Projects

Within the context of complex human-environmental systems - the interaction of bio-physical and social-economic systems - current projects are linked by common methodological approaches and research themes including improving sustainability, understanding resilience, mapping dynamic responses. Our more theoretical projects are related to understanding the properties of components within the larger systems we study. 

 

Marine environments:

 

Terrestrial environments:

Theory:

POSEIDON - management of marine fisheries

We are using a combination of theoretical modelling and empirical data analysis approaches to investigate new practical approaches to fisheries management in a range of contexts. The extraction of ocean resources plays out as a complex interaction between ocean ecology, collections of individual fishermen, economics and policy. Finding management solutions which engender sustainability both ecologically and economically, in conditions which are dynamic and heterogeneous on a variety of temporal and spatial scales, is non-trivial and is the key problem that drives this research. We have built a sophisticated fisheries model (including ocean ecology, an individual-based fishing fleet, market interactions and policy) - POSEIDON. One use of this model is as a ‘flight simulator’ for fisheries management strategies in both data-rich and data-poor contexts. We also use state of the art computational approaches, analysis of decision-making, and extensive engagement with stakeholders and policy-makers, to optimise and create policies for a range of sustainable target conditions. Model applications have so far included the US West Coast Groundfish, Indonesian Snapper and Pacific Tuna.

Left: a schematic represent of the main elements of the POSEIDON model, including the policy analysis and optimisation components

Centre: an example output showing boats operating without constraint in an open-access fishery. Expanding fishing fronts naturally develops, where the trade-off between fuel costs and the exploration of virgin stock is balanced at different radial distances for larger and smaller boats.  See Bailey et al. (2018) for further examples and discussion.

Right: An example from an ongoing project in which we are adapting POSEIDON to simulate the Eastern Pacific tuna fishery. This fishery is characterized by the use of fish aggregating devices (FAD) and months-long ocean trips by purse seine vessels, requiring us to add models of oceanic currents and long-term agent planning capabilities to POSEIDON. The image shows the model domain and both boats and FADs distributed in the ocean.

This project is part of the Oxford Martin School Programme on Sustainable Oceans, and run in close collaboration with Ocean Conservancy, along with partners in UC Santa Barbara, George Mason University Arizona State University.

Group members involved:

Richard Bailey, Ernesto Carrella, Jens Madsen, Nicolas Payette

 

OSIRIS - Ocean Systems Interactions, Risks, Instabilities and Synergies

Marine biological communities are affected by a combination of changes that affects their composition and ecosystem functioning and services in a significant way. While habitat destruction and overfishing are critical and immediate short-term threats, there is growing concern that changes in underlying global ocean conditions (e.g. water temperature changes, acidification due to increased climate change effects) will combine with these local/regional direct drivers to push marine systems to new, and potentially dysfunctional, states. 

Much work on single species, or particular groups (e.g. corals), have been done to increase knowledge of how individual stressors (such as acidification) impacts these organisms. In particular, the empirical advances in our understanding of combined multiple stressors’ influence have shown that the combinations of stressors are not always additive(f(A) + f(B) = f(A+B), but can be antagonistic, where the sum of the effects is less than expected based on the estimated single effects (f(A) + f(B) > f(A + B)), or synergistic (f(A) + f(B) < f(A + B)), where the sum of the effects is larger than expected based on the estimated single effects. However, most of these studies consider pair-wise stressors, while natural systems rarely experience just two combined stressors; it is assumed that more than two stressors generally result in (stronger) synergistic effects. Recognising and incorporating these types of effects are important due to their consequences for species/groups’ performance and survival. However, controlled experiments varying two or more stressors and studying the effects on survival and fitness are difficult if not impossible for some species. It is impossible to do these experiments for whole ecological communities, where changes in relative abundance and biomass can result in changes in species interactions and mitigate or amplify environmental changes. These changes can have large-scale consequences in coastal and oceanic communities and the ecosystem services they provide for people. 

It is unlikely that multifactor, controlled experiments can be implemented for multispecies or whole ecological communities. To supplement the empirical work, there is a strong need for an effective analytical framework to quantify the role of multiple stressors and to evaluate the consequences of interacting climate and non-climate stressors in marine systems. If synergistic effects prove to have large ecosystem consequences, an additional level of caution in how we manage our oceans will likely be warranted. Furthermore, if synergies are common and important, international carbon dioxide emissions targets (set at 2˚C) may still be too high to maintain ocean functionality. In response to these needs, we have developed a new model framework (OSIRIS – Ocean Systems Interactions, Risks, Instabilities and Synergies) to help explore possible system-level consequences of multiple external environmental forcings. 

                                                                                                         Example results: the harmful effects of increased

                                                                                                                                       synergies and noise in forcing
                     An example modelled ecosystem

We currently are using OSIRIS in two ecosystems, the California Current System in the eastern Pacific, and the Chukchi and Beaufort Seas in the Pacific Arctic. Both projects investigate tipping points and system resilience, and incorporate (local) policy effects to mitigate or slow-down system changes. This project is run in close collaboration with Ocean Conservancy, along with Centre for Ocean Solutions at Stanford University.

Group members involved:

Richard Bailey, Jesse van der Grient

 

Ocean Plastics

 

This work is being undertaken in close collaboration with partners at SYSTEMIQ Ltd and Pew, and has the aim of developing a global roadmap for reducing leakage of plastics to the oceans to near zero by 2040. Our role in this work is to support the data analysis and model development for the assessment of economically feasible solutions.

 

Group members involved:

Richard Bailey, Toby Pilditch

Identifying robust policies against deforestation

Agriculture is one of the main drivers of deforestation, which contributes to carbon emissions and biodiversity loss. Finding ways to increase the productivity of agriculture on the same amount of land while protecting areas of existing forest can bring economic benefits and reduce deforestation. My work focuses on understanding agricultural production systems, particularly for high-value cash crops, and encoding these systems in computational simulations. It is possible to use these simulations to test the effect of different policies such as increasing fertilizer use and mechanization affect the profitability of agriculture and the pattern of deforestation. The simulations can also test the influence of uncertainties such as commodity prices on policy outcomes. As a result, decision makers can identify optimal policies which are robust to uncertainties. The advantage to a simulation is that it lets decision makers test these policies in advance before making financial commitments, and avoid potentially costly mistakes. I am also exploring how new data from satellites, drones, and sensors, as well as the application of machine learning algorithms to these data can inform these simulations. See the videos below for an example simulation and more about the methods.

This work is funded by the Green Templeton College DPhil Scholarship.

Group members involved:

Adam Formica, Richard Bailey, Richard Grenyer

 

Optimization of anti-poaching measures

 

Poaching of African elephants Loxodonta africana is one of today’s most

pressing and widely-publicized conservation issues. The international

demand for ivory – a substance so valuable it is sometimes referred to as

‘white gold’ – has led to the rapid decline of many elephant populations.

There are numerous controversies and uncertainties surrounding elephant

poaching and the ivory trade, and debates on how to mitigate the poaching

crisis continue to the present day. 

 

Agent-based models are promising tools to both inform decision-making

and improve our understanding of elephant poaching, because they acc-

ount for the complex and dynamic interplays between elephants, poachers,

and law enforcement. This project explores how various policy and manage-

ment interventions affect levels of poaching. With this model, we hope to

structure the debate around this complex and controversial issue, and help

guide future research or policymaking. This model could also be adapted

in the future to fit the specific conditions of a particular park or country, thus

providing greater insights into the potential outcomes of different

interventions.  

Group members involved: Emily Neil, Richard Bailey, Ernesto Carrella, Jens Koed Madsen

Desert landscape evolution and stability

Dryland regions are characterised by patchy vegetation, erodible surfaces and erosive aeolian processes. These components of the dryland system interact dynamically through a variety of feedbacks. Wind erosion models play a key role in simplifying wind flow and sediment transport processes on partly vegetated surfaces. However, most existing models do not recognise the heterogeneous nature of vegetated desert surfaces; those that do are often computationally expensive to run.

 

 

 

 

 

The dryland system in its simplified form.

To fill this gap, we developed the cellular automaton Vegetation and Sediment Transport model (ViSTA), which couples a sophisticated vegetation distribution model with a sediment transport model. This allows us to explicitly link vegetation growth, wind flow dynamics and sediment flux over any dryland surface. ViSTA requires only a few simple inputs, and can be forced with a variety of climate and land use change scenarios, to characterise possible transition scenarios between environmental states. Since vegetated semi-arid landscapes are often used for pastoralism, agriculture and habitation, the model output has direct relevance to land management policies in some of the world’s most vulnerable environments.

 

 

Examples of model output.

This project was funded by the Natural Environment Research Council. The full ViSTA model code is freely available on GitHub.

Group members involved:

Jerome Mayaud (co-supervised with Prof. Giles Wiggs), Richard Bailey

 
 

Stability of mutualistic ecological systems

 

Conserving ecosystem function and associated services requires deep understanding of the underlying basis of system stability. While the study of ecological dynamics is a mature and diverse field, the lack of a general model that predicts a broad range of theoretical and empirical observations has allowed unresolved contradictions to persist. In this work we provide a general model of mutualistic ecological interactions between two groups and show for the first time how the conditions for bi-stability, the nature of critical transitions, and identifiable leading indicators in time-series can be derived from the basic parameters describing the underlying ecological interactions. Strong mutualism and nonlinearity in handling-time are found to be necessary conditions for the occurrence of critical transitions. We used the model to resolve open questions concerning the effects of heterogeneity in inter-species interactions on both resilience and abundance, and discuss these in terms of potential trade-offs in real systems. This framework provides a basis for rich investigations of ecological system dynamics, and may be generalisable across many ecological contexts.

For further details see Feng & Bailey (2018).

This project was funded by the University of Oxford John Fell Fund.

Project now complete - Group members involved

Wenfeng Feng, Richard Bailey, Kirsty McGregor

Belief persistence in social networks

Social networks include structures such as social media and Internet forums. While they are a tremendous boon for sharing information and generating social contacts, they are also subject to the spread and maintenance of misinformation, manipulation, and alternative facts. This type of information represents a serious problem, as these beliefs can spread, solidify and persist in social networks.

           The project explores how these beliefs can emerge, spread and be maintained through social network interactions. We approach this through rational processes (such as Bayesian belief revision) and Agent-Based Models. Given the process approach, we can test possible interventions to influence beliefs and behaviour in social networks. The project targets belief diffusion in diverse areas such as political campaigns, the climate change debate, and conspiratorial thinking. 

Group members involved:

Jens Madsen, Richard Bailey, Toby Pilditch

 

© 2019 CoHESyS-Lab