Macroinfauna responses and recovery trajectories after an oil spill differ from those following saltmarsh restoration.

Given the severity of injuries to biota in coastal wetlands from the Deepwater Horizon oil spill (DWH) and the resulting availability of funding for restoration, information on impacted salt marshes and biotic development of restored marshes may both help inform marsh restoration planning in the near term and for future spills. Accordingly, we performed a meta-analysis to model a restoration trajectory of total macroinfauna density in constructed marshes (studied for ~30 y), and with a previously published restoration trajectory for amphipods, we compared these to recovery curves for total macroinfauna and amphipods from DWH impacted marshes (over 8.5 y). Total macroinfauna and amphipod densities in constructed marshes did not consistently reach equivalency with reference sites before 20 y, yet in heavily oiled marshes recovery occurred by 4.5 y post spill (although it is unlikely that macroinfaunal community composition fully recovered). These differences were probably due to initial conditions (e.g., higher initial levels of belowground organic matter in oiled marshes) that were more conducive to recovery as compared to constructed marshes. Furthermore, we found that amphipod trajectories were distinctly different in constructed and oiled marshes as densities at oiled sites exceeded that of reference sites by as much as 20x during much of the recovery period. Amphipods may have responded to the rapid increase and high biomass of benthic microalgae following the spill. These results indicate that biotic responses after an oil spill may be quantitatively different than those following restoration, even for heavily oiled marshes that were initially denuded of vegetation. Our dual trajectories for oil spill recovery and restoration development for macroinfauna should help guide restoration planning and assessment following the DWH as well as for restoration scaling for future spills.

Three probiotic strains exert different effects on plasma bile acid profiles in healthy obese adults: randomised, double-blind placebo-controlled crossover study.

Microbial metabolism in the gut may alter human bile acid metabolism in a way that beneficially affects lipid homeostasis and therefore cardiovascular disease risk. Deconjugation of bile acids by microbes is thought to be key to this mechanism but has yet to be characterised in blood and stool while observing lipid markers. The aim of this study was to determine the effect of 3 different probiotic strains on plasma and stool bile acids in the context of lipid and glucose metabolism. In this 18-week, randomised, double-blind crossover study, healthy adults (53±8 years) with a high waist circumference underwent a 1-week pre-baseline period and were then randomised to receive 1 capsule/day of Bacillus subtilis R0179 (2.5×109 cfu/capsule; n=39), Lactobacillus plantarum HA-119 (5×109 cfu/capsule; n=38), Bifidobacterium animalis subsp. lactis B94 (5×109 cfu/capsule; n=37) or placebo for 6 weeks. Following a 3-week washout and second pre-baseline week, participants were crossed to the other intervention for 6 weeks followed by a 1-week post-intervention period. Blood and stool samples were collected at the beginning and end of each intervention to measure bile acids, serum lipid profiles, and glucose and insulin levels. Data from the placebo intervention were combined for all participants for analyses. In obese participants, the difference (final-baseline) in the sum of deconjugated plasma bile acids was greater with consumption of B. subtilis (691±378 nmol/l, P=0.01) and B. lactis (380±165 nmol/l, P=0.04) than with placebo (98±176 nmol/l, n=57). No significant differences were observed for any probiotics for stool bile acids, serum lipids, blood glucose or insulin. These data suggest that B. subtilis and B. lactis had no effect on glucose metabolism or serum cholesterol but increased deconjugated plasma bile acids in obese individuals. Additional studies should be conducted to confirm these findings and explore potential mechanisms. This trial was registered at clinicaltrials.gov as NCT01879098.

Bifidobacterium bifidum R0071 decreases stress-associated diarrhoea-related symptoms and self-reported stress: a secondary analysis of a randomised trial.

Psychological stress is associated with gastrointestinal (GI) distress. This secondary analysis from a randomised, double-blind, placebo-controlled study examined whether three different probiotics could normalise self-reported stress-associated GI discomfort and reduce overall self-reported stress. Undergraduate students (n=581) received Lactobacillus helveticus R0052, Bifidobacterium longum ssp. infantis R0033, Bifidobacterium bifidum R0071, or placebo. Participants self-reported 2 outcomes for a 6-week period, which included final academic exams: daily level of stress (0=no stress to 10=extremely stressed) and weekly three diarrhoea-related symptoms (DS, 1=no discomfort to 7=severe discomfort) using the GI Symptom Rating Scale. Self-reported stress was positively related to DS (P=0.0068). Mean DS scores were lower with B. bifidum versus placebo at week 2 at the average level of stress and the average body mass index (BMI). DS scores were lower with B. bifidum at week 5 versus week 0 and 1 and with B. infantis R0033 at week 6 versus week 0. DS scores were higher when antibiotics were used in the prior week with placebo (P=0.0092). DS were not different with or without antibiotic use with the probiotics. Only B. bifidum had an effect on self-reported stress scores (P=0.0086). The self-reported stress score was also dependent on hours of sleep per day where it decreased by 0.13 for each additional hour of sleep. During a stressful period, B. bifidum R0071 decreases DS and self-reported stress scores. This trial was registered at clinicaltrials.gov as NCT01709825.

Short- and long-term movement patterns in complex confined environments in broiler chickens: the effects of distribution of cover panels and food resources.

In captivity, the positioning of structural enrichment and food resources influences behavior and space use. The aim of this experiment was to examine the influence of cover panels and the positioning of food resources on the movement and space use of domestic fowl (Gallus gallus domesticus). Eight groups of 45 male chickens were used for this study. Each group was temporarily divided into 2 groups of 20 birds; each group was used to investigate the influence of cover panels and the effects of food resources. In the cover panel treatments, 20 birds were placed in the 10-m(2) testing enclosures that contained one 2-m cover panel in the center, four 0.5-m panels in a zigzag fashion, or had no panels (controls). In the food resource treatments, the position of the feed trays varied, with 1 feed tray in the center; 2 feed trays, one at each edge; or 4 feed trays, one at each corner of the enclosure. Locations of focal birds were collected through instantaneous scan sampling that was recorded as X,Y coordinates. From these X,Y coordinates, we calculated net and total distance moved, mean and maximum step lengths, and angular dispersion of the path of movement. To calculate long-term space use, 3 replications for each of 3 cover panel and food resource treatments were placed in nine 10-m(2) enclosures for 1 wk. Locations of focal birds in each group were collected by ad libitum scan sampling and data were used to calculate core areas by kernel estimates. Mixed model ANOVA was used to determine the effects of the distribution of cover panels and positioning of food resources on movement parameters during the study period, whereas 1-way ANOVA was used for core areas. Surprisingly, our analyses showed that long-term and short-term movement was not affected by changing the location of cover panels or food resources. Only net distance seemed to be affected to a certain degree by the presence of the cover and the distinctive availability of food resources.

Inverse adaptive cluster sampling.

Consider a population in which the variable of interest tends to be at or near zero for many of the population units but a subgroup exhibits values distinctly different from zero. Such a population can be described as rare in the sense that the proportion of elements having nonzero values is very small. Obtaining an estimate of a population parameter such as the mean or total that is nonzero is difficult under classical fixed sample-size designs since there is a reasonable probability that a fixed sample size will yield all zeroes. We consider inverse sampling designs that use stopping rules based on the number of rare units observed in the sample. We look at two stopping rules in detail and derive unbiased estimators of the population total. The estimators do not rely on knowing what proportion of the population exhibit the rare trait but instead use an estimated value. Hence, the estimators are similar to those developed for poststratification sampling designs. We also incorporate adaptive cluster sampling into the sampling design to allow for the case where the rare elements tend to cluster within the population in some manner. The formulas for the variances of the estimators do not allow direct analytic comparison of the efficiency of the various designs and stopping rules, so we provide the results of a small simulation study to obtain some insight into the differences among the stopping rules and sampling approaches. The results indicate that a modified stopping rule that incorporates an adaptive sampling component and utilizes an initial random sample of fixed size is the best in the sense of having the smallest variance.

Bootstrap confidence intervals for adaptive cluster sampling.

Consider a collection of spatially clustered objects where the clusters are geographically rare. Of interest is estimation of the total number of objects on the site from a sample of plots of equal size. Under these spatial conditions, adaptive cluster sampling of plots is generally useful in improving efficiency in estimation over simple random sampling without replacement (SRSWOR). In adaptive cluster sampling, when a sampled plot meets some predefined condition, neighboring plots are added to the sample. When populations are rare and clustered, the usual unbiased estimators based on small samples are often highly skewed and discrete in distribution. Thus, confidence intervals based on asymptotic normal theory may not be appropriate. We investigated several nonparametric bootstrap methods for constructing confidence intervals under adaptive cluster sampling. To perform bootstrapping, we transformed the initial sample in order to include the information from the adaptive portion of the sample yet maintain a fixed sample size. In general, coverages of bootstrap percentile methods were closer to nominal coverage than the normal approximation.