Latitudinal and seasonal effects on growth of the Australian eastern king prawn (Melicertus plebejus)

The growth of the Australian eastern king prawn (Melicertus plebejus) is understood in greater detail by quantifying the latitudinal effect. The latitudinal effect is the change in the species’ growth rate during migration. Mark–recapture data (N = 1635, latitude 22.21°S–34.00°S) presents northerly movement of the eastern king prawn, with New South Wales prawns showing substantial average movement of 140 km (standard deviation: 176 km) north. A generalized von Bertalanffy growth model framework is used to incorporate the latitudinal effect together with the canonical seasonal effect. Applying this method to eastern king prawn mark–recapture data guarantees consistent estimates for the latitudinal and seasonal effects. For M. plebejus, it was found that growth rate peaks on 25 and 29 January for males and females, respectively; is at a minimum on 27 and 31 July, respectively; and that the shape parameter, k (per year), changes by –0.0236 and –0.0556 every 1 degree of latitude south increase for males and females, respectively.

Mixture of time-dependent growth models with an application to blue swimmer crab length-frequency data

Understanding how aquatic species grow is fundamental in fisheries because stock assessment often relies on growth dependent statistical models. Length-frequency-based methods become important when more applicable data for growth model estimation are either not available or very expensive. In this article, we develop a new framework for growth estimation from length-frequency data using a generalized von Bertalanffy growth model (VBGM) framework that allows for time-dependent covariates to be incorporated. A finite mixture of normal distributions is used to model the length-frequency cohorts of each month with the means constrained to follow a VBGM. The variances of the finite mixture components are constrained to be a function of mean length, reducing the number of parameters and allowing for an estimate of the variance at any length. To optimize the likelihood, we use a minorization–maximization (MM) algorithm with a Nelder–Mead sub-step. This work was motivated by the decline in catches of the blue swimmer crab (BSC) (Portunus armatus) off the east coast of Queensland, Australia. We test the method with a simulation study and then apply it to the BSC fishery data.

Parameter estimation and sensitivity analysis of fat deposition models in beef steers using acslXtreme.

The Davis Growth Model (a dynamic steer growth model encompassing 4 fat deposition models) is currently being used by the phenotypic prediction program of the Cooperative Research Centre (CRC) for Beef Genetic Technologies to predict P8 fat (mm) in beef cattle to assist beef producers meet market specifications. The concepts of cellular hyperplasia and hypertrophy are integral components of the Davis Growth Model. The net synthesis of total body fat (kg) is calculated from the net energy available after accounting tor energy needs for maintenance and protein synthesis. Total body fat (kg) is then partitioned into 4 fat depots (intermuscular, intramuscular, subcutaneous, and visceral). This paper reports on the parameter estimation and sensitivity analysis of the DNA (deoxyribonucleic acid) logistic growth equations and the fat deposition first-order differential equations in the Davis Growth Model using acslXtreme (Hunstville, AL, USA, Xcellon). The DNA and fat deposition parameter coefficients were found to be important determinants of model function; the DNA parameter coefficients with days on feed >100 days and the fat deposition parameter coefficients for all days on feed. The generalized NL2SOL optimization algorithm had the fastest processing time and the minimum number of objective function evaluations when estimating the 4 fat deposition parameter coefficients with 2 observed values (initial and final fat). The subcutaneous fat parameter coefficient did indicate a metabolic difference for frame sizes. The results look promising and the prototype Davis Growth Model has the potential to assist the beef industry meet market specifications.

Detection and use of QTL for complex traits in multiple environments.

QTL mapping methods for complex traits are challenged by new developments in marker technology, phenotyping platforms, and breeding methods. In meeting these challenges, QTL mapping approaches will need to also acknowledge the central roles of QTL by environment interactions (QEI) and QTL by trait interactions in the expression of complex traits like yield. This paper presents an overview of mixed model QTL methodology that is suitable for many types of populations and that allows predictive modeling of QEI, both for environmental and developmental gradients. Attention is also given to multi-trait QTL models which are essential to interpret the genetic basis of trait correlations. Biophysical (crop growth) model simulations are proposed as a complement to statistical QTL mapping for the interpretation of the nature of QEI and to investigate better methods for the dissection of complex traits into component traits and their genetic controls.

Adaptation and management of Australian buffalograss cultivars for shade and water conservation

Soft-leaf buffalo grass is increasing in popularity as an amenity turfgrass in Australia. This project was instigated to assess the adaptation of and establish management guidelines for its use in Australias vast array of growing environments. There is an extensive selection of soft-leaf buffalo grass cultivars throughout Australia and with the countrys changing climates from temperate in the south to tropical in the north not all cultivars are going to be adapted to all regions. The project evaluated 19 buffalo grass cultivars along with other warm-season grasses including green couch, kikuyu and sweet smother grass. The soft-leaf buffalo grasses were evaluated for their growth and adaptation in a number of regions throughout Australia including Western Australia, Victoria, ACT, NSW and Queensland. The growth habit of the individual cultivars was examined along with their level of shade tolerance, water use, herbicide tolerance, resistance to wear, response to nitrogen applications and growth potential in highly alkaline (pH) soils. The growth habit of the various cultivars currently commercially available in Australia differs considerably from the more robust type that spreads quicker and is thicker in appearance (Sir Walter, Kings Pride, Ned Kelly and Jabiru) to the dwarf types that are shorter and thinner in appearance (AusTine and AusDwarf). Soft-leaf buffalo grass types tested do not differ in water use when compared to old-style common buffalo grass. Thus, soft-leaf buffalo grasses, like other warm-season turfgrass species, are efficient in water use. These grasses also recover after periods of low water availability. Individual cultivar differences were not discernible. In high pH soils (i.e. on alkaline-side) some elements essential for plant growth (e.g. iron and manganese) may be deficient causing turfgrass to appear pale green, and visually unacceptable. When 14 soft-leaf buffalo grass genotypes were grown on a highly alkaline soil (pH 7.5-7.9), cultivars differed in leaf iron, but not in leaf manganese, concentrations. Nitrogen is critical to the production of quality turf. The methods for applying this essential element can be manipulated to minimise the maintenance inputs (mowing) during the peak growing period (summer). By applying the greatest proportion of the turfs total nitrogen requirements in early spring, peak summer growth can be reduced resulting in a corresponding reduction in mowing requirements. Soft-leaf buffalo grass cultivars are more shade and wear tolerant than other warm-season turfgrasses being used by homeowners. There are differences between the individual buffalo grass varieties however. The majority of types currently available would be classified as having moderate levels of shade tolerance and wear reasonably well with good recovery rates. The impact of wear in a shaded environment was not tested and there is a need to investigate this as this is a typical growing environment for many homeowners. The use of herbicides is required to maintain quality soft-leaf buffalo grass turf. The development of softer herbicides for other turfgrasses has seen an increase in their popularity. The buffalo grass cultivars currently available have shown varying levels of susceptibility to the chemicals tested. The majority of the cultivars evaluated have demonstrated low levels of phytotoxicity to the herbicides chlorsulfuron (Glean) and fluroxypyr (Starane and Comet). In general, soft leaf buffalo grasses are varied in their makeup and have demonstrated varying levels of tolerance/susceptibility/adaptation to the conditions they are grown under. Consequently, there is a need to choose the cultivar most suited to the environment it is expected to perform in and the management style it will be exposed to. Future work is required to assess how the structure of the different cultivars impacts on their capacity to tolerate wear, varying shade levels, water use and herbicide tolerance. The development of a growth model may provide the solution.

Improved estimation of size-transition matrices using tag-recapture data

We derive a new method for determining size-transition matrices (STMs) that eliminates probabilities of negative growth and accounts for individual variability. STMs are an important part of size-structured models, which are used in the stock assessment of aquatic species. The elements of STMs represent the probability of growth from one size class to another, given a time step. The growth increment over this time step can be modelled with a variety of methods, but when a population construct is assumed for the underlying growth model, the resulting STM may contain entries that predict negative growth. To solve this problem, we use a maximum likelihood method that incorporates individual variability in the asymptotic length, relative age at tagging, and measurement error to obtain von Bertalanffy growth model parameter estimates. The statistical moments for the future length given an individual’s previous length measurement and time at liberty are then derived. We moment match the true conditional distributions with skewed-normal distributions and use these to accurately estimate the elements of the STMs. The method is investigated with simulated tag–recapture data and tag–recapture data gathered from the Australian eastern king prawn (Melicertus plebejus).