Molecular Ecology Notes (2004) 4, 143 – 145 doi: 10.1046/j.1471-8286.2003.00581.x © 2004 Blackwell Publishing Ltd Blackwell Science, Ltd PROGRAM NOTE Isolation by distance, based on microsatellite data, tested with spatial autocorrelation (SPAIDA) and assignment test (SPASSIGN) SNÆBJÖRN PÁLSSON Institute of Biology, University of Iceland, Sturlugata 7, 101 Reykjavik, Iceland Abstract SPASSIGN and SPAIDA are two small programs useful to detect isolate by distance of microsat- ellite loci. The programs are written in C and are available for Linux and Windows system at http://www.hi.is/∼snaebj/programs.html. SPAIDA calculates two estimates of spatial auto- correlation, Moran’s I and Geary’s c, first by assuming the infinite allele model, and second by assuming a stepwise mutational model. SPASSIGN calculates the assignment probabilities of an individuals genotype to the location where it was sampled and compares probabilities of assignment to other locations. Genetic distances among regions based on the overall dif- ferences in likelihoods are calculated. Keywords: assignment, distance, genetics, population, spatial autocorrelation Received 30 September 2003; revision received 30 October 2003; accepted 20 November 2003 Isolation by distance (Wright 1943) results from less mixing among individuals, or pairs of populations, which are situated further apart than among those which are separated by shorter distances. This leads to a positive correlation among genetic and geographical distances, either within a continuously distributed species (e.g. Sokal & Jacquez 1991) or among populations with a discrete structure (Kimura & Weiss 1964). Microsatellites have in recent years become a popular marker of choice to address population genetics and demo- graphic questions. This interest has led further to the development of various methods for the analysis of such data. As microsatellites may carry information on the past mutational events, characterized by different number of repeats this has led to development of statistics incorporat- ing this information (stepwise mutational model, SMM) such as R ST (Slatkin 1995). How well such methods, com- pared to the ones based on the infinite allele model (IAM), will reflect the true demographic events has been a subject of several studies (see Hardy et al . 2003). Statistics based on SMM may be of choice when, for example, heterozygosity is very high, blurring the signal of subdivision (Hedrick 1999). Bertorelle & Barbujani 1995) developed the program aida to detect isolation by distance, based on correlation in allele frequencies among geographical distances. In spaida I extend this concept by adding the information obtained by differences in allelic sizes. Another class of analysis, assignment tests (e.g. Pritchard et al . 2000), which have gained increased attention lately, is based on probabilities of a certain genotype being sampled at different locations, given the population frequencies of alleles comprising the genotype. The program spassign tests how the assignment values depend on geographical distance. The software binaries for Unix, Linux and Windows can be found at http://www.hi.is/ ∼ snaebj/programs.html. Source code is available on request. Previous use of these programs can be found in Palsson (2000) and Goroposhnaya et al . (2001). The input file is similar to the one used for genepop (see Table 1). However, there should not be any commas and a space should be inserted between the alleles carried by an individual at a single locus. Missing values are noted with 0 allele size. Geographical coordinates are given in the last two columns, either as selected coordinates on a map or as latitudes and longitudes (as degree. min). The spaida program calculates two estimates of spatial autocorrelation, extended for microsatellite data, Moran’s I and Geary’s c (see Bertorelle & Barbujani 1995), first in the Correspondence: Snæbjörn Pálsson. Fax: + 354 525 4069; E-mail: snaebj@hi.is