EVALUATION OF DIVERSITY AND GENETIC STRUCTURE AS STRATEGIES FOR CONSERVATION OF NATURAL POPULATIONS OF Dalbergia nigra (Vell.) Allemão ex Benth.

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Abstract

ABSTRACT: The evaluation of diversity and genetic structure allows us to verify with precision the effect of evolutionary and anthropic processes on species. The objective of this research was to evaluate the divergence and the genetic structure of two natural populations of Dalbergia nigra, using molecular markers Inter Simple Sequence Repeats (ISSR). Leaf samples were collected from two populations, located in the National Forest of Pacotuba and the Private Natural Heritage Reserve of Cafundó. Eight ISSR primers were used, which resulted in 97 bands, with 68.04% of polymorphism. Based on the joint data, the values of 0.33 for the Nei index (H*) and 0.50 for the Shannon index (I*) indicated moderate to high genetic diversity, being influenced by the presence of genetically dissimilar individuals in the National Forest of Pacotuba. Most of the genetic divergence was intrapopulational (85.96%), with moderate differentiation between populations (ΦST = 0.1404). The estimated historical gene flow between the fragments was low (Nm = 3.21) when compared with results from other tree species, and the genetic structuring analysis separated the populations into two groups, corresponding to the two populations evaluated. The results indicate a small genetic share among populations, however, populations are becoming structured. The satisfactory levels of genetic diversity benefit the use of the trees as matrixes for programs of restoration and recovery of degraded areas, connectivity of landscapes, and sustainable use of forest resources.

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CLUMPP: a cluster matching and permutation program for dealing with label switching and multimodality in analysis of population structure.

M Jakobsson, N. Rosenberg (2007)

Clustering of individuals into populations on the basis of multilocus genotypes is informative in a variety of settings. In population-genetic clustering algorithms, such as BAPS, STRUCTURE and TESS, individual multilocus genotypes are partitioned over a set of clusters, often using unsupervised approaches that involve stochastic simulation. As a result, replicate cluster analyses of the same data may produce several distinct solutions for estimated cluster membership coefficients, even though the same initial conditions were used. Major differences among clustering solutions have two main sources: (1) 'label switching' of clusters across replicates, caused by the arbitrary way in which clusters in an unsupervised analysis are labeled, and (2) 'genuine multimodality,' truly distinct solutions across replicates. To facilitate the interpretation of population-genetic clustering results, we describe three algorithms for aligning multiple replicate analyses of the same data set. We have implemented these algorithms in the computer program CLUMPP (CLUster Matching and Permutation Program). We illustrate the use of CLUMPP by aligning the cluster membership coefficients from 100 replicate cluster analyses of 600 chickens from 20 different breeds. CLUMPP is freely available at http://rosenberglab.bioinformatics.med.umich.edu/clumpp.html.