Integrating quantitative social, ecological and mathematical sciences into landscape decision-making
Monday 7th September 2020 to Thursday 10th 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.
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INI 1 |
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| 14:00 to 14:20 |
Felix Eigenbrod (University of Southampton) Spatial/temporal scaling |
INI 1 |
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| 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.
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INI 1 |
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| 09:30 to 09:55 |
James Skates (Welsh Government) Stakeholder talk |
INI 1 |
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| 09:30 to 11:00 | Session 2: Stakeholder Perspectives - CHAIR Paula Harrison | INI 1 | |
| 09:55 to 10:20 |
Daniel McGonigle (Department for Environment, Food and Rural Affairs); Pam Berry (Department for Environment, Food and Rural Affairs); (University of Oxford) Stakeholder talk |
INI 1 |
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| 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.
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INI 1 |
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| 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.
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INI 1 |
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| 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.
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INI 1 |
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| 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.
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INI 1 |
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| 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.
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INI 1 |
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| 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.
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INI 1 |
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| 14:50 to 16:00 | Break | ||
| 16:00 to 17:30 | Workshop Quiz |
| 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.
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INI 1 |
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| 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.
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INI 1 |
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| 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.
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INI 1 |
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| 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.
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INI 1 |
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| 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
ESs)
- 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.
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INI 1 |
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| 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.
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INI 1 |
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| 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.
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INI 1 |
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| 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 |
INI 1 |
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| 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) Introduction |
INI 1 |
| 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. |
INI 1 |
| 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 |
INI 1 |
| 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. |
INI 1 |
