopy openings resulting from death of canopy trees following spruce budworm defoliation expose residual surviving trees, which have been observed to have a high incidence of decay (Stillwell, 1956), to increased wind exposure and hence increased probability of blow down.

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And yet, no other published mortality models on North American boreal forests include previous mortality of neighbouring trees. This owes in part to the lack of spatiotemporal data on dead trees (e.g., Moeur, 1993). Indeed, the NBDNR permanent sample plot data on which this study was based does not include tree coordinates and excludes dead trees from future inventory once they have been first recorded as dead (Taylor and MacLean, 2007). Operational stand dynamics models, such as STAMAN (Vanguard Forest Management Services Ltd., 1993), simulate tree growth and mortality. However, once a tree dies, it is excluded from future runs of the model, and, therefore, no further effects on other trees can be predicted. While implementation of spatially explicit data into operational growth and yield models is hampered by the cost and availability of data, the effect of past mortality at the stand level could be incorporated into distanceindependent models such as STAMAN by assuming X% of trees are damaged by falling trees in the previous period, etc. Treefall damage estimates from our study suggest X could range between 4 and 9%. Our results demonstrate that tree death events do influence survivorship of neighbouring trees in the next measurement in stands undergoing decline and breakup. The biggest economic impact of stand decline arises from its inherent uncertainty (Taylor and MacLean, 2005). As a result, the value of long-term sustained yield and annual allowable cut predictions is qualified by effects of stand decline and break-up (Baker and Baskerville, 1985). We conclude that spatiotemporal data that incorporate past mortality may improve the ability to model the processes of stand decline and break-up, and could improve the prediction of tree death events in stand dynamic models. 5. Conclusion We used a tree-by-tree approach to assess spatiotemporal dynamics and associated predictors of mortality in 50 declining balsam fir and spruce stands in relation to the last spruce budworm outbreak. Addition of a spatial component to a 15–20 year repeat-measurement PSP dataset permitted calculation of spatially explicit variables to assess the effects of tree-to-tree interactions, such as competition and contagion of death, on tree fate. Logistic regression models of individual-tree mortality included a distance-dependent competition index that outperformed all other measures of competition, and a spatially explicit mortality variable that accounted for recent mortality in a 5 m search area centred on a subject tree. The probability of a tree death event was inversely related to canopy cover, because of increased vulnerability of exposed residual canopy trees to wind-related damage in stands undergoing decline and break-up. Our results indicate that 10 years after cessation of defoliation, more open post-outbreak stands experienced 0.9 m2/ha/year of wind-related mortality. When stands reach a critical stage of decline (i.e., <40% evergreen crown closure), probability of mortality for residual trees is dramatically increased due to absence of protection from neighbouring trees and increased wind exposure, effecting rapid stand break-up.

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