Intensive Livestock Farming on New Zealand Hill Country Farms Creates Critical Source Areas of Potential Pollution

Oral Session

Intensive Livestock Farming on New Zealand Hill Country Farms Creates Critical Source Areas of Potential Pollution

Oral Session

A QTL analysis of host plant effects on fungal endophyte biomass and alkaloid expression in perennial ryegrass.

The association between perennial ryegrass (Loliumperenne L.) and its Epichloë fungal endophyte symbiont, Epichloëfestucae var. lolii, supports the persistence of ryegrass-based pastures principally by producing bioactive alkaloid compounds that deter invertebrate herbivory. The host plant genotype affects endophyte trait expression, and elucidation of the underlying genetic mechanisms would enhance understanding of the symbiosis and support improvement of inplanta endophyte performance through plant breeding. Rapid metabolite profiling and enzyme-linked immunosorbent assay were used to quantify endophyte alkaloids and mycelial mass (MM) in leaves harvested, in consecutive autumns, from an F1 mapping population hosting standard toxic endophyte. Co-aligned quantitative trait loci (QTL) on linkage groups (LG)2, LG4 and LG7 for MM and concentrations of alkaloids peramine and ergovaline confirmed host plant effects on both MM and alkaloid level and inferred the effect on alkaloids was modulated through the quantity of endophyte present in the leaf tissue. For ergovaline, host regulation independent of endophyte concentration was also indicated, by the presence of MM-independent ergovaline QTL on LG4 and LG7. Partitioning of host genetic influence between MM-dependent and MM-independent mechanisms was also observed for the alkaloid N-formylloline (NFL), in a second mapping population harbouring a tall fescue-sourced endophyte. Single-marker analysis on repeated MM and NFL measures identified marker-trait associations at nine genome locations, four affecting both NFL and MM but five influencing NFL concentration alone. Co-occurrence of QTL on LG3, LG4 and LG7 in both mapping populations is evidence for host regulatory loci effective across genetic backgrounds and independent of endophyte variant. Variation at these loci may be exploited using marker-assisted breeding to improve endophyte trait expression in different host population × endophyte combinations

Seed Transmission of Epichloë Endophytes in Lolium perenne Is Heavily Influenced by Host Genetics

Vertical transmission of symbiotic Epichloë endophytes from host grasses into progeny seed is the primary mechanism by which the next generation of plants is colonized. This process is often imperfect, resulting in endophyte-free seedlings which may have poor ecological fitness if the endophyte confers protective benefits to its host. In this study, we investigated the influence of host genetics and environment on the vertical transmission of Epichloë festucae var. lolii strain AR37 in the temperate forage grass Lolium perenne. The efficiency of AR37 transmission into the seed of over 500 plant genotypes from five genetically diverse breeding populations was determined. In Populations I–III, which had undergone previous selection for high seed infection by AR37, mean transmission was 88, 93, and 92%, respectively. However, in Populations IV and V, which had not undergone previous selection, mean transmission was 69 and 70%, respectively. The transmission values, together with single-nucleotide polymorphism data obtained using genotyping-by-sequencing for each host, was used to develop a genomic prediction model for AR37 seed transmission. The predictive ability of the model was estimated at r = 0.54. While host genotype contributed greatly to differences in AR37 seed transmission, undefined environmental variables also contributed significantly to seed transmission across different years and geographic locations. There was evidence for a small host genotype-by-environment effect; however this was less pronounced than genotype or environment alone. Analysis of endophyte infection levels in parent plants within Populations I and IV revealed a loss of endophyte infection over time in Population IV only. This population also had lower average tiller infection frequencies than Population I, suggesting that AR37 failed to colonize all the daughter tillers and therefore seeds. However, we also observed that infection of seed by AR37 may fail during or after initiation of floral development from plants where all tillers remained endophyte-infected over time. While the effects of environment and host genotype on fungal endophyte transmission have been evaluated previously, this is the first study that quantifies the relative impacts of host genetics and environment on endophyte vertical transmission

A Proposed Taxonomy of Anaerobic Fungi (Class Neocallimastigomycetes) Suitable for Large-Scale Sequence-Based Community Structure Analysis

Anaerobic fungi are key players in the breakdown of fibrous plant material in the rumen, but not much is known about the composition and stability of fungal communities in ruminants. We analyzed anaerobic fungi in 53 rumen samples from farmed sheep (4 different flocks), cattle, and deer feeding on a variety of diets. Denaturing gradient gel electrophoresis fingerprinting of the internal transcribed spacer 1 (ITS1) region of the rrn operon revealed a high diversity of anaerobic fungal phylotypes across all samples. Clone libraries of the ITS1 region were constructed from DNA from 11 rumen samples that had distinctly different fungal communities. A total of 417 new sequences were generated to expand the number and diversity of ITS1 sequences available. Major phylogenetic groups of anaerobic fungi in New Zealand ruminants belonged to the genera Piromyces, Neocallimastix, Caecomyces and Orpinomyces. In addition, sequences forming four novel clades were obtained, which may represent so far undetected genera or species of anaerobic fungi. We propose a revised phylogeny and pragmatic taxonomy for anaerobic fungi, which was tested and proved suitable for analysis of datasets stemming from high-throughput next-generation sequencing methods. Comparing our revised taxonomy to the taxonomic assignment of sequences deposited in the GenBank database, we believe that >29% of ITS1 sequences derived from anaerobic fungal isolates or clones are misnamed at the genus level

Some aspects of covariance regularisation in discriminant analysis : a thesis presented in fulfilment of the requirements for the degree of Doctor of Philosophy in Statistics at Massey University, New Zealand

Content removed due to copyright Koolaard, J.P. & Lawoko, C.R.O. (1993). Estimating error rates in discriminant analysis with correlated training observations: a simulation study. Journal of statistical computation and simulation, 48, 81-99.Statistical discriminant analysis and classification are multivariate techniques concerned with separating distinct set of objects, and with allocating new objects to previously defined populations or groups. In this process the covariance matrix plays an important role, and usually this matrix has to be estimated from sample data. In this thesis, attention is focussed on investigating the problem of (poor) estimation of the covariance structure and its effects in statistical discriminant analysis. The quality or statistical properties of these estimates usually affect the resultant classification rules which are constructed using them. Reasons for the (usually, consistent) estimators of the covariance matrices being poor are mainly to do with the quality and/or size of the training sample in relation to the number of parameters which have to be estimated. In this thesis, we are interested in investigating this problem as it occurs in the small sample, high-dimensional situation. In particular, we are interested in the problem of co-variance estimation in the situations when the sample size to dimension ratios are relatively small. The criterion used to determine the success or otherwise of various methods used to address this problem is the estimated (overall) error rate. One method of dealing with a situation which potentially results in poor estimation of the covariance matrix is to impose a prescribed (simple) structure on the covariance matrix, such as the identity matrix, or multiple of it. Another method is to make the assumption that all the groups have the same covariance matrix. The effect of such simplifying assumptions is to reduce the number of parameters to be estimated. Consequently, the (fewer) parameters are estimated with higher precision. It has been demonstrated that this may result in better statistical discriminant analysis, even if the simplifying assumptions may not be entirely correct. Of the classification rules based on the normal distribution, the quadratic discriminant function (QDF) makes no restrictions on the population parameters, and as such is the most general of this class of classification rules. However, it is also the one most affected by poor population parameter estimates. The two common simplifying techniques mentioned earlier (i.e. imposing an identity matrix structure on the covariance matrix, or assuming a common covariance among all populations) lead to two other discriminant rules, namely, the Euclidean distance function (EDF, based on the Euclidean distance between the group means) and the popular linear discriminant function (LDF, based on the Mahalanobis distance between the groups) respectively. The sample-based versions of these two classifiers are compared using expected error rates (conditional on a set of training data), and these expected error rates are obtained through the derivation of asymptotic expansions. The expansions are evaluated under a range of settings, defined by employing combinations of various values of dimension, group separation, and co-variance structure. It is shown that the simpler sample Euclidean distance function (SEDF) performs as well as or better than the sample linear discriminant function (SLDF) under most of the settings used. Exceptions occurred when the Mahalanobis distance between populations was much greater than the Euclidean distance. A flexible discrimination model, or rather, class of models, was developed by Friedman (1989), and called the regularised discriminant function (RDF). The sample version of the RDF (i.e. SRDF) model incorporates the general sample quadratic discriminant function (SQDF), the two previously-mentioned restricted models (SEDF and SLDF), as well as a wide range of models intermediate to these, through the use of additional "regularisation" parameters. The method employs two types of shrinkage of the covariance estimates - towards the pooled estimate on one hand, and towards a multiple of the identity matrix on the other. A separate regularisation parameter controls shrinkage to each. The training data is used in the model selection process to determine appropriate values for the regularisation parameters, through the use of cross-validation. The quality of model selection procedure which specifies a discriminant model is a crucial factor, since if it is performing well, it will result in a classification rule close to the optimal one from the class of models available. Through large-scale simulation studies, the performance of the sample regularised discriminant function (SRDF) is investigated and it is shown that the SRDF generally leads to lower overall error rates than the standard classification rules. This is found to be largely due to the facility which allows shrinkage of the covariance matrices to sphericity, or eigenvalue regularisation. It is also found that the SEDF performs very well in relation to the SRDF for a variety of settings. Further simulation studies show that the performance of the SRDF is more sensitive to the parameter controlling shrinkage to sphericity than the one controlling covariance mixing. Also, it is found that under some circumstances, the SRDF performs better than the other classifiers even for quite large sample size to dimension ratios. A crucial negative feature of the SRDF is its lack of scale invariance. The cause of this is eigenvalue regularisation. A modified classification rule is developed which is scale invariant, and is compared to the SRDF and the other classifiers via simulation. The modified rule omits eigenvalue regularisation, but otherwise increases sensitivity to the data by allowing for varying degrees of shrinkage to the pooled covariance for each group. It is shown that eigenvalue regularisation is generally beneficial for discrimination in medium to large dimensional problems, through its variance-reduction effect which stabilises the covariance estimates. Thus, the study concludes that scale invariance must be sacrificed in order to achieve reductions in error rate, in the absence of a suitable replacement for eigenvalue regularisation. The use of cross-validation in the model selection process of the SRDF is also investigated, for several reasons: the computational effort involved, and the fact that it rarely leads to a unique choice of model, and often uses only a small subset of the available observations, in the model selection process. Consequently, another method for determining the optimal regularisation parameters is investigated. In particular, it is investigated whether appropriate values for the regularisation parameters can be indicated from a measure of the distance between the groups. For this purpose, the Bhattacharyya distance is chosen since it comprises a term primarily pertaining to the difference between group means, and a further term which indicates the level of disparity between group covariance structures. It is shown that the magnitudes of the various components of the Bhattacharyya distance, when considered on their own and in relation to each other, do give information as to appropriate values for the regularisation parameters. A new simulation study, as well as various case studies are presented to assess the performance of a new regularised discriminant function which uses the Bhattacharyya distance estimates between groups to select regularisation parameters for given training data. This classifier is shown to perform as well as the SRDF, and is computationally much faster since it avoids any re-sampling methods. It is clear that most of the investigations and assessments of the various regularised discriminant rules have to be undertaken using Monte-Carlo simulation techniques, especially to estimate error rates. This is because exact analytical expressions for the unconditional error rate of the SRDF do not exist, except in certain limited circumstances. It has not been possible to obtain asymptotic expansions or some form of approximations of these error rates in a general context. However, an approximation which can be used to calculate algebraically the error rate of the SQDF, assuming known population parameters under (other) strict conditions, is available in the literature. This approximation is used in this thesis to further examine the effects (observed in earlier simulation work) of the covariance regularisation parameters on error rates. This is the last piece of work in the thesis and, in spite of its limited extent (because of the restricted conditions of the approximations given), it largely confirms the results which were obtained from simulation experiments in the previous parts of the thesis

Selenium-Enriched Foods Are More Effective at Increasing Glutathione Peroxidase (GPx) Activity Compared with Selenomethionine: A Meta-Analysis

Selenium may play a beneficial role in multi-factorial illnesses with genetic and environmental linkages via epigenetic regulation in part via glutathione peroxidase (GPx) activity. A meta-analysis was undertaken to quantify the effects of dietary selenium supplementation on the activity of overall GPx activity in different tissues and animal species and to compare the effectiveness of different forms of dietary selenium. GPx activity response was affected by both the dose and form of selenium (p < 0.001). There were differences between tissues on the effects of selenium supplementation on GPx activity (p < 0.001); however, there was no evidence in the data of differences between animal species (p = 0.95). The interactions between dose and tissue, animal species and form were significant (p < 0.001). Tissues particularly sensitive to changes in selenium supply include red blood cells, kidney and muscle. The meta-analysis identified that for animal species selenium-enriched foods were more effective than selenomethionine at increasing GPx activity

Sheep numbers required for dry matter digestibility evaluations when fed fresh perennial ryegrass or forage rape

Research trials with fresh forages often require accurate and precise measurement of digestibility and variation in digestion between individuals, and the duration of measurement periods needs to be established to ensure reliable data are obtained. The variation is likely to be greater when freshly harvested feeds are given, such as perennial ryegrass (Lolium perenne L.) and forage rape (Brassica napus L.), because the nutrient composition changes over time and in response to weather conditions. Daily feed intake and faeces output data from a digestibility trial with these forages were used to calculate the effects of differing lengths of the measurement period and differing numbers of sheep, on the precision of digestibility, with a view towards development of a protocol. Sixteen lambs aged 8 months and weighing 33 kg at the commencement of the trial were fed either perennial ryegrass or forage rape (8/treatment group) over 2 periods with 35 d between measurements. They had been acclimatised to the diets, having grazed them for 42 d prior to 11 days of indoor measurements. The sheep numbers required for a digestibility trial with different combinations of acclimatisation and measurement period lengths were subsequently calculated for 3 levels of imposed precision upon the estimate of mean dry matter (DM) digestibility. It is recommended that if the standard error of the mean for digestibility is equal to or higher than 5 g/kg DM, and if sheep are already used to a fresh perennial ryegrass or forage rape diet, then a minimum of 6 animals are needed and 4 acclimatisation days being fed individually in metabolic crates followed by 7 days of measurement