Introduction
Bioclim is a socalled envelopestyle method, which uses only occurrence data to define a multidimensional 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 environmental variables for all occurrences, resulting in a multidimensional rectilinear envelope. To avoid the overpredictive effect of outliers, the resulting envelope can be reduced at specified percentiles or standard deviations.
To predict the probability of species occurrences in any given location, Bioclim compares the values of the environmental variables at that location to the percentile distribution of the values from known locations. The 50th percentile refers to the median, which divides the data exactly in half. The closer the value of the environmental variable at the unknown location is to the 50th percentile, the more suitable the location is for a species to occur there, and thus the higher the probability of occurrence. Thus the probability is 1 at the 50th percentile, and because the tails of the distribution are not distinguished, the 10th percentile is treated equal to the 90th percentile and both have the same probability value. The Bioclim model combines the scores for each environmental variable into an overall probability of occurrence for each location with equal weights for all environmental variables.
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 environmental 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' packages 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 environmental variables. Values of 0 are very common as it is assigned to all cells that have at least one environmental 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 variables. It was developed in Australia under leadership of Henry Nix. The algorithm 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
Bioclim was mostly developed to model species distributions in relation to climatic variables, and thus assumes that species occurrence is influenced by climate at the scale of climate variables, and that these variables are normally distributed.
Requires absence data
No.
Configuration options
BCCVL uses the ‘dismo’ package. There are no configuration options for this algorithm.
References
 Araujo MB, Peterson AT (2012) Uses and misuses of bioclimatic envelope modeling. Ecology 93(7): 15271539.

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): 19.
 Hijmans RJ, Elith J (2015) Species distribution modeling with R.