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Key message The integration of known and latent environmental covariates within a single-stage genomic selection approach provides breeders with an informative and practical framework to utilise genotype by environment interaction for prediction into current and future environments. Abstract This paper develops a single-stage genomic selection approach which integrates known and latent environmental covariates within a special factor analytic framework. The factor analytic linear mixed model of Smith et al. (2001) is an effective method for analysing multi-environment trial (MET) datasets, but has limited practicality since the underlying factors are latent so the modelled genotype by environment interaction (GEI) is observable, rather than predictable. The advantage of using random regressions on known environmental covariates, such as soil moisture and daily temperature, is that the modelled GEI becomes predictable. The integrated factor analytic linear mixed model (IFA-LMM) developed in this paper includes a model for predictable and observable GEI in terms of a joint set of known and latent environmental covariates. The IFA-LMM is demonstrated on a late-stage cotton breeding MET dataset from Bayer CropScience. The results show that the known covariates predominately capture crossover GEI and explain 34.4% of the overall genetic variance. The most notable covariates are maximum downward solar radiation (10.1%), average cloud cover (4.5%) and maximum temperature (4.0%). The latent covariates predominately capture non-crossover GEI and explain 40.5% of the overall genetic variance. The results also show that the average prediction accuracy of the IFA-LMM is $$0.02-0.10$$ 0.02 - 0.10 higher than conventional random regression models for current environments and $$0.06-0.24$$ 0.06 - 0.24 higher for future environments. The IFA-LMM is therefore an effective method for analysing MET datasets which also utilises crossover and non-crossover GEI for genomic prediction into current and future environments. This is becoming increasingly important with the emergence of rapidly changing environments and climate change.

The effects of decomposing shark tissue on catches of benthic longlines targeting various carcharhinids were assessed to inform possible use as a semiochemical shark deterrent. During 15 nights fishing, four benthic longlines (each comprising 18–30 hooks baited with mullet, Mugil cephalus) were deployed to 12–56 m overnight for 12–21 h off eastern Australia. Two of the longlines had 2.0–3.0 kg of decomposing shark tissue placed into porous cylindrical canisters (520 × 105 mm polyvinyl chloride) secured to the mainline mostly between every three hooks (15–20 m apart), while the other two longlines had empty canisters. In total, 150 fish were caught, comprising 14 species of elasmobranchs and especially tiger sharks, Galeocerdo cuvier (31% of total). The decomposing shark tissue in the canisters had no effects on catches of any species or groups, with variability among most attributed to fishing depth (positive relationship) and also soak time (negative) for carcharhinids and G. cuvier. Irrespective of the contents of the canisters and the lack of any semiochemical effects, there was some evidence of fewer sharks caught on adjacent hooks as moonlight increased, and possibly because of a visual response. There was no depredation of any decomposing shark tissue in the canisters, but three juvenile hooked sharks were substantially depredated, and presumably by larger individuals. Most ( ∼ 70%) of the remaining hooked sharks survived. While this study showed no repelling effects of decomposing shark tissue, the conclusions are restricted to the experimental conditions, including the source of tissue and the distance between hooks, which might be used as upper limit in any future work assessing for effects.

In response to few data describing the effects of bait on trap efficiencies for blue swimmer crabs, Portunus armatus in an Australian fishery and an impetus to reduce costs, traditional baits (sea mullet, Mugil cephalus and luderick, Girella tricuspidata) were compared against a novel, less-expensive bait (European carp, Cyprinus carpio). Eight replicate traps with each bait type were fished over four days at one location in the fishery for a total of 96 trap lifts. Traps baited with sea mullet caught significantly more (by >1.5×) total blue swimmer crabs and at a greater net-profit trap deployment–1 (>3.7×) than traps with the other two baits, which produced similar catches and profits. But, the superior performance of sea mullet as bait was due to greater catches of female blue swimmer crabs (which were larger and are more valuable owing to ovarian development), with no significant difference in catches of males among bait types. Such sex specificity was attributed to possible divergent, temporal nutritional requirements including a dietary bias among mature females preparing to migrate to oceanic areas prior to reproduction. Additional data are required across larger spatio-temporal scales to further elucidate patterns; however there are clear implications for fishers that seek to maximise catches of females in the studied fishery or potentially minimise female catches which are prohibited in other Australian fisheries. Further, it is also clear that sex-specific catch data acquired during surveys should be considered for possible confounding effects of bait (and other technical factors) prior to extrapolating relative abundances.

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.

In response to an absence of size-at-age information for juvenile Chelonia mydas in the South Atlantic Ocean, but the need for such regional data to support global ecological monitoring and conservation actions, skeletochronological analyses (using humeri) were undertaken for 63 stranded dead specimens (30.0–58.0 cm curved carapace length—CCL) collected between 2008 and 2013 from 40 km of beaches off southern Brazil. Specimens were defined as being 2–8 years old and from various nesting sites (based on mitochondrial haplotypes). Parametric mixed-effect models were used to examine variation among CCL, maximum humeral length (MHL) and medial humeral width (MHW) due to age (fitted with a natural cubic smoothing spline), genetic haplotype and various random factors. Genetic haplotype was not significant and subsequently removed from analyses. All three size-at-age parameters had random variability, but changes in somatic C. mydas growth described by MHW were less representative than the others, remaining constant through a linear relationship with age. By contrast, both CCL and the preferred MHL similarly displayed positive exponential-shaped splines, with age-specific growth rates (3- to 7-year-olds) predicted at 1.4–3.4 cm year−1 CCL or 0.2–0.7 cm year−1 MHL. The observed CCL growth was comparable to recent estimates for other global populations, but with southern Brazilian C. mydas generally having larger sizes-at-age. Such intra-population differences can be attributed not only to regional prerecruitment environmental parameters and associated ecological implications (e.g. foraging ecology), but also divergent analytical methods. The choice of the latter to accurately model variable growth rates has implications for comparing size-at-age among other sea-turtle populations.