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Repeated Geographic Divergence in Behavior: A Case Study 1  

Employing Phenotypic Trajectory Analyses 2  

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Spencer J. Ingley1 *, Eric J. Billman2, Chelsey Hancock1, and Jerald B. Johnson1,3 4  

1Evolutionary Ecology Laboratories, Department of Biology, Brigham Young University, Provo, 5  

UT 84602 6  

2Department of Fisheries and Wildlife, Oregon State University, Corvallis, OR 97331 7  

3Monte L. Bean Life Science Museum, Brigham Young University, Provo, UT 84602, USA 8  

*Corresponding Author: 9  

E-mail: sjingley@byu.edu 10  

Fax: (801) 422-0090 11  

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ABSTRACT 21  

Environmental effects on behavior have long been a focus of behavioral ecologists. Among the 22  

important drivers of behavior is predation environment, which can include the presence/absence 23  

of predators, differences in resource availability, and variation in individual density. 24  

Environments with predators are often more ecologically complex and 'risky' than those without 25  

predators. Populations from these environments are sometimes more active and explorative than 26  

populations from low-risk, less complex environments. To date, most comparative studies of 27  

behavior are limited to within species comparisons of populations from divergent environments, 28  

but neglect comparisons between species following speciation, thus limiting our understanding 29  

of post-speciation behavioral evolution. Brachyrhaphis fishes provides an ideal system for 30  

studying correlations between divergent environments and behavior within and between species. 31  

Here, we test for differences in two behavioral traits -- activity and exploration -- between sister 32  

species B. roseni and B. terrabensis that occur in divergent predation environments. Species 33  

differed in activity and exploration, with higher activity and exploration levels in populations 34  

that co-occur with predators. Furthermore, we found drainage-by-species interactions, indicating 35  

that the nature of divergence varied geographically. Using the recently-developed phenotypic 36  

trajectory analysis (PTA), we quantified this difference, and found that, while the geographically 37  

isolated populations of sister 38  

species tended to evolve in parallel, the magnitude of divergence between species differed 39  

between drainages. Our results highlight the utility of PTA for multivariate behavioral data, and 40  

corroborate past predictions that complex and risky environments are correlated with increased 41  

activity and exploration levels, and that divergence continues post-speciation. 42  

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KEYWORDS 44  

Activity, exploration, behavior, Brachyrhaphis, predation, habitat complexity, phenotypic 45  

trajectory analysis46  

 

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INTRODUCTION 47  

Spatial and temporal variation in the environment has long been recognized as a major 48  

driver in behavioral divergence (Foster 1999; Foster and Endler 1999). Differences in behavior 49  

among populations from different selective environments can result from both biotic and abiotic 50  

factors, and can represent genetic divergence, phenotypic plasticity, or both (West-Eberhard 51  

2003; Foster 2013). Using a comparative framework (i.e., studying behavior in multiple 52  

populations or species from different environments), behavioral ecologists can identify 53  

mechanisms that result in geographic variation in behavior, and determine how this variation 54  

contributes to processes such as speciation. 55  

Among the most well studied drivers of phenotypic divergence among populations or 56  

species from different environments is predation (Endler 1987; Huntingford et al. 1994; Reznick 57  

1996; Johnson 2001a; Johnson and Belk 2001;). Predation can affect a variety of prey traits, 58  

including morphology (Langerhans and DeWitt 2004; Langerhans et al. 2004), life-history 59  

(Reznick and Bryga 1987; Johnson 2001a, Johnson 2002; Johnson and Zuniga-Vega 2009), and 60  

behavior (Huntingford et al. 1994; Godin and Briggs 1996; Brown and Braithwaite 2005), with 61  

behavioral studies receiving considerable recent attention. Although predation could directly 62  

affect traits such as behavior and could serve as a strong selective force, predators can also serve 63  

as a good indicator for a variety of environmental factors that are often highly correlated with the 64  

presence or absence of predators (Johnson 2002; Ingley et al. 2014). For example, in many 65  

aquatic ecosystems predators tend to occur in low elevation streams where habitats are more 66  

complex in terms of fish and invertebrate communities, available microhabitats, and prey types 67  

(Archard and Braithwaite 2011a). How behavioral traits vary among populations from different 68  

‘predation environments’ (i.e., those that are predator-naïve and occur in less complex habitats 69  

 

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versus those that are predator-exposed and occur in more complex habitats) remains a question 70  

of great interest in behavioral ecology (Archard and Braithwaite 2011a). 71  

Two axes of behavior that are likely to be correlated with differences in predation 72  

environment are (1) activity and (2) exploration level in a novel environment (Urban 2007; 73  

Millot et al. 2009; Nomakuchi et al. 2009; Wilson and Godin 2009; Wilson et al. 2010). 74  

Although patterns have been mixed, individuals from populations that occur in complex, 75  

predator rich environments are often bolder, more active, and more explorative than individuals 76  

that occur in less complex, predator free environments (Archard and Braithwaite 2011a), perhaps 77  

in an attempt to maximize current mating and feeding opportunities (see below). However, 78  

whether increased activity and exploration levels are potentially adaptive is highly context 79  

specific, particularly because both predation and other environmental factors can affect boldness 80  

between populations of a given species (Brydges et al. 2008). On one hand, increased activity 81  

levels in prey could increase their risk of predation where prey movement facilitates prey 82  

detection by predators [e.g., where predators actively pursue prey; (Kruuk and Gilchrist 1997)]. 83  

On the other hand, an individual that spends more time in ‘exposed’ areas in a given habitat (i.e., 84  

away from predator habitat) may be able to better avoid sit-and-wait ambush predators by 85  

identifying and avoiding high-risk areas. 86  

Trade-offs in life-history strategies could also explain differences in behavioral traits both 87  

among and within populations or species from divergent environments (Wolf et al. 2007). For 88  

example, despite the potential increase in predation risk associated with risky behaviors, more 89  

bold and more active individuals could increase mating opportunities and maximize current 90  

reproduction by having higher encounter rates with potential mates (Wolf et al. 2007). 91  

Increasing activity levels to increase encounters with mates could be particularly important in 92  

 

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predator environments that are often characterized by low population densities (i.e., fewer 93  

encounter opportunities with potential mates) (Abrams 1993) and high risk of predator-induced 94  

mortality. Conversely, predator-free environments often have higher population densities and 95  

limited resources, thus favoring less active individuals that can conserve their energy by reducing 96  

their activity levels. Furthermore, an increase in activity and exploration levels in complex 97  

predator environments could allow an individual to better locate and exploit the variety of 98  

microhabitat and prey types that are available. Therefore, it is reasonable to hypothesize that 99  

populations that occur in complex, predator rich environments with corresponding environmental 100  

differences (i.e., lower population densities and higher resource availability) will show increased 101  

activity and increased exploration levels compared to their predator-naïve congeners when they 102  

are in a novel environment. 103  

Aquatic systems are well suited for studying the processes that drive geographic variation 104  

in behavioral and other traits (Riechert 1999), particularly with traits related to predation 105  

environment. Abiotic and biotic factors can be characterized in aquatic systems (e.g., Johnson 106  

2002), and populations within a species can often be found in numerous water bodies that 107  

represent different predation environments. Among the most well studied aquatic groups are 108  

live-bearing fish (Poeciliidae), which have been the focus of a diversity of ecological and 109  

evolutionary studies (Johnson 2001b; Jennions and Kelly 2002; Basolo 2004; Brown and 110  

Braithwaite 2005; Mateos 2005; Jones and Johnson 2009; Wesner et al. 2011). Many of these 111  

studies have focused on adaptation to divergent predation environments, specifically life-history 112  

evolution, morphological divergence, and behavioral differences associated with different 113  

predation environments (Reznick and Bryga 1987; Reznick 1989; Rodd and Reznick 1991; 114  

Johnson and Belk 2001; Reznick et al. 2001; Brown and Braithwaite 2004; Langerhans 2009a, b; 115  

 

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Langerhans and Makowicz 2009; Wesner et al. 2011). Members of the live-bearing fish genus 116  

Brachyrhaphis have gained momentum as a model system for studying the evolution of life-117  

history (Johnson 2001a, b, 2002; Johnson and Belk 2001; Jennions et al. 2006), morphology 118  

(Langerhans and DeWitt 2004; Wesner et al. 2011) and behavior (Brown et al. 2004, 2005; 119  

Brown and Braithwaite 2005; Archard and Braithwaite 2011a, b) associated with different 120  

predation environments. Brachyrhaphis occurs primarily in lower Central America (LCA), with 121  

most species endemic to Costa Rica and Panama. Numerous species of Brachyrhaphis exhibit 122  

adaptation to divergent predation environments. Although predation pressure may be the 123  

selective force of most importance in this system, ‘predation environment’ is characterized by 124  

the presence or absence of predators and a suite of other confounded environmental factors. For 125  

example, resource availability, stream gradient, and stream width could play an important role in 126  

determining life-history, morphological, and behavioral evolution, and are known to co-vary 127  

with presence or absence of predators where populations of Brachyrhaphis occur (Johnson 128  

2002). Populations of Brachyrhaphis rhabdophora and B. episcopi, for example, show divergent 129  

behavior, morphology, and life-history strategies associated with predation environment that are 130  

similar to those observed in numerous other poeciliid species (Reznick 1996; Johnson and Belk 131  

2001). Similar patterns are also present at deeper phylogenetic levels within Brachyrhaphis (i.e., 132  

between sister species rather than among populations within a species; Ingley et al. 2014), which 133  

facilitates comparative studies evaluating adaptation both between and within species, 134  

particularly from divergent predation environments. Studies testing for differences in activity and 135  

exploration between closely related species that remain in different predation environments 136  

could provide valuable insight into the progression of divergence once speciation is complete, 137  

and would be particularly useful where patterns of behavior in related species are known. An 138  

 

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example of this pattern is the sister species pair B. roseni and B. terrabensis. These two species 139  

have similar distributions, occurring from southeastern Costa Rica to central Panama along the 140  

Pacific verdant (Bussing 1998). Although they occur within the same drainages, B. terrabensis 141  

occupies higher elevation headwater streams while B. roseni occupies lower elevation coastal 142  

streams (Bussing 1998). Consequently, B. terrabensis occurs in streams that are primarily void 143  

of piscine predators (here referred to as ‘predator-free’ sites), while B. roseni co-occurs with 144  

numerous and abundant piscine predators (here referred to as ‘predator’ sites; e.g., Hoplias 145  

microlepis). Brachyrhaphis roseni and B. terrabensis have evolved similarly divergent life 146  

histories (M. Belk et al., unpublished data) as those observed among populations of B. 147  

rhabdophora (Johnson and Belk 2001) and among populations of B. episcopi (Jennions and 148  

Telford 2002). Body shape also varies within B. rhabdophora (Langerhans and DeWitt 2004) 149  

and between B. roseni and B. terrabensis (Ingley et al. 2014), in each case as predicted by 150  

predation environment. The fact that B. roseni and B. terrabensis are sister taxa, and that they 151  

occur in geographic proximity but in divergent predation environments, suggests that the 152  

selective forces that are driving divergence among populations within B. rhabdophora and 153  

within B. episcopi (i.e., predator vs. predator-free) could also have driven divergence between B. 154  

roseni and B. terrabensis. Here, we test for divergent activity and exploration levels in B. roseni 155  

and B. terrabensis. We predict that B. roseni will exhibit higher activity and exploration levels 156  

than B. terrabensis, a pattern similar to that observed in B. episcopi from divergent predation 157  

environments (Archard and Braithwaite 2011a). 158  

The primary purpose of our study is to evaluate exploration and activity levels in sister 159  

species from divergent predation environments, and to compare them to recently published 160  

patterns observed within a closely related species that occurs in both predator and predator-free 161  

 

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environments (Archard and Braithwaite 2011a). We test for differences in activity and 162  

exploration levels between species that occur with (B. roseni) or without predators (B. 163  

terrabensis). We also discuss the potential implications of these behavioral traits on differential 164  

fitness and reproductive isolation between B. roseni and B. terrabensis. Finally, we use our data 165  

as a case study to employ a novel technique for analyzing multivariate behavioral data, the 166  

phenotypic trajectory analysis (PTA; (Collyer and Adams 2007; Adams and Collyer 2009). 167  

Although PTA has been successfully used for non-behavioral data (e.g., geometric morphometric 168  

data; Wesner et al. 2011; Ingley et al. 2014), its utility for multivariate quantitative behavioral 169  

data sets remains unexplored. We show how cryptic patterns of behavioral variation in a data set, 170  

specifically factors related to geographic variation, can be discovered using this method of 171  

analyzing evolutionary trajectories. We also demonstrate how PTA can be useful in accurately 172  

comparing multivariate behavioral data among multiple sample populations by quantifying the 173  

magnitude and direction of phenotypic change to assess divergence, convergence, or parallelism. 174  

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MATERIALS AND METHODS 176  

Live fish collection and study sites 177  

In August 2011, we used hand held seine nets to collect B. roseni and B. terrabensis from 178  

rivers in two drainage systems in Western Panama [Rio Chiriquí Viejo drainage (N 8.7924, W 179  

82.6566; N 8.5184, W 82.7115), and the Rio David drainage (N 8.6609, W 82.5206; N 8.4251, 180  

W 82.4176)]. In each drainage, B. roseni was found in downstream reaches and B. terrabensis 181  

was found in upstream reaches. After capture, fish were housed temporarily (less than 30 days) 182  

at the Naos Marine Lab at the Smithsonian Tropical Research Institute, Panama. Fish were then 183  

transported to the Evolutionary Ecology Laboratories (EEL) at Brigham Young University. Fish 184  

 

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were held under a 12L:12D light cycle, with lower light levels at the start and end of the day to 185  

mimic dawn and dusk. Fish were separated by population and divided among thirty-two 10 186  

gallon glass tanks with power filtration and aeration, water depth at ~230 mm, and water 187  

temperature at 23–24◦ C. Fish were fed flake food twice daily and supplemented every 7 days 188  

with live Daphnia sp. Fish were acclimated in the lab for a period of two months to settle after 189  

transport to BYU before being used in our experiments. Food was withheld the morning before 190  

trials, with the last feeding taking place the afternoon prior to the experiment. To prevent testing 191  

fish multiple times, individuals from a given tank were all tested sequentially. Once a fish had 192  

completed the trial, it was held temporarily in an intermediate tank. Once all the fish in a tank 193  

had participated in the experiment, they were transferred from the intermediate tank to their 194  

original holding tank. 195  

Open-field trials and data collection 196  

We tested exploration and activity levels in Brachyrhaphis roseni and B. terrabensis 197  

using an open-field trial approach. Open-field trials come in many forms, but they are all based 198  

on placing an animal into a novel open space, from which escape is prevented by a barrier, and 199  

monitoring subsequent behavior (Walsh and Cummins 1976). Recently, open-field trials have 200  

been successfully conducted on other Brachyrhaphis species using clear plastic tanks (Archard 201  

and Braithwaite 2011a). Previous studies on additional species have also shown that exploration 202  

in the lab is correlated with exploration in the wild (Kobler et al. 2009). Because we used wild-203  

caught individuals, the patterns we observe potentially reflect genetic or environmental factors, 204  

or a combination of both. Likewise, our study does not attempt to determine how fish respond to 205  

the immediate presence of a predator (e.g., do fish from predator environments reduce activity 206  

when a predator is detected, while a predator-naïve fish does not respond), but rather how 207  

 

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populations behave while exploring a novel environment. In this study, we used methods similar 208  

to those of Archard and Braithwaite (2011) on B. episcopi. We did this in part to facilitate 209  

comparison between their study and ours, in order to examine similarities between species pairs 210  

(our study) and between population pairs (Archard and Braithwaite 2011a). We used a clear, 211  

plastic tank (width 400 mm × length 400 mm × height 240 mm) with water depth 100 mm, and 212  

covered on all sides with black paper. Light levels were constant between all trials, and the tank 213  

was situated such that no shadows fell within the arena. All trials were conducted in a sound-214  

proof chamber located in the BYU EEL facilities to prevent interference from outside noise, and 215  

were monitored externally by video. For each trial, we placed a clear, colorless plastic cylinder 216  

of 80 mm diameter in the center of the tank. We placed each fish in the cylinder and allowed it 217  

to settle for a period of 2 minutes, after which the cylinder was remotely raised via a pulley. We 218  

then recorded behavior and activity of the focal fish using a video camera placed above the tank 219  

for an 8-minute observation period. The base of the arena was marked with a line 50 mm in from 220  

the perimeter of the tank to define an ‘edge’ zone and ‘center’ zone. We also placed two lines 221  

(crossing at 90◦ to one another in the center of the box) to divide the edge and center zone into 222  

quarters (Archard and Braithwaite 2011a). At the completion of each trial, standard length (SL) 223  

of the focal fish was determined to the nearest 0.5 mm. The behavior of each fish was only 224  

measured once. Although we recognize that individual behavioral plasticity could be present, we 225  

were not interested in measuring the consistence of individual differences in behavior (i.e., 226  

personalities). Our goal was to get a relative estimate of the activity and exploration levels of 227  

each population for comparative purposes. 228  

Once a trial was complete, we analyzed videos using Etholog v2.2.5 (Ottoni 2000) to 229  

quantify fish activity and exploration levels; we focused specifically on when a fish was moving 230  

 

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and when a fish moved between different quarters of the tank. All videos were analyzed by the 231  

same observer to ensure consistency, and the observer was blind with regards to the population 232  

of origin of the test subject in order to eliminate observation bias. We used these data to calculate 233  

seven measures of exploration and activity level: 1) the latency to first reach the edge zone after 234  

settling; 2) the latency to then return into the center zone; 3) the proportion of time spent in the 235  

center zone of the arena (not counting the time taken to first reach the edge zone); 4) the mean 236  

duration of visits to the center zone; 5) the proportion of time spent motionless; 6) the rate of 237  

movement between the four quarters; and 7) the rate of movement between all tank sections (i.e., 238  

both within and between quarters). These variables effectively represent exploration (variables 239  

1-4) and activity levels (variables 5-7), We conducted a total of 103 individual trials: 55 samples 240  

of B. roseni (29 from the Rio Chiriquí Viejo drainage, 26 from the Rio David drainage) and 48 241  

samples of B. terrabensis (28 from the Rio Chiriquí Viejo drainage, 23 from the Rio David 242  

drainage). We used roughly equal numbers of males and females for each sample populations, 243  

with at least 10 individuals from each sex for each population. All collecting and experimental 244  

procedures were approved by the BYU IACUC (protocol number 11-0901). 245  

Phenotypic trajectory analysis 246  

Phenotypic trajectory analysis (PTA) can be used on data sets that consist of multivariate 247  

data and have a two-factor design (e.g., predation environment and drainage in our study), with a 248  

significant interaction between factors (Collyer and Adams 2007; Adams and Collyer 2009). To 249  

our knowledge the PTA has never been used for behavioral data sets. Phenotypic trajectory 250  

analysis tests whether the significant interaction between main effects and the ‘index variable’ 251  

(defined below) resulted from differences in magnitude (MD) or direction (Θ) of phenotypic 252  

(behavioral) change, or both (see Adams and Collyer, 2009; Collyer and Adams, 2007 for 253  

 

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computational details). In this study, trajectory magnitude is a measure of phenotypic change in 254  

behavior; trajectories with similar behaviors for each level (e.g., drainage or species) will have a 255  

smaller magnitude than trajectories with extremely different behaviors for each level. Trajectory 256  

direction compares the orientation of trajectories in the multivariate trait space; those that have 257  

similar behavioral changes across levels will not have significant differences in trajectory 258  

direction. However, where behaviors become more similar (i.e., convergence in behavioral traits) 259  

or more dissimilar (i.e., divergence in behavioral traits) across levels, the direction will be 260  

significantly different between trajectories. 261  

To analyze our multivariate data set, we first conducted two principal component 262  

analyses (PCA): one for the four variables describing exploration and one for the three variables 263  

describing activity. These PCAs were performed on a correlation matrix with standardized 264  

variables. Using PC scores facilitates the visual interpretation of the phenotypic landscape 265  

without altering the outcome of the PTA (Dennis et al. 2011). We then analyzed the data (using 266  

all PC scores as the response variables) using mixed model multivariate analyses of variances 267  

(MANOVA) using proc Mixed in SAS and used model selection techniques [i.e., AIC; (Johnson 268  

and Omland 2004)] to determine the best fit model for the activity and exploration data (see 269  

Online Resources 1 and 2 for model comparisons and AIC scores). Given that PC scores are 270  

orthogonal and ordered according to the amount of variation they explain, they can be treated as 271  

repeated measures and numbered with the use of an index variable; this variable is analogous to 272  

time in a traditional repeated measures model. Thus, we treated the order number of the PC 273  

scores as an ‘index variable’ and included it in the repeated statement for mixed model analyses. 274  

In the MANOVA model for the activity and exploration data, we modeled six a priori 275  

hypotheses based on study objectives and biological relevance. We included in the models the 276  

 

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main effects of species, sex, standard length, drainage, and the index variable. Interactions of the 277  

index variable with main effect(s) are the terms of interest in the MANOVA because they test 278  

differences in the levels of the main effect(s) while allowing the magnitude and direction of 279  

differences to vary independently among principal components (Butler et al. 2009; Wesner et al. 280  

2011; Hassell et al. 2012). Therefore, we also included the 2-way interactions that included the 281  

index variable with other main effects (n=4 parameters). Three-way interactions that included the 282  

index variable (n=6) were included in the models, but we did not include 4-way or the 5-way 283  

interactions because they cannot be interpreted and thus are not biologically meaningful. The full 284  

model included all 3-way interactions; the reduced model included only the 285  

species*drainage*index variable. Four other models were generated that each removed all 3-way 286  

interactions that included one of the main effects, e.g. one model excluded all 3-way interactions 287  

that included the main effect of standard length. However, each of these reduced models retained 288  

the interaction of species*drainage*index variable as this interaction tested one of our study 289  

objectives. After running the full model, we ran reduced models to determine the best fit model 290  

for the activity and exploration data (see Online Resources 1 and 2 for model comparisons and 291  

AIC scores). 292  

We used the PTA to compare two different trajectories: a ‘species’ trajectory and a 293  

‘drainage’ trajectory (described below). Given that we found a significant interaction between 294  

species and drainage (see Results), we used the PTA to address two different, but related, 295  

questions about the evolution of activity and exploration in this group. First, our species 296  

trajectory, which focused on the magnitude and direction of change between river drainages 297  

within a species, addressed the questions: do B. roseni and B. terrabensis differ in the amount of 298  

behavioral phenotypic change that occurs between drainages, and do they evolve in parallel 299  

 

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directions? Second, our drainage trajectory, which focused on the magnitude and direction of 300  

change between species but within drainages, addressed the questions: do species show greater 301  

behavioral phenotypic divergence in one drainage than in another, and do they evolve in parallel 302  

directions? Here, we compared both size and direction of the phenotypic trajectories to test for 303  

differences in magnitude and direction of behavioral divergence and to determine if local 304  

environment affects species differently. We conducted the PTA using program R (R Core 305  

Development Team 2010). Mixed MANOVAs for the PTA were conducted in ASREML-R 306  

version 3.00 (Butler et al. 2007) within R; model results did not differ from analyses conducted 307  

using proc Mixed in SAS. For the ‘exploration’ and 'activity' data sets we plotted LS means on 308  

the first two PCs, which accounted for 63.52% and 17.74% of the observed variation for 309  

exploration, and 86.72% and 11.95% for activity, to visualize differences in magnitude and 310  

direction of behavioral change (Figures 2 and 3). For each figure we scaled the axes according to 311  

the percent of variance explained in order to more accurately represent the contribution of each 312  

PC. 313  

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RESULTS 315  

MANOVA and PTA for Activity and Exploration: Drainage and Species Trajectories 316  

As predicted, B. roseni and B. terrabensis significantly differed in both ‘activity’ and 317  

‘exploration’ (Tables 1 and 2), with B. roseni being more active and explorative overall than B. 318  

terrabensis. The best-fit model for ‘activity’ was: Index variable + Species + Sex + Drainage + 319  

SL + Index variable x Species + Index variable x Sex + Index variable x Drainage + Index 320  

variable x SL + Index variable x Species x Drainage + Index variable x Species x Sex + Index 321  

variable x Drainage x Sex (for model selection details, see Online Resources 1). The interactions 322  

 

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of Species x Index variable and Species x Drainage x Index variable were significant predictors 323  

of ‘activity’ (Table 1). This suggests that, despite overall species differences, the patterns of 324  

change found in each species differed by drainage. We ran a PTA to analyze both species 325  

trajectories and drainage trajectories for ‘activity.’ For species trajectories, B. roseni showed a 326  

greater magnitude of change in behavior between drainages than B. terrabensis (MD = 1.089; P 327  

= 0.025), with no difference in the orientation (i.e., direction) of change (θ = 168.97°; P = 0.11). 328  

For drainage trajectories, there was a greater magnitude of divergence in the Rio Chiriquí Viejo 329  

drainage than the Rio David drainage (MD = 1.456; P = 0.003), with no difference in the 330  

orientation of change (θ = 85.848°; P = 0.72). These differences in magnitude resulted from an 331  

increase in ‘activity’ in B. roseni from the Chiriquí Viejo drainage (i.e., less time spent 332  

motionless and a higher rate of movement; Table 3 and Fig. 1). Despite the large angle 333  

differences in the orientation of change, the PTA failed to detect any significant signal of non-334  

parallel divergence. 335  

The best-fit model for ‘exploration’ was: Index variable + Species + Sex + Drainage + SL + 336  

Index variable x Species + Index variable x Sex + Index variable x Drainage + Index variable x 337  

SL + Index variable x Species x Drainage + Index variable x Species x Sex + Index variable x 338  

Drainage x Sex (for model selection details, see Online Resource 2). ‘Exploration’ differed 339  

significantly for the interaction of Species x Index variable and Species x Drainage x Index 340  

variable (Table 2). This suggested that, as is the ‘activity’ data, the patterns of behavioral 341  

divergence differed between species or drainage. We ran a PTA to analyze both species 342  

trajectories and drainage trajectories for ‘exploration.’ For species trajectories, no difference in 343  

either magnitude or orientation of change was recovered (MD = 0.138, P = 0.649; θ = 140.211°, 344  

P = 0.152). For drainage trajectories, there was a greater magnitude of divergence in the Rio 345  

 

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Chiriquí Viejo drainage than the Rio David drainage (MD = 1.606; P = 0.0001), with no 346  

difference in the orientation of change (θ = 35.762°; P = 0.949). These differences were 347  

attributed to an increase in ‘exploration’ in the Chiriquí Viejo B. roseni population, and a 348  

decrease in ‘exploration’ in the Chiriquí Viejo B. terrabensis population (Table 4 and Fig. 2). 349  

Thus, our results suggest that B. roseni and B. terrabensis differed in their ‘activity’ and 350  

‘exploration’ levels, and that although the magnitude of change varied between drainages, there 351  

was not support for non-parallel divergence. 352  

353  

DISCUSSION 354  

Differential activity and exploration levels in species from divergent predation environments 355  

Our results provide evidence that both activity and exploration levels in Brachyrhaphis 356  

roseni and B. terrabensis are strongly correlated with, and could be driven by, divergence in 357  

predation environment (i.e., the presence of predators and corresponding environmental 358  

differences). Populations from more complex, predator rich environments (B. roseni) were 359  

overall more active and prone to explore a novel environment than populations from less 360  

complex, predator free environments (B. terrabensis). Our conclusions are largely consistent 361  

with studies of activity and exploration in other taxa with populations from different predation 362  

environments (Huntingford et al. 1994; Riechert and Hall 2000; Dingemanse et al. 2007), 363  

although further tests are necessary to determine which environmental differences (e.g., 364  

predation pressure, resource availability, or population density) act as the primary selective 365  

forces driving behavioral divergence. 366  

Brachyrhaphis roseni and B. terrabensis experience vastly different environments 367  

throughout their range in western Panama. Brachyrhaphis roseni occupies low-elevation streams 368  

 

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with low stream flow, warm water, complex microhabitats, and a diverse fish community that 369  

includes several types of large piscine predators (e.g., Hoplias microlepis). In contrast, B. 370  

terrabensis occupies high-elevation streams with higher stream flow, cooler water, and very low 371  

levels of habitat heterogeneity and fish diversity, with a lack of piscine predators (B. terrabensis 372  

is often the only fish species present). We predicted that these environmental differences would 373  

result in dramatically different behavioral traits, with B. roseni being more active and prone to 374  

explore a novel environment than B. terrabensis. Indeed, our results largely support this 375  

prediction. Overall, B. roseni spent more time actively exploring the arena (i.e., less time 376  

motionless) and had an increased rate of movement throughout the arena relative to B. 377  

terrabensis. Likewise, B. roseni took longer to leave the center of the arena, but returned more 378  

quickly and spent more time overall in the center than B. terrabensis. In fact, B. terrabensis often 379  

quickly left the center of the arena and then remained motionless along the side of the arena for 380  

the duration of the trial. The time that a subject spends in the open area of an arena is a standard 381  

behavioral measurement (Walsh and Cummins 1976), with the assumption that individuals 382  

perceive themselves as safer when they are adjacent to a wall rather than in the open. Thus, time 383  

spent in the center of an arena can be interpreted as a measure of boldness or propensity to 384  

engage in ‘risky’ behavior. Hence, our results suggest that B. roseni is more prone than to engage 385  

in this risky, explorative behavior than B. terrabensis when introduced to a novel environment. 386  

Our findings are remarkably similar to those of a recent study on the closely related B. 387  

episcopi, in which populations occur either with or without predators. Archard and Braithwaite 388  

(2011a) found that populations that live in complex, predator rich environments tend to be more 389  

active and prone to explore than those from less complex, predator free environments. However, 390  

unlike Archard and Braithwaite (2011a), we found that fish from our study that occur in predator 391  

 

  19  

environments (B. roseni) spent more time in the center of the arena than those that occur in 392  

predator free environments (B. terrabensis), a pattern opposite to that observed among 393  

populations of B. episcopi. Why such differences exist between these species remains unclear. 394  

In addition to differences in overall activity and exploration levels between species from 395  

different environments, we also found some differences among populations within each of our 396  

focal species, suggesting that behavior varies geographically in these species. For example, both 397  

activity and exploration levels varied significantly among species when comparing populations 398  

between drainages (i.e., significant species by drainage interaction). Phenotypic trajectory 399  

analyses revealed that this difference resulted from a difference in magnitude of behavioral 400  

change in B. roseni and B. terrabensis, with no support found for a difference in the orientation 401  

or direction of change (Tables 1 and 2; Figs 1 and 2). Similarly, interspecific levels of divergence 402  

differed between drainages, with greater interspecific divergence occurring in the Chiriquí Viejo 403  

drainage than in the David drainage, while the PTA found no evidence for differences in the 404  

angle of divergence. Our PTA, and our plotting of PC scores for univariate comparisons, 405  

revealed that B. roseni showed a greater magnitude of behavioral divergence between drainages 406  

than did B. terrabensis, although we found no statistically significant evidence that the two 407  

species evolved in different directions between drainages. Specifically, our findings related to 408  

activity levels suggest that B. roseni from the Chiriquí Viejo drainage is dramatically divergent 409  

from the other three populations sampled (Fig. 1). The pattern of divergence in exploration levels 410  

differs, in that both B. roseni populations were more prone to explore than B. terrabensis 411  

populations, although the magnitude of interspecific divergence in the David drainage was far 412  

less than that found in the Chiriquí Viejo drainage. Together, these findings suggests that some 413  

difference between drainages is driving divergence in behavioral traits in these sister species, but 414  

 

  20  

that each species reacts differently in terms of the magnitude of change they show. The exact 415  

cause of this difference in magnitude is unknown, yet provides an interesting avenue of future 416  

research. Brachyrhaphis roseni may show this greater magnitude of divergence between 417  

populations because of greater habitat heterogeneity at lower elevations than is found in 418  

upstream areas where B. terrabensis occurs. Lower elevation streams are often more complex 419  

ecosystems than higher elevation streams, and vary in terms of stream canopy cover and 420  

vertebrate communities (Angermeier and Karr 1983). Populations of the European minnow 421  

(Phoxinus phoxinus) from divergent predation environments have been shown to vary in their 422  

ability to adjust their behaviors based on the environment. Specifically, laboratory reared 423  

individuals derived from predation rich populations showed greater behavioral adjustment when 424  

exposed to a predator at a young age than laboratory reared individuals from predator-naïve 425  

populations (Magurran 1990), suggesting that populations that co-occur with predators may have 426  

a higher capacity for behavioral plasticity. If this were the case with B. roseni and B. terrabensis, 427  

we would predict that B. roseni would be more adept at adjusting its behavior in varying 428  

environments than B. terrabensis, possibly explaining the increased magnitude of divergence 429  

within B. roseni relative to B. terrabensis. Differences in behavioral divergence in populations of 430  

B. roseni could also be explained by a greater period of isolation compared to populations of B. 431  

terrabensis (i.e., less gene flow among populations within species), although our preliminary 432  

analyses indicate that this is not the case (S. Ingley, unpublished data). Overall, these findings 433  

underscore the need to evaluate behavior in multiple populations for a given species, and 434  

highlight the utility of the PTA for detecting cryptic patterns in multivariate behavioral data sets, 435  

such as the magnitude of change and whether species are diverging, converging, or evolving in 436  

parallel. 437  

 

  21  

Why do activity and exploration levels differ? 438  

What underlying processes could be driving divergent behaviors in populations from 439  

different predation environments? Individuals from populations or species that have experienced 440  

complex, predator rich habitats are often found to be more bold and prone to explore a novel 441  

environment (Krause et al. 2000; Lammers et al. 2009; Wilson et al. 2010; Archard and 442  

Braithwaite 2011a; Nannini et al. 2012), consistent with the general hypothesis that an increase 443  

in environmental risk and complexity is correlated with an increase in potentially risky behavior. 444  

Despite significant differences in behavior between drainages, our data set provides some 445  

additional support for this hypothesis, in that overall B. roseni was more active and explorative 446  

in a novel environment than B. terrabensis (Table 1). Several potential advantages to these 447  

behavioral types could stem from both biotic (e.g., presence of predators and community 448  

diversity) and abiotic (e.g., canopy cover, stream flow, microhabitat diversity, and resource 449  

availability) environmental differences in the sites where these species are found. For example, 450  

B. roseni, which occurs at lower population densities than B. terrabensis, could benefit from 451  

greater activity levels by increasing encounter rate with potential mates in an attempt to 452  

maximize current reproduction rather than future reproduction. Moreover, given that higher rates 453  

of extrinsic mortality are typically associated with increased predation threat (Johnson and 454  

Zuniga-Vega 2009), individuals may benefit from being bolder in a novel environment in order 455  

to maximize lifetime reproductive success by increasing mating opportunities (Lima and Dill 456  

1990; Wolf et al. 2007). Theory predicts that this sort of life-history trade-off could explain 457  

within-species variation in and the evolution of personality traits (Wolf et al. 2007). Although we 458  

did not explicitly test for animal personality (i.e., consistent individual differences in behavior 459  

across time and contexts) in these species, the overall differences in behavioral traits between 460  

 

  22  

species and among populations could also be explained by differences in life-history trade-offs 461  

associated with divergent environments. 462  

Differences in activity and exploration levels between B. roseni and B. terrabensis may 463  

also be influenced by resource availability and microhabitat complexity. Population densities are 464  

often greater in predator-free environments, and this increased population density is often 465  

associated with more limited resources (Reznick and Yang 1993; Grether et al. 2001; Reznick et 466  

al. 2001; Walsh and Reznick 2009). Non-density dependent environmental factors (e.g., canopy 467  

cover) can also contribute to decreased resource availability in predator-free environments 468  

(Grether et al. 2001; Johnson 2002). Therefore, reduced activity levels may limit the quantity of 469  

resources needed for growth, reproduction, and somatic maintenance, because individuals that 470  

are less active have a lower metabolic rate (Careau et al. 2008). Our data also supports this 471  

possibility, with individuals from predator-free environments (where resources are presumably 472  

lower) exhibiting lower activity levels than individuals from predator environments. In addition, 473  

differences in activity and exploration could be explained by different foraging strategies. For 474  

example, B. roseni occupies low-elevation streams, which typically have a slow flow rate and 475  

higher microhabitat complexity. On the contrary, B. terrabensis occupies high-elevation streams, 476  

which typically have a fast flow rate and lower microhabitat complexity. Brachyrhaphis roseni is 477  

known to actively forage at the water’s surface, while B. terrabensis forages in the water column 478  

in high-flow areas. When not actively foraging, B. terrabensis tends to avoid the higher flow 479  

areas by settling near the substrate or holding its position behind rocks (S. Ingley, unpublished 480  

data). Thus, the foraging strategies of each species appear to complement the observed 481  

differences in activity and exploration. 482  

483  

 

  23  

CONCLUSION 484  

Recent work on the evolution of behavior has shown that ecological factors can be key agents in 485  

driving the evolution of various behavioral traits, and that examining behavioral variation in 486  

multiple populations is crucial. Here, we show that differences in environment, including the 487  

presence or absence of predators and associated environmental differences, appear to be closely 488  

linked to divergent patterns in activity and exploration traits between sister species of 489  

Brachyrhaphis fishes, and that these patterns are consistent with those observed at the intra-490  

specific level in a closely relates species. Our observations, and the use of phenotypic trajectory 491  

analysis, point to several experiments that could explicitly address whether or not these 492  

behaviors are genetically based and adaptive. Our study also highlights the utility of the 493  

phenotypic trajectory analysis in behavioral research, a method we use here to test for shared and 494  

unique patterns of behavioral divergence among populations within river drainages. A major 495  

advantage of using PTA is that it allows researchers to compare phenotypic evolution across time 496  

or among geographic replicates (e.g., among drainages in our study), providing added flexibility 497  

and analytical power to other statistical tests, particularly when researchers are interested in 498  

studying geographic variations in behavioral traits and testing for patterns such as convergence, 499  

divergence, and parallelism. Hence, our work not only shows the impact that divergent 500  

environments can have on behavioral traits, but it also reveals how a promising new method can 501  

be applied to behavioral questions in general. 502  

503  

ACKNOWLEDGMENTS 504  

This work was supported by the Monte L. Bean Life Science Museum; a BYU MEG grant to 505  

JBJ; and the U.S. National Science Foundation (OISE 0539267, IOS-1045226 to JBJ, NSF 506  

Graduate Research Fellowship to SJI). We thank P. Johnson and M. McEntire for help in the 507  

 

  24  

field. Specimens were collected under ANAM permit no. SC/A-26-11 and exported under 508  

ANAM permit no. SEX/A-60-11. We thank the Smithsonian Tropical Research Institute for help 509  

with obtaining collecting and export permits in Panama. 510  

511  

ETHICAL STANDARDS 512  

All the experiments in this study comply with the current laws of the country in which they were 513  

performed. 514  

CONFLICT OF INTEREST 515  

The authors declare that they have no conflict of interest. 516  

517  

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 680  

 681  

 682  

 683  

 684  

685  

686  

687  

688  

689  

 

  32  

Fig. 1 Graph of least square means of principle component (PC) scores (±SE) representing 690  

‘activity’ for Brachyrhaphis roseni and B. terrabensis for the Rio Chiriquí Viejo drainage and 691  

the Rio David drainage. Brachyrhaphis roseni showed greater divergence in exploration levels 692  

between drainages relative to B. terrabensis, and species were more divergent in the Rio Chiriquí 693  

Viejo drainage than the Rio David drainage. Dotted lines represent the ‘drainage’ trajectory 694  

tested with the PTA. The aspect ratio of each axis is scaled to represent the amount of variation 695  

explained by each PC 696  

697  

698  

699  

700  

701  

 

  33  

Fig. 2 Graph of least square means of principle component (PC) scores (±SE) representing 702  

‘exploration’ for Brachyrhaphis roseni and B. terrabensis for the Rio Chiriquí Viejo drainage 703  

and the Rio David drainage. Brachyrhaphis roseni showed greater divergence in exploration 704  

levels between drainages relative to B. terrabensis, and species were more divergent in the Rio 705  

Chiriquí Viejo drainage than the Rio David drainage. Dotted lines represent the ‘drainage’ 706  

trajectory tested with the PTA. The aspect ratio of each axis is scaled to represent the amount of 707  

variation explained by each PC 708  

709  

710  

711  

712  

713  

714  

 

  34  

Table 1 Results of mixed repeated measures MANOVA testing for significant interactions for all 715  

combinations of species, sex, drainage, standard length (SL), and index variable for behavioral 716  

variables representing activity 717  

Effect DF (fm) F (a) P (a)

Index variable 2, 136 0.20 0.816

Species 1, 127 5.07 0.026

Sex 1, 127 1.28 0.260

Drainage 1, 127 5.23 0.024

Standard length (SL) 1, 127 0.31 0.578

Species × index variable 2, 136 6.37 0.002

Sex × index variable 2, 136 0.67 0.471

Drainage × index variable 2, 136 1.59 0.207

SL × Index variable 2, 136 0.21 0.810

Species x Drainage x Index variable 3, 154 3.95 0.010

Species x Sex x Index variable 3, 154 1.77 0.156

Drainage x Sex x Index variable 3, 154 0.96 0.413

718  

719  

720  

721  

 

  35  

Table 2 Results of mixed repeated measures MANOVA testing for significant interactions for all 722  

combinations of species, sex, drainage, standard length (SL), and index variable for behavioral 723  

variables representing exploration 724  

725  

Effect DF (fm) F (a) P (a)

Index variable 3, 192 0.50 0.686

Species 1, 247 24.54 < 0.001

Sex 1, 247 0.02 0.901

Drainage 1, 247 2.99 0.085

Standard length (SL) 1, 247 0.10 0.750

Species × index variable 3, 192 8.14 < 0.001

Sex × index variable 3, 192 1.26 0.290

Drainage × index variable 3, 192 0.24 0.868

SL × index variable 3, 192 0.52 0.667

Species x Drainage x index variable 4, 184 3.85 0.005

Species x Sex x Index variable 4, 184 0.98 0.420

Drainage x Sex x Index variable 4, 184 1.93 0.107

726  

727  

728  

729  

730  

 

  36  

Table 3 Component score coefficients from the principal component analysis on behaviors 731  

related to 'activity' of Brachyrhaphis terrabensis and B. roseni during open-field behavioral 732  

assays 733  

734  

Variable PC 1 PC 2

Proportion of time still 0.54 0.84

Rate of movement between quarters -0.59 0.39

Rate of movement between all divisions -0.59 0.37

735  

736  

737  

738  

739  

740  

741  

742  

743  

744  

745  

746  

747  

 

  37  

Table 4 Component score coefficients from the principal component analysis on behaviors 748  

related to 'exploration' of Brachyrhaphis terrabensis and B. roseni during open-field behavioral 749  

assays 750  

751  

Variable PC 1 PC 2

Latency to reach the edge -0.44 - 0.62

Latency to return to the center 0.45 0.46

Proportion of time spent in the center -0.55 0.38

Mean duration of visits to the center -0.54 0.50

752