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Open-Field Temporal Pattern of Ambulation in Japanese Quail Genetically Selected for Contrasting Adrenocortical Responsiveness to Brief Manual Restraint¹

J. M. Kembro^*, D. G. Satterlee^,2, J. B. Schmidt, M. A. Perillo^* and R. H. Marin^*,2

^* Instituto de Ciencia y Tecnología de los Alimentos, Cátedra de Química Biológica and Consejo Nacional de Investigaciones Científicas y Técnicas, Facultad de Ciencias Exactas, Físicas y Naturales, Universidad Nacional de Córdoba, Argentina; and Applied Animal Biotechnology Laboratories, School of Animal Sciences, Louisiana Agricultural Experiment Station, Louisiana State University Agricultural Center, Louisiana State University, Baton Rouge 70803

² Corresponding address: dsatterlee{at}agctr.lsu.edu or rmarin{at}efn.uncor.edu

	ABSTRACT

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS DISCUSSION REFERENCES

 
Japanese quail selected for a low-stress (LS), rather than a high-stress (HS), plasma corticosterone response to brief restraint have been shown to possess lower fearfulness and a nonspecific reduction in stress responsiveness. Detrended fluctuation analysis provides information on the organization and complexity of temporal patterns of behavior. The present study evaluated the temporal pattern of ambulation of LS and HS quail in an open field that represented a novel environment. Time series of 4,200 data points were collected for each bird by registering the distance ambulated every 0.5 s during a 35-min test period. Consistent with their known reduced fearfulness, the LS quail initiated ambulation significantly sooner (P < 0.02) and tended to ambulate more (P < 0.09) than did their HS counterparts. Detrended fluctuation analyses showed a monofractal series (i.e., a series with similar complexity at different temporal scales) in 72% of the birds. These birds initiated their ambulatory activity in less than 600 s. Among these birds, a lower (P < 0.03) autosimilarity coefficient () was found in the LS quail than in their HS counterparts ( = 0.76 ± 0.03 and 0.87 ± 0.03, respectively), suggesting a more complex (less regular) ambulatory pattern in the LS quail. However, when the patterns of ambulation were reexamined by considering only the active period of the time series (i.e., after the birds had initiated their ambulatory activity), monofractal patterns were observed in 97% of the birds, and no differences were found between the lines. Collectively, the results suggest that during the active period of open-field testing, during which fear responses are likely less strong and other motivations are the driving forces of ambulation, the LS and HS lines have similar ambulatory organization.

Key Words: detrended fluctuation analysis • fractal • open-field behavior • corticosterone • Japanese quail

	INTRODUCTION

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Stressors are forces external to the body that tend to displace homeostasis and produce a state of stress (Siegel, 1995). Oftentimes, stress is accompanied by increased fearfulness and hyperadrenal activity. When fear or distress persists, animal welfare and production performance can be seriously compromised (Jones, 1996a). For example, energy wastage, feather damage, reduced growth, poor feed conversion, declines in egg production and eggshell quality, injury, pain, and greater death rates have all been associated with fear and distress in poultry (Mills and Faure, 1990; Jones, 1996a,b, 1997; Jones and Hocking, 1999). Therefore, it has been suggested that genetic selection for a less pronounced fear response and decreased adrenocortical responsiveness may offer a rapid and effective method for reducing fear and distress and thereby enhance animal production performance and well-being (Jones, 1996b; Jones and Hocking, 1999; Jones et al., 2000). Indeed, Japanese quail genetically selected for low-stress (LS), rather than high-stress (HS), responsiveness based on the plasma corticosterone response to a brief mechanical restraint (Satterlee and Johnson, 1988) show 1) reduced fearfulness to restraint, novel places, objects, and human beings (Jones et al., 1992a,b, 1994a,Jones et al., b, 1999, 2000; Satterlee and Jones, 1995; Jones and Satterlee, 1996; Satterlee and Marin, 2006; Davis et al., 2008); 2) increased sociality (Jones et al., 2002); 3) lower adrenocortical responses to a wide range of stressors (handling, cold, cooping, social tension, human contact, and food and water deprivation; Satterlee and Johnson, 1988; Jones et al., 1992b, 1994b, 2000; Jones, 1996b), 4) less cortical bone porosity (Satterlee and Roberts, 1990); 5) less developmental instability (Satterlee et al., 2000, 2008); 6) less pronounced reductions in BW after exposure to multiple sequential stressors (Satterlee and Johnson, 1985); and 7) accelerated puberty and enhanced reproductive performance in both males (Satterlee et al., 2002, 2006, 2007; Marin and Satterlee, 2004; Satterlee and Marin, 2004) and females (Cadd et al., 2002; Marin et al., 2002b; Marin and Satterlee, 2006; Satterlee and Schmidt, 2008).

The open-field test is a commonly used method to assess fear and anxiety reactions to a novel environment (Jones, 1996b). Gallup and Suarez (1980) have proposed that open-field behavior in poultry represents a compromise between opposing tendencies to reinstate contact with conspecifics and to minimize detection in the face of possible predation. Assuming in gallinaceous birds that open-field behavior parallels the adaptive response to danger, Faure et al. (1983) classified the sequence of behavior patterns into 2 phases. The first phase may include either panic running or, more commonly, immobility and silence, and this phase probably reflects the inhibition of all behavioral patterns by fear. The second phase is an active phase that includes ambulation, jumps, and distress vocalizations. This latter phase may represent a waning of fear and the consequent expression of socially motivated behaviors (Faure et al., 1983). Second-phase ambulation and jumping behavioral patterns may reflect attempts to reinstate contact with conspecifics (Gallup and Suarez, 1980), which, in turn, may represent the primary motivation for open-field activity.

Some open-field responses of the LS and HS quail lines have already been studied (Jones et al., 1992b; Satterlee and Marin, 2006). Consistent with the hypothesis of reduced fear responsiveness to a novel environment during open-field tests, LS quail showed less freezing behavior and ambulated sooner (Jones et al., 1992b), and showed greater locomotor activity (Satterlee and Marin, 2006) than did their HS counterparts. However, both of these open-field studies included test durations of 10 min or less; therefore, such short durations of testing may have influenced the ambulation responses of the lines, because birds may have been in the first phase of the ambulation patterns suggested by Faure et al. (1983). Therefore, we have pondered herein whether the ambulatory pattern of the different quail stress lines may also diverge once the initial fear response to novelty has wound down. With this in mind, the present study focused on the open-field responses of previously undisturbed LS and HS quail during a longer testing time (35 min). In the data analyses, we also incorporated the use of a novel fractal analysis tool (detrended fluctuation analysis, DFA; see below) that allowed us to evaluate temporal behavioral complexity during both the total time of the trial and after the birds began their ambulation, that is, during the second phase of danger-induced adaptive ambulation behavior proposed by Faure et al. (1983).

The use of fractal analyses, such as DFA, has emerged as an effective tool to measure the temporal organizational complexity of a particular behavior (Ho et al., 1997; Peng et al., 2000). Detrended fluctuation analysis has also been proposed as an important and appropriate noninvasive tool to evaluate stress-related responses in poultry (Rutherford et al., 2003; María et al., 2004). Fractal analyses may even reveal hidden information (Peng et al., 2000) about the organization of behavior beyond that extracted by using conventional behavioral analyses, which are often limited to measures of the mean duration or frequency of particular behaviors (Rutherford et al., 2003). For example, Rutherford et al. (2003) observed that, in domestic chickens exposed to an acute stressor (restraint and blood sampling), the complexity of the vigilance behavioral pattern was increased and that it occurred independently of changes in the duration of this behavior, suggesting that DFA can reveal more subtle changes in behavioral organization during stress.

	MATERIALS AND METHODS

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Animals and Housing

Because sex differences in stress susceptibility (Marin et al., 2002a) and in open-field behavior have been reported (Jones, 1977; Jones, 1978; Jones and Faure, 1982), only female Japanese quail from generation 39 of the LS and HS lines of Satterlee and Johnson (1988) were used. The most recent genetic history of the lines, up to generation 34, is discussed in detail elsewhere (Satterlee et al., 2000; Marin and Satterlee, 2004; Satterlee et al., 2006). Although line differences in levels of plasma corticosterone were not measured in the present study, recent findings in the stress lines attest to the maintenance of divergent adrenocortical responsiveness to a variety of nonspecific systemic stressors. Indeed, Satterlee et al. (2008) have offered explanations for why the gene(s) that control the adrenocortical responsiveness trait in these lines may have become fixed.

The study quail were taken from a larger population of an approximately 400 mixed-sex bird hatch. Egg incubation, chick brooding, feeding, and lighting procedures were similar to those described by Jones and Satterlee (1996), with the exception that, at hatch, chicks were leg-banded and housed in mixed-line groups of 62 birds (31 LS + 31 HS) per compartment in each of 6 compartments (102 x 64 x 20 cm, length x width x height) of a model 2S-D, 6-deck Petersime (Petersime Incubator Co., Gettysburg, OH) brooder battery modified for quail. To maintain the line identity of each bird, leg bands were replaced with permanent wing bands at 21 d of age.

At 4 wk of age, birds were sexed by plumage coloration, and 12 females from each line were randomly cage-housed in pairs consisting of one LS and one HS female in a 4-tier cage battery that contained 48 laying cages (Alternative Design Manufacturing and Supply Inc., Siloam Springs, AR). Cages measured 50.8 x 15.2 x 26.7 cm (length x width x height). Coincident with placement in their cages, birds were switched to a quail breeder ration (21% CP; 2,750 kcal of ME/kg) and water was continued ad libitum. The birds were also subjected to a daily cycle of 14L (280 to 300 lx):10D at this time, which continued until the end of the study (at 14 wk of age). Once housed in cages, the birds then remained undisturbed, apart from routine maintenance, until testing began. One HS bird escaped from its cage shortly after housing. To maintain the same experimental conditions in all tested animals, the HS bird that escaped was not replaced, and her remaining LS bird cagemate was not tested.

General Procedure

At 32 d of age, open-field behavior (see below) of 11 quail from each of the 2 lines was evaluated. The open-field testing was conducted between 0800 and 1600 h. Each bird was tested individually and once only, and its behavior was videorecorded. The testing order of individuals was randomized.

The open-field apparatus consisted of a wooden box measuring 60 x 90 x 60 cm (width x length x height). The walls of the open field were painted matte white. If defecation occurred during a test, after completion of that test, the floor was wiped clean before reuse for the next test. The test room was maintained at an ambient temperature and light intensity similar to that in the room where the birds’ home cages were located.

To begin a test, each bird was placed near the midpoint of the open-field floor, and its behavior was recorded on videotape for 35 min by using a closed-circuit television system with a video camera suspended approximately 1.8 m directly above the open field. This arrangement made certain that the experimenter was completely hidden from the bird’s view during testing. We used digitized pixel images to analyze the ambulation of the birds in the open-field apparatus at 0.5-s intervals. In each image the X, Y coordinates of the center of the animal were recorded. The distance ambulated was defined by the distance the animal moved between 2 successive images (a time interval of 0.5 s). If the distance ambulated in a given time interval did not exceed a threshold distance of 1 cm, then the bird was considered nonambulatory in such an instance, and a 0 distance value for ambulation during that interval was recorded. Thus, a time series of the distance ambulated during the 35-min test period (i.e., 4,200 time intervals) was constructed for each bird. The following variables were also measured:

• Latency to ambulate (s): time from the start of the test until a chick showed its first ambulatory event (first time interval that showed more than 1 cm of distance ambulated).
  
• Percentage of time spent ambulating:
  
  

  

  
  where t_i is the time interval (s) in which the animal is ambulating and T is the total duration of the test (s).
  
• Distance ambulated (cm): the total (cumulative) distance ambulated by the animal during the test period.
  
• Ambulation speed (cm/s): distance ambulated divided by the time spent ambulating.
  
• Ambulation rate (cm/s):
  
  

  

  
  where D is the distance ambulated, T is the total duration of the test, and t_lat is the latency to ambulate.
  

Data Analyses

Power Spectrum Analysis. To accurately use DFA (see below), a given time series must not present periodic oscillations. Thus, the presence of periodic oscillations was evaluated by using the power spectrum analysis tool from SigmaPlot (SPSS, 2001).

DFA. A method to analyze the ambulation pattern of the birds was introduced by Peng et al. (1994), and this method has been described in detail by Kantelhardt et al. (2001). We applied this method to 2 time series of data (x_i): 1) the complete time series of the distance ambulated (all 4,200 data points), and 2) the time series that corresponded to the second (active) period of open-field ambulation behavior (wherein each test subject’s first period of nonambulation was eliminated).

The general mean value of x was calculated for each time series (equation [1]),

where N is the total number of data points. Each of the values of x_i was then subtracted from the general mean x, creating the time series, {y_i} (equation [2]),

A cumulative time series (Y_j) was then generated by adding all data points found before y_i in the time series (equation [3]),

This new time series {Y_j} was then subdivided into k nonoverlapping windows of equal length. The size of each window was represented by n (number of data points included in each window) and it decreased from N/4 to a minimum of 10 data points per window. In each window, a polynomial function was fitted, which was called the local trend of the time series (see the de-trending order below). Polynomials of the same degree were selected for all windows. The polynomial estimator of Y_j in the nth window (n = 1, . . ., k) was denoted by P_jn. Detrending occurred by subtracting the local trend in each window from the time series. Thus, the differences Z_n(j) = Y_j – P_jn were calculated, where the index j ran inside the selected window n.

In the next step, the variance inside each of the k windows was calculated by

Finally, for each size n, the DFA fluctuation function (F) was calculated as the square root of the average of the variances by

The estimation of F(n) was repeated for different window sizes, (n). Hence, once F(n) at different window sizes was calculated, the fluctuation function [F(n)] was plotted against the window size (n) on a log-log scale. A linear relationship on this log-log plot indicates a power-law relationship between the fluctuations function and the window size. Thus,

([6])

The slope of the linear log-log plot equals the power-law exponent, , which is the self-similarity parameter. The -value relates to the autocorrelation structure of the time series. If = 0.5, then the series is uncorrelated (random) or has short-range correlations (i.e., the correlations decay exponentially), whereas the situation of 0.5 < < 1 indicates that long-range autocorrelations (correlations decaying as a power-law) exist, meaning that ongoing behavior is influenced by what has occurred in the past (Kantelhardt et al., 2001). In DFA, the exponent is inversely related to a typical fractal dimension, so in this case, the value increases with increasing regularity (or decreasing complexity) in the time series (Rutherford et al., 2003).

One of the common challenges is that the autocorrelation fit is not always constant, and crossovers often exist (i.e., a change of the scaling exponent, , for different ranges of scales; Blesi et al., 2003; Ferri et al., 2005). A crossover usually can arise from actual changes in the correlation properties of the signal at different time or space scales (i.e., 2 levels of complexity), or from trends (a smooth and monotonous or slowly oscillating pattern caused by external effects) that were not correctly eliminated by the DFA (Hu et al., 2001; Kantelhardt et al., 2001). Thus, Kantelhardt et al. (2001) reported that trends within time-series data can lead to an artificial crossover in the slope of the log-log plot of F(n) versus n; that is, the slope is increased for large time scales. To determine the order of DFA that would eliminate these trends (and consequently the artificial crossovers), and thus be able to estimate the value of reliably, DFA was calculated with different detrending orders. For this purpose, linear (DFA1), square (DFA2), cubic (DFA3), and higher order polynomials were used in the fitting procedure. Because the detrending of time-series data is performed by subtracting the fits from the data, these methods differ in their capability of eliminating trends in the data. In the nth-order DFA (DFAn), trends in the profile of order n – 1 are eliminated (Kantelhardt et al., 2001). In other words, the artificial crossover (more than one linear fit) disappears when the detrending order is larger than the order of the trend. If the crossovers did not disappear after using detrending orders of a magnitude up to 5, we concluded that the crossovers were not a consequence of the presence of trends in the data and that the series were not monofractal (see next paragraph). Therefore, after evaluating the fits of all DFA performed with different detrending orders, we selected the lowest detrending order that was able to eliminate trends in all monofractal data series, and we used this order for comparison of the ambulatory pattern of LS and HS lines.

When a pattern is homogeneous—that is, when it has the same scaling properties at different time scales (only one linear fit) and is therefore characterized by only one -value—the pattern is termed monofractal (Stanley et al., 1999).

Statistical Analyses

One-way ANOVA was used to determine differences between LS and HS quail in latency to ambulate, time spent ambulating, distance ambulated, ambulation speed, and ambulation rate. In all cases, the ANOVA assumptions were verified, except for the latency to ambulate data, which were transformed to ranks to better fit ANOVA assumptions (Shirley, 1987).

To evaluate line differences in the proportion of monofractal time series, the 2-sample proportion test (Analytical Software, 2000) was used. A P-value of less than or equal to 0.05 was considered to represent significant differences.

	RESULTS

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The latency to ambulate was significantly longer (F_1,20 = 6.66, P < 0.02) and there was a tendency (F_1,20 = 3.12, P = 0.09) toward a lower percentage of time spent ambulating in female quail of the HS line compared with those of the LS line (Table 1). No significant differences were found between the stress lines in the total distance ambulated (F_1,20 = 1.02, P > 0.05), ambulation speed (F_1,20 = 0.15, P > 0.05), or ambulation rate (F_1,20 = 0.03, P > 0.05) in the open field.

View larger version (11K): [in this window] [in a new window]   Figure 1. Example of a power spectrum analysis of a complete time series of distance ambulated. All individual quail data time series showed similar patterns.

View larger version (19K): [in this window] [in a new window]   Figure 2. Example of the trend elimination capability of detrended fluctuation analysis (DFA) in time series using linear- (DFA1), quadratic- (DFA2), cubic- (DFA3), and quartic-order (DFA4) DFA data fits. Detrended fluctuation analysis 3 () was selected for group comparisons because third-order polynomials eliminated trends in the data. To improve visualization, the functions were shifted by multiples of 0.1.

View this table: [in this window] [in a new window]   Table 2. Values of the self-similarity parameter, , of female Japanese quail selected for either a reduced (LS, low-stress) or exaggerated (HS, high-stress) plasma corticosterone response to a brief restraint on evaluation in an open-field test

	DISCUSSION

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Detrended fluctuation analyses of the data series using a detrending of first and second order showed crossovers in several of the log-log plots of fluctuation versus window size that were eliminated by using higher detrending orders in the DFA procedure. These results indicate that the crossovers were not due to actual differences in the complexity of the behavior at different time scales, but to the presence of trends within the time series (a smooth and monotonous or slowly oscillating pattern caused by external effects, which can be eliminated by using a higher detrending order in the DFA procedure; Kantelhardt et al., 2001; see the materials and methods section). Detrended fluctuation analysis 3 was then selected for LS and HS line comparisons because DFA3 was the lowest detrending order that eliminated these trends in all monofractal series. Our results support the contention that, a priori, a DFA of first order should be used with caution because biological data are frequently affected by trends (Hu et al., 2001), which may lead to miscalculation of the -value. The power spectrum analyses showed no periodic oscillations in the data series, suggesting that DFA was accurately applied to our data.

When the complete data series (considering all 4,200 ambulation data points) was studied, a tendency toward a greater proportion of mononofractal series (series with similar complexity at different temporal scales) was found in LS quail (82%) than in HS quail (64%). In the monofractal time series, long-range correlations were observed in all cases (-values ranging from 0.65 to 0.97). This means that the ambulation behavior at one moment is statistically correlated with the ambulation behavior at a relatively remote time, and its influence decays in a scale-invariant, fractal manner. Long-range autocorrelations were also found in minnow reproductive behavior (Alados and Weber, 1999), chimpanzee social behavior (Alados and Huffman, 2000), domestic fowl walking and vigilant behavior (Rutherford et al., 2003; María et al., 2004), and quail home cage ambulatory behavior (Kembro et al., 2007). However, a reduced -value was found in the LS line when compared with the HS line, indicating that a more complex ambulatory behavior (a less regular activity pattern) was present in the LS quail.

Monofractal series were found only in the birds that initiated their ambulatory activity in less than 600 s (1/7 of the total duration of the trial), representing 72% of the birds. This result may suggest a priori that ambulation in birds showing long periods of inactivity (relative to the total duration of the test) are not monofractally organized; that is, their ambulation pattern cannot be described by only one self-similarity parameter, because its organization changes depending on the time scale being studied. However, it is possible that the long period of inactivity is affecting the calculation procedure, thus masking the actual temporal organization of the behavior. Interestingly, when the patterns of ambulation were reexamined by considering only the active period of the series, that is, after the birds had initiated their ambulation activity, monofractal patterns were observed in all but one (97%) of the birds, and the differences between quail of the LS and HS lines that were observed previously appeared to be lost. These results sound a cautionary note about the interpretation of fractal analysis results when, in proportion to the test duration, test animals show long periods of inactivity.

Taking into consideration the results obtained after analyzing the conventional behavioral measurements (distance ambulated, ambulation speed, and ambulation rate) and DFA, we propose that the differences found in the ambulatory pattern between quail of the LS and HS lines are primarily due to first-phase open-field behavioral responses, for which the immobility observed probably reflects the inhibition of all behavioral patterns by fear (Faure et al., 1983).

In conclusion, we submit that quail of the HS line showed exaggerated fear responses (heightened inactivity) in comparison with quail of the LS line during the first phase of open-field testing because the genomic differences in adrenocortical responsiveness were overwhelmingly in control of locomotor activity during this time. However, in the ensuing period of time, when the birds’ fear responses to the novelty of the open field were likely waning and becoming increasingly less strong, and other motivations became the predominant behavioral driving forces, both lines showed similar ambulatory patterns because genomic controls on locomotor activity had abated.

	ACKNOWLEDGMENTS

 
This research was supported by grants from Fondo Nacional de Ciencia y Tecnología, Secretaria de Ciencia y Tecnología de la Universidad Nacional de Córdoba, and Consejo Nacional de Investigaciones Científicas y Técnicas, Argentina. RHM and MAP are career members of CONICET, Argentina. JMK holds research fellowships from the latter institution and is a graduate student of the Doctorado en Ciencias Biológicas, FCE-FyN, UNC, Argentina.

	FOOTNOTES

 
¹ Approved for publication by the director of the Louisiana Agricultural Experiment Station as manuscript number 2008-230-1504.

Received for publication March 7, 2008. Accepted for publication June 13, 2008.

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