Inferential, non-parametric statistics to assess quality of probabilistic forecast systems

Many statistical forecast systems are available to interested users. In order to be useful for decision-making, these systems must be based on evidence of underlying mechanisms. Once causal connections between the mechanism and their statistical manifestation have been firmly established, the forecasts must also provide some quantitative evidence of `quality’. However, the quality of statistical climate forecast systems (forecast quality) is an ill-defined and frequently misunderstood property. Often, providers and users of such forecast systems are unclear about what ‘quality’ entails and how to measure it, leading to confusion and misinformation. Here we present a generic framework to quantify aspects of forecast quality using an inferential approach to calculate nominal significance levels (p-values) that can be obtained either by directly applying non-parametric statistical tests such as Kruskal-Wallis (KW) or Kolmogorov-Smirnov (KS) or by using Monte-Carlo methods (in the case of forecast skill scores). Once converted to p-values, these forecast quality measures provide a means to objectively evaluate and compare temporal and spatial patterns of forecast quality across datasets and forecast systems. Our analysis demonstrates the importance of providing p-values rather than adopting some arbitrarily chosen significance levels such as p < 0.05 or p < 0.01, which is still common practice. This is illustrated by applying non-parametric tests (such as KW and KS) and skill scoring methods (LEPS and RPSS) to the 5-phase Southern Oscillation Index classification system using historical rainfall data from Australia, The Republic of South Africa and India. The selection of quality measures is solely based on their common use and does not constitute endorsement. We found that non-parametric statistical tests can be adequate proxies for skill measures such as LEPS or RPSS. The framework can be implemented anywhere, regardless of dataset, forecast system or quality measure. Eventually such inferential evidence should be complimented by descriptive statistical methods in order to fully assist in operational risk management.

How accurate are the marker orders in crop linkage maps generated from large marker datasets?

Marker ordering during linkage map construction is a critical component of QTL mapping research. In recent years, high-throughput genotyping methods have become widely used, and these methods may generate hundreds of markers for a single mapping population. This poses problems for linkage analysis software because the number of possible marker orders increases exponentially as the number of markers increases. In this paper, we tested the accuracy of linkage analyses on simulated recombinant inbred line data using the commonly used Map Manager QTX (Manly et al. 2001: Mammalian Genome 12, 930-932) software and RECORD (Van Os et al. 2005: Theoretical and Applied Genetics 112, 30-40). Accuracy was measured by calculating two scores: % correct marker positions, and a novel, weighted rank-based score derived from the sum of absolute values of true minus observed marker ranks divided by the total number of markers. The accuracy of maps generated using Map Manager QTX was considerably lower than those generated using RECORD. Differences in linkage maps were often observed when marker ordering was performed several times using the identical dataset. In order to test the effect of reducing marker numbers on the stability of marker order, we pruned marker datasets focusing on regions consisting of tightly linked clusters of markers, which included redundant markers. Marker pruning improved the accuracy and stability of linkage maps because a single unambiguous marker order was produced that was consistent across replications of analysis. Marker pruning was also applied to a real barley mapping population and QTL analysis was performed using different map versions produced by the different programs. While some QTLs were identified with both map versions, there were large differences in QTL mapping results. Differences included maximum LOD and R-2 values at QTL peaks and map positions, thus highlighting the importance of marker order for QTL mapping

Queensland Coastal Wetland Resources: the Northern Territory Border to Flinders River

Protection of coastal wetland environments is an important prerequisite to effective and sustainable fisheries management and conservation of habitats for the use of future generations. Mangroves, saltmarshes and seagrasses directly support local and offshore fisheries through the provision of food, shelter, breeding and nursery grounds. As such, these vegetated wetland environments along with sandbars, mudflats, rocky foreshores and reefs have significant economic value as well as their intrinsic aesthetic and ecological values. This report summarises the results of the mapping undertaken in the Gulf of Carpentaria Region from the Queensland/Northern Territory border eastwards to the western bank of the Flinders River (hereafter called the Gulf Study Area). The study was undertaken in order to: 1. document and map coastal wetlands of the Gulf Study Area; 2. document levels of existing disturbance to and protection of these wetlands; 3. examine existing recreational, indigenous and commercial fisheries of 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/Marine Protected Area (MPA) declaration. Dataset URL Link: Queensland Coastal Wetlands Resources Mapping data. [Dataset]

Queensland Coastal Wetland Resources: Sand Bay to Keppel Bay.

Protection of coastal wetland environments is an important prerequisite to effective and sustainable inshore fisheries management and conservation of habitats for use by future generations. Mangroves, saltmarshes, seagrasses and non vegetated habitats directly support local and regional inshore and offshore fisheries through the provision of food, shelter, breeding and nursery grounds. As such, these wetland environments have significant economic value as well as their intrinsic aesthetic and ecological values. This report summarises the results of the mapping undertaken in the Central Queensland Coast from Sand Bay to Keppel Bay (hereafter referred to as the Study Area). The study was undertaken in order to: 1. document and map the coastal wetland communities along the Queensland coastline from Sand Bay (20.93°S, 149.04°E) to Keppel Bay (23.65°S, 151.07°E); 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 conservation values of the areas investigated from the viewpoint of fisheries productivity and as habitat for important and/or threatened species. Dataset URL Link: Queensland Coastal Wetlands Resources Mapping data. [Dataset]

Queensland Coastal Wetland Resources: Cape Tribulation to Bowling Green Bay

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 northern Queensland from Cape Tribulation to Bowling Green Bay (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 Study Area extends from Cape Tribulation (16o 6’S, 145o 24’E) to Bowling Green Bay (19o 30’S, 147o 24’E) in tropical north Queensland. 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. Dataset URL Link: Queensland Coastal Wetlands Resources Mapping data. [Dataset]

Coastal Wetlands Resources Investigation of the Burdekin Delta for declaration as fisheries reserves. Report to Ocean Rescue 2000

A project to investigate the coastal wetland resources of the Burdekin Delta, north Queensland, was undertaken as part of the long-term assessment of the coastal fisheries resources of Queensland. Extending from November 1993 to May 1995, fieldwork was undertaken in November 1993 and August 1994. The scope of the coastal wetlands resources investigation of the Burdekin Delta for declaration as a Fish Habitat Area was: 1. To document and map the marine wetland vegetation communities in the Burdekin River delta. 2. To document levels of existing disturbance to wetlands, existing recreational and commercial fisheries resources, and existing fishing activities. 3. To evaluate the conservation values of the areas investigated from the viewpoint of fisheries productivity and as habitat for important/threatened species. 4. To initiate Fish Habitat Area declaration under Section 120 of the Queensland Fisheries Act 1994 with formal consultation to all stakeholders. This report concentrates on Points 1 and 3, the documentation of the marine wetland vegetation communities and the evaluation of conservation values from a fisheries viewpoint. Dataset URL Link: Queensland Coastal Wetlands Resources Mapping data. [Dataset]

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]

Reassessing spatial and temporal dynamics of kangaroo populations

Long-running datasets from aerial surveys of kangaroos (Macropus giganteus, Macropus [uliginosus, Macropus robustus and Macropus rufus) across Queensland, New South Wales and South Australia have been analysed, seeking better predictors of rates of increase which would allow aerial surveys to be undertaken less frequently than annually. Early models of changes in kangaroo numbers in response to rainfall had shown great promise, but much variability. We used normalised difference vegetation index (NDVI) instead, reasoning that changes in pasture condition would provide a better predictor than rainfall. However, except at a fine scale, NDVI proved no better; although two linked periods of rainfall proved useful predictors of rates of increase, this was only in some areas for some species. The good correlations reported in earlier studies were a consequence of data dominated by large droughtinduced adult mortality, whereas over a longer time frame and where changes between years are less dramatic, juvenile survival has the strongest influence on dynamics. Further, harvesting, density dependence and competition with domestic stock are additional and important influences and it is now clear that kangaroo movement has a greater influence on population dynamics than had been assumed. Accordingly, previous conclusions about kangaroo populations as simple systems driven by rainfall need to be reassessed. Examination of this large dataset has permitted descriptions of shifts in distribution of three species across eastern Australia, changes in dispersion in response to rainfall, and an evaluation of using harvest statistics as an index of density and harvest rate. These results have been combined into a risk assessment and decision theory framework to identify optimal monitoring strategies.

Seabed discrimination data from SeaScan™

Raw data from SeaScan™ transects off Wide Bay (south Queensland) taken in August 2007 as part of a study of ecological factors influencing the distribution of spanner crabs (Ranina ranina). The dataset (comma-delimited ascii file) comprises the following fields: 1. record number 2. date-time (GMT) 3. date-time (AEST) 4. latitude (signed decimal degrees) 5. longitude (decimal degrees) 6. speed over ground (knots) 7. depth (m) 8. seabed roughness (v) 9. hardness (v) Indices of roughness and hardness (from the first and second echoes respectively) were obtained using a SeaScan™ 100 system (un-referenced) on board the Research Vessel Tom Marshall, with the ship’s Furuno FCV 1100 echo sounder and 1 kW, 50 kHz transducer. Generally vessel speed was kept below about 14 kt (typically ~12 kt), and the echo-sounder range set to 80 m. The data were filtered to remove errors due to data drop-out, straying beyond system depth limits (min. 10 m), or transducer interference.

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.

Great Barrier Reef Fish Biological Data

Biological measurements on fish sampled during the course of FRDC funded project Growth, Reproduction and Recruitment of Great Barrier Reef Food Fish Stocks (FRDC 90/18). The comma-delimited ascii file comprises the following fields: 1. Cruise number 2. Date (d-m-y) 3, Region (descriptor of part of Queensland coast or Great Barrier Reef system) 4. Reef (name or number) 5. Data source (Res=research, Rec=recreational fisher, Com=commercial fisher) 6. Capture method 7. Trap number (where appropriate) 8. Species name 9. LthStd (standard length, cm) 10. LthFrk (fork length, cm) 11. LthTot (total length, cm) 12. WtTot (approx total weight, g; weighed at sea) 13. FrameWt (weight of frame [after filleting, with viscera], g; weighed in lab) 14. Sex (macroscopic examination only) 15. GonadWt (g) Data obtained by the Department Employment, Economic Development and Innovation (formerly Primary Industries and Fisheries) between 1988 and 1993, primarily in the southern Great Barrier Reef (Capricorn-Bunker and Swain Groups), with fish traps and handlining.

Resource Assessment of the Tidal Wetland Vegetation of Western Cape York Peninsula, North Queensland, Report to Ocean Rescue 2000

A project to allow the resource assessment of tidal wetland vegetation of western Cape York Peninsula, in north Queensland, was undertaken as part of the longterm assessment of the coastal fisheries resources of Queensland. The project incorporated a littoral invertebrate fauna component. Extending from May 1993 to December 1994, fieldwork was undertaken in May 1993, November 1993 and April 1994. The aims of this project were to: • obtain baseline information on the distribution of marine plants of western Cape York Peninsula; • commence a preliminary assessment of the littoral invertebrate fauna and their habitat requirements with a view to extending knowledge of their biogeographic affinities; • perform biogeographic classification of the tidal wetlands at a meso and local scale for marine conservation planning; • evaluate the conservation values of the areas investigated from the viewpoint of fisheries productivity and as habitat for important/threatened species. Dataset URL Link: Queensland Coastal Wetlands Resources Mapping data. [Dataset]

Marine Vegetation of Cape York Peninsula. Cape York Peninsula Land Use Strategy, Office of Co-ordinator General of Queensland, Brisbane, Department of the Environment, Sport and Territories, Canberra

The Cape York Peninsula Land Use Strategy (CYPLUS) is a joint Queensland/Commonwealth initiative to provide a framework for making decisions about how to use and manage the natural resources of Cape York Peninsula in ways that will be ecologically sustainable. As part of the Natural Resources Analysis Program (NRAP) of CYPLUS, the Fisheries Division of the Queensland Department of Primary Industries has mapped the marine vegetation (mangroves and seagrasses) for Cape York Peninsula. The project ran from July 1992 to June 1994. Field work was undertaken in November 1992, May 1993, and April 1994. Final report on project: NRO6 – Marine Plan (Seagrass/Mangrove) Distribution. Dataset URL Link: Queensland Coastal Wetlands Resources Mapping data. [Dataset]

Scalloped Hammerhead microsatellite genotypes (eight loci) representing five regions on Australia's east coast and one location in central Indonesia.

Biodiversity of sharks in the tropical Indo-Pacific is high, but species-specific information to assist sustainable resource exploitation is scarce. The null hypothesis of population genetic homogeneity was tested for scalloped hammerhead shark (Sphyrna lewini, n=244) and the milkshark (Rhizoprionodon acutus, n=209) from northern and eastern Australia, using nuclear (S. lewini, eight microsatellite loci; R. acutus, six loci) and mitochondrial gene markers (873 base pairs of NADH dehydrogenase subunit 4). We were unable to reject genetic homogeneity for S. lewini, which was as expected based on previous studies of this species. Less expected were similar results for R. acutus, which is more benthic and less vagile than S. lewini. These features are probably driving the genetic break found between Australian and central Indonesian R. acutus (F-statistics; mtDNA, 0.751 to 0.903; microsatellite loci, 0.038 to 0.047). Our results support the spatially-homogeneous management plan for shark species in Queensland, but caution is advised for species yet to be studied.

Milkshark (Rhizoprionodon acutus) microsatellite genotype data (six loci) from samples collected from five locations in eastern Australia and central Indonesia.

Biodiversity of sharks in the tropical Indo-Pacific is high, but species-specific information to assist sustainable resource exploitation is scarce. The null hypothesis of population genetic homogeneity was tested for scalloped hammerhead shark (Sphyrna lewini, n=244) and the milkshark (Rhizoprionodon acutus, n=209) from northern and eastern Australia, using nuclear (S. lewini, eight microsatellite loci; R. acutus, six loci) and mitochondrial gene markers (873 base pairs of NADH dehydrogenase subunit 4). We were unable to reject genetic homogeneity for S. lewini, which was as expected based on previous studies of this species. Less expected were similar results for R. acutus, which is more benthic and less vagile than S. lewini. These features are probably driving the genetic break found between Australian and central Indonesian R. acutus (F-statistics; mtDNA, 0.751 to 0.903; microsatellite loci, 0.038 to 0.047). Our results support the spatially-homogeneous management plan for shark species in Queensland, but caution is advised for species yet to be studied.