RAD_AbundanceRichness_data

Data used for abundance and richness RAD models. Contains site identifier, site level abundance and richness, geographic coordinates, area of survey, and environmental predictors

Blois_etal_2013_PNAS_Data

Please refer to the 'README' fil

Which environmental variables should I use in my biodiversity model?

<div><p>Appropriate selection of environmental variables is critical to the performance of biodiversity models, but has received less attention than the choice of modelling method. Online aggregators of biological and environmental data, such as the Global Biodiversity Information Facility and the Atlas of Living Australia, necessitate a rational approach to variable selection. We outline a set of general principles for systematically identifying, compiling, evaluating and selecting environmental variables for a biodiversity model. Our approach aims to maximise the information obtained from the analysis of biological records linked to a potentially large suite of spatial environmental variables. We demonstrate the utility of this structured framework through case studies with Australian vascular plants: regional modelling of a species distribution, continent-wide modelling of species compositional turnover and environmental classification. The approach is informed by three components of a biodiversity model: (1) an ecological framework or conceptual model, (2) a data model concerning availability, resolution and variable selection and (3) a method for analysing data. We expand the data model in structuring the problem of choosing environmental variables. The case studies demonstrate a structured approach for the: (1) cost-effective compilation of variables in the context of an explicit ecological framework for the study, attribute accuracy and resolution; (2) evaluation of non-linear relationships between variables using knowledge of their derivation, scatter plots and dissimilarity matrices; (3) selection and grouping of variables based on hypotheses of relative ecological importance and perceived predictor effectiveness; (4) systematic testing of variables as predictors through the process of model building and refinement and (5) model critique, inference and synthesis using direct gradient analysis to evaluate the shape of response curves in the context of ecological theory by presenting predictions in both geographic and environmental space.</p> </div

Appendix D. Response functions for models of community mean trait values.

Response functions for models of community mean trait values

Appendix B. Description of environmental variables applied in modeling traits and composition.

Description of environmental variables applied in modeling traits and composition

Appendix E. The Generalized Dissimilarity Model of compositional turnover.

The Generalized Dissimilarity Model of compositional turnover

Appendix C. Comparison of mean trait values using recorded and interpolated traits.

Comparison of mean trait values using recorded and interpolated traits

Appendix I. Two maps depicting the geographic variation in species composition as represented by the three-dimensional PCA analysis of the generalized dissimilarity modeling (GDM) modeling of 4,268 vascular plant species in the case study region.

Two maps depicting the geographic variation in species composition as represented by the three-dimensional PCA analysis of the generalized dissimilarity modeling (GDM) modeling of 4,268 vascular plant species in the case study region

Appendix E. A table showing for each greenspot index percentile threshold, the number of grid cells from where the vascular plant survey plots are located, the number of plots, and the sampling proportion.

A table showing for each greenspot index percentile threshold, the number of grid cells from where the vascular plant survey plots are located, the number of plots, and the sampling proportion

Supplement 1. KMZ versions of the maps presented in Figs. 2 and 4, viewable in Google Earth.

<h2>File List</h2><p> <a href="Eco_Greenspots.kmz">Eco_Greenspots.kmz</a> (md5: eb203e80d3bbcb6fc778cd7e3ed491b6) <a href="Fire_Footprint.kmz">Fire_Footprint.kmz</a> (md5: f3ebcedec63e6d23b2a15df5f0a374f1) </p><h2>Description</h2><p>Eco_Greenspots.kmz - Ecosystem greenspot index (percentiles) for the case study region derived from fPAR time series data set. Analysis is restricted to areas with native vegetation cover. This is the same map as presented in Fig. 2 but provided here in KMZ format viewable in Google Earth.</p> <p>Fire_Footprint.kmz - Fire affected areas in the case study region revealed by the image ratio analyses of the fPAR time-series. This is the same map given in Fig. 4 but provided here in KMZ format viewable in Google Earth.</p