Role of Native Fish in Integrated Aquatic Weed and Water Quality Management within the Burdekin Irrigation Area

This report summarises work conducted by the QDPI, in partnership with the South Burdekin Water Board (SBWB) and the Burdekin Shire Council (BSC) between 2001 and 2003. The broad aim of the research was to assess the potential of native fish as biocontrol agents for noxious weeds, as part of an integrated program for managing water quality in the Burdekin Irrigation Area. A series of trials were conducted at, or using water derived from, the Sandy Creek Diversion near Groper Creek (lower Burdekin delta). Trials demonstrated that aquatic weeds play a positive role in trapping transient nutrients, until such time that weed growth becomes self-shading and weed dieback occurs, which releases stored nutrients and adversely affects water quality. Transient nutrient levels (av. TN<0.5mg/L; av. TP<0.1mg/L) found in the irrigation channel during the course of this research were substantially lower than expected, especially considering the intensive agriculture and sewage effluent discharge upstream from the study site. This confirms the need to consider the control of weeds rather than complete weed extermination when formulating management plans. However, even when low nutrient levels are available, there is competitive exploitation of habitat variables in the irrigation area leading to succession and eventual domination by certain weed species. During these trials, we have seen filamentous algae, phytoplankton, hyacinth and curled pondweed each hold competitive advantage at certain points. However without intervention, floating weeds, especially hyacinth, ultimately predominate in the Burdekin delta due to their fast propagation rate and their ability to out-shade submerged plants. We have highlighted the complexity of interactions in these highly disturbed ecosystems in that even if the more prevalent noxious weeds are contained, other weed species will exploit the vacant niche. This complexity places stringent requirements on the type of native fish that can be used as biocontrol agents. Of the seven fish species identified with herbivorous trophic niches, most target plankton or algae and do not have the physical capacity to directly eat the larger macrophytes of the delta. We do find however that following mechanical weed harvesting, inoculative releases of fish can slow the rate of hyacinth recolonisation. This occurs by mechanisms in addition to direct weed consumption, such as disturbing growth surfaces by grazing on attached biofilms. Predation by birds and water rats presents another impediment to the efficacy of large-scale releases of fish. However, alternative uses of fish in water quality management in the Burdekin irrigation area are discussed.

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.