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How stable are personalities? A multivariate view of behavioural variation over long and short timescales in the sheepshead swordtail, Xiphophorus birchmanni

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Many studies have revealed repeatable (among-individual) variance in behavioural traits consistent with variation in animal personality; however, these studies are often conducted using data collected over single sampling periods, most commonly with short time intervals between observations. Consequently, it is not clear whether population-level patterns of behavioural variation are stable across longer timescales and/or multiple sampling periods or whether individuals maintain consistent ranking of behaviours (and/or personality) over their lifetimes. Here, we address these questions in a captive-bred population of a tropical freshwater poeciliid fish, Xiphophorus birchmanni. Using a multivariate approach, we estimate the among-individual variance-covariance matrix (I), for a set of behavioural traits repeatedly assayed in two different experimental contexts (open-field trials, emergence and exploration trials) over long-term (56 days between observations) and short-term (4-day observation interval) time periods. In both long- and short-term data sets, we find that traits are repeatable and the correlation structure of I is consistent with a latent axis of variation in boldness. While there are some qualitative differences in the way individual traits contribute to boldness and a tendency towards higher repeatabilities in the short-term study, overall, we find that population-level patterns of among-individual behavioural (co)variance to be broadly similar over both time frames. At the individual level, we find evidence that short-term studies can be informative for an individual's behavioural phenotype over longer (e.g. lifetime) periods. However, statistical support is somewhat mixed and, at least for some observed behaviours, relative rankings of individual performance change significantly between data sets.
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1
2
3ORIGINAL PAPER
4How stable are personalities? A multivariate view of behavioural
5variation over long and short timescales in the sheepshead
6swordtail, Xiphophorus birchmanni
8Kay Boulton &Andrew J. Grimmer &Gil G. Rosenthal &
9Craig A. Walling &Alastair J. Wilson
10
11
12 Received: 18 September 2013 /Revised: 4 February 2014 /Accepted: 4 February 2014
13 #Springer-Verlag Berlin Heidelberg 2014
14 Abstract Many studies have revealed repeatable (among-
15 individual) variance in behavioural traits consistent with var-
16 iation in animal personality; however, these studies are often
17 conducted using data collected over single sampling periods,
18 most commonly with short time intervals between observa-
19 tions. Consequently, it is not clear whether population-level
20 patterns of behavioural variation are stable across longer time-
21 scales and/or multiple sampling periods or whether individ-
22 uals maintain consistent ranking of behaviours (and/or per-
23 sonality) over their lifetimes. Here, we address these questions
24 in a captive-bred population of a tropical freshwater poeciliid
25 fish, Xiphophorus birchmanni. Using a multivariate approach,
26 we estimate the among-individual variance-covariance matrix
27 (I), for a set of behavioural traits repeatedly assayed in two
28 different experimental contexts (open-field trials, emergence
29and exploration trials) over long-term (56 days between ob-
30servations) and short-term (4-day observation interval) time
31periods. In both long- and short-term data sets, we find that
32traits are repeatable and the correlation structure of I is con-
33sistent with a latent axis of variation in boldness. While there
34are some qualitative differences in the way individual traits
35contribute to boldness and a tendency towards higher repeat-
36abilities in the short-term study, overall, we find that
37population-level patterns of among-individual behavioural
38(co)variance to be broadly similar over both time frames. At
39the individual level, we find evidence that short-term studies
40can be informative for an individuals behavioural phenotype
41over longer (e.g. lifetime) periods. However, statistical sup-
42port is somewhat mixed and, at least for some observed
43behaviours, relative rankings of individual performance
44change significantly between data sets.
45Keywords Multivariate behaviour .Personality .
46Repeatability .Stability .Boldness .Imatrix
47Introduction
48It is now apparent that, within animal populations, individuals
49often exhibit differences in behaviour that are repeatable
50across time and context. This repeatable variation is taken as
51evidence for animal temperament (e.g. Boissy 1995; Réale
52et al. 2007), behavioural syndromes (Sih et al. 2004), coping
53styles (Koolhaas et al. 1999) or personality, the latter term
54reflecting parallels with research in human psychology
55(Budaev 1997b; Gosling 2001). A number of axes of
56among-individual behavioural variation condensed into per-
57sonality traitshave been described, including boldness-
58shyness, exploration-avoidance and general activity (Réale
59et al. 2007). Understanding the evolution of personality has
60become a major field of study in behavioural ecology (Dall
Communicated by N. Dingemanse
Craig A Walling and Alastair J Wilson made equal contributions.
Electronic supplementary material The online version of this article
(doi:10.1007/s00265-014-1692-0) contains supplementary material,
which is available to authorized users.
K. Boulton :C. A. Walling
Institute of Evolutionary Biology, University of Edinburgh, West
Mains Rd, Edinburgh EH9 3JT, UK
A. J. Grimmer :A. J. Wilson (*)
Centre for Ecology and Conservation, Biosciences, College of Life
and Environmental Sciences, University of Exeter, Cornwall
Campus, Treliever Road, Penryn, Cornwall TR10 9EZ, UK
e-mail: A.Wilson@exeter.ac.uk
G. G. Rosenthal
Department of Biology, Texas A&M University, 3258 TAMU,
College Station, TX 77843, USA
G. G. Rosenthal
Centro de Investigaciones Científicas de las Huastecas Aguazarca,
Calnali, Hidalgo, Mexico
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DOI 10.1007/s00265-014-1692-0
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61 et al. 2004; Stamps and Groothuis 2010). There is now grow-
62 ing evidence that traits relating to personality contribute to
63 fitness variation and therefore may be both adaptive and
64 generally under selection (Q1 Smith and Blumstein 2008). How-
65 ever, if natural selection occurs through variation in lifetime
66 fitness, then an important question arises: just how stable are
67 personalities over individual lifetimes? Here, we address this
68 question in a captive population of fish. We do this using a
69 novel multivariate approach that characterises personality var-
70 iation as a latent character underpinning among-individual
71 (co)variation in a suite of observed behaviours.
72 While there remains considerable disagreement over how
73 best to define individual personality traits (Réale et al. 2007;
74 Toms et al. 2010;Carteretal.2013; see below), there is a
75 broad consensus that among-individual behavioural variance
76 is the statistical signature of animal personality. Typically, this
77 is quantified as the (among-individual) repeatability, defined
78 as the proportionof observed variance explained by individual
79 identity, of one or more observed behavioural traits. Thus,
80 partitioning of observed variance into among- and within-
81 individual components (the latter arising from individual plas-
82 ticity and/or measurement errors) from repeat observations on
83 individuals is crucial to empirical studies of personality
84 (Dingemanse et al. 2012b;Brommer2013;Araya-Ajoyand
85 Dingemanse 2014). In a meta-analysis, Bell et al. (2009)
86 concluded that on average, estimates of repeatability for ob-
87 served behavioural traits decreased as the interval between
88 sampling events increased. Consequently, it may be danger-
89 ous to assume that short-term studies reflect behavioural (and
90 by implication, personality) differences that are stable over the
91 lifetime of individuals. This is potentially important since
92 short-term repeatability estimates predominate in the litera-
93 ture, although the number of studies conducted over
94 timeframes that may be considered more representative of
95 natural life spans is growing (for more recent examples, see
96 Ronning et al. 2005; Bushuev et al. 2010; Chervet et al. 2011;
97 David et al. 2012; Kanda et al. 2012). However, few studies
98 have collected repeated observations over two distinct time
99 periods from the same individual (but see for e.g. Carere et al.
100 2005) that would allow the repeatability of repeatabilityto
101 be assessed. Here, we do this, but also extend our analysis to
102 the multivariate case to ask whether patterns of among-
103 individual behavioural (co)variation reflect an underlying per-
104 sonality trait that is stable across distinct long- and short-term
105 sampling periods.
106 In what follows, we investigate the temporal stability of
107 multiple behavioural traits in the freshwater poeciliid fish,
108 Xiphophorus birchmanni to answer two complementary ques-
109 tions. Firstly, at the level of the population, how stable are the
110 patterns of among-individual trait (co)variance generated by
111 underlying personality? Secondly, at the level of the individ-
112 ual, do short term studies reveal behavioural tendencies that
113 are stable across lifetimes? To answer these questions, we
114characterise behavioural variation along what we loosely con-
115sider to be an axis of shyness-boldness. Boldness is the most
116commonly studied axis of personality in fish (Toms et al.
1172010) and positively correlates with fitness-related traits in-
118cluding reproductive success, parental provisioning, growth,
119aggression, social dominance, dispersal and proactive re-
120sponses to stressors such as predation risk (Dingemanse
121et al. 2004; Brown et al. 2005; Bell and Sih 2007;Coteetal.
1222010; Rudin and Briffa 2011; Ariyomo and Watt 2012;
123Mutzel et al. 2013). There remains, however, a lack of con-
124sensus on how best to define boldness and how it should be
125assayed (Toms et al. 2010). This raises obvious potential for
126misclassification of personality traits (Carter et al. 2013)and/
127or disagreement over appropriate experimental design (Toms
128et al. 2010).
129The present goal is to investigate stability of a personality
130trait without adding further to existing debate over issues of
131definition. Consequently we do not attempt to define boldness
132or the best way to measure it a priori; rather, we follow the
133view of others that personality traits should be considered as
134latent variables that can best be uncovered by observing
135several measurable, correlated and potentially overlapping
136behaviours across contexts (Dochtermann and Jenkins 2007;
137Dingemanse et al. 2010; Dochtermann and Roff 2010). We
138therefore make a distinction throughout between behavioural
139traitsthat are observed directly, and personality (traits),
140inferred from among-individual (co)variance in observed be-
141haviour(s). This exploratory approach, which follows
142Huntingford (1976) and others (Budaev 1997b;Moretz
1432003), is becoming more mainstream and allows the avoid-
144ance of difficulties that can arise if a single behaviour is
145chosen a priori to assay boldness. For example, a fish that
146swims a long distance in one behavioural trial may be classi-
147fied as willing to explore and therefore as bold;however,
148this behaviour could also plausibly be indicative of anxiety,
149with the animalsexplorationbeing driven by a search for
150refuge.
151Currently, the most common experimental paradigm used
152to measure boldness is that of the open-field trial (OFT), in
153which an animal is placed in an open arena and its behaviour is
154monitored for a predetermined observation period. Initially
155developed for rodent studies (Hall 1934; Walsh and Cummins
1561976), OFTs have long been applied to fish models (Warren
157and Callaghan 1975;Budaev1997b). Considered the most
158reliable way to assay boldness by some authors (Burns 2008),
159others have argued that OFTs risk conflating boldness with
160other axes of variation that are distinct (if sometimes correlat-
161ed) personality traits in their own right (e.g. exploration-
162avoidance, overall activity, Réale et al. 2007). If so, then
163simple modifications to OFTs such as providing a refuge from
164which an animal can choose to emerge and explore (emer-
165gence and exploration trial, EET) may be useful (Dingemanse
166et al. 2007).
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167 In what follows, we use both types of behavioural trial
168 mentioned above (OFT and EET) to observe how fish behave
169 in these contexts and to characterise the repeatable component
170 of multivariate behaviour. We then assess the extent to which
171 one or more major axes of variance adequately depict ob-
172 served variation. In other words, we aim to describe the
173 behavioural trait variation first, and then consider the extent
174 to which its repeatable component fits within the paradigm of
175 a major axis of personality, i.e. the boldness-shyness axis
176 (Dingemanse et al. 2010; Dochtermann and Roff 2010). We
177 then go on to address three specific questions regarding the
178 temporal stability of personality. Firstly, we ask whether re-
179 peatabilities estimated from repeated measures of individual
180 behaviours over a short time period give a misleading view of
181 the importance of among-individual variance over longer time
182 periods. Secondly, by extending our analysis to the multivar-
183 iate case, we ask whether the structure of the between-trait
184 among-individual covariance matrix denoted I, following Wil-
185 son et al. (2013), is similar when estimated from short- and
186 long-term data; i.e. do repeated empirical analyses of a single
187 population actually reveal the same major axes of among-
188 individual variation? If so, then a final question concerns the
189 extent that individuals retain the same relative ranking for
190 repeatable behaviours, and hence personality, over their
191 lifetimes.
192 Methods
193 Study species and husbandry
194 One hundred wild adult X. birchmanni were caught in the
195 Arroyo Coacuilco near the town of Coacuilco, municipality
196 of San Felipe Orizatlán, Hidalgo, Mexico (elevation 314 m
197 lat/long 21.099-98.587), and imported to the UK in Febru-
198 ary 2010. Between August 2010 and May 2011, we collect-
199 ed an offspring generation (n=384) from 13 males and 27
200 females (mean (SE) brood size of 8.86 (0.541)). Gravid
201 females were isolated and, following birth, broods were
202 immediately netted and moved to one half of a partitioned
203 30-L tank; broods of more than six offspring were split with
204 each half of the family placed in different tanks. Fry were
205 fed twice daily on a mix comprising equal quantities of
206 crushed ZM spirulina and brine shrimp flake and
207 laboratory-prepared brine shrimp nauplii. At an average of
208 17 weeks (range 12 to 27), juveniles were tagged with a
209 single elastomer injection for individual identification pur-
210 poses and transferred to mixed family rearing groups of n=
211 8. Note it is not possible to determine sex at this age in this
212 species, and therefore the sex ratio was not controlled.
213 Eight rearing groups were then kept within each of six
214 sequentially set up stacks of tanks, each stack sharing a
215 common water supply and recirculating filtration system.
216As part of a parallel study of density effects on growth,
217rearing groups were initially housed under two different
218density regimes as follows. Within each stack, four groups
219were placed in 30-L (37×37 ×22 cm) glass tanks (low-
220density treatment) with the remaining four groups in 15-L
221half tanks (high-density treatment). Half tanks were created
222by placing a black-net-covered Perspex-framed partition
223down the centre of a full-size tank. Thus, establishing a
224stack required 64 fish (i.e. 8×8) to be available for tagging
225simultaneously and this accounts for the variation in tag-
226ging age within stacks. Fish were fed twice daily with a
227standardised ration of flake food as above (morning) and a
228mixofpreviouslyfrozenbloodwormanddaphnia
229(afternoon). On the days when behavioural data was to be
230collected, the morning feed was omitted in an attempt to
231encourage exploration tendencies. Temperature was main-
232tainedbetween22and2Canda12:12-hlight-to-dark
233cycle imposed. After being housed in this manner for
23428 weeks, density was swapped for half of the tanks, thus
235creating four treatment effects with the total number of fish
236divided approximately equally between them as follows:
237low/low (n=93), low/high (n= 95), high/high (n=87) and
238high/low (n=93). Observations from individuals failing to
239reach sexual maturity by the end of the long-term study
240(50 weeks) were excluded from the analysis and the above
241breakdown (n=11).
242Behavioural data collection
243The trials were performed over two experimental study pe-
244riods, denoted long term (LT) and short term (ST). All avail-
245able fish contribute to the long-term data set (n=373) while a
246random subset of 32 fish from each of the four density treat-
247ments (low/low n=13, low/high n=4, high/high n=9,
248high/low n=6) was used for the short-term study (Table 1).
249Trials were of two types, open field (OFT) and emergence and
250exploration (EET) with multiple specific behavioural traits
251assayed in each trial type (Table 2). Overall, the long-term
252trials took 13 months to complete (May 2011 to May 2012),
253with data collected over an actual 30-week period for each
254fish. Each individual was subject to an OFT followed by an
255EET seven days later, a process that was repeated three times
256at 56-day intervals, thus yielding four OFTs and four EETs per
257fish. The short-term data set was collected in February 2013,
258with 32 individual fish subjected to alternating OFT and EET
259at 48-h intervals (i.e. 2 days between trials, 4 days between
260repeated trials of the same type) with each animal undergoing
261five trials of each type. For those 32 individuals used in both
262study periods, data was therefore collected over a timeframe
263with a mean (SE) of 531.4 (6.38) days. By comparison, the
264mean (SE) longevity of individuals with known birth and
265death dates under our laboratory conditions is 450.3 (8.10).
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266 Experimental procedures
267 Open-field trial
268 An empty 45×25× 25-cm tank was filled to a depth of 8 cm
269 with room temperature water (22 °C). The tank was lit from
270 below and visually screened by a cardboard casing to prevent
271 external laboratory disturbance. Fish were caught individually
272 from their home tank with a dip net, quickly examined for
273 identification tags and immediately placed into the centre of
274 the OFT tank. Following a 30-s acclimation period, behaviour
275 was filmed for 300 s using a Sunkwang C160 video camera
276 fitted with a 550-mm manual focus lens suspended above the
277 tank. Data were then extracted from the video using the
278 tracking software Viewer II (http://www.biobserve.com/
279 products/viewer/index.html), which was set up to divide the
280 tank basal area into two approximately equal halves (middle
281 and perimeter zones) (Fig. 1a). Water was changed between
282 individual trials to prevent chemical cues affecting behaviour.
283 Emergence and exploration trial
284 A 45× 25×25-cm tank was physically divided into three sec-
285 tions with opaque Perspex, providing a right-hand, centre and
286 left-hand chamber. A small (5 cm) opening was cut in each
287 divider, starting 2 cm from the tank edge. The openings were
288 positioned at opposite sides of the tank. The chamber on the
289 right-hand side was designated as the refuge and equipped
290with a plastic plant and several small stones. A rising trapdoor
291was rigged to a pulley above the tank, positioned inside the
292refuge and covering the exit into zone 1 (Fig. 1b). Tanks were
293filled, emptied, lit and screened as above. Fish were individ-
294ually caught and examined as before and placed directly into
295the centre of the refuge where they were allowed 30 s to
296acclimate before the trapdoor was lifted. Filming then com-
297menced for 300 s (as above), but only behaviour outside the
298refuge (i.e. in zones 1 and 2) was tracked and extracted for
299analysis.
300Behavioural traits
301The behavioural traits recorded in this study were selected as
302those likely to reflect variation along a bold-shy type person-
303ality axis. For the OFT, we predicted that fish tending toward
304boldness would actively explore the novel environment of the
305OFT by leaving the tank sides and spending more time in the
306central zone than shy fish. OFT behaviour was therefore
307quantified by four traits: track length (TL), activity (Act), area
308covered (AC) and time in middle of the tank (TIM), which we
309predicted would be positively correlated with one another. In
310the EET, we expected bold fish to locate the doorway in the
311refuge and leave through it. We recorded two traits from the
312EET: whether or not the individual emerged from the relative
313safety of the refuge (emergence) and latency in seconds to do
314so. We predicted positive within-individual correlations be-
315tween emergence from the refuge and the OFT traits, with
316negative correlations between latency to emerge and all other
317traits. Note that the EET tank was set up with the area outside
318the refuge further divided into two zones (1 and 2; Fig 1b). In
319the EET, we had initially planned to use latency to enter zone
3202(distal to the refuge) as an additional trait in our analyses;
321however, in practice, this became a redundant trait due to a
322low frequency of fish entering this area.
323Statistical analyses
324All data were modelled using restricted maximum likelihood
325mixed effects models implemented in ASReml V3 (Gilmour
326et al. 2009). Prior to analysis, data for the OFT trait time in
t2:1Ta b l e 2 Behavioural traits recorded in OFT and EET
t2:2Trial type Measured trait Definition
t2:3OFT TL Distance swum (cm)
t2:4OFT Act Percentage of time moving
at a minimum 1.5 cm/s (%)
t2:5OFT AC Area of tank floor covered (%)
t2:6OFT TIM Time spent in zone 2 (s, see Fig. 1)
t2:7EET Em Whether or not the fish emerged
from the refuge (binary)
OFT open-field trials, EET emergence and exploration trials, TL track
length, Act activity, AC area covered, TIM time in middle, Em emergence
t1:1Ta b l e 1 Data
Q2 set for long-term and short-term studies. Number and sex
of individuals involved. Periods of data collection and intervals between
trial pairs. Number of trials conducted; N
LT
=2,448, N
ST
=320. Mean age
of fish in days at the start of each trial pair with standard error in
parentheses
t1:2Study Number Data collection period Number of trials Mean fish age (SE)
t1:3M F T Start End Days
between trials
OFTEET12345
t1:4LT 223 150 373 May 2011 May 2012 56 1,224 1,224 203 (26.35) 259 (26.44) 372 (27.15) 427 (27.13) NP
t1:5ST 16 16 32 Feb 2013 Feb 2013 4 160 160 715 (13.36) 719 (13.36) 723 (13.36) 727 (13.36) 732 (13.36)
LT long-term, ST short-term, Mmale, Ffemale, Ttotal, OFT open-field trial, EET emergence and exploration trial, NP trial not performed
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327 middle were square-root-transformed to reduce positive skew.
328 Visual inspection of residuals suggested that the assumption
329 of residual normality was reasonable for the other traits re-
330 corded in OFT. All traits were rescaled to standard deviation
331 units prior to analysis to prevent trait scale effects from
332 influencing the structure of I (defined and estimated as de-
333 scribed below). Given that a large proportion of fish did not
334 emerge from the refuge (see results) the latency to emerge data
335 were heavily censored and we elected to use only the binary
336 variable of emergence in subsequent analyses. Emergence
337 was included in full multivariate models usingQ3 residual or
338 restricted maximum likelihood (REML) under an assumption
339 of (multivariate) residual normality. Statistical inferences on
340 this trait should therefore be treated with obvious caution.
341 While statistical approaches exist that allow non-Gaussian
342 trait distributions to be used (e.g. MCMC Bayesian ap-
343 proaches implemented in the R package MCMCglmm
344 (Hadfield, 2010)), they do not currently allow the error struc-
345 tures appropriate to our multivariate models (i.e. no definable
346 or estimable residual covariance between OFTand EET traits;
347 see below) and thus could not be used here. However, we
348 checked the validity of REML-based conclusions regarding
349 emergence by fitting additional univariate and bivariate
350 models using MCMCglmm. Specifically, we fitted a univari-
351 ate model of emergence to estimate the repeatability of this
352 trait and bivariate models of emergence with all other OFT
353 traits to estimate the covariance structure between these traits.
354 All models in MCMCglmm modelled emergence as a cate-
355 gorical trait with the residual variance fixed at 1 and all OFT
356 traits, as Gaussian. All MCMCglmm models were run for a
357 total of 1,050,000 iterations with a burn-in of 50,000 iterations
358 and a thinning interval of 1,000 iterations. The repeatability of
359 Emergence from MCMCglmm models was defined as the
360 intraclass correlation, calculated as V
I
/(V
I
+V
R
+π
2/3
), where
361 V
I
is the among-individual variance and V
R
is the residual
362 variance that in this case, is fixed to 1 (Hadfield 2010).
363 To test the hypothesis that among-individual variance for
364 behavioural traits is both present and repeatable in our fish
365 species, we first combined data from both collection periods
366and fitted a multivariate model of our observed behavioural
367traits. For each trait we included fixed effects of the mean, sex
368(a two-level factor determined from external morphology at
369maturation), home stack (a six-level factor accounting for
370differences between sets of fish sharing water supplies), trial
371number, density treatment and day order. Trial number is the
372cumulative number of trials experienced by an individual
373(fitted as a linear effect). Density treatment is a four-level
374factor describing density conditions experienced in the rearing
375stacks. Day order was modelled as a linear effect of the
376number of preceding trials conducted on any day and was
377used as a proxy for time of day. This was included to control
378for potential diurnal rhythms in fish behaviour. We also fitted
379an interaction term of trial number×density treatment, in case
380any systematic changes in observed trait means across trials
381(due to e.g., age effects, habituation etc.) are themselves
382treatment dependent. Wald Ftests were used to test the sig-
383nificance of fixed effects in the models.
384By including individual identity as a random effect, we
385then partitioned multivariate phenotypic (co)variance not ex-
386plained by the fixed effects into an among-individual and a
387within-individual (residual) component. The former is esti-
388mated as the variance-covariance matrix I, which contains
389estimates of the among-individual variance (V
I
)component
390for each trait on the diagonal and estimates of the correspond-
391ing covariance between trait pairs (COV
I
)offthediagonal.
392The within-individual component is similarly estimated as a
393residual variance-covariance matrix (R). We make the stan-
394dard assumptions that residual errors are normally distributed
395and uncorrelated across observations, and that (co)variance
396parameters in Iand Rare homogeneous across levels of the
397fixed effects (i.e. density treatments, trial number, stack etc.).
398Although the two experiment-specific sets of traits are not
399observed in the same trials, we grouped the data by trial period
400(e.g. OFT1 with EET1). Thus, we modelled a residual covari-
401ance term between OFT and EET traits observed within each
402trial period. Repeatability (R
I
) was then estimated for each trait
403as the among-individual variance (V
I
) divided by total pheno-
404typic variance (V
p
) (where V
P
is the phenotypic variance
Fig. 1 Setup of experimental tanks for aopen-field trials (OFTs) and b
emergence and exploration trials (EETs) as viewed from above. Both
tanks measured 45×25×25 cm and were filled to a depth of 8 cm. For
OFT, two zones of equal area were defined for analysis. For EET, the tank
was divided into three equal zones with fixed opaque material. The refuge
area contained a plastic plant and several small stones. A removable
doorway (hatched line) provided a means of access from the refuge to
the rest of the tank
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405 conditional on the fixed effects i.e. V
P
=V
I
+V
R
). Between each
406 pair of traits (1, 2) the among-individual covariance (COV
I
)
407 was rescaled to give the corresponding correlation r
I
(where
408 r
I(1,2)
=COV
I(1,2)
/(V
I1
×V
I2
)).
409 To test the statistical significance of among-individual be-
410 havioural variation, we compared the likelihood of our full
411 multivariate model to two further models. In the first of these,
412 we fitted Ias a diagonal matrix such that the model allows
413 among-individual variance V
I
for each trait, but assumes
414 COV
I
is zero between all trait pairs. In the second, a null
415 model, we removed the random effect of individual identity
416 completely. Comparison of the diagonal model with the null
417 model using likelihood ratio tests (LRTs) allows a global test
418 of the significance of among individual behavioural variance
419 (Wilson et al. 2010). Comparison of the full model with the
420 diagonal model, again by LRT, allows a statistical test of
421 whether Icontains significant between-trait covariance struc-
422 ture (Wilson et al. 2013). LRTs were performed by estimating
423 χ
2
nDF
as twice the difference in model log likelihoods, with
424 the number of degrees of freedom (n) equal to the number of
425 additional parameters to be estimated in the more complex
426 model.
427 The above analyses were then repeated using long- and
428 short-term data subsets to estimate the corresponding matrices
429 I
LT
and I
ST
and associated parameters. Note that, following the
430 conclusion of the LT, the density treatments were no longer
431 applied, and the 32 fish used in the STwere housed together in
432 the same stack. Therefore, the fixed effect stack was redundant
433 and omitted from the models for the short-term subset analy-
434 ses. To further investigate the structure of I,I
LT
and I
ST
,we
435 subjected each matrix to eigenvector (EV) decomposition.
436 This allowed us to examine the following: (a) how much
437 variance is captured by the first axis (eigenvector 1, EV1) of
438 multivariate behaviour in each case (b) whether factor load-
439 ings of individual traits onto EV1 are consistent with an
440 interpretation of boldness-shynessand (c) whether EV1 is
441 similar in I
LT
and I
ST
. To provide a quantitative measure of
442 how similar the multivariate behavioural axes emerging from
443 the long- and short-term data sets were, we calculated the
444 angle (θ) between the first eigenvectors of I
LT
and I
ST
.An
445 angle of θ=0° equates to the vectors being perfectly aligned,
446 meaning that EV1 i.e. the axes of multivariate behavioural
447 variation in I
LT
and I
ST
are identical. Conversely, an angle of
448 θ=90° would indicate the vectors are orthogonal (and thus
449 maximally differentiated) to each other across the two differ-
450 ent time periods (i.e. the major axis of behavioural variation
451 across the two studies are independent).
452 Uncertainty around the factor loadings for individual traits
453 on EV1 (for Imatrix) and around θwas estimated using a
454 parametric bootstrap approach (similar to that outlined in the
455 appendix of Morrissey et al. (2012)). We simulated 5,000
456 replicate draws of I,I
LT
and I
ST
from multivariate normal
457 distributions using the maximum likelihood estimates of these
458matrices as the means and the variance-covariance matrices of
459their elements to define the variances. In each case, the 5,000
460simulated matrices were subject to eigen decomposition. Un-
461certainty around the point estimates of trait-specific factor
462loadings was then described using the 95 % highest probabil-
463ity density interval the simulated values of these loadings (for
464I,I
LT
and I
ST
, respectively). Note that these intervals should be
465viewed as approximate as they are vulnerable to departures
466from multivariate normal assumptions. By comparing 5,000
467pairs of simulated LT and ST matrices, we similarly estimated
468the uncertainty around our point estimate of θ. Note however
469that since θcannot be less than zero, we also generated a null
470distribution for the estimator in the absence of any difference
471between (true) Imatrices. This was done by comparing the
472leading eigenvector of each of the 500 replicate draws of I
LT
473(simulated as described above), to the leading eigenvector of a
474second matrix, simulated with the same mean (i.e. the REML
475point estimate of I
LT
) but a variance equal to the estimated
476variance-covariance matrix from the short-term study. Thus
477the null distribution represents θestimates given that (i) the
478angle is zero since true Imatrices are identical (and equal to
479the REML estimate of I
LT
), but (ii) the second (short-term)
480matrix (and so its leading eigenvector) is estimated with
481greater uncertainty due to the lower sample size.
482Finally, we compared V
I
estimates in LT and ST data
483subsets, and tested the among-individual, across data subset
484correlations (r
I(LT,ST)
). For each behavioural trait (x), we used a
485likelihood ratio test to compare a bivariate model of x
LT
and
486x
ST
where V
I
is constrained to be equal, to a model where it is
487free to vary. This tests the hypothesis that among individual
488variance differs across data sets (note that since traits are
489analysed in observed standard deviation units, V
I
can also be
490interpreted as the repeatability estimate unconditional on fixed
491effects). We then expanded this model to estimate the among-
492individual, across data subset correlation (r
I(LT,ST)
) and tested
493this against null hypotheses of both r
I
=0 and r
I
=+1. Estima-
494tion of this correlation is possible since the 32 fish used in the
495short-term study were a subset of the long-term study. If r
I
=+
4961, then this indicates that the ranking of phenotypic merits (i.e.
497each individuals repeatable component of the observed trait)
498is the same across data sets. However, if r
I
=0, then an indi-
499vidual phenotypic merit in the long-term study is uncorrelated
500with the repeatable component of that same behaviour ob-
501served over a short time period in later life.
502Results
503In total, 1,235 sets of behavioural observations were conduct-
504ed from a possible 1,492, the difference being due to mortality
505of some fish over the study period. Summary data for all
506behavioural traits are presented in the supplemental materials,
507Fig. S1. In EET, the number of fish emerging from the refuge
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508 within the observation period was lower than anticipated
509 based on pilot data (LT=526/2,448, ST=100/318), resulting
510 in severe censoring of latency to emerge data. We therefore
511 elected to use only the binary emergence trait from this trial
512 type in our analyses.
513 Analysis of full data set
514 There was significant among-individual variance in multivar-
515 iate behaviour (diagonal model versus null model, χ
2
5
=125.6,
516 P<0.001), as well as among-individual covariance among
517 traits (diagonal model versus full model, χ
2
10
=101.8,
518 P<0.001). Estimates of individual repeatability (R
I
(±SE))
519 were low to moderate, ranging across traits from 0.055
520 (±0.024) for emergence (on the observed scale, estimated by
521 REML) to 0. 192 (±0.029) for time in middle (Table 3). Based
522 on univariate models, V
I
was statistically significant at
523 P<0.05 for all traits (Supplemental Table S2). The estimated
524 fixed effects are not directly relevant to present objectives;
525 however they are presented in full in the supplemental mate-
526 rials (Supplemental Table S3).
527 Between traits, the signs of all among-individual cor-
528 relations (r
I
) were positive, consistent with our a priori
529 expectations (Table 3). The OFT traits track length,
530 activity and area covered were all strongly correlated
531 (and nominally significant based on |r
I
|>two standard
532 errors); however, while time in middle was strongly
533correlated with area covered (r
I
=0.653± 0.075, Table 3),
534it was only weakly associated with the other OFT traits.
535The EET trait emergence was positively correlated with
536each OFT trait (r
I
estimates ranging from 0.304 with
537track length to 0.577 with activity, Table 3).
538Eigen analysis of I, estimated from the full data set revealed
539that the first two vectors explained 64 % (eigenvector 1, EV1)
540and 26 % (eigenvector 2, EV2) of the repeatable among-
541individual variation, respectively (Fig. 2). The trait loadings
542on the dominant vector EV1 are consistent with an interpre-
543tation of this axis of variation as boldness (or arguably explo-
544ration and/or general activity; see discussion). Thus, individ-
545uals that tended to emerge repeatedly in the EET swim longer
546distances, are more active, explore more area and spend more
547time in the middle of the OFT tank. By comparison, EV2 trait
548loadings show this axis to be dominated by the time spent in
549the middle of the tank. Track length and activity load on this
550vector to a lesser extent and with an opposing sign to time in
551middle, while the other traits show limited contributions to
552EV2 (Fig. 2b).
553As noted earlier, our REML analysis makes an assumption
554of (multivariate) residual normality that is violated by inclu-
555sion of the binary trait emergence. Univariate analysis of
556emergence using MCMCglmm, calculated following Equa-
557tion 15 of Nakagawa and Schielzeth (2010), yielded a slightly
558higher estimate of repeatability (on the liability scale) with a
559posterior mode of R=0.090, 95 % Q5highest probability density
t3:1Ta b l e 3 Among-individualQ4 variance/covariance matrix (I) from the multivariate analysis of (a) all data, (b) long-term study and (c) short-term study
t3:2TL Act AC TIM Em
t3:3(a) All Data
t3:4TL 0.130 (0.025)0.865 (0.033)0.750 (0.069)0.162 (0.117)0.304 (0.198)
t3:5Act 0.124 (0.024)0.159 (0.026)0.731 (0.065)0.241 (0.106)0.577 (0.182)
t3:6AC 0.097 (0.022)0.104 (0.022)0.128 (0.026)0.653 (0.075)0.414 (0.202)
t3:7TIM 0.026 (0.019)0.042 (0.020)0.102 (0.023)0.192 (0.029)0.540 (0.180)
t3:8Em 0.026 (0.017)0.054 (0.018)0.035 (0.018)0.056 (0.019)0.055 (0.024)
t3:9(b) Long term
t3:10 TL 0.143 (0.028)0.892 (0.030)0.777 (0.069)0.238 (0.118)0.272 (0.192)
t3:11 Act 0.137 (0.026)0.164 (0.028)0.708 (0.072)0.314 (0.106)0.539 (0.180)
t3:12 AC 0.108 (0.025)0.106 (0.025)0.136 (0.030)0.704 (0.075)0.458 (0.208)
t3:13 TIM 0.041 (0.022)0.058 (0.022)0.118 (0.026)0.207 (0.033)0.607 (0.181)
t3:14 Em 0.027 (0.020)0.058 (0.020)0.045 (0.021)0.073 (0.022)0.071 (0.028)
t3:15 (c) Short term
t3:16 TL 0.458 (0.155)0.926 (0.041)0.640 (0.182)0.247 (0.256)1.070 (0.513)
t3:17 Act 0.381 (0.137)0.369 (0.134)0.812 (0.112)0.017 (0.274)1.001 (0.502)
t3:18 AC 0.188 (0.095)0.214 (0.097)0.188 (0.089)0.492 (0.222)0.545 (0.524)
t3:19 TIM 0.083 (0.089)0.005 (0.084)0.106 (0.079)0.248 (0.101)0.667 (0.557)
t3:20 Em 0.165 (0.080)0.139 (0.073)0.054 (0.056)0.076 (0.059)0.052 (0.066)
Estimates of variance (V
I
, ital) with among-individual between-trait covariances (COV
I
, bold) and among-individual between-trait correlations (r
I
,bold
ital). Standard errors are shown in parentheses for all parameter estimates
TL track length, Act activity, AC area covered, TIM time in middle, Em emergence
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560 (HPD) interval 0.0240.177, Table S1. While noting that
561 interval will never span zero since Ris constrained to lie in
562 positive parameter space, the posterior mode is nonetheless
563 distinctfrom zero (Supplemental material, Fig. S2). Bivar-
564 iate models (i.e. the use of one OFT trait plus emergence as the
565 phenotypic variates) also confirmed the presence of strong
566 positive among-individual correlations (r
I
) between emer-
567 gence and OFT traits. Thus, the MCMCglmm analyses cor-
568 roborate the results of the REML analysis for emergence
569 (Supplemental material, Table S1).
570 Comparison of long- and short-term results
571 In both the long- and short-term studies, the presence of
572 repeatable variance was statistically supported (comparisons
573 of null and diagonal model: LT χ
2
5
=77.0, P<0.001; ST χ
2
5
=
574 29.7, P<0.001) as was the presence of between-trait among-
575 individual covariance structure (comparisons of diagonal and
576 full multivariate model: LT χ
2
10
=95.0, P<0. 001; ST χ
2
10
=
577 54.9, P<0.001). Univariate models confirmed that V
I
was
578statistically significant for all OFT traits in both LT and ST,
579but not for emergence in ST (Supplementary Table S2).
580The estimate of I
LT
is very similar to that obtained using all
581data (as described above), not unexpected given that the long-
582term study contributes the bulk of the total data set. However,
583comparison of I
LT
and I
ST
(and derived parameters thereof)
584indicates some differences in the structure of among-
585individual behavioural variation as estimated from our long-
586and short-term studies (Table 3). Note that the smaller size of
587the short-term data set means that the estimates are less precise
588for this study; this is reflected in the larger standard errors
589associated with the parameters. Repeatability estimates (R)
590were higher in the short-term study across all traits. However
591the increased Rfrom ST was particularly striking for track
592length (Table 3,Fig.3). For this trait, along with activity and
593area covered the null hypothesis of equality of (V
I
)acrossdata
594sets could be rejected (comparison of bivariate models with
595homogeneous and heterogeneous V
I
,P<0.05, Fig. 3).
596The among-individual between-trait correlations (r
I
)reveal
597a broadly similar structure for the long- and short-term studies
598(Table 3). Thus, estimates for ST largely confirm our a priori
a)
b)
-0.4
-0.2
0
0.2
0.4
0.6
0.8
1
Eigenvector loading
ALL (64%)
LT (66%)
ST (73%)
-1.5
-1
-0.5
0
0.5
1
1.5
2
2.5
3
Track
Len
g
th
Activity Area
Covered
Time in
Middle
Emergence
Track
Length
Activity Area
Covered
Time in
Middle
Emergence
Eigenvector loading
ALL (26%)
LT (24%)
ST (26%)
Fig. 2 Eigenvector
decomposition of Ifor all data
combined (All), long-term (LT)
and short-term (ST) data sets,
with percentage of variance
explained in parentheses. Shown
are the trait loadings in standard
deviation units for the first (a)and
second (b) eigenvectors. Error
bars show 95 % HPD intervals
from the parametric bootstrap (see
text for details). Note that the
point estimates of EV1 loadings
on emergence in All and LT
datasets actually lie outside the
simulated intervals. This reflects
sensitivity of intervals estimates
to departures from multivariate
normality assumed in the
bootstrap
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599 expectation of positive correlation structure between the OFT
600 traits and emergence. One qualitative exception to the expect-
601 ed pattern is provided by time in middle. In LT, this trait is
602 positively correlated with all other traits as expected; however,
603 in ST, the sign of r
I
is negative (but not significant) between
604 time in middle and track length and emergence (Table 3).
605 Eigen decomposition confirms the view that qualitative
606 differences between I
LT
and I
ST
are largely related to time in
607 middle. Thus, in both data sets, the first eigenvector again
608 dominates the variance in I(accounting for 66 and 73 % in
609 long- and short-term, respectively), consistent with an impor-
610 tant latent character underlying behavioural variation
611 (Fig. 2a). Time in middle has a strong positive loading on
612 EV1
LT
,consistentwithouraprioriexpectationthataboldfish
613 would spend more time in the middle of the open-field arena;
614 the corresponding loading coefficient is close to zero (in fact
615 slightly negative) on EV1
ST
. The angle (θ)betweenEV1
LT
616 and EV1
ST
is 34.63° (95 % HPD interval, 5.0353.09°).
617 While the point estimate of 34.63° indicates at least some
618 divergence between the leading eigenvectors on a scale from
619 0 (no difference) to 90 (axes are orthogonal), it is not signif-
620 icantly greater than the angle expected by chance if the true
621 matrices are identical (95 % HPD of the null distribution for θ
622 generated by our parametric bootstrap is from 1.54 to 69.14°).
623 While we acknowledge that our null distribution indicates low
624 statistical power to reject the null hypothesis that θ=0 (see
625 Supplemental Fig. S3), our conclusion is however that EV1
LT
626 and EV1
ST
are broadly similar, with qualitative differences
627 largely attributable to the decreased loading of TIM on EV1
ST
.
628 This is further evidenced by a drop in θfrom 34.63° to just
629 11.15° for the corresponding comparison of Iestimates ex-
630 cluding time in middle. There are also some qualitative incon-
631 sistencies evident between EV2
LT
and EV2
ST
for the OFT
632 traits, due to greater loadings on track length (changes sign),
633 activity, area covered and time in middle, while the loading on
634 emergence is reduced (also changes sign) (Fig. 2b). The angle
635 (θ)betweenEV2
LT
and EV2
ST
=48.32° (95 % HPD interval
636 25.7586.48°), which again is not significantly different from
637 null expectations.
638For those individuals tested in both long- and short-term
639studies, the among-individual correlations between LT and ST
640data sets were positive (although not always significant based
641on likelihood ratio tests) for OFT traits (Fig. 4), ranging from
6420.219 (±0.294) to 0.729 (±0.314). Estimates were significant-
643ly greater than zero for area covered and time in middle.
644However, we also found that the correlation was significantly
645less than 1 for the traits track length and activity. Thus, while
646phenotypic performance of an individual in one data set may
647be predictive of its behaviour in the other, there is also evi-
648dence that the ranking of individuals, at least for track length
649and activity, significantly differs between long- and short-term
650studies. For emergence, the corresponding among-individual
651correlation estimates between long- and short-term were ac-
652tually negative, though not significantly so. In fact, the esti-
653mate was characterised by so much uncertainty that despite
654being negative it was not possible to reject the null hypothesis
655of r=+1. We suggest this is a result of the low repeatable
656variation of emergence, and thus, little weight should be
657placed on this result.
658Discussion
659Data from our long-term (LT) and short-term (ST) studies
660provide evidence of among-individual variance in behaviour,
661both when considered separately and in combination. Of the
662five traits assayed in the two distinct types of behavioural
663trialopen field (OFT) and emergence and exploration
664(EET)repeatabilities were statistically supported in all cases.
665In addition, our analyses support the presence of a significant
666among-individual correlation structure for behavioural traits in
667I. Correlation structure is found both within and acrosscontexts
668(i.e. trial types), indicating behavioural variation among fish
669that is consistent with accepted definitions of animal personal-
670ity. We found that repeatabilities of OFT traits were higher than
671the EET though not significantly so in all cases. Our results
672therefore support the assertion of Burns (2008)thattheOFTisa
673good and reliable test of boldness and exploratory behaviour in
-0.1
0.0
0.1
0.2
0.3
0.4
0.5
0.6
Track Len
g
th Activity Area Covered Time in Middle Emer
g
ence
Repeatability
LT
ST
**
*
*
Fig. 3 Estimated trait
repeatabilities from long-term
(LT) and short-term (ST) studies.
Error bars specify one standard
error. Pvalues (**P<0.01; *P
<0.05) indicate significant
differences between V
I
based on
likelihood ratio tests (see text for
detail)
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674 small fish, although it is certainly possible that the EETcould be
675 better optimised to target the among-individual component. We
676 discuss the biological interpretation of (multivariate) variance
677 within these two trial types further below. However, here, we
678 note the pragmatic consideration that the binary distribution of
679 emergence data obtained from the EET is more difficult to
680 analyse and interpret while the censoring of latency to emerge
681 created a data distribution not readily modelled in any software.
682 Although such problems are likely surmountable by modifica-
683 tion of the behavioural assay (e.g. using an extended observa-
684 tion time to eliminate or at least reduce censoring), at least in
685 this case, it is not clear to us that the EET provides additional
686 biological insight.
687 Comparison of long- and short-term data sets suggested
688 that the patterns of individual (co)variance between traits
689 frequently used to define boldness are relatively stable.
690 Nevertheless, as predicted a priori we found a tendency
691 for the magnitude of R
I
to decrease with a higher interval
692 between observations, at least in OFT trials. For example,
693 repeatabilities for OFT traits ranged from 0.188 to 0.458
694 in the short-term data (with repeat observations at an
695 average interval of 4 days) but 0.136 to 0.207 in the
696 long-term data (average interval of 56 days). In a meta-
697 analysis of behavioural repeatability studies that included
698 either long-term (i.e. >1 year) or short-term (i.e. <1 year)
699 intervals between observations, the average (median)
700 across all estimates was 0.37 (Bell et al. 2009). Here,
701 our repeatability estimates pertain to correlated traits and
702 are therefore not independent. Nevertheless, apart from our
703 short-term study estimates for track length and activity, we
704 note that our estimates for all other traits were lower than
705 those of the meta-analysis average. Repeatability estimates
706 from short-term studies in the meta-analysis (Bell et al.
707 2009) outnumbered those from long-term studies by 11:1;
708 however, our study considers observations collected within
709 two distinctly separate periods across individual lifetimes.
710Arguably, the more important question to be asked of our
711long- and short-term data sets concerns the stability of corre-
712lation structure within the multivariate Imatrix and the inter-
713pretation of boldness from its eigenvector decomposition. As
714seen with the single-trait repeatabilities, the structure of I
LT
715mirrored that of Iestimated from all data combined. This is
716unsurprising given that the long-term data comprised a much
717greater number of individuals and will thus dictate patterns in
718the combined dataset. I
LT
is dominated by a single vector that
719is broadly consistent with our expectations of boldness. Sig-
720nificant within- and between-trial type correlations indicate
721that individuals emerging from the EET refuge are more likely
722to have high scores for all OFT traits, thus matching our
723expectation of bold behaviour.
724Though not statistically significant, qualitative differences
725between I
LT
and I
ST
were apparent. These differences were
726focussed around the sign and strength of correlations between
727time in middle and traits from both trial types, indicating that
728both bold and shy individuals from the short-term study spent
729a similar amount of time in the middle, whereas in the long-
730term study, shy fish had behaved in a more thigmotaxic
731manner. This pattern was reflected in comparisons of the
732major eigenvectors of long- and short-term data, where a
733moderate, albeit not statistically significant, angle (θ)between
734the first long- and short-term axes was estimated. Further-
735more, if time in middle is dropped from the calculation, the
736estimated angle is reduced by more than half. Thus, our
737interpretation is that both data sets reveal a major vector of
738among-individual (co)variance in observed behavioural traits.
739This vector is similar in the two data sets and can be
740interpreted as a latent personality traitnamely boldness. In
741both data sets, bolder individuals tend to swim longer dis-
742tances, be more active and explore more area (in the OFT) and
743are more likely to emerge from a refuge (in the EET). How-
744ever, tendency to spend more time in the middle of the OFT
745arena appears not be a reliable indicator of boldness as it was
-1.5
-1
-0.5
0
0.5
1
1.5
Track-length Activity Area Covered Time in Middle Emergence
Estimated among-individual correlations
* *
Fig. 4 Estimated among-
individual correlations (r
I
)
between LT and ST data sets for
each observed trait, with standard
error bars. Each correlation was
tested against two null hypotheses
of interest: (i) r
I
=1.0 (*P<0.05)
and (ii) r
I
=0.0 (
P<0.05), using
likelihood ratio tests to compare
unconstrained and constrained
models (see text for details)
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746 only associated with this vector in the LT study. Indeed, this
747 trait was the major source of qualitative difference between
748 the two matrices.
749 In the current study, it is not possible to distinguish whether
750 higher repeatabilities and the changing structure of Iwith
751 regard to time in middle are a consequence of the sampling
752 period (long- versus short-term) or potentially reflect interest-
753 ing, possibly even species-specific, biological changes that
754 happen with age and/or trial experience. Note, however, that
755 our analyses control for any habituation effects on mean
756 behaviour, and that we found little statistical support for
757 individual-by-trial-number interactions (results not shown).
758 More generally, some authors have argued that individual
759 behaviour is likely to become more rigid and follow more
760 set patterns over time (Roberts and DelVecchio 2000). If so,
761 we would predict increasing repeatabilities with age (here,
762 confounded with time scale of data collection). Conversely,
763 others suggest that in the absence of any disturbance (e.g. in a
764 constant laboratory environment), expectations of changes to
765 individual patterns of behaviour formed in early life are ill
766 founded (Stamps and Groothuis 2010). While no overall
767 differences were found between juvenile and adult behaviour-
768 al repeatabilities in the Bell et al. (2009) meta-analysis, a
769 subset of data suggested juvenile behaviour to have higher
770 repeatability than that of adults. However, the meta-analysis
771 contained only three studies that included observations fol-
772 lowing individuals through from juvenile to adult status. Thus,
773 direct comparison of age classes is not straightforward. Clear-
774 ly, more empirical studies of how repeatability changes with
775 age would be valuable, as indeed would parallel studies ex-
776 ploring environmental dependence. Here, we assumed homo-
777 geneous variance structures across environments (density
778 treatments, stacks) and other fixed effects (sexes, day order)
779 for simplicity. These assumptions can be relaxed in the statis-
780 tical models to test for and quantify individual by environment
781 (I×E) as changes in the among-individual variance (or struc-
782 ture of Iin the multivariate case) (Dingemanse et al. 2010).
783 Here, post hoc analyses of the LT data set provides some
784 evidence of heterogeneous repeatabilities across density treat-
785 ment classes (see Supplemental Table S4). Though not ex-
786 pected to bias current conclusions (parameter estimates pre-
787 sented are effectively averaged across treatments), if robust,
788 this effect may certainly be biologically interesting.
789 The population level patterns of among-individual
790 (co)variances between traits were broadly similar between
791 LT
I
and ST
I
, albeit with some differences as described above.
792 However, by using the same individuals in both long- and
793 short-term studies, we were able to address the question of
794 whether the relative ranking of individuals with respect to
795 their behavioural tendencies was stable. The estimates of r
I
796 for each observed behavioural trait between the long- and
797 short-term datasets provide a mixed answer to this question.
798 Positive correlations for the OFT traits do show a degree of
799stability in (repeatable) behavioural tendencies across the data
800sets though statistical support was mixed and it appears indi-
801viduals were more likely to maintain a consistent ranking for
802some traits (e.g. area covered) than others (e.g. track length).
803We previously stated it is not our intention to be prescrip-
804tive about what boldness is or how it should be assayed.
805Nevertheless, a priori, we anticipated that in the OFT, bold
806fish would travel long distances and be willing to visit a large
807area of the tank including the central zone and that these traits
808would correlate significantly with whether individuals
809emerged in the EET. However, this depiction requires that
810the bold individual is also active and/or exploratory. Above,
811we have noted that the major axis of variation in Iis largely
812consistent with expectations of a bold-shy continuum as the
813terminology is used in the literature; however, the strength of
814among-individual correlations suggests that it could equally
815be called exploration or general activity in a novel environ-
816ment. Nevertheless, as qualitatively, almost all the variance
817loads onto this single axis of variance; we conclude that these
818continuums (personality axes) are, at least in our study spe-
819cies, either the same entity or so tightly correlated that
820attempting to distinguish between them may have little prac-
821tical value. Indeed, Burns (2008) concluded that emergence
822from a refuge was difficult to interpret strictly as either bold-
823ness or exploration, even though it has been described as
824boldness only by others (e.g. Budaev 1997a; Brown et al.
8252005). Exploring the functional significance of the conse-
826quences of this behavioural variance in wild populations is
827likely to yield more insight than further debate with regard to
828terminology (e.g. Dingemanse et al. 2012a; Kurvers et al.
8292012; Carvalho et al. 2013). Nonetheless, we have sufficient
830statistical support in our results to conclude that both trial
831types revealed behaviours characteristic of boldness, evident
832from the strong among-individual correlations between all the
833observed traits. This again leads us in the direction of Burns
834(2008) view that in practice, the OFT offers the most useful
835test arena for this axis of personality. Here, we have obtained
836repeated measures of multiple behavioural traits during two
837test types and across two distinct sampling periods (long
838versus short term), something that has seldom been accom-
839plished in the literature. In practical terms, we conclude that
840the OFT is preferable to the EET as an experimental test for
841investigating boldness, and we show how eigen decomposi-
842tion of an Imatrix can usefully identify latent personality
843traits. This multivariate approach is broadly similar to that
844used in several other recent studies (Budaev 2010;Carteretal.
8452013; Araya-Ajoy and Dingemanse 2014). Our study also
846provides information about the stability of personality, both
847in terms of population level patterns and individual differ-
848ences. We find that observed behavioural traits are repeatable
849over long time periods as well as when observations are made
850over only a few weeks, although there is a tendency for short-
851term estimates to be higher. Taking a multivariate approach,
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852 we show that Iis dominated by a single vector through
853 phenotypic space that is similar across the two study periods
854 and can be interpreted as boldness. We note, however, there
855 are at least some qualitative differences in the relationships of
856 observed behaviours to this vector. At the individual level, we
857 also find qualified support for the proposition that short-term
858 studies are informative for an individuals behavioural pheno-
859 type over longer (e.g. lifetime) periods.
860 Acknowledgments The authors would like to thank Niels Dingemanse
861 and three anonymous reviewers for the helpful comments that have
862 helped to improve this manuscript. Additionally, we thank Jarrod Had-
863 field for advice with MCMCglmm software. This work was supported by
864 an EPSRC studentship to KB and a BBSRC fellowship to AJW. CAW
865 was supported by a NERC Junior Research Fellowship.
866
867 Ethical standards Ethical review committees at the Universities of
868 Edinburgh and Exeter approved all work in this study, which was carried
869 out under licence granted by the Home Office (UK) under the Animals
870 (Scientific Procedures) Act 1986.
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AUTHOR QUERIES
AUTHOR PLEASE ANSWER ALL QUERIES.
Q1. Smith and Blumstein 2008is cited in the body but its bibliographic information is missing.
Kindly provide its bibliographic information. Otherwise, please delete it from the text/body.
Q2. Please check if the modifications in Tables 1 and 2 are appropriate.
Q3. Please check if the captured expansion "residual or restricted maximum likelihood" for the
abbreviation "REML" is correct.
Q4. Kindly check if the modifications in Table 3 are correct.
Q5. Kindly check if the captured expansion "highest probability density" for the abbreviation "HPD" is
correct.
Q6. Please provide an access date for the reference "Hadfield J (2010)".
... These differences are mirrored by differences in boldness (Sih et al., 2004;Wilson et al., 1994;Toms et al., 2010). Xiphophorus birchmanni are bolder than X. malinche and show repeatable within-individual covariance among measures of boldness (Boulton et al., 2014;Johnson et al., 2015). In addition to sexual cues, swordtails attend to social information outside the context of mating (Wong and Rosenthal, 2005;Coleman and Rosenthal, 2006), suggesting that boldness might also be sensitive to social experience. ...
... Once all females had matured (approximately 9 months), we tested female preference for olfactory cues of X. birchmanni and X. malinche. In addition, we recorded measures of shy-bold behavior commonly used in swordtails and other fish (Boulton et al., 2014;Dingemanse et al., 2007). After all behavioral trials, we returned females to their respective treatment for an additional 2 months prior to tissue collection. ...
... In addition, we measured the amount of time females spent within the shelter provided during preference trials (T s ). We chose these measures as they have each previously been suggested to be reliable indicators of boldness (Boulton et al., 2014;Dingemanse et al., 2007). We used unpaired t-tests to test for differences between exposure groups in both D t and T s . ...
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... 43% at trial 1, 51% at trial 11) but broadly comparable to estimates reported from similar assays designed to test cognitive variation (see Cauchoix et al., 2017 for an overview). We note that a contributing factor is likely to be the short interobservation period (here 24 h) typical of cognitive studies, since behavioural repeatabilities generally decline as this increases (Boulton et al., 2014). ...
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... The use of different tests to measure the same behaviour in similar taxonomic groups, however, may yield inconsistent results. Furthermore, tests intended to measure a specific behaviour are not necessarily interchangeable because they could equally be used to measure other behaviours (exploration or activity; Boulton, Grimmer, Rosenthal, Walling, & Wilson, 2014). For example, in a latency to reach a novel food item (food neophobia) test in the mealworm (T. ...
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A recent discussion in this journal (Dingemanse et al. Behav Ecol Sociobiol 66: 1543–1548, 2012; Garamszegi and Herczeg Behav Ecol Socibiol 66:1651–1658, 2012) deals with a core issue in animal personality research: Can animal personality research quantify correlated behaviors on the between-individual level, or is this too demanding in terms of design and analysis of the data, and should behavioral ecologists therefore take the “individual gambit” and work on the phenotypic level only. Taking this gambit implies accepting that the between-individual correlation in behavioral traits (which is the correlation of interest) may be masked by a residual correlation of different magnitude or sign. Understanding (co)variances on different levels is the main thrust of quantitative genetics, and animal personality research can make good use of the plethora of ideas and analytical approaches developed in this field. I, here, outline reasons why the “individual gambit” may or may not work out and its relationship to the quantitative genetic “phenotypic gambit”. I especially emphasize the meaning of residuals and phenotypic plasticity which has not been fully appreciated in the debate thus far. I conclude that instead of a priori assuming that between-individual correlations are captured sufficiently well by the phenotypic correlation, animal personality researchers should set up more ambitious data collection and analysis designs to critically test this conjectured equality.
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Although our understanding of how animal personality affects fitness is incomplete, one general hypothesis is that personality traits (e.g. boldness and aggressiveness) contribute to competitive ability. If so, then under resource limitation, personality differences will generate variation in life history traits crucial to fitness, like growth. Here, we test this idea using data from same-sex dyadic interaction trials of sheepshead swordtails (Xiphophorus birchmanni). In males, there was evidence of repeatable variation across a suite of agonistic contest behaviours, while repeatable opponent effects on focal behaviour were also detected. A single vector explains 80 % of the among-individual variance in multivariate phenotype and can be viewed as aggressiveness. We also find that aggressiveness predicts dominance—the repeatable tendency to win food in competition—and dominant individuals show faster post-trial weight gain (independently of initial size). In females, a dominance hierarchy predictive of weight gain was also found, but there was no evidence of variation in aggressiveness. While size often predicts contest outcome, our results show that individuals may sometimes grow larger because they are behaviourally dominant rather than vice versa. When resources are limited, personality traits such as aggression can influence growth, life history, and fitness through impacts on resource acquisition.
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Biologists often study phenotypic evolution assuming that phenotypes consist of a set of quasi-independent units that have been shaped by selection to accomplish a particular function. In the evolutionary literature, such quasi-independent functional units are called 'evolutionary characters', and a framework based on evolutionary principles has been developed to characterize them. This framework mainly focuses on 'fixed' characters, i.e. those that vary exclusively between individuals. In this paper, we introduce multi-level variation and thereby expand the framework to labile characters, focusing on behaviour as a worked example. We first propose a concept of 'behavioural characters' based on the original evolutionary character concept. We then detail how integration of variation between individuals (cf. 'personality') and within individuals (cf. 'individual plasticity') into the framework gives rise to a whole suite of novel testable predictions about the evolutionary character concept. We further propose a corresponding statistical methodology to test whether observed behaviours should be considered expressions of a hypothesized evolutionary character. We illustrate the application of our framework by characterizing the behavioural character 'aggressiveness' in wild great tits, Parus major.
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Assessing behavioural consistency is crucial to understand the evolution of personality traits. In the present study, we examined the short- and long-term repeatability and stability of two unrelated personality traits – exploratory tendencies and struggling rate – using captive female zebra finches (Taeniopygia guttata). We performed two experimental sessions of behavioural tests with a 7-mo interval, which represents up to one quarter of a zebra finch’s life expectancy. We showed that, overall, exploratory tendencies and struggling rate were significantly repeatable in the short term. However, only exploratory tendencies were repeatable in the long term. We found interindividual differences in short-term stability of exploratory tendencies, but not struggling rate, providing evidence for differences in intraindividual variability. In the long term, struggling rate significantly decreased between the two experimental sessions, whereas exploratory tendencies remained stable. Finally, the amount of interindividual variation measured at both sessions did not differ. Our results suggest that short- and long-term repeatability and stability of personality may vary between individuals, depending on the behavioural trait under scrutiny. As a consequence, deducing personality from measures realized earlier in a subject’s life should be performed with caution. We discuss the implications of inter- and intraindividual variation in personality consistency on individual fitness.
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Boldness and aggressiveness are two behavioural traits that have received extensive attention in the field of animal behaviour. However, relatively little is known about how these traits are maintained in populations and the fitness of individuals that exhibit them. We tested the effect of boldness and aggressiveness on the reproductive success of zebrafish, Danio rerio. Using behavioural tests, we established groups of males that varied consistently in their boldness (bold, middling and shy) as well as groups of males that differed in their aggressiveness (aggressive, middling and nonaggressive) and paired them with randomly selected females. We found no difference in the total number of eggs laid by females mated to bold or aggressive males. However, the number of fertilized eggs differed, with the boldest and the most aggressive males fertilizing more eggs than the other groups. Furthermore, the proportion of fertilized eggs differed between groups. These results show that an individual's reproductive fitness can be associated with behavioural variations in boldness and aggressiveness.