TY - JOUR

T1 - Are expert-based ecosystem services scores related to biophysical quantitative estimates?

AU - Roche, P.K.

AU - Campagne, C.S.

N1 - Funding Information: We thank Petteri Vihervaara for a review and comments on a draft version of this paper. Funding: This study is part of a regional ES assessment supported by a research contract from the Haut de France DREAL (Regional Direction for Environment, Planning and Housing).

PY - 2019/11

Y1 - 2019/11

N2 - Among the different approaches developed to assess ecosystem services (ES), the capacity matrix is flexible and quick to implement. The matrix is a look-up table that assigns each ecosystem type a score expressing its ES capacity. Using expert elicitation enables resource efficient and integrative ES scoring that can meet general demand for ES mapping and assessment at different scales. There is an implicit consideration that data from proxies or models would provide better estimates of ES biophysical value as expert-based scores are subjective and depend on expert preferences and therefore unreliable. To test this assumption, we compared using linear and geographically weighted regression (GWR) to compare ES scores provided by an expert panel for seven ES with eight spatial quantitative biophysical indicators at landscape scale for the French Hauts-de-France Region. We obtained statistically significant linear regression r 2 between 0.03 and 0.76 and GWR r 2 between 0.56 and 0.81. The hot cold maps produced using expert scores and quantitative indicators were highly correlated. We conclude that using expert knowledge through the matrix approach yields results very close to those from quantitative proxies or biophysical models for the evaluation of ES at the regional level, particularly when there is a need to evaluate many ES or in a data scarce region.

AB - Among the different approaches developed to assess ecosystem services (ES), the capacity matrix is flexible and quick to implement. The matrix is a look-up table that assigns each ecosystem type a score expressing its ES capacity. Using expert elicitation enables resource efficient and integrative ES scoring that can meet general demand for ES mapping and assessment at different scales. There is an implicit consideration that data from proxies or models would provide better estimates of ES biophysical value as expert-based scores are subjective and depend on expert preferences and therefore unreliable. To test this assumption, we compared using linear and geographically weighted regression (GWR) to compare ES scores provided by an expert panel for seven ES with eight spatial quantitative biophysical indicators at landscape scale for the French Hauts-de-France Region. We obtained statistically significant linear regression r 2 between 0.03 and 0.76 and GWR r 2 between 0.56 and 0.81. The hot cold maps produced using expert scores and quantitative indicators were highly correlated. We conclude that using expert knowledge through the matrix approach yields results very close to those from quantitative proxies or biophysical models for the evaluation of ES at the regional level, particularly when there is a need to evaluate many ES or in a data scarce region.

KW - Assessment

KW - Biophysical indicators

KW - Capacity matrix

KW - Ecosystem service mapping

KW - Look-up table

KW - Regional level

UR - http://www.scopus.com/inward/record.url?scp=85067886280&partnerID=8YFLogxK

U2 - 10.1016/j.ecolind.2019.05.052

DO - 10.1016/j.ecolind.2019.05.052

M3 - Article

VL - 106

JO - Ecological indicators

JF - Ecological indicators

SN - 1470-160X

M1 - 105421

ER -