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Application of Ecogene Model to Study Genetic Diversity in Species

January 27, 2019 0 Comment

Ecogene Model:

An example of the application of the Ecogene model to management Degen. Roubik and Loveless (2002) used field data on tree spatial distribution in the intensive study plots in the Tapajos National Forest to examine the effects of logging and of fragmentation on genetic diversity in Jacaranda copaiba one of Dendrogene’s seven focal species.

They created populations with different densities and spatial patterns, representing: a control, using inventory data for one 400 ha block; logging (removal) of all individuals greater than 31 cm DBH; fragmentation to give the same residual population size (90 flowering trees greater than 20 cm DBH) as in the logging scenario, reducing the area of the forest habitat to 140 ha (35 percent of its original size).

Flowering phenology, pollinator movement patterns and seed dispersal distributions were identical in all three simulations.

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The seeds produced from one episode of mating in these populations were then analysed for 13 standard genetic measures, including effective population sizes, fraction of selling. Mean pollen dispersal distance, observed and expected heterozygosities and allelic diversity. Each simulation was run 30 times, and means and standard deviations (sd) were used to compare the results between the control population and the two different treatment scenarios.

In both treatments. 11 of 13 indices of genetic diversity were significantly different from those of the control population. Effective population size was most severely affected by alterations in tree spatial distribution. It was estimated at 161.81 (sd = 7.34) individuals in the control population, 59.22 (sd = 4.16) individuals in the logged population and 44.80 (sd = 4.84) individuals in the fragmented population.

Self-pollination increased significantly in the treatment scenarios, and mean pollen dispersal distance also increased. Expected heterozygosis (also known as gene diversity) was significantly reduced in the treatment simulations. The results also suggest that some genetic variables are more sensitive than others in demonstrating genetic differences under different management regimes.

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