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Current modelling approaches to predict spatially explicit biodiversity responses to climate change mainly focus on the direct effects of climate on species. Integration of spatiotemporal land-cover scenarios is still limited. Current approaches either regard land cover as constant boundary conditions, or rely on general, typically globally defined land-use scenarios. This is problematic as it disregards the complex synergistic effects of climate and land use on biodiversity at the regional scale, as biophysical, economic, and social issues important for regional land-use decisions are also affected by climate change. To realistically predict climate impacts on biodiversity, it is therefore necessary to consider both, the direct effect of climate change on biodiversity, and its indirect effect on biodiversity via land-use change. In this review and perspective paper, we outline how biodiversity models could be better integrated with regional, climate-driven land-use models. We provide an overview of empirical and modelling approaches to both land-use (LU) and biodiversity (BD) change, focusing on how integration has been attempted. We then analyse how LU and BD model properties, such as scales, inputs, and outputs, can be matched and identify potential integration challenges and opportunities. We found LU integration in BD models has been frequently attempted. By contrast, integrating the role of BD in models of LU decisions is largely lacking. As a result, bi-directional effects remain largely understudied. Only few integrated LU-BD socio-ecological models have assessed climate change effects on LU and no study has yet investigated the relative contribution of direct vs. indirect effects of climate change on BD. There is a large potential for model integration given the overlap on spatial scales, although challenges remain with respect to spatial scale, temporal dynamics, investigation of indirect effects, and bi-directionality, including feeding back to climate models. Efforts to better understand human decisions, eco-evolutionary dynamics, connection between terrestrial and aquatic systems, and format standardization of modelling outputs and empirical data should improve future models. Integrating biodiversity feedbacks into land-use and climate models requires modelling innovations, but should be feasible.
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The road to integrate climate change effects on land-use change in
regional biodiversity models
Juliano Sarmento Cabral1, Alma Mendoza-Ponce2,3, Andr´e Pinto da Silva4,5, Johannes
Oberpriller6, Anne Mimet7, Julia Kieslinger8, Thomas Berger9, Jana Blechschmidt1,
Maximilian Br¨onner8, Alice Classen10, Stefan Fallert1, Florian Hartig6, Christian Hof7,
Markus Hoffmann11, Thomas Knoke12, Andreas Krause13 , Anne Lewerentz1, Perdita
Pohle8, Uta Raeder11, Anja Rammig13, Sarah Redlich10 , Sven Rubanschi7, Christian
Stetter14, Wolfgang Weisser7, Daniel Vedder1,15,16,17, Peter H. Verburg18, and Damaris
Zurell19
1Ecosystem Modelling, Center for Computational and Theoretical Biology (CCTB),
University of Wurzburg
2Research Program on Climate Change, Universidad Nacional Aut´onoma de M´exico
3International Institute for Applied Systems Analysis, Laxenburg, Austria
4Department of Ecology and Genetics, Animal Ecology, Evolutionary Biology Centre,
Uppsala University
5Centre for Ecology, Evolution and Environmental Changes (cE3c), Faculdade de Ciˆencias,
Universidade de Lisboa
6Theoretical Ecology Lab, University of Regensburg
7Technical University of Munich, Terrestrial Ecology Research Group, Department of Life
Science Systems, School of Life Sciences
8Chair of Human Geography and Development Studies, Institute of Geography,
Friedrich-Alexander University Erlangen-Nuernberg
9Land-Use Economics in the Tropics and Subtropics, Hans-Ruthenberg Institute,
Hohenheim University
10Department of Animal Ecology and Tropical Biology, Biocentre, University of Wurzburg
11Technical University of Munich, Limnologische Station Iffeldorf, Chair of Aquatic
Systems Biology, Department of Life Science Systems, School of Life Science
12Technical University of Munich, Institute of Forest Management, Department of Life
Science Systems, School of Life Sciences
13Technical University of Munich, Land Surface-Atmosphere Interactions, Department of
Life Science Systems, School of Life Sciences
14Technical University of Munich, Agricultural Production and Resource Economics,
School of Life Sciences
15Helmholtz Center for Environmental Research - UFZ, Department of Ecosystem Services
16Institute of Biodiversity, Friedrich Schiller University Jena
17German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig
18Institute for Environmental Studies, VU University Amsterdam
19Inst. for Biochemistry and Biology, University of Potsdam
1
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February 28, 2022
Juliano Sarmento Cabral1, Alma Mendoza-Ponce2,3, Andr´e Pinto da Silva4,5, Johannes Oberpriller6, Anne
Mimet7, Julia Kieslinger8, Thomas Berger9, Jana Blechschmidt1, Maximilian Br¨onner8, Alice Classen10 , Ste-
fan Fallert1, Florian Hartig6, Christian Hof7, Markus Hoffmann11, Thomas Knoke12, Andreas Krause13, Anne
Lewerentz1, Perdita Pohle8, Uta Raeder11, Anja Rammig13 , Sarah Redlich10, Sven Rubanschi7, Christian
Stetter14, Wolfgang Weisser7, Daniel Vedder1,15,16,17 , Peter H. Verburg18, Damaris Zurell19
1Ecosystem Modelling, Center for Computational and Theoretical Biology (CCTB), University of W¨urzburg,
Klara-Oppenheimer-Weg 32, 37074, W¨urzburg, Germany
2Research Program on Climate Change, Universidad Nacional Aut´onoma de M´exico, Mexico City, Mexico
3International Institute for Applied Systems Analysis, Laxenburg, Austria
4Department of Ecology and Genetics, Animal Ecology, Evolutionary Biology Centre, Uppsala University,
Uppsala, Sweden
5Centre for Ecology, Evolution and Environmental Changes (cE3c), Faculdade de Ciˆencias, Universidade de
Lisboa, Lisbon, Portugal
6Theoretical Ecology Lab, University of Regensburg, Universit¨atsstraße 31, 93053 Regensburg, Germany
7Technical University of Munich, Terrestrial Ecology Research Group, Department of Life Science Systems,
School of Life Sciences, 84354 Freising, Germany
8Institute of Geography, Friedrich-Alexander University Erlangen-Nuernberg, Wetterkreuz 15, 91058 Erlan-
gen, Germany
9Land-Use Economics in the Tropics and Subtropics, Hans-Ruthenberg Institute, Hohenheim University,
Hohenheim, Germany
10 Department of Animal Ecology and Tropical Biology, Biocentre, University of W¨urzburg, Am Hubland,
97074 W¨urzburg, Germany
11 Technical University of Munich, Limnologische Station Iffeldorf, Chair of Aquatic Systems Biology, De-
partment of Life Science Systems, School of Life Science,
Hofmark 1-3, 82393 Iffeldorf, Germany
12 Technical University of Munich, Institute of Forest Management, Department of Life Science Systems,
School of Life Sciences, 58354 Freising, Germany
13 Technical University of Munich, Land Surface-Atmosphere Interactions, Department of Life Science Sys-
tems, School of Life Sciences, 85354 Freising, Germany
14 Agricultural Production and Resource Economics, School of Life Sciences, Technical University of Munich,
84354 Freising, Germany
15 Helmholtz Center for Environmental Research - UFZ, Department of Ecosystem Services, Permoserstr.
15, 04318 Leipzig, Germany
16 Institute of Biodiversity, Friedrich Schiller University Jena, Dornburger Straße 159, 07743 Jena, Germany
17 German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Puschstr. 4, 04103 Leipzig,
Germany
18 Institute for Environmental Studies, VU University Amsterdam, De Boelelaan 1111, 1081 HV Amsterdam,
The Netherlands
2
Posted on Authorea 28 Feb 2022 | The copyright holder is the author/funder. All rights reserved. No reuse without permission. | https://doi.org/10.22541/au.164608831.19029067/v1 | This a preprint and has not been peer reviewed. Data may be preliminary.
19 Ecology & Macroecology, Inst. for Biochemistry and Biology, University of Potsdam, Am Neuen Palais
10, 14469 Potsdam, Germany
Article type: review/perspective
Abstract
1. Current modelling approaches to predict spatially explicit biodiversity responses to climate change
mainly focus on the direct effects of climate on species. Integration of spatiotemporal land-cover sce-
narios is still limited. Current approaches either regard land cover as constant boundary conditions, or
rely on general, typically globally defined land-use scenarios. This is problematic as it disregards the
complex synergistic effects of climate and land use on biodiversity at the regional scale, as biophysi-
cal, economic, and social issues important for regional land-use decisions are also affected by climate
change. To realistically predict climate impacts on biodiversity, it is therefore necessary to consider
both, the direct effect of climate change on biodiversity, and its indirect effect on biodiversity via
land-use change.
2. In this review and perspective paper, we outline how biodiversity models could be better integrated
with regional, climate-driven land-use models. We provide an overview of empirical and modelling
approaches to both land-use (LU) and biodiversity (BD) change, focusing on how integration has been
attempted. We then analyse how LU and BD model properties, such as scales, inputs, and outputs,
can be matched and identify potential integration challenges and opportunities.
3. We found LU integration in BD models has been frequently attempted. By contrast, integrating the
role of BD in models of LU decisions is largely lacking. As a result, bi-directional effects remain largely
understudied. Only few integrated LU-BD socio-ecological models have assessed climate change effects
on LU and no study has yet investigated the relative contribution of direct vs. indirect effects of climate
change on BD.
4. There is a large potential for model integration given the overlap on spatial scales, although chal-
lenges remain with respect to spatial scale, temporal dynamics, investigation of indirect effects, and
bi-directionality, including feeding back to climate models. Efforts to better understand human de-
cisions, eco-evolutionary dynamics, connection between terrestrial and aquatic systems, and format
standardization of modelling outputs and empirical data should improve future models. Integrating
biodiversity feedbacks into land-use and climate models requires modelling innovations, but should be
feasible.
Keywords: agent-based models, biodiversity response, environmental change, indirect effects, integrative
approaches, mechanistic models, socio-ecological systems, species richness
1 | INTRODUCTION
Biodiversity is under multiple threats, with land use being a key current stressor (B¨uhne et al., 2021; IPBES,
2019) and climate change effects likely to intensify in the future (Pereira et al., 2020). A challenge to
disentangling the effect of these two stressor groups is that while climate is a key determinant of biodiversity
patterns in general (Kreft et al., 2007), it is also a key driver of human land use (Yamaura et al., 2011).
Consequently, changes in climate can be expected to exert multifold effects on biodiversity (Arneth et al.,
2020; Lecl`ere et al., 2020). These effects can follow direct and indirect pathways, with indirect pathways
happening via climate-driven changes in land use (Fig. 1).
Most biodiversity and ecosystem assessments focus on the direct effects of climate change. Indeed, following
the development of climate models and climate change projections at the global scale (e.g. Hijmans et
al., 2005; with more recent projections by Fick & Hijmans, 2017; Karger et al., 2020), there has been a
large production of biodiversity assessments under climate change. These assessments have mostly kept land
use/cover and other global change drivers constant (e.g. Anderson et al., 2013; Sarmento Cabral et al.,
2013; Titeux et al., 2016). Even when climate change is combined with land cover change, the latter is not
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modelled as a consequence of the former (e.g. Travis, 2003) and land-atmosphere feedbacks have been ignored
(Wulfmeyer et al., 2018). Therefore, the indirect effects of climate change on biodiversity via its effects on
land-use change (Fig. 1) remain underexplored.
Considering that land use is arguably the strongest driver of biodiversity change to date (IPBES, 2019), un-
derstanding the potential effects of climate change on land use is of high importance (Titeux et al., 2017). In
this sense, global land-use models and integrated assessment models (IAMs – see Weyant 2017 for a review)
allow assessing the impacts of climate on land-use changes, as well as quantifying potential climate feedbacks
through greenhouse gas emissions. These models may consider economic rules of production and demand for
commodities that utilize land, like agriculture, pasture and forestry (Havl´ık et al., 2011; Lotze-Campen et
al., 2008). However, the typically coarse spatial resolutions and economic focus of these global models do not
account for the diversity of farmer behaviours, decision-making strategies, and governance structures at the
local to regional scale (Arneth et al., 2014; Rounsevell et al., 2014), and thus produce less precise rates of land
conversion at the regional scales (Bayer et al., 2020). In addition, profit maximization, as it is assumed in
many of the models, does not capture the complex socio-ecological systems that involve organized sustaina-
ble behaviour at the local and regional scales (Ostrom, 2009; see also Ceddia et al., 2015 for the specific
consideration of forest rights). The standard models also ignore time lags arising from knowledge diffusion
until informed decision-taking (Brown et al., 2018). Moreover, market-integrated landowners can consider
fluctuations in market prices, costs, and yields in their decisions on local land allocation, although there
can be other motivations, attitudes, and even contract obligations complicating land-use change processes
(Debonne et al., 2021; Malek et al., 2019). As persons who are generally averse to risks and uncertainties
(Pichon, 1997), landowners may decide to diversify their land-use types to buffer against risk and ambiguity
such as that arising from climate change (Eisele et al., 2021; Knoke et al., 2011). Importantly, these decisions
may depend on the regional context, be it traditional or cultural. Challenges for modelling land-use changes
and their impacts on biodiversity at the regional scale thus lie in plausible climate-integrated, socio-economic
models to simulate regional land allocation.
It is important to acknowledge that focusing on regional scales can be key to tackle the above-mentioned
challenges. Indeed, climate change is spatially heterogeneous (Bowler et al., 2020), and species, ecosystems,
political jurisdiction, and land users’ responses and decisions tend to be region-specific. Additionally, em-
pirical biodiversity assessments and conservation policies are mostly designed to address adaptation and
mitigation options at regional scales (see Glossary for definitions). Only the regional scale enables working
with fine-scale (spatial and thematic resolution) and land-use description (e.g. potentially integrating small
landscape elements), while offering the possibility to explore biodiversity responses from the local (populati-
ons, communities) to the regional scale (species pool, homogenization). Therefore, besides the direct effects
of climate and land use on regional biodiversity, integrating land-use change induced by climate change on
biodiversity assessments seems paramount to support forward-looking conservation policies.
In this perspective paper, we argue for an integration of indirect pathways of climate change effects on
regional biodiversity via connection between biodiversity and land-use models. To this end, we first review
empirical and modelling studies of the effects of climate and climate change on regional land use, followed by
studies of the effects of land use on regional biodiversity. The empirical overview provides insights on aspects
not yet modelled, whereas the modelling overview highlights possible routes of how land-use and biodiversity
models can be integrated by matching the resolution and input/output of the various modelling approaches.
Finally, we discuss potential challenges and opportunities of feeding biodiversity effects back into land-use
and climate models. This would effectively mean integrating all three modelling approaches at the regional
scale, with three key (bi-directional) links between climate change, land-use change and biodiversity change
models (Fig. 1). Our empirical and modelling overview as well as proposed integration links ultimately foster
the dialogue between the research communities focusing on land-use and biodiversity modelling to achieve
truly integrated assessments at regional scales.
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1.1 | Glossary
Local scales/models : focused on single or small mosaics of populations, communities, stakeholders or habitats
(i.e. from a few meters to several kilometers). Local models include population viability analysis (PVA)
models, metapopulation models, reserve prioritization approaches (several small or single large debate), all
of which typically focus on particular habitats or habitat networks or on particular local populations or
communities.
Regional scales/models : anything from landscape to continental spatial extents, including pronounced and
multiple environmental gradients (i.e. tens to thousands km). Approaches that calculate local variables
but can be projected at any scale (e.g. dynamic vegetation models, species distribution models), including
regional grid extents, are treated as regional. Most biodiversity models already fall within this category and
typically focus on metapopulation dynamics across environmental gradients, on species ranges, on diversity
distributions or on distribution of ecosystem functions.
Global scales/models : anything at global spatial extents (e.g. global circulation models). Biodiversity models
at global scale often use coarse resolution and might focus on entire taxonomic groups or ecological guilds.
Similarly, global land-use models often distinguish a number of world regions.
Biodiversity measures : any component or aspect defining variability of ecological entities (individuals, po-
pulations, communities, ecosystems), which may happen over space, time or within/across entities (i.e.
intraspecific variability, population structure). Important ways to quantify aspects of biodiversity involve
abundances, species richness, composition and function metrics. At the regional scales, components such as
alpha (local), beta (internal turnover) and gamma (regional total) diversity are relevant for the different
aspects.
Biodiversity models : models defining dynamics of any biodiversity component or aspect. These models should
normally integrate information on spatial and temporal variation of environmental conditions and of model
agents, which drive the variability of ecological entities (i.e. biodiversity).
Phenomenological models : models in which a variable state is correlated to other variables, which can be
done by machine-learning, econometric, and statistical relationships.
Land use: economic and social functions of a land (Haines-Young, 2009). For simplicity, water use is excluded,
but land use can affect adjacent water bodies.
Management: diversity of practices applied to a land to reach the targeted purpose of the land, e.g. cutting,
fertilizing, removing deadwood. Management practices can be classified into input and output categories.
Mechanistic models : models in which the state of a variable is explicitly influenced by factors via causal
relationships, often dynamic ones. Rule-, equation-, and agent-based models as well as cellular automata are
typical examples with such relationships.
Integrative models : models that integrate models from different fields of research, such as land use, biodi-
versity and/or climate.
Hybrid models : models that combine different modelling methods, such as agent-based and correlative
components.
Land-use type :large category of land use such as agriculture, forestry, or urban settlements and infrastruc-
tures, characterized by certain types of input and output types and intensity of use.
Land cover: physical surface characteristics of land (Haines-Young, 2009).
Model input : any data read in from files during initialization and during model iterations.
Model output : data generated by the model and saved to file.
5
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2 | LAND USE AT REGIONAL SCALE
2.1 | Climate change effects on land use
Regional land use, such as agriculture, forestry, and hunting practices, strongly depends on climate conditions.
This is because different plants and animals, including both target product and potential pests, have different
environmental preferences for optimal productivity. Consequently, environmental change strongly influences
productivity and the stakeholder’s decision on land use, both of which in turn affect regional economies. For
instance, field experiments show that maize yields decline more (39%) under water stress than wheat yields
(21%) (Daryanto et al., 2016). In addition, multiple lines of evidence suggest that the four major crops wheat,
maize, rice, and soybean all respond negatively to temperature increases (Zhao et al., 2017), while irrigation
represents an effective adaptation option if irrigation costs remain covered by the profit (Agnolucci et al.,
2020). Globally, inter-annual climate variability accounts for around one third of the observed yield variability
(Ray et al., 2015), whereas historical climate change (1974-2008) reduced consumable calories of the ten most
important crops by 1% (Ray et al., 2019). The effects seem more drastic when looking on productivity, with
indications that anthropogenic climate change has reduced global agricultural productivity by around 21%
since 1961, with more severe consequences for warmer regions such as Africa and Latin America (Ortiz-Bobea
et al. 2021). For Europe, climate trends since 1989 have slightly increased continent-wide maize and sugar
beet yields, but significantly reduced, albeit with large spatial variability, wheat and barley yields (Moore &
Lobell, 2015). Such climate-driven changes in crop yields directly affect regional land-use patterns. According
to Zaveri et al. (2020), repeated dry anomalies have been responsible for around 9% of the rate of cropland
expansion in developing countries over the last two decades. Furthermore, land-use patterns are likely to be
affected by extreme weather events, with the little current evidence indicating that farmers temporarily and
dynamically shift land use after weather shocks - e.g. away from cash and permanent crops one year after a
drought, and away from horticulture and permanent crop after a flood (He & Chen, 2022; Olesen & Bindi,
2002; Ramsey et al., 2021; Salazar-Espinoza et al., 2015).
Climate change will continue over the next decades, thereby affecting agricultural productivity and triggering
additional shifts in land-use patterns and crop choice (Alexander et al., 2018; Pugh et al., 2016). Strong shifts
are likely to be caused by changes in precipitation patterns (Malek et al., 2018). Whereas irrigation may
partially mitigate land-use changes, land-use change also depends on soil conditions as well as national or
regional policies, agricultural prices, subsidies, consumer behaviour, and the structure of agricultural and
silvicultural actors (the so-called Shared Socio-economic Pathways - SSPs, O’Neil et al., 2014). Unlike climate,
economic or agricultural systems are very adaptive and strongly driven by human expectations and decisions.
As a consequence, changes in these systems are even more difficult to project into the future than in natural
systems due to the large uncertainties involved (Troost & Berger, 2015). For example, Ramsey et al. (2021)
found that land-use responses to changing weather patterns vary across time and space. Indeed, it is crucial
to determine the temporal scale at which these systems are satisfactorily predictable (‘forecasting horizon‘).
Considering all these aspects, it seems mandatory that adequate modelling approaches should decide on
what type of model is required for which problem and on which scale (Levin, 1992). Processes involving
water supply at the regional (watershed) scale, impacts of yield change at the policy relevant scales, and
adaptation measures at farm holding level are all necessary at the regional scale. Thus, responses of regional
land use to climate change are complex and are driven not only by the natural conditions, but also by the
socio-economic context.
2.2 | Modelling regional land-use change
Regional land-use models reflect the local or regional contexts more accurately than the global approaches.
Current models include a continuum from mechanistic to phenomenological approaches (Table 1; see also the
Summary for definitions). Phenomenological models statistically relate a set of explanatory socio-economic or
biophysical variables to transitions in local land use (e.g. Verburg & Overmars, 2009). In these approaches, the
quantity of change is given by transition matrices of two historical land-use maps and the allocation of changes
6
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vary depending on the model. In other approaches, global economic or integrated assessment models are used
to determine the region-level demands. Some of these models have applied neural networks approaches to
produce a probability map of land-use changes (Dai et al., 2005; Qiang & Lam, 2015). Mechanistic models
emulate processes in which drivers of change (e.g. sequence of land uses) interact, sometimes based on rules
(St´ephenne & Lambin, 2001; Rouget et al., 2003), sometimes based on cellular automata (Liu et al., 2017;
Clarke, 2008; Diogo et al., 2015). Agent-based models appear to be best suited to mechanistically integrate
the individual farmer behaviour, policies, and biophysical tags (Murray-Rust et al., 2014a,b). Such models
can be modified to include regional trade-offs among a large variety of socioeconomic input variables, to
identify the main drivers of land-use change. However, they usually do not consider the impacts of climate
change because their future projections are at short- to medium-term (i.e. few years or decades – Table 1),
much shorter than the projection horizon of the climate models. Moreover, climate change projections are
more global-based in nature with substantially less accuracy at regional scales, which is why such projections
may have little relevance to individual land-users (e.g. farmers) and their decision making process (Morton et
al., 2015, 2017). Nevertheless, there are few regional land-use change models that have projected long-term
land-use trajectories integrating climate change (e.g. Mendoza-Ponce et al., 2018, 2019).
While most land-use models focus on biophysical and socioeconomic drivers, some models have integrated
incipient connections to biodiversity. For example, vegetation recovery rates or dynamics can influence the
use of fuelwood (St´ephenne & Lambin, 2001; Kiruki et al., 2019) or management strategies (Verburg &
Overmars, 2009; Murray-Rust et al., 2014b). Other models integrated information on ecosystem threats,
such as alien species, to model transformed rates and areas (Rouget et al., 2003). These connections of
biodiversity elements to land use mostly focus on plant or vegetation patterns, but a few examples also
consider animal diversity as a driver (Soares-Filho et al., 2013; Dai et al., 2015). To summarize, while there
are a few approaches that consider some aspect of biodiversity as a factor influencing regional land-use
change, most models do not.
3 | LAND-USE EFFECTS ON REGIONAL BIODIVERSITY
3.1 | Land use effects on biodiversity
Land use has been the main driver of biodiversity decline over the past 50 years (IPBES, 2019; Pereira et
al., 2012). Whereas physical actions on land as direct drivers of ecosystem change (e.g. agriculture, forestry,
or urbanization) operate at the local level (Lambin & Meyfroidt, 2010), impacts of land use also operate at
larger spatial scales (Haines-Young, 2009). These impacts include also aquatic ecosystems, such as rivers,
with sediment and nutrient discharges affecting entire catchment areas (Alin et al., 2002; Bussi et al., 2016).
In addition, the complexity of land-use-biodiversity relationships arises from the multidimensionality of both
land use (e.g. type, management, intensity Erb et al., 2013; Kuemmerle et al., 2013) and biodiversity
(e.g. taxonomic, functional, and phylogenetic diversity Devictor et al., 2010), with various multidirectional
impacts overlapping, reinforcing, or mitigating each other (Haines-Young, 2009). Ecologists developed two
main types of approaches to deal with this complexity.
The “management-oriented” approaches link the management dimensions of land use to biodiversity at local
scale (Paillet et al., 2010), sometimes integrating the landscape-scale effects of land use (M¨uller et al., 2007).
The studies using such approaches usually focus on a single land-use type (e.g. agriculture or forestry), and
rely on detailed evaluation of the management practices and their intensity (Herzog et al., 2006; Jeliazkov
et al., 2016). These approaches have addressed multiple biodiversity dimensions. For example, taxonomic
diversity can show strongly decreasing or flat responses to management intensity (Allan et al., 2015; Simons &
Weisser, 2017; Tsiafouli et al., 2015). These observed patterns result from the loss of ecological opportunities
(i.e. resource depletion, habitat degradation, micro-habitat loss) linked to the changes in abiotic and biotic
conditions and in the disturbance regime.
The “type-oriented” approaches look for general impacts of land use on biodiversity, usually focusing on spatial
7
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intensification and limiting their description of land use to one (e.g. Clavero & Brotons, 2010) or several
(e.g. Herrera et al., 2016; Mimet et al., 2014; Uhler et al., 2021) land-use types. These approaches often
consider the landscape effects of land use on biodiversity through the description of landscape composition,
configuration, and connectivity (e.g. Clavero & Brotons, 2010; Fahrig et al., 2011). They show how landscape-
level land use influences the functional composition and structure of species communities. For example, the
homogenization of the landscape through intensive land use is related to lower beta-diversity (Gossner et
al., 2016; Jeliazkov et al., 2016), whereas land-use intensification simplifies food webs (Jeliazkov et al., 2016;
Mendoza & Ara´ujo, 2019; Pellissier et al., 2017; Tsiafouli et al., 2015). Through changes in pressures and
connectivity, land-use intensification can also induce phenotypic adaptation, e.g. via changes in phenology
(Barbaro & Halder, 2009; Mimet et al., 2009) and trait evolution (e.g. dispersal ability Martin et al., 2017).
Moreover, the type-oriented approach is particularly prevalent in studying aquatic ecosystems, as aquatic
biodiversity can change due to agricultural and urban land-use changes (Allan, 2004; Polasky et al., 2011).
Finally, few studies assess simultaneously the roles of both land-use type and intensity, showing that land-
use type mainly drives the functional and taxonomic composition, while land-use intensity rather drives the
functional redundancy of species (Birkhofer et al., 2017; Lalibert´e et al., 2010).
3.2 | Modelling regional biodiversity change
Several approaches have been proposed to model biodiversity at regional scales, varying from phenomenologi-
cal (e.g. macroecological analyses, species distributions models) to mechanistic or process-based models (see
Dormann et al., 2012; Zurell et al., 2016 for comparisons in the field of niche models). Whereas both types
of models can assess potential effects of land-use and climate change, the mechanistic models can further
account for transient, non-equilibrium, and novel conditions by explicitly simulating eco-evolutionary proces-
ses (Cabral et al., 2017; Dormann et al., 2012). For instance, ecophysiological models integrate processes on
the basis of metabolic theories describing life-history of species, such as energy uptake, growth, respiration,
and thermoregulation (e.g. Cabral et al., 2019; Kearney, 2012; Leidinger et al., 2021), and of ecosystem-level
processes, such as carbon assimilation and metabolic costs (the so-called general ecosystem models GEMs
for both autotrophic and heterotrophic biodiversity e.g. Harfoot et al., 2014; and the dynamic vegetation
models DVMs e.g. Sakschewski et al., 2015). The ecosystem-level GEMs and DVMs that are agent-based
and trait-based can account for functional diversity by defining functional types (e.g. SEIB-DGVM Sato et
al., 2007; JeDi Pavlick et al., 2013; LPJml-FIT – Sakschewski et al., 2015) and have been applied to regional
extents (e.g. Lautenbach et al., 2017; Sato & Ise, 2012; Thonicke et al., 2020) despite being also largely used
in global studies. These ecosystem-level ecophysiological models often lack cross-region, spatial processes
(e.g. disturbances, dispersal). To this end, models further based on metapopulation and metacommunity
theories can also integrate demographic processes, such as reproduction, mortality, density-dependence, and
dispersal, as well as biotic interaction processes, such as resource competition and trophic interactions (Cabral
& Kreft, 2012; Hagen et al., 2021; Harfoot et al., 2014; Urban et al., 2016). Some models focus entirely on
region-wide spatial processes, such as connectivity, which can be based for example on graph (e.g. Foltˆete
et al., 2012) or circuit (e.g. McRae et al., 2008) theories and focus on particular species or group of species.
These mechanistic models thus predict abundances, demographic rates, and connectivity, all of which have
a higher information value than just presence probabilities (Ehrl´en & Morris, 2015).
Mechanistic models jointly addressing climate change and land-use effects on biodiversity have been proposed
almost two decades ago (Travis, 2003), but their application to real-world systems has so been limited, partly
due to low species-specific data availability and computational runtimes which are unfeasible for automatic
optimization (Cabral et al. 2017, Dormann et al. 2012). Still, several models can already use real-world
environmental data as input (e.g. Hagen et al., 2021; Higgins et al., 2020; Malchow et al., 2021; McIntyre
& Lavorel, 2007; Sarmento Cabral et al., 2013), which should promote their application for addressing land-
use effects. Indeed, biodiversity models including land use vary from hypothetical virtual experiments to
real-world applications across regional scales (Table 2). These models address e.g. temporal dynamics and
coexistence of functional groups under land-use management in complex landscapes (Boulangeat et al., 2014;
Lautenbach et al., 2017; Qu´etier et al., 2007), or the geographical range of focal species (e.g. Bocedi et al.,
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2014, 2021; Faleiro et al., 2013; Sales et al., 2020; Zamora-Gutierrez et al., 2021). The partial effect of
land use on biodiversity (i.e disentangling its effect from other disturbances such as climate change) has
been considered in few studies. For instance, Travis (2003) showed the proportion of habitat loss influences
the threshold of response to climate change. Sarmento Cabral et al. (2013) compared simulations with
and without habitat loss, revealing that land use negatively influences local abundances while not strongly
affecting range size of shrubs. Synes et al. (2019) went further and compared uni- and bidirectional effects
of crop yields on pollinator populations, demonstrating that the inclusion of bidirectional feedbacks revealed
much stronger loss in crop yields. Considering high resolution can also improve performance of biodiversity
predictions (Marshall et al., 2021). To summarize, there are a few approaches integrating both climate
change and land-use effects on biodiversity, but the incorporation of land use in biodiversity dynamics under
climate change has not always modified results (Dullinger et al., 2020), possibly due to the fact that climate
change-driven changes in land use are not considered.
4 | THE ROADMAP TO INTEGRATE CLIMATE CHANGE WITH
SOCIO-ECOLOGICAL SYSTEMS
4.1 | Integrating climate-driven biodiversity change into land-use models
Differently from biodiversity models, which often integrate land use (Table 2 and previous section), land use
models rarely consider biodiversity (Table 1). Hence, the integration of land-use models with biodiversity
models is largely missing, although land users may not solely prioritize profits and may as well weigh
biodiversity and ecosystem opportunity costs (foregoing an attractive alternative). In fact, it is unlikely that
land users would only respond to prices and costs to maximize land rent a key motivation for developing
agent-based land-use models (Berger & Troost, 2014). While it is clear that landowners indeed respond
to economic opportunities and risks, assuming pure profit-maximizing behaviour will not suffice to analyze
land-use and associated human-driven biodiversity changes (Berger, 2001; Castro et al., 2018; Lambin et
al., 2001). For example, subsistence farmers need to sustain their families and might thus not be profit
maximizers, but risk minimizers, going for guaranteed results. They might even have ecological objectives
(Knoke et al., 2014), but have to sustain their families. In contrast, industrial-style farmers are mostly profit
maximizers, which explains the expansion of oil palms (Fisher et al., 2011), soy, or rubber (Warren-Thomas
et al., 2018). The land-use change models currently focus on the relationships among farmers decisions and
biophysical elements (Table 1). This reflects the fact that individual decisions, cultural practices, and regional
policies on subsidies underlie the land-use change processes. In this regard, biodiversity is often not included
in the land-use change models because it is not yet considered a key driver of the decision of farmers or even
of human population size, dietary preferences, economy, climate change, and technology. However, regional
models should start integrating biodiversity change beyond simply considering vegetation cover. The ability
of biodiversity models to predict abundances, demographic rates, and connectivity may be important for
land-use decisions. Indeed, biodiversity can lead to ecosystem modifications at the local level, like influencing
the selective extraction of valuable species (Cazzolla Gatti et al., 2015; Poudyal et al., 2019) or modifying
crop yields (Synes et al., 2019). Moreover, biodiversity loss may affect consumer behaviour if correctly
communicated (Schaffner et al., 2015), which has been, for example, used for palm oil-free products and
many dedicated product certifications. Biodiversity economic value may be also evoked and could become
a driver in economic land allocation models (e.g. Bateman et al., 2013), while the resulting relationships
between comprehensive economic ecosystem value and biodiversity are variable (Paul et al., 2020). All these
aspects indicate multiple ways to integrate biodiversity into land-use models, from influencing the decisions
of subsistence farmers to improving yields, product demand, and biodiversity-influenced regional policies.
Some ways have been already tackled, although not in land-use models addressing climate change effects
(and thus not featured in Table 1).
Regional or global land-use change models commonly integrate biodiversity in two ways: 1) as a restriction for
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anthropogenic land-use expansion through limitation rules inside protected areas or intact forests (Alexander
et al., 2018; Schmitz et al., 2014) or 2) a post hoc overlap analysis of model outputs over biodiversity-rich
areas (Kobayashi et al., 2019; Powers & Jetz, 2019). As an alternative, land allocation approaches building
on multiple criteria methods allow for integration of biodiversity as an own objective function, for example
to represent the preferences of conservationists (e.g. Knoke, Gosling, et al., 2020; Knoke, Paul, et al., 2020).
Moreover, mathematical models have been developed to assess forest enrichment with coarse woody debris
to elevate biodiversity at minimum costs in forest enterprises (H¨artl & Knoke, 2019). Further integration
of biodiversity could be achieved through modelling the expansion of common plant species (e.g. via range
models Sarmento Cabral et al., 2013; Bocedi et al., 2014, 2021) that would directly impact the land cover.
Another flexible and theoretically consistent framework to model land allocation decisions might be the use
of household models (Singh et al., 1986), which account for departures from traditional profit-maximization.
The household is assumed to maximize utility, which is a function of many aspects beyond profit and
weather such as, e.g., cultural practices, subsistence, biodiversity, leisure, or environmental protection. This
framework allows for integration of biodiversity aspects into land-use modelling from a behavioural point
of view, which could have positive feedbacks to biodiversity. Indeed, focusing landscape management based
both on legislation and traditional agriculture can have positive effects on biodiversity, especially on specialist
species (Santos et al., 2016).
Some challenges to integrate biodiversity model output as land-use model input lies on the differences and
incompatibilities of spatial and temporal scales. This demands particular attention across land-use models,
which often apply and report different resolution formats and units (compare geographical resolutions across
models in Table 1). Besides, a key epistemological issue is that many mechanistic biodiversity models run
in hypothetical landscapes and ecological systems (e.g. Travis, 2003). Moreover, in many hypothetical and
real-world biodiversity models, the spatial resolution is a grid cell and temporal resolution is a generation.
To make the output of such biodiversity models useful for land-use models, the species parameters and
landscape scales must be adequately calibrated. For example, explicitly calibrating a grid cell to 1 km2
to match a given land-use model should be accompanied by adequate dispersal ability, carrying capacity,
and local population dynamics of the target species, community, or functional group. Furthermore, to fully
integrate the two modelling approaches, it is necessary to relate individual land users’ actions and decisions,
which may take place at local scales (e.g. the field level for farmers), to biodiversity outcomes (i.e. extirpation
of local populations) that may vary in scale from local to regional.
4.2 | Integrating climate-driven land-use change into biodiversity models
We found four main approaches for the inclusion of land use in biodiversity modelling:
1. In the first approach, authors perform an overlay of the outputs of both land-use and biodiversity
models to identify land-use change within important biodiversity areas or to identify suitable areas for
biodiversity (e.g. Faleiro et al. 2013, Martinuzzi et al. 2014, Sales et al. 2020). Thus, in this approach
only the modelling results are analysed together.
2. In the second approach, authors apply land-use field data or land-use model outputs from previously
developed models as input for a biodiversity model (e.g. Struebig et al. 2015). This input may include
time-series of land-use changes.
3. A third approach uses a land-use model where each land-use type is associated with biodiversity values
calibrated on literature data (e.g. Santos et al. 2016, Koch et al 2019). In this approach, land use
itself can be interpreted as specific calibration for biodiversity models.
4. Finally, we found simulations of land use and biodiversity in the same study through model coupling
(e.g. Bastos et al. 2018, Marshall et al. 2020, Redhead et al. 2020). This latter type of approach
constitutes the most integrative one, in which both models are simultaneously simulated. However,
this integration remains largely uni-directional, with bi-directional feedbacks between land use and
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biodiversity rarely attempted (but see Synes et al., 2019). In fact, uni-directionally coupled models
may miss important dynamics, as bi-directional models revealed greater influence of land-use change
(Synes et al., 2019).
Most of the identified studies only account for LU effects on habitat availability or suitability (e.g. Travis
2003). This has also been highlighted in recent reviews (Santos et al. 2021; Davidson et al. 2021). Land-use
effects on demography have been considered by Quetier et al. (2007) through variation of fecundity rates,
dispersal ability, and mortality (via disturbances) in plant functional groups, while Sarmento Cabral et al.
(2013) considered the loss of habitat to reduce local carrying capacity of studied species. Effects of land use on
dispersal have been explored by Bocedi et al. (2014) when looking at how anthropogenic disturbance effects
vary depending on individuals’ settlement rules during dispersal. The inclusion of climate change effects was
mainly done through assessment of direct effects on biodiversity (see Struebig et al., 2014; Zamora-Gutierrez
et al. 2018). There was only one example of simultaneous inclusion of direct and indirect effects of climate
change on biodiversity via climate-driven land-use change (Dullinger et al. 2020 - a study with type 4
approach). Nevertheless, there is still a large disconcert of modelling studies in integrating climate, climate
change, and climate-change induced land-use change (Table 2, Fig. 2a), with explicit comparisons as well as
relative quantification of direct vs. indirect effects of climate change has yet to be investigated.
How can the relative role of direct and indirect effects of climate change on biodiversity be quantified? For
a start, we suggest increasing focus on model coupling , as this has been successfully done both with
correlative (Dullinger et al. 2020) and process-based (Synes et al. 2019) biodiversity models. Agent-based
models seem a straightforward way for this integration as the framework is used by both the land-use and
biodiversity communities (Tables 1-2; Fig. 2a). We consider the following factors to be key to successful
model integration:
Spatial resolution : A key direction for improving model integration is the harmonization of spatial scales
(units and file formats), but this is not a limiting factor since land-use and biodiversity models considerably
overlap in terms of spatial extents and resolutions (Fig. 2b-c). Most land-use models do generate outputs
that could be used as input by biodiversity models to some extent (compare output variable in Table 1 with
input variables in Table 2). Despite the improvement in relation to the coarse resolution used in global
models, regional land-use models still vary considerably in the spatial resolution (Table 1), from polygon-
based models (particularly useful when farmers are the agents) to raster-based. Considering that polygons
and raster can be quickly converted back and forth, biodiversity models would require a raster conversion
plugin to readily use different output and input formats of land-use models. This can be easily done, but
most current biodiversity models do not have such plugins and thus any format synchronization needs to be
done before simulation.
Temporal resolution : Moreover, temporal resolution and extent seem to require further attention. Most land-
use change studies focus on relatively short temporal extents (e.g. 20 years), with few time steps. Whereas
this reflects inherent uncertainty of the behaviour of human agents and of socioeconomic dynamics, it also
limits its utility to biodiversity models, which often require yearly (and sometimes even coarser) resolution
for longer time period (compare temporal extents in Table 1-2). For generational-based biodiversity models
(e.g. Sarmento Cabral et al., 2013), matching temporal resolution of biodiversity and land-use models require
considering particular years or dates as references. The best direction here is that both models converge to
generation- and agent-agnostic time steps, such as year.
Indirect effects of climate change : Disentangling direct and indirect effects of climate change on biodiver-
sity revealed to be a largely understudied subject. Though we have not found studies to have tackled it
through mechanistic approaches, this seems achievable as mechanistic land-use models that consider climate
input have been already coupled with mechanistic biodiversity models (see Synes et al. 2019 combining
RangeShifter, Bocedi et al., 2014, with CRAFTY, Murray-Rust et al., 2014a). Furthermore, an avenue to
explore such indirect effects at regional level is becoming more feasible as mechanistic land-use models that
incorporate climate scenarios are being applied to larger extents (Brown et al. 2021).
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Bi-directional feedbacks : The simultaneous simulation of land use and biodiversity with process-based models
should allow for the inclusion of bi-directional feedback between models (Fig. 3). This would account for
emergent interactions between both systems, something that has been recently called for (Urban et al.,
2021). Therefore, the current knowledge of land-use effects is in fact mostly based on the unidirectional effect
from land use on biodiversity. In fact, all integrated approaches were primarily those mostly focusing on
biodiversity. The land-use-focused paper that came closest to an integrated approach coupled an ecosystem
model to simulate crop yield and natural vegetation, although number of species per se was not explicitly
modelled (Murray-Rust et al. 2014a).
4.3 | The full integration: challenges to integrate socio-ecological change into
climate models
The influence of land use and land-use change on climate is already well known (e.g. Deng et al., 2013),
but the integration of biodiversity and per extension also of socio-ecological models into climate models
deserves further attention (see Fig. 3). The effects of land-use change on regional and global climate have
been assessed focusing on biogeochemical and biogeophysical feedbacks (Pongratz et al., 2010, 2018). Land-
use change impacts climate by affecting extreme temperatures (Findell et al., 2017; Wang et al., 2015),
precipitation (Woldemichael et al., 2012), evapotranspiration (Krause et al., 2017; N´obrega et al., 2017),
or surface runoffs (Guzha et al., 2018; Krause et al., 2017). For example, afforestation can take up carbon
from the atmosphere, while also cool down regional temperature by absorbing radiation and increasing
transpiration (Betts, 2011). This effect of vegetation on climate has been studied and exploited across scales,
from decreasing urban heat islands to feedbacks between the terrestrial biosphere and the climate. Whereas
vegetation models typically do not really account for biodiversity (see Section 3.2), by simulating forest
growth and carbon assimilation among other ecosystem-level processes, these models consider important
ecosystem functions such as net primary production. Considering that biodiversity has positive relationships
with ecosystem function, the so-called biodiversity-ecosystem function (BEF) relationships (see van der
Plas, 2019 for a review), we can already assume that maintaining high biodiversity can be central to carbon
sequestration and thus also to climate mitigation. In this regard, landscape-level forest models (see Petter
et al., 2020 for a comparison), functional-structural forest models (e.g. Petter et al., 2021), or trait-based
models in general (see Zakharova et al., 2019 for a review and many of the models in Table 2) better capture
biodiversity, as different trait combinations can represent different species. These models have not yet been
coupled with land-use and climate models, but we suggest integrating ecosystems’ productivity with changes
in tree diversity and composition as one promising way to fully integrate biodiversity, land-use, and climate
models. A concept showing how to achieve this has recently been proposed (Bendix et al., 2021). That is,
biodiversity modelling would link not only to climatic conditions but also to deforestation, afforestation, and
agriculture management. This would also represent an improvement over integrated assessment models, which
are typically applied to evaluate proposed climate mitigation but still lack explicit biodiversity integration
despite attempts of using IAMs for assessing biodiversity loss (e.g. Veerkamp et al., 2020). Ultimately, with
fully integrated models including bi-directional effects (Fig. 3), we could explore scenarios considering not
only climate mitigation, but also e.g. biodiversity, ecosystem functions, and sustainable development.
4.4 | Directions to modelling developments
Current models can already tackle a series of both land-use and biodiversity processes. However, the full
spectrum of climate change effects on land use and land-use effects on biodiversity has not been fully modelled,
as revealed by our empirical review. Here, we identify avenues for development in modelling independent of
integration across research fields.
For land-use models, there is a need to identify the regional drivers of land-use change to implement well-
tailored concepts to simulate human-related changes in the composition of landscapes, ecosystems, and,
as argued in previous sections, biodiversity. For climate drivers, further evidence is needed to understand
how land users respond to weather extremes in various contexts. For socioeconomic drivers, this means
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going beyond simulating agricultural or livestock expansion to integrate production of specific commodities
and to address possible responses of decision-makers to biodiversity changes, as they will consider their
livelihood demands (Affholder et al., 2013) and/or economic opportunities and risks (Lambin et al., 2001).
Via biodiversity-economic value functional relationships (Paul et al., 2020), biodiversity could be integrated
in such models as a factor of production. In addition, biodiversity indices could be used to represent the
preferences of conservationists (Knoke, Gosling, et al., 2020). For example, changes in the productivity of
some land-use types could lead to reallocation of land, which could help understand the relationship among
national or regional policies, subnational, or international demand of products and prices. This understanding
will help land-use models to go beyond the analysis of historical trends to improve projections, including
scenarios that integrate farmers’ decisions linked to a globalized world and biodiversity elements as inputs.
In addition, common strategies to deal with increasing uncertainty, such as overproduction (Fuss et al.,
2015), land-use diversification (Rosa et al., 2019), or the diversion of the available labour to obtain off-farm
income (Shannon & Motha, 2015) will influence land-use change processes and associated biodiversity. For
instance, off-farm income may reduce crop diversification at the farm level (Ochoa et al., 2019). Accounting
for changes in the objectives of farmers or land-use planners is also influential for simulating land allocation
(Castro et al., 2018). This may be particularly important if we want to improve land-use practices by
considering biodiversity information or other novel decision-criteria such as environmental costs when losing
biodiversity, something commonly disregarded in real-world decision-making.
Another characteristic of current land-use models is their limited ability to project far into the future, giving
their high demand for data in the fitting process and the above-mentioned socio-economic uncertainties. In
this sense, the deficit of integrating uncertainty into land-use models is evident, particularly when historical
data are not reliable for quantifying uncertainties. We will then need robust modelling approaches providing
acceptable solutions over large uncertainty spaces, which would possibly include future conditions (Gorissen
et al., 2015). Extreme events, such as severe floods or droughts, may represent “black swans” (Taleb, 2007)
for landowners. “Black swans” are historically seen as often highly improbable, but of utmost economic
consequences when occurring.
For biodiversity models, the inclusion of evolutionary dynamics at ecological time frames remains understu-
died (but see Leidinger et al., 2021), even though human activities can trigger evolutionary response. Another
direction is to integrate climate-driven behaviour of human agents on the biodiversity. For example, Cabral
et al. (2011) assessed the effects of harvesting wild flowers by reducing the number of produced offspring.
However, it is unclear how human behaviour may change in the future. Will humans stop harvesting in
the wild due to conservation policies, to pressures for decreasing carbon footprint via embargoing overseas
flower export, or both? In fact, direct resource exploitation often targets demographic or growth processes in
biodiversity assessments, e.g. in fisheries (Melbourne-Thomas, Johnson, Ali˜no, et al., 2011; Salihoglu et al.,
2017), forestry (Albert et al., 2008; Bottalico et al., 2016), sport hunting (Mattsson et al., 2012), or grassland
management (Johst et al., 2006; Rolinski et al., 2018; Schr¨oder et al., 2008). In forest management, for in-
stance, forest owners will adapt forest structure and management to enhance the resistance (by establishing
mixed forests) and resilience (by enhancing the structural diversity). Planting mixed forests will positively
influence species richness (Knoke et al., 2008) and managing forests to increase structural diversity will also
increase biodiversity (Schall et al., 2018). However, the biodiversity impact of climate mitigation policies
must also be investigated, as typically suggested bioenergy crop expansion can actually be detrimental for
biodiversity (Hof et al., 2018).
Another important research direction is to improve harmonization of complex relevant data (often done for
occurrence or occupancy, but rarely for demographic rates, dispersal ability traits, and genetic diversity).
Data harmonization and standard formats should be also strived across biodiversity models. This means to
implement models that consider input and generate output matching current trends in empirical biodiversity
assessments to follow the Essential Biodiversity Variables (EBVs) framework (Pereira et al., 2013; Urban et
al., 2021). This should promote a better integration across models and data-model integration. Most common
model outputs such as species occurrence and abundance (Table 2) are already part of the EBV class ‘species
populations’. Although this already allows to connect environmental change metrics, standardized biodiver-
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sity metrics, and model-data, EBVs are rarely mentioned in biodiversity models. Community composition,
another EBV class, is often addressed only through taxonomic diversity (i.e. species richness). However,
other community facets typically quantified in empirical assessments such as functional and phylogenetic
diversity are far less common (Table 2), although they can be, to some extent, easily derived from merging
species occurrence metrics with phylogenetic and trait data from online databases (e.g. PanTHERIA Jones
et al., 2009; and PHYLACINE Faurby et al., 2018). The EBV classes such as genetic composition, species
traits, and ecosystem functioning are yet rarely reported in biodiversity models. Although modelling of
such components may be challenging as it requires additional input data and simulating microevolutionary,
niche-based, and metabolic-based processes, recent advances are making the simulation of such EBV classes
easier (Leidinger et al., 2021; Zurell et al., 2021).
A few model development trends are common for both land-use and biodiversity models. For example, a
hurdle to overcome is linking of terrestrial and aquatic systems (but see Harfoot et al., 2014 for biodiversity
models). This is due to fundamental differences in ecological processes and biodiversity and ecosystem
functions (Daam et al., 2019). In particular, sediment
and nutrient inputs can be main drivers of aquatic biodiversity (Fern´andez et al., 2021), which has led to a
bias in the selection of land-use types considered in previous studies, with agricultural land being the focus
of most analyses. In addition, socio-economic influences on aquatic ecosystems were often ignored or only
considered as disturbing factors, with the interactions between aquatic ecosystems and socio-economic effects
still largely unassessed by either empirical or modelling studies. Another general necessary development
is improving resolution at regional scales. Whereas land-use models can already generate high resolution
regional data (Fig. 2b), output from high resolution regional climate models are not yet commonly used in
either biodiversity or land-use modelling, including for stakeholder decision-making (Gutowski et al., 2020).
This stresses the necessity to synchronize model developments between the biodiversity, land-use, and climate
change fields.
5 | CONCLUSIONS
While biodiversity models have become quite sophisticated in predicting biodiversity change due to clima-
te change, efforts must be taken to integrate this development considering climate-driven land-use change.
Because land use itself is strongly influenced by climate change, integrating both climate-driven biodiversity
and land-use models can tackle the complete appraisal of both direct and indirect effects of climate change
on biodiversity. However, disentangling these effects has yet to be addressed by dedicated simulation expe-
rimental designs. First attempts to integrate biodiversity and land-use models show that the integration is
attainable, although model integration has happened only in studies focused on biodiversity change. In this
sense, integrating biodiversity feedbacks into land-use and climate models requires further modelling inno-
vations, but should be feasible. Still, major shortcomings include a better concerted effort in standardizing
outputs and resolution, as well as methods to simultaneously optimize multiple outputs (e.g. species num-
ber, stakeholder profits, carbon balance, and temperature) in fully integrated, climate-land-use-biodiversity
models.
ACKNOWLEDGEMENTS
AC, AK, AL, AR, CH, CS, FH, JB, JK, JSC, JO, MB, PP, SB, SR, TK, WWW acknowledge funding by
the Bavarian Ministry of Science and the Arts in the context of the Bavarian Climate Research Network
(BayKliF); APS was supported by DAAD; SF acknowledges funding by the DBU. DZ acknowledges funding
by the DFG (ZU 361/1-1). DV acknowledges the support of iDiv funded by the German Research Foundation
(DFG– FZT 118, 202548816). This manuscript is a product of the workshop “How to predict future land use,
biodiversity and ecosystem change under climatic change at the regional level ” - an initiative of the BayKlif
consortium BLIZ (Blick in die Zukunft). We thank Heiko Paeth for comments.
CONFLICT OF INTEREST
14
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The authors declare no conflict of interest.
AUTHOR’S CONTRIBUTIONS
JSC, with contributions of AR, FH, TK, WW, designed the study; JSC, AM, AMP, APS, JK, JO led the
writing of sections; JSC wrote the first draft; AK, AMP, APS, AL, DV, JB, JO, SB, SF led the literature
search, writing and editing of the tables; DV led the Fig. 1; JSC led Fig. 2, SF led Fig. 3; all authors
commented and contributed with the written text, tables and figures.
DATA AVAILABILITY STATEMENT
This manuscript does not include any data.
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SUPPLEMENTARY MATERIAL
Supplementary Material S1 - Details on literature search and model
classification
Table 1: Examples of regional land-use model implementations, with their respective model ty-
pe, scales, input variables, climate change impact, outputs, ecosystem/biodiversity parameters
and key findings . For details on literature search, inclusion criteria and classification, see Supplementary
Material S1. Note that scales often depend on the input, with provided scales reflecting the study system
of the particular study or suggested by authors. Abbreviations: PHENO: phenomenological; MECHA: me-
chanistic; GE: geographical extent, GR: geographical resolution, TE: temporal extent, BP: biophysical, SE:
socioeconomic, CC: climate change, LU: land use, BD: biodiversity, NA: not applicable.
Model Type Study
Region
and
Scales
Input
variables
What is
modified
by CC?
CC
effects
on LU
BD-
related
parame-
ter
Output
variables
CLUE-S
(Verburg et
al., 2002)
PHENO GE: Sibuyan
island,
Klang-
Langat
watershed
(456, 4,300
km2); GR:
150m; TE:
1997-2017,
1989-1999
BP: altitude,
slope,
aspect,
geology,
erosion,
distance to
stream and
coast; SE:
population
density,
distances to
roads,
towns, and
ports
Nothing NA None Cover type
(e.g. forest,
grassland,
urban,
coconut and
palm oil
plantations,
rice fields)
30
Posted on Authorea 28 Feb 2022 | The copyright holder is the author/funder. All rights reserved. No reuse without permission. | https://doi.org/10.22541/au.164608831.19029067/v1 | This a preprint and has not been peer reviewed. Data may be preliminary.
Dyna-CLUE
(Verburg &
Overmars,
2009)
PHENO GE: Europe
(27
countries);
GR: 1 km2;
TE:
2000-2030
BP: water
deficit,
potential
evapotran-
spiration,
tempera-
ture, water
logging
occurrence;
SE: regional
demand of
agricultural
products
Land
allocation
and natural
succession of
abandoned
lands
Dry or cold
climates
lower
succession
speed
Natural
vegetation
succession
Abandonment
areas linked
to regrowth
of natural
vegetation,
agricultural
intensification
SALU
(St´ephenne
& Lambin,
2001)
MECHA GE: Burkina
Faso
(274,200
km2); GR:
2.5 x 3.75o;
TE:
1960-1997
BP: precipi-
tation;
Socioeco-
nomic:
human
population,
livestock,
cereals
imports
Yearly
changes in
land-use
allocation
Rainfall
determines
the
productivity.
If it
decreases, it
is compen-
sated by LU
expansion.
Vegetation
recovery
rates for
producing
fuelwood
Areas of LU
expansion
and intensi-
fication,
pastures,
fallow and
fuelwood
extraction
FLUS (Liu
et al., 2017)
MECHA GE: China
(9.56 Mi
km2); GR: 1
km2; TE:
2010-2050
BP: soil,
elevation,
tempera-
ture,
precipita-
tion; SE:
population,
GDP and
technologi-
cal
innovations
Land
allocation
Annual
precipitation
and
temperature
Ecological
regions
Extent and
location of
cultivated
areas,
forests,
grassland
and urban
covers.
DINAMICA-
EGO
(Soares-
Filho et al.,
2013)
PHENO GE:
Brazilian
amazon
(619,946
km2); GR: 1
km2; TE:
2003-2050
BP: soil,
vegetation,
slope,
elevation,
distance to
rivers; SE:
distance to
deforested
areas, roads,
towns,
protected
areas
Probability
of land use
None Distribution
of mammals
Deforestation
area linked
to reduced
mammals
distribution
and carbon
emissions
SLEUTH
(Clarke,
2008)
MECHA GE: Mainly
focused on
US cities;
GR:
variable;
TE: variable
Biophysical:
slope,
hillshade;
SE: distance
to roads
Nothing NA None Urban
expansion
and other
LU related
to cities
31
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Aporia
(Murray-
Rust et al.,
2014b)
MECHA GE: Aurau
Valley,
Switzerland
(99 km2)
and Lanan
Catchment
(132 km2);
GR: variable
(farm);
TE:2000-
2020
BP: soil,
slope,
nitrogen;
SE: farmers’
decisions
(e.g. biofuel
harvest,
food
production,
traditional
practices,
diversity of
rotation)
Output
yields
Model
presentation
Directly via
vegetation
modelling;
indirectly
via
biophysical
and policy
tags
Land
management
practices,
ecosystem
service
indicators,
market data
with prices
Agent-based
Rural Land
Use New
Zealand
(Morgan &
Daigneault,
2015)
MECHA GE:
Hurunui and
Waiau
Catchments
in New
Zealand;
GR: variable
(farm); TE:
2010-2060
BP: soil,
available
water; SE:
market
prizes,
productivity
current
enterprise,
social
network for
imitation
and
endorsement
Productivity
of the farm
Dairy and
forest
enterprises
will increase
None LU, farm
net revenue,
greenhouse
gas
emissions
CPV
Analysis
Model (Dai
et al., 2005)
PHENO GE: Pearl
river delta
(10,851
km2); GR: -;
TE:
1985-2000
BP: climate,
soil water,
vegetation,
relief; SE:
population,
technology,
policy,
profits
Potential
change of
land-use
system
Main
drivers:
rapid
economic
growth,
population,
infrastruc-
ture; low CC
effects
Vegetation,
species
diversity
Dominance
of land use,
patches,
fragmentation
Qiang &
Lam (2015)
Hybrid GE: Lower
Mississippi
Basin
(48,000
km2); GR:
30m; TE:
1996-2006
BP:
elevation,
soil, distance
to water;
SE: distance
to roads,
human
settlements,
and
pipelines
Nothing NA None LU maps
32
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Diogo et al.
(2015)
MECHA GE:
Netherlands
(41,543
km2); GR:
100m; TE:
2007-2012
BP: climate,
soil,
elevation,
hydrology;
SE:
population
growth, diet
preferences,
access to
financing,
technology
(rotating
scheme),
political
factors, land
tenure,
fertilizer use
Biophysical
suitability
Changes in
crop yields
and
productivity
None LU maps
pixelwise
Hietel et al.
(2004)
PHENO GE: Erda,
Eibelshausen
(11 km2, 9
km2); GR:
1:5,000; TE:
1945-1998
BP:
elevation,
slope,
aspect,
available
water, soil
texture; SE:
land
management
Available
water
capacity
Change from
arable land
to grassland
with lower
water
capacity
None Suitability
maps
Rouget et
al. (2003)
Hybrid GE: Cape
Region
(129,462
km2); GR:
1‘; TE: 20 yr
BP: habitat,
alien species,
geology,
distance to
coastline,
altitude,
slope,
roughness,
bioclimatic
variables;
SE: urban
area,
distance to
roads
Nothing NA Broad
habitat
units, alien
species
threat
Land-cover
maps with
percentage
of
transformed
area
33
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CRAFTY-
EU
(Brown et
al., 2019)
MECHA GE: EU
together
with
Norway,
Switzer-
land and
the UK
but
excluding
Croatia;
GR: 10’;
TE:
2016–2086
SE: five
capitals
(natural,
human,
social,
manufac-
tured, and
financial),
timber
demand,
meat,
crops,
carbon
sequestra-
tion,
landscape
diversity,
recreation
Natural
capital
Differences
in land
systems
mainly
driven by
SE
scenarios,
but also
by CC
Ecosystem
services,
including
landscape
diversity
and
recreation
value
Transitions
between
eight
land-use
types
Table 2. Examples of biodiversity models which include land use effects, with their re-
spective type, scales, studied taxa, input variables, climate change impact, outputs, ecosys-
tem/biodiversity parameters and key findings . Studies that simultaneously apply LU and BD models
are defined as integrative in the LU approach column. Approaches that combine both phenomenological and
mechanistic components are termed hybrid. For details on literature search, inclusion criteria, and classifi-
cation, see Supplementary Material S1. Abbreviations: PHENO: phenomenological, MECHA: mechanistic,
gen: generations, yr: year, HS: hypothetical system, RWS: real-world system, GE: geographical extent, GR:
geographical resolution, TE: temporal extent; TR: temporal resolution, NP: National Park, BP: biophysical,
SE: socioeconomic, BD: biodiversity, LU: land use, SG: study group, CC: climate change, LUC: land-use
change, SSP: socioeconomic pathway, NAv: not available.
Model LU
ap-
proach
Type
of the
BD
model
Study
re-
gion,
scales
and
group
Input
vari-
ables
Output
vari-
ables
What
is
modi-
fied
by
LU?
Key
find-
ings
(LU
effects
on
BD)
CC CC -
driven
LUC
Travis
(2003)
No
explicit
model,
includes
random
habitat
loss
MECHA,
HP
GE: 2000
grid cells;
GR: grid
cell; TE:
100s gen;
TR: gen;
SG:
virtual
species
BP:
habitat
type; SE:
habitat or
not
Occupied
grid cells,
spatial
occupancy
Habitat
loss
Thresholds
to species
survival,
with
combined
CC and
LU
showing
the lowest
thresholds
Yes No
34
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LoLiPop
(Sarmento
Cabral et
al., 2013)
No
explicit
model,
reads in
habitat
loss data
Hybrid,
RWS
GE: Cape
Region
(11,000
km2); GR:
1’ x 1’;
TE: 10s
gen; TR:
gen; SG:
plants
BP:
climate,
soil,
suitability;
SE:
habitat
loss
Spatial
abundance
distribu-
tion, range
size, range
filling
Carrying
capacity
Lower
abun-
dances,
ranges less
affected;
highlands
act as
refugia
under CC
due to
lower LU
Yes No
RangeShifter
(Bocedi et
al., 2014)
No
explicit
model,
reads in
habitat
loss data
MECHA,
RWS
GE and
GR:
variable;
TE 100s
yr; TR:
yr; SG:
virtual
species
BP:
suitability;
SE:
habitat
loss
Spatial
abundance
distribu-
tion,
range size
Habitat
suitability,
movement
cost
LU affects
abun-
dances
and con-
nectivity
between
populations
Yes No
FATE-HD
(Boulangeat
et al.,
2014)
No
explicit
model,
reads-in
pasture
and field
data
Hybrid,
RWS
GE:
´
Ecrins NP
(2,700
km2); GR:
100 m;
GE: 925
km2; TE:
100s yr;
SG: plants
BP: topo-
climatic,
climatic,
soil
variables;
SE:
grazing
and
mowing
intensity
Spatial
abundance
distribu-
tion,
population
structure
Habitat
area,
dispersal,
distur-
bance
(affects
abun-
dances,
seed bank,
fecundity)
LU effects
can at
least
partly be
simulated
through
disturbance
Yes No
Kallimanis
et al.
(2005)
No
explicit
model,
includes a
distur-
bance
submodel
MECHA,
HP
GE:
65,536
grid cells:
GR: grid
cell; TE:
1000s gen;
TR: gen;
SG:
virtual
species
BP:
habitat;
SE:
disturbance
Spatial
distribu-
tion of
occupied
grid cells
Grid cell
occupancy
Extinction
risk higher
for low
dispersal
rates, LU
pattern
affects
population
survival
No No
LandSHIFT
Koch et
al. (2019)
Integrative:
MECHA
MECHA,
RWS
GE:
Africa
(30.3 Mi
km2); GR:
5’x 5’; TE:
2000-2030;
TR: yr;
SG:
vertebrates
BP: forest
and
vegetation
types,
abundance
per LU
type; SE:
LU
suitability,
human
population
BD
Intactness
Index
(BII)
Population
density,
livestock
density,
crop pro-
duction,
calories
availabil-
ity,
BII
Land
sparing
more
effective
for
conserving
biodiver-
sity (and
food
production)
No No
35
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Dullinger
et al.
(2020)
Integrative:
MECHA
PHENO,
RWS
GE: Part
of
Austrian
Alps
(1,426
km2); GR:
0.01 km2;
TE:
current-
2050; TR:
yr; SG:
plants
BP: tem-
perature
variables,
precipita-
tion, solar
radiation,
bedrock;
SE: SSP,
land cover
type, LU
class
Habitat
suitability,
range size,
species
richness
and LU
distribu-
tion, LU
intensifi-
cation and
homogeneity
Habitat
suitability
LU and
CC both
affect
species
habitat
suitability,
LU
stronger
Yes Yes
Zamora-
Gutierrez
et al.
(2021)
No
explicit
model,
reads in
LU data
PHENO,
RWS
GE:
Mexico (2
Mi km2);
GR: 5’ x 5
‘; TE:
current-
2050; TR:
yr; SG:
bats
BP: tem-
perature
and pre-
cipitation
variables;
SE: LU
type, SSP
Habitat
suitability
Habitat
suitability
Vulnerability
of bats to
CC and
LUC very
high
Yes No
Bastos
et al.
(2018)
Integrative:
MECHA
MECHA,
RWS
GE:
North-
east
Portu-
gal (6.6
km2);
GR: 1
km2;
TE:
current-
2050;
TR: yr;
SG:
raptors
BP:
disper-
sal
corri-
dors,
tem-
pera-
ture,
land-
scape
struc-
ture,
fire,
mois-
ture,
NPP;
SE:
land
cover
Minimum
local
biomass
index
Landscape
compo-
sition,
local
surface
temperature
Disruptive
effect
of LUC
in the
spatio-
temporal
distri-
bution
of top
preda-
tors’
biomass
No No
36
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Bonnot et
al. (2013)
No
explicit
model,
applies
human
impacts
scenarios
Hybrid,
RWS
GE:
Central
Hard-
woods
Bird Con-
servation
(0.3 Mi
km2); GR:
30x30 m;
TE: 100
yr; TR:
yr;SG:
birds
BP: grid
cell and
landscape
attributes,
habitat
suitability,
relative
productiv-
ity; SE:
restora-
tion,
communi-
cation
tower
strategies
Spatial
abundance
distribution
Carrying
capacity,
reproduc-
tive rate,
survival
rate
Habitat
conserva-
tion must
be
strategic;
source-
sink
dynamics
and
dispersal
influence
population
survival
No No
Faleiro et
al. (2013)
Integrative:
PHENO
Hybrid,
RWS
GE:
Cerrado
biome (2
Mi km2);
GR: 0.1°;
TE:
2002-2050;
TR: yr;
SG:
non-flying
mammals
BP:
climate;
SE: envi-
ronmental
and infras-
tructure
variables,
past LU
Potential
species
distribu-
tion,
spatial
conserva-
tion
plan
Spatial
conserva-
tion
prioritization
LUC
altered
spatial
location of
conserva-
tion
priority
sites
Yes No
Struebig
et al.
(2015)
No
explicit
model,
reads in
land-cover
data
PHENO,
RWS
GE:
Borneo
(743,330
km2); GR:
1 km2;
TE:
1950-2080;
TR: 30 yr;
SG: orang-
utans
BP:
climate,
rugged-
ness,
distance
to water
and to
karst
forest; SE:
land-cover
class,
human
popula-
tion,
deforesta-
tion
rate
Habitat
suitability
Habitat
suitability
Most
suitable
habitat
expected
to decline
due to
CC, even
if LUC
towards
more
protection
Yes No
37
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Santos et
al. (2016)
Integrative:
MECHA
PHENO,
HS
GE:
Northwest
Iberia
(100
km2); GR:
1 ha; TE:
1960-2040;
TR: 40 yr;
SG: birds
BP: patch
attributes;
SE: soil
use, man-
agement
strategy,
human
population
trend
Cover
type, bird
diversity
(richness,
specialist
richness,
total
abundance)
Cover
type
LU inten-
sification
homoge-
nizes
landscape,
with
negative
impacts on
biodiversity.
No No
Redhead
et al.
(2020)
Integrative:
MECHA
PHENO,
RWS
GE:
Great
Britain
(209,331
km2);
GR: 1
km2;
TE
and
TR: 1
yr; SG:
benefi-
cial
insects
BP:
cli-
mate,
suit-
ability
factors;
SE:
land
cover,
pro-
tected
area,
prior-
ity,
crop-
ping
intensity
Probability
of
occur-
rence;
poten-
tial
rich-
ness,
poten-
tial
func-
tional
diversity
Habitat
suitability
Arable
land
expan-
sion
lowers
species
rich-
ness,
even
under
less in-
tensive
cropping
No No
LAMOS-
FATE
(Qu´etier
et al.,
2007)
No
explicit
model,
applies
LUC
scenarios
and stake-
holder
assessments
MECHA,
RWS
GE:
Romanche
River
headwater
(7,000
grid cells);
GR: grid
cell (ca.
42x42 m);
TE and
TR: Nav;
SG: plant
functional
types
BP: pro-
ductivity;
SE: LU,
fertiliza-
tion,
manage-
ment
scenario
Abundance
of plant
functional
types,
ecosystem
services to
people
Dispersal,
fecundity,
distur-
bance
regime
(mowing,
fertiliza-
tion,
grazing)
Subalpine
grasslands
is sensitive
to
land-use
change
No No
38
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Sales et al.
(2020)
No
explicit
model,
applies
LUC
scenarios
Hybrid,
RWS
GE:
Tropical
South
America
(17.8 Mi
km2); GR:
10’; TE:
2030-2090;
TR: yr;
SG:
terrestrial
vertebrates
BP:
climate,
vegetation
and
habitat
types; SE:
land-use
and
land-cover
types
Potential
species
distribu-
tion,
potential
alpha and
beta
richness
Potential
species
distribution
Climate
and
land-use
change act
synergisti-
cally, with
high
turnover
rates for
ecotonal
fauna
Yes No
Martinuzzi
et al.
(2014)
Integrative:
PHENO
PHENO,
RWS
GE:
Con-
tiguous
USA
(30,700
km2);
GR: 1
ha;
TE:
2001-
2051;
TR: 5
yr; SG:
fresh-
water
vertebrates
BP:
water-
shed
area,
water
quality,
natural
cover;
SE:
past
LU
change,
eco-
nomic
re-
turns,
conver-
sion
costs
Land
use
type
rarity-
weighted
species
rich-
ness,
threat
to
fresh-
water
diversity
Water
quality
Urban
expan-
sion as
major
threat
in
species-
rich
regions
or
severe
water
quality
problems
No No
Marshall
et al.
(2021)
Integrative:
MECHA
PHENO,
RWS
GE:
Belgium
(9 Mi
km2); GR:
1 km2;
TE:
2010-2035;
TR: yr;
SG:
bumblebees
BP: None;
SE:
land-use
class, crop
type
Habitat
suitability
Habitat
suitability
Using
more LU
predictors
improved
perfor-
mance.
Arable
and urban
land were
mostly
negative.
No No
39
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RangeShifter-
CRAFTY
(Synes et
al., 2019)
Integrative:
MECHA
MECHA,
HS
GE: 10000
grid cells;
GR: grid
cell (ca.
500 x 500
m); TE:
50 yr; TR:
yr; SG:
pollinators
BP: pro-
ductivity;
SE: land
use,
demand
Spatial
abundance
distribu-
tion,
land-use
type, crop
yield
Carrying
capacity
Crop-
pollinator
system
showed
greater
changes in
bi-
directionally
coupled
models
No No
Graphab
(Foltˆete et
al., 2012)
No
explicit
model,
reads in
land cover
layers
MECHA,
RWS
GE:
section of
Franche-
Comt´e
(252 Mi
pixels);
GR: 10 x
10 m; TE
and TR: -;
SG: tree
frog
BP:
habitat
characteri-
sation;
SE: land
cover/LU
resistances
Species
distribu-
tion,
landscape
connectiv-
ity
metrics
Resistance
values;
habitat
availabil-
ity;
carrying
capacity
LU inten-
sification
can reduce
connectiv-
ity, with
negative
effects on
species
abundance
and
distribution
No No
Lautenbach
et al.
(2017)
No
explicit
model,
applies af-
forestation
scenarios
MECHA,
RWS
GE:
Mulde
Basin
(5,744
km2); GR:
1 km2;
TE: 500
yr; TR: 1
yr; SG:
plants
BP: biocli-
matic
variables,
soil
texture;
SE:
protected
area, land
use, land
cover
Species
richness,
richness of
functional
groups,
carbon
storage
Habitat
suitability
Non-linear
relation-
ships of
species
richness
with
afforested
area and
land use
configuration.
No No
40
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Figure 1. Biodiversity change (BDC), land-use change (LUC), and climate change (CC) all
interact. In addition to the bidirectional interactions (solid arrows), there are additive and multiplicative
effects of LUC and CC on BDC (dashed arrows). Studies on biodiversity response to climate change have
largely focused on the direct link of CC to BDC. Biodiversity assessments considering indirect effects of CC
on BDC via CC-driven LUC are largely lacking. References: 1) IPBES (2019); 2) B¨uhne et al. (2021); 3)
Seddon et al. (2020); 4) Dale et al. (2011); 5) Chausson et al. (2020); 6) Oliver & Morecroft (2014).
Figure 2. Properties of land-use and biodiversity models . a) Ordination of retrieved models from
Tables 1-2 in regard to field of research (land use or biodiversity), spatial scale (resolution and extent),
study system (hypothetical or real world), method (phenomenological or mechanistic/Agent-based), as well
as whether they use climate, climate change (CC) and CC-induced land-use (LU) change. Expect for spatial
scales axes (see panels b and c), all these characteristics were classified with yes/no. Ordination axes are
colored blue, whereas studies are given in grey or green font. Green colored studies highlight integrative
approaches, i.e. simulating both land use and biodiversity. Studies fill the ordination space very well, with
ordination arrows pointing to different directions (variation explained by the first two ordination axes <
41
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35%, with five dimensions necessary to reach a stress < 0.05 and stress with 11 dimensions = 0.003). This
indicates a high diversity of proposed models and that relevant modelling and experimental aspects (e.g.
integrating climate, climate change and climate-change-induced land-use change) are not yet often combined.
b-c) Spatial scale properties of land-use (b) and biodiversity c) models. Note that the scales in b-c) are in
orders of magnitude of km, with several models overlap scale properties and could thus be readily integrated.
We added jitter in a) and vertical spacing in b) to improve visualization. The principal component analysis
was performed with ‘smacof’ and ‘vegan’ R packages, using Bray-Curtis dissimilarity matrices.
Figure 3. Examples of how climate, biodiversity and land-use models have been integrated.
Climate models often provide the basic drivers for both land-use and biodiversity models (black arrow;
see Tables 1-2 for climate variables used as input). The blue arrow shows an additional one way coupling
(unidirectional) between the output of a land-use model which is used as input for the biodiversity model (e.g.
Dullinger et al., 2020) and the red arrow shows an additional integration of the biodiversity model output
as additional input for the land-use model. The red and blue arrows together act as a loose bi-directional
coupling, therefore creating a feedback loop between the models (e.g. Synes et al., 2019). The yellow dotted
arrow displays a possible integration from the biodiversity models, back to the climate model, creating a fully
integrated system. To our knowledge, there are yet no regional studies which integrate such a feedback. Note
that climate models already integrate land-use model output (not illustrated, but see Pongratz et al., 2018),
thus the yellow arrow can be achievable if climate models use the output of both land-use and biodiversity
outputs from bi-directionally integrated models.
Supplementary Material S1 - Details on literature search and model
classification
This SI accompanies the paper
The road to integrate climate change effects on land-use change in regional biodiversity models
Juliano Sarmento Cabral, Alma Mendoza-Ponce, Andr´e Pinto da Silva, Johannes Oberpriller, Anne Mimet,
Julia Kieslinger, Thomas Berger, Jana Blechschmidt, Maximilian Br¨onner, Alice Classen, Stefan Fallert,
Florian Hartig, Christian Hof, Markus Hoffmann, Thomas Knoke, Andreas Krause, Anne Lewerentz, Perdita
Pohle, Uta Raeder, Anja Rammig, Sarah Redlich10, Sven Rubanschi, Christian Stetter, Wolfgang Weisser,
Daniel Vedder, Peter H. Verburg, Damaris Zurell
42
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1| Details on literature search for Table 1
1.1|Literature search and inclusion criteria
We first searched the literature for land-use models using web of science with the search string “land (use
OR cover) AND model” and classified them by reading the title and abstract as regional (i.e. maximal
spatial extent on a continental extent). Furthermore, we also included models that did not come up in
the search, but that we knew existed beforehand. This research was conducted over the scope of one week
(02.03.2021-11.03.2021).
1.2| Model classification
After thoroughly reading the papers, the model type was classified in phenomenological (e,g, empirical data
was used to find statistical correlations) or mechanistic. Mechanistic ones included mathematical descriptions
of processes between input and output, but also cellular automata (state of cell at a given time step depends
on the state of the cell itself and neighboring cells at the previous time step), agent-based (individuals or
collective entities and their decisions connect input and output) and combinations of the previous types. We
further compiled the scales (spatial extent and resolution, temporal extent and resolution), input (variables
read-in during initialization or simulation), output (reported metrics), whether the study included climate
change effects directly on land use, whether the study included biodiversity variables. For input variables, we
distinguished between biophysical (e.g. soil, climate) and socioeconomic (e.g. price, land cover type) variables.
Further, we noted whether the study included climate change, what variable is modified by climate change,
and whether there are climate change effects on land-use change.
2| Details on literature search for Table 2
2.1|Literature search and inclusion criteria
The following is a list of keywords that we entered to web of knowledge’s search engine (webofknowledge.com).
Results were not filtered by time, though we did pay extra attention to newer papers, since the field of
ecological modelling is still relatively young, and more papers come out each year. We tried multiple different
key word combinations, most of which lead to hugely over 1000 results. For these papers, we sorted by newest,
and skimmed through the first pages. In the end, our final key words consisted of “(mechanistic OR agent*
OR process*) AND model* AND (land AND (*use OR *cover)) AND biodiv* AND region* AND change”
which led to a total of 374 papers (final search string listed below). Their abstracts were read to determine
if they fitted the scope of this paper, and if so, the paper was read thoroughly and included in this table if
fitting. Other papers included in this table were either from other key word combinations as briefly explained
above, or were other modelling papers that we knew of independent of this literature research.
This research was conducted over the scope of one week (01.02.2021-08.02.2021). Below we provide a list of
search strings and the respective number of hits (Table S1).
Table S1. Key word list (search strings) and their respective results (number of hits).
Search string Hits
model* AND (landscape* OR use OR *cover)
AND climate AND biodiv
5059
model* AND (land*use OR land*cover) AND
biodiv*
86
model* AND (landscape AND use OR *cover)
AND biodiv
7021
model* AND (land* AND (use OR *cover)) AND
biodiv
8187
43
Posted on Authorea 28 Feb 2022 | The copyright holder is the author/funder. All rights reserved. No reuse without permission. | https://doi.org/10.22541/au.164608831.19029067/v1 | This a preprint and has not been peer reviewed. Data may be preliminary.
Search string Hits
(mechanistic OR agent-based OR
population-based OR process-based OR
individual-based) AND model* AND (land* AND
(use OR *cover)) AND biodiv
287
(mechanistic OR agent* OR population* OR
process* OR individual*) AND model* AND
(land* AND (use OR *cover)) AND biodiv
4052
(mechanistic OR agent* OR population* OR
process* OR individual*) AND model* AND
(land* AND (use OR *cover)) AND biodiv AND
climate
1248
model* AND (land* AND (use OR *cover)) AND
biodiv AND climate
2596
(mechanistic OR agent* OR process*) AND
model* AND (land* AND (use OR *cover)) AND
biodiv
2019
(mechanistic OR agent* OR process*) AND
model* AND (land AND (use OR *cover)) AND
biodiv
1549
(mechanistic OR agent* OR process* OR
individual*) AND model* AND (land AND (use
OR *cover)) AND biodiv AND (human OR
anthropogenic)
623
(mechanistic OR agent* OR process* OR
individual*) AND model* AND (land AND (use
OR *cover)) AND biodiv AND (human impact)
262
model* AND (land AND (use OR *cover)) AND
biodiv and search within results for human impact
781
model* AND (land AND (use OR *cover)) AND
biodiv AND ”human impact”
77
model* AND (land* AND (use OR *cover)) AND
biodiv AND region* (mechanistic OR agent* OR
process*) model* AND (land AND (use OR
*cover)) AND biodiv AND region*
552
model* AND (land* AND (use OR *cover)) AND
biodiv AND region* (mechanistic OR agent* OR
process*) model* AND (land AND (use OR
*cover)) AND biodiv AND region* AND change
374
2.2| Model classification
Models were classified by land-use approach (if explicitly simulating a land-use model, classified as integra-
tive), type of biodiversity model component (mechanistic, experimental-statistic, or hybrid when combining
both; real vs. virtual system), scales (spatial extent and resolution, temporal extent and resolution), study
biological group (e.g. taxon or ecological guild), input (variables read-in during initialization or simula-
tion, either empirical or generated by land-use model), output (reported land-use and biodiversity metric),
whether the study included climate change effects directly on biodiversity or via climate-induced land-use
change. For input variables, we distinguished between biophysical (e.g. soil, climate) and socioeconomic
(e.g. price, land cover type) variables. Further, we noted what variable is modified by land use, whether
44
Posted on Authorea 28 Feb 2022 | The copyright holder is the author/funder. All rights reserved. No reuse without permission. | https://doi.org/10.22541/au.164608831.19029067/v1 | This a preprint and has not been peer reviewed. Data may be preliminary.
there is climate change effects whether there is a comparison of land-use change with and without climate
change effects, and what land-use effects there are on biodiversity.
45
... However, coupling our ecological model with a land-use change model would be a promising avenue to deepening our understanding of possible conservation trajectories in the Taita Hills (cf. Cabral et al., 2022). ...
Article
Full-text available
Introgressive hybridisation is a process that enables gene flow across species barriers through the backcrossing of hybrids into a parent population. This may make genetic material, potentially including relevant environmental adaptations, rapidly available in a gene pool. Consequently, it has been postulated to be an important mechanism for enabling evolutionary rescue, i.e. the recovery of threatened populations through rapid evolutionary adaptation to novel environments. However, predicting the likelihood of such evolutionary rescue for individual species remains challenging. Here, we use the example of Zosterops silvanus, an endangered East African highland bird species suffering from severe habitat loss and fragmentation, to investigate whether hybridisation with its congener Zosterops flavilateralis might enable evolutionary rescue of its Taita Hills population. To do so, we employ an empirically parameterised individual‐based model to simulate the species’ behaviour, physiology and genetics. We test the population’s response to different assumptions of mating behaviour as well as multiple scenarios of habitat change. We show that as long as hybridisation does take place, evolutionary rescue of Z. silvanus is likely. Intermediate hybridisation rates enable the greatest long‐term population growth, due to trade‐offs between adaptive and maladaptive introgressed alleles. Habitat change did not have a strong effect on population growth rates, as Z. silvanus is a strong disperser and landscape configuration is therefore not the limiting factor for hybridisation. Our results show that targeted gene flow may be a promising avenue to help accelerate the adaptation of endangered species to novel environments, and demonstrate how to combine empirical research and mechanistic modelling to deliver species‐specific predictions for conservation planning.
Article
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Article
Full-text available
Reliably modelling the demographic and distributional responses of a species to environmental changes can be crucial for successful conservation and management planning. Process-based models have the potential to achieve this goal, but so far they remain underused for predictions of species' distributions. Individual-based models offer the additional capability to model inter-individual variation and evolutionary dynamics and thus capture adaptive responses to environmental change. We present RangeShiftR, an R implementation of a flexible individual-based modelling platform which simulates eco-evolutionary dynamics in a spatially explicit way. The package provides flexible and fast simulations by making the software RangeShifter available for the widely used statistical programming platform R. The package features additional auxiliary functions to support model specification and analysis of results. We provide an outline of the package's functionality, describe the underlying model structure with its main components and present a short example. RangeShiftR offers substantial model complexity, especially for the demographic and dispersal processes. It comes with elaborate tutorials and comprehensive documentation to facilitate learning the software and provide help at all levels. As the core code is implemented in C++, the computations are fast. The complete source code is published under a public licence, making adaptations and contributions feasible. The RangeShiftR package facilitates the application of individual-based and mechanistic modelling to eco-evolutionary questions by operating a flexible and powerful simulation model from R. It allows effortless interoperation with existing packages to create streamlined workflows that can include data preparation, integrated model specification and results analysis. Moreover, the implementation in R strengthens the potential for coupling RangeShiftR with other models.
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