A Temporally Explicit Production Efficiency Model for Fuel Load Allocation in Southern Africa

Abstract We present a regional fuel load model (1 km² spatial resolution) applied in the southern African savanna region. The model is based on a patch-scale production efficiency model (PEM) scaled up to the regional level using empirical relationships between patch-scale behavior and multi-source remote sensing data (spatio-temporal variability of vegetation and climatic variables). The model requires the spatial distribution of woody vegetation cover, which is used to determine separate respiration rates for tree and grass. Net primary production, grass and tree leaf death, and herbivory are also taken into account in this mechanistic modeling approach. The fuel load model has been calibrated and validated from independent measurements taken from savanna vegetation in Africa southward from the equator. A sensitivity analysis on the effect of climate variables (incoming radiation, air temperature, and precipitation) has been conducted to demonstrate the strong role that water availability has in determining productivity and subsequent fuel load over the southern African region. The model performance has been tested in four different areas representative of a regional increasing rainfall gradient—Etosha National Park, Namibia, Mongu and Kasama, Zambia, as well as in Kruger National Park, South Africa. Within each area, we analyze model output from three different magnitudes of canopy coverage (<5, 30, and 50%). We find that fuel load ranges predicted by the model are globally in agreement with field measurements for the same year. High rainfall sustains green herbaceous production late in the dry season and delays tree leaf litter production. Effect of water on production varies across the rainfall gradient with delayed start of green material production in more arid regions.     

Advantages of Task-Specific Multi-Objective Optimisation in Evolutionary Robotics

The application of multi-objective optimisation to evolutionary robotics is receiving increasing attention. A survey of the literature reveals the different possibilities it offers to improve the automatic design of efficient and adaptive robotic systems, and points to the successful demonstrations available for both task-specific and task-agnostic approaches (i.e., with or without reference to the specific design problem to be tackled). However, the advantages of multi-objective approaches over single-objective ones have not been clearly spelled out and experimentally demonstrated. This paper fills this gap for task-specific approaches: starting from well-known results in multi-objective optimisation, we discuss how to tackle commonly recognised problems in evolutionary robotics. In particular, we show that multi-objective optimisation (i) allows evolving a more varied set of behaviours by exploring multiple trade-offs of the objectives to optimise, (ii) supports the evolution of the desired behaviour through the introduction of objectives as proxies, (iii) avoids the premature convergence to local optima possibly introduced by multi-component fitness functions, and (iv) solves the bootstrap problem exploiting ancillary objectives to guide evolution in the early phases. We present an experimental demonstration of these benefits in three different case studies: maze navigation in a single robot domain, flocking in a swarm robotics context, and a strictly collaborative task in collective robotics.     

Lower Neighborhood Socioeconomic Status Associated with Reduced Diversity of the Colonic Microbiota in Healthy Adults

In the United States, there are persistent and widening socioeconomic gaps in morbidity and mortality from chronic diseases. Although most disparities research focuses on person-level socioeconomic-status, mounting evidence suggest that chronic diseases also pattern by the demographic characteristics of neighborhoods. Yet the biological mechanisms underlying these associations are poorly understood. There is increasing recognition that chronic diseases share common pathogenic features, some of which involve alterations in the composition, diversity, and functioning of the gut microbiota. This study examined whether socioeconomic-status was associated with alpha-diversity of the colonic microbiota. Forty-four healthy adults underwent un-prepped sigmoidoscopy, during which mucosal biopsies and fecal samples were collected. Subjects’ zip codes were geocoded, and census data was used to form a composite indicator of neighborhood socioeconomic-status, reflecting household income, educational attainment, employment status, and home value. In unadjusted analyses, neighborhood socioeconomic-status explained 12–18 percent of the variability in alpha-diversity of colonic microbiota. The direction of these associations was positive, meaning that as neighborhood socioeconomic-status increased, so did alpha-diversity of both the colonic sigmoid mucosa and fecal microbiota. The strength of these associations persisted when models were expanded to include covariates reflecting potential demographic (age, gender, race/ethnicity) and lifestyle (adiposity, alcohol use, smoking) confounds. In these models neighborhood socioeconomic-status continued to explain 11–22 percent of the variability in diversity indicators. Further analyses suggested these patterns reflected socioeconomic variations in evenness, but not richness, of microbial communities residing in the sigmoid. We also found indications that residence in neighborhoods of higher socioeconomic-status was associated with a greater abundance of Bacteroides and a lower abundance of Prevotella, suggesting that diet potentially underlies differences in microbiota composition. These findings suggest the presence of socioeconomic variations in colonic microbiota diversity. Future research should explore whether these variations contribute to disparities in chronic disease outcomes.     

Automated extraction of bank angles from bird‐borne video footage using open‐source software

The use of miniaturized video cameras to study the at‐sea behavior of flying seabirds has increased in recent years. These cameras allow researchers to record several behaviors that were not previously possible to observe. However, video recorders produce large amounts of data and videos can often be time‐consuming to analyze. We present a new technique using open‐source software to extract bank angles from bird‐borne video footage. Bank angle is a key facet of dynamic soaring, which allows albatrosses and petrels to efficiently search vast areas of ocean for food. Miniaturized video cameras were deployed on 28 Wandering Albatrosses (Diomedea exulans) on Marion Island (one of the two Prince Edward Islands) from 2016 to 2018. The OpenCV library for the Python programming language was used to extract the angle of the horizon relative to the bird’s body (= bank angle) from footage when the birds were flying using a series of steps focused on edge detection. The extracted angles were not significantly different from angles measured manually by three independent observers, thus being a valid method to measure bank angles. Image quality, high wind speeds, and sunlight all influenced the accuracy of angle estimates, but post‐processing eliminated most of these errors. Birds flew most often with cross‐winds (58%) and tailwinds (39%), resulting in skewed distributions of bank angles when birds turned into the wind more often. Higher wind speeds resulted in extreme bank angles (maximum observed was 94°). We present a novel method for measuring postural data from seabirds that can be used to describe the fine‐scale movements of the dynamic‐soaring cycle. Birds appeared to alter their bank angle in response to varying wind conditions to counter wind drift associated with the prevailing westerly winds in the Southern Ocean. These data, in combination with fine‐scale positional data, may lead to new insights into dynamic‐soaring flight.     

A comparison of ferric-chelate reductase and chlorophyll and growth ratios as indices of selection of quince,pear and olive genotypes under iron deficiency stress

Quince (Cydonia oblonga Mill.), pear (Pyrus communis L.) and olive (Olea europaea L.) genotypes were evaluated for their tolerance to iron deficiency stress by growing young plants in three types of aerated nutrient solutions: (1) with iron, (2) without iron or (3) low in iron and with 10 mM bicarbonate. Plants were obtained either from rooted softwood cuttings or from germination of seeds. The degree of tolerance was evaluated with several indices: (1) the chlorophyll content, (2) the root Fe³⁺ reducing capacity and (3) the whole plant relative growth. Fifteen hours before Fe³⁺ reducing capacity determination, iron was applied to the roots of plants with iron-stress, since this method resulted in increasing the reductase activity. All quince and pear genotypes increased the root Fe³⁺ reducing capacity when grown in the treatments for iron-stress, in relation to control plants of the same genotypes. In olive cultivars, the Fe³⁺ reducing capacity was lower in the iron-stress treatments than in the control one. Studying the relationship between relative growth and chlorophyll content for each genotype under iron-stress, in relation to both indices in control plants, a classification of species and genotypes was established. According to that, most olive cultivars and some pear rootstocks and cultivars appear more iron-efficient than quince rootstocks. Our study shows that in some woody species, determining root Fe³⁺ reducing capacity is not the best method to establish tolerance to iron deficiency stress.     

Presence of prostaglandins E2 and A2 in canine gastric secretions

The presence of prostaglandins (PGs) was determined in gastric juice obtained from 3 conscious dogs, provided with a chronic gastric fistula. Outputs of acid (mequiv min^?1) and PGs (pg min^?1) were measured in gastric secretions stimulated by pentagastrin (100 or 200 ng kg^?1min^?1). Prostaglandin activity was estimated, after extraction and thin layer chromatography, by radioimmuno-assay of the PGB formed by treatment with alkali. Tritiated PGs were added to gastric juice for the purpose of correcting for PGs recovery. Using this method, the minimum mass of PGB which could be satisfactorily distinguished from zero was 25 pg. Prostaglandins A₂ and E₂ were present in pentagastrin-activated gastric secretions and averaged (mean ± SE, n = 8) 200.7 ± 18.1 and 260.1 ± 18.0 pg min^?1 respectively. The identity of PGA₂ and PGE₂ was confirmed by gas liquid chromatography combined with mass spectrometry. The amount of PGE₂ converted to PGA₂ during extraction, separation and conversion procedures was estimated from the amount of [³H] PGA₂ found when only [³H] PGE₂ had been added to a sample of gastric juice and averaged 14.5% ± 2.0. Our preliminary results support the possibility that PGE₂ and PGA₂ may be of physiological importance in the regulation of canine gastric secretions.