Using self-organizing maps to investigate spatial patterns of non-native species

Our ability to demonstrate statistical patterns of invasion by non-native species will determine the success of future management projects. We investigated the suitability of self-organizing maps (SOM, neural network) for patterning habitat invasion by exotic fish species at the regional scale (Southwest France), using a binary dataset of species occurrences. The SOM visualization can be used as an analytical tool to bring out relationships between sample locations and biological variables, but in addition the weight of each species in the output of the SOM can be interpreted as its occurrence probability in various geographic areas. After training the SOM with fish presence/absence data, the k-means algorithm helped to derive three major clusters of sites (headwater, montane, and plain areas). Each cluster was divided into two subsets of sites according to non-native fish, because assemblage compositions delineated different geological areas: Pyrenees Mountains, Massif Central Mountains, and alluvial plain. Occurrence probabilities of species within our study stream system were roughly influenced by river typology, with a higher occurrence probability for most species (i.e. a greater risk) in downstream sections. Conversely, headwater streams at the highest elevations in the study area showed the lowest risk of invasion. Efficient analytical tools such as SOM may thus help to yield explicit schemes that could influence the measures to be taken in the latter phase of conservation plans.