# An introduction to SiTree

#### Clara Antón Fernández

#### 03-08-2021

# Introduction

The sitree package provides a framework to implement Single Tree forest growth models in a fast and memory efficient way. It keep tracks of all alive, dead, and removed trees in a robust and efficient way. SiTree is designed to run single tree simulations where trees can be defined by two time-dependent variables (such as diameter (or basal area), and height), and on time-independent variable, such as tree species. SiTree simulates birth, growth, and death of trees as well as management. Functions can also be defined that affect characteristics of the stand (external modifiers), such as climate change, or fertilization.

The easiest way to start with your own simulation is probably to modify the example functions provided (see the Test Equations vignette).

# Input data

Two types of input are required by SiTree: tree level and stand level. Tree level information is passed in `tree.df`, while stand level information is passed in `stand.df`.

`tree.df` should be a data frame with four columns named `plot.id`, `treeid`, `dbh`, `height`, and `tree.sp`, which correspond to a stand/plot ID, a tree ID, diameter, height, and tree species.

Plot and stand data is passed in `stand.df`, which should be a data frame or a list, with at least a column or element named `plot.id` which should contain all the plot IDs present in `tree.df`. Typical information provided in `stand.df` are plot size, elevation, site index, plot coordinates, distance to road, temperature or precipitation.

An example of tree data and stand data are provided.

    library(sitree)

    ## Loading required package: ggplot2

    head(tr)

    ##   plot.id treeid dbh height tree.sp
    ## 1      91 108286 149    118      53
    ## 2      91 137120 133    107      53
    ## 3      91 108287  55     52      53
    ## 4      91 108268 235    151      49
    ## 5      91 137124 187    137      53
    ## 6      91 108271 162    124      49

    head(fl)

    ## $plot.id
    ##   [1]   1   2   3   4   5   6   7   8   9  10  11  12  13  14  15  16  17  18
    ##  [19]  19  20  21  22  23  24  25  26  27  28  29  30  31  32  33  34  35  36
    ##  [37]  37  38  39  40  41  42  43  44  45  46  47  48  49  50  51  52  53  54
    ##  [55]  55  56  57  58  59  60  61  62  63  64  65  66  67  68  69  70  71  72
    ##  [73]  73  74  75  76  77  78  79  80  81  82  83  84  85  86  87  88  89  90
    ##  [91]  91  92  93  94  95  96  97  98  99 100
    ##
    ## $SI.m
    ##   [1] 11 11  8 11  8  8  6 14 11 11  8 23  6  6 11 11 17 14  8  8  8  6 17  8  6
    ##  [26]  6 20 11 11  8 11 11  8 11 17  8  8  8 14 11 14 14 11 17 11 11 26 14 11 14
    ##  [51] 11 11  6 14 17  8  8 11  8 17 14 14 14 14 17  8  8  8 14 11 11 20 17 17 11
    ##  [76] 14 11  8 11  8  8  8  8 17  8 11  8  6 11  6  8  8  8 23 17 11  8 14  8  8
    ##
    ## $SI.spp
    ##   [1] 2 2 3 3 3 3 3 3 3 3 2 1 2 3 3 3 1 3 3 3 3 2 3 3 2 2 2 2 3 2 2 2 2 2 1 2 2
    ##  [38] 3 3 3 2 3 3 3 3 3 1 3 2 2 3 3 3 3 1 3 2 2 3 3 3 3 2 3 1 3 3 3 2 2 2 1 2 1
    ##  [75] 3 3 3 2 3 3 2 2 3 3 3 3 2 3 3 2 3 3 3 1 3 2 3 1 2 1
    ##
    ## $prop.plot
    ##   [1] 5 5 0 0 0 0 5 0 5 0 0 0 0 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 0 0 0 0 0 0 0
    ##  [38] 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 7 3 0 0 0 0 0 0 6
    ##  [75] 4 5 0 0 0 0 0 0 0 0 0 0 0 7 0 0 0 0 0 5 7 0 0 0 0 0
    ##
    ## $ha2total
    ##   [1]  450.6190  450.6190  901.2380  901.2380  901.2380  901.2380  450.6190
    ##   [8]  901.2380  450.6190  901.2380  901.2380  901.2380  901.2380  630.8666
    ##  [15]  901.2380  901.2380  901.2380  901.2380  901.2380  901.2380  901.2380
    ##  [22]  901.2380  901.2380  901.2380  901.2380  901.2380  901.2380  901.2380
    ##  [29]  901.2380  360.4952  901.2380  901.2380  901.2380  901.2380  901.2380
    ##  [36]  901.2380  901.2380  901.2380  901.2380  901.2380  901.2380  901.2380
    ##  [43]  901.2380  901.2380  901.2380  270.3714  901.2380  901.2380  901.2380
    ##  [50]  901.2380  901.2380  901.2380  901.2380  901.2380  901.2380  901.2380
    ##  [57]  901.2380  901.2380  901.2380  901.2380  901.2380  901.2380  901.2380
    ##  [64]  901.2380  901.2380  630.8666  270.3714  901.2380  901.2380  901.2380
    ##  [71]  901.2380  901.2380  901.2380  540.7428  360.4952  450.6190  901.2380
    ##  [78]  901.2380  901.2380  901.2380  901.2380  901.2380  901.2380  901.2380
    ##  [85]  901.2380  901.2380  901.2380  630.8666  901.2380  901.2380  901.2380
    ##  [92] 2693.6380  901.2380  450.6190  630.8666  901.2380  901.2380  901.2380
    ##  [99]  901.2380  901.2380
    ##
    ## $tree2ha
    ##   [1]  80.00000  80.00000  40.00000  40.00000  40.00000  40.00000  80.00000
    ##   [8]  40.00000  80.00000  40.00000  40.00000  40.00000  40.00000  57.14286
    ##  [15]  40.00000  40.00000  40.00000  40.00000  40.00000  40.00000  40.00000
    ##  [22]  40.00000  40.00000  40.00000  40.00000  40.00000  40.00000  40.00000
    ##  [29]  40.00000 100.00000  40.00000  40.00000  40.00000  40.00000  40.00000
    ##  [36]  40.00000  40.00000  40.00000  40.00000  40.00000  40.00000  40.00000
    ##  [43]  40.00000  40.00000  40.00000 133.33333  40.00000  40.00000  40.00000
    ##  [50]  40.00000  40.00000  40.00000  40.00000  40.00000  40.00000  40.00000
    ##  [57]  40.00000  40.00000  40.00000  40.00000  40.00000  40.00000  40.00000
    ##  [64]  40.00000  40.00000  57.14286 133.33333  40.00000  40.00000  40.00000
    ##  [71]  40.00000  40.00000  40.00000  66.66667 100.00000  80.00000  40.00000
    ##  [78]  40.00000  40.00000  40.00000  40.00000  40.00000  40.00000  40.00000
    ##  [85]  40.00000  40.00000  40.00000  57.14286  40.00000  40.00000  40.00000
    ##  [92]  40.00000  40.00000  80.00000  57.14286  40.00000  40.00000  40.00000
    ##  [99]  40.00000  40.00000

## Calculation of input variables required by the main functions: The fn.prep.common.vars function

Many of the main functions in a simulation use plot-level variables, like competition indices (e.g. plot-level basal area). In order to make the code more transparent, compact, and robust all variables required in the sub-models that can be estimated from tree, stand and plot variables are calculated in one place. The `fn.prep.common.vars` function.

For example, if diameter increment is estimated as a function of initial diameter, stand basal area and number of trees per ha, both stand basal area variable and number of trees per ha should be calculated in `fn.prep.common.vars`. Other typical examples of variables calculated in the `fn.prep.common.vars` are top height (the mean height of the trees with the largest diameter in a stand), basal area of larger trees, or tree volume.

The `fn.prep.common.vars` function should be provided by the user, so it fits the particular needs of the growth model selected and the data. Calculating the most common variables used in forestry should be straight forward as they are already provided in either the SiTree or the SiTreeE package.

An example of a `fn.prep.common.vars` is provided in SiTree.

    prep.common.vars.fun

    ## function (tr, fl, i.period, this.period, common.vars, vars.required,
    ##     period.length, n.periods, ...)
    ## {
    ##     if (length(common.vars) > 1)
    ##         res <- common.vars
    ##     else res <- list()
    ##     others <- list(...)
    ##     all.plot.vars <- data.table(plot.id = fl$plot.id, SI.m = fl$SI.m,
    ##         SI.spp = fl$SI.spp, tree2ha = fl$tree2ha, ha2total = fl$ha2total,
    ##         kom = fl$kom)
    ##     res$i.stand <- match(tr$data[["plot.id"]], fl[["plot.id"]])
    ##     res$i.tree <- match(fl$plot.id, tr$data$plot.id)
    ##     res$tree.BA.m2 <- pi * (tr$data[["dbh.mm"]][, this.period]/1000/2)^2
    ##     res$SBA.m2.ha <- tapply(res$tree.BA.m2 * fl[["tree2ha"]][res$i.stand],
    ##         list(plot.id = tr$data[["plot.id"]]), FUN = sum)
    ##     i.match.tapply <- match(tr$data[["plot.id"]], names(res$SBA.m2.ha))
    ##     res$SBA.m2.ha <- as.vector(res$SBA.m2.ha[i.match.tapply])
    ##     res$spp <- sp.classification(tree.sp = tr$data[["tree.sp"]],
    ##         species.spruce = c(1, 2, 3), species.pine = c(10, 11,
    ##             20, 21, 29), species.harw = c(30, 31))
    ##     all.tree.vars <- data.table(treeid = tr$data$treeid, plot.id = tr$data$plot.id,
    ##         dbh.mm = tr$data$dbh.mm[, this.period], height.dm = tr$data$height.dm[,
    ##             this.period], tree.sp = tr$data$tree.sp)
    ##     all.tree.vars[all.plot.vars, `:=`(tree2ha, i.tree2ha), on = list(plot.id)]
    ##     all.tree.vars[, `:=`(BA.m2, pi * (dbh.mm/1000/2)^2)]
    ##     QMD.cm <- tapply(tr$data[["dbh.mm"]][, this.period], list(tr$data[["plot.id"]]),
    ##         function(x.mm) {
    ##             x.mm <- x.mm[is.finite(x.mm)]
    ##             (sqrt(sum((x.mm/10)^2)/length(x.mm)))
    ##         })
    ##     res$QMD.cm <- as.vector(QMD.cm[i.match.tapply])
    ##     tph <- tapply(fl[["tree2ha"]][res$i.stand], tr$data[["plot.id"]],
    ##         sum)
    ##     res$tph <- as.vector(tph[i.match.tapply])
    ##     res$SDI <- res$tph * (res$QMD.cm/(10 * 2.54))^1.605
    ##     pr.spp.ba <- data.frame(spru = rep(0, length(res$i.stand)),
    ##         pine = 0, harw = 0, birch = 0, other = 0)
    ##     pr.spp.ba$spru[res$spp == "spruce"] <- 1
    ##     pr.spp.ba$pine[res$spp == "pine"] <- 1
    ##     pr.spp.ba$birch[res$spp %in% c("birch")] <- 1
    ##     pr.spp.ba$other[res$spp %in% c("other")] <- 1
    ##     pr.spp.ba$harw[res$spp %in% c("birch", "other")] <- 1
    ##     pr.spp.ba <- pr.spp.ba * res$tree.BA.m2
    ##     dum.s <- tapply(pr.spp.ba$spru, tr$data$plot.id, sum)
    ##     dum.p <- tapply(pr.spp.ba$pine, tr$data$plot.id, sum)
    ##     dum.h <- tapply(pr.spp.ba$harw, tr$data$plot.id, sum)
    ##     dum.b <- tapply(pr.spp.ba$birch, tr$data$plot.id, sum)
    ##     dum.o <- tapply(pr.spp.ba$other, tr$data$plot.id, sum)
    ##     pr.spp.ba <- data.frame(spru = as.vector(dum.s), pine = as.vector(dum.p),
    ##         harw = as.vector(dum.h), birch = as.vector(dum.b), other = as.vector(dum.o))
    ##     pr.spp.ba <- pr.spp.ba/with(pr.spp.ba, spru + pine + harw)
    ##     pr.spp.ba <- pr.spp.ba[match(tr$data$plot.id, names(dum.s)),
    ##         ] * 100
    ##     res$pr.spp.ba <- pr.spp.ba
    ##     rm(pr.spp.ba)
    ##     res$PBAL.m2.ha <- ave(res$tree.BA.m2 * 10000/fl[["plot.size.m2"]][res$i.stand],
    ##         tr$data$plot.id, FUN = function(X) {
    ##             ord.x <- order(X)
    ##             X <- sum(X[ord.x]) - cumsum(X[ord.x])
    ##             X <- X[match(1:length(X), ord.x)]
    ##             return(X)
    ##         })
    ##     previous.period <- paste0("t", i.period - 1)
    ##     if ("stand.age.years" %in% names(fl)) {
    ##         if (i.period == 0 & !is.data.frame(fl$stand.age.years)) {
    ##             my.age <- fl$stand.age.years
    ##             fl$stand.age.years <- data.frame(matrix(NA, ncol = n.periods,
    ##                 nrow = length(fl$plot.id)))
    ##             names(fl$management) <- paste0("t", 1:n.periods)
    ##             fl$stand.age.years[, this.period] <- my.age
    ##         }
    ##         if (i.period > 0 & "stand.age.years" %in% names(fl)) {
    ##             fl$stand.age.years[, this.period] <- fl$stand.age.years[,
    ##                 previous.period] + 5
    ##             stand.age.dt <- data.table(plot.id = fl$plot.id,
    ##                 SI.spp = fl$SI.spp, SI.m = fl$SI.m, stand.age.years = fl$stand.age.years[,
    ##                   this.period], waiting.time = 15)
    ##             if (any(!is.na(fl$management[, this.period]))) {
    ##                 stand.age.dt[, `:=`(stands.ff, !substr(fl$management[,
    ##                   this.period], 1, 1) %in% c("0", "3"))]
    ##                 stand.age.dt[stands.ff == TRUE, `:=`(stand.age.years,
    ##                   -waiting.time + period.length/2)]
    ##                 stand.age.dt[, `:=`(dev.class, calculate.development.class(SI.spp = SI.spp,
    ##                   SI.m = SI.m, stand.age.years = stand.age.years))]
    ##             }
    ##             fl$stand.age.years[, this.period] <- stand.age.dt[,
    ##                 stand.age.years]
    ##             if (any(is.na(stand.age.dt$stand.age.years)))
    ##                 browser()
    ##         }
    ##         res$dev.class <- calculate.development.class(SI.spp = fl$SI.spp,
    ##             SI.m = fl$SI.m, stand.age.years = fl$stand.age.years[,
    ##                 this.period])
    ##         res$tree.age <- data.table(age.years = fl$stand.age.years[,
    ##             this.period][match(tr$data$plot.id, fl$plot.id)],
    ##             treeid = tr$data$treeid)
    ##         table(fl$stand.age.years[, this.period], useNA = "always")
    ##     }
    ##     vuprha.m3.ha <- NULL
    ##     all.tree.vars[all.plot.vars, `:=`(kom, kom), on = "plot.id"]
    ##     all.tree.vars[tree.sp == 12, `:=`(tree.sp, 10)]
    ##     all.tree.vars[dbh.mm > 0, `:=`(c("vol.w.tr.m3", "vol.wo.tr.m3"),
    ##         volume.norway(dbh.mm, height.dm, as.numeric(levels(tree.sp))[tree.sp],
    ##             kom))]
    ##     vuprha.m3.ha <- NULL
    ##     all.tree.vars[all.plot.vars, `:=`(kom, i.kom), on = "plot.id"]
    ##     all.tree.vars[tree.sp == 12, `:=`(tree.sp, 10)]
    ##     all.tree.vars[dbh.mm > 0, `:=`(c("vol.w.tr.m3", "vol.wo.tr.m3"),
    ##         volume.norway(dbh.mm, height.dm, as.numeric(levels(tree.sp))[tree.sp],
    ##             kom))]
    ##     all.tree.vars[, `:=`(vuprha.m3.ha, vol.wo.tr.m3 * tree2ha)]
    ##     vuprha.m3.ha <- all.tree.vars[, sum(vuprha.m3.ha, na.rm = TRUE),
    ##         by = plot.id]
    ##     all.plot.vars[vuprha.m3.ha, `:=`(vuprha.m3.ha, V1), on = "plot.id"]
    ##     all.plot.vars[is.na(vuprha.m3.ha), `:=`(vuprha.m3.ha, 0)]
    ##     res$vuprha.m3.ha <- all.plot.vars$vuprha.m3.ha
    ##     res$vol.wo.tr.m3.ha <- all.tree.vars$vuprha.m3.ha
    ##     if (i.period == 0) {
    ##         time.intern <- rep(NA, length.out = length(fl$plot.id))
    ##     }
    ##     else {
    ##         time.intern <- fl$time.since.final.felling
    ##         harv.last <- substr(fl$management[, this.period], 1,
    ##             1) %in% c("1", "2")
    ##         time.intern[harv.last] <- period.length/2
    ##         time.intern[!harv.last & !is.na(time.intern)] <- time.intern[!harv.last &
    ##             !is.na(time.intern)] + period.length
    ##     }
    ##     fl$time.since.final.felling <- time.intern
    ##     invisible(list(res = res, fl = fl))
    ## }
    ## <environment: namespace:sitree>

# Tree lists: the `trList` and `trListDead` classes

In order to efficiently store the list of all individual live and dead (and removed) trees, two Reference classes (or refclasses) are defined in SiTree. Refclases is chosen instead of S3 or S4 classes because refclasses objects are mutable and the usual R copy on modify semantics do not apply. When simulating for long periods, or for large datasets (e.g. a whole national forest inventory) the risk of running out of memory is not negligible. Using refclasses aim at maintaining the memory needs to the minimum by using mutable objects for storing the larger objects such as represented by the tree lists.

There are two Reference Classes implemented in the sitree package, one for live trees (trList) and other for dead trees (trListDead).

- trList This class has two fields, data and nperiods. Under data basic information for each tree is stored (a unique stand ID plot.id, a unique tree ID treeid, and dbh and height for each period, dbh.mm, and height.dm). The nperiods field is an integer that stores the number of periods to be simulated.

- trListDead This class extends trList. DBH and heights measured while the tree was alive are stored under the data field. Also under this field information on how long the tree has been in the simulation can be found (yrs.sim). In this class the new field last.measurement stores the dimensions of the tree when it died or was removed. How these dimensions are calculated is defined on the dead.trees.growth function.

Reference Classes objects are mutable, they don’t use R’s usual copy-on-modify semantics, but are modified in place.

We have provided a function to convert the `sitree()` output containing trList and *trListDead** objects to a data frame, the `sitree2dataframe` function. The resulting data frame follows the usual R copy on modify semantics, and most users might be more comfortable with it.

    result.sitree <- sitree (tree.df   = stand.west.tr,
                               stand.df  = stand.west.st,
                               functions = list(
                                 fn.growth     = 'grow.dbhinc.hgtinc',
                                 fn.mort       = 'mort.B2007',
                                 fn.recr       = 'recr.BBG2008',
                                 fn.management = NULL,
                                 fn.tree.removal = NULL,
                                 fn.modif      = NULL,
                                 fn.prep.common.vars = 'prep.common.vars.fun'
                               ),
                               n.periods = 12,
                               period.length = 5,
                               mng.options = NA,
                               print.comments = FALSE,
                               fn.dbh.inc    = 'dbhi.BN2009',
                               fn.hgt.inc    = 'height.korf'
                             )
    str(result.sitree$live)

    ## Reference class 'trList' [package "sitree"] with 2 fields
    ##  $ data    :List of 6
    ##   ..$ plot.id  : num [1:849] 2 2 2 2 2 2 2 2 2 2 ...
    ##   ..$ treeid   : int [1:849] 2 3 4 5 6 9 10 11 12 13 ...
    ##   ..$ dbh.mm   :'data.frame':    849 obs. of  13 variables:
    ##   .. ..$ t0 : num [1:849] 50 50 50 50 116 84 68 90 58 115 ...
    ##   .. ..$ t1 : num [1:849] 61 61 61 61 138 101 83 108 71 137 ...
    ##   .. ..$ t2 : num [1:849] 71 71 71 71 157 116 96 124 82 156 ...
    ##   .. ..$ t3 : num [1:849] 80 80 80 80 174 130 108 138 92 173 ...
    ##   .. ..$ t4 : num [1:849] 89 89 89 89 189 142 119 151 102 188 ...
    ##   .. ..$ t5 : num [1:849] 97 97 97 97 203 154 129 163 111 202 ...
    ##   .. ..$ t6 : num [1:849] 105 105 105 105 216 165 139 174 120 215 ...
    ##   .. ..$ t7 : num [1:849] 113 113 113 113 228 175 148 185 128 227 ...
    ##   .. ..$ t8 : num [1:849] 120 120 120 120 239 185 157 195 136 238 ...
    ##   .. ..$ t9 : num [1:849] 127 127 127 127 250 195 166 205 144 249 ...
    ##   .. ..$ t10: num [1:849] 134 134 134 134 260 204 174 214 152 259 ...
    ##   .. ..$ t11: num [1:849] 141 141 141 141 270 213 182 223 159 269 ...
    ##   .. ..$ t12: num [1:849] 148 148 148 148 279 221 190 232 166 278 ...
    ##   ..$ height.dm:'data.frame':    849 obs. of  13 variables:
    ##   .. ..$ t0 : num [1:849] 42 21 8 42 113 81 81 104 60 113 ...
    ##   .. ..$ t1 : num [1:849] 52 31 18 52 128 94 94 118 72 128 ...
    ##   .. ..$ t2 : num [1:849] 61 40 27 61 140 105 104 129 81 140 ...
    ##   .. ..$ t3 : num [1:849] 69 48 35 69 150 115 113 138 89 150 ...
    ##   .. ..$ t4 : num [1:849] 76 55 42 76 158 123 121 146 97 158 ...
    ##   .. ..$ t5 : num [1:849] 82 61 48 82 165 130 128 153 104 165 ...
    ##   .. ..$ t6 : num [1:849] 88 67 54 88 171 136 135 159 110 171 ...
    ##   .. ..$ t7 : num [1:849] 94 73 60 94 177 142 141 165 115 177 ...
    ##   .. ..$ t8 : num [1:849] 99 78 65 99 182 147 146 170 120 182 ...
    ##   .. ..$ t9 : num [1:849] 104 83 70 104 187 152 151 175 125 187 ...
    ##   .. ..$ t10: num [1:849] 109 88 75 109 191 157 156 179 130 191 ...
    ##   .. ..$ t11: num [1:849] 114 93 80 114 195 161 160 183 134 195 ...
    ##   .. ..$ t12: num [1:849] 118 97 84 118 199 165 164 187 138 199 ...
    ##   ..$ yrs.sim  : num [1:849] 60 60 60 60 60 60 60 60 60 60 ...
    ##   ..$ tree.sp  : Factor w/ 29 levels "1","2","3","10",..: 1 1 1 1 1 1 1 1 1 1 ...
    ##  $ nperiods: int 12
    ##  and 19 methods, of which 5 are  possibly relevant:
    ##    addTrees, as.list, extractTrees, getTrees, show#envRefClass

    head(sitree2dataframe(result.sitree$live))

    ##   treeid plot.id tree.sp period dbh.mm height.dm
    ## 1      2       2       1     t0     50        42
    ## 2      3       2       1     t0     50        21
    ## 3      4       2       1     t0     50         8
    ## 4      5       2       1     t0     50        42
    ## 5      6       2       1     t0    116       113
    ## 6      9       2       1     t0     84        81

# The sitree() function

The `sitree()` function is the core function of the SiTree package. It is the function that runs the simulations. It requires tree data (`tree.df`), stand/plot data (`stand.df`), and a list of functions to be used in the simulation (`functions`), the number of periods for which to run the simulation (`n.periods`), and the period length (`period.length`). Management options can be passed through the `mng.options` argument, and it is also possible to print comments about the progress of the simulation selecting `print.comments = TRUE`. Additional arguments needed by the selected functions go into the ellipsis (‘…’) and can be retrieved by simply converting it to a list, e.g. `arguments <- list(...)`.

The `functions` argument must be a list containing at least 7 elements:

- `fn.growth` the name of the function that implements the growth sub-model
- `fn.mort` the name of the function that implements the mortality sub-model
- `fn.recr` the name of the function that implements the recruitment sub-model: ingrowth, and natural and artificial regeneration
- `fn.management` the name of the function that implements the management (e.g. harvest), can be `NULL`
- `fn.tree.removal` the name of the function that implements the management at the tree level, that is the selection of trees to be fell, can be NULL.
- `fn.modif` the name of the function to calculate the effect of external modifiers, can be `NULL`
- `fn.prep.common.vars` the name of the function to calculate auxiliary variables, such as basal area of the stand (see ‘The fn.prep.common.vars’ subsection above) .

Further details on the requirements of the functions listed above can be found under the section “The user-defined functions”.

# The user-defined functions

The `sitree()` function is a flexible framework for forest growth simulations. Any growth sub-model, mortality sub-model, management, etc. can be used. Some examples are provided in SiTree and in SiTreeE, but generally, the submodels functions need to be provided by the user. The examples provided in SiTree and in SiTreeE can be used as a template. To debug the user-defined functions we suggest to use the provided example as a starting point, set `print.comments = TRUE` and switch the submodels functions one by one to test them.

An example of how the list provided in the `functions` argument of `sitree` should look like is given below, and further details on each of the functions are provided next.

The `fn.growth` function should return a data frame with two columns giving diameter increment (`dbh.inc.mm`) and height increment (`hgt.inc.dm`) of all live trees. This data frame should only contain numerical data (no missing data allowed). Care must be taken to ensure that the order matches that of the tree list. Examples of the growth functions are provided as `grow.dbhinc.hgtinc`, `dbhi.BN2009`, and `height.korf`.

The `fn.mort` function should return a `TRUE`/`FALSE` vector of same length as the number of trees in the tree list. `TRUE` indicates a tree that will die before the next period, and `FALSE` indicates a tree that will stay alive. An example of a `fn.mort` function is provided in `mort.B2007`.

The `fn.recr` function is the function that estimates recruitment, the new trees for the next period. This function should return a list of new trees (or an empty list if there are no new trees) with elements `plot.id`, `treeid`, `dbh.mm`, `height.dm`, `yrs.sim` (indicates when are the trees incorporated to the plot, for example, in the middle of the period), and `tree.sp`.An example of a `fn.recr` function is provided in `recr.BBG2008`.

`fn.management` is optional. It should return a list, with at least one element called `management` which should be a vector with length equal to the number of plots in `stand.df`. The example we provide uses a simple code to define management (a five characters string indicating with a binary code (1 = present, 0 = absent) the treatments to be executed: harvest-thinning-fertilization-pruning-other), but any other way to code management can be used, as far as `fn.management` returns a vector. When no management will take place during the simulation `fn.management` can be set to `NULL`. An example of a `fn.management` function is provided in `management.prob`.

`fn.tree.removal` is optional. It should return a `TRUE`/`FALSE` vector indicating which trees are to be removed. The vector should have the same length as the number of trees alive at the current period. When no tree removal will take place during the simulation (no harvest is allowed) `fn.tree.removal` can be set to NULL. An example of a `fn.tree.removal` function is provided in `mng.tree.removal`.

`fn.modif` is a function that can be used to modify characteristics of the plot or stand, such as site index. This function is optional, and no example is provided in the current version of the package. It should return a list with names matching some of those in the `stand.df` data frame. After the external modifiers are calculated with the function defined as `fn.modif`, the elements in the plot data that matches those of the results of `fn.modif` are replaced before the rest of the simulation continues. For example, if the plot has been fertilized and we can assume that SI has increased by 2 meters, the `fn.modif` function needs to return a list with a SI element with all SI as in the plot data frame except for those that have changed.

`fn.prep.common.vars` is the function used to calculate everything needed for the `fn.growth`, `fn.mort`, etc to be calculated. For example, the `fn.prep.common.vars` function is the place to calculate stand competition indices, volume, stand age, etc. An example is given in the function `prep.common.vars.fn`.