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Established for biodiversity conservation, protected areas (PAs) have been downsized, downgraded, and/or degazetted for socioeconomic development including plantation agriculture. Although studies have highlighted causes, implications for biodiversity conservation, and the need for policies governing Protected Area Downsizing, Downgrading, and Degazettement (PADDD), no study has proposed a methodology to inform PADDD events to help decision making that balance economic growth pursuit with ecological and environmental commitments. A methodology based on land use change and forest patch analysis has been applied to Buvuma Island to guide the choice of PAs that can undergo PADDD as well as identification of new areas that can be declared as PAs. Our results revealed that, over the last decade, natural vegetation of Buvuma Island has been highly degraded with forest depletion from 45.0% in 2007 to 15.8% in 2016. About 65% of the initial forest cover were lost. The average yearly forest loss rate was 3.2% or 712.8 ha. A total number of 19 PAs covering 3103 ha including 2816 ha of existing PAs and 287 ha of identified forest patches were selected for biodiversity conservation. This flexible methodology can be applied at various spatial and temporal scale to ensure sustainable plantation agriculture development.

 

Post-Wilsonian physics views theories not as isolated points but elements of bigger universality classes, with effective theories emerging in the infrared. This paper makes initial attempts to apply this viewpoint to homogeneous geometries on group manifolds, and complexity geometry in particular. We observe that many homogeneous metrics on low-dimensional Lie groups have markedly different short-distance properties, but nearly identical distance functions at longer distances. Using Nielsen's framework of complexity geometry, we argue for the existence of a large universality class of definitions of quantum complexity, each linearly related to the other, a much finer-grained equivalence than typically considered in complexity theory. We conjecture that at larger complexities, a new effective metric emerges that describes a broad class of complexity geometries, insensitive to various choices of 'ultraviolet' penalty factors. Finally we lay out a broader mathematical program of classifying the effective geometries of right-invariant group manifolds. 

Instrument fidelity in message testing research hinges upon how precisely messages operationalize treatment conditions. However, numerous message testing studies have unmitigated threats to validity and reliability because no established procedures exist to guide construction of message treatments. Their construction typically occurs in a black box, resulting in suspect inferential conclusions about treatment effects. Because a mixed methods approach is needed to enhance instrument fidelity in message testing research, this article contributes to the field of mixed methods research by presenting an integrated multistage procedure for constructing precise message treatments using an exploratory sequential mixed methods design. This work harnesses the power of integration through crossover analysis to improve instrument fidelity in message testing research through the use of natural language processing (NLP). 

Abstract. Understanding the sources and sinks of methane (CH4)is critical to both predicting and mitigating future climate change. Thereare large uncertainties in the global budget of atmospheric CH4, butnatural emissions are estimated to be of a similar magnitude toanthropogenic emissions. To understand CH4 flux from biogenic sourcesin the United States (US) of America, a multi-scale CH4 observationnetwork focused on CH4 flux rates, processes, and scaling methods isrequired. This can be achieved with a network of ground-based observationsthat are distributed based on climatic regions and land cover. To determinethe gaps in physical infrastructure for developing this network, we need tounderstand the landscape representativeness of the current infrastructure.We focus here on eddy covariance (EC) flux towers because they are essentialfor a bottom-up framework that bridges the gap between point-based chambermeasurements and airborne or satellite platforms that inform policydecisions and global climate agreements. Using dissimilarity,multidimensional scaling, and cluster analysis, the US was divided into 10clusters distributed across temperature and precipitation gradients. Weevaluated dissimilarity within each cluster for research sites with activeCH4 EC towers to identify gaps in existing infrastructure that limitour ability to constrain the contribution of US biogenic CH4 emissionsto the global budget. Through our analysis using climate, land cover, andlocation variables, we identified priority areas for research infrastructureto provide a more complete understanding of the CH4 flux potential ofecosystem types across the US. Clusters corresponding to Alaska and theRocky Mountains, which are inherently difficult to capture, are the mostpoorly represented, and all clusters require a greater representation ofvegetation types.