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**Model description paper**
08 Mar 2019

**Model description paper** | 08 Mar 2019

LPJ-GM 1.0

^{1}Dynamic Macroecology/Land Change Science, Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Birmensdorf, Switzerland^{2}Department of Physical Geography and Ecosystem Science, Lund University, Lund, Sweden^{3}Centre for Mathematical Sciences, Department for Mathematics Lund University, Lund, Sweden

^{1}Dynamic Macroecology/Land Change Science, Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Birmensdorf, Switzerland^{2}Department of Physical Geography and Ecosystem Science, Lund University, Lund, Sweden^{3}Centre for Mathematical Sciences, Department for Mathematics Lund University, Lund, Sweden

Abstract

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Dynamic global vegetation models are a common tool to assess the effect of climate and land use change on vegetation. Though most applications of dynamic global vegetation models use plant functional types, some also simulate species occurrences. While the current development aims to include more processes, e.g. the nitrogen cycle, the models still typically assume an ample seed supply allowing all species to establish once the climate conditions are suitable. Pollen studies have shown that a number of plant species lag behind in occupying climatological suitable areas (e.g. after a change in the climate) as they need to arrive at and establish in the newly suitable areas. Previous attempts to implement migration in dynamic vegetation models have allowed for the simulation of either only small areas or have been implemented as a post-process, not allowing for feedbacks within the vegetation. Here we present two novel methods simulating migrating and interacting tree species which have the potential to be used for simulations of large areas. Both distribute seeds between grid cells, leading to individual establishment. The first method uses an approach based on fast Fourier transforms, while in the second approach we iteratively shift the seed production matrix and disperse seeds with a given probability. While the former method is computationally faster, it does not allow for modification of the seed dispersal kernel parameters with respect to terrain features, which the latter method allows.

We evaluate the increase in computational demand of both methods. Since dispersal acts at a scale no larger than 1 km, all dispersal simulations need to be performed at maximum at that scale. However, with the currently available computational power it is not feasible to simulate the local vegetation dynamics of a large area at that scale. We present an option to decrease the required computational costs through a reduction in the number of grid cells for which the local dynamics are simulated only along migration transects. Evaluation of species patterns and migration speeds shows that simulating along transects reduces migration speed, and both methods applied on the transects produce reasonable results. Furthermore, using the migration transects, both methods are sufficiently computationally efficient to allow for large-scale DGVM simulations with migration.

How to cite

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How to cite.

Lehsten, V., Mischurow, M., Lindström, E., Lehsten, D., and Lischke, H.: LPJ-GM 1.0: simulating migration efficiently in a dynamic vegetation model, Geosci. Model Dev., 12, 893-908, https://doi.org/10.5194/gmd-12-893-2019, 2019.

1 Introduction

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A large suite of dynamic global vegetation models (DGVMs) is currently used to simulate the effects of climate and/or land use change on vegetation and ecosystem properties. These simulations result in projections (or hindcasts) of species ranges as well as changes in ecosystem properties such as carbon stocks and fluxes. Examples of these DGVMs include ORCHIDEE (Yue et al., 2018), LPJ-GUESS (Sitch et al., 2003), and IBIS (Foley et al., 1998; Sato et al., 2007); for a review of DGVM features, see Quillet et al. (2010).

While most DGVM applications use plant functional types (groups of plant species with similar traits and responses to environmental conditions), here we only consider applications which explicitly simulate tree species (e.g. Hickler et al., 2012). These models typically assume that species can establish at any site once the environmental conditions become suitable. However, in real ecosystems species not only need to establish and replace existing vegetation, which the processes in gap models describe successfully, but they also need to have a sufficient amount of seeds present at a given location to successfully establish. Implicitly, current DGVMs assume that ample amounts of seeds of all species are present in every location.

While this approach might seem reasonable in cases in which the vegetation can
keep up with climate change (i.e. moving sufficiently fast to occupy areas
which become suitable), there have been a number of instances reported in
which
a considerable migration lag occurred. For instance, *Fagus sylvatica*
has been shown to have a considerable migration lag and is currently still in
the process of occupying its climatological optimum (Bradshaw and Lindbladh,
2005).

The implementation of migration into dynamic vegetation models is not only of interest for the simulation of historical species ranges, but it is also of interest for the projection of ecosystem properties in the future since migration lags might lead to uncertainties in projected ecosystem properties if the wrong species community is predicted to occur at a certain site (Neilson et al., 2005). Especially, given that the speed at which environmental conditions change is currently unprecedented, at least over the last centuries, effects of the migration lag of key species should be evaluated when projecting ecosystem properties. This holds in particular for projections over several centuries. For periods of less than 50–100 years ahead, which corresponds to at most a few generations of most tree species, the explicit modelling of seed dispersal might be less important for simulating tree distributions, in particular when taking into account the overwhelming influence of human activities.

Migration lags can be caused by different factors. Seed transport might only occur over limited distances. But low seed amounts and, in particular, long generation times can also slow down migration. Seed amount and generation time depend on the competition with other trees: a free-standing tree starts to produce seeds earlier and produces more than a tree of the same age in a closed forest. The competitors, however, are also migrating, which leads to feedbacks between the species (Snell et al., 2014).

Thus, for simulations over large areas covering long time spans, species migration – consisting of (a) local dynamics influenced by the environment, (b) competition between species, and (c) seed dispersal – has to be taken into account simultaneously for several species.

Species migration has been implemented successfully in dynamic vegetation models working on smaller extents and finer scales than DGVMs typically use, e.g. forest landscape models (FLMs; review in Shifley et al., 2017) such as TreeMig, (Lischke et al., 2006), LandClim (Schumacher et al., 2004), Landis (Mladenoff, 2004), and Iland (Seidl et al., 2012) or spatially explicit individual-based models such as LAVESI (Kruse et al., 2018).

In these models, seed dispersal is modelled in a straightforward way: seeds are distributed from each producing to each receiving cell with a distance-dependent probability. However, transferring these approaches to DGVMs is problematic due to a number of conceptual and technical difficulties. DGVMs usually operate on a coarse spatial resolution to reduce computational load and input data requirements. This neglects the spatial heterogeneity within the grid cells. Additionally, and even more critical for implementing migration, it leads to discretization errors: if it is assumed that the forest representing the grid cell is located in the centre of the cell, the seeds cannot move far enough to leave the cell (given a typical cell size of 50 km×50 km or 10 km×10 km). If, on the other hand, a uniformly distributed forest in the cell is assumed in the simulation some seeds reach the neighbour cell with each time step, leading to a resolution-dependent speed-up of migration.

Also, some specifics of model implementations might complicate the inclusion of migration in some DGVMs. Many DGVM implementations are done in a way that for each grid cell all years are simulated before the simulation of the next cell is started. This is done to minimize input–output effort since all climate data for each cell are read in at once and it also eases parallelization for multicore computers, since in this case each node is assigned a number of grid cells which the node calculates independently of the other nodes without communication. However, for simulating seed dispersal, all cells need to be annually evaluated. In addition to the reasons mentioned before, most DGVM applications use plant functional types, which typically comprise species with very different traits with respect to migration (e.g. dispersal vectors or seed properties). Hence, introducing migration would require splitting up PFTs into smaller groups and parameterizing the additional properties.

There have been a number of attempts to integrate species migration in DGVMs
(see Snell et al., 2014, and the Discussion section). For example, Sato and
Ise (2012) developed a DGVM in which species could potentially migrate between
neighbouring cells with a fixed rate of about 1 km yr^{−1}, while
Snell (2014) simulated migration as an infection process between patches and
within each grid cell.

However, to the knowledge of the authors, there is no implementation of a migration scheme into a DGVM which allows for simulations with a large extent, takes migration within the grid cell into account, and includes feedbacks between all simulated species.

Here we present two methods to fill this gap, i.e. allow for the simultaneous simulation of the migration of several species. The methods are implemented into the LPJ-GUESS DGVM but can potentially also be implemented into other DGVMs. Though they are tested here using a virtual landscape, they can be applied for simulations of large areas given current computing resources.

2 Methods

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LPJ-GUESS is a flexible framework for modelling the dynamics of terrestrial ecosystems from landscape to global scales (Sitch et al., 2003; Smith et al., 2001). Similar to most other DGVMs, it requires time series of climate data (precipitation, air temperature, and shortwave radiation), soil conditions, and carbon dioxide concentrations as input and explicitly simulates vegetation cover. While it uses plant functional types in most applications, some applications simulate tree species (e.g. Hickler et al., 2012; Lehsten et al., 2015). LPJ-GUESS explicitly simulates canopy conductance, photosynthesis, phenology, and carbon allocation. It uses a detailed individual-based representation of forest stand structure and dynamics. Each species (or PFT) has a specific growth form, leaf phenology, life history, and bioclimatic limits determining its performance and competitive interactions under the forcing conditions and realized ecosystem state of a particular grid cell (Sitch et al., 2003). A large body of publications describes the features of LPJ-GUESS in detail; here we concentrate on the changes that were applied to LPJ-GUESS version 4.0 (Lindeskog et al., 2013; Smith et al., 2014). To differentiate between the original version of LPJ-GUESS and our extended version (in which we implemented the migration module) we refer to the extended version as LPJ-GM (short for LPJ-GUESS-MIGRATION).

Standard LPJ-GUESS simulations are typically performed at a computing cluster with cells running on different nodes of the cluster without any interaction of the nodes. We implemented a distributed simulation using a message-passing interface (MPI) (Clarke et al., 1994) with the grid cells communicating with a master process.

Seeds are potentially produced in each grid cell at the end of each year after the first 100 years (see below). The number of seeds produced is sent to the node computing the dispersal, while all nodes wait for this master node to finish the calculation. This node sends the number of seeds that arrive at each grid cell back to all nodes to continue the calculation.

Similar to the standard version of LPJ-GUESS (Sitch et al., 2003; Smith et al., 2001), in the first 100 years no seed dispersal is performed and all species are allowed to establish and grow without seed limitation and without N limitation to equilibrate the soil pools with carbon and nitrogen. This time period is used to sample NPP given a certain N deposition and climate to subsequently equilibrate the N pools of the soil and a fast spin-up of 40 000 years approximated using the sampled rates of C assimilation (Smith et al., 2014). After this initialization period all vegetation is killed and succession starts from a bare soil with seed limitation active. In LPJ-GM seed dispersal is done on an annual basis. The amount of seeds produced is communicated to the master node at the end of each year. The master node redistributes seeds over the whole spatial domain according to the dispersal algorithm and communicates the amounts of arriving seeds back to each grid cell. Seeds transferred to the grid cells are added to the seed bank, which determines establishment probability in environmentally suitable cells (environmental suitability is determined by means of environmental envelopes containing, amongst others, minimum survival and establishment temperatures; see Smith et al., 2001). All communications between the processes are done via MPI protocol (Clarke et al., 1994).

LPJ-GUESS is a gap model with the typical successional vegetation changes. To
even out successional-based fluctuations in ecosystem properties and to
simulate disturbances, most previous applications simulate a certain
number of replicate patches per grid cell. All patches share the same climate
but potentially differ in their successional stage due to different timing of
disturbances and stochastic mortality. Conceptually, each patch has a size of
1000 m^{2} but represents an area depending on the resolution of the grid
cell. Patches have no spatial position with respect to each other and do not
interact (Smith et al., 2001). In LPJ-GM we reduced the number of patches to
one but achieved the representative averaging by using explicitly placed
small grid cells instead of statistical units (replicate patches). For each
large grid cell in the climate grid we simulate a large number of cells of
1 km^{2} area, resulting in a more than sufficient averaging of
successional stages. LPJ-GUESS simulations are typically performed with patch
numbers around 10 (e.g. Smith et al., 2001), but depending on the aim of the
simulation patch numbers have even been increased to 500 (e.g. Lehsten et
al., 2016). In our set-up even with 50 km corridors LPJ-GM represents a
0.5×0.5^{∘} cell with 200 simulation cells ranging at the higher
end of the patch number per area compared to previous simulations. We
demonstrate this in Fig. 3 in which a single large grid cell is
separated into 11×11 small grid cells with the same climate, each of which is
1 km×1 km. The local
dynamics and seed production are only simulated along the transects (grey or
green cells in left panel of Fig. 3). As a next step the seed production is
interpolated onto all cells for which no local dynamics were calculated, and
the seed dispersal is simulated. Finally, seedling establishment is
simulated, but only in the grid cells on the corridors (more details for the
different steps are given below).

The seed production starts once the tree reaches maturity height and is
scaled linearly with leaf area up to maximum (LAI).
The seed number produced per tree is calculated as the product of the maximum
fecundity multiplied by the proportion of the current LAI to the maximum LAI
and multiplied by the area per grid cell. For example, the maximum fecundity
of beech is 29 000, the maximum LAI is 5 m^{2} m^{−2}, and the maturity
height is 14.4 m. Hence, a tree of 15 m height is above the maturity height,
and with an LAI of 2.5 m^{2} m^{−2} it will produce $\mathrm{29}\phantom{\rule{0.125em}{0ex}}\mathrm{000}\times \mathrm{0.5}/\mathrm{5}=\mathrm{14}\phantom{\rule{0.125em}{0ex}}\mathrm{500}$ seeds. No specific age of maturity is taken into account.

All seeds of a species produced $S({x}^{\prime},{y}^{\prime})$ at a location $({x}^{\prime},{y}^{\prime})$ within a year are available for seed dispersal. Once seeds have entered the seed bank, no further dispersal is possible (they remain in the seed bank). Though LPJ-GUESS keeps track of carbon allocated to the main plant compartments and even allocates a certain amount of carbon to seeds (which is transferred to the litter pool, the soil pool, and finally the atmosphere), for simplicity we decided not to relate the seed production to the carbon accounting at this point. Allocation rules, including seed production and even mast fruiting effects (synchronized strong increases in seed production e.g. similar to Lischke et al., 2006), could be included in the future.

The produced seeds are distributed according to

$$\begin{array}{}\text{(1)}& {\displaystyle}{S}_{\mathrm{d}}\left(xy\right)=\int S({x}^{\prime},{y}^{\prime}){k}_{\mathrm{s}}(x-{x}^{\prime},y-{y}^{\prime})\phantom{\rule{0.125em}{0ex}}\mathrm{d}{x}^{\prime}\phantom{\rule{0.125em}{0ex}}\mathrm{d}{y}^{\prime}.\end{array}$$

$S\left({x}^{\prime}{y}^{\prime}\right)$ is the seed production, and ${k}_{\mathrm{s}}(x-{x}^{\prime},y-{y}^{\prime})$ the seed
dispersal kernel in Euclidean coordinates. The seed distribution
*S*_{d}(*x*,*y*), i.e. the input of seeds in location *x*, *y*, is then
obtained by integrating over all possible locations ${x}^{\prime},{y}^{\prime}$ for arriving at
*x*,*y*.

Thus, the seed distribution is given by the convolution $(\ast \ast )$ of the seed production and the seed dispersal kernel:

$$\begin{array}{}\text{(2)}& {\displaystyle}{S}_{\mathrm{d}}=S\phantom{\rule{0.125em}{0ex}}\ast \ast {k}_{\mathrm{s}}.\end{array}$$

For this study we used the seed dispersal kernel and parameterization for
*Fagus sylvatica* from TreeMig (Lischke et al., 2006). The seed
dispersal kernel defines the probability of seeds arriving at a sink cell
(*x*,*y*) from the source cell $\left({x}^{\prime},{y}^{\prime}\right)$ with a certain distance
$z=\sqrt{{\left(x-{x}^{\prime}\right)}^{\mathrm{2}}+{\left(y-{y}^{\prime}\right)}^{\mathrm{2}}}$.

The kernel is specified in a polar coordinate system, *k*_{s}(*z**θ*)=*k*_{s}(*z*|*θ*)*k*_{s}(*θ*), with the radial
distance *z*. The seeds follow a mixture of two exponential distributions,
the short- and the long-term dispersal, while the angular dispersion,
*θ*, is uniform in all directions (in our case the angular dispersion
*θ* is uniform, but if one is interested e.g. in implementing wind
directions this can be changed). Thus, the radial component of the kernel is
given by

$$\begin{array}{}\text{(3)}& {\displaystyle}{k}_{\mathrm{s}}\left(z\mathrm{|}\mathit{\theta}\right)=(\mathrm{1}-\mathit{\kappa}){\displaystyle \frac{\mathrm{1}}{{\mathit{\alpha}}_{\mathrm{s},\mathrm{1}}}}{e}^{-\frac{z}{{\mathit{\alpha}}_{\mathrm{s},\mathrm{1}}}}+\mathit{\kappa}{\displaystyle \frac{\mathrm{1}}{{\mathit{\alpha}}_{\mathrm{s},\mathrm{2}}}}{e}^{-\frac{z}{{\mathit{\alpha}}_{\mathrm{s},\mathrm{2}}}},\phantom{\rule{1em}{0ex}}\mathit{\kappa}\in (\mathrm{0},\mathrm{1}),\end{array}$$

while the angular term is given by

$$\begin{array}{}\text{(4a)}& {\displaystyle}& {\displaystyle}{k}_{\mathrm{s}}\left(\mathit{\theta}\right)={\displaystyle \frac{\mathrm{1}}{\mathrm{2}\mathit{\pi}}}\phantom{\rule{0.25em}{0ex}}\text{for}\phantom{\rule{0.25em}{0ex}}\mathit{\theta}\in [\mathrm{0},\mathrm{2}\mathit{\pi}],\text{(4b)}& {\displaystyle}& {\displaystyle}{k}_{\mathrm{s}}\left(\mathit{\theta}\right)=\mathrm{0}\phantom{\rule{0.25em}{0ex}}\text{otherwise}.\end{array}$$

The dispersal kernel is defined by the species-specific values for the
proportion of long-distance dispersal *κ* and the species expected
dispersal distances *α*_{s,1} and *α*_{s,2} for the
two kernels.

The species-specific values for these parameters (0.99 for
*κ*_{s} and 25 and 200 m for the two mean dispersal distances
*k*_{s} for *Fagus sylvatica*) were taken from by Lischke et
al. (2006).

The number of the seeds in the seed bank (i.e. the dormant seeds in the soil
that can germinate in subsequent years in each cell) is increased by the
influx *S*_{d} of seeds according to Eq. (1) and reduced by the
yearly loss of germinability (caused by the decay of seeds; see Supplement S4 for
parameter values) and the amount of germinated seeds at the end of each
simulated year, similar to TreeMig (Lischke et al., 2006).

For each grid cell and each year we prescribe whether the species requires seeds to establish. By not requiring seeds for establishment we define refugia, or we stipulate that the species' seeds are known to be very far dispersed and hence no explicit simulation of establishment by seeds is required for this species. Technically this is implemented by reading in a list for each cell containing a year starting from which species establishment is not limited by the availability of seeds.

LPJ-GUESS is a gap model and in the original version the number of newly established saplings only depends on the amount of light reaching the forest floor (given that the cell has a suitable climate). In LPG-GM we additionally limit the establishment of seedlings depending stochastically on the number of available seeds. Hence, the seed limitation is applied before the light limitation. The probability that a species establishes is given in Eq. (5).

$$\begin{array}{}\text{(5)}& {\displaystyle}{P}_{\mathrm{est}}=S{p}_{x}{P}_{\mathrm{germ}}\end{array}$$

*P*_{est} is the probability of the species establishing,
*S* is the number of seeds, and *P*_{germ} is the seed germination
proportion. The extra parameter *p*_{x} takes (implicitly) the area of each
grid cell into account. In our case we fixed this parameter to 0.01 after
initial testing. Hence, if in a certain year 100 seeds are in the seed bank
and the germination rate is 0.71 (value for *Fagus sylvatica*) the
probability of establishment is $\mathrm{0.01}\times \mathrm{100}\times \mathrm{0.71}=\mathrm{0.71}$.

One way to simulate seed dispersal is to calculate the convolution of the matrix containing the seed production and the seed dispersal kernel (specified in Eqs. 1 and 3). However, evaluating the convolution explicitly can be computationally expensive for seed dispersal kernels with a long range.

An alternative is based on the convolution theorem and the fast Fourier transform (FFT), a technique commonly used in physics, image processing, and engineering (Strang, 1994), but rarely in ecology; see e.g. Powell (2001), Shaw et al. (2006), and Pueyo et al. (2008).

This approach carries out the computations in the frequency domain; see
Gonzales and Woods (2002). Here we use the notation $F\mathit{\left\{}S\mathit{\right\}}=\int {e}^{-iux-ivy}S(x,y)\phantom{\rule{0.125em}{0ex}}\mathrm{d}x\phantom{\rule{0.125em}{0ex}}\mathrm{d}y$ to denote the two-dimensional
Fourier transform of *S* and correspondingly *F**{**k*_{s}*}*,
the two-dimensional Fourier transform of *k*_{s}. It then follows that
the Fourier transform of the convolution equals the product of the Fourier
transforms.

$$\begin{array}{}\text{(6)}& {\displaystyle}F\mathit{\{}S\phantom{\rule{0.125em}{0ex}}\ast \ast \phantom{\rule{0.125em}{0ex}}{k}_{\mathrm{s}}\mathit{\}}=F\mathit{\left\{}S\mathit{\right\}}F\mathit{\left\{}{k}_{\mathrm{s}}\mathit{\right\}}\end{array}$$

Thus, it is possible to compute the convolution by applying the inverse Fourier transform to the products of the Fourier transforms.

$$\begin{array}{}\text{(7)}& {\displaystyle}S\phantom{\rule{0.125em}{0ex}}\ast \ast \phantom{\rule{0.125em}{0ex}}{k}_{\mathrm{s}}={F}^{-\mathrm{1}}\mathit{\left\{}F\mathit{\right\{}S\mathit{\left\}}F\mathit{\right\{}{k}_{\mathrm{s}}\mathit{\left\}}\mathit{\right\}}\end{array}$$

This equation must be discretized before evaluating it on a computer. The
discrete Fourier transform is computed using the fast Fourier transform
(Cooley and Tukey, 1965), which has a computational cost of *O*(*N*^{2}log ^{2}(*N*)) in two
dimensions. The discrete approximation of *S*_{d} is then given by

$$\begin{array}{}\text{(8)}& {\displaystyle}{S}_{\mathrm{d}}={F}^{-\mathrm{1}}\mathit{\left\{}F\mathit{\right\{}S\mathit{\}}\phantom{\rule{0.125em}{0ex}}\odot F\mathit{\{}{k}_{\mathrm{s}}\mathit{\left\}}\mathit{\right\}},\end{array}$$

where ⊙ is the element-wise (Hadamard product) multiplication of matrices.

Nowadays, software packages for FFT typically only compute positive
frequencies. That means that we have to shift the frequencies prior to the
element-wise multiplication of *F**{**S**}* and *F**{**k*_{s}*}* This is
illustrated in Fig. 1; see also Supplement S2.

While this method allows for the inclusion of different wind distributions by changing the seed dispersal kernel (as long as they are valid for the whole simulated area), it does not allow us to use different seed dispersal kernels at different locations, e.g. due to prevailing wind directions in valleys, due to barriers to animal transport like a motorway, or due to lower transport permeability in already forested areas.

Another way to simulate seed dispersal is to simulate the seed movement between the cells explicitly by shifting the matrix containing the produced seeds by one position (repeatedly in all directions of the Moore neighbourhood; i.e. the surrounding eight cells) and simulating the transport of a certain proportion of the seeds into the next cell. Each move can be viewed as an independent random variable. Repeating these moves thus corresponds to a random walk process. The Lindeberg condition for sequences for sums of independent random variables ensures that the kernel will be Gaussian under general conditions (Shiryaev, 2016), with the expected value given by the sum of expected values for each random variable and similarly for the variance (see Supplement S1 for a formal proof and a derivation of the parameters of the resulting normal distribution).

If this is done repeatedly it allows for an easy implementation of spatial explicit differences in seed dispersal kernel distributions by adjusting the proportions of seeds being transported into the next cell according to a similarly sized matrix containing the area roughness or permeability. Through this approach, barriers and even wind speeds in latitudinal and longitudinal directions can be implemented by adjusting the dispersal probabilities accordingly. After the distribution of the dispersed seeds is calculated, the seeds are added to the seed bank. An example calculation of the first three steps of the SMSM (in the final simulation 10 steps are performed) is given in the Supplement S3.

Seed dispersal acts at a rather fine scale compared to the usual scale at
which DGVMs are run (LPJ-GUESS is typically run at a 0.5 to 0.1^{∘}
longitude–latitude scale), though some regional applications use finer grids
(e.g. Scherstjanoi et al., 2014). Given that the average long-distance seed
dispersal, for example for *Fagus sylvatica*, is 200 m (representing
0.002^{∘} longitude–latitude at the Equator), simulations at such a
coarse scale will not be able to capture this process.

As a compromise between currently available computing resources and required simulation detail we choose a 1 km scale at which we performed our simulations. However, even at this scale, simulating large areas, for example within the European continent, would result in a high computational effort.

Given that in some areas the landscape is rather homogenous while other areas have a variable terrain (or land use conditions), we test whether for homogenous landscapes it is sufficient to simulate the local dynamics only in latitudinal, longitudinal, and diagonal transects (i.e. north–south, east–west, northeast–southwest, and northwest–southeast corridors) and how this will influence the migration speed. The corridors are one grid cell wide and regularly placed in the simulation domain. Their density can be chosen by defining the distance between the latitudinal and longitudinal corridors.

Although LPJ-GM only simulates local dynamics in the cells along the corridors, the seed matrix needed to be filled for the dispersal calculation using the FFTM or the SMSM algorithm. We applied a nearest-neighbour interpolation of the seed production before performing the seed dispersal calculation (theoretical considerations show that a distance-weighted average would strongly speed up the migration).

To test our newly developed migration module we simulated the spread of a
single late successional species (*Fagus sylvatica*) through an area
covered by an early successional species (*Betula pendula*). The
species-specific parameters for both species are given in the Supplement S4.
All grid cells and all years in the simulated area had a static climate
suitable for both species. Though the simulated domain is quadratic in our
case, it could have any shape. Each cell in the simulated domain has been
simulated independently (except for the influx and outflux of seeds) from
the others. For one specific simulation using the SMSM method we assumed
differences in the dispersal ability (e.g. more or less permeable areas or
physical barriers), while the climate on all grid cells is still static and
favourable. The differences in tree dispersal ability from landscape barriers
are displayed in Fig. 2. Areas coloured white have zero permeability, and hence no
seeds can reach these areas.

Figure 3 demonstrates the sequence of simulating vegetation dynamics on the corridors, interpolation of seed production, and seed dispersal on the entire grid and back via the seed input on the transects.

Given the uniformity of the climate, there should be no variability in the
migration speed caused by differences in climatic conditions. We simulated
the spread of *F. sylvatica* from a single grid cell in the corner of
the study area, which represents the refugium. We tested several corridor
distances (between the parallel and between the diagonal corridors) for their
effect on the migration speed. To calculate the migration speed we first
determined the migration distance. This was the distance between the start
point of the migration and the 95th percentile farthest point in the virtual
landscape where the leaf area index (LAI) of *F. sylvatica* was larger
than 0.5. This migration distance was subsequently divided by the simulated
time elapsed since the start of the migration. To avoid founder effects we
neglected all points within first 5 km from the starting location (the
refugium). The simulations were performed over 3000 years and over an area of
100×100 cells of 1 km^{2}. Finally, we ran one simulation in which we did
not calculate the seed dispersal (but performed all communication between
cells and one run even without the communication), hence allowing us to
estimate the computation time demand for the seed dispersal calculation.

To estimate the performance of our methods against an implementation in which
all grid cells exchange seeds with each other, we developed a
MATLAB^{®} script, since initial testing had
shown that such a procedure would be too slow to be implemented in LPJ-GUESS.
Hence, when evaluating the performance differences from the script one has to
bear in mind that these are calculated in a different environment. However, in
a general sense we can see no reason why they should not reflect the
performance differences between the algorithms. The whole
MATLAB^{®} script testing the performance,
including the graphs, is part of the Supplement.

3 Results

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The study comparing the performance of different migration mechanisms without
the vegetation dynamics, implemented in
MATLAB^{®}, has shown that both the FFTM and
the SMSM performed faster than the explicit dispersal from all grid
cells to each other within the range of the dispersal (Fig. S2.6 in
Supplement S2). This was especially pronounced if the area to be simulated
was increased. Though faster than the explicit dispersal method, the SMSM was
still up to an order of magnitude slower than the FFTM, in particular for
large simulation domains in MATLAB^{®}, while
the FFTM and the SMSM required relatively similar amounts of time in the
implementation in LPJ-GUESS (Table 1).

Using the parameterization from TreeMig in a complete (no corridors)
simulation area of 100×100 grid cells with a size of 1 km^{2} each
resulted in a migration speed of 34 m per year for *Fagus sylvatica*
(Fig. 4).

Though the establishment in the model is stochastic, the simulated spread was
relatively smooth. The corridor distances of 10, 20, and 50 km resulted in a
reduced migration rate of 26, 28, and 28 m yr^{−1} (compared to a simulation without corridors), respectively
(Fig. 4, lower three rows of panels). While in the simulation without
corridors the variability of the migration speed was relatively low (dots
under the red line in the upper left panel of Fig. 4), this variability was
strongly increased when corridors were simulated. This was caused by
*F. sylvatica* migrating along the diagonal, reaching the endpoint of
the diagonal, and then migrating along the longitudinal and latitudinal
corridors into cells which actually had a shorter distance to the refugia than
the endpoint of the diagonal.

The simulation time per grid cell in the whole area (range for which the seed dispersal was computed) was increased by 12 % by simulating the FFTM, but by using the corridors it was reduced to 36 %, 22 %, and 12 % compared to simulating the full area (Table 1, col. 7). The proportion of computation time used to perform the FFTM increased from 11 % without corridors to 18 %, 29 %, and 29 % for simulations with corridors every 10, 20, and 50 km. This estimate only includes the required time for computing the FFT-based seed dispersal since the control run without seed dispersal still contained all communication between cells. For the control run seeds were produced and sent to the master but the master did not compute the seed dispersal, though it still communicated with all other nodes to allow for a fair assessment of the computation time demand of the two methods (see Table 1). An additional run without any communication resulted in a computation time similar to the run with communication.

Initial testing of the probability parameter for the SMSM suggested a value
of $p=\mathrm{5}\times {\mathrm{10}}^{-\mathrm{7}}$ to generate a migration speed comparable to the
migration speed for the FFTM based on the TreeMig parameterization. Using the
derivation presented in Supplement S2, it is possible to calculate this
parameter for a Gaussian dispersal kernel. One can approximate any dispersal
kernel by adding several Gaussian kernel; however, this would increase
calculation time since the SMSM would have to be performed several times.
Therefore, we decided to choose a parameter for the SMSM approximating the
migration speed rather than the seed dispersal kernel used in Lischke et
al. (2006). This resulted in a migration speed of 39 m yr^{−1} for the
filled area and 27, 29, and 30 m yr^{−1} for the
10, 20, and 50 km corridors (Fig. 5).

Similarly to the FFTM simulations, the migration speed was reduced for simulations with transects (see Table 1 for a summary). Also comparable to the FFTM-based seed dispersal computation, calculation time per grid cell in the whole area (range for which the seed dispersal is computed) was increased by 16 % by the simulation of dispersal, but reduced to 35 %, 19 %, and 11 % by using the corridors. The proportion of calculation time spent for simulating the seed dispersal is comparable to the proportion using the FFT; it was 16 %, 19 %, close to 23 %, and 32 % (see Table 1).

Since the SMSM allows for adjusting the probability depending on the seed transport permeability of the terrain, we also simulated the migration within a non-homogenous dispersal area. The results of this simulation are displayed in Fig. 6. The total computation time for this simulation was 46 000 CPU h for 6000 years.

Though all cells of the virtual landscape had a similar climate, some cells were never occupied (see Fig. 6) because the seeds were not able to reach them due to the different permeability (which might not be reasonable for real-world simulations but demonstrates the method). Migration speed was different in different parts of the simulated area.

4 Discussion

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To our knowledge, in our study for the first time (tree) species migration has been implemented in a DGVM in a way that allows for simulations of simultaneously migrating and interacting species for large areas.

The presented new methods for simulating migration in DGVMs showed a promising performance in different aspects.

The first is the gain in efficiency with the FFTM and the SMSM methods
compared to the traditional, straightforward approach of evaluating the seed
transport from all cells to each other (Fig. S2.6 in Supplement S2). A two-dimensional FFT can be obtained by
successive passes of the one-dimensional FFT, and hence the complexity will be
the one-dimensional complexity squared
(Gonzalez and Woods, 2002). The computational
complexity for the FFTM is *O*(*N*^{2}log ^{2}(*N*)) for an *N* ×*N*
grid discretizing the seed distribution, while the complexity of the direct
implementation of the convolution approach in the SMSM is *O*(2*K**R**N*^{2}) for an *N* ×*N* grid discretizing the seed distribution and
*R* ×*R* kernel, with *K* being the number of iterations of the SMSM
(for the derivation see Supplement S1). This can be computationally
comparable to the FFTM for kernels with a short range of *R*. Secondly,
simulating the local dynamics only along the corridors instead of in the full
area resulted in a similar migration pattern, and the simulated migration
speed was similar to that of the simulation with full grid cell cover (though
it is slower, caused by the stochasticity of the establishment; see Table 1),
but needed much less computing time (reduction of 88 % for the corridors
every 50 km).

In this study we present two alternative methods for simulating dispersal, which differ in their properties. While the FFTM allows any type of seed dispersal kernel, the SMSM corresponds to a normal distribution kernel. Although other shapes of dispersal kernels can be approximated by weighted sums of normal distributions, each of which has to be simulated by an own SMSM, this will cause strong increases in computational demand. Additionally, the SMSM restricts the long tail of the distributions by the number of iterations, as the seeds can travel only one grid cell per iteration step.

On the other hand, the advantage of the SMSM lies in its ability (contrary to the FFTM) to modify the parameters of the seed dispersal kernel spatially, depending on the terrain. If instead of applying a single permeability for all directions, a different permeability is applied for each of the eight directions (e.g. north, northeast, east, etc.), this method also allows for a spatially explicit consideration of wind directions (which is not possible for the FFTM, as it relies on a universal kernel applied to the entire area). Hence, depending on the aim of the analysis, one algorithm or the other, or a combination of the algorithms, is most suitable.

While not implemented here, it should be theoretically possible to use the FFTM (preferably with corridors) for some homogenous parts of the simulated area and the SMSM for the remaining part in a single simulation. As long as the seed donor areas for both methods are exclusive, and the areas in which the seeds are allowed to disperse overlap at least the width of the kernel, we can see no reasons why this should not be feasible.

Our new species migration submodule FFTM uses for the first time an algorithm based on fast Fourier transform to simulate dispersal in a DGVM. Due to its efficiency, the FFT is one of the “workhorses” in mathematics, physics, and signal processing (Strang, 1994). In ecology, there have been a few applications using FFTs to simulate the dispersal of pollen (e.g. for risk analysis, Shaw et al., 2006; seeds, Pueyo et al., 2008; even in a course compendium, Powell, 2001), but not as a standard technique in DGVMs.

The SMSM, in turn, mimics the seed transport process itself in a simple and straightforward way, which to our knowledge has also not been implemented in DGVMs.

Both approaches are combined with features of modelling species migration that are already used in other dynamic vegetation models (see Snell, 2014).

The cellular automaton KISSMig (Nobis and Normand, 2014) simulates the spread of single species driven by a spatio-temporal grid of suitability and by transitions to the nearest-neighbour cells, which is similar to one iteration in the SMSM. The suitability-based models CATS (Dullinger et al., 2012) and MigClim (Engler and Guisan, 2009) simulate a simple demography of single species and spread explicitly based on a seed dispersal kernel.

To also account for ecophysiology, the CATS model was combined with LPJ-GUESS in a post-processing approach (Lehsten et al., 2014). A spatio-temporally explicit suitability for a single species was estimated from LPJ-GUESS simulations of the productivity of this species, assuming the presence of the other species. This suitability was subsequently used within CATS to simulate migration. Such a post-processing approach, however, does not include interactions among several migrating species.

Forest landscape models have been developed to integrate such feedbacks between species and dispersal (He et al., 2017; Shifley et al., 2017). These models simulate local vegetation dynamics with species interactions and dispersal by explicit calculation of seed or seedling transport probabilities with dispersal kernels of different shapes (e.g. LandClim, Schumacher et al., 2004, Landis, Mladenoff, 2004, Iland, Seidl et al., 2012). To capture spatial heterogeneity, they run at a comparably fine spatial resolution (about 20–100 m grid cells), allowing only for the simulation of relatively small areas due to computational demands.

To overcome such computational limits, several approaches for a spatial upscaling of the models have been put forward. For example, the forest landscape model TreeMig can operate at a coarser resolution (grid cell size 1000 m) because it aggregates within-stand heterogeneity by dynamic distributions and height classes (Lischke et al., 1998), which allows for applications at a larger scale, e.g. over all of Switzerland (Bugmann et al., 2014) or on a transect through Siberia (Epstein et al., 2007). Another upscaling of TreeMig was achieved by the D2C method (Nabel, 2015; Nabel and Lischke, 2013), which simulates local vegetation dynamics only in a subset of cells that are dynamically determined as representative for classes of similar cells. This method led to a computing time reduction of 30 %–85 % compared to the full simulation. This reduction is in a similar range for our transect method depending on the configuration of the corridors.

In DGVMs, the discretization problem resulting from the need to upscale from
the fine scale at which migration processes act to the scale at which DGVMs
work is very pronounced because they are designed to operate over very large
extents (continents or the entire globe). Given the computational demands of
the simulations, they are therefore typically running at a coarse resolution,
for example 0.5 or 0.1^{∘} longitude–latitude, and simulate the
vegetation dynamics at the centre of each of these grid cells, assuming this
point to be representative for the entire cell.

Snell (2014) approached the discretization problem for the DGVM LPJ-GUESS by assuming that the numerous replicates of the vegetation dynamics on a patch are randomly distributed over the area of the grid cell (using 400 patches). Migration within the grid cell is treated similarly to an infection process, whereby the probability of a patch becoming infected (e.g. of the migrating species being able to establish) depends only on the number of already invaded patches within the grid cell. Only once a migrating species has managed to establish in a certain proportion of the patches of the simulated grid cell is further dispersal (explicit via a dispersal kernel) into surrounding grid cells possible. Yet, there is no spatial orientation of the patches within the grid cell and all simulations in this approach are strongly resolution dependent. Simulations of large areas such as continents remain computational challenging with this approach.

Our transect approach, similarly to the approach of Snell (2014), uses smaller representative spatial units of 1 km cells for a spatial upscaling. Since these small grid cells are arranged in contiguous corridors, the migration along these corridors can be simulated without or with only a small discretization error. The results indicate that the error potentially introduced by the interpolation to the rest of the area is also small.

The two approaches that we present differ in their ability to simulate heterogeneous landscapes (in terms of permeability). We suggest using the FFTM with corridors in homogenous landscapes (to speed up the computation) and using the SMSM without corridors in heterogeneous landscapes. In cases in which parts of the domain are heterogeneous (e.g. the regions around a mountainous area) and others are homogenous (e.g. lowlands), the cells can be arranged in a way that they cover the whole area in the heterogeneous part and only corridors in the homogenous part. In this setting the SMSM can still be used for the whole domain and an improvement of computation time can be achieved by only simulating the local vegetation dynamics in the homogenous parts of the domain. Thus, with our approaches, we have combined several advantages of the before-mentioned approaches: the seed dispersal from forest landscape models, improved by the novel FFTM or SMSM and the ecophysiology, and the structure and community dynamics of LPJ-GUESS. We furthermore found a compromise between discretization and efficiency by using the corridor method.

Despite the satisfying performance of the new methods in these first tests, some aspects require further development.

Even with the computing time reduction by the corridor approach using a
corridor distance of 50 km, the computing time required for the simulations
including dispersal was still considerable. The reason is that the number of
cells on the corridors (where the local dynamics are simulated) is larger
than the number of replicates usually used in all the 1 or 0.5^{∘} grid
cells simulated in traditional DGVMs. For large-scale applications, the
approach should be further optimized, e.g. by choosing corridors even further
apart from each other in homogenous areas and adapting the corridor density
to the large-scale (between-grid-cell) heterogeneity of the terrain. The
within-grid-cell heterogeneity in turn can be accounted for by deriving seed
dispersal permeability that can be used in the SMSM approach. Another area
of improvement lies in the technical implementation of the seed dispersal
algorithm. In the current implementation, seed dispersal is performed at
a single CPU, while all other CPUs wait until they receive the seeds. There
are certainly ways to perform the seed dispersal computation on several nodes
to decrease the waiting time. Furthermore, in multispecies simulations the
dispersal has to be calculated for each migrating species. In this case, the
dispersal of different species should be calculated on separate nodes.
Enlarging simulation areas generally resulted in longer runtimes for all
methods. Sometimes, however, the runtimes decreased in a pronounced way for
the FFTM (Supplement S2). A cause for these decreases is that the efficiency
of the FFT depends on possible factorizations of the domain size (Bronstein
et al., 1995). For example, it is most efficient for domain sizes of 2^{n}.
Thus, a careful choice of the domain size or of an FFT code doing that
automatically promises to speed up the FFTM. Figure S2.6 in Supplement
S2 does not represent the differences in
computation time between SMSM and FFTM as they are measured on the computing
cluster when performing the actual simulation. While in Table 1 there was
only a marginal difference between the calculations of the two methods, the
differences in the MATLAB^{®} implementation
presented in Supplement S2 are up to an order of magnitude. It seems that
MATLAB^{®} uses a different optimization for
calculating the fast Fourier transform even though both
MATLAB^{®} and the FFT libraries used on
the computing cluster are based on the libraries provided by
http://fftw.org (last access: 4 March 2019).

It is to be expected that any sub-cell assumption results in discretization
errors. In our case the assumption of a corridor reduced the migration
speed. This needs to be taken into account when evaluating the result of
such studies. The design of the corridors might also not have been optimal;
a corridor wider than a single cell might result in less of a decrease in
migration speed. However, these types of analysis are outside the scope of
this study. One other aspect of using the corridors is that while a late
successional species (in our case *F. sylvatica*) certainly has no problems to establish
below the early successional species, in the case of an early successional
species (e.g. *B. pendula*) migrating into an area occupied by a late successional
species, the corridors might decrease the migration speed even more. An
early successional species can only establish after sufficient light reaches
the ground, either due to the senescence of a tree from the established
species or a disturbance event. The narrow corridors might have strongly
limited the availability of such grid cells. However, since early
successional species typically have a good dispersal ability, this should
not influence simulations of tree migration following climate change (e.g.
after the last glaciation).

In this study the focus was on developing and testing novel methods; i.e.
we did not attempt to correctly simulate the spread of *F. sylvatica*
over a defined time period. The calculated spread rates were well below most
of the spread rates in the literature. *F. sylvatica* has been
estimated to migrate with ca. 100 m per year based on pollen analyses by
Bradshaw and Lindbladh (2005). Although such estimated high migration speeds
could also be the result of glacial refugia located further north than
assumed (Feurdean et al., 2013), our estimated migration speeds of
20–30 m yr^{−1} still seem rather low. However, in this paper we aimed
to implement tree migration by using the parameterization of TreeMig in a
DGVM and thereby allow for large-scale simulations. Our estimated migration rates
of 20–30 m yr^{−1} are very close to the migration rates estimated for
this parameterization for TreeMig by Meier et al. (2012), who estimated a
value of 22 m yr^{−1}. Hence, though we implemented the migration module
into a conceptually very different model, the resulting migration rate
remained relatively similar.

To perform model runs estimating the migration speed of any species would require a fine tuning of the age of maturity, seed production, dispersal parameters, germination rates, and seed survival (which are very rough estimates in TreeMig; Lischke et al., 2006) to generate the observed migration e.g. by comparing to migration rates based on pollen records. Unfortunately, though all of these parameters are most likely strongly influencing migration rates, they are not only hard to find in a study performed with similar methods for all tree species, but they are also likely to be highly variable depending on growth conditions and even the provenance of the individual tree. However, for a large-scale application at least the sensitivity of these parameters should be evaluated.

In our model, we assumed seed production to start at a fixed, species-specific height of maturity, which accounts for a developmental threshold but also growth and thus environmental conditions (similar to TreeMig, Lischke et al., 2006). Other studies used age of maturity as a trigger to start seed production, which has been shown to be important in determining tree migration rates (e.g. Nathan et al., 2011). The aim of this study was not a full sensitivity analysis but a study showing that a similar approach to that of Lischke et al. (2006) results in comparable migration rates. We will implement the option to use age of maturity in the next version of LPJ-GM.

Applications of our approach to simulate migration in the future are only
suitable if the migration speed of any species is substantially faster than
the migration speed that we reach for *F. sylvatica* (due to the time
the period for which climate projections are available). Furthermore,
independent of the model used, migration simulations are only suitable if the
species are not typically planted, as in many commercial forests.

The test simulations were performed on a virtual landscape of 100 km × 100 km, but eventually the method is aimed to allow for large-scale simulations over several millennia. Regarding memory requirements, this is possible with currently available hardware: test runs with landscapes of 4000 × 4000 grid cells (i.e. the size of Europe) were performed without technical problems, at least regarding the memory requirement (given 62 GB of RAM). The considerable computational cost, however, requires a relatively high amount of computing time, which might be reduced by efforts to speed up (due to efficient parallelization) the FFTM (currently the FFTM is performed on a single node, while the remaining nodes are idle; one could use all nodes to perform the FFTM) or by even further apart corridors.

5 Conclusions

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The presented novel approaches offer high potential to simulate the spatio-temporal dynamics of species which are migrating and interacting with each other simultaneously. The approaches are not restricted to LPJ-GUESS, but can in principle be applied to other DGVMs or FLMs which simulate seed (or seedling) production and explicit regeneration. The presented methods need to be improved in terms of computing performance to allow for simulations of tree migration at continental scale and over paleo-timescales. Our study also shows that estimates for seed dispersal kernels for the major tree species need to be revised to allow for simulations of forest development, for example, over the Holocene.

Code and data availability

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Code and data availability.

The data behind all figures as well as the code for Supplement S2 are published on the DataGURU server (http://dataguru.lu.se, last access: 4 March 2019; https://doi.org/10.18161/migration_lehsten_2018, Lehsten et al., 2019a; https://doi.org/10.18161/seed_disp_code_2018, Lehsten et al., 2019b).

Supplement

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Supplement.

The Supplement contains a derivation of the variance of the seed dispersal kernel for the SMSM (Supplement S1) and an example evaluation of computation time difference between FFTM and the traditional method (Supplement S2). In this Supplement, an example code for the FFTM is given, together with code demonstrating the required transformation of the seed kernel for the FFTM, and an example calculation of the SMSM (Supplement S3). Species-specific parameters within the simulation are also given (Supplement S4). The supplement related to this article is available online at: https://doi.org/10.5194/gmd-12-893-2019-supplement.

Author contributions

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Author contributions.

VL, DL, and HL designed the study, and VL performed the simulations and the statistical analysis. MM and EL contributed to the study design, and MM also performed large parts of the coding. EL developed the formal proof in Supplement S1 and the computation-performance-related estimates in the Conclusion. All authors contributed to the writing of the paper.

Competing interests

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Competing interests.

The authors declare that they have no conflict of interest.

Acknowledgements

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Acknowledgements.

This study was funded by the Swiss National Science Foundation project
CompMig, no. 205321_163223.

Edited by:
Christoph Müller

Reviewed by: Julia Nabel and two anonymous
referees

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Short summary

To assess the effect of climate on vegetation, dynamic vegetation models simulate their response e.g. to climate change. Most currently used dynamic vegetation models ignore the fact that for colonization of a new area not only do the climatic conditions have to be suitable, but seeds also need to arrive at the site to allow the species to migrate there. In this paper we are developing a novel method which allows us to simulate migration within dynamic vegetation models even at large scale.

To assess the effect of climate on vegetation, dynamic vegetation models simulate their response...

Geoscientific Model Development

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