analysis of the growth of native black poplars populus ... fileacrocephalus data services ltd...
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Data analysis, modelling & design services
Analysis of the growth of native black poplars Populus nigra betulifolia using R
David Max, Acrocephalus Data Services Ltd
Summary
� Sixteen male native black poplars Populus nigra betulifolia (clone 25) were planted in a
strip approximately 60 metres by 12.
� Two female native black poplars (clone 32) are also planted in the same strip.
� Trees are planted in mounds to prevent waterlogging of the roots.
� Nearest-neighbour gaps are set at 8 metres.
� Planting in mounds increased annual growth by 0.38 metres.
� Annual growth of trees in mounds averages 1.07 metres per year.
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Purpose
This study investigates whether planting methods were effective in improving survival rates and
growth rates of young native black poplars.
Contents
Introduction: native black poplars on the meadow at Hillside House ................................................................... 2
Methods ................................................................................................................................................................ 3
Planting stages: the A-group trees ............................................................................................................... 3
Planting stages: the B-group trees ............................................................................................................... 3
Planting mounds ........................................................................................................................................... 3
Planting stages: the C-group trees ............................................................................................................... 4
DNA tests ...................................................................................................................................................... 4
Final layout of poplars on east side of meadow, 2017 ................................................................................. 4
Data analysis ................................................................................................................................................ 4
Results .................................................................................................................................................................. 4
Effect on growth of growing trees in mounds ............................................................................................... 4
Differences in annual growth increment between year ................................................................................ 5
Overall annual growth rates .......................................................................................................................... 5
Conclusions .......................................................................................................................................................... 5
References ........................................................................................................................................................... 5
Appendix: R script and output .............................................................................................................................. 6
Figures
Figure 1 A-group tree in 2015 (tree A02) .............................................................................................................. 3
Figure 2 Final layout ............................................................................................................................................. 4
Figure 3 Growth of poplars ................................................................................................................................... 5
Introduction: native black poplars on the meadow at Hillside House
A project to develop a small strip plantation of native black poplar Populus nigra betulifolia began
in 2014.
This project followed the suggestion of Oliver Rackham in some of his classic publications (e.g.
[1]). The original plan was to grow perhaps 20 or more native black poplars on a meadow site at
Hillside House, Norfolk, UK.
Low-lying parts of the eastern counties of England probably once had extensive woods or forests
partly composed of this now rare native subspecies.
The planting site appears to be almost ideal for native black poplar. The soil at the site is quite peaty
near the surface. Further down is a more gravelly layer containing a lot of finely divided flint. The
ground in the planting area is damp for much of the year, and in the winter months the water table is
often only 10 to 20 cm below the soil surface. However, since 2013, flooding on this meadow site
has never been more than local and short-term.
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Methods
Planting stages: the A-group
trees
The original planting was of 50 trees
in autumn 2014.
The first group of trees was placed in
a strip on the west side of the
meadow.
Trees were obtained as bareroot
whips from British Hardwood Tree
Nursery Ltd, DN21 4TZ, UK.
The picture shows the typical features
of the original batch of plants (the A-
group). The leaves are rather stiff,
flat, and 'solid'-looking, with small
rounded teeth (not obviously hooked).
The laminas tend to be rather dark
green. The petioles are hairy, though
not excessively so (a handlens is
required to see the hairs).
These A-group plants have gradually
developed Pemphigus galls, though
none were present at first.
After the initial planting, survival of
these trees was poor and only 16
(31%) were still alive by the end of summer 2015.
The most likely explanation for the poor survival is that that some of the whips were originally put
into ground that was too wet.
Planting stages: the B-group trees
After the poor return on the A-group trees, more whips were sourced and planted in winter 2015/16.
The intention at this stage was to end up with around 40+ good trees. The new material came from
a nursery in Norfolk.
Spacing between nearest neighbours was set at around 3.5 to 4 metres. Later, after visiting sites
where mature native black poplars were present, it became obvious that these gaps were too tight.
Planting mounds
In an attempt to improve survival rates, the new B-group trees were planted in shallow mounds of
soil, to prevent the young roots from becoming waterlogged. The mounds were approx. 30 cm high
and were covered over with turves to stop them eroding away and collapsing. Mounds were
surrounded by protective wire-netting guards (or fences), partly to stop rabbit-damage but also to
prevent them from collapsing.
This work entailed moving substantial amounts of sand and soil to the planting area (at least 200 kg
per tree). The new trees also went into the best patches of the meadow, where roots would initially
Figure 1 A-group tree in 2015 (tree A02)
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be clear of the top of the water-table. Once established the young
trees were expected to be able to tolerate occasional wetter
conditions after heavy rain.
Planting stages: the C-group trees
In addition to the male trees, two female trees were obtained from
Nowton Park near Bury St Edmunds in Suffolk. The whips, labelled
C01, C02, are derived from cuttings taken from a well-known
veteran tree located in the Abbey Gardens, Bury St Edmunds. The
whips were kindly supplied by Dwaine Gray of Nowton Park.
DNA tests
DNA tests were performed by Stuart A’Hara at the Northern Forest
Research Station (Forest Research, Roslin, Scotland).
DNA testing showed that the A-group trees were genuine
P.n.betulifolia clone 25 (a male clone).
The DNA tests on trees from the B-group showed them to be of
uncertain provenance. One tree from this group (B21) was retained
as it was indicated to be pure P.nigra but with some genetic
contribution from 'Vereecken', a fast-growing form of P.nigra. (This
tree is not however a native black poplar P.n.betulifolia.)
After losses, the total number of trees relevant to P.n.betulifolia
conservation here is 18. Tree B21 is also included at certain points
in the analysis.
Final layout of poplars on east side of meadow, 2017
In early 2017 all the surviving poplars were moved to the eastern side of the meadow.
The reasons for moving the trees are given in detail on
http://hillside.acrocephalus.com/HH.poplars.html#translocation. The choice of the new location was
not directly related to growth conditions for the native black poplars.
The 2017 transplanting work was left rather too late in the 'bare-root' season for comfort, but was
complete by March 30th 2017. By late April all the trees were showing good leaf development and
appeared to be in good health.
In the layout plan shown (Figure 2), distances are in metres and the positive y-axis corresponds
roughly to due north.
Nearest-neighbour gaps were set at 8 metres.
Data analysis
Results were analysed using R.
Results
Output from the analysis using R is listed in the Appendix.
Effect on growth of growing trees in mounds
Model lm2016.pnb1 considers P.n.betulifolia trees in a single year (2016) to test whether planting in
mounds affected growth rates. Growth was greater in mounds than not in mounds (F1,14 = 5.955, p =
Figure 2 Final layout
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0.0286). The effect of planting in
mounds was to yield an
additional 0.379 metres annual
growth (s.e. 0.155).
Differences in annual
growth increment between
year
The boxplot (Figure 3) suggests
that growth rates might be lower
after 2016. However, the linear
mixed-effects model lme1
(Pinheiro & Bates 2000, [2])
indicates rates were not
significantly different between
years (t-values:
2017: t18 = -0.5391, p = 0.5955;
2018: t18 = -1.5531, p = 0.1353).
[Even a model treating the
growth measurements as
independent data (model lm1)
fails to show evidence for a
difference between years
(regression F2,39 = 1.82, p =
0.1755).]
Overall annual growth rates
Mean annual growth increment for 19 trees was 1.068 metres (summary statistics: minimum 0.880,
Q1 0.988, median 1.050, mean 1.068, Q3 1.100, maximum 1.400).
Conclusions
Although DNA tests showed the B-group trees to be of uncertain provenance, the trees did provide
good evidence that survival of trees planted in mounds was much better. Thus the measures taken
(mounds, stakes, wire-netting etc) have clearly been effective.
Annual growth of trees grown in mounds was approximately 38 cm greater than for trees grown on
flat ground.
Mean annual growth increment of native black poplars grown in mounds was 1.07 metres.
References
[1] Rackham, O. 1986. THE HISTORY OF THE COUNTRYSIDE. J.M.Dent, London.
[2] Pinheiro, J.C. & Bates, D.M. 2000. Mixed-Effects Models in S and S-PLUS. Springer-Verlag.
(ISBN 0-387-98957-9)
Figure 3 Growth of poplars
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Appendix: R script and output
> source('LOAD.POPLARS7.R', echo=T)
> ##-----------------------------------------------------------------------------
---
> ## LOAD.POPLARS7.R
> ## Collation of growth data for native bla .... [TRUNCATED]
> str(POP1901)
'data.frame': 373 obs. of 14 variables:
$ date : Factor w/ 17 levels "01/02/2016","03/01/2017",..: 13 13 13 13 13 13
13 13 13 13 ...
$ ID : Factor w/ 102 levels "A01","A02","A03",..: 1 2 3 4 5 6 7 8 9 10 ...
$ x : num NA NA NA NA NA NA NA NA NA NA ...
$ y : num NA NA NA NA NA NA NA NA NA NA ...
$ mound : Factor w/ 2 levels "none","yes": 1 1 1 1 1 1 1 1 1 1 ...
$ G2016 : num NA NA NA NA NA NA NA NA NA NA ...
$ G2017 : num NA NA NA NA NA NA NA NA NA NA ...
$ G2018 : num NA NA NA NA NA NA NA NA NA NA ...
$ coord : int 0 0 0 0 0 0 0 0 0 0 ...
$ alive : Factor w/ 4 levels "","-9999","N",..: 4 4 4 4 4 4 4 4 4 4 ...
$ alive2 : Factor w/ 2 levels "alive","dead": 1 1 1 1 1 1 1 1 1 1 ...
$ leaves : Factor w/ 3 levels "-9999","glabrous",..: 1 1 1 1 1 1 1 1 1 1 ...
$ condition: Factor w/ 5 levels "dead","good",..: NA NA NA NA NA NA NA NA NA NA
...
$ notes : Factor w/ 50 levels "","(already removed)",..: 30 30 30 30 30 30 30
30 30 30 ...
> ## leave out some of the cols in POP1901, not needed here...
> POP1901.ss <- subset(POP1901, select=-c(alive, alive2, leaves, condition,
notes))
> str(POP1901.ss)
'data.frame': 373 obs. of 9 variables:
$ date : Factor w/ 17 levels "01/02/2016","03/01/2017",..: 13 13 13 13 13 13 13
13 13 13 ...
$ ID : Factor w/ 102 levels "A01","A02","A03",..: 1 2 3 4 5 6 7 8 9 10 ...
$ x : num NA NA NA NA NA NA NA NA NA NA ...
$ y : num NA NA NA NA NA NA NA NA NA NA ...
$ mound: Factor w/ 2 levels "none","yes": 1 1 1 1 1 1 1 1 1 1 ...
$ G2016: num NA NA NA NA NA NA NA NA NA NA ...
$ G2017: num NA NA NA NA NA NA NA NA NA NA ...
$ G2018: num NA NA NA NA NA NA NA NA NA NA ...
$ coord: int 0 0 0 0 0 0 0 0 0 0 ...
> ## TREES is the table of non-varying data (source, type, etc)
> TREES <- read.table('TREES.txt', head=T, skip=0, sep='\t')
> str(TREES)
'data.frame': 102 obs. of 7 variables:
$ ID : Factor w/ 102 levels "A01","A02","A03",..: 1 2 3 4 5 6 7 8 9 10 ...
$ type : Factor w/ 4 levels "Alnus","P.n.betulifolia",..: 2 2 2 2 2 2 2 2 2 2
...
$ group : Factor w/ 4 levels "A","alders","B",..: 1 1 1 1 1 1 1 1 1 1 ...
$ sex : Factor w/ 2 levels "female","male": 2 2 2 2 2 2 2 2 2 2 ...
$ clone : int 25 25 25 25 25 25 25 25 25 25 ...
$ source: Factor w/ 4 levels "B.H.N.","F.F.N.",..: 1 1 1 1 1 1 1 1 1 1 ...
$ notes : Factor w/ 7 levels "(null)","DNA tested at Roslin",..: 1 2 1 1 1 1 1 1
1 1 ...
> ## database JOIN operation...
> POP1901.aug <- merge( x = POP1901.ss, y = TREES, by.x = 'ID', by.y = 'ID')
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> date.ISO <- as.Date(POP1901.aug$date, format='%d/%m/%Y')
> POP1901.aug <- data.frame(POP1901.aug, date.ISO=date.ISO)
> POP1901.aug <- subset(POP1901.aug, select=-c(notes)) ## leave out 'notes'
> str(POP1901.aug)
'data.frame': 373 obs. of 15 variables:
$ ID : Factor w/ 102 levels "A01","A02","A03",..: 1 1 1 1 1 1 1 1 2 2 ...
$ date : Factor w/ 17 levels "01/02/2016","03/01/2017",..: 13 15 17 4 6 10 9
8 8 4 ...
$ x : num NA NA NA 2 1.7 NA NA NA NA 3.1 ...
$ y : num NA NA NA 1.9 1.8 NA NA NA NA 8.9 ...
$ mound : Factor w/ 2 levels "none","yes": 1 1 NA 1 1 2 NA 2 2 1 ...
$ G2016 : num NA 0.74 NA NA NA NA NA NA NA NA ...
$ G2017 : num NA NA NA NA NA 1.19 NA NA NA NA ...
$ G2018 : num NA NA NA NA NA NA NA 0.7 0.65 NA ...
$ coord : int 0 0 0 1 1 0 0 0 0 1 ...
$ type : Factor w/ 4 levels "Alnus","P.n.betulifolia",..: 2 2 2 2 2 2 2 2 2 2
...
$ group : Factor w/ 4 levels "A","alders","B",..: 1 1 1 1 1 1 1 1 1 1 ...
$ sex : Factor w/ 2 levels "female","male": 2 2 2 2 2 2 2 2 2 2 ...
$ clone : int 25 25 25 25 25 25 25 25 25 25 ...
$ source : Factor w/ 4 levels "B.H.N.","F.F.N.",..: 1 1 1 1 1 1 1 1 1 1 ...
$ date.ISO: Date, format: "2014-11-21" "2016-12-22" ...
> POP1901.aug
ID date x y mound G2016 G2017 G2018 coord type
1 A01 21/11/2014 NA NA none NA NA NA 0 P.n.betulifolia
2 A01 22/12/2016 NA NA none 0.74 NA NA 0 P.n.betulifolia
3 A01 30/05/2016 NA NA <NA> NA NA NA 0 P.n.betulifolia
4 A01 05/01/2017 2.0 1.9 none NA NA NA 1 P.n.betulifolia
5 A01 09/12/2016 1.7 1.8 none NA NA NA 1 P.n.betulifolia
6 A01 12/01/2018 NA NA yes NA 1.19 NA 0 P.n.betulifolia
7 A01 11/06/2016 NA NA <NA> NA NA NA 0 P.n.betulifolia
8 A01 11/01/2019 NA NA yes NA NA 0.70 0 P.n.betulifolia
9 A02 11/01/2019 NA NA yes NA NA 0.65 0 P.n.betulifolia
10 A02 05/01/2017 3.1 8.9 none NA NA NA 1 P.n.betulifolia
11 A02 12/01/2018 NA NA yes NA 1.37 NA 0 P.n.betulifolia
12 A02 30/05/2016 NA NA <NA> NA NA NA 0 P.n.betulifolia
13 A02 21/11/2014 NA NA none NA NA NA 0 P.n.betulifolia
14 A02 09/12/2016 3.3 8.7 none NA NA NA 1 P.n.betulifolia
15 A02 11/06/2016 NA NA <NA> NA NA NA 0 P.n.betulifolia
16 A02 22/12/2016 NA NA none 0.45 NA NA 0 P.n.betulifolia
17 A03 11/01/2019 NA NA yes NA NA 1.15 0 P.n.betulifolia
18 A03 30/05/2016 NA NA <NA> NA NA NA 0 P.n.betulifolia
19 A03 21/11/2014 NA NA none NA NA NA 0 P.n.betulifolia
20 A03 11/06/2016 NA NA <NA> NA NA NA 0 P.n.betulifolia
21 A03 12/01/2018 NA NA yes NA 0.95 NA 0 P.n.betulifolia
22 A03 09/12/2016 2.1 17.1 none NA NA NA 1 P.n.betulifolia
23 A03 22/12/2016 NA NA none 0.27 NA NA 0 P.n.betulifolia
24 A04 11/01/2019 NA NA yes NA NA 0.81 0 P.n.betulifolia
25 A04 21/11/2014 NA NA none NA NA NA 0 P.n.betulifolia
26 A04 30/05/2016 NA NA <NA> NA NA NA 0 P.n.betulifolia
27 A04 12/01/2018 NA NA yes NA 1.25 NA 0 P.n.betulifolia
28 A04 09/12/2016 4.2 19.2 none NA NA NA 1 P.n.betulifolia
29 A04 11/06/2016 NA NA <NA> NA NA NA 0 P.n.betulifolia
30 A04 22/12/2016 NA NA none 0.60 NA NA 0 P.n.betulifolia
31 A05 09/12/2016 4.8 21.8 yes NA NA NA 1 P.n.betulifolia
32 A05 11/06/2016 NA NA <NA> NA NA NA 0 P.n.betulifolia
33 A05 11/01/2019 NA NA yes NA NA 1.27 0 P.n.betulifolia
34 A05 12/01/2018 NA NA yes NA 0.99 NA 0 P.n.betulifolia
35 A05 22/12/2016 NA NA yes 0.99 NA NA 0 P.n.betulifolia
36 A05 21/11/2014 NA NA none NA NA NA 0 P.n.betulifolia
37 A05 30/05/2016 NA NA <NA> NA NA NA 0 P.n.betulifolia
38 A06 21/11/2014 NA NA none NA NA NA 0 P.n.betulifolia
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39 A06 30/05/2016 NA NA <NA> NA NA NA 0 P.n.betulifolia
40 A06 09/12/2016 NA NA <NA> NA NA NA 0 P.n.betulifolia
41 A07 30/05/2016 NA NA <NA> NA NA NA 0 P.n.betulifolia
42 A07 11/01/2019 NA NA yes NA NA 0.75 0 P.n.betulifolia
43 A07 11/06/2016 NA NA <NA> NA NA NA 0 P.n.betulifolia
44 A07 22/12/2016 NA NA yes 1.23 NA NA 0 P.n.betulifolia
45 A07 09/12/2016 4.9 25.2 yes NA NA NA 1 P.n.betulifolia
46 A07 12/01/2018 NA NA yes NA 1.32 NA 0 P.n.betulifolia
47 A07 21/11/2014 NA NA none NA NA NA 0 P.n.betulifolia
48 A08 11/06/2016 NA NA <NA> NA NA NA 0 P.n.betulifolia
49 A08 30/05/2016 NA NA <NA> NA NA NA 0 P.n.betulifolia
50 A08 21/11/2014 NA NA none NA NA NA 0 P.n.betulifolia
51 A08 12/01/2018 NA NA yes NA 1.10 NA 0 P.n.betulifolia
52 A08 11/01/2019 NA NA yes NA NA 0.85 0 P.n.betulifolia
53 A08 22/12/2016 NA NA none 1.37 NA NA 0 P.n.betulifolia
54 A08 09/12/2016 6.4 24.8 none NA NA NA 1 P.n.betulifolia
55 A09 30/05/2016 NA NA <NA> NA NA NA 0 P.n.betulifolia
56 A09 12/01/2018 NA NA yes NA 0.64 NA 0 P.n.betulifolia
57 A09 11/06/2016 NA NA <NA> NA NA NA 0 P.n.betulifolia
58 A09 22/12/2016 NA NA yes 1.29 NA NA 0 P.n.betulifolia
59 A09 21/11/2014 NA NA none NA NA NA 0 P.n.betulifolia
60 A09 09/12/2016 4.6 29.6 yes NA NA NA 1 P.n.betulifolia
61 A09 11/01/2019 NA NA yes NA NA 1.03 0 P.n.betulifolia
62 A10 11/01/2019 NA NA yes NA NA 1.39 0 P.n.betulifolia
63 A10 22/12/2016 NA NA none 0.66 NA NA 0 P.n.betulifolia
64 A10 21/11/2014 NA NA none NA NA NA 0 P.n.betulifolia
65 A10 30/05/2016 NA NA <NA> NA NA NA 0 P.n.betulifolia
66 A10 12/01/2018 NA NA yes NA 1.41 NA 0 P.n.betulifolia
67 A10 11/06/2016 NA NA <NA> NA NA NA 0 P.n.betulifolia
68 A10 09/12/2016 12.5 33.5 none NA NA NA 1 P.n.betulifolia
69 A11 11/06/2016 NA NA <NA> NA NA NA 0 P.n.betulifolia
70 A11 30/05/2016 NA NA <NA> NA NA NA 0 P.n.betulifolia
71 A11 22/12/2016 NA NA yes NA NA NA 0 P.n.betulifolia
72 A11 12/01/2018 NA NA yes NA 0.90 NA 0 P.n.betulifolia
73 A11 11/01/2019 NA NA yes NA NA 0.80 0 P.n.betulifolia
74 A11 21/11/2014 NA NA none NA NA NA 0 P.n.betulifolia
75 A11 12/12/2016 NA NA yes 1.26 NA NA 0 P.n.betulifolia
76 A11 09/12/2016 4.3 33.5 <NA> NA NA NA 1 P.n.betulifolia
77 A12 22/12/2016 NA NA none NA NA NA 0 P.n.betulifolia
78 A12 21/11/2014 NA NA none NA NA NA 0 P.n.betulifolia
79 A12 11/06/2016 NA NA <NA> NA NA NA 0 P.n.betulifolia
80 A12 30/05/2016 NA NA <NA> NA NA NA 0 P.n.betulifolia
81 A12 12/01/2018 NA NA yes NA 1.06 NA 0 P.n.betulifolia
82 A12 11/01/2019 NA NA yes NA NA 1.14 0 P.n.betulifolia
83 A12 09/12/2016 6.0 33.5 none 1.07 NA NA 1 P.n.betulifolia
84 A12 12/12/2016 NA NA none NA NA NA 0 P.n.betulifolia
85 A13 22/12/2016 NA NA yes NA NA NA 0 P.n.betulifolia
86 A13 12/01/2018 NA NA yes NA 1.01 NA 0 P.n.betulifolia
87 A13 11/06/2016 NA NA <NA> NA NA NA 0 P.n.betulifolia
88 A13 11/01/2019 NA NA yes NA NA 1.35 0 P.n.betulifolia
89 A13 30/05/2016 NA NA <NA> NA NA NA 0 P.n.betulifolia
90 A13 21/11/2014 NA NA none NA NA NA 0 P.n.betulifolia
91 A13 09/12/2016 4.1 37.7 <NA> NA NA NA 1 P.n.betulifolia
92 A13 12/12/2016 NA NA yes 1.09 NA NA 0 P.n.betulifolia
93 A14 12/01/2018 NA NA yes NA 1.05 NA 0 P.n.betulifolia
94 A14 11/01/2019 NA NA yes NA NA 1.09 0 P.n.betulifolia
95 A14 21/11/2014 NA NA none NA NA NA 0 P.n.betulifolia
96 A14 30/05/2016 NA NA <NA> NA NA NA 0 P.n.betulifolia
97 A14 21/12/2016 2.8 49.7 none NA NA NA 1 P.n.betulifolia
98 A14 22/12/2016 NA NA none 1.06 NA NA 0 P.n.betulifolia
99 A14 09/12/2016 2.8 49.6 <NA> NA NA NA 1 P.n.betulifolia
100 A15 11/01/2019 NA NA yes NA NA 0.75 0 P.n.betulifolia
101 A15 22/12/2016 NA NA none 0.94 NA NA 0 P.n.betulifolia
102 A15 12/01/2018 NA NA yes NA 1.62 NA 0 P.n.betulifolia
103 A15 30/05/2016 NA NA <NA> NA NA NA 0 P.n.betulifolia
104 A15 21/11/2014 NA NA none NA NA NA 0 P.n.betulifolia
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105 A15 09/12/2016 5.4 58.4 none NA NA NA 1 P.n.betulifolia
106 A16 21/11/2014 NA NA none NA NA NA 0 P.n.betulifolia
107 A16 09/12/2016 NA NA <NA> NA NA NA 0 P.n.betulifolia
108 A16 30/05/2016 NA NA <NA> NA NA NA 0 P.n.betulifolia
109 A17 30/05/2016 NA NA <NA> NA NA NA 0 P.n.betulifolia
110 A17 22/12/2016 NA NA none 0.51 NA NA 0 P.n.betulifolia
111 A17 11/01/2019 NA NA yes NA NA 0.83 0 P.n.betulifolia
112 A17 21/11/2014 NA NA none NA NA NA 0 P.n.betulifolia
113 A17 12/01/2018 NA NA yes NA 0.93 NA 0 P.n.betulifolia
114 A17 09/12/2016 NA NA none NA NA NA 0 P.n.betulifolia
115 A18 11/01/2019 NA NA none NA NA 0.72 0 P.n.betulifolia
116 A18 30/05/2016 NA NA <NA> NA NA NA 0 P.n.betulifolia
117 A18 12/01/2018 NA NA yes NA 1.03 NA 0 P.n.betulifolia
118 A18 22/12/2016 NA NA none NA NA NA 0 P.n.betulifolia
119 A18 09/12/2016 NA NA none 1.05 NA NA 0 P.n.betulifolia
120 A18 21/11/2014 NA NA none NA NA NA 0 P.n.betulifolia
121 A19 30/05/2016 NA NA <NA> NA NA NA 0 P.n.betulifolia
122 A19 21/11/2014 NA NA none NA NA NA 0 P.n.betulifolia
123 A20 30/05/2016 NA NA <NA> NA NA NA 0 P.n.betulifolia
124 A20 21/11/2014 NA NA none NA NA NA 0 P.n.betulifolia
125 A21 30/05/2016 NA NA <NA> NA NA NA 0 P.n.betulifolia
126 A21 21/11/2014 NA NA none NA NA NA 0 P.n.betulifolia
127 A22 21/11/2014 NA NA none NA NA NA 0 P.n.betulifolia
128 A22 30/05/2016 NA NA <NA> NA NA NA 0 P.n.betulifolia
129 A23 30/05/2016 NA NA <NA> NA NA NA 0 P.n.betulifolia
130 A23 21/11/2014 NA NA none NA NA NA 0 P.n.betulifolia
131 A24 30/05/2016 NA NA <NA> NA NA NA 0 P.n.betulifolia
132 A24 21/11/2014 NA NA none NA NA NA 0 P.n.betulifolia
133 A25 21/11/2014 NA NA none NA NA NA 0 P.n.betulifolia
134 A25 30/05/2016 NA NA <NA> NA NA NA 0 P.n.betulifolia
135 A26 30/05/2016 NA NA <NA> NA NA NA 0 P.n.betulifolia
136 A26 21/11/2014 NA NA none NA NA NA 0 P.n.betulifolia
137 A27 21/11/2014 NA NA none NA NA NA 0 P.n.betulifolia
138 A27 30/05/2016 NA NA <NA> NA NA NA 0 P.n.betulifolia
139 A28 21/11/2014 NA NA none NA NA NA 0 P.n.betulifolia
140 A28 30/05/2016 NA NA <NA> NA NA NA 0 P.n.betulifolia
141 A29 21/11/2014 NA NA none NA NA NA 0 P.n.betulifolia
142 A29 30/05/2016 NA NA <NA> NA NA NA 0 P.n.betulifolia
143 A30 30/05/2016 NA NA <NA> NA NA NA 0 P.n.betulifolia
144 A30 21/11/2014 NA NA none NA NA NA 0 P.n.betulifolia
145 A31 30/05/2016 NA NA <NA> NA NA NA 0 P.n.betulifolia
146 A31 21/11/2014 NA NA none NA NA NA 0 P.n.betulifolia
147 A32 21/11/2014 NA NA none NA NA NA 0 P.n.betulifolia
148 A32 30/05/2016 NA NA <NA> NA NA NA 0 P.n.betulifolia
149 A33 30/05/2016 NA NA <NA> NA NA NA 0 P.n.betulifolia
150 A33 21/11/2014 NA NA none NA NA NA 0 P.n.betulifolia
151 A34 21/11/2014 NA NA none NA NA NA 0 P.n.betulifolia
152 A34 30/05/2016 NA NA <NA> NA NA NA 0 P.n.betulifolia
153 A35 30/05/2016 NA NA <NA> NA NA NA 0 P.n.betulifolia
154 A35 21/11/2014 NA NA none NA NA NA 0 P.n.betulifolia
155 A36 21/11/2014 NA NA none NA NA NA 0 P.n.betulifolia
156 A36 30/05/2016 NA NA <NA> NA NA NA 0 P.n.betulifolia
157 A37 21/11/2014 NA NA none NA NA NA 0 P.n.betulifolia
158 A37 30/05/2016 NA NA <NA> NA NA NA 0 P.n.betulifolia
159 A38 30/05/2016 NA NA <NA> NA NA NA 0 P.n.betulifolia
160 A38 21/11/2014 NA NA none NA NA NA 0 P.n.betulifolia
161 A39 21/11/2014 NA NA none NA NA NA 0 P.n.betulifolia
162 A39 30/05/2016 NA NA <NA> NA NA NA 0 P.n.betulifolia
163 A40 21/11/2014 NA NA none NA NA NA 0 P.n.betulifolia
164 A40 30/05/2016 NA NA <NA> NA NA NA 0 P.n.betulifolia
165 A41 30/05/2016 NA NA <NA> NA NA NA 0 P.n.betulifolia
166 A41 21/11/2014 NA NA none NA NA NA 0 P.n.betulifolia
167 A42 30/05/2016 NA NA <NA> NA NA NA 0 P.n.betulifolia
168 A42 21/11/2014 NA NA none NA NA NA 0 P.n.betulifolia
169 A43 21/11/2014 NA NA none NA NA NA 0 P.n.betulifolia
170 A43 30/05/2016 NA NA <NA> NA NA NA 0 P.n.betulifolia
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171 A44 30/05/2016 NA NA <NA> NA NA NA 0 P.n.betulifolia
172 A44 21/11/2014 NA NA none NA NA NA 0 P.n.betulifolia
173 A45 21/11/2014 NA NA none NA NA NA 0 P.n.betulifolia
174 A45 30/05/2016 NA NA <NA> NA NA NA 0 P.n.betulifolia
175 A46 21/11/2014 NA NA none NA NA NA 0 P.n.betulifolia
176 A46 30/05/2016 NA NA <NA> NA NA NA 0 P.n.betulifolia
177 A47 30/05/2016 NA NA <NA> NA NA NA 0 P.n.betulifolia
178 A47 21/11/2014 NA NA none NA NA NA 0 P.n.betulifolia
179 A48 21/11/2014 NA NA none NA NA NA 0 P.n.betulifolia
180 A48 30/05/2016 NA NA <NA> NA NA NA 0 P.n.betulifolia
181 A49 21/11/2014 NA NA none NA NA NA 0 P.n.betulifolia
182 A49 30/05/2016 NA NA <NA> NA NA NA 0 P.n.betulifolia
183 A50 21/11/2014 NA NA none NA NA NA 0 P.n.betulifolia
184 A50 30/05/2016 NA NA <NA> NA NA NA 0 P.n.betulifolia
185 A51 30/05/2016 NA NA <NA> NA NA NA 0 P.n.betulifolia
186 A51 21/11/2014 NA NA none NA NA NA 0 P.n.betulifolia
187 A52 21/11/2014 NA NA none NA NA NA 0 P.n.betulifolia
188 A52 30/05/2016 NA NA <NA> NA NA NA 0 P.n.betulifolia
189 alder1 09/12/2016 1.7 20.2 <NA> NA NA NA 1 Alnus
190 B01 09/12/2016 -1.8 -1.8 <NA> NA NA NA 1 unknown
191 B01 30/05/2016 NA NA <NA> NA NA NA 0 unknown
192 B01 01/02/2016 NA NA <NA> NA NA NA 0 unknown
193 B01 20/12/2016 NA NA none NA NA NA 0 unknown
194 B01 22/12/2016 NA NA none 0.58 NA NA 0 unknown
195 B02 09/12/2016 NA NA <NA> NA NA NA 0 unknown
196 B02 01/02/2016 NA NA <NA> NA NA NA 0 unknown
197 B02 05/01/2017 5.5 0.5 yes NA NA NA 1 unknown
198 B02 20/12/2016 NA NA none NA NA NA 0 unknown
199 B02 30/05/2016 NA NA <NA> NA NA NA 0 unknown
200 B02 04/01/2017 4.0 0.0 yes NA NA NA 1 unknown
201 B03 05/01/2017 12.4 1.9 yes NA NA NA 1 unknown
202 B03 30/05/2016 NA NA <NA> NA NA NA 0 unknown
203 B03 01/02/2016 NA NA <NA> NA NA NA 0 unknown
204 B03 04/01/2017 10.0 1.0 yes 1.28 NA NA 1 unknown
205 B03 09/12/2016 NA NA <NA> NA NA NA 0 unknown
206 B04 05/01/2017 8.3 1.9 yes NA NA NA 1 unknown
207 B04 30/05/2016 NA NA <NA> NA NA NA 0 unknown
208 B04 01/02/2016 NA NA <NA> NA NA NA 0 unknown
209 B04 09/12/2016 NA NA <NA> NA NA NA 0 unknown
210 B04 04/01/2017 7.0 3.0 yes NA NA NA 1 unknown
211 B05 09/02/2016 NA NA <NA> NA NA NA 0 unknown
212 B05 04/01/2017 4.9 5.0 yes 1.03 NA NA 1 unknown
213 B05 30/05/2016 NA NA <NA> NA NA NA 0 unknown
214 B05 09/12/2016 4.9 5.0 <NA> NA NA NA 1 unknown
215 B05 05/01/2017 4.7 5.4 yes NA NA NA 1 unknown
216 B06 09/12/2016 NA NA <NA> NA NA NA 0 unknown
217 B06 30/05/2016 NA NA <NA> NA NA NA 0 unknown
218 B06 04/01/2017 7.0 6.0 yes NA NA NA 1 unknown
219 B06 05/01/2017 8.7 6.8 yes NA NA NA 1 unknown
220 B06 09/02/2016 NA NA <NA> NA NA NA 0 unknown
221 B07 30/05/2016 NA NA <NA> NA NA NA 0 unknown
222 B07 05/01/2017 12.4 6.9 yes NA NA NA 1 unknown
223 B07 09/12/2016 NA NA <NA> NA NA NA 0 unknown
224 B07 09/02/2016 NA NA <NA> NA NA NA 0 unknown
225 B07 04/01/2017 10.0 5.0 yes 1.48 NA NA 1 unknown
226 B08 09/12/2016 NA NA <NA> NA NA NA 0 unknown
227 B08 30/05/2016 NA NA <NA> NA NA NA 0 unknown
228 B08 04/01/2017 10.0 9.0 yes 0.77 NA NA 1 unknown
229 B08 09/02/2016 NA NA <NA> NA NA NA 0 unknown
230 B08 05/01/2017 12.4 11.2 yes NA NA NA 1 unknown
231 B09 10/02/2016 NA NA <NA> NA NA NA 0 unknown
232 B09 22/12/2016 NA NA none NA NA NA 0 unknown
233 B09 09/12/2016 1.3 23.9 <NA> NA NA NA 1 unknown
234 B09 30/05/2016 NA NA <NA> NA NA NA 0 unknown
235 B10 09/12/2016 8.1 23.0 <NA> NA NA NA 1 unknown
236 B10 22/12/2016 NA NA none 0.17 NA NA 0 unknown
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237 B10 10/02/2016 NA NA <NA> NA NA NA 0 unknown
238 B10 30/05/2016 NA NA <NA> NA NA NA 0 unknown
239 B11 22/12/2016 NA NA yes 0.29 NA NA 0 unknown
240 B11 09/12/2016 12.0 25.2 <NA> NA NA NA 1 unknown
241 B11 11/06/2016 NA NA <NA> NA NA NA 0 unknown
242 B11 30/05/2016 NA NA <NA> NA NA NA 0 unknown
243 B11 10/02/2016 NA NA <NA> NA NA NA 0 unknown
244 B12 09/12/2016 1.3 27.8 <NA> NA NA NA 1 unknown
245 B12 22/12/2016 NA NA none NA NA NA 0 unknown
246 B12 30/05/2016 NA NA <NA> NA NA NA 0 unknown
247 B12 10/02/2016 NA NA <NA> NA NA NA 0 unknown
248 B13 30/05/2016 NA NA <NA> NA NA NA 0 unknown
249 B13 22/12/2016 NA NA none 0.91 NA NA 0 unknown
250 B13 09/12/2016 7.9 27.1 <NA> NA NA NA 1 unknown
251 B14 30/05/2016 NA NA <NA> NA NA NA 0 unknown
252 B14 22/12/2016 NA NA none 0.83 NA NA 0 unknown
253 B14 09/12/2016 8.0 31.1 <NA> NA NA NA 1 unknown
254 B15 30/05/2016 NA NA <NA> NA NA NA 0 unknown
255 B15 09/12/2016 0.6 31.7 <NA> NA NA NA 1 unknown
256 B15 22/12/2016 NA NA none NA NA NA 0 unknown
257 B16 30/05/2016 NA NA <NA> NA NA NA 0 unknown
258 B16 09/12/2016 11.2 34.5 <NA> NA NA NA 1 unknown
259 B16 22/12/2016 NA NA yes NA NA NA 0 unknown
260 B16 12/12/2016 NA NA <NA> NA NA NA 0 unknown
261 B17 09/12/2016 0.4 35.4 <NA> NA NA NA 1 unknown
262 B17 30/05/2016 NA NA <NA> NA NA NA 0 unknown
263 B17 22/12/2016 NA NA none 0.95 NA NA 0 unknown
264 B18 30/05/2016 NA NA <NA> NA NA NA 0 unknown
265 B18 09/12/2016 7.8 35.5 <NA> NA NA NA 1 unknown
266 B18 22/12/2016 NA NA none NA NA NA 0 unknown
267 B18 12/12/2016 NA NA <NA> NA NA NA 0 unknown
268 B19 22/12/2016 NA NA yes NA NA NA 0 unknown
269 B19 12/12/2016 NA NA <NA> NA NA NA 0 unknown
270 B19 09/12/2016 11.0 37.7 <NA> NA NA NA 1 unknown
271 B19 30/05/2016 NA NA <NA> NA NA NA 0 unknown
272 B20 22/12/2016 NA NA none 0.28 NA NA 0 P.nigra
273 B20 30/05/2016 NA NA <NA> NA NA NA 0 P.nigra
274 B20 20/12/2016 NA NA none NA NA NA 0 P.nigra
275 B20 11/06/2016 NA NA <NA> NA NA NA 0 P.nigra
276 B20 09/12/2016 0.1 39.7 <NA> NA NA NA 1 P.nigra
277 B21 11/01/2019 NA NA yes NA NA 1.20 0 P.nigra
278 B21 30/05/2016 NA NA <NA> NA NA NA 0 P.nigra
279 B21 11/06/2016 NA NA <NA> NA NA NA 0 P.nigra
280 B21 09/12/2016 7.6 40.5 <NA> NA NA NA 1 P.nigra
281 B21 20/12/2016 NA NA none NA NA NA 0 P.nigra
282 B21 12/01/2018 NA NA yes NA 0.78 NA 0 P.nigra
283 B22 30/05/2016 NA NA <NA> NA NA NA 0 unknown
284 B22 09/12/2016 4.0 42.2 <NA> NA NA NA 1 unknown
285 B22 22/12/2016 NA NA none 0.65 NA NA 0 unknown
286 B22 11/06/2016 NA NA <NA> NA NA NA 0 unknown
287 B23 30/05/2016 NA NA <NA> NA NA NA 0 unknown
288 B23 03/01/2017 NA NA yes 1.25 NA NA 0 unknown
289 B23 09/12/2016 11.0 42.0 <NA> NA NA NA 1 unknown
290 B24 09/12/2016 0.0 43.4 <NA> NA NA NA 1 unknown
291 B24 22/12/2016 NA NA none 0.37 NA NA 0 unknown
292 B24 30/05/2016 NA NA <NA> NA NA NA 0 unknown
293 B24 11/06/2016 NA NA <NA> NA NA NA 0 unknown
294 B25 12/12/2016 NA NA none 0.97 NA NA 0 unknown
295 B25 09/12/2016 7.5 44.0 <NA> NA NA NA 1 unknown
296 B25 30/05/2016 NA NA <NA> NA NA NA 0 unknown
297 B25 22/12/2016 NA NA none 0.99 NA NA 0 unknown
298 B26 30/05/2016 NA NA <NA> NA NA NA 0 unknown
299 B26 22/12/2016 NA NA none NA NA NA 0 unknown
300 B26 09/12/2016 4.1 45.8 <NA> NA NA NA 1 unknown
301 B27 22/12/2016 NA NA yes 1.46 NA NA 0 unknown
302 B27 09/12/2016 10.5 46.8 <NA> NA NA NA 1 unknown
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303 B27 30/05/2016 NA NA <NA> NA NA NA 0 unknown
304 B28 11/06/2016 NA NA <NA> NA NA NA 0 unknown
305 B28 09/12/2016 0.1 47.2 <NA> NA NA NA 1 unknown
306 B28 21/12/2016 NA NA none 0.74 NA NA 0 unknown
307 B28 30/05/2016 NA NA <NA> NA NA NA 0 unknown
308 B29 30/05/2016 NA NA <NA> NA NA NA 0 unknown
309 B29 22/12/2016 NA NA none NA NA NA 0 unknown
310 B29 09/12/2016 7.5 48.3 <NA> NA NA NA 1 unknown
311 B30 11/06/2016 NA NA <NA> NA NA NA 0 unknown
312 B30 30/05/2016 NA NA <NA> NA NA NA 0 unknown
313 B30 21/12/2016 NA NA none 0.95 NA NA 0 unknown
314 B30 09/12/2016 3.8 49.7 <NA> NA NA NA 1 unknown
315 B31 09/12/2016 10.0 50.4 <NA> NA NA NA 1 unknown
316 B31 30/05/2016 NA NA <NA> NA NA NA 0 unknown
317 B31 03/01/2017 NA NA yes 0.60 NA NA 0 unknown
318 B32 09/12/2016 0.0 51.2 <NA> NA NA NA 1 unknown
319 B32 30/05/2016 NA NA <NA> NA NA NA 0 unknown
320 B32 21/12/2016 NA NA none 0.82 NA NA 0 unknown
321 B33 09/12/2016 3.8 54.0 <NA> NA NA NA 1 unknown
322 B33 21/12/2016 NA NA none 0.57 NA NA 0 unknown
323 B33 30/05/2016 NA NA <NA> NA NA NA 0 unknown
324 B34 21/12/2016 NA NA none NA NA NA 0 unknown
325 B34 09/12/2016 7.0 53.5 <NA> NA NA NA 1 unknown
326 B34 30/05/2016 NA NA <NA> NA NA NA 0 unknown
327 B35 21/12/2016 NA NA none 0.45 NA NA 0 unknown
328 B35 09/12/2016 0.0 55.2 <NA> NA NA NA 1 unknown
329 B35 30/05/2016 NA NA <NA> NA NA NA 0 unknown
330 B35 20/12/2016 NA NA none NA NA NA 0 unknown
331 B36 09/12/2016 10.0 55.4 <NA> NA NA NA 1 unknown
332 B36 30/05/2016 NA NA <NA> NA NA NA 0 unknown
333 B36 21/12/2016 NA NA none 0.87 NA NA 0 unknown
334 B37 30/05/2016 NA NA <NA> NA NA NA 0 unknown
335 B37 09/12/2016 0.0 59.1 <NA> NA NA NA 1 unknown
336 B37 20/12/2016 NA NA none NA NA NA 0 unknown
337 B38 09/12/2016 9.6 60.7 <NA> NA NA NA 1 unknown
338 B38 30/05/2016 NA NA <NA> NA NA NA 0 unknown
339 B39 30/05/2016 NA NA <NA> NA NA NA 0 unknown
340 B39 09/12/2016 3.7 61.7 <NA> NA NA NA 1 unknown
341 B39 21/12/2016 NA NA none 1.21 NA NA 0 unknown
342 B40 30/05/2016 NA NA <NA> NA NA NA 0 unknown
343 B40 09/12/2016 0.0 63.3 <NA> NA NA NA 1 unknown
344 B40 20/12/2016 NA NA none NA NA NA 0 unknown
345 B41 30/05/2016 NA NA <NA> NA NA NA 0 unknown
346 B41 20/12/2016 NA NA none NA NA NA 0 unknown
347 B41 09/12/2016 NA NA none 0.76 NA NA 0 unknown
348 B42 30/05/2016 NA NA <NA> NA NA NA 0 unknown
349 B42 20/12/2016 NA NA none NA NA NA 0 unknown
350 B42 09/12/2016 NA NA <NA> NA NA NA 0 unknown
351 B43 30/05/2016 NA NA <NA> NA NA NA 0 unknown
352 B43 20/12/2016 NA NA none NA NA NA 0 unknown
353 B43 09/12/2016 NA NA <NA> NA NA NA 0 unknown
354 B44 22/12/2016 NA NA none NA NA NA 0 unknown
355 B44 30/05/2016 NA NA <NA> NA NA NA 0 unknown
356 B44 09/12/2016 NA NA <NA> NA NA NA 0 unknown
357 B44 30/01/2016 NA NA none NA NA NA 0 unknown
358 B45 30/05/2016 NA NA <NA> NA NA NA 0 unknown
359 B45 09/12/2016 NA NA <NA> NA NA NA 0 unknown
360 B45 30/01/2016 NA NA none NA NA NA 0 unknown
361 B45 22/12/2016 NA NA none 0.23 NA NA 0 unknown
362 B46 30/01/2016 NA NA none NA NA NA 0 unknown
363 B46 09/12/2016 NA NA <NA> NA NA NA 0 unknown
364 B46 30/05/2016 NA NA <NA> NA NA NA 0 unknown
365 B46 22/12/2016 NA NA none 0.61 NA NA 0 unknown
366 B47 30/05/2016 NA NA <NA> NA NA NA 0 unknown
367 B47 09/12/2016 NA NA <NA> NA NA NA 0 unknown
368 B47 30/01/2016 NA NA none NA NA NA 0 unknown
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369 B47 22/12/2016 NA NA none 0.46 NA NA 0 unknown
370 C01 11/01/2019 NA NA yes NA NA 0.96 0 P.n.betulifolia
371 C01 12/01/2018 NA NA yes NA 1.17 NA 0 P.n.betulifolia
372 C02 12/01/2018 NA NA yes NA 1.33 NA 0 P.n.betulifolia
373 C02 11/01/2019 NA NA yes NA NA 1.17 0 P.n.betulifolia
group sex clone source date.ISO
1 A male 25 B.H.N. 2014-11-21
2 A male 25 B.H.N. 2016-12-22
3 A male 25 B.H.N. 2016-05-30
4 A male 25 B.H.N. 2017-01-05
5 A male 25 B.H.N. 2016-12-09
6 A male 25 B.H.N. 2018-01-12
7 A male 25 B.H.N. 2016-06-11
8 A male 25 B.H.N. 2019-01-11
9 A male 25 B.H.N. 2019-01-11
10 A male 25 B.H.N. 2017-01-05
11 A male 25 B.H.N. 2018-01-12
12 A male 25 B.H.N. 2016-05-30
13 A male 25 B.H.N. 2014-11-21
14 A male 25 B.H.N. 2016-12-09
15 A male 25 B.H.N. 2016-06-11
16 A male 25 B.H.N. 2016-12-22
17 A male 25 B.H.N. 2019-01-11
18 A male 25 B.H.N. 2016-05-30
19 A male 25 B.H.N. 2014-11-21
20 A male 25 B.H.N. 2016-06-11
21 A male 25 B.H.N. 2018-01-12
22 A male 25 B.H.N. 2016-12-09
23 A male 25 B.H.N. 2016-12-22
24 A male 25 B.H.N. 2019-01-11
25 A male 25 B.H.N. 2014-11-21
26 A male 25 B.H.N. 2016-05-30
27 A male 25 B.H.N. 2018-01-12
28 A male 25 B.H.N. 2016-12-09
29 A male 25 B.H.N. 2016-06-11
30 A male 25 B.H.N. 2016-12-22
31 A male 25 B.H.N. 2016-12-09
32 A male 25 B.H.N. 2016-06-11
33 A male 25 B.H.N. 2019-01-11
34 A male 25 B.H.N. 2018-01-12
35 A male 25 B.H.N. 2016-12-22
36 A male 25 B.H.N. 2014-11-21
37 A male 25 B.H.N. 2016-05-30
38 A male 25 B.H.N. 2014-11-21
39 A male 25 B.H.N. 2016-05-30
40 A male 25 B.H.N. 2016-12-09
41 A male 25 B.H.N. 2016-05-30
42 A male 25 B.H.N. 2019-01-11
43 A male 25 B.H.N. 2016-06-11
44 A male 25 B.H.N. 2016-12-22
45 A male 25 B.H.N. 2016-12-09
46 A male 25 B.H.N. 2018-01-12
47 A male 25 B.H.N. 2014-11-21
48 A male 25 B.H.N. 2016-06-11
49 A male 25 B.H.N. 2016-05-30
50 A male 25 B.H.N. 2014-11-21
51 A male 25 B.H.N. 2018-01-12
52 A male 25 B.H.N. 2019-01-11
53 A male 25 B.H.N. 2016-12-22
54 A male 25 B.H.N. 2016-12-09
55 A male 25 B.H.N. 2016-05-30
56 A male 25 B.H.N. 2018-01-12
57 A male 25 B.H.N. 2016-06-11
58 A male 25 B.H.N. 2016-12-22
59 A male 25 B.H.N. 2014-11-21
60 A male 25 B.H.N. 2016-12-09
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61 A male 25 B.H.N. 2019-01-11
62 A male 25 B.H.N. 2019-01-11
63 A male 25 B.H.N. 2016-12-22
64 A male 25 B.H.N. 2014-11-21
65 A male 25 B.H.N. 2016-05-30
66 A male 25 B.H.N. 2018-01-12
67 A male 25 B.H.N. 2016-06-11
68 A male 25 B.H.N. 2016-12-09
69 A male 25 B.H.N. 2016-06-11
70 A male 25 B.H.N. 2016-05-30
71 A male 25 B.H.N. 2016-12-22
72 A male 25 B.H.N. 2018-01-12
73 A male 25 B.H.N. 2019-01-11
74 A male 25 B.H.N. 2014-11-21
75 A male 25 B.H.N. 2016-12-12
76 A male 25 B.H.N. 2016-12-09
77 A male 25 B.H.N. 2016-12-22
78 A male 25 B.H.N. 2014-11-21
79 A male 25 B.H.N. 2016-06-11
80 A male 25 B.H.N. 2016-05-30
81 A male 25 B.H.N. 2018-01-12
82 A male 25 B.H.N. 2019-01-11
83 A male 25 B.H.N. 2016-12-09
84 A male 25 B.H.N. 2016-12-12
85 A male 25 B.H.N. 2016-12-22
86 A male 25 B.H.N. 2018-01-12
87 A male 25 B.H.N. 2016-06-11
88 A male 25 B.H.N. 2019-01-11
89 A male 25 B.H.N. 2016-05-30
90 A male 25 B.H.N. 2014-11-21
91 A male 25 B.H.N. 2016-12-09
92 A male 25 B.H.N. 2016-12-12
93 A male 25 B.H.N. 2018-01-12
94 A male 25 B.H.N. 2019-01-11
95 A male 25 B.H.N. 2014-11-21
96 A male 25 B.H.N. 2016-05-30
97 A male 25 B.H.N. 2016-12-21
98 A male 25 B.H.N. 2016-12-22
99 A male 25 B.H.N. 2016-12-09
100 A male 25 B.H.N. 2019-01-11
101 A male 25 B.H.N. 2016-12-22
102 A male 25 B.H.N. 2018-01-12
103 A male 25 B.H.N. 2016-05-30
104 A male 25 B.H.N. 2014-11-21
105 A male 25 B.H.N. 2016-12-09
106 A male 25 B.H.N. 2014-11-21
107 A male 25 B.H.N. 2016-12-09
108 A male 25 B.H.N. 2016-05-30
109 A male 25 B.H.N. 2016-05-30
110 A male 25 B.H.N. 2016-12-22
111 A male 25 B.H.N. 2019-01-11
112 A male 25 B.H.N. 2014-11-21
113 A male 25 B.H.N. 2018-01-12
114 A male 25 B.H.N. 2016-12-09
115 A male 25 B.H.N. 2019-01-11
116 A male 25 B.H.N. 2016-05-30
117 A male 25 B.H.N. 2018-01-12
118 A male 25 B.H.N. 2016-12-22
119 A male 25 B.H.N. 2016-12-09
120 A male 25 B.H.N. 2014-11-21
121 A male 25 B.H.N. 2016-05-30
122 A male 25 B.H.N. 2014-11-21
123 A male 25 B.H.N. 2016-05-30
124 A male 25 B.H.N. 2014-11-21
125 A male 25 B.H.N. 2016-05-30
126 A male 25 B.H.N. 2014-11-21
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127 A male 25 B.H.N. 2014-11-21
128 A male 25 B.H.N. 2016-05-30
129 A male 25 B.H.N. 2016-05-30
130 A male 25 B.H.N. 2014-11-21
131 A male 25 B.H.N. 2016-05-30
132 A male 25 B.H.N. 2014-11-21
133 A male 25 B.H.N. 2014-11-21
134 A male 25 B.H.N. 2016-05-30
135 A male 25 B.H.N. 2016-05-30
136 A male 25 B.H.N. 2014-11-21
137 A male 25 B.H.N. 2014-11-21
138 A male 25 B.H.N. 2016-05-30
139 A male 25 B.H.N. 2014-11-21
140 A male 25 B.H.N. 2016-05-30
141 A male 25 B.H.N. 2014-11-21
142 A male 25 B.H.N. 2016-05-30
143 A male 25 B.H.N. 2016-05-30
144 A male 25 B.H.N. 2014-11-21
145 A male 25 B.H.N. 2016-05-30
146 A male 25 B.H.N. 2014-11-21
147 A male 25 B.H.N. 2014-11-21
148 A male 25 B.H.N. 2016-05-30
149 A male 25 B.H.N. 2016-05-30
150 A male 25 B.H.N. 2014-11-21
151 A male 25 B.H.N. 2014-11-21
152 A male 25 B.H.N. 2016-05-30
153 A male 25 B.H.N. 2016-05-30
154 A male 25 B.H.N. 2014-11-21
155 A male 25 B.H.N. 2014-11-21
156 A male 25 B.H.N. 2016-05-30
157 A male 25 B.H.N. 2014-11-21
158 A male 25 B.H.N. 2016-05-30
159 A male 25 B.H.N. 2016-05-30
160 A male 25 B.H.N. 2014-11-21
161 A male 25 B.H.N. 2014-11-21
162 A male 25 B.H.N. 2016-05-30
163 A male 25 B.H.N. 2014-11-21
164 A male 25 B.H.N. 2016-05-30
165 A male 25 B.H.N. 2016-05-30
166 A male 25 B.H.N. 2014-11-21
167 A male 25 B.H.N. 2016-05-30
168 A male 25 B.H.N. 2014-11-21
169 A male 25 B.H.N. 2014-11-21
170 A male 25 B.H.N. 2016-05-30
171 A male 25 B.H.N. 2016-05-30
172 A male 25 B.H.N. 2014-11-21
173 A male 25 B.H.N. 2014-11-21
174 A male 25 B.H.N. 2016-05-30
175 A male 25 B.H.N. 2014-11-21
176 A male 25 B.H.N. 2016-05-30
177 A male 25 B.H.N. 2016-05-30
178 A male 25 B.H.N. 2014-11-21
179 A male 25 B.H.N. 2014-11-21
180 A male 25 B.H.N. 2016-05-30
181 A male 25 B.H.N. 2014-11-21
182 A male 25 B.H.N. 2016-05-30
183 A male 25 B.H.N. 2014-11-21
184 A male 25 B.H.N. 2016-05-30
185 A male 25 B.H.N. 2016-05-30
186 A male 25 B.H.N. 2014-11-21
187 A male 25 B.H.N. 2014-11-21
188 A male 25 B.H.N. 2016-05-30
189 alders <NA> NA Woodland Trust 2016-12-09
190 B <NA> NA F.F.N. 2016-12-09
191 B <NA> NA F.F.N. 2016-05-30
192 B <NA> NA F.F.N. 2016-02-01
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193 B <NA> NA F.F.N. 2016-12-20
194 B <NA> NA F.F.N. 2016-12-22
195 B <NA> NA F.F.N. 2016-12-09
196 B <NA> NA F.F.N. 2016-02-01
197 B <NA> NA F.F.N. 2017-01-05
198 B <NA> NA F.F.N. 2016-12-20
199 B <NA> NA F.F.N. 2016-05-30
200 B <NA> NA F.F.N. 2017-01-04
201 B <NA> NA F.F.N. 2017-01-05
202 B <NA> NA F.F.N. 2016-05-30
203 B <NA> NA F.F.N. 2016-02-01
204 B <NA> NA F.F.N. 2017-01-04
205 B <NA> NA F.F.N. 2016-12-09
206 B <NA> NA F.F.N. 2017-01-05
207 B <NA> NA F.F.N. 2016-05-30
208 B <NA> NA F.F.N. 2016-02-01
209 B <NA> NA F.F.N. 2016-12-09
210 B <NA> NA F.F.N. 2017-01-04
211 B <NA> NA F.F.N. 2016-02-09
212 B <NA> NA F.F.N. 2017-01-04
213 B <NA> NA F.F.N. 2016-05-30
214 B <NA> NA F.F.N. 2016-12-09
215 B <NA> NA F.F.N. 2017-01-05
216 B <NA> NA F.F.N. 2016-12-09
217 B <NA> NA F.F.N. 2016-05-30
218 B <NA> NA F.F.N. 2017-01-04
219 B <NA> NA F.F.N. 2017-01-05
220 B <NA> NA F.F.N. 2016-02-09
221 B <NA> NA F.F.N. 2016-05-30
222 B <NA> NA F.F.N. 2017-01-05
223 B <NA> NA F.F.N. 2016-12-09
224 B <NA> NA F.F.N. 2016-02-09
225 B <NA> NA F.F.N. 2017-01-04
226 B <NA> NA F.F.N. 2016-12-09
227 B <NA> NA F.F.N. 2016-05-30
228 B <NA> NA F.F.N. 2017-01-04
229 B <NA> NA F.F.N. 2016-02-09
230 B <NA> NA F.F.N. 2017-01-05
231 B <NA> NA F.F.N. 2016-02-10
232 B <NA> NA F.F.N. 2016-12-22
233 B <NA> NA F.F.N. 2016-12-09
234 B <NA> NA F.F.N. 2016-05-30
235 B <NA> NA F.F.N. 2016-12-09
236 B <NA> NA F.F.N. 2016-12-22
237 B <NA> NA F.F.N. 2016-02-10
238 B <NA> NA F.F.N. 2016-05-30
239 B <NA> NA F.F.N. 2016-12-22
240 B <NA> NA F.F.N. 2016-12-09
241 B <NA> NA F.F.N. 2016-06-11
242 B <NA> NA F.F.N. 2016-05-30
243 B <NA> NA F.F.N. 2016-02-10
244 B <NA> NA F.F.N. 2016-12-09
245 B <NA> NA F.F.N. 2016-12-22
246 B <NA> NA F.F.N. 2016-05-30
247 B <NA> NA F.F.N. 2016-02-10
248 B <NA> NA F.F.N. 2016-05-30
249 B <NA> NA F.F.N. 2016-12-22
250 B <NA> NA F.F.N. 2016-12-09
251 B <NA> NA F.F.N. 2016-05-30
252 B <NA> NA F.F.N. 2016-12-22
253 B <NA> NA F.F.N. 2016-12-09
254 B <NA> NA F.F.N. 2016-05-30
255 B <NA> NA F.F.N. 2016-12-09
256 B <NA> NA F.F.N. 2016-12-22
257 B <NA> NA F.F.N. 2016-05-30
258 B <NA> NA F.F.N. 2016-12-09
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259 B <NA> NA F.F.N. 2016-12-22
260 B <NA> NA F.F.N. 2016-12-12
261 B <NA> NA F.F.N. 2016-12-09
262 B <NA> NA F.F.N. 2016-05-30
263 B <NA> NA F.F.N. 2016-12-22
264 B <NA> NA F.F.N. 2016-05-30
265 B <NA> NA F.F.N. 2016-12-09
266 B <NA> NA F.F.N. 2016-12-22
267 B <NA> NA F.F.N. 2016-12-12
268 B <NA> NA F.F.N. 2016-12-22
269 B <NA> NA F.F.N. 2016-12-12
270 B <NA> NA F.F.N. 2016-12-09
271 B <NA> NA F.F.N. 2016-05-30
272 B <NA> NA F.F.N. 2016-12-22
273 B <NA> NA F.F.N. 2016-05-30
274 B <NA> NA F.F.N. 2016-12-20
275 B <NA> NA F.F.N. 2016-06-11
276 B <NA> NA F.F.N. 2016-12-09
277 B <NA> NA F.F.N. 2019-01-11
278 B <NA> NA F.F.N. 2016-05-30
279 B <NA> NA F.F.N. 2016-06-11
280 B <NA> NA F.F.N. 2016-12-09
281 B <NA> NA F.F.N. 2016-12-20
282 B <NA> NA F.F.N. 2018-01-12
283 B <NA> NA F.F.N. 2016-05-30
284 B <NA> NA F.F.N. 2016-12-09
285 B <NA> NA F.F.N. 2016-12-22
286 B <NA> NA F.F.N. 2016-06-11
287 B <NA> NA F.F.N. 2016-05-30
288 B <NA> NA F.F.N. 2017-01-03
289 B <NA> NA F.F.N. 2016-12-09
290 B <NA> NA F.F.N. 2016-12-09
291 B <NA> NA F.F.N. 2016-12-22
292 B <NA> NA F.F.N. 2016-05-30
293 B <NA> NA F.F.N. 2016-06-11
294 B <NA> NA F.F.N. 2016-12-12
295 B <NA> NA F.F.N. 2016-12-09
296 B <NA> NA F.F.N. 2016-05-30
297 B <NA> NA F.F.N. 2016-12-22
298 B <NA> NA F.F.N. 2016-05-30
299 B <NA> NA F.F.N. 2016-12-22
300 B <NA> NA F.F.N. 2016-12-09
301 B <NA> NA F.F.N. 2016-12-22
302 B <NA> NA F.F.N. 2016-12-09
303 B <NA> NA F.F.N. 2016-05-30
304 B <NA> NA F.F.N. 2016-06-11
305 B <NA> NA F.F.N. 2016-12-09
306 B <NA> NA F.F.N. 2016-12-21
307 B <NA> NA F.F.N. 2016-05-30
308 B <NA> NA F.F.N. 2016-05-30
309 B <NA> NA F.F.N. 2016-12-22
310 B <NA> NA F.F.N. 2016-12-09
311 B <NA> NA F.F.N. 2016-06-11
312 B <NA> NA F.F.N. 2016-05-30
313 B <NA> NA F.F.N. 2016-12-21
314 B <NA> NA F.F.N. 2016-12-09
315 B <NA> NA F.F.N. 2016-12-09
316 B <NA> NA F.F.N. 2016-05-30
317 B <NA> NA F.F.N. 2017-01-03
318 B <NA> NA F.F.N. 2016-12-09
319 B <NA> NA F.F.N. 2016-05-30
320 B <NA> NA F.F.N. 2016-12-21
321 B <NA> NA F.F.N. 2016-12-09
322 B <NA> NA F.F.N. 2016-12-21
323 B <NA> NA F.F.N. 2016-05-30
324 B <NA> NA F.F.N. 2016-12-21
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325 B <NA> NA F.F.N. 2016-12-09
326 B <NA> NA F.F.N. 2016-05-30
327 B <NA> NA F.F.N. 2016-12-21
328 B <NA> NA F.F.N. 2016-12-09
329 B <NA> NA F.F.N. 2016-05-30
330 B <NA> NA F.F.N. 2016-12-20
331 B <NA> NA F.F.N. 2016-12-09
332 B <NA> NA F.F.N. 2016-05-30
333 B <NA> NA F.F.N. 2016-12-21
334 B <NA> NA F.F.N. 2016-05-30
335 B <NA> NA F.F.N. 2016-12-09
336 B <NA> NA F.F.N. 2016-12-20
337 B <NA> NA F.F.N. 2016-12-09
338 B <NA> NA F.F.N. 2016-05-30
339 B <NA> NA F.F.N. 2016-05-30
340 B <NA> NA F.F.N. 2016-12-09
341 B <NA> NA F.F.N. 2016-12-21
342 B <NA> NA F.F.N. 2016-05-30
343 B <NA> NA F.F.N. 2016-12-09
344 B <NA> NA F.F.N. 2016-12-20
345 B <NA> NA F.F.N. 2016-05-30
346 B <NA> NA F.F.N. 2016-12-20
347 B <NA> NA F.F.N. 2016-12-09
348 B <NA> NA F.F.N. 2016-05-30
349 B <NA> NA F.F.N. 2016-12-20
350 B <NA> NA F.F.N. 2016-12-09
351 B <NA> NA F.F.N. 2016-05-30
352 B <NA> NA F.F.N. 2016-12-20
353 B <NA> NA F.F.N. 2016-12-09
354 B <NA> NA F.F.N. 2016-12-22
355 B <NA> NA F.F.N. 2016-05-30
356 B <NA> NA F.F.N. 2016-12-09
357 B <NA> NA F.F.N. 2016-01-30
358 B <NA> NA F.F.N. 2016-05-30
359 B <NA> NA F.F.N. 2016-12-09
360 B <NA> NA F.F.N. 2016-01-30
361 B <NA> NA F.F.N. 2016-12-22
362 B <NA> NA F.F.N. 2016-01-30
363 B <NA> NA F.F.N. 2016-12-09
364 B <NA> NA F.F.N. 2016-05-30
365 B <NA> NA F.F.N. 2016-12-22
366 B <NA> NA F.F.N. 2016-05-30
367 B <NA> NA F.F.N. 2016-12-09
368 B <NA> NA F.F.N. 2016-01-30
369 B <NA> NA F.F.N. 2016-12-22
370 C female 32 Nowton Park 2019-01-11
371 C female 32 Nowton Park 2018-01-12
372 C female 32 Nowton Park 2018-01-12
373 C female 32 Nowton Park 2019-01-11
> ## year growth data (growth) are stored in separate columns for each year
> ## (years 2016, 2017...)
> vnames.old <- c('ID', 'type', 'clone', 'mound .... [TRUNCATED]
> vnames <- vnames.old
> g2016 <- cbind(POP1901.aug[!is.na(POP1901.aug$G2016), vnames], year=2016)
> vnames <- ifelse(vnames.old != 'G2016', vnames.old, 'G2017')
> g2017 <- cbind(POP1901.aug[!is.na(POP1901.aug$G2017), vnames], year=2017)
> vnames <- ifelse(vnames.old != 'G2016', vnames.old, 'G2018')
> g2018 <- cbind(POP1901.aug[!is.na(POP1901.aug$G2018), vnames], year=2018)
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> vnames.new <- c(ifelse(vnames.old != 'G2016', vnames.old, 'growth'), 'year')
> vnames.new <- ifelse(vnames.new != 'ID', vnames.new, 'tree')
> names(g2016) <- vnames.new
> names(g2017) <- vnames.new
> names(g2018) <- vnames.new
> popg <- rbind(g2016, g2017, g2018)
> popg <- data.frame(popg, yearf=factor(popg$year))
> ## Exclude trees of unknown provenance...
> ss1 <- popg[substr(popg[,'type'],1,1) == 'P',]
> table(ss1$mound, ss1$year, ss1$type)
, , = Alnus
2016 2017 2018
none 0 0 0
yes 0 0 0
, , = P.n.betulifolia
2016 2017 2018
none 11 0 1
yes 5 18 17
, , = P.nigra
2016 2017 2018
none 1 0 0
yes 0 1 1
, , = unknown
2016 2017 2018
none 0 0 0
yes 0 0 0
> ## Location plots. x _and_ y must be present...
> poploc <- POP1901.aug[!is.na(POP1901.aug$x) & !is.na(POP1901.aug$y),]
> poploc
ID date x y mound G2016 G2017 G2018 coord type
4 A01 05/01/2017 2.0 1.9 none NA NA NA 1 P.n.betulifolia
5 A01 09/12/2016 1.7 1.8 none NA NA NA 1 P.n.betulifolia
10 A02 05/01/2017 3.1 8.9 none NA NA NA 1 P.n.betulifolia
14 A02 09/12/2016 3.3 8.7 none NA NA NA 1 P.n.betulifolia
22 A03 09/12/2016 2.1 17.1 none NA NA NA 1 P.n.betulifolia
28 A04 09/12/2016 4.2 19.2 none NA NA NA 1 P.n.betulifolia
31 A05 09/12/2016 4.8 21.8 yes NA NA NA 1 P.n.betulifolia
45 A07 09/12/2016 4.9 25.2 yes NA NA NA 1 P.n.betulifolia
54 A08 09/12/2016 6.4 24.8 none NA NA NA 1 P.n.betulifolia
60 A09 09/12/2016 4.6 29.6 yes NA NA NA 1 P.n.betulifolia
68 A10 09/12/2016 12.5 33.5 none NA NA NA 1 P.n.betulifolia
76 A11 09/12/2016 4.3 33.5 <NA> NA NA NA 1 P.n.betulifolia
83 A12 09/12/2016 6.0 33.5 none 1.07 NA NA 1 P.n.betulifolia
91 A13 09/12/2016 4.1 37.7 <NA> NA NA NA 1 P.n.betulifolia
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97 A14 21/12/2016 2.8 49.7 none NA NA NA 1 P.n.betulifolia
99 A14 09/12/2016 2.8 49.6 <NA> NA NA NA 1 P.n.betulifolia
105 A15 09/12/2016 5.4 58.4 none NA NA NA 1 P.n.betulifolia
189 alder1 09/12/2016 1.7 20.2 <NA> NA NA NA 1 Alnus
190 B01 09/12/2016 -1.8 -1.8 <NA> NA NA NA 1 unknown
197 B02 05/01/2017 5.5 0.5 yes NA NA NA 1 unknown
200 B02 04/01/2017 4.0 0.0 yes NA NA NA 1 unknown
201 B03 05/01/2017 12.4 1.9 yes NA NA NA 1 unknown
204 B03 04/01/2017 10.0 1.0 yes 1.28 NA NA 1 unknown
206 B04 05/01/2017 8.3 1.9 yes NA NA NA 1 unknown
210 B04 04/01/2017 7.0 3.0 yes NA NA NA 1 unknown
212 B05 04/01/2017 4.9 5.0 yes 1.03 NA NA 1 unknown
214 B05 09/12/2016 4.9 5.0 <NA> NA NA NA 1 unknown
215 B05 05/01/2017 4.7 5.4 yes NA NA NA 1 unknown
218 B06 04/01/2017 7.0 6.0 yes NA NA NA 1 unknown
219 B06 05/01/2017 8.7 6.8 yes NA NA NA 1 unknown
222 B07 05/01/2017 12.4 6.9 yes NA NA NA 1 unknown
225 B07 04/01/2017 10.0 5.0 yes 1.48 NA NA 1 unknown
228 B08 04/01/2017 10.0 9.0 yes 0.77 NA NA 1 unknown
230 B08 05/01/2017 12.4 11.2 yes NA NA NA 1 unknown
233 B09 09/12/2016 1.3 23.9 <NA> NA NA NA 1 unknown
235 B10 09/12/2016 8.1 23.0 <NA> NA NA NA 1 unknown
240 B11 09/12/2016 12.0 25.2 <NA> NA NA NA 1 unknown
244 B12 09/12/2016 1.3 27.8 <NA> NA NA NA 1 unknown
250 B13 09/12/2016 7.9 27.1 <NA> NA NA NA 1 unknown
253 B14 09/12/2016 8.0 31.1 <NA> NA NA NA 1 unknown
255 B15 09/12/2016 0.6 31.7 <NA> NA NA NA 1 unknown
258 B16 09/12/2016 11.2 34.5 <NA> NA NA NA 1 unknown
261 B17 09/12/2016 0.4 35.4 <NA> NA NA NA 1 unknown
265 B18 09/12/2016 7.8 35.5 <NA> NA NA NA 1 unknown
270 B19 09/12/2016 11.0 37.7 <NA> NA NA NA 1 unknown
276 B20 09/12/2016 0.1 39.7 <NA> NA NA NA 1 P.nigra
280 B21 09/12/2016 7.6 40.5 <NA> NA NA NA 1 P.nigra
284 B22 09/12/2016 4.0 42.2 <NA> NA NA NA 1 unknown
289 B23 09/12/2016 11.0 42.0 <NA> NA NA NA 1 unknown
290 B24 09/12/2016 0.0 43.4 <NA> NA NA NA 1 unknown
295 B25 09/12/2016 7.5 44.0 <NA> NA NA NA 1 unknown
300 B26 09/12/2016 4.1 45.8 <NA> NA NA NA 1 unknown
302 B27 09/12/2016 10.5 46.8 <NA> NA NA NA 1 unknown
305 B28 09/12/2016 0.1 47.2 <NA> NA NA NA 1 unknown
310 B29 09/12/2016 7.5 48.3 <NA> NA NA NA 1 unknown
314 B30 09/12/2016 3.8 49.7 <NA> NA NA NA 1 unknown
315 B31 09/12/2016 10.0 50.4 <NA> NA NA NA 1 unknown
318 B32 09/12/2016 0.0 51.2 <NA> NA NA NA 1 unknown
321 B33 09/12/2016 3.8 54.0 <NA> NA NA NA 1 unknown
325 B34 09/12/2016 7.0 53.5 <NA> NA NA NA 1 unknown
328 B35 09/12/2016 0.0 55.2 <NA> NA NA NA 1 unknown
331 B36 09/12/2016 10.0 55.4 <NA> NA NA NA 1 unknown
335 B37 09/12/2016 0.0 59.1 <NA> NA NA NA 1 unknown
337 B38 09/12/2016 9.6 60.7 <NA> NA NA NA 1 unknown
340 B39 09/12/2016 3.7 61.7 <NA> NA NA NA 1 unknown
343 B40 09/12/2016 0.0 63.3 <NA> NA NA NA 1 unknown
group sex clone source date.ISO
4 A male 25 B.H.N. 2017-01-05
5 A male 25 B.H.N. 2016-12-09
10 A male 25 B.H.N. 2017-01-05
14 A male 25 B.H.N. 2016-12-09
22 A male 25 B.H.N. 2016-12-09
28 A male 25 B.H.N. 2016-12-09
31 A male 25 B.H.N. 2016-12-09
45 A male 25 B.H.N. 2016-12-09
54 A male 25 B.H.N. 2016-12-09
60 A male 25 B.H.N. 2016-12-09
68 A male 25 B.H.N. 2016-12-09
76 A male 25 B.H.N. 2016-12-09
83 A male 25 B.H.N. 2016-12-09
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91 A male 25 B.H.N. 2016-12-09
97 A male 25 B.H.N. 2016-12-21
99 A male 25 B.H.N. 2016-12-09
105 A male 25 B.H.N. 2016-12-09
189 alders <NA> NA Woodland Trust 2016-12-09
190 B <NA> NA F.F.N. 2016-12-09
197 B <NA> NA F.F.N. 2017-01-05
200 B <NA> NA F.F.N. 2017-01-04
201 B <NA> NA F.F.N. 2017-01-05
204 B <NA> NA F.F.N. 2017-01-04
206 B <NA> NA F.F.N. 2017-01-05
210 B <NA> NA F.F.N. 2017-01-04
212 B <NA> NA F.F.N. 2017-01-04
214 B <NA> NA F.F.N. 2016-12-09
215 B <NA> NA F.F.N. 2017-01-05
218 B <NA> NA F.F.N. 2017-01-04
219 B <NA> NA F.F.N. 2017-01-05
222 B <NA> NA F.F.N. 2017-01-05
225 B <NA> NA F.F.N. 2017-01-04
228 B <NA> NA F.F.N. 2017-01-04
230 B <NA> NA F.F.N. 2017-01-05
233 B <NA> NA F.F.N. 2016-12-09
235 B <NA> NA F.F.N. 2016-12-09
240 B <NA> NA F.F.N. 2016-12-09
244 B <NA> NA F.F.N. 2016-12-09
250 B <NA> NA F.F.N. 2016-12-09
253 B <NA> NA F.F.N. 2016-12-09
255 B <NA> NA F.F.N. 2016-12-09
258 B <NA> NA F.F.N. 2016-12-09
261 B <NA> NA F.F.N. 2016-12-09
265 B <NA> NA F.F.N. 2016-12-09
270 B <NA> NA F.F.N. 2016-12-09
276 B <NA> NA F.F.N. 2016-12-09
280 B <NA> NA F.F.N. 2016-12-09
284 B <NA> NA F.F.N. 2016-12-09
289 B <NA> NA F.F.N. 2016-12-09
290 B <NA> NA F.F.N. 2016-12-09
295 B <NA> NA F.F.N. 2016-12-09
300 B <NA> NA F.F.N. 2016-12-09
302 B <NA> NA F.F.N. 2016-12-09
305 B <NA> NA F.F.N. 2016-12-09
310 B <NA> NA F.F.N. 2016-12-09
314 B <NA> NA F.F.N. 2016-12-09
315 B <NA> NA F.F.N. 2016-12-09
318 B <NA> NA F.F.N. 2016-12-09
321 B <NA> NA F.F.N. 2016-12-09
325 B <NA> NA F.F.N. 2016-12-09
328 B <NA> NA F.F.N. 2016-12-09
331 B <NA> NA F.F.N. 2016-12-09
335 B <NA> NA F.F.N. 2016-12-09
337 B <NA> NA F.F.N. 2016-12-09
340 B <NA> NA F.F.N. 2016-12-09
343 B <NA> NA F.F.N. 2016-12-09
> nrow(poploc)
[1] 66
> ## the most recent location coordinates
> lastxy <- by(poploc$date.ISO, list(poploc$ID), max)
> as.numeric(lastxy)
[1] 17171 17171 17144 17144 17144 NA 17144 17144 17144 17144 17144 17144
[13] 17144 17156 17144 NA NA NA NA NA NA NA NA NA
[25] NA NA NA NA NA NA NA NA NA NA NA NA
[37] NA NA NA NA NA NA NA NA NA NA NA NA
[49] NA NA NA NA 17144 17144 17171 17171 17171 17171 17171 17171
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[61] 17171 17144 17144 17144 17144 17144 17144 17144 17144 17144 17144 17144
[73] 17144 17144 17144 17144 17144 17144 17144 17144 17144 17144 17144 17144
[85] 17144 17144 17144 17144 17144 17144 17144 17144 17144 NA NA NA
[97] NA NA NA NA NA NA
> poploc.dates <- data.frame(tree = row.names(lastxy),
+ xydate = as.Date(as.numeric(lastxy), origin='1970-01-01'))
> str(popg)
'data.frame': 83 obs. of 8 variables:
$ tree : Factor w/ 102 levels "A01","A02","A03",..: 1 2 3 4 5 7 8 9 10 11 ...
$ type : Factor w/ 4 levels "Alnus","P.n.betulifolia",..: 2 2 2 2 2 2 2 2 2 2
...
$ clone : int 25 25 25 25 25 25 25 25 25 25 ...
$ mound : Factor w/ 2 levels "none","yes": 1 1 1 1 2 2 1 2 1 2 ...
$ growth: num 0.74 0.45 0.27 0.6 0.99 1.23 1.37 1.29 0.66 1.26 ...
$ sex : Factor w/ 2 levels "female","male": 2 2 2 2 2 2 2 2 2 2 ...
$ year : num 2016 2016 2016 2016 2016 ...
$ yearf : Factor w/ 3 levels "2016","2017",..: 1 1 1 1 1 1 1 1 1 1 ...
> ## ss1 is Populus nigra or P.n.betulifolia (NW Europe 'subspecies')
> str(ss1)
'data.frame': 55 obs. of 8 variables:
$ tree : Factor w/ 102 levels "A01","A02","A03",..: 1 2 3 4 5 7 8 9 10 11 ...
$ type : Factor w/ 4 levels "Alnus","P.n.betulifolia",..: 2 2 2 2 2 2 2 2 2 2
...
$ clone : int 25 25 25 25 25 25 25 25 25 25 ...
$ mound : Factor w/ 2 levels "none","yes": 1 1 1 1 2 2 1 2 1 2 ...
$ growth: num 0.74 0.45 0.27 0.6 0.99 1.23 1.37 1.29 0.66 1.26 ...
$ sex : Factor w/ 2 levels "female","male": 2 2 2 2 2 2 2 2 2 2 ...
$ year : num 2016 2016 2016 2016 2016 ...
$ yearf : Factor w/ 3 levels "2016","2017",..: 1 1 1 1 1 1 1 1 1 1 ...
> print(ss1)
tree type clone mound growth sex year yearf
2 A01 P.n.betulifolia 25 none 0.74 male 2016 2016
16 A02 P.n.betulifolia 25 none 0.45 male 2016 2016
23 A03 P.n.betulifolia 25 none 0.27 male 2016 2016
30 A04 P.n.betulifolia 25 none 0.60 male 2016 2016
35 A05 P.n.betulifolia 25 yes 0.99 male 2016 2016
44 A07 P.n.betulifolia 25 yes 1.23 male 2016 2016
53 A08 P.n.betulifolia 25 none 1.37 male 2016 2016
58 A09 P.n.betulifolia 25 yes 1.29 male 2016 2016
63 A10 P.n.betulifolia 25 none 0.66 male 2016 2016
75 A11 P.n.betulifolia 25 yes 1.26 male 2016 2016
83 A12 P.n.betulifolia 25 none 1.07 male 2016 2016
92 A13 P.n.betulifolia 25 yes 1.09 male 2016 2016
98 A14 P.n.betulifolia 25 none 1.06 male 2016 2016
101 A15 P.n.betulifolia 25 none 0.94 male 2016 2016
110 A17 P.n.betulifolia 25 none 0.51 male 2016 2016
119 A18 P.n.betulifolia 25 none 1.05 male 2016 2016
272 B20 P.nigra NA none 0.28 <NA> 2016 2016
6 A01 P.n.betulifolia 25 yes 1.19 male 2017 2017
11 A02 P.n.betulifolia 25 yes 1.37 male 2017 2017
21 A03 P.n.betulifolia 25 yes 0.95 male 2017 2017
27 A04 P.n.betulifolia 25 yes 1.25 male 2017 2017
34 A05 P.n.betulifolia 25 yes 0.99 male 2017 2017
46 A07 P.n.betulifolia 25 yes 1.32 male 2017 2017
51 A08 P.n.betulifolia 25 yes 1.10 male 2017 2017
56 A09 P.n.betulifolia 25 yes 0.64 male 2017 2017
66 A10 P.n.betulifolia 25 yes 1.41 male 2017 2017
72 A11 P.n.betulifolia 25 yes 0.90 male 2017 2017
81 A12 P.n.betulifolia 25 yes 1.06 male 2017 2017
86 A13 P.n.betulifolia 25 yes 1.01 male 2017 2017
93 A14 P.n.betulifolia 25 yes 1.05 male 2017 2017
102 A15 P.n.betulifolia 25 yes 1.62 male 2017 2017
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113 A17 P.n.betulifolia 25 yes 0.93 male 2017 2017
117 A18 P.n.betulifolia 25 yes 1.03 male 2017 2017
282 B21 P.nigra NA yes 0.78 <NA> 2017 2017
371 C01 P.n.betulifolia 32 yes 1.17 female 2017 2017
372 C02 P.n.betulifolia 32 yes 1.33 female 2017 2017
8 A01 P.n.betulifolia 25 yes 0.70 male 2018 2018
9 A02 P.n.betulifolia 25 yes 0.65 male 2018 2018
17 A03 P.n.betulifolia 25 yes 1.15 male 2018 2018
24 A04 P.n.betulifolia 25 yes 0.81 male 2018 2018
33 A05 P.n.betulifolia 25 yes 1.27 male 2018 2018
42 A07 P.n.betulifolia 25 yes 0.75 male 2018 2018
52 A08 P.n.betulifolia 25 yes 0.85 male 2018 2018
61 A09 P.n.betulifolia 25 yes 1.03 male 2018 2018
62 A10 P.n.betulifolia 25 yes 1.39 male 2018 2018
73 A11 P.n.betulifolia 25 yes 0.80 male 2018 2018
82 A12 P.n.betulifolia 25 yes 1.14 male 2018 2018
88 A13 P.n.betulifolia 25 yes 1.35 male 2018 2018
94 A14 P.n.betulifolia 25 yes 1.09 male 2018 2018
100 A15 P.n.betulifolia 25 yes 0.75 male 2018 2018
111 A17 P.n.betulifolia 25 yes 0.83 male 2018 2018
115 A18 P.n.betulifolia 25 none 0.72 male 2018 2018
277 B21 P.nigra NA yes 1.20 <NA> 2018 2018
370 C01 P.n.betulifolia 32 yes 0.96 female 2018 2018
373 C02 P.n.betulifolia 32 yes 1.17 female 2018 2018
> ##----------------------------------------------------------------------
> ## fit models; first, some lm() models
> ##
> ## Check effect of mound on .... [TRUNCATED]
> lm2016.pnb0 <- lm(growth ~ 1, data = g2016.pnb)
> lm2016.pnb0
Call:
lm(formula = growth ~ 1, data = g2016.pnb)
Coefficients:
(Intercept)
0.9113
> lm2016.pnb1 <- lm(growth ~ mound, data = g2016.pnb)
> lm2016.pnb1
Call:
lm(formula = growth ~ mound, data = g2016.pnb)
Coefficients:
(Intercept) moundyes
0.7927 0.3793
> summary(lm2016.pnb0)
Call:
lm(formula = growth ~ 1, data = g2016.pnb)
Residuals:
Min 1Q Median 3Q Max
-0.6412 -0.2662 0.1087 0.2137 0.4587
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.91125 0.08309 10.97 1.46e-08 ***
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---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.3324 on 15 degrees of freedom
> summary(lm2016.pnb1)
Call:
lm(formula = growth ~ mound, data = g2016.pnb)
Residuals:
Min 1Q Median 3Q Max
-0.52273 -0.18468 0.00264 0.17477 0.57727
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.79273 0.08688 9.124 2.87e-07 ***
moundyes 0.37927 0.15542 2.440 0.0286 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.2882 on 14 degrees of freedom
Multiple R-squared: 0.2984, Adjusted R-squared: 0.2483
F-statistic: 5.955 on 1 and 14 DF, p-value: 0.02857
> anova(lm2016.pnb0, lm2016.pnb1)
Analysis of Variance Table
Model 1: growth ~ 1
Model 2: growth ~ mound
Res.Df RSS Df Sum of Sq F Pr(>F)
1 15 1.6570
2 14 1.1625 1 0.49448 5.955 0.02857 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
> ## moundyes adds 0.379 metres of annual growth.
>
> ##----------------------------------------------------------------------
> ## Check for year di .... [TRUNCATED]
> ss1m
tree type clone mound growth sex year yearf
35 A05 P.n.betulifolia 25 yes 0.99 male 2016 2016
44 A07 P.n.betulifolia 25 yes 1.23 male 2016 2016
58 A09 P.n.betulifolia 25 yes 1.29 male 2016 2016
75 A11 P.n.betulifolia 25 yes 1.26 male 2016 2016
92 A13 P.n.betulifolia 25 yes 1.09 male 2016 2016
6 A01 P.n.betulifolia 25 yes 1.19 male 2017 2017
11 A02 P.n.betulifolia 25 yes 1.37 male 2017 2017
21 A03 P.n.betulifolia 25 yes 0.95 male 2017 2017
27 A04 P.n.betulifolia 25 yes 1.25 male 2017 2017
34 A05 P.n.betulifolia 25 yes 0.99 male 2017 2017
46 A07 P.n.betulifolia 25 yes 1.32 male 2017 2017
51 A08 P.n.betulifolia 25 yes 1.10 male 2017 2017
56 A09 P.n.betulifolia 25 yes 0.64 male 2017 2017
66 A10 P.n.betulifolia 25 yes 1.41 male 2017 2017
72 A11 P.n.betulifolia 25 yes 0.90 male 2017 2017
81 A12 P.n.betulifolia 25 yes 1.06 male 2017 2017
86 A13 P.n.betulifolia 25 yes 1.01 male 2017 2017
93 A14 P.n.betulifolia 25 yes 1.05 male 2017 2017
102 A15 P.n.betulifolia 25 yes 1.62 male 2017 2017
113 A17 P.n.betulifolia 25 yes 0.93 male 2017 2017
117 A18 P.n.betulifolia 25 yes 1.03 male 2017 2017
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282 B21 P.nigra NA yes 0.78 <NA> 2017 2017
371 C01 P.n.betulifolia 32 yes 1.17 female 2017 2017
372 C02 P.n.betulifolia 32 yes 1.33 female 2017 2017
8 A01 P.n.betulifolia 25 yes 0.70 male 2018 2018
9 A02 P.n.betulifolia 25 yes 0.65 male 2018 2018
17 A03 P.n.betulifolia 25 yes 1.15 male 2018 2018
24 A04 P.n.betulifolia 25 yes 0.81 male 2018 2018
33 A05 P.n.betulifolia 25 yes 1.27 male 2018 2018
42 A07 P.n.betulifolia 25 yes 0.75 male 2018 2018
52 A08 P.n.betulifolia 25 yes 0.85 male 2018 2018
61 A09 P.n.betulifolia 25 yes 1.03 male 2018 2018
62 A10 P.n.betulifolia 25 yes 1.39 male 2018 2018
73 A11 P.n.betulifolia 25 yes 0.80 male 2018 2018
82 A12 P.n.betulifolia 25 yes 1.14 male 2018 2018
88 A13 P.n.betulifolia 25 yes 1.35 male 2018 2018
94 A14 P.n.betulifolia 25 yes 1.09 male 2018 2018
100 A15 P.n.betulifolia 25 yes 0.75 male 2018 2018
111 A17 P.n.betulifolia 25 yes 0.83 male 2018 2018
277 B21 P.nigra NA yes 1.20 <NA> 2018 2018
370 C01 P.n.betulifolia 32 yes 0.96 female 2018 2018
373 C02 P.n.betulifolia 32 yes 1.17 female 2018 2018
> nrow(ss1m)
[1] 42
> table(ss1m$year)
2016 2017 2018
5 19 18
> by1 <- by(ss1m$growth, list(ss1m$tree), mean)
> summary(by1[!is.na(by1)])
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.8800 0.9883 1.0500 1.0677 1.1000 1.4000
> ## lm() model, ignoring grouping structure
> lm1 <- lm(growth ~ yearf, data = ss1m)
> lm1
Call:
lm(formula = growth ~ yearf, data = ss1m)
Coefficients:
(Intercept) yearf2017 yearf2018
1.17200 -0.06147 -0.17811
> summary(lm1)
Call:
lm(formula = growth ~ yearf, data = ss1m)
Residuals:
Min 1Q Median 3Q Max
-0.47053 -0.17637 -0.02221 0.15361 0.50947
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.17200 0.10145 11.552 3.69e-14 ***
yearf2017 -0.06147 0.11402 -0.539 0.593
yearf2018 -0.17811 0.11468 -1.553 0.128
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
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Residual standard error: 0.2269 on 39 degrees of freedom
Multiple R-squared: 0.08536, Adjusted R-squared: 0.03845
F-statistic: 1.82 on 2 and 39 DF, p-value: 0.1755
> ##----------------------------------------------------------------------
> ## nlme models
> ## Form a groupedData object for this subset, check year .... [TRUNCATED]
> ss1m.gd <- groupedData(formula = growth ~ yearf | tree, data = ss1m,
+ outer = ~ 1 |sex)
> lme1 <- lme(growth ~ yearf, data = ss1m.gd, random = ~ 1 | tree)
> lme1
Linear mixed-effects model fit by REML
Data: ss1m.gd
Log-restricted-likelihood: -1.2058
Fixed: growth ~ yearf
(Intercept) yearf2017 yearf2018
1.17200000 -0.06147368 -0.17811111
Random effects:
Formula: ~1 | tree
(Intercept) Residual
StdDev: 3.367691e-06 0.2268515
Number of Observations: 42
Number of Groups: 19
> summary(lme1)
Linear mixed-effects model fit by REML
Data: ss1m.gd
AIC BIC logLik
12.4116 20.72941 -1.2058
Random effects:
Formula: ~1 | tree
(Intercept) Residual
StdDev: 3.367691e-06 0.2268515
Fixed effects: growth ~ yearf
Value Std.Error DF t-value p-value
(Intercept) 1.1720000 0.1014511 21 11.552367 0.0000
yearf2017 -0.0614737 0.1140212 21 -0.539143 0.5955
yearf2018 -0.1781111 0.1146791 21 -1.553126 0.1353
Correlation:
(Intr) yr2017
yearf2017 -0.890
yearf2018 -0.885 0.787
Standardized Within-Group Residuals:
Min Q1 Med Q3 Max
-2.07416004 -0.77745556 -0.09789489 0.67714391 2.24584667
Number of Observations: 42
Number of Groups: 19
> ## Parameter estimates from lm() and lme() models are same but p-values
> ## are larger (less significant) for lme model
> ## Conclusion: growth see .... [TRUNCATED]
: A01
[1] 2.63
------------------------------------------------------------
: A02
[1] 2.47
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------------------------------------------------------------
: A03
[1] 2.37
------------------------------------------------------------
: A04
[1] 2.66
------------------------------------------------------------
: A05
[1] 3.25
------------------------------------------------------------
: A06
[1] NA
------------------------------------------------------------
: A07
[1] 3.3
------------------------------------------------------------
: A08
[1] 3.32
------------------------------------------------------------
: A09
[1] 2.96
------------------------------------------------------------
: A10
[1] 3.46
------------------------------------------------------------
: A11
[1] 2.96
------------------------------------------------------------
: A12
[1] 3.27
------------------------------------------------------------
: A13
[1] 3.45
------------------------------------------------------------
: A14
[1] 3.2
------------------------------------------------------------
: A15
[1] 3.31
------------------------------------------------------------
: A16
[1] NA
------------------------------------------------------------
: A17
[1] 2.27
------------------------------------------------------------
: A18
[1] 2.8
------------------------------------------------------------
: A19
[1] NA
------------------------------------------------------------
: A20
[1] NA
------------------------------------------------------------
: A21
[1] NA
------------------------------------------------------------
: A22
[1] NA
------------------------------------------------------------
: A23
[1] NA
------------------------------------------------------------
: A24
[1] NA
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------------------------------------------------------------
: A25
[1] NA
------------------------------------------------------------
: A26
[1] NA
------------------------------------------------------------
: A27
[1] NA
------------------------------------------------------------
: A28
[1] NA
------------------------------------------------------------
: A29
[1] NA
------------------------------------------------------------
: A30
[1] NA
------------------------------------------------------------
: A31
[1] NA
------------------------------------------------------------
: A32
[1] NA
------------------------------------------------------------
: A33
[1] NA
------------------------------------------------------------
: A34
[1] NA
------------------------------------------------------------
: A35
[1] NA
------------------------------------------------------------
: A36
[1] NA
------------------------------------------------------------
: A37
[1] NA
------------------------------------------------------------
: A38
[1] NA
------------------------------------------------------------
: A39
[1] NA
------------------------------------------------------------
: A40
[1] NA
------------------------------------------------------------
: A41
[1] NA
------------------------------------------------------------
: A42
[1] NA
------------------------------------------------------------
: A43
[1] NA
------------------------------------------------------------
: A44
[1] NA
------------------------------------------------------------
: A45
[1] NA
------------------------------------------------------------
: A46
[1] NA
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------------------------------------------------------------
: A47
[1] NA
------------------------------------------------------------
: A48
[1] NA
------------------------------------------------------------
: A49
[1] NA
------------------------------------------------------------
: A50
[1] NA
------------------------------------------------------------
: A51
[1] NA
------------------------------------------------------------
: A52
[1] NA
------------------------------------------------------------
: alder1
[1] NA
------------------------------------------------------------
: B01
[1] 0.58
------------------------------------------------------------
: B02
[1] NA
------------------------------------------------------------
: B03
[1] 1.28
------------------------------------------------------------
: B04
[1] NA
------------------------------------------------------------
: B05
[1] 1.03
------------------------------------------------------------
: B06
[1] NA
------------------------------------------------------------
: B07
[1] 1.48
------------------------------------------------------------
: B08
[1] 0.77
------------------------------------------------------------
: B09
[1] NA
------------------------------------------------------------
: B10
[1] 0.17
------------------------------------------------------------
: B11
[1] 0.29
------------------------------------------------------------
: B12
[1] NA
------------------------------------------------------------
: B13
[1] 0.91
------------------------------------------------------------
: B14
[1] 0.83
------------------------------------------------------------
: B15
[1] NA
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------------------------------------------------------------
: B16
[1] NA
------------------------------------------------------------
: B17
[1] 0.95
------------------------------------------------------------
: B18
[1] NA
------------------------------------------------------------
: B19
[1] NA
------------------------------------------------------------
: B20
[1] 0.28
------------------------------------------------------------
: B21
[1] 1.98
------------------------------------------------------------
: B22
[1] 0.65
------------------------------------------------------------
: B23
[1] 1.25
------------------------------------------------------------
: B24
[1] 0.37
------------------------------------------------------------
: B25
[1] 1.96
------------------------------------------------------------
: B26
[1] NA
------------------------------------------------------------
: B27
[1] 1.46
------------------------------------------------------------
: B28
[1] 0.74
------------------------------------------------------------
: B29
[1] NA
------------------------------------------------------------
: B30
[1] 0.95
------------------------------------------------------------
: B31
[1] 0.6
------------------------------------------------------------
: B32
[1] 0.82
------------------------------------------------------------
: B33
[1] 0.57
------------------------------------------------------------
: B34
[1] NA
------------------------------------------------------------
: B35
[1] 0.45
------------------------------------------------------------
: B36
[1] 0.87
------------------------------------------------------------
: B37
[1] NA
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------------------------------------------------------------
: B38
[1] NA
------------------------------------------------------------
: B39
[1] 1.21
------------------------------------------------------------
: B40
[1] NA
------------------------------------------------------------
: B41
[1] 0.76
------------------------------------------------------------
: B42
[1] NA
------------------------------------------------------------
: B43
[1] NA
------------------------------------------------------------
: B44
[1] NA
------------------------------------------------------------
: B45
[1] 0.23
------------------------------------------------------------
: B46
[1] 0.61
------------------------------------------------------------
: B47
[1] 0.46
------------------------------------------------------------
: C01
[1] 2.13
------------------------------------------------------------
: C02
[1] 2.5
> ## Total growth recorded for each tree over up to 3 years
> by1 <- by(popg$growth, list(popg$tree), sum)
> bytemp <- data.frame(tree=names(by1), growth = as.vector(by1),
+ years = as.vector(table(popg$tree)))
> ## Total tree growth sorted in descending order, with number of
> ## years for which data (of trees in mounds) are available
> bytemp[sort.list(-byt .... [TRUNCATED]
tree growth years
10 A10 3.46 3
13 A13 3.45 3
8 A08 3.32 3
15 A15 3.31 3
7 A07 3.30 3
12 A12 3.27 3
5 A05 3.25 3
14 A14 3.20 3
9 A09 2.96 3
11 A11 2.96 3
18 A18 2.80 3
4 A04 2.66 3
1 A01 2.63 3
102 C02 2.50 2
2 A02 2.47 3
3 A03 2.37 3
17 A17 2.27 3
101 C01 2.13 2
74 B21 1.98 2
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78 B25 1.96 2
60 B07 1.48 1
80 B27 1.46 1
56 B03 1.28 1
76 B23 1.25 1
92 B39 1.21 1
58 B05 1.03 1
70 B17 0.95 1
83 B30 0.95 1
66 B13 0.91 1
89 B36 0.87 1
67 B14 0.83 1
85 B32 0.82 1
61 B08 0.77 1
94 B41 0.76 1
81 B28 0.74 1
75 B22 0.65 1
99 B46 0.61 1
84 B31 0.60 1
54 B01 0.58 1
86 B33 0.57 1
100 B47 0.46 1
88 B35 0.45 1
77 B24 0.37 1
64 B11 0.29 1
73 B20 0.28 1
98 B45 0.23 1
63 B10 0.17 1
6 A06 NA 0
16 A16 NA 0
19 A19 NA 0
20 A20 NA 0
21 A21 NA 0
22 A22 NA 0
23 A23 NA 0
24 A24 NA 0
25 A25 NA 0
26 A26 NA 0
27 A27 NA 0
28 A28 NA 0
29 A29 NA 0
30 A30 NA 0
31 A31 NA 0
32 A32 NA 0
33 A33 NA 0
34 A34 NA 0
35 A35 NA 0
36 A36 NA 0
37 A37 NA 0
38 A38 NA 0
39 A39 NA 0
40 A40 NA 0
41 A41 NA 0
42 A42 NA 0
43 A43 NA 0
44 A44 NA 0
45 A45 NA 0
46 A46 NA 0
47 A47 NA 0
48 A48 NA 0
49 A49 NA 0
50 A50 NA 0
51 A51 NA 0
52 A52 NA 0
53 alder1 NA 0
55 B02 NA 0