DOI: 10.5061/dryad.rn8pk0p64
title: The drivers and functions of rock juggling in otters

description: Object play refers to the seemingly non-functional manipulation of
 inanimate items when in a relaxed state. In juveniles, object play may
 help develop skills to aid survival. However, why adults show object play
 remains poorly understood. We studied potential drivers and functions of
 the well-known object play behaviour of rock juggling in Asian
 small-clawed (Aonyx cinereus) and smooth-coated (Lutrogale perspicillata)
 otters. These are closely related species, but Asian small-clawed otters
 perform extractive foraging movements to exploit crabs and shellfish while
 smooth-coated otters forage on fish. We thus predicted that frequent rock
 jugglers might be better at solving extractive foraging puzzles in the
 first species, but not the latter. We also assessed whether species, age,
 sex and hunger correlated with rock-juggling frequency. We found that
 juvenile and senior otters juggled more than adults. However,
 rock-juggling frequency did not differ between species or sexes. Otters
 juggled more when “hungry”, but frequent jugglers did not solve food
 puzzles faster. Our results suggest that rock juggling may be a
 misdirected behaviour when hungry and may facilitate juveniles’ motor
 development, but it appears unrelated to foraging skills. We suggest
 future studies to reveal the ontogeny, evolution and welfare implications
 of this object play behaviour.
description: <em>Study sites and
 test subjects</em> We collected data at three sites
 in the United Kingdom: New Forest Wildlife Park, Newquay Zoo and Tamar
 Otter and Wildlife Centre. At New Forest Wildlife Park, four groups of
 Asian small-clawed otters (“ASC”; N = 4, 4, 2, 1) and two groups of
 smooth-coated otters (“SCO”: N = 4, 2) were studied. Newquay Zoo held one
 group of Asian small-clawed otters (N = 12). Tamar Otter and Wildlife
 Centre had three groups of Asian small-clawed otters (N = 15, 3, 3).
 Across the three sites, we studied a total of 23 males (ASC: N = 21; SCO:
 N = 2) and 27 females (ASC: N = 23; SCO: N = 4). Ages ranged from 6 months
 to 19 years for Asian small-clawed otters and 3 months to 5 years for
 smooth-coated otters (for group compositions, see supplementary materials
 table S1). To
 address the research questions on individual and species differences in
 rock juggling frequency, we first collected observational data on each
 individual to determine when and how often rock juggling was performed. To
 address whether rock juggling facilitates extractive foraging behaviour,
 we then presented each otter group with novel extractive food puzzles to
 solve. <i>Rock-juggling
 observations</i> Prior to data collection, we
 carried out preliminary observations to compile an ethogram (supplementary
 materials table S2). Data collection dates and times
 differed between study sites due to the location and availability of the
 establishment and feeding times. At New Forest Wildlife park, we collected
 data between August 5<sup>th</sup> and September
 4<sup>th </sup>2018, between 1000 – 1400 hours. At Newquay
 Zoo, we collected data between October 17<sup>th</sup> 2018
 and February 14<sup>th</sup> 2019, between 1030 – 1230 hours
 and 1430 – 1630 hours. At Tamar Otter and Wildlife Centre, we collected
 data between December 10<sup>th</sup> 2018 and January
 15<sup>th</sup> 2019, between 0900 – 1630 hours. We identified
 otters using differences in body size, shape, fur colour and nose
 patterns. We conducted observational scans every 3 minutes over a one-hour
 period. This was repeated to give a total of 12 hours of observation per
 otter. To assess the influence of hunger levels, we constructed a schedule
 to observe each otter prior, during and post the establishment’s regular
 feeding times (supplementary materials table S3). Otters were deemed to be
 satiated for two hours post feed, as food is reported to take two hours to
 pass from mouth to spraint in otters (Heap et al. 2008). <i>Den Camera
 Trap</i> To
 ensure we did not bias our rock juggling observations by only watching
 otter behaviour outdoors, a motion-triggered camera trap was installed in
 the indoor enclosure at Newquay Zoo between July
 25<sup>th</sup> and 28<sup>th</sup> 2018. Each
 motion-triggered recording was 15 seconds in length and contained a date
 and time stamp. The “hunger level” of the otters could thus be inferred by
 time since last feed. Due to the low resolution of the recordings, we
 could not ID the otters. Body size was used to differentiate adults from
 pups; otters ≤1 year old were considered pups. We scored the number of
 adult otters and pups present, and whether individuals were showing rock
 juggling. <i>Novel Extractive Food
 Puzzles</i> We designed three novel
 extractive food puzzles to quantify individual variation in extractive
 foraging performance. These puzzle types were designed to be novel to the
 otters, to control for prior experiences that could influence results and
 included: (a) 50ml white opaque screw-top medicine bottles (radius=~2cm,
 height=~7cm including lid), (b) tennis balls with an 8cm diameter cross
 cut in, with a 1.5 x 1.5cm square hole in the middle of the cross and (c)
 pairs of bright green Duplo® bricks (each brick measuring: 3.1cm x 6.4cm x
 2.4cm) stacked on top of each other (Figure 1). We filled each puzzle with 10g of
 5% fat lean minced meat. We placed mince inside the bottles and loosely
 secured the lid to test general dexterity and digit strength. The same
 experimenter closed all bottles to ensure consistency. We placed mince
 inside the hole of the tennis balls to replicate foraging in nooks and
 crannies. We stuffed mince between the upper and lower Duplo® brick to
 mimic the extraction of mussels or clams. We also placed monkey nut shells
 between the bricks to prevent the bricks from being closed too tightly by
 the otters. This produced a consistent gap of ~1mm between the
 bricks. The
 puzzle types were presented to each group in a random order. We provided
 twice as many individual puzzles as there were otters in each study group
 (e.g. groups of four otters were presented with eight puzzles) to prevent
 individuals from monopolising the puzzles and increase the opportunity for
 all otters to solve the puzzles. Puzzles were dropped in at three separate
 points at each otter enclosure to further facilitate all individuals
 accessing the puzzles and to aid in individual identification for data
 collection. At the start of every session, we filled three puzzles in
 sight of the otters to demonstrate that there was food inside to motivate
 the otters to extract it. Puzzles were presented ca. one
 hour before a feed to ensure that otters were both active and food
 motivated. Puzzle trials lasted until all puzzles were “solved”, i.e. the
 food reward was extracted, or until 20 minutes had elapsed. Each puzzle
 session was recorded with two Panasonic video cameras positioned around
 the circumference of the enclosure. An additional camera (Fujifilm Finepix
 HS20 EXR) was used freehand to facilitate the identification of each
 otter. From each puzzle trial video, we scored each otter’s: (i) latency
 to first interact with a puzzle, (ii) time spent interacting with a puzzle
 until the otter solved it and (iii) total time from puzzles being dropped
 into the enclosure until the otter solved a puzzle. We noted if an otter
 did not solve the puzzle, in which case the “total time” was the same as
 the trial duration (1200 seconds). <i>Statistical
 Analyses</i> <i>Rock Juggling
 Observational Data</i> To address whether otter species,
 age or sex were associated with differences in rock juggling frequency, we
 first summed all observations of rock juggling to provide a total count of
 rock juggling observations for each otter. The total number of behaviour
 scans per otter (regardless of behaviour performed) was also noted, as we
 could not observe each individual for a total of 252 times (i.e. every 3
 min for 12 hours, started at 0 min). Missing observations were due to
 unforeseen circumstances, such as the transfer of an individual to another
 zoo. The otter with the lowest observation count of &lt;230 behaviour
 scans was removed from the sample. An extreme outlier with an unusually
 high propensity to rock juggle (i.e. a total count of 59 while across all
 others, average±SD = 12.77±14.95) was also removed from analyses. Asian
 small-clawed otters at the New Forest Wildlife Park were considerably
 older (all &gt;11 years old) than Asian small-clawed otters at both
 Newquay Zoo and Tamar Otter and Wildlife Park (all ≤11 years old). To
 account for this confounding factor in analyses, we categorised otters as
 either “young” (≤11 years) or “old” (&gt;11 years). This age threshold
 was informed by the 11 year average life expectancy of captive Asian
 small-clawed otters (Crandall, 1964) and the reproductive status of the
 otters to reflect the senescent individuals in the sample. To test how species, age and sex
 were associated with rock juggling frequency, we used a negative binomial
 generalized linear model as clumping in the count data resulted in an
 over-dispersed Poisson model. An ‘offset’ for total observation count was
 included to inform the model of variance in observation effort. Rock
 juggling count was the response variable and species, age, age category,
 sex and site were the fixed effects. Site was included in the model to
 account for variation in otter behaviour between the zoos. The model also
 included interactions between age and age category, and age and site.
 Stepwise backwards elimination of least significant terms was conducted to
 produce a minimal adequate model (MAM). For each non-significant
 predictor, the z- or t- and p-values are reported from the model summary
 before it was removed from the model, while for significant predictors we
 also report the slope estimates ± SE from the MAM summary. For each
 significant predictor, a likelihood ratio test using command ‘anova’ was
 run to compare the MAM to a model where that fixed effect was removed.
 These ANOVAs provided the likelihood ratio test/Chisquare and p-values
 reported and indicate whether there is a statistical difference in the
 amount of deviance explained by the two models including vs. excluding the
 predictors of interest. To test whether hunger levels (as
 inferred by time since last feed) were associated with differences in
 juggling frequency, we summed the number of rock juggling observations
 when otters were assumed to be hungry vs. full, as well as the total
 number of behaviour scans when otters were hungry/full. A negative
 binomial generalized linear mixed-effects model was run that offset total
 observation count, had rock juggling as the response variable, hunger
 level as the fixed effect and individual ID as a random effect. Model
 selection was conducted as described above. <i>Den Camera Trap
 Data</i> The camera trap generated 556
 usable 15-sec video clips. To test whether hunger levels were associated
 with rock juggling frequency in Newquay Zoo otters whilst they spent time
 inside, we used known feeding times and the time of the recording to infer
 whether otters were relatively hungry or full by calculating time since
 last feed. As individuals could not be identified, a Pearson’s chi-squared
 test was run on a contingency table of rock juggling occurrences vs.
 absences when hungry vs. full. Observed values were then compared to
 expected values to assess the direction of the effect. To test whether pup presence
 influenced rock juggling frequency in adults (e.g. because adults might
 demonstrate rock juggling to their pups), a Pearson’s chi-squared test was
 run on a contingency table of rock juggling occurrences vs. absences when
 pups were present vs. absent. Observed values were then compared to
 expected values to assess the direction of the effect. To control for confounding
 factors, the influence of time since last feed (i.e. “hunger”) on pup
 presence in the den was assessed. A Pearson’s chi-squared test was run on
 a contingency table of pup presence vs. absence when otters were
 relatively hungry vs. full. Observed values were compared to expected
 values to assess the direction of the effect. <i>Novel Extractive Food
 Puzzle Data</i> To address whether rock juggling
 frequency was associated with extractive foraging performance on the food
 puzzles, we first removed an outlier from the dataset. This outlier
 represented an individual that was present in the enclosure but did not
 interact with any of the tennis ball puzzles for the duration of the trial
 and thus had a latency of 1200 seconds, while all other otters had
 latencies of 90 seconds or less. In addition, for some video trials
 individuals could not be identified or moved out of camera shot and so did
 not have solving success, latencies or interaction times accurately
 recorded. These NAs were removed from the dataset before analyses. As rock
 juggling was used as a fixed effect in analyses to predict puzzle solving
 performance, an offset could not be used to control for variance in
 observer effort. Instead, a juggle rate was calculated by dividing the
 total count of rock juggling by the total number of observation points for
 each otter. Personal observation suggested that juggle rate differed
 between species, and this species difference was found to be significant.
 We therefore included this juggle rate-species interaction in the
 statistical models of puzzle interaction data. However, we omitted it from
 the puzzle solve analyses as it caused major model convergence
 issues. To test
 whether latency to first interaction with each food puzzle type was
 shorter for younger, and possibly more explorative, otters, and/or for
 those who rock juggled more frequently, we used a linear mixed-effects
 model. The response variable was each otter’s latency to first interact
 with each food puzzle, fixed effects were juggle rate, species and their
 interaction, age, sex, puzzle type and site, and random effects were
 individual ID nested within group ID. As plotting the data suggested that
 the species showed large differences in their latency to start interacting
 with the different puzzle types, we explored these patterns further by
 running the same model again, but on the data for each species
 separately. Solving success was noted as a
 binary category with “1” representing success while “0” represented an
 otter’s failure to solve a puzzle (i.e. extract the food reward) during
 the trial. To test whether otters that rock juggled at a higher frequency
 were more successful at solving puzzles, we initially used a generalized
 linear mixed-effects models with binomial error structure. Solving success
 was the response variable, fixed effects were juggle rate, species and
 their interaction, latency to first interaction with the task, age, sex,
 puzzle type and site, and individual ID nested within group ID were random
 effects. However, this model failed to converge until we simplified it to
 include only the main predictors of interest: species, juggle rate and
 puzzle type. To
 test whether otters that rock juggled more frequently required less
 interaction time to solve puzzles, we used a linear mixed-effects model on
 data including only puzzle solves, and excluding puzzle solve failures to
 avoid ceiling effects. Interaction time was the response variable, juggle
 rate, species, the interaction between juggle rate and species, age, sex,
 site and puzzle type were fixed effects, and individual ID nested within
 group ID were random effects. Model selection was
 conducted as described above for the rock juggling observational data. We
 conducted all statistical analyses in RStudio version 3.6.0.