# SMARTscape: An agent-based model to analyse rural landscapes multifunctionality

Clémence Vannier, Klervi Choulette, Dan Richards, Alan Heays, Alexander

Herzig, Sandra Lavorel

► To cite this version:

Clémence Vannier, Klervi Choulette, Dan Richards, Alan Heays, Alexander Herzig, et al. SMARTscape: An agent-based model to analyse rural landscapes multifunctionality. Environmental Modelling and Software, 2026, 195, pp.106732. 10.1016/j.envsoft.2025.106732. hal-05389338

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# SMARTscape: An agent-based model to analyse rural landscapes multifunctionality

Clémence Vannier<sup>a, *, †</sup>, Klervi Choulette<sup>b</sup>, Dan Richards<sup>a</sup>, Alan Heays<sup>a</sup>, Alexander Herzig<sup>a</sup>, Sandra Lavorel<sup>a, c</sup>

## ARTICLE INFO

Dataset link: https://github.

com/manaakiwhenua/agent_template

(Original data)

Keywords:

Landscape performance

Land use management

Landscape intensity

Decision making process

Behaviour mixes

New Zealand

## ABSTRACT

Land-use changes and behavioural adaptation will be essential in global efforts to address environmental crises. For this, landscape multifunctionality will become critical to support a wide range of ecosystem services (ES), land productivity, and economic profitability. This study aims to explore pathways towards achieving sustainable food production while enabling multifunctionality. We developed SMARTscape, an empirically stylized agent-based model (ABM) developed to simulate interactions between land-use patterns, ES supply, and decision-making across a range of stylized landscapes inspired by New Zealand rural systems. SMARTscape combines three agent behavioural types—business-as-usual (BAU), profit-oriented (PO), and environment-oriented (EO)—with three decision-making rules of increasing social complexity. Agents interact with four collective entities (neighbours, peers, industry bodies, government), allowing simulation of bottom-up and top-down influence mechanisms. We designed a factorial experiment of 60 scenarios, from the combination of five landscape compositions, three decision rules, four agent-population behaviour mixes, and tested cluster size effect (on 10 cluster sizes from a very fragmented landscape to very aggregated) on 15 out of the 60 scenarios. Results show that land-use intensity and fragmentation are primary drivers of multifunctionality. However, profit-oriented behaviours can outperform environment-oriented strategies in intensive contexts, particularly through adoption of perennial systems, and behavioural diversity enhances trade-off management. Combined decision-making consistently led to higher multifunctionality, highlighting the importance of social influence. SMARTscape demonstrates the value of integrating social and spatial dynamics in a flexible modelling platform, offering both theoretical insights and a tool for exploring sustainable land-use transitions in data-poor but policy-relevant contexts.

## 1. Introduction

Land use change (LUC) and adaptation behaviours are critical components of global efforts to address pressing environmental crises, namely climate change and biodiversity loss (Pörtner et al., 2021; Verburg et al., 2015). As ecosystems face unprecedented shifts due to rising temperatures, altered precipitation patterns, and habitat degradation, land managers and stakeholders are compelled to make adaptive decisions to mitigate negative impacts and ensure long-term sustainability (Forzieri et al., 2022; Pecl et al., 2017; Psistaki et al., 2024). These decisions are often complex, influenced by socio-economic factors, ecological conditions and policies, highlighting the need for integrated

approaches to adaptation (Barbanente and Grassini, 2022; Medina Hidalgo et al., 2021). Understanding the drivers of LUC and the behaviours of decision-makers is essential for developing effective strategies that promote resilience in the face of global environmental challenges (Brown and Roper, 2017; Cook-Patton et al., 2021; Montes de Oca Munguia et al., 2021).

Rural landscapes, particularly in agricultural regions, are at the forefront of these environmental and socio-economic pressures, facing mounting challenges in maintaining and enhancing natural resources, biodiversity and ecosystem services while ensuring profitability. The increasing frequency of extreme weather events, alongside long-term shifts in climate, is disrupting traditional agricultural practices,

<footnote><sup>a</sup> Manaaki Whenua-Landcare Research Group, Bioeconomy Science Institute, New Zealand</footnote>

<footnote><sup>b</sup> Ecole Normale Supérieure de Rennes, University of Rennes 1, Rennes, France</footnote>

<footnote><sup>c</sup> Laboratoire d'Ecologie Alpine, Université Grenoble Alpes, Université Savoie Mont Blanc, CNRS, Grenoble, France</footnote>

<footnote>* Corresponding author.</footnote>

<footnote>E-mail address: vannierc@landcareresearch.co.nz (C. Vannier).</footnote>
leading to declining crop yields and increased vulnerability of food systems (Cook-Patton et al., 2021; Manes et al., 2022; Wakweya, 2023). At the same time, market pressures for higher productivity and lower costs conflict with the need to preserve biodiversity and natural resources, creating a trade-off between short-term economic gains and long-term sustainability (Lankoski and Thiem, 2020; Lechenet et al., 2014). In the face of these tensions, farmers need supportive policies, incentives and innovation to implement adaptive and sustainable practices while maintaining profitability in a rapidly changing world (Alt et al., 2024; Griffin et al., 2023).

The management and transformation of rural landscapes shape the biodiversity and ecological processes that are critical to long-term sustainability, because these determine synergies and trade-offs among ecosystem services, and thereby landscape multifunctionality (Lavorel et al., 2022). Ecosystem services (ES) are the various benefits that humans derive from ecosystems (Millennium Ecosystem Assessment - MEA, 2005). The ES framework has advocated the understanding, assessment and decision support for the relationships between human well-being and the health of ecosystems (Dick et al., 2018). In this study, we use landscape performance as a quantitative approach to assess the level of provision of multiple ES and land indicators – such as land use or intensity (Yang, 2020). This approach embeds all the socio-eco-environmental variables of a rural landscape to be represented in a modelling and computational exercise (Dezhkam et al., 2017; Sakieh and Salmanmahiny, 2016). Landscape performance, as a measure of the collective delivery of multiple ecosystem services, reflects the overall functionality, resilience, and sustainability of a landscape (Hölting et al., 2019a, 2019b; Lavorel et al., 2022). Multifunctionality, the simultaneous provision of multiple ES (Mastrangelo et al., 2014), emerges from interactions between individual ecosystem services (Lavorel et al., 2022). To implement an effective land use transition, it is essential to identify possible, available, and desired options for change that would promote multiple goals and to ensure that these options and the means of achieving them are accepted by local stakeholders (MacLeod et al., 2022).

While land use and its intensity are the primary determinants of multifunctionality, spatial configuration of land use is also critical to their achievement, especially in intensively used landscapes (Grass et al., 2019; Lavorel et al., 2022). In particular, the spatial scale of land use heterogeneity, typically characterised by patch size, determines which ecosystem services are better supported, and ultimately multifunctionality (Lavorel et al., 2022; Richards et al., 2024). In productive landscapes where several critical services such as pollination, pest control, or interception of nutrient or sediment for regulation of water quality are supported by small patches of native vegetation, fine-grained landscapes may achieve greater multifunctionality than landscapes with larger patches (Lavorel et al., 2022; Richards et al., 2024).

Scenario simulations are widely used for land use modelling approaches to spatialise and quantify land use and land cover alternatives and their associated ES and landscape performance (Felix et al., 2022; Vannier et al., 2019, 2022). Scenario simulation can also integrate human aspects explicitly, for example, for the management and protection of ecosystems by considering ecosystem services (Nápoles-Vértiz and Caro-Borrero, 2024; Locatelli et al., 2024). Several land use modelling approaches are commonly used for these projections, ranging from statistical models of land use allocation to mechanistic models that account for human decision processes. Agent-Based Models (ABM) are used to represent and simulate complex systems in which agents make autonomous decisions by interacting with their environment (Brugière et al., 2022; Le et al., 2010; Matthews et al., 2007). ABM have been widely applied to agriculture and the environment to explore the influence of decision mechanisms on environmental and economic outcomes (Brown et al., 2018; Caillault et al., 2013; Morgan and Daigneault, 2015; Yletyinen et al., 2021). Early ABM were strongly based on the influence of economic or agronomic mechanisms on agent decisions. For example, Morgan and Daigneault (2015) showed how the

economic, environmental and land use impact of policy influence individual farmers' decisions in terms of farming systems and land use. Likewise, Caillault et al. (2013) assessed the influence of incentive networks on decision-making at several scales and their consequences for landscape dynamics in a virtual stylized mixed farming system. Recent ABM have considered more complex social behaviour towards environmental goals. For example, Brown et al. (2018) showed that while the macroeconomic context (e.g. trading conditions) remained the dominant driver of LUC pathways, behavioural effects could accelerate, mitigate or counteract their effects on multifunctionality. Yletyinen et al. (2021) explored the capacity of social influence to generate nature conservation outcomes from voluntary private land conservation.

Yet, after initial interest in applying ABMs to simulate LUC and substantial development of simulation tools, further development has now stagnated (O'Sullivan et al., 2016). The critical challenge facing ABMs is that they are typically highly parameterised for specific applications or case study regions based on empirical data. This specialisation means that the resulting model is not generalisable, and cannot deal with unforeseen and unparameterized future changes, such as new policies, agricultural production opportunities, or climate change-driven events (Verburg et al., 2019).

In New Zealand (NZ), the agricultural sector contributes to 53 % of GHG emissions, including 43 % of methane and 5.5 % of nitrous oxide (New Zealand's Greenhouse Gas Inventory, 2024). The adoption of climate change mitigation and adaptation strategies for the agricultural sector has become a priority, organised around the maintenance of agricultural productivity and the elimination of net emissions by 2050 (Agriculture, 2022; Ministry for the Environment, 2019). Meanwhile the NZ agricultural sector is also the highest merchandise export contributor (80.9 %) for the country (Ministry for Primary Industries, 2024). In this context, addressing environmental challenges while ensuring profitability is fundamental.

As in many other countries, New Zealand faces the urgent challenge of reconciling or maintaining agricultural productivity with environmental sustainability, driving a growing demand for rural landscapes that deliver multiple ES (Maes et al., 2020; Ministry for the Environment & Stats NZ, 2025). The aim of this study is to (i) develop a flexible ABM to design multifunctional rural landscapes applicable in a wide range of rural landscapes; (ii) run multiple scenarios for a range of landscapes with varying topography, management intensity and spatial configuration under alternative decision-making rules and behaviour mixes, (iii) deliver a spatially-explicit evaluation of these scenarios in terms of landscape composition, ES and resulting multifunctionality for identifying best-performing scenarios.

Recent work has emphasised that spatial fragmentation and configuration of land use play a critical role in determining multifunctionality outcomes. For example, Lavorel et al. (2022) proposed spatially differentiated templates to support multifunctional landscape design; Neyret et al. (2024) showed that restoring landscape connectivity entails trade-offs across biodiversity and ecosystem service bundles; and Richards et al. (2024) demonstrated through virtual landscape experiments that random or fine-grained restoration patterns often outperform block or contiguous configurations in achieving multifunctionality. Building on those previous simulation studies to examine the role of land-use composition and fragmentation in shaping multifunctionality (Lavorel et al., 2022; Neyret et al., 2024), decision-making processes and social heterogeneity still need to be integrated into scenario explorations. While past work using virtual landscapes has deepened our mechanistic understanding of how spatial patterns influence the provision and interaction of ecosystem services (ES), it did not account for how actors' behaviours and governance rules shape land use outcomes. To address this, we developed the SMARTscape ABM to complement spatial simulations with diverse decision-making logic. SMARTscape was designed to fill this gap by testing how multiscale decision-making variability, in combination with spatial gradients of intensity and fragmentation, influences multifunctionality across rural landscapes.
SMARTscape combines a variety of decision-making rules reflecting generic behaviour types to project land use and ES for landscapes of varying management intensity and spatial configuration. We demonstrate SMARTscape’s capabilities for NZ-based landscape configurations and actors. We tested the following hypotheses of the effects of landscape intensity and spatial configuration, decision making scales and combinations of individual behaviour on ES supply and multifunctionality:

- Extensive landscapes supply more ES than intensive landscapes, but this pattern may change when considering both sustainability and profitability (Hypothesis 1).

- The cumulative effects of decision-making scales lead to better supply of ES and multifunctionality (Hypothesis 2).

- The effect of behavioural types on multifunctionality varies with landscape intensity; profit-oriented behaviours perform better in intensive systems, while behavioural diversity improves overall balance (Hypothesis 3).

- Multifunctionality in terms of both environmental sustainability and profitability is primarily determined by land use intensity and fragmentation (Hypothesis 4).

- Fragmented landscapes perform less effectively for ES supply and multifunctionality, but this depends on initial land use intensification (Hypothesis 5).

## 2. Materials and methods

We used the Overview, Design concepts and Details (ODD) protocol for describing the model (Grimm et al., 2020), as described in the following section. The ODD protocol is a standardised framework providing a consistent, logical and readable account of the structure and dynamics of ABMs. We then describe the simulation experiments and data analysis.

### 2.1. ODD description

#### 2.1.1. Purpose and patterns

The SMARTscape model aims to explore the combinations of landscapes, behaviours and levels of decision-making needed to achieve multifunctionality by maximising the performance of a set of 10 indicators (Fig. 1). SMARTscape is designed as a generative model for theory development in socio-ecological systems. It employs stylized representations of landowners, decision rules, and landscapes to explore how patterns of multifunctionality emerge from individual behaviours and multiscale influences (Schlüter et al., 2025). SMARTscape operates as a knowledge-support model that facilitates insight into the causal interactions and trade-offs shaping rural landscape dynamics under varied socio-environmental conditions.

The model was implemented using the Netlogo software (Wilensky, 2004). All details of the model installation, user guide, parameters and computations are described in Appendix 1 (Model Description).

#### 2.1.2. Entities, state variables and scales

##### 2.1.2.1. Agents. Agents represent the human landowners and decision-makers. There are five types of landowners in the model: growers, livestock farmers, foresters, protected land managers and property developers (Fig. 1). Each type is associated to a land use (Table 2).

Agents respond to information and incentives from collective entities. Agents own one piece of land represented by a patch. Land patches are homogeneous units, like parcels or plots, with one type of land use. To compute landscape indicators, the size of one patch is fixed at 1 ha.

Agents are assigned one of three types of predefined behaviour: Business as Usual (BAU), Profit Oriented (PO), and Environment Oriented (EO) (Table 1).

##### 2.1.2.2. Land use representation. The land is represented as a spatial grid

<figure><img src="image_4.png" /><figcaption>Fig. 1. – Schematic overview of the SMARTscape model.</figcaption></figure>
<table><caption>**Table 1**
Agent behaviours.</caption><thead><tr><td>Behaviour type</td><td>Description</td><td>Assumption</td></tr></thead><tbody><tr><td>Business as Usual (BAU)</td><td>Keep going, impact of climate change or other disruptors will be balanced on my operation anyway.</td><td>Agents decide by themselves and comply with legislation. No major change in the way the agent is operating.</td></tr><tr><td>Profit Oriented (PO)</td><td>Keep growing, I have strong optimism for the future of my financial benefits and performances.</td><td>Agent decides according to profits. Choices are guided by economic profits, according to the best possible outcomes of the plot.</td></tr><tr><td>Environment Oriented (EO)</td><td>Engage the change, adapt, and give back to the land and community. These agents are the most inventive and take risks for adaptation.</td><td>Mitigation is not enough, and adaptation of the farming system is required. Strong choices are taken without option to go back.</td></tr></tbody></table>

<table><caption>**Table 2**
Land use correspondence (simple or complex) from the NZ GIS database, and the agent type and associated use in the model.</caption><thead><tr><td>Land use</td><td>Description</td><td>Agent type</td></tr></thead><tbody><tr><td>Artificial Water</td><td>Town, peri-urban area<br>Lake, river, recreation Wetland, restoration</td><td>Property developer<br>no agent</td></tr><tr><td>Annual crop</td><td>Arable production</td><td>Grower (arable)</td></tr><tr><td>Perennial crop</td><td>Horticultural production</td><td>Grower (perennial)</td></tr><tr><td>Scrub</td><td>Unproductive land, restoration, protection, recreation</td><td>no agent</td></tr><tr><td>Intensive pasture</td><td>Dairy and grazing production</td><td>Livestock farmer (dairy)</td></tr><tr><td>Extensive pasture</td><td>Beef, lamb, deer and other meat production</td><td>Livestock farmer (sheep and beef)</td></tr><tr><td>Native forest</td><td>Unproductive land, protection, recreation</td><td>Protected land managers</td></tr><tr><td>Exotic forest</td><td>Wood production, recreation</td><td>Forester</td></tr></tbody></table>

of patches. Land Use is derived from National GIS database (Land Cover Database and Land Use Map, accessible using LRIS Portal) (Table 2).

Each land use is assigned an age, a yield, a monetary value, an emission and carbon stock value, which are used to compute landscape indicators. Default land use parameters are described in Appendix 1 (Model Description). Default values can be replaced by user customised values.

The land can be represented as a virtual or realistic landscape in the model. A virtual landscape is a grid of patches with land use randomly assigned and configured (Lavorel et al., 2022). Patches can be clustered to aggregate identical land use cells to simulate landscapes of varying fragmentation, considering here clusters from 1 to 10 patches. Realistic landscapes can be generated by using GIS data.

### 2.1.3. Process overview and scheduling

At each time step, each agent analyses the age of its current land use. If it is due to review, each agent collects information of (i) its current land use, the land use of (ii) their neighbours and (iii) their peers, and incentives from (iv) the industry and (v) the government. The list of all land use options encourages him to keep or adopt a new land use type. A set of decision rules is used to simulate the decision-making of farmers to choose a land use type for the next time step based on the received information and incentives. The landscape is updated with the new land uses implemented within each patch. Landscape indicators are computed accordingly (Fig. 1). The simulation is run for 30 time steps, representing 30 years of decision making.

### 2.1.4. Design concepts

#### 2.1.4.1. Basic principles.
Land is owned by agents. The decision of changing land use is made by the agent owning the patch. Multiple

scales are interacting in the decision-making process of each agent. Decision-making by individual agents influences the landscape patterns, ES and overall landscape performance.

#### 2.1.4.2. Emergence.
The model produces patterns that are not directly programmed but emerge from the interaction of agent behaviours, land-use decisions, and collective incentives. Emergence occurs by specific combinations of initial situations and multiscale incentives. Homogenisation of land use is produced by conservative BAU agent behaviours to reinforce dominant land uses across neighbouring patches. In contrast, PO and EO behaviours generate fragmented or clustered patterns, depending on the mix of social and environmental incentives. The neighbourhood tends to homogenise land use locally. The social network is as spatial and influences the land use of groups of farmers, hence tends to homogenise land use generally.

The generated landscape configuration is not predictable and depends on the initial randomised distribution of social networks and on the initial randomised landscape configuration. According to the initial conditions and the number and type of active networks during the simulation, landscape patterns could emerge from individual based decisions.

Multifunctionality emerges as a spatial pattern in which areas of high or low ecosystem service supply form patches or gradients across the landscape. These are identified by calculating landscape-level multifunctionality scores (average of normalised ES values) and mapping their spatial distribution over time. Patterns where different ES show contrasting supply (e.g., high GHG emissions co-occurring with high profitability) also emerged under certain intensification scenarios. These emergent patterns result from the model's decentralised decision-making and land use interactions rather than top-down control, illustrating how agent-level rules can lead to landscape-level trade-offs or synergies.

#### 2.1.4.3. Adaptation.
Agents have a range of possible LUC decisions. They adopt the land use type suggested by the majority of the collective entities if it is consistent with their current land use or the land use change rule. If not, the current land use type is kept or a random choice within possible land use types is adopted.

Landscape performance indicators analysis enables quantifying and characterising the changes. Indicators, such as financial performance or GHG emissions, are communicated to the industry and government entities respectively. To maintain a desired level of performance of the overall landscape, feedback loops enable collective entities (i.e. industry and government) to incentivise the agents taking action.

#### 2.1.4.4. Objectives.
Collective entities incite agents to adopt i) the land use type majority, ii) land use options that will enhance the landscape performance.

#### 2.1.4.5. Learning.
There is no learning process for the agents in the model.

#### 2.1.4.6. Prediction.
There is no prediction process conducted by the agents in the model.

#### 2.1.4.7. Sensing.
Agents directly receive a list of land use types from their collective entities. They determine the major land use type in their list and use the result as the recommendation to adopt.

#### 2.1.4.8. Interaction.
The agents' actions during the simulation depend on a combination of their current land use, their behaviour type and interactions with other agents and entities. Agents interact with four types of collective entities that influence their land-use decisions: neighbours, peers, industry bodies, and government. These interactions occur at each time step during the simulation:
- Neighbours are defined by the Moore neighbourhood (eight surrounding patches) and influence agents to adopt the most common land use type within this local spatial network.

- Peers form an aspatial social network in which agents are randomly connected; they influence land use decisions based on the most frequent land use among the peer group.

- Industry bodies incentivise agents toward land uses that maximise landscape profitability.

- Government entities provide incentives based on the landscape's environmental performance, promoting land uses that increase carbon storage and reduce greenhouse gas (GHG) emissions.

Each agent compiles land use suggestions from these collective entities and integrates them into a decision list. The final decision depends on the agent's behaviour type:

- Agents with a BAU behaviour will most of the time maintain the current land use.

- Agents with Profit Orientated behaviour will choose the land use that generates the most monetary profitability.

- Agents with Environmental Orientated behaviour will select the land use that favours increased carbon storage or a sustainable production method.

If no consensus or majority exists among the suggestions, the agent either retains the current land use or selects a new one randomly from the feasible options. These interactions determine spatial dynamics and emergent landscape configurations.

### 2.1.4.9. Stochasticity.
When using virtual landscapes, the landscape configuration at the setup has a degree of randomness. Land Use composition (% of each land use type) is set by the user. Its spatial allocation is random. The age attribute of the land use is randomly allocated.

Agent behaviour proportion in the agent population is set by the user. It is spatially randomly allocated.

Randomness can also occur when running the model: when the current land use is not a possible option for the next time step, or when there is no majority of land use listed for the next time step, the agent randomly chooses a land use from its list for the next time step.

### 2.1.4.10. Collectives.
To realistically capture the multilevel influences shaping land use decisions in NZ's agricultural sector, we integrate four types of collective entities - neighbours, social peers, industry bodies, and government -reflecting the interplay of local imitation, social affiliation, market-driven incentives, and policy frameworks, as highlighted in both empirical studies and simulation-based research on incentive networks (Caillault et al., 2013; Ministry for Primary Industries, 2024).

Agents are influenced by four collective entities to take action on the future use of their land:

- Their neighbours which influence them to mimic their behaviour.

- Their peers (e.g. social connections from different networks) which influence them to mimic their behaviour.

- Industry bodies which incentivise the agents to meet a large-scale profitability objective.

- The government which creates incentives in response to the overall environmental impact of the simulated landscape.

The two higher-scale networks (e.g. industry and government) send incentives for change (new legislation, opportunity, options) to the agents, with their own specific objectives. Agent behaviour types (BAU, PO, EO) determine how they respond to incentives from higher network entities.

The suggestions and incentives are implemented in NetLogo through

lists that store the recommended land uses, which are then filtered based on allowed transitions and chosen either deterministically or randomly depending on the presence/absence of a majority.

### 2.1.4.11. Observation.
We modelled six provisioning and supporting ES that proved to be essential for sustainability, and four landscape indicators that describes the land composition and proved to be essential to capture the landscape intensity sustainability (Caillault et al., 2013; Crouzat et al., 2015; Lavorel et al., 2022; McGarigal et al., 2012). At each time step, a list of 10 output indicators (Table 3) is computed to evaluate the landscape performance (i.e., land use area, agricultural production (crop and livestock), pollination, GHG emissions and carbon stock, mean patch size, diversity index, habitat quality for native birds,

<caption>Table 3</caption>

Output indicators for land performances evaluation during the simulation process.

<table><thead><tr><th>Type</th><th>Output indicator</th><th>Method</th><th>Reference</th></tr></thead><tbody><tr><td>Landscape indicator</td><td>Land use area (ha)</td><td>Number of hectares of each type of land use</td><td>-</td></tr><tr><td>Ecosystem Service</td><td>Agricultural production: Crop yield (t/ha/year) from annual and perennial crops</td><td>Proxy (land area*yield) From mean of cereal production in tonne/hectare; and mean of horticultural production in tonne/hectare.</td><td>Vannier et al. (2022)</td></tr><tr><td>Ecosystem Service</td><td>Agricultural production: Livestock yield (t/ha/an/year)</td><td>Proxy (land area*yield) From mean milk solids in kg/ animal/year; mean meat from beef production in kg/ animal, and mean meat from sheep production in kg/animal.</td><td>Vannier et al. (2022)</td></tr><tr><td>Ecosystem Service</td><td>Pollination (service provided 1, or not provided 0)</td><td>Land cover capacity to provide habitat for pollinators, and crop requirement for pollination</td><td>(Richards et al., 2023; Richards, 2023)</td></tr><tr><td>Ecosystem Service</td><td>Carbon stock (CO<sub>2</sub> equiv.)</td><td>Sum of CO<sub>2</sub> equivalent stored on patches of land from look-up table relating the land cover type of each pixel to an estimated value of carbon stocks.</td><td>(Richards et al., 2023; Richards, 2023)</td></tr><tr><td>Landscape indicator</td><td>Greenhouse Gas (GHG) emission (CO<sub>2</sub> equiv.)</td><td>Sum of GHG-equivalent carbon emissions of patches from look-up table relating the land cover type of each pixel to an estimated value of carbon emissions.</td><td>(Vannier et al., 2022; Richards et al., 2023; Richards, 2023)</td></tr><tr><td>Landscape indicator</td><td>Mean Patch Size (ha)</td><td>Average number of patches in a contiguous cluster.</td><td>McGarigal et al. (2012)</td></tr><tr><td>Landscape indicator</td><td>Diversity index (0-1)</td><td>Shannon index for land uses</td><td>McGarigal et al. (2012)</td></tr><tr><td>Ecosystem Service</td><td>Habitat quality (service provided 1, or not 0)</td><td>Bird habitat suitability models a simple relative habitat suitability for two NZ indigenous bird species of significant conservation interest (Kiwi, Kereru): 20 ha contiguous patches of perennial crop or forest.</td><td>(Richards et al., 2023; Richards, 2023)</td></tr><tr><td>Ecosystem Service</td><td>Profitability: total value (NZ$/year)</td><td>Sum of product values for all patches.</td><td>Vannier et al. (2022)</td></tr></tbody></table>
and profitable value). Details of formulas are described in Appendix 1 (Model Description).

### 2.1.5. Initialisation

At setup, the landscape is generated using predefined land use percentages or using GIS data.

Agents are generated on their patch, a behaviour is assigned to them, an age, a yield and a monetary value are assigned to each patch according to its land use.

To avoid border effects, the local network is defined by the Moore neighbourhood of each cell within a torus space (Kimura, 2002).

### 2.1.6. Input data

Realistic landscapes, in the form of a GIS dataset can be uploaded as a vector or raster layer (Appendix 1).

### 2.1.7. Sub-models

A set of land use change rules are implemented as sub-models. For each agent, the behaviour and current land use provides a list of potential land use change options (Fig. 2, TableA1.6 to A1.10). If the current land use is available, this choice is taken, otherwise the agent picks one option randomly. If no option is available, no action is taken by the agent. This is the baseline rule, described in Fig. 2.

On top of the baseline rule, neighbours, network, industry, and government can influence the list of land use options an agent is given. If a) the current land use is available, this choice is taken, or if b) the current land use is not available, and there is a majority of incentives for one particular land use (e.g., the most common land use among neighbours), this land use is chosen, or if c) the current land use is not available, and there is no majority of incentive for one particular land use option, the agent picks one land use option randomly. If no option is available, no action is taken by the agent.

## 2.2. Simulation experiments

### 2.2.1. Landscape intensity

To test the model under different initial landscape configurations, five landscapes of varying intensity were simulated: an extensive

landscape, an intermediate-extensive landscape, an intermediate-intensive landscape, an intensive landscape (adapted from Lavorel et al., 2022) and a NZ representative landscape (analysis of the NZ Land Cover Data Base 2020 – LCDB, (LCDB v5.0, 2024). Each landscape is defined in terms of land-use composition (Table 4).

### 2.2.2. Scenarios

First, a series of 60 scenarios were designed as a full factorial experiment to test the effects on landscape performance of varying combinations of the following parameters: (i) initial land use composition (1–5), (ii) composition of agent population according to the three behaviours (a to d), (iii) decision rules (i to k) (Fig. 3). For each of the five landscapes, variations within the agent population in relation to their behaviours were tested, either with an equal proportion of the three behaviours (a), or with a majority of one behaviour (i.e. 50 % for the majority and 25 % for the other two, repeated according to the three behaviour options-b, c, or d). The three combinations of decision rules are tested: (i) baseline, (j) baseline and neighbourhood, (k) baseline, neighbourhood and social network.

Second, the effect of landscape fragmentation was tested on 15 out of the 60 scenarios: all 5 landscapes, 3 rules and population behaviour (a) (Fig. 3). For these, 10 landscape cluster size scenarios tested factorial combinations of (i) initial land use composition (1–5), (ii) cluster size (patches aggregated from 1\*1 to 10\*10), (iii) decision rules (i, j, k), and (iv) population behaviour (a). A cluster size of 2, for example, means that patches of the same type will be assigned as clusters of 2\*2 patches. The prescribed average size of the plots at the start of the simulation will therefore be larger as cluster size increases, representing a landscape that is less and less fragmented.

### 2.2.3. Simulation

Model simulations were run using the BehaviorSpace interface (Wilensky and Shargel, 2002) which produces sets of simulations with varying input parameters (land use composition, decision rule, population behaviour, and cluster size - see Fig. 3). Each scenario was run over a period of thirty years, with 100 replicates per scenario sharing the same input prescribed parameters (land use composition, agent population, rules, cluster size) with only land use configuration changing

<figure><img src="image_5.png" /><figcaption>**Fig. 2.** – Land use change baseline rule by agent behaviour type. An agent can always keep its current land use. For example, if an agent has a profit orientated behaviour and is a crop grower, it can keep its current land use (annual crop) or choose to change to perennial crop or intensive pasture. Other land use options are not available for their configuration.</figcaption></figure>
<caption>**Table 4**</caption>

Land use composition (percentage of each land use) for the five landscape intensity levels (adapted from Lavorel et al., 2022; LCDB 2020).

<table><thead><tr><th>Land use</th><th>1 - Extensive</th><th>2 - Intermediate-extensive</th><th>3 - Intermediate-intensive</th><th>4 - Intensive</th><th>5 - NZ</th></tr></thead><tbody><tr><td>1. Artificial</td><td>1</td><td>1</td><td>3</td><td>3</td><td>1</td></tr><tr><td>2. Water</td><td>1</td><td>1</td><td>2</td><td>2</td><td>2</td></tr><tr><td>3. Annual crop</td><td>1</td><td>1</td><td>5</td><td>6</td><td>1</td></tr><tr><td>4. Perennial crop</td><td>1</td><td>1</td><td>4</td><td>6</td><td>1</td></tr><tr><td>5. Scrub</td><td>10</td><td>10</td><td>10</td><td>5</td><td>3</td></tr><tr><td>6. Intensive pasture</td><td>8</td><td>26</td><td>36</td><td>45</td><td>23</td></tr><tr><td>7. Extensive pasture</td><td>48</td><td>30</td><td>20</td><td>18</td><td>24</td></tr><tr><td>8. Native forest</td><td>20</td><td>20</td><td>10</td><td>5</td><td>38</td></tr><tr><td>9. Exotic forest</td><td>10</td><td>10</td><td>10</td><td>10</td><td>7</td></tr></tbody></table>

<figure><img src="image_6.png" /><figcaption>**Fig. 3.** – Set of scenarios tested for the SMARTscape model experimentation.</figcaption></figure>

randomly at each initialisation. To minimise simulation times, we used the NeSi high performance computer cluster (https://www.nesi.org.nz/).

## 2.3. Data analysis

Analyses of simulation results were performed using R (R Core Team, 2024).

### 2.3.1. Stability and sensitivity assessment

To assess the stability and the sensitivity of the model and the decision-making rules, we analysed the statistical differences between identical simulations. One scenario was simulated by fixing the land use configuration parameters (fixed seed). In this case, each simulation was run with the same initial configuration and land use configuration. Then, to explore the impact of spatial variations on the model's results, the same approach was repeated without fixing the random parameters, thus allowing variations in landscape configurations while retaining the same behaviour and rules proportions defined in the input.

In both cases, analyses of variance (ANOVA) were carried out to test differences between the simulation means for each output variable and each scenario. Homogeneity of variance was verified by Bartlett tests and normality by Shapiro-Wilk tests. The simulations were independent of each other. Kruskal-Wallis tests were performed when the application

conditions were not met.

As outlined in Table 3, output indicators have been chosen from already published work, allowing qualitative comparison of our overall result. Crossovers with other landscape analysis and ES works developed in the same context are discussed to validate the model global structure.

### 2.3.2. Statistical description

First, we modelled the nine output indicator variables (2 landscape indicators and 7 ecosystem service indicators) as a function of landscape intensity, rule, and behaviour, using general linear models (LMs). To test for possible interactive effects between these three explanatory variables, we included all two-way interactions within the linear models. Linear models were fitted using the R statistical software (R Core Team, 2024).

We then used Principal Component Analysis (PCA) to describe the multivariate structure of indicators at the end of the simulation. This analysis aimed to identify statistically close groups of scenarios according to landscapes, interaction rules and behaviours. All indicators were included in the analysis. Two dimensionality criteria were used to establish the number of factorial axes to be extracted: the Kaiser-Guttman criterion (Kaiser, 1960) and Horn's parallel analysis (Horn, 1965). The variables contributing to the factorial axes were determined by selecting those with an absolute contribution greater than the threshold of 1 on the number of variables. A priori groupings were
applied to see whether the simulated landscapes had different output values depending on whether they belonged to an intensity level, an agent population composition or an interaction rule set.

### 2.3.3. Landscape performance analysis

We analysed the performance of the simulated landscapes using landscape performance indicator values at the end of the simulation. Two measures were used to assess landscape performance:

- The multifunctionality score is defined as the average of the stand-ardised values for the seven landscape performance indicators (Richards et al., 2024). ES and landscape indicator values are standardised between 0 and 1, where 1 represents the maximum supply of an ES and 0 no supply. GHG emission values, considered to have a negative impact on ecosystem performance, were inverted so that a high score reflects a positive impact, i.e., a low GHG emission value (Lavorel et al., 2022). The mean and standard error of the standardised indicators were calculated to compare multi-functionality between landscapes.

- Pielou's equitability index (Pielou, 1966) provides an assessment of equity in the distribution of different indicators within a landscape (Richards et al., 2018).

## 3. Results

### 3.1. Model stability and sensitivity

Over the 100 simulations tested with fixed random parameters, the model outputs are always identical. The internal structure of the model and the implementation of the decision rules and behaviours are therefore defined as stable.

The distribution results and the ANOVAs on the random landscape configurations show that the minimum, maximum, quartiles, median and mean of the model outputs show low variability between the 100 simulations of each scenario (Figures A2.1, A2.2 and A2.3). All simulations follow the same trends according to their initial parametrisation, validating the internal structure of the model.

### 3.2. Descriptive analysis of model results

#### 3.2.1. Hierarchy and importance in individual performance indicators

Nine linear models (LMs) with output landscape indicators and ecosystem services as dependent variables have been computed (Appendix Table A2.1). Landscape intensity had a significant impact on all landscape indicators and ecosystem services, as well as decision rules and behaviours. Landscape intensity is the primary determinant, followed by rule j (baseline + neighbourhood) and behaviour d (environment-oriented behaviour majority). Landscape 3 and 4 (intermediate-intensive and intensive) have the strongest positive effects on crop and livestock yield, and pollination index (Appendix Table A2.1). Landscapes 3 and 4 also produced greater economic profitability, but lower total carbon stock, and increased total emissions (Appendix Table A2.1).

Rules j and k had significant positive effects on most indicators, although Rule j had a significant negative effect on the carbon stock and bird suitability index (Appendix Table A2.1). Behaviours b and c negatively impacted the crop yield, pollination index, diversity index, bird suitability index, and economic value, and significantly increased carbon emissions (Appendix Table A2.1). Behaviour d showed mixed effects, balancing agricultural productivity with environmental conservation by increasing crop yield, pollination, carbon stock, bird suitability index and total economic value, reducing emissions but also livestock yield (Appendix Table A2.1).

There were significant interactions between landscape intensity and behaviours. For example, under landscapes 2, 3, 4 and 5, behaviour b and d had significant negative interaction effects on crop yield, pollination, bird suitability and total economic value; while behaviour b, c

and d have a negative interaction effect with the total carbon stock (Appendix Table A2.1). Under rule j, positive interaction effects on environmental conservation were shown by behaviour b and c; while under rule k only behaviour b shows those positive interactions (Appendix Table A2.1). Interactions between landscape intensity and rules show strong negative effects. For example, rules j and k have a negative interaction effect under landscapes 2, 3, 4 and 5 with the crop yield, pollination and diversity index (Appendix Table A2.1). Rule k has also significant negative interaction with livestock yield and carbon emissions, while rule j has a significant negative interaction effect with the total economic value (Appendix Table A2.1).

#### 3.2.2. Multivariate variation in performance indicators

The PCA captured 88.6 % of the variance on the first 3 factorial axes chosen according to the Kaiser-Guttman criterion and Horn's parallel analysis. Axis 1 (54.6 % of total variance) represents the dominant effect of the degree of landscape intensification, while axis 2 (17.3 % of total variance) separates livestock production (positive values) from crops and afforestation (negative values). Finally, axis 3 represents effects of landscape fragmentation, nearly as important as those of axis 2 (16.6 % of total variance) (Figs. 4 and 5).

Simulation results projected on axes 1 and 3 are divided into 5 groups representing the five landscapes. Axis 1 highlights the contrast between extensive landscapes on the left and intensive landscapes on the right, mainly explained by scores for pollination, plot value, GHG emissions, artificial areas, annual and perennial cropping and intensive grazing. Axis 3 highlights the difference between Extensive and Intermediate-extensive landscapes from NZ landscape. Axis 3 mostly contrasts LU5 (shrubland) and 9 (exotic forest) from LU8 (native forest). Extensive and Intermediate-extensive landscapes are differentiated by the increasing area of native forest and patch size. The extensive and intermediate-extensive landscapes are mainly associated with greater carbon stocks and more numerous patches of scrub and exotic forest.

The structuring of the results according to the decision rules can be seen on axis 2 (Fig. 5). The landscapes simulated with rules j and k are opposite, with rule i being intermediate. Collaboration with neighbours (j rule) increases the area of extensive grazing in extensive landscapes (left of axis 1). In intensive landscapes (right of axis 1) this behaviour increases intensive grazing area, along with livestock yield and GHG emissions. In contrast, the combination of institutional and neighbour influences (k rule) increased habitat quality, exotic forest area and carbon storage in extensive landscapes (left of axis 1) and increased the area of perennial crops along with crop yields in more intensive landscapes.

We also explored a priori grouping according to the proportions of behaviours, but there was no visible difference between the four groups when analysed using the overall dataset. Clear grouping of behaviour types was only relevant when analysing the dataset within landscape intensity and within rules.

### 3.3. Landscape performance analysis

The averages of each performance indicator were analysed to identify highest and lowest scores according to the different landscape intensity, rules and behaviours (Table 5, Fig. 6).

First, unlike the intermediate-intensive and intensive landscapes, which have the most balanced indicator averages (Table 5), the extensive landscapes has very high averages for the ecological services of carbon storage (0.52), bird suitability (0.61) and GHG emissions (0.86) and low averages for the services of pollination (0.13), yield (crop yield 0.11, livestock yield 0.13) and profitability (0.08). The NZ landscape is close to the extensive and intermediate extensive landscape (Fig. 6), consistent with its high cover of native forest (38 %). Second, the combination of all decision rules (baseline, neighbourhood and social network) resulted in the highest landscape performance scores, except for the livestock yield (Table 5). Third, we analysed the landscape
<figure><img src="image_7.png" /><figcaption>**Fig. 4.** – Principal component analysis (PCA) of model output values at year 30 for all scenarios. A: Representation of data on factorial axes 1 and 3 (71.3 % of total variance explained) with a priori grouping according to landscapes (1: Extensive, 2: Intermediate-extensive, 3: Intermediate-intensive, 4: Intensive, 5: NZ). B: Histogram of the eigenvalues of the factorial axes with the percentage of variance explained, the red line represents the random eigenvalues for Horn's parallel analysis. C: Absolute contribution of variables to axis 1. D: Absolute contribution of variables to axis 3. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)</figcaption></figure>

performance scores according to agent behaviour population (Fig. 6C). Populations with a majority of Profit Oriented agents scored highest overall for each landscape indicator and ES (Table 5), with the exception of livestock yield, regardless of decision-making rules.

Multifunctionality scores were analysed across landscapes, rules and behaviours to complete the PCA results and to determine which configurations favour the highest ES supply (Fig. 7). Bartlett tests rejected

variance homogeneity ($P < 0.001$ for each of landscape, rules and behaviours).

The Kruskal-Wallis analysis revealed significant differences between mean multifunctionality scores across landscapes (Kruskal-Wallis $\chi^2 = 2610.9$, ddl = 4, $P < 0.001$), decision rules (Kruskal-Wallis $\chi^2 = 2013.1$, ddl = 2, $P < 0.001$) and population behaviours (Kruskal-Wallis $\chi^2 = 146.04$, ddl = 3, $P < 0.001$). The intensive landscape produced the
<figure><img src="image_8.png" /><figcaption>**Fig. 5.** – Principal component analysis (PCA) of model output values at year 30 for all scenarios. A: Representation of data on factorial axes 1 and 2 (71.9 % of total variance explained) with a priori grouping according to rules. B: Histogram of the eigenvalues of the factorial axes with the percentage of variance explained of each axis, the red line represents the random eigenvalues for Horn's parallel analysis. D: Absolute contribution of variables to axis 2. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)</figcaption></figure>

highest mean score (0.52 ± 0.003), while the extensive landscape produced the lowest mean score (0.35 ± 0.003). The combination of baseline, neighbourhood and social network rules produced the highest score (0.5 ± 0.003) over the three decision rules. The differences between scores across population behaviours is low with BAU majority scoring the lowest (0.41 ± 0.003) and PO majority scoring the highest (0.45 ± 0.003) across the four population behaviour mixes.

The Pielou index (Fig. 7B) was similar for the intermediate-extensive, intermediate-intensive and intensive landscapes. Those three landscapes have a similar balance between indicator values with the intermediate-intensive landscape being the most balanced (0.96 ± 0.002). In contrast, the extensive landscape differs significantly from the other five and is characterised by the lowest index (0.82 ± 0.003), suggesting that strong contrasts exist for one or more indicators within
<caption>**Table 5**</caption>

Average standardised performance indicator value by landscape, decision rules and population behaviour. Scores range between 0 and 1, where 0 means the Ecosystem Service is not provided and 1 it is fully provided. Scores higher than 0.5 are provided in bold.

<table><thead><tr><th>Ecosystem Services</th><th></th><th>Pollination</th><th>GHG emissions</th><th>Value $</th><th>Carbon stock</th><th>Crop Yield</th><th>Livestock Yield</th><th>Bird suitability</th></tr></thead><tbody><tr><td rowspan="5">Landscape</td><td>Extensive</td><td>0.13</td><td><strong>0.86</strong></td><td>0.08</td><td><strong>0.52</strong></td><td>0.11</td><td>0.13</td><td><strong>0.61</strong></td></tr><tr><td>Intermediate-extensive</td><td>0.29</td><td><strong>0.66</strong></td><td>0.21</td><td>0.42</td><td>0.25</td><td>0.33</td><td><strong>0.61</strong></td></tr><tr><td>Intermediate-intensive</td><td><strong>0.58</strong></td><td><strong>0.51</strong></td><td><strong>0.62</strong></td><td>0.23</td><td>0.48</td><td>0.44</td><td>0.45</td></tr><tr><td>Intensive</td><td><strong>0.67</strong></td><td>0.36</td><td><strong>0.78</strong></td><td>0.13</td><td><strong>0.67</strong></td><td><strong>0.55</strong></td><td>0.46</td></tr><tr><td>NZ</td><td>0.2</td><td><strong>0.77</strong></td><td>0.21</td><td>0.39</td><td>0.28</td><td>0.21</td><td><strong>0.84</strong></td></tr><tr><td rowspan="3">Decision Rule</td><td>baseline</td><td>0.35</td><td><strong>0.66</strong></td><td>0.36</td><td>0.31</td><td>0.35</td><td>0.3</td><td><strong>0.64</strong></td></tr><tr><td>Baseline and neighbours</td><td>0.28</td><td><strong>0.51</strong></td><td>0.33</td><td>0.25</td><td>0.26</td><td>0.48</td><td>0.43</td></tr><tr><td>baseline, neighbours and social network</td><td>0.49</td><td><strong>0.72</strong></td><td>0.44</td><td>0.46</td><td>0.46</td><td>0.21</td><td><strong>0.71</strong></td></tr><tr><td rowspan="4">Population<br>Behaviour</td><td>Equal proportion</td><td>0.38</td><td><strong>0.63</strong></td><td>0.38</td><td>0.34</td><td>0.36</td><td>0.33</td><td><strong>0.59</strong></td></tr><tr><td>BAU majority</td><td>0.36</td><td>0.6</td><td>0.37</td><td>0.32</td><td>0.33</td><td>0.37</td><td><strong>0.53</strong></td></tr><tr><td>PO majority</td><td>0.4</td><td><strong>0.66</strong></td><td>0.41</td><td>0.39</td><td>0.38</td><td>0.29</td><td><strong>0.65</strong></td></tr><tr><td>EO majority</td><td>0.37</td><td><strong>0.63</strong></td><td>0.37</td><td>0.31</td><td>0.35</td><td>0.33</td><td><strong>0.59</strong></td></tr></tbody></table>

<figure><img src="image_9.png" /><figcaption>**Fig. 6.** – Standardised average of the values of each landscape performance indicator according to landscape (A), decision rules (B), population behaviour (C), where 1: Extensive, 2: Intermediate-extensive, 3: Intermediate-intensive, 4: Intensive, 5: NZ, ; i: baseline rule, j: baseline and neighbourhood rules, k: baseline, neighbourhood and social network rules; and a: equal proportion of all behaviours, b: business as usual behaviour majority, c: profit oriented behaviour majority, d: environment oriented behaviour majority. Standard errors of the means are less than 0.006.</figcaption></figure>

this landscape. These contrasts are highlighted in the spider graphs presented in Fig. 6A. While the Pielou index varied significantly across landscapes, the Bartlett analysis did not reveal any significant difference of the Pielou index across rules (i and j = 0.9 ± 0.003, k = 0.93 ± 0.002) or behaviours (a, b, c = 0.93 ± 0.002, d = 0,9 ± 0.003).

### 3.4. Effect of fragmentation

In this analysis, a cluster size of 1 represents a highly fragmented landscape, while a cluster size of 10 represents a highly aggregated one. The variation in the initial size of land clusters influenced simulation outcomes for all landscapes, rules and behaviours (Fig. 8). Variation in land clustering at the input leads to variation in the ES supply and
<figure><img src="image_10.png" /><figcaption>Fig. 7. – Average Pielou index per landscape (A); then multifunctionality scores (average scores) according to landscapes (B), decision rules (C), and population behaviour (D). Where 1: Extensive, 2: Intermediate-extensive, 3: Intermediate-intensive, 4: Intensive, 5: NZ, i: baseline rule, j: baseline and neighbourhood rules, k: baseline, neighbourhood and social network rules, a: equal proportion of all behaviours, b: business as usual behaviour majority, c: profit-oriented behaviour majority, d: environment-oriented behaviour majority.</figcaption></figure>

overall multifunctionality score in the output. Most of the time, an increase of land clustering leads to a decrease of the multifunctionality score. This is particularly true for intensive and intermediate-intensive landscapes, i and k rules, a and d population behaviour.

In intensive and intermediate-intensive landscapes, the pollination index is more sensitive to the initial land cluster, as this ES requires a good representation of annual and perennial crops throughout the whole landscape to be better delivered. For the NZ and intermediate-extensive landscapes, the initial land cluster doesn't influence the multifunctionality score. For the extensive landscape, the higher the initial land cluster is, the higher will be the multifunctionality score, with especially bird suitability index and total carbon stock increase when the land clustering increases. The pollination service is better provided in more fragmented landscapes. In contrast, the three ES indicators (habitat quality, carbon storage and GHG emissions) showed better service provision with increasing cluster size, reaching a threshold effect at cluster size 4. Livestock yields decreased until reaching a threshold at cluster size 4, while crop yields increased slightly with the initial land cluster increase.

In decision rules i and k, the multifunctionality score decreases regularly when the initial land clustering increases. All the decision rules follow the same tendencies of increase (bird suitability index, total carbon stock, total emissions) or decrease (pollination, total livestock yield) with higher initial land clustering. Signals are less easy to interpret when it comes to total crop yield or total value, which are independent of any decision rules in their computation and the initial landscape, compared to land use representation at the end of the simulation that are the first determinants of ES supply. However, the effect of rule k, bringing together the baseline, neighbourhood and networks, scores higher than other configurations, whatever the initial land cluster.

# 4. Discussion and conclusion

## 4.1. Model stability

ABMs such as SMARTscape are highly complex, making validation complex (Collins et al., 2024; Parry and Bithell, 2012). It is therefore the role of sensitivity analyses (Collins et al., 2024) to understand and describe the behaviour of the model compared to the rules developed to characterise the margins of uncertainty and stability (Razavi et al., 2021; Saltelli, 2004). Other methods require independent and quantitative input parameters, which is not the case of this model. New methods adapted to correlated inputs are increasingly being developed (Razavi et al., 2021) but were not used in this study, with a preference for a step-by-step analysis more focused on the thematic hypotheses. The randomness of the model comes from the initial landscape configuration and the LUC decisions of the agents. The variation indicated by the ANOVAs is generally due to the small variation in the values of an indicator within a scenario. As soon as a simulation presents slightly different values, it is considered to be significantly different.

## 4.2. Importance and hierarchy of simulation factors

The GLM results highlight the trade-offs between productivity and environmental outcomes (Hypothesis 1). More intensive landscapes showed high yields but negative effects on carbon stock and emissions. More complex rules increased landscape diversity (mean patch size, diversity index). Behaviours impacted on biodiversity (mean patch size, diversity index and bird suitability index), with some enhancing diversity while others reduced it. Carbon stock reduction was associated with higher emissions and lower biodiversity. Strong negative coefficients for carbon stock indicate that intensive farming reduced carbon storage potential.

Interactions between factors matter. Our results show that landscape
<figure><img src="image_11.png" /><figcaption>**Fig. 8.** Average standardised values of each performance indicator and multifunctionality score for all landscapes (A) and rules (B) under equal proportion of all behaviour (a). Error bars representing the standard errors of each mean have been displayed. *Where 1: Extensive, 2: Intermediate-extensive, 3: Intermediate-intensive, 4: Intensive, 5: NZ; i: baseline rule, j: baseline and neighbourhood rules, k: baseline, neighbourhood and social network rules.*</figcaption></figure>

intensity interacts with behaviour and rules in non-linear ways. Some combinations of intensive landscape with business-as-usual population behaviours further reduced environmental quality (lower carbon stock and bird suitability). Other combinations of intermediate-extensive landscapes and more complex decision rules showed improvements in biodiversity but at the cost of lower yield. Interactions between population behaviour and rules are statistically significant and have mixed effects. Under a simple neighbourhood rule (j), positive interaction effects on environmental conservation are shown by business-as-usual and profit-oriented behaviours, while under a more complex decision rule (k) only the business-as-usual behaviour (b) showed these positive interactions. Our results are consistent with the CRAFTY model

(Murray-Rust et al., 2014) showing that within specific circumstances (i. e., landscape, rules) the effects of population behaviour are strong and diverse, and can mitigate the impacts of other drivers (Brown et al., 2018).

Unsurprisingly, more intensive landscapes delivered high yield but were environmentally costly. Interactions between complex rules had mixed effects, enhancing yield but sometimes worsening environmental outcomes. Business-as-usual and profit-oriented population behaviours reduced ES like biodiversity and bird habitat provision. However, strong interactions exist between land, rules and behaviours, creating complex trade-offs.
### 4.3. Rules exploration

The cumulative effects of decision-making options led to better supply of ES and thereby multifunctionality (Hypothesis 2). The different rules for agent interactions significantly modified landscape performance. The combination of all the rules (rule k) led to better landscape performance as compared to individual behaviour (rule i). Conversely, the introduction of neighbour interactions only (rule j) decreased landscape performance. Thus, the cumulative effects of the three decision-making levels resulted in greater multifunctionality. This suggests that integrating individual logic with social and networked influences enhances adaptive land-use outcomes. The decision rules also influenced landscape fragmentation. Consistent with the IRIUS model (Impact of Networks of Influence on Land Use) (Caillault et al., 2013), the combination of NB and NT networks increased mean patch size, while landscapes were more fragmented in the absence of a network.

### 4.4. Behaviour effects

The initial hypothesis (Hypothesis 3) on population behaviours were expected to lead to different landscape performances depending on the initial landscape intensity. The behavioural proportions in the agent population did not influence landscape overall performance. However, the effect of behaviour depended on landscape intensity, confirming our initial hypothesis. In intensive landscapes, a majority of environment-oriented behaviours did not lead to the expected better provision of ES; instead, a majority of profit-oriented behaviours delivered better performance for all indicators except cattle yield. This can be explained by the behaviour of PO agents seeking to maximise their profitability and turning to more profitable uses, in particular perennial crops. These crops are not only more profitable, but also have high values for pollution, habitat quality and GHG emissions.

When considering specific behaviours, environment-oriented agents underperformed under certain conditions. In particular, a majority of EO agents did not yield greater ES outcomes in highly intensive settings. This may result from their reluctance to shift away from less productive practices when structural constraints dominate the landscape. Profit-oriented agents leveraged perennial crops for dual gains. PO agents often adopted perennial cropping systems, boosting both profitability and regulating services. This finding highlights the potential for market-driven pathways to align with sustainability goals under specific configurations. Overall, behavioural diversity influenced trade-off management. Scenarios with mixed behaviour types showed more balanced outcomes across indicators than those with homogenous populations. Diversity in decision-making logics appears to buffer against extreme trade-offs and foster resilience in ES supply.

Thus, SMARTscape simulations highlight that achieving multifunctionality in real-world rural landscapes depends not only on land-use patterns but also on the diversity of decision-making behaviours and the structure of social and institutional incentives. Findings indicate that socially networked, profit-oriented behaviours can, under specific configurations, deliver both economic and environmental benefits—particularly through the adoption of high-value perennial systems. In the New Zealand context, where the dual imperative of maintaining agricultural productivity and reducing environmental impacts is particularly acute, diversified behaviour types, collaborative governance, and place-based planning are critical for advancing multifunctional and sustainable landscapes.

### 4.5. Ecosystem services and landscape performance

SMARTscape simulations confirmed differences in performance between extensive and intensive landscapes (Hypothesis 4). However, intensive landscapes were found to provide better ES landscape performance overall than extensive landscapes, contrary to expectations. One of the most unexpected outcomes of the SMARTscape simulations

was the high performance of scenarios dominated by Profit-Oriented agents, which in several cases outperformed those dominated by Environment-Oriented agents, especially in intensive landscapes. While EO behaviours were designed to prioritise environmental sustainability, PO agents—driven by profit-maximising strategies—often achieved better trade-offs between profitability and ecosystem service (ES) provision. This suggests that under certain land use intensities, market-based motivations can align with improved multifunctionality outcomes, particularly when accounting for economic and food production parameters, and when supported by combined decision-making rules, which integrates individual logic, neighbourhood influence, and social networks. This rule consistently led to the best-performing scenarios across indicators, highlighting the importance of multi-scale, socially mediated decision-making. In contrast, Lavorel et al. (2022) showed that intensification of the agricultural landscape led to an overall reduction in the ES considered and, in landscape, multifunctionality. These different conclusions reflect the indicators considered in each study (Neyret et al., 2024). Here, we included land yield and profitability as landscape performance indicators, in contrast to a greater focus on indicators of regulating (erosion control, regulation of nutrient leaching) and cultural (recreation) ES, which are supplied by extensive grasslands, shrublands and forest, in previous analyses. These two economic indicators provide a holistic view of agricultural landscapes and ensure that the primary objective of this type of landscape is considered. Further implementation of SMARTscape will integrate more regulating and cultural ES to analyse trade-offs among production and environmental objectives than just climate change mitigation.

Fragmented landscapes delivered lower ES supply and multifunctionality, but this outcome was influenced by initial land use intensification (Hypothesis 5). Another surprising result from the SMARTscape simulations was the role of landscape fragmentation, which has been associated with negative impacts in the biodiversity conservation and ecosystem service literature (Fahrig et al., 2019; Mitchell et al., 2015). In SMARTscape, however, greater fragmentation improved ES delivery and multifunctionality in intensive landscapes (Hypothesis 4), likely due to increased spatial heterogeneity and functional complementarities. By contrast, in extensive landscapes, larger, more cohesive patches performed better. These findings underscore the importance of considering the interactions between landscape structure, decision-making processes, and behaviour types, rather than evaluating them in isolation. They also challenge linear assumptions about behaviour and sustainability, revealing that in complex socio-ecological systems, the relationships between actors, rules, and spatial patterns are often non-additive and context-dependent. Our results align with the study performed by Richards et al. (2024) on the restoration of intensive landscapes, considering the profitability of the land, which suggested that a random distribution of restoration plots leads to greater multifunctionality. Conversely, a reduction in fragmentation and an increase in the size of patches in an extensive landscape increased ES and therefore landscape performance. In the same study, pollination decreased with intensification, contrary to our simulations. However, like Lavorel et al. (2022), pollination decreased with plot size and the effect was greater for an intensive landscape; fragmentation also attenuated the effect of intensification.

### 4.6. Future developments

The greatest added value of SMARTscape lies in its unique integration of spatial landscape dynamics, agent-level behavioural diversity, and multi-scale social influence, within a stylized yet empirically informed modelling framework that enables systematic exploration of landscape multifunctionality. Drawing on the conceptual framing of empirically stylized models from Schlüter et al. (2025), SMARTscape offers a middle-ground modelling approach that generalises insights from real-world agricultural systems—particularly those of New Zealand—while abstracting from case-specific data to support theory
development and transferable scenario exploration.

SMARTscape aims to explore scenarios for achieving multi-functionality by maximising landscape performance in multiple ES. First, studying real landscapes based on GIS data will allow a further level of exploration to confirm our initial results. Second, to better encompass all the ES, the integration of indicators such as recreation, erosion and nitrogen leaching into the model could be developed. These two steps appear fundamental to offer a more generalisable use of the model and more general value of the proposed results.

In fact, despite the growing adoption of ABM to understand socio-environmental systems in several fields, their use is still generally applied to specific case studies. The impossibility of generalising these models to other non-parameterised situations raises questions about the relevance of creating new ABMs (O'Sullivan et al., 2016; Verburg et al., 2019). SMARTscape offers a stylized ABM that helps in navigating between empirical ABM and theory by providing a tool for empirically stylized modelling (Schlüter et al., 2025). SMARTscape is designed as a generative platform for theory exploration. It draws from empirical knowledge of rural socio-ecological dynamics but abstracts from case-specific data to reveal how broad patterns of land use, agent behaviour, and institutional context shape multifunctionality outcomes. The main advantage of SMARTscape in this context is its design, which enables its application to a wide range of landscapes representing various worldwide configurations under a wide range of socio-environmental conditions, without needing to train the model (Murray-Rust et al., 2014). Future developments of SMARTscape will therefore focus on: 1) analysing the reproducibility and transposability of the model in different landscape and socio-environmental contexts; and 2) adapting it as a decision-making tool to help local and regional stakeholders understand and build their own multifunctional landscapes. While stylized models such as SMARTscape offer advantages in generalisability and theory development, stakeholders may perceive them as lacking realism. To address this, we suggest using SMARTscape as a knowledge support facilitating dialogue, reflection, and shared interpretation of complex trade-offs in landscape management in a participatory approach. Through its interactive interface, collaborative narrative and real time simulation could help bridge the gap between abstraction and stakeholder relevance. Used as a participatory tool, these approaches have always improved stakeholder engagement, thus directly benefiting informed, more effective and better accepted decision-making.

### CRediT authorship contribution statement

**Clémence Vannier:** Writing – review & editing, Writing – original draft, Visualization, Validation, Supervision, Project administration, Methodology, Investigation, Formal analysis, Data curation, Conceptualization.
**Klervi Choulette:** Writing – review & editing, Writing – original draft, Visualization, Validation, Formal analysis.
**Dan Richards:** Writing – review & editing, Supervision, Methodology, Investigation, Formal analysis, Conceptualization.
**Alan Heays:** Writing – review & editing, Writing – original draft, Software, Investigation, Formal analysis, Data curation.
**Alexander Herzig:** Writing – review & editing, Resources, Methodology, Investigation, Funding acquisition, Conceptualization.
**Sandra Lavorel:** Writing – review & editing, Writing – original draft, Conceptualization.

### Software and Data availability

Software required is NetLogo version 6.4. NetLogo is an open-source software that can be accessed at NetLogo Web

SMARTscape code can be accessed and downloaded at GitHub - manaakiwhenua/agent_template.

Code developer: Alan Heays.

Contact: heaysa@landcareresearch.co.nz.

Date first available: April 15, 2024.

Program language: Logo.

A detailed documentation for application installation, testing, and deployment can be found at GitHub - manaakiwhenua/agent_template.

No data are required for local installation and use of software. See Data Availability Statement.

### Funding sources

The work was supported by the Ministry for Business Innovation and Employment (Strategic Science Investment Funding), The Ecole Normale Superieure de Rennes (France), the International Research Project of CNRS (France) ‘Agri4Adapt’, Biodiversa+ RECONNECT project funded by ANR Agence Nationale de la Recherche (ANR-22-EBIP-0009-06).

### Declaration of competing interest

All authors declare that they have no conflict of interest.

### Acknowledgements

The author(s) wish to acknowledge the use of New Zealand eScience Infrastructure (NeSI) high performance computing facilities, consulting support and/or training services as part of this research. New Zealand's national facilities are provided by NeSI and funded jointly by NeSI's collaborator institutions and through the Ministry of Business, Innovation & Employment's Research Infrastructure programme (URL: http://www.nesi.org.nz). The authors thank Jing Guo for his technical help in running NeSI, and Pr Andrew Sturman for the English editing.

## Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.envsoft.2025.106732.

### Data availability

Data are available on github: https://github.com/manaakiwhenua/agent_template
https://github.com/manaakiwhenua/agent_template (Original data) (github)

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