Long-Term Effects of Deforestation on Genetic Diversity: A Comparison of Old Growth and Secondary Red Oak Populations

EPA Grant Number: U915790
Title: Long-Term Effects of Deforestation on Genetic Diversity: A Comparison of Old Growth and Secondary Red Oak Populations
Investigators: Gerwein, Joel B.
Institution: University of Massachusetts - Boston
EPA Project Officer: Jones, Brandon
Project Period: June 1, 2000 through June 1, 2002
Project Amount: $74,200
RFA: STAR Graduate Fellowships (2000) RFA Text |  Recipients Lists
Research Category: Academic Fellowships , Ecological Indicators/Assessment/Restoration , Fellowship - Ecology and Ecosystems


Most forests of New England were cleared in the last 200 years. The genetic consequences of this massive deforestation are unknown. This study compares levels of genetic diversity of red oak (Quercus rubra) in old-growth and secondary forests to assess effects of different land-use histories.


Leaf tissue was collected from five pairs of old-growth and secondary stands located in Central and Western Massachusetts. Secondary stands are on land that was cleared for pasture or tillage. Two age classes (100+ years old, <50 years old) were sampled in both old-growth and secondary stands. Thirty trees per age class were sampled. Samples were stored at -80 ?C until extracted. DNA was extracted using Quiagen's Plant DNeasy kit. Allele size of individual oaks will be determined at nuclear and chloroplast microsatellite loci previously identified in oak and other species (Steinkellner, et al., 1997; Dumolin-Lapegue, et al., 1999; Weising and Gardner, 1999; Dow, et al., 1995; Isagi and Suhandono, 1997). Allele size is determined by end labeling primers with fluorescent dyes and separating fragments on an automated DNA sequencer (Applied Biosystems 377). Using 1830s maps, the investigator will reconstruct the pattern of regional forest cover around the peak of deforestation. Forest cover will be reconstructed from these maps following the method of Golodetz and Foster (1997), which involves matching 1830s maps to current map projections using a zoom transfer scope and ARC/INFO (ESRI, 1995). Regional forest cover, mean fragment isolation, and mean fragment size will be calculated using FRAGSTAT software (McGarigal and Marks 1995). Using valuation and other historic records, forest cover will be estimated by region at from 1800-1990. This data will give an indication of the pace of forest recovery, useful for interpreting differences in genetic diversity between age classes. Mean number of alleles per locus (A), and fixation index (Rst , Fst) will be calculated for each age class and population (Slatkin, 1995; Hartl and Clark, 1997). Rst and Fst will be calculated for the old-growth stands separately and for all the stands taken as a whole. These calculations will allow for a description of the genetic structure in the stands studied, fulfilling the study's first objective. The investigator will test for the significance of Rst using bootstrapping (Goodman, 1997). The differences in A, Rst and Fst between old-growth and secondary stands using bootstrapping (Sokal and Rohlf, 1995) will be tested. If Rst and Fst are significantly different from zero, pairwise comparisons will be made. Otherwise, an unpaired comparison will be made. This will fulfill the study's second objective, the comparison of genetic diversity in old-growth and secondary stands. Testing for differences in A and Rst between age classes using bootstrapping will fulfill the study's third objective, the determination of effects of deforestation level at time of recruitment on genetic diversity. Testing for the correlation of A, Rst and Fst with isolation and peak deforestation level using the rank correlation test (Sokal and Rohlf, 1995) will fulfill the study's fourth objective, determining the effect of historic level and pattern of deforestation on genetic diversity. Finally, testing for difference in Fst / Rst between chloroplast loci and nuclear loci using confidence limits calculated through jackknifing (Weir, 1990) will fulfill the study's fifth objective, the comparison of differentiation at chloroplast and nuclear loci.

Expected Results:

The investigator predicted that old-growth stands would be more diverse and less differentiated than secondary stands, that younger cohorts in secondary stands would be more diverse than older cohorts, and that areas that underwent less severe deforestation will show less of a difference in genetic diversity between old-growth and secondary stands.

Supplemental Keywords:

deforestation, genetic diversity, Quercus rubra, genetics, terrestrial, northeast, Massachusetts, MA, EPA Region 1., RFA, Scientific Discipline, Geographic Area, Ecosystem Protection/Environmental Exposure & Risk, Ecology, Ecosystem/Assessment/Indicators, Ecosystem Protection, State, Forestry, Ecological Effects - Environmental Exposure & Risk, Environmental Monitoring, Ecology and Ecosystems, EPA Region, Ecological Indicators, ecosystem assessment, forest ecosystems, Massachusetts (MA), forest land cover, ecological assessment, ecosystem indicators, forests, changes in forest cover, deforestation, ecological research, forest conservation, Region 1