136
for toxicity bioassays, in particular as it is very
sensitive to subtle behavioural changes that may
be undetectable to other behavioural variables
(Rutherford et al. 2004; Coughlin et al. 1992).
Zooplankton behavioural complexity typically
decreases under stress (Seuront 2010a, b, 2012;
Seuront and Leterme 2007; Michalec et al.
2013a, b). Increases in complexity have, however, also been observed under certain stress
conditions (Shimizu et al. 2002; Michalec et al.
2013a, b),
5
although such results seem to occur in
response to acute or stimulatory challenges, quite
apart from the chronic or inhibitory stressors
that are associated to reduction in complexity
(Alados et al. 1996; Seuront 2010a, b, 2012).
As shown for several fractal and multifractal
measures of environmental complexity (Seuront
2010a), regardless of the direction, it is ultimately the relative differences between the fractal
and multifractal exponents observed for a given
species under stressful and non-stressful conditions that may be more informative on the related
behavioural changes.
Note that the approach described in this
contribution is not limited to behavioural ecotoxicology but can be generalised to assess relative
changes in the behavioural complexity of marine
invertebrates in a wide range of ecologically
relevant situation related to, e.g., the quality and
the quantity or food and the presence of mates or
predators (Seuront 2010a, b; Schmitt et al. 2006).
It is finally stressed that the application of fractals
to zooplankton behavioural ecology in general
(Rutherford et al. 2004; Seuront 2011) and to
zooplankton ecotoxicology in particular (Seuront
2010a, b, 2012; Shimizu et al. 2002; Michalec
et al. 2013a, b; Seuront and Leterme 2007) is,
however, still in its infancy. Further work is
needed to entangle the fractal complexity of
behavioural properties and to generalise the use
of fractal and multifractal approaches to stress
assessment in marine invertebrates.
5 It is worth noting that the increase in the complexity of
Daphnia magna trajectories in contaminated waters must
be treated with caution as some of the fractal dimensions
reported fall outside the theoretical range 1 ≤ D ≤ 2, i.e.
D > 2 (Shimizu et al. 2002).
References
Alados CL, Escos JM, Emlen JM (1996) Fractal structure
of sequential behaviour patterns: an indicator of stress.
Anim Behav 51:437–443
Asher L et al (2009) Recent advances in the analysis of
behavioural organization and interpretation as indicators of animal welfare. J Roy Soc Interf 6:1103–1119
Coughlin DJ, Strickler JR, Sanderson B (1992) Swimming
and search behaviour in clownfish, Amphiprion
perideraion, larvae. Anim Behav 44:427–440
Dur G et al (2010) The different aspects in motion
of the three reproductive stages of Pseudodiaptomus
annandalei (Copepoda, Calanoida). J Plankton Res
32:423–440
Garaventa F et al (2010) Swimming speed alteration of
Artemia sp. and Brachionus plicatilis as a sub-lethal
behavioural end-point for ecotoxicological surveys.
Ecotoxicology 19:512–519
Goldberger AL, Rigney DR, West BJ (1990) Chaos and
fractal in human physiology. Sci Am 363:43–49
Goldberger AL et al (2000) Physiobank, physiotoolkit,
and physionet: components of a new research resource
for complex physiological signals. Circulation
101:215–220
Ivanov PC et al (1999) Multifractacilty in human heartbeat dynamics. Nature 399:461–465
Mandelbrot BB (1982) The fractal geometry of nature.
Freeman, New York
Michalec FG et al (2010) Differences in behavioral
responses of Eurytemora affinis (Copepoda,
Calanoida) reproductive stages to salinity variations.
J Plankton Res 32:805–813
Michalec FG et al (2013a) Behavioral responses of the
estuarine calanoid copepod Eurytemora affinis to
sub- lethal concentrations of waterborne pollutants.
Aquat Toxicol 138/139:129–138
Michalec FG et al (2013b) Changes in the swimming
behavior of Pseudodiaptomus annandalei (Copepoda,
Calanoida) adults exposed to the diatom toxin 2-trans,
4-trans decadienal. Harmful Algae 30:56–64
Mishima M et al (1999) Complexity of terminal airspace
geometry assessed by lung computed tomography in
normal subjects and patients with chronic obstructive
pulmonary disease. Proc Natl Acad Sci U S A
96:8829–8834
Moison M, Schmitt FG, Souissi S (2012) Effect of temperature on Temora longicornis swimming behaviour:
illustration of seasonal effects in a temperate ecosystem. Aquat Biol 16:149–162
Rutherford KMD et al (2004) Fractal analysis of animal
behaviour as an indicator of animal welfare. Anim
Welf 13:99–103
Schmitt FG et al (2006) Scaling of swimming sequences
in copepod behavior: data analysis and simulation.
Physica A 364:287–296
Seuront L (2010a) Fractals and multifractals in ecology
and aquatic sciences. CRC Press, Boca Raton
L. Seuront
for toxicity bioassays, in particular as it is very
sensitive to subtle behavioural changes that may
be undetectable to other behavioural variables
(Rutherford et al. 2004; Coughlin et al. 1992).
Zooplankton behavioural complexity typically
decreases under stress (Seuront 2010a, b, 2012;
Seuront and Leterme 2007; Michalec et al.
2013a, b). Increases in complexity have, however, also been observed under certain stress
conditions (Shimizu et al. 2002; Michalec et al.
2013a, b),
5
although such results seem to occur in
response to acute or stimulatory challenges, quite
apart from the chronic or inhibitory stressors
that are associated to reduction in complexity
(Alados et al. 1996; Seuront 2010a, b, 2012).
As shown for several fractal and multifractal
measures of environmental complexity (Seuront
2010a), regardless of the direction, it is ultimately the relative differences between the fractal
and multifractal exponents observed for a given
species under stressful and non-stressful conditions that may be more informative on the related
behavioural changes.
Note that the approach described in this
contribution is not limited to behavioural ecotoxicology but can be generalised to assess relative
changes in the behavioural complexity of marine
invertebrates in a wide range of ecologically
relevant situation related to, e.g., the quality and
the quantity or food and the presence of mates or
predators (Seuront 2010a, b; Schmitt et al. 2006).
It is finally stressed that the application of fractals
to zooplankton behavioural ecology in general
(Rutherford et al. 2004; Seuront 2011) and to
zooplankton ecotoxicology in particular (Seuront
2010a, b, 2012; Shimizu et al. 2002; Michalec
et al. 2013a, b; Seuront and Leterme 2007) is,
however, still in its infancy. Further work is
needed to entangle the fractal complexity of
behavioural properties and to generalise the use
of fractal and multifractal approaches to stress
assessment in marine invertebrates.
5 It is worth noting that the increase in the complexity of
Daphnia magna trajectories in contaminated waters must
be treated with caution as some of the fractal dimensions
reported fall outside the theoretical range 1 ≤ D ≤ 2, i.e.
D > 2 (Shimizu et al. 2002).
References
Alados CL, Escos JM, Emlen JM (1996) Fractal structure
of sequential behaviour patterns: an indicator of stress.
Anim Behav 51:437–443
Asher L et al (2009) Recent advances in the analysis of
behavioural organization and interpretation as indicators of animal welfare. J Roy Soc Interf 6:1103–1119
Coughlin DJ, Strickler JR, Sanderson B (1992) Swimming
and search behaviour in clownfish, Amphiprion
perideraion, larvae. Anim Behav 44:427–440
Dur G et al (2010) The different aspects in motion
of the three reproductive stages of Pseudodiaptomus
annandalei (Copepoda, Calanoida). J Plankton Res
32:423–440
Garaventa F et al (2010) Swimming speed alteration of
Artemia sp. and Brachionus plicatilis as a sub-lethal
behavioural end-point for ecotoxicological surveys.
Ecotoxicology 19:512–519
Goldberger AL, Rigney DR, West BJ (1990) Chaos and
fractal in human physiology. Sci Am 363:43–49
Goldberger AL et al (2000) Physiobank, physiotoolkit,
and physionet: components of a new research resource
for complex physiological signals. Circulation
101:215–220
Ivanov PC et al (1999) Multifractacilty in human heartbeat dynamics. Nature 399:461–465
Mandelbrot BB (1982) The fractal geometry of nature.
Freeman, New York
Michalec FG et al (2010) Differences in behavioral
responses of Eurytemora affinis (Copepoda,
Calanoida) reproductive stages to salinity variations.
J Plankton Res 32:805–813
Michalec FG et al (2013a) Behavioral responses of the
estuarine calanoid copepod Eurytemora affinis to
sub- lethal concentrations of waterborne pollutants.
Aquat Toxicol 138/139:129–138
Michalec FG et al (2013b) Changes in the swimming
behavior of Pseudodiaptomus annandalei (Copepoda,
Calanoida) adults exposed to the diatom toxin 2-trans,
4-trans decadienal. Harmful Algae 30:56–64
Mishima M et al (1999) Complexity of terminal airspace
geometry assessed by lung computed tomography in
normal subjects and patients with chronic obstructive
pulmonary disease. Proc Natl Acad Sci U S A
96:8829–8834
Moison M, Schmitt FG, Souissi S (2012) Effect of temperature on Temora longicornis swimming behaviour:
illustration of seasonal effects in a temperate ecosystem. Aquat Biol 16:149–162
Rutherford KMD et al (2004) Fractal analysis of animal
behaviour as an indicator of animal welfare. Anim
Welf 13:99–103
Schmitt FG et al (2006) Scaling of swimming sequences
in copepod behavior: data analysis and simulation.
Physica A 364:287–296
Seuront L (2010a) Fractals and multifractals in ecology
and aquatic sciences. CRC Press, Boca Raton
L. Seuront