DOI: 10.5061/dryad.4mw6m90dr
title: Landscape structure, predictability of forest regeneration trajectories, and recovery rate on secondary forests

description: Abandonment of agricultural lands promotes the global expansion of
 secondary forests, which are critical for preserving biodiversity and
 ecosystem functions and services. Such roles largely depend, however, on
 two essential successional attributes, trajectory and recovery rate, which
 are expected to depend on landscape-scale forest cover in non- linear
 ways. This dataset is the synthesis outcome of 22 independent databases
 from studies of woody plant species recovery as part of the research
 project entitled "Impacts of landscape structure on secondary
 tropical forest regeneration". This work aimed to understand the
 effect of landscape-level disturbance on forest regeneration, specifically
 through the predictability of trajectories and the recovery rate of these
 forests. Using a multiscale approach and a large vegetation dataset (843
 plots, 3511 tree species) from 22 secondary forest chronosequences
 distributed across the Neotropics, we show that successional trajectories
 of woody plant species richness, stem density, and basal area are less
 predictable in landscapes (4-km radius) with intermediate (40-60%) forest
 cover than in landscapes with high (&gt;60%) forest cover. This
 supports theory suggesting that high spatial and environmental
 heterogeneity in intermediately deforested landscapes can increase the
 variation in key ecological factors for forest recovery (e.g. seed
 dispersal, seedling recruitment), increasing the uncertainty of
 successional trajectories. Regarding the recovery rate, only the species
 richness is positively related to forest cover in relatively small (1-km
 radius) landscapes. These findings highlight the importance of using a
 spatially-explicit landscape approach in restoration initiatives and
 suggest that these initiatives can be more effective in more forested
 landscapes, especially if implemented across spatial extents of 1-4 km
 radius. 
description: We compiled 22 independent databases from studies of woody plant
 species recovery across five Neotropical countries. Each study included
 plots established in secondary forest stands of different ages forming a
 chronosequence. We used taxonomic species richness, density of
 individuals, and total basal area per plot to evaluate the successional
 trajectories and recovery rate of vegetation structure. We calculated the
 extrapolated values of species richness considering the maximum sample
 coverage (?n = 1.0), following the protocols proposed by Chao &
 Jost (2012). To assess the predictability of successional trajectories, we
 related each community attribute (species richness, density of
 individuals, and basal area) to stand age for each chronosequence (n =
 22). We derived the adjusted R²adj  values from generalized additive
 models (GAMs) to use as a proxy for predictability of the successional
 trajectories. Because R²adj represents the fraction of the variance in the
 dependent variable that is explained by the independent variable (Crawley
 2012), this parameter can be used as a proxy of the predictability of the
 relationship between each vegetation attribute and stand age.  To assess
 the recovery rate of successional trajectories, we extracted the predicted
 value of the GAM relating each vegetation attribute and stand age for the
 fixed age of 15 and 20 years of succession. Then, we calculated the
 recovery rate values through the equation: [15 ????? ????????? ????? ― 20
 ????? ????????? ????? / 5 ] , where 5 corresponds  to the age interval in
 years. This measure was established under the assumption that five years
 is a short interval of recovery and therefore presents a lineal
 behavior.  Imagery selection and
 pre-processing For each site we defined a landscape of
 10-km radius from the centroid of the set of plots from each study. We
 choose this radius to standardize landscape size and enable an adequate
 analysis of landscape-level orest cover for the chronosequence stands in
 all databases. We obtained Landsat ETM+ and Landsat 8 satellite imagery
 with 30-m spatial resolution in the multispectral bands from the United
 States Geological Survey database (USGS, https://glovis.usgs.gov/app).
 Images were selected based on the location of the landscape of interest,
 the year of vegetation inventories of each study, and cloudiness. For
 databases containing data collected in different years, we selected
 imagery corresponding to the median year of the study and containing less
 than 10% cloudiness. During pre-processing of images, we corrected for
 cloud cover by creating both a cloud and a cloud shadow mask using the
 Cloud Masking tool and fmask function in QGIS 2.18.14 software (Quantum
 GIS Development Team 2009) as recommended for Landsat TM/ETM +/OLI/TIRS
 images (Zhu & Woodcock 2012). For each image in a landscape, we
 conducted panchromatic and spectral image fusion (i.e. pansharpend
 compound) to improve spatial resolution in the Landsat image using the IHS
 (Intensity Hue Saturation) method (Zhang 2002). Due to a failure in the
 Scan Line Corrector (SLC) of the Landsat ETM satellite sensor since May
 2003, some images have wedge-shaped gaps on each side, resulting in the
 loss of ca. 22% of information. To correct this, we applied the Gapfill
 tool with the ENVI 4.7 program (ENVI 2008) according to the filling
 technique developed by Scaramuzza et al. (2004). This technique fills gaps
 in a Landsat image with data from another image and applies a linear
 transformation to adjust the corrected image based on the standard
 deviation and mean values of each band of each scene (Scaramuzza et al.
 2004). Image classification and estimation of percent
 forest cover We carried out a supervised classification
 of images based on training data and validation. We considered three
 categories of land cover in the classification: native forest cover,
 agricultural lands, and other land covers (e.g. water, human settlements).
 Forest cover included both old-growth and late successional second-growth
 forests because vegetation structure in the later forest type is quite
 similar to old-growth forests (Poorter et al. 2016; Rozendaal et al.
 2019). First, we selected regions of interest based on expert knowledge
 (i.e. polygons with land cover information) of the raster layer as a
 reference to classify unknown pixels by comparing the digital value of
 pixels with training data (Canty 2007). To this end, we used the Support
 Vector Machine non-parametric method for non-linear data (SVM). This
 method uses the Kernel class of algorithm (Foody & Mathur 2004;
 Tamma et al. 2013). Overall satellite image classification accuracy was
 relatively high (&gt;85%). To reduce the salt and pepper effect, we
 applied post-classification Majority/Minority analysis. Next, we used the
 classified vectors to estimate the percent forest cover within each study
 landscape, using ten differently sized buffers, ranging from 1 to 10 km,
 at 1 km intervals (corresponding to landscapes of 314.1 to 31,415.6 ha).
 We next calculated forest cover for each buffer using the Dinamica EGO 4
 program (http://csr.ufmg.br/dinamica). All classified vectors were sent to
 the authors of each database for revision and approval before estimation
 of the percent forest cover.