Ecology and Evolution. 2021;00:1–25.
Received: 23 November 2020
Revised: 4 February 2021
Accepted: 20 February 2021
DOI: 10.1002/ece 3.7391
Tackling unresolved questions in forest ecology:
The past and future role of simulation models
Isabelle Maréchaux1* | Fanny Langerwisch2,3* | Andreas Huth4,5,6 |
Harald Bugmann7 | Xavier Morin8 | Christopher P.O. Reyer9 | Rupert Seidl10,11 |
Alessio Collalti12,13 | Mateus Dantas de Paula14 | Rico Fischer4 | Martin Gutsch9 |
Manfred J. Lexer15 | Heike Lischke16 | Anja Rammig11 | Edna Rödig4 |
Boris Sakschewski9 | Franziska Taubert4 | Kirsten Thonicke9 | Giorgio Vacchiano17 |
Friedrich J. Bohn4*
1INRAE, CIRAD, CNRS, AMAP, Univ Montpellier, Montpellier, France
2Department of Ecology and Environmental Sciences, Palacký University Olomouc, Olomouc, Czech Republic
3Department of Water Resources and Environmental Modeling, Czech University of Life Sciences, Prague, Czech Republic
4Helmholtz Centre for Environmental Research - UFZ, Leipzig, Germany
5German Centre for Integrative Biodiversity Research (iDiv) Halle- Jena- Leipzig, Leipzig, Germany
6Institute of Environmental Systems Research, Osnabrück University, Osnabrück, Germany
7Forest Ecology, Institute of Terrestrial Ecosystems, ETH Zürich, Zurich, Swit zerland
8EPHE, CEFE, CNRS, Univ Montpellier, Univ Paul Valéry Montpellier, IRD, Montpellier, France
9Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association, Potsdam, Germany
10Institute of Silviculture, University of Natural Resources and Life Sciences (BOKU), Vienna, Austria
11TUM School of Life Sciences, Technical University of Munich, Freising, Germany
12Forest Modelling Lab, Institute for Agriculture and Forestry Systems in the Mediterranean, National Research Council of Italy (CNR- ISAFOM), Lamezia Terme, Italy
13Depar tment of Innovation in Biological, Agro- food and Forest Systems, University of Tuscia, Viterbo, Italy
14SBiK- F - Senckenberg Biodiversity and Climate Research Centre, Frankfurt am Main, Germany
15University of Natural Resources and Life Sciences, Vienna, Austria
16Dynamic Macroecology, Land Change Science, Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Birmensdorf, Switzerland
17DISAA, Università degli Studi di Milano, Milano, Italy
This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium,
provided the original work is properly cited.
© 2021 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd.
*Thes e authors cont ributed equal ly to this work.
Fanny Langerwisch, Palacký University
Olomouc, Department of Ecology and
Environmental Sciences, 78371 Olomouc,
European Cooperation in Science and
Technology, Grant/Award Number: COST
Action FP1304 PROFOUND; Agence
Nationale de la Recherche, Grant/Award
Number: ANR- 10- LABX- 25- 01; Evropské
strukturální a investiční fondy, Operační
1. Understanding the processes that shape forest functioning, structure, and diver-
sity remains challenging, although data on forest systems are being collected at a
rapid pace and across scales. Forest models have a long history in bridging data
with ecological knowledge and can simulate forest dynamics over spatio- temporal
scales unreachable by most empirical investigations.
2. We describe the development that different forest modelling communities have
followed to underpin the leverage that simulation models offer for advancing our
understanding of forest ecosystems.
MARÉCHAUX et Al.
1 | UNRESOLVED QUESTIONS IN FOREST
ECOLOGY: CHALLENGES AND WAYS
Forests cover about 30% of the Earth's land surface, store almost
half of the terrestrial carbon, are pivotal for the global carbon bal-
ance, supply important resources to billions of people, and host
more than half of Earth's known biodiversity (Jenkins et al., 2013;
Pan et al., 2011; Ramage et al., 2017; Vira et al., 2015). Yet, ongo-
ing and future environmental changes put forests at risk. This raises
the demand for a more detailed understanding of forest dynamics
and for assessing the future of forest ecosystems to continuously
update our knowledge base and provide information to decision-
makers (IPBES, 2016; Mori, 2017; Mouquet et al., 2015; United
Nations, 2014). Forest ecology is, however, confronted with the
challenge of investigating complex systems that are characterized
by long- term dynamics over large spatial scales, and therefore many
questions remain unresolved (Sutherland et al., 2013).
In the context of global biodiversity loss, for instance, under-
standing the link between forest biodiversity and ecosystem func-
tioning is of high interest (Naeem et al., 2009). However, long- term
effects remain underexplored and underlying mechanisms are still
under debate (Loreau et al., 2001; Scherer- Lorenzen, 2014). Similarly,
forest responses to perturbations can be complex and non- linear,
as they involve multiple processes operating at various scales, from
canopy physiology across demography to long- term adaptation and
compositional changes. As a result, forest dynamics remain difficult
to forecast (Felton & Smith, 2017; Ives & Carpenter, 2007), but un-
derstanding the underlying processes is critical in an epoch of global
change, including changes in the intensity and frequency of climate
extremes and disturbances (Field et al., 2012; Reichstein et al., 2013;
Seidl et al., 2017). As another illustration, quantifying forest carbon
stocks and fluxes and identifying their drivers are important tasks,
in particular to inform climate change mitigation policies such as
REDD (Reducing Emissions from Deforestation and Degradation;
Gibbs et al., 2007). However, substantial uncertainties remain in es-
timated carbon and other element stocks and fluxes associated with
forests locally and worldwide (Bonan, 2008; Pan et al., 2011; Ploton,
Mortier, Réjou- Méchain, et al., 2020; Réjou- Méchain et al., 2019).
Knowledge gaps may result from the lack of theoretical frame-
works (Courchamp et al., 2015; Franklin et al., 2020) and/or from the
limited availability of suitable data, which are often costly and time-
consuming to collect. As trees are typically long- lived, experiments
and field monitoring should extend over multiple decades to capture
meaningful trends, which is a temporal coverage still out of reach of
most empirical studies and prevents their repeatability (Schnitzer &
Carson, 2016). Although an increasing amount of field and remote-
sensing data have been made available at various spatial and tempo-
ral extent and resolution over the past decades, their integration into
a coherent picture remains a considerable challenge (Chave, 2013;
Estes et al., 2018; Levin, 1992).
In parallel, a variety of vegetation and forest models have been
continuously developed by different scientific communities and
for different purposes. Orchestrating the interplay of various data
and theories with forest modelling has been identified as a prom-
ising approach to tackle current research challenges (Franklin
et al., 2020; Shugart et al., 2015; van der Sande et al., 2017; Zuidema
et al., 2013). While fundamentally relying on the basic knowledge
developed through theoretical considerations or empirical studies,
models themselves represent an efficient tool to, under given as-
sumptions, generate virtual data or perform virtual experiments out
of reach of empirical investigations in terms of temporal and spatial
scope as well as number of replicates (e.g., Fyllas et al., 2017; Morin
et al., 2018; Schmitt et al., 2019). For example, using a forest dy-
namics model, Bohn and Huth (2017) created a database of 500,000
virtual forest plots varying in forest composition and structure, al-
lowing to explore the drivers of the temperature sensitivity of pro-
ductivity in temperate forests.
In addition, providing anticipatory predictions of possible fu-
tures, models can be used to test hypotheses about processes
(explanatory or corroboratory predictions; Maris et al., 2018;
Mouquet et al. 2015) by applying a range of scenarios or com-
paring different ways to model processes, for example, between
model versions or different models, and confronting them with data
program Výzkum, vý voj a vzdělávání;
German Federal Ministry of Science
and Education, Grant/Award Number:
01LS1711A; Austrian Science Fund, Grant/
Award Number: Y895- B25; BMBF- and
Belmont Forum- funded; German Federal
Ministry of Agriculture and Food and the
Federal Ministry for Environment, Nature
Conser vation and Nuclear Safet y, German
Waldklimafonds, Grant/Award Number:
2 8 W - C - 4 - 0 7 7 - 0 1
3. Using three widely applied but contrasting approaches – species distribution
models, individual- based forest models, and dynamic global vegetation models
– as examples, we show how scientific and technical advances have led models
to transgress their initial objectives and limitations. We provide an overview of
recent model applications on current important ecological topics and pinpoint ten
key questions that could, and should, be tackled with forest models in the next
4. Synthesis. This overview shows that forest models, due to their complementarity
and mutual enrichment, represent an invaluable toolkit to address a wide range of
fundamental and applied ecological questions, hence fostering a deeper under-
standing of forest dynamics in the context of global change.
MARÉCHAUX et Al.
(e.g., Collalti et al., 2019; Fisher et al., 2006; Fleischer et al., 2019;
Langan et al., 2017; Lovenduski & Bonan, 2017; Morin et al., 2021;
Sakschewski et al., 2016). For example, using different versions of
the same forest model, Collalti, Tjoelker, et al., (2020) tested two
ecological theories about plant respiration. Models can thus also
prove useful to pinpoint data and knowledge gaps and hence further
guide the design of new experiments and empirical studies (Medlyn
et al., 2016; Norby et al., 2016; Rykiel, 1996; Van Nes & Scheffer,
In the following, we evidence how the availability of various for-
est modelling approaches and decades of experience in assimilating
observational knowledge into models offer invaluable tools to ad-
dress key fundamental and applied ecological questions on forests.
To do so, we first present three widely used but contrasting mod-
elling approaches to simulate forests, namely, species distribution
models (SDMs), individual- based forest models (IBMs), and dynamic
global vegetation models (DGVMs). Our aim is to illustrate the di-
versity and complementarity of forest modelling approaches. We
then show how recent developments have allowed models to tackle
similar questions, transgressing their own historical objectives and
limitations, and paving the way to new synergies and opportunities
for forest ecology. Finally, we sketch out how forest models, singly
and in combination, could take on an increasing role in addressing a
variety of key ecological questions in the future.
2 | DIFFERENT APPROACHES TO MODEL
Different approaches have been developed to model forest eco-
systems and community dynamics, as well as forest cover and tree
species distributions. They range from basic theoretical models such
as neutral models (Hubbell, 2001), through models of growth pat-
terns of individual trees, to forest stand or landscape models (Shifley
et al., 2017), or global vegetation models (Prentice et al., 2007).
Depending on the specific objectives of the scientists, the model
representations of vegetation biodiversity, structure, or biogeo-
chemical processes have various degrees of complexity due to
different degrees of aggregation or abstraction resulting from the
differing assumptions used to construct the respective model.
The three model types we briefly present here — SDMs, IBMs,
and DGVMs — cover a gradient from models that initially focused on
a detailed representation of individual species to models that gave
initial emphasis to the representation of forest structure and tree
demography, to others that focused on the representation of bio-
geochemical processes. We chose these widely used model types to
illustrate the variety of modelling approaches that can be and have
been used to address forest ecology questions in the context of
global change. In the following, we present these three approaches
by ordering them along a gradient of decreasing resolution of biodi-
versity representation and increasing resolution of biogeochemical
process representation, acknowledging that other forms of presen-
tation could alternatively have been used.
2.1 | Species distribution models
Species distribution models (Booth et al., 2014; Guisan et al., 2017)
focus on the spatial distribution of species and how it varies with
environmental drivers. SDMs have their origin in flora distribution
maps, which laid the concepts of biogeography (Grisebach, 1872;
Humboldt, 1849). The development and increased usage of
SDMs across a wide array of taxa and environments have relied
on several technical advances (Elith & Leathwick, 2009; Guisan &
Thuiller, 2005), namely, statistical approaches (e.g., MaxEnt), meth-
ods for physical environment mapping (e.g., remote- sensing tech-
niques), and increasingly coordinated efforts to compile knowledge
on species distributions. All these approaches have been boosted by
geographic information systems (GIS).
Species distribution models rely on the concept of the ecological
niche (Guisan & Thuiller, 2005; Hutchinson, 1957; Soberón, 2007)
and can be described as a two- step process. First, the ecological
niche representation of a species is built in environmental space,
based on known records in places where environmental conditions
have been described. Second, each geographic location is assigned a
probability of occurrence for the species, based on the niche model
(Elith & Leathwick, 2009).
Species distribution models thus require little information about
the processes from which species distributions result. This can be
an advantage, for example, for poorly known taxa in demand of
conservation actions. Also, by looking for a best model fit in spe-
cies niche modelling, important environmental drivers of spatial
species patterns may be revealed (Bertrand et al., 2012; Thuiller
et al., 2003). SDMs have also been used to predict species distri-
butions under future environmental conditions, such as species
invasion or climate change (Thuiller, 2003; Thuiller et al., 2005).
However, key assumptions of SDMs, mainly that species are at equi-
librium with their environment (Václavík & Meentemeyer, 2012),
that species can always migrate to suitable environments, and that
the species– environment relationships are valid beyond the range of
model calibration, may be violated under such applications (Araújo &
Pearson, 2005; Svenning & Skov, 2004; Veloz et al., 2012). Classical
SDMs are further limited to a species- by- species approach and, thus,
typically overlook the role of species interactions in shaping species
distributions (Dormann et al., 2018), although more recent develop-
ments aim at including species interactions (e.g., Meier et al. 2011).
Additionally, the spatial autocorrelation (SAC) inherent in both spe-
cies distribution and environmental variables can bias the estimated
performance of SDMs (Bahn & McGill, 2007; Fourcade et al., 2018;
Journé et al., 2020), calling for care when using extrapolations from
SDMs (Sofaer et al., 2018). However, at the same time, accounting
for SAC in SDMs by various methods (Dormann et al., 2007; Václavík
et al., 2012) can improve their accuracy because SAC is often a result
of important ecological processes (e.g., dispersal limitation, coloniza-
tion time lag) that drive species distributions.
The integration of eco- physiological and demographic processes
into SDMs is likely critical to inferring species distributions in novel
environments or under no- present analogue conditions (Dormann
MARÉCHAUX et Al.
et al., 2012; Kearney & Porter, 2009; Urban et al., 2016). Models
that combine the traditional approach of SDMs with process- based
information (Morin & Lechowicz, 2008; Thuiller et al., 2008), such
as dispersal limitation or phenology, have been developed (Bykova
et al., 2012; Chuine & Beaubien, 2001; Duputié et al., 2015; Kleidon
& Mooney, 2000; Nobis & Normand, 2014; Stephenson, 1990).
Progress has also been made to integrate species competition
as biotic factors influencing species realized niche (Leathwick &
Austin, 2001; Meier et al., 2011) and further extend these ideas to
full ecological communities (Ferrier & Guisan, 2006).
2.2 | Individual- based forest models
There is a long tradition in ecology and forestry to use individual-
based models to answer a broad range of scientific questions. This
type of models simulates the development of each individual tree
within a forest stand. A key component is the interaction between
single trees (e.g., by shading), which is crucial for tree growth and
influences community dynamics. The simulation of individual trees
allows for capturing not only forest structure but also tree spe-
cies diversity. A widely known type of IBMs are forest gap models
(Bugmann, 2001; Huston et al., 1988; Shugart, 1984). As first de-
veloped for forest stands in North America, they have since be-
come one of the most widely used model types in ecology (Botkin
et al., 1972; Shugart et al., 2018; Shugart & West, 1977).
In the gap model approach, a forest stand is described as a mo-
saic of forest patches. The dynamics of the forest at the stand scale
emerge from the growth, mortality, establishment, and competition
of individual trees (Bugmann, 2001; Porté & Bartelink, 2002). The
vertical distribution of leaves is used to calculate the light availability
for each tree, which affects growth and mortality. Competition with
neighboring trees usually happens within a predefined competition
range, where all trees compete for resources such as light, water,
and nutrients. Due to the individual- based concept, these models
are able to describe different aspects of successional dynamics (mo-
saic dynamics, e.g., Watt, 1947) and natural heterogeneity of forest
stands (Knapp et al., 2018). The coupling of biogeochemical pro-
cesses is modelled in an aggregated way in forest gap models, using
the concept of limiting factors (affecting tree growth rates). Gap
models can simulate the impact of temperature, precipitation, CO2,
and light on tree dynamics, and thus on forest productivity, biomass,
and species composition (Overpeck, Rind, & Goldberg, 1990; Pastor
& Post, 1988; Solomon, 1986). Some early studies already included
the carbon and nutrient cycles (Pastor & Post, 1986). Gap models are
typically used with annual time steps for the demographic processes
of growth, recruitment, and mortality, with finer embedded time-
step to update the simulated environment.
Modules for forest management (Huth & Ditzer, 2001; Liu &
Ashton, 1995; Mina et al., 2017) and disturbances like fire (Fischer,
2013; Kercher & Axelrod, 1984), browsing (Didion et al., 2009;
Seagle & Liang, 2001), or windthrow (Seidl et al., 2011, 2014) have
been included in subsequent studies. Tree mortality can thus be
described as an exogenous process (e.g., by disturbances), but
also as a growth- dependent and/or intrinsic process (e.g., Keane
et al., 2001). Although gap models were first developed for temper-
ate forests in the USA, they were soon applied also for European
temperate (Bugmann, 1996; Kienast, 1987) and boreal forests
(Leemans & Prentice, 1989). Forest gap models have also been de-
veloped for tropical forests (Bossel & Krieger, 1991; Doyle, 1981;
Fischer et al., 2016; Köhler & Huth, 1998). To simplify the high spe-
cies richness of these forests, tropical gap models typically simulate
forest succession by grouping tree species that share similar ecolog-
ical features into plant functional types (PFTs). The gap model ap-
proach was also extended beyond forests, for example, to grassland
systems (Coffin & Lauenroth, 1990; Schmid et al., 2021; Taubert
et al., 2020).
From the 1990s onwards, models that keep track of the posi-
tions of each tree in a finer- grained grid (i.e., they are spatially
explicit) and thus allow for a more detailed computation of tree
light availability have been developed (Chave, 1999; Maréchaux &
Chave, 2017; Pacala et al., 1996; Pretzsch et al., 2002). Other de-
velopments have led to a more explicit representation of processes,
for example by including a more detailed temperature and CO2 de-
pendence of photosynthesis and respiration, or more detailed water
and carbon cycles or site fertility (Fischer et al., 2016; Maréchaux
& Chave, 2017). Similarly, by taking advantage of comprehensive
trait databases or long- term inventories and the detailed informa-
tion they provide on tree life histories, novel parameterizations have
allowed for simulating hundreds of species within diverse forest
communities (Maréchaux & Chave, 2017; Rüger et al., 2019). Other
stand- based models were designed to describe forest stand struc-
ture dynamics driven by eco- physiological processes in higher detail
and at finer time scales (Kramer et al., 2002; Medlyn et al., 2007;
Morales et al., 2005), although often at the cost of lower temporal or
spatial coverage. IBMs have since been used to address a wide vari-
ety of basic and applied research questions, concerning for example
forest development under climate change, assessments of man-
agement scenarios, or the drivers of tree community composition
(Bohn et al., 2014; Bugmann & Pfister, 2000; Fischer et al., 2016;
Seidl et al., 2012; Shugart et al., 2018). Modern extensions of these
models also allow for simulations of forests at large spatial scales
(i.e., from forest landscapes to entire countries or continents;
Rödig et al., 2017; Sato et al., 2007; Scherstjanoi et al., 2014; Thom
et al., 2017; Xiaodong & Shugart, 2005).
2.3 | Dynamic global vegetation models
Dynamic global vegetation models have their origin in four research
areas: plant geography, biogeochemistry, vegetation dynamics, and
biophysics (Prentice et al., 2007), with IBIS, HYBRID, and LPJ being
among the first DGVMs (Cramer et al., 2001). DGVMs have initially
been developed to represent the interaction between vegetation
and the global carbon cycle as stand- alone models, but also to rep-
resent vegetation dynamics in the context of Earth System Models,
MARÉCHAUX et Al.
that is, along with models of the atmosphere (General Circulation
Models), the oceans, and the cryosphere.
Dynamic global vegetation models simulate vegetation dynamics
from half- hourly to monthly time steps at the global scale, driven
by climate, atmospheric CO2 concentration, and soil information,
using plant physiology and biogeochemistry to explain biogeogra-
phy (Krinner et al., 2005; Sitch et al., 2003). This approach results
in the prediction of the large- scale distribution of potential natural
vegetation. The main components of DGVMs are representations
of photosynthesis, respiration, leaf transpiration, carbon alloca-
tion, mortality, and disturbance. The exchange of carbon and water
fluxes is represented at the leaf level by stomatal conductance (Ball
et al., 1987; Collatz et al., 1991; Rogers et al., 2017).
Describing vegetation dynamics at the global scale inevitably en-
tails strong model simplifications to represent vegetation. Therefore,
DGVMs use PFTs to aggregate functionally similar species to repre-
sent functional properties at the biome scale. Usually global vege-
tation is described with between 5 and 14 PFTs by differentiating
life form, leaf form, phenology, or photosynthetic pathway, for ex-
ample, tropical broad- leaved raingreen tree or C3 grasses (Prentice
et al., 2007; Woodward & Cramer, 1996). Hence, these PFTs rep-
resent a less detailed description of species diversity within forest
communities than the ones used in IBMs. Additionally, DGVMs
often are used to conduct simulations using a relatively coarse-
grained grid (typically of 0.5° lat/lon resolution) in which the char-
acteristics within each cell are assumed to be spatially homogenous,
simulating average individuals per PFT, where several of them can
compete within one grid cell. Hence, local competition processes are
simplified and the influence of spatial structure within this coarse
grid cell is neglected. Moreover, DGVMs typically apply the ‘big- leaf’
approach, whereby photosynthesis of the PFTs is simulated based
on one photosynthetic surface throughout the grid cell. Most stand-
alone DGVMs are not initialized with any observed vegetation dis-
tribution, nor with measured data for carbon and water pools. The
global PFT and carbon pool distribution is instead determined by the
given abiotic conditions and PFT- specific characteristics, that is, in
so- called “spin- up” simulations. Hence, each change in abiotic con-
ditions (e.g., climate change) results in a reaction of the vegetation.
Although DGVMs were originally developed to simulate potential
natural vegetation, including fire disturbance (Lenihan et al., 1998;
Thonicke et al., 2001), they have been advanced by simulating
land- use (Bondeau et al., 2007; Boysen et al., 2016; Langerwisch
et al., 2017; Rolinski et al., 2018), water management (Jägermeyr
et al., 2015), and forest management (Bellassen et al., 2010). In order
to account for the role of nutrient deposition in vegetation dynam-
ics and its interaction with the global carbon cycle, several DGVMs
have been further developed to include an explicit representation
of the nitrogen and phosphorus cycle (von Bloh et al., 2018; Goll
et al., 2017; Reed et al., 2015; Smith et al., 2014; Wang et al., 2010).
Similarly, a more explicit representation of tree hydraulics and water
flows has been developed in some DGVMs to better assess the effect
of climatic change on evapotranspiration and drought- related mor-
tality (Bonan et al., 2014; Hickler et al., 2006; Langan et al., 2017).
The need for a more realistic representation of vegetation structure
and biodiversity to improve the predictive power of DGVMs has
been highlighted as an important pathway to improving their pre-
dictive power (McMahon et al., 2011; Quillet et al., 2010). To achieve
this, several developments have been made to include a finer repre-
sentation of vegetation demographic processes (Fisher et al., 2018;
Hickler et al., 2012; Moorcroft et al., 2001; Smith et al., 2001) and
functional diversity (Pavlick et al., 2013; Sakschewski et al., 2015;
Scheiter et al., 2013; Verheijen et al., 2015). Lately, also seed disper-
sal of trees and therefore the ability for tree species migration has
been implemented into hybrid DGVMs, which represent a combina-
tion of a forest gap model with a DGVM (Lehsten et al., 2019; Snell
& Cowling, 2015).
We will henceforth use the terms “forest models” and “forest
modelling” to describe the variety of models that have been used
to simulate forest systems, among which the three model types de-
scribed above are widely used examples, acknowledging that each
model type is also used to simulate other ecological systems.
3 | FROM MODEL COMPLEMENTARITY TO
SYNERGIES: OPPORTUNITIES FOR FOREST
3.1 | Converging trajectories of model
As illustrated above, the different forest modelling approaches
were initially motivated by different specific objectives, leading to
different choices and simplifications in the representation of actual
vegetation (Table 1). DGVMs originally focused on bio- geochemical
processes as the exchange of carbon and water between vegetation
and atmosphere at the global scale, at the cost of a realistic repre-
sentation of forest diversity, competition, and structure. Conversely,
SDMs adopted a species- level representation of vegetation diver-
sity, but have long relied on a correlative- only approach, bypassing
the mechanistic processes underlying species distribution. Similarly,
IBMs typically used a finer- grained representation of vegetation
structure than DGVMs, as they simulate many individuals, and focus
on the competition among them, but often at the cost of a coarser
representation of some processes such as gas exchange or water
flow, typically using empirical equations fitted at a coarser temporal
resolution than DGVMs.
However, multiple scientific and technical advances (see Box 1,
Table 2) have allowed for overcoming the constraints that model-
ers initially were facing. Each of these model types has thus been
gaining in efficiency and capabilities as illustrated by the aforemen-
tioned recent model developments: next- generation DGVMs strive
to explicitly represent tree demography and diversity within PFTs,
and account for forest structure, IBMs refine their representation
of biogeochemical cycles, while SDMs endeavor to include process-
based information. In doing so, their trajectories of development
have been progressively converging. As a result, each model type
MARÉCHAUX et Al.
has broadened its field of applications beyond its initial scope, cre-
ating synergies among models, including model coupling, to address
key ecological research questions in a mutually informative way.
Box 1: New levers to foster model development for forest
Forest model development and predictive ability have been con-
strained by different factors. Forest models are data- demanding
across the different steps of model development and application,
from a robust parameterization of the multiple processes related
to plant life cycle and physiology for diverse plant types, species or
individuals, to the initialization and validation of forest simulations
over large spatial and temporal scales. Fortunately, data availability
is increasing at a high pace (Table 2). Global plant trait databases
(e.g., TRY, Kattge et al., 2011) gather data of commonly measured
traits (e.g., leaf mass per area or wood density) for a wide range of
species, and this effort is being expanded to other traits (e.g., stem
and leaf drought tolerance, Bartlett et al., 2012; Choat et al., 2012;
fine root traits, Iversen et al., 2017; litter decomposition rates,
Brovkin et al., 2012). This fosters a systematic model trait- based pa-
rameterization for a range of plant species and individuals. If data
covera ge remains incomplete however (Kat tge et al., 2020), the co m-
bination of organization principles such as natural selection- based
TABLE 1 Advantages, limitations, and challenges of three different approaches to model forests: species distribution models (SDMs),
individual- based models (IBMs), and dynamic global vegetation models (DGVMs)
SDMs IBMs DGVMs
Advantages allow a quick assessment of potential
climate- change vulnerability
can serve as a coarse filter for more
detailed/process- based approaches
are easily applicable to many taxa due to
low data and computational demands, as
well as available R- packages and methods
simulate the growth and demography
of every tree in a forest from
decades to centuries
can easily integrate field data since
forest monitoring is mainly done at
the tree level
are able to simulate dynamics of forest
structure and project changes in
species composition by including
important ecological processes (e.g.,
competition between species)
can integrate disturbances, climate
change and forest management
are mostly process- based and
therefore useful for extrapolations to
simulate vegetation on large spatial
(up to global) and temporal scales
(decades to centuries)
simulate climate impacts on
vegetation dynamics and associated
biogeochemical and water cycles
due to the process- based simulation
of stocks and fluxes
are able to consider physiological and
plant- competition processes and
increasingly plant- trait diversity
can incorporate managed grasslands
and crop growth under land- use
Limitations represent potential rather than realized
are static models, as equilibrium with
environment is assumed, which can lead
to misinterpretations by stakeholders
their accuracy depends strongly on the
spatial resolution, as there can be strong
effects of spatial autocorrelation.
are data demanding for
parameterization and initialization
can be computationally demanding
to apply at large spatial scales
(countries, continents) since millions
of trees have to be simulated in these
can raise problem of overfitting and
erroneous extrapolations when
calibrated using local field data
often represent species diversity
using plant functional types
(PFTs), and species- specific
parameterization is limited by a
lack of information for important
parameters (e.g., on ecophysiolog y).
have high computational demand at
have often a poor representation
of forest structure and certain
ecological processes (e.g., seed
dispersal, forest regrowth, tree
show – by design – no or only
rudimentary simulation of forest
deal with missing absence data
include more ecological processes and
include genetic variability
include demographic processes and
better account for the impact of extreme
speed up the parameterization step
upscale while keeping essential
improve the coupling of remote-
sensing data with model outputs
include intra- specific variation and
more realistically represent below-
increase the number of PFTs to an
optimal number, to represent major
competing functional groups
simulate actual vegetation consisting
of managed forests and remaining
natural vegetation as monitored by
forest inventories or remote sensing
improve the implementation of
vegetation structure to allow its
integration at the global scale
improve the representation of
ecological processes (e.g., vegetation
re- growth and seed dispersal)
MARÉCHAUX et Al.
TABLE 2 Types of available forest data
Short description Extent (space; time) Resolution (space; time) Examples (references and links)
Data type: experimental data
Monitoring of plant responses to a set of controlled or
manipulated biotic (e.g., competition) or abiotic (e.g.,
nutrients, climate) conditions
Very local- to- stand- scale; variable Small- scale; variable Bussotti et al., (2018); FACE experiment (Free- Air CO2
enrichments), Norby et al., (2016); rainfall manipulation,
Grossiord et al., (2018); Meir et al., (2015)
Data type: tree performance data
Direct or indirect measurements of components of tree
performance or functioning, such as tree growth (e.g., tree-
ring analysis, automatic dendrometers), resource use, (e.g.,
sapflow), or reproduction (e.g., seed traps)
Local; from snapshots to tree life
individual to forest stand;
Intra- annual to annual
tree- ring databases, Treydte et al., (2007),
https://www.ncdc.noaa.gov/data- acces s/paleo clima tolog
y- data/datas ets/tree- ring; tree sapflow database, Poyatos
et al., (2016); seed production database, Ascoli et al., (2017);
Muller- Landau et al., (2008)
Data type: trait data
Measurement of plant individual features (morphological,
physiological or phenological) which impacts components of
Local; snapshots or repeated over
e.g., season, ontogeny
Individual to species;
punctual or repeated over
e.g., season, ontogeny.
TRY, Kattge et al., (2011); sPlot, Bruelheide et al., (2019)
Data type: species presence records
Report of presence or absence of species in localities Across species range, from local to
global; snapshots or repeated over
Variable; punctual Global Biodiversity Information Facilities, GBIF, https://www.
Data type: inventory data
Systematic identification and size measurements of all trees
above a given size threshold within a forest stand
Local stands or stand network;
snapshots or repeated over longer
Individual; punctual or
typically from seasonal to
every few years.
German national inventory, https://bwi.info/start.
aspx?lang=eng; CTFS- ForestGeo, https://fores tgeo.si.edu/
Data type: eddy- flux data
Measurement of vertical turbulent fluxes of water and CO2
between the atmosphere and the vegetation layer
Stand (tower footprint of typically
few hectares); continuous
measurements over years
FLUXNET, Baldocchi et al., (2001); Pastorello et al., (2020)
Data type: remote- sensing observations
Record of vegetation characteristics and abiotic conditions
from above, based on propagated signal such as
electromagnetic waves, either active (e.g., LiDAR, RADAR) or
passive (visible light).
Regional, global; covering several
Spaceborne: down to meter-
scale; several measurements
e.g., MODIS: https://modis.ornl.gov/cgi- bin/MODIS/ globa l/
subset.pl; Justice et al., (2002); Running et al., (2004)
Stand to regional scale; snapshot
or repeated over e.g., seasons or
Airborne: down to cm- scale;
Punctual or repeated flights
https://gliht.gsfc.nasa.gov/; Goetz and Dubayah, (2011);
Zolkos et al., (2013)
Local to stand scale; snapshots
or repeated over e.g., weeks or
Drone- based: down to cm-
scale; punctual or repeated
Brede et al., (2017); Park et al., (2019); Roşca et al., (2018)
Local to stand scale; snapshots or
repeated over e.g., seasons or
Terrestrial: down to mm-
scale; mostly punctual
Disney, (2018); Takoudjou et al., (2018)
MARÉCHAUX et Al.
optimality or entropy maximization to constraint plant and eco-
system behavior can alleviate the data demand for parameteriza-
tion while improving model predictive ability (Franklin et al., 2020).
Simultaneously, networks of forest plot inventories are being com-
plemented by remote- sensing data, offering novel opportunities to
initialize and/or validate model simulation over large spatial scales
(Shugart et al., 2015) or complement predictors of SDMs (Fedrigo
et al., 2019). Recent advances in remote- sensing tools, such as the
possibility to derive tree- level information within dense canopies
(Ferraz et al., 2016) or fuse spectrometer data with co- registered
LiDAR data (Jucker et al., 2018), provide new ways to parameterize
models (e.g., allometries, Fischer et al., 2019; Jucker et al., 2017).
New tools of data processing have been developed to leverage
these new sources of data. The development of machine learning
techniques offers new possibilities to use the resulting huge data-
sets for model development and evaluation (Botella et al., 2018;
Forkel et al., 2019; Rammer & Seidl, 2019; Reichstein et al., 2019).
Additionally, Bayesian and/or inverse modelling approaches can be
used to take advantage of diverse sources of data to estimate pro-
cess parameters, calibrate entire models, and thus reduce model un-
certainty (Dietze et al., 2014; Fischer et al., 2019; Hartig et al., 2014;
Hartig et al., 2011; LeBauer, Wang, Richter, Davidson, & Dietze,
2013; Lehmann & Huth, 2015; van Oijen et al., 2013; van Oijen
et al., 2005). See also Appendix A for more studies that benefit from
increasing data availability.
Besides data availability, computing power — in terms of speed
and memory — imposes a trade- off between simulation resolution
and coverage, still today limiting large- scale applications or the fit-
ting of fine- grained models. For example, the finer- grained represen-
tation of forest biodiversity and structure recently implemented in
a DGVM model (LPJmL- FIT, Sakschewski et al., 2015) was restricted
to one biome (Tropics of South America) as opposed to the global
scale typically reached by classic DGVM simulations. However, com-
puting power will probably continue to increase in the next years
(Kurzweil, 2005), which, together with parallel processing and im-
proved algorithms, allows continuous reduction of computing time
(von Bloh et al., 2010; Snell, 2014). As an illustration, using Fast
Fourier Transformations for seed dispersal instead of modelling dis-
persal from each cell to each other increased the computing speed
by a factor of 100 (Lehsten et al., 2019). Additionally, remote- sensing
observations allow the up- scaling of IBMs at lower costs (Rödig
et al., 2017, 2018; Shugart et al., 2015). However, a fundamental
change of an algorithm in complex models can invoke unplanned side
effects, sometimes forcing modelers to invest substantial time and
effort to stabilize the new model versions. Furthermore, the devel-
opment of visualization tools to illustrate simulation results in virtual
forest scenes (e.g., Dufour- Kowalski et al., 2012; Figure 1) represents
a valuable lever to communicate on model structure, functioning,
and outputs, to inspire for new model developments and applica-
tions, but also to detect model errors. See also Appendix B for more
examples and details about technical challenges.
3.2 | Strength in unity: Insights from model inter-
comparison and coupling
Two main types of synergies among models have been increasingly
leveraged to better inform forest ecology, namely, model inter-
comparisons and model coupling efforts.
Comparing the outputs from different models that are run under
comparable or even identical conditions of driving variables offers
valuable insights beyond single- model simulations. Model compari-
sons in environmental sciences typically have two main objectives.
First, they allow for understanding differences between models by
relating the simulated pattern of each model to its underlying pro-
cesses. This can identify model structural uncertainties, which have
been highlighted as a major source of model uncertainties (Famiglietti
et al., 2020; Lovenduski & Bonan, 2017; Raiho et al., 2020), and thus
foster new model developments as well as novel empirical investiga-
tions. Although the increasing complexity of models makes the in-
terpretation of model inter- comparison results challenging (Fisher &
Koven, 2020; Appendix B), model benchmarking is facilitated by new
tools of code and data sharing (e.g., Ram, 2013) as well as the avail-
ability of detailed standardized databases (Collier et al., 2018; Reyer
et al., 2020). Additionally, simulation experiments where different
versions of a model are compared allow for insights into the effects
of specific process representation in addition to comparisons among
models. For example, using 15 different models, including DGVMs
and forest gap models, each with alternative mortality sub- models,
Bugmann et al. (2019) explored the influence of different simulated
FIGURE 1 An example of visualization
of outputs of a forest model. Visualization
of species diversity (crown colors) of a
tropical forest simulated by the FORMIND
model (Fischer et al., 2016) in the 3D
visualization center of UFZ – Helmholtz-
Centre for Environmental Research,
MARÉCHAUX et Al.
mortality processes on forest dynamics, providing insights into the
effects of process uncertainties. The second objective of model
inter- comparison is to provide ensemble simulations that allow for a
quantitative assessment of the uncertainties underlying the predic-
tions of the different models.
Model comparisons have a long history within each model com-
munity (e.g., among forest gap models — Bugmann et al., 1996 – ,
forest landscape models — Petter et al., 2020 – , stand- based eco-
physiological models — Kramer et al., 2002; Morales et al., 2005
– , DGVMs — Cramer et al., 2001; Sitch et al., 2008 – , or SDMs —
Araújo & New, 2007). More recently, the increasing ability of differ-
ent model types to use inputs and provide outputs of similar nature
and structure has allowed to compare models across model types
(Cheaib et al., 2012), and even across a wide range of sectors such as
vegetation, water, agriculture, or biodiversity to study the interac-
tion of these under climate change (Frieler et al., 2017).
Since each model has its own aim, history and therefore spe-
cific advantages and limitations (Table 1), the coupling of a model
with other types of models can be a valuable approach to expand
the initial scope of model applications, or reduce uncertainties in
model projections. For instance, several stand- scale forest mod-
els, including IBMs, have been coupled to models of emissions of
biogenic volatile organic compounds, revealing that tree species
composition and species- specific emission potentials were im-
portant drivers of the feedbacks between climate change and air
quality (Keenan et al., 2009a, 2009b; Wang et al., 2018). Similarly,
a forest demographic model has been coupled to models of soil
microbe- mediated bio- geochemistry and competition for nutrients,
revealing that spatial variation in soil properties can drive a large
variation of forest biomass and composition (Medvigy et al., 2019;
see also Sato et al., 2007). SDMs have been coupled to models of
habitat colonization in order to take into account dispersal limita-
tion in species distribution projections (Iverson et al., 2004; Nobis
& Normand, 2014; see also Franklin, 2010). Fire disturbance mod-
els have been implemented in several DGVMs (Lasslop et al., 2014;
Schaphoff et al., 2018; Yue et al., 2014, 2015); but also in forest
IBMs for a long time (Knapp et al., 2018; Pausas, 1999; Shugart &
Noble, 1981), helping to explore different modelling approaches
on the interaction between vegetation dynamics and fire (Forkel
et al., 2019; Hantson et al., 2016) to explain the declining trend in
global burnt area (Andela et al., 2017). More generally, forest models
have been coupled to models of disturbances, such as wind storms
(Seidl et al., 2011; Thom et al., 2017), allowing the investigation of
forest resilience by means of different modeling approaches (Albrich
et al., 2020). Other examples include the coupling of a DGVM to
a global economy model to dynamically include technical and so-
cietal changes in simulating future vegetation dynamics (Dietrich
et al., 2019), allowing to investigate the possible trade- offs between
bio- energy production and several sustainable development goals
(Humpenöder et al., 2018).
Model development can also take advantage of the comple-
mentarity of different vegetation model types (Table 1) by coupling
their different approaches into one model (McMahon et al., 2011).
As an illustration, the gap model approach was implemented into a
DGVM framework to better account for demographic processes and
diversity in regional- to continental- scale studies (e.g., Sakschewski
et al., 2015; Smith et al., 2001). Similarly, approaches to include
seed dispersal (Lischke et al., 2006), which originate from IBMs
(Groeneveld et al., 2009; Urban et al., 1991), can be integrated into
large- scale forest models (Lehsten et al., 2019) to account for dis-
persal limitation in predictions of species distribution changes under
climate change. SDMs have also been coupled to gap models to ac-
count for the effects of changes in species distribution at the re-
gional scale on forest composition and functioning at the local scale
(García- Valdés et al., 2020). Model coupling can thus help to improve
model realism. In all cases, uncertainties resulting from error prop-
agation across models need to be carefully assessed (e.g., Dunford
et al., 2015) to master the resulting increasing complexity while
maintaining model reliability and robustness (Famiglietti et al., 2020;
Fisher & Koven, 2020; Franklin et al., 2020; Prentice et al., 2015;
4 | FOREST MODELLING TO ADDRESS
KEY ECOLOGICAL QUESTIONS
Forest models from the different communities have been following
converging trajectories of development, leading to a generation of
models capable of addressing similar topics and taking on an impor-
tant role to address novel ecological questions that go far beyond
their traditional focus. We identified a number of ecological fields
for which we expect forest modelling to make important contribu-
tions in the next decade, by increasing our understanding of forest
ecosystems and helping generalize ecological findings. To illustrate
this, we provide examples of recent model applications to these top-
ics, from the most fundamental to applied ones, and collate ten im-
portant questions for future studies (Table 3).
4.1 | Community assembly
Understanding the drivers of community assembly, that is, the
processes that shape the number, identity and abundance of co-
occurring species, has been an important question in ecology
since its inception (Clements, 1916; Gleason, 1926; MacArthur &
Levins, 1967; McGill et al., 2006). Forest models allow for separating
the effect of different drivers through the use of null models and
sequential simulation set- ups. For instance, forest IBMs have re-
cently been used to investigate the role of trait- mediated trade- offs
and their size dependency in shaping forest community (Chauvet
et al., 2017; Falster et al., 2017; Kunstler et al., 2009). In doing so,
they used a more realistic modelling framework than most theo-
retical investigations that are generally developed to address these
questions and typically restricted to systems with few species. This
approach may be further developed and applied to various forest
communities as trait data are increasingly becoming available (Box
MARÉCHAUX et Al.
1). Modelling also helps to disentangle the contribution of stochastic
versus deterministic processes through the assessment of variability
among repeated runs (Savage et al., 2000).
Although many mechanisms have been identified empiri-
cally to contribute to species coexistence in forest communities
(Nakashizuka, 2001; Wright, 2002), their relative strengths in ob-
served communities across environmental gradients remain poorly
known. Forest modelling could help quantifying their relative con-
tributions through a combination of simple theoretical models and
data- driven simulation experiments, and exploring the debated role
of intra- specific variability on species coexistence (Hart et al., 2016;
Lischke & Löffler, 2006; Q2, Q4, Table 3). To do so, models need
to include key aspects of community assembly or known coex-
istence mechanisms, such as regeneration processes (Vacchiano
et al., 2018), negative density- dependence (Lischke & Löffler, 2006;
Maréchaux & Chave, 2017), and functional trade- offs (Sakschewski
et al., 2015) in a heterogeneous environment.
4.2 | Biodiversity and ecosystem functioning
By virtually manipulating the composition of simulated forest com-
munities, forest IBMs have proven useful in exploring the effect of
species richness and functional composition on ecosystem prop-
erties (e.g., Fischer et al., 2018). Simulations reproduced positive
relationships between (species or functional) diversity and produc-
tivity or biomass, in agreement with observed patterns (Maréchaux
& Chave, 2017; Morin et al.,2011, 2020), further motivating a
finer- grained representation of diversity in DGVMs. These studies
demonstrated how competition for light can induce this positive
effect in heterogeneous forests. Going beyond the effect of bulk
species richness, Bohn and Huth (2017) showed that this positive
effect is stronger if species are well distributed vertically across
the forest canopy. García- Valdés et al. (2018) showed that climate-
change- driven extinctions of tree species may affect forest produc-
tivity or biomass more severely than random extinctions. Schmitt
et al. (2019) found that the mechanisms through which biodiversity
influences forest functioning depend on the ecosystem state, shift-
ing from the dominance of the complementarity effect in recently
disturbed systems to the dominance of the selection effect in old
forests, suggesting a way to reconcile contrasting results obtained
with snapshots of ecosystem state in empirical studies.
A more detailed model- based investigation of the effect of tree
species diversity and species loss on other forest ecosystem func-
tions (e.g., water and nutrient cycles) should follow in the near fu-
ture (Q1, Q3, Table 3). Another potential field of model exploration
considers the influence of species diversity on crown- and surface-
fire intensity as recently investigated empirically for the boreal zone
(Rogers et al., 2015). Forest models, including flexible- trait DGVMs
(Sakschewski et al., 2015; Scheiter et al., 2013), could further inves-
tigate how functional diversity supports forest productivity and car-
bon storage under climate change, from the local to the biome scale.
4.3 | Resilience and stability
Forest models can help to disentangle the different mechanisms
shaping forest responses to perturbations through virtual experi-
ments that are beyond the reach of empirical approaches (Albrich
et al. 2020). Simulations using an individual- based and trait- based
DGVM showed that higher trait diversity increases the resil-
ience of the Amazon rainforest under future climate (Sakschewski
et al., 2016). This positive effect was attributable to ecological sort-
ing, in agreement with results from forest IBMs in temperate (Morin
et al., 2018) and tropical (Schmitt et al., 2019) forests. Higher tem-
poral stability of productivity for forests with higher diversity was
also attributed to the asynchrony of species responses to small dis-
turbances (Morin et al., 2014). Using a multi- model analysis, Radchuk
et al. (2019) showed that the multiple properties of stability, such as
resistance, recovery, or persistence, (Donohue et al., 2013) can vary
independently depending on the disturbance type.
However, we still have an insufficient understanding of forest
ecosystem stability that involves multiple processes at various spa-
tial and temporal scales (Donohue et al., 2016), and future modelling
studies should help disentangling the multiple drivers of forest resil-
ience while paying attention to the elements leading to feedbacks
(e.g., the adult – regeneration feedback). This will foster our predic-
tive ability of potential critical transitions (Q5, Q6, Q10, Table 3).
TABLE 3 Ten unresolved key questions of forest ecology
We here provide examples of key questions in high need of
research effort in forest ecology, for which modelling approaches
represent promising tools, as illustrated in the text (see section
“Forest modelling to address key ecological questions”). For a more
complete list of unanswered ecological questions regarding forest
systems, we refer the reader for example, Ammer et al. (2018) or
Sutherland et al. (2013).
Q1 How are forest functional and structural characteristics related
to climate and soil, and how does this influence forest system
functions across space and time?
Q2 Which coexistence mechanisms shape forest communities
across environmental gradients?
Q3 How important are rare species for the functioning of forest
Q4 Which forest systems and which of their proper ties are most
sensitive to changes in community composition across scales and
Q5 Which factors control the resilience of forest ecosystems to
Q6 What makes forests susceptible to rapid system shifts and how
can we project tipping points?
Q7 How do disturbance regimes and global change af fect
sustainable forest management strategies?
Q8 How do native and invasive tree species move with global
Q9 What are the main drivers of carbon allocation within plants and
Q10 Why and when do trees die?
MARÉCHAUX et Al.
4.4 | Carbon stocks and fluxes
The quantification of carbon stocks and fluxes has motivated large
ef forts of da ta co lle ctio n (Table 2) , includi ng la b o r- i ntens ive fo rest in-
ventories (Brienen et al., 2015; Ploton, Mortier, Barbier, et al., 2020),
flux measurements (Falge et al., 2002; Pastorello et al., 2020), or
remote- sensing (Running et al., 2004; Saatchi et al., 2011). Forest
models provide a framework to connect empirical data of various
nature, and this connection is even more powerful as models fea-
ture resolutions that match with a broader range of empirical data,
such as individual- based modelling approaches, including individual-
based DGVMs (Fisher et al., 2018; Rödig et al., 2017; Sakschewski
et al., 2015; Smith et al., 2001).
Models have been used to upscale and infer dynamic estimates
of forest productivity and biomass (Fischer et al., 2015) using allom-
etries from field measurements (Chave et al., 2005, 2014). Recently,
assimilation of remote- sensing data within forest models has allowed
accounting for the heterogeneity in forest structure and land- use
history in those estimates at stand to continental scales (Joetzjer
et al., 2017). For example, by using remote- sensing- derived measure-
me nts of forest he ight acros s a grid ded map over the Amaz onia n basi n
and a locally optimized gap model, it was possible to estimate the
forest successional stages of every cell in this area and derive maps
of aboveground biomass and productivity of the whole basin (Rödig
et al., 2017, 2018). Beyond estimations of carbon stocks and fluxes,
forest models can be used to understand the drivers of their spatial
variation. For example, through simulation experiments using an
IBM, Fyllas et al. (2017) showed that solar radiation an d trait va riation
driven by spatial species turnover explain the decline of forest pro-
ductivity along a tropical elevation gradient. Similarly, using a forest
demographic model, Berzaghi et al. (2019) showed that elephant dis-
turbances enhance carbon stocks in central African forests through
their effects on forest structure and composition. Models can also
prove useful to create benchmarks against which other methods to
estimate carbon stocks and fluxes can be evaluated and improved
(e.g., LiDAR, Knapp et al., 2018; eddy- flux tower, Jung et al., 2009).
Plant respiration, tree mortality, and carbon allocation are
key drivers of forest productivity and biomass (Bugmann &
Bigler, 2011; Johnson et al., 2016) but remain poorly under-
stood (Hartmann et al., 2018; Holzwarth, Kahl, Bauhus, & Wirth,
2013; Malhi et al., 2015; Merganičová et al., 2019; Collalti &
Prentice, 2019; Collalti, Ibrom, et al., 2020), and future modelling
studies should seek to foster our understanding of these critical
processes for example, through model- data fusion approaches
(Q9, Q10, Table 3).
4.5 | Forest responses to global change
Models represent a key tool to assess forest responses to the in-
teracting factors of future climate change (Bugmann, 2014; García-
Valdés et al., 2020; Medlyn et al., 2011; Sabaté et al., 2002).
Simulating the dynamics of vegetation, including forests, under
climate change is the main objective of DGVMs and has been the
focus of a sustained effort from this modelling community (Alo &
Wang, 2008; Cramer et al., 2001; Friend et al., 2014; Jarvis, 1998;
Keenan et al., 2008; Mohren et al., 1997). However, stand- scale
models, such as individual- based gap models, have also been used to
explore forest dynamics under climate- change scenarios (Bugmann
& Fischlin, 1996; Collalti et al., 2018; Fischer et al., 2014; Pastor
& Post, 1986; Reyer, 2015; Shugart et al., 2018). Such finer- scale
models can further inform the role of forest composition and struc-
ture in shaping forest responses to environmental drivers (Bohn
et al., 2018; Fyllas et al., 2017). Additionally, SDMs have been used
to project species distributions under future climate change (Noce
et al., 2017; Thuiller, 2004), although, as mentioned above, their
correlative nature has raised criticisms regarding their use for fore-
casting under no- present analogues (Table 1). Overall, a variety of
models are utilized to simulate forest responses to climate change,
allowing comparisons of different approaches and the assessment
of model uncertainties (Cheaib et al., 2012), usually showing that
process- based forest models are more conservative than correlative
SDMs (Morin & Thuiller, 2009).
Some recent model developments further aim at accounting
for othe r comp onents of globa l change (Pé rez- M éndez et al. , 2016;
Pütz et al., 2014), such as the impacts of defaunation or fragmen-
tation on forest dynamics (Dantas de Paula et al., ,,2015, 2018;
Pütz et al., 2011). Calls for a better integration of plant– animal
(Berzaghi et al., 2018) and plant– plant interactions, such as the ef-
fect of the increasing liana abundance on tree growth and survival
(Verbeeck & Kearsley, 2016), should further foster such develop-
ments (di Porcia e Brugnera et al., 2019; Pachzelt, Rammig, Higgins,
& Hickler, 2013). Another challenge is the representation of tree
species dispersal and migration of tree species at large scales
(Lehsten et al., 2019; Neilson et al., 2005; Snell et al., 2014; Q8,
Table 1), in combination with evolutionary processes to account
for species adaptive evolution and trait displacement under envi-
ronmental changes and fragmentation (DeAngelis & Mooij, 2005;
McMaho n et al., 2011; Scheiter et al ., 2013). Mor eover, accounting
for the adaptive capacity of tree individuals within their lifetime
via acclimation and phenotypic plasticity (Duputié et al., 2015;
Ri cht er et al. , 201 2) rem ain s a chall enge, as kno wled ge abo ut the se
processes remains incomplete. However, optimality principles may
provide a promising approach to predict trait variation with en-
vironmental conditions (Franklin et al., 2020). To seek additional
insight s in estimating future forest responses, a number of studies
have used forest models to estimate past forest dynamics (Heiri,
Bugmann, Tinner, Heiri, & Lischke, 2006; Lischke, 2005; Lischke
et al., 2013; Schwörer et al., 2014). Overall, while differences
among model predictions remain large (Prentice et al., 2015),
these developments, together with model benchmarking and
inter- comparisons, should help to better understand the long- term
effects of multiple interacting fac tors of global changes on forests
(Seidl et al., 2017; Q4, Q5, Q10, Table 3).
MARÉCHAUX et Al.
4.6 | Biodiversity conservation
So far, conservation efforts have not been successful to alleviate
biodiversity loss across the globe (Butchart et al., 2010), calling
for renewed efforts and biodiversity forecasts (Urban et al., 2016).
As SDMs can be calibrated for almost all species for which reli-
able distribution data are available, these models have long been
identified as tools for conservation (Araújo et al., 2019; Davis &
Zabinski, 1992; Guisan et al., 2013). Predictions of SDMs under
climate- change scenarios could be used to help refine conservation
areas (Ferrier, 2002), or predict invasion ranges of introduced spe-
cies (Broennimann et al., 2007; Thuiller et al., 2005). Although this
claim is put forward frequently (Fernandes et al., 2018), case studies
reporting applications remain sparse (Mouquet et al., 2015), likely
because of the uncertainty in SDM predictions (Barry & Elith, 2006;
Dawson et al., 2011; Journé, Barnagaud, Bernard, Crochet, & Morin,
Mixed predictions carried out jointly with different model types
(process- based or hybrid distribution models, Evans et al., 2015;
Morin & Thuiller, 2009) could provide more robust projections for
conservation managers (Thom et al., 2017). Such an approach ap-
pears especially feasible for tree species, as individual- and process-
based models are typically more abundant for forests than for
other ecosystems. Therefore, DGVMs and gap models should be
increasingly used to address the challenges of biodiversity conser-
vation planning (e.g., Fischer et al., 2016), in complement to the
species- level process- based models already available (e.g., Chuine &
Beaubien, 2001; Keenan et al., 2011; Serra- Diaz et al., 2013; Q1, Q4,
Q8, Table 3).
4.7 | Forest management
Forests provide important ecosystem services, such as timber pro-
duction, carbon sequestration, recreation and protection against
natural hazards, whose persistence or improvement is of high soci-
etal relevance (De Groot et al. 2002, MEA 2005). Sustaining these
ecosystem services is the focus of forest management (Nabuurs
et al., 2017; Yousefpour et al., 2018). Forest IBMs have a long his-
tory in helping management planning (Courbaud et al., 2001;
Hiltner et al., 2018; Huth & Ditzer, 2001; Huth et al., 2005; Keenan
et al., 2008; Mäkelä et al., 2000; Porté & Bartelink, 2002; Pretzsch
et al., 2008). As global change is challenging current and future man-
agement strategies (Seidl et al., 2014), forest model development
has aimed to help design adaptive forest management practices and
mitigation strategies under multiple disturbances (Elkin et al., 2013;
Fontes et al., 2010; Kunstler et al., 2013; Lafond et al., 2014;
Maroschek et al., 2015; Mina et al., 2017; Rasche et al., 2011; Reyer
et al., 2015; Seidl et al., 2018). DGVMs have long disregarded the
effect of forest management, as their aggregated representation
of vegetation structure typically prevents a realistic representation
of tree size distribution and density relevant to simulate silvicul-
tural practices (Table 1). However, some DGVMs used a simplified
representation of wood extraction to simulate its effect on forest
carbon stocks (Zaehle et al., 2006), and recent efforts have led to the
development of more explicit forest management modules, inspired
by finer- scale forest gap models as well as forest growth and yield
models (Bellassen et al., 2010; Collalti et al., 2018).
The integration of societal and economic dynamics generates
new challenges (Q7, Table 3), while future applications and commu-
nications with forest stakeholders will benefit from developments
regarding visualization of results from forest models (Figure 1).
5 | CONCLUSION
Forests have multiple important roles for the Earth system and
human livelihoods. Sound, quantitative knowledge of forest func-
tioning, structure, and diversity are therefore essential, especially
in times of global change. However, many scientific questions re-
garding forest properties and dynamics remain unresolved, rang-
ing from understanding tree community assembly and projecting
forest responses to environmental changes, to assessing the man-
agement of forest ecosystems. We illustrated how different forest
modelling approaches, due to their continuous development, their
complementarity, and mutual enrichment, represent an invaluable
toolkit to address ecological questions that require a renewed re-
The development of forest models crucially benefits from the
interactions among scientists from various fields, within and across
modelling communities, but also with field ecologists, physiologists,
data scientists, computer engineers, remote- sensing researchers,
and a vari ety of stakeholders . Owing to th eir long an d success ful his-
tory in integrating data and knowledge from these various sources,
the models used to simulate forests have progressively reached
maturity and can tackle a broader array of ecological problems. For
instance, forest models can disentangle the drivers of community
assembly in forest communities, thus complementing theoretical ap-
proaches that typically remain limited to simplified systems. Forest
models also provide tractable platforms to perform virtual exper-
iments still out of reach of empirical approaches in forest systems
that are characterized by slow dynamics and large spatial extents.
This notably allows shedding light on the complex links between
forest biodiversity, functioning and resilience in the long term.
Furthermore, forest models prove essential to understanding the
multiple drivers of forest productivity and biomass by combining
field and remote- sensing data across space and time, and, as a re-
sult, provide informed quantifications of carbon stocks and fluxes.
Last but not least, ongoing global change and the resulting biodi-
versity crisis as well as changing climate and disturbance regimes
crucially increase the demand of informed projections on forest
socio- ecosystems, for which forest models have a long and success-
ful history, while new developments allow for the integration of an
increasing number of interacting factors.
We demonstrated that the converging trajectories of the dif-
ferent modelling approaches used to simulate forests provide new
MARÉCHAUX et Al.
opportunities for comparisons among their outputs. This allows for
the quantification of simulation uncertainties and the identifica-
tion of their sources, and hence fosters new model developments
as well as empirical investigations. Overall, iterative model- data fu-
sion approaches and the resulting cycles of simulation- assessment-
improvement are continuously increasing the scope of model
applications while controlling for simulation uncertainties. Forest
models will thus keep contributing to a deeper understanding of for-
est structure and functioning, and they offer promising routes to fill
remaining knowledge gaps and to take on future challenges of forest
The authors represent different forest model communities. They
gathered during a workshop series “Perspectives of forest mod-
eling” supported by COST Action FP1304 PROFOUND (Toward
Robust Projections of European Forests under Climate Change), sup-
ported by COST (European Cooperation in Science and Technology).
IM acknowledges funding from an “Investissement d'Avenir” grant
managed by Agence Nationale de la Recherche (CEBA, ref. ANR- 10-
LABX- 25- 01). FL acknowledges funding from the program Evropské
strukturální a investiční fondy, Operační program Výzkum, vývoj a
vzdělávání. CPOR acknowledges funding from the German Federal
Ministry of Science and Education (BMBF grant 01LS1711A). RS ac-
knowledges funding from the Austrian Science Fund (FWF) through
START grant Y895- B25. A.C. is partially supported by resources
available from the Ministry of University and Research (FOE- 2019),
under the project “Climate Change” (CNR DTA.AD003.474). BS and
KT acknowledge funding from the BMBF- and Belmont Forum-
funded project “CLIMAX: Climate Services Through Knowledge
Co- Production: A Euro- South American Initiative For Strengthening
Societal Adaptation Response to Extreme Events” (CLIMAX).
MG acknowledges funding from the German Federal Ministry of
Agriculture and Food and the Federal Ministry for Environment,
Nature Conservation and Nuclear Safety through the project
“DENDROKLIMA”, funded within the German Waldklimafonds,
2 8 W - C - 4 - 0 7 7 - 0 1 .
CONFLICT OF INTEREST
The authors state that there is no conflict of interest.
Maréchaux I., Langerwisch F., and Bohn F. J. equally performed
conceptualization, data curation, formal analysis, investigation,
writing- original draft, and writing- review and editing. Huth A., sup-
ported for conceptualization, project administration (Lead), and
writing- review and editing. Bugmann H., Morin X., and Seidl R. sup-
ported for conceptualization and writing- review and editing. Reyer
C. P.O. supported for conceptualization, funding acquisition (Lead),
and writing- review and editing. Collalti A., Dantas de Paula, M.,
Fischer R., Gutsch M., Lexer M.J., Lischke H., Rammig A., Rödig
E., Sakschewski B., Taubert F., Thonicke K., and Vacchiano G. sup-
ported for writing- review and editing.
DATA AVAILAB ILITY STATE MEN T
No new data were collected in the course of this research.
Fanny Langerwisch https://orcid.org/0000-0003-4744-7427
Harald Bugmann https://orcid.org/0000-0003-4233-0094
Alessio Collalti https://orcid.org/0000-0002-4980-8487
Anja Rammig https://orcid.org/0000-0001-5425-8718
Albrich, K., Rammer, W., Turner, M. G., Ratajczak, Z., Braziunas, K. H.,
Hansen, W. D., & Seidl, R. (2020). Simulating forest resilience: A re-
view. Global Ecology and Biogeography, 29(12), 2082– 2096. https://
Alo, C. A., & Wang, G. (2008). Potential future changes of the terrestrial
ecosystem based on climate projections by eight general circulation
models. Journal of Geophysical Research: Biogeosciences, 113(G1),
https://doi.org/10.1029/2007J G00 0528
Ammer, C., Fichtner, A ., Fischer, A., Gossner, M. M., Meyer, P., Seidl,
R., Thomas, F. M., Annighöfer, P., Kreyling, J., Ohse, B., Berger,
U., Feldmann, E., Häberle, K.- H., Heer, K., Heinrichs, S., Huth, F.,
Krämer- Klement, K., Mölder, A., Müller, J., … Wagner, S. (2018).
Key ecological research questions for Central European forests.
Basic and Applied Ecology, 32, 3– 25. https://doi.org/10.1016/j.
Andela, N., Morton, D. C., Giglio, L., Chen, Y., van der Werf, G. R.,
Kasibhatla, P. S., DeFries, R. S., Collatz, G. J., Hantson, S., Kloster, S.,
Bachelet, D., Forrest, M., Lasslop, G., Li, F., Mangeon, S., Melton, J. R.,
Yue, C., & Randerson, J. T. (2017). A human- driven decline in global
burned area. Science, 356, 1356– 1362. https://doi.org/10.1126/scien
Araújo, M. B., Anderson, R. P., Márcia Barbosa, A., Beale, C. M., Dormann,
C. F., Early, R., Garcia, R. A., Guisan, A., Maiorano, L., Naimi, B.,
O’Hara, R. B., Zimmermann, N. E., & Rahbek, C. (2019). Standards for
distribution models in biodiversity assessments. Science Advances,
5(1), eaat4858. https://doi.org/10.1126/sciadv.aat4858
Araújo, M. B., & New, M. (2007). Ensemble forecasting of species dis-
tributions. Trends in Ecology & Evolution, 22(1), 42– 47. https://doi.
Araújo, M. B., & Pearson, R. G. (2005). Equilibrium of species’ dis-
tributions with climate. Ecography, 28(5), 693– 695. https://doi.
Ascoli, D., Maringer, J., Hacket- Pain, A., Conedera, M., Drobyshev, I.,
Motta, R., Cirolli, M., Kantorowicz, W., Zang, C., Schueler, S., Croisé,
L., Piussi, P., Berretti, R., Palaghianu, C., Westergren, M., Lageard,
J. G. A., Burkart, A., Gehrig Bichsel, R., Thomas, P. A., … Vacchiano,
G. (2017). Two centuries of masting data for European beech and
Norway spruce across the European continent. Ecology, 98(5), 1473.
htt ps://doi.org /10.1002/ec y.1785
Bahn, V., & McG ill, B. J. (2007). Can niche- based distribu tion models out-
perform spatial interpolation? Global Ecology and Biogeography, 16(6),
733– 742. https://doi.org/10.1111/j.1466- 8238.2007.00331.x
Baldocchi, D., Falge, E., Gu, L., Olson, R., Hollinger, D., Running, S., &
Wofsy, S. (2001). FLUXNET: A new tool to study the temporal and
spatial variability of ecosystem– scale carbon dioxide, water vapor,
and energy flux densities. Bulletin of the American Meteorological
Society, 82(11), 2415– 2434. https://doi.org/10 .1175/1520-
0477(2001)082<2 4 1 5 : F A N T T S >2.3.CO;2
Ball, J. T., Woodrow, I. E., & Berry, J. A. (1987). A model predicting sto-
matal conductance and its contribution to the control of photosyn-
thesis under different environmental conditions. In J. Biggins (Ed.),
Progress in photosynthesis research (pp. 221– 224). Springer. https://
d o i . o r g / 1 0 . 1 0 0 7 / 9 7 8 - 9 4 - 0 1 7 - 0 5 1 9 - 6 _ 4 8
MARÉCHAUX et Al.
Barry, S., & Elith, J. (2006). Error and uncertainty in habitat mod-
els. Journal of Applied Ecology, 43(3), 413– 423. https://doi.
org /10.1111/j.1365- 2664.20 06 .01136.x
Bartlett, M. K., Scoffoni, C., & Sack, L. (2012). The determinants of leaf
turgor loss point and prediction of drought tolerance of species and
biomes: A global meta- analysis. Ecology Letters, 15(5), 393– 405.
https://doi.or g/10.1111/j.1461- 0248.2012.01751. x
Bellassen, V., Le Maire, G., Dhôte, J. F., Ciais, P., & Viovy, N. (2010).
Modelling forest management within a global vegetation model—
Part 1: Model structure and general behaviour. Ecological
Modelling, 221(20), 2458– 2474. https://doi.org/10.1016/j.ecolm
Bertrand, R., Perez, V., & Gégout, J.- C. (2012). Disregarding the edaphic
dimension in species distribution models leads to the omission
of crucial spatial information under climate change: The case of
Quercus pubescens in France. Global Change Biology, 18(8), 2648–
2660. https://doi.org/10.1111/j.1365- 2486.2012.02679.x
Berzaghi, F., Longo, M., Ciais, P., Blake, S., Bretagnolle, F., Vieira, S.,
Scaranello, M., Scarascia- Mugnozza, G., & Doughty, C. E. (2019).
Carbon stocks in central African forests enhanced by elephant
disturbance. Nature Geoscience, 1, https://doi.org/10.1038/s4156
1 - 0 1 9 - 0 3 9 5 - 6
Berzaghi, F., Verbeeck, H., Nielsen, M. R., Doughty, C. E., Bretagnolle, F.,
Marchetti, M., & Scarascia- Mugnozza, G. (2018). Assessing the role
of megafauna in tropical forest ecosystems and biogeochemical cy-
cles – the potential of vegetation models. Ecography, 41, 1934– 1954.
Bohn, F. J., Frank, K., & Huth, A. (2014). Of climate and its resulting
tree growth: Simulating the productivity of temperate forests.
Ecological Modelling, 278, 9– 17. https://doi.org/10.1016/j.ecolm
Bohn, F. J., & Huth, A. (2017). The importance of forest structure to
biodiversity– productivity relationships. Royal Society Open Science,
4, 160521. https://doi.org/10.1098/rsos.160521
Bohn, F. J., May, F., & Huth, A. (2018). Species composition and forest
structure explain the temperature sensitivity patterns of productiv-
ity in temperate forests. Biogeosciences, 15(6), 1795– 1813. https://
d o i . o r g / 1 0 . 51 9 4 / b g - 1 5 - 1 7 9 5 - 2 0 1 8
Bonan, G. B. (2008). Forests and climate change: forcings, feedbacks,
and the climate benefits of forests. Science, 320(5882), 1444– 1449.
Bonan, G. B., Willia ms, M., Fisher, R. A., & Ol eson, K. W. (2014). Mo deling
stomatal conductance in the earth system: Linking leaf water- use ef-
ficiency and water transport along the soil– plant– atmosphere con-
tinuum. Geoscientific Model Development, 7(5), 2193– 2222. https://
d o i . o r g / 1 0 . 51 9 4 / g m d - 7 - 2 1 9 3 - 2 0 1 4
Bondeau, A., Smith, P. C., Zaehle, S., Schaphoff, S., Lucht, W., Cramer,
W., Gerten, D., Lotze- campen, H., Müller, C., Reichstein, M., & Smith,
B. (2007). Modelling the role of agriculture for the 20th century
global terrestrial carbon balance. Global Change Biology, 13, 679– 706.
Booth, T. H., Nix, H. A., Busby, J. R., & Hutchinson, M. F. (2014). bioclim:
The first species distribution modelling package, its early applica-
tions and relevance to most current MaxEnt studies. Diversity and
Distributions, 20(1), 1– 9. https://doi.org/10.1111/ddi.12144
Bossel, H., & Krieger, H. (1991). Simulation model of natural tropical
forest dynamics. Ecological Modelling, 59(1), 37– 71. https://doi.
o r g / 1 0 . 1 0 1 6 / 0 3 0 4 - 3 8 0 0 ( 9 1 ) 9 0 1 2 7 - M
Botella, C., Joly, A., Bonnet, P., Monestiez, P., & Munoz, F. (2018). Species
distribution modeling based on the automated identification of citi-
zen observations. Applications in Plant Sciences, 6(2), e1029. https://
Botkin, D. B., Janak, J. F., & Wallis, J. R. (1972). Some ecological conse-
quences of a computer model of forest growth. Journal of Ecolog y,
60(3), 849– 872. https://doi.org/10.2307/2258570
Boysen, L. R., Lucht, W., Gerten, D., & Heck, V. (2016). Impacts devalue
the potential of large- scale terrestrial CO2 removal through biomass
plantations. Environmental Research Letters, 11(9), 95010. https://doi.
org /10.1088/1748- 9326/11/9/095010
Brede, B., Lau, A., Bartholomeus, H., & Kooistra, L. (2017). Comparing
RIEGL RiCOPTER UAV LiDAR derived canopy height and DBH with
terrestrial LiDAR. Sensors, 17, 2371. https://doi.org/10.3390/s1710
Brienen, R. J. W., Phillips, O. L., Feldpausch, T. R., Gloor, E., Baker, T. R.,
Lloyd, J., Lopez- Gonzalez, G., Monteagudo- Mendoza, A., Malhi, Y.,
Lewis, S. L., Vásquez Martinez, R., Alexiades, M., Álvarez Dávila, E.,
Alvarez- Loayza, P., Andrade, A., Aragão, L. E. O. C., Araujo- Murakami,
A., Arets, E. J. M. M., Arroyo, L., … Zagt, R. J. (2015). Long- term de-
cline of the Amazon carbon sink. Nature, 519, 344– 348. https://doi.
Broennimann, O., Treier, U. A., Müller- Schärer, H., Thuiller, W., Peterson,
A. T., & Guisan, A. (2007). Evidence of climatic niche shift during
biological invasion. Ecology Letters, 10(8), 701– 709. https://doi.
org /10.1111/j.1461- 0248.2007.01060.x
Brovkin, V., van Bodegom, P. M., Kleinen, T., Wirth, C., Cornwell, W.
K., Cornelissen, J. H. C., & Kattge, J. (2012). Plant- driven variation
in decomposition rates improves projections of global litter stock
distribution. Biogeosciences, 9, 565– 576. https://doi.org/10.5194/
b g - 9 - 5 6 5 - 2 0 1 2
Bruelheide, H., Dengler, J., Jiménez- Alfaro, B., Purschke, O., Hennekens,
S. M., Chytrý, M., Pillar, V. D., Jansen, F., Kattge, J., Sandel, B., Aubin,
I., Biurrun, I., Field, R., Haider, S., Jandt, U., Lenoir, J., Peet, R. K.,
Peyre, G., Sabatini, F. M., … Zverev, A. (2019). sPlot – a new tool for
global vegetation analyses. Journal of Vegetation Science, 30(2), 161–
186. ht tps://doi.org/10.1111/jvs.12710
Bugmann, H. K. M. (1996). A simplified forest model to study species
composition along climate gradients. Ecology, 77(7), 2055– 2074.
Bugmann, H. (2001). A review of forest gap models. Climatic Change,
51(3– 4), 259– 305. https://doi.org/10.1023/A:10125 25626267
Bugmann, H. (2014). Forests in a greenhouse atmosphere: Predicting
the unpredictable? In D. A. Coomes, D. F. R. P. Burslem, & W. D.
Simonson (Eds.), Fore sts and Global Change (pp. 195– 238). .
Bugmann, H., & Bigler, C. (2011). Will the CO2 fertilization effect in for-
ests be offset by reduced tree longevity? Oecologia, 165(2), 533– 544.
h t t p s : // d o i . o r g / 1 0 . 1 0 0 7 / s 0 0 4 4 2 - 0 1 0 - 1 8 3 7 - 4
Bugmann, H., & Fischlin, A. (1996). Simulating forest dynamics in a com-
plex topography using gridded climatic data. Climatic Change, 34(2),
2 0 1 – 2 1 1 . h t t p s : / /d o i . o r g / 1 0 . 1 0 0 7/ B F 0 0 2 2 4 6 3 1
Bugmann, H., & Pfister, C. (2000). Impacts of interannual climate
variability on past and future forest composition. Regional
Environmental Change, 1(3), 112– 125. https://doi.org/10.1007/
Bugmann, H., Seidl, R., Hartig, F., Bohn, F., Brůna, J., Cailleret, M.,
François, L., Heinke, J., Henrot, A.- J., Hickler, T., Hülsmann, L., Huth,
A., Jacquemin, I., Kollas, C., Lasch- Born, P., Lexer, M. J., Merganič,
J., Merganičová, K., Mette, T., … Reyer, C. P. O. (2019). Tree mortal-
ity submodels drive simulated long- term forest dynamics: Assessing
15 models from the stand to global scale. Ecosphere, 10 (2), e02616.
Bugmann, H. K. M., Yan, X., Sykes, M. T., Martin, P., Lindner, M., Desanker,
P. V., & Cumming, S. G. (1996). A comparison of forest gap models:
Model structure and behaviour. Climatic Change, 34(2), 289– 313.
https://doi.org/10.10 07/BF002 24640
B u ss ot t i , F., P oll as tr i ni , M. , G es sl er , A. , & L u o, Z .- B . ( 201 8 ). E xp er im en ts w it h
trees: From seedlings to ecosystems. Environmental and Experimental
Botany, Experiments with Trees: From Seedlings to Ecosystems, 152, 1– 6.
Butchar t, S. H. M., Walpole, M., Collen, B., van Strien, A., Scharlemann, J.
P. W., Almond, R. E. A., Baillie, J. E. M., Bomhard, B., Brown, C., Bruno,
MARÉCHAUX et Al.
J., Carpenter, K. E., Carr, G. M., Chanson, J., Chenery, A. M., Csirke,
J., Davidson, N. C., Dentener, F., Foster, M., Galli, A ., … Watson, R.
(2010). Global biodiversity: Indicators of recent declines. Science,
328(5982), 1164– 1168. https://doi.org/10.1126/scien ce.1187512
Bykova, O., Chuine, I., Morin, X., & Higgins, S. I. (2012). Temperature de-
pendence of the reproduction niche and its relevance for plant spe-
cies distributions. Journal of Biogeography, 39(2), 2191– 2200. https://
Chauvet, M., Kunstler, G., Roy, J., & Morin, X. (2017). Using a for-
est dynamics model to link community assembly processes and
traits structure. Functional Ecology, 31( 7), 1452– 1461. https://doi.
org /10.1111/1365- 2435.12847
Chave, J. (1999). Study of structural, successional and spatial patterns in
tropical rain forests using TROLL, a spatially explicit forest model.
Ecological Modelling, 124 (2– 3), 233– 254. https://doi.org/10.1016/
S 0 3 0 4 - 3 8 0 0 ( 9 9 ) 0 0 1 7 1 - 4
Chave, J. (2013). The problem of pattern and scale in ecology: What
have we learned in 20 years? Ecology Letters, 16, 4– 16. https://doi.
Chave, J., Andalo, C., Brown, S., Cairns, M. A ., Chambers, J. Q., Eamus,
D., Fölster, H., Fromard, F., Higuchi, N., Kira, T., Lescure, J.- P., Nelson,
B. W., Ogawa, H., Puig, H., Riéra, B., & Yamakura, T. (2005). Tree al-
lometr y and improved estimation of carbon stocks and balance in
tropical forests. Oecologia, 145 (1), 87– 99. https://doi.org/10.1007/
s 0 0 4 4 2 - 0 0 5 - 0 1 0 0 - x
Chave, J., Réjou- Méchain, M., Búrquez, A., Chidumayo, E., Colgan, M.
S., Delitti, W. B. C., Duque, A., Eid, T., Fearnside, P. M., Goodman, R.
C., Henr y, M., Martínez- Yrízar, A., Mugasha, W. A ., Muller- Landau,
H. C., Mencuccini, M., Nelson, B. W., Ngomanda, A., Nogueira, E.
M., Ortiz- Malavassi, E., … Vieilledent, G. (2014). Improved allome-
tric models to estimate the aboveground biomass of tropical trees.
Global Change Biology, 20(10), 3177– 3190. https://doi.org/10.1111/
Cheaib, A., Badeau, V., Boe, J., Chuine, I., Delire, C., Dufrêne, E.,
François, C., Gritti, E. S., Legay, M., Pagé, C., Thuiller, W., Viovy,
N., & Leadley, P. (2012). Climate change impacts on tree ranges:
Model intercomparison facilitates understanding and quantifi-
cation of uncertainty. Ecology Letters, 15, 533– 544. https://doi.
org /10.1111/j.1461- 0248.2012.01764.x
Choat, B., Jansen, S., Brodribb, T. J., Cochard, H., Delzon, S., Bhaskar,
R., Bucci, S. J., Feild, T. S., Gleason, S. M., Hacke, U. G., Jacobsen, A.
L., Lens, F., Maherali, H., Martínez- Vilalta, J., Mayr, S., Mencuccini,
M., Mitchell, P. J., Nardini, A., Pittermann, J., … Zanne, A. E. (2012).
Global convergence in the vulnerability of forests to drought. Nature,
491(7426), 752– 755. https://doi.org/10.1038/natur e11688
Chuine, I., & Beaubien, E. G. (2001). Phenolog y is a major determi-
nant of tree species range. Ecology Letters, 4, 500– 510. https://doi.
Clements, F. E. (1916). Plant succession: An analysis of the development of
vegetation. Carnegie Institution of Washington.
Coffin, D. P., & Lauenroth, W. K. (1990). A gap dynamics simulation model
of succession in a semiarid grassland. Ecological Modelling, 49(3), 229–
266. https://doi.org/10.1016/0304- 3800(90)90029 - G
Collalti, A., Ibrom, A., Stockmarr, A., Cescatti, A., Alkama, R., Fernández-
Martínez, M., Matteucci, G., Sitch, S., Friedlingstein, P., Ciais, P.,
Goll, D. S., Nabel, J. E. M. S., Pongratz, J., Arneth, A., Haverd, V., &
Prentice, I. C. (2020). Forest production efficiency increases with
growth temperature. Nature Communications, 11(1), 5322. https://
d o i . o r g / 1 0 . 1 0 3 8 / s 4 1 4 6 7 - 0 2 0 - 1 9 1 8 7 - w
Collalti, A., & Prentice, I. C. (2019). Is NPP proportional to GPP? Waring’s
hypothesis 20 years on. Tree Physiology, 39(8), 1473– 1483. https://
Collalti, A., Thornton, P. E., Cescatti, A., Rita, A., Borghetti, M., Nolè,
A., Trotta, C., Ciais, P., & Matteucci, G. (2019). The sensitivity of
the forest carbon budget shifts across processes along with stand
development and climate change. Ecological Applications, 29, e01837.
Collalti, A ., Tjoelker, M. G., Hoch, G., Mäkelä, A., Guidolotti, G., Heskel,
M., Petit, G., Ryan, M. G., Battipaglia, G., Matteucci, G., & Prentice,
I. C. (2020). Plant respiration: Controlled by photosynthesis or
biomass? Global Change Biology, 26(3), 1739– 1753. https://doi.
Collalti, A., Trotta, C., Keenan, T. F., Ibrom, A., Bond- Lamberty, B., Grote,
R., Vicca, S., Reyer, C. P. O., Migliavacca, M., Veroustraete, F., Anav,
A., Campioli, M., Scoccimarro, E., Šigut, L., Grieco, E., Cescatti, A., &
Matteucci, G. (2018). Thinning can reduce losses in carbon use effi-
ciency and carbon stocks in managed forests under warmer climate.
Journal of Advances in Modeling Earth Systems, 10(10), 2427– 2452.
Collatz, G. J., Ball, J. T., Grivet, C., & Berr y, J. A. (1991). Physiological and
environmental regulation of stomatal conductance, photosynthesis
and transpiration: A model that includes a laminar boundary layer.
Agricultural and Forest Meteorology, 54( 2 – 4 ) , 1 0 7 – 1 3 6 . h t t p s : / / d o i .
o r g / 1 0 . 1 0 1 6 / 0 1 6 8 - 1 9 2 3 ( 9 1 ) 9 0 0 0 2 - 8
Collier, N., Hoffman, F. M., Lawrence, D. M., Keppel- Aleks, G., Koven, C.
D., Riley, W. J., Mu, M., & Randerson, J. T. (2018). The International
Land Model Benchmarking (ILAMB) system: Design, theory, and im-
plementation. Journal of Advances in Modeling Earth Systems, 10 (11),
2731– 2754. https://doi.org/10.1029/2018M S001354
Courbaud, B., Goreaud, F., Dreyfus, P. H., & Bonnet, F. R. (2001).
Evaluating thinning strategies using a tree distance depen-
dent growth model: Some examples based on the CAPSIS soft-
ware “uneven- aged spruce forests” module. Forest Ecology and
Management, 145(1), 15– 28. https://doi.org/10.1016/S0378
- 1 1 2 7 ( 0 0 ) 0 0 5 7 1 - 5
Courchamp, F., Dunne, J. A., Le Maho, Y., May, R. M., Thébaud, C., &
Hochberg, M. E. (2015). Fundamental ecology is fundamental.
Trends in Ecology & Evolution, 30(1), 9– 16. ht tps://doi.org/10.1016/j.
Cramer, W., Bondeau, A., Woodward, F. I., Prentice, I. C., Betts, R. A.,
Brovkin, V., & Young- Molling, C. (2001). Global response of terrestrial
ecosystem structure and function to CO2 and climate change: Results
from six dynamic global vegetation models. Global Change Biology,
7(4), 357– 373. https://doi.org/10.1046/j.1365- 2486.2001.0 0383.x
Dantas de Paula, M., Groeneveld, J., Fischer, R., Taubert, F., Martins, V. F.,
& Huth, A . (2018). Defaunation impacts on seed survival and its ef-
fect on the biomass of future tropical forests. Oikos, 127(10), 1526–
1538. https://doi.or g/10.1111/oik.05084
Dantas de Paula, M., Groeneveld, J., & Huth, A. (2015). Tropical forest
degradation and recovery in fragmented landscapes — Simulating
changes in tree community, forest hydrology and carbon bal-
ance. Global Ecology and Conservation, 3, 664– 677. https://doi.
Davis, M. B., & Zabinski, C. (1992). Changes in geographical range from
greenhouse warming: effects on biodiversity in forests. In R. L.
Peters, & T. E. Lovejoy (eds.) Global Warming and Biological Diversity
(pp. 297– 309). : Yale University Press.
Dawson, T. P., Jackson, S. T., House, J. I., Prentice, I. C., & Mace, G. M.
(2011). Beyond predictions: Biodiversity conservation in a chang-
ing climate. Science, 332, 53– 58. https://doi.org/10.1126/scien
DeAngelis, D. L., & Mooij, W. M. (2005). Individual- based modeling of
ecological and evolutionary processes. Annual Review of Ecology,
Evolution, and Systematics, 36, 147– 168. https://doi.org/10.1146/
annur ev.ecols ys.36.102003.152644
de Groot, R. S., Wilson, M. A. & Boumans, R. M. J. (2002). A typology for
the classification, description and valuationof ecosystem functions,
goods and services. Ecological Economics, 41, 393– 408.
Didion, M., Kupferschmid, A. D., Zingg, A., Fahse, L., & Bugmann,
H. (2009). Gaining local accuracy while not losing generalit y
MARÉCHAUX et Al.
— extending the range of gap model applications. Canadian Journal of
Forest Research, 39(6), 1092– 1107. https://doi.org/10.1139/X09- 041
Dietrich, J. P., Bodirsky, B. L., Humpenöder, F., Weindl, I., Stevanović,
M., Karstens, K., & Popp, A. (2019). MAgPIE 4 – a modular open-
source framework for modeling global land systems. Geoscientific
Model Development, 12(4), 1299– 1317. https://doi.org/10.5194/
g m d - 1 2 - 1 2 9 9 - 2 0 1 9
Dietze, M. C., Serbin, S. P., Davidson, C., Desai, A. R., Feng, X., Kelly,
R., Kooper, R., LeBauer, D., Mantooth, J., McHenry, K., & Wang,
D. (2014). A quantitative assessment of a terrestrial biosphere
model’s data needs across North American biomes. Journal of
Geophysical Research: Biogeosciences, 119, (3), 286– 300. https://doi.
Disney, M. (2018). Terrestrial LiDAR: A three- dimensional revolution in
how we look at trees. New Phytologist, 222, (4), 1736– 1741. https://
Donohue, I., Hillebrand, H., Montoya, J. M., Petchey, O. L., Pimm, S.
L., Fowler, M. S., Healy, K., Jackson, A. L., Lurgi, M., McClean, D.,
O'Connor, N. E., O'Gorman, E. J., & Yang, Q. (2016). Navigating the
complexity of ecological stability. Ecology Letters, 19 (9), 1172– 1185.
https://doi.or g/10.1111/ele.126 48
Donohue, I., Petchey, O. L., Montoya, J. M., Jackson, A. L., McNally, L.,
Viana, M., Healy, K., Lurgi, M., O'Connor, N. E., & Emmerson, M. C.
(2013). On the dimensionality of ecological stability. Ecology Letters,
16, 421– 429. https://doi.org/10.1111/ele.12086
Dormann, C. F., Bobrowski, M., Dehling, D. M., Harris, D. J., Hartig, F.,
Lischke, H., Moretti, M. D., Pagel, J., Pinkert, S., Schleuning, M.,
Schmidt, S. I., Sheppard, C. S., Steinbauer, M. J., Zeuss, D., & Kraan,
C. (2018). Biotic interactions in species distribution modelling:
10 questions to guide interpretation and avoid false conclusions.
Global Ecology and Biogeography, 27(9), 10 04– 1016. https://doi.
Dormann, C. F., Schymanski, S. J., Cabral, J., Chuine, I., Graham, C.,
Hartig, F., Kearney, M., Morin, X., Römermann, C., Schröder, B., &
Singer, A. (2012). Correlation and process in species distribution
models: Bridging a dichotomy. Journal of Biogeography, 39(12), 2119–
2131. https://doi .org/10.1111/j.1365 - 2699.2011.02659.x
Doyle, T. W. (1981). The role of disturbance in the gap dynamics of a
montane rain forest: An application of a tropical forest succession
model. In D. C. West, H. H. Shugart, & D. B. Botkin (Eds.), Forest
Succession: Concepts and Application (pp. 56– 73). Springer. https://
d o i . o r g / 1 0 . 1 0 0 7 / 9 7 8 - 1 - 4 6 1 2 - 5 9 5 0 - 3 _ 6
Dufour- Kowalski, S., Courbaud, B., Dreyfus, P., Meredieu, C ., & de
Coligny, F. (2012). Capsis: An open software framework and com-
munity for forest growth modelling. Annals of Forest Science, 69(6),
2 2 1 – 2 3 3 . h t t p s : / / d o i . o r g / 1 0 . 1 0 0 7 / s 1 3 5 9 5 - 0 1 1 - 0 1 4 0 - 9
Dunford, R., Harrison, P. A., & Rounsevell, M. D. A. (2015). Exploring sce-
nario and model uncertainty in cross- sectoral integrated assessment
approaches to climate change impacts. Climatic Change, 132(3), 417–
4 3 2 . h t t p s : / / d o i . o r g / 1 0 . 1 0 0 7 / s 1 0 5 8 4 - 0 1 4 - 1 2 1 1 - 3
Duputié, A., Rutschmann, A., Ronce, O., & Chuine, I. (2015). Phenological
plasticity will not help all species adapt to climate change. Global
Change Biology, 21, 3062– 3073. https://doi.org/10.1111/gcb.12914
Elith, J., & Leathwick, J. R. (2009). Species distribution models: Ecological
explanation and prediction across space and time. Annual Review
of Ecology, Evolution, and Sys tematics, 40(1), 677– 697. https://doi.
org/10.1146/annur ev.ecols ys .110308.120159
Elkin, C., Gutiérrez, A. G., Leuzinger, S., Manusch, C., Temperli, C.,
Rasche, L., & Bugmann, H. (2013). A 2°C warmer world is not safe
for ecosystem services in the European Alps. Global Change Biology,
19(6), 1827– 1840. https://doi.org/10.1111/gcb.12156
Estes, L., Elsen, P. R., Treuer, T., Ahmed, L., Caylor, K., Chang, J., Choi,
J. J., & Ellis, E. C . (2018). The spatial and temporal domains of mod-
ern ecology. Nature Ecology & Evolution, 1, https://doi.org/10.1038/
s 4 1 5 5 9 - 0 1 8 - 0 5 2 4 - 4
Evans, T. G., Diamond, S. E., & Kelly, M. W. (2015). Mechanistic species
distribution modelling as a link between physiology and conserva-
tion. Conservation Physiology, 3(1), cov056. https://doi.org/10.1093/
F. Dormann, C., M. McPherson, J., B. Araújo, M., Bivand, R., Bolliger,
J., Carl, G., G. Davies, R., Hirzel, A., Jetz, W., Daniel Kissling,
W., Kühn, I., Ohlemüller, R., R. Peres- Neto, P., Reineking, B.,
Schröder, B., M. Schurr, F., & Wilson, R. (2007). Methods to ac-
count for spatial autocorrelation in the analysis of species distri-
butional data: A review. Ecography, 30(5), 609– 628. https://doi.
Falge, E., Baldocchi, D., Tenhunen, J., Aubinet, M., Bakwin, P., Berbigier,
P., Bernhofer, C., Burba, G., Clement, R., Davis, K. J., Elbers, J. A.,
Goldstein, A. H., Grelle, A., Granier, A ., Guðmundsson, J., Hollinger,
D., Kowalski, A. S., Katul, G., Law, B. E., … Wofsy, S. (2002). Seasonality
of ecosystem respiration and gross primary production as derived
from FLUXNET measurements. Agricultural and Forest Meteorology,
113( 1 - 4 ) , 5 3 – 7 4 . h t t p s : // d o i . o r g / 1 0 . 1 0 1 6 / S 0 1 6 8 - 1 9 2 3 ( 0 2 ) 0 0 1 0 2 - 8
Falster, D. S., Brännström, A., Westoby, M., & Dieckmann, U. (2017).
Multitrait successional forest dynamics enable diverse competitive
coexistence. Proceedings of the National Academy of Sciences, 114,
E2719– E2728. https://doi.org/10.1073/pnas.16102 06114
Famiglietti, C. A., Smallman, T. L., Levine, P. A., Flack- Prain, S., Quetin, G.
R., Meyer, V., Parazoo, N. C., Stettz, S. G., Yang, Y., Bonal, D., Bloom,
A. A., Williams, M., & Konings, A. G. (2020). Optimal model complex-
ity for terrestrial carbon cycle prediction. Biogeosciences Discussions,
1– 4 2, https://doi.org/10.5194/bg- 2020- 478. [Epub ahead of print].
Fedrigo, M., Stewart, S. B., Roxburgh, S. H., Kasel, S., Bennett, L. T.,
Vickers, H., & Nitschke, C. R. (2019). Predictive ecosystem mapping
of South- Eastern Australian temperate forests using lidar- derived
structural profiles and species distribution models. Remote Sensing,
11, 93. https://doi.org/10.3390/rs110 10093
Felton, A. J., & Smith, M. D. (2017). Integrating plant ecological responses
to climate extremes from individual to ecosystem levels. Philosophical
Transactions of the Royal Society B: Biological Sciences, 372(1723),
Fernandes, R. F., Scherrer, D., & Guisan, A. (2018). How much should one
sample to accurately predict the distribution of species assemblages?
A virtual community approach. Ecological Informatics, 48, 125– 134.
Ferraz, A., Saatchi, S., Mallet, C., & Meyer, V. (2016). Lidar detection of
individual tree size in tropical forests. Remote Sensing of Environment,
183, 318– 333. https://doi.org/10.1016/j.rse.2016.05.028
Ferrier, S. (2002). Mapping spatial pattern in biodiversity for regional
conservation planning: Where to from here? Sys tem atic Biolog y, 51(2),
331– 363. https://doi.org/10.1080/10635 15025 2899806
Ferrier, S., & Guisan, A. (2006). Spatial modelling of biodiversit y at the
community level. Journal of Applied Ecology, 43, 393– 404. https://doi .
org /10.1111/j.1365- 2664.20 06 .01149.x
Field, C. B., V. Barros, T. F. Stocker, & Q. Dahe (Eds.) (2012). Managing the
risks of extreme events and disasters to advance climate change adapta-
tion. Cambridge University Press.
Fischer, F. J., Maréchaux, I., & Chave, J. (2019). Improving plant allometry
by fusing forest models and remote sensing. New Phytologist, 223(3),
1159– 1165. https://doi.org/10.1111/nph.15810
Fischer, R., Armstrong, A., Shugart, H. H., & Huth, A. (2014). Simulating
the impacts of reduced rainfall on carbon stocks and net ecosystem
exchange in a tropical forest. Environmental Modelling & Software, 52,
200– 206. https://doi.org/10.1016/j.envso ft.2013.10.026
Fischer, R., Bohn, F., Dantas de Paula, M., Dislich, C., Groeneveld, J.,
Gutiérrez, A. G., Kazmierczak, M., Knapp, N., Lehmann, S., Paulick, S.,
Pütz, S., Rödig, E., Taubert, F., Köhler, P., & Huth, A . (2016). Lessons
learned from applying a forest gap model to understand ecosys-
tem and carbon dynamics of complex tropical forests. Ecological
Modelling, next Generation Ecological Modelling, Concepts, and Theory:
MARÉCHAUX et Al.
Structural Realism, Emergence, and Predictions, 326, 124– 133. https://
Fischer, R., Ensslin, A., Rutten, G., Fischer, M., Schellenberger Costa, D.,
Kleyer, M., Hemp, A., Paulick, S., & Huth, A. (2015). Simulating car-
bon stocks and fluxes of an African tropical montane forest with an
individual- based forest model. PLoS One, 10, e0123300. https://doi.
Fis cher, R., Knapp, N., Boh n, F., Shugar t, H. H. , & Hu th, A . (2019). Th e re l-
evance of forest structure for biomass and productivity in temperate
forests: New perspectives for remote sensing. Surveys in Geophysics,
40, 7 0 9 – 7 3 4 . h t t p s : // d o i . o r g / 1 0 . 1 0 0 7 / s 1 0 7 1 2 - 0 1 9 - 0 9 5 1 9 - x
Fisc her, R., Rödi g, E. , & Huth, A. (2018 ). Cons equen ces of a red uced num-
ber of plant functional types for the simulation of forest productivity.
Forests, 9(8), 460. https://doi.org/10.3390/f9080460
Fisher, R. A., & Koven, C. D. (2020). Perspectives on the future of land
surface models and the challenges of representing complex terres-
trial systems. Journal of Advances in Modeling Ear th Systems, 12(4),
e2018MS001453. https://doi.org/10.1029/2018M S001453
Fisher, R. A., Koven, C. D., Anderegg, W. R . L., Christoffersen, B. O.,
Dietze, M. C., Farrior, C. E., Holm, J. A., Hurtt, G. C., Knox, R. G.,
Lawrence, P. J., Lichstein, J. W., Longo, M., Matheny, A. M., Medvigy,
D., Muller- Landau, H. C., Powell, T. L., Serbin, S. P., Sato, H., Shuman,
J. K., … Moorcroft, P. R. (2018). Vegetation demographics in Earth
System Models: A review of progress and priorities. Global Change
Biology, 24(1), 35– 54. https://doi.org/10.1111/gcb.13910
Fisher, R. A., Williams, M., Do Vale, R. L., Da Costa, A. L., & Meir, P.
(2006). Evidence from Amazonian forests is consistent with isohydric
control of leaf water potential. Plant, Cell & Environment, 29(2), 151–
165. https://doi.org/10.1111/j.1365- 3040.2005.01407.x
Fischer, R. (2013). Modellierung der Dynamik afrikanischer Tropenwälder.
Analyse des Einflusses von Störungen auf tropische Wälder mit Hilfe
des Waldmodells FORMIND (Dissertation, Universität Osnabrück).
Retrieved from https://repos itori um.ub.unios nabru eck.de/handl e/
u r n : n b n : d e : g b v : 7 0 0 - 2 0 1 4 0 3 2 1 1 2 3 0 2
Fleischer, K ., Rammig, A., De Kauwe, M. G., Walker, A. P., Domingues,
T. F., Fuchslueger, L., Garcia, S., Goll, D. S., Grandis, A., Jiang, M.,
Haverd, V., Hofhansl, F., Holm, J. A., Kruijt, B., Leung, F., Medlyn,
B. E., Mercado, L. M., Norby, R. J., Pak, B., … Lapola, D. M. (2019).
Amazon forest response to CO 2 fertilization dependent on plant
phosphorus acquisition. Nature Geoscience, 12(9), 736– 741. https://
d o i . o r g / 1 0 . 1 0 3 8 / s 4 1 5 6 1 - 0 1 9 - 0 4 0 4 - 9
Fontes, L., Bontemps, J. D., Bugmann, H., van Oijen, M., Gracia, C.,
Kramer, K., & Skovsgaard, J. P. (2010). Models for supporting forest
management in a changing environment. Forest Systems, 19, 8– 29.
Forkel, M., Andela, N., Harrison, S. P., Lasslop, G., van Marle, M.,
Chuvieco, E., Dorigo, W., Forrest, M., Hantson, S., Heil, A., Li, F.,
Melton, J., Sitch, S., Yue, C., & Arneth, A. (2019). Emergent relation-
ships with respect to burned area in global satellite observations and
fire- enabled vegetation models. Biogeosciences, 16(1), 57– 76. htt ps://
d o i . o r g / 1 0 . 51 9 4 / b g - 1 6 - 5 7 - 2 0 1 9
Fourcade, Y., Besnard, A. G., & Secondi, J. (2018). Paintings predict the
distribution of species, or the challenge of selecting environmental
predictors and evaluation statistics. Global Ecolog y and Biogeography,
27(2), 245– 256. https://doi.org/10.1111/geb.12684
Franklin, J. (2010). Moving beyond static species distribution models in
support of conservation biogeography. Diversity and Distributions,
16(3), 321– 330. https://doi.org/10.1111/j.1472- 4642.2010.00641.x
Franklin, O., Harrison, S. P., Dewar, R., Farrior, C. E., Brännström, Å.,
Dieckmann, U., Pietsch, S., Falster, D., Cramer, W., Loreau, M., Wang,
H., Mäkelä, A., Rebel, K. T., Meron, E., Schymanski, S. J., Rovenskaya,
E., Stocker, B. D., Zaehle, S., Manzoni, S., … Prentice, I. C. (2020).
Organizing principles for vegetation dynamics. Nature Plants, 6(5),
4 4 4 – 4 5 3 . h t t p s : // d o i . o r g / 1 0 . 1 0 3 8 / s 4 1 4 7 7 - 0 2 0 - 0 6 5 5 - x
Frieler, K., Lange, S., Piontek, F., Reyer, C., Schewe, J., Warszawski,
L., Yamagata, Y. (2017). Assessing the impacts of 1.5 °C global
warming – simulation protocol of the Inter- Sectoral Impact
Model Intercomparison Project (ISIMIP2b). Geoscientific Model
Development. https://doi.org/10.5194/gmd- 10- 4321- 11312017
Friend, A. D., Lucht, W., Rademacher, T. T., Keribin, R., Betts, R., Cadule,
P., & Woodward, F. I. (2014). Carbon residence time dominates un-
certainty in terrestrial vegetation responses to future climate and
atmospheric CO2. Proceedings of National Academy of Science USA,
111(9), 3280– 3285. https://doi.org/10.1073/pnas.12224 77110
Fyllas, N. M., Bentley, L. P., Shenkin, A., Asner, G. P., Atkin, O. K., Díaz, S.,
Enquist, B. J., Farfan- Rios, W., Gloor, E., Guerrieri, R., Huasco, W. H.,
Ishida, Y., Martin, R. E., Meir, P., Phillips, O., Salinas, N., Silman, M.,
Weerasinghe, L. K., Zaragoza- Castells, J., & Malhi, Y. (2017). Solar
radiation and functional traits explain the decline of forest primary
productivity along a tropical elevation gradient. Ecology Letters,
20(6), 730– 740. https://doi.org/10.1111/ele.12771
García- Valdés, R., Bugmann, H., & Morin, X. (2018). Climate change-
driven extinctions of tree species affect forest functioning more
than random extinctions. Diversity and Distributions, 24(7), 906– 918.
ht tps://doi.o rg/10.1111 /ddi .1274 4
García- Valdés, R., Estrada, A., Early, R., Lehsten, V., & Morin, X. (2020).
Climate change impacts on long- term forest productivity might be
driven by species turnover rather than by changes in tree growth.
Global Ecology and Biogeography, 29(8), 1360– 1372. https://doi.
org /10.1111/geb.1311 2
Gibbs, H. K., Brown, S., Niles, J. O., & Foley, J. A. (2007). Monitoring and
estimating tropical forest carbon stocks: Making REDD a reality.
Environmental Research Letters, 2(4), 45023. https://doi.org/10.10 88
/1748- 9326/2/4/0 45023
Gleason, H. A. (1926). The individualistic concept of the plant associa-
tion. Bulletin of the Torrey Botanical Club, 53(1), 7– 26. https://doi.
org /10. 2307/2479933
Goetz, S., & Dubayah, R. (2011). Advances in remote sensing technol-
ogy and implications for measuring and monitoring forest carbon
stocks and change. Carbon Management, 2(3), 231– 244. https://doi.
Goll, D. S., Vuichard, N., Maignan, F., Jornet- Puig, A., Sardans, J.,
Violette, A ., & Ciais, P. (2017). A representation of the phos-
phorus cycle for ORCHIDEE (revision 4520). Geoscientific Model
Development, 10(10), 3745– 3770. https://doi.org/10.5194/
g m d - 1 0 - 3 7 4 5 - 2 0 1 7
Grisebach, A. (1872). Die Vegetation der Erde nach ihrer klimatischen
Anordnung: Ein Abriss der vergleichenden Geographie der Pflanzen. Bd. I
und II. Verlag von Wilhelm Engelmann.
Groeneveld, J., Alves, L. F., Bernacci, L. C., Catharino, E., Knogge, C.,
Metzger, J. P., Pütz, S., & Huth, A. (2009). The impact of fragmen-
tation and density regulation on forest succession in the Atlantic
rain forest. Ecological Modelling, 220(19), 245 0– 2459. http s://doi .
Grossiord, C., Sevanto, S., Limousin, J.- M., Meir, P., Mencuccini, M.,
Pangle, R . E., Pockman, W. T., Salmon, Y., Zweifel, R., & McDowell,
N. G. (2018). Manipulative experiments demonstrate how long-
term soil moisture changes alter controls of plant water use.
Environmental and Experimental Botany, Experiments with Trees: From
Seedlings to Ecosystems, 152, 19– 27. https://doi.org/10.1016/j.envex
Guisan, A., & Thuiller, W. (2005). Predicting species distribution: Offering
more than simple habitat models. Ecology Letters, 8(9), 993– 1009.
Guisan, A ., Thuiller, W., & Zimmermann, N. E. (2017). Habitat suitability
and distribution models: With applications in R. Cambridge University
Guisan, A., Tingley, R., Baumgartner, J. B., Naujokaitis- Lewis, I., Sutcliffe,
P. R., Tulloch, A. I. T., Regan, T. J., Brotons, L., McDonald- Madden,
E., Mantyka- Pringle, C., Martin, T. G., Rhodes, J. R., Maggini,
R., Setterfield, S. A., Elith, J., Schwartz, M. W., Wintle, B. A.,
MARÉCHAUX et Al.
Broennimann, O., Austin, M., … Buckley, Y. M. (2013). Predicting spe-
cies distributions for conservation decisions. Ecology Letters, 16 (12),
1424– 1435. https://doi.org/10.1111/ele.12189
Hantson, S., Arneth, A., Harrison, S. P., Kelley, D. I., Prentice, I. C., Rabin,
S. S., Archibald, S., Mouillot, F., Arnold, S. R., Artaxo, P., Bachelet,
D., Ciais, P., Forrest, M., Friedlingstein, P., Hickler, T., Kaplan, J. O.,
Kloster, S., Knorr, W., Lasslop, G., … Yue, C. (2016). The status and
challenge of global fire modelling. Biogeosciences, 13(11), 3359– 3375.
h t t p s : // d o i . o r g / 1 0 . 5 1 9 4 / b g - 1 3 - 3 3 5 9 - 2 0 1 6
Hart, S. P., Schreiber, S. J., & Levine, J. M. (2016). How variation between
individuals affects species coexistence. Ecology Letters, 19(8), 825–
838 . https://doi.org/10.1111/ele.12618
Hartig, F., Calabrese, J. M., Reineking, B., Wiegand, T., & Huth, A.
(2011). Statistical inference for stochastic simulation models – the-
ory and application. Ecology Letters, 14 (8), 816– 827. https://doi.
org /10.1111/j.1461- 0248.2011.016 40.x
Hartig, F., Dislich, C., Wiegand, T., & Huth, A. (2014). Technical Note:
Approximate Bayesian parameterization of a process- based trop-
ical forest model. Biogeosciences, 11(4), 1261– 1272. https://doi.
o r g / 1 0 . 5 1 9 4 / b g - 1 1 - 1 2 6 1 - 2 0 1 4
Hartmann, H., Moura, C. F., Anderegg, W. R. L., Ruehr, N. K., Salmon,
Y., Allen, C. D., Arndt, S. K., Breshears, D. D., Davi, H., Galbraith, D.,
Ruthrof, K. X., Wunder, J., Adams, H. D., Bloemen, J., Cailleret, M.,
Cobb, R., Gessler, A., Grams, T. E. E., Jansen, S., … O'Brien, M. (2018).
Research frontiers for improving our understanding of drought-
induced tree and forest mortality. New Phytologist, 218(1), 15– 28.
https://doi.or g/10.1111/n ph.1504 8
Heiri, C., Bugmann, H., Tinner, W., Heiri, O., & Lischke, H. (2006). A
model- based reconstruction of Holocene treeline dynamics in the
Central Swiss Alps. Journal of Ecolog y, 94(1), 206– 216. https://doi.
Hickler, T., Prentice, I. C., Smith, B., Sykes, M. T., & Zaehle, S. (2006).
Implementing plant hydraulic architecture within the LPJ Dynamic
Global Vegetation Model. Global Ecology and Biogeography, 15(6),
567– 577. https://doi.org/10.1111/j.1466- 8238.2006.00254.x
Hickler, T., Vohland, K., Feehan, J., Miller, P. A., Smith, B., Costa, L.,
Giesecke, T., Fronzek, S., Car ter, T. R., Cramer, W., Kühn, I., & Sykes,
M. T. (2012). Projecting the future distribution of European potential
natural vegetation zones with a generalized, tree species- based dy-
namic vegetation model. Global Ecology and Biogeography, 21(1), 50–
63. https://doi.org/10.1111/j.1466- 8238.2010.00613.x
Hiltner, U., Huth, A., Bräuning, A., Hérault, B., & Fischer, R. (2018).
Simulation of succession in a neotropical forest: High selective
logging intensities prolong the recovery times of ecosystem func-
tions. Forest Ecology and Management, 430, 517– 525. https://doi.
Holzwarth, F., Kahl, A., Bauhus, J., & Wirth, C. (2013). Many ways to
die – partitioning tree mortality dynamics in a near- natural mixed
deciduous forest. Journal of Ecology, 101(1), 220– 230. https://doi.
org /10.1111/1365- 2745.12015
Hubbell, S. P. (2001). The unified n eutral theory of bio diversity and bioge og-
raphy. Princeton University Press.
Humpenöder, F., Popp, A., Bodirsky, B. L., Weindl, I., Biewald, A., Lotze-
Campen, H., Dietrich, J. P., Klein, D., Kreidenweis, U., Müller, C.,
Rolinski, S., & Stevanovic, M. (2018). Large- scale bioenergy produc-
tion: How to resolve sustainability trade- offs? Environmental Research
Letter s, 13(2), 24011. https://doi.org/10.1088/1748- 9326/aa9e3b
Huston, M., DeAngelis, D., & Post, W. (1988). New computer models
unify ecological theory. BioScience, 38(10), 682– 691. https://doi.
org /10. 23 07/1310870
Hutchinson, G. E. (1957). Concluding remarks. Cold Spring Ha rbor Symposi a
on Quantitative Biology, 22, 415– 427. https://doi.org/10.1101/
Huth, A., & Ditzer, T. (2001). Long- term impacts of logging in a tropi-
cal rain forest — a simulation study. Forest Ecology and Management,
142( 1 ) , 3 3 – 5 1 . h t t p s : / / d o i . o r g / 1 0 . 1 0 1 6 / S 0 3 7 8 - 1 1 2 7 ( 0 0 ) 0 0 3 3 8 - 8
Huth, A., Drechsler, M., & Köhler, P. (2005). Using multicriteria deci-
sion analysis and a forest growth model to assess impacts of tree
harvesting in Dipterocarp lowland rain 1250 forests. Forest Ecology
and Management, 207(1), 215– 232. https://doi.org/10.1016/j.
IPBES. (2016). Summary for policymakers of the methodological assess-
ment of scenarios and models of biodiversity and ecosystem services
of the Intergovernmental Science- Policy Platform on Biodiversity and
Ecosystem Services. Secretariat of the Intergovernmental Science-
Policy Platform on Biodiversity and Ecosystem.
Iversen, C. M., McCormack, M. L., Powell, A. S., Blackwood, C. B.,
Freschet, G. T., Kattge, J., Roumet, C., Stover, D. B., Soudzilovskaia,
N. A., Valverde- Barrantes, O. J., van Bodegom, P. M., & Violle, C.
(2017). A global Fine- Root Ecology Database to address below-
ground challenges in plant ecology. New Phytologist, 215(1), 15– 26.
https://doi.or g/10.1111/n ph.14486
Iverson, L. R., Schwartz, M. W., & Prasad, A. M. (2004). Potential coloni-
zation of newly available tree- species habitat under climate change:
An analysis for five eastern US species. Landscape Ecology, 19(7),
7 8 7 – 7 9 9 . h t t p s : / / d o i . o r g / 1 0 . 1 0 0 7 / s 1 0 9 8 0 - 0 0 5 - 3 9 9 0 - 5
Ives, A. R., & Carpenter, S. R. (2007). Stability and diversity of ecosys-
tems. Science, 317(5834), 58– 62. https://doi.org/10.1126/scien
Jägermeyr, J., Gerten, D., Heinke, J., Schaphoff, S., Kummu, M., &
Lucht, W. (2015). Water savings potentials of irrigation systems:
Global simulation of processes and linkages. Hydrology and Earth
System Sciences, 19 (7), 3073– 3091. https://doi.org/10.5194/
h e s s - 1 9 - 3 0 7 3 - 2 0 1 5
Jarvis, P. G. (Ed.). (1998). European forests and global change: The likely
impacts of rising CO2 and temperature. Cambridge University Press,
Jenkins, C. N., Pimm, S. L., & Joppa, L. N. (2013). Global patterns of ter-
restrial vertebrate diversity and conservation. Proceedings of the
National Academy of Sciences, 110(28), E2602– E2610. https://doi.
org /10.1073/pnas.13 022 51110
Joetzjer, E., Pillet, M., Ciais, P., Barbier, N., Chave, J., Schlund, M.,
& Poulter, B. (2017). Assimilating satellite- based canopy height
within an ecosystem model to estimate aboveground forest bio-
mass. Geophysical Research Letters, 44(13), 6823– 6832. https://doi.
org /10.1002/2017G L074150
Johnson, M. O., Galbraith, D., Gloor, M., De Deurwaerder, H.,
Guimberteau, M., Rammig, A., Thonicke, K., Verbeeck, H., von
Randow, C., Monteagudo, A., Phillips, O. L., Brienen, R. J. W.,
Feldpausch, T. R., Lopez Gonzalez, G., Fauset, S., Quesada, C.
A., Christoffersen, B., Ciais, P., Sampaio, G., … Baker, T. R. (2016).
Variation in stem mortality rates determines patterns of above-
ground biomass in Amazonian forests: Implications for dynamic
global vegetation models. Global Change Biology, 22(12), 3996– 4013.
Journé, V., Barnagaud, J.- Y., Bernard, C., Crochet, P.- A., & Morin, X.
(20 20 ). Cor relative cl im at ic nic he models predict real and vir tual spe-
cies distributions equally well. Ecology, 101(1), e02912. https://doi.
Jucker, T., Bo ngalov, B., Bur slem, D. F. R. P., Nilu s, R. , Dalponte, M., Lewi s,
S. L., Phillips, O. L., Qie, L., & Coomes, D. A. (2018). Topography shapes
the structure, composition and function of tropical forest landscapes.
Ecology Letters, 21(7), 989– 1000. https://doi.org/10.1111/ele.12964
Jucker, T., Caspersen, J., Chave, J., Antin, C., Barbier, N., Bongers, F.,
Dalponte, M., van Ewijk, K. Y., Forrester, D. I., Haeni, M., Higgins,
S. I., Holdaway, R. J., Iida, Y., Lorimer, C., Marshall, P. L., Momo, S.,
Moncrieff, G. R., Ploton, P., Poorter, L., … Coomes, D. A. (2017).
Allometric equations for inte gr at in g re mote sensing imagery into for-
est monitoring programmes. Global Change Biology, 23(1), 177– 190.
Jung, M., Reichstein, M., & Bondeau, A. (2009). Towards global em-
pirical upscaling of FLUXNET eddy covariance observations:
MARÉCHAUX et Al.
Validation of a model tree ensemble approach using a biosphere
model. Biogeosciences, 6(10), 2001– 2013. https://doi.org/10.5194/
b g - 6 - 2 0 0 1 - 2 0 0 9
Justice, C. O., Townshend, J. R. G., Vermote, E. F., Masuoka, E., Wolfe, R.
E., Saleous, N., Morisette, J. T. (2002). An overview of MODIS Land
data processing and product status. Remote Sensing of Environment,
the Moderate Resolution Imaging Spectroradiometer (MODIS): a New
Generation of Land Surface Monitoring, 83(1), 3– 15. https://doi.
o r g / 1 0 . 1 0 1 6 / S 0 0 3 4 - 1 3 0 6 - 4 2 5 7 ( 0 2 ) 0 0 0 8 4 - 6
Kattge, J., Bönisch, G., Díaz, S., Lavorel, S., Prentice, I. C., Leadley, P.,
Tautenhahn, S., Werner, G. D. A., Aakala, T., Abedi, M., Acosta, A . T.
R., Adamidis, G. C., Adamson, K., Aiba, M., Albert, C. H., Alcántara,
J. M., Alcázar C, C., Aleixo, I., Ali, H., … Wirth, C. (2020). TRY plant
trait database – enhanced coverage and open access. Global Change
Biology, 26(1), 119– 188. https://doi.or g/10.1111/gcb.14904
Kattge, J., Díaz, S., Lavorel, S., Prentice, I. C., Leadley, P., Bönisch, G., Garnier,
E., Westoby, M., Reich, P. B., Wright, I. J., Cornelissen, J. H. C., Violle,
C., Harrison, S. P., Van BODEGOM, P. M., Reichstein, M., Enquist, B.
J., Soudzilovskaia, N. A., Ackerly, D. D., Anand, M., … Wirth, C. (2011).
TRY – a global database of plant traits. Global Change Biology, 17(9),
2905– 2935. https://doi .org/10.1111/j.1365- 2486.2011.02451.x
Keane, R. E., Austin, M., Field, C., Huth, A., Lexer, M. J., Peters, D., &
Wyckoff, P. (2001). Tree mortality in gap models: Application to
climate change. Climatic Change, 51(3– 4), 509– 540. https://doi.
Kearney, M., & Porter, W. (2009). Mechanistic niche modelling: Combining
physiological and spatial data to predict species’ ranges. Ecology Letters,
12(4), 334– 350. https://doi.org/10.1111/j.1461- 0248.2008.01277.x
Keenan, T., Niinemets, Ü., Sabate, S., Gracia, C., & Peñuelas, J. (2009a).
Process based inventory of isoprenoid emissions from European
forests: Model comparisons, current knowledge and uncertainties.
Atmospheric Chemistry and Physics, 9(12), 4053– 4076. https://doi.
o r g / 1 0 . 5 1 9 4 / a c p - 9 - 4 0 5 3 - 2 0 0 9
Keenan, T., Niinemets, Ü., Sabate, S., Gracia, C., & Peñuelas, J. (2009b).
Seasonality of monoterpene emission potentials in Quercus ilex
and Pinus pinea: Implications for regional VOC emissions modeling.
Journal of Geophysical Research: Atmospheres, 114(D22202), https://
Keenan, T., Sabaté, S., & Gracia, C. (20 08). Forest eco- physiological
models and carbon sequestration. In: F. Bravo, R. Jandl, V.
LeMay, & K . von Gadow (Eds.) Managing Forest Ecosystems: The
Challenge of Climate Change (pp. 83– 102). : Springer. https://doi.
o r g / 1 0 . 1 0 0 7 / 9 7 8 - 1 - 4 0 2 0 - 8 3 4 3 - 3 _ 5
Keenan, T., Serra, J. M., Lloret, F., Ninyerola, M., & Sabate, S. (2011).
Predicting the future of forests in the Mediterranean under cli-
mate change, with niche- and process- based models: CO2
matters!. Global Change Biology, 17(1), 565– 579. https://doi.
Kercher, J. R., & Axelrod, M. C. (1984). Analysis of silva: A model for
forecasting the effects of SO2 pollution and fire on western co-
niferous forests. Ecological Modelling, 23(1), 165– 184. https://doi.
o r g / 1 0 . 1 0 1 6 / 0 3 0 4 - 3 8 0 0 ( 8 4 ) 9 0 1 2 4 - 8
Kienast, F. (1987). FORECE: A forest succession model for southern
Central Europe (No. ORNL/TM- 10575). Oak Ridge National Lab., TN
(USA) website: https://www.osti.gov/bibli o/5729437
Kleidon, A., & Mooney, H. A. (2000). A global distribution of biodiver-
sity inferred from climatic constraints: Results from a process- based
modelling study. Global Change Biology, 6(5), 507– 523. https://doi.
Knapp, N., Fischer, R ., & Huth, A . (2018). Linking lidar and forest mod-
eling to assess biomass estimation across scales and disturbance
states. Remote Sensing of Environment, 205, 199– 2 09. https ://doi.
Köhler, P., & Huth, A. (1998). The effects of tree species grouping in
tropical rainforest modelling: Simulations with the individual- based
model Formind. Ecological Modelling, 109(3), 301– 321. https://doi.
o r g / 1 0 . 1 0 1 6 / S 0 3 0 4 - 3 8 0 0 ( 9 8 ) 0 0 0 6 6 - 0
Kramer, K., Leinonen, I., Bartelink, H. H., Berbigier, P., Borghetti, M.,
Bernhofer, C., & Vesala, T. (2002). Evaluation of six process- based
forest growth models using eddy- covariance measurements of CO2
and H2O fluxes at six forest sites in Europe. Global Change Biology,
8(3), 213– 230. doi: 10.1046/j.1365- 2486.2002.00471.x (2002).
Evaluation of six process- based forest growth models using eddy-
covariance measurements of CO2 and H2O fluxes at six forest sites
in Europe. Global Change Biology, 8, 213– 230.
Krinner, G., Viovy, N., de Noblet- Ducoudré, N., Ogée, J., Polcher, J.,
Friedlingstein, P., Ciais, P., Sitch, S., & Prentice, I. C. (2005). A dynamic
global vegetation model for studies of the coupled atmosphere-
biosphere system. Global Biogeochemical Cycles, 19 (1), GB1015.
Kunstler, G., Allen, R. B., Coomes, D. A., Canham, C. D., & Wright, E.
F. (2013). Sustainable management, earthquake disturbances, and
transient dynamics: Modelling timber harvesting impacts in mixed-
species forests. Annals of Forest Science, 70(3), 287– 298. https://doi.
o r g / 1 0 . 1 0 0 7 / s 1 3 5 9 5 - 0 1 2 - 0 2 5 6 - 6
Kunstler, G., Coomes, D. A., & Canham, C. D. (2009). Size- dependence
of growth and mortality influence the shade tolerance of trees in
a lowland temperate rain forest. Journal of Ecology, 97(4), 685– 695.
https://doi.or g/10.1111/j.1365- 2745.2009.01482.x
Kurzweil, R. (2005). The singularit y is near: When humans transcend biol-
ogy. Penguin Books.
Lafond, V., Lagarrigues, G., Cordonnier, T., & Courbaud, B. (2014).
Uneven- aged management options to promote forest resilience for
climate change adaptation: Effects of group selection and harvest-
ing intensity. Annals of Forest Science, 71(2), 173– 186. https://doi.
o r g / 1 0 . 1 0 0 7 / s 1 3 5 9 5 - 0 1 3 - 0 2 9 1 - y
Langan, L., Higgins, S. I., & Scheiter, S. (2017). Climate- biomes, pedo-
biomes or pyro- biomes: Which world view explains the tropical
forest– savanna boundary in South America? Journal of Bioge ography,
44(10), 2319– 2330. https://doi.org/10.1111/jbi.13018
La ngerwi sch , F., Vác lavík, T., vo n Bloh, W., Vette r, T., & Thon icke, K. (2017).
Combined effects of climate and land- use change on the provision of
ecosystem services in rice agro- ecosystems. Environmental Research
Letter s, 13(1), 15003. https://doi.org /10.1088/1748- 9326/aa954d
Lasslop, G., Thonicke, K., & Kloster, S. (2014). SPITFIRE within the MPI
Earth system model: Model development and evaluation. Journal
of Advances in Modeling Earth Systems, 6(3), 740– 755. https://doi.
Leathwick, J. R., & Austin, M. P. (2001). Competitive interactions be-
tween tree species in New Zealand’s old- growth indigenous for-
ests. Ecology, 82(9), 2560– 2573. https://doi.org/10.1890/0012-
LeBauer, D. S., Wang, D., Richter, K. T., Davidson, C. C., & Dietze, M.
C. (2013). Facilitating feedbacks between field measurements and
ecosystem models. Ecological Monographs, 83(2), 133– 154. https://
Leemans, R., & Prentice, I. C. (1989). FORSKA - a general forest suc-
cession model. Meddelanden Fraan Vaextbiologiska Institutionen:
Retrieved from. http://agris.fao.org/agris - searc h/search.do?recor
Lehmann, S., & Huth, A. (2015). Fast calibration of a dynamic vegetation
model with minimum observation data. Ecological Modelling, 301, 98–
105. https://doi.org/10.1016/j.ecolm odel.2015.01.013
Lehsten, V., Mischurow, M., Lindström, E., Lehsten, D., & Lischke, H.
(2019). LPJ- GM 1.0: Simulating migration efficiently in a dynamic
vegetation model. Geoscientific Model Development, 12(3), 893– 908.
h t t p s : // d o i . o r g / 1 0 . 5 1 9 4 / g m d - 1 2 - 8 9 3 - 2 0 1 9
Lenihan, J. M., Daly, C., Bachelet, D., & Neilson, R. P. (1998). Simulating
broad- scale fire severity in a Dynamic Global Vegetation Model.
Northwest Science, 72, 91– 103.