Tree Foliar Chemistry in an African Savanna and Its Relation to Life History Strategies and Environmental Filters

<div><p>Understanding the relative importance of environment and life history strategies in determining leaf chemical traits remains a key objective of plant ecology. We assessed 20 foliar chemical properties among 12 African savanna woody plant species and their relation to environmental variables (hillslope position, precipitation, geology) and two functional traits (thorn type and seed dispersal mechanism). We found that combinations of six leaf chemical traits (lignin, hemi-cellulose, zinc, boron, magnesium, and manganese) predicted the species with 91% accuracy. Hillslope position, precipitation, and geology accounted for only 12% of the total variance in these six chemical traits. However, thorn type and seed dispersal mechanism accounted for 46% of variance in these chemical traits. The physically defended species had the highest concentrations of hemi-cellulose and boron. Species without physical defense had the highest lignin content if dispersed by vertebrates, but threefold lower lignin content if dispersed by wind. One of the most abundant woody species in southern Africa, <i>Colophospermum mopane</i>, was found to have the highest foliar concentrations of zinc, phosphorus, and δ¹³C, suggesting that zinc chelation may be used by this species to bind metallic toxins and increase uptake of soil phosphorus. Across all studied species, taxonomy and physical traits accounted for the majority of variability in leaf chemistry.</p></div

Appendix B. Supplementary figures showing bioacoustic spectra of avian vocalizations and abundance of native and exotic birds by habitat.

Supplementary figures showing bioacoustic spectra of avian vocalizations and abundance of native and exotic birds by habitat

Classification and regression tree (CART) predicting species using chemical properties.

<p>All 20 foliar chemical and elemental properties measured for each tree sample (n = 238) were used as input to the CART algorithm. The algorithm selected six properties while retaining 91% classification accuracy. This analysis illustrates the minimum number of foliar chemicals needed to classify species and the relative importance of each trait in minimizing error in the classification (in descending order, from top to bottom). The equation above each branch indicates the chemical concentration used to perform the split (e.g. “Lig < 14” means samples with lignin concentrations less than 14% by mass). The units of concentration varied for each trait as follows (all were on a mass basis): Lig = lignin (%), Hmcl = hemi-cellulose (%), Zn = zinc (μg g^-1), B = boron (μg g^-1), Mg = magnesium (%), Mn = manganese (μg g^-1). Numbers below species indicate the number of correct classifications divided by the total number of samples for that species. See text for key to species abbreviations.</p

Appendix A. Detailed methods of airborne imaging spectroscopy, LiDAR, bioacoustic recordings, and bird surveys.

Detailed methods of airborne imaging spectroscopy, LiDAR, bioacoustic recordings, and bird surveys

Nonmetric Multidimensional Scaling (NMDS) scatter plot showing dissimilarity in foliar chemistry between species.

<p>Points represent individual trees (n = 219), with 20 foliar chemical properties measured per tree. These properties were transformed using NMDS to two axes to illustrate the dissimilarity in foliar chemistry between species.</p

Mean (odd rows) and standard deviation (even rows) of 20 foliar chemical properties and two functional traits for 12 savanna woody plant species.

<p>Notes: Chl = Chlorophyll, Sol C = Soluble Carbon, Car = Carotenoids, Hemicell = Hemi-cellulose. A = Anemochory (wind), En = Endozoochory (ingestion by vertebrate animals), B = Ballochory (mechnically ejected). s.d. standard deviation, in units of each chemical. For species abbreviations, see Foliar Sampling section in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0124078#sec002" target="_blank">Methods</a>.</p><p>Mean (odd rows) and standard deviation (even rows) of 20 foliar chemical properties and two functional traits for 12 savanna woody plant species.</p

Site description by geology, precipitation, and woody plant species sampled (sample size in parantheses).

<p>Notes: MAP = Mean Annual Precipitation; MAT = Mean Annual Temperature; NWA = Nwaswitshaka (near Skukuza), LET = Letaba, LWS = Lower Sabie; <i>A</i>.<i>nig = Acacia nigrescens</i> (Fabaceae), <i>A</i>.<i>tor = A</i>.<i>tortilis</i> (Fabaceae), <i>C</i>.<i>api = Combretum apiculatum</i> (Combretaceae), <i>C</i>.<i>her = Combretum hereroense</i> (Combretaceae), <i>C</i>.<i>imb = Combretum imberbe</i> (Combretaceae), <i>C</i>.<i>mop = Colophospermum mopane</i> (Fabaceae), <i>D</i>.<i>cin = Dichrostachys cinerea</i> (Fabaceae), <i>D</i>.<i>mes = Diospyros mespiliformes</i> (Ebenaceae), <i>E</i>.<i>div = Euclea divinorum</i> (Ebenaceae), <i>S</i>.<i>afr = Spirostachys Africana</i> (Euphorbiaceae), <i>S</i>.<i>bir = Sclerocarya birrea</i> (Anacardiaceae), <i>T</i>.<i>ser = Terminalia sericea</i> (Combretaceae).</p><p>Site description by geology, precipitation, and woody plant species sampled (sample size in parantheses).</p

Multivariate regression tree (MRT) relating environmental variables to foliar chemical properties.

<p>The six foliar chemical response variables selected during the CART analysis (lignin, hemi-cellulose (Hemicell), Zn, Mn, B, Mg) were predicted using three environmental input variables (precipitation, hillslope position, and parent material). These environmental factors explained 12% of the total variance among these chemical properties. The column plots show the mean chemical concentrations of each cluster. n = number of individual tree samples; the value before n is the sum of squared errors (post-normalization) of chemical concentrations for that group.</p

Appendix A. Eight figures, providing complementary data rearranged by species or measurement technique, referenced in the main text as key additional illustrations pertinent to the interpretation of the role that species play in determining the chemical and spectral diversity of Australian tropical forests.

Eight figures, providing complementary data rearranged by species or measurement technique, referenced in the main text as key additional illustrations pertinent to the interpretation of the role that species play in determining the chemical and spectral diversity of Australian tropical forests

Appendix B. A list of species measured in the study.

A list of species measured in the study