Using harvest statistics to monitor temporal variation in kangaroo density and harvest rate

Management of the commercial harvest of kangaroos relies on quotas set annually as a proportion of regular estimates of population size. Surveys to generate these estimates are expensive and, in the larger states, logistically difficult; a cheaper alternative is desirable. Rainfall is a disappointingly poor predictor of kangaroo rate of increase in many areas, but harvest statistics (sex ratio, carcass weight, skin size and animals shot per unit time) potentially offer cost-effective indirect monitoring of population abundance (and therefore trend) and status (i.e. under-or overharvest). Furthermore, because harvest data are collected continuously and throughout the harvested areas, they offer the promise of more intensive and more representative coverage of harvest areas than aerial surveys do. To be useful, harvest statistics would need to have a close and known relationship with either population size or harvest rate. We assessed this using longterm (11-22 years) data for three kangaroo species (Macropus rufus, M. giganteus and M. fuliginosus) and common wallaroos (M. robustus) across South Australia, New South Wales and Queensland. Regional variation in kangaroo body size, population composition, shooter efficiency and selectivity required separate analyses in different regions. Two approaches were taken. First, monthly harvest statistics were modelled as a function of a number of explanatory variables, including kangaroo density, harvest rate and rainfall. Second, density and harvest rate were modelled as a function of harvest statistics. Both approaches incorporated a correlated error structure. Many but not all regions had relationships with sufficient precision to be useful for indirect monitoring. However, there was no single relationship that could be applied across an entire state or across species. Combined with rainfall-driven population models and applied at a regional level, these relationships could be used to reduce the frequency of aerial surveys without compromising decisions about harvest management.

Identification of putative virulence-associated genes of Haemophilus parasuis through suppression subtractive hybridization.

Haemophilus parasuis is the causative agent of Glässer's disease. Up to now 15 serovars of H. parasuis have been identified, with significant differences existing in virulence between serovars. In this study, suppression subtractive hybridization (SSH) was used to identify the genetic difference between Nagasaki (H. parasuis serovar 5 reference strain, highly virulent) and SW114 (H. parasuis serovar 3 reference strain, non-virulent). A total of 191 clones were obtained from the SSH library. Using dot hybridization and PCR, 15 clones were identified containing fragments that were present in the Nagasaki genome while absent in the SW114 genome. Among these 15 fragments, three fragments (ssh1, ssh13, ssh15) encode cell surface-associated components; three fragments (ssh2, ssh5, ssh9) are associated with metabolism and stress response; one fragment (ssh8) is involved in assembly of fimbria and one fragment (ssh6) is a phage phi-105 ORF25-like protein. The remaining seven fragments are hypothetical proteins or unknown. Based on PCR analysis of the 15 serovar reference strains, eight fragments (ssh1, ssh2, ssh3, ssh6, ssh8, ssh10, ssh11 and ssh12) were found in three to five of most virulent serovars (1, 5, 10, 12, 13 and 14), zero to two in three moderately virulent serovars (2, 4 and 15), but absent in the low virulent serovar (8) and non-virulent serovars (3, 6, 7, 9 and 11). In vivo transcription fragments ssh1, ssh2, ssh8 and ssh12 were identified in total RNA samples extracted from experimental infected pig lung by RT-PCR. This study has provided some evidence of genetic differences between H. parasuis strains of different virulence.

The biology of Australian weeds, Limnocharis flava

The genus name Limnocharis is derived from the Greek limno (meaning marsh or pond) and charis (meaning grace) (Haynes and Holm-Nielson 1992) and flava is Latin for yellow. The genus is generally accepted to have two species, Limnocharis flava (Linneaus) Buchenau 1868 and L. laforestii (Duchass. ex Griseb) 1858. L. flava was first named Alisma flava by Linneaus in 1753 (Haynes and Holm-Nielsen 1986). Since then, other synonyms have included Damasonium flavum Mill. 1772, Limnocharis emarginata Humb. and Bonpl. 1808, Limnocharis plumieri Rich. 1815, Limnocharis laforestii Duchas. ex Griseb (1858) and Limnocharis mattogrossensis O. Ktze. (1893) (Woodson and Schery 1943).