a tale of two forest typescaforestpestcouncil.org/wp-content/uploads/2011/03/ben-ramage.pdf ·...
TRANSCRIPT
Benjamin Ramage 1, Alison Forrestel 1,2,Max Moritz 1, and Kevin O’Hara 1
A tale of two forest types
1 Dept. of Environmental Science, Policy, and Management – UC Berkeley2 Point Reyes National Seashore
2
• Exotic pathogen of unknown origin (Phytophthora
ramorum)
• Discovered in mid ’90s in Santa Cruz and Marin Counties• “Sudden Oak Death”
= lethal trunk infection
– impacts several tree species native to California(e.g. some oaks, tanoak, madrone)
“Ramorum
Blight”= sub‐lethal foliar infection
– affects a huge number of species– pathogen is likely to persist indefinitely
The Basics of Sudden Oak Death
3
• Distribution is very patchy in CA coast ranges• Regional and local spread expected to continue
The Basics of Sudden Oak Death
(Meentemeyer
et al. 2004)(OakMapper
web application; 2006)
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Tanoak
• Ecological Significance– Prolific acorn production– Extremely shade tolerant– Very competitive in conifer forests
• Susceptibility– Genetic– Age/size classes– Environmental
tanoak could be heading towards (functional) extinction
in CA’s
coastal conifer forests
(Notholithocarpus
densiflorussyn. Lithocarpus
densiflorus)
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Potential forTrophic
Cascades
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OBJECTIVES
1.
Document and describe SOD
disease progression in the
redwood and Douglas‐fir forests
of Point Reyes National Seashore
2.
Determine which factors affect
tanoak survival probabilities
3. Simulate tanoak mortality through 2025 (using three separate models)
4. Compare baseline conditions in redwood and Douglas‐fir forests
5. Integrate these findings to discuss differences in the likely overall impact
of SOD in these two forest types
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STUDY AREA
Plots: ‐
1/20 hectare circular (12.62 m radius), all in second‐growth redwood or Douglas‐fir forest
Figure 1. Plot Locations
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STUDY DESIGN
GIS used to:‐
stratify plots by forest type
In‐field protocol used to:
‐
stratify plots by disease condition:‐
“Healthy”
(SOD symptoms scarce or non‐existent) ‐
“Diseased”
(severely impacted)(using a variant of a randomized split‐plot design, which was made possibleby the highly patchy local distribution of the disease)
‐
standardize tanoak basal area (to facilitate direct comparisons across forest types and disease conditions)
D
H
RP
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DATA COLLECTION & ANALYSIS
FIELD MEASUREMENTS: (in 2007 and 2009)
DATA ANALYSIS
(for observational component):
* Recently fallen tanoaks
were also recorded
‐
Location, DBH, and health status for all standing* trees >
3 cm DBH‐
Cover classes of all vascular plant species (visual estimates)‐
Tree regeneration tallies‐
Fuel loading (with Brown’s transects)
Generalized Linear Mixed Models‐ each plot pair (Healthy and Diseased) was treated as a random effect‐ error distribution varied with response variable‐model predictors were dependent upon the analysis
(e.g. baseline comparison of forest types, effects of SOD)
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RESULTS: Disease Progression
RW: 3.2% DF: 10.1%
RW: 8.2% DF: 26.2%
RW: 7.1% DF: 73.6%
RW: 4.8% DF: 22.3%
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RESULTS: Disease Progression
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RESULTS: Disease Progression
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2007 2009
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2007
2009
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We tested (individually and in interactions with forest type):‐ total stems, total BA, tanoak stems, tanoak BA‐mean tanoak DBH, mean DBH of all tree species ‐ California bay stems, California bay BA, richness of mature tree species ‐ slope, elevation, northness(variables that could have conceivably influenced disease development, but that were very unlikely
to have been affected by
SOD‐induced mortality)
What factors are increasing disease severity?
1. Which variables are associated with mortality patches? (i.e. H vs. D in 2007)2. Which variables are associated with higher observed mortality rates from 2007 to 2009?
RESULTS: All non‐significant
No pre‐existing plot‐level variables were related to disease severity
However…
DBH, forest type, and 2007 plot‐level tanoak mortality were all significant predictors of mortality between 2007 and 2009
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Three different models, each of which represents a different hypothesis about disease progression
slope p-valueSimple ModelIntercept 1.1326 0.0023Redwood Forest Type 1.0258 0.0574DBH (cm) -0.0626 <.0001DBH^2 0.0006 0.0343Redwood*DBH 0.0431 0.0127
slope p-valueTotal Dead Model
Intercept 0.3019 0.3414Redwood Forest Type 1.3132 0.0025DBH (cm) -0.0623 <.0001DBH^2 0.0007 0.0226Total Dead BA in 07 (m^2) -2.1384 0.0009Total Dead BA in 207^2 1.3826 0.0227Redwood*DBH 0.0434 0.0087
slope p-valueRecent Dead ModelIntercept 0.6544 0.0782Redwood Forest Type 1.2061 0.0183DBH (cm) -0.0674 <.0001DBH^2 0.0008 0.0108Recent Dead BA in 07 (m^2) -4.0060 0.0071Recent Dead BA in 07^2 7.2019 0.0285Redwood*DBH 0.0538 0.0039Recent Dead BA in 07*DBH -0.0827 0.0374
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Simulation Methods
Survival probabilities (fitted from 2007 to 2009) were used to project mortality forward in two‐year intervals to 2025
Three separate simulations
one for each model / hypothesis
One static simulation (simple model); two dynamic simulations (total dead and recent dead models)
500 runs for each simulation
Some details…
Effects of dead tanoak basal area (total or recent) were constrained to the 90th
percentile of our original (2007)
data (because our models suggested that saturation occurs at approximately this point)
Random site effects (plot and block) were ignored when predicting future survival probabilities (although we
accounted for the non‐independence of each tree when fitting models); as such, we assume that any
differences in tree‐level survival probability between individual plots and blocks are transitory and do not
reflect permanent characteristics
And an important note:
our projections assume that mortality rates between 2007
and 2009 were representative of longer‐term trends
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35%
20%
40%
0%
21
20%
0%
20%
0%
22
35%
20%
40%
0%
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20%
0%
20%
0%
5%
0%
5%
0%
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Baseline differences
between redwood and
Douglas‐fir forests
(comparison of healthy plotsin 2007)
RESULTSRW DF
Basal AreaTotal, Tanoak, Conifer (RW or DF)
Non-tanoak hardwood +California bay +
Stem CountsTotal, TanoakConifer ++Non-tanoak hardwood +California bay +
Regen (Individuals / Clumps)Total, TanoakConifer ++Non-tanoak hardwood ++California bay ++
Fuels1-hr, 10-hr, 1000-hr, litterDuff +Litter and Duff +Total +
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Baseline differences
between redwood and
Douglas‐fir forests
…continued
RESULTSRW DF
Cover ClassesShrub, Juv. Tree, ExoticHerb ++Canopy ++
RichnessHerb, Tree (Juv./Mature), ExoticShrub ++Total ( + )
EvennessTree (Juv./Mature), Exotic, TotalHerb +Shrub ( + )
DiversityHerb, Shrub, Juv. Tree, ExoticMature Tree +Total +
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• Mortality rate is much higher in Douglas‐fir forests…for reasons that are not entirely clear:
Summary
1. greater stand level abundance of CA bay?2. more conducive climatic conditions?3. greater abundance of other hosts?4. more susceptible tanoak genotypes?
• Long‐term effects may be greater in redwood forests…because:
But…
1. pre‐SOD tanoak abundances were much higher in RW forest2. there is less functional redundancy in RW forest
IMPORTANT NOTE: these results do not necessarily apply to RW and DF forests outside of PRNS,
But they do demonstrate that SOD‐induced tanoak mortality can occur very rapidly in some areas
Point Reyes National Seashore (fieldwork funding and site access)
Baker‐Bidwell Research Fellowship (funding of data analysis)
Acknowledgements
Dave Rizzo and his
students/staff [UC Davis](for testing symptomatic
samples and providing
general guidance)
And our many excellent
field volunteers
For more information,email me at: