Relationship between behavior, adrenal activity, and ...

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Susan L. Walker,4 and Katherine George1–3. 1ZSL London Zoo ... means of keeping track of animal activity on a consistent basis'' [Watters et al., 2009; p 37]. Ideally, observations are made over an extended time period, and any changes in.
Zoo Biology 30 : 1–16 (2011)

RESEARCH ARTICLE

Relationship Between Behavior, Adrenal Activity, and Environment in Zoo-Housed Western Lowland Gorillas (Gorilla gorilla gorilla) Fay E. Clark,1–3 Malcolm Fitzpatrick,1 Andy Hartley,1 Andrew J. King,2,3 Tracey Lee,1 Andrew Routh,1 Susan L. Walker,4 and Katherine George1–3 1

ZSL London Zoo, Zoological Society of London, Regent’s Park, London, United Kingdom Institute of Zoology, Zoological Society of London, Regent’s Park, London, United Kingdom 3 Royal Veterinary College, University of London, London, United Kingdom 4 Chester Zoo, North of England Zoological Society, Upton, Chester, United Kingdom 2

Monitoring adrenal activity through noninvasive fecal hormone sampling is rapidly gaining popularity as a tool to assess zoo animal welfare. However, few studies have sought to investigate the interrelationships between behavior, adrenal activity, and environment, and ask whether both behavioral and adrenal monitoring strategies are required to assess welfare sufficiently. We present the findings of a 9-month study of a small group (one male, two females) of Western lowland gorillas, Gorilla gorilla gorilla. First, we examined the effect of environmental variables on gorilla behavior. Second, we examined the effect of environmental variables on the concentration of fecal glucocorticoid metabolites (FGC) and the relationship between behavior and FGC. Environmental variables had similar effects on all three gorillas. Negative vigilance of visitors (NVV; staring, posturing, and charging at visitors) significantly increased in all subjects as environmental noise levels increased, and food-related behavior significantly decreased in all subjects as crowd size increased. Exhibit modifications had a number of positive effects on behavior. Notably, when privacy screens were used, NVV significantly decreased in two subjects. We found no significant effects of environmental variables on FGC. However, we did find significant relationships between behavior and FGC in one female. Specifically, her NVV was significantly higher one day Correspondence to: Fay E. Clark, ZSL London Zoo, Zoological Society of London, Regent’s Park, London NW1 4RY, United Kingdom. E-mail: [email protected] and Andrew Routh, ZSL London Zoo, Zoological Society of London, Regent’s Park, London NW1 4RY, United Kingdom. E-mail: [email protected]

Received 28 November 2009; Revised 18 March 2011; Accepted 11 April 2011 DOI 10.1002/zoo.20396 Published online in Wiley Online Library (wileyonlinelibrary.com).

r 2011 Wiley-Liss, Inc.

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Clark et al. before, and on the same day as, raised FGC. Also, hair plucking significantly increased in the two days following raised FGC. Overall, this study demonstrates how concurrent noninvasive fecal and behavioral monitoring can be used for

c 2011 Wiley-Liss, Inc. gorilla welfare assessment. Zoo Biol 30:1–16, 2011.

Keywords: fecal glucocorticoid metabolite; management; noninvasive monitoring; welfare

INTRODUCTION A recent special issue of Zoo Biology was dedicated to the assessment of zoo animal welfare [Watters and Wielebnowski, 2009]. Although significant progress has been made over the past decade to empirically investigate the validity, reliability, and feasibility of different welfare assessment methods in zoos, more research is encouraged [e.g. Goulart et al., 2009; Hill and Broom, 2009; Watters and Wielebnowski, 2009]. Behavior is the most common measure used to assess zoo animal welfare [Zoos Forum, 2007; Hosey et al., 2009]. Behavioral monitoring can be defined as ‘‘y a means of objectively determining what animals are doing and what they have been doing, a means of keeping track of animal activity on a consistent basis’’ [Watters et al., 2009; p 37]. Ideally, observations are made over an extended time period, and any changes in behavioral patterns (compared with an animal’s own baseline level or the considered ‘‘norm’’ for that species) can be identified and attributed to certain measured variables [Kleiman, 1992; Watters et al., 2009]. Even so, behavioral monitoring is time consuming, and interpretation of behavior can be subjective because wild animals have evolved strategies to mask signs of illness and distress [Jordan, 2005]. Zoo biologists are increasingly exploiting the benefits of physiological welfare indicators originally validated for laboratory and farm animals [see Hill and Broom, 2009, for a review]. Adrenal hormones are commonly measured because these are ‘‘front-line’’ hormones produced in response to stressful situations [Broom and Johnson, 1993; Mo¨stl and Palme, 2002]. There are two further advantages to this approach. First, glucocorticoid metabolites can be obtained noninvasively from feces [Mo¨stl and Palme, 2002; Schwarzenberger, 2007]. Second, these samples provide a measure of adrenal activity over an extended time period, e.g. over different management conditions, rather than the ‘‘snapshot in time’’ provided by blood samples [Schwarzenberger et al., 1996; Millspaugh and Washburn, 2004]. Despite the practical benefits of fecal hormone sampling, there are associated downsides. There is a species-specific time lag (usually 24–48 hr in primates) between the perception of a stressor and the excretion of glucocorticoid metabolites in the feces (the concentration of glucocorticoid metabolites in the feces is referred to as FGC hereafter) that must be taken into account [Schwarzenberger et al., 1996; Buchanan and Goldsmith, 2004; Queyras and Carosi, 2004]. It can also be difficult to determine whether an animal’s physiological response to a stressor is generally ‘‘positive’’ or ‘‘negative’’ in terms of overall welfare state. For example, significantly increased FGC may be produced in response to situations not normally regarded as stressful, such as play and mating [Broom and Johnson, 1993]. Additionally, although elevated levels of adrenal hormones are often associated with chronic stress [Sapolsky, 1992; Dallman, 1993], modifications of the hypothalamic pituitary axis over time can lead to diminished responses in chronically stressed individuals

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[Ostrander et al., 2006; Ulrich-Lai et al., 2006]. Finally, fecal hormone analysis requires technical expertise and is relatively expensive when compared with behavioral observations, and protocols must be validated [Buchanan and Goldsmith, 2004; Palme, 2005; Touma and Palme, 2005]. Overall, there is a strong argument for assessing welfare using a number of measures, in order to increase one’s confidence in the results and appreciate that animals cope with challenges both behaviorally and physiologically [Fraser, 2009]. However, there is an equally strong counterargument that, depending on the context, some methods will be more cost effective and their results easier to interpret than others in zoos. If the same or very similar conclusions about an animal’s welfare can be gained from behavioral observations and fecal sampling, the use of only one method could be justified on economical grounds. This article describes our approach to assessing the welfare of a group of Western lowland gorillas (Gorilla gorilla gorilla) at ZSL London Zoo. A study by Peel et al. [2005] was the first known to publish results on the measurement of fecal glucocorticoids in zoo-housed gorillas. Although their research provides good evidence that a combination of behavioral and adrenal data can accurately monitor stress responses, they concentrated on stress caused by social disturbances in the group. Social management factors in Western lowland gorillas have also been correlated to levels of urinary cortisol [Stoinski et al., 2002a] and salivary cortisol [Kuhar et al., 2005], but only in males. We were interested in the interrelationships between behavior, adrenal activity, and environment; namely, whether behavior and adrenal activity data would lead to similar conclusions about welfare and whether intentional changes to the environment (exhibit modifications) would have beneficial welfare effects. Because the absence of chronic stress is a key prerequisite for good welfare [Broom and Johnson, 1993; Mo¨stl and Palme, 2002], we were particularly interested in any signs of overt chronic stress identified by behavioral and adrenal monitoring. First, we examined the effect of general environmental variables (such as visitor numbers and exhibit noise level) and exhibit modifications (such as privacy screens) on gorilla behavior (Aim 1). Second, we examined the effect of environmental variables on FGC (Aim 2), and finally the relationship between behavioral expression and FGC (Aim 3).

MATERIALS AND METHODS Study Subjects and Housing The study took place between October 2007 and June 2008. Subjects were one adult male and two adult female Western lowland gorillas (Table 1) housed in the ‘‘Gorilla Kingdom’’ exhibit at ZSL London Zoo, UK. A fourth gorilla (adult female) arrived in March 2008, but was gradually introduced into the group, and therefore neither behavioral nor adrenal data could be consistently collected for this individual and she was excluded from analyses. However, her presence/absence in the exhibit was analyzed to see whether this affected the behavior or adrenal activity of the other subjects (see Statistical Analyses below). The exhibit had a naturalistic outdoor area (1,600 m2), an indoor area (120 m2) with extensive climbing structures, and an off-show indoor den area (120 m2). The gorillas were on public display between approximately 10:00 and 17:00 hr. Their diet consisted of green leafy Zoo Biology

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TABLE 1. Details of Gorillas Housed at ZSL London Zoo ID

Sex

Age

Gorilla 1 Gorilla 2 Gorilla 3

M F F

24 34 15

Rearing history

Place of acquisition

Years spent at London Zoo

Wild caught (rescued) Captive (hand reared) Captive (hand reared)

Bristol Zoo Jersey Zoo Leipzig Zoo

5 24 o1

vegetables, fruits, a variety of browse, and a commercial primate pellet. Enrichment was offered frequently and included a variety of temporary physical objects, such as plastic barrels and balls, cloth and hessian sacking, and food-based enrichment, such as frozen fruit, hidden food, log feeders, and stick tools. Behavioral Observations Behavioral observations of focal individual gorillas were made by one observer over the entire study. Four one-hour focal watches were undertaken per day, usually five days per week, using a randomized observation schedule. Using this randomized observation method, the number of 1 hr focal watches collected were 239 (Gorilla 1), 255 (Gorilla 2), and 150 (Gorilla 3). This yielded n 5 7,158 (Gorilla 1), n 5 7,642 (Gorilla 2), and n 5 4,489 (Gorilla 3) scans, using instantaneous scan sampling at 2-min intervals [Altmann, 1974; Martin and Bateson, 2007]. Observations were made from public viewing areas, and the state behavior of the focal animal (Table 2), their location within the exhibit (i.e. indoor/outdoor), and their nearest neighbor were recorded. In addition to scan data, all occurrences of event behaviors (including social interactions with group members) were recorded (Table 2). In parallel with the behavioral observations, a number of environmental variables were recorded either once daily or for each 2-min scan, as appropriate. With regards to visitor numbers, we followed the terminology of Fernandez et al. [2009]. ‘‘Visitor frequency’’ referred to the total number of people visiting the exhibit (per day), and ‘‘crowd size’’ referred to the number of people visiting the exhibit at a point in time (per scan). The total daily number of zoo visitors coming through the front gates was used as a proxy for visitor frequency, because approximately 90% of zoo visitors visited the Gorilla Kingdom exhibit [ZSL, unpublished data]. Crowd size was obtained as a head count of people within 5 m of the exhibit windows or fences. Noise levels were recorded every 2 min by ear on a scale of 1–4, and were also later collapsed into two categories: ‘‘low’’ and ‘‘high.’’ A pilot study revealed that echoing in some parts of the indoor visitor viewing area made it difficult to record noise levels on a decibel meter; hence, a meter was not used further. We also recorded the frequency of visitor’s camera flashes in the exhibit within each 2-min scan. Exhibit Modifications To test the effect of exhibit modifications on gorilla behavior (Aim 1), we made four exhibit modifications between October 2007 and January 2008 (Table 3). These were made in a stepwise manner approximately 1 month apart, so that gorilla behavior observed during the first month (approximately 4 weeks) of the modification was compared with the behavior observed during the previous month. Although these modifications might have an additive effect on behavior, this was unavoidable because modifications could not be randomized over days. All Zoo Biology

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TABLE 2. Ethogram of Broad-State and Event Behaviors Broad-state behavior Vigilance of Alert, fixed gaze toward something in the environment, including another environment gorilla, keeper/staff, or observer Vigilance of Positive vigilance of visitors (PVV): alert, fixed gaze toward visitors; with visitors relaxed posture; may include interaction with visitors through direct eye contact, mimicking, or play behavior Negative vigilance of visitors (NVV): alert, fixed gaze toward visitors; erect posture; may charge at visitors and often coincides with banging windows Rest/sleep Lie, sit, or stand Locomotion Walk, run, climb, or swing Food-related Feed or forage Maintenance Autogroom, defecate, or urinate Social/sexual Allogroom, play, aggression, or sexual contact with conspecific Aberrant Pluck haira or ‘‘peek’’a out of one small window in the indoor area. This window overlooked the outdoor area and visitor boardwalk Out-of-sight The subject cannot be observed within the exhibit Other Any behavior which does not fall into the above categories Event behavior Aggressive Affiliative Sexual Aberrant Other

Bang window, bare teeth, bite, hit or grab a conspecific, chest beat, charge, throw object, displace or supplant a conspecific, or throw vegetation Click fingers, clap hands, ‘‘kiss’’ toward visitors, mimic visitors Masturbate, display genitals, inspect genitals, slap genitals, or mount conspecific Rocka, repeatedly regurgitate and reingest food (RR)a. RR co-occurs with ‘‘peeking’’ Any other behavior which does not fall into one of the above categories, rarely occurring

a

Behaviors performed by Gorilla 2 only.

TABLE 3. Exhibit Modifications Made Between October 2008 and January 2009 Modification

Time period used (mm/dd/yy)

Description

Clover hay substrate

10/25/07–ongoing Approximately 3.5 kg clover hay was added to the indoor area daily as an additional foraging substrate External privacy screen 11/12/07–02/29/08 Two semi-opaque screens made from twigs and branches were attached to two of the four viewing windows of the indoor area. The general outline and movement of visitors could still be viewed by the gorillas through the screens and vice versa Small window covering 12/13/07–03/11/08 The small window through which Gorilla 2 ‘‘peeked’’ (refer to Ethogram, Table 2) was covered using opaque black paint Internal privacy screen 01/04/08–ongoing Three opaque screens made from cross-sections of logs were attached inside the indoor area Note that privacy screens and window coverings did not deprive gorillas from viewing visitors; rather these gave gorillas more control over viewing (and being viewed by) visitors.

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modifications were made within a 4-month period, and therefore the effects of season and other confounding factors were potentially minimized [Dawkins, 2007]. Fecal Sample Collection and FGC Extraction To test the effect of various environmental variables on FGC (Aim 2) and the relationship between behavioral expression and FGC (Aim 3), fecal samples were collected from March 17 to June 02, 2008, during the daily morning cleaning of individual off-show dens. Occasionally, the identity of the sample was uncertain, and so was not included in our analyses. Samples were immediately stored at 201C until analysis. Hormones were extracted from feces using a wet-weight shaking extraction adapted from Walker et al. [2002]. Briefly, following manual homogenization of samples, 0.5 g of wet fecal matter was combined with 90% methanol, shaken overnight at room temperature, and centrifuged for 20 min at 543 g. The supernatant was decanted, evaporated to dryness, and just before analysis extracts reconstituted in 250 ml methanol. Fecal Enzyme Immunoassay and High-Performance Liquid Chromatography FGC was analyzed using an established cortisol enzyme immunoassay [EIA; Young et al., 2004, adapted from Munro and Stabenfeldt, 1984], which used polyclonal cortisol antiserum (1:8,500; R4866; provided by CJ Munro, University of California, Davis, CA), horseradish peroxidase conjugated label (1:30,000; provided by CJ Munro, University of California, Davis, CA), and cortisol standards (SigmaAldrich, Dorset, UK). The intra- and interassay coefficients of variation were 4.4 and 7.9%, respectively. FGC was characterized using high-performance liquid chromatography (HPLC), as previously described by Young et al. [2004]. A pool of peak FCG extracts (12 extracts, 4 per gorilla) were filtered using a C-18 matrix cartridge (Thermo Hypersep C18; ThermoFisher Scientific, Runcorn, UK) and were separated on a C-18 column (ThermoFisher Scientific Hypersil Gold 150  4.6 mm, particle size 5 mm; Runcorn, UK) with a column guard (ThermoFisher Scientific Hypersil Gold 10  4 mm; Runcorn, UK) using a linear gradient of 20–100% methanol in water for more than 80 min (1 ml/min flow rate, 1 ml fractions). Additionally, reference standards known to cross-react with the cortisol EIA (cortisol, corticosterone, prednisolone, prednisone, cortisone, 21-desoxycortisone, desoxycorticosterone, progesterone) were also separated using the same reverse-phase HPLC method. All fractions were evaporated to dryness, reconstituted in 180 ml steroid buffer, and assayed for immunoreactivity, as described above. Serial dilutions of fecal extracts yielded a displacement curve parallel to the standard curve and there was no evidence of matrix interference. Consistent with other studies [Heistermann et al., 2006], only relatively small amounts of native cortisol (fractions 33–34, 2.8%) were present in gorilla feces. Large amounts of immunoreactivity were observed in other fractions (fractions 41–49, 42.6% and fractions 68–76, 19.0%), some of which were associated with some known reference standards (Fig. 1). Our assay was thus considered valid, and we proceeded with our statistical analyses. Statistical Analyses Levene’s test and Spearman’s rank were implemented in MINITAB version 15 [Minitab Inc., 2007], to assess the variance of FGC over time and the correlation between FGC and time, respectively. Generalized Linear Mixed Models (GLMMs) Zoo Biology

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Fig. 1. Reverse-phase HPLC separation of immunoreactive FGC metabolites in feces of the three study subjects. Immunoreactivity in each fraction was measured with a cortisol EIA (closed circles) and retention times for coeluted reference standards are indicated by triangles.

implemented in MLWiN version 2.02 [Rasbash et al., 2004] were then used to explore what environmental variables affect the occurrence of specific behaviors (Aim 1), what environmental variables affect variability in FGC (Aim 2), and if there is a relationship between behavior and FGC (Aim 3). Binomial models were used to test the occurrence of particular behavior (i.e. present 5 1, absent 5 0) and a normal error structure when modeling (log transformed) FGC. A full description of the model terms are shown in Table 4. All variables were entered into models and dropped sequentially until only those that explained significant variation remained (i.e. the minimal model). Each dropped variable was then put back into the model to obtain its level of nonsignificance and to check that significant variables had not been wrongly excluded. GLMMs are a useful way of analyzing data when there is potential for pseudoreplication of data points and inflated P-values [Bolker et al., 2009]. To control for potential nonindependence of behavior within observation sessions and across days, we entered ‘‘date,’’ ‘‘focal watch,’’ and ‘‘scan number’’ into our behavioral models as random effects. Because FGC is also unlikely to be independent across days, we entered ‘‘date’’ as a random effect in our models where FGC was our dependent variable. We chose to model each gorilla separately, because we were interested in each gorilla as an individual [Plowman, 2006]. Importantly, to test for the effect of environmental variables on FGC, we modeled environmental variables occurring 24 hr and 48 hr relative to the time of feces collection (0 hr). This allowed us to investigate the different potential time lags between a subject’s perception of an environmental variable as ‘‘stressful’’ and glucocorticoids being excreted in the feces. To test the effect of behavior on FGC, we modeled FGC on the same day as observed behavior (0 hr), up to two days before ( 24 hr, 48 hr) the observed behavior, and up to two days after the day of observed behavior (124 hr, 148 hr). Zoo Biology

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TABLE 4. Summary of Terms Entered Into Models to Explore the Interrelationships Between Gorilla Behavior, Adrenal Activity, and Environment

Study question/aim

Dependent variable (each modeled separately)

Fixed terms

(1) What environmental variables and exhibit modifications affect the occurrence of behavior?

Behavioral Food-related Investigatory NVV PVV Window banging Peekinga Hair pluckinga RRa Rockinga

Environmental Crowd size Visitor frequency Proportion time noise level ‘‘high’’ Exhibit modification (four types) Frequency of camera flashes Control Location within exhibit Keeper/s present at exhibit Gorilla 4 present (Y/N) Estrus state if female (Y/N) Nearest gorilla neighbor

(2) What environmental variables affect variability in FGC metabolite concentrations?

Adrenal FGC (log transformed)

Environmental Same as listed above Control Same as above

(3) Is there a relationship between behavior and FGC metabolite concentration?

Adrenal FGC (log transformed)

Behavioral Food-related Investigatory NVV PVV Window banging Peekinga Hair pluckinga RRa Rockinga Control Same as above

Separate models were run for each gorilla at different time lags (see Methods). The dependent variables of interest (behavioral, adrenal, and environmental) and ‘‘control’’ variables (which may affect behavior) entered into each model are listed. a Behaviors performed by Gorilla 2 only.

RESULTS Cortisol Profiles of Gorillas All three gorillas showed variability in their FGC throughout the study (Gorilla 1: median FGC (ng/g) 5 12.4, range 2.8–20.8; Gorilla 2: median FGC (ng/g) 5 17.7, range 8.5–120.6; Gorilla 3: median FGC (ng/g) 5 12.7, range 6.6–44.3; Fig. 2). Tests of unequal variance revealed that Gorilla 2 showed significantly larger variability in FGC than Gorilla 1 (L 5 7.24, Po0.05). Gorilla 1 was the only individual to show a time trend in FGC (FGC declined over study period: r 5 0.383, P 5 0.01; Fig. 2). Zoo Biology

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Fig. 2. Profiles of FGC metabolite concentrations (ng/g) in Gorillas 1 (grey circles), 2 (black squares), and 3 (white triangles) housed at ZSL collected over a 2 1/2 month study period. n 5 44, n 5 61, and n 5 54 fecal samples for Gorillas 1, 2, and 3, respectively.

Behavioral Profiles of Gorillas Over the course of the study period, Gorilla 2 spent 3.90% time hair plucking, 2.35% peeking, and 5.13% engaged in NVV. Gorilla 1 and 3 spent 0% time engaged in aberrant behavior, and NVV was under 5%. 116 counts of window banging, 42 of rocking, and 158 of RR were recorded for Gorilla 2. Eighty-six counts of window banging were recorded for Gorilla 1 and 41 for Gorilla 3. Aim 1. Effect of Environmental Variables on Gorilla Behavior Environmental variables (Table 4) had similar effects on the behavior of all three gorillas. In summary, negative vigilance of visitors (NVV; staring, posturing, and charging at visitors) significantly increased as environmental noise levels increased and food-related behavior significantly decreased as crowd size increased (Table 5). Exhibit modifications had a number of positive effects on behavior. Notably, when privacy screens were used, NVV significantly decreased in two subjects (Table 5). No other measured environmental variables predicted the occurrence of specific behaviors. Aim 2. Environmental Variables and FGC We found no significant effects of environmental variables upon FGC in any of the gorillas (P40.05 in all cases: Table 5). Zoo Biology

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Noise level

Crowd size

Clover hay substrate

External privacy screen

Arrows refer to significant increases or decreases in behavior. Po0.05; Po0.001.

Gorilla 3 m NVV k Food related m Food related

k NVV

Internal privacy screen

k NVV k NVV RR eliminated

Small window covering

Exhibit modifications

Gorilla 1 m NVV m NVV m Food related k Food related k NVV Gorilla 2 m NVV k Food related k NVV

Gorilla

Environmental variables

m m m m

NVV one day before NVV on the same day as Hair plucking one day after Hair plucking two days after

Increased FGC

TABLE 5. Summary of Models Reporting the Effects of Environment on Behavior and the Relationship Between FGC and Behavior

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Aim 3. Behavior and FGC We found significant relationships between behavior and FGC in Gorilla 2 (female). Specifically, her NVV was significantly higher one day before and on the same day as raised FGC. Also, hair plucking significantly increased in the two days following raised FGC (Gorilla 2 was the only subject to perform this behavior). No behaviors were significantly related to FGC in either Gorilla 1 or 3. DISCUSSION Environmental Variables and Gorilla Behavior Many studies on nonhuman primates report that zoo visitors can be a source of stress [reviewed by Hosey, 2005; Fernandez et al., 2009]. In this study, we found no evidence that visitors caused overt stress- or anxiety-related behaviors previously described for other zoo primates [e.g. Chamove et al., 1988; Blaney and Wells, 2004; Wells, 2005]. Several studies report that primates are more vigilant of noisy visitor groups [e.g. Birke, 2002; Cooke and Schillaci, 2007; Carder and Semple, 2008], but the distinction between ‘‘positive’’ and ‘‘negative’’ forms of vigilance toward visitors is seldom made. In making this distinction, we show that in our study group, NVV significantly increased as exhibit noise level increased. It is also possible that an increased level of NVV caused rather than resulted from increased visitor noise, because primates who are responsive to visitors are likely to draw bigger crowds and create more visitor activity [Mitchell et al., 1992; Hosey, 2005]. In fact, one interpretation is that visitors elicit species-typical behaviors (e.g. charging) in a relatively appropriate context, because the male was displaying behaviors related to territory defense. The fact that the male’s NVV was not associated with increased FGC levels suggest there is no welfare issue here. We did not find any significant relationships between positive vigilance of visitors and environmental variables; however, additional visitor characteristics, such as age, sex, and activity, could affect the gorillas’ vigilance and these deserve further study [Kuhar, 2008]. All gorillas showed significantly lower levels of food-related behavior as crowd size increased. Similarly, Wood [1998] found that larger and more variable weekend crowds were associated with decreased foraging in zoo chimpanzees, Pan troglodytes. We found that adding clover hay to the exhibit may help to ameliorate the visitor effect, because it significantly increased food-related behaviors in two gorillas and decreased NVV in one of these individuals. A similar increase in foraging behavior has been achieved in orangutans, Pongo pygmaeus, using browse [Birke, 2002]. Installation of the external privacy screen on windows or internal screen within the indoor exhibit had the beneficial effect of significantly reducing NVV in all gorillas. Overall, the internal privacy screen was more successful because it significantly reduced NVV in two gorillas, whereas the external screen only reduced NVV in one gorilla. Similar to our findings, Blaney and Wells [2004] found that placing a camouflage net between gorillas and zoo visitors reduced aggressive and stereotypical behavior in gorillas. Finally, by covering a small window, aberrant ‘‘peeking’’ behavior and regurgitation and reingestion of food (RR) ceased during the first month in Gorilla 2. RR is an appetitive disorder described as ‘‘statistically normal’’ for captive gorillas; however, it is considered to be undesirable [e.g. Akers and Schildkraut, 1985; Zoo Biology

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Lukas, 1999; Hill, 2009]. Gorilla 2 did not appear frustrated when the window was covered. In fact, Gorilla 2 seemed to quickly accept that this window was no longer a vantage point to watch visitors, and instead spent time in closer proximity to the other gorillas. Continued research is required to examine RR and its relationship to ‘‘peeking’’ in this particular female. Environmental Variables and FGC We found no significant relationship between day-to-day changes in environmental variables recorded (such as visitor numbers) in this study and FGC. However, there may be unmeasured environmental variables that significantly affected adrenal activity. The measurement of temperature and humidity within the indoor and outdoor areas using automated data loggers could prove insightful [Stoinski et al., 2002b; Ross et al., 2007]. More important, when exhibit modifications are made in the future, we intend to use concurrent behavioral and adrenal monitoring to assess whether more major environmental changes such as these have an effect on welfare. Behavior and FGC Our results highlighted distinct individual differences in behavior and adrenal activity, and significant relationships between behaviors and FGC were identified in one female gorilla (Gorilla 2). In this female, NVV significantly increased one day before raised FGC, suggesting that NVV could be a suitable behavioral indicator of adrenal activity taking a one-day time lag into account. The theoretical pathway in Gorilla 2 is as follows: (i) there is an immediate vigilance response to visitors (probably in response to their noise levels based on our other findings); (ii) a physiological response occurs; and (iii) raised FGC is detected in the feces 24 hr later. This matches with previous studies that report a 12–48 hr FGC time lag in primates [e.g. Whitten et al., 1998; Queyras and Carosi, 2004; Heistermann et al., 2006]. More difficult to explain is the significantly higher NVV on the same day as raised FGCs (24 hr after the occurrence of a stressor). It may indicate that NNV behavior lingers at higher levels for a day after the occurrence of an environmental stressor, or alternatively, several consecutive days of very high visitor number/noise level could contribute to several days of raised adrenal activity. Finally, hair plucking by Gorilla 2 significantly increased on the two days following raised FGC. Hair plucking is widely considered to be an indicator of stress or anxiety in captive primates [e.g. Maestripieri et al., 1992; Bloomsmith et al., 2007; Hosey and Skyner, 2007]. We hypothesize that hair plucking in this particular female may serve as a coping mechanism after the occurrence of a stressor, perhaps by reducing the concentration of circulating glucocorticoids. Moreover, because Gorilla 2 exerts a discernible behavioral and physiological stress response and that both return to baseline levels within a few days, one could argue that her welfare state is acceptable. In contrast to previous research, we did not find an effect of additional substrate (clover hay) on hair plucking [Boccia and Hijazi, 1998; Beisner and Isbel, 2008]; more research is being undertaken into the effects of enrichments that stimulate naturalistic foraging [Clark, in preparation]. Welfare Assessment in Zoo-Housed Gorillas Animal welfare is the degree to which an animal can cope with challenges in its environment, and can be determined by both psychological and physiological measures [Broom and Johnson, 1993; Barber, 2009]. The absence of chronic stress is Zoo Biology

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one prerequisite for good welfare [Broom and Johnson, 1993; Mo¨stl and Palme, 2002], and the gorillas in this study did not show signs of overt chronic stress according to our behavioral and adrenal monitoring. Aberrant behavior was only performed by one gorilla out of three, at levels under 10% of the total activity budget. In addition to this, all exhibit modifications had positive effects on gorilla behavior. In light of our results, we concur with numerous authors that no single metric provides an overall measure of welfare in captive animals [e.g. Fraser, 1995; Dawkins, 1998; Swaisgood, 2007]. More specifically, our results agree with other studies that found no clear relationship between adrenal and behavioral indicators of stress in great apes [Elder and Menzel, 2001; Kuhar et al., 2005]. In this study, behavioral and adrenal monitoring yielded very different information. For example, noise level affected NVV, but only the behavior and not the environmental variable was related to corresponding FGC. This could be explained by the frequency of sampling. Behavior was recorded for every watch throughout the day, whereas only one fecal sample was collected per day. Behavior was, therefore, useful to measure the effects of fine temporal scale environmental changes, such as the effects of crowd size and noise level. In contrast, hormone metabolites in the feces represent pooled adrenal activity over the previous several hours or days, giving a more dampened hormone profile over time with less interference from daily rhythm and acute stress [e.g. Schwarzenberger et al., 1996; Whitten et al., 1998; Mo¨stl and Palme, 2002]. Adrenal monitoring was, therefore, useful in measuring the relationships with daily levels of behavior, such as NVV and hair plucking. These inconsistencies reveal the difficulty in adopting a multimethod approach. The results from this study highlight the value of individual assessment. We found distinct individual differences in behavior and adrenal activity over the course of the study. For example, one female gorilla exhibited significantly higher variance in FGC than the other two gorillas, and was the only individual to display aberrant behavior. An animal’s behavioral and physiological response to stressors is dependent on their perceptual abilities, which may vary by age, individual, and developmental history [Moberg, 2000; Owen et al., 2004]. The impact of a stressor may also depend on the nature of the stressor (severity and duration) [Boissy, 1995; Tilbrook et al., 2000; Pacak and Palkovits, 2001]. In summary, given the differences we have uncovered among just three gorillas, it may not be appropriate to generalize these results to all gorillas housed in zoos, and instead we encourage an individualized study of gorilla welfare. CONCLUSIONS 1. Visitors did not cause overt stress- or anxiety-related behaviors in the gorilla group we studied. However, visitor crowd size and noise level did negatively affect food-related behavior and vigilance. The addition of clover hay and installation of the privacy screens decreased these ‘‘visitor effects’’ and should be continued in future gorilla management. 2. No relationships between visitor numbers and adrenal activity, or exhibit noise levels and adrenal activity, were observed in this group of zoo gorillas. 3. Behavioral and physiological measures yielded different information regarding welfare; therefore, a range of measures should ideally be applied when investigating the welfare of gorillas in zoos. Zoo Biology

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