The BCCVL currently provides 17 different algorithms across 4 different categories to run your species distribution model:

Profile models

These models only use occurrence data, and are based on the characterization of the environmental conditions of locations associated with species presence.

Statistical regression models

These models produce estimates of the effect of different environmental variables on the distribution of a species. These models use all the data available to estimate the parameters of the environmental variables, and construct a function that best describes the effect of these predictors on species occurrence. The suitability of a particular model is often defined by specific model assumptions.

Machine learning models

These models typically use one part of the dataset to ‘learn’ and describe the dataset (training) and the other part to to assess the accuracy of the model.

Geographical models

These models only use the geographic location of known occurrences of a species to predict the likelihood of presence in other locations, and do not rely on the values of environmental variables.