# Dispersal propensity in Tetrahymena thermophila ciliatesa reaction norm perspective

Frank Pennekamp, Katherine Mitchell, Alexis Chaine, Nicolas Schtickzelle

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Frank Pennekamp, Katherine Mitchell, Alexis Chaine, Nicolas Schtickzelle. Dispersal propensity in Tetrahymena thermophila ciliatesa reaction norm perspective. Evolution Agricole, 2014, pp.n/a-n/a. 10.1111/evo.12428. hal-04967754

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Dispersal propensity in *Tetrahymena thermophila* ciliates – a
reaction norm perspective

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4 Frank Pennekamp¹, Katherine A. Mitchell¹,², Alexis Chaine³ & Nicolas Schtickzelle¹

5 ¹Earth and Life Institute & Biodiversity Research Centre

6 Université Catholique de Louvain, Croix du Sud 4, L7-07-04, 1348 Louvain-la-Neuve,
Belgium

8 ² Current address: Conservation Ecology & Entomology Department, Stellenbosch

9 University, Private Bag X1, Matieland, Stellenbosch 7602, South Africa

10 ³ Station d'Ecologie Expérimentale du CNRS à Moulis USR2936, 2 route du CNRS, 09200
Moulis, France

12 Running head: Dispersal reaction norms of *T. thermophila* ciliates

13 Data archival location: intended at Dryad

14 Words: xxx (+ 201 abstract)

15 Tables: 2

16 Figures: 2

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18 Address of corresponding author:

19 Frank Pennekamp

20 Earth and Life Institute

21 Biodiversity Research Centre

22 Université catholique de Louvain

23 Croix du Sud 4, L7.07.04

24 B-1348 Louvain-la-Neuve, Belgium

25 Phone: +32 10 47 34 95

26 email: [Frank.Pennekamp@uclouvain.be](mailto:Frank.Pennekamp@uclouvain.be)
27 Contact information co-authors:

28 N. Schtickzelle: +32 10 47 20 52 [Nicolas.Schtickzelle@uclouvain.be](mailto:Nicolas.Schtickzelle@uclouvain.be)

29 K. A. Mitchell: [kmitchell@sun.ac.za](mailto:kmitchell@sun.ac.za)

30 A.S. Chaine: +33 5 61 04 03 78, [alexis.chaine@ecoex-moulis.cnrs.fr](mailto:alexis.chaine@ecoex-moulis.cnrs.fr)

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Abstract

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Dispersal and phenotypic plasticity are two main ways for species to deal with rapid changes of their environments. Understanding how genotypes (G), environments (E) and their interaction (genotype and environment; G x E) each affects dispersal propensity is therefore instrumental for predicting the ecological and evolutionary responses of species under global change. Here we used an actively dispersing ciliate to quantify the contributions of G, E, and G x E on dispersal propensity, exposing 44 different genotypes to three different environmental conditions (densities in isogenotype populations). Moreover, we assessed the condition-dependence of dispersal, i.e. whether dispersal is related to morphological, physiological or behavioral traits. We found that genotypes showed marked differences in dispersal propensity and that dispersal is plastically adjusted to density, with the overall trend for genotypes to exhibit negative density-dependent dispersal. A small, but significant G x E interaction indicates genetic variability in plasticity and therefore limited potential for dispersal plasticity to evolve. We also show evidence consistent with condition-dependent dispersal suggesting that genotypes also vary in how individual condition is linked to dispersal under different environmental contexts thereby greatly generating complex dispersal behavior due to only three variables (genes, environment, and individual condition).

Keywords: context-dependent dispersal, condition-dependent dispersal, density-dependence, phenotypic plasticity, Genotype x Environment
## 54 Introduction

Dispersal is a key trait for the ecological and evolutionary dynamics of a given species. It is broadly defined as the process of exchange of individuals between the natal and one or more reproductive sites (Matthysen 2012). Dispersal is crucially important for the spatial functioning of (meta-) populations and (meta-) communities, and can have profound impacts on both the ecological and evolutionary dynamics of populations and species (Hanski and Gaggiotti 2004; Leibold et al. 2004; Holyoak et al. 2005). Dispersal can compensate for local extinctions by recolonization of vacant habitats (Hanski and Gaggiotti 2004) or simply augment or 'rescue' small populations (Brown and Kodric-Brown 1977). The exchange of individuals between populations influences gene flow and hence has implications for local adaptation, drift, genetic diversity and population divergence of species (Ronce 2007). Understanding the factors that shape dispersal strategies and their genetic underpinnings is of great importance as dispersal is a crucially important means for species to mitigate global change both through direct movement and the subsequent gene flow that may facilitate local adaptation to new conditions (Berg et al. 2010; Chevin et al. 2010; Chaine and Clobert 2012).

In order to understand the evolution of dispersal strategies and predict their impact on the ecology and evolution of a species, it is important to disentangle the different sources of variation in dispersal behavior, i.e. the contributions of genetic (G) and environmental (E) effects and their interaction (G x E). The appropriate framework to quantify the relative contributions of G, E and G x E are dispersal reaction norms, which "characterize the array of dispersal behaviors and dispersal types of a given genotype along an environmental gradient" (Clobert et al. 2009 p. 201). Dispersal reaction norms may be used to understand the underlying genetic architecture and evolutionary potential of dispersal decisions (Ronce 2007) and are therefore increasingly applied in dispersal studies (Massot and Clobert 2000; Donohue et al. 2005; Bonte et al. 2007, 2008a,b; Chaine et al. 2013). These studies show that G, E and G x E have significant effects, but currently no study to our knowledge
quantifies the relative contributions of G, E and G x E on dispersal propensity in terms of
explained dispersal variation.

The importance of both the G and E contributions to dispersal are well supported by
empirical studies (Zera and Brisson 2012), but few have studied them simultaneously to
describe their interaction. Heritable genetic variation in dispersal propensity is the raw
material for natural selection and therefore provides an important means to adapt to
prevailing environmental conditions. Several studies on insects (planthoppers, aphids) have
shown that genetically-based population variation in dispersal propensity was linked to
habitat stability (Groeters 1989; Denno et al. 1996), providing evidence for local adaptation.
Features of the environment also affect dispersal and the role of conspecific density on
dispersal in particular has attracted both theoretical and empirical interest. Intuitively,
intraspecific competition will increase with density; hence dispersal is a means of escaping
unfavorable environmental conditions due to crowding (Bowler and Benton 2005) and can
mitigate competitive and cooperative interactions between kin (Hamilton and May 1977).
While dispersal is commonly predicted to increase with density (Gandon and Michalakis
1999; Poethke et al. 2007), Allee effects or benefits of group living can exceed the costs of
increased competition leading to negative density-dependent dispersal (Kim et al. 2009).
Indeed, empirical studies report both higher dispersal (Bengtsson et al. 1994; Hauzy et al.
2007; Bitume et al. 2013) and lower dispersal (Westerberg et al. 2008; Baguette et al. 2011;
Fellous et al. 2012) when exposed to increased conspecific densities among species. Both
positive and negative density-dependent dispersal are theoretically possible within a
population and thus forms an interesting focus for studies of genetic variation in dispersal
reaction norms. For example, both positive and negative density-dependent dispersal has
been shown in a colonial bird species (Kim et al. 2009), but such examples are rare and
have received very limited attention in the dispersal literature so far.

Empirical studies have shown that a number of different dimensions of the environment can
strongly influence dispersal decisions leading to the concepts of *context-* and *condition-*
dependent dispersal (Im and Hjermann 2001; Bowler and Benton 2005; Clobert et al. 2009). Context-dependent dispersal refers to the triggering effect of the abiotic and biotic environment, such as resource availability, predation risk, or density as described above. Condition dependence refers to the internal state or condition, such as stress level or nutrient reserves, of the potential disperser. In some cases, natural selection will favor dispersal by individuals that are best equipped to withstand the hardships encountered during the dispersal period. Indeed, consistent morphological, physiological and behavioral differences have been found between resident and disperser individuals indicating that dispersal decisions depend on the condition of an individual (Benard and McCauley 2008; Clobert et al. 2009). Theoretical work has shown that models including conditional dispersal strategies often outperform unconditional strategies (e.g. the evolution of density-dependent dispersal; Travis et al. 1999), providing theoretical support for the occurrence of adaptive dispersal plasticity in nature (Im and Hjermann 2001; Matthysen 2005). Because costs and benefits of dispersal depend on both the properties of the individual such as its internal state (condition) as well as the prevailing external context (e.g. environmental and/or social), there should be individual variation in response to environmental cues (Matthysen 2012). Thus the empirical framework for studying these effects should examine the interaction between genes and both external (E<sub>E</sub>) and internal (E<sub>I</sub>) dimensions of the environment. Accordingly, context-dependent dispersal is the significant effect of E<sub>E</sub> on dispersal, while an effect of G × E<sub>E</sub> indicates genetic variation in context-dependent dispersal. On the other hand, the significant effect of E<sub>I</sub> indicates condition-dependent dispersal, while the effect of G × E<sub>I</sub> indicates genetic variation in condition-dependent dispersal. Plasticity of dispersal is increasingly looked at but few studies investigate context- and condition-dependent dispersal simultaneously, although interactions between context and condition may explain inconsistencies in observed dispersal decisions (Clobert et al. 2009).

Genetic variation for phenotypic plasticity in dispersal is an important prerequisite for the
adaptation and evolution of dispersal reaction norms and their role in ameliorating
mismatches between phenotypes and environments (DeWitt and Scheiner 2004; Ghalambor et al. 2007). Plasticity in behavior is expected to result in a faster reaction to rapid environmental change than morphological or physiological changes and therefore may help species cope with global change (Charmantier et al. 2008; Vedder et al. 2013). Bonte and colleagues (2008b) analyzed genotype-by-environment interactions regarding thermal conditions during juvenile development and found that dispersal decisions of spiders to either perform long-distance (by ballooning) or short-distance dispersal (by rappelling) differed according to the temperature conditions experienced during the juvenile stage and that this conferred fitness benefits. These results demonstrate that phenotypic plasticity in dispersal may provide an adaptive advantage (DeWitt and Scheiner 2004) and in this specific case also when compared to a fixed dispersal strategy (Bonte et al. 2008b). To gain a better understanding of how dispersal reaction norms evolve, we need to know about the extent of genetic variability for plasticity and the shape of the reaction norm (David et al. 2004). However, the numbers of genotypes tested and number of environments used are generally low in existing experimental studies, limiting conclusions about the shape of reaction norms and variation between genotypes. Of the above mentioned studies examining the reaction norm to dispersal, only one (Bonte et al. 2008b) used a gradient of environments (five different temperatures), while all other studies contrasted only two environments of a given factor (e.g. high vs. low density, high vs. low wind velocity, herbivory vs. control, etc.).

Disentangling the contributions of G, E and G x E on dispersal propensity is a challenging task that requires controlled breeding schemes, which are more easily achieved under laboratory conditions allowing tight control of both genotypes and environments. This is particularly important when considering density-dependent dispersal and dispersal phenotypes as field conditions often involve confounding factors (Matthysen 2005). Carefully controlled density manipulations allow separation of specific density effects from confounding factors and makes measuring the phenotypes of resident and disperser
fractions of a population less difficult. Experimental dispersal systems have proven to be valuable research tools in ecological studies of dispersal (e.g. the common lizard, *Zootoca vivipara*, and the Glanville fritillary butterfly *Melitaea cinxia*) and similar systems could be used in evolutionary studies. Aquatic microbes are particularly convenient models for dispersal (Donahue et al. 2003; Fjerdingstad et al. 2007; Hauzy et al. 2007; Fellous et al. 2012) and a series of characteristics of *Tetrahymena thermophila* make the species an appropriate model for evolutionary studies that investigate the dispersal reaction norm: i) cells disperse actively, so the dispersal response is measured directly and does not depend on external dispersal vectors such as wind or currents; ii) reproduction is clonal in populations that only contain one of the seven mating types (Collins and Gorovsky 2005), hence dispersal can be measured on the population level as the population consists of cells with an identical genotype; and iii) the small cell size facilitates manipulation and a high degree of control over the environment, while dispersal can be measured over relevant spatial scales.

Here we report the results of a fully factorial G x E experiment where the dispersal reaction norms of the aquatic ciliate *Tetrahymena thermophila* were quantified in controlled laboratory conditions. Forty-four clonal cell lines (G) originating from single cells, hereafter called genetic lines or genotypes, were exposed to three different conspecific densities (E) and their dispersal rate measured in a two-patch system. This experiment aimed to provide answers to the following questions:

1) What is the relative contribution, and therefore, evolutionary importance of G, E and G x E to the phenotypic variation in dispersal propensity in *T. thermophila*?

2) What is the extent of variation in reaction norms between different genotypes and what potential may there be for further adaptation of plastic responses?
186 3) Are there consistent phenotypic (morphological or behavioral) differences between the
disperser and the resident cells that may indicate traits and trade-offs important for
187 condition-dependent dispersal and is this specific to each particular genotype?
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190 **Materials and Methods**

191 **Model organism**

192 *Tetrahymena thermophila* is a c. 50 µm unicellular, ciliated protozoan inhabiting freshwater
193 ponds and streams in the eastern part of North America where it naturally feeds on patches
194 of bacteria and dissolved nutrients (Doerder and Brunk 2012). It has long been used as a
195 model organism in molecular biology and genetics which has led to well established
196 protocols of cell culture and maintenance in the laboratory (Collins 2012). Biological samples
197 of different lines of *T. thermophila* were kindly provided by F. P. Doerder (University of
198 Illinois) or directly ordered from the National *Tetrahymena* Stock Centre
199 (tetrahymena.vet.cornell.edu), the American Type Center Collection (www.atcc.org), or the
200 Culture Collection of Algae and Protozoa (www.ccap.ac.uk).

201 We used a set of 44 genetically distinct lines which differ in geographic origin and time since
202 extraction from the field (Supplementary material, Table S1), but also in several life history
203 characteristics such as growth rate, maximum density and survival under starvation
204 conditions (Fjerdingstad et al. 2007; Pennekamp 2014). All 44 genetic lines were maintained
205 under standard culture conditions with clonal reproduction before and during the described
206 experiment: axenic liquid culture in a nutrient medium (broth consisting of 2% Proteose
207 peptone and 0.2% yeast extract [Becton Dickinson], diluted in ultrapure water), kept at
208 constant 27° C temperature in a light controlled incubator with a 14:10 hours light/dark cycle.
209 Culture stocks were renewed every 10 days by inoculating a 2 mL sample of fresh medium
210 with 100 µl of culture and maintained in 2 mL multi-well plates (CELLSTAR® multiwell
211 plates, Greiner BioOne, Belgium). All manipulations of axenic cultures were conducted under
212 sterile conditions in a laminar flow hood (Ultrasafe 218 S, Faster, Italy).

213 **Dispersal quantification system**

214 We used a standardized protocol to measure dispersal behavior of *T. thermophila* cells
215 following protocols outlined in our previous work (Fjerdingstad et al. 2007; Schtickzelle et al.
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2009; Chaine et al. 2010). The way we measured dispersal encompasses several phases of the dispersal processes such as emigration and transience and therefore cannot clearly be attributed to one of the three dispersal phases (Bowler and Benton 2005); accordingly, we will refer to our dispersal measure as 'dispersal propensity'.

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A dispersal system was constructed from two standard 1.5 mL Eppendorf® microtubes connected by a 17 mm long silicon pipe (internal diameter 4 mm; VWR, Belgium). The dispersal system was filled prior to the experiments with 3 mL of the standard medium through one tube to ensure fluid transition between the tubes. The system was then closed by placing a clamp in the middle of the connecting pipe. To start the experiment, a specific density of cells from a given genetic line was placed into the “start” tube of the system and the tube content was homogenized to encourage cells to move freely throughout the start tube. After 30 minutes of acclimation to the new medium, the clamp closing the connecting pipe was removed and cells could freely disperse between the two tubes for 6 hours.

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At the end of the experiment, the system was closed and a series of measures extracted for the “start” and “target” tubes using a digital image analysis procedure, which allows estimation of cell densities and several phenotypic traits in an objective and automated manner (Fjerdingstad et al. 2007; Pennekamp and Schtickzelle 2013). To do so, five independent samples were taken from each tube after culture homogenization, and each loaded in a chamber of a counting slide (Kima, Italy); for each chamber, a series of three dark field images (1 s time lapse) were taken using a Canon EOS 5D Mark II mounted on a Nikon Eclipse 50i microscope. Images were then treated and segmented using ImageJ software (Version 1.47, Schneider et al. 2012) to identify individual cells and extract measures. This workflow, containing several data cleaning steps to eliminate artefacts, has been carefully validated and extensively optimized to produce accurate and repeatable results (see details and measures of error rates in Pennekamp and Schtickzelle (2013)).

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Each dispersal system produced five basic measures: (1) one measure of density per tube (start and target), computed as the average of the densities measured on the first image taken on each of the five chambers; (2) one measure of dispersal propensity per system, computed as the final proportion of cells found in the target tube, i.e. $density_{target} / (density_{start} + density_{target})$; one measure of (3) average cell size, (4) average cell shape (i.e. the ratio between the length and the width of the cell) and (5) proportion of active cells (hereafter called activity) per tube. These last three measures were obtained by aggregating data extracted at the individual cell level for all cells present on the first image taken on each of the five counting chambers. A cell was classified as active if its displacement over the three sequential pictures of a given chamber was higher than 7.055 µm (i.e. 5 pixels); images 2 and 3 in a given time series for a specific chamber were used only to assess cell activity, and no other measures were extracted from them. The displacement threshold value was manually validated to distinguish between slightly moving and immobilized cells.

## 254 Experimental design

We used a fully factorial Genotype x Environment (G x E) experimental design: dispersal was quantified using systems described above for all 44 genetic lines at three different initial cell densities (100,000, 200,000 and 300,000 cells/mL), corresponding to a range of cell densities commonly observed under our culturing conditions. Our experiments used single genotypes and thus densities reflect the density of kin. Cell lines differ in how they react to non-kin and the characteristics of those other lines, thus mixed genotype populations that would represent competition among non-kin would greatly complicate the interpretation of our results and require a detailed understanding of how specific cell lines interact with other lines (which we currently lack).

In addition to the strict control of all our *T. thermophila* culture conditions, two standardization steps were performed prior to the experiment. First, a pre-culture of each genetic line was started from the stock by transferring 100 µL of culture into 2 mL of fresh medium on a 24 well plate and allowed to grow exponentially for 4 days to synchronize
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populations to the logarithmic phase of population growth (Collins 2012). Second, at the end
of this synchronization phase, cell density was estimated for each genetic line and new
cultures, to be used for the experiment, were launched at an equal starting density of 10,000
cells/mL, in culture flasks (CELLSTAR® Cell Culture Flask 50 mL, Greiner BioOne,
Belgium). These cultures grew for three days allowing cultures to reach sufficiently high cell
densities (> 300,000 cells/mL) for the experiment. Then dispersal systems were inoculated
and run for 6 hours to collect the five measures described above. 264 dispersal systems (44
lines x 3 densities x 2 replicates) were run in total; one system (replicate 1 of line D17 at
300,000 cells/mL) was defective and hence discarded.

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Generation times around 2h are reported in the literature for *T. thermophila* under optimal
conditions (35°C, constant shaking for extra O₂ supply, and food ad libitum) (Cassidy-Hanley
2012). Under such a rapid population growth, *a fortiori* if growth rate is strongly density-
dependent (i.e. different in start and target tubes), our estimates of dispersal rates could be
biased by this population growth. We controlled for this potential bias by (1) using suboptimal
culture conditions (27°C, no shaking) to increase generation time, and (2) performing an
additional experiment aimed at precisely quantifying, for each genotype, population growth
under the specific conditions of the dispersal experiment and its density-dependent variation:
growth rate was measured over six hours after cells had been diluted to a range of densities
(from 10,000 to 450,000 cells per mL) in conditions identical to those used in the dispersal
experiment except that cells had no possibility to disperse because tubes were not
connected. This experiment confirmed that multiple generations were not possible under the
experimental settings (median generation time = 5.92 hours, ranging from 3.93 to 16.24
hours) and allowed us to correct our estimates of dispersal rate for population growth (see
results). Detailed information about how we dealt with the potential bias due to population
growth and how we were able to rule it out, can be found in the Supplementary material.

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Statistical analysis

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To study the dispersal reaction norms (questions 1 and 2), a two-way crossed ANCOVA was used to test for the effects of genotype (G), initial cell density (E) and their interaction (G x E) on dispersal propensity. Genotype was considered as a fixed effect, despite its common consideration as a random effect (e.g. Crawley (2007)). This is because the set of genetic lines cannot be considered as a random sample of the genetic variation exhibited by the species in the wild (some genotypes were selected due to previous results or based on their phenotypic characteristics, some others were created by inbreeding in the laboratory).

Consequently, the results cannot be generalized to the species as a whole because the observed variation may not be representative in an ecologically relevant sense. Density was treated as a continuous covariate. The dispersal propensity was log transformed and visual inspection of residuals indicated no lack of normality and homogeneity of variance. The density was centered on the 200,000 cells/mL density to test for genetic line differences at the center of the tested E range. To assess the strength of association between the response and the effects of G, E and G x E, effect sizes were calculated using the ω² measure (Quinn and Keough 2002) that allows partitioning the total variation into the portion of variation explained by each effect.

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To study *condition-dependent dispersal* (question 3), we compared the phenotypic traits of disperser and resident fractions, dispersers being defined as cells present in the target tube and residents being the cells in the start tube at the end of the experiment. In a first step, a three-way crossed ANCOVA was used to test for the effects of genotype (G), initial cell density (E) and tube (dispersers vs. residents) on shape, size and activity (arcsine transformed). Genetic line, density and tube were modeled as fixed effects. In a second step, we used Spearman's rank correlation to relate the differences in morphology and activity (arcsine transformed) between the disperser and resident fractions to the dispersal propensity; to do so, we calculated for each dispersal system the difference between tubes
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in each of the response variables as a proportional change, i.e. $(\text{observed}_{\text{target}} - \text{observed}_{\text{start}}) / \text{observed}_{\text{start}}$.

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Because cells in the start and the target tubes differed in density after dispersal took place, we checked for a potential confounding effect, namely that phenotypic differences between dispersers and residents were due to the altered density rather than dispersal status. In the control experiment mentioned above to derive the density-dependent growth rates, the same three phenotypic traits (size, shape and activity) were measured for cells six hours after they had been diluted to a range of densities (from 10,000 to 450,000 cells per mL). For every genetic line separately, we regressed the phenotypic trait against density (in case of activity an arcsine transformation was used and density log transformed to linearize the relationship). The values of the trait were first centered to a mean of 0, so that they expressed the deviation from the mean trait due to density. Overall, these regressions showed some significant positive effect of cell density on size, shape and activity (size = $909.64 + 0.313 \times \text{density}$, $R^2 = 0.10$; shape = $1.535 + 0.0005 \times \text{density}$, $R^2 = 0.23$; arcsin(activity) = $0.704 + 0.111 \times \log(\text{density})$, $R^2 = 0.32$; respectively; all $P < 0.001$. These $R^2$ values were evaluated over all genotypes pooled, hence the considerable variation among genotypes in these traits explains why they can appear relatively low; similar regressions genotype per genotype showed higher $R^2$ values (about 0.4 on average) for all traits. Hence, we corrected the values of size, shape and activity observed in the dispersal experiment before running the two analyses presented above, by adding to every value the expected deviation from the mean due to density, as computed from a regression line computed for that genetic line. The remaining differences between residents and dispersers should therefore be a conservative estimate of the differences related to the dispersal process and not to the fact that cell density itself differed between start and target tubes.

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ANCOVAs were carried out in SAS 9.3 (www.sas.com), and all remaining analyses in R 2.15.2 (R Development Core Team 2012).

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# Results

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## Dispersal reaction norms

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We first analyzed how dispersal propensity varied between genetic lines and densities of kin using a two-way crossed ANCOVA (genetic line x density). Genetic lines varied substantially in their dispersal propensity over a 6-hour period (genotype effect: $F_{43,175} = 22.51, p < 0.001$), ranging from 15% to 95% across all densities (Figure 1). Overall, they showed a negative density-dependent modulation of their dispersal propensity (density effect: $F_{1,43} = 68.92, p = <0.001$), dispersing less when exposed to higher densities (dispersal propensity across all lines decreased from 70% at 100,000 cells/mL to 60% at 300,000 cells/mL). The interaction between genotype (G) and density (E) was significant ($F_{43,175} = 1.53, p = 0.028$), indicating that genetic lines differed in the effect of kin density on dispersal. Out of the 44 genotype slopes tested, 12 showed significant negative density-dependence, one showed significant positive density-dependence, whereas there was no evidence for density dependence in the 31 others (Figure 1). Although only 13 of the 44 slopes were significant, 39 out of 44 *T. thermophila* genetic lines had negative and only five positive reaction norm slopes of dispersal with conspecific density. The ANCOVA accounted for 80% of the observed variation in the dispersal propensity; most of the variation in dispersal propensity was due to genetic variation among clonal lines (73%), followed by the environment effect (5%) and the interaction between genotypes and the environment (2%). Because such variance partitioning can be sensitive to the number of levels per factor (Petraitis 1998; Quinn and Keough 2002), we checked for potential bias using a down-sampling procedure equalizing the number of genotypes from 44 to 3. With an equal number of levels for G and E, G accounted on average for 43%, the environment for 13% and the G x E interaction for 2% of the explained variation (for details on the down-sampling simulation, see the Supplementary material). The larger number of genotypes inflated the high proportion of variance in dispersal explained by G, but nevertheless the absolute ranking of the 3 components and
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the quite larger impact of G remained the same, meaning they were clearly not an artefact of
the experimental design.

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Applying the same analysis on dispersal rates corrected for population growth, as quantified
per genotype and tube, very similar results were obtained, leading to identical conclusions
(see Supplementary material).

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Phenotype differences between dispersers and residents

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Genetic lines significantly differed for size, shape and activity, suggesting genetically-based
trait differences between genotypes (as previously shown by Fjerdingstad et al., 2007).
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Traits further differed between the three densities in different and opposing way among
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genotypes for size and activity (significant line x density interactions, but non-significant main
density effect), but not shape (significant main density effect) (Table 1).
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Significant differences between disperser and resident cells were found for size, shape and activity, but in slightly different ways. For the three traits, these differences varied among the 44 genetic lines (significant line x tube interactions) and the three densities (significant density x tube interaction). A general pattern (significant main tube effect) was clear concerning all three traits; for the majority of the 132 genetic line x density combinations, disperser cells were significantly smaller, more elongated and active than resident cells (Figure 2). Additionally, we found that more dispersive genotypes showed a stronger cell size reduction (r= -0.39, P < 0.001; Figure 2) and activity gain (r = 0.37, P < 0.001) compared to less dispersive lines. For shape the pattern was less clear with a positive correlation between the difference in shape between tubes with dispersal propensity (r = 0.38, P < 0.001); however, in dispersive lines, disperser cells were more elongated, whereas in less dispersive lines, resident cells were more elongated (Figure 2).

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Comparing the initial cell morphology between the two replicates before dispersal took place
may provide insight whether changes in cell size and shape after dispersal are a cause or
consequence of dispersal. If differences in population level cell size between replicates are a

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cause of dispersal, we should see more dispersal in the replicate with cells being smaller prior to dispersal. A chi square test testing whether the ratio of genetic lines with smaller cells dispersed more met a 1:1 ratio, i.e. indicating no association of size with dispersal, was rejected at all densities (for 100,000, 200,000 and 300,000 cells/mL, respectively: X₁= 9, p = 0.003; X₁ = 6.182, p = 0.009; X₁ = 9, p = 0.003). Shape differences between replicates before dispersal did not indicate an association with dispersal, while activity could not be assessed because no before experiment measures were available.

The same analysis on traits not corrected for density yielded similar results (main density effect became significant for size, while interactions between genetic line x density turned non-significant for shape and activity).

## Discussion

Dispersal is a complex trait that is most likely influenced by a combination of genetic and environmental effects. Using a controlled reaction norm approach (Ronce 2007; Clobert et al. 2009) to disentangle the contributions of genotype (G), environment (E) and their interaction (G x E), we exposed 44 genetic lines of *T. thermophila* ciliates (G) to three different conspecific densities (i.e. kin) (E) and found pronounced differences in terms of dispersal propensity, indicating *context-dependent dispersal*. Importantly, we also found significant genetic variation for dispersal plasticity (G x E interaction) which provides the raw material for the evolution of dispersal reaction norms. Our findings hence show that dispersal propensity in *T. thermophila* ciliates is mainly determined by the genotype, with considerably smaller but nevertheless significant effects of the environment and the interaction between genes and environment.

## Contribution of G, E & G x E to dispersal propensity

Most of the variation in dispersal propensity was explained by the genotype effect (approximately 73% of phenotypic variation), followed by the environment (5%), whereas the
least variation was found for the G x E interaction (2%). This indicates significant genetic
differences in the sensitivity of the genotypes to conspecific density, highlighting the
importance of dispersal strategies for the fitness and survival of these genetic lines. Previous
studies have shown that intraspecific variation in dispersal rates may be an adaptation to
variation in among-population environmental conditions such as habitat stability (Ronce
2007). This has been seen previously in planthoppers, with a higher fraction of winged
individuals (i.e. potential dispersers) found in high instability coastal habitats than more
stable habitats (Denno et al. 1996). Thus different dispersal propensities observed may
reflect local adaptation of optimal dispersal propensity in the original environments of our
genetic lines. Despite extensive search (Thuillier & Schtickzelle unpublished results), we lack
detailed information about the previous environments of our *T. thermophila* genetic lines, but
certainly the stability of environmental conditions is likely to differ between differently sized
ponds or between ponds and streams. Alternatively, environmental variation could maintain
several coexisting strategies analogous to species coexistence (Chesson 2000) or within
species alternative social strategies (Oliveira et al. 2008). Collections of *T. thermophila* in the
wild often yield multiple genotypes across a small spatial scale (a few cubic meters; P.
Doerder, pers. comm. and Doerder et al. 1995), although specific information is lacking for
the genetic lines we obtained. Understanding the cause of variation in dispersal strategies
among genotypes will require a targeted study linking the ecology of collection sites with lab-
based measures of dispersal.

443 Potential for adaptation for dispersal propensity plasticity

Although there were differences in reaction norms between the genotypes, the low variance
for the G x E interaction suggests that adaptation of plastic responses in *T. thermophila* to
novel conditions would be slower due to the low standing genetic variation in reaction norms.
The higher variance in E (indicating current plastic responses) compared to the G x E
interaction indicates that perhaps this low potential may be due to significant prior selection
for plastic responses. A similar scenario of reduced genetic variation in plasticity due to prior
450

selection was reported by Charmantier et al. (2008) by comparing plasticity of breeding
dates in relation to temperature (and hence food availability) between populations of Great
tits (*Parus major*) in the UK and the Netherlands. The authors demonstrate adaptive but
reduced genetic variation in plasticity (non-significant G x E interaction) in breeding dates for
the UK population, whereas the population in the Netherlands showed a significant G x E
interaction but a less steep slope than the UK population. It is concluded that selection on
plasticity in the UK has decreased variation between individuals, whereas on-going selection
on reaction norms in the Netherlands has not yet fully decreased the variation (Charmantier
et al. 2008).

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When estimated across genotypes, dispersal decreased with increased density of kin (i.e. negatively density-dependent) and thus showed variation due to environmental conditions, i.e. context-dependent dispersal. Exhibiting plasticity in relation to density concurs with most established theory which predicts that dispersal should increase with density due to the costs of direct resource competition with conspecifics and also kin (Hamilton and May 1977; Travis et al. 1999). Empirical evidence for density effects on dispersal is somewhat mixed with the majority showing positive density-dependent dispersal (Matthysen 2005; but see Fellous et al. 2012) and a few showing negative density-dependent dispersal (Denno and Peterson 1995; Matthysen 2005; Baguette et al. 2011). Therefore, evidence from the genetic lines we studied here shows an opposite pattern to that predicted by many theoretical models and the prevailing empirical patterns. *T. thermophila* ciliates are known to show some cooperative behavior under harsh environmental conditions by exchanging growth factors that allow cell populations to be maintained at both low and high densities (Schousboe and Rasmussen 1994 and Chaine et al. 2010 respectively). Moreover, the density in our single genotype populations represents the density of kin, which may influence both dispersal and cooperation. Indeed, Chaine and colleagues (2010) found that cooperation is kin-based and that genetic lines are able to orient towards/against kin according to their cooperation strategy. A previous study found a negative association

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between the degree of cell cooperation and short distance dispersal, as dispersal is likely to disrupt group structure (Schtickzelle et al. 2009). However, specialized morphs capable of long-distance dispersal are more frequent within cooperative genotypes of *T. thermophila* (Schtickzelle et al. 2009), with similar findings also known for spiders (Corcobado et al. 2012). Therefore, a likely explanation for an overall negative density-dependent pattern of dispersal in our experiments might stem from the density-related effects of cooperation previously detected in *T. thermophila*. However, given the extensive variation in cooperation among genotypes in our system (Schtickzelle et al. 2009), we might expect to find considerable variation in the response of different genotypes to density.

Examining the significance of slopes for each genotype separately, a decrease of dispersal with conspecific density was significant for 12 genetic lines, non-significant for 31 lines, and one genetic line showed significantly increased dispersal with density. Variation in the slope of plasticity may have adaptive value depending on the environment from which our *Tetrahymena* clones originate. Plastic behaviors are beneficial in variable environments, especially when environmental variability occurs at time scales shorter than the generation time (Liefting and Ellers 2008). Genotypes living in habitats with more ephemeral resources should experience stronger variability in population density favoring highly plastic responses in dispersal, while genotypes from habitats with a more stable food supply where densities fluctuate less would benefit from less plasticity that is less sensitive to density. The limited evidence for genetic variation in reaction norms suggests that the response changing selection on reaction norms due to local conditions may be slow. New collections are needed to infer whether within site variability in reaction norms is similar to the patterns we find here and could confirm this possibility through experimental evolution, providing a rare investigation of how selection acts on standing variation of reactions norms (Scheiner 2002; David et al. 2004). Such a task would be facilitated since the reaction norms we detected were primarily linear and thus more easily analyzed under an experimental framework (David et al. 2004; Rocha and Klaczko 2012).
504 Dispersal phenotypes and trade-offs

505 Alternative dispersal strategies may arise through adaptations to mitigate the costs of 506 dispersal (Bonte et al. 2011) and improve survival during dispersal or settlement in a new 507 habitat. Such positive combinations of dispersal-related traits are known as dispersal 508 syndromes (Ronce and Clobert 2012). However, negative relationships between traits, such 509 as a trade-off between fecundity and dispersal ability (Roff 1992), are also expected and 510 reported from empirical studies (Johnson 1969; Rankin et al. 1986). The different dispersal 511 propensity observed between our lines may be the result of genetic variation in strategies 512 that result from trade-offs between life history traits such as competitive ability against 513 dispersal capacity. In the nematode *Caenorhabditis elegans* such a trade-off was proposed 514 to explain the polymorphism of solitary and gregarious genotypes, where the ability to exploit 515 resources was negatively associated with the dispersal propensity (Gloria-Soria and 516 Azevedo 2008). *T. thermophila* genetic lines also show a trade-off between the dispersal 517 propensity and peak density they can reach in standard nutrient conditions (Pennekamp 518 2014) suggesting that genetic lines that are potentially more competitive due to better 519 conversion of resources into biomass are less mobile corresponding to a competition- 520 colonization trade-off (Limberger and Wickham 2011). Likewise, a trade-off with dispersal 521 could be linked to other traits such as social cooperation (Chaine et al. 2010). Such trade- 522 offs could exist either within or between populations as described above.

523 Recent empirical work has shown that dispersers often have distinct phenotypes from non- 524 dispersers (Ims and Hjermann 2001). For example, in naked mole rats dispersers are larger 525 and have a distinct morphology from residents (O'Riain et al. 1996). Likewise, in butterflies 526 highly dispersive individuals have higher flight metabolic rates (Hanski et al. 2004; Niitepõld 527 et al. 2009) and are more active, fly better, and have a distinct morphology compared to 528 residents (Ducatez et al. 2012). Specific phenotypes optimized for dispersal could reduce 529 costs of this strategy and thus make it competitive with non-dispersers (Clobert et al. 2009). 530 In *Tetrahymena*, we found that dispersers tend to differ in morphology as well as a number
531

of different life history traits (Fjerdingstad et al. 2007; Schtickzelle et al. 2009; Pennekamp 2014). However, our experiments also revealed that these differences in morphology and activity between resident and disperser cells vary between genotypes and are also dependent on local density.

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Differences in morphology and behavior between residents and dispersers could either reflect a dispersal syndrome or be a consequence of dispersal itself. Our experimental design measures phenotypes on the population level, therefore we do not know the exact phenotype of each individual cell before and after dispersal took place and thus cannot unambiguously tell if differences are a cause or consequence of dispersal. However, circumstantial evidence supports the view that differences are most likely a cause of dispersal: differences between replicates in cell size before dispersal show that the propensity to disperse was higher in the replicate with smaller cells suggesting that small cell size is a cause rather than a consequence of dispersal.

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From a biomechanical point of view, the observed phenotype in the target tube should facilitate dispersal through reduced resistance by smaller and more elongated cells. Indeed, the small cell size of *T. thermophila* leads to low Reynolds numbers and therefore viscous-dominated regimes (Beveridge et al. 2010). Both shape and size are known to influence the drag of solid bodies in fluids as formalized by Stokes' law, in addition to the viscosity of the medium and the speed at which the body moves (Beveridge et al. 2010). Using an equation provided by Beveridge and colleagues (2010) we calculated the expected drag for a body of prolate ellipsoid shape, which is appropriate for *T. thermophila*. Evaluating this equation for size and shape changes observed between resident and dispersers in our experiment, we found that drag can decrease at the same scale as the shape (i.e. 25% less drag for dispersers than for residents) and also linearly with smaller cell sizes. However, for meaningful comparisons these values need to be expressed in comparison to the total energy expenditure of the individual cell.

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While early research on the energetic costs of movement in protists concluded that the amounts spent on locomotion were negligible (less than 1%) compared to the total metabolic energy expenditure (Fenchel and Finlay 1983), recent research showed that costs can be significant (1-10%) to substantial (10-100%) for small and fast moving ciliates or flagellates (Crawford 1992). Swimming speeds between 100 to 1000 µm/sand sizes relevant to our *Tetrahymena* clones fall within the range where negligible to significant amounts of total energy expenditure are expected for protists (Crawford 1992). Based on this information, we suggest that the variation observed between residents and dispersers in terms of size and shape can result in different locomotion and consequently dispersal, albeit the energy costs due to different morphology are probably limited. To fully understand the link between individual morphology, movement and the population-level dispersal, we would need to quantify the movement behavior and morphology of individual cells by video analysis. Although a demanding task which we are currently working on, which should allow us to elaborate on the relationship between morphology, movement behavior and movement cost in the future.

Alternative explanations could be that cells are smaller due to energetic costs associated to the dispersal process or that cells settle in the target due to lower density of conspecifics and hence the size differences result from differential cell division between tubes. However, conditions are nutrient rich throughout our experimental system so that cells are unlikely to be nutrient limited and show motility or energetic costs of dispersal as outlined above.

If our interpretation is correct, then the observed differences between tubes reflect *condition-dependent dispersal*, where certain morphological and behavioral properties influence the dispersal propensity of individuals (Clobert et al. 2009). Variation among genetic lines would then suggest that we have extensive genetic variation in the *condition-dependence* of dispersal such that some genotypes are highly condition-dependent whereas others are not.
Our experiment evaluates *context-* (density of kin) and *condition-dependence* (size, shape, activity) simultaneously, which allowed us to examine the relationship between internal and external influences on dispersal. While the dispersal propensity changed across densities in all genotypes, overall there was no consistent global trend of density on size and activity across all genotypes. There were however differences between residents and dispersers which suggest *condition-dependent dispersal* across all density contexts. The presence of significant two-way interaction effects between the tube (i.e. the contrast between residents and dispersers) and both the genotype and the density indicates that the magnitude and direction of differences between residents and dispersers varied between genotypes and across density contexts. In other words some genetic lines show *condition-dependent dispersal* that is not sensitive to density whereas other genotypes show an increase or decrease in *condition-dependent dispersal* as density increases.

Such inconsistencies are abundant in the *condition-dependent dispersal* literature and may reflect the interaction between *context*, *condition*, and genetic variation. For example, while under certain contexts individuals with higher body condition dispersed, under different contexts the opposite pattern was found (Matthysen 2012). Although puzzling at first, such inconsistencies in dispersal phenotypes may be explained by different motivations to disperse, linking the concepts of *context-* and *condition-dependent dispersal*. Indeed, individuals that disperse to avoid competition may differ from those that disperse due to other reasons such as kin structure (Clobert et al. 2009). If we also add in the influence of variation among genotypes that we detect in *Tetrahymena*, the complexity of dispersal decisions increases dramatically with just three variables (genotype, environmental context, individual condition) even when populations are composed of genetic clones.

## Acknowledgements

Virginie Thuillier and Linda Dhondt provided valuable help during the experiment and data collection. F. P. Doerder kindly provided a collection of 22 wild type genetic lines of *T. thermophila*. Comments by Caroline Nieberding, two anonymous reviewers and the Editor
609 (Jeffry Dudycha) helped to improve the overall quality of the manuscript. FP was funded by

610 Fonds Spéciaux de Recherche, Université catholique de Louvain. NS is a Research

611 Associate of the Fund for Scientific Research (F.R.S.-FNRS). Financial support for FP, NS
612 and KAM was provided by F.R.S.-FNRS and Université catholique de Louvain (ARC 10-

613 ASC received support from ANR-JCJC NetSelect and his work is part of the

614 Laboratoire d'Excellence (LABEX) entitled TULIP (ANR-10-LABX-41).
615 Tables

616 **Table 1:** Statistical analysis (three-way crossed ANCOVA) of size, shape and activity
617 according to genetic line, density (continuous co-variate) and tube (start vs. target) effects.

<table><thead><tr><th rowspan="2">Factor</th><th rowspan="2">DF for F-test</th><th colspan="2">size</th><th colspan="2">shape</th><th colspan="2">activity</th></tr><tr><th>F</th><th>p</th><th>F</th><th>p</th><th>F</th><th>p</th></tr></thead><tbody><tr><td>line</td><td>43,350</td><td>142.36</td><td><strong>&lt; 0.001</strong></td><td>108.39</td><td><strong>&lt; 0.001</strong></td><td>13.88</td><td><strong>&lt; 0.001</strong></td></tr><tr><td>density</td><td>1,43</td><td>2.26</td><td>0.134</td><td>12.95</td><td><strong>&lt; 0.001</strong></td><td>4.02</td><td><strong>0.046</strong></td></tr><tr><td>tube</td><td>1,43</td><td>378.91</td><td><strong>&lt; 0.001</strong></td><td>10.89</td><td><strong>&lt; 0.001</strong></td><td>77.44</td><td><strong>&lt; 0.001</strong></td></tr><tr><td>line x density</td><td>43,350</td><td>1.97</td><td><strong>0.001</strong></td><td>1.43</td><td><strong>0.044</strong></td><td>1.71</td><td><strong>0.005</strong></td></tr><tr><td>line x tube</td><td>43,350</td><td>7.09</td><td><strong>&lt; 0.001</strong></td><td>4.63</td><td><strong>&lt; 0.001</strong></td><td>3.18</td><td><strong>&lt; 0.001</strong></td></tr><tr><td>density x tube</td><td>1,43</td><td>7.41</td><td><strong>0.007</strong></td><td>15.73</td><td><strong>&lt; 0.001</strong></td><td>5.08</td><td><strong>0.025</strong></td></tr><tr><td>line x density x tube</td><td>1,350</td><td>0.81</td><td>0.797</td><td>1.26</td><td>0.133</td><td>0.64</td><td>0.962</td></tr></tbody></table>

618
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785

786
787 **Figure legends**

788 **Figure 1:** Dispersal propensity as a function of density for each genetic line. Each point represents one independent dispersal system. The direction of the density-dependent dispersal reaction is indicated in parentheses after the line designation when significantly different from zero at the 0.05 alpha level. Generally genetic lines showed either negative density-dependent or there was no evidence for density-dependent dispersal; only line B was an exception showing consistently positive density dependence.
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794 **Figure 2:** Relationship between the dispersal propensity (log transformed proportion of cells 795 found in the target tube after 6 h) and relative differences in cell phenotypes between start 796 and target tubes. Panel A depicts the relationship between dispersal and size, panel B 797 between dispersal and shape and panel C between dispersal and activity. Each point 798 represents one independent dispersal system (44 genetic lines x 3 densities x 2 replicates), 799 where grey scales indicate the density treatment. The lines are visual aids to indicate the 800 general trends, while the underlying analysis was based on Spearman rank correlations and 801 therefore not necessarily linear. The more dispersive a line was, the smaller, more elongated 802 and more active the cells were in the target tube.