Trees dying standing in the northeastern borealold-growth forests of Quebec: spatial patterns,rates, and temporal variation

Tuomas Aakala, Timo Kuuluvainen, Louis De Grandpré, and Sylvie Gauthier

Abstract: Spatial patterns, rates, and temporal variation of standing-tree mortality were studied in unmanaged borealold-growth forests of northeastern Quebec. The study was carried out by sampling living and dead trees within 15transects (400 m long, 40 m wide). The transects lay in stands that were classified according to their species composi-tion in three types: dominated by black spruce, Picea mariana (Mill.) BSP; mixed P. mariana and balsam fir, Abiesbalsamea (L.) Mill.; and dominated by A. balsamea. Spatial patterns were analysed using Ripley’s K function. Theyear of death was cross-dated using 190 sample discs extracted from dead standing A. balsamea and P. mariana to as-sess the rates and temporal variation of mortality. The spatial patterns of standing dead trees in P. mariana stands werepredominantly clustered. The spatial patterns of large dead trees (>19 cm diameter at breast height (1.3 m height;DBH)) in mixed and A. balsamea-dominated stands were mainly random, with few stands showing clustered patterns.Small dead trees (9–19 cm DBH) in these stands were generally more clustered than larger trees. Tree mortality variedfrom year to year, though some mortality was observed in all the studied stand types for almost every year. Standingtrees that had recently died accounted for 62%, 48%, and 51% of overall mortality in P. mariana-dominated, mixed,and A. balsamea-dominated stands, respectively. The results of this study indicate that mortality of standing trees out-side of episodic mortality events (such as insect outbreaks) is an important process in the creation of structural com-plexity and habitat diversity in these stands.

Résumé : La distribution spatiale, le taux et la variation temporelle de la mortalité des arbres sur pied ont été étudiésdans des forêts boréales anciennes et non aménagées du nord-est du Québec. L’étude a été réalisée en échantillonnantles arbres vivants et morts le long de 15 transects (400 m de longueur et 40 m de largeur). Les transects ont été établisdans des peuplements regroupés en trois types selon leur composition en espèces : peuplements dominés par Picea ma-riana (Mill.) BSP, peuplements mixtes de P. mariana et d’Abies balsamea (L.) Mill. et peuplements dominés parA. balsamea. La distribution spatiale a été analysée à l’aide de la fonction K de Ripley. L’année de la mort des arbresa été déterminée par recoupement à partir d’un échantillon de 190 disques de bois prélevés sur des arbres morts surpied de A. balsamea et de P. mariana de façon à estimer le taux et la variation temporelle de la mortalité. Les arbresmorts sur pied dans les peuplements de P. mariana étaient, dans la plupart des cas, regroupés. Les gros arbres morts(diamètre >19 cm à hauteur de poitrine (1,3 m; DHP)) dans les peuplements mixtes et les peuplements dominés parA. balsamea étaient distribués de façon aléatoire dans la plupart des cas et regroupés dans quelques peuplements. Dansces peuplements, les petits arbres morts (DHP de 9 à 19 cm) étaient généralement plus regroupés que les gros arbres.La mortalité des arbres a varié d’une année à l’autre. Toutefois, des arbres morts ont été observés presque à chaqueannée dans tous les types de peuplement étudiés. Les arbres sur pied morts récemment correspondaient respectivementà 62, 48 et 51 % de la mortalité totale dans les peuplements de P. mariana, les peuplements mixtes et les peuplementsd’A. balsamea. Nos résultats indiquent qu’à l’exception des événements épisodiques de mortalité, tels que les épidé-mies d’insectes, la mortalité des arbres sur pied est un processus important pour assurer la complexité structurale et ladiversité des habitats dans ces peuplements.

[Traduit par la Rédaction] Aakala et al. 61

Introduction

Fire has been considered the most important driving forcein the dynamics of boreal forests (Wein and MacLean 1983).However, recent studies have demonstrated the importance

of small-scale local disturbances in boreal forests(Kuuluvainen 1994; McCarthy 2001). These disturbances in-crease in importance in areas where the return interval ofstand-replacing fires exceeds the longevity of major treespecies. This leads to small-scale mortality, and its influence

Can. J. For. Res. 37: 50–61 (2007) doi:10.1139/X06-201 © 2007 NRC Canada

50

Received 4 January 2006. Accepted 17 July 2006. Published on the NRC Research Press Web site at http://cjfr.nrc.ca on13 March 2007.

T. Aakala1 and T. Kuuluvainen. Department of Forest Ecology, University of Helsinki, P.O. Box 27, FI-00014, Helsinki, Finland.L. De Grandpré and S. Gauthier. Laurentian Forestry Centre, Canadian Forest Service, Natural Resources Canada, 1055 rue duPEPS, B.P. 3800, Ste-Foy, QC G1V 4C7, Canada.

1Corresponding author (e-mail: tuomas.aakala@helsinki.fi).

on gap dynamics, becoming the dominant process drivingthe structural development of stands (Oliver and Larson 1996).

Although studies on gap dynamics in boreal regions exist(e.g., Kneeshaw and Bergeron 1998; Pham et al. 2004),knowledge about the mortality processes behind these dy-namics is still limited (Franklin et al. 1987; Palik andPederson 1996; Jonsson et al. 2005). It is known that contin-uous mortality of individuals or small groups of trees can bemore important for stand dynamics and structure than infre-quent large-scale disturbances (Harmon et al. 1986; Franklinet al. 1987; Lugo and Scatena 1996; Kuuluvainen et al.1998). However, there is little information on the role ofmortality that occurs outside discrete events such as stormsor insect outbreaks (Rouvinen et al. 2002).

The mortality of standing trees, and its importance for for-est dynamics as a component of small-scale mortality, havebeen documented for some forest types (Krasny andWhitmore 1992; Rouvinen et al. 2002; Hennon andMcClellan 2003). Generally, standing-tree mortality occursgradually, and primarily involves biotic mortality agents(Krasny and Whitmore 1992; Gale and Barfod 1999). Thus,the mortality process and its impact on the ecosystem aredifferent from the mortality that leads to trees falling overimmediately. In particular, the release of growing space isoften gradual and the changes in stand vertical structure aremore variable. The mortality of standing trees is importantalso from the point of view of biodiversity, as it createsstanding dead trees, which are a prerequisite for the presenceof a large number of organisms (Harmon et al. 1986). Majorchanges also occur on a longer time scale as the standingdead trees deteriorate and eventually fall over (Harmon et al.1986; Hennon and McClellan 2003). As a consequence,knowledge about the mortality of standing trees is requiredto increase understanding of stand structural dynamics andfacilitate the development of sustainable forest management.

In general, the aim of this study was to characterize themortality of standing trees in the northeastern boreal old-growth forests of Quebec. The specific objectives were todescribe (i) the spatial patterns, rates, and temporal variationof standing-tree mortality, and (ii) its importance as a com-ponent of the overall mortality of trees. The study was con-ducted on stands grouped according to their speciescomposition into three stand types, and the data were com-pared among the groups.

Material and methods

Study areaThe study area was located in the North Shore region of

Quebec, Canada. Sampling was conducted in two areas: inthe vicinity of Lac Dionne (50 km north of Baie-Comeau)and in the Rivière Pentecôte area (100 km northeast of Baie-Comeau) (49°30′–50°00′N, 67°30′–69°00′W). The southernpart of the area (south of 49°45′N) belongs to the Abiesbalsamea – Betula papyrifera bioclimatic domain, whereasthe northern part belongs to the Picea mariana –feathermoss domain (Robitaille and Saucier 1998). Accord-ing to the classification devised by Rowe (1972), these areasbelong to the Chibougamau–Natashquan region.

The areas are characterized by hills with moderate slopesand flattened summits, rarely exceeding 500 m a.s.l.

(Robitaille and Saucier 1998). Soils mostly consist of thinundifferentiated glacial-till deposits on gentle slopes and de-pressions. At the bottom of large valleys, soils largely con-sist of glacial fluvial sand deposits. Almost 40% of the landarea is occupied by rocky outcrops, which can be found onthe summits and steep slopes and close to water bodies(Robitaille and Saucier 1998).

The climate in the study region is classified as cold and mar-itime, with mean annual temperatures varying between –2.5and 0 °C (Robitaille and Saucier 1998). Annual precipitationvaries between 1100 and 1300 mm, of which 35% falls assnow (Robitaille and Saucier 1998).

Forests in the study area are dominated by black spruce,Picea mariana (Mill.) BSP, and balsam fir, Abies balsamea(L.) Mill., along with white spruce, Picea glauca (Moench)Voss. There is also a low preponderance of hardwoods, suchas white birch, Betula papyrifera Marsh., and trembling as-pen, Populus tremuloides Michx. The successional develop-ment of stands following a stand-replacing disturbance wasdescribed by De Grandpré et al. (2000). The sites were ini-tially dominated by P. mariana, shade-intolerant hardwoods,or mixed cover. Later on, successional development in thesesites led to the dominance of A. balsamea or P. mariana, orshared dominance of the two species in the old-growth stage(De Grandpré et al. 2000).

The disturbance regime in the North Shore region is char-acterized by a long return interval of stand-replacing forestfires. No accurate forest-fire reconstruction for the studyarea has been made. However, studies carried out in the ad-jacent region of southeastern Labrador (Foster 1983) and es-timates from the study area (De Grandpré et al. 2000;Gauthier et al. 2000) indicate a very long fire cycle. Standdynamics are thus driven by disturbances other than fire, ofwhich the most important in the region are wind and sprucebudworm (Choristoneura fumiferana (Clemens)) outbreaks(De Grandpré et al. 2000; Harper et al. 2002).

The first recorded major spruce budworm outbreak in theNorth Shore region occurred in the 1970s (Blais 1983).Though there was a smaller, localized outbreak in 1940s, nomajor outbreaks are thought to have occurred for at least130 years (Blais 1983). Abies balsamea is the preferred hostspecies for spruce budworm. However, Picea spp. can alsosuffer considerable mortality from spruce budworm whenthey are codominant with A. balsamea (MacLean 1980).

SamplingThe stands were categorized into three types according to

the species composition of the canopy dominants. Thesetypes were as follows: stands dominated by P. mariana,stands dominated by A. balsamea, and stands with shareddominance of the two species (hereinafter referred to asP. mariana stands, A. balsamea stands, and mixed stands,respectively). Stands were classified as P. mariana- orA. balsamea-dominated if the proportion of the dominantspecies was over 75% of the coniferous component. Inmixed stands the proportion of either species was less than75%. Species composition in the old-growth state is likely toreflect the productivity of the stands, as demonstrated byA. balsamea dominance in more fertile sites and P. marianadominance in poorer sites (Bergeron and Dubuc 1989;Boucher et al. 2004). Thus, a site-productivity gradient from

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Aakala et al. 51

P. mariana stands through mixed stands to A. balsameastands was assumed to exist in the study area.

Stands were selected on the basis of stand composition,density, and age by referring to forest-inventory maps com-piled by the Quebec Ministry of Natural Resources. Onlyconiferous stands with canopy coverage of over 50% and be-longing to the oldest age class (>120 years) were accepted.Sampled stands were old-growth forest, with typical featuressuch as dead wood and uneven-sized structure comprisinglarge canopy dominants and smaller trees in canopy gaps atdifferent developmental stages. Accessibility (distance fromthe nearest road or distance to navigable water) and size ofthe stand were also used as criteria for site selection. Fivestands were selected for each composition group. Three ofthe P. mariana and mixed stands were located in the LacDionne area and two at Rivière Pentecôte. Two of theA. balsamea stands were sampled at Lac Dionne and the re-maining three stands at Rivière Pentecôte. Fieldwork wasconducted during the summer of 2004. In each stand, atransect 400 m long and 40 m wide was sampled. The start-ing points and the directions of the transects were selectedrandomly, taking into account the size and shape of thestands. The starting points were located in the forest interiorto avoid edge effects.

Along each transect, all standing dead and living treeswith DBH over 19 cm were measured. Dead trees were de-fined as standing dead if they were over 1.3 m in height andstanding at an angle of more than 45°. Species, DBH,height, and decay class (DC; Table 1) were recorded. Toevaluate the importance of standing-tree mortality, trees thathad recently died were classified into three structural classesaccording to their position at death: (1) intact, for trees thathad suffered no detectable fragmentation of their tops,(2) snapped, for trees that had snapped above 1.3 m height,and (3) fallen, for trees that had uprooted, or snapped below1.3 m height. However, it was impossible to determine retro-spectively whether a snapped tree had snapped prior to or af-ter death. Therefore, structural classes 1 and 2 were regardedas one class (standing dead trees) in the analyses. Partly up-rooted trees with green foliage were classified as living.

The point patterns for the spatial analysis were deter-mined by each tree’s location. The coordinates of each treewere fixed and recorded as the distance along the transectfrom the starting point and the perpendicular distance fromthe centre line of the transect. Distance and tree height weremeasured with a Vertex hypsometer. Although the focus ofthis study was on larger trees because of their higher ecolog-ical significance (Harmon et al. 1986), smaller standing deadtrees with DBH between 9 and 19 cm were also measured.However, because of time constraints they were measuredusing 20 m as the width of the transect. Otherwise the sameinformation was recorded as for the larger trees.

To assess the rates and temporal variation of mortality,sample discs for cross-dating the year of death were ex-tracted from standing dead P. mariana and A. balsamea. Thesampling was done according to a five-stage decay classifi-cation based on the appearance of the tree (Table 1). An ef-fort was made to collect three sample discs from DC 4–7(DC 8 being too decayed for cross-dating) of the dominantspecies in the P. mariana and A. balsamea stands and fromboth species in mixed stands. For practical reasons sampling

was done systematically in each stand by starting from thepoint of easiest access to the transect. Thus, the aim was toretrieve 12 sample discs from each P. mariana andA. balsamea stand and 24 from each mixed stand. Only sam-ple discs that were judged datable were accepted. Samplediscs were extracted only from trees with DBH over 19 cm.

Data analysisSpatial patterns of standing-tree mortality were deter-

mined by analysing the spatial distribution of standing deadtrees within the transects. The patterns of small (9–19 cmDBH) and large (>19 cm DBH) dead trees were analysedseparately, using Ripley’s K function (Ripley 1981; Diggle1983), which is used to detect the randomness of a point pat-tern or its deviation from random towards regular or clus-tered for different intertree distances. Ripley’s K analysis hasbecome a standard tool for analysing spatial point patternsand is widely used in forest ecology (e.g., Sterner et al.1986; Kuuluvainen et al. 1996; Rouvinen et al. 2002). Usingthis method requires correction to be made for edge whensampling close to the sampling-plot boundary (Haase 1995).The correction factor used in this study was the weightededge correction (Diggle 1983). The recommended maximumintertree distance in the analysis is half the length of theshortest side of the sample plot (i.e., the width of thetransects in this study). Thus, for trees over 19 cm DBH themaximum distance used in our study was 20 m, and for trees9–19 cm DBH the distance was 10 m. Confidence intervals(95%) were used for testing statistical significance, and werecalculated with 500 Monte Carlo permutations for completespatial randomness. All calculations were performed usingthe SpPack add-in for MS-Excel (Perry 2004).

Dendrochronological analysis was used to assign a year ofdeath to each sampled tree. The sample discs were firstsanded so that the ring structure was clearly visible, and ringwidths were measured using a Velmex measuring bench(with an accuracy of 0.01 mm). Determining the year ofdeath was primarily done by visual dating of narrow markerrings, and the results were verified using the computer pro-gram COFECHA® (Holmes 1983; Grissino-Mayer 2001).The master chronology and a set of marker years were pro-vided by M. Simard (unpublished data) and later improvedby Périgon (2005) with samples from the same transects.The year of the outermost ring was used as the year ofdeath, though it is possible that weakening of trees might

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52 Can. J. For. Res. Vol. 37, 2007

DC Characteristics

4 Tree recently dead; small branches with at leastsome foliage still attached; wood hard

5 Foliage absent but small twigs still present; cambiumdried or absent; wood hard

6 Some larger branches still present; wood hard7 Tree snapped, possibly only the largest branches

present; some softening of wood detectable8 Tree snapped, less than 2 m high; wood soft

Note: DC, decay class. DC 1 – DC 3 are reserved for living trees andwere not used in this study. Trees in DC 4 – DC 6 may be intact orsnapped; trees in DC 7 and DC 8 are always snapped.

Table 1. Characteristics of trees in each decay class.

cause the production of annual growth rings to cease prior todeath.

The temporal variation of the mortality of standing treeswas assumed to correspond to data obtained from the samplediscs. This variation was analysed by estimating the numberof trees in a DC that died in a certain year as a proportion ofthe overall mortality in the stand for that DC. This was nec-essary because the samples collected from the field werestratified by DC and therefore represented an unequal pro-portion of the total mortality. The number of trees that diedeach year (Dx) can be expressed as

[1] D Na

nx i

i x

ii

==∑ ,

4

7

where Ni is the total number of trees in DC i, ai,x the numberof samples in DC i cross-dated to have died during year x,and ni is the total number of samples in DC i. Some of thesampled trees in DC 7 were already too decayed for analy-sis. Therefore, only half of the total number of trees in DC 7was used for the calculations.

Estimates of mortality rates were based on the results ofthe analysis of the temporal variation of mortality averagedover the previous 10 years (1994–2004). The analysis waslimited to 10 years because as time passed, an increasingnumber of trees that died standing had since fallen.

The importance of standing-tree mortality was estimatedas the number of dead trees that had recently died standingexpressed as a proportion of all recently dead trees (DC 4).It was assumed that because of the short time spent in DC 4,the differences in decay rates between standing and downdead trees were insignificant. The numbers of trees in thesetwo groups would therefore be comparable.

A one-way analysis of variance was used to test the statis-tical significance of differences between stand-type meandensities. Statistical analyses were performed using SPSS™version 12.0.1, and p < 0.05 was used as the limit for statis-tical significance.

Results

Densities of living and dead treesThe variation in the density of living trees (>19 cm DBH)

was large (Table 2), ranging from 66 trees/ha in a mixedstand at Lac Dionne to 352 trees/ha in an A. balsamea standat Rivière Pentecôte. Stand-type mean densities were 145,175, and 243 trees/ha for P. mariana, mixed, andA. balsamea stands, respectively. Variation within stand typeswas large. Therefore, the differences between stand typeswere not statistically significant (F = 1.821, p = 0.204).

Densities of standing dead trees (>9 cm DBH) variedgreatly among transects (Table 2), ranging from 89 to 229trees/ha. As with living trees, stand-type mean densities in-creased from 132 standing dead trees/ha for P. marianastands through 183 trees/ha for mixed stands to 187 trees/hafor A. balsamea stands. The differences in density betweenstand types were not significant (F = 2.391, p = 0.134).When large dead trees (>19 cm DBH) were analysed sepa-rately, mean densities were 40, 90, and 123 trees/ha inP. mariana, mixed, and A. balsamea stands, respectively. Forsmaller dead trees (9–19 cm DBH), densities were 92, 93,and 64 trees/ha for P. mariana, mixed, and A. balsameastands, respectively. The differences between mean densitieswere significant (F = 13.379, p = 0.001) for large trees butnot significant (F = 1.173, p = 0.343) for smaller trees.

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Aakala et al. 53

No. of trees/ha

Stand type Stand Location Living Standing dead

Picea mariana 1 Lac Dionne 117.5 134.4 (23.1)4 Lac Dionne 198.1 133.8 (55)5 Lac Dionne 186.9 204.4 (59.4)

11 R. Pentecôte 146.3 89.4 (46.9)13 R. Pentecôte 78.1 97.6 (13.8)

Mixed 3 Lac Dionne 135.6 229.4 (113.1)7 Lac Dionne 65.6 175.1 (58.8)8 Lac Dionne 138.1 188.8 (73.8)

10 R. Pentecôte 243.1 125 (77.5)12 R. Pentecôte 291.3 195 (127.5)

Abies balsamea 2 Lac Dionne 113.1 205.1 (116.3)6 Lac Dionne 189.4 221.9 (135.6)9 R. Pentecôte 230 104.4 (79.4)

14 R. Pentecôte 351.9 220 (135)15 R. Pentecôte 328.8 183.1 (150.6)

MeanP. mariana 145.4 131.9 (39.6)Mixed 174.7 182.7 (90.1)A. balsamea 242.6 186.9 (123.4)

Note: Values in parentheses are densities of large standing dead trees (>19 cm DBH), shown for comparisonwith living trees.

Table 2. Densities of living trees (>19 cm DBH) and standing dead trees (>9 cm DBH) in eachstand, with the mean density for each stand type.

Spatial patternsThe spatial patterns (Fig. 1) of living trees were predomi-

nantly clustered (Table 3). In P. mariana stands, the treeswere significantly clustered over all intertree distances, ex-cept for very short distances (<2 m) in two of the stands.Trees in mixed stands were not as clearly clustered as thosein P. mariana stands. In two of the mixed stands the distribu-tion differed between clustered and random for all theintertree distances studied. In three mixed stands the distri-bution of trees was random at short intertree distances butclustered at greater intertree distances. The distribution ob-served in all A. balsamea stands was clustered at longerintertree distances and random at short distances. The ob-served increase in the degree of clustering with distance wasmost pronounced between 4 and 8 m in all but oneA. balsamea stand (Table 3).

Large standing dead trees (>19 cm DBH; Table 3) inP. mariana stands were clustered over nearly all intertreedistances in three out of five stands. The distribution in oneof the remaining stands was random at short intertree dis-tances and clustered at distances greater than 12 m. OneP. mariana stand was not analysed because there were notenough large trees in it (<30). As with the distribution of liv-ing trees, the mixed stands had a less clearly clustered pat-tern of large standing dead trees than the P. mariana stands.Clustering occurred at longer intertree distances in threemixed stands (>9 and >13 m in two stands and 10–15 m inone stand). Two mixed stands had random distributions overthe whole range of intertree distances (Table 3). Abiesbalsamea stands generally had more random spatial distribu-tions of large standing dead trees than either mixed orP. mariana stands. The spatial distribution was random overall intertree distances in two of the A. balsamea stands. Sig-nificant clustering in the remaining A. balsamea stands oc-curred at shorter intertree distances, with randomdistributions at longer distances (Table 3).

Significantly clustered patterns occurred more frequentlyin smaller standing dead trees (9–19 cm DBH) than in largerstanding dead trees (>19 cm DBH). However, there was con-siderable variation in distribution patterns among the differ-ent stand types (Table 4). Three of the P. mariana stands hadsignificantly clustered patterns over all intertree distances. Inone P. mariana stand, the distribution was clustered up to7 m. Another P. mariana stand had mainly random patterns,with clustered patterns only at very short intertree distances(<2 m). In two of the mixed stands (Table 4), clustered dis-tributions of small standing dead trees (9–19 cm DBH) wereobserved over all intertree distances. Clustering was also ob-served in one stand at intertree distances up to 10 m. Thedistribution in one of the stands was clustered at distances of1, 7, and 8 m. The remaining mixed stands had clustered dis-tributions at short distances, from 1 to 4 m. Clustered distri-butions were observed in one of the A. balsamea stands overall intertree distances (except for one random observation ata distance of 3 m). One A. balsamea stand had clustered dis-tributions at intertree distances greater than 1 m and anotherat distances greater than 4 m. Two stands had too few smallstanding dead trees for proper analysis.

Tree position at deathThe proportion of overall mortality represented by the

mortality of standing trees was estimated by comparing thedensities of trees that had recently died standing with thedensities of all trees that had recently died (>9 cm DBH; Ta-ble 5). This proportion was highest in P. mariana stands (onaverage, 62% of trees that had recently died were standing),intermediate in A. balsamea stands (51%), and lowest inmixed stands (48%). However, these differences were notsignificant (analysis of variance, F = 1.001, p = 0.396).More small trees (9–19 cm DBH) than large trees (>19 cmDBH) had died standing. The proportions of small trees thathad recently died standing were 68% in P. mariana stands,

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54 Can. J. For. Res. Vol. 37, 2007

Fig. 1. Examples of stem maps of measured trees (living trees >19 cm DBH, dead trees >9 cm DBH) from Picea mariana (stand 11)(a) and mixed (stand 10) (b) and Abies balsamea (stand 15) (c) stands. A circle depicts a living tree and a cross a dead tree; the sizeof the symbol reflects DBH but is disproportionate to the coordinate axes. The broken lines show the narrower transect, used for mea-suring small dead trees (9–19 cm DBH).

67% in mixed stands, and 82% in A. balsamea stands. Thecorresponding proportions for large trees (>19 cm DBH)were 53% in P. mariana stands, 33% in mixed stands, and43% in A. balsamea stands. The differences between standtypes were not significant for small trees (F = 1.208, p =0.339) or large trees (F = 1.011, p = 0.393). Tree positionsat death for small and large trees were analysed separatelywithin each stand type also. The differences were not signif-icant for P. mariana stands (F = 1.870, p = 0.209). How-ever, the proportions in mixed stands (F = 5.608, p = 0.050)and A. balsamea stands (F = 9.795 p = 0.017) were signifi-cantly different.

Rates and temporal variation of mortalityOf the sampled trees, 190 were successfully cross-dated.

The cross-dating results (Table 6) were used for estimatingthe rates and temporal variation of mortality. The mean mor-tality rates for large standing trees (>19 cm DBH) during the

previous 10 year period (1994–2004) were 1.1 trees/ha inP. mariana stands, 1.5 trees/ha in mixed stands, and2.9 trees/ha in A. balsamea stands, corresponding to 0.8%,0.8%, and 1.2% of the total number of living trees (>19 cmDBH), respectively, in 2004. If the ratio of standing-tree tofallen-tree mortality is considered to be similar to that calcu-lated for the trees that had recently died, overall mortalityrates would be 1.2%, 1.8%, and 2.3% for P. mariana,mixed, and A. balsamea stands, respectively. However, theseare relatively crude estimates, since it is most likely that thenumber of living trees used in the calculations was not con-stant over the whole 10 year period.

The annual variations in mortality from 1980 to 2004were analysed. The highest annual observed mortality ratesfor large trees (>19 cm DBH) were 7.4 trees/ha (1996) inA. balsamea stands (Fig. 2), 4.1 trees/ha (1999) inP. mariana stands (Fig. 3), and 3.8 trees/ha (1995) in mixedstands (Fig. 4). Owing to the uncertainties in determining

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Aakala et al. 55

Distance (m)

Stand type Stand 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Living treesPicea mariana 1 * * * † † † † ‡ ‡ ‡ ‡ † ‡ ‡ ‡ ‡ ‡ ‡ ‡ ‡

4 † † * * † * † † † ‡ ‡ ‡ ‡ ‡ ‡ ‡ ‡ ‡ ‡ ‡5 * * * † † ‡ ‡ ‡ ‡ † † † ‡ ‡ ‡ ‡ ‡ ‡ ‡

11 * † ‡ ‡ ‡ ‡ ‡ ‡ ‡ ‡ ‡ ‡ ‡ ‡ ‡ ‡ ‡ ‡ ‡ ‡13 * * * * * * * * † † † † † † † † † †

Mixed 3 * * * * * * * * * * * * *7 * * * * * * * * *8 * * * * * * * * * † * * * * *

10 * † * * * * * * * * * * + † † † † † ‡12 * * * * * * * * * *

Abies balsamea 2 * * * * * * * * * † † † † † † † † *6 * * * * * * * * * *9 * * * * * * * * *

14 * * * * * * * * *15 * * * * * * * * * * * * * * *

Standing dead trees

P. mariana 1 * * * * * * * * * * * * * * * * * * * *4 * * † † * * * * * * * * † * † † † † † †5 * * * * † * * * † † † † ‡ ‡ ‡ ‡ ‡ ‡ ‡

11 * * * * * * * * * *13 Too few trees

Mixed 3 * * * * * * * * * *78

10 * * * * * * * + + + + + +12 * * * * * * * * * † † † † † † † † † †

A. balsamea 2 * * * * * * * * *6 * *9

1415 * * *

Note: A symbol denotes statistically significant clustering and an empty cell no deviation from a random distribution. The degree of clustering is indi-cated as follows: *, observations 1–2 times the upper confidence interval; †, 2–3 times the upper confidence interval; ‡, more than 3 times the upper con-fidence interval.

Table 3. Summary of the results of Ripley’s K analysis of the spatial patterns of living trees and standing dead trees >19 cm DBH asa function of distance.

the exact year of death, the longer term temporal variation ofmortality is presented using 3 year rolling averages in addi-tion to annual values (Figs. 2–4). Mortality of P. marianawas relatively constant over an extended period of time.Abies balsamea stands exhibited varying mortality trends: ahigh mortality rate was observed in the early 1980s and an-other period of higher mortality rates was observed in themid-1990s. The mortality rates for P. mariana in mixedstands were relatively constant. Besides the low mortalityrates in the early 1980s, mortality of A. balsamea in mixedstands was generally greater than that of P. mariana. More-over, A. balsamea had a more varying mortality pattern thanP. mariana in the same stands, with high mortality levels inthe mid-1990s. Overall, mortality appeared to occur rela-

tively continuously in all stand types, as there wererelatively few years in which no mortality was observed.

Discussion

Spatial patternsInterpretations of mortality processes that are based on the

distribution of dead trees are not straightforward (Moravieand Robert 2003). Mortality agents ultimately determine thepatterns of mortality but these can only work within the lim-its set by the distribution of living trees prior to the mortalityevents themselves. On the other hand, the spatial patterns ofliving trees in old-growth stands are determined by pastmortality events, subsequent regeneration, and the environ-mental conditions affecting them (Antonovics and Levin1980; Little 2002). Thus, when distributions of living treesvary, similar disturbance processes lead to different patternsof dead individuals (Barot et al. 1999).

Large living trees (>19 cm DBH) in P. mariana standshad highly clustered spatial distributions at all intertree dis-tances up to 20 m. The observed pattern has been influencedby the patterns of past mortality events. However, it is morelikely that the main contributors to the observed patternswere environmental heterogeneity, which influenced the es-tablishment of trees, and layering, which played a role in theregeneration of P. mariana. The lack of suitable establish-ment sites in these stands was reflected in the high propor-tion of canopy gaps dominated by shrubs (Périgon 2005) andthe low density of trees in the P. mariana stands in general(Harper et al. 2002, 2003). Thus, the clustered distributionof dead trees that was observed is likely to have beenstrongly influenced by the highly clustered distribution ofliving trees prior to mortality events.

The conditions for regeneration in mixed and A. balsameastands were more benign and homogeneous, as was shownby the higher density and less clustered pattern of living

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56 Can. J. For. Res. Vol. 37, 2007

Distance (m)

Stand type Stand 1 2 3 4 5 6 7 8 9 10

Picea mariana 1 † ‡ ‡ ‡ ‡ ‡ † ‡ ‡ ‡4 *5 † † † ‡ ‡ † † † † ‡

11 * * * * * † * * * †13 * * † † † * * * * †

Mixed 3 † * † † * * † † † †7 † * * * * * *8 * * * * * * *

10 * * * * * † * * * †12 * * *

Abies balsamea 2 * * * * * * * * *6 * * * * † † † † †9 Too few trees

14 * * * * † †15 Too few trees

Note: A symbol denotes statistically significant clustering and an empty cell no deviation from a random distribution. The degree of clustering is indi-cated as follows: *, observations 1–2 times the upper confidence interval; †, 2–3 times the upper confidence interval; ‡, more than 3 times the upper con-fidence interval.

Table 4. Summary of the results of Ripley’s K analysis of the spatial patterns of standing dead trees between 9 and 19 cm DBH as afunction of distance.

Stand type Stand Standing dead trees Fallen trees

Picea mariana 1 10.6 (1.9) 7.5 (3.8)4 10 (3.7) 8.1 (5.6)5 10.1 (3.8) 8.1 (6.9)

11 15.6 (8.1) 6.9 (3.1)13 11.2 (2.5) 16.9 (6.9)

Mixed 3 15.6 (4.4) 16.9 (11.9)7 10.7 (4.4) 6.9 (5.6)8 1.3 (0) 3.1 (1.9)

10 1.2 (1.2) 3.1 (3.1)12 6.9 (3.1) 11.9 (8.1)

Abies balsamea 2 9.4 (5.7) 4.4 (3.1)6 7.5 (7.5) 5.0 (5.0)9 8.8 (3.8) 3.1 (1.9)

14 10.7 (9.4) 14.4 (14.4)15 16.9 (9.4) 3.8 (2.5)

Note: Numbers in parentheses show the number of larger trees(trees >19 cm DBH/ha).

Table 5. Position at death of recently dead trees.

trees (>19 cm DBH) and the lower proportion of shrub-dominated gaps than in P. mariana stands (Périgon 2005).Thus, the predominantly random spatial distribution of largestanding dead trees (>19 cm DBH) detected in mixed andA. balsamea stands was more likely to have been influencedby the random occurrence of mortality agents and the indi-vidual tree’s progression of senescence than by the initialdistribution of living trees (compared with that inP. mariana stands). Previous studies on the mortality of can-opy dominants yielded similar results, with the spatial distri-bution of dead trees having either a random or a clusteredpattern (Parish et al. 1999; Rouvinen and Kouki 2002;Rouvinen et al. 2002). The clustered patterns of mortalityfound in these studies have often been attributed to the con-tagious properties of many mortality agents, such as patho-gens and insect outbreaks (Harmon et al. 1986). However,these patterns are scale-dependent, and it is possible that thesmall maximum scale (20 m) in the Ripley’s K analysis usedin this study might be the reason for not detecting clusteredmortality caused by the spruce budworm epidemic.

Small dead trees (9–19 cm DBH) were predominantlyclustered in all stand types. However, when comparing thepattern of small trees to the pattern of larger dead trees(>19 cm DBH), there were differences between the standtypes. Unlike those found in A. balsamea and mixed stands,small and large dead trees in P. mariana stands had similardistributions. Smaller trees in P. mariana stands were oftencanopy dominants already, whereas in mixed andA. balsamea stands small trees were often subcanopy treesregenerating in former canopy gaps. Subcanopy trees in old-growth forests are often more clustered than canopy trees(e.g., Parish et al. 1999; Rouvinen and Kouki 2002). How-ever, as small living trees were not sampled, the effect oftheir distribution on dead-tree patterns could not be deter-mined.

The role of the distribution of living trees and how theyaffect the different patterns of standing-tree mortality inP. mariana stands versus the two other stand types were evi-

© 2007 NRC Canada

Aakala et al. 57

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Fig. 2. Temporal variation of mortality of large trees (>19 cmDBH) in Abies balsamea stands from 1980 to 2004; observedvalues are shown as vertical bars and 3 year moving averages asa line plot.

dent. A highly clustered distribution of living trees, as wasobserved in P. mariana stands, leads to clustered distribu-tions of dead trees even if the mortality in the population oc-curred randomly. However, the extent to which mortalitypatterns were determined by the distribution of living trees,or by the occurrence of mortality agents, remains largely anopen question. Inferences could be made by testing the dis-tribution of dead trees against the random-mortality hypoth-esis (Kenkel 1988; Goreaud and Pelissier 2003). Thisanalysis takes into account the initial distribution of livingtrees. However, this method could not be used in the presentstudy, as there was no information on how many of the now-existing trees have grown over the 19 cm DBH thresholdsince the first recorded mortality event (Little 2002).

Tree position at deathOnly a few studies have directly quantified mortality

events leading to trees dying standing. Dobbertin et al.(2001) observed an average annual mortality rate resulting instanding dead trees to be 1.5% (compared with 0.7% forfallen dead trees) in a mountain pine, Pinus mugo var.uncinata Willk., stand in Switzerland that had been affectedby root-rot fungi. In Allegheny northern hardwood forestsaffected by beech-bark disease, Krasny and Whitmore(1992) found a higher incidence of dying trees standing thanfallen. Kneeshaw and Bergeron (1998) reported that the ma-jority of trees killed by spruce budworm died standing.These studies outline the important influence of the charac-teristics of mortality of standing dead trees in forests se-verely affected by biotic disturbances; otherwise, however,the ratio of standing to fallen dying trees has been reportedto be species-dependent (e.g., Liu and Hytteborn 1991;Siitonen et al. 2000). It is clear that species autecology isimportant in determining susceptibility to disturbance agentsand predisposing factors (Foster 1988). On the other hand,the effect of species is difficult to discern under different en-vironmental conditions.

The position of recently dead trees at death (i.e., standingor fallen) did not vary significantly among stand types whenall trees were included in the analysis. However, when largeand small dead trees were analysed separately within eachstand type, differences within mixed and A. balsamea standsemerged. Higher proportions of large trees (>19 cm DBH)had died fallen, whereas smaller trees (9–19 cm DBH) hadmore often died standing. This suggests differences betweenfactors affecting the mortality of small versus large trees inthese stand types. Larger trees have a greater sail area, andare often growing in a more exposed within-stand positionthan smaller trees. This, combined with factors related to oldage (such as the presence of root and butt rots), would makelarger trees more susceptible to wind damage. Although in-dividual mortality factors were not recorded, previous stud-ies in eastern North American boreal forests have suggesteda positive relationship for A. balsamea between tree size(Peterson 2004) or age (Ruel 2000) and susceptibility towind-induced mortality. Such a relationship would explainthe differences observed here. The lack of differences in po-sition at death between small and large trees in P. marianastands is likely related to the DBH limit used in this study.Even small trees (9–19 cm DBH) are often canopydominants in these stands. The smaller trees are thus influ-enced by mortality factors similarly to the larger trees, andthere are no clear differences in their exposure to wind.

Cross-dating the year of deathOnly 4 of the 194 sample discs collected could not be ac-

curately cross-dated. The main reason for not achieving theobjective of collecting 240 sample discs was the lack oftrees in the transects that met the criteria for adequate sam-ples. Most missing samples were from DC 4. Dead trees inthis class represent a very short window of mortality, and ina few cases were completely absent from the transects. Alarge proportion of the stems in DC 7 were judged to havedecayed too much for cross-dating. Consequently, they wererejected in the field. Thus, it is possible that the samplesfrom DC 7 that were successfully cross-dated mainly repre-

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58 Can. J. For. Res. Vol. 37, 2007

Fig. 3. Temporal variation of mortality of large trees (>19 cmDBH) in Picea mariana stands from 1980 to 2004; observed val-ues are shown as vertical bars and 3 year moving averages as aline plot.

Fig. 4. Temporal variation of mortality of large trees (>19 cmDBH) in mixed stands from 1980 to 2004, including the propor-tion of total mortality represented by Picea mariana and Abiesbalsamea; observed values are shown as vertical bars and 3 yearmoving averages as a line plot.

sent the younger parts of stems in that DC. Other reasons forrejecting sample discs in the field included damage to stemsand weathering of outer rings, both of which made accurateassessments of the last growth ring impossible.

A number of factors influenced the estimates of past mor-tality based on the cross-dated samples. First, these esti-mates became less reliable with time since death. Anincreasing number of dead and decayed trees had alreadybeen classified as DC 8, or had fallen. Consequently, therewere no cross-dating results from such trees. Similarly, partsof the trees in DC 7 were already too decayed for the proce-dure to be used. Therefore, the variation in the temporal pat-tern of mortality was most likely related to these “missing”samples. This effect became more pronounced with timesince death, which led to underestimates of actual mortalityin the past. Second, the estimates are influenced by the inac-curacies in cross-dating, particularly where the absence ofgrowth rings in the years preceding death may have biasedthe results (e.g., Cherubini et al. 2002). The third factor issampling: it is possible that some sampled trees had beenclumped, and thus possibly died in the same year. Thus, theuncertainties need to be taken into account when interpretingthe results.

Rates of standing-tree mortalityAs was expected, the absolute rates of mortality over the

previous 10 year period increased with the assumed gradientof stand productivity: from P. mariana to mixed toA. balsamea stands. However, the differences were small, ordisappeared altogether, when the mortality rates were ana-lysed relative to the number of living trees within the respec-tive stand types (1.2% in A. balsamea and 0.8% inP. mariana and mixed stands).

The rates of overall mortality estimated in the presentstudy differ slightly from those reported earlier from borealforests. In Fennoscandia, Rouvinen et al. (2002) reported acurrent rate of mortality varying between 0.6% and 1.2%in a landscape dominated by Scots pine, Pinussylvestris L. Siitonen et al. (2000) studied Fennoscandianstands dominated by Norway spruce, Picea abies (L.) Karst.,and reported an annual mortality rate of 0.43%. On the otherhand, results obtained by Senecal et al. (2004) from naturalstands dominated by P. glauca and P. tremuloides in borealQuebec showed considerably higher mortality rates. Thoseauthors observed a 3.4% mortality rate for the canopy domi-nant P. glauca (4.0% overall mortality). The lower mortalityrates in Fennoscandian forests are likely caused by differ-ences in the disturbance regime. Fennoscandian coniferousforests lack a disturbance agent equivalent to sprucebudworm (Kuuluvainen 1994). Spruce budworm was consid-ered to be a major contributor to mortality in the study bySenecal et al. (2004) and in the present study. The highermortality rates reported by Senecal et al. (2004) than in ourstudy could be explained by a younger stand age and themore synchronous senescence-related mortality.

Temporal variation of mortalityDespite the uncertainties related to the methods used for

analysing the temporal variation of mortality, some trendswere apparent: the constant mortality rate for P. mariana and

the two pronounced peaks in the incidence of mortality ofA. balsamea.

Picea mariana stands exhibited a relatively constant levelof mortality; the lower mortality rate in the early 1980s thanin later years is likely a sampling artefact because manytrees that had died standing subsequently fell over or de-cayed to DC 8 and thus were not sampled for cross-dating.The observed temporal pattern of mortality in A. balsameastands was concomitant with the past spruce budworm out-break. The outbreak began in the late 1970s (1978 and 1979being the years of most severe defoliation, as indicated bygrowth reduction; personal observation from dendrochrono-logical analysis), and resulted in a higher level of mortalityfor the years following the epidemic. This corresponds wellto the timing of peak mortality observed in previous studieson defoliator epidemics (MacLean 1980; Coulson and Witter1984). A raised level of mortality was detectable eventhough a majority of trees that had died standing had alreadydecayed too much for cross-dating or had fallen over(T. Aakala, unpublished data). Actual mortality rates directlyfollowing the spruce budworm outbreak have thus been con-siderably higher than those observed in this study.

In mixed stands, the high proportion of P. mariana mor-tality prior to the mid-1980s can be attributed to the appar-ently lower decay rates for dead standing P. mariana thanfor dead standing A. balsamea. This is because a higher pro-portion of A. balsamea had fallen or decayed past cross-dating (T. Aakala, unpublished data). The pattern ofP. mariana mortality in mixed stands was relatively constantand similar to that observed in P. mariana stands. The mor-tality rate for A. balsamea in mixed stands was not as highas that which would be expected following a sprucebudworm outbreak with a similar impact. However, the ob-served low level could also be related to the increasing pro-portion of trees falling with time. After the mid-1980s, thetrend of A. balsamea mortality in mixed stands follows thatin A. balsamea stands, with both reaching a high level (un-explained) in the mid-1990s.

Overall, very few data are available for comparing mortal-ity rates in boreal forests among years (Jonsson et al. 2005).The mortality of A. balsamea was similar to that reported bySenecal et al. (2004) for P. glauca, with distinct peaks ofmortality during, and also after, a spruce budworm epidemic.When Senecal et al. (2004) analysed mortality in relation tometeorological data, they found no dependence betweenhigh mortality rates and windstorms. This is consistent withthe data from the present study, which revealed variation inmortality rates even though fallen trees were not included inthe analysis of the temporal variation of mortality. This sug-gests that the numbers of dead trees and the variation inmortality can also be considerable, apart from that attributedto distinct events such as storms and insect outbreaks.

ConclusionsThis study demonstrates that standing-tree mortality is an

important part of overall mortality in the old-growth forestsof northeastern boreal Quebec. The mortality of standingtrees varied considerably from year to year and there weredifferences in mortality in relation to tree size in that smalltrees died standing more often than large trees. There werealso differences in spatial patterns: mortality in A. balsamea

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60 Can. J. For. Res. Vol. 37, 2007

and mixed stands led to a more random distribution of deadtrees. It is obvious that mortality of standing trees is impor-tant in creating structural complexity and habitat diversity inthese stands. This suggests that retaining standing dead treesshould be considered an important part of sustainable man-agement in the forests of the study region.

Acknowledgements

Funding for the fieldwork was provided by National Cen-tres of Excellence, Sustainable Forest Management Network(Canada). We thank Andre-Pierre Cagnon, Marie-NoelleCaron, Sophie Perigon, and Marie-Andre Vaillancourt forassistance with the fieldwork, and are grateful to MartinSimard for assistance with the dendrochronology and toChristine Simard for preparing the sample discs. Threeanonymous reviewers helped in improving an earlier versionof the manuscript.

References

Antonovics, J., and Levin, D.A. 1980. The ecological and geneticconsequences of density-dependent regulation in plants. Annu.Rev. Ecol. Syst. 11: 411–452.

Barot, S., Gignoux, J., and Menaut, J.C. 1999. Demography of asavanna palm tree: predictions from comprehensive spatial pat-tern analyses. Ecology, 80: 1987–2005.

Bergeron, Y., and Dubuc, M. 1989. Succession in the southern partof the Canadian boreal forest. Vegetatio, 79: 51–63.

Blais, J.R. 1983. Trends in the frequency, extent, and severity ofspruce budworm outbreaks in eastern Canada. Can. J. For. Res.13: 539–547.

Boucher, D., Gauthier, S., and De Grandpré, L. 2004. Structuraldynamics of coniferous stands in the northeastern boreal forestof Quebec. In Amélioration de la précision du calcul de lapossibilité forestière par une meilleure connaissance de ladynamique naturelle de la forêt boréale de la Côte-Nord.Ministère des ressources naturelles de la Faune et des Parcs,Québec, Que. pp. 95–116.

Cherubini, P., Giovanni, F., Rigling, D., Dobbertin, M., Brang, P.,and Innes, J.L. 2002. Tree-life history prior to death: two fungalroot pathogens affect tree-ring growth differently. J. Ecol. 90:839–850.

Coulson, R.N., and Witter, J.A. 1984. Forest entomology: ecologyand management. John Wiley & Sons, New York.

De Grandpré, L., Morissette, J., and Gauthier, S. 2000. Long-termpost-fire changes in the northeastern boreal forest of Quebec. J.Veg. Sci. 11: 791–800.

Diggle, P.J. 1983. Statistical analysis of spatial point patterns. Aca-demic Press, London.

Dobbertin, M., Baltensweiler, A., and Rigling, D. 2001. Tree mor-tality in an unmanaged mountain pine (Pinus mugo var.uncinata) stand in the Swiss National Park impacted by root rotfungi. For. Ecol. Manage. 145: 79–89.

Foster, D.R. 1983. The history and pattern of fire in the boreal for-est of southeastern Labrador. Can. J. Bot. 61: 2459–2471.

Foster, D.R. 1988. Species and stand response to catastrophic windin central New England, U.S.A. J. Ecol. 76: 135–151.

Franklin, J.F., Shugart, H.H., and Harmon, M.E. 1987. Tree deathas an ecological process. Bioscience, 37: 550–556.

Gale, N., and Barfod, A.S. 1999. Canopy tree mode of death in awestern Ecuadorian rain forest. J. Trop. Ecol. 15: 415–436.

Gauthier, S., De Grandpré, L., and Bergeron, Y. 2000. Differences

in forest composition in two boreal forest ecoregions of Quebec.J. Veg. Sci. 11: 781–790.

Goreaud, F., and Pelissier, R. 2003. Avoiding misinterpretation ofbiotic interactions with the intertype K12 -function: populationindependence vs. random labelling hypotheses. J. Veg. Sci. 14:681–692.

Grissino-Mayer, H.D. 2001. Evaluating crossdating accuracy: amanual and tutorial for the computer program COFECHA. Tree-Ring Res. 57: 205–221.

Haase, P. 1995. Spatial pattern analysis in ecology based onRipley’s K-function: introduction and methods of edge correc-tion. J. Veg. Sci. 6: 575–582.

Harmon, M.E., Franklin, J.F., Swanson, F.J., Sollins, P., Gregory,S.V., Lattin, J.D., Anderson, N.H., Cline, S.P., Aumen, N.G.,Sedell, J.R., Lienkaemper, G.W., Cromack, K., and Cummins,K.W. 1986. Ecology of coarse woody debris in temperate eco-systems. Adv. Ecol. Res. 15: 133–302.

Harper, K., Boudreault, C., De Grandpré, L., Drapeau, P., Gauthier,S., and Bergeron, Y. 2003. Structure, composition, and diversityof old-growth black spruce boreal forest of the Clay Belt regionin Quebec and Ontario. Environ. Rev. 11: S79–S98.

Harper, K.A., Bergeron, Y., Gauthier, S., and Drapeau, P. 2002.Post-fire development of canopy structure and composition inblack spruce forests of Abitibi, Québec: a landscape scale study.Silva Fenn. 36: 249–263.

Hennon, P.E., and McClellan, M.H. 2003. Tree mortality and foreststructure in the temperate rain forests of southeast Alaska. Can.J. For. Res. 33: 1621–1634.

Holmes, R.L. 1983. Computer-assisted quality control in tree-ringdating and measurement. Tree-Ring Bull. 43: 69–78.

Jonsson, B.G., Kruys, N., and Ranius, T. 2005. Ecology of speciesliving on dead wood — lessons for dead wood management.Silva Fenn. 39: 289–309.

Kenkel, N.C. 1988. Pattern of self-thinning in jack pine: testing therandom mortality hypothesis. Ecology, 69: 1017–1024.

Kneeshaw, D.D., and Bergeron, Y. 1998. Canopy gap characteris-tics and tree replacement in the southeastern boreal forest. Ecol-ogy, 79: 783–794.

Krasny, M.E., and Whitmore, M.C. 1992. Gradual and sudden for-est canopy gaps in Allegheny northern hardwood forests. Can. J.For. Res. 22: 139–143.

Kuuluvainen, T. 1994. Gap disturbance, ground microtopography,and the regeneration dynamics of boreal coniferous forests inFinland: a review. Ann. Zool. Fenn. 31: 35–51.

Kuuluvainen, T., Penttinen, A., Leinonen, K., and Nygren, M.1996. Statistical opportunities for comparing stand structuralheterogeneity in managed and primeval forests: an examplefrom boreal spruce forest in southern Finland. Silva Fenn. 30:315–328.

Kuuluvainen, T., Syrjen, K., and Kalliola, R. 1998. Structure of apristine Picea abies forest in northeastern Europe. J. Veg. Sci. 9:563–574.

Little, L.R. 2002. Investigating competitive interaction from spatialpatterns of trees in multispecies boreal forests: the random mor-tality hypothesis revisited. Can. J. Bot. 80: 93–100.

Liu, Q.-H., and Hytteborn, H. 1991. Gap structure, disturbance andregeneration in a primeval Picea abies forest. J. Veg. Sci. 2:391–402.

Lugo, A.E., and Scatena, F.N. 1996. Background and catastrophictree mortality in tropical moist, wet and rain forests. Biotropica,28: 585–599.

MacLean, D.A. 1980. Vulnerability of fir–spruce stands during un-controlled spruce budworm outbreaks: a review and discussion.For. Chron. 56: 213–221.

McCarthy, J. 2001. Gap dynamics of forest trees: a review withparticular attention to boreal forests. Environ. Rev. 9: 1–59.

Moravie, M.A., and Robert, A. 2003. A model to assess relation-ships between forest dynamics and spatial structure. J. Veg. Sci.14: 823–834.

Oliver, C.D., and Larson, B.C. 1996. Forest stand dynamics. JohnWiley & Sons, New York.

Palik, B.J., and Pederson, N. 1996. Overstory mortality and canopydisturbances in longleaf pine ecosystems. Can. J. For. Res. 26:2035–2047.

Parish, R., Antos, J.A., and Fortin, M.J. 1999. Stand developmentin an old-growth subalpine forest in southern interior British Co-lumbia. Can. J. For. Res. 29: 1347–1356.

Périgon, S. 2005. Dynamique de trouées dans de vieuxpeuplements résineux de la Côte-Nord. Mémoire de maîtrise,Universite du Québec à Montréal, Montréal, Que.

Perry, G.L.W. 2004. SpPack: spatial point pattern analysis in Excelusing Visual Basic for Applications (VBA). Environ. Modell.Software, 19: 559–569.

Peterson, C.J. 2004. Within-stand variation in windthrow in south-ern boreal forests of Minnesota: Is it predictable? Can. J. For.Res. 34: 365–375.

Pham, A.T., De Grandpré, L., Gauthier, S., and Bergeron, Y. 2004.Gap dynamics and replacement patterns in gaps of the northeast-ern boreal forest of Quebec. Can. J. For. Res. 34: 353–364.

Ripley, B.D. 1981. Spatial statistics. John Wiley & Sons,Chichester, UK.

Robitaille, A., and Saucier, J.-P. 1998. Paysages regionaux du Qué-bec meridional. Les publications du Québec, Québec, Que.

Rouvinen, S., and Kouki, J. 2002. Spatiotemporal availability ofdead wood in protected old-growth forests: a case study fromboreal forests in eastern Finland. Scand. J. For. Res. 17: 317–329.

Rouvinen, S., Kuuluvainen, T., and Siitonen, J. 2002. Tree mortal-ity in a Pinus sylvestris dominated boreal forest landscape inVienansalo wilderness, eastern Fennoscandia. Silva Fenn. 36:127–145.

Rowe, J.S. 1972. Forest regions of Canada. Environment Canada,Ottawa, Ont.

Ruel, J.C. 2000. Factors influencing windthrow in balsam fir for-ests: from landscape studies to individual trees studies. For.Ecol. Manage. 135: 169–178.

Senecal, D., Kneeshaw, D., and Messier, C. 2004. Temporal, spa-tial, and structural patterns of adult trembling aspen and whitespruce mortality in Quebec’s boreal forest. Can. J. For. Res. 34:396–404.

Siitonen, J., Martikainen, P., Punttila, P., and Rauh, J. 2000. Coarsewoody debris and stand characteristics in mature managed andold-growth boreal mesic forests in southern Finland. For. Ecol.Manage. 128: 211–225.

Sterner, R.W., Ribic, C.A., and Schatz, G.E. 1986. Testing for lifehistorical changes in spatial patterns of four tropical tree spe-cies. J. Ecol. 74: 621–633.

Wein, R.W., and MacLean, D.A. (Editors). 1983. An overview offire in the northern ecosystems. In The role of fire in the circum-polar ecosystems. John Wiley & Sons, Chichester, UK.

© 2007 NRC Canada

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