Computer modelling for ecosystem service assessment
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Abstract
Computer models are simplified representations of the environment that allow biophysical, ecological, and/or socio-economic characteristics to be quantified and explored. Modelling approaches differ from mapping approaches (Chapter 5) as (i) they are not forcibly spatial (although many models do produce spatial outputs); (ii) they focus on understanding and quantifying the interactions between different components of social and/or environmental systems and (iii)
by changing parameters within models, they are capable of exploring both alternative scenarios and internal model dynamics. When applied to the assessment of ecosystem
services (ES), models are important tools which can quantify the relationships that underpin ES supply, demand and flows and, in some cases, produce maps representing
these factors. Furthermore, as models can explore scenarios, trade-offs that result from different scenarios can be assessed. This chapter provides a broad overview of
different types of models that have been applied to ES assessments and discusses, with examples, the ways that these models have the potential to be used in practice. In the context of ES, there are a number of ways of distinguishing between different
types of models. Here, we distinguish between individual models focussing on single ES and modelling frameworks that can assess multiple ES within the framework of a
single modelling tool.
| Publication type | Book chapter |
|---|---|
| Publication Subtype | Book Chapter |
| Title | Computer modelling for ecosystem service assessment |
| Chapter | 4.4 |
| DOI | 10.3897/ab.e12837 |
| Year Published | 2017 |
| Language | English |
| Publisher | Pensoft Publishers |
| Publisher location | Sofia, Bulgaria |
| Contributing office(s) | Geosciences and Environmental Change Science Center |
| Description | 12 p. |
| Larger Work Type | Book |
| Larger Work Subtype | Monograph |
| Larger Work Title | Mapping ecosystem services |
| First page | 124 |
| Last page | 135 |
| Google Analytic Metrics | Metrics page |