Plant Functional Diversity and the Biogeography of Biomes in North and South America

Abstract

The concept of the biome has a long history dating back to Carl Ludwig Willdenow and Alexander von Humboldt. However, while the association between climate and the structure and diversity of vegetation has a long history, scientists have only recently begun to develop a more synthetic understanding of biomes based on the evolution of plant diversity, function, and community assembly. At the broadest scales, climate filters species based on their functional attributes, and the resulting functional differences in dominant vegetation among biomes are important to modeling the global carbon cycle and the functioning of the Earth system. Nevertheless, across biomes, plant species have been shown to occupy a common set of global functional “spectra”, reflecting variation in overall plant size, leaf economics, and hydraulics. Still, comprehensive measures of functional diversity and assessments of functional similarity have not been compared across biomes at continental to global scales. Here, we examine distributions of functional diversity of plant species across the biomes of North and South America, based on distributional information for > 80,000 vascular plant species and functional trait data for ca. 8,000 of those species. First, we show that despite progress in data integration and synthesis, significant knowledge shortfalls persist that limit our ability to quantify the functional biodiversity of biomes. Second, our analyses of the available data show that all the biomes in North and South America share a common pattern–most geographically common, widespread species in any biome tend to be functionally similar whereas the most functionally distinctive species are restricted in their distribution. Third, when only the widespread and functionally similar species in each biome are considered, biomes can be more readily distinguished functionally, and patterns of dissimilarity between biomes appear to reflect a correspondence between climate and functional niche space. Taken together, our results suggest that while the study of the functional diversity of biomes is still in its formative stages, further development of the field will yield insights linking evolution, biogeography, community assembly, and ecosystem function.

Full text

ORIGINAL RESEARCH
published: 18 December 2018
doi: 10.3389/fevo.2018.00219
Frontiers in Ecology and Evolution | www.frontiersin.org 1December 2018 | Volume 6 | Article 219
Edited by:
Daniel M. Griffith,
Oregon State University, United States
Reviewed by:
Joaquín Hortal,
Museo Nacional de Ciencias
Naturales (MNCN), Spain
Mark E. Olson,
National Autonomous University of
Mexico, Mexico
*Correspondence:
Susy Echeverría-Londoño
susyelo@gmail.com
Specialty section:
This article was submitted to
Biogeography and Macroecology,
a section of the journal
Frontiers in Ecology and Evolution
Received: 02 June 2018
Accepted: 03 December 2018
Published: 18 December 2018
Citation:
Echeverría-Londoño S, Enquist BJ,
Neves DM, Violle C, Boyle B,
Kraft NJB, Maitner BS, McGill B,
Peet RK, Sandel B, Smith SA,
Svenning J-C, Wiser SK and
Kerkhoff AJ (2018) Plant Functional
Diversity and the Biogeography of
Biomes in North and South America.
Front. Ecol. Evol. 6:219.
doi: 10.3389/fevo.2018.00219
Plant Functional Diversity and the
Biogeography of Biomes in North
and South America
Susy Echeverría-Londoño 1
*, Brian J. Enquist 2, Danilo M. Neves 2,3 , Cyrille Violle 4,
Brad Boyle 2, Nathan J. B. Kraft 5, Brian S. Maitner 2, Brian McGill 6, Robert K. Peet 7,
Brody Sandel 8, Stephen A. Smith 9, Jens-Christian Svenning 10,11 , Susan K. Wiser 12 and
Andrew J. Kerkhoff 1
1Department of Biology, Kenyon College, Gambier, OH, United States, 2Department of Ecology and Evolutionary Biology,
University of Arizona, Tucson, AZ, United States, 3Department of Botany, Federal University of Minas Gerais, Belo Horizonte,
Brazil, 4Centre d’Ecologie Fonctionnelle et Evolutive (UMR 5175), CNRS, Université de Montpellier, Université Paul Valéry,
Montpellier, France, 5Department of Ecology and Evolutionary Biology, University of California, Los Angeles, Los Angeles,
CA, United States, 6School of Biology and Ecology, University of Maine, Orono, ME, United States, 7Department of Biology,
University of North Carolina, Chapel Hill, NC, United States, 8Department of Biology, Santa Clara University, Santa Clara, CA,
United States, 9Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, MI, United States,
10 Department of Bioscience, Center for Biodiversity Dynamics in a Changing World (BIOCHANGE), Aarhus University,
Aarhus, Denmark, 11 Section for Ecoinformatics and Biodiversity, Department of Bioscience, Aarhus University, Aarhus,
Denmark, 12 Landcare Research New Zealand, Lincoln, New Zealand
The concept of the biome has a long history dating back to Carl Ludwig Willdenow
and Alexander von Humboldt. However, while the association between climate and the
structure and diversity of vegetation has a long history, scientists have only recently
begun to develop a more synthetic understanding of biomes based on the evolution of
plant diversity, function, and community assembly. At the broadest scales, climate filters
species based on their functional attributes, and the resulting functional differences in
dominant vegetation among biomes are important to modeling the global carbon cycle
and the functioning of the Earth system. Nevertheless, across biomes, plant species
have been shown to occupy a common set of global functional “spectra”, reflecting
variation in overall plant size, leaf economics, and hydraulics. Still, comprehensive
measures of functional diversity and assessments of functional similarity have not been
compared across biomes at continental to global scales. Here, we examine distributions
of functional diversity of plant species across the biomes of North and South America,
based on distributional information for >80,000 vascular plant species and functional
trait data for ca. 8,000 of those species. First, we show that despite progress in data
integration and synthesis, significant knowledge shortfalls persist that limit our ability to
quantify the functional biodiversity of biomes. Second, our analyses of the available data
show that all the biomes in North and South America share a common pattern–most
geographically common, widespread species in any biome tend to be functionally similar
whereas the most functionally distinctive species are restricted in their distribution.
Third, when only the widespread and functionally similar species in each biome are
considered, biomes can be more readily distinguished functionally, and patterns of
dissimilarity between biomes appear to reflect a correspondence between climate and

Echeverría-Londoño et al. New World Functional Diversity
functional niche space. Taken together, our results suggest that while the study of the
functional diversity of biomes is still in its formative stages, further development of the field
will yield insights linking evolution, biogeography, community assembly, and ecosystem
function.
Keywords: biogeography, biomes, functional traits, hypervolumes, macroecology, plant functional diversity
INTRODUCTION
Ecologists and biogeographers organize land plant
biodiversity into climatically-determined biomes, with
physiognomies characterized by the growth forms and
functional traits of the dominant species (Moncrieff et al.,
2016). Indeed, the biome concept has a long history dating
back to Carl Ludwig Willdenow and Alexander von Humboldt.
Willdenow recognized that similar climates support similar
vegetation forms, and Humboldt observed the widespread
association between plant distribution, physiognomy, and
environmental factors. Overall, the biome concept reflects the
assumption that similar environmental pressures select for
species with similar functional attributes, independent of their
evolutionary history. At the same time, Earth’s extant biomes
are to some extent phylogenetically distinct, with many/most
of the characteristic species being drawn from specific lineages
exhibiting key adaptations not just to climatic selection but to
additional pressures like fire or megaherbivores (Woodward
et al., 2004; Pennington et al., 2006; Donoghue and Edwards,
2014). Because biomes represent broad-scale regularities in
Earth’s vegetation, understanding functional differences among
biomes is critically important to modeling the global carbon cycle
and the functioning of the Earth system, including responses to
anthropogenic global change (Bonan et al., 2012; van Bodegom
et al., 2014; Xia et al., 2015).
The past two decades has seen rapid growth in understanding
the functional dimension of plant biodiversity across continental
scales (Swenson et al., 2012; Lamanna et al., 2014; Šímová
et al., 2018). Extensive data synthesis and analysis efforts
have defined a limited functional trait space incorporating
variation in overall plant size, leaf economics, and hydraulics,
known as functional trait spectra (Wright et al., 2004; Díaz
et al., 2016). Functional trait spectra define physiological
and ecological trade-offs that determine plant life-history
strategies (Westoby, 1998; Reich et al., 2003; Craine, 2009)
and their influence on community assembly (Kraft et al.,
2008), ecosystem function (Lavorel and Garnier, 2002; Garnier
et al., 2004; Kerkhoff et al., 2005; Cornwell et al., 2008),
and even rates of evolution (Smith and Donoghue, 2008;
Smith and Beaulieu, 2009). The generality and utility of plant
functional trait spectra has hastened their incorporation into
models of the global distribution of vegetation (van Bodegom
et al., 2014), biogeochemical cycling (Bonan et al., 2012), and
ecosystem services (Díaz et al., 2007; Cadotte et al., 2011;
Lavorel et al., 2011). However, few studies have examined
comprehensive measures of functional diversity at the biome
scale across both dominant and subordinate species and life
forms.
There are two contrasting sets of predictions about
how functional diversity varies among climatically and
physiognomically distinct biomes. On the one hand, if the
biodiversity of biomes reflects variation in the available ecological
niche space, less taxonomically diverse biomes should represent
a smaller, largely nested subset of the functional space occupied
by more diverse biomes. On the other hand, due to the global
nature of fundamental trade-offs in plant structure and function,
and similar selection pressures acting similarly across species,
assemblages occupying different environments may in fact share
similar areas of trait space (Reich et al., 2003; Wright et al., 2004;
Díaz et al., 2016). Recent studies confirm that the relationships
between climate and functional and taxonomic diversity (i.e.,
species richness) are complex and scale-dependent. In some
cases, functional diversity closely tracks climatic gradients,
with lower functional diversity in more variable and extreme
conditions (Swenson et al., 2012; de la Riva et al., 2018), and the
volume of functional space in local assemblages expands with
increasing taxonomic richness (Lamanna et al., 2014; Li et al.,
2018). However, analyses of regional-scale species pools suggest
that large changes in species richness may be associated with
minimal impacts on functional diversity (Lamanna et al., 2014;
Šímová et al., 2015). Moreover, the responses of taxonomic and
functional diversity to climate may differ among major plant
growth forms (Šímová et al., 2018), and the relative diversity
of different growth forms may change substantially along the
climate gradients that define different biomes (Engemann et al.,
2016).
Progress in analyzing functional diversity on continental
to global scales has been limited in part by data shortfalls
in cataloging the taxonomic, distributional, phylogenetic,
and functional aspects of biodiversity (Hortal et al., 2015).
Furthermore, even given the limited data available, significant
informatics challenges are associated with standardizing and
integrating large, disparate datasets describing the geographic
distributions, functional traits, and phylogenetic relationships
of species (Violle et al., 2014). Here we examine the distribution
of functional diversity of plant species across the biomes of
North and South America using the Botanical Information and
Ecology Network database (BIEN; Enquist et al., 2016; Maitner
et al., 2018), which assembles distributional and functional trait
information on >100,000 species of land plants. Specifically, we
examine variation in the functional diversity and distinctiveness
of biomes, based on land plant species distribution maps at a
scale of 100 ×100 km grid cells and a comprehensive dataset
of six functional traits that reflect the major axes of variation in
ecological strategies.
Our goals in this study are 3-fold. First, in order to highlight
persistent data shortfalls, we document the extent of the available
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Echeverría-Londoño et al. New World Functional Diversity
data characterizing the functional diversity and distinctiveness
of biomes. Second, given the data available, we characterize the
distribution of functional diversity within biomes across both
dominant and subordinate growth forms. These analyses allow
us to better quantify the functional distinctiveness of a biome
by identifying the most common functional strategies of the
most widespread species within it. Third, we ask whether biomes
are in fact characterized by functionally distinct collections of
species, based on the overlap of multidimensional hypervolumes
in functional trait space.
METHODS
Distribution Data and Biome Classification
To reduce the effects of sampling bias characteristic of occurrence
datasets compiled from multiple resources, we used the BIEN
2.0 range maps for 88,417 of plant species distributed in
North and South America (Goldsmith et al., 2016). The BIEN
database integrates standardized plant observations stemming
from herbarium specimens and vegetation plot inventories.
Species range maps in the BIEN databases were estimated using
one of three approaches, depending on the occurrences available
for each species. For species with only one or two occurrences
(c. 35% of the species), the geographic range was defined as
a square 75,000 km2area surrounding each data point. The
geographic ranges for species with three or four occurrences
were identified using a convex hull (c. 15% of the species).
Finally, the range maps for species with at least five occurrences
were obtained using the Maxent species distribution modeling
algorithm, using 19 climatic layers as predictor variables and 19
spatial eigenvectors as filters to constrain over predictions by the
models (see Goldsmith et al., 2016 for further details on range
map methodology).
We overlaid the BIEN 2.0 plant species maps on a 100
×100 km grid map with a Lambert Azimuthal Equal Area
projection to obtain a presence/absence matrix of species
for each grid cell. Based on the Olson et al. (2001) biome
classification, we assigned each matrix grid cell to one of the
biomes categories. Because of computational limitations for the
subsequent analyses, we joined some biomes based on their
similarities in climate and vegetation and literature to obtain
a broad classification as described in Table 1 (excluding Inland
Water, Rock and Ice and Mangroves). The Chaco and Caatinga
ecoregions were classified as Xeric Woodlands and Dry forest,
respectively, following Prado and Gibbs (1993),Pennington et al.
(2000),Banda et al. (2016),Silva de Miranda et al. (2018).
Species Composition Among Biomes
To understand the variation in functional trait space among
biomes, we first explored the differences in plant species
composition among them. Based on the species list for each grid
cell and biome, we defined each biome’s characteristic species
as those that have the maximum proportion of their range in
that biome. These lists of geographically predominant species
in a given biome were compared to the list of species in other
biomes. This pairwise comparison provides a simple way to
infer a directional taxonomic overlap among biomes (i.e., the
TABLE 1 | Overview of the biome classification adopted in this study and the
equivalent (Olson et al., 2001) biomes classification (excluding Inland Water, Rock
and Ice, and Mangroves).
Biomes used in this
study
Original (Olson et al., 2001) biomes
Moist Forests Tropical and Subtropical Moist Broadleaf Forests
Dry Forests Tropical and Subtropical Coniferous Forests
Tropical and Subtropical Dry Broadleaf Forests
Caatinga ecoregion
Savannas Flooded Grasslands and Savannas
Tropical and Subtropical Grasslands, Savannas and
Shrublands
Tropical Grasslands Montane Grasslands and Shrublands
Xeric Woodlands Deserts and Xeric Shrublands
Chaco ecoregion
Mediterranean
Woodlands
Mediterranean Forests, Woodlands and Scrub
Temperate Grasslands Temperate Grasslands, Savannas and Shrublands
Temperate Mixed
Forests
Temperate Broadleaf and Mixed Forests
Coniferous Forests Temperate Conifer Forests
Taiga Boreal Forests/Taiga
Tundra Tundra
Because of computer limitations, some biomes were joined based on their climatic and
vegetation similarities. The Chaco and Caatinga ecoregions were classified as Xeric
Woodlands and Dry forest, respectively, following Prado and Gibbs (1993),Pennington
et al. (2000),Banda et al. (2016),Silva de Miranda et al. (2018).
proportion of predominant species of a biome shared with
another biome). Of the 88,417 species with available range maps,
44,899 species have ranges spanning more than one biome, and
43,518 species are endemic to a specific biome.
Trait Data
We extracted all the trait information for plant species in the
New World available in the BIEN 3.0 dataset (retrieved on
7 February 2018) giving a total of 80,405 species levels trait
observations. We then filtered the information for six functional
traits: maximum plant height (m), seed mass (mg), wood density
(mg/cm3), specific-leaf-area SLA (cm2/g) and leaf phosphorus
and leaf nitrogen concentration per unit mass (mg/g). A total
of 18,192 species-level observations were left after filtering. From
these, the subset of 8,820 species with both range maps and trait
information was used for further analyses.
To estimate the functional trait space for each biome, we
required complete trait data. For this reason, we phylogenetically
imputed missing trait data (Bruggeman et al., 2009; Penone et al.,
2014; Swenson, 2014; Swenson et al., 2017) using the R package
“Rphylopars” v 0.2.9 (Goolsby et al., 2017) and the recently
published phylogeny of seed plants by Smith and Brown (2018)
as a baseline. Phylogenetic imputation is a tool for predicting
missing data in functional traits datasets based on the assumption
that closely related species tend to have similar trait values
(Swenson, 2014). We used the ALLBM tree (i.e., gene bank and
Open Tree of life taxa with a backbone provided by Magallón
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Echeverría-Londoño et al. New World Functional Diversity
et al., 2015) because it maximized the overlap between species
with available trait and distributional information. After the
trait imputation, a total of 7,842 species with complete trait
information and range maps remained for further analysis.
Trait Hypervolumes Measurement
We measured the relative functional diversity of biomes by
calculating trait hypervolumes from species pools within biomes
cells. Due to computational limitations, we constructed the trait
hypervolumes using a random sample of 20% of the cells in
each biome. The hypervolumes for each grid cell were estimated
using the extracted six functional traits and the R package
“hypervolume” (Blonder et al., 2014, 2018), using the Gaussian
KDE method with the default Silverman bandwidth estimator.
Seed mass, height and wood density were log-transformed,
whereas SLA was squared-root transformed. All traits were scaled
and centered before the analysis. Hypervolumes are reported in
units of standard deviations (sd) to the power to the number of
traits used (i.e., sd6).
Functional Distinctiveness and
Widespreadness
Because biomes are characterized by their dominant vegetation,
we also examined the geographic commonness and functional
distinctiveness of species within biomes. Using species range
maps and functional trait information, we estimated the
functional distinctiveness and widespreadness of each species in
each biome, following the conceptual framework of functional
rarity by Violle et al. (2017). Using the whole set of (measured
and imputed) traits, we first measured the Euclidean distance
in standardized trait space between all pair of species. We then
calculated the functional distinctiveness (Di) for each species
in each biome as the average functional distance of a species
to the N other species within the biome species pool. Di was
scaled between 0 and 1 within each biome with lower values
representing species that are functionally common (redundant)
and higher values representing species that are functionally
distinctive, compared to the other species in the biome. We
also estimate the geographic widespreadness (Wi) of a species
in a biome, measured as the number of grid cells occupied
by the species within a biome over the total number of cells
in that biome. A value of 1 indicates that the focal species is
present in all grid cells covered by the biome. Both functional
distinctiveness and geographic widespreadness were calculated
using the R package “funrar” (Grenié et al., 2017).
Because we lack comprehensive measures of dominance
based on local abundance or biomass, we used the measures
Di and Wi to identify for each biome the most “common”
species as those that are both geographically widespread (Wi >
0.5) and functionally similar (Di <0.25). This last threshold
was used since the third quantile of functional distinctiveness
values among biomes ranged from 0.2 to 0.3. To discriminate
functionally distinct species vs. functionally similar species in
subsequent analyses, we, therefore, used a value of 0.25 as a
cut-off (i.e., species with distinctiveness values <0.25 were
considered functional common or redundant within a specific
biome).
Functional Space and Biome Similarity
To estimate the overlap among biomes’ hypervolumes we
employed the Sørensen similarity index using (i) the total
number of species and (ii) the list of species considered as
functionally common and geographically widespread for each
biome. Functional trait space similarity was calculated as the
pairwise fractional overlap of hypervolumes among biomes. The
fractional overlap was calculated by dividing twice the volume
of the intersection of two hypervolumes by the volume of their
unions. All hypervolumes were estimated using the R package
“hypervolume” (Blonder et al., 2014, 2018), using the Gaussian
KDE method with the default Silverman bandwidth estimator.
RESULTS
We found substantial overlap in plant species composition across
biomes, based on the intersection of modeled species ranges
(see Figure 1, and Supplementary Table 1). This taxonomic
overlap is greater within tropical and temperate biomes, with
relatively few species being shared between these two climatic
zones. Interestingly, xeric woodlands share species with both
tropical and temperate biomes. Despite the high proportion
of species characteristic of tropical moist forest (83%), this
biome also shares large numbers of species with other tropical
biomes such as dry forests, savannas, xeric woodlands and
tropical grasslands. The high taxonomic overlap of temperate
biomes such as temperate grasslands, Mediterranean woodlands,
taiga and tundra suggests the potential for low functional
distinctiveness, with some temperate biomes representing a
poorer subset of the more species-rich, functionally-diverse
tropical biomes.
With fewer than 10% of the mapped species represented,
the trait data were quite sparse for our biome-scale species
assemblages (Figure 2A). Moreover, the available trait data
varied substantially across traits, plant clades, and biomes.
Phylogenetically, leaf P and wood density were particularly
poorly represented with whole clades lacking any available data.
As a result, of the approximately 2/3 of the trait values for our
7,842 species that had to be imputed, many had to be assigned
in the absence of any closely related taxa. Geographically, SLA,
height, and seed mass are undersampled in the tropics and
temperate South America, while leaf N and wood density are
more evenly sampled among regions. Leaf P is poorly sampled
across all biomes. Trait hypervolumes created from species
pools of a random selection of 20% of cells for each biome
mainly show differences between tropical and temperate/cold
biomes (Figure 2B). Trait hypervolumes tend to be larger and
more variable in tropical biomes, with the highest variation in
moist and dry tropical forests. Among temperate/cold biomes,
coniferous and temperate mixed forest support the largest
trait hypervolumes. Interestingly, xeric shrublands exhibit a
distribution of hypervolumes more similar to tropical than to
temperate biomes.
The relationship between functional distinctiveness and
geographic distribution is remarkably similar among biomes
(Figure 3). In every biome, the vast majority of species are
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Echeverría-Londoño et al. New World Functional Diversity
FIGURE 1 | Overlap of plant species among biomes of the New World. Percentage values express the fraction of species occurring in a biome that have the greatest
proportion of their geographic range in that biome, N=84,413. The base of each branched arrow is positioned to show the biome that includes the greatest
proportion of a species range, while the width represents the number of species shared with the biomes at the tips of the arrow. See Supplementary Table 1 for the
underlying matrix.
both geographically restricted and functionally similar. At the
same time, the most functionally distinctive species within every
biome were generally geographically restricted as well, and the
most geographically widespread species were almost always
functionally similar.
We found considerable variation in the proportional
distribution of growth forms within and among biomes. Woody
species represent the dominant growth forms in tropical
biomes in terms of species numbers, whereas herbaceous
species dominate in temperate environments (Figure 4). When
we considered only those species that are widespread and
functionally common in each biome, the distribution of growth
forms across biomes changed, especially in the proportion of
trees, herbs and grasses. For instance, in tropical biomes, the
proportion of trees decreased in each biome except for moist
forest. In temperate biomes, the proportion of grasses increased,
especially toward the polar regions. Interestingly, the distribution
of growth forms in xeric woodlands more closely resembles the
distribution in temperate biomes when only widespread and
functionally common species are considered.
Pairwise comparisons of species composition among biomes
reveal three main clusters representing the tropical, temperate
and polar climatic zones (Figure 5A), reflecting the high
taxonomic overlap within, but not between, these regions
(Figure 1). Pairwise comparisons of trait hypervolumes among
biomes show a less clear clustering of climatic zones (Figure 5B).
In this case, the functional space of xeric woodlands overlaps
significantly with temperate biomes. This analysis also reveals
the overlap in functional space of taigas with temperate
grasslands and mixed forests. The pairwise comparison of trait
hypervolumes among biomes using only those species considered
as functionally common and widespread shows less overlap
in trait spaces among and within climatic zones (Figure 5C).
However, even though xeric woodlands are now clustered
with the rest of tropical biomes, these habitats along with
tropical grassland exhibit great overlap in functional space
with temperate biomes such as Mediterranean woodlands and
temperate grasslands.
DISCUSSION
Our analyses yield three important insights for understanding
terrestrial biomes through a functional lens. First, we show that
despite progress in the compilation and synthesis of primary
biodiversity data, significant knowledge shortfalls persist that
may limit our ability to quantify the functional biodiversity of
biomes on continental to global scales. Second, our analyses of
the available data nevertheless show that all of the biomes in
North and South America share a remarkable common pattern
in which the most geographically widespread species in any
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Echeverría-Londoño et al. New World Functional Diversity
FIGURE 2 | (A) Proportion of species in the BIEN 3.0 database with known (gray) or missing (black) trait values. Phylogeny at the left corresponds to the seed plants
ALLBM tree from Smith and Brown (2018) (i.e., GenBank and Open Tree of life taxa with a backbone provided by Magallón et al., 2015). Maps represent the
proportion of species with trait information relative to the total number of species in the BIEN 2.0 database. (B) Distribution of trait hypervolumes of 20% of randomly
selected 100 ×100 km cells in each biome. Hypervolumes are reported in units of standard deviations to the power of the number of traits used.
biome tend to share common functional traits while the most
functionally distinctive species are invariably restricted in their
distribution. Third, when only the widespread and functionally
common species are considered, biomes can be more readily
distinguished functionally, and patterns of dissimilarity between
biomes appear to reflect a correspondence between climate and
plant functional niche space. Taken together, our results suggest
that while the study of the functional diversity of biomes is
still in its formative stages, further development of the field will
likely yield insights linking evolution, biogeography, community
assembly, and ecosystem function.
KNOWLEDGE SHORTFALLS
The BIEN database is specifically designed to close gaps in
our knowledge of plant biodiversity, yet as Hortal et al. (2015)
point out, interacting knowledge shortfalls lead to uncertainty
in quantifying biodiversity at the largest scales. For example,
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Echeverría-Londoño et al. New World Functional Diversity
FIGURE 3 | Patterns of functional distinctiveness across biomes. Distinctiveness represents how species are functionally distant from each other within a biome (i.e.,
the mean pairwise phenotypic distance from a focal species to all the others). The larger the value, the more distant a species is to the centroid of the biome’s
functional space. Widespreadness measures how geographically common a species is within a biome. A value of 0 indicates that a species is present in a single
biome cell. Bin values are the number of species on a logescale. Rectangles represent the species that are considered functionally similar and widespread in each
biome using cut-off values of 0.25 and 0.5 of distinctiveness and widespreadness, respectively.
not only did fewer than 10% of our mapped species have
both trait data and phylogenetic information available, but
those missing trait data (∼66% of species’ trait values) were
quite structured, both phylogenetically and geographically.
Thus, the Raunkiaerian shortfall in functional trait data (sensu
Hortal et al., 2015) interacts with the Wallacean shortfall
in distributional information and the Darwinian shortfall in
phylogenetic understanding. And while we can use phylogenetic
knowledge to reasonably impute those missing values (Swenson
et al., 2017), several aspects of the resulting patterns that
are important to biome classification remain uncertain. For
example, the existing growth form data suggest that woody
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Echeverría-Londoño et al. New World Functional Diversity
FIGURE 4 | Distribution of growth forms in each biome (left) using the total number of species, and (right) using only those species that are functionally similar and
widespread within the biome (see Figure 3).
species dominate in all tropical biomes, whereas the proportional
diversity of herbaceous growth forms is much higher in
temperate and polar biomes (see also Engemann et al., 2016;
Zanne et al., 2018). Does this latitudinal increase in relative
diversity of herbaceous plants reflect sampling biases and a
lack of taxonomic knowledge about tropical herbaceous plants,
or does it reflect the differing evolutionary and biogeographic
histories in the Nearctic and Neotropical realms? Dominant
growth forms are one of the key distinguishing features of
biomes, so systematic changes in the most diverse growth
forms (whether dominant or not) will necessarily influence our
predictions about how functional diversity might change across
biomes, and how those changes will affect ecosystem function and
services.
We used species range maps as input data mainly to
overcome the undeniable issue of sampling bias that is
characteristic of datasets compiled from multiple sources (see
Supplementary Figure 1). However, range maps drawn from
species distribution models could represent an additional
element of uncertainty in our results. For example, given the
geographic resolution of this study and the spatial complexity
of certain biomes, these models could have overestimated
the geographical extent of some species from cells of a
single biome to cells of nearby and interdigitated ones.
Using occurrence data did not change the general results
of this study (see Supplementary Figures 2–6); however, a
slight decrease in the functional overlap among biomes
could indeed reveal an overestimation of some species ranges
(see Supplementary Figures 2,5, and 6). Using occurrence
information also changed the general relationships between
functional distinctiveness and widespreadness in our results, due
to the relative oversampling of temperate vs. tropical regions
(see Supplementary Figure 4). In this case, our measure of
widespreadness is limited by the extremely limited sampling
of most species in tropical biomes. As datasets and sampling
improve, there is likely to be scope to reduce these uncertainties
in the future.
The other data gap that needs to be addressed to understand
the functional diversity of biomes is the Prestonian shortfall in
abundance information. Abundance is particularly important
in studies of functional diversity because the traits that matter
most for community assembly, ecosystem functioning, and
biogeochemical cycling are those of the most dominant
species. Because we based our assemblages on range maps
derived from species distribution models, we could only
address the geographic component of commonness, but the
positive relationship between local abundance and geographic
range, the so-called occupancy-abundance relationship,
suggests that the most widespread species in each biome may
frequently be among the more abundant as well (Borregaard
and Rahbek, 2010; Novosolov et al., 2017). Because biomes
are generally characterized by the dominant growth forms
in the region, integrating abundance information might
result in biomes showing less functional overlap. However,
even though BIEN has compiled voluminous plot data
that allow estimates of local abundance, making reasonable
comparisons across different regions and growth forms
is hampered by uneven sampling, regional differences in
gamma diversity, and incommensurate methods of quantifying
abundance.
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Echeverría-Londoño et al. New World Functional Diversity
FIGURE 5 | (A) Pairwise dissimilarity in species composition among biomes.
(B) Pairwise dissimilarity in trait hypervolumes (1-Sørensen similarity) among
biomes using the total number of species. (C) Pairwise dissimilarity in trait
hypervolumes (1-Sørensen similarity) among biomes using only those species
that are considered as functionally similar and widespread. The lighter the cell
the greater the dissimilarity.
COMMON PATTERNS WITHIN BIOMES
Despite the persistent gaps in the available data, our analyses
uncovered a previously undocumented relationship common to
all the biomes of the Western Hemisphere: in any biome, the
most widespread species also tend to exhibit low functional
distinctiveness, whereas the most functionally distinctive species
are almost invariably restricted in their distribution (Figure 3).
This “occupancy-redundancy” relationship may suggest that the
climatic conditions prevalent within a biome select for a set
of common characteristics, with more functionally distinctive
species being restricted to a rarer subset of habitats within the
biome. The overall prevalence of functionally similar species
in all biomes and the “occupancy-redundancy” relationship are
both consistent with a recent global analysis of community level
functional diversity that suggests that habitat filtering leads to
the coexistence of functionally similar species (Li et al., 2018)
as well as studies showing that functional redundancies increase
community stability (e.g., Walker et al., 1999; Pillar et al., 2013).
Moreover, together with the high degree of both taxonomic and
functional overlap among biomes (Figures 1,5), the fact that
common, widespread species are functionally similar reinforces
the notion that land plants across a wide range of environmental
conditions share common characteristics near the core of the
functional trait spectrum (Wright et al., 2004; Díaz et al.,
2016). Unlike Umaña et al. (2017), our results show that rare,
geographically restricted species may or may not be functionally
distinct from more widespread and common species. Thus, the
question remains open whether more functionally distinctive
species are specialists in particular environments or whether
their trait combinations result in demographic or physiological
trade-offs that limit their geographic distribution.
COMPARISONS BETWEEN BIOMES
Our comparison of trait hypervolume distributions across
biomes (Figure 2B) is consistent with the observation that more
species-rich environments are also more functionally diverse
(Swenson et al., 2012; Lamanna et al., 2014; Li et al., 2018;
Šímová et al., 2018). Our results are more equivocal concerning
the hypothesis that seasonal and extreme climatic environments
consistently limit the functional diversity of species (de la Riva
et al., 2018). All tropical biomes display higher average functional
diversity than all temperate biomes, and the polar biomes do
display the smallest hypervolumes. However, within each group,
drier or more seasonally variable biomes do not always display
smaller hypervolumes (e.g., dry forests, xeric woodlands), as
we would have expected following the tolerance hypothesis
by Currie et al. (2004), whereas optimal climatic conditions
support more combinations of physiological parameters. From
these results alone we cannot determine whether temperate
and polar biomes are less taxonomically diverse because of
limits on functional niche space, or whether their functional
hypervolumes are small because they are not taxonomically
diverse. Null-modeling approaches could potentially help to
disentangle taxonomic and functional diversity (Swenson et al.,
2012; Lamanna et al., 2014; Šímová et al., 2018), but such an
Frontiers in Ecology and Evolution | www.frontiersin.org 9December 2018 | Volume 6 | Article 219

Echeverría-Londoño et al. New World Functional Diversity
analysis was beyond the scope of this study. More importantly,
our results reinforce the importance of understanding how
evolutionary and biogeographic history shape the functional
diversity of biomes (Woodward et al., 2004; Pennington et al.,
2006; Donoghue and Edwards, 2014; Moncrieff et al., 2016). The
extensive sharing of species (and higher lineages) across biomes
within, but largely not between, different biogeographic realms
could serve both to homogenize functional diversity within
realms and to provide clues about the characteristic traits that are
selected for, or against, by different environments (Douma et al.,
2012; Zanne et al., 2014, 2018).
Despite substantial hypervolume overlap among all the
biomes (Supplementary Figure 2), tropical, temperate, and cold
biomes all appear to occupy distinguishable regions of functional
space (Figures 5B,C). The main traits differentiating biomes
appear to be traits related to overall plant size, including both
mature height and seed mass (Supplementary Figures 7–10),
rather than leaf economics traits, as observed in more local,
plot-based analyses (e.g., Douma et al., 2012). The exception is
leaf P, which displayed substantial differences between tropical
and temperate/polar biomes (Supplementary Figures 7–10), a
pattern that has been observed in other analyses based on
both species-specific values and whole ecosystem measurements
(Kerkhoff et al., 2005; Swenson et al., 2012). Because leaf P was
our most sparsely sampled trait, the differences between tropical
and temperate/polar realms might be subject to biases from the
imputation procedure. However, Leaf P does exhibit a significant
phylogenetic signal (Kerkhoff et al., 2006), which suggests that
the imputations should be unbiased (Swenson et al., 2017).
Leaf P is also significantly higher in herbaceous growth forms
(Kerkhoff et al., 2006), so the latitudinal shift from predominantly
woody to herbaceous diversity (Figure 4) may also influence this
pattern.
When we limited hypervolume analyses to only the most
widespread and functionally common species in each biome, the
individual biomes within each biogeographic realm overlapped
less in functional trait space (compare Figures 5B,C), suggesting
that these species may reflect phenotypes better adapted to
particular environments. In this context, the xeric woodland
biome is particularly interesting. In part due to the inclusion
of the Chaco ecoregion, xeric woodlands cluster with the
tropical biomes taxonomically (Figure 5A). But when the
functional hypervolumes of all of the species are considered,
they show much stronger similarity to the temperate biomes
(Figure 5B). Finally, when only the most widespread and
functionally common species are analyzed, xeric woodlands
again show increased similarity to tropical biomes, but also
they maintain high similarity with temperate grassland and
Mediterranean biomes (Figure 5C). Transitions from warm,
mesic environments to colder, drier, and more seasonal
environments are facilitated by similar traits, e.g., herbaceous
habit (Douma et al., 2012; Zanne et al., 2014, 2018), and like
colder environments, communities in dry environments also
tend to be more phylogenetically clustered (Qian and Sandel,
2017). Furthermore, the position of xeric zones at the boundary
of the inter-tropical convergence zone makes them a geographical
transition between the tropical and temperate realms. The fact
that xeric woodlands are intermediate between the tropical
and temperate realms both functionally and biogeographically
further reinforces the idea that to better understand the
functional diversity of biomes we must take into account their
biogeographic and phylogenetic histories (Pennington et al.,
2006; Donoghue and Edwards, 2014; Moncrieff et al., 2016).
CONCLUSIONS
Any classification of terrestrial biomes imposes a small number
of discrete categories on continuous gradients in climate and
species distributions, and thus represents a gross simplification
of the complex ecological landscape (Moncrieff et al., 2016, but
see Silva de Miranda et al., 2018). Yet despite their potential for
oversimplification, biomes are useful constructs for organizing
and understanding the biodiversity and functioning of Earth’s
major terrestrial ecosystems, and trait-based approaches have
high potential to help dynamically model global vegetation
distributions.
In this study we have shown that much of the taxonomic
diversity of all biomes represents species that are both narrowly
distributed and functionally similar. Further, within biomes the
most functionally distinctive species in each biome also tend to be
geographically rare, while widespread species uniformly display
low functional distinctiveness. Despite extensive taxonomic
and functional overlap among biomes, they do cluster into
distinguishable, biogeographically and climatically distinct units,
especially when the functional clustering is based on the most
widespread and functionally common species in each biome.
However, advancing a functional understanding of biomes will
require not just a better characterization of trait variation within
and between vegetation types, but also information on the
biogeographic and phylogenetic history of species assemblages
and the relative abundance of species within biomes.
DATA AVAILABILITY
Trait datasets and species range maps analyzed for this study can
be download through the R package BIEN (Maitner et al., 2018).
See http://bien.nceas.ucsb.edu/bien/ for more details about the
BIEN database.
AUTHOR CONTRIBUTIONS
SE-L, AK, BJE, DMN, and CV designed the study; SE-L and AK
analyzed the data; SE-L and AK wrote the first version of the
manuscript; CV, BB, NK, BSM, BM, RKP, BS, SS, J-CS, and SKW
contributed to the collation and creation of the traits and range
maps database. All authors contributed to the development and
writing of the manuscript.
FUNDING
SE-L, DMN, and AK were supported by a collaborative research
grant from the US National Science Foundation (DEB-1556651).
BJE was supported by National Science Foundation award
Frontiers in Ecology and Evolution | www.frontiersin.org 10 December 2018 | Volume 6 | Article 219

Echeverría-Londoño et al. New World Functional Diversity
DEB-1457812 and Macrosystems-1065861.CV was supported
by the European Research Council (Grant ERC-StG-2014-
639706-CONSTRAINTS) and by the French Foundation for
Research on Biodiversity (FRB; www.fondationbiodiversite.fr)
in the context of the CESAB project Causes and consequences
of functional rarity from local to global scales. J-CS considers
this work a contribution to his VILLUM Investigator project
Biodiversity Dynamics in a Changing World funded by VILLUM
FONDEN (grant 16549). SKW was supported by the Strategic
Science Investment Fund of the New Zealand Ministry of
Business, Innovation and Employment’s Science and Innovation
Group.
ACKNOWLEDGMENTS
We thank Dan Griffith and two reviewers for their constructive
criticism which greatly improved this manuscript. We also thank
all BIEN data contributors (see http://bien.nceas.ucsb.edu/bien/
data-contributors/ for a full list).
SUPPLEMENTARY MATERIAL
The Supplementary Material for this article can be found
online at: https://www.frontiersin.org/articles/10.3389/fevo.
2018.00219/full#supplementary-material
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Conflict of Interest Statement: The authors declare that the research was
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Frontiers in Ecology and Evolution | www.frontiersin.org 12 December 2018 | Volume 6 | Article 219