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A set of durum wheat genotypes from New South Wales (NSW, Durum Breeding Australia (DBA) Northern Program), South Australia (SA, DBA Southern Program and Australian Grain Technology), ICARDA and CIMMYT (International Centre for Research in Dryland Agriculture and International Centre for Maize and Wheat Improvement) was evaluated over 3 years (2012–2014) in field trials containing rainfed and watered blocks in Narrabri, NSW, Australia. Data on yield and other agronomic traits were analysed using a multi-environment trial approach that accommodated the factorial treatment structure (genotype by irrigation regime) within individual trials. Considerable variation was observed in the durum germplasm for productivity and grain quality traits. DBA Bindaroi (NSW) and 101042 (ICARDA) were the top yielders in watered and rainfed blocks, respectively. The yield was positively and strongly related to both harvest index and grains/m2,but grains/m2was negatively related to thousand grain weight (TGW) and positively related to screenings. TGW and screenings were strongly negatively related and TGW and grains/m2showed a weak positive relationship. Promising genotypes were identified, with superior traits to both the bread wheat check, EGA Gregory and the durum check, Caparoi. Overall, lines from SA and ICARDA were superior for yield but those from NSW were superior for quality parameters including TGW and screenings. These results suggested the possibility of developing high yielding high-quality durum varieties by crossing NSW lines with SA, CIMMYT and ICARDA lines through simultaneous selection for yield, TGW and low screenings. The results also suggested that productivity in rainfed conditions was positively related to productivity under watering, but further research is required to establish this.

The use of appropriate statistical methods has a key role in improving the accuracy of selection decisions in a plant breeding program. This is particularly important in the early stages of testing in which selections are based on data from a limited number of field trials that include large numbers of breeding lines with minimal replication. The method of analysis currently recommended for early-stage trials in Australia involves a linear mixed model that includes genetic relatedness via ancestral information: non-genetic effects that reflect the experimental design and a residual model that accommodates spatial dependence. Such analyses have been widely accepted as they have been found to produce accurate predictions of both additive and total genetic effects, the latter providing the basis for selection decisions. In this paper, we present the results of a case study of 34 early-stage trials to demonstrate this type of analysis and to reinforce the importance of including information on genetic relatedness. In addition to the application of a superior method of analysis, it is also critical to ensure the use of sound experimental designs. Recently, model-based designs have become popular in Australian plant breeding programs. Within this paradigm, the design search would ideally be based on a linear mixed model that matches, as closely as possible, the model used for analysis. Therefore, in this paper, we propose the use of models for design generation that include information on genetic relatedness and also include non-genetic and residual models based on the analysis of historic data for individual breeding programs. At present, the most commonly used design generation model omits genetic relatedness information and uses non-genetic and residual models that are supplied as default models in the associated software packages. The major reasons for this are that preexisting software is unacceptably slow for designs incorporating genetic relatedness and the accuracy gains resulting from the use of genetic relatedness have not been quantified. Both of these issues are addressed in the current paper. An updating scheme for calculating the optimality criterion in the design search is presented and is shown to afford prodigious computational savings. An in silico study that compares three types of design function across a range of ancillary treatments shows the gains in accuracy for the prediction of total genetic effects (and thence selection) achieved from model-based designs using genetic relatedness and program specific non-genetic and residual models.Supplementary materials accompanying this paper appear online.

Low temperatures during the flowering period of cereals can lead to floret sterility, yield reduction, and economic losses in Australian crops. In order to breed for improved frost susceptibility, selection methods are urgently required to identify novel sources of frost tolerant germplasm. However, the presence of genotype by environment interactions (i.e. variety responses to a change in environment) is a major constraint to select the most appropriate varieties in any given target environment. An advanced method of analysis for multi-environment trials that includes factor analytic selection tools to summarize overall performance and stability to a specific trait across the environments could deliver useful information to guide growers and plant breeding programs in providing the most appropriate decision making-strategy. In this study, the updated selection tools approached in this multi-environment trials (MET) analysis have allowed variety comparisons with similar frost susceptibility but which have a different response to changes in the environment or vice versa. This MET analysis included a wide range of sowing dates grown at multiple locations from 2010 to 2019, respectively. These results, as far as we are aware, show for the first-time genotypic differences to frost damage through a MET analysis by phenotyping a vast number of accurate empirical measurements that reached in excess of 557,000 spikes. This has resulted in a substantial number of experimental units (10,317 and 5,563 in wheat and barley, respectively) across a wide range of sowing times grown at multiple locations from 2010 to 2019. Varieties with low frost overall performance (OP) and low frost stability (root mean square deviation -RMSD) were less frost susceptible, with performance more consistent across all environments, while varieties with low OP and high RMSD were adapted to specific environmental conditions.

SummaryCanola varieties exhibit discernible variation in drought avoidance and drought escape traits, suggesting its adaptation to water-deficit environments. However, the underlying mechanisms are poorly understood.A doubled haploid (DH) population was analysed to identify QTL associated with water use efficiency (WUE) related traits. Based on the resequenced parental genome data, we developed sequence-capture based markers for fine mapping. mRNA-Seq was performed to determine the expression of candidate genes underlying QTL for carbon isotope discrimination (Δ13C).QTL contributing to main and QTL × Environment interaction effects for Δ13C and for agronomic WUE were identified. One multi-trait QTL for Δ13C, days to flower, plant height and seed yield was identified on chromosome A09, in the vicinity of ERECTA. Interestingly, this QTL region was overlapped with a homoeologous exchange event (HE), suggesting its association with the major QTL. Transcriptome analysis revealed several differentially expressed genes between parental lines, including in HE regions.This study provides insights into the complexity of WUE related genes in the context of canola adaptation to water-deficit conditions. Our results suggest that alleles for high Δ13C contribute positively to canola yield. Genetic and genomic resources developed herein could be utilised to make genetic gains for improving canola WUE.

A major challenge in the analysis of plant breeding multi-environment datasets is the provision of meaningful and concise information for variety selection in the presence of variety by environment interaction (VEI). This is addressed in the current paper by fitting a factor analytic linear mixed model (FALMM) then using the fundamental factor analytic parameters to define groups of environments in the dataset within which there is minimal crossover VEI, but between which there may be substantial crossover VEI. These groups are consequently called interaction classes (iClasses). Given that the environments within an iClass exhibit minimal crossover VEI, it is then valid to obtain predictions of overall variety performance (across environments) for each iClass. These predictions can then be used not only to select the best varieties within each iClass but also to match varieties in terms of their patterns of VEI across iClasses. The latter is aided with the use of a new graphical tool called an iClass Interaction Plot. The ideas are introduced in this paper within the framework of FALMMs in which the genetic effects for different varieties are assumed independent. The application to FALMMs which include information on genetic relatedness is the subject of a subsequent paper.

The frost susceptibility of Australian commercial cereal crops, in particular wheat and barley, has become an economically devastating issue for growers. The relative risk to frost damage of the currently available varieties is obtained through testing varieties in a series of field experiments at locations susceptible to frost events (FEs). The experimental design, measurement protocols and resultant data from these frost expression experiments (FEEs) are complex due to the unpredictability of the timing and severity of FEs, and the maturity of the plants at the time of the events. Design and protocol complexities include the use of multiple sowing dates and the recording of plant maturity. Data difficulties include a high degree of unbalance, and in the instance of multiple frosts in a FEE, there is a longitudinal aspect. A linear mixed model analysis was adopted to accommodate these characteristics of individual FEEs and the multi-environment trial analysis of 17 FEEs. Finally, an approach is demonstrated for dissemination of results that are of use to both growers and breeders.

A study was conducted during 2013-2017 to evaluate the potential of 5 cultivars/experimental lines of leucaena (Leucaena leucocephala) at 2 sites in Northern Inland NSW. In this frost-prone, summer-dominant rainfall region, all cultivars/lines established well and survival was >70% at Bingara and >95% at Manilla. Cultivars Wondergraze and Cunningham were the most productive, producing up to approximately 2.4 t DM/ha and 1.9 t DM/ha per growing season at Bingara and Manilla, respectively. Tropical grass establishment in the alleys was poor with plant productivity inversely related to leucaena productivity. Although this study has confirmed the persistence and productive potential of leucaena, the challenges around tropical grass establishment and persistence as well as the weed potential of leucaena in this region need to be addressed before broad-scale use could be recommended in Northern Inland NSW. Resumen Se realizó un estudio para evaluar el potencial de cinco cultivares/líneas experimentales de leucaena (Leucaena leucocephala) en dos sitios en la región norte del interior de NSW durante 2013-2017. En esta región, que se caracteriza por lluvias en verano y ser propensa a heladas, todos los cultivares/líneas se establecieron bien y su supervivencia fue >70% en Bingara y >95% en Manilla. Los cultivares Wondergraze y Cunningham fueron los más productivos, alcanzando hasta 2.4 t MS/ha y 1.9 t MS/ha por época de crecimiento en Bingara y Manilla, respectivamente. El establecimiento de la gramínea tropical asociada (Digitaria eriantha) fue deficiente y su producción estuvo inversamente relacionada con la de la leucaena. Aunque este estudio ha confirmado el potencial de persistencia y productividad de la leucaena, antes de poder recomendar su uso a mayor escala en el interior del norte de NSW es necesario abordar los desafíos relacionados con el establecimiento y la persistencia de las gramíneas tropicales asociadas, así como el potencial de la leucaena de volverse una maleza invasiva en esta región.

The issues surrounding illegal, unregulated and unreported fishing, and that of abandoned, lost and discarded fishing gears, leading to ghost fishing, are intensifying. Estuarine crab trapping is likely subject to high levels of illegal and potential ghost fishing, because it also has good economic incentives regarding potential catch, low gear acquisition costs and accessible fishing grounds. To improve the efficiency and effectiveness of fisheries monitoring, control and surveillance operations, the efficacy of small consumer‐grade drones for sighting traps in an estuary in NSW, Australia, was tested. Twelve sets of two flights were undertaken at 20 and 30 m altitude over a 600‐m stretch of estuary for 5 days to quantify the detectability of submerged mesh traps of three different mesh colours. The drone was able to detect the majority of traps efficiently, with depth in relation to water clarity being the main factor affecting detection. In shallow water, detection rates were high for all mesh colours, but in the slightly deeper placements, orange traps were more readily detected. This study demonstrates that drones could be an efficient and reliable tool for rapidly assessing areas for illegal and derelict traps and can be supplemented into land or vessel‐based fisheries operations.

An efficient and widely used method of analysis for multi-environment trial (MET) data in plant improvement programs involves a linear mixed model with a factor analytic (FA) model for the variety by environment effects. The variance structure is generally constructed as the kronecker product of two matrices that relate to the variety and environment dimensions, respectively. In many applications, the FA variance structure is assumed for the environment dimension and either an identity matrix or a known matrix, such as the numerator relationship matrix, is used for the variety dimension. The factor analytic linear mixed model can be fitted to large and complex MET datasets using the sparse formulation of the average information algorithm of Thompson et al. (Aust N Z J Stat 45:445–459, 2003) or the extension provided by Kelly et al. (Genet Sel Evo, 41:1186–1297, 2009) for the case of a known non-identity matrix for the variety dimension. In this paper, we present a sparse formulation of the average information algorithm for a more general separable variance structure where all components are parametric and one component has an FA structure. The approach is illustrated using a large and highly unbalanced MET dataset where there is a factorial treatment structure. Supplementary materials accompanying this paper appear online.

An 18-year field experiment was conducted on a highly acidic soil in the mixed farming zone of south-eastern Australia. The experiment was a fully phased design with three major treatment contrasts: perennial systems versus annual systems; unlimed versus limed treatments; and permanent pasture versus pasture-crop rotations. The hypotheses tested in this study were that a) perennial systems would be less acidifying than annual systems, and b) surface liming would lead to amelioration of subsurface soil acidity over time. Results showed that there was no significant difference in soil pH in terms of acidification rate and amelioration rate between perennial and annual systems, thus we rejected the first hypothesis. However, both perennial and annual systems did acidify or re-acidify soil. The re-acidification rate was at least 0.09 pH units per year in the 0–0.10 m depth under the limed treatment. The acidification rate was 0.005 pH units per year in the 0.10–0.20 m depth under the unlimed treatment. The vigorous lime regime of maintaining pH ≥5.5 (measured in 0.01 M CaCl2) in the 0–0.10 m depth increased soil pH in the 0.10–0.20 m depth at a rate of the 0.04 pH units per year, hence we accepted the second hypothesis. There was clear evidence of alkali movement vertically over time, but it was confined within the top 0.30 m depth over the 18-year period of experiment. It is recommended that soil should be limed regularly to maintain pH ≥5.5 to keep the system productive and sustainable.

Water stress during reproductive growth is a major yield constraint for wheat (Triticum aestivum L). We previously established a controlled environment drought tolerance phenotyping method targeting the young microspore stage of pollen development. This method eliminates stress avoidance based on flowering time. We substituted soil drought treatments by a reproducible osmotic stress treatment using hydroponics and NaCl as osmolyte. Salt exclusion in hexaploid wheat avoids salt toxicity, causing osmotic stress. A Cranbrook x Halberd doubled haploid (DH) population was phenotyped by scoring spike grain numbers of unstressed (SGNCon) and osmotically stressed (SGNTrt) plants. Grain number data were analyzed using a linear mixed model (LMM) that included genetic correlations between the SGNCon and SGNTrt traits. Viewing this as a genetic regression of SGNTrt on SGNCon allowed derivation of a stress tolerance trait (SGNTol). Importantly, and by definition of the trait, the genetic effects for SGNTol are statistically independent of those for SGNCon. Thus they represent non-pleiotropic effects associated with the stress treatment that are independent of the control treatment. QTL mapping was conducted using a whole genome approach in which the LMM included all traits and all markers simultaneously. The marker effects within chromosomes were assumed to follow a spatial correlation model. This resulted in smooth marker profiles that could be used to identify positions of putative QTL. The most influential QTL were located on chromosome 5A for SGNTol (126cM; contributed by Halberd), 5A for SGNCon (141cM; Cranbrook) and 2A for SGNTrt (116cM; Cranbrook). Sensitive and tolerant population tail lines all showed matching soil drought tolerance phenotypes, confirming that osmotic stress is a valid surrogate screening method.

Plant breeding programs use multi-environment trial (MET) data to select superior lines, with the ultimate aim of increasing genetic gain. Selection accuracy can be improved with the use of advanced statistical analysis methods that employ informative models for genotype by environment interaction, include information on genetic relatedness and appropriately accommodate within-trial error variation. The gains will only be achieved, however, if the methods are applied to suitable MET datasets. In this paper we present an approach for constructing MET datasets that optimizes the information available for selection decisions. This is based on two new concepts that characterize the structure of a breeding program. The first is that of “contemporary groups,” which are defined to be groups of lines that enter the initial testing stage of the breeding program in the same year. The second is that of “data bands,” which are sequences of trials that correspond to the progression through stages of testing from year to year. MET datasets are then formed by combining bands of data in such a way as to trace the selection histories of lines within contemporary groups. Given a specified dataset, we use the A-optimality criterion from the model-based design literature to quantify the information for any given selection decision. We demonstrate the methods using two motivating examples from a durum and chickpea breeding program. Datasets constructed using contemporary groups and data bands are shown to be superior to other forms, in particular those that relate to a single year alone.

Tolerance to the cereal disease Fusarium crown rot (FCR) was investigated in a set of 34 durum wheat genotypes, with Suntop, (bread wheat) and EGA Bellaroi (durum) as tolerant and intolerant checks, in a series of replicated field trials over four years with inoculated (FCR-i) and non-inoculated (FCR-n) plots of the genotypes. The genotypes included conventional durum lines and lines derived from crossing durum with 2-49, a bread wheat line with the highest level of partial resistance to FCR. A split plot trial design was chosen to optimize the efficiency for the prediction of FCR tolerance for each genotype. A multi-environment trial (MET) analysis was undertaken which indicated that there was good repeatability of FCR tolerance across years. Based on an FCR tolerance index, Suntop was the most tolerant genotype and EGA Bellaroi was very intolerant, but many durum wheats had FCR tolerance indices which were comparable to Suntop. These included some conventional durum lines, V101030, TD1702, V11TD013*3X-63 and DBA Bindaroi, as well as genotypes from crosses with 2-49 (V114916 and V114942). The correlation between FCR tolerance and FCR-n yield predictions was moderately negative indicating it could be somewhat difficult to develop high yielding FCR-tolerant genotypes. However, FCR tolerance showed a positive correlation with FCR-i yield predictions in seasons of high disease expression indicating it could be possible to screen for FCR tolerance using only FCR-i treatments. These results are the first demonstration of genetic diversity in durum germplasm for FCR tolerance and they provide a basis for breeding for this trait.

Blackleg disease, caused by the fungal pathogen Leptosphaeria maculans, continues to be a major concern for sustainable production of canola (Brassica napus L.) in many parts of the world. The deployment of effective quantitative resistance (QR) is recognized as a durable strategy in providing natural defense to pathogens. Herein, we uncover loci for resistance to blackleg in a genetically diverse panel of canola accessions by exploiting historic recombination events which occurred during domestication and selective breeding by genome-wide association analysis (GWAS). We found extensive variation in resistance to blackleg at the adult plant stage, including for upper canopy infection. Using the linkage disequilibrium and genetic relationship estimates from 12,414 high quality SNPs, GWAS identified 59 statistically significant and “suggestive” SNPs on 17 chromosomes of B. napus genome that underlie variation in resistance to blackleg, evaluated under field and shade-house conditions. Each of the SNP association accounted for up to 25.1% of additive genetic variance in resistance among diverse panel of accessions. To understand the homology of QR genomic regions with Arabidopsis thaliana genome, we searched the synteny between QR regions with 22 ancestral blocks of Brassicaceae. Comparative analyses revealed that 25 SNP associations for QR were localized in nine ancestral blocks, as a result of genomic rearrangements. We further showed that phenological traits such as flowering time, plant height, and maturity confound the genetic variation in resistance. Altogether, these findings provided new insights on the complex genetic control of the blackleg resistance and further expanded our understanding of its genetic architecture.

Fishing strategies, effort and harvests of small-scale fishers are important to understand for effective planning of regulatory measures and development programs. Gender differences in fishing can highlight inequities deserving transformative solutions, but might mask other important factors. We examined fishing modes, fishing frequency, catch-per-unit effort (CPUE), resource preferences and perceptions of fishery stock among artisanal gastropod (trochus) fishers in Samoa using structured questionnaires and mixed effects models. The fishery has an extremely modest carbon footprint of 20.4 tonnes of CO2 p.a., as few fishers used motorised boats. Trochus (Rochia nilotica), an introduced gastropod, was the second-most harvested resource, after fish, despite populations only being established in the past decade. Daily catch volume varied according to gender and villages (n = 34), and was also affected by fishing effort, experience, assets (boat), and fishing costs of fishers. Boat users had much higher CPUE than fishers without a boat. Fishers who practiced both gleaning and diving caught a greater diversity of marine resources; effects that explained otherwise seeming gender disparities. Trochus tended to be ranked more important (by catch volume) by women than men, and rank importance varied greatly among villages. Local ecological knowledge of fishers informed the historical colonisation of trochus around Samoa and current trends in population abundance. Fishing efficiency, catch diversity and perspectives about stocks were similar between fishermen and fisherwomen, when accounting for other explanatory variables. Greater importance of these shellfish to women, and gender similarities in many of the fishing responses, underscore the need to ensure equal representation of women in the decision making in small-scale fisheries.

Understanding the income and costs of fishing is fundamental to managing fisheries and planning interventions to improve efficiency and gender equity. Few studies offer data on fisher incomes and fuel use in small-scale fisheries (SSFs), and fewer have assessed factors influencing variation among fishers and between genders. We interviewed 235 artisanal fishers among 34 island villages in an artisanal sea cucumber fishery in Fiji. Linear mixed models were used to determine the effect of geographic and socioeconomic variables on incomes and fuel use from fishing sea cucumbers. Net income of sea cucumbers to fishers, averaging FJ$8, 171 year−1 (US$4, 494 year−1) (range: FJ$0–52,008 year−1), varied among villages and was 47% lower for women than men. On an average, 60% of fishers’ gross annual income came from fishing and selling sea cucumbers, although this proportion varied greatly even within villages. Fishers who practised gleaning, fished less often, or possessing numerous livelihood income streams, were less economically dependent on sea cucumbers. Men tended to estimate higher incomes for an average day of fishing than women when compared with their recall of last sale. Fuel use varied greatly among regions in Fiji but, overall, averaged 428 L fisher−1 year−1, and represented 28% of gross income. More economical fishing strategies by women resulted in lower fuel use than men per fishing day. Breath-hold divers used less fuel (compared to fishers using scuba) and fishers targeting deep-water species used more fuel than other fishers. A best approximation of 8000 t CO2 year−1 for the carbon footprint of the whole fishery suggests that some SSFs, such as the one studied here, can be significant contributors to greenhouse gas emissions, even compared to many large-scale fisheries globally. Reforms to the management of SSFs should consider regulations that minimize carbon emissions and reduce economic dependency on vulnerable marine resources.

Background The proportion of granule types in wheat starch is an important characteristic that can affect its functionality. It is widely accepted that granule types are either large, disc-shaped A-type granules or small, spherical B-type granules. Additionally, there are some reports of the tiny C-type granules. The differences between these granule types are due to its carbohydrate composition and crystallinity which is highly, but not perfectly, correlated with the granule size. A majority of the studies that have considered granule types analyse them based on a size threshold rather than chemical composition. This is understandable due to the expense of separating starch into different types. While the use of a size threshold to classify granule type is a low-cost measure, this results in misclassification. We present an alternative, statistical method to quantify the proportion of granule types by a fit of the mixture distribution, along with an R package, a web based app and a video tutorial for how to use the web app to enable its straightforward application. Results Our results show that the reliability of the genotypic effects increase approximately 60% using the proportions of the A-type and B-type granule estimated by the mixture distribution over the standard size-threshold measure. Although there was a marginal drop in reliability for C-type granules. The latter is likely due to the low observed genetic variance for C-type granules. Conclusions The determination of the proportion of granule types from size-distribution is better achieved by using the mixing probabilities from the fit of the mixture distribution rather than using a size-threshold. Electronic supplementary material The online version of this article (10.1186/s13007-017-0259-2) contains supplementary material, which is available to authorized users.

White sharks (Carcharodon carcharias) are implicated in many bites on people. Subsequently, they have become a target species for shark bite management strategies. SMART drumlines allow fast responses to, and releases of, captured animals. This research shows that white sharks’ immediate physiological response to capture on SMART drumlines is minimal., Drumlines incorporating SMART (Shark-Management-Alert-in-Real-Time) technology are a new tool used in several bather protection programmes globally. In New South Wales (NSW), Australia, the white shark (Carcharodon carcharias) is a target species for SMART drumlines because they are often involved in attacks on humans. To understand white shark sensitivity to capture and to establish protocols around acceptable timeframes for responding to alerts, 47 juvenile and subadult white sharks were caught on SMART drumlines at five locations off the east coast of Australia. There was no at-vessel mortality during the sampling period. After capture, blood was sampled from each shark to assess its acute physiological status. Of the 18 metabolites investigated, only lactate and aspartate aminotransferase exhibited significant positive relationships with the capture duration on SMART drumlines. These results indicate that the capture process is relatively benign and that the current response times used here are appropriate to minimize long-term negative impacts on released white sharks. Where white sharks are likely to interact negatively with beachgoers, SMART drumlines can therefore be a useful addition to bather protection programmes that also aim to minimize harm to captured animals. Other shark species captured on SMART drumlines should also be investigated to gain broader understanding of potential physiological consequences of using this new technology.

Tolerance to the cereal disease Fusarium crown rot (FCR) was investigated in a set of 34 durum wheat genotypes, with Suntop, (bread wheat) and EGA Bellaroi (durum) as tolerant and intolerant controls, in a series of replicated field trials over four years with inoculated (FCR-i) and non-inoculated (FCR-n) plots of the genotypes. The genotypes included conventional durum lines and lines derived from crossing durum with 2–49, a bread wheat genotype with the highest level of partial resistance to FCR. A split plot trial design was chosen to optimize the efficiency for the prediction of FCR tolerance for each genotype. A multi-environment trial (MET) analysis was undertaken which indicated that there was good repeatability of FCR tolerance across years. Based on an FCR tolerance index, Suntop was the most tolerant genotype and EGA Bellaroi was very intolerant, but some durum wheats had FCR tolerance indices which were comparable to Suntop. These included some conventional durum genotypes, V101030, TD1702, V11TD013*3X-63 and DBA Bindaroi, as well as genotypes from crosses with 2–49 (V114916 and V114942). The correlation between FCR tolerance and FCR-n yield predictions was moderately negative indicating it could be somewhat difficult to develop FCR-tolerant genotypes that are high yielding under low disease pressure. However, FCR tolerance showed a positive correlation with FCR-i yield predictions in seasons of high disease expression indicating it could be possible to screen for FCR tolerance using only FCR-i treatments. These results are the first demonstration of genetic diversity in durum germplasm for FCR tolerance and they provide a basis for breeding for this trait.

Aims We examine the average performance of Border Leicester (BL) flocks that are part of LAMBPLAN, Australia’s sheep performance recording and genetic evaluation system. We also summarise outcomes from the New South Wales Department of Primary Industry’s research on BL improvement, and discuss the performance required for the BL to continue to be a pivotal part of the Australian prime lamb industry. Key results Modest gains have been recorded in the BL in growth since 2000 and faecal egg counts have reduced, suggesting improved worm resistance. The net reproductive rate has slightly improved, while inbreeding continues to increase. In work conducted by the New South Wales Department of Primary Industries, an improved BL flock, encompassing selection for multiple births and a broadening of the breed’s genetic base, had advantages over purebred BLs in many aspects of performance, including a 19.3% advantage in the net reproductive rate. Advantages in fertility were ~5%, litter size ~7% and lamb rearing ability ~5%. BL improved × Merino and (BL improved × BL) × Merino crossbred ewes also had advantages over traditional BL × Merino ewes in their joining performance when joined to Dorset rams. Conclusions Selection in the BL needs to emphasise traits in a manner consistent with their importance to commercial lamb production. Implications Embracing this selection and broadening the BL’s genetic base will assist the BL to maintain its place in the Australian lamb industry.

Field trials for variety selection often exhibit spatial correlation between plots. When multivariate data are analysed from these field trials, there is the added complication in having to simultaneously account for correlation between the traits at both the residual and genetic levels. This may be temporal correlation in the case of multi-harvest data from perennial crop field trials, or between-trait correlation in multi-trait data sets. Use of parsimonious yet plausible models for the variance–covariance structure of the residuals for such data is a key element to achieving an efficient and inferentially sound analysis. In this paper, a model is developed for the residual variance–covariance structure firstly by considering a multivariate autoregressive model in one spatial direction and then extending this to two spatial directions. Conditions for ensuring that the processes are directionally invariant are presented. Using a canonical decomposition, these directionally invariant processes can be transformed into a set of independent separable processes. This simplifies the estimation process. The new model allows for flexible modelling of the spatial and multivariate interaction and allows for different spatial correlation parameters for each harvest or trait. The methods are illustrated using data from lucerne breeding trials at several environments.

Genomic selection in crop breeding introduces modeling challenges not found in animal studies. These include the need to accommodate replicate plants for each line, consider spatial variation in field trials, address line by environment interactions, and capture nonadditive effects. Here, we propose a flexible single-stage genomic selection approach that resolves these issues. Our linear mixed model incorporates spatial variation through environment-specific terms, and also randomization-based design terms. It considers marker, and marker by environment interactions using ridge regression best linear unbiased prediction to extend genomic selection to multiple environments. Since the approach uses the raw data from line replicates, the line genetic variation is partitioned into marker and nonmarker residual genetic variation (i.e., additive and nonadditive effects). This results in a more precise estimate of marker genetic effects. Using barley height data from trials, in 2 different years, of up to 477 cultivars, we demonstrate that our new genomic selection model improves predictions compared to current models. Analyzing single trials revealed improvements in predictive ability of up to 5.7%. For the multiple environment trial (MET) model, combining both year trials improved predictive ability up to 11.4% compared to a single environment analysis. Benefits were significant even when fewer markers were used. Compared to a single-year standard model run with 3490 markers, our partitioned MET model achieved the same predictive ability using between 500 and 1000 markers depending on the trial. Our approach can be used to increase accuracy and confidence in the selection of the best lines for breeding and/or, to reduce costs by using fewer markers.

Globally, many gillnet fisheries have excessive discards which can comprise charismatic megafauna threatened with extinction, including numerous elasmobranchs. Very few discarded elasmobranch species have been assessed for their associated fates. Here we describe the immediate mortality of several threatened species discarded from gillnets deployed off eastern Australia to target key carcharhinids and white sharks, Carcharodon carcharias (within an objective of protecting bathers) and causal factors for abundant elasmobranchs. In total, 420 animals comprising at least 22 species were gillnetted with a total immediate mortality of 49 % and group-specific estimates of 100 % for dolphins (n = 8), 100 % for teleosts (n = 16), 86 % for sharks (n = 75), 45 % for turtles (n = 20) and 36 % for rays (n = 301). Among elasmobranchs, species that were obligate ram-ventilating (e.g. great hammerhead, Sphyrna mokarran and common blacktip shark, Carcharhinus limbatus) had greater mortality (>95 %) than those species with spiracles, and likely capable of some stationary respiration (e.g. whitespotted guitar fish, Rhynchobatus australiae, whitespotted eagle ray, Aetobatus ocellatus, Australian cownose ray, Rhinoptera neglecta and to a lesser extent, pygmy devilray, Mobula kuhlii cf. eregoodootenkee) (16–74 % mortality). Mortalities among Australian cownose rays increased across longer soaks. The effect of soak time was further assessed to determine an optimal to maintain gillnet efficiency for target sharks, but minimise the absolute mortality of abundant rays and was estimated at up to three or four fishing nights (72–96 h). Other potential operational modifications, including using light and better retroactive deterrents to reduce turtle and dolphin bycatches and mortality, warrant investigation.

Genomic selection (GS) is a statistical and breeding methodology designed to improve genetic gain. It has proven to be successful in animal breeding; however, key points of difference have not been fully considered in the transfer of GS from animal to plant breeding. In plant breeding, individuals (varieties) are typically evaluated across a number of locations in multiple years (environments) in formally designed comparative experiments, called multi-environment trials (METs). The design structure of individual trials can be complex and needs to be modelled appropriately. Another key feature of MET data sets is the presence of variety by environment interaction (VEI), that is the differential response of varieties to a change in environment. In this paper, a single-step factor analytic linear mixed model is developed for plant breeding MET data sets that incorporates molecular marker data, appropriately accommodates non-genetic sources of variation within trials and models VEI. A recently developed set of selection tools, which are natural derivatives of factor analytic models, are used to facilitate GS for a motivating data set from an Australian plant breeding company. The power and versatility of these tools is demonstrated for the variety by environment and marker by environment effects.

An advanced and widely used method of analysis for multi-environment trial data involves a linear mixed model with factor analytic (FA) variance structures for the variety by environment effects. This model can accommodate unbalanced data, that is, not all varieties in all environments, it allows the use of pedigree information and appropriate accommodation of individual trial experimental designs, and most importantly the FA structure for the variety by environment effects is parsimonious and regularly results in a good fit to the data. The model provides accurate predictions of the variety effects for every environment in the data-set but this constitutes a large and unwieldly amount of information to process for the purpose of variety selection. We address this issue in the current paper by proposing factor analytic selection tools to summarise the predictions in a concise yet informative manner. The tools, which are natural derivatives of the FA structure, result in measures of overall performance and stability across the environments in the data-set. All measures are expressed on the same scale as the trait under consideration and can easily be combined to form an index for selection.

A one-stage analysis of a series of variety trials involves a combined analysis of the individual plot data across trials. Together with prudent modelling of the genetic effects across trials, this is considered to be the gold standard analysis of multi-environment field trial data. An alternative is a two-stage approach in which the variety means from an analysis of the individual trials in stage one are combined into a weighted mixed model analysis in stage two to give the full set of predicted variety by environment effects and an estimate of their associated variance structure. The two-stage analysis will exactly reproduce the one-stage analysis if the full variance-covariance matrix of the means from stage one is known and is utilised in stage two. Typically the full matrix is not stored and a diagonal approximation is used. This introduces a compromise to the full analysis. The impacts of a diagonal approximation are greater in the presence of sophisticated models for the genetic effects. A second compromise is through a loss of information in estimating the non-genetic variance parameters using the two-stage approach. In this paper we draw a direct link between the one and two-stage analysis approaches for crop variety evaluation data in Australia. We now have the computing power to analyse large and complex multi-environment variety trial data sets using the one-stage approach without the need for a two-stage approximation. This should motivate a move away from the two-stage approach in a range of contexts.

The utility of varying twine diameter (0.5-0.8 mm O) and/or mesh area (0.5:1) in south-eastern Australian recreational hoop nets was assessed in response to concerns over (1) impacts to discarded, undersize Scylla serrata L. and Portunus pelagicus Forskal and (2) marine debris (lost meshes). There was a negative relationship between O and breakage, with more in the narrowest (16.16-18.42 meshes deployment) than thickest (8.79-4.68) twines. Mesh damage was also affected by interactions between netting area and (1) soak time (only S. serrata) and (2) catches, with all nets similarly damaged during

Deep hooking is one of the key causes of mortality among angled-and-released teleosts worldwide. We assessed the effects of natural baits and other variables on the frequency of deep hooking and postrelease mortality in two Australian native freshwater fishes that are classified as vulnerable or endangered: Golden Perch Macquaria ambigua and Freshwater Catfish Tandanus tandanus. Sixty-one angled Golden Perch were released and monitored in individual submersible cages at their capture location or were held in shore-based group tanks; of these fish, 97% were hooked with natural baits, 41% were deep hooked and had their lines cut, and 44% died. Of the Golden Perch that died, 70% were deep hooked, and hook location and two associated factors (hook removal and ease of handling) were identified as significant predictors of mortality. Mortality of Golden Perch was also significantly greater during the afternoon session on the first day of angling than during other sessions and days. In contrast, of the 9...

This study investigates mortality of sharks in a commercial longline fishery in Australia. To examine the rate and biological, environmental and technological factors contributing to at-vessel mortality, four setlines with 120 gangions possessing ‘hook timers’ were deployed daily (for 7h and 14h) using conventional gears from two commercial fishing vessels during 2013. A total of 689 animals across 22 species and including 18 elasmobranchs were landed. For the five species ( Carcharhinus spp.), and one genus ( Sphyrna spp) where there were sufficient numbers for analysis, generalised linear mixed models showed that species and the elapsed time spent on the line after hooking were the strongest predictors of at-vessel mortality, with spinner ( Carcharhinus brevipinna ), blacktip ( C. limbatus ) and hammerhead ( Sphyrna spp) sharks exhibiting the highest death rates. The variables which best explained mortality, included: (i) sex of the caught sharks, and the interaction between species with (ii) capture depth, and (iii) the elapsed time spent on the line after hooking. For the subset of dusky ( C. obscurus ) and sandbar ( C. plumbeus ) sharks examined for physiological status at the point of capture, very few of the 13 chosen blood analytes varied significantly. Given the observed high mortality rates and stress associated with the time spent on the line after capture, operational changes to reduce these adverse impacts should be considered. Even simple changes such as shorter soak times could considerably mitigate these impacts.

Large-scale field evaluation of genetic material forms an important part of the selection process in the early stages of plant breeding programs. These experiments are typically designed ignoring information on genetic relatedness, often available in the form of crossing history, or plant pedigree records. This paper considers the design of plant breeding experiments where the residuals may be correlated with an assumed autoregressive process, and there is a known genetic covariance structure among genotype effects. This structure is frequently more complex than simple nested family models, arising more generally from the pedigree, or possibly identity in state measures. It is widely accepted that the analysis of these data is improved using information on related individuals. The design of these experiments exploiting known genetic relatedness is considered using three case studies from industry that differ in selection goals, genetic complexity and scale.

SUMMARYThe present paper provides an approach for the design and analysis of variety trials that are used to obtain quality trait data. These trials are multi-phase in nature, comprising a field phase followed by one or more laboratory phases. Typically the laboratory phases are costly relative to the field phase and this imposes a limit on the number of samples that can be tested. Historically, this has been achieved by sacrificing field replication, either by testing a single replicate plot for each variety or a single composite sample, obtained by combining material from several field replicates. An efficient statistical analysis cannot be applied to such data so that valid inference and accurate prediction of genetic effects may be precluded. A solution that has appeared recently in the literature is the use of partial replication, in which some varieties are tested using multiple field replicates and the remainder as single replicates only. In the present paper, an approach is proposed in which some varieties are tested using individual field replicate samples and others as composite samples. Replication in the laboratory is achieved by splitting a relatively small number of field samples into sub-samples for separate processing. It is shown that, if necessary, some of the composite samples may be split for this purpose. It is also shown that, given a choice of field compositing and laboratory replication strategy, an efficient design for a laboratory phase may be obtained using model-based techniques. The methods are illustrated using two examples. It is demonstrated that the approach provides more accurate variety predictions compared with the partial replication approach and that the gains can be substantial if the field variation is large relative to the laboratory variation.

Appropriate analysis of plant breeding trials is critical for the accurate assessment of test lines and selection decisions. The objectives of this study were two-fold: firstly, to examine the performance of two-dimensional spatial models based on the first order separable autoregressive process in comparison with randomised complete block (RCB) and randomisation based (RB) models in analysis of cotton breeding trials; secondly, to understand the presence and forms of spatial variations and their association with field layout. The different models were first used to analyse a lint yield dataset from the CSIRO cotton breeding program, which consisted of 96 trials under furrow-irrigated conditions from 1995 to 2002 and Residual Maximum Likelihood ratio test and the Akaike Information Criterion were used to identify adequate model (i.e. dataset-preferred model) for individual datasets. The spatial models fitted 62 trials adequately and outperformed the RB model (31) with the worse being RCB model (3). Spatial variations in various forms were commonly present in trials in which spatial models were adequate, and was dominant in planting row direction. Layouts with more plots in dimensional directions tended to have a higher level of spatial variation. Spatial models offered about 176 % mean relative efficiency over RCB, which was comparable with that achieved by the dataset-preferred models but about 20 % higher than the RB model. Therefore, a routine use of spatial analysis in conjunction with efficient trial designs would mitigate the impact of spatial variations on the yield estimate of cotton breeding trials and improve the accuracy of selection.

Abstract ContextA series of unprovoked shark attacks on New South Wales (Australia) beaches between 2013 and 2015 triggered an investigation of new and emerging technologies for protecting bathers. Traditionally, bather protection has included several methods for shark capture, detection and/or deterrence but has often relied on environmentally damaging techniques. Heightened environmental awareness, including the important role of sharks in the marine ecosystem, demands new techniques for protection from shark attack. Recent advances in drone-related technologies have enabled the possibility of real-time shark detection and alerting. AimTo determine the reliability of drones to detect shark analogues in the water across a range of environmental conditions experienced on New South Wales beaches. MethodsA standard multirotor drone (DJI Inspire 1) was used to detect shark analogues as a proxy during flights at 0900, 1200 and 1500 hours over a 3-week period. The 27 flights encompassed a range of environmental conditions, including wind speed (2–30.0kmh−1), turbidity (0.4–6.4m), cloud cover (0–100%), glare (0–100%), seas (0.4–1.4m), swells (1.4–2.5m) and sea state (Beaufort Scale 1–5 Bf). Key resultsDetection rates of the shark analogues over the 27 flights were significantly higher for the independent observer conducting post-flight video analysis (50%) than for the drone pilot (38%) (Wald P=0.04). Water depth and turbidity significantly impaired detection of analogues (Wald P=0.04). Specifically, at a set depth of 2m below the water surface, very few analogues were seen by the observer or pilot when water turbidity reduced visibility to less than 1.5m. Similarly, when water visibility was greater than 1.5m, the detection rate was negatively related to water depth. Conclusions The present study demonstrates that drones can fly under most environmental conditions and would be a cost-effective bather protection tool for a range of user groups. ImplicationsThe most effective use of drones would occur during light winds and in shallow clear water. Although poor water visibility may restrict detection, sharks spend large amounts of time near the surface, therefore providing a practical tool for detection in most conditions.

Two experiments were conducted to quantify the mortality of, and clinical signs of barotrauma to, Argyrosomus japonicus after retrieval from 20 m following either ‘no treatment’, ‘venting’ (a needle inserted into the swim bladder) or ‘recompression’ (released with a weighted line). In Experiment 1, 10 fish were hauled from 20 m and from 5 m (controls) and euthanised for assessment. The only effect on controls was a distended coelomic cavity, but all fish retrieved from 20 m had this symptom and a prolapsed cloaca, and many had haemorrhaging (81%), gastric herniation (70%), swimbladder rupture (50%) and affected buoyancy (66%). Another 20 fish were subjected to the treatments and, along with controls (from 5 m), were released into ‘bathy-cages’ (2.5 m × 20 m) for three days. Only two fish retrieved from 20 m died (total mortality of 3.3%). In Experiment 2, six fish retrieved from 20 m and three control fish were tagged with acoustic transmitters, released following the abovementioned treatments into the wi...

a b s t r a c t New cultivars, improved management and their interactions are key drivers of yield progress in field crops. This study examined the contribution of these factors to cotton yield increase in Australia with the adoption of 23 key locally bred cultivars developed by the Commonwealth Scientific and Industrial Research Organisation (CSIRO) cotton breeding programme up to 2006. A lint yield dataset of advanced line trials from 1980 to 2009 (325 experiments) was analysed by a linear mixed model when split into two 15-year periods. These trials were conducted at up to 15 irrigated sites each year, and best linear unbiased estimates (BLUEs) of cultivar yield and two long-term controls (Deltapine 16 and Namcala) were used to assess yield trend. Lint yield increased progressively as new cultivars were released and yield gain was almost three times as high in the recent 15-year period as the early one (18.3 versus 7.0 kg ha−1 year−1). This gain tended to be greater in cooler than warmer regions, despite lower yield in the cool. The most recent cultivars also showed improved stability. When pairs of cultivars derived from intra-cultivar selection were compared, reselected ones outyielded their parents and with higher stability. The increased cultivar yield and the reduction of genotype × region and genotype × year interaction were the main reasons behind the ongoing improved yield and stability of these released cultivars. Using yield estimates of 10 cultivars tested in both time periods, yield gain was found to be attributed by cultivar, i.e. genetics (48%), management (28%) and cultivar × management (24%). There was evidence for cultivar resistance to Verticillium wilt being a strong component of the cultivar × management interaction. It was concluded that cotton yield progress in Australia was achieved mostly through exploiting genetic variation and genotypic response to modern management.

The effects of otter boards on the catches of Metapenaeus macleayi were quantified within the broad aim of promoting more hydrodynamic and environmentally benign trawl-spreading mechanisms. Two identical trawls (7.26 m footropes) were attached to 2.89-m sweeps and simultaneously fished in a double-rig; each alternately attached to either two otter boards or a rigid 6.0-m beam with parallel sleds (configured not to impact on the behaviour of M. macleayi ). Both trawl configurations had the same total weight (143 kg) and initial wing-end spreads (predicted means of 4.33 and 4.45 m), although the otter boards caused significantly more variable wing-end spread during towing. Replacing the otter boards with the beam significantly reduced system drag (by 10%) and the weights and numbers of school prawns per hectare swept by the footropes (by 33%); with the latter attributed to the concomitant reduction in lateral bottom contact of the system. Standardised catches of the most abundant teleost Herklotsichthys castelnaui were also significantly lower (by 79%) in the beam trawl. The results support the potential for compensatory adjustments to penaeid-trawl parameters (e.g. footrope length) to offset reductions in the bottom contact of spreading mechanisms that are modified to minimise drag and/or habitat impacts.

Australian recreational and commercial fishers catch snapper ( Pagrus auratus ) from down to ∼120 m, often evoking barotrauma. Owing to minimum legal sizes and quotas or non-consumptive fishing practices, some afflicted fish are released, raising concerns over the potential for unaccounted fishing mortality. Two experiments were completed to quantify the (1) clinical signs of barotrauma among fish angled from 6 to 120 m (experiment 1) and (2) mortality (over 3 d) of fish with barotrauma that were released following either no treatment, venting (e.g. needle) or recompression (e.g. release weight) (experiment 2). In experiment 1, barotrauma was evident in some fish angled from 11 m and all from >20 m. Fish were considered to have barotrauma if they had a prolapsed cloaca combined with a distended coelomic cavity and/or gastric herniation into the buccal cavity. Despite similar clinical signs among fish in experiment 2, none died, however, the associated trauma raises welfare concerns. Both recompression and venting offered benefits over no treatment by returning fish to depth quickly and releasing gases, respectively. Nevertheless, the release method should be dictated by the clinical signs of barotrauma and extrinsic conditions, including the competency of the fisher (e.g. for venting), presence of predators (e.g. for recompression), and exposure to sun or warm water (e.g. no treatment). More selective gears, spatial and temporal closures, or possibly replacing minimum legal sizes with quotas in deep water only could also reduce the frequency of barotrauma among released P. auratus and therefore, negative impacts on stocks.

Disease resistance is often measured as plant survival, which involves taking multiple counts of plants before and after disease incidence. Often, survival data are analyzed by forming a single derived variable, namely final counts expressed as a percentage of initial counts. In this study we propose a bivariate linear mixed model approach in which the two variables are the initial and final counts. This approach is demonstrated using data from nine blackleg disease nurseries in the 2009 growing season in Australia. Replicated experiments were grown at each nursery with a mixture of commercial Australian canola cultivars and breeding lines (collectively called ‘entries’) being tested. Plant survival was determined by counting all the seedlings at emergence and then recounting the number surviving at maturity in each plot. The counts were considered as two ‘traits’, which were log transformed prior to a bivariate linear mixed model analysis. Each trait had different error variances, spatial components (both local and global) and outliers. The variance of entry effects was non-zero for both traits at all locations. The correlation of entry effects between the traits ranged from 0.218 to 0.935 across locations. Best Linear Unbiased Predictors (BLUPs) of entry effects at both sampling times provided three possible indices for selection: (log) counts at emergence, (log) counts at maturity and the difference between these two which could be exponentiated to provide percentage survival values. Thus the bivariate mixed model approach for the analysis of plant survival data provided a more detailed picture of the impact of disease resistance compared with the univariate analysis of percentage survival data. Additionally the predicted entry effects for survival were more accurate in the bivariate analysis.