Feeding ecology of Eigenmannia desantanai (Gymnotiformes: Sternopygidae) in southern Pantanal, Brazil

Aim This study investigated changes in the dietary composition and trophic niche breadth of Eigenmannia desantanai in the context of hydrological periods, rivers, and ontogeny. Methods Collections were performed monthly on the Paraguay and Amonguijá Rivers between February 2009 and January 2011. Results A total of 338 specimens of E. desantanai were collected, 224 of which came from the Amonguijá River and 114 from the Paraguay River. In all, 21 food items were identified. Fully remains of digested animals (RDA), Chironomidae, Cladocera, and Ostracoda showed greater importance in the diet of E. desantanai. Dietary composition was influenced by hydrological periods, followed by differences between sampled rivers, and ontogeny. In contrast, only hydrological periods explained variation in the E. desantanai trophic niche breadth. Conclusions The diet of E. desantanai is mainly composed of Chironomidae, Cladocera, and Ostracoda; however, individuals consumed other food items owing to changes in hydrological periods, followed by characteristics of the Amonguijá and Paraguay Rivers and ontogenetic variation. In this respect, the results of this study are germane to the food ecology of E. desantanai, providing guidelines for the management and conservation of the species.

Evaluating performance and determining optimum sample size for regression tree and automatic linear modeling

ABSTRACT This study was carried out for two purposes: comparing performances of Regression Tree and Automatic Linear Modeling and determining optimum sample size for these methods under different experimental conditions. A comprehensive Monte Carlo Simulation Study was designed for these purposes. Results of simulation study showed that percentage of explained variation estimates of both Regression Tree and Automatic Linear Modeling was influenced by sample size, number of variables, and structure of variance-covariance matrix. Automatic Linear Modeling had higher performance than Regression Tree under all experimental conditions. It was concluded that the Regression Tree required much larger samples to make stable estimates when comparing to Automatic Linear Modeling.

Microbial respiration and functional diversity of soil microbial community under treeline shifts in the Northwestern Caucasus

In mountain areas, one of the noticeable results of modern climate change is rapid shift of treelines to subalpine and alpine meadows. Such vegetation shifts is associated with a change in quality of the plant residues entering the soils, which in turn can affect the mineralization activity (basal respiration) and functional diversity of the soil microbial community. Therefore, the study was aimed at assessing the soil microbial (basal respiration and functional diversity) and chemical (C, N, C/N, pH) properties (0-10 cm) along the reserved and grazed forest-meadow transects of the Northwestern Caucasus (Karachay-Cherkess Republic), as well as evaluating an effect of vegetation type and land use on variation of these soil properties. It was found that the C and N contents (for both land usees), pH and basal respiration (reserved slope) significantly increase from forest to meadow soils. In contrary, the microbial functional diversity decreased from forest to meadow soils, which might be due to less diverse organic compounds entering the soil only with grass residues than their combination with forest litter. Two-way ANOVA showed that soil microbial functional diversity, pH, C and N along the studied forest-meadow transects was mostly associated with vegetation type (1439 % of the explained variation), and C/N and basal respiration - with land use (3336 % of the explained variation). Thus, a land use change will have a more significant effect on the mineralization activity of soil microbial community than a treeline shifts.

Honey bee (Apis mellifera) colony strength and its effects on pollination and yield in highbush blueberries (Vaccinium corymbosum)

Many pollination studies with honey bees have examined the effect of colony density on crop yield and yet overlook the effect of variation in the population size of these colonies. High colony density in northern highbush blueberry has been met with concerns from beekeepers who feel higher densities will intensify outbreaks of European foulbrood (EFB, Melissococcus plutonius, Truper and dé Clari), a honey bee brood disease. The purpose of this study was to confirm the prevalence of EFB in colonies pollinating blueberries and to determine whether field-level variation in the population of adult workers in colonies explained variation in blueberry fruit set and/or yield. We addressed these objectives over the course of two production seasons at 13 commercial blueberry fields in Oregon, USA, stocked with identical densities of 10 colonies/ha. We confirmed that all colonies had negligible symptoms of EFB at the start of blueberry pollination, but 53% of colonies in 2019 and 41% in 2020 had symptoms immediately following the pollination season. We also validated a method for rapidly assessing adult honey bee colony populations, namely by counting the rate of foragers returning to colonies, and it was found to be strongly correlated to true internal adult bee population independent of year and ambient temperature at the time of evaluation. Using returning forager counts, we determined there was considerable variation in the average population of colonies at each field, ranging from an estimated 10,300 to 30,700 adult worker bees per colony. While average colony strength did not predict variation in fruit set, it was related to variation in yield, independent of year. Our linear model of flight count (as a proxy for colony strength) predicts estimated yield increases of up to 25,000 kg/ha of blueberries could be achieved by colonies stronger than the recommended six frame minimum, suggesting that higher pollination benefits could be achieved without increasing hive density if stronger colonies are promoted.

Evaluating the Performance of Five Asset-based Wealth Indices in Predicting Socioeconomic Position in Rural Bangladesh

Social scientists have developed numerous asset-based wealth indices to assess and target socioeconomic inequalities globally. However, there are no systematic studies of the relative performance of these different measures as proxies for socioeconomic position. In this study, we compare how five asset-based wealth indices—the International Wealth Index (IWI), the Standard of Living portion of the Multi-Dimensional Poverty Index (MPI-SL), the Poverty Probability Index (PPI), the Absolute Wealth Estimate (AWE), and the DHS Wealth Index (DHS)—predict benchmarks of socioeconomic position across 11 communities in rural Bangladesh. All indices were highly correlated. The IWI best explained variation in individual and community ranking of economic well-being, while the PPI best explained variation both between and within communities for total household wealth and a general measure of subjective social status.

Change by challenge: A common genetic basis behind childhood cognitive development and cognitive training

The interplay of genetic and environmental factors behind cognitive development has preoccupied multiple fields of science and sparked heated debates over the decades. Here we tested the hypothesis that developmental genes rely heavily on cognitive challenges—as opposed to natural maturation. Starting with a polygenic score (cogPGS) that previously explained variation in cognitive performance in adults, we estimated its effect in 344 children and adolescents (mean age of 12 years old, ranging from 6 to 25) who showed changes in working memory (WM) in two distinct samples: (1) a developmental sample showing significant WM gains after 2 years of typical, age-related development, and (2) a training sample showing significant, experimentally-induced WM gains after 25 days of an intense WM training. We found that the same genetic factor, cogPGS, significantly explained the amount of WM gain in both samples. And there was no interaction of cogPGS with sample, suggesting that those genetic factors are neutral to whether the WM gains came from development or training. These results represent evidence that cognitive challenges are a central piece in the gene-environment interplay during cognitive development. We believe our study sheds new light on previous findings of interindividual differences in education (rich-get-richer and compensation effects), brain plasticity in children, and the heritability increase of intelligence across the lifespan.

Driver stopping behavior at stop-controlled intersections with sightline limitations

Though drivers approaching a stop-sign-controlled intersection are legally required to stop at the limit line if one is present, it is well established that many drivers fail to do so. At many intersections, stopping at the limit line does not afford drivers a full view of approaching traffic, so drivers must travel past the limit line to overcome sightline obstructions including vegetation, buildings, or parked vehicles. In the present observational study, typical driver stopping/slowing behavior was studied via a camera placed at three stop-sign-controlled T-intersections. The presence of buildings at the corner of two intersections, obstructing drivers’ sightlines, explained variation in stopping behaviors across intersections. While drivers were more likely to stop at these two intersections, they reached a minimum speed further past the limit line. The findings support overcoming sight restrictions as one possible reason for the commonly observed behavior of drivers slowing or stopping beyond the limit line.

EVALUATION OF RANDOM FOREST–BASED ANALYSIS FOR THE GYPSUM DISTRIBUTION IN THE ATACAMA DESERT

Gypsum-rich material covers the hillslopes above ∼ 1000 m of the Atacama and forms the particular landscape. In this contribution, we evaluate random forest-based analysis in order to predict the gypsum distribution in a specific area of ∼ 3000 km2, located in the hyperarid core of the Atacama. Therefore, three different sets of input variables were chosen. These variables reflect the different factors forming soil properties, according to digital soil mapping. The variables are derived from indices based on imagery of the ASTER and Landsat-8 satellite, geomorphometric parameters based on the Tandem-X World DEM™, as well as selected climate variables and geologic units. These three different models were used to evaluate the Ca-content derived from soil surface samples, reflecting gypsum content. All three different models derived high values of explained variation (r2 > 0.886), the RMSE is ∼ 4500 mg∙kg−1 and the NRMSE is ∼ 6%. Overall, this approach shows promising results in order to derive a gypsum content prediction for the whole Atacama. However, further investigation on the independent variables need to be conducted. In this case, the ferric oxides index (representing magnetite content), slope and a temperature gradient are the most important factors for predicting gypsum content.