Software and data downloads
The various software products, spreadsheets and data provided below are offered freely and in good faith. Please do the right thing and, where appropriate, provide due acknowledgement when using these materials. End-user licence agreements are included in some of the software products. For all other downloads no responsibility will be taken by myself or my current employer for any losses, damages or costs incurred by any person as a result of the use of these materials. Use at you own risk!
I use a range of programming tools, such as Visual Basic for Applications (VBA), R, and Gauss, however most of the software below was developed within the Borland Delphi Pascal environment. This is a fast and flexible programming language with excellent support for creating scientific applications, particularly when combined with Steema Software's TeeChart Pro graphics add-on for charting, plotting and visualisation.
Maxent Randomisation Test (MRT)
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Description | Software to perform the randomisation-based statistical test described in Roxburgh & Mokany (2009). "On testing predictions of species relative abundance from maximum entropy optimisation". Oikos. DOI: 10.1111/j.1600-0706.2009.17772.x |
| Documentation | Embedded within software. | |
| Developer | SH Roxburgh. | |
| Language | Borland Delphi Studio 2006. | |
| Requirements | Windows 95 or greater. | |
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The Digital Transferscope (TDT)
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Description | The analysis of aerial photographs has a long history in environmental management, particularly for vegetation mapping and for quantifying land cover change. The Digital Transferscope (TDT) was written as a digital alternative to the mechanical 'Zoom Transferscope' for the collection of cover data from aerial photographs. Time-series aerial photographs are first digitally scanned at a high resolution, and are then co-registered (rectified) relative to one another using a combination of user-defined control points and photographic analysis algorithms. A downhill simplex search algorithm is used to automate the rectification process, through the iterative application of either affine or projective image transformations. Scanned photos are typically around 350mb per photo, and 10,000 x 10,000 pixels resolution, therefore TDT required the development of custom algorithms for displaying and manipulating very large images. Once rectified, a user-defined sampling-grid overlay is added, allowing vegetation cover to be sampled interactively using a number of methods, e.g. at a number of points in the landscape (either on a grid, or located at random), or by recording grid-cell cover dominance. |
| Documentation | User guide with tutorial exercises [DRAFT] [ref] | |
| Developer | SH Roxburgh. | |
| Language | Borland Delphi Studio 2006. | |
| Requirements | Windows 95 or greater / Microsoft Excel. | |
| Download | TDT program, user guide & sample data (zip file; 39Mb) | |
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R code for the analyses in Roxburgh & Mokany (2007)
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Description | Supporting R program for the analyses presented in Roxburgh & Mokany (2007) |
| Documentation | See reference. | |
| Developer | SH Roxburgh. | |
| Language | R. | |
| Requirements | R programming & analysis environment . | |
| Download | R code | |
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CASS (Carbon Accounting Simulation Software)
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Description | Software to perform the randomisation-based statistical test described in Roxburgh & Mokany (2009). "On testing predictions of species relative abundance from maximum entropy optimisation". Oikos. DOI: 10.1111/j.1600-0706.2009.17772.x |
| Documentation | User guide with tutorial exercises [ref]. | |
| Developer | SH Roxburgh. | |
| Language | MS Excel spreadsheet / Visual Basic for Applications. | |
| Requirements | Windows 95 or greater / MS Excel 2003 / 2007. | |
| Download | CASS model + user guide (zip file; 4.73Mb). | |
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Range-ASSESS
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Description | Range-ASSESS is a state-and-transition based expert system for assessing the impact of changing land management practices on soil and biomass carbon across the Australian rangelands. |
| Documentation | User Guide (installed) & publication [ref 1] [ref 2] | |
| Developer | SH Roxburgh & MJ Hill. | |
| Language | Borland Delphi 7.0. | |
| Requirements | Windows 95 or greater. | |
| Download | RASetup.exe (19Mb setup file --> 125Mb installed). | |
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COINS (COmparison & INtegration Shell)
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Description | COINS is a software shell for integrating many models within the same software environment. This allows different models to be combined within the same simulation, and allows the outputs of different models, sharing the same input driver data, to be compared on an equal footing. The temporal scaling options accommodate analyses from days to centuries, and the spatial scaling is from ‘point’ to GIS-type simulations and cellular automata. Factorial and Monte Carlo capability is provided to allow sensitivity analysis of model outputs to uncertainty in model parameter. |
| Documentation | User Guide & Help (installed) & publication [ref]. | |
| Developer | ID Davies & SH Roxburgh. | |
| Language | Borland Delphi 7.0. | |
| Requirements | Windows 95 or greater. | |
| Download | Minimum installation that includes eight small examples to illustrate the major features of the software (COINS_MIN.zip; 6Mb). Full installation that includes an additional nine examples that make use of the spatial analysis capabilities of the shell, and includes the required spatial input data (COINS_FULL.zip; 296Mb. | |
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The Random Patterns Test for spatial association
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Description | Many organisms display patchiness in their distribution patterns over a wide range of spatial scales. Patchy distribution patterns, caused by processes such as growth, migration, reproduction and mortality, result in neighbouring areas being more likely to contain a species than distant areas, a phenomenon known as positive spatial autocorrelation. When species are patchily distributed the within-species spatial randomness assumptions of the standard statistical tests for detecting species associations are seriously violated. To address this problem we introduce a new test for detecting species associations - the Random Patterns test. This test takes into account spatial autocorrelation by including the characteristics of the spatial pattern of each species into the null model. This software is the implementation of the random patterns method as described in Roxburgh& Chesson (1998). Brief instructions on the use of the software for performing the random patterns statistical test are included. |
| Documentation | User Guide (included) & publications [ref 1] [ref 2]. | |
| Developer | SH Roxburgh. | |
| Language | Borland Delphi 3.0. | |
| Requirements | Windows 95 or greater. | |
| Download | RandomPatterns.zip (0.3Mb). | |
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Illustration of nonlinear averaging & ecological scaling
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Description | “Scaling up” is a commonly used phrase in carbon accounting and throughout the ecological sciences. By the term, people usually mean that measurements are made at local scales (e.g. leaves, plants), but estimates are required at larger scales, e.g. forests stands, continents, global. This disparity between what is (traditionally) measured and what is needed (i.e. stand to continental scale estimates) is often referred to as the “scaling problem”. There is little doubt that there are significant practical difficulties in making suitable measurements; however, there are also some theoretical considerations. This short note and the associated spreadsheet describes one of the significant problems – the problem of averaging in the process of scaling up. |
| Documentation | Explanatory note & Excel spreadsheet. | |
| Developer | SH Roxburgh, ML Roderick, B Barnes. | |
| Language | Microsoft Excel spreadsheet. | |
| Requirements | Windows 95 or greater. | |
| Download | Scaling.zip (0.04Mb). | |
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Illustration of scaling & self-thinning theory
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Description | Barnes, Bi & Roderick (2006) developed a theory for combining the mathematics of scaling and self-thinning into carbon accounting frameworks. The theory demonstrates how measurements made on individual trees can be used to predict biomass and carbon stocks at larger spatial scales. This work has been published in the scientific literature, but requires mathematical expertise to fully understand, and hence implement. In order to make this work more accessible, this small software tool illustrates the major features and implications of the theory, and to enable users to enter and analyse their own data. |
| Documentation | Online help. The software needs to be used in conjunction with the publication"Barnes, Bi & Roderick (2006). Application of an ecological framework linking scales based on self-thinning. Ecological Applications, 16, 133-142". | |
| Developer | SH Roxburgh. | |
| Language | Borland Delphi 7.0. | |
| Requirements | Windows 95 or greater. | |
| Download | STSetup.zip (5Mb). | |
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Animated climate viewer
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Description | This climate viewer displays month-by-month and year-by-year animations derived from historical precipitation and temperature surfaces for the Australian continent, for the period 1900-2004. The data on which these animations are based are taken from the CRC for Greenhouse Accounting monthly climate database - see below under 'Data'. |
| Documentation | ReadMe file. | |
| Developer | SH Roxburgh. | |
| Language | Borland Delphi 7.0. | |
| Requirements | Windows 95 or greater. | |
| Download | Animated Climate Viewer.zip (23Mb) | |
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OptIC (Optimisation Intercomparison Project)
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Description | This software was written to participate in the OptIC project, devised by theGlobal Carbon Project to compare different methods for numerically estimating model parameters from data (so-called model-data fusion methods). Ten contributors applied their favourite methods to a set of fiendishly tricky pseudo-observed datasets, in an attempt to uncover the sets of true but unknown model parameter values that were used to generate the pseudo-observations. My method combines two approaches - the downhill simplex, and the genetic algorithm. |
| Documentation | The optimisation method is described in the appendix to Roxburgh et al. (2006). The software is undocumented. A paper based on the intercomparison can be found here. | |
| Developer | SH Roxburgh. | |
| Language | Borland Delphi Studio 2006. | |
| Requirements | Windows 95 or greater. | |
| Download | Program and input data (7.8Mb); A summary of my results (0.5Mb); All my results (8.8Mb). | |
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DataGrabber (Beta)
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Description | Ever been in a position where you have a published bar chart or scatter plot, but what you really need is the underlying data? Of course, the easiest thing is to just ask the original author, but sometimes that's not always possible. One option is to make a photocopy and then get busy with sharp pencils, rulers and graph paper. Frustrated by the tediousness of that approach, I sat down and wrote DataGrabber as a software tool to achieve the same end. After scanning or otherwise obtaining the figure in digital form, its then a simple and painless task of setting the axes with the mouse, and then making a few clicks on the computer screen. Its much faster and more accurate than a photocopier and ruler, and much more fun! *warning* This software is still in development and contains many bugs. It is, however, useable in its current form. |
| Documentation | There is a brief tutorial which covers basic useage. | |
| Developer | SH Roxburgh. | |
| Language | Borland Delphi Studio 2006. | |
| Requirements | Windows 95 or greater. | |
| Download | Program and tutorial (0.6Mb). | |
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Create correlated random deviates
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Description | The method of Iman and Conover (Iman, R.L., Conover, W.J. 1982. A Distribution-Free Approach To Inducing Rank Correlation Among Input Variables. Commun. Statist.-Simula. Computa. 11, 311-334) is used to generate sets of inter-correlated random deviates. A typical use is for Monte-Carlo sensitivity testing of parameter values in a model, where you know that the parameters are correlated (either negatively or positively). The user selects the number of deviates, the distribution to use (from a list of 22 continuous and 6 discrete, see picture left), and the desired correlation structure (by filling in the off-diagonal elements of a correlation matrix). Sets of univariate deviates can also be generated. The algorithim underlying the analysis is that used for the Monte-Carlo analyses in the COINS software |
| Documentation | A small tutorial is provided. | |
| Developer | SH Roxburgh. | |
| Language | Borland Delphi Studio 2006. | |
| Requirements | Windows 95 or greater. | |
| Download | Program and tutorial (1.5Mb) | |
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Australian continental Net Primary Productivity (NPP) estimates
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Description | GIS data of the twelve modelled continental estimates of Australian long-term average Net Primary Productivity (NPP) reviewed by Roxburgh et al. (2004). The file format is Arcinfo ascii raster format, in geographic projection. All model estimates have been resampled to a cell size of 0.05 degrees. The units are g C m2 yr-1. The file header is: ncols 901 nrows 701 xllcorner 109.975 yllcorner -45.025 cellsize 0.05 NODATA value -100 The methods of calculation for each estimate are varied, and are summarised in Table 1of Roxburgh et al. (2004) |
| Documentation | Roxburgh et al. (2004). | |
| Download | Continental NPP data (8Mb). | |
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Gridded Australian historical monthly climate data: January 1990 – December 2004
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Description | A collection of continental-scale spatial climate data at a monthly resolution for the period January 1900 – December 2004. Climate grids were generated using a combination of data extracted from the Bureau of Meteorology (BoM) Climate Data Australia (CDA) database and the ANUSPLIN software package. Climate variables include total monthly precipitation, and minimum, maximum and mean daily temperature for each month. For each climate variable there are 1260 continental-scale grids (with an approximately 5km x 5km gridcell size) for the period January 1900 to December 2004 (105 years x 12 months per year). Data for each decade (with 120 grids per decade =12 months x 10 years) are provided as a single zip file, with each decadal file ranging in size from 20-50 Mb. Data are provided as Arc Info float format (*.hdr, *.flt). The data format is ncols 818 nrows 674 xllcorner 112.825 yllcorner -43.725 cellsize 0.050 NODATA_value -99 BYTEORDER LSBFIRST |
| Documentation | A technical report by McBeth & Roxburgh (2005) summarises the methods used to generate and validate the set of spatially interpolated (splined) historical climate surfaces for the Australian continent, and the resulting set of continental-scale gridded maps for monthly total precipitation, and mean monthly minimum and maximum temperature. | |
| Download | Data download page. | |
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Injune soil survey data
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Description | Excel spreadsheet data from soil sampling conducted at the Injune study site, as reported in Roxburgh et al. (2006). Soil organic carbon and bulk density depth profile data are given for each of 42 soil cores collected across 14 study sites, in addition to total soil carbon stocks for each site. |
| Documentation | Readme file for interpreting the spreadsheet. | |
| Download | Injune soils data.zip. | |
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Experimental data from Hely & Roxburgh (2004)
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Description | Excel spreadsheet with raw data from the experiment reported in Hely and Roxburgh (2004). This was a growth cabinet experiment investigating the competitive responses of two grasses to elevated temperature and CO2. |
| Documentation | Readme file for interpreting the spreadsheet. | |
| Download | Hely_Roxburgh.zip. | |
| Cellular automata laboratory exercise
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Description | Cellular automata are a class of mathematical model that can be used incorporate spatial information in the study of complex dynamic systems. In a cellular automaton, space is represented by an array of cells. The array can be a single line of cells, a two dimensional grid, or multi-dimensional. The essential features of a cellular automaton are (1) Space is represented by an aggregation of cells (the array). (2). At any given time, each cell is represented by a state, for example a particular species, or type of forest cover. (3) There is a neighbourhood which defines what neighbouring cells interact with the target cell. I.e., the state of a cell in the next time period depends both on its current state, and also the state of the cells in its neighbourhood. (4) There is a program, which is simply the set of rules which define how the state of a cell changes in response to its current state, and the states of its neighbours. These laboratory notes and Excel spreadsheet explore 1- and 2-dimensional cellular automata. Examples include population growth, including chaotic dynamics, and impacts of disturbance size and frequency ('fire') on biodiversity. |
| Download | CALabnotes.zip (0.1Mb). | |
| Carbon modelling laboratory exercise
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Description | The ability to mathematically model the flow of carbon into and out of ecosystems is a central component in assessing the impacts of Global Change. The first part of these lab notes gets the students to investigate the behaviour of the CASS model of terrestrial carbon dynamics. In the second part of the lab students do some ‘hands-on’ model building, where the Visual Basic for Applications (VBA) programming language is used to write and runthe students own terrestrial carbon model within Microsoft Excel. |
| Download | Carbon modelling labnotes (3.3Mb). | |
| Global productivity and carbon modelling GIS laboratory exercise
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Description | A three week laboratory using ArcView GIS software to explore global-scale patterns of vegetation growth, land-use, carbon stocks and national-scale greenhouse accounting. Co-developed with Prof. Brendan Mackey.
The aims of the exercise are to:
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| Download | Global carbon exercise (0.7Mb). | |
| ECOSIM spreadsheet exercise for modelling population growth
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Description | The spreadsheet and accompanying notes were co-developed with Dr. Peter Chesson for use in a post-graduate workshop on ecological modelling. The exercise is designed to provide an introduction to 10 population growth models and their behaviour |
| Download | Lecture notes (1.0Mb) Excel spreadsheet (0.7Mb) Spreadsheet instructions (0.4Mb) |
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| Introductory ecology lecture notes
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Description | A set of self-contained coursework notes designed for three four-hour tutorial sessions covering a range of introductory ecological concepts. These notes were used for tutorial-style classroom teaching for masters-level students. Topic covered include: What are ecological communities? Mechanisms structuring ecological communities. Methods used to describe ecological communities. What is (bio)diversity? Introduction to population dynamics. Modelling population growth. Population Viability Analysis. Landscape Ecology. Environmental variability & disturbance. Consequences of environmental variability on ecological systems. Succession. Invasion. |
| Download | Set 1 (1.0Mb; 26pp.) Set 2 (0.5Mb; 26pp.) Set 3 (0.3Mb; 18pp |
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| Laser point quadrat frame
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Description | Description of a laser-based point quadrat sampling frame, designed for sampling vegetation up to approximately 1.3m height. |
| Download | Goto description | |
| HVLauncher
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Description | HVlauncher is launching & PC shutdown software for arcade game emulators. |
| Documentation & Downloads | Goto HVLauncher web page | |
| Digital-Photo Crop Overlay (D-PCO) |
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Description | Software for aiding artistic composition of digital photographs. |
| Documentation & Downloads | Goto D-PCO web page | |
| Munsell colour interactive spreadsheet |
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Description | Excel spreadsheet for exploring the Munsell colour classification. The spreadsheet is a slightly modified version of that written by Timo Teichert, available at Paul Centore's Munsell Resources page. Also on Paul's pages can be found many explanatory materials on the Munsell system, and other useful resources. I highly reccommend taking a vist. The modifications I made to the original workbook are listed on the introductory 'Info' worksheet. Note when you open the workbook you may get a security warning as it requires a few macro's to run - so you will need to make sure macro's are enabled in Excel in order to use it |
| Download | Munsell-to-RGB-Tables-SHR.zip | |
