Topoclimatic Controls on the Distribution of Giant Sequoia: A Satellite Perspective
Eric Waller
Department of Environmental Science, Policy and Management, University of California, Berkeley, CA 94720
Question: Why is Giant Sequoia (Sequoiadendron
giganteum) generally absent from the northern Sierra
Nevada?
Sequoiadendron
giganteum
2-18-03
Historic Landsat TM/ETM+ Imagery
Reveals Consistent Pattern
3-27-99
4-11-93
Muir exaggerated glacial extent and
impact of glaciers in the northern Sierra.
California DEM
Maximum Entropy (Maxent) Modeling: Advanced Niche Modeling Requiring only Presence Data
Training data: Centroids of California GAP polygons with Giant Sequoia presence
Occur any time of year but seem especially
evident in spring.
Theory generally dismissed today:
Glacial Extent
3-16-01
Predictor variables: Monthly and annual PRISM (~4 km) minimum and maximum temperature, precipitation.
Monthly and annual Daymet (1 km) relative humidity, total radiation and precipitation frequency.
Similar cloud cover patterns consistently
buffet the abrupt west slope of the southern
Sierra.
Early Theory: Pleistocene Glaciation
(John Muir)
Glaciers no more prominent in north than
Pleistocene
in the south.
3-08-98
Models: 1) All Variables, California; 2) All Variables, Sierra Nevada (Jepson ecoregion); 3) PRISM Variables
only, Sierra Nevada; 4) All Variables, Sierra Nevada (3 outliers removed)
4-29-88
5-08-00
5-25-06
6-05-84
6-09-00
Model 1:
They can persist following local storm fronts
and, in spring, are often associated with the
passage of more northerly storm fronts (and
the northwesterly winds of the Colorado or
Tonopah Low?).
Habitat Suitability:
Warmest colors indicate
suitable conditions.
Cooler colors have very
low suitability.
White squares indicate
training data areas.
(see Phillips et al., 2006
for detailed explanation)
More Recently Posited Controls on Giant Sequoia Distribution:
Fire and soil moisture have been identified as important factors in regeneration and survival.
Soil type, leaf litter, shading, summer moisture and drainage basin configuration have also
been named.
Comparison With NOAA/AVHRR Synoptic Patterns
3-08-98
NOAA/AVHRR: Large area weather satellite
Mono Lake
These explanations don’t tend to account for the striking geographic pattern: the general
absence of Sequoia from the northern Sierra.
Support for Occupied Niche
Actual Evapotranspiration as a Driver?
-Largest, fastest-growing trees near
heart of distribution
-Optimal balance/interaction of energy (increasing
to south) and moisture (decreasing to south)?
-Stands in north appear in decline
-Precipitation increases rapidly in the northern
Sierra – unlikely that energy dropping off at the
same rate. Actual evapotranspiration based on
precipitation and temperature would be comparable
or higher at lower elevations in the northern Sierra
but there is no obvious corresponding pattern for
Sequoia distribution.
-Trees generally on south-facing
slopes in north (relatively energy
limited) and north-facing slopes in
south (relatively moisture limited)
-Trees common on both slopes in
heart of distribution, as well as
highest AND lowest occurrences
(2700’ and > 9000’)
Reveals disjunct association with northerly systems
Clearly these are not the northern extents of southerly systems
Main Giant
Sequoia Belt
San Joaquin
Valley
Adjacent Landsat Paths From Sequential Dates
Highlight the persistence of the cloud cover pattern
4-11-93
Also demonstrate pattern not confined to abrupt western slopes
(A southeastern San Joaquin Valley phenomenon: also spills
over Tehachapis into western Antelope Valley area)
All Vars,
Sierra
Nevada
Model 1: California, all climate variables.
-Climatic niche seems to be apparent in southern Sierra.
-Eastern San Gabriel mountains of southern California better
habitat than most of the northern Sierra away from existing groves.
-Hints at a temperature/energy issue? San Gabriels relatively
warmer than the northern Sierra for given ppt?
-But models weigh humidity and ppt frequency heavily.
3-16-01
-Abrupt discontinuity suggests an environmental
variable with a similarly abrupt change.
Model 3:
Model 2: Sierra Nevada, all climate variables.
PRISM
Variables,
Sierra
Nevada
3-17-01
The generally inverse geographic relationship
between Giant Sequoia and Gray/Foothill
Pine (Pinus sabiniana) suggests that there
might be a strong regional control (not
competition: they occur at different
elevations)
Model 2:
(Difficult to separate clouds from snow without shortwave IR)
-Similar results at ecoregional level as at state level.
-Models continue to point toward the greater importance of
Daymet’s humidity and precipitation frequency variables. In
particular, April, May and June humidity are important in all
cases – both at the state and ecoregional level.
Southern Sierra Nevada:
Sequoiadendron
giganteum
Model Areas:
California and the
Sierra Nevada
All Variables,
California
Model 3: Sierra Nevada, PRISM data (ppt and temperature) only
5-08-00
4-29-88
5-09-00
-Projects some possibility of Giant Sequoia on the east side of the
Sierra Nevada, particularly in the Lake Tahoe area.
-Coarseness or inaccuracy of ~4 km PRISM data?
-Or Daymet’s humidity, ppt frequency may be important, as above.
-Daymet variables are not essential to capture the latitudinal pattern.
2000’-4000’
4000’-6000’
6000’-8000’
>8000’
Pinus
sabiniana
6-09-00
Model 4:
Model 4: Sierra Nevada, all climate variables; removal of outlying Calaveras
Grove from the north and two Freeman Creek Groves in the southeast.
5-25-06
Steep Western Escarpment:
6-10-00
Bulk of Sequoia distribution (and
Foothill Pine absence) coincides with a
steep western escarpment between
2000 feet and 8000 feet. Others have
observed this connection with the
Foothill Pine absence.
All Vars,
Outliers
Removed
-Little impact on results. While northern Sierra suitability tended to drop
slightly, the Calaveras Grove area was still predicted to be an island of
relative suitability (albeit not extremely high)!
-Continued overprediction in the upper Kern River and Kings River.
-Unoccupied fundamental niche? Imprecise training data? Inaccurate climate
mapping? Missing predictor (climate?) variables?
-Latter two seem most likely. Might cloud cover frequency, per se, need to be
included in the modeling?
6-09-00
Possible Significance of Cloud Cover Patterns
Cloud Cover:
-Intermediate between full sun (damages seed embryos) and
excessive moisture (supports damaging fungus)
Cloud cover can be observed coinciding
with the abrupt escarpment and the bulk
of the Sequoia distribution.
-Delays Snowmelt (some evidence of this in weather station)
-Reduces evapotranspirative losses
Landsat TM/ETM+ scenes: Path 42,
Rows 34 and 35. Substantial west
slope coverage. Same date coverage.
Future Directions: Generation of more, and even more accurate, climate variable maps will
be helpful in determining precise controls on Giant Sequoia distribution. This should include
high resolution mapping of cloud cover patterns. These data will also be essential for an
accurate forecasting of Giant Sequoia response to future climate change.
May 26, 2006
www.wunderground.com
-Might explain the Oregon oak, Black oak, and Buckeye / Blue
oak / Foothill Pine / Valley oak that spill across Tehachapis
-Or, correlated with causal climatic factors that we can model?
References and Data Sources:
Phillips, Steven J., Robert P. Anderson, Robert E. Schapire. Maximum entropy modeling of species geographic
distributions. Ecological Modeling, Vol 190/3-4 pp 231-259, 2006.
PRISM Group, Oregon State University, http://www.prismclimate.org, created 4 Feb 2004.
http://daymet.ntsg.umt.edu/data/RecordSum.htm
http://earthexplorer.usgs.gov