Rapid assessment of genotype-by-environment interactions and heritability for growth rate in aquaculture species using in vitro fertilisation and DNA tagging

Commercial environments may receive only a fraction of expected genetic gains for growth rate as predicted from the selection environment. This fraction is result of undesirable genotype-by-environment interactions (GxE) and measured by the genetic correlation (rg) of growth between environments. Rapid estimates of genetic correlation achieved in one generation are notoriously difficult to estimate with precision. A new design is proposed where genetic correlations can be estimated by utilising artificial mating from cryopreserved semen and unfertilised eggs stripped from a single female. We compare a traditional phenotype analysis of growth to a threshold model where only the largest fish are genotyped for sire identification. The threshold model was robust to differences in family mortality differing up to 30%. The design is unique as it negates potential re-ranking of families caused by an interaction between common maternal environmental effects and growing environment. The design is suitable for rapid assessment of GxE over one generation with a true 0.70 genetic correlation yielding standard errors as low as 0.07. Different design scenarios were tested for bias and accuracy with a range of heritability values, number of half-sib families created, number of progeny within each full-sib family, number of fish genotyped, number of fish stocked, differing family survival rates and at various simulated genetic correlation levels.

Using UHF proximity loggers to quantify male–female interactions: A scoping study of estrous activity in cattle

Reproductive efficiency is an important determinant of profitable cattle breeding systems and the success of assisted reproductive techniques (ART) in wildlife conservation programs. Methods of estrous detection used in intensive beef and dairy cattle systems lack accuracy and remain the single biggest issue for improvement of reproductive rates and such methods are not practical for either large-scale extensive beef cattle enterprises or free-living mammalian species. Recent developments in UHF (ultra high frequency) proximity logger telemetry devices have been used to provide a continuous pair-wise measure of associations between individual animals for both livestock and wildlife. The objective of this study was to explore the potential of using UHF telemetry to identify the reproductive cycle phenotype in terms of intensity and duration of estrus. The study was conducted using Belmont Red (interbred Africander Brahman Hereford–Shorthorn) cattle grazing irrigated pasture on Belmont Research Station, northeastern Australia. The cow-bull associations from three groups of cows each with one bull were recorded over a 7-week breeding season and the stage of estrus was identified using ultrasonography. Telemetry data from bull and cows, collected over 4 8-day logger deployments, were log transformed and analyzed by ANOVA. Both the number and duration of bull-cow affiliations were significantly (P < 0.001) greater in estrous cows compared to anestrus cows. These results support the development of the UHF technology as a hands-off and noninvasive means of gathering socio-sexual information on both wildlife and livestock for reproductive management.

Floristic composition and pasture condition of Aristida/Bothriochloa pastures in central Queensland. II. Soil and pasture condition interactions

Sustainable management of native pastures requires an understanding of what the bounds of pasture composition, cover and soil surface condition are for healthy pastoral landscapes to persist. A survey of 107 Aristida/Bothriochloa pasture sites in inland central Queensland was conducted. The sites were chosen for their current diversity of tree cover, apparent pasture condition and soil type to assist in setting more objective bounds on condition ‘states’ in such pastures. Assessors’ estimates of pasture condition were strongly correlated with herbage mass (r = 0.57) and projected ground cover (r = 0. 58), and moderately correlated with pasture crown cover (r = 0.35) and tree basal area (r = 0.32). Pasture condition was not correlated with pasture plant density or the frequency of simple guilds of pasture species. The soil type of Aristida/Bothriochloa pasture communities was generally hard-setting, low in cryptogam cover but moderately covered with litter and projected ground cover (30–50%). There was no correlation between projected ground cover of pasture and estimated ground-level cover of plant crowns. Tree basal area was correlated with broad categories of soil type, probably because greater tree clearing has occurred on the more fertile, heavy-textured clay soils. Of the main perennial grasses, some showed strong soil preferences, for example Tripogon loliiformis for hard-setting soils and Dichanthium sericeum for clays. Common species, such as Chrysopogon fallax and Heteropogon contortus, had no strong soil preference. Wiregrasses (Aristida spp.) tended to be uncommon at both ends of the estimated pasture condition scale whereas H. contortus was far more common in pastures in good condition. Sedges (Cyperaceae) were common on all soil types and for all pasture condition ratings. Plants identified as increaser species were Tragus australianus, daisies (Asteraceae) and potentially toxic herbaceous legumes such as Indigofera spp. and Crotalaria spp. Pasture condition could not be reliably predicted based on the abundance of a single species or taxon but there may be scope for using integrated data for four to five ecologically contrasting plants such as Themeda triandra with daisies, T. loliiformis and flannel weeds (Malvaceae).

Projecting future expansion of invasive species: Comparing and improving methodologies for species distribution modeling

Modeling the distributions of species, especially of invasive species in non-native ranges, involves multiple challenges. Here, we developed some novel approaches to species distribution modeling aimed at reducing the influences of such challenges and improving the realism of projections. We estimated species-environment relationships with four modeling methods run with multiple scenarios of (1) sources of occurrences and geographically isolated background ranges for absences, (2) approaches to drawing background (absence) points, and (3) alternate sets of predictor variables. We further tested various quantitative metrics of model evaluation against biological insight. Model projections were very sensitive to the choice of training dataset. Model accuracy was much improved by using a global dataset for model training, rather than restricting data input to the species’ native range. AUC score was a poor metric for model evaluation and, if used alone, was not a useful criterion for assessing model performance. Projections away from the sampled space (i.e. into areas of potential future invasion) were very different depending on the modeling methods used, raising questions about the reliability of ensemble projections. Generalized linear models gave very unrealistic projections far away from the training region. Models that efficiently fit the dominant pattern, but exclude highly local patterns in the dataset and capture interactions as they appear in data (e.g. boosted regression trees), improved generalization of the models. Biological knowledge of the species and its distribution was important in refining choices about the best set of projections. A post-hoc test conducted on a new Partenium dataset from Nepal validated excellent predictive performance of our “best” model. We showed that vast stretches of currently uninvaded geographic areas on multiple continents harbor highly suitable habitats for Parthenium hysterophorus L. (Asteraceae; parthenium). However, discrepancies between model predictions and parthenium invasion in Australia indicate successful management for this globally significant weed. This article is protected by copyright. All rights reserved.

Projecting future expansion of invasive species: Comparing and improving methodologies for species distribution modeling

Modeling the distributions of species, especially of invasive species in non-native ranges, involves multiple challenges. Here, we developed some novel approaches to species distribution modeling aimed at reducing the influences of such challenges and improving the realism of projections. We estimated species-environment relationships with four modeling methods run with multiple scenarios of (1) sources of occurrences and geographically isolated background ranges for absences, (2) approaches to drawing background (absence) points, and (3) alternate sets of predictor variables. We further tested various quantitative metrics of model evaluation against biological insight. Model projections were very sensitive to the choice of training dataset. Model accuracy was much improved by using a global dataset for model training, rather than restricting data input to the species’ native range. AUC score was a poor metric for model evaluation and, if used alone, was not a useful criterion for assessing model performance. Projections away from the sampled space (i.e. into areas of potential future invasion) were very different depending on the modeling methods used, raising questions about the reliability of ensemble projections. Generalized linear models gave very unrealistic projections far away from the training region. Models that efficiently fit the dominant pattern, but exclude highly local patterns in the dataset and capture interactions as they appear in data (e.g. boosted regression trees), improved generalization of the models. Biological knowledge of the species and its distribution was important in refining choices about the best set of projections. A post-hoc test conducted on a new Partenium dataset from Nepal validated excellent predictive performance of our “best” model. We showed that vast stretches of currently uninvaded geographic areas on multiple continents harbor highly suitable habitats for Parthenium hysterophorus L. (Asteraceae; parthenium). However, discrepancies between model predictions and parthenium invasion in Australia indicate successful management for this globally significant weed. This article is protected by copyright. All rights reserved.

Maize yield determination in the Northern Region: Hybrid by environment by management interactions

In Maize, as with most cereals, grain yield is mostly determined by the total grain number per unit area, which is highly related to the rate of crop growth during the critical period around silking. Management practices such as plant density or nitrogen fertilization can affect the growth of the crop during this period, and consequently the final grain yield. Across the Northern Region maize is grown under a large range of plant populations under high year-to-year rainfall variability. Clear guidelines on how to match hybrids and management across environments and expected seasonal condition, would allow growers to increase yields and profits while managing risks. The objective of this research was to screen the response of commercial maize hybrids differing in maturity and prolificity (i.e. multi or single cobbing) types for their efficiency in the allocation of biomass into grain.