Practical application of spatial ecosystem service models to aid decision support

Ecosystem Service (ES) spatial modelling is a key component of the integrated assessments designed to support policies and management practices aiming at environmental sustainability. ESTIMAP (“Ecosystem Service Mapping Tool”) is a collection of spatially explicit models, originally developed to support policies at a European scale. We based our analysis on 10 case studies, and 3 ES models. Each case study applied at least one model at a local scale. We analyzed the applications with respect to: the adaptation process; the “precision differential” which we define as the variation generated in the model between the degree of spatial variation within the spatial distribution of ES and what the model captures; the stakeholders opinions on the usefulness of models. We propose a protocol for adapting ESTIMAP to the local conditions. We present the precision differential as a means of assessing how the type of model and level of model adaptation generate variation among model outputs. We then present the opinion of stakeholders; that in general considered the approach useful for stimulating discussion and supporting communication. Major constraints identified were the lack of spatial data with sufficient level of detail, and the level of expertise needed to set up and compute the models.

ZULIAN Grazia;  STANGE Erik;  WOODS Helen;  CARVALHO Laurence;  DICK Jan;  ANDREWS Christopher;  BARÒ Francesc;  VIZCAINO Maria Pilar;  BARTON David;  NOWEL Megan;  RUSCH Graciela;  AUTUNES Paula;  FERNANDES João;  FERRAZ Diogo;  FERREIRA DOS SANTOS Rui;  ASZALÓS Réka;  ARANY Ildikó;  CZÚCZ Bálint;  PRIESS Jorg;  HOYER Christian;  BÜRGER-PATRICIO Gleciani;  LAPOLA David;  MEDERLY Peter;  HALABUK Andrej;  BEZAK Peter;  KOPPEROINEN Leena;  VIINIKKA Arto; 

2017-12-31

ELSEVIER SCIENCE BV

JRC106695

2212-0416,   

http://www.sciencedirect.com/science/article/pii/S2212041617302358,    https://publications.jrc.ec.europa.eu/repository/handle/JRC106695,   

10.1016/j.ecoser.2017.11.005,