Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1080/13658816.2012.698015 | International Journal of Geographical Information Science |
Keywords | Field | DocType |
model building,classification,data model,cost effectiveness,species distribution,species distribution modelling,association analysis,conceptual model,variable selection,gradient analysis | Data mining,Environmental niche modelling,Conceptual model,Feature selection,Computer science,Artificial intelligence,Environmental data,Data model,Scatter plot,Statistical hypothesis testing,Machine learning,Ecosystem model | Journal |
Volume | Issue | ISSN |
26 | 11 | 1365-8816 |
Citations | PageRank | References |
11 | 2.48 | 5 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kristen Jennifer Williams | 1 | 16 | 4.15 |
| Lee Belbin | 2 | 12 | 3.55 |
| Michael Austin | 3 | 20 | 4.51 |
| Janet L. Stein | 4 | 11 | 2.48 |
| Simon Ferrier | 5 | 11 | 2.48 |