Pattern and Process of Tree Mortality
at Local and Regional Scales
Adrian Das
Western Ecological Research Center
Sequoia and Kings Canyon Field Station
U.S. Department of the Interior
U.S. Geological Survey
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Net Primary Productivity (Gt C yr )
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Carbon in living phytomass (Gt)
A brief mention of context . . .
40
52%
30
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Biome data from Saugier, Roy, & Mooney (2001),
Ch. 23 in Terrestrial Global Productivity
A brief mention of context . . .
• A Changing World
• Assessing Risk
By the National Assessment
Synthesis Team, US Global
Change Research Program
Published in 2000
Mortality is important . . . And yet still very
much a black box
Daniel Botkin, from 1993 Forest Dynamics Book:
“Given this situation, we set down the following concepts of
mortality processes, expecting them to be rapidly improved on . . .”
“To my surprise, those who have taken up JABOWA have left this
part of the model essentially unaltered . . .”
Dying can be complicated . . .
From Franklin et al. 1987
. . . And take a long time
Especially if you’re not a tree . . .
• Potential lifespan of a White Oak: 600 years
• Potential lifespan of Sugar Pine: 400 to 500 years
• Potential lifespan of Giant Sequoia: >3000 years
• Potential lifespan of Bristlecone Pine: >5000 years
• Potential lifespan of researcher: 120 years?
• Likely length of research career: 50 years?
• Likely length of grant: 2-5 years
Long-term Monitoring Plots
Thirty Permanent Plots (0.9 to 2.5 ha)
• Established between 1982 and 2010
• Elevation: 1500m to 3300m
• Including ponderosa pine-mixed
conifer, white fir, mixed conifer, red fir,
lodgepole pine, Jeffrey pine and
subalpine forests
• Annual Mortality Rate Data
Trees As Individuals
A Temporal Process
A Spatial Process
What Rings True?
Does History Matter (enough)?
2.5
2
Slow Average Growth
1.5
Growth Increment (mm)
1
0.5
0
1940
2.5
2
1950
1960
1970
1980
1990
2000
1970
1980
1990
2000
1970
1980
1990
2000
Declining Trend in Growth
1.5
1
0.5
0
1940
2.5
2
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1960
Abrupt Drop in Growth
1.5
1
0.5
0
1940
1950
1960
Year
Seems it does . . .
• We have built models using these additional growth
metrics for 6 prominent species across 4 different
sites. 16 combinations of site and species.
• In every case, using a more comprehensive set of
growth metrics substantially improved models of
mortality risk.
Das et. al 2007 and unpublished
Trees As Part of a Stand
Can they be used to estimate stand level
properties? Do they transfer?
• Sugar Pine Model from Sequoia:
– Blodgett Mortality Rates:
• Observed: 2.95%/year (95% CI: 2.04 to 4.37)
• Model:
3.17%/year (95% CI: 2.87 to 3.48)
• White Fir Model from Sequoia:
– Sequoia External Site Mortality Rates:
• Observed: 1.2% /year (95% CI: 1.0 to 1.4)
• Model:
1.25%/year (95% CI: 1.05 to 1.51)
– Red Fir at Sequoia Mortality Rates
• Observed: 0.81%/year (95% CI: 0.54 to 1.20)
• Model:
1.05%/year (95% CI: 0.94 to 1.18)
– Blodgett Mortality Rates
• Observed: 1.92% (95% CI: 1.65 to 2.52)
• Model:
1.46% (95% CI: 1.33 to 1.58) (Oops)
Das et. al 2007 and unpublished
Applications: Forest Health and Climate Change
2002
Vulnerability
Profiles
Number of Trees
2030
2065
More than 20% of tree
population at “high risk”
by 2100 due to climate-relate
reduction in growth
2100
95
96
97
98
Survival Probability
99
Battles et al. 2008
But what about landscapes?
Apparent Relationship between
rising temperatures and
Mortality Rate.
Can we use this
relationship to forecast
future mortality rates?
van Mantgem et al. 2009 Science 323: 521-523.
Two Non-Mutually Exclusive
Mechanisms:
1. Increasing drought stress on trees resulting
from temperature-induced increases in climatic
water deficit
2. Temperature-induced increases in the
reproduction, survivorship, and effectiveness
of insects and pathogens that kill trees
Hypothesis
Water-limitation
(Low Elevation)
Energy-limitation
(High Elevation)
Mechanism #1
(drought stress->
deficit driven)
Mechanism #2
(enemies->
temperature
driven)
Summary of Results
• Model comparisons support the hypothesis
that mechanisms driving changes in mortality
vary between water- and energy-limited
forests.
• Mortality rates increase for all model
forecasts, but we could not determine
whether those changes are best described by
absolute or relative changes in deficit.
• This leads to great uncertainty in our forecasts
What uncertainty can mean
Forecasts of Change in Mortality Rate by 2100
GFDL A2 Scenario
Back to trees and stands . . .
What have we missed?
Growth fails to adequately
capture:
• Competitive effects for small
shade tolerant trees
• Increased mortality risk due to
proximity to conspecific species
• Risk of mechanical damage
• Potential facilitative effects
Das et al. 2008
Suppression
Mechanical
Damage
Factors
Associated
with Tree
Death
Uprooted, Broken
Stem, Crushed,
Crown Damage,
Lightning, Scarring,
Cracking, Pinned,
Snow and Ice
Damage
Disease
Biotic
Factors
Insect
Vertebrate
Mistletoes
Rots
H. annosum, Armillaria,
E. tinctorium, Blackstain,
P. schweinitzii,
Cankers
C. abeitis, Cenangium,
Scleroderris, Nectria, C.
ribicola
Foliar
Diseases
Neopeckia,Mycosphaerella,
Elytroderma,Lirula,
Gymnosporangium
Bark
Beetles
D. ponderosae, D.
brevicomis, D. valens, D.
jeffreyi, S. ventralis, Ips,
Phloeosinus, P. yosemite
Defoliators
O. pseudotsugata, E.
meritana, tent
caterpillar, leaf miner, C.
pinifoliae, scales, aphids
mountain beaver,
woodpecker,
sapsucker, human,
deer, bear, gopher
Is capturing competition good enough? Probably
not
Das et al. 2011
Ok, what else matters . . .
Sampling of Future Considerations
• Improve how we measure tree health
• Improve our understanding of the factors
associated with tree mortality
• Examine linkages between background and
catastrophic mortality
• Improve our ability to use spatial information
to distinguish among mechanisms
Acknowledgements
• Funding:
– U.S. Geological Survey Pacific Southwest Area Integrated Science
Funds
– U.S. Geological Survey Western Mountain Initiative
– USDA Exotic/Invasive Pests and Disease Research Program
– California Agricultural Experiment Station
– Frank Meyers and Rosecrans Fellowships
• Data Collection
– U. S. Geological Survey
– National Park Service