skip to content

Timetable (EBDW03)

Integrating quantitative social, ecological and mathematical sciences into landscape decision-making

Monday 7th September 2020 to Thursday 10th September 2020

Monday 7th September 2020
13:30 to 13:40 Welcome and Introduction from David Abrahams (INI Director) and Jane Leeks (Newton Gateway to Mathematics) INI 1
13:40 to 14:40 Session 1 - Outputs from 2019 Research Programme - CHAIR - Viktoria Spaiser INI 1
13:40 to 14:00 Peter Challenor (University of Exeter)
Decision Making under Uncertainty
The next few years will be crucial for the future of the UK landscape. There are important decisions that need to be made about agricultural policy, nature conservation and how we respond to a changing climate. All these decisions will involve large amounts of uncertainty. How can we produce decision support tools that will help in the process? In this talk I will investigate some fo the methods currently used to aid decision makers by combining data and models and point out their advantages and disadvantages. I will also look at some other new directions that could solve some of these problems.  

14:00 to 14:20 Felix Eigenbrod (University of Southampton)
Spatial/temporal scaling
14:20 to 14:40 Paula Harrison (Centre for Ecology & Hydrology)
Coupling models to represent interactions within landscape systems
This talk will summarise discussions from the Work
Programme on ‘Mathematical and Statistical Challenges in Landscape Decision
Making’, which took place between 3 July to 2 August 2019, focusing on coupling
models to represent interactions within landscape systems. Many studies of
landscape decisions are based on models of individual sectors, such as
agriculture, forestry and water use, without considering interactions between
these sectors. Yet, many drivers (be they climate change, policies or

other) may lead to altered interactions between sectors
and scales. Coupling models across sectors and scales enables interactions,
trade-offs and synergies between different components of landscape systems to
be captured in a systemic manner. This is important because modelling
assessments that do not account for cross-sectoral or cross-scale interactions
have the potential to misrepresent impacts and thus, the need or otherwise for
adaptive action through landscape decision-making. Hence, this topic was
discussed in detail during the 2019 INI Programme. Research priorities were
divided into four

themes: (i) transparency, reproducibility and
communication in coupled models; (ii) model coupling toolbox; (iii) model
coupling technicalities; and

(iv) taking advantage of the benefits of model coupling.
The key insights that emerged in these four themes were captured within short,
medium and longer term research roadmaps.
Tuesday 8th September 2020
09:30 to 09:55 James Skates (Welsh Government)
Stakeholder talk
09:30 to 11:00 Session 2: Stakeholder Perspectives - CHAIR Paula Harrison INI 1
09:55 to 10:20 Pam Berry (Department for Environment, Food and Rural Affairs); (University of Oxford); Daniel McGonigle (Department for Environment, Food and Rural Affairs)
Stakeholder talk
10:20 to 10:45 Sue Pritchard (Food, Farming and Countryside Commission)
In the big debates about land use, whose voices count?
The Food, Farming and Countryside Commission (FFCC) has already engaged in innovative new approaches to engage citizens around the UK in the big questions about countryside, environment, climate, nature and land use. Yet it remains difficult to get diverse perspectives into research and debate. Learning from FFCC’s experiences, Sue will use her talk to explore different strategies for democratic decision making around land use in the UK.  

10:45 to 11:00 Heiko Balzter (University of Leicester)
'Building back better landscapes' - UK landscape decision making after Covid-19
The Landscape Decisions Programme aims to integrate social, ecological and mathematical sciences into landscape decision frameworks. It was initiated before the Covid-19 pandemic disrupted everybody’s lives. But how has Covid-19 changed the demand structure on UK landscapes and how we ought to adapt our decision making? This talk seeks to illuminate some of the issues emerging from the current discussion how to ‘Build Back Better’. Fundamental questions include the environmental impacts of the restrictions on movement and social distancing on landscapes, environmentally friendly pathways to economic recovery, the role of landscapes in achieving net zero carbon, air pollution in cities, disease transmission from wild animals to humans, the value of local recreational uses of landscapes to public health, social and economic inequalities, access to the countryside, changing cultural perceptions of landscapes and issues of equality and diversity. In this context it is important to understand the political, cultural and land ownership contexts in which landscape decisions in the UK are taken. A tension may arise when some ecosystem services such as air quality or flood retention benefit the wider population, but others benefit mainly the landowner or tenant. In this talk, I will reflect on how the Landscape Decisions Programme may be able to make a contribution to ‘building back better landscapes’ as we come out of the lockdown into a post-Covid world, and the contributions that social, ecological and mathematical sciences can make.

11:30 to 13:30 Break
13:30 to 14:50 Session 3: Landscape and Decisions Large Maths Call Project Presentations - CHAIR Peter Challenor INI 1
13:30 to 13:50 Mark Brewer (Biomathematics & Statistics Scotland (BioSS))
Drought risk analysis for forested landscapes: Project PRAFOR
This project aims to extend theory for probabilistic risk
analysis of continuous systems, test its use against forest data, use process
models to predict future risks, and develop decision-support tools.

Risk is commonly defined as the expectation value for
loss. Most risk theory is developed for discrete hazards such as accidents,
disasters and other forms of sudden system failure and not for systems where
the hazard variable is always present and continuously varying, with matching
continuous system response.

Risks from such continuous hazards (levels of water,
pollutants) are not associated with sudden discrete events, but with extended
periods of time during which the hazard variable exceeds a threshold. To manage
such risks, we need to know whether we should aim to reduce the probability of
hazard threshold exceedance or the vulnerability of the system.

In earlier work, we showed that there is only one
possible definition of vulnerability that allows formal decomposition of risk
as the product of hazard probability and system vulnerability. We have used
this approach to analyse risks from summer droughts to the productivity of
vegetation across Europe under current and future climatic conditions; this
showed that climate change will likely lead to greatest drought risks in
southern Europe, primarily because of increased hazard probability rather than
significant changes in vulnerability.

We plan to improve on this earlier work by: adding
exposure to hazard; quantifying uncertainties in our risk estimates for risk;
relaxing assumptions via Bayesian hierarchical modelling; testing our approach
on both observational data from forests in the U.K., Spain and Finland and on
simulated data from process-based modelling of forest response to climate
change; embedding the approach in Bayesian decision theory; and developing an
interactive web application as a tool for preliminary exploration of risk and
its components to support decision-making.

13:50 to 14:10 Richard Everitt (University of Warwick); (University of Reading)
Approximate Bayesian computation (ABC) and particle MCMC for calibrating computer models
This presentation will describe work conducted under two projects in the Landscape Decisions programme. We will outline the role we believe ABC and particle MCMC can play in calibrating landscape models, describe the current state of software being developed to allow other researchers to easily use these methods, and introduce a new technique called "rare event ABC-SMC^2" for using ABC with high-dimensional data.

14:10 to 14:30 David Large (University of Nottingham)
Developing a statistical methodology for the assessment and management of peatlands
In good condition, peatlands are the most efficient carbon store of all soils.  The UK has 2 Mha of peatlands (10% land area). 80% of these peatlands are damaged to some degree and estimated to emit 10 Mt C a-1, a similar magnitude to oil refineries or landfill sites.  Restoring degraded peatlands to halt carbon losses is an essential part of a global strategy to fight climate change. In the UK, £100s millions of public money have been pledged to restore peatland, yet we do not have a reliable and cost-effective way to direct and evaluate investment in restoration over large and often remote areas.   In a previous research project, we showed that peatland condition can be found from satellite data that measures surface motion of the peat. However, our satellite-based approach produces too much complex data that cannot be reliably and consistently analysed by eye.   To address this, we will develop a new statistical method that can robustly and consistently quantify the changes in the peatland landscape from the satellite data. This requires methods capable of handling extremely large and complex structured datasets. In statistics, a new framework, known as Object-Oriented Data Analysis (OODA), is ideally suited to achieve this purpose by building models based on suitable choices of data objects. OODA can be used for developing parsimonious models for detecting change, and for quantifying uncertainty in predictions. OODA of the satellite data as functions of space and time will enable the modelling of trends and variability in the different regions, and the detection of change in the peatland.   The result will be a series of maps that illustrate the change in peatland landscape over time that are designed to be used by land managers and policy makers to guide decision making, help reduce unnecessary spending and evaluate investment.  

14:30 to 14:50 Eleni Matechou (University of Kent)
Integrating new statistical frameworks into eDNA survey and analysis at the landscape scale
DNA-based surveys are increasingly being employed for monitoring wildlife species, while at the same time, new statistical methods are being developed for modelling species records. In this talk I will describe the rationale and idea behind our project, which aims to realise the huge potential contribution of DNA-based data to decision-making at the landscape level.

14:50 to 16:00 Break
16:00 to 17:30 Workshop Quiz
Wednesday 9th September 2020
09:30 to 10:30 Session 5: State-of-the-art Environmental modelling - CHAIR Paula Harrison INI 1
09:30 to 10:00 Mark Rounsevell (Karlsruhe Institute of Technology (KIT)); (University of Edinburgh)
An overview of land system modelling
Land system modelling is primarily focused on the question: how do land managers make decisions about the use of land resources. There is a long history of land system modelling stretching back to classical economists in the early 19th Century. Early concepts focused strongly on economic (land rent) models to explain land use distributions, but these began to also include the notion of relative location (e.g. distance to markets) in determining land use patterns. Much of the initial thinking from this time is still relevant today and shapes, to some extent, current thinking about how to model the decision-making processes of land users. However, as land use models evolved through time, non-economic aspects began to take on increasing importance. Processes such as access to information and the spatial diffusion of knowledge through space and time were shown to be critical in understanding landscape decisions. This has led to an evolution in land system modelling towards a focus on agency and social interaction in addition to economic aspects. Methods such as Agent-Based Modelling (ABM) are now able to accommodate a range of human behaviours that underpin decision making and the social interaction processes that foster knowledge exchange through cooperation or competition. In this talk I provide an overview of the evolution of land system modelling exploring the advantages and disadvantages of the many extant approaches. I also explore the considerable progress that is still needed to develop land system models further, e.g. better representing human decision-making processes, better testing against data, coupling land system models with other components of the broader environment, and endogenizing the policy making process within models.

10:00 to 10:30 Brett Day (University of Exeter)
Simulation of UK land-use policy using integrated environment-economy models
By and large, exploration of land use change using integrated environment-economy models has tended to focus on the analysis of scenarios or on the exploration of locations where land use change might deliver desired outcomes. Of course, determining where best to change land use and actually achieving that change using the policy levers open to decision-makers are two different things. In this talk we present methods and results from a series of on-going projects, that use integrated environment-economy models to examine the question of 'best' policy design. Methodologically the key innovations of the research revolve around the application of mathematical programming to identify 'best' policies, the use of agent-based modelling to examine outcomes for policies that place land owners in situations of strategic interaction and the application of methods of robust optimisation to examine decision-making under uncertainty. Our applications focus on land use change, particularly those relating to the deintensification of agriculture for multiple environmental gains and the expansion of biocrops and forest to achieve carbon reduction targets.

10:30 to 11:00 Break
11:00 to 12:00 Session 4a: State-of-the-art in quantitative social modelling - CHAIR Viktoria Spaiser INI 1
11:00 to 11:30 Gary Polhill (The James Hutton Institute)
Social simulation modelling within landscape systems
Agent-based social simulation entails the explicit, individual representation of various actors within the landscape, and the ways they affect each other. It offers a natural and powerful way to model human social systems and integrate with spatially-explicit biophysical and ecological models. In this talk, I will present an example from my own work with Alessandro Gimona and Nick Gotts (The James Hutton Institute) and Andrew Jarvis (Lancaster Environment Centre) on simulating the incentivization of biodiversity in agriculture. The talk will briefly cover some of the risks associated with coupling models to simulate landscape systems, emphasizing the importance of semantic interoperability.
11:30 to 12:00 Marc Keuschnigg (Linköpings Universitet)
Analytical Sociology and Computational Social Science
Analytical sociology focuses on social interactions among individuals and the hard-to-predict aggregate outcomes they bring about. It seeks to identify generalizable mechanisms giving rise to emergent properties of social systems which, in turn, feed back on individual decision-making. This research program benefits from computational tools such as agent-based simulations, natural language processing, and large-scale web experiments, and has considerable overlap with the nascent field of computational social science. By providing relevant analytical tools to rigorously address sociology’s core questions, computational social science has the potential to considerably advance sociology. The disciplinary relationship, however, is not a one-way street, and this talk outlines how analytical sociology, with its theory-grounded approach to computational social science, can help to move the field forward from mere descriptions and predictions to the explanation of social phenomena.

12:00 to 13:30 Break
13:30 to 16:00 Session 4b: State-of-the-art in quantitative social modelling - CHAIR Felix Eigenbrod INI 1
13:30 to 14:00 Alexis Comber (University of Leeds)
Key Considerations for integrating Quantitative Social Science within Landscape Decisions
The land resource is used to satisfy many different land
-related objectives:

food production and security, biodiversity, housing and
other developments, leisure and recreation, as well as flood protection,
biomass, energy production and waste. Landscape Decisions are fundamentally
concerned determining what to put where and have to balance competing demands
for these different Ecosystem Services. This allocation problem is further
complicated by a number of specifically social factors:

- Different actors in landscape decision making (from
policy to landowners to citizen ‘consumers’) have different objectives and
priorities and value landscape elements in different ways

- These values vary between and within groups, as well as
with socio-economic context

- Individual and institutional objectives also operate
over different time frames and spatial scales Thus some form of socio-economic
analysis or social modelling is integral to landscape decisions to

- incorporate stakeholder preferences (e.g. the relative
value of any given


- model land management behaviours (e.g. risk seekers,
consolidators, market reactors, etc)

- evaluate the socio-economic impacts of landscape
decisions (e.g. to quantify the trade-offs between food production and flood
risk mitigation) This talk will outline and illustrate the impacts of a number
of key but frequently overlooked issues associated with incorporating *any spatial
data* (including data describing social processes) into landscape decision
models, related to scale, scales of decision making and model evaluation.
14:00 to 14:30 Suzy Moat (University of Warwick); (The Alan Turing Institute)
Quantifying beautiful places and their link to health and happiness
Are beautiful environments good for our health and
happiness? In this talk, I will describe how millions of ratings from an online
game called ‘Scenic-or-Not’ and a mobile app called ‘Mappiness’ have begun to
offer new answers to this age-old question. I will explain how deep learning
can help us understand whether beautiful places are simply natural places - or
whether humans might be able to build beautiful places too.
14:30 to 15:00 Break
15:00 to 15:30 Milena Tsvetkova (London School of Economics)
Studying complex social systems with online games
Controlled experiments with human subjects provide causal answers to questions that are otherwise difficult to address with observational methods. Specifically, laboratory experiments have been crucial for improving our understanding of individual behavior. Nowadays, online experiments allow us to scale up and also study collective behavior, group-level phenomena, and complex-system dynamics such as positive feedback loops, tipping points, path dependency, and self-organization. To demonstrate the potential of this method, I will present a project that uses an online game to study how differently endowed individuals who interact with each other can produce fair outcomes. We juxtapose fairness mechanisms that individuals employ – generosity, reciprocity, and inequity aversion – with competing concepts of societal fairness – meritocracy, equality of opportunity, equality of outcomes, and Rawls’ theory of justice. The work illuminates which interventions will work better for a specific desired outcome in a company, organization, school, or community. The game and method can be adapted to study topics as diverse as urban segregation, rural development, and immigrant integration.

15:30 to 16:00 Jakub Bijak (University of Southampton)
The tale of the three landscapes: Connecting the layers through modelling
Landscape can be conceptualised through a range of interacting layers, corresponding to different aspects and features that vary across space. In this talk, we focus on three such layers: physical, human and information. By using an example of an agent-based model of migration route formation, we show how the interactions between these three layers can be modelled and analysed. We also demonstrate how the tools of uncertainty quantification can shed light on the properties and behaviour of the models and systems they represent. We conclude by reflecting on the perspectives of model-based approaches for connecting the various layers of the landscape in a coherent way, drawing from the experience of different disciplines of science

Thursday 10th September 2020
09:30 to 15:15 Session 6: Integrating social, mathematical and enviro-ecological modelling - Group Discussions
09:30 to 09:45 Paula Harrison (Centre for Ecology & Hydrology); Felix Eigenbrod (University of Southampton)
09:45 to 12:00 Formation of breakout groups & initial discussions
Breakout Room Topics:
-The role of uncertainty in decision-making
-The role of spatial-temporal dynamics in landscape decision-making   
-The role of complexities and non-linearities in landscape decision-making-The role of human processes in landscape decision-making   
-The role of social influence in landscape decision-making processes   
- Interdisciplinary integration across the social, mathematical and environmental sciences to improve the scientific evidence base supporting landscape decision-making.

12:00 to 13:30 Break
13:30 to 14:30 Reporting back from breakout groups on gaps and priorities for future research INI 1
14:30 to 14:45 Viktoria Spaiser (University of Leeds)
Summary of workshop aims
14:45 to 15:15 Peter Challenor (University of Exeter); Paula Harrison (Centre for Ecology & Hydrology); Felix Eigenbrod (University of Southampton); Viktoria Spaiser (University of Leeds)
Panel discussion – Key steps towards interdisciplinary integration across the social, mathematical and environmental sciences to improve the scientific evidence base supporting landscape decision-making.
University of Cambridge Research Councils UK
    Clay Mathematics Institute London Mathematical Society NM Rothschild and Sons