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Title: Microhabitat specialisation facilitates thermal niche evolution in

the green algal genus Halimeda

Authors: Vanessa R. Marcelino1, Lennert Tyberghein2,3, Olivier De Clerck3, Heroen Verbruggen1

Affiliations: 1 School of Botany, University of Melbourne, Australia

2 Flanders Marine Institute (VLIZ), Ostend, Belgium

3 Phycology Research Group, Ghent University, Belgium

Contact:

VRM: School of Botany, University of Melbourne, VIC 3010, Australia. [email protected]

LT: Flanders Marine Institute, Wandelaarkaai 7, 8400 Ostend Ostend, Belgium.

[email protected]

ODC: Phycology Research Group, Ghent University, Krijgslaan 281, building S8, 9000 Gent, Belgium

[email protected]

HV: School of Botany, University of Melbourne, VIC 3010, Australia.

[email protected]

Keywords: Macroecology, environmental stress, Halimeda, α-niche, species traits, evolutionary rates,

pre-adaptation, niche evolution, evolvability, niche shifts.

Running title: Traits and thermal niche evolution

Corresponding author: Vanessa Rossetto Marcelino

Abstract: 204 words.

Main text: 3821 words (5185 including references).

Number of references: 50.

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Abstract

Aims: The "out of the tropics" model explains the latitudinal diversity gradient but does not address

why some lineages are able to shift their thermal niche into temperate biomes while others fail to do so.

We investigate this question by testing whether the thermal niche of Halimeda seaweeds evolves as a

function of niche breadth and microhabitat-related lineage traits.

Location: Worldwide tropical and subtropical

Methods: We use phylogenetic comparative methods to test the effects of two types of traits –

proximity to temperate biomes (climatic niche breadth and geographical range) and microhabitat

specialisations (α-niche traits) – on thermal niche evolution of the green algal genus Halimeda.

Additionally, we use Monte Carlo simulations to test the robustness of the analyses.

Results: While proximity exhibited no clear relationship to niche evolution, we found evidence

relating microhabitat specialisation to thermal niche evolvability. Lineages associated with

exposed/harsh microhabitats have 3× faster rates of thermal niche evolution, with most change

happening from warmer to colder climates

Main conclusions: The results support the hypothesis that α-niche lineage traits can facilitate the

transition out of the tropics. Possible underlying mechanisms include adaptations to harsh habitats

constituting pre-adaptations to cooler biomes, and scenarios whereby evolutionary rates are boosted by

the available energy or other capacitors of evolutionary change.

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Introduction

Understanding why there are more species in the tropics is a central question in ecology and

evolution. One of the best supported explanations is the out of the tropics (OTT) model postulating that

species predominantly originate at low latitudes and expand towards cooler regions afterwards

(Jablonski et al., 2006). In addition to having higher speciation and lower extinction rates in the tropics,

many organisms show strong climatic niche conservatism, confining their distributions to tropical

biomes. Only a small proportion of the lineages from the tropical pool shift their climatic niche and

diversify into temperate zones (Buckley et al., 2010; Olalla-Tárraga et al., 2011; Jablonski et al.,

2013).

This begs the question why some lineages do evolve along the thermal gradient and expand

into temperate biomes while others fail to do so. Two non-mutually exclusive hypotheses based on

species traits can be invoked. We refer to the first one as the “proximity effect,” which is centred on the

assumption that lineages with larger geographical ranges or niche breadths are more likely to diversify

out of the tropics simply because they are in closer proximity to temperate habitats at their range edges.

The ocean’s cooling trend since the Cenozoic Era and the steepening of the temperature gradient

(Zachos et al., 2001; Bijl et al., 2009) further increased proximity effects by leading edge populations

to perish or adapt in the new climate. Proximity has been suggested to affect thermal niche evolution in

a number of studies (e.g. Fisher-Reid et al., 2012). The well-studied bivalves fossil record indicates

that transitions to colder climates was mediated by bridge species with expanded distributions

encompassing higher latitudes (Jablonski et al., 2013).

The second hypothesis is that thermal niche evolution is modulated by traits related to

microhabitat and use of resources at local scales (e.g. depth, substrate), commonly referred as α-niche

traits (Ackerly et al., 2006; Silvertown et al., 2006 and references therein). Two sources of evidence

point in this direction. First, α-niche traits are often correlated to climate. For example, leaf shape

(Schmerler et al., 2012) and nitrogen-phosphorus content (Reich & Oleksyn, 2004) in plants show a

remarkable correlation with temperature, and α-niche traits are related to trade-offs between utilization

of resources and performance through climate variations (Angert et al., 2009). Trait-climate

relationships could affect thermal niche evolvability either directly through natural selection/adaptation

or indirectly as a by-product of other evolutionary processes, e.g. exaptations (see Pigliucci, 2008).

Second, α-niche traits evolved earlier and remained stable in the phylogenetic history of several


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organisms while their thermal niches evolved more gradually (e.g. Ackerly et al., 2006; Emery et al.,

2012), although this trend is by no means universal (e.g. Kostikova et al., 2014). Early diversification

and subsequent stasis of α-niche traits has been successful because it facilitates species coexistence

through resource partitioning (Cavender-Bares et al., 2004; Ackerly et al., 2006; Prinzing et al., 2008;

Ackerly, 2009). Taking these two elements together, it would appear reasonable to assume that early

specialization of the α-niche scale leads to differential chances of lineages to transition into new

biomes later on. This hypothesis is surprisingly underexplored in the literature.

We use the seaweed genus Halimeda (Bryopsidales, Chlorophyta) as a model to investigate

the influence of proximity and α-niche traits on thermal niche evolution. Halimeda is unusual among

seaweeds in being siphonous (its entire body, sometimes metres in length, is composed of a single

multinucleate cell) and calcareous (Hillis, 2001). The earliest known Halimeda fossil dates back to the

Permian (Poncet, 1989) and most of the extant lineages diversified during the Cretaceous (Hillis, 2001;

Verbruggen et al., 2009a). Halimeda is found in tropical and temperate coastal regions but originated

and retained most of its species diversity in the tropics (Verbruggen et al., 2009b). Some Halimeda

lineages established in wave-affected areas, while others occupied sheltered slopes or lagoons

(Kooistra et al., 2002; Verbruggen & Kooistra, 2004). The well-known phylogeny, geographical

distribution and microhabitat characterization makes this genus a good model to test alternative

hypotheses on the influence of lineage traits on the out of the tropics process. We apply phylogenetic

comparative methods to test the relationships between thermal niche and species traits. Additionally,

we use simulations to make a critical evaluation of the statistical power of the methods to test our

hypotheses on the Halimeda dataset. We show that microhabitat specialisation, but not proximity,

relates to the pace of thermal niche evolution in Halimeda.

Material and Methods

Phylogeny

DNA sequences from nuclear (partial 18S, ITS1, 5.8S, EF1α and HSP90) and plastid (tufA,

rcbL, UCP3, UCP7) loci were generated following previously described protocols or taken from

previous work (Kooistra et al., 2002; Verbruggen et al., 2005, 2009b). Sequences generated in this


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study are available on GenBank (accession numbers to be provided). Loci were aligned in MEGA v.5

(Tamura et al., 2011), checked and corrected manually against the chromatograms. The resulting

concatenated alignment contained 7,185 bp. Suitable models of molecular evolution and a partitioning

strategy were identified using PMT (www.phycoweb.net) following a Bayesian Information Criterion

approach. The phylogenetic tree was reconstructed using 56 Halimeda species, including the vast

majority of the known species of the genus. Because we only had microhabitat data for 53 species, 3

species were excluded from the downstream analyses. The phylogeny was reconstructed with BEAST

v.1.7.5 (Drummond et al., 2012) and calibrated following Verbruggen et al. (2009a). In order to

account for phylogenetic uncertainty, we used 10,000 random trees from the posterior distribution set

(after discarding the burn-in) to test the effects of the α-niche on thermal niche evolution. The analyses

of the proximity effects are more computationally intensive, prohibiting running it with 10,000 trees,

hence we used the 10 trees with the highest posterior probabilities. For illustration purposes, we present

only the tree with the maximum sum of posterior clade probabilities.

Occurrence dataset

Species occurrence data were obtained from herbaria at Ghent University (GENT), Western

Australia Museum (WA), University of Michigan (MICH), São Paulo University (USP), Rural

University of Pernambuco (PEUFR), Nationaal Herbarium Nederland (Leiden branch; L), Smithsonian

Institution National Museum of Natural History (US) and Institute of Research for Development (IRD).

All specimens were re-identified by HV, taking into account any information about species recently

discovered using DNA barcodes that have not yet been published but are taken into consideration in

this study (in occurrence records as well as phylogeny). Uncertain identifications and duplicated

records were excluded. Coordinates for each collection were obtained from GPS records available on

the herbarium notes or georeferenced based on localities description using Google Earth. In total, we

gathered 2,508 unique occurrence records.

Thermal niche trait values

Species' thermal niches were derived from mean Sea Surface Temperature (SST) for each

locality, extracted from the Bio-ORACLE database (Tyberghein et al., 2012). In order to avoid effects

of geographic sampling bias on the obtained values, we applied geographic kernel smoothing and


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derived weighted mean SST values for each species. The exact procedures and R scripts used to derive

the thermal niche traits from occurrence records can be found in Appendix 1 in Supporting

Information. Ancestral thermal affinities were estimated from the averaged SST values with the

function ace from the R package APE (Paradis et al., 2004) and plotted along the tree with

TreeGradients v.1.03 (Verbruggen, 2012).

Proximity trait values and their impact on thermal niche evolution

To test for possible effects of proximity on thermal niche evolution, we investigated correlated

evolution between SST affinities and three features that could help bridge the gap from tropical to

temperate regions. The first of these features is "SST breadth", a proxy variable describing the width of

the distribution of SSTmean records. The second is "SST extremes", the difference between maximum

and minimum observed SSTmean values. The third feature is "distribution range", the surface area of

the smallest polygon (convex hull) containing the species occurrences. The influence of each of these

features on thermal niche evolution was evaluated with the "rate by state" test (function ratebystate in

the R package phytools (Revell, 2012)), which evaluates how the evolution of a continuous trait (here

the thermal niche) is affected by another continuous trait (each of the proximity features). This is

accomplished by regressing the squared contrasts of the thermal niche trait on the estimates of ancestral

states of the proximity variables. Simulations are then performed to test whether the influence of one

trait over the other is statistically significant. We performed 1,000 simulations for the 10 trees with

maximum posterior probabilities. The R script to calculate each proximity variable and perform the

ratebystate test can be found in Appendix 2.

α-niche trait values and their impact on thermal niche evolution

To test the influence of α-niche traits on thermal niche evolution, we selected two microhabitat

traits associated with the harshness of environmental conditions: depth – for which species were

classified as shallow or deep (threshold 1 m.), and exposure (to waves, desiccation, intertidal) – species

classified as exposed or sheltered. Depth and exposure data were gathered for each species based on

previous published work (e.g. Kooistra et al., 2002; Verbruggen & Kooistra, 2004) and personal

observations in the field (HV), and coded as binary characters. We evaluated if rates of thermal niche

evolution vary among clades with different discrete traits (i.e. exposure, depth values) by comparing


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the fit of a Brownian motion model with a single rate of thermal evolution (the null model) to that of a

model where the rate of thermal evolution depends on the state of the binary α-niche trait (alternative

model, trait-dependent rates). Both models were optimized with the brownie.lite function implemented

in phytools (Revell, 2012) and compared based on the Akaike Information Criterion (AIC). The models

were fitted to 10,000 random trees (post-burn in) to account for phylogenetic uncertainty. See

Appendix 3 for R scripts and more detailed information.

Phylogenetic Monte Carlo simulations

AIC-based model choice is prevalent in studies using phylogenetic comparative methods, but is

prone to errors when the data contain insufficient information to perform the tests or when the real

model of evolution considerably differs from the hypotheses being tested (Boettiger et al., 2012;

Pennell et al., 2014). To address this issue, we performed phylogenetic Monte Carlo (PMC, Boettiger

et al., 2012) simulations to test whether the analyses of the α-niche effects had sufficient statistical

power to distinguish between single vs. multi-rate evolutionary models. The null model is that all

Halimeda lineages have a single rate of thermal niche evolution, independent of α-niche traits. The

alternative model is that lineages with different microhabitat traits have different rates of thermal niche

evolution. The PMC simulations consist of: i) parameter estimation for both models using the original

data; ii) simulating n datasets based on the estimated parameters for each model; iii) re-estimating the

parameters and likelihood for both models based on the simulated dataset. The difference in log

likelihood (δ) of observing the data under two maximum likelihood estimated models provides the

basis for an approximation to the p-value for the test. The power of the test is the probability of

correctly rejecting the null model when the data is produced by the alternative (more complex) model.

We wrote an R function to perform PMC (Boettiger et al., 2012) for the brownie.lite function

(Appendix 4). The ancestral microhabitat traits were estimated with stochastic character mapping

(simmap) replicated 100 times (to account for the different possible histories) for each of the 10 trees

with the highest posterior probabilities. This resulted in 1,000 trees, which we refer hereafter as “trait

trees”. 1,000 PMC simulations were performed for each trait tree to obtain the statistical test for model

selection. The results were evaluated based on how many times a significant result (favoring the multi-

rate evolutionary model over the null hypothesis) was obtained for each trait tree. The R scripts for

generating the trait trees and performing PMC analyses are available in Appendix 4.


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Results

Estimation of ancestral SST affinities in Halimeda support the notion that most of the lineages

retained a tropical distribution and that thermal niche evolution happened from tropical to temperate

waters in a handful of lineages (Figs. 1 and S1, Verbruggen et al. 2009b). None of the proximity traits

(SST breadth, SST extremes or distribution range) were related to the rate of thermal niche evolution

(Table 1). In contrast, we did find a strong relationship between the α-niche and the rate of thermal

niche evolution (Table 2). Specifically, we found that lineages from exposed microhabitats have on

average 3× higher rates of thermal niche evolution when compared with lineages from sheltered

habitats (Fig. 2). The model with separate rates of thermal evolution for exposed and sheltered habitats

fits the data better than a model with a single rate across both exposure categories. Species living in

shallower waters also showed slightly faster thermal evolution, but in this case the trait-dependent

model was not preferred over the null hypothesis (Table 2).

Phylogenetic Monte Carlo simulations failed to distinguish between trait-dependent vs. single-

rate evolutionary models with depth as the predictor (Table 3). For exposure however, 75% of the

simulations on 1,000 trait trees preferred the trait-dependent model with a significant p-value,

indicating that this result is robust to variations in the topology and mapped character history and

therefore unlikely to be a false positive.

Discussion:

Our results indicate that ancestral α-niche traits can explain why some lineages are more

susceptible to thermal niche shifts than others. We found that lineages associated with exposed

microhabitats have 3× higher rates of thermal niche evolution on average (Figs. 1 and 2, Table 2).

Since Halimeda originated and largely remained in tropical waters (Verbruggen et al., 2009b), most of

the observed thermal niche evolution is towards colder climates (Fig. 1). Phylogenetic Monte Carlo

simulations suggest that the higher evolutionary rate associated with exposure is unlikely to be an

artefact of the analysis.

Unlike exposure, the depth trait does not seem to affect thermal niche in Halimeda. Exposure

and depth are naturally correlated in the sense that all exposed species are found in shallow waters, but

not vice-versa. Lagoon species, for example, inhabit shallow but much less exposed microhabitats than


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species living near the reef crest. Despite this correlation, we did not detect a significant effect of depth

on thermal evolution. PMC simulations indicate that depth-related influence is not strong enough or

our dataset is not sufficiently explanatory to inform the model choice in this case (Table 3).

Theoretically, one would expect lineages with broader climatic niches or geographical ranges

to be in closer proximity to cooler biomes, which could promote the formation of cold-adapted species

near their range edges. In Halimeda, however, we found no evidence for effects of climatic or

geographical proximity effects on the evolution of thermal niches (Table 1). In bivalves, it was shown

that lineages moving out of the tropics did so via bridge species whose distribution spans tropical and

extratropical areas (Jablonski et al., 2013). In contrast to many invertebrates that disperse with pelagic

larvae, seaweeds generally have small populations and limited dispersal (Kinlan et al., 2005), which

favors adaptive speciation. Higher levels of gene flow in bivalves may have prevented adaptive

speciation along a thermal gradient (via gene swamping, see Lenormand 2002), resulting in

eurythermal species with larger geographical ranges – the bridge species. If this scenario is correct, the

magnitude of proximity effects is influenced by dispersal strategy, leading to a situation where lineage-

specificity in rates of thermal evolution can be seen as a function of a pre-existing (non-geographic)

species trait. Regardless of the underlying mechanism, the proximity hypothesis of thermal evolution

raises a chicken or the egg dilemma where it is tricky to discern which aspect – proximity or thermal

niche – diversified first and which of them affects the other. In other words, it is difficult to discern

causality in this correlation. Approaches integrating the feedback between environment, ecology and

evolution (see Lavergne et al. 2010) could be suitable to better understand how and when one trait (or

process) shapes another.

Previous evidence shows that many α-niche traits in Halimeda have diversified soon after its

origin (Kooistra et al., 2002; Verbruggen & Kooistra, 2004; Verbruggen et al., 2009b). This helps

interpret their influence on thermal niche evolution. One obvious possibility is that adaptations to life

in harsh habitats constitute pre-adaptations to cooler biomes, facilitating the transition into it. Lineages

living in shallower and exposed microhabitats are subject to more drastic variations in temperature, and

adaptations to such conditions could leave these lineages with a higher likelihood of settling in

temperate habitats and producing temperate-water species. In this context, it is important to note that

all species inhabiting the entire intertidal and/or reef crest habitats were classified as exposed species,


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being therefore less protected from a variety of environmental stressors than their sheltered

counterparts.

Experimental studies on E. coli exposed to high temperatures reproduced in vitro evolutionary

improvement in fitness and adaptation in these temperatures (Bennett & Lenski, 1997). Interestingly,

the E. coli lineages adapted to higher temperatures also showed enhanced fitness in maltose media,

which the authors considered to be a preadaptation due to pleiotropic side effects of the selected

mutations, likely related to heat shock proteins. These proteins are known as capacitors of phenotypic

evolution because they buffer accumulated mutations and release them under environmental stress,

accelerating evolutionary rates (Rutherford & Lindquist, 1998; Sangster et al., 2004). Likewise, faster

thermal niche evolution in some Halimeda lineages may have resulted directly from adaptations to

quotidian temperature stress or indirectly by the action of ‘capacitors’ of evolvability (see Pigliucci,

2008; Masel & Trotter, 2010).

Another potential explanation involves the biologically available energy associated with the α-

niche traits analysed here. A number of studies show positive relationships between energy and

evolutionary rates (reviewed in Hawkins et al. 2003 and Wright & Rohde 2013). Lineages living in

exposed/shallow microhabitats usually receive more photosynthetically active radiation and could have

higher evolutionary rates through energy-related processes, increasing the chances of cold-adapted

species being produced. The available energy can be further increased in exposed microhabitats

through wave action, as it promotes circulation, thereby reducing the diffusion boundary layer and

loosening constraints on nutrient availability (Hurd et al., 1996).

It is interesting to note the parallel of the out of the tropics scenarios raised here and the

“shallow-towards-deep” pattern in species turnover observed in fossil records (Jablonski et al., 1983).

The mechanisms proposed to explain a possible onshore-origination trend, including higher nutrients

input, environmental stress, variability of selection pressures, rates of gene flow and phenotypic

plasticity in onshore habitats (Jablonski, 2005), also constitute plausible explanations for why only a

few lineages diversify out of the tropics. Those lineages experiencing continuous environmental

variability at α-niche scales inside the tropics may acquire adaptive traits via genetic assimilation (see

Pigliucci et al., 2006), increasing their chances of establishing in regions with similarly (or more)

stressful conditions – like the extratropics. While empirically testing these driving mechanisms remains


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a challenge, new approaches for assessing the correlation among traits and diversification constitute an

important step forward (Ng & Smith, 2014).

The approach used here also has limitations and potential caveats. Phylogenetic comparative

methods search the best fit of different evolutionary models (hypotheses) to the data, but can produce

misleading results if the real model of evolution is not among the hypotheses or when the data is not

informative enough (Boettiger et al., 2012; Pennell et al., 2014). Simulations (e.g. phylogenetic Monte

Carlo) help in the critical evaluation of the results. Our simulations demonstrated that it is impossible

to discern whether depth influences thermal niche evolutionary rates in Halimeda, even though 22% of

these simulations resulted in a significant positive answer, the power of the test is relatively low (Table

3). In other words, we cannot discern if depth has no influence on thermal niche evolution or if the

analysis was unable to resolve the differences between the proposed models because the data is not

informative enough (in terms of how the trait is structured along the phylogeny and how it relates to the

thermal niche), or the difference in evolutionary rates (σ2) between shallow and deep lineages is just too

small to be detected. We obtained much higher levels of certainty for the exposure analysis (Table 3),

suggesting that the effect of this trait on thermal niche evolution is unlikely to be an artefact of the test.

Bias may also arise from the way the variables are quantified. For example, α-niche traits could also be

measured as continuous variables if sufficient data were available for that (i.e. enough intra-specific

depth records). Using mean trait values for each species as is done here could mask the importance of

intraspecific variation and microevolution. It is reasonable to assume that the observed

macroevolutionary pattern result from microevolutionary processes (Wright & Rohde, 2013), hence

integrating comparative phylogenetic methods with experimental approaches across ecological and

evolutionary scales could bridge this knowledge gap and reduce the sources of uncertainty (Weber &

Agrawal, 2012; Verbruggen et al., 2014).

In summary, our study addresses a missing puzzle piece in explaining the out of the tropics

model: why some lineages are able to shift their thermal niche and establish in colder biomes while

others cannot. We investigated this question in the light of lineage traits using phylogenetic

comparative methods. The results do not support an influence of climatic or geographical proximity

traits on thermal evolution and we argue that it seems problematic to discern causation in proximity-

thermal niche correlations. Our data support the notion that α-niche lineage traits can have an effect on

the transition to cooler biomes, although the above-mentioned limitations should be investigated in


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more detail before drawing detailed conclusions about its mechanisms. Using PMC simulations we

observed that was not possible to assess the influence of depth with credibility, but the effect of

exposure was confirmed with substantial support. Halimeda lineages inhabiting exposed microhabitats

exhibited three-fold faster thermal niche evolution. Our model organism seems to follow Nietzsche’s

maxim “that which does not kill us makes us stronger”: lineages exposed to harsher conditions

apparently have more chances of getting out of the tropics.

Acknowledgements

We thank Sofie D'hondt for generating sequence data and Wiebe Kooistra, Claude Payri, Diane and

Mark Littler, John Huisman, Eric Coppejans, Mike Wynne, Willem Prud'homme van Reine, Mariana

C. de Oliveira, Maria Elisabeth Bandeira-Pedrosa for facilitating access to specimens and/or herbarium

collections. Funding was provided by the Research Foundation Flanders, the Australian Research

Council (FT110100585), the Australian Biological Resources Study (RFL213-08), the Erasmus

Mundus Master program in Marine Biodiversity and Conservation (EMBC), a University of Melbourne

scholarship to VRM and a SYNTHESYS grant to LT.

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Biosketch

The Verbruggen lab at Melbourne University has a broad interest in algal biology and

evolution. The ongoing research focuses on the biodiversity and evolutionary diversification of marine

algae. The group uses bioinformatics, phylogenetics, metabarcoding and genomics to integratively

approach the research questions. More info: http://www.phycoweb.net.

The Phycology Research Group at Ghent University is interest in several aspects of seaweed

evolution. The research focus includes molecular phylogenetics, reproductive biology, bacterial–algal

interactions, remote sensing, biogeography and genomics. More info: www.phycology.ugent.be.


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Supporting Information:

Appendices 1 – 4: R scripts and tutorial explaining the analyses.

Supplementary figure 1: Phylogenetic tree of the genus Halimeda including posterior probabilities.

Figures:

Figure 1: Phylogenetic tree of the seaweed Halimeda showing the evolution of its thermal niche and

exposure traits. Ancestral estate estimates of the mean Sea Surface Temperature (SST) are plotted

along the tree as a colour gradient, with warmer colours indicating warmer SST values. Pie diagrams

represent the ancestral state probabilities of living in exposed vs. sheltered microhabitats.

Figure 2: Evolutionary rates (Brownian motion parameter σ2) of the thermal niche for Halimeda

lineages living in exposed and sheltered microhabitats. The violin plots represent the distribution of

evolutionary rates of the trait-dependent model optimized for 10,000 phylogenetic trees from the

Bayesian posterior set of trees. Species inhabiting exposed habitats exhibited 3× higher (0.05 vs. 0.15)

rates of thermal niche evolution on average.


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H cuneata brazilH discoidea atl.H friabilisH scabraH tuna carH hummiiH lacunalisH tuna medH magnidiscaH cuneata africa 2H cuneata africa 1H no ID 4H discoidea ip.H gigasH cuneata australiaH magnicuneataH cuneata arabiaH taenicolaH xishaensisH macrophysaH copiosaH goreauiiH velasqueziiH renschiiH minima 1H minima 3H minima 2H minima 4H no ID 1H distorta 2H distorta 1H opuntiaH gracilis ip. 2H gracilis ip. 1H gracilis car. 1H gracilis car. 2H lacrimosaH gracilis fl.H pygmaeaH crypticaH fragilis 2H micronesicaH fragilis 1H cf. stuposaH monileH incrassataH simulansH melanesicaH borneensisH kanaloanaH heteromorphaH macrolobaH cylindracea

SST (°C)19.5 24.5 29.6

MicrohabitatShelteredExposed

Figure 1.


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0.0

0.1

0.2

0.3

0.4

0.5

sheltered exposed

Rate

s of t

herm

al n

iche

evo

lutio

n

Figure 2.


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Table 1. Relationship between proximity features and evolution of the thermal niche. The results are

based on a regression of the squared contrasts of the thermal niche on the ancestral estimates of the

proximity variables. Mean and standard deviation values are given for the analyses performed on 10

trees with maximum posterior probabilities, with 1,000 simulations per tree to assess statistical

significance. beta = mean regression coefficient. r = mean correlation coefficient. SD = standard

deviation

Proximity trait beta (SD) r (SD) p-value SST breadth -6.5 x10-3 (3.5 x10 -4) -0.02 (1.2 x10-3) 0.86 (0.01) SST extremes 0.02 (1.7 x10-4) 0.19 (1.1 x10-3) 0.16 (0.01) Distribution range 1.8 x10-6 (7.4 x10-8) 0.02 (1.0 x10-3) 0.86 (0.01)


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Table 2. Relationship between alpha niche traits and evolution of the thermal niche. Brownian motion

models were fitted to 10,000 phylogenetic trees in order to compare a single rate of evolution (σ2

single) vs. a multiple rate of evolution (σ2 multi) with two different peaks. Note that the rate of thermal

niche evolution is 3× higher for species inhabiting exposed microhabitats than for species living in

sheltered microhabitats. Δ AIC = difference between the AIC values among models (multi-rate AIC

minus single-rate AIC). Mean σ2 values are given. σ2 multi 1 = rates of thermal niche evolution for

lineages inhabiting sheltered or deep microhabitats. σ2 multi 2 = rates of thermal evolution for lineages

associated with exposed or shallow microhabitats.

α-niche trait Min. ΔAIC Max Δ AIC Δ AIC (mean) σ2 single σ2 multi 1 σ2 multi 2 Exposure -1.99 34.64 4.32 0.09 0.05 0.15

Depth -2.00 25.24 0.93 0.08 0.07 0.10


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Table 3: Phylogenetic Monte Carlo simulations performed to test the statistical significance of the

analysis relating the α-niche traits ‘exposure’ and ‘depth’ with Halimeda’s thermal niche evolution.

PMC simulations were performed for 1,000 trait trees (100 simmap trees for each 10 trees with the

maximum posterior probabilities). The average and standard deviation (SD) of p-values and power of

the test across the 1,000 trees, and the proportion of significant p-values (≤0.05) are given.

α -niche trait p-value (SD) Significant p-values Power of test (SD) Exposure 0.049 (0.1) 75% 0.573 (0.2) Depth 0.280 (0.3) 22.6% 0.337 (0.3)


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