Data from: Long term impacts of selective logging on two Amazonian tree species with contrasting ecological and reproductive characteristics: inferences from Eco-gene model simulations
Vinson, Christina C. et al. (2013), Data from: Long term impacts of selective logging on two Amazonian tree species with contrasting ecological and reproductive characteristics: inferences from Eco-gene model simulations, Dryad, Dataset, https://doi.org/10.5061/dryad.6jh51
The impact of logging and subsequent recovery after logging is predicted to vary depending on specific life history traits of the logged species. The Eco-gene simulation model was used to evaluate the long-term impacts of selective logging over 300 years on two contrasting Brazilian Amazon tree species, Dipteryx odorata and Jacaranda copaia. D. odorata (Leguminosae), a slow growing climax tree, occurs at very low densities, whereas J. copaia (Bignoniaceae) is a fast growing pioneer tree that occurs at high densities. Microsatellite multilocus genotypes of the pre-logging populations were used as data inputs for the Eco-gene model and post-logging genetic data was used to verify the output from the simulations. Overall, under current Brazilian forest management regulations, there were neither short nor long-term impacts on J. copaia. By contrast, D. odorata cannot be sustainably logged under current regulations, a sustainable scenario was achieved by increasing the minimum cutting diameter at breast height from 50 to 100 cm over 30-year logging cycles. Genetic parameters were only slightly affected by selective logging, with reductions in the numbers of alleles and single genotypes. In the short term, the loss of alleles seen in J. copaia simulations was the same as in real data, whereas fewer alleles were lost in D. odorata simulations than in the field. The different impacts and periods of recovery for each species support the idea that ecological and genetic information are essential at species, ecological guild or reproductive group levels to help derive sustainable management scenarios for tropical forests.