ArticlePDF Available

Abstract and Figures

Accurately capturing medium- to low-frequency trends in tree-ring data is vital to assessing climatic response and developing robust reconstructions of past climate. Non-climatic disturbance can affect growth trends in tree-ring-width (RW) series and bias climate information obtained from such records. It is important to develop suitable strategies to ensure the development of chronologies that minimize these medium- to low-frequency biases. By performing high density sampling (760 trees) over a ~40-ha natural high-elevation Norway spruce (Picea abies) stand in the Romanian Carpathians, this study assessed the suitability of several sampling strategies for developing chronologies with an optimal climate signal for dendroclimatic purposes. There was a roughly equal probability for chronologies (40 samples each) to express a reasonable (r = 0.3–0.5) to non-existent climate signal. While showing a strong high-frequency response, older/larger trees expressed the weakest overall temperature signal. Although random sampling yielded the most consistent climate signal in all sub-chronologies, the outcome was still sub-optimal. Alternative strategies to optimize the climate signal, including very high replication and principal components analysis, were also unable to minimize this disturbance bias and produce chronologies adequately representing climatic trends, indicating that larger scale disturbances can produce synchronous pervasive disturbance trends that affect a large part of a sampled population. The Curve Intervention Detection (CID) method, used to identify and reduce the influence of disturbance trends in the RW chronologies, considerably improved climate signal representation (from r = 0.28 before correction to r = 0.41 after correction for the full 760 sample chronology over 1909–2009) and represents a potentially important new approach for assessing disturbance impacts on RW chronologies. Blue intensity (BI) also shows promise as a climatically more sensitive variable which, unlike RW, does not appear significantly affected by disturbance. We recommend that studies utilizing RW chronologies to investigate medium- to long-term climatic trends also assess disturbance impact on those series.
Content may be subject to copyright.
1
Title: Influence of sampling and disturbance history on climatic sensitivity of temperature-limited conifers
1
2
Authors:
3
Miloš Rydvala,b, email: rydval@gmail.com, tel. (00420)735872634
4
Daniel L. Druckenbrodc
5
Miroslav Svobodaa
6
Volodymyr Trotsiuka,d,e
7
Pavel Jandaa
8
Martin Mikoláša
9
Vojtěch Čadaa
10
Radek Bačea
11
Marius Teodosiuf,g
12
Rob Wilsonb
13
a. Faculty of Forestry and Wood Sciences, Czech University of Life Sciences Prague,
14
Kamýcká 129, Praha 6Suchdol, Prague, 16521, Czech Republic
15
b. School of Earth and Environmental Sciences, University of St Andrews, UK
16
c. Department of Geological, Environmental, & Marine Sciences, Rider University,
17
Lawrenceville, NJ, USA
18
d. Swiss Federal Research Institute for Forest, Snow and Landscape Research (WSL), Birmensdorf,
19
Switzerland.
20
e. Institute of Agricultural Sciences, ETH Zurich, Switzerland
21
f. Marin DrăceaNational Research and Development Institute in Forestry, Romania
22
Voluntari, Romania
23
g. Faculty of Forestry, Ştefan cel Mare University of Suceava, Romania
24
25
26
2
ABSTRACT: Accurately capturing medium-to-low frequency trends in tree-ring data is vital to assessing
27
climatic response and developing robust reconstructions of past climate. Non-climatic disturbance can
28
affect growth trends in tree-ring width (RW) series and bias climate information obtained from such
29
records. It is important to develop suitable strategies to ensure the development of chronologies that
30
minimize these medium-to-low frequency biases. By performing high density sampling (760 trees) over a
31
~40ha natural high elevation Norway spruce (Picea abies) stand in the Romanian Carpathians, this study
32
assessed the suitability of several sampling strategies for developing chronologies with an optimal
33
climate signal for dendroclimatic purposes. There was a roughly equal probability for chronologies (40
34
samples each) to express a reasonable (r=0.3-0.5) to non-existent climate signal. While showing a strong
35
high-frequency response, older/larger trees expressed the weakest overall temperature signal. Although
36
random sampling yielded the most consistent climate signal in all sub-chronologies, the outcome was
37
still sub-optimal. Alternative strategies to optimise the climate signal, including very high replication and
38
principal component analysis, were also unable to minimize this disturbance bias and produce
39
chronologies adequately representing climatic trends, indicating that larger scale disturbances can
40
produce synchronous pervasive disturbance trends that affect a large part of a sampled population. The
41
Curve Intervention Detection (CID) method, used to identify and reduce the influence of disturbance
42
trends in the RW chronologies, considerably improved climate signal representation (from r=0.28 before
43
correction to r=0.41 after correction for the full 760 sample chronology over 1909-2009) and represents
44
a potentially important new approach for assessing disturbance impacts on RW chronologies. Blue
45
intensity (BI) also shows promise as a climatically more sensitive variable which, unlike RW, does not
46
appear significantly affected by disturbance. We recommend that studies utilizing RW chronologies to
47
investigate medium to long-term climatic trends also assess disturbance impact on those series.
48
49
KEYWORDS: disturbance detection; sampling bias; climatic signal; blue intensity; tree rings; Norway
50
spruce; Romanian Carpathian Mountains
51
52
53
3
INTRODUCTION
54
The accurate representation of climatic variability in the growth trends contained in tree-ring
55
records from climatically sensitive trees is central to assessing growth-climate response and the
56
development of robust dendroclimatic reconstructions (e.g. Anchukaitis et al., 2017; Cook et al., 2015;
57
Cook et al., 2016; D’Arrigo et al., 2006; Luterbacher et al., 2016; Wilson et al., 2016). The suitability of
58
strategically sampled tree-ring chronologies for reconstructing a particular climatic variable is typically
59
evaluated by examining the growth-climate response and the strength of this relationship. This process
60
partly relies on the assumption that chronologies are developed from a finite number of samples that
61
are representative of the population. In climatically sensitive stands (i.e. temperature sensitive trees at
62
high latitude or elevation tree-line locations) it is usually assumed that when adequate measures are
63
taken to avoid sampling trees likely affected by non-climatic influences, the common signal of the
64
sample chronology represents the common climatic signal of the population (Hughes, 2011).
65
Tree growth is the product of a range of environmental influences that are integrated into the
66
annual growth increment (Cook, 1985; Vaganov et al., 2006). Natural disturbance is one key element of
67
forest ecosystem development (Attiwill, 1994). The presence of non-climatic disturbance trends in tree
68
ring width (RW) series complicates the development of climatically sensitive tree-ring based records
69
(e.g. Briffa et al., 1996; Gunnarson et al., 2012; Rydval et al., 2016). Yet few, if any, studies explicitly
70
assess the influence of disturbance as a part of dendroclimatic research. A common presumption is that
71
the effects of disturbance are either negligible or asynchronous so that their influence is canceled out
72
through the development of a mean chronology of detrended series, or they can be minimized by
73
applying appropriate detrending techniques in cases when such trends occur systematically (Hughes,
74
2011). It has been shown that larger scale intermediate and higher severity disturbances can result in
75
synchrony of disturbance histories across the landscape on the stand level and regional spatial scales
76
(e.g. D’Amato and Orwig, 2008; Kulakowski and Veblen, 2002; Zielonka et al., 2010). While flexible data-
77
adaptive detrending approaches such as cubic smoothing splines (Cook and Peters, 1981) have been
78
utilized to limit the influence of non-climatic (e.g. disturbance) trends in RW data, a detrimental side-
79
effect of such techniques is the loss of lower frequency (i.e. multidecadal to multicentennial) climatic
80
variability.
81
4
Numerous studies have investigated dendrochronological biases and uncertainties related to
82
various methodological aspects of tree-ring data development including detrending (e.g. Briffa and
83
Melvin, 2011;Cook et al., 1995; Helama et al., 2004; Melvin and Briffa, 2008; Melvin et al., 2013), sample
84
size and signal strength (e.g. Mérian et al., 2013; Osborn et al., 1997; Wigley et al., 1984), sampling
85
design and microsite conditions (e.g. Cherubini et al., 1998; Düthorn et al., 2013, 2015; Nehrbass-Ahles
86
et al., 2014), and tree age (e.g. Carrer and Urbinati, 2004; Esper et al., 2008; Fish et al., 2010). In an
87
extensive assessment of sampling design strategies, Nehrbass-Ahles et al. (2014) highlighted that many
88
common sampling approaches used for developing representations of forest response to environmental
89
change can induce sampling related biases. However, relatively little is known about how disturbance
90
related growth trends affect the climate signal in tree ring series.
91
Time-series analysis with intervention detection (Box and Jenkins, 1970; Box and Tiao, 1975) is
92
an evolving area for studying disturbance in RW data (Druckenbrod, 2005). A time-series based method
93
called Curve Intervention Detection (CID) has been developed to characterize disturbance history and
94
quantify the effects of disturbance trends on individual RW series and overall chronology structure
95
(Druckenbrod, 2005; Druckenbrod et al., 2013). Chronology distortion and climate signal degradation,
96
due to synchronous disturbance related growth releases as a result of systematic timber felling, was
97
identified using the CID technique by Rydval et al. (2016) in Scots pine RW chronologies from Scotland.
98
However, such a technique has not previously been applied to investigate trends resulting from natural
99
sources of disturbance on the strength of the climate signal in tree-ring records.
100
Building on the work of Rydval et al. (2016), in this study we applied the CID method to a new
101
forest system and species by examining RW series from an unmanaged natural closed canopy Norway
102
spruce (Picea abies) stand in Romania (shaped by a mixed-severity natural disturbance regime with
103
partial landscape synchronization and unperturbed by human activities - Svoboda et al., 2014) to
104
examine the extent to which natural disturbance can affect climate signal strength in RW data. We
105
investigate (1) whether natural disturbance can produce widespread and synchronized trends, as those
106
resulting from human activities, that would significantly impact the expression of the climate signal in
107
tree-ring chronologies, and (2) which sampling or data processing approach best expresses the climate
108
signal. To this end, we firstly evaluated a set of sampling strategies by subsampling a large dataset of RW
109
5
data from a single stand according to a set of characteristics reflecting sampling strategies that are
110
relevant in a dendroclimatic context. The application of additional data processing techniques (including
111
disturbance trend detection and correction using the CID method, and isolation of the dominant signals
112
through principal component analysis) were investigated in an attempt to optimize the climate signal.
113
We applied the CID time-series analysis technique in order to characterize the disturbance history, its
114
impact on overall chronology structure and subsequently to reduce the influence of disturbance-related
115
trends on RW chronologies (Druckenbrod, 2005; Druckenbrod et al., 2013; Rydval et al.,2016). As an
116
alternative to RW data, a subset of chronologies was developed from series of the blue intensity (BI)
117
parameter (Björklund et al., 2014a; McCarroll et al., 2002; Rydval et al., 2014) to ascertain whether such
118
data can be used to produce proxy climate records unbiased (or less biased) by the presence of
119
disturbance trends.
120
121
METHODS
122
Sampling site
123
Samples were collected and measured from 760 high-elevation Norway spruce (Picea abies)
124
trees (cored at breast height) located in an approx. 40 ha natural Norway spruce dominant stand
125
(47°06’53”N,25°15’26”E) in Călimani National Park (hereafter Calimani) in the Eastern Carpathians of
126
northern Romania (Figure 1; see Svoboda et al. (2014) for details regarding sample collection). The
127
selected sampling site is located within an elevational range of around 1500-1650 m a.s.l., ~100-250 m
128
below the regional timberline (approx. 1780 m a.s.l.) and ~200-350 m below the treeline (approx. 1860
129
m a.s.l.) (Popa and Kern, 2009) and with slope varying from around 10° to 25°. Podzols are the
130
predominant soil type in the study region (Valtera et al., 2013). The area has a mean annual
131
temperature of 2.1-3.1°C estimated from 0.5° gridded CRU TS3.23 temperatures (based on the period
132
2005-2014 and adjusted for elevation). Over the same period, temperatures have increased by
133
approximately 1.6°C relative to the first decade of the 20th century. Mean annual precipitation is about
134
910 mm (2005-2014 mean).
135
[insert Figure 1]
136
6
Sample collection was performed in an area with no significant human activities in the past
137
(including evidence from historical documentation) and subject only to natural stand dynamics and
138
disturbance regimes (Svoboda et al., 2014). Considering the size of the sampled area and number of
139
samples collected, this high density sampling strategy, similar to that of Nehrbass-Ahles (2014), was
140
intended to provide a highly representative sample of the whole stand population by sampling a diverse
141
range of tree size and age classes. This approach makes it possible to group samples and construct
142
chronologies according to a range of characteristics.
143
CHRON.
(SUBSET)
NR. OF
SERIES
MEAN
ELEVATION
CHRON.
LENGTH
EPS ≥ 0.85
CHRON.
(BY DBH)
DBH RANGE
(CM)
CHRON.
(BY AGE)
PLOT-ALL
760
1578
1673-2009
1744-2009
110-925
PLOT-1
40
1523
1731-2009
1888-2009
DBH-1
110-235
AGE-1
PLOT-2
40
1585
1724-2009
1862-2009
DBH-2
235-265
AGE-2
PLOT-3
40
1602
1743-2009
1906-2009
DBH-3
265-280
AGE-3
PLOT-4
40
1616
1772-2009
1905-2009
DBH-4
280-300
AGE-4
PLOT-5
40
1588
1768-2009
1820-2009
DBH-5
300-320
AGE-5
PLOT-6
40
1626
1790-2009
1857-2009
DBH-6
320-340
AGE-6
PLOT-7
40
1633
1712-2009
1835-2009
DBH-7
340-360
AGE-7
PLOT-8
40
1516
1673-2009
1897-2009
DBH-8
360-380
AGE-8
PLOT-9
40
1514
1700-2009
1903-2009
DBH-9
380-400
AGE-9
PLOT-10
40
1606
1763-2009
1835-2009
DBH-10
400-415
AGE-10
PLOT-11
40
1565
1701-2009
1856-2009
DBH-11
415-430
AGE-11
PLOT-12
40
1587
1763-2009
1906-2009
DBH-12
430-450
AGE-12
PLOT-13
40
1590
1768-2009
1896-2009
DBH-13
450-480
AGE-13
PLOT-14
40
1552
1705-2009
1861-2009
DBH-14
480-500
AGE-14
PLOT-15
40
1551
1720-2009
1901-2009
DBH-15
500-520
AGE-15
PLOT-16
40
1541
1803-2009
1909-2009
DBH-16
520-550
AGE-16
PLOT-17
40
1511
1752-2009
1903-2009
DBH-17
550-590
AGE-17
PLOT-18
40
1552
1757-2009
1902-2009
DBH-18
590-650
AGE-18
PLOT-19
40
1572
1750-2009
1838-2009
DBH-19
651-925
AGE-19
144
Table 1: Site and chronology descriptive information. PLOT represents chronologies developed
145
according to sample location (i.e. plot-based), DBH chronologies are composed of samples
146
grouped according to diameter at breast height, and AGE represents chronologies with samples
147
grouped according to tree recruitment age. With the exception of the PLOT-ALL chronology, all
148
other chronologies were developed using 40 samples. (EPS = expressed population signal,
149
Wigley et al., 1984).
150
151
Data analysis
152
Sampled cores were mounted and glued on wooden mounts and subsequently surfaced with a
153
blade to enhance the visibility of ring boundaries. To help determine tree recruitment age (i.e. the
154
number of rings at coring height), pith-offset was estimated using an acetate sheet with concentric
155
circles. However, the method of sample collection specifically focused on minimizing pith-offset and so
156
the majority of samples included the pith. Ring width was then measured using a LINTAB traversing
157
7
measuring stage coupled with TsapWin (RINNTECH, Germany) measuring software to a precision of 0.01
158
mm. Sample crossdating was performed using standard dendrochronological approaches (Stokes and
159
Smiley, 1968) and crossdating of measured series was checked with CDendro (Larsson, 2015).
160
161
Disturbance detection and correction
162
Curve Intervention Detection (CID) is a time-series intervention detection method based on the
163
work of Druckenbrod (2005) and Druckenbrod et al. (2013). The method was used here to objectively
164
identify and remove disturbance trends from individual RW series following the procedure described in
165
Rydval et al. (2016), where it was used to identify and correct for growth release trends due to logging-
166
related disturbance in Scottish Scots pine (Pinus sylvestris) samples. In this study, both growth release
167
and growth suppression trends were detected and removed. Prior to the CID procedure, a constant of 1
168
mm was added to all measurements to avoid the possibility of losing tree-ring information during the
169
disturbance removal procedure (Rydval et al., 2016). As part of the CID procedure, RW measurement
170
series were first power transformed (Cook and Peters, 1997) and then detrended by fitting a negative
171
exponential or linear function. Disturbance trends were identified as outliers from 9-30 year running
172
mean distributions based on the residual series of each detrended RW series and autoregressive model
173
estimates. The identified release / suppression trend was removed by subtracting a curve (Warren,
174
1980) fitted to the series from the point where the initiation of the disturbance-related trend was
175
identified. The procedure was repeated until no further outliers were detected. The disturbance-
176
corrected series were then re-expressed in the original (non-detrended) measurement format so that
177
both the corrected and uncorrected series could then be detrended in the same way. For a detailed
178
description of the method refer to Rydval et al. (2016). CID (ver. 1.05) was used in these analyses and is
179
included in the supplemental materials as Matlab code files. A freely-available executable (ver. 1.07) is
180
available using the Matlab compiler. Contact Daniel Druckenbrod (ddruckenbrod@rider.edu) as this
181
version is dependent on operating system and Matlab release version. These time-series methods are a
182
work in progress, but we also welcome other researchers to experiment with this tool to detect and
183
isotlate disturbance events in ring-width series.
184
8
185
RW chronology development
186
Two sets of chronologies were developed with the first set composed of series prior to
187
disturbance correction using the CID method (i.e. uncorrected for the influence of disturbance pre-
188
CID) and the second set using series after correcting for disturbance trends with CID (post-CID). Using
189
ARSTAN (Cook and Krusic, 2005), both sets of RW series were power transformed to stabilize series
190
variance (Cook and Peters, 1997) and detrended by subtracting negative exponential or negatively
191
sloping linear functions. The mean chronology index was calculated using Tukey’s robust bi-weight mean
192
to reduce the influence of outlier values (Cook and Kairiukstis, 1990). Variance stabilization of the mean
193
chronology, due to changing replication, was then performed according to the procedure described in
194
Osborn et al. (1997).
195
In addition to developing an uncorrected (pre-CID) and disturbance corrected (post-CID) mean
196
chronology from all 760 samples, the entire collection of series was also divided into 19 separate sub-
197
plot chronologies (each including 40 series) compiled by grouping series according to 1) the plot location
198
where the samples were collected (PLOT) (Figure 1); 2) random sample selection without replacement
199
(RAN); 3) tree recruitment age (AGE), 4) and the diameter at breast height (DBH) (Table 1). All
200
chronologies were truncated based on an expressed population signal (EPS Wigley et al., 1984) cut-off
201
of EPS ≥ 0.85.
202
Principal Component (PC) analysis, with varimax rotation, was applied using the IBM SPSS
203
(v.20.0) statistical package (SPSS, 2011) to both pre-CID and post-CID location-based (PLOT)
204
chronologies to reduce the dimensionality of the RW predictor dataset in order to extract the dominant
205
modes of variance. Based on the temporal span of the shortest chronology (Table 1), the period 1909-
206
2009 was used in order to include all chronologies in the analysis. Only the lowest order PC scores with
207
an eigenvalue >1 were retained.
208
209
210
9
211
Blue Intensity chronology development
212
Similar to maximum latewood density, blue intensity (BI) represents summer growing
213
conditions usually reflecting a (late) summer response to temperature in conifers from temperature
214
limited locations (e.g. Björklund et al., 2014b; McCarroll et al., 2013; Rydval et al., 2014; Wilson et al.,
215
2014). BI measurements were developed for a subset of the samples (three chronologies PLOT-3,
216
PLOT-7 and PLOT-10; 40 samples each) following Rydval et al. (2014). Since, unlike other conifers such as
217
pine, Norway spruce samples do not exhibit any apparent visual colour difference between the
218
heartwood and sapwood that would affect BI measurements, chemical treatment involving sample resin
219
extraction was not performed. Such an approach was also considered adequate in a study by Wilson et
220
al. (2014) examining BI data from Engelmann spruce in British Columbia. Samples surfaced with sanding
221
paper up to 1200 grit grade were scanned using an Epson Expression 10000 XL flatbed scanner
222
combined with SilverFast Ai (v.6.6 - Laser Soft Imaging AG, Kiel, Germany) scanning software. Scanner
223
calibration was performed with the SilverFast IT8 calibration procedure using a Fujicolor Crystal Archive
224
IT8.7/2 calibration target. A resolution of 2400 dpi was used for scanning. During the scanning process,
225
samples were covered with a black cloth to prevent biases due to ambient light.
226
CooRecorder measurement software (Larsson, 2015) was used to measure BI from scanned
227
images. The BI series were then inverted according to Rydval et al. (2014) to express a positive
228
relationship with RW and instrumental temperatures and subsequently detrended by subtraction from
229
fitted negatively sloping linear functions. The mean BI chronology was calculated and truncated (EPS =
230
0.85) in the same way as the RW chronologies.
231
232
Climate data
233
For this study, in order to allow the assessment of the longest possible temporal span of the
234
tree-ring data, we used mean temperature series from a meteorological station in Sibiu, Romania
235
(hereafter SIBIU) covering the period 1851-2015 (data for 1918 were unavailable and were estimated
236
from the relevant 0.5° CRU TS3.23 grid scaled to SIBIU) located approximately 170 km to the SSW of
237
10
Calimani (Figure 1). An additional temperature record was composited using the longest Central and
238
Eastern European instrumental records in order to assess the whole span of the full 760 sample Calimani
239
chronology. This Central/East European (CEU) composite covers the period 1773-2014 and includes
240
temperature series from Prague (Czech Republic), Vienna (Austria), Kraków (Poland), Budapest
241
(Hungary), Lviv (Ukraine) and Kishinev (Moldova). The individual instrumental series were converted to
242
anomalies relative to 1961-1989 and combined as a simple average. To adjust for variance changes due
243
to the changing number of series in the composite through time, the variance of the mean series was
244
adjusted according to Osborn et al. (1997). Climate data were used to assess the strength of the climatic
245
signal in tree ring chronologies using the Pearsons correlation coefficient (r).
246
247
RESULTS
248
Four sets of chronologies developed according to various sampling strategies are presented in
249
Figure 2 with additional chronology information in Table 1. As the strongest significant chronology
250
response was observed with June-July mean temperatures (see supplementary Figure S1), RW
251
chronologies were assessed using this seasonal window. This seasonal response agrees with Sidor et al.
252
(2015) who also noted a significant June-July mean temperature signal in high-elevation spruce sites in
253
the Romanian Carpathians including Calimani.
254
The location-based ‘PLOT’ chronologies (Figure 2a see Figure 1 for plot locations) displayed a
255
large range of variability (especially before ~1960) which is also reflected in the wide range of variation
256
in correlation between each chronology and June-July average instrumental temperatures (r = 0.07 to
257
0.46; r = 0.26 Table 2). The ‘RAN’ chronologies based on random selection of samples (without
258
replacement; Figure 2b) produced a more uniform range of variability which was also observed in the
259
relationship between the chronologies and instrumental temperatures (r = 0.24 to 0.35; r = 0.26 Table
260
2). When compared with the PLOT chronologies, the correlation range of these ‘random sample’
261
chronologies against instrumental temperatures was considerably narrower, although the mean
262
correlation was virtually the same and while the very low correlations were no longer observed, the
263
higher correlations were also no longer present. When grouped according to stem size (i.e. DBH Figure
264
11
2c), chronologies displayed considerable variability particularly in the first half of the 20th century as well
265
as in the most recent period (i.e. after ~1990). Chronologies composed of series from broader-stemmed
266
(higher DBH) trees tended to correlate more weakly with instrumental temperatures (r = - 0.06 to 0.28
267
for chronologies DBH-12 DBH-19; see Table 3 for details), whereas trees with narrower stems (lower
268
DBH) appeared to exhibit higher correlations (r = 0.37 to 0.47 for chronologies DBH-1 DBH-11
269
excluding the weaker DBH-9 chronology; see Table 3). The chronologies grouped according to age
270
showed a similar range of variability to the DBH-based chronologies (Figure 2d). Although not as clear,
271
there was a tendency for younger chronologies to correlate more strongly than the oldest chronologies
272
(Figure 2d; Table 3). However, when examining only the high frequency (inter-annual) relationship
273
between the chronologies and temperature (1st differenced results in Table 3), there was little
274
difference between the young and old tree chronologies and larger trees actually displayed a stronger
275
signal than chronologies from smaller trees (r = 0.30 to 0.43, r = 0.38 for chronologies DBH-1 DBH-11;
276
r = 0.42 to 0.51, r = 0.47 for chronologies DBH-12 DBH-19). Unsurprisingly, a strong relationship (r =
277
0.63) was observed between age and DBH (Figure S2), which indicates that older trees generally also
278
tend to be larger (i.e. higher DBH) and vice-versa.
279
[insert Figure 2]
280
281
SAMPLING TYPE
PRE-CID
POST-CID
1ST DIFFERENCED
LOCATION (PLOT)
(1909-2009)
r = 0.255 ± 0.124
rrange= 0.066 - 0.461
r = 0.383 ± 0.126
rrange = 0.079 - 0.540
r = 0.422 ± 0.044
rrange = 0.337 0.530
RANDOM
(1912-2009)
r = 0.265 ± 0.057
rrange = 0.183 - 0.381
r = 0.401± 0.049
rrange = 0.329 - 0.505
r = 0.441 ± 0.032
rrange = 0.387 0.503
DBH
(1917-2009)
r = 0.265 ± 0.174
rrange= -0.067 - 0.472
r = 0.379 ± 0.062
rrange= 0.263 - 0.489
r = 0.414 ± 0.059
rrange= 0.297 0.508
AGE
(1933-2009)
r = 0.309 ± 0.153
rrange = -0.067 - 0.483
r = 0.397 ± 0.081
rrange = 0.236 - 0.509
r = 0.377 ± 0.066
rrange = 0.255 0.485
Table 2: Average correlation and correlation range of chronologies before (pre-CID) and after (post-CID)
282
disturbance correction and first differenced chronologies developed with different sampling
283
strategies, including samples grouped by location (PLOT), random sample selection (RAN),
284
grouping according to diameter at breast height (DBH), and sample age (AGE), against SIBIU
285
Jun-Jul mean instrumental temperatures. (r represents the mean correlation ± 1 standard
286
deviation, while rrange represents the full correlation range)
287
12
CHRON.
(RANDOM)
PRE-CID
CORR
1ST DIFF
CORR
CHRON.
(BY DBH)
PRE-CID
CORR
POST-CID
CORR
1ST DIFF
CORR
CHRON.
(BY AGE)
PRE-CID
CORR
POST-CID
CORR
1ST DIFF
CORR
RAN-1
0.318
0.471
DBH-1
0.473
0.453
AGE-1
0.374
0.339
0.294
RAN-2
0.239
0.503
DBH-2
0.373
0.311
AGE-2
0.483
0.451
0.421
RAN-3
0.279
0.471
DBH-3
0.393
0.418
AGE-3
0.414
0.440
0.353
RAN-4
0.218
0.480
DBH-4
0.402
0.396
AGE-4
0.473
0.452
0.431
RAN-5
0.246
0.475
DBH-5
0.402
0.389
AGE-5
0.446
0.509
0.449
RAN-6
0.248
0.415
DBH-6
0.397
0.358
AGE-6
0.470
0.489
0.414
RAN-7
0.300
0.451
DBH-7
0.406
0.368
AGE-7
0.362
0.433
0.309
RAN-8
0.381
0.417
DBH-8
0.471
0.489
AGE-8
0.370
0.323
0.321
RAN-9
0.241
0.471
DBH-9
0.196
0.327
AGE-9
0.348
0.267
0.338
RAN-10
0.228
0.397
DBH-10
0.403
0.390
AGE-10
0.237
0.310
0.352
RAN-11
0.301
0.441
DBH-11
0.357
0.409
AGE-11
0.258
0.309
0.307
RAN-12
0.334
0.439
DBH-12
0.135
0.353
AGE-12
0.227
0.334
0.255
RAN-13
0.183
0.387
DBH-13
0.275
0.353
AGE-13
0.356
0.390
0.338
RAN-14
0.359
0.414
DBH-14
0.182
0.464
AGE-14
0.414
0.489
0.381
RAN-15
0.187
0.436
DBH-15
0.054
0.394
AGE-15
0.348
0.430
0.401
RAN-16
0.247
0.429
DBH-16
0.145
0.457
AGE-16
0.034
0.470
0.485
RAN-17
0.238
0.415
DBH-17
-0.067
0.302
AGE-17
0.217
0.443
0.386
RAN-18
0.300
0.456
DBH-18
0.110
0.300
AGE-18
0.109
0.435
0.469
RAN-19
0.197
0.411
DBH-19
-0.064
0.263
AGE-19
-0.067
0.236
0.467
Table 3: Correlations of chronologies before (pre-CID) and after (post-CID) disturbance correction and
288
first differenced chronologies sampled using different sampling strategies, including random
289
sample selection (RAN), grouping according to diameter at breast height (DBH), and sample age
290
(AGE), against Jun-Jul mean instrumental temperatures from Sibiu (shading is used to aid
291
interpretation of the results with darker shades indicating higher correlations).
292
293
A summary of the general disturbance history at Calimani is provided in Figure 3a. The results
294
showed three major pulses or clusters of disturbance events, which affected a large proportion of the
295
stand, detected in the 1740s, the middle of the 19th century and the 1910s followed by growth releases
296
in the subsequent decades attributable to those disturbances. Disturbance suppression events were
297
detected in the mid/late 18th century, although the predominant release events were more prevalent
298
whereas suppression events did not appear to considerably affect the mean disturbance chronology.
299
The pre- and post- correction chronologies (Figure 3b and 3c respectively) indicated a wider spread in
300
individual pre-CID chronologies and greater deviation from the mean chronology compared to their
301
post-CID counterparts. This was also observed with the other sampling approaches (Table 2). The
302
disturbance growth chronology in Figure 3a identified periods of growth release pulses attributable to
303
disturbance which are evident in the Figure 3b mean chronology. After disturbance correction, the
304
spread of the individual chronologies was reduced as the growth release trends were removed and the
305
post-CID chronologies exhibited greater similarity to the mean chronology which did not contain the
306
growth release trends. The mean pre- and post-CID chronologies are displayed together with the SIBIU
307
13
instrumental temperature record (back to 1851) and the Central European (CEU) instrumental
308
temperature composite extending back to the 1770s (Figure 3d). The main differences between the
309
corrected and uncorrected chronologies become apparent with lower post-correction RW index values
310
in the first half of the 20th century and higher values from approximately 1770 until 1850. These results
311
also highlighted the improved agreement of the post-CID chronology with both the shorter SIBIU (rpre-CID
312
= 0.27; rpost-CID= 0.36) and longer CEU (rpre-CID = 0.14; rpost-CID= 0.26) instrumental temperature series.
313
[insert Figure 3]
314
315
The change in correlation between individual pre-CID and post-CID chronologies and the SIBIU
316
temperature series for the common 1909-2009 period (Figure 4a) showed overall improvement of the
317
mean chronology as well as all individual chronologies with the exception of PLOT-16. Similar results
318
were obtained when evaluating the full length of each chronology (Figure 4c). A comparison of the pre-
319
and post-CID root-mean-square error (RMSE) results for the common 1909-2009 period (Figure 4b) and
320
the full length of overlap (Figure 4d) between individual PLOT chronologies and SIBIU indicated a RMSE
321
decrease in nearly all post-CID chronologies. This RMSE pattern largely mirrored the correlation change
322
results and indicated chronology improvement in the sense that lower RMSE results were observed in
323
the post-correction chronologies. The results from Figure 4c were also represented spatially in Figure 1.
324
The greatest degree of post-CID chronology correlation increase with instrumental temperatures was
325
observed in chronologies from the southeastern slope, which predominantly contained growth release
326
trends in the first half of the 20th century. Chronologies showing intermediate improvement were
327
located farther north and included chronologies from the northwestern slope which predominantly
328
contained disturbance related trends in the second half of the 19th century. The least improvement was
329
observed in chronologies from the northwestern (PLOT-1) and northernmost (PLOT-18) investigated
330
locations as well as on the southern ridge (PLOT-6) and in the valley (PLOT-16), with the latter two
331
chronologies exhibiting no late 19th / early 20th century disturbance trends. Supplementary Figure S3
332
highlights in greater detail this broad spatial and temporal split in the pattern of disturbance of the
333
northwest / southeast groups and the very large percentage of trees in each group affected by these
334
two major disturbance events. Individual chronologies developed according to the other sampling
335
14
strategies showed an overall pattern of post-CID improvement similar to the location based (PLOT)
336
assessment (Table 3). The pre-CID and post-CID results from Table 3 along with their respective
337
chronologies are displayed graphically in supplementary Figure S4.
338
[insert Figure 4]
339
The principal component (PC) time-series scores of the dominant modes of variance of the pre-
340
CID dataset are presented in Figure 5a and include three PCs (loadings of the chronologies on each
341
eigenvector are presented in Table 4). PC3 showed the strongest correlation with SIBIU Jun-Jul
342
temperatures (r = 0.45) and PC1 correlated more weakly (r = 0.33), while PC2 was weakly negatively
343
correlated with temperatures (r = -0.24). When compared to the disturbance chronology in Figure 3a, a
344
strong correlation was observed with PC2 (r = 0.91). After CID correction, only two dominant PCs were
345
identified. Although the first PC was uncorrelated with temperatures, a stronger relationship was
346
observed between temperature and PC2 (r = 0.56) than was identified with any of the pre-CID PC scores.
347
Conversely, PC1 significantly correlated with the disturbance chronology (r = 0.50), whereas no
348
correlation was found with PC2.
349
350
[insert Figure 5]
351
352
CHRON.
(SUBSET)
PC1
(PRE-CID)
PC2
(PRE-CID)
PC3
(PRE-CID)
CHRON.
(SUBSET)
PC1
(POST-CID)
PC2
(POST-CID)
PLOT-10
0.911
0.163
0.275
PLOT-1
0.932
0.157
PLOT-5
0.907
0.216
0.168
PLOT-11
0.878
0.358
PLOT-19
0.885
0.302
0.265
PLOT-14
0.875
0.244
PLOT-2
0.826
0.350
0.301
PLOT-19
0.827
0.472
PLOT-1
0.813
0.528
-0.071
PLOT-6
0.791
0.508
PLOT-9
0.741
0.436
0.403
PLOT-10
0.791
0.478
PLOT-11
0.703
0.625
0.160
PLOT-2
0.777
0.530
PLOT-7
0.603
0.460
0.576
PLOT-8
0.772
0.374
PLOT-8
0.414
0.828
0.312
PLOT-5
0.721
0.402
PLOT-14
0.535
0.795
0.156
PLOT-7
0.718
0.601
PLOT-15
0.356
0.761
0.461
PLOT-9
0.714
0.585
PLOT-3
0.377
0.758
0.448
PLOT-16
0.112
0.876
PLOT-4
0.377
0.692
0.536
PLOT-4
0.337
0.858
PLOT-6
0.551
0.652
0.481
PLOT-13
0.367
0.858
PLOT-16
0.121
0.010
0.934
PLOT-17
0.438
0.815
PLOT-12
0.173
0.518
0.803
PLOT-12
0.539
0.788
PLOT-18
0.413
0.337
0.776
PLOT-15
0.523
0.780
PLOT-13
0.138
0.579
0.723
PLOT-3
0.529
0.772
PLOT-17
0.270
0.582
0.710
PLOT-18
0.587
0.635
353
Table 4: Principal Component (PC) Analysis loadings of location-based (PLOT) chronologies before (pre-
354
CID) and after (post-CID) disturbance correction on the dominant eigenvectors (results in bold
355
indicate the strongest loading of each chronology).
356
15
The restricted three site (PLOT3, 7 and 10) correlation response analysis assessing the
357
relationship between pre-CID, post-CID and BI data, with SIBIU temperatures (- Figure 6a) clearly shows
358
disturbance trends in the RW data with various degrees of post-CID improvement. In contrast to the
359
relatively narrow RW seasonal response, the BI chronology responded more strongly to a broader
360
seasonal window displaying highest correlations with mean July-September temperatures (r = 0.65). The
361
response of BI was stronger than post-CID RW with respect to the optimal season of each parameter.
362
Although improvement of the post-CID chronologies (Figures 6b, c and d) was apparent especially
363
before 1880 when the deviation of the pre-CID chronology from the instrumental record was reduced,
364
the degree of improvement was limited, particularly as periods of weaker agreement remained as
365
indicated by running correlations between the pre-/post-CID chronologies and SIBIU temperatures. In
366
contrast, the BI chronologies more closely matched the instrumental trends with running correlations
367
displaying a consistently strong relationship back into the 19th century.
368
[insert Figure 6]
369
370
DISCUSSION
371
Considering the relatively small area of the Calimani study area, it would be reasonable to
372
assume that chronologies developed from the plots would be similar in the absence of disturbance and
373
should therefore also express a very similar climate signal. However, despite the adequate replication of
374
the different chronologies, a range of chronology trends were observed (Figure 2a) expressing
375
substantial differences in correlation with temperature ranging from zero to ~0.5. The possibility of
376
developing a climatically sensitive chronology by randomly choosing and sampling all trees in a specific
377
plot would therefore depend on chance. An alternative approach, which randomly samples trees from
378
the whole stand (Figure 2b), produced a more consistent and uniform outcome, although generally
379
resulting in correlations of only ~0.3 with temperature.
380
A sampling strategy commonly applied for dendroclimatology favours the preferential selection
381
of larger / wider (i.e. higher DBH) and presumed older trees in order to extend a chronology as far back
382
in time as possible. The strong age / DBH relationship (Figure S2), would support this type of reasoning.
383
16
Forming chronologies by grouping series according to DBH, revealed that samples from the largest trees
384
expressed the weakest temperature signal (Figure 2c). Although less clear-cut than the DBH results,
385
there was also a tendency for chronologies composed of samples from old trees to produce a
386
climatically weaker signal. Yet the 1stdifferenced results indicate that there is no fundamental limitation
387
in the ability of older trees to record climatic information and that, at least at high frequencies, the
388
sensitivity of larger trees compared to smaller ones is actually greater. This observation demonstrates
389
that the overall response of trees does not simply weaken with age but is instead related to the
390
presence of disturbance related trends that bias the lower frequency expressed in the data after
391
detrending.
392
It would therefore be reasonable to conclude that the weaker performance of the older (and
393
generally larger) trees at decadal and longer timescales is at least, in part, related to the greater
394
likelihood that older trees will be affected by some disturbance event during their life than younger
395
trees. Although the Figure 2d results represent a common period of analysis in the 20th century,
396
disturbance trends in earlier parts of the older chronologies would still affect the chronology trends in
397
this recent period (i.e. by biasing the fit of the detrending functions). Therefore, without addressing
398
trend biases, sampling the largest (and perhaps oldest) trees will likely produce chronologies with poor
399
climatic sensitivity at decadal and longer timescales. This presents a problem as the oldest trees are also
400
the most valuable for studying longer-term climate. Furthermore, none of these strategies can
401
guarantee a good chronology response in terms of climate signal. We must therefore ask the question,
402
why is this the case, and does a reasonable approach exist for optimizing (maximizing) the climatic
403
potential of the population sample? If disturbance is an important factor influencing climate response,
404
then disturbance correction may be an appropriate strategy to improve calibration.
405
Previous studies have identified wind and windstorm damage as the dominant determinant of
406
large scale severe disturbance in the Romanian Carpathians and to a lesser extent insect outbreaks and
407
snow damage (e.g. Griffiths et al., 2014; Popa, 2008; Svoboda et al., 2014), which would account for the
408
observed synchronous and temporally clustered nature of disturbance (Figure 3a) and the imprinting of
409
disturbance trends in individual RW series on the mean chronologies (Figure 3b). There is also evidence
410
that large scale severe windstorm events can impact the majority of trees over relatively large areas, as
411
17
for example during the 2004 event in the Slovakian Tatra Mountains (Western Carpathians) which
412
affected 12,000 ha of montane forest stands (e.g. Holeksa et al., 2016; Zielonka et al., 2010). The
413
reduced range and more uniform trends expressed in individual PLOT chronologies, which more closely
414
matched the mean (all 760 series) chronology after CID correction in Figure 3c compared to the pre-
415
correction version in Figure 3b (particularly around the most prominent late 19th and early 20th century
416
periods of growth release), suggests that CID correction produced individual PLOT sub-chronologies that
417
more accurately approximate the larger-scale (regional population) chronology.
418
Compared to the lower mean correlation of the unfiltered pre-CID chronologies (Figure 3b, r =
419
0.28), correlations of the unfiltered post-CID chronologies (Figure 3c, r = 0.41) as well as the 1st
420
differenced pre-CID and post-CID chronology versions (r = 0.45 and 0.49 respectively) were all
421
considerably higher. This suggests that the high frequency climate signal in pre-CID chronologies was
422
unaffected by the presence of disturbance trends and that the weaker correlations of the unfiltered pre-
423
CID chronologies were related to the lower frequency trends, which was supported by the substantial
424
degree of unfiltered post-CID correlation improvement (Figure 3c). Furthermore, the long term trend of
425
the post-CID mean (all 760 series) chronology differed when compared to its pre-CID counterpart.
426
Specifically, the most apparent changes included a reduction of index values affected by growth release
427
in the first half of the 20th century and higher values before 1850 after correcting for non-climatic
428
growth suppression trends. Taken together, the above evidence suggests that a disturbance-free
429
chronology may not necessarily be achieved simply by collecting and averaging a very large number of
430
series.
431
The improvement in post-CID chronology running correlations (Figure 3e) against both SIBIU
432
and the longer CEU temperature series as well as the improved visual lower frequency trend agreement
433
with these instrumental records (Figure 3f) suggests that the CID-corrected chronology better
434
represented observed temperature trends. It should be pointed out that although CEU indicated
435
warmer temperature conditions before the mid-19th century than suggested even by the post-CID
436
chronology, early instrumental series (including those in central and eastern Europe) may contain a
437
positive warm bias as a result of measurement practices and the lack of screen use before the mid / late
438
18
19th century (Böhm et al., 2010; Moberg et al., 2003). Hence, it is unclear whether the post-CID
439
chronology indices were still too low or the instrumental record contained an early period warm bias.
440
The correlation and RMSE change results (Figure 4) indicate that, with one exception, all
441
chronologies showed some degree of improvement after CID correction. Specifically, nearly all
442
chronologies exhibited improved agreement (i.e. greater similarity) with the reference SIBIU
443
instrumental temperature series expressed by a correlation increase and reduced RMSE. However, CID
444
correction may not necessarily produce a substantial degree of improvement in all cases. In some
445
instances this may be a result of applying CID to chronologies that already expressed a strong climate
446
signal and did not exhibit any considerable degree of disturbance related trends (e.g. PLOT18 in Figure
447
4). In other cases, where only very limited improvement was observed in weakly correlating
448
chronologies with temperature (e.g. PLOT1 in Figure 4), other unidentified factors (not necessarily
449
related to disturbance) are likely responsible. In general, however, CID correction resulted in climate
450
signal improvement for RW data, which is true, not only for location-based sampling, but also the other
451
sampling strategies (Table 2 and 3). Spatially, it appears that a high severity disturbance event around
452
the 1840s and possibly others in the subsequent decades mainly affected the northern and western
453
slopes, whereas another event around the 1910s mostly affected the eastern slope. We hypothesize
454
that this distinct spatial pattern and segregation of areas affected by disturbance in these two cases may
455
point to windstorms as the most likely disturbance agent and that the spatial configuration of this
456
pattern may be indicative of the spatially distinctive impact of wind disturbance in these two instances.
457
The PC analysis (Figure 5) demonstrates that even extracting the dominant modes of variability
458
as PC scores, will not separate the climatic and non-climatic signals (i.e. this approach does not
459
guarantee best achievable results when the influence of disturbance is present). Though these are the
460
results of a local-scale analysis, it is conceivable that temporally common disturbance trends can be
461
present in chronologies even over a larger region (e.g. due to wind storms or large-scale insect
462
outbreaks). The inability to isolate the climate signal was expressed by the significant correlation of both
463
PC1 and PC3 with temperature, but to a lesser degree also through their correlation with the
464
disturbance chronology, which was mainly represented by PC2. After CID correction, a clearer
465
separation of the climatic and non-climatic signals was achieved with PC analysis as indicated by the
466
19
reduction from three dominant PCs to two and the increased correlation between PC2 and temperature.
467
Importantly, however, though weaker (compared to pre-CID), the influence of the disturbance signal
468
was reduced but not entirely removed by the CID procedure. This may be due to the relatively
469
conservative threshold (3.29 sigma) applied in the identification of release events in order to minimize
470
the likelihood of falsely identifying growth releases that are not disturbance related.
471
The parameter comparison for three sub-chronologies (PLOT 3, 7 and 10) in Figure 6 indicates
472
that BI is not only the strongest temperature proxy but could potentially serve as a disturbance-free
473
parameter, though further investigation in other locations and with additional species would be
474
required to assess whether the decreased susceptibility of this parameter to disturbance is observed
475
more generally. Kaczka and Czajka (2014) noted a similar (stronger than RW) summer temperature
476
response of Norway spruce BI from Babia Góra in southern Poland. The importance of BI (and by
477
extension maximum latewood density) to dendroclimatological research as a parameter that appears
478
generally unaffected (or less affected) by disturbance and with a stronger climate signal is clearly
479
emphasised by the evidence presented here. This may have implications for deriving chronologies free
480
of disturbance with a stronger climatic signal as one possible way to by-pass the undesirable impact of
481
disturbance on tree-ring data in dendroclimatic investigations. Furthermore, comparing RW and BI
482
chronologies may represent an additional approach to the identification of disturbance trends in RW
483
data.
484
A recent study by Rydval et al. (2016) demonstrated that disturbance related to anthropogenic
485
activities (i.e. extensive logging) can induce growth trend biases in RW chronologies. The evidence
486
presented herein demonstrates that natural disturbance can also potentially cause systematic
487
chronology biases within closed-canopy forests. This can occur even if care is taken to select seemingly
488
undisturbed sites as any evidence of disturbance occurring in the past (i.e. multiple decades or centuries
489
ago) may have been erased from the landscape and may therefore no longer be visible at the time of
490
sampling. By examining a very large number of samples, highly representative of the full stand
491
population in this study, it is clear that the strength of the climate signal expressed in a chronology from
492
a particular location can vary extensively and no sampling strategy can reliably ensure that the
493
chronology produced from any set of collected RW samples will contain a well expressed climatic signal
494
20
(i.e. the best achievable climate signal in RW data from a particular area). The development of
495
chronologies which express a sufficiently strong common population signal (i.e. assessed using the
496
widely applied EPS metric) can result in chronologies poorly correlated with climate even when the
497
relationship between climate and chronologies from other sets of samples from the same area is
498
considerably stronger. This can arise when non-climatic trends occur synchronously in those samples
499
that make up a chronology.
500
The presupposition that collecting a large number of samples and avoiding disturbance-affected
501
sampling locations can alleviate disturbance related biases in chronologies may be misleading because
502
large-scale disturbances can affect whole stands and presumably even many stands in a region (e.g. due
503
to wind disturbance or large-scale insect outbreaks). Nehrbass-Ahles (2014) performed an evaluation of
504
sampling strategies, although it mainly assessed chronologies based on various sampling techniques in
505
relation to the ‘full population’ and was also conducted in a managed stand that did not in fact display
506
much climatic sensitivity. Such an approach, however, implicitly assumes that the population itself is
507
unbiased in relation to its representation of the climate signal. Here we have demonstrated that the
508
assumption of an unbiased population may not be justified. Evaluating chronologies in relation to the
509
population (or rather a very large sample of the population) may therefore not represent a sound
510
strategy in some cases as the possible influence of disturbance should also be taken into account. This
511
finding provides some support for adopting strategies such as the careful selection (or screening) of
512
samples at the local site level, or chronologies on the multi-site network scale, by assessing their climatic
513
sensitivity in order to avoid including samples or chronologies significantly affected by disturbance in
514
dendroclimatic analyses. Such screening practices have already been commonly applied in the
515
development of reconstructions from large scale networks (e.g. Cook et al., 2013; Ljungqvist et al.,
516
2016). Nevertheless, the use of methods such as CID may be preferable as this can reduce the risk of
517
potential subjectivity and perhaps even expand the range of useable chronologies which may otherwise
518
be deemed unsuitable for dendroclimatic analysis.
519
Although this study demonstrates this issue only at a single location, there is potential for
520
systematic disturbance to affect RW chronologies in virtually any closed canopy forest ecosystem and
521
such a possibility cannot be dismissed a priori. The issues highlighted and discussed here may for
522
21
example directly affect calibration strength of reconstructions as well as the possibility of making
523
inaccurate inferences about past climatic conditions from RW-based reconstructions that may include
524
disturbance related biases. It is important to be able to perform some assessment of possible
525
disturbance effects on RW chronologies because assessing the fidelity of reconstructed climate
526
estimates before the instrumental period is difficult. We therefore recommend that all future
527
dendrochronological studies investigating medium to low frequency climatic trends should perform
528
some form of disturbance assessment and that the CID method (Druckenbrod et al., 2013; Rydval et al.,
529
2016) represents a reasonable approach.
530
531
CONCLUSION
532
In this study, we have demonstrated that natural disturbance events can act as agents which
533
significantly and systematically affect tree growth, subsequently biasing mid- to long-term RW
534
chronology trends. These disturbance trends cannot be removed using conventional detrending
535
approaches without also removing lower frequency climatic information. In closed canopy forests, the
536
oldest (and dendroclimatologically most valuable) trees are more likely to contain an embedded
537
disturbance response. It is not possible to ensure that this response can be factored out or minimized
538
simply by adopting a subjective sampling strategy or relying on a very large sample size (with respect to
539
both trees and sites). Furthermore, sampling trees across a landscape may produce a record with a
540
complex range of disturbance histories rather than reducing the disturbance signals. This important
541
finding highlights the need to develop site selection and sampling approaches for closed-canopy forests
542
that are very different from those developed by Fritts (1976) for open-canopy forests. More specifically,
543
it is imperative to develop better methods to disentangle disturbance and climate signals.
544
Disturbance detection techniques could be used, at a minimum, to identify and assess the
545
effects of disturbance on RW chronologies and (if replication permits) exclude subsets substantially
546
affected by such trends which would therefore represent a poorer expression of longer term climatic
547
variability. This also provides justification for the application of approaches such as data screening in
548
order to exclude subsets of larger datasets, which are weakly correlated with climate, from climatic
549
22
analyses. An alternative approach would include the utilization of some sort of disturbance correction
550
procedure (e.g. CID) to improve the expression of the climate signal in disturbance affected RW series.
551
Finally, other tree-ring parameters, such as BI (or maximum latewood density), which may be less prone
552
to the effects of disturbance and often express a stronger climate signal than RW (Wilson et al. 2016),
553
could also be developed.
554
The findings of this study are broadly applicable and of relevance to RW chronologies from
555
closed canopy stands. Additional larger scale investigations including various species from other
556
locations would be beneficial in assessing the relevance of our findings. Certainly, consideration should
557
be given to the possibility of disturbance related trends affecting medium to low frequency growth
558
trends in RW chronologies. We therefore recommend that some form of evaluation of this potential
559
effect should be performed as part of any dendrochronological research utilizing RW data to investigate
560
climatic trends as it may be possible to reduce this limitation and improve the expression of the climate
561
signal in such data.
562
563
564
ACKNOWLEDGEMENTS
565
The study was supported by the institutional project MSMT (CZ.02.1.01/0.0/0.0/16_019/0000803) and
566
the Czech Ministry of Education (Project INTER-COST no. LCT17055). We thank the Călimani National
567
Park authorities, especially E. Cenuşă and local foresters, for administrative support and assistance in
568
the field.
569
570
571
REFERENCES
572
Anchukaitis KJ, Wilson R, Briffa KR et al. (2017) Last millennium Northern Hemisphere summer
573
temperatures from tree rings: Part II, spatially resolved reconstructions. Quaternary Science
574
Reviews 163: 1-22.
575
23
Attiwill PM (1994) The disturbance of forest ecosystems: the ecological basis for conservative
576
management. Forest Ecology and Management 63(2): 247-300.
577
Björklund JA, Gunnarson BE, Seftigen K et al. (2014a) Blue intensity and density from northern
578
Fennoscandian tree rings, exploring the potential to improve summer temperature
579
reconstructions with earlywood information. Climate of the Past 10(2): 877-885.
580
Björklund J, Gunnarson BE, Seftigen K et al. (2014b) Using adjusted Blue Intensity data to attain high-
581
quality summer temperature information: A case study from Central Scandinavia. Holocene
582
25(3): 547-556.
583
Böhm R, Jones PD, Hiebl J et al. (2010) The early instrumental warm-bias: a solution for long central
584
European temperature series 17602007. Climatic Change 101(1-2): 41-67.
585
Box GEP and Jenkins GM (1970) Time series analysis: forecasting and control. San Francisco, CA: Holden-
586
Day, 553 pp.
587
Box GE and Tiao GC (1975) Intervention analysis with applications to economic and environmental
588
problems. Journal of the American Statistical Association 70(349): 70-79.
589
Briffa K and Melvin T (2011) A Closer Look at Regional Curve Standardization of Tree-Ring Records:
590
Justification of the Need, a Warning of Some Pitfalls, and Suggested Improvements in Its
591
Application. In: Hughes MK, Swetnam TW and Diaz HF (eds) Dendroclimatology: progress and
592
prospects. Dordrecht: Springer, pp. 113-145.
593
Briffa KR, Jones PD, Schweingruber FH et al. (1996) Tree-ring variables as proxy-climate indicators:
594
problems with low-frequency signals. In: Jones PD, Bradley S and Jouzel J (eds) Climatic
595
Variations and Forcing Mechanisms of the Last 2000 Years. Berlin Heidelberg: Springer, pp. 9-
596
41.
597
Carrer M and Urbinati C (2004) Age-dependent tree-ring growth responses to climate in Larix decidua
598
and Pinus cembra. Ecology 85(3): 730-740.
599
24
Cherubini P, Dobbertin M and Innes JL (1998) Potential sampling bias in long-term forest growth trends
600
reconstructed from tree rings: a case study from the Italian Alps. Forest Ecology and
601
Management 109(1-3): 103-118.
602
Cook ER (1985) A Time Series Analysis Approach to Tree Ring Standardization. PhD Thesis, University of
603
Arizona, Tucson, AZ, USA.
604
Cook ER and Kairiukstis LA (1990) Methods of Dendrochronology: applications in the environmental
605
sciences. Dordrecht: Kluwer Academic Publishers, 394 pp.
606
Cook ER and Krusic PJ (2005) Program ARSTAN: a tree-ring standardization program based on
607
detrending and autoregressive time series modeling, with interactive graphics. Lamont-Doherty
608
Earth Observatory, Columbia University, Palisades, NY.
609
Cook ER and Peters K (1981) The smoothing spline: a new approach to standardizing forest interior tree-
610
ring width series for dendroclimatic studies. Tree-Ring Bulletin 41: 45-53.
611
Cook ER and Peters K (1997) Calculating unbiased tree-ring indices for the study of climatic and
612
environmental change. Holocene 7(3): 361-370.
613
Cook BI, Anchukaitis KJ, Touchan R et al. (2016) Spatiotemporal drought variability in the Mediterranean
614
over the last 900 years. Journal of Geophysical Research: Atmospheres 121(5).
615
DOI:10.1002/2015JD023929.
616
Cook ER, Briffa KR, Meko DM et al. (1995) The 'segment length curse' in long tree-ring chronology
617
development for palaeoclimatic studies. Holocene 5(2): 229-237.
618
Cook ER, Krusic PJ, Anchukaitis KJ et al. (2013) Tree-ring reconstructed summer temperature anomalies
619
for temperate East Asia since 800 CE. Climate Dynamics 41(11-12): 2957-2972.
620
Cook ER, Seager R, Kushnir Y et al. (2015) Old World megadroughts and pluvials during the Common Era.
621
Science Advances 1(10). DOI:10.1126/sciadv.1500561.
622
D’Amato AW and Orwig DA (2008) Stand and landscape-level disturbance dynamics in old-growth
623
forests in Western Massachusetts. Ecological Monographs 78(4): 507522.
624
25
D'Arrigo R, Wilson R and Jacoby G (2006) On the long-term context for late twentieth century warming.
625
Journal of Geophysical Research: Atmospheres 111(D3). DOI:10.1029/2005JD006352.
626
Druckenbrod DL (2005) Dendroecological reconstructions of forest disturbance history using time-series
627
analysis with intervention detection. Canadian Journal of Forest Research 35(4): 868-876.
628
Druckenbrod DL, Pederson N, Rentch J et al. (2013) A comparison of times series approaches for
629
dendroecological reconstructions of past canopy disturbance events. Forest Ecology and
630
Management 302: 23-33.
631
Düthorn E, Holzkämper S, Timonen M et al. (2013) Influence of microsite conditions on tree-ring climate
632
signals and trends in central and northern Sweden. Trees 27(5): 1395-1404.
633
Düthorn E, Schneider L, Konter O et al. (2015) On the hidden significance of differing micro-sites on tree-
634
ring based climate reconstructions. Silva Fennica 49(1). DOI:10.14214/sf.1220.
635
Esper J, Niederer R, Bebi P et al. (2008) Climate signal age effects evidence from young and old trees
636
in the Swiss Engadin. Forest Ecology and Management 255(11): 3783-3789.
637
Fish T, Wilson R, Edwards C et al. (2010) Exploring for senescence signals in native scots pine (Pinus
638
sylvestris L.) in the Scottish Highlands. Forest Ecology and Management 260(3): 321-330.
639
Fritts HC (1976) Tree rings and climate. London: Academic Press, 567 pp.
640
Griffiths P, Kuemmerle T, Baumann M et al. (2014) Forest disturbances, forest recovery, and changes in
641
forest types across the Carpathian ecoregion from 1985 to 2010 based on Landsat image
642
composites. Remote Sensing of Environment 151: 72-88.
643
Gunnarson BE, Josefsson T, Linderholm HW et al. (2012) Legacies of pre-industrial land use can bias
644
modern tree-ring climate calibrations. Climate Research 53(1): 63-76.
645
Helama S, Lindholm M, Timonen M et al. (2004) Detection of climate signal in dendrochronological data
646
analysis: a comparison of tree-ring standardization methods. Theoretical and Applied
647
Climatology 79(3-4): 239-254.
648
26
Holeksa J, Zielonka T, Żywiec M et al. (2016) Identifying the disturbance history over a large area of
649
larchspruce mountain forest in Central Europe. Forest Ecology and Management 361: 318-
650
327.
651
Hughes MK (2011) Dendroclimatology in High-Resolution Paleoclimatology. In: Hughes MK, Swetnam
652
TW and Diaz HF (eds) Dendroclimatology: progress and prospects. Dordrecht: Springer, pp. 17-
653
34.
654
Kaczka RJ and Czajka B (2014) Intensywność odbicia światła niebieskiego jako nowy nośnik informacji w
655
badaniach dendrochronologicznych. Studia i Materiały Centrum Edukacji Przyrodniczo-Leśnej w
656
Rogowie 16(40): 274-282.
657
Kulakowski D and Veblen TT (2002) Influences of fire history and topography on the pattern of a severe
658
wind blowdown in a Colorado subalpine forest. Journal of Ecology 90(5): 806819.
659
Larsson L (2015) CooRecorder and Cdendro programs of the CooRecorder/Cdendro package version 7.8.
660
Available at: http://www.cybis.se/forfun/dendro/ (accessed 10 October 2015).
661
Ljungqvist FC, Krusic PJ, Sundqvist HS et al. (2016) Northern Hemisphere hydroclimate variability over
662
the past twelve centuries. Nature 532(7597): 94-98.
663
Luterbacher J, Werner JP, Smerdon JE et al. (2016) European summer temperatures since Roman times.
664
Environmental Research Letters 11(2). DOI: 10.1088/1748-9326/11/2/024001.
665
McCarroll D, Loader NJ, Jalkanen R et al. (2013) A 1200-year multiproxy record of tree growth and
666
summer temperature at the northern pine forest limit of Europe. Holocene 23(4): 471-484.
667
McCarroll D, Pettigrew E, Luckman A et al. (2002) Blue reflectance provides a surrogate for latewood
668
density of high-latitude pine tree rings. Arctic, Antarctic, and Alpine Research 34(4): 450-453.
669
Melvin TM and Briffa KR (2008) A “signal-free” approach to dendroclimatic standardisation.
670
Dendrochronologia 26(2): 71-86.
671
27
Melvin TM, Grudd H and Briffa KR (2013) Potential bias in ‘updating’tree-ring chronologies using
672
regional curve standardisation: Re-processing 1500 years of Torneträsk density and ring-width
673
data. Holocene 23(3): 364-373.
674
rian P, Bert D and Lebourgeois F (2013) An approach for quantifying and correcting sample size-
675
related bias in population estimates of climate-tree growth relationships. Forest Science 59(4):
676
444-452.
677
Moberg A, Alexandersson H, Bergström H et al. (2003) Were southern Swedish summer temperatures
678
before 1860 as warm as measured?. International Journal of Climatology 23(12): 1495-1521.
679
Nehrbass‐Ahles C, Babst F, Klesse S et al. (2014) The influence of sampling design on tree‐ring‐based
680
quantification of forest growth. Global Change Biology 20(9): 2867-2885.
681
Osborn TJ, Briffa KR and Jones PD (1997) Adjusting variance for sample size in tree-ring chronologies and
682
other regional mean timeseries. Dendrochronologia 15(89): 89-99.
683
Popa I (2008) Windthrow risk management. Results from Romanian forests. In: Forest disturbances and
684
effects on carbon stock: The non-permanence issue (eds Anfodillo T, Dalla Valle E and Valese E),
685
San Vito di Cadore, Italy, 9-12 June 2008,pp. 77-88. Padua: Università di Padova.
686
Popa I and Kern Z (2009) Long-term summer temperature reconstruction inferred from tree-ring records
687
from the Eastern Carpathians. Climate Dynamics 32(7-8): 1107-1117.
688
Primicia I, Camarero JJ, Janda P et al. (2015) Age, competition, disturbance and elevation effects on tree
689
and stand growth response of primary Picea abies forest to climate. Forest Ecology and
690
Management 354: 77-86.
691
Rydval M, Druckenbrod DL, Anchukaitis KJ et al. (2016) Detection and removal of disturbance trends in
692
tree-ring series for dendroclimatology. Canadian Journal of Forest Research 46(3): 387-401.
693
Rydval M, Larsson LÅ, McGlynn L et al. (2014) Blue Intensity for dendroclimatology: Should we have the
694
blues? Experiments from Scotland. Dendrochronologia 32(3): 191-204.
695
28
Sidor CG, Popa I, Vlad R et al. (2015) Different tree-ring responses of Norway spruce to air temperature
696
across an altitudinal gradient in the Eastern Carpathians (Romania). Trees 29(4): 985-997.
697
SPSS (2011) IBM SPSS statistics for Windows, version 20.0. New York: IBM Corp.
698
Stokes MA and Smiley TL (1968) An introduction to tree-ring dating. Chicago: University of Chicago Press,
699
73 pp.
700
Svoboda M, Janda P, Bače R et al. (2014) Landscape‐level variability in historical disturbance in primary
701
Picea abies mountain forests of the eastern Carpathians, Romania. Journal of Vegetation
702
Science 25(2): 386-401.
703
Vaganov EA, Hughes MK, Shashkin AV et al. (2006)Growth Dynamics of Conifer Tree Rings: Images of
704
Past and Future Environments. Berlin Heidelberg: Springer, 354 pp.
705
Valtera M, Šamonil P and Boublík K (2013) Soil variability in naturally disturbed Norway spruce forests in
706
the Carpathians: bridging spatial scales. Forest Ecology and Management 310: 134-146.
707
Warren WG (1980) On removing the growth trend from dendrochronological data. Tree-Ring Bulletin 40:
708
35-44.
709
Wigley TML, Briffa KR and Jones PD (1984) On the average of correlated time series, with applications in
710
dendroclimatology and hydrometeorology. Journal of Climate and Applied Meteorology 23(2):
711
201-213.
712
Wilson R, Anchukaitis K, Briffa KR et al. (2016) Last millennium northern hemisphere summer
713
temperatures from tree rings: Part I: The long term context. Quaternary Science Reviews
714
134(1): 1-18.
715
Wilson R, Rao R, Rydval M et al. (2014) Blue Intensity for dendroclimatology: The BC blues: A case study
716
from British Columbia, Canada. Holocene 24: 1428-1438.
717
Zielonka T, Holeksa J, Fleischer P et al. (2010) A treering reconstruction of wind disturbances in a forest
718
of the Slovakian Tatra Mountains, Western Carpathians.Journal of Vegetation Science 21(1):
719
3142.
720
29
721
722
723
Figure 1: Site location and approximate distribution of sampling plots in Calimani National Park,
724
Romania. Red shading represents post-disturbance correction correlation increase* of plot-
725
based (PLOT) chronologies (see Table 1 for details) against June-July mean instrumental
726
temperatures for the full length of chronologies same representation as Figure 4C. (*note
727
that chronology PLOT-16 shows a slight correlation decrease after disturbance correction).
728
729
730
731
732
733
734
735
736
737
738
739
740
741
30
742
743
744
745
746
747
748
749
Figure 2: Chronology plots and correlations with Jun-Jul mean temperatures from Sibiu for four
750
‘sampling’ methods including grouping according to (A) sample location (PLOT), (B) random
751
sample selection (RAN), (C) diameter at breast height (DBH) (D) and recruitment age (AGE)
752
see Table 1 for additional details. Each chronology was truncated in the year where expressed
753
population signal dropped below 0.85. (pre-CID indicates that chronologies were developed
754
with series before correcting for disturbance trends using the Curve Intervention Detection
755
method)
756
31
757
Figure 3: (A) Summary of Calimani disturbance history from Curve Intervention Detection (CID) analysis;
758
(B) chronologies before disturbance correction (pre-CID) and (C) after disturbance correction
759
(post-CID) and (D) pre-CID/post-CID chronologies with Jun-Jul temperatures from Sibiu (SIBIU)
760
and the longer central/east Europe (CEU) Jun-Jul regional composite temperature series; (E)
761
51-year running correlations between instrumental and ring-width chronologies in (D); (F) as
762
in (D) except smoothed with a 20 year low-pass Gaussian filter.
763
32
764
765
Figure 4: Comparing the (A, C) change in correlation and (B, D) root-mean-square error change of
766
Calimani plot-based (PLOT) chronologies before disturbance correction (pre-CID) vs. after
767
disturbance correction (post-CID) in relation to instrumental temperature data from Sibiu for
768
the (A, B) 1909-2009 period and (C, D) full chronology length (max. back to 1851). (The green
769
colour in A and C indicates the size of the correlation increase after disturbance correction
770
whereas red colour (only PLOT16) indicates a correlation decrease.)
771
33
772
773
Figure 5: Amplitudes of the dominant principal components (PCs) from chronologies developed (A)
774
before disturbance correction (pre-CID) and (B) after disturbance correction (post-CID), and
775
their correlation with instrumental temperatures from Sibiu and the disturbance chronology
776
in Figure 3A. (Scatterplots of significant relationships (p < 0.01) between the PCs and the
777
disturbance chronology / Sibiu temperatures are represented in supplementary figure S5).
778
34
779
Figure 6: Comparison of the PLOT-3, PLOT-7 and PLOT-10 blue intensity (BI) and ring width (RW)
780
chronologies developed before (pre-CID) and after (post-CID) disturbance correction with
781
instrumental temperatures from Sibiu (SIBIU) over the 1851-2009 period showing (A) the
782
combined correlation response of the Calimani chronologies against SIBIU temperatures; and
783
the time-series of the RW and BI chronologies together with Jun-Jul and Apr-Sep SIBIU mean
784
temperature respectively for (B) PLOT-3, (C) PLOT-7 and (D) PLOT-10. (Highlighted periods
785
indicate where expressed population signal is < 0.85.)
786
35
Supplementary figures
787
788
789
790
Figure S1: Correlation response of chronologies composed of all 760 series from Calimani (PLOT-all)
791
before (pre-CID) and after (post-CID) disturbance correction against mean monthly and
792
seasonal temperatures from Sibiu for the 1851-2011 period.
793
794
795
796
797
798
799
Figure S2: Relationship between estimated tree age and diameter at breast height (DBH).
800
801
802
803
804
805
36
806
Figure S3: Summary of Calimani disturbance history from Curve Intervention Detection (CID) analysis (A)
807
using series from plots from the northwest part of the stand (Figure 1 - PLOT 1, 2 , 5, 6, 7,
808
10, 11, 18, 19) predominantly affected by disturbance in the mid-19th century and (B) from
809
the southeast part for the stand (Figure 1 PLOT 3, 4, 8, 9, 12, 13, 14, 15, 16, 17)
810
predominantly affected by disturbance in the early 20th century. The before disturbance
811
correction (pre-CID) and after disturbance correction (post-CID) chronologies of these two
812
spatial groups are presented in (C) and (D) respectively.
813
37
814
815
816
817
818
819
Figure S4: Before disturbance correction (pre-CID) and after disturbance correction (post-CID)
820
chronology plots and correlations with Jun-Jul mean temperatures from Sibiu for four
821
‘sampling’ methods including grouping according to (A) sample location (PLOT), (B) random
822
sample selection (RAN), (C) diameter at breast height (DBH) (D) and recruitment age (AGE)
823
see Table 1 for additional details. Each chronology was truncated in the year where
824
expressed population signal dropped below 0.85.
825
826
827
828
829
830
831
832
38
833
Figure S4 continued
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
39
850
Figure S4 continued
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
40
867
Figure S4 continued
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
41
884
885
Figure S5: Scatterplots of significant relationships (p < 0.01) between the disturbance chronology in
886
Figure 3A / Jun-July average Sibiu temperatures and amplitudes of the dominant principal
887
components (PCs) from chronologies developed before disturbance correction (pre-CID) and
888
after disturbance correction (post-CID).
889
890
891
892
893
42
Curve Intervention Detection (CID) Matlab code
894
895
function [ymn,varyh,df,w,ybar,se]=bisqmean_CID(y)
896
%
897
% Biweight mean for a vector of numbers.
898
% Last revised 2011-7-09
899
% Revised by Daniel Druckenbrod 2012-1-11
900
%
901
% Source: Mosteller and Tukey (1977, p. 205, p 351-352)
902
% Cook and Kairiukstis (1990, p. 125-126)
903
%
904
%
905
%**************** INPUT *************************
906
%
907
% y (? x 1)r vector of data -- say, indices for ? cores in a year
908
%
909
%
910
%******************** OUTPUT ************************
911
%
912
% ymn (1 x 1)r biweight mean
913
% varyh (1 x 1)r asymptotic standard dev of biweight mean - p. 208,
914
% third eqn from top of page
915
% df (1x1)r degrees of freedom
916
% w (? x 1)r final weights on values in y
917
% ybar (1 x 1)r arithmetic mean corresponding to ymn
918
% se (1 x 1)r standard error of ybar
919
%
920
%******************** NOTES *********************
921
%
922
% ybar and se just included in debugging to double check
923
% on closeness of ybar to ymn, se to sqrt(varyh)
924
%
925
%*******************************************************************
926
927
928
sens = 0.001; % hard coded theshold of sensitivity for stopping
929
iterat
930
nits = 100; % max number of allowed iterations
931
932
[n,ny]=size(y);
933
if ny > 1;
934
error('y should be a vector')
935
end
936
937
if any(isnan(y));
938
error('y not permitted to have NaNs');
939
end;
940
941
if n<6; % if fewer than 6 sample size, use median
942
ymn = median(y);
943
w=[];
944
ybar=mean(y);
945
se= sqrt(var(y)/n); % standard error of mean
946
df=[];
947
varyh=NaN;
948
return;
949
end;
950
951
952
953
43
ww = 1/n; % weight for even average
954
ybar = mean(y); % arith mean
955
%ybar=median(y);
956
se= sqrt(var(y)/n); % standard error of mean
957
958
nz=0;
959
ymn = ybar; % initial biweight mean as arith mean
960
961
for i = 1: nits; % iterate max of nits times
962
ymnold = ymn; % store old value of mean
963
e = y-ymn; % deviations from mean
964
S = median(abs(e)); % median abs deviation
965
u = e / (6*S); % scaled deviations
966
967
w = (1 - u.^2).^2; % compute weights
968
L1 = abs(u)>=1; % flag huge errors
969
L1s = sum(L1);
970
if L1s>0
971
nz=0;
972
nz= nz(ones(L1s,1),:);
973
w(L1)=nz; % set weights on those obs to zero
974
end
975
w = w / sum(w); % adjust weights to sum to 1.0
976
977
ymn = sum(w .* y); % compute biweight mean
978
979
980
% Variance of estimate of biweight mean
981
ui= e / (9*S);
982
L2 = ui>1;
983
ui(L2)=[];
984
z =y(~L2);
985
nz = length(z);
986
nom1 = (z - ymn) .^2;
987
nom2 = (1-ui .^2) .^4;
988
nom = sum(nom1 .* nom2);
989
990
den1 = sum((1-ui .^2) .* (1-5*ui .^2));
991
992
varyh_hoaglin=(n^0.5)*(nom^0.5)/den1; % Dan: p. 417 3rd equation
993
den2 = -1 + sum ((1-ui .^2) .* (1-5*ui .^2));
994
% varyh = nom / (den1*den2); % variance of biweight mean
995
% last eqn, p. 208
996
997
varyh = n^.5*nom^.5 / ((den1*den2)^.5); % Dan: p.417 Kafadar
998
approach
999
1000
df = 0.7 * (nz -1); % degrees of freedom
1001
1002
1003
% if little change in mean, exit loop
1004
if abs (ymn - ymnold) < sens
1005
return
1006
end
1007
end
1008
1009
1010
1011
1012
44
% hugershoff.m
1013
% This function fits a tree ring time series to the growth trend
1014
equation
1015
% developed by Warren (1980) TRR.
1016
1017
% Function written Nov 12, 2013.
1018
% Function last revised Nov 12, 2013.
1019
1020
function qq = hugershoff(beta,x)
1021
1022
% Assign parameters from beta vector.
1023
a=beta(1);
1024
b=beta(2);
1025
c=beta(3);
1026
k=beta(4);
1027
qq=a*((x).^b).*exp(-c*(x))+k;
1028
% qq=a*((x+1).^b).*exp(-c*(x+1))+k;
1029
%q=log(a)+b*log(x)%-c*x;
1030
%qq=exp(
1031
1032
1033
1034
1035
1036
1037
1038
1039
1040
1041
1042
1043
1044
1045
1046
1047
1048
1049
1050
1051
1052
1053
1054
1055
1056
1057
1058
1059
1060
1061
1062
1063
1064
1065
1066
1067
1068
1069
1070
45
% nonlinear_exp.m
1071
% This function fits a tree ring time series to the non-linear
1072
% equation used by Ed Cook in his ARSTAN program.
1073
1074
% Function written Jan 12, 2004.
1075
% Function last revised Jan 29, 2004.
1076
1077
function qq = nonlinear_exp(beta,x)
1078
1079
% Assign parameters from beta vector.
1080
a=beta(1);
1081
b=beta(2);
1082
d=beta(3);
1083
qq = a*exp(-b*x)+d;
1084
1085
1086
1087
1088
1089
1090
1091
1092
1093
1094
1095
1096
1097
1098
1099
1100
1101
1102
1103
1104
1105
1106
1107
1108
1109
1110
1111
1112
1113
1114
1115
1116
1117
1118
1119
1120
1121
1122
1123
1124
1125
1126
1127
46
% ringwidth_import_999.m
1128
% This function imports decadal format tree ring data for manipulation
1129
% as a matrix in Matlab. The number of header lines must be specified
1130
% as an input by the user. The end of each series must be flagged
1131
% with 999. Measurements are stored as one hundreth of a
1132
% millimeter. The filename can either be specified as an input or
1133
% found using a gui. The LAST LINE of the input text file must also
1134
% be blank!
1135
1136
% Function written Mar 4, 2004.
1137
% Function last revised May 24, 2012.
1138
1139
function [col_header,rings]=ringwidth_import_999(header,varagin)
1140
1141
if nargin==1
1142
[filename,path]=uigetfile('*.txt','Select ".txt" file');
1143
elseif nargin==2
1144
filename=varagin;
1145
else
1146
disp('Too many parameters entered (DLD).')
1147
end
1148
1149
% Read in header lines
1150
headers=textread(filename, '%q',10)';
1151
% disp([headers]) % Display 1st 10 words as screen output.
1152
[label yr y0 y1 y2 y3 y4 y5 y6 y7 y8 y9]=...
1153
textread(filename,...
1154
'%8s %4d %5d %5d %5d %5d %5d %5d %5d %5d %5d %5d',...
1155
'headerlines',header);
1156
% Place decadal format widths into one matrix
1157
widths=[y0 y1 y2 y3 y4 y5 y6 y7 y8 y9];
1158
1159
% Extract unique labels of each core.
1160
importedrows=length(label);
1161
a=1;b=1;
1162
while(a<=importedrows)
1163
core(b)=label(a);
1164
corestr(:,b)=strcmp(label(a),label);
1165
a=max(find(corestr(:,b)==1))+1;
1166
b=b+1;
1167
end
1168
1169
% Find range of years over all cores and set as col 1 in rings.
1170
% As it is difficult to know how many years are in the last row
1171
% of measurments for a core, assume that the last decade has 10
1172
% measurements.
1173
rings=(min(yr):(max(yr)+10))';
1174
1175
% Transfer widths into vectors by core
1176
for i=1:length(core)
1177
core_rows=find(corestr(:,i));
1178
core_yr=yr(core_rows);
1179
core_widths=widths(core_rows,:);
1180
% Find # of measurements in a row and assign to vector series.
1181
k=1;series=0;
1182
for j=1:length(core_rows)
1183
% Look for end of series flag
1184
flag=find(core_widths(j,:)==999);
1185
if flag>0
1186
msmts=flag-1;
1187
47
elseif (ceil(core_yr(j)/10)*10)-core_yr(j)==0
1188
msmts=10;
1189
else
1190
msmts=(ceil(core_yr(j)/10)*10)-core_yr(j);
1191
end
1192
series(k:(k+msmts-1))=core_widths(j,(1:msmts));
1193
k=msmts+k;
1194
1195
end
1196
% Determine start and end of series
1197
sos=find(rings(:,1)==min(core_yr));
1198
length(series)+sos-1;
1199
eos=length(series)+sos-1;
1200
% Assign series to output matrix and convert to 1/1000th of a mm
1201
rings(sos:eos,i+1)=(series./100)';
1202
1203
1204
% Remove 999 from end of series
1205
% for j=1:length(rings(1,:))
1206
% for k=1:length(rings(:,1))
1207
% if rings(k,j)==-99.99
1208
% rings(k,j)=0;
1209
% end
1210
% end
1211
% end
1212
1213
1214
end
1215
% Construct column headers
1216
col_header(2:(length(core)+1))=core;
1217
col_header(1)={'Year'};
1218
1219
1220
1221
1222
1223
1224
1225
1226
1227
1228
1229
1230
1231
1232
1233
1234
1235
1236
1237
1238
1239
1240
1241
1242
1243
1244
1245
48
% ringwidth_import_9999.m
1246
% This function imports decadal format tree ring data for manipulation
1247
% as a matrix in Matlab. The number of header lines must be specified
1248
% as an input by the user. The end of each series must be flagged
1249
% with -9999. Measurements are stored as one thousandth of a
1250
% millimeter. The filename can either be specified as an input or
1251
% found using a gui. The LAST LINE of the input text file must also
1252
% be blank!
1253
1254
% Function written Mar 4, 2004.
1255
% Function last revised May 24, 2012.
1256
1257
function [col_header,rings,flag]=ringwidth_import_9999(header,varagin)
1258
1259
if nargin==1
1260
[filename,path]=uigetfile('*.txt','Select ".txt" file');
1261
elseif nargin==2
1262
filename=varagin;
1263
else
1264
disp('Too many parameters entered (DLD).')
1265
end
1266
1267
% Read in header lines
1268
headers=textread(filename, '%q',10)';
1269
% disp([headers]) % Display 1st 10 words as screen output.
1270
[label yr y0 y1 y2 y3 y4 y5 y6 y7 y8 y9]=...
1271
textread(filename,...
1272
'%8s %4d %5d %5d %5d %5d %5d %5d %5d %5d %5d %5d',...
1273
'headerlines',header);
1274
% Place decadal format widths into one matrix
1275
widths=[y0 y1 y2 y3 y4 y5 y6 y7 y8 y9];
1276
1277
% Extract unique labels of each core.
1278
importedrows=length(label);
1279
a=1;b=1;
1280
while(a<=importedrows)
1281
core(b)=label(a);
1282
corestr(:,b)=strcmp(label(a),label);
1283
a=max(find(corestr(:,b)==1))+1;
1284
b=b+1;
1285
end
1286
1287
% Find range of years over all cores and set as col 1 in rings.
1288
% As it is difficult to know how many years are in the last row
1289
% of measurments for a core, assume that the last decade has 10
1290
% measurements.
1291
rings=(min(yr):(max(yr)+10))';
1292
1293
% Transfer widths into vectors by core
1294
for i=1:length(core)
1295
core_rows=find(corestr(:,i));
1296
core_yr=yr(core_rows);
1297
core_widths=widths(core_rows,:);
1298
% Find # of measurements in a row and assign to vector series.
1299
k=1;series=0;
1300
for j=1:length(core_rows)
1301
% Look for end of series flag
1302
flag=find(core_widths(j,:)==-9999);
1303
if flag>0
1304
msmts=flag-1;
1305
49
elseif (ceil(core_yr(j)/10)*10)-core_yr(j)==0
1306
msmts=10;
1307
else
1308
msmts=(ceil(core_yr(j)/10)*10)-core_yr(j);
1309
end
1310
series(k:(k+msmts-1))=core_widths(j,(1:msmts));
1311
k=msmts+k;
1312
end
1313
% Determine start and end of series
1314
sos=find(rings(:,1)==min(core_yr));
1315
length(series)+sos-1;
1316
eos=length(series)+sos-1;
1317
% Assign series to output matrix and convert to 1/1000th of a mm
1318
rings(sos:eos,i+1)=(series./1000)';
1319
end
1320
% Construct column headers
1321
col_header(2:(length(core)+1))=core;
1322
col_header(1)={'Year'};
1323
1324
1325
1326
1327
1328
1329
1330
1331
1332
1333
1334
1335
1336
1337
1338
1339
1340
1341
1342
1343
1344
1345
1346
1347
1348
1349
1350
1351
1352
1353
1354
1355
1356
1357
1358
1359
1360
1361
1362
1363
50
% v105pn.m
1364
% This function extracts a single tree-ring time series from
1365
% ringwidth_import.m and places it in a vector for time series
1366
analysis.
1367
% The ‘filename’ used to load tree-ring data for processing should
1368
refer to
1369
% the file containing data imported using the function
1370
ringwidth_import.m.
1371
% Following the approach used in ARSTAN (Ed Cook, Columbia
1372
University),
1373
% the function power transforms and removes the mean to create
1374
transformed
1375
% residuals. The function then detrends with an iterative neg.
1376
exponential
1377
% fit, or if that does not fit or fails to find a solution, then a
1378
linear
1379
% regression with either a positive or negative slope is fit to the
1380
data.
1381
% Using the maximum entropy model solution otherwise known as the Burg
1382
% method, the autoregressive model that is the best fit for the series
1383
is
1384
% determined. Using the best fit model, the function searches for
1385
% autoregressive outliers iteratively. These outliers may either be
1386
pulse
1387
% events (1 yr) or CSTs (> minimum no. of yrs). After the first pass,
1388
% the outliers are removed and the series is reconstituted. The best
1389
ar
1390
% order is then redetermined and the function searches for additional
1391
% outliers. The # of iterations is set by the user (8 should be
1392
enough).
1393
% This version uses a power transformation to minimize
1394
% the heteroscedastic nature of my time series. 'fig' is a flag that
1395
% specifies whether you want a figure (=1) or not (=0). Missing years
1396
are
1397
% set to the average of neighboring rings. The central limit theorem
1398
% is used to search the residuals for trend outliers. This version
1399
also
1400
% uses David Meko's (University of Arizona) biweight mean code and
1401
% currently runs with a window of 9 to 30 yrs. Estimated values for
1402
% missing rinngs are removed in the output series. This version uses
1403
a
1404
% modified Hugershoff curve with a potentially nonzero asymptote to
1405
% detrend + and - disturbance events. It also returns the transformed
1406
% standardized series.
1407
1408
% Function written Sep 10, 2002.
1409
% Function last revised Jun 6, 2014.
1410
1411
function [YEARS,transformed,detrended,St,Str,Dtr,Atr,age,outs]=...
1412
v105pn(core,fig,iter)
1413
global PARAM; PARAM=0; % vector of parameters for best order AR model.
1414
global ORDER; ORDER=0; % best order of AR model determined by AIC.
1415
global YEARS; YEARS=0; % calendar years of tree growth from datafile
1416
1417
% Load tree-ring data (returns vars *col_header* and *rings*)
1418
load filename.mat %Insert filename here
1419
1420
% Find pointer to start and end of series
1421
sos=find(rings(:,(core+1))>0, 1);
1422
eos=find(rings(:,(core+1))>0, 1, 'last');
1423
1424
51
% Assign years and raw widths to respective vectors.
1425
YEARS=rings(sos:eos,1);
1426
raw=rings(sos:eos,core+1);
1427
1428
disp(['Core: ' char(col_header(core+1))])
1429
nyrs=length(YEARS);
1430
disp(['Total no. of measured years: ' int2str(nyrs)])
1431
disp(['First year is ' num2str(YEARS(1))])
1432
disp(['Last year is ' num2str(YEARS(nyrs))])
1433
1434
% Estimate missing ring widths using mean of neighboring rings
1435
mss=NaN(length(raw),1);
1436
1437
if find(raw==0)
1438
m1=find(raw==0);
1439
disp(['Missing rings at years ' num2str([YEARS(m1)'])])
1440
for nm=1:length(m1)
1441
prior=mean(raw(find(raw(1:m1(nm)),1,'last')));
1442
subs=mean(raw(find(raw(m1(nm):length(raw)),1,'first')+m1(nm)-
1443
1));
1444
mss(m1(nm))=mean([prior subs]);
1445
end
1446
raw=nansum([raw mss],2);
1447
end
1448
1449
% Power transformation.
1450
fdiff=0;
1451
for x=1:(length(YEARS)-1) % Calculate 1st differences
1452
fdiff(x,1)=raw(x+1);
1453
fdiff(x,2)=abs(raw(x+1)-raw(x));
1454
end
1455
1456
s=1;
1457
for q=1:(length(YEARS)-1)
1458
if (fdiff(q,1)~=0) && (fdiff(q,2)~=0)
1459
nz_fdiff(s,:)=fdiff(q,1:2);% non-zero ring widths
1460
s=s+1;
1461
end
1462
end
1463
log_fdiff=[log(nz_fdiff(:,1)) log(nz_fdiff(:,2))];
1464
1465
X=[ones(length(log_fdiff(:,1)),1) log_fdiff(:,1)];
1466
bb = regress(log_fdiff(:,2), X);
1467
optimal_line = bb(2)*log_fdiff(:,1)+bb(1);
1468
1469
optimal_pwr = 1-bb(2);
1470
disp(['Optimal Power = ' num2str(optimal_pwr)])
1471
if optimal_pwr <= 0.05
1472
transformed=log10(raw);
1473
tzero=log10(0.001);
1474
disp('Series was log10 transformed')
1475
elseif optimal_pwr>1
1476
optimal_pwr=1;
1477
transformed=(raw.^(optimal_pwr));
1478
tzero=0.001.^(optimal_pwr);
1479
disp('Series was power transformed with power =1')
1480
else
1481
transformed=(raw.^(optimal_pwr));
1482
disp(['Series was power transformed with power = ' ...
1483
num2str(optimal_pwr)])
1484
52
tzero=0.001.^(optimal_pwr);
1485
end
1486
transm=mean(transformed);
1487
1488
% Nonlinear detrending option.
1489
% Function nlinfit employs nonlinear least squares data fitting by the
1490
% Gauss-Newton Method.
1491
crashed=zeros(nyrs,1);
1492
wlngth=zeros(nyrs,1);
1493
trendtype=0; % Neg exp = 1, neg linear reg = 2, or pos linear reg = 3
1494
minyr=30; % minimum # of yrs to fit to nlinfit
1495
if minyr>nyrs
1496
disp('Insufficient # of years to fit minimum nonlinear age
1497
trend.')
1498
end
1499
b=zeros(nyrs,3);
1500
mse=NaN(nyrs,1);
1501
warning off
1502
for i=minyr:nyrs
1503
try
1504
lastwarn('')
1505
beta = [.5 .1 1];
1506
xyrs = 1:i; % set years from 1 to length of series
1507
[b(i,1:3),~,~,~,mse(i)]=nlinfit(...
1508
xyrs(1:i),transformed(1:i)','nonlinear_exp',beta);
1509
crashed(i)=1;
1510
msgstr = lastwarn;
1511
wlngth(i)=length(msgstr);
1512
catch % Stops code from crashing because of problems fitting exp
1513
curve
1514
crashed(i)=2;
1515
end
1516
end
1517
warning on
1518
i_c=0;
1519
1520
% Dissallow curve to be concave up and make sure nlinfit
1521
% converges by making b(2) sufficiently large.
1522
% constant b(3) must be >=0 in original mm
1523
i_c=find(crashed==1 & b(:,1)>=0 & b(:,2)>0.001 & b(:,3)>=tzero &
1524
wlngth==0);% & b(:,2)<0.5);
1525
[mmse,imse]=min(mse(i_c));
1526
1527
if fig==1 % fig=1 if you want a figure as output
1528
figure('Position', [10 150 600 600])
1529
subplot(3,1,1)
1530
plot(YEARS,raw,'k','LineWidth',2)
1531
ylabel('\bf Ring width (mm)')
1532
fig1atext = {['Optimal power = ', num2str(optimal_pwr,4)]};
1533
text(range(YEARS)/3+YEARS(1), max(raw)/1.2,fig1atext)
1534
end
1535
1536
if i_c(imse)>0
1537
disp(['Lowest error from fit = ' num2str(mmse)])
1538
disp(['Best age trend fit from years ' num2str(YEARS(1)) ' to '
1539
...
1540
num2str(YEARS(i_c(imse)))])
1541
disp(['Best fit extends for ' num2str(i_c(imse)) ' years'])
1542
best=b(i_c(imse),:);
1543
1544
trendtype=1;
1545
53
y_exp=nonlinear_exp(best,xyrs);
1546
detrended=transformed-y_exp';
1547
disp('Initial Age Detrending')
1548
disp(['Y = ', num2str(best(1),4), '*exp(-', num2str(best(2),4),...
1549
'*x)+', num2str(best(3),4)]);
1550
if fig==1 % fig=1 if you want a figure as output
1551
subplot(3,1,2)
1552
[h312a, h312h1, h312h2] = plotyy(YEARS,[transformed
1553
y_exp'],YEARS(i_c),mse(i_c));
1554
set(h312h1(1),'LineWidth',2)
1555
set(h312h1(1),'Color',[0 0 0])
1556
set(h312h1(2),'Color',[.2 .2 1])
1557
1558
set(h312h2(1),'LineStyle','none','Marker','.','MarkerFaceColor',[1 .2
1559
.2])
1560
fig1btext = {['Y = ', num2str(best(1),4), '*exp(-',
1561
num2str(best(2),4),...
1562
'*x)+', num2str(best(3),4)]};
1563
text(range(YEARS)/3+YEARS(1), max(transformed)/1.2,fig1btext)
1564
line([YEARS(i_c(imse)) YEARS(i_c(imse))],...
1565
[y_exp(i_c(imse))+.2
1566
y_exp(i_c(imse))+.2],'Color','k','Marker',...
1567
'v','MarkerEdgeColor',[1 .2 .2],'MarkerFaceColor',[1 .2
1568
.2])
1569
set(get(h312a(1),'Ylabel'),'String','\bf Transformed width')
1570
set(get(h312a(2),&apo