Forest dynamics
Miquel De Caceres
2026-06-18
Source:vignettes/runmodels/ForestDynamics.Rmd
ForestDynamics.RmdAbout this vignette
This document describes how to run the forest dynamics model of
medfate, described in De Cáceres et al. (2023) and
implemented in function fordyn(). This document is meant to
teach users to run the simulation model with function
fordyn(). Details of the model design and formulation can
be found at the corresponding chapters of the medfate
book.
Because the model builds on the growth and water balance models, the
reader is assumed here to be familiarized with spwb() and
growth() (otherwise read vignettes Basic
water balance and Forest
growth).
Preparing model inputs
Any forest dynamics model needs information on climate, vegetation
and soils of the forest stand to be simulated. Moreover, since models in
medfate differentiate between species, information on
species-specific model parameters is also needed. In this subsection we
explain the different steps to prepare the data needed to run function
fordyn().
Model inputs are explained in greater detail in vignettes Understanding
model inputs and Preparing
model inputs. Here we only review the different steps required
to run function fordyn().
Soil, vegetation, meteorology and species data
Soil information needs to be entered as a data frame
with soil layers in rows and physical attributes in columns. Soil
physical attributes can be initialized to default values, for a given
number of layers, using function defaultSoilParams():
examplesoil <- defaultSoilParams(4)
examplesoil## widths clay sand om nitrogen ph bd rfc
## 1 300 25 25 NA NA NA 1.5 25
## 2 700 25 25 NA NA NA 1.5 45
## 3 1000 25 25 NA NA NA 1.5 75
## 4 2000 25 25 NA NA NA 1.5 95As explained in the package overview, models included in
medfate were primarily designed to be ran on forest
inventory plots. Here we use the example object provided with
the package:
data(exampleforest)
exampleforest## $treeData
## Species DBH Height N Z50 Z95
## 1 Pinus halepensis 37.55 800 168 100 300
## 2 Quercus ilex 14.60 660 384 300 1000
##
## $shrubData
## Species Height Cover Z50 Z95
## 1 Quercus coccifera 80 3.75 200 1000
##
## attr(,"class")
## [1] "forest" "list"We can keep track of cohort age if we define a column called
Age in tree or shrub data, for example let us assume we
know the age of the two tree cohorts:
exampleforest$treeData$Age <- c(40, 24)Importantly, a data frame with daily weather for the period to be simulated is required. Here we use the default data frame included with the package:
## dates MinTemperature MaxTemperature Precipitation MinRelativeHumidity
## 1 2001-01-01 -0.5934215 6.287950 4.869109 65.15411
## 2 2001-01-02 -2.3662458 4.569737 2.498292 57.43761
## 3 2001-01-03 -3.8541036 2.661951 0.000000 58.77432
## 4 2001-01-04 -1.8744860 3.097705 5.796973 66.84256
## 5 2001-01-05 0.3288287 7.551532 1.884401 62.97656
## 6 2001-01-06 0.5461322 7.186784 13.359801 74.25754
## MaxRelativeHumidity Radiation WindSpeed
## 1 100.00000 12.89251 2.000000
## 2 94.71780 13.03079 7.662544
## 3 94.66823 16.90722 2.000000
## 4 95.80950 11.07275 2.000000
## 5 100.00000 13.45205 7.581347
## 6 100.00000 12.84841 6.570501Finally, simulations in medfate require a data frame
with species parameter values, which we load using defaults for
Catalonia (NE Spain):
data("SpParamsMED")Simulation control
Apart from data inputs, the behaviour of simulation models can be
controlled using a set of global parameters. The default
parameterization is obtained using function
defaultControl():
control <- defaultControl("Granier")Here we will run simulations of forest dynamics using the basic water
balance model (i.e. transpirationMode = "Granier"). The
complexity of the soil water balance calculations can be changed by
using "Sperry" as input to defaultControl().
However, when running fordyn() sub-daily output will never
be stored (i.e. setting subdailyResults = TRUE is
useless).
Executing the forest dynamics model
In this vignette we will fake a ten-year weather input by repeating the example weather data frame ten times.
meteo <- rbind(examplemeteo, examplemeteo, examplemeteo, examplemeteo,
examplemeteo, examplemeteo, examplemeteo, examplemeteo,
examplemeteo, examplemeteo)
meteo$dates <- as.character(seq(as.Date("2001-01-01"),
as.Date("2010-12-29"), by="day"))Now we run the forest dynamics model using all inputs (note that no
intermediate input object is needed, as in spwb() or
growth()):
fd<-fordyn(exampleforest, examplesoil, SpParamsMED, meteo, control,
latitude = 41.82592, elevation = 100)## Simulating year 2001 (1/10): (a) Growth/mortality, (b) Regeneration nT = 2 nS = 1
## Simulating year 2002 (2/10): (a) Growth/mortality, (b) Regeneration nT = 2 nS = 1
## Simulating year 2003 (3/10): (a) Growth/mortality, (b) Regeneration nT = 2 nS = 1
## Simulating year 2004 (4/10): (a) Growth/mortality, (b) Regeneration nT = 2 nS = 1
## Simulating year 2005 (5/10): (a) Growth/mortality, (b) Regeneration nT = 2 nS = 1
## Simulating year 2006 (6/10): (a) Growth/mortality, (b) Regeneration nT = 2 nS = 1
## Simulating year 2007 (7/10): (a) Growth/mortality, (b) Regeneration nT = 2 nS = 1
## Simulating year 2008 (8/10): (a) Growth/mortality, (b) Regeneration nT = 2 nS = 1
## Simulating year 2009 (9/10): (a) Growth/mortality, (b) Regeneration nT = 2 nS = 1
## Simulating year 2010 (10/10): (a) Growth/mortality, (b) Regeneration nT = 2 nS = 1It is worth noting that, while fordyn() calls function
growth() internally for each simulated year, the
verbose option of the control parameters only affects
function fordyn() (i.e. all console output from
growth() is hidden). Recruitment and summaries are done
only once a year at the level of function fordyn().
Inspecting model outputs
Stand, species and cohort summaries and plots
Among other outputs, function fordyn() calculates
standard summary statistics that describe the structural and
compositional state of the forest at each time step. For example, we can
access stand-level statistics using:
fd$StandSummary## Step NumTreeSpecies NumTreeCohorts NumShrubSpecies NumShrubCohorts
## 1 0 2 2 1 1
## 2 1 2 2 1 1
## 3 2 2 2 1 1
## 4 3 2 2 1 1
## 5 4 2 2 1 1
## 6 5 2 2 1 1
## 7 6 2 2 1 1
## 8 7 2 2 1 1
## 9 8 2 2 1 1
## 10 9 2 2 1 1
## 11 10 2 2 1 1
## TreeDensityLive TreeBasalAreaLive DominantTreeHeight DominantTreeDiameter
## 1 552.0000 25.03330 800.0000 37.55000
## 2 551.3681 25.09418 804.2767 37.62962
## 3 550.7349 25.15495 808.5316 37.70913
## 4 550.1001 25.21518 812.7693 37.78861
## 5 549.4622 25.27545 816.9949 37.86814
## 6 548.8247 25.33534 821.2003 37.94759
## 7 548.1857 25.39518 825.3936 38.02709
## 8 547.5453 25.45474 829.5711 38.10659
## 9 546.9018 25.51400 833.7308 38.18604
## 10 546.2587 25.57313 837.8791 38.26555
## 11 545.6178 25.63224 842.0073 38.34498
## QuadraticMeanTreeDiameter HartBeckingIndex ShrubCoverLive BasalAreaDead
## 1 24.02949 53.20353 3.750000 0.00000000
## 2 24.07248 52.95094 3.052800 0.03892163
## 3 24.11546 52.70256 3.066806 0.03916642
## 4 24.15824 52.45801 3.081690 0.03941073
## 5 24.20112 52.21698 3.105875 0.03976452
## 6 24.24385 51.97974 3.124224 0.03990079
## 7 24.28661 51.74580 3.136073 0.04014595
## 8 24.32928 51.51532 3.136245 0.04039126
## 9 24.37191 51.28844 3.144233 0.04074829
## 10 24.41450 51.06454 3.146162 0.04088265
## 11 24.45705 50.84402 3.152865 0.04090274
## ShrubCoverDead BasalAreaCut ShrubCoverCut
## 1 0.000000000 0 0
## 2 0.005284283 0 0
## 3 0.004704859 0 0
## 4 0.004727575 0 0
## 5 0.004768939 0 0
## 6 0.004790331 0 0
## 7 0.004815028 0 0
## 8 0.004825936 0 0
## 9 0.004844023 0 0
## 10 0.004838999 0 0
## 11 0.004818671 0 0Species-level analogous statistics are shown using:
fd$SpeciesSummary## Step Species NumCohorts TreeDensityLive TreeBasalAreaLive
## 1 0 Pinus halepensis 1 168.0000 18.604547
## 2 0 Quercus coccifera 1 NA NA
## 3 0 Quercus ilex 1 384.0000 6.428755
## 4 1 Pinus halepensis 1 167.7002 18.650187
## 5 1 Quercus coccifera 1 NA NA
## 6 1 Quercus ilex 1 383.6680 6.443996
## 7 2 Pinus halepensis 1 167.3997 18.695523
## 8 2 Quercus coccifera 1 NA NA
## 9 2 Quercus ilex 1 383.3352 6.459425
## 10 3 Pinus halepensis 1 167.0986 18.740639
## 11 3 Quercus coccifera 1 NA NA
## 12 3 Quercus ilex 1 383.0016 6.474544
## 13 4 Pinus halepensis 1 166.7960 18.785534
## 14 4 Quercus coccifera 1 NA NA
## 15 4 Quercus ilex 1 382.6662 6.489912
## 16 5 Pinus halepensis 1 166.4936 18.830241
## 17 5 Quercus coccifera 1 NA NA
## 18 5 Quercus ilex 1 382.3310 6.505098
## 19 6 Pinus halepensis 1 166.1906 18.874817
## 20 6 Quercus coccifera 1 NA NA
## 21 6 Quercus ilex 1 381.9951 6.520364
## 22 7 Pinus halepensis 1 165.8870 18.919190
## 23 7 Quercus coccifera 1 NA NA
## 24 7 Quercus ilex 1 381.6583 6.535548
## 25 8 Pinus halepensis 1 165.5820 18.963226
## 26 8 Quercus coccifera 1 NA NA
## 27 8 Quercus ilex 1 381.3198 6.550773
## 28 9 Pinus halepensis 1 165.2772 19.007234
## 29 9 Quercus coccifera 1 NA NA
## 30 9 Quercus ilex 1 380.9815 6.565898
## 31 10 Pinus halepensis 1 164.9735 19.051144
## 32 10 Quercus coccifera 1 NA NA
## 33 10 Quercus ilex 1 380.6443 6.581101
## ShrubCoverLive BasalAreaDead ShrubCoverDead BasalAreaCut ShrubCoverCut
## 1 NA 0.000000000 NA 0 NA
## 2 3.750000 NA 0.000000000 NA 0
## 3 NA 0.000000000 NA 0 NA
## 4 NA 0.033345176 NA 0 NA
## 5 3.052800 NA 0.005284283 NA 0
## 6 NA 0.005576452 NA 0 NA
## 7 NA 0.033558233 NA 0 NA
## 8 3.066806 NA 0.004704859 NA 0
## 9 NA 0.005608187 NA 0 NA
## 10 NA 0.033771071 NA 0 NA
## 11 3.081690 NA 0.004727575 NA 0
## 12 NA 0.005639663 NA 0 NA
## 13 NA 0.034077515 NA 0 NA
## 14 3.105875 NA 0.004768939 NA 0
## 15 NA 0.005687002 NA 0 NA
## 16 NA 0.034197654 NA 0 NA
## 17 3.124224 NA 0.004790331 NA 0
## 18 NA 0.005703136 NA 0 NA
## 19 NA 0.034411039 NA 0 NA
## 20 3.136073 NA 0.004815028 NA 0
## 21 NA 0.005734914 NA 0 NA
## 22 NA 0.034624582 NA 0 NA
## 23 3.136245 NA 0.004825936 NA 0
## 24 NA 0.005766681 NA 0 NA
## 25 NA 0.034933820 NA 0 NA
## 26 3.144233 NA 0.004844023 NA 0
## 27 NA 0.005814473 NA 0 NA
## 28 NA 0.035052229 NA 0 NA
## 29 3.146162 NA 0.004838999 NA 0
## 30 NA 0.005830419 NA 0 NA
## 31 NA 0.035072562 NA 0 NA
## 32 3.152865 NA 0.004818671 NA 0
## 33 NA 0.005830178 NA 0 NAPackage medfate provides a simple plot
function for objects of class fordyn. For example, we can
show the interannual variation in stand-level basal area using:
plot(fd, type = "StandBasalArea")
Tree/shrub tables
Another useful output of fordyn() are tables in long
format with cohort structural information (i.e. DBH, height, density,
etc) for each time step:
fd$TreeTable## Step Year Cohort Species DBH Height N Z50 Z95 Z100
## 1 0 NA T1_148 Pinus halepensis 37.55000 800.0000 168.0000 100 300 NA
## 2 0 NA T2_168 Quercus ilex 14.60000 660.0000 384.0000 300 1000 NA
## 3 1 2001 T1_148 Pinus halepensis 37.62962 804.2767 167.7002 100 300 NA
## 4 1 2001 T2_168 Quercus ilex 14.62362 660.7351 383.6680 300 1000 NA
## 5 2 2002 T1_148 Pinus halepensis 37.70913 808.5316 167.3997 100 300 NA
## 6 2 2002 T2_168 Quercus ilex 14.64747 661.4783 383.3352 300 1000 NA
## 7 3 2003 T1_148 Pinus halepensis 37.78861 812.7693 167.0986 100 300 NA
## 8 3 2003 T2_168 Quercus ilex 14.67099 662.2119 383.0016 300 1000 NA
## 9 4 2004 T1_148 Pinus halepensis 37.86814 816.9949 166.7960 100 300 NA
## 10 4 2004 T2_168 Quercus ilex 14.69482 662.9562 382.6662 300 1000 NA
## 11 5 2005 T1_148 Pinus halepensis 37.94759 821.2003 166.4936 100 300 NA
## 12 5 2005 T2_168 Quercus ilex 14.71845 663.6950 382.3310 300 1000 NA
## 13 6 2006 T1_148 Pinus halepensis 38.02709 825.3936 166.1906 100 300 NA
## 14 6 2006 T2_168 Quercus ilex 14.74219 664.4380 381.9951 300 1000 NA
## 15 7 2007 T1_148 Pinus halepensis 38.10659 829.5711 165.8870 100 300 NA
## 16 7 2007 T2_168 Quercus ilex 14.76586 665.1794 381.6583 300 1000 NA
## 17 8 2008 T1_148 Pinus halepensis 38.18604 833.7308 165.5820 100 300 NA
## 18 8 2008 T2_168 Quercus ilex 14.78961 665.9240 381.3198 300 1000 NA
## 19 9 2009 T1_148 Pinus halepensis 38.26555 837.8791 165.2772 100 300 NA
## 20 9 2009 T2_168 Quercus ilex 14.81324 666.6659 380.9815 300 1000 NA
## 21 10 2010 T1_148 Pinus halepensis 38.34498 842.0073 164.9735 100 300 NA
## 22 10 2010 T2_168 Quercus ilex 14.83695 667.4102 380.6443 300 1000 NA
## Age ObsID
## 1 40 <NA>
## 2 24 <NA>
## 3 40 NA
## 4 24 NA
## 5 41 NA
## 6 25 NA
## 7 42 NA
## 8 26 NA
## 9 43 NA
## 10 27 NA
## 11 44 NA
## 12 28 NA
## 13 45 NA
## 14 29 NA
## 15 46 NA
## 16 30 NA
## 17 47 NA
## 18 31 NA
## 19 48 NA
## 20 32 NA
## 21 49 NA
## 22 33 NAThe same can be shown for dead trees:
fd$DeadTreeTable## Step Year Cohort Species DBH Height N N_starvation
## 1 1 2001 T1_148 Pinus halepensis 37.62962 804.2767 0.2998357 0
## 2 1 2001 T2_168 Quercus ilex 14.62362 660.7351 0.3320154 0
## 3 2 2002 T1_148 Pinus halepensis 37.70913 808.5316 0.3004804 0
## 4 2 2002 T2_168 Quercus ilex 14.64747 661.4783 0.3328184 0
## 5 3 2003 T1_148 Pinus halepensis 37.78861 812.7693 0.3011155 0
## 6 3 2003 T2_168 Quercus ilex 14.67099 662.2119 0.3336142 0
## 7 4 2004 T1_148 Pinus halepensis 37.86814 816.9949 0.3025729 0
## 8 4 2004 T2_168 Quercus ilex 14.69482 662.9562 0.3353240 0
## 9 5 2005 T1_148 Pinus halepensis 37.94759 821.2003 0.3023696 0
## 10 5 2005 T2_168 Quercus ilex 14.71845 663.6950 0.3351965 0
## 11 6 2006 T1_148 Pinus halepensis 38.02709 825.3936 0.3029853 0
## 12 6 2006 T2_168 Quercus ilex 14.74219 664.4380 0.3359795 0
## 13 7 2007 T1_148 Pinus halepensis 38.10659 829.5711 0.3035949 0
## 14 7 2007 T2_168 Quercus ilex 14.76586 665.1794 0.3367585 0
## 15 8 2008 T1_148 Pinus halepensis 38.18604 833.7308 0.3050331 0
## 16 8 2008 T2_168 Quercus ilex 14.78961 665.9240 0.3384599 0
## 17 9 2009 T1_148 Pinus halepensis 38.26555 837.8791 0.3047963 0
## 18 9 2009 T2_168 Quercus ilex 14.81324 666.6659 0.3383059 0
## 19 10 2010 T1_148 Pinus halepensis 38.34498 842.0073 0.3037111 0
## 20 10 2010 T2_168 Quercus ilex 14.83695 667.4102 0.3372117 0
## N_dessication N_burnt N_resprouting_stumps Z50 Z95 Z100 Age ObsID
## 1 0 0 0 100 300 NA 40 NA
## 2 0 0 0 300 1000 NA 24 NA
## 3 0 0 0 100 300 NA 40 NA
## 4 0 0 0 300 1000 NA 24 NA
## 5 0 0 0 100 300 NA 41 NA
## 6 0 0 0 300 1000 NA 25 NA
## 7 0 0 0 100 300 NA 42 NA
## 8 0 0 0 300 1000 NA 26 NA
## 9 0 0 0 100 300 NA 43 NA
## 10 0 0 0 300 1000 NA 27 NA
## 11 0 0 0 100 300 NA 44 NA
## 12 0 0 0 300 1000 NA 28 NA
## 13 0 0 0 100 300 NA 45 NA
## 14 0 0 0 300 1000 NA 29 NA
## 15 0 0 0 100 300 NA 46 NA
## 16 0 0 0 300 1000 NA 30 NA
## 17 0 0 0 100 300 NA 47 NA
## 18 0 0 0 300 1000 NA 31 NA
## 19 0 0 0 100 300 NA 48 NA
## 20 0 0 0 300 1000 NA 32 NAAccessing the output from function growth()
Since function fordyn() makes internal calls to function
growth(), it stores the result in a vector called
GrowthResults, which we can use to inspect intra-annual
patterns of desired variables. For example, the following shows the leaf
area for individuals of the three cohorts during the second year:
plot(fd$GrowthResults[[2]], "LeafArea", bySpecies = T)
Instead of examining year by year, it is possible to plot the whole
series of results by passing a fordyn object to the
plot() function:
plot(fd, "LeafArea")
We can also create interactive plots for particular steps using
function shinyplot(), e.g.:
shinyplot(fd$GrowthResults[[1]])Finally, calling function extract() will extract and
bind outputs for all the internal calls to function
growth():
## # A tibble: 3,650 × 53
## date PET Precipitation Rain Snow NetRain Snowmelt Infiltration
## <date> [L/m^2] [L/m^2] [L/m^2] [L/m^… [L/m^2] [L/m^2] [L/m^2]
## 1 2001-01-01 0.883 4.87 4.87 0 3.60 0 3.60
## 2 2001-01-02 1.64 2.50 2.50 0 1.25 0 1.25
## 3 2001-01-03 1.30 0 0 0 0 0 0
## 4 2001-01-04 0.569 5.80 5.80 0 4.54 0 4.54
## 5 2001-01-05 1.68 1.88 1.88 0 0.822 0 0.822
## 6 2001-01-06 1.21 13.4 13.4 0 11.9 0 11.9
## 7 2001-01-07 0.637 5.38 0 5.38 0 0 0
## 8 2001-01-08 0.832 0 0 0 0 0 0
## 9 2001-01-09 1.98 0 0 0 0 0 0
## 10 2001-01-10 0.829 5.12 5.12 0 3.85 5.38 9.23
## # ℹ 3,640 more rows
## # ℹ 45 more variables: InfiltrationExcess [L/m^2], SaturationExcess [L/m^2],
## # Runoff [L/m^2], DeepDrainage [L/m^2], CapillarityRise [L/m^2],
## # Evapotranspiration [L/m^2], Interception [L/m^2], SoilEvaporation [L/m^2],
## # HerbTranspiration [L/m^2], PlantExtraction [L/m^2], Transpiration [L/m^2],
## # MistletoeTranspiration [L/m^2], HydraulicRedistribution [L/m^2],
## # LAI [m^2/m^2], LAIherb [m^2/m^2], LAIlive [m^2/m^2], …Forest dynamics including management
The package allows including forest management in simulations of
forest dynamics. This is done in a very flexible manner, in the sense
that fordyn() allows the user to supply an arbitrary
function implementing a desired management strategy for the stand whose
dynamics are to be simulated. The package includes, however, an in-built
default function called defaultManagementFunction() along
with a flexible parameterization, a list with defaults provided by
function defaultManagementArguments().
Here we provide an example of simulations including forest management:
# Default arguments
args <- defaultManagementArguments()
# Here one can modify defaults before calling fordyn()
#
# Simulation
fd<-fordyn(exampleforest, examplesoil, SpParamsMED, meteo, control,
latitude = 41.82592, elevation = 100,
management_function = defaultManagementFunction,
management_args = args)## Simulating year 2001 (1/10): (a) Growth/mortality & management [thinning], (b) Regeneration nT = 2 nS = 2
## Simulating year 2002 (2/10): (a) Growth/mortality & management [none], (b) Regeneration nT = 2 nS = 2
## Simulating year 2003 (3/10): (a) Growth/mortality & management [none], (b) Regeneration nT = 2 nS = 2
## Simulating year 2004 (4/10): (a) Growth/mortality & management [none], (b) Regeneration nT = 2 nS = 2
## Simulating year 2005 (5/10): (a) Growth/mortality & management [none], (b) Regeneration nT = 2 nS = 2
## Simulating year 2006 (6/10): (a) Growth/mortality & management [none], (b) Regeneration nT = 2 nS = 2
## Simulating year 2007 (7/10): (a) Growth/mortality & management [none], (b) Regeneration nT = 2 nS = 2
## Simulating year 2008 (8/10): (a) Growth/mortality & management [none], (b) Regeneration nT = 2 nS = 2
## Simulating year 2009 (9/10): (a) Growth/mortality & management [none], (b) Regeneration nT = 2 nS = 2
## Simulating year 2010 (10/10): (a) Growth/mortality & management [none], (b) Regeneration nT = 2 nS = 2When management is included in simulations, two additional tables are produced, corresponding to the trees and shrubs that were cut, e.g.:
fd$CutTreeTable## Step Year Cohort Species DBH Height N Z50 Z95 Z100
## 1 1 2001 T1_148 Pinus halepensis 37.62962 804.2767 9.749529 100 300 NA
## 2 1 2001 T2_168 Quercus ilex 14.62362 660.7351 383.667985 300 1000 NA
## Age ObsID
## 1 40 NA
## 2 24 NAManagement parameters were those of an irregular model with thinning interventions from ‘below’, indicating that smaller trees were to be cut earlier:
args$type## [1] "irregular"
args$thinning## [1] "below"Note that in this example, there is resprouting of Quercus ilex after the thinning intervention, evidenced by the new cohort (T3_168) appearing in year 2001:
fd$TreeTable## Step Year Cohort Species DBH Height N Z50 Z95
## 1 0 NA T1_148 Pinus halepensis 37.550000 800.00000 168.0000 100 300
## 2 0 NA T2_168 Quercus ilex 14.600000 660.00000 384.0000 300 1000
## 3 1 2001 T1_148 Pinus halepensis 37.629625 804.27672 157.9506 100 300
## 4 1 2001 T3_168 Quercus ilex 1.000000 47.23629 3000.0000 300 1000
## 5 2 2002 T1_148 Pinus halepensis 37.751522 810.74704 157.7808 100 300
## 6 2 2002 T3_168 Quercus ilex 1.000799 47.28542 2996.6744 300 1000
## 7 3 2003 T1_148 Pinus halepensis 37.870790 817.02913 157.6100 100 300
## 8 3 2003 T3_168 Quercus ilex 1.000799 47.28542 2994.9655 300 1000
## 9 4 2004 T1_148 Pinus halepensis 37.990316 823.29047 157.4377 100 300
## 10 4 2004 T3_168 Quercus ilex 1.000799 47.28542 2993.2428 300 1000
## 11 5 2005 T1_148 Pinus halepensis 38.110409 829.54767 157.2647 100 300
## 12 5 2005 T3_168 Quercus ilex 1.000799 47.28542 2991.5155 300 1000
## 13 6 2006 T1_148 Pinus halepensis 38.213567 834.89561 157.0909 100 300
## 14 6 2006 T3_168 Quercus ilex 1.000799 47.28542 2989.7796 300 1000
## 15 7 2007 T1_148 Pinus halepensis 38.293194 839.00656 156.9163 100 300
## 16 7 2007 T3_168 Quercus ilex 1.000799 47.28542 2988.0375 300 1000
## 17 8 2008 T1_148 Pinus halepensis 38.372664 843.09459 156.7407 100 300
## 18 8 2008 T3_168 Quercus ilex 1.000799 47.28542 2986.2856 300 1000
## 19 9 2009 T1_148 Pinus halepensis 38.452036 847.16294 156.5651 100 300
## 20 9 2009 T3_168 Quercus ilex 1.000799 47.28542 2984.5334 300 1000
## 21 10 2010 T1_148 Pinus halepensis 38.531502 851.22151 156.3899 100 300
## 22 10 2010 T3_168 Quercus ilex 1.000799 47.28542 2982.7859 300 1000
## Z100 Age ObsID
## 1 NA 40 <NA>
## 2 NA 24 <NA>
## 3 NA 40 NA
## 4 NA 24 <NA>
## 5 NA 41 NA
## 6 NA 24 NA
## 7 NA 42 NA
## 8 NA 25 NA
## 9 NA 43 NA
## 10 NA 26 NA
## 11 NA 44 NA
## 12 NA 27 NA
## 13 NA 45 NA
## 14 NA 28 NA
## 15 NA 46 NA
## 16 NA 29 NA
## 17 NA 47 NA
## 18 NA 30 NA
## 19 NA 48 NA
## 20 NA 31 NA
## 21 NA 49 NA
## 22 NA 32 NAReferences
- De Cáceres M, Molowny-Horas R, Cabon A, Martínez-Vilalta J, Mencuccini M, García-Valdés R, Nadal-Sala D, Sabaté S, Martin-StPaul N, Morin X, D’Adamo F, Batllori E, Améztegui A (2023) MEDFATE 2.9.3: A trait-enabled model to simulate Mediterranean forest function and dynamics at regional scales. Geoscientific Model Development 16: 3165-3201 (https://doi.org/10.5194/gmd-16-3165-2023).