### Supplementary Material to Manuscript Thorn et al.

### define data
### variables
### ntaxa: Number of Species per plot
### year.f: year as factor 
### manage: treatment: m=managed, n= unmanaged
### X: longitude
### Y: latitude
### Plot: Name of plot 
### phyl_div_ab: Effect size of phylogenetic diversity

data_org <-
structure(list(ntaxa = c(17L, 33L, 42L, 39L, 33L, 45L, 34L, 35L, 
23L, 42L, 50L, 43L, 23L, 48L, 52L, 6L, 30L, 37L, 46L, 35L, 52L, 
50L, 51L, 38L, 16L, 13L, 34L, 46L, 57L, 46L, 41L, 60L, 34L, 38L, 
27L, 41L, 52L, 64L, 42L, 45L, 56L, 31L, 16L, 34L, 17L, 48L, 21L, 
30L, 28L, 14L, 35L, 21L, 38L, 37L, 42L, 37L, 3L, 49L, 63L, 11L, 
38L, 42L, 45L, 21L, 77L, 45L, 68L, 89L, 56L, 36L, 64L, 37L, 56L, 
35L, 44L, 68L, 59L, 62L, 59L, 61L, 50L, 78L, 48L, 27L, 65L, 48L, 
49L, 55L, 16L, 33L, 19L, 13L, 28L, 18L, 27L, 35L, 12L, 29L, 9L, 
24L, 14L, 16L, 53L, 13L, 14L, 18L, 40L, 15L, 38L, 26L, 40L, 70L, 
30L, 34L, 22L, 27L, 32L, 38L, 42L, 32L, 18L, 10L, 49L, 21L, 26L, 
55L, 35L, 31L, 40L, 59L, 63L, 45L, 36L, 19L, 20L, 7L, 18L, 17L, 
25L, 29L, 22L, 21L, 22L, 30L, 7L, 28L, 31L, 17L, 19L, 49L, 25L, 
25L, 30L, 26L, 21L, 63L, 64L, 58L, 31L, 25L, 29L, 32L, 34L, 40L, 
9L, 26L, 27L, 27L, 24L, 45L, 34L, 18L, 40L, 43L, 24L, 34L), year.f = structure(c(1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L), .Label = c("2008", 
"2009", "2010", "2011"), class = "factor"), manage = structure(c(1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 
1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 
2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), .Label = c("m", 
"n"), class = "factor"), X = c(4597694, 4597813.99, 4597873.98, 
4597897.98, 4597706, 4597933.97, 4597969.97, 4597718, 4597730, 
4597765.99, 4597789.99, 4593742, 4593775.87, 4593782.03, 4593785.11, 
4593791.27, 4593751.24, 4593754.32, 4593891.99, 4593991.98, 4595540.7, 
4595587.08, 4595610.27, 4595633.46, 4595378.38, 4595424.76, 4595447.95, 
4595471.14, 4595140.61, 4595186.3, 4595209.14, 4595254.83, 4595300.52, 
4595323.37, 4595026.38, 4595072.07, 4595094.92, 4596192.32, 4596229.86, 
4596267.41, 4596304.96, 4596342.5, 4596380.05, 4596417.6, 4597694, 
4597813.99, 4597873.98, 4597897.98, 4597706, 4597933.97, 4597969.97, 
4597718, 4597730, 4597765.99, 4597789.99, 4593742, 4593775.87, 
4593782.03, 4593785.11, 4593791.27, 4593751.24, 4593754.32, 4593891.99, 
4593991.98, 4595540.7, 4595587.08, 4595610.27, 4595633.46, 4595378.38, 
4595424.76, 4595447.95, 4595471.14, 4595140.61, 4595186.3, 4595209.14, 
4595254.83, 4595300.52, 4595323.37, 4595026.38, 4595072.07, 4595094.92, 
4596192.32, 4596229.86, 4596267.41, 4596304.96, 4596342.5, 4596380.05, 
4596417.6, 4597694, 4597813.99, 4597873.98, 4597897.98, 4597706, 
4597933.97, 4597969.97, 4597718, 4597730, 4597765.99, 4597789.99, 
4593742, 4593775.87, 4593782.03, 4593785.11, 4593791.27, 4593751.24, 
4593754.32, 4593891.99, 4593991.98, 4595540.7, 4595587.08, 4595610.27, 
4595633.46, 4595378.38, 4595424.76, 4595447.95, 4595471.14, 4595140.61, 
4595186.3, 4595209.14, 4595254.83, 4595300.52, 4595323.37, 4595026.38, 
4595072.07, 4595094.92, 4596192.32, 4596229.86, 4596267.41, 4596304.96, 
4596342.5, 4596380.05, 4596417.6, 4597694, 4597813.99, 4597873.98, 
4597897.98, 4597706, 4597933.97, 4597969.97, 4597718, 4597730, 
4597765.99, 4597789.99, 4593742, 4593775.87, 4593782.03, 4593785.11, 
4593791.27, 4593751.24, 4593754.32, 4593891.99, 4593991.98, 4595540.7, 
4595587.08, 4595610.27, 4595633.46, 4595378.38, 4595424.76, 4595447.95, 
4595471.14, 4595140.61, 4595186.3, 4595209.14, 4595254.83, 4595300.52, 
4595323.37, 4595026.38, 4595072.07, 4595094.92, 4596192.32, 4596229.86, 
4596267.41, 4596304.96, 4596342.5, 4596380.05, 4596417.6), Y = c(5437002, 
5436516.61, 5436273.92, 5436176.84, 5436953.46, 5436031.22, 5435885.6, 
5436904.92, 5436856.38, 5436710.77, 5436613.69, 5440069, 5440617.96, 
5440717.77, 5440767.67, 5440867.48, 5440218.72, 5440268.62, 5440066.89, 
5440065.48, 5441159.32, 5441070.73, 5441026.43, 5440982.13, 5441469.41, 
5441380.81, 5441336.51, 5441292.22, 5441108.72, 5441019.77, 5440975.29, 
5440886.34, 5440797.39, 5440752.91, 5441331.1, 5441242.15, 5441197.67, 
5440730.19, 5440822.87, 5440915.55, 5441008.24, 5441100.92, 5441193.6, 
5441286.29, 5437002, 5436516.61, 5436273.92, 5436176.84, 5436953.46, 
5436031.22, 5435885.6, 5436904.92, 5436856.38, 5436710.77, 5436613.69, 
5440069, 5440617.96, 5440717.77, 5440767.67, 5440867.48, 5440218.72, 
5440268.62, 5440066.89, 5440065.48, 5441159.32, 5441070.73, 5441026.43, 
5440982.13, 5441469.41, 5441380.81, 5441336.51, 5441292.22, 5441108.72, 
5441019.77, 5440975.29, 5440886.34, 5440797.39, 5440752.91, 5441331.1, 
5441242.15, 5441197.67, 5440730.19, 5440822.87, 5440915.55, 5441008.24, 
5441100.92, 5441193.6, 5441286.29, 5437002, 5436516.61, 5436273.92, 
5436176.84, 5436953.46, 5436031.22, 5435885.6, 5436904.92, 5436856.38, 
5436710.77, 5436613.69, 5440069, 5440617.96, 5440717.77, 5440767.67, 
5440867.48, 5440218.72, 5440268.62, 5440066.89, 5440065.48, 5441159.32, 
5441070.73, 5441026.43, 5440982.13, 5441469.41, 5441380.81, 5441336.51, 
5441292.22, 5441108.72, 5441019.77, 5440975.29, 5440886.34, 5440797.39, 
5440752.91, 5441331.1, 5441242.15, 5441197.67, 5440730.19, 5440822.87, 
5440915.55, 5441008.24, 5441100.92, 5441193.6, 5441286.29, 5437002, 
5436516.61, 5436273.92, 5436176.84, 5436953.46, 5436031.22, 5435885.6, 
5436904.92, 5436856.38, 5436710.77, 5436613.69, 5440069, 5440617.96, 
5440717.77, 5440767.67, 5440867.48, 5440218.72, 5440268.62, 5440066.89, 
5440065.48, 5441159.32, 5441070.73, 5441026.43, 5440982.13, 5441469.41, 
5441380.81, 5441336.51, 5441292.22, 5441108.72, 5441019.77, 5440975.29, 
5440886.34, 5440797.39, 5440752.91, 5441331.1, 5441242.15, 5441197.67, 
5440730.19, 5440822.87, 5440915.55, 5441008.24, 5441100.92, 5441193.6, 
5441286.29), Plot = structure(c(1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 
9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L, 
22L, 23L, 24L, 25L, 26L, 27L, 28L, 29L, 30L, 31L, 32L, 33L, 34L, 
35L, 36L, 37L, 38L, 39L, 40L, 41L, 42L, 43L, 44L, 1L, 2L, 3L, 
4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 
18L, 19L, 20L, 21L, 22L, 23L, 24L, 25L, 26L, 27L, 28L, 29L, 30L, 
31L, 32L, 33L, 34L, 35L, 36L, 37L, 38L, 39L, 40L, 41L, 42L, 43L, 
44L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 
14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 24L, 25L, 26L, 
27L, 28L, 29L, 30L, 31L, 32L, 33L, 34L, 35L, 36L, 37L, 38L, 39L, 
40L, 41L, 42L, 43L, 44L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 
10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 
23L, 24L, 25L, 26L, 27L, 28L, 29L, 30L, 31L, 32L, 33L, 34L, 35L, 
36L, 37L, 38L, 39L, 40L, 41L, 42L, 43L, 44L), .Label = c("FAE_1", 
"FAE_11", "FAE_16", "FAE_18", "FAE_2", "FAE_21", "FAE_24", "FAE_3", 
"FAE_4", "FAE_7", "FAE_9", "FKN_1", "FKN_12", "FKN_14", "FKN_15", 
"FKN_17", "FKN_4", "FKN_5", "FKO_4", "FKO_6", "LAO_10", "LAO_12", 
"LAO_13", "LAO_14", "LAO_3", "LAO_5", "LAO_6", "LAO_7", "LAW_10", 
"LAW_12", "LAW_13", "LAW_15", "LAW_17", "LAW_18", "LAW_5", "LAW_7", 
"LAW_8", "T3_51", "T3_52", "T3_53", "T3_54", "T3_55", "T3_56", 
"T3_57"), class = "factor"), phyl_div_ab = c(0.887817913, -0.057214305, 
0.231648224, -1.12788543, 0.050134297, -0.103010893, 0.22311053, 
0.84983028, 0.084495899, -0.498347155, -0.951004036, 1.452853502, 
-0.825628094, -2.236668246, -1.699953798, -0.728893794, 0.587143462, 
0.650036195, -0.47969927, 0.107632991, -3.106711681, -4.280499885, 
-3.154372866, 0.313415329, -1.92883057, 0.481782296, -0.160160076, 
-3.038991896, -1.691009515, 0.371862353, -2.132710876, -0.773893512, 
-1.736066886, -0.882847975, -0.350287689, -1.114510264, -1.955745464, 
-2.121159619, -2.007567762, -3.509303875, -0.753091482, -3.543739792, 
-3.047101599, -3.7069813, 1.032170008, 1.21906781, -1.561035756, 
-0.301854215, 0.477451271, -4.984271691, -0.563664831, 0.067605154, 
-1.229593207, -0.966118752, -0.233650707, -2.413201026, -2.142342106, 
-1.724080244, -0.547753709, 0.125231917, 1.03851521, -0.980486706, 
0.247869919, 1.069437781, -6.11127315, -4.610789847, -0.404167815, 
0.045921689, -0.706621321, -6.27596649, -4.586679363, -4.274432326, 
-4.64636566, -4.724244464, -1.352778145, 2.113868553, -2.225859429, 
-3.965008465, -2.719045033, -1.815592393, -3.503895609, 2.511011391, 
-2.218693756, -3.778781084, 0.648957743, -3.55896329, -3.040285858, 
-1.432351231, -0.382171436, 0.14663518, -0.779063097, 0.654831994, 
0.559624326, -0.9312478, -1.730226967, -0.788189181, -0.618703787, 
-1.279544352, 0.353167592, -1.105527467, -1.782143776, -0.028046868, 
-1.223572418, 1.466454156, -0.312979339, -0.189200887, 0.322011286, 
0.747058778, 0.31321308, 0.08246737, -0.864520007, -2.630368348, 
-5.135193502, -1.121276074, -3.685521716, 0.723903368, -0.655279103, 
-0.488231255, -0.809064119, -0.8212666, -0.155413505, -1.920562385, 
0.595087222, -3.041664, -2.645729777, -3.724513448, -1.102405434, 
-1.783714586, -1.393949322, -0.933980466, -3.769891483, -2.912849034, 
-0.838765306, 0.178876316, -1.28327955, -1.831317684, -1.671325427, 
-1.130519702, -0.947247378, -0.214421721, 0.136175944, -0.194362588, 
0.221340315, -0.323675179, 0.334714105, 1.563840599, -0.615135325, 
-0.839563836, -0.116472639, -1.344926354, 1.969335362, 0.060852782, 
-0.184314998, 0.85921089, -0.126202144, -0.306863063, -2.984402725, 
-2.821104412, 0.060678249, -2.257118754, 0.439526247, 0.251705237, 
0.231967461, 0.308063377, -0.197744583, -2.306001439, -1.410364627, 
-1.677714242, -0.518578054, -0.217978784, -0.360037101, -0.849240732, 
-1.261087423, -2.423420351, -0.89965069, -0.668714496)), .Names = c("ntaxa", 
"year.f", "manage", "X", "Y", "Plot", "phyl_div_ab"), class = "data.frame", row.names = c(NA, 
-176L))

library("multcomp")
library("lme4")
set.seed(290875)

### define contrast matrix for comparison of management type for each year
### and year differences for each management type
K <- structure(
    c( 1,  0,  0,  0, -1,  0,  0,  0, 
       0,  0,  0,  1,  0,  0,  1, -1, 
       0,  0,  0,  0,  0,  0,  1,  0, 
       0,  1, -1,  0,  0,  0,  0,  0, 
       0,  1,  0,  0,  1,  0,  0,  0, 
      -1,  0,  0,  0,  0,  0,  0, -1, 
       0,  0,  0, -1,  0,  0,  0,  0, 
       0,  1, -1,  0,  0,  0, -1,  0, 
       0,  0,  0,  0,  1, -1,  0,  0, 
       0, -1,  0,  0,  0,  0,  0,  1), .Dim = c(10L, 8L),
    .Dimnames = list(c(
        "year.f1:managem - year.f1:managen",
        "year.f2:managem - year.f2:managen", 
        "year.f3:managem - year.f3:managen",
        "year.f4:managem - year.f4:managen", 
        "year.f2:managem - year.f1:managem",
        "year.f3:managem - year.f2:managem", 
        "year.f4:managem - year.f3:managem",
        "year.f2:managen - year.f1:managen", 
        "year.f3:managen - year.f2:managen",
        "year.f4:managen - year.f3:managen"), 
    c(
        "year.f1:managem", 
        "year.f2:managem",
        "year.f3:managem", 
        "year.f4:managem", 
        "year.f1:managen", 
        "year.f2:managen",
        "year.f3:managen", 
        "year.f4:managen"
    ))
)

###############################Number of species#############################################
### fit mixed model
erg.ntaxa <- lmer(ntaxa ~ year.f:manage + (1|X) + (1|Y) + (1|X*Y) + (1|Plot) - 1, 
                  data = data_org) 
### estimate contrasts
ergmc <- glht(erg.ntaxa, linfct = K)
### simultaneous inference
summary(ergmc)

##############################Phylogenetic diversity#########################################
### fit mixed model
erg.phyl_div_ab <- lmer(phyl_div_ab ~ year.f:manage + (1|X) + (1|Y) + (1|X*Y) + (1|Plot) - 1, 
                        data = data_org)
### estimate contrasts
ergmcabu <- glht(erg.phyl_div_ab, linfct = K)
### simultaneous inference
summary(ergmcabu)

sessionInfo()