Responses of woody vegetation to exclusion of large herbivores in semi‐arid savannas

The Nkuhlu large‐scale long‐term exclusion experiment in Kruger National Park was designed to study the long‐term effects of large herbivores on vegetation. One treatment excludes elephants, another excludes all herbivores larger than hares and another one comprises an open, control area. Vegetation monitoring was implemented in 2002 when a baseline survey was conducted prior to exclusion. Monitoring was repeated 5 years after exclusion. Data from the surveys were analysed to establish how structure and composition of woody vegetation had changed 5 years after herbivore exclusion. The analysis showed that neither plant assemblage nor mean vegetation height had changed significantly since exclusion. However, both species richness and density of woody plants increased 5 years after exclusion of all large herbivores, but not after the exclusion of elephants alone. One already common species, Dichrostachys cinerea, became more common after excluding all large herbivores compared with either no exclusion or elephant exclusion, possibly leading to competitive suppression of other species. Species other than D. cinerea tended to either increase or decrease in density, but the changes were insufficient to induce significant shifts in the overall assemblage of woody plants. The results indicate that after 5 years of exclusion, the combined assemblage of large herbivores, and not elephants alone, could induce changes in species richness and abundances of woody plants, but the effect was so far insufficient to induce measureable shifts in the assemblages of woody plants. It is possible that assemblages will change with time and increasing elephant numbers may amplify future changes.     

Virtual reality 3D echocardiography in the assessment of tricuspid valve function after surgical closure of ventricular septal defect

Background

Chronic right ventricular apical pacing may have detrimental effect on left ventricular function and may promote to heart failure in adult patients with left ventricular dysfunction.

Methods

A group of 99 pediatric patients with previously implanted pacemaker was studied retrospectively. Forty-three patients (21 males) had isolated congenital complete or advanced atrioventricular block. The remaining 56 patients (34 males) had pacing indication in the presence of structural heart disease. Thirty-two of them (21 males) had isolated structural heart disease and the remaining 24 (13 males) had complex congenital heart disease. Patients were followed up for an average of 53 ± 41.4 months with 12-lead electrocardiogram and transthoracic echocardiography. Left ventricular shortening fraction was used as a marker of ventricular function. QRS duration was assessed using leads V₅ or II on standard 12-lead electrocardiogram.

Results

Left ventricular shortening fraction did not change significantly after pacemaker implantation compared to preimplant values overall and in subgroups. In patients with complex congenital heart malformations shortening fraction decreased significantly during the follow up period. (0.45 ± 0.07 vs 0.35 ± 0.06, p = 0.015). The correlation between the change in left ventricular shortening fraction and the mean increase of paced QRS duration was not significant. Six patients developed dilated cardiomyopathy, which was diagnosed 2 months to 9 years after pacemaker implantation.

Conclusion

Chronic right ventricular pacing in pediatric patients with or without structural heart disease does not necessarily result in decline of left ventricular function. In patients with complex congenital heart malformations left ventricular shortening fraction shows significant decrease.     

Effects of Roads on Endangered San Joaquin Kit Foxes

ABSTRACT San Joaquin kit foxes (Vulpes macrotis mutica) occur in central California, USA, and are endangered due to habitat loss and degradation. As the human population of California grows, more roads are being constructed in remaining kit fox habitat. We examined effects of 2-lane roads on demographic and ecological patterns of kit foxes on the Lokern Natural Area (LNA) from August 2001 to June 2004. Of 60 radiocollared kit foxes, only one was struck by a vehicle. Foxes were assigned to 1 of 3 risk categories (high, medium, or low) based on proportion of time spent in road-effect zones, which were defined by the probability of a fox encountering a road during nocturnal movements. Fox survival probabilities, reproductive success, litter size, nocturnal movements, and den placement all were similar among risk categories. Nocturnal locations of foxes were closer to roads than were den locations, and den fidelity was lowest for medium-risk foxes and highest for low-risk foxes but intermediate for high-risk foxes. Food availability and use were not affected by proximity to roads. We were unable to detect any significant detrimental effects from 2-lane roads on kit fox demography and ecology. Our results suggest that standard mitigation strategies, such as crossing structures and exclusionary fencing, would not benefit kit foxes on the LNA.     

A phasing and imputation method for pedigreed populations that results in a single-stage genomic evaluation

Background

Efficient, robust, and accurate genotype imputation algorithms make large-scale application of genomic selection cost effective. An algorithm that imputes alleles or allele probabilities for all animals in the pedigree and for all genotyped single nucleotide polymorphisms (SNP) provides a framework to combine all pedigree, genomic, and phenotypic information into a single-stage genomic evaluation.

Methods

An algorithm was developed for imputation of genotypes in pedigreed populations that allows imputation for completely ungenotyped animals and for low-density genotyped animals, accommodates a wide variety of pedigree structures for genotyped animals, imputes unmapped SNP, and works for large datasets. The method involves simple phasing rules, long-range phasing and haplotype library imputation and segregation analysis.

Results

Imputation accuracy was high and computational cost was feasible for datasets with pedigrees of up to 25 000 animals. The resulting single-stage genomic evaluation increased the accuracy of estimated genomic breeding values compared to a scenario in which phenotypes on relatives that were not genotyped were ignored.

Conclusions

The developed imputation algorithm and software and the resulting single-stage genomic evaluation method provide powerful new ways to exploit imputation and to obtain more accurate genetic evaluations.     

The importance of information on relatives for the prediction of genomic breeding values and the implications for the makeup of reference data sets in livestock breeding schemes

Background

The theory of genomic selection is based on the prediction of the effects of genetic markers in linkage disequilibrium with quantitative trait loci. However, genomic selection also relies on relationships between individuals to accurately predict genetic value. This study aimed to examine the importance of information on relatives versus that of unrelated or more distantly related individuals on the estimation of genomic breeding values.

Methods

Simulated and real data were used to examine the effects of various degrees of relationship on the accuracy of genomic selection. Genomic Best Linear Unbiased Prediction (gBLUP) was compared to two pedigree based BLUP methods, one with a shallow one generation pedigree and the other with a deep ten generation pedigree. The accuracy of estimated breeding values for different groups of selection candidates that had varying degrees of relationships to a reference data set of 1750 animals was investigated.

Results

The gBLUP method predicted breeding values more accurately than BLUP. The most accurate breeding values were estimated using gBLUP for closely related animals. Similarly, the pedigree based BLUP methods were also accurate for closely related animals, however when the pedigree based BLUP methods were used to predict unrelated animals, the accuracy was close to zero. In contrast, gBLUP breeding values, for animals that had no pedigree relationship with animals in the reference data set, allowed substantial accuracy.

Conclusions

An animal''s relationship to the reference data set is an important factor for the accuracy of genomic predictions. Animals that share a close relationship to the reference data set had the highest accuracy from genomic predictions. However a baseline accuracy that is driven by the reference data set size and the overall population effective population size enables gBLUP to estimate a breeding value for unrelated animals within a population (breed), using information previously ignored by pedigree based BLUP methods.     

Accuracy of pedigree and genomic predictions of carcass and novel meat quality traits in multi-breed sheep data assessed by cross-validation

Background

Genomic predictions can be applied early in life without impacting selection candidates. This is especially useful for meat quality traits in sheep. Carcass and novel meat quality traits were predicted in a multi-breed sheep population that included Merino, Border Leicester, Polled Dorset and White Suffolk sheep and their crosses.

Methods

Prediction of breeding values by best linear unbiased prediction (BLUP) based on pedigree information was compared to prediction based on genomic BLUP (GBLUP) and a Bayesian prediction method (BayesR). Cross-validation of predictions across sire families was used to evaluate the accuracy of predictions based on the correlation of predicted and observed values and the regression of observed on predicted values was used to evaluate bias of methods. Accuracies and regression coefficients were calculated using either phenotypes or adjusted phenotypes as observed variables.

Results and conclusions

Genomic methods increased the accuracy of predicted breeding values to on average 0.2 across traits (range 0.07 to 0.31), compared to an average accuracy of 0.09 for pedigree-based BLUP. However, for some traits with smaller reference population size, there was no increase in accuracy or it was small. No clear differences in accuracy were observed between GBLUP and BayesR. The regression of phenotypes on breeding values was close to 1 for all methods, indicating little bias, except for GBLUP and adjusted phenotypes (regression = 0.78). Accuracies calculated with adjusted (for fixed effects) phenotypes were less variable than accuracies based on unadjusted phenotypes, indicating that fixed effects influence the latter. Increasing the reference population size increased accuracy, indicating that adding more records will be beneficial. For the Merino, Polled Dorset and White Suffolk breeds, accuracies were greater than for the Border Leicester breed due to the smaller sample size and limited across-breed prediction. BayesR detected only a few large marker effects but one region on chromosome 6 was associated with large effects for several traits. Cross-validation produced very similar variability of accuracy and regression coefficients for BLUP, GBLUP and BayesR, showing that this variability is not a property of genomic methods alone. Our results show that genomic selection for novel difficult-to-measure traits is a feasible strategy to achieve increased genetic gain.     

Computational investigation of epithelial cell dynamic phenotype in vitro

Background  

When grown in three-dimensional (3D) cultures, epithelial cells typically form cystic organoids that recapitulate cardinal features of in vivo epithelial structures. Characterizing essential cell actions and their roles, which constitute the system's dynamic phenotype, is critical to gaining deeper insight into the cystogenesis phenomena.