Alleles In Space (AIS): Computer Software for the Joint Analysis of Interindividual Spatial and Genetic Information

Genetic analyses of natural populations have historically relied on statistical procedures based on the concept that distinct ‘‘populations’’ of a species exist across a landscape. Invariably, commonly used analyses reduce to approaches that treat collections of individuals (‘‘populations’’) as independent/causative variables and allele frequencies as dependent/ response variables. Examples of these procedures include Wright’s FST and its variants (Excoffier et al. 1992; Nei 1973; Slatkin 1995; Weir and Cockerham 1984), contingency table procedures (Raymond and Rousset 1995; Roff and Bentzen 1989), and measures of genetic distances among populations (e.g., Nei 1972, 1978; Reynolds et al. 1983). These analyses qualitatively or explicitly test null hypotheses of homogeneity of allele frequencies between or among populations. Although almost universally applied, the analyses mentioned above are not necessarily appropriate in many situations. For example, highly mobile organisms such as large mammals or birds can occupy continuous habitats over large spatial scales. Plants may also occupy large continuous habitats, as can species inhabiting marine or aquatic systems. In these cases, objectively designating groups of individuals at population levels for use in genetic analyses may prove difficult, if not impossible. Clearly, an important consideration in these situations is the spatial extent of the ‘‘populations’’ that are chosen for analyses. If groups of organisms are defined over larger than appropriate spatial scales, resulting measures of genetic differentiation may actually provide ambiguous or misleading results (Miller et al. 2002). To address many of these issues, I have developed a new software package entitled ‘‘Alleles In Space’’ (AIS). This program, rather than implementing methodology that relies on arbitrary groupings of individuals, instead has the ability to perform joint analyses of interindividual spatial and genetic information that can be applied at virtually any spatial scale. These approaches specifically lend themselves to analyses of genetic data when one or a few individuals are sampled from large numbers of collection sites. Moreover, the program is designed to handle a wide variety of genetic data types, including codominant marker systems, dominant marker systems, and DNA sequences. Thus AIS will likely be useful for elucidation of patterns in diverse study types ranging from local analyses of genetic structure, phylogeographical studies, and studies encompassing aspects of the emerging field of landscape genetics (Manel et al. 2003).