Description Usage Arguments Details Value Note Author(s) References See Also Examples
There is a split of behaviour of this function according parameter black
.
1 2 3 4 
data 
A data.frame with series as columns and years as rows such as that produced by read.* function of dplR . 
prior 

change 

m1 
Determines the number of years to be averaged (including target year) for period prior the potential releas. 
m2 
Determines the number of years to be averaged (including target year) for period prior the potential releas. 
boundary 
Boundary line function of one argument, eg. 
buffer 
Number of years determining how close to one another two releases can be. 
criteria 
Threshold for detection of moderate release 
criteria2 
Threshold for detection of major release. 
segment 
Determines length of the segment on which prior growth will be divided 
segment2 
Determines length of the segment on which first mm of prior growth will be divided. 
black 
If TRUE Black and Abrams 2003 method used else Nowacki and Abrams 1997. 
gfun 
Determines if M1 and M2 values are mean or median for selected period. 
length 
Determines how many years have to be given critera exceeded to be considered as release. 
notop 
Number of highest data points for fitting the boundary line. 
notop2 
Number of highest data points for fitting the boundary line in the segments for first mm. 
storedev 
Format for saving the graphical outputs, eg. pdf or jpeg. 
If black=TRUE: Boundaryline method scales the percent growth change of Nowacki & Abrams (1997) according to growth rate prior to disturbance. In their example, Black & Abrams (2003) defined moderate and major releases as those falling within 2049.9%, and 50100% of the boundary line, respectively. Advantage of the boundaryline is standardization, which takes into account the relationships among tree age, size, and canopy class determining radial growth rate (Black et al. 2004). On the downside, Black et al. (2009) suggest approximately 50000 ring width measurements is necessary for boundary line determination for a given species (Black et al. 2009).
If black=FALSE: Radialgrowth averaging criteria developed by Nowacki & Abrams (1997) is one of the most often used techniques from this category. This method computes the percentage growth change (%GC) between average radial growth over the preceding 10year period, M1 (including the target year), and average radial growth over the subsequent 10year period, M2 (excluding the target year): %GC = [(M2M1)/M1] * 100. Minimum threshold for release is 25% growth change for moderate and >50% for major release. The advantage of this method is its broad applicability even for a small number of samples, and that information about species autecology is not necessary. On the other hand, this generality of radialgrowth averaging may lead to detection of false releases and missing of true releases (Black & Abrams 2003; Fraver & White 2005). These inaccuracies are primarily caused by different growth rates in young, small, and suppressed trees when compared to older, larger and dominant trees.
Return list object with
releases 
By length and buffer filtred percent growth change (PGC). 
years 
Release years per tree. 
change 
Original PGC. 
pgc 
Reduced releases values per tree and year. 
years_list_total 
Number of releases per year. 
all_releases 
All PGC above criteria. 
Rather use functions with ALL suffix.
Pavel Fibich <pavel.fibich@prf.jcu.cz>, Jan Altman <altman.jan@gmail.com>, Tuomas Aakala <tuomas.aakala@helsinki.fi>, Jiri Dolezal <jiriddolezal@gmail.com>
Nowacki, G.J. & Abrams, M.D. 1997. Radialgrowth averaging criteria for reconstructing disturbance histories from presettlementorigin oaks. Ecological Monographs, 67, 225249.
Black, B.A. & Abrams, M.D. (2003) Use of boundaryline growth patterns as a basis for dendroecological release criteria. Ecological Applications, 13, 17331749.
Black, B.A., Abrams, M.D., Gagen, M., Daniels, L.D., Kipfmueller, K.F., Speer, J.H. & Anchukaitis, K.J. (2004) Development and application of boundaryline release criteria. Dendrochronologia, 22, 3142.
Black, B.A., Abrams, M.D., Rentch, J.S. & Gould, P.J. (2009) Properties of boundaryline release criteria in North American tree species. Annals of Forest Science, 66.
growthAveragingALL
, boundaryLineALL
, plotRelease
, reduceByLB
1 2 3 4 5 6  data(relData)
rna<noblabrams(relData1,black=FALSE) # for Nowacki and Abrams 1997
rba<noblabrams(relData1,black=TRUE) # Black and Abrams 2003
plotRelease(relData1,rna$change,rna, 1, method="NowackiAbrams",addHLines=c(0.2))
plotRelease(relData1,rba$change,rba, 1, method="BlackAbrams",addHLines=c(0.2,0.5))

[1] "## Nowacki & Abrams analysis!"
[1] "Criteria 0.25 Criteria2 0.5 m1 10 m2 10 Buffer 2 Length 2"
[1] "Total number of releases >= 0.25 & < 0.5 is 13"
inyears
1888 1899 1920 1936 1938 1971 1978 1979 1980 1986 1995
1 1 1 1 2 1 1 1 2 1 1
[1] "Total number of releases >= 0.5 is 30"
inyears
1895 1896 1899 1903 1927 1935 1936 1938 1939 1942 1947 1978 1984 1986 1988 1990
1 3 1 1 1 5 5 3 1 1 1 2 1 1 1 1
1993
1
[1] "## Black & Abrams analysis!"
[1] "Criteria 0.25 Criteria2 0.5 m1 10 m2 10 Buffer 2 Length 2 Segment 0.5 Segment2 0.5"
[1] "Summary of y=a+bx fit."
Call:
lm(formula = tops ~ segments, data = boundaries)
Residuals:
1 2 3 4 5 6 7
2.1637 1.7473 1.3473 0.1650 0.1749 0.2988 0.2922
Coefficients:
Estimate Std. Error t value Pr(>t)
(Intercept) 2.6631 1.0656 2.499 0.0545 .
segments 0.7266 0.5287 1.374 0.2277

Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.399 on 5 degrees of freedom
Multiple Rsquared: 0.2742, Adjusted Rsquared: 0.129
Fstatistic: 1.889 on 1 and 5 DF, pvalue: 0.2277
[1] "Summary of y=a+bx+cx^2 fit."
Call:
lm(formula = tops ~ segments + I(segments^2), data = boundaries)
Residuals:
1 2 3 4 5 6 7
1.2627 1.7473 0.8067 0.8858 0.7155 0.2988 0.6088
Coefficients:
Estimate Std. Error t value Pr(>t)
(Intercept) 1.1766 1.5661 0.751 0.494
segments 1.7961 2.0898 0.859 0.439
I(segments^2) 0.7208 0.5796 1.244 0.282
Residual standard error: 1.328 on 4 degrees of freedom
Multiple Rsquared: 0.4765, Adjusted Rsquared: 0.2148
Fstatistic: 1.821 on 2 and 4 DF, pvalue: 0.274
[1] "Summary of y=ae^bx fit."
Formula: tops ~ a * exp(b * segments)
Parameters:
Estimate Std. Error t value Pr(>t)
a 2.4721 1.4860 1.664 0.157
b 0.3441 0.4231 0.813 0.453
Residual standard error: 1.48 on 5 degrees of freedom
Number of iterations to convergence: 19
Achieved convergence tolerance: 7.85e06
[1] "y=c+ae^bx nls error: step factor 0.000488281 reduced below 'minFactor' of 0.000976562"
[1] "y=c+dx+ae^bx nls error: singular gradient"
[1] "y=ae^bx+ce^dx nls error: Missing value or an infinity produced when evaluating the model"
[1] "Summary of y=a+blog(x) fit."
Formula: tops ~ a + b * log(segments)
Parameters:
Estimate Std. Error t value Pr(>t)
a 1.5195 0.6389 2.378 0.0633 .
b 0.4241 0.7248 0.585 0.5839

Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.588 on 5 degrees of freedom
Number of iterations to convergence: 1
Achieved convergence tolerance: 8.688e09
[1] "Summary of y=a+bx+clog(x)+dxlog(x) fit."
Call:
lm(formula = tops ~ segments + log(segments) + segments:log(segments),
data = boundaries)
Residuals:
1 2 3 4 5 6 7
0.007628 0.001627 0.189574 0.418157 0.170181 0.193449 0.129045
Coefficients:
Estimate Std. Error t value Pr(>t)
(Intercept) 30.061 4.285 7.016 0.00595 **
segments 26.547 4.156 6.388 0.00777 **
log(segments) 14.067 1.957 7.189 0.00555 **
segments:log(segments) 10.384 1.852 5.606 0.01121 *

Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.313 on 3 degrees of freedom
Multiple Rsquared: 0.9782, Adjusted Rsquared: 0.9564
Fstatistic: 44.86 on 3 and 3 DF, pvalue: 0.005431
[1] "The fitted boundary line summary!"
[1] "Logarithmic model y=a+bx+clog(x)+dxlog(x) was the best!"
Call:
lm(formula = tops ~ segments + log(segments) + segments:log(segments),
data = boundaries)
Residuals:
1 2 3 4 5 6 7
0.007628 0.001627 0.189574 0.418157 0.170181 0.193449 0.129045
Coefficients:
Estimate Std. Error t value Pr(>t)
(Intercept) 30.061 4.285 7.016 0.00595 **
segments 26.547 4.156 6.388 0.00777 **
log(segments) 14.067 1.957 7.189 0.00555 **
segments:log(segments) 10.384 1.852 5.606 0.01121 *

Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.313 on 3 degrees of freedom
Multiple Rsquared: 0.9782, Adjusted Rsquared: 0.9564
Fstatistic: 44.86 on 3 and 3 DF, pvalue: 0.005431
[1] "Total number of releases >= 0.25 & < 0.5 is 14"
inyears
1888 1897 1899 1934 1935 1938 1941 1944 1967 1969
1 1 2 1 2 2 2 1 1 1
[1] "Total number of releases >= 0.5 is 37"
inyears
1890 1893 1895 1896 1899 1903 1910 1935 1936 1938 1939 1940 1941 1942 1943 1944
1 3 1 3 2 2 1 6 1 3 2 1 2 1 1 2
1947 1978 1980 1982
1 2 1 1
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.