Introduction

Bioclim is a so-called envelope-style method, which uses only occurrence data to define a multi-dimensional environmental space in which a species can occur. This environmental space is constructed as a bounding box around the minimum and maximum values of the predictor variables for all occurrences, resulting in a multi-dimensional rectilinear envelope. To avoid the overpredictive effect of outliers, the resulting envelope can be reduced at specified percentiles or standard deviations.


The Bioclim algorithm gives equal weight to all predictor variables, and compares the values of predictor variables at an unknown location to a percentile distribution of the values at known occurrence locations. The closer to the 50th percentile (the median), the more suitable the location is for a species to occur.


In the ‘dismo’ implementation that the BCCVL uses, predicted values larger than 0.5 are subtracted from 1 to transform upper tail values to the lower tail. Then the minimum percentile score across all predictor variables is used to obtain the overall score for an unknown location. By using the minimum across all variables, the model predicts that a species will only occur at sites suitable to the most limiting factor. The final score is subtracted from 1 and then multiplied by two so that the results are between 0 and 1. The developers of the ‘dismo’ package have implemented this scaling to make the results more similar to other species distribution modelling methods and easier to interpret. Values of 1 will rarely be observed, as it would require a location that has the optimal (median) value for all predictor variables. Values of 0 are very common as it is assigned to all cells that have at least one predictor variable with a value outside the percentile distribution.


Bioclim was the first species distribution modelling package that linked spatially explicit species occurrence data with maps of environmental predictors. It was developed in Australia under leadership of Henry Nix. It is still widely used, because it is easy to understand, but it is generally acknowledged that it does not perform as good as some other modelling methods.


Advantages

  • Simple and intuitive
  • Presence only model, no absence data needed
  • Provides ranking of environmental predictor variables
  • Useful in teaching species distribution modelling

Limitations

  • Susceptible to overprediction
  • Does not account for interactions between predictors
  • Cannot use categorical variables
  • Does not make quantitative predictions or provide confidence levels

Assumptions

N/A


Requires absence data

No


Configuration options

BCCVL uses the ‘dismo’ package. There are no configuration options for this algorithm.


References

  • Booth TH, Nix HA, Busby JR, Hutchinson MF (2014) BIOCLIM: the first species distribution modelling package, its early applications and relevance to most current MAXENT studies. Diversity and Distributions, 20(1), 1-9.
  • Hijmans RJ, Elith J (2015) Species distribution modeling with R.