analysis of the growth of native black poplars populus ... fileacrocephalus data services ltd...

Acrocephalus Data Services Ltd

PNB.2019.doc [1/33]

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 ...



$ 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)


$ date : Factor w/ 17 levels "01/02/2016","03/01/2017",..: 13 13 13 13 13 13 13

13 13 13 ...


$ 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 ...



$ 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)



$ 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)



$ 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 ...


...

$ group : Factor w/ 4 levels "A","alders","B",..: 1 1 1 1 1 1 1 1 1 1 ...


$ 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


6 A01 12/01/2018 NA NA yes NA 1.19 NA 0 P.n.betulifolia


8 A01 11/01/2019 NA NA yes NA NA 0.70 0 P.n.betulifolia























31 A05 09/12/2016 4.8 21.8 yes NA NA NA 1 P.n.betulifolia




35 A05 22/12/2016 NA NA yes 0.99 NA NA 0 P.n.betulifolia





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71 A11 22/12/2016 NA NA yes NA NA NA 0 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


85 A13 22/12/2016 NA NA yes NA NA NA 0 P.n.betulifolia





















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115 A18 11/01/2019 NA NA none NA NA 0.72 0 P.n.betulifolia

























































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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


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



197 B02 05/01/2017 5.5 0.5 yes NA NA NA 1 unknown







204 B03 04/01/2017 10.0 1.0 yes 1.28 NA NA 1 unknown










214 B05 09/12/2016 4.9 5.0 <NA> NA NA NA 1 unknown
























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239 B11 22/12/2016 NA NA yes 0.29 NA NA 0 unknown




















259 B16 22/12/2016 NA NA yes NA NA NA 0 unknown









268 B19 22/12/2016 NA NA yes 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


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




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






















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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


PNB.2019.doc [17/33]

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


PNB.2019.doc [18/33]

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




> ## 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')


> vnames <- ifelse(vnames.old != 'G2016', vnames.old, 'G2018')



PNB.2019.doc [19/33]

> vnames.new <- c(ifelse(vnames.old != 'G2016', vnames.old, 'growth'), 'year')

> vnames.new <- ifelse(vnames.new != 'ID', vnames.new, 'tree')

> names(g2016) <- 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













83 A12 09/12/2016 6.0 33.5 none 1.07 NA NA 1 P.n.betulifolia



PNB.2019.doc [20/33]




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
















































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


PNB.2019.doc [21/33]

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


PNB.2019.doc [22/33]

[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)


$ tree : Factor w/ 102 levels "A01","A02","A03",..: 1 2 3 4 5 7 8 9 10 11 ...


...

$ clone : int 25 25 25 25 25 25 25 25 25 25 ...


$ growth: num 0.74 0.45 0.27 0.6 0.99 1.23 1.37 1.29 0.66 1.26 ...


$ 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)


$ tree : Factor w/ 102 levels "A01","A02","A03",..: 1 2 3 4 5 7 8 9 10 11 ...


...

$ clone : int 25 25 25 25 25 25 25 25 25 25 ...


$ growth: num 0.74 0.45 0.27 0.6 0.99 1.23 1.37 1.29 0.66 1.26 ...


$ 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




35 A05 P.n.betulifolia 25 yes 0.99 male 2016 2016












272 B20 P.nigra NA none 0.28 <NA> 2016 2016
















PNB.2019.doc [23/33]



282 B21 P.nigra NA yes 0.78 <NA> 2017 2017

371 C01 P.n.betulifolia 32 yes 1.17 female 2017 2017


















277 B21 P.nigra NA yes 1.20 <NA> 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 ***


PNB.2019.doc [24/33]

---

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:


-0.52273 -0.18468 0.00264 0.17477 0.57727

Coefficients:


(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


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























PNB.2019.doc [25/33]

282 B21 P.nigra NA yes 0.78 <NA> 2017 2017


















277 B21 P.nigra NA yes 1.20 <NA> 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:


-0.47053 -0.17637 -0.02221 0.15361 0.50947

Coefficients:


(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


PNB.2019.doc [26/33]


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


PNB.2019.doc [27/33]

------------------------------------------------------------

: 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


PNB.2019.doc [29/33]

------------------------------------------------------------

: 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


PNB.2019.doc [32/33]

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


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57 B04 NA 0

59 B06 NA 0

62 B09 NA 0

65 B12 NA 0

68 B15 NA 0

69 B16 NA 0

71 B18 NA 0

72 B19 NA 0

79 B26 NA 0

82 B29 NA 0

87 B34 NA 0

90 B37 NA 0

91 B38 NA 0

93 B40 NA 0

95 B42 NA 0

96 B43 NA 0

97 B44 NA 0

> sink()

analysis of the growth of native black poplars populus ... fileacrocephalus data services ltd...

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