Current velocity and spatial scale as determinants of the distribution and abundance of two rheophilic herbivorous insects

Organisms frequently show marked preferences for specific environmental conditions, but these preferences may change with landscape scale. Patterns of distribution or abundance measured at different scales may reveal something about an organism's perception of the environment. To test this hypothesis, we measured densities of two herbivorous aquatic insects that differed in body morphology and mobility in relation to current velocity measured at different scales in the upper Colorado River (Colorado, USA). Streambed densities of the caddisfly larva Agapetus boulderensis (high hydrodynamic profile, low mobility) and mayfly nymph Epeorus sp. (low hydrodynamic profile, high mobility) were assessed at 3 spatial scales: whole riffles, individual cobbles within riffles, and point locations on cobbles. Riffles were several meters in extent, cobbles measured 10–30 cm in size, and the local scale was within a few centimeters of individual larvae (themselves ca. 0.5–1.0 cm in size). We also quantified the abundance of periphytic food for these herbivores at the cobble and riffle scales. Agapetus favored slow current (<30 cm s^–1) across all scales. Epeorus, by contrast, favored fast current (60–80 cm s^–1) at the local and riffle scale, but not at the cobble scale. Only Agapetus showed a significant relationship to current at the cobble scale, with greatest larval densities occurring at velocities near 30 cm s^–1. We had predicted an inverse correlation between grazer density and periphytic abundance; however, this occurred only for Agapetus, and then only at the cobble scale. These data suggest that organisms respond to environmental gradients at different spatial scales and that the processes driving these responses may change with scale, e.g., shifting from individual habitat selection at local and cobble scales to population responses at the riffle scale. This study also highlights the importance of using the appropriate scale of measurement to accurately assess the relationship between organisms and environmental gradients across scale.     

Culture-positive sepsis in neonatal camelids: 21 cases

BACKGROUND: There is limited literature on neonatal bacterial sepsis in New World (NW) camelids. HYPOTHESIS: Bacterial culture-positive crias have clinical differences based on the specific bacterial genera isolated. ANIMALS: Bacterial culture-positive NW camelid crias <21 days of age from 1990 to 2005 were included. METHODS: Historic physical examination and cliniopathologic data were retrieved from medical records as were the identity and antibiograms of bacterial isolates. Cases were categorized by outcome (survival versus nonsurvival) and type of sepsis (gram-negative or gram-positive). Kruskal-Wallis and chi-square testing were used to evaluate differences between groups. RESULTS: Twenty-one crias met the inclusion criteria. Median age was 2 days. Failure of passive transfer was common. There were few differences identified on the basis of outcome or type of sepsis. Crias without gastrointestinal or central nervous system involvement survived in greater numbers. Forty-six percent of isolates were gram-positive. The most common isolates were the following: Escherichia coli, Enterococcus spp., Listeria monocytogenes, and Citrobacter spp. Overall survival was 67% (14/21). CONCLUSIONS AND CLINICAL IMPORTANCE: Crias with sepsis do not appear to present with major biochemical, hematologic, or blood gas abnormalities, potentially complicating diagnosis. Affected crias may not have localizing signs at presentation and are not usually febrile, although hypothermia, tachypnea, and tachycardia are relatively common. Total protein concentration was not a substitute for immunoglobulin G measurement in septic crias in this study. Familiarity with the clinical presentation and common pathogens isolated should improve early recognition and treatment and ultimately outcome of crias with sepsis.     

IMAGING DIAGNOSIS—USE OF RADIOGRAPHY AND COMPUTED TOMOGRAPHY IN THE DIAGNOSIS OF A MINERALIZED YOLK SAC IN A BROWN KIWI (APTERYX MANTELLI)

A 12‐day‐old Brown Kiwi (Apteryx mantelli) was presented with anorexia, torticollis, head‐tilt, and coelomic distension. Radiographs showed an ill‐defined, fat‐opaque, coelomic mass displacing viscera craniodorsally. Curvilinear mineral opacities were superimposed over the ventral aspect of the mass. Computed tomography demonstrated the presence of mineral within the periphery of a fat attenuating mass consistent with a retained yolk sac. A deutectomy (yolk sac excision) was performed. Histopathology of the excised tissue confirmed the diagnosis of a retained yolk sac with multifocal mineralization.     

Primary corneal melanocytoma in a Collie

A 6‐year‐old female, spayed Collie was referred to the Western College of Veterinary Medicine for a 12‐month history of a progressive right corneal mass. A superficial keratectomy was performed and histopathology revealed a corneal melanocytoma with complete excision. There has been no recurrence of the neoplasm to date (12 months). This is the first known report of an isolated corneal melanocytoma in a canine.     

Aconitase couples metabolic regulation to mitochondrial DNA maintenance

Mitochondrial DNA (mtDNA) is essential for cells to maintain respiratory competency and is inherited as a protein-DNA complex called the nucleoid. We have identified 22 mtDNA-associated proteins in yeast, among which is mitochondrial aconitase (Aco1p). We show that this Krebs-cycle enzyme is essential for mtDNA maintenance independent of its catalytic activity. Regulation of ACO1 expression by the HAP and retrograde metabolic signaling pathways directly affects mtDNA maintenance. When constitutively expressed, Aco1p can replace the mtDNA packaging function of the high-mobility-group protein Abf2p. Thus, Aco1p may integrate metabolic signals and mtDNA maintenance.     

A robust design mark-resight abundance estimator allowing heterogeneity in resighting probabilities

This article introduces the beta-binomial estimator (BBE), a closed-population abundance mark-resight model combining the favorable qualities of maximum likelihood theory and the allowance of individual heterogeneity in sighting probability (p). The model may be parameterized for a robust sampling design consisting of multiple primary sampling occasions where closure need not be met between primary occasions. We applied the model to brown bear data from three study areas in Alaska and compared its performance to the joint hypergeometric estimator (JHE) and Bowden’s estimator (BOWE). BBE estimates suggest heterogeneity levels were non-negligible and discourage the use of JHE for these data. Compared to JHE and BOWE, confidence intervals were considerably shorter for the AIC_c model-averaged BBE. To evaluate the properties of BBE relative to JHE and BOWE when sample sizes are small, simulations were performed with data from three primary occasions generated under both individual heterogeneity and temporal variation in p. All models remained consistent regardless of levels of variation in p. In terms of precision, the AIC_c model-averaged BBE showed advantages over JHE and BOWE when heterogeneity was present and mean sighting probabilities were similar between primary occasions. Based on the conditions examined, BBE is a reliable alternative to JHE or BOWE and provides a framework for further advances in mark-resight abundance estimation.     

Quantification of Odours from Piggery Effluent Ponds using an Electronic Nose and an Artificial Neural Network

An Artificial Neural Network (ANN) and an electronic nose, AromaScan, were used to predict the piggery odour concentrations emanating from an effluent pond and to develop a confident, rapid, and cost-effective technique for odour measurement. Odour samples from five different piggery effluent ponds were analysed using the AromaScan and dynamic dilution olfactometry. The resulting sensor data were used to train the artificial neural network to correlate the responses to the odour concentrations measured by olfactometry. Effectiveness was evaluated through simulation with various pre-processing techniques and network architectures. The simulation results have shown that a two-layer back-propagation neural network, which has a tan-sigmoid transfer function in the hidden layer and a linear transfer function in the output layer, could be trained to predict piggery odour concentrations with high value of the correlation coefficient R of 0·984 under the best network performance. The results from the application of scaling and principal component analysis suggest that these techniques are necessary not only to avoid the failure of the network caused by saturation but also to enhance performance. An early stopping technique was shown to provide benefits to the network performance in terms of a decrease in computation time and overfitting. It was found that the optimal number of hidden neurons for the network was 20. Odour concentration of unknown samples were able to be predicted with significant accuracy.     

Novel expression of Haemonchus contortus vaccine candidate aminopeptidase H11 using the free-living nematode Caenorhabditis elegans

With the problem of parasitic nematode drug resistance increasing, vaccine development offers an alternative sustainable control approach. For some parasitic nematodes, native extracts enriched for specific proteins are highly protective. However, recombinant forms of these proteins have failed to replicate this protection. This is thought to be due to differences in glycosylation and/or conformation between native and recombinant proteins. We have exploited the free-living nematode Caenorhabditis elegans to examine its suitability as an alternative system for recombinant expression of parasitic nematode vaccine candidates. We focussed on Haemonchus contortus aminopeptidase H11 glycoprotein, which is enriched in a gut membrane fraction capable of inducing significant protection against this important ovine gastrointestinal nematode. We show that H. contortus H11 expressed in C. elegans is enzymatically active and MALDI mass spectrometry identifies similar di- and tri-fucosylated structures to those on native H11, with fucose at the 3- and/or 6-positions of the proximal GlcNAc. Some glycan structural differences were observed, such as lack of LDNF. Serum antibody to native H11 binds to C. elegans recombinant H11 and most of the antibody to rH11 or native H11 is directed to glycan moieties. Despite these similarities, no reduction in worm burden or faecal egg count was observed following immunisation of sheep with C. elegans-expressed recombinant H11 protein. The findings suggest that the di- and tri-fucosylated N-glycans expressed on rH11 do not contribute to the protective effect of H11 and that additional components present in native H11-enriched extract are likely required for enhancing the antibody response necessary for protection.     

Evaluation of a local regression kriging approach for mapping apparent electrical conductivity of soil (ECa) at high resolution

Apparent electrical conductivity of soil (ECa) is a property frequently used as a diagnostic tool in precision agriculture, and is measured using vehicle‐mounted proximal sensors. Crop‐yield data, which is measured by harvester‐mounted sensors, is usually collected at a higher spatial density compared to ECa. ECa and crop‐yield maps frequently exhibit similar spatial patterns because ECa is primarily controlled by the soil clay content and the interrelated soil moisture content, which are often significant contributors to crop‐yield potential. By quantifying the spatial relationship between soil ECa and crop yield, it is possible to estimate the value of ECa at the spatial resolution of the crop‐yield data. This is achieved through the use of a local regression kriging approach which uses the higher‐resolution crop‐yield data as a covariate to predict ECa at a higher spatial resolution than would be prudent with the original ECa data alone. The accuracy of the local regression kriging (LRK) method is evaluated against local kriging (LK) and local regression (LR) to predict ECa. The results indicate that the performance of LRK is dependent on the performance of the inherent local regression. Over a range of ECa transect survey densities, LRK provides greater accuracy than LK and LR, except at very low density. Maps of the regression coefficients demonstrated that the relationship between ECa and crop yield varies from year to year, and across a field. The application of LRK to commercial scale ECa survey data, using crop yield as a covariate, should improve the accuracy of the resultant maps. This has implications for employing the maps in crop‐management decisions and building more robust calibrations between field‐gathered soil ECa and primary soil properties such as clay content.