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Forest biomass in subtropical Andes: Plots data

Citation

Blundo, Cecilia; Malizia, Agustina; Malizia, Lucio R.; Lichstein, Jeremy W. (2020), Forest biomass in subtropical Andes: Plots data, Dryad, Dataset, https://doi.org/10.5061/dryad.0p2ngf1z0

Abstract

Forest biomass plays an important role in the global carbon cycle. Therefore, understanding the factors that control forest biomass stocks and dynamics is a key challenge in the context of global change. We analyzed data from 60 forest plots in the subtropical Andes (22-27.5° S and 300-2300 m asl) to describe patterns and identify drivers of aboveground biomass (AGB) stocks and dynamics. We found that AGB stocks remained roughly constant with elevation due to compensating changes in basal area (which increased with elevation) and plot-mean wood specific gravity (which decreased with elevation). AGB gain and loss rates both decreased with elevation and were explained mainly by temperature and rainfall (positive effects on both AGB gains and losses). AGB gain was also correlated with forest use history and weakly correlated with forest structure. Mean annual temperature and rainfall showed minor effects on AGB stocks and AGB change (gains minus losses) over recent decades. Although AGB change was only weakly correlated with climate variables, increases in AGB gains and losses with increasing rainfall – together with observed increases in rainfall in the subtropical Andes – suggest that these forests may become increasingly dynamic in the future.

Methods

Plots were established between 300 and 2300 m asl, and between 22° and 27.5° S to cover the wide environmental variation of the subtropical Andes in northwest Argentina. Plot establishment and measurement were performed between 2002 and 2017, and followed standardized protocols for Andean forests (Osinaga-Acosta et al. 2014). Database include plot-level data for 60 permanent plots, 45 of them measured twice with a mean of 6.6 years between censuses (29 plots with 4-6 years between census 1 and 2, and 16 plots with 9-10 years between census 1 and 2).

Plots were established in well-conserved and mature forests. However, some plots have occasional grazing, and some plots had low-intensity selective logging prior to the initial census. In summary, 48 out of 60 plots were old-growth forests (28 with grazing and 20 without), and 12 out of 60 plots were forests with low-intensity logging (> 20 years since the last selective logging).

For each plot, we obtained climatic data at 1-km resolution from the CHELSA database (Karger et al. 2017). We obtained wood specific gravity (WSG) for 74% of species using local databases (Easdale et al. 2007, INTI-CITEMA 2007; https://www.inti.gob.ar/publicaciones/descargac/365), and we used an international database when local data were unavailable (Chave et al. 2006). For unidentified individuals, we used the plot-mean WSG value (see details below) of the plot where that individual occurred.

We calculated plot-level basal area, stem density of large trees and plot-mean WSG to describe forest structure of each plot. Basal area of individual trees was calculated as: BA = π/4 × DBH2. Tree-level BA values (m2) were summed to estimate plot-level BA (m2/ha). Stem density of large trees (SD50) is the density of all stems ≥ 50 cm in DBH measured in the plot (stem/ha). Plot-mean WSG (WSGµ) in each plot was calculated by weighting the WSG of each species (g/cm3) by its plot basal area.

AGB was estimated from the equation developed by Chave et al. (2014) for cases where tree height was unavailable (as is this case): AGB = exp (–1.803 – 0.976 × E + 0.976 × ln(WSG) + 2.673 × ln(DBH) – 0.0299 × (ln(DBH))2). The environmental factor E was calculated from plot-level climate variables as follows: E = (0.178 × Temperature seasonality – 0.938 × Climatic Water Deficit – 6.61 × Precipitation seasonality) × 10-3). We extracted the Climatic Water Deficit index from Chave et al. (2014) (http://chave.ups-tlse.fr/pantropical_allometry.htm). Tree-level AGB values (Mg biomass) were summed to estimate plot-level AGB (Mg biomass/ha).

To quantify AGB dynamics within each plot, we calculated plot-level AGB change, AGB loss, and AGB gain (Mg biomass/ha/yr). AGB change = (AGB2 − AGB1)/T, where AGB1 and AGB2 are AGB at the time of the first and second measurements, respectively, and T is the census interval (years). AGB loss (due to tree mortality) is the AGB of trees alive at time 1 but dead at time 2, annualized by T. Finally, based on the formula for AGB dynamics, AGB2 = AGB1 + (AGB gain – AGB loss) × T, we obtained annualized AGB gain (due to tree recruitment and growth), as: AGB gain = (AGB2 − AGB1)/T + AGB loss.

Funding

Consejo Nacional de Investigaciones Científicas y Técnicas, Award: Cooperation International UF-CONICET 526/15, and PICTO 2014-0059