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The TROLL model

TROLL models each tree individually in a located environment. Thus TROLL model, alongside with SORTIE (Pacala et al. 1996; Uriarte et al. 2009) and FORMIND (Köhler & Huth 1998; Fischer et al. 2016), can be defined as an individual-based and spatially explicit forest growth model. TROLL simulates the life cycle of individual trees from recruitment, with a diameter at breast height (dbh) above 1 cm, to death with growth and seed production. Trees are growing in a spatialized light environment explicitly computed within voxels of 1 m3. Each tree is consistently defined by its age, diameter at breast height (dbh), height (h), crown radius (CR), crown depth (CD) and leaf area (LA). Tree geometry is calculated with allometric equations but leaf area vary dynamically within each crown following carbon allocations. Voxels resolution of 1 m3 allow the establishment of a maximum one tree by 1x1 m pixels. Each tree is flagged with a species label inherited from the parent tree through the seedling recruitment. A species label is associated with a number of species specific parameters (see table below) related to functional trait values which can be sampled on the field.

Carbon assimilation is computed over a half-hourly period of a representative day. Then allocation is computed to simulate tree growth from an explicit carbon balance (in contrast to previous models). Finally the environment is updated at each timestep set to one month. Seedlings are not simulated explicitly but as a pool. In addition belowground processes, herbaceous plants, epiphytes and lianas are not simulated inside TROLL.

Species-specific parameters used in TROLL from Maréchaux & Chave (2017). Data originates from the BRIDGE (Baraloto et al. 2010) and TRY (Kattge et al. 2011) datasets.
Abbreviation Description Units
LMA leaf mass per area g.m2
Nm leaf nitrogen content per dry mass mg.g1
Pm leaf phosphorus content per dry mass mg.g1
wsg wood specific gravity g.cm3
dbhthresh diameter at breast height threshold m
hlim asymptotic height m
ah parameter of the tree-height-dbh allometry m

Abiotic environment

A voxel space, with a resolution of 1 m3, is used to explicitly model the abiotic environment. For each tree crown, leaf area density is calculated on tree geometry assuming a uniform distribution across voxels occupied by the crown. Leaf area density is computed within each voxel summing all tree crowns inside the voxel v, and is denoted LAD(v) (leaf area per voxel in m².m^{-3}). The vertical sum of LAD from voxel v to the ground level defines LAI(v) (leaf area per ground area in m^2.m^{-2} commonly called leaf area index):

LAI(v) = \sum _{v'=v} ^\infty LAD(v')

Daily variations in light intensity (photosynthetic photon flux density PPFD in \mu mol_{photons}.m^{-2}.s^{-1}), temperature (T in degrees Celsius), and vapour pressure deficit (VPD in kPA) are computed to assess carbon assimilation within each voxel of the canopy and for a representative day per month (see Appendix 1 from @Li for further details). Variation of PPFD Within the canopy is calculated as a local Beer-Lambert extinction law:

PPFD_{max,month}(v) = PPFD_{top,max,month}*e^{-k*LAI(v)}

The daily maximum incident PPFD at the top of canopy PPFD_{top,max,month} is given as input. The extinction rate k is assumed as constant, besides its variation with zenith angle and species leaf inclination angle (Meir, 2000). Moreover only vertical light diffusion is considered ignoring lateral light diffusion, which can have an important role especially in logging gaps. Finally, intra-day variation at half hour time steps t for a representative day every month are used to compute PPFD_{month}(v,t), T_{month}(v,t) and VPD_{month}(v,t). Water and nutrient processes both in soil and inside trees are not simulated.

Photosynthesis

Theory

Troll simulates the carbon uptake of each individual with the Farquhar, von Caemmerer and Berry model of C3 photosynthesis (Farquhar et al. 1980). Gross carbon assimilation rate (A in \mu mol~CO_2. m^{-2}.s^{-1}) will be the minimum of either Rubisco activity (A_v) or RuBP generation (A_j):

A=min(A_v, A_j)~|~A_v=V_{cmax}*\frac{c_i-\Gamma^*}{c_i+K_m}~;~A_j=\frac{J}{4}*\frac{c_i-\Gamma^*}{c_i+2*\Gamma^*}

V_{cmax} is the maximum rate of carboxylation (\mu mol~CO_2.m^{-2}.s^{-1}). c_i is the CO_2 partial pressure at carboxylation sites. \Gamma^* is the CO_2 compensation point in absence of dark respiration. K_m is the apparent kinetic constant of the Rubisco. And J is the electron transport rate (\mu mol e^-.m^{-2}.s^{-1}). J depends on the light intensity with PPFD:

J = \frac{1}{2*\theta}*[\alpha*PPFD+J_{max}-\sqrt{(\alpha*PPFD+J_{max})^2}-4*\theta*\alpha*PPFD*J_{max}]

J_{max} is the maximal electron transport capacity (\mu mol e^-.m^{-2}.s^{-1}). \theta is the curvature factor. And \alpha is the apparent quantum yield to electron transport (mole^-.mol~photons^{-1}).

Carbon assimilation by photosynthesis will then be limited by the CO_2 partial pressure at carboxylation sites. Stomata controls the gas concentration at carboxylation sites through stomatal transport:

A = g_s*(c_a-c_i)

g_s is the stomatal conductance to CO_2 (molCO_2.m^{-2}.s^{-1}). TROLL simulates stomatal conductance g_s with the model from (Medlyn et al. 2011):

g_s = g_0 + (1 + \frac{g_1}{\sqrt{VPD}})*\frac{A}{c_a}

g_0 and g_1 are parameters from the model. TROLL model assume g_0 \approx 0 (empirically tested and considered as reasonable).

Parametrization

Leaf traits can be used as a proxy of photosynthesis, especially leaf nutrient content which directly plays a role in it (Wright et al. 2004). Domingues et al. (2010) suggested that V_{cmac} and J_{max} were both limited by the leaf concentration of nitrogen N and phosphorus P (mg.g^{-1}):

log_{10} V_{cmax-M} = min(\begin{array}{c} -1.56+0.43*log_{10} N-0.37*log_{10} LMA \\ -0.80+0.45*log_{10} P-0.25*log_{10} LMA \end{array})

log_{10} J_{max-M} = min(\begin{array}{c} -1.50+0.41*log_{10} N-0.45*log_{10} LMA \\ -0.74+0.44*log_{10} P-0.32*log_{10} LMA \end{array}

V_{cmax-M} and J_{max-M} are the photosynthetic capacities at 25^\circ C of mature leaves per leaf dry mass (resp. \mu mol CO_2.g^-1.s^{-1} and \mu mol e^-.g^{-1}.s^{-1}). LMA is the leaf mass per are (g.cm^{-2}). V_{cmax} and J_{max} are calculated by multiplying V_{cmax-M} and J_{max-M} by LMA. V_{cmax} and J_{max} variations with temperature are calculated with Bernacchi et al. (2003).

TROLL computes leaf carbon assimilation A_l combining equations from for each tree crown voxel within in each crown layer l:

A_l = \frac{1}{n_v*t_M} * \sum_v \sum^{t_M}_{t=1} A(PPFD_{month}(v,t),VPD_{month}(v,t),T_{month}(v,t))

PPFD_{month}(v,t), VPD_{month}(v,t) , and T_{month}(v,t) are derived from microclimatic data. n_v is the number of voxels within crown layer l. And the sum is calculated over the t_M half-hourly intervals t of a typical day.

Autotrophic respiration

A large fraction of plants carbon uptake is actually used for plant maintenance and growth respiration. The autotrophic respiration can represent up to 65% of the gross primary productivity but varies strongly among species, sites, and environments.

TROLL uses Atkin et al. (2015) database of mature leaf dark respiration and associated leaf traits to compute leaf maintenance respiration:

R_{leaf-M} = 8.5431-0.1306*N-0.5670*P-0.0137*LMA+11.1*V_{cmax-M}+0.1876*N*P

R_{leaf-M} is the dark respiration rate per leaf dry mass at a temperature of 25^\circ C (nmolCO_2.g^{-1}.s^{-1}). TROLL assumes leaf respiration during daylight to be 40% of leaf dark respiration, and computes total leaf respiration by accounting for the length of daylight.

TROLL model stem respiration (R_{stem} in \mu molC.s^{-1}) with a constant respiration rate per volume of sapwood:

R_{stem} = 39.6*\pi*ST*(dbh-ST)*(h-CD)

dbh, h, CD and ST are tree diameter at breast height, height, crown depth and sapwood thickness, respectively (m). TROLL assumes ST=0.04~m when dbh>30~cm and an increasing ST for lower dbh.

Finally, TROLL computes both fine root maintenance respiration, as half of the leaf maintenance respiration. Whereas coarse root and branch maintenance respiration is computed as half of the stem respiration. And growth respiration (R_{growth}) is assumed to account for 25% of the gross primary productivity minus the sum of maintenance respiration.

Net carbon uptake

Net primary production of carbon for one individual NPP_{ind} (gC) is computed by the balance between gross primary production GPP_{ind} and respiration R:

NPP_{ind} = GPP_{ind} - R_{maintenance} - R_{growth}

TROLL partitions individuals’ total leaf area LA into three pools for different leaf age classes corresponding to different photosynthesis efficiency (young, mature and old leaves with LA_{young}, LA_{mature}, and LA_{old} respectively). Consequently we can compute growth primary production for one individual as:

GPP_{ind} = 189.3 * \Delta t * \sum _{l= \lfloor h-CD \rfloor +1} ^{\lfloor h \rfloor} [A_l] * (\frac{LA_{young}}{2} + LA_{mature} + \frac{LA_{old}}{2})

h and CD are tree height and crown depth, respectively (m). \lfloor x \rfloor is the rounding function. \Delta t is the duration of a timestep (year).

Thus, TROLL can compute carbon allocation to wood into an increment of stem volume \Delta V (m^3):

\Delta V = 10^{-6} * \frac{f_{wood}*NPP_{ind}}{0.5*wsg}*Senesc(dbh)

f_{wood} is the fixed fraction of NPP allocated to stem and branches. wsg is the wood specific gravity (g.cm^{-3}, see @ref(tab:traits)). TROLL assume large trees less efficient to convert NPP as growth by using a size-related growth decline with function Senesc after a specific diameter at breast height threshold dbh_{thresh}:

Senesc(dbh) = max(0;3-2*\frac{dbh}{dbh_{thresh}})

Finally, TROLL can compute carbon allocation to canopy with canopy NPP fraction denoted f_{canopy} and decomposed into leaf, twig and fruit production. Carbon allocation to leaf results in a new young leaf pool, whereas other leaf pools are updated as follow:

\begin{array}{c} \\ \Delta LA_{young} = \frac{2*f_{leaves}*NPP_{ind}}{LMA}-\frac{LA_{young}}{\tau_{young}} \\ \Delta LA_{mature} = \frac{LA_{young}}{\tau_{young}} - \frac{LA_{mature}}{\tau_{mature}}\\ \Delta LA_{old} = \frac{LA_{mature}}{\tau_{mature}} - \frac{LA_{old}}{\tau_{old}} \end{array}

\tau_{young}, \tau_{mature}, and \tau_{old} are species’ residence times in each leaf pool (years). The sum of residency time thus defined the leaf lifespan LL = \tau_{young} + \tau_{mature} + \tau_{old} (years). \tau_{young} is set to one month and \tau_{mature} is set to a third of leaf lifespan LL. Belowground carbon allocation is not simulated inside TROLL.

Tree growth

Once the increment of stem volume \Delta V is calculated, TROLL converts it into an increment of tree diameter at breast height denoted \Delta dbh. TROLL infer tree height from dbh using a Michaelis-Menten equation:

h = h_{lim}*\frac{dbh}{dbh + a_h}

On the other hand, we have the trunk volume V = C * \pi * (\frac{dbh}{2})^2*h, thus:

\begin{array}{c} \\ \Delta V = C*\frac{1}{2}*\pi*h*dbh*\Delta dbh + C * \pi * (\frac{dbh}{2})^2*h \\ \Delta V = V*\frac{\Delta dbh}{dbh}*(3-\frac{dbh}{dbh + ah})\end{array}

Next, TROLL used the new trunk dimension (dbh and h) to update tree crown geometry using allometric equations (Chave et al. 2005):

\begin{array}{c} \\ CR = 0.80 + 10.47*dbh - 3.33*dbh^2\\ CD = -0.48 + 0.26*h~;~CD = 0.13 + 0.17*h~(h<5~m)\end{array}

Finally, TROLL computes the mean leaf density within the crown (LD in m^2.m^{-3}) assuming a uniform distribution:

LD = \frac{LA_{young}+LA_{mature}+LA_{old}}{\pi*CR^2*CD}

Mortality

Mortality is partitioned in two factors inside TROLL: background death d_b and treefall death d_t.

Chave et al. (2009) advocated for a wood economics spectrum opposing fast growing light wood species with high risk of mortality to slow growing dense wood species with reduced risk of mortality. Hence, background mortality is derived from wood specific gravity wsg inside TROLL:

d_b = m*(1-\frac{wsg}{wsg_{lim}})+d_n

m (events.year^{-1}) is the reference background death rate for lighter wood species (pioneers). d_n represents death by carbohydrates shortage. If the number of consecutive days with NPP_{ind} < 0 is superior to tree leaf lifespan d_n is set to 1 and remains null in other cases.

Mortality by treefall inside TROLL depends on a specific stochastic threshold \theta:

\theta = h_{max}*(1-v_T*|\zeta|)

h_{max} is the maximal tree height. v_T is the variance term set to 0.3. |\zeta| is the absolute value of a random centred and scaled Gaussian. If the tree height h is superior to \theta then the tree may fall with a probability 1-\theta/h (Chave 1999). The treefall direction is random (drawn from a uniform law (\mathcal{U}[0,2\pi]). All trees in the trajectory of the falling tree will be hurted through a variable denoted hurt, incremented by fallen tree height h. If a tree height is inferior than its hurt values then it may die with a probability 1-\frac{1}{2}\frac{h}{hurt}. hurt variable is reset to null at each timestep (month).

Recruitment

Once the tree becomes fertile they will start to disperse seeds. TROLL consider tree as fertile after a specific height threshold h_{mature} (Wright et al. 2005):

h_{mature} = -11.47+0.90*h_{max}

But TROLL is not considering seed directly through a seedbank, instead seed might be interpreted as a seedling recruitment opportunity. The number of reproduction opportunities per mature tree is denoted n_s and set to 10 for all species. This assumption originates from a trade-off between seed number and seed size resulting in equivalent survival and recruitment probability. All n_s events are dispersed with a distance randomly drawn from a Gaussian distribution. Additionally, TROLL model consider external seedrain through n_{ext} events of seed immigration:

n_{ext} = N_{tot}*f_{reg}*n_{ha}

N_{tot} is the external seedrain per hectare (number of reproduction opportunities). f_{reg} is the species’ regional frequency. n_{ha} is the simulated plot size in ha.

Finally, a bank of seedlings to be recruited is defined for each pixel. If the ground-level light reaches a species light compensation point LCP the species will be recruited:

LCP = \frac{R_{leaf}}{\phi}

R_{leaf} is the leaf respiration for maintenance. \phi is the quantum yield (\mu mol C.\mu mol~photon) set to 0.06. If several species reach their LCP, one is picked at random. Seedlings are recruited with following initial geometry:

\begin{array}{c} \\ dbh = \frac{a_h}{h_{max} - 1}\\ h = 1~m\\ CR = 0.5~m\\ CD = 0.3~m\\ LD = 0.8~m^2.^{-3} \end{array}

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