Genecological Responses in Western Conifers to
Climate Changes Over the Past Two Millennia
Bob Westfall, Connie Millar, and Diane Delany
USDA Forest Service, Pacific Southwest Research Station,
Albany, California, USA
Niche Space and Climate
From Jackson & Overpeck 2000
Niche Spaces and Individual Species Response:
Whitewing Mtn
Deadwood Species, 3051 m
Whitebark Pine krumm
W White Pine ↓ 250 m
Lodgepole Pine ↓ 250 m
Jeffrey Pine
↓ 500 m
Mtn Hemlock
↓ 250 m
Sugar Pine
↓ 600 m
Mixed Conifer Forest
800-1350 CE
• Range Maps: California Gap Analysis Project (UCSB)
Polygons selected from co-dominant species in primary-to-secondary cover types; Sierra
Nevada and Eastern Sierra Jepson eco-regions
<http://www.biogeog.ucsb.edu/projects/gap/gap_home.html>
• Climate: 4 km PRISM Gridded Data (Spatial Climate Analysis Service)
Minimum and maximum temperature, precipitation (30 yr averages); Annual, January, and July
< http://www.ocs.orst.edu/prism/>
Joint climate space
Medieval vs Present
Ann ppt -24 mm
Ann minT +3.2°C
Ann maxT +2.3°C
Millar,
et
al.2006
“This is déjà vu all over again”
- Yogi Berra
0
Niche Breadth
Mean Character
Niche Space in Genetic Context
Environmental Tolerance
vt
Spatial Position, x
• Environmental Tolerance: how rapidly fitness decreases away from the
optimal spatial location
• Niche Breadth:
how much the fitness of a phenotype adapted to one
environment is reduced if grown in another environment
From Pease et al 1989
Notes: Graph represents a Gaussian fitness model of the
correlation between a phenotypic character and an environmental
gradient (in this case, climate change) with respect to fitness.
Contour intervals represent mean fitness with the maximum at
mean phenotype 0 and spatial position vt.
Adaptation to Climate Change
Dieback of Limber Pine Forests
# Dead
Trees
Tmin/
Tmax
Precip
Millar et al., 2007
Tree ring width
Living trees
Dead trees
Interannual variance
in growth
Estimated by Generalized
Autoregressive Conditional
Heterrscedasticiy (GARCH)
Growth significantly greater (by
paired T-test) in dead trees during
18th and 18th centuries; significantly
greater in living trees during 20th
Interannual variance significantly
greater in dead tress in 18th and 19th
centuries, not in 20th
Interactions between temperature and precip and with demographic classes
Statistically-significant: Min temp and precip; demographic cl;ass by temp and precip
Mean Character
Populations Will Lag Climate Change
!0.5
Lz ! s !x " vt"
x
vt
Spatial Position , x
Notes: Lz represent the amount that the
mean phenotype deviates that from the
optimum for the changed climate at
spatial position vt
From Pease et al 1989
Populations are away from their optima
In common garden provenance tests, trait maxima are often in populations
lower in latitude or in elevations than that of the test locations (reviewed in
Wright 1976).
Rehfeldt, et al. (1999) examined 20-yr data from over 120 populations of
Pinus contorta in 60 test plantations in British Columbia (the Illingworth
tests).
Reaction norms for height for populations
growing along a temperature gradient.
Solid arrow indicates a population whose growth
maximum is at about 1o C; dotted arrow indicates
the population with the growth maximum for
populations growing at at that temperature, but
with a growth maximum at a warmer
temperature.
Adapted from Rehfeldt et al. 2001
Population optima were from warmer
environments that those currently occupied.
Difference between the mean temperature at
the locally inhabited environment and that
where the growth is maximum. Difference
was 0.5oC at low latitudes, 7oC at high
latitudes
Adapted from Rehfeldt et al. 1999
These populations not only lag
current climatic change, but did
not fully adapt to past changes
(i.e., Little Ice Age)
Environmental Tolerance and Population
Persistence
Genetic differences between krummholz and upright forms of
Pinus albicualis (Rogers et al. 1999)
Krummholz (prostrate growth form)
At tree line, longevity of patches (especially
layered ones) in excess of 500 years and up
to 1700 years (King and Graumlich 1998)
Upright clumps
Occupy lower elevations, those below
krummholz are young (<100 years)
0.052 >Fst < 0.062 between krummholz
and upright forms
Conclusion: The two forms are likely to have originated from
different source populations resulting in a genetic mosaic
Effects of Pollen and Seed Dispersal
• Gene flow will decrease populations’ adaptive optima
From García-Ramos & Kirkpatrick 1997
• Gene flow can limit species ranges
From Kirkpatrick & Barton 1997
• Gene flow will reduce the rate of adaptation to climate change.
From Pease et al 1989; García-Ramos & Rodríguez 2002
Dispersal Functions
0.020
Normal Distribution
0.56419 b 0.5 !"b x
2.
frequency
0.015
0.005
Exponential Distribution
0.5 b
1.
0.010
0.000
0
"b x 1.
!
100
150
Distance
0.0020
Long"tailed Distribution
0.5
0.0015
frequency
0.25 b 2. !"b x
50
0.0010
0.0005
Recent estimates indicate highly leptokurtic (long-tailed }
distributions, in some plant species,with shape parameters
less than 0.5 (in the example above). In long-tail
distributions, most of the dispersal is near the parent, but are
non-zero at long distances from the parent.
0.0000
0
50
100
Distance
150
200
• There is an optimum dispersal rate after which the evolutionary
rate declines.
• Numerical simulations show that populations spread in density
and adaptational waves under conditions of low environmental
heterogeneity or high genetic variation.
From García-Ramos & Rodríguez 2002
Spatial autocorrelation plots of coast redwood
genotypes in four stands
0.4
0.4
0.3
0.3
0.2
0.2
0.1
0.1
0
0
-0.1
-0.1
0
50
100
150
Feet
200
250
300
0
Site 3
0.5
0.4
0.4
0.3
0.3
0.2
0.2
0.1
0.1
0
0
-0.1
-0.1
0
Sites 1&2: Lowland
Sites 3&4: Upland
50
100
150
Feet
50
100
200
250
300
150
Feet
200
250
300
200
250
300
Site 4
0.5
r
r
Site 2
0.5
r
r
Site 1
0.5
0
50
100
150
Feet
Note wave patterns, especially in upland sites. Also note high correlations in the upland
sites. This indicates relatively recent establishment of the stands.
Error bars are 95% CIs; dotted lines ar 95% CI around zero correlation.
From Rogers & Westfall 2008
Recruitment of Subalpine Conifers
Research Questions:
The nature of limber pine recruitment (<120 yrs) relative to:
-- elevation (upper, mid, lower treeline)
-- species: BCP (Pilo) vs LP (Pifl)
-- soils & aspect
-- vegetation structure
upper
mid
lower
FIELD METHODS
-30m wide belt transects x 30 m plots
-Tallied all live & dead trees & diameters
-Aged all LP < 30 cm diameter
Recruit Results
low
upper
mid
Plot Density LP by Age Class, 7 Sites
- LP recruiting 100 m above current BCP upper treeline
- 300 m above current LP treeline
- Recruit pulse concentrated 1976-1991
- Dolomitic or non-dolomitic soils
- Former forest (upper); meadow (mid); ravines (low)
-
Limber Pine recruit – all sites
Climate Analyses - Methods
Composite Climate Indices from Three Instrumental Stations
Mina, NV; Independence, Tahoe, Yosemite NP, CA; temperature, precipitation
Period-of-Record: 1906 – 2005
Reanalysis Data (700 hPa)
From Kalnay et al., 1996; NOAA/ESRL; http://www.cdc.noaa.gov/
Climate and Recruit
Simple Correlation Analysis
Multiple Regression Analysis:
2nd Order Least Squares Response-Surface Model
Recruitment-Climate Correlations
Fit of overall model, R2 = 0.77
Temperature
Annual Min Temp
Annual MaxTemp
Dec Max Temp
Jan Max Temp
May Min Temp
June Min Temp
June Max Temp
Sept Min Temp
r
p
Precipitation
0.58
0.38
0.32
0.36
0.45
0.58
0.37
0.45
<0.001
<0.001
0.002
<0.001
<0.001
<0.001
<0.001
<0.001
Annual Precip
WY Precip
Sept Precip
Sept Precip -1yr
…More recruit with warmer years,
esp warmer springs & summers
…More recruit with wetter autumns
r
p
0.08
NS
0.06
NS
0.15 0.17
0.28 <0.01
Interactions of Recruitment with Climate
Ann min Temp x Recruit x (Ann Precip, July Precip, & Sept Precip 1-yr prior)
July Precip
log Recruit
Annual Precip
Blue curves = higher Tmin
Red curves = lower Tmin
1-yr prior Sept
Precip
Atmospheric Pressure – Reanalysis Data (700 hPa -- 3000 m asl)
Correlations between number of recruits per year and atmospheric pressure
April to June 1-yr Prior
June Recruit Yr
Dec to Feb Prior Winter
Aug to Oct Recruit Yr
Aug-Oct 2 yrs Post Recruit
Modeled LP recruitment 1910-2001 Linear & Spline Fits
Annual Min Temp
July to Oct Precip
Limber Pine Recruit and the Atlantic Multidecadal Oscillation
Limber Pine Recruit, all sites
Note: recruit data have periodicity of 2.5 yrs, reflecting mast years for seed production
Summary
1. Treeline shift individualistic by species in White Mtns
2. LP recruit 300m above current LP and 100m above current
BCP treelines
3. LP recruit below lower treeline in ravines
4. LP & BCP recruit into sage meadows at mid-elevation
5. Recruit episodic & complexly related to climate
6. LP recruit favored under warming temps, wet autumns
7. Overall elevation gain proportional to temp increase(1.95°C
lapse; 2.0°C composite instrumental)
Conclusions
• Episodic, threshold, and reversible changes are
more common responses to late Quaternary
climate changes in mountain ecosystems than
are linear or gradual changes -- These have
resulted in non-analogous vegetation assemblages,
like those at Whitewing Mt.
• As a result, populations will lag genetic (or
adaptational) responses to not only current, but
to past climatic changes (as with the Red Queen
or Court Jester).
Literature cited
• García-Ramos, G., and M. Kirkpatrick. 1997. Genetic
models of adaptation and gene flow in peripheral
populations. Evolution 51:21-28.
• García-Ramos, G., and D. Rodríguez. 2002. Evolutionary
speed of species invasions. Evolution 56:661-668.
• Kirkpatrick, M., and N. H. Barton. 1997. Evolution of a
species' range. American Naturalist 150:1-23.
• Pease, C. M., R. Lande, and J. J. Bull. 1989. A model of
population growth, dispersal and evolution in a changing
environment. Ecology 70:1657-1664.
• Davis, M. B., R. G. Shaw, and J. R. Etterson. 2005.
Evolutionary responses to changing climate. Ecology
86:1704-1714. -- A review, including those of results
from the papers above.
Haldane’s Law: “The universe is not
only queerer than we imagine, it’s
queerer than we CAN imagine.”
The Ultimate Principle: “By definition,
when you are investigating the
unknown you don’t know what you
will find.”
Coauthors:
•
Harry A. Alden. The Smithsonian Institution, Washington, DC, USA
•
John.C. King, Lone Pine Research, Bozeman, Montana, USA
Field Assistance:
Veronique Greenwood
Karolyn Wyneken
Special thanks to:
Dan Cayan
Mike Dettinger
Connie
Karolyn
Wyneken
John King
“…For the uncertain future has yet to
come, with every possible variety of
fortune...”
- From Plutarch's “Solon”