Leakey Foundation Final Report

Fernando A. Campos, University of Calgary

1.1 Brief summary

The central aim of my research is to understand the effects of environmental change on behavioralflexibility and adaptation in the white-faced capuchin (Cebus capucinus), a medium-sized Neotrop-ical primate, across a range of spatial and temporal scales. I collected behavioral data during 18months of field work in Sector Santa Rosa (SSR) of the Área de Conservación Guanacaste (ACG),a UNESCO World Heritage site in Costa Rica that comprises a complex mosaic of tropical dry for-est in diverse stages of regeneration. I combined my behavioral observations with long-term censusand demographic data, satellite-based assessments of landscape spatial pattern, climate records, andsystematic botanical measurements to investigate several outstanding questions about how primatescope with ecological variability. Predation risk and foraging efficiency showed strong spatiotempo-ral variation depending on both climate and habitat structure, while capuchins’ movement patternsand population dynamics reflected these pressures at larger scales. This research contributes to ourunderstanding of how primates adapt their behavior to thrive in changing environments.

1.2 Publication summary and plans

My Leakey-funded field work has resulted in three peer-reviewed publications in journals withbroad readership.

1. “Drivers of home range characteristics across spatiotemporal scales in a neotropical primate,Cebus capucinus” published in Animal Behaviour: doi:10.1016/j.anbehav.2014.03.007.

2. “Spatial ecology of perceived predation risk and vigilance behavior in white-faced capuchins”published in Behavioral Ecology: doi:10.1093/beheco/aru005.

3. “Urine-washing in white-faced capuchins: a new look at an old puzzle” published in Be-haviour: doi:10.1163/1568539X-000030800.

I have completed two additional manuscripts that I plan to submit for publication in the com-ing weeks. The first is titled “A multi-decade investigation of primate population dynamics: theeffects of climatic oscillations and forest regeneration” and the second is titled “Energy returns onforaging in white-faced capuchins, Cebus capucinus.”

1.3 Detailed Summary

1.3.1 Introduction and relevance to the study of human origins

One of the major goals of paleoanthropology is to understand the role of environmental factors inshaping the course of human evolution. Most prevailing hypotheses that attempt to link the envi-

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ronment with human evolution ascribe central importance to landscape change and heterogeneity(Vrba, 1980; Potts, 1996; Kingston, 2007). A growing body of evidence suggests that many earlyhominins exploited a variety of habitats within mosaic landscapes, and that many of these habitatsexperienced strong shifting or oscillating climatic conditions (reviewed by Reed et al., 2005; Elton,2008). Potts (1998) argues further that the appearances of many important hominin adaptationscoincide with periods of substantial habitat diversity and environmental remodeling. However,there are significant limitations on what can be learned about human evolution from archeologicaland paleoenvironmental data alone. For example, the fitness landscapes associated with individualforaging strategies, ranging behaviors, daily activity patterns, and habitat and resource utilizationdepend critically on interactions between individuals and the particular biotic and abiotic elementsof their local surroundings and can probably never be fully contextualized within a reconstructedancient environment based on archeological data.

One approach to dealing with these limitations is to use living nonhuman primates as models(Whiten et al., 2010). Capuchins (Cebus and Sapajus spp.) show many behavioral and anatomi-cal convergences with great apes and humans, including a large brain-to-body size ratio, long lifespans, violent coalitionary aggression, the capacity for tool use, and social traditions. Like early ho-minins, C. capucinus possess a remarkable ability to exploit varied and varying environments: theyare generalists in both habitat and diet, and they thrive in a broad range of environmental condi-tions (Fragaszy et al., 2004). The landscape at SSR and the numerous convergences that capuchinsshare with hominins makes this population an excellent model system for exploring some of theecological and demographic pressures that may have shaped humans’ evolutionary trajectory. Myfield observations and my broad analytical approach—integrating landscape ecology, spatial statis-tics, and GIS/remote sensing— provide a comprehensive examination of a primate population’s re-sponses to its changing environment. The results of this research provide valuable information thatmay help to deconstruct and quantify the key components of the “habitat heterogeneity” paradigmin human evolution, which remains central but vaguely-defined in current anthropological theory.My principle findings are summarized below.

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1.3.2 Perceived predation risk and vigilance behavior

Predation pressure is believed to be a fundamental evolutionary driver in primate socioecology, yetour understanding of how fine-scale variation in perceived predation risk affects primates’ short-term space use patterns and predator avoidance strategies remains limited. Perceived predationrisk can be estimated relatively easily in some nonhuman primates due to their distinct and eas-ily recognizable alarm-call responses to different predator guilds (Seyfarth et al., 1980; Digweed etal., 2005; Fichtel et al., 2005). I calculated encounter frequencies with different predator guilds—raptors, snakes, and terrestrial quadrupeds—based on alarm calls and direct observation, and Icharacterized these encounters using a range of ecological variables. Alarm-calling bouts directed atbirds were more likely to originate in high forest strata, whereas alarm-calling bouts at snakes andterrestrial quadrupeds were more likely to originate near the ground. Using an analytical approachintroduced by Willems and Hill (2009), I related the spatial distribution of alarm calls to the spa-tial distribution of systematically collected ranging data to create predator guild-specific estimatesof perceived predation risk, and I explored the distribution of these perceived risk functions acrossthe heterogeneous landscape. The relative risk maps revealed that high-risk areas for birds and forall guilds combined consisted of more mature forest, whereas low-risk areas for these predatorsconsisted of relatively younger forest (fig. 1.1).

1197000

1198000

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Snake Terrestrial

650500 651500 652500

Bird Combined

−2.0

−1.5

−1.0

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2.0

Figure 1.1: Log-relative risk functions for each predator guild based on alarm-calling data and given the underlying pattern of habitat

usage, with asymptotic tolerance contours showing zones of heighted risk (solid) and reduced risk (dashed). Small gray crosses indi-

cate the locations of recorded alarm-calling bouts. The “combined” guild includes the other 3 guilds as well as all alarm-calling bouts

that could not be confidently assigned to a guild.

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Finally, I analyzed the spatiotemporal occurrence of a putative antipredator behavior, vigilance,to determine whether the perceived risk functions were informative for predicting vigilance behav-ior relative to null models of uniform risk or habitat-specific risk. Capuchins were most vigilantnear the ground, which may reflect greater perceived exposure to snakes and terrestrial predators inlower forest strata. Incorporating the combined (bird + snake + terrestrial quadruped) risk func-tion into a predictive model of vigilance behavior improved prediction relative to null models ofuniform risk or habitat-specific risk (fig. 1.2).

Coefficient Estimates−0.5 0.0 0.5 1.0

Habitat Intermediate

Habitat Mature

Level Middle

Level High

Snake Risk

Terrestrial Risk

Bird Risk

Combined Risk

m12

m11

m10

m9

m8

Figure 1.2: Estimated coefficients for models for the occurrence of vigilance behavior that include vertical level, one guild or habitat

risk scenario, and no interaction effects. Estimates are relative to the baseline categories “Low” for vertical level and “Early Forest”

for habitat. Vigilance was coded as 1 (vigilant) or 0 (not vigilant). All models include the random effects focal sample number and focal

animal. Thick lines show±1 standard deviation; thin lines show±2 standard deviations. Model m12 received greatest empirical

support.

The results of this study demonstrate that my study animals perceived reduced predation riskin the high and middle forest layers, and they adjust their vigilance behavior to small-scale spatialvariation in perceived risk. My findings suggest a possible benefit to C. capucinus from exploitingyoung, disturbed habitats: perceived predation risk from aerial predators and overall predator en-counter rates are reduced in younger habitats. Thus, the ability to exploit disturbed buffer or edgehabitats, which are often given low conservation priority, may provide unexpected advantages toadaptable species, such as refuge from some types of predators.

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1.3.3 Small-scale energetic returns on foraging behavior

Natural selection should strongly favor efficiency in individual foraging behavior (Emlen, 1966;Pyke et al., 1977). Using brief foraging bouts as the unit of analysis, I quantified energy intake rateusing the measured energy content of ingested food items, and I quantified energy expenditureusing estimates of basal and locomotor energy costs based on continuous changes in activity andtime-matched movement trajectories. Capuchins’ small-scale foraging behavior shifted along withchanges in their energetic resource base. Energy expenditure rates during foraging bouts were in-versely proportional to food abundance, and travel velocities slowed as they incorporated a greaterfraction of less-predictable invertebrates in their diet (fig. 1.3).

Early Intermediate Mature

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porti

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Early Intermediate Mature

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Velo

city

(m/m

in)

a)

b)

Figure 1.3: Concurrent changes in the energetic resource base and travel velocities, separated by season and habitat type. a) Propor-

tion of the total energy ingested from insects and invertebrates. The proportions do not sum to one because of the small contributions

of seeds, flowers, and other animal food items. b) Violin plots and box plots of overall travel velocities during focal samples, each of

which contributes one point to the plot.

Expected energetic returns on foraging bouts were driven primarily by extrinsic constraints suchas seasonality and habitat characteristics rather than by intrinsic constraints such as sex or socialgroup size. I found that, from a foraging energetics standpoint, mature habitats were consistentlysuperior to younger habitats, enabling higher energy intake rates and lower energy expenditure

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rates for greater overall net energy returns. This mature-forest advantage varied seasonally and wasmost evident during the dry season, when ecological contrasts between habitat types are most pro-nounced (fig. 1.4).

Energy Expenditure Rate

Season Wet:Habitat Intermediate

Season Wet:Habitat Mature

Habitat Mature

Habitat Intermediate

Season Wet

Sex Male

Group Mass

−0.2 −0.1 0.0 0.1 0.2

Energy Intake Rate

Season Wet:Habitat Intermediate

Season Wet:Habitat Mature

Habitat Mature

Habitat Intermediate

Season Wet

Sex Male

Group Mass

−1.0 −0.5 0.0 0.5 1.0

Net Energy Return

Season Wet:Habitat Intermediate

Season Wet:Habitat Mature

Habitat Mature

Habitat Intermediate

Season Wet

Sex Male

Group Mass

−0.25 0.00 0.25

0.00 0.25 0.50 0.75 1.00Importance

a)

b)

c)

Figure 1.4: Model-averaged regression coefficients and relative variable importance for linear mixedmodels of a) energy expenditure

rate, b) energy intake rate, and c) net energy return. Thick error bars show±1 SD and thin bars show 95% confidence intervals. The

predictor variables were scaled to facilitate direct comparison of their relative effects. All models included the random effects focal

individual and focal group. The relative importance of each predictor variable was calculated as the sum of the Akaike weights for each

model in which the variable appeared.

Thermoregulatory needs compel capuchins to increase resting time during the dry season (Cam-pos & Fedigan, 2009), but when they do forage during the dry season, they must expend greaterenergy per unit of energy consumed. This finding reinforces the idea that the dry season is a chal-lenging time for capuchins in SSR and that their long-term population dynamics are to some de-gree contingent on multi-year trends in dry season severity (see below). In light of my other find-ings that capuchins in SSR experience greater perceived predation risk in mature forest, I infer aninteresting ecological dynamism in which the study animals must balance the counteracting pres-sures of more productive foraging conditions with greater predation risk.

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1.3.4 Differential use of space

The factors that drive within-species variation in animal space use remain poorly understood. Agrowing body of evidence suggests that both home range attributes and biological interpretationsof the home range may depend fundamentally on the scale of analysis. I used a multi-scale mixedeffects modelling framework to examine how seasonal fluctuations in climate, food resource abun-dance, and group mass affected variance in home range area and the maturity stage of forest usedby capuchins in SSR. I combined the location data that I collected during my field work withdata from several other researchers to produce an 8-year data set representing over 20,000 contacthours. I estimated home ranges for seven social groups at four nested temporal scales and threenested spatial scales using a movement-based kernel method (fig. 1.5).

Monthly Quarterly Half-yearly Yearly

Figure 1.5: Illustration of themovement-based kernel method used for estimating home ranges of white- faced capuchins in this study.

Home ranges shown are for group BH at each of four nested temporal scales. The upper panels show the recorded location points

(small dots) and the inferredmovement paths (black line segments) of BH group during the relevant period. The colored shading

represents the utilization distribution. The lower panels show the three home range zones delineated for each home range: core zone

(50% isopleth, solid line); primary ranging zone (70% isopleth, dashed line); and total home range zone (95% isopleth; dotted line).

Group mass was consistently the most important predictor of home range size in the modelsfor monthly and quarterly ranges, and its effects were relatively insensitive to spatial or temporalscale. Mean daily maximum temperature was an influential factor in shaping monthly range area,with hotter weather favoring smaller home range area. Greater fruit availability was also associ-ated with smaller monthly range area. The effects of temperature and fruit availability were bothscale dependent: the impact of both variables was greatest on the core zone (fig. 1.6). The differ-ent study groups showed marked variation in the habitat composition of their home ranges, but inall groups, higher-use zones consisted of older, more evergreen forest. The habitat composition ofdifferent home range zones was driven primarily by climatic seasonality, with hotter temperaturespredicting increased use of mature evergreen forest (fig. 1.7).

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Core Primary Total

Mean max temperature

Fruit biomass

Group mass

Mean max temperature

Fruit biomass

Group mass

Monthly

Quarterly

−0.2 0.0 0.2 −0.2 0.0 0.2 −0.2 0.0 0.2

Regression estimates

0.00 0.25 0.50 0.75 1.00Importance

Figure 1.6: Model-averaged regression coefficients and relative variable importance for linear mixedmodels of home range size. Thin

error bars show 95% confidence intervals, and thick bars show±1 SD.

High Use Medium Use Low Use

Mean max temperature

Fruit biomass

Group mass

Mean max temperature

Fruit biomass

Group mass

Monthly

Quarterly

−0.02 0.00 0.02 −0.02 0.00 0.02 −0.02 0.00 0.02

Regression estimates

0.00 0.25 0.50 0.75 1.00Importance

Figure 1.7: Model-averaged regression coefficients and relative variable importance for linear mixedmodels of mean forest maturity

index containedwithin the home range.

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1.3.5 Long-term population dynamics in relation to forest regeneration and climatechange

Number of Groups

Mean Group Size

Total Population Size

Immature:Female Ratio

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1.31.41.51.61.71.81.9

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(b)

(a)

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Figure 1.8: Trends in the capuchin population from 1983 to 2013,

including (a) the total number of groups, (b) mean group size, (c)

total population size, and (d) immature to adult female ratio. Loess

smoothers added to aid visual interpretation of the trends.

Long-term monitoring is necessary for under-standing the extent to which small or threat-ened animal populations can cope with chang-ing environmental conditions (Sinclair & By-rom, 2006; Clutton-Brock, 2012). Most studiesof long-lived nonhuman primates have notbeen long enough in duration to investigatethese important questions, and consequently,there have been few studies on the long-termdynamics of primate populations in relation toquantitative data on ecosystem change. Draw-ing together various long-term data sets, I ex-amined how climatic fluctuations and land-scape structural dynamics have affected thenatural recovery process of a population ofwhite-faced capuchins over a 30-year period inrelation to quantitative information on howthe landscape and climate changed during thesame period. The population’s rapid initialgrowth and later stabilization suggests that itwas below the habitat’s carrying capacity at thetime of the conservation area’s establishment(fig. 1.8). Most of the population growth inrecent decades has occurred in a sub-region ofSSR that experienced greater gains in forestcover with medium- to high-degree of ever-greenness, which is an important resource for primates during the severe dry season (fig. 1.9). Theavailability evergreen habitats varied with the strength of the previous wet season, which in turnwas strongly coupled with global climatic and oceanic cycles. Following extreme drought periods,population growth slowed, mean group size decreased, and reproductive rate declined. If droughtyears become increasingly common as the global climate warms, many animals in seasonally dryforests may be negatively affected. These findings suggest that extreme drought years may challengesmall capuchin populations by disrupting tree phenology cycles and reducing the availability offavorable habitat during critical times of year.

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Plateau

Carbonal

Naranjo

Santa Elena

Nancite

Cuajiniquil

Plateau

Carbonal

Naranjo

Santa Elena

Nancite

Cuajiniquil

Evergreenness None Medium High Modis Transects Sub-regionLow

1985

2011

Figure 1.9: Landsat-derivedmaps of the study area during peak dry season inMarch 1985 (top) andMarch 2011 (bottom) showing

topography, vegetation greenness, MODIS transects, and sub-regions of relatively high capuchin density. The uniformly dark area

in the foreground is the Pacific Ocean. The elevation data are exaggerated by a factor of four, and they come from the ASTERGlobal

Digital ElevationModel, Version 2, a product ofMETI andNASA.

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1.3.6 Population health and genetic diversity genetic diversity

The population genetics project described in my original proposal to the Leakey Foundation hasnot been completed, but this research is well underway. The project’s goal is to quantify geneticdiversity in three Neotropical primate species and to assess the landscape’s permeability to geneflow along an ecological gradient from sea level to 1,100 m in the ACG. We have collected fecalsamples for genetic analysis from unhabituated groups of capuchins (89 samples), howler monkeys(109 samples), and spider monkeys (56 samples) all across the ACG (fig. 1.10). My collaborators atthe University of Tokyo are currently genotyping these samples. I expect the results of this researchto provide unique insights into landscape-scale ecological and behavioral processes related to pri-mate population health, life history, community ecology, and conservation.

!(

!(

!(

!(

!(

!(

!(

!(

Naranjo (2 m)

Nancite (2 m)

Cacao (1100 m)

Maritza (600 m)

El Hacha (272 m)

Murcielago (38 m)

Horizontes (165 m)

Santa Rosa (300 m)

Sample collection

Cebus

Alouatta

Ateles

!( Biological Stations (elevation)

Figure 1.10: Map of DNA sample collection sites for three primate species in the Área de Conservación Guanacaste.

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References

Campos, F. A. & Fedigan, L. M. (2009). Behavioral adaptations to heat stress and water scarcity inwhite-faced capuchins (Cebus capucinus) in santa rosa national park, costa rica.American Jour-nal of Physical Anthropology, 138(1), 101–111. doi:10.1002/ajpa.20908

Clutton-Brock, T. (2012). Long-term, individual-based field studies. In P. M. Kappeler & D. P. Watts(Eds.), Long-term field studies of primates (pp. 437–449). Springer Berlin Heidelberg.

Digweed, S. M., Fedigan, L. M., & Rendall, D. (2005). Variable specificity in the anti-predator vocaliza-tions and behaviour of the white-faced capuchin, Cebus capucinus. Behaviour, 142(8), 997–1021.doi:10.1163/156853905774405344

Elton, S. (2008). The environmental context of human evolutionary history in eurasia and africa. Jour-nal of Anatomy, 212(4), 377–393. doi:10.1111/j.1469-7580.2008.00872.x

Emlen, J. M. (1966). The role of time and energy in food preference. The American Naturalist, 100(916),611–617. doi:10.1086/282455

Fichtel, C., Perry, S., & Gros-Louis, J. (2005). Alarm calls of white-faced capuchin monkeys: an acousticanalysis.Animal Behaviour, 70(1), 165–176. doi:10.1016/j.anbehav.2004.09.020

Fragaszy, D. M., Fedigan, L. M., & Visalberghi, E. (2004). The complete capuchin: the biology of thegenus Cebus.New York: Cambridge Univ Press.

Kingston, J. D. (2007). Shifting adaptive landscapes: progress and challenges in reconstructing earlyhominid environments. Yearbook of Physical Anthropology, 50, 20–58. doi:10.1002/ajpa.20733

Potts, R. (1998). Environmental hypotheses of hominin evolution. Yearbook of Physical Anthropology,41, 93–136.

Potts, R. (1996). Evolution and climate variability. Science, 273(5277), 922–923. doi:10.1126/science.273.5277.922

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Pyke, G. H., Pulliam, H. R., & Charnov, E. L. (1977). Optimal foraging: selective review of theory andtests. Quarterly Review of Biology, 52(2), 137–154.

Reed, K. E., Fish, J. L., Brockman, D. K., & van Schaik, C. P. (2005). Tropical and temperate seasonalinfluences on human evolution. In D. K. Brockman & C. P. van Schaik (Eds.), Seasonality inprimates: studies of living and extinct human and non-human primates (pp. 489–518). New York:Cambridge Univ Press.

Seyfarth, R. M., Cheney, D. L., &Marler, P. (1980). Monkey responses to three different alarm calls:evidence of predator classification and semantic communication. Science, 210(4471), 801–803.doi:10.1126/science.7433999

Sinclair, A. R. E. & Byrom, A. E. (2006). Understanding ecosystem dynamics for conservation of biota.Journal of Animal Ecology, 75(1), 64–79. doi:10.1111/j.1365-2656.2006.01036.x

Vrba, E. S. (1980). Evolution, species and fossils: how does life evolve. South African Journal of Science,76 (2), 61–84.

Whiten, A., McGrew, W. C., Aiello, L. C., Boesch, C., Boyd, R., Byrne, R. W., Dunbar, R. I. M., Mat-suzawa, T., Silk, J. B., Tomasello, M., van Schaik, C. P., &Wrangham, R. (2010). Studying extantspecies to model our past. Science, 327(5964), 410, 410. doi:10.1126/science.327.5964.410-a

Willems, E. P. &Hill, R. A. (2009). Predator-specific landscapes of fear and resource distribution: ef-fects on spatial range use. Ecology, 90(2), 546–555. doi:10.1890/08-0765.1

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