Integrating different approaches in the definition of biological stocks: A northern Australian multi-jurisdictional fisheries example using grey mackerel, Scomberomorus semifasciatus

A holistic approach to stock structure studies utilises multiple different techniques on the same individuals sampled from selected populations and combines results across spatial and temporal scales to produce a weight of evidence conclusion. It is the most powerful and reliable source of information to use in formulating resource management and monitoring plans. Few examples of the use of a holistic approach in stock structure studies exist, although more recently this is changing. Using such an approach makes integration of results from each technique challenging. An integrated stock definition (ISD) approach for holistic stock structure studies was developed in this study to aid in the appropriate interpretation of stock structure results to guide the determination of fishery management units. The ISD approach is applied herein to a study of the northern Australian endemic grey mackerel, Scomberomorus semifasciatus (Scombridae). Analyses of genetic (mitochondrial DNA and microsatellites), parasite, otolith stable isotope, and growth data are synthesised to determine the stock structure of S. semifasciatus across northern Australia. Integration of the results from all techniques identified at least six S. semifasciatus stocks for management purposes. Further, the use of the ISD approach provided a simple basis for integrating multiple techniques and for their interpretation. The use of this holistic approach was a powerful tool in providing greater certainty about the appropriate management units for S. semifasciatus. Future stock structure studies investigating spatial management questions in the fisheries context should adopt a holistic approach and apply the ISD approach for a more accurate definition of biological stocks to improve fisheries management.

Commercial-scale Alternaria brown spot resistance screening as the first step in breeding new mandarins for Australia

Rapid screening tests and an appreciation of the simple genetic control of Alternaria brown spot (ABS) susceptibility have existed for many years, and yet the application of this knowledge to commercial-scale breeding programs has been limited. Detached leaf assays were first demonstrated more than 40 years ago and reliable data suggesting a single gene determining susceptibility has been emerging for at least 20 years. However it is only recently that the requirement for genetic resistance in new hybrids has become a priority, following increased disease prevalence in Australian mandarin production areas previously considered too dry for the pathogen. Almost all of the high-fruit-quality parents developed so far by the Queensland-based breeding program are susceptible to ABS necessitating the screening of their progeny to avoid commercialisation of susceptible hybrids. This is done effectively and efficiently by spraying 3-6 month old hybrid seedlings with a spore suspension derived from a toxin-producing field isolate of Alternaria alternate, then incubating these seedlings in a cool room at 25°C and high humidity for 5 days. Susceptible seedlings show clear disease symptoms and are discarded. Analysis of observed and expected segregation ratios loosely support the hypothesis for a single dominant gene for susceptibility, but do not rule out the possibility of alternative genetic models. After implementing the routine screening for ABS resistance for three seasons we now have more than 20,000 hybrids growing in field progeny blocks that have been screened for resistance to the ABS disease.

Estimation and evaluation of multi-decadal fire severity patterns using Landsat sensors

Retrospective identification of fire severity can improve our understanding of fire behaviour and ecological responses. However, burnt area records for many ecosystems are non-existent or incomplete, and those that are documented rarely include fire severity data. Retrospective analysis using satellite remote sensing data captured over extended periods can provide better estimates of fire history. This study aimed to assess the relationship between the Landsat differenced normalised burn ratio (dNBR) and field measured geometrically structured composite burn index (GeoCBI) for retrospective analysis of fire severity over a 23 year period in sclerophyll woodland and heath ecosystems. Further, we assessed for reduced dNBR fire severity classification accuracies associated with vegetation regrowth at increasing time between ignition and image capture. This was achieved by assessing four Landsat images captured at increasing time since ignition of the most recent burnt area. We found significant linear GeoCBI–dNBR relationships (R2 = 0.81 and 0.71) for data collected across ecosystems and for Eucalyptus racemosa ecosystems, respectively. Non-significant and weak linear relationships were observed for heath and Melaleuca quinquenervia ecosystems, suggesting that GeoCBI–dNBR was not appropriate for fire severity classification in specific ecosystems. Therefore, retrospective fire severity was classified across ecosystems. Landsat images captured within ~ 30 days after fire events were minimally affected by post burn vegetation regrowth.

Highly heritable resistance to root-lesion nematode (Pratylenchus thornei) in Australian chickpea germplasm observed using an optimised glasshouse method and multi-environment trial analysis

Pratylenchus thornei is a root-lesion nematode (RLN) of economic significance in the grain growing regions of Australia. Chickpea (Cicer arietinum) is a significant legume crop grown throughout these regions, but previous testing found most cultivars were susceptible to P. thornei. Therefore, improved resistance to P. thornei is an important objective of the Australian chickpea breeding program. A glasshouse method was developed to assess resistance of chickpea lines to P. thornei, which requires relatively low labour and resource input, and hence is suited to routine adoption within a breeding program. Using this method, good differentiation of chickpea cultivars for P. thornei resistance was measured after 12 weeks. Nematode multiplication was higher for all genotypes than the unplanted control, but of the 47 cultivars and breeding lines tested, 17 exhibited partial resistance, allowing less than two fold multiplication. The relative differences in resistance identified using this method were highly heritable (0.69) and were validated against P. thornei data from seven field trials using a multi-environment trial analysis. Genetic correlations for cultivar resistance between the glasshouse and six of the field trials were high (>0.73). These results demonstrate that resistance to P. thornei in chickpea is highly heritable and can be effectively selected in a limited set of environments. The improved resistance found in a number of the newer chickpea cultivars tested shows that some advances have been made in the P. thornei resistance of Australian chickpea cultivars, and that further targeted breeding and selection should provide incremental improvements.

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.