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Article

The Influence of the Recording Time in Modelling the Swimming Behaviour of the Freshwater Inbenthic Copepod Bryocamptus pygmaeus

by
Mattia Di Cicco
1,*,
Marco Uttieri
2,
Tiziana Di Lorenzo
3,4,
Barbara Fiasca
1,
Ilaria Vaccarelli
1,5,
Agostina Tabilio Di Camillo
6 and
Diana Maria Paola Galassi
1
1
Department of Life, Health and Environmental Sciences, University of L’Aquila, Via Vetoio, 67100 L’Aquila, Italy
2
Department of Integrative Marine Ecology, Stazione Zoologica Anton Dohrn, Villa Comunale, 80121 Naples, Italy
3
Research Institute on Terrestrial Ecosystems of the National Research Council, Via Madonna del Piano 10, Sesto Fiorentino, 50019 Firenze, Italy
4
“Emil Racovita” Institute of Speleology, Romanian Academy, Clinicilor 5, 400006 Cluj Napoca, Romania
5
Scuola Universitaria Superiore IUSS Pavia, 27100 Pavia, Italy
6
CNR-IRSA, Viale Francesco De Blasio, 5, 70132 Bari, Italy
*
Author to whom correspondence should be addressed.
Water 2022, 14(13), 1996; https://doi.org/10.3390/w14131996
Submission received: 31 May 2022 / Revised: 20 June 2022 / Accepted: 21 June 2022 / Published: 22 June 2022
(This article belongs to the Special Issue Groundwater and Surface Water Ecosystem Modeling: Current Status and Future Perspectives)
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Abstract

:
The analysis of copepod behaviour gained an increasing impetus over the past decade thanks to the advent of computer-assisted video analysis tools. Since the automated tracking consists in detecting the animal’s position frame by frame and improving signals corrupted by strong background noise, a crucial role is played by the length of the video recording. The aim of this study is to: (i) assess whether the recording time influences the analysis of a suite of movement descriptive parameters; (ii) understand if the recording time influences the outcome of the statistical analyses when hypotheses on the effect of toxicants/chemicals on the freshwater invertebrate behaviour are tested. We investigated trajectory parameters commonly used in behavioural studies—swimming speed, percentage of activity and trajectory convex hull—derived from the trajectories described by the inbenthic–interstitial freshwater copepod Bryocamptus pygmaeus exposed to a sub-lethal concentration of diclofenac. The analyses presented in this work indicate that the recording time did not influence the outcome of the results for the swimming speed and the percentage of activity. For the trajectory convex hull area, our results showed that a recording session lasting at least 3 min provided robust results. However, further investigations are needed to disentangle the role of concurrent factors, such as the behavioural analysis of multiple individuals simultaneously, whether they are of the same or opposite sex and the implications on sexual behaviour, competition for resources and predation.

1. Introduction

The analysis of animal behaviour tackles the great challenge of understanding how individuals respond to changes occurring at a much faster timescale than evolutionary processes, but it is also useful to unravel the causal links between behavioural adaptations and the underlying genetic, cellular and physiological processes [1]. In zooplankton ecology, trajectory analysis has gained increasing impetus over past decades thanks to the advent of computer-assisted video analysis tools, allowing users to adopt a more quantitative, objective and consistent approach to describe behavioural parameters (e.g., speed, percentage of activity, area exploitation, thigmotaxis, phototaxis, chemotaxis and encounter probability) [2,3,4]. This quantitative approach also gave the possibility to explore and statistically analyse the behavioural alterations induced by the exposure to specific compounds [5,6], increasing salinity [7,8] or temperature fluctuations [9]. With few exceptions, nowadays tracking analysis relies on digital video-based recording permitting a frame-by-frame software encryption of the animal movement pattern into a grid of pixels. Different tracking software, either commercial (e.g., [1]) or open source (e.g., [10]), are equipped with algorithms optimised to distinguish the tracked animals from the background field by automated or semi-automated settings that work on pixel intensity threshold and saturation values. For each tracked individual, the software detects the average frame by frame surface of the animal to calculate its centroid (mathematical equivalent of the centre of gravity). The simplest output achievable with a tracking analysis software consists of time series of the centroid coordinates that can be used to obtain a quantitative set of positional data describing the animal swimming behaviour (e.g., average speed, percentage of activity, total travelled distance, inter-individual distance, path tortuosity, explored area, total number of encounters).
While a great deal of literature focuses on ecotoxicological standard species such as zebrafish (e.g., [11,12,13,14,15,16]) and crustacean daphnids (e.g., [2,17,18,19]), relatively fewer video-assisted behavioural studies have been conducted on non-standard aquatic invertebrate species, many of whom are copepods. Copepods are known to provide important ecosystem services such as carbon recycling and bioturbation [20,21] and are important links in marine [22,23,24,25,26] and freshwater food webs [27,28]. Freshwater harpacticoid copepods have a slender body form allowing them to often have primacy in terms of species richness and abundances in the surface layers of sediments in springs, streams, rivers and lakes, and are also preadapted to live in the interstices among sediment particles, even deep in the sediment layers beneath surface lentic and lotic waters [29]. Their attitude to dispersal is lower if compared to other small invertebrates [30,31], thus limiting their ability to move away from a disturbing factor, either physical (for example, an increase in water temperature) or chemical (the localised presence of one or more environmental pollutants), or both conjunctively. Thus, sub-lethal physical and chemical disturbances can potentially lead to local population declines [3,4].
The few applications of tracking analysis on freshwater inbenthic invertebrates can be explained by the numerous technical challenges that users must overcome to perform a correct video analysis, such as the preliminary assessment of light source and orientation, water quality and temperature, interactions between water and air, sediment size and sterilisation, arena shape and dimension and expensive video cameras. A clear understanding of how benthic/interstitial copepods move is, however, crucial because of their ecological importance in freshwater inbenthic environments [29,32]. Among the several variables to consider in behavioural studies, the recording time is the one most lacking standardisation, making any comparative analysis difficult if not unrealistic. In the trajectory analyses of copepods, the time span adopted in various experiments is extremely variable, ranging from a few seconds [33,34] to several minutes [3,35,36,37].
Given the emerging needs to evaluate the effects of environmental contaminants, even at sub-lethal concentrations which can alter the behaviour of benthic/interstitial species, affecting various aspects of their fitness, and considering that the experimental time commonly adopted is relevant (up to 5 min in [4]), this study aims to: (i) assess whether the recording time influences the analysis of a suite of parameters obtained by modelling the swimming behaviour of the harpacticoid copepod Bryocamptus pygmaeus based on automated tracking; (ii) evaluate if the recording time influences the outcome of the statistical analyses when hypotheses on the effect of a pollutant on the swimming behaviour are tested. The species B. pygmaeus has been selected as the test species for several reasons [4] and references therein: (i) it is widely distributed in Europe; (ii) it is ubiquitous in interstitial freshwater environments; (iii) its feeding habits (detritus and microbial biofilm feeder) make this species a good candidate for laboratory culture.
The motion of B. pygmaeus can be reduced to a 2D process, but the realisation of experimental setups requires specific skills to reconstruct the proper background to let the copepods behave as naturally as possible. Pelagic copepods move in a real 3D environment, and the recording of their motion requires more complex video equipment [35]. The movement of inbenthic species is instead much less studied [3,4]. The two different environments can nonetheless benefit from the reciprocal integration, and future efforts should be addressed to the creation of common frameworks both in terms of laboratory setups and numerical approaches.
We focused on three of the most studied trajectory parameters—average swimming speed, percentage of activity and trajectory convex hull—to address the listed issues, using the video tracking data previously assembled by [4], investigating the behavioural effects of a sub-lethal concentration of diclofenac (DCF) at different times of recording on the species B. pygmaeus.

2. Material and Methods

2.1. Data Set

The adults of the harpacticoid copepod B. pygmaeus were sorted from inbenthic samples collected in autumn 2020 at the Vera Spring, central Italy (coordinates: 42°22′21.42′′ N, 13°27′30.51′′ E; altitude 664 m asl) with a hand net (mesh size: 60 µm). The site is pristine and is one of the main basal springs of the Gran Sasso karst aquifer (Central Italy) [38]. In the laboratory (University of L’Aquila, Central Italy), living individuals of B. pygmaeus were sorted under a Leica M205 stereomicroscope at 12× magnification and stored in several Petri dishes filled with 20 mL filtered spring water and fine sterile quartz sediment (0.1 g, φ = 0.25 mm) (Mapei S.p.A.®, Milan, Italy). Due to the negative phototaxis displayed by the specimens during the sorting phase ([4] and authors’ personal observation), vials were kept in total darkness in a thermostatic cabinet (Velp ScientificaTM FOC 120E Cooled Incubator, Usmate Velate (MB), Italy) at the sampling site mean temperature (8.0 ± 0.2 °C). Culture medium was renewed once a week, and the individuals were fed with particulate organic matter (POM) from the sampling site (46.75 g L−1 ± 15.12 g L−1). Before video recording, a gradual transition from culture medium to dilution water was accomplished in 48 h. The dilution water (pH = 7.6, electrical conductivity = 234 µS cm−1, total hardness = 90.71 mg L−1, CaSO4 2H2O = 60 mg L−1, MgSO4 = 60 mg L−1, NaHCO3 = 96 mg L−1, KCl = 4 mg L−1) was obtained by remineralising MILLIPORE® MILLI-Q® deionised water [4,39]. The 50 µg L−1 diclofenac (DCF) stock solution needed for the treatment individuals was prepared by dissolving 50 mg of salt in 1 L of reconstituted water; scalar dilutions of 100×, 10× and 1× were prepared to obtain the nominal concentration of 50 μg L−1 DFC, 10 min prior to the sub-lethal exposure test according to [4].

2.2. Behavioural Test and Video Recording

A total of 80 adult individuals of B. pygmaeus was used for the tests (female length = 0.62 mm ± 0.07 mm; female width = 0.14 mm ± 0.02 mm; male length = 0.55 mm ± 0.07 mm; male width = 0.12 mm ± 0.02 mm). The experiment was performed in four independent runs within 21 days and the difference in age between individuals (21 days in the worst-case scenario) was considered negligible since B. pygmaeus can live for over 4 months in laboratory conditions [4]. The experiment was split into 4 runs to avoid unbalanced DCF exposure times between the first and the last recorded individuals. In each run, both for control and treatment, 10 adult individuals were tested, and the sex ratio was kept close to 1 in each run (40 individuals were recorded for the control group and 40 were recorded for the treatment group). Each run lasted 72 h and was performed in a 5 cm Petri dish containing 0.1 g of fine sterile quartz sediment and 10 mL of test solution. The entire experiment was performed in permanent darkness at 8.0 ± 0.2 °C. At the end of the 72 h, the individuals were individually transferred to a circular glass arena (diameter = 1.5 cm, height = 0.1 cm) filled with 150 µL of test medium without sediments. After transferring individuals, the arenas were placed again in the thermostatic cabinet for 60 min [3,40] in order to rebalance the temperature. For recording the swimming behaviour of B. pygmaeus, the arenas were one at a time placed above an infrared (IR) light panel (470 mm × 210 mm × 20 mm; wavelength = 850 nm) (Loligo Systems®, Viborg, Denmark) under a 30 fps infrared-sensitive digital camera (IDS camera, Loligo Systems®) mounted on a Leica M205 trinocular stereomicroscope (field of view = 1.6 cm) equipped with an aspherical lens to avoid postprocessing edge correction. The absence of light was necessary [4] to avoid (a) DCF photodegradation; (b) additional stress caused by visible light on B. pygmaeus. The individuals were recorded at 8× magnification for 5 min and the temperature was kept stable with a lab-made temperature-controlled cooling system (temperature fluctuations in the range of ±0.5 °C). Live recordings of the animals were acquired at the same time of day and saved on a solid-state disc (SSD) using the uEye Cockpit software (IDS Imaging Development Systems GmbH®, Obersulm, Germany).

2.3. Video Processing and Trajectory Analysis

The swimming behaviour video for each adult individual was imported into the open-source software Openshot video editor 2.5.1 to cut and export video portions relative to the first 60 (Factor 1: level A), 120 (Factor 1: level B), 180 (Factor 1: level C), 240 (Factor 1: level D) and 300 (Factor 1: level E) seconds, respectively, of the recorded trajectory. After, each video (400 in total at the end of the video editing procedure; 5 videos × 40 individuals for each treatment condition) was analysed in LoliTrack v.5 (Loligo Systems®) to obtain the frame-by-frame animal centroid position (x,y). The xlsx outputs of the coordinates were converted into csv and analysed in R Studio ver. 4.0.3 [41] using the package “trajr” [42].
For each of the 400 digitalised trajectories, we calculated three parameters:
(i) Average speed (AS) calculated as in Equation (1):
AS = ( ∑ i = 1 n s i ) ∗ n − 1
where s i is the instantaneous speed expressed in mm s−1 of the i-th pair of frames; n is the total number of pairs of frames.
(ii) Percentage of activity (PA) calculated as in Equation (2):
PA = a f n
where a f is the sum of the frames of animal activity. The animal was considered active when its instantaneous speed in the i-th frame was ≥0.15 mm s−1 [4].
(iii) Trajectory convex hull (CH) defined as the minimum area of the convex polygon containing all the coordinates of the trajectory expressed in mm2 (for the CH mathematical computation, see [3] and references therein).
A scheme of the experimental design from the video recording to the trajectory analysis is presented in Figure 1.

2.4. Statistical Analysis

Differences between levels of Factor 1 (Time: A, B, C, D, E), Factor 2 (Condition: Control and Treatment) and Factor 3 (Sex: Male and Female) were tested with a three-way PERMANOVA with interaction (permutations = 9999, α = 0.05, input distances = Euclidean) [43]. To stabilise the variance of the variables before the analysis, a square root transformation for AS and CH values was applied, while an arcsine transformation was applied for PA values. Since the PERMANOVA outcome is sensitive to differences in multivariate dispersion among groups (Figure S1), we tested the dispersion effect of each factor with a permutational Levene’s test centred on the medians (PERMDISP routine) (α = 0.05, permutations = 9999) [44,45]. When the null hypothesis was rejected by PERMANOVA for Factor 1, pairwise comparisons among all possible pairs of levels of Factor 1 were carried out with permutational t-tests (α = 0.05).

3. Results

The values of each trajectory variable (Figure S2, Tables S1–S3) are shown in the statistical summary in Table 1. Variables are expressed as mean ± SD for each possible combination of the factors’ levels.
The PERMDISP analysis highlighted a significant inequality of variances for Factor 3 (Sex) for the three parameters analysed (AS, PA, CH). In addition, for CH the PERMDISP test also rejected the null hypothesis for Factor 1 (Time) (Table 2).
For AS and PA values, the PERMANOVA analysis rejected the null hypothesis for Factor 2 (Condition) and Factor 3 (Sex), highlighting significant differences between males and females and between controls and DCF-treated individuals (Table 3; Figure 2). The AS and PA measurements of B. pygmaeus were not influenced by the recording time since no significant differences were detected by the PERMANOVA analysis for Factor 1 (Time) and for all the interactions between factors (Table 3; Figure 2). The PERMANOVA test for CH values rejected the null hypothesis for Factor 2 (Condition), Factor 3 (Sex) and for their interaction (Table 3; Figure 3).
The post hoc permutational t-test for CH (Figure 2; Table 4) indicated that PERMANOVA significance is linked to the significant differences between one minute of recording (Factor 1, level A) and the other levels (Factor 1, levels B, C, D and E). The CH post hoc analysis rejected the null hypothesis for the difference between 2 and 5 min of recording (namely, Factor 1, level B and Factor 1, level E) and for 2 and 4 min of recording (namely, Factor 1, level B and Factor 1, level D) (Figure 2; Table 4). These results indicated that after 3 min of recording no sensible differences emerge in CH calculation.

4. Discussion

Swimming behavioural analysis is being largely applied in modern aquatic ecotoxicology [46,47]. The growing interest in behavioural video tracking is attributable to the relevance of swimming behavioural changes as an optimal sub-lethal endpoint [48], to technological advances in the past decade and to the easy accessibility of computational software [49]. The present work represents a first step towards understanding how the observation time can influence the analysis of the main behavioural parameters defining the swimming patterns of freshwater inbenthic microcrustaceans used to assess sub-lethal effects of pollutants. Indeed, the timing of recording may affect the results obtained when analysing movement between sexes, or among different developmental stages (e.g., juveniles versus adults) belonging to the same species.
We selected as target variables those most studied in video tracking behavioural aquatic research: the average swimming speed (AS), the percentage of activity (PA) and the area of the trajectory convex hull (CH). At first glance, AS and PA could appear as a redundant way to measure the same aspect of the swimming behaviour. Despite the AS calculation being influenced by the inactivity frames of the individuals, a decrease in AS can occur even if the overall PA remains the same between controls and treatments (i.e., the animal exhibits lower instantaneous speed values without an increase in the total inactive frames [4]). Hence, accounting both for AS and PA is a reliable way to have a clear and complete scenario about the dynamics shaping the overall swimming behaviour.
The AS and the PA have been proved to be the most robust and explanatory traits of invertebrate swimming behaviour to assess sub-lethal effects of toxic compounds or physical changes (e.g., increasing water temperature) in the environment where a species lives [3,4,19,50,51,52,53,54,55,56]. Swimming speed and swimming activity are fundamental behavioural traits which may affect individual interactions at population and community levels (e.g., conspecific encounter, predation avoidance, individual feeding rate, escape behaviour).
The convex hull area offers a simplistic measure for the extent to which the members of a particular species aggregate or spread out in space at a particular time [57]. The CH estimation is an important swimming behavioural trait that can be used as a proxy to estimate different animal characteristics such as sheltering behaviour [58], thigmotaxis [4,59], phototaxis [60], space usage and overall path tortuosity [61,62].
The lack of standardisation makes it difficult to validate the results of different studies obtained with different experimental designs [63]. The time span during which the animal behaviour is recorded (often referred as recording time) is a key factor in video tracking analysis. Faimali et al. [64] highlighted the extreme variability in the recording time adopted in different experiments performed with marine invertebrate species. For copepods, the recording times range from seconds [56] to several hours [54,65]. The importance of the recording time and its influence on the outcome of behavioural analyses by video recording have already been addressed by Kane et al. [66] regarding target fish species. Our findings suggest that the recording time does not play a major role in altering the statistical power of the tests when testing differences in the base parameters between control and treatment individuals (p-value for Condition was <0.05 for each swimming parameter), or between males and females (p-value for Sex was <0.05 for each swimming parameter). Nevertheless, the PERMDISP results for the AS, PA and CH showed a dispersion effect for Factor 2 (Condition). This outcome could be explained by the existence of an intrinsic sexual behavioural difference, resulting in the heterogeneity of the variable variance [67,68]. Even if the heteroscedasticity of our data did not play a key role in highlighting a sex-dependent effect of DCF on B. pygmaeus individuals (p-value for the interaction between Condition and Sex > 0.05 for each swimming parameter), we cannot exclude that a sex-dependent response could emerge when testing sub-lethal effects of toxic compounds [60,69]. Conceivably, the null hypothesis for Factor 1 (Time) was only rejected for the CH since both controls and treatments showed the tendency to explore and occupy larger areas in direct proportion with the recording time, making it difficult to estimate the true “base level” of this parameter for B. pygmaeus both in control and treatment conditions. Nonetheless, the post hoc permutational t-test revealed that no significant differences are observable after three minutes of recording; hence, this limit could suffice to obtain statistically consistent results. Our findings are in line with the results obtained from mouse behavioural analysis [70,71] for which recording times of 3–5 min have been found sufficient to produce accurate and consistent results.
Pelagic copepods tend to adapt their space usage to the size of the testing vessels [55]. In our study, focusing on an inbenthic species, this process occurred and reached a plateau in three minutes for both treatment and control individuals. Since it has been shown that the arena size under the same recording time can produce different results in crustacean amphipods [60] and branchiopods [72], we would highlight that a key role could be played by the ratio between recording time and the area of the arena when investigating sub-lethal effects of pollutants on the explorative ability of the animal. In our case, consistent results for the CH in terms of null hypothesis rejection could be affected by the ratio between the recording time and the arena surface (≤102.85 s cm−2). However, this value could change greatly depending on the intrinsic characteristic of the studied species (e.g., maximum velocity, dispersion ability, presence/absence of prey and predators in the arena). If the abovementioned ratio is in favour of time (e.g., small arenas or long recording times), it is possible that pollutant-exposed individuals may be able to explore the overall available surface like the controls even when the toxic substance has adverse effects. This condition explains the lower statistical power of the CH between controls and treatments despite the null hypothesis being rejected for the remaining swimming variables [3].

5. Conclusions

This contribution represents the first step towards understanding how the recording time may influence swimming behavioural studies in inbenthic/interstitial copepods, consequently determining changes in the results obtained when the swimming behaviour is investigated in both natural and polluted conditions.
If the test protocol is of primary importance to ensure the reproducibility of the method adopted, the time factor in the movement analysis is not far behind. Indeed, some motion attributes can remain relatively unchanged over time, others are much more time dependent. The analyses presented in this work indicate that recording sessions lasting 3 min each provide robust results, while shorter or longer sessions may introduce artifacts or cause misinterpretation of the movement. However, further investigations are needed to disentangle the role of concurrent factors, such as the behavioural analysis of multiple individuals simultaneously, whether they are of the same or opposite sex and the implications on sexual behaviour, competition for resources and predation.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/w14131996/s1, Figure S1: Schematic explanation of PERMANOVA and PERMDISP routine from PERMANOVA + manual chapter 2 (Anderson et al., 2008) (Schematic diagram showing two groups of samples in a bivariate system (two dimensions) that (a) do not differ in either location or dispersion, (b) differ only in their location in multivariate space, (c) differ only in their relative disperesions and (d) differ in both their location and in their relative dispersion); Figure S2: Trajectories examples for five mnutes of recording (A = Control Male, B = Control female, C = treatment Male, D = Treat-ment female); Table S1: Individuals’ swimming speeds; Table S2: Individuals’ percentage of activity; Table S3: Individuals’ trajectory convex hull.

Author Contributions

Conceptualisation and methodology: M.D.C., D.M.P.G., T.D.L. and M.U.; data analysis: M.D.C., T.D.L. and I.V.; data collection: M.D.C., B.F. and A.T.D.C.; validation: M.U., D.M.P.G. and B.F.; writing—review and editing: D.M.P.G., B.F., T.D.L. and M.D.C. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the European Community (LIFE12 BIO/IT/000231 AQUALIFE).

Data Availability Statement

The data presented in this study are available in the Supplementary Materials.

Acknowledgments

We would like to take this opportunity to acknowledge the time and effort devoted by the four reviewers to improving the quality of the published work.

Conflicts of Interest

The authors declare no conflict of interest.

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Water 14 01996 g001 550
Figure 1. Graphical representation of the experimental design: from the trajectory recording to the computation of the behavioural parameters. A = one minute of recording (1800 video frames); B = two minutes of recording (3600 video frames); C = three minutes of recording (5400 video frames); D = four minutes of recording (7200 video frames); E = five minutes of recording (9000 video frames) for both controls and treatments.
Figure 1. Graphical representation of the experimental design: from the trajectory recording to the computation of the behavioural parameters. A = one minute of recording (1800 video frames); B = two minutes of recording (3600 video frames); C = three minutes of recording (5400 video frames); D = four minutes of recording (7200 video frames); E = five minutes of recording (9000 video frames) for both controls and treatments.
Water 14 01996 g001
Water 14 01996 g002 550
Figure 2. Bar plots of the studied trajectory parameters (mean ± SD; n = 20). M: males; F: females. Dark grey: controls; light grey: treatments. A = one minute of recording; B = two minutes of recording; C = three minutes of recording; D = four minutes of recording; E = five minutes of recording. * = significant differences for Factor 1 (Time); ■ = significant differences for Factor 2 (Condition); ▲ = significant differences for Factor 3 (Sex).
Figure 2. Bar plots of the studied trajectory parameters (mean ± SD; n = 20). M: males; F: females. Dark grey: controls; light grey: treatments. A = one minute of recording; B = two minutes of recording; C = three minutes of recording; D = four minutes of recording; E = five minutes of recording. * = significant differences for Factor 1 (Time); ■ = significant differences for Factor 2 (Condition); ▲ = significant differences for Factor 3 (Sex).
Water 14 01996 g002
Water 14 01996 g003 550
Figure 3. CH value interaction plot for each level of Factor 1 (Time, (A) = one minute of recording; (B) = two minutes of recording; (C) = three minutes of recording; (D) = four minutes of recording; (E) = five minutes of recording. Red = females; Blue = males (mean ± SD; n = 20).
Figure 3. CH value interaction plot for each level of Factor 1 (Time, (A) = one minute of recording; (B) = two minutes of recording; (C) = three minutes of recording; (D) = four minutes of recording; (E) = five minutes of recording. Red = females; Blue = males (mean ± SD; n = 20).
Water 14 01996 g003
Table
Table 1. Statistical summary of the analysed trajectory parameters (mean ± SD; n = 20). AS = average speed; PA = percentage of activity; CH = trajectory convex hull area. A = 1 min of recording, B = 2 min of recording, C = 3 min of recording, D = 4 min of recording, E = 5 min of recording. CON = control, TRE = treatment. F = female; M = male.
Table 1. Statistical summary of the analysed trajectory parameters (mean ± SD; n = 20). AS = average speed; PA = percentage of activity; CH = trajectory convex hull area. A = 1 min of recording, B = 2 min of recording, C = 3 min of recording, D = 4 min of recording, E = 5 min of recording. CON = control, TRE = treatment. F = female; M = male.
TimeConditionSexAS (mm s−1)PA (%)CH (mm2)
ACONF0.85 ± 0.3748.27 ± 6.7168.78 ± 40.49
ACONM0.74 ± 0.3342.82 ± 13.0652.03 ± 41.57
ATREF0.41 ± 0.2038.69 ± 12.1711.91 ± 31.25
ATREM0.56 ± 0.2238.53 ± 7.3260.64 ± 28.14
BCONF0.79 ± 0.3545.75 ± 8.27103.40 ± 44.25
BCONM0.76 ± 0.2643.25 ± 12.0888.83 ± 39.82
BTREF0.47 ± 0.2039.91 ± 9.2849.93 ± 31.84
BTREM0.61 ± 0.2437.47 ± 8.1779.88 ± 33.83
CCONF0.76 ± 0.3545.35 ± 8.74108.66 ± 37.46
CCONM0.78 ± 0.2642.39 ± 12.41103.57 ± 39.89
CTREF0.45 ± 0.1939.94 ± 7.5765.23 ± 35.37
CTREM0.53 ± 0.2437.30 ± 8.6396.41 ± 30.90
DCONF0.69 ± 0.3244.75 ± 8.18110.10 ± 33.30
DCONM0.77 ± 0.2542.09 ± 11.81105.36 ± 38.13
DTREF0.43 ± 0.1940.00 ± 6.4787.57 ± 35.92
DTREM0.44 ± 0.2537.16 ± 8.7299.34 ± 29.51
ECONF0.69 ± 0.3244.61 ± 7.90110.25 ± 24.35
ECONM0.77 ± 0.2442.15 ± 11.39109.74 ± 38.25
ETREF0.45 ± 0.1940.67 ± 5.8496.55 ± 36.09
ETREM0.46 ± 0.2636.90 ± 9.28102.46 ± 29.98
Table
Table 2. PERMDISP analysis results for each factor of the studied trajectory parameters. Significant results in bold.
Table 2. PERMDISP analysis results for each factor of the studied trajectory parameters. Significant results in bold.
Degrees of FreedomSums of SquaresMean SquarePseudo Fp-Value
Average speed (mm s−1)Time40.03220.00806060.5910.655
Residuals3955.11420.0136379
Total3995.1464
Condition10.03750.0374853.11920.091
Residuals3984.54260.12018
Total3994.5801
Sex10.05720.0572264.14080.041
Residuals3985.22400.013820
Total3995.2813
Percentage of activity (%)Time40.013390.00334790.58530.656
Residuals3952.145020.0057200
Total3992.15841
Condition10.000330.00032920.05640.821
Residuals3982.204900.0058331
Total3992.20523
Sex10.063170.06316711.4480.002
Residuals3982.085680.005518
Total3992.14885
Convex hull area (mm2)Time484.8921.22176.1177<0.001
Residuals3951300.853.4689
Total3991385.74
Condition15.745.74311.28620.235
Residuals3981687.824.4651
Total3991693.56
Sex117.2217.22214.21780.038
Residuals3981543.464.0832
Total3991560.68
Table
Table 3. Three-way interaction PERMANOVA summary for the three studied trajectory parameters (9999 permutations; α = 0.05, input distance matrix = Euclidean); significant differences in bold.
Table 3. Three-way interaction PERMANOVA summary for the three studied trajectory parameters (9999 permutations; α = 0.05, input distance matrix = Euclidean); significant differences in bold.
Degrees of FreedomSums of SquaresMean SquarePseudo Fp-Value
Average speed (mm s−1)Time40.00770.001910.0640.989
Cond12.57772.5777286.033<0.001
Sex10.19980.199756.6670.013
Time × Cond40.03010.007530.2510.889
Time × Sex40.00110.000290.0100.999
Cond × Sex40.03990.039931.3330.258
Time × Cond × Sex40.03530.008830.2950.894
Residuals38010.78630.02996
Total39913.678
Percentage of activity (%)Time40.00310.0007810.07540.980
Cond10.27520.27521526.5828<0.001
Sex10.8120.811537.83850.006
Time × Cond40.1220.0030590.29550.887
Time × Sex40.00060.0001540.01491.000
Cond × Sex40.0360.0036110.34880.554
Time × Cond × Sex40.1800.0044980.43450.786
Residuals3803.72710.010275
Total3994.1211
Convex hull area (mm2)Time4405.76101.44115.6249<0.001
Cond1111.92111.92017.2391<0.001
Sex117.7617.7602.73550.091
Time × Cond417.084.2690.65760.645
Time × Sex413.613.4030.52420.724
Cond × Sex451.3551.3467.90890.009
Time × Cond × Sex49.472.3680.36740.836
Residuals3802337.216.492
Total3992964.16
Table
Table 4. Factor 1 (Time) pairwise permutational t-test outcome for the trajectory convex hull area. A = one minute of recording; B = two minutes of recording; C = three minutes of recording; D = four minutes of recording; E = five minutes of recording. Statistically significant results in bold.
Table 4. Factor 1 (Time) pairwise permutational t-test outcome for the trajectory convex hull area. A = one minute of recording; B = two minutes of recording; C = three minutes of recording; D = four minutes of recording; E = five minutes of recording. Statistically significant results in bold.
ComparisonPermutational tp-Value
A–B−2.9920.002773
A–C−4.4538.48 × 10−6
A–D−5.2781.308 × 10−9
A–E−5.8185.953 × 10−9
B–C−1.6350.1021
B–D−2.6470.008119
B–E−3.3070.0009434
C–D−1.0750.2822
C–E−1.7750.07582
D–E−0.69520.487
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Di Cicco, M.; Uttieri, M.; Di Lorenzo, T.; Fiasca, B.; Vaccarelli, I.; Tabilio Di Camillo, A.; Galassi, D.M.P. The Influence of the Recording Time in Modelling the Swimming Behaviour of the Freshwater Inbenthic Copepod Bryocamptus pygmaeus. Water 2022, 14, 1996. https://doi.org/10.3390/w14131996

AMA Style

Di Cicco M, Uttieri M, Di Lorenzo T, Fiasca B, Vaccarelli I, Tabilio Di Camillo A, Galassi DMP. The Influence of the Recording Time in Modelling the Swimming Behaviour of the Freshwater Inbenthic Copepod Bryocamptus pygmaeus. Water. 2022; 14(13):1996. https://doi.org/10.3390/w14131996

Chicago/Turabian Style

Di Cicco, Mattia, Marco Uttieri, Tiziana Di Lorenzo, Barbara Fiasca, Ilaria Vaccarelli, Agostina Tabilio Di Camillo, and Diana Maria Paola Galassi. 2022. "The Influence of the Recording Time in Modelling the Swimming Behaviour of the Freshwater Inbenthic Copepod Bryocamptus pygmaeus" Water 14, no. 13: 1996. https://doi.org/10.3390/w14131996

APA Style

Di Cicco, M., Uttieri, M., Di Lorenzo, T., Fiasca, B., Vaccarelli, I., Tabilio Di Camillo, A., & Galassi, D. M. P. (2022). The Influence of the Recording Time in Modelling the Swimming Behaviour of the Freshwater Inbenthic Copepod Bryocamptus pygmaeus. Water, 14(13), 1996. https://doi.org/10.3390/w14131996

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