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Age distribution patterns in open boreal Dahurican larch forests of Central Siberia
Pores~~~ology Management Forest Ecology
and Management
93 (1997)
20.5-214
Age distribution patterns in open boreal Dahurican larch forests of Central Siberia Alexander Bondarev The Sukachev
Institute
of Forests,
Accepted
22 October
Krasnoyarsk,
Russia
1996
Abstract Tree age distribution determines stand size structure and is related to disturbance history and stand dynamics. Data are presented from 40 pure larch stands from six locations on the Taimyr peninsula and Evenk region in the northern open forests of Russian Siberia. A Weibull density function was used to describe actual age distribution and to simulate age distribution. Larch age distributions have similar patterns in different locations over the study area. All stands were found to have a multi-aged structure. The average coefficient of variation for age is about 48%. The range of tree ages exceed 400 years in the oldest forests. The patterns of age distribution change for different age groups. For the youngest stands (40-80 years old) the age distribution was leptokurtic and positively skewed, whereas for middle-aged forests (80- 180 years old) it tended to be more mesokurtic and symmetric. In the oldest stands (more than 180 years old) the distribution had a platykurtic form. A high correlation was found between the Weibull function coefficients and the coefficient of age variation and for the mean age. The oldest forests were found in river valleys. Middle-aged forests occur more commonly on middle slopes and the youngest forests occupy top slopes and uplands. A more normal tree age distribution assumes a study area less damaged by forest fires than in the more southern parts of the boreal forest. 0 1997 Elsevier Science B.V. Keywords:
Weibull
distribution;
Sparse forests;
Simulation;
Age variation;
1. Introduction
Dahurican larch is the common name for two larch species which together occupy not less than 2.39 million km2 of central and eastern Siberia (Pozdnyakov, 1975): L.urix gmelinii (Ruppr.) Ruppr., which spans vast highlands and mountain systems in central Siberia and Lurid cujunderi, the most common tree species in eastern Siberia. Open or sparse boreal forests cover more than 0.650 million km2 of central Siberia and extend from the Enisei River valley in the west to the Lena River valley
in the east. The northern
0378-I 127/97/$17.00 0 1997 Elsevier PII SO3781127(96)03952-7
border of these
Science
B.V. All rights reserved.
Multi-aged
structure;
Tree limit;
Larix;
Russia
forests defines a tree limit at 70-72”N in the Asian portion of Russia, while the southern border occurs at 65-67”N. Larch covers 95% of the forest territory. These forests are poorly stocked. Most of the stands have a growing stock of 20-30 m3 ha-‘. However, the growing stock may be as high as 60-70 m3 ha- ’ in river valleys, while on upper slopes and highlands it decreases to 5- 10 m3 ha- ‘. The average diameter at breast height (DBH) range is 5-15 cm and mean tree height ranges from 3 to 8 m. Moss-shrub and lichen-shrub forest types dominate. The study area has been little investigated. Most
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Ecology
and Management
of the information about these forests are botanical descriptions that do not contain data about forest structure, forest growth and dynamics. This article presents the first data on age structure in the northernmost forests of the world. Nowhere else in the world does the forest border extend so far to the north as on the Taimyr peninsula, where it reaches 72”N. Another objective of this research was to find an appropriate approach to estimate tree age distributions in remote regions and to develop models for their simulation. We also examined geographical patterns in tree age distribution. The study of tree age distribution is very important, especially for multi-aged stands. It aids in understanding stand size structure and is related to the forest disturbance history (Ross et al., 1982) and stand dynamics.
Table I Geographic LoCation
93 (1997)
location
205-2
14
of the sampled
stands
Lat. (N)
Long (E)
Elevation Cm a.s.l.1
72%’ 72”30’ 71”17’ 70”53 68O44’ 66’52’
I OY20’ I 0 l”50’ 99”30 102”56 lW20’ io2°20’
40 30 40 300 I 00 510
No. af StiMds :;ampled l___- . I0 I!)
.; 5
were sampled for study. Table 1 shows the geographical coordinates of the six locations.
3. Materials
and methods
3.1. Sample plot and single-tree inventor?, 2. Study areas Forty pure larch stands in six different locations of the Taimyr peninsula and Evenk regions (Fig. 1)
Fig. 1. Location
(numbers)
Different sampling designs were used for the sample stands. The first stage included the use of topographic maps and satellite imagery to find the
of sampled
stands.
A. Bondarev
/Forest
Ecology
and Management
most common landscapes for sampling. Because of the remoteness of the study area, transportation was also taken into consideration. Most stands (locations 1, 2 and 4) were analysed by establishing rectangular permanent plots. Their usual size was 0.5 ha, except in very sparse stands where a 1.0 ha size was used.
Table 2 Characteristics Location 1
2
3
4
5
6
a Normal
of the sampled Stand 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 1 2 3 1 2 3 4 5 1 2 3 4 5
(Pressler’s)
93 (1997)
20.5-214
201
At other locations, denser stands were studied with circular plots. Seven to ten circular plots, each with not less than 30 trees per plot, were created in every stand. Depending on tree quantity, plot sizes ranged from 100 to 500 m2 for a combined total of 144 circular plots.
stands
No. of stems ha-’ 580 420 312 464 560 135 324 324 313 238 380 662 422 436 770 500 428 596 1034 430 1156 694 592 825 708 433 552 402 181 528 486 3186 3243 2575 658 797 1189 1166 1532 850 form factor
As
DBH
Cm)
Basal area (m* ha-‘)
Form a height (m)
Canopy
(years) (cm)
203 204 208 64 186 150 139 164 110 154 194 214 191 210 120 159 188 160 110 135 230 171 239 166 156 226 265 97 213 292 257 129 140 171 213 175 154 102 154 172
12.6 11.0 9.2 7.6 9.4 10.3 10.0 11.8 8.0 10.3 11.2 8.3 9.2 10.5 6.0 9.1 9.6 7.9 5.6 7.1 11.0 10.1 13.4 8.4 7.3 13.4 14.1 7.7 7.8 13.4 15.6 6.9 7.0 7.2 13.8 10.5 9.5 7.1 7.7 12.3
6.5 5.3 4.5 4.4 5.0 4.9 5.2 5.3 4.3 5.0 4.8 4.5 4.7 5.4 3.6 4.7 4.8 4.8 4.5 4.9 6.5 5.7 6.6 4.4 3.5 6.7 7.3 3.8 3.6 6.2 9.2 5.3 5.3 4.8 8.0 6.0 5.6 4.4 4.5 7.5
7.00 3.64 1.94 1.98 3.12 1.07 2.42 3.40 1.52 2.70 3.56 3.47 2.66 3.63 2.04 3.12 2.96 2.92 2.61 1.70 10.50 5.56 1.92 4.31 3.50 5.78 8.26 1.78 0.83 7.10 8.88 11.43 12.00 10.00 9.33 6.61 8.00 4.50 6.95 9.66
3.5 3.1 2.7 3.0 2.9 2.9 3.0 3.0 2.1 2.8 2.6 2.7 2.6 2.9 2.4 2.8 2.7 2.8 2.8 2.7 4.0 3.5 4.1 2.8 3.0 3.9 4.2 2.5 2.2 3.6 5.1 3.9 3.6 3.6 4.9 3.9 3.6 2.9 3.4 4.8
0.35 0.19 0.12 0.21 0.25 0.08 0.14 0.18 0.10 0.15 0.19 0.17 0.11 0.22 0.15 0.20 0.15 0.15 0.18 0.12 0.36 0.24 0.28 0.14 0.20 0.25 0.35 0.18 0.06 0.30 0.20 0.36 0.42 0.41 0.24 0.16 0.25 0.18 0.23 0.28
was used to calculate
Height
form height.
closure
Growing stock cm3 ha- ’ ) 24.3 11.2 5.2 5.9 10.7 3.1 7.2 10.2 4.2 7.7 9.3 9.5 7.0 10.4 4.9 8.7 8.0 8.2 1.2 4.6 42.3 19.3 32.6 12.1 10.7 22.3 34.9 4.4 1.8 25.1 45.7 44.3 43.3 35.9 45.5 25.7 29.1 12.9 23.8 46.3
208
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All trees with DBH greater than 1.5 cm and height more than 1.3 m were numbered and their diameter (DBH) and height were measured. A subsample of 35-40 trees was systematically sampled (each Zth tree up to a total of not less than 35-40 trees) in each stand for age determination. A total of 1360 sampled trees were aged. Two permanent plots at location 2 were chosen to estimate the accuracy of sampling design. The age of 180 trees was determined in each permanent plot. To determine tree age, we took cores from sampled trees at stump height (l- 10 cm depending on tree size), and dried, sanded and counted the annual rings using a binocular microscope. Data from five plots (two in location 2 and three in location 4) were chosen for estimating the bias in the distribution simulation and were not included in further analyses. 3.2. Statistical analysis To estimate the bias in the samples, we created sub-samples from two plots (Positions 4 and 8 in location 1) in which all trees were aged. Every fifth tree was chosen for sub-sampling. A t-test was used to estimate the mean age between sample groups and the tota group, while the Kolmogorov-Smirnov (KS) test (Hollander and Wolfe, 1973) was applied to discover similarity in distribution patterns. Statistical analysis was done using STATGRAPHICS” software (Ver. 3.0). The t-test results revealed an absence of significant differences between mean age for total and sub-sampled populations: 1. forstand4, t=OSll,d.f.=36, P=O.613; 2. for stand 8, t = -0.594, d.f. = 36, P = 0.556. The KS test supported similarity in distribution patterns: 1. for stand 4, KS = 0.16 (critical value 0.221) for both stands for 36 d.f., P = 0.41; 2. for stand 8, KS = 0.11, P = 0.99. Basic statistics were computed for each sampled stand. Table 2 presents the mean and total characteristics of the sampled stands. We used the Weibull two-parameter function , _ e-( t/PI-
(1) to describe the actual age distribution and to simulate age distribution for model stands. Interval values
and
Management
93 (1997)
205-214
were calculated as a difference ( N2 - N,) between the following expressions N, = 1 - e-‘X,/p’”
(21
and N = 1 _ ,-c~i-/~)” (3) 2 where N, and N2 are values at the beginning and end of age intervals xl and x2, which are, respectively, age at the beginning and end of the interval. Some reasons predetermined the choice of the Weibull function. It has been reported (Bailey and Dell, 1973; Svalov, 1982; Little, 1983; Ganina, 1984; Kaplunov and Kuzmichev, 1985; Khatouri and Dennis, 1990; Popov, 1993) that the Weibull function is appropriate and flexible for describing distribution patterns in biological systems. It would be possible to find well fitted, different functions for each age distribution but we would then be unable to solve the problem of distribution simulation. Regressionanalysis was used to estimate the correlation between the curve shape( p coefficient) and mean age, coefficient ct, which determines curve scale, and the coefficient of age variation (Cy, 1.
4. Results 4.1. Statistics and distribution patterns To simplify presentation, we combined all stands in 40-year age classesby mean stand age. Table 3 showsthe resultsof smoothing the basic statistics for these groups. Mean age in the first column defines the average age of each class. All standswere found Table 3 Results of basic statistical
calculations
for age groups
x
STD
cv
Ex
AS
Min
Max
R
R,,
60 100 140 180 220 260
29 48 67 85 104 122
49 48 48 47 47 47
1.47 0.78 0.16 -0.05 -0.18 -0.65
1.03 0.95 0.98 0.78 0.25 0.19
34 44 52 59 65 71
174 252 322 387 448 506
140 208 270 328 383 435
2.33 2.08 I .93 1.83 1.74 1.68
x, group variation; maximum:
mean age; STD, standard deviation; CV, coefficient of Ex. kurtosis; As, skewness: Min, minimum; Max, R, range; R,e,, relative range CR,, = R/a.
-
A. Bondarev
40
80
120
160
200
/ Forest Ecology
and Management
00;
20 Coeftkient
and fitted age distribution
205-214
209
240
Age,years
Fig. 2. Actual
93 (1997)
for stand 2-8.
40
60
80
of wriation,
Fig. 4. LY and /?I coefficients mean age.
to have a multi-aged structure. Fig. 2 shows the common picture for age distribution in the study area. The absolute range for the oldest stands can extend to over 400 years while the largest relative range is more common for the youngest forests. The coefficient of variation sometimes exceeds 60% (Fig. 3) and has a very small tendency to decline with increasing mean age. The largest differences in the coefficient of variation (22-61%) were found for youngest stands. The youngest forests have a positively skewed leptokurtic (spiked) distribution. Beginning at the age of 180 years, distribution tends to be platykurtic (flatter) because of increasing range and declining population in central classes. Changes in homogeneity are also apparent. Skewness of distribution decreases slightly as well. The cr and p coefficients for the Weibull function were calculated for each stand using the least square method in STATGRAPHICS’“. Fig. 4 shows the relationship between /3 and mean age, LY and
"OW
100 %
as a function
100 we.”
300 age,
years
of age variation
and
CV’. Linear regression equations were computed for both coefficients 1 - = 0.101 + 0.00708 X CV, i- 0.054 (4) aA
and PA = 1.143 XA & 12years
(5) The correlation is stronger for the /3 coefficient and mean age, (adj. R* = 0.97) while age variability explains about 69% of the coefficient (Y variance (adj. R* = 0.69). The p coefficient increases with increasing mean age. This results in reducing the population in the middle age classes and forms a more platykurtic distribution. Curve scale (Y, is more stable and has less influence on the shape of the curve. While CV’ has a small tendency to decline with mean age, the coefficient (Y increases and forms a less positively skewed curve. To illustrate the results of distribution simulation
80
I OO
100 Mean
Fig. 3. Relationship variation.
between
200
300
age,yem
mean
age and coefficient
Age, years
of age
Fig. 5. Results tions.
of Weibull
simulation
for different
age distribu-
210
A. Bon&rev/Forest
Table 4 The accuracy estimation Weibull function Plot no.
Ecology
and Management
4.2. Geographical patterns in the age distribution of actual
Simulated coefficient
age distribution
KS test
using
cy
P
KS,<
KS,,
1.7909
244.94
0.184
0.221
9.6
9.5
2-3 4-l
2.7768 1.6643
218.66 111.25
2.0540 1.9959
311.58 333.98
0.221 0.207 0.227
1.8 19.5
4-2 4-3
0.083 0.229 0.113
6.0 7.8 3.8
0.140
0.231
KS test, Kolmogorov-Smimov
the
,y’ test
2-2
X&’
2.0 3.9
x;
6.0
test
for the age groups, we calculated (Y and p coefficients using data in Table 3. Fig. 5 shows modelled distributions for some age groups. As we had rather similar CV’ values, the main difference between curves was determined by the p coefficient. The preliminary results of distribution analysis are supported by the simulation model. The older the stands were, the more platykurtic the curve of the distribution. To test Eqs. (4) and (5) we used data from five stands selected to estimate the distribution prediction. The results, displayed in Table 4, show a good agreementbetween actual age distribution and simulation by Weibull function when IY and p coefficients are calculated using Eqs. (4) and (5). Only in one case, for the stand occurring at the tree line in the mountains (plot 4-l), was the critical value of test estimatorsgreater than the actual value.
Table 5 MANOVA
results for estimating
Source of variance Covariance variables Latitude Elevation Slope Qualitative factors Exposure Relief Location Qualitative factor interaction Residual variance Total
93 (19971205-214
site location
factor
influence
on mean age variance
Variance
Factor (%‘c)
705.0
32.2
335.3 493.5 123.6 586.9
15.3 12.5 5.6 26.8 13.8 9. I
302.5 200.3 247.8 122.0 778.5 2192.4
Early in the research,we found that mean age and age structure were in&renced by site locations. To quantify this, we examined the structure of sampled stands by their geographical locations. elevations. and slope exposure or aspect. A confidence interval method in an MANOVA routine (Bock, 1975; Mardia et al., 1979; Stehman and Meredith, 1995) was used to estimate the influence of geographic factors on age distribution. All analysed factors were combined into two groups. Latitude, elevation, and slope composed a quantitative group, and aspect, relief, and location representedqualitative factors. Eight aspect groups were determined: N (north), NE (northeast), E (east), SE (southeast), S (south). SW (southwest), W (west), and NW (northwest). Relief was divided into three groups: (1) plains; (2) highlands: (3) mountains. Three groups were established for location: (1) valley; (2) slope; (3) upland. The results from examining these geographical factors are described below. 1. Aspect: the influence of this factor was 28% and was not significant at the 95% probability level. Confidence intervals for each group were overlapping. 2. Relief: the influence of this factor was 6% and was not significant at the 95% probability level. Confidence intervals for each group were overlapping.
11.3 5.6 35.4 100.0
weight
F-test
Prob.
6.0 8.6
0.003 0.007
12.6 3.2
0.001 0.087 0.095 0.661 0.098
7.3 0.7 2.6 3.2 1.6 .-
0.060 0.230
value
A. Bondareu / Forest Ecology and Management 93 (1997) 205-214
3. Location: the influence of this factor was 21% and was significant. Confidence intervals for ‘ valley’ and ‘uplands’ groups were not overlapping. Confidence intervals for ‘slope’ and ‘upland’ groups had a 21% overlap. Mean tree age for ‘upland’ was determined to be 153 years; for ‘slope’, 191 years; and for ‘valley’, 234 years. Table 5 illustrates the results of the estimatesfor the combined influence of the qualitative and quantitative groups. Quantitative factors were used as covariance variables. In spite of a residual variance shareof only 35%, most of the analysed factors and their interaction were not significant. This is shown by F-test and probability values that generally exceed the 0.005 level. The significant factors were latitude and elevation but their total weight did not exceed 38% of the total variance.
5. Discussion A multi-aged tree age pattern is very common for Siberian larch forests. One reasonis a long period of forest growth-generally hundredsof years. Another reason, and more important from our point of view, are that forest fires occur several times during the life of the forest (Kolesnikov, 1947; Falaleev, 1957; Faialeev and Machemis, 1975; Ditrich, 1970, etc.). Estimates for multi-aged composition of Siberian larch forests differ. In the Lake Baikal region, it was found to be about 82% (Babintseva, 1990) and 84% (Svalov, 1963) and in the Krasnoyarsk region about 43% (Shanin, 1967). The remaining forests were even-aged.The total shareof multi-aged larch forests in Siberia hasbeen found to range from 75% (Svalov, 1963) to 53% (Shanin, 1967). These estimatesdiffer owing to inconsistenciesin which territories are included in the analysis and which kinds of forest are uneven-aged. In our investigation, all samplestands were multi-aged. Full-aged or multi-aged structure is common for the boreal forests of the Northern Hemisphere. The main problems under discussionare the reasonsfor the multi-aged structure and the dynamics of these forests. Two approachescan be discussed.First, a multi-aged pattern reflects severe past disturbances within a stand (Kolesnikov, 1947; Ditrich, 1970; Ross et al., 1982; Lorimer, 1985; Stewart, 1986;
211
Despontsand Payette, 1992). A secondpoint of view is based on the suggestion that the northern boreal forests are not so heavily influenced by catastrophic forest fires (Quinby, 1991). It hasbeen hypothesised by Dzedzylya (1969) that theseforests preserve their structure over centuries without the occurrence of catastrophic disturbances.Very low natural mortality is compensatedfor by continuous recruitment so that their mean parameters (diameter, height, growing stock) and distribution pattern does not change significantly. Yet, in northern forests, fire disturbances are more evident as permafrost causesroot systems to be exposed to greater damage.Post-fire regeneration here yields an even-aged forest structure instead of a continuous recruitment that would yield a multi-aged forest. Holla and Knowles (1988) emphasized that a multi-aged distribution points to continual recruitment as the major component of population dynamics, while fire would be a minor component. They describe consistent biomasspatterns for White Pine (Pinus strobus) populations in northwestern Ontario. A similar conclusion was made for Scats pine (Pinus syluestris) growing just below the tree-limit in the Swedish Scandes(Kullman, 1987). Kullman (1987) recorded a continuous regeneration pattern even during climatically unfavourable periods. Johnson et al. (1994) pointed to a short post-fire period of high recruitment in a Pinus contorta-Picea engelmmnii forest in Canada followed by low and sporadic recruitment for the remaining life of the stands. Engelmark et al. (1994) assumedthat a J-shaped age structure in pines of northern Sweden was due to a stationary forest pattern. A J-shapewould mean a negative exponential age distribution curve which assumesthat most of the population occurs at the beginning of the distribution curve. Suffling (1983) showed that 80% of the typical age-class distributions of boreal and Great Lakes forests had a similar negative exponential age distribution. The samedistribution pattern was found by Pitcher (1987) in red fir ( Abies mgnifica) forests within Sequoia National Park, California. These patterns differ from the age distribution patterns in Dahurican larch forests above the Arctic Circle in central Siberia. Our investigation hasshown a more normally distributed age population (Fig. 2).
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A. Bondarm
/ Forest
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This Gaussian age distribution also has been observed in multi-aged larch forests in the Magadan region of the Russian Far East (Podmasko, 1973). The age distribution of a forest reflects its dynamics and history of disturbances. The differences found in coefficient of age variation, especially in youngest forests, are the results of different succession scenarios. When most trees die in the first few years after severe forest fires, new regeneration forms a positively skewed age distribution that has less age variation. The more trees that survive after fires, the more broadly distributed the age population is. As a result, the coefficient of variation increased greatly. During the post-fire period, variation gradually decreases due to increasing mean age. We hypothesise that a negative exponential age distributions are a result of periodic catastrophic fires occurring on most of the territory of the northern boreal forests. The restoration of forest vegetation takes place generally by even-age regeneration which forms major peak on the age distribution curve. The surviving trees are a minority of the population. Normally distributed populations are due to continuous recruitment. This scenario suggests a low frequency and less dramatic forest fire history in the northern part of the boreal zone. Some geographical patterns in age distribution were found during investigation. Mean age and age distribution correlate with stand location. The oldest stands with platykurtic age distributions occur in the river valleys and lower terraces and are never observed on the upper slopes or uplands, while the youngest forests with leptokurtic distribution are more often on upper slopes or upland sites and are observed on river banks only as a result of regeneration after clear cutting and fires. The most common explanation for the youngest forest occurrence on the uplands, where they define tree limit is the forest border invasion due to a global warming (Ball, 1986; Kullman and Engelmark, 1990; Kullman, 1992; McDonald et al.. 1993; Payette et al., 1994). However, other warm periods have occurred in the past, which casts some doubt on this explanation. During these past warmer periods there was the unrealised potential to form old-growth stands at upland sites. We hypothesise that harsh winds also determine age distribution. Only a few trees older than 200
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93 (1997)
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years were found without central pith decay. The oldest trees usually have the biggest size so they are affected and damaged by strong winds more at the open upland sites than in sheltered river valleys.
6. Conclusions 1. Larch age distributions have similar patterns in different locations all over the study area. All stands had a multi-aged structure. 2. The average coefficient of variation for age is about 48%. The range of tree ages exceed 400 years in the oldest forests. 3. The age distribution pattern changes for different age groups. For youngest stands (40-80 years) the age curve is spiked or leptokurtic and positively skewed while for middle-aged forests (80180 years) the curve tends to be more mesokurtic and symmetric. In the oldest forests (over 180 years old) the curve has a flatter or platykurtic form. 4. The actual age distribution can be simulated using a two-parameter Weibull function. A high correlation was observed for the cy coefficient, which defines the curve shape and the coefficient of age variation, and for the p coefficient, which defme curve scale and the mean age. 5. The oldest forests were found in river valleys and lower river terraces. Middle-aged forests occur more commonly on middle slopes and the youngest forests occupy top slopes and uplands. Mean tree age is poorly correlated with latitude and elevation. 6. A more normal age distribution for a tree population assumes a study area less damaged by forest fires than in the more southern parts of the horeal forest. Forest fires are less important for forest succession above the Arctic Circle than below it. 7. Age distribution patterns can be used for the analysis and prognosis of fire history and disturbance over the boreal forest. 8. Age occurrence in forests is a function of position in the landscape which has implications for the modelling of global changes including global warming.
A. Bondarev
/ Forest Ecology
9. The age structure of forests at the tree limit is the result not only of tree invasion or climatic change, but other ecological factors including strong winds which destroy the largest and oldest trees in open forest landscapes.
Acknowledgements I would like to thank Thomas Stone (The Woods Hole Research Center) for his assistance in preparing this manuscript. I am also appreciative of Richard Houghton and George Woodwell for their comments on the manuscript. Special thanks to Andy Gillespie and Thomas Jacob (US Forest Service) for their advice and criticism. Support for the preparation of this article while at the Woods Hole Research Center came from the John D. and Catherine T. MacArthur Foundation and the Trust for Mutual Understanding.
References Babintseva, R.M., 1990. Regeneration in regional system of forest management. Abstract of Doctoral Thesis, Forest and Wood Institute, Krasnoyarsk. (In Russian.) Bailey, R.L. and Dell, T.R., 1973. Quantifying diameter distributions with the Weibull function. For. Sci., 19: 97-104. Ball. T.F.. 1986. Historical evidence and climatic implications of a shift in the boreal forest tundra transition in central Canada. Climate Change, 8f.2): 121-134. Bock, R.D., 1975. Multivariate Statistical Methods in Behavioral Research. McGraw-Hill, New York. Desponts, M. and Payette, S., 1992. Recent dynamics of jack pine at its northern distribution limit in northern Quebec. Can. J. Bot., 7d6): 1157-l 167. Ditrich, V.I., 1970. Structure, growth dynamics, and inventory patterns of larch forests in Irkutsk region. Abstract of Doctoral Thesis, Leningrad Technology Institute, Leningrad. (In Russian.) Dzedzylya, A.A., 1969. Silviculture-inventory patterns in larch forests of the Chantaika river basin. Abstract of Doctoral Thesis, Siberian Technology Institute, Krasnoyarsk. (In Russian.) Engelmark, O., Kullman, L. and Bergeron, Y., 1994. Fire and age structure of Scats pine and Norway spruce in northern Sweden during the past 700 years. New Phytol., 126(l): 163-168. Falaleev, E.N., 1957. Age structure of Enisei ridge larch forests. Collect. Articles Siberian Technol. Inst. Krasnoyarsk. 16(4): 18-23. (In Russian.) Falaleev. E.N. and Machernis, P.I., 1975. Inventory features in
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uneven-aged larch forests. Collect. Articles Siberian Technol. Inst. Krasnoyarsk, 4: 15-18. (In Russian.) Ganina, N.V., 1984. Diameter distribution using Weibull distribution function. For. Sci., N2: 65-70. (In Russian.) Holla, T.A. and Knowles, P., 1988. Age structure analysis of a virgin white pine, Pinus strobes, population. Can. Field Nat., 102(2): 221-226. Hollander, M. and Wolfe, D.A., 1973. Nonparametric Statistical Methods. John Wiley, New York. Johnson, E.A., Miyanishi, K. and Kleb, H., 1994. The hazards of interpretation of static age structures as shown by stand reconstructions in a Pinus contorta-Picea engelmannii forest. J. Ecol.. 82(4): 923-93 1. Kaplunov, V.Y. and Kuzmichev, V.V., 1985. The relationships between distribution sets on diameter, basal area and volume. In: Structure, Growth and Inventory of the Stands. Institute of Forest and Wood, Krasnoyarsk, pp. 46-52. (In Russian.) Khatouri, M. and Dennis, B., 1990. Growth-and-yield model for uneven-aged Cedrus atlantica stands in Morocco. For. Ecol. Manage., 36(2): 253-266. Kolesnikov, B.P., 1947. Larch forests of the Middle-Amur plain. Primizdat, Vladivostok. (In Russian.) Kullman, L., 1987. Long-term dynamics of high-altitude populations of Pinus qhestris in the Swedish Scandes. J. Biogeogr., 14(l): l-8. Kullman, L., 1992. Holocene thermal trend inferred from tree-limit history in the Scandes Mountains. Global Ecol. Biogeogr. Lett., 2(6): 181-188. Kullman, L. and Engelmark, O., 1990. A high Late Holocene tree-limit and the establishment of the spruce forest-limit-a case study in northern Sweden. Boreas, 19(4): 323-331. Little, S.N., 1983. Weibull diameter distributions for mixed stands of western conifers. Can. J. For. Res., 13: 85-88. Lorimer, C.G., 1985. Methodological considerations in the analysis of forest disturbance history. Can. J. For. Res., 15( 1): 200-213. Mardia, K.V., Kent, J.T. and Bibby, J.M., 1979. Multivariate Analysis. Academic Press, London. McDonald, G.M., Edwards, T.W.D., Moser. K.A., Pienitz, R. and Smol, J.P., 1993. Rapid response of treeline vegetation and lakes to past climate warming. Nature, 361 (6409): 243-246. Payette, S., Delwaide, A., Momeau, C. and Lavoie, C., 1994. Stem analysis of a long-lived black spruce clone at treeline. Amt. Alp. Res., 26(l): 56-59. Pitcher, D.C., 1987. Fire history and age structure in red fir forests of Sequoia National Park, California. Can. J. For. Res.. 17(7): 582-587. Podmasko, B.I., 1973. Larch forest inventory m the north of the USSR Far East using aircraft image interpretation methods. Abstract of Doctoral Thesis, Moscow Forest Technology Institute, Moscow. (In Russian.) Popov, V.E., 1993. The structure of cedar (Pinus sibirica) forests on Lena-Angara Plato. In: The Structure and Growth of Siberian Forests. Krasnoyarsk publication, pp. 69-87. (In Russian.)
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Pozdnyakov, L.K., 1975. Dahurican larch. Nauka, Moscow. (In Russian.) Quinby, P.A., 1991. Self-replacement in old-growth white pine forests of Temagami, Ontario. For. Ecol. Manage., 41(1-2): 95-109. Ross, MS., Sharik, T.L. and Smith, D.W.. 1982. Age-structure relationships of tree species in an Appalachian oak forest in Southwest Virginia. Bull. Torrey Bot. Club, 109(3): 287-298. Shanin, S.S., 1967. Structural features of Siberian pine and larch forests. Abstract of Doctoral Thesis, Forest and Wood Institute, Krasnoyarsk. (In Russian.) Stehman, S.V. and Meredith, M.P., 1995. Practical analysis of factorial experiments in forestry. Can. J. For. Res., 25: 4466 461.
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Stewart, G.H., 1986. Population dynamics of a montane conifer forest, western Cascade Range, Oregon, USA. Ecology, 67(2): 534-544. Sufffing, R., 1983. Stability and diversity in boreal and mixed temperate forests: a demographic approach. Environ. Manage.. 17(4):
359-371.
Svalov, N.N.. 1963. Forest management basics in taiga regiom. Goslesbumizdat, Moscow. (In Russian.) Svalov, S.N., 1982. The estimating of Weibull distribution function for stand structure description. Sci. Articles Moscow For. Technol. Inst.. 139: 172-174. (In Russian.1
and Management
93 (1997)
20.5-214
Age distribution patterns in open boreal Dahurican larch forests of Central Siberia Alexander Bondarev The Sukachev
Institute
of Forests,
Accepted
22 October
Krasnoyarsk,
Russia
1996
Abstract Tree age distribution determines stand size structure and is related to disturbance history and stand dynamics. Data are presented from 40 pure larch stands from six locations on the Taimyr peninsula and Evenk region in the northern open forests of Russian Siberia. A Weibull density function was used to describe actual age distribution and to simulate age distribution. Larch age distributions have similar patterns in different locations over the study area. All stands were found to have a multi-aged structure. The average coefficient of variation for age is about 48%. The range of tree ages exceed 400 years in the oldest forests. The patterns of age distribution change for different age groups. For the youngest stands (40-80 years old) the age distribution was leptokurtic and positively skewed, whereas for middle-aged forests (80- 180 years old) it tended to be more mesokurtic and symmetric. In the oldest stands (more than 180 years old) the distribution had a platykurtic form. A high correlation was found between the Weibull function coefficients and the coefficient of age variation and for the mean age. The oldest forests were found in river valleys. Middle-aged forests occur more commonly on middle slopes and the youngest forests occupy top slopes and uplands. A more normal tree age distribution assumes a study area less damaged by forest fires than in the more southern parts of the boreal forest. 0 1997 Elsevier Science B.V. Keywords:
Weibull
distribution;
Sparse forests;
Simulation;
Age variation;
1. Introduction
Dahurican larch is the common name for two larch species which together occupy not less than 2.39 million km2 of central and eastern Siberia (Pozdnyakov, 1975): L.urix gmelinii (Ruppr.) Ruppr., which spans vast highlands and mountain systems in central Siberia and Lurid cujunderi, the most common tree species in eastern Siberia. Open or sparse boreal forests cover more than 0.650 million km2 of central Siberia and extend from the Enisei River valley in the west to the Lena River valley
in the east. The northern
0378-I 127/97/$17.00 0 1997 Elsevier PII SO3781127(96)03952-7
border of these
Science
B.V. All rights reserved.
Multi-aged
structure;
Tree limit;
Larix;
Russia
forests defines a tree limit at 70-72”N in the Asian portion of Russia, while the southern border occurs at 65-67”N. Larch covers 95% of the forest territory. These forests are poorly stocked. Most of the stands have a growing stock of 20-30 m3 ha-‘. However, the growing stock may be as high as 60-70 m3 ha- ’ in river valleys, while on upper slopes and highlands it decreases to 5- 10 m3 ha- ‘. The average diameter at breast height (DBH) range is 5-15 cm and mean tree height ranges from 3 to 8 m. Moss-shrub and lichen-shrub forest types dominate. The study area has been little investigated. Most
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and Management
of the information about these forests are botanical descriptions that do not contain data about forest structure, forest growth and dynamics. This article presents the first data on age structure in the northernmost forests of the world. Nowhere else in the world does the forest border extend so far to the north as on the Taimyr peninsula, where it reaches 72”N. Another objective of this research was to find an appropriate approach to estimate tree age distributions in remote regions and to develop models for their simulation. We also examined geographical patterns in tree age distribution. The study of tree age distribution is very important, especially for multi-aged stands. It aids in understanding stand size structure and is related to the forest disturbance history (Ross et al., 1982) and stand dynamics.
Table I Geographic LoCation
93 (1997)
location
205-2
14
of the sampled
stands
Lat. (N)
Long (E)
Elevation Cm a.s.l.1
72%’ 72”30’ 71”17’ 70”53 68O44’ 66’52’
I OY20’ I 0 l”50’ 99”30 102”56 lW20’ io2°20’
40 30 40 300 I 00 510
No. af StiMds :;ampled l___- . I0 I!)
.; 5
were sampled for study. Table 1 shows the geographical coordinates of the six locations.
3. Materials
and methods
3.1. Sample plot and single-tree inventor?, 2. Study areas Forty pure larch stands in six different locations of the Taimyr peninsula and Evenk regions (Fig. 1)
Fig. 1. Location
(numbers)
Different sampling designs were used for the sample stands. The first stage included the use of topographic maps and satellite imagery to find the
of sampled
stands.
A. Bondarev
/Forest
Ecology
and Management
most common landscapes for sampling. Because of the remoteness of the study area, transportation was also taken into consideration. Most stands (locations 1, 2 and 4) were analysed by establishing rectangular permanent plots. Their usual size was 0.5 ha, except in very sparse stands where a 1.0 ha size was used.
Table 2 Characteristics Location 1
2
3
4
5
6
a Normal
of the sampled Stand 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 1 2 3 1 2 3 4 5 1 2 3 4 5
(Pressler’s)
93 (1997)
20.5-214
201
At other locations, denser stands were studied with circular plots. Seven to ten circular plots, each with not less than 30 trees per plot, were created in every stand. Depending on tree quantity, plot sizes ranged from 100 to 500 m2 for a combined total of 144 circular plots.
stands
No. of stems ha-’ 580 420 312 464 560 135 324 324 313 238 380 662 422 436 770 500 428 596 1034 430 1156 694 592 825 708 433 552 402 181 528 486 3186 3243 2575 658 797 1189 1166 1532 850 form factor
As
DBH
Cm)
Basal area (m* ha-‘)
Form a height (m)
Canopy
(years) (cm)
203 204 208 64 186 150 139 164 110 154 194 214 191 210 120 159 188 160 110 135 230 171 239 166 156 226 265 97 213 292 257 129 140 171 213 175 154 102 154 172
12.6 11.0 9.2 7.6 9.4 10.3 10.0 11.8 8.0 10.3 11.2 8.3 9.2 10.5 6.0 9.1 9.6 7.9 5.6 7.1 11.0 10.1 13.4 8.4 7.3 13.4 14.1 7.7 7.8 13.4 15.6 6.9 7.0 7.2 13.8 10.5 9.5 7.1 7.7 12.3
6.5 5.3 4.5 4.4 5.0 4.9 5.2 5.3 4.3 5.0 4.8 4.5 4.7 5.4 3.6 4.7 4.8 4.8 4.5 4.9 6.5 5.7 6.6 4.4 3.5 6.7 7.3 3.8 3.6 6.2 9.2 5.3 5.3 4.8 8.0 6.0 5.6 4.4 4.5 7.5
7.00 3.64 1.94 1.98 3.12 1.07 2.42 3.40 1.52 2.70 3.56 3.47 2.66 3.63 2.04 3.12 2.96 2.92 2.61 1.70 10.50 5.56 1.92 4.31 3.50 5.78 8.26 1.78 0.83 7.10 8.88 11.43 12.00 10.00 9.33 6.61 8.00 4.50 6.95 9.66
3.5 3.1 2.7 3.0 2.9 2.9 3.0 3.0 2.1 2.8 2.6 2.7 2.6 2.9 2.4 2.8 2.7 2.8 2.8 2.7 4.0 3.5 4.1 2.8 3.0 3.9 4.2 2.5 2.2 3.6 5.1 3.9 3.6 3.6 4.9 3.9 3.6 2.9 3.4 4.8
0.35 0.19 0.12 0.21 0.25 0.08 0.14 0.18 0.10 0.15 0.19 0.17 0.11 0.22 0.15 0.20 0.15 0.15 0.18 0.12 0.36 0.24 0.28 0.14 0.20 0.25 0.35 0.18 0.06 0.30 0.20 0.36 0.42 0.41 0.24 0.16 0.25 0.18 0.23 0.28
was used to calculate
Height
form height.
closure
Growing stock cm3 ha- ’ ) 24.3 11.2 5.2 5.9 10.7 3.1 7.2 10.2 4.2 7.7 9.3 9.5 7.0 10.4 4.9 8.7 8.0 8.2 1.2 4.6 42.3 19.3 32.6 12.1 10.7 22.3 34.9 4.4 1.8 25.1 45.7 44.3 43.3 35.9 45.5 25.7 29.1 12.9 23.8 46.3
208
A. Bondarel~/Forest
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All trees with DBH greater than 1.5 cm and height more than 1.3 m were numbered and their diameter (DBH) and height were measured. A subsample of 35-40 trees was systematically sampled (each Zth tree up to a total of not less than 35-40 trees) in each stand for age determination. A total of 1360 sampled trees were aged. Two permanent plots at location 2 were chosen to estimate the accuracy of sampling design. The age of 180 trees was determined in each permanent plot. To determine tree age, we took cores from sampled trees at stump height (l- 10 cm depending on tree size), and dried, sanded and counted the annual rings using a binocular microscope. Data from five plots (two in location 2 and three in location 4) were chosen for estimating the bias in the distribution simulation and were not included in further analyses. 3.2. Statistical analysis To estimate the bias in the samples, we created sub-samples from two plots (Positions 4 and 8 in location 1) in which all trees were aged. Every fifth tree was chosen for sub-sampling. A t-test was used to estimate the mean age between sample groups and the tota group, while the Kolmogorov-Smirnov (KS) test (Hollander and Wolfe, 1973) was applied to discover similarity in distribution patterns. Statistical analysis was done using STATGRAPHICS” software (Ver. 3.0). The t-test results revealed an absence of significant differences between mean age for total and sub-sampled populations: 1. forstand4, t=OSll,d.f.=36, P=O.613; 2. for stand 8, t = -0.594, d.f. = 36, P = 0.556. The KS test supported similarity in distribution patterns: 1. for stand 4, KS = 0.16 (critical value 0.221) for both stands for 36 d.f., P = 0.41; 2. for stand 8, KS = 0.11, P = 0.99. Basic statistics were computed for each sampled stand. Table 2 presents the mean and total characteristics of the sampled stands. We used the Weibull two-parameter function , _ e-( t/PI-
(1) to describe the actual age distribution and to simulate age distribution for model stands. Interval values
and
Management
93 (1997)
205-214
were calculated as a difference ( N2 - N,) between the following expressions N, = 1 - e-‘X,/p’”
(21
and N = 1 _ ,-c~i-/~)” (3) 2 where N, and N2 are values at the beginning and end of age intervals xl and x2, which are, respectively, age at the beginning and end of the interval. Some reasons predetermined the choice of the Weibull function. It has been reported (Bailey and Dell, 1973; Svalov, 1982; Little, 1983; Ganina, 1984; Kaplunov and Kuzmichev, 1985; Khatouri and Dennis, 1990; Popov, 1993) that the Weibull function is appropriate and flexible for describing distribution patterns in biological systems. It would be possible to find well fitted, different functions for each age distribution but we would then be unable to solve the problem of distribution simulation. Regressionanalysis was used to estimate the correlation between the curve shape( p coefficient) and mean age, coefficient ct, which determines curve scale, and the coefficient of age variation (Cy, 1.
4. Results 4.1. Statistics and distribution patterns To simplify presentation, we combined all stands in 40-year age classesby mean stand age. Table 3 showsthe resultsof smoothing the basic statistics for these groups. Mean age in the first column defines the average age of each class. All standswere found Table 3 Results of basic statistical
calculations
for age groups
x
STD
cv
Ex
AS
Min
Max
R
R,,
60 100 140 180 220 260
29 48 67 85 104 122
49 48 48 47 47 47
1.47 0.78 0.16 -0.05 -0.18 -0.65
1.03 0.95 0.98 0.78 0.25 0.19
34 44 52 59 65 71
174 252 322 387 448 506
140 208 270 328 383 435
2.33 2.08 I .93 1.83 1.74 1.68
x, group variation; maximum:
mean age; STD, standard deviation; CV, coefficient of Ex. kurtosis; As, skewness: Min, minimum; Max, R, range; R,e,, relative range CR,, = R/a.
-
A. Bondarev
40
80
120
160
200
/ Forest Ecology
and Management
00;
20 Coeftkient
and fitted age distribution
205-214
209
240
Age,years
Fig. 2. Actual
93 (1997)
for stand 2-8.
40
60
80
of wriation,
Fig. 4. LY and /?I coefficients mean age.
to have a multi-aged structure. Fig. 2 shows the common picture for age distribution in the study area. The absolute range for the oldest stands can extend to over 400 years while the largest relative range is more common for the youngest forests. The coefficient of variation sometimes exceeds 60% (Fig. 3) and has a very small tendency to decline with increasing mean age. The largest differences in the coefficient of variation (22-61%) were found for youngest stands. The youngest forests have a positively skewed leptokurtic (spiked) distribution. Beginning at the age of 180 years, distribution tends to be platykurtic (flatter) because of increasing range and declining population in central classes. Changes in homogeneity are also apparent. Skewness of distribution decreases slightly as well. The cr and p coefficients for the Weibull function were calculated for each stand using the least square method in STATGRAPHICS’“. Fig. 4 shows the relationship between /3 and mean age, LY and
"OW
100 %
as a function
100 we.”
300 age,
years
of age variation
and
CV’. Linear regression equations were computed for both coefficients 1 - = 0.101 + 0.00708 X CV, i- 0.054 (4) aA
and PA = 1.143 XA & 12years
(5) The correlation is stronger for the /3 coefficient and mean age, (adj. R* = 0.97) while age variability explains about 69% of the coefficient (Y variance (adj. R* = 0.69). The p coefficient increases with increasing mean age. This results in reducing the population in the middle age classes and forms a more platykurtic distribution. Curve scale (Y, is more stable and has less influence on the shape of the curve. While CV’ has a small tendency to decline with mean age, the coefficient (Y increases and forms a less positively skewed curve. To illustrate the results of distribution simulation
80
I OO
100 Mean
Fig. 3. Relationship variation.
between
200
300
age,yem
mean
age and coefficient
Age, years
of age
Fig. 5. Results tions.
of Weibull
simulation
for different
age distribu-
210
A. Bon&rev/Forest
Table 4 The accuracy estimation Weibull function Plot no.
Ecology
and Management
4.2. Geographical patterns in the age distribution of actual
Simulated coefficient
age distribution
KS test
using
cy
P
KS,<
KS,,
1.7909
244.94
0.184
0.221
9.6
9.5
2-3 4-l
2.7768 1.6643
218.66 111.25
2.0540 1.9959
311.58 333.98
0.221 0.207 0.227
1.8 19.5
4-2 4-3
0.083 0.229 0.113
6.0 7.8 3.8
0.140
0.231
KS test, Kolmogorov-Smimov
the
,y’ test
2-2
X&’
2.0 3.9
x;
6.0
test
for the age groups, we calculated (Y and p coefficients using data in Table 3. Fig. 5 shows modelled distributions for some age groups. As we had rather similar CV’ values, the main difference between curves was determined by the p coefficient. The preliminary results of distribution analysis are supported by the simulation model. The older the stands were, the more platykurtic the curve of the distribution. To test Eqs. (4) and (5) we used data from five stands selected to estimate the distribution prediction. The results, displayed in Table 4, show a good agreementbetween actual age distribution and simulation by Weibull function when IY and p coefficients are calculated using Eqs. (4) and (5). Only in one case, for the stand occurring at the tree line in the mountains (plot 4-l), was the critical value of test estimatorsgreater than the actual value.
Table 5 MANOVA
results for estimating
Source of variance Covariance variables Latitude Elevation Slope Qualitative factors Exposure Relief Location Qualitative factor interaction Residual variance Total
93 (19971205-214
site location
factor
influence
on mean age variance
Variance
Factor (%‘c)
705.0
32.2
335.3 493.5 123.6 586.9
15.3 12.5 5.6 26.8 13.8 9. I
302.5 200.3 247.8 122.0 778.5 2192.4
Early in the research,we found that mean age and age structure were in&renced by site locations. To quantify this, we examined the structure of sampled stands by their geographical locations. elevations. and slope exposure or aspect. A confidence interval method in an MANOVA routine (Bock, 1975; Mardia et al., 1979; Stehman and Meredith, 1995) was used to estimate the influence of geographic factors on age distribution. All analysed factors were combined into two groups. Latitude, elevation, and slope composed a quantitative group, and aspect, relief, and location representedqualitative factors. Eight aspect groups were determined: N (north), NE (northeast), E (east), SE (southeast), S (south). SW (southwest), W (west), and NW (northwest). Relief was divided into three groups: (1) plains; (2) highlands: (3) mountains. Three groups were established for location: (1) valley; (2) slope; (3) upland. The results from examining these geographical factors are described below. 1. Aspect: the influence of this factor was 28% and was not significant at the 95% probability level. Confidence intervals for each group were overlapping. 2. Relief: the influence of this factor was 6% and was not significant at the 95% probability level. Confidence intervals for each group were overlapping.
11.3 5.6 35.4 100.0
weight
F-test
Prob.
6.0 8.6
0.003 0.007
12.6 3.2
0.001 0.087 0.095 0.661 0.098
7.3 0.7 2.6 3.2 1.6 .-
0.060 0.230
value
A. Bondareu / Forest Ecology and Management 93 (1997) 205-214
3. Location: the influence of this factor was 21% and was significant. Confidence intervals for ‘ valley’ and ‘uplands’ groups were not overlapping. Confidence intervals for ‘slope’ and ‘upland’ groups had a 21% overlap. Mean tree age for ‘upland’ was determined to be 153 years; for ‘slope’, 191 years; and for ‘valley’, 234 years. Table 5 illustrates the results of the estimatesfor the combined influence of the qualitative and quantitative groups. Quantitative factors were used as covariance variables. In spite of a residual variance shareof only 35%, most of the analysed factors and their interaction were not significant. This is shown by F-test and probability values that generally exceed the 0.005 level. The significant factors were latitude and elevation but their total weight did not exceed 38% of the total variance.
5. Discussion A multi-aged tree age pattern is very common for Siberian larch forests. One reasonis a long period of forest growth-generally hundredsof years. Another reason, and more important from our point of view, are that forest fires occur several times during the life of the forest (Kolesnikov, 1947; Falaleev, 1957; Faialeev and Machemis, 1975; Ditrich, 1970, etc.). Estimates for multi-aged composition of Siberian larch forests differ. In the Lake Baikal region, it was found to be about 82% (Babintseva, 1990) and 84% (Svalov, 1963) and in the Krasnoyarsk region about 43% (Shanin, 1967). The remaining forests were even-aged.The total shareof multi-aged larch forests in Siberia hasbeen found to range from 75% (Svalov, 1963) to 53% (Shanin, 1967). These estimatesdiffer owing to inconsistenciesin which territories are included in the analysis and which kinds of forest are uneven-aged. In our investigation, all samplestands were multi-aged. Full-aged or multi-aged structure is common for the boreal forests of the Northern Hemisphere. The main problems under discussionare the reasonsfor the multi-aged structure and the dynamics of these forests. Two approachescan be discussed.First, a multi-aged pattern reflects severe past disturbances within a stand (Kolesnikov, 1947; Ditrich, 1970; Ross et al., 1982; Lorimer, 1985; Stewart, 1986;
211
Despontsand Payette, 1992). A secondpoint of view is based on the suggestion that the northern boreal forests are not so heavily influenced by catastrophic forest fires (Quinby, 1991). It hasbeen hypothesised by Dzedzylya (1969) that theseforests preserve their structure over centuries without the occurrence of catastrophic disturbances.Very low natural mortality is compensatedfor by continuous recruitment so that their mean parameters (diameter, height, growing stock) and distribution pattern does not change significantly. Yet, in northern forests, fire disturbances are more evident as permafrost causesroot systems to be exposed to greater damage.Post-fire regeneration here yields an even-aged forest structure instead of a continuous recruitment that would yield a multi-aged forest. Holla and Knowles (1988) emphasized that a multi-aged distribution points to continual recruitment as the major component of population dynamics, while fire would be a minor component. They describe consistent biomasspatterns for White Pine (Pinus strobus) populations in northwestern Ontario. A similar conclusion was made for Scats pine (Pinus syluestris) growing just below the tree-limit in the Swedish Scandes(Kullman, 1987). Kullman (1987) recorded a continuous regeneration pattern even during climatically unfavourable periods. Johnson et al. (1994) pointed to a short post-fire period of high recruitment in a Pinus contorta-Picea engelmmnii forest in Canada followed by low and sporadic recruitment for the remaining life of the stands. Engelmark et al. (1994) assumedthat a J-shaped age structure in pines of northern Sweden was due to a stationary forest pattern. A J-shapewould mean a negative exponential age distribution curve which assumesthat most of the population occurs at the beginning of the distribution curve. Suffling (1983) showed that 80% of the typical age-class distributions of boreal and Great Lakes forests had a similar negative exponential age distribution. The samedistribution pattern was found by Pitcher (1987) in red fir ( Abies mgnifica) forests within Sequoia National Park, California. These patterns differ from the age distribution patterns in Dahurican larch forests above the Arctic Circle in central Siberia. Our investigation hasshown a more normally distributed age population (Fig. 2).
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Ecology
This Gaussian age distribution also has been observed in multi-aged larch forests in the Magadan region of the Russian Far East (Podmasko, 1973). The age distribution of a forest reflects its dynamics and history of disturbances. The differences found in coefficient of age variation, especially in youngest forests, are the results of different succession scenarios. When most trees die in the first few years after severe forest fires, new regeneration forms a positively skewed age distribution that has less age variation. The more trees that survive after fires, the more broadly distributed the age population is. As a result, the coefficient of variation increased greatly. During the post-fire period, variation gradually decreases due to increasing mean age. We hypothesise that a negative exponential age distributions are a result of periodic catastrophic fires occurring on most of the territory of the northern boreal forests. The restoration of forest vegetation takes place generally by even-age regeneration which forms major peak on the age distribution curve. The surviving trees are a minority of the population. Normally distributed populations are due to continuous recruitment. This scenario suggests a low frequency and less dramatic forest fire history in the northern part of the boreal zone. Some geographical patterns in age distribution were found during investigation. Mean age and age distribution correlate with stand location. The oldest stands with platykurtic age distributions occur in the river valleys and lower terraces and are never observed on the upper slopes or uplands, while the youngest forests with leptokurtic distribution are more often on upper slopes or upland sites and are observed on river banks only as a result of regeneration after clear cutting and fires. The most common explanation for the youngest forest occurrence on the uplands, where they define tree limit is the forest border invasion due to a global warming (Ball, 1986; Kullman and Engelmark, 1990; Kullman, 1992; McDonald et al.. 1993; Payette et al., 1994). However, other warm periods have occurred in the past, which casts some doubt on this explanation. During these past warmer periods there was the unrealised potential to form old-growth stands at upland sites. We hypothesise that harsh winds also determine age distribution. Only a few trees older than 200
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93 (1997)
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years were found without central pith decay. The oldest trees usually have the biggest size so they are affected and damaged by strong winds more at the open upland sites than in sheltered river valleys.
6. Conclusions 1. Larch age distributions have similar patterns in different locations all over the study area. All stands had a multi-aged structure. 2. The average coefficient of variation for age is about 48%. The range of tree ages exceed 400 years in the oldest forests. 3. The age distribution pattern changes for different age groups. For youngest stands (40-80 years) the age curve is spiked or leptokurtic and positively skewed while for middle-aged forests (80180 years) the curve tends to be more mesokurtic and symmetric. In the oldest forests (over 180 years old) the curve has a flatter or platykurtic form. 4. The actual age distribution can be simulated using a two-parameter Weibull function. A high correlation was observed for the cy coefficient, which defines the curve shape and the coefficient of age variation, and for the p coefficient, which defme curve scale and the mean age. 5. The oldest forests were found in river valleys and lower river terraces. Middle-aged forests occur more commonly on middle slopes and the youngest forests occupy top slopes and uplands. Mean tree age is poorly correlated with latitude and elevation. 6. A more normal age distribution for a tree population assumes a study area less damaged by forest fires than in the more southern parts of the horeal forest. Forest fires are less important for forest succession above the Arctic Circle than below it. 7. Age distribution patterns can be used for the analysis and prognosis of fire history and disturbance over the boreal forest. 8. Age occurrence in forests is a function of position in the landscape which has implications for the modelling of global changes including global warming.
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9. The age structure of forests at the tree limit is the result not only of tree invasion or climatic change, but other ecological factors including strong winds which destroy the largest and oldest trees in open forest landscapes.
Acknowledgements I would like to thank Thomas Stone (The Woods Hole Research Center) for his assistance in preparing this manuscript. I am also appreciative of Richard Houghton and George Woodwell for their comments on the manuscript. Special thanks to Andy Gillespie and Thomas Jacob (US Forest Service) for their advice and criticism. Support for the preparation of this article while at the Woods Hole Research Center came from the John D. and Catherine T. MacArthur Foundation and the Trust for Mutual Understanding.
References Babintseva, R.M., 1990. Regeneration in regional system of forest management. Abstract of Doctoral Thesis, Forest and Wood Institute, Krasnoyarsk. (In Russian.) Bailey, R.L. and Dell, T.R., 1973. Quantifying diameter distributions with the Weibull function. For. Sci., 19: 97-104. Ball. T.F.. 1986. Historical evidence and climatic implications of a shift in the boreal forest tundra transition in central Canada. Climate Change, 8f.2): 121-134. Bock, R.D., 1975. Multivariate Statistical Methods in Behavioral Research. McGraw-Hill, New York. Desponts, M. and Payette, S., 1992. Recent dynamics of jack pine at its northern distribution limit in northern Quebec. Can. J. Bot., 7d6): 1157-l 167. Ditrich, V.I., 1970. Structure, growth dynamics, and inventory patterns of larch forests in Irkutsk region. Abstract of Doctoral Thesis, Leningrad Technology Institute, Leningrad. (In Russian.) Dzedzylya, A.A., 1969. Silviculture-inventory patterns in larch forests of the Chantaika river basin. Abstract of Doctoral Thesis, Siberian Technology Institute, Krasnoyarsk. (In Russian.) Engelmark, O., Kullman, L. and Bergeron, Y., 1994. Fire and age structure of Scats pine and Norway spruce in northern Sweden during the past 700 years. New Phytol., 126(l): 163-168. Falaleev, E.N., 1957. Age structure of Enisei ridge larch forests. Collect. Articles Siberian Technol. Inst. Krasnoyarsk. 16(4): 18-23. (In Russian.) Falaleev. E.N. and Machernis, P.I., 1975. Inventory features in
and Management
93 (1997)
205-214
213
uneven-aged larch forests. Collect. Articles Siberian Technol. Inst. Krasnoyarsk, 4: 15-18. (In Russian.) Ganina, N.V., 1984. Diameter distribution using Weibull distribution function. For. Sci., N2: 65-70. (In Russian.) Holla, T.A. and Knowles, P., 1988. Age structure analysis of a virgin white pine, Pinus strobes, population. Can. Field Nat., 102(2): 221-226. Hollander, M. and Wolfe, D.A., 1973. Nonparametric Statistical Methods. John Wiley, New York. Johnson, E.A., Miyanishi, K. and Kleb, H., 1994. The hazards of interpretation of static age structures as shown by stand reconstructions in a Pinus contorta-Picea engelmannii forest. J. Ecol.. 82(4): 923-93 1. Kaplunov, V.Y. and Kuzmichev, V.V., 1985. The relationships between distribution sets on diameter, basal area and volume. In: Structure, Growth and Inventory of the Stands. Institute of Forest and Wood, Krasnoyarsk, pp. 46-52. (In Russian.) Khatouri, M. and Dennis, B., 1990. Growth-and-yield model for uneven-aged Cedrus atlantica stands in Morocco. For. Ecol. Manage., 36(2): 253-266. Kolesnikov, B.P., 1947. Larch forests of the Middle-Amur plain. Primizdat, Vladivostok. (In Russian.) Kullman, L., 1987. Long-term dynamics of high-altitude populations of Pinus qhestris in the Swedish Scandes. J. Biogeogr., 14(l): l-8. Kullman, L., 1992. Holocene thermal trend inferred from tree-limit history in the Scandes Mountains. Global Ecol. Biogeogr. Lett., 2(6): 181-188. Kullman, L. and Engelmark, O., 1990. A high Late Holocene tree-limit and the establishment of the spruce forest-limit-a case study in northern Sweden. Boreas, 19(4): 323-331. Little, S.N., 1983. Weibull diameter distributions for mixed stands of western conifers. Can. J. For. Res., 13: 85-88. Lorimer, C.G., 1985. Methodological considerations in the analysis of forest disturbance history. Can. J. For. Res., 15( 1): 200-213. Mardia, K.V., Kent, J.T. and Bibby, J.M., 1979. Multivariate Analysis. Academic Press, London. McDonald, G.M., Edwards, T.W.D., Moser. K.A., Pienitz, R. and Smol, J.P., 1993. Rapid response of treeline vegetation and lakes to past climate warming. Nature, 361 (6409): 243-246. Payette, S., Delwaide, A., Momeau, C. and Lavoie, C., 1994. Stem analysis of a long-lived black spruce clone at treeline. Amt. Alp. Res., 26(l): 56-59. Pitcher, D.C., 1987. Fire history and age structure in red fir forests of Sequoia National Park, California. Can. J. For. Res.. 17(7): 582-587. Podmasko, B.I., 1973. Larch forest inventory m the north of the USSR Far East using aircraft image interpretation methods. Abstract of Doctoral Thesis, Moscow Forest Technology Institute, Moscow. (In Russian.) Popov, V.E., 1993. The structure of cedar (Pinus sibirica) forests on Lena-Angara Plato. In: The Structure and Growth of Siberian Forests. Krasnoyarsk publication, pp. 69-87. (In Russian.)
214
A. Bondarer,
/ Forest Ecology
Pozdnyakov, L.K., 1975. Dahurican larch. Nauka, Moscow. (In Russian.) Quinby, P.A., 1991. Self-replacement in old-growth white pine forests of Temagami, Ontario. For. Ecol. Manage., 41(1-2): 95-109. Ross, MS., Sharik, T.L. and Smith, D.W.. 1982. Age-structure relationships of tree species in an Appalachian oak forest in Southwest Virginia. Bull. Torrey Bot. Club, 109(3): 287-298. Shanin, S.S., 1967. Structural features of Siberian pine and larch forests. Abstract of Doctoral Thesis, Forest and Wood Institute, Krasnoyarsk. (In Russian.) Stehman, S.V. and Meredith, M.P., 1995. Practical analysis of factorial experiments in forestry. Can. J. For. Res., 25: 4466 461.
and Management
93 (1997)
205-214
Stewart, G.H., 1986. Population dynamics of a montane conifer forest, western Cascade Range, Oregon, USA. Ecology, 67(2): 534-544. Sufffing, R., 1983. Stability and diversity in boreal and mixed temperate forests: a demographic approach. Environ. Manage.. 17(4):
359-371.
Svalov, N.N.. 1963. Forest management basics in taiga regiom. Goslesbumizdat, Moscow. (In Russian.) Svalov, S.N., 1982. The estimating of Weibull distribution function for stand structure description. Sci. Articles Moscow For. Technol. Inst.. 139: 172-174. (In Russian.1