A Statistical Model for Stock Assessment of Southern Bluefin Tuna with Temporal Changes in Selectivity

Assessment of the status of southern bluefin tuna (SBT) by Australia and Japan has used a method (ADAPT) that imposes a number of structural restrictions, and is similar to methods used for a number of stocks world-wide. A flexible method for assessment of the SBT population is presented that is much less restrictive and has potentially wide applicability. The three key features are: (1) all fitting to data is within the context of maximum likelihood, (2) catch-at-age data are not assumed to be without error (as in existing methods), but rather to be random variables, while age-specific selectivity is allowed to change over time within the bounds of specific structure, and (3) autocorrelation in recruitment processes is considered within the likelihood framework of the model. While the results suggest the stock has been depleted considerably from its virgin biomass, and are generally consistent with previous assessments, they also indicate that it is not as much below the biomass that will produce maximum suitable yield as previously estimated and that the extent of stock rebuilding necessary may not be as large as has been argued. The available data are shown to provide little information on the steepness parameter of the stock-recruitment function, and hence on sustainable catch levels for the stock.     

Artificial Neural Networks as a Method of Spatial Interpolation for Digital Elevation Models

This paper examines the performance of artificial neural networks (ANNs) as a method of spatial interpolation, when presented with irregular and regular samples of elevation data. The results of the ANN interpolation are compared with results obtained by kriging. Tests of spatial bias in the systematic errors contained in each of the neural network-derived DEMs were conducted using four attributes: slope, aspect, average direction and average distance from the nearest sampled value. Based on RMS and other evaluation measures, the accuracy of estimated DEMs from regular and irregular sample distributions using neural networks is lower than the accuracy level derived from kriging. The accuracy level of the ANN interpolators also decreases as the range of elevation values in DEMs increases. As reported in the literature, ANNs are approximate interpolators, and the pattern of under-prediction and over-prediction of elevation values in this study revealed that all estimated values fell within the range of sample elevations. Neural networks cannot predict values outside the range of elevation values contained in the sample, a property shared by other interpolators such as inverse weighted distance.     

Spatial Resolution and Algorithm Choice as Modifiers of Downslope Flow Computed from Digital Elevation Models

This research investigated the dependence of computing downslope flow from digital elevation models (DEMs) on two basic elements of the flow calculation: the spatial resolution of the DEM and the algorithm used to compute the downslope flow, specifically how it defines topographic slope and aspect. Six algorithms for downslope flow and downslope accumulation were implemented in MATLAB using different assumptions about components of the computation from the terrain analysis literature. We analyzed the results of downslope flow statistically, thresholded the values to yield streams, and compared the different results with the actual surface streams in the National Hydrographic Database. We repeated the computations using DEMs of 3-m, 30-m, and 90-m resolutions, covering a test area near Santa Barbara, California. We conclude that downslope flow computations are overestimates, and that they are fraught with critical algorithmic assumptions and scale effects. They should be approached with great caution in GIS-based analyses.     

Assessment of Shuttle Radar Topography Mission Elevation Data Based on Topographic Maps in Turkey

Depending on scale, topographic maps depicting the shape of the land surfaces of the Earth are produced from different data sources. National topographic maps at a scale of 1:25 000 (25K maps) produced by General Command of Mapping are used as the base map set in Turkey. This map set, which consists of approximately 5500 sheets, covers the whole country and is produced using photogrammetric methods. Digital Elevation Models (DEMs) created from these maps are also available. Recently, another data source, Synthetic Aperture Radar (SAR) interferometric data, has become more important than those produced by conventional methods. The Shuttle Radar Topography Mission (SRTM) contains elevation data with 3 arc-second resolution and 16 m absolute height error (90 percent confidence level). These data are freely available via the Internet for approximately 80 percent of the Earth's land mass. In this study, SRTM DEM was compared with DEM derived from 25K topographic maps for different parts of Turkey. The study areas, each covering four neighboring 25K maps, and having an area of approximately 600 km², were chosen to represent various terrain characteristics. For the comparison, DEMs created from the 25K maps were obtained and organized as files for each map sheet in vector format, containing the digitized contour lines. From these data, DEMs in the resolution of 3 arc-second were created (25K-DEM), in the same structure as the SRTM DEM, allowing the 25K-DEMs and the SRTM DEM to be compared directly. The results show that the agreement of SRTM DEM to the 25K-DEM is within about 13 m, which is less than the SRTM's targeted error of 16 m. The spatial distribution of the height differences between SRTM-DEM and the 25K-DEM and correlation analysis show that the differences were mainly related to the topography of the test areas. In some areas, local height shifts were determined.     

Exploring the Hidden Potential of Common Spatial Data Models to Visualize Uncertainty

Common Spatial Data Models (SDMs) such the vector, raster, and quadtree have well understood and widely practiced conventions of storage and visualization. This paper explores what happens when the conventions of visualization are not strictly adhered to, and the SDMs are used in an atypical fashion. A framework based on a quasi similarity measure is presented, which quantifies (in terms of "distance") the relationship between the storage format and the visualization output, following an accepted protocol. This research used a transformation process (Tp) to define this distance. Then, the atypical use of the quadtree SDM to represent choropleth spatial boundary uncertainty and attribute uncertainty was quantified using the same framework. This research shows that if a SDM is used outside of its original context, then the distance between the storage format and its visual output can alter; in our case, the distance decreased. This result was interpreted as evidence for the creation of a new spatial data structure. The formalization of the relationship between an SDM and its visual output will be valuable for future exploration of the non-conventional visualization of common SDMs.     

Atlas Mapping in the 21st Century

Atlases are changing. The paper paradigm of maps and atlases has pervaded recent cartographic history. By nature that paradigm serves, as well as defines, a specific audience in terms of use and presentation. In the lab and at the printing press, the paper paradigm demands certain design and production flows that will drastically change through evolutions in data structures, mapmaking techniques, and presentation methods. Technological transformations in mapping influence much of this change. The implications for the creation and distribution of atlases are significant. With that in mind, this paper addresses a number of issues that relate to the technological evolution of atlases from paper to digital products: the distinction between paper and digital products and services and the implications of that distinction for atlas design—particularly for web-based services; facilitating data and its application to the evolution of map/atlas products; and a redefinition of "atlas" and the "audience" for an atlas.     

On the Uncertainty of Local Shape of Lines and Surfaces

Modeling line or surface phenomena digitally involves two tasks: discretization of the phenomenon, which yields a finite set of data, and subsequent interpolation, which reconstructs the continuum. Many mathematical techniques exist for the latter task, and most methods require a number of parameters to be specified. The shape of digital line or surface models between the data points (that is, the local shape) and the information derived from these models both depend on the selected method and, possibly, on the specification of parameters. The reconstruction of the continuum thus introduces uncertainty. This paper examines the sources and effects of this type of uncertainty. For this purpose, the modeling of lines and surfaces is separated into an abstraction, an implementation, and measurement. The individual factors affecting uncertainty of local shape in each step are identified and discussed. The paper concludes that local shape uncertainty, unlike positional uncertainty of given data, cannot be numerically assessed. Instead, measures of plausibility have to be used to denote the quality of digital models of lines and surfaces. Finally, the concept and potential problems of future empirical investigations are discussed.     

The Discontinuous Nature of Kriging Interpolation for Digital Terrain Modeling

Kriging is a widely employed method for interpolating and estimating elevations from digital elevation data. Its place of prominence is due to its elegant theoretical foundation and its convenient practical implementation. From an interpolation point of view, kriging is equivalent to a thin-plate spline and is one species among the many in the genus of weighted inverse distance methods, albeit with attractive properties. However, from a statistical point of view, kriging is a best linear unbiased estimator and, consequently, has a place of distinction among all spatial estimators because any other linear estimator that performs as well as kriging (in the least squares sense) must be equivalent to kriging, assuming that the parameters of the semivariogram are known. Therefore, kriging is often held to be the gold standard of digital terrain model elevation estimation. However, I prove that, when used with local support, kriging creates discontinuous digital terrain models, which is to say, surfaces with "rips" and "tears" throughout them. This result is general; it is true for ordinary kriging, kriging with a trend, and other forms. A U.S. Geological Survey (USGS) digital elevation model was analyzed to characterize the distribution of the discontinuities. I show that the magnitude of the discontinuity does not depend on surface gradient but is strongly dependent on the size of the kriging neighborhood.     

Contemporary Statistical Maps—Evidence of Spatial and Graphic Ignorance

Statistical maps are now produced in greater numbers and by persons in a wider range of disciplines than ever before. It is unfortunate that this increased production has not been accompanied by an improvement in map quality. All too frequently, contemporary mapmakers evidence a lack of understanding of the primary function of statistical maps, the symbolic language of mapping, and the effect that data manipulation plays in map communication. Coupled with these deficiencies there is a general lack of appreciation of the basic elements of graphic design. Furthermore, the expanded use of the computer in mapmaking seems to be related to and may even foster low quality since programmers are generally untutored in cartographic design and communication.     

Early growth rate and feeding of a small group of laboratory-reared saury,Scomberesox saurus scombroides (Pisces: Scomberesocidae)

Sea-spawned saury eggs collected 20 km off the west coast of the Cape Peninsula were hatched in laboratory tanks and the larvae reared. The larvae were fed initially on nauplii of cultured copepods, Tisbe holothuriae Humes and Pseudodiaptomus hessei (Mrázek). Growth rate during the first 47 days post-hatch averaged 0,62 mm·day^?1 (at 18—19°C) and between the 47th and 120th day averaged 0,45 mm·day^?1 (at 14,8—15,5°C). Some observations on feeding behaviour are included.