Are ecosystem services adequately quantified?
- Quantification of ecosystem services (ES) is an important step in operationalizing the concept for management and decision-making. With the exponential increase in ES research, ES have become a ‘catch-all phrase’, which some suggest has led to a poorly defined, impractical and ambiguous concept. An overview of the methods used in ES quantification is needed to examine their scientific rigour and provide guidelines for selecting appropriate measures.
- We present a systematic review of 405 peer-reviewed ES research papers to address the question: ‘Is the biophysical and socio-economic reality of ES adequately quantified? First, we considered whether ES measures are scientifically rigorous enough by considering four predefined criteria (the type of data used, quantification of uncertainty, validation done and data reported). Secondly, using a novel approach, we determined which part of the ES cascade was measured: the ecosystem property, function, service, benefit or value.
- Our results showed that each of the 21 ES analysed had on average 24 different measures, which may indicate the complex reality of ES and/or suggest a potential lack of consensus on what constitutes an ES. We found that uncertainty is often not included and validation mostly missing.
- When analysing which part(s) of the ES cascade each measure corresponded to, we found that for regulating ES, ecosystem properties and functions (ecological aspects) are more commonly quantified (67% of measures). Conversely for provisioning ES, benefits and values (socio-economic aspects) are more commonly quantified (68%). Cultural ES are predominantly quantified using scores (35%).
- In conclusion, ES appear to be poorly quantified in many cases, as often only one side of the cascade is considered (either the ecological or socio-economic side) and oversimplified and variable indicators are often used.
- Policy implications. This review provides a detailed overview of ecosystem services (ES) quantification (ranging from simple scores to advanced methods) with the aim to support future ES quantification and ultimately the successful application of the ES concept.