Gene interactions constrain the course of evolution of phosphine resistance in the lesser grain borer, Rhyzopertha dominica

Phosphine, a widely used fumigant for the protection of stored grain from insect pests, kills organisms indirectly by inducing oxidative stress. High levels of heritable resistance to phosphine in the insect pest of stored grain, Rhyzopertha dominica have been detected in Asia, Australia and South America. In order to understand the evolution of phosphine resistance and to isolate the responsible genes, we have undertaken genetic linkage analysis of fully sensitive (QRD14), moderately resistant (QRD369) and highly resistant (QRD569) strains of R. dominica collected in Australia. We previously determined that two loci, rph1 and rph2, confer high-level resistance on strain QRD569, which was collected in 1997. We have now confirmed that rph1 is responsible for the moderate resistance of strain QRD369, which was collected in 1990, and is shared with a highly resistant strain from the same geographical region, QRD569. In contrast, rph2 by itself confers only very weak resistance, either as a heterozygote or as a homozygote and was not discovered in the field until weak resistance (probably due to rph1) had become ubiquitous. Thus, high-level resistance against phosphine has evolved via stepwise acquisition of resistance alleles, first at rph1 and thereafter at rph2. The semi-dominance of rph2 together with the synergistic interaction between rph1 and rph2 would have led to rapid selection for homozygosity. A lack of visible fitness cost associated with alleles at either locus suggests that the resistance phenotype will persist in the field.

Co-evolution of wild rabbits (Oryctolagus cuniculus) and RHDV: resistance and virulence

Rabbit Haemorrhagic Disease Virus (RHDV) was introduced to Australia in 1995 for the control of wild rabbits. Initial outbreaks greatly reduced rabbit numbers and the virus has continued to control rabbits to varying degrees in different parts of Australia. However, recent field evidence suggests that the virus may be becoming less effective in those areas that have previously experienced repeated epizootics causing high mortality. There are also reports of rabbits returning to pre-1995 density levels, Virus and host can be expected to co-evolve. The host will develop resistance to the virus with the virus subsequently changing to overcome that resistance. It has been 12 years since the release of RHDV and it is an opportune time to examine where the dynamic currently stands between RHDV and rabbits. Laboratory challenge tests have indicated that resistance to RHDV has developed to different degrees in populations throughout Australia. In one population a low dose (1:25 dilution) of Czech strain RHDV failed to infect a single susceptible rabbit, yet infected a low to high (up to 73%) percentage across other populations tested. Different selection pressures are present in these populations and will be driving the level of resistance being seen. The mechanisms and genetics behind the development of resistance are also important as the on-going use of RHDV as a control tool in the management of rabbits relies on our understanding of factors influencing the efficacy of the virus. Understanding how resistance has developed may provide clues on how best to use the virus to circumvent these mechanisms. Similarly, it will help in managing populations that have yet to develop high levels of resistance.

Simulating the evolution of glyphosate resistance in grains farming in northern Australia.

Background and Aims: The evolution of resistance to herbicides is a substantial problem in contemporary agriculture. Solutions to this problem generally consist of the use of practices to control the resistant population once it evolves, and/or to institute preventative measures before populations become resistant. Herbicide resistance evolves in populations over years or decades, so predicting the effectiveness of preventative strategies in particular relies on computational modelling approaches. While models of herbicide resistance already exist, none deals with the complex regional variability in the northern Australian sub-tropical grains farming region. For this reason, a new computer model was developed. Methods: The model consists of an age- and stage-structured population model of weeds, with an existing crop model used to simulate plant growth and competition, and extensions to the crop model added to simulate seed bank ecology and population genetics factors. Using awnless barnyard grass (Echinochloa colona) as a test case, the model was used to investigate the likely rate of evolution under conditions expected to produce high selection pressure. Key Results: Simulating continuous summer fallows with glyphosate used as the only means of weed control resulted in predicted resistant weed populations after approx. 15 years. Validation of the model against the paddock history for the first real-world glyphosate-resistant awnless barnyard grass population shows that the model predicted resistance evolution to within a few years of the real situation. Conclusions: This validation work shows that empirical validation of herbicide resistance models is problematic. However, the model simulates the complexities of sub-tropical grains farming in Australia well, and can be used to investigate, generate and improve glyphosate resistance prevention strategies.

Dosage consistency is the key factor in avoiding evolution of resistance to phosphine and population increase in stored-grain pests

BACKGROUND Control of pests in stored grain and the evolution of resistance to pesticides are serious problems worldwide. A stochastic individual-based two-locus model was used to investigate the impact of two important issues, the consistency of pesticide dosage through the storage facility and the immigration rate of the adult pest, on overall population control and avoidance of evolution of resistance to the fumigant phosphine in an important pest of stored grain, the lesser grain borer. RESULTS A very consistent dosage maintained good control for all immigration rates, while an inconsistent dosage failed to maintain control in all cases. At intermediate dosage consistency, immigration rate became a critical factor in whether control was maintained or resistance emerged. CONCLUSION Achieving a consistent fumigant dosage is a key factor in avoiding evolution of resistance to phosphine and maintaining control of populations of stored-grain pests; when the dosage achieved is very inconsistent, there is likely to be a problem regardless of immigration rate. © 2012 Society of Chemical Industry

The rph1 Gene Is a Common Contributor to the Evolution of Phosphine Resistance in Independent Field Isolates of Rhyzopertha Dominica

Phosphine is the only economically viable fumigant for routine control of insect pests of stored food products, but its continued use is now threatened by the world-wide emergence of high-level resistance in key pest species. Phosphine has a unique mode of action relative to well-characterised contact pesticides. Similarly, the selective pressures that lead to resistance against field sprays differ dramatically from those encountered during fumigation. The consequences of these differences have not been investigated adequately. We determine the genetic basis of phosphine resistance in Rhyzopertha dominica strains collected from New South Wales and South Australia and compare this with resistance in a previously characterised strain from Queensland. The resistance levels range from 225 and 100 times the baseline response of a sensitive reference strain. Moreover, molecular and phenotypic data indicate that high-level resistance was derived independently in each of the three widely separated geographical regions. Despite the independent origins, resistance was due to two interacting genes in each instance. Furthermore, complementation analysis reveals that all three strains contain an incompletely recessive resistance allele of the autosomal rph1 resistance gene. This is particularly noteworthy as a resistance allele at rph1 was previously proposed to be a necessary first step in the evolution of high-level resistance. Despite the capacity of phosphine to disrupt a wide range of enzymes and biological processes, it is remarkable that the initial step in the selection of resistance is so similar in isolated outbreaks.

Managing glyphosate resistance in Australian cotton farming: modelling shows how to delay evolution and maintain long-term population control

Glyphosate resistance is a rapidly developing threat to profitability in Australian cotton farming. Resistance causes an immediate reduction in the effectiveness of in-crop weed control in glyphosate-resistant transgenic cotton and summer fallows. Although strategies for delaying glyphosate resistance and those for managing resistant populations are qualitatively similar, the longer resistance can be delayed, the longer cotton growers will have choice over which tactics to apply and when to apply them. Effective strategies to avoid, delay, and manage resistance are thus of substantial value. We used a model of glyphosate resistance dynamics to perform simulations of resistance evolution in Sonchus oleraceus (common sowthistle) and Echinochloa colona (awnless barnyard grass) under a range of resistance prevention, delaying, and management strategies. From these simulations, we identified several elements that could contribute to effective glyphosate resistance prevention and management strategies. (i) Controlling glyphosate survivors is the most robust approach to delaying or preventing resistance. High-efficacy, high-frequency survivor control almost doubled the useful lifespan of glyphosate from 13 to 25 years even with glyphosate alone used in summer fallows. (ii) Two non-glyphosate tactics in-crop plus two in-summer fallows is the minimum intervention required for long-term delays in resistance evolution. (iii) Pre-emergence herbicides are important, but should be backed up with non-glyphosate knockdowns and strategic tillage; replacing a late-season, pre-emergence herbicide with inter-row tillage was predicted to delay glyphosate resistance by 4 years in awnless barnyard grass. (iv) Weed species' ecological characteristics, particularly seed bank dynamics, have an impact on the effectiveness of resistance strategies; S. oleraceus, because of its propensity to emerge year-round, was less exposed to selection with glyphosate than E. colona, resulting in an extra 5 years of glyphosate usefulness (18 v. 13 years) even in the most rapid cases of resistance evolution. Delaying tactics are thus available that can provide some or many years of continued glyphosate efficacy. If glyphosate-resistant cotton cropping is to remain profitable in Australian farming systems in the long-term, however, growers must adapt to the probability that they will have to deal with summer weeds that are no longer susceptible to glyphosate. Robust resistance management systems will need to include a diversity of weed control options, used appropriately.

APSIM – Evolution towards a new generation of agricultural systems simulation

Agricultural systems models worldwide are increasingly being used to explore options and solutions for the food security, climate change adaptation and mitigation and carbon trading problem domains. APSIM (Agricultural Production Systems sIMulator) is one such model that continues to be applied and adapted to this challenging research agenda. From its inception twenty years ago, APSIM has evolved into a framework containing many of the key models required to explore changes in agricultural landscapes with capability ranging from simulation of gene expression through to multi-field farms and beyond. Keating et al. (2003) described many of the fundamental attributes of APSIM in detail. Much has changed in the last decade, and the APSIM community has been exploring novel scientific domains and utilising software developments in social media, web and mobile applications to provide simulation tools adapted to new demands. This paper updates the earlier work by Keating et al. (2003) and chronicles the changing external challenges and opportunities being placed on APSIM during the last decade. It also explores and discusses how APSIM has been evolving to a “next generation” framework with improved features and capabilities that allow its use in many diverse topics.