pollinator adaptation and the evolution of floral nectar ... · composition of nectar among plants...

Pollinator adaptation and the evolution of floral nectar sugarcomposition

S. ABRAHAMCZYK*, M. KESSLER† , D . HANLEY‡ , D . N. KARGER† , M. P. J . M €ULLER† ,A . C. KNAUER† , F . KELLER§ , M. SCHWERDTFEGER¶ & A. M. HUMPHREYS**††*Nees Institute for Plant Biodiversity, University of Bonn, Bonn, Germany

†Institute of Systematic and Evolutionary Botany, University of Zurich, Zurich, Switzerland

‡Department of Biology, Long Island University - Post, Brookville, NY, USA

§Institute of Plant Science, University of Zurich, Zurich, Switzerland

¶Albrecht-v.-Haller Institute of Plant Science, University of Goettingen, Goettingen, Germany

**Department of Life Sciences, Imperial College London, Berkshire, UK

††Department of Ecology, Environment and Plant Sciences, University of Stockholm, Stockholm, Sweden

Keywords:

asterids;

fructose;

glucose;

phylogenetic conservatism;

phylogenetic constraint;

pollination syndrome;

sucrose.

Abstract

A long-standing debate concerns whether nectar sugar composition evolves

as an adaptation to pollinator dietary requirements or whether it is ‘phylo-

genetically constrained’. Here, we use a modelling approach to evaluate the

hypothesis that nectar sucrose proportion (NSP) is an adaptation to pollina-

tors. We analyse ~ 2100 species of asterids, spanning several plant families

and pollinator groups (PGs), and show that the hypothesis of adaptation

cannot be rejected: NSP evolves towards two optimal values, high NSP for

specialist-pollinated and low NSP for generalist-pollinated plants. However,

the inferred adaptive process is weak, suggesting that adaptation to PG only

provides a partial explanation for how nectar evolves. Additional factors are

therefore needed to fully explain nectar evolution, and we suggest that

future studies might incorporate floral shape and size and the abiotic envi-

ronment into the analytical framework. Further, we show that NSP and PG

evolution are correlated – in a manner dictated by pollinator behaviour.

This contrasts with the view that a plant necessarily has to adapt its nectar

composition to ensure pollination but rather suggests that pollinators adapt

their foraging behaviour or dietary requirements to the nectar sugar compo-

sition presented by the plants. Finally, we document unexpectedly sucrose-

poor nectar in some specialized nectarivorous bird-pollinated plants from

the Old World, which might represent an overlooked form of pollinator

deception. Thus, our broad study provides several new insights into how

nectar evolves and we conclude by discussing why maintaining the concep-

tual dichotomy between adaptation and constraint might be unhelpful for

advancing this field.

Introduction

Understanding the evolution of floral rewards is central

to understanding the evolution of plant–pollinator inter-actions. Nectar is the main floral reward provided by the

vast majority of modern angiosperms (70–80%,

extracted from Heywood et al., 2007), but despite coad-

aptation between plants and their animal pollinators

being thought to be one of the key mechanisms

Correspondence: Stefan Abrahamczyk, Nees Institute for Plant

Biodiversity, University of Bonn, Meckenheimer Allee 170, 53115

Bonn, Germany.

Tel.: +49 228 734649, fax:+49 228 733120; e-mail: stefan.

[email protected]

Aelys M. Humphreys, Department of Ecology, Environment and Plant

Sciences, University of Stockholm, SE–10691, Stockholm, Sweden.

Tel.: +46 (0)8 163 771; e-mail: [email protected]

112ª 2016 EUROPEAN SOC I E TY FOR EVOLUT IONARY B IO LOGY . J . E VOL . B I OL . 3 0 ( 2 0 1 7 ) 1 1 2 – 12 7

JOURNAL OF EVOLUT IONARY B IOLOGY ª 2016 EUROPEAN SOC I E TY FOR EVOLUT IONARY B IO LOGY

doi: 10.1111/jeb.12991

responsible for angiosperm evolution and floral diversifi-

cation (Stebbins, 1970; Harrison et al., 1999), our under-

standing of the evolution of floral nectar remains poor.

On the one hand, it has been suggested that the chemi-

cal composition of nectar, an aqueous solution compris-

ing primarily the monosaccharides glucose and fructose

and the disaccharide sucrose (Baker & Baker, 1982), is

as an adaptation to the nutritional constraints or prefer-

ences of a plant’s pollinator(s) as well as to flower mor-

phology (Baker & Baker, 1975, 1982, 1983a). On the

other hand, the sugar composition of floral nectar has

been found to be relatively invariable within plant

clades, with any interspecific differences being indepen-

dent of the plants’ main pollinators (e.g. van Wyk, 1993;

Galetto et al., 1998; Nicolson & van Wyk, 1998; Galetto

& Bernadello, 2003; Rodr�ıguez-Ria~no et al., 2014). These

seemingly contradictory perspectives call into question

our understanding of what drives the evolution of floral

rewards and, in particular, floral nectar.

Early studies documented convergence in the sugar

composition of nectar among plants pollinated by the

same pollinator group (PG; Baker & Baker, 1982,

1983a). Various groups of insects (bees, wasps, butter-

flies, moths and some groups of flies) and several

groups of vertebrates (e.g. hummingbirds, sunbirds,

honeyeaters, phyllostomid and pteropodid bats) are

obligate nectar feeders and, by visiting flowers regu-

larly, often also act as pollinators (referred to as special-

ists; Fleming & Muchhala, 2008). In addition, a

surprising variety of unspecialised vertebrates and

insects (songbirds, geckos, mice, kinkajous, short-ton-

gued flies and some groups of beetles) are known to

feed on nectar occasionally (referred to as generalists,

e.g. van Tets & Nicolson, 2007; Nicolson, 2002; Hansen

et al., 2006; Johnson & Nicolson, 2008). These animals

lack special morphological adaptations to feed on nec-

tar, but are still known to be effective pollinators. How-

ever, their nutritional requirements differ from those of

nectar-feeding specialists with respect to sugar concen-

tration (in solution) or composition (relative contribu-

tion of sucrose and the two hexoses, fructose and

glucose, to total sugar content). Although most special-

ist PGs, such as moths, bees or hummingbirds, prefer

nectar with a high sucrose proportion (Nicolson et al.,

2007; Johnson & Nicolson, 2008), nectar-feeding bats

prefer a low sucrose proportion (Baker et al., 1998).

Similarly, sucrose-rich nectar cannot be digested as effi-

ciently by, or is even toxic to, some generalists

(Mart�ınez del Rio, 1990; Mart�ınez del Rio et al., 1992).

Therefore, it has frequently been proposed that inter-

specific differences in nectar sugar composition and

concentration, especially in nectar sucrose proportion

(NSP), reflect adaptations to the dietary requirements

of different PGs (e.g. Heynemann, 1983; Mart�ınez del

Rio et al., 1992; Baker et al., 1998).

In addition, there is a long-recognized correlation

between floral shape and NSP, such that deep or

tubular flowers tend to have high NSP and shallow

flowers tend to have low NSP (high hexose proportion;

Percival, 1961). This correlation has been attributed to

the environment, because open flowers run greater risk

of nectar evaporation, rendering them useless to polli-

nators (Baker & Baker, 1983a). Hexose solutions have

higher osmolarity, and therefore lower evaporation

rates, than sucrose solutions, and this is thought to

explain the correlation between shallow flowers and

nectars with a high proportion of hexose (Nicolson

et al., 2007). However, high-hexose nectars also tend to

require more water. In Mediterranean regions, there is

a predominance of tubular flowers with high-sucrose

nectars, suggested to be the result of selection against

high-hexose nectars in a warm, dry climate (Petanidou,

2005; Nicolson et al., 2007). This correlation reflects the

physical constraints of sugar solutions themselves, but

it is also thought to constitute evidence for adaptation

to pollinators because a plant will only be regularly vis-

ited by efficient pollinators, whose requirements of suf-

ficiently dilute, easily accessible nectar are fulfilled

(Nicolson et al., 2007).

Given the obvious adaptive advantage of a good fit

between floral traits and animal pollinators, it is sur-

prising that more recent studies of plant species polli-

nated by different PGs have failed to find NSP values

characteristic of the individual PGs (e.g. van Wyk,

1993; Galetto et al., 1998; Nicolson & van Wyk, 1998;

Galetto & Bernadello, 2003). Instead, similar NSP has

been recorded for closely related species in the same

plant family (e.g. in Gesneriaceae, Proteaceae and Scro-

phulariaceae), and in aloes and relatives, NSP has been

found to be conserved within but not among genera,

irrespective of PG (e.g. van Wyk et al., 1993; Nicolson

& Thornburg, 2007; Rodr�ıguez-Ria~no et al., 2014).

These findings have led to the suggestion that there is a

‘phylogenetic constraint’ (sic) on the adaptation of NSP

to pollinators (Galetto & Bernadello, 2003; Thornburg,

2007; Rodr�ıguez-Ria~no et al., 2014). For example, it has

been suggested that the differences in NSP between

plants pollinated by hummingbirds and passerine birds

might be because they belong to different plant clades

rather than due to any innate differences in the

requirements of the pollinators themselves (Nicolson &

Thornburg, 2007).

Considerable attention has been paid to the question

of what causes interspecific differences in the relative

proportions of fructose, glucose and sucrose in nectar.

To date, the debate has generally been centred on a

perceived dichotomy between adaptation to pollinators

and phylogenetic constraints (e.g. Schmidt-Lebuhn

et al., 2006; Nicolson et al., 2007). We are not aware of

any explicit, biological mechanism generating the con-

straint being proposed and believe that focus on the

perceived dichotomy between adaptation and conser-

vatism may have hampered progress into understand-

ing of how floral nectar evolves. In general,

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Evolution of floral nectar sugar composition 113

phylogenetic conservatism or constraint1 is often

invoked to explain the lack of variation among close

relatives or the tendency of closely related species to

retain their ancestral state over time (e.g. Wiens & Gra-

ham, 2005; Cooper et al., 2010; Wiens et al., 2010; Crisp

& Cook, 2012). However, this conveys only that there

is limited trait variation among close relatives, not what

the causal explanation for the observed pattern might

be (Westoby et al., 1995; Blomberg & Garland, 2002;

Losos, 2011). In the case of nectar, documentation of

phylogenetic conservatism in sugar composition sug-

gests limited variation among closely related plant spe-

cies, irrespective of their pollinators, but provides no

insight into how floral nectar evolves, nor does it con-

stitute evidence for or against adaptation (Leroi et al.,

1994; Blomberg & Garland, 2002; Ackerly, 2003, 2004;

Crisp & Cook, 2012). Both stasis and change can result

from both adaptive and nonadaptive processes; for

example, stabilizing selection provides an adaptive

explanation for the pattern of stasis and retention of

the ancestral state does not mean that the trait in ques-

tion is not an adaptation to something (Westoby et al.,

1995; Ackerly, 2004; Losos, 2011; Hansen, 2014). For

instance, if a correlation between NSP and PG was

rejected, the hypothesis that NSP evolves as an adapta-

tion to PG might be rejected, but that would not pre-

clude that nectar sugar composition was an adaptation

to something else, say, corolla shape and size (Nicolson,

2002; Witt et al., 2013). Alternatively, a nonadaptive

hypothesis might be favoured if change in NSP was

found to be stochastic with respect to any environmen-

tal variable to which it was hypothesized to be an adap-

tation, for example if it were found to be drifting

according to the allometric constraints of floral shape

and size or the physical constraints of the environment.

Thus, mechanistic explanations do not require infer-

ence of conservatism and can be distinguished using

comparative methods, by setting up testable hypotheses

to be evaluated in a model comparison framework

(Hansen, 1997; Butler & King, 2004). Here, we use this

approach to evaluate the hypothesis that nectar sugar

composition, in particular NSP, is an adaptation to pol-

linator preferences. We compile a data set that is

unprecedented in scope for this purpose, with nectar

sugar composition, pollinator and phylogenetic data

broadly sampled for the asterids, a major angiosperm

clade of about 80 000 species (Bremer, 2009), including

the carrots, daisies, heathers and mints. We define the

sugar composition of nectar as the trait and PG as the

environment. We then test for a correlation between

the trait and the environment, such that there is con-

vergence of the trait in relation to the environment

(i.e. convergence of nectar sugar composition for plants

pollinated by the same PG; Leroi et al., 1994; Ackerly,

2004) and such that an adaptive shift in the trait is

associated with a shift in function (i.e. a new pollina-

tion syndrome; Hansen, 1997; Butler & King, 2004).

We also explore the nature of the hypothesized adapta-

tion by asking whether the trait or the environment

changes first (Pagel, 1994; Ackerly, 2004)?

Pollinators are thought mainly to be sensitive to the

proportion of total sugar constituted by sucrose (NSP),

although that by fructose (NFP) and that by glucose

(NGP) are thought not to provide reliable evidence of

pollination syndromes (Baker et al., 1998). Based on

this, we analysed the proportional content of each

sugar in turn to evaluate support for the following

hypotheses: H1: nectar sucrose is an adaptation to polli-

nators. This would be supported if a correlation

between NSP and PG cannot be rejected, if there is evi-

dence that adaptation to the environment is rapid and

accurate and if change between NSP and PG is corre-

lated, such that change in PG (the environment) pre-

cedes or accompanies change in NSP (the trait); and

H2: nectar fructose and glucose are not adaptations to

pollinators. This would be supported if a correlation

between the trait (NFP or NGP) and the environment

(PG) is rejected or if there is evidence that shifts in the

trait in relation to the environment are slow and

achieved by stochastic, rather than adaptive, change.

Materials and methods

Nectar data

We compiled nectar sugar composition data for asterids

using published records and, focusing on previously

neglected taxa (e.g. from the Old World tropics), col-

lected and analysed new nectar samples for this study.

We focused on the proportional content of the three

main nectar sugars (fructose, glucose and sucrose) and

chose the asterids because of their diversity in floral

structure, geographical range and pollinators. Further-

more, a wealth of published data on pollination ecology

is available for this clade, accessed by searching for

publications in ISI Web of Knowledge and Google

Scholar using the terms ‘nectar sugar composition’ and

‘nectar sugar content’. In contrast to nectar volume and

total sugar concentration, which are heavily influenced

by water availability and microclimate, nectar sugar

composition is relatively constant within plant species

(Baker & Baker, 1983a; Schwerdtfeger, 1996; Torres &

Galetto, 1998; Nicolson & Thornburg, 2007). The rela-

tively low level of intraspecific variation that has been

documented is mainly due to differences among

1Phylogenetic constraint, phylogenetic conservatism and phyloge-

netic inertia are here used interchangeably to describe a pattern of

evolutionary stasis, lack of variation among close relatives, and

over time, or retention of ancestral traits. We are aware that use

varies among authors and may even refer to the process of failing

to change, adapt to some factor or occupy some habitat or region

(Wiens & Graham, 2005; Ackerly, 2009; Cooper et al., 2010;

Wiens et al., 2010; Losos, 2011; Crisp & Cook, 2012).

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114 S. ABRAHAMCZYK ET AL.

individuals and only to a small degree due to differ-

ences between populations in the wild or between

plants growing in the wild or in botanical gardens (e.g.

Freeman & Wilken, 1987; Vickery & Sutherland, 1994;

Lanza et al., 1995; Gijbels et al., 2014). We further

reduced the potential for variation in nectar sugar com-

position by collecting nectar only from young flowers

to minimize the impact of bacteria and yeasts, which

are able to transfer sucrose to hexoses (Nicolson &

Thornburg, 2007). We collected nectar between

September 2007 and August 2011 in the field in Eur-

ope, South-East Asia and South America and in botani-

cal gardens. We used glass capillaries to collect nectar

from at least three young flowers per plant species. The

minimum volume of nectar collected per species was

1 lL (Morrant et al., 2009). We placed the nectar on fil-

ter paper, air-dried it and stored it in silica gel for up to

a few months to prevent microbial decomposition of

the sugars. Overall sugar concentration (%) and com-

position (relative proportions of fructose, glucose and

sucrose) were determined using standard protocols (SI

text). The proportional content of each sugar was nor-

malized by logit transformation prior to analysis.

Definition of pollinator groups

Due to the large variety of concepts for defining PGs,

these were considered carefully. Because plants that are

mainly pollinated by unspecialized nectar-feeding birds

(generalists) contrast in their morphology and physiol-

ogy with plants that are mainly pollinated by obligate

nectar-feeding birds (specialists; Johnson & Nicolson,

2008), we treated specialized and unspecialized bird

species as different PGs. Generalist passerine and non-

passerine birds that feed on nectar, as well as on larger

amounts of fruits, seeds or insects, are referred to as

unspecialized (e.g. orioles [Oriolidae], bulbuls [Pyc-

nonotidae], white-eyes [Zosteropidae], Hawaiian hon-

eycreepers [Drepaniidae] and flowerpeckers

[Dicaeidae]; Amadon, 1950; Stiles, 1981; Johnson &

Nicolson, 2008; del Hoyo et al., 2008). Some of these

birds (thrushes, starlings and mockingbirds; families in

the Muscicapoidea) lack the ability to digest sucrose

(Mart�ınez del Rio, 1990), whereas others (waxwings;

Bombycilidae) are able to digest sucrose but not as effi-

ciently as they are able to digest hexoses (Mart�ınez del

Rio et al., 1992). In contrast, the bird species we refer

to as specialized, such as New World hummingbirds

(Trochilidae) and Old World sunbirds (Nectariniidae),

sugarbirds (Promeropidae), and some small-bodied gen-

era of honeyeaters (Meliphagidae) and lorikeets

(Psittacidae; Hopper & Burbridge, 1979; Pyke, 1980;

Stiles, 1981; Tjørve et al., 2005; Johnson & Nicolson,

2008), take most of their energy from nectar and are

able to digest sucrose efficiently. Behavioural differ-

ences between specialized nectar-feeding birds in the

Old and New Worlds are associated with different

characteristics of their food plants. Therefore, we

treated specialized, nectar-feeding birds from the Old

and New Worlds as separate PGs.

Several concepts for insect pollination systems exist.

The distinction between plants pollinated by short-

tongued bees and butterflies, short-tongued bees or

long-tongued bees (e.g. Baker & Baker, 1975, 1982,

1983a,b) can be difficult to detect in nature (Schwerdt-

feger, 1996). Also, the details of the pollinators of these

plants are largely unknown. Instead, we adopted the

two categories developed by Schwerdtfeger (1996): (i)

typical bee-pollinated, often zygomorphic, flowers are

referred to as bee-pollinated, and (ii) relatively small,

open flowers, providing access to the nectar for a wide

range of insects (e.g. small bees and butterflies, wasps,

flies and beetles), are referred to as generalist insect-pol-

linated. Similarly, we recognized a butterfly-pollinated

and a moth-pollinated group, mainly distinguishing the

species pollinated during the day and night, respectively

(Schwerdtfeger, 1996). This contrasts with the various

groups of plants pollinated by different groups of butter-

flies recognized by some authors (Baker & Baker, 1975,

1982, 1983a,b). In the end, we defined nine pollinator

groups: generalist insects, bees and wasps, specialized

flies, butterflies, moths, New World bats, unspecialized

birds, specialized Old World birds and hummingbirds.

We obtained information on each asterid species’

affiliation to one pollinator group from the same publi-

cations used to extract the published data on nectar

sugar composition. For the newly generated data

(~ 30% of species), we used pollinator observations

from the literature (e.g. J€ager & Rothmaler, 2011) or

our own field observations. For species where the polli-

nator is unknown, we used categories based on floral

morphology, coloration and scent (Faegri & van der

Pijl, 1978) because this is known to be a reliable

method for identifying the most effective, main PGs of

a plant (Fenster et al., 2004; Rosas-Guerrero et al.,

2014). For example, brightly coloured, often red, scent-

less flowers with long, relatively wide corolla tubes,

pending stigmas and stamens without a landing plat-

form were scored as pollinated by specialized Old World

birds or, in the Americas, hummingbirds. Flowers with

a landing platform, long, narrow corolla tubes and

bright colours were scored as butterfly-pollinated.

Phylogenetic information

Published, DNA-based phylogenies for most species in

the nectar data set are available, but the DNA markers

used differ among studies, rendering data coverage for

individual genes highly incomplete. Therefore, we used

phylogenetic information from published studies for all

sampled asterid species to generate a summary phy-

logeny. At the higher taxonomic level, we referred to

Smith et al. (2011) and the phylomatic webpage (Webb

et al., 2008). For resolution among and within genera,




we used published phylogenies based on multiple genes

(supplementary data, S1; Abrahamczyk et al., 2016) to

manually place species using Mesquite 2.74 (Maddison

& Maddison, 2009). Polytomies were randomly resolved

100 times to generate a set of 100 trees that reflect

some, shallower phylogenetic uncertainty (e.g. within

genera). The final set of trees comprised 2063 species,

for which both nectar and PG data were available.

Many trait evolution analyses rely on phylogenetic

distances (e.g. all models founded in Brownian motion;

e.g. Pagel, 1999; Thomas & Freckleton, 2012), and

because the assembled tree lacked branch-length infor-

mation, three approaches for providing branch lengths

were explored (Fig. S1). The most realistic distribution

of branch lengths, representing the results of numerous

independent studies (Table S1), was obtained by setting

all branch lengths to 1.0 and then scaling the tree

height to absolute time using 110 published age con-

straints (Table S1) in pathd8 (Britton et al., 2007).

Model fitting in a Brownian motion framework:adaptive and nonadaptive models

We devised a series of models (Table 1) that allow

change in nectar sugar to depart in various ways from

the null expectation of constant change that is random

in direction (Brownian motion, BM; Schluter et al.,

1997). The first departure allowed the rate of change in

nectar sugar to vary over time and among clades (‘dif-

ferential rates’), independently of PG. The number of

rate shifts was increased incrementally until no better

models were found (i.e. all shifts supported by

DAICc ≥ 6 were identified; Thomas & Freckleton,

2012). For practicality and to avoid inferring spurious

shifts, the minimum clade size for detecting shifts was

set to 100.

Next, a series of Ornstein–Uhlenbeck (OU) models

(Hansen, 1997; Butler & King, 2004) was fitted. These

allow change in nectar sugar to be directional, towards

one or more optimal values, at a rate dictated both by

the strength of the pull (rate of adaptation) and the rate

of (stochastic) change towards the optimum. The first

model allowed directional change towards a single opti-

mum value (OU-1), the second allowed the mean opti-

mum to differ among species in different clades (OU-

Clades; with clades defined as having uniform rates

change in the differential rates analysis; 16 putative

optima; see Results), and the third allowed the mean

optimum to differ among species pollinated by different

PGs (OU-PG; nine putative optima). More complex

Table 1 Models founded in Brownian motion compared for nectar sucrose proportion (NSP), nectar fructose proportion (NFP) and nectar

glucose proportion (NGP).

Model Hypothesis Free parameters AICc (NSP) AICc (NFP) AICc (NGP)

BM Nectar sugar evolves independently of PG and is

random in direction, with a mean change of zero

and a rate of change that is constant over time

and among lineages

k = 2 (root state and rate of change) 10539.4 8820.2 9901.5

Differential rates As BM but the rate of change may vary over time

and among lineages

k = 2 + 2n (root state, n number of

rate shifts and n + 1 number of rates)

9931.7 8004.3 9220.2

OU-1 Nectar sugar evolves independently of PG but is

directional, towards a global optimum state; the

strength of the pull towards and rate of

(stochastic) change towards the optimum are

constant over time and among lineages

k = 3 (rate of change, strength of pull

[=adaptive change] and 1 trait

optimum)

10160.5 8417.5 9398.0

OU-Clades As OU-1 but directional change is towards several

optima, which may differ among clades (defined

as having their own rate of change in the

differential rates analysis; Table S3)

k = 18 (as OU-1 and 16 putative

optima)

10134.5 8357.8 9342.8

OU-PG As OU-1 but directional change is towards several

optima, which may differ among PGs; thus,

nectar sugar evolution is dependent on PG

k = 11 (as OU-1 and nine putative

optima)

9149.1 8395.8 9344.6

OU-2 As OU-PG but directional change is towards two

optima, which may differ between generalist and

specialist PGs

k = 4 (as OU-1 and 2 putative optima) 10096.3 – –

OU-2V As OU-2 but the rate of change may differ

between optima

k = 5 (as OU-2 and 2 putative rates of

stochastic change)

10098.2 – –

OU-2A As OU-2 but the strength of pull may differ

between optima

k = 5 (as OU-2 and 2 putative rates of

adaptive change [=pull])

10096.9 – –

BM, Brownian motion; OU, Ornstein–Uhlenbeck.AICc values in bold denote the best-fitting model.




models in which the adaptive pull or stochastic rate

may vary among optima (Beaulieu et al., 2012) were

explored, but with 9 or 16 putative optima, such mod-

els soon become highly parameter-rich. Although the

current data set is large, not all putative optima are rep-

resented by many data points (Table S8), and prelimi-

nary findings indicated that one or more parameters

could not be reliably estimated. This resulted in a sub-

optimal model overall. Results for these more complex

models are therefore considered unreliable and are not

reported. Instead, to further test our hypotheses, three

simpler models based on the results of the OU-PG

model (see Results) were devised for NSP only

(Table 1): one in which the mean optimum was

allowed to differ between specialist and generalist PGs

(OU-2), one in which rates of stochastic change

towards those optima may also differ (OU-2V) and one

in which the strength of the pull towards those optima

may also differ (OU-2A).

All models were fitted using maximum likelihood

(ML) in R (R Development Core Team 2014), and

model fit was compared using sample-size corrected

AIC values (AICc; Burnham & Anderson, 2002). The

BM and ‘differential rates’ models were fitted using

‘transformPhylo.ML’ in motmot (Thomas & Freckleton,

2012), and the OU models were fitted using OUwie

(Beaulieu & O’Meara, 2015). Ancestral states, which

determine the phylogenetic distribution of each puta-

tive selective regime, were determined using the equal-

rates model in the ‘ace’ function of ape (Paradis et al.,

2004) for PG and manually for plant clade.

Model fitting in a continuous-time Markovframework: correlation analyses

The results of the above analyses suggested that NSP

(but not NFP or NGP) is an adaptation to PG (see

Results). To further explore this, we tested for corre-

lated change between the NSP and PG. We treated each

variable as binary, with PG coded as ‘specialist’ (bats,

specialized flies, bees and wasps, specialized birds, but-

terflies, moths and hummingbirds) or ‘generalist’ (gen-

eralist insects and unspecialized birds) and NSP as

‘high’ (>0.45) or ‘low’ (≤ 0.45, the 84th percentile

[mean + 1 standard deviation]) for all generalist-polli-

nated plants; Table S4). Next, we used two continuous-

time Markov models for discrete traits to test whether

change in one trait depends on the state of the second

trait (Pagel, 1994; Pagel & Meade, 2006). The first

model states that rates of change in one trait are inde-

pendent of the other trait (i.e. 0 ? 1 and 1 ? 0 transi-

tions occur at the same rate irrespective of whether the

second trait is in state 0 or 1). The second model states

that rates of change in one trait are dependent on the

other trait (i.e. 0 ? 1 and 1 ? 0 transitions in one trait

may differ depending on whether the second trait is in

state 0 or 1). There are eight possible transitions in the

dependent model and four in the independent model

(Table 2). Models were fitted with 10 mL iterations for

each of the 100 trees using the discrete functions in

BayesTraits V2.0 (Quad Precision version for large trees;

available from www.evolution.rdg.ac.uk/BayesTraits.

html), and fit was compared using a likelihood ratio

(LR) test with four degrees of freedom (d.f.) on each

tree.

Robustness of results to phylogenetic uncertaintyand scale and compared to simulated data

Three sets of analyses were performed to test the effect

of (i) phylogenetic uncertainty (by comparing the phy-

logenetic signal of each nectar sugar and pollinator data

across the set of 100 trees), (ii) phylogeny alone (by

comparing results of the differential rates analysis to

those for simulated data) and (iii) phylogenetic scale

(by comparing best-fitting models across the asterids as

a whole to those for a set of less inclusive clades).

Details of these analyses are provided in the SI text.

Table 2 Definition of rate parameters compared in the correlation

analyses.

Parameter Evolutionary transition* Estimate† (median [95% CI])

Forward shifts (0 ? 1)

q12 Shift from specialist to

generalist in high sucrose

background

0.00077 (0.00048–0.010)

q13 Shift from high to low sucrose

in specialist background

0.018 (0.017–0.019)

q24 Shift from high to low sucrose

in generalist background

0.45 (0.015–3.1)

q34 Shift from specialist to

generalist in low sucrose

background

0.0044 (0.0033–0.0058)

Reverse shifts (1 ? 0)

q21 Shift from generalist to

specialist in high sucrose

background

0.00 (0.00–1.51)

q31 Shift from low to high sucrose

in a specialist background

0.054 (0.050–0.058)

q42 Shift from low to high sucrose

in a generalist background

0.045 (0.0083–0.48)

q43 Shift from generalist to

specialist in low sucrose

background

0.030 (0.022–0.043)

*Eight transitions are possible under the dependent model because

transition rates in one trait may vary depending on state of the

second trait. In the independent model, transition rates in one trait

are the same irrespective of the state of the second trait. Thus,

q12 = q34, q21 = q43, q13 = q24 and q31 = q42, and the indepen-

dent model has four rate parameters.

†Rate estimates shown are summaries across the 100 trees; com-

parisons reported in the text were performed across each tree indi-

vidually and cannot be read directly from this table.




http://www.evolution.rdg.ac.uk/BayesTraits.html

http://www.evolution.rdg.ac.uk/BayesTraits.html

Results

Variation in nectar sugar composition in relation topollinator group

The nectar sugar composition data set comprised 2116

species and subspecies of asterids, representing 660 gen-

era, 55 families and 13 of the 16 orders. Roughly two-

thirds of these data (1480 species) were taken from the

literature (see supplementary data, S2; Abrahamczyk

et al., 2016). Data for the remaining 636 species were

generated for this study (577 species sampled from

Botanical Gardens, 59 from the wild, with samples from

the wild spanning a range of families and pollinator

groups [Data S2; Abrahamczyk et al., 2016]). A ternary

plot shows separation of nectar sugar composition along

the sucrose axis, with plants pollinated by generalists

being found at lower NSP than plants pollinated by spe-

cialists (Figs 1a and S2). Only hummingbird-pollinated

plants show a skew with respect to the two hexoses,

being shifted towards fructose. There is a high degree of

variation in the NSP of species pollinated by most PGs,

especially by specialized flies, Old World birds and but-

terflies, and much overlap among them (Fig. 1b). In

particular, we documented unexpectedly sucrose-poor

nectar for some species pollinated by Old World special-

ized birds that have nectar with a low overall sugar

concentration (Fig. S3).

Best-fitting evolutionary models for NSP, NFP andNGP

The best-fitting model for NSP was OU-PG and for NFP

and NGP differential rates (Table 1). Two independent

optima were inferred for NSP: low NSP for plants

pollinated by generalist birds and unspecialized insects

and high NSP for plants pollinated by all other PGs

studied here (Fig. 2). This model is a much better fit to

the data (DAICc ≥ 783) than any of the other BM-

based models. However, the parameter estimates from

this model suggest that the adaptive process is weak

(Table 3). The stationary variance, a measure of the

rate of drift relative to the strength of selection, is very

high (r2/2a = 2.5 9 105), and the phylogenetic half-

life, defined as the time needed to evolve half the dis-

tance from the ancestral state to the trait optimum, is

extremely long (ln[2]/a = 4.0 9 105 Ma). The simpler

models (OU-2, OU-2V and OU-2A), although a worse

fit to the data (third best overall; Table 1), confirm that

generalist-pollinated plants are evolving towards a

lower NSP optimum than specialist-pollinated plants

and yield phylogenetic half-life estimates of ~ 10 Ma,

that is, suggesting an adaptive process in which species

are closer to their optimum and that is achievable

within about 10% of the age of the asterids (Table S2).

The second best model for NSP was the differential

rates model, in which 15 rate shifts were identified

(Fig. 3, Table S3). Seven shifts were slowdowns and

eight speedups; several shifts were nested and most shifts

were found in Ericales, where rates generally increased

in shallower clades, or Gentianales + Lamiales, where

rates generally decreased in shallower clades. Most clades

identified correspond to, or almost to, major named

clades. This could be an artefact of the relatively sparse

sample analysed here or, alternatively, suggests corre-

spondence between evolutionary processes and named

clades that we are only beginning to be able to detect

(Smith et al., 2011; Humphreys & Barraclough, 2014).

Group means

Nectar Sugar Composition

Fructose Glucose Pollinator group

Sucrose Generalist insects

Specialized flies

Bees and wasps

Butterflies

Moths

Unspecialized birds

Specialized OW birds

Hummingbirds

NW bats

0

0.2

0.4

0.6

0.8

1

Suc

rose

pro

porti

on

(a) (b)

Fig. 1 Composition of nectar sugars and variation among pollinator groups. (a) Ternary plot of the relative contributions of fructose,

glucose and sucrose to the nectar sugar utilized by each pollinator group (PG). All groups separate along the sucrose axis with generalist

pollinator groups tending towards lower nectar sucrose proportion (NSP; main plot: all data, n = 2116; inset: group means; see Fig. S2 for

details). NW = New Word; OW = Old World. See main text for definitions of PGs. (b) Distribution of NSP among plants pollinated by

different PGs. Horizontal line = median, boxes = upper and lower quartiles, circles = outliers (defined as lying beyond 1.5 9 the

interquartile range [Q3–Q1]). Outliers represent several plant families and are based on data sampled from both wild and cultivated plants.




Fifteen rate shifts were also found under the overall best

model for NFP (DAICc ≥ 354; Tables 1 and S3). Half of

these occur at exactly the same node as for NSP, and the

other half occur only one or a few nodes away. Under

the overall best model for NGP (DAICc ≥ 122), there was

support for 11 rate shifts. Again, shifts tended to occur at

the same nodes as for NSP, NFP or both. The pattern of

slowdowns and speedups was the same for all three sug-

ars: decreases tended to occur in Gentianales and Lami-

ales and speedups in Ericales. The second best model for

both NFP and NGP was OU-Clades.

Correlation analyses: nectar sucrose proportion andpollinator group

When NSP and PG are coded as binary variables, there

is significant dependency in the data (P < 0.0001, Fish-

er’s exact test, two-tailed; Table S4). Specialist PGs are

more likely to be associated with high than low NSP,

and very few generalist PGs are associated with high

NSP (specialists are three times more likely to pollinate

high-NSP flowers and generalists six times more likely

to pollinate low-NSP flowers). This association does not

hold the other way round: both high-NSP and low-NSP

flowers are more likely to be pollinated by a specialist

than a generalist pollinator (68 and four times more

likely, respectively).

More formally, the model of dependent evolution

between NSP and PG could not be rejected for any of

the 100 trees (P < 0.0001, LR tests with 4 d.f.). A

comparison of rate parameter estimates reveals the nat-

ure of this dependency: a high-NSP, specialist-polli-

nated plant is more likely to change into a low-NSP,

specialist-pollinated plant than into a high-NSP general-

ist-pollinated plant (q13 > q12; 98% of trees; Table 2).

This suggests that nectar shifts first from the ancestral

state. In addition, shifts from a specialist to generalist

PG are more likely in a low sugar background

(q34 > q12; 94% of trees) and shifts from high to low

NSP are more likely in a generalist PG background

(q24 > q13; 96% of trees). Rate estimates for the

reverse transitions were indistinguishable.

Robustness to phylogenetic uncertainty and scaleand compared to simulated data

Estimates of phylogenetic signal were constant across

the set of 100 trees for all three sugars and PG (SI text,

Table S5). Thus, our findings are robust to some phylo-

genetic uncertainty. The results of the differential rates

analysis differed for empirical and simulated data

(Fig. S4). Thus, overall, the pattern of rate increases

and decreases is not an artefact of the phylogeny and

NSP is evolving differently to expectations for a neutral

trait. However, some shift positions were recovered for

both empirical and simulated data (Fig. 3 SI text;

Table S3) and these should be interpreted with caution

because they can apparently be recovered with any

trait. Finally, analysis of less inclusive clades revealed a

strong effect of phylogenetic scale. For NSP, the find-

ings for the asterids overall were confirmed, but, in

addition, differences among specialist PGs as well as

among clades were found (Table 3, Figs S5 and S6).

Evidence for adaptation, for example as indicated by

short phylogenetic half-lives relative to overall tree

height, was much stronger for clades analysed sepa-

rately than for asterids overall. These findings were lar-

gely mirrored for NFP and NGP, thus contradicting

results across the asterids as a whole for these two sug-

ars (SI text, Tables S6 and S7, Fig. S5).

Discussion

Unexpected variation in nectar sugar compositionfor many pollinator groups

Based on the results of Baker & Baker (1982), we

expected plants pollinated by one PG to have converged

on the optimum NSP for that PG. Instead, we found

high variability in the NSP of plant species pollinated

by most PGs, most notably by specialized flies, bees and

wasps, butterflies and specialized Old World birds

(Fig. 1). The variability for these insect-pollinated

plants may be because of different preferences in males

and females (Rusterholz & Erhardt, 1997, 2000), differ-

ent requirements for different pollinator subgroups

–30

–20

–10

0

10

20

Pollinator group

Logi

t NS

C

Fig. 2 Selective optima inferred for nectar sucrose content (NSP) in

relation to pollinator group (PG) in asterids. The main differences

are found between generalist and specialist PGs, with plants

pollinated by generalist insects and unspecialized birds tending

towards lower NSP optima than species pollinated by other PGs. See

Table 3 for details. Colours and symbols as in Fig. 1.




Table

3Parameter*

estim

atesforthebest-fittingOU

modelforasteridsoverallandforeach

oftheless

inclusivecladesanalysedseparately,where

such

amodelcould

notberejected

(seeTable

S6).

Clade

Model†

r†

a

h–

generalist

insects

h–bats

h–

bees

and

wasp

s

h–

specialized

flies

h–

specialized

Old

World

bird

s

h–

unsp

ecialized

bird

s

h–

butterflies

h–

moths

h–

humming-

bird

st 1/2(th)

v y

Asterid

sOU-PG

0.87

<0.001

<0.001

45.3

78.1

99.9

53.3

<0.001

15.6

100

99.8

4.0

9105(114)

2.5

9105

Fouqueria

ceae+

Polemoniaceae+

Prim

ulaceae

OU-1

0.19

0.087

––

53.7

––

–53.7

–53.7

7.98(67)

1.11

Core

Eric

aceae

OU-1

5.65

0.11

––

85.7

–85.7

––

–85.7

6.37(49)

25.9

Core

Asteraceae

OU-1

1.12

0.062

23.6

–23.6

––

–23.6

–23.6

11.1

(39)

8.98

Core

Solanaceae

OU-1

3.38

0.15

–34.7

34.7

34.7

––

34.7

34.7

34.7

4.53(28)

11.0

Gelsemiaceae+

Gentianaceae+

Apocynaceae

OU-1

1.80

0.057

–82.4

82.4

82.4

––

82.4

82.4

82.4

12.1

(62)

15.7

Rubiaceae

OU-1

1.37

0.12

––

78.2

78.2

––

78.2

78.2

78.2

5.78(56)

5.72

Gesn

erio

ideae

OU-PG

23.4

9.98

–36.2

88.5

––

––

–77.8

0.070(31)

1.17

Core

Plantaginaceae

OU-PG

0.69

0.068

<0.001

–57.8

––

––

–74.8

10.2

(46)

5.07

Core

Acanthaceae

OU-1

0.67

0.053

––

60.5

––

–60.5

–60.5

13.0

(34)

6.26

Ajugoideae+

Lamioideae

OU-PG

81.9

9.74

––

82.4

–28.8

–74.2

–91.8

0.071(34)

4.20

Nepetoideae

OU-PG

15.5

5.59

8.16

–70.0

83.9

––

––

78.1

0.12(38)

1.38

Campanulaceae

OU-PG

0.35

0.057

–6.30

23.3

––

1.15

––

77.7

12.2

(56)

3.09

Boraginales

BM

0.39

NA

––

NA

––

––

––

NA(57)

NA

*r2is

therate

ofstoch

astic

change(e.g.underdrift

orin

relationto

anunmeasuredfactor),ais

thestrength

ofthe‘pull’towardstheselectiveoptima(rate

ofadaptation)in

Ma�1,h

isthemeantraitoptimum,t 1/2

isthephylogenetichalf-lifein

Mawithtotaltreeheight(th)in

Main

brackets

(ln(2)/a;

thetimeneededto

movefrom

theancestralstate

tothenew

optimum)andv y

isthestationary

variance

(r2/2a;

thebalance

betw

een

theaction

controlledbyaandthatcontrolledbyr).

Optimum

valuesare

shown

asback-transform

edmean

estim

ates(%

NSP;seeFig.S5forvariationaroundthemean).

†OU-PG

isamodelin

whichthemeanoptimum

maydifferamongpollinatorgroups(PGs).NotallPGsare

presentin

allclades(seeTable

S8)so

thenumberofoptimadiffers

among

clades.

Best

fittingmodels

andcolumnswithtw

ogeneralist

PGsare

bolded.




(Goldblatt & Manning, 1999; Petanidou, 2005) or

different optimal NSP values in different plant clades

(explored further below). The variability for plants pol-

linated by specialized Old World nectarivorous birds

probably requires a different explanation. Nectarivorous

birds are known to prefer sucrose-rich nectars with a

high overall sugar concentration, but experiments have

shown that they prefer nectar composed of hexoses if

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itecn

a m

acro

phyl

la

Cre

scen

tia a

lata

Cre

scen

tia c

ujet

e

Cre

scen

tia d

acty

lon

Jaca

rand

a m

imos

ifolia

Tour

retti

a la

ppac

ea

Ecc

rem

ocar

pus

scab

er

Cam

psis

gra

ndifl

ora

Cam

psis

spe

c

Cam

psis

radi

cans

Teco

ma

stan

s

Teco

ma

cape

nsis

Teco

ma

fulv

a

Teco

ma

x sm

ithii

Inca

rville

a ar

guta

Inca

rville

a de

lava

yi

Inca

rvill

ea o

lgae

Podr

anea

rica

solia

na

Dep

lanc

hea

glab

ra

Teco

man

the

volu

bilis

Teco

man

the

venu

sta

Cam

psid

ium

val

divi

anum

Prosta

nthe

ra ro

tund

ifolia

Prosta

nthe

ra m

agnif

ica

Prosta

nthe

ra c

unea

ta

Prosta

nthe

ra st

riatifo

lia

Wes

tring

ia fr

utico

sa

Conge

a to

men

tosa

Vitex a

gnus

castu

s

Vitex t

rifoli

a

Viticipr

emna

que

ensla

ndica

Vitex d

onian

a

Ballota acetabulosa

Ballota hispanica

Marrubium peregrin

um

Marrubium supinum

Marrubium vulgare

Lamium album

Lamium maculatum

Lamium orvala

Lamium flavidum

Lamium galeobdolon

Lamium garganicum

Lamium amplexicaule

Leucas zeylanica

Leucas aspera

Leonotis nepetifolia

Leonotis leonitis

Leonotis intermedia

Leonotis leonurus

Leonotis ocymifolia var ra

ineriana

Leonotis ocymifolia var schinzii

Leonotis spec

Leonurus cardiaca

Leonurus cardiaca ssp villosus

Leonurus cardiaca ssp turke

stanicus

Leonurus sibiricusPhlomis f

ruticosa

Phlomis visco

sa

Phlomis tuberosa

Phlomis russeliana

Phlomis herba ve

nti

Betonica m

acrantha

Betonica offic

inalis

Galeopsis bifid

a

Galeopsis te

trahit

Galeopsis se

getum

Galeopsis pubesc

ens

Melittis

melissophyllu

m

Prasium m

ajus

Sideritis m

acrosta

chys

Sideritis g

lacialis

Sideritis h

ysso

pifolia

Sideritis s

yriaca

Stachys

recta

Sideritis s

pec

Stachys

alpina

Stachys

lamioides

Stachys

annua

Stachys

cassi

a

Stachys

plumosa

Stachys

aegyptia

ca

Stachys

elliptica

Stachys

palustris

Stachys sylv

atica

Stenogyne m

inutiflora

Stachys cocci

nea

Stachys

cretica

Physo

stegia vi

rginiana

Colquhounia cocc

inea

Gomphostemma sp

ec

Gomphostemma st

robilinum

Pogostemon ca

blin

Holmsk

ioldia

sang

uinea

Holmsk

ioldia

tetten

sis

Scutella

ria la

teriflora

Scutella

ria alpina

Scutella

ria co

ccinea

Scutella

ria cf

atriplic

ifolia

Scutella

ria albida

Scutella

ria in

carn

ata

Scutella

ria sp

ec

Scutella

ria sp

eciosa

Scutella

ria sc

ordifolia

Scutella

ria in

tegrifolia

Scutella

ria vi

olacea

Scutella

ria co

lumnea

Scutella

ria co

staric

ana

Scutel

laria

altiss

ima

Scutel

laria

galer

iculat

a

Rothe

ka se

rrata

Teuc

rium

poli

um ss

p ca

pitat

um

Teuc

rium

rotu

ndifo

lium

Teuc

rium

cham

aedr

ys

Teuc

rium

frut

icans

Teuc

rium

mon

tanu

m

Teuc

rium

hirc

anicu

m

Teuc

rium

scor

dium

Teuc

rium

mar

um

Teuc

rium

scor

odon

ia

Teuc

rium

het

erop

hyllu

m

Trich

oste

ma

lanat

um

Ajuga

repe

ns

Clerod

endr

um in

erme

Clerod

endr

um tr

ichoto

mum

Clerod

endr

um pa

nicula

tum

Clerod

endru

m spec

iosiss

imum

Clerod

endru

m indic

um

Clerod

endr

um ca

pitatu

m

Clerod

endr

um th

omso

niae

Clerod

endr

um sp

lende

ns

Clerod

endr

um d

eflex

um

Clerod

endr

um cf

villo

sum

Clerod

endr

um tr

acya

num

Lavandula angustifolia

Lavandula spica

Lavandula spec

Collinsonia canadensis

Plectranthus barbatus var barbatus

Plectranthus reflexus

Plectranthus amboinicus

Plectranthus ambiguus

Plectranthus zuluensis

Plectranthus fruticosus

Plectranthus ciliatus

Plectranthus epilithicus

Plectranthus ecklonii

Plectranthus saccatus

Plectranthus hilliardiae

Dauphinea brevilabra

Pycnostachys eminii

Pycnostachys reticulataOcimum selloi

Ocimum basilicum

Ocimum spec minimum

Orthosiphon thymiflorus

Orthosiphon tubiformis

Orthosiphon aristatus

Orthosiphon spec

Elsholtzia stauntonii

Agastache mexicana

Agastache aurantiaca

Agastache urticifolia

Agastache foeniculum

Agastache rupestris

Dracocephalum grandiflorum

Dracocephalum moldavica

Dracocephalum nutans

Dracocephalum ruyschiana

Hyssopus officinalis

Glechoma hederacea

Glechoma hirsuta

Cedronella canariensis

Nepeta grandiflora

Nepeta cataria

Nepeta nepetella

Nepeta parnassica

Nepeta sibirica

Nepeta x faasenii

Nepeta nudaLycopus europaeus var exaltatus

Lycopus uniflorus

Horminum pyrenaicum

Prunella grandiflora

Lepechinia conferta

Lepechinia floribunda

Melissa officinalis

Melissa officinalis ssp altissima

Mentha cf spicata

Mentha pulegiumMentha x piperitaMentha rotundifoliaMentha cervinaMentha aquaticaMentha spec

Monardella macranthaMonardella nanaMonarda didyma

Clinopodium nepetaClinopodium nepeta ssp sylvaticaClinopodium vulgare

Hedeoma hyssopifolia

Pycnanthemum pilosumPycnanthemum virginianum

Monarda fistulosaMonarda russelianaConradina grandiflora

Cuminia eriantha

Micromeria croaticaMicromeria dalmatica

Thymus praecoxThymus capitatusThymus serpyllumThymus pulegioides ssp carniolicus

Origanum vulgare

Thymbra capitata

Satureja montanaSatureja hortensisSatureja thymbraSatureja spec

Salvia sphacelifolia

Salvia trachyphylla

Salvia spathaceaSalvia microphylla

Salvia squalens

Salvia styphelusSalvia coahuilensis

Salvia divinorum

Salvia strictaSalvia spec IISalvia oppositiflora

Salvia amplifrons

Salvia amarissimaSalvia mexicanaSalvia greggii

Salvia fulgens

Salvia iodantha

Salvia purpurea

Salvia farinaceaSalvia reglaSalvia macrophyllaSalvia heerii

Salvia confertiflora Salvia karwinskii

Salvia coccinea

Salvia guaraniticaSalvia corrugataSalvia spec ISalvia tortuosa

Salvia uliginosaSalvia pichinchensis

Salvia lasiocephalaSalvia pauciserrata

Salvia spruceiSalvia patens

Salvia iodochroa

Salvia semiatrataSalvia elegans

Salvia discolor

Salvia gesneraefloraSalvia lemmoniSalvia lavanduloides

Salvia splendens

Salvia glutinosa

Salvia fruticosaSalvia bulleyana

Rosmarinus officinalis

Perovskia atriplicifolia

Perovskia abrotanoides

Perovskia scrophulariifolia

Salvia argenteaSalvia ringensSalvia aurita

Salvia multiorrhiza

Salvia verbenaca

Salvia virgataSalvia verticillataSalvia canariensisSalvia scabra

Salvia aethiopis

Salvia taraxacifoliaSalvia indicaSalvia roemeriana

Salvia judaica

Salvia jurisiciiSalvia officinalis

Salvia trilobaSalvia nemorosa

Salvia pratensis

Salvia broussonetiiSalvia namaensis

Salvia stepposaSalvia hierosolymitana

Salvia viridis

Salvia sclareaSalvia austriaca

Salvia scala

Salvia nutansSalvia somalensis

Aloy

sia g

ratis

sima

Aloy

sia w

right

ii

Lipp

ia ju

nellia

na

Phyla

filifo

rmis

Lant

ana

cam

ara

Lant

ana

fuca

taJu

nellia

juni

perin

a

June

llia tr

iden

s

June

llia c

onna

tibra

ctea

ta

June

llia s

uccu

lent

ifolia

June

llia m

ulin

oide

s

June

llia lig

ustri

na

June

llia th

ymifo

lia

Verb

ena

offic

inal

is

Verb

ena

hast

ata

Verb

ena

bona

riens

is

Verb

ena

stric

tG

land

ular

ia c

rithm

ifolia

Gla

ndul

aria

aur

antia

ca

Gla

ndul

aria

tene

ra

Gla

ndul

aria

per

uvia

na

Gla

ndul

aria

lacin

iata

Gla

ndul

aria

flava

Gla

ndul

aria

mac

rosp

erm

aDiost

ea ju

ncea

Rhaph

itham

nus

venu

stus

Stac

hyta

rphe

ta fr

antz

ii

Stac

hyta

rphe

ta s

pec

Stac

hyta

rphe

ta in

dica

Duran

ta re

pens

Dur

anta

tria

cant

ha

Petre

avo

lubi

lis

arolf

idna

rgai

debo

csE

vra

mury

pma

leM

esne

esne

tarp

mury

pma

leM

ercit

alai

stra

Bat

anor

aisa

rhpu

Ean

aivo

kts ro

tcel

asu

htna

nih

Rsu

hpol

osu

nitor

essu

htna

nih

Rro

nim

suht

nani

hR

sirts

ulap

siral

ucid

eP

fissifajellitsa

Cailocitoirtap

ajellitsaC

aanatno

mortsuaajellitsa

Canal

ajellitsaC

atgetniajellitsa

Cailofir

Cas

tille

ja to

men

tosaC

astilleja tenuifloraC

astilleja sessiliflora

Castilleja irasuensis C

astil

leja

ort

egae

Cas

tille

ja li

near

ifoliaC

astilleja integra

egsi

nilag

Aail

ofits

inad

igir

sinil

agA

ofiht

nani

hrai

xuor

uoma

Lailat

agri

vai

xuor

uoma

L

Orobanche alba mu

rocu

leh

cnab

orOO

robanche hederae

itul

gai

nna

mhe

Rnosa

ale

ainn

amh

eR

atkzes

aip

ainn

amh

eR

iiot

ain

wolu

aP

mentosa

Mazus reptans

Mim

ulus aurantiacusM

imulus guttatus

Mim

ulus flemingii silan

idra

csu

lumi

M

naia

nsu

lumi

Mdinussn

egnir

sulu

miM

suec

inup

sulu

miM

Mim

ulus luteus

Pinguicula m

acrophyllaP

inguicula hemiepiphytica

Pinguicula leptoceras

Pinguicula longifolia ssp caussensis

Pinguicula alpina

Pinguicula m

oranensis

Pinguicula laueana

Pinguicula rectifolia

Pinguicula m

octezumae

Pinguicula gigantea

Pinguicula ehlersae

Pinguicula esseriana

Pinguicula oblongiloba

Pinguicula zecheri

Utricularia vulgaris

Utricularia gibba ssp exoleta

Utricularia alpina

Utricularia reniform

is

Utricularia nephrophylla

Ibicella luteaP

roboscidea fragrans

Proboscidea louisiana

Halleria lucida

Diascia vigilis

Myoporum

acuminatum

Sutera cordata

Sutera roseoflava

Scrophularia orientalis

Scrophularia sciophila

Scrophularia nodosa

Scrophularia vernalis

Scrophularia grandiflora

Scrophularia pyrenaica

Scrophularia sm

ithii

Teedia lucida

Derm

atobotrys saundersii

Phygelius aequalis

Phygelius capensis

Buddleja globosa

Buddleja japonica

Buddleja m

endozensis

Buddleja forrestii

Buddleja crispa

Buddleja colvilei

Buddleja albiflora

Buddleja davidii

Buddleja lindleyana

Ourisia poeppigii

Stemodia suffruticosa

Gratiola officinalis

Bacopa spec

Asarina procum

bensM

abrya coccineaM

abrya acerifolia

Mabrya geniculata ssp geniculata

Mabrya rosei

Mabrya erecta

Lophospermum

purpusii

Lophospermum

turneri

Lophospermum

erubescens

Lophospermum

atrosanguineum

Lophospermum

scandens

Maurandya barclaiana

Maurandya scandens

Maurandella antirrhiniflora

Cym

balaria pallida

Neogaerrhinum

filipesM

isopates orontium

Pseudorontium

cyathiferum

Galvezia fruticosa

Galvezia leucantha

Galvezia speciosa

Gam

belia juncea

Gam

belia speciosa

Mohavea breviflora

Antirrhinum

latifolium

Antirrhinum

coulterianum ssp orcuttianum

Antirrhinum

siculum

Antirrhinum

majus

Antirrhinum

majus var cirrhigerum

Chaenorrhinum

origanifolium

Kickxia spuria

Linaria alpina

Linaria vulgarisLinaria triornithophora

Linaria dalmatica

Linaria purpurea

Linaria saxatilis

Isoplexis isabelliana

Isoplexis canariensis

Isoplexis chalcantha

Digitalis trojana

Digitalis obscura

Digitalis parviflora

Digitalis grandiflora

Digitalis purpurea

Wulfenia carinthiaca

Veronica bachofeniiVeronica m

issurica

Veronica missurica ssp stellata

Veronica spicataVeronica saturejoides

Veronica austriaca ssp austriaca

Veronica teucriumVeronica polita

Veronica persica

Veronica filiformis

Veronica salicifolia

Veronica speciosa

Veronica elliptica

Veronica diosmifolia

Veronicastrum virginica

Globularia alypum

Cam

pylanthus salsoloides

Tetranema roseum

Russelia equisetiform

isR

usselia villosa

Russelia w

orthingtonii

Russelia verticillata

Russelia sarm

entosa

Collinsia heterophylla

Keckiella antirrhinoides

Chelone glabra

Chelone lyonii

Penstemon confertus

Penstemon alam

osensis

Penstemon barbatus

Penstemon w

islizenii

Penstemon eatoni

Penstemon pseudospectabilis

Penstemon digitalis

Penstemon m

ultiflorus

Penstemon sm

allii

Penstemon spectabilis

Penstemon virgatus

Penstemon strictus

Penstemon centhranthifolius

Penstemon superbus

Penstemon parryi

Penstemon hartw

egii

Penstemon isophyllus

Penstemon cam

panulatus

Penstemon kunthii

Penstemon ram

osus

Penstemon pinifolius

Penstemon linarioides

Penstemon dasyphyllus

Penstemon lanceolatus

Penstemon serrulatus

Penstemon laetus

Penstemon ovatus

Penstemon pallidus

Penstemon stenophyllus

Penstemon spec I

Penstemon spec II

Penstemon flavescens

Besleria barbata

Besleria modica

Besleria columneoides

Besleria reticulata

Besleria formosaGasteranthus corallinus

Gasteranthus macrocalyx

Gasteranthus quitensis

Gasteranthus pansamalanus

Rhytidophyllum spec

Rhytidophyllum exsertum

Rhytidophyllum tomentosum

Gesneria libanensis

Gesneria ventricosa

Gesneria pedunculosa

Achimenes candida

Achimenes grandiflora

Achimenes flava

Achimenes longiflora

Achimenes erecta

Achimenes misera

Diastema scabrum

Diastema vexans

Solenophora calycosa

Pearcea hypocyrtiflora

Pearcea rhodotricha

Pearcea sprucei

Pearcea spec

Kohleria inaequalis var ocellata

Kohleria strigosa

Kohleria spicata

Kohleria elegans

Kohleria villosa

Kohleria hirsuta

Kohleria amabilis var bogotensis

Kohleria amabilis var amabilis

Kohleria affinis

Kohleria tigridia

Koellikeria erinoides

Gloxinia erinoides

Gloxinia sylvatica

Gloxinia gymnostoma

Gloxinia nematanthodes

Heppiella ulmifolia

Mitraria coccinea

Asteranthera ovata

Rhabdothamnus solandri

Sinningia schiffneri

Sinningia gerdtiana

Sinningia basiliensis

Sinningia araneosa

Sinningia valsuganensis

Sinningia carangolensis

Sinningia aggregata

Sinningia tubiflora

Sinningia sellovii

Sinningia warmingii

Sinningia allagophylla

Sinningia spec I

Sinningia sceptrum

Sinningia incarnata

Sinningia elatior

Sinningia nordestina

Sinningia barbata

Sinningia aghensis

Sinningia helioana

Sinningia pusilla

Sinningia villosa

Sinningia speciosa

Sinningia guttata

Sinningia lindleyi

Sinningia gigantifolia

Sinningia cochlearis

Vanhouttea spec nov indet II

Vanhouttea spec nov indet III

Vanhouttea spec nov indet I

Vanhouttea lanata

Sinningia hirsutaPaliavana sericiflora

Paliavana prasinata

Sinningia reitzii

Sinningia lineata

Sinningia macrostachya

Sinningia eumorpha

Sinningia conspicua

Sinningia cardinalis

Sinningia magnifica

Sinningia bulbosa

Sinningia hatschbachii

Sinningia cooperi

Sinningia iarae

Sinningia micans

Sinningia macropoda

Sinningia canescens

Sinningia douglasii

Sinningia calcaria

Sinningia piresiana

Sinningia rupicola

Sinningia insularis

Sinningia nivalis

Sinningia leucotricha

Sinningia spec alata aff

Sinningia spec nov ined

Sinningia spec II

Paradrymonia ciliosa

Paradrymonia hypocyrta

Nautilocalyx forgetii

Nautilocalyx bullatus

Nautilocalyx melittifolius

Nautilocalyx lynchii

Nautilocalyx spec

Codonanthe erubescens

Codonanthe gracilis

Nematanthus gregarius

Nematanthus fissus

Nematanthus crassifolius

Nematanthus saracoba

Nematanthus corticola

Nematanthus brasiliensis

Nematanthus spec

Nematanthus hirtellus

Nematanthus glabratus

Nematanthus strigillosus

Nematanthus m

aculatus

Chrysothemis pulchella

Chrysothemis friedrichsthaliana

Corytoplectus speciosus

Glossolom

a sprucei

Glossolom

a tetragonoides

Alloplectus tetragonus

Alloplectus ichthyoderma

Allopectus andinus

Allopectus spec I

Allopectus spec II

Neomortonia rosea

Monophyllea horsfieldii

Columnea byrsina

Columnea virginica

Columnea hirta

Columnea lepidocaulis

Columnea spathulata

Columnea crassifolia

Columnea rubriacuta

Colum

nea medicinalis

Columnea dielsii

Colum

nea spec III

Colum

nea hirsutissima

Colum

nea herthae

Colum

nea oerstediana

Colum

nea microphylla

Colum

nea spec I

Colum

nea querceti

Colum

nea oxyphylla

Colum

nea purpurata

Colum

nea gloriosa

Colum

nea spec II

Colum

nea orientandinaC

olumnea arguta

Colum

nea strigosa

Colum

nea magnifica

Colum

nea tulae

Colum

nea schiedeana

Colum

nea linearis

Colum

nea hirta var mortonii

Columnea argentea

Columnea inaequilatera

Columnea m

inor

Columnea nicaraguensis

Columnea m

icrocalyx

Drymonia fim

briata

Drymonia spec I

Drymonia m

arnier lapostollea

Drymonia spec IV

Drymonia spec V

Drymonia rhodolom

a

Drymonia conchocalyx

Drymonia m

ultiflora

Drymonia dodsonii

Drymonia coccinea

Drymonia serrulata

Drymonia urceolata

Drymonia warszewicziana

Drymonia spectabilis

Drymonia spec II

Drymonia spec III

Cobananthe calochlamys

Alsobia diamanthiflora

Alsobia punctata

Episcia cupreata

Episcia cupreata var viridifolia

Rhynchoglossum gardneri

Rhynchoglossum spec

Streptocarpus caulescens

Streptocarpus saxorum

Streptocarpus stomandrus

Streptocarpus wendlandii

Streptocarpus polyanthus

Streptocarpus eylesii

Briggsia muscicola

Aeschynanthus evrardii

Aeschynanthus tricolor

Aeschynanthus parasiticus

Aeschynanthus spec

Aeschynanthus parviflora

Aeschynanthus radicans

Aeschynanthus longicalyx

Aeschynanthus rhododendron

Aeschynanthus lanceolatus

Aeschynanthus micranthus

Aeschynanthus speciosus

Agalmyla chorisepalaCyrtandraspec III

Cyrtandraspec I

Cyrtandraspec II

Didymocarpus hispida

Didymocarpus crinita

Didymocarpus atrosanguineus

Didymocarpus reptans

Syringa pubescens ssp microphyllaSyringa wolfii

Syringa vulgaris

Syringa tomentella ssp yunnanensis

Syringa x chinensisSyringa x persicaLigustrum sempervirens

Ligustrum sinense

Ligustrum tschonoskii

Jasminum polyanthum

Jasminum odoratissimum

Jasminum didymum

Menodora robusta

Jasminum nudiflorum

Jasminum parkeri

Jasminum sambac

Jasminum fruticans

Jasminum humile

Jasminum mesnyi

Mandevilla cf sanderi

Mandevilla pentlandianaMandevilla petreaMandevilla hirsuta

Mandevilla angustifoliaMandevilla spec I

Mandevilla laxaParsonsia heterophylla

Trachelospermum jasminoides

Apocynum androsaemifolium

Pachypodium baronii

Pachypodium succulentum

Pachypodium bispinosum

Pachypodium decaryi

Pachypodium hybrid

Pachypodium densiflorum

Pachypodium lamerei

Acokanthera oppositifolia

Kibatalia spec I

Kibatalia spec II

Adenium obesum

Stephanostema stenocarpumWrightia spec I

Wrightia spec II

Allamanda catharica

Allamanda schottiiCerbera manghas

Cerbera odollam

Thevetia ahouai

Thevetia peruviana

Thevetia bicornuta

Thevetia neriifolia

Thevetia ovata

Amsonia tabernaemontana

Kopsia fruticosaKopsia griffithii

Kopsia arborea Vinca minor

Catharanthus roseusRauvolfia sumatrana

Rauvolfia cf ligustrina

Rauvolfia serpentina

Tabernaemontana catharinensis

Tabernaemontana pachysiphon

Marsdenia spec II

Telosma procumbensDischidia hirsuta

Dischidia ovataHoya imperialisHoya multiflora

Hoya bella

Hoya australis

Hoya kerrii

Hoya carnosaHoya kentiana

Hoya thomsoniiHoya serpens

Hoya longifolia

Hoya lacunosa

Hoya pentaphlebia

Ceropegia sandersonii

Araujia sericifera

Morrenia brachystephana

Philibertia gilliesii

Amblyopetalum coccineum

Oxypetalum arnottianum

Oxypetalum coeruleum

Vincetoxicum hirundinaria

Asclepias angustifolia

Asclepias syriaca

Asclepias fruticosa

Asclepias curassavica

Asclepias linaria

Gelsemium sempervirensFagraea fragrans

Fagraea racemosa

Lisianthus nigrescens

Symbolanthus calygonus

Symbolanthus pulcherrimus

Symbolanthus spec

Symbolanthus spec nov ined

Chelonanthus alatus

Macrocarpaea arborescensMacrocarpaea harlingii

Macrocarpaea sodiroana

Macrocarpaea noctiluca

Halenia asclepiadeaHalenia spec

Halenia weddelliana Gentianella germanica

Gentianella hirculus

Gentianella specGentiana sedifoliaGentiana walujewii

Gentiana tibeticaGentiana lutea

Gentiana pneumonantheGentiana dinarica

Gentiana acaulis

Gentiana angustifolia

Gentiana verna

Virectaria majorSabicea panamensis

Pseudomussaenda flava

Mussaenda phillipicaMussaenda angustisepala

Mussaenda aliciaMussaenda erythrophyllaMussaenda nervosaMussaenda mutabilis

Ixora chinensisIxora SunkistIxora finlaysonianaIxora coccineaIxora javanicaIxora coccinea f lutea

Alberta magnaBurchellia capensis

Pavetta revoluta

Randia scortechiniiRandia spinosa

Oxyanthus formosus

Gardenia thunbergiaGardenia tubiferaTocoyena formosa

Warszewiczia coccine

Pentagonia macrophyllaDioicodendron dioicum

Ladenbergia sp ILadenbergia sp II

Stilpnophyllum oellgaardiiIsertia laevis

Hoffmannia nebulosa

Hoffmannia ghiesbreghtiiHoffmannia refulgens

Hamelia patensHillia macrophylla

Hillia wurdackiiHillia parasitica

Cosmibuena macrocarpaChione costaricensis

Cephalanthus occidentalisRondeletia odorata

Timonius spec Gonzalagunia dependens

Gonzalagunia rosea

Arachnothryx lojensisGuettarda speciosa

Notopleura vargasiana

Heterophyllaea pustulataOreopolus glacialis

Coccocypselum condalia

Faramea cf glandulosa

Faramea unifloraFaramea coerulescens

Morinda citrifolia

Psychotria nervosa

Psychotria acuminataPsychotria capensisPsychotria elata

Psychotria reticulata

Psychotria aubletiana

Psychotria carthagenensisPsychotria tomentosa

Psychotria specMyrmecodia spec

Psychotria poeppigianaPsychotria tinctoria

Rudgea ciliata

Palicourea angustifolia

Palicourea lyristipula

Palicourea subtomentosa

Palicourea canarina

Palicourea sulphureaPalicourea heterochroma

Palicourea spec I

Palicourea cf weberbaueriPalicourea andreiPalicourea spec II

Palicourea rigidaPalicourea lobbii

Palicourea calycinaPalicourea thyrsiflora

Palicourea luteonivea

Palicourea sp nov ined

Leptodermis specPhuopsis stylosa

Pentas lanceolataPentas schimperiana

Arcytophyllum capitatumArcytophyllum filiforme

Arcytophyllum macbridei

Arcytophyllum vernicosumArcytophyllum thymifolium

Arcytophyllum setosumArcytophyllum peruvianum

Arcytophyllum ciliolatum

Arcytophyllum lavarumArcytophyllum rivetii

Manettia cordifoliaManettia spec I

Manettia spec IIIManettia spec IIManettia inflataBouvardia bouvardioidesBouvardia laevisBouvardia glabberimaBouvardia ternifolia

Cuscu

ta od

orata

Convo

lvulus

canta

bricu

s

Convo

lvulus

arvens

is

Convo

lvulus

cneo

rum

Calyste

gia se

pium

Merre

mia sp

ec

Ipomoe

a spe

c

Ipomoe

a hier

onym

i

Ipomoea indica

Ipomoea acuminata

Ipomoea rubrifl

ora

Ipomoea mauriti

ana

Ipomoea cairic

ari

Ipomoea arboresc

ensIpomoea alba

Ipomoea purpurea

Ipomoea nil

Ipomoea hederifolia

Ipomoea lobata

Ipomoea quamoclit

Maripa

nica

ragu

ensis

Petunia axillaris

Petunia spec

Fabiana imbric

ata

Fabiana denudata

Fabiana patagonica

Brunfelsia americ

ana

Brunfelsia m

ire

Brunfelsia paucif

lora

Vestia fo

etidaCestr

um nocturnum

Cestrum elegans

Cestrum aurantia

cum

Cestrum co

rymbosu

m

Cestrum cf

bracteatum

Cestrum purpureum

Cestrum parqui

Cestrum psit

tacinum

Cestrum fa

scicu

latum

Cestrum la

urifoliu

m

Salpiglossis

sinuata

Streptoso

len jameso

nii

Browallia americ

ana

Browallia sp

eciosa

Physochlaina orientalis

Hyoscyamus albus

Hyoscyamus aureus

Atropa belladonna

Jaborosa integrifolia

Lycium gilliesianum

Lycium chilense var fil

ifolium

Lycium chilense var m

inuitifolium

Lycium chilense var chilense

Lycium chilense var ve

rgarae

Lycium chilense var descolei

Lycium chilense var confertifolium

Lycium ciliatum

Lycium tenuispinosum var calycinum

Lycium tenuispinosum var te

nuispinosum

Lycium elongatum

Lycium americanum

Lycium infaustum

Lycium cuneatum

Lycium cestroides

Lycium ameghinoi

Lycium morongii

Lycium vimineum

Lycium nodosum

Lycium rachidocladum

Exodeconus miersii

Solandra maxima

Solandra grandiflora

Solandra specJuanulloa aurantiaca

Juanulloa speciosa

Trianaea nobilisMarkaea spec

Markea neurantha

Mandragora officinalis

Capsicum annuum

Salpichroa origanifolia

Salpichroa spec II

Salpichroa spec I

Cuatresia harlingiana

Cuatresia spec

Withania adpressa

Witheringia mexicana

Physalis heterophylla

Physalis crassifolia

Physalis peruviana

Iochroma coccineaIochroma cyaneum

Iochroma spec I

Iochroma fuchsioides

Iochroma calycina

Iochroma spec II

Vassobia breviflora

Dunalia fasciculataDunalia australis

Dunalia spec II

Dunalia spec III

Saracha spec

Brugmansia aurea

Brugmansia vulcanicola

Brugmansia suaveolens

Brugmansia sanguineaDatura meteloides

Datura metelDatura cf arborea

Datura wrightii

Datura stramonium

Nicotia

na otophora

Nicotia

na tabacu

m

Nicotia

na raim

ondii

Nicotia

na rustic

a

Nicotia

na cordifo

lia

Nicotia

na africana

Nicotia

na excelsio

r

Nicotia

na petunioides

Nicotia

na noctiflora

Nicotia

na glauca

Nicotia

na sylvestr

is

Nicotiana lo

ngiflora

Nicotiana plumbaginifolia

Nicotiana bonariensis

Nicotiana forgetiana

Nicotiana langsdorffii

Nicotiana alata

Nicotia

na mutabilis

Schiza

nthus pinnatus

Phac

elia

leuc

ophy

lla

Phac

elia

tana

cetif

olia

Nem

ophi

la m

acul

ata

Hyd

roph

yllu

m v

irgin

ianu

m

Cor

dia

sebe

sten

a

Cor

dia

spec

Wig

andi

a ca

raca

sana

Wig

andi

a cr

ispa

Ehre

tia d

icks

onii

Ehre

tia d

icks

onii

var j

apon

ica

Hel

iotro

pium

eur

opae

um

Hel

iotro

pium

frut

icos

um

Hel

iotro

pium

hirs

utis

sim

um

Borag

o of

ficin

alis

Symph

ytum

cor

datu

m

Symph

ytum

offi

cinal

e

Symph

ytum

x u

plan

dicu

m

Pulm

onar

ia o

fficin

alis

Pulmon

aria

obsc

ura

Pulmon

aria

moll

is

Pulm

onar

ia ru

bra

Nonea

lute

a

Cynog

lottis

bar

relie

ri

Cynog

lottis

chet

ikian

a

Anchu

sa p

usilla

Anchu

sa st

ylosa

Anchu

sa th

essa

la

Anchu

sella

cret

ica

Anchu

sa ca

lcare

a

Anchu

sa lit

tore

a

Anchu

sa a

rven

sis

Anchu

sa va

riega

ta

Anchu

sa o

fficina

lis

Anchu

sa lim

bata

Anchu

sa cr

ispa

Anchu

sa fo

rmos

a

Anchu

sa ca

pellii

Anchu

sa ca

pens

is

Anchu

sa se

mpe

rvire

ns

Anchu

sa hy

brida

Anchu

sa b

arre

lieri

Anchu

sa g

meli

nii

Anchu

sa le

ptop

hylla

Lyco

psis

arve

nsis

Anchu

sa a

egyp

tica

Horm

uzak

ia ag

greg

ata

Anchu

sa a

zure

a

Trac

hyste

mon

orie

ntali

s

Alka

nna

orie

ntal

is

Alka

nna

tinct

oria

Mac

rom

eria

viri

diflo

rus

Lith

ospe

rmum

pur

puro

caer

uleu

m

Cerin

the

maj

or

Para

mol

tkia

doe

rfler

i

Lith

odor

a ol

eifo

lia

Ono

sma

polyp

hylla

Ono

sma

aren

aria

Echi

um v

ulga

re

Echi

um c

retic

um

Echiu

m d

ecai

snei

Echiu

m s

impl

ex

Echiu

m p

inin

ana

Echiu

m w

ildpr

etii s

sp tr

ichos

ipho

n

Echiu

m w

ildpr

etii s

sp w

ildpr

etii

Echiu

m b

revir

ame

Echiu

m h

ierre

nse

Echiu

m v

iresc

ens

Echiu

m g

igan

teum

Echiu

m c

andi

cans

Echiu

m a

uber

ianu

m

Echi

um g

entia

noid

es

Echi

um s

pec

Echi

um it

alicu

mC

ynog

loss

um g

erm

anic

um

Cyn

oglo

ssum

offi

cina

le

Lind

elof

ia lo

ngifl

ora

Mer

tens

ia p

rimul

oide

s

Mer

tens

ia v

irgin

ica

Tric

hode

sma

bois

sier

i

Aucu

ba ja

poni

ca

Bar

nade

sia

arbo

rea

Bar

nade

sia

spin

osa

Barnadesia polyacantha

Barnadesia odorata

Barnadesia parviflora iu

eiss

ujag

ariuq

uhC

Ech

inop

s m

icro

ceph

alus

Ech

inop

s sp

haer

ocep

halu

s ss

p al

bidu

s

Ech

inop

s sp

haer

ocep

halu

s

Ono

pord

um a

cant

hium

Alfr

edia

niv

eaCen

taur

ea p

tero

caul

a

Cen

taur

ea a

sper

a

Cen

taur

ea u

ligin

osa

Cen

taur

ea m

onta

na v

ar n

igro

fimbr

ia

Cen

taur

ea o

rient

alis

Cen

taur

ea m

acro

ceph

ala

Cen

taur

ea c

ana

Cen

taur

ea n

igre

scen

s

Cen

taur

ea p

hryg

ia

Cen

taur

ea tr

ium

fetti

i

Cen

taur

ea o

rpha

nide

a ss

p or

phan

idea

Cen

taur

ea b

ract

eata

Cen

taur

ea o

ssic

a

Cen

taur

ea s

pinu

losa

Cen

taur

ea d

epre

ssa

Cen

taur

ea p

seud

ophr

ygia

Cen

taur

ea p

hryg

ia s

sp s

teno

lepi

s

Cen

taur

ea ja

cea

Cen

taur

ea th

raci

ca

Cen

taur

ea d

ebea

uxii

Cen

taur

ea c

anar

iens

is

Cen

taur

ea n

igra

Cen

taur

ea c

heira

nthi

folia

Cen

taur

ea g

last

ifolia

Cen

taur

ea s

cabi

osa

Cen

taur

ea s

empe

rvire

ns

Cen

taur

ea n

icae

ensi

s

Cen

taur

ea c

yanu

s

Cen

taur

ea ra

phan

ina

ssp

mix

ta

Cen

taur

ea m

onta

na

Cen

taur

ea s

cabi

osa

ssp

alpe

stris

Car

tham

us ti

ncto

rius

Car

dunc

ellu

s m

itiss

imus

Ser

ratu

la q

uinq

uefo

lia

Arc

tium

tom

ento

sum

Cou

sini

a hy

strix

Ptil

oste

mon

afe

r

Cyn

ara

card

uncu

lus

Car

duus

def

lora

tus

Car

duus

aca

ntho

ides

Car

duus

per

sona

ta

Car

duus

nut

ans

Cirs

ium

eris

ithal

es

Cirs

ium

vul

gare

Cirs

ium

tube

rosu

m

Cirs

ium

mon

spes

sula

num

Cirs

ium

ole

race

um

Sco

lym

us h

ispa

nicu

s

Mos

char

ia p

inna

tifid

a

Trix

is d

ivar

icat

a

Trix

is d

ivar

icat

a ss

p di

scol

or

Mut

isia

dec

urre

ns

Mut

isia

coc

cine

a

Mut

isia

retro

rsa

Mut

isia

ham

ata

Mut

isia

ala

ta

Mut

isia

mic

roce

phal

aM

utis

ia s

pino

saM

utis

ia s

inua

ta

Mut

isia

lehm

anni

i

Mut

isia

vic

iifol

iaA

cour

tia m

icho

acan

a

Tric

hocl

ine

rept

ans

Stif

ftia

chry

sant

ha

Hya

lose

ris r

ubic

unda

Arc

totis

fast

uosa

Vern

onia

nov

ebor

acen

sis

Vern

onia

mol

lissi

ma

Vern

onia

nud

iflor

aVe

rnon

ia w

akef

ield

ii

Less

ingi

anth

us m

ollis

sim

us

Trag

opog

on p

orrif

oliu

s ss

p po

rrifo

lius

Trag

opog

on p

rate

nsis

Hie

raci

um to

men

tosu

m

Hie

raci

um p

ilose

lla

Hie

raci

um u

mbe

llatu

mC

icho

rium

inty

bus

Son

chus

asp

er

Den

dros

eris

litto

ralis

Rei

char

dia

picr

oide

s

Leon

todo

n hi

spid

us

Leon

todo

n hi

spid

us s

sp h

yose

roid

es

Leon

todo

n hi

spid

us s

sp d

anub

ialis

Ago

seris

gla

uca

Hyp

ocha

eris

radi

cata

Hyp

ocha

eris

ach

yrop

horu

sA

pose

ris fo

etid

a

Lact

uca

tata

rica

Tara

xacu

m o

ffici

nale

Cre

pis

mol

lisC

repi

s sp

ec

Cal

endu

la a

rven

sis

Cal

endu

la o

ffici

nalis

Leon

topo

dium

alp

inum

Hel

ichr

ysum

sto

echa

s

Tana

cetu

m c

orym

bosu

mA

chill

ea m

illef

oliu

mLe

ucan

them

um v

ulga

re muiranorocmu

mehtnasyrhC San

tolin

a ch

amae

cypa

rissu

s

Kal

imer

is m

ongo

lica

Ast

er a

mel

lus

Ast

er s

pec

Sol

idag

o ca

nade

nsis

Sol

idag

o ch

ilens

isS

olid

ago

glom

erat

aS

olid

ago

virg

aure

a

Erig

eron

pin

natis

ectu

sG

rinde

lia d

isco

idea

Sym

phyo

tric

hum

nov

i bel

gii

Pyr

roco

ma

croc

ea

Eur

ybia

con

spic

uaE

uryb

ia m

acro

phyl

la

tatsahoicenesara

Psuatatned

airalugiL

cepsairalugiL

kslawezrp

airalugiLiiairod

oiceneS

sumirregetni

oiceneS

sneloevausaloetsa

Hsutavo

oiceneS

diuqeanioicene

Sen

s sudilauqsoicene

Sinotrebrab

oiceneS

cus

Sen

ecio

pam

pean

usS

enec

io v

erna

lis

Ade

nost

yarbalg

sel

sutalucitraoicene

S

whcsainiel

Kiihtrufnie

eglufainiel

Kns avivrep

mesainiel

K

aeabocajoicene

Sallyhpohpro

midarekca

P

Inul

a sa

licin

a

Inul

a he

leni

um

Inul

a en

sifo

liaIn

ula

spira

eifo

liaP

alle

nis

spin

osa

Bup

htha

lmum

sal

icifo

lium

Aca

ntho

sper

mum

his

pidu

m

Tage

tes

erec

taTa

gete

s m

inut

a

Arn

ica

long

ifolia

Ste

via

satu

reifo

lia

Mik

ania

per

iplo

cifo

liaM

ikan

ia u

rtic

aefo

lia

Liat

ris s

pica

ta

Eup

ator

ium

inul

ifoliu

m

Eup

ator

ium

sub

hast

atum

Eup

ator

ium

hoo

keria

num

Eup

ator

ium

arn

ottia

num

Bid

ens

pilo

sa

Dah

lia m

erck

ii

Cos

mos

sul

phur

eus

Bid

ens

laev

isB

iden

s sp

ecC

oreo

psis

lanc

eola

taC

oreo

psis

maj

or

Hym

enox

ys g

rand

iflor

aH

ymen

oxys

hoo

pesi

i

Hel

eniu

m a

rgen

tinum

Gai

llard

ia m

egap

otam

ica

Gai

llard

ia p

ulch

ella

Wed

elia

bup

htal

mifl

ora

Zex

men

ia b

upht

halm

iflor

aA

ngel

phyt

um a

spili

oide

s

Ver

besi

na a

ltern

ifolia

Ech

inac

ea la

evig

ata

Ech

inac

ea te

nnes

seen

sis

Ech

inac

ea p

urpu

rea

Hel

iops

is h

elia

ntho

ides

Hel

iops

is h

elia

ntho

ides

var

sca

bra

Wye

thia

am

plex

icau

lis

Zin

nia

peru

vian

a

Rud

beck

ia la

cini

ata

Rud

beck

ia la

cini

ata

var

hum

ilis

Hel

iant

hella

qui

nque

nerv

is

Acm

ella

dec

umbe

ns v

ar a

ffini

s

Tith

onia

div

ersi

folia

Hel

iant

hus

annu

us

Hel

iant

hus

mic

roce

phal

us

irek

oohf

faal

ovea

cS

lep

seht

nayn

eM

atat ail

ofirt

seht

nayn

eM

atall

yhpo

rdyh

sedi

ohp

myN

sedi

ohp

myN

atatl

ep

Lobelia columnaris

Lobelia rhynchopetalum

Lobelia validaLobelia sessilifolia

Cyanea hirtella

Cyanea angustifolia

Cyanea leptostegia

Clerm

ontia hawaiiensis

Clerm

onia kohalae

Clerm

ontia montis loa

Clerm

ontia persicifoliaC

lermontia spec

Clerm

onia parvifloraC

lermontia arborescens

Clerm

ontia kakeana

Lobelia yuccoidesLobelia hypoleuca

nitne

gra

suly

pma

cohp

iS

us

Siphocam

pylus esi

sner

odau

cS

iphocampylus lucidus

Siphocam

pylus manettiaeflorus

Siphocam

pylus nemoralis

suso

ilof

suly

pma

cohp

iS S

iphocampylus gigan

teusS

iphocampylus spec

Burm

eistera anderssonii

Burm

eistera sodiroanaB

urmeistera resupinata

Burm

eistera specceps

aimo

pisy

Lsu

naze

abfc

nogo

port

neC

ama

lat

nogo

port

neC

ncensismu

ecar

hco

nogo

port

neC

em

nogo

port

neC

asud su

etul

nogo

port

neC

ogop

ortn

eC

silifi

rbal

gn

ogop

ortn

eC

suil

ofina

los

nog

opor

tne

Csu

solu

narg

n

unic

ylac

nogo

port

neC

s

ogop

ortn

eC

VIce

psn

Ice

psno

gopo

rtne

C

suta

ucra

nogo

port

neC

gopo

rtne

Cir

elav

nogo

port

neC

Vce

psno

gopo

rtne

Csu

htna

ire

noce

psno

gopo

rtne

C II

ceps

nogo

port

neC

III

suni

dnab

usno

gopo

rtne

C

Lobelia tupaLobelia excelsa

Pratia spec

Pratia m

ontana

Lobelia tenuior

Isotoma axillaris

Lobelia elegans

Lobelia cardinalis

Hippobrom

a longifloraLobelia siphiliticaLobelia laxiflora

Lobelia erinus

Cyphia crenata

Codonopsis viridiflora

Canaria canariensis

Nesocodon m

auritianus

Wahlenbergia fernandeziana

Musschia aurea

Trachelium caeruleum

Cam

panula rapunculoides

Cam

panula drabifolia ssp drabifolia

Michauxia cam

panuloides

Cam

panula versicolor

Cam

panula trachelium

Azorina vidalii

Cam

panula thyrsoides

Cam

panula alliariifolia

Cam

panula latifoliaC

ampanula caucasica

Sym

phyandra hofmannii

Edraianthus gram

inifolius

Cam

panula baumgartenii

Cam

panula rotundifoliaC

ampanula rapunculus

Cam

panula portenschlagianaC

ampanula persicifolia

Asyneum

a canescens

Phyteum

a humile

Phyteum

a spicatumP

etromarula pinnata

Escallonia bifida

Escallonia rubra

Escallonia callcottiae

Itea virginicaC

olumellia oblonga ssp sericea

Desfontainia spinosa

Eryngium

amethystinum

Eryngium

campestre

Eryngium

bourgatii

Eryngium

giganteum

Ferula comm

unis

Tinguarra m

ontana

Thapsia garganica

Scandix australis ssp australis

Carum

carvi

Anethum

graveolens

Coriandrum

sativum

Peucedanum

cervaria

Heracleum

sphondylium

Tordylium apulum

Bupleurum

salicifolium

Astydam

ia latifolia

Hym

enosporum flavum

Pittosporum

crassifolium

Pittosporum

tenuifolium

Pittosporum

eugenioides

Pittosporum

undulatum

Pittosporum

revolutum

Pittosporum

tobira

Pittosporum

xylocarpu

Pittosporum

macrantha

Pittosporum

heterophyllum

Hedera helix

Polyscias sambucifolia

Sambucus ebulus

Viburnum carlesii

Viburnum tinus

Viburnum davidii

Viburnum opulus

Viburnum henryi

Abelia engleriana

Abelia parvifolia

Abelia m

acrotera

Abelia triflora

Abelia x grandiflora

Kolkwitzia am

abilis

Dipelta floribunda

Morina longifolia

Cephalaria am

brosioides

Cephalaria leucantha

Dipsacus laciniatus

Dipsacus pilosus

Dipsacus sativus

Knautia arvensis

Knautia spec

Succisa inflexa

Succisa pratensis

Lomelosia palestina

Pterocephalus papposum

Scabiosa atropurpurea

Scabiosa gram

inifolia

Scabiosa colum

baria

Scabiosa triandra

Scabiosa farinosa

Scabiosa canescens

Scabiosa ochroleuca

Centranthus ruber

Valeriana alliariifolia

Valeriana pyrenaica

Valeriana phu

Valeriana officinalis

Heptacodium

miconioides

Leycesteria formosa

Symphoricarpos albus

Lonicera ligustrina sspyunnanensis

Lonicera acuminata

Lonicera arizonica

Lonicera pilosa

Lonicera implexa

Lonicera periclymenum

Lonicera caprifolia

Lonicera fragrantissima

Lonicera tangutica

Lonicera caerulea

Lonicera pyrenaica

Lonicera alpigena

Lonicera alpigena var webbiana

Lonicera involucrata

Lonicera japonica

Lonicera rupicola

Lonicera myrtillus

Lonicera ruprechtiana

Lonicera maackii

Lonicera tatarica

Lonicera altmannii

Lonicera morrow

ii

Lonicera tomentella

Triosteum him

alayanumD

iervilla lonicera

Weigela decora

Weigelia hybrida

Weigela subsessilis

Weigela coraeensis

Weigelia praecox

Ilex mitis

Ilex cornuta

Ilex latifoliaIlex aquifolium

Ilex paraguariensis

Helw

ingia japonic

Eschweilera ovata

Diospyros lotus

Diospyros virginiana

Sarracenia minorSarracenia flava

Sarracenia alata

Sarracenia purpurea

Dimorphanthera spec

Agapetes buxifolius

Agapetes macrantha

Agapetes incurvata

Agapetes hosseana

Agapetes lobbii

Agapetes serpens

Vaccinium vitis idaea

Gaylussacia densa

Gaylussacia chamissonis

Gaylussacia jordanensis

Orthaea cf abbreviata

Vaccinium myrtillus

Vaccinium consanguineum

Vaccinium spec

Vaccinium uliginosum

Semiramisia speciosa

Thibaudia floribunda

Thibaudia martiniana

Sphyrospermum buxifolium

Sphyrospermum cordifolium

Oreanthes hypogaeus

Ceratostema reginaldii

Ceratostema alatumPsammisia guianensis

Psammisia ecuadorensis

Psammisia ramiflora

Psammisia spec

Macleania insignis

Macleania glabra

Macleania smithiana

Macleania mollis

Satyria warszewiczii

Cavendishia costaricensis

Cavendishia melastomoides

Cavendishia callista

Cavendishia capitulata

Cavendishia endresii

Cavendishia crassifolia

Cavendishia quereme

Cavendishia bracteata

Cavendishia smithii

Cavendishia complectens

Cavendishia nobilis var capitata

Gaultheria eriophylla

Gaultheria serrata

Gaultheria sleumeriana

Andromeda polifolia

Pieris japonicaAgarista hispidula

Agarista oleifolia

Dracophyllum secundum

Acrotriche patula

Leucopogon parviflorus

Rhododendron russatum

Rhododendron glariflorum

Rhododendron zoelleri

Rhododendron robinsonii

Rhododendron malayanum

Rhododendron multicolor

Rhododendron tuba

Rhododendron subcrenulatum

Rhododendron macgregoriae

Rhododendron gaultheriifolium

Rhododendron brassii

Rhododendron flavoviride

Rhododendron javanicum

Rhododendron inundatum

Rhododendron phaeochitum

Rhododendron haematophthalmum

Rhododendron verstegii

Rhododendron keleticum

Rhododendron cuneatum

Rhododendron intricatum

Rhododendron lepidostylum

Rhododendron burmanicum

Rhododendron viriosum

Rhododendron ciliatum

Rhododendron keiskei

Rhododendron championiae

Rhododendron moulmainense

Rhododendron canadense

Rhododendron luteum

Rhododendron occidentale

Rhododendron simiarum

Rhododendron meddianum

Rhododendron arboreum

Rhododendron barbatumRhododendron bakeri

Rhododendron atlanticum

Rhododendron neriiflorum

Rhododendron arborescens

Rhododendron ponticum

Rhododendron williamsianum

Rhododendron calophytum

Rhododendron strigillosum

Rhododendron kiusianum

Rhododendron simsii

Rhododendron hongkongense

Rhododendron vaseyi

Rhododendron farrerae

Ledum palustre

Erica ampullacea

Erica terminalis

Erica fastigiata

Erica mam

mosa

Erica pinea

Erica longifolia

Erica incarnata

Erica discolor

Erica colorans

Erica verticillata

Erica urna viridis

Erica coccinea

Erica densifolia

Erica glomiflora

Erica brachialis

Erica fascicularis

Erica plukenetii

Erica bibax

Erica perlata

Erica regia

Erica tumida

Erica diaphana

Erica blandfordia

Erica monadelphia

Erica sessiliflora

Erica viridiflora

Erica baccans

Erica thomae

Erica grandiflora

Erica vestita

Erica fourcadei

Erica cylindrica

Erica versicolor

Erica phylicifolia

Erica sitiens

Erica bauera

Erica gilva

Erica patersonia

Erica leucotrachela

Erica caffra

Erica curviflora

Erica foliacea

Erica haematocodon

Erica banksia

Erica massonii

Erica carnea

Erica cerinthoides

Erica perspicua

Erica conspicua

Erica verecunda

Erica glandulosa

Erica porteri

Erica sphaeroidea

Bejaria racemosa

Arbutus andrachne

Arbutus xalapensis

Arctostaphylos uva ursi

Enkianthus campanulatus

Pterostyrax corymbosus

Styrax japonicus

Styrax officinalis

Symplocos spec

Camellia japonicaCamellia cuspidata

Camellia salicifolia

Camellia tsaii

Camellia sinensis

Stewartia pseudocamellia

Visnea mocaner

Eurya japonica

Fouquieria diguetii

Fouquieria splendensCobaea scandens

Bonplandia geminifloraCantua candelillaCantua buxifolia

Cantua pyrifolia

Cantua quercifolia

Loeselia amplectensLoeselia mexicana

Loeselia ciliata

Ipomopsis aggregata

Ipomopsis macombii

Ipomopsis longiflora

Ipomopsis rubra

Ipomopsis thurberi

Collomia linearis

Gilia tricolor

Phlox drummondiiPhlox divaricata var laphamii

Phlox paniculata

Phlox subulata

Phlox nivalis

Polemonium caeruleum var acutiflorum

Polemonium caeruleum

Polemonium reptans

Polemonium carneum

Polemonium pauciflorum

Maesa argenteaMaesa hupehensis

Maesa perlariaDeherainia smaragdina

Jacquinia aurantiacaCoris monspeliensis

Androsace vitalianaSoldanella alpina

Dionysia aretioides

Primula boveanaPrimula verticillata

Primula edelbergiiPrimula rosea

Primula florindae

Primula vialiiPrimula capitata

Primula denticulata

Primula darialica

Primula frondosaPrimula halleri

Primula farinosa

Primula verisPrimula vulgaris

Primula juliae

Primula elatior

Primula japonica

Primula pulverulenta

Primula allionii

Primula marginataPrimula pedemontana

Primula palinuri

Primula auricula

Primula hirsuta

Primula rusbyi var ellisiae

Primula rusbyi ssp rusbyi

Primula malacoides

Primula forbesii

Primula jesoana

Primula polyneura

Primula bulleyana ssp beesiana

Primula obconica

Anagallis tenella

Marcgravia browneiMarcgravia nepenthoides

Marcgravia specNorantea brasiliensis

Norantea spec

Impatiens oncidioides

Impatiens montalbanaImpatiens eriospermaImpatiens elephanticeps

Impatiens paucidentataImpatiens parvifloraImpatiens noli tangere

Impatiens textoriImpatiens parasiticaImpatiens balsaminaImpatiens flaccida

Impatiens repens

Impatiens mackeyana ssp claeri

Impatiens mackeyana ssp zenkeri

Impatiens hians

Impatiens sakeriana

Impatiens platypetalaImpatienshawkeri white

Impatienshawkeri red

Impatiens pseudoviola

Impatiens kilimanjari

Impatiens sodeniiImpatiens burtonii

Impatiens auricoma

Impatiens catatii

Impatiens hochstetteri

Impatiens niamniamensis

Impatiens nyungwensis

Impatiens morsei

Impatiens irangiensis

Impatiens zombensis

Impatiens marianae

Impatiens namchabarwensis

Alangium platanifoliumCornus orbiculatusCornus nuttallii

Cornus sanguineaCornus macrophyllaCornus sericea

Hydrangea anomalaHydrangea quercifolia

Deutzia crenataDeutzia purpurascensDeutzia gracilisDeutzia glabrataKirengeshoma palmata

Philadelphus coronariusPhiladelphus schrenkiiPhiladelphus sericanthus

Jamesia americana

Eucnide aurea

Loasa argentinaLoasa triphyllaNasa speciosa

2069

2092

22282276

2277

2389

2672

2824

3112

3241

3438

3820

3821

3825

3832

LAMIALES

GE

NTIA

NA

LES

SOLANALES

ASTERALES

ERIC

ALES

CO

RN

ALE

S*

GARRYALES +

BORAGINALES AQUIFOLIALES

+ DIPSACALES

Mean rate:

0.25 0.8 2.15 2.85 3.35 5.75 8.64

Fig. 3 Phylogenetic position of rate shifts inferred in the differential rates analysis for nectar sucrose proportion (NSP). Branch colours

show rates as exceptionally high (red) to exceptionally low (blue), through intermediate rates (yellow then green), compared to

background rates (black). Major sampled orders are named. *The line not named (next to Dipsacales + Aquifoliales) is

Apiales + Escalloniales + Bruniales. Clades at which rate shifts occur are numbered; details are provided in Table S3. Based on a support

measure (shift support) calculated from the likelihood of finding a shift at each node, three shifts should be treated with caution: core

Solanaceae (node 2389, SS = 0.53), core Plantaginaceae (node 2276, SS = 0.85) and Gesnerioideae (node 3438, SS = 0.92). Shift support

(SS) is 1– fexp, where fexp is the expected frequency of shifts at that node based on results for simulated data (SI text; Fig. S4). The

differential rates analysis for nectar glucose (NGP) and nectar fructose proportion (NFP) revealed similar results as for NSP (Table S3).




the overall sugar concentration of the nectar is low

(5%; Brown et al., 2010). This is because the birds are

sensitive to small changes in osmolarity of the nectar

and hexose solutions have a higher osmolarity than

sucrose solutions (Nicolson & Fleming, 2003). Our find-

ing, that about a third of the species pollinated by Old

World nectarivorous birds produce nectar with low

overall sugar concentration (< 20%) that is extremely

sucrose poor (NSP < 0.20; Fig. S3), corroborates the

results of Brown et al. (2010) and suggests that this

phenomenon may be more widespread in nature than

previously thought. It is possible that this represents an

alternative, energy-saving pollination strategy, which is

deceptive to the pollinators because of the compara-

tively energy-poor nectar they are rewarded with. Simi-

lar deception strategies probably occur in many plant

species belonging to several genera both within and

outside the asterids (e.g. van Wyk et al., 1993; Nicolson

& van Wyk, 1998; this study). Further studies are

needed to test this hypothesis.

Nectar sucrose proportion as an adaptation topollinator group

We tested the hypothesis that NSP is an adaptation to

PG (e.g. Heynemann, 1983; Mart�ınez del Rio et al.,

1992; Baker et al., 1998). At first glance, our results

suggest that this hypothesis cannot be rejected: we

found strong statistical support for convergence on dif-

ferent NSP optima among different PGs and for corre-

lated evolution between NSP and PG. But to what

extent do the details of our findings constitute evidence

for NSP being an adaptation to PG?

The best model for NSP evolution overall is where

NSP is allowed to evolve towards different optima for

each of the nine PGs studied here (Table 1). Inspection

of the inferred optima reveals that they in fact converge

on only two independent optimal values, one for plants

pollinated by generalists and one for plants pollinated

by specialists (Fig. 2). Lack of differences among plants

pollinated by most obligate nectar feeders implies that

the NSP requirements of the individual specialist PGs,

although being generally high, are indistinguishable.

This could be one reason why previous studies (van

Wyk, 1993; Galetto et al., 1998; Nicolson & van Wyk,

1998; Galetto & Bernadello, 2003) have failed to detect

significant differences in NSP among (specialist) PGs.

Further inspection of the best-fitting model reveals a

weak adaptive process (Table 3). The phylogenetic half-

life is unrealistically long, suggesting that species are far

from their optimum and unlikely ever to reach it (Han-

sen, 1997; T.F. Hansen, pers. comm.). The stochastic

variance is also very high, suggesting that any adaptive

evolution towards the NSP optima is overwhelmed by

stochastic movement and that a large amount of the

residual variance of the model is not explained by the

two optima. One interpretation of such a model is that

it does not represent adaptation to peaks in the

adaptive landscape in the strict sense but rather an

adaptive trend of increasing divergence from the ances-

tral state (Hansen, 1997, 2012; T.F. Hansen, pers.

comm.). Such an interpretation has been invoked for

body size evolution in relation to dietary niches in ceta-

ceans and habitat in monitor lizards (Slater et al., 2010;

Collar et al., 2011). For nectar evolution, this would

suggest slow movement towards low NSP in generalist-

pollinated plants and high NSP in specialist-pollinated

plants. However, the optima inferred from these models

tend to lie outside currently occupied ranges, perhaps

themselves therefore representing unreachable adaptive

peaks. The optima inferred for NSP approach the limits

of the occupied range but are not unrealistic (Table 3).

Another possible interpretation, therefore, is that NSP is

evolving as an adaptation to other, unmeasured vari-

ables (Labra et al., 2009; Hansen, 2012), for example

corolla shape and size (Nicolson, 2002; Witt et al.,

2013), perhaps as a function of the abiotic environment

(Petanidou, 2005; Nicolson et al., 2007). A third possi-

ble interpretation is that NSP is drifting, bounded by

the allometric constraints of floral shape and size,

which in turn could be correlated with PG. Such an

explanation has been invoked for body shape evolution

in three-spined stickleback (Voje et al., 2013). Without

formally incorporating these variables into the hypothe-

sis-testing framework, these alternatives cannot be dis-

tinguished but results from the simpler OU models

(OU2, OU2A and OU2V; Table S2) do offer some addi-

tional insight: these models suggest a much stronger

adaptive process, at the coarse level of generalist PGs

versus specialist PGs. This supports the interpretation of

adaptation rather than that of drift. However, because

the simpler models are a much worse fit to the data,

they do not capture the entire story. The more complex

model is a far superior fit to the data but one that nev-

ertheless explains less of the residual variance.

Together, these results suggest that PG is an important

component of NSP evolution, but it does not act

directly as hypothesized here – other factors are needed

to fully explain how NSP evolves.

Is nectar sucrose proportion the trait or theenvironment?

The dependency in the NSP and PG data coded as binary

variables suggests that a flower is more likely to be polli-

nated by a specialist pollinator, irrespective of its NSP

(Table S4). In contrast, a specialized nectar feeder is

more likely to pollinate a high-NSP flower and a general-

ist feeder is more likely to pollinate a low-NSP flower.

This asymmetry is explained by the finding that NSP is

the first to shift from the ancestral state of high NSP/spe-

cialist PG (q13 > q12; Table 2); that is, changes in NSP

may occur without a simultaneous or preceding change

in PG. Once variation in NSP has been established, shifts




from a specialist to generalist PG are more likely in lin-

eages with low-NSP nectar and further shifts from high

to low NSP are more likely in generalist-pollinated lin-

eages. Thus, the evolution of NSP and PG is correlated in

a way that means that certain shifts are more likely than

others, but not in a way that requires simultaneous

change, as would be expected under strict coevolution

(Janzen, 1980). The finding that NSP changes first sug-

gests that the nature of the correlation is primarily dic-

tated by pollinator behaviour rather than vice versa. In

other words, it suggests that pollinating animals ‘capital-

ize’ on the NSP presented by the plant and that the

plants do not necessarily adapt their NSP to the local pol-

linator guild (cf. ‘diffuse coevolution’ of Janzen (1980)).

This contrasts with the view that a plant must adapt its

nectar composition to ensure regular visitation by effi-

cient pollinators (Nicolson et al., 2007). A conceptual

consequence of this is that NSP should perhaps be trea-

ted as the environment and PG the trait – in order to be

fully understood, the floral rewards–pollinator interac-

tion might better be viewed from the opposite perspec-

tive to that often presented in the literature (and this

study; Nicolson et al., 2007 and references therein).

Different processes in different clades and fordifferent sugars?

Analysis of less inclusive clades revealed several impor-

tant insights. Evidence for adaptation was much stronger

for individual clades than for asterids as a whole: an

adaptive model could not be rejected for 12 of 13 clades

(Table S6) and a much more rapid and precise adaptive

process was inferred from these models than from the

model for asterids overall (Table 3). This supports the

idea that, at the broadest scale, several factors are needed

to explain nectar evolution, only one of which is adapta-

tion to the broadly defined PGs analysed here. Certain

clades, however, revealed strong evidence for adaptation

to PG in some clades and none at all in other clades.

Multiple-optima models tended to corroborate the find-

ing of independent optima for generalist and specialist

PGs (Fig. S5) but also found differences among specialist

PGs. One interesting example is the low-NSP optimum

inferred for specialized nectarivorous birds, providing

further support for the hypothesis of deception elabo-

rated upon above. In contrast, single-optimum models

revealed different optima in different clades, irrespective

of the main PGs. A high optimum was inferred for the

heather family (Ericaceae) and a low optimum for the

daisy family (Asteraceae). Ericaceae are pollinated

entirely by specialist PGs (bees and wasps, specialized

OW birds and hummingbirds sampled here), whereas

Asteraceae are pollinated by both specialist and general-

ist PGs (generalist insects, bees and wasps, butterflies

and hummingbirds; Table S8). Ericaceae tend to have

tubular flowers and Asteraceae small, open flowers, sug-

gesting that the different optima inferred for these two

clades could be governed by floral shape and size, rather

than the main PGs of each clade. These results further

support both conclusions above that PG alone cannot

explain how interspecific differences in NSP evolve and

that the relationship between NSP and PG is perhaps not

governed by adaptations of the plant but by pollinator

behaviour and dietary requirements.

Finally, these findings hold true not only for NSP but

for NFP and NGP as well (Tables S6 and S7, Fig. S5).

This contrasts with findings for asterids as a whole,

where an adaptive model was rejected for these two

sugars (Table 1), and suggests either that pollinators are

sensitive to the proportion of the two hexoses in nectar

(contra Baker et al., 1998) or, because our analyses were

based on proportions, that the signal in NSP cannot be

independent of that in NFP and/or NGP, even if differ-

ent processes govern the relative proportions of each

sugar. Future (experimental) work may shed further

light on these alternatives.

Adaptation and conservatism in nectar evolutionand beyond

Much of the historical debate surrounding the evolu-

tion of nectar sugar composition has centred on the

dichotomy between adaptation to pollinator dietary

requirements and there being a ‘phylogenetic con-

straint’, limiting the amount of variation that can accu-

mulate within and among clades (Baker & Baker, 1975;

Nicolson et al., 2007). Indeed, results such as those

above, of different processes operating in different

clades and of clade-specific NSP optima that are inde-

pendent of pollinator diversity, are likely to underlie

some earlier claims of phylogenetic conservatism

(Galetto & Bernadello, 2003; Thornburg, 2007;

Rodr�ıguez-Ria~no et al., 2014). However, although we

found that adaptation to pollinators is not a sufficient

explanation on its own, phylogeny is merely a depiction

of patterns and cannot in itself constrain or explain

anything (e.g. Losos, 2011). Many empirical studies

have invoked both adaptation and conservatism in

cases such as ours, where there is some evidence for

adaptation, but where the specific hypothesis being

tested leaves some observed variance unexplained

(Ackerly, 2004; Cattin et al., 2004; Escudero et al.,

2012; Hansen, 2014). However, in these studies, phylo-

genetic conservatism is not invoked as an alternative to

adaptation but to describe a strong historical signal not

directly related to the hypothesis being tested. In other

words, conservatism is invoked in lieu of the full mech-

anistic explanation, just as appears to be the case in the

nectar literature.

Given the long-standing clarity, far beyond the nectar

literature, of the inadequacy of the adaptation/conser-

vatism dichotomy (e.g. Leroi et al., 1994; Blomberg &

Garland, 2002; Ackerly, 2004; Losos, 2011), why does

it persist? We suggest that there are several reasons.




1 As a result of (na€ıve) interpretation of phylogenetic

patterns of trait similarity and divergence, often mea-

sured as phylogenetic signal (Losos, 2008; Cooper

et al., 2010; Crisp & Cook, 2012), as process. How-

ever, although it has long been clear that patterns

cannot simply be read from phylogenies and inter-

preted as processes (e.g. Blomberg & Garland, 2002),

the extent to which phylogenetic signal is discon-

nected from any underlying process has only become

clear relatively recently (e.g. Revell et al., 2008; Bou-

cher et al., 2014; M€unkem€uller et al., 2015). There-

fore, the practice of inferring process from

phylogenetic patterns remains.

2 Because of the cladistic tradition of recognizing only

autapomorphies as adaptations (Hansen, 2014). This

means that a retained ancestral state (plesiomorphy)

cannot be interpreted as an adaptation. The counter-

argument is that retained plesiomorphies must be

adaptations for something or they would have suc-

cumbed to selection pressures to change (Losos,

2011; Hansen, 2014). Thus, retention of the ancestral

state is a pattern that says nothing of its generating

or maintaining evolutionary process.

3 Because of the terminology used in the OU frame-

work of adaptive models (‘adaptation–inertia mod-

els’; e.g. Pienaar et al., 2013). This use may certainly

be perceived as conceptually confusing but inference

of ‘inertia’ over ‘adaptation’ does not necessarily

mean that the trait in question is not an adaptation

at all, only that the specific hypothesis under study is

rejected (Hansen, 2014). The trait may still be an

adaptation to an environment that itself is evolving

in a drift-like manner or to another, unmeasured

variable that shows strong similarity among closely

related species (Labra et al., 2009; Hansen, 2012).

Thus, although model inferences may be described as

either ‘adaptation’ or ‘inertia’, interpretation is not

necessarily dichotomous.

4 Due to the development of the conceptual frame-

works for studying adaptation and conservatism as

largely different fields. This is most likely historical: as

one field was realizing the challenges involved in

inferring adaptation in a comparative framework

(Baum & Larson, 1991; Leroi et al., 1994; Hansen,

1997), another, dedicated to detecting (niche) conser-

vatism (Harvey & Pagel, 1991; Wiens & Graham,

2005; Crisp & Cook, 2012), was born. Increasingly,

these fields make use of the same models (cf. e.g.

Kozak & Wiens, 2010; M€unkem€uller et al., 2015), butbecause they are developing in parallel (one is con-

cerned with adaptation [Hansen, 1997; Butler & King,

2004; Beaulieu et al., 2012; ], the other with a ‘failure

to adapt’ [Wiens et al., 2010; Kozak & Wiens, 2010; ]),

reconciliation of how adaptation and conservatism

can be interpreted together has received much less

attention than each phenomenon has separately (but

see Ackerly, 2003, 2004; Labra et al., 2009).

Conclusion

We present evidence that NSP is an adaptation to PG in

asterids but, importantly, that this is not the whole

story. Future studies may increase mechanistic under-

standing of how plant–pollinator interactions via floral

rewards evolve by focusing on dense sampling of nar-

rower clades, finer divisions of PGs and incorporating

additional factors into the hypothesis-testing frame-

work, along with consideration of how the floral nec-

tar–pollinator interaction arises, without the need to

invoke phylogenetic constraints or conservatism.

Acknowledgments

The botanical gardens of Berlin, Bonn, Freiburg, G€ottin-gen, Halle, Heidelberg, Jena, Munich, M€unster,Osnabr€uck, Potsdam, Basel, Bern, St. Gallen, Zurich,

Leiden, Kew, Graz, Cochabamba, Santa Cruz, Singa-

pore, Bogor and Cibodas and the ‘Sukkulentensamm-

lung’ Zurich provided permission to collect nectar. We

are grateful to B. Steudel, J. Kluge, E.A. Tripp, H. Mar-

tinez Piedra, H. Hentrich and J.-M. Tompkins for help

during data collection, M. Weist and A. Schmidt-

Lebuhn for help during nectar collection, P. Endress, J.

de Vos, R. Carter, G. Thomas and P. Kevan for helpful

suggestions and advice, B. Keller, S.W. Peters and A.

Egert for help during laboratory work, E. Benetti and

B. Seitz L€udi for help during literature collection,

Palack�y University for space and D. Ackerly, L. Bun-

nefeld and several anonymous reviewers for comment-

ing on earlier drafts. SA was funded by the Konrad-

Adenauer Stiftung, the Swiss Nationalfond and the Stif-

tung zur F€orderung der Pflanzenkenntnis. DH was

funded by the European Social Fund and the State

Budget of the Czech Republic (project no. CZ.1.07/

2.3.00/30.0041). AMH was funded by a Swiss NF Fel-

lowship for Prospective Researchers (grant PBZHP3-

133420) and by Carl Tryggers Stiftelse (CTS 12:409).

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Supporting information

Additional Supporting Information may be found

online in the supporting information tab for this article:

Appendix S1 Supporting figures (S1-S5) and tables

(S1-S8)

Table S1 Age constraints

Table S2 Parameter estimates from simpler OU models

Table S3 Detailed results of the differential rates analy-

sis

Table S4 Contingency table for NSP and PG coded as

binary variables

Table S5 Estimates of phylogenetic signal across 100

trees

Table S6 Model fit for less inclusive clades analyzed

separately

Table S7 Parameter estimates for OU models for less

inclusive clades (NFP and NGP)

Table S8 Distribution of PGs in asterids and less inclu-

sive clades

Figure S1 Lineages-through-time plots for three differ-

ent branch length distributions

Figure S2 Ternary plots for each PG separately

Figure S3 Sucrose proportion and sugar concentration

for specialized OW birds

Figure S4 Significance of rate shifts for simulated data

Figure S5 Selective optima inferred for NSP, NFP and

NGP in asterid subclades

Data S1 Literature used for constructing the asterid

summary phylogeny

Data S2 Nectar sugar concentration and composition

data newly generated for this study; and literature

sources for published data analyzed in this study

Data deposited at Dryad: doi: 10.5061/dryad.1r45h

Received 20 June 2016; revised 8 October 2016; accepted 12 October

2016




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