Fast, Easy, and Publication-Quality Ecological Analyses with PC-ORD

Transcription

1 Emerging Technologies Fast, Easy, and Publication-Quality Ecological Analyses with PC-ORD JeriLynn E. Peck School of Forest Resources, Pennsylvania State University, University Park, Pennsylvania USA Abstract The recent version 6 release of the software PC-ORD Multivariate Analysis of Ecological Data (MjM Software ) earns a solid A grade for ease of use, comprehensiveness of analysis tools, and excellent graphing capabilities. New features in version 6 include the addition of several analysis tools (PCoA, RDA, SumF), the enhancement of others (partial Mantel test, orthogonal rotation in NMS), additional graphing features (boxplots, convex hulls) and new options that finally make importing data from Excel easy. Description The combined effort of Dr. Bruce McCune (Oregon State University) and MjM Software, the PC- ORD software is an integrated compilation of data management, exploration, graphing, and analysis tools applicable to the kinds of data collected by ecologists. Data are stored in a spreadsheet format that can accommodate up to 32,000 rows and columns (23,170 if a distance matrix will be calculated) and can be imported directly from an Excel spreadsheet or from text files of various formats. Options are available to assist users with structuring their data matrices (e.g., appending matrices, deleting rows and columns), summarizing data (e.g., sums, diversity indices, species lists), graphically exploring data (e.g., boxplots, scatterplots, distribution curves), and modifying data (e.g., common transformations and relativizations). Analysis tools, with both reasonable default settings and the ability to tailor input parameters as needed, are included to address both common and complex analyses questions: How does species composition vary along known gradients? o polar ordination (Bray-Curtis ordination) How can sample units be ordered based on the relative abundance of indicator species? o weighted averaging ordination How does species composition or habitat condition vary among samples taken along unknown gradients? o principal components analysis and nonmetric multidimensional scaling, as well as 298 Bulletin of the Ecological Society of America

2 reciprocal averaging, detrended correspondence analysis, and principal coordinates analysis How can new sample units be ordered to fit within the space of an existing ordination model? o predictive nonmetric multidimensional scaling (NMS scores) How does species composition change when specific explanatory variables are manipulated? o canonical correspondence analysis, redundancy analysis Does species composition or habitat condition vary among locations or change following a disturbance or as a result of applying a treatment? o multiresponse permutation procedure, distance-based MANOVA, SumF Which species behave similarly across a data set? o agglomerative clustering, two-way clustering, TWINSPAN Which samples units can be classified into the same groups based on species or habitat data? o agglomerative clustering, two-way clustering Which species are most abundant and frequent in treatment or habitat groups? o indicator species analysis Do two data sets show similarity of compositional structure? o Mantel test An extensive and easy to use 2-D and 3-D ordination graphics interface enables the production of tailored, publication-quality diagrams and includes a range of helpful interpretative options. Review Although the menu-driven user interface is easy enough to navigate and the help menu is extensive, the first hurdle is simply getting the data into PC-ORD. Because the software operates on *.wk1 formatted spreadsheet files, which Microsoft Excel no longer supports, most users will not be able to simply open their data files but must instead import them from an Excel file or from a text file that has been exported from some other software (e.g., a database). Fortunately, version 6 now includes options to do this relatively painlessly. However, once the data are in PC-ORD, which only saves files in the *.wk1 format, users must export the data if they wish to make complex edits using Excel (such as using formulas). Most users will require a few back-and-forths before they get their data just they way they want them. From there out, use of the drop-down menus is straightforward and strategically placed help buttons can answer many questions. Users who are not sure where to start may take advantage of the advisor wizard, which asks a series of questions to lead users through a dichotomous key to determine which data manipulations or analyses are most appropriate. Although the questions posed by the wizard may be difficult for beginning users, they force the user to think through important decisions in their analysis pathway, and help is provided in the form of page numbers for relevant reading in the 2002 Analysis of Ecological Communities book by Bruce McCune and James Grace. Once users have their data in the software and have become accustomed to the interface, they will be pleasantly surprised at the range of tools available in PC-ORD that tailor to ecological data sets. While basic data management options are annoyingly lacking (e.g., copy and paste, aggregating across subsets of data), PC-ORD includes tools useful for ecologists that are not commonly found in statistical software packages, such as the ability to construct species area curves, plot smoothed variable distributions, create species lists, or conduct analyses such as polar ordination (which they term Bray-Curtis ordination), Emerging Technologies July

3 indicator species analysis, and SumF. In addition, all of the most common ordination, classification, and group-testing tools are available, and many can be run using default settings or easily tailored to fit data set constraints and analysis objectives. For example, simple mouse clicks in the setup window are all that is needed to run a PCA with a variance/covariance cross-products matrix, include biplot scores for species, and add a randomization test. Similarly, NMS can be run using default settings (in Autopilot mode) or the user can define input (e.g., number of axes) and output (e.g., orthogonal rotation) settings. The information provided following most procedures in the result file is so extensive that the beginning user may be a bit overwhelmed, but more advanced users will appreciate having access to details that can be cumbersome to program in other software. Almost all users will find the graphics interface a welcome contrast to those available in the most common statistical software packages. After running procedures that produce output that can be graphed (e.g., ordinations, clustering dendrograms, species area curves), users enter a graphics interface that is like a program within a program, in that it is not possible to access the data files or main program menus while graphing output. Once there, however, drop-down menus or toolbars provide many options for viewing output optimally, and extensive user preference options can be used to tailor graphics precisely for the desired medium. Nice features include the ability to define and save preference sets that control features such as plot type, fonts, colors, and line and symbol types: users can apply one set (e.g., in black and white) for manuscripts and another (e.g., in color) for presentations. Upgrading from version 5 to version 6 is probably worth it. A notable improvement is the ability to now import/export spreadsheet data (as *.xls Excel files) without including the special formatting information that PC-ORD requires at the top of the datafile. In addition, several new analysis tools have been added, including the metric multidimensional scaling procedure principal coordinates analysis (PCoA), the linear equivalent to CCA in redundancy analysis (RDA), and a permutation test based on aggregated F statistics called SumF. Version 6 also includes additional graphing options, such as creating boxplots and drawing convex hulls, which connect the dots around groups in ordination space (Fig. 1). Many of the tools within PC-ORD are available in other software. For instance, other commercial products have comparable versions of PCA (CANOCO, PRIMER, SAS, S+, etc.) and clustering (PRIMER, SAS, SPSS, S+, etc.), and these and other tools are also available in freeware (e.g., the VEGAN packing in R). While a few users may prefer options only available in other software packages (e.g., including covariates in CCA within CANOCO, applying distanced-based MANOVA to some unbalanced designs in PRIMER, or exploring species area relationships in depth in EstimateS), the vast majority of users will find the tools available in PC-ORD to be equivalent to, or better than, versions available elsewhere. For instance, PC-ORD provides users the greatest flexibility available for running NMS and provides the most detailed output, necessary for the proper interpretation of this complex tool. The single greatest argument for PC-ORD, however, is simply that most of the multivariate analysis tools needed to explore species data sets are present in the same software package, eliminating the time-consuming need to learn multiple programs, convert data sets to different formats for software entry, and search around for the best tool for making publication-quality graphics. As such, PC-ORD is competitively priced for student and single-user licenses; site licenses, however, 300 Bulletin of the Ecological Society of America

4 can be quite expensive, as the fee increases linearly with the number of potential simultaneous users. Although available to order online, the software is not downloadable, which netbook users (and procrastinators) may find inconvenient. Mac-only users are also out of luck, as PC-ORD only runs on the Microsoft Windows platform (Windows 98 and later). The software does not come with a manual, but the extensive help menu contains a wealth of descriptions, explanations, equations, and citations. In addition, a new companion book is now available (Peck 2010) that guides beginning users through the analysis process using the tools available in version 6. Conclusion Overall, PC-ORD is the best currently available one-stop-shopping option for the multivariate analysis of species community data and is helpful for a variety of other data types as well. I recommend it as a starting place for beginning users of these analysis tools; most users will find it satisfies nearly all of their multivariate analysis needs. Literature cited McCune, B., and J. Grace Analysis of ecological communities. MjM Software Design, Gleneden Beach, Oregon, USA. Peck, J. E Multivariate analysis for community ecologists: step-by-step using PC-ORD. MjM Software Design, Gleneden Beach, Oregon, USA. Fig. 1. PC-ORD can be used to draw convex hulls connecting sample units to the same group. Here the four groups with three reps each represent four different periods of time. Emerging Technologies July

5 302 Bulletin of the Ecological Society of America