Landscape urbanization threatens plant phylogenetic diversity in the Brazilian Atlantic Forest

Urbanization causes species loss around the world, but its effects on phylogenetic diversity are poorly known in tropical forests. Using a patch-landscape approach in an urbanizing region of the Brazilian Atlantic Forest, we tested whether the increase in landscape urbanization reduces plant species density, phylogenetic richness and divergence, and increases the relatedness among co-occurring individuals and species. We assessed plant responses to urbanization in adult (diameter at breast height?>?10 cm) and sapling communities (2.5–10 cm diameter) separately, as saplings are proxies of the future flora. We sampled 2860 woody plants belonging to 155 species in nine circular landscapes with urbanization level varying from 0% to 45%, and estimated the relatedness among the species that have increased and decreased in relative abundance in more urbanized landscapes (winner and losers, respectively). As expected, species density and phylogenetic richness decreased with the increase in urbanization. These responses were consistent for adult and sapling communities, suggesting a persistent loss of species and lineages in more urbanized landscapes. Contrary to our expectations, phylogenetic divergence and structure did not respond to urbanization, indicating that the more urbanized landscapes still retain much evolutionary history. However, because the relatedness among winners was greater than among losers, it is likely that the phylogenetic divergence gradually reduces and the relatedness increases, resulting in impoverished forests with uncertain ability to provide ecosystem services such as carbon storage and pest control. This environmental cost should be taken into account to align urban sprawl with biodiversity conservation.     

The Structured Interview of Family Assessment Risk: Convergent Validity,Inter-rater Reliability and Structural Relations

The quality of parenting, composition and socio-economic status are family risk or protective factors on juvenile delinquency, however, there is not an instrument to help psychologists and social workers to specifically assess the adolescent offender’s family. This study set out to accomplish an additional validation study of the structured interview of family assessment risk (SIFAR), a structured professional judgment tool for the assessment of family risk and protective factors of juvenile delinquents. The statistical analyses included inter-rater reliability, convergent validity with YLS/CMI, the Partial Least Squares approach to structural equation modeling and receiving operator characteristics (ROC) analysis. A sample of 130 male adolescent delinquents detained in Portuguese forensic facilities and their parents, were paired analyzed. The YLS/CMI was used to analyze the convergent validity with SIFAR. SIFAR shows a strong correlation with the YLS/CMI family context, moderate to high values of inter-rater reliability; SIFAR factors show that they are predictive determinants of the Moderate Four risk factors. ROC analysis shows adequate accuracy power. The findings show that SIFAR it is useful as an additional assessment tool for structured risk assessment instruments since it allows understanding the vulnerabilities and strengths of the delinquent’s family.     

Noncrossing structured additive multiple-output Bayesian quantile regression models

Quantile regression models are a powerful tool for studying different points of the conditional distribution of univariate response variables. Their multivariate counterpart extension though is not straightforward, starting with the definition of multivariate quantiles. We propose here a flexible Bayesian quantile regression model when the response variable is multivariate, where we are able to define a structured additive framework for all predictor variables. We build on previous ideas considering a directional approach to define the quantiles of a response variable with multiple outputs, and we define noncrossing quantiles in every directional quantile model. We define a Markov chain Monte Carlo (MCMC) procedure for model estimation, where the noncrossing property is obtained considering a Gaussian process design to model the correlation between several quantile regression models. We illustrate the results of these models using two datasets: one on dimensions of inequality in the population, such as income and health; the second on scores of students in the Brazilian High School National Exam, considering three dimensions for the response variable.     

Biodiversity conservation in urban parks: a study of ground-dwelling ants (Hymenoptera: Formicidae) in Rio de Janeiro City

Urban Ecosystems - Urban green recreational spaces may provide habitat for animals and plants. We investigated the importance of urban public parks in the city of Rio de Janeiro with regard to the...     

Affiliative Structures in Preschool Peer Groups.

Changes in affiliative organization of 15 age-graded toddler and preschool play groups were examined in terms of assessed similarity in patterns of playmate association. Measures of peer association were derived from direct observation of social interaction during free play. The degree of between subject similarity in association profiles was derived using complete linkage hierarchical clustering procedures. Findings revealed distinct social subgroups in all social groups. Secondary analyses showed a linear increase in the size of affiliative subgroups as a function of age. Measures of interactive reciprocity within social subgroups suggested progressive consolidation of affiliative structures with age. Among older children, membership within affiliative subgroups was associated with more frequent preferences for subgroup members. Findings are discussed in terms of how children's insertion within the affiliative network of their peer group constrain socialization of their behavior and provide specific experiences that serve as contexts for the construction of more intimate interpersonal relationships.     

Urban or rural fragments: which soil use in forest surroundings induces greater litter deposition?

Urban Ecosystems - The soil use in surroundings of forest remnants, as well as, variations in seasonality and rainfall totals from each year might influence the composition, structure, and...     

Extreme Risk Analysis of Interdependent Economic and Infrastructure Sectors

Willful attacks or natural disasters pose extreme risks to sectors of the economy. An extreme-event analysis extension is proposed for the Inoperability Input-Output Model (IIM) and the Dynamic IIM (DIIM), which are analytical methodologies for assessing the propagated consequences of initial disruptions to a set of sectors. The article discusses two major risk categories that the economy typically experiences following extreme events: (i) significant changes in consumption patterns due to lingering public fear and (ii) adjustments to the production outputs of the interdependent economic sectors that are necessary to match prevailing consumption levels during the recovery period. Probability distributions associated with changes in the consumption of directly affected sectors are generated based on trends, forecasts, and expert evidence to assess the expected losses of the economy. Analytical formulations are derived to quantify the extreme risks associated with a set of initially affected sectors. In addition, Monte Carlo simulation is used to handle the more complex calculations required for a larger set of sectors and general types of probability distributions. A two-sector example is provided at the end of the article to illustrate the proposed extreme risk model formulations.     

Narrating political opportunities: explaining strategic adaptation in the climate movement

This article advances theory on social movements’ strategic adaptation to political opportunity structures by incorporating a narrative perspective. Our theory explains how people acquire and use knowledge about political opportunity structures through storytelling about the movement’s past, present, and imagined future. The discussion applies the theory in an ethnographic case study of the climate movement’s mobilization around the UN Climate Summit in Paris, 2015. This analysis demonstrates how a dominant narrative of defeat about the prior protest campaign in Copenhagen, 2009 shaped the strategizing process. While those who experienced Copenhagen as a success preferred strategic continuity, those who experienced defeat developed a “Copenhagen narrative” to advance strategic adaptation by projecting previously experienced threats and opportunities onto the Paris campaign. Yet by relying on a retrospective narrative, movement actors tended to overlook emerging political opportunities. We demonstrate that narrative analysis is a useful tool for understanding the link between structure and agency in social movements and other actors affected by (political) opportunity structures.

     

Modeling Uncertainties in Workforce Disruptions from Influenza Pandemics Using Dynamic Input‐Output Analysis

Influenza pandemic is a serious disaster that can pose significant disruptions to the workforce and associated economic sectors. This article examines the impact of influenza pandemic on workforce availability within an interdependent set of economic sectors. We introduce a simulation model based on the dynamic input‐output model to capture the propagation of pandemic consequences through the National Capital Region (NCR). The analysis conducted in this article is based on the 2009 H1N1 pandemic data. Two metrics were used to assess the impacts of the influenza pandemic on the economic sectors: (i) inoperability, which measures the percentage gap between the as‐planned output and the actual output of a sector, and (ii) economic loss, which quantifies the associated monetary value of the degraded output. The inoperability and economic loss metrics generate two different rankings of the critical economic sectors. Results show that most of the critical sectors in terms of inoperability are sectors that are related to hospitals and health‐care providers. On the other hand, most of the sectors that are critically ranked in terms of economic loss are sectors with significant total production outputs in the NCR such as federal government agencies. Therefore, policy recommendations relating to potential mitigation and recovery strategies should take into account the balance between the inoperability and economic loss metrics.     

International Trade Inoperability Input-Output Model (IT-IIM): Theory and Application

The inoperability input-output model (IIM) has been used for analyzing disruptions due to man-made or natural disasters that can adversely affect the operation of economic systems or critical infrastructures. Taking economic perturbation for each sector as inputs, the IIM provides the degree of economic production impacts on all industry sectors as the outputs for the model. The current version of the IIM does not provide a separate analysis for the international trade component of the inoperability. If an important port of entry (e.g., Port of Los Angeles) is disrupted, then international trade inoperability becomes a highly relevant subject for analysis. To complement the current IIM, this article develops the International Trade-IIM (IT-IIM). The IT-IIM investigates the resulting international trade inoperability for all industry sectors resulting from disruptions to a major port of entry. Similar to traditional IIM analysis, the inoperability metrics that the IT-IIM provides can be used to prioritize economic sectors based on the losses they could potentially incur. The IT-IIM is used to analyze two types of direct perturbations: (1) the reduced capacity of ports of entry, including harbors and airports (e.g., a shutdown of any port of entry); and (2) restrictions on commercial goods that foreign countries trade with the base nation (e.g., embargo).