A spatially explicit multi-competitor coexistence model of penaeid (shrimp) distribution on the Australian Great Barrier Reef

A spatially explicit multi-competitor coexistence model was developed for meta-populations of prawns (shrimp) occupying habitat patches across the Great Barrier Reef, where dispersal was localised and dispersal rates varied between species. Prawns were modelled as individuals moving to and from patches or cells according to pre-set decision rules. The landscape was simulated as a matrix of cells with each cell having a spatially explicit survival index for each species. Mixed species prawn assemblages moved over this simplified spatially explicit landscape. A low level of chronic random environmental disturbance was assumed (cyclone and tropical storm damage) with additional acute spatially confined disturbance due to commercial trawling, modelled as an increase in mortality affecting inter-specific competition. The general form of the results was for increased disturbance to favour good-colonising "generalist" species at the expense of good-competitor "specialists". Increasing fishing mortality (local patch extinctions) combined with poor colonising ability resulted in low equilibrium abundance for even the best competitor, while in the same circumstances the poorest competitor but best coloniser could have the highest equilibrium abundance. This mimics the switch from high-value prawn species to lower-value prawn species as trawl effort increases, reflected in historic catch and effort logbook data and reported anecdotaly from the north Queensland trawl fleet. To match the observed distribution and behaviour of prawn assemblages, a combination inter-species competition, a spatially explicit landscape, and a defined pattern of disturbance (trawling) was required. Modelling this combination could simulate not only general trends in spatial distribution of each of prawn species but also localised concentrations observed in the survey data

Spatial and temporal variation and effects of changes in management in discard rates from the commercial reef line fishery of the Great Barrier Reef, Australia

Discarding in commercially exploited fisheries has received considerable attention in the last decade, though only more recently in Australia. The Reef Line fishery (RLF) of the Great Barrier Reef (GBR) in Australia is a large-scale multi-sector, multi-species, highly regulated hook and line fishery with the potential for high levels of discarding. We used a range of data sources to estimate discard rates and discard quantities for the two main target groups of the RLF, the coral trout, Plectropomus spp, and the red throat emperor, Lethrinus miniatus, and investigated possible effects on discarding of recent changes in management of the fishery. Fleet-wide estimates of total annual quantities discarded from 1989 to 2003 were 292-622 t and 33-95 t for coral trout and red throat emperor, respectively. Hypothetical scenarios of high-grading after the introduction of a total allowable commercial catch for coral trout resulted in increases in discard quantities up to 3895 t, while no high-grading still meant 421 t were discarded. Increasing the minimum size limit of red throat emperor from 35 to 38 cm also increased discards to an estimated 103 t. We provide spatially and temporally explicit estimates of discarding for the two most important species in the GBR RLF of Australia to demonstrate the importance of accounting for regional variation in quantification of discarding. Effects of management changes on discarding are also highlighted. This study provides a template for exploring discarding levels for other species in the RLF and elsewhere.

Sustainable grazing management for temporal and spatial variability in north Australian rangelands – a synthesis of the latest evidence and recommendations

Rainfall variability is a major challenge to sustainable grazing management in northern Australia, with management often complicated further by large, spatially-heterogeneous paddocks. This paper presents the latest grazing research and associated bio-economic modelling from northern Australia and assesses the extent to which current recommendations to manage for these issues are supported. Overall, stocking around the safe long-term carrying capacity will maintain land condition and maximise long-term profitability. However, stocking rates should be varied in a risk-averse manner as pasture availability varies between years. Periodic wet-season spelling is also essential to maintain pasture condition and allow recovery of overgrazed areas. Uneven grazing distributions can be partially managed through fencing, providing additional water-points and in some cases patch-burning, although the economics of infrastructure development are extremely context-dependent. Overall, complex multi-paddock grazing systems do not appear justified in northern Australia. Provided the key management principles outlined above are applied in an active, adaptive manner, acceptable economic and environmental outcomes will be achieved irrespective of the grazing system applied.

Integrating different approaches in the definition of biological stocks: A northern Australian multi-jurisdictional fisheries example using grey mackerel, Scomberomorus semifasciatus

A holistic approach to stock structure studies utilises multiple different techniques on the same individuals sampled from selected populations and combines results across spatial and temporal scales to produce a weight of evidence conclusion. It is the most powerful and reliable source of information to use in formulating resource management and monitoring plans. Few examples of the use of a holistic approach in stock structure studies exist, although more recently this is changing. Using such an approach makes integration of results from each technique challenging. An integrated stock definition (ISD) approach for holistic stock structure studies was developed in this study to aid in the appropriate interpretation of stock structure results to guide the determination of fishery management units. The ISD approach is applied herein to a study of the northern Australian endemic grey mackerel, Scomberomorus semifasciatus (Scombridae). Analyses of genetic (mitochondrial DNA and microsatellites), parasite, otolith stable isotope, and growth data are synthesised to determine the stock structure of S. semifasciatus across northern Australia. Integration of the results from all techniques identified at least six S. semifasciatus stocks for management purposes. Further, the use of the ISD approach provided a simple basis for integrating multiple techniques and for their interpretation. The use of this holistic approach was a powerful tool in providing greater certainty about the appropriate management units for S. semifasciatus. Future stock structure studies investigating spatial management questions in the fisheries context should adopt a holistic approach and apply the ISD approach for a more accurate definition of biological stocks to improve fisheries management.

Statistical methods for analysis of multi-harvest data from perennial pasture variety selection trials

Variety selection in perennial pasture crops involves identifying best varieties from data collected from multiple harvest times in field trials. For accurate selection, the statistical methods for analysing such data need to account for the spatial and temporal correlation typically present. This paper provides an approach for analysing multi-harvest data from variety selection trials in which there may be a large number of harvest times. Methods are presented for modelling the variety by harvest effects while accounting for the spatial and temporal correlation between observations. These methods provide an improvement in model fit compared to separate analyses for each harvest, and provide insight into variety by harvest interactions. The approach is illustrated using two traits from a lucerne variety selection trial. The proposed method provides variety predictions allowing for the natural sources of variation and correlation in multi-harvest data.