An Invasive Grass Increases Live Fuel Proportion and Reduces Fire Spread in a Simulated Grassland

Fire is a globally important ecosystem process, and invasive grass species generally increase fire spread by increasing the fuel load and continuity of native grassland fuelbeds. We suggest that invasive grasses that are photosynthetically active, while the native plant community is dormant reduce fire spread by introducing high-moisture, live vegetation gaps in the fuelbed. We describe the invasion pattern of a high-moisture, cool-season grass, tall fescue (Schedonorus phoenix (Scop.) Holub), in tallgrass prairie, and use spatially explicit fire behavior models to simulate fire spread under several combinations of fuel load, invasion, and fire weather scenarios. Reduced fuel load and increased extent of tall fescue invasion reduced fire spread, but high wind speed and low relative humidity can partially mitigate these effects. We attribute reduced fire spread to asynchrony in the growing seasons of the exotic, cool-season grass, tall fescue, and the native, warm-season tallgrass prairie community in this model system. Reduced fire spread under low fuel load scenarios indicate that fuel load is an important factor in fire spread, especially in invaded fuel beds. These results present a novel connection between fire behavior and asynchronous phenology between invasive grasses and native plant communities in pyrogenic ecosystems.     

Prevalence of chronic obstructive pulmonary disease and variation in risk factors across four geographically diverse resource-limited settings in Peru

Background

It is unclear how geographic and social diversity affects the prevalence of chronic obstructive pulmonary disease (COPD). We sought to characterize the prevalence of COPD and identify risk factors across four settings in Peru with varying degrees of urbanization, altitude, and biomass fuel use.

Methods

We collected sociodemographics, clinical history, and post-bronchodilator spirometry in a randomly selected, age-, sex- and site-stratified, population-based sample of 2,957 adults aged ≥35 years (median age was 54.8 years and 49.3% were men) from four resource-poor settings: Lima, Tumbes, urban and rural Puno. We defined COPD as a post-bronchodilator FEV₁/FVC < 70%.

Results

Overall prevalence of COPD was 6.0% (95% CI 5.1%–6.8%) but with marked variation across sites: 3.6% in semi-urban Tumbes, 6.1% in urban Puno, 6.2% in Lima, and 9.9% in rural Puno (p < 0.001). Population attributable risks (PARs) of COPD due to smoking ≥10 pack-years were less than 10% for all sites, consistent with a low prevalence of daily smoking (3.3%). Rather, we found that PARs of COPD varied by setting. In Lima, for example, the highest PARs were attributed to post-treatment tuberculosis (16% and 22% for men and women, respectively). In rural Puno, daily biomass fuel for cooking among women was associated with COPD (prevalence ratio 2.22, 95% CI 1.02–4.81) and the PAR of COPD due to daily exposure to biomass fuel smoke was 55%.

Conclusions

The burden of COPD in Peru was not uniform and, unlike other settings, was not predominantly explained by tobacco smoking. This study emphasizes the role of biomass fuel use, and highlights pulmonary tuberculosis as an often neglected risk factor in endemic areas.     

Analysis of Longitudinal Clinical Trials with Missing Data Using Multiple Imputation in Conjunction with Robust Regression

Summary In a typical randomized clinical trial, a continuous variable of interest (e.g., bone density) is measured at baseline and fixed postbaseline time points. The resulting longitudinal data, often incomplete due to dropouts and other reasons, are commonly analyzed using parametric likelihood‐based methods that assume multivariate normality of the response vector. If the normality assumption is deemed untenable, then semiparametric methods such as (weighted) generalized estimating equations are considered. We propose an alternate approach in which the missing data problem is tackled using multiple imputation, and each imputed dataset is analyzed using robust regression (M‐estimation; Huber, 1973 , Annals of Statistics 1, 799–821.) to protect against potential non‐normality/outliers in the original or imputed dataset. The robust analysis results from each imputed dataset are combined for overall estimation and inference using either the simple Rubin (1987 , Multiple Imputation for Nonresponse in Surveys, New York: Wiley) method, or the more complex but potentially more accurate Robins and Wang (2000 , Biometrika 87, 113–124.) method. We use simulations to show that our proposed approach performs at least as well as the standard methods under normality, but is notably better under both elliptically symmetric and asymmetric non‐normal distributions. A clinical trial example is used for illustration.     

Managing private,commercial rangelands for agricultural production and wildlife diversity in Namibia and Zambia

The private game industry has grown across Africa since the mid-20th century. While considerable research has documented wildlife production on commercial land in many eastern and southern African countries, few studies have focused specifically on the integration of livestock and game production in Namibia and Zambia. This paper reports a survey of 43 commercial conservancy members in Namibia and 23 game farmers in Zambia conducted between September 2004 and June 2005. The survey was based on inductive sampling theory and queried farmers on how they have integrated wildlife production into their management practices. Farmers in each country reported considerable integration of wildlife conservation and agricultural production. Namibian farmers reported substantial problems with bush encroachment, whereas none of their Zambian counterparts raised similar complaints. This paper describes the state of rangeland management on commercial farms in Namibia and Zambia and identifies important areas where further research can contribute to the enhancement of this conservation-production system.     

B cell genomics behind cross-neutralization of SARS-CoV-2 variants and SARS-CoV

1. Download : Download high-res image (219KB)
2. Download : Download full-size image

     

Assessment of a livestock GPS collar based on an open‐source datalogger informs best practices for logging intensity

Ecologists have used Global Positioning Systems (GPS) to track animals for 30 years. Issues today include logging frequency and precision in estimating space use and travel distances, as well as battery life and cost. We developed a low‐cost (~US$125), open‐source GPS datalogger based on Arduino. To test the system, we collected positions at 20‐s intervals for several 1‐week durations from cattle and sheep on rangeland in North Dakota. We tested two questions of broad interest to ecologists who use GPS collars to track animal movements: (1) How closely do collared animals cluster in their herd? (2) How well do different logging patterns estimate patch occupancy and total daily distance traveled? Tested logging patterns included regular logging (one position every 5 or 10 min), and burst logging (positions recorded at 20‐s intervals for 5 or 10 min per hour followed by a sleep period). Collared sheep within the same pasture spent 75% of daytime periods within 51 m of each other (mean = 42 m); collared cattle were within 111 m (mean = 76 m). In our comparison of how well different logging patterns estimate space use versus constant logging, the proportion of positions recorded in 1‐ and 16‐ha patches differed by 2%–3% for burst logging and 1% for regular logging. Although all logging patterns underestimated total daily distance traveled, underestimations were corrected by multiplying estimations by regression coefficients estimated by maximum likelihood. Burst logging can extend battery life by a factor of 7. We conclude that a minimum of two collars programmed with burst logging robustly estimate patch use and spatial distribution of grazing livestock herds. Research questions that require accurately estimating travel of individual animals, however, are probably best addressed with regular logging intervals and will thus have greater battery demands than spatial occupancy questions across all GPS datalogger systems.     

Naringenin, a flavanone inhibits the proliferation of cerebrally implanted C6 glioma cells in rats

Tumor cells are able to survive and proliferate in spite of their increased oxidative stress. This was taken as a hint for the implication of oxidants/antioxidants in the proliferation of glial-tumor cells. In the present study, an anti-proliferative effect of Naringenin, an antioxidant against cerebrally implanted C6 glioma cells in rats has been investigated. The status of lipid peroxidation/antioxidants, expressions of protein kinase C, nuclear factor κB, cyclin D1, cyclin dependent kinase 4, proliferating cell nuclear antigen, vascular endothelial growth factor, argyophillic nucleolar organizing regions and histopathology of brain tissues of control and experimental rats were analyzed. On supplementation of naringenin (50mg/kg BW for 30 days) to glioma induced rats, there was a reduction in lipid peroxidation with an increased antioxidant status. There was a significant decrease in the expressions of protein kinase C, nuclear factor κB, cyclin D1 and cyclin dependent kinase 4 on naringenin treatment. Further, the drug could modulate the glial-tumor cell proliferation as evidenced from the histopathological findings, argyophillic nucleolar organizing regions staining, proliferating cell nuclear antigen and vascular endothelial growth factor immunostaining. The findings suggest that naringenin could underlie the inhibition of glial tumor cell proliferation in C6 glioma models of rat.