Consequences of a large-scale fragmentation experiment for Neotropical bats : disentangling the relative importance of local and landscape-scale effects

Context Habitat loss, fragmentation and degradation are widespread drivers of biodiversity decline. Understanding how habitat quality interacts with landscape context, and how they jointly affect species in human-modified landscapes, is of great importance for informing conservation and management. Objectives We used a whole-ecosystem manipulation experiment in the Brazilian Amazon to investigate the relative roles of local and landscape attributes in affecting bat assemblages at an interior-edge-matrix disturbance gradient. Methods We surveyed bats in 39 sites, comprising continuous forest (CF), fragments, forest edges and intervening secondary regrowth. For each site, we assessed vegetation structure (local-scale variable) and, for five focal scales, quantified habitat amount and four landscape configuration metrics. Results Smaller fragments, edges and regrowth sites had fewer species and higher levels of dominance than CF. Regardless of the landscape scale analysed, species richness and evenness were mostly related to the amount of forest cover. Vegetation structure and configurational metrics were important predictors of abundance, whereby the magnitude and direction of response to configurational metrics were scale-dependent. Responses were ensemble-specific with local-scale vegetation structure being more important for frugivorous than for gleaning animalivorous bats. Conclusions Our study indicates that scale-sensitive measures of landscape structure are needed for a more comprehensive understanding of the effects of fragmentation on tropical biota. Although forest fragments and regrowth habitats can be of conservation significance for tropical bats our results further emphasize that primary forest is of irreplaceable value, underlining that their conservation can only be achieved by the preservation of large expanses of pristine habitat

Performance of layers fed diets containing different oils

A trial was carried out at the Animal Science Department of the Federal University of Viçosa to evaluate the performance of heavy and light layers. During four periods of twenty-eight days, 432 layers in the second production cycle, being 216 Hy Line W36 (light birds) and 216 Hy Line Brown (heavy birds), were used. A completely randomized experimental design with a 1+(4x2x2) factorial arrangement (one control treatment, four oil sources, two inclusion levels, and two genetic lines) with four replicates of six birds per experimental unit, was applied. The treatments consisted of diets containing 16.5% crude protein, formulated to supply the nutritional requirements, and the inclusion of each oil source at 2 or 4%. Soybean, canola, linseed and fish oils were used. The control diet did not contain any oil. The studied parameters were: feed intake (g/hen/day), egg production (%), egg weight (g), egg mass (g/hen/day), feed conversion ratio (kg feed /dozen of eggs and per kg eggs). Oil levels and sources did not influence egg mass, feed conversion ratio (per kg eggs and dozen eggs), or egg production. During the first three periods, independently of oil source or genetics, eggs from layers fed 4% oil were the heaviest, except for those of light birds fed soybean oil during the third period, which egg weight was similar for the 2 and 4% levels. Light layers presented lower feed intake and better feed conversion ratio (per kg eggs and dozen eggs) as compared to heavy layers, and there was no influence of the evaluated oil sources on feed intake

Fatty acid profile of eggs of semi-heavy layers fed feeds containing linseed oil

The incorporation of polyunsaturated fatty acids in chicken eggs by adding oils to the diets has been extensively studied. This experiment aimed at evaluating possible changes in the fatty acid profile of the eggs of layers fed diets supplemented with linseed and soybean oils. The experiment was performed using 192 29 week-old laying hens, distributed in a completely randomized design, into six treatments with four replicates of eight birds each. Treatments consisted of a control diet (no vegetable oil) and diets including 2% of vegetable oil. Linseed oil replaced 0, 25, 50, 75, and 100% soybean oil in the diets, corresponding to 0.0, 0.5, 1.0, 1.5, and 2.0% of linseed oil in the diet. A pool of two egg yolks from each treatment was submitted to lipid extraction and fatty acid methylation, and subsequent gas chromatography (GC) analysis to detect seven fatty acids. Saturated (myristic and palmitic) fatty acids concentration was affected by lipid dietary source, with the lowest concentration in birds were fed feeds containing linseed oil. Polyunsaturated fatty acids (PUFA) concentration in the eggs was influenced by different levels of linseed oil inclusion. Linoleic acid egg content increased when linseed oil was used on diet as compared to the control diet. Linseed oil was considered an excellent source of linolenic acid incorporation in the eggs

Nonparametric estimation of probabilistic sensitivity measures

Computer experiments are becoming increasingly important in scientific investigations. In the presence of uncertainty, analysts employ probabilistic sensitivity methods to identify the key-drivers of change in the quantities of interest. Simulation complexity, large dimensionality and long running times may force analysts to make statistical inference at small sample sizes. Methods designed to estimate probabilistic sensitivity measures at relatively low computational costs are attracting increasing interest. We first, propose new estimators based on a one-sample design and building on the idea of placing piecewise constant Bayesian priors on the conditional distributions of the output given each input, after partitioning the input space. We then present two alternatives, based on Bayesian non-parametric density estimation, which bypass the need for predefined partitions. Quantification of uncertainty in the estimation process through is possible without requiring additional simulator evaluations via Bootstrap in the simplest proposal, or from the posterior distribution over the sensitivity measures, when the entire inferential procedure is Bayesian. The performance of the proposed methods is compared to that of traditional point estimators in a series of numerical experiments comprising synthetic but challenging simulators, as well as a realistic application

Discounting Health and Money: New Evidence Using A More Robust Method

International audienceThis study compares discounting for money and health in a field study. We applied the direct method, which measures discounting independent of utility, in a representative French sample, interviewed at home by professional interviewers. We found more discounting for money than for health. The median discount rates (6.5% for money and 2.2 % for health) were close to market interest rates suggesting that the direct method solves the puzzle of unrealistically high discount rates typically observed in applied economics. Constant discounting fitted the data better than hyperbolic discounting. The substantial individual heterogeneity in discounting could be explained by age and occupation