Abstract: Assembling spatial units into meaningful clusters is a challenging task, as it must cope with a consequential computational complexity while controlling for the modifiable areal unit problem (MAUP), spatial autocorrelation and attribute multicolinearity. Nevertheless, these effects can reveal significant interactions among diverse spatial phenomena, such as segregation and economic specialization. Various regionalization methods have been developed in order to address these questions, but key fundamental properties of the aggregation of spatial entities are still poorly understood. In particular, due to the lack of an objective stopping rule, the question of determining an optimal number of clusters is yet unresolved. Therefore, we develop a clustering algorithm which is sensitive to scalar variations of multivariate spatial correlations, recalculating PCA scores at several aggregation steps in order to account for differences in the span of autocorrelation effects for diverse variables. With these settings, the scalar evolution of correlation, compactness and isolation measures is compared between empirical and 120 random datasets, using two dissimilarity measures. Remarkably, adjusting several indicators with real and simulated data allows for a clear definition of a stopping rule for spatial hierarchical clustering. Indeed, increasing correlations with scale in random datasets are spurious MAUP effects, so they can be discounted from real data results in order to identify an optimal clustering level, as defined by the maximum of authentic spatial self-organization. This allows singling out the most socially distressed areas in Greater Santiago, thus providing relevant socio-spatial insights from their cartographic and statistical analysis. In sum, we develop a useful methodology to improve the fundamental comprehension of spatial interdependence and multiscalar self-organizing phenomena, while linking these questions to relevant real world issues. (c) 2015 Elsevier Ltd. All rights reserved.

Abstract: Soil ecosystem dynamics are influenced by the composition of bacterial communities and environmental conditions. A common approach to study bacterial successional dynamics is to survey the trajectories and patterns that follow bacterial community assemblages; however early successional stages have received little attention. To elucidate how soil type and chemical amendments influence both the trajectories that follow early compositional changes and the architecture of the community bacterial networks in soil bacterial succession, a time series experiment of soil microcosm experiments was performed. Soil bacterial communities were initially perturbed by dilution and subsequently subjected to three amendments: application of the pesticide 2,4-dichlorophenoxyacetic acid, as a pesticide-amended succession; application of cycloheximide, an inhibitor affecting primarily eukaryotic microorganisms, as a eukaryotic-inhibition bacterial succession; or application of sterile water as a non-perturbed control. Terminal restriction fragment length polymorphism (T-RFLP) analysis of the 16S rRNA gene isolated from soil microcosms was used to generate bacterial relative abundance datasets. Bray-Curtis similarity and beta diversity partition-based methods were applied to identify the trajectories that follow changes in bacterial community composition. Results demonstrated that bacterial communities exposed to these three conditions rapidly differentiated from the starting point (less than 12 h), followed different compositional change trajectories depending on the treatment, and quickly converged to a state similar to the initial community (48-72 h). Network inference analysis was applied using a generalized Lotka-Volterra model to provide an overview of bacterial OTU interactions and to follow the changes in bacterial community networks. This analysis revealed that antagonistic interactions increased when eukaryotes were inhibited, whereas cooperative interactions increased under pesticide influence. Moreover, central OTUs from soil bacterial community networks were also persistent OTUs, thus confirming the existence of a core bacterial community and that these same OTUs could plastically interact according to the perturbation type to quickly stabilize bacterial communities undergoing succession.

Abstract: The aim of this article is to examine the validity of elevation gain measures in mountain activities, such as hiking and mountain running, using different wearable devices and post-processing procedures. In particular, a total of 202 efforts were recorded and evaluated using three standard devices: GPS watch, GPS watch with barometric altimeter, and smartphone. A benchmark was based on orthorectified aerial photogrammetric survey conducted by the Chilean Air Force. All devices presented considerable elevation gain measuring errors, where the barometric device consistently overestimated elevation gain, while the GPS devices consistently underestimated elevation gain. The incorporation of secondary information in the post-processing can substantially improve the elevation gain measuring accuracy independently of the device and altitude measuring technology, reducing the error from -5% to -1%. These results could help coaches and athletes correct elevation gain estimations using the proposed technique, which would serve as better estimates of physical workload in mountain physical activities.