This section discusses the different modelling algorithms, they fall into two different categories; namely inductive and deductive. Inductive modelling depends on occurrence data, generated from sampling species in the field to inform the model. Deductive modelling takes place when element-environment relationships that have been discovered through research are used to inform the model.
Before running the modelling program: Before you run your chosen modelling program, consider how you will test the results of the model.
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Please note that this list is in no way an exhaustive list of all modeling packages, nor does it attempt to promote or elevate the status of those packages listed below. |
http://ecobas.org/www-server/mod-info/index.html
Download CLIMEX 3.0.2 here .
Presence data only
Description
In this technique two different methods are considered. Firstly the convex hull method creates the smallest possible polygon to enclose all of the sample points. This method will represent irregular shapes, however outliers could cause the polygon to be extremely large. Furthermore clusters and sample density are not taken into account .
The second method is kernel mapping, this method creates a continuous density surface where the density of sample points are represented by 3-dimensional peaks whose height is determined by the number of sample points in that locality. This method represents irregular shapes and allows the prediction of occurrence over the sample area .
Download scripts for ESRI software here .
Presence data only
Description
The user sets maximum and minimum values for each environmental predictor where the species is known to occur, thereby creating a rectangular environmental envelope. The software then uses these values to predict in what areas the species can be found. The Mahalonobis technique can be used to create an oblique ellipse environmental envelope based on the environmental variables supplied .
Download BIOCLIMav (v1.2) here.
Download Mahalonobis Distances extension for ArcView here.
Presence and absence data
Description
This technique allows the user to formulate a relationship between species occurrence and the environmental aspects. The function consists of a response(dependant) variable, predictor (independent) variables and a link (relationship) function, which describes the relationship between the variables. Generalized additive models (GAMs) bring additional smoothing functions into the relationship function, but require large sample sizes.
Most standard statistical packages offer logistic regression.
Download BIOMOD for R here .
Download the StatMod Zone extension to ArcView for SAS here.
Download ArcView-SDM for ordinary regression here .
Presence and absence data
Description
This technique creates a classification or regression tree where occurrence data is split into mostly present and mostly absent groups depending on the environmental value given. This process happens recursively until the data cannot be split any further or until the stop value is reached.Most advanced statistical packages offer classification techniques.
Download the StatMod Zone extension to ArcView 3X for SAS here .
Description
This technique uses the maximum value of entropy to estimate the most uniform distribution of the occurrence data over the study area. This uniform distribution is constrained by the environmental values or proportion of occurrence points in a category. The resulting predicted distribution is regularized to avoid over fitting. The output values are represented as percentages with 100% being most suitable and 0% being least suitable.
Download MaxEnt here
Description
BioMapper is a kit of GIS- and statistical tools designed to build habitat suitability (HS) models and maps for any kind of animal or plant. It is centred on the Ecological Niche Factor Analysis (ENFA) that allows it to compute habitat suitablility models without the need of absence data.
Download BioMapper here .
Presence and absence data
Description
Canonical Correspondence Analysis (CCA) is a widely used method for direct gradient analysis. CCA assumes that species have a distribution with one mode. This means that the species has one optimal environmental condition. If any aspect of the environment is greater or lesser than this optimum, the species will perform more poorly (i.e. it will have a lesser abundance).
Some statistical packages offer Ordination (DA – Splus)
Presence and absence data
Description
This technique requires a target variable and a set of predictor variables. Irrelevant predictors are removed and interactions between predictors are discovered. The model tests itself to avoid over fitting and handles missing variables by creating a missing value indicator.
Download MARS here .