Queensland Coastal Wetland Resource Investigation of the Bowen Region: Cape Upstart to Gloucester Island

The wetland resources of the Queensland coastline have been mapped as a baseline dataset for Marine Protected Area investigation and particularly Fish Habitat Area (FHA) declaration, Ramsar site nomination and continued monitoring of these important fish habitats. This report summarises the results of the mapping undertaken in the Bowen region from the East Coast of Cape Upstart (Abbot Bay) to Gloucester Island (encompassing Edgecumbe Bay). The study was undertaken in order to: 1. document and map the coastal wetland communities within the Bowen region; 2. document levels of existing disturbance to and protection of the wetlands; 3. examine existing recreational and commercial fisheries in the region; and 4. evaluate the significance of the coastal wetlands in the region. Dataset URL Link: Queensland Coastal Wetlands Resources Mapping data. [Dataset]

Evolutionary relationships between bat coronaviruses and their hosts

Recent studies have suggested that bats are the natural reservoir of a range of coronaviruses (CoVs), and that rhinolophid bats harbor viruses closely related to the severe acute respiratory syndrome (SARS) CoV, which caused an outbreak of respiratory illness in humans during 2002-2003. We examined the evolutionary relationships between bat CoVs and their hosts by using sequence data of the virus RNA-dependent RNA polymerase gene and the bat cytochrome b gene. Phylogenetic analyses showed multiple incongruent associations between the phylogenies of rhinolophid bats and their CoVs, which suggested that host shifts have occurred in the recent evolutionary history of this group. These shifts may be due to either virus biologic traits or host behavioral traits. This finding has implications for the emergence of SARS and for the potential future emergence of SARS-CoVs or related viruses.

Queensland Coastal Wetlands Resources Mapping Data

1:100,000 coastal wetland vegetation mapping for Queensland including mangrove communities, saltpans and saline grasslands. Mapping taken from Landsat TM images with ground truthing. Additional metadata is available for details of techniques and accuracy for each section of coastline. Data Currency for each section of coast: NT border to Flinders River - 1995 SE Gulf of Carpentaria - 1987, 1988, 1991, 1992 Cape York Peninsula - 1986-88, 1991 Cape Trib to Bowling Green Bay - 1997-99 The Burdekin Region - 1991 The Bowen Region - 1994-95 The Whitsunday Region - 1997 Repulse Bay - 1989 Central Qld - 1995, 1997 The Curtis Coast Region - 1997 Round Hill Head to Tin Can Inlet - 1997 Moreton Region - 1995. Article Links: 1/ #1662. Queensland Coastal Wetland Resources: the Northern Territory Border to Flinders River. Project Report. Information Series QI00099. 2/ #1663. Queensland Coastal Wetland Resources: Sand Bay to Keppel Bay. Project Report. Information Series QI00100. 3/ #1664. Queensland Coastal Wetland Resources: Cape Tribulation to Bowling Green Bay. Project Report. Information Series QI01064. 4/ #1666. Coastal Wetlands Resources Investigation of the Burdekin Delta for declaration as fisheries reserves. Report to Ocean Rescue 2000. Project Report. 5/ #1667. Queensland Coastal Wetland Resource Investigation of the Bowen Region: Cape Upstart to Gloucester Island. Project Report. 6/ #1784. Resource Assessment of the Tidal Wetland Vegetation of Western Cape York Peninsula, North Queensland, Report to Ocean Rescue 2000. Project Report. 7/ #1785. Marine Vegetation of Cape York Peninsula. Cape York Peninsula Land Use Strategy. Project Report. 8/ #3544. Queensland Coastal Wetland Resources: The Whitsunday Region. Project Report.Information Series QI01065. 9/ #3545. Queensland Coastal Wetland Resources: Round Hill Head to Tin Can Inlet. Project Report. Information Series QI99081.

Queensland Coastal Wetland Resources: The Whitsunday Region

This report provides key resource data for the ongoing assessment of the requirement for additional Marine Protected Areas (e.g. FHAs under the Queensland Fisheries Act 1994) in regions of high fish habitat value in the Whitsunday Region from Gloucester Island to Cape Hillsborough (hereafter referred to as the Study Area). The study also provides baseline information on the coastal wetlands within this Study Area for consideration in the Ramsar site nomination process. The project aimed to: 1. document and map the coastal wetland communities of the Study Area; 2. document levels of existing disturbance to and protection of the wetlands; 3. examine existing recreational, indigenous and commercial fisheries resources in the region; 4. evaluate the conservation values of the areas investigated from the viewpoint of fisheries productivity and as habitat for important and/or threatened species for future FHA/MPA declaration.

Isolation of multiple novel paramyxoviruses from pteropid bat urine

Bats have been found to harbor a number of new emerging viruses with zoonotic potential and there has been a great deal of interest in identifying novel bat pathogens to determine risk to human and animal health. Many groups have identified novel viruses in bats by detection of viral nucleic acid, however virus isolation is still a challenge and there are few reports of viral isolates from bats. In recent years, our group has developed optimized procedures for virus isolation from bat urine, including the use of primary bat cells. In previous reports we have described the isolation of Hendra virus, Menangle virus and Cedar virus, in Queensland, Australia. Here, we report the isolation of four additional novel bat paramyxoviruses from urine collected from beneath pteropid bat (flying fox) colonies in Queensland and New South Wales during 2009-2011.

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.