Appendix A. Comparison between PCA results using all measured traits and a subset excluding theoretically interdependent traits.

Comparison between PCA results using all measured traits and a subset excluding theoretically interdependent traits

Spearman's <i>ρ</i> between descriptive <i>R</i>² and predictive <i>R</i>²_CV values calculated for all linear models resulting from modeling each vegetation attribute as a function of one, two and three textural variables (TV).

<p>See <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0030506#pone-0030506-t001" target="_blank">Table 1</a> for vegetational attributes abbreviations and Methods for explanation of <i>R</i>²_CV calculation.</p

Predicting Tropical Dry Forest Successional Attributes from Space: Is the Key Hidden in Image Texture?

<div><p>Biodiversity conservation and ecosystem-service provision will increasingly depend on the existence of secondary vegetation. Our success in achieving these goals will be determined by our ability to accurately estimate the structure and diversity of such communities at broad geographic scales. We examined whether the texture (the spatial variation of the image elements) of very high-resolution satellite imagery can be used for this purpose. In 14 fallows of different ages and one mature forest stand in a seasonally dry tropical forest landscape, we estimated basal area, canopy cover, stem density, species richness, Shannon index, Simpson index, and canopy height. The first six attributes were also estimated for a subset comprising the tallest plants. We calculated 40 texture variables based on the red and the near infrared bands, and EVI and NDVI, and selected the best-fit linear models describing each vegetation attribute based on them. Basal area (<em>R</em>² = 0.93), vegetation height and cover (0.89), species richness (0.87), and stand age (0.85) were the best-described attributes by two-variable models. Cross validation showed that these models had a high predictive power, and most estimated vegetation attributes were highly accurate. The success of this simple method (a single image was used and the models were linear and included very few variables) rests on the principle that image texture reflects the internal heterogeneity of successional vegetation at the proper scale. The vegetation attributes best predicted by texture are relevant in the face of two of the gravest threats to biosphere integrity: climate change and biodiversity loss. By providing reliable basal area and fallow-age estimates, image-texture analysis allows for the assessment of carbon sequestration and diversity loss rates. New and exciting research avenues open by simplifying the analysis of the extent and complexity of successional vegetation through the spatial variation of its spectral information.</p> </div

Observed (x-axes) vs. estimated (y-axes) values for the best descriptive (▴) and predictive (red +) linear models for vegetation attributes.

<p>See <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0030506#pone-0030506-t001" target="_blank">Table 1</a> for vegetational attributes abbreviations. Digits 1, 2, and 3 refer to the number of textural variables included in the model as explanatory variables.</p

Vegetational attributes used in the analysis and their abbreviations.

<p>Vegetational attributes used in the analysis and their abbreviations.</p

Fraction of the variation in vegetation attributes (median and range) explained by the descriptive (—♦—), predictive (- -○- -) and null (—▴—) models using a different number of textural attributes as explanatory variables.

<p>For the descriptive and null models conventional <i>R</i>² values are reported, while for the predictive models <i>R</i>²_CV is reported, so the values are not strictly comparable (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0030506#s2" target="_blank">Methods</a> for explanation).</p

Functional trait data

Functional trait dat

Species abundances

Contains the number of individuals in old-growth and secondary forest for each species at each site

Site locations

Latitude and longitude of sites used in this study

Above-ground biomass of Neotropical secondary forests database

This database is the product of the 2ndFOR collaborative research network on secondary forests. The database contains aboveground biomass data (in Mg/ha) for 1334 secondary forest plots differing in time since abandonment. The plots belong to different chonosequence studies in the Neotropics. For a description of the database, see Poorter et al. 2016. Biomass resilience of Neotropical secondary forests. Nature doi:10.1038/nature16512