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.     

Evaluation of the exercise intervention with the "Back School" education program in a Brazilian company of cigars: a case study

The purpose of this study was to assess the benefits of an exercise program associated to the educational phase of the "Back School Program" in the quality of life of workers from a cigar factory. This is a case study conducted over four months with three activities a week, two times a day, with the third day devoted to educational activities, focusing on healthy lifestyle, correct posture, postural orientations, daily living and work activities, work motivation, and other issues.     

Association between handgrip strength,walking, age-related illnesses and cognitive status in a sample of Portuguese centenarians

Background

Centenarians are a growing population in Europe and present significant variability in motor and cognitive functions. The aim of our study was to characterize health status, as well as cognitive and motor functions in a group of Portuguese centenarians. In addition, our study also aimed at analyzing the relationship between cognitive functions and the burden of diseases affecting the elderly.

Methods

Fifty-two centenarians were evaluated using the Mini-Mental State Examination, short version. Walking-related parameters (velocity and time spent in the 3 m walk test), grip strength and number of age-related illnesses were also measured. The relationship between cognitive scores and time spent in the three metre walk test, velocity, grip strength and number of diseases was analysed.

Results

Cognitive scores showed a positive correlation with both handgrip strength and time spent in the three metre walk. In contrast, no association was found between cognitive scores and the presence/absence of disease, walking velocity or number of diseases present.

Conclusions

These results suggest that in centenarians, cognitive functions may be related with motor functions.

     

Effectiveness of FISK,an Invasiveness Screening Tool for Non‐Native Freshwater Fishes,to Perform Risk Identification Assessments in the Iberian Peninsula

Risk assessments are crucial for identifying and mitigating impacts from biological invasions. The Fish Invasiveness Scoring Kit (FISK) is a risk identification (screening) tool for freshwater fishes consisting of two subject areas: biogeography/history and biology/ecology. According to the outcomes, species can be classified under particular risk categories. The aim of this study was to apply FISK to the Iberian Peninsula, a Mediterranean climate region highly important for freshwater fish conservation due to a high level of endemism. In total, 89 fish species were assessed by three independent assessors. Results from receiver operating characteristic analysis showed that FISK can discriminate reliably between noninvasive and invasive fishes for Iberia, with a threshold of 20.25, similar to those obtained in several regions around the world. Based on mean scores, no species was categorized as “low risk,” 50 species as “medium risk,” 17 as “moderately high risk,” 11 as “high risk,” and 11 as “very high risk.” The highest scoring species was goldfish Carassius auratus. Mean certainty in response was above the category “mostly certain,” ranging from tinfoil barb Barbonymus schwanenfeldii with the lowest certainty to eastern mosquitofish Gambusia holbrooki with the highest level. Pair‐wise comparison showed significant differences between one assessor and the other two on mean certainty, with these two assessors showing a high coincidence rate for the species categorization. Overall, the results suggest that FISK is a useful and viable tool for assessing risks posed by non‐native fish in the Iberian Peninsula and contributes to a “watch list” in this region.     

The Gamma-count distribution in the analysis of experimental underdispersed data

Event counts are response variables with non-negative integer values representing the number of times that an event occurs within a fixed domain such as a time interval, a geographical area or a cell of a contingency table. Analysis of counts by Gaussian regression models ignores the discreteness, asymmetry and heteroscedasticity and is inefficient, providing unrealistic standard errors or possibly negative predictions of the expected number of events. The Poisson regression is the standard model for count data with underlying assumptions on the generating process which may be implausible in many applications. Statisticians have long recognized the limitation of imposing equidispersion under the Poisson regression model. A typical situation is when the conditional variance exceeds the conditional mean, in which case models allowing for overdispersion are routinely used. Less reported is the case of underdispersion with fewer modeling alternatives and assessments available in the literature. One of such alternatives, the Gamma-count model, is adopted here in the analysis of an agronomic experiment designed to investigate the effect of levels of defoliation on different phenological states upon the number of cotton bolls. Data set and code for analysis are available as online supplements. Results show improvements over the Poisson model and the semi-parametric quasi-Poisson model in capturing the observed variability in the data. Estimating rather than assuming the underlying variance process leads to important insights into the process.     

Likelihood analysis for a class of beta mixed models

Beta regression is a suitable choice for modelling continuous response variables taking values on the unit interval. Data structures such as hierarchical, repeated measures and longitudinal typically induce extra variability and/or dependence and can be accounted for by the inclusion of random effects. In this sense, Statistical inference typically requires numerical methods, possibly combined with sampling algorithms. A class of Beta mixed models is adopted for the analysis of two real problems with grouped data structures. We focus on likelihood inference and describe the implemented algorithms. The first is a study on the life quality index of industry workers with data collected according to an hierarchical sampling scheme. The second is a study assessing the impact of hydroelectric power plants upon measures of water quality indexes up, downstream and at the reservoirs of the dammed rivers, with a nested and longitudinal data structure. Results from different algorithms are reported for comparison including from data-cloning, an alternative to numerical approximations which also allows assessing identifiability. Confidence intervals based on profiled likelihoods are compared with those obtained by asymptotic quadratic approximations, showing relevant differences for parameters related to the random effects. In both cases, the scientific hypothesis of interest was investigated by comparing alternative models, leading to relevant interpretations of the results within each context.     

Strong formulation for the spot 5 daily photograph scheduling problem

Earth observation satellites, such as the SPOT 5, take photographs of the earth according to consumers’ demands. Obtaining a good schedule for the photographs is a combinatorial optimization problem known in the literature as the daily photograph scheduling problem (DPSP). The DPSP consists of selecting a subset of photographs, from a set of candidates, to different cameras, maximizing a profit function and satisfying a large number of constraints. Commercial solvers, with standard integer programming formulations, are not able to solve some DPSP real instances available in the literature. In this paper we present a strengthened formulation for the DPSP, based on valid inequalities arising in node packing and 3-regular independence system polyhedra. This formulation was able, with a commercial solver, to solve to optimality all those instances in a short computation time.     

A class of bivariate regression models for discrete and/or continuous responses

ABSTRACT

A general class of models for discrete and/or continuous responses is proposed in which joint distributions are constructed via the conditional approach. It is assumed that the distributions of one response and of the other response given the first one belong to exponential family of distributions. Furthermore, the marginal means are related to the covariates by link functions and a dependency structure between the responses is inserted into the model. Estimation methods, diagnostic analysis and a simulation study considering a Bernoulli-exponential model, a particular case of the class, are presented. Finally, this model is used in a real data set.     

Hypothesis test for paired samples in the presence of missing data

Missing data are present in almost all statistical analysis. In simple paired design tests, when some subject has one of the involved variables missing in the so-called partially overlapping samples scheme, it is usually discarded for the analysis. The lack of consistency between the information reported in the univariate and multivariate analysis is, perhaps, the main consequence. Although the randomness on the missing mechanism (missingness completely at random) is an usual and needed assumption for this particular situation, missing data presence could lead to serious inconsistencies on the reported conclusions. In this paper, the authors develop a simple and direct procedure which allows using the whole available information in order to perform paired tests. In particular, the proposed methodology is applied to check the equality among the means from two paired samples. In addition, the use of two different resampling techniques is also explored. Finally, real-world data are analysed.     

Confidence Intervals for the Hyperparameters in Structural Models

This article deals with the bootstrap as an alternative method to construct confidence intervals for the hyperparameters of structural models. The bootstrap procedure considered is the classical nonparametric bootstrap in the residuals of the fitted model using a well-known approach. The performance of this procedure is empirically obtained through Monte Carlo simulations implemented in Ox. Asymptotic and percentile bootstrap confidence intervals for the hyperparameters are built and compared by means of the coverage percentages. The results are similar but the bootstrap procedure is better for small sample sizes. The methods are applied to a real time series and confidence intervals are built for the hyperparameters.