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Data from: Soil is the main predictor of secondary rain forest estimated aboveground biomass across a neotropical landscape

Citation

Santiago-García, Ricardo J.; Finegan, Bryan; Bosque-Pérez., Nilsa A. (2018), Data from: Soil is the main predictor of secondary rain forest estimated aboveground biomass across a neotropical landscape, Dryad, Dataset, https://doi.org/10.5061/dryad.cm94q95

Abstract

We studied the relative effects of landscape configuration, environmental variables, forest age and spatial variables on estimated aboveground biomass (AGB) in Costa Rican secondary rain forests patches. We measured trees > 5 cm dbh in 24, 0.25 ha plots and estimated AGB for trees 5-24.9 cm dbh and for trees > 25 cm dbh using two allometric equations based on multispecies models using tree dbh and wood specific gravity. AGB averaged 87.3 Mg/ha for the 24 plots (not including remnant trees) and 123.4 Mg/ha including remnant trees (20 plots). There was no effect of forest age on AGB. Variation partitioning analysis showed that soils, climate, landscape configuration and space together explained 61% of tree AGB variance. When controlling for the effects of the other three variables, only soils remained significant. Soil properties, specifically K and Cu, had the strongest independent effect on AGB (variation partitioning, R2=0.17, p=0.0310), indicating that in this landscape, AGB variation in secondary forest patches is influenced by soil chemical properties. Elucidating the relative influence of soils in AGB variation is critical for understanding changes associated to land cover modification across neotropical landscapes, as it could have important consequences for land use planning since secondary forests are considered carbon sinks.

Usage Notes

Funding

National Science Foundation, Award: 0903479 and 1313824

Location

San Juan La Selva Biological Corridor
Costa Rica