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Dispersal of white spruce seed in mature aspen
stands
James D. Stewart, Edward H. Hogg, Patrick A. Hurdle, Kenneth J. Stadt, Peter
Tollestrup, and Victor J. Lieffers
Abstract: The dispersal of white spruce (Picea glauca (Moench) Voss) seed through trembling aspen (Populus tremuloides
Michx.) forests was investigated by releasing artificial seed (confetti) from different heights on a meteorological tower, and,
secondly, by observing the distribution of spruce regeneration along transects radiating out from small isolated patches of
mature spruce seed trees. Mean dispersal distance of confetti increased with height of release. Before leaf fall of the aspen
canopy, most confetti landed close to and in all directions around the tower. After leaf fall, no confetti was observed upwind
from the tower and the mean dispersal distance increased, with peak densities occurring at a distance of 15 m in the downwind
direction. The rate of decrease in regeneration density with distance from patches of mature, seed-bearing white spruce was
much less than that observed during confetti release experiments. Furthermore, regeneration densities were significantly
greater in the prevailing downwind direction (toward the east). The results indicate that stronger than average winds, primarily
from the northwest, west, and southwest, play a major role in the dispersal of white spruce seed. Simulation modelling of the
observed distribution of regeneration suggests that long-distance (>250 m) dispersal may be an important mechanism for the
persistence of white spruce in the fire-prone boreal forest of western Canada.
Key words: seed dispersal, boreal forest, mixedwood, wind dispersal, artificial seed.
Résumé : Les auteurs ont étudié la dispersion des graines de l’épinette blanche (Picea glauca (Moench) Voss) dans des forêts
de peupliers faux-trembles (Populus tremuloides Michx.) premièrement, en relâchant des graines artificielles (confetti) à
diverses hauteurs à partir d’une tour météorologique, et deuxièmement, en observant la distribution de la régénération en
épinettes le longs de transects s’irradiant à partir de petits groupes d’épinettes matures produisant des graines. La distance
moyenne de dispersion des confetti augmente avec la hauteur de relâchement. Avant la chute des feuilles de la canopée des
peupliers, la plupart des confetti atterrissent près et tout autour de la tour. Après la chute des feuilles, on ne trouve pas de
confetti dans la direction d’où origine du vent, et dans la direction opposée, la distance moyenne de dispersion augmente avec
un pic de densité à 15 m de la tour. Le taux de décroissance de la densité de régénération avec la distance à partir des groupes
d’épinettes matures porteuses de graines est de beaucoup inférieur à celui observé dans les expériences de relâchement de
confetti. De plus, les densités de régénération sont significativement plus élevées en aval du vent prédominant (vers l’est). Les
résultats indiquent que des vents plus forts que la moyenne, venant surtout du nord-ouest, ouest et sud-ouest, jouent un rôle
majeur dans la dispersion des graines d’épinette blanche dans cette région. Un modèle de simulation de la distribution de la
régénération observée suggère que la dispersion sur de longues distances (>250 m) pourrait être un important mécanisme pour
assurer la persistance de l’épinette blanche dans la forêt boréale susceptible au feu, dans l’ouest canadien.
Mots clés : dispersion des graines, forêt boréale, forêt mixte, dispersion par le vent, graines artificielles.
[Traduit par la rédaction]
Introduction
White spruce (Picea glauca (Moench) Voss) is widespread
across Canada and is one of the dominant trees in the mixedwood section of the western boreal forest (Rowe 1972). Dispersal of spruce seed, along with patterns of disturbance and
establishment site quality, is likely one of the major factors in
determining the pattern and composition of stands in the boreal
landscape. Unlike most other major boreal tree species, white
spruce has no mechanism for in situ regeneration following
fire, such as cone serotiny or root suckering (Heinselman
1981). White spruce often establishes naturally under trembling aspen (Populus tremuloides Michx.) stands (e.g., Rowe
1955; Lieffers and Stadt 1994), and recruitment can continue
for many decades after the canopy closure of the aspen
(Lieffers et al. 1996). Since white spruce seed loses viability in
Received May 26, 1997.
J.D. Stewart,1 K.J. Stadt, P. Tollestrup,2 and V.J. Lieffers. Department of Renewable Resources, University of Alberta, Edmonton,
AB T6G 2E3, Canada.
E.H. Hogg and P.A. Hurdle. Natural Resources Canada, Canadian Forest Service, Northern Forestry Centre, Edmonton, AB T6H 3S5,
Canada.
1
2
Author to whom all correspondence should be addressed. Present address: Alberta Research Council, Postal Bag 4000, Vegreville,
AB T9C 1T4, Canada. e-mail: [email protected]
Present address: Industrial Forestry Service Ltd., 1595 Fifth Avenue, Prince George, BC V2L 3L9, Canada.
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the seedbank within a few years (Nienstadt and Zasada 1990),
white spruce seed must have dispersed through the aspen canopy rather than into openings following disturbance.
Seed dispersal has been studied most often by recording
seed densities in seed traps placed at different distances from
a natural seed source (e.g., Isaac 1930; Roe 1967; Ford et al.
1983; Greene and Johnson 1996). In these studies, it was possible to relate the distance of dispersal to average wind and
stand conditions for the intervals between seed trap counts. To
relate seed dispersal to particular wind conditions and heights
of release, more controlled studies have been undertaken
where natural or artificial seed is released from known heights
on towers (Schlesinger 1970; Augspurger and Franson 1987;
Greene and Johnson 1989, 1995) or from kites or balloons
(Isaac 1930). Usually this has been done in clearings, which
provided a less complex wind environment than within stands.
An alternative approach was to visually follow the flights of
individual seeds released from a spruce tree (Zasada and Lovig
1983). This method is impossible within a forest unless the
seeds are large and brightly coloured, as was used in studies
by Augspurger and Franson (1987) in a tropical forest.
For white spruce, dispersal from natural seed sources has
been studied only in clearings (e.g., Roe 1967; Dobbs 1976;
Youngblood and Max 1992). However, we know that dispersal
within stands differs from dispersal in clearings or into clearings from neighbouring stands, and that dispersal from point
sources and area sources also differ (Greene and Johnson
1996). To our knowledge, dispersal of white spruce seed
within stands has not been studied.
We approached this study from three perspectives. First, we
intended to release seed from various heights in an aspen forest, and observe their dispersal around and away from the point
source. However, the wings of white spruce are very fragile
and are easily damaged (Schlesinger 1970 and personal observation), and it was logistically impossible for us to obtain a
large enough supply of undamaged seed. Fortunately, work by
Greene and Johnson (1990) showed that seeds with different
wing morphologies and autorotative characteristics behave
similarly during wind dispersal; therefore, any heavy particle
with a terminal descent velocity similar to that of spruce seed
would serve as a model (e.g., paper confetti). Secondly, as a
proxy for seed dispersal, we studied the distributions of white
spruce regeneration surrounding isolated stands of seed trees
growing in pure aspen stands. Thirdly, we developed simulation models as a means of interpreting both parts of the study.
The modelling objectives were (i) to test the ability of wind
data to predict the dispersal pattern of seed surrogates around
the tower (point) source and (ii) develop and test a model of
seed dispersal into pure aspen stands from isolated patch
sources of white spruce.
Materials and methods
Dispersal of surrogate seeds
We released surrogate “seeds” from a meteorological tower in a mature aspen-dominated stand near Spring Creek, Alberta (54°55′N,
117°43′W). The stand was 16.3 m tall with 2000 trees/ha, and
39 m2/ha basal area. The 30 m tall tower was erected in the midst of
the stand without disturbing the canopy. We placed three wind
measurement stations on the tower below the canopy (9.5 m), in midcanopy (15.0 m), and above the canopy (19.4 m). Horizontal wind
Can. J. Bot. Vol. 76, 1998
velocity and direction were measured with RM Young model 05103
wind monitors, and vertical windspeeds were measured with RM
Young model 27100 gill propeller anemometers (22 cm diameter,
30 cm pitch propeller). A Campbell Scientific CR–21X datalogger
stored data from the sensors, averaged at 2-s intervals during each
release of confetti. Steel pans, 80 × 80 × 1 cm deep, were attached to
the downwind side of the tower within (14.5 m), at the top of
(16.8 m), and above the canopy (19.5 m).
The surrogate seed was ordinary circular paper confetti (7 mm
diameter, 76 mg/100 pieces) dyed with fabric dyes. Different colours
were used for different heights of release. We measured the descent
velocity of the confetti by timing its fall 10 m in still air in an enclosed
stairwell. Mean descent velocity was 0.887 ± 0.067 m/s (mean ± SD),
compared with a mean of 0.54 ± 0.120 m/s for natural white spruce
seed (Schlesinger 1970). The confetti was released by sprinkling a
thin layer of it onto the pans, which were gently vibrated to keep the
confetti above the boundary layer of the pans and accessible to be
blown off by the wind. There was a pronounced relationship between
the rate of confetti dispersal and the windspeed, providing an approximation of the “biased anemometer” effect of seed abscission noted by
Greene and Johnson (1992). The layer of confetti was replaced as
necessary, with pauses at intervals at the top two heights to maintain
synchrony with the lowest height. During each drop event, 283.5 g
(10 oz.), or about 373 000 pieces, were released from each height.
The duration of the releases ranged from 21 to 52 min.
The dispersed confetti of each colour were counted on traps laid
out in radii around the tower. The traps were positioned at 30° intervals 2.5 m from the tower, and at 15° intervals at 5 and 10 m. In the
downwind direction and 15° and 30° on either side of it, additional
traps were laid out at distances of 15, 20, 30, and every 15 m beyond
that to 210 m from the tower base. The traps were pieces of pulp sheet
covered with a light coating of spray adhesive (Super 77, 3M Canada
Inc., London, Ont.) to minimize the chances of confetti blowing off
after landing on the trap. Although the size of trap increased from 0.16
to 2.56 m2 with distance it was not possible to maintain a constant area
sampling rate, because of the geometric increase in area with distance,
and practical constraints. The area sampling rate diminished from
1.6% nearest the tower, to 0.1% at 210 m distance.
The drops were made during the seed-cast season, three before leaf
fall (August 30 and 31 and September 6) and two after all the leaves
had fallen (both on October 12). The dates selected had average windspeeds similar to the seasonal normals (Environment Canada 1982).
Seed densities (counts divided by trap area) were used in all analyses. Wind direction during the releases of confetti was variable; therefore, the downwind dispersal of confetti was averaged over the two or
three transects that showed the furthest evidence of dispersal. We
calculated mean dispersal distances using a method similar to
Augspurger and Franson (1987), except that we standardized trap
counts to a square-metre basis to account for different trap sizes. We
also weighted the resulting density values by the proportion of the
area that the trap represented compared with the total area represented
by the transect, to account for the increasing area with distance along
the transect.
Seed dispersal was also simulated using the wind data that was
collected every 2 s during each of the five releases of confetti. First,
vertical and horizontal windspeeds and directions were estimated for
each 0.5-s time interval by linear interpolation of the wind data sets.
During simulations, the number of seeds released in each time interval
was assumed to be proportional to the square of measured horizontal
windspeed (Greene and Johnson 1996). The wind data were then used
to estimate seed trajectories, assuming a fall rate of either 0.887 m/s
(for confetti) or 0.54 m/s (for white spruce seed). Every 0.5 s, the
height, distance, and direction of the seed from the seed source was
calculated. When simulated seed height was between the top and
bottom anemometers, wind speed and direction (horizontal and vertical) for the next 0.5 s was estimated by linear interpolation of the
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Stewart et al.
mometer, we assumed that increases in both vertical and horizontal
wind velocity (u) with height (z) are proportional to ln((z – d)/zo),
based on a canopy height (H) of 16.5 m with a zero-plane displacement (d) of 0.78H and a roughness length (zo) of 0.075H (Jones 1992).
For seed heights less than the lower anemometer, we assumed a linear
reduction in horizontal and vertical wind velocities to zero at the soil
surface. After each simulated seed reached the ground, its distance
and direction from the seed source was recorded. Seed densities at
different distances from the seed source (tower) were then estimated
after normalizing the data so that the total number of seeds released
was equal to that used in each confetti release experiment; i.e., 373 000
“seeds.” For purposes of comparison with field results, calculations
of seed densities were based on the area within 15° of the prevailing
downwind direction during each experiment.
Distribution of naturally regenerated seedlings
We examined the dispersal of white spruce seed indirectly, by determining the distribution of white spruce natural regeneration surrounding isolated stands of white spruce seed trees growing in relatively
homogeneous pure aspen forests. High-quality seedbeds such as rotten wood or exposed mineral soil result from small-scale disturbances, which were not obviously related to distance from the seed
source trees. Therefore, we assumed that variation in seedbed quality
would be randomly distributed at the scale we worked with. Thus, the
effect of seed dispersal would be evident when data from many transects were averaged.
From aerial reconnaissance, we selected isolated groups of at least
10 dominant or codominant spruce seed trees growing in relatively
uniform, 30- to 60-year-old aspen stands in the Lac La Biche
(54°45′–55°13′N, 111°46′–112°14′W) and Whitecourt (53°55′–
54°20′N, 115°35′–115°46′W) Forests. Some stands with heavy grass
or shrub cover were eliminated, because there was insufficient spruce
regeneration to give a useful distribution.
In each surveyed stand, we ran out transects in each cardinal and
subcardinal direction where there was at least 500 m to the next possible seed source. The number of transects surveyed varied from 2 to
8 in each of the 13 sites, averaging 5 in the 6 Lac La Biche sites, and
4 in the 7 Whitecourt sites. There were two to five replicates of each
of the eight directions. Starting at 10 m from the edge of the seed tree
island, we located stations at 20-m intervals, out to 250 m. Each station was composed of two circular plots, tangential to the transect line
at the station point, and whose radii increased with distance from the
seed source stand. The area sampled was 16% of the total area. In each
plot the number of stems of naturally regenerated white spruce ≤10 m
in height were counted.
To characterize the white spruce seed source stands, we counted
the number of seed trees, measured the heights of two representative
seed trees, and estimated their ages from increment cores (Table 1).
We used these data as a basis for the parameters used in the patch
source model. We also measured the height and age of two representative aspen from the surrounding stand. As with the confetti data,
densities (counts divided by plot area) were used in all our analyses.
A simulation model of dispersal from patch sources was developed based on the following micrometeorological model of seed dispersal from a point source (Greene and Johnson 1989), which gives a
lognormal distribution of seed frequency with respect to distance of
dispersal (dQ/dx):
2

 
 

  −  
Q
dQ
− 1 ln  xF  
=
exp
dx xσu(2π)1/2
 2σu2   Hu−g  

 
 
where Q is the total number of seeds dispersed, x is the distance (m)
from the seed source, (u−g) is the abscission-weighted, mean geometric
−
wind speed, (σu) is the standard deviation of ln(u−g), F is the mean fall
rate of spruce seed (0.54 m/s), and H is the height of seed release
(20 m). The values of seed frequency (dQ/dx) from this equation were
Table 1. The estimated number of spruce seed trees, heights and
ages of the white spruce seed source stands and the surrounding
aspen stands, and the mean height differences between the spruce
and aspen stands.
Whitecourt
No. of spruce seed trees
Spruce ages (years)
Spruce heights (m)
Aspen ages (years)
Aspen heights (m)
Height differences (m)
Lac La Biche
Mean
SE
Mean
SE
30
89
24
43
18
6.3
10.0
7.0
0.9
3.4
0.9
0.8
36
94
23
46
18
5.3
8.9
6.1
2.8
1.6
1.4
1.6
divided by 0.5πx to give seed densities as a function of distance in
either the upwind or downwind direction (90° quadrant). The patch
model was then constructed by arranging 29 individual trees on a 4-m
grid to approximate a circular patch source with a diameter of 24 m.
A transect was then defined, starting at one of the outermost trees and
extending away from the patch. During the simulations, the density of
regeneration (i.e., “successful seeds”) was calculated at each point on
the transect by summing the seed density contributions from each of
the trees in the patch. The values of Q, u−g, and σu were determined by
iteration to give optimum fit to the observed mean regeneration densities on the downwind transects.
Results
Dispersal of surrogate seeds
Wind regimes
The mean windspeeds above the canopy ranged from 1.7 m/s
on September 6 to 3.6 m/s on October 12 during our confetti
releases (Fig. 1). These were slightly lower than the 30-year
mean windspeeds for August through October (3.9 m/s) measured at Grande Prairie, 80 km west of our Spring Creek tower
(Table 2). However, the maximum gust speeds (6.4–10.9 m/s)
were less than a third of the maximum indicated in the 30-year
record (31 m/s; Fig. 1, Table 2).
Horizontal windspeed increased with height, as expected
(Fig. 1). During August and September when the aspen were
in leaf, horizontal windspeeds within and below the canopy
were 38 and 10%, respectively, of the windspeeds above the
canopy. In October, after the leaves had fallen, windspeeds
within and below the canopy were about 56 and 33%, respectively, of windspeed above the canopy. Winds were from the
west on all release dates, except for September 6, when winds
were easterly.
There was a trend for a slightly negative mean vertical
windspeed, and gust maxima were generally greater downward than upward (Fig. 1). However, on each date, the maximum observed updraft windspeed was greater (1.8–2.2 m/s)
than the confetti fall rate (0.887 m/s).
Confetti dispersal
Before leaf fall, confetti dispersed in all directions, although
the majority of confetti moved downwind (Fig. 2). After leaf
fall, this effect was less pronounced with very little confetti
moving more than 90° away from the downwind direction. The
pattern of radial dispersal of confetti measured at the 10-m ring
of traps was irregular for all three heights of confetti release,
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Can. J. Bot. Vol. 76, 1998
Fig. 1. Horizontal, updraft, and downdraft windspeeds measured
above (19.4 m, open bars), in the middle of (15.0 m, solid bars),
and below (9.5 m, hatched bars) an aspen canopy during five
releases of confetti. Means, SDs, and extrema are indicated by
histogram bars, lines with crossbars, and lines without crossbars,
respectively. There were two confetti releases on Oct. 12 (Oct. 12a
and Oct. 12b).
Fig. 2. Radial dispersal of confetti following release from a
meteorological tower in a mature aspen stand. The three heights
were 2 m above (19.5 m, solid line), at (16.8 m, broken line), and
2 m below (14.5 m, broken and dotted line) the top of the aspen
canopy. Confetti densities were measured at 15° intervals at 10 m
from the tower base. Three releases were made during the leaf-on
period and two during the leaf-off period.
Table 2. Average wind regimes for the seed dispersal months of
August, September, and October for the three study areas.
the leaves had fallen, dispersal distances increased. Confetti
released at and above canopy height had distinct peaks of density at 15 m (Fig. 3). Mean dispersal distance on the downwind
transects during the leaf-on period was 24.2, 14.2, and 10.8 m
for confetti released from above, at, and below canopy height,
respectively; whereas, after leaf-fall, the corresponding values
increased to 37.9, 27.0, and 20.7 m, respectively.
The results of the model simulations for seed (confetti) dispersal using the wind data from the tower are shown in Fig. 4.
For the leaf-on period, the pattern and magnitude of modelled
confetti densities were generally similar to those observed.
However, the simulated fall rate for confetti (0.887 m/s) resulted in a maximum dispersal distance of 78 m, which was
much less than that observed (>200 m). When the simulation
was repeated using the slower fall rate for spruce seed
(0.54 m/s), the modelled dispersal distances increased to a
maximum of 356 m, giving a dispersal pattern similar to that
observed for the confetti release experiment. For the leaf-off
period, the peak in modelled densities occurred at a greater
Maximum gust
Maximum hourly
Mean prevailing
Mean
Grande Prairie
Whitecourt
Lac La Biche
31.0
20.3
5.5
3.9
22.8
16.9
2.9
2.5
27.7
19.3
4.4
3.0
Note: Values (m/s) were derived from 30-year normals (1951–1980)
from airport meteorological records (Environment Canada 1982).
probably as a result of local microscale heterogeneity of the
stand surrounding the tower.
During the leaf-on period, most of the confetti from all
release heights landed within 15 m of the tower, although confetti was found on the traps at very low densities as far as
210 m away (Fig. 3). Observation beyond the traplines indicated that some confetti was travelling at least 250 m. After
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Fig. 3. Downwind confetti dispersal following release from
positions 2 m above (d), at (j), and 2 m below (m) mean canopy
height in a mature aspen stand. Data are averages from the two or
three transects that exhibited the longest dispersal distances during
each release. Three releases were made during the leaf-on period
and two during the leaf-off period.
Fig. 4. Simulation models for dispersal from a point source in an
aspen canopy before and after leaf fall. Confetti data (d) is
compared with models using the confetti fall rate of 0.887 m/s
(broken and dotted line), and that for white spruce, 0.54 m/s
(broken line).
distance from the tower (ca. 20–40 m) than that observed
(15 m), but modelled and observed densities were generally
comparable at distances greater than 100 m. During the simulations, most seeds followed a continuously downward trajectory but updrafts during each simulation resulted in maximum
seed heights of between 25 and 57 m, and generally much
greater than average dispersal distances.
250 m radius of the source stands were an order of magnitude
lower (Fig. 5). The average densities tended to be lower in the
Lac La Biche stands than in the Whitecourt stands, except for
the northeastern (45°) transects (Fig. 5), although significant
differences were found only in the north, east, south, and
southwest directions (overall significance at p ≤ 0.05 for all
tests, probabilities for individual directions corrected by the
Bonferroni inequality to p ≤ 0.006 25). The seed source stands
and their surrounding aspen stands were not different in
heights or ages between the two regions (Table 1).
The distribution of white spruce regeneration with distance
from the seed source was different depending on whether the
transect ran upwind toward the prevailing wind direction
(i.e., northwest, west, and southwest), or downwind with the
prevailing wind (i.e., northeast, east, and southeast) (Fig. 6).
Mean regeneration densities of white spruce from upwind transects were less than 500 stems/ha adjacent to the seed source
stand and tended to decline slightly, but erratically, as the distance increased. On downwind transects, regeneration densities averaged over 2500 stems/ha adjacent to the seed source
and declined linearly to less than 500 stems/ha at 90 m. Past
90 m, the densities declined gradually and were not signifi-
Distribution of naturally regenerated seedlings
The radial distribution of white spruce regeneration in the aspen stands surrounding the seed sources showed that more
white spruce have established downwind than upwind, relative
to the prevailing westerly wind direction in both regions
(Environment Canada 1982) (Fig. 5). The average regeneration density and the wind direction frequencies were positively
correlated for Whitecourt (r = 0.80, p = 0.017). For Lac La Biche,
the correlation was not significant (r = 0.39, p = 0.340) because
of the northern and northeastern transect results; however, regeneration density and wind direction frequency were strongly
correlated in the other six directions (r = 0.91, p = 0.012). We
found regeneration densities as high as 12 000 stems/ha in individual plots in both regions, but average densities within the
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Fig. 5. Radial distribution of white spruce regeneration in mature
aspen stands surrounding isolated spruce seed trees in two regions,
Lac La Biche (open bars) and Whitecourt (hatched bars). Values
are means and SEs from transects radiating out from the source
trees. The frequency of occurrence of winds for different
downwind directions is also presented for Lac La Biche (solid line)
and Whitecourt (broken line).
Can. J. Bot. Vol. 76, 1998
height of mature, seed-bearing spruce in source stands
(Table 1). During initial simulations, however, it became apparent that the magnitude of observed regeneration densities
was always much greater than expected at distances greater
than 100 m. This was especially true on the transects located
upwind from the source stand, where there was an actual increase in the estimated total number of spruce seedlings per
1-m increase in distance from the patch. This indicated that
there was a “background density” of white spruce seedlings
that had originated from dispersal over longer distances from
other seed sources in the region. The magnitude of this background density could not be determined precisely but was
probably about 100/ha, based on the minimum densities recorded on the transects (Fig. 6). When this was included in the
patch source model, the best fit with the observed regeneration
densities on the downwind transects (Fig. 6) was obtained using the following parameters: Q = 1725, u−g = 2.5 m/s and
σu = 0.9, where Q is the total number of seedlings in the downwind quadrant that originated from the patch. For the upwind
transects, the best fit to the observed regeneration densities was
obtained by reducing the value of Q to 250 and using the same
values of u−g and σu.
Discussion
Fig. 6. Distribution of white spruce regeneration with distance into
and with the prevailing wind direction in mature aspen stands
surrounding isolated spruce seed trees. Symbols represent means
(with SEs) of field observations from transects in the easterly
(northeast, east, southeast) directions against prevailing winds (j),
and westerly directions (northwest, west, southwest) with
prevailing winds (d). Lines represent the simulation model results
described in the text (solid, downwind; broken, upwind).
cantly different from those found on the westerly transects
(p = 0.100).
The model parameters describing dispersal from a patch
source appeared to be realistic, in terms of the number and
Factors affecting seed dispersal
The results of our confetti release experiments have shown the
importance of in-canopy wind regimes in determining the pattern of white spruce seed dispersal within aspen stands. The
presence of aspen leaves results in strong reductions in horizontal wind speed, especially in the lower canopy, which leads
to shorter dispersal distances during the leaf-on period than
after leaf fall (Fig. 3). Greater variability in wind direction
associated with lower windspeeds (Pasquill and Smith 1983)
within and below the canopy may also have contributed to
shorter dispersal distance and deposition upwind of the tower
in the releases before leaf fall (Fig. 2).
How well do our experimental confetti release results compare with the distribution of spruce regeneration from real seed
sources? The curve for confetti dispersal differed from the
distribution of regeneration in that a greater proportion of the
confetti fell nearer to the source and less fell at the greater
distances (Fig. 7). This difference remains even when considering the greater fall rate of the confetti compared with real
spruce seed (Fig. 4). When making such comparisons, it is also
important to consider the influence of source geometry on the
shape of the distance–dispersal curves (Greene and Johnson
1996). In the present study, the confetti was released from a
point source (the tower), whereas the white spruce regeneration originated from patch sources. However, the patches of
mature white spruce were chosen to be small (<20 m radius)
relative to the surrounding areas of pure aspen (500 m radius).
Thus, the observed pattern of regeneration density should not
deviate significantly from that expected around a point source,
except at locations immediately adjacent to the mature spruce.
Therefore, the difference in source geometry cannot explain
the abundance of spruce seedlings at large (>100 m) distances
from the seed source, relative to that expected from the confetti
release experiments.
The most likely explanation is that most white spruce seed
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Stewart et al.
Fig. 7. Comparison of our confetti dispersal in leaf-on (s) and
leaf-off (h) periods, and sapling regeneration with distance from
seed source (m) with published models of white spruce seed
dispersal into clearings in British Columbia (Dobbs 1976) and
Alaska (Youngblood and Max 1992).
is dispersed at windspeeds much higher than those observed
during our confetti release experiments. This is consistent with
the results of Greene and Johnson (1992), who found that the
probability of abscission of Acer saccharinum L. samaras increased as a power function of windspeed, with an exponent >2. Furthermore, our application of the patch source
model to the observed distribution of regeneration suggests
that very strong wind events must be much more common from
the west, northwest, and southwest than from the east, northeast, and southeast (by a factor of seven, based on the best
fitting values of Q used above). Extreme wind events in the
study areas are more common from the prevailing direction
than from the opposite direction by at least this factor (Flesch
and Wilson 1993).
Our results suggest that simulation modelling of seed dispersal using tower-based wind data can be a useful means of
exploring the dynamics of tree seed dispersal. However, it is
apparent that an improved understanding of the factors controlling seed abscission is critical to the successful prediction
of seed dispersal patterns, even over short time periods. In
addition to the effect of horizontal wind speed, the direction
and magnitude of vertical windspeeds may also be important.
We do not know, for example, if white spruce seed is more
likely to be dislodged from cones during strong updrafts. In
the confetti release experiment, we observed that gusts of high
vertical windspeeds were responsible for carrying off a disproportionately large part of the confetti, although this relationship was not quantified. The strongest vertical winds were
downward (Fig. 1), and this may have contributed to the high
rate of deposition close to the tower (Fig. 3), since such downward gusts move the confetti into lower air layers where horizontal windspeeds are less. Also, the pans used during the
experiment may have obstructed updrafts from carrying off the
confetti. Thus, future experiments of this type could greatly
benefit from more carefully controlled conditions of seed re-
187
lease. A more intensive design for the sampling of wind regimes may also be necessary to accurately characterize seed
trajectories within forest canopies. This could include measurements of three-dimensional wind velocity at a higher frequency (1–10 Hz), over a greater range of heights (e.g., from
the understory shrub layer to at least one tree height above the
canopy), and at more than one tower location within the stand.
Most other studies of seed dispersal of Picea species have
shown that, like our results, the bulk of the seed falls within a
few tens of metres from the source stand and that densities drop
rapidly to about 100 m (Alexander and Edminster 1983; Alexander 1986; Dobbs 1976; McCaughey and Schmidt 1987; Roe
1967; Youngblood and Max 1992). Figure 7 shows two typical
examples of seed dispersal from mature white spruce stands
into adjacent clearings in central British Columbia (Dobbs
1976) and Alaska (Youngblood and Max 1992). It is interesting to note that the pattern of relative dispersal densities into
the clearings were similar to our results for downwind, withinstand dispersal based on our observed distributions of white
spruce regeneration. However, a more rigorous comparison
would also need to include the influence of differences in
source geometry (Greene and Johnson 1996), even after all
other relevant factors such as tree height, seed fall rate, and
wind regime have been considered.
Ecological and forest management implications
The results of our regeneration distribution surveys indicate
that the influence of patches of white spruce seed trees becomes indistinguishable from background levels of seed dispersal at distances greater than about 100–150 m downwind,
relative to the prevailing wind direction, and at much smaller
distances upwind. This background seed rain can result in a
potential minimum density of about 100 stems/ha of spruce
regeneration in the normal forest floor conditions of an aspen
understory, even at distances of 250 m or more from major
seed sources (Fig. 6). In Alberta boreal forests, Eberhart and
Woodard (1987) found that, even in the largest burns, only
14% of the burned area was located more than 500 m from
residual unburned forest. This suggests that most sites in the
boreal mixed-wood forest will experience some white spruce
seed rain after fire. Other studies have observed low-density,
long-distance dispersal in burns and clearings (MacArthur
1964; Greene and Johnson 1995; Galipeau et al. 1997). Such
long-distance dispersal may also permit the recruitment of
white spruce into pure aspen stands. If these trees survive to
become seedbearers, Quaite (1956) suggested that as few as
15 stems/ha would be sufficient to produce a fully stocked
spruce understory in the next generation.
The success of natural seeding of white spruce in mixedwood stands depends on the number of seeds dispersed, the
aerodynamic effect of the aspen canopy (Greene and Johnson
1996), and the quality of seedbeds (Roe 1967). Our data suggest that in typical mixed-wood sites, 500 or more seedlings
per hectare will become established within about 80–100 m
downwind and about 10 m upwind of a seed tree island. Given
the relatively lower densities of seed rain upwind, we suggest
that forest managers may wish to leave seed trees no more than
100 m apart if heavy stands of spruce are desired, unless there
is a high coverage of more receptive seedbeds such as rotten
wood or exposed mineral soil. To develop mixed-wood stands
with lower densities of white spruce, patches of spruce seed
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188
trees several hundred metres apart may be adequate. The importance of seedbed quality cannot be ignored, however, as
some of the seed tree islands that we visited had little regeneration, notably those with a dense cover of Calamagrostis
canadensis (Michx.) Beauv., Alnus crispa (Ait.) Pursh, or Corylus cornuta Marsh.
It is also clear that the height of seed release above the
surrounding canopy plays a major role in determining the potential distances over which white spruce recruitment can occur. Dispersal distances will be enhanced if seed trees are tall
or at elevated topographic locations relative to the surrounding
vegetation. Thus, dominant trees contribute the most to dispersal not only by producing the most seed (Waldron 1965) but
also because they release more of their seed above the canopy.
In 10 years of observation of a southern boreal forest in
Manitoba, Waldron (1965) recorded that the timing of peak
seed rains varied from late August to early October. Leaf fall
for aspen begins in early September, peaks in late September,
and is nearly complete by early October (personal observations). From these data we can expect that some seed dispersal
will occur before aspen leaf fall and some after. In those years
with delayed spruce seed development and abscission, early
aspen leaf fall, or insect defoliation of the forest canopy, wider
dispersal of white spruce could be anticipated.
Acknowledgments
We thank Simon Landhäusser for discussions and suggestions
on this study, Dan MacPherson for providing the confetti traps,
Kim Krause and Simon Landhäusser for assistance in the field,
the Alberta Forest Service for field accommodations, and
Richard Rothwell and Robert Swanson for permitting us to use
the Spring Creek tower. We also thank Dr. David Greene and
an anonymous reviewer for their comments on an earlier draft
of this paper. Funding was provided through a collaborative
grant from Alberta-Pacific Forest Industries, Inc., the Canadian
Forest Service, the Natural Sciences and Engineering Research
Council, and from the Sustainable Forest Management
Network of Centres of Excellence.
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