The Genetic Architecture of Climatic Adaptation of Tropical Cattle

Adaptation of global food systems to climate change is essential to feed the world. Tropical cattle production, a mainstay of profitability for farmers in the developing world, is dominated by heat, lack of water, poor quality feedstuffs, parasites, and tropical diseases. In these systems European cattle suffer significant stock loss, and the cross breeding of taurine x indicine cattle is unpredictable due to the dilution of adaptation to heat and tropical diseases. We explored the genetic architecture of ten traits of tropical cattle production using genome wide association studies of 4,662 animals varying from 0% to 100% indicine. We show that nine of the ten have genetic architectures that include genes of major effect, and in one case, a single location that accounted for more than 71% of the genetic variation. One genetic region in particular had effects on parasite resistance, yearling weight, body condition score, coat colour and penile sheath score. This region, extending 20 Mb on BTA5, appeared to be under genetic selection possibly through maintenance of haplotypes by breeders. We found that the amount of genetic variation and the genetic correlations between traits did not depend upon the degree of indicine content in the animals. Climate change is expected to expand some conditions of the tropics to more temperate environments, which may impact negatively on global livestock health and production. Our results point to several important genes that have large effects on adaptation that could be introduced into more temperate cattle without detrimental effects on productivity.

Population dynamics of Thaumastocoris peregrinus in Eucalyptus plantations of South Africa

Thaumastocoris peregrinus is a sap-sucking insect that infests non-native Eucalyptus plantations in Africa, New Zealand, South America and parts of Southern Europe, in addition to street trees in parts of its native range of Australia. In South Africa, pronounced fluctuations in the population densities have been observed. To characterise spatiotemporal variability in T. peregrinus abundance and the factors that might influence it, we monitored adult population densities at six sites in the main eucalypt growing regions of South Africa. At each site, twenty yellow sticky traps were monitored weekly for 30 months, together with climatic data. We also characterised the influence of temperature on growth and survival experimentally and used this to model how temperature may influence population dynamics. T. peregrinus was present throughout the year at all sites, with annual site-specific peaks in abundance. Peaks occurred during autumn (February–April) for the Pretoria site, summer (November–January) for the Zululand site and spring (August–October) for the Tzaneen, Sabie and Piet Retief monitoring sites. Temperature (both experimental and field-collected), humidity and rainfall were mostly weakly, or not at all, associated with population fluctuations. It is clear that a complex interaction of these and other factors (e.g. host quality) influence population fluctuations in an annual, site specific cycle. The results obtained not only provide insights into the biology of T. peregrinus, but will also be important for future planning of monitoring and control programs using semiochemicals, chemical insecticides or biological control agents.

Indigofera linnaei-a problem legume across northern pastures

Indigofera linnaei (or Birdsville Indigo) is a native legume with widespread abundance in pastures across northern Australian, and occurs in all northern regions of Australia from the tropical Kimberleys and arid central Australia to subhumid coastal Queensland (Figure 1). I. linnaei in central Australia has been linked to canine fatalities due to the toxin indospicine. Indospicine, an analog of arginine, is an unusual non-protein amino acid found only in a number of Indigofera species including I. linnaei. Dogs are particularly sensitive to the heptatoxicity of indospicine, and while they do not themselves consume the plant, dogs have been poisoned indirectly through the consumption of indospicine-contaminated meat from horses and camels grazing in regions where I. linnaei is common (Hegarty and Pound 1988, FitzGerald et al 2011). I. linnaei is observed to occur in various forms from strongly prostrate in south-east Queensland to an erect shrub-like form growing to more than 50cm in height in some northern regions. It mostly occurs as a minor proportion of native pasture but denser stands develop under certain circumstances. The indospicine content of I. linnaei has not previously been reported outside of central Australia, and in this study we investigate the indospicine content of plant samples collected across various regions, including both prostrate and upright forms. All samples were collected in March-July, dried, milled and analysed by UPLC-MS/MS in an adaption of our method (Tan et al 2014). Indospicine was determined in all I. linnaei plant samples regardless of region or growth form (Table 1). Measured levels were in the range 159.5 to 658.8 mg/kg DM and indicate that this plant may pose a similar problem in all areas dependent on local seasonal abundance.

Improving the effectiveness of age-abundance indicators in the management of fisheries in Queensland, Australia

The development of fishery indicators is a crucial undertaking as it ultimately provides evidence to stakeholders about the status of fished species such as population size and survival rates. In Queensland, as in many other parts of the world, age-abundance indicators (e.g. fish catch rate and/or age composition data) are traditionally used as the evidence basis because they provide information on species life history traits as well as on changes in fishing pressures and population sizes. Often, however, the accuracy of the information from age-abundance indicators can be limited due to missing or biased data. Consequently, improved statistical methods are required to enhance the accuracy, precision and decision-support value of age-abundance indicators.