Micro-personality traits and their implications for behavioural and movement ecology research

Many animal personality traits have implicit movement‐based definitions, and can directly or indirectly influence ecological and evolutionary processes. It has therefore been proposed that animal movement studies could benefit from acknowledging and studying consistent inter-individual differences (personality), and, conversely, animal personality studies could adopt a more quantitative representation of movement patterns. Using high-resolution tracking data of three-spined stickleback fish (Gasterosteus aculeatus), we examined the repeatability of four movement parameters commonly used in the analysis of discrete time-series movement data (time stationary, step-length, turning angle, burst frequency), and four behavioural parameters commonly used in animal personality studies (distance travelled, space use, time in free water, time near objects). Fish showed repeatable inter-individual differences in both movement and behavioural parameters when observed in a simple environment with two, three, or five shelters present. Moreover, individuals that spend less time stationary, take more direct paths and less commonly burst travel (movement parameters), were found to travel farther, explored more of the tank, and spent more time in open water (behavioural parameters). Our case-study indicates that the two approaches – quantifying movement and behavioural parameters – are broadly equivalent, and we suggest that movement parameters can be viewed as "micro-personality" traits that give rise to broad-scale consistent inter-individual differences in behaviour. This finding has implications for both personality and movement ecology research areas. For example, the study of movement parameters may provide a robust way to analyse individual personalities in species that are difficult or impossible to study using standardised behavioural assays.Movement parameters and behavioural parameters for individual fish, across two weeks of testing (week 1, week 2) in three environments (1 – 2 shelters; 2 – 3 shelters; 3 – 5 shelters) are provided. Details of movement and behavioural paramaters are described in the Methods above. Sinuosity is addionally provided, measured as as: S =(-2 log (R) / sl)^0.5, where R = mean resultant vector of turning angles, and sl = mean step length. Funding provided by: Deutsche ForschungsgemeinschaftCrossref Funder Registry ID: http://dx.doi.org/10.13039/501100001659Award Number: FU‐985/1‐1Funding provided by: Natural Environment Research CouncilCrossref Funder Registry ID: http://dx.doi.org/10.13039/501100000270Award Number: NE/H016600/3Funding provided by: Natural Environment Research CouncilCrossref Funder Registry ID: http://dx.doi.org/10.13039/501100000270Award Number: NE/M015351/1Funding provided by: Royal SocietyCrossref Funder Registry ID: http://dx.doi.org/10.13039/501100000288Award Number: RG 110401See published paper for full details. In brief: Fish were filmed using a Panasonic HDC-SD60 HD video camera (Panasonic Corporation of North America, Seraucus, NJ, USA). Video recordings were processed using IDTracker (Perez-Escudero et al. 2014) to generate x, y coordinates for fish, frame by frame (25 Hz recording). Movement was therefore considered to be formed by a discrete step-turn process. Data was then manually checked and a value of 5mm/s was chosen as a threshold to determine movement, which represented movement across frames of less than a pixel (Duteil et al. 2016). A sub-sampling rate of 2.5 Hz was used to prevent false large turns which can occur due to the processing of the video recording (Delcourt et al. 2013). The movement threshold and sub-sampling rates are in essence arbitrary values but were chosen to retain as much information about the movement path, whilst minimising any causal effects such smoothing can have on characteristics of movement trajectories (Bovet & Benhamou, 1988; Codling & Hill, 2005; Gurarie & Ovaskainen 2011; Benhamou 2014; Bailey et al, 2020); different combinations of thresholds and subsampling did not affect our findings (Supplementary Material Figures S2-S10). For each fish and for each trial we then calculated the following movement parameters: (i) Time Stationary (% of trial), (ii) Step Length (mean across trial, mm), (iii) Turning Angle (mean cosine turn angle, Ө), and (iv) Burst Frequency (the relative frequency of periods of movement with a speed above 3 s.d's of the mean step-length of the fish when moving (Kane et al, 2004), and the following behavioural parameters: (v) Distance Travelled (total distance travelled during trial) (vi) Space Use (proportion of tank two-dimensional space explored), (vii) Time Near Object (% of time 'near' an object during the trial), and (viii) Time in Free Water (% time away from tank edges and shelters). Near (or away) from objects was considered as within 7cm (larger than fish body length which is on average 5.3cm in our study population; Fürtbauer et al. 2015); other distances were considered from 2cm to 15cm, but results were quantitatively similar