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First Time At Niche Modeling?

This is a blog on the power of numeracy. My first book — Niche Modeling — is now in print.

The first six chapters are tutorial topics in R programming and theoretical topics in niche modeling: functions, data, spatial, topology, environmental data collections, and examples. The last six chapters are about using niche modeling to detect errors: bias, autocorrelation, non-linearity, long term persistence, circularity and fraud - useful information for any biological modeler.

July 31, 2006

Examples of Ecological Niche Models from GARP

Filed under: Uncategorized, Ecological Niche Models — admin @ 1:10 am

GARP is an acronym for Genetic Algorithm for Rule Set Production. GARP is an algorithm primarily designed for predicting the potential distribution of biological entities from raster based environmental and biological data. This post describes examples of the interpretation of different sets of rules developed by GARP.

Abundance of Greater Glider

The Greater Glider (Petauroides volans) is a species of gliding possum found extensively in old-growth forest regions of South Eastern Australia. It nests in hollows created by the broken limbs of eucalyptus trees, and feeds on eucalyptus leaves of a variety of species. The species is of interest for conservation because their presence is an indicator of the presence of a suite of arboreal marsupial species.

The Waratah Creek data set is a mapping of an area 1600 ha in extent, in a 20×20 grid, located at Waratah Ck. It contains eight data layers. The first is the density of Greater Gliders at four levels, while the remaining variables are based on forest inventory variables known to be relevant to possum density. The data set and comparison of the performance of a number of other Artificial Intelligence methods is described in Stockwell et.al. (1990). The variables are shown, in row-column order in Figure 1.

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Figure 1. The variables in the Waratah Creek data set in row-columns order are GG Density, Dev development, (road corridors, pine plantations), StC stream corridor (proximity), SdC stand condition (merchantable timber) , StQ site quality (productivity), FlN floristic nutrients (based on vegetation types), Slp slope, and Ero erosion potential. Dark squares are low values, and lighter squares are higher values.

The data set is a useful small, test data set for comparing predictive algorithms, and is included in the distribution of the GARP program. It is particularly useful for testing predictive algorithms because there are complex combinations of ecological relationships within it. (more…)

July 28, 2006

Bayesian Networks

Filed under: Statistics, Ecological Niche Models, Climate Change — admin @ 4:25 am

The problem with many models, from climate systems to multiple species and ecosystems processes, to consumer purchasing behaviour is that we often have very little understanding of the actual relationships between the variables in the system.

From our limited vantage point as observers of and not experimenters on systems we only see many weakly correlated variables, often drawn from incomplete samples and widely ranging sources.

We need an automated method of developing structure from the given data that explicitly quantifies our belief that a model that captures the behaviour of the system. Bayesian nets, Beliefs nets or graphical models begin to do this, by assigning a level of belief to each of the possible values of parameters. That is, while a conventional simulation of climate say has at most one value at each simulation, a Bayesean network would represent the distribution of possible values for each parameter at each point in time.

Belief net construction can involve a manual process of knowledge engineering. Examples of systems for graphically structuring models are the Ptolemy project for modeling, simulation, and design of concurrent, real-time, embedded systems, or the freely available ’scientific workflow’ tool called Kepler where the flow of data from one analytical step to another is captured in a formal workflow language. Recent advances in machine learning and data mining have also yielded efficient methods for creating belief nets directly from data (Cooper and Herskovits, 1992). (more…)