STICS - Stirling Conservation Science
Stirling University
ConFooBio
Resolving conflicts between food security and biodiversity conservation under uncertainty (ConFooBio)
ConFooBio is funded by an EU H2020 ERC Starting Grant to Nils Bunnefeld (2016-2022) (Link to official EU project website)
Postdocs/Fellows: Dr Sarobidy Rakotonarivo, Dr Jeremy Cusack, Dr Brad Duthie, Dr Jeroen Minderman, Dr Isabel Jones, Dr Rocio Pozo, Dr Laura Thomas-Walters, Dr Isla Hodgson
News (24th May 2022): New art-science collaboration page: The Wild Goose Chase
ConFooBio background
Conflicts between food security and biodiversity conservation are increasing in scale and intensity and have been shown to be damaging for both biodiversity and human livelihoods. Uncertainty, for example from climate change, decreases food security, puts further pressure on biodiversity and exacerbates conflicts.
ConFooBio proposes to develop management strategies for conflicts between biodiversity conservation and food security under uncertainty. ConFooBio will integrate game theory and social-ecological modelling to develop new theory to mitigate conservation conflicts. ConFooBio will implement a three-tiered approach 1) characterise and analyse 7 real-world conservation conflicts impacted by uncertainty; 2) develop new game theory that explicitly incorporates uncertainty; and 3) produce and test a flexible social-ecological model, applicable to real-world conflicts where stakeholders operate under conditions of extreme uncertainty. ConFooBio focuses on the following 7 case studies:
The project has importance for society at large because ecosystems and their services are central to human wellbeing. Managing a specific natural resource often results in conflict between those stakeholders focussing on improving food security and those focussed on biodiversity conversation. ConFooBio will illuminate new strategies to such conflicts by showing how to achieve synergies that protect biodiversity and secure livelihoods. This project will develop a practical, transparent and flexible model for the sustainable future of natural resources that is also robust to uncertainty (e.g., climate change). The project aims to be relevant for environmental negotiations among stakeholders with competing objectives from local to global (e.g. the negotiations to implement the United Nations Sustainable Development Goals).
For updates and publications follow the link to official EU project website.
The ConFooBio project has worked on three main themes, 1) characterise and analyse real-world conservation conflicts impacted by uncertainty over time; 2) develop new approaches to collecting data in the field based on game theory that explicitly incorporates uncertainty; and 3) produce and test a flexible social-ecological model for conservation conflict management.
1) Our time series analysis of conservation conflicts has focused on geese and cranes in Northern Europe assessing management responses to increasing populations. Our paper, published in Conservation Letters in 2018 shows a mismatch between population changes and the management responses (i.e. compensation payments, scaring, lethal control and hunting) with a time lag of one to three years for the management action to adapt to changes in the population numbers. Our findings highlight the need for more adaptive and timely responses of management to changes in population numbers so as not to increase social conflicts and jeopardize the status of wildlife populations and local livelihoods. In an additional publication, we have shown that where there are conflicts over wildlife management, we require novel approaches to untangle the complexity of decision making under uncertainty. In the publication that arose from this work we explored the use of Value of Information approaches for goose management in Scotland.
1) Our time series analysis of conservation conflicts has focused on geese and cranes in Northern Europe assessing management responses to increasing populations. Our paper, published in Conservation Letters in 2018 shows a mismatch between population changes and the management responses (i.e. compensation payments, scaring, lethal control and hunting) with a time lag of one to three years for the management action to adapt to changes in the population numbers. Our findings highlight the need for more adaptive and timely responses of management to changes in population numbers so as not to increase social conflicts and jeopardize the status of wildlife populations and local livelihoods. In an additional publication, we have shown that where there are conflicts over wildlife management, we require novel approaches to untangle the complexity of decision making under uncertainty. In the publication that arose from this work we explored the use of Value of Information approaches for goose management in Scotland.
2) We have developed and implemented 216 game workshops with 864 stakeholders across three countries (Scotland, Gabon and Madagascar) to evaluate the impacts of different conflict interventions (subsidies, compensation payments) on stakeholders’ decision making (scaring, killing, habitat conservation). These games take into account the uncertainty of where and when wildlife occurs and on which land parcels. Along with the games we have administered questionnaire surveys with the same 864 participants across the same countries (Scotland, Gabon and Madagascar) to identify key determinants of individual and group behaviour (values, attitudes, local perceptions of equity and trust, socio-demographics). We are currently in the process of analysing the data and preparing a manuscrips.
3) We have developed a novel modelling framework to predict wildlife management outcomes in the presence of stakeholder disagreement and conflict. Our new software for social-ecological modelling called Generalised Management Strategy Evaluation (GMSE) and underlying code is freely available as a package in the statistical software R and on GitHub and has been downloaded over 8 thousand times. The description of the package and its capabilities was published in the peer-reviewed journal Methods in Ecology and Evolution in 2018. The next steps will be to show the broad and realistic applicability of the GMSE model and to apply it to real world case studies.
We have developed and tested a novel application of genetic algorithms for social-ecological modeling. This allows stakeholders in the modeling framework to exhibit goal-orientated behaviour, to adapt their decision making to a dynamic ecosystem as well as to other stakeholders. This allows us to study the behaviour of stakeholders with conflicting goals, for example when one set of stakeholders aims to produce food and the other set aims to conserve biodiversity and wildlife populations. Thus, we have combined computer science with ecology and human behaviour research to develop an interdisciplinary modeling approach to tackle conflicts in biodiversity conservation and food security. The software is called Generalised Management Strategy Evaluation (“GMSE”) and the underlying code is freely available as a package in the statistical software R and on GitHub.
We have also developed a novel game-based approach using tablet computers, where people make decisions about wildlife populations that damage their agricultural crops. Decisions range from scaring to killing to setting aside habitat for conservation under treatments of compensation payments and subsidies. These games are accessible to illiterate and innumerate participants and capturing spatial and temporal dynamics of wildlife and decisions made to help people voice their needs and stimulate discussion on their motives and decision processes in conflicts between conservation of biodiversity and food production and security.
STICS - Stirling Conservation Science 