scholarly journals Using Meta-Analysis for Large-Scale Ecosystem Service Valuation: Progress, Prospects, and Challenges

2019 ◽  
Vol 49 (1) ◽  
pp. 23-63
Author(s):  
Robert J. Johnston ◽  
Dana Marie Bauer
Keyword(s):  
Water Quality ◽  
Ecosystem Services ◽  
Ecosystem Service ◽  
Regression Models ◽  
Large Scale ◽  
Meta Analysis ◽  
Riparian Buffer ◽  
Ecosystem Service Valuation ◽  
Validity Criteria ◽  
Meta Regression

AbstractThis article discusses prospects and challenges related to the use of meta-regression models (MRMs) for ecosystem service benefit transfer, with an emphasis on validity criteria and post-estimation procedures given sparse attention in the ecosystem services literature. We illustrate these topics using a meta-analysis of willingness to pay for water quality changes that support aquatic ecosystem services and the application of this model to estimate water quality benefits under alternative riparian buffer restoration scenarios in New Hampshire's Great Bay Watershed. These illustrations highlight the advantages of MRM benefit transfers, together with the challenges and data needs encountered when quantifying ecosystem service values.

scholarly journals A Global Meta-Analysis for Estimating Local Ecosystem Service Value Functions

2021 ◽  
Author(s):  
Luiz Norberto Lacerda Magalhães Filho ◽  
Peter Cornelis Roebeling ◽  
Maria Isabel Bastos ◽  
Waldecy Rodrigues ◽  
Giulia Claudia Ometto
Keyword(s):  
Ecosystem Services ◽  
Ecosystem Service ◽  
Explanatory Variable ◽  
Meta Analysis ◽  
Explanatory Power ◽  
Value Functions ◽  
Explanatory Variables ◽  
Regression Functions ◽  
Meta Regression ◽  
Meta Analyses

Meta-analysis has increasingly been used to synthesize the ecosystem services literature, with some testing of the use of such analyses to transfer benefits. These are typically based on local primary studies. However, meta-analyses associated with ecosystem services are a potentially powerful tool for transferring benefits, especially for environmental assets for which no primary studies are available. In this study we use the Ecosystem Service Valuation Database (ESVD), which brings together 1350 value estimates from more than 320 studies around the world, to estimate meta-regression functions for provisioning, regulating & maintenance and cultural ecosystem services across 12 biomes. We tested the reliability of these meta-regression functions and found that even using variables with high explanatory power, transfer errors could still be large. We show that meta-analytic transfer performs better than simple value transfer and, in addition, that local meta-analytical transfer (i.e. based on local explanatory variable values) provides more reliable estimates than global meta-analytical transfer (i.e. based on mean global explanatory variable values). Thus, we conclude that when taking into account the characteristics of the study area under analysis, including explanatory variables such as income, population density and protection status, we can determine the value of ecosystem services with greater accuracy.

scholarly journals A Global Meta-Analysis for Estimating Local Ecosystem Service Value Functions

Environments ◽  
2021 ◽  
Vol 8 (8) ◽  
pp. 76
Author(s):  
Luiz Magalhães Filho ◽  
Peter Roebeling ◽  
Maria Isabel Bastos ◽  
Waldecy Rodrigues ◽  
Giulia Ometto
Keyword(s):  
Ecosystem Services ◽  
Ecosystem Service ◽  
Explanatory Variable ◽  
Meta Analysis ◽  
Explanatory Power ◽  
Value Functions ◽  
Explanatory Variables ◽  
Regression Functions ◽  
Meta Regression ◽  
Meta Analyses

The Meta-analysis has increasingly been used to synthesize the ecosystem services literature, with some testing of the use of such analyses to transfer benefits. These are typically based on local primary studies. However, meta-analyses associated with ecosystem services are a potentially powerful tool for transferring benefits, especially for environmental assets for which no primary studies are available. In this study we use the Ecosystem Service Valuation Database (ESVD), which brings together 1350 value estimates from more than 320 studies around the world, to estimate meta-regression functions for Provisioning, Regulating and maintenance, and Cultural ecosystem services across 12 biomes. We tested the reliability of these meta-regression functions and found that even using variables with high explanatory power, transfer errors could still be large. We show that meta-analytic transfer performs better than simple value transfer and, in addition, that local meta-analytical transfer (i.e., based on local explanatory variable values) provides more reliable estimates than global meta-analytical transfer (i.e., based on mean global explanatory variable values). Thus, we conclude that when taking into account the characteristics of the study area under analysis, including explanatory variables such as income, population density, and protection status, we can determine the value of ecosystem services with greater accuracy.

Riparian Buffer Zone and Whole Watershed Influences on River Water Quality: Implications for Ecosystem Services near Megacities

2016 ◽  
Vol 3 (2) ◽  
pp. 277-305 ◽  
Author(s):  
Mariana Z. Nava-López ◽  
Stewart A. W. Diemont ◽  
Myrna Hall ◽  
Víctor Ávila-Akerberg
Keyword(s):  
Water Quality ◽  
Ecosystem Services ◽  
River Water ◽  
Buffer Zone ◽  
River Water Quality ◽  
Riparian Buffer ◽  
Riparian Buffer Zone

Developing a Technical Framework for Integrating Ecosystem Service into Strategic Environmental Assessment

2014 ◽  
Vol 1010-1012 ◽  
pp. 339-343
Author(s):  
Run He Cheng
Keyword(s):  
Ecosystem Services ◽  
Environmental Assessment ◽  
Ecosystem Service ◽  
Strategic Environmental Assessment ◽  
High Rate ◽  
Evaluation Procedure ◽  
Spatial And Temporal Scales ◽  
Biological Resources ◽  
Survival And Development ◽  
Ecosystem Service Valuation

Ecosystem services (ES) of the biological resources are the foundation for human survival and development, but due to frequent interferences of human activities and high rate of population increasing, biological resources have been severely damaged. The occupation of the value of ecosystem services makes the biodiversity receiving more and more threats. Therefore, protection and management of ecosystem services has become more and more urgent. Through evaluation of the values of ecosystem services at the spatial and temporal scales in a strategic environmental assessment (SEA) framework, we may assess the influences of government policies, programs and plans on ecosystem services more efficiently. In this paper we presented on the method for ecosystem service valuation and involving stakeholders and decision-makers in SEA Processes. Then put forward the evaluation procedure framework of integrating ecosystem service into strategic environmental assessment.

scholarly journals Infrastructure and Trade: A Meta-Analysis

REGION ◽  
2015 ◽  
Vol 1 (1) ◽  
pp. 25 ◽  
Author(s):  
Guney Celbis ◽  
Peter Nijkamp ◽  
Jacques Poot
Keyword(s):  
Regression Models ◽  
Meta Analysis ◽  
Public Infrastructure ◽  
Transport Costs ◽  
Panel Data Estimation ◽  
Regression Techniques ◽  
Meta Regression ◽  
Low Levels ◽  
The Impact ◽  
Data Estimation

Low levels of infrastructure quality and quantity can create trade impediments through increased transport costs. Since the late 1990s an increasing number of trade studies have taken infrastructure into account. The purpose of the present paper is to quantify the importance of infrastructure for trade by means of meta-analysis and meta-regression techniques that synthesize various studies. The type of infrastructure that we focus on is mainly public infrastructure in transportation and communication. We examine the impact of infrastructure on trade by means of estimates obtained from 36 primary studies that yielded 542 infrastructure elasticities of trade. We explicitly take into account that infrastructure can be measured in various ways and that its impact depends on the location of the infrastructure. We estimate several meta-regression models that control for observed heterogeneity in terms of variation across different methodologies, infrastructure types, geographical areas and their economic features, model specifications, and publication characteristics. Additionally, random effects account for between-study unspecified heterogeneity, while publication bias is explicitly addressed by means of the Hedges model.  After controlling for all these issues we find that a 1 percent increase in own infrastructure increases exports by about 0.6 percent and imports by about 0.3 percent. Such elasticities are generally larger for developing countries, land infrastructure, IV or panel data estimation, and macro-level analyses. They also depend on the inclusion or exclusion of various common covariates in trade regressions.

scholarly journals Irrigation and Ecosystem Services: development of an irrigation model for the LUCI ecosystem services framework

2021 ◽  
Author(s):  
◽  
Stuart Easton
Keyword(s):  
Water Quality ◽  
New Zealand ◽  
Ecosystem Services ◽  
Water Use ◽  
Ecosystem Service ◽  
Overland Flow ◽  
Irrigation System ◽  
Data Availability ◽  
Spatially Explicit ◽  
Irrigation Application

<p>Poor water quality is currently a major environmental issue worldwide and in New Zealand, where reactive Nitrogen (N) and Phosphorous (P) lost from agricultural fields are significant drivers of water quality degradation in rural catchments. Irrigation application to crops is essential to agricultural production however irrigation inputs can increase N and P losses to waterways via drainage and/or overland flow directly and as a result of reduced soil capacity to buffer rainfall events. Indirect nutrient losses are also increased following irrigation implementation due to amplified farming intensity. Furthermore, irrigation applications represent the world’s greatest consumptive use of water. Improving irrigation efficiency with regard to water use represents a synergistic opportunity for the improvement of a number of different ecosystem services including water quality, water supply, and food production.  Spatially explicit modelling of irrigation is needed to determine inefficiencies in water delivery and target these inefficiencies for management or mitigation at sub-field scales. A complimentary need exists for irrigation modelling within ecosystem service decision support tools so that nutrient and water movement can be accurately quantified in irrigated environments.   This thesis describes the development and implementation of SLIM – the Spatially-explicit LUCI Irrigation Model. SLIM adapts existing lumped hydrological and irrigation modelling techniques and practices to a fully distributed, spatially explicit framework, so that sub-field variations in water flows resulting from variable soil properties are accounted for. SLIM is generally applicable across New Zealand, using readily available national scale datasets and literature derived parameters. SLIM is capable of predicting irrigation depth and timing based on common management strategies and irrigation system characteristics, or can replicate irrigation applications where information is available. Outputs from SLIM are designed to assist irrigation management decisions at the field level, and to inform the hydrology component of the Land Utilisation and Capability Indicator (LUCI) ecosystem service assessment framework. Standalone SLIM outputs include time-series files, water balance plots, and raster maps describing the efficiency and efficacy of the modelled irrigation system.   SLIM has been applied in three different agroecosystems in New Zealand under surface, micro, and spray irrigation systems, each characterised by different levels of data availability. Results show that SLIM is able to accurately predict the timing of irrigation applications and provide usable information to inform irrigation application decisions. SLIM outputs emphasise the importance of soil variability with regard to water loss and risk of nutrient leaching. Opportunity exists for irrigation water use efficiency to be improved through targeted management at sub-field scales in New Zealand farming systems.</p>

scholarly journals Irrigation and Ecosystem Services: development of an irrigation model for the LUCI ecosystem services framework

2021 ◽  
Author(s):  
◽  
Stuart Easton
Keyword(s):  
Water Quality ◽  
New Zealand ◽  
Ecosystem Services ◽  
Water Use ◽  
Ecosystem Service ◽  
Overland Flow ◽  
Irrigation System ◽  
Data Availability ◽  
Spatially Explicit ◽  
Irrigation Application

<p>Poor water quality is currently a major environmental issue worldwide and in New Zealand, where reactive Nitrogen (N) and Phosphorous (P) lost from agricultural fields are significant drivers of water quality degradation in rural catchments. Irrigation application to crops is essential to agricultural production however irrigation inputs can increase N and P losses to waterways via drainage and/or overland flow directly and as a result of reduced soil capacity to buffer rainfall events. Indirect nutrient losses are also increased following irrigation implementation due to amplified farming intensity. Furthermore, irrigation applications represent the world’s greatest consumptive use of water. Improving irrigation efficiency with regard to water use represents a synergistic opportunity for the improvement of a number of different ecosystem services including water quality, water supply, and food production.  Spatially explicit modelling of irrigation is needed to determine inefficiencies in water delivery and target these inefficiencies for management or mitigation at sub-field scales. A complimentary need exists for irrigation modelling within ecosystem service decision support tools so that nutrient and water movement can be accurately quantified in irrigated environments.   This thesis describes the development and implementation of SLIM – the Spatially-explicit LUCI Irrigation Model. SLIM adapts existing lumped hydrological and irrigation modelling techniques and practices to a fully distributed, spatially explicit framework, so that sub-field variations in water flows resulting from variable soil properties are accounted for. SLIM is generally applicable across New Zealand, using readily available national scale datasets and literature derived parameters. SLIM is capable of predicting irrigation depth and timing based on common management strategies and irrigation system characteristics, or can replicate irrigation applications where information is available. Outputs from SLIM are designed to assist irrigation management decisions at the field level, and to inform the hydrology component of the Land Utilisation and Capability Indicator (LUCI) ecosystem service assessment framework. Standalone SLIM outputs include time-series files, water balance plots, and raster maps describing the efficiency and efficacy of the modelled irrigation system.   SLIM has been applied in three different agroecosystems in New Zealand under surface, micro, and spray irrigation systems, each characterised by different levels of data availability. Results show that SLIM is able to accurately predict the timing of irrigation applications and provide usable information to inform irrigation application decisions. SLIM outputs emphasise the importance of soil variability with regard to water loss and risk of nutrient leaching. Opportunity exists for irrigation water use efficiency to be improved through targeted management at sub-field scales in New Zealand farming systems.</p>

scholarly journals Neurotrophic Factor Levels in Preterm Infants: A Systematic Review and Meta-Analysis

2021 ◽  
Vol 12 ◽  
Author(s):  
Francieli Cristina Krey ◽  
Bruna Alvim Stocchero ◽  
Kerstin Camile Creutzberg ◽  
Bernardo Aguzzoli Heberle ◽  
Saulo Gantes Tractenberg ◽  
...  
Keyword(s):  
Systematic Review ◽  
Preterm Infants ◽  
Neurotrophic Factor ◽  
Regression Models ◽  
Meta Analysis ◽  
Effect Sizes ◽  
Sample Type ◽  
Term Infants ◽  
Significant Difference ◽  
Meta Regression

Objectives: Through a systematic review and meta-analysis of the literature we aimed to compare the levels of BDNF, NGF, NT-3, NT-4, and GDNF between human term and preterm infants, and investigate factors implicated in the variability of effect size estimates.Methods: The analysis was performed in three online databases, MEDLINE Complete, PsycINFO, and CINAHL. A random effects model was used to calculate the standardized mean difference (SMD) of neurotrophic factor levels in preterm infants vs. term within a 95% confidence interval (CI). To explore sources of heterogeneity meta-regression models were implemented.Results: Sixteen studies were included in this meta-analysis. A combined sample of 1,379 preterm and 1,286 term newborns were evaluated. We identified significant lower BDNF (SMD = −0.32; 95% CI: −0.59, −0.06; p = 0.014) and NT-3 (SMD = −0.31; 95% CI: −0.52, −0.09; p = 0.004) levels in preterm compared to term infants. No significant difference was observed in NGF and NT-4 levels between groups. Given that only two effect sizes were generated for GDNF levels, no meta-analytical model was performed. Meta-regression models revealed sample type (placental tissue, cerebrospinal fluid, peripheral blood, and umbilical cord blood) as a significant moderator of heterogeneity for BDNF meta-analysis. No significant associations were found for gestational week, birth weight, and clinical comorbidity of newborns with effect sizes.Conclusions: Our findings indicated that lower BDNF and NT-3 levels may be associated with preterm birth. Future studies with larger samples sizes should investigate neurodevelopmental manifestations resulting from neurotrophic factor dysregulation among preterm infants.

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