Examining the Recent Climate through the Lens of
Ecology: Inferences from Temporal Pattern Analysis.
Paul Hessburg & Ellen Kuhlmann, USDA Forest Service
Pacific Northwest Research Station, Wenatchee, WA, and
Thomas Swetnam, University of Arizona, Tree-Ring Research
Laboratory
‰
The climate of a region exerts top-down
control on regional ecosystem patterns and
processes across space and through time
‰
Knowing this climatologists want to program their
GCC models to realistically grow & burn regional
landscapes under varying climatic regimes
‰
This has important ramifications for carbon
budgeting in future climate modeling
‰
Ecologists and climatologists now want to
know how fire regimes, sites, tree growth,
and regeneration will vary with the climate
‰
One might begin by correlating past climate
anomalies with natural records of those
patterns or processes.
For example, information useful to interpreting
fire frequency, severity, and spatial extent is
recorded in fire scars of living and dead trees.
Other useful information is recorded in the
regenerated cohorts that fill in the landscape
between the recorder trees.
1975
1950
1925
1900
1875
1850
1825
1800
1775
1750
1725
1700
1675
1650
1625
1600
(h)
(1680-96)
(1712-34)
(1696-1712)
(1734-58)
(1798-1816) (1816-40)
(1758-98)
(1840-1923)
(1923-46)
(1946-77)
(1977- )
(g)
(1600s)
(1630s)
(1640s-60s)
(1680s-90s)
(1660s-80s)
(1690s-1710s)
(1770s)
(1680s-90s) (1700s-20s)
(1820s)
(1810s)
(1770s-1820s)
(1720s-70s)
(1850s)
(1820s-70s)
(1870s-1920s)
(1870s-80s)
(1880s-1920s)
(1920s-30s)
(1920s-40s)
(f)
(1980s-90s)
(1940s-70s)
(e)
(1646-68)
(1777-88)
(1688-1738)
(1739-44) (1756-60)
(1745-55) (1761-76)
(1682-87)
(1669-81)
(1800-38)
(1839-53) (1854-69) (1870-93)
(1894-1916)
(1917-36)
(1795-99)
(1789-94)
(d)
(1745-69)
(1785-1809)
(1885-1909)
(1825-49)
(1920s-30s)
(c)
(1922-31)
(1977)
(b)
(1600-50)
(1650-90)
(1700-60)
(1860-90)
(1920-79)
(a)
1975
1950
(1919-40)
1925
1875
1850
1825
1800
1775
1750
1725
1700
1675
1650
1900
(1900-19)
approximate starting/ending of cool or wet phase
cool or wet phase
aproximate starting/ending of warm or dry phase
warm or dry phase
1625
1600
-
Available studies…
1975
1950
1925
1900
1875
1850
1825
1800
1775
1750
1725
1700
1675
1650
1625
1600
1) Lacked pre-defined climatic regions
2) Did not identify climatic regions that
coherent with respect to the signals
they were interested in
3) Lacked specific start and end dates for
climatic periods, and
4) Lacked information on the unique
features of each climatic regime
(h)
(1680-96)
(1712-34)
(1696-1712)
(1734-58)
(1798-1816) (1816-40)
(1758-98)
(1840-1923)
(1923-46)
(1946-77)
(1977- )
(g)
(1600s)
(1630s)
(1640s-60s)
(1680s-90s)
(1660s-80s)
(1690s-1710s)
(1770s)
(1680s-90s) (1700s-20s)
(1820s)
(1810s)
(1770s-1820s)
(1720s-70s)
(1850s)
(1820s-70s)
(1870s-1920s)
(1870s-80s)
(1880s-1920s)
(1920s-30s)
(1920s-40s)
(f)
(1980s-90s)
(1940s-70s)
(e)
(1646-68)
(1777-88)
(1688-1738)
(1739-44) (1756-60)
(1745-55) (1761-76)
(1682-87)
(1669-81)
(1800-38)
(1839-53) (1854-69) (1870-93)
(1894-1916)
(1917-36)
(1795-99)
(1789-94)
(d)
(1745-69)
(1785-1809)
(1885-1909)
(1825-49)
(1920s-30s)
(c)
(1922-31)
(1977)
(b)
(1600-50)
(1650-90)
(1700-60)
(1860-90)
(1920-79)
(a)
1975
1950
(1919-40)
1925
1875
1850
1825
1800
1775
1750
1725
1700
1675
1650
1900
(1900-19)
approximate starting/ending of cool or wet phase
cool or wet phase
aproximate starting/ending of warm or dry phase
warm or dry phase
1625
1600
-
Evaluating Temporal Patterns of Drought:
1) Pros and cons of PCA and FA
‰ PCA and FA are helpful for extracting
variance held in common among a set of
related sites.
‰ These approaches assume that climatic
signals will be expressed in common
modes of variance or in shared variance.
‰ But what if the signals are not just
monotonic but also mixed and based on
amplitude differences?
‰ Are there more appropriate multivariate
techniques for finding these?
Evaluating Temporal Patterns of Drought:
1) Pros and cons of PCA and FA
2) Why correspondence analysis?
‰ Correspondence analysis, also an
eigenanalysis method, is better suited to
nonlinear and nonmonotonic variables.
‰ We used correspondence analysis as
implemented in TWINSPAN because it
combined ordination with a classification
function, allowing direct group formation.
Flow of Analysis
(1) Obtain modeled grid point PDSI
reconstructions for the CONUS (Cook et
al.1999, Cook 2000)
(2) Regionalize PDSI grid points into
climatic regions
(3) Establish a Northwestern United States
(NW) climatic region, including grid points
1-3, 8-10, and 16-18 (Fig. 2)
(4) Convert the PDSI values for the 9 grid
point time series in the one matrix to
presence/absence values corresponding
with 4 defined severity ranges, making 4
matrices from the one (Figs. 4 and 5)
MildExtreme
ModerateExtreme
SevereExtreme
Extreme
PDSI
≤ -1
-1 to 1
≥1
PDSI
≤ -2
-2 to 2
≥2
PDSI
≤ -3
-3 to 3
≥3
PDSI
≤ -4
-4 to 4
≥4
Grid Points from other
climate zones
STOP
Combine the 9 grid
point time series for
the NW into a single
matrix, with years
as rows and annual
PDSI values as
columns
(1) Obtain modeled grid point PDSI
reconstructions for the CONUS (Cook et
al.1999, Cook 2000)
(11) Perform ARIMA analysis on the
timelines derived by PCA, FA, and
TWINSPAN; using only the 20thcentury portion of the timelines, test
the shifts identified by each method as
intervention variables on the PDO time
series of Mantua et al. (1997, Table 1).
STOP
(5) Convert the presence/absence values to
proportions of 1, 2, and 3 decade forward
moving bins (Fig. 6)
1d
1d
1d
1d
2d
2d
2d
Mod
Mild
Sev
Ext2d
3d
3d
3d
Mod
Mild
Sev
Ext3d
Mod
Mild
Sev
Ext
(6) Conduct TWINSPAN analysis on the 12
resulting matrices, considering 4 dry/wet
severity levels and 3 bin sizes (Fig. 6)
(7) Evaluate the robustness of the 12 sets of
TWINSPAN groupings via discriminant
analysis with 4-fold cross-validation.
(2) Regionalize PDSI grid points into
climatic regions
(10) Conduct PCA and FA using the
same matrices analyzed by TWINSPAN
in step 6; compare the PCA, FA,and
TWINSPAN results (Fig. 8)
(9) Characterize differences among the
various regimes, especially among the
anomalies (Fig. 9)
(3) Establish a Northwestern United States
(NW) climatic region, including grid points
1-3, 8-10, and 16-18 (Fig. 2)
(8) Plot the 12 DISCRIM validated
groupings of years to a timeline (Fig. 7)
and estimate the most appropriate
start/stop dates of each period using
TWINSPAN Signal Strength (TwSS)
and TWINSPAN Intervention Analysis
(TwIN) analysis methods.
A
BB
C
C1 NWT
C2 NCA
C3 ENI
GP1
GP2
GP3
GP8
GP9
GP10
GP16
GP17
GP18
GP4
GP5
GP6
GP11
GP12
GP13
GP19
GP20
GP21
GP27
GP28
GP29
GP38
C4 WMT
GP25
GP26
GP34
GP35
C5 EMT
GP36
GP44
GP45
GP46
GP54
GP55
GP56
GP65
C6 NSD
GP64
GP66
GP67
GP75
GP76
GP77
GP78
GP79
Flow of Analysis
(1) Obtain modeled grid point PDSI
reconstructions for the CONUS (Cook et
al.1999, Cook 2000)
(2) Regionalize PDSI grid points into
climatic regions
(3) Establish a Northwestern United States
(NW) climatic region, including grid points
1-3, 8-10, and 16-18 (Fig. 2)
(4) Convert the PDSI values for the 9 grid
point time series in the one matrix to
presence/absence values corresponding
with 4 defined severity ranges, making 4
matrices from the one (Figs. 4 and 5)
MildExtreme
ModerateExtreme
SevereExtreme
Extreme
PDSI
≤ -1
-1 to 1
≥1
PDSI
≤ -2
-2 to 2
≥2
PDSI
≤ -3
-3 to 3
≥3
PDSI
≤ -4
-4 to 4
≥4
Grid Points from other
climate zones
STOP
Combine the 9 grid
point time series for
the NW into a single
matrix, with years
as rows and annual
PDSI values as
columns
(11) Perform ARIMA analysis on the
timelines derived by PCA, FA, and
TWINSPAN; using only the 20thcentury portion of the timelines, test
the shifts identified by each method as
intervention variables on the PDO time
series of Mantua et al. (1997, Table 1).
STOP
(5) Convert the presence/absence values to
proportions of 1, 2, and 3 decade forward
moving bins (Fig. 6)
1d
1d
1d
1d
2d
2d
2d
Mod
Mild
Sev
Ext2d
3d
3d
3d
Mod
Mild
Sev
Ext3d
Mod
Mild
Sev
Ext
(6) Conduct TWINSPAN analysis on the 12
resulting matrices, considering 4 dry/wet
severity levels and 3 bin sizes (Fig. 6)
(7) Evaluate the robustness of the 12 sets of
TWINSPAN groupings via discriminant
analysis with 4-fold cross-validation.
(10) Conduct PCA and FA using the
same matrices analyzed by TWINSPAN
in step 6; compare the PCA, FA,and
TWINSPAN results (Fig. 8)
(9) Characterize differences among the
various regimes, especially among the
anomalies (Fig. 9)
(8) Plot the 12 DISCRIM validated
groupings of years to a timeline (Fig. 7)
and estimate the most appropriate
start/stop dates of each period using
TWINSPAN Signal Strength (TwSS)
and TWINSPAN Intervention Analysis
(TwIN) analysis methods.
Combine the 9 grid
point time series for
the NW into a single
matrix, with years
as rows and annual
PDSI values as
columns
YEAR
1675
1676
1677
1678
1679
1680
1681
1682
1683
1684
1685
1686
1687
1688
1689
1690
.
.
.
GP1
0.4
2.1
-1.8
-0.1
-1.3
0.3
-0.4
-1.2
-0.3
1.2
0.4
0.5
-2.2
1.4
0.0
0.4
.
.
.
GP2
1.3
1.7
-2.3
0.3
-0.8
0.2
0.7
-1.3
-0.1
0.5
0.9
0.3
-1.2
0.5
0.2
0.0
.
.
.
PDSI Value
GP3
-0.5
0.9
-1.6
-0.5
-1.3
0.4
-0.4
-0.4
-0.1
1.3
0.4
-0.8
-0.9
1.8
0.5
0.2
.
.
.
GP8
1.0
-0.7
-2.9
2.4
-1.1
-0.5
3.4
-2.9
3.0
-2.9
1.4
-0.2
1.0
-2.6
0.9
-0.3
.
.
.
GP9
0.4
-0.9
-2.0
0.5
-0.8
-1.1
2.1
-0.5
1.0
0.7
0.9
-1.0
2.3
-1.4
2.3
0.3
.
.
.
…
…
…
…
…
…
…
…
…
…
…
…
…
…
…
…
…
Flow of Analysis
(1) Obtain modeled grid point PDSI
reconstructions for the CONUS (Cook et
al.1999, Cook 2000)
(2) Regionalize PDSI grid points into
climatic regions
(3) Establish a Northwestern United States
(NW) climatic region, including grid points
1-3, 8-10, and 16-18 (Fig. 2)
(4) Convert the PDSI values for the 9 grid
point time series in the one matrix to
presence/absence values corresponding
with 4 defined severity ranges, making 4
matrices from the one (Figs. 4 and 5)
MildExtreme
ModerateExtreme
SevereExtreme
Extreme
PDSI
≤ -1
-1 to 1
≥1
PDSI
≤ -2
-2 to 2
≥2
PDSI
≤ -3
-3 to 3
≥3
PDSI
≤ -4
-4 to 4
≥4
Grid Points from other
climate zones
STOP
Combine the 9 grid
point time series for
the NW into a single
matrix, with years
as rows and annual
PDSI values as
columns
(11) Perform ARIMA analysis on the
timelines derived by PCA, FA, and
TWINSPAN; using only the 20thcentury portion of the timelines, test
the shifts identified by each method as
intervention variables on the PDO time
series of Mantua et al. (1997, Table 1).
STOP
(5) Convert the presence/absence values to
proportions of 1, 2, and 3 decade forward
moving bins (Fig. 6)
1d
1d
1d
1d
2d
2d
2d
Mod
Mild
Sev
Ext2d
3d
3d
3d
Mod
Mild
Sev
Ext3d
Mod
Mild
Sev
Ext
(6) Conduct TWINSPAN analysis on the 12
resulting matrices, considering 4 dry/wet
severity levels and 3 bin sizes (Fig. 6)
(7) Evaluate the robustness of the 12 sets of
TWINSPAN groupings via discriminant
analysis with 4-fold cross-validation.
(10) Conduct PCA and FA using the
same matrices analyzed by TWINSPAN
in step 6; compare the PCA, FA,and
TWINSPAN results (Fig. 8)
(9) Characterize differences among the
various regimes, especially among the
anomalies (Fig. 9)
(8) Plot the 12 DISCRIM validated
groupings of years to a timeline (Fig. 7)
and estimate the most appropriate
start/stop dates of each period using
TWINSPAN Signal Strength (TwSS)
and TWINSPAN Intervention Analysis
(TwIN) analysis methods.
(4) Convert the PDSI values for the 9 grid
point time series in the one matrix to
presence/absence values corresponding
with 4 defined severity ranges, making 4
matrices from the one (Figs. 4 and 5)
MildExtreme
ModerateExtreme
SevereExtreme
Extreme
PDSI
≤ -1
-1 to 1
≥1
PDSI
≤ -2
-2 to 2
≥2
PDSI
≤ -3
-3 to 3
≥3
PDSI
≤ -4
-4 to 4
≥4
(5) Convert the presence/absence values to
proportions of 1, 2, and 3 decade forward
moving bins (Fig. 6)
1d
1d
2d
2d
Mod
Mild
3d
3d
Mod
Mild
Mod
Mild
1d
2d
Sev
3d
Sev
Sev
1d
Ext2d
Ext3d
Ext
Value of the Palmer Drought Severity Index (PDSI)
Dry/Wet Severity
-1
Temperate
Portion
0
Mild-Extreme
-6
Dry Portion
-5 -4 -3 -2
1
Wet Portion
2 3 4 5
6
Moderate- Extreme
-6
-5
-4
-3
-2
-1
0
1
2
3
4
5
6
Severe-Extreme
-6
-5
-4
-3
-2
-1
0
1
2
3
4
5
6
Extreme
-6
-5
-4
-3
-2
-1
0
1
2
3
4
5
6
Flow of Analysis
(1) Obtain modeled grid point PDSI
reconstructions for the CONUS (Cook et
al.1999, Cook 2000)
(2) Regionalize PDSI grid points into
climatic regions
(3) Establish a Northwestern United States
(NW) climatic region, including grid points
1-3, 8-10, and 16-18 (Fig. 2)
(4) Convert the PDSI values for the 9 grid
point time series in the one matrix to
presence/absence values corresponding
with 4 defined severity ranges, making 4
matrices from the one (Figs. 4 and 5)
MildExtreme
ModerateExtreme
SevereExtreme
Extreme
PDSI
≤ -1
-1 to 1
≥1
PDSI
≤ -2
-2 to 2
≥2
PDSI
≤ -3
-3 to 3
≥3
PDSI
≤ -4
-4 to 4
≥4
Grid Points from other
climate zones
STOP
Combine the 9 grid
point time series for
the NW into a single
matrix, with years
as rows and annual
PDSI values as
columns
(11) Perform ARIMA analysis on the
timelines derived by PCA, FA, and
TWINSPAN; using only the 20thcentury portion of the timelines, test
the shifts identified by each method as
intervention variables on the PDO time
series of Mantua et al. (1997, Table 1).
STOP
(5) Convert the presence/absence values to
proportions of 1, 2, and 3 decade forward
moving bins (Fig. 6)
1d
1d
1d
1d
2d
Mod
2d
2d
Mild
Sev
Ext2d
3d
Mod
3d
3d
Mild
Sev
Ext3d
Mod
Mild
Sev
Ext
(6) Conduct TWINSPAN analysis on the 12
resulting matrices, considering 4 dry/wet
severity levels and 3 bin sizes (Fig. 6)
(7) Evaluate the robustness of the 12 sets of
TWINSPAN groupings via discriminant
analysis with 4-fold cross-validation.
(10) Conduct PCA and FA using the
same matrices analyzed by TWINSPAN
in step 6; compare the PCA, FA,and
TWINSPAN results (Fig. 8)
(9) Characterize differences among the
various regimes, especially among the
anomalies (Fig. 9)
(8) Plot the 12 DISCRIM validated
groupings of years to a timeline (Fig. 7)
and estimate the most appropriate
start/stop dates of each period using
TWINSPAN Signal Strength (TwSS)
and TWINSPAN Intervention Analysis
(TwIN) analysis methods.
(6) Conduct TWINSPAN analysis on the 12
resulting matrices, considering 4 dry/wet
severity levels and 3 bin sizes (Fig. 6)
(7) Evaluate the robustness of the 12 sets of
TWINSPAN groupings via discriminant
analysis with 4-fold cross-validation.
(9) Characterize differences among the
various regimes, especially among the
anomalies (Fig. 9)
(8) Plot the 12 DISCRIM validated
groupings of years to a timeline (Fig. 7)
and estimate the most appropriate
start/stop dates of each period using
TWINSPAN Signal Strength (TwSS)
and TWINSPAN Intervention Analysis
(TwIN) analysis methods.
Flow of Analysis
(1) Obtain modeled grid point PDSI
reconstructions for the CONUS (Cook et
al.1999, Cook 2000)
(2) Regionalize PDSI grid points into
climatic regions
(3) Establish a Northwestern United States
(NW) climatic region, including grid points
1-3, 8-10, and 16-18 (Fig. 2)
(4) Convert the PDSI values for the 9 grid
point time series in the one matrix to
presence/absence values corresponding
with 4 defined severity ranges, making 4
matrices from the one (Figs. 4 and 5)
MildExtreme
ModerateExtreme
SevereExtreme
Extreme
PDSI
≤ -1
-1 to 1
≥1
PDSI
≤ -2
-2 to 2
≥2
PDSI
≤ -3
-3 to 3
≥3
PDSI
≤ -4
-4 to 4
≥4
Grid Points from other
climate zones
STOP
Combine the 9 grid
point time series for
the NW into a single
matrix, with years
as rows and annual
PDSI values as
columns
(11) Perform ARIMA analysis on the
timelines derived by PCA, FA, and
TWINSPAN; using only the 20thcentury portion of the timelines, test
the shifts identified by each method as
intervention variables on the PDO time
series of Mantua et al. (1997, Table 1).
STOP
(5) Convert the presence/absence values to
proportions of 1, 2, and 3 decade forward
moving bins (Fig. 6)
1d
1d
1d
1d
2d
2d
2d
Mod
Mild
Sev
Ext2d
3d
3d
3d
Mod
Mild
Sev
Ext3d
Mod
Mild
Sev
Ext
(6) Conduct TWINSPAN analysis on the 12
resulting matrices, considering 4 dry/wet
severity levels and 3 bin sizes (Fig. 6)
(7) Evaluate the robustness of the 12 sets of
TWINSPAN groupings via discriminant
analysis with 4-fold cross-validation.
(10) Conduct PCA and FA using the
same matrices analyzed by TWINSPAN
in step 6; compare the PCA, FA,and
TWINSPAN results (Fig. 8)
(9) Characterize differences among the
various regimes, especially among the
anomalies (Fig. 9)
(11) Perform ARIMA analysis on the
timelines derived by PCA, FA, and
TWINSPAN; using only the 20thcentury portion of the timelines, test
the shifts identified by each method as
intervention variables on the PDO time
series of Mantua et al. (1997, Table 1).
STOP
(8) Plot the 12 DISCRIM validated
groupings of years to a timeline (Fig. 7)
and estimate the most appropriate
start/stop dates of each period using
TWINSPAN Signal Strength (TwSS)
and TWINSPAN Intervention Analysis
(TwIN) analysis methods.
(10) Conduct PCA and FA using the
same matrices analyzed by TWINSPAN
in step 6; compare the PCA, FA,and
TWINSPAN results (Fig. 8)
Results
High/Mixed
Moderate/Mixed
Low/Dry
PACIFIC
1975
1950
1925
1900
1875
1850
1825
1800
1775
1750
1725
1700
1675
Twinspan Signal Strength Analysis
(1675-1714) (1715-30) (1739-48) (1756-65)
(1766-1921 )
(1922-43) (1944-72) (1973-)
(1675-1714) (1715-30) (1731-55) (1756-65)
(1766-1925)
(1926-43) (1944-76) (1977-)
Twinspan Intervention Analysis
Factor Analysis
1716
1742
1801
1742
1801
1852
1917
1940
1917
1940
High/Mixed
Moderate/Mixed
Low/Dry
PACIFIC
1975
1950
1925
1900
1875
1850
1825
1800
1775
1750
1725
1700
1675
Principal Components Analysis
tEXT/DW
t MOD/DW
1736-48
1756-65
1715-30
tLOW/D
tTEMP
1922-43
1977-
Moderate/Mixed
Low/Dry
PACIFIC
1975
1950
1925
1900
1875
1850
1825
1800
1775
1750
1725
1700
1675
High/Mixed
High/Mixed
-6
Pacific
4
PDSI
2
0
-2
-4
Low/Dry
-6
Moderate/Mixed
Mean Dry/Wet Percent Composition
80
70
*
60
50
40
30
*
- PDSI
20
10
+PDSI
0
Low/Dry
Low/D
Mod/Mixed
Mod/DW High/Mixed
Ext/DW
PACIFIC
Temp
PDSI – Tree rings
1900–1921
Pacific
1922–1943
(PDSI
– Tree
Low /Dry
1930
rings)
1944–1973
Pacific
1973–1978
Moderate/Mixed
1950
1960
1980
PC1 of the PDO
4
2
0
-2
-4
1900
1910
1920
1940
1970
Year
1925†
shift
1947†
shift
1977†,
shift
‡
1990
2000
Table 1. Comparison of PDO phase shift dates of Mantua et. al. (1997) to the dates
derived by TWINSPAN Signal Strength, PCA Intervention, and FA Intervention
methods. Each set of shift dates were tested as intervention variables on the same
PDO time series used in Mantua et. al. (1997). Intervention analysis methodology
follows that of Box and Tiao (1976).
TWINSPAN
PCA
Mantua et.al
Signal Strength Intervention
(1997)
PDO phase shift
date p value date p value date p value
cool to warm 1925 0.017* 1922 0.003 1917 0.460
warm to cool 1947 0.000 1943 0.000 1940 0.260
cool to warm 1977 0.000 1973 0.000
FA
Intervention
date p value
1917 0.460
1940 0.260
*Value reported here differs from that published in Mantua et.al. (1997), however it is
correct (personal communication, Stephen Hare)
PDSI – Instrumental
1977–1986
High/Mixed
(PDSI – Instrumental)
1924
1986–1994
Pacific
1944
1900–1924
Pacific
1924–1944
Moderate/Dry
1910
1930
1994–1995
Moderate/Dry
1986
1994
1944–1977
Pacif ic
PC1 of the PDO
4
2
0
-2
-4
1900
1920
1940
1950
1960
1970
1980
1990
Year
1925†
step
1947†
step
1977†, ‡
step
1989‡
step
2000
Table 2. Comparison of PDO phase shift dates of Mantua et. al. (1997) to
the dates derived by TWINSPAN Signal Strength analysis of the Cook
(2000) instrumental PDSI time series for the northwestern US. Each set of
shift dates were tested as intervention variables on the same PDO time
series used in Mantua et. al. (1997). Intervention analysis methodology
follows that of Box and Tiao (1976).
PDO phase shift
Cool to warm
Warm to cool
Cool to warm
Warm to cool
Cool to warm
Mantua et. al. (1997)
date
p value
1925
0.017*
1947
0.000
1977
0.000
TWINSPAN
Signal Strength
date
p value
1924
0.000
1944
0.000
1977
0.000
1986
0.021
1994
0.473
*Value reported here differs from that published in Mantua et. al. (1997),
however it is correct (Stephen Hare, personal communication).
Ecological Applications:
‰ Low Energy/Dry anomaly (1922-1943) corresponds
well with period of strong mountain hemlock
establishment (1921-1945, Woodward et al. 1995)
tEXT/DW
t MOD/DW
1736-48
1756-65
1715-30
tLOW/D
tTEMP
1922-43
1977-
1975
1950
1925
1900
1875
1850
1825
1800
1775
1750
1725
1700
1675
Ecological Applications:
‰ Low Energy/Dry anomaly (1922-1943) corresponds
well with period of strong mountain hemlock
establishment (1921-1945, Woodward et al. 1995)
‰ Our results align well with those of Keen (1937) who
found periods of poor ponderosa pine growth from
1739-44, 1756-1760 with adjacent periods of good
growth from 1745-55 and 1761-76.
tEXT/DW
t MOD/DW
1736-48
1756-65
1715-30
tLOW/D
tTEMP
1922-43
1977-
1975
1950
1925
1900
1875
1850
1825
1800
1775
1750
1725
1700
1675
Ecological Applications:
Recently, we reconstructed fire severity of pre-management
era (A.D. 1900) forests of 3 eco-subregions in the eastern
Cascades of OR & WA using forest structural attributes.
ESR5
ESR11
ESR13
(Hessburg et al. 2000)
Ecological Applications (examples):
We found that regardless of forest type or biophysical
setting, most forests of the pre-management era were
influenced by mixed severity fire (MSF)
High
16%
Low
12%
Low
9%
High
44%
Mixed
47%
Mixed
72%
ESR 5 - E. WA Cacades
High
27%
Low
11%
Mixed
62%
Study Area - E. WA Cacades
ESR 11 - E. WA Cacades
High
20%
Low
13%
Mixed
67%
ESR 13 - E. WA Cacades
100
ESR5
Low
Mixed
High
80
60
40
20
0
*
*
wd-PP/DF/GF
cm-DF/GF
Total
60
Percentage area
50
ESR11
40
30
20
10
0
80
70
60
50
40
30
20
10
0
wd-PP/DF/GF
cm-DF/GF
Total
wd-PP/DF/GF
cm-DF/GF
Total
(warm-dry)
(cool moist)
ESR13
Percentage area
70
60
Study Area
50
40
30
20
*
*
10
0
wd-PP/DF/GF
cm-DF/GF
(warm-dry)
(cool moist)
Total
45
Eastern OR & WA Study Area
MSF, wd-PP/DF/GF
40
%Patches
%Area
30
25
20
15
10
Overstory canopy percent
91-100%
81-90%
71-80%
61-70%
51-60%
0
41-50%
5
31-40%
Percentage
35
Ecological Applications:
These results suggested that non-equilibrium
rather than equilibrium fire dynamics were at work.
Occurrence of equilibrium dynamics would be
represented by strong dominance of low severity
fires (LSF). With LSF dominating, we’d see
abundant large tree and fire tolerant species
dominated forest structures.
High/Mixed
-6
Pacific
4
PDSI
2
0
-2
-4
Low/Dry
-6
Moderate/Mixed
The observation of non-equilibrium fire dynamics is consistent
with the recent 300 yr PNW climatic history: one dominated by
a low variance but mixed background Pacific climate that is
punctuated with moderate and high variance anomalies.
Conclusions:
‰ TWINSPAN applied to temporal pattern analysis
was useful; it provided start and end dates, and
features of signals for comparison with natural
records of ecological phenomena.
‰ Unexpected finding – the variance level of a
climate anomaly can be a primary dimension
and hence it is an important descriptor.
t EXT/DW
t MOD/DW
1736-48
t LOW/D
1756-65
1715-30
t TEMP
1922-43
1977-
1975
1950
1925
1900
1875
1850
1825
1800
1775
1750
1725
1700
1675
Recommendations for Future Studies:
‰ Stratify data prior to pattern analysis by climatic region
‰ Relax assumptions about the features of climate
signals.
‰ Employ a variety of classification and ordination
methods in pattern analysis, see more dimensions.
‰ Consider the characteristics of the background climate
and how anomalies may stand out in contrast.
t EXT/DW
t MOD/DW
1736-48
t LOW/D
1756-65
1715-30
t TEMP
1922-43
1977-
1975
1950
1925
1900
1875
1850
1825
1800
1775
1750
1725
1700
1675