Spatial evolutionary and ecological vicariance analysis (SEEVA), a novel approach to biogeography...

SPECIALPAPER

Spatial evolutionary and ecologicalvicariance analysis (SEEVA), a novelapproach to biogeography and speciationresearch, with an example from BrazilianGentianaceae

Lena Struwe1,2*, Peter E. Smouse1, Einar Heiberg3, Scott Haag4

and Richard G. Lathrop1,4

1Department of Ecology, Evolution & Natural

Resources, Rutgers University, New Brunswick,

NJ 08901-8551, USA, 2Department of Plant

Biology & Pathology, Rutgers University, New

Brunswick, NJ 08901-8551, USA, 3Department

of Clinical Physiology, Lund University, SE-221

85 Lund, Sweden, 4Center for Remote Sensing

& Spatial Analysis, Rutgers University, New

Brunswick, NJ 08901-8551, USA

*Correspondence: Lena Struwe, Department of

Ecology, Evolution & Natural Resources,

Rutgers University, 14 College Farm Road, New

Brunswick, NJ 08901-8551, USA.

E-mail: [email protected]

ABSTRACT

Aim Spatial evolutionary and ecological vicariance analysis (SEEVA) is a simple

analytical method that evaluates environmental or ecological divergence associated

with evolutionary splits. It integrates evolutionary hypotheses, phylogenetic data,

and spatial, temporal, environmental and geographical information to elucidate

patterns. Using a phylogeny of Prepusa Mart. and Senaea Taub. (Angiospermae:

Gentianaceae), SEEVA is used to describe the radiation and ecological patterns of

this basal gentian group across south-eastern Brazil.

Location Latin America, global.

Methods Environmental data for 151 geolocated botanical collections,

associated with specimens from seven species, were compiled with ArcGIS, and

were matched with geolocated base layers of eight climatological variables, as well

as one each of geological, soil type, elevational and vegetation variables. Sister

groups were defined on the basis of the six nested nodes that defined the

phylogenetic tree of these two genera. A (0, 1)-scaled divergence index (D) was

defined and tested for each of 12 environmental and for each of the six

phylogenetic nodes, by means of contingency analyses. We contrast divergence

indices of nested clades, allopatric and sympatric sister clades.

Results The level of ecological divergence between sister clades/species, defined in

terms of D measures, was substantial for five of six nodes, with 21 of 72

environmental comparisons having D > 0.75. Soil types and geological age of

bedrock were strongly divergent only for basal nodes in the phylogeny, by contrast

with temperature and precipitation, which exhibited strong divergence at all nodes.

There has been strong divergence and progressive occupation of wetter and colder

habitats throughout the history of Prepusa. Nodes separating allopatric sister clades

exhibited larger niche divergence than did those separating sympatric sister clades.

Main conclusions SEEVA provides a multi-source, direct analysis method for

correlating field collections, phylogenetic hypotheses, species distributions and

georeferenced environmental data. Using SEEVA, it was possible to quantify and

test the divergence between sister lineages, illustrating both niche conservatism

and ecological specialization. SEEVA permits elucidation of historical and

ecological vicariance for evolutionary lineages, and is amenable to wide

application, taxonomically, geographically and ecologically.

Keywords

Angiosperms, ecological vicariance, Gentianaceae, GIS, historical biogeography,

niche conservatism, phylogeography, Prepusa, Senaea, sympatric speciation.

Journal of Biogeography (J. Biogeogr.) (2011) 38, 1841–1854

ª 2011 Blackwell Publishing Ltd http://wileyonlinelibrary.com/journal/jbi 1841doi:10.1111/j.1365-2699.2011.02532.x

INTRODUCTION

Geographical (area-based) vicariance analysis has played a

prominent role in biogeography and speciation theory for

many years (e.g. Humphries & Parenti, 1999). The ecological

component of vicariance has not received the attention it

warrants, although Hardy & Linder (2005) defined ecological

vicariance as niche divergence of sister species, due to

ecological specialization. Ecological vicariance has also been

used in a slightly different sense, indicating climate or other

ecological changes leading to an increasingly fragmented

habitat, with subsequent speciation in separate fragments

(e.g. Cronk, 1992; Haffer, 1997; Escudero et al., 2009). Gentry

(1981) hypothesized that small-scale ecological (e.g. soil)

differences in the habitat of tropical rain forest plants provided

opportunities for specialization that have led to speciation

within many plant groups (i.e. an example of ecological

vicariance in our sense), but the phenomenon is more pervasive

than that (Young & Leon, 1989). One can envisage adaptation

to new habitats through physiological or morphological

changes, niche separation due to competition, or development

of more specialized pollination or dispersal features. One such

example is the niche separation along elevational gradients in

Himalayan chats (Aves), where closely related species have

developed both morphological differences and divergent veg-

etational preferences (Landmann & Winding, 1993). In general,

one should expect coarse-scale ecological vicariance along

elevational or climatic gradients, representing changes in

photoperiod, rainfall, temperature and/or seasonality.

Coarse-scale spatial separation, such as that typically

invoked for classic vicariance analysis (historical biogeogra-

phy), does not readily account for the micro-habitat features

and fine-scale spatial differences that separate species within a

particular region. Spatial separation may be coincident with

ecological vicariance, but ecologically similar habitats some-

times exist as small fragments, interspersed with other habitats,

over relatively short distances. In fact, biogeographical anal-

yses, such as ancestral area analysis and dispersal–vicariance

analysis (DIVA; Ronquist, 1997) often oversimplify distribu-

tion patterns, and they seldom take ecological data into

account. Species and individuals typically interact with and

react to their local environments, not their spatial positions per

se. Ecological vicariance analysis can elucidate both niche

similarities (niche conservatism, plesiomorphic ecological

traits) and niche differences (adaptation and niche specializa-

tion, apomorphic ecological traits).

Because new species probably occupy ecological niches that

have diverged gradually and minimally from those of their

immediate ancestors, current habitat preferences can be

expected to reflect ancestral preferences to a first approxima-

tion (niche conservatism; e.g. Prinzing et al., 2001; Martınez-

Meyer et al., 2004; Wiens & Graham, 2005). It is widely

recognized that occupation of novel habitats and development

of drastic ecological shifts has led to new lineages and species

through adaptive radiation and acquisition of new genetic

traits. It is possible to reconcile both forms of vicariance by

combining phylogenetic (temporal), geographical (spatial) and

ecological data, all matched (specimen-by-specimen) with geo-

referenced records, using standard GIS methodology, yielding

an integrated approach to spatial, environmental and ecolog-

ical vicariance analysis. Previous methods have used ecological

traits generally mapped onto phylogenies, providing insight

into niche evolution, ecological vicariance and/or ancestral

ecological traits (e.g. Ladiges et al., 1987; Hardy & Linder,

2005; Frasier et al., 2008), but without the power of detailed,

multiple georeferenced specimens and without the level of

statistical analysis presented here. Our objective here is to

present a new methodology, spatial evolutionary and ecological

vicariance analysis (SEEVA), along with newly developed and

freely available software, to begin that synthesis. We highlight

this new approach with an example from the flowering plant

family Gentianaceae, specifically focusing on a sister pair of

Neotropical genera and on hypotheses related to their speciation

history and biogeography, and the evolution of their ecological

niches.

Prepusa Mart. and Senaea Taub. (Gentianaceae: Helieae)

contain seven partly disjunct and endangered flowering plant

species, endemic to south-eastern and eastern Brazil’s frag-

mented, mid- to high-elevation campo rupestre and campo de

altitude vegetation types (Calio et al., 2008). Prepusa montana

and Senaea coerulea are restricted to campo rupestre, and the

other five species are found in campo de altitude. Prepusa and

Senaea (together) form the sister group for all other taxa in the

Neotropical Helieae, a gentian tribe of over 220 species (Struwe

et al., 2009a), widely distributed throughout wet and moist

climate areas of the Neotropics. All the species of this ancient

lineage are of biogeographical interest, exhibiting fragmented

distributions and high endemism in an area of high (but

currently threatened) biodiversity on Gondwanic Precambrian

bedrock (Olson & Dinerstein, 2002). Both campo rupestre and

campo de altitude vegetation have been widespread since the

Miocene, but repeated glacial cycles during the Quaternary

have restricted both vegetation types to small, isolated

‘vegetative islands’ (Safford, 1999). Such geographical vicari-

ance has led to fragmented habitats with rich, highly endemic

floras (Safford & Martinelli, 2000; Alves & Kolbek, 2004). The

species from these two genera probably represent in situ

relictual lineages, still found within the common ancestral area

for the tribe. There are narrow mountain endemics of both

Prepusa and Senaea in the state of Rio de Janeiro, exhibiting

terrestrial (but otherwise classical) island biogeography, as well

as species in the Brazilian Highlands (in Espırito Santo, Minas

Gerais and Bahia). The entire group provides an excellent

model system for testing historical ecological niche vicariance.

In the absence of an appropriate methodology, however,

no one has included environmental and ecological data in a

phylogeographical reconstruction of either genus.

With SEEVA, it is now possible to evaluate niche separation

that either accompanied or followed speciation, comparing

ecological and spatial data for each node within the phylogeny,

yielding a sequence of realized ecological shifts throughout the

phylogenetic history of the group. In larger context, we will

L. Struwe et al.

1842 Journal of Biogeography 38, 1841–1854ª 2011 Blackwell Publishing Ltd

focus on four general questions. (1) How does one measure

and compare the ecological divergence of phylogenetic nodes

in standardized fashion, throughout the phylogenetic tree? (2)

Is it possible to distinguish between niche conservatism and

niche divergence throughout an evolutionary lineage? (3)

What sort of ecologically relevant variables exhibit the most

notable divergence (ecological vicariance) between sister

clades? (4) Are allopatric sister clades associated with larger

or smaller ecological niche divergence than sympatric sister

clades? In the specific phylogenetic context of Prepusa and

Senaea, we examine three additional questions. (5) Is the inter-

generic split accompanied by ecological niche divergence? (6)

What ecological characteristics show the greatest divergence

between allopatric campo de altitude and campo rupestre clades?

(7) Within sympatric clades, is there evidence for ecological

niche separation that could have driven speciation?

MATERIALS AND METHODS

Geolocation of taxonomic specimens

All known (167) herbarium collection records for Prepusa and

Senaea were entered into an existing customized FileMaker Pro

9.0 database (ClarisWorks, Santa Clara, CA, USA), georefer-

encing each locality by hand, using printed and online maps,

atlases and gazettes. Fernanda Calio (University of Sao Paulo,

Sao Paulo, Brazil) identified all herbarium sheets, and the

collections are listed in Calio et al. (2008). Simultaneously, we

coded data quality for each collection record, based on the

degree of precision for each latitude/longitude geographical

coordinate pair: (1) coordinates undeterminable (locality too

vague), (2) location known to nearest degree only, (3) location

known to nearest minute, (4) location known to nearest

second, and (5) label with GPS coordinates recorded. We

included only those records graded (‡ 3), retaining 141

collections for Prepusa and 10 for Senaea. To ensure accuracy,

latitude and longitude decimal degrees were used as coordi-

nates, which were converted to a point shapefile in ArcGIS v.

9.2 (ESRI, Redlands, CA, USA). The distribution map for these

151 collections is shown in Fig. 1. We can anticipate that

location information, and (by extension) all the associated

environmental/ecological information, will improve as GPS

technology is applied to new collections, but these historical

specimens (and their geographical coordinates) are currently

the best collections available for these taxa.

Our 151 collections inevitably sample only a portion of the

total range distributions of these species, but it is probable that

new collections will be found in areas with ecological features

similar to the samples already collected within each species

(Martınez-Meyer et al., 2004; Wiens & Graham, 2005). It is

also noteworthy that these collections are from areas that have

been rather well explored over the past 300 years (the

collection artefact), and finding new localities of these rare

species in an increasingly urbanized and agriculturally devel-

oped landscape has become progressively unlikely. Prepusa

alata and S. janeirensis are known from a single locality

(mountain) each; however, there is a very recent unconfirmed

report that S. janeirensis has been now found in Espırito Santo

(M. F. Calio, pers. comm.). Senaea coerulea has not been

re-collected since 1982, possibly having become extinct in the

interim. Of the seven species, four were considered critically

endangered (P. alata, P. connata, S. coerulea, S. janeirensis);

two endangered (P. hookeriana, P. viridiflora); and one vul-

nerable (P. montana) by Calio et al. (2008; not yet included in

IUCN’s database), based on the IUCN’s Red List v. 3.1

categories and criteria of population size and range, fragmen-

tation and immediate threats (IUCN, 2001). Traditional

statistical tests require that samples be randomly drawn from

the total distribution. Based on the considerations above, and

also in view of the fact that the sample sizes for some species

are small, statistical inferences should be viewed with some

degree of caution. The exercise is nevertheless worthwhile,

both as a means of illustrating what one can expect to learn

from SEEVA, and as a way of shedding light on these two

genera, both in danger of extirpation.

Phylogenetic data and distributions

We employed the single most parsimonious reconstruction of

the phylogeny of Prepusa and Senaea from Calio et al. (2008)

as our phylogenetic tree for these seven taxa, importing a

Nexus-format tree file into seeva v. 1.01 (superimposed on

distribution in Fig. 1). This particular tree was based on

morphological data only, because specimens for the rare and

endangered species were unavailable or unsuitable for DNA

work (Calio et al., 2008). We view the phylogeny as reliable

and unambiguous, because all branches are supported by

strong morphological synapomorphies, and because all but

one node had Bayesian posterior probability values in excess of

0.85. The inclusion of all species, even those poorly sampled, is

necessary to provide a proper evaluation of the evolutionary

and ecological history.

Allopatry and sympatry

Mapped collections were also used to identify spatially

sympatric, partially sympatric and allopatric species and clade

distributions for later SEEVA comparison. Geographically

sympatric species might be in similar environments due to

spatial contiguity, but in an area with steep mountain slopes

this might not always be the case. Sympatry and allopatry are

commonly inferred from total species distribution maps,

rather than from individual collection locales, which may

differ in their ecological niches; we address the ecological

issues with SEEVA. When the geolocated records are overlaid

on a topographic relief map of south-eastern Brazil (Fig. 1),

the intraspecific fragmentation within and interspecific spatial

separation among all seven taxa are evident, apart from four

sympatric species in the mountains north of Rio de Janeiro.

This best supported phylogenetic hypothesis for the Pre-

pusa–Senaea clade shows a fully resolved evolutionary tree.

Node 1 provides the split between the two genera, Prepusa

SEEVA methodology

Journal of Biogeography 38, 1841–1854 1843ª 2011 Blackwell Publishing Ltd

ancestor ?

Figure 1 Distribution map of Prepusa and Senaea in south-eastern Brazil, showing geolocated collections of the seven species. The

phylogenetic relationships are superimposed as white lines; note that the ancestor’s location is hypothetical. The bottom inset map is a

close-up of the coastal mountains near Rio de Janeiro, home to four of the seven species.

L. Struwe et al.


versus Senaea, two subclades that are partially sympatric in

their geographical distribution (Fig. 1). Within Prepusa there

are four subsequent nodes, with P. montana Mart. diverging

first (node 2, allopatric), P. viridiflora Brade diverging next

(node 3, allopatric), P. alata Porto & Brade diverging third

(node 4, sympatric), and finally P. hookeriana Gardn. and

P. connata Gardn. separating (node 5, sympatric). Node 6 is

the divergence between the two extant and allopatric species of

Senaea, S. janeirensis Brade and S. coerulea Taub. Of these

species, P. montana and S. coerulea are generally found in

campo rupestre vegetation, and the other five in campo de

altitude habitats.

Environmental data

Using collection record point locations, we extracted the

coincident environmental variables from 12 layers in ArcGIS,

having created an Arc Macro Language (AML) script to

automate this extraction for both vector and raster environ-

mental datasets. The environmental data from ArcGIS were

exported to an Excel worksheet for subsequent SEEVA statistical

analysis. We acquired primary GIS data layers for temperature

from WorldClim v. 1.4 (http://www.worldclim.org; Hijmans

et al., 2005), including: maximum temperature of the warmest

month (in �C · 10; format, 1-km grid); minimum temperature

of the coldest month (in �C · 10; format, 1-km grid); annual

temperature range (in �C; 1-km grid); annual mean tempera-

ture (in �C · 10; 1-km grid). We also extracted precipitation

data from WorldClim, including: average annual precipitation

(mm precipitation; 1-km grid); precipitation during the driest

quarter (mm precipitation; 1-km grid); precipitation during the

wettest month (mm precipitation; 1-km grid); precipitation

during the wettest quarter (mm precipitation; 1-km grid).

Additional base-layer data were extracted for: elevation (source

data, US Geological Survey, Schenk et al., 1997; format, grid;

scale, 30 arc second); vegetation type (source data, ESRI, 1996;

format, polygon; scale, 1:20,000,000); soil type (source, ESRI,

1996; format, vector; scale, 1:5,000,000–10,000,000); geology

(age of bedrock; source, US Geological Survey, Schenk et al.,

1997; format, vector; scale, 1:1,000,000–5,000,000).

Our characterization of environmental variation is spatially

rather coarse, as are the original GIS database layers, but the

objective is to elucidate broad patterns; as with any geospatial

analysis, there is a trade-off between high spatial resolution and

broad spatial coverage. We used the highest-resolution sources

available. Our current environmental features have the advan-

tage of being mapped in a consistent fashion across the entire

study area (all of eastern Brazil). Inevitably, correlations

between phylogenetic divergence, ecological divergence and

environmental divergence must be interpreted as trends. In

addition, even minute-level spatial resolution for collections is

of limited precision, as we cannot pinpoint a plant location to a

specific 1-km grid cell, but only to a small neighbourhood of

grid cells. As environmental characteristics are broadly catego-

rized as well, and not at scales varying within < 1 km, seeva

evaluation is as precise as the current spatial resolution of both

ecological variation and location data allow. Both the spatial

resolution of new field collections and that of environmental

base layers are continually improving, so one can anticipate

steadily improving precision in the future. Finally, the object

here is spatial, environmental and ecological ‘pattern assess-

ment’, not experimental demonstration of the impact of

particular variables on adaptation of these taxa. SEEVA can

point the way forward, however, by indicating likely environ-

mental pressures that may well have been adaptive, and in spite

of the current coarseness of resolution, it reveals substantial and

edifying ecological associations that warrant further attention.

Rationale for SEEVA

The central premise of SEEVA is that cladistic splits (nodes)

are at least as much a reflection of ecological splits (ecological

vicariance) as they are of spatial separation (spatial vicariance).

That fact should be reflected in ecological divergence of the

resulting clades, which can be expected to linger for a long time

in the phylogenetic record. From a statistical point of view,

that leads to the expectation that cladistic splits will be

associated with specific environmental splits. We evaluate that

expectation, using a null hypothesis that ecological and

phylogenetic factors are completely independent (no ecological

vicariance). It is understood that ecological features are

correlated, both with each other and with geographical

separation, sometimes highly so (e.g. rainfall and tempera-

ture); but we show that separate analysis of single ecological

indicators is itself revealing, indicating which environmental,

geological and ecological features are divergent between sister

clades emerging from each phylogenetic node, and which are

not, providing some useful clues as to where to look for

ecological niche separation. Taken collectively, it is clear (see

below) that the internal ecological radiation of this (collective)

basal clade of the Helieae has been substantial, and that some

of the obvious geological, climatic and environmental features

have probably been more relevant than others.

Assessing environmental associations

To test for ecological vicariance associated with any particular

phylogenetic split, we tally each of the specimens within the

two daughter clades for each of several environmental features

(used as ecological indicators), each characterized as a K-state

variable, thus reducing the data for each character to the form

of a (2 · K) contingency table at each node of the phylogeny

(Table 1). For the seven species of this study, that reduces the

analysis of each environmental character to a set of six contin-

gency tables, one table for each phylogenetic node (each sta-

tistically independent of the others). We are led naturally to a

set of six hypothesis tests for each environmental feature, one

per node. The null hypothesis is that there is no divergence of

feature states between the two sister clades, but significant

divergence indicates an association between phylogenetic and

ecological splits. The sample sizes for some nodes are inevitably

small, so we use a sample-permuted version of Fisher’s (1958)

SEEVA methodology


exact test to compute the test criteria and P-values in all cases

(see Metha & Patel, 1986). Because we compute six (indepen-

dent) tests for each environmental feature, we have used a

Bonferroni correction (cf. Rice, 1989), which amounts to declar-

ing a significant result for a particular node only if P £ 0.0085,

which is equivalent to an experiment-wise error rate of a = 1 )(1 ) 0.0085)6 � 6 (0.0085) = 0.05 for the set of six indepen-

dent nodal tests. The procedure is conservative, and reduces the

rate of false positives (requiring follow-up) to a minimum.

Measuring the strength of the association

Statistical testing aside, we need some sense of the strength of

the phylogenetic–ecological associations we uncover. Several

comparison indices have been developed for contingency

tables, but none is ideal under all circumstances. There are

alternative pairwise test criteria that one could use. Most

common is the traditional contingency criterion that we have

used elsewhere (Struwe et al., 2009b). Given a 2 · K contin-

gency table of the form shown in Table 1, we compare a pair of

species or sister clades, via the traditional 2 · K contingency

test criterion,

v2 ¼ nA!nB!X�1!X�2!��X�K !

n!xA1!xB1!xA2!xB2!��xAK !xBK !

� �ð1Þ

for the data configuration itself. By permuting membership of

each of the specimens between clades, while holding the

marginal totals constant, we can evaluate the fraction (P) of

permuted (null hypothesis) splits that yield a v2-criterion that

is at least as large as the actual split (Fisher, 1958). Struwe et al.

(2009b) used a derivative impact index (IAB), adjusted for the

sample size (n) and the degrees of freedom (K ) 1),

IAB ¼ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi

v2

n � ðK � 1Þ

sð2Þ

to gauge the size of the effect. The IAB index is bounded below

by 0, but its maximum size depends on the evenness of

representation of the various states within the combined

dataset for the particular node in question, as well as the

relative sample sizes of the two clades, and it is difficult to

gauge how large the association is, relative to how large it

could be.

To circumvent this problem, we have implemented a

proportional measure, bounded on the interval (0, 1), and

traceable to the Horn (1966) index of proportional overlap

(see also Chao et al., 2008; Jost, 2008). We begin with the same

2 · K contingency table as above, but we define an index of

divergence as:

DAB ¼ 1� 2qAB

qAA þ qBB; ð3Þ

where

qAA ¼XK

k¼1

xAk

nA

� �2

; qAB ¼XK

k¼1

xAk�xBk

nA � nB; qBB ¼

XK

k¼1

xBk

nB

� �2

: ð4Þ

This second index scales all contrasts from 0.0 (no difference

between sister clades) to 1.0 (maximum possible difference),

regardless of the sample configuration at each node. We report

and stress the divergence index (D) here; (0, 1)-scaling conveys

the magnitude of divergence better, and we subscript it for

each node (D1, D2,…, D6) to avoid confusion.

Formal analysis and SEEVA software

The seeva software was written by Einar Heiberg in matlab

(http://www.mathworks.com), and is available as a precom-

piled, stand-alone program that can be run on a PC with

Windows 7, Vista, XP, NT or ME. It is also available in a

source-code format for users who want to modify or contrib-

ute to the software project. The source-code version can be run

on any platform where matlab is available. The seeva

software is free for all academic users, provided that they cite

this publication in scientific presentations or scientific publi-

cations, and can be downloaded from http://seeva.heiberg.se. A

manual and additional documentation is provided on the

SEEVA webpage: http://www.rci.rutgers.edu/~struwe/seeva.

The current version (v. 1.01) imports raw data as Excel files

tabulating individuals and their environmental (or other)

variables, and phylogenetic trees in the NEXUS format, and

calculates I, D and associated P-values for each node, or other

(specified) pairwise comparisons. The tabulated output file can

be opened in Excel.

Quantitative variables (temperature, elevation and precipi-

tation) were scored as ordered, continuous data. In view of the

coarseness of the available base layers, we chose to divide the

relevant scales into four quartile classes (the default setting in

seeva, although the number of classes is adjustable). Quali-

tative variables (soil, geology and vegetation type) were treated

as non-ordered, categorical data, and when state frequencies

were very low, we combined related types or (rarely) omitted

the very few records with rare types present in fewer than four

individuals (see data files at http://www.rci.rutgers.edu/~

Table 1 Numeric tally for an ecological feature exhibiting K

different eco-states.

Clade

Eco-state

(1) (2) (3) (4) Total

A xA1 xA2 xA3 xA4 nA

B xB1 xB2 xB3 xB4 nB

Node total XÆ1 XÆ2 XÆ3 XÆ4 n

n total specimens divided into a pair of sister groups (A and B), the

derivative sister clades derived from a particular phylogenetic split/

node.

xAk and xBk are the tallies of specimens within each of the two clades

that exhibit the kth state (k = 1,…, K = 4); the sum of the state-tallies

(xAk) is the total sample size (nA) for clade A, and similarly for clade B;

the total tallies for the K = 4 states are indicated by the bottom line of

the table, XÆ1, XÆ2, etc., and those tallies sum to the total sample size for

the node (n).

L. Struwe et al.


struwe/seeva for categories and types for each variable).

Fisher’s exact tests, divergence indices (D) and associated P-

values were computed as described above. The raw data

worksheet, tree file, seeva raw output file and Excel formatted

result file are available on request from the authors.

RESULTS

Ecological vicariance analysis

We used seeva to analyse the six phylogenetic nodes for each

of 12 environmental features; 40 of the 72 comparisons were

significant (P £ 0.0085, indicated by *) after Bonferroni

correction. Overall, the degree of ecological divergence between

sister clades/species, indicated by the sizes of the (0, 1)-scaled D

measures, was substantial for all but node 5. Sample sizes and

divergence indices (D) are presented for each variable and

node: temperature variables in Table 2, precipitation variables

in Table 3 and non-climatic variables in Table 4.

For the temperature variables (Table 2), Prepusa and Senaea

(node 1) were moderately divergent for all features

(D1 = 0.38*–0.49*). The split within Senaea exhibited a wide

range of divergence indices across temperature variables

(D6 = 0.21–0.76), but sample sizes were small for both of

Table 2 Index of divergence (D) from phylogeny-based seeva evaluation of Prepusa and Senaea from south-eastern Brazil, using four

temperature-based features.

Phylogenetic node

Number of

samples Index of divergence (D)

nA nB

Maximum temperature

warmest month

Minimum temperature

coldest month

Temperature

annual range

Mean annual

temperature

Prepusa versus Senaea

1 10 141 0.42* 0.43* 0.49* 0.38*

Within Prepusa

2 63 78 0.49* 0.83* 0.73* 0.56*

3 9 69 0.67* 0.45* 0.25 0.60*

4 5 64 0.89* 0.60* 0.49 0.60*

5 16 48 0.09 0.00 0.38* 0.00

Within Senaea

6 5 5 0.47 0.76 0.21 0.76

Nodal averages across features – – 0.51 0.51 0.43 0.48

Nodes correspond to those in Fig. 2. Features with significant D-values > 0.75 are listed in bold face. The numbers of samples for the two sister clades

are indicated by nA and nB.

*Nodes showing significant differences between sister groups using a Bonferroni criterion of P £ 0.0085.

Table 3 Index of divergence (D) from phylogeny-based seeva evaluation of Prepusa and Senaea from south-eastern Brazil, using four

precipitation-based features.

Phylogenetic node

Number of

samples Index of divergence (D)

nA nB

Average annual

precipitation

Precipitation,

wettest month

Precipitation,

driest month

Precipitation,

wettest quarter


1 10 141 0.69* 0.37* 0.28 0.29

Within Prepusa

2 63 78 0.85* 0.92* 0.95* 0.92*

3 9 69 0.92* 1.00* 0.11 1.00*

4 5 64 0.53 0.40 0.51 0.44

5 16 48 0.12* 0.00 0.12* 0.00

Within Senaea

6 5 5 0.00 0.76 1.00* 1.00*

Nodal average across features – – 0.52 0.57 0.50 0.61

Nodes correspond to those in Fig. 3. Features with significant D-values > 0.75 are listed in bold face. The numbers of samples for the two sister clades

are indicated by nA and nB.

*Nodes showing significant differences between sister groups using a Bonferroni criterion of P £ 0.0085.

SEEVA methodology


these rare taxa, so none of the D6 values was significant.

Temperature divergence varied widely within Prepusa, ranging

from (D5 = 0.00, minimum temperature during the coldest

month, and mean annual temperature) to (D4 = 0.89*,

maximum temperature during the warmest month). As shown

for minimum average temperature for the coldest month

(Fig. 2), there is a strong ecological split between cold-tolerant

and warm-tolerant clades at the base of Prepusa (D2) and at the

base of Senaea (D6), and there is also a tendency towards

progressively cold-tolerant taxa as one moves up the phylogeny

of Prepusa (nodes 2 fi 5, see relative distribution of observa-

tions in the species histograms).

Analysis of the four precipitation variables (Table 3) indi-

cates that the two basal nodes within Prepusa exhibit the largest

divergence, with D2 and D3 = 0.85*–1.00* for all but one

measure (precipitation during the driest month, D3 = 0.11).

By contrast with the temperature variables (Table 2), all

precipitation variables showed strong separation of the two

Senaea species (D6 = 0.76*–1.00*), except for average annual

precipitation, which exhibited no ecological divergence within

Senaea (D6 = 0.0). Overall, there was strong divergence and

progressive occupation of wetter habitats in Prepusa (Fig. 3).

The progression towards wetter habitats is seen by comparing

the relative proportions of precipitation states in the histo-

grams of each of the terminal species in Fig. 3.

Elevation, vegetation type and soil type are complex

features, all cross-correlated with each other and strongly

associated with temperature and precipitation regimes

(Table 4). Vegetation type is a result of the interaction

between many environmental and biological factors, and this

feature showed strong divergence for several nodes

(D1 = 0.77*, D2 = 1.00*, D4 = 0.75*, D6 = 0.76*). For eleva-

tion, nodes 3 fi 5 within Prepusa and node 6 within Senaea

showed strong divergence (D = 0.43–1.00*), but the most

basal nodes, D1 = 0.33 and D2 = 0.35, did not. There was also

a large range of elevations within single species (e.g. P. mon-

tana) and no consistent directional trends in elevational

distributions within Prepusa (Fig. 4). For soil type, Senaea

showed maximum divergence between its two species

(D6 = 1.00*), but there were no particularly useful patterns

within Prepusa. Only the most basal node (D2 = 0.54*) in

Prepusa showed any useful bedrock substrate divergence; with

that single exception, all individuals in the study were found

on the same bedrock substrate. The limited ecological

divergence associated with the latter two variables can be

explained by very coarse base-layer data, and/or strong and

persistent niche conservatism.

Allopatric versus sympatric clades

Many environmental features show strong divergence between

allopatric sister clades, one in the campo de altitude and one in

the campo rupestre zones (nodes 2 and 6), involving many

ecological distinctions. Although exhibiting greater spatial

separation, however, allopatric clades do not always show

the largest divergence for specific environmental features,

such as maximum temperature during the warmest month

and elevation (Fig. 5), indicating that particular ecological

niches can diverge more for sympatric than for allopatric sister

clades.

For sympatric splits of campo de altitude species (nodes 4

and 5), we see distinctive ecological niche separation at closer

spatial quarters, indicated by modest divergence indices for

maximum and minimum temperatures and precipitation

variables. All these variables show strong elevational influences.

Sympatric niche divergence could be the result of adaptation

through specialization, following local vertical fragmentation

and isolation of populations.

Table 4 Index of divergence (D) from phy-

logeny-based seeva evaluation of Prepusa

and Senaea from south-eastern Brazil, using

four non-climatic environmental features

(elevational zone, vegetation type, soil type

and geological bedrock age).Phylogenetic node

Number

of samples Index of divergence (D)

nA nB Elevation

Vegetation

type Soil type

Geological age

of bedrock


1 10 141 0.33 0.77* 0.34* 0.10

Within Prepusa

2 63 78 0.35* 1.00* 1.00* 0.54*

3 9 69 0.54* 0.28 0.00 0.00

4 5 64 0.73* 0.75* 0.00 0.00

5 16 48 0.43* 0.03 0.00 0.00

Within Senaea

6 5 5 1.00* 0.76 1.00* 0.00

Nodal average

across features

– – 0.56 0.60 0.39 0.11

Nodes correspond to those in Fig. 4. Features with significant D-values > 0.75 are listed in bold

face. The numbers of samples for the two sister clades are indicated by nA and nB.

*Nodes showing significant differences between sister groups using a Bonferroni criterion of

P £ 0.0085.

L. Struwe et al.


Ecological vicariance has not developed uniformly across

nodes for all ecological features, but all ecological features

(barring age of bedrock) have substantial D-values when

averaged across nodes (bottom rows of Tables 2–4). Details

aside, seeva also indicates (on average) that ecological diver-

gence of spatially allopatric clades (D2 = 0.76, D3 = 0.48,

D6 = 0.64) is slightly greater than that of partially sympatric

clades (D1 = 0.41) or sympatric clades (D4 = 0.50, D5 =

0.10), and averaging broadly, we see that Dallopatric > Dpartial >

Dsympatric.

Ecological and evolutionary integration

Although there is variation in which environmental features

are most important at any particular node, speciation generally

involves ecological vicariance, whether or not it also involves

geographical fragmentation. It is also noteworthy that these

environmental features are partially to strongly correlated

among themselves; that translates into the observation that

ecological vicariance is a coordinated adaptive process with

multiple overt signatures. Those signatures are evident in

environmental features that can be accessed in the form of GIS

base layers.

Understanding of the evolutionary trends in ecological

patterns requires tree-thinking (Baum et al., 2005) and map-

ping of D-values from many environmental variables onto a

phylogenetic tree. This visualizes and highlights the complex

interaction between environmental characters and the phylo-

genetic and biogeographical history of a particular taxonomic

group. D-values need to be compared between nodes, between

variables, and for their relative (temporal) nodal position in

the phylogeny. This is exemplified in Fig. 5, which provides a

detailed, but overall, pattern analysis of ecological vicariance

throughout the history of this group.

As also seen in Fig. 5, there is large-scale ecological

vicariance at all but node 5, the most recent split in Prepusa.

Interestingly, however, the deepest split (between Prepusa and

Senaea) does not exhibit great ecological divergence, so

divergence does not necessarily increase with time since

separation. Beyond the average levels of divergence for

particular nodes, averaged over features, it is noteworthy that

each node shows divergence for a different combination of

campo rupestre campo de altitude

< 8.6 °C (coldest)8.6-11.0 °C11.0-13.0 °C>13.0 °C (warmest)

S. j

anei

rens

is ( n

=5)

S. c

oeru

lea

(n=5

)

P. a

lata

(n=5

)

P. c

onna

ta (n

=16)

P.h

ooke

riana

(n=4

8)

P. m

onta

na (n

=63)

P. v

iridi

flora

(n=9

)warmer«

D1=0.43*

D4=0.60

D3=0.45*

D6=0.76 D2=0.83*

D5=0.00

Minimum average temperatureof coldest month

»colder

warmer«

»colderallopatric

allopatric

allopatric

sympatric

sympatric

partially sympatric

Figure 2 Phylogenetic hypothesis for Prepusa and Senaea from south-eastern Brazil, based on Calio et al. (2008; outgroups excluded).

Numbered nodes correspond to nodes in the spatial evolutionary and ecological vicariance analysis (SEEVA), and geographic sympatry/

allopatry is indicated at each node. The SEEVA results for the variable ‘minimum average temperature of the coldest month’ (coded as four

qualitative states, using seeva software) are shown as histograms for each species. Total height of each histogram bar equals 100% of

observations for each species; number of observations (n) is indicated after each species name; greyscale colours of histograms represent the

four different states. Divergence indices are provided for each node; *P £ 0.0085 (significant difference between clades after Bonferroni

correction).

SEEVA methodology


features. Certain features (e.g. soil) show larger divergence

lower in the phylogeny than for more recent nodes. Other

features (elevation) show large differences throughout the

entire phylogeny. Although many of these environmental

features are intercorrelated, nodal divergence indices for

different features are not tightly coupled across the phylogeny.

There are some average trends, but the details matter for each

node.

Prepusa and Senaea diverge significantly and strongly in

about two-thirds of the variables investigated, with the

strongest patterns found in vegetation type and annual

precipitation (Fig. 5), but all temperature variables also show

significant differences. However, the degree of divergence at

this basal node is less than the most basal nodes within both

genera. The two spatially disjunct Senaea species are highly

divergent from each other in all climatic variables, but show no

divergence in soil type and bedrock age. Within Prepusa, the

three basal nodes (nodes 2–4) all have strong divergence

patterns, with trends indicating stronger climate seasonality

divergence towards the base (e.g. minimum temperature

during the coldest month, and precipitation amounts during

the wettest quarter, wettest month and driest quarter). For the

more recent splits (nodes 4 and5), most climatic variables

taper off in their divergence patterns, but elevation is still

highly divergent, indicating species niches that have diverged

elevationally.

DISCUSSION

Ecological niche evolution

Ecological niche conservatism should manifest as low diver-

gence indices for ecological features for major sister clades,

whereas relatively higher divergence indices within subclades

could be the result of novel niche adaptation or specialization

within existing ancestral niches, due to competition, dispersal

or vicariance. This is seen with variables such as precipitation

during the wettest quarter and driest month, which show lower

D-values at node 1 than at nodes 2, 3 and 6.

Geological age of bedrock is the feature that shows a very

low average D-value, while the average D-values for climate-

related features (rainfall and temperature) are much higher,

suggesting strong differences in how these species manage

climatic seasonality. Soil type showed only one node with a

high divergence index (node 2, separating P. montana from the

rest of the genus Prepusa), possibly indicating a strong (and

<736 mm (driest)737-1358 mm1358-1687 mm>1688 mm (wettest)

S. j

anei

rens

is (n

=5)

S. c

oeru

lea

(n=5

)

P. a

lata

(n=5

)

P. c

onna

ta (n

=16)

P.h

ooke

riana

( n=4

8)

P. m

onta

na (n

=63)

P. v

iridi

flora

(n=9

)drier«

D1=0.69*

D4=0.53

D3=0.92*

D6=0.00 D2=0.85

D5=0.12*

Annual precipitation »wetter

drier«

drier«

»wetter

drier« »wettercirtapollacirtapolla

allopatric

sympatric

sympatric

partiallysympatric

Figure 3 Spatial evolutionary and ecological vicariance analysis (SEEVA) results for the variable ‘annual precipitation’ coded as four

qualitative states, using seeva software. Total height of each histogram bar equals 100% of observations for each Prepusa and Senaea species

from south-eastern Brazil, with number of observations (n) indicated after each species name; greyscale colours of histograms represent the

four different states. Divergence indices are noted for each node; *P £ 0.0085 (significant difference between clades after Bonferroni

correction).

L. Struwe et al.


ancestral) tribal preference for ferralitic and allitic soils, rich in

humus (umbric ferrisols). Prepusa montana is the only species

in this study found on subtropical and tropical brown forest

soils (eutric cambisols) and low-humus, ferruginated, savanna

soils (chromic cambisol). This is compatible with the idea that

soil characteristics are important for defining ancestral and

current habitats for the tribe as a whole, where many basal

lineages are restricted to nutrient-poor, low-pH soils (Struwe

et al., 2002, 2009a; Frasier et al., 2008).

Sampling issues

In this pilot study, we used a small group (seven species) with a

limited number of geolocated collections (151) to illustrate the

opportunities provided by SEEVA. As always, the utility of

analysis is strongly dependent on the relevance and quality of

the input data. Are these the right variables for this problem,

and are the data accurate enough, geospatially, to support the

enterprise? For Prepusa and Senaea, the climatic, elevation and

vegetation features exhibit overt signals at several phylogenetic

nodes, both within and between the two genera. Soil type and

age of bedrock, on the other hand, were of minimal utility

because they were of reduced relevance; because the available

characterizations of them were inadequate; or because the

coarseness of the available base layers was a poor match for the

geolocation accuracy of the botanical collections. These

specimens were collected over a time span of 300 years, with

limited spatial accuracy (by modern standards), but the results

are nevertheless revealing. With fresh collections, tightly GPS-

localized, the accuracy of both geolocation and the derivative

base layers can only be expected to improve.

The statistical power of SEEVA increases with the number of

collection records mapped for each species. Having acknowl-

edged that, we have shown here that inclusion of only a small

number of records for rare species, while less than statistically

compelling, can shed some useful light on the complete history

of a particular taxonomic group. With SEEVA’s phylogenetic

component, inclusion of numerous species, even the rarer

species, is both possible and desirable. It is common in

phylogenetic analyses to exclude the rare species due to lack of

(particularly) molecular data, but we do not recommend

incomplete taxon sampling. The number of taxa is not a limit

to this method, because sister groups are compared, and the

number of nodes increases as the number of species increases.

The number of available specimens will remain a statistical

concern (limited statistical power), but inclusion of the few

available data points for these species is better than excluding

taxa from the phylogeny. The resulting divergence indices for

<788 m (lowest)788-966 m966-1084 m>1084 m (highest)

S. j

anei

rens

is (n

=5)

S. c

oeru

lea

(n=5

)

P. a

lata

(n=5

)

P. c

onna

ta (n

=16)

P.h

ooke

riana

( n=4

8)

P. m

onta

na (n

=63)

P. v

iridi

flora

(n=9

)

D1=0.33

D4=0.73*

D3=0.54*

D6=1.00 D2=0.35*

D5=0.43

Elevationlower« »higher »lower

»higherlower«

cirtapolla cirtapolla

allopatric

sympatric

sympatric

partiallysympatric

Figure 4 Spatial evolutionary and ecological vicariance analysis (SEEVA) results for the variable elevation coded as four qualitative

states, using seeva software. Total height of each histogram bar equals 100% of observations for each species, and number of observations

(n) is indicated after each Prepusa or Senaea species from south-eastern Brazil; greyscale colours of histograms represent the four different

states. Divergence indices are noted for each node; *P £ 0.0085 (significant difference between clades after Bonferroni correction).

SEEVA methodology


nodal splits that involve clades with small sample sizes should

be interpreted with appropriate statistical caution.

CONCLUSIONS

The SEEVA methodology provides a standardized, compara-

tive measure for qualitative and quantitative differences

between sister groups and clades, and has been demonstrated

to be a useful tool to evaluate niche conservatism as well as

niche divergence (question 1). The largest divergence was

found in climatic characters related to precipitation seasonal-

ity, and the lowest differences were found for soil type and

geological age of bedrock (questions 2 and 3). Nodes dividing

two allopatric sister clades show generally larger divergence

than partially sympatric sister clades, which showed larger

divergence than sympatric clades, with some exceptions

(question 4). Sympatric clades generally show distinct

divergence in elevation and annual temperature. The

generic split between Prepusa and Senaea is highlighted by

large, but not the highest, divergences in environmental

S. j

anei

rens

is

S. c

oeru

lea

P. a

lata P

. con

nata

P.

hook

eria

na

P. m

onta

na

P. v

iridi

flora

node 1

node 4

node 3

node 2node 6

node 5

Annual Mean TemperatureTemperature Annual RangeMin. Temperature Coldest MonthMax. Temperature Warmest MonthPrecipitation Wettest QuarterPrecipitation Driest MonthPrecipitation Wettest MonthAnnual PrecipitationGeologic Age of BedrockSoil TypeVegetation TypeElevation

0.35 *1.00 *1.00 *

0.54 *

0.49 *0.83 *

0.73 *0.56 *

0.85 *

0.95*0.92 *

0.92 *

0.73 *0.75 *

0.000.00

0.89 *0.60*

0.490.60*

0.53

0.510.40

0.44

0.54 *0.28

0.000.00

0.67 *0.45 *

0.250.60 *

0.92 *

0.111.00 *

1.00 *

0.43*0.030.000.00

0.090.00

0.38*0.00

0.12*

0.12 *0.00

0.00

1.00*0.76

1.00*0.00

0.470.76

0.210.76

0.00

1.00*0.76

1.00*

0.330.77 *

0.34 *0.10

0.69 *0.37*

0.280.29

0.42 *0.43 *0.49 *

0.38 *

Figure 5 Divergence indices for 12 environmental variables for each node of the phylogeny of Prepusa and Senaea species from south-

eastern Brazil, shown as histograms of divergence indices (scales range from 0–1). *Statistically significant divergence indices after Bon-

ferroni correction (P £ 0.0085), showing which variables are significantly different between the sister clades derived from each nodal split.

L. Struwe et al.


characteristics (question 5). The highest divergences in the

environmental patterns are found between the two Senaea

species, and between P. montana and the rest of the species of

Prepusa. The phylogenetic split between taxa in campo rupestre

versus campo de altitude are associated with high divergence in

soil types, precipitation variables related to seasonality in

rainfall, and minimum temperature during the coldest month,

which is also an indication of climatic seasonality (question 6).

Within the clade separating the sympatric species P. connata

and P. hookeriana, most variables show low or no divergence,

but elevation and annual temperature range are significantly

divergent, suggesting possible ecological niche vicariance even

between such closely related and co-occurring species (ques-

tion 7).

The close interaction between species and their environment

has been an important tenet of our understanding of evolution

for over 200 years. SEEVA yields an analysis of direct

observation data from individuals, allowing examination of

variation within and among clades, avoiding the necessary loss

of information that accompanies traditional averaging. The

main problems remain how to analyse variables that are

mutually interdependent; how to include detailed, individu-

alized information (as opposed to species-wide ‘averages’);

and how to incorporate phylogenetic time depth as a factor in

such multi-feature analyses. We will need future refinements

in SEEVA to accommodate correlated ecological features and

the distinction between continuous and unordered state

variables. Our phylogenetic analysis has thus far been

restricted to imposing a splitting sequence, determined from

other data, and there is (as yet) no provision for ambiguous

phylogenies, nor have we incorporated refinements relating

to the time depth of the various nodes. While there is

ample room for further development of this methodology,

even this first step towards combining evolutionary, ecological

and geographical information with SEEVA has already

rendered complex ensembles of information amenable to

productive analysis.

ACKNOWLEDGEMENTS

The authors would like to thank J. Bognar, J. Burkhalter, M.F.

Calio, C. Kovach-Orr, P. Miarmi and W. Rosica for help with

geolocation data, species determinations, figure preparation

and cartography, and other earlier parts of this study. L.S. and

P.E.S. were funded by National Science Foundation, Division

of Environmental Biology grants (NSF-DEB-317612 and NSF-

DEB-0514956, respectively) and US Department of Agriculture

awards (USDA/NJAES-NJ17112 and USDA/NJAES-17111,

respectively; S.H. and R.L. were funded by the New Jersey

Agricultural Experiment Station; E.H. was funded by a Swedish

Research Council award (no. 621-2008-2949). L.S. and E.H.

would like to dedicate this article in the memory of their

recently deceased uncle, Olle Edqvist, a scientist and policy-

maker who through his whole life promoted intellectual

curiosity and ethical scientific research across all borders and

disciplines globally.

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speciation and vicariance in neotropical Macrocarpaea

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BIOSKETCH

Lena Struwe is an associate professor at Rutgers University

and Director of the Chrysler Herbarium. Her research interests

concern the taxonomy, evolution and biogeography of Gen-

tianales, the biogeographical history of the Neotropics, and

global biodiversity research on ethnobotanically used plants.

The SEEVA development team includes evolutionary biolo-

gists Peter Smouse and Lena Struwe, ecologist and GIS

researcher Richard Lathrop, and GIS analyst Scott Haag at

Rutgers University, as well as medical image researcher and

computer programmer Einar Heiberg at Lund University.

They have been developing SEEVA as a method for integration

and exploration of individual-based quantitative and qualita-

tive data (environmental or otherwise) with evolutionary

species data since 2005. The SEEVA team website is http://

www.rci.rutgers.edu/~struwe/seeva.

Author contributions: L.S., R.L. and P.S. conceived the ideas

(with L.S. responsible for most of ecological and evolutionary

input, R.H. for GIS and P.S. for statistics); L.S. and S.H.

collected the data; E.H. programmed the software and

provided mathematical ideas; L.S., E.H. and P.S. analysed

the data; L.S. and P.S. led the writing.

Editor: Pauline Ladiges

L. Struwe et al.


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