Florivory by Cebus Capucinus: How Variation in Food Abundance and Colour Vision Affect Foraging Strategies

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2015-01-23 Florivory by Cebus capucinus: How variation in food abundance and colour vision affect foraging strategies.

Hogan, Jeremy

Hogan, J. (2015). Florivory by Cebus capucinus: How variation in food abundance and colour vision affect foraging strategies. (Unpublished master's thesis). University of Calgary, Calgary, AB. doi:10.11575/PRISM/26189 http://hdl.handle.net/11023/2021 master thesis

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UNIVERSITY OF CALGARY

Florivory by Cebus capucinus: How variation in food abundance and colour vision affect

foraging strategies.

Jeremy David Hogan

A THESIS

SUBMITTED TO THE FACULTY OF GRADUATE STUDIES

IN PARTIAL FULFILMENT OF THE REQUIREMENTS FOR THE

DEGREE OF MASTER OF ARTS

GRADUATE PROGRAM IN ANTHROPOLOGY

CALGARY, ALBERTA

JANUARY, 2015

Abstract

Many primates consume flowers in small proportions annually but increase their use seasonally. Food used during “crunch” periods may exert selection pressure for traits that improve foraging efficiency. High quality foods are believed to select for detection and harvesting adaptations, including colour vision. This study was designed to determine what leads to flower consumption by white-faced capuchin monkeys, a species that has polymorphic colour vision and consumes flowers rarely, but in high proportions seasonally. I compared flower foraging behaviour to fruit, flower, and invertebrate abundance, and obtained nutritional and spectral reflectance data for all flower foods. Flowers were consumed in higher frequencies during periods of low invertebrate abundance. Flower foods are of relatively high nutritional quality, have chromatic properties predicted to be more visible to trichromats, and were consumed more frequently by trichromats, suggesting they may play a role in the evolution and maintenance of polymorphic colour vision.

Acknowledgements

I would be remiss to start my acknowledgements with anyone besides my incredible co- supervisors, Drs. Linda Fedigan and Amanda Melin. Thank you both for placing your trust in me as a graduate student, and for all of your guidance, patience, and support in all forms over the last two-plus years. It has been an absolute honour to be a part of the Santa Rosa team and learn from such accomplished mentors; I will forever be grateful for the opportunity.

This project would never have been possible without the sweat, blood, and occasional tears of three incredibly dedicated and hardworking field assistants: Michelle Janzen, Saul

Chevez, and Shakib Khondker. Thank you all for your friendship and for your willingness to spend hundreds of hours chasing monkeys down escarpments and through vines with me. It is because of you all that I have both a thesis to write and my sanity partially intact.

To that end, thank you to the monkeys for their tolerance of our presence, for always providing enough entertainment to make the time fly by even on the longest days, and for finally eating at least some flowers.

Thank you to Dr. John Addicott for the countless hours of patient instruction and advice on data collection and management. I am sure I would still be staring cross-eyed at broken excel tables without your interventions. Thank you to Dr. Tak Fung for then taking the time to work with me and that well organized data to design my analyses.

The amount of support I received from all corners of the globe to make this project happen is truly awe-inspiring, and I have many people to thank for getting me this far. First, thank you to the University of Calgary Department of Anthropology and Archaeology for the opportunity to be a graduate student in such a welcoming environment, and academic and financial support. There are simply too many staff and faculty who provided support to

iii acknowledge individually, so thank you all. Thank you also to all of the students who shared the grotto with me and offered timely advice and frequent morale boosts. A special thank you to all of my Santa Rosa lab mates for the training, support and camaraderie: Mackenzie Bergstrom, Dr.

Fernando Campos, Krisztina Mosdossy, Monica Myers, Liz Sargeant, and Dr. Eva Wikberg.

Thank you to our counterparts at the University of Tokyo for the genetics support: Dr. Shoji

Kawamura and Yuka Matsushita. I owe my success at colour vision modelling to the scripting prowess of Dr. Chihiro Hiramatsu of Kyushu University, thank you for walking me through my numerous errors with such cheerful aplomb! My knowledge of Santa Rosa plant life largely stems from the patient instruction of Adrian Guadamuz Chavarria, to whom I owe a great deal.

Of course, this project doesn’t happen without the opportunity to conduct research in Santa Rosa, therefore I am indebted to the gracious hospitality of Roger Blanco and the rest of the ACG staff.

Thank you for the providing me with the amazing opportunity to experience your park for the better part of a year, it will forever be a sacred place to me.

Thank you to my family and friends for not forgetting me while I was in the bush, and for supporting me through this entire process. I am incredibly lucky to have such a strong support network, and I look forward to many more years of regaling you all with “a monkey pooped in my coffee” stories. One of the highlights of my time in Santa Rosa was introducing my parents, brother, and extended family to the monkeys, thank you all for taking the chance on celebrating a

Tico Christmas with me.

Finally, thank you to my incredibly supportive wife, Dr. Caroline Turner, for not only tolerating long bouts of separation, but for being my biggest cheerleader every step of the way.

Thank you for your love, friendship, and support, not to mention the numerous ways you directly contributed to and improved my project.

Dedication

To Caroline: for believing in me, pushing me, supporting me, and making me a better researcher and person. I love you with all my heart.

Table of Contents

Abstract ...... ii Acknowledgements ...... iii Dedication ...... v Table of Contents ...... vi List of Tables ...... ix List of Figures and Illustrations ...... xi

CHAPTER ONE: GENERAL INTRODUCTION ...... 1 1.1 Background ...... 1 1.1.1 Primates and flowers ...... 1 1.1.2 Polymorphic colour vision ...... 2 1.2 Objectives ...... 3 1.3 General Methods ...... 4 1.3.1 Study site ...... 4 1.3.2 Study species and subjects ...... 6 1.3.3 Study period ...... 9 1.3.4 Plant sample collection ...... 10 1.4 Overview ...... 10

CHAPTER TWO: WHY DO MONKEYS EAT FLOWERS? ...... 13 2.1 Background ...... 13 2.1.1 A tale of two Luehea ...... 13 2.1.2 Are flowers fallback foods? ...... 14 2.1.3 Flower use by primates ...... 16 2.1.4 Nutritional quality of flowers ...... 17 2.1.5 White-faced capuchin consumption of flowers and invertebrates...... 18 2.2 Research Objectives and Hypotheses ...... 21 2.3 Methods ...... 23 2.3.1 Study site ...... 23 2.3.2 Study species ...... 23 2.3.3 Study groups ...... 23 2.3.4 Phenological data collection ...... 24 2.3.5 Invertebrate abundance ...... 25 2.3.6 Behavioural data collection ...... 25 2.3.7 Nutritional data ...... 28 2.3.8 GPS data ...... 29 2.3.9 Data analysis ...... 30 2.3.10 Phenology scores ...... 30 2.3.11 Flower consumption in relation to food abundance indices ...... 31 2.3.12 Behavioural changes and flower foraging ...... 33 2.3.13 Energy intake rates ...... 34 2.3.14 Daily travel rate ...... 34 2.4 Results ...... 34

2.4.1 Research question one: the annual and seasonal importance of flowers in the diet ...... 34 2.4.2 Research questions two and three: the effects of fruit, flower and invertebrate abundance on flower foraging ...... 36 2.4.3 Research question four: relative and absolute flower nutritional quality ...... 37 2.4.4 Energy intake rates ...... 41 2.4.5 Research question five: how florivory affects behaviour ...... 42 2.5 Discussion ...... 44 2.5.1 Role of flower foods across the annual cycle ...... 44 2.5.2 Seasonal importance of flowers ...... 46 2.5.3 Flower consumption rates were not affected by fruit or flower abundance, but were negatively related to invertebrate abundance...... 46 2.5.4 Flowers are nutritionally comparable to many fruit species ...... 47 2.5.5 Differences in flower quality of the two Luehea species do not explain seasonal foraging patterns ...... 48 2.5.6 Behavioural change was associated with flower consumption ...... 49 2.5.7 Categorizing flowers on the fallback/preferred foods spectrum ...... 49 2.6 Conclusions ...... 51

CHAPTER THREE: POLYMORPHIC COLOUR VISION AND THE VARIATION IN FLOWER COLOUR SIGNALS ...... 52 3.1 Background ...... 52 3.1.1 Defining colour ...... 52 3.1.2 Defining colour vision ...... 52 3.1.3 Vertebrate colour vision biology and physiology ...... 54 3.1.4 Evolution of vertebrate colour vision ...... 55 3.1.5 Polymorphic colour vision ...... 56 3.1.6 Evolution of routine trichromacy in primates ...... 57 3.1.7 Benefits of colour vision ...... 57 3.1.8 Polymorphism as a natural experiment for testing hypotheses of colour vision evolution ...... 59 3.1.9 Flower foods and colour vision ...... 62 3.2 Research Objectives and Hypotheses ...... 64 3.3 Methods ...... 65 3.3.1 Study species ...... 65 3.3.2 Study site and animals ...... 66 3.3.3 Genotyping ...... 67 3.3.4 Spectroscopy ...... 70 3.3.5 Chromaticity models ...... 71 3.3.6 Support Vector Machine modelling ...... 72 3.3.7 Just Noticeable Difference modelling ...... 73 3.3.8 Behavioural observations ...... 75 3.3.9 Scan sampling ...... 75 3.3.10 FLPV sampling ...... 76 3.3.11 Data analysis ...... 77 3.3.12 Differences in flower foraging frequencies between phenotypes ...... 77

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3.3.13 Differential utilization of small patches between phenotypes ...... 78 3.4 Results ...... 78 3.4.1 Support Vector Machine modelling results ...... 78 3.4.2 Short distance foraging simulation using Just Noticeable Difference modelling results ...... 88 3.4.3 Long distance foraging simulation using Just Noticeable Difference modelling results ...... 90 3.4.4 Trichromats consume flowers more frequently than dichromats do ...... 92 3.4.5 Small patch visits ...... 93 3.5 Discussion ...... 94 3.5.1 Most flowers are more visible to trichromats than to dichromats ...... 94 3.5.2 Variable luminance increases the value of trichromacy ...... 95 3.5.3 Trichromats eat flowers more frequently than dichromats do ...... 97 3.6 Conclusions ...... 98

CHAPTER FOUR: GENERAL DISCUSSION ...... 100 4.1 Research Summary ...... 100 4.2 Future Directions ...... 102 4.2.1 Annual variation of florivory patterns and food abundances ...... 102 4.2.2 Long distance detection and leadership into patches ...... 103 4.2.3 Probable pollination of Luehea speciosa ...... 104 4.2.4 Crop destruction by capuchins ...... 105 4.2.5 Flowers as training food for infants ...... 106 4.3 Conclusions ...... 107

REFERENCES ...... 108

APPENDIX I: ETHOGRAM ...... 118

APPENDIX II: MACRO- AND MICRONUTRIENT CONTENTS OF FLOWER FOODS ...... 120

APPENDIX III: JUST NOTICEABLE DIFFERENCE (JND) MODELLING RESULTS BY FLOWER PART FOR ALL MODELLED SCENARIOS ...... 121

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List of Tables

Table 1.3.1. Number of capuchins in the three habituated study groups during the data collection period, May 2013-March 2014...... 8

Table 2.1.1. Flower species recorded to be consumed by C. capucinus in Santa Rosa. Data adapted from Melin et al. 2014a and Fedigan unpublished data...... 20

Table 2.4.1. Flower species consumed by white-faced capuchins, the number of flower patch visits (FLPVs) per species, and the months during which foraging was observed. Data based on 1157 hours of observation...... 36

Table 2.4.2. Concentration of macronutrients as a percent of the total dry weight, and gross energy per gram by dry weight for each flower species consumed by capuchins during the observation period. Luehea candida and Luehea speciosa were also analyzed, but are compared separately in Figure 2.4.4. Manilkara chicle data were obtained from Bergstrom et al. (2014a in prep.). The calculated average includes Luehea speciosa...... 39

Table 2.4.3. The consumption rate and energy intake rate of flowers observed to be consumed during focal sampling. Luehea speciosa was not completely consumed during foraging, therefore the energy intake rate is likely an overestimate of actual energy gained by capuchins while foraging on this species...... 42

Table 2.5.1. Comparison of the proportion of FLPVs to observed flower food species in 2007-2008 (Melin et al. 2014a) and 2013-2014 (Hogan)...... 45

Table 3.3.1. The group affiliation, sex, age class, colour vision type, and the predicted peak spectral sensitivities of the M/L cones of all monkeys observed in this study (N= 51)...... 68

Table 3.3.2. The proportion of each colour vision phenotype in the study population of capuchins, for individuals that have been genotyped (n= 50)...... 69

Table 3.3.3. Frequency of each allele in the study population of capuchins for all individuals that have been genotyped (n= 50)...... 70

Table 3.3.4. The formulas used to model how the three major colour vision pathways are stimulated by an object, adapted from Hiramatsu et al. (2008). QS is not included in the luminance calculation due to the relatively small proportion of S-cones found in mammalian eyes compared to M and L opsins...... 72

Table 3.4.1. The success of Support Vector Machine (SVM) analysis at categorizing a given flower part for each colour vision phenotype, when luminance is included (white columns) and excluded (shaded columns). “Middles” is a category used to denote the middle, non-petal structures of a flower, typically this was the reproductive organs...... 87

Table 3.4.2. Pairwise comparison of the overall visual performance for all capuchin colour vision phenotypes, measured using Just Noticeable Difference (JND) values.

Percentages reflect the number of flower parts for which JND values were at least 1 JND greater when modelling the phenotype in the row compared to the phenotype in the column. JNDs were calculated by contrasting the chromatic values of 21 flower parts and their upper and lower leaf surfaces. If the percentage of flower parts a phenotype had a JND advantage for differed between upper and lower leaf surface simulations, the lower leaf surface percentage is included in parenthesis...... 90

Table 3.4.3. Pairwise comparison of the overall visual performance for all white-faced capuchin colour vision phenotypes, measured using Just Noticeable Difference (JND) modelling. Percentages reflect the number of flower parts for which JND values were at least 1 JND greater for the phenotype in the row compared to the phenotype in the column. JNDs were calculated by contrasting the chromatic values of 21 flower parts from 14 species to the mean chromatic value of upper and lower leaf surfaces from 28 plant species. If the percentage of flower parts a phenotype had a JND advantage for differed between upper and lower leaf surface simulations, the lower leaf surface percentage is included in parenthesis...... 92

List of Figures and Illustrations

Figure 1.3.1. Location of Sector Santa Rosa, Área de Conservación Guanacaste, Costa Rica. Image obtained from Google Earth®...... 5

Figure 1.3.2. Monthly mean high and low temperatures and monthly precipitation accumulation for the study period (April 2013 – March 2014) in Sector Santa Rosa, Área de Conservación Guanacaste, Costa Rica...... 6

Figure 1.3.3. Home range utilization of the three capuchin study groups, January 2013 to March 2014. Graphic used with permission courtesy of Dr. Fernando Campos...... 9

Figure 2.4.1. Distribution of flower patch visits (green dots represent one FLPV) by RM group on December 12, 2013. A total of 49 FLPVs occurred: 48 to Luehea speciosa and 1 to Diphysa americana. Only one tree was revisited and accounted for two FLPVs, with the second FLPV occurring over an hour later...... 35

Figure 2.4.2. Flower patch visit (FLPV) rate per month (green bars) in relation to ripe fruit (red line) and flower (blue line) abundance for that month. Flower foraging rates were not related to the abundance of ripe fruit or flowers in the environment. No behavioural data were collected August-October (shaded area), although previous research at this site suggests flowers are not consumed during this time (Melin et al. 2014a). Behavioural data collection for January was limited to two weeks...... 37

Figure 2.4.3. Average proportion of dry matter consisting of fat, protein, and water soluble carbohydrates for each major food category. Data for fruit, seeds, and invertebrates adapted from Bergstrom et al. (2014a in prep.)...... 40

Figure 2.4.4. The macronutrient composition on a dry weight basis of the two major parts categories for Luehea speciosa and Luehea candida. “Middles” includes all structures located in the center of the flower above the petals, which is mostly the reproductive organs of the flower...... 41

Figure 2.4.5. The estimated marginal mean frequency of flower patch visits (FLPVs) for the 22 dichromatic individuals including in this study, per sex/age class, with one standard error. The flower foraging frequency was calculated by dividing the total number of scans involving flower foraging by the total number of scans recorded per individual, per cycle (n= 286). There were no significant differences in FLPV rates for any sex/age class...... 43

Figure 3.4.1. The Support Vector Machine (SVM) success rate of each modeled colour vision phenotype at identifying flower spectra (N= 21) from background leaf spectra (N= 28). During SVM trials, any flower part sample that was correctly categorized by the machine as a flower following hyperplane creation was considered a successful ID...... 80

Figure 3.4.2. Chromaticity plot displaying the relative intensity of luminance (measured as the log of M+L values) and blue-yellow colour cues (S/(M+L)) of leaves (grey

triangles) compared to various leaf parts (red stars: petals; yellow circles: immature samples; blue squares: “middle” structures, mostly reproductive organs; green squares: bracts). The faded area within the grey box represents the luminance value range for leaves; any flower parts outside of this range (9/21 samples) have brightness cues different from leaf samples, although some may not be sufficient to be discriminable to dichromats...... 81

Figure 3.4.3. The Support Vector Machine (SVM) modelling results for all three dichromat phenotypes, with luminance included (left panels) and excluded (right panels) in the models. The X-axis is the log shifted values of luminance scores for each sample, while the Y axis represents the relative intensity of the blue-yellow colour channel signal. Green triangles are leaf samples, red closed circles are flower part samples. Open green boxes indicate an object was identified as a leaf by the SVM, while an open red circle indicates the SVM classified the object as a flower. Percentages above each graph represent the rate at which the SVM correctly categorized flower parts for each modelling scenario...... 82

Figure 3.4.4. The Support Vector Machine (SVM) analysis results modelling trichromat phenotype 532/543, with luminance included (left panels) and excluded (right panels) in the model. In all figures, the Lightness(log) axis represents the log-corrected luminance values, the S/(L+M) axis is relative intensity of the blue-yellow colour channel signal, and the L/(L+M) axis is the relative intensity of the red-green colour channel signal. Green triangles are leaf samples, red closed circles are flower part samples. Open green boxes indicate an object was identified as a leaf by the SVM, while an open red circle indicates the SVM classified the object as a flower. When luminance was included, the SVM correctly classified 13/21 (61.90%) of flower samples, improving to 17/21 (80.95%) when luminance was not included...... 83

Figure 3.4.5. The Support Vector Machine (SVM) modelling results of trichromat phenotype 532/561, with luminance included (left panels) and excluded (right panels) in the model. In all figures, the Lightness(log) axis represents the log-corrected luminance values, the S/(L+M) axis is the relative intensity of the blue-yellow colour channel signal, and the L/(L+M) axis is the relative intensity of the red-green colour channel signal. Green triangles are leaf samples, red closed circles are flower part samples. Open green boxes indicate an object was identified as a leaf by the SVM, while an open red circle indicates the SVM classified the object as a flower. When luminance was included, the SVM correctly classified 15/21 (71.43%) of flower samples, improving to 16/21 (76.19%) when luminance was not included...... 84

Figure 3.4.6. The SVM analysis results of trichromat phenotype 543/561, with luminance included (left panels) and excluded (right panels) in the model. In all figures, the Lightness(log) axis represents the log-corrected luminance values, the S/(L+M) axis is relative intensity of the blue-yellow colour channel signal, and the L/(L+M) axis is the relative intensity of the red-green colour channel signal. Green triangles are leaf samples, red closed circles are flower part samples. Open green boxes indicate an object was identified as a leaf by the SVM, while an open red circle indicates the SVM classified the object as a flower. When luminance was included, the SVM correctly xii

classified 14/21 (66.67%) of flower samples, improving to 15/21 (71.43%) when luminance was not included...... 85

Figure 3.4.7. Mean Just Noticeable Difference (JND) scores for the six capuchin colour vision phenotypes for 21 flower part samples compared to the mean chromatic values of upper and lower surfaces of conspecific leaves, simulating a close up or short range foraging situation...... 89

Figure 3.4.8. The mean JND scores for the six capuchin colour vision phenotypes, calculated for all 21 flower parts compared to the mean chromatic values of upper and lower leaf surfaces of 28 plant species, simulating a long-distance viewing scenario...... 91

Figure 3.4.9. The estimated marginal mean flower eating frequency (number of flowers consumed as a proportion of flower eating scans per behavioural “cycle”) of dichromats and trichromats over the duration of the study, with one standard error...... 93

Figure 3.4.10. The mean number of flower patch visits by dichromatic and trichromatic individuals to species that produce small flower crops, with one standard error...... 94

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Chapter One: General introduction

1.1 Background

1.1.1 Primates and flowers

Until recently, flowers have been considered to be either too small or simply not nutritious enough for most primates to use as foods in any meaningful amounts. These assumptions have since been proven false; many primates consume flowers, sometimes in large quantities. A recent literature review reported that 165 primate species have been observed to consume flowers, including representatives from every primate family except for Tarsiidae

(Heymann 2011). Additionally, due to the historical tendency of combining rarely consumed food items into a catch-all “other” category, it is likely that researchers have underestimated the frequency of florivorous behaviour by primates (Lappan 2009). There is good reason for primates to consume flowers, as they have several nutritional qualities that make them useful food items. For example, flowers are often produced in higher abundances than fruits (Morgan

1993); they lack physical defenses that would be of concern to primates and are thus easy to process and consume (Bandeili and Muller 2010); and they have macro- and micronutrient compositions that are comparable to other plant foods (McCabe and Fedigan 2007). While some primate species consume flowers constantly throughout the year in relatively stable frequencies,

Heymann (2011) notes that most primates consume flowers in a highly seasonal manner. For many species, flowers represent less than 10% of their annual plant food intake, yet they become one of or the most important food items for a delimited period. Although florivory rates may simply vary in accordance with the availability of flower foods, it is also possible that flower foraging is a response to fluctuating abundances of other more frequently consumed food sources.. Foods that are only consumed specifically to survive “crunch” periods of reduced overall food abundance are hypothesized to exert disproportionate selective pressure relative to

their use, promoting adaptations that improve foraging efficiency and ultimately survival

(Marshall and Wrangham 2007).

1.1.2 Polymorphic colour vision

The ability to discriminate between the chromatic properties of objects is inferred to have evolved early in the vertebrate lineage, and many variations of the colour vision discrimination system have evolved since then (Collin and Trezise 2004). While most mammals are dichromatic, possessing two photoreceptors capable of processing chromatic signals, primates are unique in that many species have evolved trichromacy, and are thus capable of finer colour discrimination (Regan et al. 2001). The possession of a third photoreceptor, coupled with the peak spectral sensitivities of these photoreceptors in primates, is believed to greatly improve the ability to detect reddish colours from greens, as is the case with humans (Mollon 1989).

Numerous theories have been proposed as to why this ability would be beneficial to primates, mostly focusing on improved food detection (Dominy and Lucas 2001, Osorio and Vorobyev

1996), predator detection (Pessoa et al. 2014), or communication (Changizi et al. 2006).

However, it is extremely difficult to directly measure whether this improved colour discrimination ability actually leads to chromatic detection differences, particularly in wild primates. One method of controlling many of the confounds prevalent in such studies is by conducting research on populations with both dichromatic and trichromatic group members.

However, outside of humans, functionally dichromatic individuals in species with routine trichromacy are exceedingly rare (Regan et al. 2001). Most New World monkeys and several strepsirrhine species are polymorphic for colour vision, and these wild populations are frequently composed of both trichromats and dichromats (Jacobs 1984, Veilleux and Bolnick 2009). For species with polymorphic colour vision, colour vision ability is multi-allelic and X-linked, therefore all males and homozygous females are blue-yellow dichromats, while heterozygous 2

females are trichromatic with red-green colour vision (Hiramatsu et al. 2005). Such polymorphisms are rare in nature, and are believed to only persist if there is a net selective advantage to the maintenance of multiple alleles in the population, rather than selection towards the fittest allele (Hiwatashi et al. 2010). There are four main explanations for such balancing selection to persist: heterozygotic advantage, niche partitioning, negative frequency-dependent selection, and mutual benefit of association (Mollon et al. 1984). Of these scenarios, heterozygotic advantage has long been hypothesized to be the primary factor maintaining polymorphic colour vision in primates, and such an advantage has been detected in captive studies (Caine and Mundy 2000, Riba-Hernandez et al. 2004, Smith et al. 2003), although it has never been detected amongst wild-living species (Caine et al. 2010, Hiramatsu et al. 2008, Melin et al. 2009, Vogel et al. 2007). In fact, several studies have detected dichromatic advantages in certain foraging tasks, and it appears that niche partitioning or mutual benefit of association may be the underlying causes (Caine et al. 2010, Melin et al. 2007, Melin et al. 2012).

1.2 Objectives

While flower foraging by white-faced capuchins has been reported by numerous researchers (Chapman and Fedigan 1990, McCabe and Fedigan 2007, Melin 2011), this study is the first to explicitly focus on it. Therefore, my main objectives are to understand why capuchins are consuming flowers, and what factors influence the frequency of florivory. Specifically, I attempt to determine (1) why capuchins consume flowers, especially in the seasonal fashion that they do, and (2) how colour vision differences amongst individuals influences flower foraging behaviours. By answering these questions, I aim to contribute to our understanding of how foods that are consumed infrequently across the annual cycle may still have evolutionary implications due to their seasonal importance. My further objective is to contribute to our growing knowledge

of the conditions that favour colour vision polymorphism by investigating an oft-ignored food source.

1.3 General Methods

1.3.1 Study site

All data were collected in Sector Santa Rosa (Santa Rosa), Área de Conservación

Guanacaste (ACG), a world heritage site in northwest Costa Rica (Figure 1.3.1). Originally established as a national park in 1971 to preserve a culturally significant homestead predominantly surrounded by cleared agricultural land, Santa Rosa is now largely tropical dry forest in various stages of regeneration, and also contains some stands of primary forest. Like all tropical dry forests, Santa Rosa is characterized by extreme precipitation seasonality (Murphy and Lugo 1986). During the wet season, May-November in Santa Rosa, rain falls almost daily, and monthly precipitation accumulation is typically over 100 mm (Figure 1.3.2). Conversely, rainfall almost never occurs throughout the dry season (December-April), resulting in the temporary loss of most standing water sources throughout the park (Campos and Fedigan 2009).

Many plant species respond to this prolonged, predictable drought by being deciduous, with up to 80% of trees in Santa Rosa shedding their leaves for at least a portion of the dry season

(Janzen 1988). Temperatures, while both slightly hotter and more variable during the dry season, are relatively consistent annually, while the interannual variation in the intensity and duration of rainfall is highly variable and dependent on southern oscillation cycles (“El-Niño” events)

(Campos unpublished data). Seasonal water stress results in comparatively less plant biodiversity in tropical dry forests than wet forests, but tropical dry forests are animal rich (Ceballos and

Garcia 1995). In Santa Rosa numerous mammal species have been observed, including three sympatric primate species: white-faced capuchins, black-handed spider monkeys (Ateles

geoffroyi), and mantled howler monkeys (Alouatta palliata). Tropical dry forests are of special concern to researchers, as they are the most endangered forest structure on Earth, with only

0.02% of the historical range remaining intact (DeGama-Blanchet and Fedigan 2005). Recently established protected areas, including ACG, have demonstrated that successful regrowth is possible (Moline 1999). In Costa Rica 16% of the remaining tropical dry forest is now protected, which is the highest proportion of any country in the Americas (Portillo-Quintero and Sánchez-

Azofeifa 2010).

Figure 1.3.1. Location of Sector Santa Rosa, Área de Conservación Guanacaste, Costa Rica.

Image obtained from Google Earth®.

400.00 34.00 350.00 32.00

300.00 C)

30.00 ° 250.00 28.00 200.00

150.00 26.00 Temperature Temperature (

100.00 24.00 MonthlyPrecipitation (mm) 50.00 22.00

0.00 20.00

Month

TotalRainfall Average High Temperature Average Low Temperature

Figure 1.3.2. Monthly mean high and low temperatures and monthly precipitation accumulation for the study period (April 2013 – March 2014) in Sector Santa Rosa, Área de Conservación

Guanacaste, Costa Rica.

1.3.2 Study species and subjects

White-faced capuchin monkeys (Cebus capucinus) are medium sized (2-4 kg) moderately sexually dimorphic New World monkeys of the family Cebidae (Fragaszy et al.

2004). White-faced capuchins are omnivorous, intelligent, and behaviourally flexible, which has allowed them to occupy a large diversity of habitats and to stretch their distribution across most of Central America and northern Columbia and Ecuador (Fragaszy et al. 2004). C. capucinus are highly active and often destructive foragers, using a variety of techniques to locate and extract food items (Melin et al. 2014b). This generalist approach is thought to be a driving factor in their cognitive evolution, and they are often considered to be one of the more intelligent monkey species (Melin et al. 2014b). There is a high degree of intra- and interannual variation in their

diet, but it primarily consists of fruits and invertebrates (Chapman and Fedigan 1990). In Santa

Rosa capuchins spend about 20% of their foraging time searching for plant foods and up to 67% searching for invertebrates, but fruit comprises about 60% of their energetic intake (Bergstrom et al. 2014a in prep.). While they are primarily arboreal, capuchins utilize all dimensions of their habitat and often spend considerable time on the ground or in the understory (Fragaszy et al.

2004), making behavioural observation relatively easy compared to other primate species that exclusively inhabit the canopy.

White-faced capuchins live in multi-male, multi-female groups averaging 10-30 individuals, within which linear dominance hierarchies form for both sexes (Fragaszy et al.

2004). This can have direct influence on food access (Vogel 2005), although no fitness measures have been found to correlate with rank for females (Fedigan et al. 2008). Typically females remain in their natal group while males disperse at an average age of four years (Jack and

Fedigan 2004a). This often occurs soon after a group takeover by extragroup males (which occurs on average every 4.5 years), and maintains gene flow throughout the community (Jack and Fedigan 2004b).

White-faced capuchins have polymorphic colour with three X-linked alleles controlling the peak spectral sensitivity of their mid- to long- wavelength sensitive cones (M/L cones), resulting in six possible colour vision phenotypes: three dichromatic and three trichromatic

(Hiramatsu et al. 2005). Since colour vision phenotype is directly controlled by these alleles, colour vision of an individual can be determined through genetic analysis of fecal samples collected non-invasively, making them ideal subjects for colour vision research.

I studied three C. capucinus groups that have been subject to long-term observation under the supervision of Dr. Linda Fedigan and her students since 1983: groups Administration (AD),

Los Valles (LV), and Rosa Maria (RM). Groups AD and RM were previously one group known as Cerca de Piedra (CP), but CP fissioned during 2012 and early 2013, a process that was complete by the onset of this study. All three groups have overlapping home ranges of mainly secondary dry forest (Figure 1.3.3). I selected these three groups due to the pre-existing phenology system within their home ranges, which was established by researchers in 2008 and has been used to collect systematic phenological data on common capuchin plant foods. All individuals within these groups are recognizable to researchers due to characteristic differences in body size, pelage patterns and scarring, making capuchins ideal subjects for behavioural observation. During the study, group sizes fluctuated slightly with births and disappearances, ranging from 12-21 individuals per group (Table 1.3.1).

Table 1.3.1. Number of capuchins in the three habituated study groups during the data collection period, May 2013-March 2014.

Group Size During Study AD 19 - 22 LV 11 - 14 RM 18

Due to known differences in the foraging behaviours of small juveniles and infants, only individuals older than three years old at the onset of the study were included in scan and focal sampling data collection (Fragaszy et al. 2004). Initially a total of 37 individuals were included in focal and scan sampling. However, two adult females (NE and SA) disappeared in October

2013, therefore all data collected from these individuals were excluded from analysis. Most individuals had been colour vision phenotyped prior to this study, and I collected fecal samples for any new group members opportunistically throughout the entire data collection period. All six of the possible colour vision phenotypes are represented in these study groups, although not

in equal proportions, (33 dichromats and 17 trichromats). Detailed group affiliation, colour vision phenotype, and sex/age class characterization of each individual is available in Chapter

Three (Table 3.3.1).

Figure 1.3.3. Home range utilization of the three capuchin study groups, January 2013 to March

2014. Graphic used with permission courtesy of Dr. Fernando Campos.

1.3.3 Study period

I collected all data for this project between May 2013 and March 2014, with no data collected in August and September 2013. Previous researchers reported no flower consumption

by the study groups during this time. I also did not collect behavioural data between December

20, 2013, and January 10, 2014. I collected behavioural data on a total of 107 full days (44 AD,

35 RM, 28 LV), for a total of 1157 observation hours. On these days I conducted 1689 scan samples, from which I obtained 14701 data points. I also collected a total of 268 hours of focal sampling records. The ethogram I used for both focal and scan sampling is attached as Appendix

I. Spectroscopic data are supplemented by data collected in 2007-2008 by A. Melin from plants at the same field site, and nutritional data for Manilkara chicle flowers were obtained from data collected by M. Bergstrom in 2010. Detailed descriptions of relevant data collection methods are included with each data chapter (Chapters Two and Three).

1.3.4 Plant sample collection

Flower samples for nutritional and spectroscopic analysis were collected whenever a species was observed to be consumed by capuchins. For nutritional analysis, only life stages at which capuchins would consume them were collected. I collected separate spectroscopic data for all flower parts that were different colours to the human eye.

1.4 Overview

The purpose of this study was to determine what factors affected the frequency of flower food use by white-faced capuchins. In Chapter Two I report on the general trends of florivory by

C. capucinus. I quantify what flower species were consumed, in what frequencies, by which monkeys, and during which time periods. Using behavioural data I compare florivory rates to the seasonal variations in fruit, flower, and invertebrate abundances in Santa Rosa to determine if such external factors significantly affect florivory. Based on observations from previous research, I predicted that flowers that bloomed in different seasons would not differ significantly nutritionally or in terms of relative abundance, and that the observed seasonality of florivory was

instead a response to external factors, most likely a decrease in ripe fruit abundance in the dry season. If this prediction was confirmed flowers could be considered fallback foods, suggesting that because they are critical for survival during a “crunch” period they may exert high selection pressure disproportionate to their importance in the diet (Marshall and Wrangham 2007).

Also in Chapter Two I report on the nutritional composition of all flowers observed to serve as capuchin foods during this study. I also report on the nutritional composition of one non-consumed flower species (Luehea candida) that is closely related to the most-consumed species (Luehea speciosa) as a comparison point to determine if flower nutrition affects capuchin flower food choice. I predicted that flower nutritional quality would not differ between any analyzed flower species, but that flowers in general would be of lower nutritional quality than ripe fruit foods, implying that flowers are not selected due to their quality.

In Chapter Three I address how differences in an individual’s colour vision abilities affects their flower foraging behaviours. Based on the fact that flowers often utilize chromatic signals to attract pollinators, I predicted that trichromatic capuchins would have an advantage in detecting flower food items amongst leafy backgrounds over dichromats, and that this would result in them consuming flowers more frequently. To test this I collected spectroscopic data on the flowers and leaves of all species observed to serve as flower foods during this study, then tested perception of these signals by the different capuchin colour vision phenotypes using computer simulation models. I also compared the frequency of flower foraging for dichromatic and trichromatic individuals using behavioural data that I collected.

In Chapter Four I summarize and synthesize my research findings, discuss its relevance to the existing body of research, and explain the significance of my conclusions. I also address some limitations to this study, and suggest future directions for advancing the topics covered.

Chapter Two: Why do monkeys eat flowers?

2.1 Background

2.1.1 A tale of two Luehea

Luehea candida and L. speciosa are two woody plants in the family Malvaceae (formerly

Tiliaceae); both can be abundant in tropical dry forests and are morphologically similar to the extent that it is difficult to distinguish between them in the absence of fruits. Both species produce flowers of similar colours, structures and crop sizes, and both undergo nocturnal anthesis, fully opening shortly after sunset and only remaining viable until mid-morning the next day (Haber and Frankie 1982). While the flowers of L. candida are slightly larger than L. speciosa (mean diameter L. candida = 80 mm, L. speciosa = 50 mm), L. speciosa produces slightly more nectar per flower on average (L. candida = 206 μL, L. speciosa = 242 μL, Haber and Frankie 1982). However, the phenological cycles of these two Luehea species are quite different. For example, in the Santa Rosa tropical dry forest in northwest Costa Rica, L. candida produces its flowers in the early wet season (May to July), whereas L. speciosa blooms in the early dry season (December to January).

Despite the similarities of these two flowers, a population of white-faced capuchin monkeys (Cebus capucinus) that inhabit the tropical dry forests of Santa Rosa, Costa Rica rarely forage on the wet season blooming L. candida flowers, whereas flowers of L. speciosa, which bloom in the dry season, are heavily consumed (3.3% of the annual plant diet, 10th most consumed plant food, Melin 2011). While this is the most obvious example, previous research at this site has revealed a consistent yet unexplained pattern of heavier flower consumption by capuchins during the dry season versus the wet season regardless of species (Melin unpublished data). There are two possible explanations for this pattern: (1) dry season flowers are inherently different from and relatively “better” (i.e., they are more energetically dense or more

proteinaceous) than species that bloom in the wet-season; or (2) capuchins are responding to ecological or climatic factors characteristic of the dry season, which is hypothesized to be a stressful “crunch” period for capuchins, by increasing their reliance on flowers. Whether or not there are significant nutritional differences between the two Luehea species has not yet been investigated. If there are no significant nutritional differences then this would suggest that there are other reasons why capuchins exploit one flower species more frequently than the other. It is possible that capuchins consume flowers not because of their inherent value, but in response to other, external factors that differ between seasons, such as the abundance of highly preferred foods. In the latter case, the most likely explanation for seasonal variation in the consumption of flowers is that they are utilized by capuchins as fallback foods during times of fruit scarcity

(Marshall and Wrangham 2007, Marshall et al. 2009).

2.1.2 Are flowers fallback foods?

Food type, abundance and availability all directly shape the evolution of the diverse morphological, social and ecological strategies we see amongst extant primates today (Hohmann et al. 2006). Therefore, why primates choose specific foods has been a central focus of research in primate evolutionary ecology (Felton et al. 2009, Marshall and Wrangham 2007). Historically, many field studies have focused on understanding the role of food items that make up the largest proportion of a species’ diet in terms of overall intake (Janson and Chapman 1999). While there is no doubt that the proportional dietary contribution of a particular food is important to consider when studying primate diets, it is certainly not the only facet of a food’s impact on a species’ life history and evolution.

Many primate species’ diets are seasonally variable, and some food items with low annual importance are highly important over shorter temporal periods when preferred foods are

less abundant. Such resources are often referred to by anthropologists as “fallback foods”, and while the definition of the concept has varied, a common definition is that a fallback food is a resource whose use is negatively related to the abundance of preferred foods (Marshall and

Wrangham 2007, Marshall et al. 2009). Preferred foods, in turn, are defined as those that are consumed disproportionately often relative to their abundance in the habitat (Leighton 1993,

Marshall et al. 2009). A food that is consumed frequently but is rare is said to be highly preferred, whereas a highly abundant food item that is rarely consumed is said to be avoided or under-selected (Leighton 1993). Preferred foods are typically highly nutritious and easy to access, process and digest in comparison to other food items (Marshall and Wrangham 2007,

Felton et al. 2009). Researchers also note that preferences vary among species, groups, individuals, and time, making measurement extremely difficult (Felton et al. 2009).

Use of fallback foods assists primates in persisting through periods of low preferred food abundance, during which time they may otherwise face starvation and potentially extirpation.

Because of this, fallback foods are hypothesized to exert selective pressure disproportionate to their annual importance, promoting adaptations that improve foraging efficiency specifically for that resource (Marshall and Wrangham 2007). The evolutionary influence of fallback foods varies with its quality and importance in the diet (Marshall et al. 2009). “Staple” fallback foods are defined as foods that can make up 100% of the diet seasonally and are generally abundant in the environment and therefore are easily discovered, but they are also often lower quality. Since they are not difficult to locate but can be difficult to consume or digest efficiently, staple fallbacks are believed to exert selective pressure that promotes processing adaptations that allow for better nutrient access and digestion (Wrangham and Marshall 2007). “Filler” fallback foods, conversely, never constitute 100% of the diet, and are typically more patchily distributed but of

higher nutritional quality. Like preferred foods, they are hypothesized to promote behavioural and anatomical adaptations related to locating and harvesting foods, such as tool use and colour vision, rather than anatomical food processing adaptations, such as thick enamel (Marshall and

Wrangham 2007, Marshall et al. 2009). In addition to food switching, these seasonal “crunch” periods may induce other behavioural responses from primates that are intended to further alleviate the seasonal stress (Hemingway and Bynum 2005). There are two common patterns of behavioural change employed by primates to cope with these crunch periods: (1) temporarily increasing energetic output to visit more food patches over a larger area (“effort maximizing”); or (2) temporarily reducing overall activity rates, therefore lowering required energy intake

(“energy minimizing”) (Hemingway and Bynum 2005).

2.1.3 Flower use by primates

Most primate species are commonly classified as either frugivorous or folivorous. This broad categorization, while useful for some purposes, masks the contribution of many other food items consumed in smaller proportions, such as flowers (Lappan 2009). As a result, there is a misconception that most primates do not eat flowers, and the ones that do, do so rarely. A recent literature review by Heymann (2011) revealed that flower consumption has been reported in 165 primate species, and that members of every family except Tarsiidae have been recorded as florivores. Lappan (2009) speculates that even this value may underestimate the frequency of flower eating amongst primates due to many instances of flower foraging being hidden within an

“other” category in the primate literature.

Despite the fact that flower foraging is common amongst primates, there is a dearth of studies focusing on how flowers are being utilized. Heymann (2011) notes that some trends can be inferred; for example, larger-bodied (>5 kg) primates are significantly more likely to consume

entire flowers, whereas smaller-bodied primates typically consume only nectar. However, body size does not appear to be a limiting factor for the consumption of flowers, as even the largest apes have been noted to eat them, some species in high volumes periodically (Newton-Fisher

1999, Rothman et al. 2006). While flowers comprise less than 10% of the foraging budget for most primate species, flowers are often seasonally important (Heymann 2011). In many cases, flowers are the first or second most consumed food item during a certain season, and can comprise up to 85% of the primate’s diet during that period (Heymann 2011). Of the species reported by Heymann (2011) to consume flowers seasonally, the majority are generally categorized as frugivorous. It is possible that these primates are utilizing flowers as fallback foods, and indeed this has been hypothesized to be the case for several primate species

(Hemingway and Bynum 2005). In tropical dry forests, most plant species produce flowers during the dry season, partially because the reduced leaf cover results in enhanced visibility of flowers to pollinators, increasing the display effectiveness (Frankie et al. 1974). Their abundance in the dry season may make flowers attractive targets as fallback foods for primates living in tropical dry forests.

2.1.4 Nutritional quality of flowers

Flowers have also been neglected in primate foraging studies based on the assumption that they are too small or simply not nutritious enough to be useful to most primates, particularly larger-bodied ones. In the case of capuchins this does not appear to be true, since the macronutrient density of select flowers known to serve as food items are comparable to fruit foods (Bergstrom et al. 2014a in prep., McCabe and Fedigan 2007). While flowers are often smaller and lighter than fruits, they are also typically much more abundant, with many species overproducing flowers in relation to their subsequent fruit set (Morgan 1993, Wise and Cummins

2002). In addition to their abundance, many flowers have nutritional advantages in comparison to fruits. Fruits often have high proportions of indigestible fibrous materials, whereas nectar usually does not, and nectar can be rich in easily accessed sugars such as sucrose, glucose and fructose (Heymann 2011). Although it is typically dilute, nectar is often produced in copious amounts by plant species looking to attract larger pollinators (Carthew and Goldingjay 1997,

Willmer 2011). Pollen is protein rich and often produced in sufficient quantities to be a worthwhile food source for many primate species (McConkey et al. 2003, Roulston and Cane

2000). Fruits, by comparison, often contain low concentrations of protein (Janson and Chapman

1999). Flowers are also known as sources of many micronutrients such as phosphorus, potassium, and iron (Whigham et al. 2013, McCabe and Fedigan 2007, Bergstrom et al. 2014a in prep.). Typically, petals are less defended by physical structures such as trichromes than other plant structures, and have thinner cell walls that are easier to consume and digest (Bandeili and

Muller 2010, Whigham et al. 2013, Matter et al. 1999). Floral nectar is produced specifically to attract pollinators, and while secondary compounds in nectar are known to exist, they have only been documented in a limited number of species, and the toxicity of these compounds has been questioned (Adler 2000). Overall, many flowers are abundant and easily accessed sources of highly digestible carbohydrates, proteins, and micronutrients, making them a good food source for energetic primates.

2.1.5 White-faced capuchin consumption of flowers and invertebrates

White-faced capuchins are omnivorous and exhibit significant dietary variation across groups, habitats and seasons (Chapman and Fedigan 1990, Fragaszy et al. 2004, McCabe and

Fedigan 2007, Melin et al. 2014a, Melin et al. 2014b). They are generally classified as frugivore- insectivores and are known to prefer ripe fruit (Fragaszy et al. 2004). They have also been

observed to consume flowers, although never in quantities larger than 10% of their annual diet

(Chapman and Fedigan 1990, McCabe and Fedigan 2007, Melin 2011). The capuchins in Santa

Rosa have been observed to consume a highly diverse flower diet of 25 species from 18 plant families (Table 2.1.1), most of which are consumed only rarely (Melin 2011). Although flowers do not appear to be important to capuchins on an annual level, in a capuchin population living in

Santa Rosa there is noticeable seasonality to flower foraging, with the highest rates occurring in the dry season, a time of reduced plant productivity (Melin et al. 2014a). The relative use, diversity and importance of flowers to capuchins has been largely unexplored. The fact that flowers are consumed in a highly seasonal fashion suggests that they may be used as a fallback food, a hypothesis that I test with this study.

Table 2.1.1. Flower species recorded to be consumed by C. capucinus in Santa Rosa. Data adapted from Melin et al. 2014a and Fedigan unpublished data.

Family Species Annonaceae Anonna purpurea Apocynaceae Stemmadenia donnell-smithii Stemmadenia obovata Bignoniaceae Pithecoctenium crucigerum Tabebuia ochracea Boraginaceae Cordia guanacastensis Burseracea Bursera simaruba Chrysobalanaceae Licania arborea Cochlospermaceae Cochlospermum vitifolium Dilleniaceae Curatella americana Euphorbiaceae Euphorbia schlechtendalii Fabaceae Bauhinia ungulata Centrosema molle Cassia grandis Hymenaea courbaril Malvaceae Luehea candida Luehea speciosa Malvaviscus arboreus Myrtaceae Callistemon viminalis Orchidaceae Brassia spp. Papilionaceae Diphysa americana Rubiaceae Calycophyllum candidissimum Salicaceae Casearia arguta Sapotaceae Manilkara chicle Viscaceae Phoradendron quadrangulare

Invertebrates are an important component of the capuchin diet, making up 25-45% of the annual diet and accounting for 67% of their overall foraging effort (Bergstrom et al. 2014b in prep., Melin et al. 2007, Melin et al. 2008, Melin et al. 2010, Mosdossy et al. in review). While invertebrates are generally abundant throughout the annual cycle, there is measurable seasonality in overall abundance and species composition, and capuchins exhibit seasonal variation in their invertebrate foraging patterns (Melin et al. 2014b, Mosdossy et al. in review). Because capuchin

foraging efforts on invertebrates increase during periods of reduced plant availability, embedded and well-defended invertebrates have been hypothesized to act as fallback foods for C. capucinus

(Mosdossy et al. in review, Melin et al. 2014b). Invertebrates consumed by capuchins in Santa

Rosa contain relatively higher protein densities (as a percentage of dry matter) than do fruits

(Bergstrom et al. 2014a in prep.). Pollen, also, has been noted to be very protein rich (Roulston and Cane 2000). Understanding the use of flowers in relation to the abundance of invertebrates, in addition to fruit, is therefore important for examining the foraging choices and behaviours of capuchins.

2.2 Research Objectives and Hypotheses

The purpose of this study is to gain a better understanding of the florivorous behaviours of white-faced capuchins. Specifically, I aim to determine how important flowers are in the capuchin diet annually and seasonally, what the nutritional compositions of flower foods are, and what ecological conditions correspond to increased flower foraging activity. I hypothesize that flowers act as a filler fallback food for capuchins. To determine whether flowers were being used in this manner, I obtained data to answer the following questions and test the associated predictions derived from the concept of filler fallback food use.

1. What proportion of the white-faced capuchin diet consists of flowers? If flowers fit the definition of fallback foods, then I predict they should be consumed in relatively low amounts across the annual cycle, but show a predictable seasonal period of increased consumption. I predict flowers should never comprise 100% of the diet for any time period, satisfying the definition of a filler fallback food.

2. Is flower consumption related to flower abundance? If flowers are fallback foods, I predict there should be no correlation between the abundance of flowers and their consumption.

3. Is flower consumption related to ripe fruit abundance? If flowers are fallback foods, I predict they should be consumed in higher volumes during periods of low abundance of preferred ripe fruit foods.

4. Is flower consumption related to invertebrate abundance? I predict that flowers may be consumed as a protein replacement source, and that their consumption is negatively related to invertebrate abundance.

5. How do the nutritional qualities of flower foods compare relative to other capuchin food types? If flowers are fallback foods, I predict the species of flowers consumed should be (a) of lower nutritional quality than preferred ripe-fruit foods (although some filler fallback foods are high quality) and (b) of similar quality to non-consumed flower species (i.e., the flower species consumed are not chosen because of their inherent quality). I test Part B of this prediction by comparing the nutritional quality of the two Luehea species.

6. Are there any behavioural differences associated with periods of increased flower consumption? If flowers are consumed as a response to food scarcity capuchins may exhibit behavioural changes that provide further evidence of a “crunch” period occurring. Depending on the strategy that white-faced capuchins use to minimize nutritional stress, these changes may manifest as increased time spent foraging,, increased time spent resting, and/or changes in daily travel distance.

2.3 Methods

2.3.1 Study site

All data were collected in Sector Santa Rosa, Área de Conservación Guanacaste (ACG),

Costa Rica (see Chapter One for detailed site conditions). Like other tropical dry forests, Santa

Rosa experiences extreme seasonality of precipitation and plant phenology (Janzen 1988,

Murphy and Lugo 1986, Melin et al. 2014b). In Santa Rosa the dry season typically occurs from

December to April. During this time rainfall averages only 0-20 mm/month, approximately 80% of the trees shed their leaves for at least a portion of this period (Janzen 1988), and above ground water sources are mostly absent (Campos and Fedigan 2009). In contrast, during the wet months from May through November, rainfall averages 130-450 mm per month, and most trees retain full leaf coverage. Although temperature varies less than rainfall does throughout the year, the dry season is warmer on average and has a wider daily range (Figure 1.3.2).

2.3.2 Study species

White-faced capuchins are medium-sized (2-4 kg) New World monkeys inhabiting a diverse array of habitats throughout Central and South America (for detailed species description, see Chapter One). They are omnivorous, with the bulk of their diet composed of many species of fruit and invertebrates. While they are known to prefer ripe fruit foods (Melin et al. 2014a), and spend the bulk of their foraging time searching for and consuming invertebrates (Melin et al.

2007, Melin et al. 2008, Bergstrom et al. 2014b in prep.), capuchins have been observed to consume flowers from several plant species (Table 2.1.1).

2.3.3 Study groups

I studied three groups of C. capucinus in Santa Rosa (AD, LV, RM). These groups have been under long term observation for many years, and are habituated to researcher presence.

Using identifying characteristics such as sex, age, scarring, pigmentation, and pelage patterns, all individuals are recognizable. All three groups live in close proximity and have some degree of home range overlap (Figure 1.3.3). I chose to collect data from these three groups out of the five currently studied at our site to ensure that phenological data were relevant, as their home ranges overlap with an existing phenology trail system. Groups were of different sizes, and some fluctuated slightly during the study (AD: 19-22, LV: 11-14, RM: 18).

2.3.4 Phenological data collection

Since many flower species are highly ephemeral (Willmer 2011), monthly phenology data collection (the standard at our field site) risked missing flowering events for some species, therefore we recorded phenology data every two weeks. On a phenology data collection day, typically on or as close as possible to the 1st and 15th of every month, my assistants and I collected phenological data from 408 representative plants of 55 capuchin food species. The majority of phenology plants have been observed for several years by previous researchers, but prior to this study only fruit food species had been included in the phenology system, therefore we added eight species (Bauhinia ungulata, Calycophyllum candidissimum, Cordia guanacastensis, Cassia grandis, Diphysa americana, Hymenaea courbaril, Licania arborea, and

Luehea speciosa) from which capuchins had been observed to consume flowers but not fruits.

All observers were trained and tested by experienced field personnel prior to collecting phenology data independently. During each assessment observers recorded the percentage of leaf, fruit, and flower coverage for each plant using a 4-point scale (0= 0%, 1= 1-25%, 2= 26-

50%, 3= 51-75%, 4= 76-100%). Observers also recorded the percentage of leaf, fruit, and flower cover that was mature using the same scale. Plants that were observed to be dead or severely damaged were removed from subsequent phenology cycles and replaced by a representative

plant of the same species; researchers made an effort to find a replacement located as close to the original plant as possible. During the field season we recorded a total of 13 phenology cycles.

2.3.5 Invertebrate abundance

I obtained invertebrate abundance data from a study conducted in 2011 and 2012 by K.

Mosdossy (2013). Although it would have been ideal to conduct the invertebrate study simultaneously with the present research to eliminate any potential effects from interannual variation in invertebrate abundance, data collected by Mosdossy conform to the invertebrate abundance trends noted by other researchers at this site (Janzen 1973, Janzen 1988, Janzen 1993) and were therefore used. During the Mosdossy study, pan and malaise invertebrate traps were installed in four locations throughout Santa Rosa in representative forest age classes and heights.

Invertebrates were collected from these traps up to three times per week, identified to the family level whenever possible, and each family was assigned a monthly abundance estimate, averaged within trap types across sites. From these data, I summed the total number of invertebrates collected monthly, regardless of taxa, to create an overall monthly invertebrate abundance index.

2.3.6 Behavioural data collection

I collected data from May 26, 2013, to March 23, 2014, for a total of 1157 observation hours over 107 days: 44 days with AD (489 hours), 28 with LV (286 hours), and 35 with RM

(382 hours). I did not collect behavioural data from July 26 to October 24, 2013, or December

20, 2013 to January 10, 2014. The principal investigator was the same throughout the study, while three different field assistants also contributed to research throughout this period. All researchers had a minimum of one month of field observation on the study groups prior to the start of data collection. We routinely measured inter-observer reliability of monkey and food species identification, as well as behavioural state classifications, by sporadic testing in the field.

Observers followed each group within the seven days preceding or following a phenology day to ensure that behavioural observations were relevant to the collected phenological data. We followed groups continuously from sunrise to sunset whenever possible (typically 06:00 to

18:00), and unless a group could not be found for one or more days, each group was initially followed for three days per phenology cycle. During the study, it became clear that differences in group size were resulting in uneven sampling of individuals. Following this realization, the data collection cycles were adjusted and researchers spent two days with LV, three days with RM, and four with AD per cycle.

We used three main data collection methods during group follows: all-occurrence “flower patch visit” (FLPV) sampling, scan sampling, and focal sampling (Altmann 1974). Observers recorded all data using handheld PSION electronic devices and Loggy software. Each line of data entered into Loggy was time and date stamped to the nearest second.

Rare events, such as flower consumption by C. capucinus, are thought to be underreported using standardized field methods such as scan or focal sampling (Leighton 1993).

One sampling method designed to capture such rare, fleeting behaviours is “patch visit” sampling (Leighton 1993, Melin 2014a). We recorded a flower patch visit (FLPV) whenever any monkey was observed to be consuming flowers, regardless of the number of monkeys participating, the number of flowers consumed, or the length of the foraging bout. For each

FLPV, we recorded the following information: time of FLPV onset, flower species being consumed, and the location of the patch (marked via GPS point). We treated individual plants as separate FLPVs, and the same tree was recorded as a new FLPV only if more than one hour elapsed between visits by any group member.

We only recorded scan samples when both a field assistant and I were present, typically from 07:00 to 15:30 each field day. This ensured that peripheral group members were well represented in scan data, as we could devote search effort to locating these cryptic members when two observers were present. Small juveniles and infants are known to have significantly different behavioural patterns, particularly with regards to foraging, therefore only adults, subadult males, and large juveniles were included in scan sampling (Fragaszy et al. 2004). Every

30 minutes we located each eligible monkey in the group and recorded their behavioural state

(see ethogram, Appendix I). To prevent observer bias, I chose a random monkey to initiate each scan sample. We ceased a scan sample following the discovery of every monkey in the group, or after 10 minutes if some group members had not yet been located. If monkeys were interacting with a food item, we recorded the type of food item (by species and part, whenever possible). If, during a scan, flower consumption was noted, one researcher recorded an FLPV simultaneously.

We conducted focal sampling from 07:00 to 15:30 daily, in the time intervals between scan samples. I created a randomized list prior to each field day, and monkeys were each observed individually for 10 minute focal periods, in the order of the list as closely as possible.

During the focal period, one researcher described all behavioural states and events in which the focal monkey engaged (see ethogram, appendix I), and the second researcher entered these data into a custom data-logging program, which recorded all entered data with a timestamp to the nearest second. During focal observations researchers recorded every “eat” event that occurred while a monkey was consuming a food item. I used eat events to compare intake rates between food species, and I only recorded them when food consumption was directly observed (i.e. chewing and swallowing). I also recorded whenever a monkey switched between individual food items whenever possible, although at times food items were too small to differentiate. If a

monkey was interacting with a food item, I recorded the species and part of that item when possible. Researchers attempted to keep a focal monkey in sight at all times. If a monkey was not fully visible I considered it out of sight, and we ceased data collection until clear sight was re- established. We attempted to relocate a lost focal individual for the full focal time remaining, regardless of the length of time spent out of sight.

Despite my best efforts, there is reason to suspect that my data may be underestimating the true rate of flower foraging for some food species. Specifically, due to the leafiness of L. speciosa, the location of the flowers in the trees, the low amount of light during the early morning, and the manner in which the capuchins foraged on these flowers, it was at times impossible to obtain accurate identifications of the individuals involved for scan and focal samples, and many FLPVs were likely missed altogether.

2.3.7 Nutritional data

I designed flower collection protocols to follow the procedures of Rothman et al. (2012).

Once a flower species was confirmed as a food item during my study, I collected flowers from a minimum of three different representative plants, then counted, weighed and dried them at 35-

40º C using a Nesco-American Harvest FD-1010 1000 Watt Gardenmaster dehydrator. Although

Luehea candida was not a species observed to be consumed by capuchins during this study, I also collected samples of it to compare as an out-group with Luehea speciosa. Previous researchers had noted that capuchins appeared to target only the middle portions of Luehea speciosa flowers when foraging (Bergstrom, Melin, Myers, pers. comm.), although it was unclear whether capuchins were consuming the reproductive structures in the middle of the flower or simply ingesting nectar, which is produced at the base of these structures and rests on the petals. To account for this, both Luehea species were divided into two parts for analysis: the

structures protruding from the middle of the flowers (referred to in this study as “middles”), and petals. I weighed and began dehydration of all samples within one hour of collection to minimize water loss during transport. I only collected flowers once they were at a maturity stage that the monkeys were observed to consume, and only analyzed the parts of the flowers noted to be consumed by monkeys. Processing was kept to a minimum to ensure structural integrity of the samples. I dehydrated flowers for a minimum of five days, up to a maximum of seven days if they did not appear to be sufficiently dried. Following dehydration, I placed samples in Ziploc® bags, then weighed, labelled, and stored them alongside silica gel for a maximum of three months. I then shipped all samples to Dairy One Laboratories in Ithaca, New York, where they were analyzed for their gross energy contents using standardized laboratory procedures (Dairy

One 2014). Dairy One also determined the samples’ proportions of moisture and the following macro- and micronutrients: crude protein, neutral-detergent fibre, non-fibre carbohydrates, water soluble carbohydrates, crude fat, calcium, phosphorus, magnesium, potassium, and sodium.

Finally, Dairy One investigated samples for their concentrations of: iron, zinc, copper, manganese, and molybdenum. All reported nutritional results are based on analysis of one submitted sample, therefore no uncertainty estimates are reported.

2.3.8 GPS data

While with a study group I recorded a GPS waypoint every 30 minutes using Garmin

GPSMAP 62s handheld GPS units, which recorded my UTM coordinates +/- a maximum of 10 m. I recorded waypoints as central to the group as possible, or, if only a small proportion of the group was visible due to dense foliage or high group spread, a waypoint was collected directly under one monkey. I also recorded sleep tree or group discovery locations, as well as locations of

any FLPVs throughout the day. I recorded FLPV points as near to the base of the patch as possible.

2.3.9 Data analysis

I conducted all analyses using IBM SPSS Statistics 21.0.0.2 software, with a minimum significance threshold of p<0.05. If multiple post-hoc analyses were performed on the same dataset (such as comparing age and sex classes to each other), I applied Bonferroni corrections to the required significance threshold.

2.3.10 Phenology scores

I calculated ripe fruit indices and ripe flower indices for each phenological cycle using the following methodology:

1. I calculated ripe fruit and flower scores by multiplying the cover score by the maturity score for every plant observed on a given phenology day.

2. Due to an uneven number of representative plants per species in the phenology, I calculated an average ripe fruit and flower score within each species for each phenology cycle.

3. To control for interspecific differences in plant size and abundance within the park, I multiplied the average fruit and flower ripeness scores for each species by the biomass per hectare for that species, as estimated by Melin (2011) using methodology adapted from Peters et al. (1988) (Equations 2.3.1, 2.3.2).

1.9 Equation 2.3.1. Fi=47(DBH)

Equation 2.3.2. Biomassi= ((Σn(Fi))/Areatransects)*1 hectare

Equation 2.3.1 calculates the estimated fruit production in grams (F) for a given tree (i) based on its diameter at breast height (DBH), in metres. Equation 2.3.2 sums fruit productivity within each species for all individuals found within transects of known areas, then standardizes the fruit productivity score by dividing the species sum by the total transect area and multiplying by one hectare. Although Equation 2.3.1 was designed to measure fruit productivity and not flower productivity, since flower phenology reflects fruit cycles much closer than leaf cycles, it should provide an appropriate estimate of flower abundance.

4. Due to known errors associated with estimating the fruit productivity of wind-dispersed species using this method (Peters et al. 1988), I excluded five species (Apeiba tibourbou,

Bauhinia ungulata, Guazuma ulmifolia, Luehea candida, and Luehea speciosa) from ripe fruit index calculations but left them in ripe flower index calculations.

5. I then determined the total ripe fruit and mature flower indices for each cycle by summing all species’ biomass-adjusted ripeness scores (Equation 2.3.3). I only used species that produced fruit known to be capuchin food to calculate ripe fruit indices, while I used all species monitored for phenology to calculate ripe flower indices, regardless of whether the flowers were consumed by capuchins. I chose to leave non-flower foods in my phenology estimates because I am interested in investigating how capuchins respond to overall flower abundance rather than the specific abundances of known food species.

푐표푣푒푟푎푔푒 ∗푚푎푡푢푟푖푡푦 Equation 2.3.3. 푅퐹퐼 = ∑푛 퐴푣푔 ( 푖 푖) ∗ 퐵푖표푚푎푠푠 푐푦푐푙푒 푖=푠푝푒푐푖푒푠 푖 4 푖

2.3.11 Flower consumption in relation to food abundance indices

To compare the daily rate of flower foraging to the abundance of flowers, preferred ripe fruit foods, and invertebrates, I used a generalized estimating equation (GEE). Because I was 31

aiming to determine how white-faced capuchins in general change their flower foraging habits based on food abundance rather than how different groups or individuals react, a GEE was the appropriate statistical analysis for this dataset, as GEEs are able to account for repeated measures within a variable and measure the average effect of the dataset (Liang and Zeger 1986).

Exploratory analysis revealed a significant effect of observer hours and number of FLPVs recorded (B= -0.030, SE= 0.0139, χ2(1)= 4.587, p= 0.032), therefore standardization of FLPVs was necessary. To account for differences in daily observer effort, I divided the total number of

FLPVs observed on a given day by the hours of observation to create an FLPV rate, with the observation day being the unit of analysis. Unfortunately, this prevented the use of negative- binomial distribution for the GEEs, which, due to zero-inflation concerns is the ideal distribution for my dataset (Sileshi 2006, Sileshi 2008). However, GEEs are robust to slight offsets in data distribution (Ghisletta and Spini 2004), so I do not anticipate this to have a strong impact on my conclusions. Given these parameters and because the data are continuous, I specified a Gaussian distribution.

The introduction of too many predictor variables into a GEE with a small data set is known to negatively affect statistical power (Dr. Tak Fung pers. comm.), thus, due to my relatively small sample size of observation days (N= 107) and phenology cycles (N= 13 for fruit and flowers, N= 7 for invertebrates), I conducted separate analyses for the effects of flower, fruit and invertebrate abundance on flower foraging. Visual inspection of the dataset revealed no correlation between fruit and flower abundances, therefore running these analyses independently should not have affected my results. Since invertebrate data were tabulated monthly, I analyzed two cycles of behavioural data with each invertebrate abundance index. Because several data points are compared to the same phenological scores within a given period, in all equations I

included the study period cycle and group membership (“group by cycle”) as variables with covariance structures of AutoRegressive Order 1 (AR(1)). This is the appropriate covariance structure because a given phenology cycle is more related to the cycles immediately before and after it, and more weight is applied to this correlation than to cycles several months apart

(Hanley et al. 2003).

2.3.12 Behavioural changes and flower foraging

Since the unit of analysis of FLPV data is at the group level rather than the individual, I analyzed scan sample data to determine whether behavioural states varied in any way with flower foraging. I summed scan sample data for each individual within each phenological cycle, resulting in 13 data points for each individual (N= 455). Using these data, I calculated flower foraging frequency per individual per cycle by dividing the total number of flower feeding states observed by the total number of scan samples collected from that individual within a cycle. I calculated similar frequencies for resting, for which I included passive social behaviours such as grooming and hand-sniffing, and visually scanning, which I used to estimate foraging effort. I then used a GEE to determine whether flower foraging was correlated with either resting or visual foraging. I also analyzed whether flower foraging frequencies differed between sex and age classes. Colour vision effects can be misconstrued as sex effects (Melin et al. 2010), therefore I only included dichromatic females to males (Melin et al. 2010). Due to the fact that all juvenile females were trichromats, only adult females could be compared to males. I also compared adult female trichromats to juvenile female trichromats in a separate analysis. In all

GEEs, each individual was treated as the unit of analysis and was assigned an AR(1) covariance structure to account for the repeated measure of each individual over time and temporal changes in behaviour.

2.3.13 Energy intake rates

I determined the average number of ingest events per minute and ingest events per individual food item for each flower food species from the focal data. Using values obtained from laboratory analysis of flower samples, I assigned each species an average energy intake rate by dividing the gross energy per flower by the average number of ingest events recorded per item, then multiplying this by the average number of ingests per minute.

2.3.14 Daily travel rate

I measured the total distance travelled by a capuchin group using Garmin Basecamp software by measuring the linear distance between each 30 minute waypoint and summing the distance travelled by day. To compensate for variation in observation day length, I divided the total daily travel distance by the hours observers were present for each day, yielding a daily travel rate. To determine if there was any correlation between FLPV rate and daily travel rate, I performed a GEE analysis. In this analysis, group membership was included as a repeated measure of AR(1) covariance structure.

2.4 Results

2.4.1 Research question one: the annual and seasonal importance of flowers in the diet

I recorded a total of 156 FLPVs to seven plant species (Table 2.4.1). Flower foraging was almost exclusively a dry season strategy: 93.5% of FLPVs were observed between mid-

December and March. A FLPV occurred on 32% of observation days, but with a strong seasonal bias: flower eating was observed on 58% of dry season days, but only 7% of wet season days.

One species, Luehea speciosa, which the monkeys foraged on in December, accounted for 72% of all FLPVs. One day in December is particularly noteworthy: group RM had 49 FLPVs to 48

different trees in a single day (31% of total FLPVs), all but one of which were Luehea speciosa trees (Figure 2.4.1).

We rarely recorded flower foraging during scan sampling. Monkeys were observed to consume or process flowers only 49 times during scan samples, which constituted 0.3% of all scans. Study subjects were recorded as actively feeding (i.e., biting, swallowing) on plant matter in 9% of scan samples, 3.5% of which were flowers. Luehea speciosa comprised 2% of the plant diet in terms of annual intake, which makes it the 19th most important food item of the 55 plant foods eaten during scan sampling. Diphysa americana was the 24th most consumed plant food, making up 0.9% of the overall diet. Combined, less than 0.5% of the plant diet consisted of

Callistemon viminalis, Centrosema macrocarpum, Malvaviscus arboreus, and Manilkara chicle flower species. Although flowers comprised only a small proportion of the annual plant diet,

Luehea speciosa and Diphysa americana were the second and fifth most important food items in

December, respectively, accounting for 25% of the diet.

Table 2.4.1. Flower species consumed by white-faced capuchins, the number of flower patch visits (FLPVs) per species, and the months during which foraging was observed. Data based on

1157 hours of observation.

Species Family Number of FLPVs Months consumed

Bauhinia ungulata Fabaceae 2 January

Callistemon viminalis Mytaceae 6 February- March

Centrosema macrocarpum Fabaceae 17 January-March

Diphysa americana Fabaceae 5 December, March

Luehea speciosa Malvaceae 112 December

Malvaviscus arboreus Malvaceae 12 February- March

Manilkara chicle Sapotaceae 2 June

Total 156

2.4.2 Research questions two and three: the effects of fruit, flower and invertebrate abundance on flower foraging

December had the highest rate of flower foraging of any month, and very low ripe fruit and invertebrate indices. Despite the strong seasonal trend in flower foraging behaviour, there was no significant relationship between the daily FLPV rate and ripe fruit (B= -0.002, SE=

0.0071, χ2(1)=0.063, p= 0.801) or flower (B= 0.007, SE= 0.0059, χ2(1)= 1.261, p= 0.261)

abundances (Figure 2.4.2). However, flower foraging was significantly negatively related to invertebrate abundance (B= -0.006, SE= 0.0028, χ2(1)= 5.034, p= 0.025).

25 120

100 20

80 15 No behavioural data collected 60 10

40 ofFLPVs Number Ripe Fruit and Flower and Index Flower RipeFruit 5 20

0 0 Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar FLPVs Fruit Flowers

August-October (shaded area), although previous research at this site suggests flowers are not consumed during this time (Melin et al. 2014a). Behavioural data collection for January was limited to two weeks.

2.4.3 Research question four: relative and absolute flower nutritional quality

Based on gross energy measurements, all flower species analyzed were energetically similar; every species contained between 19-21 kJ of gross energy per gram of dry weight, which is within the range of tested fruit foods, but amongst the bottom 50% (Bergstrom et al. 2014a in

prep., see Appendix II for individual macro- and micronutrient compositions per flower species).

Flowers also have the highest moisture content of any food category; an average of 81% of a flower food’s weight is water. This high moisture content results in relatively low energetic values per flower compared to other food items, ranging from 0.25 kJ per flower for Diphysa americana to 16.57 kJ per flower for Callistemon viminalis. In terms of macronutrient content on a dry matter basis, flowers ranged from 2-5% fat, 25-46% water soluble carbohydrates, and 8-

24% crude protein (Table 2.4.2). These values are consistent with previous research, and are similar in quality to previously analyzed fruit foods (Bergstrom et al. 2014a in prep.). Flowers are among the most protein-rich plant foods in terms of macronutrient content (Figure 2.4.3).

Specifically, Centrosema macrocarpum, Diphysa americana, and Malvaviscus arboreus flowers were in the top ten of analyzed plant foods in terms of protein content (Bergstrom et al. 2014a in prep.). Compared to invertebrates analyzed by Bergstrom et al. (2014a in prep.), flowers contain a greater amount of water soluble carbohydrates on a dry matter basis, but less protein and fat

(Figure 2.4.3). Aside from potassium, which was found in concentrations ranging from 1.6-2.2%, no other micronutrient was found in concentrations greater than 0.7% (Appendix II). Manilkara chicle flowers have the highest iron content of any capuchin plant food from Santa Rosa that has been analyzed, at concentrations of 891 parts per million (ppm).

Species Crude Water Soluble Crude Gross Protein Carbohydrates Fat Energy (%) (%) (%) (kJ/g) Callistemon viminalis 10.4 37.4 4.1 19.24 Centrosema macrocarpum 19.3 25.4 3.9 19.70 Diphysa americana 24.4 27.2 3.1 20.68 Malvaviscus arboreus 17.9 24.9 4.2 19.11 Manilkara chicle 7.7 45.9 1.9 20.35 Average (consumed flowers) 15.3 33.3 3.4 19.96

100 Crude Fat Crude Protein Water Soluble Carbohydrates 90

30 Dry Matter (%) Proportion Dry

0 Flowers Fruit Seeds Invertebrates Food Item Category

Figure 2.4.3. Average proportion of dry matter consisting of fat, protein, and water soluble carbohydrates for each major food category. Data for fruit, seeds, and invertebrates adapted from

Bergstrom et al. (2014a in prep.).

Nutritional analysis revealed little difference between Luehea candida and Luehea speciosa flowers (Figure 2.4.4). Overall, L. candida is heavier, has a higher moisture content, and has more protein. However, L. candida also contains a lower concentration of water soluble carbohydrates, and there were no major differences in micronutrient composition (Figure 2.4.4, see Appendix II for micronutrient data).

60 Crude Fat Crude Protein Water Soluble Carbohydrates

20 Proportion of Dry Weight (%) Weight Dry of Proportion 10

0 Luehea candida Luehea speciosa Luehea candida Luehea speciosa middles middles petals petals Species and Part

2.4.4 Energy intake rates

Foraging strategies and intake rates varied considerably among flower species. Small flowers, such as Bauhinia ungulata, Centrosema macrocarpum, and Diphysa americana were observed to be eaten whole, while capuchins appeared to target specific parts of Callistemon viminalis, Luehea speciosa, and Manilkara chicle, often causing little to no observable damage to the flower. This suggests that capuchins were likely targeting nectar or pollen from these species, and as a result, energy intake rates for these species, which are calculated using the gross energy estimates for the entire flower, are likely to overestimate the actual energy intake. Flower foraging was rarely observed during focal sampling; only five species of flowers were ever

observed to be eaten during a focal sample and monkeys were only observed foraging in flower patches for a total of 24.35 minutes over the entire study (0.11% of total focal time). Less than

30 seconds of foraging behaviour was observed during focal data collection for both Callistemon viminalis and Malvaviscus arboreus, therefore these species were excluded from energy intake rate calculations. Nevertheless, some trends are observable. In general, flowers were quick to process and consume, and monkeys were able to forage at a rate of 2-22 flowers/minute depending on the species, with energy intake rates ranging from 5.12-22.98 kJ/minute (Table

2.4.3).

Species Consumption rate Energy intake rate (flowers/minute) (kJ/minute) Centrosema macrocarpum 2.4 5.12

Diphysa americana 22.0 5.48

Luehea speciosa 4.0 22.98

2.4.5 Research question five: how florivory affects behaviour

The daily travel rates of capuchin groups were not significantly correlated with the rate of flower foraging (B= -0.002, S.E.= 0.0027, χ2(1)= 0.353, p= 0.553). There were no significant differences in flower foraging rates between sex or age classes for either trichromats (B= 0.002,

S.E.= 0.0014, χ2(1)= 2.388, p= 0.122) or dichromats (Sex: B= 0.000, S.E.= 0.0012, χ2(1)= 0.009, p= 0.925, Age: B= 0.001, S.E.= 0.0012, χ2(2)= 0.806, p= 0.369, Figure 2.4.5). Resting

frequencies were significantly negatively related to flower consumption (B= -1.147, S.E.=

0.1898, χ2(1)= 212.459, p< 0.001), while visual foraging frequency was significantly positively correlated with flower consumption (B= 0.833, S.E.= 0.2847, χ2(1)= 8.562, p= 0.003).

0.004

0.0035

0.003

0.0025

0.002

0.0015

0.001

Frequency patch visitsflower of Frequency 0.0005

0 Adult Female Adult Male Subadult Male Juvenile Male Sex/Age Class

2.5 Discussion

2.5.1 Role of flower foods across the annual cycle

Overall, flowers are a rarely consumed resource. Consumption of all flower species combined accounted for 3.5% of the annual plant diet. This is in line with previous estimates for flower use by capuchins (Chapman and Fedigan 1990, McCabe and Fedigan 2007, Melin 2011), as well as the majority of frugivorous primates for whom flower foraging has been recorded

(

(Campos pers. comm.), potentially making the timing and productivity of any given food crop unpredictable and therefore unreliable.

Despite this study being specifically designed to capture flower foraging behaviours, we only noted consumption of seven flower species. However, exploration of other flower consumption records at this site reveals that a total of 25 species have been recorded as flower foods for C. capucinus (Table 2.1.1). Most flower species appear to be consumed rarely, but many are more important in certain years or seasons. Aside from L. speciosa, which was the most important flower food to capuchins during both this study and a 2007-2008 field study

(Melin et al. 2014a), there was almost no overlap between studies in the proportion of FLPVs for any other flower species (Table 2.5.1). It is important to note that while similar methods were used for both the Melin et al. (2014a) study and this one, two extra capuchin groups living in different habitats were included in the 2011 investigation, and the Melin et al. (2014a) study

included months for which I was unable to collect data, resulting in the capture of more food diversity in 2007-2008.

Table 2.5.1. Comparison of the proportion of FLPVs to observed flower food species in 2007-

2008 (Melin et al. 2014a) and 2013-2014 (Hogan).

Proportion Proportion Months Species Family of FLPVs of FLPVs Consumed (2007-2008) (2013-2014) Bauhinia ungulata Fabaceae 8.0% 1.3% Dec-Jan Brassica spp. Orchidacea 1.8% 0.0% Unknown Callistemon viminalis Myrtaceae 0.4 % 3.8% Jan-Mar Calycophyllum candidissimum Rubiaceae 1.8% 0.0% Oct, Dec Centrosema macrocarpum Fabaceae 0.0% 10.9% Jan-Mar Cordia guanacastensis Boraginaceae 1.1% 0.0% Jul-Sept Diphysa americana Fabaceae 0.4% 3.2% Nov-Mar Hymenaea courbaril Fabaceae 6.2% 0.0% May-June Licania arborea Chrysobalanaceae 0.4% 0.0% Feb, May Luehea candida Malvaceae 2.2% 0.0% May Luehea speciosa Malvaceae 73.0% 71.8% Dec-Jan Malvaviscus arboreus Malvaceae 2.2% 7.7% All year Manilkara chicle Sapotaceae 0.0% 1.3% June Pithecoctineum crucigerum Bignoniaceae 2.5% 0.0% Feb-Mar

Capuchins are widely regarded as one of the most intelligent monkey species, and it is likely that their highly active generalist foraging strategy is what allows them to succeed in harsh environments such as tropical dry forest (Fragaszy et al. 2004, Melin et al. 2014b). Although flowers constitute only a small proportion of the diet, they likely act as another layer of food security in an unpredictable environment, and they can account for 25% of the plant diet in periods of low food abundance.

2.5.2 Seasonal importance of flowers

As predicted, flowers were more important in the capuchin diet seasonally. Flower eating was observed on most dry season days, and 93.5% of all FLPVs were recorded during the dry season. These data support the hypothesis of research question one, that flower consumption would show a predictable increase in consumption seasonally. In one two-week period in

December Luehea speciosa and Diphysa americana were the second and fifth most important food items respectively, comprising 25% of the plant diet. This intensified flower foraging occurred during one of the lowest ripe fruit abundance periods observed during this study. It is also worth noting that the first and fourth most important plant foods consumed during this period were fruits from two Ficus (fig) species, which are asynchronous fruit producers (Parr et al. 2011). Excluding figs, flowers comprised 51% of the plant diet during this period, and in some years flowers may be the only reliable plant food source available to capuchins at this time.

2.5.3 Flower consumption rates were not affected by fruit or flower abundance, but were negatively related to invertebrate abundance.

FLPV rates were not related to the abundance of flowers, suggesting that capuchins were not flower foraging as a response to flower availability, which supports the predictions associated with research question two. However, FLPV rates were also not significantly related to the abundance of fruit foods, suggesting that flower consumption was not a direct response to fruit dearth alone. This does not my predictions associated with research question three. Flower foraging was, however, negatively related to overall invertebrate abundance (research question four), therefore flower consumption patterns may be responsive to dietary items beyond fruit.

Invertebrates are a main source of protein for capuchins, and many flower species analyzed during this study had high protein concentrations, more so than most other plant foods previously

analyzed (Bergstrom et al. 2014a in prep.). Thus, flowers may be used as a protein supplement during periods of low invertebrate abundance. There is some evidence that the macronutrient proportions of flowers affected the foraging strategy employed by capuchins: of all the flowers tested, the highest protein concentrations were found in Diphysa americana (24.4%) and

Centrosema macrocarpum (19.3%). Both are species that were consumed whole, and both were eaten in the late dry season, a period of time during which capuchins are known to have reduced invertebrate capture success (Melin et al. 2014b).

2.5.4 Flowers are nutritionally comparable to many fruit species

All flower species analyzed were within the range of nutritional variation found in previously analyzed fruits consumed by capuchins in Santa Rosa, suggesting they are as nutritious as many preferred foods, although they would fit in amongst the bottom half

(Bergstrom et al. 2014a in prep.). On a per item basis, flowers contain less energy than most fruits, and energy intake rates reflect this: consuming flowers results in lower caloric gains per minute than consuming most fruits (Bergstrom et al. 2014a in prep.). While all of this may suggest that flower foods are not of particularly high quality, their abundance and ease of consumption may compensate for any shortcomings. Many preferred fruit foods require extensive processing, and as a result, capuchins have to spend extra time and energy to obtain the edible portions, whereas flowers require very little, if any, manipulation before consumption.

Flowers also offered nutritional advantages, in that they are more protein rich than most fruit foods. Finally, flowers are generally overproduced in relation to the subsequent fruit set by most plants (Morgan 1993). Flowering trees were often large enough to support several monkeys for extended periods of time, with several foraging bouts lasting over 20 minutes. This crop

abundance likely sufficiently compensates for the relatively lower energy per flower, making flower foraging a worthwhile endeavour for capuchins.

2.5.5 Differences in flower quality of the two Luehea species do not explain seasonal foraging patterns

Evidence from our comparative case study of flowers from two species in the genus

Luehea suggests that differential flower quality is not likely the cause of the seasonal flower foraging behaviours observed by capuchins, as there were no noticeable differences in their nutritional composition. These results should be interpreted cautiously however, as capuchins were almost always observed foraging on L. speciosa in a manner that minimized structural damage to the flower, and it is likely that they were exclusively targeting nectar or pollen. Both of these substances were produced in such low quantities that it was too difficult to obtain sufficient volumes during this study; therefore, the nutritional analysis of these species was conducted on the full flower structure, which is not entirely reflective of the nutritional gain by capuchins. Despite these limitations, since both Luehea species were collected and processed in an identical fashion, it is reasonable to infer that direct comparison between them is appropriate, and that the observed lack of difference in nutritional composition between them is real. Haber and Frankie (1982) also report that while the nectar from both species is similar, there is a measurable difference between the sugar ratios in the nectar of the two species, with L. candida

(the non-consumed species) having a much higher sucrose to hexose ratio. Although sugar ratio differences do not significantly alter the overall energetic availability of nectar, they are known to affect the attractiveness of the nectar to some pollinators (Southwick et al. 1981). It is possible that capuchins are capable of detecting such differences, although we do not yet have evidence that primates exhibit this type of fine sensitivity to sugar ratios.

2.5.6 Behavioural change was associated with flower consumption

While resting frequencies decreased and foraging effort increased in relation to FLPV rates, there was no relationship between travel rate and FLPV rates, although home range use is known to decrease in the dry season (Campos et al. 2014), when flower foraging is at its peak.

Taken together, this is best interpreted as a hybrid of the energy minimization and effort maximization responses to food shortage: capuchins spend more time looking for food to satisfy their minimum nutritional requirements, but may do so in an energy minimizing way, by exploiting resources in a localized area. Although evidence from our phenology data suggests that fruit foods are not as scarce in the dry season as expected, there are other dry season concerns that capuchins must contend with that are unrelated to food abundance, including extreme temperatures, little to no shade cover, and extremely reduced access to water sources

(Campos and Fedigan 2009). Previous research in Santa Rosa indicates that capuchins spend more time resting and less foraging or travelling as temperature increases (Campos and Fedigan

2009). Flower foraging was observed primarily in the early morning hours, the time of day when temperatures were lowest. It is possible that capuchins were exploiting flowers while the day was cool as a means to achieving their minimum daily energy and moisture requirements. This is especially effective since many plant species attempt to cope with the day’s heat in a similar fashion, reducing the risk of desiccation by having nocturnal or early morning anthesis patterns, with flowers remaining open and nectar being produced in the early morning hours, only to wilt later in the day (Bullock and Solis-Magallanes 1990).

2.5.7 Categorizing flowers on the fallback/preferred foods spectrum

Capuchins were observed to consume flowers almost exclusively in the dry season, a period during which fruits and invertebrates are known to be in reduced abundance for some

periods (Melin et al. 2014a, Melin et al. 2014b). However, flower foraging rates did not significantly vary with changes in preferred, ripe fruit abundance, so flowers do not appear to be meeting the definition of a fallback food as set out by Marshall et al. (2009). Furthermore, high visitation rates to L. speciosa relative to its abundance in the habitat indicate this flower might be a preferred food relative to other options concurrently available (Melin 2011; this study). But, despite potential preference for L. speciosa, capuchins rarely consume the nutritionally similar L. candida, a species that flowers during seasons when both ripe fruits and invertebrates are abundant, suggesting that, overall, flowers of the Luehea genus are not consistently preferred foods. Based on the highly seasonal nature of capuchin flower foraging behaviour and the importance of certain flower food species (which are consumed frequently but never for 100% of the diet) during harsh annual periods, as well as the high nutritional quality of most flowers analyzed in this study, I suggest the best categorization of flower foods is at the intersection of

“high quality filler fallback foods” and “low quality preferred foods” along the spectrum proposed by Marshall et al. (2009). High quality filler foods, in addition to preferred foods, are hypothesized to select for adaptations that improve locating and harvesting foods, whereas staple fallback foods often promote processing adaptations, such as thick dental enamel (Marshall and

Wrangham 2007, Marshall et al. 2009). Flowers are not well physically defended, are relatively easy to access and process (Bandeili and Muller 2010, Whigham et al. 2013), and have many nutritional qualities that suggest they are high quality foods in sufficient quantities. Based on this, there is likely little to be gained from improved processing abilities, and selection is more likely to act upon harvesting advantages that improve flower locating. If flowers are operating at this filler fallback/preferred food threshold, and they are important during annual “crunch” periods, it is possible that they promote the evolution of harvesting traits. One such harvesting

adaptation may be colour vision (Marshall and Wrangham 2007), a hypothesis I address in

Chapter Three.

2.6 Conclusions

Overall, this research indicates that while flowers constitute a small proportion of the annual capuchin diet, they play an important seasonal role, and may affect the life histories and behaviours of capuchins in Santa Rosa. Flower foraging also has the potential for broad community effects by inhibiting or promoting the reproduction of some plant species, ultimately shaping their distribution and abundance (McCall and Irwin 2006, Chapman et al. 2013a). This has important implications for the conservation and management of an endangered ecosystem such as the tropical dry forest, and this research further enhances our ability to protect what little remains.

Future studies may benefit from analyzing the nutritional composition of various flower species abundant in Santa Rosa that are not consumed by capuchins for a more in depth comparison. The limits of this investigation also prevented exploration of the potential for secondary compounds to affect foraging decisions. Although flowers are not specifically highly chemically defended compared to other plant parts (McCall and Fordyce 2010), secondary compounds have been noted in large concentrations of the flowers of many plant species (Adler

2000), and primates are known to make foraging decisions based on the concentrations of certain secondary metabolites (Welker et al. 2007). It is possible that flowers produced in the early wet season contain secondary compounds unpalatable to capuchins.

Chapter Three: Polymorphic colour vision and the variation in flower colour signals

3.1 Background

3.1.1 Defining colour

One of the hallmarks of primate evolution has been the development of, and reliance on, a specialized visual system, including stereoscopic depth perception, high acuity, and, for many primate species, heightened colour discrimination abilities (Martin and Ross 2005). Although the evolutionary history of primate colour vision is still debated, the most parsimonious explanation for the existing pattern of species with trichromatic colour vision is that trichromacy has evolved repeatedly in the primate lineage, suggesting that it plays an important role in primate life

(Martin and Ross 2005, Melin et al. 2013, Regan et al. 2001). Despite this, the benefits to being trichromatic are not entirely understood (Dominy et al. 2003). An object’s colour property is determined by the interaction between it and photons of light striking its surface that are then absorbed, transmitted, or reflected (Hunt and Pointer 2011). Colour is determined by two main aspects of the reflected light: luminance and chromaticity. Luminance refers to the energetic intensity of the reflected light waves, whereas chromaticity is determined by the specific wavelengths of the radiation.

3.1.2 Defining colour vision

Wavelengths that are reflected by an object can then strike the visual organs of an animal and induce physiological responses, which, in vertebrates possessing at least two photoreceptors of different sensitivity, results in the perception of colour. Receivers will only detect and process reflected light within a small range of wavelengths that stimulates their photoreceptors, referred to as the “visible spectrum”, therefore colour vision is subjective. For humans and other primates, this visible spectrum is 400-700 nm, while many other animals are capable of detecting wavelengths in the near ultraviolet range, down to 200 nm (Jacobs 1996, reviewed by Melin 52

2011). Colour as it is perceived is influenced by three major factors of the absorbed wavelengths: brightness, saturation, and hue. Brightness is the subjective interpretation of luminance, although it does not linearly correlate with luminance values in humans (Osorio et al. 1998). Hue and saturation are the observer’s interpretations of chromaticity. Hue refers to how the colour associated with a specific wavelength is interpreted; common use of descriptors such as red, blue, or yellow are typically referring to hues. Saturation is the interpretation of the amount of different wavelengths being reflected by the object; an object reflecting only a narrow range of wavelengths is said to be saturated, whereas one that reflects a large band of wavelengths has low saturation. Together, hue, saturation and brightness all influence how an observer perceives the chromaticity and luminance of an object, and their interpretation by the receiver is highly context dependent on both environmental conditions and the colour vision system of the observer.

In addition to context dependency, various “noise” effects influence whether the chromatic difference between two objects is actually discriminable to an individual. The minimum chromatic distance where two objects are noted to be discernibly different colours is defined as one “just-noticeable difference” (JND) (Osorio et al. 2004). The magnitude of the noise effects on the visual system is determined by both the physical structure of the eye and the physiological limitations of the associated neural pathways. In vertebrate visual systems, the largest contributor of noise is due to the mid- and long- wavelength sensitive photopigments that function as both chromatic and luminance signal receptors, leading to “cross-talk” and signal corruption (Osorio et al. 2004). The limitations of such signal corruption are significant: Osorio et al. (2004) hypothesize that the spectral tuning of the mid- and long- wavelength sensitive

opsins in the vertebrate eye have evolved under the constraint of limiting signal corruption rather than maximizing chromatic signal discrimination ability.

3.1.3 Vertebrate colour vision biology and physiology

There are two main photoreceptor types responsible for vision in vertebrates: rods and cones. Both function in primarily the same way: photons are absorbed by a protein in the photoreceptor known as an opsin. Different opsins are sensitive to different wavelengths, although each photoreceptor has only one signal response (known as univariance) (Naka and

Rushton 1966). Therefore, one photoreceptor working alone is not capable of providing colour information (Naka and Rushton 1966). When sufficient photons have been absorbed, a signal cascade is triggered, eventually leading to signal transmission to the visual cortex of the brain

(Collin et al. 2009). Rods are significantly more photosensitive than cones and are capable of activation following absorption of a single photon (Okawa and Sampath 2007). Due to this extreme sensitivity they are primarily responsible for scotopic (night) vision. This sensitivity also means that rods have virtually no use during colour vision, except possibly under low light conditions when absorbed wavelengths are within the range that stimulates both rods and the short-wavelength sensitive (S) cones (Pokorny et al. 2008). Cones require more light but have a faster reset rate following activation, allowing them to perceive higher levels of detail (Okawa and Sampath 2007). There are multiple types of opsins, each associated with a different cone types (Kelber et al. 2003). Each opsin is responsive to a different range of wavelengths, and the sensitivities of all of the opsins overlap to some extent. This allows for antagonistic signalling: a light at a certain wavelength stimulates different opsins to different degrees (Kelber et al. 2003).

The neurons associated with cones are also different from those triggered by rods in that they are subject to spectral opponency. Certain wavelengths of photons stimulate a cone, resulting in the

suppression of some neurons while activating others, which allows for differential response to different wavelengths (Regan et al. 2001). These differential responses, when coupled with antagonistic firing of different cones, are the basis of colour vision: every different cone type present in the retina multiplies the colour vision discrimination ability of the observer by approximately 100 (Osorio and Vorobyev 2008). Generally, colour vision is thought to have evolved in environments with highly variable (and therefore unreliable) luminance cues such as leafy forests or shallow waters, as an adaptation providing an alternative system for object differentiation (Collin and Trezise 2004).

3.1.4 Evolution of vertebrate colour vision

The evolution of different opsins (and therefore colour vision), appears to have occurred early during vertebrate evolution. Current evidence suggests that the early predecessors to the mammalian order were tetrachromats, possessing four cone types and a rod-like photoreceptor

(Collin and Trezise 2004). The opsin associated with rods is hypothesized to have evolved from wavelength sensitive photopsins, suggesting that early vertebral visual systems evolved in diurnal living animals (Collin et al. 2009). Tetrachromacy is still common to fish, birds and reptiles today, although the diversification of colour vision has continued throughout the evolutionary history of these lineages, resulting in highly differentiated and specialized colour vision systems (Osorio and Vorobyev 2008).

As mammals evolved and speciated they primarily occupied nocturnal niches, a condition that is known to favor a reduction in cone diversity and density (Mollon 1989). Early in their evolutionary history the mammalian order lost two cone types, becoming functional dichromats.

Of the extant living placental mammals, trichromacy is only regularly found in the primate order, although not all species are trichromatic. Evidence suggests that early primates were nocturnal

dichromats (Heesy and Ross 2001), and although the origins of colour vision are still debated, recent evidence suggests that trichromacy may have arisen among nocturnal species that benefitted from trichromacy during crepuscular periods and well moonlit nights (Melin et al.

2013).

3.1.5 Polymorphic colour vision

A type of trichromacy known as polymorphic trichromacy has evolved amongst the platyrrhines (New World monkeys), the result of mutation of the X-chromosome without gene duplication. Aside from the routinely trichromatic howler monkeys (Alouatta species) and the monochromatic owl monkeys (Aotus species), which have a non-functioning S-cone, all New

World monkeys have an S-cone as well as multiple alleles for a mid- to long- wavelength sensitive (M/L) cone type, each with slightly shifted peak spectral sensitivities. As a result of this differentiation, any female that has two different X-chromosomes is trichromatic, while all males and homozygous females are dichromats (Hiramatsu et al. 2005). This phenomenon was first observed in the 1980s by laboratory researchers, who noted that some female New World monkeys were able to discriminate red food objects much more effectively than some of their female and male counterparts (Jacobs 1984). Polymorphic trichromacy has since been confirmed genetically and through in-vitro reconstitution of the photopigments (Jacobs 1996). The spectral sensitivity of the M/L cones in species with polymorphic colour vision is controlled by a single gene, therefore genotype at this loci and colour vision phenotype are directly linked (Hiramatsu et al. 2005). Amongst New World monkeys, there are 2-4 alleles per species, resulting in three, six, or ten colour vision phenotypes, respectively. Several species of strepsirrhines have also been discovered to have polymorphic colour vision (Veilleux and Bollnick 2009). Although debated, based on phylogenetic evidence it appears that polymorphic colour vision has evolved

in the primate order multiple times, suggesting that colour discrimination is an important facet of primate life (Surridge et al. 2003).

3.1.6 Evolution of routine trichromacy in primates

Following their divergence from strepsirrhines and platyrrhines, a gene duplication on the

X-chromosome occurred in the catarrhine (Old World primates) lineage, resulting in two X- linked cone types with different peak sensitivities (Regan et al. 2001). A similar but independent duplication event has given rise to routine trichromacy in howler monkeys (Genus Alouatta)

(Matsushita et al. 2014). The spectral tuning of catarrhines and howler monkeys is highly convergent: both groups have cones with peak sensitivities at approximately 430 nm, 535 nm and 560 nm (Dominy and Lucas 2001).

3.1.7 Benefits of colour vision

While the benefits of colour vision to wild primates continue to be debated, there are three main selective pressures that researchers have focused on: receiving signals from conspecifics, detecting predators, and finding food (Pessoa et al. 2014). All trichromatic primates, whether routine or polymorphic, possess cones that are spectrally tuned to discriminate both the blue-yellow continuum typical of dichromatic mammals, as well as a red-green channel unique to trichromats. In the colour vision literature an item that can be differentiated from background scenes based on its chromatic properties is defined as “conspicuous”. Many items

(particularly those considered yellow or red to the human eye) are believed to be conspicuous to trichromats but not dichromats when viewed against a green background (Melin et al. 2014a).

Primates are highly social animals, and rely on their ability to receive and interpret social signals from conspecifics to mediate group living (Changizi et al. 2006). In addition to physical signals such as teeth baring or body posturing, many species use skin colouration to communicate. These

signals, such as flushing bare skin by increasing blood flow to the region, are more conspicuous to trichromats, potentially allowing primates to communicate without becoming conspicuous to their dichromatic predators (Changizi et al. 2006). Many primate predators (e.g., many members of the Felidae family) sport pelage patterns thought to be cryptic to dichromats, but which are conspicuous to trichromatic primates, an advantage that results in faster detection rates by human trichromats in laboratory studies (Pessoa et al. 2014). Although both communication and predator detection may have played important roles in the evolution of trichromacy, food has long been hypothesized to be the major driving factor (Regan et al. 2001). Based on chromatic analysis of many fruits consumed by primates, trichomatic vision is believed to be advantageous in locating fruit against a green, leafy background (Mollon 1989, Osorio and Vorobyev 1996,

Melin et al. 2009, Surridge et al. 2003). Additionally, many fruits change colour from green to red to signal ripeness. Trichromatic primates may exploit this cue to be more efficient at selecting ripe fruits from a patch, and ripe fruits are known to be more nutritious and easier to process and digest than unripe fruits for most species (Sumner and Mollon 2000). However, fruit is often darker than the surrounding foliage and can have colouration equally discriminable to dichromats (Hiramatsu et al. 2008), therefore trichromacy may not offer sufficient advantage to have evolved solely as a response to frugivory. Furthermore, many trichromatic primate species are not especially frugivorous, raising the possibility that trichromacy may not have evolved specifically to find ripe fruits (Dominy and Lucas 2001). Folivory has also earned consideration as a strong selection pressure. Many leaf species change colour from red to green as they mature, and leaves are often more nutritious and less defended by chemical or physical means while they are young (Lucas et al. 2003). Several species of primate consume leaves almost exclusively, and many others are known to fall back on leaves when preferred fruits are unavailable (see Chapter

Two for a discussion of fallback foods). The argument that folivory may select for trichromacy is bolstered by the fact that the most folivorous New World monkeys, Alouatta species, are also the only ones to have evolved routine trichromacy. Regardless of the food source, it appears that trichromacy has evolved in a manner that is specifically tuned for discriminating reddish objects against a green background, and many researchers have hypothesized that it is the background, rather than the specific food, which may be the major selection pressure at play (Sumner and

Mollon 2000). If this is true, the type of food, whether it be fruit or leaf, is irrelevant to the evolution of colour vision as long as the food item is more conspicuous to trichromats, and indeed different foods may be responsible for maintaining trichromacy in different primate species.

Melin et al. (2014a) suggest that long-distance detection of small food patches may be an understudied but crucial aspect of colour vision evolution. From a distance, many of the cues dichromats could use to compensate for their reduced chromatic discrimination ability (e.g. olfaction) are ineffective. Furthermore, many plant foods exploited by primates occur in small patches, reducing the likelihood of contest competition in group living primates and increasing the “finders reward” benefits of discovering the patch, increasing the selective advantage of long distance detection (Bunce et al. 2011, Melin et al. 2014a).

3.1.8 Polymorphism as a natural experiment for testing hypotheses of colour vision evolution

Although there is strong theoretical support for food, predation risk and communication strategies influencing the evolution of colour vision, there is scant evidence of trichromacy providing a clear fitness advantage to primates. Polymorphic colour vision in a species provides an elegant natural experiment for testing these hypotheses, as monkey groups often have several dichromatic and trichromatic individuals cohabitating and competing for food resources.

Polymorphic colour vision also presents an evolutionary puzzle in itself. Polymorphisms do not typically last over evolutionary time, and in most circumstances selection acts to drive the least fit alleles from existence. Situations when multiple alleles persist in an intermediate proportion are considered to be under pressure from balancing selection rather than directional selection, and polymorphisms shared across species, as is the case with polymorphic colour vision, are extremely rare (Hedrick 2007). One possible explanation is that polymorphic colour vision is relatively new, and therefore the fittest phenotype simply has not had sufficient time to be directionally selected for. However, genetic analysis of two New World monkey species suggests that this is not the case, and that balancing selection is indeed in action (Hiwatashi et al.

2010). For balancing selection to be maintained there must be a fitness advantage to multiple alleles persisting within a population. Currently there are four major non-mutually exclusive hypotheses invoked: heterozygotic advantage, niche divergence, mutual benefit of association, and negative frequency-dependence selection (Mollon et al. 1984).

Heterozygote advantage refers to situations where even though homozygotic individuals are less fit, at least one type of heterozygote in the population is the fittest possible genotype.

This causes multiple alleles to remain in the population. Since routine colour vision has evolved in several primate lineages and therefore appears to be adaptive, heterozygotic advantage has long been considered the most likely explanation for polymorphic colour vision (Osorio and

Vorobyev 1996). If this is the case, the advantage for females with two different opsins is expected to be sufficiently large enough over dichromatic females to maintain the allelic diversity, even at the expense of all males being dichromats (Mollon 1989). Experiments using polymorphic trichromats have demonstrated that trichromatic females from several species of

Callitrichids have a detection and selection advantage for artificial food sources that were

reddish in colouration and more nutritious (Caine and Mundy 2000, Smith et al. 2003). Despite this, attempts to reveal a trichromatic foraging advantage for wild primate populations have found little evidence. In many cases trichromats are more efficient at a task, but dichromats are able to compensate through more effort or by using alternative cues, such as luminance (Melin et al. 2009, Hiramatsu et al. 2008). Other studies have demonstrated that dichromats and trichromats each have foraging advantages for acquiring certain foods, and a long-term life history study on white-faced capuchins found no significant difference between colour vision types for any fitness markers explored (Melin et al. 2007, Vogel et al. 2007, Melin et al. 2010,

Fedigan et al. 2014). In light of all of this, the heterozygotic advantage hypothesis does not appear to be well supported.

The niche divergence theory hypothesizes that polymorphic colour vision may allow members of the same species to coexist in groups while specializing on different foods, reducing intragroup competition. While dichromats and trichromats each appear to have advantages in relation to certain foods, currently there is no evidence suggesting that they differ in their food selection, the amount of time spent foraging for any specific food item, or their microhabitat selection, therefore this hypothesis is not well supported although it has not received extensive attention (Melin et al. 2008, Caine et al. 2010). However, several studies have revealed subtle foraging differences between dichromats and trichromats, and Melin et al. (2012) hypothesize that niche divergence could be better revealed through exploration at finer scales than have previously been employed in wild studies. Negative frequency-dependence selection theory shares many of the characteristics of niche divergence theory, but is believed to be the least likely mechanism maintaining polymorphic trichromacy in primates (Hiwatashi et al. 2010)

The mutual benefit of association hypothesis has also received little attention, but recent evidence suggests that it may be at least partly responsible for the maintenance of balancing selection, particularly with regards to predator detection. Pessoa et al. (2014) hypothesize that trichromats likely have an advantage in detecting mammalian carnivores such as felids, which are major predators for many New World monkeys. Dichromats, meanwhile, are hypothesized to have an advantage in breaking camouflage patterns, largely due to their increased reliance on achromatic signals and fewer colour confusion effects (Morgan et al. 1992, Caine et al. 2010,

Melin et al. 2007). This may result in improved detection of cryptic predators, such as snakes.

The ability to detect different foods from long distance may also result in mutual benefits if discovery of food by one individual leads to access for other group members. However, to date, no studies have attempted to directly examine the mutual benefit of association hypothesis.

Whatever mechanism is maintaining balancing selection on polymorphic colour vision, it appears that it is not heterozygotic advantage, which suggests that, for New World monkeys at least, trichromacy is not universally adaptive.

3.1.9 Flower foods and colour vision

Although typically comprising less than 10% of the annual diet, flowers are seasonally important foods for many primates, including capuchins, and have been hypothesized to play a role as fallback foods for some species (Heymann 2011). Due to their higher use during what is thought to be a highly stressful annual period, capuchins may have evolved traits that improve flower foraging efficiency (see Chapter Two for a detailed review). Since flowers are generally not difficult to process (Merwin and Perrella 2011, Bandeili and Muller 2010), traits improving detection are the most likely to be selected upon. One such adaptation may be colour vision. A number of plants that rely on animals for pollination services have evolved colourful floral

displays to attract their pollinators, many of which have evolved specialized colour vision abilities (Chittka et al. 1993, Willmer 2011). For example, both hummingbirds (family

Trochilidae) and honeybees (Apis mellifera) have highly derived colour vision systems, possessing photosensitive pigments with peak spectral sensitivities of 340 nm and 370 nm respectively (Chittka et al. 1994, Lunau et al. 2011). While it is unlikely that trichromatic primates have developed colour vision specifically as an adaptation towards florivory, the links between primate evolution and angiosperm radiation have long been hypothesized (Sussman

1991). Many flowers known to be consumed by capuchins are suspected to have colour cues conspicuous to trichromats but not dichromats based on how they appear to human trichromats.

For species with polymorphic colour vision such as white-faced capuchins, trichromacy may provide an advantage for detecting flowers from a distance, improving their ability to find and consume flower food species and ultimately contributing to the maintenance of the polymorphism in the population.

Although there are fewer species in peak fruit production during the dry season in Santa

Rosa (Melin et al. 2014a), most plants shed their leaves for at least a portion of this season

(Janzen 1988), and many plants produce flowers during this period because they are much more visible to pollinators at this time (Frankie et al. 1974). As discussed in Chapter Two, in Santa

Rosa flowers are consumed by capuchins significantly more during the dry season. If plants that produce flowers in the dry season are doing so to increase visibility to pollinators they are likely to use colour to advertise; how well capuchins can detect and subsequently consume flowers may be influenced by their colour vision phenotype.

3.2 Research Objectives and Hypotheses

The purpose of this study is to investigate whether differences in colour discrimination ability affect how white-faced capuchins interact with flowers. Specifically, I address the following questions and test the following predictions:

1. Are the types of flowers consumed by capuchins more visible to trichromats than to

dichromats when viewed against a background of various leaf spectra, emulating both long

distance and close up viewing scenarios? Because many flowers appear to have chromatic

properties that differ from leaves in the red-green dimension to the human eye I predicted

that trichromatic monkeys would have a detection advantage over dichromats, the latter of

which only possess luminance and blue-yellow colour vision. I test this by using models of

conspicuity for flower foods in the colour space of different monkeys.

2. Are there differences in the frequency of flower consumption, or the types of flowers

targeted, between capuchins with different colour vision phenotypes? I predicted that

trichromatic individuals would consume more flowers than dichromatic monkeys would, and

that “conspicuous” flowers would account for a higher proportion of the flower diet of

trichromats. Conversely, I predicted that dichromats would consume greater numbers of

inconspicuous flowers. I test this by examining the flower foraging activity budget of

dichromats and trichromats.

3. Are there differences in the rate of discovery of small patches of flowers that colour vision

modelling predicts would be more visible to trichromats? I predicted that trichromatic

individuals have a relatively higher number of flower patch visits to these conspicuous

species than do dichromats. I test this by looking at the number of small patch flower food

discoveries by individuals of each colour vision phenotype.

3.3 Methods

3.3.1 Study species

White-faced capuchins are highly active, visually-oriented foragers, spending upwards of

50% of their time searching for and consuming food (Fragaszy et al. 2004). They are omnivorous, primarily eating ripe-fruits and invertebrates, but they also regularly consume flowers, which can comprise up to 25% of their plant diet during some time periods (Chapter

Two). Capuchin groups typically range in size from 10-30 individuals and are female philopatric, with males changing groups on average every 4.5 years, maintaining gene flow (Fragaszy et al.

2004, Jack and Fedigan 2004b). In many cases this results in related individuals with different colour vision phenotypes living in the same group, enabling observation of how colour vision differences affect foraging decisions with natural controls for some confounds.

White-faced capuchins have polymorphic colour vision, with three known M/L cone types with peak sensitivities at 532 nm, 543 nm, and 561 nm (Hiramatsu et al. 2005). These sensitivity variations are a direct result of differences in the amino acids present at exons 3 (site

180, Alanine or Serine) and 5 (site 277, Phenylalanine or Tyrosine; site 285, Alanine or

Threonine) on the X chromosome, so it is possible to determine an individual’s colour vision phenotype via genetic analysis. Females have two copies of the X chromosome, therefore these three alleles result in six possible phenotypes for females: three dichromatic and three trichromatic (Hiramatsu et al. 2005). Since males only have one copy of the X chromosome they are always one of the three dichromatic phenotypes. In the wild, trichromats are more efficient at selecting some ripe fruits but dichromats and trichromats do not have different rates of overall

fruit consumption (Melin et al. 2009, Hiramatsu et al. 2008) and do not consume grossly different food items (Melin et al. 2008). Trichromats are, however, hypothesized to have detection advantages for certain fruit species (Melin et al. 2014a).

3.3.2 Study site and animals

All behavioural and spectral data was obtained from Sector Santa Rosa (Santa Rosa),

Área de Conservación Guanacaste (ACG), Costa Rica. Santa Rosa has been the site of long term field studies on C. capucinus under the direction of Dr. Linda Fedigan since 1983. It is primarily tropical dry forest, most of which is secondary growth at varying stages of maturity on reclaimed farmland (Moline 1999). Tropical dry forest is characterized by high average temperatures annually and extreme variation in precipitation (Murphy and Lugo 1986). In Santa Rosa, the wet season is typically from May-November. During this season an average of 130-450 mm of rain falls per month and temperatures are typically between 22-32° C. Although there can be up to 20 mm of precipitation monthly in the dry season months of December-April, most days have no rain and are hot, with temperatures occasionally rising above 40° C. Due to this prolonged annual period of drought plant productivity is severely reduced in the dry season, with up to 80% of trees losing their leaves for at least some portion of the season (Janzen 1988).

I studied three habituated groups of white-faced capuchins (AD, LV, RM) between May

2013 and March 2014, with no data collected between August and October 2013, or December

20, 2013 to January 10, 2014. Individuals are identifiable using physical characteristics such as their sex, age class, pelage patterns, and scarring, therefore behavioural data and fecal samples for genetic analysis could reliably be collected from specific individuals.

3.3.3 Genotyping

Many of the individuals in the study groups had been previously genotyped by other researchers during previous investigations (Table 3.3.1). However, due to births and male immigration to our study groups, the colour vision phenotypes of some of our monkeys was not known, therefore my research assistants and I needed to collect additional fecal samples. For each sample, we collected approximately 1 g of feces in sterile 15 ml screw-cap plastic vials, which had each been pre-loaded with 5 ml of RNAlater® solution to preserve the sample. To prevent sample contamination, investigators wore masks and sterile gloves, collected feces within five minutes of defecation, and only collected samples when they were very confident of the individual’s identity. We then sealed samples in paraffin wax and stored them at room temperature until transport to the genetics laboratory was possible. Samples were shipped to the laboratory of Dr. Shoji Kawamura at the University of Tokyo in Tokyo, Japan, where they were processed, and DNA was extracted, amplified, and sequenced using previously established standardized techniques (Hiramatsu et al. 2005; Melin et al. 2014a). We were unable to collect sufficient samples from one infant (AN), who is therefore excluded from analyses. Field researchers were not informed of the colour vision phenotypes of any monkeys until the conclusion of the observation period to avoid any potential biases during behavioural data collection. Individuals of all six possible colour vision phenotypes reside within these study groups, although in unbalanced proportions (Tables 3.3.2, 3.3.3).

Table 3.3.1. The group affiliation, sex, age class, colour vision type, and the predicted peak spectral sensitivities of the M/L cones of all monkeys observed in this study (N= 51).

Monkey Group Sex Age Class Colour Vision Predicted Peak Type Spectral Sensitivity AB AD Female Adult Trichromat 532/561 BO AD Female Adult Dichromat 561/561 BT AD Female Adult Trichromat 532/561 BZ AD Male Adult Dichromat 532 CI AD Male Large Juvenile Dichromat 543 FE AD Female Small Juvenile Dichromat 561/561 FG AD Male Large Juvenile Dichromat 561 LM AD Female Large Juvenile Trichromat 532/561 MI AD Female Large Juvenile Trichromat 532/561 ME AD Male Large Juvenile Dichromat 532 NT AD Male Large Juvenile Dichromat 532 PY AD Male Large Juvenile Dichromat 543 TI AD Female Adult Dichromat 561/561 TS AD Female Large Juvenile Trichromat 532/561 TD AD Male Small Juvenile Dichromat 561 TY AD Male Subadult Dichromat 543 UR AD Male Large Juvenile Dichromat 561 WK AD Male Infant Dichromat 561 ZA AD Female Adult Dichromat 561/561 BD LV Male Infant Dichromat 561 CE LV Female Adult Dichromat 561/561 CH LV Female Adult Trichromat 543/561 CL LV Female Infant Trichromat 532/561 CT LV Female Small Juvenile Dichromat 561/561 EP LV Male Infant Dichromat 532 MQ LV Male Adult Dichromat 532 OR LV Female Adult Trichromat 532/561 SJ LV Female Small Juvenile Dichromat 561/561 SA LV Female Adult Trichromat 532/543 SF LV Female Large Juvenile Trichromat 543/561 TH LV Female Large Juvenile Trichromat 543/561 TL LV Male Adult Dichromat 561

Monkey Group Sex Age Class Colour Vision Predicted Peak Type Spectral Sensitivity VN LV Female Small Juvenile Dichromat 561/561 AN RM Female Infant Unknown Unknown BD RM Male Small Juvenile Dichromat 561 CG RM Male Small Juvenile Dichromat 561 DU RM Female Small Juvenile Trichromat 532/561 ED RM Female Adult Dichromat 561/561 FT RM Female Large Juvenile Trichromat 532/561 JA RM Male Subadult Dichromat 561 KI RM Female Adult Dichromat 561/561 LA RM Female Large Juvenile Trichromat 532/561 LB RM Male Large Juvenile Dichromat 561 LE RM Male Adult Dichromat 532 LU RM Female Large Juvenile Trichromat 532/561 MD RM Female Small Juvenile Trichromat 532/561 PD RM Female Small Juvenile Trichromat 532/561 RF RM Male Adult Dichromat 561 SH RM Female Adult Dichromat 561/561 SI RM Female Adult Dichromat 561/561 WE RM Male Infant Dichromat 561

Table 3.3.2. The proportion of each colour vision phenotype in the study population of capuchins, for individuals that have been genotyped (n= 50).

Phenotype Percentage of population Dichromat 532 12% Dichromat 543 6% Dichromat 561 48% Trichromat 532/543 2% Trichromat 532/561 26% Trichromat 543/561 6%

Table 3.3.3. Frequency of each allele in the study population of capuchins for all individuals that have been genotyped (n= 50).

Allele Percentage of population 532 25% 543 9% 561 66%

3.3.4 Spectroscopy

I collected spectroscopic data using an Ocean Optics Inc. Jaz EL-200 spectrometer with a

PX pulsed xenon light source, calibrated to read wavelengths between 200-850 nm, using the reflectance function of Ocean Optics SpectraSuite software (64-bit version 1.6.0.11). I calibrated the spectrometer before every use using a WS-1-SL white reflectance standard with Spectralon®, and recalibrated every 20 minutes during sampling to prevent drift. When I observed a flower species to be a food item for white-faced capuchins, I collected five representative samples of that species’ flowers and leaves from five different plants for spectroscopic analysis. I conducted analysis within an hour of sample collection to prevent desiccation and colour change. If I observed flowers to be consumed when immature, I also collected five immature flowers for spectroscopy. Similarly, if different flower parts displayed different colouration, I analyzed five samples for each part. In total, I divided flowers into four categories: immature flowers, petals, bracts, and “middle” parts, which was a general term I used to refer to the reproductive structures of a flower. Due to scarcity of available samples, only two flowers and three leaves of Bauhinia ungulata, and four flowers and no leaves of Manilkara chicle were collected. The upper and lower leaf surfaces are often very different colours, therefore I conducted spectroscopic analysis on each separately for each leaf sample. Because relatively few flowers were observed to be used as food during this study, I chose to augment my dataset with flower species that have been

observed to be food items in other years. Specifically, I collected and analyzed mature flowers of

Stemmadenia obovata, and incorporated spectroscopic data collected by Melin in 2007-2008 for the following flowers: mature Cassia grandis, immature Curatella americana, mature

Cochlospermum vitifolium, immature Manilkara chicle, immature Tabebuia ochracea, and stamens of Vachellia collinsii, as well as leaf samples for these species. Finally, to improve the accuracy of colour vision simulations by providing more data for the modelling of background spectra, additional leaf samples were included from 14 tree species that are abundant in Santa

Rosa but are not known to be flower food species (five samples per species). SpectraSuite produced a reflectance spectra for each sample, which was the relative intensity of reflectance recorded at each wavelength within the measured 200-850 nm range. I then uploaded the reflectance spectra for each sample into a Microsoft Access database using a macro, which uploaded the data in 1 nm increments and adjusted the readings to account for drift by making the lowest recorded reflectance percentage act as the zero point for that sample, and adjusting all other wavelengths up or down accordingly.

3.3.5 Chromaticity models

I modelled colour vision using Mathworks MatLab R2014b software. I calculated estimates of the quantum catch of each photoreceptor using the following formula (from Riba-

Hernandez et al. 2004):

ퟕퟎퟎ Formula 3.3.1. Qi = 퐑(훌)퐈(훌)퐒 (훌)퐝훌 ∫ퟒퟎퟎ 풊

This equation calculates the quantum catch (Q) of an object’s reflectance R(λ) for photoreceptor “i” within the range of primate vision capabilities (400-700 nm) based on the irradiance spectrum I(λ) of the environment and the spectral sensitivity of the photoreceptor

Si(λ). I used luminance data obtained by Hiramatsu et al. (2008) in Santa Rosa forest under shaded conditions to enable comparison between samples and other studies. Knowing the quantum catch of each photopigment, I then calculated how each item would stimulate the blue- yellow and luminance systems in both dichromats and trichromats, as well as the red-green pathway found only in trichromats (Table 3.3.4).

Vision Pathway Dichromats Trichromats

Blue-Yellow QS/(2QL) or QS/(2QM) QS/(QL+QM)

Red-Green Not Present QL/(QL+QM)

Luminance QL+QM QL+QM

3.3.6 Support Vector Machine modelling

To determine how trichromats and dichromats may differ in ability to detect flowers from long distances against leafy backgrounds, I used Support Vector Machine (SVM) modelling, a system of machine learning object classification (Cortes and Vapnik 1995). For colour vision modelling, the major benefit to SVM analysis is that it does not rely on the arbitrary, predetermined, and often biased classifications of spectral data that are based on the human visual system to estimate the detectability of target objects against a specified background for a given colour vision phenotype (Melin et al. 2014a). I used the LIBSVM (Chang and Lin 2011) extension of MATLAB to create a hyperplane based on analyzed data points of known classification, then used that hyperplane to predict what class a data point of unknown classification should be considered. The hyperplane created by the machine was the one with the

largest margins between data points of different classes. SVM analysis also allows for soft margins in situations where some classes have a degree of overlap in their data points, as is the case with the chromaticity of different plant parts. For this analysis, the SVM constructed hyperplanes using blue-yellow chromaticity data for both dichromats and trichromats, and also red-green chromaticity data for trichromats. I used two classes of data: leaves (upper and lower), and flowers (bracts, reproductive organs, petals, and immature). In total I included spectral data on 21 parts from 14 flower species, as well as leaves from these 14 species plus 14 others to ensure that the leaf spectrum had sufficient variation to be representative of background leaf noise. Although most other studies have not examined the impact of luminance values during colour vision modelling, luminance cues can be important short-distance foraging cues and may provide added information. To explore their potential contribution I ran the analysis twice for each phenotype, once including luminance in the hyperplane creation and once without. Using the radial basis kernel function and “leave-one-out” methodology the SVM creates a hyperplane using all relevant data except for one plant item. Following hyperplane creation, the SVM attempts to predict whether the final unlabeled object is a flower or a leaf. This is repeated for every plant part and colour vision phenotype combination.

3.3.7 Just Noticeable Difference modelling

Just Noticeable Difference (JND) modelling is used to predict whether or not a target item is discriminable from background objects (Riba-Hernandez et al. 2004, Osorio et al. 2004).

Objects with increasingly higher JND scores are presumed to be visible under less ideal situations. JND analysis uses values determined with human subjects under laboratory conditions, but has also been used widely for comparisons among primates (Osorio et al. 2004).

JND scores are determined by the minimum chromatic distance (ΔS) between a target object

(flowers in this instance) and its background (leaves) required for differentiation. In this study, I calculated JND values using two different background (leaf) colour values. To simulate close up foraging scenarios, I directly compared flowers to the upper and lower surfaces of leaves from their own species. To better approximate long distance detection differences, when the leaves of many species would be contributing to the background colour, I calculated a mean upper and lower leaf score for all species, and then calculated the chromatic distance between a flower part and this mean value. With only one chromatic pathway, the minimum chromatic distance for dichromats is shown in Formula 3.3.2:

ퟐ ퟐ (휟풇푳−휟풇푺) Formula 3.3.2. 횫푺 = ퟐ ퟐ (휟훚푺) +(휟훚푳)

Trichromats have three different photopigments, therefore their chromatic distance is calculated using Formula 3.3.3:

ퟐ ퟐ ퟐ ퟐ ퟐ ퟐ ퟐ 훚푺 (휟풇푳−휟풇푴) +훚푴 (휟풇푳−휟풇푺) +훚푳 (휟풇푴−휟풇푴) Formula 3.3.3. 횫푺 = ퟐ ퟐ ퟐ (훚푺훚푴) +(훚푺훚푳) +(훚푴훚푳)

In both equations, ωi represents the Weber fractions (which act as the “noise” calculation) associated with human photopigments (S:0.08, M:0.02, L:0.02; Wyszecki and Styles 1982). The difference between the natural logs of the quantum catches for the target object and the background for a given photopigment (e.g., ΔfL= ln(QL(FLOWER)) – ln(QL(LEAVES))) is represented by Δfi. Although natural variation in the environment may prevent the perfect classification of the 1 JND threshold for flower food items, modelling expected JND scores allows for comparison between phenotypes, and provides a framework for exploring how different flowers are detected by capuchins. In addition to using JND to predict which flowers were conspicuous to which phenotypes, I determined which phenotypes had a detection advantage for each flower species by comparing the JND values of the three dichromats (532, 543 and 561) and the three

trichromat (532/543, 532/561, and 543/561) phenotypes with each other. To be considered to have an advantage for discriminating a flower species, JND scores for a phenotype had to be at least 1 JND higher than the phenotype to which it was being compared.

3.3.8 Behavioural observations

To determine predicted differences in detectability of flowers between colour vision phenotypes translated to differentiated use of flower resources by capuchins, my research assistants and I conducted scan sampling to determine activity budgets (Altmann 1974). Prior to the initiation of the sampling regime all investigators were trained on the applicable data collection and field techniques for a minimum of one month, and assistants were then routinely tested on their abilities throughout their time in the field. We followed each group from May to

July 2013 for three consecutive days every two weeks, from sleep tree to sleep tree (typically 12 hours/day). Following this initial field season we determined that group size differences were resulting in unbalanced sampling, therefore we changed the number of days spent with each group every two weeks to: four (AD), three (RM) and two (LV).

While with a capuchin group, we also collected data for this research objective using a form of all-occurrences sampling of flower foraging I refer to as “flower patch visit sampling”

(FLPV) sampling. We recorded all data on handheld Motorola PSION computers, using custom- built data-logging software, which recorded a timestamp for every input to the second.

3.3.9 Scan sampling

Every 30 minutes, investigators located as many group members as possible, recording their identity and behavioural state when they were found (See ethogram, Appendix I). If a monkey was foraging we recorded the type of food item being consumed or processed, for plant foods this was to the species and part level whenever possible. We terminated scan sampling

once all group members were located, or after a maximum of 10 minutes if some monkeys were not found. To prevent oversampling central group members we used a randomly-chosen monkey to initiate every scan sample, and we only collected scan sample data when at least two investigators were present. If flower foraging occurred during scan sampling, one researcher would record FLPVs while the second continued searching for monkeys. Due to known foraging differences between certain age classes, we only included large juveniles, subadult males and adults during scan sampling data collection (Fragaszy et al. 2004).

3.3.10 FLPV sampling

Flower eating by C. capucinus is rare, and frequently occurs rapidly, therefore the likelihood is low of observing florivory using traditional ethological sampling methods (e.g., focal and scan sampling). FLPV sampling is specifically designed to capture such rare, fleeting behaviours (Melin et al. 2014a, Leighton 1993). While a primate group was under observation, we recorded any visit to a flower patch that resulted in flower consumption as one FLPV, regardless of the number of monkeys involved, the number of flowers consumed, or the size of the flower patch. We treated individual plants as separate FLPVs regardless of their proximity, provided they were identifiably independent, and a second FLPV could occur in the same patch provided the monkeys left the area for more than one hour prior to returning to forage. When an

FLPV was noted, whenever possible observers recorded the flower species being consumed and the individual monkeys involved in the patch visit. Monkeys that could not be identified were recorded as “unknown individuals”. If flower foraging occurred during a scan sampling event, one observer recorded the FLPV while the other continued the scan sample.

3.3.11 Data analysis

I performed all data analysis using IBM SPSS Statistics 21.0.0.2 software, using a minimum significance threshold of p< 0.05 for all analyses.

3.3.12 Differences in flower foraging frequencies between phenotypes

I analyzed the flower foraging frequency for each phenotype by comparing the frequency of flower eating events that were observed during scan sampling per monkey. Since monkeys were not consistently found in every scan sample, and some monkeys were located more during scan sampling than others, I standardized the flower foraging frequency by dividing the number of flower eating events observed per monkey per two week study period (“cycle”) by the total number of scan samples that monkey was observed in. Using this standardized frequency, I used a generalized estimating equation (GEE) to determine whether the frequency of flower consumption per monkey was influenced by colour vision ability (trichromat or dichromat).

GEEs are appropriate for this study because they are capable of analyzing the mean response of the data and account for repeated measures within the data pool, even if the correlation structure is not accurately known (Hanley et al. 2003, Ghisletta and Spina 2004). The mean response is appropriate because I am interested in the variation between phenotypes, not individual monkeys. Since sex (B= 0.000, S.E.= 0.0012, χ2(1)= 0.009, p= 0.925) or age class (dichromats:

B= 0.001, S.E.= 0.0012, χ2(1)= 0.806, p= 0.369; trichromats: B= 0.002, S.E.= 0.0014, χ2(1)=

2.388, p= 0.122) did not affect florivory rates when controlling for colour vision type (see

Chapter 2), I included all individuals in the model who were present for the duration of the study and for whom scan data were collected (N= 35 individuals). Although the flower foraging frequency is zero-biased and a negative binomial distribution would be ideal, differences in sample size amongst individuals require standardization of the data, making the dataset

incompatible with negative binomial distribution, therefore Gaussian distribution was used

(Sileshi 2006, Sileshi 2008, Dr. Tak Fung pers. comm.). In all analyses, individual monkeys were treated as repeated measures, with AR(1) covariance structure used to account for the likelihood of correlation between closer data cycles.

3.3.13 Differential utilization of small patches between phenotypes

To determine if trichromats were better able to locate and therefore consume small, high finder-reward flower patches more effectively than dichromats, I compared the number of times dichromats and trichromats were observed to be involved during FLPVs using a GEE. Large patch access can potentially be decided mainly by memory and contest competition rather than the novel discovery and scramble competition important to small patch location (Bunce et al.

2011, Di Bitetti and Janson 2001), therefore FLPVs to Diphysa americana, Luehea speciosa, and

Manilkara chicle were excluded from this analysis. In this model, the total number of “small- patch” FLPVs per individual monkey was the dependent variable, with colour vision phenotype acting as the predictor variable. I treated group membership as a repeated measure of unstructured covariance, and due to the large number of zeroes (most monkeys were never observed to discover a small patch) and the fact that the data were count data (Sileshi 2006,

Sileshi 2008), a negative-binary distribution was used.

3.4 Results

3.4.1 Support Vector Machine modelling results

There were differences in the success of discrimination for every colour vision phenotype modelled using Support Vector Machine (SVM) modelling. Success also varied for every phenotype depending on whether or not luminance was included in the SVM. In all instances, models using trichromatic phenotypes were more successful at categorizing flowers (Figure

3.4.1). Luminance values for flower part samples fell within the range of leaf luminance values for 12 of 21 flower parts (Figure 3.4.2). All three trichromatic phenotypes were more successful than all three dichromats, regardless of whether luminance was included in the trial or not.

However, adding luminance greatly increased the success of dichromats, while decreasing trichromatic success.

SVM models using dichromat phenotypes were highly unsuccessful at categorizing flowers when luminance was excluded from the analysis: phenotype 532 correctly identified only one flower sample (4.8%), while phenotypes 543 and 561 did not correctly identify a single sample (Figure 3.4.3). When luminance was included, the success rate of dichromats rose sharply and the phenotypes reversed in order of success: phenotype 561 succeeded in identifying

52.4% of samples, 543 succeeded 38% of the time, and 532 correctly identified just 14.3% of samples.

Without luminance, the SVM was most successful when modelling trichromat 532/543 with 80.95% of flowers correctly categorized (Figure 3.4.4). The success rate using phenotype

532/561 was 76.2% (Figure 3.4.5), and the SVM succeeded in classifying 71.4% of flowers correctly while modelling phenotype 543/561 (Figure 3.4.6). The inclusion of luminance decreased the success of all three trichromats slightly. When luminance was included in the model trichromat 532/561 had the highest success rate, correctly identifying 71.4% of flower samples, while the 532/543 trichromatic phenotype was the least successful, only identifying

61.9% of flowers correctly.

100 90 80 With Luminance 70 Without Luminance 60 50 40 30 20

Proportion of successful IDs (%) IDs successful of Proportion 10 0 Dichromat Dichromat Dichromat Trichromat Trichromat Trichromat 532 543 561 532/543 532/561 543/561

Phenotype

Figure 3.4.1. The Support Vector Machine (SVM) success rate of each modeled colour vision phenotype at identifying flower spectra (N= 21) from background leaf spectra (N= 28). During

SVM trials, any flower part sample that was correctly categorized by the machine as a flower following hyperplane creation was considered a successful ID.

Figure 3.4.2. Chromaticity plot displaying the relative intensity of luminance (measured as the log of M+L values) and blue-yellow colour cues (S/(M+L)) of leaves (grey triangles) compared to various leaf parts (red stars: petals; yellow circles: immature samples; blue squares: “middle” structures, mostly reproductive organs; green squares: bracts). The faded area within the grey box represents the luminance value range for leaves; any flower parts outside of this range (9/21 samples) have brightness cues different from leaf samples, although some may not be sufficient to be discriminable to dichromats.

Luminance Included Luminance Excluded

Figure 3.4.3. The Support Vector Machine (SVM) modelling results for all three dichromat phenotypes, with luminance included (left panels) and excluded (right panels) in the models. The

X-axis is the log shifted values of luminance scores for each sample, while the Y axis represents the relative intensity of the blue-yellow colour channel signal. Green triangles are leaf samples, red closed circles are flower part samples. Open green boxes indicate an object was identified as a leaf by the SVM, while an open red circle indicates the SVM classified the object as a flower.

Percentages above each graph represent the rate at which the SVM correctly categorized flower parts for each modelling scenario.

Trichromat 532/543 Luminance Included Luminance Excluded Conspicuous ratio: 61.9% Conspicuous ratio: 80.95%

S/(L+M) axis is relative intensity of the blue-yellow colour channel signal, and the L/(L+M) axis is the relative intensity of the red-green colour channel signal. Green triangles are leaf samples, red closed circles are flower part samples. Open green boxes indicate an object was identified as a leaf by the SVM, while an open red circle indicates the SVM classified the object as a flower.

When luminance was included, the SVM correctly classified 13/21 (61.90%) of flower samples, improving to 17/21 (80.95%) when luminance was not included.

Trichromat 532/561 Luminance Included Luminance Excluded Conspicuous ratio: 71.43% Conspicuous ratio: 76.19%

Figure 3.4.5. The Support Vector Machine (SVM) modelling results of trichromat phenotype

532/561, with luminance included (left panels) and excluded (right panels) in the model. In all figures, the Lightness(log) axis represents the log-corrected luminance values, the S/(L+M) axis is the relative intensity of the blue-yellow colour channel signal, and the L/(L+M) axis is the relative intensity of the red-green colour channel signal. Green triangles are leaf samples, red closed circles are flower part samples. Open green boxes indicate an object was identified as a leaf by the SVM, while an open red circle indicates the SVM classified the object as a flower.

When luminance was included, the SVM correctly classified 15/21 (71.43%) of flower samples, improving to 16/21 (76.19%) when luminance was not included.

Trichromat 543/561 Luminance Included Luminance Excluded Conspicuous ratio: 66.67% Conspicuous ratio: 71.43%

SVM correctly classified 14/21 (66.67%) of flower samples, improving to 15/21 (71.43%) when luminance was not included.

No flower parts were correctly classified by the SVM for all six phenotypes with either luminance included or excluded (Table 3.4.1). One sample, the middle structures from Luehea candida, was never successfully categorized by the SVM for any colour vision phenotype with or without luminance included. Luehea species in general were not well identified by the SVM, as the five samples from these two species were only correctly classified as flowers for 14% of all possible opportunities. For trichromats, adding luminance to the analysis largely negatively affected the success rate of categorizing green or white parts, while flowers that would be considered red or yellow remained detected. Conversely, luminance effects on dichromat success were enhanced across all colour categories. Twelve samples, from 10 of 14 flower species were correctly categorized for all trichromat phenotypes regardless of luminance. The SVM never correctly identified nine flower parts from eight different species for any dichromat, with or without luminance. The Centrosema macrocarpum reproductive organs were the only flower part correctly categorized for any of the dichromatic phenotypes by the SVM when luminance was not included, and this only occurred when modelling using phenotype 532.

Peak Spectral Sensitivity 532 532 543 543 561 561 532/ 532/ 532/ 532/ 543/ 543/ 543 543 561 561 561 561 Luminance Included? No Yes No Yes No Yes No Yes No Yes No Yes Flower Species and Part Bauhinia ungulata- Middles ------+ - + - - + Callistemon viminalis- Middles ------+ + + + + + Cassia grandis- Petal - - - + - + + + + + + + Centrosema macrocarpum- Bract - - - + - + + + + + + + Centrosema macrocarpum- Petal + + - - - - + + + + + + Centrosema macrocarpum- Middles - - - + - + + + + + + - Cochlospermum vitifolium- Petal - - - - - + + + + + + + Curatella americana- Immature ------+ - + + + + Diphysa americana- Petal - - - + - + + + + + + + Luehea candida- Bract ------+ - + - - + Luehea candida- Immature - - - + - + - - - + - - Luehea candida- Petal - - - + - + ------Luehea candida- Middles ------Luehea speciosa- Bract - - - - - + ------Luehea speciosa- Petal ------+ - - - - - Malvaviscus arboreus- Petal ------+ + + + + + Manilkara chicle- Immature ------+ + + + + + Manilkara chicle- Petal - - - - - + + + + + + + Stemmedenia obovata- Petal - - - + - + + + + + + + Tabebuia ochracea- Immature - - - + - + + + + + + + Vachellia collinsii- Stamen ------+ + + + + +

3.4.2 Short distance foraging simulation using Just Noticeable Difference modelling results

All flower parts were discriminable from both the upper and lower surfaces of conspecific leaves by at least 1 Just Noticeable Difference (JND) for all plant species when modelled for trichromats with 532/561 and 543/561 phenotypes (see Appendix III for JND values per flower part for all phenotypes). With one exception, JND values for flower parts relative to upper leaf surfaces were > 1 JND for trichromat phenotype 532/543. However, JND values for 5 of 21 flower parts were < 1 JND when compared to lower leaf surfaces for this phenotype. All three dichromatic phenotypes had JND values > 1 JND for 19 of 21 flower parts relative to upper leaf surfaces, while 7 of 21 flower parts had values of < 1 JND compared to lower leaf surfaces. Overall, JND values were highest when modelling the trichromat 532/561 phenotype, and the average JND values for all flower parts were higher for all three trichromat phenotypes than the three dichromat phenotypes (Figure 3.4.7). For trichromats, the most visible flower part for all three phenotypes when contrasted against both upper and lower leaf surfaces was Malvaviscus arboreus petals (JNDs of 12-28 compared to upper and lower leaves).

Dichromat success varied by phenotype and leaf surface. JND values were highest for

Centrosema macrocarpum relative to upper leaf surfaces (JNDs of 8) when modelling dichromat phenotypes 532 and 543, while Manilkara chicle petals were most the most visible flower part compared to upper leaf surfaces for phenotype 561, with a JND of 7. The highest JND values for flower parts compared to lower leaf surfaces was Diphysa americana petals for all three dichromats (JNDs of 9).

7 Mean values compared to upper leaf surfaces 6 Mean values compared to lower leaf surfaces

2 Mean JND Value Mean JND 1

0 532 543 561 532/543 532/561 543/561 Colour Vision Phenotype

The JND values for most flower parts were higher for trichromat phenotypes 532/561 and

543/561 compared to all dichromat phenotypes by at least 1 JND (Table 3.4.2). Trichromat phenotype 532/543 is predicted to only have detection advantages for one to four flower parts compared to dichromats, and was outperformed by both other trichromatic phenotypes for 7-14 flower parts. Dichromatic phenotype 532 had detection advantages for one to two flower parts compared to the other dichromat phenotypes, while dichromat phenotype 561 never had any detection advantages compared to any phenotype.

Table 3.4.2. Pairwise comparison of the overall visual performance for all capuchin colour vision phenotypes, measured using Just Noticeable Difference (JND) values. Percentages reflect the number of flower parts for which JND values were at least 1 JND greater when modelling the phenotype in the row compared to the phenotype in the column. JNDs were calculated by contrasting the chromatic values of 21 flower parts and their upper and lower leaf surfaces. If the percentage of flower parts a phenotype had a JND advantage for differed between upper and lower leaf surface simulations, the lower leaf surface percentage is included in parenthesis.

Spectral 532 543 561 532/543 532/561 543/561 Sensitivity 532 X 5% 10% 0% 0% 0% 543 0% X 10% 0% 0% 0% 561 0% 0% X 0% 0% 0% 532/543 19% 5% (10%) 14% X 0% 0% 532/561 67% (76%) 71% (76%) 71% (67%) 67% (62%) X 48% (38%) 543/561 54% (62%) 57% 52% 43% (33%) 0% X

3.4.3 Long distance foraging simulation using Just Noticeable Difference modelling results

Long range JND results followed similar trends, although JND values for many flower parts improved for trichromats (see Appendix III for JND values per flower part for all modelled phenotypes). JND values for all flower parts compared to the mean chromatic values of all upper and lower leaves, which was designed to simulate a long-distance viewing scenario, were > 1

JND for all three trichromat phenotypes. Conversely, when contrasted with upper leaf surfaces, three flowers parts had JND values < 1 JND when modelling for dichromat phenotypes 532 and

561, and four flower parts were < 1 JND for phenotype 543. Contrasted with lower leaf surfaces,

JNDs were < 1 JND for: 6 of 21 flower parts when modelling dichromat phenotype 532, 5 of 21 for phenotype 543, and 4 of 21 for phenotype 561. The mean JND values of all flower parts were

90 higher for all three trichromatic phenotypes compared to the three dichromat phenotypes, for both upper and lower leaf surfaces (Figure 3.4.8). The most visible flower compared to both upper and lower leaf surfaces for trichromats was Malvaviscus arboreus (JNDs of 11-28);

Diphysa americana was the most visible flower for dichromats (JNDs of 8-10).

7 Mean against upper leaf surfaces 6 Mean against lower leaf surfaces 5 4

3 JND Score JND 2 1 0 532 543 561 532/543 532/561 543/561 Phenotype

The JND values for most flower parts compared to the mean chromatic value of upper and lower leaf surfaces were at least 1 JND higher for the trichromatic phenotype 532/561.

Conversely, only four to six flower parts tested were >1 JND higher for trichromat phenotype

532/543 compared to dichromats (Table 3.4.3). Trichromat 532/561 also had JND advantages of at least 1 JND for most flower parts compared to trichromat 532/543, and outperformed trichromat 543/561 as well. All three dichromats had JND advantages > 1 JND compared to at least one other dichromat phenotype for one flower sample each.

Table 3.4.3. Pairwise comparison of the overall visual performance for all white-faced capuchin colour vision phenotypes, measured using Just Noticeable Difference (JND) modelling.

Percentages reflect the number of flower parts for which JND values were at least 1 JND greater for the phenotype in the row compared to the phenotype in the column. JNDs were calculated by contrasting the chromatic values of 21 flower parts from 14 species to the mean chromatic value of upper and lower leaf surfaces from 28 plant species. If the percentage of flower parts a phenotype had a JND advantage for differed between upper and lower leaf surface simulations, the lower leaf surface percentage is included in parenthesis.

Spectral 532 543 561 532/543 532/561 543/561 Sensitivity 532 X 5% 5% 0% 0% 0% 543 0% X 5% 0% 0% 0% 561 0% (5%) 0% (5%) X 0% 0% 0% 532/543 29% 24% (19%) 19% X 0% 0% 532/561 71% (62%) 71% (57%) 71% (57%) 71% (52%) X 48% (38%) 543/561 57% (43%) 57% (38%) 48% (38%) 43% (29%) 0% X

3.4.4 Trichromats consume flowers more frequently than dichromats do

Flower foraging was the recorded behavioural state for 49 of 14701 scans (see Chapter

Two for detailed flower foraging behaviour analysis). Luehea speciosa was the most consumed flower, representing 58% of flower foraging bouts observed during scan sampling, and 72% of the 156 flower patch visits observed during 1157 hours of observation. Trichromatic individuals consumed flowers significantly more frequently than dichromats did (B= 0.002, SE= 0.0010,

χ2(1)= 5.134, p= 0.023) (Figure 3.4.9), with trichromats accounting for 53% of flower foraging observations during scan samples despite their smaller population size. This was largely the

92 result of differential use of L. speciosa, as 70% of flower eating events towards this species were by trichromats.

0.005

0.004

0.003

0.002

Flower Foraing Frequency Foraing Flower 0.001

0 Dichromat Trichromat Colour Vision Phenotype

3.4.5 Small patch visits

A total of 45 FLPVs to species with small patches were recorded, with 22 individual monkeys participating (13 dichromats and 9 trichromats). A total of 28 monkeys were never observed to consume a flower from a small patch (20 dichromats, 7 trichromats, and 1 infant female of unknown phenotype). Of these patch visits, 24 (53%) of FLPVs were by dichromats, while the remaining 21 (47%) were all trichromats. Trichromats were observed in these small patch species more than dichromats relative to their populations (Figure 3.4.10), but the difference was not significant (B= 0.402, S.E.= 0.2308, χ2(1)= 3.036, p= 0.081). In 9 instances

(20%) two monkeys were in a small patch concurrently, and we could not determine which

93 individual was the patch discover, therefore despite our best efforts social foraging may have affected these results

1.6 1.4 1.2 1 0.8 0.6 0.4 0.2

Mean number of small flower patch flower small of visitsMean number 0 Dichromat Trichromat Colour Vision Phenotype

Figure 3.4.10. The mean number of flower patch visits by dichromatic and trichromatic individuals to species that produce small flower crops, with one standard error.

3.5 Discussion

3.5.1 Most flowers are more visible to trichromats than to dichromats

SVM and JND modelling suggest that trichromats have both long and short range flower detection advantages over dichromats when attempting to locate most flower species against a green, leafy background. As predicted, trichromats have the greatest advantage when detecting flowers with reddish colouring, such as Callistemon viminalis, Cassia grandis and Malvaviscus arboreus. However, trichromatic advantage also extends to many flower species that appear white to human eyes, such as Manilkara chicle. Dichromats did not have a detection advantage for any flower species. While short range detection advantages are potentially valuable for

94 trichromats, there is ample evidence that in most cases, dichromats are able to compensate for their reduced colour detection ability through the use of other sensory cues, or via increased foraging effort (Dominy et al. 2001, Hiramatsu et al. 2008, Melin et al. 2009). From a distance, many of the cues that could be used when in close proximity to potential food items (such as luminance or tactile cues) are not available, therefore having enhanced colour discrimination ability is likely to be most adaptive in the realm of long distance detection (Melin et al. 2014a).

This competitive advantage is amplified when food sources are found in relatively small patches that are not worth memorizing the location of, as the reward is likely to be monopolized by the finder (Bunce et al. 2011, Di Bitetti and Janson 2001). Many flowers consumed by capuchins, such as Callistemon viminalis, Centrosema macrocarpum, Malvaviscus arboreus and Vachellia collinsii, are found in very small patches of only a few flowers, presenting a high incentive for detection from a distance. The differences between JND scores for dichromats and trichromats for these species were amongst the highest observed in the study, suggesting trichromats should have a competitive advantage in locating and consuming these flower species. However, our analysis of FLPVs to small patch species showed only a non-significant trend in this direction.

This is likely because with the small sample size of such patch visits I lack the power to detect a significant effect of colour vision.

3.5.2 Variable luminance increases the value of trichromacy

While luminance improved the SVM results of the dichromatic phenotypes, there is some evidence that luminance is too variable in natural forested habitats to be relied upon as a food cue (Osorio et al. 2004). Particularly from a long distance, there is a high likelihood of differential sunlight penetration in the forest canopy, effectively eliminating the use of luminance. Nevertheless, due to reduced colour variation acting as noise, dichromats are likely to

95 be more sensitive to more subtle luminance cues, and at short distances, luminance has been shown to be a valuable cue for fruit detection (Hiramatsu et al. 2008). Luminance may play a similar role for flowers: many flowers had luminance values that fell outside the range of leaves included in this analysis (Figure 3.4.2).

With luminance included, SVMs modelling trichromatic phenotypes had decreased success at classifying flower parts that would be categorized as white or green by human standards, while flowers with red and yellow cues remained largely unaffected. The decrease in trichromatic accuracy suggests that luminance may confuse target identification by introducing non-useful noise into the visual information system. Many lower leaf surfaces in Santa Rosa are pale or creamy white, and may therefore be confused for flowers based solely on their chromatic signals, particularly with luminance variation in the field of view. Conversely, even with luminance included and increasing the noise of the system, many red and yellow flowers remain visible to trichromats but not dichromats in the SVM analysis, a clear indication that enhanced colour vision is a valuable foraging tool.

Both SVM and JND modelling predict a clear trichromat advantage at detecting many flowers against a leafy background, regardless of luminance input. However, there is some discrepancy between SVM and JND analysis. Some species, such as Bauhinia ungulata, were predicted to be visible to trichromats but not dichromats by SVM analysis, but received similar

JND scores using both long range and short range leaf chromatic distance values, suggesting these flowers should be equally visible to all colour vision types. Conversely, some species predicted not to be visible to a phenotype by SVM modelling received relatively high JND scores. This variation in part reflects differences in how the two analyses compare flowers to leaves. SVM analysis compares the reflectance spectra of each flower to the mean chromatic

96 distance value of every individual leaf species, whereas JND compares flowers only to leaves from their own species for short-range modelling, or a mean of all leaves for long range modelling. If a leaf from any one species has similar chromatic values to a flower sample from a different species, this overlap can be enough to make the SVM incorrectly classify the flower part, while JND is less sensitive to such effects. This is likely what causes the analyzed parts of both Luehea species to be so poorly classified by the SVM for most colour vision phenotypes.

It is also prudent to remember that colour is not the only cue flowers exhibit that may allow monkeys to recognize them as food: it is unlikely that even if leaves have significant chromatic overlap with a flower species it makes a flower “invisible” to monkeys, as they are still able to discriminate based on physical characteristics such as shape and size. Luehea trees produce large synchronous crops of flowers on terminal branches, making them very visible from a distance to humans and likely monkeys. At short and long distances, odour cues may also be important. Many flowers produce very strong odours to attract pollinators (Willmer 2011).

Some primates are very sensitive to certain food odors, and are known to use olfaction to make foraging decisions (Bolen and Green 1997, Dominy et al. 2001, Laska et al. 2000). Researchers frequently noted strong flower odours while working in Santa Rosa, it is possible that capuchins are able to locate large flower patches via olfaction.

3.5.3 Trichromats eat flowers more frequently than dichromats do

In accordance with my prediction, capuchins with trichromatic colour vision consumed flowers at a significantly higher frequency than dichromats, suggesting they are utilizing resources differentially based on their colour vision ability. This may be interpreted as a type of foraging niche divergence. Surprisingly, the flowers most frequently consumed by both dichromats and trichromats were not of colours that conferred the strongest trichromatic

97 detection advantage. For example, Luehea speciosa was the most consumed flower but was also one of the only species not predicted by SVM analysis to be more effectively detected by trichromats.

Colour is a common signalling strategy used by flowers to attract pollinators, and many species known to be important pollinators have enhanced colour vision (Chittka and Menzel

1992). While it is unlikely that capuchins have coevolved with flowers as a pollinator, this study suggests that the evolution of trichromatic colour vision may have been favoured via an increased ability of trichromats to effectively detect colour cues produced by flowers. Although flowers do not appear to be classically defined fallback foods (Chapter 2), they are a highly seasonal resource that is consumed significantly more during a period of low abundance of other capuchin food types (Melin et al. 2014a). Such predictable seasonal stress is hypothesized to apply strong selective pressure, leading to the evolution of behaviours or morphology specifically adapted to compensate (Marshall et al. 2009). Flower consumption also appears to be inversely related to invertebrate abundance in Santa Rosa. Invertebrates are an important food items for capuchins, and important sources of protein (see Chapter Two for a detailed discussion). Importantly, trichromatic monkeys have been previously shown to have a decreased foraging efficiency for gleaning invertebrates (Melin et al. 2010). Because flowers are a high source of protein, increased flower consumption could be a more profitable foraging strategy for trichromats, again suggestion some degree of niche divergence between phenotypes.

3.6 Conclusions

This study demonstrates not only that capuchins with trichromatic colour vision are likely able to detect flowers from various distances more effectively than dichromats, but that they may use this advantage to consume flowers more frequently. Care must be taken in not conflating

98 trichromatic advantage at a particular task (in this case flower foraging) with heterozygotic advantage as a maintenance mechanism for polymorphic colour vision. This study, combined with others from Santa Rosa, suggest that rather than a specific phenotype having a competitive advantage in the wild, colour vision polymorphism may persist by improving the fitness of all individuals in a mixed group of dichromats and trichromats, either through niche partitioning or via mutual benefit of association. Long term life history data from this study population indicates that trichromacy does not result in significantly lower inter-birth intervals or age at first birth, and trichromats do not live longer than dichromats, suggesting that colour vision phenotype has no net bearing on an individual’s fitness (Fedigan et al. 2014). Furthermore, dichromatic capuchins have been noted to be more efficient at capturing cryptic invertebrates (Melin et al.

2008). Group living has long considered to be an evolutionary trade-off between reducing predation risk at the expense of increasing feeding competition (Wrangham 1980, reviewed in

Clutton-Brock and Janson 2012). Polymorphic colour vision potentially improves both aspects of this equation: trichromats and dichromats can rely on different foods during periods of low food abundance, with dichromats exploiting cryptic invertebrates and trichromats consuming flowers, while also specializing in the detection of different predators (Melin et al. 2008, Mollon et al.

1984, Morgan et al. 1992, Pessoa et al. 2014). Group benefits may extend to long range detection as well: trichromats benefit from exploiting small patches, while dichromats benefit through the detection of large flower patches by trichromatic group members.

Chapter Four: General Discussion

The types of foods consumed by primates have direct and indirect evolutionary consequences, and shape all aspects of an individual’s life history (Hohmann et al. 2006). Even less frequently consumed foods may still exert high selection pressure if they are an important food item used to survive predictable seasonal “crunch” periods, during which time more typical foods are not available or are present in reduced abundances (Marshall et al. 2009). High quality foods are typically easy to process but more rare and patchy, and they are therefore hypothesized to be particularly likely to promote adaptations improving food detection or harvesting; one such adaptation has been proposed to be colour vision (Marshall and Wrangham 2007). Amongst placental mammals, trichromacy has evolved almost exclusively within the primate order, and is hypothesized to be advantageous in detecting and selecting food items from a leafy background, amongst other benefits (Regan et al. 2001). Species with polymorphic colour vision offer an opportunity to explore what factors underlie the evolution of trichromacy. Dichromatic and trichromatic individuals coexist within groups, allowing for the explicit comparison of foraging behaviours and strategies employed by each phenotype, while avoiding the confounding factors common to interspecific comparison. Despite this, the advantages of trichromacy have yet to be demonstrated in wild primates (for a detailed review see Melin et al. 2012). In fact, many studies suggest that polymorphic colour vision persists due to benefits accrued by both dichromats and trichromats, not just trichromats (Caine et al. 2010, Melin et al. 2007).

4.1 Research Summary

In this research project, my aim was to determine (1) if the florivory patterns exhibited by white-faced capuchins support the hypothesis that flower consumption is a response to harsh

100 periods of reduced preferred food abundance; and (2) whether such flower foraging may play a role in the maintenance and persistence of polymorphic trichromacy colour vision.

In Chapter Two, I discussed the annual and seasonal trends of flower foraging by C. capucinus, and what factors affected the use of flowers as food. I predicted that flowers were not consumed due to their nutritional attributes, but rather as a response to declining preferred ripe fruit abundance in the dry season. As predicted, flowers that were consumed by capuchins did not have obviously different macro- or micronutrient proportions when compared to similar, non-consumed flowers. While flower quality or abundance did not explain the rate of florivory by C. capucinus, neither did the abundance of preferred fruit foods. Flower foraging rates did, however, increase in accordance with reduced abundance of invertebrates, which are always present, but in fluctuating quantities seasonally (Mosdossy 2013). Invertebrates are, on average, significantly more proteinaceous proportionally than are plant foods, and capuchins spend the majority of their foraging time budget searching for the former (Bergstrom et al. 2014a in prep.).

Flower species consumed by capuchins during this study were found to contain some of the highest proportions of protein of any plant food analyzed. During seasons of low invertebrate abundance, flowers may serve as one of the best protein sources available to capuchins. Their importance during such periods highlights the need to consider the influence of all food sources on primate evolution, regardless of annual proportion in the diet.

Since flowers are not particularly difficult for a monkey to harvest or process (Bandeili and Muller 2010, Whigham et al. 2013), if flower foraging abilities are under selective pressure it is likely the detection abilities of consumers that are selected upon. In Chapter Three I explored whether differences in colour vision resulted in differential flower detection capabilities by using computer simulations of white-faced capuchin colour vision phenotypes, and I

101 predicted that trichromats would have a detection advantage for most flower species. I found that in every colour vision modelling scenario, trichromats outperformed dichromats for many flower species, suggesting that the former have an advantage for detecting flowers in natural, leafy settings. To investigate whether this predicted detection advantage translated into measurable differences in flower food use, I also compared the frequency of flower foraging by trichromats and dichromats in Chapter Three. As predicted, trichromats consumed flowers more frequently.

Evidence from flower food species that produce only small flower patches (all of which were predicted to be more visible to trichromats), demonstrated a similar pattern of more utilization by trichromats, although not in statistically significant proportions. Overall, these findings supported my predictions that trichromats are better able to visually identify floral food resources, and subsequently take advantage of them more frequently than do their dichromatic cohorts. I interpret these results as support for the niche divergence or mutual benefit of association hypotheses of polymorphic colour vision persistence, and for the idea that flower consumption may be at least partially responsible for the evolution and maintenance of polymorphic trichromacy in C. capucinus.

4.2 Future Directions

4.2.1 Annual variation of florivory patterns and food abundances

While this study offers insight into the underlying factors affecting white-faced capuchin florivory strategies, and the ultimate consequences of such behaviour, there still remains much to learn about primate/flower interactions. First, as noted in Chapter Two, there is interannual variation in what flower species are included in the capuchin diet, as well as in the importance of each flower species in a given year. Many primate habitats, including Santa Rosa, experience interannual ecological variation due to changes in climatic patterns, plant phenology cycles, and

102 stochastic factors over time. Knowing how capuchins use flowers in response to extreme conditions, such as during a very poor fruit year or a particularly dry wet season, would perhaps reveal significant effects that were not observed during this study. Similarly, this research relies on invertebrate abundances from one year of intensive sampling. Further years of invertebrate abundance data collection would be valuable in confirming the observed trends, and would also allow interannual comparisons. Measuring the abundance of invertebrate taxa (such as caterpillars) within specific areas concurrently with the collection of foraging records would also allow categorization of capuchin preferences for specific invertebrate taxa. This would be important for conclusively determining whether flowers can be considered fallback foods, as researchers could determine if flower use varies in relation to the abundance of preferred invertebrate foods (Marshall et al. 2009).

4.2.2 Long distance detection and leadership into patches

While trichromats consumed flowers significantly more frequently than did dichromats during this study, this pattern did not prove significant when I only compared the frequency of visits to small, conspicuous patches for which long distance detection advantages and subsequent rewards should be highest (Bunce et al. 2011, Di Bitetti and Janson 2001, Melin et al. 2014a).

However, trichromats were involved in FLPVs to such flower species more frequently than dichromats were, and supplementation of the dataset with further behavioural observation periods may prove that there are real, measurable differences in flower use by dichromats and trichromats. This would be an important piece of evidence supporting niche divergence as an underlying maintenance mechanism of polymorphic colour vision. Further support for the niche divergence hypothesis may also be detected by identifying leadership into larger patches, which

103 is difficult to determine in the field but may be detectable using modern radio telemetry techniques (Kays et al. 2011).

4.2.3 Probable pollination of Luehea speciosa

Although many primates have been hypothesized to behave in ways that may facilitate pollination while flower foraging, direct evidence of primates affecting fruit set is sparse: only one study has presented evidence of improved fruit set following primate visitation (Gautier-

Hion and Maisels 1994). One of the major hurdles in obtaining such data is that verifying pollination and isolating the effects of one animal species is difficult in wild settings (Carthew and Goldingjay 1997). Because of this, animal behaviours that might lead to successful pollination are often used as proxy measures (Fleming and Sosa 1994). Specifically, there is a stereotyped sequence of behaviours that is considered to be the minimum required precursor to pollination. First, the animal must forage in a manner that would allow uptake of pollen onto the body. During foraging, the plant’s reproductive organs must remain viable; this condition is typically considered to be met if destruction of less than 50% of the plant’s flowering crop remains intact following foraging bouts. Finally, the animal must subsequently visit conspecific flowers; if the plant species is self-incompatible then the animal must visit flowers of a new conspecific individual. During this study, when capuchins could be identified and therefore tracked, they were observed to satisfy these requirements during 96% of foraging bouts in

Luehea speciosa, with 32% of individuals being observed to forage in another L. speciosa tree within one hour of the first foraging record. Based on the sucrose to hexose ratio (known to be an accurate indicator of which pollinator syndrome is in operation, Southwick et al. 1981), Haber and Frankie (1982) hypothesized that mammals are most likely the intended pollinators for L. speciosa. Since L. speciosa flowers open at night and only produce nectar until the early morning

104 of the following day, Haber and Frankie further suggested that bats were the most likely pollinators for this species. However, following all-night focal tree observations at several sites in northwestern Costa Rica, bats were never observed to visit L. speciosa trees, despite their presence at nearby flowering trees of other species (Haber and Frankie 1982). Based on these observations, it may be possible that monkeys, rather than bats, are serving as pollinators for L. speciosa in this region. Non-flying mammal pollinators have been observed to be more important pollinators in ecosystems that lack more typical pollinators; often times these habitats are isolated and harsh, such as Australian desert or South African fynbos (Goldingjay et al. 1991). In

Santa Rosa habitat loss to agriculture in the preceding centuries coupled with extreme seasonality may have resulted in a dearth of non-primate pollinators. Further studies investigating the reproductive success of Luehea speciosa when visited by capuchins may be revealing. This would best be accomplished through exclusion studies, as well as by comparing

L. speciosa fruit set success in habitats with capuchins and without. Detailed focal follows of individual capuchins would also allow for improved estimates of how likely pollination is, and how frequently these behaviours occur. Flower foraging visits to Callistemon viminalis and

Manilkara chicle by capuchins also satisfied the behavioural requirements necessary to consider an animal to be a potential pollinator, but very few visits to these species were observed.

4.2.4 Crop destruction by capuchins

Capuchin foraging behaviours directed at the flowers of Bauhinia ungulata, Centrosema macrocarpum and Malvaviscus arboreus were highly destructive, and typically resulted in the destruction of most, if not all of the plant’s flower crop. Flower crop destruction by primates is not uncommon, and has been hypothesized to reduce fruit set and, in some instances, to reduce targeted plant species’ abundance within primate home ranges (Riba-Hernandez and Stoner

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2005, Chapman et al. 2013b). Both crop destruction and pollinator services by capuchins have the potential to shape their habitat and community structure, which can affect all trophic levels.

Primates have recently been suggested to act as ecosystem engineers in some ecosystems, particularly through folivorous and florivorous behaviours (Chapman et al. 2013a). During this study we noted the total destruction of several flower crops, as well as behaviours that may conversely facilitate pollination for other species. Aside from Malvaviscus arboreus, capuchins were never observed to consume the fruits of the species whose flower crops they destroyed, whereas the fruits of Luehea speciosa and Manilkara chicle are both commonly consumed foods when available. Capuchins may have evolved to exploit these flowers in a way that minimizes their destruction, ensuring that subsequent fruit sets are not impacted by their florivory, or are even enhanced through increased pollination. Florivory by primates may be another mechanism through which they actively shape, alter or maintain their environment.

4.2.5 Flowers as training food for infants

Throughout the study, infants and small juveniles were frequently observed to participate in FLPVs, and many of these individuals were rarely observed consuming any other food items.

Unlike fruits, which are often either too heavy for infants to handle or are protected by thick protective outer layers that are difficult to penetrate, flowers are lightweight, thin-walled, and unprotected by mechanical defenses, allowing for easy harvesting and processing (Bandeili and

Muller 2010, Whigham et al. 2013). Because of this, flowers may serve as “training” food for infants, allowing them to learn basic foraging techniques while gaining some nutritional benefit.

Similar flower foraging behaviour by infants has been observed in other primates, including chimpanzees (Pan troglodytes) (Badescu pers. comm.), and future studies may benefit from

106 conducting focal observations on these young primates to determine what types of food are introduced as they are weaned and gain independence.

4.3 Conclusions

Although flowers are not consumed in large proportions on an annual basis by white- faced capuchins, their increased use in the dry season as well as their importance during times of reduced invertebrate abundances suggest that they may be influential foods, affecting both the immediate health of capuchins and their evolutionary history. Flowers are likely used as a replacement protein source during periods of reduced invertebrate abundance. Some flower species, specifically Luehea speciosa, also provide reliable, large patches of easily accessed and processed energy during the dry season, which may help capuchins survive and persist in Santa

Rosa tropical dry forests. In turn, flower use may have contributed to the evolution of polymorphic colour vision, and differential flower use by dichromats and trichromats may partially explain why this balanced polymorphic system persists. Overall, flowers play a significant role in the capuchin diet even when used in small proportions, and likely exert selective pressure disproportionate to their annual importance.

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APPENDIX I: ETHOGRAM

States- duration recorded

Affiliative: other affiliative behaviours such as hugging or momentary touch

Aggressive: display, threaten, chase, bite, swing, hold, push, threat face, vocalizations

Carry: holding a food item while moving

Close inspect: close examination of a specific food items

Contact rest: rest in contact with another monkey

Drink: consumption of water

Extractive: picking at substrate, breaking branches, peeling bark or tool use to obtain food items

Feed: actively eating a food item (i.e. chewing, swallowing)

Follow: animal closely follows behind conspecific

Fur rub: rubbing plant or animal substances into fur

Groom: licking, sniffing, touching own fur or social partner’s fur

Locomote: high speed travel in a specific direction over long distance

Mobbing behaviour: participation in group mobbing of predator

Solitary play: play behaviour without partner(s)

Food Process: manual treatment of food before consumption (ie hair/exoskeleton removal)

Rest: lying down on a branch, being inactive

Search: manipulating arboreal leaves, sifting through leaf litter or looking in tree holes

Sexual Behaviour: solicit, copulation, vocalizations

Scan: anti-predator or extragroup vigilance not in context of foraging

Social play: play behaviour with one or more partners

Submissive: vocalizations, facial expressions, avoid, leave

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Visual Scan: visually scanning immediate area for food items, could occur while moving

Watch: intently observing the foraging or food processing behaviour of a conspecific

Events- only frequency recorded

Alarm Call: short, abrupt call directed at a predator

Bite: sink teeth into food item, in non-feeding contexts (i.e. processing or close inspecting)

Catch: seizure of an animal prey item

Defecate

Eat: place food item into mouth, chewing and swallowing, tallied by hand to mouth movements

Fecal Wash: rubbing fecal matter on hands and feet

Lick: pass tongue over food item

Lost call: vocalization made when individual is separated from group

Miss: failed attempt to catch invertebrate prey

Move: move to another foraging location within the same tree/patch

Other: behaviour that does not fit into above categories

Visual inspect: close examination by looking at food or background items

Sniff: sniffing/ smelling food item

Tapping: tapping substrate during foraging usually accompanied by listening

Theft: seizure of food item from unwilling conspecific: note if successful or attempted

Touch: touch food item with hand or foot (i.e. while close inspecting)

Reject: item dropped, discarded, abandoned or not consumed after close inspection

Urine wash: rubbing urine over hands and feet

Urinate

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APPENDIX II: MACRO- AND MICRONUTRIENT CONTENTS OF FLOWER FOODS

WM/Fl: Wet mass/flower; DM/FL: Dry mass/flower; GE: Gross energy; CP: Crude protein; WSC: Water soluble carbohydrates; NFC: Non-fibre carbohydrates; NDF: Neutral-detergent fibres; CF: Crude Fat. Species and part WM/Fl DM/Fl Moisture GE GE/Fl CP WSC NFC NDF CF (g) (g) (%) (kJ/g) (kJ) (%) (%) (%) (%) (%) Callistemon viminalis 3.25 0.86 73.5 19.25 16.56 10.4 37.4 61.4 19.4 4.1 Centrosema macrocarpum 0.67 0.11 84.0 19.70 2.11 19.3 25.4 40.9 31.7 3.9 Diphysa americana 0.06 0.01 81.4 20.68 0.25 24.4 27.2 46.1 22.9 3.1 Luehea candida- middle parts 1.86 0.28 85.0 20.27 5.64 14.3 33.4 48.3 32.7 2.0 Luehea candida- petals 1.61 0.19 88.0 20.83 4.01 12.6 36.5 56.9 20 5.1 Luehea speciosa- middle parts 0.51 0.13 74.2 20.69 2.71 12.8 36.7 53.7 26.6 2.4 Luehea speciosa- petals 0.72 0.14 80.0 20.71 2.97 11.6 40.9 63.6 16.3 3.7 Malvaviscus arboreus 0.22 0.04 82.3 19.11 0.73 17.9 24.9 60.9 16.5 4.2 Manilkara chicle* 0.07 0.02 74.2 20.35 0.41 7.7 45.9 71.7 14.8 1.9

Micronutrients, labelled using standard periodic table abbreviations. Species and part Ca (%) P (%) Mg (%) K (%) Na (%) Fe Zn Cu Mn Mo (ppm) (ppm) (ppm) (ppm) (ppm) Callistemon viminalis 0.43 0.12 0.15 1.62 0.015 43 14 15 17 0.1 Centrosema macrocarpum 0.62 0.23 0.26 1.86 0.007 60 33 12 44 0.8 Diphysa americana 0.35 0.37 0.21 2.18 0.008 90 44 11 24 2.7 Luehea candida- middle parts 0.25 0.30 0.26 2.37 0.002 52 19 24 48 0 Luehea candida- petals 0.11 0.23 0.18 2.06 0.005 41 13 16 21 0.3 Luehea speciosa- middle parts 0.21 0.23 0.20 1.55 0.004 36 18 16 55 0 Luehea speciosa- petals 0.12 0.18 0.17 1.83 0.006 35 14 13 31 0.3 Malvaviscus arboreus 0.67 0.3 0.21 2.07 0.009 111 31 11 14 0.3 Manilkara chicle* 0.24 0.13 0.16 1.59 0.018 891 12 6 22 0.6 *Manilkara chicle data from Bergstrom et al. 2014a (in prep.)

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APPENDIX III: JUST NOTICEABLE DIFFERENCE (JND) MODELLING RESULTS

BY FLOWER PART FOR ALL MODELLED SCENARIOS

Short range modelling (flower vs. conspecific leaves), upper leaves

Phenotype Modelled Species Part 532/543 532/561 543/561 532 543 561 Bauhinia ungulata Middles 4.21 4.19 4.19 4.12 4.20 4.18 Callistemon viminalis Petal 2.31 4.56 3.47 2.05 1.91 1.58 Cassia grandis Petal 2.71 5.98 4.03 1.51 1.75 2.14 Centrosema macrocarpum Bract 5.28 5.65 5.49 5.28 5.24 5.03 Centrosema macrocarpum Petal 8.41 14.56 12.02 8.05 7.72 6.64 Centrosema macrocarpum Middles 6.85 6.79 6.86 6.79 6.86 6.77 Cochlospermum vitifolium Petal 6.19 9.73 7.82 5.39 5.70 6.26 Curatella americana Immature 4.30 4.34 4.42 4.28 4.31 4.17 Diphysa americana Petal 6.47 8.86 7.63 5.99 6.23 6.68 Luehea candida Bract 3.17 4.71 3.98 3.08 2.98 2.66 Luehea candida Immature 2.68 4.02 3.42 2.61 2.52 2.25 Luehea candida Petal 1.41 4.08 2.92 0.85 0.72 0.40 Luehea candida Middles 2.78 4.52 3.74 2.67 2.57 2.24 Luehea speciosa Bract 3.08 3.11 3.13 3.07 3.08 3.00 Luehea speciosa Petal 6.56 6.56 6.54 6.43 6.54 6.54 Malvaviscus arboreus Petal 12.24 28.71 17.34 5.11 3.84 1.93 Manilkara chicle Immature 7.18 9.79 8.03 6.35 6.68 7.14 Manilkara chicle Petal 1.77 2.88 2.19 1.44 1.55 1.71 Stemmedenia obovata Petal 3.98 8.19 5.93 2.82 3.11 3.66 Tabebuia ochracea Immature 0.59 1.48 1.15 0.50 0.46 0.34 Vachellia collinsii Middles 4.93 8.28 6.17 3.91 4.22 4.69

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Short range modelling (flower vs. conspecific leaves), lower leaves

Phenotype Modelled Species Part 532/543 532/561 543/561 532 543 561 Bauhinia ungulata Middles 1.99 1.98 2.02 1.97 1.99 1.94 Callistemon viminalis Petal 1.52 4.06 2.82 0.94 0.80 0.49 Cassia grandis Petal 2.90 6.19 4.19 1.70 1.95 2.35 Centrosema macrocarpum Bract 0.98 2.42 1.76 0.74 0.66 0.48 Centrosema macrocarpum Petal 4.66 12.92 9.70 3.50 3.14 2.09 Centrosema macrocarpum Middles 2.27 2.25 2.30 2.25 2.28 2.22 Cochlospermum vitifolium Petal 7.46 9.60 8.34 6.91 7.18 7.61 Curatella americana Immature 3.98 3.98 4.07 3.95 3.98 3.86 Diphysa americana Petal 8.93 10.56 9.65 8.53 8.78 9.17 Luehea candida Bract 0.67 2.03 1.37 0.12 0.04 0.11 Luehea candida Immature 0.63 1.57 1.10 0.35 0.41 0.52 Luehea candida Petal 2.35 3.24 2.70 2.11 2.21 2.37 Luehea candida Middles 0.75 2.14 1.49 0.29 0.37 0.53 Luehea speciosa Bract 1.80 1.83 1.83 1.80 1.80 1.75 Luehea speciosa Petal 1.85 2.67 2.09 1.56 1.66 1.79 Malvaviscus arboreus Petal 12.07 27.67 16.65 5.42 4.19 2.37 Manilkara chicle Immature 6.29 9.36 7.39 5.38 5.71 6.20 Manilkara chicle Petal 1.05 2.73 1.87 0.47 0.57 0.77 Stemmedenia obovata Petal 2.63 6.39 4.42 1.40 1.64 2.09 Tabebuia ochracea Immature 0.88 1.33 1.19 0.88 0.86 0.76 Vachellia collinsii Middles 4.72 7.81 5.86 3.78 4.07 4.52

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Long range modelling (flower vs. mean of 28 leaf species), upper leaves

Phenotype Modelled Species Part 532/543 532/561 543/561 532 543 561 Bauhinia ungulata Middles 2.71 3.11 2.99 2.72 2.69 2.53 Callistemon viminalis Petal 3.74 10.20 6.76 1.00 0.73 0.71 Cassia grandis Petal 2.20 6.36 4.21 0.58 0.34 0.13 Centrosema macrocarpum Bract 2.08 3.77 2.98 1.89 1.79 1.52 Centrosema macrocarpum Petal 5.61 13.93 10.63 4.55 4.17 3.06 Centrosema macrocarpum Middles 3.39 3.73 3.61 3.34 3.35 3.21 Cochlospermum vitifolium Petal 6.70 9.27 7.81 6.08 6.36 6.82 Curatella americana Immature 3.17 3.51 3.47 3.17 3.16 2.99 Diphysa americana Petal 8.77 10.82 9.69 8.30 8.57 9.02 Luehea candida Bract 1.75 3.20 2.57 1.49 1.41 1.20 Luehea candida Immature 1.65 2.36 2.08 1.63 1.59 1.43 Luehea candida Petal 3.07 4.23 3.67 2.86 2.94 3.04 Luehea candida Middles 3.97 5.35 4.66 3.68 3.81 4.00 Luehea speciosa Bract 1.14 2.39 1.83 0.88 0.89 0.87 Luehea speciosa Petal 3.39 3.41 3.46 3.36 3.39 3.30 Malvaviscus arboreus Petal 11.66 28.42 16.86 1.54 0.75 1.63 Manilkara chicle Immature 5.43 9.95 7.31 4.11 4.48 5.10 Manilkara chicle Petal 2.01 4.34 3.26 1.22 1.21 1.34 Stemmedenia obovata Petal 4.61 7.47 5.90 3.81 4.00 4.37 Tabebuia ochracea Immature 2.93 3.35 3.25 2.92 2.91 2.73 Vachellia collinsii Middles 3.49 8.19 5.61 1.85 2.16 2.72

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Long range modelling (flower vs. mean of 28 leaf species), lower leaves

Phenotype Modelled Species Part 532/543 532/561 543/561 532 543 561 Bauhinia ungulata Stamen 2.09 2.18 2.18 2.09 2.09 2.01 Callistemon viminalis Petal 3.40 9.21 6.02 0.60 0.57 0.99 Cassia grandis Petal 1.93 5.41 3.51 0.08 0.26 0.65 Centrosema macrocarpum Bract 1.44 2.68 2.08 1.26 1.19 1.00 Centrosema macrocarpum Petal 4.99 12.85 9.76 3.93 3.57 2.54 Centrosema macrocarpum Stamen 2.79 3.03 2.90 2.71 2.74 2.69 Cochlospermum vitifolium Petal 7.19 9.04 7.94 6.71 6.96 7.34 Curatella americana Immature 2.56 2.63 2.67 2.54 2.56 2.47 Diphysa americana Petal 9.31 10.76 9.93 8.93 9.17 9.54 Luehea candida Bract 1.20 2.19 1.75 0.90 0.90 0.85 Luehea candida Immature 1.01 1.30 1.19 1.00 0.98 0.91 Luehea candida Petal 3.14 3.76 3.44 2.98 3.06 3.15 Luehea candida Stamen 4.04 4.92 4.43 3.81 3.93 4.10 Luehea speciosa Bract 1.10 1.65 1.38 0.97 1.01 1.07 Luehea speciosa Petal 2.81 2.81 2.80 2.73 2.79 2.78 Malvaviscus arboreus Petal 11.39 27.47 16.16 0.91 0.87 2.16 Manilkara chicle Immature 5.80 9.42 7.18 4.74 5.09 5.62 Manilkara chicle Petal 1.70 3.47 2.59 1.11 1.20 1.34 Stemmadenia obovata Petal 4.93 6.99 5.78 4.32 4.54 4.89 Tabebuia ochracea Immature 2.31 2.44 2.44 2.29 2.31 2.21 Vachellia collinsii Stamen 3.70 7.47 5.26 2.48 2.77 3.25

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