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Sequential power analysis framework in assessing social forestry outcomes.

ABSTRACT: We extend the Actor-Centred Power framework to consider dimensions beyond the life of community natural resource management partnership initiatives by examining social forestry partnership projects in Indonesia. We do this by examining how power constellations realign across the temporal phases that operationalize project partnerships. We propose a sequential power analysis framework that examines power in three parts. The framework first proposes a method for historicizing actors into their power background. Second, we present mode for examining the arrival of a partnership scheme, which we call the power delivery phase. Third, we highlight approaches for examining the way power relations are adjusted, whether reinforced or reconfigured, by introducing an approach for examining programmatic outcomes of social forestry partnership schemes. This article thus provides broadly applicable but targeted guide for the researchers collecting data and seeking to make sense of power relations on community forest partnership schemes in various contexts. This framework is particularly useful for analysing equity and justice dimensions by highlighting who benefits and who loses.•Sequential Power Analysis (SPA) methodology is rooted in interest based and historical power framework.•SPA is consisted of three parts: power background, power delivery, and power adjustment•SPA framing provides a protocol for researchers to collect data.

SUBMITTER: Sahide MAK

PROVIDER: S-EPMC7248237 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Sequential power analysis framework in assessing social forestry outcomes.

Sahide Muhammad Alif K MAK Fisher Micah R MR Verheijen Bart B Maryudi Ahmad A Kim Yeon-Su YS Wong Grace Y GY

MethodsX 20200515

We extend the Actor-Centred Power framework to consider dimensions beyond the life of community natural resource management partnership initiatives by examining social forestry partnership projects in Indonesia. We do this by examining how power constellations realign across the temporal phases that operationalize project partnerships. We propose a sequential power analysis framework that examines power in three parts. The framework first proposes a method for historicizing actors into their pow ...[more]

PMID: 32477895

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Project description:BackgroundBiomarker identification is one of the major and important goal of functional genomics and translational medicine studies. Large scale -omics data are increasingly being accumulated and can provide vital means for the identification of biomarkers for the early diagnosis of complex disease and/or for advanced patient/diseases stratification. These tasks are clearly interlinked, and it is essential that an unbiased and stable methodology is applied in order to address them. Although, recently, many, primarily machine learning based, biomarker identification approaches have been developed, the exploration of potential associations between biomarker identification and the design of future experiments remains a challenge.MethodsIn this study, using both simulated and published experimentally derived datasets, we assessed the performance of several state-of-the-art Random Forest (RF) based decision approaches, namely the Boruta method, the permutation based feature selection without correction method, the permutation based feature selection with correction method, and the backward elimination based feature selection method. Moreover, we conducted a power analysis to estimate the number of samples required for potential future studies.ResultsWe present a number of different RF based stable feature selection methods and compare their performances using simulated, as well as published, experimentally derived, datasets. Across all of the scenarios considered, we found the Boruta method to be the most stable methodology, whilst the Permutation (Raw) approach offered the largest number of relevant features, when allowed to stabilise over a number of iterations. Finally, we developed and made available a web interface ( https://joelarkman.shinyapps.io/PowerTools/ ) to streamline power calculations thereby aiding the design of potential future studies within a translational medicine context.ConclusionsWe developed a RF-based biomarker discovery framework and provide a web interface for our framework, termed PowerTools, that caters the design of appropriate and cost-effective subsequent future omics study.

Project description:Industrial forestry typically leads to a simplified forest structure and altered species composition. Retention of trees at harvest was introduced about 25 years ago to mitigate negative impacts on biodiversity, mainly from clearcutting, and is now widely practiced in boreal and temperate regions. Despite numerous studies on response of flora and fauna to retention, no comprehensive review has summarized its effects on biodiversity in comparison to clearcuts as well as un-harvested forests. Using a systematic review protocol, we completed a meta-analysis of 78 studies including 944 comparisons of biodiversity between retention cuts and either clearcuts or un-harvested forests, with the main objective of assessing whether retention forestry helps, at least in the short term, to moderate the negative effects of clearcutting on flora and fauna. Retention cuts supported higher richness and a greater abundance of forest species than clearcuts as well as higher richness and abundance of open-habitat species than un-harvested forests. For all species taken together (i.e. forest species, open-habitat species, generalist species and unclassified species), richness was higher in retention cuts than in clearcuts. Retention cuts had negative impacts on some species compared to un-harvested forest, indicating that certain forest-interior species may not survive in retention cuts. Similarly, retention cuts were less suitable for some open-habitat species compared with clearcuts. Positive effects of retention cuts on richness of forest species increased with proportion of retained trees and time since harvest, but there were not enough data to analyse possible threshold effects, that is, levels at which effects on biodiversity diminish. Spatial arrangement of the trees (aggregated vs. dispersed) had no effect on either forest species or open-habitat species, although limited data may have hindered our capacity to identify responses. Results for different comparisons were largely consistent among taxonomic groups for forest and open-habitat species, respectively. Synthesis and applications. Our meta-analysis provides support for wider use of retention forestry since it moderates negative harvesting impacts on biodiversity. Hence, it is a promising approach for integrating biodiversity conservation and production forestry, although identifying optimal solutions between these two goals may need further attention. Nevertheless, retention forestry will not substitute for conservation actions targeting certain highly specialized species associated with forest-interior or open-habitat conditions. Our meta-analysis provides support for wider use of retention forestry since it moderates negative harvesting impacts on biodiversity. Hence, it is a promising approach for integrating biodiversity conservation and production forestry, although identifying optimal solutions between these two goals may need further attention. Nevertheless, retention forestry will not substitute for conservation actions targeting certain highly specialized species associated with forest-interior or open-habitat conditions.

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Sequential power analysis framework in assessing social forestry outcomes.

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Sequential power analysis framework in assessing social forestry outcomes.

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