In the problem of reconstructing full sib pedigrees from DNA marker data, three existing algorithms and one new algorithm are compared in terms of accuracy, efficiency and robustness using real and simulated data sets. An algorithm based on the exclusion principle and another based on a maximization of the Simpson index were very accurate at reconstructing data sets comprising a few large families but had problems with data sets with limited family structure, while a Markov Chain Monte Carlo (MCMC) algorithm based on the maximization of a partition score had the opposite behaviour. An MCMC algorithm based on maximizing the full joint likelihood performed best in small data sets comprising several medium-sized families but did not work well under most other conditions. It appears that the likelihood surface may be rough and presents challenges for the MCMC algorithm to find the global maximum. This likelihood algorithm also exhibited problems in reconstructing large family groups, due possibly to limits in computational precision. The accuracy of each algorithm improved with an increasing amount of information in the data set, and was very high with eight loci with eight alleles each. All four algorithms were quite robust to deviation from an idealized uniform allelic distribution, to departures from idealized Mendelian inheritance in simulated data sets and to the presence of null alleles. In contrast, none of the algorithms were very robust to the probable presence of error/mutation in the data. Depending upon the type of mutation or errors and the algorithm used, between 70 and 98% of the affected individuals were classified improperly on average.

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Microsatellite genetic markers are becoming increasing important tools in the investigation of alternate reproductive strategies in wild plants and animals, and in the implementation of optimal breeding programs for endangered species, and managed cultured populations. Overall, little attention is paid to the frequency and impact of scoring errors and mutations on the resolution and accuracy of such analyses. Here, parentage of 792 Atlantic salmon (Salmo salar) reared communally were determined using di-and tetranucleotide microsatellites. Over 99 . 5% of the offspring could be unambiguously matched to one set of parents in the original 12 (1´1) experimental cross (each of 12 males uniquely crossed to one of 12 females) and in a simulated 36 (1´1) cross (involving additional parents), and over 80% in a 12´12 cross (all 12 males crossed to all 12 females). Mutations were rare (»3 . 4´10 ±4 ), though scoring errors were relatively common (2±3% per allele scored), with the rate of error varying among loci. Approximately 90% of scoring errors (or mutations) are expected to be detected in this analysis, and of those that are not, fewer than 0 . 5% should lead to a false or incorrect determinations of parentage. Based on several indices, we expect that greater than 99 . 7% of offspring assayed were matched to their true parents.

Abstract. A strong and significant positive correlation was observed between condition factor and total lipid content in immature Atlantic salmon, Salmo salar L., parr (0+) sampled at the same time. Condition factor can thus be used as a convenient non‐lethal indicator of energy reserve status among immature salmonids.