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

Synchronized peak-rate years of global resources use

Many separate studies have estimated the year of peak, or maximum, rate of using an individual resource such as oil. However, no study has estimated the year of peak rate for multiple resources and investigated the relationships among them. We exploit time series on the appropriation of 27 global renewable and nonrenewable resources. We found 21 resources experienced a peak-rate year, and for 20 resources the peak-rate years occurred between 1960-2010, a narrow time window in the long human history. Whereas 4 of 7 nonrenewable resources show no peak-rate year, conversion to cropland and 18 of the 20 renewable resources have passed their peak rate of appropriation. To test the hypothesis that peak-rate years are synchronized, i.e., occur at approximately the same time, we analyzed 20 statistically independent time series of resources, of which 16 presented a peak-rate year centered on 2006 (1989-2008). We discuss potential causal mechanisms including change in demand, innovation and adaptation, interdependent use of resources, physical limitation, and simultaneous scarcity. The synchrony of peak-rate years of multiple resources poses a greater adaptation challenge for society than previously recognized, suggesting the need for a paradigm shift in resource use toward a sustainable path in the Anthropocene

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

Large scale land acquisitions and REDD+: a synthesis of conflicts and opportunities

Large scale land acquisitions (LSLA), and Reducing Emissions from Deforestation and forest Degradation (REDD+) are both land based phenomena which when occurring in the same area, can compete with each other for land. A quantitative analysis of country characteristics revealed that land available for agriculture, accessibility, and political stability are key explanatory factors for a country being targeted for LSLA. Surprisingly LSLA occur in countries with lower accessibility. Countries with good land availability, poor accessibility and political stability may become future targets if they do not already have LSLA. Countries which high levels of agriculture-driven deforestation and LSLA, should develop interventions which reduce forest loss driven either directly or indirectly by LSLA as part of their REDD+ strategies. Both host country and investor-side policies have been identified which could be used more widely to reduce conflicts between LSLA and REDD+. Findings from this research highlight the need for and can inform the development of national and international policies on land acquisitions including green acquisitions such as REDD+. Land management must be considered with all its objectives—including food security, biodiversity conservation, and climate change mitigation—in a coherent strategy which engages relevant stakeholders. This is not currently occurring and might be a key ingredient to achieve the targets under the Sustainable Development Goals 2 and 15 and 16 (related to food security and sustainable agriculture and the protection of forests) among others

glUV: a global UV-B radiation data set for macroecological studies

1. Macroecology has prospered in recent years due in part to the wide array of climatic data, such as those provided by theWorldClim and CliMond data sets, which has become available for research. However, important environmental variables have still been missing, including spatial data sets on UV-B radiation, an increasingly recognized driver of ecological processes. 2. We developed a set of globalUV-B surfaces (glUV) suitable to match common spatial scales in macroecology. Our data set is based on remotely sensed records fromNASA’sOzone Monitoring Instrument (Aura-OMI). Following a similar approach as for theWorldClim and CliMond data sets, we processed daily UV-B measurements acquired over a period of eight years into monthly mean UV-B data and six ecologically meaningful UV-B variables with a 15-arc minute resolution. These bioclimatic variables represent Annual Mean UV-B, UV-B Seasonality, Mean UV-B of Highest Month, Mean UV-B of Lowest Month, Sum of Monthly Mean UV-B during Highest Quarter and Sum of Monthly Mean UV-B during Lowest Quarter. We correlated our data sets with selected variables of existing bioclimatic surfaces for land and with Terra–MODIS Sea Surface Temperature for ocean regions to test for relations to known gradients and patterns. 3. UV-B surfaces showed a distinct seasonal variance at a global scale, while the intensity of UV-B radiation decreased towards higher latitudes and was modified by topographic and climatic heterogeneity. UV-B surfaces were correlated with global mean temperature and annual mean radiation data, but exhibited variable spatial associations across the globe.UV-B surfaces were otherwise widely independent of existing bioclimatic surfaces. 4. Our data set provides new climatological information relevant for macroecological analyses. As UV-B is a known driver of numerous biological patterns and processes, our data set offers the potential to generate a better understanding of these dynamics inmacroecology, biogeography, global change research and beyond. The glUV data set containing monthly mean UV-B data and six derived UV-B surfaces is freely available for download at: http://www.ufz.de/gluv