Multivariate Analysis of Ecological Data. MjM Software, Gleneden Beach, Oregon, U.S.A. Cover: The essence of multivariate analysis is the. Please download the latest?xes from our website pcord.com>, and determine whether the problem still exists in the latest version. Publication-quality graphics. 2D and 3D graphs. 3D graph animation. Analysis advisor wizard. Very large data sets. PC-ORD is BIO-LOGICAL. Over 5,000 articles in biology journals contain graphs & multivariate analyses produced by PC-ORD.

Publications with HyperNiche HyperNiche is user-friendly software for nonparametric regression. Our primary purpose is to provide a flexible tool for multiplicative habitat modeling – habitat models where the predictors are combined multiplicatively rather than additively. This is a flexible and powerful approach to habitat modeling. For in-depth explanation see • • • • • Simple, ecologically reasonable response surfaces pose difficult challenges to traditional habitat modeling tools.

HyperNiche provides tools that easily find complex response surfaces, such as this hump that combines sigmoid and Gaussian curves. HyperNiche has many potential uses: • Build habitat models for species presence-absence (estimate likelihood of occurrence in relation to multiple habitat parameters or other predictors). • Build habitat models for species abundance (estimate abundance in relation to predictors). • Estimate physiological response surfaces in relation to 1, 2, or more environmental parameters. Open your physiological variables as the response matrix and your environmental variables as the predictor matrix. • Build empirical models of species diversity in relation to multiple predictors.

Place your diversity measures (calculated with PC-ORD or a spreadsheet) in the response matrix. • Relate community ordination scores to multiple environmental variables.

Save your ordination scores in a spreadsheet, then open this as the response matrix and your environmental variables as the predictors. • Optimize sample stratification – choose combinations of variable to maximize differences in a response variable among strata. Place potential stratifying variables in the predictor matrix, then conduct a free search to find the best combination of variables for predicting the response. • Build multiple regression models for any nonlinear response to multiple interacting factors.

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