Using tickler chains to promote more environmentally benign trawls in an Australian penaeid fishery

There has been an increasing emphasis on reducing the environmental impacts of penaeid trawls by modifying their anterior sections. In one Australian estuary, this approach has culminated in a new generic trawl comprising a slightly smaller mesh size, and steeper and shorter side panels than traditional trawls, combined with a top panel extending forwards of the bottom panel (termed ‘lead-ahead’) and no headline floats. This study sought to investigate if an additional simple, cumulative modification (tickler chain) might help promote adoption of the new trawl, via improved penaeid catches. Six volunteer fishers towing pairs of either the traditional or new trawls in double rigs were asked if an observer could accompany fishing trips, and alternately attach a light tickler chain (~3 kg) anterior to the ground gear of one trawl. Regardless of the trawl design, the tickler chains significantly increased the catch weights of penaeids, but relatively more so in the new trawls (by 1.11 × vs 1.08 × ). The tickler chains also significantly increased the number and weight of total bycatch in the traditionaltrawls (by up to 1.22 × ), but not in the new trawls—although there were variable species-specific effects. In terms of total effects, the data support using tickler chains to increase the efficiency of the new trawls and their cumulative benefits, and could be promoted to facilitate broader inter- and intra-fleet adoption

Genomic prediction models, selection tools and association studies for genotype by environment data

Plant breeding is complicated by the fact that genotypes respond differently to different environments, a phenomenon known as genotype by environment interaction (GEI). Despite its importance, however, many plant breeding programmes still use inefficient methods for handling GEI. This thesis develops a wide-array of methods that leverage GEI for efficient prediction, selection and discovery in plant breeding. The methods are demonstrated using a collaborating cotton breeding dataset from Bayer CropScience as well as publicly available and simulated datasets. Chapter 1 presents a brief overview of plant breeding design and analysis, with a focus on genomic prediction models, selection tools and association studies for genotype by environment data. Chapter 2 develops genomic prediction models that predict the response of different genotypes across different growing environments. The models are referred to as integrated factor analytic (IFA) models. The IFA models integrate known genotypic covariates derived from marker data and known environmental covariates derived from weather and soil data along with latent environmental covariates estimated directly from the phenotypic data. These models have great potential to improve predictive plant breeding in the presence of GEI. Chapter 3 develops selection tools that provide breeders with information to select and deploy well-adapted genotypes to their target environments. The tools provide measures of overall performance and stability, which summarise average genotype performance across environments and the variability in performance. A new directional stability measure is also introduced that partitions genotype stability into components that reflect favourable and unfavourable adaptation. These tools are becoming increasingly important with the presence of rapidly changing environments amidst climate change. Chapters 4 and 5 develop fast exact methods for conducting genome-wide association studies (GWAS). The methods produce all required test statistics from the fit of a single linear mixed model, instead of a very large number of models for all markers of interest. Fast methods are also introduced for GWAS using complex models for GEI. These methods have great potential to improve discovery in a wide-array of genetic studies, particularly with the advent of large-scale datasets and complex genotype by environment interactions. Chapter 6 develops a general framework for simulating GEI using the class of multiplicative models. The framework can be used to simulate realistic multi-environment trial (MET) datasets and model breeding programmes that better reflect the complexity of real-world settings. This framework provides a general basis for plant breeders and researchers to evaluate different breeding methods in the presence of GEI. Chapter 7 presents a discussion and concluding remarks, with a focus on placing the thesis in the wider agricultural community

FieldSimR: an R package for simulating plot data in multi-environment field trials

This paper presents a general framework for simulating plot data in multi-environment field trials with one or more traits. The framework is embedded within the R package FieldSimR, whose core function generates plot errors that capture global field trend, local plot variation, and extraneous variation at a user-defined ratio. FieldSimR's capacity to simulate realistic plot data makes it a flexible and powerful tool for a wide range of improvement processes in plant breeding, such as the optimisation of experimental designs and statistical analyses of multi-environment field trials. FieldSimR provides crucial functionality that is currently missing in other software for simulating plant breeding programmes and is available on CRAN. The paper includes an example simulation of field trials that evaluate 100 maize hybrids for two traits in three environments. To demonstrate FieldSimR's value as an optimisation tool, the simulated data set is then used to compare several popular spatial models for their ability to accurately predict the hybrids' genetic values and reliably estimate the variance parameters of interest. FieldSimR has broader applications to simulating data in other agricultural trials, such as glasshouse experiments.</p

Genomic selection using random regressions on known and latent environmental covariates

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 [Formula: see text] higher than conventional random regression models for current environments and [Formula: see text] 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00122-022-04186-w

Additive and non-additive genetic variance in juvenile Sitka spruce (Picea sitchensis Bong. Carr)

Many quantitative genetic models assume that all genetic variation is additive because of a lack of data with sufficient structure and quality to determine the relative contribution of additive and non-additive variation. Here the fractions of additive (fa) and non-additive (fd) genetic variation were estimated in Sitka spruce for height, bud burst and pilodyn penetration depth. Approximately 1500 offspring were produced in each of three sib families and clonally replicated across three geographically diverse sites. Genotypes from 1525 offspring from all three families were obtained by RADseq, followed by imputation using 1630 loci segregating in all families and mapped using the newly developed linkage map of Sitka spruce. The analyses employed a new approach for estimating fa and fd, which combined all available genotypic and phenotypic data with spatial modelling for each trait and site. The consensus estimate for fa increased with age for height from 0.58 at 2 years to 0.75 at 11 years, with only small overlap in 95% support intervals (I95). The estimated fa for bud burst was 0.83 (I95=[0.78, 0.90]) and 0.84 (I95=[0.77, 0.92]) for pilodyn depth. Overall, there was no evidence of family heterogeneity for height or bud burst, or site heterogeneity for pilodyn depth, and no evidence of inbreeding depression associated with genomic homozygosity, expected if dominance variance was the major component of non-additive variance. The results offer no support for the development of sublines for crossing within the species. The models give new opportunities to assess more accurately the scale of non-additive variation

FieldSimR: an R package for simulating plot data in multi-environment field trials

This paper presents a general framework for simulating plot data in multi-environment field trials with one or more traits. The framework is embedded within the R package FieldSimR, whose core function generates plot errors that capture global field trend, local plot variation, and extraneous variation at a user-defined ratio. FieldSimR’s capacity to simulate realistic plot data makes it a flexible and powerful tool for a wide range of improvement processes in plant breeding, such as the optimisation of experimental designs and statistical analyses of multi-environment field trials. FieldSimR provides crucial functionality that is currently missing in other software for simulating plant breeding programmes and is available on CRAN. The paper includes an example simulation of field trials that evaluate 100 maize hybrids for two traits in three environments. To demonstrate FieldSimR’s value as an optimisation tool, the simulated data set is then used to compare several popular spatial models for their ability to accurately predict the hybrids’ genetic values and reliably estimate the variance parameters of interest. FieldSimR has broader applications to simulating data in other agricultural trials, such as glasshouse experiments

Bedform migration in a mixed sand and cohesive clay intertidal environment and implications for bed material transport predictions

Many coastal and estuarine environments are dominated by mixtures of non-cohesive sand and cohesive mud. The migration rate of bedforms, such as ripples and dunes, in these environments is important in determining bed material transport rates to inform and assess numerical models of sediment transport and geomorphology. However, these models tend to ignore parameters describing the physical and biological cohesion (resulting from clay and extracellular polymeric substances, EPS) in natural mixed sediment, largely because of a scarcity of relevant laboratory and field data. To address this gap in knowledge, data were collected on intertidal flats over a spring-neap cycle to determine the bed material transport rates of bedforms in biologically-active mixed sand-mud. Bed cohesive composition changed from below 2 vol% up to 5.4 vol% cohesive clay, as the tide progressed from spring towards neap. The amount of EPS in the bed sediment was found to vary linearly with the clay content. Using multiple linear regression, the transport rate was found to depend on the Shields stress parameter and the bed cohesive clay content. The transport rates decreased with increasing cohesive clay and EPS content, when these contents were below 2.8 vol% and 0.05 wt%, respectively. Above these limits, bedform migration and bed material transport was not detectable by the instruments in the study area. These limits are consistent with recently conducted sand-clay and sand-EPS laboratory experiments on bedform development. This work has important implications for the circumstances under which existing sand-only bedform migration transport formulae may be applied in a mixed sand-clay environment, particularly as 2.8 vol% cohesive clay is well within the commonly adopted definition of “clean sand”

Incretin Receptor Null Mice Reveal Key Role of GLP-1 but Not GIP in Pancreatic Beta Cell Adaptation to Pregnancy

Islet adaptations to pregnancy were explored in C57BL6/J mice lacking functional receptors for glucagon-like peptide 1 (GLP-1) and gastric inhibitory polypeptide (GIP). Pregnant wild type mice and GIPRKO mice exhibited marked increases in islet and beta cell area, numbers of medium/large sized islets, with positive effects on Ki67/Tunel ratio favouring beta cell growth and enhanced pancreatic insulin content. Alpha cell area and glucagon content were unchanged but prohormone convertases PC2 and PC1/3 together with significant amounts of GLP-1 and GIP were detected in alpha cells. Knockout of GLP-1R abolished these islet adaptations and paradoxically decreased pancreatic insulin, GLP-1 and GIP. This was associated with abolition of normal pregnancy-induced increases in plasma GIP, L-cell numbers, and intestinal GIP and GLP-1 stores. These data indicate that GLP-1 but not GIP is a key mediator of beta cell mass expansion and related adaptations in pregnancy, triggered in part by generation of intra-islet GLP-1

Genomic epidemiology of SARS-CoV-2 in a UK university identifies dynamics of transmission

AbstractUnderstanding SARS-CoV-2 transmission in higher education settings is important to limit spread between students, and into at-risk populations. In this study, we sequenced 482 SARS-CoV-2 isolates from the University of Cambridge from 5 October to 6 December 2020. We perform a detailed phylogenetic comparison with 972 isolates from the surrounding community, complemented with epidemiological and contact tracing data, to determine transmission dynamics. We observe limited viral introductions into the university; the majority of student cases were linked to a single genetic cluster, likely following social gatherings at a venue outside the university. We identify considerable onward transmission associated with student accommodation and courses; this was effectively contained using local infection control measures and following a national lockdown. Transmission clusters were largely segregated within the university or the community. Our study highlights key determinants of SARS-CoV-2 transmission and effective interventions in a higher education setting that will inform public health policy during pandemics.</jats:p

Sex-specific vulnerability of Portunus armatus to capture in round traps with traditional and novel fish baits

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 (>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