Tree Seedling Distributions Across a
Gradient of Phytophthora ramorum-infected
Coast Live Oak/Bay Forests1
Letty Brown2 and Barbara Allen-Diaz2
Abstract
The effects of sudden oak death (SOD) on coast live oak/bay forest succession are still largely
unknown. One scenario suggests that bay will become a greater component of the overstory in
infected coast live oak-bay forests and that these communities will succeed to bay forests over
time. We investigated tree seedling densities of coast live oak and bay in forty plots in eight
sites over a three-year period across a gradient of SOD-infected oak woodland/bay forests.
Classification and regression tree analysis (CART) and analysis of variance models were used
to investigate a number of stand and landscape variables as predictors of seedling numbers
over the infection gradient. Results indicate that a great deal of variability exists in seedling
numbers for both oak and bay. Though other variables play smaller roles, site and year are the
significant indicators of seedling densities for coast live oak, and site alone is the important
indicator of bay seedling densities. Based on this study, we did not find bay seedlings in
greater proportion to coast live oak seedlings in stands impacted by SOD. We do not believe
that P. ramorum and the associated SOD complex is, at this time, causing succession towards
bay-dominated forests. Rather the interplay between favorable weather years, herbivory, and
other site factors will determine the competitive advantage of coast live oak or bay seedlings
and the outcome for dominance of California’s coast live oak/bay woodlands.
Keywords: Forest response, Quercus, seedling dynamics, sudden oak death, succession,
Umbellularia.
Introduction
Sudden oak death (SOD), caused by Phytophthora ramorum, has killed thousands of
oaks (Quercus spp.) and tanoaks (Lithocarpus densiflorus) in the coast ranges of
California (Rizzo and Garbelotto 2003). The ecological implications of this pathogen
on western forests, in terms of potential changes to forest structure and composition,
are still not well understood. One scenario suggests that California bay laurel
(Umbellularia californica) will become a greater component of the overstory in
infected coast live oak-bay forests. McBride (1974) showed that in coast live oak/bay
forests of the San Francisco Bay Area, coast live oak (Quercus agrifolia) naturally
gives way to bay during normal succession with the removal of certain disturbances
like livestock grazing. Thus, in forest stands where bay and coast live oak are
codominant and coast live oak is killed by P. ramorum, bay should become a
dominant component of the stand. Research has shown that in some areas of P.
ramorum infection, the basal area of coast live oak has decreased by as much as 55
percent, while the BA of bay has increased from 25 percent to over 45 percent of the
total basal area of the site (Brown and Allen-Diaz 2005). Other studies have shown
substantial losses of coast live oak in terms of both basal area and numbers of
1
An abbreviated version of this paper was presented at the Sixth California Oak Symposium: Today’s
Challenges, Tomorrow’s Opportunities, October 9-12, 2006, Rohnert Park, California.
2
Graduate Student and Professor, respectively, Department of Environmental Science, Policy, and
Management, Division of Ecosystem Sciences. University of California, Berkeley, CA 94720-3110.
229
GENERAL TECHNICAL REPORT PSW-GTR-217
individuals (Kelly and Meentemeyer 2002, McPherson and others 2005, Swiecki and
others 2005).
Bay is not only the codominant tree with coast live oak in many areas of the
coast range, it is also an important foliar host of the SOD disease (Davidson and
others 2005). Mature bay trees exhibit high rates of P. ramorum sporulation, but as
foliar and not stem hosts of the pathogen, they do not die from the infection.
Therefore, bay is recognized as an important source of inoculum that contributes to
oak mortality in coast live oak-bay woodlands. A relationship between bay and coast
live oak mortality has also been demonstrated at the landscape scale in which density
of bays was an important predictor of oak mortality (Kelly and Meentemeyer 2002).
Swiecki and Bernhardt (2002) also described a spatial association between bay and
P.ramorum infection on coast live oak.
Though mature bays may capture gap openings as coast live oaks are removed
by SOD, the question remains whether and which species of tree seedling will be able
to exploit the canopy openings created by the death of mature coast live oaks.
Seedling survival in the understory is central to an understanding of forest succession
as those trees capable both of surviving as understory plants and responding to
release to reach overstory size will inevitably form a major portion of the dynamic
forest community. Woody seedling germination and survival in oak/bay forests is a
combination of a great number of factors. As well as water, light and nutrient needs
critical for germination and growth, other factors such as distance from seed source,
seedbed suitability, and predation at both the seed and seedling level are important. In
this study, we compared environmental variables that addressed many of these
factors and tested for differences between coast live oak and bay seedling densities in
several P. ramorum-infected coast live oak/bay forests over a two- to three-year
period.
Methods
Field Methods
We conducted this study at eight sites in the greater San Francisco Bay Area in
California (Marin, Sonoma, and Contra Costa counties) on a mix of public and
private lands. Sites were chosen to represent a gradient of visibly manifested signs of
SOD infection, and were established to monitor vegetation change over this gradient
(fig. 1). Skywalker (SKY) and both China Camp sites (CC1, CC2) show the greatest
mortality from SOD while both Briones sites (EB1, EB2) show none. Olompoli
(OLO), Fairfield Osborn Preserve (FOP), and Bouverie (BVR) are intermediate. At
these eight 1-ha sites, five 0.08-ha plots were randomly located. In each plot, four 16
x 1m belt transects were established, radiating from the plot center stake and running
in the cardinal directions. All seedlings <30-cm tall and <1-cm basal diameter were
counted by species and transect (e.g., north, south, east, west) in the spring. These
counts occurred in 2002, 2003, and 2004 for five of our sites (Skywalker, the China
Camp sites, and the Briones sites), and in 2003 and 2004 for three of our sites
(Olompoli, Fairfield Osborn, and Bouverie). We did not tag and track individuals
over time.
230
Tree Seedling Distributions Across a Gradient of Phytophthora ramoruminfected Coast Live Oak/Bay Forests—Brown
Figure 1—Map of study sites.
In each plot, many environmental variables were measured. Slope and aspect
were measured at plot center. Basal area (BA) of all trees, snags and logs by species
was recorded throughout the entire .08-ha plot. Log basal area was estimated at 1.37
m, based on the height of the remaining stump plus distance on the log to 1.37 m.
Depths of litter and duff were measured to the nearest centimeter at 1, 11, and 16
meters along each transect, following Brown (1974).
Coarse woody debris (CWD) was measured using a variation on Waddell
(2002), in which CWD was taken at each plot along the four 16-m transects for each
piece of wood whose central axis crossed the tape and was larger than 8 cm in
diameter. For each eligible piece of wood, the diameters of the largest and smallest
ends (with a minimum of 8 cm) were recorded to the nearest 2.5 cm. The length of
the log was recorded to the nearest 30 cm, not including the log portion smaller than
8 cm in diameter. To convert each piece of wood measured to a volume (m3/ha), the
following equation was used (from Waddell 2002):
where Ds and DL are the diameters of the smallest and largest end of the log and l is
log length. Comparisons between coarse woody debris loads were made based on
these cubic volumes.
Leaf area index (LAI) was averaged from multiple readings taken in each plot in
mid-late October. LAI data was collected from Skywalker, China Camp Miwok,
China Camp Back Meadows, Briones Bear Creek, and Briones Alhambra in 2002,
Olompoli, Bouverie, and Fairfield Osborne in 2003, and all sites in 2004. At each
231
GENERAL TECHNICAL REPORT PSW-GTR-217
site, above canopy (A) and below canopy (B) readings were obtained using the LAI2000 Plant Canopy Analyzer optical sensor (Li-Cor, Inc, Lincoln, NE). In accordance
with LAI-2000 protocol, A readings were taken in a clearing, while B readings were
taken manually at six different points beneath the canopy in each of the five plots at a
site. Three readings were taken at each of six locations per plot and averaged.
Pathology data was taken in all plots in the spring of 2003 and 2004. Nine bay
leaves (three leaves x three trees) were tested per plot. Leaf tissue was tested for
presence or absence of P. ramorum using PCR methods (processed by Garbelotto
lab, UC Berkeley).
Statistical Methods
An index of SOD impact was developed to create a ranking of SOD severity on our
sites (Apigian 2005). To do this, we used PC-ORD to run a principle components
analysis (PCA) (McCune and Mefford 1999) to construct a composite variable (based
on PCA scores) to which we categorically assigned a SOD infection score for each
plot (5 plots x 8 sites = 40 plots). We used five environmental variables as important
indicators of SOD infection: percent of total basal area composed of dead coast live
oak snags, percent of total basal area in live coast live oak trees, LAI, CWD volume,
and percent of all coast live oak stems that were symptomatic of SOD (based on
stems that exhibited seeping, that were found in plots that tested positive for P.
ramorum). These variables were selected because they represent a range of impacts
that SOD may have on a stand, as opposed to using any single metric. The variables
used meet the assumptions of PCA (McCune and Grace 2002).
We used a classification and regression tree analysis (CART) to model
relationships between tree seedling densities and landscape variables. CART models
are developed by recursively partitioning the response variable into increasingly
homogenous subsets based on critical threshholds in continuous or categorical
variables (Crawley 2002). Tree-based models are graphically displayed so that one
can follow the tree node through a series of binary splits on the predictor variable to
an end node. We used a regression tree analysis with tree seedling density as our
output variable. The estimate for all observations that follow the same lineage of
branching to an end node is given by the mean x-value for that set of observations.
The Tree Model function in S PLUS 6.1 (SAS 2002) was used for our analysis.
After developing species-specific models for different years separately, a model was
developed that pooled all years together, using density of seedlings as the dependent
variable and the corresponding values of each landscape or stand variable for model
development. Default settings were used. The following equation shows the
independent variables used in these final models, where the dependent variable was
either coast live oak or bay seedling density:
•
Seedling density = f (LAI, BA live coast live oak m2/ha, BA live bay
m2/ha, duff depth, litter depth, CWD m3/ha, northness index, slope, plotbased SOD index, site, year)
The northness index was derived using the following equation, starting with
aspect in radians: Northness = cosine(aspect) . The plot-based SOD index was the
index derived from PCA scores.
To prune the CART trees, the cost-complexity setting was employed which
generated a table that showed how the model deviance decreased as the number of
232
Tree Seedling Distributions Across a Gradient of Phytophthora ramoruminfected Coast Live Oak/Bay Forests—Brown
nodes changed (Crawley 2002). Using these data, a deviance of 0.1 was chosen. This
number encompassed the largest jumps in deviance and was used for all models.
Additionally, ANOVA in S PLUS 6.1 (SAS 2002) was employed using
independent variables identified by the first and second runs of CART to determine
the degree of significance among plots.
Results
We found that large amounts of spatial and temporal variability existed in seedling
densities (table 1). Results indicated that coast live oak, bay, and toyon (Heteromeles
arbutifolia) were consistently the predominant woody species represented in the
seedling layer. There were few seedlings found of the other tree species in the plot
including black oak (Quercus kellogii), Pacific madrone (Arbutus menziesii), Oregon
oak (Quercus garryana), Big leaf maple (Acer macrophyllum), California buckeye
(Aesculus californica), and Douglas fir (Pseudotsuga menziesii). Certain sites tended
to have more total seedlings (table 1 and 2), as well as more seedlings of certain
species. The two China Camp sites had the highest mean densities of seedlings (table
2); however these high numbers were largely driven by toyon which comprised 97
and 99 percent of site totals in 2003 (table 1).
Skywalker consistently had the highest numbers of coast live oak seedlings; it
had over 20 times more coast live oak seedlings than other sites in 2002, and had
higher numbers than all other sites in the other two years of the study. Bouverie, the
second highest-producing site for coast live oak seedlings, had 5.5 times more
seedlings in 2003 and 2004 than all other sites, besides Skywalker. Bay, too,
exhibited a large variation in mean seedling densities among sites. Briones Bear
Creek had consistently higher densities than all other sites; it had up to 33 times more
bay seedlings than the China Camp sites and Fairfield Osborn.
Table 1—Total number of seedlings by species per site including coast live oak (Quag), bay
(Umca), toyon (Hear), and ‘other’ species in 2002, 2003, 2004. ‘Other’ includes black oak
(Quercus kellogii), Oregon oak (Q. garryana), Big leaf maple (Acer macrophyllum), Pacific
madrone (Arbutus menziesii), California buckeye (Aesculus californica) and Douglas fir
(Pseudotsuga menziesii). Seedling numbers were counted along a total of twenty 16 m2 belt
transects per site (320 m2 total area).
Site
Sum
(‘02)
Qu
ag
Sum
(‘03)
Qu
ag
Sum
(‘04)
Qu
ag
Sum
(‘02)
Um
ca
Sum
(‘03)
Um
ca
Sum
(‘04)
Um
ca
Sky
349
177
34
110
97
61
0
0
0
0
0
0
828
CCMiwok
24
20
13
12
14
12
33
1100
20
0
0
0
1248
CCBackMdw
2
3
3
6
5
1
19
742
59
0
0
0
840
BrionesAlh
13
7
1
69
60
66
1
0
0
0
0
0
217
BrionesBrCk
0
22
23
246
190
239
8
18
7
1
0
0
754
Olompoli
na
19
20
na
90
83
na
15
4
na
4
6
241
Bouverie
na
147
37
na
123
110
na
45
25
na
24
9
520
FOP
na
42
27
na
19
16
na
1
1
na
6
5
117
total
388
437
158
443
598
588
61
1921
116
1
34
20
4765
Sum Sum Sum Sum Sum Sum
(‘02) (‘03) (‘04) (‘02) (‘03) (‘04) Grand
He ar He ar He ar Other Other Other Total
233
GENERAL TECHNICAL REPORT PSW-GTR-217
Table 2—Average seedling density (all species) per site. For sites Skywalker, China Camp
Miwok, China Camp Back Meadows, Briones Alhambra, and Briones Bear Creek, these
values represent densities from ‘02-’04. For sites Olompoli, Bouverie, and Fairfield Osborn,
values represent only ‘03 and ’04 numbers. In the statistical differences column, sites w/the
same symbol (+, ++, and –) are not statistically different from one another.
Site
Sky
CCMiwok
CCBackMdw
BrionesAlh
BrionesBearCrk
Olompoli
Bouverie
FOP
mean
seedlings per
m2
1.38
2.08
1.39
0.37
1.32
0.72
1.32
0.30
n
15
15
15
15
15
10
10
10
SE
0.26
0.72
0.66
0.03
0.17
0.08
0.17
0.08
Statistical
Differences
+
++
+
+
+
+
-
Table 3—Total seedlings counted at each site, by year.
2002
2003
2004
Site
total
total
total
Sky
459
274
95
CCMiwok
69
1134
45
CCBackMdw
27
750
63
BrionesAlh
83
67
67
BrionesBearCrk
255
230
269
Olompoli
na
128
113
Bouverie
na
339
181
FOP
na
68
49
Total
893
2990
882
Principal Components Analysis indicated that the first axis was the best synthesis of
the variables potentially associated with SOD. The eigenvalue of 3.724 was much
greater than all other axes indicating that axis 1 was the only significant axis for
interpretation (McCune and Grace 2002). This axis represents a strong SOD gradient
from high SOD impact (low proportion of live oaks, high proportion of dead oaks,
high light penetration, high woody debris volume, and high incidence of SOD) to low
impact of SOD. We adjusted the axis scores by taking the inverse of the PCA values
so that the index would run from least affected to most affected plots. We then took
the absolute value of the PC 1 axis values, setting the least affected plot to 1 with the
most affected plot receiving a score of 8.89. These adjustments did not change the
scale of the original PCA axis and were done simply to ease interpretation and
analysis.
234
Tree Seedling Distributions Across a Gradient of Phytophthora ramoruminfected Coast Live Oak/Bay Forests—Brown
Coast Live Oak
The model included 983 coast live oak seedlings recorded over three years. CART
analyses showed that site and year were the most important variables explaining the
number of coast live oak seedlings. Skywalker was initially separated from the other
sites, and then Bouverie (fig. 2). Both of these sites had high mean seedling densities
while the other sites had lower mean densities (0.86 seedlings/plot). Seedling
densities varied greatly by year (table 1) and the CART analysis validated the
importance of the year effect. For example, Skywalker (fig. 2) had the highest mean
seedling densities in 2002 (17.45 seedlings/plot), followed by 2003 (8.85
seedlings/plot), and the lowest density in 2004 (1.70 seedlings/plot).
All other sites
All other sites
Skywalker
Site:bcdefgh
|
Bouverie
2002
Site:bcdefh
0.8594
(80)
Year<2002.5
4.6000
(10)
2003
2004
Year<2003.5
17.4500
(5)
8.8500
(5)
1.7000
(5)
Figure 2—Final CART model chosen for coast live oak. Numbers in parentheses
below the end node represent the number of plots in each subset.
Figure 3, below, shows that after excluding site and year from the CART model,
northness and bay basal area were the most important variables predicting coast live
oak seedling densities. The highest mean coast live oak seedling density (15.71
seedlings/plot) occurred on south facing slopes, in which bay basal areas were >0.79
m2/ha. For the remaining plots, live bay basal area was again an important predictor
of coast live oak seedling density. On these sites, on more north-facing slopes, a
lower live bay basal area (<0.52 m2/ha) and a lower SOD index (<1.93) predicted
higher seedling densities (4.160 seedlings/plot) compared to plots with higher SOD
index (1.01 seedlings/plot).
235
GENERAL TECHNICAL REPORT PSW-GTR-217
other aspects
south-facing aspects
North.ness..real.<-0.91654
|
live.Umca.BA<0.787619
live.Umca.BA<0.520926
Plot.SOD.index<1.93422
0.8273
(55)
4.1670
1.0130
(20)
(12)
North.ness..real.<-0.975401
6.6250
(6)
0.8333
(6)
15.7100
(6)
Figure 3—Second CART model without site and year. Northness, live bay basal
area, and SOD index were the most important predictor variables for coast live oak
seedling numbers. Numbers in parentheses below the end node represent the
number of plots in each subset.
Table 4 shows ANOVA results run on the important variables identified by the
first and second models of CART. As expected, both site and year are significant
predictors of seedling numbers of coast live oak. There is also a significant
interaction between site and year, indicating that these two variables are not
consistent, that is they do not vary together in the same way (see table 1). The
remaining environmental variables were not significant.
Table 4—ANOVA results for coast live oak seedling densities over the 2002-2004 time
period. Variables chosen for the model were from CART models (fig. 2 and 3).
Site
Year
Northness
liveUmcaBA
plotSODindex
Site:Year
Residuals
236
Df
7
1
1
1
1
7
86
Sum of
Sq
982.4363
184.5043
5.0635
5.3601
0.676
514.7816
826.4184
Mean Sq
140.348
184.5043
5.0635
5.3601
0.676
73.5402
9.6095
F Value
14.60511
19.20017
0.52693
0.5578
0.07035
7.65285
Pr(F)
0.0000000
0.0000331
0.4698722
0.4571869
0.7914614
0.0000004
Tree Seedling Distributions Across a Gradient of Phytophthora ramoruminfected Coast Live Oak/Bay Forests—Brown
Bay
The bay model analyzed 1,629 seedlings as response variables over the three-year
period. The final model deviance was set to 0.1, and showed that site, northness, and
duff depth had the highest explanatory value (fig. 4). Of these, site was the most
important, separating Briones Bear Creek from the other sites, and then separating
the two China Camp sites and Fairfield Osborn Preserve, with a lower mean seedling
density (0.53 seedlings/plot) from the other sites (fig. 4). At the next node, average
duff depth dictated whether the mean bay seedling density was 7.25/plot (less duff
depth), or 3.79/plot (higher duff depth). For Briones Bear Creek, northness was the
key determinant of seedling density. Lower northness determined a higher mean
(17.5 seedlings/plot), while higher northness meant a lower mean density of bay
seedlings (7.08 seedlings/plot).
All other sites
Briones Bear Creek
Site:abcdfgh
|
Sky., Briones Alh.,
Olompoli, Bouverie
China Camp sites,
Fairfield Osborn
Site:bch
North.ness..real.<0.129497
Avg.duff.depth..cm.<0.340909
0.5313
(40)
7.2500
(8)
3.7920
(42)
17.5000
(6)
7.0830
(9)
Figure 4—CART model chosen for bay. Numbers in parentheses below the end
node represent the number of plots in each subset.
As with coast live oak seedling analysis, a second CART model was constructed
eliminating site, northness, and average duff depth from the model in order to explore
further important relationships (fig. 5). LAI was the most important variable
predicting bay seedling density, showing that more seedlings occur in areas with
higher LAI (and thus more shade). The highest mean seedling density (17.5/plot)
represented sites whose LAI value was higher than 2.98, and whose bay basal area
was higher than 0.79 m2/ha but less than 1.03 m2/ha.
237
GENERAL TECHNICAL REPORT PSW-GTR-217
higher light
lower light
LAI<2.9775
|
live.Quag.BA<2.30167
live.Umca.BA<0.790504
live.Umca.BA<1.25224
1.087
(49)
5.667
(6)
6.214
(7)
live.Umca.BA<1.02569
4.634
(28)
17.500
(6)
4.639
(9)
Figure 5—CART model chosen for bay, run without the variables in the first model.
Here LAI, live coast live oak basal area, and live bay basal area are the most
important predictors of densities of bay seedlings. Numbers in parentheses below the
end node represent the number of plots in each subset.
Table 5 shows ANOVA results run on the variables identified by the first and
second models of CART. As expected, site and LAI are significant predictors of
seedling numbers of California bay. The remaining environmental variables were not
significant.
Table 5—ANOVA results explaining variability in bay seedlings over the 2002-2004 time
period. Variables in the model were those identified by the two CART models run.
Sum of
Sq
Df
Mean Sq
F Value
Pr(F)
Site
7
1317.229 188.1756 23.25514
0
Duffdepth
1
1.625
1.6248
0.2008
0.655133
Northness
1
5.678
5.6784
0.70175 0.404368
LAI
1
32.873
32.8734
4.06257 0.046761
liveQuagBA
1
0.003
0.0033
0.0004
0.984036
liveUmcaBA
1
29.911
29.9115
3.69652 0.057619
Residuals
92
744.444
8.0918
Discussion
The effects of SOD on coast live oak/bay forest succession are still largely unknown,
particularly in these early stages of the disease. McBride (1974) suggested that coast
238
Tree Seedling Distributions Across a Gradient of Phytophthora ramoruminfected Coast Live Oak/Bay Forests—Brown
live oak/bay communities, in the absence of disturbances like livestock grazing,
would naturally succeed to bay forests over time. With the death of coast live oak,
successional processes toward bay could be accelerated.
Our results indicate that a great deal of spatial and temporal variability exists for
both coast live oak and bay seedlings. Site is the most important indicator of seedling
density for both our coast live oak and bay models, possibly indicating that
environmental factors accounting for the variance in seedling densities were not
measured in this study.
Year is a significant indicator of seedling density in the coast live oak model but
not in the bay model. This finding is commensurate with the literature, as oaks are
known masting species and therefore acorn production varies between years and is
cyclic, with large-scale weather patterns influencing production levels (Koenig and
others 1996). The lack of a year effect on bay seedlings in our models suggests that
bays do not appear to be as subject to temporal asynchrony as the oaks.
One environmental variable was statistically significant in the ANOVA models.
We found leaf area index to be a predictor of bay seedling densities, but not of coast
live oak seedling densities. The CART model demonstrates that bay seedling
densities are higher in areas with less light. This finding could be an artifact of
lowered light levels beneath mature bay trees, indicating that distance to seed source
is an important variable for bay seedlings, rather than light levels per se. Further,
mature bay basal area was almost significant at the 95% level in the bay ANOVA
model. However, either of these scenarios is important in predicting seedling
dynamics in gap openings produced by SOD, as it may mean that bay seedlings are
more limited than oak seedlings by light levels or distance to seed source.
Little literature exists on bay seedling requirements to support or refute these
claims. Mature bays are generally classified as shade-tolerant, but tolerance levels are
not well defined (USDA 1965). McBride showed that bay seedlings can invade open
grasslands and brushlands in the Berkeley hills (McBride 1974); similar capabilities
were observed in the Santa Cruz mountains (Unsicker 1974).
Particularly amidst concern that certain oak species are not replacing
themselves, several studies have evaluated shade-tolerance levels in coast live oak
seedlings. Coast live oak seedlings are exceptionally shade-tolerant compared to
seedlings of some other California oak species, and are able to maintain high
photosynthesis capacities and root elongation in low light (Callaway 1992). Many
studies have found that coast live oak seedlings are spatially associated with
shrublands (McBride 1974, Muick and Bartolome 1987, Callaway and D’Antonio
1991, Callaway and Davis 1998). One study showed that seedling survival is
enhanced by artificial shade (Muick 1991). However, whether this is due to shade
tolerance factors (i.e., moisture requirements) in coast live oak seedlings, or other
factors such as seedbed suitability or protection from herbivory is unclear.
Other parameters will be important in determining the successional outcome at
any coast live oak/bay site and future studies should account for these. Bay seedlings
can be foliar hosts of P. ramorum which may affect leaf retention in seedlings as it
does in mature bays (Davidson and others 2005), potentially affecting bay seedling
survival. Both bay and coast live oak seedlings are susceptible to herbivory from a
variety of vertebrate and invertebrate grazing animals. Tyler and others (2002) found
that seed predation and herbivory by small mammals significantly reduced coast live
oak seedling recruitment in a study conducted at the Sedgwick Reserve in Santa
239
GENERAL TECHNICAL REPORT PSW-GTR-217
Barbara County. Both coast live oak and bay seedlings are palatable to deer
(Sampson and Jesperson 1963, McBride 1974). An additional factor for future
investigation is an analysis of whether CWD will provide refugia for oak seedling
survival, similar to Callaway and D’Antonio’s (1991) determination that shrubs
facilitate coast live oak seedling survival.
Future studies should include measurements of individual seedling survival and
growth rates as seedling growth rates vary. Some studies suggest that bay seedlings
grow fast, attaining up to 30-cm growth per year on favorable sites (USDA 1965).
One study on restoration of coast live oak found that in irrigated tree shelters (a
favorable site), coast live oak may attain 38-cm growth per year (Plumb and De
Lasaux 1996), but under normal field conditions coast live oaks were found to grow
much more slowly often persisting at heights of approximately 10 cm for many years
(Muick 1997).
Conclusions
In these early stages of the disease, we do not believe that P. ramorum and the
associated SOD complex is accelerating successional processes toward baydominated forests. In our study, numbers of coast live oak and bay seedlings,
evaluated over three years in the early stages of SOD, did not appear to be affected
by the presence of SOD. Our gradient of infection from little-to-no presence of P.
ramorum in Briones sites to considerable presence and visible effects of oak die-off
at Skywalker and China Camp did not correlate with coast live oak or bay seedling
numbers. However, seedling survival is only one part of this picture. Recruitment
into saplings, pole and, finally, tree-sized classes are important components of stand
succession. The temporal variability of SOD pressure is also important. Thus the
interplay between favorable weather years, herbivory, site factors, and variation in
SOD pressure will determine the competitive advantage of coast live oak or bay
seedlings and the outcome for dominance of California’s coast live oak/bay
woodlands.
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