Forest dynamics
Miquel De Caceres
2026-10-16
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$StandSummary## Step NumTreeSpecies NumTreeCohorts NumShrubSpecies NumShrubCohorts
## 1 0 2 2 1 1
## 2 1 2 2 1 1
## 3 2 2 2 1 1
## 4 3 2 2 1 1
## 5 4 2 2 1 1
## 6 5 2 2 1 1
## 7 6 2 2 1 1
## 8 7 2 2 1 1
## 9 8 2 2 1 1
## 10 9 2 2 1 1
## 11 10 2 2 1 1
## TreeDensityLive TreeBasalAreaLive DominantTreeHeight DominantTreeDiameter
## 1 552.0000 25.03330 800.0000 37.55000
## 2 551.3673 25.14663 802.6468 37.59922
## 3 550.7312 25.26037 805.2904 37.64849
## 4 550.0916 25.37443 807.9294 37.69779
## 5 549.4469 25.48875 810.5718 37.74726
## 6 548.8004 25.60370 813.2023 37.79659
## 7 548.1505 25.71886 815.8317 37.84602
## 8 547.4970 25.83417 818.4594 37.89553
## 9 546.8383 25.94945 821.0822 37.94505
## 10 546.1778 26.06533 823.6969 37.99451
## 11 545.5175 26.18162 826.3135 38.04412
## QuadraticMeanTreeDiameter HartBeckingIndex ShrubCoverLive BasalAreaDead
## 1 24.02949 53.20353 3.750000 0.00000000
## 2 24.09764 53.05850 3.980119 0.03899540
## 3 24.16602 52.91486 4.229441 0.03938881
## 4 24.23459 52.77267 4.494970 0.03978613
## 5 24.30337 52.63149 4.777099 0.04029770
## 6 24.37245 52.49213 5.078686 0.04059290
## 7 24.44168 52.35396 5.398639 0.04100207
## 8 24.51102 52.21701 5.737831 0.04141446
## 9 24.58044 52.08155 6.097254 0.04194485
## 10 24.65015 51.94760 6.480797 0.04224967
## 11 24.72003 51.81444 6.887360 0.04243836
## ShrubCoverDead BasalAreaCut ShrubCoverCut
## 1 0.000000000 0 0
## 2 0.005923307 0 0
## 3 0.006293002 0 0
## 4 0.006687784 0 0
## 5 0.007127848 0 0
## 6 0.007556200 0 0
## 7 0.008032917 0 0
## 8 0.008538383 0 0
## 9 0.009099863 0 0
## 10 0.009645135 0 0
## 11 0.010193119 0 0Species-level analogous statistics are shown using:
fd$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 1 167.6997 18.620014
## 5 1 Quercus coccifera 1 NA NA
## 6 1 Quercus ilex 1 383.6676 6.526616
## 7 2 Pinus halepensis 1 167.3978 18.635232
## 8 2 Quercus coccifera 1 NA NA
## 9 2 Quercus ilex 1 383.3334 6.625137
## 10 3 Pinus halepensis 1 167.0942 18.650177
## 11 3 Quercus coccifera 1 NA NA
## 12 3 Quercus ilex 1 382.9975 6.724253
## 13 4 Pinus halepensis 1 166.7880 18.664895
## 14 4 Quercus coccifera 1 NA NA
## 15 4 Quercus ilex 1 382.6589 6.823857
## 16 5 Pinus halepensis 1 166.4810 18.679274
## 17 5 Quercus coccifera 1 NA NA
## 18 5 Quercus ilex 1 382.3194 6.924428
## 19 6 Pinus halepensis 1 166.1723 18.693435
## 20 6 Quercus coccifera 1 NA NA
## 21 6 Quercus ilex 1 381.9781 7.025424
## 22 7 Pinus halepensis 1 165.8620 18.707363
## 23 7 Quercus coccifera 1 NA NA
## 24 7 Quercus ilex 1 381.6351 7.126809
## 25 8 Pinus halepensis 1 165.5490 18.720899
## 26 8 Quercus coccifera 1 NA NA
## 27 8 Quercus ilex 1 381.2893 7.228549
## 28 9 Pinus halepensis 1 165.2352 18.734161
## 29 9 Quercus coccifera 1 NA NA
## 30 9 Quercus ilex 1 380.9426 7.331167
## 31 10 Pinus halepensis 1 164.9214 18.747449
## 32 10 Quercus coccifera 1 NA NA
## 33 10 Quercus ilex 1 380.5961 7.434170
## 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 0.033340488 NA 0 NA
## 5 3.980119 NA 0.005923307 NA 0
## 6 NA 0.005654907 NA 0 NA
## 7 NA 0.033613364 NA 0 NA
## 8 4.229441 NA 0.006293002 NA 0
## 9 NA 0.005775450 NA 0 NA
## 10 NA 0.033888300 NA 0 NA
## 11 4.494970 NA 0.006687784 NA 0
## 12 NA 0.005897831 NA 0 NA
## 13 NA 0.034259197 NA 0 NA
## 14 4.777099 NA 0.007127848 NA 0
## 15 NA 0.006038503 NA 0 NA
## 16 NA 0.034444547 NA 0 NA
## 17 5.078686 NA 0.007556200 NA 0
## 18 NA 0.006148358 NA 0 NA
## 19 NA 0.034725615 NA 0 NA
## 20 5.398639 NA 0.008032917 NA 0
## 21 NA 0.006276453 NA 0 NA
## 22 NA 0.035008254 NA 0 NA
## 23 5.737831 NA 0.008538383 NA 0
## 24 NA 0.006406204 NA 0 NA
## 25 NA 0.035389283 NA 0 NA
## 26 6.097254 NA 0.009099863 NA 0
## 27 NA 0.006555566 NA 0 NA
## 28 NA 0.035578422 NA 0 NA
## 29 6.480797 NA 0.009645135 NA 0
## 30 NA 0.006671250 NA 0 NA
## 31 NA 0.035669190 NA 0 NA
## 32 6.887360 NA 0.010193119 NA 0
## 33 NA 0.006769166 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$TreeTable## Step Year Cohort Species DBH Height N Z50 Z95 Z100
## 1 0 NA T1_158 Pinus halepensis 37.55000 800.0000 168.0000 100 300 NA
## 2 0 NA T2_179 Quercus ilex 14.60000 660.0000 384.0000 300 1000 NA
## 3 1 2001 T1_158 Pinus halepensis 37.59922 802.6468 167.6997 100 300 NA
## 4 1 2001 T2_179 Quercus ilex 14.71708 661.7987 383.6676 300 1000 NA
## 5 2 2002 T1_158 Pinus halepensis 37.64849 805.2904 167.3978 100 300 NA
## 6 2 2002 T2_179 Quercus ilex 14.83420 663.5957 383.3334 300 1000 NA
## 7 3 2003 T1_158 Pinus halepensis 37.69779 807.9294 167.0942 100 300 NA
## 8 3 2003 T2_179 Quercus ilex 14.95130 665.3902 382.9975 300 1000 NA
## 9 4 2004 T1_158 Pinus halepensis 37.74726 810.5718 166.7880 100 300 NA
## 10 4 2004 T2_179 Quercus ilex 15.06830 667.1809 382.6589 300 1000 NA
## 11 5 2005 T1_158 Pinus halepensis 37.79659 813.2023 166.4810 100 300 NA
## 12 5 2005 T2_179 Quercus ilex 15.18566 668.9785 382.3194 300 1000 NA
## 13 6 2006 T1_158 Pinus halepensis 37.84602 815.8317 166.1723 100 300 NA
## 14 6 2006 T2_179 Quercus ilex 15.30284 670.7708 381.9781 300 1000 NA
## 15 7 2007 T1_158 Pinus halepensis 37.89553 818.4594 165.8620 100 300 NA
## 16 7 2007 T2_179 Quercus ilex 15.41979 672.5574 381.6351 300 1000 NA
## 17 8 2008 T1_158 Pinus halepensis 37.94505 821.0822 165.5490 100 300 NA
## 18 8 2008 T2_179 Quercus ilex 15.53650 674.3381 381.2893 300 1000 NA
## 19 9 2009 T1_158 Pinus halepensis 37.99451 823.6969 165.2352 100 300 NA
## 20 9 2009 T2_179 Quercus ilex 15.65351 676.1243 380.9426 300 1000 NA
## 21 10 2010 T1_158 Pinus halepensis 38.04412 826.3135 164.9214 100 300 NA
## 22 10 2010 T2_179 Quercus ilex 15.77027 677.9035 380.5961 300 1000 NA
## Age ObsID
## 1 40 <NA>
## 2 24 <NA>
## 3 40 NA
## 4 24 NA
## 5 41 NA
## 6 25 NA
## 7 42 NA
## 8 26 NA
## 9 43 NA
## 10 27 NA
## 11 44 NA
## 12 28 NA
## 13 45 NA
## 14 29 NA
## 15 46 NA
## 16 30 NA
## 17 47 NA
## 18 31 NA
## 19 48 NA
## 20 32 NA
## 21 49 NA
## 22 33 NAThe same can be shown for dead trees:
fd$DeadTreeTable## Step Year Cohort Species DBH Height N N_starvation
## 1 1 2001 T1_158 Pinus halepensis 37.59922 802.6468 0.3002785 0
## 2 1 2001 T2_179 Quercus ilex 14.71708 661.7987 0.3324241 0
## 3 2 2002 T1_158 Pinus halepensis 37.64849 805.2904 0.3019443 0
## 4 2 2002 T2_179 Quercus ilex 14.83420 663.5957 0.3341701 0
## 5 3 2003 T1_158 Pinus halepensis 37.69779 807.9294 0.3036184 0
## 6 3 2003 T2_179 Quercus ilex 14.95130 665.3902 0.3359265 0
## 7 4 2004 T1_158 Pinus halepensis 37.74726 810.5718 0.3061375 0
## 8 4 2004 T2_179 Quercus ilex 15.06830 667.1809 0.3386189 0
## 9 5 2005 T1_158 Pinus halepensis 37.79659 813.2023 0.3069907 0
## 10 5 2005 T2_179 Quercus ilex 15.18566 668.9785 0.3394701 0
## 11 6 2006 T1_158 Pinus halepensis 37.84602 815.8317 0.3086879 0
## 12 6 2006 T2_179 Quercus ilex 15.30284 670.7708 0.3412560 0
## 13 7 2007 T1_158 Pinus halepensis 37.89553 818.4594 0.3103878 0
## 14 7 2007 T2_179 Quercus ilex 15.41979 672.5574 0.3430473 0
## 15 8 2008 T1_158 Pinus halepensis 37.94505 821.0822 0.3129476 0
## 16 8 2008 T2_179 Quercus ilex 15.53650 674.3381 0.3457910 0
## 17 9 2009 T1_158 Pinus halepensis 37.99451 823.6969 0.3138015 0
## 18 9 2009 T2_179 Quercus ilex 15.65351 676.1243 0.3466520 0
## 19 10 2010 T1_158 Pinus halepensis 38.04412 826.3135 0.3137821 0
## 20 10 2010 T2_179 Quercus ilex 15.77027 677.9035 0.3465509 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 × 54
## date PET Precipitation Rain Snow NetRain Snowmelt Runon
## <date> [L/m^2] [L/m^2] [L/m^2] [L/m^2] [L/m^2] [L/m^2] [L/m^2]
## 1 2001-01-01 0.883 4.87 4.87 0 3.66 0 0
## 2 2001-01-02 1.64 2.50 2.50 0 1.30 0 0
## 3 2001-01-03 1.30 0 0 0 0 0 0
## 4 2001-01-04 0.569 5.80 5.80 0 4.60 0 0
## 5 2001-01-05 1.68 1.88 1.88 0 0.862 0 0
## 6 2001-01-06 1.21 13.4 13.4 0 12.0 0 0
## 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 3.91 5.38 0
## # ℹ 3,640 more rows
## # ℹ 46 more variables: Infiltration [L/m^2], 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], …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$CutTreeTable## Step Year Cohort Species DBH Height N Z50 Z95 Z100
## 1 1 2001 T1_158 Pinus halepensis 37.59922 802.6468 9.162901 100 300 NA
## 2 1 2001 T2_179 Quercus ilex 14.71708 661.7987 383.667576 300 1000 NA
## Age ObsID
## 1 40 NA
## 2 24 NAManagement parameters were those of an irregular model with thinning interventions from ‘below’, indicating that smaller trees were to be cut earlier:
args$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 Z100
## 1 0 NA T1_158 Pinus halepensis 37.55000 800.00000 168.0000 100 300 NA
## 2 0 NA T2_179 Quercus ilex 14.60000 660.00000 384.0000 300 1000 NA
## 3 1 2001 T1_158 Pinus halepensis 37.59922 802.64683 158.5368 100 300 NA
## 4 1 2001 T3_179 Quercus ilex 1.00000 47.23629 3000.0000 300 1000 NA
## 5 2 2002 T1_158 Pinus halepensis 37.71758 808.91651 158.3659 100 300 NA
## 6 2 2002 T3_179 Quercus ilex 1.00000 47.23629 2998.2956 300 1000 NA
## 7 3 2003 T1_158 Pinus halepensis 37.80710 813.63671 158.1941 100 300 NA
## 8 3 2003 T3_179 Quercus ilex 1.00000 47.23629 2996.5829 300 1000 NA
## 9 4 2004 T1_158 Pinus halepensis 37.85627 816.22107 158.0214 100 300 NA
## 10 4 2004 T3_179 Quercus ilex 1.00000 47.23629 2994.8618 300 1000 NA
## 11 5 2005 T1_158 Pinus halepensis 37.90545 818.80107 157.8491 100 300 NA
## 12 5 2005 T3_179 Quercus ilex 1.00000 47.23629 2993.1434 300 1000 NA
## 13 6 2006 T1_158 Pinus halepensis 37.95467 821.37705 157.6765 100 300 NA
## 14 6 2006 T3_179 Quercus ilex 1.00000 47.23629 2991.4231 300 1000 NA
## 15 7 2007 T1_158 Pinus halepensis 38.00381 823.94367 157.5038 100 300 NA
## 16 7 2007 T3_179 Quercus ilex 1.00000 47.23629 2989.7009 300 1000 NA
## 17 8 2008 T1_158 Pinus halepensis 38.05303 826.50823 157.3305 100 300 NA
## 18 8 2008 T3_179 Quercus ilex 1.00000 47.23629 2987.9720 300 1000 NA
## 19 9 2009 T1_158 Pinus halepensis 38.10222 829.06645 157.1575 100 300 NA
## 20 9 2009 T3_179 Quercus ilex 1.00000 47.23629 2986.2460 300 1000 NA
## 21 10 2010 T1_158 Pinus halepensis 38.15135 831.61555 156.9853 100 300 NA
## 22 10 2010 T3_179 Quercus ilex 1.00000 47.23629 2984.5275 300 1000 NA
## Age ObsID
## 1 40 <NA>
## 2 24 <NA>
## 3 40 NA
## 4 24 <NA>
## 5 41 NA
## 6 24 NA
## 7 42 NA
## 8 25 NA
## 9 43 NA
## 10 26 NA
## 11 44 NA
## 12 27 NA
## 13 45 NA
## 14 28 NA
## 15 46 NA
## 16 29 NA
## 17 47 NA
## 18 30 NA
## 19 48 NA
## 20 31 NA
## 21 49 NA
## 22 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).