Towards responsible native fish stocking: identifying management concerns and appropriate research methodologies

A major outcome of this project has been the identification and prioritisation of the major management issues related to the ecological impacts of fish stocking and the elucidation of appropriate research methodologies that can be used to investigate these issues. This information is paramount to development of the relevant research projects that will lead to stocking activities aligned with world’s best practice, a requisite for ecologically sustainable recreational freshwater fisheries. In order to quantify the major management issues allied to the sustainability of freshwater fish stocking, stakeholders from around Australia were identified and sent a questionnaire to determine which particular issues they regarded as important. These stakeholders included fisheries managers or researchers from Federal, Territory and State jurisdictions although others, including representatives from environment and conservation agencies and peak recreational fishing and stocking groups were also invited to give their opinions. The survey was completed in late 2007 and the results analysed to give a prioritized list of key management issues relating to the impacts of native fish stocking activities. In the analysis, issues which received high priority rankings were flagged as potential topics for discussion at a future expert workshop. Identified high priority issues fell into the following core areas: marking techniques, genetics, population dynamics, introduction of pathogens and exotic biological material and ecological, biological and conservation issues. The next planned outcome, determination of the most appropriate methodologies to address these core issues in research projects, was addressed through the outputs of an expert workshop held in early 2008. Participants at this workshop agreed on a range of methodologies for addressing priority sustainability issues and decided under what circumstances that these methodologies should be employed.

CRC50089 Grain Insect Ecology

Resistance to phosphine in target pests threatens market access for Australian grain. While the grains industry is now attempting to develop an effective and sustainable strategy to manage this resistance, action is severely limited by significant gaps in our knowledge of the key ecological factors that influence the development of resistance. There is a need to research this information as a foundation for a rational approach to managing phosphine resistance in the Australian grains industry. Research outcomes: The project has provided critical research methodologies and preliminary data to fill the large gaps in our knowledge of the ecology of two key pests, Rhyzopertha dominica and Tribolium castaneum, and how this may drive the development of phosphine resistance. This information will contribute to the groundwork for future research needed to provide a scientific basis for a rational resistance management strategy.

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.