alouatta palliata travel time predicts fecal glucocorticoid levels

Travel Time Predicts Fecal Glucocorticoid Levelsin Free-Ranging Howlers (Alouatta palliata)

Jacob C. Dunn & Jurgi Cristóbal-Azkarate &

Björn Schulte-Herbrüggen & Roberto Chavira &

Joaquím J. Veà

Received: 5 August 2012 /Accepted: 20 December 2012 /Published online: 23 January 2013# Springer Science+Business Media New York 2013

Abstract Environmental stressors impact physiology in many animal species.Accordingly, the monitoring of fecal glucocorticoid metabolites (fGCM) has beenincreasingly used to evaluate the physiological costs of habitat disturbance on wildanimal populations, providing a powerful tool for conservation and management.Several studies have suggested that primates in forest fragments have higher fGCMlevels than those in continuous forests, yet the proximate causes of fGCM variationremain to be identified. In previous studies of Mexican howlers (Alouatta palliatamexicana) in Los Tuxtlas, Mexico, we found that individuals living in a smaller andmore disturbed forest fragment consumed significantly less fruit and had a signifi-cantly higher feeding effort than those living in a bigger, more conserved forestfragment. Here, we aimed to examine the effects of fruit consumption and travel timeon fGCM levels in the same two groups of howlers, during three sampling sessions

Int J Primatol (2013) 34:246–259DOI 10.1007/s10764-013-9657-0

Electronic supplementary material The online version of this article (doi:10.1007/s10764-013-9657-0)contains supplementary material, which is available to authorized users.

J. C. Dunn (*) : J. Cristóbal-AzkaratePrimate Immunogenetics and Molecular Ecology Research Group, Division of BiologicalAnthropology, University of Cambridge, Cambridge( CB2 3QY( UKe-mail: [email protected]

J. C. DunnCentro de Investigaciones Tropicales, Ex-Hacienda Lucas Martín, C.P. 91019, Xalapa, Veracruz,México

B. Schulte-HerbrüggenUnited Nations Environment Programme World Conservation Monitoring Centre, EcosystemAssessment Programme, Cambridge( CB3 0DL( UK

R. ChaviraDepartamento de Biología de la Reproducción, Instituto de Ciencias Médicas y Nutrición SalvadorZubirán, C.P. 14000, Mexico, D.F., México

J. J. VeàCentre Especial de Recerca en Primats, Facultat de Psicologia, Universitat de Barcelona, Barcelona08035 Spain

http://dx.doi.org/10.1007/s10764-013-9657-0

that differed markedly in fruit availability. We found that fGCM levels (N=202 fecalsamples) were higher in the howler group living in the smaller forest fragment andvaried seasonally in both focal groups, being lowest when fruit consumption washighest. However, our results suggest that travel time is the main factor predictingfGCM levels in howlers, and that although fruit consumption may be negativelyrelated to fGCM levels, this relationship is probably mediated by the strong effect thatfruit consumption has on travel time. Our results provide important insight into theproximate causes of fGCM variation in primates in fragments and highlight thepotential conservation significance of studies showing that habitat loss and transfor-mation can lead to increases in travel time in wild primates.

Keywords Activity . Alouatta palliata mexicana . Diet . Fecal glucocorticoids . ForestFragmentation . Fruit . Howler . Metabolic stress

Introduction

Animals living in the wild face stress through competition for food, mates, andterritories, as well as that imposed by the physical environment and exposure todisease. Any of these stressors may disrupt physiological homeostasis, leading towhat is commonly termed the stress response (Sapolsky 2002). Although thismechanism is adaptive in the short term, if a stressor persists too frequently, orfor too long, the resulting high levels of glucocorticoids (GCs), stress hormonesthat function to optimize energy availability (Sapolsky 2002), can have deleteri-ous effects on reproduction, immune function, and survival (Sapolsky 2002;Sapolsky et al. 2000; Wingfield 2005; Wingfield and Romero 2001). Becauseof this, GC monitoring has been increasingly used to evaluate the physiologicalcosts of habitat disturbance, thereby providing a powerful tool for the conserva-tion and management of wild vertebrate populations (Busch and Hayward 2009).

Despite the enormous potential of this tool, determining the proximate causesof GC variation is complicated by the presence of multiple potentially stressfulstimuli. Traditionally, endocrinological studies of wild primates have focused onpsychological stressors, such as dominance rank (Abbott et al. 2003; Gesquiereet al. 2011a; Sapolsky 1993, 1994). However, ecological factors affecting theavailability of food resources, such as habitat characteristics (Behie et al. 2010;Chapman et al. 2006; Jaimez et al. 2012; Martínez-Mota et al. 2007; Rangel-Negrín et al. 2009) and resource seasonality (Beehner and McCann 2008;Carnegie et al. 2011; Gesquiere et al. 2008; 2011b; Pride 2005a), may representsignificant sources of stress in primates. For example, decreases in the availabil-ity (Behie et al. 2010; Foerster and Monfort 2010; Pride 2005a) and consump-tion (Champoux et al. 1993; Foerster et al. 2012; Muller and Wrangham 2004;Pride 2005b) of ripe fruits, which are rich in usable energy, have been related toincreases in GC levels in primates. Further, increases in energetically costlyactivities such as travel, which may be related to ecological factors, are alsoreported to have positive effects on GC levels (Cavigelli 1999; Gesquiere et al.2011b; Girard and Garland 2002; Kraemer et al. 2008; Muller and Wrangham2004; Tharp 1975).

Fecal Glucocorticoid Levels in Free-Ranging Howlers 247

In previous studies of Mexican howlers (Alouatta palliata mexicana), which arelisted as critically endangered by the IUCN (Cuarón et al. 2008), we found thatindividuals living in a smaller and more disturbed forest fragment consumed signif-icantly less fruit and exhibited a significantly higher feeding effort than those living ina bigger, more conserved forest fragment (Dunn et al. 2009, 2010). Here, our aim wasto improve our understanding of the proximate mechanisms underlying GC variationin primates in fragments by examining the effects of travel time and fruit consump-tion on fecal glucocorticoid metabolite (fGCM) levels in the same two groups ofhowlers previously studied (Dunn et al. 2009, 2010), during three periods thatdiffered markedly in fruit availability.

Based on published evidence about the relationships between activity pattern,fruit consumption, habitat characteristics, and fGCM levels in primates, weproposed four hypotheses and used an information-theoretic approach to evaluatethe relative support for each of them: 1) fGCM levels are positively related totravel time; 2) fGCM levels are negatively related to fruit consumption; 3) fGCMlevels are higher during periods of low fruit availability; and 4) fGCM levels arehigher in a group living in a smaller and more disturbed forest fragment than agroup living in a larger, more conserved forest fragment.

Materials and Methods

Study Site

The Los Tuxtlas Biosphere Reserve (18º37 –18º35 N, 95º08 -95º05 W; 0–400 m asl)is the northernmost limit for populations of Alouatta palliata (Estrada and Coates-Estrada 1988) and represents the most northerly tropical rain forest distribution in theAmerican continent (Dirzo and Garcia 1992). The dominant vegetation in LosTuxtlas was originally tropical rain forest, but this region has been severely frag-mented over the last 60 yr (Guevara et al. 2004). Therefore, the remaining popula-tions in this region are isolated in an archipelago of forest fragments that vary in size,isolation distance, and habitat quality (Arroyo-Rodríguez et al. 2008; Cristóbal-Azkarate et al. 2005).

Local climate is warm and humid with a mean annual temperature of 25 °C andmean annual rainfall of 4900 mm (Soto and Gama 1997). There are three distinctperiods of fruit availability reported in the region: a primary peak at the end of the dryseason/beginning of the rainy season (April–June), a less intense secondary peak inthe wet season (August–October), and a period of fruit scarcity from November toMarch (Dunn et al. 2010).

Focal Groups

The RH group resided in a 244-ha forest fragment inhabited by five groups ofhowlers and the total population density was 0.12 ind/ha (Cristóbal-Azkarate et al.2005). The RC3 group was the only group found in a 7.2-ha fragment and the totalhowler population density was 1.11 ind/ha. The home range of the RH group wasmarkedly larger than that of the RC3 group (89.5 ha vs. 5.8 ha; Dunn et al. 2009) and

248 J.C. Dunn et al.

was more characteristic of an old-growth forest, with a greater availability of fruit andbig trees, whereas the home range of the RC3 group was more perturbed, with agreater number of pioneer species (Dunn et al. 2009).

We selected the focal groups to control for variation in GC levels that may resultfrom differences in group size and composition. The RH group consisted of threeadult males, three adult females, and one infant at the start of the study, and one infantwas born during the study. The RC3 group also consisted of three adult males, threeadult females, and one infant, as well as one juvenile, and two infants were bornduring the study. We collected data only from adult individuals. Both groups havebeen observed almost continuously since 1999 (Asensio et al. 2009; Cristóbal-Azkarate et al. 2005; Dunn et al. 2009, 2010, 2012) and the individuals were,therefore, known to us and habituated to our presence.

Sampling Sessions

We collected data during three different sampling sessions between February2006 and February 2007: Sampling Session 1 (SS1: February 4–March 2, 2006and January 20– February 9, 2007), during the annual period of fruit scarcityreported for Los Tuxtlas; Sampling Session 2 (SS2: April 24–May 20, 2006and May 28–June 14, 2006), during the primary peak in fruit productionreported for the region; and Sampling Session 3 (SS3: August 9–August 27,2006 and September 6–September 26, 2006), during the secondary peak in fruitproduction reported for the region (see Dunn et al. 2010 for a review of localplant phenology).

Diet and Activity Pattern

The methods used to collect data on diet and activity pattern have been described indetail elsewhere (Dunn et al. 2010). Briefly stated, we collected data from each groupon 16 nonconsecutive days in each sampling session, for a total of 48 d and 288observation hours per group. We used focal observations of 2-h continuous samplingon randomly selected adult individuals between 07:00 h and 15:00 h. We categorizedbehavioral observations into resting (sleep or static without interaction), feeding(inspection of food, bringing food to mouth, chewing and swallowing, moving whilefeeding within a food patch), traveling (movement to a new area or tree), and otherbehavior (remaining activities not categorized as resting, feeding, or traveling).During feeding we also recorded the food item consumed: fruits, leaves, and otheritems (flowers, petioles, or bark). We reported diet and activity as percentages of totalobservation time (Fig. 1).

We used observational data and the birth dates of infants to evaluate the repro-ductive status of females, which included data from the biweekly monitoring of alladult females during the 12 mo after the end of our study period (Cristóbal-Azkarateet al. 2011), basing our calculations on the mean howler gestation period of 6 mo(Crockett and Rudran 1987; Glander 1980; Strier et al. 2001). We categorizedfemales into one of three reproductive categories: pregnant (defined as starting fromthe day of estimated conception to the day before parturition), lactating (defined byeither observations of lactation, or, in case of uncertainty, as starting from the day of


parturition until 20 mo, when latest weaning occurs; Domingo-Balcells and Vea-Baro2009), and other (neither pregnant nor lactating).

Fecal Collection and Glucocorticoid Assay

We collected the first defecation of the morning from each individual to controlfor diurnal variations in fGCM excretion. Overall we analyzed 202 fecal samples(N=112 RC3, 90 RH): 58 in SS1 (33 RC3, 25 RH), 81 in SS2 (43 RC3, 38 RH),and 63 in SS3 (36 RC3, 27 RH), which were evenly distributed among the adultindividuals (mean±SD samples/individual: RC3=18.6±2.5; RH=15.0±2.7) andbetween sexes (94 female samples, 108 male samples). We collected samplesonly if free of urine and other impurities (mud, dust, rain, etc.) in 10-mlpolypropylene tubes, which we labeled with the time and date of collectionand the identity of the individual, and immediately stored them in a cooler withfrozen gel packs. Once back at the field station each afternoon, we stored thesamples in a freezer at −20 °C. Freezing samples is reported to have a minimaleffect on fGCM (Hunt and Wasser 2003; Khan et al. 2002) and we storedsamples for a maximum of 12 mo at −20 °C. At the end of the study, welyophilized the samples to eliminate the water content. To be sure that all thewater was removed from the samples, we repeatedly weighed 30 of the biggestsamples during lyophilization until there was no variation in their weights.

We extracted fGCMs using a modified version of the method described in Wasseret al. (2000), which has previously been used for Mexican howlers (Cristóbal-Azkarate et al. 2007). Briefly stated, 0.7 g of pulverized dry feces was shaken for20 h in 4.0 ml of methanol analytical grade reagent. We then centrifuged (3000 rpm)extracts for 20 min and recovered and evaporated the supernatants containing the

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

RC3 RH RC3 RH RC3 RH

SS1 SS2 SS3

Ob

serv

atio

n T

ime

Sampling Session / Group

Leaf

Fruit

Rest

Travel

Fig. 1 The percentage of total observation time spent traveling, resting, and feeding (fruit and leaves) bytwo groups of howlers in different forest fragments in Los Tuxtlas, Mexico, during three different samplingsessions (SS1, SS2, and SS3).


steroids. This extraction technique has proven consistently to recover 84.3±2 % ofthe steroids in the original sample (Wasser et al. 2000).

We performed glucocorticoid quantification by radioimmunoassay (RIA). Wereconstituted the extracts with a phosphate assay buffer free of steroids (DPC, LosAngeles, CA) and used a corticosterone COAT-A-COUNT RIA kit (DPC, LosAngeles, CA), which is a solid-phase I125 RIA method. The calibration range forthe assay was 20–2000 ngml–1 (57.7–5772 nmoll–1), with a 2-h incubation time atroom temperature (15–28 °C). Finally, we quantified fGCM levels in a gammacounter for 1 min and report values in nanograms per gram of dry feces (ngg–1).Corticosterone antibodies have proven to be more effective than other antibodies formeasuring glucocorticoid metabolites in the feces of many different mammal species(Wasser et al. 2000).

We were unable to conduct an adrenocorticotropic hormone (ACTH) chal-lenge test to validate our method, as Mexican howlers are currently listed asCritically Endangered by the IUCN (Cuarón et al. 2008) and their capture forunwarranted invasive practices is prohibited. However, Aguilar-Cucurachi et al.(2010) conducted biological validations with Alouatta palliata mexicana for thesame extraction and fGCM quantification method. They evaluated fGCM levelsbefore, during, and after an artificial stressor (capture and translocation) andfound a clear peak in corticosterone immediately after the stressor, indicatingthat corticosterone assays provide a reliable adrenal stress signal in thissubspecies.

We ran the extracts in duplicate assays together with standards. We ran atotal of three assays of fGCM levels and the intra- and interassay coefficientsof variation were 2.4 % and 3.1 % respectively. Cross-reactivities of theantibody with other steroids is <0.34 %. We validated hormone assays viaparallelism and accuracy tests. We performed parallelism by comparing theslope of a serial dilution curve of pooled howler fecal extracts to the slopeof the standard curve using a Student’s t-test (Zar 2009). The difference wasnot significant (t=0.25, P=0.94, N=10), indicating that the two slopes wereparallel. For accuracy, in addition to the standard curve, a second set ofstandards was “spiked” with a constant amount of pooled howler fecal extract.We examined accuracy by subtracting the value of the pooled extract from the“spiked” samples (pooled extract+standard tubes) and by calculating the slopethat results from plotting the values of the standards and the “spiked” standards.In our case, the slope that results from plotting these values was B=0.999,indicating high accuracy.

Data Analyses

We used generalized linear mixed effect models (GLMMs) to analyze the effect ofindependent variables (forest fragment, sampling session, travel time, and fruitconsumption) on a dependent continuous variable (fGCM levels; Table I). GLMMsallow for the analysis of non-normally distributed data and the specification of arandom effect to account for the nonindependence of repeated measures. All statis-tical analyses were performed in the R environment, version 2.9.2 (R DevelopmentCore Team 2011).


Models included in the analyses excluded combinations containing both fruitconsumption and travel time, as these variables were not independent, leading tocollinearity (see Mundry 2010 for discussion of why this is problematic), andexcluded interaction terms, which were not relevant to our hypotheses. We specifiedindividuals within forest fragments as hierarchical random effects to control for therepeated sampling of fecal samples from the same individual. Further, as pregnancyand lactation have been found to significantly increase fGCM levels in femaleprimates (Engh et al. 2006; Foerster et al. 2012; Mastorakos and Ilias 2003;Setchell et al. 2008), we evaluated fGCM levels among reproductive categories. Aspregnant and lactating females were found to have substantially higher fGCM levelsthan nonreproducing females and males (Fig. S1), we controlled for this variable inall models (Table I).

We based model evaluation on the information-theoretic approach usingAkaike’s information criterion (AIC) to infer the relative support for alternativemodels. This approach stems from the recognition that data rarely provide absolutesupport for a single hypothesis; rather, data can influence only the extent to whichany given hypothesis is supported (relative to competing explanations). Theapproach has become increasingly popular in behavioral ecology and is particu-larly well suited for studying ecological systems in which multiple hypothesescould be proposed to explain observed phenomena (Lukacs et al. 2007; Richards etal. 2010). We based the interpretation of GLMM results on model ΔAICi, i.e., AICof respective model – AIC of best model. Following the guidelines published byBurnham and Anderson (2002), we considered models having ΔAICi≤2 to receivesubstantial support, those having ΔAICi within 4–7 of the best model to receiveconsiderably less support, and models with ΔAICi>10 to have essentially nosupport. We further examined all interpretations of relative support for individualvariables by assessing their respective effect sizes and standard errors. We tested

Table I Variables used in GLMMs

Variable Definition

Dependent variables

fGCM level (ngg–1) Continuous variable (min=4.2; max=337.3; mean=117.1)

Independent variables (fixed effects)

Forest fragment Factor variable: 2 levels (RC3, RH)

Sampling session Factor variable: 3 levels (SS1, SS2, SS3)

Travel (% total observation time) Continuous variable (min=0.6; max=31; mean=12)

Fruit consumption (% total observation time) Continuous variable (min=0; max=100; mean=56)

Forest fragment Factor variable: 2 levels (RC3, RH)

Independent variables (random effects)

Individual Factor variable: 12 levels (12 individuals)

Independent variables (control variables)

Reproductive status Factor variable: binary response (a) lactating orreproducing females, (b) neither lactating norreproducing females, and males


the validity of models regarding the assumed normal distribution of intragrouperrors and randomly distributed random effects, qualitatively by plotting intragroupresiduals, which provide a good surrogate for intragroup errors, and inspection offitted versus residual plots (Zuur et al. 2009).

Results

Travel time was present in all four of the best-supported models and was foundto have a strong positive effect on fGCM levels (Tables II and III). Forexample, an increase in daily travel time from 10 % to 20 % of observationtime (total range: 1 %–31 %), equated to a ca. 50 % increase in fGCM levels(Fig. 2). The effect of fruit consumption on fGCM levels received very littlesupport compared to the alternative models. This was confirmed by a low effectsize compared to its standard error and less support for the single parametermodel than the null model (Table II).

There was some support for an effect of sampling session on fGCM levels,based on the fact that sampling session was present in two of the three best-performing models (Table II). The effect sizes of the best-supported modelsshow that fGCM levels were higher in SS1 than in SS2 or SS3 (Table III).However, the single parameter model of sampling session received very littlesupport compared to travel time (Table II). Similarly, forest fragment waspresent in the third best performing model (Δi=2.6), providing some supportfor an effect of forest fragment on fGCM levels (Table II). The effect sizes ofthis model suggest that fGCM levels might have been higher in the smallerforest fragment (RC3) than in the larger one (RH); however, the effect size was

Table II Results of generalized linear mixed-effect models investigating the effects of travel time and fruitconsumption on fGCM levels in two groups of howlers in different forest fragments (FF) in Los Tuxtlas,Mexico, during each of three different sampling sessions (SS)

Model Delta AIC(Δi)

Log likelihood(Li)

Relative likelihood(li)

Akaike weight(wi)

Evidence ratio(wi/wj)

Travel+SS 0 −1073.7 1.00 0.51

Travel 1 −1076.2 0.61 0.31 1.65

Travel+SS+FF 2.6 −1074.0 0.27 0.14 3.67

Travel+FF 4.9 −1077.1 0.09 0.04 11.59

FF+SS 12 −1079.7 <0.01 0.00 403.43

SS 12.5 −1080.9 <0.01 0.00 518.01

Fruit+FF+SS 13.2 −1079.2 <0.01 0.00 735.10

Fruit+SS 13.3 −1080.3 <0.01 0.00 772.78

FF 32.9 −1092.1 <0.01 0.00 1.39E+07

Null model 33.2 −1093.2 <0.01 0.00 1.62E+07

Fruit+FF 34.5 −1091.9 <0.01 0.00 3.10E+07

Fruit 34.9 −1093.1 <0.01 0.00 3.79E+07


low compared to its standard error (Table III) and the single-parameter modelof forest fragment received little support (Table II). Data on mean (± SE)fGCM levels for each group in each of the three sampling sessions is givenin Fig. 3, and data on mean (± SE) fGCM levels, fruit consumption, and traveltime for all individuals is given in Table SI.

Table III Effect size and stan-dard error (SE) of individual vari-ables in the three best-supportedmodels from Table II

Model Effect size SE

Travel+sampling session

(Intercept) 94.1 18.4

Travel 3.6 1.0

Sampling session (SS2) −20.2 10.5


Reproductive status (pregnant) 24.3 8.3

Travel


Travel 3.1 0.5


Travel+fragment+sampling session


Travel 3.0 0.7

Forest fragment (RH) −9.4 13.7




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Discussion

The results of this study strongly suggest that increases in travel time result inincreases in fGCM levels in free-ranging howlers. Minimizing energy expenditureis thought to be fundamental to the foraging strategy of howlers as a result of theirhighly folivorous diet (Milton 1980). However, to our knowledge, this is the first timethat an indicator of energy expenditure has been related to any measure of physio-logical stress in this primate genus. Our results agree with the observations of otherauthors who have suggested that increases in travel can lead to increases in stress inwild primates (Cavigelli 1999; Foerster and Monfort 2010; Muller and Wrangham2004) and bring insight into the proximate mechanisms underlying increases in GClevels in primates in fragments (Chapman et al. 2006; Martínez-Mota et al. 2007;Rangel-Negrín et al. 2009). Moreover, they highlight the potential conservationsignificance of studies showing that habitat loss and transformation can lead toincreases in travel time in wild primates (Donati et al. 2011; Dunn et al. 2009,2010; Gonzalez-Zamora et al. 2011; Hardus et al. 2012).

Fecal glucocorticoid metabolite levels were highest in both groups during SS1(Fig. 3), the period of lowest fruit consumption (Fig. 1). This supports the findings ofBehie et al. (2010), who proposed that fruit availability is an important factor predictingstress in howlers, as well as the growing recognition (Behie et al. 2010; Dunn et al.2009, 2010) that howlers, traditionally described as primarily folivorous, may be morereliant on fruit than previously believed. However, our models showed no support for aneffect of fruit consumption per se on fGCM levels compared to alternative hypotheses(Table II). We argue that the relationship between fruit availability and glucocorticoidlevels previously reported for howlers (Behie et al. 2010) is probably mediated by thenegative effect that fruit consumption has on travel time (present study; cf.Asensio et al.2007; Dunn et al. 2009, 2010; Rodríguez-Luna et al. 2003).

The mean fGCM levels of the RH group, in a bigger, more conserved forestfragment, were lower than those of the RC3 group, in a smaller and more disturbedforest fragment, particularly during SS1 (Fig. 3). This is consistent with previousstudies showing that howlers inhabiting smaller forest fragments have higher fGCMlevels (Martínez-Mota et al. 2007). However, the direct effect of forest fragment on

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Fig. 3 Mean (± SE) fGCM levels for two groups of howlers in different forest fragments in Los Tuxtlas,Mexico, during each of three different sampling sessions (SS1, SS2, and SS3).


fGCM levels received little support in our models, suggesting that the difference instress levels between the two focal groups result mainly from the effect of habitatcharacteristics on travel time. Indeed, howlers inhabiting smaller and more degradedforest fragments have been reported to consume less fruit (Asensio et al. 2007; Dunnet al. 2009, 2010; Juan et al. 2000), feed from more secondary food resources (Dunnet al. 2012), and feed from smaller trees (Dunn et al. 2009) than groups in biggerfragments, all of which result in increases in travel time (Dunn et al. 2009, 2010,2012). As the availabilities of fruit (Dunn et al. 2009, 2010) and big trees (Lauranceet al. 2000) are negatively affected by habitat loss and fragmentation, the results ofour study have important implications for the conservation and management ofprimates in fragments and highlight the importance of protecting large, undisturbedforested areas. Our results also suggest that researchers examining stress levelsamong primate populations across different habitats should carry out their fieldworkduring periods of resource scarcity, when they would be likely to obtain the clearestdifference among groups. This is important, as field endocrinology is costly in termsof both time and resources.

Other factors, including exposure to tourists (Behie et al. 2010) and parasitization(Chapman et al. 2006), may also affect the stress response in primates. However,neither of our study groups is exposed to tourists or high levels of human contact.Further studies are needed to determine how parasite infection may relate to fGCMlevels in our focal groups.

Finally, it is important to note that a year-long study in two forest fragments may notbe sufficient for deriving general conclusions about the responses of howlers to forestfragmentation. Therefore, our results should be interpreted as preliminary. However, ourresults suggest that forest fragmentation, by limiting the availability of fruit and bigtrees, and forcing howlers to increase travel time, may lead to increases in stress andlimit the long-term viability of populations. GC levels have been related to survivorshipin studies of several vertebrate species (Bonier et al. 2009; Romero andWikelski 2001).However, long-term studies showing a direct cause–effect relationship between fGCMlevels and survival in primates are lacking. Therefore, determining how relevant ourresults are for the survivorship and/or fitness of howlers in forest fragments will requirelong-term studies of demographic change and fGCM levels.

Acknowledgments We are thank L. Mendoza and B. Gomez for their help in the field; G. García-Lopez,R. Valenzuela, L. Boeck, and E. O. Ameca for their assistance in the laboratory; C. McOwen for statisticaladvice; C. Huber and the Palacios family for access to their land; and R. Coates for logistical support. Wealso thank the Fundación BBVA, which provided a studentship for J. Dunn and financial support for theproject and E. Rodríguez-Luna and the Universidad Veracruzana for the use of their facilities. Finally, wethank three anonymous reviewers and the Associate Editor, Oliver Schülke, for their very helpful andconstructive comments on a previous version of this manuscript.

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