Overview of the functions in the gdm package

Description

Generalized Dissimilarity Modeling is a statistical technique for modelling spatial variation in biodiversity between pairs of geographical locations. The gdm package currently provides basic functions to fit, summarize, and plot Generalized Dissimilarity Models and to make predictions (in both space and time) and map biological patterns by transforming environmental predictor variables. Future updates will incorporate support for genomic data.

Details

The functions in the gdm package provide the tools necessary for fitting GDMs, including functions to prepare biodiversity and environmental data. Major functionality includes:

• Formatting various types of biodiversity and environmental data to gdm's site-pair format used in model fitting

• Fitting GDMs using geographic and environmental distances between sites

• Plotting fitted functions & extracting I-spline values

• Predicting pairwise dissimiliarites between sites or times and transforming envirnmental predictors to biological importance and mapping these patterns.

To see the preferable citation of the package, type citation("gdm").

I. Formatting input data

GDM fits biological distances to pairwise site geographical and environmental distances. Most users will need to first format their data to gdm's site-pair table format:

II. Model fitting and summary

III. Model prediction and transformation of environmental data

IV. Plotting model output and fitted functions

Author(s)

The gdm development team is Glenn Manion, Matt Lisk, Simon Ferrier, Diego Nieto-Lugilde, and Matt Fitzpatrick. Where others have contributed to individual functions, credits are provided in function help pages.

The maintainers of the R version of gdm are Matt Fitzpatrick <mfitzpatrick@al.umces.edu> and Matt Lisk <mlisk@al.umces.edu>.

Results