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The road to integrate climate change effects on land-use change in regional biodiversity models

February 2022

DOI:10.22541/au.164608831.19029067/v1

Authors:

Juliano Sarmento Cabral

University of Bonn

Alma Mendoza-Ponce

UNAM

André P. Silva

Stockholm Resilience Centre

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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.

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) Bühne et al. (2021); 3) Seddon et al. (2020); 4) Dale et al. (2011); 5) Chausson et al. (2020); 6) Oliver & Morecroft (2014).

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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.

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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.

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

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

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

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.

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.

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

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

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.,

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

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

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).

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

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-

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

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?

eﬀects

on LU

BD-

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)

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

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

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

transformed

area

CRAFTY-

(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

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

model

Study

re-

gion,

scales

and

group

Input

vari-

ables

Output

vari-

ables

What

modi-

ﬁed

LU?

Key

ﬁnd-

ings

(LU

eﬀects

BD)

CC CC -

driven

LUC

Travis

(2003)

explicit

model,

includes

random

habitat

loss

MECHA,

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

showing

the lowest

thresholds

Yes No

LoLiPop

(Sarmento

Cabral et

al., 2013)

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)

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)

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,

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)

explicit

model,

includes a

distur-

bance

submodel

MECHA,

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:

suitability,

human

population

Intactness

Index

(BII)

Population

density,

livestock

density,

crop pro-

duction,

calories

availabil-

ity,

BII

Land

sparing

eﬀective

for

conserving

biodiver-

sity (and

food

production)

No No

Dullinger

et al.

(2020)

Integrative:

MECHA

PHENO,

RWS

GE: Part

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,

stronger

Yes Yes

Zamora-

Gutierrez

et al.

(2021)

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

Bonnot et

al. (2013)

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

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)

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

protection

Yes No

Santos et

al. (2016)

Integrative:

MECHA

PHENO,

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;

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

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)

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

land-use

change

No No

Sales et al.

(2020)

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

change,

eco-

nomic

re-

turns,

conver-

sion

costs

Land

use

type

rarity-

weighted

species

rich-

ness,

threat

fresh-

water

diversity

Water

quality

Urban

expan-

sion as

major

threat

species-

rich

regions

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

RangeShifter-

CRAFTY

(Synes et

al., 2019)

Integrative:

MECHA

MECHA,

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)

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)

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

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 <

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

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*

model* AND (landscape AND use OR *cover)

AND biodiv

7021

model* AND (land* AND (use OR *cover)) AND

biodiv

8187

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”

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

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.

Life-history adaptation under climate warming magnifies the agricultural footprint of a cosmopolitan insect pest

Article

Full-text available

Jan 2025

Climate change is affecting population growth rates of ectothermic pests with potentially dire consequences for agriculture and global food security. However, current projection models of pest impact typically overlook the potential for rapid genetic adaptation, making current forecasts uncertain. Here, we predict how climate change adaptation in life-history traits of insect pests affects their growth rates and impact on agricultural yields by unifying thermodynamics with classic theory on resource acquisition and allocation trade-offs between foraging, reproduction, and maintenance. Our model predicts that warming temperatures will favour resource allocation towards maintenance coupled with increased resource acquisition through larval foraging, and the evolution of this life-history strategy results in both increased population growth rates and per capita host consumption, causing a double-blow on agricultural yields. We find support for these predictions by studying thermal adaptation in life-history traits and gene expression in the wide-spread insect pest, Callosobruchus maculatus; with 5 years of evolution under experimental warming causing an almost two-fold increase in its predicted agricultural footprint. These results show that pest adaptation can offset current projections of agricultural impact and emphasize the need for integrating a mechanistic understanding of life-history evolution into forecasts of pest impact under climate change.

Predicting extinctions with species distribution models

Article

Full-text available

Feb 2023

Predictions of species-level extinction risk from climate change are mostly based on species distribution models (SDMs). Reviewing the literature, we summarise why the translation of SDM results to extinction risk is conceptually and methodologically challenged and why critical SDM assumptions are unlikely to be met under climate change. Published SDM-derived extinction estimates are based on a positive relationship between range size decline and extinction risk, which empirically is not well understood. Importantly, the classification criteria used by the IUCN Red List of Threatened Species were not meant for this purpose and are often misused. Future predictive studies would profit considerably from a better understanding of the extinction risk–range decline relationship, particularly regarding the persistence and non-random distribution of the few last individuals in dwindling populations. Nevertheless, in the face of the ongoing climate and biodiversity crises, there is a high demand for predictions of future extinction risks. Despite prevailing challenges, we agree that SDMs currently provide the most accessible method to assess climate-related extinction risk across multiple species. We summarise current good practice in how SDMs can serve to classify species into IUCN extinction risk categories and predict whether a species is likely to become threatened under future climate. However, the uncertainties associated with translating predicted range declines into quantitative extinction risk need to be adequately communicated and extinction predictions should only be attempted with carefully conducted SDMs that openly communicate the limitations and uncertainty.

Hybridisation May Aid Evolutionary Rescue of an Endangered East African Passerine

Article

Full-text available

Jul 2022
EVOL APPL

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.

Life-history adaptation under climate warming magnifies the agricultural footprint of a cosmopolitan insect pest

Preprint

Full-text available

Mar 2024

Climate change is affecting the distributions and growth rates of cold-blooded pest species, with dire consequences for agriculture and global food security. To address this challenge, it is essential to forecast how pests will respond to changing climates. However, current projection models typically overlook the potential for climate change adaptation. By unifying life-history theory with thermodynamics based on first principles, we here show that pest species' climate adaptation can cause substantial increases in their agricultural impact via changes in temperature-dependent resource acquisition and allocation strategies. We test these predictions by studying thermal adaptation in life-history traits and gene expression in the cosmopolitan insect pest, Callosobruchus maculatus. Five years of adaptation to simulated warming caused an almost two-fold increase in the predicted agricultural footprint of C. maculatus, while adaptation to cold temperature had little effect. Consistent with the theoretical predictions, the magnified impact under warming was driven by synergistic increases in larval resource acquisition and adult reproductive potential, resulting in a predicted double blow on agricultural yields. This suggests that adaptation in insect life-history will significantly worsen agricultural loss under future climate change and emphasizes the need for integrating a mechanistic understanding of life-history evolution into ecological forecasts of pests.

Spatiotemporal dynamics of freshwater macrophytes in Bavarian lakes under environmental change

Thesis

Full-text available

Jan 2022

Anne Lewerentz

Macrophytes are key components of freshwater ecosystems because they provide habitat, food, and improve the water quality. Macrophyte are vulnerable to environmental change as their physiological processes depend on changing environmental factors, which themselves vary within a geographical region and along lake depth. Their spatial distribution is not well understood and their importance is publicly little-known. In this thesis, I have investigated the spatiotemporal dynamics of freshwater macrophytes in Bavarian lakes to understand their diversity pattern along different scales and to predict and communicate potential consequences of global change on their richness. In the introduction (Chapter 1), I provide an overview of the current scientific knowledge of the species richness patterns of macrophytes in freshwater lakes, the influences of climate and land-use change on macrophyte growth, and different modelling approaches of macrophytes. The main part of the thesis starts with a study about submerged and emergent macrophyte species richness in natural and artificial lakes of Bavaria (Chapter 2). By analysing publicly available monitoring data, I have found a higher species richness of submerged macrophytes in natural lakes than in artificial lakes. Furthermore, I showed that the richness of submerged species is better explained by physio-chemical lake parameters than the richness of emergent species. In Chapter 3, I considered that submerged macrophytes grow along a depth gradient that provides a sharp environmental gradient on a short spatial scale. This study is the first comparative assessment of the depth diversity gradient (DDG) of macrophytes. I have found a hump-shaped pattern of different diversity components. Generalised additive mixed-effect models indicate that the shape of the DDG is influenced mainly by light quality, light quantity, layering depth, and lake area. I could not identify a general trend of the DDG within recent years, but single lakes show trends leading into different directions. In Chapter 4, I used a mechanistic eco-physiological model to explore changes in the distribution of macrophyte species richness under different scenarios of environmental conditions across lakes and with depths. I could replicate the hump-shaped pattern of potential species richness along depth. Rising temperature leads to increased species richness in all lake types, and depths. The effect of turbidity and nutrient change depends on depth and lake type. Traits that characterise “loser species” under increased turbidity and nutrients are a high light consumption and a high sensibility to disturbances. “Winner species” can be identified by a high biomass production. In Chapter 5, I discuss the image problem of macrophytes. Unawareness, ignorance, and the poor accessibility of macrophytes can lead to conflicts of use. I assumed that an increased engagement and education could counteract this. Because computer games can transfer knowledge interactively while creating an immersive experience, I present in the chapter an interactive single-player game for children. Finally, I discuss the findings of this thesis in the light of their implications for ecological theory, their implications for conservation, and future research ideas (Chapter 6). The findings help to understand the regional distribution and the drivers of macrophyte species richness. By applying eco-physiological models, multiple environmental shaping factors for species richness were tested and scenarios of climate and land-use change were explored.

Relationship of insect biomass and richness with land use along a climate gradient

Article

Full-text available

Oct 2021

Recently reported insect declines have raised both political and social concern. Although the declines have been attributed to land use and climate change, supporting evidence suffers from low taxonomic resolution, short time series, a focus on local scales, and the collinearity of the identified drivers. In this study, we conducted a systematic assessment of insect populations in southern Germany, which showed that differences in insect biomass and richness are highly context dependent. We found the largest difference in biomass between semi-natural and urban environments (−42%), whereas differences in total richness (−29%) and the richness of threatened species (−56%) were largest from semi-natural to agricultural environments. These results point to urbanization and agriculture as major drivers of decline. We also found that richness and biomass increase monotonously with increasing temperature, independent of habitat. The contrasting patterns of insect biomass and richness question the use of these indicators as mutual surrogates. Our study provides support for the implementation of more comprehensive measures aimed at habitat restoration in order to halt insect declines.

RangeShiftR: an R package for individual‐based simulation of spatial eco‐evolutionary dynamics and species' responses to environmental changes

Article

Full-text available

Aug 2021
ECOGRAPHY

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.

gen3sis: A general engine for eco-evolutionary simulations of the processes that shape Earth’s biodiversity

Article

Full-text available

Jul 2021
PLOS BIOL

Understanding the origins of biodiversity has been an aspiration since the days of early naturalists. The immense complexity of ecological, evolutionary, and spatial processes, however, has made this goal elusive to this day. Computer models serve progress in many scientific fields, but in the fields of macroecology and macroevolution, eco-evolutionary models are comparatively less developed. We present a general, spatially explicit, eco-evolutionary engine with a modular implementation that enables the modeling of multiple macroecological and macroevolutionary processes and feedbacks across representative spatiotemporally dynamic landscapes. Modeled processes can include species’ abiotic tolerances, biotic interactions, dispersal, speciation, and evolution of ecological traits. Commonly observed biodiversity patterns, such as α, β, and γ diversity, species ranges, ecological traits, and phylogenies, emerge as simulations proceed. As an illustration, we examine alternative hypotheses expected to have shaped the latitudinal diversity gradient (LDG) during the Earth’s Cenozoic era. Our exploratory simulations simultaneously produce multiple realistic biodiversity patterns, such as the LDG, current species richness, and range size frequencies, as well as phylogenetic metrics. The model engine is open source and available as an R package, enabling future exploration of various landscapes and biological processes, while outputs can be linked with a variety of empirical biodiversity patterns. This work represents a key toward a numeric, interdisciplinary, and mechanistic understanding of the physical and biological processes that shape Earth’s biodiversity.

Temporal environmental variation may impose differential selection on both genomic and ecological traits

Article

Full-text available

May 2021
OIKOS

The response of populations and species to changing conditions determines how community composition will change functionally, including via trait shifts. Selection from standing variation has been suggested to be more efficient than acquiring new mutations. Yet, studies on community trait composition and trait selection largely focus on phenotypic variation in ecological traits, whereas the underlying genomic traits remain understudied. Using a genome-explicit, niche-and individual-based model, we address the potential interactions between genomic and ecological traits shaping communities under an environmental selective forcing, namely temporal positively autocorrelated environmental fluctuation. In this model, all ecological traits are explicitly coded by the genome. For our experiments, we initialized 90 replicate communities , each with ca 350 initial species, characterized by random genomic and ecological trait combinations, on a 2D spatially explicit landscape with two orthogonal gradients (temperature and resource use). We exposed each community to two contrasting scenarios: without (i.e. static environments) and with temporal variation. We then analyzed emerging compositions of both genomic and ecological traits at the community, population and genomic levels. Communities in variable environments were species poorer than in static environments, and populations more abundant, whereas genomes had lower genetic linkage, mean genetic variation and a non-significant tendency towards higher numbers of genes. The surviving genomes (i.e. those selected by variable environments) coded for enhanced environmental tolerance and smaller biomass, which resulted in faster life cycles and thus also in increased potential for evolutionary rescue. Under temporal environmental variation, larger, less linked genomes retained more variation in mean dispersal ability at the population level than at genomic level, whereas the opposite trend emerged for biomass. Our results provide clues to how sexually-reproducing diploid plant communities might react to variable environments and highlights the importance of genomic traits and their interaction with ecological traits for eco-evolutionary responses to changing climates.

Anthropogenic climate change has slowed global agricultural productivity growth

Article

Full-text available

Apr 2021
Nat. Clim. Change.

Agricultural research has fostered productivity growth, but the historical influence of anthropogenic climate change (ACC) on that growth has not been quantified. We develop a robust econometric model of weather effects on global agricultural total factor productivity (TFP) and combine this model with counterfactual climate scenarios to evaluate impacts of past climate trends on TFP. Our baseline model indicates that ACC has reduced global agricultural TFP by about 21% since 1961, a slowdown that is equivalent to losing the last 7 years of productivity growth. The effect is substantially more severe (a reduction of ~26–34%) in warmer regions such as Africa and Latin America and the Caribbean. We also find that global agriculture has grown more vulnerable to ongoing climate change. Agricultural productivity has increased historically, but the impact of climate change on productivity growth is not clear. In the last 60 years, anthropogenic climate change has reduced agricultural total factor production globally by 21%, with stronger impacts in warmer regions.

Vulnerability of bat‐plant pollination interactions due to environmental change

Article

Full-text available

Mar 2021

Plant‐pollinator interactions are highly relevant to society as many crops important for humans are animal pollinated. However, changes in climate and land use may put such interacting patterns at risk by disrupting the occurrences between pollinators and the plants they pollinate. Here, we analyse how the co‐occurrence patterns between bat pollinators and 126 plant species they pollinate may be disrupted given changes in climate and land use, and we forecast relevant changes of the current bat‐plant co‐occurrence distribution patterns for the near future. We predict under RCP8.5 21% of the territory will experience a loss of bat species richness, plants with C3 metabolism are predicted to reduce their area of distribution by 6.5%, CAM species are predicted to increase their potential area of distribution up to 1% and phanerophytes are predicted to have a 14% reduction in their distribution. The potential bat‐plant interactions are predicted to decrease from an average of 47.1 co‐occurring bat‐plant pairs in the present to 34.1 in the pessimistic scenario. The overall changes in suitable environmental conditions for bats and the plant species they pollinate may disrupt the current bat‐plant co‐occurrence network and will likely putt at risk the pollination services bat species provide.

How Bayesian Are Farmers When Making Climate Adaptation Decisions? A Computer Laboratory Experiment for Parameterising Models of Expectation Formation

Article

Full-text available

Feb 2021

As the consequences of climate change for agricultural production slowly unfold at the local level (sometimes with contradicting signals), farmers’ information processing and decision making become more relevant for policy analysis and modelling. The major challenge is to reveal patterns in the way farmers form expectations about future production outcomes and to encode these findings into models of heterogeneous expectation formation. We developed and tested a payout‐motivated field experiment to observe farmer decision‐making under climate change and to examine how they form their expectations in a recursive‐dynamic context. Participants were exposed to ambiguity and acquired incremental evidence about the true distribution of possible climate outcomes through repeated random draws. Simulation models used in agricultural and environmental research usually implement simple forms of adaptive agent expectation or completely neglect this issue by assuming perfect foresight or constant expectations. Our computer laboratory experiments with blue‐ and white‐collar farmers from Southwest Germany (n = 97) suggest that expectation behaviour of a large share of farmers can be well replicated with Bayesian types of expectation models.

Weather, cropland expansion, and deforestation in Ethiopia

Article

Nov 2021
J ENVIRON ECON MANAG

We use high-resolution weather data and rich Ethiopian household- and plot-level data in 2011/12, 2013/14, and 2015/16 growing seasons to investigate the impact of weather shocks on agricultural producers' cropland expansion and land conversion behaviors. We find that weather above 32 °C is harmful to crop growth. Household-level analysis shows that each additional average harmful growing degree day (defined as temperature above 32 °C) leads to a 17.2%, 20.1%, and 20.0% increase in total land holdings, cropland, and cropland allocated to cereal production, respectively. Each additional average harmful growing degree day reduces households' forest by 24.7% for households with some forest in the baseline growing season. Further analysis shows that farmers' cropland expansion substitutes migration and off-farm employment. The significant impacts of weather shocks on cropland expansion are only significant for households with relatively fewer assets but not for households with more assets, which suggests that only households without enough resources would expand cropland. These findings highlight the need to identify and facilitate coping strategies that are sustainable in the long run.

Coding for Life: Designing a Platform for Projecting and Protecting Global Biodiversity

Article

Oct 2021

Time is running out to limit further devastating losses of biodiversity and nature's contributions to humans. Addressing this crisis requires accurate predictions about which species and ecosystems are most at risk to ensure efficient use of limited conservation and management resources. We review existing biodiversity projection models and discover problematic gaps. Current models usually cannot easily be reconfigured for other species or systems, omit key biological processes, and cannot accommodate feedbacks with Earth system dynamics. To fill these gaps, we envision an adaptable, accessible, and universal biodiversity modeling platform that can project essential biodiversity variables, explore the implications of divergent socioeconomic scenarios, and compare conservation and management strategies. We design a roadmap for implementing this vision and demonstrate that building this biodiversity forecasting platform is possible and practical.

Modelling the long-term dynamics of tropical forests: From leaf traits to whole-tree growth patterns

Article

Sep 2021
ECOL MODEL

Tropical forests are the most diverse terrestrial ecosystems and home to numerous tree species competing for resources in space and time. Functional traits influence the ecophysiological performance of tree species, yet the relationship between traits and emergent long-term growth patterns is poorly understood. Here, we present a novel 3D forest stand model in which growth patterns of individual trees and forest stands are emergent properties of leaf traits. Individual trees are simulated as 3D functional-structural tree models (FSTMs), considering branches up to the second order and leaf dynamics at a resolution of one cubic meter. Each species is characterized by a set of leaf traits that corresponds to a specific position on the leaf economics spectrum and determines light-driven carbon assimilation, respiration and mortality rates. Applying principles of the pipe model theory, these leaf scale processes are coupled with within-tree carbon allocation, i.e., 3D tree growth emerges from low-level processes. By integrating these FSTMs into a dynamic forest stand model, we go beyond modern stand models to integrate structurally detailed internal physiological processes with interspecific competition and interactions with the environment in diverse tree communities. For model calibration and validation, we simultaneously compared a large number of emergent patterns at both the tree and forest levels in a pattern-oriented modelling framework. At the tree level, the specific leaf area and correlated leaf traits determined the maximum height and age of trees, as well as their size-dependent growth rate and shade tolerance. Trait variations along the leaf economics spectrum led to a continuous transition from fast-growing, short-lived and shade-intolerant to slow-growing, long-lived and shade-tolerant trees. These emerging patterns resemble well-known functional tree types, indicating a fundamental impact of leaf traits on long-term growth patterns. At the forest level, a large number of patterns taken from lowland Neotropical forests were reproduced, indicating that our forest model simulates structurally realistic forests over long time spans. Our ecophysiological approach improves the understanding of how leaf level processes scale up to the tree and the stand level, and facilitates the development of next-generation forest models for species-rich forests in which tree performance emerges directly from functional traits.

The road to integrate climate change effects on land-use change in regional biodiversity models

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