DOI: 10.5061/dryad.0p2ngf1z0
title: Forest biomass in subtropical Andes: Plots data

description: 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.
description: 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 <i>et
 al.</i> 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 (&gt; 20 years since the last selective
 logging). For each plot, we obtained climatic data at
 1-km resolution from the CHELSA database (Karger <i>et al.</i>
 2017). We obtained wood specific gravity (WSG) for 74% of species using
 local databases (Easdale <i>et al.</i> 2007, INTI-CITEMA 2007;
 https://www.inti.gob.ar/publicaciones/descargac/365), and we used an international database when local data were unavailable (Chave <i>et al.</i> 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 × DBH<sup>2</sup>. Tree-level BA values (m<sup>2</sup>) were summed to estimate plot-level BA (m<sup>2</sup>/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/cm<sup>3</sup>) by its plot basal area. AGB was estimated from the equation developed by Chave <i>et al.</i> (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))<sup>2</sup>). 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<sup>-3</sup>). We extracted the Climatic Water Deficit index from Chave <i>et al.</i> (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 = (AGB<sub>2</sub> − AGB<sub>1</sub>)/T, where AGB<sub>1</sub> and AGB<sub>2</sub> 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, AGB<sub>2</sub> = AGB<sub>1</sub> + (AGB gain – AGB loss) × T, we obtained annualized AGB gain (due to tree recruitment and growth), as: AGB gain = (AGB<sub>2</sub> − AGB<sub>1</sub>)/T + AGB loss.