NMπ 2.0: software update to minimize the risk of false positives among determinants of reproductive success.

In plant populations, parentage analysis helps understand factors shaping individual reproductive success. However, estimating reproductive success determinants based on parentage counts requires decoupling the effects of individual fecundity and propagule dispersal. The neighborhood model implemented in the NMπ software provides a standard solution for this problem based on the fixed-effects regression-like approach. Nonetheless, it has been recently shown that the method is prone to false discoveries when important fecundity determinants are omitted. To account for the unexplained variance in fecundity, the Bayesian approach was developed based on the new model (the hierarchical neighborhood model; HNM). Here, I present the NMπ software update that allows the HNM approach to be used in the framework of a friendly interface. More importantly, the HNM approach is now made available for both dispersed (seedlings) and non-dispersed (seeds with known mothers) progeny data. The Bayesian approach, among others, selects significant fecundity determinants, estimates the proportion of variance in reproductive potential explained by selected determinants (R 2 ), and provides individual female and male fecundity values. Although the software was designed to handle microsatellite marker data, a solution is proposed for large sets of single nucleotide polymorphisms. The program can be run on Windows (using either a terminal or a graphical interface) as well as (using a terminal) on Linux, or macOS platforms. In any case, NMπ can utilize multi-core processors to speed up the analysis. The updated package containing the code, the executable file, the user manual, and example data is available at https://www.ukw.edu.pl/pracownicy/plik/igor_chybicki/3694/.