Core principles for integrated models for ecosystem management
Lead Research Organisation:
University of Kent
Department Name: Sch of Maths Statistics & Actuarial Sci
Abstract
Human society and our economy have been built to make use of the natural world. However, as the Dasgupta report has highlighted these activities have damaged our environment and wild populations. Repairing this damage and rebuilding our society to sustainably make use of natural resources will require significant investment. It is important to ensure that this effort is applied effectively otherwise we may do further harm. However, the complexity of the natural world makes it hard to predict how our interventions will affect it.
Mathematics provides a language in which to describe how systems change over time. If a system can be described sufficiently accurately in mathematical terms, it becomes possible to test how it would respond to a given change. Thus, mathematical models have the potential to provide tools that would allow us to design conservation and restoration activities which will be effective. However, while a variety of mathematical models exist for individual environmental, ecological, and socio-economic systems, unified models, which describe how all of these aspects come together are relatively rare. Furthermore, it is not clear how to best go about designing such models to support decision-makers.
This project will address this problem by producing a guide to the development of models to inform management decisions. This will be achieved by assessing the decisions made in constructing the existing wealth of published models to determine how they influenced the overall effectiveness of those models as decision-making tools. From this analysis we will synthesize a set of general model design principles and demonstrate how design decisions follow through into practical consequences. In the process we will identify common modelling approaches between different fields of study and, thus, strategies for the development of wholistic models.
Finally, we will demonstrate how to apply this guide in practice by developing outlines of models for the management of agricultural ecosystems and river ecosystems.
Agricultural ecosystems are large, potentially encompassing an entire country or countries. As a result, their behaviour varies considerably across the system as a whole; this presents a substantial modelling challenge. Furthermore, decisions are typically made for individual farms without necessarily considering how they will affect the overall health of the ecosystem. The model developed needs to bridge this gap.
River ecosystems support agriculture, transport and power generation and are essential in flood management. They also have a complex spatial structure which is affected by many of the uses we put them to. This structure also plays an important role in the health of the wildlife communities which inhabit them. Again, our model for the management of these systems need to account for how local decisions influence the overall health of the ecosystem.
Mathematics provides a language in which to describe how systems change over time. If a system can be described sufficiently accurately in mathematical terms, it becomes possible to test how it would respond to a given change. Thus, mathematical models have the potential to provide tools that would allow us to design conservation and restoration activities which will be effective. However, while a variety of mathematical models exist for individual environmental, ecological, and socio-economic systems, unified models, which describe how all of these aspects come together are relatively rare. Furthermore, it is not clear how to best go about designing such models to support decision-makers.
This project will address this problem by producing a guide to the development of models to inform management decisions. This will be achieved by assessing the decisions made in constructing the existing wealth of published models to determine how they influenced the overall effectiveness of those models as decision-making tools. From this analysis we will synthesize a set of general model design principles and demonstrate how design decisions follow through into practical consequences. In the process we will identify common modelling approaches between different fields of study and, thus, strategies for the development of wholistic models.
Finally, we will demonstrate how to apply this guide in practice by developing outlines of models for the management of agricultural ecosystems and river ecosystems.
Agricultural ecosystems are large, potentially encompassing an entire country or countries. As a result, their behaviour varies considerably across the system as a whole; this presents a substantial modelling challenge. Furthermore, decisions are typically made for individual farms without necessarily considering how they will affect the overall health of the ecosystem. The model developed needs to bridge this gap.
River ecosystems support agriculture, transport and power generation and are essential in flood management. They also have a complex spatial structure which is affected by many of the uses we put them to. This structure also plays an important role in the health of the wildlife communities which inhabit them. Again, our model for the management of these systems need to account for how local decisions influence the overall health of the ecosystem.
Organisations
| Description | The project set out to identify effective mathematical ways to describe the interactions between the natural environment and economic activities. Our intention was to review the considerable amount of work that has been done in this area, pick out the most effective approaches, and thus provide guidance for future work to manage these interactions. We discovered that there is a considerably greater variety in approaches taken than we had anticipated. In particular, in this area, it appears to be relatively uncommon for researchers to re-use/develop the work of others; instead new methods are developed for each problem. This occurs despite the fact that the majority of the research we reviewed could have been re-used or developed further and sufficient detail was provided to make doing so relatively straightforward. The reasons for this are unclear but we suggest the following possibilities: 1) Researchers may struggle more to identify the usable parts of the research of others than they do to develop their own approach. 2) Researchers may prefer to develop their own approaches due to academic incentives, e.g. the perceived requirement for "novel" approaches. 3) We may have missed the relevant literature, e.g. if the re-use/development of these mathematical approaches is occuring in government departments or industrial applications. |
| Exploitation Route | The observation that the economic-ecological literature is relatively fragmented is an important one. A shift towards a more consistent set of approaches would, potentially, allow progress to be made more swiftly and efficiently. We would recommend that the community collectively seek to develop a common framework for modelling of these complex systems. Funders may also wish to encourage the reuse of existing research over the development of new approaches. |
| Sectors | Agriculture, Food and Drink,Environment,Financial Services, and Management Consultancy |