Uncertainty of Monetary Valued Ecosystem Services – Value Transfer Functions for Global Mapping

<div><p>Growing demand of resources increases pressure on ecosystem services (ES) and biodiversity. Monetary valuation of ES is frequently seen as a decision-support tool by providing explicit values for unconsidered, non-market goods and services. Here we present global value transfer functions by using a meta-analytic framework for the synthesis of 194 case studies capturing 839 monetary values of ES. For 12 ES the variance of monetary values could be explained with a subset of 93 study- and site-specific variables by utilizing boosted regression trees. This provides the first global quantification of uncertainties and transferability of monetary valuations. Models explain from 18% (water provision) to 44% (food provision) of variance and provide statistically reliable extrapolations for 70% (water provision) to 91% (food provision) of the terrestrial earth surface. Although the application of different valuation methods is a source of uncertainty, we found evidence that assuming homogeneity of ecosystems is a major error in value transfer function models. Food provision is positively correlated with better life domains and variables indicating positive conditions for human well-being. Water provision and recreation service show that weak ownerships affect valuation of other common goods negatively (e.g. non-privately owned forests). Furthermore, we found support for the shifting baseline hypothesis in valuing climate regulation. Ecological conditions and societal vulnerability determine valuation of extreme event prevention. Valuation of habitat services is negatively correlated with indicators characterizing less favorable areas. Our analysis represents a stepping stone to establish a standardized integration of and reporting on uncertainties for reliable and valid benefit transfer as an important component for decision support.</p></div

Range of monetary valued ES.

<p>Represents the database of unit-adjusted monetary values of standardized ES types [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0148524#pone.0148524.ref037" target="_blank">37</a>] from peer reviewed data collections [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0148524#pone.0148524.ref013" target="_blank">13</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0148524#pone.0148524.ref036" target="_blank">36</a>]. The coloured bar charts reflect the total number of monetary values per ES type; the grey boxplots represent the variability of economic values. ES in bold font indicate the selection of 12 ES types (839 values) that were used for the value transfer functions.</p

Global spatial distribution of monetary estimates and uncertainties.

<p>The bivariate maps show the extrapolated relative monetary values (yellow to green) and uncertainties (yellow to red) of the meta-analytic value transfer functions for the ES: A) food provision, B) water provision, C) climate regulation, D) extreme events regulation, E) recreation service and F) habitat service. Monetary values and uncertainties are grouped into three classes (low, medium, high) accordingly to the spatial extrapolations of the optimized value transfer functions respectively the confidence intervals of transferred monetary values (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0148524#sec002" target="_blank">method</a> section). The classes were defined by equal interval distances for each ES separately. Accordingly, classes between ES contain different ranges of values. However, a standardized color code (0–1) was used for simplicity of visualization.</p

Overview of input data and characteristics of value transfer functions for 12 ES.

<p>The table shows for each ES the number of case studies and monetary values (2^nd column). In the 3^rd column pie charts reflect the relative influence (importance) of groups of covariates expressed in percentage values and number of covariates (number in brackets) in these groups. The importance of covariates is illustrated by the size of the pie slide and quantified in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0148524#pone.0148524.s002" target="_blank">S2 Fig</a>. The bluish bar charts in column 4 represent the model quality based on percentage of variance explained by the model (R-squared). Additionally, column 4 shows the percentage area of terrestrial earth surface covered accordingly to uncertainty classes (low, medium, high).</p

Workflow from data compilation to uncertainty estimation.

<p>The diagram shows different steps of data preparation and analysis (grey boxes): i) synthesis of monetary values (response variable), ii) compilation of covariates that are supposed to affect the variance of monetary values; and iii) development of value transfer functions. The bluish boxes show (interim-) results of the different steps and refer to figures that visualize these outputs.</p

Appendix C. Detailed description of explanatory variables.

Detailed description of explanatory variables

Appendix B. The list of plant species analyzed.

The list of plant species analyzed

Appendix A. Parsimonious regression tree, magnitude of the coefficients, and the probability of significance for parsimonious models.

Parsimonious regression tree, magnitude of the coefficients, and the probability of significance for parsimonious models

Appendix E. Missing data imputation procedure and results.

Missing data imputation procedure and results

Appendix D. References used for the year of the first occurrence in the wild of central European species in North America.

References used for the year of the first occurrence in the wild of central European species in North America