BioGeoSim simulates the stochastic origin and spread of species geographic ranges
in a heterogeneous landscape (Rahbek et al. 2007). The landscape is represented as
a gridded domain map, with the rows and columns representing the geographic coordinates
of the domain. Entries of 0 indicate grid cells that are not part of the domain (e.g.,
ocean or water grid cells for a terrestrial domain). Positive real-number entries
represent grid cell values of environmental variables (e.g., temperature or NPP),
measured for each grid cell in the domain. Each species geographic range is mapped
in a separate gridded file, with 1s and 0s corresponding to presence or absence of
a species at a grid cell in the domain.
Citing BioGeoSim: Gotelli, N.J., G.L. Entsminger, C. Rahbek, & G. R. Graves. 2013.
BioGeooSim: Biogeography software for Ecologists. Acquired Intelligence Inc. & Kesey-Bear.
Jericho, VT 05465. http://garyentsminger.com/biogecosim.htm.
BioGeoSim uses these data to simulate the stochastic placement of each species within
the domain. In simple models, all grid cells are equiprobable. In more complex models,
the probability of occurrence in a grid cell is proportional to the environmental
variable in that grid cell. Geographic ranges may spread through strict range cohesion,
complete scatter of occurrences, or a Poisson dispersal model, in which each subsequent
cell is colonized by dispersal from an occupied cell.
BioGeoSim calculates the observed species richness in each cell, once all species
have been distributed in the model. After a given number of stochastic replications,
BioGeoSim calculates the average species richness per grid cell. These expected values
are then used in an additional set of stochastic iterations to calculate a goodness-of-fit
deviation and compare it to the deviations from the observed data (i.e., a classic
null model test).
Finally, BioGeoSim calculates a simple linear regression statistic of observed versus
predicted species richness for all grid cells. If the model fits the data perfectly,
the fitted regression line would have a slope of 1.0 and an r2 value of 1.0.