Infection and pathology in Queensland grouper, Epinephelus lanceolatus, (Bloch), caused by exposure to Streptococcus agalactiae via different routes

Since 2007, 96 wild Queensland groupers, Epinephelus lanceolatus, (Bloch), have been found dead in NE Australia. In some cases, Streptococcus agalactiae (Group B Streptococcus, GBS) was isolated. At present, a GBS isolate from a wild grouper case was employed in experimental challenge trials in hatchery-reared Queensland grouper by different routes of exposure. Injection resulted in rapid development of clinical signs including bilateral exophthalmia, hyperaemic skin or fins and abnormal swimming. Death occurred in, and GBS was re-isolated from, 98% fish injected and was detected by PCR in brain, head kidney and spleen from all fish, regardless of challenge dose. Challenge by immersion resulted in lower morbidity with a clear dose response. Whilst infection was established via oral challenge by admixture with feed, no mortality occurred. Histology showed pathology consistent with GBS infection in organs examined from all injected fish, from fish challenged with medium and high doses by immersion, and from high-dose oral challenge. These experimental challenges demonstrated that GBS isolated from wild Queensland grouper reproduced disease in experimentally challenged fish and resulted in pathology that was consistent with that seen in wild Queensland grouper infected with S. agalactiae.

Statistical methods for analysis of multi-harvest data from perennial pasture variety selection trials

Variety selection in perennial pasture crops involves identifying best varieties from data collected from multiple harvest times in field trials. For accurate selection, the statistical methods for analysing such data need to account for the spatial and temporal correlation typically present. This paper provides an approach for analysing multi-harvest data from variety selection trials in which there may be a large number of harvest times. Methods are presented for modelling the variety by harvest effects while accounting for the spatial and temporal correlation between observations. These methods provide an improvement in model fit compared to separate analyses for each harvest, and provide insight into variety by harvest interactions. The approach is illustrated using two traits from a lucerne variety selection trial. The proposed method provides variety predictions allowing for the natural sources of variation and correlation in multi-harvest data.